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72 results for “vegetation mapping”
30 m Normalized Difference Vegetation Index Maps of Pure Pixels over China for Estimation of Fractional Vegetation Cover (2014, 2018, 2022)
<p>Using multi-angle remote sensing data, we generated 30-m maps for the normalized difference vegetation index (NDVI) of fully-covered vegetation (<em>Vv</em>) and bare soils (<em>Vs</em>) across China in 2014, 2018 and 2022. These pixel-wise <em>Vv</em> and <em>Vs</em> maps can be integrated with the vegetation index (VI)-based model to facilitate the accurate and rapid estimation of fractional vegetation cover (FVC) across various spatial resolutions and large scales. The products were produced using a multi-angle algorithm (MultiVI), which effectively addressed the spatial variability inherent in <em>Vv</em> and <em>Vs</em> and enhanced the accuracy of FVC estimations in comparison to traditional statistical methods. The estimated FVC demonstrated a root mean square deviation (RMSD) of approximately 0.1 when evaluated against field-measured FVC across different experimental sites.</p>
Distribution maps of vegetation alliances in Europe
<p>This dataset contains information on the occurrence of phytosociological alliances in European territorial units. The first version, including a description of the methods, was published by Preislerová et al. (2022).</p> <p>Version 2 of the dataset contains data on 1115 alliances (as opposed to 1105 in the first version) in 82 European territorial units. These changes reflect the concepts accepted in version 3 of the EuroVegChecklist (Mucina et al. 2016) published on https://floraveg.eu/download/. This version contains syntaxonomic changes in the vegetation of coastal dunes (classes <em>Ammophiletea arundinaceae</em>, <em>Helichryso-Crucianelletea maritimae</em> and <em>Honckenyo peploidis-Leymetea arenarii</em>), Mediterranean pine forests (order <em>Pinetalia halepensis</em>) and bogs (class <em>Oxycocco-Sphagnetea</em>), which were adopted by the European Vegetation Classification Committee in January 2024 following the proposals published by Marcenò et al. (2018, 2024), Bonari et al. (2021) and Jiroušek et al. (2022), respectively.</p> <p>The data include a spreadsheet with the database and a set of 1115 maps as individual image files.</p> <p><strong>Recommended citation of the dataset</strong></p> <p>Preislerová Z., Jiménez-Alfaro B., Mucina L., Berg C., Bonari G., Kuzemko A., Landucci F., Marcenò C., Monteiro-Henriques T., Novák P., Vynokurov D., Bergmeier E., Dengler J., Apostolova I., Bioret F., Biurrun I., Campos J.A., Capelo J., Čarni A., Çoban S., Csiky J., Ćuk M., Ćušterevska R., Daniëls F.J.A., De Sanctis M., Didukh Ya., Dítě D., Fanelli F., Golovanov Y., Golub V., Guarino R., Hájek M., Iakushenko D., Indreica A., Jansen F., Jašková A., Jiroušek M., Kalníková V., Kavgacı A., Kucherov I., Küzmič F., Lebedeva M., Loidi J., Lososová Z., Lysenko T., Milanović Đ., Onyshchenko V., Perrin G., Peterka T., Rašomavičius V., Rodríguez-Rojo M.P., Rodwell J.S., Rūsiņa S., Sánchez Mata D., Schaminée J.H.J., Semenishchenkov Y., Shevchenko N., Šibík J., Škvorc Ž., Smagin V., Stešević D., Stupar V., Šumberová K., Theurillat J.-P., Tikhonova E., Tzonev R., Valachovič M., Vassilev K., Willner W., Yamalov S., Večeřa M. & Chytrý M. (2022). Distribution maps of vegetation alliances in Europe. <em>Applied Vegetation Science</em>, 25, e12642. https://doi.org/10.1111/avsc.12642</p>
Mapping the global distribution of C4 vegetation using observations and optimality theory
<p>This dataset includes annual C4 vegetation distribution and its uncertainty from 2001 to 2019. We also provide the distribution of C4 natural grasses and C4 crops during the same period, as well as the code and interim dataset to generate the main figures. Please refer to manuscript for more details:</p> <p>Luo, X., Zhou, H., Satriawan, T.W., Tian, J., Zhao, R., Keenan, T.F., Griffith, D. M., Sitch, S. Smith, N.G. & Still, C.J. (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory. <em>Nature Communications.</em> https://doi.org/10.1038/s41467-024-45606-3.</p> <p><strong>Update (Nov 2023): </strong>we have updated the observational constraint from a linear model to a non-linear model - logistic curve, to better depict how C4 photosynthetic advantage translates into C4 grass coverage changes (C4_distribution_NUS_v2.2.nc).</p> <p><strong>Update (August 2023): </strong>we corrected the issue caused by a bias in the remote sensing grassland base map, and released the version 2 of the C4 vmap (C4_distribution_NUS_v2.nc).</p> <p><strong>Update (June 2023): </strong>we noticed there is a critical issue in the version 1 of our C4 map, due to the quality of remote sensing grassland base map used. We are now working on providing a new version (V2) in the next few months (Jun 2023).</p>
Gran Paradiso - Nivolet: binary map vegetated/not vegetated (2016)
<p>A vegetated/not vegetated binary map for the Nivolet area in Gran Paradiso (Italy) PA, detected by a thresholding on NDVI spectral index extracted from a Sentinel-2A image dated 13 August 2016, at 10 meters spatial resolution, projected in WGS84/UTM32N. <br> The map has binary values where value 1 indicates pixels of vegetation whereas value 0 indicates No vegetation pixels.<br> </p>
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra. in Fauna and landscape-zonal distribution of Orthoptera in the Komi Republic (Russia)
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra.
Maps of the diversity and distribution of Raunkiær's life forms in European vegetation
<p>This repository contains raster files (TIF format) with a 50 km × 50 km resolution (over UTM grid EPSG:32633), showcasing the diversity and distribution of Raunkiær’s life forms in European vegetation. The maps are based on two key metrics: (i) the proportion (%) of species within each life form and (ii) the diversity of life forms, including richness and evenness.</p> <p>To generate these maps, we averaged plot-level metric values across a comprehensive dataset comprising 546,501 vegetation plots sourced from the European Vegetation Archive (EVA; Project 163; <a href="https://euroveg.org" target="_new">https://euroveg.org</a>). These plots cover diverse habitats, including 173,190 forests, 260,884 grasslands, 52,517 scrubs, and 59,910 wetlands.</p> <p>The maps encompass the entire dataset, offering a visualization of the geographical distribution patterns of life forms across Europe. Additionally, we created habitat-specific maps by subsetting the dataset to explore unique patterns within each habitat type (forest, grassland, scrub, and wetland).</p> <p>Furthermore, we generated additional maps based on standardised effect sizes (SES) of diversity metrics. Through 500 species identity shuffles without replacement, specific to each habitat type, we examined the deviations from random expectations. SES values outside the range of -1.96 to 1.96 indicate significantly lower or higher metric values than expected at random, respectively. </p> <p> </p> <table> <tbody> <tr> <td><strong>Folder name</strong></td> <td><strong>Description of TIF raster values</strong></td> </tr> <tr> <td>full.div</td> <td>Mean richness and evenness of life forms across all habitat types</td> </tr> <tr> <td>full.mean.rel.prop</td> <td>Mean proportion of each life form across all habitat types</td> </tr> <tr> <td>habitat.div</td> <td>Mean richness and evenness of life forms across separate habitat types (forest, grassland, scrub, and wetland)</td> </tr> <tr> <td>habitat.mean.rel.prop</td> <td>Mean proportion of each life form across separate habitat types (forest, grassland, scrub, and wetland)</td> </tr> <tr> <td>SES.full.div</td> <td>Mean richness and evenness of life forms across all habitat types measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.full.mean.rel.prop</td> <td>Mean proportion of each life form across all habitat types measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.habitat.div</td> <td>Mean richness and evenness of life forms across separate habitat types (forest, grassland, scrub, and wetland) measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.habitat.mean.rel.prop</td> <td>Mean proportion of each life form across separate habitat types (forest, grassland, scrub, and wetland) measured with standardized effect sizes (SES)</td> </tr> </tbody> </table> <p><br>Additional information is available in our publication:<br><br>Midolo, G., Axmanová, I., Divíšek, J., Dřevojan, P., Lososová, Z., Večeřa, M., Karger, D. N., Thuiller, W., Bruelheide, H., Aćić, S., Attorre, F., Biurrun, I., Boch, S., Bonari, G., Čarni, A., Chiarucci, A., Ćušterevska, R., Dengler, J., Dziuba, T., Garbolino, E., Jandt, U., Lenoir, J., Marcenò, C., Rūsiņa, S., Šibík, J., Škvorc, Ž., Stančić, Z., Stanišić-Vujačić, M., Svenning, J. C., Swacha, G., Vassilev, K., & Chytrý, M. (2024) Diversity and distribution of Raunkiær’s life forms in European vegetation.<em> Journal of Vegetation Science. </em>Accepted on the 10th of December 2023</p>
Vegetation Maps of the Early Pleistocene Guadix-Baza Basin
<p>Following a methodology based on fossil material, paleogeographic data and paleoclimate calculations allows generating maps of the Early Pleistocene vegetation units of Guadix-Baza Basin for both glacial and interglacial scenarios.</p> <p>The resulting vegetation maps represent a great diversity of vegetation types in the Guadix-Baza Basin, with seven different units which change their distribution according to climatic changes, i.e., dry (glacial) and humid (interglacial) periods. During dry periods the dominant vegetation type is the steppe, with Mediterranean woodlands and deciduous and conifer forests largely reduced and restricted to valleys or mountainous areas. During humid periods, the steppes are replaced by open Mediterranean woodlands, while deciduous and conifer forests occupy larger areas in the mountain ranges.</p> <p>For additional information check the publication: Altolaguirre, Y., Schulz, M., Gibert, L., Bruch, A.A., 2021. Mapping Early Pleistocene environments and the availability of plant food as a potential driver of early <em>Homo</em> presence in the Guadix-Baza Basin (Spain). Journal of Human Evolution, ----.</p>
A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types
<p>Dataset accompanying manuscript <em>"A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types". </em>Datasets contain a wall-to-wall map of vegetation types covering the study area of terrestrial Norway, produced using three methods for assembling individual predictions from Distribution models (<em>probability-based method</em>, <em>performance-based method</em> and <em>prevalence-based method</em>). </p>
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3.
Pacific Atoll Vegetation Maps
Open the record for dataset details and reuse information.
Plant Survey of Current Vegetation: MAP OF SONORAN DESERT PLANT COMMUNITY DISTRIBUTION IN THE CAPLTER STUDY AREA, PHOENIX, ARIZONA
This study represents an effort to map the distribution of plant community types across the Central Arizona - Phoenix Long Term Ecological Research (CAP-LTER) site centered in metropolitan Phoenix using Landsat ETM data. Vegetation classification was carried out using field data collected from within the study area describing woody plant species. A system was devised which represented a compromise between providing floristic information and enabling maximum spectral discrimination between community types. Image classification used reference spectra derived from training sites in the field and was carried out on subsets defined by soil surface texture in order to control for the strong background soil signature inherent to arid regions. While groundtruthing revealed that vegetation on clayey soils was mapped to 91% accuracy, other sections produced maps with less accuracy. The results of this study demonstrate that image classification of desert vegetation using only Landsat ETM data is problematic and may not be practical without other supporting data, such as radar imaging.This project attempts to produce a vegetation distribution map across undeveloped parcels of outlying desert wilderness, as well as remnant mountain parks throughout the city, contained within the Central Arizona Phoenix Long Term Ecological Research (CAPLTER) study area. This effort seeks to create the first successful classification map of Sonoran Desert vegetation derived from satellite imagery. The map would also be the first fine-scale map of plant community types in the Phoenix region. The depiction would allow for a calculation of the land area covered by each vegetation class, and which communities are exposed to development pressures. This map potentially provides a basis from which researchers can measure vegetative biomass distribution across the landscape and attempt to incorporate this component into ecological models of energy flows and biogeochemical cycling in the CAP-LTER site. I
A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types
<p>A 10 m resolution land cover map of the Tibetan Plateau with 12 vegetation types and 3 non-vegetation types for the year 2022 (TP_LC10-2022) by leveraging state-of-the-art remote sensing approaches including the Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and Random Forest model using Google Earth Engine platform.</p>
Map of the Vegetation of the Natural Reserve of Monte Catillo (central Italy)
<p><strong>Map of the Vegetation of the Natural Reserve of Monte Catillo (central Italy)</strong></p> <p><strong> </strong></p> <p><strong>Authors:</strong></p> <p>Sabina Burrascano, Lorenzo Caucci, Giulio Ferrante </p> <p>Department of Environmental Biology, Sapienza University of Rome</p> <p>Maria Vinci </p> <p>Città metropolitana di Roma Capitale</p> <p><strong> </strong></p> <p><strong>Reference system:</strong> EPSG 3004 - Monte Mario / Italy zone 2</p> <p><strong>Spatial scale: </strong>the map was drawn at 1:2,000</p> <p><strong>Minimum Mapping Unit:</strong> 0.2 hectares</p> <p><strong>Provided styles:</strong> use "Carta_vegetazione_aprile2024.qml" for Italian legend; or "Carta_veg_ENG.qml" for English legend</p> <p><strong> </strong></p> <p>Vegetation maps has the purpose of assessing and representing the distribution and extension of the types of vegetation of an area, after investigating it through field surveys, both physiognomic (annotation of the dominant species) and floristic-vegetational (complete censuses of all vascular plant species). Vegetation maps have gained a major role in the Geographical Information Systems of national and local administrations, especially in protected areas, due to their usefulness as tools for environmental monitoring, management and territorial planning.</p> <p>The collaboration between the Metropolitan City of Rome Capital and the Department of Environmental Biology of Sapienza University of Rome has led, among other results, to the creation of a novel map of the vegetation of the Monte Catillo Natural Reserve, updating the pre-existing one which dates back to 20051. The new map was created at a spatial scale of 1:2,000 based on both orthophotos, i.e. aerial photos taken in 2014, and satellite images taken in 2022 and 2023. The minimum mapping unit was set at 0.2 hectares.</p> <p>A EUNIS code was assigned to each type of vegetation based on the correspondence between field observations and the habitat descriptions reported in the EUNIS database. </p> <p>EUNIS (European Nature Information System) is a classification system developed by the European Environmental Agency for the description and categorization of habitat types in Europe to facilitate the exchange of information between European countries and improve the management and conservation of biodiversity at the continental level.</p> <p>The EUNIS system is used to classify natural, semi-natural and artificial habitats, as well as species and habitat types of importance for biodiversity conservation. It follows a hierarchical scheme that allows different levels of detail to be applied to each habitat based on the degree of correspondence between what was detected in the field and what is described in the classification. For example, a Mediterranean scrub habitat dominated by Pistacia lentiscus and Phyllyrea sp. could be classified as follows:</p> <p>Level 1: S - Heaths, scrub and tundra;</p> <p>Level 2: S5 - Maquis, shrub matorral and thermo-Mediterranean maquis;</p> <p>Level 3: S51 - Mediterranean scrub and shrubby matorral;</p> <p>Level 4: S512 - Shrubby matorral of Olea europaea and Pistacia lentiscus;</p> <p>Level 5: S5123 - Pistacia lentiscus and Phillyrea shrub matorral.</p> <p>If the species composition detected in the field does not reflect any of the features described at level 5, the habitat will be reported with a level 4 and so on.</p> <p>Due to the biogeographical peculiarities of the Monte Catillo Natural Reserve, a lack of correspondence was found for several vegetation types occurring within the Reserve. For example, no code has been found in the EUNIS system that identifies the cork oak (Quercus suber) forest with Styrax officinalis understorey. The same was true for the pseudomaquis vegetation for which the most detailed code indicates strictly Balkan species not present in Italy.</p> <p>The map was developed in a GIS (Geographic Information System) environment, and allows the extraction and analysis of quantitative data, such as the extension of the main types of vegetation which can thus be monitored over time even in response to specific disturbances such as intense fires that periodically affect the Reserve.</p> <p> </p> <p><strong>References</strong></p> <p>1. Provincia di Roma, (2006). Piano di assetto della Riserva Naturale di Monte Catillo. Approvato con Deliberazione del Commissario ad acta del 26 Novembre 2015 pubblicato sul BURL del 19 Gennaio 2016, n. 5, supplemento 2.</p> <p> </p>
Assessment of Vegetation Indices for Mapping Burned Areas Using a Deep Learning Method and a Comprehensive Forest Fire Dataset from Landsat Collection.
<p>This repository contains a dataset focused on the delineation of burned areas (BA) in forests, created from Landsat satellite images covering the period from 1985 to 2021. The study also explores the integration of vegetation spectral indices (VIs) within a Convolutional Neural Network (CNN) detector, utilizing U-Net architecture. Along with the dataset of historical BA in Galicia from 1985, we provide the necessary images and code to facilitate the analysis and application of these methods. This repository aims to serve as a valuable resource for researchers and professionals in the field of forest fire management and remote sensing, highlighting the potential advantages of using VIs for improved burned area detection and analysis.</p> <p>DOI for published article: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2024.12.001" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.asr.2024.12.001</span></span></a></p>
A probabilistic map of Costa Rican peatlands based on vegetation, ecosystem, and soil inventories
<p>There are the GIS files that accompany a peer-reviewed article.</p> <p>This is the first published effort aimed at developing a peatland map for Costa Rica. A probabilistic approach using vegetation, ecosystem, and soil datasets was used to predict the distribution and extent of peatlands below 700 m in elevation. High-elevation sites found in the Talamanca Mountains were visually identified using satellite imagery; those peatlands are small in size (< 0.05 km<sup>2</sup>). Our analysis produced an estimated low-elevation peatland extent of 1433 km<sup>2</sup> and a high-elevation peatland extent of 23.08 km<sup>2</sup>, yielding an estimated total extent of 1456 km<sup>2</sup> for Costa Rica. This figure is in line with previously published extent estimates for this country (577-2670 km<sup>2</sup>). As for all maps, we stress that the accuracy of this product is ultimately limited by data availability and quality, as well as ground-referencing information. Still, the new map can provide guidance for land management, policymaking, and future science endeavors.</p>
New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities
<p>The dataset is the vegetation type map for India as per <a href="https://www.sciencedirect.com/science/article/pii/S0303243415000574?via%3Dihub">Roy et 2015 "<span>New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities". </span></a></p> <p><span>The dataset consistes of two files- (1) a raster GIS file at 60m spatial resolution (EPSG 32643 WGS 84/ UTM Zone 34) in which each pixel value means a vegetation class as defined and mapped in Roy et al., 2015 and (2) a csv file which contains information matching the pixel value with the vegetation type. <br><br>For all additional information, please refer to the pper reviewed publication. </span></p>
Soil moisture maps of Ukraine based on SMAP satellite data, vegetation season 2018
<p>A set of soil moisture maps of Ukraine based on SMAP satellite data</p> <p>Product: SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture V001</p> <p>Vegetation season 2018</p> <p> </p>
Text-fig. 1 Map of the area surrounding Bohutín and the Litavka River. PA – locality. www.mapy.cz in Reconstruction Of Vegetation Development On The Floodplain Of The Litavka River In The Holocene (Central Bohemia, Brdy Mts.)
Text-fig. 1 Map of the area surrounding Bohutín and the Litavka River. PA – locality. www.mapy.cz
ScienceDex guides
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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.