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59 results for “Temperature records”
Data and code to accompany 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record'
<p>Data and analysis code to accompany the manuscript 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record', published in <em>Oceanography and Marine Biology: An Annual Review </em>(<a href="https://doi.org/10.1201/9781003363873-2">https://doi.org/10.1201/9781003363873-2</a>). Data files include records of sea surface temperature (SST) collected in Pacific Grove, California, USA from January 20, 1919 to the end of 2020. The analysis code produces a continuous 100+ year record with adjustments made for time of day the data were collected, and filling gaps in the data set where necessary. </p> <p>This dataset makes use of an earlier 83-year version of the sea surface temperatures produced by Breaker et al. 2005 available at <a href="https://aquadocs.org/handle/1834/20890">https://aquadocs.org/handle/1834/20890</a>, with the data file available at <a href="https://purl.stanford.edu/rc833pc4972">https://purl.stanford.edu/rc833pc4972</a> </p>
Highly consistent brightness temperature fundamental climate data record from SSM/I and SSMIS
<p>The highly consistent brightness temperature (TB) fundamental climate data record (FCDR) comprises intercalibrated TBs from SSM/I on F11 and F13, and SSMIS on board F17. It covers the time period from December 1991 to December 2021. It provides homogenized and intercalibrated TBs in a user-friendly data format (HDF5). SSM/I and SSMIS data are used for various applications, such as analyses of the hydrological cycle. The improved homogenization and inter-calibration procedure ensure the long-term stability of the FCDR for climate related applications. <br> This data files contain daily TBs data on 1°×1° grid-level of satellite F11, F13 and F17 (Level 2A).<br> It is worth noting that the original sensor TB data are provided by Level-1C dataset. The Level-1C data record is complemented with scan status, quality flags, sun glint angles, and earth incidence angles.</p>
Analytical and supplemental data from: Ammonites as paleothermometers: Isotopically reconstructed temperatures of the Western Interior Seaway track global records
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Recorded temperature data of a reinforced concrete, 1.2-m long, beam-slab model and associated meteorological data.
<div><strong>The data contain internal and surface temperature measurements of a 1.2 m-long, reinforced concrete girder-slab element exposed to the environmental conditions of the city of Bucaramanga, Colombia (Coordinates: 7°08'35", 73°07'18"). In addition, meteorological variables of the environment near the element were recorded. The data was measured from July 7, 2023, to November 6, 2023.</strong></div> <div><strong>The data were obtained by:</strong></div> <ul> <li> <div><strong>Internal Temperature (file name: InternalTemperature): 40 thermocouples were embedded in the element (Fig. 1) to collect data every 30 minutes from 6:30 AM on July 10, 2023 to 11:30 PM on November 6, 2023. Temperatures are in degrees Celsius (°C).</strong></div> </li> <li> <div><strong>Surface Temperature (file name: SurfaceTemperatureCameraEast and SurfaceTemperatureCameraWest): A FLIR E6 XT thermal camera was used to record Surface temperature of 10 points on the EAST (Fig. 2) and WEST (Fig. 3) faces of the element from 6:30 AM on June 29, 2023 to 4 PM on October 31, 2023. Twenty daily measurements were recorded every 30 minutes. Temperatures are in degrees Celsius (°C).</strong></div> </li> <li> <div><strong>Surface Temperature (file name: SurfaceTemperatureDroneEast): A DJI MAVIC 2 ENTERPRISE ADVANCED (EU) drone equipped with a thermal imaging camera measured the surface temperature of 10 points on the EAST side of the element (Fig. 4) every 30 minutes, from 6:30 AM on September 9 to 4 PM on October 31. Temperatures are in degrees Celsius (°C).</strong></div> </li> <li><strong>Meteorological variables (file name: WeatherStationData): A DAVIS VANTAGE PRO-2 WLRS weather station recorded data every 30 minutes from 12 AM on July 10, 2023, to 12:30 PM on November 6, 2023. The data included the average, maximum, and minimum ambient temperature (°C); relative humidity (%); speed (m/s) and wind direction (cardinal direction); atmospheric pressure (mb); precipitation (mm) and average and maximum solar radiation (W/m<sup>2</sup>).</strong></li> </ul>
SeaTemCon_R code for "The Holocene temperature conundrum answered by mollusk records from East Asia"
<p>This repository includes the code that can be used to calculate the contribution percentages of seasonal temperatures to the annual temperature for the paper entitled "The Holocene temperature conundrum answered by the mollusk records from East Asia".</p>
Data repository for "The Holocene temperature conundrum answered by mollusk records from East Asia"
<p>This repository includes the data that can be used to reproduce the figures for the paper entitled "The Holocene temperature conundrum answered by the mollusk records from East Asia".</p>
Compilation of δO2/N2 records, and accumulation rate and temperature reconstructions from various polar ice cores
<p>Compilation of δO2/N2 records, and accumulation rate and temperature reconstructions from various polar ice cores. The first file 'Compilation_Acc_T_2024.xlsx' contains published temperature and accumulation rate reconstructions which were used to make Figure 3 in the associated paper. The datasets are included as published (i.e., no interpolation onto age or depth scales). Depth/age ranges considered in study are indicated in Table 3 in associated paper. The second file 'Compilation_O2N2_2024.xlsx' contains published and unpublished δO2/N2 data. </p>
Recordings from the C. borealis Stomatogastric Nervous System at different temperatures in the decentralized condition
<p>As a courtesy please inform us if you are planning to work with these data (marder@brandeis.edu).</p> <p>These data are collected from the stomatogastric nervous system of the crab <em>Cancer borealis</em> in the decentralized condition (modulatory inputs cut) at different temperatures. For data collection methods please see Haddad & Marder, 2018 (DOI: <a href="https://doi.org/10.1016/j.neuron.2018.08.035">10.1016/j.neuron.2018.08.035</a>). These data are also among the larger data set described in Gorur-Shandilya et al, 2021 (https://www.biorxiv.org/content/10.1101/2021.07.06.451370v1.full.pdf). </p> <p>In data sets 845_082 and 845_078 there are two preparations recorded from in each file. Please see notes to clarify which channels belong to which preparations. Please see end for descriptions of nerve/neuron abbreviations. </p> <p>Data set 845_082_0044 (11°C) and 845_082_0064 (27°C):</p> <p> Preparation 1 = Channels 1 (lvn), 6 (lpn), 2 (pyn), 4 (pdn)</p> <p> Preparation 2 = Channels 7 (lvn), 10 (lpn), i2/9 (pdn), 13 (lgn)</p> <p>Data set 857_016_0049 (11°C) and 857_016_0069 (27°C):</p> <p> Preparation 1 = Channels 2 (LG), 7 (lvn), 4 (lpn), 9 (pyn), 8 (pdn), 15 (lgn), 6 (mvn), 11 (dgn)</p> <p>Data set 845_078_0027 (11°C) and 845_078_0040 (27°C)</p> <p> Preparation 1 = Channels 1 (lvn, upper), 6 (lpn), 4 (pyn), 2 (lvn lower)</p> <p> Preparation 2 = Channels 7 (lvn), 10 (lpn), i2/9 (pyn), 13 (pdn)</p> <p>lvn = lateral ventricular nerve, lpn = lateral pyloric nerve, pyn = pyloric nerve, pdn = pyloric dilator nerve, lgn = lateral gastric nerve, mvn = median ventricular nerve, dgn = dorsal gastric nerve, LG= lateral gastric neuron.</p>
Mast seeding records in North American Pinaceae and summer temperature data (1960-2014)
<p>Mast seeding database compilation for conifer tree reproduction in North America (belonging to genus: <em>Abies</em>, <em>Picea</em>, <em>Pinus</em>, <em>Tsuga</em>). All data included in analyses met the criteria that they: i) had at least 6 years of mast seeding data for a species of coniferous tree in North America, ii) data were collected on a continuous scale (e.g., based on seed traps, visual cone counts, or cone scars), iii) occurred between 1960-2014, and iv) for a taxon to be included in the study, there was a minimum of 10 separate time series at the level of the genus. Data from distinct sites or on different species were included separately. Data are standardized to values between 0-100 for each dataset.</p> <p>Summer temperature data are based on ClimateNA, including mean annual July temperatures from 1960-2014 for each location where there is mast-seeding data, and the difference in consecutive summer mean July temperatures.</p>
Biomarker indices and concentrations and biomarker-based temperature estimates from the Iberian Margin core MD95-2042, composite atmospheric temperature record from Greenland, and stacks of delta 18Oice and atmospheric temperature records from three Antarctic sites
<p>Core MD95-2042 alkenone and GDGT data: This dataset provides the following information for core MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT-2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. Sediment samples were taken every 5 cm from core MD95-2042 and homogenized before lipid extraction. The lipid extracts were splitted into two fractions: one for alkenone analysis by gas chromatography coupled to a flame ionization detector, and the other for GDGT analysis by high-performance liquid chromatography coupled to mass spectrometry. All GDGT analyses were done in duplicate. The 1σ analytical uncertainties from 37 replicate analyses of the core catcher sample from core MD95-2042 are 0.007 (0.4 °C) for RI-OH, 0.008 (0.2 °C) for RI-OH′, 0.003 (0.2 °C) for TEX<sub>86</sub>, 0.238 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for U<sup>K′</sup><sub>37</sub>. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX<sub>86</sub><sup>H</sup>-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX<sub>86</sub><sup>H</sup> + 33.0 (Darfeuil et al., 2016). U<sup>K′</sup><sub>37</sub>-SST estimates are from the following global calibration: SST = 29.876 × U<sup>K′</sup><sub>37</sub> − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX<sub>86</sub>-SST and TEX<sub>86</sub>-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for U<sup>K′</sup><sub>37</sub>-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Alkenone data covering the 160–70 and 70–0 ka BP periods are from Davtian et al. (2021) and Darfeuil et al. (2016), respectively. GDGT data covering the 160–45 ka BP period are from Davtian et al. (2021). The age model of core MD95-2042 for the 160–43 and 43–0 ka BP periods was obtained by tuning to Chinese speleothems (Cheng et al., 2016) and by recalibrating existing <sup>14</sup>C ages with the Marine20 calibration curve (Heaton et al., 2020), respectively. MIS, Marine Isotope Stage; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available.</p> <p>Greenland atmospheric temperature record: This dataset consists in a composite Greenland atmospheric temperature record, which was built with the following records: the GISP2 atmospheric temperature record by Kobashi et al. (2017) for the 10–0 ka BP period, the NGRIP atmospheric temperature record by Kindler et al. (2014) for the 120–10 ka BP period, and the NEEM atmospheric temperature record by NEEM community members (2013) for the 129–120 ka BP period. The NEEM temperature anomalies obtained by NEEM community members (2013) were shifted by –31 °C to obtain absolute air temperatures. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p> <p>Antarctic δ<sup>18</sup>O<sub>ice</sub> and atmospheric temperature stacks: This dataset consists in two stacks of three Antarctic records (EDC, EDML, and WD), one for δ<sup>18</sup>O<sub>ice</sub> and the other for atmospheric temperature: both stacks are provided with their stacking uncertainties. To build the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were resampled every 10 years before centering to zero means and normalization to unit standard deviations over the 140–0 ka BP period (68–0 ka BP for WD). To optimize the continuity between the portions with and without the WD ice core, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were centered to zero means over the 68–67 ka BP period. The resulting Antarctic δ<sup>18</sup>O<sub>ice</sub> records were then averaged and stacking uncertainties were calculated as the pooled standard deviation of the stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records divided by the square root of the number of stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records. The final Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, expressed in ‰, has the same standard deviation as the δ<sup>18</sup>O<sub>ice</sub> record from EDML over the 140–0 ka BP period, and has a zero mean over the 1–0 ka BP. The Antarctic atmospheric temperature stack was built like the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, except that the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were corrected for seawater δ<sup>18</sup>O<sub>ice</sub> variations before conversion into atmospheric temperature. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p>
Gokyo Lake 4 temperature and pressure records
<p>Temperature and pressure time series collected in Gokyo Lake 4, in Sagarmatha, Nepal, between June 6, 2018 and September 1, 2019. A taut-line mooring held 9 HOBO temperature loggers bounded by 2 pressure sensors at the top and bottom of the mooring. Matlab files hold each dataset (pressure, temperature) separately. dt variable is Matlab date time, and sensor depths are in centimeters above bottom.</p>
A seven-year record of fluctuating core body temperatures of nesting leatherback and hawksbill sea turtles
<p>Sea turtles experience considerable changes in water temperatures during migrations and seasonal movements that will influence their body temperatures. Nothing is known of how sea turtles' core body temperatures vary from season to season at nesting sites. Over seven consecutive seasons we measured the surface temperatures of freshly laid eggs as proxies of core body temperatures of sea turtles using non-contact infrared thermometers. We estimated the temperatures of two species that have contrasting migration patterns - leatherbacks, which are adapted to migrate between tropical breeding sites to cold temperate waters, and hawksbills that are confined to the tropics and sub-tropics. We found considerable year-to-year variations in temperatures in both species (means ranging from 30.4°C to 31.5°C in leatherbacks), hawksbills the more so (28.1°C to 30.3°C). These differences will likely be modified by both natural seasonal variations and anthropogenic changes in global ocean temperatures and resulting changes in currents and water temperatures local to nesting beaches. These previously unrecognised fluctuations in body temperatures of nesting turtles have, it is argued, potential for predicting environmental tolerances, reproductive success, and nest site selection by sea turtles, and contribute to predictions of which rookeries may remain viable or not during future ocean warming.</p>
A suspended hot drop of 1,3-propanediol with 4.1 mm base diameter, 190oC core temperature, and approaching air flow at 0.34 m/s, recorded at 10,000 fps and 99 ms exposure time, with default video playback speed 30 fps
<p>A suspended hot drop of 1,3-propanediol with 4.1 mm base diameter, 190<sup>o</sup>C core temperature, and approaching air flow at 0.34 m/s, recorded at 10,000 fps and 99 ms exposure time, with default video playback speed 30 fps</p>
A seven-year record of fluctuating core body temperatures of nesting leatherback and hawksbill sea turtles
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Ice gliding diatoms establish record-low temperature limits for motility in a eukaryotic cell
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Mast seeding records in North American Pinaceae and summer temperature data (1960-2014)
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Data from: Nacre microstructure records spatiotemporal variation in temperature in the modern ocean
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Global record-breaking recurrence rates indicates more widespread and intense surface air temperature and precipitation extremes
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Synchrotron Diffraction Data Recorded during High Temperature Deformation of Ti-64
<p>A caked synchrotron X-ray diffraction (SXRD) dataset, recording diffraction pattern rings during the high temperature deformation of a Ti-64 sample. The sample was deformed in uniaxial tension at 950°C and a strain rate of about 0.02 s-1, using an electro-thermal mechanical tester (ETMT) mounted on the I12:JEEP beamline at Diamond Light Source. Results were recorded using a high energy 89 keV synchrotron X-ray beam and a fast acquisition rate (10 Hz) detector. The results of this experiment are presented in the paper; </p> <p>C.S. Daniel, C.-T. Nguyen, M.D. Atkinson, J.Q. da Fonseca, Direct Evidence for Dynamic Phase Transformation during High Temperature Deformation in Ti-64, MATEC Web Conf. 321 (2020) 12037. <a href="https://doi.org/10.1051/matecconf/202032112037">10.1051/matecconf/202032112037</a></p> <p> </p>
Consistent ground surface temperature records for the CMA stations over China for 1956–2022 via numerical simulation
<p>The ground surface represents the land-atmosphere interface and plays a crucial role in exchanging energy, matter, and biochemical fluxes. The ground surface temperature (T<sub>s</sub>) is hence widely investigated as an indicator to understand the thermal state of soil in a warming world. However, regular and continuous T<sub>s</sub> measurements are rare worldwide, and the early T<sub>s</sub> records were derived from snow surface measurements and are not comparable with the measurements of the modern automatic systems. In this dataset, we reconstructed the T<sub>s</sub> records of the China Meteorological Administration (CMA) for 1956–2022 by numerical simulation.</p>
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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.