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1,034 results for “Empirical”
Dataset for publication: EMPIR_19ENG02_PartialDischargeChargeEstimationInGasInsulatedSubstationsUsingElectricandMagneticAntennasData
<p>Dataset for the conference paper entitled: Partial Discharge Charge Estimation In Gas-Insulated<br> Substations Using Electric and Magnetic Antennas</p>
Empirical CDF for Luria-Delbrück distribution from rSalvador package
<p>Simulation data used for article "The number of neutral mutants in an expanding Luria-Delbrück population is approximately Frechet". Analyze with Julia software code at <a href="https://github.com/evolbio/FrechetLD">https://github.com/evolbio/FrechetLD</a>.</p>
An empirical study on the influence of developers' experience on software test code quality
<p>Software developers' engagement in open-source software projects lies in different levels of participation, e.g., core or peripheral developers. Recent studies have investigated the role of developers' contributions and their influence on software quality. However, few studies investigate the relationship between the developers' experience and test code quality in software projects. In this study, we aim to investigate the relationship between the developers' experience and the test code quality from the perspective of test smells. We performed an empirical study to analyze the insertion and removal of test smells in four open-source Java projects. We collected 18 test smells and calculated their authorship through the projects' Tags. The four software projects contain 386 test classes and 5,178 test smells. We found out that the insertion of 67.28\% of test smells occurs during the test class creation, and the removal of 20.88\% of test smells occurs during the evolution of projects. In addition, core developers are responsible for inserting 88.91\% and removing 89.82\% test smells. Core developers insert and remove more test smells than the peripheral developers. Most test smell removal is due to test code deletion, which may indicate that both core and peripheral developers are unaware of test smells in test code.</p>
Simulation data for a semi-empirical approach for tsunami inundation in South Italy
<p>The archive contains data used for the simulations devised for a semi-empirical method for the evaluation of tsunami wave propagation and inundation along the coasts of South Italy.</p> <p>UPDATE - October 19, 2022<br> - Added readme files (in English and Italian) with an explanation of the files of the repository.<br> - Added text files for values of alpha also for the case of perpendicular tsunami fronts (as per Figures 5d and 5f of the paper); only files for realistic tsunami fronts (as per Figures 5c and 5e of the paper) were present in the previous version of the repository. </p>
Empire State Building
The Empire State Building is a 102-story Art Deco skyscraper in Midtown Manhattan in New York City, United States. It was designed by Shreve, Lamb & Harmon and built from 1930 to 1931. Its name is derived from "Empire State", the nickname of the state of New York. The building has a roof height of 1,250 feet (380 m) and stands a total of 1,454 feet (443.2 m) tall, including its antenna. Kindly support me on [Patreon](http:///www.patreon.com/HUSSEN?fan_landing=true) and [Artsation](http://www.artstation.com/mohamedhussien1) I really need your support to continue. 😁 Source: Objaverse 1.0 / Sketchfab
Late Roman Empire Sarcophagus
High relief carved in marble imported from Rome. Dated from the Early IV c. AD (310 A.D.), reveals the assimilation of Christianism by the Hispanic elites (not everyone can afford such a piece and material imported from Rome). The relief depicts differents events from the Old and New Testament. For more details, check the annotations in the model. This piece was placed in the cathedral of Astorga, Spain. It is assumed that was used to keep king Alfonso III (the Great) corpse, dead in the early X c. The piece is today exposed in the Museo Arqueológico Nacional of Madrid ( nº inv. 50310) Biblical quotes are from the "Christian Standard Bible" (CSB). Further readings: **Huguet**, P. B. (1943). Fragmentos de un sarcófago paleo-cristiano con escenas de la historia de San Pedro, descubiertos en Tarragona. Butlletí Arqueològic. Reial Societat Arqueològica Tarraconense, (1-2), 12-17. **Desken-Weiland**, J (2018). Christian Sarcophagi from Rome. In The Routledge Handbook of Early Christian Art, pp. 39-55 Source: Objaverse 1.0 / Sketchfab
Curated dataset of bug fix commits from "An Empirical Study on Real Bug Fixes"
<p>To cite it:</p> <p><code>@misc{bfdataset,<br> author = {Martin Monperrus},<br> title = {Curated dataset of bug fix commits from "An Empirical Study on Real Bug Fixes"},<br> year = 2017,<br> doi = {10.5281/zenodo.1004734},<br> url = {https://doi.org/10.5281/zenodo.1004734}<br> }</code></p> <pre> </pre> <p> </p>
Replication Kit for Paper: "Are There Any Unit Tests? An Empirical Study on Unit Testing in Open Source Python Projects"
<p>Replication Kit for the Paper "Are there any Unit Tests? An Empirical Study on Open Source Python Projects" by Fabian Trautsch, Jens Grabowski.</p> <p>You can cite the paper via:</p> <p>@inproceedings{trautsch2017there,<br> title={Are There Any Unit Tests? An Empirical Study on Unit Testing in Open Source Python Projects},<br> author={Trautsch, Fabian and Grabowski, Jens},<br> booktitle={Proceedings of the IEEE International Conference on Software Testing, Verification and Validation (ICST)},<br> pages={207--218},<br> year={2017},<br> organization={IEEE}<br> }</p> <p> </p> <p>Contents:<br> 1) Used version of the vcsSHARK<br> - located in “vcsSHARK”<br> 2) Used version of the testImpSHARK<br> - located in “testImpSHARK”<br> 3) Analysis implementations<br> - located in “testImpSHARK/testimpshark/analysis”<br> 4) Raw Data CSV Files<br> - located in “testImpSHARK/testimpshark/analysis/data<br> 5) Raw MongoDB<br> - located in “mongo_backup”</p> <p><br> Usage:<br> 1) Usage instructions for the vcsSHARK is given on its github homepage (http://ftrautsch.github.io/vcsSHARK/index.html) or directly in the “vcsSHARK/pyvcsshark/main.py” file</p> <p>2) Usage instructions for the testImpSHARK:<br> - if only one revision should be analyzed use “testImpSHARK/main.py”<br> - if all revisions should be analyzed use “testImpSHARK/execution.py”<br> - in both files concrete instructions can be found</p> <p>3) Each analysis file is commented. For some of them (rq1_boxplot.py and rq4.py the connection to the MongoDB must be changed). For the R files, the path to the data must be adapted. Otherwise, the files can be directly executed.</p> <p>4) The MongoDB can be restored via:<br> mongorestore --gzip --archive=smartshark040816.gz --db smartshark --host <HOST> --port <PORT> --username <USERNAME> --password <PASSWORD> --authenticationDatabase <AUTHENTICATION_DATABASE></p> <p><br> Tests:<br> 1) The tests can be run directly via the unittest framework of python: e.g., python -m unittest tests/test_common.py</p>
Data for: Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma using reinforcement learning
<p>This contains data used for a paper titled: Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma using reinforcement learning.</p> <p>Numerous data sets are included, the main being `main.csv` which includes the fixation counts for a number of Moran processes between pairs of players from the Axelrod library.</p> <p>The source code and explanation of the data is here: https://github.com/Axelrod-Python/axelrod-moran. </p> <p> </p>
Replication Package for "An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems"
<p>This is the replication package for the article "An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems" published in the Empirical Software Engineering journal.</p> <p>This package requires Python 3.5 and all the dependencies that are listed in "requirements.txt".<br> The notebooks (in "notebooks" folder) should be opened and executed with Jupyter.</p> <p>The notebooks require the graphs (in "graphs" folder) to be computed first. To do so, execute "helpers.py" with Python.<br> The graphs are built using the data provided by https://libraries.io under CC BY-SA<br> https://creativecommons.org/licenses/by-sa/4.0/<br> Those data can be found in the "data" folder.</p> <p> </p>
Agile Social Software Engineer: An Empirical Approach to Mitigate and Manage Social Debts in Agile Environments
<p>Replication package description:<br> "DataCollection.xlsx": Survey responses from Agile teams in weeks 1, 2, 3, 4 and 5.<br> <br> "DataAnalysis.xlsx": Analysis of the answers from the survey to answer the research questions proposed in this work.<br> Team-working issues that could lead to community smells can be found in TeamworkingIssues Tab.<br> The root causes of community smells in agile teams over weeks can be found in Rootcauses Tab.<br> Requests for Agile social guardian intervention can be found in Requestagileteams Tab.<br> Recurring needs and concerns in agile teams can be found in Needs_Concerns Tab.</p>
Replication data for "Learning When to Quit: An Empirical Model of Experimentation in Standards Development"
<p>This is the replication data for the article "Learning When To Quit: An Empirical Model of Experimentation in Standards Development," published in the <em>American Economic Journal: Microeconomics</em>. </p> <p>A detailed description of the data construction can be found in the Data Appendix (Section E in the Online Appendix published with the article's supplementary material).</p> <p>The file _readme-LWTQ-data.xls contains a description of the variables.</p> <p>If you use the data, cite the paper!</p> <p><strong>Ganglmair, Bernhard, Timothy Simcoe, and Emanuele Taranntino (2024): "Learning When To Quit: An Empirical Model of Experimentation in Standards Development," <em>American Economic Journal: Microeconomics</em>, forthcoming<em>.</em></strong></p>
WP5 Datasets of empirical testing of co-created tools (Task 5.1, Task 5.4, D5.4)
<p>The datasets are part of WP5 and the Report on the evaluation of effectiveness of tools (D5.4). This includes one study on the forum tool and one study on an energy community website on which the handbook tool is based (Task 5.4). Additionally, there is one follow up study on the predictors of interest in such an energy community website and a survey of already available energy community and energy citizenship tools by energy community members (Task 5.1). </p>
Supplementary materials to the article "Practices and attitudes toward replication in empirical translation and interpreting studies"
<p><strong>Abstract of the paper:</strong></p> <p>This article presents the results of three studies on practices in and attitudes toward replication in empirical translation and interpreting studies. The first study reports on a survey in which 52 researchers in translation and interpreting with experience in empirical research answered questions about their practices in and attitudes toward replication. The survey data were complemented by a bibliometric study of publications indexed in the Bibliography of Interpreting and Translation (BITRA) (Franco Aixelá 2001–2019) that explicitly stated in the title or abstract that they were derived from a replication. In a second bibliometric study, a conceptual replication of Yeung’s (2017) study on the acceptance of replications in neuroscience journals was conducted by analyzing 131 translation and interpreting journals. The article aims to provide evidence-based arguments for initiating a debate about the need for replication in empirical translation and interpreting studies and its implications for the development of the discipline.</p> <p><strong>Description of the supplementary materials:</strong></p> <ul> <li>Material 1: questionnaire in English</li> <li>Material 2: questionnaire in Spanish</li> <li>Material 3: results of both questionnaires</li> <li>Material 4: corpus of identified replications in Translation and Interpreting Studies (TIS)</li> <li>Material 5: acceptance of replications in TIS journals</li> </ul>
Artifact to accompany "An Empirical Examination of Fuzzer Mutator Performance" (ISSTA 2024 article)
<p>This artifact contains scripts and data to supplement the ISSTA 2024 article "An Empirical Examination of Fuzzer Mutator Performance":</p> <p> </p> <p>Over the past decade, hundreds of fuzzers have been published in top-tier security and software engineering conferences. Fuzzers are used to automatically test programs, ideally creating high-coverage input corpora and finding bugs. Modern "greybox'' fuzzers evolve a corpus of inputs by applying <em>mutations</em> to inputs and then executing those new inputs while collecting coverage. New inputs that are "interesting'' (e.g. reveal new coverage) are saved to the corpus. Given their non-deterministic nature, the impact of each design decision on the fuzzer's performance can be difficult to predict. Some design decisions (e.g. "should the fuzzer perform deterministic mutations of inputs'') are exposed to end-users as configuration flags, but others (e.g. "what kinds of random mutations to apply to inputs?'') are typically baked-in to the fuzzer code itself. This paper describes our over 12.5-CPU-year evaluation of the set of mutation operators employed by the popular AFL++ fuzzer including the <em>havoc</em> phase, splicing and \redqueen, exploring the impact of adjusting some of those unexposed configurations.</p> <p>In this experience paper, we propose a methodology for determining different fuzzers' behavioral diversity with respect to branch coverage and bug detection using rigorous statistical methods. Our key finding is that, across a range of targets, disabling certain mutation operators (some of which were previously "baked-in'' to the fuzzer) resulted in inputs that cover different lines of code and reveal different bugs. A surprising result is disabling certain mutators leads to more diverse<strong> </strong>coverage and allows the fuzzer to find more bugs faster. We call for researchers to investigate seemingly simple design decisions in fuzzers more thoroughly and encourage fuzzer developers to expose more configuration parameters pertaining to these design decisions to end-users.</p>
Dataset for FEM modelling of emission reference material (ERM) developed in the EMPIR project 20NRM04 MetrIAQ
<p>This dataset contains the numerical data used in the project 20NRM04 MetrIAQ for the FEM model simulations on the emission reference material (ERM) with the software COMSOL 6.2. The data published here are freely accessible with a text editor.<br>The accompanying document '<em>FEM data and file name explanations.pdf'</em> contains additional information for each data file.<br>A detailed description of the development of the FEM model can be found in the '<em>Guideline with recommendations on the customised design and dimensioning of the ERM, including the uncertainty of the numerical model developed for the prediction of the emission rate. The numerical model will (i) support the customised generation of the ERM (ii) simulate the transport processes inside the ERM and the compound release into test chamber air and (iii) enable the prediction of the emissions for each of the selected target VOC</em>' (<a href="https://doi.org/10.5281/zenodo.12749007" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12749007</a>).</p>
Deep learning based Image Compression for Microscopy Images: An Empirical Study
<p>This dataset supports the manuscript titled "Deep Learning Based Image Compression for Microscopy Images: An Empirical Study." The materials provided are essential for replicating the experiments in the study. The dataset is divided into three main parts:</p> <ol> <li>Config: train and test yaml config files for both 2D and 3D mmv_im2im labelfree task</li> <li>Sample_data: sample data to test the compression and the downstream labelfree code, in both 2D and 3D case.</li> <li>Model: fine-tuned 2D/3D labelfree model and 3D compression model. We use pre-trained 2D compression models from CompressAI</li> </ol> <p> </p>
An Empirical Study on Challenges of Event Management in Microservice Architectures
<p>Data set of the submitted manuscript "An Empirical Study on Challenges of Event Management in Microservice Architectures"</p>
Patent Working Requirements and Complex Products: An Empirical Assessment of India's Form 27 Practice and Compliance
<p>Dataset of the first comprehensive and systematic analysis of 4,916 valid Statements of Working (Form 27) corresponding to 3,126 patents pertaining to mobile technology in India. Released under the Creative Commons-Attribution-Share Alike 4.0 (CC-BY-SA 4.0) License as a part of the <strong>Pervasive Technologies</strong> project at the <strong>Centre for Internet and Society, India</strong>.</p> <p>For the detailed methodology used for this study, refer to <a href="http://cis-india.org/a2k/blogs/methodology-statements-of-working-form-27-of-indian-mobile-device-patents">Methodology: Statements of Working (Form 27) of Indian Mobile Device Patents</a>.</p> <p>For information on how to read this dataset, refer to the section "Detailed legend and process of logging the results" in the methodology.</p> <p>A paper titled "<a href="http://jipel.law.nyu.edu/vol-7-no-1-1-contreras/"><strong>Patent Working Requirements and Complex Products</strong></a>" authored by Prof. Jorge Contreras and Rohini Lakshané, first published in July 2017 presents an analysis of the dataset.</p> <p>The patents chosen to be included in this dataset are a subset of the patents found in another study by the same authors, Patents and Mobile Devices in India: An Empirical Survey.</p> <p>Data is provided AS-IS, without warranty as to accuracy or completeness.</p> <p>All uses of the accompanying data and modifications and derivatives thereof must contain the following attribution: "Data provided by Jorge L. Contreras and Rohini Lakshané (2017)"</p>
Dataset of Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software
<p>Dataset of Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software, SCAM'13.</p> <p>https://www.monperrus.net/martin/companion-diversity-api-usages</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.