A document by Shelley Stall. Click on the document to view its contents.

NIST is an important leader in the US for setting standards that inform best practices for scientific research and innovation. AGU's response to the RFI is based on our experience working with Earth, space, and environmental science researchers as well as computer and information scientists and data and software experts in the work that we are doing in promoting FAIR and open software sharing in AGU journals and in the wider community. 

A document by Kristina Vrouwenvelder. Click on the document to view its contents.

Within the climate modeling community, the complex citation issue has been discussed for a decade in the context of research traceability and data citation/data impact. The traceability requires fine-granular information on individual datasets, whereas meaningful data impact analysis relies on data citations on large data collections of data belonging to and individual model run or to a model experiment. To this date, it is not yet possible to achieve both goals with one technical solution. Suggestions for combinations of DOIs on data collections and user-defined PID collections of data subsets across several DOIs have not been taken up (see Stockhause et al., 2013).The IPCC FAIR Guidelines introduced in the Sixth Assessment Report (AR6) aimed to enhance the transparency of the AR6 and its outcomes by documenting the figure creation process (Pirani et al., 2022). Many figures are based on large numbers of datasets hosted in various repositories. Citing every dataset in the captions is not feasible. User-defined data collections utilizing data provenance records could be included in a caption, but lack the information about the authors and funders of the individual objects required for data citation and data impact analysis.The exchange on complex citation difficulties intensified at the AGU 2020 within the Community of Practice and led to the establishment of the RDA Complex Citation Working Group (WG). The WG brings all stakeholders together. It aims to provide recommendations for citing a large number of existing objects in a way that allows to properly assign credit for individual objects.

A document by Shelley Stall. Click on the document to view its contents.

A document by Shelley Stall. Click on the document to view its contents.

A document by Kristina Vrouwenvelder. Click on the document to view its contents.

Software and data citation are emerging best practices in scholarly communication. This article provides structured guidance to the academic publishing community on how to implement software and data citation in publishing workflows. These best practices support the verifiability and reproducibility of scientific results; sharing and reuse of valuable data and software tools, and attribution to the creators of the software and data. While data citation is increasingly well-established, software citation is rapidly maturing. With the current intensive use of software, including specialized tools and models for scientific research problems, the research community has begun to recognize that software, as a key research result and resource, requires the same level of transparency, accessibility, and disclosure as data. Software and data that support scientific results should be preserved and shared in scientific repositories for discovery, transparency, and use by other researchers. These goals can be supported by citing these products in the Reference Section of papers and effectively associating them to the software and data preserved in scientific repositories. Publishers need to mark up these references in a specific way to enable downstream processes, specifically those that enable automated attribution. Academic publishers wishing to stay current with best practices in the field are encouraged to follow the guidance provided here.

Research data are a vital component of the scientific record. Discovering and assessing data for possible reuse in future research is challenging. The Belmont Forum has recently awarded funds to three international teams as part of a four-year Collaborative Research Action (CRA) on Science-driven e-Infrastructure Innovation (SEI) for the Enhancement of Transnational, Interdisciplinary and Transdisciplinary Data Use to improve data management practices that will increase data reuse. One of these awardees, PARSEC, comprises two interwoven strands, one focused on improving data practices for reuse and credit, and one for synthesis science. The data specialists work alongside synthesis science researchers as they determine the influence of natural protected areas on socioeconomic outcomes for local communities. They collaborate with the researchers to better understand their motivations and work practices, and to aid them in the data-related steps that need to be taken during the research lifecycle. This will ensure their data and code are FAIR-compliant and thus enhance the likelihood of their data being reused and their analyses reproducible. The PARSEC team is working with Research Data Alliance (RDA), Earth Science Information Partners (ESIP), DataCite and ORCID to build awareness of the elements required for data creators to receive credit and automated attribution for their data contributions, and the tools that will make it easier to observe usage. Credit for data is an important incentive for researchers to make their data reusable. When data are FAIR and cited, their related publications have higher visibility. We shall discuss various ways in which we are working across the science-data interface in our multi-country and multi-disciplinary working environment to improve data (and code) reuse through better management and crediting. Make your Data FAIR, Cite your Data, Get Credit, Increase Reuse and reap the rewards!

Data that are FAIR demonstrate specific characteristics including: ease of discovery, ability to access, community acceptable formats allowing interoperability, and information that supports the decision for reuse. The process used to determine data reuse is commonly called “fit for purpose” or “fit for use”. These criteria are defined using relevant factors established by the community for which the data was originally created, and also a “best effort” for criteria needed by other research communities. The FAIR Data Principles support robust documentation of datasets to include the necessary information for reuse. An important part of that documentation, or metadata, is clear documentation of the quality and uncertainty related to the data being considered. When this information is not complete, data has a higher tendency of being used incorrectly leading to inaccurate research, rejected papers, or even retracted papers. The importance of data creators to make their data FAIR – including uncertainty information – directly improves the transparency and integrity of our science today and into the future.

Researchers are challenged with preparing and complying with data management plans and open data mandates. The ocean science repository community has demonstrated the benefits of providing FAIR (findable, accessible, interoperable, and reusable) data to its researchers through innovative alliances, robust metadata, and semantic connectivity. Challenges still exist in coordinating science policy, and research services across funders, institutions, and publishers that support the researcher’s data management needs. The convenors of this session request abstracts that focus on the value of FAIR data to the research community and the tools that support data discovery, compliance with data management plans, transparency, reproducibility, and research integrity.

This paper discusses a project aimed at detecting whether protected areas (PAs) influence the socioeconomic well-being of adjacent communities. The Belmont Forum funded PARSEC project is using satellite images and deep learning algorithms to predict socioeconomic conditions. In this paper we show our ongoing work for the selection of PAs, development of methodology using deep learning to detect socioeconomic indicators from remote sources, facilitation in data management and the approach used to handle a complex inter-disciplinary and trans-national team. We note the challenges in selecting case studies with examples from Australia, Brazil, Japan and the USA, and meshing remote sensed data with census data. We discuss the advantages of good data management for the individual and for the project and some simple steps to make this easy.