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 ID: 430939
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Slide1
Today’s Research Data Environment
The context for Social Science DataSlide2
International Polar Year (IPY) experience
2Slide3
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 dataResource collections: community-level dataReference collections: multiple communities
3Slide4
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.”
4Slide5
Lessons learned from the IPY
Established a data policy around five data principles:
Discoverable
OpenLinked
Useful
Safe
“[M]ust consider the data ecosystem as a whole.”Need for a “keystone species” in the data ecosystem5Slide6
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.”
6Slide7
Local research data landscapes
Large
data
centres for single
projects
Project-level repositories (e.g.,
Islandora)Institutional and domain repositoriesGovernment agencies with dataData library servicesResearchers
without infrastructure
A
patchwork of
“
entities
”
that are largely unconnected
7Slide8
Global research data landscape
Networks
of data
archives
Inter- and non-governmental
organizations with warehouses of
dataInternational social science projectsNational and pan-national statistical organizations
A
patchwork of
“
entities
”
that are loosely connected
8Slide9
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
9Slide10
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
10Slide11
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.
11Slide12
How does it
all fit
together?
Data
centre
OAIS
Data
centre
Web
site
Web
site
Web
site
OAIS
OAIS
OAIS
Data
library
Data
library
12Slide13
A research data infrastructure
OAIS
OAIS
OAIS
OAIS
13Slide14
Connect data repositories
OAIS
OAIS
OAIS
OAIS
14Slide15
Distribute OAIS functions
AIP
AIP
DIP
SIP
SIP: submission information package
AIP: archival information package
DIP: dissemination information package
15Slide16
Share OAIS services
OAIS
OAIS
OAIS
Delivery
Protection
Interpretation
Application
Interoperation
Authentication
Find
Method
Linkage
OAIS
Community Cloud
16Slide17
GRDI2020 Digital Science Ecosystem
17Slide18
Cyberinfrastructure
18Slide19
Data Services and Infrastructure
Data Services
19Slide20
Jim Gray’s e-Science Vision
20