Erases data curation altogether, as usual. Scientists suck.

Policy can articulate the scope of work. For repositories that house data, policy can help users manage expectations, especially for individuals who are new to data sharing, or where expectations for sharing data have changed. We cover some of the current literature around the process for writing policy, specifically focusing on policy for data collections and repositories, factors that encouraged us to create a repository policy, our collaborative process for creating the policy, and lessons learned. We hope that others can use our processes to build their own policy that reflects the needs of their campuses and scholars and further moves the needle toward the “Library as Publisher” model.

The increase in research data-sharing practices has highlighted a growing number of ethical challenges related to the sharing and publication of datasets. In order to support the stakeholders involved in the publication of research data, the FORCE11 Research Data Publishing Ethics Working Group, in collaboration with Committee on Publication Ethics (COPE), has developed recommendations toward best practices for handling ethical cases related to the sharing and publication of research data. The recommendations posted are available together as PDF "Recommendations for the handling of ethical concerns relating to the publication of  research data" or broken apart by categories of considerations. Each include background information, examples, and recommended workflows for cases that may arise during the review & publication processes or post-publication.

Profit-friendly data privacy laws in the U.S. are out of step with public sentiment and hinder uses the public supports, from reducing opioid overdose deaths to curbing the COVID-19 pandemic.

Plain text data consists of a sequence of encoded characters or “code points” from a given standard such as the Unicode Standard. Some of the most common file formats for digital data used in eScience (CSV, XML, and JSON, for example) are built atop plain text standards. Plain text representations of digital data are often preferred because plain text formats are relatively stable, and they facilitate reuse and interoperability. Despite its ubiquity, plain text is not as plain as it may seem. The set of standards used in modern text encoding (principally, the Unicode Character Set and the related encoding format, UTF-8) have complex architectures when compared to historical standards like ASCII. Further, while the Unicode standard has gained in prominence, text encoding problems are not uncommon in research data curation. This primer provides conceptual foundations for modern text encoding and guidance for common curation and preservation actions related to textual data.

Early last October my phone rang. On the line was a researcher calling from Today, the BBC’s agenda-setting morning radio programme. She told me that something strange had happened, and she hoped I…

Adoption of good research data management practices is increasingly important for research teams. Despite the work the research community has done to define best data management practices, these practices are still difficult to adopt for many research teams. Universities all around the world have been offering Research Data Services to help their research groups, and libraries are usually an important part of these services. A better understanding of the pressures and factors that affect research teams may help librarians serve these groups more effectively. The social interactions between the members of a research team are a key element that influences the likelihood of a research group successfully adopting best practices in data management. In this article we adapt the Unified Theory of the Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) to explain the variables that can influence whether new and better, data management practices will be adopted by a research group. We describe six moderating variables: size of the team, disciplinary culture, group culture and leadership, team heterogeneity, funder, and dataset decisions. We also develop three research group personas as a way of navigating the UTAUT model, and as a tool Research Data Services practitioners can use to target interactions between librarians and research groups to make them more effective.

College and university research departments often collaborate on data collection and analysis, with the aim of a more thorough and expedient validatio

The Association of American Universities (AAU) and the Association of Public and Land-grant Universities (APLU) have released their Guide to Accelerate Public Access to Research Data, the result of two...

This study aimed to assess the frequency of receiving requested data for a systematic review and associated factors. We contacted the authors of studies in need of additional data via e-mail. The primary outcome was the success in receiving the requested data according to the time until receipt. We …

Article: Sample Identifiers and Metadata to Support Data Management and Reuse in Multidisciplinary Ecosystem Sciences

This guide covers the basics and best practices for data management for individuals who are new to the research and data-collecting process.

Adoption of good research data management practices is increasingly important for research teams. Despite the work the research community has done to define best data management practices, these practices are still difficult to adopt for many research teams. Universities all around the world have been offering Research Data Services to help their research groups, and libraries are usually an important part of these services. A better understanding of the pressures and factors that affect research teams may help librarians serve these groups more effectively. The social interactions between the members of a research team are a key element that influences the likelihood of a research group successfully adopting best practices in data management. In this article we adapt the Unified Theory of the Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) to explain the variables that can influence whether new and better, data management practices will be adopted by a research group. We describe six moderating variables: size of the team, disciplinary culture, group culture and leadership, team heterogeneity, funder, and dataset decisions. We also develop three research group personas as a way of navigating the UTAUT model, and as a tool Research Data Services practitioners can use to target interactions between librarians and research groups to make them more effective.