Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

MESA model files and data for: 'Stellar Neutrino Emission Across The Mass-Metallicity Plane'

Example MESA model files and stellar evolution tracks for download from "Stellar Neutrino Emission Across The Mass-Metallicity Plane".

opencc-by-4.0Sep 2023View details →

Data and Models from the study entitled, "Large-area automatic detection of shoreline stranded marine debris using deep learning"

This repository contains data and models used in the study entitled, "Large-area automatic detection of shoreline stranded marine debris using deep learning". This study can be accessed as an open access publication at the following location: https://doi.org/10.1016/j.jag.2023.103515. The data set is comprised of 1,587 images (512 pixels x 512 pixels) which contains 10,703 individual bounding box labels of marine debris objects. The imagery was collected over the State of Hawai'i in 2015 at 2 centimeter resolution (ground spacing distance). The classification scheme consists of 8 labeled classes: unidentified object, processed wood, metal, vessel, net/cloth, buoy, tire, and line fragments.

opencc-by-4.0Aug 2023View details →

Coupled climate-glacier modelling of the last glaciation in the Alps: modelling data

This dataset contains key distributed 2D variables resulting from the modelling of the Alpine Ice Field over the last glacial cycle from Jouvet and al. (2023, 10.1017/jog.2023.74), including basal surface topography, ice thickness, pressure-adjusted basal temperature, basal and surface ice flow speeds. The results are given on a raster grid in UTM system of coordinate with a spatial resolution of 2 km and a temporal resolution of 100 year. The data are compiled in netCDF. As explained in the paper, the model was designed to match LGM evidence. Modelled results related to intermediate states and to the Holocene must be interpreted with caution considering the relative coarse resolution (2 km). Small ice caps aside the main Alpine Icefield (except the Jura) were excluded.

opencc-by-4.0Oct 2023View details →

D^2EPC BIM-based Digital Twin data model example and real-time building measurements

An example building digital twin data model, developed within the H2020 project D^2EPC, corresponding to the first out of six Case Studies (CERTH nZEB Smart House DIH). The following files are provided:i) The BIM-based data model of the building parameters (.json file)ii) Building real-time collected measurements within the project (in separate .json files):<ul><li>Living room: CO2, temperature, humidity, luminance, presence, PM2.5, TVOCs, loudness, smoke</li><li>Office: temperature, humidity, luminance, presence</li><li>Entire ground floor: HVAC system electrical energy consumption</li><li>Entire first floor: HVAC system electrical energy consumption</li><li>Entire building: electrical energy consumption (lighting & appliances)</li><li>Building PV installation: electrical energy production</li></ul>

openCC (other)Apr 2022View details →

Hydrodynamic modeling data for Synthesis of Juvenile Steelhead Responses to Hydrodynamic Conditions in the Sacramento-San Joaquin Delta

This data release includes the output variables extracted from the UnTRIM Bay-Delta hydrodynamic model (hydrodynamic model) for use in evaluating the effects of hydrodynamics on the behavior of acoustically-tagged juvenile steelhead in the Sacramento-San Joaquin Delta. Work was funded by Proposition 1 and completed by Anchor QEA, LLC, and U.S. Geological Survey for the State Water Contractors under a Proposition 1 Grant (Evaluating Juvenile Salmonid Behavioral Responses to Hydrodynamic Conditions in the Sacramento-San Joaquin Delta), contracted by Delta Stewardship Council. Not all the hydrodynamic model output variables in the output provided with this memorandum were used in the final fish models used to analyze steelhead responses. Model output for additional variables and locations were included for completeness and to make these output files more broadly useful to researchers interested in other locations or variables in the Sacramento-San Joaquin Delta. Hydrodynamic model simulations were conducted for 2011, 2012, 2013, 2014, 2015, and 2016, with hydrodynamic model output variables provided at the same locations for each period simulated. The model simulation for each year spanned the full period of steelhead detections in the telemetry data collected during that year.

openCC0Feb 2019View details →

Data for Lake Mendota Phosphorus Cycling Model

There is an opportunity to advance both prediction accuracy and scientific discovery for phosphorus cycling in Lake Mendota (Wisconsin, USA). Twenty years of phosphorus measurements show patterns at seasonal to decadal scales, suggesting a variety of drivers control lake phosphorus dynamics. Our objectives are to produce a phosphorus budget for Lake Mendota and to accurately predict summertime epilimnetic phosphorus using a simple and adaptable modeling approach. We combined ecological knowledge with machine learning in the emerging paradigm, theory-guided data science (TGDS). A mass balance model (PROCESS) accounted for most of the observed pattern in lake phosphorus. However, inclusion of machine learning (RNN) and an ecological principle (PGRNN) to constrain its output improved summertime phosphorus predictions and accounted for long term changes missed by the mass balance model. TGDS indicated additional processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve our mass balance modeling approach.

openCC (other)Apr 2019View details →

Plot-level field data and model simulation results, archived to accompany Turner et al. manuscript; reports data from summer 2017 sampling of short-interval fires that burned during summer 2016 in Greater Yellowstone.

Subalpine forests in the northern Rocky Mountains have been resilient to stand-replacing fires that historically burned at 100–300-yr intervals. Fire intervals are projected to decline drastically as climate warms, and forests that reburn before recovering from previous fire may lose their ability to rebound. We studied recent fires in Greater Yellowstone (Wyoming, USA) and asked whether short-interval (less than 30 yrs) stand-replacing fires can erode lodgepole pine (Pinus contorta var. latifolia) forest resilience via increased burn severity, reduced early postfire tree regeneration, reduced carbon stocks, and slower carbon recovery. During 2016, fires reburned young lodgepole pine forests that regenerated after wildfires in 1988 and 2000. During 2017, we sampled 0.25-ha plots in stand-replacing reburns (n=18) and nearby young forests that did not reburn (n=9). We also simulated stand development with and without reburns to assess carbon recovery trajectories. Nearly all prefire biomass was combusted ("crown fire plus") in some reburns in which prefire trees were dense and small (≤ 4 cm basal diameter). Postfire tree seedling density was reduced six-fold relative to the previous (long-interval) fire, and high-density stands (greater than 40,000 stems ha-1) were converted to sparse stands (less than 1,000 stems ha-1). In reburns, coarse wood biomass and aboveground carbon stocks were reduced by 65% and 62%, respectively, relative to areas that did not reburn. Increased carbon loss plus sparse tree regeneration delayed simulated carbon recovery by greater than 150 yrs. Forests did not transition to nonforest, but extreme burn severity and reduced tree recovery foreshadow an erosion of forest resilience.

openCustomMay 2025View details →

Virgina Coast Reserve LTER Externally-hosted Models and Data

This dataset contains a listing of models and data associated with Virginia Coast Reserve Long-Term Ecological Research that are not otherwise available in the VCR/LTER data archive. It includes links to models and data that have been made available by researchers that are not in the VCR/LTER archive because they are deposited in a specialized system specifically tailored to a specific type (e.g. models) or in another archive due to requirements and constraints imposed by funders and journals. It takes the form of a spreadsheet including for each item the type (i.e., model or data), Internet link, name, brief description and optionally a link to a publication that used the data.

opencc-by-4.0Jan 2020View details →

Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS-v.2.0

This database can be used as the input runoff files in the RAPID model [David et al., 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.0 [Rodell et al., 2004] LSM outputs, available at; <a href="http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi">http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi</a> The GLDAS-v.2.0 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files;         GLDAS.2.0_NOAHres_3H_yyyy.tar.gz           (Note: res = 10 or 025; yyyy = 2000 to 2009)   Note: These runoff data were used by Sikder et al. [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.   Other necessary links associated with this database: RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a> RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a> GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a>   References: David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a> Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a> Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a>

opencc-zeroFeb 2020View details →

EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database

EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.

opencc-by-4.0Mar 2020View details →

Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020 --- Data Files

New  data  on github: https://github.com/sesterhenn/Corona-DataAssimilation             doi://10.5281/zenodo.3732292 https://zenodo.org/record/3733244

opencc-by-4.0May 2020View details →

Models and source data for MuML dipole fitting

Source data, models, and scripts necessary to reproduce the results of: "Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles" (M. Veit, D. M. Wilkins, Y. Yang, R. A. DiStasio Jr., M. Ceriotti, arXiv: 2003.12437). The model is a combination of symmetry-adapted Gaussian process regression (SA-GPR) for atomic dipoles and scalar GPR for atomic partial charges, which are fit together to reproduce the molecule's total dipole moment. Source data, kernel matrices, weights, residuals, and scripts for fitting and plotting the results are included.

opencc-by-4.0Jun 2020View details →

Model data from GRL paper: "Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone"

This folder includes monthly model data (experiments using SC-WACCM4 and E3SMv1) of temperature (TEMP) and sea level pressure (SLP) that were used in the Geophysical Research Letters paper "Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone", # 2020GL088583. See also for additional information/data: <a href="https://zenodo.org/record/3066448">https://zenodo.org/record/3066448</a> Labe, Z., Peings, Y., & Magnusdottir, G. (2020). Warm Arctic , cold Siberia pattern : role of full Arctic amplification versus sea ice loss alone. Geophysical Research Letters, 1–26. <a href="https://doi.org/10.1029/2020GL088583">https://doi.org/10.1029/2020GL088583</a> <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL088583">[Paper]</a><a href="https://sites.uci.edu/zlabe/arctic-amplification/">[Plain Language Summary]</a><a href="https://github.com/zmlabe/AA">[GitHub]</a>

opencc-by-4.0Aug 2020View details →

Occurrence data used to create species distribution models and apply an evaluation method

These two files containing a table with three columns: species names, longitude, latitude. Each row of the tables represents a georeferenced presence record for the corresponding species. The original presence data were downloaded from the GBIF database and after going through a cleaning process, we ended with these records that passed all the tests. These datasets were used to create species distribution models (SDMs) that were then used to apply a new method to evaluate the performance of different SDMs. Jiménez & Soberón (2020)

opencc-by-4.0Feb 2020View details →

JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.

The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that (1) gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute (2) theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes We deliver (1) data collection and (2) numerical codes in the following files attached: (1) Data collection: <ul> <li>A PDF file named JUMP-zonal-jets-data-collection-GRL.pdf that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li>JUMP-JetsInTheLab.zip</li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li>uvData-SRS-istep-312000-nstep-50-niz-12.nc</li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter <ul> <li>uvData-JupObs-istep-0-nstep-4-niz-1.nc</li> <li>StatisticalData-JupObs.nc</li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li>IPV-QGPV-Jupiter-Saturn.zip</li> </ul> </li> </ul> (2) Numerical codes: <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul>   The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver (1) velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and (2) guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.   Acknowledgments This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.

opencc-by-4.0Oct 2020View details →

Brazilian Earth System Model: CMIP5 Sea ice concentration and Air Temperature data

The Brazilian Earth System Model, Version 2.5 (BESM-OAV2.5) used here is a global climate coupled ocean-atmosphere-sea ice model, and is part of CMIP5 project. The atmospheric component of BESM-OAV2.5 is BAM (Brazilian Atmospheric Model) and was described in detail by Figueroa et al., (2016). BAM, developed at Center for Weather Forecasting and Climate Studies of the National Institute for Space Research CPTEC-INPE has been constantly reformulated over the last years (Figueroa et al., 2016; Nobre et al., 2013). The lastest version, used here and described by Veiga et al., (2019), has spectral horizontal representation truncated at triangular wave number 62, grid resolution of approximately 1.875∘×1.875∘, and 28 sigma levels in the vertical, with unequal increments between the vertical levels (i.e., a T62L28). The oceanic component of BESM-OAV2.5 is the Modular Ocean Model, Version 4p1, from National Oceanic and Atmospheric Administration-Geophysical Fluid Dynamics Laboratory (MOM4p1/NOAA-GFDL), described in detail by Griffies, (2009). The MOM4p1 includes a Sea Ice Simulator (SIS) built-in ice model (Winton 2000). The SIS has five ice thickness categories and three vertical layers (one snow and two ice). To calculate ice internal stresses are used the elastic-viscous-plastic technique described by Hunke and Dukowicz, (1997). The thermodynamics is given by a modified Semtner’s three-layer scheme (Semtner, 1976). SIS is able to calculate sea ice concentration, snow cover, thickness, brine content and temperature. Furthermore, SIS calculates ice-ocean fluxes and transmits fluxes between atmosphere and ocean.  The horizontal grid resolution of MOM4p1 in the longitudinal direction is a set to 1˚. The latitudinal direction varies uniformly, in both hemispheres, from 1∕4o between 10o S and 10o N to 1o of resolution at 45o and to 2o of resolution at 90o. The vertical axis has 50 levels (upper 220m, has 10 m resolution, increasing to about 360 at deeper levels. The MOM4p1 and BAM models were coupled using FMS coupler.  FMS coupled was developed by NOAA-GFDL. The BAM model receives SST and ocean albedo from MOM4p1 and SIS (hour by hour). The MOM4p1 receives momentum fluxes, specific humidity, pressure, heat fluxes, vertical diffusion of velocity components and freshwater.  This study used two numerical experiments from CMIP5: (i) piControl: it runs for 700 years, forced by invariant pre-industrial atmospheric CO2 concentration level  (280ppmv) and (ii) Abrupt 4xCO2: it runs for 460 years, comprising an abrupt instantaneous quadrupling of atmospheric CO2 level concentration from the piControl simulation. The design of both experiments follows the CMIP5 protocol (Taylor et al., 2012).

Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).

Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted). This folder contains the files needed to start each of the models described in the paper. The files were created using MOE 2020 software made by Chemical Computing Group and run on NAMD2. The models identifiers in the paper correspond to the following terms in the code: Substrate: "13_5_coa" Product: "13_5_coa_desat_fe3" Apoprotein: "13_5_no_ligand" Saturated Lipid: "13_5_nocoa" Desaturated Lipid: "13_5_nocoa_desat_fe3" CoA model: "13_5_coa_nolipid" Substrate-waterbox model: "13_5_coa_waterbox" Saturated Lipid-waterbox: "13_5_nocoa_waterbox"

2d U-net models trained to segment human placental maternal/fetal blood volumes and blood vessels from syncrotron micro-CT data along with a sample data volume.

This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at <a href="https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1">https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1</a>. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at <a href="https://github.com/DiamondLightSource/placental-segmentation-2dunet">https://github.com/DiamondLightSource/placental-segmentation-2dunet</a> and is published at <a href="https://doi.org/10.5281/zenodo.4252562">https://doi.org/10.5281/zenodo.4252562</a>

VR-Together Pilot 3: 3D Character Models and Animation Data

VR-Together Pilot 3 Character and Animation Dataset. This dataset contains the 3D characters and animations as used in <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>. It contains the 4 characters of the associated experience and their post-processed motion capture animation data in the FBX format, as well as the texture data in the PNG format.  The data contained in this dataset was prepared for the Unity game engine, but should be usable in other content creation systems without issue.  VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.

Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al.

Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al. which is to be submitted to Atmospheric Measurement Techniques.  A comprehensive inter-comparison of seven radiative transfer models in the limb scattering geometry has been performed. Every model is capable of accounting for polarisation within a fully spherical atmosphere. Three models (GSLS, SASKTRAN-HR, and SCIATRAN) are deterministic, and four models (MYSTIC, SASKTRAN-MC, Siro, and SMART-G) are statistical using the Monte Carlo technique.  This dataset consists of the raw radiance data used to perform the intercomparisons, atmospheric input data for the optical properties of the atmosphere, and data specifying the geometry of the test cases. Data is provided in NetCDF4 format with documentation present inside the variable attributes. More detail on the comparison scenarios can be found within the published article.  (Link to be added when available).