Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,433
datasets available to search
ShareScore release 0.9.0
Dataset results
1,433 results for “mask”
Japanese Tengu Mask
**Tengu** are creatures found in Japanese folklore. Tengu are considered a type of *yokai* This mask was made as a study. The design is entirely my own, based on ancient as well as contemporary stylings. This is free to download and free use for any purpose as long as I am given some form of credit, and if the the work is non-commercial. Source: Objaverse 1.0 / Sketchfab
The Mask of Agamemnon
Source: Objaverse 1.0 / Sketchfab
Kulango People Dance Mask
Scanned at the Broward County Library in Ft. Lauderdale, FL. Source: Objaverse 1.0 / Sketchfab
Terracotta Mask
Location: Museo Archeologico di Aidone, Aidone, Sicily, Italy Catalogue Number: 69-360 Description: Terracotta mask, Veiled goddess. Data Capture Method: Photogrammetry Processing Software: Reality Capture Citation: The data for this project were originally collected as a joint effort between the University of Catania and CVAST at the University of South Florida (USF), with the collaboration of the Fondazione Bruno Kessler (FBK). Dr. Mariarita Sgarlata and Dr. Herbert Maschner, Principal Investigators. We gratefully acknowledge the participation of the administrators of the Villa Romana del Casale and the Museo Archeologico di Aidone. The data have been transferred to Global Digital Heritage (GDH) for processing and analysis. Funding for this project, both at USF and at GDH, has been provided by the Hitz Foundation, Herbert Maschner, Principal Investigator. Source: Objaverse 1.0 / Sketchfab
Маска из Маданга | Mask from Madang Province
Этнография Маска ритуальная, середина XX вв. Новая Гвинея, провинция Маданг Материал: панцирь черепахи, волокна растительные, раковины, Глина окрашеннная КП НВФ-19313 Хранитель: Милоcердов Д.Ю. Сканирование выполнил: Ахтамзян А.И. Source: Objaverse 1.0 / Sketchfab
Anthropomorphic mask - 2014 - 1
Timiryazevskiy-I burial site Source: Objaverse 1.0 / Sketchfab
Mask, Roger Brown Study Center
Mask located at the Roger Brown Study Center in Chicago. Source: Objaverse 1.0 / Sketchfab
Wooden Mask Masque Man 3D Scan
Photogrammetry Wooden Mask Masque Man Lowpoly Model Source: Objaverse 1.0 / Sketchfab
Whole-body X-ray images of laying hens with keel bone annotations and masks for training deep learning models
<p>This dataset contains whole-body x-ray images of laying hens (n=1051), with the corresponding keel bone annotations and masks. This dataset was basically used to train deep learning models to segment the keel bone from the whole-body x-ray images. But can be used by others for further developments of similar models. This dataset was generated during the research project funded by Svenska Forskningsrådet Formas (2019-02116). The project aimed to develop a digital tool to assess bones of laying hens using x-ray imaging. All images are in JPEG format. All images are named with informative codes indicating bird ID, date and time of x-raying. For instance, in this "001_20230419_0957_PM.Dicom.jpg" image name, "001" stands for the bird ID, "20230419" for the x-raying date, "0957" for the x-raying time, and "PM.Dicom.jpg" indicates that the bird was x-rayed postmortem "PM" with "Dicom" file output and converted to the ".jpg" format. </p> <p>Update on 28/08/2024: This dataset is linked to publication</p> <p>Sallam et al. (2024) Research Note: A deep learning method segments chicken keel bones from whole-body X-ray images,<br>Poultry Science, Volume 103, Issue 11, 2024, 104214, ISSN 0032-5791,<br>https://doi.org/10.1016/j.psj.2024.104214</p>
Analyzing evolutionary game theory in epidemic management: A study on social distancing and mask-wearing strategies
<p>When combating a respiratory disease outbreak, the effectiveness of protective measures hinges on spontaneous shifts in human behavior driven by risk perception and careful cost-benefit analysis. In this study, a novel concept has been introduced, integrating social distancing and mask-wearing strategies into a unified framework that combines evolutionary game theory with an extended classical epidemic model. To yield deeper insights into human decision-making during COVID-19, we integrate both the prevalent dilemma faced at the epidemic's onset regarding mask-wearing and social distancing practices, along with a comprehensive cost-benefit analysis. We explore the often-overlooked aspect of effective mask adoption among undetected infectious individuals to evaluate the significance of source control. Both undetected and detected infectious individuals can significantly reduce the risk of infection for non-masked individuals by wearing effective facemasks. When the economic burden of mask usage becomes unsustainable in the community, promoting affordable and safe social distancing becomes vital in slowing the epidemic's progress, allowing crucial time for public health preparedness. In contrast, as the indirect expenses associated with safe social distancing escalate, affordable and effective facemask usage could be a feasible option. In our analysis, it was observed that during periods of heightened infection risk, there is a noticeable surge in public interest and dedication to complying with social distancing measures. However, its impact diminishes beyond a certain disease transmission threshold, as this strategy cannot completely eliminate the disease burden in the community. Maximum public compliance with social distancing and mask-wearing strategies can be achieved when they are affordable for the community. While implementing both strategies together could ultimately reduce the epidemic's effective reproduction number (Re) to below one, countries still have the flexibility to prioritize either of them, easing strictness on the other based on their socio-economic conditions.</p>
viruses_masking:v21.1.1
<p>kraken2 DB for viruses built with masking option. Contains 11968 viruses. </p> <p>Built in Jan. 2021</p>
Dataset of "Lesson learned from the COVID-19 pandemic: toddlers learn earlier to read emotions with face masks"
<p>Dataset used for statistical analyeses.</p> <p> </p> <p>Subject_ID: individual code</p> <p>Age: Age (years old)</p> <p>Group: Toddlers. Adults</p> <p>Emotion: S = Sadness, A = Anger, H = Happiness, F = fear, N = Neutral</p> <p>Mask: yes = presence of mask, no = lack ok mask</p> <p>Correct_Answer: 1 = correct, 0 = wrong </p>
human_masking:v21.1.1
<p>kraken2 DB for human with masking option created in 2021. Based on 639 unique accession numbers.</p> <p> </p>
CARS-mask-sunglass
<p>This is a dataset of faces with masks and sunglasses. </p> <p>The mask dataset is divided into three categories:</p> <p>0: No mask (2,093 images), 1: Partially wearing a mask (1,856 images), 2: Fully wearing a mask (1,196 images).</p> <p> The sunglasses dataset is divided into two categories:</p> <p>0: No sunglasses (23,809 images), 1: Wearing sunglasses (17,118 images).</p>
A 2D hyperspectral library of mineral reflectance, from 900 to 2500nm - Masked high dynamic range data
<p>Each <strong>zip</strong> file contains the text description file: <strong>description.txt</strong>. It also contains the data of one or two measurement, <strong>A</strong> and possibly B. The files are named as follows (<strong>####</strong> is the sample ID, <strong>@</strong> the letter of the measurement):</p> <ul> <li><strong>####-@.ply</strong> and <strong>####-@.png</strong>: 3D reconstruction in Stanford PLY format, and associated texture file.</li> <li><strong>@-im-000.jpg</strong>, <strong>@-im-010.jpg</strong>, ..., <strong>@-im-360.jpg</strong>: JPEG images of the sample, the angle is in degrees. 0 degree correspond to the position used during scanning.</li> <li><strong>@.mhdr.h5</strong>: HDF5 file containing masked HDR raw scan data. Masked values are represented by nan.</li> </ul> <p>The zip files correspond to the sample IDs, according to the following table:</p> <pre><code>Mineral Sample IDs Datapoints ---------------------------------------------------------------- Actinolite 0020, 0021, 0064 56840 Albite 0107 22521 Almandine 0025, 0026, 0073, 0074 17425 Andalusite 0014 18862 Anhydrite 0004 30832 Apatite 0089(2), 0090(2) 69062 Aragonite 0061 40111 Arsenopyrite 0087(2) 66333 Augite 0038 8503 Barite 0006 37130 Beryl 0075(2) 37743 Biotite 0049(2), 0050 45400 Blende 0086(2) 15596 Bronzite 0112 50452 Bytownite 0103 14666 Calcite 0010, 0011, 0052, 0078, 0079 112501 Cassiterite 0119, 0120 38535 Celestite 0000, 0001, 0002 56075 Chalcedony 0108(2), 0109(2) 96431 Chalcopyrite 0106 19375 Chlorite 0013 75802 Clinochlore 0126 45301 Coal 0081, 0082 44664 Copper 0101 14556 Diopside 0069 58959 Dolomite 0091(2), 0092(2) 76810 Enstatite 0047 31072 Epidote 0023, 0024 47306 Fluorite 0003(2), 0012(2) 98638 Galena 0053, 0054 17931 Garnet 0115 27331 Glaucophane 0016, 0017, 0076, 0077 200830 Goethite 0114 10717 Graphite 0083, 0084 20047 Grossular 0030, 0031 46592 Gypsum 0005(2), 0007, 0063 159829 Halite 0008, 0056 66201 Halloysite 0121, 0122 102028 Hematite 0039(2), 0085(2), 0095, 0096 165228 Hornblende 0046 9175 Hypersthene 0111 62932 Ilmenite 0116 16256 Kaolinite 0113 20764 Kyanite 0029 12320 Labradorite 0088(2), 0104(2) 62762 Limonite 0128, 0129 64550 Magnetite 0055, 0072 7103 Microcline 0071 60699 Montmorillonite 0123, 0124, 0125 87986 Muscovite 0034 62481 Nepheline 0097(2) 51930 Olivine 0065 7965 Omphacite 0019, 0067 108032 Opal 0102 34404 Orthoclase 0057 49824 Phlogopite 0045, 0070 105119 Pyrite 0042, 0048 31598 Pyrolusite 0117, 0118 45534 Pyrrhotite 0051 21572 Quartz 0009(2), 0035 126760 Rutile 0093 4959 Sanidine 0099(2) 49260 Serpentine 0018, 0068 78937 Siderite 0080 11651 Silicified wood 0127(2) 92935 Sillimanite 0032, 0033 70546 Sodalite 0043, 0060 61852 Sphalerite 0105 27485 Staurolite 0027, 0028, 0066 30628 Sulfur 0036, 0037 34751 Talc 0022, 0040, 0041, 0058, 0059 138317 Titanite 0094 4121 Tourmaline 0044, 0062 68419 Tremolite 0098(2), 0100 70913 Zircon 0110(2) 5110 </code></pre> <p> </p>
Global Seasonal Mountain Snow Mask from MODIS MOD10A2
<p>Seasonal Mountain Snow (SMS) mask derived from MODIS MOD10A2 snow cover extent and GTOPO30 digital elevation model produced at 30 arcsecond spatial resolution.</p> <p>Three datasets are provided: the Seasonal Mountain Snow mask (MODIS_mtnsnow_classes), a seasonal snow cover classification (MODIS_snow_classes), and cool-season cloud percentages (MODIS_clouds). The classification systems are as follows:</p> <p>MODIS_snow_classes:</p> <ul> <li>0: Little-to-no snow</li> <li>1: Indeterminate due to clouds</li> <li>2: Ephemeral snow</li> <li>3: Seasonal snow</li> </ul> <p>MODIS_mtnsnow_classes:</p> <ul> <li>0: Mountains with little-to-no snow</li> <li>1: Indeterminate due to clouds</li> <li>2: Mountains with ephemeral snow</li> <li>3: Mountains with seasonal snow</li> </ul> <p>MODIS_clouds</p> <ul> <li>0: < 5% of clouds during the cool season (defined Oct.-Mar. for Northern Hemisphere and Apr.-Sep. for Southern Hemisphere)</li> <li>1: 5% cool-season days with cloud cover</li> <li>2: 10% cool-season days with cloud cover</li> <li>3: 20% cool-season days with cloud cover</li> <li>4: 30% cool-season days with cloud cover</li> <li>5: 40% cool-season days with cloud cover</li> <li>6: 50% cool-season days with cloud cover</li> </ul> <p> </p> <p>For manuscript "Characterizing biases in mountain snow accumulation from global datasets" submitted to WRR</p>
mmu39_masking:v24.6.30
<p>Kraken2 Database for Mus Musculus GRC39 built with masking option, in june 2024. </p> <p>ref from : Mus_musculus.GRCm39.dna.primary_assembly.fa</p>
Masked Conditional Diffusion Model with GNN for Spatial Transcriptomics Data Imputation
Open the record for dataset details and reuse information.
Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - ALL 2011 & 2014 AUDIO DATA
<p><strong>Description of the data and file structure<br></strong>This record contains all 2011 & 2014 audio data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:</p> <p> Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. <em>Global Change Biology</em>.<strong> </strong>In press.</p> <p>The data include the following: the 2011 & 2014 audio files from analyzed Dtag depoyments. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13333019 - all 2009 & 2010 audio data; 10.5281/zenodo.13308835 - (1) all calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above.</p> <p>These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p> </p> <p><strong>Description of audio data files</strong><br>The data files contain the .dtg extension. This is the compressed raw data from all analyzed deployments. Once files are downloaded, they will need to be decompressed, which is done using the tagtools tool kit for Matlab, R or Octave, available at https://github.com/animaltags .</p> <p>Each deployment is named using the first letter of the genus and species name ("oo" for Orcinus orca), followed by the two-digit year (e.g., 09 for 2009), followed by the 3-digit Julian day (e.g., 246), followed by a letter denoting the population (a-d for Northern Residents, m for Southern Residents), followed by a series of numbers that denote the specific block (on the tag memory board) from which the data came. All files from a deployment should be put within a folder for that deployment, so that the functions within the tagtools tool kit can locate them.</p> <p>Once the .dtg files are decompressed, there will be 4 new files for every decompressed file, with extensions as follows: .wav (audio) as well as .pk, .swv, .txt. The audio files are ready to use in .wav form, and can be viewed using any audio software. We recommend using Matlab with the tagtools tool kit, or viewing the files in batch mode within RavenPro (https://store.birds.cornell.edu/collections/raven-sound-software).</p> <p>We provide calibrated movement data (see DOI: 10.5281/zenodo.13308835). However, if users wish to run their own calibration from raw movement data, the .swv files are used for this purpose along with the tagtools tool kit in Matlab, R or Octave, available at https://github.com/animaltags .</p>
Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - ALL 2009 & 2010 AUDIO DATA
<p><strong>Description of the data and file structure<br></strong>This record contains all 2009 & 2010 audio data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:</p> <p> Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. <em>Global Change Biology</em>.<strong> </strong>In press.</p> <p>The data include the following: the 2009 & 2010 audio files from analyzed Dtag depoyments. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13328931 - all 2011 & 2014 audio data; 10.5281/zenodo.13308835 - (1) all calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above.</p> <p>These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p> </p> <p><strong>Description of audio data files</strong><br>The data files contain the .dtg extension. This is the compressed raw data from all analyzed deployments. Once files are downloaded, they will need to be decompressed, which is done using the tagtools tool kit for Matlab, R or Octave, available at https://github.com/animaltags .</p> <p>Each deployment is named using the first letter of the genus and species name ("oo" for Orcinus orca), followed by the two-digit year (e.g., 09 for 2009), followed by the 3-digit Julian day (e.g., 246), followed by a letter denoting the population (a-d for Northern Residents, m for Southern Residents), followed by a series of numbers that denote the specific block (on the tag memory board) from which the data came. All files from a deployment should be put within a folder for that deployment, so that the functions within the tagtools tool kit can locate them.</p> <p>Once the .dtg files are decompressed, there will be 4 new files for every decompressed file, with extensions as follows: .wav (audio) as well as .pk, .swv, .txt. The audio files are ready to use in .wav form, and can be viewed using any audio software. We recommend using Matlab with the tagtools tool kit, or viewing the files in batch mode within RavenPro (https://store.birds.cornell.edu/collections/raven-sound-software).</p> <p>We provide calibrated movement data (see DOI: 10.5281/zenodo.13308835). However, if users wish to run their own calibration from raw movement data, the .swv files are used for this purpose along with the tagtools tool kit in Matlab, R or Octave, available at https://github.com/animaltags .</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.