today’s research data environment

Today’s Research Data Environment

The context for Social Science Data

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International Polar Year (IPY) experience

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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

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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.”

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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

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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.”

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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

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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

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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

WebsiteFTP site

Research web portals

Data libraries

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Data landscape entities

Access Function

Individual Centric

Domain Centric

Institutional Centric

Long-term access

Short to mid-term access

Immediate access Website

sFTP sites

Domain web

portals

Data centres

Domain archives

Datalibraries

Staging repositori

es

Institutional

repositories

Sust

aina

bilit

y

Warehouses

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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.

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How does it all fit together?

Datacentre

OAIS

Datacentre

Website

Website

Website

OAIS

OAIS

OAIS

Datalibrary

Datalibrary

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A research data infrastructure

OAIS

OAIS

OAIS

OAIS

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Connect data repositories

OAIS

OAIS

OAIS

OAIS

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Distribute OAIS functions

AIP

AIP

DIP

SIP

SIP: submission information packageAIP: archival information packageDIP: dissemination information package

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Share OAIS services

OAIS

OAIS

OAIS

DeliveryProtectionInterpretationApplicationInteroperation Authenticati

onFindMethodLinkage

OAIS

Community Cloud

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GRDI2020 Digital Science Ecosystem

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Cyberinfrastructure

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Data Services and Infrastructure

Data Services

• Local• Technology

• Social• Global

Distributed

Preservation

Backbone

Data Management Plans

Data Citation Training

DataVerse Instance

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Jim Gray’s e-Science Vision