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Python scripts for input and post-processing of fuzz sputtering TRI3DYN simulations
<p>The influence of a fuzzy surface on the physical sputtering of Mo in He plasmas has been studied with hyperspectral imaging (HSI) measurements and simulations that couple the TRI3DYN code with an impurity transport code. The 2D profiles of the Mo I line emission intensity from HSI images reveal that the sputtering yield, Y, is reduced to ~40% of the smooth-surface value due to the presence of a fuzz layer, while the angular distribution of the sputtered Mo atoms might not change significantly. The simulations reproduce the Y reduction successfully, but indicate that fuzz causes an increase in the small-angle distribution of sputtered atoms. However, the increase is too small to produce an observable change in the Mo I emission profiles. A simple analytical model that assumes a single collision mean free path for a fuzz layer and considers only the primary sputtering events qualitatively reproduces the Y reduction and the small-angle distribution enhancement, explaining the geometrical effect of fuzz on physical sputtering.</p>
Data for: Predation and biophysical context control long-term carcass nutrient inputs in an Andean ecosystem
<p>Animal carcass decomposition is an often-overlooked component of nutrient cycles. The importance of carcass decomposition for increasing nutrient availability has been demonstrated in several ecosystems, but impacts in arid lands are poorly understood. In a protected high desert landscape in Argentina, puma predation of vicuñas is a main driver of carcass distribution. Here, we sampled puma kill sites across three habitats (plains, canyons, and meadows) to evaluate the impacts of vicuña carcass and stomach decomposition on soil and plant nutrients up to 5 years after carcass deposition. Soil beneath both carcasses and stomachs had significantly higher soil nutrient content than adjacent reference sites in arid, nutrient-poor plains and canyons, but not in moist, nutrient-rich meadows. Stomachs had greater effects on soil nutrients than carcasses. However, we did detect higher plant N concentrations at kill sites. The biogeochemical effects of puma kills persisted for several years and increased over time, indicating that kills do not create ephemeral nutrient pulses, but can have lasting effects on the distribution of soil nutrients. Comparison to broader spatial patterns of predation risk reveals that puma predation of vicuñas is more likely in nutrient-rich sites, but carcasses have the greatest effects on soil nutrients in nutrient-poor environments, such that carcasses increase localized heterogeneity by generating nutrient hotspots in less productive environments. Predation and carcass decomposition may thus be important overlooked factors influencing ecosystem functioning in arid environments.</p>
Inputs for ECF Model for case study Vienna
<p>The datasets comprise of:</p> <ol> <li>Cooling demand density and Gross Floor area distribution from https://www.hotmaps.eu/</li> <li>Pipe Costs Estimates for district cooling networks from sEEnergies_D4.5_DH_investment_costs_and_allocation_local_ressources_final_version.pdf</li> <li>EU.11 Cooling Technologies cost data from "Pathways for energy efficient heating and cooling.” EU-Comission, Forthcoming final report, 2023 </li> </ol>
MINI-EX version 2.2 input files (MiMB, lab protocol series book chapter)
<p>Wendrich et al. (2020) Arabidopsis root scRNA-seq dataset, processed for MiMB (lab protocol series) book chapter: "MINI-EX version 2: cell-type-specific gene regulatory network inference using an integrative single-cell transcriptomics approach"</p> <p>Contains:</p> <ul> <li>Seurat object of single-cell dataset</li> <li>Extracted input files for MINI-EX</li> </ul>
Input parameters and output trajectory files for manuscript "Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CLPro: Insights from the Computational and Experimental Investigations"
<p>Input parameters used for molecular dynamics simulations on GROMACS 2022 software and the output trajectory files for calculating binding free energy, in the manuscript with the title: "Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CL<sup>Pro</sup>: Insights from the Computational and Experimental Investigations"</p>
The MOE Waste Input-Output table for Japan, 2011
<p>The uploaded files provide supplementary data related to the journal article:</p> <p>Nakamura, Shinichiro. "Tracking the product origins of waste for treatment using the WIO data developed by the Japanese Ministry of the environment." Environmental Science & Technology 54, no. 23 (2020): 14862-14867. <a href="https://doi.org/10.1021/acs.est.0c06015">https://doi.org/10.1021/acs.est.0c06015</a></p> <p>Please, refer to the abovementioned article, particularly the Supporting Information, for further description of the provided data. </p> <p>Specifically, refer to Table S4 for the notation used to represent secondary waste items obtained post-treatment, such as through shredding. </p> <p>For a waste item 'z,' the notation z(k) represents 'z' after undergoing treatment 'k,' as outlined below:</p> <p>(i): after intermediate treatment<br>(d): after dehydration<br>(c): after concentration<br>(s): after shredding<br>(f): after filtration</p>
HANZE v2.2 flood impact model input data
<p>This dataset provides input data needed to run HANZE v2.2 model. The ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" of the HANZE model (variable "repo_path" at the beginning of the file).</p>
Supplementary supply chain (input) data
<p>This document provides supplementary information on the Conference Paper entitled 'Ensuring supply for emergency services –<br>modeling supply chains with incomplete sets of data'' submitted by Kippenberger et al. for the Simulation Notes Europe (SNE)</p> <p> </p>
Input and enriched files for the pf1600 dataset - ENPKG
<p>Input files (.mgf spectra and .tsv metadata) and enriched files (metabolite annotation, molecular networks and taxonomical resolution results) for the pf1600 dataset described at <a href="https://doi.org/10.1093/gigascience/giac124">https://doi.org/10.1093/gigascience/giac124</a> and used prior to their semantic enrichment and conversion to knowledge graphs as described in <a href="https://doi.org/10.1021/acscentsci.3c00800">https://doi.org/10.1021/acscentsci.3c00800</a>.</p> <p> </p> <p>A typical directory looks like :</p> <p> </p> <p>├── neg<br>│ ├── isdb <br>│ │ ├── config.yaml<br>│ │ ├── VGF159_A02_isdb_neg.tsv<br>│ │ ├── VGF159_A02_isdb_reweighted_flat_neg.tsv<br>│ │ ├── VGF159_A02_isdb_reweighted_neg.tsv<br>│ │ ├── VGF159_A02_treemap_chemo_counted_neg.html<br>│ │ └── VGF159_A02_treemap_chemo_intensity_neg.html<br>│ ├── molecular_network<br>│ │ ├── config.yaml<br>│ │ ├── VGF159_A02_mn_metadata_neg.tsv<br>│ │ └── VGF159_A02_mn_neg.graphml<br>│ ├── VGF159_A02_features_ms2_neg.mgf<br>│ ├── VGF159_A02_features_quant_neg.csv<br>│ ├── VGF159_A02_lcms_method_params_neg.txt<br>│ ├── VGF159_A02_lcms_processing_params_neg.xml<br>│ ├── VGF159_A02_sirius_neg.mgf<br>│ └── VGF159_A02_WORKSPACE_SIRIUS<br>│ ├── canopus_compound_summary.tsv<br>│ ├── canopus_formula_summary_adducts.tsv<br>│ ├── canopus_formula_summary.tsv<br>│ ├── canopus_neg.tsv<br>│ ├── canopus_npc_neg.tsv<br>│ ├── canopus_npc.tsv<br>│ ├── canopus.tsv<br>│ ├── compound_identifications_adducts.tsv<br>│ ├── compound_identifications.tsv<br>│ ├── csi_fingerid_neg.tsv<br>│ ├── csi_fingerid.tsv<br>│ ├── formula_identifications_adducts.tsv<br>│ ├── formula_identifications.tsv<br>│ ├── params.yml<br>│ └── report.mztab<br>├── pos<br>│ ├── isdb<br>│ │ ├── config.yaml<br>│ │ ├── VGF159_A02_isdb_pos.tsv<br>│ │ ├── VGF159_A02_isdb_reweighted_flat_pos.tsv<br>│ │ ├── VGF159_A02_isdb_reweighted_pos.tsv<br>│ │ ├── VGF159_A02_treemap_chemo_counted_pos.html<br>│ │ └── VGF159_A02_treemap_chemo_intensity_pos.html<br>│ ├── molecular_network<br>│ │ ├── config.yaml<br>│ │ ├── VGF159_A02_mn_metadata_pos.tsv<br>│ │ └── VGF159_A02_mn_pos.graphml<br>│ ├── VGF159_A02_features_ms2_pos.mgf<br>│ ├── VGF159_A02_features_quant_pos.csv<br>│ ├── VGF159_A02_lcms_method_params_pos.txt<br>│ ├── VGF159_A02_lcms_processing_params_pos.xml<br>│ ├── VGF159_A02_sirius_pos.mgf<br>│ └── VGF159_A02_WORKSPACE_SIRIUS<br>│ ├── canopus_neg.tsv<br>│ ├── canopus_summary_adducts.tsv<br>│ ├── canopus_summary.tsv<br>│ ├── canopus.tsv<br>│ ├── compound_identifications_adducts.tsv<br>│ ├── compound_identifications.tsv<br>│ ├── csi_fingerid_neg.tsv<br>│ ├── csi_fingerid.tsv<br>│ ├── formula_identifications_adducts.tsv<br>│ ├── formula_identifications.tsv<br>│ ├── npc_summary.csv<br>│ ├── params.yml<br>│ └── report.mztab<br>├── rdf<br>│ └── graph_params.yaml<br>├── taxo_output<br>│ ├── params.yaml<br>│ ├── VGF159_A02_species.json<br>│ ├── VGF159_A02_taxo_metadata.tsv<br>│ └── VGF159_A02_taxon_info.json<br>└── VGF159_A02_metadata.tsv</p>
NetCDF input files to run Ichthyop examples
<p>This dataset contains the NetCDF input files to run the Ichthyop model. These files have been removed from the Ichthyop Git repository to save space.</p>
ChemDyg input data
<p>Seven observations and reanalysis datasets used in the diagnostics sets of ChemDyg v1.0.0 are pre-processed, and the reference path is default assigned corresponding to different DOE machines. We provide the original data in NetCDF or text format. Please check https://github.com/E3SM-Project/ChemDyg for more information about ChemDyg. </p>
WaterFutures/BoN2024: BWDF input data and results
Open the record for dataset details and reuse information.
Fives Input dataset (Cobalt & Darshan traces, combined and preprocessed)
<p>Dataset made of aggregated and curated Cobalt and Darshan logs from the Theta HPC platform at ALCF.</p> <p>Cobalt and Darshan logs were obtained from ALCF Public Data repository (https://reports.alcf.anl.gov/data/index.html) and cover the year 2022. This data was generated from resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. In order to use the scripts contained within this archive, these datasets must be downloaded and placed in the directory '2022' at the root of the extracted archive.</p> <p>The Darshan logs used in this datasets are originillay available in an aggregated form. The levels of details are usually the following : </p> <ul> <li>job (reservation made to a resource manager for some platform resources)</li> <li>application run (application running inside the job, on the reserved resources ; there may be multiple ones, sequentially or in parallel, during a job's execution)</li> <li>I/O operation (read or write registered to a file from a process of an application)</li> </ul> <p>Darshan CSV files for Theta contain job and application runs informations, but individual I/O of each application run is aggregated into a single entry.</p> <p>This resource is organised as a single archive containing:</p> <ul> <li>YAML files with our datasets, at various granularity levels (in 'preprocessed_datastets' directory): <ul> <li>48 files containing each<strong> 1 month worth of job traces</strong> for one of <strong>3 job classes</strong> (4 files per month, one per job class and one with all job classes) </li> <li>4 files containing each the entire year worth of job traces ; 1 file per job class, 1 file with all job classes.</li> </ul> </li> <li>A Jupyter Lab notebook, which contains the necessary routines to create aformentionned datasets from raw logs files from ALCF, for the Theta system</li> <li>A requirements.txt file, describing required Python packages and their versions.</li> <li>Various empty directories meant to receive outputs from the Jupyter notebook.</li> </ul>
HANZE v2.3 flood impact model input data
<p>This dataset provides input data needed to run HANZE v2.3 model. The ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" of the HANZE model (variable "repo_path" at the beginning of the file).</p>
input K490 file for iCORAL in iLOVECLIM
<p>Input files for iCORAL emmbedded in iLOVECLIM :</p> <p>- Kd490 : diffuse attenuation coefficient at 490 nm taken from the Level-3 binned MODIS-Aqua products in the OceanColor database (available at: <a href="http://oceancolor.gsfc.nasa.gov">http://oceancolor.gsfc.nasa.gov</a>). The MODIS data are taken from the entire mission composite at 9km resolution, encompassing 15 years from 2002 to 2016, and has been regridded on the clio grid (3° by 3°).</p>
MUFFIN : A suite of tools for the analysis of functional sequencing data - Example input data
<p>This repository contains the data required to run the example notebooks and to reproduce the figures from the paper : </p> <div> <div><strong>MUFFIN : A suite of tools for the analysis of functional sequencing data</strong></div> </div> <div><em>Pierre de Langen, Benoit Ballester</em></div> <div>bioRxiv 2023.12.11.570597; doi: <a href="https://doi.org/10.1101/2023.12.11.570597" target="_blank" rel="noopener">https://doi.org/10.1101/2023.12.11.570597</a></div> <div> </div> <div>Source code is located here :</div> <div><a href="https://github.com/pdelangen/Muffin" target="_blank" rel="noopener">https://github.com/pdelangen/Muffin</a></div> <div> </div> <ul> <li><strong>10k_pbmc_gene/ </strong>contains the data for 10k pbmc dataset in standard 10x sparse count table format.</li> <li><strong>genome_annot/</strong> contains gencode v38 and chromosomes for the human (used for gene set enrichment analyses)</li> <li><strong>GO_files/</strong> contains gene set information retrieved from the g:ProfileR website.</li> <li><strong>immune_chip/ </strong>contains the data required to re-run the ChIP-seq analyses, it will require to also launch the dl_data.smk to retrieve the data from ENCODE.</li> <li><strong>tcga_atac/</strong> contains the sample-genomic region ATAC tag count table, as well as the sample metadata and a gene set file of cancer hallmark genes.</li> <li><strong>scATAC/</strong> contains the cell barcode-genomic region ATAC tag count table, as well as the barcode metadata and the 10k pbmc dataset pre-analyzed in h5 AnnData format.</li> </ul> <div> </div>
Spatiotemporal changes in riverine input into the Eocene North Sea revealed by strontium isotope and barium analysis of bivalve shells
<p>Extended data and calculations belonging to this study are summarized in the supplementary material. These supplements contain the following supplementary data files: </p> <ul> <li>Supplementary material: supplementary Figure S1</li> <li>Supplementary data 1: Element and isotope data for each individual shell</li> <li>Supplementary data 2: <sup>87</sup>Sr/<sup>86</sup>Sr salinity variability reconstruction</li> <li>Supplementary data 3: Stratigraphy for sampling locations</li> <li>Supplementary data 4: <sup>87</sup>Sr/<sup>86</sup>Sr data for recent oyster shells</li> <li>Supplementary data 5: Extended strontium isotope data</li> </ul>
Revised TOPAS input files for the simulation of WAXS, SAXS and PDF
<p>TOPAS input files for discrete and low-dimensional structure models: (1) a benzene molecule, (2) a PbS quantum dot, (4) a hydroxyapatite nano-fibril and (5) turbostratic carbon</p> <p>TOPAS calculations can be performed with the blank data file or <em>yobs_eqn =1; min 0 max 157.5204 del 0.1</em></p> <p>the file path of the blank data file has to be updated</p> <p>models can be found at <a href="https://doi.org/10.5281/zenodo.8169025" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8169025</a></p>
RAPID input and output files corresponding to "Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset consists of output files of the study reported in:</p> <ul> <li>Tom, M., David, C.H., Marlis, K.M., Zimdars, P.A., Bonassies, Q., Wade, J., Cerbelaud, A., Pavelsky T., Huang, T. (In Review), Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary<br></strong><br>This dataset contains the results of RAPID river discharge simulations (January 1980, February 1980) for the Mississippi river basin using the CURRNT framework. The surface and subsurface runoff data were retrieved from NASA GLDAS Phase 2 dataset (Rodell et al., 2004) at a 3-hourly temporal resolution.<strong><br></strong></p> <p><strong>Software</strong></p> <p>The software used to produce the files in this dataset is available at <a href="https://github.com/czarmanu/currnt" target="_blank" rel="noopener">https://github.com/czarmanu/currnt</a>.</p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to Mississippi River Basin.</p> <p><strong>Description of files</strong></p> <p>All files below were prepared by Manu Tom, using the software mentioned above.<br><br>1980-01</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for January 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the initial state of RAPID (zeros populated for Qout).</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for January 1980 from RAPID corresponding to the downstream point of each reach.</li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for January 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-02</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for February 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-01-31.</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for February 1980 from RAPID corresponding to the downstream point of each reach. </em></li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for February 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-03</p> <ul> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-03.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-02-29.<br><br></em></li> </ul> <p><strong>Other necessary links associated with this dataset:</strong></p> <p>RAPID model (David et al., 2011): <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RRR: RAPID model pre-processor (David et al., 2019): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS VIC 3H v2.0 outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0</a></p> <p>NSIDC Earthaccess library: <a href="https://github.com/nsidc/earthaccess">https://github.com/nsidc/earthaccess</a><br><br><strong>References</strong></p> <p>David, C. H., Maidment, D. R., Niu, G. Y., Yang, Z. L., Habets, F., and Eijkhout, V. (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></p> <p>David, C. H., Hobbs, J., Turmon, M., Emery, C., Reager, J. T., Famiglietti, J. (2019). Analytical propagation of runoff uncertainty into discharge uncertainty through a large river network. Geophys. Res. Lett. 46, 8102–8113, <a href="https://doi.org/10.1029/2019GL083342.492">https://doi.org/10.1029/2019GL083342.492</a></p> <p>Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., 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></p>
Input data and modelling files for a model of the Finnish energy system with focus on cascade hydropower and the addition of a hydrogen storage system realised in Backbone
<p>The files show the input data and modelling files used for the publication "Cascade hydropower integration in a techno-economic power system model: A study of Finnish hydropower plants" (Kiehle et al., 2025 - submitted). The paper's <a title="Preprint on SSRN" href="https://dx.doi.org/10.2139/ssrn.4971685" target="_blank" rel="noopener">preprint</a> is available. A model of the Finnish energy system in 2022 was built in the techno-economic modelling framework Backbone (available on GitLab: https://gitlab.vtt.fi/backbone/backbone). The focus was on implementing cascading hydropower plants in a power system model, including individual reservoirs, generation and spillage capacities. </p> <p>"ModellingFiles_Debug" are GAMS-based data that can be used to run the scenario in Backbone or display the results. "ModellingResults" are gdx files that purely list the results. Those are also presented in more detail in the scientific paper. The Excel files present the input data used for modelling and can also be used to run the model. </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.