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Conference Presentations 2013

  • IASSIST 2013-Data Innovation: Increasing Accessibility, Visibility, and Sustainability, Cologne, Germany
    Host Institution: GESIS – Leibniz Institute for the Social Sciences

B2: Research Data Management Infrastructures: Facilitating Access and Preservation (Wed, 2013-05-29)
Chair:Stuart Macdonald

  • Research Data Management using CKAN: A Datastore, Data Repository and Data Catalogue
    Joss Winn (University of Lincoln)


    This paper offers a full and critical evaluation of the open source CKAN software ( for use as a Research Data Management (RDM) tool within a university environment. It presents a case study of CKAN's implementation and use at the University of Lincoln, UK, and highlights its strengths and current weaknesses as an institutional Research Data Management tool. The author draws on his prior experience of implementing a mixed media Digital Asset Management system (DAM), Institutional Repository (IR) and institutional Web Content Management System (CMS), to offer an outline proposal for how CKAN can be used effectively for data analysis, storage and publishing in academia. This will be of interest to researchers, data librarians, and developers, who are responsible for the implementation of institutional RDM infrastructure. This paper is presented as part of the dissemination activities of the Jisc-funded Orbital project (

  • Harnessing Data Centre Expertise to Drive Forward Institutional Research Data Management: A Case Study from the University of Essex
    Thomas Ensom (UK Data Archive)


    The Research Data @Essex project, funded under the Jisc MRD Program, piloted a research data management and sharing infrastructure at the University of Essex. The project team was led by the UK Data Archive in collaboration with support services at the University. It built on the Archive's extensive experience in enabling data re-use, now being carried forward by the new UK Data Service. The project demonstrated that an exchange of knowledge between data centers and institutional data services is mutually beneficial, particularly in accelerating institutional infrastructure development. A major focus was the development of an institutional research data repository based on the EPrints software. Key among our innovations has been the expansion of the EPrints metadata profile, to allow the capture of detail necessary for describing diverse research data, while also meeting relevant standards. The metadata profile adopted is compliant with DataCite and INSPIRE schemas, and also leverages the descriptive power of the Data Documentation Initiative (2.1) schema, in a novel use of metadata developed within the social science community. Our solution offers a full-featured and easy to deploy data repository package, now being explored as replacement for the technology behind the UK Data Service's ESRC Data Store self-archiving facility.


B3: Harnessing the Power of Data: Expanding Linkages (Wed, 2013-05-29)
Chair:Stefan Kramer

  • Indicator-Based Monitoring of an Interdisciplinary Field of Science: the Example of Educational Research
    Andreas Oskar Kempf (GESIS - Leibniz Institute for the Social Sciences)
    Ute Sondergeld (German Institute for International Educational Research (DIPF))


    Monitoring of an interdisciplinary field of research is challenged on several levels: Different scholarly communication cultures come into effect, while on the other hand, knowledge stored in disciplinary databases is indexed in different ways. To create an analytical basis for such a field of science, e.g. educational research, it is necessary to clarify heterogeneous metadata by taking into account distinct semantic spheres of concepts and understanding. Scholarly databases contain a multitude of information that is relevant for monitoring. However, owing to conventional user surfaces, databases are usually not interlinked for analytic purposes. Hence, they can only inform about changes in research and publication if respective indicators are deduced, standardized and visualized. The contribution presents findings from the interdisciplinary scientometric project "Educational Research Monitoring" (MoBi). Based on multi-dimensional analyses of projects and publications, MoBi identifies adequate characteristics for describing the field of research and output, and its reception. In a second step, such aspects are translated into standard indicators for the description of science, e.g. research activity, networking and proportion of external funding. These indicators provide a conceptual basis for a web-based monitoring service. MoBi is collaboratively conducted by the Leibniz institutes GESIS, DIPF and ZPID, in co-operation with iFQ.

  • DataBridge: Building an E-science Collaboration Environment Tool for Linking Diverse Datasets into a Socio-metric Network
    Jonathan Crabtree (Odum Institute UNC Chapel Hill)


    There are currently thousands of scientists creating millions of data sets describing an increasingly diverse matrix of social and physical phenomena. This rapid increase in both amount and diversity of data implies a corresponding increase in the potential of data to empower important new collaborative research initiatives. However, the sheer volume and diversity of data presents a new set of challenges in locating all of the data relevant to a particular line of research. Taking full advantage of the unique data managed by the "long-tail of science" requires new tools specifically created to assist scientists in their search for relevant data sets. DataBridge is an e-science collaboration environment designed specifically for the exploration of a rich set of socio-metric tools and the corresponding space of relevance algorithms, and their adaptation to define semantic bridges that link large numbers of diverse datasets into a socio-metric network. Data from large NSF funded projects will be analyzed to develop relevance-based data discovery methods. This paper will discuss the design of DataBridge and the Socio-metric network analysis algorithms that will be used to explore the space of relevancy by metadata and ontology, by pattern analysis and feature extraction, and via human connections.

  • Terra Populus-Integrated Data for Population and Environmental Research
    Peter Clark (University of Minnesota)
    Alex Jokela (University of Minnesota)


    Terra Populus (TerraPop) is one of several projects funded by the National Science Foundation under the DataNet initiative. This initiative seeks to build a network of partners that will create infrastructure and tools for long-term digital data preservation, access, and re-use. The specific goal of Terra Populus is to lower barriers to conducting interdisciplinary human-environment research by making data from different domains easily interoperable. Building on the Minnesota Population Center's past experience with IPUMS and NHGIS, TerraPop will incorporate census, geospatial, land use, land cover, and climate data, along with other environmental, agricultural, and economic datasets. These data currently exist with disparate formats and structures, have generally inadequate metadata, and have incompatible geographic identifiers. This session will focus on the technology infrastructure developed for the initial beta release of the TerraPop system. We'll provide an overview of the software architecture and data models underlying the system, encompassing micro-data, area-level data, raster data, and associated metadata. We'll discuss the structure of the initial data available in the prototype, why these datasets were chosen, and how they're linked. Lastly, we'll discuss the kinds of research that we hope to support via the beta release.


B4: Qualitative and Atypical Data: Expanding and Facilitating Usage (Wed, 2013-05-29)
Chair:Oliver Watteler

  • What Do They Do With It? How People Re-Use Qualitative Data from the UK Data Service
    Libby Bishop (UK Data Archive)


    Re-use of qualitative data has grown significantly in recent years as demonstrated by numerous publications, conference sessions, and funded projects. At the UK Data Service, over 1000 qualitative data collections were delivered to users in 2009-2010. For the first time, we have undertaken a systematic analysis of how people are re-using qualitative data collections. Analysis included type of user (e.g., student, higher education staff member, government staff, etc.), discipline, whether the re-use is for education; such as post-graduate theses or teaching; or for new research, and whether the topic of the re-use is related to the original research, or for entirely different investigations. Initially, the data were collected in order to provide feedback to data creators about how their data have been repurposed. The response to these reports was very positive; data creators want to know what happens to their data. Some intend to use the report as part of their own impact reporting for research assessment. We value this information for internal purposes as well, such as better understanding usage levels of our holdings, locating innovative uses to develop into case studies, and fostering relationships between data creators and users.

  • Sharing Qualitative Data of Business and Organizational Research Problems and Solutions, Bielefeld University
    Tobias Gebel (The German Data Service Center for Business and Organizational Data (DSZ-BO))


    In German empirical organizational research, qualitative methods are used predominantly. For this specific field of research it is typical that the samples are often very small and sensitive. Also, the data often are not usable for other researchers. Consequences are the non-exhaustion of the analysis potential of interesting research data, recurring interviews and a strain on the field. That causes an ongoing decline of the willingness of respondents to participate in interviews. Data sharing can contribute to relieve overstressed research populations and to exhaust the analysis potential of available data. Nevertheless, data sharing has no tradition in qualitative organizational research. Our presentation focuses on four central requirements for data sharing: visibility of data in its existence, data documentation, data protection and data access. We will address the specific features of data documentation, as well as the data protection procedure. Additionally, we would like to discuss specifics and possible solutions for those requirements of qualitative organizational studies.

  • Use with Caution: A Multi-disciplinary Analysis of Data Use and Access Conditions
    Tiffany Chao (University of Illinois at Urbana-Champaign, Graduate School of Library and Information Science)


    The rising expectations for public sharing of research data have triggered a greater awareness for describing and documenting data for potential use by a global community. For researchers, it is an opportunity to detail special notes, limitations, and conditions to facilitate appropriate reuse. This study examines what conditions for use and access of publicly available data are made visible through metadata description and how these limitations may differ across disciplines and data types. Content for analysis consists of data records from the Earth and social sciences, which are drawn from the Global Change Master Directory (, a public metadata repository for data. The comparative investigation not only brings forth prevalent issues associated with data within a domain research area, but also what commonalities may exist across fields and the types of data produced and used. The content of these use and access descriptions also serves as a point of comparison with findings in the literature from studies on domain-specific data practices and related perceptions. By bringing together these different perspectives, a more cohesive multi-disciplinary data landscape is developed that can inform curation support structures for the stewardship of research data.


B5: Data Citation: In Principle and Practice (Wed, 2013-05-29)
Chair:Kristin Partlo

  • Data Citation in Australian Social Science Research: Results of a Pilot Study
    Steven McEachern (Australian Data Archive)


    The importance of data citation for understanding the impact of social surveys has becoming increasingly recognized as a priority concern among research infrastructure providers and funders (ANDS, 2012, NSF, 2012; Ball and Duke, 2012). For data archives, data citation provides a mechanism to understand the dissemination activities of the archive, particularly in enabling access to data for secondary use. While social science data archives have long recommended or required the use of citations as a condition of access to datasets, the compliance with this condition is minimal (Piwowar, 2011). For this reason, many data archives and repositories have implemented or are currently exploring new mechanisms for enabling data citation, such as DOIs. Such a pilot study being conducted by the Australian Data Archive (ADA). This project involves three elements: - a review of the current literature on data citation practices in Australian and international social science - a survey of current practice among users of 5 major Australian social science data sets - a pilot study of the use of DOIs with ADA datasets. The paper will present the current results of this project, recommendations for the ADA regarding data citation, and implications for data archives and repositories, more generally.

  • Making Data Citable. The Technical Architecture of the da|ra Information System
    Dimitar Dimitrov (GESIS - Leibniz Institute for the Social Sciences)
    Erdal Baran (GESIS - Leibniz Institute for the Social Sciences)
    Dennis Wegener (GESIS - Leibniz Institute for the Social Sciences)


    Today, exact citation and referencing of datasets used for research becomes more and more important due to the enormous growth of data we are experiencing. Identification mechanisms such as DOI can be used to uniquely identify datasets in a persistent way. However, users need support by information systems for attaching identifiers and discovering data. The da|ra information system is a system that allows registering research datasets, searching for registered datasets, and following links to the landing pages of the registered datasets. We present the technical architecture of the da|ra information system and the 3rd party services the system is based on. The architecture is based on SOA principles and is implemented based on the Grails framework. The functionality of the information system is exposed by the da|ra portal as well as by service interfaces, which allow users to build their own tools for registering datasets or to integrate the functionality into existing environments.

  • IASSIST Quarterly

    Publications Special issue: A pioneer data librarian
    Welcome to the special volume of the IASSIST Quarterly (IQ (37):1-4, 2013). This special issue started as exchange of ideas between Libbie Stephenson and Margaret Adams to collect


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