Category Archives: News

Corporate Digital Engagement and volunteering through Zooniverse

Over the years a growing number of companies have included Zooniverse in their digital engagement and volunteer efforts, connecting their employee network with real research projects that need their help.

It’s been lovely hearing the feedback from employees:

“This was an awesome networking event where we met different team members and also participated in a wonderful volunteer experience. I had so much fun!”

“This activity is perfectly fitted to provide remote/virtual support. You can easily review photos from anywhere. Let’s do this again!”

“Spotting the animals was fun; a nice stress reliever!’

The impact of these partnerships on employees and on Zooniverse has been tremendous. For example, in 2020 alone, 10,000+ Verizon employees contributed over a million classifications across dozens of Zooniverse projects. With companies small to large incorporating Zooniverse into their volunteer efforts, this new stream of classifications has been a tremendous boon for helping propel Zooniverse projects towards completion and into the analysis and dissemination phases of their efforts. And the feedback from employees has been wonderful — participants across the board express their appreciation for having a meaningful way to engage in real research through their company’s volunteer efforts. 

A few general practices that have helped set corporate volunteering experiences up for success:

  • Focus and choice: Provide a relatively short list of recommended Zooniverse projects that align with your company’s goals/objectives (e.g., topic-specific, location-specific, etc.), but also leave room for choice. We have found that staff appreciate when a company provides 3-6 specific project suggestions (so they can dive quickly into a project), as well as having the option to choose from the full list of 70+ projects at zooniverse.org/projects
  • Recommend at least 3 projects: This is essential in case there happens to be a media boost for a given project before your event and the project runs out of active data*. Always good to have multiple projects to choose from. 
  • Team building: Participation in Zooniverse can be a tremendous team building activity. While it can work well to just have people participate individually, at their own convenience, it also can be quite powerful to participate as a group. We have created a few different models for 1-hour, 3-hour, and 6-hour team building experiences. The general idea is that you start the session as a group to learn about Zooniverse and the specific project you’ll be participating in. You then set a Classification Challenge for the hour (e.g., as a group of 10, we think we can contribute 500 classifications by the end of the hour). You play music in the background while you classify and touch base halfway through to see how you’re doing towards your goal (by checking your personal stats at zooniverse.org) and to share interesting, funny, and/or unusual images you’ve classified. At the end of the session, you celebrate reaching your group’s Classification Challenge goal and talk through a few reflection questions about the experience and other citizen science opportunities you might explore in the future. 
  • Gathering stats: Impact reports have been key in helping a company tell the story of the impact of their corporate volunteering efforts, both internally to their employee network and externally to their board and other stakeholders. 
    • Some smaller companies (or subgroups within a larger company) manually gather stats about their group’s participation in Zooniverse. They do this by taking advantage of the personal stats displayed within the Zooniverse.org page (e.g., number of classifications you’ve contributed). They request that their staff register and login to Zooniverse before participating and send a screenshot of their Zooniverse.org page at the end of each session. The team lead then adds up all the classifications and records the hours spent as a group participating in Zooniverse. 
    • If manual stats collection is not feasible for your company, don’t hesitate to reach out to us at contact@zooniverse.org to explore possibilities together. 

We’ve also created a variety of bespoke experiences for companies who are interested in directly supporting the Zooniverse. Please email contact@zooniverse.org if you’re interested in exploring further and/or have any questions. 

If you’re a teacher, school administrator, student, or anyone else who might be interested in having Zooniverse help you in fulfilling student volunteer or service hour requirements, please check out https://blog.zooniverse.org/2020/03/26/fulfilling-service-hour-requirements-through-zooniverse/ 

*Zooniverse project datasets range in size; everything from a project’s dataset being fully completed within a couple weeks (e.g., The Andromeda Project) to projects like Galaxy Zoo and Snapshot Serengeti that have run and will continue to run for many years. But even for projects that have data that will last many months or years, standard practice is to upload data in batches, lasting ~2-4 months. When a given dataset is completed, this provides an opportunity for the researchers to share updates about the project, interim results, etc. and encourage participation in the next cycle of active data. 

New Results for Milky Way Project Yellowballs!

What are “Yellowballs?” Shortly after the Milky Way Project (MWP) was launched in December 2010, volunteers began using the discussion board to inquire about small, roundish “yellow” features they identified in infrared images acquired by the Spitzer Space Telescope. These images use a blue-green-red color scheme to represent light at three infrared wavelengths that are invisible to our eyes. The (unanticipated) distinctive appearance of these objects comes from their similar brightness and extent at two of these wavelengths: 8 microns, displayed in green, and 24 microns, displayed in red. The yellow color is produced where green and red overlap in these digital images. Our early research to answer the volunteers’ question, “What are these `yellow balls’?” suggested that they are produced by young stars as they heat the surrounding gas and dust from which they were born. The figure below shows the appearance of a typical yellowball (or YB) in a MWP image.  In 2016, the MWP was relaunched with a new interface that included a tool that let users identify and measure the sizes of YBs. Since YBs were first discovered, over 20,000 volunteers contributed to their identification, and by 2017, volunteers catalogued more than 6,000 YBs across roughly 300 square degrees of the Milky Way. 

New star-forming regions. We’ve conducted a pilot study of 516 of these YBs that lie in a 20-square-degree region of the Milky Way, which we chose for its overlap with other large surveys and catalogs. Our pilot study has shown that the majority of YBs are associated with protoclusters – clusters of very young stars that are about a light-year in extent (less than the average distance between mature stars.) Stars in protoclusters are still in the process of growing by gravitationally accumulating gas from their birth environments. YBs that represent new detections of star-forming regions in a 6-square-degree subset of our pilot region are circled in the two-color (8 microns: green, 24 microns: red) image shown below. YBs present a “snapshot” of developing protoclusters across a wide range of stellar masses and brightness. Our pilot study results indicate a majority of YBs are associated with protoclusters that will form stars less than ten times the mass of the Sun.

YBs show unique “color” trends. The ratio of an object’s brightness at different wavelengths (or what astronomers call an object’s “color”) can tell us a lot about the object’s physical properties. We developed a semi-automated tool that enabled us to conduct photometry (measure the brightness) of YBs at different wavelengths. One interesting feature of the new YBs is that their infrared colors tend to be different from the infrared colors of YBs that have counterparts in catalogs of massive star formation (including stars more than ten times as massive as the Sun). If this preliminary result holds up for the full YB catalog, it could give us direct insight into differences between environments that do and don’t produce massive stars. We would like to understand these differences because massive stars eventually explode as supernovae that seed their environments with heavy elements. There’s a lot of evidence that our Solar System formed in the company of massive stars.

The figure below shows a “color-color plot” taken from our forthcoming publication. This figure plots the ratios of total brightness at different wavelengths (24 to 8 microns vs. 70 to 24 microns) using a logarithmic scale. Astronomers use these color-color plots to explore how stars’ colors separate based on their physical properties. This color-color plot shows that some of our YBs are associated with massive stars; these YBs are indicated in red. However, a large population of our YBs, indicated in black, are not associated with any previously studied object. These objects are generally in the lower right part of our color-color plot, indicating that they are less massive and cooler then the objects in the upper left. This implies there is a large number of previously unstudied star-forming regions that have been discovered by MWP volunteers. Expanding our pilot region to the full catalog of more than 6,000 YBs will allow us to better determine the physical properties of these new star-forming regions.

Volunteers did a great job measuring YB sizes!  MWP volunteers used a circular tool to measure the sizes of YBs. To assess how closely user measurements reflect the actual extent of the infrared emission from the YBs, we compared the user measurements to a 2D model that enabled us to quantify the sizes of YBs. The figure below compares the sizes measured by users to the results of the model for YBs that best fit the model. It indicates a very good correlation between these two measurements. The vertical green lines show the deviations in individual measurements from the average. This illustrates the “power of the crowd” – on average, volunteers did a great job measuring YB sizes!

Stay tuned…  Our next step is to extend our analysis to the entire YB catalog, which contains more than 6,000 YBs spanning the Milky Way. To do this, we are in the process of adapting our photometry tool to make it more user-friendly and allow astronomy students and possibly even citizen scientists to help us rapidly complete photometry on the entire dataset.

Our pilot study was recently accepted for publication in the Astrophysical Journal. Our early results on YBs were also presented in the Astrophysical Journal, and in an article in Frontiers for Young Minds, a journal for children and teens.

Researchers working to improve participant learning through Zooniverse

Our research group at Syracuse University spends a lot of time trying to understand how participants master tasks given the constraints they face. We conducted two studies as a part of a U.S. National Science Foundation grant to build Gravity Spy, one of the most advanced citizen science projects to date (see: www.gravityspy.org). We started with two questions: 1) How best to guide participants through learning many classes? 2) What type of interactions do participants have that lead to enhanced learning?  Our goal was to improve experiences on the project. Like most internet sites, Zooniverse periodically tries different versions of the site or task and monitors how participants do.

We conducted two Gravity Spy experiments (the results were published via open access: article 1 and article 2). Like in other Zooniverse projects, Gravity Spy participants supply judgments to an image subject, noting which class the subject belongs to. Participants also have access to learning resources such as the field guide, about pages, and ‘Talk’ discussion forums. In Gravity Spy, we ask participants to review spectrograms to determine whether a glitch (i.e., noise) is present. The participant classifications are supplied to astrophysicists who are searching for gravitational waves. The classifications help isolate glitches from valid gravitational-wave signals.

Gravity Spy combines human and machine learning components to help astrophysicists search for gravitational waves. Gravity Spy uses machine learning algorithms to determine the likelihood of a glitch belonging to a particular glitch class (currently, 22 known glitches appear in the data stream); the output is a percentage likelihood of being in each category.

Figure 1. The classification interface for a high level in Gravity Spy

Gradual introduction to tasks increases accuracy and retention. 

The literature on human learning is unclear about how many classes people can learn at once. Showing too many glitch class options might discourage participants since the task may seem too daunting, so we wanted to develop training while also allowing them to make useful contributions. We decided to implement and test leveling, where participants can gradually learn to identify glitch classes across different workflows. In Level 1, participants see only two glitch class options; in Level 2, they see 6; in Level 3, they see 10, and in Level 4, 22 glitch class options. We also used the machine learning results to route more straightforward glitches to lower levels and the more ambiguous subjects to higher workflows. So participants in Level 1 only saw subjects that the algorithm was confident a participant could categorize accurately. However, when the percentage likelihood was low (meaning the classification task became more difficult), we routed these to higher workflows.

We experimented to determine what this gradual introduction into the classification task meant for participants. One group of participants were funneled through the training described above (we called it machine learning guided training or MLGT);  another group of participants was given all 22 classes at once.  Here’s what we found:  

  • Participants who completed MLGT were more accurate than participants who did not receive the MLGT (90% vs. 54%).  
  • Participants who completed MLGT executed more classifications than participants who did not receive the MLGT (228 vs. 121 classifications).
  • Participants who completed MLGT had more sessions than participants who did not receive the MLGT (2.5 vs. 2 sessions). 

The usefulness of resources changes as tasks become more challenging

Anecdotally, we know that participants contribute valuable information on the discussion boards, which is beneficial for learning. We were curious about how participants navigated all the information resources on the site and whether those information resources improved people’s classification accuracy. Our goal was to (1) identify learning engagements, and (2) determine if those learning engagements led to increased accuracy. We turned on analytics data and mined these data to determine which types of interactions (e.g., posting comments, opening the field guide, creating collections) improved accuracy. We conducted a quasi-experiment at each workflow, isolating the gold standard data (i.e., the subjects with a known glitch class). We looked at each occasion a participant classified a gold standard subject incorrectly and determined what types of actions a participant made between that classification and the next classification of the same glitch class. We mined the analytics data to see what activities existed between Classification A and Classification B. We did some statistical analysis, and the results were astounding and cool. Here’s what we found:  

  • In Level 1, no learning actions were significant. We suspect this is because the tutorial and other materials created by the science team are comprehensive, and most people are accurate in workflow 1 (~97%).
  • In Level 2 and Level 3, collections, favoriting subjects, and the search function was most valuable for improving accuracy. Here, participants’ agency seems to help to learn. Anecdotally, we know people collect and learn from ambiguous subjects.
  • In Level 4, we found that actions such as posting comments and, viewing the collections created by other participants were most valuable for improving accuracy. Since the most challenging glitches are administered in workflow 4, participants seek feedback from others.

The one-line summary of this experiment is that when tasks are more straightforward, learning resources created by the science teams are most valuable; however, as tasks become more challenging, learning is better supported by the community of participants through the discussion boards and collections. Our next challenge is making these types of learning engagements visible to participants.

Note: We would like to thank the thousands of Gravity Spy participants without whom this research would not be possible. This work was supported by a U.S. National Science Foundation grant No. 1713424 and 1547880. Check out Citizen Science Research at Syracuse for more about our work.

supernova hunters and nine lessons for curious people

At the weekend, a bunch of us had fun with a timely challenge – trying to find and follow-up supernovae with supernova hunters as part of the Nine Lessons and Carols for Curious People 24 hour science/music/comedy show organised by Robin Ince and the Cosmic Shambles Network in support of various good causes. Robin and Brian Cox normally run a huge show at the Hammersmith Apollo theatre at this time of year, but this socially distant, marathon show was a suitable replacement.

Robin and musician Steve Pretty somewhere in the middle of the 24 and a bit hour long show – they were on stage throughout! Credit: Cosmicshambles.com

In the run up to the show there was some concern that poor weather in Hawai’i – where the PanSTARRS telescope that provides data for Supernova Hunters is located – might prevent us getting enough data, but in the event skies were clear. Very clear. Which caused a problem as the extra data took a while to get to the servers at Queen’s University Belfast and from there to us, but thanks to heroic efforts from the Supernova Hunters team, I was able to zoom into the show early on and pointed the viewers to the supernovahunters.org site, and classifications started to flow in.

Supernova hunting is a competitive sport these days, and though the early results from volunteers were encouraging, most of what we found was either too faint to make follow-up easy with the telescopes we had on stand by or were objects already identified by other surveys (including the Zooniverse’s friends at ZTF). A brief reappearance on the Nine Lessons big screen (and an email to existing volunteers asking for help) later and we finally had a set of good candidates.

Liverpool Telescope in the Canary Islands, which was responsible for our first follow-up observations. Credit: Liverpool Telescope.

The team – especially Ken Smith and Darryl Wright – worked overnight to arrange follow-up. When I emerged from a few hours sleep observers at the Liverpool Telescope had checked out our most promising candidate – but it turned out not to be a supernova, but rather a less extreme cosmic explosion known as a cataclysmic variable. I marvelled at the fact Robin was still awake – and was coherently interviewing cosmologists, brain scientists and the odd astronaut – and gave an update.

Just after I finished, Belfast’s Ken Smith popped up with the news that observers in Hawai’i using the SNIFS instrument had followed up other targets – and one of them was a real supernova! Better, it was a type 1a – the kind of supernova that can be used to measure the expansion rate of the Universe. Admittedly it was a type 1a-91bg, a rarer type of supernova which is fainter than a normal type 1a, but still useful, and this gave us a payoff for the show.

Spectrum confirming our candidate is a SN1a-91bg associated with a galaxy at redshift z=0.061 – light from an explosion that happened nearly a billion years ago.

Using only that supernova, a bit of maths on the back of an envelope and a few fairly shaking assumptions, we calculated that the Universe was 12.8 billion years old, about a billion short of the commonly accepted value. I wouldn’t throw out the careful systematic analysis of populations of supernova for this simple calculation – but we did get to announce to a bleary eyed comedian that the Universe might be (a little bit) younger than expected.

Just as I went on air a message from Mark Huber, the observer providing data from Hawai’i, confirmed a second supernova – this one a type II, an exploding massive star. It might even be of the same type as the famous 1987A which was spotted in a satellite galaxy of the Milky Way, the Large Magellanic Cloud. Trying to take this in, and convey what was happening quickly was bit much for my sleep-deprived brain but hopefully people realised we confirmed a second supernova!

More importantly, we’ve recorded the results of all of our discoveries in a Astronote published on the Transient Name Server website (the worldwide clearing house for such discoveries). You can read the result of a Supernova Hunters weekend here – and rejoice in the fact that Robin Ince and some of the Cosmic Shambles team are now coauthors on a scientific publication!

I’ll post links to clips from the show when they’re available too, and if you fancy supernova hunting yourself there will be more data on the supernovahunters.org site soon!

Chris

PS Thanks a million to the Supernova Hunters volunteers, and to the team that made it happen – Brooke Simmons (Lancaster), Ken Smith (Belfast), Darryl Wright (Mayo Clinic), Coleman Krawczyk (Portsmouth) and Grant Miller and Belinda Nicholson (Oxford). Michael Fulton and Shubham Srivastav from QUB took the Liverpool Telescope observations, and Michael also led the publication of our AstroNote.

PPS This gives Robin Ince a Erdös Number of, I think, no higher than 5. His Bacon number (according to the Infinite Monkey Cage) is no higher than 3, so this gives him a Bacon-Erdös number of no more than 15! More importantly, as he’s performed music on stage, he must have a Sabbath number, though finding out what it is requires further work – making him one of the rare number of individuals with EBS numbers. A suitable reward for 24 hours of effort.

Into the Zooniverse: Vol II now available!

For the second year in a row, we’re honoring the hundreds of thousands of contributors, research teams, educators, Talk moderators, and more who make Zooniverse possible. This second edition of Into the Zooniverse highlights another 40 of the many projects that were active on the website and app in the 2019 – 20 academic year.

Image of Into the Zooniverse book

In that year, the Zooniverse has launched 65 projects, volunteers have submitted more than 85 million classifications, research teams have published 35 papers, and hundreds of thousands of people from around the world have taken part in real research. Wow!

To get your copy of Into the Zooniverse: Vol II, download a free pdf here or order a hard copy on Blurb.com. Note that the cost of the book covers production and shipping; Zooniverse does not receive profit through sales. According to the printer, printing and binding take 4-5 business days, then your order ships. To ensure that you receive your book before December holidays, you can use this tool to calculate shipping times.

Read more at zooniverse.org/about/highlights.

News from the Etchiverse – our first results!

Just over three years ago we launched the first Etch A Cell project (https://www.zooniverse.org/projects/h-spiers/etch-a-cell). The project was the first of its kind on the Zooniverse: never before had we asked volunteers to help draw around the small structures inside of cells (also known as ‘manual segmentation of organelles’) visualised with very high-powered electron microscopes. We even had to develop a new tool type on the Zooniverse to do this – a drawing tool for annotating images.

In this first Etch A Cell project, the organelle we asked Zooniverse volunteers to help examine was the nuclear envelope (as you can see shown in green in the image below). The nuclear envelope is a large membrane found within cells. It surrounds the nucleus, which is the part of the cell that contains the genetic material. It’s an important structure to study as it’s known to be involved in a number of diseases, including cancer, and it’s often the first structure research teams inspect in a new data set.

This gif shows an image of a cell taken with an electron microscope. This particular cell is a HeLa cell, a type of cancer cell that is widely used in scientific research. The segmented nuclear envelope is shown in green.

The results…

Earlier this year, we published the first set of results from this project. I’ve summarised some of our most exciting findings below, but if you’d like to take a look at the original paper, you can access it here (https://www.biorxiv.org/content/10.1101/2020.07.28.223024v1.full).

1. Zooniverse volunteers dedicated a huge amount of effort! Zooniverse volunteers submitted more than 100,000 segmentations across the 4000 images analysed in this first Etch A Cell project. Through this effort, the nuclear envelopes of 18 cells were segmented (shown below in green) from our original data block (shown below).

2. Volunteers were very good at segmenting the nuclear envelope. As you can see in the gif and images below, most classifications submitted for each image were really good! Manual segmentation isn’t an easy task to do, even for experts, so we were really impressed!

An unannotated image is shown on the left. The image on the right shows an overlay of all the volunteer segmentations received for this image. As you can see, most volunteers did a great job at segmenting the nuclear envelope.

3. There’s power in a crowd! The image below shows an overlay of every single segmentation for one of the nuclei studied in Etch A Cell. As you can see, through the collective effort of Zooniverse volunteers, something beautiful emerges – by overlaying everyone’s effort like this, you can see the shape of the nuclear envelope begin to appear!

To make sense of all of this data, we developed an analysis approach that took all of these lines and averaged them to form a ‘consensus segmentation’ for each nuclear envelope. This consensus segmentation, produced through the collective effort of volunteers, was incredibly similar to that produced by an expert microscopist. You can see this in the image below: on the left (in yellow) you can see the expert segmentation of the nuclear envelope of one cell compared to the volunteer segmentation (in green). The top image shows a single slice from the cell, the bottom image shows the 3D reconstruction of the whole nuclear envelope.

4. Volunteer segmentations can be used to train powerful new algorithms capable of segmenting the nuclear envelope. We found that volunteer data alone, with no expert data at all, could be used to train computer algorithms to perform the task of nuclear envelope segmentation to a very high standard. In the gif below you can see the computer predicted nuclear envelope segmentation for each of the cells in pink.

5. Our algorithm works surprisingly well on other data sets. We ran this new algorithm on other datasets that had been produced under slightly different experimental conditions. Because of these differences, we didn’t expect the algorithm to perform very well, however, as you can see in the images below, it did a very good job at identifying the location of the nuclear envelope. Because of this transferability, members of our research team have already begun using this algorithm to aid their new research projects.

The future…

We’re so excited to share these results with you, our volunteer community, and the research communities we collaborate with, and we’re looking forward to building on these findings in the future. The algorithms we’ve been able to produce from this effort are already being used by research teams at the Crick, and we’ve already launched multiple new projects asking for your help to look at other organelles – The Etchiverse is expanding!

You can access all our current Etch A Cell projects through the Etch A Cell Organisation page

Zooniverse Mobile App Release v2.8.2!

Now it’s even easier to contribute to science from your phone!

On any crowded public bus (before the pandemic), people sat next to each other, eyes fixed on their phones, smiling, swiping. 

What were they all doing? Using a dating app, maybe. Or maybe they were separating wildcam footage of empty desert from beautiful birds. Maybe they were spotting spiral arms on faraway galaxies.

Maybe one of them was you!  

We’ve loved seeing the participation in the Zooniverse through the mobile app (available for iOS and Android) over the past two years. So we made it even easier for you to do that wherever you swipe these days—a park bench, or maybe your home. (Please don’t swipe and drive). 

Right now, you can go into the app and contribute to Galaxy Zoo Mobile, Catalina Outer Solar System Survey, Disk Detective, Mapping Historic Skies, Nest Quest Go, or Planet Four: Ridges. And we have more projects on the way!

What’s new in the app

When you update to version 2.8.2, you’ll notice a slick new look. At the very top, there’s now an “All Projects” category. This will show you everything available for mobile—with the projects that need your help the most sorted at the very top! You can also still choose a specific discipline, of course.

That’s it for features that are totally new, but a lot of features in this version are fixed. No more crashing when you tap on browser projects. A lot fewer project-related crashes. Animated gifs, which previously worked only on iOS, now also work on Android—so researchers can show you an image that changes over time.  

What’s more—and you’ll never see this, but it’s important to us, the developers—we’ve made a lot of changes that help us keep improving the app. We have better crash reporting mechanisms and more complete automated testing. We also updated all of our documentation so that developers from outside our team can contribute to the app, too! We’d love to be a go-to open source project for people who are learning, or working in, React Native (the platform on which our app is built).

Aggregate Functionality

The full list of functionalities now includes:

  • Swipe (binary question [A or B.] response)
  • Single-answer question (A, B, or C)
  • Multi-answer question (any combination of A, B, and C.)
  • Rectangle drawing task (drawing a rectangle around a feature within a subject)
  • Single-image subjects
  • Multi-image subjects (e.g. uploading 2+ images as a single subject; users swipe up/down to display the different images)
  • Animated gifs as subjects
  • Subject auto-linking (automatically linking subjects retired from one workflow into another workflow of interest on the same project)
  • Push notifications (sending messages/alerts about new data, new workflows, etc., via the app)
  • Preview (an owner or collaborator on a project in development being able to preview a workflow in the ‘Preview’ section of the mobile app)
  • Beta Review (mobile enabled workflows are accessible through the ‘Beta Review’ section of the app for a project in the Beta Review process; includes an in-app feedback form)
  • Ability to see a list of all available projects, as well as filter by discipline (with active mobile app workflows listed at the top)

We also carried out a number of infrastructure improvements, including: 

  • Upgrades to the React Native libraries we use
  • Created a staging environment to test changes before they are implemented in full production
  • Additional test coverage
  • Implemented bug reporting and tracking
  • Complete documentation, so open source contributors can get the app running from our public code repository
  • And a myriad of additional improvements like missing icons no longer crashing the app, improvements to the rectangle drawing task, etc.

Note: we will continue developing the app; this is just the end of this phase of effort and a great time to share the results.

If you’re leading a Zooniverse project and have any questions about where in the Project Editor ‘workflow’ interface to ‘enable on mobile’, don’t hesitate to email contact@zooniverse.org. And/or if you’re a volunteer and wonder if workflow(s) on a given project could be enabled on mobile, please post in that project’s Talk to start the conversation with the research team and us. The more, the merrier!

Looking forward to having more projects on the mobile app!

A Few Stats of Interest:

  • Since Jan 1, 2020: 
    • 6.2 million classifications submitted via the app (that’s 7% of 86.7 million classifications total through Zooniverse projects)
    • 18,000 installations on iOS + 17,000 on Android
  • Current Active Users (people who have used the app in the last 30 days):
    • 1,800 on iOS + 7,700 on Android

Previous Blog Posts about the Zooniverse Mobile App:

project completed: The American Soldier in wwII

This is a guest post from the research team behind The American Soldier in WWII.

As challenges press upon all of us in the midst of the pandemic, the team behind The American Soldier in World War II has some good news to share. 

When we initially launched our project on Zooniverse on VE Day 2018, our goal was to have all 65,000 pages of commentaries on war and military service written by soldiers in their own hands transcribed and annotated within a 2-year window – in triplicate, for quality-control purposes. We not only hit that milestone in May 2020, but last week we completed an additional 4th round. 

Attracting 3,000-plus new contributors, this extension of the transcription drive took only six months. Beyond allowing more people to engage with these unique and revealing wartime documents, the added round is improving our final project output. Within the next week or so, our top Zooniverse transcribers will begin final, manual verification of these transcriptions and annotations, which have been cleaned algorithmically. If you are a consistent project contributor and interested in helping with final validation, please do let us know by signing up here.

As we move forward with the project, we have created a Farewell Talk board. Since we have had so many incredible contributors to The American Soldier, we would love to hear any parting words our volunteers would like to share with the team and with fellow contributors about your experiences or most memorable transcriptions. 

We are so incredibly grateful for the international team of researchers, data and computer scientists, designers, educators, and volunteers who have gotten the project to where it is and in spite of the great upheaval. Thanks to their hard work and dedication, the project’s open-access website remains on track for a spring 2021 launch. 

We look forward to sharing more news with you soon. Until then, be well and safe. 

The American Soldier in WWII Team

NASA and Zooniverse Announce Partnership

We’re very happy to announce a new partnership between NASA and our Zooniverse teams at the Adler Planetarium and the University of Minnesota. This new partnership advances and deepens our existing relationship and efforts with NASA. Our team will work together with NASA to create new opportunities for the Zooniverse volunteer community to engage and participate in projects that span the wide range of NASA’s science divisions: astrophysics, heliophysics, planetary science, and earth science.

This new NASA grant will enable new projects as well as provide support for our developers to maintain our research-enabling platform. This support is very welcome, and will help us share our platform with a growing number of scientists who want to unlock data from NASA’s missions, centers, and projects. We’re really looking forward to building and launching these new projects, but don’t worry — nothing else will change. The platform will still be a welcome home to a wide range of research and projects.

It’s been more than a decade now since the Zooniverse launched, and it’s exciting to have reached the point where the Zooniverse platform, research teams, and AMAZING community of volunteers are consistently recognized as valuable contributors and collaborators in research.  The Zooniverse team is excited for this partnership and for the future ahead — here’s to lots more adventures to come!

The Zooniverse: A Quick starter guide for research teams

Over the past several months, we’ve welcomed thousands of new volunteers and dozens of new teams into our community.

This is wonderful.

Because there are new people arriving every day, we want to take this opportunity to (re)introduce ourselves, provide an overview of how Zooniverse works, and give you some insight on the folks who maintain the platform and help guide research teams through the process of building and running projects.

Who are we?

The core Zooniverse team is based across three institutions:

  • Oxford University, Oxford UK
  • The Adler Planetarium, Chicago IL
  • The University of Minnesota-Twin Cities, Minneapolis MN

We also have collaborators at many other institutions worldwide. Our team is made up of web developers, research leads, data scientists, and a designer.

How we build projects

Research teams can build Zooniverse projects in two ways.

First, teams can use the Project Builder to create their very own Zooniverse project from scratch, for free. In order to launch publicly and be featured on zooniverse.org/projects, teams must go through beta review, wherein a team of Zooniverse volunteer beta testers give feedback on the project and answer a series of questions that tell us whether the project is 1) appropriate for the platform; and 2) ready to be launched. Anyone can be a beta tester! To sign up, visit https://www.zooniverse.org/settings/email. Note: the timeline from requesting beta review to getting scheduled in the queue to receiving beta feedback is a few weeks. It can then take a few weeks to a few months (depending on the level of changes needed) to improve your project based on beta feedback and be ready to apply for full launch. For more details and best practices around using the Project Builder, see https://help.zooniverse.org/getting-started/.

The second option is for cases where the tools available in the Project Builder aren’t quite right for the research goals of a particular team. In these cases, they can work with us to create new, custom tools. We (the Zooniverse team) work with these external teams to apply for funding to support design, development, project management, and research.

Those of you who have applied for grant funding before will know that this process can take a long time. Once we’ve applied for a grant, it can take 6 months or more to hear back about whether or not our efforts were successful. Funded projects usually require at least 6 months to design, build, and test, depending on the complexity of the features being created. Once new features are created, we then need additional time to generalize (and often revise) them for inclusion in the Project Builder toolkit.

To summarize:

Option 1: Project Builder

  • Free!
  • Quick!
  • Have to work with what’s available (no customization of tools or interface design)

Option 2: Custom Project

  • Funding required
  • Can take a longer time
  • Get the features you need!
  • Supports future teams who may also benefit from the creation of these new tools!

We hope this helps you to decide which path is best for you and your research goals.