Research Data Management for Researchers: Module 1: Intro to Data, Metadata and the Research Data...

Research Data Management for Researchers: Module 1: Intro to Data, Metadata and the Research Data Lifecycle

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Research Data Management for Researchers:Module 1: Intro to Data, Metadata and the Research Data Lifecycle

DRAFT

Glen NewtonPaul Budkewitsch

[email protected]@nrcan-rncan.gc.ca

Outline

Some definitions

Data Sharing

Research & ResearchData Lifecycle

Research DataComplexity

Data Archiving

Data Management Roles

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Some definitionsData SharingResearch & Research Data LifecycleResearch Data ComplexityData ArchivingData Management Roles

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What is:n Research?n Research Data?n Research & Research Data Life Cycles?

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Data is becoming more important

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In the past, more emphasis was given to publications.This is changing.

Diepenbroek, M., Schindler, U., Grobe, H. 2008.PANGAEA - An ICSU World Data Center as a Networked Publication and Library System for Geoscientific Data

http://hdl.handle.net/10013/epic.28613

Research Data Disappears

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n The status quo is for most research data to (eventually) disappear:except for large well organized projects, historically most research

data collected has already disappeared.n Not through malice, just through mismanagement or more

accurately a lack of management

Degradation in information content associated with data and metadata over timeStatus quo

Time of publication

Specific details about problems with individual items or specificdates of collection are lost relatively rapidly

General details about the data collection are lostthrough time

Accident may destroydata and documentation

Retirement or career change makes access byscientists to mental storage difficult or unlikely

Death of investigator and subse-quent loss of remaining records

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Timede la Sablonniere, Auger, Sabourin and Newton. 2010. Facilitating Data Sharing

in the Behavioral Sciences. Submitted to Data Science Journal.After Michener et al. 1997, Nongeospatial Metadata for the Ecological Sciences.

Ecological Applications 7:1:330-34210.1890/1051-0761(1997)007[0330:NMFTES]2.0.CO;2

Why Share data?

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n encourages scientific enquiry and debaten enables scrutiny of research outcomesn facilitates research beyond the scope of the original research

n leads to new collaborations between data users and data creatorsn reduces the cost of duplicating data collectionn provides important resources for education and trainingn encourages the improvement and validation of research methods

n promotes the research that created the data and its outcomesn can provide a direct credit to the researcher as a research output in

its own right

Benefits of Data Sharing

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Within this new technological context, more widespread and efficient accessto and sharing of research data will have substantial benefits for publicscientific research. Open access to, and sharing of, data reinforces openscientific inquiry, encourages diversity of analysis and opinion, promotes newresearch, makes possible the testing of new or alternative hypotheses andmethods of analysis, supports studies on data collection methods and

measurement, facilitates the education of new researchers, enables theexploration of topics not envisioned by the initial investigators, and permits

the creation of new data sets when data from multiple sources are combined.Sharing and open access to publicly funded research data not only helps to

maximize the research potential of new digital technologies and networks, butprovides greater returns from the public investment in research.

OECD. 2003. Promoting Access to Public Research Data for Scientific, Economic, and Social Development: OECD FollowUp Group on Issues of Access to Publicly Funded Research Data.

http://dataaccess.ucsd.edu/Final Report 2003.pdf

Unpredicted reuse of data

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n Data often has value beyond that planned or even imagined by thecollector of the data

n And combining it with other data can often support the discovery ofemergent processes

Unpredicted reuse of data

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What is the following?

Page from a ships log

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New Zealand, October 1769

These are Captain James Cooks logs

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His Majestys Bark [a type of ship] Endeavour on Her Passage On theCoast of New Zealand from Poverty Bay to SouthwOctober 15th 1769; Course: S 20 E; Winds: Vary; Location:

39

50

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51; Moderate and fair weather...thunder and spitting

rain... Log 39, page 79. UK National Archives

n Record of date, time, location (lat/long), the sea conditions andlocal weather conditions

n Now being mined by JISC, the University of Sunderland, the MetOffice Hadley Centre and the British Atmospheric Data Centre forclimate change research1

1http://www.nationalarchives.gov.uk/news/stories/371.htm

Research & Research Data Lifecycle

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Research & Research Data Lifecycle

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Various perspectives

Pepe,A. & Mayernik, M & Borgman, C. & Van de Sompel, H.Technology to Represent Scientific Practice: Data, Life Cycles, and Value Chains

http://arxiv.org/abs/0906.2549

Lord, P., A. Macdonald, L. Lyon & D. Giarretta. 2004.From Data Deluge to Data Curation. In Proceedings of the UK e-science

http://www.ukoln.ac.uk/ukoln/staff/e.j.lyon/150.pdf

Lyon, L. 2007. Dealing with Data: Roles, Rights, Responsibilities and Relationshipshttp://www.ukoln.ac.uk/ukoln/staff/e.j.lyon/reports/dealing with data report-final.pdf

Humphrey, C. 2006. e-Science and the Life Cycle of Researchhttp://datalib.library.ualberta.ca/humphrey/lifecycle-science060308.doc

Interagency Working Group on Digital Data. 2009. Harnessing the Power of Digital Data for Science and Societyhttp://www.whitehouse.gov/files/documents/ostp/opengov inbox/harnessing power web.pdf

Very complete view:

Bechhofer, S. & D. Roure & M. Gamble & C. Goble & I. Buchan. 2010.Research Objects: Towards Exchange and Reuse of Digital Knowledge.

Nature Preceedings.http://dx.doi.org/10.1038/npre.2010.4626.1

Research Data Complexity

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n Datan Metadatan Transformations (derived data/metadata), combinationsn More Metadata


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Real research projects can have extremely complex data collection andmanagement needs.

Wallis, J. 2008. Moving Archival Practices Upstream: An Exploration of the Life Cycleof Ecological Sensing Data in Collaborative Field Research Lineage

Retrieval for Scientific Data Processing: A Survey.The International Journal of Digital Curation 1:3

http://www.ijdc.net/index.php/ijdc/article/view/67


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Data collection and management complexity are effected by a numberof factors. For example:n multiple researchersn multiple teamsn inter and multi disciplinaryn multiple jurisdictionsn multiple funding agenciesn multiple types of funding agencies (government, industry, philanthropic)n long term projectsn human subjectsn multiple/competing vocabularies/ontologies/metadata standards

n data formats, data transformation, data quality control

What is Metadata?

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Metadata is structured information that describes, explains, locates, orotherwise makes it easier to retrieve, use, or manage an information

resource. Metadata is often called data about data or information aboutinformation.

NISO. 2004. Understanding Metadata.http://http://www.ijdc.net/index.php/ijdc/article/view/67

What is Metadata?

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Three main types of metadata:n Descriptive metadata describes a resource for purposes such as

discovery and identification. It can include elements such as title,abstract, author, and keywords.

n Structural metadata indicates how compound objects are puttogether, for example, how pages are ordered to form chapters.

n Administrative metadata provides information to help manage aresource, such as when and how it was created, file type and other

technical information, and who can access it.


What is Metadata?

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Administrative metadata usually divided into two types:n Rights management metadata, which deals with intellectual

property rights.n Preservation metadata, which contains information needed to

archive and preserve a resource.



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Real research projects often have data that is described by manymetadata standards

Brandt, D.S. 2007. Data, research, metadata, metaresearch.ACRL/STS, ALA annual meeting

http://ala.org/ala/mgrps/divs/acrl/about/sections/sts/programs/annual2007programs/brandt.pdf


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As data is transformed, translated, filtered, combined with other data ina research data workflow, lineage or provenance metadata can capture

the nature of these changes.

Bose, R. & Frew, J. 2005. Lineage Retrieval for Scientific Data Processing: A Survey.ACM Computing Surveys 37:1

http://dx.doi.org/10.1145/1057977.1057978


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Some of these work flows can be very complex.

Davidson, S. & Freire, J. 2008. Provenance and scientific workflows: challenges and opportunities.SIGMOD 08: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data

http://dx.doi.org/10.1145/1376616.1376772

Freire,J. & Koop, D. & Santos, E. & Silva, C.T. 2008. Provenance for Computational Tasks: A Survey.Computing in Science & Engineering

http://dx.doi.org/10.1109/MCSE.2008.79

Barga, R. & Digiampietri,L. 2008. Automatic capture and efficient storage of e-Science experiment provenance.Concurrency and Computation: Practice and Experience 20:5:419-429

http://dx.doi.org/10.1002/cpe.1235

Bowers, S. & McPhillips, T. & Ludscher, B. 2008. Provenance in collection-oriented scientific workflows.Concurrency and Computation: Practice and Experience 20:5:519-529

http://dx.doi.org/10.1002/cpe.1235


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Some transformations can cause metadata to become data!

Jones, M. and Schildhauer, P. and Reichman, O. and Bowers, Shawn. 2006.The New Bioinformatics: Integrating Ecological Data from the Gene to the Biosphere.

Annual Review of Ecology, Evolution, and Systematics 37:1:519-544.http://dx.doi.org/10.1146/annurev.ecolsys.37.091305.110031

Data Archiving

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n Mediumn Migration

Medium

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n The physical storage medium both for analog and digital storageof information has an expected lifespan.

n Digital media can deteriorate and alter the underlying data (bits) offiles well before their expected end of life

Miller, S. 2002. Bridging the Gap between Libraries and Data Archives: Progress Report.Presentation at Joint Informations Systems Committee (JISC, UK) and NSF Digital Libraries Initiative All Projects Meeting, Edinburgh, Scotland.

http://gdc.ucsd.edu:8080/digarch/about-project/presentations/edinburgh2002/view

Medium

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Any single project can have a number of initial physical media.



Migration

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n Before the endoflife of a medium, its contents need to be copiedreliably (bits verified) to a new medium (the same kind or different)

n The provenance metadata needs to be updated when this occursn Sometimes the ability to read the old medium is difficult or not

possible, as the technology has progressed and due to the lack ofavailability of the appropriate working readers (i.e. 9track tape

readers)


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Understanding roles in the research data workflow is helpful insucccessfully managing data.


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One view:

Pryor, G. & Donnelly, M. 2009. Skilling Up to Do Data: Whose Role, Whose Responsibility, Whose Career?International Journal of Digital Curation 4:2

http://www.ijdc.net/index.php/ijdc/article/view/126

Roles & Responsibilities: Another view

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Liz Lyon, Director, United Kingdom Office for Library and InformationNetworking (UKOLN)

n Scientist: creation and use of datan Institution: curation of and access to data

n Data centre: curation of and access to datan User: use of 3rd party datan Funder: set/react to public policy drivers

n Publisher: maintain integrity of the scientific record

Directly from: Lyon, L. 2007. Dealing with Data: Roles, Rights, Responsibilities and Relationshipshttp://www.ukoln.ac.uk/ukoln/staff/e.j.lyon/reports/dealing with data report-final.pdf

Roles & Responsibilities: Scientist

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n Rights:u Of first use.u To be acknowledged.u To expect IPR to be honoured.u To receive data training and advice.

n Responsibilities:u Manage data for life of project.u Meet standards for good practice.u Comply with funder / institutional data policies and respect IPR of

others.u Work up data for use by others.


Roles & Responsibilities: Scientist (cont.)

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n Relationships:u With institution as employee.u With subject communityu With data centre.u With funder of work.


Roles & Responsibilities: Institution

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n Rights:u To be offered a copy of data.

n Responsibilities:u Set internal data management policy.u Manage data in the short term.u Meet standards for good practice.u Provide training and advice to support scientists.u Promote the repository service.

n Relationships:u With scientist as employer.u With data centre through expert staff.


Roles & Responsibilities: Data Centre

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n Rights:u To be offered a copy of data.u To select data of long-term value.

n Responsibilities:u Manage data for the long-term.u Meet standards for good practice.u Provide training for deposit.u Promote the repository service.u Protect rights of data contributors.u Provide tools for re-use of data.


Roles & Responsibilities: Data Centre (cont.)

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n Relationships:u With scientist as clientu With user communities.u With institution through expert staff.u With funder of service.


Roles & Responsibilities: User

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n Rights:u To re-use data (non-exclusive licence).u To access quality metadata to inform usability.

n Responsibilities:u Abide by licence conditions.u Acknowledge data creators / curators.u Manage derived data effectively.

n Relationships:u With data centre as supplier.u With institution as supplier.


Roles & Responsibilities: Funder (1/2)

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n Rights:u To implement data policies.u To require those they fund to meet policy obligations.

n Responsibilities:u Consider wider public-policy perspective & stakeholder needs

u Participate in strategy co-ordination.u Develop policies with stakeholders.u Participate in policy co-ordination, joint planning & fund service

delivery.u Monitor and enforce data policies.


Roles & Responsibilities: Funder (2/2)

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n Responsibilities (cont.):u Resource post-project long-term data management.u Act as advocate for data curation & fund expert advisory service(s).

u Support workforce capacity development of data curators.n Relationships:

u With scientist as funder.u With institution.u With data centre as funder.u With other funders.u With other stakeholders as policy-maker and funder of services.


Roles & Responsibilities: Publisher

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n Rights:u To expect data are available to support publication.u To request pre-publication data deposit in long-term repository.

n Responsibilities:u Engage stakeholders in development of publication standards.

u Link to data to support publication standards.u Monitor & enforce public. standards.

n Relationships:u With scientist as creator, author and reader.u With data centres and institutions as suppliers.


Research Lifecycle

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Where is the researcher in the lifecycle?

Interagency Working Group on Digital Data. 2009. Harnessing the Power of Digital Data for Science and Societyhttp://www.whitehouse.gov/files/documents/ostp/opengov inbox/harnessing power web.pdf

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The Research Lifecycle needs to evolve to support Data Management...

Lord, P. & A. Macdonald. 2003.e-Science Curation Report: Data curation for e-Science in the UK: an audit to establish requirements for future curation and provision

http://www.jisc.ac.uk/uploaded documents/e-ScienceReportFinal.pdf







Research Data Commons

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The future suggested by the Australian National Data Service is perhapsthe most complete vision, as it includes in the greater societal context.

ANDS Technical Working Group. 2007. Towards the Australian Data Commons: A proposal for an Australian National Data Servicehttp://pfc.org.au/pub/Main/Data/TowardstheAustralianDataCommons.pdf

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Not covered (brief introduction to...):n Module 2: More hands on

u Barriers to sharingu Indepth data and metadata examplesu Research Data Planu Data formats

n Module 3: Canadian and International Research Data Activities andOrganizationsu CNC/CODATAu National Consultation Access to Scientific Research Data (2004)

u Research Data Canadau International

Acknowledgments

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CNC/CODATA Committee; Margaret Haines, Carleton University;NRC/CISTI; Research Data Canada; Larry Speers.

Contact and license

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n Contact: Glen Newton [email protected] License: Creative Commons Attribution-Noncommercial-Share Alike

2.5 Canada License;Paternit-Pas dUtilisation Commerciale-Partage des Conditions Initiales

lIdentique 2.5 Canadan Copyright: c2009-2010 National Research Council, Natural

Resources Canada, Glen Newtonn Note: Various components copyright their respective owners

OutlineSome definitionsSome definitions

Data SharingData is becoming more importantResearch Data LifecycleResearch Data DisappearsfooWhy Share data?Benefits of Data SharingUnpredicted re--use of dataUnpredicted re--use of datafooPage from a ship's logfoofooThese are Captain James Cook's logsfoo

Research & Research Data LifecycleResearch & Research Data LifecycleResearch LifecycleResearch LifecycleResearch Data LifecycleResearch Data LifecycleResearch Data LifecycleResearch Data Lifecycle

Research Data ComplexityResearch Data ComplexityResearch Data ComplexityDataResearch Data ComplexityWhat is Metadata?What is Metadata?What is Metadata?Research Data ComplexityMore MetadataResearch Data ComplexityMore MetadataResearch Data ComplexityMore MetadataMore MetadataMore MetadataMore MetadataResearch Data ComplexityMore Metadata

Data ArchivingData ArchivingMediumResearch Data LifecycleMediumResearch Data LifecycleMigration

Data Management RolesData Management RolesData Management RolesRoles: One viewRoles & Responsibilities: Another viewRoles & Responsibilities: ScientistRoles & Responsibilities: Scientist (cont.)Roles & Responsibilities: InstitutionRoles & Responsibilities: Data CentreRoles & Responsibilities: Data Centre (cont.)Roles & Responsibilities: UserRoles & Responsibilities: Funder (1/2)Roles & Responsibilities: Funder (2/2)Roles & Responsibilities: PublisherResearch LifecycleResearch LifecycleResearch LifecycleResearch LifecycleResearch LifecycleResearch LifecycleResearch Data LifecycleResearch Data CommonsResearch Data Commons

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Research Data Management for Researchers: Module 1: Intro to Data, Metadata and the Research Data...

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