Enetwild Project
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The Enetwild Project eNETwild Wildlife: collecting and sharing data on wildlife populations, transmitting animal and human disease agents Standards for data collection on wildlife distribution and abundance Guillaume Body, 17/01/2017, Parma 1 Plan 1. What is a data standard 2. Why do we need them 3. On what do they apply 4. Existing data standards 5. Current framework 2 1 Data standard: definition 1. What is a data standard ? 3 1. Data standard: definition “Standards” are documented agreements containing technical specifications or other precise criteria to be used consistently as rules, guidelines, or definitions of characteristics to ensure that materials, products, processes, and services are fit for their purpose The challenge remains for any community of practice to develop community based vocabularies and content standards through identifying the important features and their properties within a particular domain and express these using GML application schemas http://www.eubon.eu/getatt.php?filename=EU%20BON_D2.2_Data%20sharing%20tools_13350.pdf https://www.iso.org/standards.html http://tdwg.org/ http://geobon.org/essential-biodiversity-variables/guidance/standards-overview/ 4 2 1. Data standard: definition Exemple of standards From INSPIRE “Species distribution” It contains: List of variables Their relations List of accepted values Format of values UML class diagram of INSPIRE corresponding to the Species Distribution (from Figure 7 in INSPIRE Data specification)5 1. Data standard: definition Exemple of standards From INSPIRE “Species distribution” It contains: List of variables Their relations List of accepted values Format of values UML class diagram of INSPIRE corresponding to the Species Distribution (from Figure 7 in INSPIRE Data specification)6 3 1. Data standard: definition Exemple of standards From INSPIRE “Species distribution” It contains: List of variables Their relations List of accepted values Format of values UML class diagram of INSPIRE corresponding to the Species Distribution (from Figure 7 in INSPIRE Data specification)6 1. Data standard: definition CountingMethodValue Exemple of standards counted The units defined by the countUnitValues have been counted. From INSPIRE estimated The units defined by the countUnitValues have been estimated. “PopulationSizeType” calculated The units defined by the countUnitValues have been calculated using a modelling technique. GeneralCountingUnitValue colonies individual organisms of the same species living closely together, usually for mutual benefit individuals single, genetically distinct member of a population juvenile not sexually mature individual larvae a distinct juvenile form many animals undergo before metamorphosis into adults pairs mated pairs shoal A cluster of internal self-coordinated moving individuals, e.g. a fish flock. shoots Shoots are counted when it is not possible to distinguish individuals, e.g. due to clonal growth. groups of plants of a single species growing so closely together that it is impossible to distinguish single tufts individuals without destroying the occurrence 7 [1] http://inspire.ec.europa.eu/codelist/CountingMethodValue [2] http://inspire.ec.europa.eu/codelist/GeneralCountingUnitValue 4 1. Data standard: definition Development of standards for sampling-event data 8 2. Data standard: the need 2. Why do we need them ? 9 5 2. Data standard: the need To be in capacity to use data ! It is the common language 10 2. Data standard: the need E.g: Wolf communal synthetic data ETAT_DDT_biennale2017_DDT DPT INSEE_ NOM_COMMUNE ZONE 01 01187 HOTONNE Avérée 01 01292 Le PETIT-ABERGEMENT Avérée 01 01273 NEUVILLE-SUR-AIN Avérée 01 01176 Le GRAND-ABERGEMENT Non-avérée 04 04006 ALLOS Avérée 04 04020 BARLES Avérée 04 04023 BAYONS Avérée Fichier zonage2004 N°Dpt N°INSEE Nom commune Statut exercice 2017 04 04005 ALLONS Régulière 04 04006 ALLOS Régulière 04 04007 ANGLES Régulière 04 04008 ANNOT Régulière 04 04013 AUBIGNOSC Occasionnelle 11 6 2. Data standard: the need E.g: Wolf communal synthetic data ETAT_DDT_biennale2017_DDT DPT INSEE_ NOM_COMMUNE ZONE 01 01187 HOTONNE Avérée 01 01292 Le PETIT-ABERGEMENT Avérée 01 01273 NEUVILLE-SUR-AIN Avérée 01 01176 Le GRAND-ABERGEMENT Non-avérée 04 04006 ALLOS Avérée 04 04020 BARLES Avérée 04 04023 BAYONS Avérée Fichier zonage2004 N°Dpt N°INSEE Nom commune Statut exercice 2017 04 04005 ALLONS Régulière 04 04006 ALLOS Régulière 04 04007 ANGLES Régulière 04 04008 ANNOT Régulière 04 04013 AUBIGNOSC Occasionnelle 12 2. Data standard: the need E.g. : « oiseaux de passage ONCFS-FNC-FDC » bird survey Somme contact Code carte IGN Annee Nom espece Code espece auditif X Y XL93 YL93 0316 1999 GRIVE DRAINE GD 0 68837,58 2415701 120489,1 6853408 0317 1999 GRIVE DRAINE GD 0 70842,47 2397795 0415 1999 GRIVE DRAINE GD 0 100160,1 2432499 151922,7 6869944 0416 1999 GRIVE DRAINE GD 3 98639,5 2413531 150251,5 6851002 0417 1999 GRIVE DRAINE GD 3 97163,03 2395435 148631,5 6832930 0418 1999 GRIVE DRAINE GD 3 95477,88 2376366 146795,2 6813888 0419 1999 GRIVE DRAINE GD 1 93813,82 2355890 144968,8 6793441 13 7 2. Data standard: the need E.g. : « oiseaux de passage ONCFS-FNC-FDC » bird survey Somme contact Code carte IGN Annee Nom espece Code espece auditif X Y XL93 YL93 0316 1999 GRIVE DRAINE GD 0 68837,58 2415701 120489,1 6853408 0317 1999 GRIVE DRAINE GD 0 70842,47 2397795 0415 1999 GRIVE DRAINE GD 0 100160,1 2432499 151922,7 6869944 0416 1999 GRIVE DRAINE GD 3 98639,5 2413531 150251,5 6851002 0417 1999 GRIVE DRAINE GD 3 97163,03 2395435 148631,5 6832930 0418 1999 GRIVE DRAINE GD 3 95477,88 2376366 146795,2 6813888 0419 1999 GRIVE DRAINE GD 1 93813,82 2355890 144968,8 6793441 14 2. Data standard: the need We need data standard to: - store data : same list of variables same vocabulary - compare data : same signification same unit - keep the integrity of data : known referentials - process data : complete list of variables 15 8 2. Data standard: the need We also need metadata standards ! Metadata = data about data a description of the context of the dataset Species referential Administrative referential Geographic projection Investigator Methodology Sampling effort 16 2. Data standard: the need We need metadata standards to: - track data : who did the job when for what how to access them how to cite them - understand data : which kind of data which methodology - find data : period covered by the dataset area covered by the dataset taxonomic group covered by the dataset 17 9 2. Data standard: the need If you need something, it must be in the data/metadata standard Everything which is not in the standard, does not exist. Everything which is in plain text, does not exist. 18 The eNETwild project Cas A: A validated protocole exist for the species: the data-standard including the description of the protocole is established, then littérature review start and fill in the database C B that can be use for modelling A Case B: The data standard is only developped Database census: when a protocole is validated for a species. Littérature review and filling up the database Year 1 Year Continuous method only start after hte format is defined development to Case C: A protocole cannot be validated to assess abundance evaluate the parameter (ex: abundance): a standard that record the different unvalidated protocole may be developped, and the PROJECT MANAGEMENT PROJECT Continuous Data format standard database not filled up as the data could not be used. Networking and Ad-hoc Stakeholder Preliminary models & maps to identify Years 2 Years metadata standard technical analysis where we have no or scarce data & Strategic plan development Cost/effective approach recording scientific for species with organizations in countries to (i) use CTs advice to - Development 5 of harmonized accepted protocole to target specific areas to confirm EFSA protocols spp. presence for data and (ii) to diagnose specific disease (Citizen (one per Validation & Data collection Science) year) quality assessment by Integration into the wildlife database professionals Predictive modelling of wildlife Year 6 Year Prepare maps and populations and diseases charts 19 10 2. Data standard: the need FAIR principles: Findable : role of metadata Accessible : role of the database or metadata Interoperable : role of the data standard Reusable: role of the data sharing agreement What data standards do not do: Fiability of the methodology Adequation between the data and its use 20 3. Data standard: application 3. On what do they apply ? 21 11 3. Data standard: application Raw/primary data Occurrence distance sampling Hunt kill CMR road count Processed data Distribution Abundance index Hunt bag Density Metadata 22 3. Data standard: application 23 12 3. Data standard: application Raw/primary data Occurrence distance sampling Hunt kill CMR road count Processed data Distribution Abundance index Hunt bag Density Metadata 24 4. Data standard: the existant 4. Existing data standards 25 13 4. Data standard: the existant 26 3. Data standard: the existant Darwin Core + DarwinCore Event extension ABCD Access to Biological Collections Data INSPIRE directive EML Ecological Metadata Language DarwinCore and ABCD are minimally restrictive, so they need to be explained 27 14 4. Data standard: the existant Raw/primary data Occurrence DarwinCore / ABCD distance sampling none CMR none road count DarwinCore Event ? Processed data Distribution INSPIRE Abundance index none Density none Metadata EML / ABCD 28 5. Data standard: current framework 5. Current framework for developping standards 29 15 3. Data standard: