Mark A. Parsons and Peter A. Fox 19 December 2014 Why Data Citation Currently Misses the Point References: 1 Joint Declaration of Data Citation Principles, https://www.force11.org/datacitation 2 Chawla D S. 2014. Could digital badges clarify the roles of co-authors? ScienceInsider http://news.sciencemag.org/scientific-community/2014/11/could-digital-badges-clarify-roles- co-authors. See also http://projectcredit.net. 3 ESIP Data Stewardship Committee http://wiki.esipfed.org/index.php/ Preservation_and_Stewardship 4 Donovan C and S Hanney. 2011. The payback framework explained. Research Evaluation 20 (3): 181-183. http://dx.doi.org/10.3152/095820211X13118583635756 What’s the use case? In recent years, the data management community has begun to codify data citation practices and associated technologies, especially persistent identifiers. Nonetheless, digital data sets are rarely cited formally or specifically. Moreover, citation has done little to open up data in the unindexed deep Web. There are many reasons for this, but we believe one problem is that data managers expect too much from classic bibliographic- style data citation and assume false parallels between data and literature. The idea of formalizing data citation emerged in the 1990s, primarily as a mechanism to encourage and reward data sharing by giving credit to data “authors”. The approach seemed a logical extension of the publishing incentive, but it has not provided a strong incentive to share or done much to expose hidden data. We need to reconsider the use case, but instead work-around efforts such as “data journals” have emerged to try and make data publishing more akin to literature publishing. Meanwhile, the community is recognizing other purposes of citation, notably to help ensure a scientific result can be verified. After much discussion amongst competing views, the community converged around a core set of data citation principles 1 . These principles are an important step forward, but they are primarily oriented toward formal, scholarly citation. They hint at, but do not fully consider, the myriad ways data are used. In this poster, we take a broader view on what we are trying to accomplish with data citation by exploring several use cases around attribution, provenance, and impact. We seek to start a conversation on how we can robustly address the myriad use cases that begin to uncover the deep Web. We suggest that we need more sophisticated, diverse, and nuanced approaches to actually address the many use cases of identifying, tracking, and enhancing data use. 1. Attribution and Credit Provide fair and recognized attribution for all personnel involved in creating a data set. Some concerns: • Who is the “author” of a data set? • What is the appropriate credit mechanism for all involved? • Who gets credit for what? Some ideas: Project CRediT has defined a taxonomy of contributor roles for publications and suggests using digital badges that detail what each author did for the work and link to their profiles elsewhere on the Web 2 . Can we do this for data? Project CRediT (Contributor Roles Taxonomy) why not change the world? ® 2. Tracking and Provenance Identify and trace all observations used in forcing and constraining a model run. Some concerns: • How to capture the purpose of the data in the model e.g. forcing, assimilation, boundary conditions? • How to reference the precise version and subset used. • How far back does one need to go (see figure below). • What are references (PIDs) pointing too? Some ideas: This is really an issue of provenance not just reference. Full reproducibility requires being able to trace data, processes, and tools. While persistent identifiers are crucial they are insufficient. A fuller semantic description of the provenance is required as well as richer context description. See provenance work of ESIP 3 . 3. Impact and Return on Investment Provide a means to track the use, impact, and value of a data set. Some concerns: • Data are used in many contexts that do not result in a formal article, e.g. land use planning, disaster response, agricultural prediction, policy analysis, education, etc. • How to attribute a particular outcome to a particular person. • Qualitative impact may be as important as quantitative, but it is hard to measure. Consider the impact of this Apollo 8 image on public consciousness. Some ideas: In health and social sciences researchers have developed a “Payback Framework” with a logical model of the complete research process and categories of payback from research 4 . Can we extend this and apply it to data? If we assign credit badges as suggested in use case 1, can we aggregate the links to those badges through search engines rather than relying on constrained citation indices? “Payback Categories” 1. Knowledge 2. Benefits to future research and research use 3. Benefits from informing policy and product development 4. Environmental and public sector benefits 5. Broader economic benefits Rensselaer Polytechnic Institute — rpi.edu Data citation in theory and practice The Data Citation Principles cover purpose, function and attributes of citations. These principles recognize the dual necessity of creating citation practices that are both human understandable and machine- actionable. 1. Importance Data should be considered legitimate, citable products of research. Data citations should be accorded the same importance in the scholarly record as citations of other research objects, such as publications. 2. Credit and Attribution Data citations should facilitate giving scholarly credit and normative and legal attribution to all contributors to the data, recognizing that a single style or mechanism of attribution may not be applicable to all data. 3. Evidence In scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited. 4. Unique Identification A data citation should include a persistent method for identification that is machine actionable, globally unique, and widely used by a community. 5. Access Data citations should facilitate access to the data themselves and to such associated metadata, documentation, code, and other materials, as are necessary for both humans and machines to make informed use of the referenced data. 6. Persistence Unique identifiers, and metadata describing the data, and its disposition, should persist -- even beyond the lifespan of the data they describe. 7. Specificity and Verifiability Data citations should facilitate identification of, access to, and verification of the specific data that support a claim. Citations or citation metadata should include information about provenance and fixity sufficient to facilitate verifying that the specific timeslice, version and/ or granular portion of data retrieved subsequently is the same as was originally cited. 8. Interoperability and Flexibility Data citation methods should be sufficiently flexible to accommodate the variant practices among communities, but should not differ so much that they compromise interoperability of data citation practices across communities. Joint Declaration of Data Citation Principles 1 Figure courtesy Curt Tilmes, NASA 1. Conceptualization 2. Methodology 3. Software 4. Validation 5. Formal analysis 6. Investigation 7. Resources 8. Data curation 9. Writing – original draft 10. Writing – review & editing 11. Visualization 12. Supervision 13. Project administration 14. Funding acquisition Initial Conclusions • Much data use and production occur outside of the regular scholarly discourse (i.e. the literature). • The principles of data citation are strong, and the increasing use of persistent identifiers is a significant advance, but we must think beyond bibliographic-style citation. • It is important to have a citation approach that can readily be accepted by scholarly publishers, but we should not assume that that approach addresses other concerns. We must separate the various concerns around citation by considering multiple use cases. • Indeed we must consider use cases in the first place! What problem are we truly trying to solve? • Other disciplines are taking a more nuanced look at these issues outside the realm of publication. Geosciences should too. Stage 0 ID Topic Stage 1 Inputs Stage 2 Research process Stage 3 Primary outputs Stage 4 Secondary outputs: policy, products Stage 5 Adoption Stage 6 Final outcomes Stock or reservoir of knowledge Interface A project specification Interface B dissemination direct impact from processes and outputs to adoption The political , professional, and industrial environment and wider society direct feedback paths The logical model of the Payback Framework PROJECTCREDITNET (CONTRIBUTOR TAXONOMY): J. SCOTT, L. ALLEN, A. BRAND ET AL.; BIOMED CENTRAL DESIGN (BADGE DESIGNS)/ CREATIVE COMMONS 4.0
Why Data Citation Currently Misses the Point
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Mark A. Parsons and Peter A. Fox
19 December 2014
Why Data Citation Currently Misses the Point
References: 1Joint Declaration of Data Citation Principles, https://www.force11.org/datacitation 2Chawla D S. 2014. Could digital badges clarify the roles of co-authors? ScienceInsider http://news.sciencemag.org/scientific-community/2014/11/could-digital-badges-clarify-roles-co-authors. See also http://projectcredit.net. 3ESIP Data Stewardship Committee http://wiki.esipfed.org/index.php/Preservation_and_Stewardship 4Donovan C and S Hanney. 2011. The payback framework explained. Research Evaluation 20 (3): 181-183. http://dx.doi.org/10.3152/095820211X13118583635756
What’s the use case?
In recent years, the data management community has begun to codify data citation practices and associated technologies, especially persistent identifiers. Nonetheless, digital data sets are rarely cited formally or specifically. Moreover, citation has done little to open up data in the unindexed deep Web. There are many reasons for this, but we believe one problem is that data managers expect too much from classic bibliographic-style data citation and assume false parallels between data and literature.
The idea of formalizing data citation emerged in the 1990s, primarily as a mechanism to encourage and reward data sharing by giving credit to data “authors”. The approach seemed a logical extension of the publishing incentive, but it has not provided a strong incentive to share or done much to expose hidden data. We need to reconsider the use case, but instead work-around efforts such as “data journals” have emerged to try and make data publishing more akin to literature publishing. Meanwhile, the community is recognizing other purposes of citation, notably to help ensure a scientific result can be verified. After much discussion amongst competing views, the community converged around a core set of data citation principles1. These principles are an important step forward, but they are primarily oriented toward formal, scholarly citation. They hint at, but do not fully consider, the myriad ways data are used.
In this poster, we take a broader view on what we are trying to accomplish with data citation by exploring several use cases around attribution, provenance, and impact. We seek to start a conversation on how we can robustly address the myriad use cases that begin to uncover the deep Web.
We suggest that we need more sophisticated, diverse, and nuanced approaches to actually address the many use cases of identifying, tracking, and enhancing data use.
1. Attribution and Credit Provide fair and recognized attribution for all personnel involved in creating a data set.
Some concerns: • Who is the
“author” of a data set?
• What is the appropriate credit mechanism for all involved?
• Who gets credit for what?
Some ideas: Project CRediT has defined a taxonomy of contributor roles for publications and suggests using digital badges that detail what each author did for the work and link to their profiles elsewhere on the Web2. Can we do this for data?
Project CRediT (Contributor Roles Taxonomy)
why not change the world? ®
2. Tracking and Provenance Identify and trace all observations used in forcing and constraining a model run.
Some concerns: • How to capture the
purpose of the data in the model e.g. forcing, assimilation, boundary conditions?
• How to reference the precise version and subset used.
• How far back does one need to go (see figure below). • What are references (PIDs) pointing too?
Some ideas: This is really an issue of provenance not just reference. Full reproducibility requires being able to trace data, processes, and tools. While persistent identifiers are crucial they are insufficient. A fuller semantic description of the provenance is required as well as richer context description. See provenance work of ESIP3.
3. Impact and Return on Investment Provide a means to track the use, impact, and value of a data set.
Some concerns: • Data are used in many contexts that do not result in a formal
article, e.g. land use planning, disaster response, agricultural prediction, policy analysis, education, etc.
• How to attribute a particular outcome to a particular person. • Qualitative impact may
be as important as quantitative, but it is hard to measure. Consider the impact of this Apollo 8 image on public consciousness.
Some ideas: In health and social sciences researchers have developed a “Payback Framework” with a logical model of the complete research process and categories of payback from research4. Can we extend this and apply it to data? If we assign credit badges as suggested in use case 1, can we aggregate the links to those badges through search engines rather than relying on constrained citation indices?
“Payback Categories” 1. Knowledge 2. Benefits to future research and research use 3. Benefits from informing policy and product development 4. Environmental and public sector benefits 5. Broader economic benefits
Rensselaer Polytechnic Institute — rpi.edu
Data citation in theory and practice
The Data Citation Principles cover purpose, function and attributes of citations. These principles recognize the dual necessity of creating citation practices that are both human understandable and machine-actionable. 1. Importance
Data should be considered legitimate, citable products of research. Data citations should be accorded the same importance in the scholarly record as citations of other research objects, such as publications.
2. Credit and Attribution Data citations should facilitate giving scholarly credit and normative and legal attribution to all contributors to the data, recognizing that a single style or mechanism of attribution may not be applicable to all data.
3. EvidenceIn scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited.
4. Unique IdentificationA data citation should include a persistent method for identification that is machine actionable, globally unique, and widely used by a community.
5. Access Data citations should facilitate access to the data
themselves and to such associated metadata, documentation, code, and other materials, as are necessary for both humans and machines to make informed
use of the referenced data. 6. Persistence
Unique identifiers, and metadata describing the data, and its disposition, should persist -- even beyond the lifespan of the data they describe.
7. Specificity and Verifiability Data citations should facilitate identification of, access to, and verification of the specific data that support a claim. Citations or citation metadata should include information about provenance and fixity sufficient to facilitate verifying that the specific timeslice, version and/or granular portion of data retrieved subsequently is the same as was originally cited.
8. Interoperability and Flexibility Data citation methods should be sufficiently flexible to accommodate the variant practices among communities, but should not differ so much that they compromise interoperability of data citation practices across communities.
Joint Declaration of Data Citation Principles1
Figure courtesy Curt Tilmes, NASA
1. Conceptualization 2. Methodology 3. Software 4. Validation 5. Formal analysis 6. Investigation 7. Resources 8. Data curation
9. Writing – original draft 10. Writing – review &
editing 11. Visualization 12. Supervision 13. Project administration 14. Funding acquisition
Initial Conclusions • Much data use and production occur outside of the regular scholarly discourse (i.e. the literature). • The principles of data citation are strong, and the increasing use of persistent identifiers is a
significant advance, but we must think beyond bibliographic-style citation. • It is important to have a citation approach that can readily be accepted by scholarly publishers, but we
should not assume that that approach addresses other concerns. We must separate the various concerns around citation by considering multiple use cases.
• Indeed we must consider use cases in the first place! What problem are we truly trying to solve? • Other disciplines are taking a more nuanced look at these issues outside the realm of publication.
Geosciences should too.
Stage 0 ID Topic
Stage 1 Inputs
Stage 2 Research process
Stage 3 Primary outputs
Stage 4 Secondary
outputs: policy,
products
Stage 5 Adoption
Stage 6 Final
outcomes
Stock or reservoir of knowledge
Interface A project
specification
Interface B dissemination
direct impact from processes and outputs to adoption
The political , professional, and industrial environment and wider society
direct feedback paths
The logical model of the Payback Framework
PROJECTCREDITNET (CONTRIBUTOR TAXONOMY): J. SCOTT, L. ALLEN, A. BRAND ET AL.; BIOMED CENTRAL DESIGN (BADGE DESIGNS)/CREATIVE COMMONS 4.0
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