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.
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 firstname.lastname@example.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 email@example.com if you’re interested in exploring further and/or have any questions.
*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.
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.
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.
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.
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).
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)
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 firstname.lastname@example.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:
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!
In the beginning of April 2020, we were notified that subjects from one Zooniverse project were appearing in the subject set of a separate project where they did not belong. In our investigation of the issue, our team determined that this behavior was being caused by a Caesar configuration mistake that used an incorrect Subject Set ID. Project owners using Caesar were able to create Subject Rule Effects that added subjects to collections or subject sets, even without proper subject set editing permissions. We have rectified the issue surrounding Subject Rule Effects and eliminated this vulnerability, and would like to share the details for anyone who is interested.
The issue was raised by project lead James Perry (@JamesPerry), who reported that subjects that didn’t belong to his project were appearing in his subject sets. Due to a mistyped subject set ID in a Caesar `add_to_subject_set` effect for an unrelated project, that Subject Rule Effect was sending subjects from that project to one of James’s subject sets instead of the correct target.
Our immediate course of action was to fix the project impacted by the vulnerability, and push out a temporary code fix to prevent the vulnerability from being exploited.
To fix the affected project, we updated the incorrect subject set id for the project that was incorrectly sending subjects to the wrong project and removed the unwanted subjects from the set.
On April 3rd we deployed a temporary code fix to disable Subject Rule Effect creation and modification for all but admin users (see PR #1109). This change was communicated to affected teams that were most impacted by the change, and teams that reached out after seeing our notification banner or encountering a Caesar interface error.
On May 15th we pushed out a permanent fix that checked the user has permissions to send data to the target subject set or collection. Specifically, the updated validation code checks that the user has update permissions on the project the subject set or collection is linked to. (see PRs #1115, #1129 and #1131).
For anyone running their own hosted copy of Caesar, we recommend pulling these changes as soon as you’re able.
The following is an update from the SuperWASP Vairable Stars research team. Enjoy!
Welcome to the Spring 2020 update! In this blog, we will be sharing some updates and discoveries from the SuperWASP Variable Stars project.
What are we aiming to do?
We are trying to discover the weirdest variable stars!
Stars are the building blocks of the Universe, and finding out more about them is a cornerstone of astrophysics. Variable stars (stars which change in brightness) are incredibly important to learning more about the Universe, because their periodic changes allow us to probe the underlying physics of the stars themselves.
We have asked citizen scientists to classify variable stars based on their photometric light curves (the amount of light over time), which helps us to determine what type of variable star we’re observing. Classifying these stars serves two purposes: firstly to create large catalogues of stars of a similar type which allows us to determine characteristics of the population; and secondly, to identify rare objects displaying unusual behaviour, which can offer unique insights into stellar structure and evolution.
We have 1.6 million variable stars detected by the SuperWASP telescope to classify, and we need your help! By getting involved, we can build up a better idea of what types of stars are in the night sky.
What have we discovered so far?
We’ve done some initial analysis on the first 300,000 classifications to get a breakdown of how many of each type of star is in our dataset.
So far it looks like there’s a lot of junk light curves in the dataset, which we expected. The programme written to detect periods in variable stars often picks up exactly a day or a lunar month, which it mistakes for a real period. Importantly though, you’ve classified a huge number of real and exciting light curves!
We’re especially excited to do some digging into what the “unknown” light curves are… are there new discoveries hidden in there? Once we’ve completed the next batch of classifications, we’ll do some more to see whether the breakdown of types of stars changes.
An exciting discovery…
In late 2018, while building this Zooniverse project, we came across an unusual star. This Northern hemisphere object, TYC-3251-903-1, is a relatively bright object (V=11.3) which has previously not been identified as a binary system. Although the light curve is characteristic of an eclipsing contact binary star, the period is ~42 days, notably longer than the characteristic contact binary period of less than 1 day.
Spurred on by this discovery, we identified a further 16 candidate near-contact red giant eclipsing binaries through searches of archival data. We were excited to find that citizen scientists had also discovered 10 more candidates through this project!
Of the 10 candidate binaries discovered by citizen scientists, we were happy to be able to take spectroscopic observations for 8 whilst in South Africa, and we have confirmed that at least 2 are, in fact, binaries! Thank you citizen scientists!
Why is this discovery important?
The majority of contact or near-contact binaries consist of small (K/M dwarf) stars in close orbits with periods of less than 1 day. But for stars in a binary in a contact binary to have such long periods requires both the stars to be giant. This is a previously unknown configuration…
Interestingly, a newly identified type of stellar explosion, known as a red nova, is thought to be caused by the merger of a giant binary system, just like the ones we’ve discovered.
Red novae are characterised by a red colour, a slow expansion rate, and a lower luminosity than supernovae. Very little is known about red novae, and only one has been observed pre-nova, V1309 Sco, and that was only discovered through archival data. A famous example of a possible red nova is the 2002 outburst in V838 Mon. Astronomers believe that this was likely to have been a red nova caused by a binary star merger, forming the largest known star for a short period of time after the explosion.
So, by studying these near-contact red giant eclipsing binaries, we have an unrivalled opportunity to identify and understand binary star mergers before the merger event itself, and advance our understanding of red novae.
What changes have we made?
Since the SuperWASP Variable Stars Zooniverse project started, we’ve made a few changes to make the project more enjoyable. We’ve reduced the number of classifications needed to retire a target, and we’ve also reduced the number of classifications of “junk” light curves needed to retire it. This means you should see more interesting, real, light curves.
We’ve also started a Twitter account, where we’ll be sharing updates about the project, the weird and wacky light curves you find, and getting involved in citizen science and astronomy communities. You can follow us here: www.twitter.com/SuperWASP_stars
We still have thousands of stars to classify, so we need your help!
Once we have more classifications, we will be beginning to turn the results into a publicly available, searchable website, a bit like the ASAS-SN Catalogue of Variable Stars (https://asas-sn.osu.edu/variables). Work on this is likely to begin towards the end of 2020, but we’ll keep you updated.
We’re also working on a paper on the near-contact red giant binary stars, which will include some of the discoveries by citizen scientists. Expect that towards the end of 2020, too.
Otherwise, watch this space for more discoveries and updates!
We would like to thank the thousands of citizen scientists who have put time into this Zooniverse project. If you ever have any questions or suggestions, please get in touch.
Over the past week a number of students and organizations have reached out to us to see if Zooniverse participation can fulfill volunteering/service hour requirements for graduation, scholarships, etc.
The short answer is — Yes! Many organizations welcome and encourage Zooniverse participation as a way to fulfill service hour requirements.
We recommend that organizations place at the forefront what students/participants get out of these experiences beyond contributing time and classifications. Rather than creating busy work, we favor a model where participants take time to reflect on how their efforts (and the community’s collective efforts) are contributing to our understanding of our world and the broader universe.
Here is one approach for constructing a productive and rewarding volunteer experience for your organization:
Step 1: Share this opportunity with your Organization
Email your organization to see if participation in Zooniverse can be used to fulfill volunteering or other participation requirements. Share this blog post with them so they understand what you would be doing and how you’ll ‘document’ your participation (see Step 8 below).
Step 2: Register at Zooniverse.org
Create a Zooniverse account by clicking ‘Register’ in the upper-right of the Zooniverse.org homepage (only a name and email are required).
Registering is not required to participate in Zooniverse. But it is useful in this case in order to provide a record of participation.
Step 3: Zooniverse background info
Watch this brief animation and video for background/context about the Zooniverse, the world’s largest platform for people-powered research, with 100 active projects and 2 million people around the world participating. Every Zooniverse project is led by a different research team, spanning a wide range of subjects that include: identifying planets around distant stars (PlanetHunters.org), studying the impact of climate change on animals (SnapshotSafari.org) and plants (FloatingForests.org), tracking resistance to antibiotics (Bash the Bug), transcribing handwritten documents (antislaverymanuscripts.org), and more. The collective efforts of Zooniverse projects have resulted in over 200 research publications to date.
Step 4: Choose your project(s)
Choose from the full list of ~100 active Zooniverse projects (see zooniverse.org/projects) or choose from the curated lists of projects below that tend to work well with different age groups, as selected by the Zooniverse team:
Consider these Reflection Questions, or other similar questions. The questions explore the ‘why’ behind this experience. Why do the researchers need your help? How might the results help science? Are you interested in participating in other projects of this type, and why or why not?
For Organizations: Consider sending these via a Google Form or other survey tool for participants to submit responses to these questions. Note: before using the example form above, make a copy of the Google form and send the survey from your own account to make sure you can access the responses.
Each project has a ‘Talk’ discussion forum associated with it (e.g., https://www.zooniverse.org/projects/mrniaboc/bash-the-bug/talk). This is where the researchers and participants from around the world chat with each other — asking questions about the science, weird things people see while classifying, new discoveries, & more. First, explore the discussion threads and check out some of the questions other people have asked. If you’re feeling comfortable, ask the researchers a question about the science, being a scientist, etc. You might start with a question you asked as part of the ‘Reflection Questions’ activity above. The researchers are keen to hear your questions and engage with you. Check back later to see the response, or watch for Talk email notifications, if you’ve enabled them.
Step 8: Document your participation to fulfill your requirements
Once signed in at Zooniverse.org, you’ll see your display name and your total classification count. (If you hover over the doughnut-ribbon in the center top of the page, you’ll see the classification counts for each specific project you’ve participated in.)
Please note that there is no built-in time-tracker within Zooniverse. Many organizations encourage participants to use the number of classifications they’ve contributed as a proxy for time spent on the site. On average, a person contributes 20-75 classifications/hour on most projects (this ranges widely depending on the difficulty of the tasks, the number of tasks for a given classification, etc.).
For example, if someone has done 100 classifications, you can estimate that they’ve spent ~2 hours classifying on Zooniverse; e.g., 2 hours x 50 classifications / hour = 100 classifications. The Organization should add ~45 minutes to this time estimate for the time it takes to carry out the additional ‘meta’ elements of the experience outlined above.
Please note – because we are a small organization and 1000s of students each week are participating in Zooniverse as volunteers, we (the internal Zooniverse team) are not able to sign individual’s ‘certificates of completion’ or other records of that type for volunteer hours. Instead, organizations encourage their participants to take a screenshot of their signed-in Zooniverse.org page showing their personal stats. This screenshot serves as a proxy for documentation of your effort.
Another option is to participate in either of the following specific Zooniverse projects. The research teams leading those efforts have the capacity to provide certificates.
For Organizations: Consider using a Google Form or other survey instrument for participants to submit their classification count and a screenshot of their Zooniverse.org page. Note: make a copy of the Google form and send it from your account so you can access the responses.
If you need to reference a 501(c)(3):
While Chicago’s Adler Planetarium, one of the hosts of the Zooniverse web development team, is a 501(c)(3), the Zooniverse is not. Organizations that need to link explicitly to a 501(c)(3) for their volunteering efforts use the Adler Planetarium as the reference. Documentation of the Adler Planetarium’s 501(c)(3) status is provided here.
We recognize it would be helpful to have an easier way to share participation information with organizations for these purposes (though this will need to be done in a very thoughtful way). Please note that because we are a grant-funded web development team, enhancements of this type take time to design, build and implement. If you or your organization have suggestions for how best to share this information, or are interested in helping to support this effort via collaborative grant-writing or otherwise, please let us know.
As always, please don’t hesitate to reach out to email@example.com if you have any questions or suggestions.
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