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1,028 results for “simulation model”
Model simulation data used in "Impacts of ice-nucleating particles on cirrus clouds and radiation derived from global model simulations with MADE3 in EMAC" (Beer et al., Atmos. Chem. Phys., 2024)
<p>This dataset contains the namelist setup and the output of the EMAC global model simulations analysed and discussed in Beer et al. (<i>Atmos. Chem. Phys.</i>, 2024). For details see the README.md file and Table 2 in the paper.</p>
The dataset of article "Analysis of Uncertainties and Associated Convective Processes in Simulations of Extreme Precipitation over Cities with a Regional Earth System Model: A Case Study"
<p>The "out<i>EXP02.nc" and "out</i>EXP11.rar" are two examples of 11 simulations' output file in the article, in each of the file, 9 variables (horizontal wind u, horizontal wind v, vertical speed, height, temperature, pressure, precipitation, longitude and latitude) from outputs of simulation are included, the time interval is 2 hours.</p><p>The MERRA-2 dataset provides the initial and boundary conditions of chemical fields in simulations.</p><p>The era5 dataset provides the meteorological initial and boundary conditions in simulations.</p><p> </p>
Data release for "Consistent eccentricities for gravitational wave astronomy: Resolving discrepancies between astrophysical simulations and waveform models"
<div> <p>This is a data release to accompany <a href="https://arxiv.org/abs/2402.07892">arXiv:2402.07892</a> "Consistent eccentricities for gravitational wave astronomy: Resolving discrepancies between astrophysical simulations and waveform models".</p> <p>See also <a href="../doi/10.5281/zenodo.10974974">https://zenodo.org/doi/10.5281/zenodo.10974974</a>. Please cite the paper (<a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240207892V/abstract">https://ui.adsabs.harvard.edu/abs/2024arXiv240207892V/abstract</a>) if you use this in a publication or any other scientific work.</p> <p><br>The file can be read in using<br><br>```<br>import pandas</p> <p>data = pandas.read_hdf("CMC_eccentricities_standardized.hdf5")<br>```<br><br>The description of the different keys in the file are as follows:<br><br>- `m1`: source-frame mass of the primary object in the binary in units of Msun<br>- `m2`: source-frame mass of the secondary object in the binary in units of Msun<br>- `chi1`: dimensionless spin of the primary object in the binary<br>- `chi2`: dimensionless spin of the secondary object in the binary<br>- `z`: cosmological redshift<br>- `a0`: Initial separation in AU. This typically corresponds to the stopping criterion in astrophysical simulations.<br>- `e0`: The eccentricity at the initial separation `a0`<br>- `f0`: 22 mode frequency corresponding to the initial separation.<br>- `channel`: Takes values between 1 and 5. <br> - 1: Ejected mergers <br> - 2: In-cluster (two-body) mergers<br> - 3: Binary-Single Encounters<br> - 4: Binary-Binary Encounters<br> - 5: Single-Single Encounters<br>- `cluster_weight`: Weight given to each binary based on cluster properties (mass and metallicity), assuming some initial mass function for the cluster and metallicity evolution as a function of redshift.<br>- `cosmo_weight`: Weight given to each binary based on the redshift, to account for cosmological volume.<br>- `e_W03_*Hz`: Eccentricities extracted from the Wen 2003 prescription at the reference peak frequency specified.<br>- `e_t_2PN_10Hz`: Eccentricities extracted from the prescription in Vijaykumar et. al. 2024 at reference 22 mode frequency of 10 Hz. <br>- `e_t_2PN_Mf_1000HzMsun`: Eccentricities extracted from the prescription in Vijaykumar et. al. 2024 at reference 22 mode frequency corresponding to `M \times f = 1000 Hz Msun`. This ensures that eccentricity is defined at a fixed number of cycles before merger, independent of the cosmological redshift.<strong> We strongly recommend that all eccentricity estimates from astrophysical simulations are quoted at a reference frequency corresponding to fixed `M \times f`.</strong><br>- `total_weight`: `cosmo_weight \times cluster_weight`<br><br><strong>NOTE</strong>: For mergers belonging to `channel=1`, we straightaway set the `e_t_*` estimates to zero. This is because mergers from this channel will not be eccentric at `f>10 Hz`, and due to its large initial separation is computationally intensive to evolve using the PN evolution equations.</p> <p> </p> </div>
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)
<p>As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.</p> <p>This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 1 and the representative concentration pathway (RCP) used was RCP 2.6. More information about SSP and RCP can be referred to O'Neill et al. (2020).</p> <p>Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.</p> <p>More information about the six selected CMIP6 GCMs:</p> <p>ACCESS-CM2 -<br>http://dx.doi.org/10.1071/ES19040<br>BCC-CSM2-MR -<br>https://doi.org/10.5194/gmd-14-2977-2021<br>CNRM-ESM2-1-<br>https://doi.org/10.1029/2019MS001791<br>MPI-ESM1-2-HR -<br>https://doi.org/10.5194/gmd-12-3241-2019<br>MRI-ESM2-0 -<br>https://doi.org/10.2151/jmsj.2019-051<br>NorESM2-MM -<br>https://doi.org/10.5194/gmd-13-6165-2020</p> <p>Additional references:<br>O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br>Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br>Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.</p> <p><strong>Please cite the following if this data is used in any research or project:</strong></p> <p><em><strong>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a></strong></em></p> <p> </p> <p><strong>Cross-Model Version:</strong></p> <div> <div> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>] </p> <p> </p> <p><strong>Model-specific Version:</strong></p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>] </p> </div> </div> <p> </p> <p><strong>Representative Cities Version:</strong></p> <p>Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]</p>
Soil N2O emissions from global land ecosystems simulated by the LPJ-GUESS model
<p>This file includes the input and output data for global soil N2O simulations performed with the LPJ-GUESS model. It also contains site-level observations of N2O fluxes from natural vegetation and cropland, sourced from the literature and used for model evaluation. Additional details are available in our recent paper published in <strong>Geoscientific Model Development</strong><em> </em>(<a href="https://doi.org/10.5194/gmd-18-3131-2025">https://doi.org/10.5194/gmd-18-3131-2025</a>).</p>
Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Model Simulation Outputs
<p>This data sets are model outputs from CMAQ simulations. The output contains only PM2.5 variable after combining related aerosol species. File format is netCDF binary. File naming convention for Domain 1 (D1) is D1_EXP_DATE_TIME_e000.nc where EXP is the control experiment (CTR) or the assimilation experiments (DA_icbc), DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. Also, file naming convention for Domain 2 (D2) is D2_EXP_CASE_DATE_TIME_e000 where EXP is the control experiment (CTR) or the assimilation experiments (DA_ic and DA_icbc), CASE is the simulation cases for ANL or PRD, DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. For the processed and assimilated observation data in this study for D1 and D2, the file names are D1_OBS_DATA_YYYYMMDDhh.txt and D2_OBS_DATA_YYYYMMDDhh.txt, respectively, where YYYYMMDD is date format and hh is UTC.</p>
Road traffic simulator using the Intelligent Driver Model
<p>Road traffic simulator using the Intelligent Driver Model</p>
Simulation Cases of the Martian MPB Model of Wang et al [2022]
<p>The detailed information for all the simulation cases and the corresponding fitting results of the Martian MPB Model of Wang et al [2022] is listed in this supplementary document.</p>
Supporting datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS"
<p>This archive contains datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS".</p>
Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations
<p>The data provided here has been used to create the figures in the paper submitted to Biogeosciences titled <em>Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations.</em></p>
Daily climate and rainfall data for Niger 1983-2021, for use in SARRA-O crop simulation model
<p>This dataset contains daily rainfall and climate data for Niger, that can be used as input of the <a href="https://github.com/SARRA-cropmodels/SARRA-O">SARRA-O spatialized crop simulation model</a>. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.</p> <p>The archive contains :</p> <ul> <li>AgERA5 (doi:<a href="https://doi.org/10.24381/cds.6c68c9bb">10.24381/cds.6c68c9bb</a>) climatic data for Niger, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²) at 0.1° spatial resolution from 01/01/1981 to 31/12/2021</li> <li>TAMSAT v3.0 (doi:<a href="http://doi.org/10.1038/sdata.2017.63">10.1038/sdata.2017.63</a>) satellite rainfall estimation data for Niger (mm), with daily geotiff files at 0.0375° spatial resolution from 01/01/1983 to 31/12/2021</li> <li>CHIRPS v2.0 (doi:<a href="https://doi.org/10.1038/sdata.2015.66">10.1038/sdata.2015.66</a>) satellite rainfall estimation data for Niger (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/1981 to 31/12/2022</li> </ul> <p>This data has been extracted from their original sources using the <a href="https://github.com/SARRA-cropmodels/SARRA-data-download">SARRA-data-downloader tool</a>, on June 14th and 15th, 2023.</p> <p>The applicable licences are the licences of the respective datasets.</p>
Daily climate and rainfall data for northern Cameroon 2020-2022, for use in SARRA-Py crop simulation model
<p>This dataset contains daily rainfall and climate data for north Cameroon, that can be used as input of the SARRA-Py spatialized crop simulation model. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.</p> <p>The archive contains :</p> <ul> <li>AgERA5 (doi:<a href="https://doi.org/10.24381/cds.6c68c9bb">10.24381/cds.6c68c9bb</a>) climatic data for north Cameroon, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²/d) at 0.1° spatial resolution from 01/01/2020 to 31/12/2022</li> <li>CHIRPS v2.0 (doi:<a href="https://doi.org/10.1038/sdata.2015.66">10.1038/sdata.2015.66</a>) satellite rainfall estimation data for north Cameroon (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/2020 to 31/12/2022</li> </ul> <p>This data has been extracted from their original sources using the <a href="https://github.com/SARRA-cropmodels/SARRA-data-download">SARRA-data-downloader tool</a>.</p> <p>The applicable licences are the licences of the respective datasets.</p>
Bigraphs Model and Simulation for RPL
<p>Dataset for the experiment comparing RPL bigraphs model with RPL-Lite</p>
Inlists for paper: Simulating a stellar contact binary merger – I. Stellar models
<p>We study the initial conditions of a common envelope (CE) event resulting in a stellar merger. A merger’s dynamics could be understood through its light curve, but no synthetic light curve has yet been created for the full evolution. Using the smoothed particle hydrodynamics (SPH) code StarSmasher, we have created three-dimensional (3D) models of a 1.52 M<sub>⊙</sub> star that is a plausible donor in the V1309 Sco progenitor. The integrated total energy profiles of our 3D models match their initial one-dimensional (1D) models to within a 0.1 per cent difference in the top 0.1 M<sub>⊙</sub> of their envelopes. We have introduced a new method for obtaining radiative flux by linking intrinsically optically thick SPH particles to a single stellar envelope solution from a set of unique solutions. For the first time, we calculated our 3D models’ effective temperatures to within a few per cent of the initial 1D models, and found a corresponding improvement in luminosity by a factor of ≳10<sup>6</sup> compared to ray tracing. We let our highest resolution 3D model undergo Roche lobe overflow with a 0.16 M<sub>⊙</sub> point-mass accretor (<em>P</em> ≃ 1.6 d) and found a bolometric magnitude variability amplitude of ∼0.3 – comparable to that of the V1309 Sco progenitor. Our 3D models are, in the top 0.1 M<sub>⊙</sub> of the envelope and in terms of total energy, the most accurate models so far of the V1309 Sco donor star. A dynamical simulation that uses the initial conditions we presented in this paper can be used to create the first ever synthetic CE evolution light curve.</p>
Simulation results of an agent-based model of civil violence with the effect of introducing a small world network
<p>The data set contains the simulation results of an agent-based model of civil violence with the effect of introducing a small world network. Some details on the agent-based model (without small world network) can be found in [Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham, Nonlinear Dynamics of Crime and Violence in Urban Settings, Journal of Artificial Societies and Social Simulation, 15(1), 2, http://jasss.soc.surrey.ac.uk/15/1/2.html, DOI: 10.18564/jasss.1921].</p> <p>Files in folder "Appropriate_Rate_of_Violence" are related to the case with the appropriate rate of violence.</p> <p>Subfolder NoSWN_CitVis_14 is for the case with no small world network and the citizen vision of 14.</p> <p>Subfolder SWN_CitVis_13.16 is for the case with small world network and the citizen vision of 13.16.</p> <p>Subfolder SWN_CitVis_14 is for the case with small world network and the citizen vision of 14.</p> <p>Each file with name starting with Act has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. These files are provided for lattice sizes from 100x100 to 300x300 and for different random seeds.</p> <p>Files in folder "High_Rate_of_Violence" are related to the case with the high rate of violence.</p> <p>Subfolder Beta0.2 is for the case with small world network and \beta=0.2.</p> <p>Subfolder Beta0.8 is for the case with small world network and \beta=0.8.</p> <p>Subfolder NoSWN is for the case with no small world network.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. These files are provided for lattice sizes from 100x100 to 300x300 and for different random seeds.</p>
A subsection of England and Wales EPC households, joined with PPD data, used for simulation modelling
<p>If you want to give feedback on this dataset, or wish to request it in another form (e.g csv), please fill out this survey <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqCAoQt4AzuGH8Th5tJjnkGP956Fgc6O8T6wJaM7Nhd_nRdg/viewform?usp=pp_url&entry.1276408097=10.5281/zenodo.7322967">here</a>. We are a not-for-profit research organisation keen to see how others use our open models and tools, so all feedback is appreciated! It's a short form that takes 5 minutes to complete. </p> <p><strong>Important Note: Before downloading this dataset, please read the License and Software Attribution section at the bottom.</strong></p> <p>This dataset aligns with the work published in Centre for Net Zero's report "Hitting the Target". In this work, we simulate a range of interventions to model the situations in which we believe the UK will meet its 600,000 heat pump installation per year target by 2028. For full modelling assumptions and findings, read our <a href="https://www.centrefornetzero.org/res/hitting-the-target/">report on our website</a>.</p> <p>The code for running our simulation is open source <a href="https://github.com/centrefornetzero/domestic-heating-abm">here</a>.</p> <p>This dataset contains over 9 million households that have been address matched between Energy Performance Certificates (EPC) data and Price Paid Data (PPD). The code for our address matching is <a href="https://github.com/centrefornetzero/epc-ppd-address-matching">here</a>. Since these datasets are Open Government License (OGL), this dataset is too. We basically model specific columns from various datasets, as set out in our methodology section in our report, to simplify and clean up this dataset for academic use. License information is also available in the appendix of our report above.</p> <p>The EPC data loaders can be found <a href="https://github.com/centrefornetzero/epc-england-wales-parquet">here</a> (the data is <a href="https://epc.opendatacommunities.org/">here</a>) and the rest of the schemas and data download locations can be found <a href="https://github.com/centrefornetzero/bigquery-schemas">here</a>.</p> <p>Note that this dataset is not regularly maintained or updated. It is correct as of January 2022. The data was curated and tested using dbt via <a href="https://github.com/centrefornetzero/domestic-heating-data">this Github repository</a> and would be simple to rerun on the latest data.</p> <p>The schema / data dictionary for this data can be found <a href="https://github.com/centrefornetzero/domestic-heating-data/blob/main/cnz/models/marts/domestic_heating/domestic_heating.yml#L5">here</a>.</p> <p>Our recommended way of loading this data is in Python. After downloading all "parts" of the dataset to a folder. You can run:</p> <p>```</p> <p>import pandas as pd</p> <p>data = pd.read_parquet("path/to/data/folder/")</p> <p>```</p> <p> </p> <p><strong>Licenses and software attribution</strong>:</p> <p><em>For EPC, PPD and UK House Price Index data</em>:</p> <p>For the EPC data, we are permitted to republish this providing we mention that all researchers who download this dataset follow <a href="https://epc.opendatacommunities.org/docs/copyright">these copyright restrictions</a>. We <strong>do not explicitly release any Royal Mail address data</strong>, instead we use these fields to generate a pseudonymised "address_cluster_id" which reflects a unique combination of the address lines and postcodes, as well as other metadata. When viewing <a href="https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/what-is-personal-data/what-is-personal-data/">ICO and GDPR guidelines</a>, this still counts as personal data, but we have gone to measures to pseudonymise as much as possible to fulfil our obligations as a data processor. You <strong>must read this carefully before downloading the data</strong>, and ensure that you are using it for the research purposes as determined by this copyright notice.</p> <p>Contains HM Land Registry data © Crown copyright and database right 2021. This data is licensed under the Open Government Licence v3.0.</p> <p>Contains OS data © Crown copyright and database right 2022.</p> <p>Contains Office for National Statistics data licensed under the Open Government Licence v.3.0.</p> <p>The OGL v3.0 license states that we are free to:</p> <ul> <li>copy, publish, distribute and transmit the Information;</li> <li>adapt the Information;</li> <li>exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application.</li> </ul> <p>However we must (where we do any of the above):</p> <ul> <li>acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence;</li> </ul> <p>You can see more information <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">here</a>.</p> <p><em>For XOServe Off Gas Postcodes</em>:</p> <p>This dataset has been released openly for all uses <a href="https://www.cse.org.uk/projects/view/1259#GB_postcodes_off_the_mains_gas_grid">here</a>.</p> <p><em>For the address matching:</em></p> <p>GNU Parallel: O. Tange (2018): GNU Parallel 2018, March 2018, https://doi.org/10.5281/zenodo.1146014</p>
Micelle size screening - Gwalp tail anchor dimer simulation - 60 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data with dimers of a given peptide.</p>
Micelle size screening - yFis1 simulation - 45 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data.</p>
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
<p>A highly granular calorimeter, similar to CALICE is simulated in Geant4. The calorimeters showers are represented as either images or point clouds. Two state-of-the-art score based diffusion models, on image based and the other point cloud based, are trained on the same set of calorimeter simulations and directly compared to each other.<br> <br> These files include the original Geant4 simulation, an intermediate form of the data required for training, and the samples generated by the image and point cloud models.</p>
The Robot Basal Ganglia: Simulated Dopamine Regulation of Behaviour in a Neurorobotic Model
<p>Supplementary Video for the article "Simulated dopamine modulation of a neurorobotic model of the basal ganglia"</p> <p>00:07. Part 1: Rat behaviour in an open arena.<span> </span></p> <p>00:47. Part 2: Robot behavior.<span> </span></p> <p>02:08. Part 3: Inside the model.<span> </span></p> <p>04:00. Part 4: Low simulated dopamine—Slowness and cessation of movement.<span> </span></p> <p>04:38. Part 5: Low simulated dopamine—A behavioral trap.<span> </span></p> <p>05:11. Part 6: High simulated dopamine—Distortion causes behavioral disintegration.</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.