Scientific data from and for the citizen
First Monday

Scientific data from and for the citizen by Sven Schade, Chrisa Tsinaraki, and Elena Roglia



Abstract
Powered by advances of technology, today’s Citizen Science projects cover a wide range of thematic areas and are carried out from local to global levels. This wealth of activities creates an abundance of data, for example, in the forms of observations submitted by mobile phones; readings of low-cost sensors; or more general information about peoples’ activities. The management and possible sharing of this data has become a research topic in its own right. We conducted a survey in the summer of 2015 in order to collectively analyze the state of play in Citizen Science. This paper summarizes our main findings related to data access, standardization and data preservation. We provide examples of good practices in each of these areas and outline actions to address identified challenges.

Contents

1. Introduction
2. Background
3. Summary of our approach
4. Highlights from the survey
5. Conclusion

 


 

1. Introduction

Citizen Science, i.e., the active and cautious participation of people in scientific research that is outside their professional expertise (our working definition), has a long tradition in areas such as environmental monitoring (Davies, et al., 2011; Roy, et al., 2012; Gabrys, 2016) or astronomy (Christian, et al., 2012; Raddick, et al., 2013). As a phenomenon, it continues to rise in popularity, also powered by the increase in technical support that helps reaching out to more people and increasing the possibilities for participation (Grey, 2011). Today’s richness and diversity becomes, for example, visible when browsing the rich offers of crowdsourcing platforms, such as Zooniverse (2017), or searching dedicated catalogues of Citizen Science activities, such as SciStarter (2017). More in-depth investigations of the rich landscape of Citizen Science projects were carried out recently by Wiggins and Crowston (2015).

Over the past few years, mainly from 2014 onwards, the Citizen Science landscape did not only grow and contribute to diversity itself; it also changed from decoupled communities to increased coordination, re-use of methods and tools, and wider data sharing. These developments are especially supported by the U.S.-based Citizen Science Association (CSA, 2017), the European Citizen Science Association (ECSA, 2017) and their Australian counterpart, the Australian Citizen Science Association (ACSA, 2017). Recent debates cover a rich set of complementary aspects, such as (i) the use of Citizen Science for policy (European Commission, 2013; Haklay, 2015); (ii) longitudinal studies about the phenomenon itself (Riesch and Potter, 2014); and (iii) the re-use of data and tools for new projects (Wang, et al., 2015).

One of the central aspects in all of these debates is data, especially those data sets and streams that are either gathered or analyzed with the engagement of citizens. Our work investigates particular needs for the scientific data that is handled and (ideally) shared by Citizen Science projects, including, for example, observations submitted by mobile phones, the readings of low-cost sensors, or more general information about peoples’ activities. Among other aspects, we are particularly interested in specificities of projects within the European Union (EU) and their positioning in a global setting.

In the past, we already identified the lack of communication, exchange and use (interoperability) of data across projects, as well as the guaranteed long-term availability of data and data management tools (sustainability) as major challenges (Craglia and Granell, 2014). In this paper, we underpin these general statements with concrete findings that address aspects of (open) access and reuse conditions, standardization (including comparability, quality, etc.) and long-term data provision.

Most of the findings presented here are derived from an international survey that we carried out in the summer of 2015. A detailed report analyzing all survey questions has already been published (Schade and Tsinaraki, 2016a) — together with the anonymized replies and the script that we used for the automated part of the analysis (Schade and Tsinaraki, 2016b). In addition, this article outlines already existing examples and activities that might be used to overcome the identified issues, and we briefly outline our future work.

 

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2. Background

2.1. On Citizen Science

Citizen Science is not a new phenomenon, since non-professionals have contributed to scientific studies for centuries. The history and rise of Citizen Science has already been well explained by others (Silvertown, 2009; Bonney, et al., 2009). In a nutshell, volunteering in scientific work has already a long tradition. Only the term Citizen Science came to use relatively recently. Digital transformation influenced its evolution, sometimes called Citizen Cyber Science (Grey, 2011). And, last but not least, we are today in a dual situation, in which several Citizen Science activities are moving into more organized and coordinated structures, while, at the same time individuals and communities are increasingly capable of defining and following their own scientific questions and this (Extreme) Citizen Science cannot be followed and monitored (Haklay, 2013). In our work, we most often refer to the work of Craglia and Shanley (2015) that integrates several of the previous categorizations into a combined scheme. Most notably, Citizen Science is positioned into a space of Citizens’ active contributions, collaborations or co-creations, which may have different motivations (see also Figure 1).

 

Unifying categorizations of Citizen Science
 
Figure 1: Unifying categorizations of Citizen Science. Source: Craglia and Shanley (2015).

 

Considering the work presented here, we did not impose any definition of Citizen Science when preparing and distributing our survey. Thus, we assume that people participated because they considered their own project(s) to involve Citizen Science. Notably, this implicitly restricted our target audience. From those replying to our survey, we can say that it is primarily the environmental sector, and within that the biodiversity community that consider themselves as Citizen Science practitioners. This can be partially explained by the tradition of involving lay people in the observation of species of various kinds (one of the most famous examples being the Christmas Bird Count; see Butcher and Niven, 2007) and in environmental issues more generally (considering weather conditions, see Bell, et al., 2013). Furthermore, the notion “Citizen Science” itself was primarily coined by the environmental community, it was especially taken up early by bird watchers (Butcher and Niven, 2007), the ecology community in general (Dickinson, et al., 2011) and recently also in order to discover invasive alien species (Adriaens, et al., 2015). Other communities use other terms, such as Do-It-Yourself (DIY) Science (Nascimento, et al., 2014) or Volunteered Geographic Information (VGI) (See, et al., 2016).

2.2. On data management

With the increasing availability and use of data — including open data that is, for example, released by public authorities (G8, 2013; Open Data Barometer, 2017) but also rich collections in the private sector that allow for new forms of business intelligence (Arnott, et al., 2017) — it becomes even more important to manage raw data sets and the (intermediate) products created thereof. This holds also for research data that underpins new scientific insights, should allow for the repeatability and reproducibility of experiments, and ultimately contributes to the reputation of individual researchers and whole scientific institutions (Molloy, 2011; Nosek, et al., 2015). As related data collections might be of large sizes and might depend on specific technologies to be handled, data management can be considered a new form of art and a highly requested skill within and beyond scientific communities.

While for some fields, such as particle physics and astronomy, the need for data management is not new, it now expands into many more thematic areas and more general solutions are requested. Thanks to the help of the sectors where data management already became a tradition, and with the support of modern librarians, several principles, guidelines and best practices for (research) data management exist. To date, the FAIR principles (Wilkinson, et al., 2016) might be the most prominent. This set of principles particularly focuses on findability, accessibility, interoperability and reuse.

Whereas guidance and principles on data management and Open Data can be defined at a reasonably generic level, they also require translation into specific contexts. It needs to be evaluated if a particular domain has additional needs and how it might interpret high-level recommendations. Although Citizen Science per se is not bound to any particular theme or topic, it is easy to imagine that projects that deal in any form with citizens and data might share some challenges and possible solutions. The existing data management guide for public participation in scientific research (DataONE, 2013) already highlights some of the specificities.

2.3. On data management for Citizen Science

Considering Citizen Science projects, the existing data management issues seem to be similar to what is discussed at a more general level (DataONE, 2013). As part of previous work (Craglia and Granell, 2014), we already saw a series of examples from the Citizen Science and Smart Cities communities, that data interoperability and sustainability are two central issues. Interoperability considers the possibilities to exchange and integrate data and metadata (data about data, i.e., data descriptions) between different projects. Sustainability of the developed solutions addresses the continuous (long-term) access provision for the data collected.

Due to the intrinsic characteristic of inclusiveness, it might furthermore be assumed that Citizen Science might be leading on issues related to open access, licensing and citation (as a form of acknowledgement). However, so far these are assumptions and gut feelings.

Given these known issues and early guidance and recommendations, we are interested to take stock of the actual practices in Citizen Science projects. We want to get a better understanding of the already existing practices and available solutions, but also the existing pitfalls and needs for improvement.

 

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3. Summary of our approach

In order to shed some light into the state of play in data management of Citizen Science projects, we ran an open survey over the summer of 2015. In a nutshell, we followed a straightforward approach as indicated in Figure 2.

 

Sketch of methodology
 
Figure 2: Sketch of methodology. Source: Schade and Tsinaraki (2016a).

 

A questionnaire was set up based on the data management principles that were at that time discussed by the Group of Earth Observations (GEO, 2015). Our questions tried to cover the complete spectrum from discovery and access to usability and preservation.

A draft questionnaire was discussed with several experts from the Citizen Science community, and the survey was then widely distributed using networks such as national and international Citizen Science associations, Infrastructure for Spatial Information in the European Community (INSPIRE, 2017), European Association of Geographers (EUROGEO, 2017), and the Partnership for European Environmental Research (PEER, 2017).

Over a six-week period we received input from 121 different projects. The responses were pre-processed only to harmonize spelling and remove typos before analyzing the results. We generated summary graphics where possible and interpreted the results accordingly. The analysis of the replies to all the individual questions and of a selected subset of combinations was then published in the full survey report. Also the anonymized replies and the (R) script that we used for generating our graphics were published with an open source license. Information about the complete collection has been made available on the Open Data Catalogue of the European Commission’s Joint Research Centre — JRC (Schade and Tsinaraki, 2016b), see also Figure 3.

 

Screenshot of the survey metadata as published in the JRC Data Catalogue
 
Figure 3: Screenshot of the survey metadata as published in the JRC Data Catalogue.

 

We highlight the core messages of the full report below, together with additional insights that we gained in the many discussions with Citizen Science practitioners and project managers that followed our survey.

 

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4. Highlights from the survey

Below, we highlight a few findings from the complete survey report (Schade and Tsinaraki, 2016a) and complement the discovered shortcomings with brief summaries of the state of play and pointers to future readings related to data access, standardization and long-term preservation.

4.1. General characteristics of participating projects

Projects could indicate to work on multiple topics and themes. The majority of replies (84 percent) covered environmental topics, followed by earth science (23 percent), social sciences (10 percent) and space science (six percent). Within the most prominent topic (102 participants selected environment), 68 percent related themselves to the theme of biodiversity, followed by water quality (25 percent), land cover (21 percent), land use (18 percent), air quality (17 percent) and noise (nine percent). Apart from the unexpected strong focus on environmental sciences (as compared to astronomy) and the less unexpected clustering of biodiversity related projects, the geographic coverage of the survey is remarkable. We found a good distribution across administrative levels and also between projects from within and from outside the EU (Table 1). Interestingly, project durations did not only increase when moving from the neighborhood to the continent level, but also when leaving EU territory (Table 2).

 

Table 1: Answers to survey question 2: “Which geographic extent does the project cover?”
Source: Schade and Tsinaraki, 2016a.
ExtentInside EUOutside EU
Neighborhood2224
City level2930
Regional3139
Country3730
Continental2726
Total coverage146149

 

 

Table 2: Answers to survey question 2: “Which geographic extent does the project cover?” including projects that are set up for more than four years (numbers on the left in each cell) and percentage.
ExtentInside EUOutside EU
Neighborhood8/22 (36%)15/24 (62%)
City level10/29 (34%)19/30 (63%)
Regional14/31 (45%)24/39 (62%)
Country19/37 (51%)20/30 (67%)
Continental16/27 (59%)18/26 (69%)
Total coverage67/146
(46%)
96/149
(64%)

 

4.2. On open access

One of our assumptions was that Citizen Science leads when it comes to the open access to data. Indeed, the respondents indicated a strong willingness to provide the data from the Citizen Science projects openly, some on the level of the raw data gathered by the projects, and some on a more aggregated level (Figure 4). However, the amount of projects that do not provide any access to the data that they collect is not marginal. Furthermore, the practice of introducing embargo periods — often to retain control of the data for peer-reviewed scientific publications — predominates.

 

Combined analysis of survey question 9
 
Figure 4: Combined analysis of survey question 9: “Do you provide access to raw datasets or aggregated values?” and question 13: “Do you make the data available for re-use?” Source: Schade and Tsinaraki, 2016a.

 

When getting into some more detail, the desired licensing schemes for the different contributing projects mirror these intentions, but also provide a more diversified view (Figure 5). Less restrictive licenses dominate, especially among those projects that intend to provide access to their data directly after it has been gathered. However, when considering also the replies to a free text question “Which license do you use?” (survey question 13.2), we found that for many projects, the decision about licensing is still to come and that discussions about licensing schemes are still ongoing. Furthermore, for most of the projects, we identified mismatches between the intended conditions for data access and use, and the actually chosen license.

 

Combined analysis of survey question 13
 
Figure 5: Combined analysis of survey question 13: “Do you make the data available for re-use?” and question 13.1: “Which are the conditions for re-use?”

 

Whereas the overall willingness to provide free and open access to data appears strong within the respondents, we investigated the issues further with a dedicated study that focused solely on existing Citizen Science projects that use mobile phone apps for the reporting of invasive alien species. Independently from our survey, we followed a baseline provided by Adriaens and others in 2015 (Adriaens, et al., 2015), updated by Schneider (2016) and investigated a set of 25 projects in respect to data accessibility. Considering language barriers that made it difficult to find all relevant information, we only succeeded to at least view the collected data sets in eight cases. For six of these, we only succeeded to view the collected data on the Web, and only for two we could actually download the relevant data. Only one (IASTracker, 2017) licenses the data for free and open access. Although these investigations were brief and we might have missed a few cases due to language issues, this indicates that the current reality differs from the survey results, at least in relation to invasive alien species monitoring.

Overall, there seems to remain a need for the promotion of open access, but also for guidance about license use, as well for best practices on licensing and possibly re-licensing of already collected data. Related to the provision of open data, the guidance of Open Data Commons (2017) can be recommended. Furthermore, the Global Biodiversity Information Facility (GBIF), for example, just completed a three-year exercise to re-license several million observations that were already included in their database into open access (GBIF, 2016). Examples like this can help others to change or to set up their license models from the beginning.

4.3. On standardization

For us it was important to better understand the status of interoperability arrangements, particularly related to the use of standards for data and for metadata. Here, we found that almost half of all respondents indicated that their project follows data as well as metadata standards. However, this leaves open which exact standards are already used and what is at all considered to be standard (Figure 6). Notably, many more projects replied to the question considering data, as compared to the ones related to metadata (i.e., data/descriptions about data). Overall, the responses from those projects that provide direct access to their data differed in their distribution from the responses of the projects following other access policies (Figure 6).

 

Combined analysis of survey question 13
 
Figure 6: Combined analysis of survey question 13: “Do you make the data available for re-use?”, question 17.1: “Are these metadata based on international or community-approved (non-proprietary) standards?” and question 16: “Is the data collected by the project structured based on international or community-approved (non-proprietary) standards?”

 

In fact, following a series of happenings that took place in parallel to the survey itself, the topic of standardization is increasingly recognized, much progress has been made and future roadmaps are currently formulated. Public Participation in Scientific Research (PPSR)-Core has been proposed as a model for Citizen Science project metadata, i.e., structured descriptions about Citizen Science projects themselves, some time ago (DataONE, 2013). In the interim, SciStarter, the Atlas of Living Australia (ALA, 2017) and others keep developing a de facto standard for that area. Standards for observation data from Citizen Science exist in parts, e.g., Darwin Core (2017) for the biodiversity domain, but standards for geospatial data are still under development. Here, especially within the newly created Citizen Science Domain Working Group (DWG) of the Open Geospatial Consortium (OGC, 2017), which just published a discussion paper on the topic (Simonis and Atkinson, 2017), and in the context of the international Data and Metadata Working Group of CSA (CSA, Data and Metadata Working Group, 2017). Several book chapters are underway to provide more examples and to elaborate especially on descriptions of data quality (Bastin, et al., submitted-a; Bastin, et al., submitted-b).

Although standardization activities are increasing, there is still a long way to go, especially considering the diversity of Citizen Science projects, their application domains and the expertise required to follow widely recognized standards for data discovery, exchange and re-use. Already our survey indicated that many of the participating projects did not apply data or metadata standards, which might at least in parts be traced back to the knowledge and resources required for implementing existing standards. Even if projects indicated that they do follow a standard, it can be assumed that many projects follow different standards, i.e., their outcomes are not per se interoperable. Challenges to identify widely applicable, easy to use and yet valuable standards for comparing and integrating data across Citizen Science projects are being addressed.

Overall, as also in standardization activities that are not related to Citizen Science, we can roughly distinguish three different approaches emerging:

De facto standards that all Citizen Science projects within a certain community follow, usually because there is a predominant central system in place that all project activities depend on. This is, for example the case for biodiversity related Citizen Science projects in Australia, where the ALA provides the required research infrastructure in a centralized manner. Another example is Open Street Map (OSM), which provides one single platform for storing and editing geographic information. Whereas this was initially focused on transportation networks, it now features a much richer set of features.

An imposed single set of standards that a community adheres to, but implements over loosely connected distributed data management systems. Here, a highly prominent example comes from outside the Citizen Science domain and addresses geospatial information that is provided by public authorities in the European Union (EU). The INSPIRE Directive legally mandates the member states of the EU to share data related to a rich set of 34 environment-related topics (themes) in particular manners. Though the concrete data models and exact technical solutions have been developed in close collaboration with the authorities and experts that are affected by the INSPIRE Directive, this still results in a set of standards that have to be followed in order to meet legal requirements. The related process of standards development and stepwise implementation spans over more than 20 years. Notably, this is indeed one of the most (of not the most) complex example of a single set of standards that requires a high level of expertise to follow, with the advantage of being distributed by interoperable data management nodes.

A brokering approach that accepts data in any form and mediates between connected de-centralized systems by transforming the different formats used on the fly. This approach is, for example, followed within GEOSS (Nativi, et al., 2012). In theory, connected systems do not have to apply any standard to be connected, but in such a case connectors have to be put in place on a case by case basis. However, this approach can be powerful when combining it with a different set of standards, for example, it might accept data that is compliant to INSPIRE, data from OSM, or data in Darwin Core and help integrating them into a single system.

It will be up to the Citizen Science community which of these models, or combinations they will follow.

4.4. On data preservation

Considering the sustainability of research results, 70 participants (58 percent of all survey respondents) replied that they intend to provide access beyond the lifetime of the project. Out of those, a majority (47) projects are themselves set up to run for a period longer than four years (see also Figure 7).

 

Combined analysis of survey question 3
 
Figure 7: Combined analysis of survey question 3: “What is the planned duration of the project?” and “For how long do you ensure the access to the data from you project?”

 

Investigating this further, we discovered an inverse tendency between those projects that receive funding from the EU (e.g., via the current framework program for research and innovation, Horizon 2020), and those that do not rely on such funding. Less than one third of the 23 projects relying (at least in parts) on EU funding plan to provide data access beyond the lifetime of their project, whereas more other projects are committed to do so (Figure 8). This might very well be due to the nature of research projects.

 

Combined analysis of survey question 5
 
Figure 8: Combined analysis of survey question 5: “How is this citizen science project financed?” and “For how long do you ensure the access to the data from you project?”

 

In terms of storage facilities, we found that most respondents rely on remote servers that are provided by one of the project partners. Projects that are set up for a longer time period (more than four years) depend on public repositories. Our complementary investigations related to invasive alien species, support these needs in the sense that some data might simply not be accessible anymore because the projects were completed and possibly existing data access could not be guaranteed.

We believe that this situation will change, since there exist now some services, such as figshare (2017), which allow users, academic institutions and publishers to preserve research datasets. In addition to services with no specific audience, there exist also solutions for audiences that share specific features, including the ones listed below:

OpenAIRE (2017), an infrastructure developed at the EU level in order to make openly available publications and data that are outputs of research projects funded by the European Commission (EC). OpenAIRE is supported by the Zenodo repository (Zenodo, 2017) that has been developed by CERN. It is a major contributor to the ongoing discussions of developing a European Open Science Cloud (EOSC, 2016).

The Open APC initiative (2017), which releases datasets on fees paid for open access journal articles by universities and research institutions under an open database license.

IEEE DataPort (2017), a repository of datasets and data analysis tools developed by IEEE that is currently offered gratis since it in beta testing.

Corporate solutions that allow the staff and/or the members of an organization to deposit and/or publish their data. Such examples, at the EC level, are the JRC Data Catalogue (2017), which publishes data from the Joint Research Canter of the EC, European Union Open Data Portal (2017), which publishes data from the EC and European Data Portal (2017), which publishes data from both the EC and the EU member states.

In some domains, well recognized data management initiatives include contributions from Citizen Science projects as part of their data sources. The previously introduced GBIF provides one of the largest examples of including observations from citizen scientists, researchers and automated monitoring programs. On a smaller yet recognizable scale, the data collected via the My Ocean Sampling Day (MyOSD, 2017) initiative are fed into well-established data centers of the marine domain. Those data centers were originally created to collect and preserve research data about oceans and the marine environments, without explicitly accounting for Citizens’ contributions. However, Citizen Science was recognized as an additional data source.

Domain specific frameworks, that allow the members of a Citizen Science community formed on common interest to deposit their data and, in some cases, collaborate on data identification, description, analysis etc. Such examples are common in the environmental domain, and include iNaturalist (2017), a citizen science framework that invites users to participate in its projects, share their relevant observations and work on them together with other interested users; and myObservatory (2017), an environmental information management system that also provides its users with analysis tools, and many others in a centralized system. Also, the previously mentioned ALA and to some extent the well-known Open Street Map (2017) fall into this category. Whereas ALA provides a central facility for creating projects using common tools and storage facilities, Open Street Map provides one unifying platform for accessing and editing geographic information.

Having this rich set of already existing efforts, we still lack a gap analysis of the data preservation needs that cannot be covered by the current services. Awareness raising actions and guidelines of using the already available solutions seem to be necessary.

 

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5. Conclusion

Following the overall recognition of data management challenges related to Citizen Science Projects, the survey helped to advance our understanding about the state of play and directions taken by current initiatives. Beyond this initial scope, the survey helped us to get involved in many of the ongoing discussions that revealed a set of good practices and lessons to learn from. Still, we see particular issues related to access provisions (especially licensing), standardization and data preservation, but could identify a rich set of emerging activities that provide leading examples and best practices.

We see a strong need for the following actions:

  • Not only promote Open Data and Open Science, but also to provide guidelines and best practices, e.g., for the use of licenses and re-licensing. It will be most important to make such information discoverable and accessible to interested parties, i.e., current and future Citizen Science projects. Access provision via recognized Citizen Science associations (national and international) seems most appropriate.
  • Follow a similar exercise for data preservation and sustainability. This may also include best practices on how existing infrastructures could already be used, as well as a gap analysis of the indeed missing components that would make Citizen Science data more sustainable. We might think about parallel work on sustaining communities, and models for providing continuous support.
  • Continue to interconnect the ongoing standardization efforts, especially within technical working groups of Citizen Science associations, OGC, and GEO/GEOSS. Thematic examples from the area of biodiversity should be closely involved so that already existing community standards are considered and can be connected to new developments.

We will continue to work on these items, especially in the context of the European Commission’s Environmental Knowledge Community (EKC), by continuing our collaborations with the already existing working groups, and by establishing connections and discussions with other relevant organizations. End of article

 

About the authors

Sven Schade. Sven’s professional career focuses on geospatial information sharing and use — from regional to global levels, and vice versa. Since September 2013, he is working as a scientific and technical officer for the European Commission’s Joint Research Centre (JRC), where he already worked for three years as a post-doc (2009–2012) and six month as an intern (2005). Before re-joining the JRC, Sven was employed by the European Environment Agency (EEA) as project manager for the Shared Environmental Information System (SEIS). In his early career, Sven spent 10 years with the Institute for Geoinformatics (IfGI) of the University of Münster — where he completed his diploma and Ph.D. studies (in 2004 and 2009, respectively) and contributed to numerous national and European-level research projects in the area of geospatial information science. Sven has authored and co-authored more than 100 publications in the fields of Geospatial Semantic Web, Observation Web and Digital Earth, which have received more than 1,000 citations.
Direct comments to: s [dot] schade [at] ec [dot] europa [dot] eu

Chrisa Tsinaraki. The common denominator in Chrisa’s scientific career is knowledge representation and management, applied in different disciplines like open data, interoperability support, big data, digital libraries, citizen science, cultural heritage, multimedia semantics, multilingualism, event modeling and visualization. She holds the diploma (1995), M.Eng. (2000) and Ph.D. degrees (2008) in computer engineering from the Electronics & Computer Engineering Department of the Technical University of Crete. Since September 2013 she is working in the Joint Research Centre of the European Commission. Before that, Chrisa has worked as a researcher at the Technical University of Crete and the University of Trento, and has been involved in many R&D projects. She has also been a visiting lecturer at the Technical University of Crete. She has authored and co-authored more than 60 peer-reviewed publications, which have received more than 1,000 citations.
E-mail: chrysi [dot] tsinaraki [at] ec [dot] europa [dot] eu

Elena Roglia. Elena received her degree in mathematics from the University of Turin, Department of Mathematics Science, in 2003. After a period in the private sector, she started a Ph.D. in Science and High Technology at the Department of Computer Science, University of Turin that she completed in 2011. She is working in the Joint Research Centre of the European Commission since 2012, where she has been involved in different projects related to Global Earth Observation System of Systems. Her researches focus mainly in data integration, interoperability and analysis.
E-mail: elena [dot] roglia [at] ec [dot] europa [dot] eu

 

Acknowledgements

The views expressed in this paper are purely those of the authors and may not in any circumstances be regarded as stating official positions of the European Commission.

This work would not have been possible without the support of the participating Citizen Science projects, we are very satisfied and grateful about the many replies that we received. The high number of responses could only be achieved thanks to the many organizations and individuals that helped distributing our call for participation, here we particularly acknowledge the support of the INSPIRE, PEER and EUROGEO communities, Software Sustainability Institute, ECSA, CSA, ACSA and the national Citizen Science networks of Germany, Switzerland and Austria, as well as the projects SOCIENTIZE, EUBON, Citizen Sense and the Citizens’ Observatories. Special thanks also to the following power users on twitter: @EUexpo2015, @MozillaScience, @SciStarter, @ICTscienceEU and @john_magan. The preparation of the survey was greatly supported by Jose Miguel Rubio, Anne Bowser, Muki Haklay, Claudia Göbel, Arne Berre, Jaume Piera, Max Craglia, Andrea Perego and Cristina Rosales Sanchez.

 

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Editorial history

Received 20 April 2017; accepted 13 June 2017.


Creative Commons License
This paper is in the Public Domain.

Scientific data from and for the citizen
by Sven Schade, Chrisa Tsinaraki, and Elena Roglia.
First Monday, Volume 22, Number 8 - 7 August 2017
http://firstmonday.org/ojs/index.php/fm/article/view/7842/6326
doi: http://dx.doi.org/10.5210/fm.v22i7.7842





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