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.
6,281
datasets available to search
ShareScore release 0.7.1
Dataset results
6,281 results for “Landscape”
Landscape Position Project at North Temperate Lakes LTER: Lake Characteristics 1998 - 2000
Parameters characterizing the chemical limnology and spatial attributes of 47 lakes were surveyed as part of the Landscape Position Project. Lake characteristics compiled here include lake area and perimeter, catchment area, mean and maximum depth, shoreline development factor, elevation and percent wetlands within catchment area. Lake order was determined using a modification of the method of Riera et al. (2000). Lake order is a numerical surrogate for groundwater influx and hydrological position along a drainage network, with the highest number indicating the lake lowest in a watershed. Lake order for each lake was determined by field visit with presence/absence of streams confirmed, not base solely on topographic maps. Riera, Joan L., John J. Magnuson, Tim K. Kratz, and Katherine E. Webster. 2000. A geomorphic template for the analysis of lake districts applied to Northern Highland Lake District, Wisconsin, U.S.A. Freshwater Biology 43:301-18. Number of sites: 49
Landscape Position Project at North Temperate Lakes LTER: Vertical Lake Profiles 1998 - 1999
Parameters characterizing the chemical limnology and spatial attributes of 45 lakes were surveyed as part of the Landscape Position Project. Parameters are measured at or close to the deepest part of the lake. A vertical profile of temperature, dissolved oxygen, and conductivity are collected at 1 meter increments Sampling Frequency: generally monthly for one summer; for some lakes, one or two samples in one summer Number of sites: 45
Landscape Position Project at North Temperate Lakes LTER: Benthic Invertebrate Abundance 1998 - 1999
Benthic invertebrate assemblages of 32 lakes were surveyed as part of the Landscape Position Project. We used modified Hester-Dendy colonization substrates to sample benthic invertebrate communities. Each sampling device consisted of a 3"x3" top plate, alternating layers of course and fine mesh, a ''choreboy'' commercial scrubbing puff, alternating layers of coarse (6.35 mm) and fine (3.18 mm) black plastic mesh, and a 3"x3" bottom plate. Two Hester-Dendy samplers were set at a depth of one meter on each of three substrate types (cobble, sand and silt) within each lake for four weeks in late June through late July in either 1998 or 1999. Within each lake, areas of different substrate types were identified using WI-DNR depth contour lake maps, and substrate type was verified by direct observation. Different substrates were sampled to account for invertebrate associations with specific substrate characteristics. Lake order was determined using a modification of the method of Riera et al. (2000). Lake order is a numerical surrogate for groundwater influx and hydrological position along a drainage network, with the highest number indicating the lake lowest in a watershed. Riera, Joan L., John J. Magnuson, Tim K. Kratz, and Katherine E. Webster. 2000. A geomorphic template for the analysis of lake districts applied to Northern Highland Lake District, Wisconsin, U.S.A. Freshwater Biology 43:301-18. Sampling Frequency: one survey on each lake in late June through late July of 1998 or 1999 Number of sites: 32
Landscape Position Project at North Temperate Lakes LTER: Fish Growth and Mercury Contaminant Data 1998 - 1999
As part of the Landscape Position Project, yellow perch were collected for mercury and isotope analysis by a combination of angling, beach seining, vertical gill net, fyke net and electrofishing in the summers of 1998 and 1999. A total of 86 yellow perch from 25 lakes were analyzed. Scales were used to determine age and length at ages 1 to 3 years. The nitrogen stable isotope signature indicates the relative food-web position of the fish relative to cladocerans collected from the same lake. The N_SIGNATURE value divided by 3.2 gives trophic position relative to cladoceran Sampling Frequency: one survey on each lake in late June through late July of 1998 or 1999 Number of sites: 25
Landscape Position Project at North Temperate Lakes LTER: Fish Mercury Level 1998 - 1999
As part of the Landscape Position Project, yellow perch were collected for mercury and isotope analysis by a combination of angling, beach seining, vertical gill net, fyke net and electrofishing in the summers of 1998 and 1999. A total of 183 yellow perch from 43 study lakes with approximate length of 150 mm were analyzed. Sampling Frequency: one survey on each lake in late June through August of 1998 or 1999 Number of sites: 43
AIRBORNE SPECTROMETER MEASUREMENTS FMOM BOREAL SNOW-COVERED LANDSCAPE
<p>The dataset contains 10 meter resolution reflectance data from boreal snow-covered landscape. The purpose of the airborne measurements was to investigate the effect of forest canopy on optical remote sensing signals for snow-covered surfaces. The hyperspectral airborne data was acquired with an AisaDUAL imaging spectrometer on March 18 and on March 21, 2010 in Sodankylä, Finland. The image swath was 240 meters and flight lines were several kilometers long. The original spatial resolution of the data is 80 cm x 80 cm, but it was resampled to pixel size of 10 m x 10 m. All measurements were carried out in non-cloudy conditions (0/8 to 2/8 cloud cover). On 18 March, the tree canopy was snow-free and snow on the ground was several days old, while on 21 March, the tree canopy was snow-covered and snow on ground was fresh. The data contains mosaics of the flight lines for the bands 555 nm, 645 nm, 858.5 nm and 1640 nm for both days 18 March 2010 and 21 March 2010.</p>
Mast-borne spectral reflectance measurements of boreal landscape during spring
<p>This dataset contains mast-borne spectral reflectance measurements (350-2500 nm / 350-1000 nm) measured with an ASD Field Spec Pro JR spectroradiometer and digital images of the measurement areas from the time of the measurements. The measurement targets are a boreal sparse pine forest and a forest opening located at the premises of the Arctic Space Centre of the Finnish Meteorological Institute in Sodankylä, northern Finland (N67.361833, E26.634154, WGS84).</p> <p>The dataset covers spring time periods during years 2010-2018 from the dry snow period until some time after the snow disappearance. The temporal coverage vary from year to year depending on the mounting date and due to technical problems. Measurements have been conducted every 30 min during fixed day-time period and based on set weather threshold values.</p> <p>The spectral reflectance data are organized in yearly CSV files the metadata information attached in the file header. Accordingly, the digital images from the measurement areas are organized in yearly folders and packed into zip files.</p> <p>For this version a data example plot (Data_example_mast.png) was added to have a quick visualisation of the sort of the data available.</p> <p>For further information contact Henna-Reetta Hannula (henna-reetta.hannula@fmi.fi) or Kirsikka Heinilä (kirsikka.heinila@ymparisto.fi)</p>
Data from: Spatial and host-related variation in prevalence and population density of wheat curl mite (Aceria tosichella) cryptic genotypes in agricultural landscapes
<p><strong>Filename: coord.csv</strong></p> <p>Names of the sampling locations and their geographic coordinates.</p> <ol> <li>Name - sampling locality identifier</li> <li>Lat - latitude</li> <li>Long - longitude</li> </ol> <p> </p> <p><strong>Filename: lineages.csv</strong></p> <ol> <li>id.sample - sample identifier</li> <li>host - host species (Arrela=<em>Arrhenantherum elatius</em>, Avesat=<em>Avena sativa</em>, Broine=<em>Bromus inermis</em>, Elyres=<em>Elymus repens</em>, Horvul=<em>Hordeum vulgaris</em>, Seccer=<em>Secale cereale</em>, Triaes=<em>Triticum aestivum</em>, Tririm=<em>Triticale rimpaui</em></li> <li>x, y - geodetic coordinates</li> <li>stems - no. of stems in a sample</li> <li>leaves - no. of leaves in a sample</li> <li>MT.01 to MT.27 - no. of mites belonging to each genetic lineage</li> </ol>
Data from: Sex-specific recombination landscape in a species with holocentric chromosomes
<p>Male and female meiosis typically exhibit significant differences in crossover locations along chromosomes. It has been suggested that higher recombination rates at chromosome centers in females counteract centromere-associated meiotic drivers, increasing their chances of segregating into the oocyte rather than to the non-viable polar bodies. Our research, employing the first sex-specific recombination map for an organism lacking defined centromeres revealed parallel recombination landscapes across the sexes, supporting the meiotic drive hypothesis.</p>
Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.
<p>Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.</p> <p>Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.</p>
GraspOS landscape survey on Reforming Research Assessment
<p><span>This dataset is related to GraspOS Deliverable D2.1 "OS-aware RRA approaches landscape report" (<a title="https://zenodo.org/records/11098095" href="../records/11098095" target="_blank" rel="noreferrer noopener">https://zenodo.org/records/11098095</a>), Annex 1. GraspOS landscape survey on Reforming Research Assessment.</span></p>
GraspOS landscape survey for pilots
<p>This dataset is related to GraspOS Deliverable D2.1 "OS-aware RRA approaches landscape report" (<a title="https://zenodo.org/records/11098095" href="../records/11098095" target="_blank" rel="noreferrer noopener">https://zenodo.org/records/11098095</a>), Annex 4. The questionnaire for GraspOS landscape survey for pilots.</p>
Regional landform and landscape digital maps for the Eastern Guiana Shield
<p>Archive containing digital <strong>maps of 'landform types' and 'landscape units' for French Guiana and the State of Amapa (Brazil).</strong> These maps accompany the paper 'Using textural analysis for regional landform and landscape mapping, Eastern Guiana Shield', <em>Geomorphology</em> (doi:10.1016/j.geomorph.2 018.03.017) and have been produced according to the methods presented therein.</p> <p><br> </p>
Tree-covered and intact forest landscapes BC1000, 1995, 2000, 2005, 2010, 2013, 2016 at 250 m
<p>Based on the <a href="http://www.unep-wcmc.org/resources-and-data/generalised-original-and-current-forest">UNEP historic forest cover map</a>, ESA land cover time series and <a href="http://www.intactforests.org/data.ifl.html">intact forest landscape (IFL 2000, 2013 and 2016) data</a>. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>ldg = theme: land degradation,</li> <li>forest.cover = variable: forest / tree cover,</li> <li>esacci.ifl = determination method: combination of ESA land cover and IFL maps,</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1995 = time reference: year 1995,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Global restoration opportunities in tropical rainforest landscapes - Supplementary Materials - Spatial Data Layers
<p><strong>Global restoration opportunities in tropical rainforest landscapes</strong></p> <p><strong>Sci Adv 5 (7), eaav3223</strong></p> <p><strong>DOI: 10.1126/sciadv.aav3223</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/5/7/eaav3223">https://advances.sciencemag.org/content/5/7/eaav3223</a></strong></p> <p><strong>Supplementary Materials</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1">https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1</a></strong></p> <p><strong>Spatial Data layers:</strong></p> <p><strong><a href="https://doi.org/10.5281/zenodo.3233495">https://doi.org/10.5281/zenodo.3233495</a></strong></p> <p><strong>_OutR10:</strong></p> <p><strong>r_10.img → Global restoration opportunity score (ROS)</strong></p> <p><strong>r_10_sc.img → Global restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_aa_sc.img → Australiasia restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_at_sc.img → Afro Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_im_sc.img → Indo Malay restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p> </p> <p><strong>_OutBasics:</strong></p> <p><strong>r_1.img → Study Area</strong></p> <p><strong>r_2.img → Restorable Area</strong></p> <p><strong>r_3.img → Restoration Benefits</strong></p> <p><strong>r_4.img → Restoration feasibility</strong></p> <p><br> <strong>_OutCountry:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for country XXX – rescaled 0-1</strong></p> <p><br> <strong>_OutHotspots:</strong></p> <p><strong>r_10_hotspot_XXX_hotspot_area_sc.tif → restoration opportunity score (ROS) for conservation hotspot area XXX – rescaled 0-1</strong></p> <p><strong>r_10_hotspots_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in conservation hotspots</strong></p> <p><br> <strong>_OutKBA:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for Key Biodiversity Area XXX – rescaled 0-1</strong></p> <p><strong>r_10_kba_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Key Biodiversity Areas</strong></p> <p><br> <strong>_OutAichi:</strong></p> <p><strong>r_10_aichi_XXX.tif → Top 15% area of with highest restoration opportunity score (ROS) in country XXX</strong></p> <p><strong>r_10_aichi.img → Top 15% area of with highest restoration opportunity score (ROS) global</strong></p> <p><br> <strong>_OutBonn:</strong></p> <p><strong>r_10_XXX_Bonn.img → Area with highest restoration opportunity score (ROS) in country XXX according to their Bonn Challenge commitments</strong></p> <p> </p> <p><strong>_OutParis:</strong></p> <p><strong>r_10_at_paris.img → Area with highest restoration opportunity score (ROS) in Afro Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_im_paris.img → Area with highest restoration opportunity score (ROS) in Indo Malay Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_nt_paris.img → Area with highest restoration opportunity score (ROS) in Neo Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><br> <strong>_OutTEOW:</strong></p> <p><strong>r_10_ECOREGION_XXX_sc.tif → restoration opportunity score (ROS) for Ecoregion XXX – rescaled 0-1</strong></p> <p><strong>r_10_ECOREGION_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Ecoregions</strong></p> <p> </p> <p><strong>_OutAll</strong></p> <p><strong>alltargets.img → Area with highest restoration opportunity score (ROS) according to all targets (excluded from the paper)</strong></p> <p> </p>
Questions for future developments in the preprints landscape
<p>This submission includes one file complementing the F1000Research article "Preprints and Scholarly Communication: Adoption, Practices, Drivers and Barriers" - <a href="https://doi.org/10.12688/f1000research.19619.1">https://doi.org/10.12688/f1000research.19619.1</a></p> <p>The table '<strong>Questions for future developments in the preprints landscape</strong>' lists a number of key questions that we believe need to be addressed so that preprints can be supported sustainably in the future, along with their owners.</p> <p>More information on this study is also available in the form of a <a href="http://doi.org/10.5281/zenodo.3357727">report</a>.</p>
Dietary fibers boost gut microbiome-produced B vitamin pool and alter host immune landscape
<p>This dataset contains fcs files of lymphocytes from the colonic lamina propria, lungs, and spleens of specific-pathogen-free (SPF), gnotobiotic (14-member synthetic microbiota, 14SM) or germ-free (GF) mice fed five distinct rodent diets (Standard chow 1, SC1; Standard chow 2, SC2; Fiber-supplemented diet, FS; Inulin-supplemented diet, IN; or Fiber-free diet, FF), analysed by mass cytometry. Three million cells per organ per animal were transferred into 15 mL conical tubes. For live/dead staining, cells were incubated with 5 μM cisplatin for 5 minutes. Cells were washed, and cell surface staining mix was added containing pre-conjugated antibodies for 30 minutes at room temperature. Samples were washed twice with FACS buffer, then fixed using the FoxP3 Fix/Perm kit (eBiosciences) for 45 minutes at 4°C, followed by permeabilization wash. Samples were then incubated with the intracellular staining mix for 30 minutes at room temperature. Cells were washed with FACS buffer twice, and pellets were resuspended in Cell-ID™ Intercalator-Ir (Fluidigm) in MaxPar fixation solution (Fluidigm, catalogue no. 201192B) and refrigerated overnight, or for up to five days. Prior to acquisition, samples were washed twice with 1X PBS, and then washed twice with deionized water. Cell pellets were further resuspended in deionized water at 0.5 × 10^6 cells/mL and topped up with 10% calibration beads (EQ Four Element Calibration Beads, Fluidigm). All samples were acquired on the Helios Mass Cytometer (Fluidigm). Effector immune populations and activated T cells in the gut accumulate in a microbiota-dependent manner. Shifts in the microbiome according to dietary fiber source and content result in altered concentrations of B vitamins available to the host, which is tied to distinct alterations in innate and adaptive immune populations. </p>
Protected planet (protected areas), forests and intact forest landscapes at 100 m, 250 m to 1 km resolution
<p><a href="https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA">Protected planet</a> (protected areas; version Oct 2024) and <a href="https://intactforests.org/data.ifl.html">intact forest landscapes</a> (2000, 2013, 2016 and 2020) rasterized to 100 m, 250 m and 1 km resolutions. The aggregated map contains all pixels that are either protected or intacts. To use these resources please refer to original data producers:</p> <ul> <li>Defourny, P., Lamarche, C., Bontemps, S., De Maet, T., Van Bogaert, E., Moreau, I., Brockmann, C., Boettcher, M., Kirches, G., Wevers, J., Santoro, M., Ramoino, F., & Arino, O. (2017). Land Cover Climate Change Initiative - Product User Guide v2. Issue 2.0. <a href="http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf">http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf</a></li> <li>Olsson, E., Albrecht, R., & Golden Kroner, R.E. (2021). PADDDtracker Data Release Version 2.1: Technical Notes. Conservation International, Arlington, VA. DOI: 10.5281/zenodo.4749615.</li> <li>Potapov, P., Hansen, M. C., Laestadius L., Turubanova S., Yaroshenko A., Thies C., Smith W., Zhuravleva I., Komarova A., Minnemeyer S., Esipova E. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. <a href="http://advances.sciencemag.org/content/3/1/e1600821">Science Advances, 2017; 3:e1600821</a></li> <li>UNEP-WCMC and IUCN (2024), Protected Planet: The World Database on Protected Areas (WDPA) [Online], October 2024, Cambridge, UK: UNEP-WCMC and IUCN. Available at: <a title="Visit Protected Planet" href="http://protectedplanet.net/" target="_blank" rel="noopener">www.protectedplanet.net</a>.</li> </ul> <p>The time-series of forest areas (<strong>forest.areas_esa.cci_p</strong>) are based on the <a href="https://climate.esa.int/en/odp/#/project/land-cover">ESA CCI Land Cover time-series</a> (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling. Two maps (<strong>forest.cover.sum_esa.cci_p_250m</strong> and <strong>forest.cover.diff_esa.cci_p_250m</strong>) show long term cumulative forest cover and difference in forest cover for 2022 vs 2000.</p> <p>The protected planet areas and intact forest landscapes were rasterized using:</p> <pre><code>## https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA for(j in 0:2){ system(paste0('gdal_rasterize -ot Byte -a_nodata 0 -burn 100 -where "IUCN_CAT LIKE \'I%\'" /data/CCI_LandCover/WDPA_Oct2024_Public_shp_', j, '/WDPA_Oct2024_Public_shp-polygons.shp WDPA_Oct2024_Public_shp_', j, '_1km.tif -tr 0.008333333 0.008333333 -te -180 -65.00208 180 87.37 -co COMPRESS=DEFLATE -a_srs EPSG:4326')) } s = sds(rast("WDPA_Oct2024_Public_shp_ALL_0_1km.tif"), rast("WDPA_Oct2024_Public_shp_ALL_1_1km.tif"), rast("WDPA_Oct2024_Public_shp_ALL_2_1km.tif")) dg.x = app(s, fun=max, na.rm=TRUE, cores = 32) dg.x0 = terra::ifel(is.na(dg.x), 0, dg.x, filename="protected.areas_wdpa.all_p_1km_s_2023_2024_go_epsg4326_v20241025.tif", wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT2S'), overwrite=TRUE) ## https://intactforests.org/data.ifl.html for(j in c(2000,2013,2016,2020)){ system(paste0('gdal_rasterize -ot Byte -a_nodata 0 -burn 100 -l \"ifl_', j, '\" /mnt/lacus/raw/protectedplanet/ifl_', j, '.shp intact.forest_gfw_p_1km_s_', j, '0101_', j, '1231_go_epsg4326_v20241025.tif -tr 0.008333333 0.008333333 -te -180 -65.00208 180 87.37 -co COMPRESS=DEFLATE -a_srs EPSG:4326')) } ## Combination IFL & WPDA b = sds(rast("protected.areas_wdpa.all_p_1km_s_2023_2024_go_epsg4326_v20241025.tif"), rast("intact.forest_gfw_p_1km_s_20200101_20201231_go_epsg4326_v20241025.tif")) bg.x = app(b, fun=max, na.rm=TRUE, cores = 32) bg.x0 = terra::ifel(is.na(bg.x), 0, bg.x, filename="protected.intact.areas_wdpa.ifl_p_1km_s_2020_2024_go_epsg4326_v20241025.tif", wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT2S'), overwrite=TRUE)</code></pre>
Raw data for Infrastructure and Awareness Landscape Analysis in Latin America
<p>Persistent Identifiers (PIDs), such as Digital Object Identifiers (DOIs), are foundational to connecting and enhancing the visibility of Latin American research within a global framework. Although the region is rich in diverse and impactful research, many repositories remain only partially integrated into international registries and aggregators, limiting their discoverability and reach. The adoption of PIDs across repositories in Latin America varies widely, underscoring the need for increased awareness about the role of open PIDs in advancing research accessibility and visibility.</p> <p>This dataset offers a comprehensive overview of the current landscape of repositories, publishing systems, and Open Science policies across Latin America, shedding light on the institutional and national efforts that support an open and inclusive research infrastructure. It highlights the importance of collaboration among researchers, institutions, funders, librarians, and government agencies in fostering Open Science practices and encouraging strategic PID adoption. By expanding these open practices and strengthening PID adoption, Latin American research can achieve greater integration and impact within the global research ecosystem.</p> <p>You can read the full report titled "Infrastructure and Awareness Landscape Analysis in Latin America" at <a href="https://doi.org/10.5281/zenodo.14010858" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14010858</a> </p>
Landscape classes of combinations of elevation, slope angle, and aspect, for the Ilirney Lake System Region, Chukotka, Russia
<p>The elevation was accessed for the area of interest in 90 m spatial resolution from the TanDEM-X 90 m digital elevation model (DEM) product (Krieger et al, 2013). Prior to spatial topographical parameters extraction, the DEM was resampled from the 90-m cell spacing to a 30-m resolution. The result was classified into 589 different possible combinations of elevation, slope angle, aspect. For the classification we used the possible combinations of elevation, slope, and aspect which were grouped into the following categories:</p> <p>Elevation:</p> <ul> <li>0-400 m</li> <li>400-450m</li> <li>450-500m</li> <li>500-600m</li> <li>600-650m</li> <li>650-700m</li> <li>700-1000m</li> <li>1000-1500m</li> </ul> <p>Slope:</p> <ul> <li>0-2°</li> <li>2-4°</li> <li>4-6°</li> <li>6-8°</li> <li>8-10°</li> <li>10-12°</li> <li>12-16°</li> <li>16-18°</li> <li>18-20°</li> <li>20-25°</li> <li>25-50°</li> </ul> <p>Aspect:</p> <ul> <li>0-45°</li> <li>45-90°</li> <li>90-135°</li> <li>135-180°</li> <li>180-225°</li> <li>225-270°</li> <li>270-315°</li> <li>315-360°</li> </ul> <p>Format: Geotiff; projection UTM58N and 30x30 m tiles; extent: 642010.1, 654910.1, 7462218, 7492908 m (xmin, xmax, ymin, ymax)</p>
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.