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Dataset results
90 results for “model transformation”
Data for the manuscript entitled "AMOC variability and watermass transformations in the AWI climate model" by Sidorenko et al. 2021, submitted to JAMES
<p>Data is stored in a SHELVE persistent storage as produced in Python 3.7.4. The visualisation example is provided in a Jupyter Python Notebook.</p>
Transformer Models for Disconnection-Aware Triple Transformer Loop
<p>Models of the Triple Transformer Loop for retrosynthesis trained using OpenNMT.</p> <p>Full details in <a href="https://doi.org/10.1039/d3sc01604h">Chemical Science</a>.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <div> <div> <div class="highlighter--icon highlighter--icon-copy"> </div> <div class="highlighter--icon highlighter--icon-change-color"> </div> <div class="highlighter--icon highlighter--icon-delete"> </div> </div> </div>
Quantum model transformation
<p>KDM to UML: A Quantum Model Transformation</p>
Construct Validity of a Large Loop Excision of the Transformation Zone (LLETZ) Training Model
ClinicalTrials.gov study NCT02476500. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Swapping birth and death: symmetries and transformations in phylodynamic models
Open the record for dataset details and reuse information.
All figures and tables for "Optimized approximate inverse Laplace transform for geo-deformation computation in viscoelastic earth model"
<p>It is the data set of all figures and tables in the paper with title "Optimized approximate inverse Laplace transform for geo-deformation computation in viscoelastic earth model".</p>
Data Set for Predicting the Performance of ATL Model Transformations Based on Generated Models
<p>Predicting the execution time of model transformations can help to understand how a transformation reacts to a given input model without creating and transforming the respective model.</p> <p>In our previous data set (https://doi.org/10.5281/zenodo.8385957), we have documented our experiments in which we predict the performance of ATL transformations using predictive models obtained from training linear regression, random forest and support vector regression. As input for the prediction, our approach uses a characterization of the input model. In these experiments, we only used data from real models.</p> <p>However, a common problem is that transformation developers do not have enough models available to use such a prediction approach. Therefore, in a new variant of our experiments, we investigated whether the three considered machine learning approaches can predict the performance of transformations if we use data from generated models for training. We also investigated whether it is possible to achieve good predictions with smaller training data. The dataset provided here offers the corresponding raw data, scripts, and results.</p> <p>A detailed documentation is available in documentaion.pdf.</p>
Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling
Open the record for dataset details and reuse information.
Data for "No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models"
<p>Datasets to reproduce the experiments associated with the paper: https://doi.org/10.48550/arXiv.2307.06440</p> <p>The readme contains instructions for how to use them: https://github.com/JeanKaddour/NoTrainNoGain/blob/main/bert/README.md</p> <p>c4-subset-random.tar.bz2 is a subset of the C4 dataset (https://arxiv.org/abs/1910.10683), licensed under ODC-BY 1.0.</p>
Efficacy of BET protein proteolysis targeted chimera-based combinations against novel patient-derived models of Richter Transformation-Diffuse Large B-Cell Lymphoma [RNA-Seq]
GEO Series GSE154462. Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
Pharmacological modulation of H3K9me2 deposition in human intestinal models and transformed ES cells
GEO Series GSE154057. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
Identification of transformation-related pathways in a breast epithelial cell model using a ribonomics approach.
GEO Series GSE12215. Homo sapiens. 12 samples. Type: Expression profiling by array.
E6/E7 from Beta-2-HPVs 122, 38b and 107 possess transforming properties in a fibroblast model in vitro
GEO Series GSE191090. Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing.
Transformation of the Fallopian Tube Secretory Epithelium Leads to High-grade Serous Ovarian Cancer in BRCA/P53/PTEN Models
GEO Series GSE49827. Mus musculus. 3 samples. Type: Genome variation profiling by genome tiling array.
A genetically engineered ovarian cancer mouse model based on fallopian tube transformation mimics human high-grade serous carcinoma development
GEO Series GSE52011. Mus musculus. 12 samples. Type: Expression profiling by array.
Mixture models and wavelet transforms reveal high confidence RNA-protein interaction sites in MOV10 PAR-CLIP data
GEO Series GSE37524. Homo sapiens. 2 samples. Type: Other.
A CRISPR/Cas9-engineered ARID1A-deficient human gastric cancer organoid model reveals essential and non-essential modes of oncogenic transformation
GEO Series GSE164179. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Transforming properties of E6/E7 genes from Beta HPV80 in a fibroblast model in vitro
GEO Series GSE279652. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Dissecting the early steps of MLL induced leukaemogenic transformation using a new mouse model of AML [ATAC-seq]
GEO Series GSE141353. Mus musculus. 15 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Data for Investigating the Technical Debt in Procedural Model Transformation Languages
<p>The content presents the data for Investigating the Technical Debt in Procedural Model Transformation Languages </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.