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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

H1: Data Management in the Research Process (Fri, 2017-05-26)
Chair:John Heintz

  • Putting Metadata on the Map - Producing Enhanced Geospatial Visualisations from Open-source Tools to Encourage Metadata Creation Earlier in the Data Lifecycle
    Samuel Spencer (Aristotle Metadata Registry)


    Librarians and archivists have long known the benefits of metadata in for improving discoverability and understanding of datasets. However, with some open-data portals when depositing guidelines are based on policy or legislative requirements metadata quality may be an additional burden for data depositors with lower concern for long-term archival strategies. What is needed is a way to demonstrate short-term benefits for depositors that improve metadata quality while reducing the perceived burden of metadata production. 
    To improve the quality of data and metadata records for, we explored methods to improve production of rich geospatial visualisations to show users the immediate benefit of structural metadata, while also showing how metadata improved the quality of deposited data records. Additionally, we offered minimal machine-readable metadata profiles that could be created in common office-suite tools to maximise utility while minimising authoring time.
    This talk covers the challenges of producing structural metadata from records available in the CKAN data repository, methods for importing this into an open-source Aristotle Metadata Registry and how we connected these to produce metadata-driven interactive maps on NationalMap using Lastly, we look at how this data-visualisation focus engagement strategy has improved the quality of open government data.

  • From Administrative Burden to Research Excellence: Getting Researchers to Take Data Management Seriously
    Alexandra Stam (FORS)


    Driven in part by the open access movement, recent years have seen the expansion of initiatives that aim to promote data sharing, with increasing awareness among stakeholders of the importance of making data publicly available. Consequently, many funders have implemented formal data management plans as part of the proposal process, while data repositories and libraries have developed services, guidelines and trainings to help researchers fulfill funders’ requirements and apply good data management practices so that data can be shared. However, these same parties sometimes neglect the fact that many researchers do not take data management seriously, and perceive it and treat it at best as a form of administrative burden, and at worst as an obstacle to doing research. We will call for the repositioning of data management to the heart of the research process, irrespective of data sharing. Researchers should first and foremost see the value of good data management for their own research, as a way to achieve research excellence. We will share some reflections as to how this could be achieved by reconsidering the roles of funding agencies, data repositories, and librarians in encouraging and supporting good data management practices, beyond the goal of data sharing.

  • Moving Data around: Integrated Research Workflows for Curating and Publishing Data
    Maude Frances (University Library, UNSW Sydney)
    Steven McEachern (Australian Data Archive, Australian National University)
    Daniel Bangert (University Library, UNSW Sydney)
    Carolien van Ham (School of Social Sciences, UNSW Sydney)
    Janet McDougall (Australian Data Archive, Australian National University)
    Luc Betbeder-Matibet (Research Infrastructure, UNSW Sydney)


    The presentation draws on a use case from political science to demonstrate integrated scholarly processes for curating and publishing data. Curation activities are distributed across institutional and national repositories, archives, registries and websites. In workflows which prioritise existing research practice and disciplinary standards, researchers provide structured metadata to the Australian Data Archive (ADA) using a template based on the Data Documentation Initiative (DDI). These metadata are mapped to RDF and RIF-CS standards and sent to the institutional repository at UNSW Australia, for publication and further dissemination. The primary role of the institutional repository is to move the data around – to apply standards and protocols that enable the data to be widely and openly accessible.The implementation supports researchers in comprehensively describing their research methods and data according to a widely-adopted disciplinary standard, and reduces their workload relating to institutional reporting and dissemination. The integrity of the institutional data repository is increased by its direct integration with the rich descriptions of data in the disciplinary archive. Added value for the institution is derived from the reporting capabilities of the repository, which links to enterprise systems to generate statistics about the University’s research assets.


H2: Research Data Management Strategies & Opportunities (Fri, 2017-05-26)
Chair:Spencer Acadia

  • Spreading the Knowledge: Overviewing the University of Alberta Libraries’ Research Data Management Services
    James Doiron (University of Alberta Libraries)


    In June 2016 the Tri-Council Agencies, a major source of research funding for post-secondary institutions in Canada, released a Statement of Principles on Digital Data Management which identifies research data management (RDM) as being an essential and shared responsibility between researchers, research communities, research institutions, and research funders. As a major international research library, University of Alberta Libraries (UAL) offers expertise, resources, and services for supporting sound RDM throughout the research lifecycle. In alignment with the Tri-Council statement, UAL has adopted a holistic approach to education and delivering of RDM knowledge and resources across campus, focusing upon a variety of stakeholders. Examples of this include a running series of applied RDM training sessions for liaison librarians, customized information sessions both for Research Services Office and Research Ethics Office staff, and collaborative RDM events and training sessions delivered to researchers and students across campus. Some specific services and platforms offered by UAL include the Portage Data Management Planning (DMP) Assistant, Dataverse, and an open access Education and Research Archive for promoting research discovery, archival, and preservation. This session will provide a brief overview of UAL’s RDM services, methods employed for their delivery and uptake, and both current and emerging RDM initiatives.

  • Across Canada, across Disciplines: Research Data Management Practices and Needs in the Social Sciences and Humanities
    Leanne Trimble (University of Toronto)
    Dylanne Dearborn (University of Toronto)
    Tatiana Zaraiskaya (Queen's University)
    Jane Burpee (McGill University)
    Eugene Barsky (University of British Columbia)
    Catie Sahadath (University of Ottawa)


    Across Canada, ten universities (to date) have worked together to survey their research communities in order to better understand research data management practices and needs. This work builds on a previous collaborative effort designed to delve into RDM habits of researchers in engineering and science, by expanding to researchers in the humanities and social sciences. This session will discuss the survey results from participating universities, providing insight into the Canadian RDM landscape while highlighting disciplinary differences and notable results. Survey sections include working with research data, data sharing, funding mandates and research data management services. Information generated by this survey will help inform Canadian institutional services, infrastructure and policies. Participating universities at the time of writing include: Dalhousie University, McGill University, Queen’s University, Ryerson University, University of Alberta, University of British Columbia, University of Ottawa, University of Toronto, University of Waterloo, and the University of Windsor. The session will also discuss the collaboration process, which resulted in the development of a clearinghouse of generic survey documents (questionnaires, ethics review documents) that will be housed by Portage, Canada’s emerging national RDM infrastructure project. These documents can be used by other institutions to conduct similar studies. Future initiatives include a further survey of researchers in the health and medical sciences.

    Additional authors are Marjorie Mitchell, University of British Columbia and Matthew Gertler, University of Toronto.

  • In Aggregate: Trends, Needs, and Opportunities from Faculty Research Data Management Surveys
    Abigail Goben (University of Illinois-Chicago)
    Tina Griffin (University of Illinois-Chicago)


    A popular starting point for libraries engaging in research data management (RDM) services is a faculty needs assessment. Preliminary reviews of the literature identified almost fifty individual institutional results, mostly surveys from Highest or Higher Research Activity institutions. Henderson and Knott (2015) explicitly argue that no further surveys are needed because of the breadth and depth already covered by these studies. However, no overarching analysis has yet been conducted to examine cross-institutional trends and identify best practices or gaps in the literature. To address these issues, the authors will compare published faculty RDM needs assessments. Studies to be included will be US-based, in order to retain homogeneity regarding research institution classification and funding mandates. Studies must be specifically about RDM needs and/or services, as opposed to broader library services, and should not be only evaluations of implemented library RDM services. Research questions for this project include: identifying question overlaps; identifying which research data issues are common across institutions; determining if graduate students and research staff were considered in the needs assessments; and what gaps in the literature yet remain. 
    Henderson ME, Knott TL. Starting a Research Data Management Program Based in a University Library. Medical Reference Services Quarterly. 2015;34(1):47-59. doi:10.1080/02763869.2015.986783.


H3: Is Bigger Always Better? Examining Big Data’s Limits in Utility, Quality, and Security (Fri, 2017-05-26)
Chair:Meryl Brodsky

  • Would Big Data Replace Marketing and Social Surveys? - Potential Usage of Big Data in Marketing and Social Surveys: In Case of Mongolia
    Davaasuren Chuluunbat (MMCG Company, Mongolia)


    In recent years there have been debates on “Would big data replace marketing and social surveys” among marketing and social research communities in international level. The most trending idea is that big data will not replace MR, but that it will give support to the research. The main explanation of this idea is that big data could not reveal customers’ insights. However they agree that big data could help to conduct MR in an effective way. There is lots of evidence. Therefore MR societies are considering cooperating with big data owners, hiring the new skills of data analysts, combining quantitative and qualitative data for making value together with big data owners, and investing in big data platforms, etc. In this paper, I will outline possible links between big data and MR and usage of big data in MR. I will also discuss some cases known in the global level, showing how big data and MR are combined. For Mongolia, which shifted to a market economy after the socialistic regime was destroyed, big data is quite new concept, even though the value of market research has been recognized by businesses a few years ago. In the international MR communities, big data usage is trending topic. We have to follow the trend, and hope that it will be realized soon by our communities. There are some cases of using big data in marketing and social research, especially in the research design. Because big data is quite a new concept for us, we have opportunities and challenges to use big data. So I will include practices of using big data in our work and opportunities and challenges to use it in this paper. Then I will suggest solution to use opportunities in an effective way and solve challenges in our case.

  • Data Quality, Transparency and Reproducibility in Large Bibliographic Datasets
    Angela Zoss (Duke University)
    Trevor Edelblute (Indiana University)
    Inna Kouper (Indiana University)


    Increasingly, large bibliographic databases are hosted by dedicated teams that commit to database quality, curation, and sharing, thereby providing excellent sources of data. Some databases, such as PubMed or HathiTrust Digital Library, offer APIs and describe the steps to retrieve or process their data. Others of comparable size and importance to bibliographic scholarship, such as the ACM digital library, still forbid data mining. The additional cleaning and expansion steps required to overcome barriers to data acquisition must be reproducible and incorporated into the curation pipeline, or the use of large bibliographic databases for analysis will remain costly, time-consuming, and inconsistent. In this presentation, we will describe our efforts to create reproducible workflows to generate datasets from three large bibliographic databases: PubMed, DBLP (as a proxy for the ACM digital library), and HathiTrust. We will compare these sources of bibliographic data and address the following: initial download and setup, gap analysis, supplemental sources for data retrieval and integration. By sharing our workflows and discussing both automated and manual steps of data enhancements, we hope to encourage researchers and data providers to think about sharing the responsibility of openness, transparency and reproducibility in re-using large bibliographic databases.

  • Secure Data Solutions for Social Media Data Analysis
    David Schiller (GESIS)
    Katharina Kinder-Kurlanda (GESIS)


    Social media are used by more and more people. It influences how people communicate, how they gather information and how they behave – not only on social media platforms but in general.For example, WeChat is used by 700 million people in China. Additional services such as payment systems, language training etc. are incorporated into this app, resulting in a huge source of personal information. It is likely that such repositories will be one future of data collections.Research in the social sciences must be enabled to analyse social media data to understand how societies in general are changing and developing. This personal data should not only be used by commercial companies. The social sciences therefore need to build methods and infrastructures to access social media data sources and to support analyses. For several reasons (e.g. privacy concerns, proprietary data) existing secure data solutions seem to be particularly interesting to facilitate social media data access and sharing. Secure access solutions would also ensure proper documentation and quality of the data and the reproducibility of results. The talk will present first approaches to adapt and develop the Secure Data Center at GESIS also as a platform for social media data analysis.

  • 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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