data management: international challenges, national infrastructure, and institutional responses

Data Management: International challenges, National Infrastructure, and Institutional Responses - an Australian Perspective

Dr Andrew TreloarDirector of Technology

Australian National Data Service

INTERNATIONAL CHALLENGES

Inconvenient data

DOI: 10.1098/rsta.2005.1569

Imprisoneddata

DOI 10.1098/rsta.2006.1793

Invisible data

DOI 10.1098/rsta.2006.1793

Inaccessible data

Incomprehensible data

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Survey ID Ind. Cat.(O) T-PC F-Views A-Convenience

12345 O Y a sa

Date Depth (m) Temperature (Celsius) Salinty (ppt) Sigma -T (kgm-3)

30/10/80 10 -1.875 34.555 27.841

Date Depth Temperature Salinity Density

30/10/80 10 -1.875 34.555 27.841

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Summary Not a first class object Unmanaged Disconnected Unfindable Unreusable

Why re-use data? Efficiency Validation Integrity Value for money Self-interest

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Astronomy case study Hubble Space Telescope (HST) operating since 1990 Observations are proposed, and if accepted, data is collected and

made available to the proposers – who then write a research paper

Each year around 1,000 proposals are reviewed and approximately 200 are selected, for a total of 20,000 individual observations

Data is stored at the Space Telescope Science Institute and made available after embargo period

There are now more research papers written by “second use” of the research data, than by the use initially proposed

11Source: http://archive.stsci.edu/hst/bibliography/pubstat.html

Cancer micro-array trial case study Piwowar, et. al., “Sharing Detailed Research Data Is

Associated with Increased Citation Rate” http://www.plosone.org/article/info:doi/10.1371/journal.pone.

0000308 Looked at the citation history of cancer microarray

clinical trial publications Found that publicly available data was associated with

a 69% increase in citations, independent of journal impact factor, date of publication, and author country of origin

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Alzheimer’s Disease NeuroImaging Initiative Collaborative effort to find

brain biomarkers for Alzheimer’s disease

Key: All brain scans and other data freely available to scientific community without embargo.

Over 3K full downloads and 1M scan downloads by over 400 investigators world-wide

Over 100 publications13

http://www.fnih.org/work/areas/chronic-disease/adni

Institut Douglas CC BY-NC-ND

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NATIONAL INFRASTRUCTURE

National approaches Number of different countries: UK, US, DE, NL Different environments => different ecosystems

and so some local tradeoffs But some common themes emerging:

Do the things that only you can do Be the ‘voice for data’ Prime the pump

Australian National Data Service An initiative of the Australian Government being

conducted as part of the National Collaborative Research Infrastructure Strategy ($A24M) and the Super Science Initiative ($A48M)

A collaboration between Monash University, the Australian National University and CSIRO

Nearly 50 staff, funded to mid 2013 More researchers re-using more data more often Data as a first-class object

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ANDS is enabling the transformation of:

Data that are: Unmanaged Disconnected Invisible Single use

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Collections that are: Managed Connected Findable Reusable

so that Australian researchers can easily discover, access and re-use data

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Defining characteristics of ANDS Building national services Engaging with institutions not researchers (mostly) Working within funding constraints

use, not amount! Building the Australian Research Data Commons

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ANDS Programs Frameworks and Capability Seeding the Commons Data Capture Metadata Stores ARDC Core Public Sector Data Applications

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Spending profile

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RDA Demo http://www.google.com/

INSTITUTIONAL RESPONSES

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Driven by Australian Code for Responsible Conduct of Research Equivalent of UKRIO’s Code of Practice for Research:

Promoting good practice and preventing misconduct Takes significant time to get accepted ANDS providing models of good practice Seeding the Commons U->M

Data management policy and planning

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Retrospective data description Different selection mechanisms Seeding the Commons U->M

Fixing the past

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Improving internal CRIS systems Better integration Moving beyond publications Better links to data collection descriptions Seeding the Commons, Metadata Stores D->C

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Facilitating easier/better capture of data and metadata from selected ‘instruments’

Making the right thing easier Improving quality of metadata Data Capture U->M S->R

Fixing the future

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Describing institutions research data assets Series of metadata stores rollouts plus some

ancillary activity Metadata Stores, Seeding the Commons, Data

Capture D->C I->F

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ONGOING ISSUES

Country-Institution-Discipline Who wins? Who should win?

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Sustainability, sustainability, sustainability… Institutional activity National services/resources Developed software

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Priming the pump, or continuing to pump? If institutions/researchers/disciplines don’t care,

why should the funders?

Role of Government

Questions/Links ands.org.au services.ands.org.au

andrew.treloar@ands.org.au @atreloar andrew.treloar.net