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133 results for “natural environment”
Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia
<p>This dataset consists of raw RGB and NIR images captured using the DJI Inspire 1 UAV equipped with interchangeable RGB and NDVI-modified cameras. Data were collected across 27 diverse study sites in Serbia, representing a variety of ecological and landscape features. The UAV flights followed pre-defined grid missions, capturing high-resolution imagery with a ground sampling distance (GSD) of 3–4 cm. The collected images were processed to produce georeferenced orthomosaics, which serve as the basis for detailed land cover classification and analysis.</p>
Where nature and poverty meet: developing a multidimensional environment-poverty measure
<p>Dataset for the article: where nature and poverty meet: developing a multidimensional environment-poverty measure by Schleicher et al., 2024</p>
Natural variation further increases resilience of sorghum bred for chronically drought-prone environments
<p>Moisture stress is one of the major constraints for crop production in African Sahel. Here, we explore the potential to use natural genetic variation to build on the inherent drought tolerance of an elite sorghum cultivar (Teshale) bred for Ethiopian conditions including chronic drought, evaluating a backcross nested-association mapping population using 12 diverse founder lines crossed with Teshale under three drought-prone environments in Ethiopia. All twelve populations averaged higher head exsertion and lower leaf senescence than the recurrent parent in the two highest-stress environments, reflecting new drought resilience mechanisms from the donors. 154 QTLs were detected for eight drought responsive traits – the validity of these were supported in that 113 (73.4%) overlapped with QTLs previously detected for the same traits, concentrated in regions previously associated with 'stay-green' traits. Allele effects show that some favorable alleles are already present in the Ethiopian cultivar, however the exotic donors offer rich scope for increasing drought resilience. Using model-selected SNPs associated with eight traits in this study and three in a companion study, phenotypic prediction accuracies for grain yield were equivalent to genome-wide SNPs and were significantly better than random SNPs, indicating that these studied traits are predictive of sorghum grain yield.</p>
Data from: Correlation of shell phenotype and local environment suggests a role for natural selection in the evolution of Placostylus snails
The giant edible Placostylus snails of New Caledonia occur across a wide range of environmental conditions, from the dry southwest to the wetter central and northeastern regions. In large, slow-moving animals such as Placostylus, speciation could be assumed to be largely driven by allopatry and genetic drift as opposed to natural selection. We examined variation in shell morphology using geometric morphometrics and genetic structure within two species of Placostylus (P. fibratus, P. porphyrostomus), to determine the drivers of diversity in this group. Despite the current patchy distribution of snails on New Caledonia, both mtDNA and nuclear SNP data sets (>3000 loci) showed weak admixing between populations and species. Shell morphology was concordant with the genetic clusters we identified and had a strong relationship with local environment. The genetic data, in contrast to the morphological data, did not show concordance with climatic conditions, suggesting the snails are not limited in their ability to adapt to different environments. In sympatry, P. fibratus and P. porphyrostomus maintained genetic and morphological differences, suggesting a genetic basis of phenotypic variation. Convergence of shell shape was observed in two adjacent populations that are genetically isolated but experience similar habitat and climatic conditions. Conversely, some populations in contrasting environments were morphologically distinct although genetically indistinguishable. We infer that morphological divergence in the Placostylus snails of New Caledonia is mediated by adaptation to the local environment.
Risk Classification of contaminates sites in Anderstorp using the German Einzelfallbewertung Altlastenstandorte (EB) method from the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG)
<p>This data set includes the documents for risk classifying contaminated sites in Anderstorp, Sweden using the German Einzelfallbewertung Altlastenstandorte (EB) method from the Hessian Agency for Nature Conservation, Environment, and Geology (HLNUG).</p>
RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments
<p><strong>License + Attribution</strong></p> <p>This dataset is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>. Commercial usage is not permitted. If you use this dataset or the code in a scientific publication, please cite the following <a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Tobias_Fischer_RT-GENE_Real-Time_Eye_ECCV_2018_paper.html">paper</a>:</p> <blockquote> <p>@inproceedings{FischerECCV2018,<br> author = {Tobias Fischer and Hyung Jin Chang and Yiannis Demiris},<br> title = "{RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments}",<br> booktitle = {European Conference on Computer Vision},<br> year = {2018},<br> month = {September},<br> pages = {339--357}<br> }</p> </blockquote> <p>This work was supported in part by the Samsung Global Research Outreach program, and in part by the EU Horizon 2020 Project PAL (643783-RIA).</p> <p>More information can be found on the Personal Robotic Lab's website: <a href="https://www.imperial.ac.uk/personal-robotics/software/">https://www.imperial.ac.uk/personal-robotics/software/</a>.</p> <p><strong>Overview</strong></p> <p>The dataset consists of two parts: 1) One where the eyetracking glasses were worn (and thus ground truth labels for head-pose and eye gaze are available; suffix <em>_glasses</em>), and 2) One with natural appearances (no eyetracking glasses are worn; suffix <em>_noglasses</em>). The <em>_noglasses</em> images were used to train subject-specific GANs, and these GANs were used to inpaint the region covered by the eyetracking glasses in the <em>_glasses</em> images.</p> <p>There is code accompanying this dataset: <a href="https://github.com/Tobias-Fischer/rt_gene">https://github.com/Tobias-Fischer/rt_gene</a>. Please use the issue tracker in the code respository if you have questions regarding the dataset.</p> <p><strong>Subjects / 3-Fold evaluation</strong></p> <p>15 participants were recorded in 17 sessions. Session 014 is a second recording of participant 002, and session 015 is a second recording of participant 005 (different days and different camera poses were used).</p> <p>We used a 3-fold evaluation, with the three folds consisting of the following sessions (test on one of the groups, training with the remaining two groups):</p> <ol> <li>'s001', 's002', 's008', 's010'</li> <li>'s003', 's004', 's007', 's009'</li> <li>'s005', 's006', 's011', 's012', 's013'</li> </ol> <p>The validation set consists of sessions 's014', 's015' and 's016'.</p> <p>While the MATLAB script (<em>prepare_dataset.m</em>; see code repository) creates train and test images for each subject, all images were used for the evaluation (see <em>evaluate_model.py</em>).</p> <p><strong>Labeled dataset (sXYZ_glasses)</strong></p> <p>The file for each subject contains the following information:</p> <ul> <li>label_combined.txt This is the main file containing labels. The formatting is as follows:<br> seq_number, [head pose: right(pos) / left(neg), up (pos) / down(neg)], [gaze: right(pos) / left(neg), up(pos) / down(neg)], timestamp</li> <li>label_headpose.txt This file contains more detail about the head pose of the subject.<br> seq_number, [head pose translation: further(pos) / closer(neg), left(pos) / right(neg), up(pos) / down(neg)], [head pose rotation: roll right(pos) / roll left(neg), down(pos) / up(neg), rotate left(pos), rotate right(neg)], timestamp</li> <li>kinect2_calibration.yaml<br> The kinect2_calibration.yaml file contains the camera projection matrix in ROS format (this file should not be required).</li> <li>kinect2_pose.txt<br> The kinect2_pose.txt file contains the pose of the Kinect with respect to the motion capture system (this file should not be required).</li> <li>"original" folder <ul> <li>The face_before_inpainting folder contains the face with a large margin to the left and right.</li> <li>The mask folder contains images indicating the regions of the eyetracking glasses, aligned with the images in the face_before_inpainting folder.</li> <li>The overlay folder contains images where the mask was overlaid on the face_before_inpainting images.</li> <li>The face folder contains the face image extracted using MTCNN with a tighter margin.</li> <li>The left and right folders contain the left and right eye image areas.</li> </ul> </li> <li>The face, left and right images were used as baseline comparison in the paper (Fig. 7 without inpainting).</li> <li>"inpainted" folder <ul> <li>The face_after_inpainting folder contains images corresponding to the ones in the face_before_inpainting folder after applying the inpainting.</li> <li>Then, the images contained in the face, left and right folders were extracted using MTCNN as above.</li> </ul> </li> </ul> <p><strong>Unlabeled dataset (sXYZ_noglasses)</strong></p> <ul> <li>kinect2_calibration.yaml<br> This file contains the camera projection matrix in ROS format (this file should not be required).</li> <li>kinect2_pose.txt<br> This file contains the pose of the Kinect with respect to the motion capture system (this file should not be required).</li> <li>"face" folder<br> This folder contains the faces that can be used to train the GANs (without eyetracking glasses being worn).</li> </ul>
Fig. 3 in Discoderus LeConte Species Exhibit Episodic Emergences in both Urban and Natural Environments of Arizona, USA (Coleoptera: Carabidae)
Fig. 3. Syntype specimens of Discoderus species described by Casey from Arizona. All scale bars 5 mm. A) D. aequalis Casey, 1914, B) D. congruens Casey, 1914, C) D. obsidianus Casey, 1914, D) D. papagonis Casey, 1924, E) D. hesperus Casey, 1914, junior subjective synonym of D. parallelus, F) D. pinguis Casey, 1884, G) D. subviolaceus Casey, 1914, H) D. symbolicus Casey, 1914.
Fig. 1 in Discoderus LeConte Species Exhibit Episodic Emergences in both Urban and Natural Environments of Arizona, USA (Coleoptera: Carabidae)
Fig. 1. Discoderus obsidianus emergence in 2022. A) Dorsal habitus, female from Mesa, AZ (scale bar 5 mm), B) Accumulation of suspected D. obsidianus in Scottsdale, AZ on a front porch over a single night.
Fig. 2 in Discoderus LeConte Species Exhibit Episodic Emergences in both Urban and Natural Environments of Arizona, USA (Coleoptera: Carabidae)
Fig. 2. Distribution map of Discoderus obsidianus. Localities based on digital records from SCAN (circles), GBIF (triangles), BugGuide (diamonds), and physical specimens from this event (squares). The Texas record is an outlier for which the identification was not confirmed by the authors.
Data from: Host species and environmental effects on bacterial communities associated with Drosophila in the laboratory and in the natural environment
The fruit fly Drosophila is a classic model organism to study adaptation as well as the relationship between genetic variation and phenotypes. Although associated bacterial communities might be important for many aspects of Drosophila biology, knowledge about their diversity, composition, and factors shaping them is limited. We used 454-based sequencing of a variable region of the bacterial 16S ribosomal RNA gene to characterize the bacterial communities associated with wild and laboratory Drosophila isolates. In order to specifically investigate effects of food source and host species on bacterial communities, we analyzed samples from wild Drosophila melanogaster and D. simulans collected from a variety of natural substrates, as well as from adults and larvae of nine laboratory-reared Drosophila species. We find no evidence for host species effects in lab-reared flies; instead, lab of origin and stochastic effects, which could influence studies of Drosophila phenotypes, are pronounced. In contrast, the natural Drosophila–associated microbiota appears to be predominantly shaped by food substrate with an additional but smaller effect of host species identity. We identify a core member of this natural microbiota that belongs to the genus Gluconobacter and is common to all wild-caught flies in this study, but absent from the laboratory. This makes it a strong candidate for being part of what could be a natural D. melanogaster and D. simulans core microbiome. Furthermore, we were able to identify candidate pathogens in natural fly isolates.
Data from: Short-term microbial effects of a large-scale mine-tailing storage facility collapse on the local natural environment
We investigated the impacts of the Mount Polley tailings impoundment failure on chemical, physical, and microbial properties of substrates within the affected watershed, comprised of 70 hectares of riparian wetlands and 40 km of stream and lake shore. We established a biomonitoring network in October of 2014, two months following the disturbance, and evaluated riparian and wetland substrates for microbial community composition and function via 16S and full metagenome sequencing. A total of 234 samples were collected from substrates at 3 depths and 1,650,752 sequences were recorded in a geodatabase framework. These data revealed a wealth of information regarding watershed-scale distribution of microbial community members, as well as community composition, structure, and response to disturbance. Substrates associated with the impact zone were distinct chemically as indicated by elevated pH, nitrate, and sulphate. The microbial community exhibited elevated metabolic capacity for selenate and sulfate reduction and an abundance of chemolithoautotrophs in the Thiobacillus thiophilus/T. denitrificans/T. thioparus clade that may contribute to nitrate attenuation within the affected watershed. The most impacted area (a 6km stream connecting two lakes) exhibited 30% lower microbial diversity relative to the remaining sites. The tailings impoundment failure at Mount Polley Mine has provided a unique opportunity to evaluate functional and compositional diversity soon after a major catastrophic disturbance to assess metabolic potential for ecosystem recovery.
Video Data: Spatiotemporal visual statistics of aquatic environments in the natural habitats of zebrafish
<p>Video dataset accompanying " Spatiotemporal visual statistics of aquatic environments in the natural habitats of zebrafish." See accompanying <a href="https://github.com/eacooper/ZebrafishAquaticVisualStatsCode">Github repository</a> for more documentation and analysis code.</p>
Effects of Natural Sounds on Attention Restoration in Noisy Environment
ClinicalTrials.gov study NCT05009784. IPD Sharing: NO. Countries: 1. Publications: 1.
Impacts of Nitrogen Deposition in the Natural Environment on Pollen Allergy in Belgium
ClinicalTrials.gov study NCT06714149. IPD Sharing: NO. Countries: 1. Publications: 2.
Explore the Effects of Virtual Reality Natural Environment of Older People
ClinicalTrials.gov study NCT05803460. IPD Sharing: NO. Countries: 1. Publications: 0.
Stroke Rehabilitation Utilizing Therapeutical Methods Designed for Nature Environments
ClinicalTrials.gov study NCT06633146. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Alcohol Impaired Driving: From the Laboratory to the Natural Environment
ClinicalTrials.gov study NCT03846050. IPD Sharing: NO. Countries: 1. Publications: 1.
Silver Agri Age: Longevity, Intrinsic Capacity and Cognitive Impairment Into Farmlands and Natural Environments: Updating Contexts Following the "Montessori" Action
ClinicalTrials.gov study NCT06754202. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Coral reef carbonate budgets and ecological drivers in the central Red Sea – a naturally high temperature and high total alkalinity environment
Open the record for dataset details and reuse information.
Providing virtual nature experiences to incarcerated men reduces stress and increases interest in the environment
Open the record for dataset details and reuse information.
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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.