{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T20:12:06Z","timestamp":1786219926462,"version":"build-2736575974"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"DOI":"10.1038\/s41597-025-05045-1","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T11:31:01Z","timestamp":1745926261000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing"],"prefix":"10.1038","volume":"12","author":[{"given":"Qixu","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5687-803X","authenticated-orcid":false,"given":"Jinwei","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3759-9403","authenticated-orcid":false,"given":"Ping","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qu","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9495-1226","authenticated-orcid":false,"given":"Jun","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helen","family":"Gurgel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linsheng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"5045_CR1","doi-asserted-by":"publisher","first-page":"299","DOI":"10.2147\/CLEP.S34440","volume":"5","author":"NE Murray","year":"2013","unstructured":"Murray, N. E., Quam, M. B. & Wilder-Smith, A. Epidemiology of dengue: past, present and future prospects. Clin Epidemiol 5, 299\u2013309, https:\/\/doi.org\/10.2147\/CLEP.S34440 (2013).","journal-title":"Clin Epidemiol"},{"key":"5045_CR2","doi-asserted-by":"publisher","unstructured":"Venkatesan, P. Global upsurge in dengue in 2024. The Lancet Infectious Diseases 24, https:\/\/doi.org\/10.1016\/S1473-3099(24)00609-1 (2024).","DOI":"10.1016\/S1473-3099(24)00609-1"},{"key":"5045_CR3","doi-asserted-by":"publisher","first-page":"1508","DOI":"10.1038\/s41564-019-0476-8","volume":"4","author":"JP Messina","year":"2019","unstructured":"Messina, J. P. et al. The current and future global distribution and population at risk of dengue. Nature Microbiology 4, 1508\u20131515, https:\/\/doi.org\/10.1038\/s41564-019-0476-8 (2019).","journal-title":"Nature Microbiology"},{"key":"5045_CR4","doi-asserted-by":"publisher","unstructured":"Haider, N., Hasan, M. N., Onyango, J. & Asaduzzaman, M. Global landmark: 2023 marks the worst year for dengue cases with millions infected and thousands of deaths reported. IJID Regions 13, https:\/\/doi.org\/10.1016\/j.ijregi.2024.100459 (2024).","DOI":"10.1016\/j.ijregi.2024.100459"},{"key":"5045_CR5","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1038\/d41586-024-00626-3","volume":"627","author":"M Lenharo","year":"2024","unstructured":"Lenharo, M. Brazil\u2019s record dengue surge: why a vaccine campaign is unlikely to stop it. Nature 627, 250\u2013251, https:\/\/doi.org\/10.1038\/d41586-024-00626-3 (2024).","journal-title":"Nature"},{"key":"5045_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01312-7","volume":"9","author":"SR da Silva Neto","year":"2022","unstructured":"da Silva Neto, S. R. et al. Arboviral disease record data - Dengue and Chikungunya, Brazil, 2013\u20132020. Scientific Data 9, 198, https:\/\/doi.org\/10.1038\/s41597-022-01312-7 (2022).","journal-title":"Scientific Data"},{"key":"5045_CR7","doi-asserted-by":"publisher","first-page":"e0004705","DOI":"10.1371\/journal.pntd.0004705","volume":"10","author":"GE Coelho","year":"2016","unstructured":"Coelho, G. E., Leal, P. L., Cerroni Mde, P., Simplicio, A. C. & Siqueira, J. B. Jr. Sensitivity of the Dengue Surveillance System in Brazil for Detecting Hospitalized Cases. PLoS Negl Trop Dis 10, e0004705, https:\/\/doi.org\/10.1371\/journal.pntd.0004705 (2016).","journal-title":"PLoS Negl Trop Dis"},{"key":"5045_CR8","doi-asserted-by":"publisher","unstructured":"Codeco, C. et al. Infodengue: A nowcasting system for the surveillance of arboviruses in Brazil. Revue d'\u00c9pid\u00e9miologie et de Sant\u00e9 Publique 66, https:\/\/doi.org\/10.1016\/j.respe.2018.05.408 (2018).","DOI":"10.1016\/j.respe.2018.05.408"},{"key":"5045_CR9","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1029\/2019GH000186","volume":"3","author":"I Ogashawara","year":"2019","unstructured":"Ogashawara, I., Li, L. & Moreno-Madrinan, M. J. Spatial-Temporal Assessment of Environmental Factors Related to Dengue Outbreaks in Sao Paulo, Brazil. Geohealth 3, 202\u2013217, https:\/\/doi.org\/10.1029\/2019GH000186 (2019).","journal-title":"Geohealth"},{"key":"5045_CR10","doi-asserted-by":"publisher","unstructured":"Lowe, R. et al. Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil. Elife 5, https:\/\/doi.org\/10.7554\/eLife.11285 (2016).","DOI":"10.7554\/eLife.11285"},{"key":"5045_CR11","doi-asserted-by":"publisher","first-page":"e0308271","DOI":"10.1371\/journal.pone.0308271","volume":"19","author":"KU Salim","year":"2024","unstructured":"Salim, K. U. et al. Socioeconomic and environmental factors associated with dengue fever incidence in Guatemala: Rising temperatures increase dengue risk. PloS one 19, e0308271, https:\/\/doi.org\/10.1371\/journal.pone.0308271 (2024).","journal-title":"PloS one"},{"key":"5045_CR12","doi-asserted-by":"publisher","first-page":"e0011700","DOI":"10.1371\/journal.pntd.0011700","volume":"17","author":"X Seposo","year":"2023","unstructured":"Seposo, X., Valenzuela, S. & Apostol, G. L. Socio-economic factors and its influence on the association between temperature and dengue incidence in 61 Provinces of the Philippines, 2010\u20132019. PLoS neglected tropical diseases 17, e0011700\u2013e0011700, https:\/\/doi.org\/10.1371\/journal.pntd.0011700 (2023).","journal-title":"PLoS neglected tropical diseases"},{"key":"5045_CR13","doi-asserted-by":"publisher","DOI":"10.1590\/0037-8682-0080-2024","volume":"57","author":"B Geraldini","year":"2024","unstructured":"Geraldini, B., Johansen, I. C. & Justus, M. Influence of temperature and precipitation on dengue incidence in Campinas, S\u00e3o Paulo State, Brazil (2013-2022). Revista da Sociedade Brasileira de Medicina Tropical 57, e007102024, https:\/\/doi.org\/10.1590\/0037-8682-0080-2024 (2024).","journal-title":"Revista da Sociedade Brasileira de Medicina Tropical"},{"key":"5045_CR14","doi-asserted-by":"publisher","unstructured":"Borges, I. V. G. et al. Analysis of the interrelationship between precipitation and confirmed dengue cases in the city of Recife (Brazil) covering climate and public health information. Frontiers in public health 12, https:\/\/doi.org\/10.3389\/fpubh.2024.1456043 (2024).","DOI":"10.3389\/fpubh.2024.1456043"},{"key":"5045_CR15","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.actatropica.2016.12.034","volume":"168","author":"I Alkhaldy","year":"2017","unstructured":"Alkhaldy, I. Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia\u2014A generalised linear model with break-point analysis. Acta tropica 168, 9\u201315, https:\/\/doi.org\/10.1016\/j.actatropica.2016.12.034 (2017).","journal-title":"Acta tropica"},{"key":"5045_CR16","doi-asserted-by":"publisher","unstructured":"Figueredo, M. B. et al. Analysis of the correlation between climatic variables and Dengue cases in the city of Alagoinhas\/BA. Scientific Reports 2023 13:1 13, https:\/\/doi.org\/10.1038\/s41598-023-34349-8 (2023).","DOI":"10.1038\/s41598-023-34349-8"},{"key":"5045_CR17","doi-asserted-by":"publisher","unstructured":"Zhu, B. et al. Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008\u20132016). PLOS ONE 14, https:\/\/doi.org\/10.1371\/journal.pone.0225811 (2019).","DOI":"10.1371\/journal.pone.0225811"},{"key":"5045_CR18","doi-asserted-by":"publisher","unstructured":"G\u00f3mez, R. E. G. et al. Association between Climate Factors and Dengue Fever in Asuncion, Paraguay: A Generalized Additive Model. International Journal of Environmental Research and Public Health 19, https:\/\/doi.org\/10.3390\/ijerph191912192 (2022).","DOI":"10.3390\/ijerph191912192"},{"key":"5045_CR19","doi-asserted-by":"publisher","first-page":"e209","DOI":"10.1016\/S2542-5196(20)30292-8","volume":"5","author":"R Lowe","year":"2021","unstructured":"Lowe, R. et al. Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study. The Lancet. Planetary health 5, e209\u2013e219, https:\/\/doi.org\/10.1016\/S2542-5196(20)30292-8 (2021).","journal-title":"The Lancet. Planetary health"},{"key":"5045_CR20","doi-asserted-by":"publisher","unstructured":"Soukavong, M. et al. Bayesian spatio-temporal analysis of dengue transmission in Lao PDR. Scientific Reports 2024 14:1 14, https:\/\/doi.org\/10.1038\/s41598-024-71807-3 (2024).","DOI":"10.1038\/s41598-024-71807-3"},{"key":"5045_CR21","doi-asserted-by":"publisher","unstructured":"Tewari, P. et al. Associations between Dengue Incidence, Ecological Factors, and Anthropogenic Factors in Singapore. Viruses 15, https:\/\/doi.org\/10.3390\/v15091917 (2023).","DOI":"10.3390\/v15091917"},{"key":"5045_CR22","doi-asserted-by":"publisher","unstructured":"Nouzova, M., Clifton, M. E. & Noriega, F. G. Mosquito adaptations to hematophagia impact pathogen transmission. Current Opinion in Insect Science 34, https:\/\/doi.org\/10.1016\/j.cois.2019.02.002 (2019).","DOI":"10.1016\/j.cois.2019.02.002"},{"key":"5045_CR23","doi-asserted-by":"publisher","first-page":"S106","DOI":"10.1016\/j.ijid.2023.04.263","volume":"130","author":"K Marczell","year":"2023","unstructured":"Marczell, K. et al. The Impact of Dengue Fever on Gross Domestic Product: Case Studies from Thailand and Brazil. International journal of infectious diseases 130, S106\u2013S106, https:\/\/doi.org\/10.1016\/j.ijid.2023.04.263 (2023).","journal-title":"International journal of infectious diseases"},{"key":"5045_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2022.107518","volume":"169","author":"Y Wang","year":"2022","unstructured":"Wang, Y. et al. Impact of extreme weather on dengue fever infection in four Asian countries: A modelling analysis. Environ Int 169, 107518, https:\/\/doi.org\/10.1016\/j.envint.2022.107518 (2022).","journal-title":"Environ Int"},{"key":"5045_CR25","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s42489-022-00121-7","volume":"72","author":"Y Loukili","year":"2022","unstructured":"Loukili, Y., Lakhrissi, Y. & Ali, S. E. B. Geospatial Big Data Platforms: A Comprehensive Review. KN - Journal of Cartography and Geographic Information 72, 293\u2013308, https:\/\/doi.org\/10.1007\/s42489-022-00121-7 (2022).","journal-title":"KN - Journal of Cartography and Geographic Information"},{"key":"5045_CR26","doi-asserted-by":"crossref","unstructured":"Li, Z. in High Performance Computing for Geospatial Applications (eds Wenwu Tang & Shaowen Wang) 53-76 (Springer International Publishing, 2020).","DOI":"10.1007\/978-3-030-47998-5_4"},{"key":"5045_CR27","doi-asserted-by":"publisher","unstructured":"Liu, P. A survey of remote-sensing big data. Frontiers in Environmental Science 3, https:\/\/doi.org\/10.3389\/fenvs.2015.00045 (2015).","DOI":"10.3389\/fenvs.2015.00045"},{"key":"5045_CR28","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.future.2014.10.029","volume":"51","author":"Y Ma","year":"2015","unstructured":"Ma, Y. et al. Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems 51, 47\u201360, https:\/\/doi.org\/10.1016\/j.future.2014.10.029 (2015).","journal-title":"Future Generation Computer Systems"},{"key":"5045_CR29","doi-asserted-by":"publisher","unstructured":"ArmbrustMichael et al. A view of cloud computing. Communications of the ACM, https:\/\/doi.org\/10.1145\/1721654.1721672 (2010).","DOI":"10.1145\/1721654.1721672"},{"key":"5045_CR30","doi-asserted-by":"publisher","first-page":"5326","DOI":"10.1109\/jstars.2020.3021052","volume":"13","author":"M Amani","year":"2020","unstructured":"Amani, M. et al. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, 5326\u20135350, https:\/\/doi.org\/10.1109\/jstars.2020.3021052 (2020).","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"5045_CR31","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","volume":"164","author":"H Tamiminia","year":"2020","unstructured":"Tamiminia, H. et al. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing 164, 152\u2013170, https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.04.001 (2020).","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"5045_CR32","doi-asserted-by":"publisher","unstructured":"Wimberly, M. C., Beurs, K. M. d., Loboda, T. V. & Pan, W. K. Satellite Observations and Malaria: New Opportunities for Research and Applications. Trends in Parasitology 37, https:\/\/doi.org\/10.1016\/j.pt.2021.03.003 (2021).","DOI":"10.1016\/j.pt.2021.03.003"},{"key":"5045_CR33","doi-asserted-by":"publisher","unstructured":"Wimberly, M. C. et al. Cloud-based applications for accessing satellite Earth observations to support malaria early warning. Scientific Data 2022 9:1 9, https:\/\/doi.org\/10.1038\/s41597-022-01337-y (2022).","DOI":"10.1038\/s41597-022-01337-y"},{"key":"5045_CR34","doi-asserted-by":"publisher","unstructured":"Belaunzar\u00e1n-Zamudio, P. F. et al. Frontiers | Comparison of the Impact of Zika and Dengue Virus Infection, and Other Acute Illnesses of Unidentified Origin on Cognitive Functions in a Prospective Cohort in Chiapas Mexico. Frontiers in Neurology 12, https:\/\/doi.org\/10.3389\/fneur.2021.631801 (2021).","DOI":"10.3389\/fneur.2021.631801"},{"key":"5045_CR35","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1111\/tmi.12227","volume":"19","author":"C Barcellos","year":"2014","unstructured":"Barcellos, C. & Lowe, R. Expansion of the dengue transmission area in Brazil: the role of climate and cities. Tropical Medicine & International Health 19, 159\u2013168, https:\/\/doi.org\/10.1111\/tmi.12227 (2014).","journal-title":"Tropical Medicine & International Health"},{"key":"5045_CR36","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.epidem.2018.05.011","volume":"25","author":"CT Code\u00e7o","year":"2018","unstructured":"Code\u00e7o, C. T., Villela, D. A. M. & Coelho, F. C. Estimating the effective reproduction number of dengue considering temperature-dependent generation intervals. Epidemics 25, 101\u2013111, https:\/\/doi.org\/10.1016\/j.epidem.2018.05.011 (2018).","journal-title":"Epidemics"},{"key":"5045_CR37","doi-asserted-by":"publisher","first-page":"e0006070","DOI":"10.1371\/journal.pntd.0006070","volume":"11","author":"RM Lana","year":"2017","unstructured":"Lana, R. M., Gomes, M. F. D. C., Lima, T. F. M. D., Hon\u00f3rio, N. A. & Code\u00e7o, C. T. The introduction of dengue follows transportation infrastructure changes in the state of Acre, Brazil: A network-based analysis. PLOS Neglected Tropical Diseases 11, e0006070, https:\/\/doi.org\/10.1371\/journal.pntd.0006070 (2017).","journal-title":"PLOS Neglected Tropical Diseases"},{"key":"5045_CR38","doi-asserted-by":"publisher","first-page":"m4385","DOI":"10.1136\/bmj.m4385","volume":"371","author":"R Lowe","year":"2020","unstructured":"Lowe, R. et al. Emerging arboviruses in the urbanized Amazon rainforest. BMJ 371, m4385, https:\/\/doi.org\/10.1136\/bmj.m4385 (2020).","journal-title":"BMJ"},{"key":"5045_CR39","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-56044-y","volume":"14","author":"C Barcellos","year":"2024","unstructured":"Barcellos, C., Matos, V., Lana, R. M. & Lowe, R. Climate change, thermal anomalies, and the recent progression of dengue in Brazil. Scientific Reports 14, 5948, https:\/\/doi.org\/10.1038\/s41598-024-56044-y (2024).","journal-title":"Scientific Reports"},{"key":"5045_CR40","doi-asserted-by":"publisher","first-page":"e0007012","DOI":"10.1371\/journal.pntd.0007012","volume":"13","author":"M Churakov","year":"2019","unstructured":"Churakov, M., Villabona-Arenas, C. J., Kraemer, M. U. G., Salje, H. & Cauchemez, S. Spatio-temporal dynamics of dengue in Brazil: Seasonal travelling waves and determinants of regional synchrony. PLOS Neglected Tropical Diseases 13, e0007012, https:\/\/doi.org\/10.1371\/journal.pntd.0007012 (2019).","journal-title":"PLOS Neglected Tropical Diseases"},{"key":"5045_CR41","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1016\/s1473-3099(14)70781-9","volume":"14","author":"R Lowe","year":"2014","unstructured":"Lowe, R. et al. Dengue outlook for the World Cup in Brazil: an early warning model framework driven by real-time seasonal climate forecasts. The Lancet Infectious Diseases 14, 619\u2013626, https:\/\/doi.org\/10.1016\/s1473-3099(14)70781-9 (2014).","journal-title":"The Lancet Infectious Diseases"},{"key":"5045_CR42","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.73","volume":"5","author":"AS Siraj","year":"2018","unstructured":"Siraj, A. S. et al. Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia. Sci Data 5, 180073, https:\/\/doi.org\/10.1038\/sdata.2018.73 (2018).","journal-title":"Sci Data"},{"key":"5045_CR43","doi-asserted-by":"publisher","unstructured":"Sorichetta, A. et al. High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020. Scientific Data 2015 2:1 2, https:\/\/doi.org\/10.1038\/sdata.2015.45 (2015).","DOI":"10.1038\/sdata.2015.45"},{"key":"5045_CR44","doi-asserted-by":"publisher","unstructured":"Mu\u00f1oz Sabater, J. et al. ERA5-land post-processed daily-statistics from 1950 to present. https:\/\/doi.org\/10.24381\/cds.e9c9c792 (2024).","DOI":"10.24381\/cds.e9c9c792"},{"key":"5045_CR45","doi-asserted-by":"publisher","unstructured":"Mu\u00f1oz Sabater, J. ERA5-Land hourly data from 1950 to present. https:\/\/doi.org\/10.24381\/cds.e2161bac (2019).","DOI":"10.24381\/cds.e2161bac"},{"key":"5045_CR46","doi-asserted-by":"publisher","unstructured":"Rahman, K. M. et al. An Association between Rainy Days with Clinical Dengue Fever in Dhaka, Bangladesh: Findings from a Hospital Based Study. Int J Environ Res Public Health 17, https:\/\/doi.org\/10.3390\/ijerph17249506 (2020).","DOI":"10.3390\/ijerph17249506"},{"key":"5045_CR47","doi-asserted-by":"publisher","unstructured":"Bolton, D. The Computation of Equivalent Potential Temperature. Monthly Weather Review 108,\u00a0https:\/\/doi.org\/10.1175\/1520-0493(1980)1081046:TCOEPT2.0.CO;2.","DOI":"10.1175\/1520-0493(1980)1081046:TCOEPT2.0.CO;2"},{"key":"5045_CR48","doi-asserted-by":"publisher","unstructured":"Abatzoglou, J. T. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958\u20132015. Scientific Data 2018 5:1 5, https:\/\/doi.org\/10.1038\/sdata.2017.191 (2018).","DOI":"10.1038\/sdata.2017.191"},{"key":"5045_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.landurbplan.2021.104255","volume":"216","author":"MdCM Cunha","year":"2021","unstructured":"Cunha, Md. C. M. et al. Disentangling associations between vegetation greenness and dengue in a Latin American city: Findings and challenges. Landscape and Urban Planning 216, 104255, https:\/\/doi.org\/10.1016\/j.landurbplan.2021.104255 (2021).","journal-title":"Landscape and Urban Planning"},{"key":"5045_CR50","doi-asserted-by":"publisher","first-page":"e469","DOI":"10.1016\/S2542-5196(19)30215-3","volume":"3","author":"D Rojas-Rueda","year":"2019","unstructured":"Rojas-Rueda, D., Nieuwenhuijsen, M. J., Gascon, M., Perez-Leon, D. & Mudu, P. Green spaces and mortality: a systematic review and meta-analysis of cohort studies. Lancet Planet Health 3, e469\u2013e477, https:\/\/doi.org\/10.1016\/S2542-5196(19)30215-3 (2019).","journal-title":"Lancet Planet Health"},{"key":"5045_CR51","doi-asserted-by":"publisher","unstructured":"Chen, J. et al. Global 1\u2009km\u2009\u00d7\u20091\u2009km gridded revised real gross domestic product and electricity consumption during 1992\u20132019 based on calibrated nighttime light data. Scientific Data 2022 9:1 9, https:\/\/doi.org\/10.1038\/s41597-022-01322-5 (2022).","DOI":"10.1038\/s41597-022-01322-5"},{"key":"5045_CR52","unstructured":"European Commission: Joint Research, C. GHSL data package 2023. (Publications Office of the European Union, 2023)."},{"key":"5045_CR53","doi-asserted-by":"publisher","unstructured":"Pesaresi M., P. P. GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975\u20132030). https:\/\/doi.org\/10.2905\/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA (2023).","DOI":"10.2905\/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA"},{"key":"5045_CR54","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.15091652","author":"Q Zhu","year":"2025","unstructured":"Zhu, Q. et al. Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing. Zenodo https:\/\/doi.org\/10.5281\/zenodo.15091652 (2025)."},{"key":"5045_CR55","doi-asserted-by":"publisher","DOI":"10.1029\/2021MS002502","volume":"13","author":"JA Weyn","year":"2021","unstructured":"Weyn, J. A., Durran, D. R., Caruana, R. & Cresswell-Clay, N. Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models. Journal of Advances in Modeling Earth Systems 13, e2021MS002502, https:\/\/doi.org\/10.1029\/2021MS002502 (2021).","journal-title":"Journal of Advances in Modeling Earth Systems"},{"key":"5045_CR56","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1002\/qj.3654","volume":"146","author":"JR Eyre","year":"2020","unstructured":"Eyre, J. R., English, S. J. & Forsythe, M. Assimilation of satellite data in numerical weather prediction. Part I: The early years. Quarterly Journal of the Royal Meteorological Society 146, 49\u201368, https:\/\/doi.org\/10.1002\/qj.3654 (2020).","journal-title":"Quarterly Journal of the Royal Meteorological Society"},{"key":"5045_CR57","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1175\/2008JHM1068.1","volume":"10","author":"G Balsamo","year":"2009","unstructured":"Balsamo, G. et al. A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System. Journal of Hydrometeorology 10, 623\u2013643, https:\/\/doi.org\/10.1175\/2008JHM1068.1 (2009).","journal-title":"Journal of Hydrometeorology"},{"key":"5045_CR58","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-015-0348-x","volume":"13","author":"MRT Nunes","year":"2015","unstructured":"Nunes, M. R. T. et al. Emergence and potential for spread of Chikungunya virus in Brazil. BMC Medicine 13, 102, https:\/\/doi.org\/10.1186\/s12916-015-0348-x (2015).","journal-title":"BMC Medicine"},{"key":"5045_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2022.157566","volume":"847","author":"W Wint","year":"2022","unstructured":"Wint, W., Jones, P., Kraemer, M., Alexander, N. & Schaffner, F. Past, present and future distribution of the yellow fever mosquito Aedes aegypti: The European paradox. Science of The Total Environment 847, 157566, https:\/\/doi.org\/10.1016\/j.scitotenv.2022.157566 (2022).","journal-title":"Science of The Total Environment"},{"key":"5045_CR60","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1002\/qj.3803","volume":"146","author":"H Hersbach","year":"2020","unstructured":"Hersbach, H. et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999\u20132049, https:\/\/doi.org\/10.1002\/qj.3803 (2020).","journal-title":"Quarterly Journal of the Royal Meteorological Society"}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-05045-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-05045-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-05045-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T11:31:14Z","timestamp":1745926274000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-05045-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,29]]},"references-count":60,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["5045"],"URL":"https:\/\/doi.org\/10.1038\/s41597-025-05045-1","relation":{"references":[{"id-type":"doi","id":"10.5281\/zenodo.15091652","asserted-by":"subject"}]},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,29]]},"assertion":[{"value":"25 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"712"}}