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677 results for “Replication package”
Replication package for: "P-Hacking, Data Type and Data-Sharing Policy".
<p>See Readme file. Journal article abstract: "This paper examines the relationship between p-hacking, publication bias, and data-sharing policies. We collect 38,876 test statistics from 1,106 articles published in leading economic journals between 2002--2020. We find that while data-sharing policies increase the provision of data, they do not decrease the extent of p-hacking and publication bias. Similarly, articles that use hard-to-access administrative data or third-party surveys, as compared to those that use easier-to-access (e.g., author-collected) data are not different in their p-hacking and publication extent. Voluntary provision of data by authors on their homepages offers no evidence of reduced p-hacking."</p>
Replication package for: "Economic Distress and Children's Mental Health: Evidence from the Brazilian High Risk Cohort Study for Mental Conditions"
<p>This package replicates the results of "Economic distress and children's mental health: evidence from the Brazilian High Risk Cohort Study for Mental Conditions" (and its respective online appendix) using mostly Stata. The package does not include the paper's main dataset, which is confidential.</p>
Replication package of article "Screening Articles for Systematic Reviews with ChatGPT"
<ul><li>/experiments - Full experimental data</li><li>/prompts - Full prompts of the six experiments</li><li>/results - Results: data and plots<ul><li>rq1.xlsx - Data related to RQ1</li><li>rq2.xlsx - Data related to RQ2</li><li>significance.xlsx - Results of Spearman's rank test</li></ul></li><li>/topics-and-exclusion-criteria - Topics and exclusion criteria of the five datasets</li></ul>
Replication Package for 'Lessons Learned from Mining the Hugging Face Repository'
<p>Replication Package attached to the 'Lessons Learned from Mining the Hugging Face Repository' article. Within the README and accompanying scripts, you will find detailed instructions to guide you through the analysis conducted in the article. Please, remember to cite the original paper if the replication package is used.</p>
VulSATDˆ{DS}: a dataset for vulnerabilities and self-admitted technical debt and its replication package
<p>VulSATDˆ{DS} is a dataset of functions in C/C++ extracted from 12 open-source projects and labelled for the existence of self-admitted technical debt (SATD) and vulnerability. It was presented in the paper "VulSATDˆ{DS}: a dataset for vulnerabilities and self-admitted technical debt." This upload contains both the dataset (file: VulSATDDS.zip) and the replication package that allows the recreation of the dataset from its sources (replication.zip).</p>
Replication package for "Solar Eclipses and the Origins of Critical Thinking and Complexity".
<p>This replication package accompanies Litina and Roca-Fernández. “Solar Eclipses and the Origins of Critical Thinking and Complexity”. The Economic Journal. (forthcoming)</p>
Replication Package for ML-EUP Conversational Agent Study
<p>This is the replication package of the paper <a href="https://conf.researchr.org/details/icse-2024/icse-2024-research-track/5/How-to-Support-ML-End-User-Programmers-through-a-Conversational-Agent">How to Support ML End-User Programmers through a Conversational Agent</a>, published at ICSE 2024.</p> <p><strong>Replication Package Files</strong></p> <ul> <li><strong>Readme.pdf:</strong> document that describes the replication package and indicates how to use it. </li> <li> <p><strong>1. Forms.zip: </strong>contains the forms used to collect data for the experiment.</p> </li> <li> <p><strong>2. Experiments.zip: </strong>contains the participants’ and sandboxers’ experimental task workflow with Newton.</p> </li> <li> <p><strong>3. Responses.zip: </strong>contains the responses collected from participants during the experiments.</p> </li> <li> <p><strong>4. Analysis.zip:</strong> contains the data analysis scripts and results of the experiments.</p> </li> <li> <p><strong>5. newton.zip: </strong>contains the tool we used for the WoZ experiment.</p> </li> <li> <p><strong>Interactions.pdf: </strong>explains Figure 4 of the paper in detail by depicting the interactions of P4.</p> </li> <li> <p><strong>TutorialStudy.pdf:</strong> script used in the experiment with and without Newton to be consistent with all participants.</p> </li> <li> <p><strong>Woz_Script.pdf:</strong> script wizard used to maintain consistent Newton responses among the participants.</p> </li> <li><strong>Dockerfile</strong>: docker definition of newton-docker.tar.gz.</li> <li> <p><strong>newton-docker.tar.gz:</strong> docker image that contains both the tool and the analysis files.</p> </li> <li><strong>LICENSE: </strong>license file describing the license of data and code files.</li> </ul> <p> </p> <p><strong>1. Forms.zip</strong></p> <p>The forms zip contains the following files:</p> <ul> <li> <p><strong>Demographics.pdf: </strong>a PDF form used to collect demographic information from participants before the experiments</p> </li> <li> <p><strong>Post-Task Control (without the tool).pdf:</strong> a PDF form used to collect data from participants about challenges and interactions when performing the task without Newton </p> </li> <li> <p><strong>Post-Task Newton (with the tool).pdf:</strong> a PDF form used to collect data from participants after the task with Newton.</p> </li> <li> <p><strong>Post-Study Questionnaire.pdf:</strong> a PDF form used to collect data from the participant after the experiment.</p> </li> </ul> <p> </p> <p><strong>2. Experiments.zip</strong></p> <p>The experiments zip contains two types of folders:</p> <ul> <li> <p><strong>exp[participant’s number]-c[number of dataset used for control task]e[number of dataset used for experimental task]</strong>. Example: exp1-c2e1 (experiment participant 1 - control used dataset 2, experimental used dataset 1)</p> </li> <li> <p><strong>sandboxing[sandboxer’s number].</strong> Example: sandboxing1 (experiment with sandboxer 1)</p> </li> </ul> <p> </p> <p>Every experiment subfolder contains:</p> <ul> <li> <p><strong>warmup.json: </strong>a JSON file with the results of Newton-Participant interactions in the chat for the warmup task.</p> </li> <li> <p><strong>warmup.ipynb: </strong>a Jupyter notebook file with the participant’s results from the code provided by Newton in the warmup task.</p> </li> <li> <p><strong>sample1.csv: </strong>Death Event dataset.</p> </li> <li> <p><strong>sample2.csv: </strong>Heart Disease dataset.</p> </li> <li> <p><strong>tool.ipynb: </strong>a Jupyter notebook file with the participant’s results from the code provided by Newton in the experimental task.</p> </li> <li> <p><strong>python.ipynb: </strong>a Jupyter notebook file with the participant’s results from the code they tried during the control task.</p> </li> <li> <p><strong>results.json:</strong> a JSON file with the results of Newton-Participant interactions in the chat for the task with Newton.</p> </li> </ul> <p> </p> <p>To load an experiment chat log into Newton, add the following code to the notebook:</p> <pre><code>import anachat import json with open("result.json", "r") as f: anachat.comm.COMM.history = json.load(f) </code></pre> <p>Then, click on the notebook name inside Newton chat</p> <p>Note 1: the subfolder for P6 is exp6-e2c1-serverdied because the experiment server died before we were able to save the logs. We reconstructed them using the notebook newton_remake.ipynb based on the video recording.</p> <p>Note 2: The sandboxing occurred during the development of Newton. We did not collect all the files, and the format of JSON files is different than the one supported by the attached version of Newton.</p> <p> </p> <p><strong>3. Responses.zip</strong></p> <p>The responses zip contains the following files:</p> <ul> <li> <p><strong>demographics.csv: </strong>a CSV file containing the responses collected from participants using the demographics form</p> </li> <li> <p><strong>task_newton.csv: </strong>a CSV file containing the responses collected from participants using the post-task newton form.</p> </li> <li> <p><strong>task_control.csv:</strong> a CSV file containing the responses collected from participants using the post-task control form.</p> </li> <li> <p><strong>post_study.csv:</strong> a CSV file containing the responses collected from participants using the post-study control form.</p> </li> </ul> <p> </p> <p><strong>4. Analysis.zip</strong></p> <p>The analysis zip contains the following files:</p> <ul> <li> <p><strong>1.Challenge.ipynb:</strong> a Jupyter notebook file that performs the statistical tests and creates the perceptions of challenges figure.</p> </li> <li> <p><strong>2.Interactions.py: </strong>a Python file that creates the participants’ JSON files.</p> </li> <li> <p><strong>3.Interactions.Graph.ipynb: </strong>a Jupyter notebook file that creates the participant’s interaction figure.</p> </li> <li> <p><strong>4.Interactions.Count.ipynb:</strong> a Jupyter notebook file that counts participants’ interaction with each figure.</p> </li> <li> <p><strong>config_interactions.py:</strong> this file contains the definitions of interaction colors and grouping</p> </li> <li> <p><strong>interactions.json: </strong>a JSON file with the interactions during the Newton task of each participant based on the categorization.</p> </li> <li> <p><strong>requirements.txt: </strong>dependencies required to run the code to generate the graphs and json analysis.</p> </li> </ul> <p> </p> <p>To run the analyses, please follow the steps:</p> <p>1- Extract Analysis.zip and cd into the directory</p> <p>2- Install Python 3.10, and then the analysis dependencies with the following command:</p> <pre><code>pip install -r requirements.txt</code></pre> <p>3- Run Jupyter Notebook/Lab and execute all cells of <strong>1.Challenge.ipynb</strong>. It will generate the challenges figure.</p> <p>4- Run <strong>2.Interactions.py</strong> using the following command:</p> <pre><code>python 2.Interactions.py</code></pre> <p>This file was created manually by individually categorizing each interaction of the participants. The execution will generate the file interactions.json with the graph definitions of the interactions.</p> <p>5- Run Jupyter Notebook/Lab and execute all cells of <strong>3.Interactions.Graph.ipynb</strong>. It will create the interactions graph visualization.</p> <p>6- Run Jupyter Notebook/Lab and execute all cells of <strong>4.Interactions.Count.ipynb</strong>. It will create the interactions table.</p> <p> </p> <p><strong>5. newton.zip</strong></p> <p>The newton zip contains the <strong>source code of the Jupyter Lab extension</strong> we used in the experiments. Read the <strong>README.md</strong> file inside it for instructions on how to install and run it.</p> <p> </p> <p><strong>6. newton-docker.tar.gz</strong></p> <p>This file contains the Docker image with the replication package in a configured environment for both the tool and the dat analyses.</p> <p>To import the image, run:</p> <pre><code><span>docker load <</span> <span>newton-docker.tar.gz</span></code></pre> <p>Then, start the container by running:</p> <pre><code><span>docker run -p 8888:8888 -it newton</span></code></pre> <p>Finally, start Jupyter Lab:</p> <pre><code><span>jupyter lab --collaborative --ip="*" --port=8888 --allow-root</span></code></pre> <p> </p>
Replication package for Fine-grained, accurate and scalable source differencing
<p>Replication package for the article <em><strong>Fine-grained, accurate and scalable source differencing</strong></em> accepted at the 46th International Conference on Software Engineering (ICSE 2024).</p> <p>List of files:</p> <ul> <li><strong>dataset</strong>: the folder containing the four datasets used in the paper, including the file-pairs and the script to regenerate them.</li> <li><strong>qualitative_experiment_diffs</strong>: the folder containing the GUI used by the participants of the qualitative experiment on the 100 cases.</li> <li><strong>gumtree</strong>: the folder containing the code of GumTree used to run the experiments.</li> <li><strong>analysis</strong>: the folder containing the CSV files and notebooks for the analysis of the results.</li> </ul>
Replication Package of the Paper: "Using Large Language Models for Commit Message Generation: A Preliminary Study"
<p>This replication package contains the evaluation data and script files used in the paper "Using Large Language Models for Commit Message Generation: A Preliminary Study". We provide below a brief description of each folder:</p><ul><li><strong>experiment_data/human_evaluation</strong>: Human evaluation results from two participants.</li><li><strong>experiment_data/msg</strong>: Generated commit messages of each method (baselines & LLMs) and human-written commit messages.</li><li><strong>script</strong>: Evaluation metric script, parallel inference script (for using OpenAI API).</li></ul>
Replication package for: Labor Market Screening and the Design of Social Insurance: An Equilibrium Analysis of the Labor Market for the Disabled
<p>This is a replication package for "Labor Market Screening and the Design of Social Insurance: An Equilibrium Analysis of the Labor Market for the Disabled." It contains all the necessary codes and data to replicate empirical and quantitative findings in the paper.</p>
Replication Package "Evaluating Search-Based Software Microbenchmark Prioritization"
<p>Replication package for the paper "Evaluating Search-Based Software Microbenchmark Prioritization".</p>
Replication Package for "Data-Driven Evidence-Based Syntactic Sugar Design"
<p>Replication Package supplementing the submission of "Data-Driven Evidence-Based Syntactic Sugar Design"</p>
Replication Package for 'Analyzing the Evolution and Maintenance of ML Models on Hugging Face'
<p>Replication Package attached to the 'Analyzing the Evolution and Maintenance of ML Models on Hugging Face' article. Within the README and accompanying scripts, you will find detailed instructions to guide you through the analysis conducted in the article.</p>
Detecting Usage of Deprecated Web APIs via Tracing: Replication Package
<p>Replication package for "Detecting Usage of Deprecated Web APIs via Tracing", published at 21st IEEE International Conference on Software Architecture (ICSA 2024)</p> <p> </p> <p>Changes from v2 to v3:</p> <ul> <li>readme.md: The requirements are now stated and explanations are given in more detail. Some typos in commands are fixed.</li> </ul> <p> </p> <p>Changes from v1 to v2:</p> <ul> <li>DATA: In addition to the intermedia data (output.json), the overall result files (results.json) are now provided as well for all example projects.</li> <li>IMPLEMENTATION: An incomplete version of the DeprecationDetector was present in v1. The new version includes extended matching functionality for specifications and improved export formatting.</li> <li>PROJECT: The server.jar in the client-server example project has been improved.</li> </ul>
Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements (Replication Package)
<p>Replication package for Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements</p>
Replication package for `How Do Voters Respond to Welfare vis-a-vis Public Good Programs? Theory and Evidence of Political Clientelism'
<p>Contents of Replication Package:</p> <p>Readme file, Data Dictionary, Household Codes (do, csv and dta files), VillageYear Codes (do, csv and data files), folders containing maps, survey details and replication output</p>
Replication package for: Market Structure and Extortion: Evidence from 50,000 Extortion Payments
<p>The package contains all the code necessary to reproduce the figures and tables in "Market Structure and Extortion: Evidence from 50,000 Extortion Payments."</p>
Replication Package for Data Quality Assessment in the Wild: Findings from GitHub
<p>This contains the data and code used in the EASE 2014 paper "Data Quality Assessment in the Wild: Findings from GitHub."</p>
Replication package for "U.S. Market Concentration and Import Competition"
<p>Amiti, Mary, and Sebastian Heise, "U.S. Market Concentration and Import Competition," Review of Economic Studies.</p>
Replication Package for Political Language In Economics
<p>Replication Data for "Political Language in Economics" by Zubin Jelveh, Bruce Kogut, and Suresh Naidu</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.