3TU.Datacentrum3TU.Datacentrum3 Universities joined forces to support research with data-labs and archiving

Jeroen Rombouts, TU Delft Library, March 28, 2011

OutlineOutline

1. Introduction3TU.Datacentrum background

2. Organisational challengesRoles and responsibilities divided among institutesFocus on research support

3. Service challengesAdd value for differing needs

BackgroundBackground

• Delft University of Technology16,400 students 4,700 staff (204 professors)

• Eindhoven University of Technology7,000 students 3,100 staff (169 professors)

• University of Twente8,400 students 3,000 staff (180 professors)

• 3TU.Datacentrum initiated 2008 by University libraries as 3yr project by 3TU.Federation

• Current year for transition to going concernStructural funding, staff, QA, promotion focus on data consumers

• Challenge: ‘Competitors’ trusting each other!

Organisational solutionsOrganisational solutions

• New ‘flag’: 3TU

• Front offices– Local 3TU.Datacentrum staff as liaison and ‘primary’ data stewards

Few people at every institute with basic knowledge of data management and 3TU.Datacentrum product catalogue

• Back office– Special expertise and archive at TU Delft

TU Delft has national task and builds on two previous projects: E-Archiving – digital depot, Darelux – Data Archiving River Environment LuxemburgOther projects at other universities

• Data-labs– Consult, support, build platforms for on-going research (projects)

Trying Data Verse Network and supporting 2 community platforms

ExperienceExperience

• Front office– Being (physically) close helps building trust– Huge ‘disciplinary’ (individual) differences in openness and data

management level– Need more than a few (trained) people

• Back office– Wide array of skills required (legal, it,

management, digital curation, research tools, training, …)

– Trade-off between long term preservationand (re-)use

– Balancing generic and discipline specific

• Data labs– Value for acquisition and standardisation

Researcher NeedsResearcher Needs

• Security– Long term, source preservation, backup, …

• Data exchange– Visibility, access, enable sharing, efficient distribution, …

• Storage space– Finished project data, …

• Claim– Pre-publication data sharing, verification, …

• Quality– Standards, …

• (Access) Efficiency– Data modelling, retrieval, …

• ‘Simple’ data setsSingle file (BagIt) per data set (can be a ‘zipped’ collection).Standard (self)upload form and descriptive information,

• Special collectionsRelation network of data sets, instruments, time, locations and areas – formalised in RDF.Negotiate: deposit procedure, descriptive information (xml, picture, preview), data model, …

• Querying for large (array) data sets (OPeNDAP)

• We offer tailor-made if …– The data collection fits the objects + datastreams + relations setup– Your functional requests has (expected) general applicability– You do not require a different look & feel”

Service/technical solutionsService/technical solutions

CollectieC1

ApparaatA1

Is gemeten door

Is lid van

Periode(dag)

Gaat over tijd

Staat op plaats

lengtegraad 4.3742Periode(maand)

Is deel van

Plaats(gebied)

Is deel van

breedtegraad

51.9973

naamDak van EWI

naam windmeter

naam Delft

DatasetD2Is berekend uit

Periode(jaar)

Is deel van

naam

...

maker...Dataset

D1

Plaats(punt)

DATA

DATA

DATA

Special collectionsSpecial collections

• Standard (bibliographical) meta data • Single datastream “BAG” (BagIT)

zipfile containing data en technical meta data

Most cases only data required.Meta data for long term preservation:- checksums- mapping of file extensions to mime-types

Simple data setsSimple data sets

ExampleExample

ExperienceExperience

• Advice by 3TU.Datacentrum is much appreciated

• Digital Object Identifiers (DOIs) as ‘carrots’(TU Delft Library is a DataCite partner)

• Difficult to grasp relational data model

ConclusionsConclusions

Evaluation• Opportunities for university libraries• People training (data librarians & data

scientists) required• Data acquisition/ingest, training, raising

awareness, cultural change are all slow processes

• High IT ‘awareness’ researchers makes life easy & difficult

Plans•Expand front offices•Discipline archive collaboration •Expand staff and skills•Data consumers•Funding

Questions & DiscussionQuestions & Discussion

LinksLinks

• Main website: datacentrum.3tu.nl• Data website: data.3tu.nl

• Example special collections:– OPeNDAP + picture ‘quicklook’: IDRA drizzle radar data:

http://dx.doi.org/10.4121/uuid:5f3bcaa2-a456-4a66-a67b-1eec928cae6d

– XML view: Hospital event log: http://dx.doi.org/10.4121/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54

• Simple data sets:– Laser measurements flame: http://dx.doi.org/10.4121/uuid:cb9c1edd-1120-4a05-

b28a-6091c15545c7

• DataCite: datacite.org