The document discusses dataset identification and citation using DataCite and EZID. It provides an introduction to DataCite, which helps researchers find, access, and reuse data, and EZID, which allows for easy creation and management of DataCite DOIs and other identifiers. The document outlines the DataCite metadata schema, describes functionality available through EZID, and recommends next steps for using these services.
Persistent Identifiers, Herbarium workshop at Kongsvold, September 1 to 4, 2014Dag Endresen
Implementation of persistent and globally unique identifiers for specimens held in natural history collections worldwide will open up new opportunities for referring to these physical resources in an interlinked digital context such as the Internet. Here, we will describe the approach for persistent identification of collection specimens developed and implemented at the Natural History Museum in Oslo (NHM-UiO) by the the Norwegian participant node to the Global Biodiversity Information Facility (GBIF-Norway). The Norwegian university museums are invited to use our resolver service at "http://purl.org/gbifnorway/id/<uuid>" when publishing biodiversity data to GBIF. All occurrence records published through GBIF-Norway, with appropriate PURL-UUID identifiers mapped to the Darwin Core occurrenceID, will automatically be added to our resolver service and kept updated.
The explosion of data creation across all scholarly disciplines necessitates corresponding efforts to create new solutions for its management and use. Ever-growing repositories and datasets within require organization, identification, description, publication, discovery, citation, preservation, and curation to allow these materials to realize their potential in support of data-driven, often interdisciplinary research. What infrastructures and technical environments are required for this work? Can new approaches, specifications, standards and best practices be created? Are there partnerships and collaborations that exist or can be pursued? This webinar, Part 2 of a two-part NISO series on data, will explore these and other questions
These are the slides for Robert H. McDonald for the Future Trends Panel Presentation at the the Inter-institutional Approaches to Supporting Scholarly Communication Symposium held on August 16, 2012 at the Georgia Institute of Technology.
Persistent Identifiers, Herbarium workshop at Kongsvold, September 1 to 4, 2014Dag Endresen
Implementation of persistent and globally unique identifiers for specimens held in natural history collections worldwide will open up new opportunities for referring to these physical resources in an interlinked digital context such as the Internet. Here, we will describe the approach for persistent identification of collection specimens developed and implemented at the Natural History Museum in Oslo (NHM-UiO) by the the Norwegian participant node to the Global Biodiversity Information Facility (GBIF-Norway). The Norwegian university museums are invited to use our resolver service at "http://purl.org/gbifnorway/id/<uuid>" when publishing biodiversity data to GBIF. All occurrence records published through GBIF-Norway, with appropriate PURL-UUID identifiers mapped to the Darwin Core occurrenceID, will automatically be added to our resolver service and kept updated.
The explosion of data creation across all scholarly disciplines necessitates corresponding efforts to create new solutions for its management and use. Ever-growing repositories and datasets within require organization, identification, description, publication, discovery, citation, preservation, and curation to allow these materials to realize their potential in support of data-driven, often interdisciplinary research. What infrastructures and technical environments are required for this work? Can new approaches, specifications, standards and best practices be created? Are there partnerships and collaborations that exist or can be pursued? This webinar, Part 2 of a two-part NISO series on data, will explore these and other questions
These are the slides for Robert H. McDonald for the Future Trends Panel Presentation at the the Inter-institutional Approaches to Supporting Scholarly Communication Symposium held on August 16, 2012 at the Georgia Institute of Technology.
Data Equivalence
Mark Parsons, Lead Project Manager, Senior Associate Scientist, National Snow and Ice Data Center
Data citation, especially using persistent identifiers like Digital Object Identifiers (DOIs), is an increasingly accepted scientific practice. Recently, several, respected organizations have developed guidelines for data citation. The different guidelines are largely congruent in that they agree on the basic practice and elements of data citation, especially for relatively static, whole data collections. There is less agreement on the more subtle nuances of data citation that are sometimes necessary to ensure precise reference and scientific reproducibility--the core purpose of data citation. We need to be sure that if you follow a data reference you get to the precise data that were used or at least their scientific equivalent. Identifiers such as DOIs are necessary but not sufficient for the precise, detailed, references necessary. This talk discusses issues around data set versioning, micro-citation, and scientific equivalence. I propose some interim solutions and suggest research strategies for the future.
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And here is the abstract of the talk:
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Data Equivalence
Mark Parsons, Lead Project Manager, Senior Associate Scientist, National Snow and Ice Data Center
Data citation, especially using persistent identifiers like Digital Object Identifiers (DOIs), is an increasingly accepted scientific practice. Recently, several, respected organizations have developed guidelines for data citation. The different guidelines are largely congruent in that they agree on the basic practice and elements of data citation, especially for relatively static, whole data collections. There is less agreement on the more subtle nuances of data citation that are sometimes necessary to ensure precise reference and scientific reproducibility--the core purpose of data citation. We need to be sure that if you follow a data reference you get to the precise data that were used or at least their scientific equivalent. Identifiers such as DOIs are necessary but not sufficient for the precise, detailed, references necessary. This talk discusses issues around data set versioning, micro-citation, and scientific equivalence. I propose some interim solutions and suggest research strategies for the future.
Carmen O'Dell and Barbara Sen JIBS-RLUK event July 2012sherif user group
RDM Rose by Carmen O'Dell and Barbara Sen, (University of Sheffield). Presentation at Demystifying Research Data: don’t be scared be prepared: A joint JIBS/RLUK event, Tuesday 17th July 17th July 2012, Brunei Gallery at SOAS (School of Oriental and African Studies), London.
NSF Workshop Data and Software Citation, 6-7 June 2016, Boston USA, Software Panel
FIndable, Accessible, Interoperable, Reusable Software and Data Citation: Europe, Research Objects, and BioSchemas.org
Alexandra Basford, InCoB 2011: A Journal’s Perspective on Data Standards and ...GigaScience, BGI Hong Kong
Alexandra Basford's talk in the curation session at the InCoB meeting in Kuala Lumpar, 30/11/11 on: GigaScience: A Journal’s Perspective on Data Standards and Biocuration
A huge amount of incredibly diverse research data remains beyond the reach of internet search engines, peer review processes, and systematic cataloging. The ability by consumers to annotate data is an important mitigation, harnessing "the crowd" to make it easier for everyone to discover and re-use data.
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
Data Science: Origins, Methods, Challenges and the future?Cagatay Turkay
Slides for my talk at City Unrulyversity on 18.03.15 in London. Discuss the term Data Science, touch upon the origins and the data scientist types. A longer discussion on the Data Science process and challenges analysts face.
And here is the abstract of the talk:
Data Science ... the term is everywhere now, on the news, recruitment sites, technology boards. "Data scientist" is even named to be sexiest job title of the century. But what is it, really? Is it just a hype or a term that will be with us for some time?
This session will investigate where the term is originating from and how it relates to decades of research in established fields such as statistics, data mining, visualisation and machine learning. We will investigate how the field is evolving with the emergence of large, heterogeneous data resources. We will discuss the objectives, tools and challenges of data science as a practice, and look at examples from research and industrial applications.
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1. Dataset Identification & Citation:
DataCite and EZID
Joan Starr
California Digital Library
October, 2011
2. Dataset Identification & Citation
Introduction
The Researchers’ Challenge
Identifiers are a tool for researchers
DataCite
“Helping you find, access and reuse data.”
EZID
Easy creation and management of DataCite DOIs and other
identifiers.
Next steps
For DataCite, EZID and you!
6. Early in the research life cycle
Data-intensive research + Writing up the results
Where’s
the data? What if I
move it?
PERSISTENT IDENTIFIERS
make the difference
by Dave Rogers http://www.flickr.com/photos/dave-rogers/2815036285/
9. Meeting funder requirements
• Data-intensive research + • Grantor requirements
for data management
What do we plan
put here?
How do we
track the data?
PERSISTENT IDENTIFIERS
make the difference
By David Mellis, http://www.flickr.com/photos/mellis/7675610/
10.
11. DataCite
German National Library of Economics (ZBW) Canada Institute for Scientific and Technical Information
German National Library of Science and Technology (TIB) (CISTI)
German National Library of Medicine (ZB MED) Technical Information Center of Denmark
GESIS - Leibniz Institute for the Social Sciences, Germany Institute for Scientific & Technical Information (INIST-
Australian National Data Service (ANDS) CNRS), France
ETH Zurich, Switzerland TU Delft Library, The Netherlands
The Swedish National Data Service (SNDS)
The British Library , UK
California Digital Library (CDL), USA
Office of Scientific & Technical Information (OSTI), USA
Purdue University Library
12. DataCite Metadata V. 2.2
• Small required set = citation elements
• Optional descriptive set:
– extendable lists
– can refer to other standards, schemes
– domain-neutral
– rich ability to describe relationships to other
digital objects
• Metadata Search (MDS) is full-text indexed
13. DataCite Metadata V. 2.2
Required properties Optional properties
1. Identifier (with type attribute) 6. Subject (with schema attribute)
2. Creator (with name identifier 7. Contributor (with type & name identifier
attributes) attributes)
3. Title (with optional type attribute) 8. Date (with type attribute)
4. Publisher 9. Language
5. PublicationYear 10. ResourceType (with description
attribute)
11. AlternateIdentifier (with type attribute)
12. RelatedIdentifier (with type &relation
type attributes)
13. Size
14. Format
15. Version
16. Rights
17. Description (with type attribute)
24. Next Steps
DataCite
• Dublin Core application profile
• Content Service
• Metadata v. 2.3
EZID
•UI redesign
•Automated link checking
•Exposure for citations
By Nicola Whitaker http://www.flickr.com/photos/nicolawhitaker/111009156/
25. Next Steps for you
• Get more information, and
• Try EZID for yourself!
By Nicola Whitaker http://www.flickr.com/photos/nicolawhitaker/111009156/
26. For more information
EZID
EZID application: http://n2t.net/ezid/
EZID website: http://www.cdlib.org/services/uc3/ezid/
UC3 website: http://www.cdlib.org/services/uc3/
DataCite
DataCite Home: http://datacite.org/
DataCite Metadata Schema:
http://schema.datacite.org/meta/kernel-2.2/index.html
DataCite Metadata Search: http://search.datacite.org
Contact Joan Starr at uc3@ucop.edu
27. Questions?
by Horia Varlan
http://www.flickr.com/photos/horiavarlan/4273168957/in/photostream/
Editor's Notes
Thank you for this opportunity to speak with you today about Dataset Identification & Citation.
My library:Serving the 10 UC campuses226,000 students 134,000 faculty and staffWorking collaborativelylibrariesdata centersmuseums, archivesfaculty and researchersCDL has historically provided strategic, integrated technical and program services in a broad portfolio, including:Groundbreaking licensing agreementsUnion bibliographic servicesData curation & preservation toolsOpen access publishing servicesCDL: http://www.cdlib.org/
My group:The UC Curation Center is creative partnership between the CDL, the ten UC campuses, and peer institutions in the community.An evolving community of shared concern and practice; bringing together diverse experience, expertise, and resources; providing robust curation solutions.
Let’s start out by taking a look at some common challenges in data-intensive research today.
Researchers doing data-intensive research and writing. Want to refer to the dataset right now even though they haven't yet found a permanent "home" for the data. If they get a persistent identifier for that dataset now, they will have reference that can be used in the paper. When the papers are published and the data is moved, the researcher simply updates the target URL, and the reference will still work.
RESEARCH TEAMS, work ACROSS REGIONS OR COUNTRIES where a data is hosted REMOTELY. Let’s assume the database is stored on someone’s departmental web server, but the server is getting old, soon to be replaced. The team can get an identifier now +circulate it to colleagues + the entire data federation. When the infrastructure is replaced, the team updates the location details so that references to the database continues to work perfectly.
Researchers who have published extensively and who want to be able to move around in their career, also may want to take their data with them.They can get identifiers for the work AND the datasets that go with it. With persistent identifiers, the references are never broken, because the researcher can keep the target URLs and other metadata up to date even as she moves.
As the NSF and other funders issue requirements for data management plans, scientists have to be able to answer basic questions like, How will you name and organize the data files? Persistent identifiers provide a ready answer to this requirement.
To address this challenge, DataCite was formed in 2009 by 10 Libraries and Research Centers.
The number has now grown to 15. In addition there are 3 associate members, including the Korea Institute of Science and Technology Information, so there is a presence in Asia.Mission: “"Helping you find, access, and reuse data"Advocacy, citationTo support citation, access and finding, you need…Metadata
MDS=Metadata Search
The 5 Required properties = basic citation elements[click]Optional elementsThe Family Jewels = RelatedIdentifer, relationTypeIsCitedBy & Cites IsSupplementTo & IsSupplementedByIsContinuedBy & Continues IsNewVersionOf & IsPreviousVersionOf IsPartOf & HasPart IsDocumentedBy & Documents isCompiledBy & CompilesIsVariantFormOf & IsOriginalFormOfCOMING IN 2.3: IsIdenticalTo
Now that we’ve discussed identifiers, how do you get them? How do you keep them up to date?EZIDA service to make and manage actionable idsCan manage identifiers under different schemes:ARKs, DOIs, and more to comeUser and programming interfacesPartnering for replication
How to use the UI to test EZIDARKs and DOIsARKsFlexibleCase-sensitiveSpecial features support granularityCan be deletedInexpensiveDOIsEstablished brand in publishingIndexed by major A&I citation databases DataCite policies applyCannot be deletedMore costlyDOIs should be assigned to objects that are under good long-term management, and where there is an intention is to make the object persistently available.DOIs must be registered exclusively with metadata that is available to public view.Can DOIs and ARKs work together?Yes. For example, researchers may choose to use ARKs for unpublished materials associated with an object that has been registered with a DOI. These two identifier schemes can work well together, and EZID offers them both, along with policy support consistent across both schemes.
Let’s take a look at the UI now. I would give you a live demo, but I’m afraid that it might have some difficulties traveling over SKYPE. I’ve made some key captures here, and I think it will work fairly well for us.So, thisis our User Interface. EZID also has a machine-to-machine interface, an API, and a link to the documentation is here.If you’d like to try EZID, simply click on the help tab [CLICK] here.
Let’s take a look at the UI now. I would give you a live demo, but I’m afraid that it might have some difficulties traveling over SKYPE. I’ve made some key captures here, and I think it will work fairly well for us.So, thisis our User Interface. EZID also has a machine-to-machine interface, an API, and a link to the documentation is here.If you’d like to try EZID, simply click on the help tab [CLICK] here.
On the Help screen, you have the choice of creating a test ARK or DOI.[CLICK] Click the Create button
On the Help screen, you have the choice of creating a test ARK or DOI.[CLICK] Click the Create button
EZID creates the identifier and sends you to the MANAGE tab where you have the opportunity to enter a target URL and other metadata as we’ve seen earlier.
EZID creates the identifier and sends you to the MANAGE tab where you have the opportunity to enter a target URL and other metadata as we discussed earlier. The EZID UI allows the entry of DataCite’s required set, and you can submit a full record using the API.
So here is what this means. Here is an example of a data set deposited with one of our clients, Dryad.Dryad is an international repository of data underlying peer-reviewed articles in the basic and applied biosciences.
Dublin Core application profile available for the DataCite Metadata Schema; we’ll keep it up to date and in-sync. From the DCMI: “A DCAP is designed to promote interoperability within the constraints of the Dublin Core model and to encourage harmonization of usage and convergence on "emerging semantics" around its edges.”Content Service exposes our metadata stored in the DataCite Metadata Store (MDS) using multiple formats Alpha version: The service can be accessed at http://data.datacite.orgEZID: UI redesignActivity reportingBrowse & searchEnhanced persistence supportAutomated link checkingTombstone pages (a web page returned for a resource no longer found at its target location of record. The tombstone may provide “last known” metadata, including the original owner.)Exposure for citationsThomson-Reuters (Web of Knowledge)Elsevier (Scopus)OAI? RSS?GoogleScholar