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datasets available to search
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Dataset results
575 results for “Modeling Codes”
A primary hierarchically organized patient-derived model enables in depth interrogation of stemness driven by the coding and non-coding genome [ATAC-Seq]
GEO Series GSE211498. Homo sapiens. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Expressing profiles of small non-coding RNA in mouse models of oxygen-induced retinopathy (OIR)
GEO Series GSE84303. synthetic construct; Mus musculus. 6 samples. Type: Non-coding RNA profiling by array.
Multibeam bathymetry data, multi-channel seismic reflection profiles and pore water modeling code
<p>Supplementary material for "Complex architecture of mud volcano systems: new insights on flow pathways unravel intricate fluid circulation"</p>
When Large Language Models Meet Fragile Code Completion Dataset
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Data and code for: Modeling algal defenses under multiple stressors: impacts on explanatory and predictive performance
<p>First release for Zenodo</p>
Model code and observed dataset
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Combining Large Language Models with Static Analyzers for Code Review Generation
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CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models
<p>This repository contains prompts generated by CodeIPPrompt, a platform used to assess potential intellectual property infringement risks associated with the output of code language models. The source code of the platform can be found at our GitHub repository: <a href="https://github.com/zh1yu4nyu/CodeIPPrompt">https://github.com/zh1yu4nyu/CodeIPPrompt</a>. Detailed information regarding the datasets, as well as usage instructions, can be found in the README.md file.</p> <p>The paper has been accepted by International Conference on Machine Learning (ICML) 2023. If you find this work helpful, please cite us as follows:</p> <pre><code class="language-markdown">@inproceedings{yu2023codeipprompt, title={CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models}, author={Yu, Zhiyuan and Wu, Yuhao and Zhang, Ning and Wang, Chenguang and Vorobeychik, Yevgeniy and Xiao, Chaowei}, booktitle={International Conference on Machine Learning}, year={2023}, organization={PMLR} }</code></pre>
Data and codes for Landslide hazard spatiotemporal modelling
<p>Data and codes for submissions</p>
Serotonergic-dependent effects of exercise and elevated stress hormone on small non-coding RNA transcriptomics and proteomics in a mouse model of affective disorders
GEO Series GSE247679. Mus musculus. 31 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Small non-coding RNAs in extracellular vesicles derived from equine synovial fluid and plasma in a model of equine osteoarthritis
GEO Series GSE195807. Equus caballus. 78 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Microarray analysis of hub genes, non-coding RNAs and pathways in lung after whole body irradiation in a mouse model
GEO Series GSE202586. Mus musculus. 72 samples. Type: Non-coding RNA profiling by array.
Construction of a Diagnosis Model for Tuberculosis based on Long Non-coding RNA
GEO Series GSE289620. Homo sapiens. 24 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation
<p>Please find the most up to date version of the dataset at<br>https://github.com/RISElabQueens/forge24-code-translation</p> <p> </p> <p>Thank you</p>
Paper samples for the SLR "A systematic literature review on the impact of AI models on the security of code generation"
<p>Here we provide the whole list of papers that were queried for the SLR "A systematic literature review on the impact of AI models on the security of code generation", their acceptance/rejection with the reason and the final sample after reading the whole text and applying a quality review. </p> <p> </p> <p>Updated version at this link:https://zenodo.org/records/11092334</p>
Model codes and data for ``Low-cost High-Speed Photogrammetry for Measuring Dynamic Flow Deposits"
<p>The dataset contains the raw and processed data of the experiment, and the Matlab code is used to synchronize the cameras. The content includes: </p> <p><br>1. CatchFlashlights.m: Matlab algorithm to identify flashlight time steps for camera synchronization.<br>2. cameras_01_10.rar, cameras_11_20.rar, ..., cameras_61_70.rar: videos for the experiment, including calibration, flashlights, and fan evolution for the 70 cameras.<br>3. targets.txt: xyz data for 100 reference points.<br>4. DEM_tif_600_699.rar, DEM_tif_700_799.rar, and DEM_tif_800_900.rar: DEMs for every second during 600-900 seconds for the debris flow fan experiment described in the manuscript.</p>
Model code and Data for manuscript 'The Role of Wave-induced Mixing in Spring Phytoplankton Bloom in the South Yellow Sea'
<p>This repository contains four files:</p> <p>(1) Wave-induced mixing data for China seas;</p> <p>(2) Water transparency data for the South Yellow Sea;</p> <p>(3) Observation data used in this study; and</p> <p>(4) Model code for our ecological model and model experiments, including configuration data file. </p>
Code and data from : Using a spatially explicit population model to evaluate cost-effective management scenarios for an invasive deer population
<p>This record contains the following:</p> <p>-"WoJ SEPM.Rmd": Script used to build our spatially explicit population model, run simulations for our scenarios and calculate population summary statistics</p> <p>-"WoJ cpue.Rmd": Script used to run our catch-per-unit-effort model that estimates relationship between deer density and number of deer shot per hour</p> <p>-"Cost estimates.Rmd": Script used to calculate costs for each scenario</p> <p>-"Costs functions.R": Functions that are called in the "Cost estimates" script.</p> <p> </p> <p>In addition, all datafiles required to run the scrips are included here.</p>
MITgcm simulations of sea level response to freshwater injected at the surface and at depth in southern high latitudes: Model output and analysis code
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RNA-sequencing analysis of aberrantly expressed long non-coding RNAs and mRNAs in a mouse model of ventilator-induced lung injury
GEO Series GSE114132. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
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.