Find research datasets worth reusing

Impact of Passive Voice in Requirements Engineering

This repository contains instrumentation material and results for the experiment described in the paper "On The Impact of Passive Voice Requirements on Domain Modelling" by Henning Femmer, Jan Kucera, and Antonio Vetrò from Technische Universität München.

Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"

This dataset is associated with the following publication: Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, Astronomy and Astrophysics, vol. 665, 2022. doi:10.1051/0004-6361/202142493. In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with: <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations: - 'ds_sel': contains monthly timeseries of selected intersecting data - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10) - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10) - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10)  The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2. The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error. The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures. Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a>

ePol: Impacts of Diversity on Software Teams Dataset

Data set that has all the records of the activities carried out in the ePol Project, with their respective attributes. Additionally, it also includes code that does a cleanup according to some criteria. After the cleaning performed by the aforementioned code, the results are also available in the "dataset_epol_result.csv" file. For more information read: SOUZA, NATAN. MASSONI, TIAGO. SARMENTO, CAMILLA. Impactos da Diversidade em Equipes de Software, 2023.

IMMERSE Horizon 2020 Project Downstream User Toolbox – data for tutorial on impact of wave coupling on surface particle dispersion simulations

Exemplary data for tutorial on impact of wave coupling on surface particle dispersal simulations <a href="https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU">https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU</a> created as part of the downstream user toolbox of the IMMERSE Horizon 2020 project (<a href="https://immerse-ocean.eu/">https://immerse-ocean.eu/</a>). In the tutorial the impact of new options for the representation of wave-current interactions in the NEMO ocean model (<a href="https://www.nemo-ocean.eu/">https://www.nemo-ocean.eu/</a>) on surface particle simulations are tested in a case study for the Mediterranean Sea. The tutorial consists of two jupyter notebooks: Parcels_CalcTraj.ipynb and CompTraj_uncoupledVScoupled.ipynb. Parcels_CalcTraj.ipynb calculates Lagrangian particle trajectories based on velocity output  from ocean only as well as coupled ocean-wave model simulation by making use of the OceanParcels software (<a href="https://oceanparcels.org/">https://oceanparcels.org/</a>). CompTraj_uncoupledVScoupled.ipynb compares dispersal statistics of Lagrangian particle trajectories calculated from ocean-only vs coupled ocean-wave model simulations. This repository contains the surface velocity and ocean model grid data needed to run Parcels_CalcTraj.ipynb, as well as the trajectory data produced by Parcels_CalcTraj.ipynb, which is needed to run CompTraj_uncoupledVScoupled.ipynb. The surface velocity data stems from two simulations with a regional high-resolution (1/24° horizontal resolution) model configuration for the Mediterranean Sea: a coupled ocean-wave model simulation and a complimentary ocean-only simulation. These model simulations make use of the NEMO v4.2-RC ocean model, the Wave Watch 3 v.6.07 wave model, the OASIS3-MCT coupler, and ECMWF atmospheric fields; they are described in detail in IMMERSE deliverable D5.7 “Assessment of wave-current effects on the circulation in theMed-MFC system”.

SM: Economy-wide impacts of socio-politically driven net-zero energy in Europe

Supplementary Material (SM): Economy-wide impacts of socio-politically driven net-zero energy in Europe Two zipped folders (a) Euro-Calliope.zip includes -Energy system configurations by storyline (market-driven, government-directed, people-powered) and year (2030, 2050). (b) WEGDYN.zip includes -Supplementary Material (SM_Regionaleconomiceffects.pdf) -Processed Euro-Calliope output data to WEGDYN input data (EC2WD_data.xlsx) -WEGDYN results (WEGDYN_data.xlsx) -WEGDYN resolution, nesting trees, elasticities (WEGDYN_model.xlsx)

opencc-by-4.0Feb 2023View details →

An Empirical Study of Container Image Configurations and Their Impact on Start Times (Container Image Data)

Dataset with the container image metadata used for our IEEE/ACM CCGRID 2023 paper "An Empirical Study of Container Image Configurations and Their Impact on Start Times". Abstract of the paper: A core selling point of application containers is their fast start times compared to other virtualization approaches like virtual machines. Predictable and fast container start times are crucial for improving and guaranteeing the performance of containerized cloud, serverless, and edge applications. While previous work has investigated container starts, there remains a lack of understanding of how start times may vary across container configurations. We address this shortcoming by presenting and analyzing a dataset of approximately 200,000 open-source Docker Hub images featuring different image configurations (e.g., image size and exposed ports). Leveraging this dataset, we investigate the start times of containers in two environments and identify the most influential features. Our experiments show that container start times can vary between hundreds of milliseconds and tens of seconds in the same environment. Moreover, we conclude that no single dominant configuration feature determines a container's start time and that hardware and software parameters must be considered together for an accurate assessment. Dataset description: Our images dataset contains 200,986 entries with 21 features associated to each container image. In the following, we describe the meaning of each feature. Further information is available in <a href="https://github.com/opencontainers/image-spec">OCI Image Specification</a> and the <a href="https://docs.docker.com/engine/reference/run/">Docker Run Documentation</a>. Besides the 20 features grouped in the five categories below, each dataset entry has a image_id, which is used to uniquely identify the dataset entry. Features Metadata features (prefix: meta) <ul> <li>meta_repo_digest : The repo digest is a SHA-256 hash which is used to uniquely identify and pull the image from Docker Hub</li> <li>meta_architecture : The CPU architecture which the binaries in the image are built to run on</li> <li>meta_os : The name of the operating system which the image is built to run on</li> <li>meta_docker_version : The Docker version used to built this image</li> </ul> I/O stream features (prefix: io) <ul> <li>io_attach_stdin : boolean setting to determine whether the console should be attached to the process stdin stream</li> <li>io_attach_stdout : boolean setting to determine whether the console should be attached to the process stdout stream</li> <li>io_attach_stderr : boolean setting to determine whether the console should be attached to the process stderr stream</li> <li>io_tty : boolean setting to determine whether the console should pretend to be a TTY when attached</li> <li>io_open_std_in : boolean setting to determine whether the process stdin stream should be kept open even if console not attached</li> <li>io_std_in_once : boolean setting to determine whether the process retrieved input from the stdin stream at least once</li> </ul> Start command features (prefix: cmd) <ul> <li>cmd_args : Length of list of arguments to use as the command to execute when the container starts</li> <li>cmd_envvars : Environment variables set per default when the container starts</li> <li>cmd_additional_args : Length of list for additional arguments to the containers entrypoint</li> </ul> File system features (prefix: fs) <ul> <li>fs_volumes : Number of volumes to create/use by default</li> <li>fs_size : Size of this image in bytes</li> <li>fs_virtual_size : Virtual size of this image in bytes (equals size)</li> <li>fs_graph_driver_name : Name of the image's graph driver</li> <li>fs_root_fs_type : Name of the file system type used in the image</li> <li>fs_layers : Number of root file system layers</li> </ul> Networking features (prefix: net) <ul> <li>net_ports : Number of ports to expose per default</li> </ul>   Dataset acquisition: The dataset has been acquired from Docker Hub using a web crawler. We used substring matches with the <a href="https://hub.docker.com/explore">Docker Hub Explore function</a>. As search strings, we used all letter combination with sizes 1 to 3, meaning that our first search string was 'a' and our last was 'zzz'. We included both results from the 'recently updated' and the 'most popular' selection. We came up with an initial list of 286,294 image names. We then tested we could pull and start these images once. These tests have been conducted from April to June 2022. We sorted out all images that were either not pullable or startable and retrieved all total of 200,986 valid images. In the following, we describe the error types that we encountered and that let to the removal of the causing image from the dataset: <ul> <li>The image manifest was unknown when we tried to download it meaning that is has been renamed or deleted from the time when our web crawler was running</li> <li>The entrypoint command required a dependency that was missing in the image and therefore the container could not be started</li> <li>The image did not specify an entrypoint command and could therefore not be started</li> <li>The image declared an invalid root file system type</li> <li>The image had a malformed root file system</li> <li>The image configuration was incomplete and therefore not all required data could be obtained</li> </ul> See also our CodeOcean capsule with the processing scripts for our paper: https://doi.org/10.24433/CO.4595026.v2

opencc-by-4.0Dec 2022View details →

Break the Code? Breaking Changes and Their Impact on Software Evolution (Artefacts)

The artefacts included in this repository accompany the thesis "Break the Code? Breaking Changes and Their Impact on Software Evolution" authored by Lina María Ochoa Venegas and supervised by prof.dr. Jurgen Vinju, prof.dr. Mark van den Brand, and dr.Thomas Degueule. The thesis was developed at Eindhoven University of Technology (TU/e) in Eindhoven, The Netherlands and Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands. It was submitted to revision in 2022 and defended in 2023.   Relevant Links <ul> <li>Maracas: https://github.com/alien-tools/maracas</li> <li>BreakBot: https://github.com/alien-tools/breakbot</li> </ul>

opencc-by-4.0Feb 2023View details →

Increased impact of heat domes on 2021-like heat extremes in North America under global warming

The key codes and processed data for the paper.

Graph construction method impacts variation representation and analyses in a bovine super-pangenome

Pangenomes for minigraph, pggb, and cactus containing assemblies from <ul> <li>Hereford (cattle reference genome)</li> <li>Highland</li> <li>Brown Swiss</li> <li>Angus</li> <li>Simmental</li> <li>Original Braunvieh</li> <li>Piedmontese</li> <li>Nellore</li> <li>Brahman</li> <li>Yak</li> <li>Bison</li> <li>Gaur</li> </ul>   Also contains the genomic region classifications for the ARS-UCD1.2 reference genome for <ul> <li>Satellites</li> <li>Tandem repeats</li> <li>Low mappability</li> <li>Repetitive regions</li> <li>Normal (everything else)</li> </ul>

EO4WildFires: An Earth Observation multi-sensor, time-series machine-learning-ready benchmark dataset for wildfire impact prediction

This paper presents a benchmark dataset called EO4WildFires; a multi-sensor (multi spectral; Sentinel-2, Synthetic-Aperture Radar - SAR; Sentinel-1, meteorological parameters; NASA Power) time-series dataset that spans 45 countries, which can be used for developing machine learning and deep learning methods targeted for the estimation of the area that a forest wildfire might cover. This novel EO4WildFires dataset is annotated using EFFIS (European Forest Fire Information System) as forest fire detection and size estimation data source. A total of 31,742 wildfire events are gathered from 2018 to 2022. For each event, Sentinel-2 (multispectral), Sentinel-1 (SAR) and meteorological data are assembled into a single data cube. The meteorological parameters that are included in the data cube are: ratio of actual partial pressure of water vapor to the partial pressure at saturation, average temperature, bias corrected average total precipitation, average wind speed, fraction of land covered by snowfall, percent of root zone soil wetness, snow depth, snow precipitation, as well as percent of soil moisture. The main problem that this dataset is designed to address, is the severity forecasting before wildfires occur. The dataset is not used to predict wildfire events, but rather to predict the severity (size of area damaged by fire) of a wildfire event, if that happens in a specific place under the current and historical forest status, as recorded from multispectral and SAR images, and meteorological data. Using the data cube for the collected wildfire events, the EO4WildFires dataset is used to realize three (3) different preliminary experiments, in order to evaluate the contributing factors for wildfire severity prediction. The first experiment evaluates wildfire size using only the meteorological parameters, the second one utilizes both the multispectral and SAR parts of the dataset, while the third exploits all dataset parts. In each experiment, machine learning models are developed, and their accuracy is evaluated.

Data and code: Disentangling the impact of Atlantic Niño on sea-air CO2 flux

This repository share (1) post-processed data of Norwegian Earth system model version 2 (LM) and (2)  codes for calculating carbon system from NorESM variables (MATLAB format).

Hello, world! VIVA+ blunt impact simulations

LS-Dyna simulations files (animations[d3plots] and binary outputs [binouts]) from the publication introducing VIVA+ Human Body Models, https://www.frontiersin.org/articles/10.3389/fbioe.2022.918904/full

opencc-by-4.0Feb 2023View details →

Spray impact onto a hot solid substrate: film boiling suppression by lubricant addition. Supplementary Data

This is a Supplementary Data for a paper entitled "Spray impact onto a hot solid substrate: film boiling suppression by lubricant addition" by Gajevic Joksimovic et al. Frontiers in Physics (2023). Data used for plotting of figures Datasets for Fig. 5:  Number at the end of the file name corresponds to the volumetric lubricant concentration used. <ul> <li>      Dataset_Figure5 TimeTemperatatureFlux  Concentration 0.97.txt</li> <li>      Dataset_Figure5 TimeTemperatatureFlux Concentration 1.09.txt</li> <li>      Dataset_Figure5 TimeTemperatatureFlux Concentration 1.26.txt </li> <li>      Dataset_Figure5 TimeTemperatatureFlux Concentration 1.49.txt</li> <li>      Dataset_Figure5 TimeTemperatatureFlux Concentration 1.82.txt</li> <li>      Dataset_Figure5 TimeTemperatatureFlux Concentration 2.34.txt</li> <li>      Dataset_Figure5 TimeTemperatatureFlux Concentration 3.28.txt</li> <li>      Dataset_Figure5 TimeTemperatatureFlux Concentration 5.47.txt</li> <li>      Dataset_Figure5 TimeTemperatatureFlux PureWater.txt</li> </ul> Datasets for Fig. 11: <ul> <li>Dataset_Figure11 PHI Omega Omega nu.txt</li> </ul> Supplementary videos The supplementary videos for Fig. 8: <ul> <li>supplementary video Figure8a.mp4</li> <li>supplementary video Figure9b.mp4</li> <li>supplementary video Figure9c.mp4</li> </ul> The supplementary video for Fig. 13 <ul> <li>supplementary video Figure13.mp4</li> </ul>

opencc-by-4.0Apr 2023View details →

Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"

This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided.

Population-level health and economic impacts of introducing Vaccae vaccination in China: A modeling study

Supplementary to "Population-level health and economic impacts of introducing Vaccae vaccination in China: A modeling study"

Covid-19 - Symptoms - Impact on quality of life and needs of affected people

Dataset « Covid-19 - Symptoms - Impact on quality of life and needs of affected people ». The data came from an online study involving a sample of 639 participants resident in France affected by COVID-19 symptoms several days, weeks or months after infection. It was collected to provide characterization of a wide range of symptoms of COVID-19, their effects on quality of life and the needs of those affected.

opencc-by-4.0Jul 2023View details →

LIF-based quantification of the species transport during droplet impact onto thin liquid films (Dataset)

This database includes Supplementary Data and videos for Experiments in Fluids manuscript: LIF-based quantification of the species transport during droplet impact onto thin liquid films. Number of figure in the file name is changed: <ul> <li>fig.2 to fig. 3</li> <li>fig.3 to fig. 4</li> <li>fig.4 to fig. 5</li> <li>fig.5 to fig. 6</li> <li>fig.8 to fig. 11</li> <li>fig.9 to fig. 12</li> <li>fig.10 to fig. 13</li> <li>fig.11 to fig. 14</li> <li>fig.12 to fig. 15</li> <li>fig.13 to fig. 16</li> <li>fig.14 to fig. 17</li> </ul>

Dataset of "The Impact of Spacer Size on Charge Transfer Excitons in Dion-Jacobson and Ruddlesden-Popper Layered Hybrid Perovskites"

This dataset underpins the following article published in the Journal of Physical Chemistry Letters: "The Impact of Spacer Size on Charge Transfer Excitons in Dion-Jacobson and Ruddlesden-Popper Layered Hybrid Perovskites" DOI: 10.1021/acs.jpclett.3c01125   The dataset contains steady state absorption (UV/Vis), transient absorption (TA) and electroabsorption (EA) data acquired from experiments on 2D perovskites incorporating different organic spacers. The dataset also includes data acquired from temperature dependent measurements. The transient absorption data has been treated using a home-written matlab script in order to correct for the chirp.

Supplemental tables for a study of the seasonal Impacts of the Physical Environment on Biogeochemical Cycles in Arctic Lakes of the Mackenzie River Delta

submitted abstract We conducted two- and six-year-long deployments of continuous water samplers (OsmoSamplers) and sensors (Temperature, pressure, light level, dissolved oxygen (DO) and conductivity) in nine lakes within the mid- to outer-delta region of the Mackenzie River and documented biogeochemical fluctuations (Mn, Fe, sulfate, and DO), defined physical processes that that drive such fluctuations, and constrained the impact of lake solutes on annual riverine fluxes. Five lakes were in the mid-delta region near Inuvik, NT, two lakes were in the outer delta, and two lakes were on the Arctic coastal plain and were not impacted by the Mackenzie River. In general, temperature minima occurred in September/October, indicative of ice formation, and distinct hydrostatic pressure (water level) anomalies occurred in May/June associated with ice breakup, lasting for days to months and impacting lake levels up to 4.2 m higher than “normal”. Such anomalies coincide with a dramatic change in solute concentrations. Systematic changes in solute concentrations indicate redox-driven biogeochemical reactions, salt exclusion during ice formation, and continuous to sporadic exchange of river water. Redox reactions were regulated by DO inputs stemming from atmospheric, photosynthetic, and riverine sources. During ice-covered periods dissolved sulfate may be conservative but was generally removed. Manganese and iron concentrations showed phases of production and removal during ice-covered periods, but both were produced overall. Calculated solute fluxes from lake waters alone to the Arctic Ocean may only impact yearly riverine fluxes for solutes that exceed ten times the river concentration prior to ice breakup (e.g., Mn and Fe).

The image processing of Milani: challenges after DART impact

This dataset contain data for the paper The image processing of Milani challenges after DART impact, presented at ESA-GNC Sopot, Poland, June 2023  The .zip folder Dataset is made of 4 subfolders      -CoefficientsPCE With the PCEFull9.mat file with all the coefficients of the aPC basis and PCE listed in the paper          -TestResults With the ResultsAll.mat file that contains the predicted and true values of phase angle over the test set illustrated in the paper and the MakePlot.m script in Matlab. You can use this data to compare your method directly with the results we have obtained in this work. The script generate a simple histogram plot and compute the mean, std, Q67, and Q95 values.      -D1_with_sz_1p00s0ImagesDataset directory within this folder contains all data obtaind during the rendering phase of the 5k samples of Didymos using the polar scale of the Didymos Reference model by ESA (old values before impact).     -D1_with_sz_0p78s0 ImagesDataset directory within this folder contains all data obtaind during the rendering phase of the 5k samples of Didymos using the polar scale of the Didymos Reference model by JHUAPL (updated values after impact). This directory also contains the pre-processed input-output pairs for the WCOB, NN, and PCE (X_features, Y) and CELM, and CNN (X_images, Y) methods.  You can either decide to use the same pre-processed data we have used for training, validation, and testing or you can work with the raw data (the one from the ImagesDataset folder) to generate your own dataset.  Feel free to drop a line vie email in case you need clarification at mattia.pugliatti@polimi.it or pugliatti.mattia@gmail.com