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708 results for “Temperature, air”
Determination of Core Body Temperature in Parturient Warmed With Upper or Underbody Forced Air Cover (Bair Hugger)
ClinicalTrials.gov study NCT05314075. IPD Sharing: NO. Countries: 1. Publications: 0.
Use of Onepiece Suit or Forced Warm Air for Perioperative Temperature Conservation.
ClinicalTrials.gov study NCT02336152. IPD Sharing: Not stated. Countries: 1. Publications: 0.
In-situ relative humidity and air temperature urban microclimate data
Open the record for dataset details and reuse information.
Monthly Near-Surface Air Temperature Averages
Global surface temperatures in 2010 tied 2005 as the warmest on record. The International Satellite Cloud Climatology Project (ISCCP) was established in 1982 as part of the World Climate Research Programme (WCRP) to collect and analyze the global distribution of clouds, their properties, and their diurnal, seasonal, and interannual variations. The LAS provides data for Monthly Near-Surface Air Temperature Averages from 1994 to 2008.
GLERL Great Lakes Air Temperature/Degree Day Climatology, 1897-1983, Version 1
Daily maximum and minimum temperatures for 25 stations around the Great Lakes, 1897 to 1983, were given to NSIDC by the NOAA Great Lakes Environmental Research Laboratory (GLERL), Ann Arbor, MI. Daily data can be used to produce daily maximum, minimum, and mean temperatures, and seasonal accumulations of freezing and thawing degree days. The statistical data are archived in ASCII text files transcribed from 25 reels of 35 mm microfilm, one roll per station. Microfilm rolls are the appendices to Assel (1980), with updates covering 1978 to 1983 spliced to the end of each microfilm roll. Data sources include U.S. Department of Commerce summaries of meteorological data for Minnesota, Michigan, Wisconsin, Illinois, Pennsylvania, and New York, and the monthly meteorological observations published by Environment Canada.
AIRS/Aqua L1B Near Real Time (NRT) AMSU (A1/A2) geolocated and calibrated brightness temperatures V005 (AIRABRAD_NRT) at GES DISC
The AMSU-A Level 1B Near Real Time (NRT) product (AIRABRAD_NRT_005) differs from the routine product (AIRABRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AMSU-A instrument is co-aligned with AIRS so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. AMSU-A is primarily a temperature sounder that provides atmospheric information in the presence of clouds, which can be used to correct the AIRS infrared measurements for the effects of clouds. This is possible because non-precipitating clouds are for the most part transparent to microwave radiation, in contrast to visible and infrared radiation which are strongly scattered and absorbed by clouds. AMSU-A1 has 13 channels from 50 - 90 GHz and AMSU-A2 has 2 channels from 23 - 32 GHz. The AIRABRAD_NRT_005 products are stored in files (often referred to as "granules") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.
Air Temperatures at High Altitude, Kanchanjunga Himal, Eastern Nepal, Version 1
This data set provides air temperature (1.5 m above ground surface) data from the Kanchanjunga Himal, eastern Nepal. Air temperature was monitored from November 1998 to November 1999 at three locations (Tengkoma, Lhonak, and Ghunsa) at altitudes of 3410, 4750 and 6012 m ASL. Although temperature was measured at one-hour intervals, only daily mean values are provided.
MoHAT: Global monthly high-resolution (1 km) near-surface air temperature projections from 2001 to 2100
<p>Please be advised that our dataset previously referred to as <strong>“MoHAT: Global monthly high-resolution (1 km) near-surface air temperature projections from 2001 to 2100”</strong> has been updated and is now available under the title <strong>“MoCHAT: Global monthly CMIP6-downscaled high-resolution (1 km) near-surface air temperature projections from 1950 to 2100”</strong>.</p> <p>The latest dataset can be accessed via the following URL: [<a href="https://data.tpdc.ac.cn/zh-hans/data/40d649d6-d99e-45df-9814-c0115a109396">https://data.tpdc.ac.cn/zh-hans/data/40d649d6-d99e-45df-9814-c0115a109396</a>]</p> <p>If you have any questions when using the MoHAT dataset, please feel free to contact Miss Xuwen Lei via <a href="mailto:leixuewen22@mails.ucas.ac.cn">leixuewen22@mails.ucas.ac.cn</a>, Dr. Qingyan Meng via <a href="mailto:mengqy@radi.ac.cn">mengqy@radi.ac.cn</a>, or Mr Qikang Zhao via <a href="mailto:yc27963@umac.mo">yc27963@umac.mo</a>. </p>
FIGURE 5. Air temperature for observation periods 1–10 in An investigation of patch occupancy and dispersal by third to final instar nymphs of Brachytrupes megacephalus Lefèbvre, 1827 (Orthoptera: Gryllidae: Gryllinae) across the sand dune biotope at the Għadira Nature Reserve, Malta
FIGURE 5. Air temperature for observation periods 1–10; the highest nymph count was that of 77 individuals (OP5) and the lowest was that of 11 individuals (OP8).
Average monthly weather summaries (air temperature and precipitation) at airport Lindbergh Field, San Diego, CA, 1850 - ongoing.
Data obtained as monthly values from National Ocean and Atmosphere (NOAA) National Weather Service web site for San Diego WSO Airport, California (047740). The data, presented as two sets from 1850-1913 and from 1914-ongoing, are obtained and combined on a monthly basis and re-published.
High Mountain Asia 1 km MODIS-AIRS Gap-Filled Ground Temperatures and Permafrost Probability Maps, 2003-2016 V001
This data set consists of 1 km resolution monthly land surface temperatures (MLSTs); mean annual ground temperatures (MAGTs); and estimates of permafrost extent (PE) in the High Mountain Asia region from 1 Jan 2003 – 31 Dec 2016. The data were generated by gap-filling daily MODIS Terra/Aqua Land surface temperatures (LSTs) with downscaled Atmospheric Infra-Red Sounder (AIRS) skin surface temperatures.
High Mountain Asia Daily 5 km Downscaled SPEAR Precipitation and Air Temperature Projections V001
This data set consists of daily, 5 km resolution precipitation and mean, near-surface air temperature projections from 2015 through 2100 for the High Mountain Asia (HMA) region. The data were generated by statistically downscaling 0.5° resolution model data from the Geophysical Fluid Dynamic Laboratory (GFDL) Seamless System for Prediction and EArth System Research (SPEAR) 30-member ensemble climate model. Projections are provided for two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5 8.5. The historical model run (1990 through 2014) used to initialize the SPEAR projections is also available.
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multisource data (2001-2002)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:<span>T<sub>ave</sub>, </span><span>R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%</span><span>; T<sub>max</sub>, </span><span>R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%</span><span>; T<sub>min</sub>, </span><span>R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%</span><span>).</span></p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2013-2014)
<div> <p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p> </div>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2017-2018)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2011-2012)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2009-2010)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2007-2008)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2005-2006)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2019-2020)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
ScienceDex guides
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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.