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Conference Presentations 2016

  • IASSIST 2016-Embracing the 'Data Revolution': Opportunities and challenges for research, Bergen
    Host Institution: NSD - Norwegian Centre for Research Data

1I: Teaching data (Wed, 2016-06-01)
Chair:Laine Ruus

  • Data Management... in Writing Studies? A Case Study of Collaboration and Outreach
    Alicia Hofelich Mohr (University of Minnesota)
    Alice Motes (University of Minnesota)


    Graduate students, especially those who are beginning to learn the methods of their fields, are ideal targets for data management education, as they can integrate best practices into their developing research workflows. However, with the diversity of methods and research data being managed, it can be challenging to effectively reach students with a single workshop or series. At the University of Minnesota, we tried to customize our outreach to graduate students by targeting instructors of graduate methods courses. This was a collaborative effort between the Libraries and College of Liberal Arts (CLA), and we approached this outreach with a diverse team of support staff: a data curator with qualitative expertise, a data manager with quantitative expertise, and library liaisons from different areas. We successfully reached five courses within CLA, in the departments of statistics, journalism, communication, and writing studies. This presentation will discuss this effort, along with the surprisingly in-depth collaboration developed with a technical communication course in writing studies. It will also cover takeaways from this experience, such as the benefits of having both qualitative and quantitative viewpoints on a data management task, and how this experience will shape our approaches for providing future data management services to the humanities.

  • What Is Your 'Unit of Analysis' And, More Importantly, Why? New Tools And Methods for Teaching Undergraduate Social Science Students to Think about Data.
    Parvaneh Abbaspour (Lewis & Clark College)
    E. J. Carter Lewis (Lewis & Clark College)


    The proliferation of online datasets has created myriad opportunities for undergraduate social science students to delve into complex, quantitative analysis. While students drawn to these courses are often math and statistics savvy and relatively adept at working with statistical programs, many still lack an understanding of data creation processes such as why the data were collected, how the populations delimited and sampled, and precisely how variables are defined and measured such that they might stand-in for phenomena. Moreover, the ease of acquiring these datasets can contribute to the abstraction, and more crucially the assumptions, inherent in translating the complexity of human experience into numerical values.
    Common approaches to teaching undergraduate social science students to find data include referring them to the secondary literature, pointing them to data repositories, and walking them through a 'unit of analysis' worksheet. We argue that while such worksheets may help a student define the parameters of the data they are after, they reinforce the same abstraction inherent in the data dilemma to begin with. We present a range of tools developed this year to support data discovery with the goal of reinforcing data literacy for undergraduate social science students while helping them find the resources they need. These tools include the data review and the determinant inventory. We describe how we adapted and integrated these tools into a revised data discovery worksheet emphasizing a more holistic conception of how data models real world phenomena.


2B: Partners in research data management (Wed, 2016-06-01)
Chair:Terrence Bennett

  • The Erasmus Centre for Strategic Competitiveness Research (ECSCR) Data Centre
    Paul Plaatsman (Erasmus Data Service Centre)


    Recently the Rotterdam School of Management, the Erasmus Data Service Centre and the Research Support Office joined forces to establish the Erasmus Centre for Strategic Competitiveness Research(ECSCR). The goal is to develop a data centre with mixed data about regional competition; data from commercial vendors like Bureau van Dijk will be merged with survey data from their own generated questionnaires.
    The data were first identified through Data Management Plans (DMP's): individual, subgroup and for the whole group. Next the data have been properly described with meta data, persistent identifiers, versioning and of course securely stored. The data should still be available for analysis with e.g. Stata during the research data life cycle. Different users will have different access rights. The legal aspects about data ownership and privacy issues need to be addressed. From IT perspective we need to investigate business-, functional- and technical requirements. Obviously we need several workflows for all the above mentioned issues.
    The project wants to make Erasmus University Rotterdam's Research Data Management (RDM) policy more tangible. An official policy has been developed by a taskforce scientific integrity but implementation still needs to be done department by department. The deliverables of this project should become available for other departments as well, so generic solution are our aim. During the presentation I will inform fellow IASSIST members about the present stage of this project.

  • Embedding Metadata in the Research Process - Archives as Partners in Data Production
    Steven McEachern (Australian Data Archive)
    Janet McDougall (Australian Data Archive)
    Heather Leasor (Australian Data Archive)


    In many data archives supporting academic research data archiving, the process of collecting metadata has traditionally been the responsibility of the data archive rather than the data producer. While data producers and researchers may produce automatically generated metadata as a by-product of their work - such as in statistical data files, questionnaires or project reports - any expectations of the manual creation of metadata (such as study metadata in DDI terms) have traditionally been relatively low. However, with the growth in the volume of both research data and content from other sources, the workload demands on archives can only be expected to grow. As such, there is a significant need to reduce the processing and metadata production workload within archives. One means for achieving this is to improve the quality of the automatically generated metadata that is created by producers.

    This paper reports on two recent projects occurring at the Australian Data Archive that aim to enable this improved production - by supporting minor developments in metadata production to earlier in the data lifecycle. ADA staff have been working with two data producers involved in significant national survey projects to implement minor changes to standards and practices within their data production process. The paper will explore the changes in practices that have been proposed to the data producers, changes in work practices within the producers, and the resulting impacts on metadata quality of new content provided to the Archive.

  • Community Data Repositories Working with Libraries: Harvard Dataverse Use Case
    Eleni Castro (Harvard University)


    Since 2012 the Harvard Dataverse (, powered by the Dataverse Project open source software and developed at Harvard's Institute for Quantitative Social Science (IQSS), has been collaborating with Harvard Library to provide a solution for sharing, publishing and archiving research data for faculty and affiliated researchers. This collaboration has helped to expand the scope of the Dataverse application to better support research data beyond just the social sciences, initially with adding metadata fields to help describe datasets from the biomedical (ISA-Tab) and astronomy (Virtual Observatory) communities, and with the aim of eventually supporting more research communities such as the humanities. The Harvard Dataverse team has also extended its services to provide user support, training, and some data curation services to the Harvard community. This presentation will also highlight some current and upcoming collaborative projects which include: connecting faculty publications with their underlying research data by integrating Dataverse with Harvard's institutional repository Digital Access to Scholarship at Harvard (DASH), providing university-wide open data awareness and support via the Harvard Open Data Assistance Program (ODAP), helping researchers meet the requirements of funder mandated data management plans through customized DMPTool services, and making faculty datasets more widely discoverable by exporting metadata (MARC) into the Harvard Library Catalog, HOLLIS.

3H: DDI applications for data access (Wed, 2016-06-01)
Chair:Oliver Watteler

  • Experimenting with DDI-L at the French Center of Socio-Political Data (CDSP)
    Alexandre Mairot (CDSP - SciencePo)
    Alina Danciu (CDSP - SciencePo)


    The French Center of Socio-Political Data has presented its reflection on the process of shifting from DDI-C to DDI-L at EDDI14. This year, we will discuss the creation and storage of a DDI-L compliant XML record by capturing metadata of a nine-wave political study of the ELIPSS panel. Determining how best to recognise continuities between metadata collections within the same study, including question continuity and methodological continuities has been a primary challenge. To answer it, the starting point was the creation of a questions database. As seen at the 2014 DDI workshop in Dagstuhl, the minimum requirements that a metadata system should meet before being able to import/export DDI-L are uniqueness of items, versioning and granularity. To conceive such a database, we had to start by using simple tools. We first identified metadata in CSV files that include variable-level information. We then performed a semi-manual import from these files to the database using importing scripts. Once we removed automatically the redundancy, with a further stage of human control, we generated the structure of the DDI-L compliant xml file. Our paper will present this process and discuss its replication to other DDI-C documented studies. Author(s):

  • The UK Data Service Variable and Question Bank: Use Cases and Future Enhancements
    Hersh Mann (UK Data Archive)


    The Variable and Question Bank (VQB) is a search and browse interface that enables researchers to locate and retrieve information about variables and questions from a range of survey data collections held by the UK Data Service. Over a million variables are currently searchable from the most widely used surveys we hold. This tool also allows researchers to directly compare variables, to identify the same variable or question used across several surveys, and to detect questions that belong to a larger defined set. The VQB also enables researchers to easily view associated descriptive data and to see the variable in the wider context of the survey from which it is drawn. We have examined user experience of the tool to inform new enhancements and are working with external partners in the UK Office for National Statistics (ONS) to promote use of the VQB, to improve the visibility of particular surveys, and to support efforts in harmonisation. A metadata enrichment project utilising the power of DDI3.2 will augment the capabilities of the VQB to attribute provenance, identify variations between different versions of data collections, track changes over time, clearly map between questions and harmonised items, and create persistent identifiers.

  • The DASISH Questionnaire Design Documentation Tool: a tool for documenting questionnaire design under development
    Hilde Orten (Norwegian Social Science Data Service (NSD))
    Stig Norland (Norwegian Social Science Data Service (NSD))
    Dag Ostgulen Heradstveit (Norwegian Social Science Data Service (NSD))
    Knut Kagraff Skjak (Norwegian Social Science Data Service (NSD))


    The DASISH Questionnaire Design Documentation Tool (QDDT) is a tool under development which aim is to assist large-scale survey projects in relation to their questionnaire design and development processes. Second, researchers and students can use the tool to explore metadata from existing projects, or to design new research. Interoperability with other systems and tools, most importantly the DASISH Question Variable Database and the Translation Management Tool, both currently under development, is another key aim. The work on the QDDT started while the Data Service Infrastructure for the Social Sciences and Humanities (DASISH) project and now continues under the Synergies for European's Research Infrastructure in the Social Sciences (SERISS. The conceptual model for the tool is based on a sub-set of the DDI 3.2 specification. The tool is designed to integrate and communicate with other tools using an API. It is designed to be compatible with DDI and DDI import and export will be implemented as add-ons to the QDDT. A set of modern technologies is used in the development of the tool. This presentation of the QDDT focuses on its conceptual model, system architecture and technologies, functionalities available in the prototype of the tool, as well as plans for the further developments.

1C: Data appraisal/selection (Thu, 2016-06-02)
Chair:Bobray Bordelon

  • Data-Seeking Behavior of Economics Graduate Students: If you buy it, will they come?
    Eimmy Solis (New York University)


    Current data needs in the field of Economics are largely met through readily available open sources from government agencies, international organizations and non-profit organizations like the National Bureau of Economic Research that freely provide full-text of working papers and data. Proprietary data is also important for research in this area, but is often only available through library-licensed databases. When novice economics graduate students independently seek data, where do they look and why? Are library-licensed data sources being used in addition to widely known free web resources?

    Through a series of focus group interviews, I am investigating the strategies used to find information online by graduate and PhD students in Economics degree programs at New York University. The study hopes to understand what type of information and resources students use to conduct their research to evaluate current library and publicly available resources related to Economics. The study will assess the quality of data found, identify data trends and innovative search strategies by students. The results will enhance the library's outreach and teaching strategies to improve students' research skills in finding reliable data and lead to data-driven collection development that is more closely tied with the information seeking behavior of economics students.

  • You are the potter, data sets are the clay: Shaping a collection of small data sets
    Karen Hogenboom (University of Illinois at Urbana-Champaign)
    Michele Matz Hayslett (Universityof North Carolina, Chapel Hill)


    Library vendors are starting to compile data into searchable databases, but these will never be complete and many librarians are purchasing individual datasets. Some of the first issues that librarians need to address when starting a collection of individual datasets are the scope of their collection, and whether or not a formal collection development statement is necessary to guide the collection's development. There are issues in collecting data that are not present when collecting other types of library materials, so a general template for a collection development statement is not as helpful for data as it is for books or even other kinds of electronic resources.

    The presenters surveyed and interviewed North American academic data librarians about their data collection practices, and this presentation will describe what the study revealed about the benefits of writing a collection development statement, the issues that are addressed in data collection development statements, and what librarians use to guide their purchase and retention decisions if they do not have a collection development statement. Attendees at this presentation will learn about both a general shape of collection development statements for data as well as some specific possible points that can be included, in order to be able to plan their own collection development policies efficiently and thoroughly.

  • 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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