Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,103
datasets available to search
ShareScore release 0.9.0
Dataset results
3,103 results for “Southeast Asia”
Historical and future Lake Surface Water Temperature for 80 major lakes in Southeast Asia [LSWT-SEA]
The present dataset is part of a study delving into the intricate relationship between lake surface temperature (LSWT) and the broader context of climate change in the ecologically diverse region of Southeast Asia (SEA). Recognizing LSWT as a highly responsive indicator of climatic shifts, the research aims to shed light on the region's vulnerability to these changes. Using a suite of predictive models (namely Multilinear Regression (MLR), Multilayer perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGB), Multilayer perceptron (MLP)) the study reconstructs historical LSWT trends from 1986 to 2020 and projects future scenarios until 2100, contingent upon various Representative Concentration Pathway (RCP) trajectories. Using MODIS-derived LSWT as predicted variable. The dataset package includes the data used to carry out the research: ECMWF ERA5 and CHIRPS climatic predicting variables, MODIS-derived daytime and nighttime LSWT, historically predicted daily daytime and nighttime LSWT, future predictions of LSWT for multiple Representative Concentration Pathways (RCPs), long term historical and future trends.
Thermal changes along the urban-rural continuums in Southeast Asia
<p>This research has been published in Environmental Research Letters. Please cite it as shown below when using this dataset:</p> <p>Citation:<strong> Zhu, Y., Myint, S., Chen, J., Fan, P., Seto, K., Jain, A. K., Qi, J., & Wang, J. (2025). Thermal changes along the urban-rural continuums in Southeast Asia. Environmental Research Letters. <a href="https://doi.org/10.1088/1748-9326/adcad2">https://doi.org/10.1088/1748-9326/adcad2</a>.</strong></p> <p> </p> <p>Our study focused on 19 cities across 8 countries: Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Thailand, and Vietnam. The cities included Phnom Penh, Siem Reap, Jakarta, Surabaya, Denpasar, Vientiane, Pakse, KL, Putrajaya, Yangon, NPT, Quezon City, Iloilo, Taytay, Bangkok, Chiang Rai, HCMC, Hanoi, Cantho. </p> <p>To assess the impact of urbanization over the past two decades, we examined the patterns of Land Surface Temperature (LST) changes with Land Use and Land Cover (LULC) changes across 19 cities in SEA along the urban-rural continuums (URCs). These cities include major urban hubs such as Jakarta, Bangkok, and Ho Chi Minh City, as well as medium and smaller cities like Vientiane and Chiang Rai, reflecting a diversity of urban environments and providing a comprehensive sample of SEA’s rapid urban expansion and associated thermal impacts. The boundaries of URCs for each city were defined as an area centered within a 30 km radius from its city center point. This ensured that all cities had the same extent so that the URCs could make the comparison. The city center point was defined as either the center of the central business district or the geometric center of the city’s administrative limits (Estoque et al., 2017). We created 30 ring buffer zones around each city center point at 1 km intervals to analyze LULC and the associated LST change gradient along the URCs. After establishing these buffer zones, we excluded (1) large water bodies or oceans, (2) continuous urban areas beyond the city boundaries—for cities located close to one another, such as Kuala Lumpur and Putrajaya, and Quezon City and Taytay, the city’s URCs were adjusted to exclude overlapping administrative boundaries from neighboring cities, ensuring that the analysis was confined to each city’s specific URCs, and (3) mountains with elevations exceeding 100 m above the mean elevation of each city buffer to minimize the confounding effects of topography on LST variations to ensure that the LST changes we observed were more directly attributable to urban development rather than elevation-related climatic variations. (C. Wang et al., 2016; Z. Wang et al., 2020). These exclusions were implemented to ensure a fair comparison, avoid confusion, and reduce the influence of elevation on the analysis.</p> <p>For URCs, we retrieved LST and NDVI data for the summer months (June, July, and August) between 2000 and 2022 at a 30-meter resolution based on the Google Earth Engine Platform. This period was chosen due to peak heat-related mortality and morbidity (Hsu et al., 2021; Johnson et al., 2009). A total of 5,805 Landsat 5/7/8/9 scenes were collected to ensure full coverage of all 19 cities. Contaminated pixels (e.g., clouds and cloud shadows) were removed via quality assessment bands and Landsat-7 SLC-off stripes were eliminated. NDVI was calculated from the surface reflectance of the Red and Near-Infrared (NIR) bands. LST retrieval from Landsat data was conducted using the Statistical Mono-Window (SMW) algorithm developed by the Climate Monitoring Satellite Application Facility (CM-SAF) (Duguay-Tetzlaff et al., 2015; Freitas et al., 2013; Sun et al., 2004). Further details are provided in the Supplemental materials.</p> <p>The final datasets are LST in 2022, LST Sen's Slope (2000-2022), and NDVI Sen's Slope (2000-2022) and are shared.</p>
A study on biomedical researchers' perspectives on public engagement in Southeast Asia
<p>Survey data from biomedical researchers in Southeast Asia about their perceptions of public engagement. The survey used open and closed questions.</p>
A study on biomedical researchers' perspectives on public engagement in Southeast Asia
<p>Qualitative data on researcher's perceptions of public and community engagement in South and Southeast Asia</p>
Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways
<p>This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).</p>
High-resolution maps of rubber and rubber-related deforestation for Southeast Asia
<p>This dataset contains maps of rubber plantations in 2021, and maps of rubber-related deforestation between 1993-2016 for Southeast Asia. The rubber maps have a 10 m pixel size, and the deforestation maps have a 30 m pixel size. The dataset and the methods for generating it are described in Wang et al. 2023. High-resolution maps show that rubber causes substantial deforestation. <em>Nature</em>. <strong>Please note that an update of this dataset will follow in September 2025.</strong> </p>
Fig. 14 in A new genus of Baetidae (Insecta, Ephemeroptera) from Southeast Asia
Fig. 14. Distribution of Procerobaetis gen. nov. A. Map of Southeast Asia. B. Localities in Indonesia. C. Localities in the Philippines.
Fig. 12. Habitus, larvae. A in A new genus of Baetidae (Insecta, Ephemeroptera) from Southeast Asia
Fig. 12. Habitus, larvae. A. Procerobaetis leptobranchius gen. et sp. nov., dorsal view. B. P. leptobranchius gen. et sp. nov., lateral view. C. P. petersorum gen. et sp. nov., dorsal view. D. P. petersorum gen. et sp. nov., lateral view. Scale bars: 1 mm.
Fig. 2 in A new genus of Baetidae (Insecta, Ephemeroptera) from Southeast Asia
Fig. 2. Procerobaetis leptobranchius gen. et sp. nov., larval morphology. A. Foreleg. B. Seta at dorsal margin of femur (apex). C. Seta near ventral margin of femur. D. Seta at ventral margin and on ventral surface of tibia. E. Seta at ventral margin of tarsus. F. Fore claw. G. Tergum IV. H. Paraproct. I. Metanotum. Scale bars: 0.1 mm.
Fig. 9 in A new genus of Baetidae (Insecta, Ephemeroptera) from Southeast Asia
Fig. 9. Procerobaetis freitagi gen. et sp. nov., larval morphology. A. Foreleg. B. Seta on ventral margin of femur. C. Seta on ventral surface of tibia. D. Seta on ventral margin of tibia. E. Seta on ventral surface of tarsus. F. Seta on ventral margin of tarsus. G. Fore claw. H. Tergum IV. I. Paraproct. J. Metanotum. Scale bars: 0.1 mm.
Fig. 11 in A new genus of Baetidae (Insecta, Ephemeroptera) from Southeast Asia
Fig. 11. Procerobaetis freitagi gen. et sp. nov., larval morphology. A. Fore femur. B. Middle femur. C. Hind femur. Scale bar: 0.1 mm.
Fig. 8 in A new genus of Baetidae (Insecta, Ephemeroptera) from Southeast Asia
Fig. 8. Procerobaetis freitagi gen. et sp. nov., larval morphology. A. Labrum. B. Right mandible. C. Right prostheca. D. Left mandible. E. Left prostheca. F. Hypopharynx. G. Maxilla. H. Labium. Scale bar: 0.1 mm.
Fig. 1 in A new genus of Baetidae (Insecta, Ephemeroptera) from Southeast Asia
Fig. 1. Procerobaetis leptobranchius gen. et sp. nov., larval morphology. A. Labrum. B. Right mandible. C. Right prostheca. D. Left mandible. E. Left prostheca. F. Hypopharynx. G. Maxilla. H. Labium. Scale bar: 0.1 mm.
Fig. 5 in Phylogenetic analysis and systematic position of two new species of the ant genus Crematogaster (Hymenoptera, Formicidae) from Southeast Asia
Fig. 5. Phylogeny and distribution of the Crematogaster borneensis-group, C. khmerensisi sp. nov., C. fruhstorferi, C. ssAUS5, C. mjobergi and the C. tetracantha-group. Closed circle indicates C. khmerensis sp. nov., closed square indicates C. pfeifferi sp. nov. It is noted that C. pfeifferi sp. nov. is not represented in the phylogeny.
Fig. 4. A–D in Phylogenetic analysis and systematic position of two new species of the ant genus Crematogaster (Hymenoptera, Formicidae) from Southeast Asia
Fig. 4. A–D. Crematogaster khmerensis sp. nov., worker. A. Body in lateral view. B. Full-face view of head. C. Dorsal view of mesosoma. D. Petiole and postpetiole in dorsal view. — E–H. — Crematogaster pfeifferi sp. nov., worker. E. Body in lateral view. F. Full-face view of head. G. Dorsal view of mesosoma. H. Petiole and postpetiole in dorsal view.
Fig. 3 in Phylogenetic analysis and systematic position of two new species of the ant genus Crematogaster (Hymenoptera, Formicidae) from Southeast Asia
Fig. 3. Posterior estimates of divergence time of 24 taxa on the phylogenetic tree. Blue bars depict the 95% highest posterior density (HPD). Estimations were performed with MCMCTree using the independent rate model.
Fig. 1. Bayesian majority rule consensus tree reconstructed for 90 in Phylogenetic analysis and systematic position of two new species of the ant genus Crematogaster (Hymenoptera, Formicidae) from Southeast Asia
Fig. 1. Bayesian majority rule consensus tree reconstructed for 90 taxa using five genes (ArgK, CAD, LWRh, Top1, Wg) in a MrBayes analysis. Above node numbers indicate posterior probability. Data were partitioned by PartitionFinder v.1.1.1 and analyzed using a best fit model for each gene and codon position, with 10 million generations and a burn-in of 25 %. Area enclosed by dashed lines is enlarged on Fig. 2.
Figs 60–69 in A review of Gnathoncus of Southeast Asia (Coleoptera: Histeridae: Saprininae)
Figs 60–69. SEM micrographs of Gnathoncus semimarginatus Bickhardt, 1920. 60 – head, dorsal view; 61 – antennal club, ventral view; 62 – mentum; 63 – propygidium and pygidium; 64 – prosternum; 65 – mesoventrite; 66 – lateral disc of metaventrite and metepisternum; 67–68 – protibia (67 – dorsal view; 68 – ventral view); 69 – mesotibia, dorsal view.
Figs. 54–55 in A review of Gnathoncus of Southeast Asia (Coleoptera: Histeridae: Saprininae)
Figs. 54–55. Habitat of Gnathoncus sechuanus sp. nov. 54 – high-altitude mixed forest in Sichuan; 55 – mushroom under which Gnathoncus sechuanus nov. sp. was collected.
Figs 52–53. Pygidial microsculptures. 52 in A review of Gnathoncus of Southeast Asia (Coleoptera: Histeridae: Saprininae)
Figs 52–53. Pygidial microsculptures. 52 – Gnathoncus nannetensis (Marseul, 1862); 53 – G. rotundatus (Kugelann, 1792) (from SτοർΚΜΑΝΝ 1957).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.