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1,356 results for “Human Activities”
Figure 4 in Human activity mediates reciprocal distribution and niche separation of two sympatric mongoose species on the Pothwar Plateau, Pakistan
Figure 4. Photomicrographs of whole mount of hair structure of five different rodent species (recovered from fecal samples of gray mongoose and reference hair of rodents) consumed by the gray mongoose; A) Whole mount of recovered hair of Golunda ellioti, B) Whole mount of reference hair of Golunda ellioti, C) Whole mount of recovered hair of Tetera indica, D) Whole mount of reference hair of Tetera indica, E) Whole mount of recovered hair of Nesokia indica, F) Whole mount of reference hair of Nesokia indica, G) Whole mount of recovered hair of Rattus rattus, H) Whole mount of reference hair of Rattus rattus, I) Whole mount of recovered hair of Mus musculus, J) Whole mount of reference hair of Mus musculus.
Linked collectors and determiners for: Herbarium Specimens of Museum of Nature and Human Activities, Hyogo Pref., Japan.
Natural history specimen data linked to collectors and determiners held within, "Herbarium Specimens of Museum of Nature and Human Activities, Hyogo Pref., Japan". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/81031dbc-47f2-470d-9efa-e809b8310d62">https://bionomia.net/dataset/81031dbc-47f2-470d-9efa-e809b8310d62</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/81031dbc-47f2-470d-9efa-e809b8310d62">https://gbif.org/dataset/81031dbc-47f2-470d-9efa-e809b8310d62</a>. Formatted as a Frictionless Data package.
COESO: funding entities of citizen science activities on social sciences and humanities topics
<p>This database has been compiled by the Ibercivis team working in COESO together with contributions from other members of the COESO Consortium and representatives of the COESO Advisory Board, in addition to those who contributed information through the surveys and interviews.</p>
Figure 2 in A not so natural history of the tarantula Brachypelma vagans: Interaction with human activity
Figure 2. Preferred orientations of the burrows for Brachypelma vagans. (A) For all sites (n5110); (B) for backyards (BY1 and BY2, n550) and for football field (FG and FC, n560).
Figure 3 in A not so natural history of the tarantula Brachypelma vagans: Interaction with human activity
Figure 3. Mean diameters of the burrow entrances of Brachypelma vagans. (A) Between backyards (BY: BY1 and BY2) and football field (FB: FC and FG); (B) among individuals. ANOVA test: **P,0.01; Fisher LSD, interclass group at 1% level.
Figure 1 in A not so natural history of the tarantula Brachypelma vagans: Interaction with human activity
Figure 1. Density of Brachypelma vagans in different vegetation/land use classes representing a gradient of human activity. F: mean of MF and SF. Site class codes are referred to in the Methods section.
Tissue microarray data and processing scripts for The molecular consequences of androgen activity in the human breast
<p>This repository contains raw and processed data from the CODEX imaging dataset in this publication.</p> <p>The RAW data tables provide the resulting nuclei and membrane staining signals obtained from the nuclei segmentation described in the Methods.</p> <p>The processed data file provides the clustered and annotated version described in Methods.</p> <p>The repository also contains two scripts describing the processing of snRNA-seq and snATAC-seq data.</p>
WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32
<p><strong>WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32</strong></p> <p>This repository contains the WiFi CSI human presence detection and activity recognition datasets proposed in [1].</p> <p><strong>Datasets</strong></p> <ul> <li><strong>DP_LOS</strong> - Line-of-sight (LOS) presence detection dataset, comprised of 392 CSI amplitude spectrograms.</li> <li><strong>DP_NLOS </strong>- Non-line-of-sight (NLOS) presence detection dataset, comprised of 384 CSI amplitude spectrograms.</li> <li><strong>DA_LOS</strong> - LOS activity recognition dataset, comprised of 392 CSI amplitude spectrograms.</li> <li><strong>DA_NLOS</strong> - NLOS activity recognition dataset, comprised of 384 CSI amplitude spectrograms.</li> </ul> <p>Table 1: Characteristics of presence detection and activity recognition datasets. </p> <table> <tbody> <tr> <td><strong>Dataset</strong></td> <td><strong>Scenario</strong></td> <td><strong>#Rooms</strong></td> <td><strong>#Persons</strong></td> <td><strong>#Classes</strong></td> <td><strong>Packet Sending Rate</strong></td> <td><strong>Interval </strong></td> <td><strong>#Spectrograms</strong></td> </tr> <tr> <td>DP_LOS</td> <td>LOS</td> <td>1</td> <td>1</td> <td>6</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>392</td> </tr> <tr> <td>DP_NLOS</td> <td>NLOS</td> <td>5</td> <td>1</td> <td>6</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>384</td> </tr> <tr> <td>DA_LOS</td> <td>LOS</td> <td>1</td> <td>1</td> <td>3</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>392</td> </tr> <tr> <td>DA_NLOS</td> <td>NLOS</td> <td>5</td> <td>1</td> <td>3</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>384</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Format</strong></p> <p>Each dataset employs an 8:1:1 training-validation-test split, defined in the provided label files <em>trainLabels.csv</em>, <em>validationLabels.csv</em>, and <em>testLabels.csv</em>. Label files use the sample format [<em>i c</em>], with <em>i</em> corresponding to the spectrogram index (i.png) and <em>c </em>corresponding to the class. For presence detection datasets (DP_LOS <em>, </em>DP_NLOS), c in {0 = "no presence", 1 = "presence in room 1", ..., 5 = "presence in room 5"}. For activity recognition datasets (DA_LOS <em>, </em>DA_NLOS), c in {0="no activity", 1="walking", and 2="walking + arm-waving"}. Furthermore, the mean and standard deviation of a given dataset are provided in <em>meanStd.csv</em>.</p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] Strohmayer, Julian, and Martin Kampel. "WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32" <em>International Conference on Computer Vision Systems</em>. Cham: Springer Nature Switzerland, 2023. </p> <p>BibTeX citation:</p> <pre>@inproceedings{strohmayer2023wifi, title={WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={International Conference on Computer Vision Systems}, pages={41--50}, year={2023}, organization={Springer} }</pre>
Code repository for: Base editing mutagenesis maps functional alleles to tune human T cell activity
<p>Jupyter notebook and supplemental datasets required to created critical figures for the publication.</p>
Relationships of climate, human activity, and fire history to spatiotemporal variation in annual fire probability across California: Source Code and Core Data
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Data from: Human avoidance, selection for darkness and prey activity explain wolf diel activity in a highly cultivated landscape
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Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming
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Genomic footprints of (pre) colonialism: Population declines in urban and forest túngara frogs coincident with historical human activity
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Urbanization alters the song propagation of two human-commensal songbird species: Active space, amplitude, and attenuation code
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Raw microscopy data from: Endoplasmic reticulum stress activates human IRE1α through reversible assembly of inactive dimers into small oligomers
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Layer-dependent activity in human prefrontal cortex during working memory
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The Spring Festival Effect: the change of NO2 column concentration in China caused by the migration of human activities
<p>The Spring Festival is the most important holiday in China, human activity and population mobility may contribute greatly to air quality, especially in the megacities. According to the satellite-based tropospheric nitrogen dioxide (NO<sub>2</sub>) column and ground-based observational concentration of NO<sub>2</sub> in the megacities from 2013 to 2018 around the Spring Festival, we found that NO<sub>2</sub> concentration decreases obviously during the Spring Festival and rebounds after the Spring Festival in China, particularly in the megacities. The tropospheric NO<sub>2</sub> columns density around Beijing-Tianjin-Hebei region decreases about 40% than the period before the festival, and it in Beijing decreases by 41.6% and rebounds by 22.3%. While under the Coronavirus disease 2019 (COVID-19) pandemic progresses, the tropospheric NO<sub>2</sub> columns density in Beijing decreases by 56.2% and rebounds only by 6.8% in 2020.</p>
Output data from: From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions
<p>This repository includes output data from the following article:</p> <p><strong>Montfort, F., Bégué, A., Leroux, L., Blanc, L., Gond, V., Cambule, A.H., Remane, I.A.D., Grinand, C., 2020. From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions. <em>Land Degrad Dev</em>.; 32: 49– 65. <a href="https://doi.org/10.1002/ldr.3704">https://doi.org/10.1002/ldr.3704</a></strong></p> <p>This study aimed at characterizing and mapping the underlying factors (human or climatic) in land productivity changes over the 2000-2016 period, in order to assess land degradation in Mozambique.</p> <p>The methodology is based on remote sensing methodology. Land productivity change were first analyzed using MODIS NDVI time-series (2000–2016), and a two-step framework was then used to understand the main factors of these productivity changes, using climate times series (CHIRPS data for rainfall and CRU data for temperature), Land Use and Land Cover Change (LULCC) maps (Laurel project LULCC map), and ground knowledge.</p> <p>This repository includes raster data of annual land productivity change over the 2000-2016 period, annual land productivity climate factors, potential land productivity decrease factors and potential productivity increase factors. Data are available as GeoTIFF raster files at 250 m resolution in the UTM 37S projection (EPSG: 32737).</p>
Human activities with videos, inertial units and ambient sensors
Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot's RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results.
Dataset for: Spinal motion and muscle activity during active trunk movements - comparing sheep and humans adopting upright and quadrupedal postures
<p>Datasets used for the manuscript "Spinal motion and muscle activity during active trunk movements - comparing sheep and humans adopting upright and quadrupedal postures", accepted in PLOS ONE</p>
ScienceDex guides
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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.