Supporting the Research Data Life Cycle at CDL

Supporting the Research Data Life Cycle at CDL

University of Florida Data Management Workshop

S u m m e r, 2 0 1 2

J o a n S t a r r

Research has a life cycle.

PLANPUBLISH

SHAREMANAGE

COLLECT

Data Management Life Cycle Support

TOOLS & SERVICES• To enable data

preservation• To bake data curation

into data creation• To enhance data sharing,

collecting and gathering• To facilitate data publication

PARTNERSHIPS• To promote data discovery and access• To help researchers comply with new requirements

PLANPUBLISH

SHAREMANAGE

COLLECT

Examples from CDL & UC3

TOOLS & SERVICES• Merritt• EZID• DataUp• WAS• Data Paper model

PARTNERSHIPS• DataONE & DataCite• Data Management Plan Tool

MANAGE, SHARE

COLLECT, MANAGE, SHARE

PUBLISH

COLLECT

PLAN

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• Curation repository open to the UC community and beyond

• Discipline / content agnostic

• Micro-services architecture

• Easy-to-use UI or API

• Hosted or locally deployedPrimary Functions

1. Deposit

2. Manage (metadata, versions, etc)

3. Access (expose)

4. Share (with other researchers)

5. Preserve

• Dark archive for important digital assets

• Bright archive with direct discovery and access

• Preservation back-end for existing or new discovery and content management systems and services

• Integration with distributed data grids

https://merritt.cdlib.org/


TOOLS & SERVICES Merritt• EZID• DataUp• WAS• Data Paper model


MANAGE, SHARE


PUBLISH

COLLECT

PLAN

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EZID: long-term identifiers made easy

take control of the management

and distribution of your research,

share and get credit for it, and

build your reputation through its

collection and documentation

Primary Functions1. Create long-term identifiers2. Manage identifiers over time3. Manage associated metadata over time

http://n2t.net/ezid

What this means…

What this means…


TOOLS & SERVICES Merritt EZID• DataUp• WAS• Data Paper model


MANAGE, SHARE


PUBLISH

COLLECT

PLAN

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• Excel is the database of choice for many researchers• How to encourage data sharing, archiving, and

publishing?– Self-description– Enhance discovery– Facilitate the determination of suitability for use

Surveys indicate:• Most researchers are unaware of

preservation options• Documentation practices are poor• Excel is just one tool in workflows

Primary Functions

1. Metadata description (through extraction and augmentation)

2. Check export compatibility

3. Transfer to repository

Data Curation for Excel

Web Archiving Service (WAS)Capture today’s web, build tomorrow’s archive

Primary Functions

1. Collect web published content

2. Manage content

3. Use content for private research

4. Publish content for public access

http://webarchives.cdlib.org/

WAS: a range of uses and users

• archives for research communities

• events • web content for private

study and analysis• organization's web

presence

H A T E


TOOLS & SERVICES Merritt EZID DataUp WAS• Data Paper model


MANAGE, SHARE


PUBLISH

COLLECT

PLAN

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Vision: “data paper” • Wrap the unfamiliar in a familiar

façade• Minimally, a cover sheet and a

set of links to archived artifacts • Cover sheet contains familiar

elements: title, date, authors, abstract, identifiers

• Just enough metadata to permit basic exposure to and discovery– Indexing by services such as Web

of Science, Google Scholar– Instilling confidence in the

identifier’s stability

Data Publication


TOOLS & SERVICES Merritt EZID DataUp WAS Data Paper model


MANAGE, SHARE


PUBLISH

COLLECT

PLAN

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Working at the Network Levelenable new science and knowledge creation through universal access to data about life on earth and the

environment that sustains it

1. Build on existing cyberinfrastructure

2. Create new cyberinfrastructure

3. Create new communities of practice

DataONE’s new infrastructurehttps://www.dataone.org/

http://datacite.org/


TOOLS & SERVICES Merritt EZID DataUp WAS Data Paper model

PARTNERSHIPS DataONE & DataCite• Data Management Plan Tool

MANAGE, SHARE


PUBLISH

COLLECT

PLAN

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DMPTool

Coalition partners• CDL• DataONE• Digital Curation Centre• Smithsonian Institution• UCLA Library• UCSD Libraries• University of Illinois• University of Virginia Libraries

Meeting funding agencies data management plan requirements

Primary Functions

1. Step-by-step “wizard”

2. Templates and examples

3. Links to institutional resources and agency information

4. Plan publication and sharing

https://dmp.cdlib.org/

What can this mean for you?

• Open source– DataUp – Data Management Plan tool

• Off the shelf – Merritt– EZID– WAS

SERVICES!

& what can it mean to researchers?• For organizing their data– DataUp , EZID

• To keep their data safe– Merritt

• To help them get grants – Data Management Plan tool

• To help get their worknoticed– EZID, Data Papers

• To help them find otherdata– EZID, Data Papers

TOOLS!

But wait, there’s more: Community!

• CURATECamp: unconference events connecting practitioners & technologists interested in digital curation and data management.

• For f2f events: http://curatecamp.org/

• http://groups.google.com/group/digital-curation

courtesy of Oxnard Public Library, http://content.cdlib.org/ark:/13030/kt6c600758

and more information!http://www.cdlib.org/uc3

UC Curation [email protected]

UC3/CDLStephen Abrams David LoyPatricia Cruse Mark Reyes Scott Fisher Abhishek SalveErik Hetzner Tracy Seneca Greg Janée Joan StarrJohn KunzeMarisa Strong Perry Willett

mailto:[email protected]

…and here’s how to find me.

Joan [email protected]

@joan_starrhttp://www.slideshare.net/joanstarr

Education

Supporting the Research Data Life Cycle at CDL