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133
datasets available to search
ShareScore release 0.9.0
Dataset results
133 results for “natural environment”
Data from: Paths to selection on life history loci in different natural environments across the native range of Arabidopsis thaliana
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Data from: Is saltmarsh restoration success constrained by matching natural environments or altered succession? a test using niche models
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Data from: Artificial agri-environment scheme ponds do not replicate natural environments despite higher aquatic and terrestrial invertebrate richness and abundance
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Data from: Adaptive value of phenological traits in stressful environments: predictions based on seed production and laboratory natural selection
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Data from: Using an insect mushroom body circuit to encode route memory in complex natural environments
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Data from: Limited effects of the maternal rearing environment on the behaviour and fitness of an insect herbivore and its natural enemy
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Discrimination of cell-intrinsic and environment-dependent effects of natural genetic variation on Kupffer cell epigenomes and transcriptomes [ChIP]
GEO Series GSE216109. Mus musculus. 22 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Discrimination of cell-intrinsic and environment-dependent effects of natural genetic variation on Kupffer cell epigenomes and transcriptomes [RNA-Seq]
GEO Series GSE216114. Mus musculus. 85 samples. Type: Expression profiling by high throughput sequencing.
Gene expression of Vibrio parahaemolyticus growing in laboratory isolation conditions compared to those common in its natural ocean environment
GEO Series GSE92847. Vibrio parahaemolyticus. 6 samples. Type: Expression profiling by high throughput sequencing.
Commensal bacteria and the lung environment are responsible for Th2-mediated memory yielding natural IgE in MyD88-deficient mice
GEO Series GSE220657. Mus musculus. 54 samples. Type: Expression profiling by high throughput sequencing; Other.
The Nature and Nurture of Cell Heterogeneity: Accounting for Macrophage Gene-environment Interactions with Single-cell RNA-Seq
GEO Series GSE87849. Homo sapiens. 503 samples. Type: Expression profiling by high throughput sequencing.
Discrimination of cell-intrinsic and environment-dependent effects of natural genetic variation on Kupffer cell epigenomes and transcriptomes [ATAC-Seq]
GEO Series GSE216162. Mus musculus. 26 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Laboratory mice housed in the natural environment identifies genetic and environmental contributions to immune variation
GEO Series GSE135472. Mus musculus. 86 samples. Type: Expression profiling by high throughput sequencing.
RNA-seq Analysis of wildtype (N2) C. elegans at the L4 stage on diets from the laboratory environment and natural environment
GEO Series GSE152794. Caenorhabditis elegans. 24 samples. Type: Expression profiling by high throughput sequencing.
Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia
<p>This dataset comprises processed outputs from an unmanned aerial vehicle (UAV) image acquisition campaign conducted across 27 study sites in Serbia. Each site is organized in a separate folder, labeled by study site name, and includes the following output data for both Object-Based Image Analysis (OBIA) and Convolutional Neural Network (CNN) approaches.</p> <table> <tbody> <tr> <td> <p><span>S.No</span></p> </td> <td> <p><span>Data Alias</span></p> </td> <td> <p><span>File Type</span></p> </td> <td> <p><span>Description</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>name_of_the_study site_multiband (OBIA)</span></p> </td> <td> <p><span>.tif</span></p> </td> <td> <p><span>Five band raster orthomosaic containing RGB, DSM and NDVI layers rescaled from (0-255).</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Vectorized_r3 (OBIA+LSMS)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>The output from the segmentation process and is a basis of preparation for data labeling.</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>train_val_set (OBIA+RF)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>Contain labeled samples of land use classes for training and validation sets for respective study site.</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>ClassifiedVector (OBIA+RF)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>Classified vectorized output in rectangle shape.</span></p> </td> </tr> <tr> <td> <p><span>5</span></p> </td> <td> <p><span>ClassifiedVector_fixed (OBIA+RF)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>Final output of the classified orthomosaic in a vector file containing all the classes in the attribute table.</span></p> </td> </tr> <tr> <td> <p><span>6</span></p> </td> <td> <p><span>RandomForest (OBIA)</span></p> </td> <td> <p><span>.txt</span></p> </td> <td> <p><span>Represents trained model.</span></p> </td> </tr> <tr> <td> <p><span>7</span></p> </td> <td> <p><span>confusion_matrix (OBIA+RF)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Confusion matrix for each study site</span></p> </td> </tr> <tr> <td> <p><span>8</span></p> </td> <td> <p><span>number_of_polygons (OBIA+RF)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Contains the number of polygons marked for training and validation.</span></p> </td> </tr> <tr> <td> <p><span>9</span></p> </td> <td> <p><span>class_area_percentage (OBIA+RF)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Refers to percentage coverage of each class for a given study site.</span></p> </td> </tr> <tr> <td> <p><span>10</span></p> </td> <td> <p><span>name_of_the_study_site_result (OBIA)</span></p> </td> <td> <p><span>.png</span></p> </td> <td> <p><span>Image showing the evaluation metric values for each site.</span></p> </td> </tr> <tr> <td> <p><span>11</span></p> </td> <td> <p><span>train_val_set_CNN</span></p> </td> <td> <p><span>.geojson</span></p> </td> <td> <p><span>Bounding box labeling dataset used for CNN model training for each site.</span></p> </td> </tr> <tr> <td> <p><span>12</span></p> </td> <td> <p><span>train_parameter (CNN)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Hyperparameters used for training the CNN model.</span></p> </td> </tr> <tr> <td> <p><span>13</span></p> </td> <td> <p><span>CNN_models</span></p> </td> <td> <p><span>.h5</span></p> </td> <td> <p><span>CNN models for each site trained with defined hyperparameters.</span></p> </td> </tr> <tr> <td> <p><span>14</span></p> </td> <td> <p><span>CNN_confusion_matrix</span></p> </td> <td> <p><span>.png</span></p> </td> <td> <p><span>Confusion matrix for each study site.</span></p> </td> </tr> <tr> <td> <p><span>15</span></p> </td> <td> <p><span>Classified_rasters_CNN</span></p> </td> <td> <p><span>.tif</span></p> </td> <td> <p><span>Classified rasters through CNN model, both reclassified and colormapped.</span></p> </td> </tr> </tbody> </table>
Natural Environment Research Council (NERC) - Climate Change
Natural Environment Research Council (NERC) - Climate Change
Complex Rehabilitation Technology Enabled Physical Activity for Children With Motor Delays Via Telehealth in Natural Environments
ClinicalTrials.gov study NCT07252713. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Effects of Virtual Natural Environments on Residents' Health
ClinicalTrials.gov study NCT06586489. IPD Sharing: NO. Countries: 1. Publications: 0.
Nature and Health: How Does Lifestyle and Environment Affect Health and Wellness
ClinicalTrials.gov study NCT06001697. IPD Sharing: NO. Countries: 1. Publications: 0.
Safety and Usability of a Robotic Gait Device for Children and Adolescents With Neurological or Neuromuscular Disease in Their Natural Environment
ClinicalTrials.gov study NCT06167954. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.