April 23 NISO Virtual Conference: Dealing with the Data Deluge: Successful Techniques for Scientific Data Management
 

April 23 NISO Virtual Conference: Dealing with the Data Deluge: Successful Techniques for Scientific Data Management

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About the Virtual Conference ...

About the Virtual Conference
With the expansion of digital data collection and the increased expectations of data sharing, researchers are turning to their libraries or institutional repositories as a place to store and preserve that data. Many institutions have created such data management services and see the data curation role as a growing and important element of their service portfolio. While some of the experience in managing other types of digital resources is transferrable, the management of large-scale scientific data has many special requirements and challenges. From metadata collection and cataloging data sources, to identification, discovery, and preservation, best practices and standards are still in their infancy.
This Virtual Conference will explore in greater depth than traditional webinars some of the practical lessons from those who have implemented data management and developed best practices, as well as provide some insight into the evolving issues the community faces. It will include discussions related to certification of trusted repositories, provenance and identification issues around data, data citation, preservation, and the work of several repository networks to advance distribution of scientific information.

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  • Current archives/collections/repositories already meeting public access requirements regarding dataNACDA – NACJD – SAMHDA: examples of long term sustainabilityNAHDAP – SAMHDA – DSDR: examples of sharing of confidential dataNACJD – example of depository/researcher compliance (holding 10% of funding to PI)LGBT – MET: unique infrastructure and disseminationResearch Connections: reports and data dissemination; audiences including policymakers
  • Abstract:Decades of data citation research, initiatives and guidelines have been consolidated into a single set of Data Citation Principles, created by a synthesis group that represents more than 25 organizations. The principles are driven by the premise that "sound, reproducible scholarship rests upon a foundation of robust, accessible data" and therefore "data should be considered legitimate, citable products of research". The Dataverse repository, developed at Harvard University's IQSS, generates a data citation compliant with the Joint Principles, and provides data publishing workflows to guarantee a persistent linkage between journal articles and the underlying data. The Dataverse is open and free to all researchers.
  • We are all familiar with the metaphor of the data deluge … we are all being drowned in dataAnd we all may be drowning, period….Concerns with data capture the prior trends in significant waysAnd yet, much of that data is runoff – it is not curated, and maybe should not be keptWe need to identify what is the “right stuff” to keepThe right way to keep itAnd the right tools and services to make it useful**Data have become a critical focus for scholarly communication – but we cannot address ALL, or even very many, of those issues here. Will try to stay as narrowly focused on the issues of data citation and attribution as we can.
  • Here’s the real problem with the data deluge, and the data policies – is an utter lack of agreement on what constitutes data! Data tend to be defined by example – unacceptable in usual scholarly discourse. Would you define an animal by example?RCUK says data, specimens, models – identifying something as data, or a form of evidence, is itself a scholarly act. Marie Curie’s notebook is scientific data and also historical dataThese astronomical data can only be understood with access to the models used to generate themThe field notes in the bottom right are of little value without the research design and interpretationThe mouse is most certainly data – but getting useful information may require sending a postdoc to someone’s lab for 6 months to learn the method.
  • We’re here to talk about attribution. In CC terms, that means giving credit – but credit for what?If you have obtained data through a license, it may require that the data be cited in a certain wayThe broader issue, is attribution for what?We heard yesterday that data citation is an incentive for data release. That’s an untested hypothesis – and needs to be testedWhat we found at the symposium was that everyone down the line had their hand out! The mechanism for citation will vary by who is getting credit and the reason for making the reference. These are but a few of the many stakeholders that might deserve credit in some situations.
  • In between: Publication plus methods for longitudinal research** few researchers conduct their activities with reuse in mind – DL services has to begin at the very beginning of the process if data are to be managed and useful to anyone later.
  • Comparison of two large collaborative research sitesInterviews and ethnographic fieldworkThe data practices of CENS and SDSS researchers have implications for data curation, system evaluation, and policy.
  • Beyond the method of collection (sensor vs hand) and the domain interested (robotics, systems, app sci) there are these other dimensions along which “use” varies.
  • Some data that are important to the conduct of research are not viewed as sufficiently valuable to keep. Other data of great value may not be mentioned or cited, because those data serve only as background to a given investigation. Metrics to assess the value of documents do not map well to data.
  • The ability to discover the existence of data is a critical requirement for a data-sharing infrastructure. We can define discovery as being the ability to determine the existence of a set of data objects with specified attributes or characteristics. The attributes of interest include aspects such as the producer of the data, the date of production, the method or production, a description of its contents, its representation. Discovery may also include aspects such as levels of quality, certification, or validation by third parties. Discoverability depends both on the description and representation of data and on tools and services to search for data objects. Data rarely are self-describing . Description  and representation usually take the form of metadata, some of which may be automated if data are generated by instruments such as sensor networks or telescopes. Much metadata creation requires human intervention, making it an expensive process that is often avoided by researchers (Edwards, Mayernik, Batcheller, Bowker & Borgman, 2011, forthcoming; Mayernik, 2011; Mayernik, Batcheller & Borgman, 2011). The lack of standards and practices for citing data, akin to citing publications, is a barrier to discoverability  [cite  BRDI mtg Aug 2011]. A variety of approaches to discovery are possible. Web search engines that walk the visible internet are one possibility assuming that data descriptions are reachable via standard web protocols. With the introduction of semantic web technologies and associated crawlers and search engines, location of data-sets of interest based on semantic content becomes possible. Alternatively, more discipline-specific and structured catalogs can be created. Arguably quite a bit of data is self describing: e.g., FITS, NetCDF, … Tho even those are incomplete. The more succinct ex we can include the better. Seems to me to be a distinct issue, related to naming not discoverability? A bit of both. Let’s discuss.
  • Let’s look more closely at each of theseIdentity – unique, and in what space should it be?Generic or field specific?Persistence – not all data should be available forecver – what needs to be identified and why?
  • The ability to discover the existence of data is a critical requirement for a data-sharing infrastructure. We can define discovery as being the ability to determine the existence of a set of data objects with specified attributes or characteristics. The attributes of interest include aspects such as the producer of the data, the date of production, the method or production, a description of its contents, its representation. Discovery may also include aspects such as levels of quality, certification, or validation by third parties. Discoverability depends both on the description and representation of data and on tools and services to search for data objects. Data rarely are self-describing . Description  and representation usually take the form of metadata, some of which may be automated if data are generated by instruments such as sensor networks or telescopes. Much metadata creation requires human intervention, making it an expensive process that is often avoided by researchers (Edwards, Mayernik, Batcheller, Bowker & Borgman, 2011, forthcoming; Mayernik, 2011; Mayernik, Batcheller & Borgman, 2011). The lack of standards and practices for citing data, akin to citing publications, is a barrier to discoverability  [cite  BRDI mtg Aug 2011]. A variety of approaches to discovery are possible. Web search engines that walk the visible internet are one possibility assuming that data descriptions are reachable via standard web protocols. With the introduction of semantic web technologies and associated crawlers and search engines, location of data-sets of interest based on semantic content becomes possible. Alternatively, more discipline-specific and structured catalogs can be created. Arguably quite a bit of data is self describing: e.g., FITS, NetCDF, … Tho even those are incomplete. The more succinct ex we can include the better. Seems to me to be a distinct issue, related to naming not discoverability? A bit of both. Let’s discuss.
  • This is the demand side. It is really hard to reuse other people’s data. You need to know so much about the data to trust what you’ve got. The cases of reuse that we find are data from curated repositories, as in astronomy, surveys, and so on. Even in the big data world, they spend up to 80% of their time cleaning data to make them reusable.Reuse in clinical trials, where reproducibility is part of the paradigmRelatively little reuse of data in most areas – see our paper, just out in PLOS ONE this summer
  • The ability to discover the existence of data is a critical requirement for a data-sharing infrastructure. We can define discovery as being the ability to determine the existence of a set of data objects with specified attributes or characteristics. The attributes of interest include aspects such as the producer of the data, the date of production, the method or production, a description of its contents, its representation. Discovery may also include aspects such as levels of quality, certification, or validation by third parties. Discoverability depends both on the description and representation of data and on tools and services to search for data objects. Data rarely are self-describing . Description  and representation usually take the form of metadata, some of which may be automated if data are generated by instruments such as sensor networks or telescopes. Much metadata creation requires human intervention, making it an expensive process that is often avoided by researchers (Edwards, Mayernik, Batcheller, Bowker & Borgman, 2011, forthcoming; Mayernik, 2011; Mayernik, Batcheller & Borgman, 2011). The lack of standards and practices for citing data, akin to citing publications, is a barrier to discoverability  [cite  BRDI mtg Aug 2011]. A variety of approaches to discovery are possible. Web search engines that walk the visible internet are one possibility assuming that data descriptions are reachable via standard web protocols. With the introduction of semantic web technologies and associated crawlers and search engines, location of data-sets of interest based on semantic content becomes possible. Alternatively, more discipline-specific and structured catalogs can be created. Arguably quite a bit of data is self describing: e.g., FITS, NetCDF, … Tho even those are incomplete. The more succinct ex we can include the better. Seems to me to be a distinct issue, related to naming not discoverability? A bit of both. Let’s discuss.
  • An infrastructure for digital objects has many features – we’re concerned at this meeting with how they apply to data, attribution, and citation – but must remember that they are part of a larger internet architecture of digital objectsI will provide a brief overview of these relationships as background to the issues we will address this week
  •  Usability – really reusability – how valuable is an object if you can’t open it? Need software? Locked up in PDF?Related to discoverabiltyIPIf we could release everything under CC0 licenses, the world would be a simpler place. That won’t happenNeed to know what rights are attached, what you can do with it.Open data – in sense of no rights attached, in sense of reusable (structured vspdf)Open bib – citations per se are facts, and generally not copyrightable.Movement toward open bib – let the descriptions, the metadta go free, then others can map the world of content and ideas. Separate from the payrwalls. 
  • Provenance addresses the challenges described above. First, it helps analysts understand the data assumptions behind different simulation runs. For example, in Figure 1, nodes P2 and P4 represent runs of the multi-market model that produce outputs d3 and d5 respectively. In this simple example, d3 and d5 differ in their assessments of the economic outlook (“excellent” vs. “poor”). What might account for the differences? Data provenance allows the user to see exactly what input data and model version were used, what transformations were performed on the data, and the parameter settings used. Without automatic capture of this information as simulations and data transformation tools run, it is very difficult to recreate the provenance retrospectively by examining scripts and individual analysts’ notes.  Provenance also helps analysts find collaborators. For example, a new analyst Wilson could query to see who is using the multi-market model (Smith and Jones), how they are using it, and with what data. Alternatively, Wilson could query to see who is using order book data (Smith, Jones, and Roberts), what sources they are using for it (Thompson Reuters and Nanex), and what models they are using the data for. These queries would be very difficult to answer in a large-scale analytic environment without provenance.
  • Like a concept flow.But, YOU don’t have to know that the washington post copies from reuters, or that analyst 2 uses a person communication. It’s captured silently and built up over time.

April 23 NISO Virtual Conference: Dealing with the Data Deluge: Successful Techniques for Scientific Data Management April 23 NISO Virtual Conference: Dealing with the Data Deluge: Successful Techniques for Scientific Data Management Presentation Transcript

  • NISO Virtual Conference: Dealing with the Data Deluge: Successful Techniques for Scientific Data Management April 23, 2014 Speakers: Jan Brase, Jared Lyle, Mercè Crosas, Michael Witt, Christine Borgman, Adriane Chapman, David Wilcox, Judy Ruttenberg http://www.niso.org/news/events/2014/virtual/data_deluge/
  • NISO Virtual Conference: The Semantic Web Coming of Age: Technologies and Implementations Agenda 11:00 a.m. – 11:10 a.m. – Introduction Todd Carpenter, Executive Director, NISO 11:10 a.m. - 12:00 p.m. Keynote Speaker: DataCite – A Global Approach for Better Data Sharing Jan Brase, Ph.D., German National Library of Science and Technology 12:00 p.m. - 12:30 p.m. Guidelines and Resources for Office of Science and Technology Policy (OSTP) Data Access Plans Jared Lyle, Director of Data Curation Services, Interuniversity Consortium for Political and Social Research (ICPSR), University of Michigan 12:30 p.m. - 1:00 p.m. Joint Declaration of Data Citation Principles: Implementation and Compliance in the Dataverse Repository Mercè Crosas, Ph.D., Director of Data Science, Institute for Quantitative Social Science (IQSS), Harvard University 1:00 p.m. - 1:45 p.m. Lunch Break 1:45 p.m. - 2:15 p.m. Purdue University Research Repository (PURR): A Commitment to Supporting Researchers Michael Witt, Head, Distributed Data Curation Center (D2C2); Associate Professor of Library Science, Purdue University Research Repository (PURR) 2:15 p.m. - 2:45 p.m. The Roles of Data Citation in Data Management Christine L. Borgman, Professor & Presidential Chair in Information Studies, UCLA 2:45 p.m. - 3:15 p.m. Is This Data Fit for My Use? The Challenges and Opportunities Data Provenance Presents Adriane Chapman, MITRE 3:15 p.m. - 3:30 p.m. Afternoon Break 3:30 p.m. - 4:00 p.m. A Durable Space: Technologies for Accessing Our Collective Digital Heritage David Wilcox, Product Manager, DuraSpace 4:00 p.m. - 4:30 p.m. The SHared Access Research Ecosystem (SHARE) Project: A Joint Initiative of ARL, AAU, and APLU Judy Ruttenberg, Program Director for Transforming Research Libraries, Association of Research Libraries (ARL) 4:30 p.m. - 5:00 p.m. Conference Roundtable Moderated by Todd Carpenter, Executive Director, NISO
  • DataCite – A global approach for better data sharing Jan Brase DataCite NISO virtual conference April 23rd 2014
  • Thousand years ago: science was empirical describing natural phenomena Last few hundred years: theoretical branch using models, generalizations Last few decades: a computational branch simulating complex phenomena Today: data exploration (eScience) unify theory, experiment, and simulation Jim Gray, eScience Group, Microsoft Research 2 2 2 . 3 4 a cG a a Science Paradigms
  • Scientific Information is more than a journal article or a book Libraries should open their cataolgues to any kind of information The catalogue of the future is NOT ONLY a window to the library‗s holding, but A portal in a net of trusted providers of scientific content Consequences for Libraries
  • We do not have it BUT We know where you can find And here is the link to it!
  • 7 Simulation Scientific Films 3D Objects Grey Literature Research Data Software Including non-classical publications
  • Why is this a role for libraries? • Libraries have a history in bringing scientific information to the public • Libraries have a tendency to be persistent • A project will be forgotten in 40 years, the library will very likely still exist then • Library are very trustworthy organisations
  • DataCite
  • High visability of the content Easy re-use and verification. Scientific reputation for the collection and documentation of content (Citation Index) Encouraging the Brussels declaration on STM publishing Avoiding duplications Motivation for new research What if any kind of scientific content would be citable?
  • How to achieve this? Science is global • it needs global standards • Global workflows • Cooperation of global players Science is carried out locally • By local scientist • Beeing part of local infrastrucures • Having local funders
  • Global consortium carried by local institutions focused on improving the scholarly infrastructure around datasets and other non-textual information focused on working with data centres and organisations that hold content Providing standards, workflows and best-practice Initially, but not exclusivly based on the DOI system Founded December 1st 2009 in London DataCite
  • International DOI Foundation DataCite Member Institution Data CentreData CentreData Centre Member Institution Data CentreData CentreData Centre … Works with Managing Agent (TIB) Member Associate Stakeholder DataCite structure
  • 1. Technische Informationsbibliothek (TIB) 2. Canada Institute for Scientific and Technical Information (CISTI), 3. California Digital Library, USA 4. Purdue University, USA 5. Office of Scientific and Technical Information (OSTI), USA 6. Library of TU Delft, The Netherlands 7. Technical Information Center of Denmark 8. The British Library 9. ZB Med, Germany 10. ZBW, Germany 11. Gesis, Germany 12. Library of ETH Zürich 13. L’Institut de l’Information Scientifique et Technique (INIST), France 14. Swedish National Data Service (SND) 15. Australian National Data Service (ANDS) 16. Conferenza dei Rettori delle Università Italiane (CRUI) 17. National Research Council of Thailand (NRCT) 18. The Hungarian Academy of Sciences 19. University of Tartu, Estonia 20. Japan Link Center (JaLC) 21. South African Environmental Observation Network (SAEON) 22. European Organisation for Nuclear Research (CERN) DataCite members Affiliated members: 1. Digital Curation Center (UK) 2. Microsoft Research 3. Interuniversity Consortium for Political and Social Research (ICPS 1. Korea Institute of Science and Technology Information (KISTI) 5. Bejiing Genomic Institute (BGI) 6. IEEE 7. Harvard University Library 8. World Data System (WDS) 9. GWDG
  • IRD ( gr av/ 10 cm 3) Sand ( %) C aC O3 ( %) TOC ( %) R adio ( %/ sand) Sme c t ( %/ clay) IRD ( gr av/ 10 cm 3) Sand ( %) C aC O3 ( %) TOC ( %) R adio ( %/ sand) Sme c t ( %/ clay) IRD ( gr av/ 10 cm 3) Sand ( %) C aC O3 ( %) TOC ( %) R adio ( %/ sand) Sme c t ( %/ clay) IRD ( gr av/ 10 cm 3) Sand ( %) C aC O3 ( %) TOC ( %) R adio ( %/ sand) Sme c t ( %/ clay) IRD ( gr av/ 10 cm 3) Sand ( %) C aC O3 ( %) TOC ( %) R adio ( %/ sand) Sme c t ( %/ clay) PS 1389-3 PS 1390-3 PS 1431-1 PS 1640-1 PS 1648-1 Age (kyr) max. : 233.55 ky r PS1389-3f f 0.0 100.0 200.0 0 20 0 100 0 15 0 0. 5 0 50 0 100 0 20 0 100 0 15 0 0. 5 0 50 0 100 0 20 0 100 0 15 0 0. 5 0 50 0 100 0 20 0 100 0 15 0 0. 5 0 50 0 100 0 20 0 100 0 15 0 0. 5 0 50 0 100 54° 0' 54° 0' 54°30' 54°30' 55° 0' 55° 0' 55°30' 55°30' 11° 11° 12° 12° 13° 13° 14° 14° 15° 15° World vector shore line Grain size class KOLP A Grain size class KOEHN2 Grain size class KOEHN Geochemistry Grain size class KOLP B Grain size class KOLP DIN 20 m Scale: 1:2695194 at Latitude 0° Source: Baltic Sea Research Institute, Warnemünde. Earth quake events => doi:10.1594/GFZ.GEOFON.gfz2009kciu Climate models => doi:10.1594/WDCC/dphase_mpeps Sea bed photos => doi:10.1594/PANGAEA.757741 Distributes samples => doi:10.1594/PANGAEA.51749 Medical case studies => doi:10.1594/eaacinet2007/CR/5- 270407 Computational model => doi:10.4225/02/4E9F69C011BC8 Audio record => doi:10.1594/PANGAEA.339110 Grey Literature => doi:10.2314/GBV:489185967 Videos => doi:10.3207/2959859860 What type of data are we talking about?
  • Anything that is the foundation of further reserach is research data Data is evidence Anything that is the foundation of further reserach is research data Data is evidence
  • Over 3,200,000 DOI names registered so far. 290 data centers. 10,000,000 resolutions in 2013. DataCite Metadata schema published (in cooperation with all members) http://schema.datacite.org DataCite MetadataStore http://search.datacite.org DataCite in 2014
  • DataCite search Searchterm: * Searchterm: uploaded:[NOW-7DAY TO NOW] Searchterm: relatedIdentifier:* Searchterm: relatedIdentifier:issupplementto:10.1029* Searchterm:relatedIdentifier:*:10.1055*
  • OAI and Statistics OAI Harvester http://oai.datacite.org DataCite statistics (resolution and registration) http://stats.datacite.org
  • DataCite Content Service Service for displaying DataCite metadata Different formats (BibTeX, RIS, RDF, etc.) Content Negotation (through MIME-Typ) • Access through DOI proxy (http://dx.doi.org) • First implemented by CNRI and CrossRef: Documentation: http://www.crosscite.org/cn/
  • Content negotiation Optimized for m2m communication using the accept header of the http protocol curl -L -H "Accept: MIME_TYPE" http://dx.doi.org/DOI Try a shortcut out in any webbrowser: http://data.datacite.org/MIME_TYPE/DOI http://data.crossref.org/DOI
  • Resolving to the citation http://data.datacite.org/application/x- datacite+text/10.5524/100005 Li, j; Zhang, G; Lambert, D; Wang, J (2011): Genomic data from Emperor penguin. GigaScience. http://dx.doi.org/10.5524/100005
  • Resolving to the RDF metadata http://data.datacite.org/application/rdf+xml/10.5524/100005 <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:j.0="http://purl.org/dc/terms/" > <rdf:Description rdf:about="http://dx.doi.org/10.5524/100005"> <j.0:identifier>10.5524/100005</j.0:identifier> <j.0:creator>Li, J</j.0:creator> <j.0:creator>Zhang, G</j.0:creator> <j.0:creator>Wang, J</j.0:creator> <owl:sameAs>doi:10.5524/100005</owl:sameAs> <owl:sameAs>info:doi/10.5524/100005</owl:sameAs> <j.0:publisher>GigaScience</j.0:publisher> <j.0:creator>Lambert, D</j.0:creator> <j.0:date>2011</j.0:date> <j.0:title>Genomic data from the Emperor penguin (Aptenodytes forsteri)</j.0:title> </rdf:Description></rdf:RDF>
  • Example of use This allows persistent identification of RDF statements! Implemented for all over 65 million CrossRef and DataCite DOI names Example of use: DOI Citation Formatter http://www.crosscite.org/citeproc/
  • 2012: STM, CrossRef and DataCite Joint Statement 1. To improve the availability and findability of research data, the signers encourage authors of research papers to deposit researcher validated data in trustworthy and reliable Data Archives. 2. The Signers encourage Data Archives to enable bi- directional linking between datasets and publications by using established and community endorsed unique persistent identifiers such as database accession codes and DOI's. 3. The Signers encourage publishers and data archives to make visible or increase visibility of these links from publications to datasets and vice versa 32
  • Example The dataset: Storz, D et al. (2009): Planktic foraminiferal flux and faunal composition of sediment trap L1_K276 in the northeastern Atlantic. http://dx.doi.org/10.1594/PANGAEA.724325 Is supplement to the article: Storz, David; Schulz, Hartmut; Waniek, Joanna J; Schulz-Bull, Detlef; Kucera, Michal (2009): Seasonal and interannual variability of the planktic foraminiferal flux in the vicinity of the Azores Current. Deep-Sea Research Part I-Oceanographic Research Papers, 56(1), 107-124, http://dx.doi.org/10.1016/j.dsr.2008.08.009
  • Next steps ODIN project with ORCID. http://datacite.labs.orcid-eu.org/ MoU with Thomson reuters to cooperate on data citation index DataCite plugin for next D-Space release (early 2014)
  • Cooperation MoU with ORCID Agreement with Re3Data and DataBib to include their service in 2016 MoU with RDA to become organisational affiliate
  • 2014 Annual conference
  • Let us get back to libraries
  • The wave Growth of Information – Diversity of media types and formats User requirements – e. g. : Science 2.0, collaborative networks, social media
  • A threat? Information overload is only a problem for manual curation. Google is not complaining about data deluge—they‘re constantly trying to get more data. The more data you throw, the better the filter gets. To develop and maintain these tools is a classical tasks for libraries! Don’t turn off the taps, build boats.
  • It is not only a challenge … … it is an opportunity We all should ride the wave …
  • Thank you!
  • Guidelines and Resources for OSTP Data Access Plans NISO Webinar April 2014 www.icpsr.umich.edu/datamanagement
  • The OSTP Memo Guidelines for Response • Released February 2013, this memo directs funding agencies with an annual R&D budget over $100 million to develop a public access plan for disseminating the results of their research • ICPSR stresses that standards and guidelines for many of the requirements currently exist • The slides to follow provide an overview of the access plan elements including guidelines and resources on how to respond to meet digital data requirements in the memo
  • The OSTP Memo – A Review • Released February 22, 2013 • A concern for investment: “Policies that mobilize these publications and data for re-use through preservation and broader public access also maximize the impact and accountability of the Federal research investment.” • Federal agencies with over $100 M annually in R&D expenditures to develop plans to support increased public access to the results of research funded by the Federal Government • Plans to contain eight points
  • The Eight Points of the Plan 1. Strategy for leveraging existing archives 2. Strategy to improve the public’s ability to locate and access digital data 3. Approach to optimize search, archival, and dissemination features that encourage innovation in accessibility & interoperability and ensure long-term stewardship 4. A plan to notify awardees & researchers of their obligations 5. Strategy for measuring and enforcing compliance with the plan 6. Identification of resources within the existing agency budget to implement plan 7. Timeline for implementation 8. Identification of special circumstances that prevent the agency from meeting memo objectives
  • Data Portion of Memo - 13 Elements • The portion of the memo describing objectives for public access to data stresses 13 elements for a public access plan • The elements are also summarized online within ICPSR’s Web site: http://icpsr.umich.edu/content/datamanagement/ostp.html
  • http://sites.nationalacademies.org/DBASSE/CurrentProjects/DBASSE_0
  • http://www.icpsr.umich.edu/files/ICPSR/ICPSRComment
  • RDAP 2014 Panel: Funding agency (NOAA, NSF, NIH) responses to federal requirements for public access to research results Wendy Kozlowski (Cornell), Moderator http://www.slideshare.net/asist_org/rdap14- ostp-panel-introduction http://www.slideshare.net/asist_org/rdap-3- 2714thakur
  • Visit ICPSR Archives/Repositories already Meeting Public Access Requirements
  • ICPSR – a 50-Year History of Providing Access to Research Data Established in 1962, ICPSR maintains and shares over 8,600 research datasets and hosts 16 public- access specialized collections of data funded by various government agencies and foundations. Our mission: ICPSR advances and expands social and behavioral research, acting as a global leader in data stewardship and providing rich data resources and responsive educational opportunities for present and future generations.
  • ICPSR’s Data Management & Curation Goals • Quality - Data at ICSPR are enhanced with meaningful information to make it complete, self-explanatory, and usable for future researchers • Access – Sought by over 730 member institutions an indexed by all the major search engines, ICPSR data are easily discoverable and widely accessible to the public. • Citation - By providing standardized and well-recognized data citations, ICPSR ensures that data producers receive credit for their archived data • Preservation – For over 50 years, ICPSR has preserved its data resources for the long-term, guarding against deterioration, accidental loss, and digital obsolescence • Confidentiality - Stringent protections are in place for securing and distributing sensitive data • Educational Support – ICPSR has a long tradition of supporting training in quantitative methods, scientific data management, and resources for instruction
  • ICPSR’s Data Management & Curation Site http://www.icpsr.umich.edu/datamanagement/
  • http://icpsr.umich.edu/datamanagement/ostp.html ICPSR’s Guidelines for OSTP Data Access Plan Page
  • Data Portion of Memo - 13 Elements • The portion of the memo describing objectives for public access to data stresses 13 elements for a public access plan • The elements are also summarized online within ICPSR’s Web site: http://icpsr.umich.edu/content/datamanagement/ostp.html
  • Maximize Access "Maximize access, by the general public and without charge, to digitally formatted scientific data created with Federal funds“ • Increasing access to research data prevents the duplication of effort, provides accountability and verification of research results, and increases opportunities for innovation and collaboration. • Finding and accessing data in repositories requires descriptive metadata ("data about data") in standard, machine-actionable form. Metadata help search engines find data, and help researchers understand the context of data collections. • Standards already exist: see Data Documentation Initiative – http://www.ddialliance.org/
  • Maximize Access cont. • Access also involves knowing how to interpret the data. Incomplete data limit reuse. Obsolete data formats can be unreadable. – Repositories 'curate' or enhance data to make it complete, self-explanatory, and usable for future researchers. This includes adding descriptive labels, correcting coding errors, gathering documentation, and standardizing the final versions of files. This is called “data curation.” – Like museums that curate art or artifacts for study and understanding now and in the future, data archives curate data with the same goals. • Data curation is crucial to maximizing access. Resources for curating data: – ICPSR's Guide to Social Science Data Preparation and Archiving – UK Data Archive's Managing and Sharing Data guide.
  • Protect Confidentiality and Privacy • It is critically important to protect the identities of research subjects. • Disclosure risk is a term that is often used for the possibility that a data record from a study could be linked to a specific person. • Concerns about disclosure risk have grown as more datasets have become available online, and it has become easier to link research datasets with publicly available external databases.
  • Protect Confidentiality and Privacy cont. Protecting confidentiality of research subjects is not a viable argument for not sharing data. Infrastructure, including virtual and physical data enclaves, already exists: • Restricted-Use Data are made available for research purposes for use by investigators who agree to stringent conditions for the use of the data and its physical safekeeping. • Enclave Data are those datasets which present especially acute disclosure risks. They can be accessed only on-site in ICPSR's physical data enclave in Ann Arbor. Investigators must be approved. Their notes and analytic output are reviewed by ICPSR staff.
  • Balance Demands of Long-term Preservation and Access • Preserving digital data requires much more than storing files on a server, desktop, or in the cloud! • Digital preservation is the active and ongoing management of digital content to lengthen the lifespan and mitigate against loss, including physical deterioration, format obsolescence, and hardware and software failure.
  • Balance Demands of Long-term Preservation and Access cont. • Not all data are worth preserving indefinitely; less valuable or easily producible data may be preserved for shorter periods. • Establish selection and appraisal guidelines that make it clear what to save or discard. – Selection criteria consider factors like availability, confidentiality, copyright, quality, file format, and financial commitment.
  • Use of Data Management Plans • Data management plans describe how researchers will provide for long-term preservation of, and access to, scientific data in digital formats. • Data management plans provide opportunities for researchers to manage and curate their data more actively from project inception to completion. • See ICPSR's resource: Guidelines for Effective Data Management Plans
  • Include Cost of Data Management in Funding Proposals • Data management services carry real costs, ranging from personnel to storage to software. • Maintenance costs are routinely built into physical infrastructure development, so too should data management costs be built into data development. • Long-term access to data requires durable institutions that plan on a scale of decades and even generations. • Cost resources: – DataONE's Provide budget information for your data management plan – UK Data Archive's Costing Tool: Data Management Planning.
  • Evaluate Data Management Plans & Ensure Compliance • Plans help researchers prepare for working with and preserving data, repositories get ready to accession and provide access, and agencies to understand the community needs for archiving and access. Evaluation helps refine plans so they are realistic and attainable. • If data management plans are to be a standard component of funding applications, funding recipients should be held accountable for diversions from the originally stated plans.
  • Promote Public Deposit of Data • Public deposit of data helps to ensure the long-term accessibility and preservation of the data. • It removes the burden of ongoing maintenance and care (and user support) from the researcher and provides a stable system to which data can be entrusted. • Many sustainable online repositories are already available to host and archive research data. These may include discipline- specific repositories, archives administered by funding agencies, or institutional repositories. • Databib, a searchable directory of over 500 research data repositories, can help locate relevant repositories by subject area.
  • Preserve Intellectual Property Rights and Commercial Interests Original research may be both commercially valuable and proprietary. There are several approaches to managing these interests, including: – Tailor copyright and patent licenses, such as through Creative Commons licenses – Establish an embargo period or delayed dissemination on distribution.
  • Private-sector Cooperation to Improve Access Encourage cooperation with the private sector to improve data access and compatibility. Issues to consider: • What funding structures will be in place to ensure that both organizations involved are benefiting from the partnership? • Will the partnership require any rights to be transferred to the private organization? • How does private-sector cooperation affect access restrictions and intellectual property concerns?
  • Mechanisms for Identification & Attribution of Data • Properly citing data encourages the replication of scientific results, improves research standards, guarantees persistent reference, and gives proper credit to data producers. • Citing data is straightforward. Each citation must include the basic elements that allow a unique dataset to be identified over time: title, author, date, version, and persistent identifier. • Resources: ICPSR's Data Citations page , IASSIST's Quick Guide to Data Citation, DataCite.
  • Data Stewardship Workforce Development In coordination with other agencies and the private sector, support training, education, and workforce development related to scientific data management, analysis, storage, preservation, and stewardship. Recent data stewardship workforce development in the United States has included: • Digital Preservation Outreach and Education, from the Library of Congress • Digital Preservation Management tutorial, from Cornell University, ICPSR, and MIT • DigCCurr, from the University of North Carolina
  • Data Stewardship Workforce Development cont. ICPSR hosts data stewardship courses as part of its Summer Program in Quantitative Methods of Social Research. These include: • Curating and Managing Research Data for Re-Use • Assessing and Mitigating Disclosure Risk: Essentials for Social Science • Providing Social Science Data Services: Strategies for Design and Operation
  • Long-term Support for Repository Development • ICPSR advocates long-term funding for specialized, long-lived, trustworthy, and sustainable repositories that can mediate between the needs of scientific disciplines and data preservation requirements. • As digital data management becomes an increasingly important part of scientific research, funding agencies must contribute to the developing ecosystem of services and technologies that support access to and preservation of data. • For more information, including various long-term funding models, see ICPSR’s 2013 position paper – “The Price of Keeping Knowledge”
  • Get More information • Visit ICPSR’s Data Management & Curation site: http://www.icpsr.umich.edu/datamanagement • Contact us: – netmail@icpsr.umich.edu – (734) 647-2200
  • Acknowledgements: Linda Detterman Emily Reynolds Gavin Strassel
  • Thank you! lyle@umich.edu
  • Joint Declaration of Data Citation Principles: Implementation and Compliance in the Dataverse Repository Mercè Crosas, Ph.D. Twitter: @mercecrosas Director of Data Science Institute for Quantitative Social Science, Harvard University NISO Virtual Conference, April 23, 2014
  • A brief History of Data Citation Altman M., Crosas M., 2014, “The Evolution of Data Citation: From Principles to Implementation” IASSIST Quarterly, In Press 1906 Chicago Manual of Style Standards in Scholarly Citation: author/creator, title, dates, publisher or distributor of the work 1960 First scientific digital data archives 1977 – 1998 ASBR (“Data File” type) MARC (machine readable catalog) 1999-2014 Data Repositories (NESSTAR, Dataverse, Dryad, Figshare) DOI services(DataCite)
  • The Making of the Principles  Decades of research and practices in data citation  Consolidated to a single set of Principles  By a synthesis group representing 25+ organizations  Driven by the premise that: "sound, reproducible scholarship rests upon a foundation of robust, accessible data" and "data should be considered legitimate, citable products of research"
  • Joint Declaration of Data Citation Principles 1 Importance 2 Credit and Attribution 3 Evidence 4 Unique Identification 5 Access 6 Persistence 7 Specificity and Verifiability 8 Interoperability and flexibility Full Principles: https://www.force11.org/datacitation Endorsement: https://www.force11.org/datacitation/endorsements
  • Joint Declaration of Data Citation Principles 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.
  • Joint Declaration of Data Citation Principles 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.
  • Joint Declaration of Data Citation Principles 3. Evidence In scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited.
  • Joint Declaration of Data Citation Principles 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.
  • Joint Declaration of Data Citation Principles 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.
  • Joint Declaration of Data Citation Principles 6. Persistence Unique identifiers, and metadata describing the data, and its disposition, should persist -- even beyond the lifespan of the data they describe.
  • Joint Declaration of Data Citation Principles 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 time slice, version and/or granular portion of data retrieved subsequently is the same as was originally cited.
  • Joint Declaration of Data Citation Principles 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.
  • About Dataverse  A software framework to build data repositories.  Provides a preservation and archival infrastructure, … while researchers share, keep control of and get recognition for their data through a web interface.  Harvard Dataverse is open to all researchers and disciplines.  It contains more than 50,000 data sets.  Other large Dataverse instances throughout the world: ODUM at UNC, Dutch Universities, Scholar Portal, Fudan University.  Dataverse 4.0 (June 2014) brings an entirely new UI and improved data publishing workflows.
  • Data Citation Implementation in Dataverse The Dataverse generates a Data Citation for each deposited data set compliant with the Principles: Authors, Year, Dataset Title, DOI, Data Repository, UNF, version Example: Logan Vidal, 2013, "ANES data coding ", http://dx.doi.org/10.7910/DVN/23274 Harvard Dataverse, UNF:5:0fdUNzmCsyeqrVKtgUG74A==, V8
  • Compliant with Principle 2 Principle 2: Credit and Attribution: …facilitate giving scholarly credit and … attribution to all contributors to the data, … Authors, Year, Dataset Title, DOI, Data Repository, UNF, version
  • Compliant with Principles 4, 5, 6 Principles 4, 5, 6 Unique Identification: …machine actionable, globally unique, and widely used by a community … Access: … access to the data themselves and to such associated metadata, documentation, code, and other materials … Persistence: … even beyond the lifespan of the data they describe. Authors, Year, Dataset Title, DOI, Data Repository, UNF, version Resolves to landing page with access to metadata, docs, code and data
  • Landing Page Example: Metadata
  • Landing Page Example: Data, Code & Docs
  • Compliant with Principle 7 Principle 7 Specificity and Verifiability: …provenance and fixity sufficient to facilitate verifying that the specific time slice, version and/or granular portion of data … Authors, Year, Dataset Title, DOI, Data Repository, UNF, version Universal Numerical Fingerprint: Independent of format
  • Example of version History
  • Compliant with Principle 8 Principle 8: Interoperability and flexibility: Dataverse exports all citation metadata in XML, JSON formats
  • Implementation Suggestions for Publishers  Upgrade data citation to references section [Principle 1: Importance]  In article, cite data by claim [Principle 3: Evidence]  Provide guidelines for authors based on Principles, but customized to each journal [Principle 8: Interoperability and Flexibility]  Interoperate with, or recommend, trusted Data Repositories compliant with the Principles  Build tools to access machine-readable metadata from datasets Want to be involved? Join the Data Citation Implementation group: https://www.force11.org/datacitationimplementation
  • Remaining Challenges  Challenges of Provenance: what is the chain of ownership and transformations to the data?  Challenges of Identity: what should be cited? at what level of granularity and versioning for large, dynamic datasets?  Challenges of Attribution: How do you support attribution for hundreds/thousands contributors? Altman M., Crosas M., 2014, “The Evolution of Data Citation: From Principles to Implementation” IASSIST Quarterly, In Press
  • NISO VIRTUAL CONFERENCE APRIL 23, 2014 – SUCCESSFUL TECHNIQUES FOR SCIENTIFIC DATA MANAGEMENT Purdue University Research Repository (PURR): A Commitment to Supporting Researchers Michael Witt Head, Distributed Data Curation Center Associate Professor of Library Science http://www.lib.purdue.edu/research/witt E-mail: mwitt@purdue.edu
  • OVERVIEW 1. Preaching to the choir, but still: Data 2. Ecosystem of data repositories 3. Our campus data repository & service (PURR) a. Data management planning b. Project space for collaboration c. Publishing data d. Archiving data 4. Creating opportunities for liaison librarians & helping to operationalize library research data services 5. Roles and collaboration 6. Conclusion 104
  • DATA = EVIDENCE 105 http://epicgraphic.com/data-cake
  • FUNDING AGENCY MANDATES 106
  • ECOSYSTEM OF DATA REPOSITORIES • Publisher, e.g., Dryad • Sub/Disciplinary, e.g., RKMP • Consortium, e.g., ICPSR • Country, e.g., Research Data Australia • Government, e.g., data.gc.ca • Research center, e.g., NASA GES DISC • Instrument, e.g., CHANDRA • General-purpose, e.g., FigShare • Roll-your-own, e.g., DataVerse • University, e.g., PURR • Many others… 107
  • CAMPUS COLLABORATION The PURR service is a collaborative effort of the Purdue University Libraries, Office of the Vice President for Research, and Information Technology at Purdue. PURR is a designated university core research facility. Designated community: Purdue University faculty, staff, and graduate student researchers; their collaborators; and the current and future consumers of their data. 108
  • LIBRARY STRATEGIC PLAN Data is written into the three pillars of our strategic plan: • Learning “…information literacy defined broadly to include digital information literacy, science literacy, data literacy, health literacy, etc…” • Scholarly Communication “Lead in data-related scholarship and initiatives” • Global Challenges “We will lead in international initiatives in information literacy and e- science and … contribute to international information literacy, learning spaces, data management, and scholarly communication initiatives.” 109 https://www.lib.purdue.edu/sites/default/files/admin/plan2016.pdf
  • http://purr.purdue.edu 110
  • CURATION LIFECYCLE SERVICE MODEL 111 Witt, M. (2012). Co-designing, Co-developing, and Co-implementing an Institutional Data Repository Service. Journal of Library Administration, 52(2). DOI:10.1080/01930826.2012.655607. http://docs.lib.purdue.edu/lib_fsdocs/6/ Digital Curation Centre’s Curation Lifecycle Model: http://www.dcc.ac.uk/resources/curation-lifecycle-model
  • PURR SERVICE – INTERNAL MODEL 112 112
  • PURR SERVICE – EXTERNAL MODEL 113
  • INTRO TO PURR VIDEO 114 http://www.youtube.com/watch?v=Yw0IJj7FqA8
  • PURR POSTCARD AND POSTER 115 115
  • 116 Dimensions of Discovery (Winter 2013). Office of the Vice President for Research, Purdue University, http://www.purdue.edu/research/vpr/publications/docs/dimensions/Winter2013.pdf
  • DATA MANAGEMENT PLANS • Boilerplate text • Example DMPs • DMP Self-Assessment • DMPTool • Workshops • Tutorials • Reference and consultation with subject- specialist librarian and/or data services specialist https://purr.purdue.edu/dmp 117
  • CREATE PROJECT AND COLLABORATE Create: • any Purdue faculty, staff, or graduate student researcher can create projects • describe the project • disclaim use of sensitive or restricted data • receive a default allocation of storage • register a grant award to increase allocation • invite collaborators to join project Collaborate: • git repository to share and version files (Google Drive integration) • wiki • blog • to-do list management and project notes • newsfeed • stage data publications 118
  • SENSITIVE AND RESTRICTED DATA Sensitive data: Information whose access must be guarded due to proprietary, ethical, or privacy considerations. This classification applies even though there may not be a civil statute requiring this protection. Restricted data Information protected because of protective statutes, policies or regulations. This level also represents information that isn't by default protected by legal statue, but for which the Information Owner has exercised their right to restrict access. http://www.purdue.edu/securepurdue/policies/dataConfident/restrictions.cfm • FERPA  Registrar • HIPAA  Health Center • IRB  Human Research Protection Program • Export Control  Vice President for Research 119
  • PROJECT SPACE 121 PURR project tutorial video: http://www.youtube.com/watch?v=q5xGO_oF9uQ
  • STORAGE MENU https://purr.purdue.edu/about/pricing 122
  • DATA PUBLICATION 123 PURR publication tutorial video: http://www.youtube.com/watch?v=jYBcsfiRhio
  • PRESERVATION AND STEWARDSHIP Initial commitment of 10 years • data producer or dept can fund for longer • otherwise remanded to library collection Design guided by ISO 16363 / TRAC • Organization infrastructure • Digital object management • Technical infrastructure & Security Risk Management 124
  • ARCHIVAL INFORMATION PACKAGE Bagit “bag” contains: • bag declaration file, manifest file, data files Metadata file (XML): • METS wrapper • Dublin Core and MODS (descriptive metadata) • PREMIS (preservation metadata) MetaArchive: LOCKSS replication network (7 copies) 125
  • SUPPORTING POLICIES • Terms of Deposit • Collection Development Policy • Preservation Policy • Preservation Strategies • File Format Recommendations • Preservation Support Policy 126 https://purr.purdue.edu/legal/terms
  • REPOSITORY SOFTWARE: HUBZERO • HUBzero, open source software: http://hubzero.org • Maintained by HUBzero Foundation, originally funded by NSF • Over 50 hubs online, supporting different virtual scientific communities, hundreds of thousands of users • http://nanoHUB.org - grandfather of the hubs, exemplar • Built to facilitate virtual communities and online, scientific collaboration, research/teaching • Collaborate, develop, publish, access, execute, and manage content using a web browser • Software tools, documents, multimedia, learning objects, datasets, etc. • Social network functionality and collaboration features • LAMP stack, Joomla framework, OpenVZ and Rappture, git, etc. • EZID interface to mint DataCite DOIs (coming soon: ORCID) • Some extensions customized for PURR not in core distribution 127
  • PURR TEAM • Executive Committee: Dean of Libraries, Vice President for Research, Chief Information Officer • Steering Committee: 2 from libraries, 2 from IT, 2 from research office and sponsored programs, 3 domain faculty researchers • Personnel: Project Director (.50), Technologists (3.85), HUBzero Liaison (.35), Metadata Specialist (.20), Digital Archivist (.25), Digital Data Repository Specialist (1.0) 128
  • LIBRARIES PURR TEAM 129 PURR Project Director (50%) Michael Witt Three examples of responsibilities: • resourcing (personnel, budget, coffee, etc.) • oversees development roadmap, service definition and design • communicates across constituencies
  • LIBRARIES PURR TEAM 130 Digital Data Repository Specialist Courtney Matthews Three examples of responsibilities: • primary point of contact for helping users and librarians utilize PURR • coordinates outreach, support, and development (tons of community engagement) • helps to acquire, organize, and ingest data collections
  • LIBRARIES PURR TEAM 131 Digital Library Software Developer Mark Fisher Three examples of responsibilities: • developing a module to create archival information packages from datasets published in PURR • integrating PURR with MetaArchive, an LOCKSS preservation network • web and graphics design to keep the PURR website current and dynamic
  • LIBRARIES PURR TEAM 132 Digital Archivist (25%) Carly Dearborn Three examples of responsibilities: • define and implement AIP as well as long-term digital object management and supporting practices • lead policy development and documentation such as PURR’s preservation policy, preservation strategies, file format recommendations, and preservation support policy • consult with data producers and librarians on file formats, appraisal of data collections, and data management planning
  • LIBRARIES PURR TEAM 133 Metadata Specialist (20%) Amy Barton Three examples of responsibilities: • consult with data producers and librarians identify and apply appropriate metadata schemas and vocabularies to describe datasets • design and implement metadata for preservation, findability, and citability (i.e., DataCite DOIs) • enhance and provide quality assurance for metadata for acquired data collections
  • KEY PLAYERS: SUBJECT LIBRARIANS 134
  • KEY PLAYERS: DATA SPECIALISTS 135
  • Librarians consult on data management plans in their subject areas. Creating opportunities for librarians to interact with researchers about data 136
  • Librarian is notified by e-mail when a new project is created or a grant is awarded, based on department affiliation of Purdue project owner. Creating opportunities for librarians to interact with researchers about data 137
  • Librarian may consult or collaborate on project if needed. Creating opportunities for librarians to interact with researchers about data 138
  • Librarians review and post submitted datasets. Creating opportunities for librarians to interact with researchers about data 139
  • At the end of initial commitment (10 years), archived and published datasets are remanded to the Libraries‘ collection. A librarian working with the digital archivist selects (or not) the dataset for the collection. Creating opportunities for librarians to interact with researchers about data 140
  • CONCLUSION • Soft launch in 2012; 2013 was our first full year • PURR included in 1,040 data management plans with proposals from Purdue (tracked by our sponsored programs office) • 79 grants awarded • 1,466 registered researchers • 331 active research projects • Average project team size: 4 people • Average files per project: 67 files DMP analysis (n=111 NSF proposals from Purdue, Jan-Jun 2013) • 49% PURR • 29% Local computer or server • 14% Disciplinary repository (e.g., ICPSR, Protein Data Bank, nanoHUB, NEES) • 8% No data or not applicable 141
  • THANK YOU PURR: http://purr.purdue.edu Michael Witt Head, Distributed Data Curation Center Associate Professor of Library Science http://www.lib.purdue.edu/research/witt E-mail: mwitt@purdue.edu
  • The Roles of Data Citation in Data Management NISO Virtual Conference: Dealing with the Data Deluge: Successful Techniques for Scientific Data Management http://www.niso.org/news/events/2014/virtual/data_deluge/ Christine L. Borgman Professor and Presidential Chair in Information Studies University of California, Los Angeles hudsonalpha.org NASA Astronomy Picture of the Day
  • Deluge!!! Data! Scientists Social Scientists Funding agencies Policy makers Humanists Librarians http://www.guzer.com/pictures/suprise_suprise.jpg 14 Publishers Internet architects
  • http://www.census.gov/population/cen2000/map02.gif What are data? ncl.ucar.edu http://onlineqda.hud.ac.uk/Intro_QDA/Examples_of_Qualitative_Data.php Marie Curie‘s notebook aip.org hudsonalpha.org NASA Astronomy Picture of the Day 145
  • 146 Data are representations of observations, objects, or other entities used as evidence of phenomena for the purposes of research or scholarship. C.L. Borgman, 2014, forthcoming, Big Data, Little Data, No Data: Scholarship in the Networked World, MIT Press. hudsonalpha.org
  • Publications are arguments made by authors, and data are the evidence used to support the arguments. C.L. Borgman, 2014, forthcoming, Big Data, Little Data, No Data: Scholarship in the Networked World, MIT Press.
  • Citing publications vs. data • If publications are the stars and planets of the scientific universe, data are the ‘dark matter’ – influential but largely unobserved in our mapping process* *CODATA-ICSTI Task Group on Data Citation Standards and Practices, 2013, p. 54
  • Authorship and Attribution • Publications – Independent units – Authorship is negotiated • Data – Compound objects – Ownership is rarely clear – Attribution • Long term responsibility: Investigators • Expertise for interpretation: Data collectors and analysts hudsonalpha.org
  • Attribution of data • Legal responsibility – Licensed data – Specific attribution required • Scholarly credit: contributorship – Author of data – Contributor of data to this publication – Colleague who shared data – Software developer – Data collector – Instrument builder – Data curator – Data manager – Data scientist – Field site staff – Data calibration – Data analysis, visualization – Funding source – Data repository – Lab director – Principal investigator – University research office – Research subjects – Research workers, e.g., citizen science… 150
  • Scholarly credit • Publications • Publications • Publications • Publications • Publications • Publications • Awards and honors • Grants • Teaching • Service • Data http://blog.startfreshtoday.com/Portals/170402/images/improve-credit-score1.jpg
  • Everyone is overwhelmed with life and email and, in academia, trying to get funding and write papers. Whether something is open or not open is not highest on the priority list. There’s still need for making people aware of open science issues and making it easy for them to participate if they want to. Jonathan Eisen, genetics professor at the University of California, Davis DESPITE BEING GOOD FOR YOU AND FOR SCIENCE, TOO MANY CHALLENGES AND TOO LITTLE TIME Rewards for publications Effort to document data Competition, priority Control, ownership Slide courtesy of Merce Crosas, Harvard IQSS; Mashup of Borgman and Crosas slides 152
  • Data citation as solution to… • Credit • Attribution • Discovery
  • Research practices • Goal is publications that report the research Vs. • Goal is data that are reusable by others Image: Alyssa Goodman, Harvard Astronomy 154
  • Scientific data creation, use, and reuse* • What are the characteristics of data use and reuse within each research community? • How do characteristics of data use and reuse vary within and between research communities? Fastlizard4’s image of a Geiger counter setup to measure background radiation (flickr.com) 155 * Wynholds, L. A., Wallis, J. C., Borgman, C. L., Sands, A., & Traweek, S. (2012). Data, data use, and scientific inquiry: two case studies of data practices. In Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries (pp. 19–22). New York, NY, USA: ACM. doi:10.1145/2232817.2232822 * Wallis, J. C., Rolando, E., & Borgman, C. L. (2013). If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLoS ONE, 8(7), e67332. doi:10.1371/journal.pone.0067332
  • Research Sites • Center for Embedded Networked Sensing – Science research • Environment • Seismology – Technology research • Instrumentation • Networks – Small science – Circa 300 partners • Sloan Digital Sky Survey needs to align – Science research • Astronomy • Astrophysics – Technology research • Instrumentation • Databases – Big science – Circa 400 partners 156
  • Interview Questions Topic Question CENS SDSS Data Types Within your work, what is typically considered to be “data?” X X How do you distinguish between different levels or states of data? X DataSources What are the main sources of data for your research projects? X Do you routinely or have you ever used data that you did not generate yourself, or from beyond the immediate project team? X X Data Use When you look at data, what are you hoping to find in it? X X When, if ever, do you reuse your datasets? X X 157
  • Dimensions of Data • Observed vs. simulated data • Lab generated vs. field collected • Collected by team vs. obtained from external sources • Old vs. new data • Raw vs. processed data • Foreground vs. background data 158
  • Research findings • Uses of data vary by type of inquiry • Foreground data – Research questions – Curated – Cited • Background data – Necessary for comparison or calibration – Rarely curated – Rarely cited • Value of data lies in their use • “Use” of data is not reflected in citations 159http://drpinna.com/the-gold-standard-22948
  • Sharing and discovering data • Means to share data – Curated data archives: NASA, UKDA, ICPSR… – Contributor-curated collections – Research domain collections – University repositories – Personal websites – ftp sites • Release upon request* http://www.zippykidstore.com/ *Wallis, J. C., Rolando, E., & Borgman, C. L. (2013). If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLoS ONE, 8(7), e67332. doi:10.1371/journal.pone.0067332 160
  • Discoverability • Data are inseparable from – Code – Technical standards – Documentation – Instrumentation – Calibration – Provenance – Workflows – Local practices – Physical samples http://peacetour.org/sites/default/files/code4peace-logo2-v3-color-sm.jpg 161
  • Usability of cited objects • Identify the form and content • Interpret • Evaluate • Open • Read • Compute upon • Reuse • Combine • Describe • Annotate… 162
  • Identity and persistence of digital objects • Identity – Identifiers • DOI, Handles, URI, PURL… – Naming and namespaces • Authors/creators: ORCID, VIAF… • Generic/specific: registry number… – Description • Self-describing • Metadata augmentation • Persistence – Permanent – Long-lived – Scratch spaces http://web-interview- questions.blogspot.com/2010_06_21_archive.h tml 163
  • Intellectual property • What can I do with this object? • What rights are associated? – Reuse – Reproduce – Attribute • Who owns the rights? • How open are data? – Open data – Open bibliography 164http://pzwart.wdka.hro.nl/mdr/research/lliang/mdr/mdr_images/opencontent.jpg/
  • Implications for data management • Authors of publications – Cite publications for their data, findings, and other content – Cite your data as you wish others to cite them – Cite others’ data and publications as they wish to be cited • Data archives – Add metadata for discovery of datasets – Add metadata for interpretation and provenance • Institutional repositories, bibliographic databases – Establish standards and practices for citing data sources – Coordinate communities, e.g., telescope bibliography, IAU* 165 *IAU Working Group Libraries. (2013). Best Practices for Creating a Telescope Bibliography. IAU-Commission5 - WG Libraries. http://iau-commission5.wikispaces.com/WG+Libraries
  • Data Citation and Attribution 166 Uhlir, P. F. (Ed.). (2012). For Attribution -- Developing Data Attribution and Citation Practices and Standards: Summary of an International Workshop. Washington, D.C.: The National Academies Press. Retrieved from http://www.nap.edu/catalog.php?record_id=13564 Data Science Journal, Volume 12, 13 September 2013 2012 CODATA-ICSTI Task Group on Data Citation and Attribution. Co-Chairs: Jan Brase, Sarah Callaghan, Christine Borgman
  • Research funding acknowledgements Research reported here is supported in part by grants from the National Science Foundation and the Alfred P. Sloan Foundation: The Transformation of Knowledge, Culture, and Practice in Data-Driven Science: A Knowledge Infrastructures Perspective, Sloan Award # 20113194, CL Borgman, UCLA, PI; S Traweek, UCLA, Co-PI The Data Conservancy, NSF Cooperative Agreement (DataNet) award OCI0830976, Sayeed Choudhury, Johns Hopkins University, PI The Center for Embedded Networked Sensing (CENS) is funded by NSF Cooperative Agreement #CCR-0120778, Deborah L. Estrin, UCLA, PI Towards a Virtual Organization for Data Cyberinfrastructure, NSF #OCI-0750529, C.L. Borgman, UCLA, PI; G. Bowker, Santa Clara University, Co-PI; Thomas Finholt, University of Michigan, Co-PI Monitoring, Modeling & Memory: Dynamics of Data and Knowledge in Scientific Cyberinfrastructures: NSF #0827322, P.N. Edwards, UM, PI; Co-PIs C.L. Borgman, UCLA; G. Bowker, SCU and Pittsburgh; T. Finholt, UM; S. Jackson, UM; D. Ribes, Georgetown; S.L. Star, SCU and Pittsburgh 167
  • Finding and following digital objects • Discoverability – Identify existence – Locate – Retrieve • Provenance – Chain of custody – Transformations from original state • Relationships – Units identified – Links between units – Actions on relationships http://chicagoist.com/2008/10/09/a_gourmet_ oasis_provenance_food_and.php 168
  • Infrastructure for digital objects • Social practice • Usability • Identity • Persistence • Discoverability • Provenance • Relationships • Intellectual property • Policy http://datalib.ed.ac.uk/GRAPHICS/blue_data.gif 169
  • Social practice • Why cite data? – Reproduce research – Replicate findings – Reuse data • Why attribute data? – Social expectation – Legal responsibility • How to cite data? – Bibliographic reference – Identifier – Link 170 http://farm2.static.flickr.com/1207/707625876_46aa44851f_o.jpg
  • 171 Foreground vs Background Foreground data Background data Uses Research questions Comparison, calibration Reuses Internal data sources External data sources Disposition Retain, curate Discard Value Reference in paper Rarely cited
  • UCLA USC UCR CALTECH UCMCENTER FOR EMBEDDED NETWORKED SENSING Sensor Collected Application Data Sensor Collected Proprioceptive Data Sensor Collected Performance Data Hand Collected Application Data Flow Water depth Ammonium Ammonia Phosphate Water temp pH Temperature Conductivity Chlorophyll GPS/location Time Sap flow CO2 Humidity Rainfall Packets transmitted Packets received ORP PAR Motor speed Rudder angle Heading Roll/pitch/yaw Soil moisture Nitrate Calcium Chloride Water potential Wind speed Wind direction Wind duration Leaf wetness Routing table Neighbor table Fault detection Awake time Organism presence Organism concentration Battery voltage Mercury Methylmercury Nutrient concentration Nutrient presence LandSat images Mosscam CDOM Bird calls CENS Data: Foreground vs background
  • Astronomy data: Foreground vs. background Type Source Named Genre Catalog (Data) index SIMBAD, VizieR Obs Curated Data Collection NASA Exoplanet Database Obs Data Archive Multi-mission Archive at STScI (MAST), Infrared Science Archive (IRSA) Obs Federated Data Query Services Virtual Observatory Services (NVO, IVOA) Obs Ground Based Instruments DEep Imaging Multi-Object Spectrograph (DEIMOS), Keck Observatories, Laser Interferometer Gravitational-Wave Observatory (LIGO) Obs Ground Based Sky Surveys Deep Lens Survey, DEEP2 Galaxy Redshift Survey, Catalina Transients Survey, Palomar-Quest Survey, Sloan Digital Sky Survey (SDSS), Digitized Palomar Observatory Sky Survey (DPOSS), SDSS Value Added Catalogs Obs Physical Constants NIST Atomic Spectra Database Exp Publications Index SAO/NASA Astrophysics Data System Mixed Simulation Millennium Simulation Database Sim Space Based Instruments Chandra X-Ray Observatory, Fermi Large Area Telescope, Far Ultraviolet Spectroscopic Explorer (FUSE), Galaxy Evolution Explorer (GALEX), Hubble Space Telescope, Spitzer Space Telescope, XMM X-ray Telescope Obs Space Based Sky Surveys Two Micron All Sky Survey (2MASS), Infrared Astronomical Satellite Survey (IRAS), Wide-field Infrared Survey Explorer (WISE) Obs 173
  • © 2012 The MITRE Corporation. All rights reserved. Adriane Chapman achapman@mitre.org M. David Allen dmallen@mitre.org Barbara Blaustein bblaustein@mitre.org Is this data fit for my use? The challenges and opportunities provenance presents Information graphic courtesy of FreeDigitalPhotos.net Public Release #12-1548.
  • © 2012 The MITRE Corporation. All rights reserved. Page 175 What is Provenance? Public Release #12-1548.
  • © 2012 The MITRE Corporation. All rights reserved. Public Release #12-1548. ■Provenance can help in evaluating whether data is fit for a specific purpose – Does the data item derive from an Internet source? – Were untrusted organizations involved in producing the data item? ■Provenance “in the raw” is not always useful to users – Generally presented as a directed acyclic graph (DAG) – Many users have a good intuitive understanding of simple graphs, BUT Is Data Fit for a Specific Use? Page 176 Provenance graphs are often large and unwieldy
  • © 2012 The MITRE Corporation. All rights reserved. Use Case Page 177
  • © 2012 The MITRE Corporation. All rights reserved. Financial Systemic Risk Analysis Analysts Financial Models build and run Are there systemic risks to the health of the financial system? Decision Makers Public Release #12-3756
  • © 2012 The MITRE Corporation. All rights reserved. Systemic Risk: The IT Problem ■ To monitor systemic risk, regulators have hundreds of analysts, running hundreds of models… – …against hundreds of data sets at various time scales… – …each with thousands of different parameter settings ■ Currently, care and feeding of these models (especially data extract-transform-load) is ad hoc ■ Result: Current simulation environments don’t support analysts’ need to find and interpret data across the resulting millions of simulation executions Public Release #12-3756
  • © 2012 The MITRE Corporation. All rights reserved. Data Provenance Challenge I ran a flow of funds model from the University of Vermont back in May. Which version did I use? What transformations did I perform on the input data sets? Which model runs used the 1Q 2011 version of the FDIC’s Uniform Bank Performance Reports? Who is running Prof. Jones’ model? What input data are they using it with and with what parameters? Public Release #12-3756
  • © 2012 The MITRE Corporation. All rights reserved. Data Provenance Example d1 Filter (P1) Multi-market model (P2) Source: Thompson Reuters order book data d2 d3 Filter (P3) d4 d5 Version: 1 Time-horizon = 2016 Invoked-by: Jones Version: 2 Time-horizon = 2018 Invoked-by: Smith Multi-market model (P4) Year: 2010 Sector: Technology Time Series Normalization (P6) Link-based Classification Model (P7) d7 d8 Outlook: ―Excellent‖ Outlook: ―Poor‖ Filter (P5) Year: 2001-2010 Sector: Housing Periodicity: Quarterly Invoked-by: Roberts Outlook: ―Fair‖ d6 Source: Nanex order book data Public Release #12-3756
  • © 2012 The MITRE Corporation. All rights reserved. FitnessWidgets Page 182
  • © 2012 The MITRE Corporation. All rights reserved. Public Release #12-1548. Page 183 Ease of Use for End Users Data-centric goal: build tools and applications over provenance information to support a user’s needs. Information graphic courtesy of FreeDigitalPhotos.net
  • © 2012 The MITRE Corporation. All rights reserved. Public Release #12-1548. ■ Ad hoc, user-defined Fitness Widgets: Pre-defined queries operating over provenance graphs Page 184
  • © 2012 The MITRE Corporation. All rights reserved. Fitness Widgets: Pre-defined queries operating over provenance graphs ■ Complex, pre-defined Page 185 Public Release #12-1548.
  • © 2012 The MITRE Corporation. All rights reserved. Page 186 More Complex: Cross-organizational “double counting” Public Release #12-1548.
  • © 2012 The MITRE Corporation. All rights reserved. The Skeletons Page 187
  • © 2012 The MITRE Corporation. All rights reserved. PLUS Provenance Manager Provenance Manager PLUS Users & Applications Administrators Provenance Store (MySQL) PLUS Applications & Capture Agents Report AnnotateRetrieve Administer (access control, archiving, etc.) API (provenance-aware applications) Coordination points for automatic provenance capture Web Proxy (provenance-aware applications) Approved for Public Release 10-4145 © 2010 The MITRE Corporation. All rights reserved A. Chapman, M.D. Allen, B. Blaustein, L. Seligman, “PLUS: A Provenance Manager for Integrated Information,” IEEE Int. Conf. on Information Reuse and Integration (IRI ‘11), Las API
  • © 2012 The MITRE Corporation. All rights reserved. Architectural Options for Lineage Capture ■ ―Smart Applications‖ – Strategy: Each application calls lineage API to log whatever it thinks is important. – But, unrealistic for legacy applications ■ ―Interceptors‖ – Strategy: Listen in to whatever is happening, and log silently as it happens – Requires a small number of points of lineage capture: ESBs are ideal, since they act as central ―routers‖ ■ ―Wrappers‖ – Strategy: Write a transparent wrapper service. Make sure all orchestrations call the wrapper service with enough information for the wrapper to invoke the real thing. 189 Public Release #10-1285
  • © 2012 The MITRE Corporation. All rights reserved. Public Release #12-1548.
  • © 2012 The MITRE Corporation. All rights reserved. View Provenance The provenance graph is built automatically over time by “watching” users’ actions Public Release #12-1548.
  • © 2012 The MITRE Corporation. All rights reserved. The system can show relationship information and metadata details Get Details Public Release #12-1548.
  • © 2012 The MITRE Corporation. All rights reserved. Sort Information The system provides ways to get information “at a glance”, e.g. which organizations own the data that was used. Public Release #12-1548.
  • © 2012 The MITRE Corporation. All rights reserved. FitnessWidgets FitnessWidgets help the analyst assess data products for his specific use. Public Release #12-1548.
  • © 2012 The MITRE Corporation. All rights reserved. Annotations Annotate any node. Information can be propagated through graph. Public Release #12-1548.
  • © 2012 The MITRE Corporation. All rights reserved. ■ Provenance keeps track of who did what, when to data. ■ Provenance can help – Determine what data to use – Find data – Know what happened to the data ■ It is not a silver bullet – Capture is hard ■ Determine what pieces of information are vital to judging “fitness”, try to capture those Conclusions Page 196
  • SHARE PROJECT UPDATE Judy Ruttenberg, Program Director Association of Research Libraries NISO Virtual Conference: Dealing with the Data Deluge April 23, 2014
  • Higher education & research community • Preservation, access, and reuse of research outputs (data, articles, and more) • Interlocking layers & services to better understand what research is being produced, and to render that research as accessible as possible • Leverage existing ecosystem
  • Formation and context of SHARE • Institutional OA policies • AAU-ARL Task Force on Scholarly Communication • Funder mandates –2013 OSTP Memorandum –2014 Omnibus Appropriations –Private and other funder policies
  • Who is SHARE? Steering Group • Provost, Library directors, CIO, SRO • ARL, AAU, APLU, CNI, SPARC, NLM (federal agency liaison) Staff • Project Manager (ARL), Technical Director, Product/Community Lead, Development Team Working Groups • Repository, Workflow, Technical, Communications
  • Layers & Services of SHARE Notification Service: Project underway – Beta release fall 2014 – Full release fall 2015 Concurrent planning for interactive systems: – Registry – Discovery – Aggregation
  • SHARE Notification Service Problem Statement: • Difficult to keep abreast of the release of publications, datasets, other research outputs • No single, structured way to report research output releases in timely and ubiquitous manner
  • SHARE Notification Service Outcome & Goal: • Know that research output exists • Enable, short-term & with high-latency: –Repository Managers to identify articles/papers/reports for deposit –University and funding agency grant administrators to determine compliance with public access policies
  • SHARE Notification Service – Building Blocks
  • SHARE Notification Service – Information Flow
  • SHARE Research Release Events
  • SHARE Research Release Events
  • SHARE Registry Layer
  • SHARE Registry & Discovery Layers
  • Other Community Initiatives • CHORUS • ORCID • CrossRef • International
  • Long-term planning • Data • Author rights: An intellectual property rights strategy, including the promotion of university-based open access policies and favorable licensing terms, will be part of the scaffolding that will enable the layers of SHARE to develop
  • www.arl.org/share www.facebook.com/SHARE.research www.twitter.com/share_research share@arl.org Staying connected with SHARE:
  • NISO Virtual Conference Dealing with the Data Deluge: Successful Techniques for Scientific Data Management NISO Virtual Conference • April 23, 2014 Questions? All questions will be posted with presenter answers on the NISO website following the webinar: http://www.niso.org/news/events/2014/virtual/data_deluge/
  • Thank you for joining us today. Please take a moment to fill out the brief online survey. We look forward to hearing from you! THANK YOU