This document summarizes challenges and issues around data management and synthesis projects discussed at a workshop. Key challenges identified include a lack of metadata, limited availability of relevant open data, difficulties identifying and acquiring the right data at the appropriate spatial and temporal scales, data mismatches between available data and research questions, and reluctance from some data owners to share data. Possible actions discussed to help address these challenges include encouraging standardization, concentrating on large long-term studies, providing tools to incentivize data sharing, and changing norms around data sharing within disciplines.

1. Challenges of data
management in synthesis
projects
Summary of/reflection on afternoon session on Wed 7/5/14 at
ACEAS Grand Synthesis Workshop

2. Caveats
• I am not an ecologist
• I see most things through a data lens
• And so apologies for what I have noted about presentations this
arvo

3. Data identification and acquisition:
Seagrass
• Challenges
• Lack of metadata – need corporate knowledge
• Limited data available for open access exchange
• Lack of info about how data was collected
• Identifying relevant data sets
• Hard to identify relevant variables in some data for particular questions
• Getting data at right spatial and temporal scale
• Implications of necessary assumptions
• Data (including layers) constrains spatial resolution
• Opportunity for map improvement
• But where does the improved map end up? (c.f. data synthesis,
publication)

4. • Developing wetland plant database with range of traits
• Drawing on a number of different existing data sets
• Using a range of dispersal models
• Need for further data collection and modelling by researchers
• Data acquisition challenges
• Often sourced through personal contacts
• Populating the database with the right traits
Data identification and acquisition:
Aquatic

5. Data collation and blending: Animal
telemetry
• OzTrack platform provides a location to bring together tracking
data across disciplines
• Analysis tools are the carrot to attract the data
• Obligation to make data available (because you may have
degraded study animals QoL)
• Sourced datasets through TERN DDP ("It's awesome!")
• Challenges
• Reuse hard because original studies determine tag set up
• Raw data on its own not enough – need rich context from data
custodians/collectors
• Who owns the data?

6. Data collation and blending: Northern
Quoll
• Challenges
• Data mismatches between availability and study question (burned
patches, rockiness)
• Studies set up for different purposes, and hence produce different data

7. Data analysis and synthesis
• Challenges – endemic genetics
• Lack of adequate metadata (stuff just missing – DNA, location)
• Inadequate response from authors
• Need for format conversion
• Challenges – phenology monitoring
• Need better data => protocols and standards for data capture
• Tools for managing and sharing 1000s of images
• No global standards for phenocams
• Challenges – drought induced mortality
• Data is often biased, incomplete and patchy (but it's all we've got
sometimes)

8. Data publication and visualisation
• Challenges – aerobiology
• Different data capture technologies influence data collected
• Could only use 11 of the 17 possible data sets
• Getting the data online delayed publication of first paper
• Reluctance to release primary data (priority, errors/quality, journal
policies)
• Ignorance of data value (commercial exploitation, value adding by others)
• Challenges – indigenous knowledge
• Interaction between cultural landscape scales and cultural infrastructure

9. Overall issues
• Fitness for purpose vs. It's all we have
• When synthesising, may be constrained by lowest quality data set
• E.g. spatial resolution for seagrass, existence of presence/absence only
• Need to capture context in metadata (seagrass, telemetry,
endemics)
• Motivators for data exchange/availability
• Answer new questions through more data
• Use tools that are made available as carrot
• Data gets collected but doesn't always get published
• Some data owners are reluctant to share for understandable
human issues

10. Overall issues
• Hard to find data (if cited in paywall journals)
• Role here for DDP, Research Data Australia
• Data quality (or purpose) mismatch
• Non-interoperable data
• Academic ethos
• Hierarchical structure incompatible with data sharing
• Academia selects for possessiveness
• Underfunding => overcontribution => protectiveness

11. Possible actions
• Anticipate Reuse: get groups who collect potentially combinable
data to agree on minimum elements they will collect that will
make datasets more reusable/recombinable
• More is More: concentrate on large long-term field projects with
standardised instruments and data products
• Research Locally, Coordinate Globally: Research Data Alliance (rd-
alliance.org) provides location for working groups to reduce
barriers to data exchange
• Bribe, don't Bully: Provide tools with attractive functionality where
data sharing is easier (than what they do now)
• Change the Norms: Discussion within discipline around data-
sharing norms

12. Thank you for the opportunity to come
and listen
@atreloar
andrew.treloar@ands.org.au
andrew.treloar.net