On May 20th 2021, SSHOC hosted a Roundtable of Experts for Data Citation, to stimulate discussion on data citation in the Social Sciences and Humanities (SSH).

While stakeholders in scholarly communication generally agree on the importance of data citation, there is not consensus on where those citations should be placed within the publication – particularly when the publication is citing original data. Recently, CrossRef and the Digital Curation Center (DCC) have recommended as a best practice that original data citations appear in the works cited sections of the article. In some fields, such as the life sciences, this contrasts with the common practice of only listing data identifier(s) within the article body (intratextually). We inquired whether data citation practice has been changing in light of the guidance from CrossRef and the DCC. We examined data citation practices from 2011 to 2014 in a corpus of 1,125 articles associated with original data in the Dryad Digital Repository. The percentage of articles that include no reference to the original data has declined each year, from 31% in 2011 to 15% in 2014. The percentage of articles that include data identifiers intratextually has grown from 69% to 83%, while the percentage that cite data in the works cited section has grown from 5% to 8%. If the proportions continue to grow at the current rate of 19-20% annually, the proportion of articles with data citations in the works cited section will not exceed 90% until 2030.

New image citation standards need to be developed in order for college and graduate students to meet visual literacy standards. The  MLA Handbook , 8th edition, and  Chicago Manual of Style , 16th edition, do not adequately clarify how to caption, attribute, and cite images. Other image captioning and citing resources are available, but refer to the MLA and Chicago manuals. Image captions from scholarly journals vary widely and cannot be used as examples for students to follow. Recommendations are also provided for future editions of the  MLA Handbook  and  Chicago Manual of Style .

Software plays a significant role in modern academic research, yet lacks a similarly significant presence in the scholarly record. With increasing interest in promoting reproducible research, curating software as a scholarly resource not only promotes access to these tools, but also provides recognition for the intellectual efforts that go into their development. This work reviews existing standards for identifying, promoting discovery of, and providing credit for software development work. In addition, it shows how these guidelines have been integrated into existing tools and community cultures, and provides recommendations for future software curation efforts.

This blog post provides more detail for a short presentation I will give today at the Software Credit Workshop in London. The aim is to look at the infrastructure pieces needed for software discovery and credit, and at the workflows linking these different...

THE MODEL FOR ASSESSMENT OF RESEARCH IMPACT IS A FRAMEWORK FOR TRACKING DIFFUSION OF RESEARCH OUTPUTS AND ACTIVITIES TO LOCATE INDICATORS THAT DEMONSTRATE EVIDENCE OF BIOMEDICAL RESEARCH IMPACT.

This document identifies gaps in existing PID infrastructures, with a focus on ORCID and DataCite Metadata and links between contributors, organizations and artefacts. What prevents us from establishing interoperability and overcoming barriers between PID platforms for contributors, artefacts and organisations, and research solutions for federated attribution, claiming, publishing and direct data access? It goes on to propose strategies to overcome these gaps.

Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.

D-Lib Magazine

2014-10-17 - By Nancy K. Herther There’s little question that research data is big today. Last year an advisory committee to the director of the National Institutes of -

Read the latest article version by John Kratz, Carly Strasser, at F1000Research.

Nice discussion of who gets credit for what.

The use of published digital data, like the use of digitally published literature, depends upon the ability to identify, authenticate, locate, access, …

This post is another in my ongoing series of “basic questions about open data”, which includes “What is a Dataset?” and “What does a dataset contain?”. In this p…