The document discusses advances in data management practices and technologies for ecosystem science. It describes the role of a data manager in facilitating data management, from collecting raw data to organizing it in standard formats and metadata according to community practices. Well-managed data is stored and shared through repositories to enable discovery, access, interoperability and future reuse. Resources and experts are available to help researchers improve their data management.

1. Data management
Advances in data management practices and technologies for
ecosystem science
Ecological Society of Australia, Wed 1 October 2014
Matt Paget – AusCover data and systems coordinator
CSIRO Land & Water, Canberra

2. Introduction and Overview
Matt Paget – Data and systems coordinator for AusCover
AusCover is the satellite remote sensing data facility of TERN
Biophysical remote sensing products, Ground and Airborne validation
What is data management?
From raw data to managed data
Role of a data manager
Why managed data?
What does this mean for you?
What is a data management plan?
What does managed data look like?

3. What is data management?
Metadata
Information and context
Formats
Read and use
Location
Discover and access

4. From raw to managed data
Data
• Format
• Metadata
• Location
Individual
• Raw
• Activity
• Local

5. From raw to managed data
Data
• Format
• Metadata
• Location
Individual
• Raw
• Activity
• Local
Community
• Common, Usable
• Activity, Who, When
• Shared storage

6. From raw to managed data
Data
• Format
• Metadata
• Location
Individual
• Raw
• Activity
• Local
Community
• Common, Usable
• Activity, Who, When
• Shared storage
Multi-discipline, Published
• Standard, Open Access
• Who, What, Why, When, Where
• Managed storage

7. Role of a data manager
Liaise with researchers
Find a solution that works for everyone
Keep abreast of latest practices
Understand evolving standards
Provide tools and resources to help researchers manage
their own data
License, Metadata, Format conversion, Storage
Promote data discover and interoperability
Harvestable metadata, Web-based data access
Make data more usable and discoverable

8. Why managed data?
Interoperability
• Value adding
• Uptake and Relevance
• Alternative use
Citations and Published data
• Exposure
• Credit
• Sharing your research
Future Proof
• Safety of data
• Protect investment
• Counter memory loss
Data management plans
• ARC
• Organisations
• Collaborations
Open data, Open science, Good science citizen

9. What does this mean for you?
Help is available
Team or Community data managers
Collect metadata early
Use a field data collection tool
(ALA, ODK) or record carefully
Insist on a data management plan
What is a data management plan?
Data management is a skill
The more you do it the easier it is

10. What is a data management plan?
A data management plan or DMP is a formal document that outlines how you will
handle your data both during your research, and after the project is completed.
1 2
Data coordination
• Who will manage and
collate the data
http://en.wikipedia.org/wiki/Data_management_plan
Protocols and sampling strategy
• How will the data be collected
• Refer, cite and credit as required
Raw data to products
•What do you expect to derive
or deduce
Metadata and data
• Collate relevant information
• Consider data formats for sharing/reuse
Long-term storage and management
• Where will the data be stored
• Who will manage the data
• How will updates or changes be managed
• Who will pay for these services
3 4
Access and use policies
• License
• Embargo requirements
5 6

11. What does managed data look like? #1
Metadata headings Recommendations
Title Clear and complete. Spell out acronyms. Include
spatial reference if relevant
Abstract Summary of the work undertaken and the resulting
data product
License Select a license type. Recommend CC BY.
Get advise on the using the right words.
Point(s) of Contact Name, Institution and Email at a minimum.
Lead author and Data manager/contact.
Space and time Representative space and time bounds
Sampling strategy or algorithm Description of the sampling process or algorithm
Data quality Description of the quality, limitations and relevance
of the data
Keywords Selected keywords taken from your discipline and/or
a vocabulary (e.g., GCMD)
Link(s) to the data URL for web-enabled data access
References List of publications and other data products that
relate to this data
Primary information
Secondary
information

12. What does managed data look like? #2
Common data formats Recommendations
Notebooks, non-electronic Susceptible to data loss. Consider translating to an
electronic form
Text, CSV Mostly fine. Easily readable. Consider a ReadMe file
for metadata
MS Excel (xls) Not too bad because most people have the software.
Susceptible to change and data loss
Database Very popular. Good for stable, searchable data. Web
access. Requires knowledge of the software
ArcGIS/View, ERDAS, ENVI Proprietary GIS and raster software. Ok to work with.
Generally avoid these formats for data publishing –
expensive software required
GeoTIFF, shape files, netCDF, etc Supported by the Open Geospatial Consortium.
Multiple software and web services

13. What does managed data look like? #3
Storage locations Recommendations
Desktop and/or external hard drive Susceptible to data loss. Highly managed data and
hardware
Shared machines Mostly fine. Sharable within a group or team. Generally
managed/redundent hardware
Institution/community repository Shareable within a community. Access is variable. Easy
to divorce data from the owner
Web services Sharable to all. Machine and human access. Access
should be as easy for the owner as anyone else

14. Resources
Metadata
Data collection
• ALA field tools .. http://www.ala.org.au/get-involved/citizen-science/fielddata-software
• ODK forms .. http://www.auscover.org.au/xwiki/bin/view/Field+Sites/ODK+Forms
• AusPlots field handbook
Metadata preparation
• SHaRED .. http://www.shared.org.au
• Metadata template .. http://www.auscover.org.au/xwiki/bin/view/Product+pages/Product+page+template+-+Field+data
Formats
• Community practices
• Open Geospatial Consortium .. http://www.opengeospatial.org
Storage
• Home institution
• Australian eResearch Organisations .. http://www.aero.edu.au

15. Questions and advice
Matt Paget matt.paget@csiro.au
AusCover data and systems coordinator
Anita Smyth anita.smyth@adelaide.edu.au
AEKOS, SHaRED data facilitator
Siddeswara Guru s.guru@uq.edu.au
TERN data coordinator