{"dcterms:modified":"2025-03-19","dcterms:creator":"Harvard Dataverse","@type":"ore:ResourceMap","schema:additionalType":"Dataverse OREMap Format v1.0.0","dvcore:generatedBy":{"@type":"schema:SoftwareApplication","schema:name":"Dataverse","schema:version":"6.5 build iqss-4","schema:url":"https://github.com/iqss/dataverse"},"@id":"https://dataverse.harvard.edu/api/datasets/export?exporter=OAI_ORE&persistentId=https://doi.org/10.7910/DVN/E8Z5Q3","ore:describes":{"citation:producer":[{"citation:producerName":"Bioversity International and the International Center for Tropical Agriculture","citation:producerAbbreviation":"ABC","citation:producerURL":"https://alliancebioversityciat.org/"},{"citation:producerName":"International Center for Agricultural Research in the Dry Areas","citation:producerAbbreviation":"ICARDA","citation:producerURL":"https://www.icarda.org/"}],"citation:keyword":[{"citation:keywordValue":"abiotic stress","citation:keywordVocabulary":"AGROVOC","citation:keywordVocabularyURI":"http://aims.fao.org/aos/agrovoc/c_35768"},{"citation:keywordValue":"agroclimatic indicators","citation:keywordVocabulary":"other"},{"citation:keywordValue":"climatic data","citation:keywordVocabulary":"AGROVOC","citation:keywordVocabularyURI":"http://aims.fao.org/aos/agrovoc/c_29553"},{"citation:keywordValue":"drought","citation:keywordVocabulary":"AGROVOC","citation:keywordVocabularyURI":"http://aims.fao.org/aos/agrovoc/c_2391"},{"citation:keywordValue":"heat","citation:keywordVocabulary":"AGROVOC","citation:keywordVocabularyURI":"http://aims.fao.org/aos/agrovoc/c_3519"},{"citation:keywordValue":"flooding","citation:keywordVocabulary":"AGROVOC","citation:keywordVocabularyURI":"http://aims.fao.org/aos/agrovoc/c_2980"},{"citation:keywordValue":"spatial data","citation:keywordVocabulary":"AGROVOC","citation:keywordVocabularyURI":"http://aims.fao.org/aos/agrovoc/c_379bbe9f"},{"citation:keywordValue":"temperature","citation:keywordVocabulary":"AGROVOC","citation:keywordVocabularyURI":"http://aims.fao.org/aos/agrovoc/c_7657"},{"citation:keywordValue":"maximum temperatures","citation:keywordVocabulary":"other"},{"citation:keywordValue":"minimum temperatures","citation:keywordVocabulary":"other"},{"citation:keywordValue":"precipitation","citation:keywordVocabulary":"AGROVOC","citation:keywordVocabularyURI":"http://aims.fao.org/aos/agrovoc/c_6161"},{"citation:keywordValue":"raster","citation:keywordVocabulary":"other"},{"citation:keywordValue":"waterlogging","citation:keywordVocabulary":"AGROVOC","citation:keywordVocabularyURI":"http://aims.fao.org/aos/agrovoc/c_8333"},{"citation:keywordValue":"Latin America and the Caribbean","citation:keywordVocabulary":"Research Region"},{"citation:keywordValue":"Climate Action","citation:keywordVocabulary":"Research Lever"}],"grantNumber":{"citation:grantNumberAgency":"​Global Crop Trust​​​"},"contributor":[{"citation:contributorType":"Project Manager","citation:contributorName":"Ramirez, Julian"},{"citation:contributorType":"Project Manager","citation:contributorName":"Kehel, Zakaria"},{"citation:contributorType":"Researcher","citation:contributorName":"Sotelo, Steven"},{"citation:contributorType":"Researcher","citation:contributorName":"Hernandez, Victor"},{"citation:contributorType":"Researcher","citation:contributorName":"Aouzal, Khadija"},{"citation:contributorType":"Researcher","citation:contributorName":"Achicanoy Estrella, Harold Armando"}],"citation:dsDescription":{"citation:dsDescriptionValue":"The purpose for creating these indicators is to perform a similarity analysis of the climatic conditions (heat, drougth and waterlogging stresses) of a specific location versus the climatic conditions where the accessions of the different crops available in Genesys were collected, within the Analogues tool. The above considers climate change scenarios, thus facilitating the adaptation of crops to future conditions.\n\n<br>\n<br> \nMethodology: To calculate these agroclimatic indicators, the data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was taken as a basis, for the Shared Socioeconomic Pathway SSP2-4.5, for the time period 2041-2060 and downscaling and bias correction were applied following the methodology proposed by Navarro-Racines et al. (2020), thus obtaining daily data during the aforementioned time period for the variables: precipitation, maximum and minimum temperature at a resolution of 5 km. With the above, the indicators were calculated by month during 2041 -2060 and finally, to summarize the calculated indicators, a multi-annual aggregation of the data was carried out.","citation:dsDescriptionDate":"2025-03"},"citation:datasetContact":{"citation:datasetContactName":"Alliance Data Management","citation:datasetContactAffiliation":"Bioversity International and the International Center for Tropical Agriculture","citation:datasetContactEmail":"alliance-dm@cgiar.org"},"citation:topicClassification":{"citation:topicClassValue":"climatic data","citation:topicClassVocab":"AGROVOC","citation:topicClassVocabURI":"http://aims.fao.org/aos/agrovoc/c_29553"},"citation:dateOfCollection":{"citation:dateOfCollectionStart":"2024-01-01","citation:dateOfCollectionEnd":"2024-12-31"},"citation:distributor":[{"citation:distributorName":"International Center for Agricultural Research in the Dry Areas","citation:distributorAbbreviation":"ICARDA","citation:distributorURL":"https://www.icarda.org/"},{"citation:distributorName":"The Alliance of Bioversity International and CIAT"}],"author":{"citation:authorName":"Mora Argoti, Brayan Alexander","citation:authorAffiliation":"International Center for Tropical Agriculture - CIAT","authorIdentifierScheme":"ORCID","authorIdentifier":"https://orcid.org/0000-0002-0538-7262"},"timePeriodCovered":{"citation:timePeriodCoveredStart":"2041-01-01","citation:timePeriodCoveredEnd":"2060-12-31"},"citation:depositor":"Alliance Data Management","language":"English","dateOfDeposit":"2025-03-12","subject":["Earth and Environmental Sciences","Agricultural Sciences"],"kindOfData":["Climate Date","Geospatial Data","Breeding Data"],"citation:distributionDate":"2025-01-15","citation:productionDate":"2024-01-01","title":"North America - A Set of Agroclimatic Indicators for Identifying Abiotic Stresses (Base of the analogues tool), Calculated Based on Data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), Specifically the Shared Socioeconomic Pathway SSP2-4.5, for the Period 2041-2060.","geospatial:geographicCoverage":[{"geospatial:otherGeographicCoverage":"North America"},{"geospatial:otherGeographicCoverage":"Global"}],"geospatial:geographicBoundingBox":{"geospatial:westLongitude":"-160.25","geospatial:eastLongitude":"-52.6500","geospatial:northLatitude":"49.9999","geospatial:southLatitude":"7.19999"},"relatedDatasets":["Mora Argoti, Brayan Alexander, 2025, \"South America - A Set of Agroclimatic Indicators for Identifying Abiotic Stresses (Base of the analogues tool), Calculated Based on Data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), Specifically the Shared Socioeconomic Pathway SSP2-4.5, for the Period 2041-2060.\", https://doi.org/10.7910/DVN/JIXOGY, Harvard Dataverse","Mora Argoti, Brayan Alexander, 2025, \"Africa - A Set of Agroclimatic Indicators for Identifying Abiotic Stresses (Base of the analogues tool), Calculated Based on Data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), Specifically the Shared Socioeconomic Pathway SSP2-4.5, for the Period 2041-2060.\", https://doi.org/10.7910/DVN/ZQA9LA, Harvard Dataverse","Mora Argoti, Brayan Alexander, 2025, \"Europe - A Set of Agroclimatic Indicators for Identifying Abiotic Stresses (Base of the analogues tool), Calculated Based on Data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), Specifically the Shared Socioeconomic Pathway SSP2-4.5, for the Period 2041-2060.\", https://doi.org/10.7910/DVN/L9IDGH, Harvard Dataverse Specifically the Shared Socioeconomic Pathway SSP2-4.5, for the Period 2041-2060.","Mora Argoti, Brayan Alexander, 2025, \"Asia - A Set of Agroclimatic Indicators for Identifying Abiotic Stresses (Base of the analogues tool), Calculated Based on Data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), Specifically the Shared Socioeconomic Pathway SSP2-4.5, for the Period 2041-2060.\", https://doi.org/10.7910/DVN/WZH2TK, Harvard Dataverse","Mora Argoti, Brayan Alexander, 2025, \"Oceania - A Set of Agroclimatic Indicators for Identifying Abiotic Stresses (Base of the analogues tool), Calculated Based on Data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), Specifically the Shared Socioeconomic Pathway SSP2-4.5, for the Period 2041-2060.\", https://doi.org/10.7910/DVN/TPSCCP, Harvard Dataverse"],"@id":"https://doi.org/10.7910/DVN/E8Z5Q3","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"1.0","schema:name":"North America - A Set of Agroclimatic Indicators for Identifying Abiotic Stresses (Base of the analogues tool), Calculated Based on Data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), Specifically the Shared Socioeconomic Pathway SSP2-4.5, for the Period 2041-2060.","schema:dateModified":"2025-03-19 11:43:23.918","schema:datePublished":"2025-03-19","schema:creativeWorkStatus":"RELEASED","dvcore:termsOfUse":"<P><a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\" target=\"_blank\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br />\nThese data and documents are licensed under a <a href=\"http://creativecommons.org/licenses/by/4.0/\" target=\"_blank\"> Creative Commons Attribution 4.0 International license.</a> You may copy, distribute and transmit the data as long as you acknowledge the source through proper <a href=\"https://dataverse.org/best-practices/data-citation\" target=\"_blank\">data citation</a>.</P>","dvcore:disclaimer":"<p>The Alliance of Bioversity International and CIAT (hereinafter \"the Alliance\"), its partners, and the data authors have exercised utmost care in collecting and compiling the data. However, the data is provided \"as is\" without any express or implied warranty. Neither the Alliance, its partners, the data authors, nor any relevant funding agencies shall be held liable for any actual, incidental, or consequential damages arising from the use of this data.</p>\n\n<p>By utilizing the Alliance Dataverse, users explicitly acknowledge that the data may contain nonconformities, defects, or errors. No warranty is provided that the data will meet users' needs or expectations, nor that all nonconformities, defects, or errors can or will be corrected.</p>\n\n<p>Users are responsible for verifying the accuracy and suitability of the data for their intended use. It is strongly recommended that users refer to related publications as a baseline for their analysis whenever possible. This practice serves as an additional safeguard against misinterpretation of the data. Related publications are listed in the metadata section of the respective Dataverse study.</p>","dvcore:fileTermsOfAccess":{"dvcore:fileRequestAccess":true},"schema:includedInDataCatalog":"Harvard Dataverse","schema:isPartOf":{"schema:name":"The Alliance of Bioversity International and CIAT Dataverse","@id":"https://dataverse.harvard.edu/dataverse/AllianceBioversityCIAT","schema:description":"<strong><a href=\"https://www.bioversityinternational.org/alliance/\" style=\"color:#428BCA;\">The Alliance of Bioversity International and CIAT</a></strong>\n<P>\nToday’s global challenges of poverty, malnutrition, climate change, land degradation, and biodiversity loss call for new research, solutions, innovations, and stronger partnerships that can deliver higher impact. To respond to these challenges, and building on their complementary mandates and long collaboration, Bioversity International and the International Center for Tropical Agriculture (CIAT) have joined forces to create an Alliance.\n</P>\n\n<P>The Alliance <a href=\"https://cgspace.cgiar.org/handle/10568/106098\" target=\"_blank\" rel=\"noopener\"> delivers research-based solutions </a>that harness agricultural biodiversity and sustainably transform food systems to improve people’s lives.\n</P>\n<P>\nTo do so, the Alliance works with local, national and multinational partners across Latin America and the Caribbean, Asia and Africa, and with the public and private sectors. With partners, the Alliance generates evidence and mainstreams innovations in large-scale programmes to create food systems and landscapes that sustain the planet, drive prosperity and nourish people.\n</P>","schema:isPartOf":{"schema:name":"Harvard Dataverse","@id":"https://dataverse.harvard.edu/dataverse/harvard","schema:description":"<span><span><span><h3>Share, archive, and get credit for your data. Find and cite data across all research fields.</h3></span></span></span>"}},"ore:aggregates":[{"schema:description":"Data dictionary","schema:name":"01a. 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