Good Omens: New Services, Accurate Research Data

Of late, I’ve become a fan of the book and television series Good Omens by Terry Pratchett and Neil Gaiman. I’ve reveled in its humor, lampooning of social constructs, use of literary symbolism, and the evolving partnership between an angel and a demon that would normally not be considered collegial—even in the best of times. They’re enamored with humans, society and culture, and have carved out very good lives for themselves…until Armageddon commences and they need to find new and effective ways of collaborating to save the Earth.

Like Good Omens, data management needs strong relationships and fresh ways of managing data to assure curation and preservation, discoverability, reusability, and interoperability. The same holds true for the publications that provide access to the scholarship of this field: new paths and new collaborations to help publishers support their colleagues' scholarly and research needs.

The Journal of eScience Librarianship’s Editorial Team is excited to welcome Allie Tatarian as Data Editor. Allie is a Research & Instruction and Data Librarian from Hirsh Health Sciences Library, Tufts University. They bring experience in teaching research data management and responsible conduct in research (RCR), open access publishing, and biomedical research. In this role, Allie will ensure that datasets accompanying manuscripts meet the submission guidelines and standards of good data management practice; and will work with the editorial team to establish policies and guidelines for the submission of data articles and papers describing research data. As JeSLIB continues to receive more manuscripts with data files and an increasing interest by authors in publishing articles focusing on research datasets, it was important to provide support for these changing needs.

A new article type makes its debut in this issue of JeSLIB. The Data in Action article is used when an author wants to publish a description of library and information science research datasets with the aim of increasing visibility and transparency, supporting their reuse, and promoting reproducible research. A Data in Action Article Template was designed to be used for submitting this type of article.

In this issue, the inaugural Data in Action article, “Academic Library Pricing Dataset for SciFinder Scholar, Web of Science, and Scopus: 2018-2024” by Curtis Brundy and Joel B. Thornton, describes data that was gathered as part of a research study into pricing fairness in the academic library database market, including database pricing and agreements. The pricing data is from SciFinder, Web of Science (WoS), Chemical Abstracts Service (CAS), and Scopus. Brundy and Thornton describe the datasets, their methodology and why these were acquired, the value of the data, and directions in reproducibility.

The FAIR Principles (findability, accessibility, interoperability, and reusability) and metadata are key themes in the full-length papers “Toward Enhanced Reusability: A Comparative Analysis of Metadata for Machine Learning Objects and Their Characteristics in Generalist and Specialist Repositories” and “Identifying metadata commonalities across restricted health data sources: A mixed methods study exploring how to improve the discovery of and access to restricted datasets.” The authors in the first article researched characteristics of Machine Learning-related (ML) objects shared in generalist and specialist repositories and the extent to which repository metadata fields enabled findability and reuse of ML objects. The second article’s authors extracted datasets and accessed information from restricted data sources, identified commonalities across these data sources to develop possible metadata elements for restricted data, and mapped these metadata elements to existing metadata schemas to evaluate how well they accommodate information supplied by restricted data sources. Labou et al. and Read et al. discuss the importance of creating machine-readable metadata in meeting the FAIR Principles.

Mosha and Ngulube assessed the use of data management plans among researchers at the Nelson Mandela African Institution of Science and Technology in Tanzania. Several challenges were revealed including a lack of awareness, competence, and guidelines to assist researchers using a data management plan (DMP) for their research projects. The results provide opportunities for the Institute’s librarians to further develop research data management services, including making data management plans mandatory. Oliver, Rios, Carini, and Ly inventoried academic libraries from the U.S. Research 1 (R1) Carnegie institutions to assess the state of data services at university libraries. An increase in support for data analyses and data visualization services was discovered, but not all the libraries could provide support for these services and the authors discuss how support could be achieved.

This issue’s Curatorial Connections Column, authored by Mikala Narlock, Director of the Data Curation Network (DCN), reflects on the Community Data Toolkits Workshop (CDTW) and the role of community, data, and community data, and how change impacts these collaborations. The commentary “Back to Basics: Considering Categories of Data Services Consults” submitted by Issac Wink, Research Data Librarian, University of Kentucky, takes a deep dive into data management consultations and supporting researchers within this service.

The paths we take as research and data curation librarians are important—we need to collaborate, share data, implement FAIR principles, create accurate metadata and build secure repositories; and connect with researchers so that we can avoid data Armageddon. In Good Omens, Nightingales (Luscinia megarhynchos) are important symbols of creativity, transformation, resilience, and romance.¹ There are plenty of Nightingales to be had in research data management if one listens. To the research data world!