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

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.

Safeguarding scientific integrity: A case study in examining manipulation in the peer review process.

This case study analyzes the expertise, potential conflicts of interest, and objectivity of editors, authors, and peer reviewers involved in a 2022 special journal issue on fertility, pregnancy, and mental health. Data were collected on qualifications, organizational affiliations, and relationships among six papers' authors, three guest editors, and twelve peer reviewers. Two articles were found to have undisclosed conflicts of interest between authors, an editor, and multiple peer reviewers affiliated with anti-abortion advocacy and lobbying groups, indicating compromised objectivity. This lack of transparency undermines the peer review process and enables biased research and disinformation proliferation.Our study is limited by a few factors including: difficulty collecting peer reviewer data, potentially missing affiliations, and a small sample without comparisons. While this is a case study of one special issue, we do have suggestions for increasing integrity.

Coordinating culture change across the research landscape.

Scientific integrity necessitates applying scientific methods properly, collecting and analyzing data appropriately, protecting human subjects rightly, performing studies rigorously, and communicating findings transparently. But who is responsible for upholding research integrity, mitigating misinformation, and increasing trust in science beyond individual researchers? We posit that supporting the scientific reputation requires a coordinated approach across all stakeholders: funding agencies, publishers, scholarly societies, research institutions, and journalists and media, and policy-makers.

Examining Data Repository Guidelines for Qualitative Data Sharing.

Qualitative data provide rich information on research questions in diverse fields. Recent calls for increased transparency and openness in research emphasize data sharing. However, qualitative data sharing has yet to become the norm internationally and is particularly uncommon in the United States. Guidance for archiving and secondary use of qualitative data is required for progress in this regard. In this study, we review the benefits and concerns associated with qualitative data sharing and then describe the results of a content analysis of guidelines from international repositories that archive qualitative data. A minority of repositories provide qualitative data sharing guidelines. Of the guidelines available, there is substantial variation in whether specific topics are addressed. Some topics, such as removing direct identifiers, are consistently addressed, while others, such as providing an anonymization log, are not. We discuss the implications of our study for education, best practices, and future research.

Incorporating Data Citation in a Biomedical Repository: An Implementation Use Case.

Research data is a dynamic and evolving entity and the ability to cite such data depends on recreating the same datasets utilized in the original research. Despite the availability of several existing technologies, most data repositories lack the necessary setup to recreate a point-in-time snapshot of data, let alone long-term sustainability of dynamic data without restoring an entire database. Through this project, we adopted a subset of the Research Data Alliance data citation working group recommendations to establish a robust informatics system supporting dynamic data and its use for reproducible research within our evolving clinical data repository. We implemented key recommendations: data versioning, times-stamping, query storing, query time-stamping, query PID, and data citation in one data repository, implemented entirely at the database level, and were able to successfully reproduce a previous dataset as it existed at a specific point-in-time using only the PID as provided in a citation.