Today’s Research Data Environment The context for Social Science Data

International Polar Year (IPY) experience 2

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 3

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.” 4

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 5

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.” 6

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 7

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 8

Data landscape entities Preservation Function Individual Centric Domain Centric Institutional Centric Long-term preservation Domain archives Institutional repositories Short to mid-term preservation Data warehouses Data centres Staging repositories No preservation responsibilities Website FTP site Research web portals Data libraries 9

Data landscape entities Access Function Individual Centric Domain Centric Institutional Centric Long-term access Short to mid-term access Immediate access Websites FTP sites Domain web portals Data centres Domain archives Data libraries Staging repositories Institutional repositories Sustainability Warehouses 10

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

How does it all fit together? Data centre OAIS Data centre Web site Web site Web site OAIS Data library Data library 12

A research data infrastructure OAIS 13

Connect data repositories OAIS 14

Distribute OAIS functions AIP DIP SIP SIP: submission information package AIP: archival information package DIP: dissemination information package 15

Share OAIS services OAIS Delivery Protection Interpretation Application Interoperation Authentication Find Method Linkage OAIS Community Cloud 16

GRDI2020 Digital Science Ecosystem 17

Cyberinfrastructure 18

Data Services and Infrastructure Data Services LocalTechnology SocialGlobal Distributed Preservation Backbone Data Management Plans Data Citation Training DataVerse Instance 19

Jim Gray’s e-Science Vision 20