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Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"
<h1>Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"</h1> <p>See http://arxiv.org/abs/2411.04543.</p> <p>This data set contains the .fits files envolved in our work, from <a href="https://irsa.ipac.caltech.edu/data/Planck/" target="_blank" rel="noopener">Planck release</a> and <a href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/" target="_blank" rel="noopener">Irfan et. al., 2019</a>: </p> <p>In order to use these data files, </p> <p>please follow: (github readme)</p> <h2>Data from <em>Planck</em> release</h2> <h3><em>Planck</em> Release 1, 2013</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Planck 2013</strong></p> <p>HFI_CompMap_ThermalDustModel_2048_R1.20.fits</p> <p><a title="HFI_CompMap_ThermalDustModel_2048_R1.20.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: full-sky maps at 217 GHz with zodiacal light and without zodiacal light</strong></p> <p><strong>Relation to this work: used to filter out regions with strong zodiacal emission</strong></p> <p>HFI_SkyMap_217_2048_R1.10_nominal.fits<br><a title="HFI_SkyMap_217_2048_R1.10_nominal.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits</a></p> <p>HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits<br><a title="HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits</a></p> <h3> </h3> <h3><em>Planck</em> Release 2, 2015</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of synchrotron emission</strong></p> <p><strong>Relation to this work: used to remove synchrotron emission from full-sky maps</strong></p> <p>COM_CompMap_Synchrotron-commander_0256_R2.00.fits<br><a title="COM_CompMap_Synchrotron-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of free-free emission</strong></p> <p><strong>Relation to this work: used to remove free-free emission from full-sky maps</strong></p> <p>COM_CompMap_freefree-commander_0256_R2.00.fits<br><a title="COM_CompMap_freefree-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of carbon monoxide</strong></p> <p><strong>Relation to this work: used to remove carbon monoxide emission from full-sky maps</strong></p> <p>COM_CompMap_CO21-commander_2048_R2.00.fits<br><a title="COM_CompMap_CO21-commander_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of 94/100 GHz molecular emission lines</strong></p> <p><strong>Relation to this work: used to remove 94/100 GHz emission lines from full-sky maps</strong></p> <p>COM_CompMap_xline-commander_0256_R2.00.fits<br><a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: Galactic plane masks with no apodization</strong></p> <p><strong>Relation to this work: used to mask Galactic plane</strong></p> <p>HFI_Mask_GalPlane-apo0_2048_R2.00.fits<br><a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_GalPlane-apo0_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: point source masks</strong></p> <p><strong>Relation to this work: used to mask point sources in full-sky maps and inpaint them </strong></p> <p>HFI_Mask_PointSrc_2048_R2.00.fits<br><a title="HFI_Mask_PointSrc_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: maps for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Planck 2015 (GNILC pipeline, without CIB contamination)</strong></p> <p>COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits</a></p> <p>COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits</a></p> <p>COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits</a></p> <p><strong>Format: .FITS file (table)</strong></p> <p><strong>Type: <em>Planck</em> catalogue of compact sources at 30, 44, 70, 100, 143, 217, 353, 545, and 857 GHz</strong></p> <p><strong>Relation to this work: to mask compact sources</strong></p> <p>COM_PCCS_030_R2.04.fits<br><a title="COM_PCCS_030_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits</a></p> <p>COM_PCCS_044_R2.04.fits<br><a title="COM_PCCS_044_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits</a></p> <p>COM_PCCS_070_R2.04.fits<br><a title="COM_PCCS_070_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits</a></p> <p>COM_PCCS_100-excluded_R2.01.fits<br><a title="COM_PCCS_100-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits</a></p> <p>COM_PCCS_100_R2.01.fits<br><a title="COM_PCCS_100_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits</a></p> <p>COM_PCCS_143-excluded_R2.01.fits<br><a title="COM_PCCS_143-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits</a></p> <p>COM_PCCS_143_R2.01.fits<br><a title="COM_PCCS_143_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits</a></p> <p>COM_PCCS_217-excluded_R2.01.fits<br><a title="COM_PCCS_217-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits</a></p> <p>COM_PCCS_217_R2.01.fits<br><a title="COM_PCCS_217_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits</a></p> <p>COM_PCCS_353-excluded_R2.01.fits<br><a title="COM_PCCS_353-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits</a></p> <p>COM_PCCS_353_R2.01.fits<br><a title="COM_PCCS_353_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits</a></p> <p>COM_PCCS_545-excluded_R2.01.fits<br><a title="COM_PCCS_545-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits</a></p> <p>COM_PCCS_545_R2.01.fits<br><a title="COM_PCCS_545_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits</a></p> <p>COM_PCCS_857-excluded_R2.01.fits<br><a title="COM_PCCS_857-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits</a></p> <p>COM_PCCS_857_R2.01.fits<br><a title="COM_PCCS_857_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits</a></p> <h3> </h3> <h3><em>Planck</em> Release 3, 2018</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of CMB anisotropies (SMICA from <em>Planck</em> 2018)</strong></p> <p><strong>Relation to this work: used to remove CMB anisotropies from full-sky maps</strong></p> <p>COM_CMB_IQU-smica_2048_R3.00_full.fits<br><a title="COM_CMB_IQU-smica_2048_R3.00_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: <em>Planck</em> 2018 full-sky maps at 100, 143, 217, 353, 545, and 857 GHz</strong></p> <p><strong>Relation to this work: used to obtain dust data maps at these bands</strong></p> <p>HFI_SkyMap_100_2048_R3.01_full.fits<br><a title="HFI_SkyMap_100_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_143_2048_R3.01_full.fits<br><a title="HFI_SkyMap_143_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_217_2048_R3.01_full.fits<br><a title="HFI_SkyMap_217_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_353_2048_R3.01_full.fits<br><a title="HFI_SkyMap_353_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_545_2048_R3.01_full.fits<br><a title="HFI_SkyMap_545_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_857_2048_R3.01_full.fits<br><a title="HFI_SkyMap_857_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits</a></p> <p>HFI_RIMO_R3.00.fits<br><a title="HFI_RIMO_R3.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits</a></p> <h2> </h2> <h2>Thermal dust model from Melis O. Irfan et al. <a href="https://www.aanda.org/articles/aa/abs/2019/03/aa34394-18/aa34394-18.html" target="_blank" rel="noopener">A&A 623, A21 (2019)</a></h2> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: maps for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Melis O. Irfan et al. 2019</strong></p> <p>beta.fits<br><a title="beta.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits</a></p> <p>tau.fits<br><a title="tau.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits</a></p> <p>temp.fits<br><a title="temp.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits</a></p> <p> </p>
Time seqUential theRmal inFrared - Turbulence campaign 1 - TURF-T1 Experiment data
<p>This dataset is a thermal infrared dataset collected alongside with sonic anemometer and thermocouple data. The experiment took place on the 12/01/2019 starting at 16:21:11 NZDT and ending at 16:31:11 NZDT. The collection of the infrared data was done via uncrewed aerial vehicle with an Optris PI 450 camera flying above a TURF Sportsground located at N -43.29186830137823, E 172.6008992376682.</p>
Data from: Multimodal in situ datalogging quantifies inter-individual variation in thermal experience and persistent origin effects on gaping behavior among intertidal mussels (Mytilus californianus)
In complex habitats, environmental variation over small spatial scales can equal or exceed larger-scale gradients. This small-scale variation may allow motile organisms to mitigate stressful conditions by choosing benign microhabitats, whereas sessile organisms may rely on other behaviors to cope with environmental stresses in these variable environments. We developed a monitoring system to track body temperature, valve gaping behavior, and posture of individual mussels (Mytilus californianus) in field conditions in the rocky intertidal zone. Neighboring mussels' body temperatures varied by up to 14°C during low tides. Valve gaping during low tide and postural adjustments, which could theoretically lower body temperature, were not commonly observed. Rather, gaping behavior followed a tidal rhythm at a warm, high intertidal site; this rhythm shifted to a circadian period at a low intertidal site and for mussels continuously submerged in a tidepool. However, individuals within a site varied considerably in time spent gaping when submerged. This behavioral variation could be attributed in part to persistent effects of mussels' developmental environment. Mussels originating from a wave-protected, warm site gaped more widely, and they remained open for longer periods during high tide than mussels from a wave-exposed, cool site. Variation in behavior was modulated further by recent wave heights and body temperatures during the preceding low tide. These large ranges in body temperatures and durations of valve closure events - which coincide with anaerobic metabolism - support the conclusion that individuals experience "homogeneous" aggregations such as mussel beds in dramatically different fashion, ultimately contributing to physiological variation among neighbors.
Data from: Stingless bee foragers experience more thermally stressful microclimates but have wider thermal tolerance breadths than other worker subcastes
<p>The current state of anthropogenic climate change is of particular concern for insects, especially in the tropics where the effects are predicted to be the most deleterious. Researching climatic tolerance in social insects is challenging because adaptations can exist at both an individual level and a societal level. However, these studies are important because social insects comprise a tremendous portion of the planet's animal biomass, biodiversity, and include many important pollinators. Considering how individual physiologies construct group-level adaptations can improve the accuracy of climate change impact assessments for a variety of social species. <em>Tetragonisca angustula</em> is a neotropical stingless bee species known to exhibit particularly high worker subcaste specialization in the form of a morphologically distinct soldier caste, a trait most commonly found and studied in ants and termites. We used this model species to investigate 1) whether age- and size-differentiated task groups differ in thermal tolerance, 2) which worker subcastes operate closest to their thermal limits, and 3) the extent to which behavioral thermoregulation via shifting active foraging times can offset thermal stress in this species. We measured the thermal tolerance (CT<sub>max</sub> and CT<sub>min</sub>) of smaller-bodied foragers, and two soldier sub-castes (hovering guards and standing guards) in <em>T. angustula</em>. Despite the difference in body size between the foragers and guards, no differences in the upper or lower thermal limits were observed. However, the average thermal tolerance breadth of foragers was significantly larger than that of guards, indicating that soldiers at the nest entrance are more thermally specialized than foragers. Temperatures at foraging sites were more variable than at nest entrances, which caused warming tolerance to be significantly lower among small-bodied foragers as compared to either hovering guards or standing guards. The magnitude of warming tolerances indicated a low risk of imminent climate change impacts in this environment, but our results suggest that as temperatures increase, foragers are likely to meet their upper thermal limits before other worker subcastes. Foragers may shift the times they are active as a form of thermoregulation which could selectively impact pollination rates for plants leading to repercussions on agriculture and ecosystem functioning. This work establishes novel approaches to predicting climatic change risk in heterogeneous cooperative societies.</p>
Data for Partial Volume Deflagration Experiments with Synthesized Lithium-Ion Battery Thermal Runaway Effluent Gas.
<p>Refer to Readme.pdf or Readme.md for information on this dataset.<br><br>v1.1 - cleanup<br>v1.2 - readme updates</p>
Dominance of Auger excitation in beam heating in transmission electron microscopy: Irradiation experiments and quantitative thermal analysis of α-Al2O3
<p>The collection of uploaded files constitutes a dataset supporting our findings, titled Dominance of Auger excitation in beam heating in transmission electron microscopy: Irradiation experiments and quantitative thermal analysis of α-Al<sub>2</sub>O<sub>3</sub>, to be submitted to a scientific journal.</p> <p>The co-authors are Jihye Kwon and Hyoung Seop Kim, both at Pohang University of Science and Technology (POSTECH), Republic of Korea</p>
Data from: Stingless bee foragers experience more thermally stressful microclimates and have wider thermal tolerance breadths than other worker subcastes
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Data from: Multimodal in situ datalogging quantifies inter-individual variation in thermal experience and persistent origin effects on gaping behavior among intertidal mussels (Mytilus californianus)
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Data from: Alternative forms of brook trout nest site selection alter modeled offspring thermal experience and emergence phenology in groundwater-influenced streambeds
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Effects of thermal fluctuations on biological processes: A meta-analysis of experiments manipulating thermal variability
<p>Thermal variability is a key driver of ecological processes, affecting organisms and populations across multiple temporal scales. Despite the ubiquity of variation, biologists lack a quantitative synthesis of the observed ecological consequences of thermal variability across a wide range of taxa, phenotypic traits, and experimental designs. Here, we conduct a meta-analysis to investigate how properties of organisms, their experienced thermal regime, and whether thermal variability is experienced in either the past (prior to an assay) or present (during the assay) affect performance, relative to the performance of organisms experiencing constant thermal environments. Our results – which draw upon 1,712 effect sizes from 75 studies – indicate that the effects of thermal variability are not unidirectional and become more negative as mean temperature and fluctuation range increase. Exposure to variation in the past decreases performance to a greater extent than variation experienced in the present and increases the costs to performance more than diminishing benefits across a broad set of empirical studies. Further, we identify life history attributes that predictably modify the ecological response to variation. Our findings demonstrate that effects of thermal variability on performance are context-dependent, yet negative outcomes may be heightened in warmer, more variable climates.</p>
Is bedding material a more effective thermal insulator than trap cover for small mammal trapping? A field experiment
<p>This dataset is currently part of a manuscript undergoing a review process in the Journal of Thermal Biology. The manuscript compares the thermal insulation provided by a cover, bedding, and a combination of both when applied to small mammal traps. Treatments were tested in two different environmental conditions, one where temperature is more stable (near the sea coast), and another where temperature fluctuations are more severe (in the Pyrenees).</p> <p>The first sheet contains the data collected from four Sherman traps placed in an outside terrace. One trial lasted four days, and a second trial lasted two days. Temperature (ºC) and relative humidity (%) were recorded every 10 minutes with data loggers (Elitech RC-51) situated inside each trap. Traps were assigned to a treatment as follows: <em>Cover</em> (<em>CV</em>), trap with only the PVC cover; <em>Bedding</em> (<em>BD</em>): Trap with water-proof cotton wool inside; <em>Cover+Bedding</em> (<em>CV+BD</em>): trap with the cover and with water-proof cotton wool inside; <em>Control </em>(<em>CN</em>), a conventional trap; Another logger was placed outside to have measures of air temperature (<em>Air</em>). In the case of BD<em> </em>treatment, data-loggers were placed inside the cotton.</p> <p>The second sheet contains the data collected from three traps of three different models: Sherman, Longworth, and Heslinga. To avoid ethical concerns related with keeping wild animals trapped for long periods of time, we placed hand-warmer heat pads (<em>Terratherm</em>) – with up to 12 hours of warmth – within traps to simulate endothermic metabolism generated by a trapped small mammal (Kearney et al., 2011; Griffiths et al., 2017). Despite the covers being designed for Sherman traps, in the trials we also included two other commercial traps used in the SEMICE monitoring programme (Torre et al., 2018), <em>Longworth</em> (Longworth Scientific Instrument Co., Oxford, UK), and <em>Heslinga</em> (<a href="http://www.heslingatraps.eu/">www.heslingatraps.eu</a>) for thermal insulation capacity comparisons. The experiments were conducted in two contrasting climatic areas in Barcelona province (coastal, 22 m.a.s.l, and Pyrenees, 650 m.a.s.l), to expand the range of temporal and spatial variation in winter temperatures. We performed 63 trials combining three trap models and three treatments. Traps were placed in similar conditions and submitted to the same <em>CN</em>, <em>BD</em> and <em>CV+BD</em> treatments cited above. The <em>CV</em> treatment (trap with only the PVC cover) was excluded from the second experiment due to its poor performance in retaining temperature and humidity, as demonstrated in the first experimental session (see ‘Results’ section). Furthermore, the CV treatment does not represent a common treatment in small mammal trapping studies. Inside each trap we placed a temperature data logger (iButton, <em>Thermochron</em>) protected within an aluminium mesh, an ideal material to house the small temperature loggers (Orrock and Connolly, 2016). In the <em>Bedding</em> treatments, the mesh was placed inside the cotton. Temperature was recorded continuously every five minutes. To avoid direct heat exchange between the heat pad and the data logger, we placed the heat pad at the end of the trap close to the back door, as far away from the data logger as possible. To quantify the heat output of the heat pads through time, we placed a data logger below a hand warmer in six different trials, recording temperatures every five minutes during a 12-hour period. All experiments were conducted during winter 2021.</p>
Effects of thermal fluctuations on biological processes: A meta-analysis of experiments manipulating thermal variability
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Time seqUential theRmal inFrared - Turbulence campaign 2 - TURF-T2 Experiment data
<p>This dataset is a thermal infrared dataset collected alongside with sonic anemometer and thermocouple data. The experiment took place on the 17/01/2020 starting at 14:25:01 NZDT and ending at 14:38:01 NZDT. The collection of the infrared data was done via uncrewed aerial vehicle with an Optris PI 450 camera flying above a TURF Sportsground located at N -43.29186830137823, E 172.6008992376682.</p>
Dataset for paper "Dominance of Auger excitation in beam heating in transmission electron microscopy: Irradiation experiments and quantitative thermal analysis of α-Al2O3"
<p>The collection of uploaded files constitutes a dataset supporting our findings, titled Dominance of Auger excitation in beam heating in transmission electron microscopy: Irradiation experiments and quantitative thermal analysis of α-Al<sub>2</sub>O<sub>3</sub>, to be submitted to a scientific journal. The input file for finite element analysis, with the ".inp" extension, is also included.</p>
Thermal video of experiment to determine how fast different brands of towel dry under realistic conditions
<p>These four video files contain the raw measurements of the experiment described in this publication:</p> <p><a href="https://zenodo.org/badge/latestdoi/668661321">Measuring how fast towels dry using an infra-red camera</a></p> <p>In these experiments, I measured the surface temperature of wet and dry towels using a thermal camera to determine which brand of towels dries fastest. This was done on the request of the TV program "Kassa". The analyses of the data and the results can be found on the link above. </p>
Data from: Nonlinear averaging of thermal experience predicts population growth rates in a thermally variable environment
As thermal regimes change worldwide, projections of future population and species persistence often require estimates of how population growth rates depend on temperature. These projections rarely account for how temporal variation in temperature can systematically modify growth rates relative to projections based on constant temperatures. Here,we tested the hypothesis that time-averaged population growth rates in fluctuating thermal environments differ from growth rates in constant conditions as a consequence of Jensen's inequality, and that the thermal performance curves (TPCs) describing population growth in fluctuating environments can be predicted quantitatively based on TPCs generated in constant lab conditions. With experimental populations of the green alga Tetraselmis tetrahele, we show that nonlinear averaging techniques accurately predicted increased as well as decreased population growth rates influctuating thermal regimes relative to constant thermal regimes. We extrapolate from these results to project critical temperatures for population growth and persistence of 89 phytoplankton species in naturally variable thermal environments. These results advance our ability to predict population dynamics in the context of global change.
Data from: Local adaptation drives thermal tolerance among parasite populations: a common garden experiment
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Data from: Nonlinear averaging of thermal experience predicts population growth rates in a thermally variable environment
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Explorer-7 Thermal Radiation Experiment Selected White Sensor Temperature (Nighttime) Values V001 (EXP7L1TRTWHT) at GES DISC
Explorer-7 Thermal Radiation Experiment Selected White Sensor Temperature (Nighttime) Values product contains the temperatures measured by the white sensor at night. The white sensor was designed to measure terrestrial radiation. There is a single file for the entire mission (Nov. 15, 1959 to May 24, 1960). The data were originally written on IBM 7094 machines to magnetic tapes. In addition to the temperature values, the file contains radiance, geolocation and orbit information. The data have been restored and are archived in their original IBM EBCDIC text format.The Explorer-7 satellite was successfully launched on October 13, 1959. The radius of the circle of coverage was about 23 deg (~2500 km) at perigee and 31.5 deg (~3500 km) at apogee. Half the radiation is received from an area below the satellite with a radius of 5.3 deg (545 km) at perigee and 9 deg (~1015 km) at apogee. The Thermal Radiation Experiment successfully returned the first set of Earth looking data from space. The instrument was operational from launch until Feb. 28, 1961.The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00248 (old ID 59-009A-01A).
Explorer-7 Thermal Radiation Experiment Temperature Values from All Sensors V001 (EXP7L1TRTALL) at GES DISC
Explorer-7 Thermal Radiation Experiment Temperature Values from All Sensors product contains temperature readings from all five bolometers in order to measure solar, reflected and terrestrial radiation. There are two files for the entire mission (Oct. 19, 1959 to April 16, 1960 and April 16, 1960 to June 4, 1960. Note there is no geolocation information included with these data. The data were originally written on IBM 7094 machines on magnetic tapes. The data have been restored and are archived in their original IBM 36-bit word binary format.The Explorer-7 satellite was successfully launched on October 13, 1959. The radius of the circle of coverage was about 23 deg (~2500 km) at perigee and 31.5 deg (~3500 km) at apogee. Half the radiation is received from an area below the satellite with a radius of 5.3 deg (545 km) at perigee and 9 deg (~1015 km) at apogee. The Thermal Radiation Experiment successfully returned the first set of Earth looking data from space. The instrument was operational from launch until Feb. 28, 1961.The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00249 (old ID 59-009A-01B).
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.