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Conference Presentations 2017

  • IASSIST 2017-IASSIST 2017 – Data in the Middle: The common language of research, Lawrence
    Host Institution: University of Kansas

Workshops (Tue, 2017-05-23)

  • Collecting and Storing Data from Internet Based Sources
    Peter Smyth (UK Data Service)


    Many websites allow researchers and developers to download data from them using their Application Programming Interface (API).  This data is often in formats that social scientists are unfamiliar with (e.g. JSON).  Downloaded data can be processed immediately or stored in a database for later processing in a package like R or Stata.  Data can be collected at regular intervals over a period of time, using the built-in functionality of the Windows or Linux operating systems.
    This introductory workshop is aimed at anyone interested in collecting data from the internet via APIs.

  • Curating for Reproducibility: Why and How to Review Data and Code
    Limor Peer (Yale University)
    Florio Arguillas (Cornell University)
    Thu-Mai Christian (Odum Institute, UNC Chapel Hill)
    Joshua Dull (Yale University)


    Developments in digital scholarship, advances in computational science, mandates for open data, and the reproducibility crisis require more attention to code as a research object. We consider activities that ensure that statistical and analytic claims about given data can be reproduced with that data, curating for reproducibility (CURE). This 3-hour workshop will teach participants practical strategies for curating research materials for reproducibility. The workshop will be based on the data quality review, a framework for helping ensure that research data are well documented and usable and that code executes properly and reproduces analytic results. The workshop will introduce three models for putting this framework into practice (the Institution for Social and Policy Studies (ISPS) at Yale University, the Cornell Institute for Social and Economic Research (CISER), and the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill). Participants will learn about the basic components of the CURE workflow using examples and hands-on activities. The workshop will also demonstrate a tool that structures the CURE workflow.

A2: Data Curation: Perception and Practice (Wed, 2017-05-24)
Chair:Laine Ruus

  • Data curation: Perception and practice
    Cynthia Hudson-Vitale (Washington University in St Louis)
    Lisa Johnston (University of Minnesota)
    Jacob Carlson (University of Michigan)
    Wendy Kozlowski (Cornell University)


    Understanding the importance researchers place on specific data curation treatments (such as peer-review, persistent identifiers, chain of custody, etc.) is an essential step in building institutional curatorial services that are trusted, meet expectations, and address needs. Additionally, comparing this information to the treatments curators indicate are important, as well as the actual treatments they take locally, provides key insight into perceptions and practices related to data curation. Specifically, this information can tell us what stakeholders value in terms of curation and how this aligns with the local approach for curation services. To uncover this information, the Data Curation Network (DCN) used a mixed methods approach that included faculty focus groups, library surveys, and hands-on curation treatments. This panel will provide the detailed results and a comparative analysis of (1) both the importance of specific curatorial treatments from faculty and curator perspectives, and (2) the curation activities taking place at the six participating institutions, thus comparing perceived importance of curation activities to actual practices. This session will conclude with an audience discussion of the results, limitations of the information, and general project feedback.

A3: Documentation Challenges in Complex Data Collection Efforts (Wed, 2017-05-24)
Chair:Alicia Hofelich Mohr

  • A school survey management system for educational assessments in Switzerland
    Ingo Barkow (HTW Chur)
    Catharina Wasner (HTW Chur)


    Currently two large educational assessment programs exist in Switzerland which are institutionalized by the cantons: PISA, the well-known Program for International Student Assessment (PISA), an OECD initiative that involves a large number of nations and the Swiss National Core Skills Assessment Program (in German: ÜGK – Überprüfug der Grundkompetenzen). Following completion of the PISA 2015, the core skills assessment program was initiated to focus on the assessment based on Swiss measurement instruments to get more results with national relevance.Both programs are computer based assessments since 2016 but the IT systems for both programs are not yet optimized for supporting the fieldwork in an adequate manner. Therefore a software tool will be developed to support on the one hand the administration and the field monitoring during the data collection. On the other hand the idea is to optimize the data documentation process. In this presentation we would like to show which processes should be modeled and where documentation und metadata could be generated as a byproduct without additional effort. This includes in particular also paradata which provide interesting opportunities for analysis.

  • Mobile SMS survey data management and preservation
    Inna Kouper (Indiana University)
    Charitha Madurangi (Indiana University)
    Kunalan Ratharanjan (Indiana University)
    Tom Evans (Indiana University)
    Beth Plale (Indiana University)


    The growing availability of mobile phones provides new opportunities for data collection, particularly in developing countries where it is challenging to reach respondents in rural areas. Text messaging or short message service (SMS) allows for high-frequency, automated data collection with large sample sizes at relatively low cost. However, the use of SMS technology also raises critical data quality issues, strongly suggesting the need for ongoing data management that can help evaluate robustness of responses over time, select appropriate tools for storage and data analysis and avoid dependency on specific platforms and standards.

    In this presentation we will discuss our approach to long-term management and preservation of mobile SMS survey data that are part of a larger project that examines adaptation of small-scale farmers in Africa to climate change. We will describe the development of an automated pipeline to ingest weekly data from a cloud-based platform to local data servers, while maintaining security and confidentiality. We will also demonstrate tools to enhance survey metadata and monitor and visualize data health and trends. The audience will be invited to discuss how robust data management can serve both the needs of the research team and the needs of potential data users and stakeholders.

  • Tales from the lab: A case study of metadata & data management in complex behavioral studies
    Pernu Menheer (University of Minnesota)
    Andrew Sell (University of Minnesota)


    The nature of complex human subjects research experiments makes them inherently ambiguous and dynamic for those developing and administering the experiments. There is often no blueprint on how to best develop and program these complex experimental studies; most are undertaken precisely because they have not been done before. However, innovation in experimental data collection often comes at the expense of the ability to use established methods and tools to compile metadata and paradata. Thus, the programmers who decide how to develop the code and programs used to administer the experiments also have considerable discretion in how to collect and compile the associated data and metadata. We will describe our experiences with developing complex human subjects research experiments and how adopting good practices in metadata and data management has improved the quality of our research support.


A4: Data Instruction in the Age of Data Science (Wed, 2017-05-24)
Chair:Wendy Mann

  • Cheap, fast, or good - Pick two: Data instruction in the age of data science
    Joel Herndon (Duke University)
    Justin Joque (University of Michigan)
    Angela Zoss (Duke University)


    While data-driven research has always required training and a complex understanding of methodologies and epistemologies, the environment for data services in research libraries seems to be growing increasingly turbulent. The demand for data instruction is widening to all disciplines. Even disciplines that have strong traditions in data creation and analysis are experiencing methodological crises. Tools and best practices change rapidly and make it difficult to maintain a stable curriculum for instruction. Finally, the librarians and staff who provide data services are often doing so in an environment where they have to divide their time between many different types of services and where resources for professional development are limited.

    In this panel, librarians and staff in different data services positions will present briefly on their experiences addressing the complexities of the current data instruction environment, including teaching about data management, data gathering/creation, data cleaning and analysis, and data visualization.  The panel chair will then lead a discussion between the panelists and audience to explore other challenges and opportunities in this area. A recorder will capture discussion and resources in an openly accessible document that can serve as a resource for data services staff moving forward.


B1: Programs of Instruction (Wed, 2017-05-24)
Chair:Jungwon Yang

  • CESSDA Training for data discovery
    Jennifer Buckley (UK Data Service)
    Vanessa Higgins (UK Data Service)
    Jo Wathan (UK Data Service)


    The Council of European Social Science Data Archives (CESSDA) provides large scale, integrated and sustainable data services to the social sciences. Training for data discovery is a new area for CESSDA and aims to help researchers (or other end-users) locate and navigate data collections relevant to their own research/teaching interests; data collections may be stored in different locations and subject to different access conditions. 

  • Does graduate training in the Social Sciences prepare students for data management and sharing?
    Ashley Ebersole (ICPSR University of Michigan)
    Jai Holt (ICPSR University of Michigan)


    Graduate programs in the social sciences aim to produce well rounded scholars and researchers. Although the majority of these programs offer curriculum in research methods and data collection as well as provide opportunities for professional development it is less clear how many students leave graduate school with a background in data management and data sharing.

    For each social science discipline, including psychology, sociology, anthropology, political science, geography, and history, their ethical codes necessitate open and accessible data. This is crucial as research becomes more collaborative, and evidence suggests that the research culture can be a barrier to data sharing. Early training in data management and sharing could help alleviate this problem.

    The current study assesses how masters and doctoral level programs in the social sciences include formal and informal training in data sharing and the use of data repositories. We developed a survey instrument to measure the extent to which programs are likely to provide this training using a mix of open and closed ended questions. We sent the survey to 30 programs in the United States across the six social science disciplines. We report on our results and discuss implications for developing training materials that can be used in graduate programs.

  • Reworking the workshop: designing data management workshops to align with behavioral change models
    Elizabeth Wickes (University of Illinois at Urbana-Champaign)


    Used both to raise awareness about a variety of topics and provide important experience with tools or skills, the humble workshop is often the centerpiece of outreach repertoire.  However, it is often difficult to critically analyze the effectiveness or understand how to align content.  Behavioral change models, including self-efficacy stage models, are an adaptable framework for this assessment because they provide well-researched classification approaches and have scientific support that stage-specific interventions provide more impact than non-targeted interventions (Bamberg 2013, Nachreiner, et al., 2015, & Schwarzer, 2008). 

    This is especially true for data management training, because it attempts to promote long-term behavioral change rather than specific tool training.  Exploring a stage model toward data management behavior change immediately provides outlines for specific behavioral questions and workshop content can be critically analyzed as a targeted interventions.  This presentation will summarize the redesign process to identify and support individuals within specific stages of data management behavior change and showcase some of the open materials published to fit within this framework.  While evaluation continues to be difficult, analyzing our content within these models yielded more informed and critical judgment of content and activities and a basis for future assessment.

  • 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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