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148 results for “entropy”
DNA methylation entropy as a measure of stem cell replication and aging [RNA-seq]
GEO Series GSE213722. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
DNA methylation entropy as a measure of stem cell replication and aging
GEO Series GSE213723. Mus musculus. 144 samples. Type: Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
Data for Kinetic Entropy-Based Measures of Distribution Function Non-Maxwellianity: Theory and Simulations
<p>*************************************************<br> Data for Fig. 1:<br> *************************************************<br> nonmax_bimax_kp_1.csv is a CSV file with the data for the black curve in Figure 1. There are 1000 lines, and each line contains two numbers - the analytic value of Kaufmann and Paterson non-Maxwellianity for a bi-Maxwellian distribution and T_perp / T_parallel.</p> <p>nonmax_bimax_kp_2.csv is a CSV file with the data for the red curve in Figure 1. There are 1099 lines, and each line contains two numbers - the analytic value of Kaufmann and Paterson non-Maxwellianity for a bi-Maxwellian distribution and T_perp / T_parallel.</p> <p>*************************************************<br> Data for Fig. 2:<br> *************************************************<br> Below are the general instructions which might be useful for reproducing Fig 2 plots<br> 1. read in the .csv files and associate the variable according to the file names<br> 2. To plot Fig 2 (a), do a 2D plot of (b,Phi) vs s_Eg. b corresponds the column of s_Eg and Phi corresponds to the row of s_Eg.<br> Similarly, to plot Fig 2 (d), do a 2D plot of (b,Phi) vs Mbar_KP. Again, b corresponds the column of Mbar_KP and Phi corresponds to the row of Mbar_KP.<br> 3. Fig 2 (b) and (c) are cuts of (a) at particular values of Phi and b respectively.<br> Similarly,Fig 2 (e) and (f) are cuts of (d) at particular values of Phi and b respectively.</p> <p>*************************************************<br> Data for Fig. 3:<br> *************************************************<br> Below are the general instructions which might be useful for reproducing Fig 3 plots<br> 1. read in the .csv files and associate the variable according to the file names. Here, n_norm = n/n_infinity.<br> 2. To plot Fig 3 (a), do a 2D plot of (b,n_norm) vs s_Eg. b corresponds the column of s_Eg and n_norm corresponds to the row of s_Eg.<br> Similarly, to plot Fig 3 (d), do a 2D plot of (b,n_norm) vs Mbar_KP. Again, b corresponds the column of Mbar_KP and n_norm corresponds to the row of Mbar_KP.<br> 3. Fig 3 (b) and (c) are cuts of (a) at particular values of n_norm and b respectively.<br> Similarly, Fig 3 (e) and (f) are cuts of (d) at particular values of n_norm and b respectively.</p> <p>*************************************************<br> Data for Fig. 4:<br> *************************************************<br> nonmax_bimax_1.csv is a CSV file with the data for the black curve in Figure 4. There are 1000 lines, and each line contains two numbers - the analytic value of the non-Maxwellianity for a bi-Maxwellian distribution and T_perp / T_parallel.</p> <p>nonmax_bimax_2.csv is a CSV file with the data for the red curve in Figure 4. There are 399997 lines, and each line contains two numbers - the analytic value of non-Maxwellianity for a bi-Maxwellian distribution and the base 10 logarithm of T_perp / T_parallel.</p> <p>*************************************************<br> Mbar for Figs. 5-9:<br> *************************************************<br> The Mbar_data.csv is a CSV file saving magnetic field Bx, By, the Kaufmann and Paterson non-Maxwellianity (Mbare_KP for electrons, Mbari_KP for ions) and the new defination of non-Maxwellianity (Mbare_norm for electrons, Mbari_norm for ions) as a function of location (x,y) in the simulation domain.</p> <p>The x consists of 4096 data points from 0.00625 to 51.19375.<br> The y consists of 2048 data points from 0.00625 to 25.59375.</p> <p>The x-point location is (x0,y0)=(11.15625,19.24375). The plots in Figures 5-6,7a,8a, and 9a are used relative coordinates (x-x0,y-y0). The magnetic field data can be used to plot the field lines as contours of vector potential Az(x,y) as a function of Bx(x,y) and By(x,y) in Figures 5-6. Figures 7a, 8a, 9a are cuts at x-x0=0,8,13 respectively.</p> <p>*************************************************<br> Distribution Functions for Figs 7-9:<br> *************************************************<br> The CSV files are named as "DF_X#_Y#.csv". The two "#" are the (x-x0,y-y0) coordinates of the distribution function. In each of the files, the data includes three columns. The first two columns are either the vx and vy for Figure 7b or v_par and v_perp1 for Figures 8b and 9b. The last column is the corresponding values of the distribution functions.</p>
The dataset of predicting the potential habitat suitability of Saussurea species in China under future climate change using the optimized Maximum Entropy (MaxEnt) model
<p><strong>Description:</strong></p> <p>This dataset accompanies the study on the Saussurea species, renowned for its biodiversity and medicinal significance in high-elevation regions, which faces endangerment due to climate change and human activities. Despite its importance, conservation research on Saussurea has been limited. To address this gap, the study employed the optimized MaxEnt model to simulate Saussurea's habitat suitability and analyze key environmental factors influencing its distribution.</p> <p>The dataset includes:</p> <ol> <li><strong>Model and Parameter Optimization Code</strong>: The code used for optimizing the MaxEnt model parameters, ensuring reproducibility of the habitat suitability models.</li> <li><strong>Saussurea Distribution Points</strong>: Georeferenced points indicating the observed locations of Saussurea species.</li> <li><strong>Current Environmental Variables</strong>: Data on key environmental factors influencing Saussurea distribution, such as Elevation, Isothermality (Bio3), and Temperature Annual Range (Bio7).</li> <li><strong>Future Environmental Variables: </strong>Data on key environmental factors influencing Saussurea distribution under SSP126, SSP245, SSP370 and SSP585 in 2020-2100s.</li> </ol>
Seismic scattering regimes from multiscale entropy and frequency correlations dataset
<p>Dataset accompanying manuscript published in Geophysical Journal International entitled</p> <p><a href="doi.org/10.1093/gji/ggae098"><em>Seismic scattering regimes from multiscale entropy and frequency correlations</em></a></p> <p> </p>
Effect of Usage of Electroencephalogram (EEG) Entropy on the Incidence of Postoperative Cognitive Dysfunction in Geriatric Patients
ClinicalTrials.gov study NCT00700674. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Entropy Analysis and Complex Patient-ventilator Interactions During Mechanical Ventilation
ClinicalTrials.gov study NCT04128124. IPD Sharing: NO. Countries: 1. Publications: 0.
Romanian Entropy Clinical Study Network
ClinicalTrials.gov study NCT03210077. IPD Sharing: NO. Countries: 1. Publications: 0.
Validation of Phase Lag Entropy(PLE) as an Indicator of Depth of Sedation in Patients Undergoing Spinal Anesthesia
ClinicalTrials.gov study NCT03319004. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study of the Approximate Entropy of Adrenocorticotropic Hormone and Cortisol Secretion in Patients With Head Injury
ClinicalTrials.gov study NCT00006270. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparative Analysis of Phase Lag Entropy (PLE) and Bispectral Index (BIS) as an Anesthetic Depth Indicator
ClinicalTrials.gov study NCT03424720. IPD Sharing: NO. Countries: 1. Publications: 0.
Dexmedetomidine Different Doses as Adjuvant to Entropy-assisted General Anesthesia During Functional Endoscopy Sinus Surgery.
ClinicalTrials.gov study NCT05226325. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
End Tidal Anesthetic Concentration in Different Anesthesia Techniques Where Depth of Anesthesia Adjusted With Entropy
ClinicalTrials.gov study NCT04203290. IPD Sharing: YES. Countries: 1. Publications: 0.
CARTO-Finder Guided Ablation Versus Multiscale Entropy Guided Ablation in Persistent Atrial Fibrillation
ClinicalTrials.gov study NCT05454111. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Effect of Frontal Electromyogram (F-EMG) Activity on the Entropy Index Behavior During General Anesthesia
ClinicalTrials.gov study NCT03366662. IPD Sharing: NO. Countries: 1. Publications: 0.
Entropy and Surgical Pleth Index Guided Closed Loop Target Controlled Infusion Total Intravenous Anesthesia With Propofol
ClinicalTrials.gov study NCT03773588. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
DNA methylation entropy as a measure of stem cell replication and aging [BiSulfite-seq]
GEO Series GSE213628. Mus musculus. 120 samples. Type: Methylation profiling by high throughput sequencing.
ULtrahigh TEmperature Refractory Alloys (ULTERA) Database of High Entropy Alloys
<p>ULTERA database, developed under the <a href="https://arpa-e.energy.gov/?q=arpa-e-programs/ultimate">ARPA-E's ULTIMATE program</a>, is aimed at collecting literature data on high entropy alloys (HEAs) to facilitate rapid ML-based discovery of new ones using forward and inverse design.</p> <p>The main scope of this dataset is collecting data on compositionally complex alloys (CCAs), also known as high entropy alloys (HEAs) and multi-principle-element alloys (MPEAs), with extra attention given to (1) high-temperature (refractory) mechanical data, (2) phases present under different processing conditions. Although low-entropy alloys (incl. binaries) are typically not presented to the end-user (or counted in statistics), some are present and used in ML efforts; thus, all high-quality alloy data contributions are welcome! You can set up a contribution in as little as few minutes with <a href="https://contribute.ultera.org">this contribution repository at contribute.ultera.org</a></p> <p>As of September 2024, ULTERA contains over:</p> <ul> <li>7,900+ property-datapoints, corresponding to</li> <li>3,000+ unique HEAs, collected from</li> <li>570+ unique DOIs.</li> </ul> <p>All data is available through a high-performance API, following FAIR principles, while statistics on it can be found at our <a href="https://ultera.org">ultera.org project web page.</a> The database architecture is designed to automatically integrate starting literature data in real time with methods such as experiments, generative modeling, predictive modeling, and validations.</p> <p>Beyond large size, ULTERA has further advantage of being highly curated with many steps of data validation and then processed through <a href="https://pyqalloy.ultera.org">our abnormal data detection tools (pyqalloy.ultera.org).</a></p>
Entropy of N atoms trapped in 1D hamornic oscillator.
<p>Data contains the values of the logarithms of entropies for a 1D Bose gas trapped in the harmonic oscillator for different number of atoms.</p> <p>Columns : Number of atoms | exact value of the logarithm of the entropy | approximated value (from the Ramanujan-Hardy formula for number of partirions)</p>
Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison
In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison.
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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.