Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

An Empirical Study on the Usage and Availability of Machine Learning Libraries in Open-Source Python Projects - Dataset

This repository contains the dataset of the manuscript: "An Empirical Study on the Usage and Availability of Machine Learning Libraries in Open-Source Python Projects"

opencc-by-4.0Jun 2021View details →

Drought assessment has been outpaced by climate change: Empirical arguments for a paradigm shift

Derived datasets produced in the analysis of "Drought assessment has been outpaced by climate change: Empirical arguments for a paradigm shift".

opencc-by-4.0Sep 2021View details →

An empirical model of the dayside Martian ionosphere based on the radio occultation data from MGS, MEX, and MAVEN

This empirical model is developed from the radio occultation data to reconstruct the dayside Martian ionosphere. The observations include the measurements from MGS, MEX, and MAVEN. Based on PCA and optimized regression, this model well reproduces the peak electron density and altitude in observations.

dryad36/100

Empirical tests of habitat selection theory reveal that conspecific density and patch quality, but not habitat amount, drive long-distance immigration in a wild bird

Individuals that disperse long distances from their natal site must select breeding patches with no prior knowledge of patch suitability. Despite decades of theoretical studies examining which cues dispersing individuals should use to select breeding patches, few empirical studies have tested the predictions of these theories at spatial scales relevant to long-distance dispersal in wild animal populations. Here, we use a novel assignment model based on multiple intrinsic markers to quantify natal dispersal distances of Wood Thrush (Hylocichla mustelina) breeding in forest fragments. We show that long-distance natal dispersal in this species is more frequent than commonly assumed for songbirds and that habitat selection by these individuals is driven by density-dependence and patch quality but not the amount of habitat surrounding breeding patches. These results represent an important contribution to understanding habitat selection by dispersing individuals, especially with regards to long-distance dispersal.

opencc-zeroFeb 2022View details →

opencc-by-4.0Jun 2022View details →

Dataset of the paper "An Empirical Study on the Fault-Inducing Effect of Functional Constructs in Python"

This package contains the dataset of the manuscript "An Empirical Study on the Fault-Inducing Effect of Functional Constructs in Python"

dryad36/100

Data from: Does pH matter for ecosystem multifunctionality? An empirical test in a semi-arid grassland on the Loess Plateau

Date of data collection: 2016-2018 Geographic location of data collection: Guyuan, Ningxia, China (106°23′E, 36°15′N) These data were generated to (i) investigate the responses of soil properties, biological communities and multifunctionality to decreased soil pH; (ii) determine the potential biotic and abiotic pathways that soil pH may drive multifunctionality. In 2016, a 17 m × 40 m semi-arid grassland plot with an initial pH value of 8.05 and uniform vegetation was selected. The experiment was granted by the administration of Yunwu Mountain National Nature Reserve. A randomized block design was used with five treatments and six replicates per treatment. A total of 30 plots were established. All plots were 2 m × 2 m and separated by 1 m buffer zones. The treatments included five levels of acid addition rate (0, 0.23, 0.56, 3.60, and 9.01 mol H+ m-2) in the form of sulphuric acid solution. In late August 2017, the plant communities achieved their peak biomass, and were surveyed and harvested in a 0.5 m × 1 m quadrat in each plot to determine the plant community diversity and estimate above-ground biomass. After harvesting the plants, six soil cores (0-15 cm deep, 2.5 cm diameter) per plot in each of the six blocks were collected and pooled by plot as a replicate for further chemical analyses.

opencc-zeroApr 2022View details →

opencc-by-4.0Apr 2022View details →

MapOSR - A Mapping Review Dataset of Empirical Studies on Open Science

Research that investigates respective researchers’ engagement in Open Science varies widely in the topics addressed, methods employed, and disciplines investigated, which makes it difficult to integrate and compare its results. To investigate current outcomes of Open Science research, and to get a better understanding on topicswell-researched and on research gaps we aim at providing an openly accessible overview of empirical studies that focus on different aspects of Open Science in different scientific disciplines, academic groups and geographical regions. The present data set of studies about Open Science practices was retrieved following a PRISM approach to compile a literature review. We include studies from the Scopus and Web of Science databases with keywords relating to Open Science between the years 2000 and 2020, as well as a snowball search for relevant articles. Studies that did not investigate any aspect of Open Science, or weren’t peer reviewed were excluded, resulting in a total of 695 remaining studies. The data set was collaboratively annotated to ensure intercoder reliability of the coded data.

ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities

The dataset of paper---ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities. It includes 14,053 and 21,765 contributors, as well as 23,739 and 33,454 issues from the PyTorch and TensorFlow communities.

ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities

ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities

The dataset of paper---ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities. It includes 23,739 and 33,454 issues from the PyTorch and TensorFlow communities.

A Semi-Empirical Framework for Ice Sheet Response Analysis under Oceanic Forcing in Antarctica and Greenland

Supplementary material for Luo, X. & Lin, T. (2022). “A Semi-Empirical Framework for Ice Sheet Response Analysis under Oceanic Forcing in Antarctica and Greenland.” Climate Dynamics, in press. <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1007%2Fs00382-022-06317-x&data=05%7C01%7CXiao.Luo%40ttu.edu%7C2a6949c147dd4aa9880f08da345e55ae%7C178a51bf8b2049ffb65556245d5c173c%7C0%7C0%7C637879877007681605%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=2G3mD6n0ZOogcEFQcuCCElFmgKE0%2B%2F5KPTtzBeq8kM4%3D&reserved=0">https://doi.org/10.1007/s00382-022-06317-x</a> This repository contains data and scripts that are used to facilitate the analysis of Greenland and Antarctica ice sheet melting induced by ocean warming. The GCM directory contains ocean potential temperature time series for both Antarctica ice shelves near open sea retrieved from FGOALS-s2 under RCP 6.0 and Greenland store glacier retrieved from MIROC5 under RPC8.5 and HadGEM3-GC31-MM projections under update of RCP8.5 based on SSP5. The raw data are accessed from CMIP5 (https://esgf-node.llnl.gov/search/cmip5/) and CMIP6 (https://esgf-node.llnl.gov/search/cmip6/) projects respectively and the processed data deposited here are stored in Excel format. The WOD directory contains the NetCDF files of Present-Day ocean conditions including temperature, salinity, depth, and other variables retrieved from World Ocean Database (WOD).  The data are deposited as NetCDF format. The Analysis directory contains 3 subdirectories that illustrates the analysis workflow for both Greenland and Antarctica.          The Antarctica subdirectory contains the analysis codes to reproduce Antarctica ice shelf melting response and future projection. Antarctica_analysis.m is the main routine for computation and plotting, and the other files are input files including PISM/PICO simulations outputs and ocean temperature time series retrieved from CMIP5 project that can be found on the previous GCM directory.          The Greenland subdirectory contains the main routine store_analysis.m for Greenland Store glacier computation and plotting, the response time series (store.mat), perturbed response time series (store_pert.mat), ocean temperature projection (store_temp.mat) generated by MIROC5, and subglacial runoff time series (store_runoff.mat) estimated from annual basin surface runoff using MAR data forced by MIROC5 under RCP8.5.          The Historical subdirectory contains the data and scripts used for the historical analyses in the main text. AIS_melt_historical.m is the main script of the analysis, along with two other plotting functional scripts including plot_melt_rate.m and plot_ocean_temp.m. The input files include the PISM/PICO simulations outputs, which are the same files in Antarctica subdirectory, and other 2 input files that contain the temperature time series from 6 CMIP5 GCMs.

Data from the paper "Learning to clusterize urban areas: two competitive approaches and an empirical validation"

Data for urban clustering used in the paper "Learning to clusterize urban areas: two competitive approaches and an empirical validation". We release two datasets for urban clustering based on data acquired in Santiago de Chile. The first dataset is computed at the level of urban blocks. The second dataset is computed at the level of individuals using a uniform sample of Santiago inhabitants. Both datasets comprises features based on social characteristics (e.g., SES), land use, and aesthetic visual perception of the city. The features of each data unit (blocks or individuals) are provided using row packing (each row is a data unit) in CSV files. We release PCA (Principal Components Analysis) features for both datasets.

An Empirical Evaluation of Competitive Programming AI: A Case Study of AlphaCode

Abstract AlphaCode, the code generation system by DeepMind, is an AI technology for assisting software developers in solving competitive programming problems using natural language problem descriptions. However, there is no existing work comparing AlphaCode-generated codes with human codes. In this paper, we conduct an empirical study to find code similarities and performance differences between AlphaCode-generated codes and human codes. We collect 44 generated codes in C++ and Python languages from AlphaCode official website that solve 22 problems on Codeforces. We then retrieve 31,736 human codes by using Codeforces API. The results show that (i) the generated code from AlphaCode and the human code is not similar (i.e., the similarity of 0.26 for both C++ and Python), however, the code fragments in the generated code are comprised of various human codes (i.e., uniqueness of 3.30\% and 8.94\% for C++ and Python respectively) and (ii) the generated code performs on par with or worse than the human code in terms of execution time and memory usage. Moreover, AlphaCode employs excessive nested loops and unnecessary variable declarations  (e.g., used \textit{long long} instead of \textit{int}), which causes a low performance regarding our manual investigation.

Artefact to our paper "An Empirical Study of Automated Unit Test Generation for Python"

Artefact for “An Empirical Study of Automated Unit Test Generation for Python” Together with our paper “An Empirical Study of Automated Unit Test Generation for Python”, we provide this artefact for future use. Pynguin Version We used Pynguin 0.25.2 for our experiments. The releases of Pynguin are archieved by Zenodo, too. Pynguin 0.25.2 is available under DOI <a href="https://doi.org/10.5281/zenodo.6836225">10.5281/zenodo.6836225</a>. Preparation of the Environment We use the <a href="https://python-poetry.org"><code>poetry</code></a> dependency-management tool to manage all dependencies for this artefact. Install this tool if you have not done yet. Furthermore, let <code>poetry</code> create a virtual environment for the experiment by execution <code>poetry install</code>. Execution of the Experiment The execution scripts make several assumptions that are based on our infrastructure. We maintain a SLURM cluster infrastructure that defines different <code>constraints</code> for different machines. Furthermore, we assume some paths to be present: we assume every computing machine to have writable mount points at <code>/local/${USER}</code> and <code>/local/hdd/${USER}</code>. On our machines, both are mount points on the local hard disk/SSD of the computing machines. Additionally, we have a shared mount <code>/scratch/${USER}</code>, which is mounted via NFS from a central file server. This mount point is also mounted on all computing machines. We assume the created and packaged Docker image to be located at <code>/scratch/lukasczy/pynguin.tar</code>. You can change this path by editing the XML files. These XML files contain the basic definitions of the jobs: they specify the SLURM constraint, the version of the Pynguin Docker container, the used Pynguin configurations as well as the modules used for the experiments. These modules have to reside under <code>projects</code>, as they come with this artefact. The Python script <code>execution.py</code> generates the actual run scripts from the XML file. It generates all scripts necessary to run a SLURM array job consisting of all runs for the experiment. Further general settings for the SLURM array job are present in this file. The Bash script <code>run_experiment.sh</code> executes the full execution pipeline; one has to specify the variable <code>EXPERIMENT_NAME</code> to match the name of the respective XML file who's defined experiment shall be executed. Important: Executing the full experiment can take several days, depending on your computing infrastructure! We do therefore provide the raw result CSVs for further inspection. Data Analysis All raw data resides in the <code>data</code> folder: <ul> <li><code>loc_data.csv</code> contains all information about the lines of code in each module. This file was created using the <code>extract_locs_and_types.py</code> script in the root folder. Please note that executing this script requires that the <code>cloc</code> utility tool is installed on your system's path.</li> <li><code>types.csv</code> and <code>types_per_module.csv</code> contain type information extract from the modules at different granularity level. They are also generated using the aforementioned script.</li> <li><code>results-assertion.csv.xz</code> contains the raw results from the experiment for RQ3 that evaluates the effectiveness of the assertions.</li> <li><code>results.csv.xz</code> contains the raw results from the experiment for RQ1 and RQ2.</li> </ul> We provide the Jupyter Notebook that generated the plots, tables, and various LaTeX macros in the <code>notebooks</code> folder. Please note that if you want to reexecute this notebook, you might have to change the <code>PAPER_EXPORT_PATH</code> constant in cell <code>[2]</code> to a suitable location on your machine. Executing this notebook requires a installation of a TeX system to be available on your system because the plots are generated using <code>pdflatex</code> and <code>matplotlib</code>s pgf backend. Further Data The folder <code>projects</code> contains all the projects in the versions stated in our paper. The folder <code>run-logs</code> contains all the run logs from our experiment executions.

Data from: How does sustainable water consumption in the shower relate to different dimensions of perceived well-being? Empirical evidence from university students

Water scarcity is already a worrying issue and it is predicted to get worse in the future. This creates an imperative to use water efficiently and sustainably. In the domestic sphere, one of the main uses of water is showering, not only for hygiene reasons but also as a wellness activity. In order to gain insight into the implications of sustainable shower use, in this paper we analyse the relationship between subjective well-being and water consumption in the shower. We aim to answer the following questions: 1) How does shower water consumption relate to subjective well-being, 2) Does this relationship differ depending on showering habits (time spent in the shower, and number of showers per week), and 3) Does this relationship differ depending on the season (winter and summer). The dataset contains information on 937 students from different disciplines at the University of Granada, Spain. The different interpretations of subjective well-being considered are life satisfaction, affect, and vitality. Results suggest that there is a negative relationship between water consumption and subjective well-being, in line with the literature that identifies a well-being dividend from green behaviour (being pro-environmental helps the environment and increases happiness). All subjective well-being dimensions are negatively related to time spent in the shower, regardless of the season. In contrast, the frequency of showering is not significantly related to well-being. Therefore, it appears that higher water consumption does not translate into higher perceived well-being, indicating that there is no conflict between efficient shower water use and individual well-being.

Data for "Workhorse minimally-empirical dispersion-corrected density functional, with tests for weakly-bound systems: r2 SCAN + rVV10"

VASP inputs and outputs for the: <ul> <li>Ar2 binding energy curve (Ar2_bpara.tar.gz)</li> <li>L28 set of layered-solid geometries and interlayer binding energies (L28.tar.gz)</li> <li>S22 set of interaction energies of weakly-bound complexes (S22.tar.gz)</li> </ul> All POTCAR files have been replaced by "potcar.txt" files containing the title(s) of the POTCAR(s) needed to reproduce the calculations. A preprint is available from the arXiv:2204.11717 (<a href="https://arxiv.org/abs/2204.11717">link</a>).

opencc-by-4.0Aug 2022View details →

Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]

Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.

opencc-by-4.0Mar 2022View details →

Dataset for publication: "Empirical Characterization of Cable Effects on a Reference Lightning Impulse Voltage Divider"

Dataset for publication: "Empirical Characterization of Cable Effects on a Reference Lightning Impulse Voltage Divider". Excel file contains the data for Figures 5 and 6.