{"data":{"id":"10.4121/73060077-ec7c-4ad5-ab03-339a3430b6b5","type":"dois","attributes":{"doi":"10.4121/73060077-ec7c-4ad5-ab03-339a3430b6b5","prefix":"10.4121","suffix":"73060077-ec7c-4ad5-ab03-339a3430b6b5","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Soykan, Gürkan","nameType":"Personal","givenName":"Gürkan","familyName":"Soykan","nameIdentifiers":[{"nameIdentifier":"0000-0002-8695-7860","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Babur, Önder","nameType":"Personal","givenName":"Önder","familyName":"Babur","nameIdentifiers":[{"nameIdentifier":"0000-0002-1460-2825","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Liu, Qingzhi","nameType":"Personal","givenName":"Qingzhi","familyName":"Liu","nameIdentifiers":[{"nameIdentifier":"0000-0003-2621-9222","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Tekinerdogan, Bedir","nameType":"Personal","givenName":"Bedir","familyName":"Tekinerdogan","nameIdentifiers":[{"nameIdentifier":"0000-0002-8538-7261","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Data and Model Checkpoints from: ExoPrompt: Transformer-based greenhouse climate forecasting with structured conditioning and physics-based simulation"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2026,"subjects":[{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Agriculture, Land and Farm Management","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0701"},{"subject":"Agricultural and Veterinary Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"07"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"Microclimate"},{"subject":"Greenhouse"},{"subject":"Simulation"},{"subject":"Forecasting"},{"subject":"Transformers"},{"subject":"Conditioning"},{"subject":"Digital twins"}],"contributors":[{"name":"Wageningen University and Research","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2026-03-19","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1016/j.compag.2026.111673","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/73060077-ec7c-4ad5-ab03-339a3430b6b5.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1","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":"Data and model checkpoints accompanying the paper:\u003cbr\u003eSoykan et al. (2026). ExoPrompt: Transformer-based greenhouse climate forecasting with structured conditioning and physics-based simulation. Computers and Electronics in Agriculture, 246, 111673. https://doi.org/10.1016/j.compag.2026.111673\u003cbr\u003e--- Data ---\u003cbr\u003enew_world_sim.zipSynthetic GreenLight simulation outputs used for pretraining. Covers 9 parameter scenarios across 15 geographic locations and 2 lighting modes (LED/HPS). Each runincludes paired .csv time-series and .json exogenous parameter files.\u003cbr\u003egt.zipGround-truth climate measurements from a tomato greenhouse trial (Bleiswijk, 2009-2010) under LED and HPS lighting, reorganised into CSV format for use withthe ExoPrompt training pipeline.\u003cbr\u003ec_leakage_gt_led_conditions_csv.zipSimulation sweeps varying a single exogenous parameter (cLeakage) with aligned ground-truth subsets, used for the controlled robustness experiment in Section 3.3.\u003cbr\u003e--- Model Checkpoints ---\u003cbr\u003eckpts_pretrained_main.zipPretrained ExoPrompt and Vanilla backbones evaluated zero-shot on ground truth (Table 4).\u003cbr\u003eckpts_200k_main.zipFine-tuned checkpoints for mixed, LED-only, and HPS-only splits (Figure 5).\u003cbr\u003eckpts_cleakage.zipPretrained and fine-tuned checkpoints for the controlled cLeakage study (Section 3.3-3.4).","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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