Find research datasets worth reusing

opencc-by-4.0Jan 2019View details →

Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"

This repository contains the data for the paper: "Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019." Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper. Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)

opencc-by-4.0Aug 2019View details →

Data for the publication "Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation"

Michibata et al. (2019), currently under peer-review for publication in Geoscientific Model Development, incorporated a diagnostic tool for warm rain microphysics into the CFMIP Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011; Swales et al., 2018), designed to evaluate model representations of aerosol–cloud–precipitation interactions at a fundamental process-level. The tool automatically generates two diagnostics related to warm rain microphysics during COSP execution in a host model. One is the contoured frequency by optical depth diagram (CFODD), which visualizes a cloud-to-rain microphysical vertical structure (Suzuki et al., 2015). The other diagnostic is a global map of warm rain fraction classified as non-precipitating clouds (< –15 dBZe), drizzling clouds (–15 < dBZe< 0), and precipitating clouds (0 < dBZe). This repository contains the MIROC6/COSP2 input data and A-Train satellite statistics used in Michibata et al. (2019). A sample of the post-processing scripts for visualization using the GrADS software is also included in this repository.

opencc-by-4.0Jun 2023View details →

A new in vitro blood flow model for the realistic evaluation of antimicrobial surfaces

Dataset to the publication A new in vitro blood flow model for the realistic evaluation of antimicrobial surfaces Juliane Valtin, Stephan Behrens, André Ruland, Florian Schmieder, Frank Sonntag, Lars D. Renner, Manfred F. Maitz, Carsten Werner Adv. Healthcare Mater. 2023, 2301300. <a href="https://doi.org/10.1002/adhm.202301300">https://doi.org/10.1002/adhm.202301300</a>

opencc-by-4.0May 2024View details →

Supplementary material (part 2): "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria"

Part 2 of supplementary material for the Master's Thesis: "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria" (Wibmer 2024, available <a href="https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-151801">here</a>). Due to memory constraints, the supplementary material consists of two parts: <ul> <li>Part 1: (available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>) Includes Python scripts and model setup files, along with the first part of the datasets, including ERA-reanalysis data, observational data, and the preprocessed AROME model output (NetCDF files) of the 0.5-km simulation.</li> <li>Part 2:  Includes the preprocessed AROME model output (NetCDF files) of the 1.0-km and 2.5-km simulations (see description below).</li> </ul> To reproduce part of the figures, users must download the Python scripts and the preprocessed AROME model datasets (NetCDF files). The Python scripts should be placed in the same parent folder because some of them depend on each other (!! Important !!). Original AROME model output files (GRIB2 format) are not published due to their large size. The naming convention for the AROME simulations uses OP* (where * represents the grid spacing in meters) to differentiate the model runs based on their horizontal grid spacing: <ul> <li>OP2500: for 2.5 km</li> <li>OP1000: for 1.0 km</li> <li>OP500: for 0.5 km</li> </ul> <h3>Datasets Part 2</h3> Due to memory constraints, the datasets needed for the analyses are split up into two parts. The second part of the supplementary material contains: <ul> <li>datasets_OP1000.tar.xz: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the 1.0-km simulation. </li> <li>datasets_OP2500.tar.xz: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the 2.5-km simulation. </li> </ul> The datasets of the 0.5-km simulation (datasets_OP500.tar.xz) can be found in Part 1 of the supplementary material (available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>). The NetCDF datasets of the performed AROME-Aut simulations are packaged and compressed into <code>.tar.xz</code> files. The Python scripts, available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>, require these NetCDF datasets for plotting and analyses routines. The provided NetCDF datasets are preprocessed from the GRIB2 output of the AROME-Aut simulations. For the scripts to function properly, you need to adjust the path to the datasets within <code>path_handling.py</code>. Each <code>datasets_OP*.tar.xz</code> file contains NetCDF files for different type of levels: surface, hybridPressure (model levels), isobaricInhPa (pressure levels), meanSea (mean sea level), heightAboveGround (constant height levels).  The following naming convention for the datasets is used: <ul> <li>ds_OP*_var_hybridPressure_[lon1, lon2, lat1, lat2].nc: Contains data on hybrid pressure model levels for a specific variable (var; e.g., u, v, z, pres, q, t) for the geographical extent defined in the brackets. </li> <li>ds_OP*_interp_hybridPressure_(lon,lat).nc: Combined dataset on hybrid pressure model levels. The data is bilinearly interpolated to the specified location (lon, lat).</li> <li>ds_OP*_var_surface_whole.nc: Contains data on model surface for a specified variable (var; e.g., z, sp, t, tcc) for the whole available domain extent.</li> <li>ds_OP*_heightAboveGround_instant_whole.nc: Combined dataset on height levels (e.g., 2-m and 10-m) for the whole available domain extent.</li> <li>ds_OP*_var_meanSea_whole.nc: Contains data on mean sea level for specified variable (var; e.g., prmsl) for the whole available domain extent. </li> </ul> The original GRIB2 files are not provided due to their large size. For further information about the GRIB2 files or the NetCDF datasets, please feel free to contact me.

opencc-by-4.0Jul 2021View details →

Initial Evaluation Data for SimIMA: A Virtual Simulink Intelligent Modeling Assistant

The following is our initial dataset and evaluation materials corresponding to our development and evaluation of the <a href="https://zenodo.org/record/5123570">Simulink Intelligent Modeling Assistant (SimIMA)</a>.  We evaluate SimGestion and SimXample separately.  <ul> <li>The directory SimGestion-evaluation contains datasets, evaluation scripts, and log files involved in the evaluation of SimGestion. </li> <li>The directory SimXample-evaluation contains datasets, evaluation scripts, and log files involved in the evaluation of SimXample. </li> </ul> This is v1.0, which is the evaluation associated with the thesis "INTELLIGENT SIMULINK MODELING ASSISTANCE VIA MODEL CLONES AND MACHINE LEARNING" by Bhisma Adhikari @ Miami University , 2021.

opencc-by-4.0Nov 2022View details →

Supporting Data -- Evaluating Mask R-CNN Models to Extract Terracing across Oceanic High Islands: an example from Sāmoa.

This dataset provides supplemental information for the manuscript, "Diverse terracing practices revealed by automated lidar analysis across the Sāmoan islands", submitted to Archaeological Prospection. The dataset contains a trained Mask R-CNN deep learning model designed for detecting archaeological terracing features on the islands of American Samoa, associated training data, and the raw and cleaned output of detected terraces.

opencc-by-4.0Nov 2022View details →

Post-remediation evaluation of contaminated site using geophysical methods: Digital Elevation Model Olkusz (Poland) 20220629

The Digital Elevation Model is based on 449 aerial photos taken by a Mavic PRO Unmanned Aerial Vehicle (UAV) fitted with an FC220 camera (focal length: 35 mm; charge-coupled device: 5472 × 3078 pixels, DJI, Shenzhen, China) on 29 June 2022. The final product is a DEM with a 51.1 cm/pix raster field resolution. These products were mapped in the ellipsoid WGS 84 (EPSG:4326).  This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”

opencc-by-4.0Jan 2023View details →

Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5

The files contain 4 scripts and 6 netcdf files.  "laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1. Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc." "heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7.

openapache2.0Jun 2023View details →

Supplementary Material: A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model

This repository contains supplementary data for the journal paper: <blockquote> Mechtenberg M and Schneider A (2023) A method for the estimation of a motor unit innervation zone center position  evaluated with a computational sEMG model. Front. Neurorobot. 17:1179224. doi:  10.3389/fnbot.2023.1179224 </blockquote> It contains the configuration files for the simulator used in that publication [1]. These configuration files are to be found in the archive EMG_model_configs.zip. The files IP_tracking_opt_res.json<a href="https://zenodo.org/api/files/d21f2990-1849-40d8-90de-674fb0965938/IP_tracking_opt_res.json"> </a>and IP_tracking_opt_res.pkl contain the same information but in different file formats. In these files the results of the optimization described in the corresponding paper are stored. For each optimization condition the optimal parameters for the innervation point tracking algorithm are stored, as well as the error score for all calculated parameter combinations.   [1] Mechtenberg, Malte. (2023). UAS-Embedded-Systems-Biomechatronics/EMG-concentrated-current-sources: v0.2.1 (v0.2.1). Zenodo. https://doi.org/10.5281/zenodo.7995152

opencc-by-4.0Jul 2023View details →

Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"

This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper: "Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."

opencc-by-4.0Jul 2023View details →

Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis"

This resources provides pre-processed input data, model checkpoints and model outputs for experiments on the OpenI dataset in below study.  <blockquote> Jan Trienes, Paul Youssef, Jörg Schlötterer, and Christin Seifert. 2023. <a href="https://arxiv.org/abs/2307.12803">Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis</a>. In Proceedings of the 16th International Natural Language Generation Conference (INLG), Prague, Czech Republic. Association for Computational Linguistics. </blockquote> For more information please refer to the accompanying paper and code repository (<a href="https://github.com/jantrienes/inlg2023-radsum">https://github.com/jantrienes/inlg2023-radsum</a>). The data is structured as follows: <ul> <li><code>data/preprocessed/</code> includes the dataset(s) for each model</li> <li><code>output/</code> includes one folder for each experiment/model run. The first part of each output path indicates the dataset that was used at inference.</li> <li>For a mapping between model IDs and results in the paper, see below table. All models were also trained with the background section as input. These are available in directories with the <code>-bg-</code> qualifier. </li> </ul> <table> <thead> <tr> <th>Model name in paper</th> <th>Output directory</th> </tr> </thead> <tbody> <tr> <td>Results from Table 2</td> </tr> <tr> <td>OracleExt</td> <td>openi-unguided/oracle</td> </tr> <tr> <td>BertExt (Liu and Lapata, 2019)</td> <td>openi-unguided/bertext-default</td> </tr> <tr> <td>BertAbs (Liu and Lapata, 2019)</td> <td>openi-unguided/bertabs-default</td> </tr> <tr> <td>GSum (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>GSum w/ LR-Approx</td> <td>openi-bertext-default-clip-lrapprox/gsum-default</td> </tr> <tr> <td>GSum w/ BERT-Approx</td> <td>openi-bertext-default-clip-bertapprox/gsum-default</td> </tr> <tr> <td>GSum w/ Thresholding</td> <td>openi-bertext-default-clip-threshold/gsum-default</td> </tr> <tr> <td>WGSum (Hu et al., 2021)</td> <td>openi-wgsum/wgsum-default</td> </tr> <tr> <td>WGSum+CL (Hu et al., 2022)</td> <td>openi-wgsum-cl/wgsum-cl-default</td> </tr> <tr> <td>Results from Table 3</td> </tr> <tr> <td>Fixed (k=1)</td> <td>openi-unguided/bertext-default-clip-k1</td> </tr> <tr> <td>LR-Approx</td> <td>openi-unguided/bertext-default-clip-lrapprox</td> </tr> <tr> <td>BERT-Approx</td> <td>openi-unguided/bertext-default-clip-bertapprox</td> </tr> <tr> <td>Thresholding</td> <td>openi-unguided/bertext-default-clip-threshold</td> </tr> <tr> <td>k = |OracleExt|</td> <td>openi-unguided/bertext-default-clip-oracle</td> </tr> <tr> <td>Results from Table 4</td> </tr> <tr> <td>Fixed (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>Oracle Length</td> <td>openi-bertext-default-clip-oracle/gsum-default</td> </tr> <tr> <td>Oracle Length + Content</td> <td>openi-oracle/gsum-default</td> </tr> <tr> <td>Results from Table 5</td> </tr> <tr> <td>BertExt w/ k=[1,5]</td> <td>openi-unguided/bertext-default-clip-k{1,2,3,4,5}</td> </tr> <tr> <td>GSum w/ k=[1,5]</td> <td>openi-bertext-default-clip-k{1,2,3,4,5}/gsum-default</td> </tr> </tbody> </table>

opencc-by-4.0Mar 2023View details →

Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)

Model output of the global eddy-rich configuration used in the Geoscientific Model Development publication: "Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)"  Abstract: This paper describes the global eddying ocean-sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16° and 98 vertical levels, and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment. To access the accuracy of simulated 3D ocean fields, and highlight the relative benefits of mesoscale activities, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean-sea ice component of the fully coupled CMCC climate model.  The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling effort, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions.

opencc-by-4.0Dec 2019View details →

Benchmarks for Evaluation and Comparison of Three-way Model Merging Techniques

The attached files are intended to allow the interaction of researchers in the field of Model Merging Conflict Detection and Resolution. For people who want to add the results of evaluating a new technique and contribute to the creation of the actual body of knowledge, it is necessary to download the raw files and fill them out.

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Crater Lake GeoTIFF

An elevation model of Crater Lake, Oregon, USA Landform features: caldera, cinder cone, lava flow Resolution: 3.33 meter, 5,200 x 5,200 height samples File format: GeoTIFF This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models. When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Valdez GeoTIFF

Multiscale elevation models centered on Valdez, Alaska, USA Resolutions: 3.3, 7.5, 15, 30, 90, 250, 500, 1,000, and 2,000 meters, 1500 x 1,500 height samples each File format: GeoTIFF When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Great Sand Dunes GeoTIFF

An elevation model of Great Sand Dunes, Colorado, USA Landform features: active dune field, sand sheet, sabkha Resolution: 3.3 meter, 5,300 x 5,300 height samples File format: GeoTIFF This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models. When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Massanutten Mountain ASCII

An elevation model of Massanutten Mountain, Virginia, USA Landform features: folded ridges, hogback, water gap, meander Resolution: 10 meter, 3,900 x 3,900 height samples File format: Esri ASCII grid This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models. Version 2.0.0 replaced the previous erroneous elevation model of another geographic area. When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

opencc-by-4.0Sep 2020View details →

Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil

Red-Green-Blue (RGB) imaging was used to evaluate Aphanomyces root rot in 547 lentil accessions and lines. The root images were pre-processed by removing image background. This dataset (6,460 root images) was used to build two machine learning models — generalized linear model with elastic net regularization and convolutional neural network— to classify root images into three classes. Details about the methodology and results are described in Marzougui et al. (2020, Plant Phenomics). The excel file includes Aphanomyces root rot disease visual scores (Root_Rating), unique identifier for each lentil accession/line (Lentil_ID), unique identifier for each experiment (Experiment), and unique identifier for each image (Lab_ID).

opencc-by-4.0Dec 2020View details →

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog

An elevation model of Kočevje Rog, Slovenia Landform features: karstified plateau, karst Resolution: 2 meter, 4,500 x 4,500 height samples File format: Esri ASCII This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models. When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>