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.
708
datasets available to search
ShareScore release 0.9.0
Dataset results
708 results for “Temperature, air”
MFS-M-00216 Air temperature at 2m, raised bog (hollow in complex bog), DS18B20 (APIK)
<p>Air temperature at 2m measured in a raised bog ecosystem (sphagnum lawn) by DS18B20 (temperature logger), 2016, 30 min frequency, N60.89325 E68.68311, OTC-wet experimental site, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00368 Air temperature at 2m, raised eriophorum-sphagnum bog, DS18B20 (APIK)
<p>Air temperature at 2m measured in a raised bog ecosystem (eriophorum-Sphagnum bog) by DS18B20 (temperature logger), 2018-2020, 30 min frequency, N60.89439 E68.68113, OTC-dry experimental site,<br> as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
An All-sky 1 km Daily Surface Air Temperature Product over Mainland China
<ul> <li>An all-sky daily mean surface air temperature (T<sub>a</sub>) product at 1 km spatial resolution over mainland China for 2003–2019 has been generated mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. Three T<sub>a</sub> estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The validation results showed that R<sup>2</sup> and root mean square error (RMSE) values of the three models ranged from 0.984 to 0.986 and 1.342 K to 1.440 K, respectively, indicating that this high-resolution product has satisfactory accuracy.</li> <li>The format of the data is tif and a sample data is provided in example.zip. The dataset is stored by year, with 6 *.zip files per year.</li> <li>There is also an all-sky 0.01° daily surface air temperature product over Beijing for 2003–2019 (http://doi.org/10.5281/zenodo.4405123), which is a sub-dataset generated from this dataset for easy and convenient understanding of this dataset. And the sub-dataset has a data volume of only 264MB after compressed.</li> </ul>
High-resolution air temperature observations near the surface using fiber-optic distributed temperature sensing
<p>Time-lapse animation of air temperature observations near the surface, highlighting wave-like motion in opposite direction of the mean wind. </p> <p> </p>
Modeled and observed river water temperature and discharge in the paper "Riverine heat waves on the rise, outpacing air heat waves"
<div>The observed and modeled data – mean daily water temperature (WT, °C) and mean daily discharge (Q,<em> </em>ft<sup>3</sup>/s) modeled by an LSTM model (averaged over 5 model runs) – for 1471 sites over 1980-2022 can be found here. Out of these 1471 sites, 1276 sites had good model performance and were used to identify and analyse air and riverine heat waves (RHW) in the paper "Riverine heat waves on the rise, outpacing air heat waves". </div> <div> <div> </div> </div>
Application of artificial neural network to forecast indoor air temperature in a building with artificial ventilation: impact of early stopping.
<p>Indoor air temperature prediction can facilitate energy-saving actions without compromising the indoor thermal comfort of occupants. The aim of this study was to analyse the performance of various artificial neural networks with a view to proposing an optimal approach for predicting the indoor temperature of a tertiary building with artificial ventilation. The MLP, CNN, LSTM models and the CNN-LSTM combination (long short-term memory network) were used and coupled with the optimisation algorithms (Adam, SGD) and the independent hyper-parameters early stopping and dropout. The parameters used are outdoor ambient temperature, outdoor relative humidity, indoor relative humidity, wet bulb temperature, black globe temperature and mean radiant temperature. The data is collected in an artificially ventilated building in Yaoundé, Cameroon. A numerical code was developed in Python to run the simulations. In order to study the impact of the parameters on the prediction, two scenarios were distinguished in this work: (1) all the parameters are input to the network, (2) only the parameters whose absolute value of the correlation coefficient was greater than or equal to 0.5 were used. The impact of early stopping is assessed by distinguishing two case studies: the first without early stopping, the second with early stopping. The results showed that without early stopping, the MLP, CNN, LSTM and CNN-LSTM networks are adequate for predicting the temperature with the second scenario, mainly with both the SGD and Adam algorithms, and CNN-LSTM is the most appropriate model because the MSE and MAE values obtained in this case were closer to 0. With early stopping, the learning time is reduced and the learning curves are improved; the models optimised better with the SGD algorithm in general, but the best neural network model was obtained with the Adam algorithm and the LSTM network for the performances MSE=0.0005, MAE=0.0130 with the second scenario.</p><p><strong>Keywords: </strong>prediction, indoor temperature, artificial neural network, early stopping, artificially ventilated building.</p>
Interacting impacts of hydrological changes and air temperature warming on lake temperatures highlight the potential for adaptive management: Model output
<p>This archive includes the output from the General Ocean Turbulence Model for a series of simulations that used differing model drivers (inflow discharge, Q, and air temperature, T). The Experiment_output.zip contains text files generated from the GOTM workflow containing modelled water temperatures and the Mod_z.txt are the corresponding depths for these temperatures. </p><p>The file name corresponds to the change made to the driving data from baseline (unchanged conditions), a combination of air temperature <i>increase </i>and flow percentage change e.g. Mod_temp_T_2_Q_1.5 refers to an air temperature increase of 2 degrees Celsius and a flow increase of 50% and a Mod_temp_T_3.5_Q_0.7 refers to an air temperature increase of 3.5 degrees Celsius and a flow decrease of 30%.</p>
A thermal performance curve perspective explains decades of disagreements over how air temperature affects the flight metabolism of honey bees
<p>While multiple studies have shown that honey bees and some other flying insects lower their flight metabolic rates when flying at high air temperatures, critics have suggested such patterns result from poor experimental methods as, theoretically, air temperature should not appreciably affect aerodynamic force requirements. Here, we show that apparently contradictory studies can be reconciled by considering the thermal performance curve of flight muscle. We show that prior studies that found no effects of air temperature on flight metabolism of honey bees achieved flight muscle temperatures that were near or on equal, opposite sides of the thermal performance curve. Honey bees vary their wing kinematics and metabolic heat production to thermoregulate, and how air temperature affects the flight metabolic rate of honey bees <em>is</em> predictable using a non-linear thermal performance perspective of honey bee flight muscle.</p>
All-sky daily max ambient air temperature datasets at 1-km resolution from 2003-2012 in China
<ul> <li>All-sky daily maximum, minimum, and mean ambient air temperature datasets at 1-km resolution over two decades (2003-2022) in China have been generated by the four-dimensional spatiotemporal deep forest (4D-STDF) model. The overall RMSE values for estimates are 1.49°C, 1.53°C, and 1.18°C.</li> <li>These datasets cover mainland China, featuring high spatial resolution (1km), long temporal sequences (2003-2022), and increased accuracy. They are presented in GeoTIFF format with WGS84 projection, with data measured in 0.1 degrees Celsius (°C).</li> <li>These datasets have divided for six parts to upload Zenodo, the links as follow: <ol> <li>Daily Tmax from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>,</li> <li>Daily Tmax from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983207">https://doi.org/10.5281/zenodo.10983207</a>,</li> <li>Daily Tmin from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>,</li> <li>Daily Tmin from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983199">https://doi.org/10.5281/zenodo.10983199</a>,</li> <li>Daily Tmean from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>,</li> <li>Daily Tmean from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983177">https://doi.org/10.5281/zenodo.10983177</a></li> </ol> </li> <li>This dataset is daily Tmax from 2003-2012 (<a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>).</li> </ul>
EEAR-Clim: A high density observational dataset of daily precipitation and air temperature for the Extended European Alpine Region
<p>Data, metadata and code for paper published in Earth System Science Data:</p> <p>A high density observational dataset of daily precipitation and air temperature for the Extended Alpine Region</p> <p> </p> <p><strong>Code </strong>(working copy all written in R statistical software): scripts.zip</p> <ul> <li>to read and process data in from different sources</li> <li>to perform intra and inter-stations quality control</li> <li>to perform break detection and homogenization</li> <li>to read results of quality control and homogenization</li> </ul> <p><strong>Data</strong>:</p> <ul> <li>Daily time series of air temperature (mean, minimum and maximum) and precipitation as .zip files, grouped by data provider.</li> <li>Information on column content is provided in separate files "data_readme.txt"</li> <li>about 10000 stations from Italy, France, Switzerland, Austria, Germany, Slovenia, Croatia, Bosnia-Herzegovina, Czech Republic, Slovakia and Hungary</li> <li>Meta data (code, name, longitude, latitude, elevation, measurements availability for each variable, starting date, ending date) in "metadata.zip", including a file for each data provider</li> <li>If you <strong>use the data you agree to adhere to the respective data provider's terms</strong> as listed in "License.pdf"</li> <li>The license terms especially (and additionally to any other terms of the single data providers) include: <strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>] </li> </ul> <p> </p> <p><strong>Version history:</strong></p> <p>v1.0: initial upload</p> <p>v2.0: update of data policies; addition of France and Croatia time series</p>
Data for Minimum air temperature modeling using RS data
<p>Data for Minimum air temperature modeling using RS data</p>
The gridded 2-m air temperature data produced by Yao et al. (2021),the depth of major lakes in Wuhan, and and the land use/land cover data in 2000, 2010, and 2020
<p>The gridded 2-m air temperature data produced by Yao et al. (2021), the depth of major lakes in Wuhan, and the land use/land cover data in 2000, 2010, and 2020 used in the manuscript </p>
On-Glacier Air Temperatures for Tsanteleina Glacier, 2015
<p>Tsanteleina_Glacier_Air_Temperature_Data_2015.xlsx<br> %------------------------------------------%<br> Data Generated on 13th May 2022</p> <p>Data Curator: Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland)</p> <p>Data Provider(s): Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) thomas.shaw@wsl.ch<br> Prof. Benjamin Brock (Northumbria University, Newcastle, UK) benjamin.brock@northumbria.ac.uk</p> <p>Data period: 19th June - 15th September, 2015</p> <p><br> Details:<br> Hourly data are generated for air temperature stations ('T-Loggers') distributed across Tsanteleina Glacier, Italy (45.4812°N, 7.0618°E).<br> Air temperatures (°C) were measured using Tinytag thermistors (accuracy +/- 0.2-0.35°C) housed in naturally ventilated Campbell MET20 / MET21 radiation shields.</p> <p>Some stations fell over at times during the summer season due to differential ablation and tripod stability.<br> Filtering of this data therefore leaves gaps (NaNs) for various stations at different times of the observation period. </p> <p>Off-Glacier air temperatures in the region can be accessed from the platform of the Regione Autonoma Valle d'Aosta:<br> https://presidi2.regione.vda.it/str_dataview_station/3060. </p> <p>Additional details can be found in the article: <br> Shaw, T. E., Brock, B. W., Ayala, A., Rutter, N., & Pellicciotti, F. (2017). <br> Centreline and cross-glacier air temperature variability on an Alpine glacier: assessing temperature distribution methods and their influence on melt model calculations. <br> Journal of Glaciology, 1–16. https://doi.org/10.1017/jog.2017.65</p> <p>Please cite the above article for any usage of the dataset.</p> <p>Data are shared and compiled as part of a wider project to estimate on-glacier air temperatures from off-glacier data<br> For more details on the 'TEMPEST' project, visit: https://tempestglacier.com/</p> <p><br> </p>
Respirometry protocols for avian thermoregulation at high air temperatures: stepped and steady-state profiles yield similar results
<p>Relationships between air temperature (Tair) and avian body temperature (Tb), resting metabolic rate (RMR) and evaporative water loss (EWL) during acute heat exposure can be quantified through respirometry using several approaches. One involves birds exposed to a stepped series of progressively increasing Tair setpoints for short periods (< 20-30 min), whereas a second seeks to achieve steady-state conditions by exposing birds to a single Tair for longer periods (> 1-2 h). To compare these two approaches, we measured Tb, RMR and EWL over Tair = 28 C to 44 C in the dark-capped bulbul (Pycnonotus tricolor). The two protocols yielded indistinguishable values of Tb, RMR and EWL and related variables at most Tair values, revealing that both are appropriate for quantifying avian thermal physiology during heat exposure over the range of Tair in the present study. The stepped protocol, however, has several ethical and practical advantages. </p>
Thermal conductivity of air at different temperatures
<p><strong>Thermal conductivity of air at different temperatures</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>The thermal conductivity of a material is a measure of its ability to a particular material conduct heat. Heat transfer occurs at a lower rate in materials of low thermal conductivity than in materials of high thermal conductivity. For instance, metals typically have high thermal conductivity and are very efficient at conducting heat, while the opposite is true for insulating materials. Correspondingly, materials of high thermal conductivity are widely used in heat sink applications, and materials of low thermal conductivity are used as thermal insulation. The reciprocal of thermal conductivity is called thermal resistivity. There are several ways to measure thermal conductivity; each is suitable for a limited range of materials. Broadly speaking, there are two categories of measurement techniques: steady-state and transient. Steady-state techniques infer the thermal conductivity from measurements on the state of a material once a steady-state temperature profile has been reached, whereas transient techniques operate on the instantaneous state of a system during the approach to steady state. Lacking an explicit time component, steady-state techniques do not require complicated signal analysis. The disadvantage is that a well-engineered experimental setup is usually needed, and the time required to reach steady state precludes rapid measurement. In comparison with solid materials, the thermal properties of fluids are more difficult to study experimentally. This is because in addition to thermal conduction, convective and radiative energy transport are usually present unless measures are taken to limit these processes. The formation of an insulating boundary layer can also result in an apparent reduction in the thermal conductivity.</p> <p>Thermal conductivity (watts per meter-kelvin), Temperature (degrees kelvin)</p> <table> <tbody> <tr> <td> <p>0.00922</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>0.01375</p> </td> <td> <p>150</p> </td> </tr> <tr> <td> <p>0.0181</p> </td> <td> <p>200</p> </td> </tr> <tr> <td> <p>0.02226</p> </td> <td> <p>250</p> </td> </tr> <tr> <td> <p>0.02614</p> </td> <td> <p>300</p> </td> </tr> <tr> <td> <p>0.0297</p> </td> <td> <p>350</p> </td> </tr> <tr> <td> <p>0.03305</p> </td> <td> <p>400</p> </td> </tr> <tr> <td> <p>0.03633</p> </td> <td> <p>450</p> </td> </tr> <tr> <td> <p>0.03951</p> </td> <td> <p>500</p> </td> </tr> <tr> <td> <p>0.0456</p> </td> <td> <p>600</p> </td> </tr> <tr> <td> <p>0.0513</p> </td> <td> <p>700</p> </td> </tr> <tr> <td> <p>0.0569</p> </td> <td> <p>800</p> </td> </tr> <tr> <td> <p>0.0625</p> </td> <td> <p>900</p> </td> </tr> <tr> <td> <p>0.0672</p> </td> <td> <p>1000</p> </td> </tr> <tr> <td> <p>0.0717</p> </td> <td> <p>1100</p> </td> </tr> <tr> <td> <p>0.0759</p> </td> <td> <p>1200</p> </td> </tr> <tr> <td> <p>0.0797</p> </td> <td> <p>1300</p> </td> </tr> <tr> <td> <p>0.0835</p> </td> <td> <p>1400</p> </td> </tr> <tr> <td> <p>0.087</p> </td> <td> <p>1500</p> </td> </tr> </tbody> </table>
Adiabatic flame temperatures of common fuels in air at constant pressure
<p><strong>Adiabatic flame temperatures of common fuels in air</strong> <strong>at constant pressure</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>In the study of combustion, the adiabatic flame temperature is the temperature reached by a flame under ideal conditions. It is an upper bound of the temperature that is reached in actual processes. There are two types adiabatic flame temperature: constant volume and constant pressure, depending on how the process is completed. The constant volume adiabatic flame temperature is the temperature that results from a complete combustion process that occurs without any work, heat transfer or changes in kinetic or potential energy. Its temperature is higher than in the constant pressure process because no energy is utilized to change the volume of the system.</p> <p>Fuel, Oxidizer, Adiabatic flame temperature (degrees Celsius)</p> <p>Acetylene Air 2500</p> <p>Butane Air 4074</p> <p>Ethane Air 1955</p> <p>Ethanol Air 2082</p> <p>Gasoline Air 2138</p> <p>Hydrogen Air 2254</p> <p>Magnesium Air 1982</p> <p>Methane Air 1963</p> <p>Methanol Air 1949</p> <p>Naphtha Air 4591</p> <p>Natural gas Air 1960</p> <p>Pentane Air 1977</p> <p>Propane Air 1980</p> <p>Methylacetylene Air 2010</p> <p>Toluene Air 2071</p> <p>Kerosene Air 2093</p> <p>Bituminous Coal Air 2172</p> <p>Anthracite Air 2180</p>
Derived environmental temperatures at Jezero crater from Air Temperature Sensors' measurements on the Perseverance rover.
<p><strong>Material from Version 2</strong> extends derived Air Temperature Sensor data to the first 700 sols of the Mars 2020 mission used in the analysis of <em>Munguira et al. (2024). "One Martian Year of Near-Surface Temperatures at Jezero from MEDA measurements on Mars2020/Perseverance". Journal of Geophysical Research: Planets. [in revision]. </em>We also include the tables needed to generate and reproduce the figures in the paper. Most importantly, the tables include the results from different analyses of temperatures through Fourier series and Reynolds averaging. </p>
Fig. 4. Annual air temperature over Lake Onega catchment area for 1951–2014 in Phytoplankton Responses To Climate Change In The Large Lakes Of The Baltic Sea Basin
Fig. 4. Annual air temperature over Lake Onega catchment area for 1951–2014.
Evaluation of the influence of rain on air surface temperature measurements
<h2>Description</h2> <p>The dataset is constituted by three .csv files, which contain the measurements performed in an experiment aiming to evaluate the influence of rain on temperature readings. Two devices under tests (DUTs), one naturally ventilated and one artificially ventilated, are compared with a reference system. A .csv file is produced for DUT1, DUT2 and the reference system. Here below the content of each file is briefly described:</p> <ul> <li>Dataset_reference: accurate air temperature measurements obtained using the reference system, which is not affected by rain. The system is constituted by four aspirated thermometers (called Meteo1, Meteo2, Meteo 3, Meteo 4) manufactured at the Danish Technology Institute. The column "PT500" contains instead the rain temperature measurements. The readings are produced using a Fluke Super-DAQ (1586A). </li> <li>Dataset_DUT1: measurements of the naturally ventilated thermometer under an artificially generated rainfall. The readings are produced using the manufacturer datalogger.</li> <li>Dataset_DUT2: measurements of the artificially ventilated thermometer under an artificially generated rainfall. The readings are produced using the manufacturer datalogger.</li> </ul>
Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features
<p>This dataset supports the study titled <em>"Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features"</em>, published in <em>Urban Climate</em> (<a href="https://doi.org/10.1016/j.uclim.2024.102102" target="_new" rel="noopener">DOI: 10.1016/j.uclim.2024.102102</a>).</p> <p> </p> <p><strong>Content Overview:</strong></p> <ul> <li> <p><strong>Building Label Data for Footprint Detection</strong>:</p> <ul> <li><em>Amsterdam_BDG_Label.rar</em></li> <li><em>MiamiDade_BDG_Label.rar</em></li> </ul> <p>These are the label datasets used for training the building detection segmentation models. They have been instrumental in accurately detecting building footprints in Amsterdam.</p> </li> <li> <p><strong>Amsterdam_3D_Buildings.rar</strong>: CityGML file of 3D building models for Amsterdam, derived from LiDAR data and U-Net3+ model.</p> </li> </ul> <ul> <li> <p><strong>Morphological Features.rar</strong>: Contains urban morphological features (in raster format) extracted from LiDAR data used in the study.</p> </li> <li> <p><strong>Training and Test Data for Air Temperature Estimation</strong>:</p> <ul> <li><em>Train_Test_AvgTemp_Amsterdam.rar</em></li> <li><em>Train_Test_MaxTemp_Amsterdam.rar</em></li> <li><em>Train_Test_MinTemp_Amsterdam.rar</em></li> </ul> <p>This dataset includes training and testing data for estimating air temperatures in three scenarios: average daily temperature, minimum daily temperature, and maximum daily temperature for the city of Amsterdam.</p> </li> </ul>
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.