I have some news to break to everyone. I’ve accepted a new position at a different company, and while it’s an extremely exciting opportunity for me, it does mean that I have to step away from the Community Builder role here.
This is a bittersweet announcement for me, because as exciting as my new job is for my career, I’ve truly loved my time at the Zooniverse, helping to grow this community and our platform and getting to know so many incredible volunteers, researchers, and staff.
However, I do want to emphasize that this is definitely not goodbye! I couldn’t possibly leave completely—there are so many projects here that I enjoy doing as much as you guys do, and so many exciting developments in the pipeline that I want to see pan out. I’m not going anywhere; instead, I’m becoming one of you: a Zooniverse volunteer. I won’t be your liaison anymore, or a source for reporting your needs, but I’ll continue to be your colleague in people-powered research.
The Zooniverse is growing and changing at an incredible rate right now, and has been for much of my time here over the past 14 months. Overall, I’m blown away by what you’ve all helped us to accomplish. Projects are being launched and completed quickly, and our new research teams are more attuned to volunteers’ needs than ever before. I’ve long believed that the launch of the Project Builder would begin a process of exponentially expanding the scope of the Zoo, and we are definitely beginning to see that happening. I can’t wait to find out, along with the rest of you, what the next chapter of this story has in store for us all.
Thank you all for everything, and I’ll be seeing you all around!
Yours in people-powered research,
Darren “DZM” McRoy
Special note from the ZooTeam — Thank you Darren for all your hard work over the years! We’re so excited for you and this new opportunity. And we very much look forward to continuing to build and strengthen the relationships between our volunteers, research teams, and the Zooniverse team. Thank you all for your contributions! Onward and upward.
Trying to understand the vast proliferation of ‘citizen science’ projects is a Herculean task right now, with projects cropping up all over the place dealing with both online data analysis like that which concerns us here at the Zooniverse and with data collection and observation of the natural world via projects like iNaturalist. As the number of projects increases, so do questions about the effectiveness of these projects, and so does our desire to keep track of the impact all of the effort put into them is having.
These aren’t easy questions to answer, and an attempt to track the use of citizen science in the literature is made by Ria Follett and Vladimir Strezov, two researchers in the Department of Environmental Sciences at Macquarie University, in a recent paper published in the journal PLOS One. They look at papers including the words ‘citizen science’, and includes the surprising result that ‘online’ projects accounted for only 12% of their sample. They explain :
The missing articles dis- cussed discoveries generated using “galaxy zoo” data, rather than acknowledging the contribtions of the citizens who created this data.
This, to me, is pushing a definition to extremes. Every one of the ‘missing’ papers cited has a link to a list of volunteers who contributed; several have volunteers listed on the author list! To claim that we’re not ‘acknowledging the contribtions’ of volunteers because we don’t use the shibboleth ‘citizen science’ is ridiculous. Other Zooniverse projects, such as Planet Hunters, don’t even appear in the study for much the same reason, and it’s sad that a referee didn’t dig deeper into the limited methodology used in the article.
Part of the problem here is the age-old argument about the term ‘citizen science’. It’s not a description most of our volunteers would use of themselves, but rather a term imposed from the academy to describe (loosely!) the growing phenomenon of public participation in public research. In most of our Galaxy Zoo papers, we refer to ‘volunteers’ rather than ‘citizen scientists’ – and we believe strongly in acknowledging the contributions of everyone to a project, whatever term they choose to label themselves with.
We’re sorry to let you know that at 16:29 BST on Wednesday last week we made a change to the Panoptes code which had the unexpected result that it failed to record classifications on six of our newest projects; Season Spotter, Wildebeest Watch, Planet Four: Terrains, Whales as Individuals, Galaxy Zoo: Bar Lengths, and Fossil Finder. It was checked by two members of the team – unfortunately, neither of them caught the fact that it failed to post classifications back. When we did eventually catch it, we fixed it within 10 minutes. Things were back to normal by 20:13 BST on Thursday, though by that time each project had lost a day’s worth of classifications.
To prevent something like this happening in the future we are implementing new code that will monitor the incoming classifications from all projects and send us an alert if any of them go unusually quiet. We will also be putting in even more code checks that will catch any issues like this right away.
It is so important to all of us at the Zooniverse that we never waste the time of any of our volunteers, and that all of your clicks contribute towards the research goals of the project. If you were one of the people whose contributions were lost we would like to say how very sorry we are, and hope that you can forgive us for making this terrible mistake. We promise to do everything we can to make sure that nothing like this happens again, and we thank you for your continued support of the Zooniverse.
Today, we launchAnnoTate, an art history and transcription project made in partnership with Tate museums and archives. AnnoTate was built with the average time-pressed user in mind, by which I mean the person who does not necessarily have five or ten minutes to spare, but maybe thirty or sixty seconds.
AnnoTate takes a novel approach to crowdsourced text transcription. The task you are invited to do is not a page, not sentences, but individual lines. If the kettle boils, the dog starts yowling or the children are screaming, you can contribute your one line and then go attend to life.
The new transcription system is powered by an algorithm that will show when lines are complete, so that people don’t replicate effort unnecessarily. As in other Zooniverse projects, each task (in this case, a line) is done by several people, so you’re not solely responsible for a line, and it’s ok if your lines aren’t perfect.
Of course, if you want trace the progression of an artist’s life and work through their letters, sketchbooks, journals, diaries and other personal papers, you can transcribe whole pages and documents in sequence. Biographies of the artists are also available, and there will be experts on Talk to answer questions.
Every transcription gets us closer to the goal of making these precious documents word searchable for scholars and art enthusiasts around the world. Help us understand the making of twentieth-century British art!
An apology is owed to all Zooniverse volunteers; We incredibly underestimated the Zooniverse Community’s ability to mobilize for the Sunspotter Citizen Science Challenge. You blew our goal of 250,000 new classifications on Sunspotter in a week out of the water! It took 16 hours to reach 250,000 classifications. I’ll say that again, 16 hours!
By 20 hours you hit 350,000 classifications. That’s an 11,000% increase over the previous day. By the end of the weekend, the total count stood at over 640,000.
Let’s up the ante, shall we? Our new goal is a cool 1,000,000 classifications by Saturday September 5th. That would increase the total number of classifications since Sunspotter launched in February 2014 by 50%!
Calling all Zooniverse volunteers! As we transition from the dog days of summer to the pumpkin spice latte days of fall (well, in the Northern hemisphere at least) it’s time to mobilize and do science!
Our Zooniverse community of over 1.3 million volunteers has the ability to focus efforts and get stuff done. Join us for the Sunspotter Citizen Science Challenge! From August 29th to September 5th, it’s a mad sprint to complete 250,000 classifications on Sunspotter.
Sunspotter needs your help so that we can better understand and predict how the Sun’s magnetic activity affects us on Earth. The Sunspotter science team has three primary goals:
Hone a more accurate measure of sunspot group complexity
Improve how well we are able to forecast solar activity
Create a machine-learning algorithm based on your classifications to automate the ranking of sunspot group complexity
In order to achieve these goals, volunteers like you compare two sunspot group images taken by the Solar and Heliospheric Observatory and choose the one you think is more complex. Sunspotter is what we refer to as a “popcorn project”. This means you can jump right in to the project and that each classification is quick, about 1-3 seconds.
Let’s all roll up our sleeves and advance our knowledge of heliophysics!
In the previous post, I described the creation of the Zooniverse Project Success Matrix from Cox et al. (2015). In essence, we examined 17 (well, 18, but more on that below) Zooniverse projects, and for each of them combined 12 quantitative measures of performance into one plot of Public Engagement versus Contribution to Science:
The aim of this post is to answer the questions: What does it mean? And what doesn’t it mean?
Discussion of Results
The obvious implication of this plot and of the paper in general is that projects that do well in both public engagement and contribution to science should be considered “successful” citizen science projects. There’s still room to argue over which is more important, but I personally assert that you need both in order to justify having asked the public to help with your research. As a project team member (I’m on the Galaxy Zoo science team), I feel very strongly that I have a responsibility both to use the contributions of my project’s volunteers to advance scientific research and to participate in open, two-way communication with those volunteers. And as a volunteer (I’ve classified on all the projects in this study), those are the 2 key things that I personally appreciate.
It’s apparent just from looking at the success matrix that one can have some success at contributing to science even without doing much public engagement, but it’s also clear that every project that successfully engages the public also does very well at research outputs. So if you ignore your volunteers while you write up your classification-based results, you may still produce science, though that’s not guaranteed. On the other hand, engaging with your volunteers will probably result in more classifications and better/more science.
Surprises, A.K.A. Failing to Measure the Weather
Some of the projects on the matrix didn’t appear quite where we expected. I was particularly surprised by the placement of Old Weather. On this matrix it looks like it’s turning in an average or just-below-average performance, but that definitely seems wrongto me. And I’m not the only one: I think everyone on the Zooniverse team thinks of the project as a huge success. Old Weather has provided robust and highly useful data to climate modellers, in addition to uncovering unexpected data about important topics such as the outbreak and spread of disease. It has also provided publications for more “meta” topics, including the study of citizen science itself.
Additionally, Old Weather has a thriving community of dedicated volunteers who are highly invested in the project and highly skilled at their research tasks. Community members have made millions of annotations on log data spanning centuries, and the researchers keep in touch with both them and the wider public in multiple ways, including a well-written blog that gets plenty of viewers. I think it’s fair to say that Old Weather is an exceptional project that’s doing things right. So what gives?
There are multiple reasons the matrix in this study doesn’t accurately capture the success of Old Weather, and they’re worth delving into as examples of the limitations of this study. Many of them are related to the project being literally exceptional. Old Weather has crossed many disciplinary boundaries, and it’s very hard to put such a unique project into the same box as the others.
Firstly, because of the way we defined project publications, we didn’t really capture all of the outputs of Old Weather. The use of publications and citations to quantitatively measure success is a fairly controversial subject. Some people feel that refereed journal articles are the only useful measure (not all research fields use this system), while others argue that publications are an outdated and inaccurate way to measure success. For this study, we chose a fairly strict measure, trying to incorporate variations between fields of study but also requiring that publications should be refereed or in some other way “accepted”. This means that some projects with submitted (but not yet accepted) papers have lower “scores” than they otherwise might. It also ignores the direct value of the data to the team and to other researchers, which is pretty punishing for projects like Old Weather where the data itself is the main output. And much of the huge variety in other Old Weather outputs wasn’t captured by our metric. If it had been, the “Contribution to Science” score would have been higher.
Secondly, this matrix tends to favor projects that have a large and reasonably well-engaged user base. Projects with a higher number of volunteers have a higher score, and projects where the distribution of work is more evenly spread also have a higher score. This means that projects where a very large fraction of the work is done by a smaller group of loyal followers are at a bit of a disadvantage by these measurements. Choosing a sweet spot in the tradeoff between broad and deep engagement is a tricky task. Old Weather has focused on, and delivered, some of the deepest engagement of all our projects, which meant these measures didn’t do it justice.
To give a quantitative example: the distribution of work is measured by the Gini coefficient (on a scale of 0 to 1), and in our metric lower numbers, i.e. more even distributions, are better. The 3 highest Gini coefficients in the projects we examined were Old Weather (0.95), Planet Hunters (0.93), and Bat Detective (0.91); the average Gini coefficient across all projects was 0.82. It seems clear that a future version of the success matrix should incorporate a more complex use of this measure, as very successful projects can have high Gini coefficients (which is another way of saying that a loyal following is often a highly desirable component of a successful citizen science project).
Thirdly, I mentioned in part 1 that these measures of the Old Weather classifications were from the version of the project that launched in 2012. That means that, unlike every other project studied, Old Weather’s measures don’t capture the surge of popularity it had in its initial stages. To understand why that might make a huge difference, it helps to compare it to the only eligible project that isn’t shown on the matrix above: The Andromeda Project.
In contrast to Old Weather, The Andromeda Project had a very short duration: it collected classifications for about 4 weeks total, divided over 2 project data releases. It was wildly popular, so much so that the project never had a chance to settle in for the long haul. A typical Zooniverse project has a burst of initial activity followed by a “long tail” of sustained classifications and public engagement at a much lower level than the initial phase.
The Andromeda Project is an exception to all the other projects because its measures are only from the initial surge. If we were to plot the success matrix including The Andromeda Project in the normalizations, the plot looks like this:
Because we try to control for project duration, the very short duration of the Andromeda Project means it gets a big boost. Thus it’s a bit unfair to compare all the other projects to The Andromeda Project, because the data isn’t quite the same.
However, that’s also true of Old Weather — but instead of only capturing the initial surge, our measurements for Old Weather omit it. These measurements only capture the “slow and steady” part of the classification activity, where the most faithful members contribute enormously but where our metrics aren’t necessarily optimized. That unfairly makes Old Weather look like it’s not doing as well.
In fact, comparing these 2 projects has made us realize that projects probably move around significantly in this diagram as they evolve. Old Weather’s other successes aren’t fully captured by our metrics anyway, and we should keep those imperfections and caveats in mind when we apply this or any other success measure to citizen science projects in the future; but one of the other things I’d really like to see in the future is a study of how a successful project can expect to evolve across this matrix over its life span.
Why do astronomy projects do so well?
There are multiple explanations for why astronomy projects seem to preferentially occupy the upper-right quadrant of the matrix. First, the Zooniverse was founded by astronomers and still has a high percentage of astronomers or ex-astronomers on the payroll. For many team members, astronomy is in our wheelhouse, and it’s likely this has affected decisions at every level of the Zooniverse, from project selection to project design. That’s starting to change as we diversify into other fields and recruit much-needed expertise in, for example, ecology and the humanities. We’ve also launched the new project builder, which means we no longer filter the list of potential projects: anyone can build a project on the Zooniverse platform. So I think we can expect the types of projects appearing in the top-right of the matrix to broaden considerably in the next few years.
The second reason astronomy seems to do well is just time. Galaxy Zoo 1 is the first and oldest project (in fact, it pre-dates the Zooniverse itself), and all the other Galaxy Zoo versions were more like continuations, so they hit the ground running because the science team didn’t have a steep learning curve. In part because the early Zooniverse was astronomer-dominated, many of the earliest Zooniverse projects were astronomy related, and they’ve just had more time to do more with their big datasets. More publications, more citations, more blog posts, and so on. We try to control for project age and duration in our analysis, but it’s possible there are some residual advantages to having extra years to work with a project’s results.
Moreover, those early astronomy projects might have gotten an additional boost from each other: they were more likely to be popular with the established Zooniverse community, compared to similarly early non-astronomy projects which may not have had such a clear overlap with the established Zoo volunteers’ interests.
The citizen science project success matrix presented in Cox et al. (2015) is the first time such a diverse array of project measures have been combined into a single matrix for assessing the performance of citizen science projects. We learned during this study that public engagement is well worth the effort for research teams, as projects that do well at public engagement also make better contributions to science.
It’s also true that this matrix, like any system that tries to distill such a complex issue into a single measure, is imperfect. There are several ways we can improve the matrix in the future, but for now, used mindfully (and noting clear exceptions), this is generally a useful way to assess the health of a citizen science project like those we have in the Zooniverse.
What makes one citizen science project flourish while another flounders? Is there a foolproof recipe for success when creating a citizen science project? As part of building and helping others build projects that ask the public to contribute to diverse research goals, we think and talk a lot about success and failure at the Zooniverse.
But while our individual definitions of success overlap quite a bit, we don’t all agree on which factors are the most important. Our opinions are informed by years of experience, yet before this year we hadn’t tried incorporating our data into a comprehensive set of measures — or “metrics”. So when our collaborators in the VOLCROWE project proposed that we try to quantify success in the Zooniverse using a wide variety of measures, we jumped at the chance. We knew it would be a challenge, and we also knew we probably wouldn’t be able to find a single set of metrics suitable for all projects, but we figured we should at least try to write down onepossible approach and note its strengths and weaknesses so that others might be able to build on our ideas.
In this study, we only considered projects that were at least 18 months old, so that all the projects considered had a minimum amount of time to analyze their data and publish their work. For a few of our earliest projects, we weren’t able to source the raw classification data and/or get the public-engagement data we needed, so those projects were excluded from the analysis. We ended up with a case study of 17 projects in all (plus the Andromeda Project, about which more in part 2).
In late July I led a week-long course about crowdsourcing and data visualization at the Digital Humanities Oxford Summer School. I taught the crowdsourcing part, while my friend and collaborator, Sarah, from Google, lead the data visualization part. We had six participants from fields as diverse as history, archeology, botany and literature, to museum and library curation. Everyone brought a small batch of images, and used the new Zooniverse Project Builder (“Panoptes”) to create their own projects. We asked participants what were their most pressing research questions? If the dataset were larger, why would crowdsourcing be an appropriate methodology, instead of doing the tasks themselves? What would interest the crowd most? What string of questions or tasks might render the best data to work with later in the week?
Within two days everyone had a project up and running. We experienced some teething problems along the way (Panoptes is still in active development) but we got there in the end! Everyone’s project looked swish, if you ask me.
Participants had to ‘sell’ their projects in person and on social media to attract a crowd. The rates of participation were pretty impressive for a 24-hour sprint. Several hundred classifications were contributed, which gave each project owner enough data to work with.
But of course, a good looking website and good participation rates do not equate to easy-to-use or even good data! Several of us found that overly complex marking tasks rendered very convoluted data and clearly lost people’s attention. After working at the Zooniverse for over a year I knew this by rote, but I’d never really had the experience of setting up a workflow and seeing what came out in such a tangible way.
Despite the variable data, everyone was able to do something interesting with their results. The archeologist working on pottery shards investigated whether there was a correlation between clay color and decoration. Clay is regional, but are decorative fashions regional or do they travel? He found, to his surprise, that they were widespread.
In the end, everyone agreed that they would create simpler projects next time around. Our urge to catalogue and describe everything about an object—a natural result of our training in the humanities and GLAM sectors—has to be reined in when designing a crowdsourcing project. On the other hand, our ability to tell stories, and this particular group’s willingness to get to grips with quantitative results, points to a future where humanities specialists use crowdsourcing and quantitative methods to open up their research in new and exciting ways.
Julie A. Feldt is one of the educators behind Zooniverse.org. She first came to us in Summer 2013 as an intern at the Adler Planetarium to develop and test out Skype in the Classroom lessons and ended up joining the team the following winter. Julie was the lead educator in the development of the Planet Hunters Educators Guide. Here she shares some information on the development and contents of this resource.
In collaboration with NASA JPL, we have developed the Planet Hunters Educators Guide, which is 9 lessons aimed for use in middle school classrooms. This guide was developed for each lesson to build upon each other while also providing all the information needed to do them alone. Teacher can choose to do one lesson on its own or the entire collection. Each lesson was planned out using the 5E method and to be accomplishable in a single 45 to 60 minute class period with some Evaluate sections as take home assignments. In development we focused on the science behind Planet Hunters and utilized JPL’s Exoplanet Exploration program and tools from PlanetQuest in order to connect with our partners in this field.
Through this guide, we want to introduce teachers and their classrooms to citizen science, exoplanet discovery, and how the science behind the Planet Hunters project is conducted. Lesson 1 starts by acquainting the class with what citizen science is and looking at several projects, mostly outside of the Zooniverse. This lesson is great for teachers who just want to talk about citizen science in general and therefore it encompassesmany different types of citizen science projects. The rest of the lessons go into the understanding of exoplanets and using Planet Hunters in a classroom setting.
We wanted to give teachers the lessons they may need to build student understanding of the research and science done in Planet Hunters. Therefore, Lessons 2 through 5 focus on developing knowledge of possible life outside our solar system, the methods used to discover new worlds, and what makes those worlds habitable. For instance, in Lesson 2 students explore our own solar system with consideration of where life as we know it, directing them to the idea that there may be a habitable zone in our solar system. The students are asked to break up into groups to discuss how each of the planets compare with consideration of their location . We provided solar system information cards, see an example below, for students to be able to determine the conditions necessary for life as we know it to develop and survive.
Lesson 6 is purely about getting students acquainted with Planet Hunters, specifically how to use it and navigate the website for information. This lesson can be great for the teachers that just want to show their students how they can be a part of real scientific research. After, students use the project data to find their own results and visuals on exoplanets found in Planet Hunters. Something to note, lesson 7 and 8 are pretty similar, but Lesson 8 incorporates a higher level of math for the more adventurous or older classrooms. Lesson 9 either wraps up the guide nicely or can be a fun activity to add to your science class where the students creativity and imagination comes out through designing what they believe a real exoplanet looks like, see summary from first page below.
We hope our teachers enjoy using this product! We would love you hear how you have used it and any feedback that could be used in any future development of teacher guides for other projects.