ARIADNE is an EU-funded project that aims to facilitate sharing of archaeological datasets across Europe. The document discusses open data publication requirements, good practices, and benefits. It notes barriers to open data sharing include lack of incentives and effort required. Survey results presented on current limited sharing behavior. The benefits of open data publication are described, including increased citations and recognition for data providers. Proper preparation and documentation of datasets is important for others to find and reuse the data. Widespread open data sharing could enable new collaborative and data-driven research.

1. ARIADNE is funded by the European Commission's Seventh Framework Programme
Open Data Publication
Requirements, Good Practices, and Benefits
Open Data Publication
Requirements, Good Practices, and Benefits
EAA 2014 - Istanbul
Session: Barriers and Opportunities: Open Access
and Open Data in Archaeology
13 September 2014
Guntram Geser
Salzburg Research
EAA 2014 - Istanbul
Session: Barriers and Opportunities: Open Access
and Open Data in Archaeology
13 September 2014
Guntram Geser
Salzburg Research

2. ARIADNE
• Advanced Research Infrastructure for
archaeological Dataset Networking in Europe
– EU FP7-Infrastructures project, type „Integrating Activity“
– Runs 4 years, 02/2013-01/2017
– Focus on archaeological datasets – help overcome data
fragmentation and foster a culture of sharing and re-using
– 23 partners of 18 European countries
– Open to other participants, e.g.
• Transnational Access Programme
• Special Interest Groups
• Website: www.ariadne-infrastructure.eu
• Advanced Research Infrastructure for
archaeological Dataset Networking in Europe
– EU FP7-Infrastructures project, type „Integrating Activity“
– Runs 4 years, 02/2013-01/2017
– Focus on archaeological datasets – help overcome data
fragmentation and foster a culture of sharing and re-using
– 23 partners of 18 European countries
– Open to other participants, e.g.
• Transnational Access Programme
• Special Interest Groups
• Website: www.ariadne-infrastructure.eu

3. Main topics of this presentation
• Open Data – expectations, criteria, drivers
• Current data (non-)sharing behaviour
• Reasons for lack of open data sharing
• ARIADNE survey results on data publication
• How to benefit from open data publication
• Issue of actual data re-use
• Takeaway points
• Open Data – expectations, criteria, drivers
• Current data (non-)sharing behaviour
• Reasons for lack of open data sharing
• ARIADNE survey results on data publication
• How to benefit from open data publication
• Issue of actual data re-use
• Takeaway points

4. Open Data – expectations
• The scientific method: provide evidence for
knowledge claims („show us your data“)
• Output of publicly funded research should be openly
available, and properly preserved
• More open data sharing => better analysis => better
decision-making / solutions for critical issues
• Better return-on-investment, e.g.
– no duplication of data collection
– better exploitation of available data – therefore emphasis
on re-use
• Innovation through „data-intensive“, „data-driven“,
„big data“ (incl. archaeology?)
• The scientific method: provide evidence for
knowledge claims („show us your data“)
• Output of publicly funded research should be openly
available, and properly preserved
• More open data sharing => better analysis => better
decision-making / solutions for critical issues
• Better return-on-investment, e.g.
– no duplication of data collection
– better exploitation of available data – therefore emphasis
on re-use
• Innovation through „data-intensive“, „data-driven“,
„big data“ (incl. archaeology?)

5. Open Data – criteria
• Accessible online
– not necessarily without registration
• Reusable
– not summarized data (i.e. figures, charts,
etc.) canned in publications
– state: raw, cleaned, normalized,… (accord.
to practice)
– open format (e.g. not PDF documents)
• Openly licensed (e.g. CC-BY, if other no
NonDerivative!)
• For free – yes, but somebody has to pay
to ensure sustainability
• Accessible online
– not necessarily without registration
• Reusable
– not summarized data (i.e. figures, charts,
etc.) canned in publications
– state: raw, cleaned, normalized,… (accord.
to practice)
– open format (e.g. not PDF documents)
• Openly licensed (e.g. CC-BY, if other no
NonDerivative!)
• For free – yes, but somebody has to pay
to ensure sustainability
“Publishing data in a
reusable form to
support findings
must be mandatory”
– one of six key
areas for action
highlighted in the
The Royal Society’s
report Science as an
Open Enterprise
(2012)

6. Open Data – drivers /1
• High-level policies & initiatives
– OECD Declaration on Access to Research
Data from Public Funding (2004; Principles
and Guidelines, 2007)
– EC Communications, Open data (2011),
Towards better access to scientific
information (2012)
– Many others, e.g. Research Data Alliance –
international initiative (launched in March
2013), various working & interest groups
(archaeology not represented yet)
• High-level policies & initiatives
– OECD Declaration on Access to Research
Data from Public Funding (2004; Principles
and Guidelines, 2007)
– EC Communications, Open data (2011),
Towards better access to scientific
information (2012)
– Many others, e.g. Research Data Alliance –
international initiative (launched in March
2013), various working & interest groups
(archaeology not represented yet)
Neelie Kroes, EC
Vice-President, 2012:
“Taxpayers should
not have to pay twice
for scientific research
and they need seam-
less access to raw
data. We want to
bring dissemination
and exploitation of
scientific research
results to the next
level.”

7. Open Data – drivers /2
• Research funding agencies
– Open Access mandates extended to data
– Mandatory data management plans, i.e. data sharing must
be considered already at application stage
• Data archiving & access infrastructures put in place
– Data centres / repositories
• General : zenodo (related to OpenAIRE), Figshare, …
• Archaeology: e.g. ADS (UK), eDNA (NL), IANUS (Germany, in
preparation), MAPPA (IT), OpenContext and tDAR (USA)
– Data registries / catalogues
– Data citation standard, e.g. DataCite
• Research funding agencies
– Open Access mandates extended to data
– Mandatory data management plans, i.e. data sharing must
be considered already at application stage
• Data archiving & access infrastructures put in place
– Data centres / repositories
• General : zenodo (related to OpenAIRE), Figshare, …
• Archaeology: e.g. ADS (UK), eDNA (NL), IANUS (Germany, in
preparation), MAPPA (IT), OpenContext and tDAR (USA)
– Data registries / catalogues
– Data citation standard, e.g. DataCite

8. Open Data – drivers /3
• New publication format of „data papers“
– Describe a dataset/database, kind of extensive metadata
record or documentation, also including examples of use /
usefulness for research
– Examples of data journals in archaeology
• Journal of Open Archaeology Data, started 2012
• Internet Archaeology, started a series of data papers in 2013
• New publication format of „data papers“
– Describe a dataset/database, kind of extensive metadata
record or documentation, also including examples of use /
usefulness for research
– Examples of data journals in archaeology
• Journal of Open Archaeology Data, started 2012
• Internet Archaeology, started a series of data papers in 2013

9. Barriers to open data sharing
• Many obstacles to providing open access to reusable
data
– Priority of published papers
– Little academic reward for development and sharing of
datasets/DB
– Required effort to share re-usable data (incl. formatting,
metadata creation, licensing etc.)
– Existing copyrights, confidential and sensitive data
– Concerns that data could be scooped, misused or
misinterpreted
– Potential reputational risk (e.g. data quality, errors,…)
• Overall a bad ratio of additional effort & risks to
potential benefits
• Many obstacles to providing open access to reusable
data
– Priority of published papers
– Little academic reward for development and sharing of
datasets/DB
– Required effort to share re-usable data (incl. formatting,
metadata creation, licensing etc.)
– Existing copyrights, confidential and sensitive data
– Concerns that data could be scooped, misused or
misinterpreted
– Potential reputational risk (e.g. data quality, errors,…)
• Overall a bad ratio of additional effort & risks to
potential benefits

10. Current data (non-)sharing behaviour
• Contrary to what advocates of proper management
and sharing of data would like them to do
• According to representative surveys (PARSE.Insight
2009, Science 2011): Most data remains locked away
– On personal computers
– Portable storage carriers
– Restricted access servers
– …
• Eventually discarded as “obsolete” or lost otherwise
• Mostly not considered: Potential value of the data
for alternate and new uses by others
• Only 6-8% of researchers sometimes deposit data in
a community archive
• Contrary to what advocates of proper management
and sharing of data would like them to do
• According to representative surveys (PARSE.Insight
2009, Science 2011): Most data remains locked away
– On personal computers
– Portable storage carriers
– Restricted access servers
– …
• Eventually discarded as “obsolete” or lost otherwise
• Mostly not considered: Potential value of the data
for alternate and new uses by others
• Only 6-8% of researchers sometimes deposit data in
a community archive

11. Archiving/storing data for future use /1
PARSE.Insight survey 2009: 1202 respondents from different
research domains and countries

12. Archiving/storing data for future use /2
• “Science” journal 2011 survey of peer reviewers: 1700
responses, (international and multi-disciplinary
Where do you archive most of the data generated in your lab or
for your research?”
• “Science” journal 2011 survey of peer reviewers: 1700
responses, (international and multi-disciplinary
Where do you archive most of the data generated in your lab or
for your research?”
Note: archived ≠ curated
50.2% in our lab
38.5% university server
7.6% community repository
3.2% “other”
0.5% not stored

13. ARIADNE online survey on user needs
Participants (881 questionnaires
received, 692 with sufficient inform.)
o586 archaeological researchers
o 54 directors of research institutes
o 52 repository managers
Organisational context (640 responses)
o54% Universities
o16% Governmental organisations
o12% Private companies or institutes
o13% Self-employed
Gender (482 responses)
o57% male
o43% female
Geographic distribution (482 resp.)
o83% EU (UK: 79, France: 51, Italy:
47% NL: 35,…)
o 5% other European
o12% non-European
November-December 2013

14. Data publication

15. Barriers to data deposit/publication

16. „How would you rate the importance of the following potential
barriers to enhancing access to research data?”
European Commission: Online survey on scientific information in the digital age
(2012); 1140 participants from around Europe.
Need to make clear benefits
of open data publication!

17. Authors‘ benefits focus
• Goal = recognition and academic reward for data
providers – at least same as for other publications
• Core mechanism = citation of published data/set
– Confirms value of the data contributed
– Indicates providers of good data
– Promotes further use of the data (i.e. more citations)
– Allows the use and impact of the data to be tracked and
measured
• But data citation metrics not implemented yet
• Some indications of higher citation rates of
publications that make underlying data available
• Goal = recognition and academic reward for data
providers – at least same as for other publications
• Core mechanism = citation of published data/set
– Confirms value of the data contributed
– Indicates providers of good data
– Promotes further use of the data (i.e. more citations)
– Allows the use and impact of the data to be tracked and
measured
• But data citation metrics not implemented yet
• Some indications of higher citation rates of
publications that make underlying data available

18. How to reap the benefits? / 1
• Deposit data that underpins your research results in
a reliable, community recognised repository
– See: Data Seal of Approval; Trusted Repositories Audit &
Certification (TRAC) and other checklists
– Should provide unique persistent identifiers (e.g. DOIs)
– Require following citation standard as part of user
agreement (e.g. DataCite; citation in reference list)
• Provide good metadata – “no pain, no gain”
– Key for data re-use without direct contact with creator
– Costs of preparing data and metadata for publication
should be included in project funding
• Deposit data that underpins your research results in
a reliable, community recognised repository
– See: Data Seal of Approval; Trusted Repositories Audit &
Certification (TRAC) and other checklists
– Should provide unique persistent identifiers (e.g. DOIs)
– Require following citation standard as part of user
agreement (e.g. DataCite; citation in reference list)
• Provide good metadata – “no pain, no gain”
– Key for data re-use without direct contact with creator
– Costs of preparing data and metadata for publication
should be included in project funding

19. How to reap the benefits? /2
• Apply a license not impeding reuse (e.g. CC-BY, ODC-
BY), i.e. that that should be re-usable
• Demand proper citation by others who use your
published data
• Publish a “data paper” (about your data)
• Promote/cite your data when appropriate
• Seek collaboration and co-authoring of papers with
data re-users
• Apply a license not impeding reuse (e.g. CC-BY, ODC-
BY), i.e. that that should be re-usable
• Demand proper citation by others who use your
published data
• Publish a “data paper” (about your data)
• Promote/cite your data when appropriate
• Seek collaboration and co-authoring of papers with
data re-users

20. Issue of actual data re-use /1
• No re-use, no citation, no recognition/rewards
• Few studies on re-use outside of science and
engineering disciplines
• DIPIR project included archaeology (Faniel et al.
2013); main results:
– Key importance of contextual information
– Especially research design and data collection procedures
– Other criteria:
• institutional background of data producers (training, reputation)
• good practices of the data repository
• No re-use, no citation, no recognition/rewards
• Few studies on re-use outside of science and
engineering disciplines
• DIPIR project included archaeology (Faniel et al.
2013); main results:
– Key importance of contextual information
– Especially research design and data collection procedures
– Other criteria:
• institutional background of data producers (training, reputation)
• good practices of the data repository

21. Issue of actual data re-use /2
• Unclear level of actual re-use in the domain
• Will there be more re-use if more data is openly
available?
• Re-use for what – comparative analysis, meta-
analysis,…?
• Main forms of re-use in the domain:
– Re-use of whole datasets as is?
– Build a derivative dataset, incorporating data from others
as well as own data?
– Extract specific „data points“?
• Unclear level of actual re-use in the domain
• Will there be more re-use if more data is openly
available?
• Re-use for what – comparative analysis, meta-
analysis,…?
• Main forms of re-use in the domain:
– Re-use of whole datasets as is?
– Build a derivative dataset, incorporating data from others
as well as own data?
– Extract specific „data points“?

22. Takeaway points /1
• Researchers as open data publishers and consumers
– Publish open data to reap benefits – individually and as
research community
– Recognise colleagues who share data, cite their datasets
properly
• Research institutions
– Reward researchers who publish data
– Change mind-sets by doing (not teethless mandates)
– Offer skills development and support
• Data archives/repositories
– Important research infrastructure – need sustained
funding, but also demonstrate usage/impact
– Make open data sharing as easy as possible
• Researchers as open data publishers and consumers
– Publish open data to reap benefits – individually and as
research community
– Recognise colleagues who share data, cite their datasets
properly
• Research institutions
– Reward researchers who publish data
– Change mind-sets by doing (not teethless mandates)
– Offer skills development and support
• Data archives/repositories
– Important research infrastructure – need sustained
funding, but also demonstrate usage/impact
– Make open data sharing as easy as possible

23. Takeaway points /2
• Research funders
– Open Data mandates should come with financial coverage
of extra effort for open data publication
• All: It‘s not about data management (plans) to
comply with policies
• It‘s about …
– making published data part of the scholarly record –
persistent, citable, rewarded
– demonstrating tangible benefits of open data publication
• Research funders
– Open Data mandates should come with financial coverage
of extra effort for open data publication
• All: It‘s not about data management (plans) to
comply with policies
• It‘s about …
– making published data part of the scholarly record –
persistent, citable, rewarded
– demonstrating tangible benefits of open data publication

24. References and other literature /1
• ADS – Archaeology Data Service, http://archaeologydataservice.ac.uk
• ARIADNE First report on users needs: D2.1, April 2014, http://
www.ariadne-infrastructure.eu/Resources/D2.1-First-report-on-users-needs
• Charles Beagrie Ltd.: Keeping Research Data Safe (KRDS) Benefits Framework,
http://beagrie.com/krds-i2s2.php
• Borgman, C.L: Research Data: Who will share what, with whom, when, and why? Fifth China
– North America Library Conference 2010, Beijing, 8-12 September 2010,
http://works.bepress.com/cgi/viewcontent.cgi?article=1237&context=borgman
• CODATA - ICSTI Task Group on Data Citation Standards and Practices (2013): Out of cite, out
of mind: The current state of practice, policy, and technology for the citation of data. In:
Data Science Journal, vol.12,
https://www.jstage.jst.go.jp/article/dsj/12/0/12_OSOM13-043/_article
• Costas R., Meijer I., Zahedi Z. & Wouters P. (2013): The Value of Research Data. Metrics for
datasets from a cultural and technical point of view. Center for Science and Technology
Studies, Leiden University, http://www.knowledge-exchange.info/Default.aspx?ID=586
• Data Seal of Approval, http://www.datasealofapproval.org
• DataCite, http://www.datacite.org
• DataCite Metadata Schema for the Publication and Citation of Research Data, V3.0, July
2013, http://schema.datacite.org/meta/kernel-3/index.html
• ADS – Archaeology Data Service, http://archaeologydataservice.ac.uk
• ARIADNE First report on users needs: D2.1, April 2014, http://
www.ariadne-infrastructure.eu/Resources/D2.1-First-report-on-users-needs
• Charles Beagrie Ltd.: Keeping Research Data Safe (KRDS) Benefits Framework,
http://beagrie.com/krds-i2s2.php
• Borgman, C.L: Research Data: Who will share what, with whom, when, and why? Fifth China
– North America Library Conference 2010, Beijing, 8-12 September 2010,
http://works.bepress.com/cgi/viewcontent.cgi?article=1237&context=borgman
• CODATA - ICSTI Task Group on Data Citation Standards and Practices (2013): Out of cite, out
of mind: The current state of practice, policy, and technology for the citation of data. In:
Data Science Journal, vol.12,
https://www.jstage.jst.go.jp/article/dsj/12/0/12_OSOM13-043/_article
• Costas R., Meijer I., Zahedi Z. & Wouters P. (2013): The Value of Research Data. Metrics for
datasets from a cultural and technical point of view. Center for Science and Technology
Studies, Leiden University, http://www.knowledge-exchange.info/Default.aspx?ID=586
• Data Seal of Approval, http://www.datasealofapproval.org
• DataCite, http://www.datacite.org
• DataCite Metadata Schema for the Publication and Citation of Research Data, V3.0, July
2013, http://schema.datacite.org/meta/kernel-3/index.html

25. References and other literature /2
• Data Curation Profiles – Witt M., Carlson J., Brandt D.S & Cragin M.H. (2009): Constructing
Data Curation Profiles. In: International Journal of Digital Curation, Vol. 4, No. 3, 2009,
http://www.ijdc.net/index.php/ijdc/article/view/137
• Data Curation Profiles – website,
http://wiki.lib.purdue.edu/display/dcp/Data+Curation+Profiles
• Digital Object Identifier (DOI), http://www.doi.info
• DIPIR - Dissemination Information Packages for Information Reuse, http://dipir.org
• DRYAD, http://datadryad.org
• EC – European Commission: Online survey on scientific information in the digital age, Brussels,
2012,
http://ec.europa.eu/research/science-society/document_library/pdf_06/survey-on-scientific-infor
• EC Communication: Open data. An engine for innovation, growth and transparent governance
(12.12.2011),
http://ec.europa.eu/information_society/policy/psi/docs/pdfs/opendata2012/open_data_commu
• EC Communication: Towards better access to scientific information: Boosting the benefits of
public investments in research (17.7.2012), http://
ec.europa.eu/research/science-society/document_library/pdf_06/era-communication-towards-be
• EDNA – e-depot Nederlandse archeologie, http://www.edna.nl
• Data Curation Profiles – Witt M., Carlson J., Brandt D.S & Cragin M.H. (2009): Constructing
Data Curation Profiles. In: International Journal of Digital Curation, Vol. 4, No. 3, 2009,
http://www.ijdc.net/index.php/ijdc/article/view/137
• Data Curation Profiles – website,
http://wiki.lib.purdue.edu/display/dcp/Data+Curation+Profiles
• Digital Object Identifier (DOI), http://www.doi.info
• DIPIR - Dissemination Information Packages for Information Reuse, http://dipir.org
• DRYAD, http://datadryad.org
• EC – European Commission: Online survey on scientific information in the digital age, Brussels,
2012,
http://ec.europa.eu/research/science-society/document_library/pdf_06/survey-on-scientific-inform
• EC Communication: Open data. An engine for innovation, growth and transparent governance
(12.12.2011),
http://ec.europa.eu/information_society/policy/psi/docs/pdfs/opendata2012/open_data_commun
• EC Communication: Towards better access to scientific information: Boosting the benefits of
public investments in research (17.7.2012), http://
ec.europa.eu/research/science-society/document_library/pdf_06/era-communication-towards-bet
• EDNA – e-depot Nederlandse archeologie, http://www.edna.nl

26. References and other literature /3
• European High-level Expert Group on Scientific Data (2010): Riding the wave. How Europe can
gain from the rising tide of scientific data. A submission to the European Commission, October
2010, http://cordis.europa.eu/fp7/ict/e-infrastructure/docs/hlg-sdi-report.pdf
• Faniel I., Kansa E., Whitcher-Kansa S., Barrera-Gomez J. & Yakel E. (2013): The Challenges of
Digging Data: A Study of Context in Archaeological Data Reuse. JCDL 2013 Proceedings of the
13th ACM/IEEE-CS Joint Conference on Digital Libraries, 295-304 (preprint), http://
www.oclc.org/content/dam/research/publications/library/2013/faniel-archae-data.pdf
• Force 11: Joint Declaration of Data Citation Principles, https://www.force11.org/datacitation/
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http://www.nature.com/news/scientists-losing-data-at-a-rapid-rate-1.14416
• Goodman A., Pepe A., Blocker A. W. et al. (2014): Ten Simple Rules for the Care and Feeding
of Scientific Data. In: PLoS Computational Biology 10(4): e1003542,
http://dx.doi.org/10.1371/journal.pcbi.1003542
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Exploration of Faculty Values and Needs in Seven Disciplines. – Archaeology Case Study.
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data: Implications for archives and records management. Proceedings from the Society of
American Archivists Research Forum. San Francisco, CA: SAA, http://
files.archivists.org/conference/2008/researchforum/M-HedstromJ-Niu-SAA-ResearchPaper-2008.p
• European High-level Expert Group on Scientific Data (2010): Riding the wave. How Europe can
gain from the rising tide of scientific data. A submission to the European Commission, October
2010, http://cordis.europa.eu/fp7/ict/e-infrastructure/docs/hlg-sdi-report.pdf
• Faniel I., Kansa E., Whitcher-Kansa S., Barrera-Gomez J. & Yakel E. (2013): The Challenges of
Digging Data: A Study of Context in Archaeological Data Reuse. JCDL 2013 Proceedings of the
13th ACM/IEEE-CS Joint Conference on Digital Libraries, 295-304 (preprint), http://
www.oclc.org/content/dam/research/publications/library/2013/faniel-archae-data.pdf
• Force 11: Joint Declaration of Data Citation Principles, https://www.force11.org/datacitation/
• Gibney E. & Van Noorden R. (2013): Scientists losing data at a rapid rate. Nature, 19.12.2013,
http://www.nature.com/news/scientists-losing-data-at-a-rapid-rate-1.14416
• Goodman A., Pepe A., Blocker A. W. et al. (2014): Ten Simple Rules for the Care and Feeding
of Scientific Data. In: PLoS Computational Biology 10(4): e1003542,
http://dx.doi.org/10.1371/journal.pcbi.1003542
• Harley, Diane et al. (2010b): Assessing the Future Landscape of Scholarly Communication: An
Exploration of Faculty Values and Needs in Seven Disciplines. – Archaeology Case Study.
University of California Berkeley, http://escholarship.org/uc/item/15x7385g#page-37
• Hedstrom M., Niu J., & Marz K. (2008): Incentives for data producers to create “archive/ready”
data: Implications for archives and records management. Proceedings from the Society of
American Archivists Research Forum. San Francisco, CA: SAA, http://
files.archivists.org/conference/2008/researchforum/M-HedstromJ-Niu-SAA-ResearchPaper-2008.p

27. References and other literature /4
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• IANUS - Research Data Centre for Archaeology and Ancient Studies (Germany),
http://www.ianus-fdz.de
• Knowledge Exchange (2013): "Making Data Count: Research data availability and research
assessment", workshop, Berlin, http://www.knowledge-exchange.info/Default.aspx?ID=577
• Internet Archaeology: Data Papers, http://intarch.ac.uk/authors/data-papers.html
• Journal of Open Archaeology Data, http://openarchaeologydata.metajnl.com
• JISC / Curtis G., Hammond M. & Oppenheim C. (2013): Access to Citation Data: Cost-benefit
and Risk Review and Forward Look. JISC, September 2013,
http://repository.jisc.ac.uk/5371/1/Access-to-Citation-data-report-final.pdf
• Key Perspectives (2010): Data Dimensions: Disciplinary Differences in Research Data Sharing,
Reuse and Long Term Viability. Digital Curation Center,
http://www.dcc.ac.uk/sites/default/files/SCARP%20SYNTHESIS_FINAL.pdf
• Kvalheim V. & Kvamme T. (2014): Policies for Sharing Research Data in Social Sciences and
Humanities. A survey about research funders’ data policies. International Federation of Data
Organisations (IFDO), http://
www.ada.edu.au/documents/ifdo-report-on-policies-for-data-sharing
• Lawrence B., Jones C., Matthews B., Pepler S. & Callaghan S. (2011): Citation and peer review
of data: Moving towards formal data publication. International Journal of Digital Curation,
6(2): 4-37, http://www.ijdc.net/index.php/ijdc/article/viewFile/181/265
• Heidorn, P.B: Shedding Light on the Dark Data in the Long Tail of Science. Library Trends 57(2),
2008, http://hdl.handle.net/2142/9127
• IANUS - Research Data Centre for Archaeology and Ancient Studies (Germany),
http://www.ianus-fdz.de
• Knowledge Exchange (2013): "Making Data Count: Research data availability and research
assessment", workshop, Berlin, http://www.knowledge-exchange.info/Default.aspx?ID=577
• Internet Archaeology: Data Papers, http://intarch.ac.uk/authors/data-papers.html
• Journal of Open Archaeology Data, http://openarchaeologydata.metajnl.com
• JISC / Curtis G., Hammond M. & Oppenheim C. (2013): Access to Citation Data: Cost-benefit
and Risk Review and Forward Look. JISC, September 2013,
http://repository.jisc.ac.uk/5371/1/Access-to-Citation-data-report-final.pdf
• Key Perspectives (2010): Data Dimensions: Disciplinary Differences in Research Data Sharing,
Reuse and Long Term Viability. Digital Curation Center,
http://www.dcc.ac.uk/sites/default/files/SCARP%20SYNTHESIS_FINAL.pdf
• Kvalheim V. & Kvamme T. (2014): Policies for Sharing Research Data in Social Sciences and
Humanities. A survey about research funders’ data policies. International Federation of Data
Organisations (IFDO), http://
www.ada.edu.au/documents/ifdo-report-on-policies-for-data-sharing
• Lawrence B., Jones C., Matthews B., Pepler S. & Callaghan S. (2011): Citation and peer review
of data: Moving towards formal data publication. International Journal of Digital Curation,
6(2): 4-37, http://www.ijdc.net/index.php/ijdc/article/viewFile/181/265

28. References and other literature /5
• MAPPA Open Data (University of Pisa, Italy), http://mappaproject.arch.unipi.it/?lang=en
• OECD: Declaration on Access to Research Data from Public Funding (30.01.2004),
http://acts.oecd.org/Instruments/ShowInstrumentView.aspx?InstrumentID=157&Lang=en&Book=
• OECD: Principles and Guidelines for Access to Research Data from Public Funding (2007),
http://www.oecd.org/science/sci-tech/38500813.pdf
• Open Context (Alexandria Archive Institute, USA), http://opencontext.org
• Opportunities for Data Exchange (ODE) project / Kotarski R. et al. (2012). Report on best
practices for citability of data and on evolving roles in scholarly communication,
http://www.alliancepermanentaccess.org/index.php/community/current-projects/ode/outputs/
• PARSE.Insight: Insight into digital preservation of research output in Europe. Project
deliverable D3.4: Survey Report, 9 December 2009,
http://www.parse-insight.eu/downloads/PARSE-Insight_D3-4_SurveyReport_final_hq.pdf
• Parsons M.A. & Fox P.A. (2013): Is data publication the right metaphor? In: Data Science
Journal, vol.12, February 2013, https://www.jstage.jst.go.jp/article/dsj/12/0/12_WDS-042/_
pdf
• Piwowar H.A. & Vision T.J. (2013): Data reuse and the open data citation advantage. In:
PeerJ, 1:e175 http://dx.doi.org/10.7717/peerj.175
• MAPPA Open Data (University of Pisa, Italy), http://mappaproject.arch.unipi.it/?lang=en
• OECD: Declaration on Access to Research Data from Public Funding (30.01.2004),
http://acts.oecd.org/Instruments/ShowInstrumentView.aspx?InstrumentID=157&Lang=en&Book=F
• OECD: Principles and Guidelines for Access to Research Data from Public Funding (2007),
http://www.oecd.org/science/sci-tech/38500813.pdf
• Open Context (Alexandria Archive Institute, USA), http://opencontext.org
• Opportunities for Data Exchange (ODE) project / Kotarski R. et al. (2012). Report on best
practices for citability of data and on evolving roles in scholarly communication,
http://www.alliancepermanentaccess.org/index.php/community/current-projects/ode/outputs/
• PARSE.Insight: Insight into digital preservation of research output in Europe. Project
deliverable D3.4: Survey Report, 9 December 2009,
http://www.parse-insight.eu/downloads/PARSE-Insight_D3-4_SurveyReport_final_hq.pdf
• Parsons M.A. & Fox P.A. (2013): Is data publication the right metaphor? In: Data Science
Journal, vol.12, February 2013, https://www.jstage.jst.go.jp/article/dsj/12/0/12_WDS-042/_
pdf
• Piwowar H.A. & Vision T.J. (2013): Data reuse and the open data citation advantage. In:
PeerJ, 1:e175 http://dx.doi.org/10.7717/peerj.175

29. References and other literature /6
• Pryor, Graham (2009): Multi-scale Data Sharing in the Life Sciences: Some Lessons for Policy
Makers. International Journal of Digital Curation, Vol. 4, No 3, http://
ijdc.net/index.php/ijdc/article/view/135/178
• Research Data Alliance, https://www.rd-alliance.org
• RIN - Research Information Network & British Library (2010): Patterns of information use
and exchange: case studies of researchers in the life sciences, http://
www.rin.ac.uk/our-work/using-and-accessing-information-resources/patterns-information-use-an
• RIN - Research Information Network & Key Perspectives (2008): To Share or not to Share:
Publication and Quality Assurance of Research Data Outputs. Main Report and Annex. RIN:
London, http://
www.rin.ac.uk/our-work/data-management-and-curation/share-or-not-share-research-data-outpu
• RIN & NESTA - National Endowment for Science, Technology, and the Arts (2010): Open to
All? Case Studies of Openness in Research. Report prepared by the Digital Curation Centre.
London: RIN, http://
www.rin.ac.uk/our-work/data-management-and-curation/open-science-case-studies
• Science magazine: Science Staff introduction to the Special Issue “Dealing with Data”,
Science, Vol. 331 no. 6018, 11 February 2011, pp. 692-693,
http://www.sciencemag.org/content/331/6018/692.short
• tDAR - The Digital Archaeological Record (Digital Antiquity consortium, USA), http://
www.tdar.org
• Pryor, Graham (2009): Multi-scale Data Sharing in the Life Sciences: Some Lessons for Policy
Makers. International Journal of Digital Curation, Vol. 4, No 3, http://
ijdc.net/index.php/ijdc/article/view/135/178
• Research Data Alliance, https://www.rd-alliance.org
• RIN - Research Information Network & British Library (2010): Patterns of information use
and exchange: case studies of researchers in the life sciences, http://
www.rin.ac.uk/our-work/using-and-accessing-information-resources/patterns-information-use-and
• RIN - Research Information Network & Key Perspectives (2008): To Share or not to Share:
Publication and Quality Assurance of Research Data Outputs. Main Report and Annex. RIN:
London, http://
www.rin.ac.uk/our-work/data-management-and-curation/share-or-not-share-research-data-outpu
• RIN & NESTA - National Endowment for Science, Technology, and the Arts (2010): Open to
All? Case Studies of Openness in Research. Report prepared by the Digital Curation Centre.
London: RIN, http://
www.rin.ac.uk/our-work/data-management-and-curation/open-science-case-studies
• Science magazine: Science Staff introduction to the Special Issue “Dealing with Data”,
Science, Vol. 331 no. 6018, 11 February 2011, pp. 692-693,
http://www.sciencemag.org/content/331/6018/692.short
• tDAR - The Digital Archaeological Record (Digital Antiquity consortium, USA), http://
www.tdar.org

30. References and other literature /7
• Tenopir C., Allard S., Douglass K. et al. (2011): Data Sharing by Scientists: Practices and
Perceptions. PLoS ONE 6(6): e21101,
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021101
• The Royal Society, Science as an Open Enterprise, June 2012,
http://royalsociety.org/policy/projects/science-public-enterprise/report/
• Thessen, A.E & Patterson, D.J (2011) Data issues in the life sciences. In: ZooKeys 150: 15–51,
http://www.pensoft.net/journals/zookeys/article/1766/data-issues-in-the-life-sciences
• Uhlir, Paul F. (ed.): For attribution: Developing scientific data attribution and citation
practices and standards: Summary of an international workshop. Washington, D.C.: National
Academies Press, 2012, http://www.nap.edu/catalog.php?record_id=13564
• Vines T.H., Andrew R.L., Bock D.G. et al. (2013): Mandated data archiving greatly improves
access to research data. FASEB Journal, 27(4), http://www.fasebj.org/content/27/4/1304
• Wright S.J., Kozlowski W.A., Dietrich D. et al. (2013): Using Data Curation Profiles to Design
the Datastar Dataset Registry. D-Lib Magazine, Volume 19, Number 7/8, July/August 2013,
http://www.dlib.org/dlib/july13/wright/07wright.html
• zenodo, http://www.zenodo.org (CERN, related to OpenAIRE)
• Tenopir C., Allard S., Douglass K. et al. (2011): Data Sharing by Scientists: Practices and
Perceptions. PLoS ONE 6(6): e21101,
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021101
• The Royal Society, Science as an Open Enterprise, June 2012,
http://royalsociety.org/policy/projects/science-public-enterprise/report/
• Thessen, A.E & Patterson, D.J (2011) Data issues in the life sciences. In: ZooKeys 150: 15–51,
http://www.pensoft.net/journals/zookeys/article/1766/data-issues-in-the-life-sciences
• Uhlir, Paul F. (ed.): For attribution: Developing scientific data attribution and citation
practices and standards: Summary of an international workshop. Washington, D.C.: National
Academies Press, 2012, http://www.nap.edu/catalog.php?record_id=13564
• Vines T.H., Andrew R.L., Bock D.G. et al. (2013): Mandated data archiving greatly improves
access to research data. FASEB Journal, 27(4), http://www.fasebj.org/content/27/4/1304
• Wright S.J., Kozlowski W.A., Dietrich D. et al. (2013): Using Data Curation Profiles to Design
the Datastar Dataset Registry. D-Lib Magazine, Volume 19, Number 7/8, July/August 2013,
http://www.dlib.org/dlib/july13/wright/07wright.html
• zenodo, http://www.zenodo.org (CERN, related to OpenAIRE)

31. Disclaimer
ARIADNE is a project funded by the European Commission under the
Community’s Seventh Framework Programme, contract no. FP7-
INFRASTRUCTURES-2012-1-313193.
The views and opinions expressed in this presentation are the sole
responsibility of the authors and do not necessarily reflect the views
of the European Commission.
ARIADNE is a project funded by the European Commission under the
Community’s Seventh Framework Programme, contract no. FP7-
INFRASTRUCTURES-2012-1-313193.
The views and opinions expressed in this presentation are the sole
responsibility of the authors and do not necessarily reflect the views
of the European Commission.