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ShareScore release 0.9.0
Dataset results
23 results for “rates computation”
EOL computer vision pipelines: Classification for Image Tagging: Image Rating: Chiroptera
<p>Produced by the EOL Image Rating Classifier. Classifies images as bad or good quality (used for image gallery sorting). Dataset generated for EOL Chiroptera images. See model on the CV for <a href="https://www.kaggle.com/models/eolorg/image-quality-rating-bad-or-good" target="_blank" rel="noopener">EOL Images Model Zoo on Kaggle</a>.</p>
QM and COSMO-RS calculation results and experimental data for: Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods
<p>This dataset contains the calculation results and the experimental data compiled from literature for the manuscript "Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods". Citations should refer directly to the manuscript (Chung, Y.; Green, W. H. Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods. <em>J. Phys. Chem. A</em> <strong>2023</strong>, 127, 27, 5637–5651. doi: <a href="https://doi.org/10.1021/acs.jpca.3c01825">10.1021/acs.jpca.3c01825</a>).This includes:</p> <ul> <li>expt_data_collected.xlsx: Experimental rate constants of various liquid phase reactions collected from various sources</li> <li>For each levels of theory used for gas-phase quantum chemical calculations and COSMO-RS calculations: <ul> <li>Gas-phase quantum chemical calculation results (output log files) and computed gas phase rate constants</li> <li>COSMO-RS calculation results and computed solvation free energies</li> <li>Predicted liquid phase rate constants and relative rate constants </li> </ul> </li> </ul> <p> </p>
Dataset on computational and experimental external dose rates from nuclear medicine patients.
<p><strong>A journal paper published in EJNMMI Physics details the method to create the data, which support also the results described in the paper. The paper describes the development and validation of an advanced computational framework for the assessment of external dose rates from nuclear medicine patients. </strong></p>
Atmospheric Response Matrices (ARMs) computed with AtRIS in: The Atmospheric Influence on Cosmic-Ray-Induced Ionization and Absorbed Dose Rates
<p>Ionization and Dose Atmospheric Response Matrices (ARMs) computed with the Atmspheric Interaction Radiation Simulator (AtRIS) and used in the following publication: <br>The Atmospheric Influence on Cosmic-Ray-Induced Ionization and Absorbed Dose Rates.<br><br>All files are in txt format.<br>Files ending in '_energy_bins.txt', contain the informations about the energy binning of the primary particles used in the AtRIS simulations. <br>Files ending in '_ioni.txt', contain the ionization ARMs computed with the above mentioned energy binning.<br>Files ending in '_dose.txt', contain the ICRU water sphere dose ARMs computed with the above mentioned energy binning.</p>
Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD
<p>Set of data and scripts for the analysis of RAMD dissociation trajectories generated for BN-NS and BT/BCT-BPTI mutants, reported in the manuscript:</p> <p><br>"Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD"</p> <p>by Giulia D'Arrigo, Daria B. Kokh, Ariane Nunes-Alves and Rebecca C. Wade</p> <p> </p> <ol> <li>RAMD movies of WT Bn-Bs and D35Abn mutant dissociation pathway (WT-diss.mp4 and D35Abn-diss.mp4)</li> <li>Protein-Protein.zip contains:</li> </ol> <ul> <li>README - set of instructions to go through files and for the use of the scripts</li> <li>Jupyter notebooks: tauRAMD_PP_Residence_time.ipynb and tauRAMD_PP_Unbinding_pathways.ipynb</li> <li>Scripts used for analysis and postprocessing RAMD simulations - for the correct usage of the scripts check the corresponding file header</li> <li>System input files in BN-BS/ and BT_BCT-BPTI/</li> <li>Experimental data used</li> <li>2 RAMD movies (.mp4) showing dissociation of WT Bn-Bs and D35Abn mutant</li> </ul>
Data from: Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates
We propose a two-step procedure for estimating multiple migration rates in an approximate Bayesian computation (ABC) framework, accounting for global nuisance parameters. The approach is not limited to migration, but generally of interest for inference problems with multiple parameters and a modular structure (e.g. independent sets of demes or loci). We condition on a known, but complex demographic model of a spatially subdivided population, motivated by the reintroduction of Alpine ibex (Capra ibex) into Switzerland. In the first step, the global parameters ancestral mutation rate and male mating skew have been estimated for the whole population in Aeschbacher et al. (Genetics 2012; 192: 1027). In the second step, we estimate in this study the migration rates independently for clusters of demes putatively connected by migration. For large clusters (many migration rates), ABC faces the problem of too many summary statistics. We therefore assess by simulation if estimation per pair of demes is a valid alternative. We find that the trade-off between reduced dimensionality for the pairwise estimation on the one hand and lower accuracy due to the assumption of pairwise independence on the other depends on the number of migration rates to be inferred: the accuracy of the pairwise approach increases with the number of parameters, relative to the joint estimation approach. To distinguish between low and zero migration, we perform ABC-type model comparison between a model with migration and one without. Applying the approach to microsatellite data from Alpine ibex, we find no evidence for substantial gene flow via migration, except for one pair of demes in one direction.
Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)
<p><span>State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (</span><em><span>λ</span></em><sub><span>1</span></sub><span> / <em>λ</em><sub>0 </sub></span><span>= 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s <em>D</em>) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.</span></p>
Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)
<p>State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (<em>λ</em><sub>1</sub> / <em>λ</em><sub>0 </sub>= 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s <em>D</em>) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.</p>
Heart Rate Controller in Computed Tomography Coronary Angiography
ClinicalTrials.gov study NCT05261464. IPD Sharing: Not stated. Countries: 1. Publications: 21.
Data from: Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates
Open the record for dataset details and reuse information.
Data from: Reproducibility of volumetric computed tomography of stable small pulmonary nodules with implications on estimated growth rate and optimal scan interval
Open the record for dataset details and reuse information.
Electronic transport computation in thermoelectric materials: from ab initio scattering rates to nanostructures
Open the record for dataset details and reuse information.
AlphaBeta: Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants
GEO Series GSE153055. Arabidopsis thaliana. 13 samples. Type: Methylation profiling by high throughput sequencing.
Impact of Computer-aided Detection System on the Proximal Adenoma Miss Rate
ClinicalTrials.gov study NCT07308743. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Heart Rate Variability in Duchenne Muscular Dystrophy During Computer Task
ClinicalTrials.gov study NCT04607824. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Miss Rate of Gastric Neoplasms Under Computer-aided Endoscopy
ClinicalTrials.gov study NCT06495645. IPD Sharing: NO. Countries: 1. Publications: 0.
Role of Computer-Aided Detection Colonoscopy in Polyp Detection Rate
ClinicalTrials.gov study NCT07171333. IPD Sharing: YES. Countries: 1. Publications: 0.
Transparent Cap-Assisted Colonoscopy Combined With Computer-Aided Detection in Improving the Detection Rate of Colorectal Adenomas
ClinicalTrials.gov study NCT07097350. IPD Sharing: YES. Countries: 1. Publications: 0.
Adenoma Detection Rate in Water and Air Colonoscopy Using Computer-aided System
ClinicalTrials.gov study NCT05448300. IPD Sharing: Not stated. Countries: 2. Publications: 0.
The Effects of Chewing Gum on a Computer Task and Liking Ratings of Ice Cream
ClinicalTrials.gov study NCT02198911. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.