So how long do researchers have to keep records of experiments? We had that question while reading the retraction notice for a 2007 paper in the Journal of Clinical Investigation:

Today we’re releasing a new open source project, which will enable any organization with a DocumentCloud account to do crowdsourcing using documents.

D-Lib Magazine

We’re surrounded by data—more and more of it is created every day about every person and every topic. Accompanying this cascade is an open data...

Author(s): Hampton, Stephanie; Strasser, Carly; Tewksbury, Joshua; Gram, Wendy; Budden, Amber; Batcheller, Archer; Duke, Clifford; Porter, John | Abstract: The need for sound ecological science has escalated alongside the rise of the information age and “big data” across all sectors of society. Big data generally refer to massive volumes of data not readily handled by the usual data tools and practices and present unprecedented opportunities for advancing science and inform- ing resource management through data-intensive approaches. The era of big data need not be propelled only by “big science” – the term used to describe large-scale efforts that have had mixed success in the individual-driven culture of ecology. Collectively, ecologists already have big data to bolster the scientific effort – a large volume of distributed, high-value information – but many simply fail to contribute. We encourage ecologists to join the larger scientific community in global initiatives to address major scientific and societal problems by bringing their distributed data to the table and harnessing its collective power. The scientists who contribute such information will be at the forefront of socially relevant science – but will they be ecologists?

Marketers must stop abusing the term "Big Data" in order to harness it. Here's how.

Last year, we reported on two retractions and two corrections by a team at the University of Utah. The retractions were because “some of the original data were inappropriately removed from th…

Webex by Cisco is the leading enterprise solution for video conferencing, online meetings, screen share, and webinars. Web conferencing, cloud calling, and equipment.

The Data Intelligence 4 Librarians course was developed by 3TU.Datacentrum at the end of 2011 to provide online resources and training for digital preservation practitioners, specifically for library staff. The course objectives are to transfer and exchange knowledge about data management, and to provide participants with the skills required to advise researchers or research groups on efficient and effective ways of adding value to their data. The paper describes the process of creating the course, the methodology and the results of the first pilot, which took place from February to June 2012. It also demonstrates the choices made during the design process and discusses the implications of the evaluation of the pilot course for further development: in particular, how the course might be expanded to more disciplines and other data repositories.

After a spectacular case of data fraud in the field of social psychology surfaced in The Netherlands in September 2011, the Dutch research community was confronted with a number of questions. Is this an isolated case or is scientific fraud with data more common? Is the scientific method robust enough to uncover the results of misconduct and to withstand the breach of trust that fraud causes? How responsible and reliable are researchers when they collect, process, analyse and report on data? How can we prevent data fraud? Do we need to adapt the codes of conduct for researchers or do we need stricter rules for data management and data sharing?This paper discusses the conclusions and recommendations of two reports that were published recently in consequence of this data fraud. The reports are relevant for scientific integrity and trustworthy treatment of research data. Next, this paper reports on the outcomes of enquiries in data cultures in a number of scientific disciplines. The concluding section of this paper contains a number of examples that show that the approach towards data sharing is improving gradually. The data fraud case can be regarded as a wake-up call.

Software metadata.

I have developed 15 principles for my daily work as a data scientist. These are the principles  that I personally follow : 1- Do not lie with data and do not bullshit: Be honest and frank about emp…