IFLA Warsaw • 16 – 17August 2017

New Data, Same Skills: Applying Core Principles to New Needs in Data Curation

Lynn Silipigni Connaway, PhD

Senior Research Scientist & Director of User Research, OCLC

connawal@oclc.org

@LynnConnaway

• Data curation involves different stakeholders

• Data governance & framework

• Critical factors in data governance

• Business objectives

• LIS objectives

• Data life-cycle

Data Curation

Data Curation & Roles

• Data management objects can be text, numbers,

images, video, audio, software, algorithms,

reports, models & other forms

• Data management = all activities that related to

maintaining, preserving, & adding value during

lifecycle of digital data

• Emphasis on adding value for reuse of data

ROLES OF DATA CURATORS

Lewis (2010) proposed possible areas for library

involvement with RDM

• Influence national data policy

• Lead on local (institutional) data policy

• Develop local data curation capacity

• Identify required data skills with LIS schools

• Bring data into UG research-based training

• Teach data literacy to postgraduate students

• Develop LIS workforce data confidence

• Provide researcher data advice

• Develop researcher data awareness

Roles and responsibilities of RDM librarians

• Provide researcher data advice

• Teach data literacy

• Develop researcher data awareness

• Support local data curation

• Embedded in R&D data flow & related to data life-cycle

• Includes

• R&D management & data utilization perspectives (Koltay 2015, Hagen-MacIntosh 2016 and Carlson & Johnston 2016)

• Practical skills such as data analysis, description

& tools

Data Literacy

Successful librarians can strengthen or develop

present roles in the following areas:

• Assessment

• Education

• Curation

• Environment

Challenges

• Difficult to get engagement from senior managers who may

not see the importance of DRM

• Difficult to maintain levels of funding required to run services without

engagement from senior management

• Critical to include activities that link to university strategies &

funder policies (Bellanger et al., 2017)

• Maintain balance of services – do not skew towards those

researchers who speak the loudest

DATA REUSE

Key Barriers

• Low levels of user trust in information resources of every kind (Yoon, 2016; Yoon, 2017; Carlson & Anderson, 2007)

• Current models, especially those emphasizing metadata quality

over primary data quality, may not be as effective as initially

expected

• Repositories are clearinghouses, not archives

• Preservation for reusability is not enough

Key Barriers

• Primary user groups are not known data creators but unknown data

reusers

• Approach effectively returns data librarianship to Ranganathan’s view:

information resources of all kinds are for use

• Research data are for reuse

• Every reuser his/her data

• Every dataset its reuser

• If data are not reused, then data curators must redouble their efforts to

connect data with actual reusers

• Ranganathan’s third law: Find reusers for your data

(Connaway & Faniel, 2014)

Updating Ranganathan

Increase the discoverability, access and

use of resources within users’ existing

workflows.(Connaway and Faniel, 2014, p. 74)

Updating Ranganathan

Reuser-centric Framework

• Integrate repository ingest into development of data reuse plans

• Build data literacy programs around an institutional repository

service

• Integrate data curation training into communities of practice

• Promote reuse from the beginning of research cycle

• Build research data models with

• designated reuses

• specific reuse communities in mind

TECHNOLOGY & TRAINING

EDUCATION & TRAINING

• Often must retrain librarians on staff as RDM

librarians – not able to hire RDM experts

• Utilize librarians’ skills & competences

• Teaching of information literacy

• Development & management of collections

• Conducting reference interviews

• Familiarization with publications repository management &

Open Access

• Training needed by RDM librarians

• Often do not have RDM knowledge & skills

• Data processing

• Data analysis

• Data handling

• Need education & training addressing research data lifecycle

• Plan

• Collect

• Organize

• Store

• Preserve

• Share

• Assess

• Key areas for RDM librarian development

• Develop protocols & infrastructures for description, discovery,

retrieval, & citation of research data

• Simplify compliance regulations & articulate rationale

• Adapt archival practices for data “at rest”

• Assess & identify trustworthy repositories

• Review & propose institutional policies

• Apply data mining & analytics to demonstrate evidence of

faculty productivity, research impact, trends & rankings

REPOSITORY PLATFORMS

• Most platforms designed to work with common use cases

& do not fully address the needs of research data

• Data modelling requirements often complex & difficult to

represent in traditional folder hierarchies

• Diverse & sometimes proprietary file formats not always

supported

• Metadata presents a major obstacle

• Diversity across disciplines difficult to consolidate in single

system

CONCLUSIONS &

RECOMMENDATIONS

Conclusions

• Lack of policy, unclear responsibilities, mismatch in quality &

demand of staff’s data literacy, & lack of professional education

• Stakeholders not fully engaged in data curation

• Need framework to establish clear organizational structure &

division of responsibilities, & clarify relationships between

stakeholders

• More emphasis on data reutilization in the field of data curation

• Universities expected to demonstrate accountability

• Academic librarians can be critical players

• Librarians should be integral to the academy to leverage

its knowledge assets & empower its community

• Results will create relationships not only across

academy, but with public & private sectors

• Collaborative innovation & governance will strengthen

university’s research infrastructure

Conclusions

Recommendations: Cultivate Professionals

• Become proactive designers of services that enable productive

knowledge workers

• Clarify job responsibilities & professional quality

• Establish relevant courses by promoting both theory & practice

• Accelerate development of standards & policy

• Promote innovation of data curation

• Strengthen data consciousness & data ethics education

• Provide legal framework

• Be aware of tools used by researchers to analyze data

• Establish discipline-specific data literacy training

• Strengthen data consciousness & data ethics education

• Share project management roles to increase research team

productivity

• Be change agents that build evidence to monitor efficiencies &

gauge impact

• Partner in knowledge-generating activities

Recommendations: Know Users

Recommendations: Develop Communities

• Develop intra-organizational collaboration (Pinfield, Cox, and Smith, 2014,

p7)

• Shift to more entrepreneurial roles & partnerships

• Cooperate & communicate with professional departments in

universities

Reordering Ranganathan

(Connaway & Faniel, 2014)

“Perhaps the most convenient method of

studying the consequences of this law will

be to follow the reader from the moment

he enters the library to the moment he

leaves it…”

(Ranganathan 1931, 337)

References

Bellanger, S., Higman, R., Imker, H., Jones, B., Lyon, L., Stokes, P., Teperek, M., Verdicchio, D.

(2017). Strategies for engaging senior leadership with RDM – IDCC discussion.

https://unlockingresearch.blog.lib.cam.ac.uk/?p=1435 (accessed 26th May 2017).

Carlson, J, Johnston, L.(2015). Data information literacy: Librarians, data, and the education of a

new generation of researchers. Purdue Information Literacy Handbooks. West Lafayette, Indiana:

Purdue University Press.

Carlson, S. & Anderson, B. (2007). What are data? The many kinds of data and their implications

for data re‐use. Journal of Computer‐Mediated Communication, 12(2), 635-651.

Connaway, Lynn Silipigni, and Ixchel M. Faniel. 2014. Reordering Ranganathan: Shifting user

behaviors, shifting priorities. Dublin, OH: OCLC Research.

http://www.oclc.org/content/dam/research/publications/library/2014/oclcresearch-reordering-

ranganathan-2014.pdf.

Hagen-McIntosh, J. (2016). Information and data literacy: The role of the library. Oakville, ON,

Canada Waretown, NJ, USA: Apple Academic Press.

References

Koltay, T. (2015). Data Literacy: In Search of a Name and Identity. Journal of Documentation, 71(2):

401–415.

Lewis, M.J. (2010) Libraries and the management of research data. In: McKnight, S, (ed.)

Envisioning Future Academic Library Services. Facet Publishing , London , pp. 145-168.

Pinfield, S., Cox, A.M., & Smith, J. (2014). Research data management and libraries: Relationships,

activities, drivers and influences. PLOS ONE.

https://doi.org/10.1371/journal.pone.0114734 (accessed 16th August 2017).

Yoon, A. (2016). Red flags in data: Learning from failed data reuse experiences. Proceedings of the

Association for Information Science and Technology, 53(1), 1-6.

Yoon, A. (2017). Data reusers & trust development. Journal of the Association for Information

Science and Technology, 68(4), 946-956.

ACKNOWLEDGEMENTS

I would like to thank Brittany Brannon and Erin M. Hood

for their assistance in preparing this presentation.

Questions &

Discussion

Lynn Silipigni Connaway, PhD

Senior Research Scientist & Director of User

Research

connawal@oclc.org

@LynnConnaway