Category Archives: CitizenScience

The Universe Inside Our Cells

Below is the first in a series of guest blog posts from researchers working on one of our recently launched biomedical projects, Etch A Cell.

Read on to let Dr Martin Jones tell you about the work they’re doing to further understanding of the universe inside our cells!

– Helen

 

Having trained as a physicist, with many friends working in astronomy, I’ve been aware of Galaxy Zoo and the Zooniverse from the very early days. My early research career was in quantum mechanics, unfortunately not an area where people’s intuitions are much use! However, since I found myself working in biology labs, now at the Francis Crick Institute in London, I have been working in various aspects of microscopy – a much more visual enterprise and one where human analysis is still the gold standard. This is particularly true in electron microscopy, where the busy nature of the images means that many regions inside a cell look very similar. In order to make sense of the images, a person is able to assimilate a whole range of extra context and previous knowledge in a way that computers, for the most part, are simply unable to do. This makes it a slow and labour-intensive process. As if this wasn’t already a hard enough problem, in recent years it has been compounded by new technologies that mean the microscopes now capture images around 100 times faster than before.

Picture1
Focused ion beam scanning electron microscope

 

Ten years ago it was more or less possible to manually analyse the images at the same rate as they were acquired, keeping the in-tray and out-tray nicely balanced. Now, however, that’s not the case. To illustrate that, here’s an example of a slice through a group of cancer cells, known as HeLa cells:

Picture2

We capture an image like this and then remove a very thin layer – sometimes as thin as 5 nanometres (one nanometre is a billionth of a metre) – and then repeat… a lot! Building up enormous stacks of these images can help us understand the 3D nature of the cells and the structures inside them. For a sense of scale, this whole image is about the width of a human hair, around 80 millionths of a metre.

Zooming in to one of the cells, you can see many different structures, all of which are of interest to study in biomedical research. For this project, however, we’re just focusing on the nucleus for now. This is the large mostly empty region in the middle, where the DNA – the instruction set for building the whole body – is contained.

Picture3

By manually drawing lines around the nucleus on each slice, we can build up a 3D model that allows us to make comparisons between cells, for example understanding whether a treatment for a disease is able to stop its progression by disrupting the cells’ ability to pass on its genetic information.

Nucleus3D-1.gif

Animated gif of 3D model of a nucleus

However, images are now being generated so rapidly that the in-tray is filling too quickly for the standard “single expert” method – one sample can produce up to a terabyte of data, made up of more than a thousand 64 megapixel images captured overnight. We need new tricks!

 

Why citizen science?

With all of the advances in software that are becoming available you might think that automating image analysis of this kind would be quite straightforward for a computer. After all, people can do it relatively easily. Even pigeons can be trained in certain image analysis tasks! (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141357). However, there is a long history of underestimating just how hard it is to automate image analysis with a computer. Back in the very early days of artificial intelligence in 1966 at MIT, Marvin Minsky (who also invented the confocal microscope) and his colleague Seymour Papert set the “summer vision project” which they saw as a simple problem to keep their undergraduate students busy over the holidays. Many decades later we’ve discovered it’s not that easy!

Picture4

(from https://www.xkcd.com/1425/)

Our project, Etch a Cell is designed to allow citizen scientists to draw segmentations directly onto our images in the Zooniverse web interface. The first task we have set is to mark the nuclear envelope that separates the nucleus from the rest of the cell – a vital structure where defects can cause serious problems. These segmentations are extremely useful in their own right for helping us understand the structures, but citizen science offers something beyond the already lofty goal of matching the output of an expert. By allowing several people to annotate each image, we can see how the lines vary from user to user. This variability gives insight into the certainty that a given pixel or region belongs to a particular object, information that simply isn’t available from a single line drawn by one person. Difference between experts is not unheard of unfortunately!

The images below show preliminary results with the expert analysis on the left and a combination of 5 citizen scientists’ segmentations on the right.

Screen Shot 2017-06-21 at 15.29.00
Example of expert vs. citizen scientist annotation

In fact, we can go even further to maximise the value of our citizen scientists’ work. The field of machine learning, in particular deep learning, has burst onto the scene in several sectors in recent years, revolutionising many computational tasks. This new generation of image analysis techniques is much more closely aligned with how animal vision works. The catch, however, is that the “learning” part of machine learning often requires enormous amounts of time and resources (remember you’ve had a lifetime to train your brain!). To train such a system, you need a huge supply of so-called “ground truth” data, i.e. something that an expert has pre-analysed and can provide the correct answer against which the computer’s attempts are compared. Picture it as the kind of supervised learning that you did at school: perhaps working through several old exam papers in preparation for your finals. If the computer is wrong, you tweak the setup a bit and try again. By presenting thousands or even millions of images and ensuring your computer makes the same decision as the expert, you can become increasingly confident that it will make the correct decision when it sees a new piece of data. Using the power of citizen science will allow us to collect the huge amounts of data that we need to train these deep learning systems, something that would be impossible by virtually any other means.

We are now busily capturing images that we plan to upload to Etch a cell to allow us to analyse data from a range of experiments. Differences in cell type, sub-cellular organelle, microscope, sample preparation and other factors mean the images can look different across experiments, so analysing cells from a range of different conditions will allow us to build an atlas of information about sub-cellular structure. The results from Etch a cell will mean that whenever new data arrives, we can quickly extract information that will help us work towards treatments and cures for many different diseases.

Stargazing Live 2017: Thank you all!

Breaking news… Zooniverse volunteers on Exoplanet Explorers have discovered a new 4-planet system!

simoneAnimation
Computer animation of the 4-planet system. Planet orbits are to scale and planet sizes are to scale with each other, but not with the star and the size of the orbits. Credit: Simone Duca.

Congratulations to all* who directly classified the light curves for this system, bringing it to the attention of the research team. And an enormous *thank you* to the 14,000+ volunteers who provided over 2 million classifications in just three days to make this discovery possible. This is equivalent to 3.4 years of full time effort. I *heart* people-powered research! It’s also amazing how quickly we were able to get these data to the eyes of the public — the Kepler Space Satellite observed this star between December 15 and March 4, 2017.  Data arrived on Earth on March 7th and Zooniverse volunteers classified it April 3-5, 2017. I *heart* Zooniverse.

ExoplanetExplorers.org was the featured project for our inaugural ABC Australia Stargazing Live 3-day, prime-time TV event, which just ended yesterday and through which this discovery was made. Over the years, we’ve partnered with the BBC as part of their Stargazing Live event in the UK. On night 1, Chris Lintott, our intrepid leader, invites the million+ viewers to participate in that year’s featured Zooniverse project, on night 2 he highlights interesting potential results coming through the pipeline, and on night 3, if science nods in our favor, he has the pleasure of announcing exciting discoveries you all, our volunteers, have made (for example, last year’s pulsar discovery and the supernova discovery from a couple years back). 

This year we partnered with both the UK’s BBC and Australia’s ABC TV networks to run two Stargazing Live series in two weeks. We’re exhausted and exhilarated from the experience! We can imagine you all are as well (hats off to one of our volunteers who provided over 15,000 classifications in the first two days)!

Stargazing Live epitomizes many of our favorite aspects of being a member of the Zooniverse team – it’s a huge rush, filled with the highs and lows of keeping a site up when thousands of people are suddenly providing ~7000 classifications a minute at peak. We’re so proud of our web development team and their amazing effort; their smart solutions, quick thinking, and teamwork. The best part is that we collectively get to experience the joy, wonder, and discovery of the process of science right alongside the researchers. Each year the research teams leading each project have what is likely among the most inspiring (and intense) 3-days of their careers, carrying out the detective work of following up each potential discovery at breakneck speed.

planet9stats
Over 2 million classifications in just 1 day on planetninesearch.org!

talk

Brad Tucker and his team leading PlanetNineSearch.org featured in the BBC Stargazing Live event this year checked and rechecked dozens of Planet 9 candidates orbital parameters and against known object catalogs, making sure no stone was left unturned. We were bolstered throughout with re-discoveries of known objects, including many known asteroids and Chiron, a minor planet in the outer Solar System, orbiting the Sun between Saturn and Uranus.

chiron
The red, green, and blue dots in the lower left quadrant show Chiron as it moved across the Australian night sky during the Skymapper Telescope Observations for planetninesearch.org.

Even though Planet 9 hasn’t been discovered yet, it’s huge progress for that field of research to have completed a thorough search through this Skymapper dataset, which allows us to probe out to certain distances and sizes of objects across a huge swath of the sky. Stay tuned for progress at planetninesearch.org and through the related BackyardWorlds.org project, searching a different parameter space for Planet 9 in WISE data.

Also, and very importantly, the BBC Stargazing Live shows gave the world an essential new member of the Twitterverse:

liftoff-3
Understanding this inside joke alone makes it worth watching the show!

The Exoplanet Explorers team, led by Ian Crossfield, Jessie Christiansen, Geert Barentsen, Tom Barclay, and more were also up through much of each night of the event this week, churning through the results. Because the Kepler Space Telescope K2 dataset is so rich, there were dozens of potential candidates to triple check in just 3 days. Not only did our volunteers discover the 4-planet system shown above, but 90 new and true candidate exoplanets! That’s truly an amazing start to a project.

gumballs
Chris Lintott shows Brian Cox and Julia Zemiro the possible planets we’ve found so far, using the nearby town’s entire stock of gumballs. 

Once you all, our amazing community, have classified all the images in this project and the related PlanetHunters.org, the researchers will be able to measure the occurrence rates of different types of planets orbiting different types of stars. They’ll use this information to answer questions like — Are small planets (like Venus) more common than big ones (like Saturn)? Are short-period planets (like Mercury) more common than those on long orbits (like Mars)? Do planets more commonly occur around stars like the Sun, or around the more numerous, cooler, smaller “red dwarfs”?

There’s also so much to learn about the 4-planet system itself. It’s particularly interesting because it’s such a compact system (all orbits are well within Mercury’s distance to our Sun) of potentially rocky planets. If these characteristics hold true, we expect they will put planet formation theories to the test.

A fun part of our effort for the show was to create visualizations for this newly discovered system. Simone, one of our developers, used http://codepen.io/anon/pen/RpOYRw to create the simulation shown above. We welcome all to try their hand using this tool or others to create their favorite visualization of the system. Do post your effort in the comments below. To set you on the right path, here are our best estimates for the system so far:

Fun facts:

  • In 2372 years, on July 9, 4388AD, all four planets will transit at the same time.
  • If you’re standing on planet e, the nearest planet would appear bigger than the full moon on the sky. Apparent size of other planets while standing on e = 10 arcmin, 16 arcmin, 32 arcmin.
  • If you’re on planet e, the star barely appears to rotate: you see the same side of it for many “years,” because the star rotates just as quickly as planet “e” goes around it.

This post wouldn’t be complete without a thank you to Edward Gomez for following up candidates with the Los Cumbres Observatory Robotic Telescope Network. Not only is LCO a great research tool, but it provides amazing access to telescopes and quality curricular materials for students around the world.

*And a special thanks to the following volunteers who correctly identified at least one the planets in the newly discovered 4-planet system:
Joshua Kusch
Edward Heaps
Ivan Terentev
TimothyCatron
James Richmond
Alan Patricio Zetina Floresmarhx
sankalp mohan
seamonkeyluv
traumeule
B Butler
Nicholas Sloan
Kerrie Ryan
Huskynator
Lee Mason
Trudy Frankensteiner
Alan Goldsmith
Gavin Condon
Simon Wilde
Sharon McGuire
helenatgzoo
Melina Thévenot
Niamh Claydon-Mullins
ellieoban
Anastasios D. Papanastasiou
AndyGrey
Angela Crow
Dave Williams
Throbulator
Tim Smith
Erin Thomas
Valentina Saavedra
Carole Riley
sidy2001
bn3
ilgiz
Antonio Pasqua
Peter Bergvall
Stephen Hippisley
sidy2001
bn3
Michael Sarich

Studying the Impact of the Zooniverse

Below is a guest post from a researcher who has been studying the Zooniverse and who just published a paper called ‘Crowdsourced Science: Sociotechnical epistemology in the e-research paradigm’. That being a bit of a mouthful, I asked him to introduce himself and explain – Chris.

My name is David Watson and I’m a data scientist at Queen Mary University of London’s Centre for Translational Bioinformatics. As an MSc student at the Oxford Internet Institute back in 2015, I wrote my thesis on crowdsourcing in the natural sciences. I got in touch with several members of the Zooniverse team, who were kind enough to answer all my questions (I had quite a lot!) and even provide me with an invaluable dataset of aggregated transaction logs from 2014. Combining this information with publication data from a variety of sources, I examined the impact of crowdsourcing on knowledge production across the sciences.

Last week, the philosophy journal Synthese published a (significantly) revised version of my thesis, co-authored by my advisor Prof. Luciano Floridi. We found that Zooniverse projects not only processed far more observations than comparable studies conducted via more traditional methods—about an order of magnitude more data per study on average—but that the resultant papers vastly outperformed others by researchers using conventional means. Employing the formal tools of Bayesian confirmation theory along with statistical evidence from and about Zooniverse, we concluded that crowdsourced science is more reliable, scalable, and connective than alternative methods when certain common criteria are met.

In a sense, this shouldn’t really be news. We’ve known for over 200 years that groups are usually better than individuals at making accurate judgments (thanks, Marie Jean Antoine Nicolas de Caritat, aka Marquis de Condorcet!) The wisdom of crowds has been responsible for major breakthroughs in software development, event forecasting, and knowledge aggregation. Modern science has become increasingly dominated by large scale projects that pool the labour and expertise of vast numbers of researchers.

We were surprised by several things in our research, however. First, the significance of the disparity between the performance of publications by Zooniverse and those by other labs was greater than expected. This plot represents the distribution of citation percentiles by year and data source for articles by both groups. Statistical tests confirm what your eyes already suspect—it ain’t even close.

Influence of Zooniverse Articles

We were also impressed by the networks that appear in Zooniverse projects, which allow users to confer with one another and direct expert attention toward particularly anomalous observations. In several instances this design has resulted in patterns of discovery, in which users flag rare data that go on to become the topic of new projects. This structural innovation indicates a difference not just of degree but of kind between so-called “big science” and crowdsourced e-research.

If you’re curious to learn more about our study of Zooniverse and the site’s implications for sociotechnical epistemology, check out our complete article.

Pop-ups on Comet Hunters

pasted-image-at-2016_10_20-11_05-am

 

We’re testing out a new feature of our interface, which means if you’re classifying images on Comet Hunters you may see occasional pop-up messages like the one pictured above.

The messages are designed to give you more information about the project. If you do not want to see them, you have the option to opt-out of seeing any future messages. Just click the link at the bottom of the pop-up.

You can have a look at this new feature by contributing some classifications today at www.comethunters.org.

Emails from the Zooniverse

pasted-image-at-2016_09_16-02_48-pm
Click this image to be taken to your Zooniverse email settings

We’re cleaning up our email list to make sure that we do not email anyone who does not want to hear from us. You will have got an email last week asking you if you want to stay subscribed. If you did not click the link in that email, then you will have received one today saying you have been unsubscribed from our main mailing list. Don’t worry! If you still want to receive notifications from us regarding things like new projects, please go to www.zooniverse.org/settings/email and make sure you’re subscribed to general Zooniverse email updates.
NOTE: This has not affected emails you get from individual Zooniverse projects.

Asteroid Zoo Paused

The AsteroidZoo community has exhausted the data that are available at this time. With all the data examined we are going to pause the experiment, and before users spend more time we want to make sure that we can process your finds through the Minor Planet Center and get highly reliable results.

We understand that it’s frustrating when you’ve put in a lot of work, and there isn’t a way to confirm how well you’ve done. But please keep in mind that this was an experiment – How well do humans find asteroids that machines cannot?

Often times in science an experiment can run into dead-ends, or speed-bumps; this is just the nature of science. There is no question that the AsteroidZoo community has found several potential asteroid candidates that machines and algorithms simply missed. However, the conversion of these tantalizing candidates into valid results has encountered a speed bump.

What’s been difficult is that all the processing to make an asteroid find “real” has been based on the precision of a machine – for example the arc of an asteroid must be the correct shape to a tiny fraction of a pixel to be accepted as a good measurement. The usual process of achieving such great precision is hands-on, and might take takes several humans weeks to get right. On AsteroidZoo, given the large scale of the data, automating the process of going from clicks to precise trajectories has been the challenge.

While we are paused, there will be updates to both the analysis process, and the process of confirming results with the Minor Planet Center. Updates will be posted as they become available.

https://talk.asteroidzoo.org/
http://reporting.asteroidzoo.org/

Thank you for your time.

What is Penguin Watch 2.0?

We’re getting through the first round of Penguin Watch data- it’s amazing and it’s doing the job we wanted, which is to revolutionise the collection and processing of penguin data from the Southern Ocean – to disentangle the threats of climate change, fishing and direct human disturbance. The data are clearly excellent, but we’re now trying to automate processing them so that results can more rapidly influence policy.

In “PenguinWatch 2.0”, people will be able to see the results of their online efforts to monitor and conserve Antarctica’s penguins colonies. The more alert among you will notice that it’s not fully there yet, but we’re working on it!

We have loads of ideas on how to integrate this with the penguinwatch.org experience so that people are more engaged, learn more and realise what they are contributing to!

unnamed

For now, we’re doing this the old-fashioned way; anyone such as schools who want to be more engaged, can contact us (tom.hart@zoo.ox.ac.uk) and we’ll task you with a specific colony and feedback on that.

Lost Classifications

We’re sorry to let you know that at 16:29 BST on Wednesday last week we made a change to the Panoptes code which had the unexpected result that it failed to record classifications on six of our newest projects; Season Spotter, Wildebeest Watch, Planet Four: Terrains, Whales as Individuals, Galaxy Zoo: Bar Lengths, and Fossil Finder. It was checked by two members of the team – unfortunately, neither of them caught the fact that it failed to post classifications back. When we did eventually catch it, we fixed it within 10 minutes. Things were back to normal by 20:13 BST on Thursday, though by that time each project had lost a day’s worth of classifications.

To prevent something like this happening in the future we are implementing new code that will monitor the incoming classifications from all projects and send us an alert if any of them go unusually quiet. We will also be putting in even more code checks that will catch any issues like this right away.

It is so important to all of us at the Zooniverse that we never waste the time of any of our volunteers, and that all of your clicks contribute towards the research goals of the project. If you were one of the people whose contributions were lost we would like to say how very sorry we are, and hope that you can forgive us for making this terrible mistake. We promise to do everything we can to make sure that nothing like this happens again, and we thank you for your continued support of the Zooniverse.

Sincerely,

The Zooniverse Team

One line at a time: A new approach to transcription and art history

Today, we launch AnnoTate, an art history and transcription project made in partnership with Tate museums and archives. AnnoTate was built with the average time-pressed user in mind, by which I mean the person who does not necessarily have five or ten minutes to spare, but maybe thirty or sixty seconds.

AnnoTate takes a novel approach to crowdsourced text transcription. The task you are invited to do is not a page, not sentences, but individual lines. If the kettle boils, the dog starts yowling or the children are screaming, you can contribute your one line and then go attend to life.

The new transcription system is powered by an algorithm that will show when lines are complete, so that people don’t replicate effort unnecessarily. As in other Zooniverse projects, each task (in this case, a line) is done by several people, so you’re not solely responsible for a line, and it’s ok if your lines aren’t perfect.

Of course, if you want trace the progression of an artist’s life and work through their letters, sketchbooks, journals, diaries and other personal papers, you can transcribe whole pages and documents in sequence. Biographies of the artists are also available, and there will be experts on Talk to answer questions.

Every transcription gets us closer to the goal of making these precious documents word searchable for scholars and art enthusiasts around the world. Help us understand the making of twentieth-century British art!

Get involved now at anno.tate.org.uk

Sunspotter Citizen Science Challenge: 29th August – 6th September

Calling all Zooniverse volunteers!  As we transition from the dog days of summer to the pumpkin spice latte days of fall (well, in the Northern hemisphere at least) it’s time to mobilize and do science!

Sunspotter Citizen Science Challenge

Our Zooniverse community of over 1.3 million volunteers has the ability to focus efforts and get stuff done. Join us for the Sunspotter Citizen Science Challenge! From August 29th to September 5th, it’s a mad sprint to complete 250,000 classifications on Sunspotter.

Sunspotter needs your help so that we can better understand and predict how the Sun’s magnetic activity affects us on Earth. The Sunspotter science team has three primary goals:

  1. Hone a more accurate measure of sunspot group complexity
  2. Improve how well we are able to forecast solar activity
  3. Create a machine-learning algorithm based on your classifications to automate the ranking of sunspot group complexity
Classifying on Sunspotter
Classifying on Sunspotter

In order to achieve these goals, volunteers like you compare two sunspot group images taken by the Solar and Heliospheric Observatory and choose the one you think is more complex.  Sunspotter is what we refer to as a “popcorn project”.  This means you can jump right in to the project and that each classification is quick, about 1-3 seconds.

Let’s all roll up our sleeves and advance our knowledge of heliophysics!