Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Data-driven model enhancement of late-life lithium-ion batteries

Suplement dataset for the paper "Data-driven model enhancement of late-life lithium-ion batteries"

opencc-by-4.0Jun 2023View details →

Data supporting 'Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model'

The dataset supporting our publication 'Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model' in Geophysical Research Letters. The main .zip archive contains a set of NetCDF files detailing: <ul> <li>Initial optimised glacier states (geology-optimized...)</li> <li>Simulation results (Prog20...)</li> </ul> Initial states and results are given by cluster (see Figure 1 in the paper), as shown in all filenames (C1 through to C12). Prognostic simulation filenames additionally distinguish between runs between 1999 and 2019 (Prog2020) and between 2020 and 2050 (Prog2050). 'NV'/'NoVel' and 'NT'/'NoThk' refer to simulations using the partial optimisation (optimisation without including velocity/thickness observations) as detailed in the paper. 'AV' at the end of the filename denotes the integrated area/volume results file, as opposed to the 2D raster results file. A 'V' before the cluster designation shows that the simulation used the variable SMB as opposed to the fixed SMB (see the paper for details). 'ID' before the cluster designation shows that the simulation was using extrapolated SMB based on the trend in SMB since 2000, instead of assuming the continuation of the current SMB. 'ID' on its own denotes linear extrapolation and 'IDQ' denotes quadratic extrapolation (not used in the published paper). 'SMBF' in the filename shows that the simulation used the SMB-elevation feedback. The additional .zip archive contains the code of IGM v1.0 used to produce the model results. For details on installing and using IGM, please see the Github page at <a href="https://github.com/jouvetg/igm.The">https://github.com/jouvetg/igm</a>. A further .zip archive (in version 3 - Sims2010-2022.zip) contains the simulations based on linear extrapolation of the observed trend in SMB between 2010 and 2022, following the same nomenclature as in the principal archive (see above). Version 4 contains an additional mosaicked DEM of the results for the whole Alps with the ice removed to give the complete basal topography (kindly processed by T. Léger at UNIL) using the Japan Aerospace Exploration Agency (2021) ALOS World 3D 30 meter DEM. V3.2, Jan 2021. Distributed by OpenTopography. <a title="https://doi.org/10.5069/G94M92HB" href="https://doi.org/10.5069/G94M92HB" target="_blank" rel="noreferrer noopener">https://doi.org/10.5069/G94M92HB</a>. Accessed: 2024-09-09.

Monthly, Seasonal and Yearly Net Primary Productivity (NPP) data of India from 2003-2020 modelled using the CASA model

We estimated monthly Net Primary Productivity (NPP) at 1km spatial resolution for India using the Carnegie-Ames-Stanford Approach (CASA) Model. The CASA is a light use efficiency (LUE) based model that simulates NPP driven by remote sensing and meteorological data inputs. NPP is calculated as a product of the light use efficiency (LUE) and absorbed photosynthetically active radiation (APAR). The seasonal and annual data are prepared by aggregating the monthly data. The India Meteorological Department (IMD) recognizes four seasons in India based on climate conditions. The four seasons are defined as Winter (January-February), Pre-monsoon (March-May), Monsoon (June-September), and Post-monsoon (October-December). The seasonal data is prepared according to the above classification of the seasons.

The International Transport Energy Modeling (iTEM) Open Data & Harmonized Transport Database

This dataset and documentation contains detailed information of the iTEM Open Database, a harmonized transport data set of historical values, 1970 - present. It aims to create transparency through two key features: <ul> <li>Open-Data: Assembling a comprehensive collection of publicly-available transportation data</li> <li>Open-Code: All code and documentation will be publicly accessible and open for modification and extension. <a href="https://github.com/transportenergy">https://github.com/transportenergy</a></li> </ul> The iTEM Open Database is comprised of individual datasets collected from public sources. Each dataset is downloaded, cleaned, and harmonised to the common region and technology definitions defined by the iTEM consortium https://transportenergy.org. For each dataset, we describe the name of the dataset, the web link to the original source, the web link to the cleaning script (in python), variables, and explain the data cleaning steps (which explains the data cleaning script in plain English). Shall you find any problems with the dataset, please report the issues here <a href="https://github.com/transportenergy/database/issues">https://github.com/transportenergy/database/issues</a>.

NASA Eulerian Snow On Sea Ice Model Version 1.1 (NESOSIMv1.1) data: 1980 - 2024

Repository updates Update on Sep 12th 2024: The repository now includes NESOSIM v1.1 output from September 1st 2022 to April 30th 2023 and September 1st 2023 to April 30th 2024  Update on Sep 5th 2022: The repository now includes NESOSIM v1.1 output from September 1st 2021 to April 30th 2022 Update on June 7th 2022: The repository now includes NESOSIM v1.1 output from September 1st 2021 to March 31st 2022 Update on March 8th 2021: The gridded forcing files are now available in the gridded_forcings.zip file. Data are stored as Python pickles which can be easily read in by the core NESOSIM source code.  Update on March 8th 2021: The repository now includes zip files of gridded forcing (snowfall, winds, ice drift, ice concentration, initial conditions) as well as gridded Operation IceBridge snow depths.  Update on January 30th 2021: The repository now also includes a NESOSIM v1.1. daily gridded snow climatology using the mean (np.nanmean) of all data available between September 1 2010 and April 30 2020. Overview NESOSIM is a three-dimensional, two-layer (vertical), Eulerian snow on sea ice budget model developed with the primary aim of producing daily estimates of the depth and density of snow on sea ice across the polar oceans through the winter accumulation season, generally September through April (Petty et al., 2018). This repository contains model output from September 1st 1980 to April 30th 2021 [and September 1st 2021 to March 31st 2022 as of June 7th 2022] based on the NESOSIM v1.1 code release which is available on GitHub (https://github.com/akpetty/NESOSIM/tree/v1.1) and archived through Zenodo (10.5281/zenodo.4448355). More information about changes between the v1.0 and v1.1 model framework can be found in those links. A preprint is now available in The Cryosphere Discuss explaining these upgrades and their impacts on ICESat-2 winter Arctic sea ice thickness estimates (Petty et al., 2022).  Data production: Data are re-initialized at the end of summer each year (September 1st) using summer near-surface air temperature-scaled initial snow depths and run through until the end of April of the following year. The 1987-1988 winter is missing due to the lack of passive-microwave derived ice concentration data available during this period. Daily data are generated on a 100 km x 100 km North Polar Stereographic grid (EPSG: 3413) across the entire Arctic Ocean including the peripheral seas. Forcings: NB: Recent year runs often require the use of near-real-time data products, so the underlying forcings used in this v1.1 release can change in time, as noted below: <ul> <li>Snowfall: European Center for Medium Range Weather Forecasts (ECMWF) ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards2) + CloudSat scaling (Cabaj et al., 2020).</li> <li>Near-surface winds: ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> <li>Sea ice drift: NSIDC Polar Pathfinder v4 (https://nsidc.org/data/nsidc-0116, September 1 1980 to April 30 2019), OSI SAF merged (https://osi-saf.eumetsat.int/products/osi-405-c, September 1 2019 onwards).</li> <li>Sea ice concentration: Final v3 NSIDC Climate Data Record (https://nsidc.org/data/g02202/versions/3/, September 1 1980 to December 31 2020), and near-real-time v2 NSIDC Climate Data Record (https://nsidc.org/data/g10016, January 1 2021 onwards).</li> <li>Near-surface air temperature (to derive temperature-scaled initial conditions): ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> </ul> The forcings used to generate each winter dataset are described in a new 'forcings' variable in each NetCDF file.  Operation IceBridge snow depths: The repository now also includes the gridded Operation IceBridge snow depths we used for calibration purposes, as described in Petty et al., (2022). The data contained within gridded_oib_snowdepths.zip includes the daily gridded data on the NESOSIM v1.1 100 km domain, ordered by day of collection. Data are stored as Python pickles and text files and include estimates derived from the following snow depth algorithms: SRLD (2009-2015): snow radar layer detection, JPL (2009-2015): Jet Propulsion Laboratory, GSFC (2009-2015): Goddard Space Flight Center, NSIDC (2009-2012): archived NASA GSFC data on the NSIDC, QL (2013-2019): NSIDC quick-look data based on the GSFC algorithm. MEDIAN (2010-2015): consensus snow depth from median of GSFC, JPL and SRLD.  References: Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426. Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018. Petty A. A., N. Keeney, A. Cabaj, P. Kushner, M. Bagnardi (2023), Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, doi: 10.5194/tc-17-127-2023.

opengpl-3.0-or-laterSep 2024View details →

Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)

This repository concerns the supplementary material data of the research article entitled "Personalised in silico model for radiation-induced pulmonary fibrosis" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.

Experimental data for validation of a variational RANS level III flow model: water waves over an array of obstacles and Ogee weir flows

Experimental dataset for the validation of a variational RANS level III flow model. The experimental data correspond to experiments on unsteady of water waves over an array of obstacles and steady curved flows over an Ogee weir. The experiments were conducted at the Hydraulics Laboratory at the Univeristy of Córdoba.

Data for "SeaMoon: from protein language models to continuous structural heterogeneity"

Datasets used for development of SeaMoon:  <a href="https://github.com/PhyloSofS-Team/seamoon">https://github.com/PhyloSofS-Team/seamoon</a>. This upload contains the following data: <ul> <li>precomputed_emb.tar.gz is a compressed archive containing the precomputed data used for training and testing the models of the SeaMoon method, in Torch .pt format.  The file prefixes consist of two IDs, "ID1_ID2_", identifying the <a href="https://github.com/PhyloSofS-Team/DANCE">DANCE</a> [1] protein conformational collection used for its generation. "ID1" represents the first member of the collection in alphabetical order, while "ID2" is the reference conformation for the structural alignment. The "ESM_data" or "ProstT5_data" suffixes designate the type of embeddings, generated by either ESM2 [2] or ProstT5 [3]. The dictionnary contains the following keys: <ul> <li>emb: The per-residue embedding.</li> <li>data: A tuple containing "ID2" (the reference), the amino acid sequence, and the coverage of the positions in the original DANCE collection.</li> <li>eigvect: The eigenvectors of the covariance matrix of the "ID1_ID2" collection, centered on reference conformaton "D2".</li> <li>eigval: The associated eigenvalues.</li> <li>ref: The coordinates of the C-alpha atoms of the reference conformaton "ID2".</li> </ul> </li> <li>train_list.txt, train_list_5ref.txt, val_list.txt and test_list.txt contain the identifiers of the samples used for training and evaluating the SeaMoon models. In the "5ref" setting, we used up to 5 reference conformations per collection. </li> </ul> For details on SeaMoon see: <div> <div>SeaMoon: Prediction of molecular motions based on language models</div> </div> <div>Valentin Lombard, Dan Timsit, Sergei Grudinin, Elodie Laine</div> <div>bioRxiv 2024.09.23.614585; doi: https://doi.org/10.1101/2024.09.23.614585</div> <div> </div> <div>For more information on data usage and generation please see <a href="https://github.com/PhyloSofS-Team/seamoon">https://github.com/PhyloSofS-Team/seamoon</a>.</div> <div> </div> <div>Abstract:</div> How protein move and deform determines their interactions with the environment and is thus of utmost importance for cellular functioning. Following the revolution in single protein 3D structure prediction, researchers have focused on repurposing or developing deep learning models for sampling alternative protein conformations. In this work, we explored whether continuous compact representations of protein motions could be predicted directly from protein sequences, without exploiting nor sampling protein structures. Our approach, called SeaMoon, leverages protein Language Model (pLM) embeddings as input to a lightweight (~1M trainable parameters) convolutional neural network. SeaMoon achieves a success rate of up to 40% when assessed against ~1,000 collections of experimental conformations exhibiting a wide range of motions. SeaMoon capture motions not accessible to the normal mode analysis, an unsupervised physics-based method relying solely on a protein structure's 3D geometry, and generalises to proteins that do not have any detectable sequence similarity to the training set. SeaMoon is easily retrainable with novel or updated pLMs.    [1] Lombard, V.; Grudinin, S.; Laine, E. Explaining Conformational Diversity in Protein Families through Molecular Motions. Scientific Data 2024, 11, 752. [2] Lin, Z.; Akin, H.; Rao, R.; Hie, B.; Zhu, Z.; Lu, W.; Smetanin, N.; Verkuil, R.; Kabeli, O.; Shmueli, Y.; Dos Santos Costa, A.; Fazel-Zarandi, M.; Sercu, T.; Candido, S.; Rives, A. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 2023, 379, 1123–1130. [3] Heinzinger, M.; Weissenow, K.; Sanchez, J. G.; Henkel, A.; Steinegger, M.; Rost, B. ProstT5: Bilingual language model for protein sequence and structure. bioRxiv 2023, 2023–07.

Urban Vegetation Data - Canopy Height Model (Brussels Capital Region, 2021)

This GIS dataset was created for the following scientific publication, as part of the EU-funded <a href="https://coolschools.eu/">Cool Schools</a> research project (under Grant Agreement No. 101003758) : Gallez, E., Canters, F., Gadeyne, S., & Baró, F. (2024). <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000846?via%3Dihub">A multi-indicator distributive justice approach to assess school-related green infrastructure benefits in Brussels - ScienceDirect</a>. Ecosystem Services, 70, 101677. https://doi.org/10.1016/j.ecoser.2024.101677.  Very-High Resolution Canopy Height Model (resolution : 25cm), distinguishing between 4 vegetation types (trees, high shrubs, low shrubs and grass) in the Brussels Capital Region. Coordinate system : Lambert_Belge_72. The CHM was built on : <ul> <li>VHR aerial orthophotos (visible RGB and NIR) (“UrbIS-Ortho N-S, 2021”) for the Brussels Capital Region, of 5x5cm resolution  Source: Paradigm. (2021). UrbIS-Ortho N-S. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/fec72767-d6b6-41b9-a767-616df2779aae#access">https://datastore.brussels/web/urbisdownload</a>.  and;</li> <li>digital terrain models (DSM and DTM) of 50x50cm, captured on 22/09/2021. Paradigm.Brussels. Source: Paradigm. (2021). DSM / DTM. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/1d7bd49d-fe83-4388-af85-6f5dc8ec7909#access">https://datastore.brussels/web/urbisdownload.</a></li> </ul> Both the orthophotos and digital terrain models were resampled to a 25x25cm resolution, using a bilinear interpolation method. The Canopy Height Model was then created by selecting NDVI values of 0.2 and higher, - a commonly used threshold value to distinguish vegetated land from built land (Hashim et al., 2019) -, and vegetation height thresholds of < 0.5m (for grass), 0.5 - 2m (for low shrubs), 2 - 5m (for high shrubs), and > 5m (for trees) (Derkzen et al., 2015; Sankey et al., 2018). Green roofs were excluded.The CHM raster was then converted to polygon features.  Classification : <ul> <li>From 0 to 0.5 m (nDSM value) : gridcode 1 = grass</li> <li>From 0.5 to 2 m (nDSM value): gridcode 2 = low shrubs</li> <li>From 2 to 5 m (nDSM value): gridcode 3 = high shrubs</li> <li>From 5 to 113.96 m (nDSM value): gridcode 4 = trees</li> </ul>

Data from: A robust model for the assessment of oil spill hazards over land and water bodies

This repository contains all the data required to generate the results and figures reported in the article: A robust model for the assessment of oil spill hazards over land and water bodies.  Pablo Vallés, Sergio Martínez-Aranda, Reinaldo García & Pilar García-Navarro  Fluid Dynamic Technologies TFD-I3A, Universidad de Zaragoza, Spain, 2024 Author: Sergio Martínez Aranda Email: sermar@unizar.es Summary of the content: *FILE* BSLmodel_code.c  :  Implementation of the BSL model in the software OILFlow2D (Hydronia LLC) *ZIP-FOLDER* testOilChannel  :  Synthetic test 1: Oil spill over water channel with parabolic velocity profile     Contains:     *FILE* plotter2D.m  :  Matlab file for plotting the article figures     *FILE* readVTK_hu.m  : Ad-hoc Matlab function for reading VTK files and extract arrays of x, y, h, modU variables at cells     *FOLDER* graphics  :  Contains the output figures for the article     *FILE* free_surface_profiles_impCent.mat  :  Matlab structure containing the water level results along the longitudinal center profile for all the cases tested     *FILE* vel_profiles_impCent.mat  :  Matlab structure containing the velocity results along the cross-section x=900m for all the cases tested     *FOLDER* hydro_shear_layer  :  Folder with the 2D hydrodynamics fields for the Bottom Shear Layer used in the simulations     *FOLDER* BSL_disabled  :  Folders containing the raw simulation results with the BSL model disabled          Contains:         *FILES* stgpuXX.vtk  :  VTK files with the 2D fields of the oil layer variables at different times         *FILE* deltat.out  :  File with the evolution of the time step and the inlet-outlet discharges         *FOLDERS* BSL_impCent_CdXpXXXX  :  Folders containing the raw simulation results with the BSL model enabled for different drag coefficients Cd         Contains:         *FILES* stgpuXX.vtk  :  VTK files with the 2D fields of the oil layer variables at different times         *FILE* deltat.out  :  File with the evolution of the time step and the inlet-outlet discharges          *ZIP-FOLDER* testOilBay  :  Synthetic test 2: Oil spill from land to a rotating water bay      Contains:     *FILE* plotter2D.m  :  Matlab file for plotting the article figures     *FILE* readVTK_zhvel.m  : Ad-hoc Matlab function for reading VTK files and extract arrays of x, y, z, h, u, v variables at cells     *FOLDER* graphics  :  Contains the output figures for the article.     *FOLDER* hydro_shear_layer :  Folder with the 2D hydrodynamics rotating fields, including VTK files, for the Bottom Shear Layer used in the simulations     *FOLDER* BSL_disabled  :  Folders containing the raw simulation results with the BSL model disabled          Contains:         *FILES* stgpuXX.vtk :  VTK files with the 2D fields of the oil layer variables at different times         *FILE* deltat.out  :  File with the evolution of the time step and the inlet-outlet discharges             *FOLDERS* BSL_impCent_CdXpXXXX  :  Folders containing the raw simulation results with the BSL model enabled for different drag coefficients Cd         Contains:         *FILES* stgpuXX.vtk :  VTK files with the 2D fields of the oil layer variables at different times         *FILE* deltat.out  :  File with the evolution of the time step and the inlet-outlet discharges                   *ZIP-FOLDER* caseSpillTilenga  :  Realistic case: Oil spill hazard assessment in the White Nile - Tilenga Project      Contains:     *FILE* Qgis_project.qgz  :  Portable QGIS project for plotting the article figures     *FOLDER* geoData  :  Contains the georeferenced data used for the simulation setup     *FOLDER* images  :  Contains the output figures for the article     *FOLDER* hydro_shear_layer :  Folder with the 2D hydrodynamics fields for the Bottom Shear Layer used in the simulations     *FOLDER* spills  :  Folders containing the OilFlow2D project files to perform the simulation of the six spill scenarios reported in the article         *FOLDERS* spill_XXX_XX  :  Folders containing raster files with the oil spreading results at different times for the six spill scenarios reported in the article

Model data and code for "Freeze-thaw effects on daily sediment transport in an Alpine river"

Supporting information for the research article "Freeze-thaw effects on daily sediment transport in an Alpine river" by Skålevåg et al., submitted to Water Resources Research. This data repository contains the processed data, model code, and results presented in the research article. Please refer to the article and its supplementary information for details on primary data.   Contents: <ul> <li>processed data: <ul> <li>Standardised target and predictor variables, in addition to non-standardised data used for freeze-thaw state classification <a href="https://zenodo.org/api/records/13928999/draft/files/model_variables.csv/content" target="_blank" rel="noopener noreferrer">model_variables.csv</a></li> <li>Means and standard deviations of standardised variables <a href="https://zenodo.org/api/records/13928999/draft/files/regression_variables_mean_std.csv/content" target="_blank" rel="noopener noreferrer">regression_variables_mean_std.csv</a></li> </ul> </li> <li>model code: <ul> <li>final model presented in research article: <a href="https://zenodo.org/api/records/13928999/draft/files/model.py/content" target="_blank" rel="noopener noreferrer">model.py</a></li> <li>model comparison performed as part of model development: <a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_predictors_and_segmentation.html/content" target="_blank" rel="noopener noreferrer">model_comparison_predictors_and_segmentation.html</a></li> </ul> </li> <li>results: <ul> <li>final model: <ul> <li>Inference trace from the pymc model <a href="https://zenodo.org/api/records/13928999/draft/files/inference.nc/content" target="_blank" rel="noopener noreferrer">inference.nc</a></li> <li>Summary table of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary.csv/content" target="_blank" rel="noopener noreferrer">inference_summary.csv</a></li> <li>Visualisation of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_trace.png/content" target="_blank" rel="noopener noreferrer">inference_trace.png</a></li> </ul> </li> <li>other models: <ul> <li>non-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_SRC.nc/content" target="_blank" rel="noopener noreferrer">inference_SRC.nc</a> and <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_SRC.csv</a></li> <li>non-segmented "pooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_nonsegmented.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_nonsegmented.csv</a></li> <li>freeze-thaw-state-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_segm_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_segm_SRC.csv</a></li> <li>freeze-thaw-state-segmented "unpooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_unpooled.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_unpooled.csv</a></li> </ul> </li> <li>model comparison: <ul> <li><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_waic.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_waic.csv</a></li> <li> <div><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_loo.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_loo.csv</a></div> </li> </ul> </li> </ul> </li> </ul>

Data for: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models"

This repository contains the raw data to reproduce the paper: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models". Within the .tar.gz file, you will find the directory structure described above. <h2>Directory Structure</h2> <h3>`data`</h3> Contains the data to reproduce all figures in the manuscript. Used primarily by the Jupyter Notebooks that plot the data from the paper. <h3>`eval`</h3> Contains the predicted energies according to a MACE model for the following systems and facets: - covsplit (100, 111, 211, 331, 410, 711): The NN model is trained on low-coverage structures and tested on high-coverage structures for a single facet - evencov (100, 111, 211, 331, 410, 711): The NN is trained on even coverages and tested on odd coverages for a single facet - facet (100, 111, 211, 331, 410, 711): the NN is trained on the facet indicated by the folder name (e.g., facet-100 means that the model was trained on Cu(100)) and tested on all of the other facets. - full: the model was trained on all facets and all coverages - slopes (various versions and configurations): the models were trained with different body-order correlation (v) for the Cu(711) facet and tested only on the Cu(711) facet - Rh111: Energies for the Rh(111) + CHOH + CO systems. <h3>`mcmc`</h3> Contains the data for MCMC (Markov Chain Monte Carlo) evaluations for two systems: Cu and Rh - copper-mcmc-public.tar.gz - rhodium-mcmc-public.tar.gz <h3>`models`</h3> Contains the weights and parameters of the best-performing MACE models trained in this work, as selected by the validation loss: File formats: `.model` and `_swa.model` relate to the first-stage of training and the second-stage of training. <h3>`pyscripts`</h3> Python scripts to perform the MCMC sampling given the custom configuration file `sample_cfg.json`. <h3>`scripts`</h3> Shell scripts for evaluation and training the MACE models, along with the hyperparameters used in doing so. - Evaluation scripts (eval-*.sh) - Training scripts (train-*.sh) <h3>`train`</h3> Training, validation, and testing data for all Cu and Rh facets in this work, according to the naming scheme described above. - Rh111 - covsplit - evencov - facet - full - slopes

Data from: Habitat suitability models reveal extensive distribution of deep warm water coral frameworks in the Red Sea

Deep-sea coral frameworks are understudied in the Red Sea, where conditions in the deep are conspicuously warm and saline compared to other basins. Habitat suitability models can be used to predict the distribution pattern of species or assemblages where direct observation is difficult. Here we show how coral frameworks, built by species within the families Caryophylliidae and Dendrophylliidae, are distributed between water depths of 150 m and 700 m in the northern Red Sea and Gulf of Aqaba. To extrapolate the known (ground-truthed) positions of these deep frameworks, we use environmental and geomorphometric variables to inform well-performing maximum entropy models. Over 250 km2 of seafloor in our study area are identified as suitable for such frameworks, equivalent to at least 35% of the area of photic-zone coral reefs in the same region. We hence contend that deep-water coral frameworks are an important and underappreciated repository of Red Sea biodiversity.

opencc-by-4.0Jul 2021View details →

RDF version of the data from Choi, JS. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources (2018)

This is an RDFied version of the dataset published in Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018) The original dataset publication DOI: <a href="https://doi.org/10.1038/s41598-018-24483-z">https://doi.org/10.1038/s41598-018-24483-z</a> The Original publication authors: Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun

opencc-by-4.0Nov 2021View details →

RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).

This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021). The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a> The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki

Data, Analytical Code, and Model Outputs From: "Green is the New Black: Outcomes of Post-Fire Tree Planting Across the Interior West, USA"

This archive includes data (locations of tree plantings, one-year survival records, remotely sensed canopy cover change), statistical code, and model outputs from Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive

opencc-by-4.0Nov 2024View details →

Spatial clustering of Neobuccinum eatoni occurrence data for potential distribution modeling

The occurrence dataset for Neobuccinum eatoni was compiled through filtration process, starting with records from the Global Biodiversity Information Facility (GBIF) and supplemented by museum specimens and additional sources like SOMBASE, iBOL, NIWA, ANTABIF, and SCAR-AntOBIS. Further data were sourced from the National Museum of Natural History in Paris, the University of Vigo, and recent fieldwork in Antarctica, Heard Island, and Kerguelen Island. Records were meticulously screened to remove misidentified specimens, inaccurate locations, duplicates, and outdated entries, ensuring accuracy and relevance. To address spatial autocorrelation, clustering methods divided the data into distinct geographic clusters, producing a refined dataset used to model N. eatoni's potential distribution with enhanced predictive reliability by reducing spatial autocorrelation effects.

opencc-by-4.0Jun 2021View details →

Mapping Across OpenAIRE And Argos Data Models And The DMP Common Standard

This is the outcome of the mapping activity across the data models of Argos and the OpenAIRE Research Graph and the RDA DMP Common Standard.

opencc-by-4.0Jun 2021View details →

Electrochemical and Spectroscopic Data supported by Computational Models for Exploring the Metal- and Ligand-Based Oxidation of Mackinawite Nanoparticles

Supporting information to our study, where under anaerobic conditions, ferrous iron reacts with sulfide producing FeS precipitate, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases such as gregite or pyrite. The dataset provide information about the chemical speciation of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Nanoparticulate FeS was found to get oxidized to a Fe3+ containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7) and in a concomitant oxidation step, polysulfides are proposed to give a material described as Fe2+(1−3x)Fe3+(2x)S2-(1-y)(Sn2-)y. The thermodynamic differences between ligand- and metal-based oxidation processes from density functional theory can be used to describe one- and two-electron electronic and structural transformations. These findings together point to the existence of a previously unknown, metastable FeS phase located between FeS and greigite (Fe2+Fe3+2S2-4) along a metal oxidation path, and Fe2+S2- and pyrite (Fe2+S22-) along a ligand oxidation path, respectively.