Find research datasets worth reusing

Research Data for "Evaluating the electronic structure and stability of epitaxially grown Sr-doped LaFeO3 perovskite alkaline O2 evolution model electrocatalysts"

This is the research data supporting figures and tables for the paper "Evaluating the electronic structure and stability of epitaxially grown Sr-doped LaFeO3 perovskite alkaline O2 evolution model electrocatalysts" appearing in RSC Applied Interfaces under DOI <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4LF00260A">https://doi.org/10.1039/D4LF00260A</a>.

Research Data for "Electronic structure and stability of the active surface phase of NixCo3-xO4 spinel alkaline O2 evolution electrocatalysts: from an epitaxial model catalyst perspective"

These datasets support main text and supplementary figures and tables of the paper "Electronic structure and stability of the active surface phase of NixCo3-xO4 spinel alkaline O2 evolution electrocatalysts: from an epitaxial model catalyst perspective" appearing in ACS Applied Energy Materials under DOI: <a href="https://doi.org/10.1021/acsaem.4c01688">https://doi.org/10.1021/acsaem.4c01688 </a>

Data for An Integrative Data-driven Model Simulating C. elegans Brain, Body and Environment Interactions

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Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (SeisRox Pro modelling projects - supplementary data)

This dataset includes pre-loaded and integrated data sets used for seismic modelling based on geomodels created by interpretation of DOMs. The dataset includes: <ul> <li>Five different SeisRox modelling projects of the Landnørdingsvika model;</li> </ul> 1) base-case model and base-case+Noise in one project, 2) 30Hz model, 3) no_thin_units model, 4) no_karst model, 5) perfect_illumination model. <ul> <li>Two different SeisRox modelling projects of the Treskelodden model;</li> </ul> 1) base-case model, base-case+Noise, 30Hz model, perfect_illumination model all in one project, 2) model with 10x thicker Kapp Starostin layer in the overburden model. This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.

Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Synthetic seismic models - supplementary data)

This dataset includes pre-loaded seismic models based on digital outcrop models (DOMs) representing 11 different case scenarios with varying signal processing setting (frequency, illumination angle, noise) and different geological models for the DOMs. The case scenarios are set up to investigate detection tresholds in seismic models of thin beds with a high degree of lithological variation.  The dataset includes: <ul> <li>A Petrel 2022 project with 11 pre-loaded seismic models based on two different geomodels presented in the paper.</li> <li>The input SGY files for each seismic section and PSF used.</li> </ul> This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.

Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Well data for elastic rock parameters - supplementary data)

This dataset includes pre-loaded and integrated data sets of the well data from boreholes on Spitsbergen. it also includes various maps of Svalbard to see where the boreholes are located. Rock parameters were plotted and determined using the blueback toolbox add-on to Petrel.  The dataset used for rock parameter statisitcs includes: <ul> <li>A Petrel 2022 project with well data (gamma ray, density, sonic, P-velocity, lithology) from the two boreholes Reindalspasset I (7816/2-1) and Tromsøbreen II (7617/1-2).</li> <li>Excel sheets with the wireline-log data used from the two boreholes. </li> </ul> This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.

Thermal evolution of dark matter in the early universe from a symplectic glueball model---Data release

This is the data release to reproduce the plots shown in "Thermal evolution of dark matter in the early universe from a symplectic glueball model".

Data of Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction

Using the dense ground-based GNSS receiver network and ionosonde data from East and Southeast Asia during 2010-2021, a novel Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction (STEP) was developed. The model integrates the convolutional neural network (CNN) and long short-term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities. This dataset includes both the model and the results generated by STEP. The parameters provided are: UT (hours), Latitude (°), Longitude (°), Date, Y_pred (TECU/min), and Y_true (TECU/min). The dimensions of Y_pred and Y_true are 10812 x 610, where 10812 represents the product of the number of date and the number of UT (minus 18), and 610 corresponds to the product of the number of Latitude and Longitude. The model with a .pth extension can be loaded using PyTorch.

Data of 3D PPMLR-MHD model Simulation for manuscript "Formation and Evolution of Nightside Transpolar arc and Its Relationship with Energetic Plasma in the Magnetotail Lobe"

Data of 3D PPMLR-MHD model Simulation for manuscript "Formation and Evolution of Nightside Transpolar arc and Its Relationship with Energetic Plasma in the Magnetotail Lobe" These data come from a fully run of a 3D MHD Simulation model that is named PPMLR-MHD model (detailed descriptions below). There are 2 types of data files: 1) X15dXXXX.mat is saved simulation parameters. 2) Xing15XXXX_heatflux.mat is saved heat flux from simulation parameters. XXXX is the number of files, and files with the same serial number correspond to the same time.   The first type files of data include the following parameters: time, x, y, z, logrho, Vx, Vy, Vz, Bx, By, Bz, Pr, Jx, Jy, Jz Where, time is simulation time, which need to plus the start time to transfer them to universal time: time+16:00.             (x,y,z) are the three components of the position of simulation point in GSM coordinates;             logrho is the plasma density at the simulation point;             (Vx, Vy,Vz) are the three components of plasma velocity at the simulation point in GSM coordinates;            (Bx, By,Bz) are the three components of magnetic field at the simulation point in GSM coordinates;             Pr is the plasma dynamic presure at the simulation point;            (Jx, Jy,Jz) are the three components of plasma electric current at the simulation point in GSM coordinates;      The second type file of data includes the simulated heat flux along the magnetic field lines at the simulation point in GSM coordinates.  PPMLR-MHD model The PPMLR-MHD model is on the basis of an extension of the piecewise parabolic method (1) with a Lagrangian remap to magnetohydrodynamics (MHD) (2, 3). It is a three-dimensional MHD model, designed specially for the solar wind–magnetosphere–ionosphere system (4-6). The model possesses a high resolution in capturing MHD shocks and discontinuities and a low numerical dissipation in examining possible instabilities inherent in the system (4). The model uses a Cartesian coordinate system with the Earth’s center at the origin and X, Y, and Z axes pointing towards the Sun, the dawn-dusk direction, and the north, respectively. The size of the numerical box extends from 25 RE to –100 RE along the Sun-Earth line and from –50 RE to 50 RE in Y and Z directions, with 240×240×240 grid points and a minimum grid spacing of 0.2 RE. An inner boundary of radius 3 RE is set for the magnetosphere to avoid the complexities associated with the plasmasphere and large MHD characteristic velocity from the strong magnetic field (6). An electrostatic ionosphere shell with height-integrated conductance is imbedded, allowing an electrostatic coupling process introduced between the ionosphere and the magnetospheric inner boundary. The Earth’s magnetic field is approximated by a dipole field with a dipole moment of 8.06×1022 A/m in magnitude. The model is run to solve the whole system by inputting the real interplanetary conditions for the current event.

Computational modeling and analytical validation of singular geometric effects in fault data using a combinatorial approach - Input and processed data

The archive contains the input and processed data for the companion manuscript. The input data contains XYZ coordinates of points documenting the investigated interfaces. The output datasets contain directional data from applying the combinatorial algorithm to point data sets. We have also included .VTU and .PVSM files for visualization of the geological settings in ParaView.

Data in support of Plume Dynamics Reduced-Order Models

This split tarball contains the data used in experiments in the paper "Coarse Graining and Reduced Order Models for Plume Dynamics".  Unzipping the tarball in the experiment repository, or symlinking from the repository to the unzipped directory "plume_videos", will allow experiments to run. Data is laid out as it is in our machine.  This includes movie files, numpy arrays of movies, and pickled coordinates of plume origins.  Tarball is fragmented using the unix split command.  Run `cat x* | tar -xzvf -`.  See https://unix.stackexchange.com/questions/61774/create-a-tar-archive-split-into-blocks-of-a-maximum-size

opencc-by-4.0Jul 2024View details →

Supporting Data for "The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change"

Code and netcdf files of processed X-SHiELD and CMIP6 simulations to reproduce the figures of the revised submission of Timothy M. Merlis, Ilai Guendelman, Kai-Yuan Cheng, Lucas Harris, Yan-Ting Chen, Christopher S. Bretherton, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, and Stephan Fueglistaler (2024): "The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change".

opencc-by-4.0Jun 2024View details →

Data and materials for the "Steady-State Mixing State of Black Carbon Aerosols from a Particle-Resolved Model"

Data and sripts for the "Steady-State Mixing State of Black Carbon Aerosols from a Particle-Resolved Model"

Supplementary, raw data, and model for manuscript titled Coupling the TKE-ACM2 Planetary Boundary Layer Scheme with the Building Effect Parameterization Model

This file contains 1. raw data simulated by WRF and PALM, and LiDAR and surface station measurements (<a href="https://zenodo.org/api/records/13959541/draft/files/Raw%20data%20PALM_WRF_Obs.zip/content" target="_blank" rel="noopener noreferrer">Raw data PALM_WRF_Obs.zip</a>); 2. supplementary drawing modeled and simulated U10, T2, and RH2 time series at surface stations (<a href="https://zenodo.org/api/records/13959541/draft/files/Supplementary.docx/content" target="_blank" rel="noopener noreferrer">Supplementary.docx</a>); 3. WRF version containing the TKE-ACM2+BEP (<a href="https://zenodo.org/api/records/13959541/draft/files/WRF433_TKE-ACM2+BEP.tar.gz/content" target="_blank" rel="noopener noreferrer">WRF433_TKE-ACM2+BEP.tar.gz</a>).

opencc-by-4.0Sep 2024View details →

Personalized genomes for DL models supporting data

Archive of models and data associated with our manuscript <a href="https://www.biorxiv.org/content/10.1101/2024.10.15.618510v1">"Training deep learning models on personalized genomic sequences improves variant effect prediction"</a>. Code for training and benchmarking LCL models is available at <a href="https://github.com/Danko-Lab/clipnet_ablation">https://github.com/Danko-Lab/clipnet_ablation</a>, whereas code for training and benchmarking K562 models is available at <a href="https://github.com/Danko-Lab/clipnet_k562/">https://github.com/Danko-Lab/clipnet_k562/</a>. Model files & metadata: <ul> <li>n{i}_run{j}.tar <ul> <li>CLIPNET LCL models trained on i individuals</li> </ul> </li> <li>subsample_individuals_ids.tar <ul> <li>text files containing lists of the individuals used to train the above models.</li> </ul> </li> <li>reference_models.tar <ul> <li>CLIPNET LCL model trained on data from 67 PRO-cap libraries, but using hg38 sequences instead of personal genomes.</li> </ul> </li> <li>clipnet_k562_reference.tar <ul> <li>hg38-trained model described above transfer learned to K562.</li> </ul> </li> </ul> Benchmark data: <ul> <li>across_loci_metrics.tar <ul> <li>benchmarks of LCL models at predicting transcription initiation at individual CREs within the genome</li> </ul> </li> <li>qtl_metrics.tar <ul> <li>benchmarks of LCL models at predicting differences in transcription initiation between individuals at initiation QTLs</li> </ul> </li> <li>k562_data.tar <ul> <li>benchmarks of the reference-trained K562 model and <a href="https://zenodo.org/records/11196189">one transferred over from the personalized CLIPNET model</a> on MPRA data from <a href="https://www.biorxiv.org/content/10.1101/2024.05.05.592437v1">https://www.biorxiv.org/content/10.1101/2024.05.05.592437v1</a></li> </ul> </li> </ul>

Training data for muTOV using a piecewise polytope model

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Data for training BNN model

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Hybrid Model Sample Data

Sample data to illustrate the format in which the hybrid model expects the input and output data for training and inference. <a href="https://zenodo.org/api/records/14043079/draft/files/era5_y2003.tar.gz/content" target="_blank" rel="noopener noreferrer">era5_y2003.tar.gz</a> contains ERA5 data for atmospheric variables for the year 2003. <a href="https://zenodo.org/api/records/14043079/draft/files/era5_sst_y2003.tar.gz/content" target="_blank" rel="noopener noreferrer">era5_sst_y2003.tar.gz</a> contains ERA5 sea surface temperature data from 2003. <a href="https://zenodo.org/api/records/14043079/draft/files/era5_precip_y2003.tar.gz/content" target="_blank" rel="noopener noreferrer">era5_precip_y2003.tar.gz</a> contains ERA5 precipitation data for 2003. <a href="https://zenodo.org/api/records/14043079/draft/files/toa_isr_y2003.tar.gz/content" target="_blank" rel="noopener noreferrer">toa_isr_y2003.tar.gz</a> contains the top of the atmosphere incident solar radiation data. <a href="https://zenodo.org/api/records/14043079/draft/files/ohtc300.tar.gz/content" target="_blank" rel="noopener noreferrer">ohtc300.tar.gz</a> contains the ORAS5 heat content of the upper 300 m deep ocean. All data sets are regridded to the SPEEDY model grid.

Data set: Modeling of Magnesium Intercalation into Chevrel Phase Mo6S8: Report on improved cell design.

Dataset of the continuum simulations generated and used within the paper "Modeling of Magnesium Intercalation into Chevrel Phase Mo6S8: Report on improved cell design", published in Batteries & Supercaps  (2023, 6 (5), e202200562, DOI: 10.1002/batt.202200562). A good understanding of the limiting processes in rechargeable magnesium batteries is key to develop novel highcapacity/high-voltage cathode materials. Thereby, the performance of magnesiumion batteries can strongly depend on the morphology of the intercalation cathode. Moreover, high mass loadings are essential for commercialization. In this work the influence of different mass loadings are studied in addition to the impact of the particle size distribution of the active material. Therefore, a detailed continuum model is developed, which is able to describe the complex intercalation of magnesium into a Chevrel phase (CP) cathode. The model considers the thermodynamics, kinetics and interplay of the two energetically different intercalation sites of Mo6S8, which results from its unique crystal structure, as well as the impact of the desolvation on the electrochemical reactions and possible ion agglomeration. Ideal combinations of mass loading and electrolyte concentration as well as the desired CP particle size are determined for the state-of-the-art magnesium tetrakis(hexafluoroisopropyloxy)borate Mg[B(hfip)4]2 electrolyte.

No increase is detected and modelled for the seasonal cycle amplitude of δ13C of atmospheric carbon dioxide: scripts and data to prepare figures

The file contains the scripts and data to plot the graphics displayed in Joos et al., No increase is detected and modelled for the seasonal cycle amplitude of δ13C of atmospheric carbon dioxide, Biogeosciences, in press, November 2024.