The Library of Congress and its digital preservation partners from the federal, library, creative, publishing, technology, and copyright communities are working to develop a national strategy to collect, archive, and preserve digital content.

With sophisticated data mining, campaign research pushes the bounds of privacy.

Background The widespread reluctance to share published research data is often hypothesized to be due to the authors' fear that reanalysis may expose errors in their work or may produce conclusions that contradict their own. However, these hypotheses have not previously been studied systematically.   Methods and Findings We related the reluctance to share research data for reanalysis to 1148 statistically significant results reported in 49 papers published in two major psychology journals. We found the reluctance to share data to be associated with weaker evidence (against the null hypothesis of no effect) and a higher prevalence of apparent errors in the reporting of statistical results. The unwillingness to share data was particularly clear when reporting errors had a bearing on statistical significance.   Conclusions Our findings on the basis of psychological papers suggest that statistical results are particularly hard to verify when reanalysis is more likely to lead to contrasting conclusions. This highlights the importance of establishing mandatory data archiving policies.

Research Data MANTRA (or Management Training) is a labour of love. It has been an integral part of the University of Edinburgh's Research Data Management (RDM) programme since 2012. The staff at EDINA and Data Library at the University of Edinburgh has been curating this resource, based on internal and external feedback and has just published its fourth release since 2011. MANTRA is an open, web-based training course intended for self-paced learning by PhD students and early career researchers or others who manage digital data as part of a research project. It informs about good practice in research data management with real life stories. MANTRA’s approach is to be fun, relevant, useful, interactive and timely (FRUIT!). Librarians’ training needs are catered for through a companion resource, the DIY RDM Training Kit for Librarians.

The University of Illinois at Urbana-Champaign (UIUC) Library conducted an analysis of 1,260 Data Management Plans (DMPs) submitted in National Science Foundation (NSF) proposals from July 2011 through November 2013. Each DMP was assigned controlled vocabulary and keyword terms which summarized the proposed data management mechanisms for storing and sharing data. A database composed of the proposal’s title, PI (Principal Investigator), PI’s department and college, NSF grant number, funded status, assigned DMP vocabulary, and keyword terms was constructed. As of May 2014, a total of 298 of these UIUC proposals had been funded by the NSF. Our analysis of this sample revealed no significant statistical differences in proposed data storage and reuse venues between funded and unfunded proposals. However, there was a statistically higher frequency of use of the campus institutional repository and disciplinary repositories in proposals submitted after October 2012.

Secret passwords sent in e-mails, lack of encryption, the list goes on.

A Hack Education Project

Yesterday Uber again caught the eye of the public when surge pricing went into effect in Sydney, Australia, during a hostage situation with people trying to leave the area. Keeping in mind that the…

Google is not among the companies directly backing Microsoft in its fight over foreign data privacy.

Competition and disorganization within their disciplines prevent many researchers from making their data publicly available, which is stunting scientific progress.

Museums are mining detailed information from visitors, raising questions about the use of Big Data in the arts.

The Quarterly Journal of Political Science has a policy of in-house review of all statistical code. This is expensive and hard to do. Is it worth it? According to Nick Eubank, an unambiguous yes: Of the 24 empirical papers subject to in-house replication … Continue reading →

As more and more details from the Sony Pictures hack seep out into the internet, it's been easy—and to be honest a little fun—to take the voyeur's view. Dumb corporate powerpoints! Passwords in a folder literally called Password! Paul Blart 2 anything! But then you actually look at the full scope of what's out there.

The management of research data is now a major challenge for research organisations. Vast quantities of born-digital data are being produced in a wide variety of forms at a rapid rate in universities. This paper analyses the contribution of academic libraries to research data management (RDM) in the wider institutional context. In particular it: examines the roles and relationships involved in RDM, identifies the main components of an RDM programme, evaluates the major drivers for RDM activities, and analyses the key factors influencing the shape of RDM developments. The study is written from the perspective of library professionals, analysing data from 26 semi-structured interviews of library staff from different UK institutions. This is an early qualitative contribution to the topic complementing existing quantitative and case study approaches. Results show that although libraries are playing a significant role in RDM, there is uncertainty and variation in the relationship with other stakeholders such as IT services and research support offices. Current emphases in RDM programmes are on developments of policies and guidelines, with some early work on technology infrastructures and support services. Drivers for developments include storage, security, quality, compliance, preservation, and sharing with libraries associated most closely with the last three. The paper also highlights a ‘jurisdictional’ driver in which libraries are claiming a role in this space. A wide range of factors, including governance, resourcing and skills, are identified as influencing ongoing developments. From the analysis, a model is constructed designed to capture the main aspects of an institutional RDM programme. This model helps to clarify the different issues involved in RDM, identifying layers of activity, multiple stakeholders and drivers, and a large number of factors influencing the implementation of any initiative. Institutions may usefully benchmark their activities against the data and model in order to inform ongoing RDM activity.