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90 results for “model transformation”
Data supporting "Transformer Model Generated Bacteriophage Genomes are Compositionally Distinct from Natural Sequences"
<p>Sequence and composition data supporting doi: <a href="https://doi.org/10.1101/2024.03.19.585716" target="_blank" rel="noopener">10.1101/2024.03.19.585716</a>. Uncompressed file size is ~5.8GB.</p> <p>Data in zip files is organized by sequence provenance (generRNA, natural, or transformer (megaDNA)). Common file types between folders include:</p> <ul> <li>Multi-record fasta file: Sequence data for all sequences of a given provenance. For generRNA sequences, these are found within the `seq` column of file "MFE_distribution_Fig4a.csv"</li> <li>Composition files: Individual sequence level compositional metrics for sliding 120 bp windows. Only structural metrics were used in this study.</li> <li>Genomad: Results from the genomad pipeline (https://portal.nersc.gov/genomad/)</li> <li>Stats: Aggregate statistics for all sequences of a given provenance.</li> </ul> <p>The natural folder also has a metadata file detailing the taxonomy for all natural sequences.<br><br>Figure datasets are the cleaned (sometimes aggregated) datasets that underly specific figures in the manuscript. The figure designations are based on the order in: https://www.biorxiv.org/content/10.1101/2024.03.19.585716v1.</p>
Web-based Editor for Entity-Relationship Modeling with SQL Transformation Algorithm
<p>With the daily growth of data in all kinds of sectors, such as <span>Information Technolo</span><span>gies (IT)</span>, healthcare, education, commerce or telecommunication, it becomes important to use a high-performance system to manage all this data in the best possible way. Indeed, in the absence of good data management, it can be difficult for these sectors to prevent data loss, ensure safe maintenance or guarantee data security. For this reason, an effective data management system is the Entity-Relationship model. Indeed, thanks to this model, all kinds of sectors have the possibility of designing and organizing their data in relational databases, thus improving data security and better internal communication. Furthermore, it is interesting to modernize the classic approach of the Entity-Relationship model and its visual representations of data in the present day. The technologies of Augmented and Virtual Reality respond precisely to this challenge of innovation in the Entity-Relationship model. Therefore, the first objective of this thesis is to implement the Entity-Relationship model in a Meta-Modeling Platform for Augmented and Virtual Reality. The second objective is to implement an algorithm that transforms an Entity-Relationship model into SQL statements to create database tables. The Entity-Relationship model is used to design the logic of a database, and the implementation of the algorithm is used to create the database.</p>
Transformers for Modeling Physical Systems
<p>Data set associated with the publication <a href="https://arxiv.org/abs/2010.03957">Transformers for Modeling Physical Systems</a>. Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performance for many language related tasks, their applicability outside of the natural language processing field has been minimal. In this work, we propose the use of transformer models for the prediction of dynamical systems representative of physical phenomena. </p> <p>This data set includes data in HDF5 files for:</p> <p>Lorenz ODE:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_training_rk.tar.gz?versionId=c4bd1230-3b22-4d2e-83f0-357146a90423">lorenz_training_rk.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_valid_rk.tar.gz?versionId=3cf95dac-a75d-42d8-85ab-615f7a2ad67f">lorenz_valid_rk.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_test_rk.tar.gz?versionId=bbb4bd3d-33c9-4903-98ed-f7a926dc95db">lorenz_test_rk.tar.gz</a></li> </ul> <p>Flow Around a Cylinder:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_training.tar.gz?versionId=25bd1f3a-03b7-44d0-aa3f-afaffa6cd706">cylinder_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_valid.tar.gz?versionId=58a6e98d-be41-4603-91cb-9522d848cf57">cylinder_valid.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_test.tar.gz?versionId=a3c03293-0278-40ee-b181-2eb53a8d0b47">cylinder_test.tar.gz</a></li> </ul> <p>Gray-Scott Reaction-Diffusion:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_training.tar.gz">grayscott_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_valid.tar.gz?versionId=3bb8aa25-c9c8-494e-a206-e8d1fdb8a88f">grayscott_valid.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_test.tar.gz?versionId=d9cee8a6-b22f-44b9-ae1b-2433caf77e34">grayscott_test.tar.gz</a></li> </ul> <p>Rossler ODE:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/rossler_training.tar.gz?versionId=d44be9cf-8fa7-4eb2-8b6e-8ac129822f51">rossler_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/rossler_valid.tar.gz?versionId=93c658e3-235a-4150-b233-ed514d6c7467">rossler_valid.tar.gz</a></li> </ul> <p>As well as several pretrained embedding models for the Google Collab notebooks on <a href="https://github.com/zabaras/transformer-physx/">Github</a>:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_lorenz_pretrained.pth?versionId=cd30ff4e-34b3-4070-b346-718fb8526dac">embedding_lorenz_pretrained.pth</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_cylinder_pretrained.pth?versionId=69552efb-09e4-49d9-a224-ccc7cff91b86">embedding_cylinder_pretrained.pth</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_rossler_pretrained.pth?versionId=e7d1f4aa-3f31-45fe-91e4-8411e9cd634f">embedding_rossler_pretrained.pth</a></li> </ul> <p>See the Github repository for code base: <a href="https://github.com/zabaras/transformer-physx/">https://github.com/zabaras/transformer-physx/</a></p>
Dataset used in the publication "Using of Transformers Models for Text Classification to Mobile Educational Applications"
<p>Dataset used in the publication "Using of Transformers Models for Text Classification to Mobile Educational Applications".</p> <p>More info about the dataset can be found in the published article.</p>
Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Refinement dataset SPIRA
<p>This dataset was collected over the internet and in hospital wards with the goal of detecting respiratory insufficiency (typically caused by COVID-19). This data collection is part of the SPIRA Project, whose goal is developing a system for recognizing respiratory insufficiency through speech analysis. The datasets presented here were used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger.</p> <p>The spira_trimmed_data file contains the original ~1 hour dataset collected over the internet (control) and in hospital wards (patients) by the SPIRA Project. This is as described in the paper: Deep learning against COVID-19: Respiratory insufficiency detection in Brazilian Portuguese Speech. We include it here for completeness.</p> <p>The spira_control_full_mp3 file contains the complete ~18 hours control data collected over the internet by the SPIRA project. While not useful for respiratory insufficiency detection, the dataset may be used for identifying age and gender as we mention in our paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers.</p>
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
<p>The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. </p> <p>Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (<em>Grus grus</em>) in a spatio-temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. </p> <p>Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.</p> <p>Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability, but can be a useful approach to improving insight gained from model outputs. </p>
Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Pretraining Datasets raw audios from CORAA
<p>This repository contains all the pretraining datasets used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger. These datasets are part of a collection of datasets from the TaRSila project (see https://sites.google.com/view/tarsila-c4ai). The audios published here were in part also published with annotations and transcriptions as the CORAA dataset (see https://github.com/nilc-nlp/CORAA). Here we publish the original raw audios from the following datasets (without transcriptions) - ALIP, C-Oral, SP2010, NURC-Recife, NURC-São Paulo and Programa Certas Palavras. In total, the datasets contain about 800 hours of Brazilian Portuguese Speech.</p> <p>The audios have been converted to mp3 to facilitate the upload. ALIP, C-Oral and SP2010 are integrally contained in one file each. Programa Certas Palavras and NURC-Recife are split in 3 parts each, while NURC-SP is split in 7 parts of roughly equal size. More information on the datasets can be found in the paper Acoustic models of Brazilian Portuguese Speech based on Neural Transformers as well as on the original references which created these datasets.</p>
Figure 1: Abstract Meta-Model-UNDERSTANDING SERVICE COMPOSITION WITH NON-FUNCTIONAL PROPERTIES USING DECLARATIVE MODEL-TO-MODEL TRANSFORMATIONS
<p>To de¯ne the model we ¯rst de¯ned the abstract meta-model which con-<br> tains all the processes which can be used in the application model. These are<br> used to model the behaviour of the application (see Figure 1).</p>
Transformers Model Zoos and Soups: A Population of Language and Vision Models
<p>Model Zoos submitted to the NeurIPS 2024 Dataset & Benchmark track: "<em>Transformer Model Zoos and Soups: A Population of Language and Vision Models</em>"</p> <p>We generate two model zoos, one for computer vision built on the ViT-S architecture, and one for language modeling based on the BERT architecture. For each, we train several backbone models with varying hyperparameters, and further fine-tune them using multiple hyperparameter combinations. We further annotate every model with performance metrics. These include test accuracy and F1-score, as well as the generalization gap. For the vision models, we also include the robust accuracy after a FGSM attack.</p>
Vision-Transformer, ViT, model validation dataset
<p><span>The U.S. cotton industry is highly concerned with removing plastic contamination from cotton lint. A major source of this </span><span>contamination is the plastic used to wrap cotton modules produced by John Deere round module harvesters. A machine-vision </span><span>detection and removal system has been developed to address this problem, using low-cost color cameras to detect plastic in the </span><span>cotton stream and remove it. However, the system requires a lot of calibration and is difficult for cotton gin workers to operate due to </span><span>its reliance on custom machine-vision classifier running on low-cost ARM computers running Linux. This research aims to make the system more user-friendly by adding an </span><span>auto-calibration feature that can track cotton colors and avoid plastic images, reducing the need for skilled personnel to operate the </span><span>system and making it easier for the cotton ginning industry to adopt. This image dataset was created to validate several Vision-</span><span>Transformer, ViT, AI models that in combination provides the key enabling technology for the auto-calibration code.</span></p>
Input data and results of the RECC v2.5 model for the transformation scenarios of the global building stock
<p>This dataset contains the input data and core results of the RECC v2.5 model for the transformation scenarios of the global building stock. For details abou the RECC model, see DOI <a href="https://doi.org/10.1111/jiec.13023" target="_blank" rel="noopener">https://doi.org/10.1111/jiec.13023</a> and the RECC model landing page: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc" target="_blank" rel="noopener">https://www.industrialecology.uni-freiburg.de/odym-recc</a></p> <p>The following data are included in this dataset:</p> <ul> <li>The entire model input database (120 model parameters)</li> <li>The parameters for the sensitivity analysis (8 parameters)</li> <li>The 70 folders with the core results</li> <li>The master classification file RECC_Classifications_Master_V2.0.xlsx</li> <li>The model config file RECC_Config.xlsx</li> <li>The list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The result compilation and exporting configuration file RECCv2.5_EXPORT_Combine_Select.xlsx</li> <li>The main result summary file (extracted from the 70 result folders) Results_Extracted_RECCv2.5_10Regs_sep.xlsx</li> <li>The result summary file for comparison with the CRAFT model timber supply RECCv2.5_10Regs_CRAFT_Coupling_SHARE.xlsx</li> <li>The results of the sensitivity analysis: Results_Extracted_RECCv2.5_10Regs_Sensitivity_sep.xlsx</li> </ul> <p>Note that the result folders of the sensitivity analysis are not archived here (too little information in relation to the data volume). They can be requested from the author. The results can also be recreated by running the RECC model with the sensitivity analysis parameters.</p> <p>The model itself is available as Python code from <a href="https://github.com/IndEcol/RECC-ODYM" target="_blank" rel="noopener">https://github.com/IndEcol/RECC-ODYM</a></p>
Science ready spectra, their best-fitting models and results of Jeans axisymmetric modelling described in the research paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al.
<p>This package contains data presented in the paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al. (2021 Nature Astronomy in press). The dataset can be used to reproduce Figures 3 and 4 from the main manuscript and Extended Data Figures 1, 2, 4, 5 from the Supplementary Information.</p> <p>(1) Python scripts and data points required to reproduce Figure 4 in the manuscript and Extended Data Figure 5 from the Supplementary Information. The data and scripts are presented in a combined .zip archive for both figures.</p> <p>(2) One-dimensional spectra extracted within 1 half-light radius from long-slit Binospec spectra and multi-wavelength far-UV-to-near-IR broadband spectral energy distributions (SEDs) assembled from the photometric measurements extracted within the same aperture for 11 galaxies from the main sample (9 in the Coma cluster and 2 in the Abell 2147 cluster) and 5 galaxies from the supplementary (auxiliary) list. The spectra and SEDs are accompanied with their best-fitting stellar population models and parameters determined by the NBursts+phot algorithm: radial velocity, velocity dispersion, truncation age, final stellar metallicity. The templates are MILES-based models with self-consistent chemical evolution presented in Grishin et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019arXiv190913460G/abstract). The filenames contain the coefficient for galactic winds and the mass fraction of stars in the final starburst, e.g. _l15_60 means lambda=1.5, SSP_frac=60 per cent. The files are presented as binary FITS tables with the fields annotated using unified content descriptors (UCDs) from the list established by the International Virtual Observatory Alliance and physical units where applicable.</p> <p>(3) Two-dimensional profiles of internal kinematics (radial velocity and velocity dispersion) and stellar population properties (truncation age and final stellar metallicity) derived from the analysis of long-slit Binospec spectra for 12 galaxies after adaptive binning, 11 from the main sample and GMP3016 in the Coma cluster from the supplementary sample; best-fitting Jeans axisymmetric models without adaptive binning, i.e. full profiles along the slit. The data are presented in binary FITS tables in the two FITS extensions, one for the profiles derived from the corresponding spectra and the second one for dynamical models.</p>
Reuse of Model Transformations for Propagating Variability Annotations in Annotative Software Product Lines - Evaluation Data
<p>This package contains all data that was produced for and used in the doctoral thesis for evaluating commutativity of propagating annotations in model-driven product lines.<br> This includes the implementation that conducts the evaluation, the measured results, and the input subjects.</p>
Figure data to "Continuous similarity transformation for critical phenomena: easy-axis antiferromagnetic XXZ model"
<p>This collection of data is complementary to the publication "Continuous similarity transformation for critical phenomena: easy-axis antiferromagnetic XXZ model", Matthias R. Walther, Dag-Björn Hering, Götz S. Uhrig, Kai P. Schmidt, arXiv:2211.05689 (https://arxiv.org/abs/2211.05689).</p> <p>It contains the data points calculated by the method of Continuous Similarity Transformation(CST) used in Figures 3,5,6,7 and 8 in the CSV-Format.</p> <p>For details on the CST, the used error estimates and physical quantities we refer the the publication.</p> <p>For details on the format we recommend the README.md file.</p>
Genome report: Genome sequence of 1S1, a transformable and highly regenerable diploid potato for use as a model for gene editing and genetic engineering
<p>Generation of a genomic resource for a readily transformable diploid potato would provide a resource for high throughput functional analysis in potato. The heterozygous <em>Solanum tuberosum</em> Group Phureja clone 1S1 has a high regeneration rate, self-fertility, desirable tuber traits and is amenable to <em>Agrobacterium</em>-mediated transformation. To create a contiguous genome assembly, a homozygous doubled monoploid of 1S1 (DM1S1) was sequenced using 44 Gbp of long reads generated from Oxford Nanopore Technologies (ONT), yielding a 736 Mb assembly that encoded 31,145 protein-coding genes. The final assembly for DM1S1 represents a nearly complete genic space, shown by the presence of 99.6% (C:99.5%[S:97.8%, D:1.7%],F:0.1%,M:0.4%,n:1614) of the Benchmarking Universal Single Copy Orthologs. Variant analysis with Illumina reads from 1S1 was used to deduce its alternate haplotype using the variant calling tools Strelka2 (v2.9.10), GATK's Haplotypecaller (v4.1.4.1), and Freebayes (v1.3.2). These variants were used to create consensus fasta sequences with the DM1S1 assembly using bcftools (v1.9.64).</p>
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
Open the record for dataset details and reuse information.
Genome report: Genome sequence of 1S1, a transformable and highly regenerable diploid potato for use as a model for gene editing and genetic engineering
Open the record for dataset details and reuse information.
Data from: Protein Set Transformer: A protein-based genome language model to power high diversity viromics
Open the record for dataset details and reuse information.
Surface Ozone, NO2, and PM2.5 Concentrations Estimated by the Deep Learning model (Air Transformer) based on Satellite data.
<p>Surface ozone, NO2, and PM2.5 concentrations Estimated by the deep learning model (Air Transformer) based on massive ground-level monitoring, satellite observations, meteorological conditions, dynamic industrial emissions, and other ancillary data from May 2018 to June 2021.</p>
Code generation for classical-quantum software systems modelled in UML - Dataset and EGL Transformation
<p>This dataset contains all the elements necessary for carry out the EGL transformation from UML models to Hybrid and Quantum code, as well as to carry its validation. </p> <blockquote> <p><em>Quantum computing is gaining an increasing interest since it can solve certain problems exponentially faster than classical computing. Thus, many organizations are researching and launching investments for integrating quantum software into their existing systems. Software modernization (as based on Model-Driven Engineering) has been proposed to migrate from/to the so-called hybrid software systems, which integrate classical and quantum software. In that process, both, reverse engineering and restructuring phases, have already been investigated. However, forward engineering phase for generating hybrid source code from high-level design models has not yet been addressed. Thus, this research proposes a quantum code generation technique from extended UML design models. It consists of a set of Model-to-Text transformations (defined through Epsilon Generation Language) to generate both Python and Qiskit code, which respectively integrate classical and quantum code. The transformation has been validated through a multi-case study with 7 hybrid software systems modelled in UML, which demonstrated that the transformation is effective and efficient. The implication of this work is that the software modernization process for hybrid software systems can be completed by tackling forward engineering phase, and that Model-Driven Engineering can therefore globally facilitate industry adoption of quantum software.</em></p> </blockquote>
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.