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Workshop 3: Calibration, Sample Prep and Scanning

Drafted by Gretchen Gehrke, Stevie Lewis, Liz Barry.

Why (The Situation): We want to calibrate the spectrometers we built. We want to prepare our oil samples for scanning. We want to learn how to use spectralworkbench.org. We want to scan known samples of oil to test the function of the equipment itself. We want to scan unknown samples and assemble their spectra into sets for comparing and contrasting with known samples to attempt categorization.

When: two and a half (2.5) hour workshop, part of a four-part series.

Where: a room with long tables, chairs, power outlets, and internet connection.

What (the content): calibrate a spectrometer using a fluorescent lightbulb; safely work with ultraviolet (UV) lasers; the features of spectralworkbench.org; troubleshoot your equipment to get a clear high-quality scan;

For What (Achievement Based Objectives):

In completing this four hour workshop, you will:

• Meet others attending the workshop
• Share with each other what motivated you to to participate
• Connect your spectrometer to the computer using the webcam’s cord & USB plug
• Login to spectralworkbench.org
• Look at a flourescent light bulb through the spectrometer
• Use spectralworkbench.org’s interface to set the calibration of the spectrometer
• Squeeze eyedroppers to put oil samples into cuvettes (small tubes with square sides)
• Shine a blue laser through a small container of oil, and capture the spectra the oil emits with your spectrometer and spectralworkbench.org
• Assign your calibration to your scan of oil
• “Read the rainbow” AKA the spectrum of fluorescence produced by oil
• Gain necessary information for use in the 4th and final workshop in this series

Notes for Facilitators:

Materials Needed:

• a large clean space for people to work (long tables work well)
• one computer per person or team constructing the spectrometer
• internet connection (preferably with google chrome)
• a power source that can support all your computers
• newspaper to cover the tables
• 3 poster boards
• markers and pens
• sticky notes
• paper research notes (insert link)
• a plugged in compact fluorescent light bulb
• the Public Lab Spectrometer 3.0 that was constructed in workshop 2
• the oil testing kit add on (if you do not have the add-on kit, including:
  • a 405nm blue/violet laser pen: $3 from Alibaba, or for purchasing singles here
  • cuvettes (1-2 per sample)
  • droppers (1 per sample)
  • protective eye gear
  • gloves
• known samples such as specific oil samples, for example 80W90 and 20W50 (these also come with the kit)
• unknown sample (such as runoff water from the road, black tar like substances found on the beach etc.)

Setting up the event:

• Line your tables with newspaper
• Two of the poster boards should be prepared with the words “Notes” “Questions” “Ideas” evenly spaced down the left hand side. Each poster boards will also carry one of the titles: “Calibration” and “Scanning.” Put these on the wall. Leave the third poster board blank and hang it on the wall as well.
• Set up computer stations for each working group along with the spectrometers they built in workshop 2. Provide each group with the oil testing kit add-on or materials in it.
• Put pens, markers, paper research notes and sticky notes around for people to use to take notes.

Workshop Schedule:

1. Introduction (10 minutes)
   1. Who’s in the room
   2. Introduction to the event
2. Calibrating your spectrometer (20 minutes)
   1. Logging into spectralworkbench
   2. The calibration process
3. Sampling (1 hour 30 minutes)
   1. Setting up for safe scanning!
   2. Scanning samples
   3. Creating sets
4. Wrap up (10 minutes)

Workshop Outline

1. Introduction

1.1 Who’s in the room? (10 minutes)

Go around the room, with each person introducing themselves with their name, where they’re from, and the reason they are interested in being here today. If there are really a lot of people, then as a large group say names only, and then break into smaller groups at tables to share reasons, hopes, and expectations for attending.

1. If you have not done so yet, introduce yourself:
   1. why you are interested in this project and
   2. a little bit about Public Lab.
2. Give an overview of the event goals and structure (at the top of this page)
3. Emphasize “the tools, technology and learning that happens here is always under development. One of the major outcomes of the event is to provide constructive feedback on the learning, the activities and the tool we will build in order to improve it for future participants.”
4. Introduce the things in the room:
   1. Highlight the posters, markers and sticky pads available for people to put up their questions, comments, and ideas on as they work through the event.
   2. Identify the paper research notes for those who would like to take in depth notes on their steps for sharing back with the Public Lab community.

2. Calibrating your spectrometer

Needs brief intro

2.1 Logging into spectralworkbench.org

• Ask your group if there’s anyone with a publiclab.org username.
• If no one has one, choose someone to create one for themselves at https://publiclab.org/signup
• Go to spectralworkbench.org and log in. You will be directed through the Publiclab.org site, just keep clicking through until you are back to spectralworkbench.org and your username shows in the upper righthand corner.

2.2 The calibration process

FACTS: each spectrometer has to be calibrated before it is used.

Choose one person at the table to read the following text out loud:

Calibration helps us to make sure the readings of an instrument are consistent. For example, if this were a scale, we would want to make sure that when it read that something weighed two pounds, the item you were weighing was really two pounds. Because we know certain colors always show up at the same place on the nanometer scale (recall from workshop 2), the way we calibrate a spectrometer is by using a known type of light. In this case, we use a compact fluorescent light where the green light wave always peeks at 546 nanometers. By shining the compact fluorescent light into our spectrometer, and graphing the color peaks on SpectralWorkbench.org we can measure our future samples against that known calibration of the compact florescent light. In this way, we know our future scans will be accurate.

Individually, write down any questions you have about these concepts and discuss as a group. Going through the activity may help clarify, and afterwards if you still have questions, you can email plots-spectrometry@googlegroups.com.

Follow the following 4 steps to connect your spectrometer to your computer:

1. Plug the cord into a USB port on your computer
2. In an internet browser (we strongly recommend Chrome for reasons that will later become clear), navigate to http://spectralworkbench.org (make sure you’re logged in! See 2.1)
3. On Spectral Workbench, click “Capture Spectra” (the live capture drop down).
4. Make sure it is working:
   1. If you see black or just a light line of color, you will know you are using the correct camera (i.e. not the built-in one on your computer) and you are ready to proceed.
   2. If you see yourself, you need to switch the input camera by using the button on the right under the picture that says “change camera.”
   3. If you still see yourself, use Chrome’s address bar to “allow” or “enable” the camera on your spectrometer from the browser’s address bar. Once you are seeing the rainbow image from your spectrometer on SpectralWorkbench, you are ready to proceed.

Follow the following 4 steps to take your first scan of a fluorescent lightbulb:

1. Hold the front end of the spectrometer (where the slit is) towards the lit compact fluorescent light bulb. Make sure the color line that appears on your image appears straight, as in the example from the calibration page on SpectralWorkbench.
   1. Troubleshooting information: if the colors are really not straight, you can open up your spectrometer and try to move the angle of the DVD slightly.
2. Once your color line is straight, click the middle of it to position the yellow line directly through it, as in the example.
3. Click the button “begin capture”, and once you do so, you (and perhaps a partner) will need to hold the spectrometer steady.
4. Once you click “begin capture”, you will see a new page where the “live feed” of your spectra is coming through. You may have to work to re-align your equipment to see a clear spectra of all the colors -- then, click the blue “save” on the left of the screen.
5. This will open a new page where you will see your calibration and can fill in some information about it. Title the spectra “CFL Calibration” and click the button “save and calibrate later.”
6. On the new page, click the blue “calibrate” button below your sample. This will walk you through matching your sample (the above bar) to the example scan (the below bar) so the colors line up.
7. Leave these pages open in your browser.

Additional calibration directions can be found on this wiki: http://publiclab.org/wiki/spectral-workbench-calibration

Once you have finished calibrating your spectrometers, take five minutes and brainstorm notes, questions, and ideas on the calibration process. Post these up on the poster board. These will be reviewed and compiled at the end of the workshop and posted back to Public Lab. If you still have questions, you can email plots-spectrometry@googlegroups.com.

------ (10 minute break) ------

3 Sampling

3.1 Setting up for safe scanning! (90 minutes)

YOU MUST REVIEW AND FOLLOW THESE SAFETY GUIDES (10 minutes)

• Do not turn on the laser unless it is in your testing frame and be sure to never look directly at the light. Even looking at the beam indirectly can be damaging to your eye.
• Always wear your safety glasses when working with oil samples, suspected oil samples and the laser.
• The materials that come in the beta kit are hazardous and should only be handled with gloves in a well ventilated area. Here are the Material Safety Data Sheet (MSDS) for each oil type included in the Oil Testing Kit: MSDS_5W30_motor_oil_Hess.pdf, MSDS_20W50_Gulfpride.pdf, MSDS_crude_oil_sweet_Hess.pdf, MSDS_80W90_Lucas.pdf, MSDS_Diesel_fuels_Hess.pdf, MSDS_gasoline_Hess.pdf

3.2 Scanning samples (50 minutes)

The Public Lab community advises to scan each sample 3 times. Following this rule, a rough time estimate would be that a first time user can achieve the scanning of 3 unique samples in an hour. As we learned in Workshop 1 (Experimental Design), taking triplicate samples of each one of your materials will increase the accuracy of your results.

1. Identify which of your oil samples are “knowns” and begin with them.
2. Use the dropper to fill the cuvette with your sample to the line above where it curves in (just over halfway full)
3. Place your sample in the sample holder on your oil testing kit add-on
4. Place the cover over the sample

5. Press and hold the button on the laser.

6. Return to the open “capture” tab on spectral workbench. There you will see your spectra. Ideally, you want your spectra line to fall between 25-75%.

   1. Look at the top line of the graph -- Is it yellow? It turns yellow when your are peaks touching or getting cut off by this line. If the top line is yellow, you need to dim your spectra by using the physical dimmer on the oil testing kit add-on. The higher you pull the dimmer, the less light it lets in.
7. Once you’ve captured a good spectra, click “Save” and title it the name (whatever it is you’re sampling) and the number 1.
8. Leave this tab open and repeat steps 1-5 with the same samples naming the consecutive ones (Sample name)2, and (Sample name)3. For example Diesel1, Deisel2 and Diesel3.

3.3 Creating sets (30 minutes)

1. Once you have all three scans of one sample, click the “Save as a set” button on your most recent scan (Sample3). This will start a page for you where you can add your samples to one graph -- see https://publiclab.org/wiki/spectral-workbench-usage#Sets.
2. On the page that comes up, give some information about the set you’re creating. For example, you’ll want to title it with the sample name for example (Diesel Set for Oil Testing or Unknown Set for Oil Testing).
3. Return to your open tab for the sample2 you’ve just created (sample2) and click the “add to set” button.
4. This will pull up a search bar where you will need to find the set you created for your first sample (in this example we used “Diesel Set for Oil Testing). Find that set, click “Add spectrum to this set”)
5. Do the same for sample1
6. Find the tab that has the set you’re creating, and refresh it. This should show you all three samples on one graph.
7. Press the “Equalize Area” Button which will make sure the light intensities of your three samples are the same.
8. In the “more tools” button, click the “Find graph centers.” The closer these lines are together, generally the more similar your sample spectra are to each other.
9. Take a screenshot of your graph and save it with the name of your sample in a place you will be able to find it later. We will return to these graphs in Workshop 4 to publish your results research note on Public Lab.

Follow all the steps of activity 3B for all of your samples. More notes on scanning can be found on this wiki: https://publiclab.org/wiki/oil-testing-kit#Scan)

Once you have finished scanning your samples, take five minutes and brainstorm notes, questions, and ideas on the scanning process. Have participants post these up on the poster board. These will be reviewed compiled on the end of the workshop and posted back to Public Lab

Individually, what do you remember from Workshop 1 about taking multiple samples? Why is this important?

4. Reflection and Wrap up (10 minutes)

Send one person from the entire group to take notes on the poster board while everyone reflects on the day’s activities through the following questions:

• what was hard?
• what was easy?
• what questions are you left with?
• what questions are you inspired to explore?
• any other takeaways you’d like to share with the Public Lab community?

Choose someone from the group to write up their experience as a Public Lab Research Note.

Back to Homebrew Workshops Overview

Workshop 3: Calibration, Sample Prep and Scanning

Drafted by Gretchen Gehrke, Stevie Lewis, Liz Barry.

Why (The Situation): We want to calibrate the spectrometers we built. We want to prepare our oil samples for scanning. We want to learn how to use spectralworkbench.org to scan known samples of oil to test the function of the equipment itself. We want to scan unknown samples and assemble their spectra into sets for comparing and contrasting with known samples to attempt categorization.

When: two and a half (2.5) hour workshop, part of a four-part series.

Where: a room with long tables, chairs, power outlets, and internet connection.

What (the content): calibrate a spectrometer using a fluorescent lightbulb; safely work with ultraviolet (UV) lasers; the features of spectralworkbench.org; troubleshoot your equipment to get a clear high-quality scan;

For What (Achievement Based Objectives):

In completing this four hour workshop, you will:

• Meet others attending the workshop
• Share with each other what motivated you to to participate
• Connect your spectrometer to the computer using the webcam’s cord & USB plug
• Login to spectralworkbench.org
• Look at a flourescent light bulb through the spectrometer
• Use spectralworkbench.org’s interface to set the calibration of the spectrometer
• Squeeze eyedroppers to put oil samples into cuvettes (small tubes with square sides)
• Shine a blue laser through a small container of oil, and capture the spectra the oil emits with your spectrometer and spectralworkbench.org
• Assign your calibration to your scan of oil
• “Read the rainbow” AKA the spectrum of fluorescence produced by oil
• Gain necessary information for use in the 4th and final workshop in this series

Notes for Facilitators:

Materials Needed:

• a large clean space for people to work (long tables work well)
• one computer per person or team constructing the spectrometer
• internet connection (preferably with google chrome)
• a power source that can support all your computers
• newspaper to cover the tables
• 3 poster boards
• markers and pens
• sticky notes
• paper research notes (insert link)
• a plugged in compact fluorescent light bulb
• the Public Lab Spectrometer 3.0 that was constructed in workshop 2
• the oil testing kit add on (if you do not have the add-on kit, including:
  • a 405nm blue/violet laser pen: $3 from Alibaba, or for purchasing singles here
  • cuvettes (1-2 per sample)
  • droppers (1 per sample)
  • protective eye gear
  • gloves
• known samples such as specific oil samples, for example 80W90 and 20W50 (these also come with the kit)

• unknown sample (such as runoff water from the road, black tar like substances found on the beach etc.) Setting up the event:

• Line your tables with newspaper

• Two of the poster boards should be prepared with the words “Notes” “Questions” “Ideas” evenly spaced down the left hand side. Each poster boards will also carry one of the titles: “Calibration” and “Scanning.” Put these on the wall. Leave the third poster board blank and hang it on the wall as well.
• Set up computer stations for each working group along with the spectrometers they built in workshop 2. Provide each group with the oil testing kit add-on or materials in it.
• Put pens, markers, paper research notes and sticky notes around for people to use to take notes.

Workshop Schedule:

1. Introduction (10 minutes)
   1. Who’s in the room
   2. Introduction to the event
2. Calibrating your spectrometer (20 minutes)
   1. Logging into spectralworkbench
   2. The calibration process
3. Sampling (1 hour 30 minutes)
   1. Setting up for safe scanning!
   2. Scanning samples
   3. Creating sets
4. Wrap up (10 minutes)

Workshop Outline

1. Introduction

1.1 Who’s in the room? (10 minutes)

Go around the room, with each person introducing themselves with their name, where they’re from, and the reason they are interested in being here today. If there are really a lot of people, then as a large group say names only, and then break into smaller groups at tables to share reasons, hopes, and expectations for attending.

1. If you have not done so yet, introduce yourself:
   1. why you are interested in this project and
   2. a little bit about Public Lab.
2. Give an overview of the event goals and structure (at the top of this page)
3. Emphasize “the tools, technology and learning that happens here is always under development. One of the major outcomes of the event is to provide constructive feedback on the learning, the activities and the tool we will build in order to improve it for future participants.”
4. Introduce the things in the room:
   1. Highlight the posters, markers and sticky pads available for people to put up their questions, comments, and ideas on as they work through the event.
   2. Identify the paper research notes for those who would like to take in depth notes on their steps for sharing back with the Public Lab community.

2. Calibrating your spectrometer

Needs brief intro

2.1 Logging into spectralworkbench.org

• Ask your group if there’s anyone with a publiclab.org username.
• If no one has one, choose someone to create one for themselves at https://publiclab.org/signup
• Go to spectralworkbench.org and log in. You will be directed through the Publiclab.org site, just keep clicking through until you are back to spectralworkbench.org and your username shows in the upper righthand corner.

2.2 The calibration process

FACTS: each spectrometer has to be calibrated before it is used.

Choose one person at the table to read the following text out loud:

Calibration helps us to make sure the readings of an instrument are consistent. For example, if this were a scale, we would want to make sure that when it read that something weighed two pounds, the item you were weighing was really two pounds. Because we know certain colors always show up at the same place on the nanometer scale (recall from workshop 2), the way we calibrate a spectrometer is by using a known type of light. In this case, we use a compact fluorescent light where the green light wave always peeks at 546 nanometers. By shining the compact fluorescent light into our spectrometer, and graphing the color peaks on SpectralWorkbench.org we can measure our future samples against that known calibration of the compact florescent light. In this way, we know our future scans will be accurate.

Individually, write down any questions you have about these concepts and discuss as a group. Going through the activity may help clarify, and afterwards if you still have questions, you can email plots-spectrometry@googlegroups.com.

Follow the following 4 steps to connect your spectrometer to your computer:

1. Plug the cord into a USB port on your computer
2. In an internet browser (we strongly recommend Chrome for reasons that will later become clear), navigate to http://spectralworkbench.org (make sure you’re logged in! See 2.1)
3. On Spectral Workbench, click “Capture Spectra” (the live capture drop down).
4. Make sure it is working:
   1. If you see black or just a light line of color, you will know you are using the correct camera (i.e. not the built-in one on your computer) and you are ready to proceed.
   2. If you see yourself, you need to switch the input camera by using the button on the right under the picture that says “change camera.”
   3. If you still see yourself, use Chrome’s address bar to “allow” or “enable” the camera on your spectrometer from the browser’s address bar. Once you are seeing the rainbow image from your spectrometer on SpectralWorkbench, you are ready to proceed.

Follow the following 4 steps to take your first scan of a fluorescent lightbulb:

1. Hold the front end of the spectrometer (where the slit is) towards the lit compact fluorescent light bulb. Make sure the color line that appears on your image appears straight, as in the example from the calibration page on SpectralWorkbench.
   1. Troubleshooting information: if the colors are really not straight, you can open up your spectrometer and try to move the angle of the DVD slightly.
2. Once your color line is straight, click the middle of it to position the yellow line directly through it, as in the example.
3. Click the button “begin capture”, and once you do so, you (and perhaps a partner) will need to hold the spectrometer steady.
4. Once you click “begin capture”, you will see a new page where the “live feed” of your spectra is coming through. You may have to work to re-align your equipment to see a clear spectra of all the colors -- then, click the blue “save” on the left of the screen.
5. This will open a new page where you will see your calibration and can fill in some information about it. Title the spectra “CFL Calibration” and click the button “save and calibrate later.”
6. On the new page, click the blue “calibrate” button below your sample. This will walk you through matching your sample (the above bar) to the example scan (the below bar) so the colors line up.
7. Leave these pages open in your browser.

Additional calibration directions can be found on this wiki: http://publiclab.org/wiki/spectral-workbench-calibration

Once you have finished calibrating your spectrometers, take five minutes and brainstorm notes, questions, and ideas on the calibration process. Post these up on the poster board. These will be reviewed and compiled at the end of the workshop and posted back to Public Lab. If you still have questions, you can email plots-spectrometry@googlegroups.com.

------ (10 minute break) ------

3 Sampling

3.1 Setting up for safe scanning! (90 minutes)

YOU MUST REVIEW AND FOLLOW THESE SAFETY GUIDES (10 minutes)

• Do not turn on the laser unless it is in your testing frame and be sure to never look directly at the light. Even looking at the beam indirectly can be damaging to your eye.
• Always wear your safety glasses when working with oil samples, suspected oil samples and the laser.
• The materials that come in the beta kit are hazardous and should only be handled with gloves in a well ventilated area. Here are the Material Safety Data Sheet (MSDS) for each oil type included in the Oil Testing Kit: MSDS_5W30_motor_oil_Hess.pdf, MSDS_20W50_Gulfpride.pdf, MSDS_crude_oil_sweet_Hess.pdf, MSDS_80W90_Lucas.pdf, MSDS_Diesel_fuels_Hess.pdf, MSDS_gasoline_Hess.pdf

3.2 Scanning samples (50 minutes)

The Public Lab community advises to scan each sample 3 times. Following this rule, a rough time estimate would be that a first time user can achieve the scanning of 3 unique samples in an hour. As we learned in Workshop 1 (Experimental Design), taking triplicate samples of each one of your materials will increase the accuracy of your results.

1. Identify which of your oil samples are “knowns” and begin with them.
2. Use the dropper to fill the cuvette with your sample to the line above where it curves in (just over halfway full)
3. Place your sample in the sample holder on your oil testing kit add-on
4. Place the cover over the sample

5. Press and hold the button on the laser.

6. Return to the open “capture” tab on spectral workbench. There you will see your spectra. Ideally, you want your spectra line to fall between 25-75%.

   1. Look at the top line of the graph -- Is it yellow? It turns yellow when your are peaks touching or getting cut off by this line. If the top line is yellow, you need to dim your spectra by using the physical dimmer on the oil testing kit add-on. The higher you pull the dimmer, the less light it lets in.
7. Once you’ve captured a good spectra, click “Save” and title it the name (whatever it is you’re sampling) and the number 1.
8. Leave this tab open and repeat steps 1-5 with the same samples naming the consecutive ones (Sample name)2, and (Sample name)3. For example Diesel1, Deisel2 and Diesel3.

3.3 Creating sets (30 minutes)

1. Once you have all three scans of one sample, click the “Save as a set” button on your most recent scan (Sample3). This will start a page for you where you can add your samples to one graph -- see https://publiclab.org/wiki/spectral-workbench-usage#Sets.
2. On the page that comes up, give some information about the set you’re creating. For example, you’ll want to title it with the sample name for example (Diesel Set for Oil Testing or Unknown Set for Oil Testing).
3. Return to your open tab for the sample2 you’ve just created (sample2) and click the “add to set” button.
4. This will pull up a search bar where you will need to find the set you created for your first sample (in this example we used “Diesel Set for Oil Testing). Find that set, click “Add spectrum to this set”)
5. Do the same for sample1
6. Find the tab that has the set you’re creating, and refresh it. This should show you all three samples on one graph.
7. Press the “Equalize Area” Button which will make sure the light intensities of your three samples are the same.
8. In the “more tools” button, click the “Find graph centers.” The closer these lines are together, generally the more similar your sample spectra are to each other.
9. Take a screenshot of your graph and save it with the name of your sample in a place you will be able to find it later. We will return to these graphs in Workshop 4 to publish your results research note on Public Lab.

Follow all the steps of activity 3B for all of your samples. More notes on scanning can be found on this wiki: https://publiclab.org/wiki/oil-testing-kit#Scan)

Once you have finished scanning your samples, take five minutes and brainstorm notes, questions, and ideas on the scanning process. Have participants post these up on the poster board. These will be reviewed compiled on the end of the workshop and posted back to Public Lab

Individually, what do you remember from Workshop 1 about taking multiple samples? Why is this important?

4. Wrap up

Send one person from the entire group to take notes on the poster board while everyone discusses the following questions: Reflecting on today’s event: what was hard? what was easy? what questions are you left with? what questions are you inspired to explore? any other takeaways you’d like to share with the Public Lab community? Choose someone from the group to write up their experience as a Public Lab Research Note.

Facilitator’s notes for after the event: Compile the notes that were left on the poster boards and your experiences facilitating this event. Post them to the Public Lab wiki and put a link to it on the bottom of this page: https://publiclab.org/wiki/oil-testing-event

Back to Homebrew Workshops Overview

Workshop 3: Calibration, Sample Prep and Scanning

Drafted by Gretchen Gehrke, Stevie Lewis, Liz Barry.

Why (The Situation): We want to calibrate the spectrometers we built. We want to prepare our oil samples for scanning. We want to learn how to use spectralworkbench.org to scan known samples of oil to test the function of the equipment itself. We want to scan unknown samples and assemble their spectra into sets for comparing and contrasting with known samples to attempt categorization.

When: two and a half (2.5) hour workshop, part of a four-part series.

Where: a room with long tables, chairs, power outlets, and internet connection.

What (the content): calibrate a spectrometer using a fluorescent lightbulb; safely work with ultraviolet (UV) lasers; the features of spectralworkbench.org; troubleshoot your equipment to get a clear high-quality scan;

For What (Achievement Based Objectives):

In completing this four hour workshop, you will:

• Meet others attending the workshop
• Share with each other what motivated you to to participate
• Connect your spectrometer to the computer using the webcam’s cord & USB plug
• Login to spectralworkbench.org
• Look at a flourescent light bulb through the spectrometer
• Use spectralworkbench.org’s interface to set the calibration of the spectrometer
• Squeeze eyedroppers to put oil samples into cuvettes (small tubes with square sides)
• Shine a blue laser through a small container of oil, and capture the spectra the oil emits with your spectrometer and spectralworkbench.org
• Assign your calibration to your scan of oil
• “Read the rainbow” AKA the spectrum of fluorescence produced by oil
• Gain necessary information for use in the 4th and final workshop in this series

Notes for Facilitators:

Materials Needed:

a large clean space for people to work (long tables work well) one computer per person or team constructing the spectrometer internet connection (preferably with google chrome) a power source that can support all your computers newspaper to cover the tables 3 poster boards markers and pens sticky notes paper research notes (insert link) a plugged in compact fluorescent light bulb the Public Lab Spectrometer 3.0 that was constructed in workshop 2 the oil testing kit add on (if you do not have the add-on kit, below are the materials in it that you will need for the event: a 405nm blue/violet laser pen: $3 from Alibaba, or for purchasing singles here cuvettes (1-2 per sample) droppers (1 per sample) protective eye gear gloves known samples such as specific oil samples, for example 80W90 and 20W50 (these also come with the kit) unknown sample (such as runoff water from the road, black tar like substances found on the beach etc.) Setting up the event:

Line your tables with newspaper Two of the poster boards should be prepared with the words “Notes” “Questions” “Ideas” evenly spaced down the left hand side. Each poster boards will also carry one of the titles: “Calibration” and “Scanning.” Put these on the wall. Leave the third poster board blank and hang it on the wall as well. Set up computer stations for each working group along with the spectrometers they built in workshop 2. Provide each group with the oil testing kit add-on or materials in it. Put pens, markers, paper research notes and sticky notes around for people to use to take notes.

Workshop Schedule:

1. Introduction (10 minutes)
   1. Who’s in the room
   2. Introduction to the event
2. Calibrating your spectrometer (20 minutes)
   1. Logging into spectralworkbench
   2. The calibration process
3. Sampling (1 hour 30 minutes)
   1. Setting up for safe scanning!
   2. Scanning samples
   3. Creating sets
4. Wrap up (10 minutes)

Workshop Outline

1. Introduction

1.1 Who’s in the room? (10 minutes)

Go around the room, with each person introducing themselves with their name, where they’re from, and the reason they are interested in being here today. If there are really a lot of people, then as a large group say names only, and then break into smaller groups at tables to share reasons, hopes, and expectations for attending.

1. If you have not done so yet, introduce yourself:
   1. why you are interested in this project and
   2. a little bit about Public Lab.
2. Give an overview of the event goals and structure (at the top of this page)
3. Emphasize “the tools, technology and learning that happens here is always under development. One of the major outcomes of the event is to provide constructive feedback on the learning, the activities and the tool we will build in order to improve it for future participants.”
4. Introduce the things in the room:
   1. Highlight the posters, markers and sticky pads available for people to put up their questions, comments, and ideas on as they work through the event.
   2. Identify the paper research notes for those who would like to take in depth notes on their steps for sharing back with the Public Lab community.

2. Calibrating your spectrometer

Needs brief intro

2.1 Logging into spectralworkbench.org

• Ask your group if there’s anyone with a publiclab.org username.
• If no one has one, choose someone to create one for themselves at https://publiclab.org/signup
• Go to spectralworkbench.org and log in. You will be directed through the Publiclab.org site, just keep clicking through until you are back to spectralworkbench.org and your username shows in the upper righthand corner.

2.2 The calibration process

FACTS: each spectrometer has to be calibrated before it is used.

Choose one person at the table to read the following text out loud:

Calibration helps us to make sure the readings of an instrument are consistent. For example, if this were a scale, we would want to make sure that when it read that something weighed two pounds, the item you were weighing was really two pounds. Because we know certain colors always show up at the same place on the nanometer scale (recall from workshop 2), the way we calibrate a spectrometer is by using a known type of light. In this case, we use a compact fluorescent light where the green light wave always peeks at 546 nanometers. By shining the compact fluorescent light into our spectrometer, and graphing the color peaks on SpectralWorkbench.org we can measure our future samples against that known calibration of the compact florescent light. In this way, we know our future scans will be accurate.

Individually, write down any questions you have about these concepts and discuss as a group. Going through the activity may help clarify, and afterwards if you still have questions, you can email plots-spectrometry@googlegroups.com.

Follow the following 4 steps to connect your spectrometer to your computer:

1. Plug the cord into a USB port on your computer
2. In an internet browser (we strongly recommend Chrome for reasons that will later become clear), navigate to http://spectralworkbench.org (make sure you’re logged in! See 2.1)
3. On Spectral Workbench, click “Capture Spectra” (the live capture drop down).
4. Make sure it is working:
   1. If you see black or just a light line of color, you will know you are using the correct camera (i.e. not the built-in one on your computer) and you are ready to proceed.
   2. If you see yourself, you need to switch the input camera by using the button on the right under the picture that says “change camera.”
   3. If you still see yourself, use Chrome’s address bar to “allow” or “enable” the camera on your spectrometer from the browser’s address bar. Once you are seeing the rainbow image from your spectrometer on SpectralWorkbench, you are ready to proceed.

Follow the following 4 steps to take your first scan of a fluorescent lightbulb:

1. Hold the front end of the spectrometer (where the slit is) towards the lit compact fluorescent light bulb. Make sure the color line that appears on your image appears straight, as in the example from the calibration page on SpectralWorkbench.
   1. Troubleshooting information: if the colors are really not straight, you can open up your spectrometer and try to move the angle of the DVD slightly.
2. Once your color line is straight, click the middle of it to position the yellow line directly through it, as in the example.
3. Click the button “begin capture”, and once you do so, you (and perhaps a partner) will need to hold the spectrometer steady.
4. Once you click “begin capture”, you will see a new page where the “live feed” of your spectra is coming through. You may have to work to re-align your equipment to see a clear spectra of all the colors -- then, click the blue “save” on the left of the screen.
5. This will open a new page where you will see your calibration and can fill in some information about it. Title the spectra “CFL Calibration” and click the button “save and calibrate later.”
6. On the new page, click the blue “calibrate” button below your sample. This will walk you through matching your sample (the above bar) to the example scan (the below bar) so the colors line up.
7. Leave these pages open in your browser.

Additional calibration directions can be found on this wiki: http://publiclab.org/wiki/spectral-workbench-calibration

Once you have finished calibrating your spectrometers, take five minutes and brainstorm notes, questions, and ideas on the calibration process. Post these up on the poster board. These will be reviewed and compiled at the end of the workshop and posted back to Public Lab. If you still have questions, you can email plots-spectrometry@googlegroups.com.

------ (10 minute break) ------

3 Sampling

3.1 Setting up for safe scanning! (90 minutes)

YOU MUST REVIEW AND FOLLOW THESE SAFETY GUIDES (10 minutes)

• Do not turn on the laser unless it is in your testing frame and be sure to never look directly at the light. Even looking at the beam indirectly can be damaging to your eye.
• Always wear your safety glasses when working with oil samples, suspected oil samples and the laser.
• The materials that come in the beta kit are hazardous and should only be handled with gloves in a well ventilated area. Here are the Material Safety Data Sheet (MSDS) for each oil type included in the Oil Testing Kit: MSDS_5W30_motor_oil_Hess.pdf, MSDS_20W50_Gulfpride.pdf, MSDS_crude_oil_sweet_Hess.pdf, MSDS_80W90_Lucas.pdf, MSDS_Diesel_fuels_Hess.pdf, MSDS_gasoline_Hess.pdf

3.2 Scanning samples (50 minutes)

The Public Lab community advises to scan each sample 3 times. Following this rule, a rough time estimate would be that a first time user can achieve the scanning of 3 unique samples in an hour. As we learned in Workshop 1 (Experimental Design), taking triplicate samples of each one of your materials will increase the accuracy of your results.

1. Identify which of your oil samples are “knowns” and begin with them.
2. Use the dropper to fill the cuvette with your sample to the line above where it curves in (just over halfway full)
3. Place your sample in the sample holder on your oil testing kit add-on
4. Place the cover over the sample

5. Press and hold the button on the laser.

6. Return to the open “capture” tab on spectral workbench. There you will see your spectra. Ideally, you want your spectra line to fall between 25-75%.

   1. Look at the top line of the graph -- Is it yellow? It turns yellow when your are peaks touching or getting cut off by this line. If the top line is yellow, you need to dim your spectra by using the physical dimmer on the oil testing kit add-on. The higher you pull the dimmer, the less light it lets in.
7. Once you’ve captured a good spectra, click “Save” and title it the name (whatever it is you’re sampling) and the number 1.
8. Leave this tab open and repeat steps 1-5 with the same samples naming the consecutive ones (Sample name)2, and (Sample name)3. For example Diesel1, Deisel2 and Diesel3.

3.3 Creating sets (30 minutes)

1. Once you have all three scans of one sample, click the “Save as a set” button on your most recent scan (Sample3). This will start a page for you where you can add your samples to one graph -- see https://publiclab.org/wiki/spectral-workbench-usage#Sets.
2. On the page that comes up, give some information about the set you’re creating. For example, you’ll want to title it with the sample name for example (Diesel Set for Oil Testing or Unknown Set for Oil Testing).
3. Return to your open tab for the sample2 you’ve just created (sample2) and click the “add to set” button.
4. This will pull up a search bar where you will need to find the set you created for your first sample (in this example we used “Diesel Set for Oil Testing). Find that set, click “Add spectrum to this set”)
5. Do the same for sample1
6. Find the tab that has the set you’re creating, and refresh it. This should show you all three samples on one graph.
7. Press the “Equalize Area” Button which will make sure the light intensities of your three samples are the same.
8. In the “more tools” button, click the “Find graph centers.” The closer these lines are together, generally the more similar your sample spectra are to each other.
9. Take a screenshot of your graph and save it with the name of your sample in a place you will be able to find it later. We will return to these graphs in Workshop 4 to publish your results research note on Public Lab.

Follow all the steps of activity 3B for all of your samples. More notes on scanning can be found on this wiki: https://publiclab.org/wiki/oil-testing-kit#Scan)

Once you have finished scanning your samples, take five minutes and brainstorm notes, questions, and ideas on the scanning process. Have participants post these up on the poster board. These will be reviewed compiled on the end of the workshop and posted back to Public Lab

Individually, what do you remember from Workshop 1 about taking multiple samples? Why is this important?

4. Wrap up

Send one person from the entire group to take notes on the poster board while everyone discusses the following questions: Reflecting on today’s event: what was hard? what was easy? what questions are you left with? what questions are you inspired to explore? any other takeaways you’d like to share with the Public Lab community? Choose someone from the group to write up their experience as a Public Lab Research Note.

Facilitator’s notes for after the event: Compile the notes that were left on the poster boards and your experiences facilitating this event. Post them to the Public Lab wiki and put a link to it on the bottom of this page: https://publiclab.org/wiki/oil-testing-event

Back to Homebrew Workshops Overview

Facilitator’s notes for before the event: Sample Prep (https://publiclab.org/wiki/oil-testing-kit#Collect)

Workshop 3: Calibration, Sample Prep and Scanning

Drafted by Gretchen Gehrke, Stevie Lewis, Liz Barry.

Why (The Situation): We want to calibrate the spectrometers we built. We want to prepare our oil samples for scanning. We want to learn how to use spectralworkbench.org to scan known samples of oil to test the function of the equipment itself. We want to scan unknown samples and assemble their spectra into sets for comparing and contrasting with known samples to attempt categorization.

When: two and a half (2.5) hour workshop, part of a four-part series.

Where: a room with long tables, chairs, power outlets, and internet connection.

What (the content): calibrate a spectrometer using a fluorescent lightbulb; safely work with ultraviolet (UV) lasers; the features of spectralworkbench.org; troubleshoot your equipment to get a clear high-quality scan;

For What (Achievement Based Objectives):

In completing this four hour workshop, you will:

• Meet others attending the workshop
• Share with each other what motivated you to to participate
• Connect your spectrometer to the computer using the webcam’s cord & USB plug
• Login to spectralworkbench.org
• Look at a flourescent light bulb through the spectrometer
• Use spectralworkbench.org’s interface to set the calibration of the spectrometer
• Squeeze eyedroppers to put oil samples into cuvettes (small tubes with square sides)
• Shine a blue laser through a small container of oil, and capture the spectra the oil emits with your spectrometer and spectralworkbench.org
• Assign your calibration to your scan of oil
• “Read the rainbow” AKA the spectrum of fluorescence produced by oil
• Gain necessary information for use in the 4th and final workshop in this series

Notes for Facilitators:

Materials Needed:

a large clean space for people to work (long tables work well) one computer per person or team constructing the spectrometer internet connection (preferably with google chrome) a power source that can support all your computers newspaper to cover the tables 3 poster boards markers and pens sticky notes paper research notes (insert link) a plugged in compact fluorescent light bulb the Public Lab Spectrometer 3.0 that was constructed in workshop 2 the oil testing kit add on (if you do not have the add-on kit, below are the materials in it that you will need for the event: a 405nm blue/violet laser pen: $3 from Alibaba, or for purchasing singles here cuvettes (1-2 per sample) droppers (1 per sample) protective eye gear gloves known samples such as specific oil samples, for example 80W90 and 20W50 (these also come with the kit) unknown sample (such as runoff water from the road, black tar like substances found on the beach etc.) Setting up the event:

Line your tables with newspaper Two of the poster boards should be prepared with the words “Notes” “Questions” “Ideas” evenly spaced down the left hand side. Each poster boards will also carry one of the titles: “Calibration” and “Scanning.” Put these on the wall. Leave the third poster board blank and hang it on the wall as well. Set up computer stations for each working group along with the spectrometers they built in workshop 2. Provide each group with the oil testing kit add-on or materials in it. Put pens, markers, paper research notes and sticky notes around for people to use to take notes.

Workshop Schedule:

1. Introduction (10 minutes)
   1. Who’s in the room
   2. Introduction to the event
2. Calibrating your spectrometer (20 minutes)
   1. Logging into spectralworkbench
   2. The calibration process
3. Sampling (1 hour 30 minutes)
   1. Setting up for safe scanning!
   2. Scanning samples
   3. Creating sets
4. Wrap up (10 minutes)

Workshop Outline

1. Introduction

1.1 Who’s in the room? (10 minutes)

Go around the room, with each person introducing themselves with their name, where they’re from, and the reason they are interested in being here today. If there are really a lot of people, then as a large group say names only, and then break into smaller groups at tables to share reasons, hopes, and expectations for attending.

1. If you have not done so yet, introduce yourself:
   1. why you are interested in this project and
   2. a little bit about Public Lab.
2. Give an overview of the event goals and structure (at the top of this page)
3. Emphasize “the tools, technology and learning that happens here is always under development. One of the major outcomes of the event is to provide constructive feedback on the learning, the activities and the tool we will build in order to improve it for future participants.”
4. Introduce the things in the room:
   1. Highlight the posters, markers and sticky pads available for people to put up their questions, comments, and ideas on as they work through the event.
   2. Identify the paper research notes for those who would like to take in depth notes on their steps for sharing back with the Public Lab community.

2. Calibrating your spectrometer

Needs brief intro

2.1 Logging into spectralworkbench.org

• Ask your group if there’s anyone with a publiclab.org username.
• If no one has one, choose someone to create one for themselves at https://publiclab.org/signup
• Go to spectralworkbench.org and log in. You will be directed through the Publiclab.org site, just keep clicking through until you are back to spectralworkbench.org and your username shows in the upper righthand corner.

2.2 The calibration process

FACTS: each spectrometer has to be calibrated before it is used.

Choose one person at the table to read the following text out loud:

Calibration helps us to make sure the readings of an instrument are consistent. For example, if this were a scale, we would want to make sure that when it read that something weighed two pounds, the item you were weighing was really two pounds. Because we know certain colors always show up at the same place on the nanometer scale (recall from workshop 2), the way we calibrate a spectrometer is by using a known type of light. In this case, we use a compact fluorescent light where the green light wave always peeks at 546 nanometers. By shining the compact fluorescent light into our spectrometer, and graphing the color peaks on SpectralWorkbench.org we can measure our future samples against that known calibration of the compact florescent light. In this way, we know our future scans will be accurate.

Individually, write down any questions you have about these concepts and discuss as a group. Going through the activity may help clarify, and afterwards if you still have questions, you can email plots-spectrometry@googlegroups.com.

Follow the following 4 steps to connect your spectrometer to your computer:

1. Plug the cord into a USB port on your computer
2. In an internet browser (we strongly recommend Chrome for reasons that will later become clear), navigate to http://spectralworkbench.org (make sure you’re logged in! See 2.1)
3. On Spectral Workbench, click “Capture Spectra” (the live capture drop down).
4. Make sure it is working:
   1. If you see black or just a light line of color, you will know you are using the correct camera (i.e. not the built-in one on your computer) and you are ready to proceed.
   2. If you see yourself, you need to switch the input camera by using the button on the right under the picture that says “change camera.”
   3. If you still see yourself, use Chrome’s address bar to “allow” or “enable” the camera on your spectrometer from the browser’s address bar. Once you are seeing the rainbow image from your spectrometer on SpectralWorkbench, you are ready to proceed.

Follow the following 4 steps to take your first scan of a fluorescent lightbulb:

1. Hold the front end of the spectrometer (where the slit is) towards the lit compact fluorescent light bulb. Make sure the color line that appears on your image appears straight, as in the example from the calibration page on SpectralWorkbench.
   1. Troubleshooting information: if the colors are really not straight, you can open up your spectrometer and try to move the angle of the DVD slightly.
2. Once your color line is straight, click the middle of it to position the yellow line directly through it, as in the example.
3. Click the button “begin capture”, and once you do so, you (and perhaps a partner) will need to hold the spectrometer steady.
4. Once you click “begin capture”, you will see a new page where the “live feed” of your spectra is coming through. You may have to work to re-align your equipment to see a clear spectra of all the colors -- then, click the blue “save” on the left of the screen.
5. This will open a new page where you will see your calibration and can fill in some information about it. Title the spectra “CFL Calibration” and click the button “save and calibrate later.”
6. On the new page, click the blue “calibrate” button below your sample. This will walk you through matching your sample (the above bar) to the example scan (the below bar) so the colors line up.
7. Leave these pages open in your browser.

Additional calibration directions can be found on this wiki: http://publiclab.org/wiki/spectral-workbench-calibration

Once you have finished calibrating your spectrometers, take five minutes and brainstorm notes, questions, and ideas on the calibration process. Post these up on the poster board. These will be reviewed and compiled at the end of the workshop and posted back to Public Lab. If you still have questions, you can email plots-spectrometry@googlegroups.com.

------ (10 minute break) ------

3 Sampling

3.1 Setting up for safe scanning! (90 minutes)

YOU MUST REVIEW AND FOLLOW THESE SAFETY GUIDES (10 minutes)

• Do not turn on the laser unless it is in your testing frame and be sure to never look directly at the light. Even looking at the beam indirectly can be damaging to your eye.
• Always wear your safety glasses when working with oil samples, suspected oil samples and the laser.
• The materials that come in the beta kit are hazardous and should only be handled with gloves in a well ventilated area. Here are the Material Safety Data Sheet (MSDS) for each oil type included in the Oil Testing Kit: MSDS_5W30_motor_oil_Hess.pdf, MSDS_20W50_Gulfpride.pdf, MSDS_crude_oil_sweet_Hess.pdf, MSDS_80W90_Lucas.pdf, MSDS_Diesel_fuels_Hess.pdf, MSDS_gasoline_Hess.pdf

3.2 Scanning samples (50 minutes)

FYI You can anticipate getting through 9 scans in an hour. Most people suggest running each sample 3 times, so if you follow this rule, you can anticipate running 3 different samples in an hour. As we learned in Workshop 1 (Experimental Design), taking triplicate samples of each one of your materials will help to ensure accurate results.

1. Identify which of your oil samples are “knowns” and begin with them.
2. Use the dropper to fill the cuvette with your sample to the line above where it curves in (just over halfway full)
3. Place your sample in the sample holder on your oil testing kit add-on
4. Place the cover over the sample

5. Press and hold the button on the laser.

6. Return to the open “capture” tab on spectral workbench. There you will see your spectra. Ideally, you want your spectra line to fall between 25-75%.

   1. Look at the top line of the graph -- Is it yellow? It turns yellow when your are peaks touching or getting cut off by this line. If the top line is yellow, you need to dim your spectra by using the physical dimmer on the oil testing kit add-on. The higher you pull the dimmer, the less light it lets in.
7. Once you’ve captured a good spectra, click “Save” and title it the name (whatever it is you’re sampling) and the number 1.
8. Leave this tab open and repeat steps 1-5 with the same samples naming the consecutive ones (Sample name)2, and (Sample name)3. For example Diesel1, Deisel2 and Diesel3.

3.3 Creating sets (30 minutes)

1. Once you have all three scans of one sample, click the “Save as a set” button on your most recent scan (Sample3). This will start a page for you where you can add your samples to one graph.
2. On the page that comes up, give some information about the set you’re creating. For example, you’ll want to title it with the sample name for example (Diesel Set for Oil Testing or Unknown Set for Oil Testing).
3. Return to your open tab for the sample2 you’ve just created (sample2) and click the “add to set” button.
4. This will pull up a search bar where you will need to find the set you created for your first sample (in this example we used “Diesel Set for Oil Testing). Find that set, click “Add spectrum to this set”)
5. Do the same for sample1
6. Find the tab that has the set you’re creating, and refresh it. This should show you all three samples on one graph.
7. Press the “Equalize Area” Button which will make sure the light intensities of your three samples are the same.
8. In the “more tools” button, click the “Find graph centers.” The closer these lines are together, generally the more similar your sample spectra are to each other.
9. Take a screenshot of your graph and save it with the name of your sample in a place you will be able to find it later. We will return to these graphs in Workshop 4 to publish your results research note on Public Lab.

Follow all the steps of activity 3B for all of your samples. More notes on scanning can be found on this wiki: https://publiclab.org/wiki/oil-testing-kit#Scan)

Once you have finished scanning your samples, take five minutes and brainstorm notes, questions, and ideas on the scanning process. Have participants post these up on the poster board. These will be reviewed compiled on the end of the workshop and posted back to Public Lab

Individually, what do you remember from Workshop 1 about taking multiple samples? Why is this important?

4. Wrap up

Send one person from the entire group to take notes on the poster board while everyone discusses the following questions: Reflecting on today’s event: what was hard? what was easy? what questions are you left with? what questions are you inspired to explore? any other takeaways you’d like to share with the Public Lab community? Choose someone from the group to write up their experience as a Public Lab Research Note.

Facilitator’s notes for after the event: Compile the notes that were left on the poster boards and your experiences facilitating this event. Post them to the Public Lab wiki and put a link to it on the bottom of this page: https://publiclab.org/wiki/oil-testing-event

Follow the following 4 steps to connect your spectrometer to your computer:

1. Plug the cord into a USB port on your computer
2. In an internet browser (we strongly recommend Chrome for reasons that will later become clear), navigate to http://spectralworkbench.org (make sure you’re logged in! See 2.1)

3. On Spectral Workbench, click “Capture Spectra” (the live capture drop down).

4. Make sure it is working:

   1. If you see black or just a light line of color, you will know you are using the correct camera (i.e. not the built-in one on your computer).
   2. If you see yourself, you need to switch the input camera by using the button on the right under the picture that says “change camera.”
   3. If you still see yourself, use Chrome’s address bar to “allow” or “enable” the camera on your spectrometer from the browser’s address bar. Once you’re seeing the rainbow image from your spectrometer on SpectralWorkbench, you are ready to proceed.

*

3. Use of spectrometers in environmental science

3.1 Background reading (45 minutes)

Read these short synopses about how spectrometry has been used in environmental science, and what types of data and advocacy can result from the use of a spectrometer.

One sentence overview: Fluorescence spectroscopy is a widely useful technique. Similarly to how blood or bodily fluids at a crime scene are revealed by shining a UV light, oil also fluoresces. 

from https://publiclab.org/notes/eustatic/4-20-2012/370nm-uv-detection-corexit-dispersed-oil-gulf-coast-beaches

From the texbook, Oil Spill Science and Technology chapters 4, 5, 7, we learn about more about this method to identify oil in the environment:

The combination of spectroscopy and UV lasers is used to identify oil in the environment. Oil has a “unique oil fluorescence spectral signature” (p 171); when ultraviolet light (300 to 355 nm) is shined on petrochemicals, they fluoresce (release) light in visible wavelengths that are specific to the kind of oil it is. Chlorophyll and other biological materials also fluoresce, but (fortunately) at significantly different wavelengths to avoid confusion. 

From the abstract of the 2012 article, “Findings of Persistency of Polycyclic Aromatic Hydrocarbons in Residual Tar Product Sourced from Crude Oil Released during the Deepwater Horizon MC252 Spill of National Significance” produced by James H “Rip” Kirby III, of the University of South Florida Dept of Geology, and also of The Emerald Coast Chapter of Surfrider:

The use of ultraviolet light equipment in the field showed distinct fluorescent responses to illumination by a 370nm UV light source. UV light equipment was found to be very efficient in identifying tar product on the beach for evaluating the visual level of contamination on the beach. Fluorescent responses from tar product found in the field and laboratory created tar product were measured by fluorometry equipment. [link](http://surfrider.org/images/uploads/publications/Corexit_Connections.pdf)

From the 2012 article, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill”, we learn how different methods are used in times of a spill:

This technical article begins by stating how valuable people are in times of disaster: “experienced observers are a spill response’s mainstay.” Since there are few experienced observers available, and the weather and environmental conditions present access challenges to getting a holistic view of the situation, responding to the Deepwater Horizon oil disaster also involved extensive airborne and spaceborne passive and active remote sensing. An airplane carried a Visible/Infrared Spectrometer over the spill to derive oil slick thickness and oil-to-water emulsion ratios. Other equipment on planes and satellites helped extrapolate this understanding to the geographic extent of the entire spill, find the extent of burned oil carried into the air as smoke, and track oil as it sunk to the seafloor. http://www.sciencedirect.com/science/article/pii/S0034425712001563 

There was a January 2016 discussion on the plots-spectrometry mailing list that referenced the 2011 article "Prediction of crude oil properties and chemical composition by means of steady-state and time-resolved fluorescence." by p.3600 of Pantoja, Patricia A., et al. Energy & Fuels 25.8 (2011): 3598-3604:

This graph from the article shows peak shift due to dilution of crude oil:


Public Lab Oil Testing Fellow Ethan has been working on a dilution test to see if diluting oil samples with mineral oil changes their spectrum, and if there's a way to adjust for that. The main gist of this article is about exactly the kind of fluorescence based oil differentiation we're attempting with the Oil Testing Kit. They use a 337nm excitation light source, not the 405nm that Public Lab has been working with, but the peaks in the graph occur at wavelengths longer than 405nm, so some active developers of the spectrometer feel this is not improbably related to the blue=>red shift we're looking for in lighter=>heavier oils:

As a group, discuss these brief summaries and your ideas.

3.2 Defining your problem

Workshop 1, covered what is required in designing a scientific experiment. This workshop has presented how spectroscopy is used to detect oil in the environment. Now we will put these elements together. Below are options for what kinds of experiments are known to be possible with the current state of development on Public Lab’s spectrometer 3.0 and oil testing kit. Discuss as a group which topic you would like to design an experiment around:

• If you have an unknown substance you think might be oil, you can tell if the substance definitely isn’t oil.
• If you have different oil samples, you can compare and contrast these samples (for example diesel, crude, fish, and motor oils).
• You can compare known oil samples with an unknown sample. You should be able to say if your unknown sample has a spectrum similar to or different from your known sample.
• You can compare oily materials to tell if they are similar or different.

After the group has chosen which experiment they would like to design, design a hypothesis (if needed, refresh your memory by reviewing Workshop 1: 2.3 and 2.4). Record the hypothesis on the large chart paper so that everyone can see it. Discuss as a large group.

Return to small groups and work through designing the experiment. Ask yourselves, what needs to be done to test the hypothesis? Record the steps you specify for the experiment on the large chart paper.

3. Use of spectrometers in environmental science

3.1 Background reading (45 minutes)

Read these short synopses about how spectrometry has been used in environmental science, and what types of data and advocacy can result from the use of a spectrometer.

One sentence overview: Fluorescence spectroscopy is a widely useful technique. Similarly to how blood or bodily fluids at a crime scene are revealed by shining a UV light, oil also fluoresces. 

from https://publiclab.org/notes/eustatic/4-20-2012/370nm-uv-detection-corexit-dispersed-oil-gulf-coast-beaches

From the texbook, Oil Spill Science and Technology chapters 4, 5, 7, we learn about more about this method to identify oil in the environment:

The combination of spectroscopy and UV lasers is used to identify oil in the environment. Oil has a “unique oil fluorescence spectral signature” (p 171); when ultraviolet light (300 to 355 nm) is shined on petrochemicals, they fluoresce (release) light in visible wavelengths that are specific to the kind of oil it is. Chlorophyll and other biological materials also fluoresce, but (fortunately) at significantly different wavelengths to avoid confusion. 

From the 2012 article, “Findings of Persistency of Polycyclic Aromatic Hydrocarbons in Residual Tar Product Sourced from Crude Oil Released during the Deepwater Horizon MC252 Spill of National Significance” produced by James H “Rip” Kirby III, of the University of South Florida Dept of Geology, and also of The Emerald Coast Chapter of Surfrider. From the abstract:

The use of ultraviolet light equipment in the field showed distinct fluorescent responses to illumination by a 370nm UV light source. UV light equipment was found to be very efficient in identifying tar product on the beach for evaluating the visual level of contamination on the beach. Fluorescent responses from tar product found in the field and laboratory created tar product were measured by fluorometry equipment. [link](http://surfrider.org/images/uploads/publications/Corexit_Connections.pdf)

From the 2012 article, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill”, we learn how different methods are used in times of a spill:

This technical article begins by stating how valuable people are in times of disaster: “experienced observers are a spill response’s mainstay.” Since there are few experienced observers available, and the weather and environmental conditions present access challenges to getting a holistic view of the situation, responding to the Deepwater Horizon oil disaster also involved extensive airborne and spaceborne passive and active remote sensing. An airplane carried a Visible/Infrared Spectrometer over the spill to derive oil slick thickness and oil-to-water emulsion ratios. Other equipment on planes and satellites helped extrapolate this understanding to the geographic extent of the entire spill, find the extent of burned oil carried into the air as smoke, and track oil as it sunk to the seafloor. http://www.sciencedirect.com/science/article/pii/S0034425712001563 

There was a January 2016 discussion on the plots-spectrometry mailing list that referenced the 2011 article "Prediction of crude oil properties and chemical composition by means of steady-state and time-resolved fluorescence." by p.3600 of Pantoja, Patricia A., et al. Energy & Fuels 25.8 (2011): 3598-3604:

This graph form the article shows peak shift due to dilution of crude oil:

Public Lab Oil Testing Fellow Ethan has been working on a dilution test to see if diluting oil samples with mineral oil changes their spectrum, and if there's a way to adjust for that. The main gist of this article is about exactly the kind of fluorescence based oil differentiation we're attempting with the Oil Testing Kit. They use a 337nm excitation light source, not the 405nm that Public Lab has been working with, but the peaks in the graph occur at wavelengths longer than 405nm, so some active developers of the spectrometer feel this is not improbably related to the blue=>red shift we're looking for in lighter=>heavier oils:

As a group, discuss these brief summaries and your ideas.

2.2 Explore and measure a rainbow (20 minutes)

This activity is taken from physicscentral.com - Aliya Merali

Resources needed: A shallow pan, water, small mirror, flashlight, paper

• Fill the pan with 2 inches of water
• Insert the mirror at an angle and hold the paper above the mirror
• Shine your flashlight at the intersection on the water and the mirror as shown below:

http://physicscentral.com/experiment/physicsathome/rainbow.cfm

Choose another person at each table to read each of the following sentences out loud, pausing frequently for discussion:

The light coming from the flashlight is refracted by water (which means the water is acting like a prism), then reflected by the mirror onto the paper above. 

Question for the group: In what order are the colors displayed? Have you ever seen a rainbow in a different order of colors?

nm = nanometer, which is one billionth of a meter

Rainbows manifest from violet to red. Why? Because colors travel at specific wavelengths: violet has the shortest wavelength we can see, blue is slightly longer, and green slightly longer all the way up to red, which has the longest wavelengths that humans can see. 

Fun fact: wavelengths can be measured … in nanometers! Red is in the 620–750 nm range on the visible spectrum whereas violet is always in the 380–450 nm range. This is what this looks like on a chart:

https://en.wikipedia.org/wiki/Light

Although most of us can see colors with the naked eye, we are not very adept at measuring color or color intensities.

**A spectrometer is a device which splits colors apart, like a prism, and measures the strength of each color.** 

In its simplest form, a spectrometer is an optical device, like a prism, which separates light into separate wavelengths so you can observe and measure the amount of light energy at each frequency. For more information, see the [spectrometer curriculum](/wiki/spectrometer-curriculum)

Look at the example of soybean oil below:

https://spectralworkbench.org/spectrums/44929

Above, notice how much of each color this particular soybean oil is giving off. The peaks and dips in the chart that show the intensity of that particular color. Consider, how can this be useful? For instance, Crude Oil and Synthetic gear oil 80w90 often appear to our human eyes to be the same color, even though they are two different substances. Below, see an example of how a spectrometer can read their spectral signatures more precisely than our human eyes:

2: Learning about Spectrometry

2.1 Seeing in color

Choose one person to read out loud:

_“Light is made up of waves, and we see longer waves and shorter waves as different colors.”_ - Randall Munroe in Thing Explainer: Complicated Stuff in Simple Words, 2015.

When light is bent (AKA refracted), each color will separate out according to its wavelength at a different angle. Let’s explore this idea in the next task.

Workshop Schedule

1. Introduction (20 minutes total)
   1. Who’s in the room (10 minutes)
   2. Introduction to the event (10 minutes) 2: Learning about Spectrometry (25 minutes total)
   3. Seeing in color
   4. Explore and measure a rainbow (activity from physicscentral.com) (20 minutes)
   5. What we can learn from light (5 minutes)
2. Use of spectrometers in environmental science
   1. Background Reading
   2. Defining your problem
3. Building your Spectrometer (1 hour 20 minutes)
   1. Tools you will need
4. Reflection and wrap-up (10 minutes)

Workshop Outline

1. Introduction

1.1 Who is here today?

As a whole group, take turns introducing yourself by saying your name, where you’re from, and your reason for coming to the workshop today.

1.2 What are we doing today?

Take a minute to read through the achievement based objectives on page 1 of your handout.

1. If you have not done so yet, introduce yourself:
   1. why you are interested in this project, and
   2. a little bit about Public Lab.
2. Give an overview of the event goals and structure (at the top of this page)
3. Emphasize “the tools, technology and learning that happens here is always under development. One of the major outcomes of the event is to provide constructive feedback on the learning, the activities and the tool we will build in order to improve it for future participants.”
4. Introduce the things in the room:
   1. Highlight the posters, markers and sticky pads available for people to put up their questions, comments, and ideas on as they work through the event.
   2. Identify the paper research notes for those who would like to take in depth notes on their steps for sharing back with the Public Lab community.

Workshop Schedule

1. Introduction (20 minutes total)
   1. Who’s in the room (10 minutes)
   2. Introduction to the event (10 minutes) 2: Learning about Spectrometry (25 minutes total)
   3. Seeing in color
   4. Explore and measure a rainbow (activity from physicscentral.com) (20 minutes)
   5. What we can learn from light (5 minutes)
2. Use of spectrometers in environmental science
   1. Background Reading
   2. Defining your problem
3. Building your Spectrometer (1 hour 20 minutes)
   1. Tools you will need
4. Reflection and wrap-up (10 minutes)

Workshop Outline

1. Introduction

1.1 Who is here today?

As a whole group, take turns introducing yourself by saying your name, where you’re from, and your reason for coming to the workshop today.

*

1.2 What are we doing today?

Facilitator’s speaking notes: Take a minute to read through the achievement based objectives on page 1 of your handout.

If you have not done so yet, introduce yourself: why you are interested in this project, and a little bit about Public Lab. Give an overview of the event goals and structure (at the top of this page) Emphasize “the tools, technology and learning that happens here is always under development. One of the major outcomes of the event is to provide constructive feedback on the learning, the activities and the tool we will build in order to improve it for future participants.” Introduce the things in the room: Highlight the posters, markers and sticky pads available for people to put up their questions, comments, and ideas on as they work through the event. Identify the paper research notes for those who would like to take in depth notes on their steps for sharing back with the Public Lab community.

Back to Homebrew Workshops Overview

Workshop 2: Spectrometry & Building a Spectrometer

Drafted by Gretchen Gehrke, Stevie Lewis, Liz Barry.

Why (The Situation): We want to learn what spectroscopy is, and we want to understand the purpose spectroscopy serves in environmental science. We want to join the development community around Public Lab’s prototype spectrometer and work toward realizing its promise of collecting data that will be useful in pollution cases.

When: 2.5 hour workshop, part of a four-part series.

Where: a room with long tables, chairs, power outlets, water source, and internet connection.

What (the Content): what kinds of things can be learned from light; how to measure a rainbow; what the unit “nanometer” refers to; the working parts of a spectrometer; how to position a sample in front of the spectrometer.

For What (Achievement Based Objectives): In completing this four hour workshop, you will:

• Meet others attending the workshop
• Share with each other what motivated you to to participate
• Read 4 brief stories from spectrometry experiments in environmental science
• Measure a rainbow (in nanometers!)
• Study what wavelengths belong to which colors
• Fold paper pieces into a working spectrometer
• Fold paper pieces into a holder for lasers and sample containers
• Critically review the documentation you were provided -- what worked, what didn’t?
• Post your critical review as a research note on publiclab.org

Notes for Facilitators:

Estimated Time: 2h 30min.

Materials Needed:

• large clean space for people to build their kits (long tables work well)
• 2 poster boards one with the words “Notes” “Questions” “Ideas” evenly spaced down the left hand side.
• markers and pens
• sticky notes
• paper research notes https://i.publiclab.org/system/images/photos/000/005/815/original/paper-research-note.pdf
• a shallow pan
• water (enough to fill the pan with 2in of water)
• a small mirror
• a flashlight
• a piece of white paper
• one computer per person or team constructing the spectrometer
• internet connection
• a power source that can support all your computers
• one Public Lab Spectrometer (version 3.0) and oil testing kit addition per person or team
  • The oil testing kit add-on pieces can be found on these download files that you can print out on heavy card stock or a cereal box and cut the pieces out.
  • (https://i.publiclab.org/system/images/photos/000/009/589/original/all-together-9-8.511.pdf),
  • here (https://i.publiclab.org/system/images/photos/000/011/023/original/light-flap.svg)
  • and here (https://i.publiclab.org/system/images/photos/000/013/171/original/all-together-10-2.pdf), a camera (to document)

Setting up the event:

• Set your tables up near a power source that can be used by all the computers
• Put up the 2 poster boards on the wall. One poster board will be blank, on the other, put the words “Notes, Questions and Ideas” evenly spaced down the left hand side.
• Put post-its, markers and pens on each table.
• Put copies of the hand written research notes on each table for people to include more in depth information on what participants explored.
• Set one Spectrometer kit, one Oil Testing Kit (or the supplies for these), and one computer, on each station people will work from. (Suggested group size is 2-3 people -- you can divide your chairs, tables and participants up accordingly)

Workshop Schedule

1. Introduction (20 minutes total)
   1. Who’s in the room (10 minutes)
   2. Introduction to the event (10 minutes) 2: Learning about Spectrometry (25 minutes total)
   3. Seeing in color
   4. Explore and measure a rainbow (activity from physicscentral.com) (20 minutes)
   5. What we can learn from light (5 minutes)

2. Use of spectrometers in environmental science

   1. Background Reading

3. Building your Spectrometer (1 hour 20 minutes)

4. Reflection and wrap-up (10 minutes)

Back to Homebrew Workshops Overview

Workshop 1: Designing an experiment

Drafted by Gretchen Gehrke, Stevie Lewis, Liz Barry.

Why (the Situation): In order to confidently answer the questions we have about our environment, we want to learn how to structure our questions, use DIY tools, conduct experiments following the scientific method, and understand the capabilities and limitations of our data.

When: a 3.5 hour workshop in four sections, with a ten minute break after each section.

Where: a room with tables and chairs, with participants sitting in small groups.

What (the content): The basics of designing an effective experiment; transforming guesses into testable hypotheses; assessing precision and reproducibility; interpreting data based on data quality.

For What (Achievement Based Objectives): By the end of this workshop, you will have:

• Met others attending the workshop
• Shared with each other what motivated you to to participate
• Written down your expectations for your time
• Discussed examples of scientific questions that interest you in small groups
• Read about the elements needed for good experimental design
• Noted the important points of designing a clear experiment
• Drafted your own questions and transform them into hypotheses
• Explored the concept of proof versus likelihood (facilitated discussion)
• Discussed the importance of precision, resolution, and accuracy in any data set
• Reflected upon how experimentation relates to your own interests/work

Notes for Facilitators:

Materials Needed:

• Markers, pens
• blank 8.5”x11” printer paper
• blank index cards
• blank large chart paper
• tape
• printed copies of this handout

Setting up the event:

• Write out the 7 steps of the scientific method written out on index cards, one set per table in scattered order:
  • asking a question
  • gathering background information
  • developing a hypothesis
  • conducting an experiment to test the hypothesis
  • analyzing the experimental results
  • communicating the experimental results
  • retesting the hypothesis (or a new one, if necessary)

Workshop Schedule:

1. Introduction
   1. Who is here today? (20 minutes)
   2. Introductions among tables (10 minutes)
   3. Everyone asks questions, but how?
   4. Step by step
2. Developing hypotheses
   1. From observing to questioning
   2. Evidence versus proof
   3. Let’s turn guesses into testable hypotheses
   4. Turn your own question into a hypothesis
3. Experiment design -- Testing your hypotheses
   1. Address the question
   2. Concept of using a known sample as a baseline for identifying unknowns
   3. Precision and resolution, Part 1
   4. Precision and resolution, Part 2
   5. Reproducibility
4. Wrap up / Congrats you made it!
   1. Relating all this to our real lives

Workshop Outline

1. Introduction

1.1 Who is here today?

As a whole group, take turns introducing yourself by saying your name, where you’re from, and your reason for coming to the workshop today.

1.2 What are we doing today?

Take a minute to read through the achievement based objectives on page 1 of your handout, and then each table should choose one person to read through the following out loud:

You may have heard people talk about the Scientific Method. The Scientific Method is a general guideline for the steps to take in order to answer a question that is based on an observation. Historically, the scientific method originated as a knowledge production technique, and it endures into the present day as a technique you can potentially use in your work, in your community, to help take control of a situation.

1.3 Everyone asks questions, but how?

Think of a situation in which have you wondered about something (how it works, why it exists, etc). How have you tried to answer your question? Think about the specific steps you have taken, and write them down.

Take five (5) minutes to make notes on your own paper, summarizing your individual thoughts as a five-step process. Share with your table. Next, each table will make a brief poster presentation back to the whole group. Finally, discuss your perceptions of how the EPA, or even law enforcement, goes about answering questions.

1.4 Step by step

Look at the index cards on your table, and notice that they are in no particular order.

There are seven basic steps listed for what is commonly understood as the “scientific method”. It is important to note that this is not a linear, one-time series of steps, but rather, it is cyclical. For example, a given hypothesis might require several different experiments to adequately test that hypothesis, or test results may indicate that the original hypothesis is false and you need to develop a new hypothesis.

Each step in the basic scientific method is important and challenging, and deserves special attention. In this workshop we are going to focus mostly on developing a hypothesis, and designing an experiment to test the hypothesis. During this series of workshops, you will conduct an experiment, and by the end, be able to analyze and communicate your results.

A couple of important points that we want to highlight that are crucial to scientific inquiry and experimental design are: 

1. the concept of proof versus likelihood, and 
2. the importance of knowing your analyses’ precision. 

We’ll cover these topics in the following sections.

Take fifteen (15) minutes to work through the following prompt as a group:

In what order would you imagine proceeding through these steps? Place them in order, then discuss:

• Would an experiment be a straight path through these steps?
• Would you repeat certain parts, or occasionally take one out of order?
• Would you ever need to repeat the entire sequence?
• Why?

Return to what you wrote down during the previous activity (1.3) about what steps you took to answer your own question. Based on this new knowledge, name 2 or 3 things you might change about your original ideas.

Send one representative from your group to go up to the wall and tape your steps in order. Each group should line up their rows to facilitate comparison; discuss.

------ (10 minute break) ------

2. Developing Hypotheses

2.1 From observing to questioning

In this task, we are going to come up with research questions for our own observations. Choose one person at the table to read the following text out loud:

Before developing a hypothesis, the very first step in a scientific inquiry is asking a question. Scientific questions usually arise from observations. For example, we might observe that “The sky is blue,” which prompts us to wonder, “Why is the sky blue?” We might notice that “A lot of kids have asthma here,” which compels us to ask, “Why do so many kids have asthma in this neighborhood?” If we notice that “this substance looks like oil,” we ask, “Could this be oil?” These are all good questions based on an initial observation.

On a large piece of paper, draw a vertical line through the middle to make two columns, AKA a “T Chart”:

• Label the top of the left-hand column “Observations”
• Label the top of the right-hand column “Research Questions”

Individually, on sticky notes:

• Write observations that you have had about your yard, your neighborhood, your landscape, local industrial activity, etc. One per note.
• For each observation, take another sticky note & write a research question based on it.

After a few minutes, everyone can put up their sticky notes up on the “T chart” in the appropriate columns.

Review similarities and differences in the framing of research questions, and make a presentation about this to the whole group.

2.2 Evidence versus proof

Choose one person at the table to read the following text out loud:

A key difference between science and mathematics is that there is no such thing as absolute proof in science.

In mathematics, you can have a proven theorem because you are dealing with a closed system where all of the information is available and controlled, and the proof is final. As a result, there can be a binary, “yes or no”, “proven or disproven” set of logic in math.

In science, we don’t have the luxury of an absolute proof because not all of the information is known. We discover new relevant information constantly, and never deal with a truly closed circuit where all information is known. In science, knowledge is tentative, based on the information available, and we gather evidence that suggests **a likelihood** that something is true.

Everything in science is tentative, and is based on the best available evidence, but cannot be based on absolute proof. This is extremely important for our experimental design and how we talk about results.

Quick group check in: What is the difference between the kind of answers that are possible to arrive at for questions in the domain of math versus the domain of science?

Based on what we just read, we can begin designing our experiments with the understanding that any answers we reach through scientific research will be evidence-based but not an absolute truth. The basic sequence is to

Make an observation
Ask a related question
From that question, develop a hypothesis that can be tested.

What is a hypothesis? Your hypothesis is an educated guess about the answer to your question, but is different from a basic guess in two important ways: first, a hypothesis is based upon existing evidence (albeit a limited amount), and second, a hypothesis can be tested such that new evidence can be gathered that directly supports or refutes the hypothesis.

The steps for turning a question into a hypothesis are:

Starting with your observation, get as specific as possible (or as specific as you’d like to be, based on the scope of your research).

With your specific observation, brainstorm ideas that could be the cause of what you are observing. This is your “basic guess.” 

Make sure you have some evidence, or there is existing information, that supports your guess. If there is not, then familiarize yourself with the information about the issue that is available, and adjust your guess/idea accordingly. This saves you time by helping you develop a more likely hypothesis. 

To turn your guess into a real hypothesis, you must have a testable statement, with tests that can be observed and measured, and/or compared against a “known” value or entity. This often will be in the form of “X is more than/less than/similar to Y”, where X and Y can be observed and compared, or one of the two is already “known.” 

Here is an example of turning a guess into a hypothesis:

Observation: The sidewalks on this tree-lined street are cracked and bumpy. Get more specific Revised Observation: The sidewalks on this tree-lined street are more cracked and bumpy than the sidewalks on the street without trees. Good. Now guess why that is the case. Guess: I think trees made sidewalks bumpy. Make it more specific and testable Hypothesis: I think tree roots grow under sidewalks, and as they get bigger, they can push up the cement sidewalks. Thus, sidewalks near bigger trees are likely to have more cracked and bumpy sidewalks than sidewalks on streets with small trees or without trees.

2.3 Let’s turn guesses into testable hypotheses

Individually, turn these guesses into testable hypotheses:

Guess: Cats like wet cat food.
Hypothesis: _______________________________

Guess: Breathing fumes from cleaning products is bad.
Hypothesis: _______________________________

Guess: Strip mining damages the environment.
Hypothesis: _______________________________

2.4 Turn your own question into a hypothesis

Each person take a look back at the observations you made in section 2.1.

• Chose one of your questions, transform it into a hypothesis, and write your hypothesis on a blank index card.
• In small groups, talk about how testing this hypothesis might or might not be helpful in your own work or pursuits.
• Consider kinds of tests are possible for this question. What tests or data are available? Which ones are used by environmental regulators?
• Write out variations on your question, like "in a worst case scenario, like a windy day" or "measure particulates" vs. "measure PM2.5" and try out "daily average" vs. "on a bad day".

------ (10 minute break) ------

3. Experimental Design – Testing your Hypothesis

3.1 Address the question

Individually, take five or ten minutes to read the following text:

The first step in designing your experiment is to make sure that it will allow you to address your hypothesis. 

Your goal is to gather evidence that either directly supports or directly refutes your hypothesis, so the more specific your hypothesis is, the more tailored and efficient your experiment can and should be, and the more clearly it can answer your question. Broader questions and hypotheses can be very useful and provide a wealth of evidence, but do require more comprehensive investigations. 

For example, with a broad hypothesis such as _“I think tomato plants grow well in sunlight,”_ your experiment will have to encompass several varieties of tomato plants in several different potential growing conditions (e.g. different kinds of soil and watering patterns), and different sunlight exposures, and you would have to evaluate different aspects of growth (growth rate, fruit abundance, fruit quality, etc). 

If your hypothesis were more specific, such as _“I think beefsteak tomato plants growing in sandy soils under a variety of watering conditions grow faster in the sun than in the shade,”_ then your experiment need only include a few types of beefsteak tomato plants growing in one type of soil under either sunny or shady conditions, with variable watering patterns. 

Take into consideration that the second experimental design would be inadequate to address the first hypothesis, while the first experimental design would be excessive to address the second hypothesis. The second experiment isolates one difference between the two scenarios -- it's a **comparison** -- which is an easier type of experiment.

As a group, discuss:

• Would it make sense for either of the two hypotheses written above to test eggplant growth in addition to tomato plant growth?
• Would it make sense to test growth under drought conditions?
• Would it make sense to test tomato plant growth in predominantly cloudy conditions?

Each person should individually write:

• one alternative hypothesis with experimental design that expands the scope of the scientific inquiry.
• one alternative hypothesis with experimental design that narrows the scope of the scientific inquiry.

As a group, go around and have each person can share how they expanded or narrowed the inquiry. Discuss what kind of answer you can expect from the modified question.

3.2 Concept of using a known sample as a baseline for identifying unknowns

Each table should choose one person to read through the following text out loud:

In many scientific inquiries, identification or classification of an unknown object or compound is accomplished through comparison with known compounds. 

When designing an experiment to identify or classify unknown objects or compounds, you should choose relevant known samples against which to compare. _“How will I know what’s relevant?”,_ you might ask. Well, if the question is _“Is this mucky stuff actually oil?”,_ then obtain known samples of the oil carried on nearby train tracks, pipelines, etc, as well as commonly available consumer oils such as for vehicles or machinery. Once you have relevant known compounds available, you can assess your unknown compound through comparing similarities and differences to the “knowns”. You need to analyze known compounds to demonstrate that your method is valid and is able to correctly identify, classify, or quantify the known sample. 

If your method can’t tell that oil is oil, your method is not valid (for using to identify anything else).

The assessment of whether a compound is ***similar enough*** to a known compound that you conclude they are in the same category, will depend on your method’s precision and resolution, which are discussed below. 

As a group, discuss:

• When have you been faced with an unknown substance and gone about trying to figure out what it was?
• What other thoughts and ideas do these concepts bring up for you, especially related to unknown substances?

3.3 Precision and Resolution Part 1

Choose one person at the table to read the following text out loud:

In scientific experiments, we are assessing the likelihood of an assertion to be true, such as whether an unknown oily residue is in a certain class of oil. To do this, we have to know *how well we know* our own data. That knowledge depends on accuracy, precision, and resolution.

Each table should choose one person to read the following situation out loud:

mg = milligram, which is one thousandth of a gram;
L = liter.
Units of mg/L read as “milligrams per liter” are a measure of concentration.

You are interested in whether or not an industrial facility violated their permit by discharging more than 1.8 mg/L ammonia in their effluent. 

* Let’s say your instrument measured 2 mg/L ammonia, but the resolution of your instrument is 1 mg/L, meaning that the only readings you can obtain are 0, 1, 2, 3… mg/L. Can you be certain that your measurement of 2 mg/L is definitely higher than 1.8 mg/L? Could the true value of your measurement be 1.6 mg/L and still read as 2 mg/L? _(pause for discussion)_
* What if your tool could reliably measure 0.1 mg/L differences, and using it, you took three measurements which were 1.6, 2.2, and 1.8 mg/L. How well do you know what the “true” value is? _(pause for discussion)_
* What if your three measurements were 1.9, 1.8, and 1.9 mg/L? How well do you know what the “true” value of those measurements is? _(pause for discussion)_

Each table should choose another person to continue reading:

The example above illustrates that your ability to evaluate your own data depends on:

* knowing your instrument’s resolution (resolution means the smallest distinguishable difference from a given value)
* knowing your method’s precision (precision is the variability in values recorded for a given true value)
* knowing your method’s accuracy (accuracy means how close a measured value is to the true value, often based on the mean of several measurements) 

The numbers or values that our equipment give us have a specific relationship to reality, and how well we can describe that relationship is dictated by resolution, precision, and accuracy. Describing your data’s accuracy*, precision, and resolution are part of** determining the probability that your original assertion is true. 

_*Note: that in the example above, it is assumed that your instrument was reading fairly accurate results because it had been appropriately calibrated. We will discuss calibration more in a future workshop._

_**Note: There are several factors that will influence probability, and especially in larger data sets, more statistical analyses are needed to adequately describe the data. In this workshop we are focusing more conceptually rather than mathematically. If you want to do more statistics, here is a useful introductory guide for statistical analyses: http://www.robertniles.com/stats/._

3.4 Precision and Resolution Part 2

Everyone get a piece of paper and a writing implement, and get ready to draw!

Draw a few visual representations to describe what we learned above about accuracy, precision, and resolution. Draw a representation of measurements with:

• high precision but low accuracy
• low precision but high accuracy
• low precision and low accuracy
• high precision and high accuracy
• high precision but low resolution
• low precision but high resolution

Share your drawings with your group members and discuss them.

Next, as a table, refresh your memories about the ammonia concentrations measured in the effluent of the industrial facility (above).

Draw representations of the data from the example to explain whether or not you are able to determine if the discharge is above the 1.8 mg/L ammonia limit for each scenario listed (and remember to assume that your measurements are accurate).

3.5 Reproducibility

Each table should choose one person to read through the following out loud:

To demonstrate the validity of your experiment and your results, you must be able to show that you can achieve same results multiple times, and that another person could achieve the same results by following your protocol. This reproducibility of results is important for three reasons: 

1. to help you assess your own precision, as discussed above, 
2. to ensure that you provide enough information for other people to replicate your experiment and thus grow scientific knowledge and capacity, and 
3. to demonstrate the validity of the data by asserting that it is reliably reproducible. 

In cases of scientific fraud that have occurred (thankfully rarely), they have most often been caught by another researcher attempting to reproduce an experiment’s results, and alerting people that the initial results must have been falsified. 

In Workshop 3, we will follow procedures to take each oil spectrum three times. By virtue of posting our oil spectra freely and openly to the Web, we are contributing to a massive body of experimental results, and both demonstrating and evaluating the reproducibility of those results. 

Individually, and then as a group:

Think ahead towards future research projects you might have in mind. When do you think you might have to be aware of resolution, precision, and accuracy?

------ (10 minute break) ------

4. Wrap-up / Congrats we made it!

4.1 Relating all this to our real lives

Each table should choose one person to read through the following out loud:

In our daily lives, we make observations constantly. Think about the observations you listed in section 2.1, and others that cross your mind: 

* Have you ever noticed that grass pops up through cracks in the sidewalk in some places but not others? 
* Have you ever been struck by an odd smell in a new house or new car? 

Your take home assignment, should you choose to accept it, is to think of something that you have observed in an environment that matters to you. Write down your observation, and come up with a hypothesis that could be tested, or at least some baseline ideas that could become hypotheses after reading relevant background information. Remember that your hypothesis must be testable, and that the outcomes of your test will offer a likelihood of an answer, but not definitive proof. For the hypothesis that you create, define how precisely you are going to need to know your answer. Also define the purpose(s) you intend to use the data you collect for.

Congratulations! You have embarked upon the process of scientific inquiry!

Back to Homebrew Workshops Overview

Workshop 1: Designing an experiment

Drafted by Gretchen Gehrke, Stevie Lewis, Liz Barry.

Why (the Situation): In order to confidently answer the questions we have about our environment, we want to learn how to structure our questions, use DIY tools, conduct experiments following the scientific method, and understand the capabilities and limitations of our data.

When: a 3.5 hour workshop in four sections, with a ten minute break after each section.

Where: a room with tables and chairs, with participants sitting in small groups.

What (the content): The basics of designing an effective experiment; transforming guesses into testable hypotheses; assessing precision and reproducibility; interpreting data based on data quality.

For What (Achievement Based Objectives): By the end of this workshop, you will have:

• Met others attending the workshop
• Shared with each other what motivated you to to participate
• Written down your expectations for your time
• Discussed examples of scientific questions that interest you in small groups
• Read about the elements needed for good experimental design
• Noted the important points of designing a clear experiment
• Drafted your own questions and transform them into hypotheses
• Explored the concept of proof versus likelihood (facilitated discussion)
• Discussed the importance of precision, resolution, and accuracy in any data set
• Reflected upon how experimentation relates to your own interests/work

Notes for Facilitators:

Materials Needed:

• Markers, pens
• blank 8.5”x11” printer paper
• blank index cards
• blank large chart paper
• tape
• printed copies of this handout

Setting up the event:

• Write out the 7 steps of the scientific method written out on index cards, one set per table in scattered order:
  • asking a question
  • gathering background information
  • developing a hypothesis
  • conducting an experiment to test the hypothesis
  • analyzing the experimental results
  • communicating the experimental results
  • retesting the hypothesis (or a new one, if necessary)

Workshop Schedule:

1. Introduction
   1. Who is here today? (20 minutes)
   2. Introductions among tables (10 minutes)
   3. Everyone asks questions, but how?
   4. Step by step
2. Developing hypotheses
   1. From observing to questioning
   2. Evidence versus proof
   3. Let’s turn guesses into testable hypotheses
   4. Turn your own question into a hypothesis
3. Experiment design -- Testing your hypotheses
   1. Address the question
   2. Concept of using a known sample as a baseline for identifying unknowns
   3. Precision and resolution, Part 1
   4. Precision and resolution, Part 2
   5. Reproducibility
4. Wrap up / Congrats you made it!
   1. Relating all this to our real lives

Workshop Outline

1. Introduction

1.1 Who is here today?

As a whole group, take turns introducing yourself by saying your name, where you’re from, and your reason for coming to the workshop today.

1.2 What are we doing today?

Take a minute to read through the achievement based objectives on page 1 of your handout, and then each table should choose one person to read through the following out loud:

You may have heard people talk about the Scientific Method. The Scientific Method is a general guideline for the steps to take in order to answer a question that is based on an observation. Historically, the scientific method originated as a knowledge production technique, and it endures into the present day as a technique you can potentially use in your work, in your community, to help take control of a situation.

1.3 Everyone asks questions, but how?

Think of a situation in which have you wondered about something (how it works, why it exists, etc). How have you tried to answer your question? Think about the specific steps you have taken, and write them down.

Take five (5) minutes to make notes on your own paper, summarizing your individual thoughts as a five-step process. Share with your table. Next, each table will make a brief poster presentation back to the whole group. Finally, discuss your perceptions of how the EPA, or even law enforcement, goes about answering questions.

1.4 Step by step

Look at the index cards on your table, and notice that they are in no particular order.

There are seven basic steps listed for what is commonly understood as the “scientific method”. It is important to note that this is not a linear, one-time series of steps, but rather, it is cyclical. For example, a given hypothesis might require several different experiments to adequately test that hypothesis, or test results may indicate that the original hypothesis is false and you need to develop a new hypothesis.

Each step in the basic scientific method is important and challenging, and deserves special attention. In this workshop we are going to focus mostly on developing a hypothesis, and designing an experiment to test the hypothesis. During this series of workshops, you will conduct an experiment, and by the end, be able to analyze and communicate your results.

A couple of important points that we want to highlight that are crucial to scientific inquiry and experimental design are: 

1. the concept of proof versus likelihood, and 
2. the importance of knowing your analyses’ precision. 

We’ll cover these topics in the following sections.

Take fifteen (15) minutes to work through the following prompt as a group:

In what order would you imagine proceeding through these steps? Place them in order, then discuss:

• Would an experiment be a straight path through these steps?
• Would you repeat certain parts, or occasionally take one out of order?
• Would you ever need to repeat the entire sequence?
• Why?

Return to what you wrote down during the previous activity (1.3) about what steps you took to answer your own question. Based on this new knowledge, name 2 or 3 things you might change about your original ideas.

Send one representative from your group to go up to the wall and tape your steps in order. Each group should line up their rows to facilitate comparison; discuss.

------ (10 minute break) ------

2. Developing Hypotheses

2.1 From observing to questioning

In this task, we are going to come up with research questions for our own observations. Choose one person at the table to read the following text out loud:

Before developing a hypothesis, the very first step in a scientific inquiry is asking a question. Scientific questions usually arise from observations. For example, we might observe that “The sky is blue,” which prompts us to wonder, “Why is the sky blue?” We might notice that “A lot of kids have asthma here,” which compels us to ask, “Why do so many kids have asthma in this neighborhood?” If we notice that “this substance looks like oil,” we ask, “Could this be oil?” These are all good questions based on an initial observation.

On a large piece of paper, draw a vertical line through the middle to make two columns, AKA a “T Chart”:

• Label the top of the left-hand column “Observations”
• Label the top of the right-hand column “Research Questions”

Individually, on sticky notes:

• Write observations that you have had about your yard, your neighborhood, your landscape, local industrial activity, etc. One per note.
• For each observation, take another sticky note & write a research question based on it.

After a few minutes, everyone can put up their sticky notes up on the “T chart” in the appropriate columns.

Review similarities and differences in the framing of research questions, and make a presentation about this to the whole group.

2.2 Evidence versus proof

Choose one person at the table to read the following text out loud:

A key difference between science and mathematics is that there is no such thing as absolute proof in science.

In mathematics, you can have a proven theorem because you are dealing with a closed system where all of the information is available and controlled, and the proof is final. As a result, there can be a binary, “yes or no”, “proven or disproven” set of logic in math.

In science, we don’t have the luxury of an absolute proof because not all of the information is known. We discover new relevant information constantly, and never deal with a truly closed circuit where all information is known. In science, knowledge is tentative, based on the information available, and we gather evidence that suggests **a likelihood** that something is true.

Everything in science is tentative, and is based on the best available evidence, but cannot be based on absolute proof. This is extremely important for our experimental design and how we talk about results.

Quick group check in: What is the difference between the kind of answers that are possible to arrive at for questions in the domain of math versus the domain of science?

Based on what we just read, we can begin designing our experiments with the understanding that any answers we reach through scientific research will be evidence-based but not an absolute truth. The basic sequence is to

Make an observation
Ask a related question
From that question, develop a hypothesis that can be tested.

What is a hypothesis? Your hypothesis is an educated guess about the answer to your question, but is different from a basic guess in two important ways: first, a hypothesis is based upon existing evidence (albeit a limited amount), and second, a hypothesis can be tested such that new evidence can be gathered that directly supports or refutes the hypothesis.

The steps for turning a question into a hypothesis are:

Starting with your observation, get as specific as possible (or as specific as you’d like to be, based on the scope of your research).

With your specific observation, brainstorm ideas that could be the cause of what you are observing. This is your “basic guess.” 

Make sure you have some evidence, or there is existing information, that supports your guess. If there is not, then familiarize yourself with the information about the issue that is available, and adjust your guess/idea accordingly. This saves you time by helping you develop a more likely hypothesis. 

To turn your guess into a real hypothesis, you must have a testable statement, with tests that can be observed and measured, and/or compared against a “known” value or entity. This often will be in the form of “X is more than/less than/similar to Y”, where X and Y can be observed and compared, or one of the two is already “known.” 

Here is an example of turning a guess into a hypothesis:

Observation: The sidewalks on this tree-lined street are cracked and bumpy. Get more specific Revised Observation: The sidewalks on this tree-lined street are more cracked and bumpy than the sidewalks on the street without trees. Good. Now guess why that is the case. Guess: I think trees made sidewalks bumpy. Make it more specific and testable Hypothesis: I think tree roots grow under sidewalks, and as they get bigger, they can push up the cement sidewalks. Thus, sidewalks near bigger trees are likely to have more cracked and bumpy sidewalks than sidewalks on streets with small trees or without trees.

2.3 Let’s turn guesses into testable hypotheses

Individually, turn these guesses into testable hypotheses:

Guess: Cats like wet cat food.
Hypothesis: _______________________________

Guess: Breathing fumes from cleaning products is bad.
Hypothesis: _______________________________

Guess: Strip mining damages the environment.
Hypothesis: _______________________________

2.4 Turn your own question into a hypothesis

Each person take a look back at the observations you made in section 2.1.

• Chose one of your questions, transform it into a hypothesis, and write your hypothesis on a blank index card.
• In small groups, talk about how testing this hypothesis might or might not be helpful in your own work or pursuits.
• Consider kinds of tests are possible for this question. What tests or data are available? Which ones are used by environmental regulators?
• Write out variations on your question, like "in a worst case scenario, like a windy day" or "measure particulates" vs. "measure PM2.5" and try out "daily average" vs. "on a bad day".

------ (10 minute break) ------

3. Experimental Design – Testing your Hypothesis

3.1 Address the question

Individually, take five or ten minutes to read the following text:

The first step in designing your experiment is to make sure that it will allow you to address your hypothesis. 

Your goal is to gather evidence that either directly supports or directly refutes your hypothesis, so the more specific your hypothesis is, the more tailored and efficient your experiment can and should be, and the more clearly it can answer your question. Broader questions and hypotheses can be very useful and provide a wealth of evidence, but do require more comprehensive investigations. 

For example, with a broad hypothesis such as _“I think tomato plants grow well in sunlight,”_ your experiment will have to encompass several varieties of tomato plants in several different potential growing conditions (e.g. different kinds of soil and watering patterns), and different sunlight exposures, and you would have to evaluate different aspects of growth (growth rate, fruit abundance, fruit quality, etc). 

If your hypothesis were more specific, such as _“I think beefsteak tomato plants growing in sandy soils under a variety of watering conditions grow faster in the sun than in the shade,”_ then your experiment need only include a few types of beefsteak tomato plants growing in one type of soil under either sunny or shady conditions, with variable watering patterns. 

Take into consideration that the second experimental design would be inadequate to address the first hypothesis, while the first experimental design would be excessive to address the second hypothesis. The second experiment isolates one difference between the two scenarios -- it's a **comparison** -- which is an easier type of experiment.

As a group, discuss:

• Would it make sense for either of the two hypotheses written above to test eggplant growth in addition to tomato plant growth?
• Would it make sense to test growth under drought conditions?
• Would it make sense to test tomato plant growth in predominantly cloudy conditions?

Each person should individually write:

• one alternative hypothesis with experimental design that expands the scope of the scientific inquiry.
• one alternative hypothesis with experimental design that narrows the scope of the scientific inquiry.

As a group, go around and have each person can share how they expanded or narrowed the inquiry. Discuss what kind of answer you can expect from the modified question.

3.2 Concept of using a known sample as a baseline for identifying unknowns

Each table should choose one person to read through the following text out loud:

In many scientific inquiries, identification or classification of an unknown object or compound is accomplished through comparison with known compounds. 

When designing an experiment to identify or classify unknown objects or compounds, you should choose relevant known samples against which to compare. _“How will I know what’s relevant?”,_ you might ask. Well, if the question is _“Is this mucky stuff actually oil?”,_ then obtain known samples of the oil carried on nearby train tracks, pipelines, etc, as well as commonly available consumer oils such as for vehicles or machinery. Once you have relevant known compounds available, you can assess your unknown compound through comparing similarities and differences to the “knowns”. You need to analyze known compounds to demonstrate that your method is valid and is able to correctly identify, classify, or quantify the known sample. 

If your method can’t tell that oil is oil, your method is not valid (for using to identify anything else).

The assessment of whether a compound is ***similar enough*** to a known compound that you conclude they are in the same category, will depend on your method’s precision and resolution, which are discussed below. 

As a group, discuss:

• When have you been faced with an unknown substance and gone about trying to figure out what it was?
• What other thoughts and ideas do these concepts bring up for you, especially related to unknown substances?

3.3 Precision and Resolution Part 1

Choose one person at the table to read the following text out loud:

In scientific experiments, we are assessing the likelihood of an assertion to be true, such as whether an unknown oily residue is in a certain class of oil. To do this, we have to know *how well we know* our own data. That knowledge depends on accuracy, precision, and resolution.

Each table should choose one person to read the following situation out loud:

mg = milligram, which is one thousandth of a gram;
L = liter.
Units of mg/L read as “milligrams per liter” are a measure of concentration.

You are interested in whether or not an industrial facility violated their permit by discharging more than 1.8 mg/L ammonia in their effluent. 

* Let’s say your instrument measured 2 mg/L ammonia, but the resolution of your instrument is 1 mg/L, meaning that the only readings you can obtain are 0, 1, 2, 3… mg/L. Can you be certain that your measurement of 2 mg/L is definitely higher than 1.8 mg/L? Could the true value of your measurement be 1.6 mg/L and still read as 2 mg/L? _(pause for discussion)_
* What if your tool could reliably measure 0.1 mg/L differences, and using it, you took three measurements which were 1.6, 2.2, and 1.8 mg/L. How well do you know what the “true” value is? _(pause for discussion)_
* What if your three measurements were 1.9, 1.8, and 1.9 mg/L? How well do you know what the “true” value of those measurements is? _(pause for discussion)_

Each table should choose another person to continue reading:

The example above illustrates that your ability to evaluate your own data depends on:


* knowing your instrument’s resolution (resolution means the smallest distinguishable difference from a given value)
* knowing your method’s precision (precision is the variability in values recorded for a given true value)
* knowing your method’s accuracy (accuracy means how close a measured value is to the true value, often based on the mean of several measurements) 

The numbers or values that our equipment give us have a specific relationship to reality, and how well we can describe that relationship is dictated by resolution, precision, and accuracy. Describing your data’s accuracy*, precision, and resolution are part of** determining the probability that your original assertion is true. 

_*Note: that in the example above, it is assumed that your instrument was reading fairly accurate results because it had been appropriately calibrated. We will discuss calibration more in a future workshop._

_**Note: There are several factors that will influence probability, and especially in larger data sets, more statistical analyses are needed to adequately describe the data. In this workshop we are focusing more conceptually rather than mathematically. If you want to do more statistics, here is a useful introductory guide for statistical analyses: http://www.robertniles.com/stats/._

3.4 Precision and Resolution Part 2

Everyone get a piece of paper and a writing implement, and get ready to draw!

Draw a few visual representations to describe what we learned above about accuracy, precision, and resolution. Draw a representation of measurements with:

• high precision but low accuracy
• low precision but high accuracy
• low precision and low accuracy
• high precision and high accuracy
• high precision but low resolution
• low precision but high resolution

Share your drawings with your group members and discuss them.

Next, as a table, refresh your memories about the ammonia concentrations measured in the effluent of the industrial facility (above).

Draw representations of the data from the example to explain whether or not you are able to determine if the discharge is above the 1.8 mg/L ammonia limit for each scenario listed (and remember to assume that your measurements are accurate).

3.5 Reproducibility

Each table should choose one person to read through the following out loud:

To demonstrate the validity of your experiment and your results, you must be able to show that you can achieve same results multiple times, and that another person could achieve the same results by following your protocol. This reproducibility of results is important for three reasons: 

1. to help you assess your own precision, as discussed above, 
2. to ensure that you provide enough information for other people to replicate your experiment and thus grow scientific knowledge and capacity, and 
3. to demonstrate the validity of the data by asserting that it is reliably reproducible. 

In cases of scientific fraud that have occurred (thankfully rarely), they have most often been caught by another researcher attempting to reproduce an experiment’s results, and alerting people that the initial results must have been falsified. 

In Workshop 3, we will follow procedures to take each oil spectrum three times. By virtue of posting our oil spectra freely and openly to the Web, we are contributing to a massive body of experimental results, and both demonstrating and evaluating the reproducibility of those results. 

Individually, and then as a group:

Think ahead towards future research projects you might have in mind. When do you think you might have to be aware of resolution, precision, and accuracy?

------ (10 minute break) ------

4. Wrap-up / Congrats we made it!

4.1 Relating all this to our real lives

Each table should choose one person to read through the following out loud:

In our daily lives, we make observations constantly. Think about the observations you listed in section 2.1, and others that cross your mind: 

* Have you ever noticed that grass pops up through cracks in the sidewalk in some places but not others? 
* Have you ever been struck by an odd smell in a new house or new car? 

Your take home assignment, should you choose to accept it, is to think of something that you have observed in an environment that matters to you. Write down your observation, and come up with a hypothesis that could be tested, or at least some baseline ideas that could become hypotheses after reading relevant background information. Remember that your hypothesis must be testable, and that the outcomes of your test will offer a likelihood of an answer, but not definitive proof. For the hypothesis that you create, define how precisely you are going to need to know your answer. Also define the purpose(s) you intend to use the data you collect for.

Congratulations! You have embarked upon the process of scientific inquiry!

Back to Homebrew Workshops Overview

Workshop 1: Designing an experiment

Drafted by Gretchen Gehrke, Stevie Lewis, Liz Barry.

Why (the Situation): In order to confidently answer the questions we have about our environment, we want to learn how to structure our questions, use DIY tools, conduct experiments following the scientific method, and understand the capabilities and limitations of our data.

When: a 3.5 hour workshop in four sections, with a ten minute break after each section.

Where: a room with tables and chairs, with participants sitting in small groups.

What (the content): The basics of designing an effective experiment; transforming guesses into testable hypotheses; assessing precision and reproducibility; interpreting data based on data quality. ^

For What (Achievement Based Objectives): By the end of this workshop, you will have:

• Met others attending the workshop
• Shared with each other what motivated you to to participate
• Written down your expectations for your time
• Discussed examples of scientific questions that interest you in small groups
• Read about the elements needed for good experimental design
• Noted the important points of designing a clear experiment
• Drafted your own questions and transform them into hypotheses
• Explored the concept of proof versus likelihood (facilitated discussion)
• Discussed the importance of precision, resolution, and accuracy in any data set
• Reflected upon how experimentation relates to your own interests/work

Notes for Facilitators:

Materials Needed:

• Markers, pens
• blank 8.5”x11” printer paper
• blank index cards
• blank large chart paper
• tape
• printed copies of this handout

Setting up the event:

• Write out the 7 steps of the scientific method written out on index cards, one set per table in scattered order:
  • asking a question
  • gathering background information
  • developing a hypothesis
  • conducting an experiment to test the hypothesis
  • analyzing the experimental results
  • communicating the experimental results
  • retesting the hypothesis (or a new one, if necessary)

Workshop Schedule:

1. Introduction
   1. Who is here today? (20 minutes)
   2. Introductions among tables (10 minutes)
   3. Everyone asks questions, but how?
   4. Step by step
2. Developing hypotheses
   1. From observing to questioning
   2. Evidence versus proof
   3. Let’s turn guesses into testable hypotheses
   4. Turn your own question into a hypothesis
3. Experiment design -- Testing your hypotheses
   1. Address the question
   2. Concept of using a known sample as a baseline for identifying unknowns
   3. Precision and resolution, Part 1
   4. Precision and resolution, Part 2
   5. Reproducibility
4. Wrap up / Congrats you made it!
   1. Relating all this to our real lives

Workshop Outline

1. Introduction

1.1 Who is here today?

As a whole group, take turns introducing yourself by saying your name, where you’re from, and your reason for coming to the workshop today.

1.2 What are we doing today?

Take a minute to read through the achievement based objectives on page 1 of your handout, and then each table should choose one person to read through the following out loud:

You may have heard people talk about the Scientific Method. The Scientific Method is a general guideline for the steps to take in order to answer a question that is based on an observation. Historically, the scientific method originated as a knowledge production technique, and it endures into the present day as a technique you can potentially use in your work, in your community, to help take control of a situation.

1.3 Everyone asks questions, but how?

Think of a situation in which have you wondered about something (how it works, why it exists, etc). How have you tried to answer your question? Think about the specific steps you have taken, and write them down.

Take five (5) minutes to make notes on your own paper, summarizing your individual thoughts as a five-step process. Share with your table. Next, each table will make a brief poster presentation back to the whole group. Finally, discuss your perceptions of how the EPA, or even law enforcement, goes about answering questions.

1.4 Step by step

Look at the index cards on your table, and notice that they are in no particular order.

There are seven basic steps listed for what is commonly understood as the “scientific method”. It is important to note that this is not a linear, one-time series of steps, but rather, it is cyclical. For example, a given hypothesis might require several different experiments to adequately test that hypothesis, or test results may indicate that the original hypothesis is false and you need to develop a new hypothesis.

Each step in the basic scientific method is important and challenging, and deserves special attention. In this workshop we are going to focus mostly on developing a hypothesis, and designing an experiment to test the hypothesis. During this series of workshops, you will conduct an experiment, and by the end, be able to analyze and communicate your results.

A couple of important points that we want to highlight that are crucial to scientific inquiry and experimental design are: 

1. the concept of proof versus likelihood, and 
2. the importance of knowing your analyses’ precision. 

We’ll cover these topics in the following sections.

Take fifteen (15) minutes to work through the following prompt as a group:

In what order would you imagine proceeding through these steps? Place them in order, then discuss:

• Would an experiment be a straight path through these steps?
• Would you repeat certain parts, or occasionally take one out of order?
• Would you ever need to repeat the entire sequence?
• Why?

Return to what you wrote down during the previous activity (1.3) about what steps you took to answer your own question. Based on this new knowledge, name 2 or 3 things you might change about your original ideas.

Send one representative from your group to go up to the wall and tape your steps in order. Each group should line up their rows to facilitate comparison; discuss.

------ (10 minute break) ------

2. Developing Hypotheses

2.1 From observing to questioning

In this task, we are going to come up with research questions for our own observations. Choose one person at the table to read the following text out loud:

Before developing a hypothesis, the very first step in a scientific inquiry is asking a question. Scientific questions usually arise from observations. For example, we might observe that “The sky is blue,” which prompts us to wonder, “Why is the sky blue?” We might notice that “A lot of kids have asthma here,” which compels us to ask, “Why do so many kids have asthma in this neighborhood?” If we notice that “this substance looks like oil,” we ask, “Could this be oil?” These are all good questions based on an initial observation.

On a large piece of paper, draw a vertical line through the middle to make two columns, AKA a “T Chart”:

• Label the top of the left-hand column “Observations”
• Label the top of the right-hand column “Research Questions”

Individually, on sticky notes:

• Write observations that you have had about your yard, your neighborhood, your landscape, local industrial activity, etc. One per note.
• For each observation, take another sticky note & write a research question based on it.

After a few minutes, everyone can put up their sticky notes up on the “T chart” in the appropriate columns.

Review similarities and differences in the framing of research questions, and make a presentation about this to the whole group.

2.2 Evidence versus proof

Choose one person at the table to read the following text out loud:

A key difference between science and mathematics is that there is no such thing as absolute proof in science.

In mathematics, you can have a proven theorem because you are dealing with a closed system where all of the information is available and controlled, and the proof is final. As a result, there can be a binary, “yes or no”, “proven or disproven” set of logic in math.

In science, we don’t have the luxury of an absolute proof because not all of the information is known. We discover new relevant information constantly, and never deal with a truly closed circuit where all information is known. In science, knowledge is tentative, based on the information available, and we gather evidence that suggests **a likelihood** that something is true.

Everything in science is tentative, and is based on the best available evidence, but cannot be based on absolute proof. This is extremely important for our experimental design and how we talk about results.

Quick group check in: What is the difference between the kind of answers that are possible to arrive at for questions in the domain of math versus the domain of science?

Based on what we just read, we can begin designing our experiments with the understanding that any answers we reach through scientific research will be evidence-based but not an absolute truth. The basic sequence is to

Make an observation
Ask a related question
From that question, develop a hypothesis that can be tested.

What is a hypothesis? Your hypothesis is an educated guess about the answer to your question, but is different from a basic guess in two important ways: first, a hypothesis is based upon existing evidence (albeit a limited amount), and second, a hypothesis can be tested such that new evidence can be gathered that directly supports or refutes the hypothesis.

The steps for turning a question into a hypothesis are:

Starting with your observation, get as specific as possible (or as specific as you’d like to be, based on the scope of your research). With your specific observation, brainstorm ideas that could be the cause of what you are observing. This is your “basic guess.” Make sure you have some evidence, or there is existing information, that supports your guess. If there is not, then familiarize yourself with the information about the issue that is available, and adjust your guess/idea accordingly. This saves you time by helping you develop a more likely hypothesis. To turn your guess into a real hypothesis, you must have a testable statement, with tests that can be observed and measured, and/or compared against a “known” value or entity. This often will be in the form of “X is more than/less than/similar to Y”, where X and Y can be observed and compared, or one of the two is already “known.”

Here is an example of turning a guess into a hypothesis:

Observation: The sidewalks on this tree-lined street are cracked and bumpy. Get more specific Revised Observation: The sidewalks on this tree-lined street are more cracked and bumpy than the sidewalks on the street without trees. Good. Now guess why that is the case. Guess: I think trees made sidewalks bumpy. Make it more specific and testable Hypothesis: I think tree roots grow under sidewalks, and as they get bigger, they can push up the cement sidewalks. Thus, sidewalks near bigger trees are likely to have more cracked and bumpy sidewalks than sidewalks on streets with small trees or without trees.

2.3 Let’s turn guesses into testable hypotheses

Individually, turn these guesses into testable hypotheses:

Guess: Cats like wet cat food.
Hypothesis: _______________________________

Guess: Breathing fumes from cleaning products is bad.
Hypothesis: _______________________________

Guess: Strip mining damages the environment.
Hypothesis: _______________________________

2.4 Turn your own question into a hypothesis

Each person take a look back at the observations you made in section 2.1.

• Chose one of your questions, transform it into a hypothesis, and write your hypothesis on a blank index card.
• In small groups, talk about how testing this hypothesis might or might not be helpful in your own work or pursuits.
• Consider kinds of tests are possible for this question. What tests or data are available? Which ones are used by environmental regulators?
• Write out variations on your question, like "in a worst case scenario, like a windy day" or "measure particulates" vs. "measure PM2.5" and try out "daily average" vs. "on a bad day".

------ (10 minute break) ------

3. Experimental Design – Testing your Hypothesis

3.1 Address the question

Individually, take five or ten minutes to read the following text:

The first step in designing your experiment is to make sure that it will allow you to address your hypothesis. 

Your goal is to gather evidence that either directly supports or directly refutes your hypothesis, so the more specific your hypothesis is, the more tailored and efficient your experiment can and should be, and the more clearly it can answer your question. Broader questions and hypotheses can be very useful and provide a wealth of evidence, but do require more comprehensive investigations. 

For example, with a broad hypothesis such as _“I think tomato plants grow well in sunlight,”_ your experiment will have to encompass several varieties of tomato plants in several different potential growing conditions (e.g. different kinds of soil and watering patterns), and different sunlight exposures, and you would have to evaluate different aspects of growth (growth rate, fruit abundance, fruit quality, etc). 

If your hypothesis were more specific, such as _“I think beefsteak tomato plants growing in sandy soils under a variety of watering conditions grow faster in the sun than in the shade,”_ then your experiment need only include a few types of beefsteak tomato plants growing in one type of soil under either sunny or shady conditions, with variable watering patterns. 

Take into consideration that the second experimental design would be inadequate to address the first hypothesis, while the first experimental design would be excessive to address the second hypothesis. The second experiment isolates one difference between the two scenarios -- it's a **comparison** -- which is an easier type of experiment.

As a group, discuss:

• Would it make sense for either of the two hypotheses written above to test eggplant growth in addition to tomato plant growth?
• Would it make sense to test growth under drought conditions?
• Would it make sense to test tomato plant growth in predominantly cloudy conditions?

Each person should individually write:

• one alternative hypothesis with experimental design that expands the scope of the scientific inquiry.
• one alternative hypothesis with experimental design that narrows the scope of the scientific inquiry.

As a group, go around and have each person can share how they expanded or narrowed the inquiry. Discuss what kind of answer you can expect from the modified question.

3.2 Concept of using a known sample as a baseline for identifying unknowns

Each table should choose one person to read through the following text out loud:

In many scientific inquiries, identification or classification of an unknown object or compound is accomplished through comparison with known compounds. 

When designing an experiment to identify or classify unknown objects or compounds, you should choose relevant known samples against which to compare. _“How will I know what’s relevant?”,_ you might ask. Well, if the question is _“Is this mucky stuff actually oil?”,_ then obtain known samples of the oil carried on nearby train tracks, pipelines, etc, as well as commonly available consumer oils such as for vehicles or machinery. Once you have relevant known compounds available, you can assess your unknown compound through comparing similarities and differences to the “knowns”. You need to analyze known compounds to demonstrate that your method is valid and is able to correctly identify, classify, or quantify the known sample. 

If your method can’t tell that oil is oil, your method is not valid (for using to identify anything else).

The assessment of whether a compound is ***similar enough*** to a known compound that you conclude they are in the same category, will depend on your method’s precision and resolution, which are discussed below. 

As a group, discuss:

• When have you been faced with an unknown substance and gone about trying to figure out what it was?
• What other thoughts and ideas do these concepts bring up for you, especially related to unknown substances?

3.3 Precision and Resolution Part 1

Choose one person at the table to read the following text out loud:

In scientific experiments, we are assessing the likelihood of an assertion to be true, such as whether an unknown oily residue is in a certain class of oil. To do this, we have to know *how well we know* our own data. That knowledge depends on accuracy, precision, and resolution.

Each table should choose one person to read the following situation out loud:

mg = milligram, which is one thousandth of a gram;
L = liter.
Units of mg/L read as “milligrams per liter” are a measure of concentration.

You are interested in whether or not an industrial facility violated their permit by discharging more than 1.8 mg/L ammonia in their effluent. 

* Let’s say your instrument measured 2 mg/L ammonia, but the resolution of your instrument is 1 mg/L, meaning that the only readings you can obtain are 0, 1, 2, 3… mg/L. Can you be certain that your measurement of 2 mg/L is definitely higher than 1.8 mg/L? Could the true value of your measurement be 1.6 mg/L and still read as 2 mg/L? _(pause for discussion)_
* What if your tool could reliably measure 0.1 mg/L differences, and using it, you took three measurements which were 1.6, 2.2, and 1.8 mg/L. How well do you know what the “true” value is? _(pause for discussion)_
* What if your three measurements were 1.9, 1.8, and 1.9 mg/L? How well do you know what the “true” value of those measurements is? _(pause for discussion)_

Each table should choose another person to continue reading:

The example above illustrates that your ability to evaluate your own data depends on:


* knowing your instrument’s resolution (resolution means the smallest distinguishable difference from a given value)
* knowing your method’s precision (precision is the variability in values recorded for a given true value)
* knowing your method’s accuracy (accuracy means how close a measured value is to the true value, often based on the mean of several measurements) 

The numbers or values that our equipment give us have a specific relationship to reality, and how well we can describe that relationship is dictated by resolution, precision, and accuracy. Describing your data’s accuracy*, precision, and resolution are part of** determining the probability that your original assertion is true. 

_*Note: that in the example above, it is assumed that your instrument was reading fairly accurate results because it had been appropriately calibrated. We will discuss calibration more in a future workshop._

_**Note: There are several factors that will influence probability, and especially in larger data sets, more statistical analyses are needed to adequately describe the data. In this workshop we are focusing more conceptually rather than mathematically. If you want to do more statistics, here is a useful introductory guide for statistical analyses: http://www.robertniles.com/stats/._

3.4 Precision and Resolution Part 2

Everyone get a piece of paper and a writing implement, and get ready to draw!

Draw a few visual representations to describe what we learned above about accuracy, precision, and resolution. Draw a representation of measurements with:

• high precision but low accuracy
• low precision but high accuracy
• low precision and low accuracy
• high precision and high accuracy
• high precision but low resolution
• low precision but high resolution

Share your drawings with your group members and discuss them.

Next, as a table, refresh your memories about the ammonia concentrations measured in the effluent of the industrial facility (above).

Draw representations of the data from the example to explain whether or not you are able to determine if the discharge is above the 1.8 mg/L ammonia limit for each scenario listed (and remember to assume that your measurements are accurate).

3.5 Reproducibility

Each table should choose one person to read through the following out loud:

To demonstrate the validity of your experiment and your results, you must be able to show that you can achieve same results multiple times, and that another person could achieve the same results by following your protocol. This reproducibility of results is important for three reasons: 

1. to help you assess your own precision, as discussed above, 
2. to ensure that you provide enough information for other people to replicate your experiment and thus grow scientific knowledge and capacity, and 
3. to demonstrate the validity of the data by asserting that it is reliably reproducible. 

In cases of scientific fraud that have occurred (thankfully rarely), they have most often been caught by another researcher attempting to reproduce an experiment’s results, and alerting people that the initial results must have been falsified. 

In Workshop 3, we will follow procedures to take each oil spectrum three times. By virtue of posting our oil spectra freely and openly to the Web, we are contributing to a massive body of experimental results, and both demonstrating and evaluating the reproducibility of those results. 

Individually, and then as a group:

Think ahead towards future research projects you might have in mind. When do you think you might have to be aware of resolution, precision, and accuracy?

------ (10 minute break) ------

4. Wrap-up / Congrats we made it!

4.1 Relating all this to our real lives

Each table should choose one person to read through the following out loud:

In our daily lives, we make observations constantly. Think about the observations you listed in section 2.1, and others that cross your mind: 

* Have you ever noticed that grass pops up through cracks in the sidewalk in some places but not others? 
* Have you ever been struck by an odd smell in a new house or new car? 

Your take home assignment, should you choose to accept it, is to think of something that you have observed in an environment that matters to you. Write down your observation, and come up with a hypothesis that could be tested, or at least some baseline ideas that could become hypotheses after reading relevant background information. Remember that your hypothesis must be testable, and that the outcomes of your test will offer a likelihood of an answer, but not definitive proof. For the hypothesis that you create, define how precisely you are going to need to know your answer. Also define the purpose(s) you intend to use the data you collect for.

Congratulations! You have embarked upon the process of scientific inquiry!

URL: http://publiclab.org/wiki/oil-testing-workshop-design-an-experiment

Back to Homebrew Workshops Overview

Workshop 1: Designing an experiment

Drafted by Gretchen Gehrke, Stevie Lewis, Liz Barry.

Why (the Situation): In order to confidently answer the questions we have about our environment, we want to learn how to structure our questions, use DIY tools, conduct experiments following the scientific method, and understand the capabilities and limitations of our data.

When: a 3.5 hour workshop with a 10 minute break at the midpoint.

Where: a room with tables and chairs, with participants sitting in small groups.

What (the content): The basics of designing an effective experiment; transforming guesses into testable hypotheses; assessing precision and reproducibility; interpreting data based on data quality. ^

For What (Achievement Based Objectives): By the end of this workshop, you will have:

• Met others attending the workshop
• Shared with each other what motivated you to to participate
• Written down your expectations for your time
• Discussed examples of scientific questions that interest you in small groups
• Read about the elements needed for good experimental design
• Noted the important points of designing a clear experiment
• Drafted your own questions and transform them into hypotheses
• Explored the concept of proof versus likelihood (facilitated discussion)
• Discussed the importance of precision, resolution, and accuracy in any data set
• Reflected upon how experimentation relates to your own interests/work

Notes for Facilitators:

Materials Needed:

• Markers, pens
• blank 8.5”x11” printer paper
• blank index cards
• blank large chart paper
• tape
• printed copies of this handout

Setting up the event:

• Write out the 7 steps of the scientific method written out on index cards, one set per table in scattered order:
  • asking a question
  • gathering background information
  • developing a hypothesis
  • conducting an experiment to test the hypothesis
  • analyzing the experimental results
  • communicating the experimental results
  • retesting the hypothesis (or a new one, if necessary)

Workshop Schedule:

1. Introduction
   1. Who is here today? (20 minutes)
   2. Introductions among tables (10 minutes)
   3. Everyone asks questions, but how?
   4. Step by step
2. Developing hypotheses
   1. From observing to questioning
   2. Evidence versus proof
   3. Let’s turn guesses into testable hypotheses
   4. Turn your own question into a hypothesis
3. Experiment design -- Testing your hypotheses
   1. Address the question
   2. Concept of using a known sample as a baseline for identifying unknowns
   3. Precision and resolution, Part 1
   4. Precision and resolution, Part 2
   5. Reproducibility
4. Wrap up / Congrats you made it!
   1. Relating all this to our real lives

Workshop Outline

1. Introduction

1.1 Who is here today?

As a whole group, take turns introducing yourself by saying your name, where you’re from, and your reason for coming to the workshop today.

1.2 What are we doing today?

Take a minute to read through the achievement based objectives on page 1 of your handout, and then each table should choose one person to read through the following out loud:

You may have heard people talk about the Scientific Method. The Scientific Method is a general guideline for the steps to take in order to answer a question that is based on an observation. Historically, the scientific method originated as a knowledge production technique, and it endures into the present day as a technique you can potentially use in your work, in your community, to help take control of a situation.

1.3 Everyone asks questions, but how?

Think of a situation in which have you wondered about something (how it works, why it exists, etc). How have you tried to answer your question? Think about the specific steps you have taken, and write them down.

Take five (5) minutes to make notes on your own paper, summarizing your individual thoughts as a five-step process. Share with your table. Next, each table will make a brief poster presentation back to the whole group. Finally, discuss your perceptions of how the EPA, or even law enforcement, goes about answering questions.

1.4 Step by step

Look at the index cards on your table, and notice that they are in no particular order.

There are seven basic steps listed for what is commonly understood as the “scientific method”. It is important to note that this is not a linear, one-time series of steps, but rather, it is cyclical. For example, a given hypothesis might require several different experiments to adequately test that hypothesis, or test results may indicate that the original hypothesis is false and you need to develop a new hypothesis.

Each step in the basic scientific method is important and challenging, and deserves special attention. In this workshop we are going to focus mostly on developing a hypothesis, and designing an experiment to test the hypothesis. During this series of workshops, you will conduct an experiment, and by the end, be able to analyze and communicate your results.

A couple of important points that we want to highlight that are crucial to scientific inquiry and experimental design are: 

1. the concept of proof versus likelihood, and 
2. the importance of knowing your analyses’ precision. 
We’ll cover these topics in the following sections.

Take fifteen (15) minutes to work through the following prompt as a group:

In what order would you imagine proceeding through these steps? Place them in order, then discuss:

• Would an experiment be a straight path through these steps?
• Would you repeat certain parts, or occasionally take one out of order?
• Would you ever need to repeat the entire sequence?
• Why?

Return to what you wrote down during the previous activity (1.3) about what steps you took to answer your own question. Based on this new knowledge, name 2 or 3 things you might change about your original ideas.

Send one representative from your group to go up to the wall and tape your steps in order. Each group should line up their rows to facilitate comparison; discuss.

------ (10 minute break) ------

2. Developing Hypotheses

2.1 From observing to questioning

In this task, we are going to come up with research questions for our own observations. Choose one person at the table to read the following text out loud:

Before developing a hypothesis, the very first step in a scientific inquiry is asking a question. Scientific questions usually arise from observations. For example, we might observe that “The sky is blue,” which prompts us to wonder, “Why is the sky blue?” We might notice that “A lot of kids have asthma here,” which compels us to ask, “Why do so many kids have asthma in this neighborhood?” If we notice that “this substance looks like oil,” we ask, “Could this be oil?” These are all good questions based on an initial observation.

On a large piece of paper, draw a vertical line through the middle to make two columns, AKA a “T Chart”:

• Label the top of the left-hand column “Observations”
• Label the top of the right-hand column “Research Questions”

Individually, on sticky notes:

• Write observations that you have had about your yard, your neighborhood, your landscape, local industrial activity, etc. One per note.
• For each observation, take another sticky note & write a research question based on it.

After a few minutes, everyone can put up their sticky notes up on the “T chart” in the appropriate columns.

Review similarities and differences in the framing of research questions, and make a presentation about this to the whole group.

2.2 Evidence versus proof

Choose one person at the table to read the following text out loud:

A key difference between science and mathematics is that there is no such thing as absolute proof in science.

In mathematics, you can have a proven theorem because you are dealing with a closed system where all of the information is available and controlled, and the proof is final. As a result, there can be a binary, “yes or no”, “proven or disproven” set of logic in math.

In science, we don’t have the luxury of an absolute proof because not all of the information is known. We discover new relevant information constantly, and never deal with a truly closed circuit where all information is known. In science, knowledge is tentative, based on the information available, and we gather evidence that suggests **a likelihood** that something is true.

Everything in science is tentative, and is based on the best available evidence, but cannot be based on absolute proof. This is extremely important for our experimental design and how we talk about results.

Quick group check in: What is the difference between the kind of answers that are possible to arrive at for questions in the domain of math versus the domain of science?

Based on what we just read, we can begin designing our experiments with the understanding that any answers we reach through scientific research will be evidence-based but not an absolute truth.

Step 1: make an observation
Step 2: ask a related question
Step 3: from that question, develop a hypothesis that can be tested.

What is a hypothesis? Your hypothesis is an educated guess about the answer to your question, but is different from a basic guess in two important ways: first, a hypothesis is based upon existing evidence (albeit a limited amount), and second, a hypothesis can be tested such that new evidence can be gathered that directly supports or refutes the hypothesis.

The steps for turning a question into a hypothesis are:

Starting with your observation, get as specific as possible (or as specific as you’d like to be, based on the scope of your research). With your specific observation, brainstorm ideas that could be the cause of what you are observing. This is your “basic guess.” Make sure you have some evidence, or there is existing information, that supports your guess. If there is not, then familiarize yourself with the information about the issue that is available, and adjust your guess/idea accordingly. This saves you time by helping you develop a more likely hypothesis. To turn your guess into a real hypothesis, you must have a testable statement, with tests that can be observed and measured, and/or compared against a “known” value or entity. This often will be in the form of “X is more than/less than/similar to Y”, where X and Y can be observed and compared, or one of the two is already “known.”

Here is an example of turning a guess into a hypothesis:

Observation: The sidewalks on this tree-lined street are cracked and bumpy. Get more specific Revised Observation: The sidewalks on this tree-lined street are more cracked and bumpy than the sidewalks on the street without trees. Good. Now guess why that is the case. Guess: I think trees made sidewalks bumpy. Make it more specific and testable Hypothesis: I think tree roots grow under sidewalks, and as they get bigger, they can push up the cement sidewalks. Thus, sidewalks near bigger trees are likely to have more cracked and bumpy sidewalks than sidewalks on streets with small trees or without trees.

2.3 Let’s turn guesses into testable hypotheses

Individually, turn these guesses into testable hypotheses:

Guess: Cats like wet cat food.
Hypothesis: _______________________________

Guess: Breathing fumes from cleaning products is bad.
Hypothesis: _______________________________

Guess: Strip mining damages the environment.
Hypothesis: _______________________________

2.4 Turn your own question into a hypothesis

• Chose one of your questions, transform it into a hypothesis, and write your hypothesis on a blank index card.
• In small groups, talk about how testing this hypothesis might or might not be helpful in your own work or pursuits.
• Consider kinds of tests are possible for this question. What tests or data are available? Which ones are used by environmental regulators?
• Write out variations on your question, like "in a worst case scenario, like a windy day" or "measure particulates" vs. "measure PM2.5" and try out "daily average" vs. "on a bad day".

------ (10 minute break) ------

3. Experimental Design – Testing your Hypothesis

3.1 Address the question

Individually, take five or ten minutes to read the following text:

The first step in designing your experiment is to make sure that it will allow you to address your hypothesis. 

Your goal is to gather evidence that either directly supports or directly refutes your hypothesis, so the more specific your hypothesis is, the more tailored and efficient your experiment can and should be, and the more clearly it can answer your question. Broader questions and hypotheses can be very useful and provide a wealth of evidence, but do require more comprehensive investigations. 

For example, with a broad hypothesis such as _“I think tomato plants grow well in sunlight,”_ your experiment will have to encompass several varieties of tomato plants in several different potential growing conditions (e.g. different kinds of soil and watering patterns), and different sunlight exposures, and you would have to evaluate different aspects of growth (growth rate, fruit abundance, fruit quality, etc). 

If your hypothesis were more specific, such as _“I think beefsteak tomato plants growing in sandy soils under a variety of watering conditions grow faster in the sun than in the shade,”_ then your experiment need only include a few types of beefsteak tomato plants growing in one type of soil under either sunny or shady conditions, with variable watering patterns. 

Take into consideration that the second experimental design would be inadequate to address the first hypothesis, while the first experimental design would be excessive to address the second hypothesis. The second experiment isolates one difference between the two scenarios -- it's a **comparison** -- which is an easier type of experiment.

As a group, discuss:

• Would it make sense for either of the two hypotheses written above to test eggplant growth in addition to tomato plant growth?
• Would it make sense to test growth under drought conditions?
• Would it make sense to test tomato plant growth in predominantly cloudy conditions?

Each person should individually write:

• one alternative hypothesis with experimental design that expands the scope of the scientific inquiry.
• one alternative hypothesis with experimental design that narrows the scope of the scientific inquiry.

Discuss your alternative hypotheses as a group.

3.2 Concept of using a known sample as a baseline for identifying unknowns

Each table should choose one person to read through the following text out loud:

In many scientific inquiries, identification or classification of an unknown object or compound is accomplished through comparison with known compounds. 

When designing an experiment to identify or classify unknown objects or compounds, you should choose relevant known samples against which to compare. _“How will I know what’s relevant?”,_ you might ask. Well, if the question is _“Is this mucky stuff actually oil?”,_ then obtain known samples of the oil carried on nearby train tracks, pipelines, etc, as well as commonly available consumer oils such as for vehicles or machinery. Once you have relevant known compounds available, you can assess your unknown compound through comparing similarities and differences to the “knowns”. You need to analyze known compounds to demonstrate that your method is valid and is able to correctly identify, classify, or quantify the known sample. 

If your method can’t tell that oil is oil, your method is not valid (for using to identify anything else).

The assessment of whether a compound is ***similar enough*** to a known compound that you conclude they are in the same category, will depend on your method’s precision and resolution, which are discussed below. 

As a group, discuss:

• When have you been faced with an unknown substance and gone about trying to figure out what it was?
• What other thoughts and ideas do these concepts bring up for you, especially related to unknown substances?

3.3 Precision and Resolution Part 1

Choose one person at the table to read the following text out loud:

In scientific experiments, we are assessing the likelihood of an assertion to be true, such as whether an unknown oily residue is in a certain class of oil. To do this, we have to know *how well we know* our own data. That knowledge depends on accuracy, precision, and resolution.

Each table should choose one person to read the following situation out loud:

mg = milligram, which is one thousandth of a gram;
L = liter.
Units of mg/L read as “milligrams per liter” are a measure of concentration.

You are interested in whether or not an industrial facility violated their permit by discharging more than 1.8 mg/L ammonia in their effluent. 

* Let’s say your instrument measured 2 mg/L ammonia, but the resolution of your instrument is 1 mg/L, meaning that the only readings you can obtain are 0, 1, 2, 3… mg/L. Can you be certain that your measurement of 2 mg/L is definitely higher than 1.8 mg/L? Could the true value of your measurement be 1.6 mg/L and still read as 2 mg/L? (pause for discussion)
* What if your tool could reliably measure 0.1 mg/L differences, and using it, you took three measurements which were 1.6, 2.2, and 1.8 mg/L. How well do you know what the “true” value is? (pause for discussion)
* What if your three measurements were 1.9, 1.8, and 1.9 mg/L? How well do you know what the “true” value of those measurements is? (pause for discussion)

Each table should choose another person to continue reading:

The example above illustrates that your ability to evaluate your own data depends on:


* knowing your instrument’s resolution (resolution means the smallest distinguishable difference from a given value)
* knowing your method’s precision (precision is the variability in values recorded for a given true value)
* knowing your method’s accuracy (accuracy means how close a measured value is to the true value, often based on the mean of several measurements) 

The numbers or values that our equipment give us have a specific relationship to reality, and how well we can describe that relationship is dictated by resolution, precision, and accuracy. Describing your data’s accuracy*, precision, and resolution are part of** determining the probability that your original assertion is true. 

_*Note: that in the example above, it is assumed that your instrument was reading fairly accurate results because it had been appropriately calibrated. We will discuss calibration more in a future workshop._

_**Note: There are several factors that will influence probability, and especially in larger data sets, more statistical analyses are needed to adequately describe the data. In this workshop we are focusing more conceptually rather than mathematically. If you want to do more statistics, here is a useful introductory guide for statistical analyses: http://www.robertniles.com/stats/._

3.4 Precision and Resolution Part 2

Everyone get a piece of paper and a writing implement, and get ready to draw!

Draw a few visual representations to describe what we learned above about accuracy, precision, and resolution. Draw a representation of measurements with:

• high precision but low accuracy
• low precision but high accuracy
• low precision and low accuracy
• high precision and high accuracy
• high precision but low resolution
• low precision but high resolution

Share your drawings with your group members and discuss them.

Next, as a table, refresh your memories about the ammonia concentrations measured in the effluent of the industrial facility (above).

Draw representations of the data from the example to explain whether or not you are able to determine if the discharge is above the 1.8 mg/L ammonia limit for each scenario listed (and remember to assume that your measurements are accurate).

3.5 Reproducibility

Each table should choose one person to read through the following out loud:

To demonstrate the validity of your experiment and your results, you must be able to show that you can achieve same results multiple times, and that another person could achieve the same results by following your protocol. This reproducibility of results is important for three reasons: 

1. to help you assess your own precision, as discussed above, 
2. to ensure that you provide enough information for other people to replicate your experiment and thus grow scientific knowledge and capacity, and 
3. to demonstrate the validity of the data by asserting that it is reliably reproducible. 

In cases of scientific fraud that have occurred (thankfully rarely), they have most often been caught by another researcher attempting to reproduce an experiment’s results, and alerting people that the initial results must have been falsified. 

In Workshop 3, we will follow procedures to take each oil spectrum three times. By virtue of posting our oil spectra freely and openly to the Web, we are contributing to a massive body of experimental results, and both demonstrating and evaluating the reproducibility of those results. 

Individually, and then as a group:

Think ahead towards future research projects you might have in mind. When do you think you might have to be aware of resolution, precision, and accuracy?

------ (10 minute break) ------

4. Wrap-up / Congrats we made it!

4.1 Relating all this to our real lives

Each table should choose one person to read through the following out loud:

In our daily lives, we make observations constantly. Think about the observations you listed in section 2.1, and others that cross your mind: 

* Have you ever noticed that grass pops up through cracks in the sidewalk in some places but not others? 
* Have you ever been struck by an odd smell in a new house or new car? 

Your take home assignment, should you choose to accept it, is to think of something that you have observed in an environment that matters to you. Write down your observation. Now, come up with a hypothesis that could be tested, or at least some baseline ideas that could become hypotheses after reading relevant background information. Remember that your hypothesis must be testable, and that the outcomes of your test will offer a likelihood of an answer, but not definitive proof. For the hypothesis that you create, define how precisely you are going to need to know your answer. Also define the purpose(s) you intend to use the data you collect for.

You have embarked upon the process of scientific inquiry!