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1,782 results for “Algorithm”
Data from: No silver bullets in correlative ecological niche modeling: insights from testing among many potential algorithms for niche estimation
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Data from: Fast dating using least-squares criteria and algorithms
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Data from: Expert, crowd, students or algorithm: who holds the key to deep-sea imagery ‘big data’ processing?
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Software for control of autonomous robots using fuzzy logic controllers tuned by genetic algorithms
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Algorithms for determining transposable genes in a genome
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Data from: Interpreting the FLOCK algorithm from a statistical perspective
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Data from: An SSR based approach incorporating a novel algorithm for identification of rare maize genotypes facilitates criteria for landrace conservation in Mexico
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Data from: Estimating field capacity from volumetric soil water content time series using automated processing algorithms
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Data from: Can the use of digital algorithms improve quality care? An example from Afghanistan
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Data from: QTG-Finder2: a generalized machine-learning algorithm for prioritizing QTL causal genes in plants
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Data for training AMSR2-CNN and its corresponding machine learning algorithm
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Data from: Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types
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Data from: A segmentation algorithm for characterizing Rise and Fall segments in seasonal cycles: an application to XCO2 to estimate benchmarks and assess model bias
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Algorithm
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Synthetic river datasets built for testing and development of the Surface Water and Ocean Topography mission discharge algorithms
<p><strong>1.Summary</strong></p> <p>Datasets used for testing the performance of discharge estimation algorithms built in support of the Surface Water and Ocean Topography satellite mission. The benchmarking manuscript entitled “Exploring the factors controlling the performance of the Surface Water and Ocean Topography mission discharge algorithms” is currently under review at Water Resources Research. Once the manuscript is accepted, its DOI will be included here.</p> <p><strong>2.File description</strong></p> <p>The dataset is divided into four groups: 1-Ideal data, 2-Varying Temporal Sampling, 3-Measurement Uncertainty, and 4-SWOT Sampling and Uncertainty. Ideal data contains daily measurements with no observational uncertainty. Varying Temporal Sampling downsamples the ideal measurements considering different temporal frequencies with complete sets assuming: 1 measurement every 2 days, 3 days, 4 days, 5 days, 7 days, 10 days, and 21 days. The measurement uncertainty set adds errors to cross-sectional heights and widths, which are used to compute reach average height, width, and slope considering error corruption. The final set SWOT Sampling and Uncertainty accounts for SWOT temporal sampling and measurement uncertainty. Sets containing uncertainty have extra height, width, and slope attributes with the word true appended to the attribute name. Such attributes represent the uncorrupted measurements at the cross-section and reach scales. Height, width, and slopes for the SWOT sampling and Uncertainty dataset containing the value of negative 9999 denote points that are not observed at a particular location and time step.</p> <p>Data will be contained in one NetCDF file per river. The file contains the following groups and variables:</p> <p><strong>/River_Info/</strong></p> <p>Name: River name, data type: char</p> <p>QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)</p> <p>rch_bnd: Reach boundaries measured in meters from the upstream end of the model</p> <p>gdrch: Reaches used in the study. Used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.</p> <p><strong>/XS_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>Z: Bed elevation in meters. Dimension: Cross-section, time step.</p> <p>xs_rch: Reach number for each cross-section. Dimension: Cross-section,1.</p> <p>X: Flow distance measured from the most upstream end of the model to the cross-section (meters). Dimension: Cross-section, 1.</p> <p>longitude: Cross-section longitude in decimal degrees. Dimension: Cross-section,1.</p> <p>latitude: Cross-section latitude in decimal degrees. Dimension: Cross-section,1.</p> <p>W: River width in meters. Dimension: Cross-section, time step.</p> <p>Wtrue: River width in meters. Dimension: Cross-section, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.</p> <p>Q: Discharge (m<sup>3</sup>/s). Dimension: Cross-section, time step.</p> <p>H: Water surface elevation in meters. Dimension: Cross-section, time step.</p> <p>Htrue: Water surface elevation in meters. Dimension: Cross-section, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.</p> <p>A: Cross-sectional area of flow in m<sup>2</sup>. Dimension: Cross-section, time step.</p> <p>P: Wetted perimeter in meters. Dimension: Cross-section, time step.</p> <p>n: Manning’s roughness. Dimension: Cross-section, time step.</p> <p><strong>/Reach_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>W: Reach averaged river width in meters. Dimension: Reach, time step.</p> <p>Wtrue: Reach averaged river width in meters. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the width value with no uncertainty.</p> <p>Q: Reach averaged discharge (m<sup>3</sup>/s). Dimension: Reach, time step.</p> <p>H: Reach averaged water surface elevation in meters. Dimension: Reach, time step.</p> <p>Htrue: Reach averaged water surface elevation in meters. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.</p> <p>S: Reach averaged water surface slope in meters per meter. Reach, time step.</p> <p>Strue: Reach averaged water surface slope in meters per meter. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the slope value with no uncertainty.</p> <p>A: Reach averaged area of flow in m<sup>2</sup>. Dimension: Reach, time step.</p> <p><strong>References</strong></p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: <a href="https://doi.org/10.1016/j.gloplacha.2014.01.011">https://doi.org/10.1016/j.gloplacha.2014.01.011</a>.</p> <p> </p>
TECPR2 protein models predicted by three algorithms
<p>This ZIP-file contains the files used for TECPR2 protein modeling and resulting PDB (Protein Data Bank) files from three algorithms/pipelines (GalaxyWEB, trRosetta, SWISS-MODEL) used for clustering analysis and visualization in Neuser et al. ("Clinical, neuroimaging and molecular spectrum of <em>TECPR2-</em>associated hereditary sensory and autonomic neuropathy with intellectual disability").<br> We always used standard parameters and the respective top model ("model_1 | model01 | model1") for each algorithm/pipeline and/or downstream steps.</p>
An algorithm to quantify intratumor heterogeneity based on alterations of gene expression profiles: Data availability
<p>DEPTH evaluates the tumor heterogeneity level of each tumor sample based on gene expression profiles Heterogeneity score. </p>
CompressGraph: Knowledge about compression algorithms, tools, formats, ratios and runtime behaviour
<p>Machine-readable dataset to determine the best compression settings for a given goal such as "fastest compression" or "fastest search over compressed data". Graph model (JSON + auto-derived Dot format) along with CSV measurement results of 13 compression tools and 30 tool-configuration-search combinations.</p> <p>Associated publication: Josef Spillner, «Comparison and Model of Compression Techniques for Smart Cloud Log File Handling», CCCI 2020.</p> <p>This dataset also contains a snapshot of the associated software prototypes.<br> </p>
Matlab Algorithm for Systematic Vertical Separation Measurements of Tectonic Fault Scarps
<p>We have studied the topography along the Lost River Fault in Idaho (USA) and in particular the rupture zone generated by the strong 1983 Borah Peak earthquake (M<sub>w</sub> 6.9) almost 40 years after its occurrence. Our first reason for acquiring these data was to systematically measure vertical separation (VS) along the fault.<br> To do this we have developed and implemented a scarp analysis algorithm in MATLAB (www.mathworks.com) which enables users to measure the vertical separation from a topographic profile. The code constructs a topographic profile along the preferred trend (generally perpendicular to the fault trace) and projects the best-fit lines to each of the hanging wall and footwall flat to the fault location. The vector difference between the intersection of these lines with a vertical plane at the fault location is the vertical separation. Please see the code guide for additional detail. </p>
EVISAN – a dataset for multi-sperm detection and tracking algorithm development
<p><strong>P</strong><strong>urpose</strong><strong>:</strong> Automatic tracking and detection of motile cells in time-lapse mode during microscopy is an important requirement in many biological applications. Although significant developments have been made in cell tracking algorithms, current datasets are limited in size and diversity, especially for data-dependent generalized deep learning models. In this paper, we introduce a new and larger standard sperm tracking dataset with a useful framework for assessing motile cell tracking algorithms.</p> <p><strong>Acquisition and validation methods: </strong>Semen samples were used to obtain image datasets. The sequences of motile sperm data were acquired using a microscope. We recorded motile sperm from different donors under different magnification, to provide a suitable sperm pool and achieve a degree of image heterogeneity. Sperm images were manually annotated and checked by several biologists using semi-automatic software to generate the dataset.</p> <p><strong>Data format and usage notes: </strong>We present a new dataset, EVISAN – Expert Visual Sperm Annotation, comprising partially annotated sperm images from different donors at a varying magnification that is readily usable as training data for computer vision applications. With 6,000 images, our collection is an unparalleled heterogeneous dataset for deep learning sperm detection application development. All images are stored in XML and JPEG formats. We also provide a standard evaluation framework for the proposed dataset.</p> <p><strong>Potential applications: </strong>Our standard dataset is highly suitable for quantitatively evaluating state-of-the-art, multi-target tracking and detection algorithms. We also foresee the clinical application of these algorithms in assisted reproduction in humans, such as real-time computer-assisted sperm analysis.</p>
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.