FAIR Data and Services in Biodiversity Science and                     Geoscience

Lannom, Larry; Koureas, Dimitrios; Hardisty, Alex

doi:10.1162/dint_a_00034

Cited by 56 publications

(42 citation statements)

References 9 publications

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“…Making resources available for the community means ensuring that data (and related materials) are findable and accessible on the Web (Mabile et al 2016), and that they comply with adopted international standards making them interoperable and reusable by others (Hansen et al 2018, Lannom et al 2019. This aligns with the FAIR data principles that have been proposed as part of the international initiative FORCE11, 1 to make data Findable, Accessible, Interoperable and Reusable (FAIR data principles from Wilkinson et al, 2016).…”

Section: Introductionmentioning

confidence: 90%

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

David

Mabile

Specht

et al. 2020

Data Science Journal

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The SHARC Interest Group of the Research Data Alliance was established to improve research crediting and rewarding mechanisms for scientists who wish to organise their data (and material resources) for community sharing. This requires that data are findable and accessible on the Web, and comply with shared standards making them interoperable and reusable in alignment with the FAIR principles. It takes considerable time, energy, expertise and motivation. It is imperative to facilitate the processes to encourage scientists to share their data. To that aim, supporting FAIR principles compliance processes and increasing the human understanding of FAIRness criteria-i.e., promoting FAIRness literacy-and not only the machine-readability of the criteria, are critical steps in the data sharing process. Appropriate human-understandable criteria must be the first identified in the FAIRness assessment processes and roadmap. This paper reports on the lessons learned from the RDA SHARC Interest Group on identifying the processes required to prepare FAIR implementation in various communities not specifically data skilled, and on the procedures and training that must be deployed and adapted to each practice and level of understanding. These are essential milestones in developing adapted support and credit back mechanisms not yet in place.

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Section: Introductionmentioning

confidence: 90%

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

David

Mabile

Specht

et al. 2020

Data Science Journal

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“…The DiSSCo 45 (biodiversity), ELIXIR 46 (biomedical research), E-RIHS 47 (cultural heritage), and EISCAT 48 (atmospheric research) initiatives provide excellent examples about the challenges and needs which many scientific disciplines are faced with [26,27]. At the bottom layer of the scientific knowledge space are the digital representations of large numbers of physical objects such as, for example, biological specimens [28], or observations of phenomena such as caused by, for example, diseases, treatments of diseases, chemical processes in the atmosphere, and many others. Exemplars of the corresponding digital objects are hosted in many institutions and labs worldwide.…”

Section: Stable Domain Of Scientific Entities and Relationshipsmentioning

confidence: 99%

FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Smedt¹,

Koureas²,

Wittenburg³

2020

Preprint

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Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

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“…The Distributed System of Scientific Collections (DiSSCo) repository is a collection of information about existing biological and geological specimens that are widely distributed across museums, universities, botanical gardens, and other institutions throughout Europe [22]. This repository unifies scientific assets under common curation and makes the data more FAIR.…”

Section: Research Infrastructure For Natural Science Collectionsmentioning

confidence: 99%

Implementing FAIR data for people and machines: Impacts and implications - results of a research data community workshop

Borycz

Carroll²

2020

ISU

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The Implementing FAIR Data for People and Machines: Impacts and Implications workshop was organized by the Board on Research Data and Information of the National Academies of Sciences, Engineering, and Medicine (NASEM), the CENDI Federal Information Managers Group, the Research Data Alliance (RDA), and the National Federation of Advanced Information Services (NFAIS), and held at NASEM’s Keck Center in Washington, DC on September 11, 2019. The goals of the Implementing FAIR Data workshop were to discuss the current status of FAIR data implementation, share what is being done to encourage scientists to share data in machine-readable formats, and examine the implications of FAIR data implementation for people and machines. FAIR data policies, tools, and measures of FAIR data compliance were considered from multiple perspectives. Marcia McNutt, President of the National Academy of Sciences (NAS), offered opening remarks, and the keynote address was presented by Barend Mons, Professor of Bioinformatics at Leiden University Medical Center and President of the International Science Council’s Committee on Data (CODATA). Three panel discussions addressed (1) the perspectives of scientists and administrators from U.S. federal agencies, (2) case studies on the implementation of FAIR data practices, and (3) principles and methods of measuring FAIR data compliance. The automation of scientific workflows was discussed by Stuart Feldman, Chief Scientist of Schmidt Futures, a philanthropic organization devoted to investing in research, technology, and science. The workshop closed with highlights and takeaways from each session as summarized by the moderators, followed by general questions.

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FAIR Data and Services in Biodiversity Science and Geoscience

Cited by 56 publications

References 9 publications

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Implementing FAIR data for people and machines: Impacts and implications - results of a research data community workshop

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