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Managing data. Why do it?. DATA Data was once an innocuous android instead of a method of judging researchers. What is “managing” data?. Data Accessible? Enough storage? Backed up? Metadata Change history?

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Managing data why do it

Managing data. Why do it?


Data was once an innocuous android

instead of a method of judging researchers

What is managing data
What is “managing” data?

  • Data

    • Accessible?

    • Enough storage?

    • Backed up?

  • Metadata

    • Change history?

    • Link to research proposal/experimental design/analysis tools/published papers/...?

    • Will it be needed for 5, 10, 20 years/lawsuits?

I already know how to do it
I already know how to do it

Yes, but.. have you considered moving:

  • From the desktop to the (semantic!) web

  • From small-scale to continental-scale

  • From disciplinary to multi-disciplinary

  • From a private collection to a public library

  • From unfunded to funded

  • From the academic realm to popular science

In other words data sharing
In other words.. data sharing

Data sharing matters even if you do not share

  • We all use shared data to some degree

  • Data sharing is de rigueur in some disciplines

  • Data sharing is legally required in some cases

  • Data sharing is necessary for open science

  • Data sharing is a minimum for collaborations

Benefits of data sharing
Benefits of data sharing

Public benefits

  • New research: new results from data reuse

  • Reproducibility: access to provenance info

  • Economies of scale: fund projects ones

    Private benefits

  • Prestige: promote one’s work in new ways

  • Career dev: count data as research output

  • Efficiency: division of labour, streamline research

This requires changes to how data is managed
This requires changes to how data is managed

  • Data need to be discoverable

  • Data need to be standardised

  • Data need to be put in context

  • Data need to acknowledge contributors

  • Data need to include use conditions

  • Data need to link to other relevant resources

  • (Data need to be peer-reviewed?)

Case study
Case study

Environmental acoustics

  • Emerging, inter-disciplinary research

  • Environmental research using sound recordings of the environment

  • Used to monitor, analyse the environment

  • Recordings require sophisticated statistics

  • No existing theory, method, tool or standard

Growing pains
Growing pains

Started on a small scale

  • Initially used to monitor individual sites

  • Opportunistic deployment of recorders

  • Ad hoc management of data

  • Closed system, inaccessible data

  • Serves the needs of a small group at QUT

  • Competes with similar efforts elsewhere

A new opportunity
A new opportunity

Became part of the Australian supersite Network

  • Data part of a long-term monitoring program

  • Deploy recorders at continental scale

  • Interest in integrating with other data sources

  • Obligation to publicise and describe data

  • Requires collaboration with partner sites

  • Need for storage capacity and server software

From managing data to eresearch
From managing data to eResearch

Develop better tools for science

  • Two workshops to define data standards

  • Build a dedicated acoustic repository

  • Develop a library of acoustic data tools

  • Embed recordings with environmental data

  • Release data under Creative Commons

  • Release platform as open source


New research opportunities

  • Approached by potential collaborators

  • Partnerships with community groups (research + linkage)

  • Special issue of Ecological Informatics

    Raise public profile of image

  • Newspaper articles

  • Radio interviews

  • Documentary

    Access to in-kind support

  • Funding of national deployment of equipment

  • Access to existing infrastructure and services