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Effective Data Management NISO Virtual Conference Effective data management is widely recognized as a necessity, including the development of robust strategies for data collection, as well as ensuring appropriate management, handling, and preservation of those data. But is this being achieved? What practices are perceived by the research community as worthwhile and fit to purpose? Are there speed bumps in our processes that need smoothing out or eliminating? Do the available platforms provide the right services to the right people at an affordable price? What emerging challenges do we need to start addressing? This event will establish the state of current practice and identify potential areas of concern. Confirmed speakers include Kristi Holmes, Director, Galter Health Sciences Library, Northwestern University; Kristin Lee, Librarian for Research Data,Tufts University; Clara Llebot Lorente, Data Management Specialist, Oregon State University; Maria Praetzellis, Product Manager, Research Data Management, UC Curation Center, California Digital Library; Carly Strasser, Program Manager, Open Science. Chan Zuckerberg Initiative and Keith Webster, Dean of University Libraries, Carnegie Mellon University.
Open and inclusive science and FAIR Practices (Findable Accessible Interoperable Reusable) are increasingly a requirement of good scholarship, driven by changing funder and cultural expectations to research access. This presentation will address openness in the context of knowledge equity and translation, explore tools and strategies to establish and advance local data policy priorities, and also introduce the InvenioRDM software project and collaborative open source community. We’re leveraging InvenioRDM, a turnkey born-interoperable research data management (RDM) repository and data index to empower discovery, reuse, and impact of a wide range of digital artifacts through best practice standards and technologies. Development is carried out by a multi-national partnership which includes CERN, Northwestern University, and over 20 other collaborators, representing academic, research, cultural, funding, and industry collaborators from around the world.
data sharing
data sharing
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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