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1,433 results for “masks”
Figure 2 in Biology and management of the masked chafer Cyclocephala disticcta Burmeister &Melolonthidae, Dynastinae, Cyclocephalini)
Figure 2. Larval stage of Cyclocephala disticcta bred in captivity. A, Larva of the 2nd instar defecating &mean size: 1.3 mm); B, Cephalic capsule of the 1st, 2nd and 3rd instars, respec* tively; C, Transition between the 3rd instar &below) and pre*pupa &above).
Image segmentation masks for curved arrows on molecular images from chemical reaction mechanism images
<p>The dataset presented herein is designed as a ground truth for image segmentation tasks focused on noise extraction in Optical Chemical Structure Recognition (OCSR) processes. It comprises 73 manually extracted and annotated images from real reaction mechanism images, along with 5320 synthetic molecular images generated using RDKit, each featuring computer-drawn curved arrows on random locations on the molecular image pertinent to their respective tasks. Curved arrows are prevalent in chemical reaction mechanism images and significantly impact the accuracy of molecular identity recognition. This dataset aims to enhance OCSR tasks by enabling the pretreatment of molecular images to remove noise, thereby improving molecular recognition accuracy.</p>
Fig. 1 in Common occurrence of Enterocytozoon bieneusi genotypes SHR1 and PL2 in farmed masked palm civet (Paguma larvata) in China
Fig. 1. Phylogenetic relationships of the E. bieneusi genotypes. The relationships were inferred using NJ analysis of the ITS rRNA gene and the values generated greater than 70% are shown beside the nodes. Genotypes with hollow circles and filled circles are known and novel genotypes identified in this study, respectively.
protozoa_masking:v24.8.1
<p>kraken2 DB for protozoa built with masking option. Contains 154,589 accession numbers corresponding to 111 unique taxons.</p> <p> </p>
plasmid_masking:v21.1.1
<div> <p>kraken2 DB for plasmids built with masking option in Jan 2021. Contains 22283 accession numbers corresponding to 3357 taxons.</p> <p> </p> </div>
Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders
<p>#########</p> <p>Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders</p> <p>#########</p> <p>Authors: Lucas Stoffl, Andy Bonnetto, Stéphane D'Ascoli & Alexander Mathis</p> <p>Affiliation: Ecole Polytechnique de Lausanne (EPFL)</p> <p>Date: 25/09/2024</p> <p>Link to the BiorXiv article : https://doi.org/10.1101/2024.08.06.606796</p> <p>-----------------</p> <h2>Provided data (hBehaveMAE checkpoints)</h2> <p>We provide a collection of pre-trained models that were reported in our paper, allowing you to reproduce our results for MABe22, hBABEL and Shot7M2 datasets.</p> <p>Note that you can <a href="https://huggingface.co/datasets/amathislab/SHOT7M2">download Shot7M2</a> on HuggingFace and <a href="https://github.com/amathislab/BehaveMAE/tree/main/hBABEL">generate hBABEL</a> by following the instructions on the <a href="https://github.com/amathislab/BehaveMAE">github page.</a></p> <ul> <li><strong>hBehaveMAE_hBABEL.pth </strong>: checkpoint for the hBehaveMAE pre-trained on the hBABEL dataset</li> <li><strong>hBehaveMAE_Shot7M2.pth</strong> : checkpoint for the hBehaveMAE pre-trained on the Shot7M2 dataset</li> <li><strong>hBehaveMAE_MABe22.pth</strong>: checkpoint for the hBehaveMAE pre-trained on the MABe22 dataset</li> </ul> <h2>References</h2> <p>If you find our code, weights or ideas useful, please cite:</p> <table> <tbody> <tr> <td>@article {Stoffl2024hBehaveMAE,<br> author = {Stoffl, Lucas and Bonnetto, Andy and d{\textquoteright}Ascoli, St{\'e}phane and Mathis, Alexander},<br> title = {Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders},<br> elocation-id = {2024.08.06.606796},<br> year = {2024},<br> doi = {10.1101/2024.08.06.606796},<br> publisher = {Cold Spring Harbor Laboratory},<br> URL = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.06.606796},<br> eprint = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.06.606796.full.pdf},<br> journal = {bioRxiv}<br>}</td> </tr> </tbody> </table>
Daily Anomalies and High Productivity Zone Mask for Northern Peruvian Coastal Marine Ecosystem during the 2017 Coastal El Niño (December 2016 - May 2017)
<p>This dataset is part of the manuscript entitled "Chlorophyll Response and High Productivity Zone Contraction in Northern Perú During the 2017 Coastal El Niño."</p> <p>The dataset is designed to assess the atmospheric and oceanographic drivers of productivity changes during the 2017 Coastal El Niño. It allows detailed analysis of the interactions between physical processes (e.g., wind-driven upwelling rates, heat flux changes) and biological responses (e.g., chlorophyll concentration variations) in a region highly susceptible to ENSO-related variability. This comprehensive dataset provides valuable insight into the physical-biological coupling and the impacts of rapid climate events on marine ecosystems. The dataset, covering the period from December 1, 2016, to May 31, 2017, includes:</p> <p>1. Chlorophyll-a Anomalies (chla): Represents deviations in surface chlorophyll concentrations, a proxy for phytoplankton biomass, highlighting variations in primary productivity during the event.</p> <p>2. Sea Surface Temperature Anomalies (sst): Captures changes in sea surface temperatures relative to the climatological mean, providing insight into the warming pattern typical of marine heatwaves associated with the Coastal El Niño.</p> <p>3. Sea Level Anomaly (sla): Indicates changes in sea surface height, which reflects thermal expansion of water masses and potential contributions from coastal trapped waves propagating along the Peruvian coast.</p> <p>4. Wind Component Anomalies (u,v): Daily anomalies for both zonal (east-west) and meridional (north-south) wind components, which are critical for understanding changes in wind patterns including upwelling and Ekman transport processes.</p> <p>5. Ekman Pumping Anomalies (w): Represents variations in vertical water movement forced by wind stress curl, highlighting the suppression or enhancement of upwelling during the event.</p> <p>6. Latent Heat Flux Anomalies (lathf): Indicates deviations in heat loss from the ocean surface due to evaporation, affecting surface temperature regulation.</p> <p>7. Shortwave Radiation Anomalies (swrad): Shows changes in solar radiation (and also a proxy for PAR) reaching the ocean surface, influencing upper ocean heat content and the light availability for phytoplankton.</p> <p>8. High Productivity Zone (mask): A binary mask with daily values of 1 indicating areas meeting the HPZ criterion and 0 otherwise, allowing for spatial tracking of the HPZ's extent during the period of study.</p> <p> </p>
Drainage System Masks in the Transantarctic Mountains, Antarctica
<p>This data product is a set of drainage system boundary masks in the Transantarctic Mountains, Antarctica. These masks were developed to support a landscape evolution study but could be useful in other Antarctic applications. My regions of interest were determined by locating where topography converges to a single point, similar to the methodology used for delineating fluvial watersheds, except over ice instead of water. Masks were manually delineated using the Reference Elevation Model of Antarctica, a high-resolution DEM of the Antarctic Surface and the United States Geologic Survey (USGS) topography maps both provided by the <a href="https://www.pgc.umn.edu/">Polar Geospatial Center</a>. There are 36 drainage system masks in total merged into one shapefile. The shapefile attribute table contains 4 parameters: BasinValue, BasinName, Shape_Leng, and Shape_Area. BasinValue column contains a unique integer for each drainage area from 1-38 while BasinName contains the name of the closest major glacier for reference. </p> <p>Spatial Reference System: WGS 84 / Antarctic Polar Stereographic (<a href="https://epsg.io/3031">EPSG:3031</a>)</p> <p>Data Format: Shapefile</p>
Atchafalaya UAVSAR interferograms, coherence files and 3-class masks
<p>The dataset contains,</p> <ol> <li>UAVSAR interferograms and coherence files generated with SAR data acquired over Atchafalaya basin in coastal Louisiana, USA.</li> <li>Three class (wetland, open water, intermittent flow) maps generated using interferometric coherence for high (rising-to-high) and low (ebbing-to-low) tide conditions.</li> </ol>
Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks
<p><strong>General information</strong></p> <p>The dataset consists of 4403 labelled subscenes from 155 Sentinel-2 (S2) Level-1C (L1C) products distributed over the Northern European terrestrial area. Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels subscenes. 6 L1C S2 products were labelled fully. Among other 149 S2 products the most challenging ~10 subscenes per product were selected for labelling. In total the dataset represents 4403 labelled Sentinel-2 subscenes, where each sub-tile is 512 x 512 pixels at 10 m resolution. The dataset consists of around 30 S2 products per month from April to August and 3 S2 products per month for September and October. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations in Northern Europe.</p> <p>The classification pixel-wise map consists of the following categories:</p> <ul> <li>0 – MISSING: missing or invalid pixels;</li> <li>1 – CLEAR: pixels without clouds or cloud shadows;</li> <li>2 – CLOUD SHADOW: pixels with cloud shadows;</li> <li>3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km).</li> <li>4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km).</li> <li>5 – UNDEFINED: pixels that the labeler is not sure which class they belong to.</li> </ul> <p>The dataset was labelled using Computer Vision Annotation Tool (<a href="https://github.com/openvinotoolkit/cvat">CVAT</a>) and <a href="https://segments.ai/">Segments.ai</a>. With the possibility of integrating active learning process in Segments.ai, the labelling was performed semi-automatically.</p> <p>The dataset limitations must be considered: the data is covering only terrestrial region and does not include water areas; the dataset is not presented in winter conditions; the dataset represent summer conditions, therefore September and October contain only test products used for validation. Current subscenes do not have georeferencing, however, we are working towards including them in next version.</p> <p>More details about the dataset structure can be found in README. </p> <p><strong>Contributions and Acknowledgements</strong></p> <p>The data were annotated by Fariha Harun and Olga Wold. The data verification and Software Development was performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold. Methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich.<br> We would like to thank Segments.ai annotation tool for instant and an individual customer support. We are grateful to European Space Agency for reviews and suggestions. We would like to extend our thanks to Prof. Gholamreza Anbarjafari for the feedback and directions.<br> The project was funded by<strong><em> European Space Agency</em></strong>, Contract No. 4000132124/20/I-DT.</p>
A 10 m land mask for the Nuup Kangerlua, Kobbefjord, and Ameralik fjord systems in southwest Greenland
<p>This dataset consists of a 10 m land mask of the Nuup Kangerlua, Kobbefjord, and Ameralik fjord systems in southwest Greenland. The land mask is based on two Sentinel-2 MSI images of the area (tiles WDS and WES) that were acquired at high tide on 30 July 2017. Land was masked by applying a threshold of 0.1 Wm<sup>-2</sup> to the short-wave infrared (SWIR) band at 1614 nm. Mountain lakes and shaded coastal regions were masked using the TanDEM-X digital elevation model. Some inland waters were masked manually. </p> <p>Land and water were assigned values of 0 and 1, respectively, and are provided in geotiff format projected to UTM Zone 22 (WGS 84).</p> <p>The boundaries of the mask area extend from 63.90°N, 51.999°W in the southwest to 64.01°N, 51.302°W in the northeast.</p>
From high masked to high realized genetic load in inbred Scandinavian wolves
<p><span>When new mutations arise at functional sites they are more likely to impair than improve fitness. If not removed by purifying selection, such deleterious mutations will generate a genetic load that can have negative fitness effects in small populations and increase the risk of extinction. This is relevant for the highly inbred Scandinavian wolf (<em>Canis</em> <em>lupus</em>) population, founded by only three wolves in the 1980s and suffering from inbreeding depression. We used functional annotation and evolutionary conservation scores to study deleterious variation in a total of 209 genomes from both the Scandinavian and neighboring wolf populations in northern Europe. The masked load (deleterious mutations in heterozygote state) was highest in Russia and Finland with deleterious alleles segregating at lower frequency than neutral variation. Genetic drift in the Scandinavian population led to the loss of ancestral alleles, fixation of deleterious variants and a significant increase in the per-individual realized load (deleterious mutations in homozygote state; an increase by 45% in protein-coding genes) over five generations of inbreeding. Arrival of immigrants gave a temporary genetic rescue effect with ancestral alleles re-entering the population and thereby shifting deleterious alleles from homozygous into heterozygote genotypes. However, in the absence of permanent connectivity to Finnish and Russian populations, inbreeding has then again led to the exposure of deleterious mutations. These observations provide genome-wide insight into the magnitude of genetic load and genetic rescue at the molecular level, and in relation to population history. They emphasize the importance of securing gene flow in the management of endangered populations.<br></span></p>
Data for Numerical Study on the Impact of Large Air Purifiers, Physical Distancing, and Mask Wearing in Classrooms
<p>This is a reference case setup to re-generate all the data for the paper titled "Numerical Study on the Impact of Large Air Purifiers, Physical Distancing, and Mask Wearing in Classrooms"<br> </p>
L-TOWN simulated measurement without faults or cyber-attacks for scenarios with masking
<p>Additional resources for repository <a href="https://github.com/asztyber/wdn-simulation">asztyber/wdn-simulation</a></p> <p>Required to run scenarios with masking.</p>
Training patches and prediction codes of deep learning (LANA) model for Landsat 8/9 cloud/shadow mask
<p>This dataset includes (i) the image patches dataset and (ii) application/prediction (not training) codes for Landsat 8 cloud and cloud shadow masking used in a paper in review and uploaded here: </p> <p>Hankui Zhang, Dong Luo, David Roy, A learning attention network algorithm (LANA) for accurate Landsat-8 cloud and shadow masking, <em>Remote Sensing of Environment</em> </p> <p>The documentation is in <a href="https://zenodo.org/api/files/5462baa5-2bba-4b0f-92aa-c17681b6464b/l8_training_data_readme_new.pdf?versionId=95253efb-6447-46d8-ae4b-ac0c93b43532">l8_training_data_readme_new.pdf</a>. </p>
R10 region mask based on IPCC AR6 WG3 and ISIMIP
<p>region_classification.tsv: Tab-separated value file of ISO3 code, country name and the R10 mapping used.</p> <p>r10masks_fractional.nc: 0.5° grid of fractions of grid cell that is part of one of 10 world regions as defined by IPCC Working Group 3.</p> <p>The "region" variable is a dimension (11, 360, 720) variable. The first axis is the fraction (0-1 scale) of the grid cell at latitude and longitude (defined by second and third axis) falling into each world region.</p> <p>Values of first axis correspond to following regions defined in the country mappings file:</p> <ul> <li>0=South-East Asia and developing Pacific</li> <li>1=Eurasia</li> <li>2=Asia-Pacific</li> <li>3=Africa</li> <li>4=Middle East</li> <li>5=Latin America and Caribbean</li> <li>6=North America</li> <li>7=Eastern Asia</li> <li>8=Southern Asia</li> <li>9=Europe</li> <li>10=World (all land, excluding Antarctica, and the sum of fractions in 0-9).</li> </ul> <p>Oceans and inland lakes are not counted within countries/regions.</p> <p>The starting point for this data is the 0.5° country mask from Perrette (2023).</p> <p>Country mappings in the TSV file follow the prescription of IPCC Working Group 3 Annex II (Al Khourdajie et al. 2022) where possible. Some ambiguities exist with regards to post-colonial, geographically detached, and disputed territories which are not explicitly defined in Annex II. These have been grouped by geographic rather than political region (example: French Guiana is classified as Latin America & Caribbean, which it is most definitely geographically part of, rather than Europe, which it is politically part of).</p> <p> </p> <p>Perette, 2023: ISI-MIP/isipedia-countries (v2.6). GitHub repository. <a href="https://github.com/ISI-MIP/isipedia-countries/releases/tag/v2.6">https://github.com/ISI-MIP/isipedia-countries</a><a href="https://github.com/ISI-MIP/isipedia-countries/releases/tag/v2.6">/releases/tag/v2.6</a></p> <p>Al Khourdajie et al., 2022: Annex II: Definitions, Units and Conventions [Al Khourdajie, A., R. van Diemen, W.F. Lamb, M. Pathak, A. Reisinger, S. de la Rue du Can, J. Skea, R. Slade, S. Some, L. Steg (eds)]. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.021</p>
Masked Emotion FilmClip Dataset (MEFD): Emotion Elicitation with Facial Coverings
<p><strong>Masked Emotion FilmClip Dataset (MEFD): Emotion Elicitation with Facial Coverings</strong></p><p> </p><p>The Masked Emotion FilmClip Dataset (MEFD) stands as an avant-garde assembly of emotion-inducing video clips tailored for a unique niche - the elicitation of emotions in individuals wearing facial masks. This dataset emerges in response to the global need to understand emotional cues and expressions in the backdrop of widespread facial mask usage. Assembled by leveraging the synergies between cinematography and psychological research, MEFD serves as an invaluable trove for researchers, especially those in AI, seeking to decode emotions even when a significant portion of the face is concealed.</p><p><strong>Dataset Highlights</strong>:</p><ul><li><strong>Facial Masks</strong>: All subjects in the video clips are seen wearing facial masks, replicating real-world scenarios and augmenting the dataset's relevance.</li><li><strong>Film Titles</strong>: The title of each selected film enriching the context of the emotional narrative.</li><li><strong>Emotion Label</strong>: Clear emotion classification associated with every clip, ensuring replicability in emotional elicitation.</li><li><strong>Clip Duration</strong>: Precise duration details ensuring standardized exposure and consistent emotion elicitation.</li><li><strong>Curated with Expertise</strong>: Clips have undergone rigorous evaluation by seasoned psychologists and film critics, affirming their effectiveness in eliciting the designated emotion.</li><li><strong>Consent and Ethics</strong>: The dataset respects and upholds privacy and ethical standards. Every participant provided informed consent. This endeavor has received the green light from the Ethics Committee at the University of Granada, documented under the reference: 2100/CEIH/2021.</li></ul><p><strong>Emotion-Eliciting Video Clips within Dataset</strong>:</p><p>Film Targeted Emotion Duration (seconds) The Lover Baseline 43 American History X Anger 106 Cry Freedom Sadness 166 Alive Happiness 310 Scream Fear 395</p><p>A paramount feature of MEFD is its emphasis on "key moments". These timestamps, a product of collective expertise from psychologists and film critics, guide the researcher to intervals of heightened emotional resonance within the clips, especially challenging to discern with masked faces.</p><p><strong>Key Emotional Moments within Dataset</strong>:</p><p>Film Targeted Emotion Key moment timestamps (seconds) American History X Anger 36, 57, 68 Cry Freedom Sadness 112, 132, 154 Alive Happiness 227, 270, 289 Scream Fear 23, 42, 79, 226, 279, 299, 334</p><p> </p><p>-----------------</p><p>DATA STRUCTURE<br>-----------------</p><p>SADNESS_XXX.CSV<br>timestamp emotion<br>1625062890.938222 NEUTRAL --> Initial time start for the neutral video<br>1625062932.567609 SADNESS --> Initial time start for the EMOTION video</p><p><br>Notes:<br>** Subject id 15: FEAR label started to fast; Neutral data very few<br>-----------------</p><p> </p><p><i>The ethical consent for this dataset was provided by La Comisión de Ética en Investigación de la Universidad de Granada, as documented in the approval titled: 'DETECCIÓN AUTOMÁTICA DE LAS EMOCIONES BÁSICAS Y SU INFLUENCIA EN LA TOMA DE DECISIONES MEDIANTE WEARABLES Y MACHINE LEARNING' registered under 2100/CEIH/2021.</i></p><p>MEFD is more than just a dataset; it is a testament to human resilience and adaptability. As facial masks become ubiquitous, understanding the nuances of masked emotional expressions becomes imperative. MEFD rises to this challenge, bridging gaps and pioneering a new frontier in emotion research.</p>
Red-footed and masked boobies stable isotope data
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From high masked to high realized genetic load in inbred Scandinavian wolves
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Hemodynamic responses link individual differences in informational masking to the vicinity of superior temporal gyrus
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.