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1,433 results for “mask”
Data from: Group size differences may mask underlying similarities in social structure: a comparison of female elephant societies
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Pupil and masking responses to light as functional measures of retinal degeneration in mice Mus Musculus
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Data from: Why does noise reduce response to alarm calls? Experimental assessment of masking, distraction and greater vigilance in wild birds
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Data from: Maladaptive plasticity masks the effects of natural selection in the red-shouldered soapberry bug
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Data from: Current geography masks dynamic history of gene flow during speciation in northern Australian birds
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Data from: Unrecognized coral species diversity masks differences in functional ecology
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Data for: Apparent stability masks underlying change in a mule deer herd with unmanaged chronic wasting disease
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Data from: Post-hatching parental care masks the effects of egg size on offspring fitness: a removal experiment on burying beetles
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Data from: Ultra-long telomeres shorten with age in nestling great tits but are static in adults and mask attrition of short telomeres
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Analyzing evolutionary game theory in epidemic management: A study on social distancing and mask-wearing strategies
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Data from: Hybridization masks speciation in the evolutionary history of the Galápagos marine iguana
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Data from: Parental care masks a density-dependent shift from cooperation to competition among burying beetle larvae
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Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Cloud Mask R01
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Cloud Mask (PREFIRE_SAT2_2B-MSK) contains a binary clear/cloud indicator and probabilities derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_2B-MSK is derived from mid-infrared and far-infrared measurements collected globally by TIRS-PREFIRE aboard PREFIRE-SAT2 using a Machine Learning technique. Specifically, inputs are PREFIRE Spectral Radiance (PREFIRE_SAT2_1B-RAD) from PREFIRE Satellite 2 (PREFIRE_SAT2_2B-RAD) and PREFIRE Auxiliary Meteorology Data created for PREFIRE Satellite 2 (PREFIRE_SAT2_AUX-MET). PREFIRE_SAT2_2B-MSK is used to identify cloudy and clear scenes in TIRS-PREFIRE data, which supports the production of other Level 2 PREFIRE products. These include variables such as top of atmosphere spectral flux, surface spectral emissivity, and atmospheric properties such as temperature and water vapor profiles. Science data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The cloud identification data for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-MSK collection.
ECOSTRESS Cloud Mask Daily L2 Global 70m V001
The ECO2CLD Version 1 data product was decommissioned on May 21, 2025. Users are encouraged to use the [ECO_L2_CLOUD](https://doi.org/10.5067/ECOSTRESS/ECO_L2_CLOUD.002) Version 2 and [ECO_L2G_CLOUD](https://doi.org/10.5067/ECOSTRESS/ECO_L2G_CLOUD.002) Version 2 data products.The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO2CLD Version 1 data product provides a cloud mask that can be used to determine cloud cover for the ECO1BRAD, ECO2LSTE, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products. The ECOSTRESS Level 2 cloud product is derived using the five calibrated thermal bands in a multispectral cloud-conservative thresholding approach. The details of the algorithm are provided in the Algorithm Theoretical Basis Document (ATBD). The corresponding [ECO1BGEO](https://doi.org/10.5067/ECOSTRESS/ECO1BGEO.001) data product is required to georeference the ECO2CLD data product.The ECO2CLD Version 1 data product contains a single cloud mask layer. Information on how to interpret the bit fields in the cloud mask is provided in section 3.1 of the User Guide.Known Issues* Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.* Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.
EMIT L2A Estimated Surface Reflectance and Uncertainty and Masks 60 m V001
The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) [EMIT Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/).The EMIT Level 2A Estimated Surface Reflectance and Uncertainty and Masks (EMITL2ARFL) Version 1 data product provides surface reflectance data in a spatially raw, non-orthocorrected format. Each EMITL2ARFL granule consists of three Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Reflectance (EMIT_L2A_RFL), Reflectance Uncertainty (EMIT_L2A_RFLUNCERT), and Reflectance Mask (EMIT_L2A_MASK). The Reflectance file contains surface reflectance maps of 285 bands with a spectral range of 381-2493 nanometers (nm) at a spectral resolution of ~7.5 nm, which are held within a single science dataset layer (SDS). The Reflectance Uncertainty file contains uncertainty estimates about the reflectance captured as per-pixel, per-band, posterior standard deviations. The Reflectance Mask file contains six binary flag bands and two data bands. The binary flag bands identify the presence of features including clouds, water, and spacecraft which indicate if a pixel should be excluded from analysis. The data bands contain estimates of aerosol optical depth (AOD) and water vapor.Each NetCDF4 file holds a location group containing a geometric lookup table (GLT) which is an orthorectified image that provides relative x and y reference locations from the raw scene to allow for projection of the data. Along with the GLT layers, the files will also contain latitude, longitude, and elevation layers. The latitude and longitude coordinates are presented using the World Geodetic System (WGS84) ellipsoid. The elevation data was obtained from Shuttle Radar Topography Mission v3 (SRTM v3) data and resampled to EMIT’s spatial resolution.Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules at the end of an orbit segment reaching 150 km in length.Known Issues:* Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.* Possible Reflectance Discrepancies: Due to changes in computational architecture, EMITL2ARFL reflectance data produced after December 4, 2024, with Software Build 010621 and onward may show discrepancies in reflectance of up to 0.8% in extreme cases in some wavelengths as compared to values in previously processed data. These discrepancies are generally lower than 0.8% and well within estimated uncertainties. Between earlier builds and Build 010621, neither resulting output should be interpreted as more ‘correct’ than the other, as their results are simply convergence differences from an optimization search. Most users are unlikely to observe the impact.
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Cloud Mask R01
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Cloud Mask (PREFIRE_SAT1_2B-MSK) contains a binary clear/cloud indicator and probabilities derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT1_2B-MSK is derived from mid-infrared and far-infrared measurements collected globally by TIRS-PREFIRE aboard PREFIRE-SAT1 using a Machine Learning technique. Specifically, inputs are PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) from PREFIRE Satellite 1 (PREFIRE_SAT1_2B-RAD) and PREFIRE Auxiliary Meteorology Data created for PREFIRE Satellite 1 (PREFIRE_SAT1_AUX-MET). PREFIRE_SAT1_2B-MSK is used to identify cloudy and clear scenes in TIRS-PREFIRE data, which supports the production of other Level 2 PREFIRE products. These include variables such as top of atmosphere spectral flux, surface spectral emissivity, and atmospheric properties such as temperature and water vapor profiles. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The cloud identification data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-MSK collection.
KITTI-Masks
<p><strong>KITTI Masks Dataset</strong></p> <p>This Dataset consists of 2120 sequences of binary masks of pedestrians. The sequence length varies between 2-710. For details, we refer to our paper. It is based on the original KITTI Segmentation challenge which can be found at https://www.vision.rwth-aachen.de/page/mots </p> <p> </p> <p>A detailed description can be found at: https://openreview.net/pdf?id=EbIDjBynYJ8</p> <p>An example dataloader can be found at: https://github.com/bethgelab/slow_disentanglement/blob/26eef4557ad25f1991b6f5dc774e37e192bdcabf/scripts/dataset.py#L875 </p>
Data from: Size evolution in microorganisms masks trade-offs predicted by the growth rate hypothesis
Adaptation to local resource availability depends on responses in growth rate and nutrient acquisition. The growth rate hypothesis (GRH) suggests that growing fast should impair competitive abilities for phosphorus and nitrogen due to high demand for biosynthesis. However, in microorganisms, size influences both growth and uptake rates, which may mask trade-offs and instead generate a positive relationship between these traits (size hypothesis, SH). Here, we evolved a gradient of maximum growth rate (μmax) from a single bacterium ancestor to test the relationship among μmax, competitive ability for nutrients and cell size, while controlling for evolutionary history. We found a strong positive correlation between μmax and competitive ability for phosphorus, associated with a trade-off between μmax and cell size: strains selected for high μmax were smaller and better competitors for phosphorus. Our results strongly support the SH, while the trade-offs expected under GRH were not apparent. Beyond plasticity, unicellular populations can respond rapidly to selection pressure through joint evolution of their size and maximum growth rate. Our study stresses that physiological links between these traits tightly shape the evolution of competitive strategies.
Data from: Logistic regression analysis of factors influencing the effectiveness of intensive sound masking therapy in patients with tinnitus
Objectives: To investigate factors influencing the effectiveness intensive sound masking therapy on tinnitus using Logistic Regression Analysis. Design: The study used a retrospective cross-section analysis. Participants: 102 patients with tinnitus were recruited at the Sun Yat-sen Memorial Hospital of Sun Yat-sen University, China. Intervention: Intensive sound masking therapy was used as an intervention approach for patients with tinnitus. Primary and secondary outcome measures: participants underwent audiological investigations and tinnitus pitch and loudness matching measurements, followed by intensive sound masking therapy. The Tinnitus Handicap Inventory (THI) was used as the outcome measure pre- and post-treatment. Multivariate logistic regression was performed to investigate the association of demographic and audiological factors with effective therapy. Results: According to the THI score changes pre-and post-sound masking intervention, fifty-one participants were categorised into an effective group, the remaining 51 participants were placed in a non-effective group. Those in the effective group were significantly younger than those in the non-effective group (p=0.012). Significantly more participants had flat audiogram configurations in the effective group (p=0.04). Multivariable logistic regression analysis showed that age (OR=0.96, 95% CI: 0.93, 0.99, p=0.007), audiometric configuration (p=0.027) and THI score pre-treatment (OR=1.04, 95% CI: 1.02, 1.07, p<0.001) were significantly associated with therapeutic effectiveness. Further analysis showed that patients with flat audiometric configurations were 5.45 times more likely to respond to intervention than those with high-frequency steeply sloping audiograms (OR=5.45, 95% CI: 1.67, 17.86, p=0.005). Conclusion: Audiometric configuration, age and THI scores appear to be predictive for the effectiveness of sound masking treatment. Gender, tinnitus characteristics and hearing threshold measures seem not to be related to treatment effectiveness. Further randomized control study is needed to provide further evidence of the effectiveness of prognostic factors in tinnitus interventions.
Calavera Mask
Source: Objaverse 1.0 / Sketchfab
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Allen Brain Atlas
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