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With what types of data will I be working?

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  • Determine what kind(s) of data you will be managing
  • Review case studies to find examples of data management in specific disciplinary domains



  • Purdue University Libraries, and University of Illinois Urbana-Champaign Graduate School of Information and Library Science. “Data Curation Profiles Toolkit.” Accessed September 24, 2013.

Data Curation Profiles are designed to capture requirements for specific data generated by researchers as articulated by the researchers themselves. They are also intended to enable librarians and others to make informed decisions in working with data of this form, from this research area or sub-discipline. Data Curation Profiles employ a standardized set of fields to enable comparison. They are also designed to be flexible enough for use in any domain or discipline.



Gives a briefing on how the project is acquiring and preserving at-risk digital opinion polls, voting records, large-scale surveys and other social science research data.

Geospatial data is information such as maps, imagery and data sets that help us better understand, manage and monitor change in the present while providing insight into the past.  From the first colonial maps to the time-sequenced satellite imagery of the 21st century, cartographic information has helped define our view of the country and the world.  Today, cartographic materials in digitized form are being collected across a broad spectrum of types.  Preserving these digitized images in distributed Internet accessible archives will ensure perpetual access to data vital for disaster relief, resource management, management of environmental policy, analysis of population demographics, education and teaching, plus countless other areas of public interest. To preserve this enormous amount of digitized data, the Library of Congress, the University of California, Santa Barbara and Stanford University have partnered to form the National Geospatial Digital Archive.



  • Cragin, Melissa H., Carole L. Palmer, and Tiffany C. Chao. “Relating Data Practices, Types, and Curation Functions: An Empirically Derived Framework.” Proceedings of the American Society for Information Science and Technology 47, no. 1 (November 2010): 1–2. doi:10.1002/meet.14504701426.

We present a general conceptual framework that maps relationships and dependencies among scientific data practices, types of data produced and used, and associated curation activities. As part of the Data Conservancy initiative, the framework is being elaborated through empirical studies of data practices in the earth sciences and life science and validated against use cases as curatorial services are developed around data being prepared for ingest into the repository. The framework can be applied more broadly for identifying and representing curation requirements and to support description and assessment of existing or planned curation infrastructure and services. It will support full accounts of the data products and workflows required to maintain the coherence and context of complex data collections.

The DCC SCARP project investigated attitudes and approaches to data deposit, sharing and reuse, curation and preservation over a range of research fields in differing disciplines. The aim was to investigate research practitioners’ perspectives and practices in caring for their research data, and the methods and tools they use to that end. Objectives included identification and promotion of ‘good practice’ in the selected research domains, as expressed in DCC tools and resources such as the Curation Lifecycle Model. The approach combined case study methods with a survey of the literature relevant to digital curation in the selected fields. A range of methods was applied to fit the differing research contexts.

  • Witt, Michael, Jacob Carlson, D. Scott Brandt, and Melissa H. Cragin. “Constructing Data Curation Profiles.” International Journal of Digital Curation 4, no. 3 (December 7, 2009). doi:10.2218/ijdc.v4i3.117.

This paper presents a brief literature review and then introduces the methods, design, and construction of the Data Curation Profile, an instrument that can be used to provide detailed information on particular data forms that might be curated by an academic library. These data forms are presented in the context of the related sub-disciplinary research area, and they provide the flow of the research process from which these data are generated.

Social science data

Data-PASS is a voluntary partnership of organizations created to archive, catalog and preserve data used for social science research.  Examples of social science data include: opinion polls; voting records; surveys on family growth and income; social network data; government statistics and indices; and GIS data measuring human activity.

Physical science data

All the latest launches in the world of electronic lab notebooks (ELNs).

The IUPAC International Chemical Identifier is a non-proprietary identifier for chemical substances that can be used in printed and electronic data sources thus enabling easier linking of diverse data compilations.

A broader system of exchange protocols based on data dictionaries and relational rules expressible in different machine-readable manifestations, including, but not restricted to, Crystallographic Information File and XML.

  • Thermodynamics Research Center.  An XML-Based IUPAC Standard for Storage and Exchange of Experimental Thermophysical and Thermochemical Property Data

This page contains links to ThermoML files, which represent experimental thermophysical and thermochemical property data reported in the corresponding articles published by major journals in the field.

Humanities data

Provides information about the Stanford work in the categories of Topology, Topics, Documents, Data, and Further Research.

Interdisciplinary data

  • Parsons, Mark A., Oystein Godoy, Ellsworth LeDrew, Taco de Bruin, Bruno Danis, Scott Tomlinson, and David Carlson. “A Conceptual Framework for Managing Very Diverse Data for Complex, Interdisciplinary Science.” Journal of Information Science 37, no. 6 (December 2011): 555–569. doi:10.1177/0165551511412705.

Much attention has been given to the challenges of handling massive data volumes in modern data-intensive science. This paper examines an equally daunting challenge - the diversity of interdisciplinary data, notably research data, and the need to interrelate these data to understand complex systemic problems such as environmental change and its impact. We use the experience of the International Polar Year 2007-8 (IPY) as a case study to examine data management approaches seeking to address issues around complex interdisciplinary science.


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