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3,264 results for “FAST”
STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale (validation data)
<p>Raw and refined data for the validation tests performed in the paper "STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale":<br> - rallpack 1, analytical, STEPS3, STEPS4, mesh scaling with STEPS4. It is so simple that I provide only the raw data. The space saving using refined data is minimal</p> <p>- rallpack 3, STEPS 3, STEPS 4. Both raw and refined data are provided.</p> <p>- caburst, STEPS 3, STEPS 4. Both raw and refined data are provided.</p>
Fast kinetics data of binding of azide to myoglobin, training data -- MetBio practicals
<p>These data files were acquired during the course of the 3rd <a href="http://frenchbic.cnrs.fr/">FrenchBIC</a> <a href="http://frenchbic.cnrs.fr/2021/01/25/3rd-frenchbic-summer-school-on-methods-for-studying-metals-in-biology/">MetBio summer school</a> taking place in and around Marseille. The data were acquired during the “stopped flow” practicals.</p> <p>The data correspond to the reaction of Myoglobin with various concentrations of azide, triggered using a stopped-flow apparatus and followed by UV/Visible spectroscopy.</p> <p>The concentrations of azide can be deduced from the names of the files and are expressed in millimolar. The concentrations of myoglobin are variable but always much lower than that of azide.</p> <p>The purpose of this dataset is to be used as training data for analysing multiwavelength kinetic data. It will be the subject of a data analysis tutorial using the free software <a href="https://bip.cnrs.fr/groups/bip06/software/">QSoas</a>, to be published <a href="https://vince-debian.blogspot.com/">there</a>.</p> <p>With the exception of the <code>WTV-Azide-0.5mm_3.txt</code>, all the files are in a “matrix” format, in which the first column gives the time and each column after the first corresponds to the absorbances over time of a single wavelength.</p> <p>The <code>WTV-Azide-0.5mm_3.txt</code> uses a different format, in which each line corresponds to a <em>wavelength</em> <em>time</em> <em>absorbance</em> triplet.</p> <p>Useful background reading:</p> <ul> <li> <p>Coletta <em>et al</em>, <strong>1996</strong>, DOI: 10.1111/j.1432-1033.1996.00049.x</p> </li> <li> <p>De Sanctis <em>et al</em>, <strong>2007</strong>, DOI: 10.1529/biophysj.106.098764</p> </li> </ul>
Data for 'VespaG: Expert-guided protein language models enable accurate and blazingly fast fitness prediction'
<div>Datasets used for development of VespaG and VespaG predictions generated with <a href="https://github.com/JSchlensok/VespaG">https://github.com/JSchlensok/VespaG</a>. </div> <div> </div> <div>Uploads contain:</div> <div> <ol> <li><strong>Performance</strong> summaries for ProteinGym [1]:<br>- Spearman and Pearson correlation for VespaG: <em>proteingym_performance_vespag.csv </em>(columns: 'DMS_id', 'Spearman', 'Pearson')<br>- Spearman correlation for evaluated methods VespaG, GEMME [2], VESPA [3], TranceptEVE [4], AlphaMissense [5], PoET [6]: <em>proteingym_spearman_allmethods.csv </em>(columns: 'DMS_id', 'Trancept EVE-L', 'VESPA', 'VespaG', 'GEMME', 'AlphaMissense', 'PoET', 'UniProt_ID', 'coarse_selection_type' (function), 'taxon')</li> <li><strong>Fasta</strong> files with sequences for all train sets (<em>vespag_fasta_training_datasets.zip</em> with seq_all9k.fasta, seq_human5k.fasta, seq_droso4k.fasta, seq_ecoli2k.fasta, seq_virus1k.fasta) and test set (<em>proteingym_217.fasta</em>)</li> <li><strong>VespaG</strong> <strong>Predictions</strong> for test set: <em>vespag_proteingym_rawpreds_by_training_dataset.zip</em> with raw_preds_ecoli.csv, raw_preds_human.csv, raw_preds_virus.csv, raw_preds_all.csv, raw_preds_droso.csv (columns: 'DMS_id', 'mutation', 'DMS_score', 'VespaG'). Predictions are based on different training data, the final model VespaG was trained on a subset of the human proteome and <strong>raw VespaG predictions</strong> <strong>for</strong> <strong>the</strong> <strong>ProteinGym benchmark are in raw_preds_human.csv </strong>(used to calculate the performances above).</li> <li><strong>GEMME predictions</strong> for train sets: <em>vespag_proteingym_rawpreds_by_training_dataset.zip </em>with folders 'human', 'droso', 'ecoli', 'virus', 'all' for respective fasta file (each containing GEMME mutational landscape output files named '<em>ID' + '</em>_normPred_evolCombi.txt')</li> <li><strong>ESM-2</strong> <strong>embeddings</strong> [7] for test set (<em>proteingym_217_esm2.h5</em>)</li> </ol> </div> <div>For details on VespaG see:</div> <div> <div> <div>VespaG: Expert-guided protein Language Models enable accurate and blazingly fast fitness prediction</div> </div> <div>Celine Marquet, Julius Schlensok, Marina Abakarova, Burkhard Rost, Elodie Laine</div> <div>bioRxiv 2024.04.24.590982; doi: https://doi.org/10.1101/2024.04.24.590982</div> <div> </div> <div>For more information on data usage and generation please see <a href="https://github.com/JSchlensok/VespaG">https://github.com/JSchlensok/VespaG</a>.</div> <div> </div> <div>Abstract:</div> <div>Exhaustive experimental annotation of the effect of all known protein variants remains daunting and expensive, stressing the need for scalable effect predictions. We introduce VespaG, a blazingly fast single amino acid variant effect predictor, leveraging embeddings of protein Language Models as input to a minimal deep learning model. To overcome the sparsity of experimental training data, we created a dataset of 39 million single amino acid variants from the human proteome applying the multiple sequence alignment-based effect predictor GEMME as a pseudo standard-of-truth. Assessed against the ProteinGym Substitution Benchmark (217 multiplex assays of variant effect with 2.5 million variants), VespaG achieved a mean Spearman correlation of 0.48 +/- 0.01, matching state-of-the-art methods such as GEMME, TranceptEVE, PoET, AlphaMissense, and VESPA. VespaG reached its top-level performance several orders of magnitude faster, predicting all mutational landscapes of the human proteome in 30 minutes on a consumer laptop (12-core CPU, 16 GB RAM).</div> <div> </div> <div>[1] Notin, Pascal, et al. "ProteinGym: large-scale benchmarks for protein fitness prediction and design." <em>Advances in Neural Information Processing Systems</em> 36 (2024).<br>[2] Laine, Elodie, Yasaman Karami, and Alessandra Carbone. "GEMME: a simple and fast global epistatic model predicting mutational effects." <em>Molecular biology and evolution</em> 36.11 (2019): 2604-2619.</div> <div>[3] Marquet, Céline, et al. "Embeddings from protein language models predict conservation and variant effects." <em>Human genetics</em> 141.10 (2022): 1629-1647.</div> <div>[4] Notin, Pascal, et al. "TranceptEVE: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction." <em>bioRxiv</em> (2022): 2022-12.</div> <div>[5] Cheng, Jun, et al. "Accurate proteome-wide missense variant effect prediction with AlphaMissense." <em>Science</em> 381.6664 (2023): eadg7492.</div> <div>[6] Truong Jr, Timothy, and Tristan Bepler. "PoET: A generative model of protein families as sequences-of-sequences." <em>Advances in Neural Information Processing Systems</em> 36 (2024).</div> <div>[7] Lin, Zeming, et al. "Evolutionary-scale prediction of atomic-level protein structure with a language model." <em>Science</em>379.6637 (2023): 1123-1130.</div> </div>
Data and codes for "Decay-protected superconducting qubit with fast control enabled by integrated on-chip filters"
<p>Data and codes for "Decay-protected superconducting qubit with fast control enabled by integrated on-chip filters".</p>
Do flower-colonizing microbes influence floral evolution? A test with fast-cycling Brassica
<p>Pollinators are thought to be the main drivers of floral evolution. Flowers are also colonized by abundant communities of microbes that can affect the interaction between plants and their pollinators. Very little is known, however, about how flower-colonizing microbes influence floral evolution. Here we performed a six-generation experimental evolution study using fast-cycling <em>Brassica rapa</em>, in which we factorially manipulated the presence of pollinators and flower microbes to determine how pollinators and microbes interact in driving floral evolution. We measured the evolution of six morphological traits, as well as plant mating system and flower attractiveness. Only one of the six traits (flower number) evolved in response to pollinators, while microbes did not drive the evolution of any trait, nor did they interact with pollinators in driving evolution of morphological traits. Moreover, we did not find evidence that pollinators or microbes affected the evolution of flower attractiveness to pollinators. However, we found an interactive effect of pollinators and microbes on the evolution of autonomous selfing, a trait that is expected to evolve in response to pollinator limitation. Overall, we found only weak evidence that microbes mediate floral evolution. However, our ability to detect an interactive effect of pollinators and microbes might have been limited by weak pollinator-mediated selection in our experimental setting. Our results contrast with previous (similar) experimental evolution studies, highlighting the susceptibility of such experiments to drift and to experimental artefacts.</p>
Data from: Fast mvSLOUCH: Multivariate Ornstein-Uhlenbeck-based models of trait evolution on large phylogenies
<p>The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for a wide range of phylogenetic Gaussian models. Taking advantage of it, we redesigned the R package mvSLOUCH. Here, we demonstrate how the new version of the package can be used to thoroughly examine the evolution and adaptation of traits in a large dataset of 1252 vascular plants through the use of multivariate Ornstein-Uhlenbeck processes. The results of our analysis demonstrate the ability of the modeling framework to distinguish between various alternative hypotheses regarding the evolution of functional traits in angiosperms.</p>
Dataset: Fastenal Company (FAST) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig 3 in In situ fast marking study of manila clams (Ruditapes philippinarum)
Fig 3: Magnification of a ventral margin in Manila clam: green fluorescent mark produced following 30 minutes calcein solution exposure (100 mg L−1) and observed growth increments before and after marking. Along a growth axis, grey levels have been measured (AU: arbitrary unit) and each grey peak is identified (blue circle) as the limit between each opaque band and each hyaline band, reflecting each one growth increment.
Fig 2 in In situ fast marking study of manila clams (Ruditapes philippinarum)
Fig 2: A Valve of Manila Clam with the line which represents the direction of growth (DOG); B) Thick section of shell (thickness of 0.2 mm); C) Ventral margin of a calcein-marked shell with a fluorescent mark (Calcein exposure time and concentration are indicated for each staining experiment)
Python scripts and datasets used in the article "Investigating the off-axis GRB afterglow scenario for extragalactic fast X-ray transients"
<p>This package includes datasets and python scripts used in the analysis and creation of figures in the A&A paper "Investigating the off-axis GRB afterglow scenario for extragalactic fast X-ray transients" (Wichern et al. 2024).</p>
Figures 4-5. 4 in The fast-calling short-tailed cricket Anurogryllus celerinictus Walker, 1973 (Orthoptera: Gryllidae) occurs in Cuba, Greater Antilles
Figures 4-5. 4 Geographical distribution of Anurogryllus celerinictus: previous records (red symbols) and new records (yellow symbols). Image frame = 2,000 x 1,000 km. 5 Habitat and microhabitat of Anurogryllus celerinictus at Havana City, Cuba. This section of the grassy parterre yielded 12 of the examined specimens in three consecutive nights.
Figure 1 in The fast-calling short-tailed cricket Anurogryllus celerinictus Walker, 1973 (Orthoptera: Gryllidae) occurs in Cuba, Greater Antilles
Figure 1. Live Anurogryllus celerinictus from Cuba, photographed in captivity on white background: a) adult male; b– c) same adult female, d) juvenile. Note "short-winged" male and that same female individual displays either "shortwinged" or "long-winged" condition.
Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the all-sky infrared radiative transfer algorithm
<p>Dataset for AMT-2017-261 by DeSouza-Machado et. al.<br> <br> 1D-variational retrievals of temperature and moisture fields from<br> hyperspectral infrared satellite sounders use cloud-cleared radiances<br> as their observation. These derived observations allow the use of<br> clear-sky only radiative transfer in the inversion for geophysical<br> variables but at reduced spatial resolution compared to the native<br> sounder observations. Cloud-clearing can introduce various errors,<br> although scenes with large errors can be identified and<br> ignored. Information content studies show that when using multi-layer<br> cloud liquid and ice profiles in infrared hyperspectral radiative<br> transfer codes, there are typically only 2-4 degrees of freedom of<br> cloud signal. This implies a simplified cloud representation is<br> sufficient for some applications which need accurate radiative<br> transfer. Here we describe a single-footprint retrieval approach for<br> clear and cloudy conditions, which uses the thermodynamic and cloud<br> fields from Numerical Weather Prediction (NWP) models as a first<br> guess, together with a simple cloud representation model coupled to a<br> fast scattering radiative transfer algorithm (RTA). The NWP model<br> thermodynamic and cloud profiles are first co-located to the<br> observations, after which the N-level cloud profiles are<br> converted to two slab clouds (typically one for ice and one for water<br> clouds). From these, one run of our fast cloud representation model<br> allows an improvement of the \emph{a-priori} cloud state by comparing the<br> observed and model simulated radiances in the thermal window<br> channels. The retrieval yield is over 90\%, while the degrees of<br> freedom correlate with the observed window channel brightness<br> temperature which itself depends on the cloud optical depth. The cloud<br> representation/scattering package is bench-marked against radiances<br> computed using a Maximum Random Overlap cloud scheme. All-sky infrared<br> radiances measured by NASA’s Atmospheric Infrared Sounder (AIRS) and<br> NWP thermodynamic and cloud profiles from the European Center for<br> Medium Range Weather Forecasting (ECMWF) forecast model are used in<br> this paper.</p> <p> </p>
Spontaneous Low-Voltage Fast Onset Seizures in Human Mesial Temporal Lobe Epilepsy Are Caused by Specific Inhibitory/Excitatory Imbalance
<p>Local field potential recordings during low voltage fast seizures recorded from microelectrodes in patients with medically refractory temporal lobe epilepsy</p>
Dataset for "Comparison of the Fast and Slow Climate Response to Three Radiation Management Geoengineering Schemes"
<p>Reproducible dataset for "Comparison of the Fast and Slow Climate Response to Three Radiation Management Geoengineering Schemes"</p>
Dataset (encounter histories of female bats) used in the analysis for the paper Culina et al.: Live fast, don't die young: survival reproduction trade-offs in long-lived income breeders
<p>Two data files (Md_capture_H, Mn_capture_H) represent the capture histories of females of two species (Myotis daubentonii = Md, and M. nattereri=Mn). These capture histories were used to run multi-event-capture-mark-recapture model, as described in the paper, and in the readme document in this data package.<br> </p>
Data for figures in "Reproducibility in Benchmarking Parallel Fast Fourier Transform based Applications"
<p>FFT benchmark data and Python plotting programs</p>
A Methodology for the Fast Identification and Monitoring of Microplastics in Environmental Samples using Random Decision Forest Classifiers
<p>This short video shows the results of the application of a classifier for microplastics as described by Hufnagl et al. (2019).</p> <p> </p> <p>If you reuse this video please cite</p> <p> </p> <p>Hufnagl, B., Steiner, D., Renner, Löder, M. G. J., Laforsch, C. and Lohninger, H. <em>A Methodology for the Fast Identification and Monitoring of Microplastics in</em><em> Environmental Samples using Random Decision Forest Classifiers,</em> Analytical Methods, 2019, DOI:10.1039/C9AY00252A</p>
Feature count data for Love et al. 2019 analysis for "Using equivalence class counts for fast and accurate testing of differential transcript usage" paper
<p>Feature count data for Love et al. 2019 analysis used in the "Using equivalence class counts for fast and accurate testing of differential transcript usage" paper. For reproducing the analyses and figures using the <a href="https://github.com/Oshlack/ec-dtu-paper/">ec-dtu-paper</a> code.</p> <p>Contains:</p> <ul> <li>Equivalence class count matrix for all 24 samples (using counts from Salmon)</li> <li>Salmon quantification results for all 24 samples</li> <li>Exon counts for all 24 samples using DEXSeq-count</li> </ul>
Sodium Fast Reactor Database
<p>This database contains reactor physic data gathered from high-fidelity sodium fast reactor MCNP models. Each reactor design contains values such as k-eff, beta-eff, sodium void coefficient, Doppler coefficient, etc. The data is stored as an h5 database and can easily be converted to a Pandas dataframe for manipulation.</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.