Call for Participation in Vespucci Training School on Digital Transformations in Citizen Science and Social Innovation – January 2019

Apply until 31 October at https://www.cs-eu.net/events/internal/vespucci-training-school-digital-transformations-citizen-science-and-social 

The Role of Digital Technologies in Engaging Citizens (not only Citizen Scientists) in Social Innovation

Mini BioBlitz at Teppes de Verbois Nature ReserveWith the widespread availability of cheap, ubiquitous and powerful tools like the internet, the world-wide-web, social media and smartphone apps, new ways of carrying out both citizen science and social innovation have become possible. Often this means that barriers for citizens to engage in both science and social innovation have been lowered in terms of communication, outreach and scaling and thresholds for participation have also been lowered. There is an enormous potential for these technologies to strengthen the role of intermediary civil organizations and communities, and thereby to re-balance the playing field in favour of a broader range of actors – even those who do not use Information and Communication Technologies (ICT). ICTs can also help citizen engagement in policy framing by facilitating their involvement throughout the policy cycle, from agenda setting to service design and provision up to policy impact evaluation, creating new roles for stakeholders and enabling new power relations. However, digital technology should also be put in context, as it is often not leading edge but existing off-the-shelf technologies that are used in social innovation. Thus, technology must always be seen in its close intertwinement with the actual world of people, places, and digital skills people may or may not have.

Aim and Goals of the Training School

This training school is a five-day event for doctoral students, researchers, policymakers, civic entrepreneurs, designers, and civil servants who are interested in exploring and learning about:

  1. how citizen science can be understood and/or used as a strategic or intentional approach to social innovation;
  2. the intertwining of social innovation with socio-technical developments, including the impacts of digital transformation;
  3. the relationship between policy framing, participatory research, and social innovation.

All that, with the principles of the Vespucci Initiative – slow learning, long discussion, and collaborative learning where everyone is respected and expected to contribute and learn.

expected outcome(s) of the Training School:

Participants will learn about new forms of collaborative socio-technical development for social innovation, analyze case studies, and apply what they have learned by building a real collaborative socio-technical development for involving citizens and other stakeholders. As a result, participants will learn new skills and, more importantly, they will know new people, peers to collaborate with and/or other professionals who can help their projects.
The program is built upon three main tracks. The first three days will be devoted to introducing participants to these tracks (one track per day). The last two days will be devoted to group work.

  1. Overview of citizen science in research and innovation.
  2. Citizen science, social innovation, and policy-framing.
  3. Digital technologies in citizen science and social innovation: opportunities and risks.

Organization Committee:
Sven Schade, European Commission DG Joint Research Centre (JRC), Ispra, Italy
Marisa Ponti, European Commission DG Joint Research Centre (JRC), Ispra, Italy
Cristina Capineri, University of Siena, Italy (local organiser)

Lecturers/Facilitators:

  • Muki Haklay, University College London, UK
  • Mara Balestrini, CEO Ideas For Change, Barcelona, Catalonia, Spain
  • Stefan Daume, Founder and Chief Data Wrangler at the Scitingly Project, Stockholm Sweden
  • Sven Schade, JRC
  • Cristina Capineri, University of Siena, Italy
  • Marisa Ponti, JRC

A training school co-funded by JRC (www.vespucci.org) and COST Action 15212 Citizen Science to promote creativity, scientific literacy, and innovation throughout Europe

Date: January 21-25, 2019
Venue: Fattoria di Maiano, Via Benedetto da Maiano, 11, 50014 Fiesole FI, Italy
Nearest airports: Florence and Pisa; Nearest railway station: Florence.
Language of the training school: English
Maximum Number of Participants: 20

Apply until 31 October at https://www.cs-eu.net/events/internal/vespucci-training-school-digital-transformations-citizen-science-and-social – You don’t need to be part of the Cost Action on citizen science to apply! 

More information is available here.

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Non-traditional data approaches and the Sustainable Development Goals workshop

The workshop took place in IIASA, which is located in Laxenburg in Austria. The workshop was hosted by the earth observation and citizen science group at IASSA. The workshop focus on the interface between citizen science, earth observation, and traditional data collection methods in the context of monitoring and contributing to the Sustainable Development Goals (SDGs). A contextual/perspective academic paper is an expected output of the workshop, so this post is only a summary of the opening presentations. There is also an overlap with the aim of the WeObserve project and the communities of practice in it.

The Earth Observation community geared up already to how it can contribute to the SDGs. EuroGEOSS workshop identified several SDGs where there can be a contribution of citizen science: No. 3 in wealth and wellbeing (e.g. greenspace in cities), No. 4 on quality of education, No. 5 in gender equality, No. 6 on water quality and flood management, No. 11 on sustainable cities – air quality, noise, empty houses, No. 14 – plastics, and No. 15 in species monitoring, disease, and finally on Global Partnership (No. 17).

DSCN3119Australian Citizen Science Association view – some awareness to SDGs and few projects that are linked to SDGs explicitly, though there is an issue of details. From the US CSA, the view is that there are projects that can be linked – water monitoring, CoCoRHaS, phenology, and eBird. Examples also include grassroots environmental monitoring, or the Humanitarian OpenStreetMap Team. CitizenScience.Asia is a new network – in the context of China, people collect data to understand the environment, to collect evidence and protect rights, and for pure curiosity. The Blue-map used to report water pollution, it then goes to the government, and after being vetted it is shown, and some of it does not show. There are contributory DNA commercial project, but also “China Nature Watch” or Bauhinia Genome project that asks people to share information in Hong Kong. There are bottom-up projects, which include selling test kits for water which is used by people who share it on an online map – after 400-500 data points, the website was shut down by the government. There is also links to Public Lab – creating an automatic water monitor for flow. DSCN3122 Citizen Science Africa Association (CitSAF) – in Kenya the SDGs is getting attention (following the MDGs). NGOs activities are not synced with the government. Government pay attention to health, water, and education. CitSAF emerged from links to UNEP and focused on Kenya – air quality, some research on Malaria, and they can see interest in Nigeria, South Africa and other countries. CitSAF wants to increase the involvement and responsibility of citizens in African countries towards their natural and socio-cultural environment, especially in monitoring the SDGs. The SDG/CS Maximisation group which works across the citizen science associations (which Libby Hepburn coordinates) pointed out that the challenge is the bottom-up – from practitioners, and top-down from the UN and different countries. There is work on the credibility aspects of citizen science. There are is a need for facilitation between the CS community and the SDG community to progress things. The Citizen Science Global Partnership – launched in December 2017, as a network of networks to support citizen science activities. The global partnership has ideas and interest in working with the SDG but they are aspirational at the moment. They include – a platform for coordinating citizen science under the banner of SDG.

The Stockholm Environment Institute analysis of citizen science and SDGs: SEI has worked on environment/development over 30 years with many participatory activities, and worked explicitly on citizen science for the past 10 years. In the analysis they identified that citizen science can be used to refine and define goals; then monitoring; and even for achieving – e.g. in education, gender. The Citizen Science Centre in Zurich focuses on a platform – to allow projects, knowledge in the area, community of citizens and scientists, and projects. The open seventeen challenge is a good example for challenge-based workshops that help people to develop projects. There is an aim for developing an SDG citizen science toolkit. The Joint Research Centre of the European Commission has created an inventory of citizen science activities and mapping them against the SDGs with results being published soon. In addition, there is an effort of a standard for citizen science data and metadata with links to COST Action effort. There is a potential for recording aspects of participants if that is appropriate in the metadata. There is a specific effort of developing guidelines for environmental reporting in a process that will allow it to be cross EU.

SDSN – Sustainable Development Solution Network set up by the UN for the implementation of the sustainable development, with 800 members of universities, and other groups. Within that, the TRENDS group focuses on data governance? How people can integrate data from new sources. 20 expert members and focus on strengthening the data ecosystem, improve learning and data sharing, developing policies, and inform investment. The work is framed around data governance and use. The POPGRID project is attempting to reconcile different sources of data to get good population estimates. Another UN effort is the UN-GGIM have done work on identifying geospatial sources that can be used in SDG with an analysis to understand the indicators at different tiers – the http://ggim.un.org/UNGGIM-wg6. There is an opportunity to understand which indicators information is considered relevant, and where are data gaps. The thinking about crowdsourced and citizen science data is how to find it how to have metadata, understanding comparability and good usability for an SDG indicator. The is an issue about the global spatial data infrastructure for citizen science and crowdsourced data. There is a need to budget for data management, metadata recording and sharing of information from crowdsourced projects. There is a call for good practises and lessons learnt about the SDG indicators in the sustainable development knowledge platform.

UN Environment pointed that the SDGs includes 244 indicators, and they were developed through the inter-agency and expert group on SDG indicators (IAEG-SDG). The custodian agency is developing a methodology, improving capacity, and getting and using the data. The three types of data include country submission of data, data that is complimented with international estimates, and some global data products. There is an effort to consider a mapping exercise and then think where it can be used. A way forward is to identify one indicator, and try to get it accepted – need to be Tier 3. So the opportunity for citizen science is in an indicator that needs to be tier 3, but without an internationally established methodology or standard.

 

Papers from PPGIS 2017 meeting: state of the art and examples from Poland and the Czech Republic

dsc_0079About a year ago, the Adam Mickiewicz University in Poznań, Poland, hosted the PPGIS 2017 workshop (here are my notes from the first day and the second day). Today, four papers from the workshop were published in the journal Quaestiones Geographicae which was established in 1974 as an annual journal of the Faculty of Geographical and Geological Sciences at the university.

The four papers (with their abstracts) are:

Muki Haklay, Piotr Jankowski, and Zbigniew Zwoliński: SELECTED MODERN METHODS AND TOOLS FOR PUBLIC PARTICIPATION IN URBAN PLANNING – A REVIEW “The paper presents a review of contributions to the scientific discussion on modern methods and tools for public participation in urban planning. This discussion took place in Obrzycko near Poznań, Poland. The meeting was designed to allow for an ample discussion on the themes of public participatory geographic information systems, participatory geographic information systems, volunteered geographic information, citizen science, Geoweb, geographical information and communication technology, Geo-Citizen participation, geo-questionnaire, geo-discussion, GeoParticipation, Geodesign, Big Data and urban planning. Participants in the discussion were scholars from Austria, Brazil, the Czech Republic, Finland, Ireland, Italy, the Netherlands, Poland, the United Kingdom, and the USA. A review of public participation in urban planning shows new developments in concepts and methods rooted in geography, landscape architecture, psychology, and sociology, accompanied by progress in geoinformation and communication technologies.
The discussions emphasized that it is extremely important to state the conditions of symmetric cooperation between city authorities, urban planners and public participation representatives, social organizations, as well as residents”

Jiří Pánek PARTICIPATORY MAPPING IN COMMUNITY PARTICIPATION – CASE STUDY OF JESENÍK, CZECH REPUBLIC “Community participation has entered the 21st century and the era of e-participation, e-government and e-planning. With the opportunity to use Public Participation Support Systems, Computer-Aided Web Interviews and crowdsourcing mapping platforms, citizens are equipped with the tools to have their voices heard. This paper presents a case study of the deployment of such an online mapping platform in Jeseník, Czech Republic. In total, 533 respondents took part in the online mapping survey, which included six spatial questions. Respondents marked 4,714 points and added 1,538 comments to these points. The main aim of the research was to find whether there were any significant differences in the answers from selected groups (age, gender, home location) of respondents. The results show largest differences in answers of various (below 20 and above 20 year) age groups. Nevertheless, further statistical examination would be needed to confirm the visual comparison”.

Edyta Bąkowska-Waldmann, Cezary Brudka, and Piotr Jankowski: LEGAL AND ORGANIZATIONAL FRAMEWORK FOR THE USE OF GEOWEB METHODS FOR PUBLIC PARTICIPATION IN SPATIAL PLANNING IN POLAND: EXPERIENCES, OPINIONS AND CHALLENGES “Geoweb methods offer an alternative to commonly used public participation methods in spatial planning. This paper discusses two such geoweb methods – geo-questionnaire and geo-discussion in the context of their initial applications within the spatial planning processes in Poland. The paper presents legal and organizational framework for the implementation of methods, provides their development details, and assesses insights gained from their deployment in the context of spatial planning in Poland. The analysed case studies encompass different spatial scales ranging from major cities in Poland (Poznań and Łódź) to suburban municipalities (Rokietnica and Swarzędz in Poznań Agglomeration). The studies have been substantiated by interviews with urban planners and local authorities on the use and value of Geoweb methods in public consultations.”

Michał Czepkiewicz, Piotr Jankowski, and Zbigniew Zwoliński: GEO-QUESTIONNAIRE: A SPATIALLY EXPLICIT METHOD FOR ELICITING PUBLIC PREFERENCES, BEHAVIOURAL PATTERNS, AND LOCAL KNOWLEDGE – AN OVERVIEW “Geo-questionnaires have been used in a variety of domains to collect public preferences, behavioural patterns, and spatially-explicit local knowledge, for academic research and environmental and urban planning. This paper provides an overview of the method focusing on the methodical characteristics of geo-questionnaires including software functions, types of collected data, and techniques of data analysis. The paper also discusses broader methodical
issues related to the practice of deploying geo-questionnaires such as respondent selection and recruitment, representativeness, and data quality. The discussion of methodical issues is followed by an overview of the recent examples of geo-questionnaire applications in Poland, and the discussion of socio-technical aspects of geo-questionnaire use in spatial planning”

These papers provide examples from Participatory GIS in Poland and the Czech Republic, which are worth examining, as well as our review of the major themes from the workshop. All the papers are open access.

Identifying success factors in crowdsourced geographic information use in government

GFDRRA few weeks ago, the Global Facility for Disaster Reduction and Recovery (GFDRR), published an update for the report from 2014 on the use of crowdsourced geographic information in government. The 2014 report was very successful – it has been downloaded almost 1,800 times from 41 countries around the world in about 3 years (with more than 40 academic references) which showed the interests of researchers and policymakers alike and outlined its usability. On the base of it, it was pleasing to be approached by GFDRR about a year ago, with a request to update it.

In preparation for this update, we sought comments and reviews from experts and people who used the report regarding possible improvements and amendments. This feedback helped to surface that the seven key factors highlighted by the first report as the ones that shaped the use of VGI in government (namely: incentives, aims, stakeholders, engagement, technical aspects, success factors, and problems) have developed both independently and in cross-cutting modes and today there is a new reality for the use of VGI in government.

Luckily, in the time between the first report and the beginning of the new project, I learned about Qualitative Comparative Analysis (QCA) in the Giving Time event and therefore we added Matt Ryan to our team to help us with the analysis. QCA allowed us to take 50 cases, have an intensive face to face team workshop in June last year to code all the cases and agree on the way we create the input to QCA. This helped us in creating multiple models that provide us with an analysis of the success factors that help explain the cases that we deemed successful. We have used the fuzzy logic version of QCA, which allowed a more nuanced analysis.

Finally, in order to make the report accessible, we created a short version, which provides a policy brief to the success factors, and then the full report with the description of each case study.

It was pleasure working with the excellent team of researchers that worked on this report: Vyron Antoniou, Hellenic Army Geographic Directorate, Sofia Basiouka, Hellenic Ministry of Culture and Sport, Robert Soden, World Bank, Global Facility for Disaster Reduction & Recovery (GFDRR), Vivien Deparday, World Bank, Global Facility for Disaster Reduction & Recovery (GFDRR). Matthew Ryan, University of Southampton, and Peter Mooney, National University of Ireland, Maynooth. We were especially lucky to be helped by Madeleine Hatfield of Yellowback Publishing who helped us in editing the report and making it better structured and much more readable.

The full report, which is titled “Identifying success factors in crowdsourced geographic information use in government” is available here.

And the Policy Brief is available here. 

Citizen Science & Scientific Crowdsourcing – week 5 – Data quality

This week, in the “Introduction to Citizen Science & Scientific Crowdsourcing“, our focus was on data management, to complete the first part of the course (the second part starts in a week’s time since we have a mid-term “Reading Week” at UCL).

The part that I’ve enjoyed most in developing was the segment that addresses the data quality concerns that are frequently raised about citizen science and geographic crowdsourcing. Here are the slides from this segment, and below them a rationale for the content and detailed notes

I’ve written a lot on this blog about data quality and in many talks that I gave about citizen science and crowdsourced geographic information, the question about data quality is the first one to come up. It is a valid question, and it had led to useful research – for example on OpenStreetMap and I recall the early conversations, 10 years ago, during a journey to the Association for Geographic Information (AGI) conference about the quality and the longevity potential of OSM.

However, when you are being asked the same question again, and again, and again, at some point, you start considering “why am I being asked this question?”. Especially when you know that it’s been over 10 years since it was demonstrated that the quality is beyond “good enough”, and that there are over 50 papers on citizen science quality. So why is the problem so persistent?

Therefore, the purpose of the segment was to explain the concerns about citizen science data quality and their origin, then to explain a core misunderstanding (that the same quality assessment methods that are used in “scarcity” conditions work in “abundance” conditions), and then cover the main approaches to ensure quality (based on my article for the international encyclopedia of geography). The aim is to equip the students with a suitable explanation on why you need to approach citizen science projects differently, and then to inform them of the available methods. Quite a lot for 10 minutes!

So here are the notes from the slides:

[Slide 1] When it comes to citizen science, it is very common to hear suggestions that the data is not good enough and that volunteers cannot collect data at a good quality, because unlike trained researchers, they don’t understand who they are – a perception that we know little about the people that are involved and therefore we don’t know about their ability. There are also perceptions that like Wikipedia, it is all a very loosely coordinate and therefore there are no strict data quality procedures. However, we know that even in the Wikipedia case that when the scientific journal Nature shown over a decade ago (2005) that Wikipedia is resulting with similar quality to Encyclopaedia Britannica, and we will see that OpenStreetMap is producing data of a similar quality to professional services.
In citizen science where sensing and data collection from instruments is included, there are also concerns over the quality of the instruments and their calibration – the ability to compare the results with high-end instruments.
The opening of the Hunter et al. paper (which offers some solutions), summarises the concerned that are raised over data

[Slide 2] Based on conversations with scientists and concerned that are appearing in the literature, there is also a cultural aspect at play which is expressed in many ways – with data quality being used as an outlet to express them. This can be similar to the concerns that were raised in the cult of the amateur (which we’ve seen in week 2 regarding the critique of crowdsourcing) to protect the position of professional scientists and to avoid the need to change practices. There are also special concerns when citizen science is connected to activism, as this seems to “politicise” science or make the data suspicious – we will see next lecture that the story is more complex. Finally, and more kindly, we can also notice that because scientists are used to top-down mechanisms, they find alternative ways of doing data collection and ensuring quality unfamiliar and untested.

[Slide 3] Against this background, it is not surprising to see that checking data quality in citizen science is a popular research topic. Caren Cooper have identified over 50 papers that compare citizen science data with those that were collected by professional – as she points: “To satisfy those who want some nitty gritty about how citizen science projects actually address data quality, here is my medium-length answer, a brief review of the technical aspects of designing and implementing citizen science to ensure the data are fit for intended uses. When it comes to crowd-driven citizen science, it makes sense to assess how those data are handled and used appropriately. Rather than question whether citizen science data quality is low or high, ask whether it is fit or unfit for a given purpose. For example, in studies of species distributions, data on presence-only will fit fewer purposes (like invasive species monitoring) than data on presence and absence, which are more powerful. Designing protocols so that citizen scientists report what they do not see can be challenging which is why some projects place special emphasize on the importance of “zero data.”
It is a misnomer that the quality of each individual data point can be assessed without context. Yet one of the most common way to examine citizen science data quality has been to compare volunteer data to those collected by trained technicians and scientists. Even a few years ago I’d noticed over 50 papers making these types of comparisons and the overwhelming evidence suggested that volunteer data are fine. And in those few instances when volunteer observations did not match those of professionals, that was evidence of poor project design. While these studies can be reassuring, they are not always necessary nor would they ever be sufficient.” (http://blogs.plos.org/citizensci/2016/12/21/quality-and-quantity-with-citizen-science/)

[Slide 4] One way to examine the issue with data quality is to think of the clash between two concepts and systems of thinking on how to address quality issue – we can consider the condition of standard scientific research conditions as ones of scarcity: limited funding, limited number of people with the necessary skills, a limited laboratory space, expensive instruments that need to be used in a very specific way – sometimes unique instruments.
The conditions of citizen science, on the other hand, are of abundance – we have a large number of participants, with multiple skills, but the cost per participant is low, they bring their own instruments, use their own time, and are also distributed in places that we usually don’t get to (backyards, across the country – we talked about it in week 2). Conditions of abundance are different and require different thinking for quality assurance.

[Slide 5] Here some of the differences. Under conditions of scarcity, it is worth investing in long training to ensure that the data collection is as good as possible the first time it is attempted since time is scarce. Also, we would try to maximise the output from each activity that our researcher carried out, and we will put procedures and standards to ensure “once & good” or even “once & best” optimisation. We can also force all the people in the study to use the same equipment and software, as this streamlines the process.
On the other hand, in abundance conditions we need to assume that people are coming with a whole range of skills and that training can be variable – some people will get trained on the activity over a long time, while to start the process we would want people to have light training and join it. We also thinking of activities differently – e.g. conceiving the data collection as micro-tasks. We might also have multiple procedures and even different ways to record information to cater for a different audience. We will also need to expect a whole range of instrumentation, with sometimes limited information about the characteristics of the instruments.
Once we understand the new condition, we can come up with appropriate data collection procedures that ensure data quality that is suitable for this context.

[Slide 6] There are multiple ways of ensuring data quality in citizen science data. Let’s briefly look at each one of these. The first 3 methods were suggested by Mike Goodchild and Lina Li in a paper from 2012.

[Slide 7] The first method for quality assurance is crowdsourcing – the use of multiple people who are carrying out the same work, in fact, doing peer review or replication of the analysis which is desirable across the sciences. As Watson and Floridi argued, using the examine of Zooniverse, the approaches that are being used in crowdsourcing give these methods a stronger claim on accuracy and scientific correct identification because they are comparing multiple observers who work independently.

[Slide 8] The social form of quality assurance is using more and less experienced participants as a way to check the information and ensure that the data is correct. This is fairly common in many areas of biodiversity observations and integrated into iSpot, but also exist in other areas, such as mapping, where some information get moderated (we’ve seen that in Google Local Guides, when a place is deleted).

[Slide 9] The geographical rules are especially relevant to information about mapping and locations. Because we know things about the nature of geography – the most obvious is land and sea in this example – we can use this knowledge to check that the information that is provided makes sense, such as this sample of two bumble bees that are recorded in OPAL in the middle of the sea. While it might be the case that someone seen them while sailing or on some other vessel, we can integrate a rule into our data management system and ask for more details when we get observations in such a location. There are many other such rules – about streams, lakes, slopes and more.

[Slide 10] The ‘domain’ approach is an extension of the geographic one, and in addition to geographical knowledge uses a specific knowledge that is relevant to the domain in which information is collected. For example, in many citizen science projects that involved collecting biological observations, there will be some body of information about species distribution both spatially and temporally. Therefore, a new observation can be tested against this knowledge, again algorithmically, and help in ensuring that new observations are accurate. If we see a monarch butterfly within the marked area, we can assume that it will not harm the dataset even if it was a mistaken identity, while an outlier (temporally, geographically, or in other characteristics) should stand out.

[Slide 11] The ‘instrumental observation’ approach removes some of the subjective aspects of data collection by a human that might make an error, and rely instead on the availability of equipment that the person is using. Because of the increase in availability of accurate-enough equipment, such as the various sensors that are integrated in smartphones, many people keep in their pockets mobile computers with the ability to collect location, direction, imagery and sound. For example, images files that are captured in smartphones include in the file the GPS coordinates and time-stamp, which for a vast majority of people are beyond their ability to manipulate. Thus, the automatic instrumental recording of information provides evidence for the quality and accuracy of the information. This is where the metadata of the information becomes very valuable as it provides the necessary evidence.

[Slide 12] Finally, the ‘process oriented’ approach bring citizen science closer to traditional industrial processes. Under this approach, the participants go through some training before collecting information, and the process of data collection or analysis is highly structured to ensure that the resulting information is of suitable quality. This can include the provision of standardised equipment, online training or instruction sheets and a structured data recording process. For example, volunteers who participate in the US Community Collaborative Rain, Hail & Snow network (CoCoRaHS) receive standardised rain gauge, instructions on how to install it and online resources to learn about data collection and reporting.

[Slide 13]  What is important to be aware of is that methods are not being used alone but in combination. The analysis by Wiggins et al. in 2011 includes a framework that includes 17 different mechanisms for ensuring data quality. It is therefore not surprising that with appropriate design, citizen science projects can provide high-quality data.

 

 

Citizen Science for Observing and Understanding the Earth

Since the end of 2015, I’ve been using the following mapping of citizen science activities in a range of talks:

Range of citizen science activities
Explaining citizen science

The purpose of this way of presentation is to provide a way to guide my audience through the landscape of citizen science (see examples on SlideShare). The reason that I came up with it, is that since 2011 I give talks about citizen science. It started with the understanding that I can’t explain extreme citizen science when my audience doesn’t understand what citizen science is, and that turned into general talks on citizen science.

Similarly to Caren Cooper, I have an inclusive approach to citizen science activities, so in talks, I covered everything – from bird watching to DIY science. I felt that it’s too much information, so this “hierarchy” provides a map to go through the overview (you can look at our online course to see why it’s not a great typology). It is a very useful way to go through the different aspects of citizen science, while also being flexible enough to adapt it – I can switch the “long-running citizen science” fields according to the audience (e.g. marine projects for marine students).

An invitation for Pierre-Philippe Mathieu (European Space Agency) in 2015 was an opportunity to turn this mapping and presentation into a book chapter. The book is dedicated to “Earth Observation Open Science and Innovation and was edited by Pierre-Philippe and Christoph Aubrecht.

When I got to writing the chapter, I contacted two researchers with further knowledge of citizen science and Earth Observation – Suvodeep Mazumdar and Jessica Wardlaw. I was pleased that they were happy to join me in the effort.

Personally, I’m very pleased that we could include in the chapter the story of the International Geophysical Year, (thank Alice Bell for this gem), with Moonwatch and Sputnik monitoring.

The book is finally out, it is open access, and you can read our chapter, “Citizen Science for Observing and Understanding the Earth” for free (as well as all the other chapters). The abstract of the paper is provided below:

Citizen Science, or the participation of non-professional scientists in a scientific project, has a long history—in many ways, the modern scientific revolution is thanks to the effort of citizen scientists. Like science itself, citizen science is influenced by technological and societal advances, such as the rapid increase in levels of education during the latter part of the twentieth century, or the very recent growth of the bidirectional social web (Web 2.0), cloud services and smartphones. These transitions have ushered in, over the past decade, a rapid growth in the involvement of many millions of people in data collection and analysis of information as part of scientific projects. This chapter provides an overview of the field of citizen science and its contribution to the observation of the Earth, often not through remote sensing but a much closer relationship with the local environment. The chapter suggests that, together with remote Earth Observations, citizen science can play a critical role in understanding and addressing local and global challenges.

 

Citizen Science & Scientific Crowdsourcing – week 2 – Google Local Guides

The first week of the “Introduction to Citizen Science and Scientific Crowdsourcing” course was dedicated to an introduction to the field of citizen science using the history, examples and typologies to demonstrate the breadth of the field. The second week was dedicated to the second half of the course name – crowdsourcing in general, and its utilisation in scientific contexts. In the lecture, after a brief introduction to the concepts, I wanted to use a concrete example that shows a maturity in the implementation of commercial crowdsourcing. I also wanted something that is relevant to citizen science and that many parallels can be drawn from, so to learn lessons. This gave me the opportunity to use Google Local Guides as a demonstration.

My interest in Google Local Guides (GLG) come from two core aspects of it. As I pointed in OpenStreetMap studies, I’m increasingly annoyed by claims that OpenStreetMap is the largest Volunteered Geographical Information (VGI) project in the world. It’s not. I guessed that GLG was, and by digging into it, I’m fairly confident that with 50,000,000 contributors (of which most are, as usual, one-timers), Google created the largest VGI project around. The contributions are within my “distributed intelligence” and are voluntary. The second aspect that makes the project is fascinating for me is linked to a talk from 2007 in one of the early OSM conferences about the usability barriers that OSM (or more general VGI) need to cross to reach a wide group of contributors – basically about user-centred design. The design of GLG is outstanding and shows how much was learned by the Google Maps and more generally by Google about crowdsourcing. I had very little information from Google about the project (Ed Parsons gave me several helpful comments on the final slide set), but by experiencing it as a participant who can notice the design decisions and implementation, it is hugely impressive to see how VGI is being implemented professionally.

As a demonstration project, it provides examples for recruitment, nudging participants to contribute, intrinsic and extrinsic motivation, participation inequality, micro-tasks and longer tasks, incentives, basic principles of crowdsourcing such as “open call” that support flexibility, location and context aware alerts, and much more. Below is the segment from the lecture that focuses on Google Local Guides, and I hope to provide a more detailed analysis in a future post.

The rest of the lecture is available on UCLeXtend.