Citizen Science & Open Science: Synergies & Future Areas of Work

Citizen Science (CS) and Open Science (OS) are among the most discussed topics in current research and innovation policy, and are becoming increasingly related. This policy brief was developed with contributions from a mixed group of experts from both fields. It aims at informing decision makers who have adopted Citizen Science or Open Science on the synergies between these approaches and the benefits of considering them together. By showcasing initiatives implemented in Europe, this document highlights how Citizen Science and Open Science together can address grand challenges, respond to diminishing societal trust in science, contribute to the creation of common goods and shared resources, and facilitate knowledge transfer between science and society to stimulate innovation. The issues of openness, inclusion and empowerment, education and training, funding, infrastructures and reward systems are discussed regarding critical challenges for both approaches. The document concludes by recommending to consider Citizen Science and Open Science jointly, to strengthen synergies by building on existing initiatives, launching targeted actions regarding education and training, and infrastructures. This policy brief was developed within the framework of the Horizon 2020 project ‘Doing It Together Science’ (DITOs) to establish a collaborative network with external organisations and decision makers throughout Europe

Using new data sources for policymaking

This JRC technical report synthesises the results of our work on using new data sources for policy-making. It reflects a recent shift from more general considerations in the area of Big Data to a more dedicated investigation of Citizen Science, and it summarizes the state of play. With this contribution, we start promoting Citizen Science as an integral component of public participation in policy in Europe. The particular need to focus on the citizen dimension emerged due to (i) the increasing interest in the topic from policy Directorate-Generals (DGs) of the European Commission (EC), (ii) the considerable socio-economic impact policy making has on citizens’ life and society as a whole, and (iii) the clear potentiality of citizens’ contributions to increase the relevance of policy making and the effectiveness of policies when addressing societal challenges. We explicitly concentrate on Citizen Science (or public participation in scientific research) as a way to engage people in practical work, and to develop a mutual understanding between the participants from civil society, research institutions and the public sector by working together on a topic that is of common interest.JRC.B.6-Digital Econom

Citizen Science and its Impacts on Spatial Data Infrastructure Research

Citizen Science crystallised as an umbrella term for public participation in scientific research, Volunteered Geographic Information (VGI), Citizens’ Observatories, and many more. Technological advancements clearly power the wealth and spread of related initiatives, and organisational structures could be established over the past few years. On the one hand, most of the data collected or analysed by these initiatives, such as biodiversity records, air quality information or waste maps, have a spatio-temporal component. On the other hand, many Citizen Science initiatives reply on spatial data in order to plan or carry out their activities. Thus it is legitimate to ask if and how these recent developments might influence Spatial Data infrastructure (SDI) research. In 2018, the International Journal of Spatial Data Infrastructure Research (IJSDIR) for the first time showcases possible future scenarios in a dedicated Special Section on Citizen Science. The editorial at hand sets the scene for this Special Section

Still in Need of Norms: The State of the Data in Citizen Science

This article offers an assessment of current data practices in the citizen science, community science, and crowdsourcing communities. We begin by reviewing current trends in scientific data relevant to citizen science before presenting the results of our qualitative research. Following a purposive sampling scheme designed to capture data management practices from a wide range of initiatives through a landscape sampling methodology (Bos et al. 2007), we sampled 36 projects from English-speaking countries. The authors used a semi-structured protocol to interview project proponents (either scientific leads or data managers) to better understand how projects are addressing key aspects of the data lifecycle, reporting results through descriptive statistics and other analyses. Findings suggest that citizen science projects are doing well in terms of data quality assessment and governance, but are sometimes lacking in providing open access to data outputs, documenting data, ensuring interoperability through data standards, or building robust and sustainable infrastructure. Based on this assessment, the paper presents a number of recommendations for the citizen science community related to data quality, data infrastructure, data governance, data documentation, and data access

Research data management challenges in citizen science projects and recommendations for library support services. A scoping review and case study.

Citizen science (CS) projects are part of a new era of data aggregation and harmonisation that facilitates interconnections between different datasets. Increasing the value and reuse of CS data has received growing attention with the appearance of the FAIR principles and systematic research data management (RDM) practises, which are often promoted by university libraries. However, RDM initiatives in CS appear diversified and if CS have special needs in terms of RDM is unclear. Therefore, the aim of this article is firstly to identify RDM challenges for CS projects and secondly, to discuss how university libraries may support any such challenges. A scoping review and a case study of Danish CS projects were performed to identify RDM challenges. 48 articles were selected for data extraction. Four academic project leaders were interviewed about RDM practices in their CS projects. Challenges and recommendations identified in the review and case study are often not specific for CS. However, finding CS data, engaging specific populations, attributing volunteers and handling sensitive data including health data are some of the challenges requiring special attention by CS project managers. Scientific requirements or national practices do not always encompass the nature of CS projects. Based on the identified challenges, it is recommended that university libraries focus their services on 1) identifying legal and ethical issues that the project managers should be aware of in their projects, 2) elaborating these issues in a Terms of Participation that also specifies data handling and sharing to the citizen scientist, and 3) motivating the project manager to good data handling practises. Adhering to the FAIR principles and good RDM practices in CS projects will continuously secure contextualisation and data quality. High data quality increases the value and reuse of the data and, therefore, the empowerment of the citizen scientists

D4.2 Policy Briefs 2

The second batch of DITOs policy briefs focuses on four themes: Brief 1 - Environmental sustainability: This brief follows up the policy brief #1 on BioBlitzes and focuses on the pilot study conducted to develop a common evaluation framework for the City Nature Challenge (CNC) 2018 in Europe. Brief 2 - Biodesign: This brief follows up the policy brief #1 on Do It Yourself Biotechnology (DIYBio). It assesses the potential and challenges of biodesign citizen science for education and how it can contribute to achieving the Sustainable Development Goals (SDGs). Brief 3 - RRI indicators that reflect the practices of public engagement organisations: This third brief on the overarching topic of RRI is focussed on enriching the conversation and applications of RRI frameworks, in particular how they can move from being used as tools for assessment by funders and evaluators to being useful guidelines for personal and organisational learning and development. The brief presents results from in-depth conversations with facilitators and insights from reviewing RRI indicators in a way that reflects their practices. Brief 4 - RRI - linking Citizen Science and Open Science: This second policy brief on the topic of RRI is focussed on relations between Citizen Science and Open Science. It draws on initiatives implemented in Europe to identify synergies and future areas of work. In response to request from the mid-term project review for more evidence on inclusion impacts of the project, we have decided to diversify the types of policy briefs we will produce. In addition to ‘classic’ policy briefs aimed at giving an introduction and overview of a given topic (Brief 2 and 4) we now also offer ‘Research Insights’ that are based on gathering more thorough evidence from within the project and providing it to decision-makers (Brief 1 and 3). Like the first batch of briefs, a community-oriented approach was chosen for defining the specific topics of each brief and elaborating the content. Brief 1 has been developed by the ECSA working group on BioBlitzes, Brief 2 in cooperation with the in ECSA working group on Citizen Science for Learning and Education. Brief 3 draws on collaborative evaluation work within the DITOs consortium. Brief 4 was created together with the ECSA working group on Citizen Science and Open Science. The timeline of each policy brief has been adapted to be responsive to schedules of contributors, political dynamics and external demands. Brief 4 was already launched in February 2018. Brief 3 is finished and will be designed and published in the next weeks. Brief 1 and 2 are presented as an advanced draft version. Their final review will be conducted in workshops with the respective working groups and external experts at the International ECSA Conference in Geneva next week. This deliverable concludes the successful second stage of WP4 facilitating policy engagement for RRI. The final batch of policy briefs (M36) will further expand this work on biodesign, environmental sustainability and additional aspects of RRI. DITOs ‘Policy Briefs 2’ is Deliverable 4.2 (D4.2) from the coordination and support action (CSA) Doing It Together science (DITOs), grant agreement 709443

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.</jats:p

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices

Data Management Documentation in Citizen Science Projects: Bringing Formalisation and Transparency Together

Citizen science (CS) is a way to open up the scientific process, to make it more accessible and inclusive, and to bring professional scientists and the public together in shared endeavours to advance knowledge. Many initiatives engage citizens in the collection or curation of data, but do not state what happens with such data. Making data open is increasingly common and compulsory in professional science. To conduct transparent, open science with citizens, citizens need to be able to understand what happens with the data they contribute. Data management documentation (DMD) can increase understanding of and trust in citizen science data, improve data quality and accessibility, and increase the reproducibility of experiments. However, such documentation is often designed for specialists rather than amateurs. This paper analyses the use of DMD in CS projects. We present analysis of a qualitative survey and assessment of projects’ DMD, and four vignettes of data management practices. Since most projects in our sample did not have DMD, we further analyse their reasons for not doing so. We discuss the benefits and challenges of different forms of DMD, and barriers to having it, which include a lack of resources, a lack of awareness of tools to support DMD development, and the inaccessibility of existing tools to citizen scientists without formal scientific education. We conclude that, to maximise the inclusivity of citizen science, tools and templates need to be made more accessible for non-experts in data management

Citizen Science and Geospatial Capacity Building

This book is a collection of the articles published the Special Issue of ISPRS International Journal of Geo-Information on “Citizen Science and Geospatial Capacity Building”. The articles cover a wide range of topics regarding the applications of citizen science from a geospatial technology perspective. Several applications show the importance of Citizen Science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities, which are covered in the book. Particular emphasis is given to various problems encountered in the CitSci and VGI projects with a geospatial aspect, such as platform, tool and interface design, ontology development, spatial analysis and data quality assessment. The book also points out the needs and future research directions in these subjects, such as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders