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84 results for “descent”
Time series of high-frequency profiles of depth, temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, turbidity, pH, oxidation-reduction potential, photosynthetically active radiation, colored dissolved organic matter, phycocyanin, phycoerythrin, and descent rate for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025
Depth profiles of water biogeochemical properties were collected with SeaBird Electronics (SBE) Conductivity, Temperature, and Depth (CTD) profilers from 2013-2025 at five drinking water reservoirs in southwestern Virginia, USA. The study reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of CTD depth profiles measured at the deepest site of each reservoir adjacent to the dam as well as other upstream reservoir sites. The profiles were collected approximately fortnightly in the spring months, weekly in the summer and early autumn, and monthly in the late autumn and winter. Beaverdam Reservoir, Carvins Cove Reservoir, and Falling Creek Reservoir were sampled every year in the dataset (2013-2025); Spring Hollow Reservoir was only sampled 2013-2017 and 2019; and Gatewood Reservoir was only sampled in 2016. Data availability differs across years due to additional sensors that have been added or replaced over time. From 2013-2016, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor and an ECO FLNTU sensor for turbidity and chlorophyll. From 2017-2025, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor, an ECO FLNTU sensor for turbidity and chlorophyll, a PAR-LOG ICSW sensor for photosynthetically active radiation, and a SBE 27 pH and ORP (oxidation-reduction potential) sensor. In 2022 and 2023, profiles were also taken with an additional CTD equipped with an SBE 43 Dissolved Oxygen sensor; an ECO Triplet Scattering Fluorescence sensor for
Parish church (Parochiekerk Sint-Pieter). Triptych. Descent of the Cross ("Edelheeretriptiek"). Rogier Van der Weyden. 1443. Central panel.
<u>File Name</u>: PM_141421_B_Leuven <br><u>Sublocation</u>: Parochiekerk Sint-Pieter <br><u>Location</u>: Leuven <br><u>Province</u>: Vlaams Brabant <br><u>Country</u>: Belgium <br><u>Header</u>: Triptiek "Edelheeretriptiek", Rogier Van der Weyden, 1443, middenpaneel <br><u>Description</u>: Parish church (Parochiekerk Sint-Pieter). Triptych. Descent of the Cross ("Edelheeretriptiek"). Rogier Van der Weyden. 1443. Central panel. <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert; pmrmaeyaert@gmail.com <br><u>Keywords</u>: Europe|Belgium; Europe|Belgium|Vlaams-Brabant; Europe|Belgium|Vlaams-Brabant|Leuven; Cultural heritage <br><u>Date of Generation</u>: 2021-10-03T14:09:18+02:00
Parish church (Parochiekerk Sint-Pieter). Triptych. Descent of the Cross ("Edelheeretriptiek"). Copy after Rogier Van der Weyden. 1443.
<u>File Name</u>: PM_141417_B_Leuven <br><u>Sublocation</u>: Parochiekerk Sint-Pieter <br><u>Location</u>: Leuven <br><u>Province</u>: Vlaams Brabant <br><u>Country</u>: Belgium <br><u>Header</u>: Triptiek "Edelheeretriptiek", school Rogier Van der Weyden, 1443 <br><u>Description</u>: Parish church (Parochiekerk Sint-Pieter). Triptych. Descent of the Cross ("Edelheeretriptiek"). Copy after Rogier Van der Weyden. 1443. <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert; pmrmaeyaert@gmail.com <br><u>Keywords</u>: Europe|Belgium; Europe|Belgium|Vlaams-Brabant; Europe|Belgium|Vlaams-Brabant|Leuven; Cultural heritage <br><u>Date of Generation</u>: 2021-10-03T14:02:31+02:00
Triptych of the Descent from the Cross. Dieric Bouts. Ca 1450-1458.
<u>File Name</u>: PM_152222_E_Granada <br><u>Sublocation</u>: Cabildo de la Capilla Real <br><u>Location</u>: Granada <br><u>Province</u>: Andalucía, Granada <br><u>Country</u>: Spain <br><u>Header</u>: Triptico de la descenso cruzado/triptiek van de kruisafneming, Dieric Bouts, ca 1450-1458 <br><u>Description</u>: Triptych of the Descent from the Cross. Dieric Bouts. Ca 1450-1458. <br><u>Author</u>: Dieric Bouts (ca 1410/1415-1475) <br><u>Author Mail</u>: pmrmaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert <br><u>Keywords</u>: Cultural heritage|Techniques|Painting; Cultural heritage|Techniques; Cultural heritage; Cultural heritage|Museum/private collection; Europe|Spain; Europe|Spain|Andalucía; Europe|Spain|Andalucía|Granada; Europe|Spain|Andalucía|Granada|Granada <br><u>Date of Generation</u>: 2023-12-24T14:46:58.045+02:00
Data For: Identifying rare variants inconsistent with identity-by-descent in population-scale whole-genome sequencing data
<p>Simulation output and Genome-wide scan for nIBD variants in UK10K data as reported in:</p> <p>Identifying rare variants inconsistent with identity-by-descent in population-scale whole-genome sequencing data</p> <p>Johnson KE, Adams CJ, Voight BF. Methods Ecol Evol 2022 Nov;13(11): 2429–2442.</p> <p>Code available at: https://github.com/kelsj/EVICORD</p>
Code outputs and figures from "Efficient high-resolution refinement in cryo-EM with stochastic gradient descent"
<p>Code outputs and figures for the numerical experiments on preconditioned SGD for cryo-EM reconstruction for reproducing the results in the article:</p> <blockquote> <p><a title="doi" href="https://doi.org/10.1107/S205979832500511X" target="_blank" rel="noopener"><code><em>Efficient high-resolution refinement in cryo-EM with stochastic gradient descent</em>.</code></a></p> <p><em>Bogdan Toader, Marcus A. Brubaker, Roy R. Lederman</em></p> <pre>Acta Crystallographica Section D, 2025</pre> </blockquote> <p>The outputs are obtained by running the Jupyter notebooks in the <em>notebooks/preconditioned_sgd</em> directory in the GitHub repository (release v0.2):</p> <blockquote> <p><a title="github link" href="https://github.com/bogdantoader/simplecryoem">https://github.com/bogdantoader/simplecryoem</a></p> </blockquote> <div>The particle images used for these experiments can be downloaded from <a title="empiar-10076 link" href="https://www.ebi.ac.uk/empiar/EMPIAR-10076">EMPIAR-10076</a> and require inverting the contrast. The file containing the pose variables and CTF parameters is the <em>particles_file/my_particles_8.star </em>file in the attached archive.</div>
Figure 6. Performance plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 5 we can see that the mse curve reaches the In performance goal but it does not<br> decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these<br> results one can say that RNN is better choice than Feedforward MLP in prediction purpose.</p>
Figure 5. Performance plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 5 we can see that the mse curve reaches the In performance goal but it does not<br> decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these<br> results one can say that RNN is better choice than Feedforward MLP in prediction purpose.</p>
Figure 3. Regression plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 3 depicts the regression plot for the feedforward MLP network, analyzing it we can<br> say that Y=T regression is not so good.</p>
Figure 4. Regression plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 4,depicts the regression plot for the Timedelay RNN network, analyzing it we can<br> say that Y=T regression is totally fit.<br> This paper also comprises of comparative study of performance(mse) plot of both network.</p>
Figure 2.Flow Chart for Data preprocessing & Training-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Methodology<br> This paper develops an ANN based comparative predictive model for NASDAQ stock<br> prediction. The first ANN model is developed with Multi-Layer Feed forward Network<br> Architecture & the second model is developed with Recurrent Neural Network Architecture. In this<br> paper gradient descent based back propagation learning algorithm is used for the supervised<br> learning of the predictive network.</p>
Enhanced Dataset of Digitized Screen-film Mammograms of African Descent
<p>This dataset presents the enhanced version of digitized Screen-film Mammograms of African Descent. It contains mamographic images of 78 African cancer patients</p>
Emergence of kinship structures and descent systems: multi-level evolutionary simulation and empirical data analyses
Open the record for dataset details and reuse information.
Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing
<p>Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most non-model wild populations. Specifically, large-sized or panmictic populations may be characterized by low variance in genetic relatedness among individuals which in turn, can prevent accurate estimation of quantitative genetic parameters. We used estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci to estimate quantitative genetic parameters for ecologically important traits in nine-spined sticklebacks (<em>Pungitius pungitius</em>) from a large, outbred population. Using empirical and simulated datasets, with varying sample sizes and pedigree complexity, we assessed the performance of different crossing schemes in estimating additive genetic variance and heritability for all traits. We found that low variance in relatedness characteristic of wild outbred populations with high migration rate can impair the estimation of quantitative genetic parameters and bias heritability estimates downwards. On the other hand, the use of a half-sib/full-sib design allowed precise estimation of genetic variance components, and revealed significant additive variance and heritability for all measured traits, with negligible dominance contributions. Genome-partitioning and QTL mapping analyses revealed that most traits had a polygenic basis and were controlled by genes at multiple chromosomes. Furthermore, different QTL contributed to variation in the same traits in different populations suggesting heterogenous underpinnings of parallel evolution at the phenotypic level. Our results provide important guidelines for future studies aimed at estimating adaptive potential in the wild, particularly for those conducted in outbred large-sized populations.</p>
Erill la Vall Descent from the Cross (fragment)
This is a test using a mobile-phone camera. Model is not complete because was placed near a wall. Most acurare photos with a pro camera and complete 360 tour of course will produce beter results. About sculpture: Erill la Vall Descent from the Cross is a set of seven 12th-century wooden sculptures from the Pyrenees village of Erill la Vall in Catalonia, originally painted, comprising a complete Romanesque Descent from the Cross sculpture group, which in Catalonia has the peculiarity that it is made up of seven figures. Two of the carvings are kept at the National Art Museum of Catalonia (MNAC) and the rest at the Vic Episcopal Museum. +info: https://en.m.wikipedia.org/wiki/Erill_la_Vall_Descent_from_the_Cross Source: Objaverse 1.0 / Sketchfab
Dataset of Digitized Screen-film Mammograms of African Descent
<p>This dataset contains 78 mammographic Images obtained from Cancer Patients</p>
Dataset of Digitized Screen-film Mammograms of African Descent
<p>This dataset contains 78 mammographic images of African cancer patients</p>
RZooRoH: an R package to characterize individual genomic autozygosity and identify homozygous-by-descent segments
<p>these are two files used to test the RZooRoH package in the manuscript:<br> RZooRoH: an R package to characterize genomic autozygosity and identify homozygous-by-descent segments<br> A.R. Bertrand, N.K. Kadri, L. Flori, M. Gautier and T. Druet</p> <p>Each row represents a SNP. The files contain first 5 information columns followed by the genotyes (1 row per individual):<br> 1 Chromosome number<br> 2 Marker name<br> 3 Position in bp<br> 4 Allele 1<br> 5 Allele 2</p> <p>Genotypes are coded as 0, 1 and 2 for AA, AB and BB and 9 for missing.</p> <p>The file geno_sim15_5col.txt contains the simulated genotypes: 25000 markers and 500 individuals.<br> The file filtered_soay_5col.txt contains the sheep genotype data: 47365 markers and 110 individuals.</p> <p>The origin of the files is described in the paper.<br> The simulated data set is from Druet and Gautier (2017) whereas the sheep data comes from Kijas et al. (2012).</p>
Pedestrian single file movement on stairway: Investigating the impact of stair construct on pedestrian ascent and descent fundamental diagram
<p>Pedestrian single-file movement on stairs</p>
Data used in article "Self-calibration of UAV Thermal Imagery Using Gradient Descent"
<p>This repository contains data allowing for reproduction of research described in article "Self-calibration of UAV Thermal Imagery Using Gradient Descent".</p> <p>Each zip archive available in repository contains:</p> <ul> <li>Thermal images produced using Zenmuse H20T camera (rjpg directory)</li> <li>Geojson vector shape of river centerline (centerline.json file)</li> <li>Configuration file used by Python implementation available at https://github.com/radekszostak/aerial-thermal-tuner (config.py file)</li> </ul> <p> </p>
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International Brain Laboratory public data
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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.