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677 results for “Replication package”
Replication package for: "The Smoot-Hawley Trade War"
<p>Replication package for: Mitchener, K.J., O'Rourke, K.H., and Wandschneider, K. (2022), "The Smoot-Hawley Trade War".</p>
Replication package for: The Value of Political Connections: Evidence from China's Anti-Corruption Campaign
<p>The replication materials contain this README file, two do-files <br> ("ChinaConnections_Alonso_etal_dofilePREP.do" and "ChinaConnections_Alonso_etal_dofileRESULTS.do"),<br> two excel datasets ("CPI" and "DataPrep_POLITICIANS"), and the Appendix to the paper. <br> In order to run the codes, the user will need additional proprietary data from CSMAR that we cannot share. </p> <p>Alonso, M., Palma, N. and Simon-Yarza, B. (2022). The Value of Political Connections: Evidence from China's Anti-Corruption Campaign. Journal of Institutional Economics, forthcoming</p>
Replication Package for: Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching
<p>This ZIP archive is the replication package of the manuscript `Yogurts chooses consumers ? Estimation of Random-Utility Models via Two-Sided Matching`, by Odran Bonnet, Alfred Galichon, Yu-Wei Hsieh, Keith O'Hara, Matt Shum.</p> <p>It contains data, analysis code and outputs corresponding to the 7 tables in the manuscript and appendix.</p>
Dataset for AutoPruner's Replication Package
<p>Dataset for AutoPruner's Replication Package</p>
Replication Package for "Improving and Evaluating Code Recommender Systems"
<p>Replication Package for "Improving and Evaluating Code Recommender Systems"</p>
Replication package for: We are all behavioral, more or less: A taxonomy of consumer decision making
<p>Code and data for reproducing tables in the paper.</p>
Replication Package of the paper "Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software"
<p># Replication Package of the paper "Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software"<br> ## SDC-Scissor (Self-Driving Car coSt-effeCtIve teSt SelectOR)</p> <p><br> ### Structure<br> - datasets: the datasets we used in our study for training and evaluating ML Models<br> - RQs: the extended results and analysis for each of the RQs mentioned in the paper </p> <p>More information on each topic can be found in the README file in the subfolders.<br> </p>
Replication package for: "Finance and Green Growth"
<p>De Haas, R. and A. Popov (2022), <em>Finance and Green Growth</em>, The Economic Journal, forthcoming.</p>
Replication package for "Testing Models of Strategic Uncertainty: Equilibrium Selection in Repeated Games"
<p>This package contains the data, analysis code, and experimental software to replicate manuscript "<em>Testing Models of Strategic Uncertainty: Equilibrium Selection in Repeated Games</em>" by the Boczon, Vespa, Weidman and Wilson forthcoming at JEEA.</p>
Replication Package for "The Rise of Pass Throughs: An Empirical Investigation"
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Computer Science Students Perceptions Regarding AI Open-Source Repositories Replication Package
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Replication Package: Unboxing Default Argument Breaking Changes in 1 + 2 Data Science Libraries in Python
<p><strong>Replication Package</strong></p> <p>This repository contains data and source files needed to replicate our work described in the paper "Unboxing Default Argument Breaking Changes in Scikit Learn".</p> <p><strong>Requirements</strong></p> <p>We recommend the following requirements to replicate our study:</p> <ol> <li>Internet access</li> <li>At least 100GB of space</li> <li>Docker installed</li> <li>Git installed</li> </ol> <p><strong>Package Structure</strong></p> <p>We relied on Docker containers to provide a working environment that is easier to replicate. Specifically, we configure the following containers:</p> <ul> <li><code>data-analysis</code>, an R-based Container we used to run our data analysis.</li> <li><code>data-collection</code>, a Python Container we used to collect Scikit's default arguments and detect them in client applications.</li> <li><code>database</code>, a Postgres Container we used to store clients' data, obtainer from Grotov et al.</li> <li><code>storage</code>, a directory used to store the data processed in <code>data-analysis</code> and <code>data-collection</code>. This directory is shared in both containers.</li> <li><code>docker-compose.yml</code>, the Docker file that configures all containers used in the package.</li> </ul> <p>In the remainder of this document, we describe how to set up each container properly.</p> <p><strong>Using VSCode to Setup the Package</strong></p> <p>We selected VSCode as the IDE of choice because its extensions allow us to implement our scripts directly inside the containers. In this package, we provide configuration parameters for both <code>data-analysis</code> and <code>data-collection</code> containers. This way you can directly access and run each container inside it without any specific configuration.</p> <p>You first need to set up the containers</p> <pre><code>$ cd /replication/package/folder $ docker-compose build $ docker-compose up # Wait docker creating and running all containers </code></pre> <p>Then, you can open them in Visual Studio Code:</p> <ol> <li>Open VSCode in project root folder</li> <li>Access the command palette and select "Dev Container: Reopen in Container" <ol> <li>Select either <em>Data Collection</em> or <em>Data Analysis</em>.</li> </ol> </li> <li>Start working</li> </ol> <p>If you want/need a more customized organization, the remainder of this file describes it in detail.</p> <p><strong>Longest Road: Manual Package Setup</strong></p> <p><strong>Database Setup</strong></p> <p>The database container will automatically restore the dump in <code>dump_matroskin.tar</code> in its first launch. To set up and run the container, you should:</p> <p>Build an image:</p> <pre><code>$ cd ./database $ docker build --tag 'dabc-database' . $ docker image ls REPOSITORY TAG IMAGE ID CREATED SIZE dabc-database latest b6f8af99c90d 50 minutes ago 18.5GB </code></pre> <p>Create and enter inside the container:</p> <pre><code>$ docker run -it --name dabc-database-1 dabc-database $ docker exec -it dabc-database-1 /bin/bash root# psql -U postgres -h localhost -d jupyter-notebooks jupyter-notebooks=# \dt List of relations Schema | Name | Type | Owner --------+-------------------+-------+------- public | Cell | table | root public | Code_cell | table | root public | Md_cell | table | root public | Notebook | table | root public | Notebook_features | table | root public | Notebook_metadata | table | root public | repository | table | root </code></pre> <p>If you got the tables list as above, your database is properly setup.</p> <p>It is important to mention that this database is extended from the one provided by <a href="https://markdowntohtml.com/">Grotov et al.</a>. Basically, we added three columns in the table <code>Notebook_features</code> (<code>API_functions_calls</code>, <code>defined_functions_calls</code>, and<code>other_functions_calls</code>) containing the function calls performed by each client in the database.</p> <p><strong>Data Collection Setup</strong></p> <p>This container is responsible for collecting the data to answer our research questions. It has the following structure:</p> <ul> <li><code>dabcs.py</code>, extract DABCs from Scikit Learn source code, and export them to a CSV file.</li> <li><code>dabcs-clients.py</code>, extract function calls from clients and export them to a CSV file. We rely on a modified version of <a href="https://markdowntohtml.com/">Matroskin</a> to leverage the function calls. You can find the tool's source code in the `matroskin`` directory.</li> <li><code>Makefile</code>, commands to set up and run both <code>dabcs.py</code> and <code>dabcs-clients.py</code></li> <li><code>matroskin</code>, the directory containing the modified version of matroskin tool. We extended the library to collect the function calls performed on the client notebooks of Grotov's dataset.</li> <li><code>storage</code>, a docker volume where the data-collection should save the exported data. This data will be used later in <a href="https://markdowntohtml.com/#data-analysis-setup">Data Analysis</a>.</li> <li><code>requirements.txt</code>, Python dependencies adopted in this module.</li> </ul> <p>Note that the container will automatically configure this module for you, e.g., install dependencies, configure matroskin, download scikit learn source code, etc. For this, you must run the following commands:</p> <pre><code>$ cd ./data-collection $ docker build --tag "data-collection" . $ docker run -it -d --name data-collection-1 -v $(pwd)/:/data-collection -v $(pwd)/../storage/:/data-collection/storage/ data-collection $ docker exec -it data-collection-1 /bin/bash $ ls Dockerfile Makefile config.yml dabcs-clients.py dabcs.py matroskin storage requirements.txt utils.py </code></pre> <p>If you see project files, it means the container is configured accordingly.</p> <p><strong>Data Analysis Setup</strong></p> <p>We use this container to conduct the analysis over the data produced by the <a href="https://markdowntohtml.com/#data-collection-setup">Data Collection</a> container. It has the following structure:</p> <ul> <li><code>dependencies.R</code>, an R script containing the dependencies used in our data analysis.</li> <li><code>data-analysis.Rmd</code>, the R notebook we used to perform our data analysis</li> <li><code>datasets</code>, a docker volume pointing to the <code>storage</code> directory.</li> </ul> <p>Execute the following commands to run this container:</p> <pre><code>$ cd ./data-analysis $ docker build --tag "data-analysis" . $ docker run -it -d --name data-analysis-1 -v $(pwd)/:/data-analysis -v $(pwd)/../storage/:/data-collection/datasets/ data-analysis $ docker exec -it data-analysis-1 /bin/bash $ ls data-analysis.Rmd datasets dependencies.R Dockerfile figures Makefile </code></pre> <p>If you see project files, it means the container is configured accordingly.</p> <p>A note on <code>storage</code> shared folder</p> <p>As mentioned, the <code>storage</code> folder is mounted as a volume and shared between <code>data-collection</code> and <code>data-analysis</code> containers. We compressed the content of this folder due to space constraints. Therefore, before starting working on <a href="https://markdowntohtml.com/#data-collection-setup">Data Collection</a> or <a href="https://markdowntohtml.com/#data-analysis-setup">Data Analysis</a>, make sure you extracted the compressed files. You can do this by running the <code>Makefile</code> inside <code>storage</code> folder.</p> <pre><code>$ make unzip # extract files $ ls clients-dabcs.csv clients-validation.csv dabcs.csv Makefile scikit-learn-versions.csv versions.csv $ make zip # compress files $ ls csv-files.tar.gz Makefile</code></pre>
Replication package for: "Relative Income and Mental Health in Couples"
<p>Getik, Demid. 2024. "Relative Income and Mental Health in Couples". <em>The </em><em>Economic Journal.</em></p>
Replication Package: 3D Shear‐wave Velocity and Density Modelling of the Northern Cascadia Subduction Zone
<p>This work is done as part of the <a href="https://metrovanmicromap.ca/" target="_blank" rel="noopener noreferrer">Metro Vancouver Seismic Microzonation Project</a> which aims to improve our understanding of seismic hazards in the Georgia Basin and explore opportunities to reduce their impact through mitigation.<br><br>Accurate shear wave velocity images of the subsurface is required to achieve this, hence we developed a new 3D shear-wave velocity (Vs) model for the region from ambient seismic noise and earthquake data recorded by temporary and permanent seismic stations in the last two decades as well as a new 3D density model from a publicly available gravity dataset.<br><br>Here, we share a replication package that includes datasets (seismic and gravity), computer codes and scripts adopted and developed for this research work. The package also includes intermediate result files and final 3D models and GMT script used to plot each figure in the associated manuscript (Submitted to JGR Solid Earth). Our goal is to support research transparency and reproducibility of our work.</p>
Replication Package for Terminal Lucidity
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Replication Package for "Life out of the Shadows: The Impacts of Regularization Program on the Lives of Forced Migrants"
<p>This package contains the data, programs and instructions to replicate manuscript "Life out of the Shadows: The Impacts of Regularization Program on the Lives of Forced Migrants" by Ana María Ibáñez, Andrés Moya, María Adelaida Ortega, Sandra V. Rozo, and María José Urbina forthcoming at JEEA.</p>
Replication package with data used in the study: The effect of code smells and design patterns on two change-related metrics: An exploratory study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells and design patterns on two change-related metrics: An exploratory study"</p> <p>This dataset contains the following folders:</p> <ul> <li>Aggregated Results Per System <ul> <li> For each subject system (AOI, Jedit, JHotDraw), we identify the following datasets: DP, nDP, S, nS ,SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</li> <li> Those csv files include raw data about every class in every release, the csv files also include columns which represent: <ul> <li>- CHURN (CLPLPR(C)*100): defined as the sum of added and deleted lines in a class in a release, adjusted to the size of the class and to the number of revisions in the release;</li> <li>- and FREQ (MTPR(C)*100): defined as the average number of changes made to a class in a release, adjusted to the number of revisions in the release</li> </ul> </li> </ul> </li> <li>Detailed Results Per Smell Or Pattern <ul> <li> For each specific code smell (S) in each public release (Rel) of all subject systems, we identify SDP and SnDP datasets. Each dataset is in a separate .csv file</li> <li> For each specific design pattern (DP) in each public release (Rel) of all subject systems, we identify SDP and nSDP </li> </ul> </li> <li>Plots<br> We also include QQ plots for CHURN, FREQ values for every dataset in every system, that could serve as a supplementary data for the paper.</li> </ul>
Replication package for "A Model of Demonstrated Inexpertise of Software Developers"
<p>Replication package for "A Model of Demonstrated Inexpertise of Software Developers"</p>
Replication Package for: Scalable and Reliable Multi-Dimensional Aggregation of Sensor Data Streams
<p>This repository contains a replication package and experimental results for our study on <em>Scalable and Reliable Multi-Dimensional Aggregation of Sensor Data Streams</em>.</p> <p>It features the presented implementation with Kafka Streams, tools for load generation and data collection, scripts for executing the presented evaluations as well as our raw results and script for analysis. A detailed description is given in the top-level README.md file.</p>
Replication Package for "Catching Smells in the Act: A GitHub Actions Workflow Investigation" - Thesis
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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.