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260 results for “Empirical Study”
Appendix of Empirical Study of Co-Renamed Identifiers
<p>This data contains four CSVs: <strong>Rename.csv, Rename_Ignore_Inflection.csv, Relation.csv, Relation_Ignore_Inflection.csv.</strong></p> <p> </p> <p><strong>Rename.csv</strong> and <strong>Rename_Ignore_Inflection.csv</strong> contain renamings used in our study.</p> <p>The difference between these CSVs is whether we ignored inflections. </p> <p>Each row represents a renaming.</p> <p>Columns descriptions are below.</p> <ul> <li><strong>repository</strong>: repository name</li> <li><strong>commit</strong>: commit hash</li> <li><strong>filepath</strong>: location of the renamed identifier</li> <li><strong>oldname</strong>: identifier name before the renaming</li> <li><strong>newname</strong>: identifier name after the renaming</li> <li><strong>type</strong>: identifier type (Class, Method, Attribute, Parameter, Variable)</li> <li><strong>operational_chunk</strong>: operational chunks of the renaming</li> <li><strong>meaningful_rename_set_id</strong>: meaningful rename set IDs (independent for each repository)</li> </ul> <p> </p> <p><strong>Relation.csv</strong> and <strong>Relation_Ignore_Inflection.csv</strong> contain relationships detected in our study.</p> <p>The difference between these CSVs is whether we ignored inflections. </p> <p>Each row represents a relationship between renamed identifier 1 and renamed identifier 2.</p> <p>Columns descriptions are below.</p> <ul> <li><strong>repository</strong>: repository name</li> <li><strong>meaningful_rename_set_id</strong>: meaningful rename set IDs (independent for each repository)</li> <li><strong>commit</strong>: commit hash</li> <li><strong>relation:</strong> type of relationship</li> <li><strong>file{1,2}</strong>: location of the identifier</li> <li><strong>oldname{1,2}</strong>: identifier name before the renaming</li> <li><strong>newname{1,2}</strong>: identifier name after the renaming</li> <li><strong>type{1,2}</strong>: identifier type (Class, Method, Attribute, Parameter, Variable)</li> </ul>
Voting Behaviour - Knowledge from empirical studies
<p>This file contains primary data for our study that compares voting behaviour under first-past-the-post, evaluative voting, approval voting and D21 – Janeček method on the primary dataset consisting of 31 elections in the Czech Republic and secondary dataset of 11 elections from various countries. Our analysis aims to explore how voters use multiple positive and negative votes and what effects these voting rules have on different types of candidates. We also focus on strategic voting and on the factors influencing the number of votes cast by a voter. Our data show significant disparities in use of negative votes under voting methods offering more resources than FPTP but not in the positive votes. Furthermore, there are more misaligned voters under those methods than under plurality voting, however the ratio of strategic votes is comparable. Last but not least, we find that these methods favour medium and unknown candidates instead of polarising and unpopular candidates like plurality voting.</p>
Replication package for the Helm charts empirical study
<p><strong>Helm Charts for Kubernetes Applications: Evolution, Outdatedness and Security Risks</strong></p> <p>This repository represents a replication package for our MSR study on Helm charts.</p> <p>This replication package requires Python 3.5+ to be installed, and all the dependencies listed in ``requirements.txt``.</p> <p>They can be automatically installed using ``pip install -r requirements.txt``. <br> These experiments were executed on a Linux Ubuntu OS.</p> <p>This replication package contains three folders:<br> - notebooks: contains notebooks where we analyze data. <br> - figures: contains figures saved from the notebooks<br> - datasets: contains all datasets required</p> <p>To obtain the analysis used in the paper, one should execute ``jupyter notebook`` at the root of this replication package, and open the notebook contained in ``notebooks``.</p> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license. The source code is under the GNU General Public License.</p>
Replication package for "Method Chaining Redux: An Empirical Study of Method Chaining in Java, Kotlin, and Python"
<p>This dataset includes scripts and data files used to generate all analysis and results from the paper. A <strong>README.md</strong> file is included for details on using the scripts.</p> <p>The dataset is quite large. It is broken down into three archives. All scripts are in <strong>replication-pkg.zip</strong> and the other 2 files only contain data. So if you want to just inspect the analysis, you only need that single zip.</p> <p>If you grab the <strong>data-cached.zip</strong> file and extract it, it will need around 3GB of space. This is the processed dataset stored in Parquet files. Use this if you want to just recreate the tables/figures from the paper.</p> <p>If you want to make changes to the analyses, you will need the raw data in <strong>data-raw.zip</strong>. This will need around 29GB of space once extracted. If you then generate the CSV files from those TXT files (which you will need to do for any custom analysis), you will need an additional 22GB of space.</p>
DATASET - On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code
<p>Study package containing raw and analyzed data from the work entitled "On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code".</p> <p>The package comprises: 1) a summary of the information extracted from all papers mentioned in the Background; 2) the source code snippets used in the three studies; 3) the consent and characterization forms distributed to the participants; 4) the raw data, the aggregated data and other material generated with the collected data.</p>
What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study
<p><strong>What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study </strong></p> <p>This repository contains data and code for the paper <a href="https://arxiv.org/abs/2110.04845">What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study</a>.</p> <p>We hope that this work will spur further research on understanding sentence--sentence relatedness, methods of sentence representation, measures of semantic relatedness, and their applications.</p> <p><strong>Citing our work</strong><br> Please use the following BibTex entry to cite us if you use our dataset or any of the associated analyses:</p> <blockquote>@inproceedings{abdalla2023makes,<br> title={What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study},<br> author={Abdalla, Mohamed and Vishnubhotla, Krishnapriya and Mohammad, Saif M.},<br> year={2023},<br> address = {Dubrovnik, Croatia},<br> publisher = "Association for Computational Linguistics",<br> booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume"<br> }</blockquote> <p><strong>Dataset Description</strong></p> <p>The dataset consists of 5500 English sentence pairs that are scored and ranked on a relatedness scale ranging from 0 (least related) to 1 (most related).</p> <p><strong>Why Semantic Relatedness?</strong><br> Closeness of meaning can be of two kinds: semantic relatedness and semantic similarity. Two sentences are considered semantically similar when they have a paraphrasal or entailment relation, whereas relatedness accounts for all of the commonalities that can exist between two sentences. Semantic relatedness is central to textual coherence and narrative structure. Automatically determining semantic relatedness has many applications such as question answering, plagiarism detection, text generation (say in personal assistants and chat bots), and summarization.</p> <p>Prior NLP work has focused on semantic similarity (a small subset of semantic relatedness), largely because of a dearth of datasets. In this paper, we present the first manually annotated dataset of sentence--sentence semantic relatedness. It includes fine-grained scores of relatedness from 0 (least related) to 1 (most related) for 5,500 English sentence pairs. The sentences are taken from diverse sources and thus also have diverse sentence structures, varying amounts of lexical overlap, and varying formality.</p> <p><strong>Comparative Annotations and Best-Worst Scaling</strong><br> Most existing sentence-sentence similarity datasets were annotated, one item at a time, using coarse rating labels such as integer values between 1 and 5 @ representing coarse degrees of closeness. It is well documented that such approaches suffer from inter- and intra-annotator inconsistency, scale region bias, and issues arising due to the fixed granularity.</p> <p>The relatedness scores for our dataset were, instead, obtained using a <em>comparative</em> annotation schema. In comparative annotations, two (or more) items are presented together and the annotator has to determine which is greater with respect to the metric of interest.</p> <p>Specifically, we use Best-Worst Scaling, a comparative annotation method}, which has been shown to produce reliable scores with fewer annotations in other NLP tasks. We use scripts from https://saifmohammad.com/WebPages/BestWorst.html to obtain relatedness scores from our annotations.</p> <p><br> <strong>Loading the Dataset</strong><br> - The sentence pairs, and associated scores, are in the file sem_text_rel_ranked.csv in the root directory. The CSV file can be read using:</p> <pre><code class="language-python">python import pandas as pd str = pd.read_csv('sem_text_rel_ranked.csv') row = str.loc[0] sent1, sent2 = row['Text'].split("\n") score = row['Score']</code></pre> <p>- Relevant columns:</p> <p> - Text: Sentence pair, separated by the newline character.<br> - Score: The semantic relatedness score between 0 and 1.</p> <p>- Additionally:<br> - the SourceID column indicates the source dataset from which the sentence pair was drawn (see Table 2 of our paper)<br> - The SubsetID column indicates the sampling strategy used for the source dataset<br> - and the PairID is a unique identifier for each pair that also indicates its Source and Subset.</p> <p><br> <strong>Raw Annotations from Amazon Mechanical Turk</strong></p> <p>- The `mturk_data/` subdirectory provides the raw MTurk annotations obtained with our comparative annotation setup.<br> - Each row of `mturk_data/bws_annotations.csv` consists of four sentence pairs along with human annotations for the most related (column `BestItem`) and the least related (column `WorstItem`) pair.<br> - File `mturk_data/id2sents.csv` pairs each sentence pair with the corresponding SourceID, SubsetID, and PairID that indicates the source dataset (see Table 2 of our paper).<br> - See file `mturk_data/task_intructions.txt` for the instructions provided to annotators for our task.</p> <p><br> <strong>Datasheet for STR-2022</strong><br> The datasheet for our dataset is in the document `STR2022-datastatement.pdf` in the root folder of this repository.</p> <p><strong>Ethics Statement</strong><br> Any dataset of semantic relatedness entails several ethical considerations. We talk about this in Section 8 of our paper.</p> <p><strong>Creators</strong><br> - <a href="https://www.cs.toronto.edu/~msa/index.html">Mohamed Abdalla</a> (University of Toronto)<br> - <a href="https://priya22.github.io/">Krishnapriya Vishnubhotla</a> (University of Toronto)<br> - <a href="http://saifmohammad.com/">Saif M. Mohammad</a> (National Research Council Canada)</p> <p><strong>Contact:</strong> msa@cs.toronto.edu, vkpriya@cs.toronto.edu, saif.mohammad@nrc-cnrc.gc.ca</p> <p> </p>
An Empirical Study to Evaluate the Impact of Mindfulness at a Software Company (Laboratory Package)
<p>For the empirical study carried out in an Andalusian software company to evaluate the effects of the practise of mindfulness, this laboratory package contains the experimental material used during its execution, the data sets (both "csv" and "sav" files) derived from the experimental tasks, and the results of the statistical analysis (both scripts and plots).</p>
An Empirical Study of Vulnerabilities in Edge Frameworks to Support Security Testing Improvement
<p>This package contains the material collected to perform the empirical study described in our paper entitled ‘An Empirical Study of Vulnerabilities in Edge Frameworks to Support Security Testing Improvement’ authored by Jahanzaib MALIK (<a href="mailto:jahanzaib.malik@uni.lu">jahanzaib.malik@uni.lu</a>) and Fabrizio PASTORE (fabrizio.pastore@uni.lu).</p> <p> </p>
Dataset for the paper "The Relationship Between Different Python Argument-Passing Mechanisms and Fixes: An Empirical Study"
<p>This is the replication package of the article</p> <p>The Relationship Between Different Python Argument-Passing Mechanisms and Fixes: An Empirical Study</p> <p> </p> <p>Abstract:</p> <p>Modern programming languages (such as Python, but not only) have introduced a variety of constructs and syntactical elements to make software development more efficient and concise. Examples include lambda functions, comprehension collections, of mechanisms to facilitate the passing of arguments to a function. While many of such constructs may, in principle, be beneficial for developers, recent studies have shown that certain programming constructs introduce atoms of confusion, and may therefore affect program understanding. Moreover, recent work has shown that changes to some constructs, namely functional constructs of Python, induce more fixes than other changes. This paper studies the effect of different Python argument-passing mechanisms to investigate their relationship with code proneness to be fixed. Specifically, we study the fix-proneness for what concerns the function definition, as well as its invocation. This is done by analyzing the evolutionary history of 200 Python projects, for a total of about 3M functions and 12M call sites. While there are varying effects for what concerns parameter declaration mechanisms, we found evidence that keyword-based argument passing is less defect prone than positional argument passing, and this is not affected by size-related confounding factors.</p>
Data from: Behavioral thermoregulation of caterpillars is altered by temperature, but not parasitism: an empirical field study
<p>Laboratory assays show that parasites often have lower heat tolerance than their hosts. But how physiological tolerances and behavioral responses of hosts and parasites combine to affect their ecological interactions in heterogeneous field environments is largely unknown. We addressed this challenge using the model insect system of the braconid wasp parasitoid, <em>Cotesia congregata</em>, and its caterpillar host, <em>Manduca sexta</em>. We used experimental manipulations of microclimate in the field to determine how elevated daytime temperatures altered the behavior, performance, and survival of the host and parasite. Our experimental manipulation increased daily maximum temperatures on host plants, but had negligible effects on overall mean temperature. These increased maximum temperatures resulted in subtle, biologically relevant, changes in physiology and behavior of the host and parasitoid. We found that parasitism by the wasp did not significantly alter caterpillar thermoregulatory behavior, while experimentally increased daily maximum temperatures resulted in both parasitized and unparasitized caterpillars being found more frequently in cooler microhabitats. Overall, we did not observe the complete parasitoid mortality seen at extreme temperatures in laboratory studies, but gained insight into sublethal effects of increased daily maximum temperatures on host and parasitoid behavior and physiology. Climate change will alter both the biotic and abiotic environments that organisms face, and we show here that empirical experiments in the field are important for understanding organismal response to these new environments. </p>
Dataset: What can be concluded from User Feedback? - An Empirical Study
<p>In 2022, we conducted a study on the awareness of users concerning existing software in order to compare users' description and their feedback.</p> <p>This data set contain 100 participants' description and feedback about the Komoot hiking app. The aspects were manually coded in the data set.</p>
Data for study "Digital tools for innovative higher education teaching - a Scoping Review of Empirical Studies".
<p>The goal of this research is to point out, by a scoping review of the papers published between 2020 and 2023, the digital tools used in the teaching process at the higher education level. The dataset was collected using Web of Science and Scopus databases.</p> <p>The dataset contains the following elements:</p> <ol> <li>paper details (Authors, Publication Year, Article Title, DOI),</li> <li>OECD classification,</li> <li>teaching tool assigned to paper.</li> </ol>
Reproduction package for "Revisiting the reproducibility of empirical software engineering studies based on data retrieved from development repositories"
<p>Reproduction package for "Revisiting the reproducibility of empirical software engineering studies based on data retrieved from development repositories", published in Information and Software Technology, Volume 164, December 2023. DOI: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.infsof.2023.107318" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.infsof.2023.107318</span></span></a></p>
Evaluating the Impact of Third-Party Library Reuse in Java Projects: An Empirical Study
<p>Update, we have added the following:<br> - `dataset.ipynb`: Computes all the dataset used for this study. The user needs to use a call graph generator like OPAL/WALA.</p> <p>- `loc.ipynb`: Computes a dataset for LOC information given a call graph dataset</p> <p>- `called-reuse.ipynb`: Computing a dataset for all called functions and LOC in third-party libraries</p> <p>- `megamorphic.ipynb`: Empirically decide the megamorphic call sites. <br> <br> This part of the submission related to the paper "Evaluating the Impact of Third-Party Library Reuse in Java Projects: An Empirical Study". Here is a table of contents:</p> <ul> <li>`census.csv`: extracted data from the Census-II</li> <li>`releases_all.csv`: all releases available from Maven Coordinates in Census-II</li> <li>`releases_jar.csv`: dataset of all releases with a POM and JAR file</li> <li>`releases_jar_6_months.csv`: releases selected when studiying the 6-month interval</li> <li>`releases_jar_6_months.csv`: releases selected when comparing consecutive versions per artefactid and groupid</li> <li>`loc_composition.csv`: data on the ratio of insourced and outsourced code in project releases</li> <li>`loc_desc_without_lambdas.csv`: mined data on LOC information and class information.</li> <li>`loc_reuse_lower_bound.csv`: data on the ratio of reused LOC and functions of outsourced code, lower-bound</li> <li>`loc_reuse_upper_bound.csv`: data on the ratio of reused LOC and functions of outsourced code, upper-bound</li> </ul>
Empirical primary studies where gamification is used to study energy behavior
<p>The data set is used to conduct a literature review on primary studies where gamification is used to study energy behavior; it contains original search results and after-filter results. The data set also includes a Bib file for all reviewed studies.</p>
The Moderating Effect of Employee Agility on the Link between Employee Vitality, Digital Literacy and Transformational Leadership with Job Performance: An Empirical Study of HR Practitioners in the Manufacturing Sector of Northern Malaysia.
<p>This is a dataset for a study that examines the effects of employee vitality, digital literacy, and transformational leadership on job performance. Additionally, it investigates the moderating role of employee agility in these relationships. Data were collected from HR practitioners in manufacturing companies in the northern region of Malaysia for analysis. The results indicate that the job performance of HR practitioners is positively influenced by employee vitality, digital literacy and transformational leadership. </p>
Rapid Empiric Treatment With Oseltamivir Study (RETOS)
ClinicalTrials.gov study NCT01248715. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Data from: implications of methodologies for integrating empirical kinships into ex situ population management using PMx: a case study of Baer’s Pochard (Aythya baeri) in North America
Open the record for dataset details and reuse information.
Genotype-environment interaction and the maintenance of genetic variation: an empirical study of Lobelia inflata (Campanulaceae)
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Protein quantification in ecological studies: a literature review and empirical comparisons of standard methodologies
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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.