Now, more than ever, the information community is thinking about data and how best to support its creation, stewardship, and use Whether we are talking about content as data, research data, or data pertaining to online use and behaviors, we continue to amass ever-more data with no plan for storing it, no good way to parse it, and nothing but a vague hope of "automating" the hard work of cleaning it up, which only creates problems down the road. This webinar will assess the data needs of researchers and the response from the information community, from a variety of perspectives.
Confirmed speakers include: Bob Boissy, Director of Account Development, Springer-Nature; Gwen Evans, Vice President of Global Library Relations, Elsevier, Lisa Federer, NLM Data Science and Open Science Librarian, Office of Strategic Initiatives, National Library of Medicine; Srinivasan KG Govindarajan, Senior Vice President, Strategy & Solutions of Straive; Graham Smith, Research Data Manager, Editorial Operations, Springer-Nature; and Jennifer Thoegersen, researcher and PhD candidate, Oslo Metropolitan University.
Moderated by Todd Carpenter, Executive Director, NISO, the discussion encompasses the following:
Please be prepared to provide the NISO audience with a brief description of your background, your role in your organization or institution and provide the audience with a sense of the perspective you are bringing to this conversation.
At what point in the lifecycle of data, are you or your organization faced with the need to handle or assist with that data? What does your organization/institution perceive as being its role or responsibility in supporting the flow of data across the various stages? What are the challenges?
In the context of the collection of data and creation of a dataset, where do we believe researchers need the most support or assistance?
If current resistance to data-sharing exists due to the day-to-day constraints or burdens that it puts on researchers’ workflow, whether in terms of time, staff, etc., what might allow the broader community of stakeholders to relieve that pressure? How might the broader community work towards a better or more appropriate allocation of roles and responsibilities?
As a normal element of the data lifecycle, we know that data will need to be cleaned, normalized. Where should that process begin? On whose shoulders should the responsibility rest? Where are the resources for that? If training is needed, how might that be handled?
What is the role of automation in this process? What tools are needed in support of creation, curation, and re-use of data?
In particular, for some disciplines, there are very real concerns about the need to protect and minimize the risk to the data associated with human subjects. Additionally, there are concerns about sharing of data with regard to public-private research collaborations (Note: Example would be engineering schools working with auto-manufacturers in building and testing self-driving cars). What are the challenges for content- and service providers in the context of the need to secure data and ensure privacy of individuals in research datasets?
What are some of the current challenges in making data sets discoverable? What are the implications for hosting datasets in repositories and on various platforms? What are the long-term challenges of storage and retention of datasets?
Where does the funding come from for support of the various forms of data-sharing? Does this need to be built into grant applications? Is this something that should rest with the research institutions themselves? How much responsibility should service or content providers be expected to assume for long-term access to datasets?
What are the next steps that the community sees as key to making progress? What need is there for standards in this context and how might organizations such as NISO help in satisfying that need?
Resources shared by our panel:
Research Data Management Librarian Academy - Free online professional development program for librarians, information professionals, and other professionals who work in a research-intensive environment throughout the world.
The HEADT Centre - The Humboldt-Elsevier Advanced Data and Text Centre (HEADT Centre)
Practical challenges for researchers in data sharing - 2018 White Paper by Springer Nature
Supplemental Information to the NIH Policy for Data Management and Sharing: Selecting a Repository for Data Resulting from NIH-Supported Research - Guidance Regarding Desirable Characteristics for Repositories
Data Discovery Paradigms IG - RDA Data Discovery Group
Forecasting Costs for Preserving, Archiving, and Promoting Access to Biomedical Data - NLM funded study by the National Academies
RDMF19 - Costing data management - Research Data Management Forum (RDMF)
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