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authors were most likely to share data if they had prior experience sharing or reusing data, if their study was published in an open access journal or a journal with a relatively strong data sharing policy, or if the study was funded by a large number of NIH grants. Authors of studies on cancer and human subjects were least likely to make their datasets available.

Abstract: The deluge of scientific research data has excited the general public, as well as the scientific community, with the possibilities for better understanding of scientific problems, from climate to culture. For data to be available, researchers must be willing and able to share them. The policies of governments, funding agencies, journals, and university tenure and promotion committees also influence how, when, and whether research data are shared. Data are complex objects. Their purposes and the methods by which they are produced vary widely across scientific fields, as do the criteria for sharing them. To address these challenges, it is necessary to examine the arguments for sharing data and how those arguments match the motivations and interests of the scientific community and the public. Four arguments are examined: to make the results of publicly funded data available to the public, to enable others to ask new questions of extant data, to advance the state of science, and to reproduce research. Libraries need to consider their role in the face of each of these arguments, and what expertise and systems they require for data curation.

Proposes a new way of looking at library organization and services, with three specific areas, reduce redundant collection and storage with a "national network of "last copy" print repositories," throw efforts toward National Digital Library such as proposed by Darnton, coordinate depository and data sharing initiatives, consolidate technical services operations regionally, and establish an LIS R&D for data collection, data sharing, decision making and development of useful information applications.

"this report: 1) describes data-centric architectures; 2) gives some examples of how organisations are already sharing data and discusses this from a data-centric perspective; 3) introduces some tools and technologies that can support data-centric architectures as well as some new models of data management; 4) concludes with a look at the direction of travel. This report also provides a glossary to help clarify key terms and a 'References' section listing works cited."

Scientific Data Sharing and Archiving at UC3/CDL: the Excel Add-in Project and More John Kunze, California Digital Library/California Curation Center and Tricia Cruse, California Digital Library/California Curation Center

article by Mercè Crosas, Institute for Quantitative Social Science, Harvard University, Harvard. Demonstrates application's utility for data sharing, preservation, permanence, interoperability, ease of use and maintenance, among other considerations

In this paper we make the case for a scientific data publication model on top of linked data and introduce the notion of Research Objects as first class citizens for sharing and publishing.

from the description; "he theme of this international symposium is the promotion of greater sharing of scientific data for the benefit of research and broader development, particularly in the developing world."

"A Data Curation Profile is a resource for Library and Information Science professionals, Archivists, IT professionals, Data Managers, and others who want information about the specific data generated and used in research areas and sub-disciplines that may be published, shared, and preserved for re-use."

The key to the Alzheimer's project was an agreement as ambitious as its goal: not just to raise money, not just to do research on a vast scale, but also to share all the data, making every single finding public immediately, available to anyone with a computer anywhere in the world.

Calls for an inventory of federally-funded research datasets toward better data stewardship

From the abstract: ", we propose a social framework based on crowdsourced annotations of scholars, designed to keep up with the rapidly evolving disciplinary and interdisciplinary landscape. We describe a system called Scholarometer, which provides a service to scholars by computing citation-based impact measures. This creates an incentive for users to provide disciplinary annotations of authors, which in turn can be used to compute disciplinary metrics. We first present the system architecture and several heuristics to deal with noisy bibliographic and annotation data. We report on data sharing and interactive visualization services enabled by Scholarometer. Usage statistics, illustrating the data collected and shared through the framework, suggest that the proposed crowdsourcing approach can be successful. Secondly, we illustrate how the disciplinary bibliometric indicators elicited by Scholarometer allow us to implement for the first time a universal impact measure proposed in the literature. Our evaluation suggests that this metric provides an effective means for comparing scholarly impact across disciplinary boundaries."

IU Bloomington School of Informatics and Computing associate professor Johan Bollen and the National Information Standards Organization (NISO) will share the Mellon Foundation grant designed to build upon the Metrics from Scholarly Usage of Resources (MESUR) project that Bollen began in 2006

highlights a number of initiatives in the Universities of Edinburgh and Oxford, showing how research data repository infrastructures can be effectively realized through collaboration and sharing of expertise.

"led by the Open Knowledge Foundation, Open Institute, Fundar, Sunlight Foundation and the World Wide Web Foundation. It mission is to share principles and resources for governments and societies on how to best harness the opportunities created by opening government data."

Implications of new federal directives for data sharing, privacy, compliance, access and good practices for libraries collaborating with researchers

The 34 page report on key challenges and recommendations to NSF on "Digital Research Data Sharing and Management" was released for comment on December 14, 2011. Comments accepted through January 18, 2012.

From Elsevier, S&T Journals, Content Innovation. Offers models of dataset linking and application-based dataset linking to scientific journal articles