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645 results for “Spatial distributions”
Spatial soil properties distribution in “Hoya del río Suárez” region in Colombia.
There is a spatial soil properties distribution surface in raster format. This is the result of a study about digital soil mapping. Implementing geoestatistics (regression kriging RK) and machine learning algorithms (random forest RF, support vector machines SVM, and ensemble models), the study found the best performance for 5 soil properties (clay fraction, bulk density, total porosity, pH, and cation exchange capacity). The study was located in a region named “Hoya del río Suárez”, which is the main sugarcane-producing region in Colombia, and its land area is around 47000 hectares. Those raster surfaces were carried out in 2021, and the database used for doing this study was compiled between 2015 and 2016.
Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution
<p>Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution based on a compilation of data sets (Biome6000k, Geo-Wiki, LandPKS, mangroves soil database, and from various literature sources; total of about 65,000 training points). We used a comparable thematic legend used to produce the Dynamic Land Cover 100m: Version 2. Copernicus Global Land Operations product (Buchhorn et al. 2019), which is based on the UN FAO Land Cover Classification System (LCCS), so that users can compare actual (https://lcviewer.vito.be/) vs potential (this data set) land cover. Two classes not available in the LCCS were added: "subtropical/tropical mangrove vegetation" and "sub-polar or polar barren-lichen-moss, grassland". The map was created using relief and climate variables representing conditions the climate for the last 20+ years and predicted at 250 m globally using an Ensemble Machine Learning approach as implemented in the mlr package for R. Processing steps are described in detail <a href="https://github.com/Envirometrix/PNVmaps"><strong>here</strong></a>. Maps with "_sd_" contain estimated model errors per class. Antarctica is not included.</p> <p>Produced for the needs of the <a href="https://naturemap.earth/"><strong>NatureMap</strong></a> which is project run by the <strong>International Institute for Applied Systems Analysis</strong> (IIASA), the <strong>International Institute for Sustainability</strong> (IIS), the <strong>UN Environment Programme World Conservation Monitoring Centre</strong> (UNEP-WCMC), and the <strong>UN Sustainable Development Solutions Network</strong> (SDSN). NatureMap is funded by Norway’s International Climate Initiative (NICFI).</p> <p>Maps will also be made available via: <a href="https://OpenLandMap.org">OpenLandMap.org</a>. These are initial predictions for testing purposes only. A publication explaining all processing steps is pending.</p> <p>If you discover a bug, artifact or inconsistency in the predictions, 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://github.com/Envirometrix/PNVmaps/issues">https://github.com/Envirometrix/PNVmaps/issues</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>pnv = theme: potential natural vegetation,</li> <li>potential.landcover = variable: potential land cover type (e.g. "open forest, evergreen needleleaf"),</li> <li>probav.lc100 = classification model: ProbaV-based land cover mapping legend (LCCS),</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: period 2000-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Spatial Distribution of the International Food Prices: Unexpected Heterogeneity and Randomness
<p>Global <a href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/food-prices">food prices</a> are typically analysed in a time-series framework. We complement this approach by focusing on the spatial <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/price-dispersion">price dispersion</a> of the country-pair bilateral trade in the international food trade network (<em>IFTN</em>), for ten relevant commodities. The main purposes are to verify if the <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/price-convergence">Law of One Price</a> (<em>LOP</em>) holds and to investigate the emergence of randomness in the price-formation mechanism.</p> <p>We distinguish between the “internal” variance, which indicates the magnitude of price discrimination, and the “external” variance, that is a measure of price dispersion. We find that, for some commodities, spatial price dispersion is remarkable and persistent over time (i.e., failure of the <em>LOP</em>) and that there exists a strict correlation between price spikes and peaks in spatial price variability.</p> <p>We test whether the price distribution can be replicated through a <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/stochastic-process">stochastic process</a> of extraction. Surprisingly, the actual distribution of prices, for several commodities, is well described by a random distribution. Then, the process of data aggregation is not neutral because the information at the micro-level scale might be lost at the macro-scale, due to the complexity of the <em>IFTN</em>. Finally, we discuss some possible economic explanations of these outcomes and the main methodological, environmental, and policy consequences.</p>
Data from: Colony size affects breeding density, but not spatial distribution type, in a seabird
<p>The spatial distribution of individuals within populations can result in fine-scale density-dependence and affect the social environment that is encountered. As such, it is important to quantify within-population spatial structuring and understand the factors that shape it. In this study, we make use of point process statistics to test whether colony size affects the statistical type of spatial nest distribution produced by common terns (<i>Sterna hirundo</i>) breeding at identical man-made rectangular and homogeneous islands of fixed physical size. Comparing sub-colonies of variable density both within and across years, we find that inter-nest distances are smaller at higher local and overall breeding density, but that the spatial distribution type does not vary across the observed densities. This suggests that the birds' main settlement rules do not depend on density. In our case, analyses of fine-scale density-dependence or potential social effects therefore do not need to account for between-individual heterogeneity in settlement decision rules or acceptance of these rules. We urge, however, other studies to similarly test for density-dependence of the spatial distribution of individuals before undertaking such 'down-stream' analyses.</p>
Data package from "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images"
<p>This data package contains the very high resolution maps of canopy palms from the paper "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images". These maps have been produced with two GeoEye-1 very high resolution images (0.5 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. The total size of the decompressed archive is 2.56 Go and is distributed in two shapefiles, one for each GeoEye-1 image. When using this dataset, please cite the original article https://doi.org/10.3390/rs12142225</p>
Data from: Overlap of spatial and temporal spawning distributions of spring and summer Chinook Salmon results in hybridization in the upper Columbia River
<p>The upper Columbia River in Washington State (main-stem and tributary habitat between McNary and Chief Joseph dams) is inhabited by two major lineages of Chinook Salmon (<i>Oncorhynchus tshawytscha</i>); endangered spring Chinook Salmon and summer Chinook Salmon which are not ESA listed. The lineages are highly genetically divergent from one another and historically spatial and temporal isolating mechanisms maintained these genetic differences. Both lineages occur in the Entiat River, a system where anthropogenic activity has changed habitat, flows, species composition, and the distribution of the two lineages over the past century. We examined the spatial and temporal overlap in spawning distributions between Entiat River spring and summer Chinook Salmon and we used genetic markers to assess the level of introgression between lineages. Redd surveys were conducted from 2003 to 2017 to describe spatial and temporal spawning patterns of both lineages. We genotyped sub-yearling juvenile Chinook Salmon captured in the Entiat River from 2009–2014 at 90 SNP loci to determine lineage and hybridization status. There was temporal overlap in spawning between lineages in several years and considerable spatial overlap in redd locations annually. Genetic analysis revealed hybridization between lineages does occur, albeit at relatively low rates (2.6% of sub-yearling juveniles genotyped). We detected hybrids each year samples were collected and they were distributed throughout the Entiat River basin. Hybridization between lineages of Chinook Salmon could result in introgression and a loss of genetic diversity between the lineages, and/or, a loss of production by ESA-listed spring Chinook Salmon. The presence of hybrids warrants concern for ESA-listed spring Chinook Salmon in both the Entiat River system and throughout the upper Columbia River basin.</p>
Disentangling drivers of spatial autocorrelation in species distribution models
<p>Species distribution models (SDMs) are frequently used to understand the influence of site properties on species occurrence. For robust model inference, SDMs need to account for the spatial autocorrelation of virtually all species occurrence data. Current methods do not routinely distinguish between extrinsic and intrinsic drivers of spatial autocorrelation, although these may have different implications for conservation. Here, we present and test a method that disentangles extrinsic and intrinsic drivers of spatial autocorrelation using repeated observations of a species. We focus on unknown habitat characteristics and conspecific interactions as extrinsic and intrinsic drivers, respectively. We model the former with spatially correlated random effects and the latter with an autocovariate, such that the spatially correlated random effects are constant across the repeated observations whereas the autocovariate may change. We tested the performance of our model on virtual species data and applied it to observations of the corncrake Crex crex in the Netherlands. Applying our model to virtual species data revealed that it was well able to distinguish between the two different drivers of spatial autocorrelation, outperforming models with no or a single component for spatial autocorrelation. This finding was independent of the direction of the conspecific interactions (i.e., conspecific attraction versus competitive exclusion). The simulations confirmed that the ability of our model to disentangle both drivers of autocorrelation depends on repeated observations. In the case study, we discovered that the corncrake has a stronger response to habitat characteristics compared to a model that did not include spatially correlated random effects, whereas conspecific interactions appeared to be less important. This implies that future conservation efforts should primarily focus on maximizing habitat availability. Our study shows how to systematically disentangle extrinsic and intrinsic drivers of spatial autocorrelation. The method we propose can help to correctly identify the main drivers of species distributions.</p>
Data from: Effects of roads and land use on frog distributions across spatial scales and regions in the eastern and central United States
Aim: Understanding the scales over which land use affects animal populations is critical for conservation planning, and it can provide information about the mechanisms that underlie correlations between species distributions and land use. We used a citizen-science database of anuran surveys to examine the relationship between road density, land use, and the distribution of frogs and toads across spatial scales and regions of the United States. Location: Eastern and Central United States Methods: We compiled data on anuran occupancy collected from 1999-2013 across 13 states in the North American Amphibian Monitoring Program, a citizen science survey of calling frogs. These data were indexed to measures of land use within buffers ranging from 300 m to 10 km. Results: The negative effects of road density and development on anuran richness were strongest at the smallest scales (300 – 1000 m), and this pattern was consistent across regions. In contrast, the relationships of anuran richness to agriculture and forest cover were similar across local scales but varied among regions. Richness had a negative relationship with agriculture/ forest loss in the Midwest but a positive relationship with agriculture in the Northeast. Anuran richness was more closely related to primary/secondary road density than to rural road density, and the negative effects of larger roads increased at smaller scales. Individual species differed in the scales over which roads and development affected their distributions, but these differences were not closely related to either body size or movement ability. Main conclusions: This study further refines our understanding of the relationship between roads and amphibian populations and highlights the need for research into the specific mechanisms by which roads affect amphibians. Additionally, we find that relationships between land use and species richness can differ substantially across regions, demonstrating that one should use caution in generalizing from one region to another, even when species composition is similar.
Data for: The meta-analysis of the effects of spatial sampling bias correction on presence only species distribution models
<p>This dataset contains information extracted from 70 studies identified through a systematic review of the peer-reviewed literature (Web of Science and SCOPUS databases both searched on the 13/02/2023) to evaluate the effect of spatial sampling bias correction methods in presence-only species distribution models.</p>
Environmental DNA reflects spatial distribution of a rare turtle in a lentic wetland assisted colonisation site
<p>Conservation translocations require robust post-release monitoring to evaluate their success, which can be challenging to implement and maintain. Monitoring techniques that can account for the dispersal and cryptic nature of translocated animals are necessary to provide critical information on persistence and distribution. In this study, we developed a highly sensitive environmental DNA (eDNA) assay specific to the Critically Endangered western swamp turtle (<em>Pseudemydura umbrina</em>), a species currently undergoing trials of assisted colonisation. Actively filtering sufficient volumes of water in lentic systems is difficult due to high concentrations of clogging particulates, therefore we assessed the viability of passive sampling in a controlled environment by submerging filter membranes and directly extracting DNA. Active sampling detected <em>P. umbrina</em> with a 97.6% detection rate, whereas passive sampling resulted in an 8.3% detection rate. We then used a fine-scale eDNA sampling design and radio tracked translocated <em>P. umbrina</em> at the assisted colonisation wetland to investigate eDNA dispersal and spatial monitoring resolution. We detected <em>P. umbrina</em> at 42% (7 / 17) of eDNA sample sites, and the probability of a positive eDNA detection was negatively associated with the distance of <em>P. umbrina</em> from the sampling site, indicating limited eDNA dispersal from the source. Systems with low natural mixing and limited eDNA dispersal provide an opportunity for high resolution spatial and temporal monitoring via targeted eDNA approaches. This is beneficial for monitoring rare species in these systems, as such high-resolution results can provide insights on species presence, distribution, and microhabitat use.</p>
Spatially explicit habitat selection: testing contagion and the ideal free distribution with culex mosquitoes
<p>Since its inception, attempts have been made to improve Ideal Free Distribution (IFD) Theory in order make it better fit real-world data. Spatial contagion is a newer ecological concept that suggests the perceived quality of a patch can be affected by the quality of its neighbor patches. Here, we present a series of experiments testing for potential contagion effects, examining how contagion can interact with the IFD, and determining whether spatial context affects assessment of habitat quality. First, we tested whether the presence of conspecific competitors negatively impacts oviposition habitat selection by female mosquitoes (<em>Culex restuans</em>). We then used a more complex spatial landscape to determine whether competition can create a spatial contagion effect. Finally, we examined whether the density of conspecifics can adjust the contagion effect of nutrient availability. We found that while females avoided patches containing conspecifics, there was no effect of competition/density on neighboring patches. Additionally, we found that resource availability was a significant predictor of where egg rafts were laid, but resource availability did not have a contagion effect. These results provide further support for the utility of the IFD, as individuals were able to accurately assess patch-level habitat quality.<br> </p>
The global distribution of plants used by humans datasets: list of utilised species, occurrence data and model outputs at 10 arc-minutes spatial resolution
<p>Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders <em>raw_data</em> and <em>processed_data</em> containing respectively the list of utilised plant species modelled -<em>utilised_plants_species_list.csv</em>-, and their occurrence data -<em>occurrence_data.zip-</em> and predicted distribution -<em>species_proba_per_cell.rds-.</em></p> <p> </p> <ul> <li>The file <em>utilised_plants_species_list.csv</em> in the <em>raw_data</em> folder contains a<strong> </strong>list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:</li> </ul> <p><strong>plant_ID:<em> </em></strong>plant identifier number ranging from between 1-35687</p> <p><strong>binomial_acc_name:</strong> binomial accepted name of the plant species</p> <p><strong>author_acc_name</strong>: name of the author(s)</p> <p><strong>is_hybrid:</strong> logical TRUE or FALSE indicating whether the species is an hybrid or not.</p> <p><strong>AnimalFood:</strong> forage and fodder for vertebrate animals only.</p> <p><strong>EnvironmentalUses:</strong> examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water.</p> <p><strong>Fuels:</strong> charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials.</p> <p><strong>GeneSources:</strong> wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs.</p> <p><strong>HumanFood:</strong> food for humans only, including beverages and food additives.</p> <p><strong>InvertebrateFood:</strong> plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs.</p> <p><strong>Materials:</strong> woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products.</p> <p><strong>Medicines:</strong> both human and veterinary.</p> <p><strong>Poisons:</strong> plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides.</p> <p><strong>SocialsUses:</strong> plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance.</p> <p><strong>Totals:</strong> total number of uses recorded for a species</p> <p> </p> <ul> <li>The zipfile <em>occurrence_data.zip</em> in the <em>processed_data</em> folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to build species distribution models with the following 14 fields:</li> </ul> <p><strong>Species:</strong> the binomial accepted name of the species</p> <p><strong>Fullname:</strong> same as species</p> <p><strong>decimalLongitude:</strong> the geographic longitude of the occurrence records of the species in decimal degrees</p> <p><strong>decimalLatitude:</strong> the geographic latitude of the occurrence records of the species in decimal degrees</p> <p><strong>countryCode:</strong> a three-letter standard abbreviation for the country of the occurrence locality</p> <p><strong>coordinateUncertaintyinMeters</strong>: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location</p> <p><strong>year:</strong> year of the observation of the occurrence record of the species</p> <p><strong>individualCount:</strong> the number of individuals present at the time of the observation</p> <p><strong>gbifID:</strong> unique identifier number for the occurrence from the original database</p> <p><strong>basisOfRecords:</strong> the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen</p> <p><strong>institutionCode</strong>: the name of the institution or organization listed as the data publisher on GBIF</p> <p><strong>establishmentMeans:</strong> statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans</p> <p><strong>is_cultivated_observation:</strong> whether or not an organism is cultivated</p> <p><strong>sourceID:</strong> name of the source database</p> <p> </p> <ul> <li>The file <em>species_proba_per_cell.rds</em> in the <em>processed_data</em> folder is<em> a R Data Serialization </em>(RDS) file containing a data.table object with the following 3 fields:</li> </ul> <p><strong>plant_ID:</strong><em> </em>plant identifier number ranging from between 1-35687</p> <p><strong>proba:</strong> species occurrence probability</p> <p><strong>cell:</strong><em> </em>raster grid cell number between 1-2251762</p> <p>This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.</p>
A matter of scale: Identifying the best spatial and temporal scale of environmental variables to model the distribution of a small cetacean
<p>The importance of scale when investigating ecological patterns and processes is recognised across many species. In marine ecosystems, the processes that drive species distribution have a hierarchical structure over multiple nested spatial and temporal scales. Hence, multi-scale approaches should be considered when developing accurate distribution models to identify key habitats, particularly for populations of conservation concern. Here, we propose a modelling procedure to identify the best spatial and temporal scale for each modelled and remotely sensed oceanographic variable to model harbour porpoise (<em>Phocoena phocoena</em>) distribution. Harbour porpoise sightings were recorded during dedicated line-transect aerial surveys conducted in the summer of 2016, 2021 and 2022 in the Northeast Atlantic. Binary generalised additive models were used to assess the relationships between porpoise presence and oceanographic variables at different spatial (5, 20 and 40 km) and temporal (daily, monthly and across survey period) scales. Selected variables included sea surface temperature, thermal fronts, chlorophyll-a, sea surface height, mixed layer depth and salinity. A total of 30,514 km was covered on-effort with 216 harbour porpoise sightings recorded. Overall, the best spatial scale corresponded to the coarsest resolution considered in this study (40 km), while porpoise presence showed stronger association with oceanographic variables summarised at a longer temporal scale (monthly and averaged over survey period). Habitat models including covariates at coarse spatial and temporal scales may better reflect the processes driving availability and abundance of prey resources at the large scales covered during the surveys. These findings support the hypothesis that a multi-scale approach should be applied when investigating species distribution. Identifying suitable spatial and temporal scale would improve the functional interpretation of the underlying relationships, particularly when studying how a small marine predator interacts with its environment and responds to climate and ecosystem changes. </p>
MuAP Spatial distribution of various air pollutants in China at 1 km(NO2 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(NO2)</p> <p>Multiple air pollutions dataset (MuAP) </p> <p>Time frame: 2021-2023<br>Area: Most of China<br>Resolution: about 1km<br>File storage format: .xz and GeoTIFF<br>Spatial projection: WGS84<br>Daily file name: year_doy.tif (Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.)</p> <p>Monthly file name: year_month.tif</p> <p>Yearly file name: year_month.tif</p> <p>Unit: Please divide by 10 when using. (ug/m3)</p> <p>When you download and use our data, please cite:</p> <ol> <li>Chi, Y., Zhan, Y., Wang, K., and Ye, H.: Sequential spatiotemporal distribution of PM<sub>2.5</sub>, SO<sub>2</sub> and Ozone in China from 2015 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-76, in review, 2023.</li> <li>Chi, Y., Zhan, Y., Wang, K., & Ye, H. (2023). Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020. Remote Sensing, 15(24). doi:10.3390/rs15245705</li> </ol> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.</p> <ul> <li>PM2.5:https://zenodo.org/records/8093749</li> <li>O3:https://zenodo.org/records/8180923</li> <li>SO2:https://zenodo.org/records/8093749</li> <li>NO2:Please contact the author at fjcyfeng@qq.com.</li> </ul> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
Spatial distribution of naming patterns in Russian law firms through text embeddings: data and code
<p>Data and code for a paper about spatial analysis of law firms naming in Russia. See also the attached paper's DOI and URL as well as the link to the GitHub repository.</p>
Spatial distributions of XCO2 seasonal cycle amplitude and phase over northern high-latitude regions
<p>This dataset is the GEOS-Chem model output used in the following publication, <br> Jacobs, N., Simpson, W. R., Graham, K. A., Holmes, C., Hase, F., Blumenstock, T., Tu, Q., Frey, M., Dubey, M. K., Parker, H. A., Wunch, D., Kivi, R., Heikkinen, P., Notholt, J., Petri, C., and Warneke, T.: Spatial distributions of <em>X<sub>CO</sub></em><sub>2</sub> seasonal cycle amplitude and phase over northern high latitude regions, <em>Atmos. Chem. Phys. Discuss.</em> [preprint], https://doi.org/10.5194/acp-2021-185, in review, 2021.</p> <p>The version 2 zip file contains three directories.<br> CO2_tracers_daily/GEOSChem.taggedCO2.YYYYMMDD.nc files: this directory contains the CO<sub>2</sub> simulation as defined by Jacobs et al. (2021). YYYY indicates year, MM indicates month, and DD indicates day.<br> CO2_tracers_monthly/GEOSChem.taggedCO2.YYYYMM.nc files: this directory contains the CO<sub>2</sub> simulation as defined by Jacobs et al. (2021). YYYY indicates year and MM indicates month.<br> TT_tracers/GEOSChem.TTtagged.YYYYMM.nc files: this directory contains the tagged tracers simulation as defined by Jacobs et al. (2021). YYYY indicates year and MM indicates month.</p> <p>The version 1 zip file contains only the monthly mean directories.</p>
FIGURE 1 in Phytoseiid Mite Diversity (Acari: Mesostigmata) And Assessment Of Their Spatial Distribution In French Apple Orchards
FIGURE 1: French regions sampled (with the number of orchards considered) and: a – proportions of dominant species in each region in regards to mite densities; b – proportions of dominant species in each region in regards to number of plots occupied.
Fig. 11 in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Fig. 11. Size structure of cray fish by locations.
Fig. 10 in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Fig. 10. Isobath maps (depth, m), isotach map (velocity, m/s) — autumn.
Fig. 7 in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Fig. 7. Typical crayfish habitats in the Gorovnik.
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.