Engaging Crowds: new options for subject delivery & interaction

Since its founding, a well-known feature of the Zooniverse platform has been that volunteers see (& interact with) image, audio, or video files (known as ‘subjects’ in Zooniverse parlance) in an intentionally random order. A visit to help.zooniverse.org provides this description of the subject selection process:

[T]he process for selecting which subjects get shown to volunteers is very simple: it randomly selects an (unretired, unseen) subject from the linked subject sets for that workflow.


For some project types, this method can help to avoid bias in classification. For other project types, however, random subject delivery can make the task more difficult.

Transcription projects frequently use a single image as the subject-level unit. These images most often depict a single page of text (i.e., 1 subject = 1 image = 1 page of text). Depending on the source material being transcribed, that unit/page is often only part of a multi-page document, such as a letter or manuscript. In these cases, random subject delivery removes the subject (page) from its larger context (document). This can actually make successful transcription more difficult, as seeing additional uses of a word or letter can be helpful for deciphering a particular hand.

Decontextualized transcription can also be frustrating for volunteers who may want greater context for the document they’re working on. It’s more interesting to be able to read or transcribe an entire letter, rather than snippets of a whole.

This is why we’re exploring new approaches to subject delivery on Zooniverse as part of the Engaging Crowds project. Engaging Crowds aims to ‘investigate the practice of citizen research in the heritage sector‘ in collaboration with the UK National Archives, the Royal Botanic Garden Edinburgh, and the National Maritime Museum. The project is funded by the UK Arts & Humanities Research Council as one of eight foundational projects in the ‘Towards a National Collection: Opening UK Heritage to the World‘ program.

As part of this research project, we have designed and built a new indexing tool that allows volunteers to have more agency around which subject sets—and even which subjects—they want to work on, rather than receiving them randomly.

The indexing tool allows for a few levels of granularity. Volunteers can select what workflow they want to work on, as well as the subject set. These features are currently being used on HMS NHS: The Nautical Health Service, the first of three Engaging Crowds Zooniverse projects that will launch on the platform before the end of 2021.

Subject set selection screen, as seen in HMS NHS: The Nautical Health Service.

Sets that are 100% complete are ‘greyed’ out, and moved to the end of the list — this feature was based on feedback from early volunteers who found it too easy to accidentally select a completed set to work on.

In the most recent iteration of the indexing tool, selection happens at the subject level, too. Scarlets and Blues is the second Engaging Crowds project, featuring an expanded indexing tool from the version seen in HMS: NHS. Within a subject set, volunteers can select the individual subject they want to work on based on the metadata fields available. Once they have selected a subject, they can work sequentially through the rest of the set, or return to the index and choose a new subject.

Subject selection screen as seen in Scarlets and Blues.

On all subject index pages, the Status column tells volunteers whether a subject is Available (i.e. not complete and not yet seen); Already Seen (i.e. not complete, but already classified by the volunteer viewing the list); or Finished (i.e. has received enough classifications and no longer needs additional effort).

A major new feature of the indexing tool is that completed subjects remain visible, so that volunteers can retain the context of the entire document. When transcribing sequentially through a subject set, volunteers that reach a retired subject will see a pop-up message over the classify interface that notes the subject is finished, and offers available options for how to move on with the classification task, including going directly to the next classifiable subject or returning to the index to choose a new subject to classify.

Subject information banner, as seen in Scarlets and Blues.

As noted above, sequential classification can help provide context for classifying images that are part of a group, but until now has not been a common feature of the platform. To help communicate ordered subject delivery to volunteers, we have included information about the subject set–and a given subject’s place within that set–in a banner on top of the image. This subject information banner (shown above) tells volunteers where they are within the order of a specific subject set.

Possible community use cases for the indexing tool might include volunteers searching a subject set in order to work on documents written by a particular author, written within a specific year, or that are written in a certain language. Some of the inspiration for this work came from Talk posts on the Anti-Slavery Manuscripts project, in which volunteers asked how they could find letters written by certain authors whose handwriting they had become particularly adept at transcribing. Our hope is that the indexing tool will help volunteers more quickly access the type of materials in a project that speak to their interests or needs.

If you have any questions, comments, or concerns about the indexing tool, please feel free to post a comment here, or on one of our Zooniverse-wide Talk boards. This feature will not be immediately available in the Project Builder, but project teams who are interested in using the indexing tool on a future project should email contact@zooniverse.org and use ‘Indexing Tool’ in the subject line. We’re keen to continue trying out these new tools on a range of projects, with the ultimate goal of making them freely available in the Project Builder.

Frequently Asked Questions: Indexing Tool + Sequential Classification

“Will all new Zooniverse projects use this method for subject selection and sequential classification?”

No. The indexing tool is an optional feature. Teams who feel that their projects would benefit from this feature can reach out to us for more information about including the indexing tool in their projects. Those who don’t want the indexing tool will be able to carry on with random subject delivery as before.

“Why can’t I refresh the page to get a new subject?”

Projects that use sequential classification do not support loading new subjects on page refresh. If the project is using the indexing tool, you’ll need to return to the index and choose a new page. If the project is not using the indexing tool, you’ll need to classify the image in order to move forward in the order of sequence. However, the third Engaging Crowds project (a collaboration with the Royal Botanic Garden Edinburgh) will include the full suite of indexing tool features, plus an additional ‘pagination’ option that will allow volunteers to move forwards and backwards through a subject set to decide what to work on see preview image below). We’ll write a follow-up to this post once that project has launched.

A green banner with the name of the subject set and Previous and Next buttons
Subject information banner, as seen in the forthcoming Royal Botanic Garden Edinburgh project.

“How do I know if I’m getting the same page again?”

The subject information banner will give you information about where you are in a subject set. If you think you’re getting the same subject twice, first start by checking the subject information banner. If you still think you’re getting repeat subjects, send the project team a message on the appropriate Talk board. If possible, include the information from the subject information banner in your post (e.g. “I just received subject 10/30 again, but I think I already classified it!”).


What’s interesting? Or rather, what’s most interesting? This most fundamental of questions isn’t one we often directly address when thinking about scientific data, when we’re usually concerned with classification or deriving some global property of the data. But interestingness is important – in my own work with large surveys of the Universe, how interesting a new object is – an exploding star, or a strange galaxy – may determine whether we point telescopes at it, or whether it will languish, unobserved, in a catalogue for decades.

Hanny’s Voorwerp – a light echo lit up by activity in a now-faded quasar – was found early in the Galaxy Zoo project, providing a timely reminder of the importance of finding the unusual things in large datasets!

We’ve learnt how important serendipitous discoveries can be from previous astronomical Zooniverse projects, ranging from Galaxy Zoo’s Green Peas to Boyajian’s Star, ‘the most interesting star in the Milky Way’ (even if it turns out not to host an alien megastructure. With new projects such as the Vera Rubin Observatory’s LSST survey nearly ready to provide an unprecedented flood of information, astronomers around the world are honing their techniques for getting the most out of such large datasets – but the problem of preparing for surprise has been neglected.

In part because it turns out it’s hard to get funding for a search for the unusual, where by definition I can’t say in advance what it is that we’ll find. I’m therefore very pleased the team have received a new grant from the Alfred P. Sloan Foundation to build on the Zooniverse to provide tools designed for serendipity. My hunch is that, as we’ve learnt from so many Zooniverse projects before, a combination of human and machine intelligence is needed for the task; while modern machine learning is good at finding the unusual, working out which unusual things are actually interesting is best left to human intuition and intelligence.

If we think about being ‘unusual’ and being ‘interesting’ as different axes, an interesting space on which to plot our data appears. Modern machine learning is best suited to finding the unusual – but most unusual things are boring artefacts.

The project won’t stop at astronomy. In combination with Prof Kate Jones‘ team at UCL and elsewhere, we’ll look for surprises in audio recordings from ecological monitoring projects, testing whether identifying rare events – such as gunshots – might contribute to assessments of the health of an ecosystem. (You might remember Kate – she ran the Bat Detective project on the Zooniverse) And with the Science Scribbler team (particually Michele Darrow and Mark Basham) based at the Rosalind Franklin Institute we’ll use the latest high resolution imaging to use these techniques to spot structures in cells.

In doing all of this we can build on our galaxy-classifying Zoobot, the work on glitch identification from Gravity Spy (recent results!), friends and collaborators like Kate Storey-Fischer and Michelle Lochner, whose Astronomaly concept seems right up our street, and of course the insights and efforts of the two million strong Zooniverse army. Who knows what we might find together?


PS If you have a PhD in a relevant scientific discipline, or in computer science, then we’re advertising a postdoc – see here for details, or get in touch via Twitter or email to discuss.

Zooniverse Volunteers Discover More New Worlds

The volunteers on our Planet Hunters TESS project have helped discover another planetary system! The new system, HD 152843, consists of two planets that are similar in size to Neptune and Saturn in our own solar system, orbiting around a bright star that is similar to our own Sun. This exciting discovery follows on from our validation of the long-period planet around an evolved (old) star, TOI-813, and from our recent paper outlining the discovery of 90 Planet Hunters TESS planet candidates, which gives us encouragement that there are a lot more exciting systems to be found with your help!

Figure: The data obtained by NASA’s Transiting Exoplanet Survey Satellite which shows two transiting planets. The plot shows the brightness of the star HD 152843 over a period of about a month. The dips appear where the planets passed in front of the star and blocked some of its light from getting to Earth.

Multi-planet systems, like this one, are particularly interesting as they allow us to study how planets form and evolve. This is because the two planets that we have in this system must have necessarily formed out of the same material at the same time, but evolved in different ways resulting in the different planet properties that we now observe.

Even though there are already hundreds of confirmed multi-planet systems, the number of multi-planet systems with stars that are bright enough such that we can study them using ground-based telescopes remains very small. However, the brightness of this new citizen science found system, HD 152843, makes it an ideal target for follow-up observations, allowing us to measure the planet masses and possibly even probe their atmospheric composition.

This discovery was made possibly with the help of tens of thousands of citizen scientists who helped to visually inspect data obtained by NASA’s Transiting Exoplanet Survey Satellite, in the search for distant worlds. We thank all of the citizen scientists taking part in the project who continue to help with the discovery of exciting new planet systems and in particular to Safaa Alhassan, Elisabeth M. L. Baeten, Stewart J. Bean, David M. Bundy, Vitaly Efremov, Richard Ferstenou, Brian L. Goodwin, Michelle Hof, Tony Hoffman, Alexander Hubert, Lily Lau, Sam Lee, David Maetschke, Klaus Peltsch, Cesar Rubio-Alfaro, Gary M. Wilson, the citizen scientists who directly helped with this discovery and who have become co-authors of the discovery paper.

The paper has been published by the Monthly Notices of the Royal Astronomical Society (MNRAS) journal and you can find a version of it on arXiv at: https://arxiv.org/pdf/2106.04603.pdf.

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!


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.

Fixed Cross-Site Scripting Vulnerability on Zoomapper App

On 9 November 2020, a security researcher notified us of a cross-site scripting (XSS) vulnerability on our zoomapper application. This service hosts tile sets that are used to render maps for a small number of other Zooniverse applications, but is not connected to any critical Zooniverse infrastructure. This XSS vulnerability could have allowed users to execute malicious code on the zoomapper application in the browser.

We were able to remediate the vulnerability within hours of the report by disabling the browser GUI for zoomapper (see PR #6). The GUI had been turned on by default for the zoomapper app, but is not necessary to fulfill the app’s intended role.

Additional notes on the incident:

  • The vulnerability existed since the app was first deployed on September 15th 2020.
  • The vulnerability was located in the underlying Tileserver-GL dependency.
  • No Zooniverse user or project data was vulnerable or exposed by this vulnerability.

We’d like to thank Rachit Verma (@b43kd00r) for bringing this issue to our attention and for following responsible disclosure by reporting it to us in private, as requested on our security page.

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

The Zooniverse Blog. We're the world's largest and most successful citizen science platform and a collaboration between the University of Oxford, The Adler Planetarium, and friends