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85 results for “Translation models”
Finite element method (FEM) models for translational research in non-invasive brain stimulation
<p>Finite element method (FEM) models for non-invasive brain stimulation modeling using SimNIBS or other compatible software.<br> The mouse and monkey models are described in detail in Alekseichuk et al., Comparative modeling of transcranial magnetic and electric stimulation in mouse, monkey, and human, NeuroImage 2019.<br> The Petri dish model follows a typical experimental setup for in-vitro TMS, similar to what is described in Lenz et al. Repetitive magnetic stimulation induces plasticity of inhibitory synapses, Nature Communications 2016.<br> <br> The following files are included:<br> 1. Brain tissue slice in a Petri dish.<br> 2. Normal adult male nude mouse "Digimouse" (brain volume of 0.38 cm3).<br> 3. Normal adult male capuchin monkey "S" (brain volume of 68.31 cm3).<br> <br> The models include the following tissues (coded with numbers):<br> 1. White matter volume<br> 2. Grey matter volume<br> 3. CSF volume<br> 4. Skull volume<br> 5. Soft tissues volume<br> 8. Eyeballs volume<br> 1001. White matter outer surfaces<br> 1002. Grey matter outer surfaces<br> 1003. CSF outer surfaces<br> 1004. Skull outer surfaces<br> 1005. Soft tissues outer surfaces<br> 1008. Eyeballs outer surfaces<br> <br> With any questions, please, contact the corresponding authors of the relevant papers or <a href="mailto:aopitz@umn.edu">aopitz@umn.edu</a> (Alexander Opitz).</p>
Figure data and model used in Stranded fossil-fuel assets translate to major losses for investors in advanced economies
<p>The package contains i) the figure code and underlying data to create all figures in the main paper and supplementary information of the journal article and ii) the network and imputation model used to calculate the shock calculation.</p>
GLM2_modified and Results as used in Ma et al: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 2020
<p>Code modified GLM2, scripts and result as used in Ma et al 2019, Ma et al 2019, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-146</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>
Integrative modeling results of in-cell architecture of an actively transcribing-translating expressome
<p>Repository containing good-scoring models, input files, modeling and analysis protocols for the integrative modeling of the M. pneumoniae expressome from in-cell cryo-electron tomography and crosslinking mass spectrometry using IMP.</p>
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>
Specification-Driven Code Translation By Large Language Models: How Far Are We?
<p>The artifacts and dataset for "Specification-Driven Code Translation By Large Language Models: How Far Are We?"</p>
Lost in Translation? Not for Large Language Models: Automated Divergent Thinking Scoring Performance Translates to Non-English Contexts (Datasets)
<p>Datasets for: Zielińska, A., Organisciak, P., Dumas, D., & Karwowski, M. (2023). Lost in translation? Not for large language models: Automated divergent thinking scoring performance translates to non-English contexts. <em>Thinking Skills and Creativity, 50</em>, 101414. https://doi.org/10.1016/j.tsc.2023.101414</p>
AF2 models for "Co-translational assembly promotes functional diversification of paralogous proteins" by Mallik, et al.
<p>AF2 models used for analyses presented in "Co-translational assembly promotes functional diversification of paralogous proteins" by Saurav Mallik, Angel F. Cisneros, Christian R. Landry, and Emmanuel D. Levy.</p> <p>These models were used to analyze the structural divergence of 3703 Obligatory Homomer, 697 Mixed, and 181 Obligatory Heteromer pairs.</p> <p>Folders are separated into different categories:<br>- Models of homomers (from Schweke et al., 2024. Cell):<br> . AF2_HM_full_models: Full structures of homodimeric models.<br> . AF2_HM_nodiso3: Core structures of homomeric models, trimmed using scripts from Schweke et al. (2023).</p> <p>- Models of heteromers (generated in this work):<br> . AF2_HET_full_models: Full structures of heterodimeric models.<br> . AF2_HET_nodiso3: Core structures of heteromeric models, trimmed using a modified version of the code from Schweke et al. (2023) to work with heterodimers.</p>
Mining Proteomic Databases of a Model Plant Medicago truncatula for Mosaic Proteins and Other Unconventional Translation Products
<p><strong>How many different proteins can be produced from a single spliced transcript? Genome annotation projects do not consider the coding potential of reading frames other than that of the reference open reading frames (refORFs). Recently, alternative open reading frames (altORFs) and their translational products, alternative proteins (altProts), have been shown to carry out important functions in various organisms. Overlapping altORFs may be involved in one fundamental mechanism so far overlooked. A few years ago, it was proposed that altORFs may act as building blocks for chimeric (mosaic) polypeptides, which are produced via multiple ribosomal frameshifting events from a single mature transcript. We adopt terminology from that earlier discussion and call this mechanism mosaic translation. This way of extracting and combining genetic information may significantly increase proteome diversity. Thus, we hypothesize that this mechanism may have contributed to the flexibility and adaptability of organisms to a variety of environmental conditions. The idea of mosaic translation is a testable hypothesis, although its direct demonstration is technically very challenging. If confirmed, this concept will revolutionize modern genetics. In this project, we would like to follow a unique strategy for the detection of mosaic proteins in proteomic databases publicly available for a very important model plant <em>Medicago truncatula</em>. The proposed analysis will be based on our own preliminary data already generated in the course of an ongoing TÜBİTAK1002 project. Regardless of whether the evidence for mosaic translation is found in this study, this effort will help identify such proteins later when more proteomic data become available. Finally, our approach can reveal unconventional frameshifting products that derive from the omission of several nucleotides by ribosomes (for example, +2 to +16 frameshifts). Regardless of whether such frameshifted products are parts of mosaic proteins, the potential for their detection makes this project very novel, because frameshifts longer than one nucleotide in the forward direction have not been described so far.</strong></p>
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>This repository (and several sub-repositories) contains the data for the manuscript "His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models"</p> <p>The current repository contains the de-identified metadata of WSIs. Additionally, it contains Supplementary Data S1-S7.</p> <p>Due to the large size of WSIs, the archives have been divided into several parts to satisfy the size limit of Zenodo.</p> <p>HMU-C dataset:</p> <p>part a: <a href="../doi/10.5281/zenodo.12636965">https://zenodo.org/doi/10.5281/zenodo.12636965</a></p> <p>part b: <a href="../doi/10.5281/zenodo.12705912">https://zenodo.org/doi/10.5281/zenodo.12705912</a></p> <p>HMU-1st dataset:</p> <p>part a: <a href="../doi/10.5281/zenodo.12710399">https://zenodo.org/doi/10.5281/zenodo.12710399</a></p> <p>part b: <a href="../doi/10.5281/zenodo.12723825">https://zenodo.org/doi/10.5281/zenodo.12723825</a></p> <p>part c: <a href="../doi/10.5281/zenodo.12724507">https://zenodo.org/doi/10.5281/zenodo.12724507</a></p> <p>part d: <a href="../doi/10.5281/zenodo.12726785">https://zenodo.org/doi/10.5281/zenodo.12726785</a></p> <p>Please fully download all the parts and concatenate them before extraction.</p> <p>Supplementary Data S1-S7:</p> <p><a href="https://zenodo.org/doi/10.5281/zenodo.16763510">https://zenodo.org/doi/10.5281/zenodo.16763510</a></p>
Paradise fish (Macropodus opercularis) as novel translational model for emotional and cognitive function - RAW DATA
<p>The data of the study under the title "Paradise fish (Macropodus opercularis) as novel translational model for emotional and cognitive function". The structure of the data follows the structure of the results section of the paper. Data sheets are labeled according to figure numbers of the papers.</p>
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
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Measuring the aggregated impact of research: Establishing criteria for coding Translational Science Benefits Model data
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A White Matter Atlas and Common Connectivity Space Facilitate the Pig as a Translational Model in Neuroscience
<p>Dataset accompanying the preprint <em>"A White Matter Atlas and Common Connectivity Space Facilitate the Pig as a Translational Model in Neuroscience"</em>. Contained here are the files used for the cross-species browser spec files, as well as the raw and preprocessed pig diffusion-weighted data, and their raw anatomical files and surfaces. </p> <p>The preprint is available here: <a href="https://www.biorxiv.org/content/10.1101/2020.10.13.337436">https://www.biorxiv.org/content/10.1101/2020.10.13.337436</a></p> <p>And the associated code and lighter files can be found here: <a href="https://github.com/neurabenn/pig_connectivity_bp_preprint">https://github.com/neurabenn/pig_connectivity_bp_preprint</a></p> <p> </p>
Are researchers moving away from animal models as a result of poor clinical translation in the field of stroke? an analysis of opinion papers
<p>Objectives</p> <p>Despite decades of research using animals to develop pharmaceutical treatments for stroke patients, few therapeutic options exist. The vast majority of interventions successful in preclinical animal studies have turned out to have no efficacy in humans, or to be harmful to humans. In view of this we explore whether there is evidence of a move away from animal models in this field.</p> <p>Methods</p> <p>We used an innovative methodology, the analysis of opinion papers. Although we took a systematic approach to literature searching and data extraction, this is not a systematic review because the study involves the synthesis of opinions, not research evidence. Data were extracted from retrieved papers in chronological order and analysed qualitatively and descriptively.</p> <p>Results</p> <p>Eighty eligible papers, published between 1979 and 2018, were identified. Most authors were from academic departments of neurology, neuroscience or stroke research. Authors agreed that translational stroke research was in crisis. They held diverse views about the causes of this crisis, most of which did not fundamentally challenge the use of animal models. Some, however, attributed the translational crisis to animal-human species differences and one to a lack of human in vitro models. Most of the proposed solutions involved fine-tuning animal models but authors disagreed about whether such modifications would improve translation. A minority suggested using human in vitro methods alongside animal models. One proposed focusing only on human based in vitro methods.</p> <p>Conclusion</p> <p>Despite recognising that animal models have been unsuccessful in the field of stroke, most researchers exhibited a strong resistance to relinquishing them. Nevertheless there is an emerging challenge to the use of animal models, in the form of human focused in vitro approaches. For the sake of stroke patients there is an urgent need to revitalise translational stroke research and explore the evidence for these new approaches.</p>
Translated spherical harmonic gravitational field models for Phobos and 433 Eros
<p>The dataset contains the spherical harmonic gravitational field models for the Martian moon Phobos and the asteroid 433 Eros, which are translated from the original models derived in the respective coordinate systems.</p> <p>The gravity model files contain the normalized spherical harmonic coefficients in the standard format in the folder "gravity". The files named with the prefix "sphcfs\_" are for: 1. base models tied to the respective shape models of Phobos and Eros; 2. four translated models for the northern and southern halves of Phobos and Eros. The models are derived via boundary-value problems. The translated models are each accompanied by an analytically transformed model for comparison. The corresponding data files are identified with the prefix "tsphcfs\_".</p> <p>The triangulated shape model for Phobos interpolated using the spherical harmonic series by Willner et al. (2014), Planetary and Space Science, vol. 102, pp. 51-59, DOI:10.1016/j.pss.2013.12.006 is given in the ".obj" format. The shape model for Eros by Thomas et al. (2002), Icarus, vol. 155, pp. 18-37, DOI:10.1006/icar.2001.6755 used in the study belongs to the ``NEAR Collected Shape and Gravity Models'' (NEAR-A-5-COLLECTED-MODELS-V1.0) at https://sbn.psi.edu/pds/resource/nearbrowse.html (retrieved in Mar. 2024), and is not included. The dissected shape models are provided for both objects, which are used for model evaluation over the northern and southern halves of the bodies.</p> <p>The parameters of the Brillouin spheres are provided in the folder "sphere".</p> <p>The numerical results in the folder "result" are modeled gravitational potentials alongside the true values around and on the surface of the bodies. The results for the ellipsoidal harmonic models are provided for comparison.</p>
Survey Results for: MM-LDTF: Multi-Model Legal Document Translation Framework for Vernacular Languages
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His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>partab of His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models</p>
Exposure to a Vitamin D Best Practices Toolkit, Model, & E-Tools Increases Knowledge, Confidence, and Translation of Research to Public Health and Practice
<p>Preventable vitamin D deficiency (VDD) is a global health concern. Prevention, early detection, and treatment of vitamin D deficiency aligning with serum 25-hydroxyvitamin D recommendations of 40-60 ng/ml (100-150 nmol/L) by an international panel of 48 vitamin D researchers would result in significant health benefits and cost-savings to individuals and society. However, research shows healthcare professionals lack knowledge and confidence in vitamin D best practices. A vitamin D toolkit was developed, including a model for decision-making support, e-tools, and accompanying resources, and implemented using an online, asynchronous learning management system. The pre-, post-test, and follow-up survey study design aimed to increase nurses’ and dietitians' levels of vitamin D knowledge, confidence, translation of evidence into spheres of practice and influence, and identify translation barriers. Toolkit completion increased participants’ (n=119) knowledge, 31% to 65% (p <0.001) and confidence 2.0 to 3.3 (p<0.001) on a scale of 1-5. Respondents reported using the model (100%) as a framework successfully guiding the translation of vitamin D knowledge into their sphere of influence or practice (94%) and identified translation barriers. The toolkit should be included in interdisciplinary continuing education, research/quality improvement initiatives, healthcare policy, and institutions of higher learning to increase movement of research to practice.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.