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4,669 results for “Environment;”
Figure 1 from: Costa BG, Pellegrini TG, Bernardi LFO, Ferreira RL (2017) Notes on predator-prey relationships among Tanypodinae larvae (Diptera, Chironomidae) and mites (Acariformes) in Brazilian subterranean aquatic environments. Subterranean Biology 22: 67-74. https://doi.org/10.3897/subtbiol.22.13925
Figure 1 - Tanypodinae preying on mites at each studied cave.
Figure 4 from: Visagie CM, Yilmaz N, Renaud JB, Sumarah MW, Hubka V, Frisvad JC, Chen AJ, Meijer M, Seifert KA (2017) A survey of xerophilic Aspergillus from indoor environment, including descriptions of two new section Aspergillus species producing eurotium-like sexual states. MycoKeys 19: 1-30. https://doi.org/10.3897/mycokeys.19.11161
Figure 4 - Chemical structure of major compounds produced by A. mallochii and A. megasporus.
Figure 1c from: Visagie CM, Yilmaz N, Renaud JB, Sumarah MW, Hubka V, Frisvad JC, Chen AJ, Meijer M, Seifert KA (2017) A survey of xerophilic Aspergillus from indoor environment, including descriptions of two new section Aspergillus species producing eurotium-like sexual states. MycoKeys 19: 1-30. https://doi.org/10.3897/mycokeys.19.11161
Figure 1c - Continued.
Figure 1b from: Visagie CM, Yilmaz N, Renaud JB, Sumarah MW, Hubka V, Frisvad JC, Chen AJ, Meijer M, Seifert KA (2017) A survey of xerophilic Aspergillus from indoor environment, including descriptions of two new section Aspergillus species producing eurotium-like sexual states. MycoKeys 19: 1-30. https://doi.org/10.3897/mycokeys.19.11161
Figure 1b - Continued.
Figure 1a from: Visagie CM, Yilmaz N, Renaud JB, Sumarah MW, Hubka V, Frisvad JC, Chen AJ, Meijer M, Seifert KA (2017) A survey of xerophilic Aspergillus from indoor environment, including descriptions of two new section Aspergillus species producing eurotium-like sexual states. MycoKeys 19: 1-30. https://doi.org/10.3897/mycokeys.19.11161
Figure 1a - Continued.
DATASET: Exploring the Complexity of Adolescent Aggression: The Interplay of Family Environment in Peru
Open the record for dataset details and reuse information.
Figure 2 from: Suárez D, Martín S, Naranjo M (2018) First report of the invasive alien species Caenoplana coerulea Moseley, 1877 (Platyhelminthes, Tricladida, Geoplanidae) in the subterranean environment of the Canary Islands. Subterranean Biology 26: 67-74. https://doi.org/10.3897/subtbiol.26.25921
Figure 2 Individual of C.coerulea preying on an isopod (red circle).
Figure 1 from: Serafim-Júnior M, Perbiche-Neves G, Lansac-Toha F (2019) Environments and macrophytes as main variables controlling rotifers in a river/lake system before Porto Primavera Reservoir construction. Zoologia 36: 1-8. https://doi.org/10.3897/zoologia.36.e24191
Figure 1 Sampling area and sampling sites.
Figure 4 from: Serafim-Júnior M, Perbiche-Neves G, Lansac-Toha F (2019) Environments and macrophytes as main variables controlling rotifers in a river/lake system before Porto Primavera Reservoir construction. Zoologia 36: 1-8. https://doi.org/10.3897/zoologia.36.e24191
Figure 4 Mean values of Shannon-Wienner index diversity and eveness among the sampling months.
Figure 3 from: Serafim-Júnior M, Perbiche-Neves G, Lansac-Toha F (2019) Environments and macrophytes as main variables controlling rotifers in a river/lake system before Porto Primavera Reservoir construction. Zoologia 36: 1-8. https://doi.org/10.3897/zoologia.36.e24191
Figure 3 Mean values of richness and total abundance among the sampling months.
Fig. 6 in Microfacies Analysis And Depositional Environments Of The Tithonian-Valanginian Limestones From Dâmbovicioara Gorges (Cheile Dâmbovicioarei), Getic Carbonate Platform, Romania
Fig. 6 Peritidal limestones from the Dâmbovicioara Gorges.
Figure 2 in Spatiotemporal distribution, abundance, and species-environment relationships of Scyphozoa (Cnidaria) species in Hisarönü, Marmaris, and Fethiye bays (Muğla, Turkey
Figure 2. Diagram of quadrat method of counting C. andromeda.
FORMATION OF A COMPETITIVE ENVIRONMENT IN THE PRACTICE OF MANAGEMENT OF SMALL BUSINESS UNITS IN THE CONDITIONS OF ECONOMY MODERNIZATION
<p><em><span>This article presents conclusions on the formation of a competitive environment in the management practice of small business entities in the context of economic modernization.</span></em></p>
Multimodal Sensor Dataset from a Forest Environment in Adões using a Unitree Go1 Platform equipped with a Robosense BPearl LiDAR
<p><strong>Dataset Description:</strong></p> <p>This dataset was collected using a Unitree Go1 robot equipped with multiple sensors:</p> <ul> <li><strong>LiDAR Data:</strong> Acquired from a Robosense Bpearl.</li> <li><strong>IMU Data:</strong> Available through the <code>/high_state</code> topic published by the Unitree Go1.</li> <li><strong>RGB Images:</strong> Captured using a Realsense D435i camera.</li> </ul> <p>To process this dataset, you will need the <code>Unitree_transformer.py</code> Python script, available at the provided <a href="https://github.com/pedrotomas27/python_executables/tree/252ecf0feb9c3e8652daa2e6def7f4c94667207e">URL</a>, along with the necessary <a href="https://github.com/unitreerobotics/unitree_legged_sdk">Unitree package</a>.</p> <p><strong>Note:</strong> The transform for the LiDAR sensor is not included in the rosbag. To include it, manually add the following static transform to your ROS setup:<br><node pkg="tf" type="static_transform_publisher" name="rslidar_broadcaster" args="0.15 0 0.13 0 0.309017 0 base rslidar 0.1" /><br><br><strong>Important:</strong> There was a camera malfunction midway through the dataset collection.</p>
Multimodal Sensor Dataset from a Forest Environment in Adões using a Unitree Go1 Platform equipped with a Velarray M1600 LiDAR
<p><strong>Dataset Description:</strong></p> <p>This dataset was collected using a Unitree Go1 robot equipped with various sensors:</p> <ul> <li><strong>LiDAR Data:</strong> Captured with a Velodyne Velarray M1600.</li> <li><strong>IMU Data:</strong> Available via the <code>/high_state</code> topic published by the Unitree Go1.</li> <li><strong>RGB Images:</strong> Streamed from a Realsense D435i camera.</li> </ul> <p>To process this dataset, you will need the <code>Unitree_transformer.py</code> Python script, which can be found at the provided <a href="https://github.com/pedrotomas27/python_executables/tree/252ecf0feb9c3e8652daa2e6def7f4c94667207e">URL</a>. Ensure that the <a href="https://github.com/unitreerobotics/unitree_legged_sdk">Unitree package</a> is also installed.</p> <p><strong>Note:</strong> The LiDAR transform is not included in the rosbag file. To incorporate this transform, manually add the following static transform to your ROS setup:<br><br><node pkg="tf" type="static_transform_publisher" name="lidar_0_broadcaster" args="0.15 0 0.13 0 0 0 base lidar_0 0.1" /><br><br></p>
Speaking with Robots in Noisy Environments
<p>The dataset contains training, validation and test data used for training the StoRM and DFNet 3 models for speech enhancement along with a README file on how to use this dataset to reproduce the enhancement and word error rate results presented in the paper "Speaking with Robots in Noisy Environments" at HRI, 2025. </p>
Hexafluorophosphate-Triggered Hydrogen Isotope Exchange (HIE) in Fluorinated Environments: A Platform for the Deuteration of Aromatic Compounds via Strong Bond Activation
<p>The data set contains DFT with the program package ORCA to calculate the free enthalpy of the reaction for the hydrolysis of PF6-activated with hexafluoroisopropanol (HFIP) and reaction barriers with the help of nudged elastic band (NEB) calculation. The data in Simulated_IR contains DFT optimization and frequency calculations performed using Gaussian16 package.</p>
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>
Data from: Structured inhibitory activity dynamics in new virtual environments
<p>Inhibition plays a powerful role in regulating network excitation and plasticity; however, the activity of defined interneuron types during spatial exploration remain poorly understood. Using two-photon calcium imaging, we recorded hippocampal CA1 somatostatin- and parvalbumin-expressing interneurons as mice performed a goal-directed spatial navigation task in new visual virtual reality (VR) contexts. Activity in both interneuron classes was strongly suppressed but recovered as animals learned to adapt the previously learned task to the new spatial context. Surprisingly, although there was a range of activity suppression across the population, individual somatostatin-expressing interneurons showed consistent levels of activity modulation across exposure to multiple novel environments, suggesting context-independent, stable network roles during spatial exploration. This work reveals population-level temporally dynamic interneuron activity in new environments, within which each interneuron shows stable and consistent activity modulation.</p>
How do users interact with Virtual Geographic Environments? Users' behavior evaluation in urban participatory planning [Dataset]
<p>VGEs used in the experimental study.</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.