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Moving beyond preservation: Developing a platform to enable complex data reuse Dr. David Turner Eco-informatics facility, Terrestrial.

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Presentation on theme: "Moving beyond preservation: Developing a platform to enable complex data reuse Dr. David Turner Eco-informatics facility, Terrestrial."— Presentation transcript:

1 Moving beyond preservation: Developing a platform to enable complex data reuse Dr. David Turner Eco-informatics facility, Terrestrial Ecosystem Research Network, University of Adelaide, Australia

2 Data services Data & information management Knowledge modelling Data relationship management Licensing, citations and condition of use Informatics and community practices User support Usage statistics to support our data contributors plot complex well-described integrated ecology

3 ÆKOS’s Niche N sites, surveys, plots complex well-described integrated ÆKOS Data Primary ecology ÆKOS’s Niche

4 The data revolution

5 An emerging consensus? Free of financial barriers for any researcher to contribute to for any user to access immediately on publication Made available without restriction on reuse for any purpose subject to proper attribution Quality-assured and published in a timely manner Archived and made available in perpetuity International Council for Science (ISCU) 2 September 2014 Published data should be independently understandable Peer (2014) International Journal of Digital Curation Published data should be independently understandable Peer (2014) International Journal of Digital Curation

6 Are there unique challenges in ecology? “Our extensive experience … collecting empirical data is that large data sets are often nuanced and complex, and appropriate analysis of them requires intimate knowledge of their context and substance to avoid making serious mistakes in interpretation.” David Lindenmayer and Gene E. Likens Benchmarking Open Access Science Against Good Science. Bulletin of the Ecological Society of America 94:338–340.

7 Ecological complexity

8 Reusing data Search for data Acquire data Assess suitability Prepare data

9 Barriers to reuse Dispersed: Data is stored in many storage locations and formats Source:Forestcheck: Complex: Data usually needs explanation and context before it can be accurately used Diverse and fragmented: Ecological data covers a wide range of topics and there are many different ways of measuring, observing and expressing different concepts * Rapidly evolving with few measurement standards

10 Empowering researchers Discovery Comprehension Extraction Access Integration Publication -Article -Data and citation to AEKOS Publication -Article -Data and citation to AEKOS Consider your users

11 Discovery through traditional metadata An example of a textual abstract for a data set: Otway Ranges Orchid Recovery Program The aim of the project is to compile and implement recovery plans for nationally threatened native orchids occurring in the Otway Ranges region of Victoria. Populations are monitored to gauge the current threats or causes of decline and the effectiveness of recovery actions. Species studied include: Caladenia argocalla, Pterostylis bryophylla, Thelymitra cyanapicata and Caladenia rigida. This dataset contains records collected from 1966 to present. The fields in this dataset include: Species name, GPS reading and datum, start and end dates, historical records, population size, key threatening processes, number of flowering individuals, number of flowers and the number of individual plants aborted, grazed, pollinated, hand pollinated, damaged, spent and caged. Images of the species and recovery activities are also available. source: Metadata from Flora Information System, Information Services Section (ISS) of the Victorian Department of Sustainability and Environment.

12 Creating structure Collection: Otway Ranges Orchid Recovery Program Subject Keywords : EARTH SCIENCE - BIOLOGICAL CLASSIFICATION – PLANTS - ORCHIDACEAE Subject Keywords : EARTH SCIENCE - BIOSPHERE - ECOLOGICAL DYNAMICS – COMMUNITY DYNAMICS READE, J. (2010) Population Trends and Key Management Actions for Otway Ranges Threatened Orchid Species. DSE, 265pp. Organisation: v.au/dse/ ISO Keywords: BIOTA Subject Keywords: EARTH SCIENCE - BIOSPHERE - ECOLOGICAL DYNAMICS - SPECIES_THREATENING PROCESS URL: HTM rights spatial coverage citation location subject is managed by is owned by Person: Joe Reade subject Full Description: {abstract – as before} description Spatial Coverage: (38.4  S – 38.9  S, 143  E – 144  W) Derived from: ANDS RIF-CS format metadata

13 Incorporate observations and description Collection: Otway Ranges Orchid Recovery Program species coverage Observed Entity: Organism Measure Coverage: Organism Absence Time Coverage: Jun Spatial Coverage: Polygon: «sub- coastal area around Cape Otway» spatial coverage time coverage measure coverage Entity coverage Measure Coverage: Organism Presence measure coverage Species Target: Spider Orchid Family Target: Orchidaceae species target Species Coverage: Caladenia argocalla Species Coverage: Pterostylis bryophylla Species Coverage: Thelymitra cyanapicata species coverage Species Coverage: Caladenia rigid Method Coverage: Visual Observation method coverage Measure Coverage: Organism Population measure coverage

14 Define relationships and vocabularies Common Name: Orchid Common Name: Spider Orchid Genus: Caladenia Genus: Pterostylis Genus: Thelymitra equates to Species: Caladenia argocalla equates to Spatial Coverage: Polygon: «sub- coastal area around Cape Otway» Place: Otway Ranges covers place Species Target: Orchid Family Target: Orchidaceae Organisation: Department for Sustainability and Environment, VIC. Person: Joe Reade Person: Dr. Joe Reade member of Land Use: Forestry

15 AEKOS discovery

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17 Access

18 The comprehension challenge

19 Data entropy

20 The information landscape Plants Birds Bats © eResearchSA

21 Embedding context Observation Observed Entity Observation Process observed under part of related to self-observed measurements of targeted things + observation of contextual things measurements of effort + description of method context ‘document‘ of observed things + context Observation Set (Collection) in data set with associated description metadata

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24 Integration ÆKOS applies a flexible knowledge representation approach Collection  Book Graph  Chapter Observation  Section Entity  Paragraph on common subject Statement  Sentence Value  Object of a Sentence Metadata  Front Matter/Edition Notice Ontology  Grammar Vocabulary  Dictionary + Thesaurus (Data) Collection Graph Observation Entity Statement Value

25 Concept alignment Overlapping concepts Measurement standards Classification systems Preserve complexity in the data Squash variation for discovery

26 Study location Sampled area Landscape features Sampling unit Organism group (vegetation association) Organism group (individual tree) EntityAttributeValue Org_gp 0001Tree height8 Org_gp 0001SpeciesE. camaldulensis Org_gp 0001DBH45 Org_gp 0001Life stageMature Org_gp 0001ConditionGood Org_gp 0001FloristicsFlowering Org_gp 0001ShapeC Representing data as information

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28 Integration

29 Agile data management

30 ÆKOS’s coverage

31 Level of Data Complexity (Richness) DataOne Nature ALA (Species Data) No data ANDS- RDA, TDDP Vegbank Pangaea Other Atlases ÆKOS Researcher Datasets (SHaRED) ÆKOS (Site Data) Level of data integration Fully Integration ÆKOS in the data landscape

32 Level of Description DataOne Nature ALA (species data) No data ANDS- RDA, TDDP Vegbank Pangaea Other Atlases ÆKOS Researcher Datasets (SHaRED) ÆKOS Integrated Site Data Fully Integration Level of data integration

33 Science impact

34 / Infrastructure uptake International National

35 Feedback and collaborators wanted Website:


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