The application of archival concepts to a data-intensive environment: working with scientists to understand data management and preservation needs

The collection, organization, and long-term preservation of resources are the raison d’être of archives and archivists. The archival community, however, has largely neglected science data, assuming they were outside the bounds of their professional concerns. Scientists, on the other hand, increasingly recognize that they lack the skills and expertise needed to meet the demands being placed on them with regard to data curation and are seeking the help of “data archivists” and “data curators.” This represents a significant opportunity for archivists and archival scholars but one that can only be realized if they better understand the scientific context.

[1]  Tony Hey,et al.  The Fourth Paradigm: Data-Intensive Scientific Discovery , 2009 .

[2]  Matthew S. Mayernik,et al.  Moving Archival Practices Upstream: An Exploration of the Life Cycle of Ecological Sensing Data in Collaborative Field Research , 2008, Int. J. Digit. Curation.

[3]  J. Berson.  Memory Practices in the Sciences (review) , 2009 .

[4]  Kalpana Shankar.  Order from chaos: The poetics and pragmatics of scientific recordkeeping , 2007 .

[5]  Barbara P. Buttenfield,et al.  Digital Libraries and Collaborative Knowledge Construction , 2003 .

[6]  Anthony J. G. Hey,et al.  The Fourth Paradigm: Data-Intensive Scientific Discovery [Point of View] , 2011 .

[7]  Jeremy P. Birnholtz,et al.  Data at work: supporting sharing in science and engineering , 2003, GROUP.

[8]  Mark Pendergast,et al.  Proceedings of the 2003 International ACM SIGGROUP Conference on Supporting Group Work, GROUP 2003, Sanibel Island, Florida, USA, November 9-12, 2003 , 2003, GROUP.

[9]  Peter Botticelli.  Records Appraisal in Network Organizations , 2000 .

[10]  W. King.  The Project on the History of Recent Physics in the United States , 2010 .

[11]  A. Curry,et al.  Rescue of old data offers lesson for particle physicists. , 2011, Science.

[12]  R. Yin,et al.  Case Study Research: Design and Methods (4th ed. , 2009 .

[13]  Ann Zimmerman,et al.  New Knowledge from Old Data , 2008 .

[14]  Jean Hartley,et al.  Case study research , 2004 .

[15]  Larry Hackman,et al.  The documentation strategy process: a model and a case study , 2010 .

[16]  R. Yin.  Case Study Research: Design and Methods , 1984 .

[17]  Peter L. Pulsifer,et al.  Today's Data are Part of Tomorrow's Research: Archival Issues in the Sciences , 2007 .

[18]  Nancy A. Vanhouse,et al.  Digital Library Use: Social Practice in Design and Evaluation , 2003 .

[19]  Helen Willa Samuels,et al.  Appraising the Records of Modern Science and Technology: A Guide , 1985 .

[20]  J. Warnow-Blewett,et al.  AIP study of multi-institutional collaborations: Phase 1, high-energy physics , 1992 .

[21]  G. King,et al.  Ensuring the Data-Rich Future of the Social Sciences , 2011, Science.

[22]  Michael Witt,et al.  Data sharing, small science and institutional repositories , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[23]  Wendy W. Chapman,et al.  Public sharing of research datasets: A pilot study of associations , 2010, J. Informetrics.

[24]  Francine Berman,et al.  Got data?: a guide to data preservation in the information age , 2008, CACM.

[25]  Clark A. Elliott.  Experimental Data as a Source for the History of Science , 2010 .

[26]  Noel Enyedy,et al.  Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries , 2007, International Journal on Digital Libraries.

[27]  Sarita Albagli,et al.  Memory Practices in the Sciences , 2008 .