Archive for the 'Data Curation, Open Data, and Research Data Management' Category

"The ‘Digital’ Scholarship Disconnect"

Posted in Data Curation, Open Data, and Research Data Management, Digital Humanities, Emerging Technologies, Research Libraries on June 17th, 2014

Clifford Lynch has published "The 'Digital' Scholarship Disconnect" in EDUCAUSE Review.

Here's an excerpt:

Still, in all of these examples of digital scholarship, a key challenge remains: How can we curate and manage data now that so much of it is being produced and collected in digital form? How can we ensure that it will be discovered, shared, and reused to advance scholarship? We are struggling through the establishment of institutions, funding models, policies and practices, and even new legal requirements and community norms—ranging from cultural changes about who can use data (and when) to economic decisions about who should pay for what. Some disciplines are less contentious than others: for example, astronomy data is technically well-understood and usually not terribly sensitive. Reputation, rather than commercial reward, is wrapped up in astronomical discoveries, and there is no institutional review board to ensure the safety and dignity of astronomical objects. On the other hand, human subjects and their data raise an enormous number of questions about informed consent, privacy, and anonymization; when there are genetic markers or possible treatments to be discovered or validated, serious high-value commercial interests may be at stake. All of these factors tend to work against the free and convenient sharing of data.

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"The University Library as Incubator for Digital Scholarship"

Posted in Data Curation, Open Data, and Research Data Management, Digital Humanities, Emerging Technologies, Research Libraries on June 17th, 2014

Bryan Sinclair has published "The University Library as Incubator for Digital Scholarship" in EDUCAUSE Review.

Here's an excerpt:

The campus of the future will be increasingly connected and collaborative, and the library can be the community center and beta test kitchen for new forms of interdisciplinary inquiry. Libraries have always been in the business of knowledge creation and transfer, and the digital scholarship incubator within the library can serve as a natural extension of this essential function. In an age of visualization, analytics, big data, and new forms of online publishing, these central spaces can facilitate knowledge creation and transfer by connecting people, data, and technology in a shared collaborative space.

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"Developing a Research Data Management Service—A Case Study"

Posted in Data Curation, Open Data, and Research Data Management, Research Libraries on June 5th, 2014

Jeff Moon has published "Developing a Research Data Management Service—A Case Study" in Partnership.

Here's an excerpt:

Publicly-funded, researcher-generated data has been on the front burner lately, driven by a variety of factors, including evolving funding-agency policies and journal publisher requirements. In this context, Queen's University Library (QUL) developed and implemented a Research Data Management (RDM) Service to meet researchers' needs. This process is described here, framed around four main themes: planning, building, educating, and doing.

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"Research Data Sharing: Developing a Stakeholder-Driven Model for Journal Policies"

Posted in Data Curation, Open Data, and Research Data Management, Open Access, Open Science, Publishing, Scholarly Journals on June 5th, 2014

Paul Sturges et al. have self-archived "Research Data Sharing: Developing a Stakeholder-Driven Model for Journal Policies."

Here's an excerpt:

The Journal Research Data (JoRD) Project was a JISC (Joint Information Systems Committee) funded feasibility study on the possible shape of a central service on journal research data policies. The objectives of the study included, amongst other considerations: to identify the current state of journal data sharing policies and to investigate the views and practices of stakeholders to data sharing. The project confirmed that a large percentage of journals do not have a policy on data sharing, and that there are inconsistencies between the traceable journal data sharing policies. Such a state leaves authors unsure of whether they should deposit data relating to articles and where and how to share that data. In the absence of a consolidated infrastructure for the easy sharing of data, a journal data sharing model policy was developed. The model policy was developed from comparing the quantitative information gathered from analysing existing journal data policies with qualitative data collected from the stakeholders concerned. This article summarises the information gathered, outlines the process by which the model was developed and presents the model journal data sharing policy in full.

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"PLOS Data Policy: Catalyst for a Better Research Process"

Posted in Data Curation, Open Data, and Research Data Management, Open Access, Open Science, Publishing, Scholarly Journals on June 3rd, 2014

Emma Ganley has published "PLOS Data Policy: Catalyst for a Better Research Process" in College & Research Libraries News.

Here's an excerpt:

PLOS is seeking to ensure the ongoing utility of research, as making a paper openly accessible is enhanced enormously if that paper is linked seamlessly to the data from which it was constructed. In a time when post-publication peer review is more prevalent and data frequently come under intense public scrutiny, with whistle-blowers, blogs, and websites dedicated to investigating the validity and veracity of scientific publications, requiring access to the relevant data leads to a more rigorous scientific record.

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"PyRDM: A Python-Based Library for Automating the Management and Online Publication of Scientific Software And Data"

Posted in Data Curation, Open Data, and Research Data Management on May 30th, 2014

Christian T. Jacobs et al. have self-archived "PyRDM: A Python-Based Library for Automating the Management and Online Publication of Scientific Software And Data."

Here's an excerpt:

The recomputability and reproducibility of results from scientific software requires access to both the source code and all associated input and output data. However, the full collection of these resources often does not accompany the key findings published in journal articles, thereby making it difficult or impossible for the wider scientific community to verify the correctness of a result or to build further research on it. This paper presents a new Python-based library, PyRDM, whose functionality aims to automate the process of sharing the software and data via online, citable repositories such as Figshare. The library is integrated into the workflow of an open-source computational fluid dynamics package, Fluidity, to demonstrate an example of its usage.

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"Peer Review of Datasets: When, Why, and How"

Posted in Data Curation, Open Data, and Research Data Management, Publishing on May 14th, 2014

Matthew S. Mayernik et al. have published "Peer Review of Datasets: When, Why, and How" in the Bulletin of the American Meteorological Society.

Here's an excerpt:

This paper discusses issues related to data peer review, in particular the peer review processes, needs, and challenges related to the following scenarios: 1) Data analyzed in traditional scientific articles, 2) Data articles published in traditional scientific journals, 3) Data submitted to open access data repositories, and 4) Datasets published via articles in data journals.

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U.S. Open Data Action Plan

Posted in Data Curation, Open Data, and Research Data Management, Open Access on May 12th, 2014

The White House has released the U.S. Open Data Action Plan.

Here's an excerpt:

The Smithsonian Cooper-Hewitt National Design Museum Collection plans to make all digitized collections metadata public domain, and digitized collection images without copyright or other restriction publicly available at the highest available resolution for non-commercial, educational use. . . .

The Smithsonian Freer Gallery of Art and Arthur M. Sackler Gallery plans to make all digitized collections metadata public domain, and digitized collection images without copyright or other restriction publicly available at the highest available resolution for non-commercial, educational use. . . .

After a successful limited release of an API of the Smithsonian American Art Museum collection and hackathon that resulted in a number of working prototypes, the Smithsonian American Art Museum is planning a staged release, from open metadata, like artist or medium, to an open API of digitized collections images without copyright or other restriction available for non- commercial, educational use.

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Big Data: Seizing Opportunities, Preserving Values

Posted in Data Curation, Open Data, and Research Data Management, Emerging Technologies on May 6th, 2014

The Executive Office of the President has released Big Data: Seizing Opportunities, Preserving Values.

Here's an excerpt:

On January 17, in a speech at the Justice Department about reforming the United States' signals intelligence practices, President Obama tasked his Counselor John Podesta with leading a comprehensive review of the impact big data technologies are having, and will have, on a range of economic, social, and government activities. Podesta was joined in this effort by Secretary of Commerce Penny Pritzker, Secretary of Energy Ernest Moniz, the President's Science Advisor John Holdren, the President's Economic Advisor Jeffrey Zients, and other senior government officials. The President's Council of Advisors for Science & Technology conducted a parallel report to take measure of the underlying technologies. Their findings underpin many of the technological assertions in this report.

This review was conceived as fundamentally a scoping exercise. Over 90 days, the review group engaged with academic experts, industry representatives, privacy advocates, civil rights groups, law enforcement agents, and other government agencies. The White House Office of Science and Technology Policy jointly organized three university conferences, at the Massachusetts Institute of Technology, New York University, and the University of California, Berkeley. The White House Office of Science & Technology Policy also issued a "Request for Information" seeking public comment on issues of big data and privacy and received more than 70 responses. In addition, the WhiteHouse.gov platform was used to conduct an unscientific survey of public attitudes about different uses of big data and various big data technologies. A list of the working group's activities can be found in the Appendix.

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What Drives Academic Data Sharing?

Posted in Data Curation, Open Data, and Research Data Management on May 2nd, 2014

RatSWD has released What Drives Academic Data Sharing?.

Here's an excerpt:

Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration.

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"Data Publication Consensus and Controversies"

Posted in Data Curation, Open Data, and Research Data Management, Publishing, Scholarly Journals on April 30th, 2014

F1000Research has released an eprint of "Data Publication Consensus and Controversies."

Here's an excerpt:

As data publication venues proliferate, significant debate continues over formats, processes, and terminology. Here, we present an overview of data publication initiatives underway and the current conversation, highlighting points of consensus and issues still in contention.

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How to Discover Requirements for Research Data Management Services

Posted in Data Curation, Open Data, and Research Data Management on April 9th, 2014

The DCC and DataONE have released How to Discover Requirements for Research Data Management Services.

Here's an excerpt:

This guide is meant for people whose role involves developing services or tools to support research data management (RDM) and digital curation, whether in a Higher Education Institution or a project working across institutions. Your RDM development role might be embedded with the research groups concerned, or at a more centralised level, such as a library or computing service. You will need a methodical approach to plan, elicit, analyse, document and prioritise a range of users' requirements.

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