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52 results for “Programming Languages”
Image-based Many-language Programming Language Identification - Replication Package
<p>This dataset contains the data, software, and instructions needed to replicate the findings of the paper:</p> <p>Francesca Del Bonifro, Maurizio Gabbrielli, Antonio Lategano, and Stefano Zacchiroli. Image-based Many-language<br> Programming Language Identification. <a href="https://peerj.com/computer-science/"><em>PeerJ Computer Science</em></a>, 2021 (to appear). DOI: <a href="https://dx.doi.org/10.7717/peerj-cs.631">10.7717/peerj-cs.631</a></p> <p>After retrieving the full dataset, extract the replication-package.zip archive and follow the instructions described in the README.md file.</p>
Natural Language-Guided Programming User Study
<p>In this dataset you find the user study data that was used in the <strong><em>Natural Language-Guided Programming</em></strong> paper, which is accepted for Onward! 2021. A preprint can be found here <a href="https://arxiv.org/pdf/2108.05198.pdf">https://arxiv.org/pdf/2108.05198.pdf</a>. The dataset consists of the following files:</p> <ul> <li> <p>benchmark.json contains 201 test cases. Each test case consists of context, a natural language intent and target code. The test cases are intended to evaluate a model that can predict code giving a piece of context code and a natural language intent. The test cases were derived from Jupyter notebooks that were crawled from Github projects with permissive licenses. In the project_metadata field you find information about the original project such as its git url and license.</p> </li> <li> <p>predictions-annotated.json contains predictions of the three models used in the paper for 100 test cases in benchmark.json. Each prediction is accompanied with qualitive assesments from three annotators.</p> </li> <li> <p>train-index.jsonl is the list of github projects that were used for training the models.</p> </li> <li> <p>eval-index.jsonl is a list of github projects that we kept separate for evaluation. The benchmark.json was created from a random subset of the projects in this list.</p> </li> </ul> <p>For more details we refer to the paper.</p>
Replication package for "Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation"
<p>This repository contains the replication package for the paper "Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation" by Fernando Vallecillos Ruiz, Anastasiia Grishina, Max Hort and Leon Moonen, accepted for publication in ACM Transactions on Software Engineering and Methodology on 2025-10-09.</p> <p>A preprint is deposited on arXiv with DOI: <a href="https://doi.org/10.48550/arXiv.2401.07994">10.48550/arXiv.2401.07994</a>.</p> <p>The replication package is archived on Zenodo with DOI: <a href="https://doi.org/10.5281/zenodo.10500593">10.5281/zenodo.10500593</a>. It is maintained on GitHub at <a href="https://github.com/secureIT-project/RTT_for_APR">https://github.com/secureIT-project/RTT_for_APR</a>.</p> <p>This project builds on code from the <a href="https://github.com/lin-tan/clm/">clm</a> project, which is (c) 2023, The ASSET research group led by Lin Tan, Purdue University, licensed under the BSD 3-Clause License (see jasper/LICENSE.BSD). All modifications and new contributions are (c) 2025 by the authors of this replication package and distributed under the MIT License (see LICENSE.MIT). The data, models and preprint are distributed under the CC BY 4.0 license.</p> <h2>Citation<code> </code></h2> <p>If you build on this data or code, please cite this work by referring to the paper:</p> <div> <pre><code>@article{ruiz2025:rtt, title = {Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation}, author = {Vallecillos Ruiz, Fernando and Anastasiia Grishina and Max Hort and Leon Moonen}, journal = {ACM Transactions on Software Engineering and Methodology (TOSEM)}, year = {2025}, publisher = {{ACM}} }</code></pre> </div> <h2>Organization</h2> <p>The replication package is organized as follows:</p> <ul> <li>clm-apr <ul> <li>plbart: code to generate patches with PLBART models.</li> <li>codet5: code to generate patches with CodeT5 models.</li> <li>transcoder: code to generate patches with the TransCoder model.</li> <li>incoder: code to generate patches with InCoder models.</li> <li>santacoder: code to generate patches with the SantaCoder model.</li> <li>starcoder: code to generate patches with the StarCoderBase model.</li> <li>quixbugs: code to validate patches generated for the QuixBugs benchmark.</li> <li>defects4j: code to validate patches generated for any of the Defects4J benchmarks.</li> <li>humaneval: code to validate patches generated for the HumanEval-Java benchmark.</li> </ul> </li> <li>humaneval-java: the HumanEval-Java benchmark proposed by Jiang et al. 2023</li> <li>jasper: a Java tool to parse Java programs needed to preprocess input.</li> <li>model: folder to download the language models.</li> <li>analysis_wandb: data from WandB and Jupyter notebook to create graphs.</li> <li>tmp_benchmarks: folder for temporary files used in patch validation. The folder may contain pairs of `paralell’ folders src and src_org for each benchmark, used to replace buggy code with candidate patches.</li> </ul> <h2>Replication</h2> <h3>Prerequisites</h3> <ul> <li>Python version: 3.8—3.10.</li> <li><a href="https://git-lfs.com/">Git LFS</a> is required for model downloading.</li> </ul> <h4>Weight and Biases (WandB)</h4> <ol> <li>Create an account on <a href="https://wandb.ai/">Weights and Biases</a></li> <li>Install the <a href="https://docs.wandb.ai/ref/python">Weights and Biases</a> library</li> <li>Run <code>wandb login</code> and follow the instructions</li> </ol> <h4>Set up OpenAI access</h4> <p>OpenAI account is needed with access to <code>gpt-3.5-turbo</code> and <code>gpt-4</code> . The <code>OPENAI_API_KEY</code> environment variable should be set to your OpenAI API access token.</p> <h3>Dependencies</h3> <ul> <li><a href="https://github.com/rjust/defects4j">Defects4J</a> - To generate inputs for the Defects4J datasets or to validate them, you need to have installed <a href="https://github.com/rjust/defects4j">their tool</a>.</li> <li>Java 8</li> <li>Apache Maven</li> </ul> <h3>Setup</h3> <p>We recommend the use of the setup script:</p> <pre><code>setup.sh </code></pre> <p>which performs the following:</p> <ol> <li>Creates a virtual environment for Python and activate it.</li> <li>Install the packages in <code>requirements.txt</code>.</li> <li>Compiles Jasper.</li> <li>Downloads parsers.</li> <li>Check if the Defects4J installation is correct.</li> </ol> <h3>Download models</h3> <p>The following bash script contains the code to download all of the models used:</p> <pre><code>models/download_models.sh </code></pre> <p>We recommend downloading only the models you are going to use due to their size</p> <pre><code>cd models chmod +x download_models.sh ./download_models.sh </code></pre> <p>To run one specific model, for example, PLBART (C#), use the following commands:</p> <pre><code>cd models git lfs install git clone https://huggingface.co/uclanlp/plbart-java-cs git clone https://huggingface.co/uclanlp/plbart-cs-java cd ../.. </code></pre> <h3>Step 1: Preprocessing and Prompting:</h3> <p>Each script in each <code>clm-apr/[model]</code> folder connects one or more models with<br>one dataset. These scripts follow the template: [benchmark]_[model]_[technique].py.<br>The scripts first create an <code>[model]_input.json</code> file with the preprocessed<br>input. Then generate outputs based on that file with one or more models.<br>For example:</p> <pre><code>cd clm-apr/plbart python quixbugs_plbart_round.py # Generates input for QuixBugs and generate patches using Java<->C# RTT. python quixbugs_plbart_round_nl.py # Generates input for QuixBugs and generate patches using Java<->NL RTT. </code></pre> <p>Optionally, use argument <code>--device_map cpu</code> if you wish to run the script on<br>CPU, for example:</p> <pre><code>python quixbugs_plbart_round.py --device_map cpu </code></pre> <p>Otherwise, the script will be run on all available CUDA GPU’s.</p> <p>We have commented the generation of inputs in the scripts. Users are free to<br>uncomment this method and try for themselves. It is easily recognizable by<br>their name template <code>[model]_[benchmark]_input()</code>. In the previous case:</p> <pre><code>quixbugs_plbart_input() </code></pre> <h3>Step 2 and 3: Round Trip Translation and Postprocessing</h3> <p>These steps are also included in the [benchmark]_[model]_[technique].py<br>script mentioned above. They are modularized in the method recognizable by<br>their name template [model]_[benchmark]_output().<br>For example:</p> <pre><code>quixbugs_incoder_output() </code></pre> <p>This method:</p> <ol> <li>Reads the input json file.</li> <li>Generates outputs through the LLM.</li> <li>Postprocess the output (extract the patch, clean up extra token, etc.).</li> <li>Creates [model]_output_[technique]_[extra].json.</li> </ol> <p>The last 3 steps are repeated according to the number of runs set to performed<br>(10 in our experiments). Each run will produce a different file with the seed<br>used in its generation. For example, <code>quixbugs\_plbart\_round.py</code> and<br><code>quixbugs\_plbart\_round_nl.py</code> scripts create:</p> <pre><code>clm-apr/quixbugs/plbart_results/run_0/plbart_java_cs_java_output_round_csharp_batch.json clm-apr/quixbugs/plbart_results/run_0/plbart_java_nl_java_output_round_nl_batch.json </code></pre> <h3>Step 4: Evaluation of RTT Results:</h3> <p>The last step evaluates the generated outputs against the test-suites of each<br>benchmark. This script reads the previous outputs files and generates a new one<br>with the results of the test for one model. Furthermore, it connects with the<br><em>WandB</em> tool to calculate metrics and send them to analyze.</p> <p>Following the previous examples, to validate the results previously obtained,<br>we execute the following:</p> <pre><code>cd clm-apr/quixbugs python validate_quixbugs_parallel.py </code></pre> <p>Given the included JSON, this script would create:</p> <pre><code>clm-apr/quixbugs/plbart_results/run_0/plbart_java_cs_java_validate_round_csharp_batch.json </code></pre> <p>We have disabled <em>WandB</em> in the script to allow users to try the script first.<br>However, it can be easily activated by changing the parameter <code>mode="disabled"</code><br>to <code>mode="online"</code>.<br>We have set the variable <code>total_runs = 1</code>, as well as <code>input_file</code> and <code>output_file</code><br>to the results included. They should be modified accordingly to validate more runs<br>or to validate other files/models.</p> <h3>Included Results</h3> <p>We include two CSV files obtained through WandB.</p> <pre><code>'data_cleaned_grouped.csv': Aggregated metrics of the 25 outputs for all runs. 'full_data_all_runs.csv': All metrics for all outputs on all runs. </code></pre> <h2>Changelog</h2> <ul> <li>v1.0 - updates corresponding to the accepted version of the manuscript in TOSEM</li> <li>v0.1 - initial replication package corresponding to v1 of arXiv deposit: includes raw data, code, and example outputs.</li> </ul> <h2>References</h2> <p>Jiang, N.; Liu, K.; Lutellier, T.; and Tan, L. 2023. Impact of Code Language<br>Models on Automated Program Repair. In 45th International Conference on<br>Software Engineering (ICSE), 1430–1442. IEEE. ISBN 978-1-66545-701-9.</p> <div> </div>
Automated Programming Exercise Generation in the Era of Large Language Models
<p>Lecturers are increasingly attempting to use large language models (LLMs) to simplify and make the creation of exercises for students more efficient. Efforts are also being made to automate the exercise creation process in software engineering (SE) education. This study explores the use of advanced LLMs, including GPT-4 and LaMDA, for automated programming exercise creation in higher education and compares the results with related work using GPT-3.5-turbo. Utilizing applications such as ChatGPT, Bing AI Chat, and Google Bard, we identify LLMs capable of initiating different exercise designs. However, manual refinement is crucial for accuracy. Common error patterns across LLMs highlight challenges in complex programming concepts, while specific strengths in various topics showcase model distinctions. This research underscores LLMs' value in exercise generation, emphasizing the critical role of human supervision in refining these processes. Our concise insights cater to educators, practitioners, and other researchers seeking to enhance SE education through LLM applications.</p>
Figure 3. Logoped 1.0 program-Modern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>The program was designed for the therapy of logoneurosis, while being equally useful for the therapeutic activities used in the treatment of dyslexic-dysgraphic disorders. Logoped 1.0 provides a vast lexical material, which is organised into several sections: exercises involving reading the syllables and the words, sentences reading, followed by phrase and text reading. The colourful design of the words and sentences, the attractive way in which they are displayed on the monitor, and the fact that it allows choosing the exercises level of difficulty render the reading activity much more attractive for the pupil (Tobolcea, 2001). Its functional schema is presented in figure 3.</p>
Programing language & Games
<p>With the development of science and technology, there are more and more electronic games on the market. The types of electronic games have also become more diversified. At present, there are many programming languages on the market that can be used to develop games. As a beginner of game development, it is difficult for us to choose an appropriate programming language to develop specific types of games. So we investigate some famous game and the programing languages they use.</p>
Reddit and StackOverflow dataset (Programming languages)
<p>This data set contains anonymized data collected from Reddit (via the <a href="https://api.pushshift.io/redoc">Pushshift API</a>) and StackOverflow (from <a href="https://www.kaggle.com/datasets/stackoverflow/stackoverflow">Kaggle's dataset</a>).</p> <p>Each folder includes the data split by trimester. The schema of StackOverflow and Reddit-related files follows:</p> <ul> <li>Fields from StackOverflow <ul> <li>question_id</li> <li>answer_id</li> <li>creation_date - answer creation_date</li> <li>score - score of the question/answer</li> <li>tags - all tags flagged for a question</li> <li>answer_count - number of answers for a question</li> <li>start_question - question's time of creation</li> <li>last_activity_date - last update on the question</li> <li>new_id - hashed id of the answerer</li> <li>q_new_id - hashed id of the questioner</li> </ul> </li> <li>Fields from Reddit <ul> <li>comment_id</li> <li>submission_id</li> <li>score - score of the question/submission</li> <li>subreddit</li> <li>created_utc - time of creation (unrelated to last modified comments)</li> <li>new_id - hashed id</li> </ul> </li> </ul> <p>The .txt files represent the structure of the corresponding hypergraphs.</p>
"Constructing Temporal Networks of OSS Programming Language Ecosystems" reproducibility package
<p>Reproducibility package for the paper submission "Constructing Temporal Networks of OSS Programming Language Ecosystems". Contains the dataset, extracted and external 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>
Metrics of Collaboration Across Programming Paradigms and Languages: Comprehensive Dataset
<h3>[New version] : </h3> <p>Following a recent revision of our taxonomy of programming paradigms, we have observed slight changes in the data of our dataset. Consequently, to ensure accuracy and relevance, the dataset has been updated to reflect these changes. We encourage you to download the latest version to benefit from the updated data.</p> <h3>Description :</h3> <p>This dataset provides a comprehensive analysis of collaboration metrics across various programming paradigms and languages, aimed at researchers and practitioners interested in the interoperability and integration challenges within multi-language development environments. The data encapsulates both raw and cleaned metrics detailing potential collaboration avenues and interoperability between different programming paradigms, as well as between distinct programming languages, with and without redundancy.</p> <h3>Contents:</h3> <p>CollaborationMetric_Paradigms_Raw.csv - Contains the raw metrics of collaboration potential between programming paradigms based on a novel collaboration metric developed in our research. This dataset encompasses various paradigms' compatibility and integration potential, offering a wide-angle view on the landscape of programming paradigm interoperability.<br>CollaborationMetric_Paradigms_Cleaned.csv - A cleaned and refined version of the raw paradigm collaboration metrics, with redundant data removed for clarity and ease of analysis. This file is optimized for researchers seeking to delve directly into the significant findings of paradigm compatibility without the need for further data cleaning.<br>CollaborationMetric_Languages_Raw.csv - Offers raw collaboration metrics between different programming languages, providing insights into how languages may synergize or face integration challenges within multi-language projects. The data reflects an extensive analysis of language interoperability, grounded in the theoretical framework outlined in our associated research paper.<br>CollaborationMetric_Languages_Cleaned.csv - A streamlined version of the language collaboration metrics, free from redundancies, and focused on the core insights into language interoperability. This dataset is intended for immediate application in research and practical scenarios, facilitating quick identification of potential language synergies or conflicts.</p> <h3><br>Methodology:</h3> <p>The datasets were generated through a rigorous quantitative analysis, employing a collaboration metric that considers structural characteristics, kinship relationships, and fundamental computational and behavioural properties of programming paradigms and languages. Detailed methodology and theoretical underpinnings are described in our associated research paper, "[Towards a quantitative evaluation of paradigmatic collaboration: insights from Prolog and beyond]," intended for submission to the International Conference on Logic Programming (ICLP).</p>
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
<p>As the current existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles.</p> <p>We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006--2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. This document is the data companion for a paper published elsewhere and presents a detailed list of the selected papers. Besides, the document also presents the form of our questionnaire-based survey. </p> <p>We also include the filled in questionnaires and raw data of the referred survey. To validate the SMS results we conducted a survey (in November 2018) with 28 HPC experts involved in the cHiPSet COST action to which we added, in October 2019, 29 HPC experts which were not involved in that COST action. Participants were recruited through convenience sampling, and contacted directly by the authors. In total, we received 57 filled survey forms.</p>
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
<p>As the current existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles.</p> <p>We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006--2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. This document is the data companion for a paper published elsewhere and presents a detailed list of the selected papers. Besides, the document also presents the form of our questionnaire-based survey. </p> <p>We also include the filled in questionnaires and raw data of the referred survey. To validate the SMS results we conducted a survey (in November 2018) with 28 HPC experts involved in the cHiPSet COST action to which we added, in October 2019, 29 HPC experts which were not involved in that COST action. Participants were recruited through convenience sampling, and contacted directly by the authors. In total, we received 57 filled survey forms.</p>
Replication Kit: "Skill Models for Programming Language Concepts"
<p><strong>Structure</strong></p> <ul> <li><strong>data</strong>: contains the data we used for our case study <ul> <li><strong>skillmodels</strong>: data sets generated from the raw data in the database</li> <li><strong>raw</strong>: raw data collected in SmartAPE [1] containing the source code of the students as well as the assessment results of the system</li> </ul> </li> <li><strong>results</strong>: contains the complete results of our case study <ul> <li>Results of AUC and RMSE for each meta-parametrization and each skill model in .csv and .Rda format</li> <li>boxplots of our AUC and RMSE distributions for each skill model and each meta-parameter in .pdf format</li> </ul> </li> <li>calculation scripts: <ul> <li><strong>sm_trainer.R</strong>: script to fit different models for different meta-parametrizations and test them using different performance metrics. Uses <em>data/skillmodels </em>as input</li> <li><strong>comparison.R</strong>: script that performs statistical tests to compare meta-parameters. Uses <em>results//results_pfa.Rda, results//results_afm.Rda, and results/results_prop.Rda</em> as input</li> </ul> </li> </ul> <p><strong>References</strong></p> <p>[1] Albrecht, Ella et al. “Experiences in Introducing Blended Learning in an Introductory Programming Course.” <em>ECSEE</em> (2018).</p>
Do Current Language Models Support Code Intelligence for R Programming Language?
<p>This is the dataset used in the paper: Do Current Language Models Support Code Intelligence for Programming Language?</p> <p> </p> <p>This dataset contains code snippets from R programming language repositories on GitHub, paired with their corresponding natural language (NL) descriptions. It was created for research in software engineering tasks like code summarization and code search. The data was collected using the GitHub REST API and includes over 1,500 public R repositories. To ensure quality, only active, well-structured R packages with proper documentation were included. Roxygen2, a popular documentation framework, was used to extract both the code and its matching NL descriptions.</p> <p>The dataset is organized into three parts: base R functions (Base), functions from the tidyverse (Tidy), and a combined set (RCombine). The dataset follows the CodeSearchNet format, with a split for training, validation, and testing data, ensuring no duplicate functions.</p>
MCMD | Multi-programming-language Commit Message Dataset
<p>A large-scale dataset in multi-programming languages and with rich information.</p> <p>This dataset is proposed in the paper "<a href="https://ieeexplore.ieee.org/document/9609189">On the Evaluation of Commit Message Generation Models: An Experimental Study</a>" accepted to ICSME 2021 and "<a href="https://link.springer.com/article/10.1007/s10664-022-10219-1">A large-scale empirical study of commit message generation: models, datasets and evaluation</a>" accepted to EMSE 2022.</p> <p><strong>Welcome to use our dataset, <a href="https://github.com/DeepSoftwareAnalytics/CommitMsgEmpirical/blob/main/dataset">MCMD</a>, and the <a href="https://github.com/DeepSoftwareAnalytics/CommitMsgEmpirical/blob/main/metrics">evaluation scripts</a> to test the performance of the commit message generation!</strong></p> <p>Citations for these two works can be found <a href="https://github.com/DeepSoftwareAnalytics/CommitMsgEmpirical#Citation">here</a>.</p>
Programming language keyword frequencies extracted from 16,000,000 public GitHub repositories (October 2016)
<p>Origin</p> <p>16,000,000 repositories on GitHub as of October 2016, classified with github/linguist and parsed with Pygments. Token.Keyword tokens were filtered and MapReduce-d. Fuzzy duplicate repositories were discarded.</p> <p>Some languages, e.g. Haskell, are parsed wrong, resulting in <strong>many</strong> keywords. Still they were not removed since we are not familiar with such languages.</p> <p>Format</p> <p>Triples [language name]\t[keyword]\t[frequency]</p> <p>Tabs and new lines in keywords are escaped as \t and \n respectively.</p>
Impact of Code Language Models on Automated Program Repair (Dataset)
<p>This is the fine-tuning dataset used in the paper Impact of Code Language Models on Automated Program Repair</p>
Diversification and change in the R programming language
<p>Languages change over time, driven by creation of new words and cultural pressure to optimise communication. Programming languages resemble written language but communicate primarily with computer hardware rather than a human audience. I tested for changes over time in use of R, a mature, open-source programming language used for scientific computing. Across 393,142 GitHub repositories published between 2014 and 2021, I extracted 143,409,288 R functions, programming "verbs", and paired linguistic and ecological approaches to estimate change in the diversity and composition of function use over time. I found that the number of R functions in use increased and underwent substantial change, driven primarily by the popularity of the "tidyverse" collection of community-written extensions. I provide evidence that users can directly change the nature of programming languages, with patterns that match known processes from natural languages and genetic evolution. In the case of R, patterns suggested there are selective pressures for increased analytic complexity and R functions in decline but not extinct ("extinction debts"). R's evolution towards the tidyverse may also represent the start of a division into two distinct dialects, which may impact the readability and continuity of analytic and scientific inquiries codified in R, as well as the language's future.</p>
JDOR - Java2D for ooRexx (and Other Programming Languages)
<p>JDOR (Java2D for ooRexx) is a Rexx command handler that allows using simple string commands to create Java2D graphics and animations. The tool is part of the open source BSF4ooRexx850 package, a bidirectional ooRexx-Java bridge, and allows in addition for recording JDOR commands and to replay them later one by one. This allows for creating Java2D graphics and running simple Java2D animations from plain text files such that programs in any programming language can take advantage of JDOR.</p> <p>The design and implementation of JDOR simplifies the interface to the Java2D classes considerably such that even students without any professional graphics background (like business administration students) can successfully take advantage of the tool to create even complex Java2D graphics and Java2D based animations which would be otherwise impossible for them as they lack the necessary Java programming and graphical skills.</p> <p>The video demonstrates some of the samples in "BSF4ooRexx850/samples" including some animation examples. In addition a Python and a Java program that create JDOR commands gets used in a pipe to a JDOR filter program to create JDOR graphics. The demonstration is run under Windows, but can be run unchanged on Linux or macOS, as can all examples of "BSF4ooRexx850".</p>
Natural Language Annotations for Reasoning about Program Semantics
<p>Natural language annotations about Python statements</p> <p>The dataset is made of pairs of files sharing the prefix of the filename</p> <p>* Annotations are in JSONL format (filenames ending with '_annot'), i.e. JSON objects separated by newline ('\n') characters</p> <p>* Reference source code files are in JSON format. (filenames ending with '_code')</p> <p> </p> <p>Source dataset : Programming Puzzles (Schuster et al. 2021, NeurIPS Dataset and benchmarks track) - MIT License - https://github.com/microsoft/PythonProgrammingPuzzles</p>
Mental Imagery Therapy for Autism (MITA) - an Early Intervention Computerized Language Training Program for Children With ASD
ClinicalTrials.gov study NCT02708290. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.