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crowd-sourcing

Challenging challenges in Earth science research at the EGU General Assembly!

Challenging challenges in Earth science research at the EGU General Assembly!

At the EGU General Assembly 2019 last month, if you walked through the dark basement and the most distant hallways of the convention centre,  into room -2.62 on Wednesday evening, you may have heard people introducing themselves followed by the words “… and I have a problem.” This may have sounded like a support group. In fact, if you had entered the room it would have been clear that you had just walked into a kind of support group – a scientific one. In the Crowd-solving Problems in Earth Sciences short course scientific, career-related and logistical problems were shared and discussed.

After the success of last year’sCrowd-Solving problems in Earth science research’ session, a group of young geomorphologists decided to organize a second crowd-solving session at the EGU 2019 meeting, but this year for a broader audience, covering various EGU divisions (including Biogeosciences, Earth Magnetism & Rock Physics, Geomorphology, Geochemistry, Mineralogy, Petrology & Volcanology).

This short course aims to provide a platform especially, but not exclusively, for early career scientists (ECS) to network and brainstorm with fellow researchers. Discussing the challenges you face in your research among your peers may help you to find the core of the problem, a path to the solution, or even other ECS that face similar problems and may become your fellows in the search for an answer.

Despite the unlucky scheduling of the session (from 19:00-20:30) 35 scientists participated this short course. In this blog, we summarize the problems highlighted in the event and share the discussions, ideas and solutions that emerged from the brainstorming session with those who didn’t find this safe place at the EGU General Assembly and the wider EGU community.

Fatherhood and parental leave: How to balance career and family in the 21st century?

Fatherhood and parental leave: How to balance career and family in the 21st century? Credit: Johannes Buckel

(Samuel Wharton, University of Leicester, United Kingdom)

One of the most important events in a man’s life is the day when he becomes a father. During these special times, it is inevitable that young fathers still want to spend time with their new-born child. However, in science, new fathers are usually early career researchers on temporary contracts and the paternity leave offered can be poor, as little as a few days to week. Thus, new fathers can often be torn between wanting to take time out to be with their child and the battle to retain job security for their new family. As a result, the majority of childcare is provided by the partner, who often ends up sacrificing their own career ambitions.

In the discussion, we found that the underlying problem is that scientific environments are built on short term contracts. This conflicts with the need for paternity leave to be more flexible, allowing men to take up to six months leave if necessary and to accommodate their partners’ ambitions. Therefore, taking time out should be considered in both partners’ CVs, so that they are not punished in their careers for producing less papers, for example. Most importantly, future fathers should not be afraid to proactively talk to their partners, supervisors and colleagues about the expectations that are placed upon them. The enjoyment of fatherhood, if granted time, could be for the benefit of every scientist.

Ground control to Major Tom: How to identify fixed reference points in a dynamic landscape?

(Eike Reinosch, TU Braunschweig, Germany)

Ground control to Major Tom: How to identify fixed reference points in a dynamic landscape? Credit: Johannes Buckel

When using satellite data in research, finding reliable and fixed reference points is essential for analysing how an object or surface moves over time. Without a reference point, the satellite data is much like ‘Major Tom’ from David Bowie’s song ‘Space Oddity’: Helplessly floating in space. Choosing a bad reference point however, could make all results invalid and completely useless. But how can we be certain, that the points we choose are reliable, even in a highly dynamic study area?

Luckily we crowd-solved some ideas and suggestions. As a first step, we can use the data available to perform a preliminary selection of reference points following a few criteria: the selected points must feature a stable backscatter signal of the satellite radar waves over time, be present and clearly visible in all images, be far away from moisture sources which could disturb the signal and, if possible, be located on bed-rock material. A second step would be to perform a statistical clustering of areas based on similar patterns and features to ensure that results are comparable.

However, during the discussion we realized that while a statistical evaluation of reference points is absolutely essential, it is just as important to verify those reference points in the field. Following field observations of potential fix points the data needs to be reprocessed with remaining reliable reference points. This should produce the best grounded result possible.

Crowdsourced data: How to use citizen science to study natural hazards in remote areas?

(Joanne Wood, King’s College London, UK)

Crowdsourced data: How to use citizen science to study natural hazards in remote areas? Credit: Johannes Buckel

Researching natural hazards in remote locations can be a challenge. Natural hazards are often only recorded if they impact humans, so records do not accurately reflect the quantity or frequency of hazards in remote regions. This means data for research into natural hazard frequency in remote regions is often incomplete.

In the brain-storming session, we talked about how citizen science provides an opportunity to bridge this gap in data availability. One of the notable outcomes of the session was the idea that citizen scientists, from children to grannies, could inspect satellite imagery from remote areas to identify the location and timing of natural hazards using online platforms. This could be supplemented with local knowledge by engaging with remote communities to map events as they happen and to help pinpoint events that have happened in the past.

We also came up with other creative sources of information, such as utilising tourist photos for high temporal resolution monitoring and even strapping cameras to animals (llamas were suggested for Jo’s case study of Peru) to access the most remote locations.

Communicating science to the public: Are we missing something?

(Stacy Phillips, The Open University, Milton Keynes, UK)

Communicating science to the public: Are we missing something? Credit: Johannes Buckel

Science communication events are becoming increasingly common and more scientists are now feeling the need to communicate science to the public. However, the parts of the public that participate in science communication events are often self-selective groups that are already interested in science. How can we reach an entire cross-section of the public?

In the discussion we didn’t find a unified approach which would enable us to reach out to the entire public, but rather decided that knowing your audience was key, that each group is different and requires a different communication style. We should remember that we, as scientists, are part of the public, and instead of ‘communicating to’ the public, we should be ‘engaging with’ the public, having two-way conversations and getting them actively involved.

Good science communication however is hard, and requires time and expertise to get it right. To improve public outreach in the future, we first need to train our scientists in communication skills at an early career stage. Science is all about communication, making such skills beneficial for your entire career. Outreach work also needs to be valued at an institutional level, required on academic CVs, and incentivised in career pathways, in order to reward those who are passionate and who excel in science communication.

Sharing is caring: How to improve accessibility to scientific infrastructure beyond national boundaries?

(Adrián Flores-Orozco, TU Wien, Austria)

Sharing is caring: How to improve accessibility to scientific infrastructure beyond national boundaries? Credit: Johannes Buckel

Geoscientists want to ensure data quality, and thus ship their equipment, and materials abroad, and prefer to analyse collected scientific samples in their own laboratories. This is a challenge when conducting research and field work beyond national boundaries, especially in remote or conflict areas (Latin America, Iran, etc.).  However, in the discussion we found out that these difficulties even arise within European countries.

There are several different kinds of research limiting issues that you can encounter when trying to get samples from across borders to your laboratory, including political restrictions, expensive shipment costs, long duration with associated delays in publications and graduation. A solution could be to improve accessibility to scientific infrastructure abroad. This would entail collaborating with local researchers and sharing equipment and laboratories. Feasible solutions could be:

  1. the creation of an international logistic consortium and a network of geoscientists working abroad,
  2. an international inventory of available infrastructure and laboratories, and
  3. convincing national or European financing agencies to invest abroad to avoid constant exportation and importation of equipment and samples.

We recognize that these are great solutions, but we need to take action to make them real. We urgently need to improve communication between researchers, stakeholders and financing agencies. To raise the pressure for change we can publish on the problem in an open access journal. We should take advantage of social media to interact among geoscientists working abroad and to share their experiences and possible solutions. We all could start caring about others, and actively share our scientific infrastructure without borders.

The mean mean: Can we trust average erosion rates?

(Günther Prasicek, University of Lausanne, Switzerland)

The mean mean: Can we trust average erosion rates? Credit: Johannes Buckel

We try to resolve the stochastic and sometimes random nature of surface processes, like erosion and sedimentation in both time and space, by averaging. By doing so we introduce biases and misleading impression. A mean thing about the mean rate is that processes might seem to be continuous, while in reality erosion and deposition rather occur as discrete pulses with hiatus, thus time spans without anything happening, in between. A common bias, such as the so-called Sadler effect, is introduced due to the temporal and spatial scales we average over.

The discussion posed a number of interesting questions: How can we approach these trust issues concerning the mean as they seem inevitable to many of Earth science research questions? Do we need methodological and conceptual frameworks which provide the bounds of the data as well as their interpretations? How can we stochastically scrutinize the data and its limit? How can we technically advance and thus trust mean rates?

To bring back this Meta discussion down to Earth, the proposed solutions are simple: let’s change the sampling strategies, sample more, spatially random and in very low erosion environments. Combine diverse methods to use varying spatial and temporal resolutions to bootstrap rates in between. And if possible, simply, develop new methods with different averaging time spans. Next steps in practice would be to first compile data of possible hiatus length and data from different methods/strategies, and then cross-compare their timespan and resulting rates at different landscape activities. We need to be ruthless with what we can actually tell with the mean data we have and should embrace low rates – as they are exciting!

We are planning on organising crowd-solving session(s) again next year. If anybody has any problems they want to solve, they can let us know!

By Eleanore Heasley (King’s College London, UK), Renee van Dongen and Anne Voigtländer (GFZ Potsdam, Germany), and Felix Nieberding, Liseth Perez and Johannes Buckel (TU Braunschweig, Germany)

Organizing team of the session also included: Harry Sanders and Richard Mason (Loughborough University, UK)

GeoTalk: A smart way to map earthquake impact

GeoTalk: A smart way to map earthquake impact

Last week at the 2016 General Assembly Sara, one of the EGU’s press assistants, had the opportunity to speak to Koen Van Noten about his research into how crowdsourcing can be used to find out more about where earthquakes have the biggest impact at the surface.

Firstly, can you tell me a little about yourself?

I did a PhD in structural geology at KULeuven and, after I finished, I started to work at the Royal Observatory of Belgium. What I do now is try to understand when people feel an earthquake, why they can feel it, how far away from the source they can feel it, if local geology affects the way people feel it and what the dynamics behind it all are.

How do you gather this information?

People can go online and fill in a ‘Did You Feel It?’ questionnaire about their experience. In the US it’s well organised because the USGS manages this system in whole of the US. In Europe we have so many institutions, so many countries, so many languages that almost every nation has its own questionnaire and sometimes there are many inquiries in only one country. This is good locally because information about a local earthquake is provided in the language of that country, but if you have a larger one that crosses all the borders of different countries then you have a problem. Earthquakes don’t stop at political borders; you have to somehow merge all the enquiries. That’s what I’m trying to do now.

European institutes that provide an online "Did You Feel the Earthquake?" inquiry. (Credit: Koen Van Noten)

European institutes that provide an online “Did You Feel the Earthquake?” inquiry. (Credit: Koen Van Noten)

There are lots of these databases around the world, how do you combine them to create something meaningful?

You first have to ask the different institutions if you can use their datasets, that’s crucial – am I allowed to work on it? And then find a method to merge all this information so that you can do science with it.

You have institutions that capture global data and also local networks. They have slightly different questions but the science behind them is very similar. The questions are quite specific, for instance “were you in a moving vehicle?” If you answer yes then of course the intensity of the earthquake has to be larger than one felt by somebody who was just standing outside doing nothing and barely felt the earthquake. You can work out that the first guy was really close to the epicentre and the other guy was probably very far, or that the earthquake wasn’t very big.

Usually intensities are shown in community maps. To merge all answers of all institutes, I avoid the inhomogeneous community maps. Instead I use 100 km2 grid cell maps and assign an intensity to every grid cell.. This makes the felt effect easy to read and allows you to plot data without giving away personal details of any people that responded. Institutes do not always provide a detailed location, but in a grid cell the precise location doesn’t matter. It’s a solution to the problem of merging databases within Europe and also globally.

Underlying geology can have a huge impact on how an earthquake is felt.  Credit: Koen Van Noten.

Underlying geology can have a huge impact on how an earthquake is felt. 2011 Goch ML 4.3 earthquake.  Credit: Koen Van Noten.

What information can you gain from using these devices?

If you make this graph for a few earthquakes, you can map the decay in shaking intensity in a certain region. I’m trying to understand how the local geology affects these kinds of maps. Somebody living on thick pile of sands, several kilometres above the hypocentre, won’t feel it because the sands will attenuate the earthquake. They are safe from it. However, if they’re directly on the bedrock, but further from the epicentre, they may still feel it because the seismic waves propagate fast through bedrock and aren’t attenuated.

What’s more, you can compare recent earthquakes with ones that happened 200 years ago at the same place. Historical seismologists map earthquake effects that happened years ago from a time when no instrumentation existed, purely based on old personal reports and journal papers. Of course the amount of data points isn’t as dense as now, but even that works.

Can questionnaires be used as a substitute for more advanced methods in areas that are poorly monitored?

Every person is a seismometer. In poorly instrumented regions it’s the perfect way to map an earthquake. The only thing it depends on is population density. For Europe it’s fine, you have a lot of cities, but you can have problems in places that aren’t so densely populated.

Can you use your method to disseminate information as well as gather it, say for education?

The more answers you get, the better the map will be. Intensity maps are easier to understand by communities and the media because they show the distribution of how people felt it, rather than a seismogram, which can be difficult to interpret.

What advice would you give to another researcher wanting to use crowd-sourced information in their research?

First get the word out. Because it’s crowd-sourced, they need to be warned that it does exist. Test your system before you go online, make sure you know what’s out there first and collaborate. Collaborating across borders is the most important thing to do.

Interview by Sara Mynott, EGU Press Assistant and PhD student at Plymouth University.

Koen presented his work at the EGU General Assembly in Vienna. Find out more about it here.

Mars Rocks – introducing a citizen science project

Mars Rocks – introducing a citizen science project

GeoLog followers will remember our previous report on Citizen Geoscience: the exciting possibilities it presents for the acquisition of data, whilst cautioning against the exploitation of volunteered labour. This blog presents a Citizen Science platform that goes beyond data collection to analysis, specifically for geological changes in remote sensing imagery of Mars. Jessica Wardlaw, a Postdoctoral Research Associate in Web GIS, at the Nottingham Geospatial Institute, introduces ‘iMars’ and explains 1) its scientific mission and 2) why imagery analysis is especially suitable for a crowd sourcing approach, so that you might consider where and how to apply it to your project.

Imagine, just for a moment, that the Mars Geological Survey invited you to an interview for the position of Scientist in Charge. Why and how would you reconstruct the geological past for a remote planet such as Mars? Where would you start? Earth is the “Goldilocks” planet, not only for human habitation but for geologists too, who can sample and test rock to understand the evolution of the Earth’s surface on which to base well-established theories such as plate tectonics. To understand the geological past and processes of remote planets, however, requires different approaches.

Planetary scientists investigate the climate and atmosphere, and the geological terrain, of planets to further understanding of our own place in the solar system. Mars provides a scintillating snapshot of early Earth; whilst some scientists contend that plate tectonics has historically happened on Mars, 70% of its surface dates from the moment it formed and provides a platform from which to view Earth in its infancy. In fact, despite our limited knowledge of Mars, it has already informed our understanding of Earth, inspiring James Lovelock’s Gaia theory. Imminent missions to the red planet are also already exploiting geological information to inform landing sites and routes of roving vehicles on Mars. The more information scientists have, the more likely missions are to land in suitable locations to successfully pursue scientific goals, such as understanding the ability of the Martian environment to support life and water, both now and in the past, which could further theories on the origins of the solar system, life on Earth, and Earth’s destiny.

Many will remember this summer for the astonishing images that arrived from Pluto, but 50 years ago, almost to the day, people celebrated the first successful fly-by mission to Mars. Mariner 4 took 21 images from a distance 6,000 miles, which, after the initial excitement, disappointingly revealed that Mars had a Moon-like cratered surface, and led to a long-held misconception of a dead, red planet. It was in 1976 that two Viking landers touched down on the red soil for the first time, paving the way for further Martian missions, with the first mission of the European Space Agency’s ExoMars programme launching next year.

The first Mars photograph and our first close-up of another planet. A representation of digital data radioed by the Mariner 4 spacecraft on 15th July 1965. (Credit: NASA/JPL-Caltech/Dan Goods)

The first Mars photograph and our first close-up of another planet. A representation of digital data radioed by the Mariner 4 spacecraft on 15th July 1965. (Credit: NASA/JPL-Caltech/Dan Goods)

Scientists analyse the size and density of craters from meteorite impacts to age the surface. The theory goes that smaller meteorites collide with a planet much more frequently than larger ones, and older surfaces have more craters because they have been exposed for longer. Advances in imaging technology since then now provide scientists with greater granularity than ever before and glimpses of other geologic features, recognisable from the surface of the Earth; sand dunes, dust devils, debris avalanches, gullies, canyons all appear and tell us about the planet’s climatic processes. The Planet Four website is just one example.

The images taken of Mars over the last forty years reveal changes on the surface that indicate invaluable information that help us to understand the climate and geology of the planet. Changes are visible in imagery over a variety of timescales, from rapidly-moving dust devils (much bigger that the one that once trapped me in Death Valley), seasonal fluctuations of the polar ice caps and recurring slope lineae (recently reported to indicate contemporary water activity) polar ice caps and the snail-slow shaping of sand dunes.

Three images of the same location taken at different times over one Martian year show how the seasonal fluctuation of the polar cap of condensed carbon dioxide (dry ice), between its solid and gaseous state, destabilises a Martian dune at high altitude to cause sand avalanches and ripple changes. (Credit: NASA/JPL/University of Arizona)

Three images of the same location taken at different times over one Martian year show how the seasonal fluctuation of the polar cap of condensed carbon dioxide (dry ice), between its solid and gaseous state, destabilises a Martian dune at high altitude to cause sand avalanches and ripple changes. (Credit: NASA/JPL/University of Arizona)

The quality and coverage of these images, however, varies greatly due to atmospheric conditions and tilt of the camera amongst other reasons. To create a consistent album of imagery, that we can confidently compare and use to identify geological changes in the images, requires considerable computational work. Images from across as much of the Martian surface as possible must be processed to remove those of poor quality and correct for different coordinate systems (co-registration) and terrain (ortho-rectification).

The iMars project is applying the latest Big Data mining techniques to over 400,000 images, so that they can be used to compute and classify changes in geological features. On a Citizen Science platform, Mars in Motion, volunteers will define the nature and scale of changes in surface features from ortho-rectified and co-registered images to a much greater detail. Human performance is inherently variable in ways we cannot fully control, either, in the same way that we can control the performance of an algorithm. Although we are investigating this too, this would require another blog post! For now I will describe the reasons why we are using a crowd-sourcing approach for this project so that you might consider how you could apply it to your research.

First of all, humans have evolved over millions of years to identify subtle variations in visual patterns to a more sophisticated level than computers currently can. Computers can execute repetitive tasks and store an infinite amount of information with far less impact on their performance than humans; the human mind, however, has proved to be too flexible and creative for computers to fully replicate, with the success of Citizen Science projects such as Galaxy Zoo, which has so far resulted in 48 academic publications. The slow seasonal shift of sand dunes on Mars, for example, would require a computer algorithm of inordinate intelligence to identify, as previous attempts to automatically detect impact craters, valley networks and sand dunes in images of Mars have found. Recent research has resulted in some very sophisticated algorithms for image analysis, but detection of changes in such a range of geological features over the range of spatial and temporal scales that we are looking to do is computationally complex and expensive. Without sending somebody to Mars, how do we know whether the computer is correct? Machine learning algorithms can only calculate what you ask them to, so are ill-equipped to make the sort of serendipitous discoveries of the unknown required in the detection of change. Volunteers in the Mars in Motion project will seek differences (Figure 4), rather than similarities, between the images and it is inherently challenging to program a computer to find something that you don’t even know to look for.

Mars in Motion: Spot the difference...on the surface of Mars!

Mars in Motion: Spot the difference…on the surface of Mars!

Secondly, we have so much data that scientists could not possibly do all of this themselves! In many areas of science and humanities, but especially in Earth and Planetary observation, Big Data capture is growing at an astronomical rate, far faster than resources and techniques for its analysis can keep up so that we are increasingly unable to handle it. This is where geoscientists have started to join the trend for recruiting volunteers to analyse imagery with some success; through large crowd-sourcing image analysis projects, like TomNod, citizens continually contribute interpretation of images for social and scientific purposes. The number of volunteers, however, is finite and the increase in data places more and more demand upon their time. Researchers using the Citizen Science approach must now carefully consider how their projects can utilise volunteers’ time effectively, efficiently and ethically.

Third and finally, a crowd-sourcing approach exposes the public to improvements in imaging technology and brings the dynamic nature of the Martian surface to life. This can only improve the chances of space exploration receiving further funding and entering classrooms through the way it combines many areas of Science, Technology, Engineering and Mathematics. Serendipitously, the engagement of the public also increases the number of pairs of eyes that analyse the images and, as such, the confidence with which scientists can use their classifications. As we collect more and more data, image analysis will necessarily require collaboration between humans and computers, as well as between volunteers and researchers, to manage it.

I hope this post gives you an insight into how we are applying the Citizen Science to consider how it might help your research too. There is actually no better time to try setting up a Citizen Science project with the launch of the Zooniverse project builder, which makes it easier than ever before to build your own project.

By Jessica Wardlaw, researcher at the University of Nottingham

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under the iMars grant agreement no. 607379.

Visit www.i-mars.eu and follow @JessWardlaw for updates on iMars and Mars in Motion.