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1,034 results for “Empirical”
An empirical approach and practical framework for a decentralized Ethereum Ecosystem Index (EEI) datasets and codes
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The Classics Never Go Out of Style: An Empirical Study of Downgrades from the Bazel Build Technology
<p>This package contains the dataset used in our study: "The Classics Never Go Out of Style: An Empirical Study of Downgrades from the Bazel Build Technology."</p><p>For more information on understanding the folder structure, please read the <i>README</i>.md.</p>
FINANCIAL LITERACY AND PATTERN OF SAVINGS, INVESTMENT BEHAVIOR OF WOMENTEACHING FACULTIES IN SAGAR REGION. AN EMPIRICAL ASSESSMENT
<p>The drive of this research paper is to examine the study of women teaching <br>faculties financial literacy along with their knowledge, attitudes, and behaviours, which has <br>considered as the central role permitting people to sort accountable decisions as women’s strive<br>to reach financial happiness. Since, as we stand alone in a developing global financial system <br>in the world financial attentiveness and knowledge is very essential especially in case of <br>teaching women. The involvement of the teaching women in economy will pay to their <br>individual wellbeing as well as nation’s economic growth. Females are accountable for the<br>household budget, outlays, and reserves for the emergency fund and also revenue generation.<br>The empirical study evidenced that it has been exposed that teaching women’s are not much<br>familiar with the credit card ideology and compound interest rate calculations. Besides, it is <br>also discovered that most of the investors handpicked financial institutions for the reason that <br>of familiarity. Several financial misconducts and fraudulent activities are witnessing in the <br>society due to lack of financial literacy and proper financial information among the <br>individuals. Therefore, the drive of this study is to know the level of financial literacy and the <br>pattern savings, investment behaviour among women teaching faculties in sagar District.</p>
Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
<p>Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center.</p> <p>Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. </p> <p><span>The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.</span></p> <p> </p>
Empirical relation among earthquake magnitude (Mw) and deformed area as imaged by InSAR
<p><span>This document presents a dataset of 96 earthquakes with available information on moment magnitude and the dimension of the deformed area, as imaged by InSAR. </span></p>
Dataset (active) - The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution
<p><strong>%%%% 26/09/2025 - NOTE: This entry only includes the active measurements conducted in the following work. </strong></p> <p><strong>The passive measurements can be found in https://doi.org/10.5281/zenodo.17208870</strong></p> <p> </p> <p>The present dataset is open-sourced with the paper "The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution".</p> <p>The paper is authored by Giuseppe Caso, Mohammad Rajiullah, Konstantinos Kousias, Usman Ali, Nadir Bouzar, Luca De Nardis, Anna Brunstrom, Ozgu Alay, Marco Neri, and Maria-Gabriella Di Benedetto</p> <p>If you use the dataset for your own research activities and publications, please consider citing the paper as follows: </p> <p><strong>G. Caso et al., "The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution," IEEE Open Journal of the Communications Society(IEEE OJ-COMS), pp. 1-21, 2024.</strong></p> <p><strong>@article{caso2024chronicles, <br>title={{The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution}}, <br>author={Caso, G. and Rajiullah, M. and Kousias, K. and Ali, U. and Bouzar, N. and De Nardis, L. and Brunstrom, A. and Alay, O. and Neri, M. and Di Benedetto, M.-G.}, <br></strong><strong>journal={IEEE Open Journal of the Communications Society},<br>pages={1--21}, <br></strong><strong>year={2024}, <br>publisher={IEEE}<br>}</strong></p> <p>A detailed description of the dataset is provided in the paper, and the README file provides additional details.</p> <p>In the paper, an in-depth performance analysis of 5G performance is carried out, also exploiting a dataset previously collected and open-sourced at https://zenodo.org/records/8224890, and fully described in the following publication:</p> <p><strong>@article{kousias2023large,<br> title={{A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements}},<br> author={Kousias, K. and Rajiullah, M. and Caso, G. and Ali, U. and Alay, O. and Brunstrom, A. and De Nardis, L. and Neri, M. and Di Benedetto, M.-G.},<br>journal={IEEE Communications Magazine},<br>volume = {62},<br>number = {5},<br>pages={44--49},<br>year={2024},<br>publisher={IEEE}<br>}</strong></p> <p>Contact Giuseppe Caso (giuseppe.caso@kau.se) for more information on the dataset(s) and potential access to additional data. </p>
An Empirical Comparative Study of Convolutional Neural Network and Support Vector Machine in Digital Signature for Digital Document Authentication
<p>Dataset dan figure of the research</p>
CARPE Database of Empirical Research on Assessment for the Leaving Certificate in Ireland (May 2024)
<p>This is the download site for the <strong>CARPE Database of Empirical Research on Leaving Certificate Assessment in Ireland (May 2024). </strong>We hope that this database will act as a guide and resource for any future research regarding the Leaving Certificate and the assessment methods used therein. If you do use this database as a reference or tool, please cite it as:</p> <p>Lehane, P., O'Connor, G. & O'Leary, M. (2024). <em>CARPE Database of Empirical Research on Leaving Certificate Assessment in Ireland </em>[Database]. Dublin City University. </p> <p>We realise that there are current and future studies that should be included in this database now and in the years to come. We intend on updating this database on an annual basis with missing or new studies. If you wish to suggest an empirical study that could be added to this database, please fill in the relevant details on this <a href="https://forms.gle/ggi53t1psYXJXqi77">Google Form</a>. </p>
An Empirical Study of Next-Line Prediction in Build Systems Using CodeGen
<p>An Empirical Study of Next-Line Prediction in BuildSystems Using CodeGen.</p> <p>Build systems play a crucial role in software development and are responsible for compiling source code into executable programs. Despite their importance, build systems often receive limited attention because their impact is not directly visible to end users. This oversight can lead to inadequate maintenance, frequent build failures, and disruptions that require additional resources. Recognising and addressing the maintenance needs of build systems is essential to preventing costly disruptions and ensuring efficient software production.</p> <p>In this paper, we explore whether applying a Large Language Model (LLM) can reduce the burden of maintaining build systems. We aim to determine whether the prior content in build specifications provides sufficient context for an LLM to generate subsequent lines accurately. We conduct an empirical study on CodeGen, a state-of-the-art Large Language Model (LLM), using a dataset of 13,343 Maven build files. The dataset consists of the Expert dataset from the Apache Software Foundation (ASF) for fine-tuning (9,426 build files) and the Generalised dataset from GitHub for testing (3,917 build files). We observe that (i) fine-tuning on a small portion of data (i.e., 11\% of the fine-tuning dataset) provides the largest improvement in performance by 13.93\% (ii) When applied to the Generalised dataset, our fine-tuned model retains 83.86\% of its performance, indicating that it is not overfitted. Upon further investigation, we classify build code content into functional and metadata subgroups based on enclosing tags. Our fine-tuned model performs substantially better in suggesting functional than metadata build code. Our findings highlight the potential of leveraging LLMs like CodeGen to relieve the maintenance challenges associated with build systems, particularly in functional content. The study highlights the limitations of large language models in suggesting the metadata components of build code. Future research should focus on developing approaches to enhance the accuracy and effectiveness of metadata generation.</p> <p> </p> <p>Replication Package Structure:</p> <p>dataset.zip: Contains dataset for Expert and Generalised dataset.</p>
Understanding the Adoption of Modern JavaScript Features: An Empirical Study on Open-Source Systems
<p>This repository contains the data and analysis from an empirical study investigating the adoption trends of modern JavaScript features introduced with ECMAScript 6 (ES6) and beyond. By mining the source code history of 158 open-source JavaScript projects, the study identifies efforts to rejuvenate legacy code by replacing outdated constructs with modern ones. The findings highlight the extensive use of modern features, their widespread adoption within one to two years after ES6's release, and ongoing trends in the rejuvenation of JavaScript codebases.<br><br></p> <ul> <li> <p><strong>scripts.zip</strong>: Contains Python scripts used to analyze data and generate the graphs presented in the study's results.</p> </li> <li><strong>scripts-threats-analysis.zip</strong>: Contains the Python scripts used to analyze the projects without applying the study's filtering criteria and to generate the table presented in the Threats to Validity section.</li> <li> <p><strong>jsminer-tool.zip</strong>: Includes the tool developed to analyze GitHub repository history and collect metrics on the adoption of modern JavaScript features.</p> </li> <li> <p><strong>jsminer_database_backup.zip</strong>: Provides a PostgreSQL database dump containing all code review comments from the repositories analyzed in the study.</p> </li> </ul>
Temporal validity of software datasets for code metrics: an empirical assessment of sampling strategies
<p>This is the repository for the scripts and data of the study "Building and updating software datasets: an empirical assessment".</p> <h2>Data collected</h2> <p>The data generated for the study it can be downloaded as a zip file. Each folder inside the file corresponds to one of the datasets of projects employed in the study (qualitas, currentSample and qualitasUpdated). Every dataset comprised three files "class.csv", "method.csv" and "sample.csv", with class metrics, method metrics and repository metadata of the projects respectively. Here is a description of the datasets:</p> <ul> <li>qualitas: includes code metrics and repository metrics from the projects in the release 20130901r of the Qualitas Corpus.</li> <li>currentSample: includes code metrics and repository metrics from a recent sample collected with our sampling procedure.</li> <li>qualitasUpdated: includes code metrics and repository metrics from an updated version of the Qualitas Corpus applying our maintenance procedure.</li> </ul> <h2>Plot graphics</h2> <p>To plot the results and graphics in the article there is a Jupyter Notebook "Experiment.ipynb". It is initially configured to use the data in "datasets" folder.</p> <h2>Replication Kit</h2> <p>For replication purposes, the datasets containing recent projects from Github can be re-generated. To do so, the virtual environment must have installed the dependencies in "requirements.txt" file, add Github's tokens in "./token" file, re-define or leave as is the paths declared in the constants (variables written in caps) in the main method, and finally run "main.py" script. The portable versions of the source code scanner <a href="https://sourcemeter.com/" target="_blank" rel="noopener">Sourcemeter</a> are located as zip files in "./Sourcemeter/tool" directory. To install Sourcemeter the appropriate zip file must be decompressed excluding the root folder "SourceMeter-10.2.0-x64-<OS>".</p> <p>The script comprise 5 steps:</p> <ol> <li>Project retrieval from Github: at first the sampling frame with projects complying with a specific quality criteria are retrieved from Github's API.</li> <li>Create samples: with the sampling frame retrieved, the current samples are selected (currentSample and qualitasUpdated). In the case of qualitasUpdated, it is important to have first the "sample.csv" file inside the qualitas folder of the dataset originally created for the study. This file contains the metadata of the projects in Qualitas Corpus.</li> <li>Project download and analysis: when all the samples are selected from the sampling frame (currentSample and qualitasUpdated), the repositories are downloaded and scanned with SourceMeter. In the cases in which the analysis is not possible, the projects are replaced with another one with similar size.</li> <li>Outlier detection: once the datasets are collected, it is necessary to manually look for possible outliers in the code metrics under study. In the notebook "Experiment.ipynb" there are specific sections dedicated for it ("Outlier detection (Section 4.2.2)").</li> <li>Outlier replacement: when the outliers are detected, in the same notebook there is also a section for outlier replacement ("Replace Outliers") where the outliers' url have to be listed to find the appropriate replacement.</li> </ol> <ul> <li>If it is required, the metrics from the Qualitas Corpus can also be re-generated. First, it is necessary to download the release 20130901r from its <a href="http://www.qualitascorpus.com/download/" target="_blank" rel="noopener">official webpage</a>. Second, decompress the .tar files downloaded. Third, make sure that the compressed files with source code from the projects (.java files) are placed in the "compressed" folder, in some cases it is necessary to read the "QC_README" file in the project's folder. Finally, run the original main script "Generate metrics for the Qualitas Corpus (QC) dataset" part of the code. </li> </ul>
Empirical Analysis of Security Vulnerabilities in Python Packages
<p>This dataset contains the data files used to analyze our RQs in the manuscript "Empirical Analysis of Security Vulnerabilities in Python Packages".</p> <p>For more information on how to understand the folder structure and dataset, please read the README.md.</p>
Distribution. Llamas are found at 3800-5000 m above sea level in the Central Andes, from C Peru to W Bolivia and N Argentina. Llama distribution reached its apex during the expansion of the Inca Empire (1470-1532 ap), when pack trains were used to carry supplies for the royal armies to S Colombia and C Chile. Although originally indigenous and endemic to South America, Llamas have now been exported to countries around the world as a companion animal, featured in livestock shows, used for trekking and backpacking, cottage industry and home use ofits wool, and in North America increasingly utilized as a guard animal for protecting sheep and goats from canid predators. in Camelidae
Distribution. Llamas are found at 3800-5000 m above sea level in the Central Andes, from C Peru to W Bolivia and N Argentina. Llama distribution reached its apex during the expansion of the Inca Empire (1470-1532 ap), when pack trains were used to carry supplies for the royal armies to S Colombia and C Chile. Although originally indigenous and endemic to South America, Llamas have now been exported to countries around the world as a companion animal, featured in livestock shows, used for trekking and backpacking, cottage industry and home use ofits wool, and in North America increasingly utilized as a guard animal for protecting sheep and goats from canid predators.
Dataset for paper "Do Performance Aspirations Matter for Guiding Software Configuration Tuning? An Empirical Investigation under Dual Performance Objectives"
<p>This contains the dataset matched with the code on the GitHub page: https://github.com/ideas-labo/aspiration-study.</p>
Master Thesis- Modeling of Electric Vehicle Charging Infrastructure and Comparison of Electric Vehicle Load Simulation with Empirical Charging Data
<p>All the data behind relevant plots in the thesis report are stored here</p>
The Dataset of the PhD thesis titled "Operationalizing Values in Mobile Applications: A Mixed-Methods Empirical Study on Agriculture Apps for Bangladeshi Female Farmers"
<p>This package includes the survey (PVQ) questionnaire, demographic questions, focus groups questionnaire, interview questionnaire, and member checking summary used in this thesis.</p>
The dataset of the paper titled "Investigating End-Users' Values for Agriculture Mobile Applications Development: A Mixed-Methods Empirical Study on Bangladeshi Female Farmers"
<p>This package includes a survey questionnaire, demographic questions, focus groups questionnaire, interview questionnaire, and 10 main values with corresponding attributes and the referred names used in this paper submitted to IST.</p>
The dataset of the paper titled "Investigating End-Users' Values in Agriculture Mobile Applications Development: An Empirical Study on Bangladeshi Female Farmers"
<p>This package includes a survey questionnaire, demographic questions, and 10 main values with corresponding attributes and the referred names used in this paper submitted to JSS.</p>
Data from: The prediction of adaptive evolution: empirical application of the secondary theorem of selection and comparison to the breeder's equation
Adaptive evolution occurs when fitness covaries with genetic merit for a trait (or traits). The breeder's equation (BE), in both its univariate and multivariate forms, allows us to predict this process by combining estimates of selection on phenotype with estimates of genetic (co)variation. However, predictions are only valid if all factors causal for trait-fitness covariance are measured. While this requirement will rarely (if ever) be met in practice, it can be avoided by applying Robertson's secondary theorem of selection (STS). The STS predicts evolution by directly estimating the genetic basis of trait-fitness covariation with out any explicit model of selection. Here we apply the BE and STS to four morphological traits measured in Soay sheep (Ovis aries) from St. Kilda. Despite apparently positive selection on heritable size traits, sheep are not getting larger. However, while the BE predicts increasing size the STS does not, a discrepancy that suggests unmeasured factors are upwardly biasing our estimates of selection on phenotype. We suggest this is likely to be a general issue, and that wider application of the STS could offer at least a partial resolution to the common discrepancy between naive expectations and observed trait dynamics in natural populations.
To Seed or Not to Seed? An Empirical Analysis of Usage of Seeds for Testing in Machine Learning Projects
<p>This contains the experimental dataset for our paper: "To Seed or Not to Seed? An Empirical Analysis of Usage of Seeds for Testing<br> in Machine Learning Projects" published at ICST 2022. Go to <a href="https://github.com/uiuc-arc/xseed">Github</a> for more details.</p>
ScienceDex guides
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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.