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170 results for “predictive mapping”
Data from: Bedrock geological map predictions for Phanerozoic fossil occurrences
<p>This is the supplementary data repository of the <em>Paleobiology</em> paper titled <em>Bedrock Geological Map Predictions for Phanerozoic Fossil Occurrences</em>. Geographically-explicit, taxonomically resolved fossil occurrences are necessary for reconstructing macroevolutionary patterns and for testing a wide range of hypotheses in the Earth and life sciences. Heterogeneity in the spatial and temporal distribution of fossil occurrences in the Paleobiology Database (PBDB) is attributable to several different factors, including turnover among biological communities, socioeconomic disparities in the intensity of paleontological research, and geological controls on the distribution and fossil yield of sedimentary deposits. Here we use the intersection of global geologic map data from Macrostrat and fossil collections in the PBDB to assess the extent to which the potentially fossil-bearing, surface-expressed sedimentary record has yielded fossil occurrences. We find a significant and moderately strong positive correlation between geologic map area and the number of fossil occurrences. This correlation is consistent regardless of map unit age and binning protocol, except at period level; the Neogene and Quaternary have non-marine map units covering large areas and yielding fewer occurrences than expected. The sedimentary record of North America and Europe yields significantly more fossil occurrences per sedimentary area than similarly-aged deposits in most of the rest of the world. However, geographic differences in area and age of sedimentary deposits lead to regionally different expectations for fossil occurrences. Using the sampling of surface-expressed sedimentary units in North America and Europe as a predictor for what might be recoverable from the surface-expressed sedimentary deposits of other regions, we find that the rest of the globe is approximately 45% as well sampled in the PBDB. Using age and area of bedrock and sampling in North America and Europe as a basis for prediction, we estimate that over 639 thousand occurrences from outside of these regions would need to be added to the PBDB to achieve global geological parity in sampling. In general, new terrestrial fossil occurrences are expected to have the greatest impact on macroevolutionary patterns. </p>
Maps of predicted carbon dioxide and methane fluxes from waterbodies in the Yukon-Kuskokwim Delta, Alaska
<p>In the Arctic, waterbodies are abundant, and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO<sub>2</sub> and CH<sub>4</sub> fluxes across the Yukon-Kuskokwim (YK) Delta of south-west AK. These datasets include carbon dioxide and methane dissolved concentrations and diffusive fluxes from a research watershed in the central YK Delta. </p>
Predicted Spatially Complete Zoning Map of North Carolina
<p>Spatially-complete zoning map of North Carolina, USA. The <strong>results </strong>folder contains results of a machine learning (random forest) model predicting 3 core district zones (residential, non-residential, and mixed use) and 13 sub-district zones (open space, industrial, commercial, office, planned use, high-density residential, medium-high-density residential, medium-density residential, medium-low-density residential, low-density residential, agricultural residential, mixed use, and downtown). Results are provided as 30-m rasters (.tif) with each value corresponding to a zoning district. Table containing zone district ID (number) and zone district name (character string) is included in <strong>zone_classification.csv</strong>. Final (spatially complete statewide maps) can be found in the <strong>final_predicted </strong>folder. This folder includes Statewide core district results in <strong>NC_predicted_core.tif</strong> and statewide sub-district results in <strong>NC_predicted_sub.tif</strong>. </p> <p>Zoning was generalized and reclassified into 3 core district zones and 13 sub-district zones (described above). Reclassified zoning data, collected from 39 counties in North Carolina is provided in the <strong>observed </strong>folder with core districts in <strong>core_district_observed_zones.tif</strong> and sub-districts in <strong>sub_district_observed_zones.tif</strong>. Also in this folder is <strong>zoning_implementation_NC.csv</strong> which includes links to the source data (zoning map and zoning ordinance) for all collected data.</p> <p>Two models were created to predict zones under different data availability scenarios (i.e., scenarios that assume different levels of data availability). Predictions labeled “within_county” utilized the within-county model which predicts zoning districts in areas where zoning data is partially available for that county. To approximate scenarios of incomplete data accessibility, 20% of the data was randomly removed from training and reserved for independent performance assessments. Predictions labeled “between-county” utilized the between-county model which predicts zoning districts in areas where zoning data is inaccessible. To approximate this scenario, multiple between-county model iterations were computed by randomly removing entire counties from the training dataset and computing performance metrics on the removed (test) counties. Predictions are provided for both core districts and sub-districts (described above). Results from these models can be found in the <strong>predicted </strong>folder. This folder contains four subfolders: <strong>core_district_within_county</strong>, <strong>sub_district_within_county</strong>, <strong>core_district_between_county</strong>, and <strong>sub_district_between_county</strong>. Within each of these folders are predicted maps 30-m raster (.tif), performance reports including precision, recall, and f1 score overall and per district (.csv), and accuracy maps (3-km grid shapefile [.shp, .shx, .prj, .dbf]) with values corresponding to the proportion of misclassified pixels within a grid cell. Multiple randomized testing county samples were conducted for the between-county models. Each random sample is labeled <strong>r*_</strong> where * is replaced with a number between 1 and 15.</p>
Data from: Predictive mapping to identify refuges for plant communities threatened by earthworm invasion
Open the record for dataset details and reuse information.
Data from: Abiotic proxies for predictive mapping of near-shore benthic assemblages: implications for marine spatial planning
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Maps of predicted carbon dioxide and methane fluxes from waterbodies in the Yukon-Kuskokwim Delta, Alaska
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Predictive mapping of tree species assemblages in an African montane rainforest
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Data from: Bedrock geological map predictions for Phanerozoic fossil occurrences
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Data from: Predictive mapping of the global power system using open data
<p>Three primary global data outputs from the research:</p> <ul> <li><strong>grid.gpkg:</strong> Vectorized predicted distribution and transmission line network, with existing OpenStreetMap lines tagged in the 'source' column</li> <li><strong>targets.tif:</strong> Binary raster showing locations predicted to be connected to distribution grid. </li> <li><strong>lv.tif:</strong> Raster of predicted low-voltage infrastructure in kilometres per cell.</li> </ul> <p>This data was created with code in the following three repositories:</p> <ul> <li>https://github.com/carderne/gridfinder</li> <li>https://github.com/carderne/predictive-mapping-global-power</li> <li>https://github.com/carderne/access-estimator</li> </ul> <p>Full steps to reproduce are contained in this file:</p> <ul> <li>https://github.com/carderne/predictive-mapping-global-power/blob/master/README.md</li> </ul> <p>The data can be visualized at the following location:</p> <ul> <li>https://gridfinder.org</li> </ul>
Data from: Monitoring and predictive mapping of floristic biodiversity along a climatic gradient in ENSO's terrestrial core region, NW Peru
<p>This is the data from the publication "Monitoring and predictive mapping of floristic biodiversity along a climatic gradient in ENSO's terrestrial core region, NW Peru" (<a href="http://onlinelibrary.wiley.com/doi/10.1111/ecog.05091/abstract">http://onlinelibrary.wiley.com/doi/10.1111/ecog.05091/abstract</a>).</p> <p>The code (including figures, appendices and the manuscript) can be found directly in the <a href="https://github.com/jannes-m/2020-enso-tdf">GitHub repository</a>.</p> <p><strong>Data sources and description</strong></p> <p>Column descriptions for all tables can be found in <em>variable_description.ods. </em>Following tables are stored in <em>tables.gpkg</em>:</p> <ol> <li>plot_species_matrix_2011: Plot species matrix recorded in 2011</li> <li>plot_species_matrix_2012: Plot species matrix recorded in 2012.</li> <li>plot_species_matrix_2016: Plot species matrix recorded in 2016.</li> <li>plot_species_matrix_2017: Plot species matrix recorded in 2017.</li> <li>lifeform: Lifeforms of the recorded species</li> <li>plot_variables: Variables specific to the plots such as height of the first tree layer, cover of dead wood, etc.</li> <li>soil: Edaphic variables.</li> <li>topography: Topographic variables.</li> <li>streets: Streets and dirt tracks in the study area.</li> <li>towns: Polygons displaying the outline of the cities Paita, Piura and Chulucanas.</li> <li>rivers: Lines displaying the major rivers in the study area.</li> <li>study_area: Outline of the study area.</li> <li>peru: Outline of Peru.</li> <li>neighbors: Outline of Peru's neighbors (Bolivia, Brazil, Chile, Colombia, Ecuador).</li> <li>coast: Coastal strip of and close to the study area.</li> <li>precipitation: Precipitation measured at the three climatic stations (Paita, Piura, Chulucanas).</li> <li>experiment_count: species counted per visit (irrigation-fertilization experiment).</li> <li>experiment_irrigation: Rain input by time during the irrigation-fertilization experiment.</li> <li>experiment_cover: Cover of each plant species per visit and per experimental plot (irrigation-fertilization experiment).</li> </ol>
Images from Newspaper Navigator predicted as maps, with human corrected labels
<p>The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (chroniclingamerica.loc.gov/). </p> <blockquote> <p>[The Newspaper Navigator dataset] consists of extracted visual content for 16,358,041 historic newspaper pages in <em>Chronicling America</em>. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the <a href="https://labs.loc.gov/work/experiments/beyond-words/">Beyond Words</a> crowdsourcing project.</p> <p>source:<a href="https://news-navigator.labs.loc.gov/"> https://news-navigator.labs.loc.gov/</a></p> </blockquote> <p>One of these categories is 'maps'. In the original training data for Newspaper Navigator, there were relatively few labelled examples of maps. The predictions for maps have an <a href="https://github.com/LibraryOfCongress/newspaper-navigator">Average Precision of 69.5%, and 34 images in the validation data</a>.</p> <p>This dataset contains a sample of these images which have been predicted as 'maps'. It also includes additional labels which indicate whether the predicted map image is a 'map' or 'not a map'. </p> <p>The data is organised as follows:</p> <ul> <li>The images themselves can be found in 'newspaper_maps.zip' </li> <li>`2020_30_10_13_19_228_sample.json` contains metadata about each image drawn from the Newspaper Navigator Dataset.</li> <li>map_labels.csv contains the labels for the images as a CSV file </li> </ul>
Data from: Exploiting Poisson additivity to predict fire frequency from maps of fire weather and land cover in boreal forests of Québec, Canada
Predictive models of fire frequency conditional on weather and land cover are essential to assess how future cover-type distributions and weather conditions may influence fire regimes. We modelled the effects of bottom-up variables (e.g. land cover) and top-down variables (e.g. fire weather) simultaneously with data aggregated or interpolated to spatial and temporal units of 100 km2 and 1yr in the boreal forest of Québec, Canada. For models of human-caused fires, we used road density as a surrogate for human access and behaviour. We exploited the additive property of Poisson distributions to estimate cover-type specific fire count rates, which would normally not be possible with data of this spatial resolution. We used piecewise linear functions to model nonlinear relations between fire weather and fire frequency for each cover-type simultaneously. The estimated conditional rates may be considered as expected mean counts per unit area and time. It follows that these rates can be rescaled to arbitrary spatial and temporal extents. Our results showed fire frequency increased nonlinearly as aridity increased and more quickly in disturbed areas than other types. Road density exerted the strongest influence on the frequency of human-caused fires, which were positively correlated with road density. The estimates may be used to parameterize the fire ignition component of spatial simulation models, which often have a resolution different from that at which the data were collected. This is an essential step in incorporating biotic and abiotic feedbacks, land-cover dynamics, and climate projections into ecological forecasting. The insight into the power of Poisson additivity to reveal high-resolution ecological processes from low-resolution data could have applications in other areas of ecology.
Data from: Is evolution predictable? quantitative genetics under complex genotype-phenotype maps
<p>A fundamental aim of post-genomic 21st century biology is to understand the genotype-phenotype map (GPM) or how specific genetic variation relates to specific phenotypic variation. Quantitative genetics approximates such maps using linear models, and has developed methods to predict the response to selection in a population. The other major field of research concerned with the GPM, developmental evolutionary biology or evo-devo, has found the GPM to be highly nonlinear and complex. Here we quantify how the predictions of quantitative genetics are affected by the complex, nonlinear maps found in developmental biology. We found that the disagreements between predicted and observed responses to selection are common, roughly in a third of generations, systematic and due to nonlinear nature of the genotype-phenotype map. They occur at all time scales, even from one generation to the next. Our results are a step towards integrating the fields studying the GPM.</p>
Sentinel-2 derived Chlorophyll-a prediction maps for high-altitude lakes in the Sierra Nevada, Spain
<p>This dataset contains chlorophyll-a (ug/L) predictions for 4 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. Predictions were made using a simple linear regression model with field sample chlorophyll-a as the dependent variable, and the following Sentinel-2 derived spectral index as the independent variable:</p><p>B3 - (B4+((B2-B4)*((665-560)/(665-490)))</p><p>Prediction maps are included as GeoTiffs and NetCDF files. Sentinel-2 data were atmospherically corrected using the following algorithms: </p><ul><li><a href="https://github.com/acolite/acolite/releases/tag/20221114.0">ACOLITE</a> (<a href="https://doi.org/10.1016/j.rse.2018.07.015">Vanhellemont & Ruddick, 2018</a>)</li><li><a href="https://grass.osgeo.org/grass83/manuals/i.atcorr.html">6SV</a> (<a href="https://doi.org/10.1109/36.581987">Vermote et al. 2006</a>)</li></ul><p><strong>Included Lakes and and their IDs:</strong></p><ul><li>Laguna de la Caldera (ID = P-2)</li><li>Laguna-embalse de las Yeguas (ID = D-6)</li><li>Laguna de Río Seco (ID = P-8)</li><li>Laguna Larga (ID = G-7)</li></ul>
The supplementary materials for "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools".
<p>This is the supplementary materials for a paper named "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools" published on the Digital Twin journal.</p>
FIGURE 3 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 3. (A) Specimen of the bromeliad Alcantarea imperialis with inflorescence photographed about 1 km from the type locality of Scinax v-signatus in the Municipality of Teresópolis, State of Rio de Janeiro. (B) A granitic outcrop with several individuals of A. imperialis, photographed in the Municipality of Miguel Pereira, State of Rio de Janeiro. (C) The same outcrop as it appears on Google Earth images. Yellow arrows indicate small whitish dots that represent individual bromeliads.
FIGURE 4 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 4. Relief map of the State of Rio de Janeiro (Modified from http://www.agritempo.gov.br/altimetria/RJ.html) with the potential distribution of Alcantarea imperialis produced based on images of Google Earth (yellow circles), occurrence of Scinax v-signatus based on voucher specimens (blue stars), localities for Scinax insperatus (gray triangle), localities we identified A. imperialis on the field but were unable to detect using Google Earth (white square), and localities Based on A. imperialis from the Herbarium on the Jardim Botânico do Rio de Janeiro (Square) – http://www.jbrj.gov.br/.
FIGURE 2 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 2. Map from GAA depicting the distribution of Scinax v-signatus. Note that the type locality (Black circle) was not included in distribution area.
FIGURE 1 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 1. Map of southeastern Brazil highlighting the area of the States of Minas Gerais, Rio de Janeiro, and Espírito Santo. Symbols indicate the type localities for Scinax v-signatus (white circle), the type locality for S. arduous (white square) and S. belloni (black square). The white hexagons indicate three new localities for S. arduous. The star indicates two localities where an undescribed species was collected in the south of the State of the Espírito Santo, and the black circle indicates the locality for another undescribed species collected in the north of the State of Rio de Janeiro.
Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps - data
<p>Data from the article<strong><em> Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps</em></strong> published in Journal of Vision.</p> <p>code: https://zenodo.org/record/5999215<br> data: https://zenodo.org/record/5999046</p> <p><br> Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> February 2022</p>
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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.