Virginia Tech University Libraries’ Data

Service Pilot with the College of Natural

Resources and Environment (CNRE)

Natsuko Nicholls, Andi Ogier and Kyrille Goldbeck DeBose

Research Data Access & Preservation (RDAP) Summit

Minneapolis, MN

April 22, 2015

Our Mission:

To deliver a suite of research data

services to the Virginia Tech Community.

Our Vision:Build Infrastructure

Advocate for Data Management

Support Collaboration

Value Partnerships

Project Highlights

Data Profiling

Data Needs Assessment

Data Interviews

5 areas of interest

5 questions

15 faculty recruited

Q1: Data Profiles

ASCII is recommended,

but it triples or

quadruples the storage

size

Companies are taking

open formats and

creating their own

proprietary standards.

Data Profiles: Summary

● Diversity of data

○ types

○ formats

○ environments

● Data States

● Raw vs.

Summarized

● Media Issues

Common Elements: Points of Interest:

Q2. Data Workflows

Fragmented workflows are

VERY problematic.

Data Management costs

30% of project time.

Need: High-quality

workflows that allow for

creativity and spontaneity.

Data Workflows: Summary

● Lifecycles are

Complicated

● Data Management

is time consuming

● Establishing and

documenting a

workflow is time

consuming

Common Elements: Points of Interest:

Q3. Data Challenges

Need: Systems that allow

algorithms and processes

to be brought to the data.

Original data isn’t always

available.

Data Challenges: Summary

● Data challenges:

○ format

○ storage

○ versioning

○ ownership

○ copyright

○ privacy

● Workflows and

tracking data

Common Elements: Points of Interest:

Q4: Data Value-Add

Harmonization of data

users and producers.

Concern: easy to mis-

interpret some data;

analysis is dependent on

specialized knowledge.

Data Value-Add: Summary

● Value-add

○ beyond research

community

○ historical data

○ public interest

● Market impacts

● Cost of data

collection is

measurable

Common Elements: Points of Interest:

Q5: Data Management Planning

DMPs seem rigid and

limited.

Standardization hinders

innovation; different

people measure different

things.

Preserve data and prevent

loss by summarization.

Data Management: Summary

● Standards vs.

specifics

● Use DMP as a tool

to find data

● Metadata is vitally

important

Common Elements: Points of Interest:

Final Thoughts

Interview process promotes partnerships,

starts conversations.

Research Management tools are needed at a

project level, not institutional/department

level. Provide information and let project

teams make the choice.

Need additional training as to why DMPs are

important to research. Many faculty still see

them as a hindrance.

Questions?

Natsuko Nicholls

Research Data Consultant

nnatsuko@vt.edu

Andi Ogier

Assistant Director

Data Curation

alop@vt.edu Kyrille Goldbeck DeBose

College Librarian for Natural

Resources & Environment

and Animal Sciences

kgoldbec@vt.edu