{"data":[{"id":"10.5281/zenodo.19466300","type":"dois","attributes":{"doi":"10.5281/zenodo.19466300","identifiers":[],"creators":[{"name":"Fehér, Zsolt Zoltán","nameType":"Personal","givenName":"Zsolt Zoltán","familyName":"Fehér","affiliation":["University of Debrecen"],"nameIdentifiers":[]}],"titles":[{"title":"Hungarian Wine Climate Atlas — district-level climate susceptibility, variety suitability and threats for the 22 Hungarian wine districts (1971–2100)"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Hungarian wine districts"},{"subject":"borvidék"},{"subject":"climate change"},{"subject":"viticulture"},{"subject":"Winkler index"},{"subject":"Huglin index"},{"subject":"FORESEE"},{"subject":"CMIP6"},{"subject":"World Bank CCKP"},{"subject":"wine"},{"subject":"grape varieties"},{"subject":"Flavescence dorée"},{"subject":"Tokaj"},{"subject":"Eger"},{"subject":"Villány"},{"subject":"drought"},{"subject":"heat stress"},{"subject":"frost"},{"subject":"RCP4.5"},{"subject":"RCP8.5"},{"subject":"SSP2-4.5"},{"subject":"SSP5-8.5"},{"subject":"climate adaptation"},{"subject":"Hungary"},{"subject":"open data"},{"subject":"reproducible research"}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Issued"}],"language":"en","types":{"ris":"COMP","bibtex":"misc","citeproc":"article","schemaOrg":"SoftwareSourceCode","resourceType":"","resourceTypeGeneral":"Software"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.1002/gdj3.22","resourceTypeGeneral":"JournalArticle","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.3389/fpls.2025.1481431","resourceTypeGeneral":"JournalArticle","relatedIdentifierType":"DOI"},{"relationType":"References","relatedIdentifier":"10.1016/j.agrformet.2003.06.001","resourceTypeGeneral":"JournalArticle","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"https://climateknowledgeportal.worldbank.org/","resourceTypeGeneral":"Dataset","relatedIdentifierType":"URL"},{"relationType":"IsDerivedFrom","relatedIdentifier":"https://odp.met.hu/","resourceTypeGeneral":"Dataset","relatedIdentifierType":"URL"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19466301","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v1.0.0","rightsList":[{"rights":"MIT License","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"mit","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"An open-source bilingual (Hungarian + English) information site that visualises climate-change susceptibility for the 22 Hungarian wine districts (borvidék). It joins three peer-reviewed climate datasets to a curated knowledge base of 58 grape varieties and surfaces the result as a publishable, citation-ready website.\nWhat is in here:\n\nA Python pipeline (analysis/src/) that extracts daily climate fields from the FORESEE v1.1 regional dataset (Dobor et al. 2015) at district level via area-weighted polygon means, computes nine viticulture indices (Winkler GDD, Huglin Index, frost days, heat-stress days, Hargreaves PET drought balance, Cool Night Index, etc.), and validates against the World Bank CCKP CMIP6 0.25° ensemble.\nA variety-suitability matcher with trapezoidal Huglin scoring, soft Winkler-class penalty, and frost/heat tolerance penalties, evaluated across four future bins (2021-2040, 2041-2060, 2061-2080, 2081-2100) and two scenarios (RCP4.5, RCP8.5).\nA curated threats dataset covering the active 2025 Flavescence dorée outbreak (NÉBIH detected in 21 of 22 wine districts), grapevine trunk diseases, late spring frost events, and EU pesticide regulation status.\n22 long-form research dossiers (~123,000 words) and two synthesis briefs, fully translated to Hungarian.\nA bilingual Next.js + MapLibre + deck.gl static site (HU + EN) with 1,091 prerendered pages, including per-district detail pages, side-by-side district comparisons, threat-ranking views, methods explainers, downloadable per-district PDFs, and a sortable comparison table.\nSHA-256 checksums and a versioned manifest.json for the curated data bundle.\nWho is it for:\n\nHungarian and international wine investors evaluating district-level climate risk\nWine growers and hegyközségi tanácsadók considering variety adaptation\nClimate impact researchers needing a reproducible Hungarian baseline\nPolicy makers and the wine-curious public\nReproducibility: Every site chart can be re-derived from the curated parquet/JSON/GeoJSON in analysis/curated/. The Python pipeline is idempotent and re-runnable; the Next.js site builds to a static export deployable to any host.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19466300","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-08T06:51:52Z","registered":"2026-04-08T06:51:52Z","published":null,"updated":"2026-04-08T06:51:52Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19466301","type":"dois","attributes":{"doi":"10.5281/zenodo.19466301","identifiers":[{"identifier":"oai:zenodo.org:19466301","identifierType":"oai"}],"creators":[{"name":"Fehér, Zsolt Zoltán","nameType":"Personal","givenName":"Zsolt Zoltán","familyName":"Fehér","affiliation":["University of Debrecen"],"nameIdentifiers":[]}],"titles":[{"title":"Hungarian Wine Climate Atlas — district-level climate susceptibility, variety suitability and threats for the 22 Hungarian wine districts (1971–2100)"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Hungarian wine districts"},{"subject":"borvidék"},{"subject":"climate change"},{"subject":"viticulture"},{"subject":"Winkler index"},{"subject":"Huglin index"},{"subject":"FORESEE"},{"subject":"CMIP6"},{"subject":"World Bank CCKP"},{"subject":"wine"},{"subject":"grape varieties"},{"subject":"Flavescence dorée"},{"subject":"Tokaj"},{"subject":"Eger"},{"subject":"Villány"},{"subject":"drought"},{"subject":"heat stress"},{"subject":"frost"},{"subject":"RCP4.5"},{"subject":"RCP8.5"},{"subject":"SSP2-4.5"},{"subject":"SSP5-8.5"},{"subject":"climate adaptation"},{"subject":"Hungary"},{"subject":"open data"},{"subject":"reproducible research"}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Issued"}],"language":"en","types":{"ris":"COMP","bibtex":"misc","citeproc":"article","schemaOrg":"SoftwareSourceCode","resourceType":"","resourceTypeGeneral":"Software"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.1002/gdj3.22","resourceTypeGeneral":"JournalArticle","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.3389/fpls.2025.1481431","resourceTypeGeneral":"JournalArticle","relatedIdentifierType":"DOI"},{"relationType":"References","relatedIdentifier":"10.1016/j.agrformet.2003.06.001","resourceTypeGeneral":"JournalArticle","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"https://climateknowledgeportal.worldbank.org/","resourceTypeGeneral":"Dataset","relatedIdentifierType":"URL"},{"relationType":"IsDerivedFrom","relatedIdentifier":"https://odp.met.hu/","resourceTypeGeneral":"Dataset","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19466300","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v1.0.0","rightsList":[{"rights":"MIT License","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"mit","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"An open-source bilingual (Hungarian + English) information site that visualises climate-change susceptibility for the 22 Hungarian wine districts (borvidék). It joins three peer-reviewed climate datasets to a curated knowledge base of 58 grape varieties and surfaces the result as a publishable, citation-ready website.\nWhat is in here:\n\nA Python pipeline (analysis/src/) that extracts daily climate fields from the FORESEE v1.1 regional dataset (Dobor et al. 2015) at district level via area-weighted polygon means, computes nine viticulture indices (Winkler GDD, Huglin Index, frost days, heat-stress days, Hargreaves PET drought balance, Cool Night Index, etc.), and validates against the World Bank CCKP CMIP6 0.25° ensemble.\nA variety-suitability matcher with trapezoidal Huglin scoring, soft Winkler-class penalty, and frost/heat tolerance penalties, evaluated across four future bins (2021-2040, 2041-2060, 2061-2080, 2081-2100) and two scenarios (RCP4.5, RCP8.5).\nA curated threats dataset covering the active 2025 Flavescence dorée outbreak (NÉBIH detected in 21 of 22 wine districts), grapevine trunk diseases, late spring frost events, and EU pesticide regulation status.\n22 long-form research dossiers (~123,000 words) and two synthesis briefs, fully translated to Hungarian.\nA bilingual Next.js + MapLibre + deck.gl static site (HU + EN) with 1,091 prerendered pages, including per-district detail pages, side-by-side district comparisons, threat-ranking views, methods explainers, downloadable per-district PDFs, and a sortable comparison table.\nSHA-256 checksums and a versioned manifest.json for the curated data bundle.\nWho is it for:\n\nHungarian and international wine investors evaluating district-level climate risk\nWine growers and hegyközségi tanácsadók considering variety adaptation\nClimate impact researchers needing a reproducible Hungarian baseline\nPolicy makers and the wine-curious public\nReproducibility: Every site chart can be re-derived from the curated parquet/JSON/GeoJSON in analysis/curated/. The Python pipeline is idempotent and re-runnable; the Next.js site builds to a static export deployable to any host.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19466301","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-08T06:51:52Z","registered":"2026-04-08T06:51:52Z","published":null,"updated":"2026-04-08T06:51:52Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19466166","type":"dois","attributes":{"doi":"10.5281/zenodo.19466166","identifiers":[],"creators":[{"name":"LIGISHA.L","nameType":"Personal","familyName":"LIGISHA.L","nameIdentifiers":[],"affiliation":[]},{"name":"Dr. G. K. JABASH SAMUEL","nameType":"Personal","familyName":"Dr. G. K. JABASH SAMUEL","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"INTELLIGENT SOLAR FORECASTING OF POWER GENERATION IN A SINGLE-AXIS SOLAR TRACKING PV SYSTEM USING A MODERN MACHINE LEARNING METHOD"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Solar Tracker"},{"subject":"MACHINE LEARNING"},{"subject":"SOLAR FORECASTING"}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19466167","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"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":"Solar energy remains one of the most promising and widely adopted renewable resources globally. In the post-fossil fuel era, solar power has gained prominence as a sustainable alternative to dwindling natural resources and as a solution to the urgent demand for carbon-neutral energy. The escalation of global warming and climate change, driven by CO2 emissions from traditional power plants, represents a critical environmental threat. Consequently, global initiatives are shifting toward the 2030 Sustainable Development Goals (SDGs) to foster environmental restoration. This project aligns with SDG 7 (Affordable and Clean Energy) by developing an optimized prototype for solar energy harvesting. Findings: While solar adoption is increasing across residential, commercial, and industrial sectors to reduce operational costs, maximizing efficiency remains a challenge. Current automation techniques, such as solar tracking—modeled after the heliotropic movement of sunflowers—significantly enhance energy capture. However, these systems face limitations due to atmospheric factors like cloud cover, dust accumulation, and seasonal shifts. Although Dual-Axis trackers (moving East-West and North-South) offer maximum efficiency, their high capital cost makes them inaccessible for many users.Methods: To address the balance between cost and performance, this research introduces a single-axis solar tracking prototype integrated with Machine Learning (ML). By utilizing ML algorithms to predict and identify the optimal angle of solar radiation, the system dynamically adjusts the panel's orientation for peak power extraction. This methodology provides a high-efficiency, cost-effective alternative to traditional tracking systems, opening new horizons for accessible solar technology.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19466166","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-04-08T06:46:29Z","registered":"2026-04-08T06:46:29Z","published":null,"updated":"2026-04-08T06:50:07Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19466167","type":"dois","attributes":{"doi":"10.5281/zenodo.19466167","identifiers":[{"identifier":"oai:zenodo.org:19466167","identifierType":"oai"}],"creators":[{"name":"LIGISHA.L","nameType":"Personal","familyName":"LIGISHA.L","nameIdentifiers":[],"affiliation":[]},{"name":"Dr. G. 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JABASH SAMUEL","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"INTELLIGENT SOLAR FORECASTING OF POWER GENERATION IN A SINGLE-AXIS SOLAR TRACKING PV SYSTEM USING A MODERN MACHINE LEARNING METHOD"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Solar Tracker"},{"subject":"MACHINE LEARNING"},{"subject":"SOLAR FORECASTING"}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19466166","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"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":"Solar energy remains one of the most promising and widely adopted renewable resources globally. In the post-fossil fuel era, solar power has gained prominence as a sustainable alternative to dwindling natural resources and as a solution to the urgent demand for carbon-neutral energy. The escalation of global warming and climate change, driven by CO2 emissions from traditional power plants, represents a critical environmental threat. Consequently, global initiatives are shifting toward the 2030 Sustainable Development Goals (SDGs) to foster environmental restoration. This project aligns with SDG 7 (Affordable and Clean Energy) by developing an optimized prototype for solar energy harvesting. Findings: While solar adoption is increasing across residential, commercial, and industrial sectors to reduce operational costs, maximizing efficiency remains a challenge. Current automation techniques, such as solar tracking—modeled after the heliotropic movement of sunflowers—significantly enhance energy capture. However, these systems face limitations due to atmospheric factors like cloud cover, dust accumulation, and seasonal shifts. Although Dual-Axis trackers (moving East-West and North-South) offer maximum efficiency, their high capital cost makes them inaccessible for many users.Methods: To address the balance between cost and performance, this research introduces a single-axis solar tracking prototype integrated with Machine Learning (ML). By utilizing ML algorithms to predict and identify the optimal angle of solar radiation, the system dynamically adjusts the panel's orientation for peak power extraction. This methodology provides a high-efficiency, cost-effective alternative to traditional tracking systems, opening new horizons for accessible solar technology.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19466167","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-04-08T06:46:28Z","registered":"2026-04-08T06:46:29Z","published":null,"updated":"2026-04-08T06:50:06Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19466040","type":"dois","attributes":{"doi":"10.5281/zenodo.19466040","identifiers":[],"creators":[{"name":"Zheng, Dongyu","nameType":"Personal","givenName":"Dongyu","familyName":"Zheng","affiliation":["Chengdu University of Technology"],"nameIdentifiers":[]},{"name":"Lipp, Alex","nameType":"Personal","givenName":"Alex","familyName":"Lipp","affiliation":["Department of Earth Sciences, University College London"],"nameIdentifiers":[]},{"name":"Farnsworth, Alexander","nameType":"Personal","givenName":"Alexander","familyName":"Farnsworth","affiliation":["University of Bristol"],"nameIdentifiers":[]},{"name":"Li, Shufeng","nameType":"Personal","givenName":"Shufeng","familyName":"Li","affiliation":["Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences"],"nameIdentifiers":[]},{"name":"Merdith, Andrew","nameType":"Personal","givenName":"Andrew","familyName":"Merdith","affiliation":["University of Adelaide"],"nameIdentifiers":[]},{"name":"Gurung, Khushboo","nameType":"Personal","givenName":"Khushboo","familyName":"Gurung","affiliation":["University of Leeds"],"nameIdentifiers":[]},{"name":"Hou, Mingcai","nameType":"Personal","givenName":"Mingcai","familyName":"Hou","affiliation":["Chengdu University of Technology"],"nameIdentifiers":[]},{"name":"Chen, Anqing","nameType":"Personal","givenName":"Anqing","familyName":"Chen","affiliation":["Chengdu University of Technology"],"nameIdentifiers":[]},{"name":"Hou, Zixi","nameType":"Personal","givenName":"Zixi","familyName":"Hou","affiliation":["Chengdu University of Technology"],"nameIdentifiers":[]},{"name":"Lunt, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Lunt","affiliation":["University of Bristol"],"nameIdentifiers":[]},{"name":"Sperling, Erik","nameType":"Personal","givenName":"Erik","familyName":"Sperling","affiliation":["Stanford University"],"nameIdentifiers":[]},{"name":"Valdes, Paul","nameType":"Personal","givenName":"Paul","familyName":"Valdes","affiliation":["University of Bristol"],"nameIdentifiers":[]},{"name":"Mills, Benjamin","nameType":"Personal","givenName":"Benjamin","familyName":"Mills","affiliation":["University of Leeds"],"nameIdentifiers":[]}],"titles":[{"title":"Dataset and Code for CIA to GMST estimates"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19466041","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"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":"# CIA–GMST Reconstruction\n\nThis repository contains the data and code used to reconstruct **Phanerozoic global mean surface temperature (GMST)** from chemical weathering indices (CIA) and paleoclimate model outputs.\n\n---\n\n## Overview\n\nUnderstanding long-term temperature evolution is critical for constraining Earth system feedbacks. This project presents a data-driven reconstruction of GMST based on:\n\n- A global compilation of **siliciclastic sediment CIA data**  - Calibration between **CIA and temperature**  - Data assimilation using **HadCM3 paleoclimate simulations**  \n\nThe workflow enables reconstruction of GMST across the Phanerozoic and provides reproducible datasets for further analysis.\n\n---\n\n##  Repository Structure├── CIA2GMST_Main.py├── Data S1. Modern river sediments CIA and local temperature.xlsx├── Data S2. SGP2 dataset and GMST estimates.xlsx├── Data S3. GMST at all periods.xlsx└── README.md\n\n---\n\n##  File Description\n\n- **CIA2GMST_Main.py**    Main script to reproduce GMST estimates using the SGP dataset and climate model outputs.\n\n- **Data S1**    Modern river sediment dataset used for CIA–temperature calibration.\n\n- **Data S2 (SGP_GMST dataset)**    Core dataset including:  - SGP Phase 2 compilation    - CIA values    - Reconstructed paleocoordinates    - Local temperature estimates    - GMST estimates  \n\n  This dataset is sufficient to reproduce all GMST results in the paper.\n\n- **Data S3**    Final GMST estimates across all geological periods.\n\n---\n\n## Reproducibility\n\nAll GMST estimates presented in the paper can be reproduced using:\n\n- `Data S2. SGP2 dataset and GMST estimates.xlsx`  - `CIA2GMST_Main.py`\n\n### Basic workflow\n\n1. Load SGP dataset (Data S2)  2. Apply data filtering (e.g., lithology, geochemistry, outliers)  3. Convert CIA to local temperature  4. Match local temperature to paleoclimate simulations  5. Extract corresponding GMST  \n\n---\n\n##  Requirements\n\n- Python ≥ 3.8  - numpy  - pandas  - matplotlib  - cartopy  \n\n ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19466040","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-04-08T06:45:58Z","registered":"2026-04-08T06:45:59Z","published":null,"updated":"2026-04-08T06:45:59Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19466041","type":"dois","attributes":{"doi":"10.5281/zenodo.19466041","identifiers":[{"identifier":"oai:zenodo.org:19466041","identifierType":"oai"}],"creators":[{"name":"Zheng, Dongyu","nameType":"Personal","givenName":"Dongyu","familyName":"Zheng","affiliation":["Chengdu University of Technology"],"nameIdentifiers":[]},{"name":"Lipp, Alex","nameType":"Personal","givenName":"Alex","familyName":"Lipp","affiliation":["Department of Earth Sciences, University College London"],"nameIdentifiers":[]},{"name":"Farnsworth, Alexander","nameType":"Personal","givenName":"Alexander","familyName":"Farnsworth","affiliation":["University of Bristol"],"nameIdentifiers":[]},{"name":"Li, Shufeng","nameType":"Personal","givenName":"Shufeng","familyName":"Li","affiliation":["Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences"],"nameIdentifiers":[]},{"name":"Merdith, Andrew","nameType":"Personal","givenName":"Andrew","familyName":"Merdith","affiliation":["University of Adelaide"],"nameIdentifiers":[]},{"name":"Gurung, Khushboo","nameType":"Personal","givenName":"Khushboo","familyName":"Gurung","affiliation":["University of Leeds"],"nameIdentifiers":[]},{"name":"Hou, Mingcai","nameType":"Personal","givenName":"Mingcai","familyName":"Hou","affiliation":["Chengdu University of Technology"],"nameIdentifiers":[]},{"name":"Chen, Anqing","nameType":"Personal","givenName":"Anqing","familyName":"Chen","affiliation":["Chengdu University of Technology"],"nameIdentifiers":[]},{"name":"Hou, Zixi","nameType":"Personal","givenName":"Zixi","familyName":"Hou","affiliation":["Chengdu University of Technology"],"nameIdentifiers":[]},{"name":"Lunt, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Lunt","affiliation":["University of Bristol"],"nameIdentifiers":[]},{"name":"Sperling, Erik","nameType":"Personal","givenName":"Erik","familyName":"Sperling","affiliation":["Stanford University"],"nameIdentifiers":[]},{"name":"Valdes, Paul","nameType":"Personal","givenName":"Paul","familyName":"Valdes","affiliation":["University of Bristol"],"nameIdentifiers":[]},{"name":"Mills, Benjamin","nameType":"Personal","givenName":"Benjamin","familyName":"Mills","affiliation":["University of Leeds"],"nameIdentifiers":[]}],"titles":[{"title":"Dataset and Code for CIA to GMST estimates"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19466040","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"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":"# CIA–GMST Reconstruction\n\nThis repository contains the data and code used to reconstruct **Phanerozoic global mean surface temperature (GMST)** from chemical weathering indices (CIA) and paleoclimate model outputs.\n\n---\n\n## Overview\n\nUnderstanding long-term temperature evolution is critical for constraining Earth system feedbacks. This project presents a data-driven reconstruction of GMST based on:\n\n- A global compilation of **siliciclastic sediment CIA data**  - Calibration between **CIA and temperature**  - Data assimilation using **HadCM3 paleoclimate simulations**  \n\nThe workflow enables reconstruction of GMST across the Phanerozoic and provides reproducible datasets for further analysis.\n\n---\n\n##  Repository Structure├── CIA2GMST_Main.py├── Data S1. Modern river sediments CIA and local temperature.xlsx├── Data S2. SGP2 dataset and GMST estimates.xlsx├── Data S3. GMST at all periods.xlsx└── README.md\n\n---\n\n##  File Description\n\n- **CIA2GMST_Main.py**    Main script to reproduce GMST estimates using the SGP dataset and climate model outputs.\n\n- **Data S1**    Modern river sediment dataset used for CIA–temperature calibration.\n\n- **Data S2 (SGP_GMST dataset)**    Core dataset including:  - SGP Phase 2 compilation    - CIA values    - Reconstructed paleocoordinates    - Local temperature estimates    - GMST estimates  \n\n  This dataset is sufficient to reproduce all GMST results in the paper.\n\n- **Data S3**    Final GMST estimates across all geological periods.\n\n---\n\n## Reproducibility\n\nAll GMST estimates presented in the paper can be reproduced using:\n\n- `Data S2. SGP2 dataset and GMST estimates.xlsx`  - `CIA2GMST_Main.py`\n\n### Basic workflow\n\n1. Load SGP dataset (Data S2)  2. Apply data filtering (e.g., lithology, geochemistry, outliers)  3. Convert CIA to local temperature  4. Match local temperature to paleoclimate simulations  5. Extract corresponding GMST  \n\n---\n\n##  Requirements\n\n- Python ≥ 3.8  - numpy  - pandas  - matplotlib  - cartopy  \n\n ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19466041","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-08T06:45:58Z","registered":"2026-04-08T06:45:58Z","published":null,"updated":"2026-04-08T06:45:58Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19466193","type":"dois","attributes":{"doi":"10.5281/zenodo.19466193","identifiers":[],"creators":[{"name":"Shiksha Mishra","nameType":"Personal","familyName":"Shiksha Mishra","nameIdentifiers":[],"affiliation":[]},{"name":"Shruti Nalikala","nameType":"Personal","familyName":"Shruti Nalikala","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"Bio-Synthetic Leaves: Rethinking Sustainable Energy  Through Nature-Inspired Design"}],"publisher":"Paramount Publishing house","container":{},"publicationYear":2026,"subjects":[{"subject":"Synthetic leaf, Artificial photosynthesis, Renewable energy, Carbon utilisation, Sustainable technology"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"CPAPER","bibtex":"article","citeproc":"","schemaOrg":"Article","resourceType":"","resourceTypeGeneral":"ConferencePaper"},"relatedIdentifiers":[{"relationType":"IsPublishedIn","relatedIdentifier":"978-93-471-4076-1","resourceTypeGeneral":"ConferenceProceeding","relatedIdentifierType":"ISBN"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19466194","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"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":"As climate change and energy shortages become increasingly difficult to ignore, there is a growing interest in technologies that work with natural processes rather than against them. One such emerging idea is the synthetic leaf. Inspired by natural photosynthesis, synthetic leaves use sunlight, water, and carbon dioxide to produce usable energy without many of the biological limitations faced by plants. Unlike natural leaves, synthetic leaves can be engineered to function faster, more efficiently, and in environments where plants cannot survive. Their compact size and flexible design distinguish them from many existing renewable energy systems that often require large infrastructure and specific locations. Recent research has improved synthetic leaf performance, resulting in greater efficiency and stability. Despite these advances, discussions around synthetic leaves largely focus on energy generation alone. Critical issues such as raw material availability, scalability, economic feasibility, environmental impact, and long-term sustainability remain insufficiently addressed. Without resolving these challenges, it is difficult to evaluate whether synthetic leaves can transition from controlled laboratory conditions to reliable real-world deployment. Beyond energy production, synthetic leaves offer opportunities to rethink carbon utilisation and energy storage. By capturing carbon dioxide and converting it into useful fuels, they may support circular carbon systems. By mimicking natural processes while enhancing them through engineering, synthetic leaves represent a promising direction for sustainable technology. This paper reviews recent developments, explains core working principles, and highlights challenges that must be addressed for real world application. This aligns with SDG 7 Clean Energy and SDG 13 Climate Action.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19466193","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-04-08T06:44:14Z","registered":null,"published":null,"updated":"2026-04-08T06:44:14Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19466194","type":"dois","attributes":{"doi":"10.5281/zenodo.19466194","identifiers":[{"identifier":"oai:zenodo.org:19466194","identifierType":"oai"}],"creators":[{"name":"Shiksha Mishra","nameType":"Personal","familyName":"Shiksha Mishra","nameIdentifiers":[],"affiliation":[]},{"name":"Shruti Nalikala","nameType":"Personal","familyName":"Shruti Nalikala","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"Bio-Synthetic Leaves: Rethinking Sustainable Energy  Through Nature-Inspired Design"}],"publisher":"Paramount Publishing house","container":{},"publicationYear":2026,"subjects":[{"subject":"Synthetic leaf, Artificial photosynthesis, Renewable energy, Carbon utilisation, Sustainable technology"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"CPAPER","bibtex":"article","citeproc":"","schemaOrg":"Article","resourceType":"","resourceTypeGeneral":"ConferencePaper"},"relatedIdentifiers":[{"relationType":"IsPublishedIn","relatedIdentifier":"978-93-471-4076-1","resourceTypeGeneral":"ConferenceProceeding","relatedIdentifierType":"ISBN"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19466193","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"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":"As climate change and energy shortages become increasingly difficult to ignore, there is a growing interest in technologies that work with natural processes rather than against them. One such emerging idea is the synthetic leaf. Inspired by natural photosynthesis, synthetic leaves use sunlight, water, and carbon dioxide to produce usable energy without many of the biological limitations faced by plants. Unlike natural leaves, synthetic leaves can be engineered to function faster, more efficiently, and in environments where plants cannot survive. Their compact size and flexible design distinguish them from many existing renewable energy systems that often require large infrastructure and specific locations. Recent research has improved synthetic leaf performance, resulting in greater efficiency and stability. Despite these advances, discussions around synthetic leaves largely focus on energy generation alone. Critical issues such as raw material availability, scalability, economic feasibility, environmental impact, and long-term sustainability remain insufficiently addressed. Without resolving these challenges, it is difficult to evaluate whether synthetic leaves can transition from controlled laboratory conditions to reliable real-world deployment. Beyond energy production, synthetic leaves offer opportunities to rethink carbon utilisation and energy storage. By capturing carbon dioxide and converting it into useful fuels, they may support circular carbon systems. By mimicking natural processes while enhancing them through engineering, synthetic leaves represent a promising direction for sustainable technology. This paper reviews recent developments, explains core working principles, and highlights challenges that must be addressed for real world application. This aligns with SDG 7 Clean Energy and SDG 13 Climate Action.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19466194","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-04-08T06:44:13Z","registered":"2026-04-08T06:44:13Z","published":null,"updated":"2026-04-08T06:44:13Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.6084/m9.figshare.31240807.v3","type":"dois","attributes":{"doi":"10.6084/m9.figshare.31240807.v3","identifiers":[],"creators":[{"name":"Feng, Yingqun","givenName":"Yingqun","familyName":"Feng","affiliation":[],"nameIdentifiers":[]},{"name":"Cheng, Jiming","givenName":"Jiming","familyName":"Cheng","affiliation":[],"nameIdentifiers":[]},{"name":"Zhang, Chao","givenName":"Chao","familyName":"Zhang","affiliation":[],"nameIdentifiers":[]},{"name":"Ma, Yujiao","givenName":"Yujiao","familyName":"Ma","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Bo","givenName":"Bo","familyName":"Wang","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Climate Modulation of Phylogenetic and Functional Constraints on Fruit Volume: A Case Study of Chinese Angiosperms"}],"publisher":"figshare","container":{},"publicationYear":2026,"subjects":[{"subject":"Community ecology (excl. invasive species ecology)","schemeUri":"http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E","subjectScheme":"ANZSRC Fields of Research","classificationCode":"310302"}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Created"},{"date":"2026-04-08","dateType":"Updated"},{"date":"2026-02-03","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.31240807","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["182916 Bytes"],"formats":[],"version":"3","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":"The data analyzed in this study were derived from the plant species checklist of the Chinese Ecosystem Research Network (CERN) (Zhang et al., 2020), which compiles plant distribution records from 22 ecological stations across China collected between 1998 and 2018. After excluding ferns and bryophytes, the final dataset comprised 2668 angiosperm species.Species names were standardized according to the \u003ci\u003eFlora of China\u003c/i\u003e. Information on fruit type (fleshy or dry), growth form (woody or herbaceous), leaf area, and fruit volume was compiled from the \u003ci\u003eFlora of China\u003c/i\u003e, the Chinese Virtual Herbarium (CVH), and the Plant Photo Bank of China (PPBC).","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://figshare.com/articles/dataset/Climate_Modulation_of_Phylogenetic_and_Functional_Constraints_on_Fruit_Volume_A_Case_Study_of_Chinese_Angiosperms/31240807/3","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-08T06:35:33Z","registered":"2026-04-08T06:35:34Z","published":null,"updated":"2026-04-08T06:35:34Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}}}},{"id":"10.6084/m9.figshare.31240807.v2","type":"dois","attributes":{"doi":"10.6084/m9.figshare.31240807.v2","identifiers":[],"creators":[{"name":"Feng, Yingqun","givenName":"Yingqun","familyName":"Feng","affiliation":[],"nameIdentifiers":[]},{"name":"Cheng, Jiming","givenName":"Jiming","familyName":"Cheng","affiliation":[],"nameIdentifiers":[]},{"name":"Zhang, Chao","givenName":"Chao","familyName":"Zhang","affiliation":[],"nameIdentifiers":[]},{"name":"Ma, Yujiao","givenName":"Yujiao","familyName":"Ma","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Bo","givenName":"Bo","familyName":"Wang","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Climate Modulation of Phylogenetic and Functional Constraints on Fruit Volume: A Case Study of Chinese Angiosperms"}],"publisher":"figshare","container":{},"publicationYear":2026,"subjects":[{"subject":"Community ecology (excl. invasive species ecology)","schemeUri":"http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E","subjectScheme":"ANZSRC Fields of Research","classificationCode":"310302"}],"contributors":[],"dates":[{"date":"2026-03-06","dateType":"Created"},{"date":"2026-04-08","dateType":"Updated"},{"date":"2026-02-03","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsPreviousVersionOf","relatedIdentifier":"10.6084/m9.figshare.31240807","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["185735 Bytes"],"formats":[],"version":"2","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":"The data analyzed in this study were derived from the plant species checklist of the Chinese Ecosystem Research Network (CERN) (Zhang et al., 2020), which compiles plant distribution records from 22 ecological stations across China collected between 1998 and 2018. After excluding ferns and bryophytes, the final dataset comprised 2668 angiosperm species.Species names were standardized according to the \u003ci\u003eFlora of China\u003c/i\u003e. Information on fruit type (fleshy or dry), growth form (woody or herbaceous), leaf area, and fruit volume was compiled from the \u003ci\u003eFlora of China\u003c/i\u003e, the Chinese Virtual Herbarium (CVH), and the Plant Photo Bank of China (PPBC).","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://figshare.com/articles/dataset/Climate_Modulation_of_Phylogenetic_and_Functional_Constraints_on_Fruit_Volume_A_Case_Study_of_Chinese_Angiosperms/31240807/2","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":13,"downloadCount":3,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-06T00:52:06Z","registered":"2026-03-06T00:52:07Z","published":null,"updated":"2026-04-08T06:35:34Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}}}},{"id":"10.6084/m9.figshare.31240807","type":"dois","attributes":{"doi":"10.6084/m9.figshare.31240807","identifiers":[],"creators":[{"name":"Feng, Yingqun","givenName":"Yingqun","familyName":"Feng","affiliation":[],"nameIdentifiers":[]},{"name":"Cheng, Jiming","givenName":"Jiming","familyName":"Cheng","affiliation":[],"nameIdentifiers":[]},{"name":"Zhang, Chao","givenName":"Chao","familyName":"Zhang","affiliation":[],"nameIdentifiers":[]},{"name":"Ma, Yujiao","givenName":"Yujiao","familyName":"Ma","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Bo","givenName":"Bo","familyName":"Wang","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Climate Modulation of Phylogenetic and Functional Constraints on Fruit Volume: A Case Study of Chinese Angiosperms"}],"publisher":"figshare","container":{},"publicationYear":2026,"subjects":[{"subject":"Community ecology (excl. invasive species ecology)","schemeUri":"http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E","subjectScheme":"ANZSRC Fields of Research","classificationCode":"310302"}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Created"},{"date":"2026-04-08","dateType":"Updated"},{"date":"2026-02-03","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["182916 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":"The data analyzed in this study were derived from the plant species checklist of the Chinese Ecosystem Research Network (CERN) (Zhang et al., 2020), which compiles plant distribution records from 22 ecological stations across China collected between 1998 and 2018. After excluding ferns and bryophytes, the final dataset comprised 2668 angiosperm species.Species names were standardized according to the \u003ci\u003eFlora of China\u003c/i\u003e. Information on fruit type (fleshy or dry), growth form (woody or herbaceous), leaf area, and fruit volume was compiled from the \u003ci\u003eFlora of China\u003c/i\u003e, the Chinese Virtual Herbarium (CVH), and the Plant Photo Bank of China (PPBC).","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://figshare.com/articles/dataset/Climate_Modulation_of_Phylogenetic_and_Functional_Constraints_on_Fruit_Volume_A_Case_Study_of_Chinese_Angiosperms/31240807","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-02-03T13:29:01Z","registered":"2026-02-03T13:29:01Z","published":null,"updated":"2026-04-08T06:35:33Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}}}},{"id":"10.5281/zenodo.19466029","type":"dois","attributes":{"doi":"10.5281/zenodo.19466029","identifiers":[{"identifier":"https://yashil-iqtisodiyot-taraqqiyot.uz/journal/index.php/GED/article/view/9864","identifierType":"URL"}],"creators":[{"name":"Musadjanova Nargiza Abduvoxid qizi","nameType":"Personal","familyName":"Musadjanova Nargiza Abduvoxid qizi","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"THE ROLE OF INNOVATION IN ENSURING COMPETITIVENESS IN THE GREEN ECONOMY"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-04-01","dateType":"Issued"}],"language":null,"types":{"ris":"JOUR","bibtex":"article","citeproc":"article-journal","schemaOrg":"ScholarlyArticle","resourceType":"","resourceTypeGeneral":"JournalArticle"},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19466030","relatedIdentifierType":"DOI"},{"relationType":"IsPartOf","relatedIdentifier":"2992-8982","resourceTypeGeneral":"Collection","relatedIdentifierType":"ISSN"}],"relatedItems":[],"sizes":[],"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 article explores the fundamental role of innovation in strengthening competitiveness within the frameworkof the green economy. In the context of global environmental challenges and the increasing need for sustainabledevelopment, the transition to a green economy has become a strategic priority for many countries and businesses.The research emphasizes that innovation serves as a key driver of this transformation, enabling enterprises to adoptenvironmentally friendly production methods, reduce resource consumption, and minimize environmental impact whilemaintaining or even enhancing their market position.The study analyzes the interconnection between innovation and competitiveness by examining how the introduction ofadvanced technologies—such as renewable energy systems, eco-efficient industrial processes, smart digital solutions,and circular economy practices—provides businesses with new opportunities to optimize costs, improve efficiency, andrespond to the growing consumer demand for sustainable goods and services. It is argued that companies investingin green innovations not only gain a competitive advantage in the short term but also ensure long-term resilience in anincreasingly environmentally conscious global market.Additionally, the article highlights the broader socio-economic implications of innovation in the green economy. Itdemonstrates that the adoption of innovative solutions contributes to the creation of green jobs, supports sustainableentrepreneurship, and promotes inclusive economic growth while addressing climate change and resource scarcity.However, the discussion also underlines the challenges businesses face, including financial constraints, limited accessto technology, and insufficient institutional support in some regions. These obstacles point to the need for developingeffective policy frameworks, strengthening public-private partnerships, and creating incentives to stimulate investment insustainable innovations.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19466029","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-04-08T06:29:22Z","registered":"2026-04-08T06:29:22Z","published":null,"updated":"2026-04-08T06:29:22Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19466030","type":"dois","attributes":{"doi":"10.5281/zenodo.19466030","identifiers":[{"identifier":"oai:zenodo.org:19466030","identifierType":"oai"},{"identifier":"https://yashil-iqtisodiyot-taraqqiyot.uz/journal/index.php/GED/article/view/9864","identifierType":"URL"}],"creators":[{"name":"Musadjanova Nargiza Abduvoxid qizi","nameType":"Personal","familyName":"Musadjanova Nargiza Abduvoxid qizi","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"THE ROLE OF INNOVATION IN ENSURING COMPETITIVENESS IN THE GREEN ECONOMY"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-04-01","dateType":"Issued"}],"language":null,"types":{"ris":"JOUR","bibtex":"article","citeproc":"article-journal","schemaOrg":"ScholarlyArticle","resourceType":"","resourceTypeGeneral":"JournalArticle"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19466029","relatedIdentifierType":"DOI"},{"relationType":"IsPartOf","relatedIdentifier":"2992-8982","resourceTypeGeneral":"Collection","relatedIdentifierType":"ISSN"}],"relatedItems":[],"sizes":[],"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 article explores the fundamental role of innovation in strengthening competitiveness within the frameworkof the green economy. In the context of global environmental challenges and the increasing need for sustainabledevelopment, the transition to a green economy has become a strategic priority for many countries and businesses.The research emphasizes that innovation serves as a key driver of this transformation, enabling enterprises to adoptenvironmentally friendly production methods, reduce resource consumption, and minimize environmental impact whilemaintaining or even enhancing their market position.The study analyzes the interconnection between innovation and competitiveness by examining how the introduction ofadvanced technologies—such as renewable energy systems, eco-efficient industrial processes, smart digital solutions,and circular economy practices—provides businesses with new opportunities to optimize costs, improve efficiency, andrespond to the growing consumer demand for sustainable goods and services. It is argued that companies investingin green innovations not only gain a competitive advantage in the short term but also ensure long-term resilience in anincreasingly environmentally conscious global market.Additionally, the article highlights the broader socio-economic implications of innovation in the green economy. Itdemonstrates that the adoption of innovative solutions contributes to the creation of green jobs, supports sustainableentrepreneurship, and promotes inclusive economic growth while addressing climate change and resource scarcity.However, the discussion also underlines the challenges businesses face, including financial constraints, limited accessto technology, and insufficient institutional support in some regions. These obstacles point to the need for developingeffective policy frameworks, strengthening public-private partnerships, and creating incentives to stimulate investment insustainable innovations.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19466030","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-08T06:29:21Z","registered":"2026-04-08T06:29:21Z","published":null,"updated":"2026-04-08T06:29:21Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19465931","type":"dois","attributes":{"doi":"10.5281/zenodo.19465931","identifiers":[{"identifier":"oai:zenodo.org:19465931","identifierType":"oai"}],"creators":[{"name":"Senthil, Aneesh","nameType":"Personal","givenName":"Aneesh","familyName":"Senthil","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"A Survey on IoT and Blockchain Integration in Smart Agriculture for Sustainable and Resilient Food Systems"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Internet of Things (IoT), Blockchain, Smart Agriculture, Supply Chain Traceability, Precision Farming, Artificial Intelligence, Smart Contracts, Agriculture 5.0, Food Security."}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Issued"},{"date":"2026-04-08","dateType":"Submitted"}],"language":"en","types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19465930","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"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":"The rapid advancement of digital technologies has significantly transformed modern agriculture, marking a shift toward systemswhere the integration of the Internet of Things (IoT) and blockchain supports more secure, transparent, and efficient agriculturaloperations. Despite this progress, traditional farming practices continue to face persistent challenges, including limited supplychain visibility, data security risks, insufficient traceability, and uncertainties arising from climate variability. Standalonetechnological solutions have, in most cases, proven inadequate in addressing these issues in a comprehensive manner.Against this backdrop, the present survey provides a systematic review of IoT–blockchain integration in smart agriculture,drawing on a curated set of 40 recent studies published between 2022 and 2026. The discussion covers architecturalframeworks, communication protocols, and consensus mechanisms that underpin these systems, while also examining theirapplication in areas such as supply chain traceability, precision farming, crop monitoring, and smart contract–driven resourceallocation. Key considerations—including scalability, interoperability, energy efficiency, and data privacy—are exploredalongside ongoing challenges such as latency constraints, the lack of standardization, and barriers to adoption amongsmallholder farmers. Looking ahead, the survey also points to emerging research directions aligned with the Agriculture 5.0paradigm, particularly those aimed at enabling sustainable, human-centric, and resilient food systems. In doing so, it bringstogether insights that may assist researchers, practitioners, and policymakers working at the intersection of agriculturaldigitalization and distributed ledger technologies","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19465931","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-04-08T06:28:06Z","registered":"2026-04-08T06:28:06Z","published":null,"updated":"2026-04-08T06:28:06Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19465930","type":"dois","attributes":{"doi":"10.5281/zenodo.19465930","identifiers":[],"creators":[{"name":"Senthil, Aneesh","nameType":"Personal","givenName":"Aneesh","familyName":"Senthil","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"A Survey on IoT and Blockchain Integration in Smart Agriculture for Sustainable and Resilient Food Systems"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Internet of Things (IoT), Blockchain, Smart Agriculture, Supply Chain Traceability, Precision Farming, Artificial Intelligence, Smart Contracts, Agriculture 5.0, Food Security."}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Issued"},{"date":"2026-04-08","dateType":"Submitted"}],"language":"en","types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19465931","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"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":"The rapid advancement of digital technologies has significantly transformed modern agriculture, marking a shift toward systemswhere the integration of the Internet of Things (IoT) and blockchain supports more secure, transparent, and efficient agriculturaloperations. Despite this progress, traditional farming practices continue to face persistent challenges, including limited supplychain visibility, data security risks, insufficient traceability, and uncertainties arising from climate variability. Standalonetechnological solutions have, in most cases, proven inadequate in addressing these issues in a comprehensive manner.Against this backdrop, the present survey provides a systematic review of IoT–blockchain integration in smart agriculture,drawing on a curated set of 40 recent studies published between 2022 and 2026. The discussion covers architecturalframeworks, communication protocols, and consensus mechanisms that underpin these systems, while also examining theirapplication in areas such as supply chain traceability, precision farming, crop monitoring, and smart contract–driven resourceallocation. Key considerations—including scalability, interoperability, energy efficiency, and data privacy—are exploredalongside ongoing challenges such as latency constraints, the lack of standardization, and barriers to adoption amongsmallholder farmers. Looking ahead, the survey also points to emerging research directions aligned with the Agriculture 5.0paradigm, particularly those aimed at enabling sustainable, human-centric, and resilient food systems. In doing so, it bringstogether insights that may assist researchers, practitioners, and policymakers working at the intersection of agriculturaldigitalization and distributed ledger technologies","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19465930","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-04-08T06:28:06Z","registered":"2026-04-08T06:28:06Z","published":null,"updated":"2026-04-08T06:28:06Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19461967","type":"dois","attributes":{"doi":"10.5281/zenodo.19461967","identifiers":[{"identifier":"oai:zenodo.org:19461967","identifierType":"oai"}],"creators":[{"name":"Kornaga, Jakub","nameType":"Personal","givenName":"Jakub","familyName":"Kornaga","affiliation":["Independent Researcher"],"nameIdentifiers":[{"nameIdentifier":"0009-0003-6137-3708","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"CORAL OPOLSKI. 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This book takes the reader through over 240 million years of history, from Triassic coral-sponge reefs, through deep Cretaceous basins, to Miocene redeposition episodes and Quaternary glacial transports. It is a story of corals that have become witnesses to climate change, tectonics, and the evolution of life. It is also a record of the work of a collector who, over the years, documented ephemeral outcrops, saving fossils from oblivion. The monograph combines scientific precision with a passion for discovery. It includes unique macro photographs, analytical sketches, and innovative 3D visualizations that reveal the beauty and complexity of ancient organisms. This is a book for geologists, paleontologists, collectors, and anyone who wants to understand how to read the Earth's history written in stone.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19461967","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-04-07T20:43:20Z","registered":"2026-04-07T20:43:21Z","published":null,"updated":"2026-04-08T06:24:55Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19461966","type":"dois","attributes":{"doi":"10.5281/zenodo.19461966","identifiers":[],"creators":[{"name":"Kornaga, Jakub","nameType":"Personal","givenName":"Jakub","familyName":"Kornaga","affiliation":["Independent Researcher"],"nameIdentifiers":[{"nameIdentifier":"0009-0003-6137-3708","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"CORAL OPOLSKI. 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This book takes the reader through over 240 million years of history, from Triassic coral-sponge reefs, through deep Cretaceous basins, to Miocene redeposition episodes and Quaternary glacial transports. It is a story of corals that have become witnesses to climate change, tectonics, and the evolution of life. It is also a record of the work of a collector who, over the years, documented ephemeral outcrops, saving fossils from oblivion. The monograph combines scientific precision with a passion for discovery. It includes unique macro photographs, analytical sketches, and innovative 3D visualizations that reveal the beauty and complexity of ancient organisms. This is a book for geologists, paleontologists, collectors, and anyone who wants to understand how to read the Earth's history written in stone.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19461966","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-04-07T20:43:21Z","registered":"2026-04-07T20:43:21Z","published":null,"updated":"2026-04-08T06:24:55Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.3929/ethz-c-000795616","type":"dois","attributes":{"doi":"10.3929/ethz-c-000795616","identifiers":[{"identifier":"0169-3298","identifierType":"ISSN"},{"identifier":"1573-0956","identifierType":"ISSN"},{"identifier":"10.1007/s10712-025-09919-2","identifierType":"other"},{"identifier":"http://hdl.handle.net/20.500.11850/795616","identifierType":"uri"}],"creators":[{"name":"Gou, Junyang","nameType":"Personal","givenName":"Junyang","familyName":"Gou","affiliation":[],"nameIdentifiers":[]},{"name":"Salberg, Arnt-Børre","nameType":"Personal","givenName":"Arnt-Børre","familyName":"Salberg","affiliation":[],"nameIdentifiers":[]},{"name":"Shahvandi, Mostafa Kiani","nameType":"Personal","givenName":"Mostafa Kiani","familyName":"Shahvandi","affiliation":[],"nameIdentifiers":[]},{"name":"Tourian, Mohammad J.","nameType":"Personal","givenName":"Mohammad J.","familyName":"Tourian","affiliation":[],"nameIdentifiers":[]},{"name":"Meyer, Ulrich","nameType":"Personal","givenName":"Ulrich","familyName":"Meyer","affiliation":[],"nameIdentifiers":[]},{"name":"Boergens, Eva","nameType":"Personal","givenName":"Eva","familyName":"Boergens","affiliation":[],"nameIdentifiers":[]},{"name":"Waldeland, Anders U.","nameType":"Personal","givenName":"Anders U.","familyName":"Waldeland","affiliation":[],"nameIdentifiers":[]},{"name":"Velicogna, Isabella","nameType":"Personal","givenName":"Isabella","familyName":"Velicogna","affiliation":[],"nameIdentifiers":[]},{"name":"Dahl, Fredrik","nameType":"Personal","givenName":"Fredrik","familyName":"Dahl","affiliation":[],"nameIdentifiers":[]},{"name":"Jäggi, Adrian","nameType":"Personal","givenName":"Adrian","familyName":"Jäggi","affiliation":[],"nameIdentifiers":[]},{"name":"Schindler, Konrad","nameType":"Personal","givenName":"Konrad","familyName":"Schindler","affiliation":[],"nameIdentifiers":[]},{"name":"Soja, Benedikt","nameType":"Personal","givenName":"Benedikt","familyName":"Soja","affiliation":[],"nameIdentifiers":[]}],"titles":[{"lang":"","title":"Uncertainty Quantification of Satellite-Based Essential Climate Variables Derived from Deep Learning"}],"publisher":"Springer","container":{},"publicationYear":2026,"subjects":[{"lang":"","subject":"Deep learning"},{"lang":"","subject":"Uncertainty quantification"},{"lang":"","subject":"Essential climate variables"},{"lang":"","subject":"Satellite observations"},{"lang":"","subject":"Snow cover"},{"lang":"","subject":"Terrestrial water storage"}],"contributors":[{"name":"ETH Zurich","affiliation":[],"contributorType":"DataManager","nameIdentifiers":[]},{"name":"ETH Zurich","affiliation":[],"contributorType":"HostingInstitution","nameIdentifiers":[]}],"dates":[{"date":"2026-02-18","dateType":"Accepted"},{"date":"2026-02-07","dateType":"Available"},{"date":"2026-02-18","dateType":"Available"},{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Review Article","resourceTypeGeneral":"Other"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["application/pdf"],"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"},{"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":[{"lang":"","description":"Accurate uncertainty information associated with essential climate variables (ECVs) is crucial for reliable climate modeling and understanding the spatiotemporal evolution of the Earth system. Recent developments in deep learning have remarkably advanced the estimation of ECVs with improved accuracy. However, the quantification of uncertainties associated with outputs of such deep learning models has yet to be widely adopted. This survey explores the types of uncertainties associated with ECVs derived from deep learning methods, including aleatoric (data) and epistemic (model) uncertainty, and the techniques to quantify them. The focus is on highlighting the importance of considering uncertainty associated with inputs in the deep learning models to account for the dynamic and multifaceted nature of satellite observations. The survey starts by clarifying the definitions of aleatoric and epistemic uncertainties and their roles in a typical satellite observation processing workflow, followed by bridging the gap between conventional statistical and deep learning views on uncertainties. Then, we comprehensively review the existing uncertainty quantification methods for deep learning algorithms and discuss their strengths and limitations. A comprehensive literature review about quantifying uncertainties in the deep learning estimates of ECVs follows the theoretical survey, covering a wide range of ECVs. The specific need for modification to fit the requirements from both the Earth observation side and the deep learning side in such interdisciplinary tasks is highlighted. We further demonstrate our findings with two selected ECV examples, snow cover and terrestrial water storage, to provide clear insights into different methods by promoting quantitative comparison. In the end, we summarize our findings and provide perspectives for future research.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://www.research-collection.ethz.ch/handle/20.500.11850/795616","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-02-18T10:17:23Z","registered":"2026-02-18T10:17:53Z","published":null,"updated":"2026-04-08T06:17:27Z"},"relationships":{"client":{"data":{"id":"ethz.e-coll","type":"clients"}}}},{"id":"10.5281/zenodo.19378149","type":"dois","attributes":{"doi":"10.5281/zenodo.19378149","identifiers":[],"creators":[{"name":"Tianjin Key Laboratory for Marine Environmental Research and Service, School of Marine Science and Technology, Tianjin University, Tianjin, China","nameType":"Organizational","nameIdentifiers":[],"affiliation":[]},{"name":"Wu, Haowen","nameType":"Personal","givenName":"Haowen","familyName":"Wu","nameIdentifiers":[{"nameIdentifier":"0009-0009-0319-2515","nameIdentifierScheme":"ORCID"}],"affiliation":[]},{"name":"Li, Wei","nameType":"Personal","givenName":"Wei","familyName":"Li","nameIdentifiers":[],"affiliation":[]},{"name":"Li, Hong","nameType":"Personal","givenName":"Hong","familyName":"Li","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"1/4° global temperature and salinity objective analysis using 4D-MGA"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Ocean temperature","subjectScheme":"GEMET"},{"subject":"Water salinity","subjectScheme":"GEMET"}],"contributors":[],"dates":[{"date":"2026-04-02","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19378150","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0","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":"A global, weekly T \u0026 S gridded dataset with a horizontal resolution of 1/4° and a depth range of 0–1500 m from 2005 to 2023 is reconstructed using a four-dimensional multigrid analysis (4D-MGA) framework. With minimal prior statistical assumptions, the 4D-MGA efficiently extracts information from in-situ T \u0026 S profiles and satellite-observed sea level anomalies by integrating multiscale spatiotemporal correlation and physical constraints. The validations show that the 4D-MGA product successfully delivers a credible, high-resolution analysis that combines robustness with reliable mesoscale information.  The dynamic height anomaly and geostrophic current derived from our product show high consistency with satellite observations and previous studies. Furthermore, the clear recirculation in the western boundary currents (WBCs) can be well captured by the 4D-MGA product. The product is then applied to investigate the linear trends of geostrophic transport within five key sections in WBCs. The 4D-MGA product is a subset of the high-resolution, objective analysis, gridded dataset for oceans, and it has the potential to advance our understanding of ocean dynamics and climate change.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19378149","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-04-08T06:13:14Z","registered":"2026-04-08T06:13:15Z","published":null,"updated":"2026-04-08T06:13:15Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19378150","type":"dois","attributes":{"doi":"10.5281/zenodo.19378150","identifiers":[{"identifier":"oai:zenodo.org:19378150","identifierType":"oai"}],"creators":[{"name":"Tianjin Key Laboratory for Marine Environmental Research and Service, School of Marine Science and Technology, Tianjin University, Tianjin, China","nameType":"Organizational","nameIdentifiers":[],"affiliation":[]},{"name":"Wu, Haowen","nameType":"Personal","givenName":"Haowen","familyName":"Wu","nameIdentifiers":[{"nameIdentifier":"0009-0009-0319-2515","nameIdentifierScheme":"ORCID"}],"affiliation":[]},{"name":"Li, Wei","nameType":"Personal","givenName":"Wei","familyName":"Li","nameIdentifiers":[],"affiliation":[]},{"name":"Li, Hong","nameType":"Personal","givenName":"Hong","familyName":"Li","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"1/4° global temperature and salinity objective analysis using 4D-MGA"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Ocean temperature","subjectScheme":"GEMET"},{"subject":"Water salinity","subjectScheme":"GEMET"}],"contributors":[],"dates":[{"date":"2026-04-02","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19378149","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0","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":"A global, weekly T \u0026 S gridded dataset with a horizontal resolution of 1/4° and a depth range of 0–1500 m from 2005 to 2023 is reconstructed using a four-dimensional multigrid analysis (4D-MGA) framework. With minimal prior statistical assumptions, the 4D-MGA efficiently extracts information from in-situ T \u0026 S profiles and satellite-observed sea level anomalies by integrating multiscale spatiotemporal correlation and physical constraints. The validations show that the 4D-MGA product successfully delivers a credible, high-resolution analysis that combines robustness with reliable mesoscale information.  The dynamic height anomaly and geostrophic current derived from our product show high consistency with satellite observations and previous studies. Furthermore, the clear recirculation in the western boundary currents (WBCs) can be well captured by the 4D-MGA product. The product is then applied to investigate the linear trends of geostrophic transport within five key sections in WBCs. The 4D-MGA product is a subset of the high-resolution, objective analysis, gridded dataset for oceans, and it has the potential to advance our understanding of ocean dynamics and climate change.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19378150","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-08T06:13:13Z","registered":"2026-04-08T06:13:13Z","published":null,"updated":"2026-04-08T06:13:13Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19465573","type":"dois","attributes":{"doi":"10.5281/zenodo.19465573","identifiers":[],"creators":[{"name":"Abdalla Ahmed, Mehad Ahmed","nameType":"Personal","givenName":"Mehad Ahmed","familyName":"Abdalla Ahmed","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"SUD_CARB: Integrated Blue Carbon Monitoring \u0026 Financial Resilience Framework for Port Sudan Mangrove Restoration"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Blue Carbon Mangrove Restoration , Nature-based Solutions, Port Sudan , Bio-Financial Resilience Index (BFRI) Environmental Data Science"}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Issued"}],"language":"en","types":{"ris":"COMP","bibtex":"misc","citeproc":"article","schemaOrg":"SoftwareSourceCode","resourceType":"","resourceTypeGeneral":"Software"},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19465574","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"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":"Project Overview: SUD_CARB is an innovative technical framework designed for the monitoring, verification, and financial modeling of Blue Carbon restoration projects in Port Sudan and the Tokar Basin. This repository integrates environmental remote sensing with economic forecasting to create a transparent ecosystem for carbon sequestration.\n\nKey Technical Components:\n\n\n\n\n\nBFRI (Biological Financial Resilience Index): A proprietary algorithmic model that quantifies ecosystem health and resilience by integrating NDVI satellite data with financial risk parameters.\n\n\n\n\nPredictive Modeling: Python-based scripts for stochastic scenario analysis, forecasting carbon stock growth and investor ROI through 2035.\n\n\n\n\nAutomated Data Pipeline: A systematic workflow that processes geospatial vegetation indices into bankable financial models.\n\n\n\nPurpose: This release provides the source code and data models required to validate the ecological and economic viability of mangrove restoration, ensuring that climate action is both scientifically sound and financially sustainable.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19465573","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-04-08T06:03:44Z","registered":"2026-04-08T06:03:44Z","published":null,"updated":"2026-04-08T06:03:44Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19465574","type":"dois","attributes":{"doi":"10.5281/zenodo.19465574","identifiers":[{"identifier":"oai:zenodo.org:19465574","identifierType":"oai"}],"creators":[{"name":"Abdalla Ahmed, Mehad Ahmed","nameType":"Personal","givenName":"Mehad Ahmed","familyName":"Abdalla Ahmed","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"SUD_CARB: Integrated Blue Carbon Monitoring \u0026 Financial Resilience Framework for Port Sudan Mangrove Restoration"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Blue Carbon Mangrove Restoration , Nature-based Solutions, Port Sudan , Bio-Financial Resilience Index (BFRI) Environmental Data Science"}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Issued"}],"language":"en","types":{"ris":"COMP","bibtex":"misc","citeproc":"article","schemaOrg":"SoftwareSourceCode","resourceType":"","resourceTypeGeneral":"Software"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19465573","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"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":"Project Overview: SUD_CARB is an innovative technical framework designed for the monitoring, verification, and financial modeling of Blue Carbon restoration projects in Port Sudan and the Tokar Basin. This repository integrates environmental remote sensing with economic forecasting to create a transparent ecosystem for carbon sequestration.\n\nKey Technical Components:\n\n\n\n\n\nBFRI (Biological Financial Resilience Index): A proprietary algorithmic model that quantifies ecosystem health and resilience by integrating NDVI satellite data with financial risk parameters.\n\n\n\n\nPredictive Modeling: Python-based scripts for stochastic scenario analysis, forecasting carbon stock growth and investor ROI through 2035.\n\n\n\n\nAutomated Data Pipeline: A systematic workflow that processes geospatial vegetation indices into bankable financial models.\n\n\n\nPurpose: This release provides the source code and data models required to validate the ecological and economic viability of mangrove restoration, ensuring that climate action is both scientifically sound and financially sustainable.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19465574","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-08T06:03:43Z","registered":"2026-04-08T06:03:44Z","published":null,"updated":"2026-04-08T06:03:44Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.34657/33309","type":"dois","attributes":{"doi":"10.34657/33309","identifiers":[],"creators":[{"name":"Germer, Jörn","nameType":"Personal","givenName":"Jörn","familyName":"Germer","affiliation":[],"nameIdentifiers":[]},{"name":"Morgenstern, Alexander","nameType":"Personal","givenName":"Alexander","familyName":"Morgenstern","affiliation":[],"nameIdentifiers":[]},{"name":"Abu Taleb, Yana","nameType":"Personal","givenName":"Yana","familyName":"Abu Taleb","affiliation":[],"nameIdentifiers":[]},{"name":"Shenker, Moshe","nameType":"Personal","givenName":"Moshe","familyName":"Shenker","affiliation":[],"nameIdentifiers":[]}],"titles":[{"lang":"eng","title":"MEWAC – Collaborative Project EXALT: Coupling thermal desalination and extraction of dewatered salt with hydroponic greenhouse cultivtion via heat pumps - Middle East Regional Water Research Cooperation Program (MEWAC)"},{"lang":"ger","title":"MEWAC – Verbundprojekt EXALT: Koppelung thermischer Entsalzung and Ausschleusung von entwässertem Salz mit hydroponischer Pflanzenproduktion mittels Wärmepumpen : Wasserforschung im Nahen und Mittleren Osten - Modul A","titleType":"AlternativeTitle"},{"lang":"","title":"Joint final project report","titleType":"Subtitle"}],"publisher":"Hannover : Technische Informationsbibliothek","container":{},"publicationYear":2026,"subjects":[{"lang":"","subject":"500 | Naturwissenschaften","subjectScheme":"ddc"},{"lang":"eng","subject":"Water scarcity","subjectScheme":"other"},{"lang":"eng","subject":"Hydroponics","subjectScheme":"other"},{"lang":"eng","subject":"Desalination","subjectScheme":"other"},{"lang":"eng","subject":"Climate control","subjectScheme":"other"},{"lang":"eng","subject":"Sustainability","subjectScheme":"other"},{"lang":"","subject":"6","subjectScheme":"sdg"}],"contributors":[{"name":"Technische Informationsbibliothek (TIB)","affiliation":[],"contributorType":"DataManager","nameIdentifiers":[]},{"name":"Technische Informationsbibliothek (TIB)","affiliation":[],"contributorType":"HostingInstitution","nameIdentifiers":[]}],"dates":[{"date":"2026-04-08","dateType":"Accepted"},{"date":"2026-04-08","dateType":"Available"},{"date":"2026-03-31","dateType":"Issued"}],"language":"en","types":{"ris":"RPRT","bibtex":"techreport","citeproc":"report","schemaOrg":"Report","resourceType":"Report","resourceTypeGeneral":"Report"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution-NonDerivs 3.0 Germany"}],"descriptions":[{"lang":"eng","description":"The EXALT project, supported by the MEWAC funding initiative, aimed to deliver practical solutions to water scarcity in the Middle East through trinational collaboration between German, Jordanian, and Israeli partners (Figure 1). Crop production in the region is constrained by chronic water scarcity, exacerbated by climate change, recurrent droughts, and salinization of freshwater and soils. In the Jordan River Basin, per capita renewable water availability has declined by more than 80% over six decades, while agriculture remains the largest consumer of water, using roughly half of total freshwater withdrawals in both Jordan and Israel. The two countries pursue divergent strategies to address water scarcity: Jordan depends on declining groundwater and transboundary surface water, supplemented by limited freshwater imports from Israel, whereas Israel relies heavily on large-scale desalination and direct wastewater reuse. In this context, EXALT developed a blueprint for innovative hydroponic greenhouse systems to overcome both water scarcity and salinity constraints. By combining advanced climate control, water recovery, and thermal desalination, the project sought to maximize water-use efficiency, reduce environmental pressures, enhance year-round productivity, and provide a scalable model for arid, subtropical regions. From the outset, the consortium emphasized integrated collaboration across technical, scientific, and capacity-building dimensions, including controlled-environment trials, field assessments, stakeholder engagement, and training of young scientists. EcoPeace Jordan supported these efforts by organizing field trips for researchers, conducting interviews, and assisting with soil sampling campaigns, while a number of universities and public institutions contributed local knowledge, sample analysis, and networking support. Research at the University of Hohenheim examined the interplay of atmospheric demand, salinity, and plant performance in hydroponic systems. Three experiments with tomato, cucumber, and quinoa demonstrated genotype-specific responses in growth, ion partitioning, and stress tolerance. Salt-tolerant tomatoes effectively sequestered sodium and chloride in the petiole, protecting metabolically active leaf tissues, while cucumbers showed lower ionic homeostasis and increased vulnerability under high vapour pressure deficit (VPD). Evapotranspiration was strongly influenced by both VPD and light regime, with LED lighting reducing water loss relative to metal halide lamps. These findings underscore the importance of coordinating humidity, light, and salinity management to optimize plant performance and water-use efficiency. At the University of Jerusalem, experiments compared nutrient uptake in recirculating deep-water culture and nutrient film technique systems. Leaf nutrient concentrations generally reflected solution composition, yet NFT systems maintained optimal nutrient levels even when solution concentrations exceeded targets. Preliminary studies on chelate dynamics suggested interactions that could limit micronutrient availability, highlighting the importance of nutrient management under low-input and saline conditions. Fraunhofer research focused on greenhouse energy efficiency and climate control (Figure 2). Thermal screens improved heat retention, LED lighting reduced electrical demand, and partial photovoltaic shading lowered heat gain while allowing recovery of water from condensation. Simulation studies of a fully controlled growing environment (FCGE) indicated potential for year-round cultivation, wastewater elimination, and substantial reduction of external water requirements. FCGE systems demonstrated high yields, significant water savings, and resilience under semi-arid and saline conditions. Collectively, the consortium showed that hydroponic systems with integrated climate and water-recovery technologies could reduce daily water demand by up to 2.25 L m⁻², nearly matching the annual irrigation allocation for protected vegetables in the Jordan Valley. Applied across an estimated 4,000 ha of greenhouse cultivation, these technologies could save roughly 19–22 million m³ of water annually - about 17% of the total supply from the King Abdullah Canal and equivalent to the freshwater needs of several hundred thousand people. At the same time, crop- and genotype-specific salt uptake was shown to enable biological removal of salts through harvested biomass, reducing or eliminating the need for energy-intensive brine management under moderate salinity conditions. Together, these findings demonstrate that closed-loop hydroponic systems can simultaneously address water scarcity and salinity constraints while substantially lowering energy demand and operational costs. Despite geopolitical tensions limiting in-region mobility and field activities, the consortium maintained continuous communication, shared methodologies, and harmonized workflows, reflecting a common understanding of regional water and agricultural challenges. Capacity-building activities included MSc, BSc, and doctoral-level research projects, remote mentoring, and cross-institutional supervision. These efforts strengthened human capital across Israel, Jordan, and Germany, creating a cadre of trained professionals ready to implement water-efficient agricultural technologies and support future science–industry collaboration. Through technical innovation, environmental monitoring, and capacity development, EXALT advanced sustainable greenhouse production, disseminated environmentally innovative technologies, and fostered long-term professional networks. By addressing both water scarcity and salinity, the project contributes to regional water security, demonstrates the value of cross-border cooperation, and provides a replicable model for sustainable agriculture under challenging conditions. Even under restricted mobility and political constraints, the consortium’s integrated approach delivered measurable contributions to technology transfer, capacity building, and resilient agricultural 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more](\u003chttps://docs.redivis.com/reference/workflows/notebooks\u003e)\n\n\n\n\n\n![map](\u003chttps://redivis.com/fileUploads/b9013038-23b7-40c9-b6bb-5092cbc795d3\u003e)\n\n","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[],"url":"https://redivis.com/workflows/x7kh-5pvd4mbf1","contentUrl":null,"metadataVersion":32,"schemaVersion":"http://datacite.org/schema/kernel-4.6","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":3,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-01-23T04:36:06Z","registered":"2025-02-01T00:00:01Z","published":null,"updated":"2026-04-08T06:00:03Z"},"relationships":{"client":{"data":{"id":"chvf.pbyfos","type":"clients"}}}},{"id":"10.5281/zenodo.19465177","type":"dois","attributes":{"doi":"10.5281/zenodo.19465177","identifiers":[],"creators":[{"name":"Obemio, Charlene Mae","nameType":"Personal","givenName":"Charlene 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