Geoscience data publication: Practices and perspectives on enabling the FAIR guiding principles

Kinkade, Danie; Shepherd, Adam

doi:10.1002/gdj3.120

Cited by 23 publications

(23 citation statements)

References 33 publications

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“…15 While these principles are becoming more widely adopted, SRP collaborative projects identified many barriers to making data FAIR and to facilitating broader data integration and sharing throughout the entire data lifecycle. Their experiences reflect commonalities with other institutions and initiatives from across a variety of disciplines who have recognized the value of the FAIR principles and sought to implement themdiscovering and clarifying the associated barriers along the wayincluding immunology, 16 plant sciences, 17 medical research, 18 chemistry, 19 geosciences, 20 computational toxicology, 21,22 psychology, 23 genomics, 24 ecology, 25 and human disease. 26 Figure 1.…”

Section: ■ Why Fair?mentioning

confidence: 73%

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

Heacock

Lopez²,

Amolegbe

et al. 2022

Environ. Sci. Technol.

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Environmental health sciences (EHS) span many diverse disciplines. Within the EHS community, the National Institute of Environmental Health Sciences Superfund Research Program (SRP) funds multidisciplinary research aimed to address pressing and complex issues on how people are exposed to hazardous substances and their related health consequences with the goal of identifying strategies to reduce exposures and protect human health. While disentangling the interrelationships that contribute to environmental exposures and their effects on human health over the course of life remains difficult, advances in data science and data sharing offer a path forward to explore data across disciplines to reveal new insights. Multidisciplinary SRP-funded teams are well-positioned to examine how to best integrate EHS data across diverse research domains to address multifaceted environmental health problems. As such, SRP supported collaborative research projects designed to foster and enhance the interoperability and reuse of diverse and complex data streams. This perspective synthesizes those experiences as a landscape view of the challenges identified while working to increase the FAIR-ness (Findable, Accessible, Interoperable, and Reusable) of EHS data and opportunities to address them.

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Section: ■ Why Fair?mentioning

confidence: 73%

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

Heacock

Lopez²,

Amolegbe

et al. 2022

Environ. Sci. Technol.

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“…This hinders data from being Open and/or Findable, Accessible, Interoperable, and Reusable (FAIR; Wilkinson et al, 2016). Further, specific criteria to implement the FAIR Guiding Principles (Gries et al, 2019;Jones et al, 2019) inevitably vary across disciplines and data types as inconsistencies in interpretations of the principles have grown (e.g., Kinkade & Shepherd, 2021;Mons et al, 2017;Stall et al, 2019). Importantly, FAIR does not mean Open; data can be Open without being FAIR, and vice versa (see What is the difference between "FAIR data" and "Open data" if there is one?).…”

Section: Current Statusmentioning

confidence: 99%

Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Hills

Damerow

Ahmmed

et al. 2022

Earth and Space Science

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This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman, et al., 2021b, https://doi. org/10.1029/2021EO153180) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing; (Section 3) Machine learning for multiscale modeling; (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Our role in ICON science therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of findable, accessible, interoperable, and reusable (FAIR) research products and practices. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi-scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery. Plain Language SummaryWe present commentaries on the state of "Integrated, Coordinated, Open, Networked (ICON) principles" in Earth and Space Science Informatics. ICON principles are meant to improve the research experience for all. Ultimately, data standardized according to community conventions and formats lead to more effective and efficient collaboration, data discovery, integration, and analyses. Data standards, tools, and machine learning developed using ICON principles enhance our understanding of Earth processes. Using ICON principles improves model results and efficacy, fosters interdisciplinary research, and provides a framework by which non-experts can confidently contribute volunteered data and findings. Standardized data also provides reliable common resources to help train and benchmark machine learning algorithms. When networked communities work together to standardize and share data openly, the resulting web of research products is more readily findable, accessible, interoperable, and reusable. Ongoing support is crucial to develop and sustain the people, systems, and tools necessary to embrace ICON principles in Earth and Space Science Informatics now and in the future.

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“…The growing awareness of Findability, Accessibility, Interoperability, and Reusability (FAIR) data principles (https://www.go-fair.org/fair-principles/) i s a n acknowledgment of these truths within the broader scientific community [1]. However, consistent application of these principles is a scientific challenge of its own, and different disciplines have made more and less progress toward these important goals [2][3][4]. Several FAIR data principles work around ensuring data and metadata are properly developed using standard vocabularies, adequately archived, and open, which ensure that data can be easily found by a human or machine.…”

Section: Introductionmentioning

confidence: 99%

Data needs to be a priority

Halford

Chen²,

Rastäetter³

2022

Front. Phys.

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Findability, Accessibility, Interoperability, and Reusability (FAIR) data are essential to heliophysics and all scientific research. The principles of FAIR data ensure the reusability and findability of data, as well as its long-term care. The goal is that data are accessible for the ongoing discovery and verification process and can be used on their own or with newly generated data in future studies leading to innovations. With the onset in the previous decades of NASA and other agencies requiring mission data to be open to the public, heliophysics has already made great strides toward FAIR data and benefited from these efforts. Continued improvements in our metadata, data archives, and data portals and the addition of DOIs for data citation will ensure data will be FAIR, enabling further scientific discoveries, reproducibility of results, longitudinal studies, and verification and validation of models. Currently, not all the data collected are findable and on open networks or archives, and not all data on archives have DOIs. Within this study, we make recommendations to prioritize resources needed to satisfy FAIR data principles, treating them as a fundamental research infrastructure rather than a simple research product.• Data collection, preparation, archiving, and accessibility need to be a priority.• Data collection, preparation, archiving, and accessibility need dedicated and sustained funding support.• Data need to be accessible through investment in infrastructure: tools to access and read the data and personnel to maintain these data and IT infrastructure.• Data need to be collected in sustained ways to enable further science and, specifically, model validation efforts.

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Geoscience data publication: Practices and perspectives on enabling the FAIR guiding principles

Cited by 23 publications

References 33 publications

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Data needs to be a priority

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