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Dataset results
330 results for “predicted distribution”
Global distribution of predicted soil types at 1 km resolution based on the WRB 2022 classification
<p>Global maps at 1 km spatial resolution of the predicted soil types (0–100% probabilities) at 1 km resolution based on the <a href="https://www.fao.org/soils-portal/data-hub/soil-classification/world-reference-base/en/">WRB 2022</a> (<strong>World Reference Base</strong> the international standard for soil classification) classification system. The training data comes from the following 3 main sources:</p> <ol> <li>WOSIS points available via: <a href="https://www.isric.org/explore/wosis">https://www.isric.org/explore/wosis</a>;</li> <li>HWSD v2 (random draw of cca 20,000 points): <a href="https://iiasa.ac.at/models-tools-data/hwsd">https://iiasa.ac.at/models-tools-data/hwsd</a>;</li> <li>Other national datasets / data from publications and projects.</li> </ol> <p>Predictions are based on using Rando Forest algorithm as implemented in the <a href="https://www.randomforestsrc.org/">randomForestSRC package</a> with cca 190 covariate layers representing soil forming factors (CHELSA Climate, Global Lithological DB GLiM, MODIS EVI and LST long-term derivatives, Digital Terrain model parameters and similar).</p> <p>All TIF files are provided as <a href="https://www.cogeo.org/">COGs</a>, which means that you can open them directly in QGIS or similar. Publication explaining all modeling steps is pending.</p> <p>Update of the predictions takes about 4–5 hrs and will be regularly run provided that new training points are available. Disclaimer: These are initial results with limited accuracy and possible issues with quality of training points, location errors and harmonization issues. Use at own risk.</p> <p>Note: original list of soil types have been subset to classes that appear at least 10 times and at least in 2 countries. If you notice an error or artifact <strong>please report via <a href="https://github.com/OpenGeoHub/SoilTypeMapping">the Github repository</a></strong>. Help us improve this dataset by contributing training points.</p>
Fully differentiable, fully distributed River Discharge Prediction: data sets
<p>This repository contains the data sets used in: Scholz et al. (2025). Fully differentiable, fully distributed River Discharge Prediction.</p> <ul> <li><code>dem_1000.h5</code> based on EU-DEM v1.1, reprojected to RADOLAN grid: <a href="https://sdi.eea.europa.eu/catalogue/srv/api/records/3473589f-0854-4601-919e-2e7dd172ff50">https://sdi.eea.europa.eu/catalogue/srv/api/records/3473589f-0854-4601-919e-2e7dd172ff50</a></li> <li><code>efas.h5</code> based on EFAS historical: <a href="https://ewds.climate.copernicus.eu/datasets/efas-historical?tab=overview">https://ewds.climate.copernicus.eu/datasets/efas-historical?tab=overview</a></li> <li><code>era5_ssrd_neckar*.nc</code> based on ERA5 provided by ECMWF, reprojected to RADOLAN grid: <a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5">https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</a></li> <li><code>radolan_neckar_*.h5</code> based on RADOLAN rw product provided by the Deutsche Wetterdienst: <a href="https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/">https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/</a></li> </ul> <p>Due to copyright, the discharge data has to be downloaded manually from the Global Runoff Data Centre (<a href="https://grdc.bafg.de/">https://grdc.bafg.de/</a>), and then preprocessed with the provided <code>bafg_parser.py</code> python script. We use the following stations in our work:</p> <ul> <li>6335290: STEIN</li> <li>6335291: GAILDORF</li> <li>6335565: BAD IMNAU</li> <li>6335600: ROCKENAU SKA</li> <li>6335601: LAUFFEN</li> <li>6335602: PLOCHINGEN</li> <li>6335603: ROTTWEIL</li> <li>6335604: KIRCHENTELLINSFURT</li> <li>6335620: MOSBACH</li> <li>6335660: PFORZHEIM</li> <li>6335665: DENKENDORF</li> <li>6335671: ALTENSTEIG</li> <li>6335675: MURR</li> <li>6335676: OPPENWEILER</li> <li>6335680: SCHWABSBERG</li> <li>6335681: UNTERGRIESHEIM</li> <li>6335690: NEUSTADT</li> </ul> <p>To preprocess the discharge data, additionally the river network data "Fließgewässer (AWGN)" provided by the Landesanstalt für Umwelt Baden-Württemberg (LUBW) is required: <a href="https://rips-metadaten.lubw.de/trefferanzeige?docuuid=7251515f-6aed-4555-8319-ab6314155ab1">https://rips-metadaten.lubw.de/trefferanzeige?docuuid=7251515f-6aed-4555-8319-ab6314155ab1</a></p> <p> </p>
Distributed Predictive Drone Swarms in Cluttered Environments
<p>This folder contains data, videos, and supplementary material for the article titled "Distributed Predictive Drone Swarms in Cluttered Environments".</p> <p>In the article, we present a Distributed Model Predictive Control (DMPC) algorithm for drone swarm navigation in two types of cluttered environments, i.e., a forest and a funnel-like environment. <br> </p> <p>The material in `zenodo_upload` is organized as follows.<br> 1. a data folder, with the logs of simulation and hardware experiments;<br> 2. an analysis folder, with Matlab scripts that analyze the logs in the data folder;<br> 3. a plotting folder, with Matlab functions used by the analysis scripts;<br> 4. an mp4 video file, on simulation and hardware experiments;<br> 5. a pdf, with supplementary materials.<br> <br> The `qp_swarm` folder contains MATLAB code for simulation experiments.</p>
Code and data for manuscript: Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir.
<p>This is the source code and data required to reproduce data analysis and figures from the manuscript, "Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir". </p>
Mapping present and future predicted distribution patterns for a meso-grazer guild in the Baltic Sea
<p>Baltic Sea communities consisting of key and endemic species are threatened by climate change. Using Ecological niche modelling, we map predicted distribution patterns under recent and future climate change scenarios (2050) for a food-web consisting of a guild of meso-grazers (Idotea spp.), their host algae (Fucus vesiculosus and F. radicans) and their fish predator (Gasterosteus aculeatus). Brackish water species depend on two important abiotic factors: temperature and salinity. We assess which of these environmental factors determines the distribution limits of the grazers in the Baltic Sea today. For species in a semi-enclosed sea area such as the Baltic Sea, climate-induced changes may lead to dramatic food-web effects. We assess the consequences of the predicted climate-induced habitat range changes for this unique Baltic community.<br /> </p>
Code and data for Bayesian joint species distribution model selection for community-level prediction
<p>Code and data for reproducing the analysis in the manuscript "Bayesian joint species distribution model selection for community-level prediction." Provided data include percent cover observations for 39 modeled vascular plant species within boreal forest understory communities and environmental model covariates. R code is provided to generate model inputs, apply alternative models, generate out-of-sample predictions, and calculate associated community and species log scores and alternative model evaluation metrics. Further, R source code is provided to implement the multinomial joint species distribution model defined in the manuscript. Details on the data, its processing, and the alternative model definitions and structure can be found in the main text of the manuscript. Provided data are currently being used in ongoing analyses and coordination with authors may be warranted to avoid duplicate publication. Potential users are encouraged to consider collaboration with authors when useful and appropriate. Misinterpretation of data may occur if used outside the context of the original analysis. All data are made available in their current state. While significant efforts have been made to ensure data accuracy, complete accuracy cannot be guaranteed. Data may be updated periodically. It is the responsibility of the data user to check for updated versions of the data.</p>
Data from: Integrated species distribution models to account for sampling biases and improve range wide occurrence predictions
<p><strong><span>Aim</span></strong></p> <p><span>Species distribution models (SDMs) that integrate presence-only and presence-absence data offer a promising avenue to improve information on species' geographic distributions. The use of such 'integrated SDMs' on a species range-wide extent has been constrained by the often-limited presence-absence data and by the heterogeneous sampling of the presence-only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map-based evaluation. We build a new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs.</span></p> <p><strong><span>Location</span></strong></p> <p><span>South and Central America.</span></p> <p><strong><span>Time period</span></strong></p> <p><span>1979-2017.</span></p> <p><strong><span>Major taxa studied</span></strong></p> <p><span>Hummingbirds.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>We build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process.</span> <span>We validate SDMs with two schemes: i) cross-validation with presence-absence data and ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set.</span></p> <p><strong><span>Results</span></strong></p> <p><span>The integrated SDM accounting for the spatially varying sampling intensity of the presence-only data was one of the top-performing models in both model validation schemes. Presence-only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence-only data for species that had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction.</span></p> <p><strong><span>Main conclusions</span></strong></p> <p><span>Integrated SDMs combining presence-only and presence-absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence-absence data.</span></p>
Modelling the carbon balance in bryophytes and lichens: Presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects
<p><strong>Premise </strong></p> <p>Bryophytes and lichens have important functional roles in many ecosystems. Insight into how their CO<sub>2</sub> exchange responds to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time-scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO<sub>2</sub> exchange rates of these poikilohydric organisms change through time as a function of weather conditions.</p> <p><strong>Methods</strong></p> <p>PoiCarb simulates diel fluctuations of CO<sub>2</sub> exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes are net photosynthesis, dark respiration, evaporation and water uptake. Measured CO<sub>2</sub>-exchange responses to light, temperature, atmospheric CO<sub>2</sub> concentration, and thallus water content (calculated in a separate module) are used to parameterise the model's carbon module. We validated the model by comparing modelled diel courses of net CO<sub>2</sub> exchange to such courses from field measurements on the tropical lichen <em>Crocodia aurata</em>. To demonstrate the model's usefulness, we simulated potential climate-change effects.</p> <p><strong>Results </strong></p> <p>Diel patterns were reproduced well and modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration.</p> <p><strong>Conclusions</strong></p> <p>Being able to reproduce the weather-dependent variation in diel carbon balances is a clear improvement compared to simple extrapolations of short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding species' conservation.</p>
Supplementary material from: Prediction of the Cold Flow Properties of Biodiesel using the FAME Distribution and Machine Learning Techniques
<p><span>The dataset is divided into three sections within the worksheet.</span></p> <p><span> </span><span>The first section contains the definition of the data's feedstock and its source reference. The reference includes the year, DOI (if available, as some are collected from books), publication journal, article title, and authors.</span></p> <p><span> </span><span>The second section describes the FAME distribution, starting from C4:0 up to C24:0, including a column of unidentified FAMEs.</span></p> <p><span><span>The third and final section describes the measured properties Cloud Point (CP), Cold Filter Plugging Point (CFPP) and Pour Point (PP).</span></span></p>
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction
<div> <div> <p>Application of artificial intelligence (AI) has been ubiquitous in the growth of research in the areas of basic sciences. Frequent use of machine learning (ML) and deep learning (DL) based methodologies by researchers has resulted in significant advancements in the last decade. These techniques led to notable performance enhancements in different tasks such as protein structure prediction, drug-target binding affinity prediction, and molecular property prediction. In material science literature, it is well-known that crystalline materials exhibit topological structures. Such topological structures may be represented as graphs and utilization of graph neural network (GNN) based approaches could help encoding them into an augmented representation space. Primarily, such frameworks adopt supervised learning techniques targeted towards downstream property prediction tasks on the basis of electronic properties (formation energy, bandgap, total energy, etc.) and crystalline structures. Generally, such type of frameworks rely highly on the handcrafted atom feature representations along with the structural representations. In this paper, we propose an unsupervised framework namely, CrysAtom, using untagged crystal data to generate dense vector representation of atoms, which can be utilized in existing GNN-based property predictor models to accurately predict important properties of crystals. Empirical results show that our dense representation embeds chemical properties of atoms and enhance the performance of the baseline property predictor models significantly.</p> </div> </div>
Archive of the analysis results of the microtremor data obtained from a seismic array with a radius of 0.58 m distributed to the participants of the blind prediction experiments for the ESG6 symposium
<p>This is a supplemental material of the paper "Array-size dependency of the upper limit wavelength normalized by array radius for the standard spatial autocorrelation method" by Ikuo Cho, published in Earth, Planets and Space. It consists of the analysis results of the microtremor data observed using a seismic array with a radius of 0.58 m, which were distributed to the participants of the blind prediction experiments in ESG6. It involves all analysis results and script files to draw Figure 1 of the paper. See the "Availability of data and materials" section of the paper to download the original observed data and analysis code. See the main text of the paper for the details of the analysis.</p>
Supplementary data from 'Predicting the distribution of Australian frogs and their overlap with Batrachochytrium dendrobatidis under climate change'
<p><strong><span>Aim: </span></strong><span>Amphibians, with over 40% of assessed species listed as threatened, are disproportionately at risk in the global extinction crisis. Among the many factors implicated in the current and ongoing loss of amphibian biodiversity are climate change and the disease chytridiomycosis, caused by the fungus <em>Batrachochytrium dendrobatidis </em>(<em>Bd</em>). These two threats are of particular concern in Australia, where <em>Bd </em>has been implicated in the declines of at least 43 frog species, and climate change is emerging as an additional threat. Here, we explore how climate change is likely to affect the distributions of Australian frog species and <em>Bd </em>to the year 2100, as well as how the spatial and climatic niche overlap between <a>chytridiomycosis-declined </a></span><span>frogs and <em>Bd</em> could shift.</span></p> <p><strong><span>Location: </span></strong><span>Australia</span></p> <p><strong><span>Methods: </span></strong><span>We used species distribution modelling to infer the current and future distribution of 141 Australian frog species and <em>Bd</em>, under two emissions scenarios. We used metrics of niche similarity, including Schoener's D and the Niche Margin Index, to quantify predicted alterations to spatial interactions between <em>Bd</em> and frog species.</span></p> <p><strong><span>Results: </span></strong><span>Climate change is likely to have a variable impact on frog distributions in Australia, with some 23 and 47 species, primarily in southern Australia, predicted to lose at least 30% of their current distributions under low and high emissions scenarios, respectively. In contrast, 69 and 68 species, respectively, have potential to increase their distributions, primarily in northern Australia. While the distribution of <em>Bd </em>is predicted to decrease, the proportional spatial and niche overlap between <em>Bd </em>and susceptible frog species is predicted to remain little changed, and in some cases, to increase.</span></p> <p><strong><span>Main conclusions: </span></strong><span>Although effects will be variable across the continent, climate change is likely to be a threatening factor to a number of Australian frog species. Additionally, chytridiomycosis is likely to remain a significant threat to many frog species, as any reductions to the pathogen's distribution largely coincide with geographic range contractions of chytridiomycosis-susceptible species.</span></p>
Potential distribution of invasive boxwood blight pathogen (Calonectria pseudonaviculata) as predicted by process-based and correlative models
<p>R project, R scripts, and data files for reproducing most of the analyses presented in a climatic suitability study for boxwood blight. The README. md file describes how to run the scripts and provides details on data inputs.</p> <p><strong>Abstract: </strong>Boxwood blight caused by <em>Cps</em> is an emerging disease that has had devastating impacts on <em>Buxus</em> spp. in the horticultural sector, landscapes, and native ecosystems. In this study, we produced a process-based climatic suitability model in the CLIMEX program and combined outputs of four different correlative modeling algorithms to generate an ensemble correlative model. All models were fit and validated using a presence record dataset comprised of <em>Cps</em> detections across its entire known invaded range. Evaluations of model performance provided validation of good model fit for all models. A consensus map of CLIMEX and ensemble correlative model predictions indicated that not-yet-invaded areas in eastern and southern Europe and in the southeastern, midwestern, and Pacific coast regions of North America are climatically suitable for <em>Cps</em> establishment. Most regions of the world where<em> Buxus</em> and its congeners are native are also at risk of establishment. These findings provide the first insights into <em>Cps</em> global invasion threat, suggesting that this invasive pathogen has the potential to significantly expand its range.</p>
Predicting potential distributions of large carnivores in Kenya: An occupancy study to guide conservation
<p><span><strong>Aim</strong>:</span><span> Species distribution maps are frequently the foundation upon which species-specific conservation strategies are developed, however, mapping species distribution is challenging, especially across large spatial extents. Our aim was to use a novel empirical approach to predict the national distribution for all six large carnivore species </span><span>found in Kenya to guide conservation and management decisions by identifying knowledge and conservation gaps.</span></p> <p><span><strong>Location</strong>:</span><span> Kenya</span></p> <p><span><strong>Methods</strong>:</span><span> Data on carnivore presence and absence were collected through questionnaires and sightings-based surveys. These data were combined and analysed using single-season false-positive occupancy models, which account for imperfect detections and false positives. </span><span>To inform conservation strategies, </span><span>we used the occupancy outputs to make predictions for unsampled areas and create occupancy-based distribution </span><span>maps, where ψ>0.50, </span><span>to</span><span> (1) quantify differences with IUCN Red List range maps, (2) quantify overlap with wildlife areas and (3) </span><span>identify areas of high carnivore richness</span><span>.</span></p> <p><span><strong>Results</strong>:</span><span> Large carnivore occupancy was associated with land conversion, habitat, and prey availability. Our results suggest that all six species are widely distributed across Kenya and reveal substantial differences in distribution maps compiled by the IUCN Red List. </span><span>More specifically, our occupancy-based distribution maps predict a </span><span>much larger distribution for African wild dog (5.09X), lion (4.77X), and leopard (1.46X), similar distribution for cheetah, and smaller distribution for spotted hyaena (0.84X) and striped hyaena (0.65X). For all large carnivores, the vast majority (~80%) of their predicted distribution falls outside wildlife areas and northern Kenya is predicted to have the highest large carnivore richness.</span></p> <p><span><strong>Main conclusions</strong>:</span> <span>Our results are encouraging as large carnivores may be widely distributed across Kenya, in some cases potentially more so than previously acknowledged. However, much of this range lies outside wildlife areas and represents areas of concern both for conservation and human livelihoods illustrating the challenges of conserving large carnivores across their range.</span></p>
Figure 5 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 5. Moving colonies to imperialist in culture and language axes (AtashpazGargari et al. 2008).
Figure 2 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 2. Generalized semivariogram showing the range of spatial dependence, nugget effect (C0) variability associated with spatial dependence (C), and sill (C + C0).
Figure 5 in Hybrid neural network with genetic algorithms for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Iran
Figure 5. Tetranychus urticae distribution maps in actual (b, d and f) and classified conditions by MLPNN (c, e and a). The maps of a, c, e and b, d, f have been drawn according to economic threshold of 4, 8 and 12, respectively.
Supplementary material 1 from: Motloung R, Robertson M, Rouget M, Wilson J (2014) Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions. NeoBiota 20: 31-48. https://doi.org/10.3897/neobiota.20.5778
Current and potential distributions of sixteen species that are not widespread in southern Africa arranged on the basis of their suitable range size : a) Acacia paradoxa, b) A. cultriformis, c) A. falciformis, d) A. pendula, e) A. rubida, f) A. stricta, g) A. retinodes, h) A. fimbriata, i) A. aneura, j) A. viscidula, k) A. acuminata, l) A. adunca, m) A. binervata, n) A. schinoides, o) A. prominens, p) A. mangium. The grey shading indicates areas that SDMs have identified as suitable by SDMs while the white ones are unsuitable.
Supplementary data for global distribution of mercury in foliage predicted by machine learning
<p>Global distribution of foliar mercury concentrations and pools with a spatial resolution of 0.25 latitude by 0.25 longitude, predicted by machine learning.</p>
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</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.