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2,591 results for “remotization”
Transformation of social relationships in COVID-19 America: Remote communication may amplify political echo chambers
<p>The COVID-19 pandemic, with millions of Americans compelled to stay home and work remotely, presented an opportunity to explore the dynamics of social relationships in a predominantly remote world. Using the 1972-2022 General Social Surveys, we found that the pandemic significantly disrupted the patterns of social gatherings with family, friends, and neighbors, but only momentarily. Drawing from the nationwide ego-network surveys of 41,033 Americans from 2020 to 2022, we found that the size and composition of core networks remained stable, though political homophily increased among non-kin relationships compared to previous surveys between 1985 and 2016. Critically, heightened remote communication during the initial phase of the pandemic was associated with increased interaction with the same partisans, though political homophily decreased during the later phase of the pandemic when in-person contacts increased. These results underscore the crucial role of social institutions and social gatherings in promoting spontaneous encounters with diverse political backgrounds.</p>
REMOTE: DEMO Greece KPIs
<p>This dataset includes the KPIs of the DEMO Greece of the REMOTE project (GA 779541)</p>
REMOTE: DEMO Spain KPIs
<p>This dataset includes the KPIs of the DEMO Spain of the REMOTE project (GA 779541)</p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
Fig. 1 in Use of filter papers to determine seroprevalence of Toxoplasma gondii among hunted ungulates in remote Peruvian Amazon
Fig. 1. Animals were hunted in the area surrounding the community of Nueva Esperanza, the only permanent settlement located along the Yavarí-Mirín River. The study site's geographic remoteness reflects extremely limited contact with domestic animals and humans, which eliminates the effect of spillover from domestic animals as a potential source of infection for wildlife. The data from this study are likely to shed light on the maintenance of T. gondii in its natural environment.
Good vibrations: Remote-tactile foraging success of wading birds is positively affected by the water content of substrates they forage in
<p>Some taxa of wading birds can locate buried prey by detecting vibratory cues in their foraging substrates while probe-foraging, using a sensory modality called "remote-touch". As more saturated substrates transmit vibrations better, we predict that these birds can detect prey in wetter substrates more easily. We used sensory assays to test whether substrate water content affects the remote-touch foraging success rate of Hadeda Ibises, <em>Bostrychia hagedash</em>. The birds were more successful at locating prey using vibratory cues than when relying on random direct contact with the beak alone. Their remote-touch foraging success rate was positively affected by increasing water contents of the soil, but water content had no effect on their direct contact foraging success (indicating this is not an artefact of ease of probing). This may partially explain the link between the range expansion of this species in southern Africa and increased soil irrigation, as it is easier for the birds to detect prey in wetter substrates. Thus, it is likely that the distribution of other remote-touch foraging birds is affected by substrate water content, and as many of these species are endangered and rely on sensitive wetland habitats, it is vital to understand their sensory requirements for foraging.</p>
Greg Kolodziejzyk's 13-year associative remote viewing experiment results
<p>The research that created this data is described in:</p> <ul> <li>Kolodziejzyk, G. (2012). Greg Kolodziejzyk's 13-year associative remote viewing experiment results. <em>The Journal of Parapsychology, 76</em>(2), 349.</li> </ul> <p>Five files are included:</p> <ul> <li>GregsARVdata2.txt - This is the raw data provided by Greg Kolodziejzyk</li> <li>Code book_0.txt - Code book (raw data)</li> <li>GregsARVdata2-dates-fixed.csv - Cleaned data (date formats standardised)</li> <li>Code book (cleaned data).pdf - Code book (cleaned data)</li> <li>Cleaning procedure_0.zip - Procedure for transforming from raw data to cleaned data.</li> </ul>
Greg Kolodziejzyk's 13-year associative remote viewing experiment results (session-level data)
<p>This dataset is a transformation of Greg Kolodziejzyk's remote viewing data (see Related datasets below). Greg used a "rapid-fire" technique whereby several short free-response remote viewing trials were completed in a single session. The trial-level data was transformed by Adrian Ryan into session-level Z-scores by exact binomial, in order that the data could be combined with those from other experiments, for the analyses reported here:</p> <ul> <li>Ryan, A., & Spottiswoode, J. (2015). Variation of ESP by Season, Local Sidereal Time, and Geomagnetic Activity. Extrasensory Perception, 377-394.</li> </ul> <p>Three files are included:</p> <ul> <li>Data file</li> <li>Code book</li> <li>Transform Procedure: The R script used to transform the original trial-level data into session-level data.</li> </ul>
Seasonal orographic effect of North American Mountain Range at different levels and its remote control on tropical climate
<p><a name="OLE_LINK36"></a><a name="OLE_LINK37"></a><a name="OLE_LINK81"></a><a name="OLE_LINK6"></a><span><span><span><span>Orography significantly influences global climate patterns. </span></span></span></span><a name="OLE_LINK32"></a><a name="OLE_LINK33"></a><span><span><span><span><span><span>Previous studies show the North American Mountain Range (NAMR) impacts regional climates seasonally but have not thoroughly illustrated the seasonally different atmospheric responses in the lower and upper troposphere, respectively. </span></span></span></span></span></span><span><span><span><span><span>Using the Community Earth System Model version 1.2 with a slab ocean configuration, we investigate the NAMR’s seasonal impacts by simulating scenarios with and without the mountain range. Our findings reveal that the NAMR induces contrasting responses in sea surface temperature (SST) and precipitation off California in different seasons, indicating different underlying mechanisms. Through analysis of large-scale circulation and local energy budgets, we find that in summer, the NAMR reinforces the North Pacific High causing SST cooling and drying off California. This cooling propagates to the equatorial Pacific via anomalous northeasterlies, influencing the Intertropical Convergence Zone and initiating a climatic signal through the Pacific Meridional Mode, which crosses the equator and affects Southern Hemisphere temperatures. In winter, the NAMR reduces wind speed and evaporation, leading to SST warming off California, amplified by SST-cloud feedback. In the upper troposphere, we observe seasonal shifts in jet stream patterns: during winter, a weakened, equatorward-shifted jet over the Pacific and a strengthened, poleward-shifted branch over the Atlantic; in summer, the jet stream intensifies over and downstream of the mountains while weakening upstream. Our research highlights distinct seasonal mechanisms by which the NAMR influence climate patterns, linking mid-latitude climate variations to equatorial, cross-hemispheric and global changes.</span></span></span></span></span></p>
SUPPLEMENTARY DATA TO: Using a citizen science approach to assess nanoplastics pollution in remote high-altitude glaciers
<p>This is the repository of the supplementary data, and it contains the following files: </p> <p>Raw data files as the original output of TD-PTR-ToF-MS for all the samples, all the blanks, all the spikes and all the calibration runs (.h5 files in three zip arcives)</p> <p>Polymer library files (a zip archive including csv files.</p> <p>A data analysis file including raw data, blank subtraction and LOD correction of all measurements (xlsx file).</p> <p>A fingerprinting result file for each plastic type (xlsx file)</p> <p>A data analysis file after plastic fingerprinting (xlsx file). </p> <p> </p> <p> </p>
Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) III: Introducing the KEN; Data for CH4
<p>We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.8-1.5 micron, with 25 evenly spaced 20%, 30%, and 40% bandpasses in this range. The SNR ranges from 3-20. We present the lower and upper wavelength per bandpass, the input CH4 value (abundance case), the retrieved CH4 value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for CH4. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) III: Introducing the KEN, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input CH4': str})</p>
A Scene-Level Method for Estimating Small River Widths in Complex Terrain Using Remote Sensing
<p><strong>Files and Descriptions:</strong></p> <p>1. <strong>TP_Lake.csv</strong>: This CSV file contains identified lakes in the Tibetan Plateau.</p> <p>2. <strong>TP_River_Monthly_Statistics.csv</strong>: Monthly statistics for river data on the Tibetan Plateau, including estimations like active channel percentage and width for different river orders.</p> <p>3. <strong>S2RiverWidth.py</strong>: The Python script that contains the main code for river width estimation model. This script includes the functions for preprocessing Sentinel-2 images and predicting river widths.</p> <p>4. <strong>best_model_vCloud10.pth</strong>: The pre-trained deep learning model weights used for river width estimation. This model is a ResNeXt model fine-tuned on our dataset. It accepts Sentinel-2 TOA image with cloud percentage <10% (SCL).</p> <p>5. <strong>Example.tif</strong>: A Sentinel-2 TOA image in .tif format, used for testing the river width estimation model.</p> <p><strong>Model Input Requirements:</strong><br>The model requires a Sentinel-2 TOA image in .tif format as input. The image should be scaled by a factor of 10,000 (with reflectance values range from 0 to 1). The `get_model_input` function automatically crops the image to 224x224 pixels around the center to fit the model's input requirements.</p>
OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application
<p>Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by <em>the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application</em> are shared. All the images have been radiometric calibrated and atmospheric corrected by the author.</p> <p>Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," <em>IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)</em>, 2021.</p> <p>More information about the author can be found at https://jianghe96.github.io/</p> <p>If this dataset is helpful please cite as:</p> <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
<p>The benchmark code is available at: <a href="https://github.com/Junjue-Wang/LoveDA">https://github.com/Junjue-Wang/LoveDA</a></p> <p><strong>Highlights: </strong></p> <ol> <li>5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan</li> <li>Focus on different geographical environments between Urban and Rural</li> <li>Advance both semantic segmentation and domain adaptation tasks</li> <li>Three considerable challenges: multi-scale objects, complex background samples, and inconsistent class distributions</li> </ol> <p><strong>Reference:</strong></p> <pre><code>@inproceedings{wang2021loveda, title={Love{DA}: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation}, author={Junjue Wang and Zhuo Zheng and Ailong Ma and Xiaoyan Lu and Yanfei Zhong}, booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks}, editor = {J. Vanschoren and S. Yeung}, year={2021}, volume = {1}, pages = {}, url={https://datasets-benchmarks proceedings.neurips.cc/paper/2021/file/4e732ced3463d06de0ca9a15b6153677-Paper-round2.pdf} }</code></pre> <p><strong>License:</strong></p> <p>The owners of the data and of the copyright on the data are RSIDEA, Wuhan University. Use of the Google Earth images must respect the "Google Earth" terms of use. All images and their associated annotations in LoveDA can be used for academic purposes only, <strong>but any commercial use is prohibited. (CC BY-NC-SA 4.0)</strong></p>
Tower-based remote sensing data for understory vegetation at Delta Junction, Alaska 2019-2020
<p> Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from August 2019-December 2020. We provide daily averaged vegetation indices for a mix of understory lichen and moss species in a black spruce dominated forest. We compute near-infrared vegetation index (NIRv), normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and chlorophyll-carotenoid index (CCI) averaged for three understory targets at NEON Delta Junction. We also provide daily averaged photosynthetically active radiation (PAR) and solar zenith angle (SZA). Finally, we provide the average diurnal profiles of all the aforementioned metrics for 4 20-day windows in winter, spring, summer, and fall. </p>
Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
<p><strong>Description</strong></p> <p>This dataset contains remote sensing data from the ESA Copernicus missions Sentinel-2 and Sentinel-5P (tropsopheric NO2 column density) in the 2018-2020 timespan. The satellite measurements each cover ~3100 locations in Europe and ~100 on the US Westcoast, each with a size of 1.2x1.2km. The locations are selected such that each measurement is centered at the location of an air quality measurement station on the ground (from the European Environment Agency or the US Environmental Protection Agency, measuring NO2). This makes it possible to analyze spatiotemporally aligned remote sensing and ground-based measurements.</p> <p> The 13 Sentinel-2 bands are upsampled (bilinear) to 10m resolution and cropped to 120x120 pixel. For some locations multiple Sentinel-2 images are available. The images are stored as binary numpy `.npy` files organized into directories based on their locations. </p> <p>The Sentinel-5P data was pre-processed by mapping the measurements from consecutive satellite overpasses onto a common rectangular grid of 0.05×0.05◦(∼5×5km) across Europe. To harmonize the Sentinel-2 (10m to 60m, upscaled to 10m) and Sentinel-5P (5×3.5km, rescaled to 5×5km) imaging resolutions, the Sentinel-5P data is linearly interpolated to 10m resolution and cropped to 120×120 pixel around the locations of interest. Additionally, all measurements with a QA flag (qa_value) below 75 were discarded, following ESA recommendations. The Sentinel-5P data are stored as `.netcdf` file, organized by location. For each location, three such files are available, containing averaged Sentinel-5P measurements at different temporal frequencies (2018-2020, quarterly, monthly).</p> <p>The <p>samples_{frequency}_{area}.csv</p> files provide a list of observations with the corresponding file paths to a (cloud-free) Sentinel-2 image, the Sentinel-5P measurement, and the average NO2 concentration measurement by the EEA or EPA ground station. These files can be used for easy data-loading.</p> <p><strong>Content</strong></p> <p>The data is organized into the following files:</p> <ul> <li>README.md - this file</li> <li>sentinel-2-eea.tar.gz [33.1GB]</li> <li>sentinel-5p-eea.tar.gz [80.1GB]</li> <li>samples_2018_2020_eea.csv </li> <li>samples_quarterly_eea.csv</li> <li>samples_monthly_eea.csv</li> <li>sentinel-2-epa.tar.gz [0.15GB]</li> <li>sentinel-5p-epa.tar.gz [1.8GB]</li> <li>samples_2018_2020_epa.csv</li> <li>samples_quarterly_epa.csv</li> <li>samples_monthly_epa.csv</li> </ul> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p><em>Scheibenreif, L., Mommert, M., Borth, D., "</em>Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space<em>", Tackling Climate Change with Machine Learning workshop at ICML 2021.</em></p> <p>Please refer to this publication for additional information on the data set.</p> <p>This data set contains modified Copernicus Sentinel data acquired in 2018-2020, processed by ESA.</p> <p> </p> <p><strong>Responsible Author</strong></p> <p>Linus Scheibenreif<br> University of St. Gallen, Institute of Computer Science<br> Chair Artificial Intelligence and Machine Learning<br> linus.scheibenreif ( at ) unisg.ch</p>
Raspberry Pi nest cameras – an affordable tool for remote behavioural and conservation monitoring of bird nests
<p><span><span><span><span><span><span><span><span><span><span><span>1. Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioural ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. Here we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof – especially to humidity and salt spray. The camera was paired with a passive infra-red sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviours and interspecific interactions of two sympatric species of breeding storm-petrel (Monteiro's storm-petrel <i>Hydrobates monteiroi</i> and Madeiran storm-petrel <i>Hydrobates castro</i>) during their chick-rearing periods.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30 s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Here we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently the camera-build methods and scripts could be applied easily to many different species that also utilise cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviours and interspecies interactions.</span></span></span></span></span></span></span></span></span></span></span></p>
Commodity Dataset | Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform
<p>(Commodity data in raster format) Supplementary materials for “Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform” that had been published on Land MDPI (2020). doi:<a href="https://doi.org/10.3390/land9100377">10.3390/land9100377</a> </p> <p>The data included:</p> <p>1) Raster data of commodity maps (TIFF Compressed in ZIP)</p> <p>2) READ ME for the dataset (DOCX)</p> <p>3) Legend for raster data in ArcGIS Format (LYR)</p> <p> </p>
Dataset from Remote analysis of Sputum Smears for Mycobacterium Tuberculosis Quantification using Digital Crowdsourcing
<p>Worldwide, TB is one of the top 10 causes of death and the leading cause from a single infectious agent. Although the development and roll out of Xpert MTB/RIF has recently become a major breakthrough in the field of TB diagnosis, smear microscopy remains the most widely used method for TB diagnosis, especially in low- and middle-income countries.</p> <p>This is a minimal dataset to reproduce our research that tests the feasibility of a crowdsourced approach to tuberculosis image analysis. In particular, we investigated whether anonymous volunteers with no prior experience would be able to count acid-fast bacilli in digitized images of sputum smears by playing an online game. Following this approach 1790 people identified the acid-fast bacilli present in 60 digitized images, the best overall performance was obtained with a specific number of combined analysis from different players and the performance was evaluated with the F1 score, sensitivity and positive predictive value, reaching values of 0.933, 0.968 and 0.91, respectively.</p> <p>The dataset includes 24 digitized images of sputum smears and the corresponding gameplays clicks. </p>
A new remote sensing benchmark dataset for machine learning applications : MultiSenGE
<p>[UPDATE] You can now access MultiSen (GE and NA) collection though this portal : <a href="https://doi.theia.data-terra.org/ai4lcc/?lang=en">https://doi.theia.data-terra.org/ai4lcc/?lang=en</a></p> <p>MultiSenGE is a new large-scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. It contains 8,157 patches of 256 * 256 pixels for Sentinel-2 L2A, Sentinel-1 GRD and a regional LULC topographic regional database. </p> <p>Every file has a specific nomenclature :</p> <ul> <li>Sentinel-1 patches: {tile}_{date}_S1_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>Sentinel-2 patches: {tile}_{date}_S2_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>Ground reference patches: {tile}_GR_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>JSON Labels: {tile}_{x-pixel-coordinate}_{y-pixel-coordinate}.json</li> </ul> <p>where <em>tile</em> is the Sentinel-2 tile number, <em>date</em> the date of acquisition of the patch, <em>x-pixel-coordinate</em> and <em>y-pixel-coordinate</em> are the coordinates of the patch in the tile.</p> <p>In addition, you can find a set of useful python tools for extracting information about the dataset on Github : <a href="https://github.com/r-wenger/MultiSenGE-Tools">https://github.com/r-wenger/MultiSenGE-Tools</a></p> <p>First experiments based on this <em>dataset</em> is in press in ISPRS Annals : <strong>Wenger, R., </strong>Puissant, A., Weber, J., Idoumghar, L., and Forestier, G.: MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 635–640, https://doi.org/10.5194/isprs-annals-V-3-2022-635-2022, 2022.</p> <p>Due to the large size of the dataset, you will only find the associated JSON files on this Zenodo repository. To download the Sentinel-1, Sentinel-2 patches and the reference data, please do so via these links: </p> <ul> <li>Sentinel-1 temporal serie patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz</a></li> <li>Sentinel-2 temporal serie patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz</a></li> <li>Ground reference patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz</a></li> <li>JSON files for each patch: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz</a></li> </ul>
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.