UVA LIBRARY SCIENTIFIC DATA

CONSULTING GROUP (SCIDAC): NEW PARTNERSHIPS AND SERVICES TO

SUPPORT SCIENTIFIC DATA IN THE LIBRARY

Andrew Sallans

Head of Strategic Data Initiatives

Sherry Lake

Senior Scientific Data Consultant

IASSIST 2011

1 June 2011

OUTLINE

Phase 1 – Research Computing Lab

Phase 2 – Scientific Data Consulting Group

1. Data assessment interviews

2. Data management planning

3. Integration of processes with IR

Partnerships

Internal

External

Challenges

Future opportunities

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BACKGROUND ON THE UNIVERSITY OF VIRGINIA

“Mr. Jefferson’s University”

Size

About 14,000 undergraduate

students

About 6,000 graduate students

About 2,000 faculty

Annual research dollars –

FY10 $375 million

DE (Ed) - $10 million

DOE- $10 million

DOD- $15 million

NSF - $29 million

DHHS - $197 million 3

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Source: “Men's Lacrosse NCAA CHAMPS! (by Matt Riley) 5/31/2011” photo gallery, http://www.virginiasports.com/

PHASE 1: RESEARCH COMPUTING LAB

Began planning in 2005.

Central IT: seeking greater

visibility.

Library: seeking new ways

to support scientific research.

Collocation provided mutual

benefits.

Staff combined in 2006,

moved to Library locations

(Research Computing Lab &

Scholars’ Lab), setup new

service points and services.5

RESEARCH COMPUTING LAB RESPONSE

Aiming to provide support across the entire

scientific research data lifecycle

Staff with expertise in:

Data

Quantitative data, statistics

Modeling, visualization

Scientific publishing

Emphasis on consulting, not drop-off services

Partnership with traditional librarians to help

ease transition to new support models

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SAMPLE RCL CONSULTATIONS

STS Undergrad Environmental Justice (2008) Development of technology solutions for empowering the

citizen scientist

Web 2.0 tools, data collection/management

Data analysis

Economics Graduate Student (2008/2009) Airline flight price modeling

Screen scraping, data collection/management

Data analysis

Mountain Lake Beetle Project (2009) Mobile data acquisition/collection solution

Database development/management, programming

Data analysis

Archiving of dissertation data (2009) EVSC student, ModelMaker 4.0 data

Biology student, IDL, Matlab, R code 7

TAKE-AWAYS

This is the future

Heavily growing space, lots of opportunity

Requires big investment and commitment, the

biggest being training and priority alignment

Libraries and institutions need to make decisions

on what to do and what not to do

It’s a culture change for both libraries,

institutions, and researchers

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PHASE 2 - SCIENTIFIC DATA CONSULTING

GROUP

December 2009/January 2010: rethinking the

model

Budgetary pressures

Changes in organizational priorities

Emerging demands in research community

Spring 2010: decision to focus on data

May 2010: close of RCL, start of SciDaC

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WHAT’S HOT IN 2010?

Open data: growing governmental interest in

making publicly-funded research more

transparent and more available (NIH, NSF)

Broader critical review: greater interest

evaluating original research data (Nature)

Technological advances: sharing of research

results easier and faster (Repositories, Web 2.0)

Reuse/preservation of research data:

increased consideration of the cost and value of

research data and need to ensure its longevity

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“SCIENTISTS SEEKING NSF FUNDING WILL SOON BE

REQUIRED TO SUBMIT DATA MANAGEMENT PLANS”Press Release 10-077, May 5, 2010

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Current Policy:

o “To advance science by encouraging data sharing among researchers”

o Data obtained with federal funds be accessible to the general public

o Grantees must develop and submit specific plans to share materials collected with NSF support, except where this is inappropriate or impossible

On or around October 2010:

o All new NSF proposals will be required to include a data management plan in the form of a 2 pg supplementary document (peer reviewed)

o New policy is meant to be a 1st step toward a more comprehensive approach to data management

o Exact requirements vague

THE CHALLENGE FOR INSTITUTIONS

Data is expensive

Time, instrumentation, inability to reproduce

Increasing regulation

Granting agencies and journals require

submission

Inadequate training

No formal data management curriculum

Preservation is not a priority

For most researchers, preservation takes time

away from the work that is rewarded

(publication, teaching) 12

SO…WHO’S GOING TO TAKE THIS ON?

Researchers?

VPR?

CIO?

OSP?

UL?

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WHY THE LIBRARY?

Neutral: works across the entire institution

Strong in relationship building: has

experience fostering discussion and relationships,

and cultivates an existing support network

Intellectual Property experts: has dealt with

copyright, can translate to data

Service-oriented: uniquely positioned as an

intellectual service unit within the institution

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GETTING STARTED…

Take what we learned in the RCL experience and

apply it to the focused demands around data

Steps:

Conduct a stakeholder analysis

Make a short term plan (12 months)

Develop clear priorities

Refine and standardize consulting methods

Communicate heavily

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STAKEHOLDER ANALYSIS (ABBREVIATED)

Internal

Researchers

Graduate Students

Grant Administrators

Deans

VP/CIO

VPR

OSP

UL

External

Funding agencies

Broader research

community

“The Public”

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SHORT TERM PLAN

Survey OSP to match grant holders with

regulations

Educate/engage subject librarians

Build political awareness/support

Build partnerships with

local/national/international groups

Resource requests:

Staffing commitment

Travel/partnership support

Promotion of initiative to institution17

CLEAR PRIORITIES

1. Data interviews/assessments

2. Response to NSF Data Management Plan

(DMP) Mandate

3. Leadership on data for the Institutional

Repository (IR)

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CONSULTING ACTIVITIES

Interviews/assessments

Data management planning templates

LOTS of documentation

Constant and continuous refinement of process

Focus on helping researchers improve process

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COMMUNICATE HEAVILY

Internal

Inform staff of processes, priorities, and progress

Keep stakeholders engaged

Reach the consumers from many angles

External

Discuss and share experiences with colleagues at other

institutions

Create partnerships to share, build upon resources and

experiences, collaborate on tools

Networking (Twitter, LinkedIn, listserves, conference calls,

conference presentations)

Bottom line: this is a big culture shift, and you do have to

say the same thing many times in different ways20

PRIORITY 1 – DATA ASSESSMENT INTERVIEWS

Initially a means of growing awareness of consulting service and doing assessment, now a means of establishing a baseline for research data management practices with any new “client”

Protocol involves:

60 minute interview discussion (researcher / SciDaCconsultants / subject librarian)

Development of a report

SciDaC consultants give researchers recommendations to improve data management

SciDaC consultants work with researchers to implement recommended solutions

Approach has proven to be very effective thus far 21

PRIORITY 2 – DATA MANAGEMENT

PLANNING

Highest priority of responding to and addressing

support needs for funding agency requirements

(ie. NSF, others)

Getting a handle on data management as a

means of institutional risk management

Coordination of effort across institution

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NSF DATA MANAGEMENT PLAN MANDATE

Official mandate became active Jan. 18, 2011

New NSF Directorates/Divisions continue to

release and specify guidelines (examples below)

Education and Human Resources (EHR)

Engineering (ENG)

Geological Sciences (GEO)

Mathematical and Physical Sciences (MPS)

Social, Behavioral, and Economic Sciences (SBE)

Researchers continue to be mostly unaware of the

mandate and how to prepare a DMP

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UVA SCIDAC NSF DMP RESPONSE

UVa Library’s Original Request

Develop boilerplate for researchers to use in proposals

SciDaC Group’s Response

No boilerplate, successful proposals need customized plans

Our approach involves:

Knowledge across many communities (“translational” opportunities)

Leadership on policy/infrastructure development

Development of a template that simplifies writing the plan

Principles

Must be easy for researcher

Must be supportable by available UVA resources/infrastructure

Must be able to be followed-through on if grant is awarded24

PRIORITY 3 – INTEGRATION WITH IR

Institutional repository “Libra” (http://libra.virginia.edu)

Built upon Hydra architecture

Three components: open access publications, data, and electronic

theses/dissertations

Working on figuring out storage and cost models to

support management of big and small data from across

institution’s research community

Will provide preservation assurance for data in form of

“blobs” or packages (bit preservation, no format migration)

Currently in process of developing user

interface/ingestion prototype that addresses needs of

small data for release in late July 201125

COLLABORATIONS

Internal

Library / VPR / CIO / OSP

Institutional Repository Team

Kuali Coeus team

External

DMP Tool

DataONE

Conference/professional networks

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CHALLENGES

Involving subject librarians?

Gaining institutional buy-in?

Meeting demand?

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HOW TO INVOLVE SUBJECT LIBRARIANS?

UVa Library Staff Model

Scientific Data Consultants

Subject Librarians

Current Training Model

Brown Bag Data CurationDiscussions

Data Interviews

Goals and Objectives

Build Data Literacy

Create Collaborative Opportunities

Establish the Library for Data Preservation

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HOW TO GAIN INSTITUTIONAL BUY-IN?

Regulations are helpful

Partnerships between key stakeholders:

University libraries (UL)

Central IT (CIO)

Research Office (VP for Research)

Sponsored Programs/Research

Strategic investment: take ownership, allocate

resources, and demonstrate capability

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HOW TO MEET DEMAND?

Time: how to best manage staff time

NSF research support alone is going to be very time consuming (UVA had about 140 proposals over the past year, 44 in November alone)

Funding: work with leaders to find money

Redirection/reallocation of grant overhead dollars

Write-in of library staff on grants

Strategy: decide how to invest

How might units be reorganized?

How do we expand to other disciplines?

How could staff resources and expertise be refocused?

What additional partnerships would add value? 31

FUTURE DIRECTIONS

Addressing data management needs of other disciplines across the institution

Integration into formal research proposal process

Broader data management education

Increased funded research project consulting

Technology consulting

Expansion of virtual organization partners and creation of research advisory board

Guiding of policy revision to address new interests in data management and preservation 32

THANK YOU!

Andrew Sallans

Head of Strategic Data Initiatives, SciDaC Group

University of Virginia Library

Email: als9q@virginia.edu

Twitter: asallans

http://www.lib.virginia.edu/brown/data

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