Digital Transformation in the PLM domain - distrib.pdf
Ebiosphere09 Vc Final
1. Data Publishing =
Scholarly Publishing ?
Vishwas Chavan
Global Biodiversity Information Facility Secretariat, Copenhagen, DENMARK
2. Significance of Biodiversity Data
Policy
development and
decision making
(at local, national,
regional, and
global levels)
Biodiversity
Data
Monitoring of status
Conservation
and trends of
and sustainable
biodiversity
use of
(sound science)
biodiversity
Chavan, June 2009
3. It is all about……
Data Data Data Data
Data
Data Data Data Content
Data
Content Content Data
Data Content
Data Data Content
Content Content Data Content
Data Content
Content Data
Data Content
Content
Data Data Data Data
Data
Data Data Data Content
Data
Data Content Data Data
Data
Chavan, June 2009
4. What is needed?
• Data Digitisation, Management and
Archiving
• Data exchange / sharing
• Digital Data Publishing
• Free and Open Access
• Data without barriers
Chavan, June 2009
5. Why should I publish data?
rm e?
e re fo
t is th
Wha
Chavan, June 2009
6. Why should I publish data?
• Recognition
rm e?
re fo
s• t he
i Opportunities
W h at
• Investment
Chavan, June 2009 Data Publishing Framework 6
7. Data Publishing Framework
• Bring in cultural change towards ‘free
and open access' to biodiversity data
• Addresses social, technical, and policy
concerns
• Answer ‘What is there for me?’ needs of
ALL
Chavan, June 2009 Data Publishing Framework 7
8. Data Publication together with scholarly
publication: ZooKeys experience
Occurrence
Data
KML file
Chavan, June 2009
10. Data Usage Index (DUI): Why?
• To demonstrate to data publishers that their
biodiversity efforts do have impact
• To encourage …
– Increase of high quality data discovery and
mobilisation
– Further usage of biodiversity data and information
in scientific work
– Formal citation behavior in research papers of
dataset
– Standardisation of dataset information
Chavan, June 2009
11. Data Usage Index (DUI): What is it?
• As set of indicators operating on data concerned
with:
– Unique Visits
– Loyal Visits (repeated visits by same IP address)
– Download of datasets & dataset records
– Volume and (rank) distributions of dataset records
per visit, visitor, dataset provider (institution,
country, region, world, theme) & period
• Indicators to be normalised (by records or MB),
relative (to world, theme) and weighted (according
to provider profile of species/taxa/themes)
Chavan, June 2009
12. Data Flow type
Digitisation
Bottom – Top
Top – Bottom
Publishing
Publishing Publishing
Publishing Toolkit
Local
Toolkit Toolkit LDUI DUIs
Toolkit LDUI
LDUI
Aggregator Aggregator
Aggregator
UNIVERSAL DUI
NDUI NDUI
Natl.,
Regional,
Aggregator Aggregator Thematic
Aggregator DUIs
TDUI
TDUI RDUI
GDUI GDUI
Mirror Mirror Global
GDUI DUIs
Chavan, June 2009 Implementation of DUI
13. Improving the relevance of Data Usage Index
Data Life Cycle
Management Access Use
DUI)
e x(
I nd
e
ag
Us
ta
Da
Phase I Phase II Phase III
Data Usage Index (DUI) implementation
Chavan, June 2009
14. DPF: Challenges
Policy and Political
Uptake
• Individual Researchers
• Scientific and Academic Institutions
• Funding and Donor Agencies
• Traditional Publishing Industry
Chavan, June 2009 GBIF Indicators
15. DPF: Challenges
Cultural and Social
Acceptance
Policy and Political
Uptake
Chavan, June 2009
16. Impact of Data Publishing Framework
Funding Agencies
es
ag
support
ur
co
en
Project res
u
Inspires another lts
in
Knowledge
Dissemination
fac
Data
ilit
• Impact Factor for Scholarly Publishingires
ate
requ
Management,
e
at
te
lit
ita
ci
& Archival
cil
Data
fa
fa
Publishing
it y
Scholarly Data Creation,
al
qu
ss ata
Publishing Collection
• Data Usage Index for Digital Data Publishing
ne d
fit es
fa
gaps
d ov
ci
n
lit
ck o
an p r
edba or
at
Im
e
ide f
e
sf
prov trategie
Increased and
s
Data Usage results in
re
su
to
l ts
in
s
ad
le
Data Usage Index
Existing cycle
Analysis, Complementary Expected cycle
Impact Factor
Interpretation
Chavan, June 2009 GBIF Indicators 16