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3,363 results for “Replication”
Reproducible Validation and Replication Studies in Nanoscale Physics (problem datasets for Rockstuhl et al. 2005 replication)
<p>Problem folders including all the input files necessary to reproduce the computations of the results related to Rockstuhl et al. 2005 on the paper: Reproducible Validation and Replication Studies in Nanoscale Physics</p>
Replication Package for the paper: Evaluating the Agreement among Technical Debt Measurement Tools: Building an Empirical Benchmark of Technical Debt Liabilities
<p>This is the replication package for the Archetypal Analysis conducted in the paper "Evaluating the Agreement among Technical Debt Measurement Tools: Building an Empirical Benchmark of Technical Debt Liabilities" accepted at Springer's EMSE Journal.</p> <p>It contains:</p> <ul> <li>The dataset with TD measurements from three TD tools for 25 Java projects</li> <li>The dataset with TD measurements from three TD tools for 25 JS projects</li> <li>The script to run the Archetypal Analysis on the two datasets</li> </ul>
On the Relationship between Refactoring Actions and Bugs: A Differentiated Replication -- Replication Package
<p>This dataset contains working data sets and analysis scripts for the paper "On the Relationship between Refactorings and Bugs: A Differentiated Replication" by Massimiliano Di Penta, Gabriele Bavota, and Fiorella Zampetti, published at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020).</p>
Replication code and data for: Recalculating ... How Uncertainty in Local Labor Market Definitions Affects Empirical Findings
<p>This repository contains the code and data to replicate all the analyses in our paper "Recalculating ... : How Uncertainty in Local Labor Market Definitions Affects Empirical Findings." Some of the data can also be used in other researchers' analyses to investigate the robustness of their results when they use commuting zones to aggregate or collect data.</p>
Code-Level Model Checking in the Software Development Workflow -- Replication Package
<p>This experience report describes a style of applying symbolic model checking developed over the course of four years at Amazon Web Services (AWS). Lessons learned are drawn from proving properties of numerous C-based systems, e.g., custom hypervisors, encryption code, boot loaders, and an IoT operating system. Using our methodology, we find that we can prove the correctness of industrial low-level C-based systems with reasonable effort and predictability. Furthermore, AWS developers are increasingly writing their own formal specifications. All proofs discussed in this paper are publicly available on GitHub. All proofs and specifications described in the paper are available, under the Apache 2.0 license, on the GitHub repository located at <a href="https://github.com/awslabs/aws-c-common/">https://github.com/awslabs/aws-c-common/</a> This is the master repository for AWS C Common library, and is in active use by the AWS C Common development team. The description of the contents of this repository are based off commit <code>b0ea9f35df8934f9e03fc3bab3919d55efd69b88</code>, although they are not expected to change significantly in the future.</p>
Replication data for: Consumer preference testing of boiled sweetpotato (Ipomoea batatas (L.) Lam.) using crowdsourced citizen science in Ghana and Uganda
<p>Crowdsourced citizen science is an emerging approach in plant sciences. The triadic comparison of technologies (tricot) approach has been successfully utilised by demand-led breeding programmes to identify varieties for dissemination suited to specific geographic and climatic regions. An important feature of this approach is the independent way in which farmers individually evaluate the varieties on their own farms as ‘citizen scientists’. In this study, we adapted this approach to evaluate consumer preferences to boiled sweetpotato (<em>Ipomoea batatas</em> (L.) Lam) roots of 21 advanced breeding materials and varieties in Ghana and 6 released varieties in Uganda. We were specifically interested in evaluating if a more independent style of evaluation (home tasting) would produce results comparable to an approach that involves control over preparation (centralised tasting). We compiled data from 1,433 participants who individually contributed to a home tasting (de-centralised) and a centralised tasting trial in Ghana and Uganda, evaluating overall acceptability, and indicating the reasons for their preferences. Geographic factors showed important contribution to define consumers’ preference to boiled sweetpotato genotypes. Home and centralised tasting approaches gave similar rankings for overall acceptability, which was strongly correlated to taste. In both Ghana and Uganda, it was possible to robustly identify superior sweetpotato genotypes from consumers’ perspectives. Our results indicate that the tricot approach can be successfully applied to consumer preference studies.</p>
Replication Package for the Paper: "An Empirical Analysis of the Manual Detection of Code Smells via Code Review"
<p>This repository contains the data and results from the paper "An Empirical Analysis of the Manual Detection of Code Smells via Code Review" submitted to SANER 2021.</p> <p> </p> <p><strong>1. "data.zip" file contains the following three folders: </strong></p> <p> </p> <p><strong>1). data folder</strong></p> <p>The data folder contains the retrieved 1,174 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2). scripts folder</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>keywords.txt</em> contains the keywords keywords asociated with code smells, such as "smell, duplication, and dead".</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>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</li> <li>keywords_improve.py is used for improving the keyword-based mining approach.</li> <li>tools.py is used for supporting the process of keywords improving.</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> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords asociated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
Replication File for Paper "The Role of Time, Weather and Google Trends in Understanding and Predicting Web Survey Response"
<p>This updated zip file contains the necessary codes and data files to reproduce all the findings presented in the accepted manuscript "The Role of Time, Weather and Google Trends in Understanding and Predicting Web Survey Response" by Qixiang Fang, Joep Burger, Ralph Meijers and Kees van Berkel. </p> <p>Compared with the last one, this new version provides more instructions in the README.txt files and indicates the exact versions of R, R studio and the R packages used.</p>
Replication as a Way of Knowing in the Digital Humanities
<p>2<sup>nd</sup> Lecture</p>
Migratory divides coincide with reproductive barriers across replicated avian hybrid zones above the Tibetan Plateau
<p>Migratory divides are proposed to be catalysts for speciation across a diversity of taxa. However, it is difficult to test the relative contributions of migratory behavior vs. other divergent traits to reproductive isolation. Comparing hybrid zones with and without migratory divides offers a rare opportunity to directly examine the contribution of divergent migratory behavior to reproductive barriers. We show that across replicate sampling transects of two pairs of barn swallow (Hirundo rustica) subspecies, strong reproductive isolation coincided with a migratory divide spanning 20 degrees of latitude. A third subspecies pair exhibited no evidence for a migratory divide and hybridized extensively. Within migratory divides, overwintering habitats were associated with assortative mating, implicating a central contribution of divergent migratory behavior to reproductive barriers. The remarkable geographic coincidence between migratory divides and genetic breaks supports a longstanding hypothesis that the Tibetan Plateau is a substantial barrier contributing to the diversity of Siberian avifauna.</p>
Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"
<p>This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". 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. main_code folder</strong></p> <ul> <li><em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <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>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020)</li> <li><em>EASE2020 - 650 non-decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020)</li> <li><em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work.</li> <li><em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019)</li> </ul> <p><strong>3. RQ1 folder</strong></p> <ul> <li><em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>4. RQ2 folder</strong></p> <ul> <li><em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>5. RQ3 folder</strong></p> <ul> <li><em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul>
Replication package for: Improving Management with Individual and Group-Based Consulting: Results from a Randomized Experiment in Colombia
<p>This package contains the data and code needed to reproduce tables and figures in Iacovone, Maloney and McKenzie (forthcoming) "</p> <p><strong>Improving Management with Individual and Group-Based Consulting: Results from a Randomized Experiment in Colombia"., <em>Review of Economic Studies</em></strong>. </p> <p>Anonymized firm data are provided for analyzing impacts on management and employment. Authorized log files and output files are provided for analysis that uses the confidential EAM data, along with instructions for applying to access these data if required.</p>
Replication package: Does the duration of rapid release cycles affect the bug handling activity?
<p>Replication package for the paper: "Does the duration of rapid release cycles affect the bug handling activity?"</p>
Replication Package for the Paper: "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community"
<p>This repository contains the data and results from the paper "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community" submitted to ICPC 2021.</p> <p> </p> <p><strong>1. "data.zip" contains the following three folders:</strong></p> <p> </p> <p><strong>1) data folder</strong></p> <p>The data folder contains the retrieved 1,190 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2) scripts folder</strong></p> <p>The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms.</p> <ul> <li> <p><em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead".</p> </li> <li> <p><em>get_changes.py</em> is used for getting code changes from OpenStack.</p> </li> <li> <p><em>get_comments.py</em> is used for getting review comments for each code change.</p> </li> <li> <p><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</p> </li> <li> <p><em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword.</p> </li> <li> <p><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</p> </li> <li> <p><em>tools.py</em> is used for supporting the process of keywords improving.</p> </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 <a href="https://www.maxqda.com/">https://www.maxqda.com/</a> for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at <a href="https://www.maxqda.com/trial">https://www.maxqda.com/trial</a> for download.</p> <ul> <li> <p><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.</p> </li> <li> <p><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.</p> </li> </ul> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords associated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
Replication package for: Credit Shocks and Equilibrium Dynamics in Consumer Durable Goods Markets
<p>Alessandro Gavazza and Andrea Lanteri, Credit Shocks and Equilibrium Dynamics in Consumer Durable Goods Markets, Review of Economic Studies</p> <p>The package includes four folders: data, stata_code, matlab_code, and figures. Please see the readme.pdf file for details.</p>
Replication Package for: Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures
<p>This repository contains a replication package and experimental results for our study <em>Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures</em>.</p> <p>The following description can also be found in the README.md file.</p> <p><strong>Repeating Benchmark Execution</strong></p> <p><em><strong>The following introduction describes how to repeat our scalability experiments. If you plan to conduct your own studies, we suggest to use the latest version of <a href="https://github.com/cau-se/theodolite">Theodolite</a> with significantly enhanced usability.</strong></em></p> <p>The Apache Kafka Streams scalability experiments of our study were executed with <a href="https://github.com/cau-se/theodolite/tree/v0.1.2">Theodolite v0.1.2</a>. To repeat our Kafka Streams experiments:</p> <ol> <li>Clone and install <a href="https://github.com/cau-se/theodolite/tree/v0.1.2">Theodolite v0.1.2</a> according to the official documentation located in <code>execution</code>.</li> <li>Copy the file <code>repeat-kstream.sh</code> into Theodolite's <code>execution</code> directory.</li> <li>Run the repetition file with <code>./repeat-kstream.sh</code> from within the <code>execution</code> directory.</li> </ol> <p>Our Apache Flink benchmark implementations are currently migrated to the latest version of Theodolite. <a href="https://github.com/cau-se/theodolite/tree/apache-flink">Theodolite's <code>apache-flink</code> Branch</a> provides the basis for our Flink scalability experiments. To repeat them:</p> <ol> <li>Clone <a href="https://github.com/cau-se/theodolite/tree/apache-flink">Theodolite's <code>apache-flink</code> Branch</a> and install Theodolite according to the official documentation located in <code>execution</code> (should be identical to the installation for Kafka Streams (see above)).</li> <li>Copy the files <code>repeat-flink-without-checkpointing.sh</code> and <code>repeat-flink-with-checkpointing.sh</code> into Theodolite's <code>execution</code> directory.</li> <li>Switch to the <code>execution</code> directory.</li> <li>Run the first repetition file with <code>./repeat-flink-with-checkpointing.sh</code>.</li> <li>Disable checkpointing by reconfiguring the Kubernetes resources <code>jobmanager-job.yaml</code> and <code>taskmanager-job-deployment.yaml</code> for each benchmark (<code>uc{1,2,3,4}-application</code>) by setting the environment variable <code>CHECKPOINTING</code> to <code>"false"</code>.</li> <li>Run the second repetition file with <code>./repeat-flink-without-checkpointing.sh</code>.</li> </ol> <p><em>Please note that the naming of our benchmarks recently changed. While our publication already uses the new naming, the corresponding Theodolite versions are is still using the old one. Specifically, this means that UC1 in the publication is UC1 in Theodolite, UC2 in the publication is UC3 in Theodolite, UC3 in the publication is UC4 in Theodolite, and UC4 in the publication is UC2 in Theodolite.</em></p> <p><strong>Raw Measurements</strong></p> <p>The results of above benchmark execution can be found in the <code>measurements</code> directory. These are CSV files, containing the measured lag trend over time for a certain subexperiment. Theodolite creates a bunch of additional files, which serve for debugging and preliminary interpretation. As these files are not required for replication, we do not included them in this package.</p> <p>The CSV files are named according to the schema <code>exp{id}_{uc}_{load}_{inst}_totallag.csv</code>, where <code>{id}</code> represents the experiment ID, assigned by Theodolite, <code>{uc}</code> the benchmark name, <code>{load}</code> the generated load, and <code>{inst}</code> the number of evaluated instances.</p> <p>The CSV table <code>experiments.csv</code> provides an overview about the configurations used in each experiment.</p> <p><strong>Reproducing Scalability Analysis</strong></p> <p><em><strong>The following introduction describes how to repeat our scalability analysis, either with our measurements or with your own. If you plan to conduct your own studies, we suggest to use the latest version of <a href="https://github.com/cau-se/theodolite">Theodolite</a> with significantly enhanced usability.</strong></em></p> <p>Analyzing the Theodolite's measurements is done using two Jupyter notebooks. In general, these notebooks should be runnable by any Jupyter server. Python 3.7 or 3.8 is required (e.g., in a virtual environment) as well as some Python libraries, which can be installed via: <code>pip install -r requirements.txt</code>. See the <a href="https://github.com/cau-se/theodolite/tree/master/analysis">Theodolite documentation</a> for additional installation guidance.</p> <p><strong>Obtaining a Scalability Graph as a CSV File</strong></p> <p>The <code>scalability-graph.ipynb</code> notebook combines the measurements (i.e., the <code>totallag.csv</code> files) of one experiment. It produces a CSV file, which provides a mapping of load intensities to minimum required resources for that load (i.e., the scalability graph). The CSV files are named according to the schema <code>exp{id}_min-suitable-instances.csv</code>, where <code>{id}</code> represents the experiment ID. Additional guidance is provided in the notebook.</p> <p><strong>Resulting Scalability Graph CSV Files</strong></p> <p>The <code>results</code> directory provides the scalability graphs for all our executed experiments.</p> <p><strong>Visualization of the Scalability Graph</strong></p> <p>The <code>scalability-graph-plotter.ipynb</code> notebook creates PDF plots of a scalability graph and allows to combine multiple scalability graphs in one plot. It can be adjusted to match the desired visualization.</p> <p><strong>Acknowledgments</strong></p> <p>This research is funded by the German Federal Ministry of Education and Research (BMBF) under grant no. 01IS17084 and is part of the <a href="https://www.industrial-devops.org">Titan project</a>.</p>
Replication Package for the Paper: Understanding Architecture Erosion: The Practitioners' Perceptive
<p>This is the replication package for the paper: "Understanding Architecture Erosion: The Practitioners' Perceptive". It contains the source code for collecting data from Stack Overflow and our dataset of this study. In the meanwhile, we provide a brief description of the files.</p> <p><strong>1. Count Results & Search Terms.xlsx</strong><br> includes the search results, count results of Table 1, and 28 search terms.</p> <p><strong>2. Dataset from online developer communities.docx</strong><br> comprises of 80 posts and blogs that discuss architecture erosion (178 pages in MS Word organized by their sources), and their URL links that were collected from several popular online developer communities.</p> <p><strong>3. Questionnaire of and dataset from surveys.docx</strong><br> includes an example of the customized emails and 10 formal surveys from the participants.</p> <p><strong>4. Dataset from Interviews.docx</strong><br> comprises of 4 interviewees from the participants.</p> <p><strong>5. Data Labelling & Encoding.mx20</strong><br> is the results of data labelling and encoding that were analyzed by the MAXQDA tool. We extracted data from the posts, labelled them according to RQs, and encoded the extracted data using Constant Comparison. The file can be opened by MAXQDA 20 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 2020, which is available at https://www.maxqda.com/trial for download.</p> <p><strong>6. SO_Spider.zip</strong><br> includes the code for data collection and filtering from Stack Overflow.</p>
Data from: Predictable adaptive trajectories of sexual coloration in the wild: evidence from replicate experimental guppy populations
The question of whether populations evolve predictably and consistently under similar selective regimes is fundamental to understanding how adaptation proceeds in the wild. We address this question with a replicated evolution experiment focused upon male sexual coloration in guppies (Poecilia reticulata). Fish were transplanted from a single high predation population in the Guanapo River to four replicate, guppy‐free low predation headwater streams. Two streams had their canopies thinned to adjust the setting under which male coloration is displayed and perceived. We assessed evolutionary divergence using second‐generation lab‐bred offspring of fish sampled four to six years following translocation. A prior experiment of the same design, performed in an adjacent drainage, resulted in the evolution of more extensive orange, black and iridescent markings. We however found evidence for expansion only in structural coloration (iridescent blue/green), no change in orange, and a reduction in black. This response amplifies earlier findings for Guanapo fish, revealing that trajectories of color elaboration differ among drainages. We also found that color phenotypes evolved more greatly at the thinned‐canopy sites. Our findings support the predictability of sexual trait evolution in the wild, and underscore the importance of signaling conditions and ornamental starting points in shaping adaptive trajectories.
Data from: Correlated divergence of female and male genitalia in replicated lineages with ongoing ecological speciation
Divergence of genital traits among lineages has the potential to serve as a reproductive isolating barrier when copulation, insemination, or fertilization are inhibited by incompatibilities between female and male genitalia. Despite widespread evidence for genital trait diversity among closely related lineages and coevolution of female and male genitalia within lineages, few studies have investigated genital evolution during the early stages of speciation. We quantified genital variation in replicated population pairs of Poecilia mexicana with ongoing ecological speciation between sulfidic (H2S-containing) and nearby non-sulfidic habitats. These analyses revealed rapid and correlated divergence of female and male genitalia across evolutionarily independent population pairs exposed to divergent selection regimes. Both sexes exhibited convergent evolution of genital traits among populations inhabiting similar habitat types. Our results demonstrate that genital evolution can occur during the early stages of speciation-with-gene-flow, potentially as a result of variation in the intensity of sexual conflict among populations. Our results suggest genitalia may contribute to early stages of divergence, and challenge the generality of previously suggested mechanisms of genital evolution in poeciliids.
Replication Code in R for "Do Insurers Compete on the Federal Heath Insurance Exchange
<p>R code for univariate and multivariate analyses to model premiums based on the number of insurers in geographic rating areas.</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.