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516 results for “human impact”
Figure 4. Distance between eyebrow and eye.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Based on what we stated above, we need to extract 28 features, which describe the distances<br> between certain points explained in the previous stage, these features are classified into six groups,<br> and each group describes the features of one face element. All features are a vertical distances<br> between two points. Group one contains seven features for mouth, groups two and three contains 14<br> features for eyes, groups four and five contain six features for eyebrows, and the last group has one<br> feature only which is the distance between the beginning of the eyebrow and the beginning of the<br> eye in same side, this is significant (from point 23 to 15) because it is used to measure the distance<br> of eyebrow from the eye. This feature is shown in Figure 4 by a line.</p>
Figure 5. ANN Structure 4.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>For classification purpose of the emotions, we use ANN of supervised learning based on<br> backpropagation algorithm. Backpropagation neural network architecture is used with its standards<br> learning function with 28 inputs representing the extracted features and 6 outputs representing 6<br> emotions, happy, sad, angry, fear, shame and disgust. the emotions. We have also a hidden layer<br> with 16 nodes selected after various trails to obtain the best results. The used ANN is depicted in<br> Figure 5.</p>
Fig. 1 in Epidemiology of Trichinella infection in wild boar from Spain and its impact on human health during the period 2006-2019
Fig. 1. Prevalence of Trichinella infection in wild boar from several Spanish autonomous communities.
Fig. 4 in Epidemiology of Trichinella infection in wild boar from Spain and its impact on human health during the period 2006-2019
Fig. 4. Box-plot and post hoc pairwise comparison via Dunn's test of the incidence. Box-plot with different letters indicate a statistically significant difference.
Fig. 3 in Epidemiology of Trichinella infection in wild boar from Spain and its impact on human health during the period 2006-2019
Fig. 3. Three-dimensional plots for the interaction effects of the total number of wild boars hunted and the prevalence of the Trichinella infection in wild boar on incidence of trichinellosis in humans.
FIGURE 2 in Human impacts and the loss of Neotropical freshwater fish diversity
FIGURE 2 | Geographical distribution of studies published in this Special Issue of Neotropical Ichthyology. Colors indicate the type of impact. Five papers are not shown in the map, because they covered large spatial extents (i.e., whole basins or the Neotropical region).
FIGURE 1 in Human impacts and the loss of Neotropical freshwater fish diversity
FIGURE 1 | Gender of authors in this Special Issue of Neotropical Ichthyology, considering all authors (n = 107) and the first author of each paper (n = 22).
FIGURE 3 in Human impacts and the loss of Neotropical freshwater fish diversity
FIGURE 3 | Main human stressors associated with the loss of Neotropical freshwater fishes, investigated by studies published in this Special Issue of Neotropical Ichthyology.
Estimating future climate change impacts on human mortality and crop yields via air pollution: supplemental files
<p>Atmospheric chemistry model output and other gridded data sets necessary to estimate human mortality and crop yield losses associated with future climate change, as used in Murray et al. [PNAS, 2024] doi:10.1073/pnas.2400117121.</p>
Molecular Dynamics Trajectories Exploring the Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution
<h3>Primary data for the publication "Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution" by de Bruyn, Dorn, Rossetti, Fernandez, Outeiro, Schulz and Carloni. Submitted to the Journal of Chemical Information and Modeling.</h3> <p>Included are all GROMACS input files, parameterised topologies, starting and final configurations, and trajectories for the lowest temperature replica (at 300 K, lowest of 32 replicas between 300-500 K exchanging according to the REST2 algorithm (Wang et al. 2011)). The data is split into three archives:</p> <ol> <li><strong>all_atom_trajectories.zip</strong> <ul> <li>contains all input files and all atom trajectories including solvent</li> <li>trajectories written at 100 ps intervals</li> </ul> </li> <li><strong>protein+ion_trajectories.zip</strong> <ul> <li>contains configuration/non-parameterised topologies and trajectories excluding solvent, but including ions</li> <li>trajectories written at 10 ps intervals</li> </ul> </li> <li><strong>additional_simulations.zip</strong> <ul> <li>contains the all atom trajectories and input files, and</li> <li>solvent-free trajectories above,</li> <li>for the additional simulations in the Supplemental Information of the article: <ul> <li>includes the DES-Amber-based simulations with 64 replicas between 300-600 K, and</li> <li>a99SB-<em>disp</em>-based simulations</li> </ul> </li> </ul> </li> </ol> <p> </p> <p>Folders are named according to the following top level scheme:</p> <ul> <li><strong>DES-Amber simulations/</strong> Simulations created using the DES-Amber force field (Tucker et al. 2022)</li> <li><strong>a99SB-<em>disp</em> simulations/</strong> SImulations created using the a99SB-<em>disp</em> force field for Intrinsically Disordered Proteins (IDPs) (Robustelli et al. 2018)</li> </ul> <p>Sub-folders follow the following scheme:</p> <ul> <li><strong>AS/</strong> Simulations of the physiological form of <em>wild-type </em>Human α-Synuclein <ul> <li>unphosphorylated</li> </ul> </li> <li><strong>pAS/</strong> Simulations of the physiological form of <em>wild-type </em>Human α-Synuclein <ul> <li>phosphorylated at S129</li> <li>with double negative charge</li> </ul> </li> <li><strong>pASH/</strong> Simulations of the physiological form of <em>wild-type </em>Human α-Synuclein (a99SB-<em>disp</em> simulations only) <ul> <li>phosphorylated at S129</li> <li>with a single negative charge (monoprotonated)</li> </ul> </li> </ul> <p> </p>
Linked collectors and determiners for: Tropical epiphyte diversity under human impact ? Comparing primary forests, secondary forests, and forest fragments in Ecuador - Otonga.
Natural history specimen data linked to collectors and determiners held within, "Tropical epiphyte diversity under human impact ? Comparing primary forests, secondary forests, and forest fragments in Ecuador - Otonga". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/08a79618-374c-4014-9e71-3194ef3cf69a">https://bionomia.net/dataset/08a79618-374c-4014-9e71-3194ef3cf69a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/08a79618-374c-4014-9e71-3194ef3cf69a">https://gbif.org/dataset/08a79618-374c-4014-9e71-3194ef3cf69a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Mammals in MZNA-VERT: project Human impacts in rivers of Navarra.
Natural history specimen data linked to collectors and determiners held within, "Mammals in MZNA-VERT: project Human impacts in rivers of Navarra". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d436cc89-f949-436a-a4c9-4b12552f760d">https://bionomia.net/dataset/d436cc89-f949-436a-a4c9-4b12552f760d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d436cc89-f949-436a-a4c9-4b12552f760d">https://gbif.org/dataset/d436cc89-f949-436a-a4c9-4b12552f760d</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Tropical epiphyte diversity under human impact ? Comparing primary forests, secondary forests, and forest fragments in Ecuador - Bilsa.
Natural history specimen data linked to collectors and determiners held within, "Tropical epiphyte diversity under human impact ? Comparing primary forests, secondary forests, and forest fragments in Ecuador - Bilsa". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0">https://bionomia.net/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0">https://gbif.org/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0</a>. Formatted as a Frictionless Data package.
Text-fig. 3. Schematic position of the human modifications (cut marks, impact marks, ochre traces, inserted bones) recognised on the canid remains from Předmostí, indicated on a wolf skeleton; the frequencies of the modifications are not shown. Modified after © 2003 ArcheoZoo.org / Michel Coutureau (Inrap). in Consumption Of Canid Meat At The Gravettian Předmostí Site, The Czech Republic
Text-fig. 3. Schematic position of the human modifications (cut marks, impact marks, ochre traces, inserted bones) recognised on the canid remains from Předmostí, indicated on a wolf skeleton; the frequencies of the modifications are not shown. Modified after © 2003 ArcheoZoo.org / Michel Coutureau (Inrap).
The impact of cefuroxime prophylaxis on human intestinal microbiota in surgical oncological patients - Dataset (FASTQ FILES)
<p>Dataset containing FASTQ files of the sequenced samples, generated by the Illumina MiSeq platform. </p> <p><span>This data is freely available under a CC-BY license; if you use it in your work, please cite our paper, "The impact of cefuroxime prophylaxis on human intestinal microbiota in surgical oncological patients" (DOI 10.3389/frmbi.2022.1092771).</span></p>
Data from: Human presence and infrastructure impact wildlife nocturnality differently across an assemblage of mammalian species
<p>Wildlife species may shift towards more nocturnal behavior in areas of higher human influence, but it is unclear how consistent this shift might be. We investigated how humans impact large mammal diel activities in a heavily recreated protected area and an adjacent university-managed forest in southwest British Columbia, Canada. We used camera trap detections of humans and wildlife, along with data on land-use infrastructure (e.g., recreation trails and restricted-access roads), in Bayesian regression models to investigate impacts of human disturbance on wildlife nocturnality. We found moderate evidence that black bears (<em>Ursus americanus</em>) were more nocturnal in response to human detections (mean posterior estimate = 0.35, 90% credible interval = 0.04 to 0.65), but no other clear relationships between wildlife nocturnality and human detections. However, we found evidence that coyotes (<em>Canis latrans</em>) (estimates = 0.81, 95% CI = 0.46 to 1.17) were more nocturnal and snowshoe hares (<em>Lepus americanus</em>) (estimate = -0.87, 95% CI = -1.29 to -0.46) were less nocturnal in areas of higher trail density. We also found that coyotes (estimate = -0.87, 95% CI = -1.29 to -0.46) and cougars (<em>Puma concolor</em>) (estimate = -1.14, 90% CI = -2.16 to -0.12) were less nocturnal in areas of greater road density. Furthermore, coyotes, black-tailed deer (<em>Odocoileus hemionus</em>), and snowshoe hares were moderately more nocturnal in areas near urban-wildland boundaries (estimates and 90% CIs: coyote = -0.29, -0.55 to -0.04, black-tailed deer = -0.25, -0.45 to -0.04, snowshoe hare = -0.24, -0.46 to -0.01). Our findings imply anthropogenic landscape features may influence medium to large-sized mammal diel activities more than direct human presence. While increased nocturnality may be a promising mechanism for human-wildlife coexistence, shifts in temporal activity can also have negative repercussions for wildlife, warranting further research into the causes and consequences of wildlife responses to increasingly human-dominated landscapes.</p>
Quantification of the global and regional impacts of gas flaring on human health via spatial differentiation
<p>Supplementary material for the associated publication.</p>
SIRIUS - Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in Permafrost Regions of AlaSka
<p>The SIRIUS inventory integrates data from (i) the Sentinel-1/2 derived Arctic coastal human impact dataset (SACHI) (Bartsch et al., 2021), (ii) OpenStreetMap dataset for the infrastructure and land use information (OpenStreetMap Contributors and Geofabrik GmbH, 2018), (iii) the pan-Arctic catchments summary database (ARCADE) for the watersheds (Speetjens et al., 2022), (iv) the modeled Northern Hemisphere permafrost map by Obu et al. (2018), and (v) the contaminated sites database and reports by the State of Alaska Department of Environmental Conservation (2023) (DEC) to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska.</p> <p>The dataset is deployed as a GeoPackage and can be imported to spatial databases (e.g. PostgreSQL/PostGIS), a Geographic Information System (e.g. QGIS), and used within geospatial processing libraries (e.g. Python's GeoPandas). All layers can be queried either in dependence or combination with one another.</p> <p>Each GeoPackage contains the following layers:</p> <ul> <li>ARCADE_WatershedsDB</li> <li>DEC_ContaminatedSitesAK</li> <li>OSM_Point_InfrastructureHIElements</li> <li>SACHI_OSM_InfrastructureHIElements</li> <li>SACHI_OSM_InfrastructureHIElements_RRNetwork</li> <li>UiO_MAGT</li> <li>UiO_PermafrostProbability</li> <li>UiO_PermafrostZones</li> </ul> <p>A corresponding manuscript, including application examples and a thorough description of the individual components, was submitted to be published in an open-access journal.</p> <p><strong>Download Data</strong></p> <ul> <li><strong>Python Scripts</strong> <ul> <li>01_InfrastructureDataETL: reprojects the input Shapefiles and raster datasets to a common coordinate system (EPSG:5936) and then clips datasets to the boundary of Alaska. It also includes a step for filtering the permafrost probability raster dataset based on a minimum probability threshold of 50% and rounds the values in the mean annual ground temperature raster dataset.</li> <li>02_OSM-aggregation: processes the OpenStreetMap (OSM) geospatial data. It imports and merges OSM polygon and point data, cleans and extracts unique values of "fclass" and "osm_type", and aggregates these values for manual categorization, based on the OSM key-value-scheme. The script assigns Land Use/Cover Area frame Statistical Survey (LUCAS) categories to the data, filters out natural objects and places, and resolves unknown categories by identifying intersections between datasets.</li> <li>03_SACHI-aggregation: assigns LUCAS categories to the SACHI dataset based on the 'Use' column.</li> <li>04_SACHI-OSM_decisiontree: performs a series of geospatial operations to determine the overlap between polygonal OSM features and SACHI features and assigns LUCAS categories to the overlapping features based on certain criteria and dissolves them. The overlapping and non-overlapping features are then combined into a single dataset: the harmonized critical infrastructure and human-impacted areas dataset.</li> <li>05_TextMiningNLTK-CSSites: performs text mining and data preprocessing on the reports of the DEC contaminated sites database. It extracts dates, calculates cleanup times for inactive sites, identifies contaminants based on abbreviations and text entries, and extracts information related to contaminants and the medium they are found in.</li> </ul> </li> <li><strong>GeoPackages</strong> <ul> <li>PermaRisk_RRNetworkLine_v01_r00.gpkg contains the rail and road network as line geometries.</li> <li>PermaRisk_RRNetworkPolygonal_v01_r00.gpkg contains the rail and road network as polygon geometries.</li> </ul> </li> </ul> <p> </p> <ul> </ul> <p> </p>
Genetic variation of Scots pine in Eurasia: Impact of postglacial recolonisation and human-mediated gene transfer
<p><strong>The dataset comprises nuclear microsatellite data (PCR products lengths) used in the paper "Genetic variation of Scots pine in Eurasia: Impact of postglacial recolonisation and human-mediated gene transfer".</strong></p> <p><strong>The pdf file includes the list of populations.</strong></p> <p>Abstract: Scots pine (<em>Pinus sylvestris</em> L.) seems to be a species of low conservation priority because it has a very wide Eurasian distribution and plays a leading role in many forest tree breeding programs. Nevertheless, considering its economic value, long breeding history, range fragmentation, and increased mortality, which is also projected in the future, it requires a more detailed description of its genetic resources. Our goal was to compare patterns of genetic variation found in biparentally inherited nuclear DNA with previous research carried out with mitochondrial and chloroplast DNA due to their different modes of transmission. We analysed the genetic variation and relationships of 60 populations across the distribution of Scots pine in Eurasia (1,262 individuals) using a set of nuclear DNA markers. We confirmed the high genetic variation and low genetic differentiation of Scots pine spanning large geographical areas. Nevertheless, there was a clear division between European and Asian gene pools. The genetic variation of Asian populations was lower than in Europe. Spain, Turkey, and the Apennines constituted separate gene pools, the latter showing the lowest values of all genetic variation parameters. The analyses showed that most populations experienced genetic bottlenecks in the distant past. Ongoing admixture was found in Fennoscandia. Our results suggest a much simpler recolonization history of the Asian than European part of the Scots pine distribution, with migration from limited sources and possible founder effects. Eastern European stands seem to have descended from the Urals refugium. It appears that Central Europe and Fennoscandia share at least one glacial refugium in the Balkans and migrants from higher latitudes, as well as from southeastern regions. The low genetic structure between Central Europe and Fennoscandia, along with their high genetic admixture, may result at least partially from past human activities related to the transfer of germplasm in the 19<sup>th</sup> and early 20<sup>th</sup> centuries. In light of ongoing climate changes and projected range shifts of Scots pine, conservation strategies are especially needed for marginal and isolated stands of this species. Genetic research should also be complemented in parts of the species distribution that have thus far been poorly studied.</p>
Impact of urban heat islands on human mortality risk in European cities
<p>These data contain estimates of temperature-related human mortality, as well as the associated economic assessments, related to urban heat islands for 85 European cities over the years 2015-2017. They are based on temperature-mortality relationships from Masselot et al. 2023 and 100m resolution UrbClim urban climate model simulations of near-surface air temperature (De Ridder et al. 2015, Hooyberghs et al. 2019), re-gridded to 500m resolution.</p> <p> </p> <p>Details of the methodology are provided in the associated paper:</p> <p>Huang, W.T.K. et al. Economic valuation of temperature-related mortality attributed to urban heat islands in European cities. <em>Nat Commun</em> <strong>14</strong>, 7438 (2023). <a href="https://doi.org/10.1038/s41467-023-43135-z">https://doi.org/10.1038/s41467-023-43135-z</a></p> <p>And associated core analysis code is available on GitHub at https://github.com/hkatty/Paper_UHI_mortality_Europe (doi:10.5281/zenodo.8429209). </p> <p> </p> <p> </p> <p>The content of the files are as follows:</p> <p><strong>spatial_timeseries</strong> zip files: These contain the most unprocessed attributable fraction estimates, with the exposure-response relationships applied to the modelled temperature, prior to any further processing.</p> <p><strong>uhi</strong> csv files: These are tables of the average mortality and years of life lost, as well as associated economic assessment, related to urban heat islands for each city. They are identical to Tables S4-S11 in the supplementary materials of the above paper.</p> <p><strong>spatial_maps_time_averaged_diff_from_rural.zip</strong>: Spatial maps showing the difference from the rural average for each day and grid box, then averaged over time. </p> <p><strong>data_urbanruralavg_timeseries.nc</strong>: Time series of urban and rural averages, as well as the difference between the two (i.e. the urban heat island effect).</p> <p><strong>avg_diff_from_rural_urbanrural.nc</strong>: The above timeseries file temporally aggregated.</p> <p><strong>simulated_urbanruraldiff_timeseries.zip</strong>: Time series of urban-rural difference in attributable fraction for 1000-member ensembles representing uncertainties in the exposure-response relationships as captured by Monte Carlo simulations.</p> <p><strong>simulated_urbanruraldiff_averaged.zip</strong>: The above simulated timeseries temporally aggregated.</p> <p> </p> <p><strong>Some variables explained:</strong></p> <p>fAF = forward attributable fraction (i.e. fraction of total mortality associated with a single day's temperature, cumulative over lag time)</p> <p>fAD = forward attributable deaths (i.e. equivalent to fAF but for number of deaths)</p> <p>tas = temperature</p> <p>heat_ex = average over heat extreme days (i.e. the warmest 2% days in 2015-2017 for the city)</p> <p>cold_ex = average over cold extreme days (i.e. as heat_ex but for the coldest 2% days)</p> <p>heat = average over days warmer than the age-dependent optimal temperature</p> <p>cold = average over days colder than the age-dependent optimal temperature</p> <p>heat_count = number of days warmer than the optimal for the age group, note that for combined 2085.1 and 2085.5 age groups, days are counted if it is considered warm for at least one age group (therefore heat_count + cold_count ≠ total days over period)</p> <p>cold_count = number of days colder than the optimal for the age group</p> <p>rural = rural average</p> <p>imd = land imperviousness</p> <p>popden = population density</p> <p>age groups: </p> <p>20 = 20 to 44<br>45 = 45 to 64<br>65 = 65 to 74<br>75 = 75 to 84<br>85 = 85 and over<br>2085.1 = all above age groups combined, weighted by the local population age structure<br>2085.5 = all above age groups combined, weighted by the 2013 European standard population age structure</p> <p> </p> <p>References:</p> <p>De Ridder, K., Lauwaet, D., and Maiheu, B., (2015): UrbClim – A fast urban boundary layer climate model. Urban Climate, 12, 21–48. <a href="https://doi.org/10.1016/J.UCLIM.2015.01.001">https://doi.org/10.1016/J.UCLIM.2015.01.001</a>.</p> <p>Hooyberghs, H., Berckmans, J., Lauwaet, D., Lefebre, F., and De Ridder, K., (2019): Climate variables for cities in Europe from 2008 to 2017. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). <a href="https://doi.org/10.24381/cds.c6459d3a">https://doi.org/10.24381/cds.c6459d3a</a>.</p> <p>Masselot et al. (2023): Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health, <a href="https://doi.org/10.1016/S2542-5196(23)00023-2">https://doi.org/10.1016/S2542-5196(23)00023-2</a>. </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.