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Today’s Research Data Environment. The context for Social Science Data. International Polar Year (IPY) experience. Data managers’ perspectives of IPY. “A Conceptual Framework for Managing Very Diverse Data for Complex, Interdisciplinary Science” reading assignment
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Today’s Research Data Environment The context for Social Science Data
Data managers’ perspectives of IPY • “A Conceptual Framework for Managing Very Diverse Data for Complex, Interdisciplinary Science” reading assignment • “This emphasis on huge data volumes has underplayed another dimension of the fourth paradigm that presents an equally daunting challenge – the diversity of interdisciplinary data and the need to interrelate these data to understand complex problems such as environmental change and its impact.” • National Science Board’s three categories of data collections: • Research collections: project-level data • Resource collections: community-level data • Reference collections: multiple communities
Data managers’ perspectives of IPY • “As data managers for IPY, we find that while technology is a critical factor to addressing the interdisciplinary dimension of the fourth paradigm, the technologies developing for exa-scale data volumes are not the same as what is needed for extremely distributed and heterogeneous data. Furthermore, as with any sociotechnical change, the greater challenges are more socio-cultural than technical.”
Lessons learned from the IPY • Established a data policy around five data principles: • Discoverable • Open • Linked • Useful • Safe • “[M]ust consider the data ecosystem as a whole.” • Need for a “keystone species” in the data ecosystem
Lessons learned from the IPY • Data realities: • “data will be highly distributed and housed at many different types of institutions,” • “the use and users of data will be very diverse and even unpredictable,” • “the types, formats, units, contexts and vocabularies of the data will continue to be very complex if not chaotic.”
Local research data landscapes • Large data centres for single projects • Project-level repositories (e.g., Islandora) • Institutional and domain repositories • Government agencies with data • Data library services • Researchers without infrastructure A patchwork of “entities” that are largely unconnected
Global research data landscape • Networks of data archives • Inter- and non-governmental organizations with warehouses of data • International social science projects • National and pan-national statistical organizations A patchwork of “entities” that are loosely connected
Data landscape entities Institutionalrepositories Domain archives Sustainability Staging repositories Warehouses Data centres Datalibraries Domain web portals WebsitesFTP sites
Data repository relationships “[T]he next step in the evolution of digital repository strategies should be an explicit development of partnerships between researchers, institutional repositories, and domain-specific repositories.” Ann Green and Myron Gutmann, “Building partnerships among social science researchers, institution-based repositories and domain specific data arrchives,”OCLC Systems & Services, Vol. 23 (1), pp. 35-53.
How does it all fit together? Web site Web site OAIS OAIS Data centre Data library OAIS Data centre OAIS Data library Web site
A research data infrastructure OAIS OAIS OAIS OAIS
Connect data repositories OAIS OAIS OAIS OAIS
Distribute OAIS functions AIP SIP AIP DIP SIP: submission information package AIP: archival information package DIP: dissemination information package
Share OAIS services Delivery Protection Interpretation Application Interoperation OAIS OAIS Authentication Find Method Linkage OAIS OAIS Community Cloud
Data Services and Infrastructure Data Services