Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

NYMPHE Horizon 2020 project Falasarna (GR) Test Sites preliminary data

The dataset is a preliminary vision on the Falasarna (GR) test site for bioremediation action in the EU funded Horizon 2020 project. The data set iwill be used to futher refine the datasets structure and to define the hyerachical data dependences.  The distribution of the typical species for phrigana habitat was investigated. The data were obtain prior application of the bioremediation measures. The data collection mission took place in October 2024. The valuee were obtaiing by direct measurement on site,

opencc-by-4.0Nov 2024View details →

Simulation and laboratory eddy current testing data - modelling compound defects via perturbation theory

This dataset serves to fit and validate a perturbation approach to the modelling eddy current signals of compound defects. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection). The simulation data was generated with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. The simulation data is supplied as csv. The laboratory data was gathered by Rainer Pohl in the eddy current laboratory of BAM, section 8.4. It is supplied in the DICONDE data format. The first frame of the pixel array in the DICONDE files corresponds to the real part and the second frame corresponds to the imaginary part of the signal. The data set is analyzed in an upcoming article.

opencc-by-4.0Sep 2024View details →

Data For Survival of the Fittest: Testing Superradiance Termination with Simulated Binary Black Hole Statistics

This repository is associated with the GitHub repository: https://github.com/jacquelynzhy/Statistical_Superradiance, which includes the code to reproduce the findings of Zhu et al. (2025). Specifically, the file "Output1.dat" here represents the output of running ZEVN with the initial conditions outlined in Section 3.1 of Zhu et al. (2025), which only included BH-BH binaries. For the values generated by a ZEVN run and instructions on how to select the type of remnants you are interested in, please refer to <a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.485..889S/abstract">Spera et al. (2019)</a> and the ZEVN GitHub page at: https://gitlab.com/sevncodes/sevn.

opencc-by-4.0May 2024View details →

FM-Tools Release 2.2: Data Set of Metadata about Tools for Formal Methods (SV-COMP 2025, Test-Comp 2025)

<h1>Collection of Information about Formal-Methods Tools</h1> <h2>Motivation</h2> There are many tools available that implement formal-methods approaches. This repository collects meta data about the tools, such that it becomes easier to reuse, integrate, and cooperate with formal-methods tools. A <a href="https://www.sosy-lab.org/research/pub/2024-Podelski65.Find_Use_and_Conserve_Tools_for_Formal_Methods.pdf">description</a> of the structure of this repository can be found in an article. A <a href="https://fm-tools.sosy-lab.org/">formatted listing</a> of some of the data in this repository can be found on a generated web site. A <a href="https://fm-tools.sosy-lab.org/schema.html">schema definition</a> of the data files in this repository can be found on a generated web site.

opencc-by-4.0Jul 2021View details →

GloMPO (Globally Managed Parallel Optimization) Benchmark Test Data

Dataset associated with:   M. Freitas Gustavo and T. Verstraelen (2021), "GloMPO (Globally Managed Parallel Optimization) - a tool for expensive, black-box optimizations: application to ReaxFF reparameterizations". Contains optimization trajectories using the CMA-ES optimizer applied to various benchmark functions and ReaxFF reparameterizations. Compares optimization results using the GloMPO framework (github.com/mfgustavo/glompo) to unmanaged optimization results.

opencc-byNov 2021View details →

MOREST Experiment Data with test sequence

MOREST experiment data that contains the test results of MOREST vs. 3 other competing methods. The json files are the raw data generated by the tool, which contains the 500 responses corresponding to bugs information.    The detailed test sequence outputs are in the "Test sequence details.zip"

opencc-by-4.0Nov 2021View details →

Training and test data for retrievals based on HATPRO observations during MOSAiC

The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of temperature (ta) and humidity (hua) profiles, integrated water vapour (prw), and liquid water path (clwvi) from brightness temperatures (tb) measured by a HATPRO (humidity and temperature profiler). A regression with quadratic terms, except for the boundary layer scan which is confined to linear terms, is performed to derive these meteorological variables. The trained retrieval is applied on the HATPRO observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on Ny-Ålesund radiosonde observations. An IDL-based radiative transfer model has been used to simulate brightness temperatures for each radiosonde. Several elevation angles (ele) are given in the file because the HATPRO radiometer performs elevation scans in between zenith scans to increase the resolution of temperature profiles in the atmospheric boundary layer.

opencc-by-4.0Jan 2022View details →

Extra Testing Data for paper "OC_Finder: A deep learning-based software for osteoclast segmentation, classification, and counting"

<pre>Here we have 9 datasets we used to validate OC_Finder's performance on various imaging settings. The 9 datasets are inside the folder named "9 datasets for validation experiment". Each dataset is composed of image files and csv files for the coordination of osteoclasts and non-osteoclasts that were manually labelled by human examiner. csv files ending "_posi" has coordination of osteoclasts and "_nega" has coordination of non-osteoclasts. Images in dataset #4, #5, #6, #7, #8, and #9 were resized so the scale of the images matched to the OC_Finder's training dataset. Images in original size before resizing are also provided in "Original images before resizing". Detailed capture setting and resizing information of images in each dataset can be found in "capture setting.xlsx". The number of images in each dataset are as following: #1: 18 #2: 18 #3: 18 #4: 36 #5: 36 #6: 36 #7: 16 #8: 16 #9: 16</pre>

opencc-by-4.0Nov 2021View details →

Training and test data for retrievals based on MiRAC-P observations during MOSAiC

The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been developed to derive the prw. The trained retrieval is applied on the MiRAC-P observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on ERA-Interim reanalysis. An IDL-based radiative transfer model has been used to simulate brightness temperatures. The elevation angle (ele) is always 90° because the MiRAC-P performed zenith scans only throughout the MOSAiC campaign.

opencc-by-4.0Jan 2022View details →

Netflow data with sampling 250 for test (D4)

NetFlow traffic generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic) NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device. NetFlow flows have been captured with sampling 250 at the packet level. A sampling means that 1 out of every X packets is selected to be flow while the rest of the packets are not valued. The version of NetFlow used to build the datasets is 5.

opencc-by-4.0Jan 2022View details →

Netflow data with sampling 500 for test (D5)

NetFlow traffic generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic) NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device. NetFlow flows have been captured with sampling 500 at the packet level. A sampling means that 1 out of every X packets is selected to be flow while the rest of the packets are not valued. The version of NetFlow used to build the datasets is 5.

Stress, Strain, and Temperature measurement data for AISI 301 and AISI 316 stainless steels from tension tests with a sudden increase of strain rate

This publication comprise data obtained during a tension test where the strain rate was suddenly changed from 10-4 s-1 to 1.3x103 s-1. The sudden increase in strain rate was carried out at different amounts of plastic strain. The tests were carried out with a modified tension Split-Hopkinson Pressure bar device. The force was measured from the stress bars using strain gages, whereas the strain was measured using high speed photography and digital image correlation. The temperature was measured using high speed infrared imaging. The data is part of the original publication in Journal of Dynamic Behavior of Materials. The original manuscript is available at https://doi.org/10.1007/s40870-022-00333-y

The Data Set for the Publication "Laboratory Testing of Small Scale Solar Facade Module with Phase Change Material and Adjustable Insulation Layer"

In the scope of the ambitious EU goals of carbon neutrality in 2050, building energy efficiency is one of the crucial segments. To ensure a faster energy transition process, innovations are needed at various built environment-related sectors - starting from building components up to urban level energy management advancements. Phase change material (PCM) enriched building components allow to shift the existing paradigm - to make a switch from the static building components to dynamic ones able to take an active part in building energy balance by ensuring energy storage in the building thermal envelope. The data set presented here supports the paper "Laboratory Testing of Small Scale Solar Facade Module with Phase Change Material and Adjustable Insulation Layer". The design of the façade module, experimental setup, used equipment, the plan of the experiment, and obtained results are described in the paper. The provided data set provides heat-flux and average temperature (in PCM) measurements.

Data of "Durability of self-healing cementitious systems with encapsulated polyurethane evaluated with a new pre-standard test method"

The dataset found here is related to the crack width measurements and water permeability tests performed in a study investigating the durability of self-healing cementitious systems with encapsulated polyurethane evaluated with a new pre-standard test method.

Xenopus tissue data for testing segmentation models

<pre>This dataset is of xenopus tissue imaged with the following settings and it comes with a trained UNET model for performing the segmentation of such tissues. In order to use the segmentation model please install the vollseg-napari plugin from the napari hub and the model will be automatically downloaded for usage. Dataset was acquired by Mari Tolonen and Jakub Sedzinski, (0000-0002-4395-9022,0000-0002-1788-0329) at the university of Copenhagen and the model was trained by Varun Kapoor at Kapoorlabs. A Z projection of 21 Z slices acquired by the ImageJ Z Projection plugin was performed on the original acquired data. ObjectiveSettings ID="Objective:0" Medium="Water" RefractiveIndex="1.333"</pre> <pre>LensNA="1.2000000000000002" Model="C-Apochromat 40x/1.2 W AutoCorr M27" NominalMagnification="40.0"</pre> <pre>Physical Size X="0.6918881841365326" Physical Size X Unit="µm" </pre> <pre>Physical Size Y="0.6918881841365326" Physical Size Y Unit="µm" </pre> <pre>Physical Size Z="2.0" Physical Size Z Unit="µm"</pre> <pre>Time interval frames 1-160: 182 sec Time interval frames 161-262: 283 sec</pre> <pre>SignificantBits="8" Type="uint8"></pre> <pre>Channel AcquisitionMode="LaserScanningConfocalMicroscopy" ExcitationWavelength="488.0" ExcitationWavelengthUnit="nm" Fluor="EGFP"</pre>

ALPPACA test data

A small test dataset of six different Klebsiella pneumoniae isolates, to be used for testing the ALPPACA pipeline after installation.

Data for the paper "Determining the value of preferred goods based on consumer demand in a home-cage based test for mice"

All data related to the paper "Determining the value of preferred goods based on consumer demand in a home-cage based test for mice" will be made available to the scientific public here. The paper will be published soon in Behavioral Research Methods.

opencc-by-4.0Sep 2021View details →

NDMAX / ECLIF2 ground test data

Data spreadsheet associated with Corbin et al., AMT 2021, Aircraft-engine particulate matter emissions from conventional and sustainable aviation fuel combustion: comparison of measurement techniques for mass, number, and size.   Manuscript URL: https://doi.org/10.5194/amt-2021-320

Training and Testing Data, Associated Code, and WRF Code for ML-based nonhydrostatic alternative scheme in dynamical core of atmosphere

Data and codes for a nonhydrostatic alternative scheme (NAS) in dynamical core of atmosphere based on machine learning. In this new version, the randomly sampled training data samples testing data samples from nonhydrostatic simulations in WRF baraclinic wave test are provided. They are processed into a new data structure, which can be directly utilized in training and testing.  Follow the instructions in README.txt and download the training and testing data, and the associated codes. Here we provide 3 parts of data and codes: 1, Training and testing data from WRF; 2, Training and testing codes for two machine learning emulators: machine learning and neural network 3, WRF application.

Thermoelastic axisymmetric reciprocity data for LIGO test mass

A compressed numpy file that contains the volumetric strain data used for reciprocity thermoelastic deformation calculations for LIGO test masses. This is expected to be used with the FINESSE 3.0 simulation software. This data is for axisymmetric deformation and heating problems. It contains data for computing up to order 30 zernike mode deformations to the LIGO test mass surfaces.