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The U.S. Association of Research Libraries (ARL) has produced a new report ( E-Science and Data Support Services ).

Data, data everywhere The Economist , 00130613, 2/27/2010, Vol. 394, Issue 8671 All too much: Monstrous amounts of data

The School of Information Studies (iSchool) at Syracuse University in Syracuse, N.Y., has introduced a new program (in collaboration with Cornell University Library) called " Building an eScience Librarianship Curriculum for an eResearch Future ". It is focused on creating librarians with a better understanding of eScience and the research process, as well as the new types of digital resources - in particular research data and their long term preservation and use - and how to manage them. Right now they have a call out for applications for scholarships that they have for this new program. The lack of eScience and research data savvy librarians is one of the gaps identified by the Research Data Canada and is the focus of its capacity working group.

Today in Washington. D.C, the CODATA organized Symposium on the Data Sharing Plans and on the Scientific Benefits of Data Sharing in GEOSS was held. Among other things, it looked at the draft GEOSS data sharing plan: The Plan, now endorsed by 80 government Members and 56 Participating Organizations, highlights the following GEOSS Data Sharing Principles: There will be full and open exchange of data, metadata, and products shared within GEOSS, recognizing relevant international instruments and national policies and legislation. All shared data, metadata, and products will be made available with minimum time delay and at minimum cost. All shared data, metadata, and products being free of charge or no more than cost of reproduction will be encouraged for research and education. Programme: Part One: Implementing the GEOSS Data Sharing Principles How We Got There and Where We're Going . Beth Greenaway. UK Environmental Observation Network An Overview of the Key Substantive Provisions

CISTI (Canada Institute for Scientific and Technical Information) is hosting the ICSTI (International Council for Scientific and Technical Information) conference " Managing Data for Science " here in Ottawa at the LAC (Library and Archives Canada). The conference is June 9-10, 1 1/2 days long with an excellent international single stream program structured into four sessions, " Foundations ", " Libraries ", " Data Services " and " Semantic Science ". I will be attending all sessions and will also be moderating the " Semantic Science " session.

The Interagency Working Group on Digital Data to the National Science and Technology Council (U.S.) has release its report, Harnessing the Power of Digital Data for Science and Society" . " The report lays out a strategic vision for ´a digital scientific data universe in which data creation, collection, documentation, analysis, preservation, and dissemination can be appropriately, reliably, and readily managed, thereby enhancing the return on our nation’s research and development investment by ensuring that digital data realize their full potential as catalysts for progress in our global information society .' "

The following excerpt from an ad for Scientific Data Management Specialist suggests it bodes well for the prospects of this (relatively) nascent profession: Processing, soliciting, and providing assistance with data submissions for scientific data from genome sequencing and genotyping experiments into existing databases , analysis pipelines and associated data flows. Developing and improving the infrastructure supporting these systems. Required Skills Formal Education PhD Scripting experience in perl or related language Experience with SQL Experience with LINUX/UNIX Ability to use Microsoft Excel and related applications Proven record solving related problems Desired Skills Knowledge of genetics, especially human genetics Experience with large data sets XML/XSLT and related web based tools Experience with array data, especially expression or genotyping data C/C++ Experience with grid computing (LSF,SunGrid, etc.) QA filtering of genotype data (HWE, non-Mendelian segregation)