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High-mountain Asia glacier elevation change trend (dh/dt) map for the period spanning 2000 to 2018
<p>See manuscript for methodology and dataset description:</p> <p>Shean DE, Bhushan S, Montesano P, Rounce DR, Arendt A and Osmanoglu B (2020) A Systematic, Regional Assessment of High-Mountain Asia Glacier Mass Balance. Front. Earth Sci. 7:363. DOI: 10.3389/feart.2019.00363</p> <p>https://www.frontiersin.org/articles/10.3389/feart.2019.00363/full</p> <p>GeoTiff header contains relevant metadata and georeferencing information (30 m pixel size, Albers Equal Area projection). Proj string is '+proj=aea +lat_1=25 +lat_2=47 +lat_0=36 +lon_0=85 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'</p> <p>External overview file (.ovr) contains pyramidal overviews for improved visualization performance at different zoom levels.</p>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in Asia
<p>Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In Asia, a diverse array of research outputs and resources are produced and published in repositories. However, a significant number of these repositories, and outputs remain undiscoverable in global registries and aggregators. <br><br>These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in Asian countries. It includes detailed records from different registry sources and repository platforms.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in Asia' at <a href="https://doi.org/10.5281/zenodo.12566244">https://doi.org/10.5281/zenodo.12566244</a></p>
Supplementary data: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's glaciers
<p>Supplementary data to <a href="http://doi.org/10.1038/nature23878"><em>Kraaijenbrink, Bierkens, Lutz and Immerzeel, 2017. Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers, Nature.</em></a> Model code can be found <a href="https://doi.org/10.5281/zenodo.2548689">here</a>.</p> <p>Please note that all data is provided in 7z-archives. To extract the data use the open source software <a href="http://www.7-zip.org/">7zip</a>.</p> <p> </p> <p><strong>Model input: </strong>Raster data</p> <p>The raster data that is required to run the model is available for the entire High Mountain Asia (<em>complete-hma.7z</em>) and for each <a href="https://www.glims.org/RGI/">RGI v5.0</a> sub-region (<<em>region-name>.7z</em>). The 7z-archives hold separate folders for each glacier, which are named by RGI glacier ID. The rasters for each glacier are in GeoTIFF format, have a 30 m resolution, are in local UTM projection (WGS84 datum), and are clipped to the RGI glacier extent.</p> <p>Rasters present for each glacier are:</p> <pre>classification.tif The debris classification made in google earth engine. debris-thickness-50cm.tif Debris thickness estimation based on Landsat 8 surface temperature. ice-thickness.tif Ice thickness determined using the Glabtop2 model ls8-composite-b456.tif Landsat 8 warmest-pixel optical composite (bands RED, NIR, SWIR1) ls8-composite-tsurf.tif Landsat 8 warmest-pixel surface temperature composite srtm-elevation.tif SRTM 1 arc second elevation data srtm-slope.tif Slope of the SRTM 1 arc second data</pre> <p> </p> <p><strong>Model input: </strong>RDS data</p> <p>The general model input data (<em>mbg-model-rds-data.7z)</em> is stored in R’s binary RDS format and <em><a href="https://www.r-project.org/">R</a></em> is required to open and read the data.</p> <p>Files present in the 7z-archive are:</p> <pre>dP_factors_2006-2100.rds Precipitation changes (delta factors) up to 2100 dT_degrees_2006-2100.rds Temperature changes (Kelvin) up to 2100 glacier-data.rds Glacier centroids with current climate and mass balance input ostrem_meancurve.rds The Östrem curve used by the model rgi-subregions.rds RGI sub-region polygons for Asia</pre> <p> </p> <p><strong>Output data</strong></p> <p>Region-aggregated output is available in ESRI Shapefile format for the RGI sub-regions, major river basins, and for a 1×1 degree grid (<em>output-shapefiles.7z</em>). The attribute tables of all shapefiles hold data on the occurrence of debris as well as current glacier area and volume, and volume projections for the end of century.</p> <p>The available shapefile attributes are:</p> <pre>count number of glaciers a_total total glacier area (m2) a_debris glacier area covered by debris (m2) a_ela glacier area below modelled ELA (m2) a_ela_deb glacier area below modelled ELA covered by debris (m2) v_total total glacier volume (m3) v_debris glacier volume covered by debris (m3) v_ela glacier volume below modelled ELA (m3) v_ela_deb glacier volume below modelled ELA covered by debris (m3) m_total_gt total glacier mass (gigaton) volST_EOC volume remaining in end of century under a stable current temperature vol15_EOC volume remaining in end of century under 1.5 degree scenario vol26_EOC volume remaining in end of century for the RCP2.6 model ensemble vol45_EOC volume remaining in end of century for the RCP4.5 model ensemble vol60_EOC volume remaining in end of century for the RCP6.0 model ensemble vol85_EOC volume remaining in end of century for the RCP8.5 model ensemble</pre>
Results for 'Health and sustainability of glaciers in High Mountain Asia'
<p>Summary table updated relative to prior version to provide more useful outputs and units according to the description below.</p> <p>Contains 1 .csv file including the glacier health metrics used in Miles and others (2021) for all RGI glacier outlines larger than 2km2 in High Mountain Asia (regions 13/14/15). The following attributes are provided in the table:</p> <ul> <li>RGIID: unique identifier from the RGI6.0</li> <li>VALID: flag to indicate if the data quality of the inputs and results was acceptable (see study Supplementary Material)</li> <li>CenLat: Latitude of glacier outline centroid from the RGI6.0</li> <li>CenLon: Longitude of glacier outline centroid from the RGI6.0</li> <li>meanSMB: Glacier mean mass balance (m w.e./year) derived by this study</li> <li>ELA: Equilibrium Line Altitude (m a.s.l.) estimated by this study</li> <li>ELAsig: Uncertainty of ELA based on 1000 Monte Carlo simulations using the derived surface mass balance uncertainty</li> <li>AAR: Accumulation Area Ration (unitless) estimated by this study</li> <li>AARsig: Uncertainty of AAR as for ELA</li> <li>totAbl: Volume of annual ablation, glacier-wide (m3/year)</li> <li>totAblsig: Uncertainty of total ablation, glacier-wide (m3/year)</li> <li>balAbl: Volume of 'balance' annual ablation, ie that compensated by net annual accumulation, glacier-wide (m3/year)</li> <li>balAblsig: Uncertainty of 'balanced' ablation, glacier-wide (m3/year)</li> <li>imbalAbl: Volume of 'imbalance' annual ablation, glacier-wide (m3/year)</li> <li>imbalAblsig: Uncertainty of imbalance ablation, glacier-wide (m3/year)</li> <li>balAblPct: Portion of annual ablation balanced by accumulation (unitless)</li> <li>balAblPctsig: Uncertainty of balance portion of ablation (unitless)</li> <li>Vol2100: Simulated glacier volume in the year 2100 under repeated application of current mass balace (m3)</li> <li>Vol2100sig: Uncertainty in Vol2100 based on current mass balance uncertainty (m3)</li> <li>PctVol2100: Simulated volume at 2100 expressed as a fraction of volume at 2000 (unitless)</li> <li>PctVol2100sig: Uncertainty in PctVol2100 based on current mass balance uncertainty (unitless)</li> </ul> <p>Also contains 1 .zip file with principal regridded inputs and results for continuity-derived glacier specific mass balances of High Mountain Asia, 2000-2016. A subdirectory contains the following for each glacier, identified by its Randolph Glacier Inventory identification number (RGIID), all in geotiff format and at the same resolution:</p> <ul> <li>'*_AW3D.tif': regridded digital elevation model based on the ASTER GDEM3 (apologies for misleading name)</li> <li>'*_debris.tif': binary rasterized debris-cover map based on the results of Scherler et al (2018)</li> <li>'*_dH.tif': regridded elevation change rate from Brun et al (2017), in m per year</li> <li>'*_dHe.tif': regridded elevation change rate uncertainty from Brun et al (2017), in m per year</li> <li>'*_FDIV.tif': raster of flux divergence, in m per year</li> <li>'*_FDIVe.tif': raster of flux divergence uncertainty, in m per year</li> <li>'*_Hdensity.tif': raster of estimated density of dH signal, in 1000 kg per m3</li> <li>'*_SMB.tif': raster of specific mass balance, in m w.e. per year</li> <li>'*_SMBe.tif': raster of specific mass balance uncertainty, in m w.e. per year</li> <li>'*_Smean.tif': raster of column-average surface speed based on regridded data from ITS_LIVE (Gardner et al, 2019), in m per year</li> <li>'*_THX.tif': raster of glacier thickness from consensus estimate of Farinotti et al (2019), in m </li> <li>'*_zFDIV.tif': raster of zonally-aggregated flux divergence, in m per year</li> <li>'*_zFDIVe.tif': raster of zonally-aggregated flux divergence uncertainty, in m per year</li> <li>'*_zones.tif': raster of elevation-based zonal segmentation for each glacier</li> <li>'*_zSMB.tif': raster of zonally-aggregated specific mass balance, in m w.e. per year</li> <li>'*_zSMBe.tif': raster of zonally-aggregated specific mass balance uncertainty, in m w.e. per year</li> </ul>
Monsoon low-pressure-system tracks over South Asia (1979-2019)
<p>Monsoon LPS tracks over South Asia, computed using ERA-Interim reanalysis data.<br> Tracking algorithm described in Hunt and Fletcher (2018) [doi:10.1007/s00382-019-04744-x] and Hunt <em>et al.</em> (2016) [doi:10.1175/MWR-D-15-0138.1].<br> <br> Description of fields:<br> <em>point_id</em>: unique integer identifier for each point<br> <em>time</em>: string with format DD/MM/YY HH:MM, denoting time of detected track point<br> <em>lon</em>: longitude of detected track point<br> <em>lat</em>: latitude of detected track point<br> <em>track_id</em>: unique integer identifier for each track (constituting a group of points)<br> <em>vort</em>: relative vorticity at 850 hPa for the given point (units: s<sup>-1</sup>)<br> <em>circulation</em>: vorticity integrated over the blob of positive vorticity containing the track point (units: arb)<br> <em>eccentricity</em>: eccentricity of the vorticity blob containing the track point<br> <em>category</em>: integer from 0-6 denoting the category of the LPS at the given point, approximately matching IMD criteria. 0: low-pressure area, 1: deep low-pressure area, 2: depression, 3: deep depression, 4: cyclonic storm, 5: severe cyclonic storm, 6: very severe cyclonic storm (and above).</p>
Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean
<p>Koptekin et al. (2022) "<strong><em>Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean</em></strong>", Current Biology <a href="https://doi.org/10.1016/j.cub.2022.11.034">https://doi.org/10.1016/j.cub.2022.11.034</a></p>
CLM/CTSM glacier input datasets used for study on evaluation variable-resolution CESM2 in High-Mountain Asia
<p><strong>General Info</strong></p> <p>This data archive contains the updated glacier-cover and glacier regions for the Community Land Model version 5 (CLM5)/Community Terrestrial Systems Model (CTSM). The updated glacier-cover and glacier regions are used for a study on the evaluation of variable-resolution (VR) CESM2 in High Mountain Asia (<a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>). The data archive also contains the model scripts and input files that have been used to create the glacier-cover dataset. The global glacier outlines used for the glacier-cover dataset were retrieved from the Randolph Glacier Inventory version 6 (RGI-Consortium, 2017). The vector data for the Greenland and Antarctic ice sheets were retrieved from the masks of Bedmachine version 4 (Morlighem et al., 2017, 2021) and version 2 (Morlighem et al., 2020; Morlighem, 2020), respectively. </p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Dataset Contents </strong></p> <pre><code>mksrf_glacier_3x3min_simyr2000.c210708.nc</code></pre> <p>The updated glacier-cover dataset, encompassing three 3-minute datasets: 1) fractional land ice coverage, including both glaciers and ice sheets (PCT_GLACIER), 2) distributions of areal glacier coverage by elevation (PCT_GLC_GIC), and 3) distributions of areal ice-sheet coverage by elevation (PCT_GLC_ICESHEET).</p> <pre><code>mksrf_GlacierRegion_10x10min_nomask_c200813.nc</code></pre> <p>The updated glacier regions, encompassing five different glacier regions (0 - Other regions, 1 - Inside standard CISM grid but outside Greenland itself, 2 - Greenland, 3 - Antarctica, and 4 - High Mountain Asia (new)), used to set the ice melt and runoff behaviour in CLM5/CTSM (more detailed information can be found in the CLM5 Documentation, <a href="https://escomp.github.io/ctsm-docs/">https://escomp.github.io/ctsm-docs/</a>)</p> <pre><code>model_scripts.tar</code></pre> <p>Model scripts used for creating the glacier-cover dataset. A README file is included that lists instructions on how to make the glacier-cover dataset. </p> <pre><code>glacier_final.tar</code></pre> <p>Input files used to create the glacier-cover dataset. The following files are included: a global 30-arcsec merged BedMachine/GMTED2010 elevation dataset (gmted_bedmachine_stitched.nc) and land-sea mask (gmted2010_modis-rawdata-lonshift.nc), Antarctica land mask (BedMachineAntarcticaRotate2RotateBack_2020-07-15_v02_lonshift.map_TO_30arcsec.nc), Greenland land mask (BedMachineGreenland-2021-04-20.map_TO_30arcsec.nc), and 30-arcsec datasets encompassing glacier-cover (30arcsec_00_rgi60_World.nc) and ice-sheet cover (30arcsec_00_BM_World.nc).</p>
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.
Whole genome sequencing of Turkish genomes reveals functional private alleles and impact of genetic interactions with Europe, Asia and Africa.
<p>BACKGROUND:</p> <p>Turkey is a crossroads of major population movements throughout history and has been a hotspot of cultural interactions. Several studies have investigated the complex population history of Turkey through a limited set of genetic markers. However, to date, there have been no studies to assess the genetic variation at the whole genome level using whole genome sequencing. Here, we present whole genome sequences of 16 Turkish individuals resequenced at high coverage (32×-48×).</p> <p>RESULTS:</p> <p>We show that the genetic variation of the contemporary Turkish population clusters with South European populations, as expected, but also shows signatures of relatively recent contribution from ancestral East Asian populations. In addition, we document a significant enrichment of non-synonymous private alleles, consistent with recent observations in European populations. A number of variants associated with skin color and total cholesterol levels show frequency differentiation between the Turkish populations and European populations. Furthermore, we have analyzed the 17q21.31 inversion polymorphism region (MAPT locus) and found increased allele frequency of 31.25% for H1/H2 inversion polymorphism when compared to European populations that show about 25% of allele frequency.</p> <p>CONCLUSION:</p> <p>This study provides the first map of common genetic variation from 16 western Asian individuals and thus helps fill an important geographical gap in analyzing natural human variation and human migration. Our data will help develop population-specific experimental designs for studies investigating disease associations and demographic history in Turkey.</p>
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>
Raincheck: A new diachronic series of rainfall maps for Southwest Asia over the Holocene - Supplementary Material
<p>Supplementary Online Material for the publication</p> <p>Hewett, Z., de Gruchy, M., Hill, D., and Lawrence, D. (forthcoming) Raincheck: A new diachronic series of rainfall maps for Southwest Asia over the Holocene. <em>Levant</em>.</p> <p>Included are all the necessary data files and scripts (R) needed to create the rainfall maps that are the subject of the article.</p>
Data used to create figures and tables in the ACP manuscript "Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality" by Gao et al. (2022)
<p>This dataset contains the original data that extracted from all collected papers refering applications of two-way coupled models in Asia. It is supplied to the review paper, which titled as "Review on two-way coupled meteorology and air quality models in Asia: impacts of aerosol feedbacks on meteorology and air quality". The dataset includes three excel files (in the format of xlsx) as follows:</p> <p>1. Basic information of literatures (Table S1.xlsx)</p> <p>2. Model performance metrics (Table S2.xlsx)</p> <p>3. Quantitative results of aerosol effects on meteorological and air quality variables (Table S3.xlsx)</p> <p>4. Basic information of model setup for two-way coupled model applications in Asia (Table S4.xlsx)</p> <p>5. Summary of aerosol-induced variations of simulated shortwave and longwave radiative forcing at the bottom and top of atmosphere and in the atmosphere in Asia (Table S5.xlsx)</p> <p>.</p>
Open database to support forensic investigation of disasters in South East Asia: FORINSEA v1.0
<p>Forensic investigation of disasters (FORIN) is a conceptual framework and research guide that focuses on the investigation of root causes of disaster risk and occurrence. Underlying the FORIN conceptual framework is the understanding that historical processes, operating asynchronously at different spatial and temporal scales, configure the specific circumstances in which disasters occur. An objective of FORIN research is to accumulate lessons and experience in a systematic way that can lead to improved choices in the future. FORINSEA aims to support this objective by providing an open database suitable to FORIN enquiries in South East Asia region. FORINSEA1.0 provides a comprehensive and coherent historical record of disasters, from 1945 until 2020, socio-economic policies and development of key infrastructure at the hydrological catchments of the Red River Delta in Vietnam and the Marikina river basin in the Philippines. The FORINSEA1.0 dataset allows researchers, for the first time, to explore and make use of geocoded data on major disasters affecting the two large and rapidly expanding cities of Hanoi and Metro Manila and their catchment areas.</p>
European Investment Bank Projects in ACP, OCT, Africa, Asia, and Latin America (1957-2024)
<p>This dataset offers a comprehensive analysis of European Investment Bank (EIB) projects in Africa, the Caribbean, and the Pacific (ACP) regions, Overseas Countries and Territories (OCT), Asia, and Latin America, spanning from 1975 to 2023. The dataset includes information on 2,558 projects; each entry in the dataset includes key project details such as the project’s sector, date of signature, and financial commitments. All numbers are in 2015 euros.</p>
Dataset associated with Banks et al.: "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol and associated radiative effects described by the paper "Radiative cooling and atmospheric perturbation effects of dust aerosol from the Aralkum Desert in Central Asia", written by Banks et al. and submitted to ACP in 2023. The paper was renamed "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia" in 2024.</p>
Estimating malaria disease burden in the Asia-Pacific
<p>This repository hosts all the Supplementary Material for the publication: Maude RJ, Mercado CEG, Rowley J, Ekapirat N, Dondorp AM. (2019) Estimating malaria disease burden in the Asia-Pacific (under review)</p>
Regional model results (combined) for the six transition potentials (one for Africa, Australia, Asia, Europe, North America, and South America)
<p>Results for the six regional models showing areas of high potential to transition from tree cover to tree cover loss to areas of low potential to transition.</p>
National Checklists 2017: Asia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>List of species from Asia inferred from individual country lists that were derived from effechecka and modified geonames polygons
National Checklists 2019: Asia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>List of species from Asia inferred from individual country lists that were derived from effechecka and modified geonames polygons
National Checklists 2019: Asia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>List of species from Asia inferred from individual country lists that were derived from effechecka and modified geonames polygons
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.