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111 results for “LLMs”

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zenodo32/100

Replication package for "Do LLMs Generate Code with Type Annotations?"

<div> See the README.md file for more details. </div>

opencc-by-4.0Nov 2024View details →

zenodo32/100

CoqPyt: Proof Navigation in Python in the Era of LLMs

Replication package with code and CompCert dataset accompanying the paper "CoqPyt: Proof Navigation in Python in the Era of LLMs". The file provided here is a Docker image. To use it, follow the steps: 1. docker load < coqpyt.tar2. docker run -it --name coqpyt -d coqpyt /bin/bash3. docker attach coqpyt  After these steps, see the README.

opencc-by-4.0Dec 2023View details →

zenodo32/100

Data of LLMs evaluation

This data is the outcome of evaluating the performance of three LLMs, including gpt-4o, gemini-1.5-pro, and claude-sonnet, on five tasks related to our system's functionalities, including generating corresponding pronunciation, example, synonyms, antonyms and contextual explanation.

opencc-by-4.0May 2024View details →

zenodo32/100

Supplementary Material for "Turning Low-Code Development Platforms into True No-Code with LLMs"

This repository contains additional material supporting the paper titled "Turning Low-Code Development Platforms into True No-Code with LLMs"

opencc-by-4.0Jul 2024View details →

zenodo32/100

Supplemental Data - LLMs for Differential Diagnostic Rare Disease

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →

zenodo32/100

Evaluating the Capability of LLMs in Identifying Compilation Errors in Configurable Systems

This dataset contains the log files of the queries performed on the LLMs, executed in the publication Albuquerque et al. 2024 (SBES-IIER 2024).

opencc-by-4.0Jul 2024View details →

zenodo32/100

Interesting Scientific Idea Generation Using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders

Dataset for the Knowledge graph used in the paper "<a href="https://arxiv.org/abs/2405.17044">Interesting Scientific Idea Generation Using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders</a>" by Xueme Gu and Mario Krenn.   Nodes represent scientific concepts extracted from 2.44 million paper titles and abstracts and edges are formed when two concepts co-occur in titles or abstracts of over 58 million papers from OpenAlex, augmented with citation information.

opencc-by-4.0Oct 2024View details →

zenodo32/100

Towards an Analogical Reasoning with LLMs in Software Engineering Education

Data collected from professors and students using our approach to teach/understand SE concepts

opencc-by-4.0Dec 2023View details →

zenodo32/100

Replication package of the paper "Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot"

<h1>Replication Package</h1> This replication package contains the necessary tools, data, and scripts for reproducing the results of our paper: "Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot". Below is a detailed description of the directory structure and the contents of this package. <h2>Contents</h2> The replication package is organized into two main directories: <ul> <li> <code>assets</code>: This directory contains all .csv files used as input for the script and the outputted .csv file used to perform the manual and automated analyses for RQ1 and RQ2. </li> <li> <code>script</code>: This directory contains all scripts for RQ1 and RQ2. </li> </ul> In the following, we describe the content of each directory: <h2><code>assets</code></h2> This directory contains the tools and resources required for our study. <h3><code>dataset</code>: Contains the main datasets used in the study.</h3> <ul> <li> <code>annotationStore.csv</code>: Input dataset for our analyses, originating from the CODESEARCHNET dataset. </li> <li> <code>queries.csv</code>: .csv file containing the queries used for the experiments filtered from the CODESEARCHNETdataset. This file contains the following columns: <ul> <li>Language: Programming language of the query</li> <li>Query: Query used for the experiment</li> <li>GitHubUrl: GitHub URL related to a snippet that addresses the query</li> <li>Relevance: Relevance of the linked GitHub snippet to the query</li> </ul> </li> </ul> <h3><code>data</code>: Contains the datasets and results of all analyses.</h3> <ul> <li> <code>queries.csv</code>: General input queries. This file contains the following columns: <ul> <li>Language: Programming language of the query</li> <li>Query: Query used for the snippet generation</li> <li>Prompt: LLM prompt generated for the query as: You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></li> </ul> </li> <li> <code>queries_filled.csv</code>: Similar to the previous file, but also containing the output produced by the LLM-based assistants. This file contains the following columns: <ul> <li>Language: Programming language of the query</li> <li>Query: Query used for the snippet generation</li> <li>Prompt: LLM prompt generated for the query as: You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></li> <li>Notes: General notes that provide additional context or information about the query or prompt.</li> <li>Gemini_Answer(n): The generated code snippets by Gemini.</li> <li>Gemini(n): The external links provided by Gemini.</li> <li>Prompt (repeated)</li> <li>Note: Notes that provide additional context or information about the query or prompt.</li> <li>Copilot_Answer(n): The generated code snippets by Bing-Copilot.</li> <li>Copilot_Bing(n): The external links provided by Bing-Copilot.</li> </ul> </li> </ul> <h4><code>copilot</code> || <code>gemini</code>: Contains the data related to the specific LLM. These two subdirectories have the same internal structure.</h4> <ul> <li><code>queries.csv</code>: The <code>queries_filled.csv</code> file, filtered for the specific LLM.</li> <li><code>queries_noTrivial.csv</code>: Contains only the queries with at least one nontrivial generated snippet.</li> <li> <code>external_links.csv</code>: External links extracted from the LLMs output. </li> <li> <code>external_links_filled.csv</code>: Snippets extracted from the external links. <ul> <li>index: Query ID</li> <li>source: Snippet ID</li> <li>url: Link URL</li> <li>note: Notes that provide additional context or information about the query or prompt</li> <li>code(n): The n-th code snippet extracted from the source</li> </ul> </li> </ul> <h4><code>manual_analysis</code>: Manual analysis results.</h4> <ul> <li><code>manual_analysis.csv</code>: <ul> <li>index: Query ID</li> <li>query: Query used for the snippet generation</li> <li>generatedsnippet(n): The n-th code snippet generated by the LLM-based assistant</li> <li>trivial_1: Manual analysis of whether or not the snippet was trivial (validator 1)</li> <li>trivial_2: Manual analysis of whether or not the snippet was trivial (validator 2)</li> <li>trivial_final: Manual analysis of whether or not the snippet was trivial (final classification if there is a disagreement)</li> <li>source: URL to analyze</li> <li>sourcetype1: Type of the source (validator 1)</li> <li>sourcetype2: Type of the source (validator 2)</li> <li>sourcetypefinal: Type of the source (final classification if there is a disagreement)</li> <li>relatedtoquery_1: Relevance of the link to the query (validator 1)</li> <li>relatedtoquery_2: Relevance of the link to the query (validator 2)</li> <li>relatedtoquery_final: Relevance of the link to the query (final classification if there is a disagreement)</li> <li>relatedtosnippets_1: Relevance of the generated snippet to those in the link (validator 1)</li> <li>relatedtosnippets_2: Relevance of the generated snippet to those in the link (validator 2)</li> <li>relatedtosnippets_final: Relevance of the generated snippet to those in the link (final classification if there is a disagreement)</li> </ul> </li> <li><code>manual_analysis_noTrivial.csv</code>: As in the previous file, but only the queries with at least one nontrivial generated code snippet.</li> </ul> <h4><code>clone_detector</code>: Output and intermediate files for clone detection with Copilot data.</h4> <ul> <li><code>copilot_tokens || gemini_tokens</code>: Contains the output the tokenization of the generated code snippets and the code snippets extracted from the external links.</li> <li><code>merged_llm_ext_link.csv</code>: All possible pairs (Cartesian product) (code snippet extracted from the external links, generated code snippet). This file is the input of the clone detection tool. <ul> <li>ID_query: Query ID</li> <li>query: Query used for the snippet generation</li> <li>language: Programming language of the query</li> <li>generated_snippet: The generated code snippet by the LLM-based assistant</li> <li>IDgensnippet: The index of the generated code snippet</li> <li>LOCgensnippet: The number of lines of code of the generated code snippet</li> <li>ID_source: Source ID</li> <li>source: Source URL</li> <li>source_snippet: Code snippet extracted from the source</li> <li>IDsourcesnippet: ID of the code snippet extracted from the source</li> <li>LOCsourcesnippet: The number of lines of code of the code snippet extracted from the source</li> <li>note: Notes that provide additional context or information about the query or prompt</li> </ul> </li> <li><code>clone_detection_output.csv</code>: Contains the clone detection results. <ul> <li>ID_query: The index of the query</li> <li>query: Query used for the snippet generation</li> <li>language: The programming language of the query</li> <li>generated_snippet: The generated code snippet by the LLM-based assistant</li> <li>IDgensnippet: The index of the generated code snippet</li> <li>LOCgensnippet: The number of lines of code of the generated code snippet</li> <li>ID_source: Source ID</li> <li>source: Source URL</li> <li>source_snippet: Code snippet extracted from the source</li> <li>IDsourcesnippet: ID of the code snippet extracted from the source</li> <li>LOCsourcesnippet: The number of lines of code of the code snippet extracted from the source</li> <li>note: Notes that provide additional context or information about the query or prompt</li> <li>clone_detected: bBolean value that indicates whether a clone has been detected (1 = detected, 0 = not detected)</li> <li>cloning_ratio: Ratio of the number of lines of code of the generated code snippet has been detected as a clone in the code snippet extracted from the source</li> <li>cloned_lines: The number of lines of code of the generated code snippet that has been detected as a clone in the code snippet extracted from the source</li> </ul> </li> </ul> <h4><code>cosine_sim</code>: Cosine similarity results.</h4> <ul> <li><code>cosine_sim_output.csv</code>: Contains the cosine similarity results <ul> <li>query_id: Query ID</li> <li>snippet_id:ID the generated code snippet</li> <li>source_id: ID of the source</li> <li>sourcesnippetid: ID of the code snippet extracted from the source <ul> <li>cosine_similarity: The cosine similarity between the generated code snippet and the code snippet extracted from the source</li> </ul> </li> </ul> </li> </ul> <h4><code>quant_analysis</code>: Quantitative analysis results.</h4> <ul> <li><code>topN_links_se.csv</code>: Contains the top-N links extracted from the search engine. <ul> <li>id: Query ID</li> <li>query: The query</li> <li>url: Link URL</li> </ul> </li> <li><code>merged_clone_cosine.csv</code>: Contains the merged results of the clone detection and cosine similarity. <ul> <li>ID_query: Query ID</li> <li>query: The query</li> <li>language: The programming language of the query</li> <li>generated_snippet: The generated code snippet by the LLM-based assistant</li> <li>IDgensnippet: The ID of the generated code snippet</li> <li>LOCgensnippet: The number of lines of code of the generated code snippet</li> <li>ID_source: The index of the source</li> <li>source: The source URL</li> <li>source_snippet: The code snippet extracted from the source</li> <li>IDsourcesnippet: The index of the code snippet extracted from the source</li> <li>LOCsourcesnippet: The number of lines of code of the code snippet extracted from the source</li> <li>note: Notes that provide additional context or information about the query or prompt</li> <li>clone_detected: Boolean value that indicates if a clone has been detected(1 = detected, 0 = not detected)</li> <li>cloning_ratio: The ratio of the number of lines of code of the generated code snippet has been detected as a clone in the code snippet extracted from the source</li> <li>cloned_lines: The number of lines of code of the generated code snippet that has been detected as a clone in the code snippet extracted from the source</li> <li>cosine_similarity: The cosine similarity between the generated code snippet and the code snippet extracted from the source</li> </ul> </li> </ul> <h4><code>other_analysis</code>: Contains more performed analysis.</h4> <ul> <li><code>sample_queries.csv</code>: Contains a sample of five queries for language used for perform the chain of thought experiment. <ul> <li>Language: Programming language of the query</li> <li>Query: Query used for the snippet generation</li> <li>Prompt: LLM prompt generated for the query as: You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></li> </ul> </li> <li><code>chain_of_thought.csv</code>: <ul> <li>Language: Programming language of the query</li> <li>Query: Query used for the snippet generation</li> <li>Prompt: LLM prompt generated for the query as: You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></li> <li>Clone_fonud: Boolean value that indicates if a clone has been detected (Yes = detected, No = not detected)</li> <li>Note: Notes that provide additional context or information about the performed analysis</li> </ul> </li> <li><code>data_check.csv</code>: <ul> <li>Link: URL of the source provided by the LLM</li> <li>Post_date: Indicates if the date of the post is before/after the date of training of the LLM (before 2023, after 2023, not provided)</li> <li>Note: Notes that provide additional context or information about the performed analysis</li> </ul> </li> </ul> <h4><code>results</code>: Final analysis results.</h4> <ul> <li><code>jaccard_analysis.csv</code>: Contains the results of the Jaccard analysis comparing the provided external links by the LLMs with the top-N links extracted from the corresponding search engine. <ul> <li>id: Query ID</li> <li>language: The programming language of the query</li> <li>llm_link: The external links provided by the LLM</li> <li>llmlinksize: The number of external links provided by the LLM</li> <li>overlap_links: The overlapping links between the LLM and the search engine</li> <li>overlap_size: The number of overlapping links between the LLM and the search engine</li> <li>nonoverlaplinks: The non-overlapping links between the LLM and the search engine</li> <li>union_size: The size of the union set links between the LLM and the search engine</li> <li>jaccard: The Jaccard similarity between the LLM and the search engine</li> </ul> </li> <li><code>merged_analysis.csv</code>: Contains the merged results of the manual and quantitative analyses. <ul> <li>id: The index of the query</li> <li>query: The query used for the experiment</li> <li>trivial_final(n): The final assignment for the triviality of the n-th generated code snippet</li> <li>source: The URL of the source</li> <li>sourcetypefinal: The final assignment for the type of the source</li> <li>relatedtoquery_final: The final assignment for the relevance of the generated code snippet to the query</li> <li>relatedtosnippets_final: The final assignment for the relevance of the generated code snippet to the source</li> <li>cloning_ratio: The maximum cloning ratio between the generated code snippet and all the code snippets extracted from the source</li> <li>cosine_similarity: The cosine similarity related to the snippets with maximum cloning ratio</li> </ul> </li> </ul> <h3><code>cccfindersw-configuration-files</code>:</h3> Contains additional configuration files for the CCFinderSW clone detection tool. The files are <code>javascript_comment.txt</code> and <code>javascript_reserved.txt</code>. They must be placed in the tool's <code>comment/</code> and <code>reserved/</code> directories. <h3><code>appendix.tex</code>: The appendix of the paper containing:</h3> <ul> <li>Table 1: Number of links of different types provided by Gemini and Bing CoPilot</li> </ul> <h3><code>appendix.pdf</code>: The appendix of the paper in PDF format.</h3> <h2><code>script</code></h2> This directory contains our scripts (mostly Python, an R script and an Applescript) to preprocess data and run the clone detection analyses. <ul> <li><code>1_dateset_filtering.py</code>: Script to filter the dataset. The input of this script is the <code>annotationStore.csv</code> file, and the output is the <code>queries.csv</code> file.</li> <li><code>2_prompt_generation.py</code>: Script to generate prompts for the LLM-based assistants. The input of this script is the <code>queries.csv</code> file, and the output is the <code>queries_filled.csv</code> file.</li> <li><code>3_gen_sheet_sources_extraction.py</code>: Script to split the external links provided by the LLM, one for each row. The input of this script is the <code>queries_filled.csv</code> file, and the output is the <code>external_link.csv</code> file.</li> <li><code>4_ext_link_snippet_extraction.py</code>: Script to extract the snippets from Web URLs. It only works for the most popular domains. The input of this script is the <code>external_links.csv</code> file, and the output is the <code>external_links_filled.csv</code> file.</li> <li><code>5_top_n_link_SearchEngine.py</code>: Script to perform top-N link search using the corresponding search engines (Google Search and Bing). The input of this script is the <code>queries_filled.csv</code> file. It executes the <code>browser_bot.scpt</code>. The output is the <code>topN_links_se.csv</code> file. <ul> <li><code>browser_bot.scpt</code>: Script for browser automation (AppleScript).</li> </ul> </li> <li><code>6_se_vs_llm.py</code>: Script to compare (using the Jaccard metric) the links returned by the corresponding search engines with those provided by the LLM-based assistants. The input of this script is the <code>external_links.csv</code> file and the <code>topN_links_se.csv</code> file. The output is the <code>jaccaard_analysis.csv</code> file.</li> <li><code>7_results_manual_analysis.py</code>: Script to extract results and statistical analyses performed on the manual analysis and reported in the tables in the paper.</li> <li><code>8_merge_gen_source_snippets.py</code>: This script takes as input: <code>{llm}/queries.csv</code> and <code>{llm}/external_link_filled.csv</code> to merge them and generates an expanded one, i.e., one in which we have on each line a snippet extracted from the source, this will be the input of our final script for clone detection. The output is the <code>merged_llm_ext_link.csv</code> file.</li> <li><code>9_clone_detection.py</code>: Script to perform clone detection. The input of this script is the <code>merged_llm_ext_link.csv</code> file, and the output is the <code>clone_detection_output.csv</code> file.</li> <li><code>10_cosine_sim_check.py</code>: Script to compute the code snippets' cosine similarity. The script takes as input the tokenized files from the <code>{llm}_tokens</code> directory. The output is the <code>cosine_sim_output.csv</code>file.</li> <li><code>11_merger_clone_cosine.py</code>: Script to merge clone detection and cosine similarity results. The input of this script is the <code>clone_detection_output.csv</code> and the <code>cosine_sim_output.csv</code> files, and the output is the <code>merged_clone_cosine.csv</code> file.</li> <li><code>12_merge_manual_quantitative_analysis.py</code>: Script to merge manual and quantitative analysis results. The inputs of this script are the <code>manual_analysis.csv</code> and the <code>merged_clone_cosine.csv</code>files, and the output is the <code>merged_analysis.csv</code> file.</li> <li><code>13_sample_for_COT_analysis.py</code>: Script to collect the sample of queries on which we perform the chain of thought analysis, the output is the <code>sample_queries.csv</code> file.</li> <li><code>14_llm_vs_csn.py</code>: Script to check the overlap between the links provided by the LLM and the one associated with the related query in the CodeSearchNet dataset. The input of this script is the <code>queries.csv</code>and <code>queries_noTrivial.csv</code> file.</li> <li><code>cloningGraph.R</code>: R script to generate the cloning graph. The input of this script is the <code>merged_analysis.csv</code> file.</li> </ul>

opencc-by-4.0Nov 2024View details →

zenodo28/100

Out of Style: Misadventures with LLMs and Code Style Transfer

CSB: A Dataset and Benchmark for Code Style Transfer Tasks

opencc-by-4.0Jan 2024View details →

zenodo28/100

Knowledge Graph Triple Validation by LLMs and Human-in-the-Loop

Suplementary material for the sumbitted article to the IPM Special issue on Large Language Models and Data Quality for Knowledge Graphs.  The dataset is an extension of [1] and includes the following columns: <ul> <li>subj the subject/head of the triple</li> <li>rel the predicate of the triple</li> <li>obj the object/tail of the triple</li> <li>support-level indicating the reliability of the triple</li> <li>ann-random[1: valid, 0: invalid], randomly selected annotation from the expert annotations avaialble in [1] </li> <li>ann-new [1: valid, 0: invalid], junior expert annotation </li> <li>gpt-4o-1 [1: valid, 0: invalid], response from 1st GPT prompt</li> <li>gpt-4o-2 [1: valid, 0: invalid], response from 2nd GPT prompt</li> <li>gpt-4o-3 [1: valid, 0: invalid], response from 3rd GPT prompt</li> <li>gpt-4o-majority [1: valid, 0: invalid], GPT annotation, computed as majority vote of gpt-4o-1,gpt-4o-2,gpt-4o-3</li> <li>claude-1 [1: valid, 0: invalid], response from 1st claude prompt</li> <li>claude-2 [1: valid, 0: invalid], response from 2nd claude prompt</li> <li>claude-3 [1: valid, 0: invalid], response from 3rd claude prompt</li> <li>claude-majority [1: valid, 0: invalid], claude annotation, computed as majority vote of claude-1,claude-2,claude-3</li> <li>llama-1 [1: valid, 0: invalid], response from 1st llama prompt</li> <li>llama-2 [1: valid, 0: invalid], response from 2nd llama prompt</li> <li>llama-3 [1: valid, 0: invalid], response from 3rd llama prompt</li> <li>llama-majority [1: valid, 0: invalid], llama annotation, computed as majority vote of llama-1,llama-2,llama-3</li> </ul> [1] https://github.com/danilo-dessi/SKG-pipeline/tree/main/eval

opencc-by-4.0Sep 2024View details →

zenodo28/100

Automating the process of Requirements Modeling with LLMs

Open the record for dataset details and reuse information.

opencc-by-4.0May 2024View details →

zenodo28/100

Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects

Contacts: website: https://labsoft-ufmg.github.io/ email: henrique.mg.bh@gmail.com

opencc-by-4.0Jun 2024View details →

zenodo28/100

Dataset | Do LLMs find the actual or the expected test oracle?

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →

zenodo28/100

Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects

Contact website: https://labsoft-ufmg.github.io/ email: henrique.mg.bh@gmail.com

opencc-by-4.0Oct 2024View details →

zenodo28/100

Data for ICSE 2025 paper "Search-Based LLMs for Code Optimization"

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2024View details →

zenodo28/100

Hyper-personalization for LLMs

Diagram showing the systems and setup required for a useful and ethical hyper-personalization of Large Language Models (LLM).

opencc-by-4.0Mar 2023View details →

ClinicalTrials.gov28/100

Multi-agent LLMs for Decision Support in Cervical Cancer During Pregnancy

ClinicalTrials.gov study NCT07318701. IPD Sharing: NO. Countries: 0. Publications: 10.

closedIPD-NOFeb 2026View details →

zenodo24/100

Analysis of the MMLU Benchmark for the evaluation of LLMs in Spanish

<h2>Description</h2> This dataset contains a set of files that had been used for an analysis of the MMLU benchmark for the Spanish evaluation of LLMs. The aim of the study was to analyze the consequences of using benchmarks designed in English and translated, by automatic translation tools, into Spanish in the evaluation of Large Language Models (LLMs). Therefore, the final objective was to highlight the importance of an accurate and specific multilingual evaluation benchmarks in order to promote the development of LLMs in other languages. To achieve this goal, a method for evaluating LLMs has been designed to extract information about how they respond to selected tests of the MMLU benchmark. The process has generated a set of files that are included in this repository. The files are divided into three folders, each of which is named after the MMLU test category that corresponds to the data it stores. The tests that have been selected are Miscellaneous, Philosophy and US_Foreign_Policy. Each folder contains ten files. First of all, the GPT-3.5-Turbo and GPT-4 model answers to the tests are included, in their original version, in a Spanish version translated with Azure Translator, and another version translated with ChatGPT. These files, which are six in total for each test, have been named according to the following structure: category-translation-responses-model.xlsx (e.g., miscellaneous-azure-responses-ChatGPT4.xlsx). The next steps of the method selected questions where the model failed in one of the translated versions and answered correctly in the original. For this selection of the GPT-4 model, a manual analysis was carried out. The analysis searched for the relationship between the model failure and the errors made in the translation. This has been included in two files for each test, one for Azure translation failures and one for ChatGPT failures. The files have been named as category_translation_manual_analysis_model.xlsx, and they also contain a classification of the linguistic fails of the automatic translation. Finally, after performing the analysis, the error selection was manually retranslated and rerun on the model to see if it changed its responses. These results are shown in the two remaining files in each folder: category_translation_corrected_questions.xlsx.

opencc-by-4.0May 2024View details →

zenodo20/100

Repository for "Evaluating LLMs for Hardware Design and Test"

<h2>Data Repository for "Evaluating LLMs for Hardware Design and Test"</h2> This repository contains the conversations and related scripts for the paper "Evaluating LLMs for Hardware Design and Test", submitted to ISLAD 2024. The repository is organized as follows: <ul> <li> `scripted-benchmarks` - this contains additional data used in the paper, which examines a more rigid process when exploring the potential applications for LLMs in hardware design. Here, each model chats are separated by subdirectory.</li> <li>`scripted-benchmarks-gpt4-tt03` - this contains just the benchmarks used in the paper, made by the first run of GPT-4, which were used for tapeout in Tiny Tapeout 3.</li> </ul> The tt03 directory contains the GitHub action scripts required to invoke OpenLane and produce synthesis files, as well as used to perform simulation tests.

opencc-by-nc-4.0Apr 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

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allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

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Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record