ARL/DLF E-Science Institute   Tags: e-research, research data management  

ARL/DLF E-Science Institute http://www.arl.org/rtl/eresearch/escien/escieninstitute/index.shtml

Purpose of the Institute

The purpose of the E-Science Institute is to help your library develop a sound strategic agenda to frame your support for e-research in general and e-science in particular. A strategic agenda articulates the priorities and ambitions, opportunities and partnerships, challenges and weaknesses of your e-research support program, but stops short of being a detailed strategic plan. After the Institute, your strategic agenda will enable you to develop a program or set of services to address the key areas in your agenda. Developing your strategic agenda requires that you develop a deeper understanding of your institutional environment for e-science/e-research. The activities are designed to help you examine your local e-research landscape, to consider the relationship between your institution as a whole and your library in terms of e-research, to identify key players in e-science and e-research at your institution, and to conduct interviews of some of them to better understand their roles.

Our working definitions of “e-science” and “e-research”

E-Science typically refers to a type of scientific research that uses large-scale computing infrastructure to process very large data sets. It’s considered a new scientific paradigm (see readings) enabled by the internet, advances in data mining techniques, etc. E-research refers more generally as the concept of research using digital technology (e.g., computing, networks, and digital data) in fields including science, social science, and the humanities. Despite its title, the ARL E-Science Institute is more focused on e-research as defined above, but will also explore e-science and other key concepts in modern, network and computer-enabled research.

Glossary:

·         e-Science

E-Science is computationally intensive science carried out in highly distributed network environments, such as science that uses immense data sets requiring grid computing or High Performance Computing to process. The term sometimes includes technologies that enable distributed collaboration, such as the Access Grid, and is sometimes used as an alternative term for Cyberinfrastructure (e.g. e-Science is the preferred term in the UK).  Examples of e-Science research include data mining, and statistical exploration of genome and other –omic structures.  

·         e-Research

The term e-Research here refers to the use of information technology to support existing and new forms of scholarly research in all academic disciplines, including the humanities and social sciences. E-research encompasses computational and e-science, cyberinfrastructure and data curation. E-Research projects often make use of grid computing or other advanced technologies, and are usually data intensive, but the concept also includes research performed digitally at any scale. E-research is useful here as a way to bridge the concept of e-science to other fields such as social science and the humanities. Just as e-science applies large-scale computing to processing vast amounts of scientific research data, e-research could include studies of large linguistic corpuses in the humanities, or integrated social policy analyses in the social sciences.

1. Foreword and Intro (Jim Gray on e-Science, A Transformed Scientific Method) from The Fourth Paradigm: Data-Intensive Scientific Discovery, Tony Hey et al. Microsoft Research, 2009   http://research.microsoft.com/en-us/collaboration/fourthparadigm/4th_paradigm_book_jim_gray_transcript.pdf  

2. e-Science and the Life Cycle of Research, Charles Humphrey.   June, 2008.  http://datalib.library.ualberta.ca/~humphrey/lifecycle-science060308.doc

3. Science Magazine, AAAS. Special Online Collection: Dealing with Data. February 11, 2011. http://www.sciencemag.org/site/special/data/  (free registration available)

Required

We have two required readings: 

• Association of Research Libraries, "Chapter 4: Applying the Scenarios to Create Strategy" in The ARL 2030 Scenarios: A User’s Guide for Research Libraries, (2010) pp. 41-46.

This chapter discusses developing a strategic agenda to drive organizational change, providing a useful definition and some guidance. NOTE:  We are not recommending or requiring that you adopt a scenarios approach to developing your strategic agenda.  We are simply pointing you to this for the discussion of what an agenda can do.  The scenarios method is a perfectly acceptable tool for planning, but in this module we have provided you with other tools to help you develop your strategic agenda. 

• H. Frank Cervone, “Strategic analysis for digital library development,” OCLC Systems & Services: International digital library perspectives
Vol. 25 No. 1, (2009), pp. 16-19.  doi: 10.1108/10650750910931887 (access requires subscription)

Cervone discusses the application of SWOT analyses in strategic planning.

Recommended

If you have time during this module, you may wish to read some of the following, which include surveys of existing e-research services as well as case studies in which authors explore the development of these services at their own institutions. 

• Jacob Carlson, Michael Fosmire, C.C. Miller, Megan Sapp Nelson, "Determining Data Information Literacy Needs: A Study of Students and Research Faculty", portal: Libraries and the Academy, Vol 11, No 2, April 2011, pp. 629-657 (access requires subscription) 

This 2011 article by Carlson, Fosmire, Miller and Sapp-Nelson in portal: Libraries and the Academy describes how "researchers increasingly need to integrate the disposition, management, and curation of their data into their current workflows. However, it is not yet clear to what extent faculty and students are sufficiently prepared to take on these responsibilities. This paper articulates the need for a data information literacy program (DIL) to prepare students to engage in such an e-research environment. Assessments of faculty interviews and student performance in a geoinformatics course provide complementary sources of information, which are then filtered through the perspective of ACRL's information literacy competency standards to produce a draft set of outcomes for a data information literacy program."

• Cornell University Library (CUL) Data Working Group (DaWG), "Digital Research Data Curation:   Overview of Issues, Current Activities, and  Opportunities for the Cornell University Library, (May 2008).  

This 2008 report contains five recommendations from the Data Working Group detailing how the Cornell University Library could engage in data curation.  Included within these recommendations is a set of services that could be provided to researchers and local infrastructure and policies needed to sustain these services.

• Melissa H. Cragin, Carole L. Palmer, Jacob R. Carlson, and Michael Witt, Data Sharing, Small Science, and Institutional Repositories (post-print) 

This 2010 article in Philosophical Transactions of the Royal Society A contains results of the Data Curation Profiles research project done by UIUC and Purdue on how faculty view and practice data sharing.

• Patricia Hswe, Michael Giarlo, Michael Furlough, Mairéad Martin. "Responding to the Call to Curate:  Digital Curation in Practice at Penn State University Libraries", The International Journal of Digital Curation, Vol 6, No 2 (2011).

This 2011 article by Hswe, Furlough and Giarlo in the The International Journal of Digital Curation presents how Pennsylvania State University Libraries established a Content Stewardship program for the university, describing the planning and staffing needed for its implementation.  They specifically address the challenges of starting and sustaining a stewardship services program.  

• Mark P. Newton, C. C. Miller, and Marianne Stowell Bracke, "Librarian Roles in Institutional Repository Data Set Collecting: Outcomes of a Research Library Task Force" Collection Management, Vol 36, No 1, (2011).  (access requires subscription) 

This 2011 article by Newton, Miller and Bracke in Collection Management describes the Purdue Libraries task force charged with building faculty-produced collections for a data repository prototype.  This project developed an inventory and characterized the resources and skills required of the libraries and its data-collecting librarians. The roles and activities of librarians identified during the project were explored.

• Catherine Soehner, Catherine Steeves, and Jennifer Ward, E-Science and Data Support Services:  A Study of ARL Member Institutions, (2010). 

This 2010 ARL report by Soehner, Steeves & Ward reviews the different approaches libraries are taking toward e-Science and data support services.  Six institutional cases studies are also provided.

• Tyler O. Walters, "Data Curation Program Development in U.S. Universities:  The Georgia Institute of Technology Example", International Journal of Digital Curation, Vol 4, No 3 (2011).

This 2011 article by Walters in The International Journal of Digital Curation presents GT’s data curation program development. The main characteristic is a program devoid of top-level mandates and incentives, but rich with independent, “bottom-up” action. The paper addresses program antecedents and context, inter-institutional partnerships that advance the library’s curation program, library organizational developments, partnerships with campus research communities, and a proposed model for curation program development.

• Brian Westra, "Data Services for the Sciences: A Needs Assessment", Ariadne, No 64 (July 2010).  

This 2010 article by Westra in Ariadne describes scientific research data management as "a fluid and evolving endeavour, reflective of the high rate of change in the information technology landscape, increasing levels of multi-disciplinary research, complex data structures and linkages, advances in data visualisation and analysis, and new tools capable of generating or capturing massive amounts of data.  These factors create a complex and challenging environment for managing data, and one in which libraries can have a significant positive role supporting e-science. A needs assessment can help to characterise scientists’ research methods and data management practices, highlighting gaps and barriers, and thereby improve the odds for libraries to plan appropriately and effectively implement services in the local setting."