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.
2,762
datasets available to search
ShareScore release 0.9.0
Dataset results
2,762 results for “Heat”
Perceptions of heat and air pollution among older adults experiencing homelessness in Phoenix, Arizona (USA) (June 2024)
This dataset consists of survey responses from 40 older adults experiencing homelessness in Phoenix, Arizona (USA), assessing the perceptions of environmental hazards—specifically heat and air pollution—and attitudes toward coping resources and behaviors. The survey includes 51 questions co-created with community members across five categories: demographics and behavior, movement/transportation, climate perceptions, resource availability, and local knowledge mapping. Surveys were conducted indoors at a local service provider over two days in June 2024, when outdoor temperatures reached 42 degrees C and 45 degrees C. The dataset offers insights into potential public service reforms to mitigate heat and air pollution risks among Arizona’s unhoused population. The survey was approved by the Institutional Review Board of Arizona State University (IRB approval number: STUDY00018399).
Urban Heat and Desert Wildlife: Rodent Body Condition Across a Gradient of Surface Temperatures in the greater Phoenix, Arizona (USA) metropolitan area (2019-2020)
We live-trapped wild rodents from seven field sites spanning three strata of land-surface temperatures in the Phoenix, Arizona (USA) metropolitan area. We captured 116 adult pocket mice (Chaetodipus spp. and Perognathus spp.) and Merriam’s kangaroo rats (Dipodomys merriami) during 2019 and 2020 from mountainous urban parks and open spaces. Animal body condition was quantified as percent body fat (i.e., fat mass divided by body mass). We used a noninvasive quantitative magnetic resonance instrument to measure body condition.
Specific Heat of Holmium in Gold and Silver at Low Temperatures - Data
<p>Data from measurements on the specific heat of a variety of Au:Ho and Ag:Ho alloys. This data is associated with the manuscript:</p> <p>Herbst, M., Reifenberger, A., Velte, C. <em>et al.</em> Specific Heat of Holmium in Gold and Silver at Low Temperatures. <em>J Low Temp Phys</em> <strong>202, </strong>106–120 (2021). https://doi.org/10.1007/s10909-020-02531-1</p> <p>For information on the motivation, measurement techniques, equipment, and data processing, please refer to this manuscript.</p>
Characterisation of Social Vulnerability to the environmental hazard of heat in Logroño, and the surrounding La Rioja region in Spain, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for Logroño, and the surrounding La Rioja region, Spain. The input variables used in this dataset come from the national census data for Spain and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Characterisation of Social Vulnerability to the environmental hazard of heat in Milan, derived from national census and EU Copernicus datasets
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Milan, Italy. The input variables used in this dataset come from the national census data for Italy and EU Copernicus data.</p> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p>
Data and software: Stress and heat flux via automatic differentiation
<h4><strong>glp-archive</strong></h4><h2><strong>Code and Data for "Stress and heat flux with automatic differentiation"</strong></h2><p>This repository contains data, code, and related artefacts supporting the following publication (<a href="https://arxiv.org/abs/2305.01401">preprint</a>):</p><p>Stress and heat flux via automatic differentiation</p><p>by Marcel F. Langer, J. Thorben Frank, and Florian Knoop</p><p><i>J. Chem. Phys.</i> 159, 174105 (2023) <a href="https://doi.org/10.1063/5.0155760">doi:10.1063/5.0155760</a></p><p>This repository is available at <a href="https://github.com/sirmarcel/glp-archive">https://github.com/sirmarcel/glp-archive</a>. Selected versions are archived on Zenodo, under <a href="https://doi.org/10.5281/zenodo.7852529">doi:10.5281/zenodo.7852529</a>.</p><h2><strong>Overview</strong></h2><p>Each subfolder in this repository contains a README.md with additional information. The subfolders are:</p><ul><li>results/: Data and code that produced the figures in the manuscript</li><li>work/: Computational workflows, models, etc.</li><li>infra/: Project-specific infrastructure code</li><li>meta/: Scripts for assembling this archive; can be ignored but is retained for transparency.</li></ul><h2><strong>Related external code</strong></h2><p>The work in this repository relies on a few tools that the authors maintain separately:</p><ul><li><a href="https://github.com/sirmarcel/glp">glp</a> implements the quantities discussed in the manuscript</li><li><a href="http://github.com/thorben-frank/mlff">mlff</a> implements the so3krates model</li><li><a href="https://github.com/flokno/tools.mlff">tools.mlff</a> provides tools for the equation of state experiments</li></ul><p>These tools were developed during the work in the manuscript. The following versions/tags reflect what was used to obtain results:</p><ul><li>glp @ v0.1.0 (tag)</li><li>mlff @ v1.0 (branch)</li><li>mlff.tools @ v0.0.1</li></ul><p>We additionally note that the GK-MD functionality has been factored out into <a href="https://github.com/sirmarcel/gkx">gkx</a>.</p><h2><strong>Versions</strong></h2><ul><li>v1.1: published version, archived at <a href="https://doi.org/10.5281/zenodo.8406532">doi:10.5281/zenodo.8406532</a></li><li>v1.0: arXiv submission v1, archived at <a href="https://doi.org/10.5281/zenodo.7852530">doi:10.5281/zenodo.7852530</a></li></ul>
Current Siberian heating is unprecedented during the past seven millennia
<p>This repository contains all the tree-ring width chronology and reconstructions data used by Hantemirov et al. (2022) to assess the annually resolved summer temperature of the past 7000 years in Siberia</p> <p>For more information, we refer the user to the readme file entitled "Hantemirov_et_al_NatCom2022_Readme.txt"</p>
Technical potential of ground-source heat pumps for Western Switzerland
<p>This dataset contains an estimation of the technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 200 x 200 m<sup>2</sup>. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to <strong>avoid the over-exploitation</strong> of the heat capacity of the ground. We consider GSHPs with <strong>vertical closed-loop borehole heat exchangers</strong> (BHE) installed at depths of 50 - 200 m. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains.</p> <p>The estimated potential accounts for:</p> <ul> <li>Norms for geothermal installations set by the Swiss Society of Engineers and Architects (SIA 384/6)</li> <li>Thermal interferences between neighbouring boreholes and their impact on the temperature change in the ground</li> <li>Topographic Landscape data to assess the available area for BHE installation</li> </ul> <p>The methodology used to generate the data is described in:</p> <p>Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. ‘Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale’. <em>Renewable Energy</em> 165 (2021): 369–80. <a href="https://doi.org/10.1016/j.renene.2020.11.019">https://doi.org/10.1016/j.renene.2020.11.019</a>.</p> <p><strong>Dataset description</strong></p> <p>As the data is targeted to large-scale applications and potential studies, it is shared in the format of <strong>pixels of 200 x 200 m<sup>2</sup></strong>. Upon request it can be provided at different aggregation levels, as it is generated at the resolution of individual building units (parcels). The potential is provided as <strong>annual</strong> <strong>values</strong>, and it can be converted to monthly values using the provided heating degree weights. For each pixel of 200 x 200 m<sup>2</sup>, we provide the following variables:</p> <ul> <li>Annual total technical heat extraction potential (in MWh)</li> <li>Potential heat delivered <em>to buildings </em>(heat pump output), assuming a heat pump performance (COP) of 4.5 (in MWh)</li> <li>Available area for GSHP installation (in m<sup>2</sup>)</li> <li>Number of installed boreholes </li> <li>Average heat extraction rate (in W/m)</li> <li>Average borehole depth (in m)</li> <li>Average borehole spacing within the parcels located in the pixel (in m)</li> <li>Heating degree weights (i.e. heat demand variation) for each month</li> </ul> <p>A description of the metadata is provided in the document <em>gshp_VD_GE_metadata_V1.pdf.</em></p> <p>This work is part of the PhD Thesis of Alina Walch. </p>
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Half-hourly growing season, chamber-based, CO2 flux data, 2009-2021
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data contains CO2 fluxes measured using an automated chamber system that measures net ecosystem CO2 exchange (NEE). Measurements are made every ~1.5 hours and modeled half-hourly. Half hour ecosystem respiration is modeled using an exponential Q10 relationship when light conditions are low (PAR<5umol/m2/s) and using a hyperbolic light relationship when PAR>5umol/m2/s. GPP is calculated as the difference between NEE and Reco.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Growing season, chamber-based, CO2 flux data, 2009-2021
This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warming affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. This data set includes measured values of CO2 fluxes during the growing season.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Seasonal water table depth data, 2012-2024
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data includes water table depth measurements collected from winter warming and control treatment plots at CiPEHR for the ice-free period of 2024. Note that the experimental warming portion of this experiment concluded in 2022. These data are a continuation of measurements taken at previously warmed plots but plots were not actively manipulated in 2023 and 2024.
MCR LTER: Coral Reef: Farmerfish gardens help buffer stony corals against marine heat waves, data for Honeycutt et al., PLOS One 2023
These data were generated in support of the manuscript: Honeycutt RC, Holbrook SJ, Brooks, AJ, and RJ Schmitt, PLOS One In Moorea, French Polynesia, we evaluated the response and fate of stony coral following a major thermal stress event in 2019 that caused a substantial amount of branching coral (dominantly Pocillopora) to bleach and die. We investigated whether Pocillopora colonies that occurred within territorial gardens protected by the farmerfish Stegastes nigricans were less susceptible to or survived bleaching better than Pocillopora on adjacent, undefended substrate. Bleaching prevalence and severity, which were quantified for >1,100 colonies shortly after they bleached, did not differ between colonies within or outside of defended gardens. By contrast, 399 focal colonies followed for one year revealed that a bleached coral within a garden was a third less likely to suffer complete colony death and, for survivors, about twice as likely to recover to its pre-bleaching cover of living tissue compared to Pocillopora outside of a farmerfish garden. Our findings indicate that while residing in a farmerfish garden may not reduce the bleaching susceptibility of a coral during thermal stress, it does help buffer a bleached coral against severe outcomes. This oasis effect of farmerfish gardens, where survival and recovery of thermally-damaged corals are enhanced, is another mechanism that helps explain why large Pocillopora colonies are far more abundant in farmerfish territories than elsewhere in the lagoons of Moorea, despite gardens being much less common. As such, farmerfish may have a growing role in maintaining the resilience of branching corals as the frequency and intensity of marine heat waves continue to increase. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued
Wind tunnel distributed temperature sensing with actively heated fibers and microstructures for detecting wind direction
<p>Wind tunnel tests were performed using distributed temperature sensing with actively heated fibers that had microstructures attached in opposing directions on neighboring fibers. These microstructures created a temperature difference between fibers that depended on wind speed, providing a prototype for distributed sensing of wind direction. These data are connected to a publication detailing this work and method, <a href="https://www.atmos-meas-tech-discuss.net/amt-2019-188/">"Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables"</a>.</p> <p>Data are stored in a netcdf format and includes the instrument reported temperature ('instr_temp') and calibrated temperature ('cal_temp') with the various parameters tested in the linked paper available as coordinates, labeled along an 'expname' dimension.</p> <p>The included ipython notebooks provide examples and explanations for using these laboratory data.</p>
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Dataset from "In silico assessment of collateral eddy current heating in biocompatible implants subjected to magnetic hyperthermia treatments"
<p>This dataset from the publication entitled "Dataset from "In silico assessment of collateral eddy current heating in biocompatible implants subjected to magnetic hyperthermia treatments" contains simulated data of magnetic hyperthermia treatments for three different indications: colorectal cancer, prostate cancer and head & neck cancer. Since the aim of the study is to evaluate the risk of thermal damage caused by the collateral heating of two common types of passive prostheses (hip and dental implants), eddy currents induced in these implants upon interacting with the externally applied ac field during treatment have been computed for all the evaluated regions. Two different alloys for the implants have been considered for each case as well: Ti6Al4V and CoCrMo. At the same time, besides temperature, the specific abosorption rate (SAR) have been also computed to work out the energy deposition in tissues.</p> <p>Calculations have been carried out using a het exchange model with and without thermoregulation.</p> <p>log-log SAR vs T plots have been obtained and proposed as a quick means to pre-check treatment feasibility in each patient. These graphs are thought to be included in treatment planning prior to the clinical procedure.</p> <p>Other parameters taken into account have been the treatment time (5 and 30 minutes), and the maximum tolerable temperature threshold (1 or 5 ºC, as indicated by the ICNIRP commission), all for three main types of tissues, namely fat, bone and muscle. Each tissue have been simulated using three different field intensities (5, 10 and 15 mT).</p> <p>The field frequency has been 300 kHz in all cases.</p> <p>The files "Dataset_description.doc" and "file_scheme.txt" contain the structure and description of the files that make up the dataset.</p> <p>UPDATES FROM PREVIOUS VERSIONS: simulations of the dental implant without thermoregulation have been added.</p>
RADIT: A Machine Learning-Reconstructed Dataset of River Discharge, Temperature, and Heat Flux into the Arctic Ocean
<p>The Reconstructed Arctic-draining river DIscharge and Temperature (RADIT) dataset provides daily records of river discharge, temperature, and heat flux for 25 major Arctic-draining rivers from 1950 to 2023. Using machine learning methods and ERA5-Land reanalysis data, we reconstructed these key hydrological variables with high accuracy (most NSEs > 0.8).</p> <p>Due to licensing restrictions and to encourage adherence to the stated licenses of the original input data, this dataset only provides the reconstructed (filled) values. Users can obtain the complete historical observational data from their original publicly available sources as detailed in our documentation. By combining these original observations with our reconstructed data, a comprehensive and continuous daily dataset from 1950 to 2023 can be assembled. Clear instructions and links for downloading the original observational data used in this study can be found at: <a href="https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data" target="_blank" rel="noopener">https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data</a>. Should you encounter any issues or have questions, please feel free to contact the first author, Zihan Wang (zhwang2018@163.com).</p>
Lagrangian Decomposition of the Meridional Heat Transport at 26.5N - Water Parcel Crossings of the RAPID 26.5N Array
<p>This dataset contains the initial and final positions and properties of Lagrangian trajectories evaluated using 5-day mean velocity and tracer fields output from the ORCA0083-N06 ocean sea-ice model hindcast (1958-2015). Numerical water parcels are initialised to sample the full-depth southward transport across the RAPID 26.5N array every month during 2004-2015. Water parcels are advected backwards-in-time using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool which enables users to specify a custom domain using a mask netCDF file.</p><p>Particles are initialised on the first-available day of each month (based on the centre of the model 5-day mean field windows) between 2004 and 2015 (inclusive) before being advected backwards-in-time within the North Atlantic Ocean until any one of four termination conditions are met: (1) water parcels reach the RAPID 26.5N array, (2) water parcels reach the OSNAP (West or East) arrays in the subpolar North Atlantic, (3) water parcels reach either the English Channel or Gibraltar Strait, or (4) particles reach the maximum advection time of 25-years. The 25-year maximum advection time ensures that we adequately resolve the subtropical gyre circulation north to the RAPID 26.5N array. The pathway transporting dense North Atlantic Deep Water from the OSNAP arrays to RAPID at 26.5N is not fully resolved in this Lagrangian experiment since these water parcels transit on multi-decadal timescales.</p><p>The number of water parcels initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 5.0 mSv (mSv == 10-3 Sv), enabling the calculation of robust Lagrangian statistics. In reality, the average. water parcel has an associated volume transport of 3.3 mSv which is conserved throughout its circulation.</p><p>Water parcel locations (converted to geographical coordinates) and properties (conservative temperature, absolute salinity, potential density [TEOS-10]) are output on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points. Here, we provide the initial and final locations and properties of all water parcels initialised from RAPID 26.5N.</p><p>All Lagrangian experiments were completed using the JASMIN High-Performance Computing facility (<a href="https://jasmin.ac.uk">https://jasmin.ac.uk</a>).</p><p><strong>For a complete description of the ORCA0083-N06 hindcast configuration see:</strong> Moat et al. (2016).</p><p><strong>For a complete description of TRACMASS v7.1 see</strong>: <a href="https://www.tracmass.org">https://www.tracmass.org</a></p>
A Danish high-resolution dataset for six office rooms with occupancy, indoor environment , heating, ventilation, lighting and room control monitoring
<p>A dataset containing measurement data for six office rooms in Aalborg Denmark.<br>All the measurements have been resampled to 5 minute resolution<br>The measurements consists of:</p> <ul> <li>BMS data for the rooms</li> <li>Occupancy for the rooms (from cameras)</li> <li>BMS data for the AHU supplying the rooms</li> <li>BMS data for the Heating system supplying the rooms</li> </ul> <p>Changes from v2<br>It was found that the pressure difference measurements across the exhaust fan was faulty and the following variables have therefore been removed:</p> <ul> <li>Ventilation:Fan__air_flow__exhaust</li> <li>Ventilation:Fan__pressure_difference__exhaust</li> </ul> <p>More data has been added, now increasing the dataset to span the rest of 2023. To better handle the changes between standard time and daylight-saving time the column named "timestamp" has been adjusted so the datetime format now follows the ISO 8601 format YYYY-MM-DDThh:mm:ss+hhmm. the +hhmm changes between 0100 (Danish standard time) and 0200 (Danish daylight-saving time).</p> <p> </p>
heat of hydrogenation for diverse organic compounds -- experimental and calculated data for 166 unique reactions
<h3>General remarks</h3> <p>The experimental data was drawn from reactions involving H2 that are available at <a href="https://webbook.nist.gov/cgi/cbook.cgi?Name=H2&Units=SI&cTR=on" target="_blank" rel="noopener">NIST</a> (accessed on 15/03/2024). Only reactions of type<strong><em> M + H2 => MH2</em></strong>, where M is a neutral, closed-shell organic molecule that accepts one equivalent of H2, were included in the collection. M corresponds to the oxidized form of the molecule ( => suffix '_ox'), MH2 to the reduced form (=> suffix '_red'). For reasons of clarity, the references to original publications were abbreviated in the main table (look up in separate table).</p> <p>For the molecules involved, Smiles were manually assigned. From those, 3D structures were generated and evaluated in order to match the thermodynamic properties as accurately as possible (for details on the procedure refer to the related work, see below).</p> <p>In addition to the experimental uncertainty, a significant scatter is seen for replicate measurements.</p> <p><strong>Please note</strong>: To compute the heat of hydrogenation from the calculated data for M/MH2 the contribution of H2 needs to be considered, take e.g. -1.164816 hartree (Energy at 298.15K, calculated at CCSD(T)=FULL/aug-cc-pVDZ) from <a href="https://cccbdb.nist.gov/energy3x.asp?method=63&basis=17&charge=0" target="_blank" rel="noopener">CCCBDB</a> (accessed on 15/03/2024).</p> <h3> </h3> <h3>Description of files</h3> <p>The file <strong>01_heat_of_hydrogenation_XP+QM.csv</strong> contains experimentally measured and calculated data.</p> <ul> <li>columns are separated by "|"</li> <li>column names and explanations: <ul> <li><strong>NIST_idx</strong> -- index of original reaction, mostly unique. In a few cases, data of the reverse reaction were subsumed under a different index</li> <li><strong>env</strong> -- if available, information about the environment a reported reaction took place in, e.g. gas phase, hexane, etc...</li> <li><strong>method</strong> -- if available, reference about the experimental technique, e.g. 'Eqk' = Heat of equilibrium, 'Cm' = Calorimetry, 'Chyd' = Calorimetry of hydrogenation</li> <li><strong>Temperature K</strong> -- if available, reported values </li> <li><strong>reference</strong> -- Abbreviation of reference to original publication</li> <li><strong>experimental heat of reaction kJ/mol</strong> -- measured value as reported by experimentalists</li> <li><strong>experimental uncertainty </strong>-- if available, uncertainty of measurement reported by experimentalists</li> <li><strong>comments</strong> -- notes relating to identification of compounds</li> <li><strong>SMILES_ox</strong> -- isomeric canonical SMILES for oxidized form M</li> <li><strong>InChI_ox</strong> -- InChI for oxidized form M </li> <li><strong>SMILES_red</strong> -- isomeric canonical SMILES for reduced form M</li> <li><strong>InChI_red </strong>-- InChI for reduced form M</li> <li><strong>reaction_index </strong>-- consequtively numbered for identical pairs (SMILES_ox, SMILES_red)<strong><br></strong></li> <li>the calculated properties are given for the oxidized and reduced form of the molecule (in hartree) <ul> <li><strong>E(B3LYP/6-31G(2df,p))</strong></li> <li><strong>E_thermal</strong></li> <li><strong>E(G4(MP2))@0K</strong></li> <li><strong>E(G4(MP2))@298K</strong></li> <li><strong>H(G4(MP2))</strong></li> <li><strong>heat_of_formation@0K</strong></li> <li><strong>heat_of_formation@298K</strong></li> </ul> </li> </ul> </li> </ul> <p><strong>02_molecules.sdf:</strong> provides for each molecule a low-energy geometry along with some descriptors and calculated energetic properties:</p> <blockquote> <ul> <li>coordinate block + bond information</li> <li>properties <ul> <li><strong>SMILES</strong> -- isomeric canonical smiles linking compound to reactions defined in 01_heat_of_hydrogenation_XP+QM.csv</li> <li><strong>radical_electrons</strong> -- number of unpaired electrons as determined by RDKit</li> <li><strong>empirical_formula</strong> -- elemental composition of molecule</li> <li><strong>molecular_weight</strong> -- as determined by RDKit in g/mol</li> <li><strong>TPSA </strong>-- topological polar surface area (<em>TPSA</em>) as determined by RDKit</li> <li><strong>logP </strong>-- octanol/water partition coefficient as predicted by RDKit</li> <li><strong>nof_heavy_atoms --</strong> number of non-hydrogen atoms in molecule</li> <li><strong>degree_of_unsaturation</strong> -- sum of multiplebonds and/or rings present in the compound</li> <li><strong>rings</strong> -- number of rings in the compound as determined by RDKit</li> <li><strong>multiplicity</strong> -- spin multiplicity for use as input for QM calculations</li> <li><strong>nof_multiple_bonds</strong> -- number of multiple bonds as determined by RDKit</li> <li><strong>Std_InChI</strong> -- standard InChi</li> <li><strong>FixedH_InChI</strong> -- variant of InChI to differentiate tautomers</li> <li><strong>tag </strong>-- dataset label</li> <li><strong>total_atoms </strong>-- total number of atoms (including H)</li> <li><strong>net_charge</strong> -- total charge of molecule in units of elementary charge</li> <li> <p>energetic properties (in hartree) </p> <ul> <li> <p><code>E(B3LYP/6-31G(2df,p))</code></p> </li> <li> <p><code>E</code><code>(HF/maug-cc-p(T+d)Z) </code></p> </li> <li> <p><code>E(HF/CBS)</code></p> </li> <li> <p><code>E(HF/maug-cc-p(Q+d)Z) </code></p> </li> <li> <p><code>E(MP2/6-31G(d))</code></p> </li> <li> <p><code>E(CCSD(T)/6-31G(d))</code></p> </li> <li> <p><code>E(HF/G3MP2LARGEXP) </code></p> </li> <li> <p><code>E(MP2/G3MP2LARGEXP)</code></p> </li> <li> <p><code>DE(MP2) hartreeDE(HF)</code></p> </li> <li> <p><code>ZPE(B3LYP) hartree</code></p> </li> <li> <p><code>ZPE_scale_factor hartree</code></p> </li> <li> <p><code>E(HLC) hartree</code></p> </li> <li> <p><code>E_thermal hartree</code></p> </li> <li> <p><code>H_thermal hartree</code></p> </li> <li> <p><code>E(G4(MP2))@0K hartree</code></p> </li> <li> <p><code>E(G4(MP2))@298K hartree</code></p> </li> <li> <p><code>H(G4(MP2)) hartree</code></p> </li> <li> <p><code>heat_of_formation@0K kcal/mol</code></p> </li> <li> <p><code>heat_of_formation@298K kcal/mol</code></p> </li> </ul> </li> </ul> </li> </ul> </blockquote> <p><strong>03_references.csv</strong> (separated by "|") lists abbreviations and corresponding full reference to original publication of individual data points.</p>
Simulated heating energy demand for two residential neighbourhoods
<p>The large-scale and comprehensive artificial dataset introduced in this research reflects the energy demands of two neighbourhoods and with some reasonable limitations mimics monitoring campaigns otherwise collected on-site from buildings in use. The monitoring campaigns are created using white-box simulation models for single-family houses representing typical neighbourhoods in Flanders. The datasets are generated using Dymola and the IDEAS package embedded in TEASER. Each house varies in geometry, size, envelope properties, occupancy schedules, and installed gas heating systems. In this research, two datasets are created, one reflecting the properties of a low-performing building stock dating before the introduction of the EPBD (2006), and the other reflecting properties of a well-performing stock built after 2006. The envelope properties for older houses are allocated using EPC data grouped in four construction periods, while for newly built houses the properties are based on EPB reports, both were collected in Flanders. The datasets include heavy-weight houses in a detached, semi-detached, or terraced typology. Furthermore, the houses are simulated as one or two-zone buildings, depending on the number of floors which range from one to three floors. In the simulations, a natural infiltration model is implemented as well as a stochastic occupant behaviour model mimicking gains from occupants and appliances. Due to the complexity of the large-scale simulation, the heating system is post-processed in a data-driven approach and the heat source for both datasets are gas-fired heating systems. In total six system configurations are considered including condensing and non-condensing boilers with three types of domestic hot water (DHW) sub-systems (no integrated DHW, direct and with a storage tank). For all configurations, a variable production efficiency is considered dependent on the load ratio. The urban-scale simulation is carried out at a 10-minute frequency for the weather data assuming the location of Heverlee (Belgium) in the year 2016.<br>The original purpose of this dataset was the development of statistical tools for the assessment of the heat loss coefficient of the building fabric. However, the generated artificial datasets provide a large spectre of usually difficult-to-measure inputs suitable to assess the importance of different components in the overall energy balance. Even though the original work looked into individual building behaviour, the datasets can be also used from an urban perspective for energy planning purposes.</p>
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.