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30 results for “reproducibility package”
Reproducibility Package for "ExpectAll: A BDD Based Approach for Link Failure Resilience in Elastic Optical Networks"
<p>This is a reproducibility package for the VMCAI'25 paper "ExpectAll: A BDD Based Approach for Link Failure Resilience in Elastic Optical Networks".</p> <p>Constantly growing demands on higher bandwidth and quality of service in modern communication networks motivate the introduction of fully optical network technologies that can eliminate the bottlenecks of optical to digital signal conversions. Recent advances in elastic optical networks enable fine-grained resource allocation technologies for traffic demands, which introduces the Routing and Spectrum Allocation (RSA) problem. In order to improve network resilience for multiple link failures while avoiding double light-spectrum allocation, we present Ex- pectAll---a novel approach and a tool for resilience and path/spectrum allocation based on binary decision diagrams (BDDs). Our method efficiently computes and stores all solutions to the RSA problem in the BDD data structure, facilitating optimal and fast failover protection for failure scenarios even with multiple failing links. ExpectAll surpasses the state- of-the-art methods in both the speed of finding a single optimal solution for a currently occurring failure scenario as well as in the preparation time required to precompute all optimal route and spectrum assignments.</p> <p> </p> <p> </p>
"Temporal Network Dataset of OSS Programming Language Ecosystems" reproducibility package
<p>Reproducibility package for the paper submission "Temporal Network Dataset of OSS Programming Language Ecosystems" to the Mining Software Repositories 2022 conference. Contains the dataset, extracted metrics, and all scripts used for the construction and the analysis done in the paper.</p> <p> </p> <p>Anonymised for the double-blind review process.</p>
LocalizeAgent Reproducibility Package
<h1>Replication Package: An LLM-Based Agent-Oriented Approach for Automated Code Design Issue Localization</h1> <p>This replication package contains the necessary code and instructions to run the experiments described in the paper, "An LLM-Based Agent-Oriented Approach for Automated Code Design Issue Localization".</p> <h2>Prerequisites</h2> <ul> <li>Python 3.12.2</li> <li>Required Python packages: <ul> <li>pip install anthropic</li> <li>pip install openai</li> <li>pip install google-generativeai</li> <li>pip install tqdm</li> <li>pip install joblib</li> <li>pip install javalang</li> <li>pip install numpy</li> </ul> </li> </ul> <h2>Installation</h2> <p>1. Clone the repository or extract the replication package.<br>2. Install the required Python packages</p> <h2>Running the Experiments</h2> <h3>Generating LLM Responses</h3> <p>To generate responses from language models (LLMs) like Claude, OpenAI, or Gemini, use the <code>run.py</code> script with the appropriate arguments.</p> <p><code> python3 run.py --MODEL_NAME --output ./openai/ --path PATH_TO_EVALUATION_DATASET</code></p> <p>For example,</p> <p><code> python3 run.py --openai --output ./openai/move_m --path EvaluationDataset/move_m/single</code></p> <ul> <li><code>--openai</code>: Use the OpenAI API (alternatively, use <code>--claude</code> or <code>--gemini</code> for other LLMs).</li> <li><code>--output</code>: Specify the directory to save the LLM responses.</li> <li><code>--path</code>: Provide the path to the directory containing the Java files.</li> </ul> <p>You can also use additional arguments like <code>--limit</code> to limit the number of files to process, <code>--context</code> to add context to the prompt, and <code>--log_file</code> to specify a log file path.</p> <h3>Evaluating LLM Responses</h3> <p>To evaluate the LLM responses by comparing function names extracted from file IDs and response texts, use the <code>eval_llms.py</code> script.</p> <p><code>python3 run.py --eval --path /openai/move_m/llm_responses.json</code></p> <ul> <li><code>--eval</code>: Enable evaluation mode.</li> <li><code>--path</code>: Provide the path to the JSON file containing the LLM responses.</li> </ul> <h3>Running the Evaluation Script</h3> <p>The <code>run_eval.sh</code> script is provided to automate the evaluation process for multiple LLM responses. It loops through a predefined set of paths and executes the evaluation command for each path.</p> <p><code>bash ./run_eval.sh</code></p> <h2>Customization</h2> <p>You can modify the source code files (<code>eval_llms.py</code>, <code>llm_interfaces.py</code>, <code>prompt_generation.py</code>, and <code>utils.py</code>) to customize the behavior or add new features as needed.</p> <h2>Additional Notes</h2> <ul> <li>The "EvaluationDataset" directory is already included in the replication package.</li> <li>The output directory specified with the <code>--output</code> argument will be created if it doesn't already exist.</li> </ul>
EditQL: A Textual Query Language for Evolving Models (Reproducibility Package)
Open the record for dataset details and reuse information.
LocalizeAgent Reproducibility Package
<h1>Replication Package: An LLM-Based Agent-Oriented Approach for Automated Code Design Issue Localization</h1> <p>This replication package contains the necessary code and instructions to run the experiments described in the paper, "An LLM-Based Agent-Oriented Approach for Automated Code Design Issue Localization".</p> <h2>Prerequisites</h2> <ul> <li>Python 3.12.2</li> <li>Required Python packages: <ul> <li>pip install anthropic</li> <li>pip install openai</li> <li>pip install google-generativeai</li> <li>pip install tqdm</li> <li>pip install joblib</li> <li>pip install javalang</li> <li>pip install numpy</li> </ul> </li> </ul> <h2>Installation</h2> <p>1. Clone the repository or extract the replication package.<br>2. Install the required Python packages</p> <h2>Running the Experiments</h2> <h3>Generating LLM Responses</h3> <p>To generate responses from language models (LLMs) like Claude, OpenAI, or Gemini, use the <code>run.py</code> script with the appropriate arguments.</p> <p><code> python3 run.py --MODEL_NAME --output ./openai/ --path PATH_TO_EVALUATION_DATASET</code></p> <p>For example,</p> <p><code> python3 run.py --openai --output ./openai/move_m --path EvaluationDataset/move_m/single</code></p> <ul> <li><code>--openai</code>: Use the OpenAI API (alternatively, use <code>--claude</code> or <code>--gemini</code> for other LLMs).</li> <li><code>--output</code>: Specify the directory to save the LLM responses.</li> <li><code>--path</code>: Provide the path to the directory containing the Java files.</li> </ul> <p>You can also use additional arguments like <code>--limit</code> to limit the number of files to process, <code>--context</code> to add context to the prompt, and <code>--log_file</code> to specify a log file path.</p> <h3>Evaluating LLM Responses</h3> <p>To evaluate the LLM responses by comparing function names extracted from file IDs and response texts, use the <code>eval_llms.py</code> script.</p> <p><code>python3 run.py --eval --path /openai/move_m/llm_responses.json</code></p> <ul> <li><code>--eval</code>: Enable evaluation mode.</li> <li><code>--path</code>: Provide the path to the JSON file containing the LLM responses.</li> </ul> <h3>Running the Evaluation Script</h3> <p>The <code>run_eval.sh</code> script is provided to automate the evaluation process for multiple LLM responses. It loops through a predefined set of paths and executes the evaluation command for each path.</p> <p><code>bash ./run_eval.sh</code></p> <h2>Customization</h2> <p>You can modify the source code files (<code>eval_llms.py</code>, <code>llm_interfaces.py</code>, <code>prompt_generation.py</code>, and <code>utils.py</code>) to customize the behavior or add new features as needed.</p> <h2>Additional Notes</h2> <ul> <li>The "EvaluationDataset" directory is already included in the replication package.</li> <li>The output directory specified with the <code>--output</code> argument will be created if it doesn't already exist.</li> </ul>
Reproducibility Package for "Design of an allosterically modulated doxycycline and doxorubicin-binding protein"
<p>Files used for MUMBO calculations depicted in the Supplementary Figure 1 of the manuscript "Design of an allosterically modulated doxycycline and doxorubicin-binding protein"</p>
R notebooks to reproduce all analyses from the manuscript "grandR: a comprehensive package for nucleotide conversion sequencing data analysis"
<p>This package contains all R notebooks to reproduce the analyses from our manuscript "grandR: a comprehensive package for nucleotide conversion sequencing data analysis".</p> <p>In the zip file you find</p> <ul> <li>several rds files in the data folder: They contain grandR objects of both simulated and real SLAM-seq data sets. You can delete them and create them again by either just "knitting" the notebooks (which will generate all data necessary for this notebook and save it into the data folder), or by executing the generateAllDataFiles.R script ("Rscript generateAllDataFiles.R"), which will generate all rds files that do not exist).</li> <li>several R notebooks (Rmd): "Knitting" them will generate all figures from the manuscript. Without the data files (rds), this will be slow!</li> <li>knit_all.bash: Execute to "knit" all notebooks</li> <li>clean.bash: Clear the output of "knitting" the notebooks</li> </ul> <p> </p>
Reproducibility package for: Assessing natural selection during range expansions: Insights from a spatially explicit ABC study
<p>Study-reproducibility set with complete source code plus simulation results for the manuscript entitled "Assessing natural selection during range expansions: Insights from a spatially explicit ABC study".</p>
Scalability Assessment of Microservice Architecture Deployment Configurations: A Domain-based Approach Leveraging Operational Profiles and Load Tests - Reproducibility Package
<p>Reproducibility package for the following publication:</p> <p>Alberto Avritzer, Vincenzo Ferme, Andrea Janes, Barbara Russo, André van Hoorn, Henning Schulz, Daniel Menasché, Vilc Rufino: <em>Scalability Assessment of Microservice Architecture Deployment Configurations: A Domain-based Approach Leveraging Operational Profiles and Load Tests</em>, Journal of Systems and Software, 2020, https://doi.org/10.1016/j.jss.2020.110564.</p>
Restricted Data of the Reproducibility Package for the paper "Higher-Order Patterns Reveal Causal Timescales of Complex Systems"
<p>Datasets which are not ours, so we do not want to publish them openly. We submitted them here as a backup.</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.