All posts by Helen Spiers

Penguins, Plastics, and Poo

This week’s guest blog post is from Dr Gemma Hall, who is leading a range of Zooniverse educational outreach initiatives in the UK. Read on to find out about the activities she led earlier this month during British Science Week.

– Helen



Penguins, Plastics, and Poo

Science Week, a week when we scientists gush about our favourite subject, attempt to explain to others what we do all day or just get plain messy with icky, sticky crowd-pleasing experiments. I think I successfully covered all these things during Science Week. And I have Zooniverse to thank for (most of) this.

I’m a STEM Ambassador (, which means I do lots of science outreach. And I’m a huge Zooniverse fan. Whether it’s bashing bugs, elephant expeditioning or galaxy-gazing, I love that anyone with a computer/internet connection can help with real people-powered research, including children.

So, for Science Week, instead of just talking about what scientists do, I used Zooniverse to get primary school children being the scientists. Children like feeling important, so key to engaging them from the outset meant emphasising the importance of helping real researchers make the world a better place. Cue dramatic gasps and disbelieving looks all around the ICT Suite!

Children also like being able to relate to what they’re learning about. And so, I introduced them to the researchers they would be helping…



 Scene: Children transfixed by a PowerPoint presentation showing a picture of Dr Tom Hart, of Penguin Watch, wrapped up against the harsh Antarctic elements, surrounded by penguins.

Tom is a Penguinologist, and I almost had to stop the teachers stampeding out of the ICT Suite in an effort to re-train so they too can get such a great job title. The children, meanwhile, were more captivated by the wondrous site of the hundreds of penguins.

I told the class that Tom’s laboratory is the Antarctic and he wears hefty, cold weather gear rather than a white lab coat. He studies penguins because they give us a really good indication of the effects humans have on the Antarctic. Tom needs to keep an eye on the penguins across many Antarctic sites, all day, every day of the year. However, I continued, Tom can’t live in the Antarctic all year because it’s too harsh and he’d miss his family. He has cameras taking hundreds of photos every day and now has so many that he needs help to analyse them.

And with that, the children keenly set about tagging penguins: Adelies, King, Gentoos, Chinstraps; adults, chicks and eggs. They were careful to observe the behaviour of the penguins and their habitats, which both gave indications of whether the penguins might be incubating eggs or caring for chicks. They imagined what working in the harsh Antarctic environment would be like and they were intrigued about what penguins get up to in the night!

The energy in the classroom could have powered the computers the children were using! And the concentration levels and tagging skills were higher than I’ve seen many adults apply (sorry, Adults!). Furthermore, they asked if they could continue helping to spot penguins at home, so huge was their passion to help.

If you want spot penguins too, go to:



Scene: Children looking at a PowerPoint presentation picture of a beach, with Peter Kohler and Ellie McKay of The Plastic Tide flying their drone.

Plastics have been in the news lots recently and these children were very clued-up, so they needed little introduction to the plastic problem. Already sufficiently motivated to help clean up our planet, they were spurred on even more by hearing that the The Plastic Tide project is the official project of British Science Week and that it was featuring on Sky News and the BBC!

I explained that Peter and Ellie needed help to tag plastic or litter in beach pictures taken by drones. Tagging the pictures teaches a computer program to recognise plastic. The more pictures that are tagged, the better the program will become. Soon, computers will be able to find the plastics themselves, aiding the creation of a global inventory of marine plastic pollution.

The children set to the task with determination, but it soon became apparent that they were not all totally happy; some were frustrated that not all the images had plastics in. After a gentle reminder that we really shouldn’t be hoping to find plastics and that it’s better to have plastic-free beaches, they returned to the task, only to exclaim later that tagging plastics was making them angry. However, this time, they were annoyed that they had spotted so much plastic and litter. Among their finds we had shoes, old toys, many bottle tops, rope, plastic bags, scores of fragments and even an old, gnarled “danger” sign.

We calculated that in one class of 30 children, each child had tagged between 10 and 20 images, so all together they had helped tag an outstanding 300–600 images. With many other schools around the country also tagging plastics, no wonder the Science Week target to tag over 250 000 images was smashed within days. In fact, by the final day, The Plastic Tide had a record-breaking 1.5 million tags—6 times their original target! That equates to 290 000 a day or 800+ tags a minute!

I spoke to Peter at the end of Science Week and he was blown away by the energy and support:

“Science Week has been a huge boost to tagging. The more tags we get, the better the computer algorithm becomes at detecting plastic. Each tag could help find millions more of the same item and will help us clean up our beaches”.

Peter also confided that there are some very exciting announcements coming soon from The Plastic Tide, so keep an eye out for those, and KEEP TAGGING!



I bet you’re wondering where the “poo” in the title comes from? Well, that’s the icky sticky crowd-pleaser I was referring to at the start. Let’s just say that soggy Weetabix squeezed through nylon tights with a hole in the toe end is a really great way to demonstrate the intestine and how it results in… poo! And I’m also told that before you see a penguin colony in the Antarctic, you smell it first.

For a cleaner approach to science, use Zooniverse!


Get tagging!

Get your children tagging!

Help with REAL research and make the world a better place.




Dr Gemma Hall is a Science & Technology Writer and STEM Ambassador. She loves explaining complex things simply, and enthusing people about the importance of science to their everyday lives. Gemma is working to develop Zooniverse in schools, enabling young people to perform real research so that they better understand what scientists do.



Twitter: @Gemma_STEM

Who’s who in the Zoo – Anabelle Cardoso

In the second of our series of ‘meet the researcher’ blog posts, meet PhD student Anabelle Cardoso, who leads the very popular elephant-spotting project, Elephant Expedition

– Helen



Anabelle (fourth from left) and the on-the-ground research team at the research station in Gabon


Project: Elephant Expedition

Researcher: Anabelle Cardoso, PhD Candidate

Location: School of Geography and the Environment, University of Oxford, UK


What are your main research interests?

‘Elephant Expedition’ is part of my PhD research at the University of Oxford, which I started in 2015. Our research is about understanding how forest elephants affect the ecosystems they live in.

Forest elephants are extremely endangered, largely due to hunting for ivory. However, because they live in such mysterious and remote forests, we don’t know as much about them as we would like to. Learning more about these important and threatened animals is critical, as the better you understand an animals ecology the more effectively you can advocate for and plan its conservation.

In our study site in Gabon, and across Africa, valuable savanna habitat is being lost due to over-expanding forests as a result of human-induced global change. Normally, you wouldn’t think of growing forests as a threat, but savanna habitat is home to most of the remaining large mammals in Africa and performs many important ecosystem functions, including carbon storage, so loss of savannas is a global concern. Elephants are ecosystem engineers meaning that they have a disproportionately large impact on the ecosystems they live in. This gives then the unique potential to affect how much forest or savanna is in a landscape, so they can help protect savannas in the face of expanding forests. Most of the research on how elephants might do this has been done on bush elephants, which are a completely different species to the forest elephants of central Africa. Our research aims to remedy this by focusing on how forest elephants affect the forest and savanna balance of the landscape they live in.

In order to better understand forest elephants, we first need to know where they are, so we’ve set up a network of hidden camera traps to photograph them as they move through the forest. Our 40 camera traps are attached to trees and take a photo when triggered by motion or heat. They are super useful for monitoring dangerous and elusive animals like forest elephants because they function 24/7 and can give us a really good idea about where in the landscape the elephants are spending their time without us having to disturb the elephants by following them on foot. This is where the citizen scientists come in – because the camera traps are quite sensitive they don’t only capture images of elephants, but also gorillas, chimpanzees, buffalo, antelope, or even passing birds and bats. The citizen scientists help us to classify all the images into categories based on what’s in them. We can then convert these classifications into data about where the elephants are at what times of year, and link it with our other environmental measurements to draw conclusions. What the citizen scientists contribute is absolutely essential to the research, and forms the backbone of everything we do.


Who else is in your project team? What are their roles?

Yadvinder Malhi (Oxford), Imma Oliveras (Oxford), William Bond (University of Cape Town), and Kate Abenermethy (University of Stirling) supervise me; and Josue Edzang-Ndong (ANPN Gabon) and David Lehmann (ANPN Gabon, University of Stirling) and Kathryn Jeffery (ANPN Gabon, University of Stirling) help managed the project on the ground in Gabon. A special mention should be made to @melvinosky and @jwidness, our wonderful project moderators.


Tell us more about the data used in your project

We have 40 motion and heat sensitive cameras set up along rainforest edges in Gabon, they take photos of all passing animals (mostly elephants, but also a lot of gorillas, chimpanzees, buffalo, leopard, and red river hogs!). These are the images that the volunteers help to classify.


How do Zooniverse volunteers contribute to your research? 

In our project, volunteers are shown an image from one of our camera traps and they have to classify it according to what animal is in it. If the image contains a forest elephant, they also have to count how many elephants they see. The project is simple, so volunteers of all ages and skill levels can join, plus they can classify hundreds of images and therefore get lots of opportunities to spot cool animals.

The project’s feasibility relies on citizen scientists – from our network of hidden camera traps in the rainforest of Gabon we have nearly 2 million photographs we need to analyse and this would be impossible without the help of our dedicated volunteers. To date, there are 10,000 citizen scientists signed up on our website from all parts of the world, as long as you have an internet connection you can join the team.

Citizen science is wonderful because everybody benefits. As researchers we can process very large data sets (like our set of elephant photos) by harnessing the power of thousands of minds all working towards a common goal. This enables us to expand our research scope far beyond what would be possible as individuals – it’s the ultimate global collaboration. The citizen scientists benefit too. Volunteers are exposed to experiences that they might not otherwise have access to, for example in Elephant Expedition you essentially go on a virtual safari through the central African rainforest looking for forest elephants, gorillas, chimpanzees, leopards or mandrills (a type of monkey) – this just isn’t something most people will ever get the chance to do in real life. The project also has a vibrant online community of volunteers. One of the volunteers is a cancer sufferer and she says that participating in our project allows her to not be excluded from doing something just because she’s sick, it gives her a way to pass the time in hospital and makes her feel part of something meaningful.

Since we have so many camera traps and they are highly sensitive, we have many photographs – nearly 2 million! The photographs have a time and location stamp, so each time a volunteer classifies an image as having an elephant in it we know when and where that elephant was sighted. This information from the volunteers is synthesised and is what we’re using to build a time series of elephant habitat use across the landscape. Without the volunteers we would have no way of analysing the images, and therefore no data with which to answer our research questions. Citizen scientists play an integral role in the success of the project, the bottom line is that without them the project wouldn’t be able to work.


What have been the biggest challenges in setting up your project?

It isn’t really a challenge, more a learning journey. I think the amount of time it takes was a challenge, that you always have to be connected to answer questions and see to issues, and of course just learning how to manage such massive data sets has been a steep learning curve! It’s been great though, and I’ve been really humbled by the experience, because all of the volunteers on the project are so lovely and helpful it’s been amazing to be a part of.


What discoveries, and other outputs, has your project led to so far?

We haven’t started doing data analysis yet but we are very excited to see the results! We will be keeping all the volunteers updated on the project page as things continue.


What’s in store for your project in the future?

We have one more small run of final photos, and then we will begin the data analysis and writing up some research! It’s all very exciting and should be coming together in the next few months.


What are your favourite other citizen research projects and why?

Oh! I loved Snapshot Serengetti!


What guidance would you give to other researchers considering creating a citizen research project?

The potential for citizen science research is truly astounding. The world is a big place and the internet is able to connect us with one another. There are millions of potential volunteers across the globe who care as much about what you are researching as you do, and citizen science is an amazing way to connect with them. The best way to make a project effective is to find clever ways of linking volunteers and researchers according to the research interests of both. I think project effectiveness can also be measured by what both researchers and volunteers gain, for example did the research fulfil its scientific aims? Was the scope of the research enhanced by being able to use a global network of volunteers? Did the volunteers feel they gained some enjoyment and knowledge from the process of engaging with it? Would volunteers educate those around them about the research?

When designing a citizen science project, we found it most important to always remember that the people who volunteer to help you are smart and they care about what you’re researching. By including them in the project they become a part of the project, so always appropriately respect their time and skills. Our project depends on people sacrificing time out of their lives to help reach a research goal, so we always make sure we put in the time to communicate with volunteers, answer questions, and just generally engage personally with the people who make the project possible.


And finally, when not at work, where are we most likely to find you?

In Oxford, writing my thesis or destressing with some yoga, or maybe at home in Cape Town, South Africa, walking on the mountain or swimming in the sea. I also love to take road-trips across Southern Africa, there’s always something beautiful to see!


To learn more about Elephant Expedition, check out Annabelle’s Twitter account (@ellieexpedition) or Instagram Page (@elephantexpedition), or click here to go directly to the project.

The future of extreme weather forecasting, preparation and response

Below is a guest post from Robbie Parks, a PhD student from Imperial College London who is studying how global climate change is influencing human mortality. 

Read on to learn more about how crowd sourcing via platforms such as the Zooniverse can help us prepare for, and respond to, extreme weather.

– Helen


The future of extreme weather forecasting, preparation and response

Robbie Parks


The extreme weather around the globe during 2017 was a grim reminder of how truly devastating extreme weather can be. The citizens of Houston, New Orleans, Mumbai, Bangladesh, Nepal, Puerto Rico, the Dominican Republic, Florida and Sierra Leone joined the swelling ranks of victims from hurricanes and floods. Hurricane Harvey was followed by the powerful storms Irma, Jose, and Maria. Heatwaves and drought around the world, from India to Italy, have also brought misery and even death to millions.

When extreme weather strikes, lives and livelihoods are ruined, infrastructure destroyed, landscapes altered (sometimes irreversibly), and communities are often left with long-term mental and physical trauma. This clearly is a challenge not just for developing countries, but also for highly industrialised nations like the United States.  What can we do about this?

Beyond avoiding the most devastating future extreme weather by curbing greenhouse gases improving forecast capabilities and capacity provides some of the best defence. National and local decision makers require reliable warnings to be able to provide emergency planning and relief in their locally minded extreme weather action plans.

Nowadays, the most modern supercomputers, armed with a suite of state-of-the art models of atmospheric processes, generate higher and higher spatial and temporal resolutions with steadily improving forecasting skill. Progress in this ‘quiet revolution’ of forecasting is significant.1 But the next 10 years could yet see another huge paradigm shift in forecast capabilities.

A critical part of this drive for improved weather forecasting is the World Weather Research Programme’s (WWRP) 10-year HIWeather (High Impact Weather) project, a consortium of over 2000 scientists from diverse fields participating from institutions worldwide. I spent some time with Dr Paolo Ruti, global chief of the WWRP at the World Meteorological Organization (WMO) in Geneva, Switzerland, to discuss advances in forecasting extreme weather through HIWeather.

According to Dr Ruti, the ambition of HIWeather is “to promote cooperative international research to achieve a dramatic increase in resilience to high impact weather”. This will translate to saving more lives on the ground in the onset of an extreme weather event, both by improved forecasting capability and better communication to decision makers who activate plans to manage such potential disasters.

First, Dr Ruti took me through the technological and analytical aspects of improving extreme weather forecasting. This includes advances in the resolution of the forecast models themselves, which enable the latest weather models to include physical processes which are critical to predicting extreme weather.

A good example (among many) of improved resolution making a difference to forecasting capability is models which allow for resolving convection processes (drawing moisture for hurricane generation at scales less than 10km spatially) to take place. The newest models are showing remarkable realism in their simulation of the potential evolution of severe storm events for up to 15 days. This additional forecast lead time and accuracy will become invaluable when planning evacuation in cities during hurricane season.

Another key component of improving forecasts is identifying what in weather patterns may indicate the onset of extremes. This includes the work of Dr Hannah Nissan, a researcher at the International Research Institute (IRI) for Climate and Society at Columbia University, New York. Dr Nissan mainly works with developing countries in Africa and South Asia to develop and improve extreme weather early warning systems. She is an expert at extending the forecast horizon of warnings by finding novel sources of predictability.

Dr Nissan and the IRI have researched the predictability of extreme heat in Bangladesh.2 Nissan has explored patterns of air transport and soil moisture, and found “heat waves are preceded by a characteristic wind pattern in the atmosphere, which can set itself up about a week to 10 days before a major heat wave.” Not only that, but reliable warnings up to 30 days in advance may be possible because as ‘soils are drier than normal for at least a month before a heat wave on average.” This will be essential to plan mid-term action plans in response to destructive heat waves.

Beyond improving modelling of weather and its extremes, challenges include data collection to power numerical and physical modelling of weather extremes. Data from the ground weather stations is the fuel for forecasting extreme weather. Developing countries like Bangladesh, however, suffer from a lack of essential weather measurement infrastructure. Dr Ruti of WWRP explains that “data, and real-time observations are a key challenge” for this purpose.

To address this challenge, Ruti said “weather data is being crowd sourced through third party networks using apps or portable weather measurement devices.” An example Ruti highlighted was of the use of mobile phones as a proxy for radar measurements to monitor and forecast rainfall. A pilot project by the French Institute for Development Research in Toulouse has demonstrated a proof-of-concept system in Burkina Faso, Niger, and Cameroon.3,4 This type of technology could emerge as especially important in parts of the world with poor ground-based weather monitoring infrastructure. With high penetration of mobile phones in most of the world,5 it is a promising example of where the next generation of weather data collection and assimilation into forecast models lies.

However, using data from public sources isn’t as simple as plugging it in to a forecasting system. Ruti warned me that “understanding the error characteristics of these data will be critical to using them effectively.” Prior to data post-processing using mobile phone data, the readings of rainfall could be two to three times the real value. Data needs to be trustworthy and processed correctly before it is used for forecast outcomes. Yet, the work is a positive move away from the expensive centralised government weather measurement projects in places which cannot necessarily afford to maintain them.

Crowd sourcing contributions in the aftermath of extreme weather is also a growing area. A leader in the field is Zooniverse, the world’s “largest and most popular platform for people-powered research.”6 The crowd sourcing of over 10,000 contributions to observing the devastation of Irma and Maria from satellite data has resulted in a damage analysis of buildings all over the Caribbean in just a few days, which would have traditionally taken a single researcher over a year. Partnering with the Machine Learning research group at Oxford, Zooniverse have built a powerful mechanism to catalogue damage from extreme weather events, which is invaluable for the clean-up and rebuilding phase.

Zooniverse continues to be involved with identifying the damage caused by the devastating hurricanes of summer 2017, as well as the historical classification of cyclones to help further the understanding of patterns and potential trends of cyclones under climate change. While these are two independent projects, they both serve the broad goal of increasing understanding and preparedness for any similar future events.

This kind of work attracts a wide range of contributors (based on a 2015 survey), with a balanced distribution of ages. Currently, those actively participating are mainly from English-speaking countries, as a combined 64% originate from the UK or the USA, with only 2% from developing countries.  Many state that contributing to the projects on Zooniverse is fun, and that contributing to scientific progress is a strong part of the appeal.

Professor Brooke Simmons, Einstein fellow at UC San Diego, and leader of the Zooniverse Analysis Group, explains that the work users are doing on Zooniverse is a making a noticeable difference on the ground, with “really good feedback” from first responders, especially in response to Irma and Maria in 2017, where Zooniverse was called in to inspect the damage.

One such first responder, Rebekah Yore, of Rescue Global, an international Non-Governmental Organisation working to reduce disaster risk reduction around the world. Having previously worked on the response to Irma and Maria in 2017, Yore explains that Rescue Global could quickly find a set of personnel via Zooniverse whereby thousands of volunteers rapidly analysed pre- and post-storm satellite images to identify “specifically features of damage and hazard from infrastructure collapse, flooding and other causes”.

Once the volunteers’ work was done, the Machine Learning department at the University of Oxford would ‘run the responses through algorithms to filter anomalous results, improve the reliability of data, and produce heat maps of identified damage and features on the ground’. This became an essential component in Rescue Global’s humanitarian response, which was involved in determining which affected populations’ needs were the most critical, and which areas may have been more dangerous to traverse for teams deployed on the ground.

From a humanitarian response perspective, and particularly from the hurricane response in the Caribbean in 2017, Zooniverse allowed a great amount of accurate information to be generated extraordinarily quickly. Yore is quite clear that without Zooniverse, the first responders could not have had this kind of information to hand. Why was this information so useful? Yore sets the scene for what greets the those who are first to such disaster zones: “villages may have disappeared, there may be new, spontaneous groups of internally-displaced people. Bridges and access roads can also be destroyed, with flooding and landslides potentially blocking access”. The insights from Zooniverse are essential for both finding vulnerable populations while also protecting members of the rescue community.

Thus, arguably the most dramatic change to preparing for extreme weather is also how responses to them are managed. HIWeather has also identified endemic challenges of what a forecast should indicate, such as how to best spread effective warnings to a vulnerable community. Social media activity is a good way to analyse how effectively an extreme weather warning is being received and followed.7

While it is one thing to create experimental forecasting products; it is another to create something which decision-makers can use without requiring very specialised knowledge, often sorely lacking at Earth’s most vulnerable areas. Dr Joy Shumake-Guillemot leads the Joint Office for Climate and Health within the WMO, which specialises in translating forecasts into actions which local decision-makers can employ in extreme weather events.

Shumake-Guillemot recognizes how important it is to translate forecasting research into something useable for those on the ground: “the last-mile user (i.e. those who have to use the results for the purposes of minimising risk for vulnerable communities) in the ministry of health is often the neglected piece of the puzzle. Without building forecasting systems that factor in decision making, cutting-edge forecast products can so often become unused, especially in developing countries.”

Dr Ruti of WWRP also made clear to me that the key to saving lives and livelihoods is not only the forecasting technology, but also the methods of communicating the hazards to a community: ‘Better prediction and communication should go hand-in-hand’. We need to understand the ‘physical and social factors limiting the capability to communicate’, as well as understanding how to find better ways of forecasting.

A critical failure in the most devastating disaster to ever hit Myanmar, Cyclone Nargis in 2008, was that although the Indian Meteorological Office had predicted the extreme with four days’ notice,8 traditional methods of disseminating the warning (such as via TV and radio) did not reach isolated communities in the low-lying regions of the country, resulting in over 100,000 potentially avoidable deaths.

The link between decision makers and predictions often is at a disconnect, and this is the mid-term key to improving action on disaster forecasts. While events like Hurricane Harvey and others are truly terrible, they provide an opportunity to remind policy makers that it is critical we get the forecasting and communication coupling right. The future of forecasting includes targeted messaging to vulnerable populations, including the elderly, the young, prisoners, and labourers. Great gains can be made with existing technology and improved communication.

Of course, key steps forward in forecasting technology will occur over the next decade. But progress will not only develop in the models’ themselves, but also in the data collection for the models by crowd sourcing, and clarifying and communicating the forecasts to decision makers and then to vulnerable communities.

The future is full of extreme weather, but we can at least know a good deal more about it before it arrives.




1         Bauer P, Thorpe A, Brunet G. The quiet revolution of numerical weather prediction. Nature 2015; 525: 47–55.

2        Nissan H, Burkart K, Mason SJ, Coughlan de Perez E, van Aalst M. Defining and predicting heat waves in Bangladesh. J Appl Meteorol Climatol DOI:10.1175/JAMC-D-17-0035.1.

3        Tollefson J. Rain forecasts go mobile. Nature 2017; 544: 4–5.

4        Doumounia A, Gosset M, Cazenave F, Kacou M, Zougmore F. Rainfall monitoring based on microwave links from cellular telecommunication networks: First results from a West African test bed. Geophys Res Lett 2014; 41: 6015–21.

5         The World Bank. Mobile cellular subscriptions (per 100 people). 2017.

6        What is the Zooniverse? Zooniverse. 2017. (accessed Sept 28, 2017).

7         Ripberger JT, Jenkins-Smith HC, Silva CL, Carlson DE, Henderson M. Social Media and Severe Weather: Do Tweets Provide a Valid Indicator of Public Attention to Severe Weather Risk Communication? Weather Clim Soc 2014; 6: 520–30.

8        IFRC. Community early warning systems. Guiding principles. 2012; : 84.




Who’s who in the Zoo – Philip Fowler

In the first of our new series of ‘meet the researcher’ blog posts, let me introduce Philip Fowler, who leads our Tuberculosis-fighting project, BashTheBug

– Helen


STJO_0130CM-square - Philip Fowler

Project: BashTheBug

Researcher: Philip Fowler, Senior Researcher

Location: John Radcliffe Hospital, University of Oxford, UK


What are your main research interests?

Antibiotic Resistance


Who else is in your project team? What are their roles?

I’m part of a project, CRyPTIC, that is collecting samples of tuberculosis around the world. So there are lots of scientists I’ve never met in labs in other countries who prepare the samples, inoculate the plates and take the photos that ultimately end up on BashTheBug.


Tell us more about the data used in your project

All the photographs you see are for a series of wells containing a single antibiotic. There are a few with 5 or 8 wells, but most antibiotics have either 6 or 7 wells. As you head from left to right, each well contains double the amount of antibiotic as the one before. Each strip of wells has been cut from a single photograph of a 96-well plate that contains 14 different anti-TB drugs in total. And each plate has been inoculated with a sample of M. tuberculosis taken from a patient somewhere in the world and incubated for two weeks. We hope to process 30,000 plates which means 6.3 million classifications over the next few years…


How do Zooniverse volunteers contribute to your research? 

They help us by deciding in which wells the bacteria are growing (and therefore the antibiotic isn’t working) and which wells they don’t grow.


What have been the biggest challenges in setting up your project?

Umm, writing the software that cuts up each photo of a 96-well plate into smaller images for uploading to the Zooniverse.


What discoveries, and other outputs, has your project led to so far?

Our volunteers have found some artefacts that we completely missed. One person spotted fuzzy patches in the last well for one drug; turns out this is the drug clofazimine crystallising in the bottom of the well as it is present at such a high concentration!

BashTheBug won the Online Community Award of the inaugural NIHR Let’s Get Digital competition back in August 2017.

Having shown the project to lots of people what has been really interesting is it ends being a test of how optimistic or conservative you are; people who are the latter will say any small spot they see is bacterial growth, whereas the former tend to say “nah, that is really small so I am going to ignore it”. Fortunately we are asking enough people that this averages out!

We are currently trying to work out how to best build a consensus from all the classifications our volunteers have done; watch this space. Anything we do intend to publish we will first submit to the biorXiv so volunteers will be able to freely download our manuscript.

Oh, and someone mentioned us in a poem.


Once you’ve finished collecting data, what research questions do you hope to be able to answer?

A subversive one: is a crowd of volunteers more accurate and/or consistent that an expert?

A left-field one: which genetic mutations in M.tuberculosis confer resistance to certain antibiotics and, equally importantly, which do not?


What’s in store for your project in the future?

Beyond a lot more data? Hmm.

We’ve got a new design of 96-well plate on the horizon, also we want to pool all our data to see if using something like Zooniverse, but in a clinical setting, could be used to help deal with difficult cases in a hospital lab.

I’m also thinking of better ways to engage with the volunteers – more soon.


What are your favourite other citizen research projects and why?

I love SETI@home as it is a bonkers idea, has been going for about 20 years and was the inspiration behind the BOINC framework (


What guidance would you give to other researchers considering creating a citizen research project?

Just try creating a test project, it is easier than you think!


And finally, when not at work, where are we most likely to find you?

On one of my bikes, or playing Minecraft with my two daughters.



Microscopy Masters draws to a close

The guest post below was written by Jacob Bruggemann, a graduate student based at the Scripps Research Institute, who helped lead the biomedical Zooniverse project, Microscopy Masters.

Read on to find out about the findings of this project, made possible by the efforts of our fantastic citizen scientist volunteers. If you would like to learn more, you can access a preprint publication about this work here 

 With thanks to Zooniverse Volunteer Becky Kennard for editing this piece. 

– Helen 


Microscopy Masters draws to a close

Microscopy Masters, the cryo-electron microscopy (cryo-EM) project building complex 3D models of proteins, is approaching its initial conclusion. Over the two years the project has been running, we’ve collected over 17,000 classifications and built a dataset of 209,696 unique protein particles. The primary dataset used in this project was the 26S proteasome lid complex generated by the Lander Lab at the Scripps Research Institute. We have also annotated other, smaller datasets.

The proteasome is a large multi-protein complex responsible for breaking down unwanted proteins into reusable parts, kind of like a large recycling center for the cell. Studying its structure could reveal the mechanisms behind how the proteasome lid only opens for proteins marked for recycling, and give insights into problems caused by the lid malfunctioning.

This project is centered on an important tenet in biology, ‘form follows function.’ On the molecular level of biology, what this means is that the shapes of large biological molecules, such as proteins and nucleic acids, are evolved to perform specific functions. By studying and understanding the structure of biological complexes, researchers can better understand how all the little moving parts of life interact, which will allow them to better combat diseases and disorders.

Scientists are often too busy in the lab to come up with catchy names, meaning that the techniques they invent are usually given pretty self-explanatory titles. In the case of cryo-EM, everything you need to know is in the name.

Imagine some scientists are interested in studying a protein, say our subject, the 26S proteasome lid. Cells containing large quantities of the protein are lysed (a scientific way to say ‘popped like balloons’) and the contents of the cells are put into a solution. That solution is purified so that it only contains the protein the scientists are interested in. The purified solution is then flash-frozen in extremely thin ice (cryo) and put under an electron microscope (EM) to obtain images of the proteins. These images are then put through sophisticated reconstruction software to obtain a detailed 3D model of the protein. This technique is so powerful, scientists can identify individual atoms in the protein complex, giving them deep insights into how it interacts with its environment.


Figure 1 A schematic of how cryo-EM is done. Taken from

Of course, there have been years of work involved in the cryo-EM that I just explained in four sentences (and some very, very expensive microscopes!). A particularly time-consuming task for cryo-microscopists is picking the individual proteins from the microscopy images (micrographs), called ‘particle picking.’

Scientists used to do this by hand, but since they often have thousands of these images to process, this can take weeks of work. So, they usually rely on software to extract the protein images. But because some proteins are so complex, it can be difficult for software to identify them in the noisy micrographs. For this reason, we decided to train citizen scientists to pick the particles from our proteasome lid data and see if it could be used to build a detailed molecular model.


Figure 2 On the left is a blank micrograph, on the right is a micrograph that has had the proteins manually picked.

Using the data from our volunteers, we made a full 3D reconstruction of the proteasome lid. We compared the model to one made with an automatic particle picker, both of which are shown below. Although they look very similar, what matters for microscopists is the ‘resolution’ of the reconstructions. What resolution means in this context is how consistent the models are when used several times.

In this case each dataset was divided into two random halves and made into two separate models, which were then compared to determine a resolution. Even though the resolution for the computer-made model is lower (better) in this case, this is partly due to the fact that the computer-picked dataset had so many more particles. For this reason, we also did a reconstruction using a subset of the computer-picked data with the same amount of particles as the crowdsourced dataset. This brought the resolution to 4.036 Å, closer to the crowdsourced dataset but still lower.


Figure 3 The final reconstructions of the crowdsourced and computational datasets. The resolutions for each are listed, the lower the better.

Even with the higher resolution, we believe this is a fantastic example of the power of citizen science. We built an entirely hand-picked dataset from people who had little to no experience with cryo-EM. This dataset allowed us to build a detailed, 3D model of a complex protein that had a similar resolution to the one built by a team of trained scientists with state-of-the-art software.

This was the first time we have run a project of this nature, and we believe that with tweaking and better feedback systems (which were only implemented by the fantastic Zooniverse team late into our project’s run) we can process data better and faster than we did in our first run.

As a side experiment to try and figure out how to better engage users, some of our project’s participants might remember being sent newsletters about ‘sprint’ datasets, which were small datasets of 15-20 images of other proteins. The use for these was not to build an entire particle dataset, but to provide data for the researchers to feed their automatic particle picking software. We found that giving the images different color schemes than the traditional black-and-white was a nice way to ‘spice’ the micrographs up for users, and we were able to provide researchers with usable data in a matter of days that they could use to start their data processing.

Although we currently have put the Microscopy Masters project on hold, we are excited with the results and in the process of submitting a publication of our initial results. I would like to thank everybody involved with the Zooniverse for building a fantastic platform to try our project. In particular, I would like to thank the Zooniverse team for answering my questions and helping me get Microscopy Masters up and working.

And lastly, thank you to all the hard-working participants in Microscopy Masters and everybody who participates in this great website!


Find out more:

You can check more of the great work done here at the Su lab on our website.

The Lander Lab is consistently pushing the work being done in cryo-EM, go see the work they are doing at their homepage.

Our publication can be previewed as a preprint on bioRxiv.



Step into the Zoo

Dr Sam Illingworth, Senior Lecturer in Science Communication and Poet, wrote the following Zooniverse inspired poem for us; ‘Research for All’.

If you’d like to read more of his work, check out Sam’s blog here.

– Helen

Research for All


Detecting bubbles in the Milky Way,

Or sorting a muon and gamma ray;

Identifying planets and their stars,

Then codifying ice geysers on Mars.


From mapping out old weather lost at sea,

To counting jungle rhythms in a tree;

With floating forests hiding in plain sight,

Sometimes research just needs a brighter light.


Etching a cell to analyse their state,

And bashing bugs to keep drugs up-to-date;

The history of what has gone before,

Can help predict what science has in store.


Transcribing ancient texts and works of art,

Unearthing words that set Shakespeare apart;

Revealing secret lives and hidden gist,

By searching for what others might have missed.


In answering the questions left to find,

We need the help of more than just one mind;

A Universe of projects yet to do,

The door is open, step into the zoo.




A Late Night at the Museum

At the end of October, The Zooniverse team was invited to the Natural History Museum in London to be part of the Museum’s monthly Lates event program.

(Photos courtesy of the Etch A Cell team)

The event was organised by the ConSciCom team who have partnered with the Zooniverse to create two very successful projects – Science Gossip and Orchid Observers. The theme for the evening was to explore the role images, such as illustrations and photographs, have played within natural history and scientific research.

From studying animal behaviour using photos taken by camera traps, to advancing our understanding of cell biology with photos from microscopes, many Zooniverse projects improve our understanding of the world around us through the help of citizen scientist volunteers.

Teams from multiple Zooniverse projects, including BashTheBug, Etch A Cell, Notes from  Nature, Orchid Observers, Science Gossip and Seabird Watch, attended the event and spent the evening speaking to people about their projects, and showing how anyone can contribute to real research through citizen science.

(Photos courtesy of the Etch A Cell team and Jim O’Donnell)

Illustrator Dr Makayla Lewis led a live gallery drawing event, asking visitors to pick up a pencil and spend 15 minutes sketching their favourite exhibits.


(Photos courtesy of Jim O’Donnell)

Thanks to everyone who got involved, including Fiona (Penguin Watch), Freddie (University of Oxford), Jim (Zooniverse Developer), Makayla (Illustrator), Martin (Etch A Cell), Nathan (University of Oxford) and Phil (BashTheBug), and especially all our volunteers who attended the event!


Six months of bashing bugs

Below is a guest blog post from Dr Philip Fowler, lead researcher on our award-winning biomedical research project Bash the Bug. Read on to find out more about this project and how you can get involved!

– Helen


Our bug-squishing project, BashTheBug, was six months old this month. Since launching on 7th April 2017, over seven thousand Zooniverse volunteers have contributed nearly half a million classifications between them, making 58 classifications per person, on average.

The bugs our volunteers have been bashing are the bacterium responsible for Tuberculosis (TB); ‘Mycobacterium Tuberculosis’. Many people think of TB as a disease of the past, to be found only in the books of Charles Dickens. However, the reality is quite different; TB is now responsible for more deaths each year than HIV/AIDS; in 2015 this disease killed 1.8 million people. To make matters worse, like all other bacterial diseases, TB is evolving resistance to the antibiotics used to treat it. It is this problem that inspired the BashTheBug project, which aims to improve both the diagnosis and treatment of TB.

At the heart of this project is the simple idea that, in order to find out which antibiotics are effective at killing a particular TB strain, we have to try growing that strain in the presence of a range of antibiotics at different doses. If an antibiotic stops the bacterium growing at a dose that can be used safely within the human body, then bingo! that antibiotic can be used to treat that strain. To make doing this simpler, the CRyPTIC project (which is an international consortium of TB research institutions), has designed a 96-well plate which has 14 different anti-TB drugs freeze-dried to the bottom of each well.

96well plate

Figure 1. A 96-well microtitre plate

These plates are common in science and are about the size of a large mobile phone. When a patient comes into clinic with TB, a sample of the bacterium they are infected with is taken, grown for a couple of weeks and then some is added to each of the 96 wells. The plate is then incubated for two weeks, and then examined to see which wells have TB growing in them and which do not. As each antibiotic is included on the plate at different doses, it is possible to work out the minimum concentration of antibiotic that stops the bug from growing.

But why are we doing this? Well, the genome of each TB sample will also be sequenced. This will allow us to build two large datasets; one of the mutations in the TB genome and another listing which antibiotics work for each sample (and which do not). Using these two datasets, we will then be able to infer which genetic mutations are responsible for resistance to specific antibiotics. With me still? Good. This will give researchers a large and accurate catalogue that would allow anyone to predict which antibiotics would work on any TB infection, simply by sequencing its genome. This is particularly important for the diagnosis and treatment of TB; currently used approaches are notoriously slow, taking up to eight weeks to identify which antibiotics can be used for effective treatment. If you were a clinician would you want to wait two months before starting your patient on treatment? Of course not.

Figure 2

Figure 2. A photograph of M. tuberculosis that has been growing on a plate for two weeks.

You might scoff at this point and say, pah, using genetics like this in hospitals will never happen. Well it already is. Since March 2017, all routine testing for Tuberculosis in England has been done by sequencing the genome of each sample that is sent to either of the two Public Health England reference laboratories. A report is returned to the clinician in around 9 days. Surprisingly, this costs less than the old, traditional methods for TB diagnosis and treatment. Sequencing TB samples also provides other valuable information, for example, you can compare the genomes of different infections to determine if an outbreak is underway, at no extra cost.

So far, so good. The main challenge to this project though, is size. We will be collecting around 100,000 samples from people with TB from around the world between now and 2020. Every single sample will have its genome sequenced and its susceptibility to different antibiotics tested on our 96-well plates. Each of these plates then need to be looked at, and any errors or inconsistencies in how this huge number of 96 well plates are read could lead to false conclusions about which mutations confer resistance, and which don’t.

This problem is why we need your help! You might not be clinical microbiologists (although a few of you no doubt are!) but there are many, many more of you than we have experienced and trained scientists. In fact, each plate will only be looked at by one, maybe two, scientists, and so it is highly likely that, without the help of volunteers, our final dataset will be riven with differences due to how different people in different labs have read the plates. The inconvenient truth, however much we’d like to think otherwise, is staring at a small white circle and deciding whether there is any M. tuberculosis growing or not is a highly subjective task. Take a look at the strip of wells below – the two wells in the top left have no antibiotic at all so give you an idea of how this strain of TB grows normally.

Figure 3

Figure 3. Is there a dose above which the bacteria doesn’t grow?

In the BashTheBug project, you are asked if there is a dose of antibiotic above which the antibiotic doesn’t grow. If you think there is, you are then asked the number of the first well that doesn’t have any TB growing. For the example image above, I might be cautious and say, well, I can see that there appears to be less and less growth as we go to the right and the dosage increases, but it never entirely goes away; there is a very, very faint dot in well #8. So I’m going to say that actually I think there is bacterial growth in all eight wells. You might be optimistic (or even just in a good mood) and disagree with me and say, yes, but by the time you get to well #6, that dot is so small compared to the growth in the control wells, either the antibiotic is doing its job, or, you know what, I’m not convinced that the dot isn’t some sediment or something else entirely.

There is no correct answer. We are probably both right to some extent; there IS something in well #8, but maybe this antibiotic would still be an effective treatment as it would be able to kill enough of the bacteria for your immune system to then be able to kill off the remainder of the infection. Therefore, the aim of BashTheBug is to identify which antibiotic dose multiple people agreed is the dose above which the bacteria no longer grows. Our result from this project is the consensus we get from showing each image to multiple people. Yes, the volunteers might, on average, take a slightly different view to an experienced clinical microbiologist, but that doesn’t matter as they will, on average, be consistent across all the plates which is vital if we are to uncover which genetic mutations confer resistance to antibiotics.

None of this would be possible without the hard work of all our volunteers. So, if you’ve done any classifications, thank you for all your help. Here’s to another six months, many more classifications, and the first results from the hard work done by the many volunteers who have taken part in the project to date.

Find out more:

  • Contribute to the project here
  • Read the official BashTheBug blog here
  • Follow @BashTheBug on Twitter here
  • BashTheBug won the Online Community Award of the NIHR Let’s Get Digital Competition, read more here

Check out other coverage of BashTheBug:

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.

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:


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.


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.


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! ( 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!



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.