Compilation of diagnostic horizons data

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Saby, Nicolas P.A.; Michéli, Erika; Csorba, Adam; Szergi, Tamás; Vadnai, Peter; Dobos, Endre; Bertuzzi, Patrick; Toutain, Benoît; Picaud, Calypso; Gay, Laura; Chenu, Jean-Philippe; Creamer, Rachel, 2020, "Compilation of diagnostic horizons data", https://doi.org/10.15454/FIUWGQ, Portail Data INRAE, V1, UNF:6:2rdG5IEFLBb+NQVA6RgIjw== [fileUNF]

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Description	This dataset corresponds to a datamart produced by the WP2 team of the Landmark H2020 project. The database was developed by using a decision tree based script which determines the presence or absence of selected WRB diagnostic units (horizons, properties and materials) based on the harmonized soil profile dataset. The python-based code was developed based on the criteria defined by the World Reference Base for Soil Resources 2014 for the selected diagnostic units, by considering the difference in the information content of the input soil profile databases. Besides the presence/absence information, the code returns a percentage of reliability which provides an estimation on the reliability of the prediction of a certain diagnostic unit. The attributes are presented in the "dh_dictionary" file.
Subject	Climate; Farming Systems and Practices; Soils and soil sciences
Keyword	Soil functions; soil; management; climate;

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	1 to 2 of 2 Files	Original Format Archival Format (.tab)
	dh_dataset.tab Tabular Data - 10.1 MB - Mar 11, 2020 - 7 Downloads 99 Variables, 15514 Observations - UNF:6:2uyBrDJb9DmyBE51OLKaoQ== CSV file with diagnostic horizons dataset.	Download Original File Format (text/csv) Tab-Delimited RData Format Variable Metadata Data File Citation RIS EndNote XML BibTeX Explore View Data TwoRavens Data Explorer
	dh_dictionary.tab Tabular Data - 6.4 KB - Mar 11, 2020 - 6 Downloads 3 Variables, 85 Observations - UNF:6:tNDDbzSOcKNdJrpIYjhSVw== CSV file with diagnostic horizon dataset attributes explanation	Download Original File Format (text/csv) Tab-Delimited RData Format Variable Metadata Data File Citation RIS EndNote XML BibTeX Explore View Data TwoRavens Data Explorer

Citation Metadata

Dataset Persistent ID	doi:10.15454/FIUWGQ
Publication Date	2020-03-11
Title	Compilation of diagnostic horizons data
Subtitle	Work package 2
Other ID	H2020-SFS-2014-2: 635201
Contact	Use email button above to contact. Nicolas Saby (INRA)
Author	Saby, Nicolas P.A. (INRA) - ORCID: 0000-0001-8964-1194 Michéli, Erika (SZIU) Csorba, Adam (SZIU) Szergi, Tamás (SZIU) Vadnai, Peter (SZIU) Dobos, Endre (SZIU) Bertuzzi, Patrick (INRA) Toutain, Benoît (INRA) Picaud, Calypso (INRA) Gay, Laura (INRA) Chenu, Jean-Philippe (INRA) Creamer, Rachel (WUR)
Contributor	Data Curator : Chenu, Jean Philippe (INRA) Data Collector : Drufin, Sébastien (INRA)
Producer	Saby, Nicolas P.A. (INRA)
Production Date	2018-02-01
Production Place	Orléans, France
Description	This dataset corresponds to a datamart produced by the WP2 team of the Landmark H2020 project. The database was developed by using a decision tree based script which determines the presence or absence of selected WRB diagnostic units (horizons, properties and materials) based on the harmonized soil profile dataset. The python-based code was developed based on the criteria defined by the World Reference Base for Soil Resources 2014 for the selected diagnostic units, by considering the difference in the information content of the input soil profile databases. Besides the presence/absence information, the code returns a percentage of reliability which provides an estimation on the reliability of the prediction of a certain diagnostic unit. The attributes are presented in the "dh_dictionary" file.
Language	English
Subject	Climate; Farming Systems and Practices; Soils and soil sciences
Keyword	Soil functions; soil; management; climate;
Kind of Data	Dataset
Data Origin	observational data; experimental data; survey data; simulation data; aggregate data
Life cycle step	Data collection; Data processing: Aggregation; Data processing: Data integration
Grant Information	EU H2020: H2020-SFS-2014-2
Depositor	Gay, Laura
Deposit Date	2019-07-24

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