Acrl march2015 final

Roles for Libraries in Providing

Research Data Management

Services

Nicole Vasilevsky, Oregon Health & Science University

Victoria Mitchell, University of Oregon

Jeremy Kenyon, University of Idaho

Nicole

VasilevskyProject Manager, Biocurator and Ontologist, Ontology Development Group,OHSU

Victoria

MitchellSocial Science Data & Government Documents Librarian, University of Oregon

Jeremy

KenyonResearch Librarian, University of Idaho Library

1 | Data services at UO Library

2 | UI support for documentation

3 | OHSU data management trainings

Do you have experience in data management training?

Why do our patrons need to know about data management?

Why?

Researcher Perspective

Version

control Track

processes for

reproducibility

Quality

Control

Stay Organized Save Time and Stress

Avoid Data Loss

Format data for reuse (by self,

team, or others)

Document for own recollection,

accountability, reuse

Funding mandates

http://www.economist.com/news/briefing/21588057-scientists-think-science-self-correcting-alarming-degree-it-not-trouble

Reproducibility

Why?

Funding mandates

Libraries can help!

At the UO Libraries

Data Services

The UO Environment

• No campus-wide research data policy

• Library leading on research data

management and preservation

• Collaborating with campus IT, Research

Services

The UO Environment

• Digital Scholarship Center• Open Access Publishing

• Digital Collections

• Institutional Repository

• Interactive Media Development

• Data Services• Science Data Services Librarian

• Social Science Data Librarian

http://digitalscholarship.uoregon.edu/

Services

• Data Management Plans

– Consultation and review

Data Management Web Pages

Services

• Consultations with faculty

• Special projects

– Southern Voting Project

Education

• Workshops

• Presentations in classes and new faculty

orientations

• 1-credit course in research data

management for grad students

Graduate Seminar in Data

Management

• 2 iterations so far

• 1st: Spring 2013 – 1 credit course, LIB 407/507

• Made it available to upper-division undergrads; none

signed up

• 2nd Spring 2014 – 1 credit course, LIB 607

Graduate Seminar in Data

Management

Based course around creation of a DMP for a

funding agency

• Students registering for the course were

strongly encouraged to have a research

project already in mind or underway

• Also used, in part and with modification, the

education modules created by DataONE

https://www.dataone.org/education-modules

• Natural disaster

• Facilities infrastructure failure

• Storage failure

• Server hardware/software

failure

• Application software failure

• External dependencies (e.g.

PKI failure)

• Format obsolescence

• Legal encumbrance

• Human error

• Malicious attack by human or

automated agents

• Loss of staffing competencies

• Loss of institutional

commitment

• Loss of financial stability

• Changes in user expectations

and requirements

Data Loss

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Slide adapted from DataONE Education Module: Why Data Management. DataOne. Retrieved March 21, 2013

Spreadsheet for Help with

OrganizingResearch Project:

[Name of research project]

Name: [Your name]

Dates:

[when you'll be conducting your research, e.g. 7/14-1/15]

Project Data Folder:

[e.g. dissertation_coldfusion_data]

Research Process/Method/ Data Source

Collection Dates Storage Format

Original Format

Working Format Access Format

Preservation Format(s)

File Naming Convention

Folder / Convention Versioning Strategy

Storage Location Who can help?

Access restrictions?

Who needs access?

Software / Tools Required

Metadata Schema Notes

LIB 607 v.3

• Changed to Data Management for the

Social Sciences (and Digital Humanities)

• Less emphasis on DMP per funder

requirements

• More time to address issues specific to the

social sciences and humanities

@ the University of Idaho Library

Research Data Services Credit: University of Idaho Creative Services

University of Idaho Characteristics:

• Public, comprehensive, land-grant university

• Strong emphasis on agriculture, environmental science, engineering

• Recent emphasis on developing research data and research cyberinfrastructure, including library research data services, INSIDE Idaho, the geospatial data repository, and NKN, a multi-disciplinary institutional data repository

How do we move from this?

To this?

Research Data Services at the U-Idaho Library

Appointments

&

ConsultationsNorthwest Knowledge

Network

(institutional data repository)

Embedded Services

(Buy-outs of librarian time)

Tool & Technology Support:

IQ-Station,

ESRI Products,

DMPTool,

Metadata editors

Website:

Data Management Best Practices

Guide

Instruction & Workshops

Many modes of service

Raise awareness of research data management & our services

Create a culture of documentation

Transform thinking across disciplines about data distribution & publishing

Focus: creating a culture of documentation

FISH502 “One-shot” Instruction Session

- Class participants: fisheries biology and statistics graduate students

- Exercise: 1) review the following spreadsheet2) identify the information needed to re-use this dataset


Research consultation: environmental modelling

Post-doc from a multi-institutional project was primary contact for several teams

Consultation on metadata was made towards the end of project

Producing 6 discrete collections of data as netCDF (format required by funder)

Repository required ISO 19115 XML metadata for describing whole collections


Challenges:

Understanding the standardAttribute Conventions for Dataset DiscoveryISO 19115-2Codelists and controlled vocabularies

Rules for free-text fieldswhat does a good title look like?

Placement of contentshould variables be listed in keywords, title, or description?

Responsibilitieswho should create XML files – the researcher or us?


Re-use and comprehension of data requires good

documentation

Researchers often have idiosyncratic and localized, i.e.

customized, documentation practices

Content standards are often not well-known among researchers

Disciplinary content standards are necessary for enabling

advanced modes of data access

Library services must emphasize documentation

Future Directions

Fienberg, S.E. et al. (1985). Sharing Research Data. Washington, D.C: National Academies Press.

http://www.nap.edu/catalog/2033/sharing-research-data

at Oregon Health & Science University

Research Data Management Efforts

What would you do with

$1k today to make

research communication

better that doesn’t involve

building another tool?

1| Workshops with the library

2| Individual consultations

Gummy Bear:

the

Groundbreaking

Paper

Your Data: Gummy Bear Raw Data

Bounces Amplitude Color

15 4 blue

43 3 red

58 9 green

75 82 purple

Materials:• Haribo Gummi Bears

Sugar Free, 5 lb bag, Amazon.com (UPC: 422384500110)

• SpringOMatic 3000 (ICanPickleThat, Portland, OR)

http://laughingsquid.com/the-anatomy-of-a-gummy-

bear-by-jason-freeny/

Figure 1. A) Gummy skeleton with belly button annotated with red arrow B) Springiness by sample color.

Methods Section: Haribo Gummi Bears (Sugar Free) were purchased from Amazon.com (UPC: 422384500110). Gummy bears were placed in the SpringOMatic 3000 (ICanPickleThat, Portland OR) according to the manufactures instructions. The Gummy Anatomy (Jason Freeny) image was cropped in PPT (Microsoft) and annotate to highlight the bellybutton.

Gummy Bear Final Figure

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blue red green purple

Spri

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bo

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Sample Color

A B Figure legends/metadat

aManipulating

images

Attribution

Metadata about research resources

Group 1: Gummy Bear Final Data

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4 3 9 82

15 43 58 75

Springiness (Bounces/Amplitude)

15 4 blue

43 3 red

58 9 green

75 82 purple

Methods: A schematic of a Gummi Bear was cropped to indicate where the belly button is located (Fig. 1). At this point, raw experimental data showing the bounce, amplitude, and color were analyzed and the springiness calculated for each color of bear. This was accomplished by dividing the bounce by the amplitude and plotting this against bear color.

Fig. 1Belly button ofHaribo Sugar FreeGummi Bear

What is missing?A. Image manipulationB. AttributionC. Figure LegendsD.Metadata about

resources

Figure 1. A) Gummy skeleton with belly button annotated with red arrow B) Springiness by sample color.

Methods Section: Haribo Gummi Bears (Sugar Free) were purchased from Amazon.com (UPC: 422384500110). Gummy bears were placed in the SpringOMatic 3000 (ICanPickleThat, Portland OR) according to the manufactures instructions.


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Sample Color

A

B


resources

Figure 2: Schematic depiction of Haribo Gummi Bear umbilical skeletal anatomy.

Methods & MaterialsGummi Bears were obtained through Amazon in 3 kg bags. Lot and temperature during transport data were not made available. Bears were housed in a plastic bowl in accordance with IACUC policy and national standards for gummi bear care. They were housed at room temperature on a natural light cycle.

Food and water were provided ad libitum (consumption was not monitored)

Each bear was sampled only once to reduce costs



resources

Belly Button

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Gummy Bear Color

(a) (b)

Fig. 1. (a) schematic of the anatomy of a gummy bear (adapted from 1). (b) springiness of bear by color using spring-o-matic.

Methods: Insert the sample of interest, specifically a colored gummy bear (Haribo, Japan). Position the probe above the sample. Press "Tickle" and the SpringOMatic (ICanPickleThat, Portland) will poke the belly button a standard depth of 1 cm. Record the number of bounces and the amplitude of the largest bounce in cm. From these values, the springiness can be calculated (bounce/amplitude).


resources


GUMMY BEARS TAUGHT US…

• People see the same data very

differently

• “Detailed” means different things…

• Metadata?!?

• File management is difficult

• Workflow

Vasilevsky N; Wirz J, Champieux R, Hannon T, Laraway B Banerjee K, Shaffer C, and Haendel M. Lions, Tigers, and Gummi Bears: Springing Towards Effective Engagement with Research Data Management (2014). Scholar Archive. Paper 3571.

CONSULTATIONS

Researcher + 2-3 from

Data Stewardship Team

Researchers DO need assistance: Finding and choosing data standards

File versioning

Applying metadata to facilitate data sharing

“Gummi Bear” themed data management exercise

resonated well with students

Lack of awareness of services and expertise

offered by the Library

Conclusions

OHSU New Directions

OHSU Library is developing

data services for researchers

BD2K educational grants in

collaboration with DMICE

www.ohsu.edu/xd/education/library/data

Acknowledgements

OHSUMelissa Haendel

Robin Champieux

Jackie Wirz

Kyle Banerjee

Bryan Laraway

Chris Shaffer

KaiserTodd Hannon

UOBrian Westra

Karen Estlund

Cathy Flynn- Purvis

John Russell

IdahoBruce Godfrey

Nancy Sprague

Lynn Baird

Greg Gollberg

Luke Sheneman

Steven Daley-Laursen

Contact usNicole Vasilevsky

[email protected]

@N_Vasilevsky

Thank you

Victoria Mitchell

[email protected]

@VictoriaStap

Jeremy Kenyon

[email protected]

@jr_kenyon

Acrl march2015 final

Data & Analytics

Transcript of Acrl march2015 final