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187
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187 results for “Language Data”
On Inter-dataset Code Duplication and Data Leakage in Large Language Models
<p>This dataset encompasses the sparse graph referenced in the publication titled "On Inter-dataset Code Duplication and Data Leakage in Large Language Models."</p> <p>This resource is a snapshot of the original <a href="https://github.com/Antolin1/code-inter-dataset-duplication">repository</a>, and the graph is preserved in the <em>interduplication.db</em> database. The schema of this database is easily understandable and is available in the original repository. Each code snippet is identified by a unique identifier (id_within_dataset) that corresponds to its identification within the dataset from which it was extracted. The complete datasets are stored in .jsonl files within their respective folders (e.g., python-150/data.jsonl, codetrans/data.jsonl, etc.).</p> <p> </p> <p> </p> <p> </p>
The origin and expansion of Pama–Nyungan languages across Australia: Cognate Data
<p>Cognate judgment data and perl script to convert the multistate cognate judgments to a binary matrix for use with Beast. Note that the perl code was developed for internal research purposes. </p>
Data-driven brain network models differentiate variability across language tasks
<p>Data and script associated with the manuscript titled "Data-driven brain network models differentiate variability<br> across language tasks". </p>
Understanding the Rare Inflammatory Disease Using Large Language Models and Social Media Data
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Data for: "Can Language Models Recognize Convincing Arguments?"
<p>For a description, see: https://go.epfl.ch/persuasion-llm</p>
Data of the research "pattern language and the datatype"
<p>Data of the research "pattern language and the datatype" </p> <p>contains the relationship of all patterns in "a pattern language"</p> <p>the results of clustering relationship between patterns by Louvain method</p> <p>the classification results of 3 datatypes </p>
Voice data in Dioula language relating to the words Yes, No, Call, Next, Previous, Dial, Stop, True, False, Enter and Exit
<p><span>For this project entitled “Development of a speech recognition model for under-resourced languages”, the collected dataset includes audio recordings of native speakers of the Dioula language pronouncing a series of specific keywords such as “Yes”, “No”, “Call”, “Next”, “Previous”, “Dial”, “Stop”, “True”, “False”, “Enter” and “Exit”. These recordings were obtained in diverse environments, with a wide variety of speakers to ensure that the different tones, accents, and phonetic variations specific to Dioula are taken into account. Each audio file was assigned to a category corresponding to the spoken word, creating a class-structured dataset. This corpus will be used to train a speech recognition model using deep learning techniques, with the objective of improving recognition accuracy in real contexts. This database is essential for the design and evaluation of a model capable of facilitating access and voice interaction for the Dioula community.</span></p>
Audio data on nominalisation for Angami-Pochuric languages
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Data for: Can language representation models think in bets?
<p>The dataset contains three files: *Item_Sets.xlsx*, *Value_Questions.xlsx*, and *Bet_Questions.xlsx*. Each of the three files corresponds to each of the three benchmarks in the manuscript currently under submission to Royal Society Open Science and is also available as a preprint: <a href="https://arxiv.org/abs/2210.07519">https://arxiv.org/abs/2210.07519</a>.</p> <p>The items in Item_Sets.xlsx is used to create the other two files. Value_Questions.xlsx is used in RQ1, and Bet_Questions.xlsx is used in both RQ2 and RQ3.</p>
Species-aware DNA language modeling - data
<p>Data accompanying the publication Species-aware DNA language modeling.</p> <p>For code, see: https://github.com/DennisGankin/species-aware-DNA-LM (for the latest version) or the code.zip file.</p> <p>The data directory contains model checkpoints, baselines models, evaluation results and datasets used for training, testing and downstream tasks. It has the following structure:</p> <p>data/ Datasets and subdirectories</p> <p> - data/results/ Results from different test runs</p> <p> - data/models/ Model checkpoints</p> <p> - data/baselines/ Baseline models</p>
Evaluation Data For "Feature-Sensitive Coverage for Conformance Testing of Programming Language Implementations"
<p>It contains the evaluation data for PLDI 2023 paper: "Feature-Sensitive Coverage for Conformance Testing of Programming Language Implementations"</p>
Data for arricle entitled 'The grammaticalization of manner expressions into complementizers: Insights from Semitic languages', published in Linguistics
<p>see publication</p>
Anonymized data for paper "Automatic Bug Fixing in the Era of Large Language Models: Interactive Simulation of Programmer Behavior" submitted to ICSE 2024
<p>The project includes the BFP benchmark used in the submitted ICSE 2024 paper titled "Automatic Bug Fixing in the Era of Large Language Models: Interactive Simulation of Programmer Behavior"</p>
Predicting brain activity from word embeddings during natural language comprehension: Example Data
<p>Example data for the "Predicting brain activity from word embeddings during natural language comprehension" tutorial.</p>
Data from: Oceanic barriers promote language diversification in the Japanese Islands
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Data from: The appropriateness of language found in research consent form templates: a computational linguistic analysis
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Data for: Can language representation models think in bets?
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Data from: Gender and language use in scientific grant writing
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Data from: Exploring media representation of the exotic pet trade, with a focus on welfare: Taxonomic, framing, and language biases in peer-reviewed publications and newspaper articles
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Systematic Mapping Study on Domain-Specific Language Development Tools - Data Repository
<p>Domain-specific languages (DSL) are programming or modeling languages devoted to a given application domain. There are many tools used to support the implementation of a DSL, making hard the decision-making process for one or another. In this sense, identifying and mapping their features is relevant for decision-making by academic and industrial initiative on DSL development. <br> Objective: The goal of this work is to identify and map the tools, Language Workbenches (LW), or frameworks that were proposed to develop DSLs discussed and referenced in publications between 2012 and 2019. <br> Method: A Systematic Mapping Study (SMS) of the literature scoping tools for DSL development. <br> Results: We identified 59 tools, including 9 under a commercial license and 41 with non-commercial licenses, and analyzed their features from 230 papers. <br> Conclusion: There is a substantial amount of tools that cover a large number of features. Furthermore, we observed that usually, the developer adopts one type of notation to implement the DSL: textual or graphical. We also discussed research gaps, such as a lack of tools that allow meta-meta model transformations and that support modeling tools interoperability. </p>
ScienceDex guides
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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.