Implementing Effective Data Practices: Stakeholder Recommendations for Collaborative Research Support

Chodacki, John; Hudson‐Vitale, Cynthia; Meyers, Natalie; Muilenburg, Jennifer; Praetzellis, Maria; Redd, Kacy; Ruttenberg, Judy; Steen, Katie; Cutcher‐Gershenfeld, Joel; Gould, Michael

doi:10.29242/report.effectivedatapractices2020

Cited by 5 publications

(4 citation statements)

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“…Additionally, the last decade has seen the growth of research information management systems that aggregate information about faculty publications or peer collaboration networks [ 4 ]. However, the large-scale discovery and aggregation of information about research data has only recently become feasible given the growth in data repositories and the adoption of digital object identifiers (DOIs) for datasets, which is thanks to several open data metrics efforts [ 5 , 6 ]. As federal funders build and implement requirements for data and other research products to be shared and made publicly accessible, institutions can start asking how well their teaching, research, and outreach mission is met by analyzing research data shared by their researchers.…”

Section: Introductionmentioning

confidence: 99%

Seek and you may (not) find: A multi-institutional analysis of where research data are shared

Johnston,

Hofelich Mohr,

Herndon

et al. 2024

PLoS ONE

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Research data sharing has become an expected component of scientific research and scholarly publishing practice over the last few decades, due in part to requirements for federally funded research. As part of a larger effort to better understand the workflows and costs of public access to research data, this project conducted a high-level analysis of where academic research data is most frequently shared. To do this, we leveraged the DataCite and Crossref application programming interfaces (APIs) in search of Publisher field elements demonstrating which data repositories were utilized by researchers from six academic research institutions between 2012–2022. In addition, we also ran a preliminary analysis of the quality of the metadata associated with these published datasets, comparing the extent to which information was missing from metadata fields deemed important for public access to research data. Results show that the top 10 publishers accounted for 89.0% to 99.8% of the datasets connected with the institutions in our study. Known data repositories, including institutional data repositories hosted by those institutions, were initially lacking from our sample due to varying metadata standards and practices. We conclude that the metadata quality landscape for published research datasets is uneven; key information, such as author affiliation, is often incomplete or missing from source data repositories and aggregators. To enhance the findability, interoperability, accessibility, and reusability (FAIRness) of research data, we provide a set of concrete recommendations that repositories and data authors can take to improve scholarly metadata associated with shared datasets.

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Section: Introductionmentioning

confidence: 99%

Seek and you may (not) find: A multi-institutional analysis of where research data are shared

Johnston,

Hofelich Mohr,

Herndon

et al. 2024

PLoS ONE

Self Cite

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“…This is why both Association of Public & Land‐grant Universities (APLU; https://www.aplu.org/) and Association of American Universities (AAU – i.e., private universities; https://www.aau.edu/) are engaged on this topic. Deans, Provosts, and Department Heads can influence their departments and communities to adopt data management and public access best practices, in partnership with their research libraries (Chodacki et al., 2020). Currently, a lot of money is going into data creation via precision agriculture but much of it is private.…”

Section: Best Practices Vs Common Practicesmentioning

confidence: 99%

“….e. private universities; https://www.aau.edu/) are engaged on this topic.Deans, Provosts, and Department Heads can influence their departments and communities to adopt data management and public access best practices, in partnership with their research libraries(Chodacki et al, 2020) Currently, a lot of money is going into data creation via precision agriculture but much of it is private. Competitions such as the Gates Foundation Grand Challenges (https://gcgh.grandchallenges.org/about) competition using Microsoft FarmBeats (https://www.microsoft.com/en-us/research/project/farmbeats-iot-agriculture/)…”

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confidence: 99%

Agricultural data management and sharing: Best practices and case study

et al. 2021

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Agricultural data are crucial to many aspects of production, commerce, and research involved in feeding the global community. However, in most agricultural research disciplines standard best practices for data management and publication do not exist. Here we propose a set of best practices in the areas of peer review, minimal dataset development, data repositories, citizen science initiatives, and support for best data management. We illustrate some of these best practices with a case study in dairy agroecosystems research. While many common, and increasingly disparate data management and publication practices are entrenched in agricultural disciplines, opportunities are readily available for promoting and adopting best practices that better enable and enhance data-intensive agricultural research and production.

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“…Investments have been made in knowledge graphs and databases (Priem, Piwowar & Orr 2022) report, Implementing Effective Data Practices: Stakeholder Recommendations for Collaborative Research Support, which collected key recommendations for effective data practices to support a more open research ecosystem, and this report also focused on using PIDs and maDMPs (Chodacki et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A Programmatic and Scalable Approach to Making Data Management Machine-Actionable

Praetzellis,

Buys,

Chen

et al. 2023

Data Science Journal

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The challenge of tracking research productivity and impact is compounded by the fragmented ecosystem in which research data outputs are managed, with stakeholders such as funders, research centers, government agencies, and academic institutions struggling to ensure compliance with mandates for data sharing and other regulatory requirements. While data management plans (DMPs) are crucial for effective research data management (RDM), their inadequate integration exacerbates fragmentation. This paper presents a robust, scalable, persistent identifier (PID)-enabled infrastructure that interlinks metadata and updates DOI records, effectively incorporating DMPs to bolster data discoverability, reusability, and compliance.

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Implementing Effective Data Practices: Stakeholder Recommendations for Collaborative Research Support

Cited by 5 publications

References 0 publications

Seek and you may (not) find: A multi-institutional analysis of where research data are shared

Seek and you may (not) find: A multi-institutional analysis of where research data are shared

Agricultural data management and sharing: Best practices and case study

A Programmatic and Scalable Approach to Making Data Management Machine-Actionable

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