Data are the infrastructure of science and they serve as the groundwork for scientific pursuits. Data publication has emerged as a game-changing breakthrough in scholarly communication. Data form the outputs of research but also are a gateway to new hypotheses, enabling new scientific insights and driving innovation. And yet stakeholders across the scholarly ecosystem, including practitioners, institutions, and funders of scientific research are increasingly concerned about the lack of sharing and reuse of research data. Across disciplines and countries, researchers, funders, and publishers are pushing for a more effective research environment, minimizing the duplication of work and maximizing the interaction between researchers. Availability, discoverability, and reproducibility of research outputs are key factors to support data reuse and make possible this new environment of highly collaborative research. An interoperable e-infrastructure is imperative in order to develop new platforms and services for to data publication and reuse. DataCite has been working to establish and promote methods to locate, identify and share information about research data. Along with service development, DataCite supports and advocates for the standards behind persistent identifiers (in particular DOIs, Digital Object Identifiers) for data and other research outputs. Persistent identifiers allow different platforms to exchange information consistently and unambiguously and provide a reliable way to track citations and reuse. Because of this, data publication can become a reality from a technical standpoint, but the adoption of data publication and data citation as a practice by researchers is still in its early stages. Since 2009, DataCite has been developing a series of tools and services to foster the adoption of data publication and citation among the research community. Through the years, DataCite has worked in a close collaboration with interdisciplinary partners on these issues and we have gained insight into the development of data publication workflows. This paper describes the types of different actions and the lessons learned by DataCite.

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.

"There's so much (data) available. To have our citizens involved coming up with innovative solutions to problems is fantastic."

In order to better understand the current state of data management education in multiple fields of science, this study surveyed scientists, including information scientists, about their data management education practices, including at what levels they are teaching data management, which topics they covering, and what barriers they experience in teaching these topics. We found that a handful of scientists are teaching data management in undergraduate, graduate, and other types of courses, as well as outside of classroom settings. Commonly taught data management topics included quality control, protecting data, and management planning. However, few instructors felt they were covering data management topics thoroughly, and respondents cited barriers such as lack of time, lack of necessary expertise, and lack of information for teaching data management. We offer some potential explanations for the existing state of data management education and suggest areas for further research.

Elevator pitches for data librarians to researchers.

Scientists in all fields face challenges in managing and sustaining access to their research data. The larger and longer term the research project, the more likely that scientists are to have resources and dedicated staff to manage their technology and data, leaving those scientists whose work is based on smaller and shorter term projects at a disadvantage. The volume and variety of data to be managed varies by many factors, only two of which are the number of collaborators and length of the project. As part of an NSF project to conceptualize the Institute for Empowering Long Tail Research, we explored opportunities offered by Software as a Service (SaaS). These cloud-based services are popular in business because they reduce costs and labor for technology management, and are gaining ground in scientific environments for similar reasons. We studied three settings where scientists conduct research in small and medium-sized laboratories. Two were NSF Science and Technology Centers (CENS and C-DEBI) and the third was a workshop of natural reserve scientists and managers. These laboratories have highly diverse data and practices, make minimal use of standards for data or metadata, and lack resources for data management or sustaining access to their data, despite recognizing the need. We found that SaaS could address technical needs for basic document creation, analysis, and storage, but did not support the diverse and rapidly changing needs for sophisticated domain-specific tools and services. These are much more challenging knowledge infrastructure requirements that require long-term investments by multiple stakeholders.

Apart from a short paper describing a heroic effort of Web archaeology, recreating Amsterdam's De Digitale Stadt , the whole second morning ...

Investigators still haven't found evidence that he leaked classified material to anyone.

Oh crap.

A blog about Digital Preservation, digital curation, long term access.

The destination and rules of the road are clear; the route you choose to get there makes a huge difference.

Which of these is true? “The PI owns the data.” “The university owns the data.” “Nobody can own it; data isn’t copyrightable.” You’ve probably heard somebody say at least one of these things — confidently. Maybe you’ve heard all of them. Maybe about the same dataset (but in that case, hopefully not from the same person). So who really owns research data? Well, the short answer is “it depends.” A longer answer is that determining ownership (and whether there’s even anything to own) can be frustratingly complicated — and, even when obvious, ownership only determines some of what can be […]

Digital curation is being recognized as a new domain that demands its own skills and expertise. The perceived or occurring professionalization of digi…

Alleging that faked pulmonary research led to grants from NIH and other federal agencies, scientist could win huge payout from false-claims suit and set a precedent