Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
128
datasets available to search
ShareScore release 0.9.0
Dataset results
128 results for “translation studies”
Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code
<p>Artifact repository for the paper <a href="http://arxiv.org/abs/2308.03109" rel="nofollow"><em>Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code</em></a>, accepted at <em>ICSE 2024</em>, Lisbon, Portugal. Authors are <a href="https://rangeetpan.github.io/" rel="nofollow">Rangeet Pan</a>* <a href="https://alirezai.cs.illinois.edu/" rel="nofollow">Ali Reza Ibrahimzada</a>*, <a href="http://rkrsn.us/" rel="nofollow">Rahul Krishna</a>, Divya Sankar, Lambert Pougeum Wassi, Michele Merler, Boris Sobolev, Raju Pavuluri, Saurabh Sinha, and <a href="https://reyhaneh.cs.illinois.edu/index.htm" rel="nofollow">Reyhaneh Jabbarvand</a>.</p> <h3>Install</h3> <p>This repository contains the source code for reproducing the results in our paper. Please start by cloning this repository:</p> <div> <pre><code>git clone https://github.com/Intelligent-CAT-Lab/PLTranslationEmpirical </code></pre> </div> <p>We recommend using a virtual environment for running the scripts. Please download <code>conda 23.11.0</code> from this <a href="https://docs.conda.io/projects/miniconda/en/latest/miniconda-other-installer-links.html" rel="nofollow">link</a>. You can create a virtual environment using the following command:</p> <div> <pre><code>conda create -n plempirical python=3.10.13 </code></pre> </div> <p>After creating the virtual environment, you can activate it using the following command:</p> <div> <pre><code>conda activate plempirical </code></pre> </div> <p>You can run the following command to make sure that you are using the correct version of Python:</p> <div> <pre><code>python3 --version && pip3 --version </code></pre> </div> <h3>Dependencies</h3> <p>To install all software dependencies, please execute the following command:</p> <div> <pre><code>pip3 install -r requirements.txt </code></pre> </div> <p>As for hardware dependencies, we used 16 NVIDIA A100 GPUs with 80GBs of memory for inferencing models. The models can be inferenced on any combination of GPUs as long as the reader can properly distribute the model weights across the GPUs. We did not perform weight distribution since we had enough memory (80 GB) per GPU.</p> <p>Moreover, for compiling and testing the generated translations, we used Python 3.10, g++ 11, GCC Clang 14.0, Java 11, Go 1.20, Rust 1.73, and .Net 7.0.14 for Python, C++, C, Java, Go, Rust, and C#, respectively. Overall, we recommend using a machine with Linux OS and at least 32GB of RAM for running the scripts.</p> <p>For running scripts of alternative approaches, you need to make sure you have installed <a href="https://github.com/immunant/c2rust">C2Rust</a>, <a href="https://github.com/gotranspile/cxgo">CxGO</a>, and <a href="https://github.com/paulirwin/JavaToCSharp">Java2C#</a> on your machine. Please refer to their repositories for installation instructions. For Java2C#, you need to create a <code>.csproj</code> file like below:</p> <div> <pre><code><Project Sdk="Microsoft.NET.Sdk"> <PropertyGroup> <OutputType>Exe</OutputType> <TargetFramework>net7.0</TargetFramework> <ImplicitUsings>enable</ImplicitUsings> <Nullable>enable</Nullable> </PropertyGroup> </Project> </code></pre> </div> <h3>Dataset</h3> <p>We uploaded the dataset we used in our empirical study to <a href="../doi/10.5281/zenodo.8190051" rel="nofollow">Zenodo</a>. The dataset is organized as follows:</p> <ol> <li><a href="https://github.com/IBM/Project_CodeNet">CodeNet</a></li> <li><a href="https://github.com/wasiahmad/AVATAR">AVATAR</a></li> <li><a href="https://github.com/evalplus/evalplus">Evalplus</a></li> <li><a href="https://github.com/apache/commons-cli">Apache Commons-CLI</a></li> <li><a href="https://github.com/pallets/click">Click</a></li> </ol> <p>Please download and unzip the <code>dataset.zip</code> file from Zenodo. After unzipping, you should see the following directory structure:</p> <div> <pre><code>PLTranslationEmpirical ├── dataset ├── codenet ├── avatar ├── evalplus ├── real-life-cli ├── ... </code></pre> </div> <p>The structure of each dataset is as follows:</p> <p>1. CodeNet & Avatar: Each directory in these datasets correspond to a source language where each include two directories <code>Code</code> and <code>TestCases</code> for code snippets and test cases, respectively. Each code snippet has an <code>id</code> in the filename, where the <code>id</code> is used as a prefix for test I/O files.</p> <p>2. Evalplus: The source language code snippets follow a similar structure as CodeNet and Avatar. However, as a one time effort, we manually created the test cases in the target Java language inside a maven project, <code>evalplus_java</code>. To evaluate the translations from an LLM, we recommend moving the generated Java code snippets to the <code>src/main/java</code> directory of the maven project and then running the command <code>mvn clean test surefire-report:report -Dmaven.test.failure.ignore=true</code> to compile, test, and generate reports for the translations.</p> <p>3. Real-life Projects: The <code>real-life-cli</code> directory represents two real-life CLI projects from Java and Python. These datasets only contain code snippets as files and no test cases. As mentioned in the paper, the authors manually evaluated the translations for these datasets.</p> <h3>Scripts</h3> <p>We provide bash scripts for reproducing our results in this work. First, we discuss the translation script. For doing translation with a model and dataset, first you need to create a <code>.env</code> file in the repository and add the following:</p> <div> <pre><code>OPENAI_API_KEY=<your openai api key> LLAMA2_AUTH_TOKEN=<your llama2 auth token from huggingface> STARCODER_AUTH_TOKEN=<your starcoder auth token from huggingface> </code></pre> </div> <p>1. Translation with GPT-4: You can run the following command to translate all <code>Python -> Java</code> code snippets in <code>codenet</code> dataset with the <code>GPT-4</code> while top-k sampling is <code>k=50</code>, top-p sampling is <code>p=0.95</code>, and <code>temperature=0.7</code>:</p> <div> <pre><code>bash scripts/translate.sh GPT-4 codenet Python Java 50 0.95 0.7 0 </code></pre> </div> <p>2. Translation with CodeGeeX: Prior to running the script, you need to clone the CodeGeeX repository from <a href="https://github.com/THUDM/CodeGeeX">here</a> and use the instructions from their artifacts to download their model weights. After cloning it inside <code>PLTranslationEmpirical</code> and downloading the model weights, your directory structure should be like the following:</p> <div> <pre><code>PLTranslationEmpirical ├── dataset ├── codenet ├── avatar ├── evalplus ├── real-life-cli ├── CodeGeeX ├── codegeex ├── codegeex_13b.pt # this file is the model weight ├── ... ├── ... </code></pre> </div> <p>You can run the following command to translate all <code>Python -> Java</code> code snippets in <code>codenet</code> dataset with the <code>CodeGeeX</code> while top-k sampling is <code>k=50</code>, top-p sampling is <code>p=0.95</code>, and <code>temperature=0.2</code> on GPU <code>gpu_id=0</code>:</p> <div> <pre><code>bash scripts/translate.sh CodeGeeX codenet Python Java 50 0.95 0.2 0 </code></pre> </div> <p>3. For all other models (StarCoder, CodeGen, LLaMa, TB-Airoboros, TB-Vicuna), you can execute the following command to translate all <code>Python -> Java</code> code snippets in <code>codenet</code> dataset with the <code>StarCoder|CodeGen|LLaMa|TB-Airoboros|TB-Vicuna</code> while top-k sampling is <code>k=50</code>, top-p sampling is <code>p=0.95</code>, and <code>temperature=0.2</code> on GPU <code>gpu_id=0</code>:</p> <div> <pre><code>bash scripts/translate.sh StarCoder codenet Python Java 50 0.95 0.2 0 </code></pre> </div> <p>4. For translating and testing pairs with traditional techniques (i.e., C2Rust, CxGO, Java2C#), you can run the following commands:</p> <div> <pre><code>bash scripts/translate_transpiler.sh codenet C Rust c2rust fix_report bash scripts/translate_transpiler.sh codenet C Go cxgo fix_reports bash scripts/translate_transpiler.sh codenet Java C# java2c# fix_reports bash scripts/translate_transpiler.sh avatar Java C# java2c# fix_reports </code></pre> </div> <p>5. For compile and testing of CodeNet, AVATAR, and Evalplus (Python to Java) translations from GPT-4, and generating fix reports, you can run the following commands:</p> <div> <pre><code>bash scripts/test_avatar.sh Python Java GPT-4 fix_reports 1 bash scripts/test_codenet.sh Python Java GPT-4 fix_reports 1 bash scripts/test_evalplus.sh Python Java GPT-4 fix_reports 1 </code></pre> </div> <p>6. For repairing unsuccessful translations of Java -> Python in CodeNet dataset with GPT-4, you can run the following commands:</p> <div> <pre><code>bash scripts/repair.sh GPT-4 codenet Python Java 50 0.95 0.7 0 1 compile bash scripts/repair.sh GPT-4 codenet Python Java 50 0.95 0.7 0 1 runtime bash scripts/repair.sh GPT-4 codenet Python Java 50 0.95 0.7 0 1 incorrect </code></pre> </div> <p>7. For cleaning translations of open-source LLMs (i.e., StarCoder) in codenet, you can run the following command:</p> <div> <pre><code>bash scripts/clean_generations.sh StarCoder codenet </code></pre> </div> <p>Please note that for the above commands, you can change the dataset and model name to execute the same thing for other datasets and models. Moreover, you can refer to <a href="https://github.com/Intelligent-CAT-Lab/PLTranslationEmpirical/blob/main/prompts/README.md"><code>/prompts</code></a> for different vanilla and repair prompts used in our study.</p> <h3>Artifacts</h3> <p>Please download the <code>artifacts.zip</code> file from our <a href="../doi/10.5281/zenodo.8190051" rel="nofollow">Zenodo</a> repository. We have organized the artifacts as follows:</p> <ol> <li>RQ1 - Translations: This directory contains the translations from all LLMs and for all datasets. We have added an excel file to show a detailed breakdown of the translation results.</li> <li>RQ2 - Manual Labeling: This directory contains an excel file which includes the manual labeling results for all translation bugs.</li> <li>RQ3 - Alternative Approaches: This directory contains the translations from all alternative approaches (i.e., C2Rust, CxGO, Java2C#). We have added an excel file to show a detailed breakdown of the translation results.</li> <li>RQ4 - Mitigating Translation Bugs: This directory contains the fix results of GPT-4, StarCoder, CodeGen, and Llama 2. We have added an excel file to show a detailed breakdown of the fix results.</li> </ol> <h3>Contact</h3> <p>We look forward to hearing your feedback. Please contact <a href="mailto:rangeet.pan@ibm.com">Rangeet Pan</a> or <a href="mailto:alirezai@illinois.edu">Ali Reza Ibrahimzada</a> for any questions or comments 🙏.</p>
Figure 5.(a)Linear (y=0.45x + 57.74) dose-response relationship between plasma propranolol to % β- adrenergeric blockade derived from healthy study participants and translate into patients with angina pectoris. This image has been adapted from(Pine et al., 1975).-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Apharmacodynamic model,with parameters in the table below, may be used to visualize the<br> propranolol concentration-effect (β-blockade) relationship in patients suffering from angina pectoris.<br> These results have been adapted from the Pine et al article published in Circulation in 1975 which<br> identified a linear relationship plasma Propranolol (ng/mL) to an effect of % β-Adrenergic Blockade<br> in a single-oral dose of 40mg Propranolol in exercising individuals (Pine et al., 1975).</p>
Yes! We're open. Open science and the future of academic practices in translation and interpreting studies - Supplementary material
<p>Supplementary material to the article "<em>Yes! We’re open</em>. Open science and the future of academic practices in translation and interpreting studies" by Christian Olalla-Soler. </p> <ul> <li>Sheet 1: Translation and Interpreting Studies journals and bibliometric indicators.</li> <li>Sheet 2: Translation and Interpreting Studies articles in Scopus.</li> <li>Sheet 3: Pre-registrations related to translation and interpreting.</li> </ul> <p>Reference:</p> <p>Olalla-Soler, Christian (2021). "<em>Yes! We’re open</em>. Open science and the future of academic practices in translation and interpreting studies". <em>Translation & Interpreting</em> 13 (2): 1-28. <a href="https://doi.org/10.12807/ti.113202.2021.a01">https://doi.org/10.12807/ti.113202.2021.a01</a></p>
Cortical correlates of a translating point-light walker; an event-related potentials study
<p>EEG recordings for all subjects (S1-S13) considered in the study "Cortical correlates of a translating point-light walker; an event-related potentials study". Data are in Brain Vision Analizer (BVA) format. They can be fully explored and analyzed using freely available software, like EEGLab, Brainstorm, Fieldtrip, BIOSIG or NPXLab. Trigger codes S 1, S 2, S 3 and S 4 respectively correspond to the condition 'cwalker' 'twalker' 'cscrambled' 'tscrambled' compared in the study.</p>
Why the Daisy sisters are different a stylometric study on the oeuvre of Swedish author Henning Mankell and the Dutch translations of his work
<ul><li>Corpus metadata.csv: metadata about the works that are included in the corpus (author, title, short title, year of first publication, translator, language, word count)</li><li>table_with_frequencies_Dutch.txt: Relative frequencies as used (and generated) by Stylo for the Dutch texts</li><li>table_with_frequencies_Swedish.txt: Relative frequencies as used (and generated) by Stylo for the Dutch texts</li><li>Zweedse originelen Mankell_Consensus_100-2000_MFWs_Culled_0-50__Classic Delta_C_0.5_EDGES.csv: CSV file of the Swedish corpus as produced by Stylo for import into Gephi.</li><li>Nederlandse vertalingen Mankell_Consensus_100-2000_MFWs_Culled_0-50__Classic Delta_C_0.5_EDGES.csv: CSV file of the translated Dutch corpus as produced by Stylo for import into Gephi.</li><li>oppose_config.txt: configurations in Stylo for the Zeta analysis</li><li>words_avoided_Dutch_random.txt: table of segments with less frequently used words for the results of the Zeta analysis of Daisy sisters versus other literary novels by Mankell in randomized word order (Dutch texts)</li><li>words_avoided_Swedish_random.txt: table of segments with less frequently used words for the results of the Zeta analysis of Daisy sisters versus other literary novels by Mankell in randomized word order (original Swedish texts)</li><li>words_preferred_Dutch_random.txt: table of segments with relatively more frequently used words for the results of the Zeta analysis of Daisy sisters versus other literary novels by Mankell in randomized word order (Dutch texts)</li><li>words_preferred_Swedish_random.txt: table of segments with relatively more frequently used words for the results of the Zeta analysis of Daisy sisters versus other literary novels by Mankell in randomized word order (Swedish texts)</li><li>stylo_config_consensus.txt: configurations Stylo for Bootstrap consensus analysis</li><li>stylo_config.txt: configurations Stylo for PCA</li></ul>
Supplementary materials to the article "Practices and attitudes toward replication in empirical translation and interpreting studies"
<p><strong>Abstract of the paper:</strong></p> <p>This article presents the results of three studies on practices in and attitudes toward replication in empirical translation and interpreting studies. The first study reports on a survey in which 52 researchers in translation and interpreting with experience in empirical research answered questions about their practices in and attitudes toward replication. The survey data were complemented by a bibliometric study of publications indexed in the Bibliography of Interpreting and Translation (BITRA) (Franco Aixelá 2001–2019) that explicitly stated in the title or abstract that they were derived from a replication. In a second bibliometric study, a conceptual replication of Yeung’s (2017) study on the acceptance of replications in neuroscience journals was conducted by analyzing 131 translation and interpreting journals. The article aims to provide evidence-based arguments for initiating a debate about the need for replication in empirical translation and interpreting studies and its implications for the development of the discipline.</p> <p><strong>Description of the supplementary materials:</strong></p> <ul> <li>Material 1: questionnaire in English</li> <li>Material 2: questionnaire in Spanish</li> <li>Material 3: results of both questionnaires</li> <li>Material 4: corpus of identified replications in Translation and Interpreting Studies (TIS)</li> <li>Material 5: acceptance of replications in TIS journals</li> </ul>
Automatic translation and multilingual cultural heritage retrieval: a case study with transcriptions in Europeana (dataset)
<p>The dataset contains all the data required to reproduce the experiments done in the paper "Automatic translation and multilingual cultural heritage retrieval: a case study with transcriptions in Europeana", published in the 25th International Conference on Theory and Practice of Digital Libraries (<a href="http://www.tpdl.eu/tpdl2021/">TPDL'21</a>). In that work we run an experiment using the Europeana CH digital library as a use case, and we evaluated the effectiveness of a multilingual information retrieval strategy using machine translations to English as pivot language. We used the CEF translation service (eTranslation) for the translation of queries and content to English (<a href="https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/eTranslation">https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/eTranslation</a>).</p> <p>The dataset is also available at <a href="https://rnd-2.eanadev.org/share/crosslingual-search/">https://rnd-2.eanadev.org/share/crosslingual-search/</a>, and it is organized in four main folders:</p> <ul> <li><strong>queries</strong>: sample of 68 queries and their translations to English. The queries were issued in languages other than English from the Europeana Portal, using the Europeana’s 1914-1918 thematic collection, between January and August 2019.</li> <li><strong>transcriptions</strong>: sample of 18,257 handwriting transcriptions and its translations to English. The transcriptions are taken from the Europeana 1914-1918 thematic collection, and obtained from the Transcribathon crowdsourcing platform (https://europeana.transcribathon.eu/).</li> <li><strong>solr_configuration</strong>: Apache Solr search engine configuration used in the experiments (which replicates the one used in Europeana).</li> <li><strong>results</strong>: manual evaluation of the query translations, and automatic evaluation of the multilingual retrieval.</li> </ul> <p> </p>
Teen Driving Translation Study (NIH)
ClinicalTrials.gov study NCT05696353. IPD Sharing: YES. Countries: 1. Publications: 28.
Translation Study of a Safe Teen Driving Intervention
ClinicalTrials.gov study NCT04317547. IPD Sharing: YES. Countries: 1. Publications: 28.
A Translational Study of Bevacizumab in Participants With Metastatic Colorectal Cancer
ClinicalTrials.gov study NCT01588990. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Randomized Translational Study to Examine the Effects of Shared Care in Management of Gestational Diabetes
ClinicalTrials.gov study NCT01565564. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Cluster-randomized Controlled Knowledge Translation Feasibility Study in Alberta Community Pharmacies
ClinicalTrials.gov study NCT02191111. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Phase II Single-Arm Study of High-Bioavailability Curcumin as Neoadjuvant Chemoradiotherapy in Mid-to-Low Rectal Cancer: Integrated Clinical and Translational Analysis of Tumor Tissue
ClinicalTrials.gov study NCT07248020. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
A Dose-Ranging Study With Vupanorsen (TRANSLATE-TIMI 70)
ClinicalTrials.gov study NCT04516291. IPD Sharing: YES. Countries: 3. Publications: 1.
Platform Study for Prostate Researching Translational Endpoints Correlated to Response to Inform Use of Novel Combinations
ClinicalTrials.gov study NCT03835533. IPD Sharing: NO. Countries: 1. Publications: 23.
Data from: STAR locally prolongs effective refractory period and increases ventricular tachycardia cycle length without short-term scar formation or functional decline: Insights from a translational porcine model study
Open the record for dataset details and reuse information.
Data from: Can the results of biodiversity-ecosystem productivity studies be translated to bioenergy production?
Biodiversity experiments show that increases in plant diversity can lead to greater biomass production, and some researchers suggest that high diversity plantings should be used for bioenergy production. However, many methods used in past biodiversity experiments are impractical for bioenergy plantings. For example, biodiversity experiments often use intensive management such as hand weeding to maintain low diversity plantings and exclude unplanted species, but this would not be done for bioenergy plantings. Also, biodiversity experiments generally use high seeding densities that would be too expensive for bioenergy plantings. Here we report the effects of biodiversity on biomass production from two studies of more realistic bioenergy crop plantings in southern Michigan, USA. One study involved comparing production between switchgrass (Panicum virgatum) monocultures and species-rich prairie plantings on private farm fields that were managed similarly to bioenergy plantings. The other study was an experiment where switchgrass was planted in monoculture and in combination with increasingly species-rich native prairie mixtures. Overall, we found that bioenergy plantings with higher species richness did not produce more biomass than switchgrass monocultures. The lack of a positive relationship between planted species richness and production in our studies may be due to several factors. Non-planted species (weeds) were not removed from our studies and these non-planted species may have competed with planted species and also prevented realized species richness from equaling planted species richness. Also, we found that low seeding density of individual species limited the biomass production of these individual species. Production in future bioenergy plantings with high species richness may be increased by using a high density of inexpensive seed from switchgrass and other highly productive species, and future efforts to translate the results of biodiversity experiments to bioenergy plantings should consider the role of seeding density.
Rapid Flow-Based Synthesis of Post-Translationally Modified Peptides and Proteins: A Case Study on MYC's Transactivation Domain
<p>Raw data for the project "Rapid Flow-Based Synthesis of Post-Translationally Modified Peptides and Proteins: A Case Study on MYC's Transactivation Domain".</p> <p>Manuscript and supporting information available on Chemical Science: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4SC00481G">https://doi.org/10.1039/D4SC00481G</a></p>
Bone Pain in Multiple Myeloma- a Translational Study
ClinicalTrials.gov study NCT04273425. IPD Sharing: NO. Countries: 1. Publications: 1.
Prospective Translational Study Investigating Predictors of Outcome in Metastatic Renal Cell Carcinoma Patients Treated With Nivolumab (I-Rene Trial)
ClinicalTrials.gov study NCT04891055. IPD Sharing: Not stated. Countries: 1. Publications: 19.
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.