With the support of a Research Data Strategy since 2010, the Library has been active in services for research data for a number of years. This work has included European-funded projects such as ODE, ODIN and THOR; Providing the DataCite UK service and involvement in other activities such as data...

The parallel rise of pervasive data collection platforms and computational methods for collecting, analyzing, and drawing inferences from large quantities of us...

Graphs/charts are less copyrightable than you might think due to "merger doctrine."

In this talk, I use Mauss' theory of the gift in order to shift how we think about data sharing and data donation, particularly in the context of biomedical res

Over the past few years, research reproducibility has been increasingly highlighted as a multifaceted challenge across many disciplines. There are socio-cultural obstacles as well as a constantly changing technical landscape that make replicating and reproducing research extremely difficult. Researchers face challenges in reproducing research across different operating systems and different versions of software, to name just a few of the many technical barriers. The prioritization of citation counts and journal prestige has undermined incentives to make research reproducible. While libraries have been building support around research data management and digital scholarship, reproducibility is an emerging area that has yet to be systematically addressed. To respond to this, New York University (NYU) created the position of Librarian for Research Data Management and Reproducibility (RDM & R), a dual appointment between the Center for Data Science (CDS) and the Division of Libraries. This report will outline the role of the RDM & R librarian, paying close attention to the collaboration between the CDS and Libraries to bring reproducible research practices into the norm.

A new study has found that as much as 80 percent of the raw scientific data collected by researchers in the early 1990s is gone forever, mostly because no one knows where to find it.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

DCAT is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web.       This document defines the schema and provides examples for its use.

Edward Snowden’s revelation about the National Security Agency’s information-gathering practices alerted the public to the nefarious uses of data ...

Revealing their location online puts their existence at risk, a group warns

Objective The purpose of this study is to describe the volume, topics, and methodological nature of the existing research literature on research data management in academic institutions.   Materials and methods We conducted a scoping review by searching forty literature databases encompassing a broad range of disciplines from inception to April 2016. We included all study types and data extracted on study design, discipline, data collection tools, and phase of the research data lifecycle.   Results We included 301 articles plus 10 companion reports after screening 13,002 titles and abstracts and 654 full-text articles. Most articles (85%) were published from 2010 onwards and conducted within the sciences (86%). More than three-quarters of the articles (78%) reported methods that included interviews, cross-sectional, or case studies. Most articles (68%) included the Giving Access to Data phase of the UK Data Archive Research Data Lifecycle that examines activities such as sharing data. When studies were grouped into five dominant groupings (Stakeholder, Data, Library, Tool/Device, and Publication), data quality emerged as an integral element.   Conclusion Most studies relied on self-reports (interviews, surveys) or accounts from an observer (case studies) and we found few studies that collected empirical evidence on activities amongst data producers, particularly those examining the impact of research data management interventions. As well, fewer studies examined research data management at the early phases of research projects. The quality of all research outputs needs attention, from the application of best practices in research data management studies, to data producers depositing data in repositories for long-term use.

ReproZip is a tool that simplifies the process of creating reproducible experiments from command-line executions, a frequent task in computational science.