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TERN ESA Workshop 2012, 'Smarter Workflows for Ecologists'

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This is a compilation of all presentations made at the TERN/ACEAS 'Smarter Workflows for Ecologists' workshop held at the ESA Conference in Melbourne, December 7th, 2012.

This is a compilation of all presentations made at the TERN/ACEAS 'Smarter Workflows for Ecologists' workshop held at the ESA Conference in Melbourne, December 7th, 2012.

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  • [Established under the federal government’s National Collaborative Research Infrastructure Strategy,] TERN is a network of Australian scientists working together to transform the way we do ecosystem science.TERN works with researchers and land managers from a range of universities and other organisations across the country.TERN’s work supports a coordinated and collaborative approach to ecosystem science across Australia.TERN is helping Australian ecosystem scientists to be more efficient and effective in their work.TERN provides what we call ‘hard’ and ‘soft’ infrastructure for researchers. The hard infrastructure is things like physical structures and tools to help with research. The soft infrastructure includes things like standard methods for doing research, capacity building, and networks for collaboration between researchers.By bringing together a broad range of researchers and providing infrastructure to support their work, TERN is enabling new advances and innovations in ecosystem science that weren’t possible before.
  • What are the benefits of TERN for Australia?TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • TERN helps researchers to be more effective and efficient, and ultimately this leads to a better understanding of Australian environments.TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • Confession of vocationA lot of what will be said may be obvious but important – need to reinforceTry to give an abstract understanding of a modelling system and the role obs can play in informing that system
  • Models incorporate our understanding of how natural systems workThe types of models that can benefit from TERN-type data
  • Take, for example, a continental scale simulation of Australian C cycle over a decade.Equifinality = underconstrainedmodelling system
  • Reasons why the modelling community get lazy

TERN ESA Workshop 2012, 'Smarter Workflows for Ecologists' TERN ESA Workshop 2012, 'Smarter Workflows for Ecologists' Presentation Transcript

  • Smarter workflows for ecologistsA pathway through the data lifecycle of an ecologist, highlightingnew initiatives that will support and enhance practice
  • AgendaTime item09:05 – 09:10 Welcome (Alison Specht)09:10 – 09:20 Introduction (Tim Clancy)09:20 – 09:40 Data Collection (Nikki Thurgate)09:40 – 09:50 Data Sharing and Citation (Alison Bradshaw)09:50 – 10:00 Data Storage and Discovery (David Turner)10:00 – 10:10 Data Analysis and Synthesis (Alison Specht)10:10 – 10:20 Modelling (Gabriel Abramowitz)
  • AgendaTime item10:25 – 11:05 Discussion and investigation of facets of workflow11:05 – 11:15 Question and answer (all presenters)11:15 – 11:25 How good knowledge of Australia’s data environment will be rewarded (Tim Clancy)
  • Smarter workflows for ecologists:what are they?Tim Clancy, Director of TERN
  • Overview• Data management in the ecological context• Data infrastructure• Why smarter workflows?• Next steps
  • Overview• Data management in the ecological context• Data infrastructure• Why smarter workflows?• Next steps
  • Sustaining Long Term Ecosystem Science & Research?
  • Ecology and the data delugeSensor Technologies
  • Ecology and the data deluge Modelled DataHigh-resolution climatesurfaces from Mike Hutchinson (ANU)
  • Assembly ofEcology and the data deluge Multi-Scale Remote Sensing DatasetsRemote Sensing /High ResMapping Airborne ARA Cessna 404 Systems and new sensor technologies Low-spatial resolution, high temporal resolution Satellite Measurements High-Resolution, Field-based Measurements Continental Dynamics in Green Cover
  • Ecology and the data delugeMeta Analyses/Synthesis Historically, competitive research advantage accrued to those individuals and groups who first conducted the experiments and captured new data, for they could ask and then answer questions before others. The rise of large-scale, shared instrumentation is necessitating new models of sharing and collaboration across disciplines and research cultures. When many groups have access to the same data, advantage shifts to those who can ask and answer better questions. -Daniel Reed, "My Scientific Big Data Are Lonely”Murphy B.P.et al.. (2011) Fire regimes: moving from a fuzzy concept to geographic entity. New Phytologist 192: 316-318.Bowman D.M.J.S. et al.. (in press) Forest fire management, climate change and the risk of catastrophic carbon losses. Frontiers in Ecology and Evolution.Murphy B.P., et al. . (in press) Fire regimes of Australia, a pyrogeographic model system. Journal of Biogeography
  • Data Policy EnvironmentExpectations of top journals
  • Data Policy EnvironmentExpectations of top journals
  • Data Policy EnvironmentExpectations of top journals
  • Overview• Data management in the ecological context• Data infrastructure• Why smarter workflows?• Next steps
  • Data PortalsIMOS Data Discovery Portal Meta-data from all Data Portals
  • Data PortalsIMOS Data Discovery Portal ALA Data Discovery Portal Meta-data Meta-data from all Data Portals all Data Portals from
  • Data PortalsIMOS Data Discovery Portal ALA Data Discovery Portal TERN Data Discovery Portal Meta-data Meta-data Meta-data from all Data Portals all Data Portals from from all Data Portals
  • Data PortalsIMOS Data Discovery Portal ALA Data Discovery Portal TERN Data Discovery Portal Meta-data Meta-data Meta-data from all Data Portals all Data Portals from from all Data Portals
  • Data PortalsIMOS Data Discovery Portal ALA Data Discovery Portal TERN Data Discovery Portal Meta-data Meta-data Meta-data from all Data Portals all Data Portals from from all Data Portals
  • Data PortalsIMOS Data Discovery Portal ALA Data Discovery Portal TERN Data Discovery Portal Meta-data Meta-data Meta-data from all Data Portals all Data Portals from from all Data Portals Data Portal Meta-data - 1 Identifier - DOI Use Licence Data Sets
  • Data PortalsIMOS Data Discovery Portal ALA Data Discovery Portal TERN Data Discovery Portal Meta-data Meta-data Meta-data from all Data Portals all Data Portals from from all Data Portals Data Portal Data Portal Meta-data - 1 Meta-data - 2 Identifier - DOI Identifier - DOI Use Licence Use Licence Data Sets Data Sets
  • Data PortalsIMOS Data Discovery Portal ALA Data Discovery Portal TERN Data Discovery Portal Meta-data Meta-data Meta-data from all Data Portals all Data Portals from from all Data Portals Data Portal Data Portal Data Portal Meta-data - 1 Meta-data - 2 Meta-data - 1 Identifier - DOI Identifier - DOI Identifier - DOI Use Licence Use Licence Use Licence Data Sets Data Sets Data Sets
  • Data PortalsIMOS Data Discovery Portal ALA Data Discovery Portal TERN Data Discovery Portal Meta-data Meta-data Meta-data from all Data Portals all Data Portals from from all Data Portals Data Portal Data Portal Data Portal Meta-data - 1 Meta-data - 2 Meta-data - 1 Identifier - DOI Identifier - DOI Identifier - DOI Use Licence Use Licence Use Licence Data Sets Data Sets Data Sets
  • What is TERN?
  • Storing, sharing and building data and knowledge• TERN is providing infrastructure to enable transformational change to ecosystem science and management in Australia• TERN enables cooperative and collaborative collection, storage, analysis and sharing of ecosystem data and knowledge.• TERN helps scientists, technicians and managers to be more effective and efficient.• TERN’s work helps to improve our understanding of Australian environments, and therefore enables the Australian community to make informed decisions about managing their environments.
  • Overview• Data management in the ecological context• Data infrastructure• Why smarter workflows?• Next steps
  • Collaborative and Networked Research Infrastructure
  • Collaborative and Networked Research Infrastructure Ecosystem Science
  • Collaborative and Networked Research Infrastructure Ecosystem Science
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Proposal and planning
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Proposal and planning
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Proposal and planning
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Proposal and planning Data collection, verification, quality assurance and control
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Proposal and planning Data collection, verification, quality assurance and control
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Proposal and planning Data collection, verification, quality assurance and control Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Proposal and planning Data collection, verification, quality assurance and control Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Proposal and planning Storage, Data collection,preservation and verification, discoverability quality assurance of data and control Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Proposal and planning Storage, Data collection,preservation and verification, discoverability quality assurance of data and control Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Data analysis, Proposal andintegration and planning synthesis Storage, Data collection,preservation and verification, discoverability quality assurance of data and control Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem Science Knowledge gap: research questions Data analysis, Proposal andintegration and planning synthesis Storage, Data collection,preservation and verification, discoverability quality assurance of data and control Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem ScienceResearch output: Knowledge gap: new data and research publications questions Data analysis, Proposal andintegration and planning synthesis Storage, Data collection,preservation and verification, discoverability quality assurance of data and control Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem ScienceResearch output: Knowledge gap: new data and research publications questions Data analysis, Proposal andintegration and planning synthesis Storage, Data collection,preservation and verification, discoverability quality assurance of data and control Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem ScienceResearch output: Knowledge gap: new data and research publications questions Data analysis, Proposal andintegration and planning synthesis Storage, Data collection,preservation and verification, discoverability quality assurance of data and control Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem ScienceResearch output: Knowledge gap: new data and research publications questions Data analysis, Proposal andintegration and planning synthesis Storage, Data collection,preservation and verification, Enables large scale and discoverability quality assurance coordinated data of data and control collection, sharing and multiple re-uses Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem ScienceResearch output: Knowledge gap: new data and research publications questions Data analysis, Proposal andintegration and planning synthesis Storage, Data collection,preservation and verification, Enables large scale and discoverability quality assurance coordinated data of data and control collection, sharing and multiple re-uses Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem Science Enhanced ability toResearch output: revise, question and Knowledge gap: new data and expand knowledge research publications questions Data analysis, Proposal andintegration and planning synthesis Storage, Data collection,preservation and verification, Enables large scale and discoverability quality assurance coordinated data of data and control collection, sharing and multiple re-uses Data + meta-data, licensing
  • Collaborative and Networked Research Infrastructure Ecosystem Science Enhanced ability toResearch output: revise, question and Knowledge gap: new data and expand knowledge research publications questions Data analysis, Proposal andintegration and planning synthesis Storage, Data collection,preservation and verification, Enables large scale and discoverability quality assurance coordinated data of data and control collection, sharing and multiple re-uses Data + meta-data, licensing
  • Overview• Data management in the ecological context• Data infrastructure• Why smarter workflows?• Next steps
  • Next Steps – Post the Workshop• Apply Best Practice Data Management• Appropriate Work Flow• Participate in the Debate• Data Discovery – AEKOS – Coming Soon• Use the Resources including: TERN, ALA, IMOS (Marine), BoM, ANDS etc.• Discuss specific data licensing requirements with relevant facility
  • The EndEnjoy the Workshopwww.tern.org.auhttp://portal.tern.org.au
  • Data Collection and SharingChallenges and case study solutionsA/Prof Nikki ThurgateUniversity of Adelaide
  • The Scientific Method• Ask a question• Do background research• Construct a hypothesis• Test your hypothesis• Analyse data• Draw conclusion• Communicate your results
  • The Scientific Method• Ask a question • What can I do that’s new?• Do background research • How can I do a better job than• Construct a hypothesis my peers?• Test your hypothesis • What cutting-edge process can I use to explain my data?• Analyse data • Can I prove existing thought• Draw conclusion wrong?• Communicate your results • Publish and gloat
  • Ecological Data Genetic SamplesSoils Photographs Basal AreaPoint Intercept Remote Sensing Spatial Coordinates Pressed Specimens
  • Ecological DataLeaf Area Index Histology Models Biogechemical Fluxes Lithology Ecological Descriptions Wet Specimens
  • Ecological DataDendrograms Chemical Analysis Genetic Samples Instruments Mapping DNA Sequences Population DynamicsLeaf Morphology Terrestrial LiDAR Dendrochronology
  • Leaf Area Index Ecological Data Analysis Chemical Genetic Samples Histology Dendrochronology DendrogramsSoils The message here is that Meteorology Photographs Basal Area Population DynamicsInstruments DNA Sequences ecologists collect lots of dataPoint Intercept Remote Sensing Models for different purposes Lithology Biogechemical Fluxes Ecological Descriptions Terrestrial LiDAR Spatial CoordinatesLeaf Morphology Mapping Specimens Pressed Wet Specimens
  • Data CollectionAd-hoc process to primary data collection• Collected samples (flora, fauna, soils, etc)• People-measured values (observations)• Digitally-measured values (stored on a device)• Where does all of this data go?• Transcription error• Does it have a use beyond the immediate project?•SHOULD it have a use beyond the immediate project?
  • Data CollectionThe answer is probably yes.• Most scientific research is directly funded by governments and should be of benefit to the community and scientific community• Analysis outputs alone are often insufficient. Primary data is required to marry new and old data or conduct a new analysis.• So what’s the problem?
  • Data Sharing ChallengesThe selfish: little incentive exists to share data• Immature citation framework (you get no credit)• Competitors can publish from your data• Competitors have the ammunition to criticise your methodology
  • Data Sharing ChallengesThe selfless: a little knowledge is a dangerous thing• Potential for data to be used to support poor science
  • Data Sharing ChallengesThe selfless: a little knowledge is a dangerous thing• Potential for data to be used to support poor science• Data can be presented as “evidence” to support perverse outcomes
  • Data Sharing ChallengesThe selfless: a little knowledge is a dangerous thing• Potential for data to be used to support poor science• Data can be presented as “evidence” to support perverse outcomes• Data may not be appropriate for public consumption for biosecurity, IP or cultural reasons
  • Data Sharing ChallengesThe selfless: a little knowledge is a dangerous thing• Potential for data to be used to support poor science• Data can be presented as “evidence” to support perverse outcomes• Data may not be appropriate for public consumption for biosecurity, IP or cultural reasons
  • Data Sharing ChallengesThe selfless: a little knowledge is a dangerous thing• Potential for data to be used to support poor science• Data can be presented as “evidence” to support perverse outcomes• Data may not be appropriate for public consumption for biosecurity, IP or cultural reasons
  • Data Sharing ChallengesThe selfless: a little knowledge is a dangerous thing• Potential for data to be used to support poor science• Data can be presented as “evidence” to support perverse outcomes• Data may not be appropriate for public consumption for biosecurity, IP or cultural reasons
  • Data Sharing ChallengesThe selfless: a little knowledge is a dangerous thing• Potential for data to be used to support poor science• Data can be presented as “evidence” to support perverse outcomes• Data may not be appropriate for public consumption for biosecurity, IP or cultural reasons
  • Data Collection ChallengesThe result:• There is a great reluctance to share.
  • Back to Data Collection• There’s lots of data being collected every day• It’s becoming expected that data will be shared• Plan what data you need• Find out what others have that you can use• Collect new data in the most sensible way• But Nikki, that’s really hard! What’s the most sensible way?
  • I’m glad you asked that question
  • The TERN Approach• Standardised methodology
  • The TERN Approach• Standardised methodology
  • The TERN Approach• Standardised methodology• Mobile data collection app
  • The TERN Approach• Standardised methodology• Mobile data collection app
  • The TERN Approach• Standardised methodology• Mobile data collection app• Sample tracking
  • The TERN Approach• Standardised methodology• Mobile data collection app• Sample tracking• Online information repository (ÆKOS)
  • The TERN Approach• Standardised methodology• Mobile data collection app• Sample tracking• Online information repository (ÆKOS)
  • Data Collection• More information about TERN (particularly ÆKOS) in David Turner’s “Data Storage and Discovery” talk• Licensing • The final consideration before you collect data • Who will own your data? • Who should be able to see it, use it or profit from it?
  • Data sharing and citationAlison Bradshaw, TERN Licensing Coordinator
  • Data Licensing ProcessGeneral process for data sharing – making dataavailable • Create data; • Identify any restrictions/conditions; • Select publisher; • Mint DOI; • Select appropriate user licence; and • Publish metadata & data with links to/detail of user licence;
  • Data Licensing ProcessGeneral process for data sharing – using data • Search data catalogue; • Consider metadata; • Check terms of licence; • Download data and use.
  • Data SharingThe key data licensing issues: • PLAN, PLAN, PLAN • Ownership • Identifying data  Digital Object Identifiers (DOIs) • Licences • Metadata
  • Data SharingIdentifying data sets: • Identify scope of data set • Identify/remove sensitive data • Clean/edit data set – quality assurance • Mint DOI
  • Data SharingLicences: • Open v conditions • Check options available  Owner limitations  Publisher limitations • Copyright?
  • Data SharingMetadata: • Comprehensive • Contemporaneous • Scope  Publisher limitations
  • Data SharingData ownership Principal Investigator
  • Data SharingData ownership Principal Investigator Employer
  • Data SharingData ownership Principal Investigator Employer Research assistant/ undergraduate student
  • Data SharingData ownership Principal Investigator Employer Research assistant Postgraduate students
  • Data OwnershipWho owns data? Principal Investigator Employer Research assistant Postgraduate students Funder
  • Data storage and discoveryDavid Turner Logos used with consent. Content of this presentation except logos is released under TERN Attribution Licence Data Licence v1.0
  • Characteristics of ecological data www.derm.qld.gov.au Complexity: Data usually needs explanation and context before it can be accurately used Diversity: Ecological Data covers aSource:Forestcheck: wide range of topics © Alamaywww.dec.wa.gov.au Fragmentation: Many different ways of measuring/observing/ expressing similar ecological concepts www.nswrail.net * Rapidly evolving Dispersal: Data is stored in many storage locations and formats
  • The Information Landscape
  • The Information Landscape © eResearchSA © eResearchSA Plants Birds Bats
  • Preservation?
  • Principles of discoverySearch needs to be intuitiveDiscovery needs to lead through to access • As few steps as possible • Access must be accessibleSufficient description for understanding • Access to SME* • Authored description is for the benefit of others • Define terms and or use vocabularies
  • Principles of capture and preservationCapture needs to ensure: • All relevant details are correctly recorded* • Information is protected against accidental lossPreservation can occur when the data: • Has value beyond its immediate intended use • Is in a readable storage format • Is richly described • Is intended to be published to facilitate discovery
  • Breakout sessionAn overview of the AEKOS system as a newparadigm for improving discovery, access andre-purposing of ecological data
  • Data analysis and synthesisAlison Specht Logos used with consent. Content of this presentation except logos is released under TERN Attribution Licence Data Licence v1.0
  • We are drowning in information while starvingfor wisdom. The world henceforth will be run bysynthesizers, people able to put together the rightinformation at the right time, think critically about it,and make important choices wisely.Edward O. Wilson (1998) Consilience: The Unity of Knowledge
  • Scientific synthesis:• provides a crucial counterweight to hyper-specialization in science• provides a method of coping with and capitalizing on the data deluge, which allows analyses at previously unimaginable scales and facilitates new discoveries• enhances the capacity for transformative research and serendipitous discoveries through the diversity of expertise, skills, and data employed• allows for the conceptualization of complex social and environmental problems beyond the scope of any one profession, discipline, data set, or research approach (Hampton and Parker 2011)
  • The model
  • The model
  • The model
  • The model
  • The model
  • An example–C&N dynamicsData from 6 long-term sites collated from many sources
  • An example–C&N dynamicsData from 6 long-term sites collated from many sourcese.g. site 3: Kidman Springs Fire Regime TrialThe Kidman Springs Fire Regime established in 1993 Afactorial trial of 4 treatments varying in frequency of fire(0, 2, 4, 6 years) and 2 treatments on season of fire – early(June), and late dry season (October).
  • An example–C&N dynamicsData from 6 long-term sites collated from many sourcese.g. site 3: Kidman Springs Fire Regime TrialThe Kidman Springs Fire Regime established in 1993 Afactorial trial of 4 treatments varying in frequency of fire(0, 2, 4, 6 years) and 2 treatments on season of fire – early(June), and late dry season (October).
  • An example–C&N dynamics Data from 6 long-term sites collated from many sources e.g. site 3: Kidman Springs Fire Regime Trial The Kidman Springs Fire Regime established in 1993 ASynthesis, analysis and modelling factorial trial of 4 treatments varying in frequency of fire (0, 2, 4, 6 years) and 2 treatments on season of fire – early (June), and late dry season (October).
  • output
  • output
  • output
  • output1 = 1-24%, 2 = 25-74%, 3= 75-89%, 4 = 90-100%.
  • outreachNo point doing the work unless it is heard.Traditional outputs: • Refereed journal articles, conference presentations‘new’ outputs: • Data deposition, portal visualisation, short reports, web site news and entries etc etc
  • outreachNo point doing the work unless it is heard.Traditional outputs: • Refereed journal articles, conference presentations‘new’ outputs: • Data deposition, portal visualisation, short reports, web site news and entries etc etc
  • outreachNo point doing the work unless it is heard.Traditional outputs: • Refereed journal articles, conference presentations‘new’ outputs: • Data deposition, portal visualisation, short reports, web site news and entries etc etc
  • Breakout sessionCheck out some of the innovative outputs of ACEAS projects and opportunity to ask questions about the process
  • Connecting data streams with the modelling communityGab AbramowitzClimate Change Research Centre, UNSWARC Centre of Excellence for Climate System Science
  • Outline• Why care about models?• Why model evaluation is complicated• How models and observations interact (and how they don’t)• How model-based experiments quantify uncertainty• Why multiple data streams are particularly important• How TERN eMAST is trying to help
  • Why care about models?• Land surface models; hydrological models; ecosystem models.• They drive: climate projections, weather forecasts, water resources assessments, impacts assessments for natural systems• Many models have a long history of development before high quality observations became available – legacy code is still a real issue.• Observations play a key role in reducing uncertainty in model predictions, yet communication channels between modelling and observational groups are often poor
  • Model evaluation is complicated• Diagnostic model evaluation is contingent parameters upon the quality of the observations used in all the four green categories. input MODEL Outputs• Model outputs may cover a wide range of s systems – e.g. carbon, water, energy states• Uncertainty in these is rarely low enough to tightly constrain simulations• Leads to “equifinality” – several different combinations of inputs / initial states / parameters give equally good results.• This makes identification of the “best” model structure very difficult NASA LIS
  • How models and observations interact• Simple comparison of observations with predictions (obs ≈ model output)• Parameter estimation (obs ≈ model output)• Direct restriction of parameter ranges (obs ≈ model parameters)• Comparison with empirical approaches (obs ≈ model input / output / params)• Data assimilation generating reanalysis products (obs ≈ model output / state) All can give diagnostic information about model structure
  • How models and observations don’t interact• Long pathways to data access / uncertainty in availability• Physical inconsistencies in data (i.e. quality control)• Data formatting and a lack of standardisation (file formats, standards within formats, time and space sampling) • e.g. CMIP5 database is anticipated to be ~50PB• Lack of (and lack of standardisation) in gap-filling (where needed)• Example – Fluxnet and the land surface modelling community
  • Gauging uncertainty in model predictions• Uncertainty in predictions is typically estimated by sampling uncertainty in parameters parameters, inputs, and/or initial states input MODEL Outputs• Multiple streams of data mean that s uncertainty ranges can be better states constrained => more reliable predictions• Examples: meteorology, C fluxes, water and heat fluxes, biomass, carbon pool sizes, physical soil properties, vegetation characteristics, soil moisture and temperature, streamflow, N, P etc NASA LIS
  • Multiple data streams – an example Model parameter estimation based on each data type separately, and in combination (error bars are uncertainty from propagated parameter uncertainty – 1σ): Prior estimate Eddy fluxes Streamflow LitterfallHaverd et al, BGD, 2012 Eddy fluxes + Litterfall Streamflow + Litterfall Streamflow + Eddy fluxes Eddy fluxes + Litterfall + Streamflow 0 1 2 3 4 -1 NPP (GtC y )
  • How TERN eMAST addresses this issueTERN eMAST has several projects aimed at bridging the observational-modelling community divide:• Convert existing datasets into standard modelling formats• Package datasets into modelling experiments that constrain aspects of a simulation so that diagnostic model evaluation is possible.• Create key products for model evaluation where they compliment important TERN datasets (high res climate forcing over Australia for 20C).
  • How TERN eMAST addresses this issueTERN eMAST has several projects aimed at bridging the observational-modelling community divide:• Convert existing datasets into standard modelling formats• Package datasets into modelling experiments that constrain aspects of a simulation so that diagnostic model evaluation is possible.• Create key products for model evaluation where they compliment important TERN datasets (high res climate forcing over Australia for 20C). One will be demonstrated in the breakout: the Protocol for the Analysis of the Land Surface (PALS) web application:
  • The last slide• Models that produce climate, weather, hydrological, ecological projections are ripe to benefit from integration of ecological data streams• “Benefit” can mean diagnostic model evaluation AND reduction in projection uncertainty• Concurrent multiple data streams are particularly useful• To date the modelling and observation communities have not communicated as well as they might – we’d like to try to help• For more information: Gab Abramowitz: gabriel@unsw.edu.au Colin Prentice: colin.prentice@mq.edu.au
  • thankyou