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184 results for “Replication Study”
Multi Interventional Study Exploring HIV-1 Residual Replication: a Step Towards HIV-1 Eradication and Sterilizing Cure
ClinicalTrials.gov study NCT02961829. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Equivalence Among Antiepileptic Drug Generic and Brand Products in People With Epilepsy: Single-Dose 6-Period Replicate Design (EQUIGEN Single-Dose Study)
ClinicalTrials.gov study NCT01733394. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of a Retroviral Replicating Vector Given Intravenously to Patients Undergoing Surgery for Recurrent Brain Tumor
ClinicalTrials.gov study NCT01985256. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Residual Replication of HIV-1 in the Gut Associated Lymphoid Tissue (GALT) of Patients on Highly Active Antiretroviral Therapy (HAART): the ANRS EP 44 Study
ClinicalTrials.gov study NCT01038401. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study the Relationship Between Obesity and Hepatitis C Replication
ClinicalTrials.gov study NCT01157975. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Study of a Retroviral Replicating Vector Combined With a Prodrug to Treat Patients Undergoing Surgery for a Recurrent Malignant Brain Tumor
ClinicalTrials.gov study NCT01470794. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Use of Labeled Glucose to Study Lymphocyte Replication and Survival in HIV-Infected Patients
ClinicalTrials.gov study NCT00001651. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: A morphometric study of species boundaries of the wild potato Solanum series Conicibaccata: a replicated field trial in Andean Peru
Open the record for dataset details and reuse information.
Data from: Quantifying the importance of geographic replication and representativeness when estimating demographic rates, using a coastal species as a case study
Open the record for dataset details and reuse information.
Restoration and replication: a case study on the value of computational reproducibility assessment
Open the record for dataset details and reuse information.
Replication Package for the paper: "Behind the Intents: An In-depth Empirical Study on Software Refactoring in Modern Code Review"
<p>This is the replication package for the paper: "Behind the Intents: An In-depth Empirical Study on Software Refactoring in Modern Code Review", published at the 17th International Conference on Mining Software Repositories (MSR ’20). </p> <p> </p> <p>It contains all the preliminary and final results of our empirical methodology. We highlight the manual classification of developers' intents behind code changes with refactoring operations. This might be used for further studies on developers' motivations when performing refactoring. </p> <p> </p> <p>Feel free to use any part of this replication package in your study, please cite as:</p> <p>Matheus Paixão, Anderson Uchôa, Ana Carla Bibiano, Daniel Oliveira, Alessandro Garcia, Jens Krinke, and Emilio Arvonio. 2020. Behind the In-tents: An In-depth Empirical Study on Software Refactoring in Modern Code Review. In 17th International Conference on Mining Software Repositories (MSR ’20), October 5–6, 2020, Seoul, Republic of Korea. ACM, New York, NY,USA, 11 pages.</p>
Reproducible Validation and Replication Studies in Nanoscale Physics (problem datasets for validation and replications from Ellis et al., 2016)
<p>Problem folders including all the input files necessary to reproduce the computations of the results related to Validation and replication of Ellis et al. 2016, on the paper: Reproducible Validation and Replication Studies in Nanoscale Physics</p>
Reproducible Validation and Replication Studies in Nanoscale Physics (repro results plots - Rockstuhl et al., 2005)
<p>This archive contains the Jupyter notebooks needed to reproduce the figures of the paper that are related to the replication of Rockstuhl et al. 2005. For further information direct to the README.md file. </p>
Replication Package for the paper: "How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study"
<p>This is the replication package for the paper: "How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study", published at the 36th International Conference on Software Maintenance and Evolution (ICSME' 20).</p> <p> </p> <p>It contains all the preliminary and final results of our empirical methodology. We highlight the manual classification of design-related and design-unrelated reviews, according to the developers’ intent of improving the structural design of the system. This might be used for further studies on the impact of design discussions on the structural quality of design.</p> <p> </p> <p>Feel free to use any part of this replication package in your study, please cite as:</p> <p>Anderson Uchôa, Caio Barbosa, Willian Oizumi, Publio Blenílio, Rafael Lima, Alessandro Garcia, and Carla Bezerra. How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study. Proceedings of the 36th International Conference on Software Maintenance and Evolution (ICSME), Adelaide, Australia, September 2020.</p>
Replication package with data used in the study: "The effect of code smells on the relationship between design patterns and defects. An empirical study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells on the relationship between design patterns and defects. An empirical study"</p> <p>This dataset contains the following folders:</p> <ul> <li> <p>"Analyzed systems" folder:</p> <ul> <li> <p>For each subject system (Ant-1.7, JEdit-4.2, Lucene-2.4, Camel-1.6, Log4j-1.2, Xalan-2.7, Poi-3.0, Ivy-2.0, Xerces-2.0, Velocity-1.6), we identify the following datasets: SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</p> </li> <li> <p>Those csv files include raw data about every class in every release. Each file includes columns which represent:</p> <ul> <li> <p>System: The analyzed system</p> </li> <li> <p>className: A fully qualified class name</p> </li> <li> <p>Pattern: if the class is part of any pattern the cell contains the name of the pattern, and “null” otherwise</p> </li> <li> <p>Smell: if the class is part of affected by any smell the cell contains the name of the smell, and “null” otherwise</p> </li> <li> <p>Bugs: Number of defects reported inside the class (extracted from the PROMISE dataset)</p> </li> </ul> </li> </ul> </li> <li> <p>A "detailed analysis" folder:</p> <ul> <li> <p>For each pattern, we report all the classes which participate in it in all the analyzed systems. The csv files inside this folder follow the same structure as the other csv files reported above</p> </li> </ul> </li> </ul> <p><br> </p>
It Takes a Village to Build a Robot: An Empirical Study of The ROS Ecosystem - Replication Package
<p>Over the past eleven years, the Robot Operating System (ROS), has grown from a small research project into the most popular framework for robotics development. Composed of packages released on the Rosdistro package manager, ROS aims to simplify development by providing reusable libraries, tools and conventions for building a robot. Still, developing a complete robot is a difficult task that involves bridging many technical disciplines. Experts who create computer vision packages, for instance, may need to rely on software designed by mechanical engineers to implement motor control. As building a robot requires domain expertise in software, mechanical, and electrical engineering, as well as artificial intelligence and robotics, ROS faces knowledge based barriers to collaboration.</p> <p>In this paper, we examine how the necessity of domain specific knowledge impacts the open source collaboration model. We create a comprehensive corpus of package metadata and dependencies over three years in the ROS ecosystem, analyze how collaboration is structured, and study the dependency network evolution. We find that the most widely used ROS packages belong to a small cluster of foundational working groups (FWGs), each organized around a different domain in robotics. We show that the FWGs are growing at a slower rate than the rest of the ecosystem, in terms of their membership and number of packages, yet the number of dependencies on FWGs is increasing at a faster rate. In addition, we mined all ROS packages on GitHub, and showed that 82% rely exclusively on functionality provided by FWGs. Finally, we investigate these highly influential groups and describe the unique model of collaboration they support in ROS.</p>
Replication Package for the Paper: "A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions: A Study of the Hibernate Developer Mailing List"
<p>This is the replication package for the paper: "A Machine Learning Based Ensemble Method for Automatic Classification of Decisions: A Study of the Hibernate Developer Mailing List". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. code folder</strong></p> <ul> <li><em>experiment.py </em>contains the source code for our experiment, which is conducted on Windows 10 and Python 3.7.0. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>decisions.xlsx </em>contains 844 labelled sentence-level decisions from the Hibernate developer mailing list.</li> </ul>
Replication Package for the Paper: "Code Smells Detection via Code Review: An Empirical Study"
<p>This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020.</p> <p> </p> <p><strong>1. data folder</strong></p> <p>The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2. scripts floder</strong></p> <p>The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>smell-term/general_smell_terms.txt</em> contains general code smell terms, such as "code smell".</li> <li><em>smell-term/specific_smell_terms.txt</em> contains specific code smell terms, such as "dead code".</li> <li><em>smell-term/misspelling_terms_of_smell.txt</em> contains the misspelling terms of 'smell', such as "ssell".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>smell_search.py</em> is used for searching review comments that contain code smell terms.</li> </ul> <p> </p> <p><strong>3. project folder</strong></p> <p>The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <ul> <li><em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool.</li> <li><em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.</li> </ul>
On the Importance and Shortcomings of Code Readability Metrics: A Case Study on Reactive Programming - replication package
<p>This is the replication package for the conference paper submission "On the Importance and Shortcomings of Code Readability Metrics: A Case Study on Reactive Programming"</p> <p><strong>Contents:</strong></p> <ul> <li>measurements.zip <ul> <li>DATASET_ORIGINAL.csv</li> <li>DATASET_REACTIVE.csv</li> </ul> </li> <li>source_code.zip <ul> <li> source_code_orig <ul> <li>Client.java</li> <li>Connection.java</li> <li>Server.java</li> <li>TcpConnection.java</li> <li>UdpConnection.java</li> </ul> </li> <li> source_code_rx <ul> <li>Client.java</li> <li>Connection.java</li> <li>Server.java</li> <li>TcpConnection.java</li> <li>UdpConnection.java</li> </ul> </li> </ul> </li> </ul> <p> </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>
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.