{"data":{"id":"10.6084/m9.figshare.7636193.v6","type":"dois","attributes":{"doi":"10.6084/m9.figshare.7636193.v6","prefix":"10.6084","suffix":"m9.figshare.7636193.v6","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Xiaolu Tang","affiliation":[],"nameIdentifiers":[]},{"name":"Shaohui Fan","affiliation":[],"nameIdentifiers":[]},{"name":"Wenjie Zhang;","affiliation":[],"nameIdentifiers":[]},{"name":"Sicong Gao","affiliation":[],"nameIdentifiers":[]},{"name":"Chen, Guo","nameType":"Personal","givenName":"Guo","familyName":"Chen","affiliation":[],"nameIdentifiers":[]},{"name":"Leilei Shi","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"A gridded dataset of belowground autotrophic respiration from 1980 to 2012 in global terrestrial ecosystems upscaling of observations"}],"publisher":"figshare","container":{},"publicationYear":2019,"subjects":[{"subject":"50301 Carbon Sequestration Science","subjectScheme":"FOR"},{"subject":"FOS: Agriculture, forestry and fisheries","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Agriculture, forestry and fisheries","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"40104 Climate Change Processes","subjectScheme":"FOR"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Earth and related environmental sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Climate Science"},{"subject":"Ecology"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2019-08-28","dateType":"Created"},{"date":"2019-08-28","dateType":"Updated"},{"date":"2019","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.7636193","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["34475478 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This data repository contains (1) yearly global autotrophic respiration (RA) dataset from 1980 to 2012 with a spatial resolution of 0.5°; (2) original field observations to develop Random Forest (RF) model; (3) main R codes to produce RA database.Model description:The globally gridded RA database was developed by Random Forest (RF) with 449 field observations (see “dataset.csv” in this repository, updated from Bond-Lamberty and Thomson, 2018) using 11 global variables, including gridded temperature, precipitation, diurnal temperature range, potential evapotranspiration, Palmer Drought Severity Index, nitrogen deposition, downward shortwave radiation, soil carbon content, soil nitrogen density, soil water content, land cover. \u003cbr\u003eDataset information:Dataset name: “Respiration_autotrophic_belowgroud_glob_1980_2012_yr_half_dgree.nc”Which means globally belowground autotrophic respiration from 1980 to 2012 with a spatial resolution of 0.5° at a yearly step.Units: g C m\u003csup\u003e-2\u003c/sup\u003e yr\u003csup\u003e-1\u003c/sup\u003eFormat: network Common Data Form (netCDF)Spatial coverage: 90S-90N, 180W-180EThe “dataset.csv” file is the field observation from peer review publications combining Global Soil Respiration Database (SRDB v4, Bond-Lamberty and Thomson, 2018), which is publicly available at https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1578. Besides, The database was further updated using observations collected from the China Knowledge Resource Integrated Database (www.cnki.net) up to November 2018 according to the criteria of SRDB. This dataset is provided in format of “.csv”.\u003cbr\u003eR codes:10fold_CV_RA.txt: 10-fold CV for RAAnnual_variability_RA.txt: annual variability for global RACMP_RA.txt: comparing RF-RA and Hashimoto2015-RA using CMP approachRa_DD_CC_plot.txt: plotting the comparing results from CMPRA_MAT_MAP_anomaly.txt: plotting and modelling the relationship between temperature/precipitation anomalies and RA RGB_plot.txt: deriving RGB plot to detecting the relative importance of temperature, precipitation and shortwave radiation. \u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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