Category Archives: Citizen Science

Civic Engagement with Data at Carnegie Library of Pittsburgh

Photo of green maple leaves in dappled sunlight

We caught up in May with Eleanor Tutt of Carnegie Library of Pittsburgh, who also has funding from IMLS to work on data but with a focus on citizen engagement with data. Here’s an excerpt from a blog post outlining some of their work:

Data seems to come up in all sorts of conversations these days, and they reach way beyond math class. For example, civic data—which includes information about our city and citizens—is a great way to engage with your community on a deeper level, and can be a powerful tool for change! Since civic data is about the people and places you see every day, it can be tough to notice. Based out of CLP-Main, the Civic Information Services team is helping to uncover and share the ways data fits into life at the Library and throughout Pittsburgh, and we have a lot of fun stuff in store.

The STEM Committee has been busy sowing the seeds for their Super Science Kits, and we just couldn’t wait to join them. Some of our favorite collaborations so far can be found in the Tree Kit. Two activities included in this kit feature data front and center: “Forest Logbook” and “Make a Tree Map.”

Have you ever kept a nature journal? Ever taken notes while walking in the woods? Surprise! You were actually collecting data. The “Forest Logbook” activity invites you to tame wild data with a pencil and paper. While on a short nature walk around the library, kids will keep a close eye on the plants and animals they encounter, making notes as they go. Collecting nature data is especially exciting because we can measure anything from the size of a tree trunk to the furriness of a squirrel’s tail. And what fun would our data be if we couldn’t share it with friends? The group is encouraged to share and compare data with each other, which gives us the chance to spot similarities and differences. This activity serves as an easy introduction to observation and collaboration, both of which are crucial steps in data collection. While the trees are busy making oxygen outside, do you ever wonder what’s up with the air in your own home? You can check out one of our Speck Air Quality Monitors for some super practical data collection.

Pittsburghers are really lucky when it comes to data, because the Western Pennsylvania Regional Data Center gives us access to a bunch of cool civic information, from playgrounds to bus stops. The dataset we use for our “Make a Tree Map” activity was created by the City of Pittsburgh. Taking a close look at data and comparing it to what we see outside is an important part of data literacy, as we can use that step to determine why and how data is collected. After that, it’s time to create our own tree maps! Because we can create our map using characteristics from climbability to circumference, each one will be a totally unique look at the same set of data.

Sound fun? Read the rest of the post here. Also, here’s some trivia: at the time of his retirement, my great-uncle was the longest-serving employee at this library system, having spent over 40 years as a bookbinder.

Image: (public domain)



2017 Conference dates chosen!

Decorative - 4T Data Literacy conference logo

We’re excited to announce that the 2017 4T Data Literacy Virtual Conference dates have been announced! We’ll meet virtually on July 20-21, 2017. This year, we’re focusing our presentations on three (and a half) themes:

  1. Big Data, including citizen science
  2. Ethical data use
  3. Personal data management

Registration and more details will be forthcoming soon. If you registered last year, you’re already on our list and will let you know when it’s time to sign up!

Using samples in citizen science

Interested in embarking on a citizen science project? One way to learn about the world around you is to take a sample. In fact, this spring the radio and podcast program, Science Friday, encouraged listeners to take samples, which sparked a variety of ideas from listeners.

So how do you go about getting a sample? As Charles Wheelan writes in Naked Statistics, it’s like soup! In all seriousness, best practice is to take a representative sample.  Wheelan explains that:

[t]he key idea is that a properly drawn sample will look like the population from which it is drawn. In terms of intuition, you can envision sampling a pot of soup with a single spoonful. If you’ve stirred the soup adequately, a single spoonful can tell you how the whole pot tastes.

This soup analogy is informative. If a sample is not representative (or the soup is not well-stirred), we cannot make generalizations. Wheelan explains:

[s]ize matters, and bigger is better…it should be intuitive that a larger sample will help smooth away any freak variation. (A bowl of soup will be an even better test than a spoonful.) One crucial caveat is that a bigger sample will not make up for errors in its composition, or “bias.” A bad sample is a bad sample.

From a sample, we may learn something about a population, but we must take care not to overgeneralize. For more on samples and other statistical concepts, Naked Statistics is a useful primer and one of the books that our team has enjoyed.


Source: Wheelan, Charles. Naked Statistics: Stripping the Dread from the Data. New York: W.W. Norton, 2014.

Image: “Pot Steaming Hot Cooking Kitchen Stove Cooker” by Republica, on Pixabay. CC0 Public Domain.

Reading Recommendation: Diary of a Citizen Scientist

One way for you and your students to get your feet wet with data is citizen science. Citizen science endeavors involve collecting data, which make such projects great activities for applying data literacy skills. In fact, citizen science is one of our themes for the second year of this project, starting in the fall!

For ideas to embark on a citizen science project, check out the book, Diary of a Citizen Scientist: Chasing Tiger Beetles and Other New Ways of Engaging the World by Sharman Apt Russell. Russell writes about her project to study the Western red-bellied tiger beetle by the Gila River (pictured above) in southwestern New Mexico. This book is mix of a diary, environmental messages, and how-to guide for being a citizen scientist. Her work will inspire you to dive into a citizen science project. Not only will you learn about Russell’s research but also about other citizen science initiatives, like Galaxy Zoo and Project FeederWatch.

Russell chronicles her successes and challenges, as well as reflects on her motivation for doing citizen science, in the book:

We all want to be part of something larger. We want to be part of a family, a community, a cause. We want to be part of something meaningful. Studies show that long-term happiness depends on this engagement. I personally want to advance conservation policy. I want to do real science. I want to learn more science.

It’s inspiring! This book is in the style of nature writing with both personal reflections and scientific information. Russell weaves stories and tips in with descriptions of her experiences. Reading her account makes a citizen science project seem manageable and provides a great introduction to citizen science.


Source: Russell, Sharman Apt. Diary of a Citizen Scientist: Chasing Tiger Beetles and Other New Ways of Engaging the World. Corvallis, OR: Oregon State University Press, 2014.

Image: Middle Fork of the Gila River, SW New Mexico” by Joe Burgess, on Wikipedia. Public Domain.

Data and the Flint Water Crisis

What's behind Flint Water Crisis?

This week I worked with U-M Library’s Emergent Research Committee to bring Marty Kaufman to the library.  He talked about the data gathering that he’s been doing to help map the locations of lead pipes in Flint’s water system.  For more on his talk see Patricia Anderson’s great Storified version  or the MLive coverage of his presentation.

Whenever I mention the Flint Water Crisis to students they become really engaged.  My colleagues and I are using the information about this issue to discuss things like the nature of authority in informational sources (“Authority is Constructed and Contextual” for my academic librarian peeps), and students seem to really catch on.  I talked with my advanced research students yesterday about the crisis, and I didn’t have to remind them in any way about what’s going on.  And just the other day, my 24 year old nephew mentioned that he had stayed up until midnight to watch the Flint Water Crisis Congressional hearings.  He says that sometimes watching election coverage seems like he’s watching some kind of movie.  But what’s happening in Flint seems “real.”

Granted, what’s happening in Flint seems very local in my community.  And the governor lives in Ann Arbor when he’s not in Lansing.  I don’t want to seem like I’m looking at what’s happening in just a clinical, removed kind of way.   This issue resonates with so many people.  And there’s a very data-related component to what’s happening.

In his presentation, Marty talked about how he and his team had to go through thousands of penciled index cards (“big” data) to determine where lead pipes are located.  He said that it’s difficult to get a clear sense of where things are because of unclear index cards (data) — yet they have to draw some kinds of conclusions.  Based on the data that they’ve gathered, the team is going to have to use predictive models to determine the likelihood of where lead pipes might be in places without clear index cards.

When asked what the general public should do, Marty made a clear case for all of us to become data collectors — He told us to look for lead in the water systems, paint, and toys within our own homes (a place where lead might be impacting all of us the most!) and record it accurately and PERMANENTLY (don’t use pencil!).  Somehow, if we can all gather this kind of data and pool our information, we can better address the serious issue of how lead exposure can impact our lives.

One of the most striking images in Marty’s presentation is the image of  undergraduates working in his GIS lab, many of them Flint-area residents.  Marty says that he had to kick the students out at the end of the day because they were so invested in their work.  Understanding that data comes from “somewhere” and that having good data can make a real impact on your life is a huge motivator.   Data and data literacy matter.

Photo: Ben Gordon, CCBY-NC-SA 2.0