TY - JOUR
T1 - Investigating FAIR data principles compliance in horizon 2020 funded Agri-food and rural development multi-actor projects
AU - Kumar, Parveen
AU - Hendriks, Tim
AU - Panoutsopoulos, Hercules
AU - Brewster, Christopher
N1 - Funding Information:
To address the barriers to data-sharing within agricultural and agri-food research, the EC is advocating for the standardization of FAIR data management and Open Science practices in EU-funded research and innovation programs ( European Commission, 2017, 2016 ; European Parliament, 2021 ). This means that all researchers who receive funding from the EU must make their research publications and data, if possible, openly accessible or FAIR. This will increase the access to the findings of these projects, consequently yielding substantial impact in practice. The primary objective behind standardizing FAIR data management and Open Science practices is to enhance the value of research. Sharing knowledge and project outputs among all parties involved in EU-funded research and innovation projects at every stage of the project helps disseminate the latest information to its intended end users (i.e., farmers, advisors, researchers, SMEs, etc.). The EC is currently supporting multiple open science initiatives that balance the protection of data and privacy with access and reuse of data to make it practical, fair, and straightforward. These initiatives come in the form of infrastructure and funding, such as the European Open Science Cloud (EOSC) and the adoption of best practices within funded projects like the FAIR Data Principles ( European Commission, 2017, 2016 ; Wilkinson et al., 2016 ). The EC also supports stakeholder initiatives, like the Code of Conduct (CoC) for Agricultural Data Sharing by Contractual Agreement ( Copa-Cogeca, 2018 ). At the same time, European Parliament and The Council of the European Union passed a regulation whereby research data produced in the course of EU-funded projects must adhere to FAIR data principles. The goal of the EOSC is to create a global scientific data infrastructure for Europe and allow European scientists and relevant stakeholders to take advantage of responsible data-driven decision and policy making ( Corcho et al., 2021 ). In addition to EU-scale efforts, many EU Member States are adopting Open Science and FAIR Data Principles at the national level, with the aim of making these principles the standard for sharing and reusing data outputs developed within EU-funded projects (European Commission, 2017, 2016; Hodson et al., 2018 ). Within the scope of agri-food and rural development, the CoC is instrumental. It establishes the data user rights as fundamental, rather than focusing on data ownership. It also assigns rights to the originator of the data, as opposed to the owner. Despite these policy frameworks, the current data-sharing landscape in this sector is loaded with challenges, including legal, technical, and financial impediments ( Kosior, 2020 , 2018). To mitigate these issues, EU Regulation 2021/695, enacted by the European Parliament and Council (2021) , projects funded under the Horizon Europe Framework Programme, mandates open access to scholarly publications and data. It also calls for FAIR data management plans, investment in open research infrastructure, and the provision of training and support for researchers.
Funding Information:
This work was conducted as part of the EUREKA project. The EUREKA project has been funded by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 862790. We extend our appreciation to the European Union for their support, which made this research possible. Additionally, I would like to acknowledge Dr. Ashley Rohde from New Mexico State University, USA for her invaluable help in reviewing and editing the paper. During the preparation of revised manuscript the author used ChatGPT-4 in order to improve and enhance the writing of some text, after using this tool, the author reviewed and edited the content as needed and take full responsibility for the content of the publication. Also, we are deeply grateful to all EUREKA project members, collaborators, and contributors for their expertise and insights into this study.
Funding Information:
The pressing question now is how the existing, relatively disconnected, data sharing system supports Open Science and FAIR data management practices and how it addresses the myriad of global challenges concerning agri-food system, particularly within research. In this study, we examine the FAIR data management practices of EU-funded MAPs within the agri-food and rural development sectors. Our primary objective is to assess the compliance of FAIR Data Principle within EU funded project outputs. Additionally, we aim to identify and understand the challenges that impede reaching optimal levels of FAIRness. Through our research, we gauge the extent to which these MAPS adhere to FAIR data management practices and pinpoint the obstacles to achieving enhanced FAIRness in their outputs. The research methodology comprises three steps: using the FAIRness evaluation framework to evaluate selected MAPs, assessing the FAIRness of the projects' output, and discussing the findings and their implications. The emphasis of this research is on agri-food and rural development projects funded under the EU Horizon 2020 Framework Programme. This research is part of the “European Knowledge Repository for Best Agricultural Practices” (EUREKA) project, which was funded under the Horizon 2020 Research and Innovation Program.
Funding Information:
This work was conducted as part of the EUREKA project. The EUREKA project has been funded by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 862790. We extend our appreciation to the European Union for their support, which made this research possible. Additionally, I would like to acknowledge Dr. Ashley Rohde from New Mexico State University, USA for her invaluable help in reviewing and editing the paper. During the preparation of revised manuscript the author used ChatGPT-4 in order to improve and enhance the writing of some text, after using this tool, the author reviewed and edited the content as needed and take full responsibility for the content of the publication. Also, we are deeply grateful to all EUREKA project members, collaborators, and contributors for their expertise and insights into this study.
Publisher Copyright:
© 2023 The Authors
PY - 2024/2/1
Y1 - 2024/2/1
N2 - CONTEXT: The agri-food and rural development sector in Europe is undergoing digitalization, and therefore becoming increasingly data-dependent. However, limited experience in implementing Findable, Accessible, Interoperable, and Reusable (FAIR) data principles has restricted knowledge sharing and reusability. OBJECTIVE: This study aims to investigate the existing FAIR Data Management practices in the agri-food and rural development sector by assessing the FAIRness of project outputs from recent H2020-funded Multi-Actor Projects (MAPs). METHODS: We conducted a FAIRness assessment of project outputs using both semi-automatic and manual framework, and we also carried out a comprehensive review of the data sharing practices among selected MAPs. RESULTS AND CONCLUSIONS: Of the investigated MAPs, <10% have achieved FAIR compliance and applied FAIR data management practices. The measured FAIRness of project products, including journal articles, conference papers, and books, is higher than that of other product types such as videos, audios, and presentations. SIGNIFICANCE: The study highlights the critical need for standardizing the adoption of FAIR data principles and data management practices across both the agri-food and rural development sector but also across bureaucratic data management throughout Europe more widely. Such a change would facilitate broader utilization and re-use of MAPs outputs and results, enhancing decision-making and agri-food and rural development practices.
AB - CONTEXT: The agri-food and rural development sector in Europe is undergoing digitalization, and therefore becoming increasingly data-dependent. However, limited experience in implementing Findable, Accessible, Interoperable, and Reusable (FAIR) data principles has restricted knowledge sharing and reusability. OBJECTIVE: This study aims to investigate the existing FAIR Data Management practices in the agri-food and rural development sector by assessing the FAIRness of project outputs from recent H2020-funded Multi-Actor Projects (MAPs). METHODS: We conducted a FAIRness assessment of project outputs using both semi-automatic and manual framework, and we also carried out a comprehensive review of the data sharing practices among selected MAPs. RESULTS AND CONCLUSIONS: Of the investigated MAPs, <10% have achieved FAIR compliance and applied FAIR data management practices. The measured FAIRness of project products, including journal articles, conference papers, and books, is higher than that of other product types such as videos, audios, and presentations. SIGNIFICANCE: The study highlights the critical need for standardizing the adoption of FAIR data principles and data management practices across both the agri-food and rural development sector but also across bureaucratic data management throughout Europe more widely. Such a change would facilitate broader utilization and re-use of MAPs outputs and results, enhancing decision-making and agri-food and rural development practices.
KW - Agri-food sector
KW - Digital innovation
KW - FAIR data principles
KW - Knowledge sharing
KW - Multi-actor projects
KW - Sustainability
U2 - 10.1016/j.agsy.2023.103822
DO - 10.1016/j.agsy.2023.103822
M3 - Article
SN - 0308-521X
VL - 214
JO - Agricultural Systems
JF - Agricultural Systems
M1 - 103822
ER -