Research Data Management and Librarians

Research Data Management
and Librarians
Presentation at Elsevier Library Connect Seminar,
6 October 2014, Johannesburg, 7 October 2014,
Durban and 9 October 2014, Cape Town
By Johann van Wyk (University of Pretoria)

Introduction
Internationally research data is increasingly recognised as a vital
resource whose value needs to be preserved for future research.
This places a huge responsibility on Higher Education Institutions to
ensure that their research data is managed in such a manner that
they are protected from substantial reputational, financial and legal
risks in the future. Librarians have a unique skillset to help these
institutions navigate this complex environment. This presentation
will highlight a number of potential roles librarians could play.

Research Data Management: A (Brave) Complex New World
Messy Complex
Small Data
Various formats
Various devices
Various Versions
Sensitive Data

What is meant by Research Data?
Research data, unlike other types of information, is
collected, observed, created or generated, for purposes
of analysis to produce original research results
http://www.docs.is.ed.ac.uk/docs/data-library/EUDL_RDM_Handbook.pdf

What is research data management?
• “the process of controlling the information generated during a
research project”
• “Managing data is an integral part of the research process.
How data is managed depends on the types of data involved,
how data is collected and stored, and how it is used -
throughout the research lifecycle”.
http://www.libraries.psu.edu/psul/pubcur/what_is_dm.html

Why Manage Research Data?
By managing research data you will:
• Meet funding body grant requirements, e.g. NSF, NIH;
• Meet publisher requirements
• Ensure research integrity and replication;
• Ensure research data and records are accurate, complete, authentic and reliable;
• Increase your research efficiency;
• Save time and resources in the long run;
• Enhance data security and minimise the risk of data loss;
• Prevent duplication of effort by enabling others to use your data;
• Comply with practices conducted in industry and commerce; and
• Protect your institution from reputational, financial and legal risk.

Designing Data Management Plans
Creating
Data
A Data Management Plan is “a formal document that outlines what you will do
with your data during and after you complete your research” (The University of Virginia
Library, 2014).
Data Management Planning Tools:
• Data Management Planning Tool (DMPTool) https://dmptool.org/
(University of California Curation Center of the California Digital Library)
• DMPonline tool https://dmponline.dcc.ac.uk/ (Digital Curation Centre, UK)
Librarians can play an advisory role

Data Capture/Collection
Creating
Data
The action or process of “gathering and measuring information on variables of
interest, in an established systematic fashion that enables one to answer stated
research questions, test hypotheses, and evaluate outcomes”
(Responsible conduct of research, n.d.; The Oxford Dictionary, 2014).
Examples of data collection methods:
Observations, textual or visual analysis, interviews, focus group interviews, surveys, tracking,
experiments, case studies, literature reviews, questionnaires, data from sensors, model outputs,
scenarios, etc.
Librarians can play their traditional role of information searching, - training
and - consultation

Data Storage and Backup
Creating
Data
Processing
Data
Analysing
Data
Data storage is the process of “preservation of data files in a secure
location which can be accessed readily” (Research Data Services,
University of Wisconsin-Madison, 2014)
Data Backup is the process of “preserving additional copies of your data
in a separate physical location from data files in storage”.
Librarians can advise researchers on File Naming Conventions

Metadata Creation
Creating
Data
Processing
Data
Analysing
Data
Preserving
Data
• Metadata is searchable, standardised and structured “information that describes a
dataset” and explains “the aim, origin, time references, geographic location, creating
author, access conditions and terms of use of a data set”
(Corti et al., 2014: 38; USGS Data Management Website, 2014)
• Examples:
- Dublin Core Metadata Element Set;
- ISO 19115: 2003(E) — Geographic Information Metadata;
- PREMIS
Librarians, especially cataloguers have the skill-set to assist with metadata
creation and to advise

Data Cleansing, Verification &
Validation
Processing
Data
Analysing
Data
• Data Cleansing
“refers to identifying incomplete, incorrect, inaccurate, irrelevant, etc. parts of the data and
then replacing, modifying, or deleting this dirty data’ (Wikipedia)
• Data Verification
“the process of evaluating the completeness, correctness, and compliance of a dataset with
required procedures to ensure that the data is what it purports to be. This can be done by persons
“who are less familiar with the data”, for example Librarians.
(Martin and Ballard, 2010: 8-9; US EPA, 2002:7)
• Data validation
process “to determine if data quality goals have been achieved and the reasons for any
deviations. Validation checks that the data makes sense”.
(Martin and Ballard, 2010: 8; US EPA 2002:15).

Data anonymisation
Processing
Data
Analysing
Data anonymisation is “the process of de-identifying sensitive data, while
preserving its format and data type” (Raghunathan, 2013: 4).
Anonymisation Techniques - Examples: Generalisation, Suppression, Permutation,
Pertubation, Substitution, Shuffling, Number and Date Variance, Nulling-out (Charles,
2012; Cormode and Srivastava, 2009; Raghunathan ,2013: 172-182; Simpson, n.d.; Vinogradov and
Pastsyak,2012: 163).
Data

Data Interpretation & Analysis
Analysing
Data
Data interpretation and analysis “is the process of assigning meaning” to
the gathered information and ascertaining “the conclusions,
significance, and implications of the findings” (Analyzing and Interpreting Data,
n.d.).

Data Publishing
Analysing
Data
Data publishing
This is the process of making research data underpinning the findings published in peer-reviewed
articles, available for readers and reviewers in an appropriate repository, or “as
supplementary materials to a journal publication” (Corti et al 2014: 197; Marques, 2013)
Data Journals
A more recent development has been the appearance of data journals. These journals publish
data papers that describe a dataset, and also give an indication in which repository the
dataset is available (Corti et al. 2014: 7-8).
Librarians can be involved in creating and managing a data repository, and can give training
and advise

Examples of Data Repository Software

Registry of Research Data Repositories
• re3data.org is a global registry of research data repositories that covers
research data repositories from different academic disciplines.
• It presents repositories for the permanent storage and access of data sets to
researchers, funding bodies, publishers and scholarly institutions.
• It can be used a tool for the easy identification of appropriate data
repositories to store research data.

Data Journals
• A list of Data Journals – available at
http://proj.badc.rl.ac.uk/preparde/blog/DataJournalsList
• Example of data journal at Elsevier: “Data in Brief”

Data Visualisation
Analysing
Data
Data Visualisation is the visual representation of data, and is used to enable
people to both understand and communicate information through graphical and
schematic avenues (Friendly, 2009: 2; Schnell and Shetterley, 2013: 3)
From Xiaoru Yuan’s presentation at CODATA Workshop on 12 June 2014

Data Archiving
Preserving
Data
Data archiving can be described as the process of retention and
storage of valuable data (this is data that will be essential for future
reference) for long-term preservation, so that the data will be
protected from risk (i.e. loss, or corruption), and will be accessible for
future use (Rouse, 2010).

Data Preservation
Preserving
Data
Data preservation is ”the process of providing enough representation
information, context, metadata, fixity, etc. to the data so that anyone other
than the original data creator can use and interpret the data” (Ruth Duerr,
National Snow and Ice Data Center as cited by Choudhury, 2014)
The Librarian can assist researchers in preparing data for long-term
preservation, by advising on metadata standards

Linking Data to research outputs
Preserving
Data
This is the process of connecting the underlying data relating to a specific
research output, e.g. journal article, thesis, etc to the research output itself.
This can be done by adding a digital object identifier (DOI) to the dataset
and including this in the metadata of the research output, or by citing the
dataset (Callaghan et al., 2013).
The Librarian can assist researchers, through training and consultation
on DOIs and data citation methods

Data Sharing
Giving
Access to
• Sharing data is the process of opening up access to research data and
making it available to other researchers (Corti et al., 2014: 2).
• Data sharing provides “opportunities for other researchers to review,
confirm or challenge research findings” (Data sharing and implementation guide,
n.d.).
Data

Data sharing Methods
The method for sharing data will depend on a variety of factors,
including size and complexity of the dataset, sensitivity of the data
collected, and anticipated number of requests for data sharing.
Researchers could
(1) Take responsibility for sharing data themselves, or
(2) Use a data archive, or
(3) Use a combination of these methods.

Data repurposing/reuse
Re-using
Data
• This is the process where secondary data (data that have been captured and
analysed by other researchers) can be re-analysed, reworked or -used for new
analyses, and compared with contemporary data (Corti et al., 2014: 169)
• This process “also enables research where the required data may be expensive,
difficult or impossible to collect”, e.g. large scale surveys, or historic data (Corti et
al., 2014: 169).

Data Citation
Re-using
Data
Data citation is the process of referencing (attributing and acknowledging)
reused data in a similar fashion as traditional sources of information (Corti et
al. 2014: 197).
Helpful Sources :
• Publication Manual of the American Psychological Association (APA, 2009)
• Oxford Manual of Style (OUP, 2012)
• Data Citation Awareness Guide (ANDS, 2011)
• Data Citation: What you Need to Know (ESRC, 2012)
The Librarian can assist researchers, through training and consultation in
data citation methods

Data Citation: DOI
Re-using
Data
DOI = Digital Object Identifier
To enable a unique and persistent identification of a digital object
A DOI is a unique alphanumeric string assigned by a registration agency (the
International DOI Foundation) to identify a digital object, e.g. a data set.
Metadata about the object is stored together with the DOI name. This may
include a location, such as a URL, where the object can be found. (Wikipedia)
For example: http://dx.doi.org/10.1000/182
DOI Registry Registrant Specific Object
The Librarian can assist researchers, through training and consultation on DOIs

Provenance of Data
• history of a data file or data set
• this includes information
o on the person(s) responsible for the data set
o context of the data set
o revision history, including additions of new data and error
corrections (Strasser et al., 2012: 7, 11)

Management of Big Data
Big data can be described in terms of its characteristics:
• Relative characteristics: denotes those datasets which cannot be acquired,
managed or processed on common devices within an acceptable time;
• Abolute chacteristics defines big data through Volume, Variety, Veracity and Velocity
(Huadong, 2014)
Big Data is part of a new science paradigm called Data Intensive Science, where
Scientists are overwhelmed with data sets from many different sources, e.g. captured
by instruments, generated by simulations, and generated by sensor networks

Absolute Characteristics of Big Data
• Volume: The scale of data that systems must ingest, process and
disseminate;
• Variety: the complexity of the types of information handled (many
sources and types of data both structured and unstructured)
• Velocity: the pace at which data flows in and out from sources like
business processes, machines, networks and human interaction with
things like social media sites, mobile devices
• Veracity: refers to the biases, noise and abnormality in data
http://inside-bigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-big-data-veracity/

Role of Librarian in Big Data
• Create awareness among researchers about Big Data Initiatives
internationally
• Create awareness among colleagues about the activities,
workgroups and task groups of CODATA (Committee on Data for
Science and Technology, of the International Council for Science)
and Research Data Alliance
• Become a member of a number of CODATA task groups

Examples of International Initiatives
Center for International Earth Science
Information Network,
EARTH INSTITUTE, COLUMBIA UNIVERSITY
Computer Network
Information Center, CAS
World Data Center for
Microorganisms
Institute of
Remote Sensing
and Digital Earth,
CAS
Dept of Earth Sciences
Institute for environment
and Human Security
Thetherless World Constellation
International Society
for Digital Earth

Pilot Projects at University of Pretoria
• The UP Library Services implemented two data management pilot projects in 2013-2014:
• Institute for Cellular and Molecular Medicine (ICMM) and the Neuro-Physio-Group
• An Open Source Document Management System was customised for this purpose
• Why Alfresco?
• Open Source
• Captured provenance of data
• Had a versioning function
• Good metadata function
• Easy to integrate with other software
• Workflow function gave supervisor overview of progress of students
• Sync function with dropbox and Google Drive
• Drag and Drop function
• File Sharing function
• Mobile App

Next Phase
Long-term Preservation
Archival Information Package (AIP)
• Bagit format (Bag-it and tag-it)
• Bagit “bag” contains:
• Bag declaration file, manifest file, data files, metadata
file (XML)
• METS wrapper
• Dublin Core and MODS(Descriptive Metadata)
• PREMIS (Preservation Metadata)

Various stakeholders in RDM
Executive Management
Deans & Dept Heads
IT Services
Research Office
Library
Principal
Investigator/Researcher
Funders
Publishers
External
(disciplinary)
data
repositories
(De Waard, Rotman and Lauruhn, 2014)

Funders Funders Requirements: USA
(Dietrich et al. 2012)
http://www.istl.org/12-summer/refereed1.html

Funders Funders’ Requirements: UK
http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies
UK Digital Curation Centre

Conclusion
This presentation showed that although the RDM environment looks
daunting the Library Professional can play an essential and much needed
role in determining the success of Research Data Management initiatives at
Higher Education Institutions.
This vast, untamed and complex environment is waiting for someone to
conquer it. Librarians have the necessary skillset to do that.
May this motto also become our victory cry:
“Veni, vidi, vici” – I came I saw I conquered

References
• Analyzing and interpreting data. Syracuse, NY: Office of Institutional Research and Assessment, Syracuse
University, n.d. [Online] available at https://oira.syr.edu/assessment/assesspp/Analyze.htm (Accessed 18
September 2014).
• CALLAGHAN, S. et al. 2013. Connecting data repositories and publishers for data publication. Presentation
delivered on 7 February 2013 at the OpenAIRE Interoperability workshop, University of Minho, held 7-8 February
2013. Braga, Portugal: University of Minho Gualtar Campus. [Online] available at
http://openaccess.sdum.uminho.pt/wp-content/
uploads/2013/02/7_SarahCallaghan_OpenAIREworkshopUMinho.pdf (Accessed 19 September 2014).
• CHARLES, K. 2012. Comparing enterprise data anonymization techniques. Newton, MA: TechTarget. [Online]
available at http://searchsecurity.techtarget.com/tip/Comparing-enterprise-data-anonymization-techniques
(Accessed 18 September 2014).

References
• CHOUDHURY, S. 2014. Public Institution perspective (Research Library). Presented at the Digital Media
Analysis, Search and Management (DMASM), 2014. [Online] available at http://dataconservancy.org/wp-content/
uploads/2014/03/DC_DMASM_2014.pdf (Accessed 24 September 2014).
• CORMODE, G. AND SRIVASTAVA, D. 2009. Anonymized data: generation, models, usage. Tutorial at
SIGMOD, July 2009. [Online] available at http://dimacs.rutgers.edu/~graham/pubs/papers/anontut.pdf
• CORTI, L. et al. 2014. Managing and sharing research data: a guide to good practice. Los Angeles: SAGE.
• Data Management Planning Tool (DMPTool). Oakland, CA: University of California Curation Center of the
California Digital Library, 2014. [Online] available at https://dmptool.org/ (Accessed 24 September
2014).
• Data sharing and implementation guide. Washington, DC: Institute of Education Sciences, U.S.
Department of Education, n.d. [Online] available at
http://ies.ed.gov/funding/datasharing_implementation.asp (Accessed 19 September 2014).

References
• DCC. 2014. Overview of funders data policies. Edinburgh, UK: Digital Curation Centre. [Online]
available at http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies
• DCC. 2014. What are metadata standards? Edinburgh, UK: Digital Curation Centre. [Online] available
at http://www.dcc.ac.uk/resources/briefing-papers/standards-watch-papers/what-are-metadata-standards
• DE WAARD, A. AND ROTMAN, D. AND LAURUHN, M. 2014. Research data management at
institutions: part 1: visions. Elsevier Library Connect, 6 February 2014. [Online] available at
http://libraryconnect.elsevier.com/articles/2014-02/research-data-management-institutions-part-1-
visions (Accessed 5 October 2014)
• DIETRICH, D. et al. 2012. De-mystifying the data management requirements of research funders.
Issues in Science and Technology Librarianship, Summer, 2012, No. 70. [Online] available at
http://www.istl.org/12-summer/refereed1.html (Accessed 22 September 2012).

References
• DMPonline tool. Edinburgh, UK: Digital Curation Centre, 2014. [Online] available at
https://dmponline.dcc.ac.uk/ (Accessed 22 September 2014).
• Edinburgh University Data Library Research Data Management Handbook, v.10, Aug, 2011.
[Online] available at http://www.docs.is.ed.ac.uk/docs/data-library/
EUDL_RDM_Handbook.pdf (Accessed 25 September 2014).
• FRIENDLY, M. 2009. Milestones in the history of thematic cartography, statistical graphics,
and data visualization. [Sl.: s.n.] [Online] available at
http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf (Accessed 19 September
2014).
• HODSON, S. 2014. Global collaboration in data science: an introduction to CODATA.
Presentation on 6 June 2014 at CODATA International Training Workshop in Big Data for
Science for Researchers from Emerging and Developing Countries, Beijing, China, 4-20 June
2014.

References
• HUADONG, G. 2014. Scientific Big Data for knowledge discovery. Presentation on 8 June 2014
at the CODATA Workshop on Big Data for International Scientific Programmes: Challenges and
Opportunities, Beijing, China, 8-9 June 2014.
• Library of Congress. 2014. PREMIS. Washington, DC: Library of Congress. [Online] available at
http://www.loc.gov/standards/premis/ (Accessed 25 September 2014).
• A list of data journals. Trac Integrated SCM and Project Management. [Online] available at
http://proj.badc.rl.ac.uk/preparde/blog/DataJournalsList (Accessed 25 September 2014).
• MARTIN, E. AND BALLARD, G. 2010. Data management best practices and standards for
Biodiversity data applicable to Bird Monitoring Data. U.S. North American Bird Conservation
Initiative Monitoring Subcommittee. [Online] available at http://www.nabci-us.org/ (Accessed
24 September 2014).

References
• MARQUES, D. 2013. Research data driving new services. Elsevier Library Connect, 25 February
2013. [Online] available at http://libraryconnect.elsevier.com/articles/best-practices/2013-
02/research-data-driving-new-services (Accessed 5 October 2014)
• NORMANDIEU, K. 2013. Beyond Volume, Variety and Velocity is the Issue of Big Data Veracity.
Inside Big Data. [Online] available at http://inside-bigdata.com/2013/09/12/beyond-volume-variety-
velocity-issue-big-data-veracity/ (Accessed 25 September 2014).
• The Oxford Dictionary. [sl.]: Oxford University Press, 2014. [Online] available at
http://www.oxforddictionaries.com/us/ (Accessed 16 September 2014).
• RAGHUNATHAN, B. 2013. The complete book of data anonymization: from planning to
implementation. Broken Sound Parkway, NW: CRC Press, Taylor and Francis Group.

References
• Research Data Services, University of Wisconsin-Madison. Madison, WI: University of Wisconsin
Madison, 201. [Online] available at http://researchdata.wisc.edu/manage-your-data/data-backup-and-integrity/
• Responsible conduct of research. DeKalb, Illinois: Northern Illinois University Faculty Development and
Instructional Design Center, n.d. [Online] available at:
http://ori.dhhs.gov/education/products/n_illinois_u/datamanagement/dctopic.html. (Accessed: 16
September 2014).
• ROUSE, M. 2010. Data archiving. Techtarget. [Online] available at
http://searchdatabackup.techtarget.com/definition/data-archiving (Accessed 19 August 2014)
• SCHNELL, K. AND SHETTERLEY, N. 2013. Understanding data visualization. [Sl.]: Accenture. [Online]
available at http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Tech-Labs-Data-
Visualization-Full-Paper.pdf (Accessed 19 September 2014).

References
• SIMPSON, J. n.d. Data Masking and Encryption Are Different. IRI Blog Articles. [Online] available at
http://www.iri.com/blog/data-protection/data-masking-and-data-encryption-are-not-the-same-things/
• STRASSER, C. et al. 2012. Primer on data management: what you always wanted to know.
[Albuquerque,NM]: DataONE, [University of New Mexico], p1-11. [Online] available at
http://www.dataone.org/sites/all/documents/DataONE_BP_Primer_020212.pdf (Accessed 28
August 2013)
• United States Environmental Protection Agency (US EPA). 2002. Guidance on Environmental Data
Verification and Data Validation: EPA QA/G-8. Washington, DC: Environmental Protection Agency.
[Online] available at http://www.epa.gov/QUALITY/qs-docs/g8-final.pdf (Accessed 24 September
2014).
• USGS Data Management. [Online] available at
http://www.usgs.gov/datamanagement/describe/metadata.php (Accessed 19 August 2014).

References
• VINOGRADOV, S AND PASTSYAK, A. 2012. Evaluation of data anonymization tools. In:
DBKDA 2012 : The Fourth International Conference on Advances in Databases,
Knowledge, and Data Applications, held 29 February-5 March, 2012, Reunion Island.
Wilmington, DE: International Academy, Research, and Industry Association (IARIA).
• What is data management? University Park, PA: Publishing and Curation Services, Penn
State University Libraries, 2014. [Online] available at
http://www.libraries.psu.edu/psul/pubcur/what_is_dm.html (Accessed 25 September
2014).
• YUAN, X. 2014. Visualization and visual analytics. Presentation 0n 12 June 2014 at
CODATA International Training Workshop in Big Data for Science for Researchers from
Emerging and Developing Countries, Beijing, China, 4-20 June 2014.

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