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Dataset results
414 results for “Generative Model”
29 Atrial Models created with a Patient-specific Augmented Atrial model Generation Tool (AugmentA)
<p>This dataset is part of the publication "AugmentA: Patient-specific Augmented Atrial model Generation Tool" (L. Azzolin et al., Preprint: <a href="https://doi.org/10.1101/2022.02.13.22270835">doi:10.1101/2022.02.13.22270835</a>). It consists of 29 statistical shape model instances derived using a non-rigid fitting algorithm to 29 magnetic resonance imaging segmentations from the University Heart Center Freiburg-Bad Krozingen. Our Patient-specific Augmented Atrial model Generation Tool (AugmentA) provided bilayer atrial models resampled with an average edge length of 0.4 mm and augmented with anatomical labels and fiber orientation so that they are ready to use for electrophysiological simulations.</p> <p> </p>
Using species distribution modeling to generate relative abundance information in unstable territories: conservation of Felidae in Mexico
<p>Raw data used in the abovementioned manuscript</p>
Refinement of an Attacker Model for Attack Paths Generation | Test Dataset
<p>This repository contains the test models and associated tests of the evaluation of the bachelor thesis "Refinement of an Attacker Model for Attack Paths Generation" / German: "Verfeinerung des Angreifermodells und Fähigkeiten in einer Angriffspfadgenerierung". The basis for the model used is the TravelPlaner CaseStudy. The tests for measuring the performance delta are also included.</p> <p>Palladio Bench 5, Java 11 and the following GitHub repositories are used for execution:<br> Analysis: https://github.com/Patrick-Spiesberger/Palladio-Addons-ContextConfidentialityAnalysis_Mitigation<br> Meta model: https://github.com/Patrick-Spiesberger/Palladio-Addons-ContextConfidentialityMetamodell_Mitigation<br> <br> Environment installation is included in the README files. See also the previous work by Maximilian Walter:<br> Analysis: https://github.com/FluidTrust/Palladio-Addons-ContextConfidentiality-Analysis<br> Meta Model: https://github.com/FluidTrust/Palladio-Addons-ContextConfidentiality-Metamodel</p>
Experimental modelling of tsunamis generated by pyroclastic density currents: the effects of particle size distribution on wave generation
<p><strong>Data set for experimental videos modelling the entrance of a fluidised granular flow into the water.</strong></p> <p>The data set includes:</p> <ul> <li>a MATLAB script to calculated theoretical volume and density of a non-fluidised mixture.</li> <li>Properties of the fluidised granular flows on the fluidising ramp: volume, density, thickness, flow and impact Froude numbers, kinetic energy of the fluidised flows.</li> <li>Properties of the surface elevation: surface elevation at x = 1.6m away from the shoreline, velocity of the leading wave, maximum/minimum amplitudes, potential energy of the generated waves and the energy transfer between the flow and the water column.</li> <li>Properties of the underwater gravity current - displacement, velocity and thickness of the underwater velocity current.</li> <li>a MATLAB script to visualise the surface elevation.</li> </ul>
Data from: A stochastic generative model for citation networks among academic papers
<p>We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model.</p>
Supplementary Materials: Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale
<p>Supplementary materials (aka data artifact or data archive) for our NAPAC22 publication: "Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale" (Paper ID: TUYE2).</p> <p>Work supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative. This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231.<br> This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.</p>
Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"
<p>Joint embedding data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"</p> <p>Code available at https://github.com/MTreppner/multiomics_dgms</p>
Dataset of AI-generated code created by various versions of GPT model
<p>This is the dataset used for the paper "<span>Human vs AI: Investigation of Security Risks in AI-generated </span><span>Code via Comparison with Human-written Code".</span></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" by Negri-Ribalta et al. The dataset provides all the information of all the papers gathered, their database of origin, and if it was accepted/rejected/duplicated. </p> <p>The file is in xls format .</p>
Detailed dataset and code generation for Artificial intelligence-based modelling of compressive strength of slurry infiltrated fiber concrete
Open the record for dataset details and reuse information.
Exploring The Potential of GPT-3-based Large Language Model For Melody Generation
<p>Here, we provide the dataset used, all generated melodies and melodies used to conduct subjective listening test.</p>
Analyzing the Dependability of Large Language Models for Code Clone Generation.
<div> <p>data.zip: <br><br>This dataset includes a collection of ten LeetCode programming problems used in the study "Analyzing the Dependability of Large Language Models for Code Clone Generation". At the top level, you will find a CSV file containing all the initial LeetCode data. Each subdirectory at this level represents a specific LeetCode problem. Within these subdirectories, you will find the original solutions, their behaviors, the input corpus, as well as folders dedicated to various temperatures, models, and code cloning tasks. Additionally, within the "repeated" folder, you will find the original LLM-generated snippets, the preprocessed snippets with the snippet behavior, and the results.</p> <p>characterizing_code_clones_project.zip: </p> <p>This zipped directory encompasses the core scripts and results used in the "Characterizing Code Clones of LLMs" research. The common folder was used to run the whole pipeline, the various parts of the pipeline are each in a folder as well as the various data analysis scripts! </p> <p> </p> </div>
Analyzing the Dependability of Large Language Models for Code Clone Generation
<p>data.zip: <br><br>This dataset includes a collection of ten LeetCode programming problems used in the study "Analyzing the Dependability of Large Language Models for Code Clone Generation". At the top level, you will find a CSV file containing all the initial LeetCode data. Each subdirectory at this level represents a specific LeetCode problem. Within these subdirectories, you will find the original solutions, their behaviors, the input corpus, as well as folders dedicated to various temperatures, models, and code cloning tasks. Additionally, within the "repeated" folder, you will find the original LLM-generated snippets, the preprocessed snippets with the snippet behavior, and the results.</p> <p>characterizing_code_clones_project.zip: </p> <p>This zipped directory encompasses the core scripts and results used in the "Characterizing Code Clones of LLMs" research. The common folder was used to run the whole pipeline, the various parts of the pipeline are each in a folder as well as the various data analysis scripts! </p>
PhysicsGen - Can Generative Models Learn from Images to Predict Complex Physical Relations?
<p>This dataset comprises 300,000 pairs of images designed for the advancement of generative model applications in physical simulations. Each pair consists of an input image and its corresponding output image that represents a physical simulation. The dataset aims to facilitate research into whether generative models can effectively learn and reproduce complex physical dynamics from visual data, potentially replacing traditional differential equation-based methods with significant computational speedups.</p> <p>Data, baseline models and evaluation code: <a href="https://www.physics-gen.org">https://www.physics-gen.org</a></p>
Supplemental materials for AaceGEN: Attention Guided Adversarial Code Example Generation for Deep Code Models
Open the record for dataset details and reuse information.
Benchmarking Study of Deep Generative Models for Inverse Polymer Design: Generation Results
Open the record for dataset details and reuse information.
Data for: Global GPP estimates at 8-day/monthly/annual temporal resolution generated by the PTEC model
<p>PTEC provides spatiotemporally estimates of Gross Primary Productivity based on a two-leaf light use efficiency model incorporating plant water status and phenology. PTEC integrates a set of satellite and climate variables within a parsimonious modeling framework to be simple yet robust and grounded on eco-physiological principles. Available at 8-day/monthly/annual and 0.05° resolution from 2001 to 2021, PTEC shows superior performance compared to benchmark products.</p>
Deep Neural Models for Medical Concept Normalization in User-Generated Texts
<p>PsyTar folds used for experiments in the paper "Deep Neural Models for Medical Concept Normalization in User-Generated Texts" to be published at ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop. </p> <p>All other datasets used in the paper can be found in the following places:</p> <p>Cadec random: https://zenodo.org/record/55013#.XPE1MC1eN24<br> Cadec custom: https://yadi.sk/d/GZoWm1wBxzyW_w</p> <p>SMM4H dataset: in the paper "Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H) - 2017 shared task"<br> <br> Bibtex:</p> <p>@inproceedings{miftahutdinov2019,<br> title = "Deep Neural Models for Medical Concept Normalization in User-Generated Texts",<br> author = "Miftahutdinov, Zulfat and Tutubalina, Elena",<br> booktitle = "Proceedings of {ACL} 2019, Student Research Workshop",<br> month = jul,<br> year = "2019",<br> address = "Florence, Italy",<br> publisher = "Association for Computational Linguistics",<br> }</p>
Replication Package: Microservice-tailored Generation of Session-based Workload Models for Representative Load Testing
<p>This is the replication package for the publication <em>Microservice-tailored Generation of Session-based Workload Models for Representative Load Testing</em>, MASCOTS 2019. It holds the experiment setup, results, and detailed analyses of the results.</p> <p>The README.md (or README.pdf) contains further descriptions and instructions.</p>
Fig. 4 Species distribution models for Vaejovis carolinianus. Results were projected onto LGM conditions from MIROC a and CCSM4 b data sources invoking the model generated using current climates data c in Pliocene origins, Pleistocene refugia, and postglacial range expansions in southern devil scorpions (Vaejovidae: Vaejovis carolinianus)
Fig. 4 Species distribution models for Vaejovis carolinianus. Results were projected onto LGM conditions from MIROC a and CCSM4 b data sources invoking the model generated using current climates data c. Localities used to test and train the model are indicated by
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.