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1,782 results for “Algorithm”
A Semi-Lagrangian Sedimentation Algorithm for Double-Moment Raindrops
<p>Data sets for plotting some figures are available, along with grads control files.</p>
A Practitioner's Guide to MDP Model Checking Algorithms (Artefact)
<p>This artefact allows to review and replicate the experiments from the TACAS'23 paper <strong>A Practitioner's Guide to MDP Model Checking Algorithms</strong>.</p> <p>The package contains all original logfiles and the scripts that extract the relevant data from those logs to generate the plots as in the paper.</p> <p>Furthermore, the artefact contains the model checking tools `Storm` and `mcsta` with their dependencies and convenient installation scripts as well as all benchmark instances.</p> <p>We provide installation instructions for those LP solvers that, due to licensing reasons, could not be included in this artefact.</p> <p>The user can thus replicate all experiments from the paper.</p> <p>An appropriate subset of the experiments is given to allow a review in a timely manner. In addition, single experiments can be handpicked for replication.</p>
Dataset: Feature embeddings from the BirdNET algorithm provide insights into avian ecology
<p>Data used for case study one in the 2023 Ecological Informatics paper "Feature embeddings from the BirdNET algorithm provide insights into avian ecology" wherein we used BirdNET feature embeddings to classify adult and juvenile Great Gray Owl vocalizations.</p>
Geomagnetic datasets of BJI station reconstructed through Artificial Neural Network improved by Genetic Algorithm in 2021
<p>Beijing station established in 1954 is one of the oldest geomagnetic observatories in China, which plays an important role in data exchange, and further provide data or standardization for satellite observation and geomagnetic model construction. With the development of urbanization, the observed data are greatly disturbed by subways, and data disturbed are almost unavailable. The dataset was reconstructed through Artificial Neural Network improved by Genetic Algorithm, including minutely data of three components (D, H and Z) in 2021. This reconstruction method has been proved to be effective.</p>
Many-core algorithms for high-dimensional gradients on phylogenetic trees
<p>XMLs, trees, and log files for "Many-core algorithms for high-dimensional gradients on phylogenetic trees". </p>
Lateral flow immunoassay-based laboratory algorithm for rapid diagnosis of diphtheria
<p><strong>Results of the ‘DIFTERIA’ Lateral Flow Immunoassay (LFIA) validation.</strong></p>
Accurate real space iterative reconstruction (RESIRE) algorithm for tomography
<p>This is the code and dataset for the paper: Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Three dataset are provided in this deposit:</p> <p>1. the simulation of vesicle model</p> <p>2. the biomineral data</p> <p>3. Amorphous Ta thinfilm data</p> <p>The algorithm are written in matlab and cuda code. Users can either use the cpu or gpu versions.</p> <p>1. The cpu code can only do tomography reconstructions with single tilt axis (say y-axis)</p> <p>2. The gpu code can do with multiple tilt axes, say ZYX<br> To use the gpu cude, you need to download and install CUDA toolkit from the NVIDA website https://developer.nvidia.com/cuda-downloads. Then, open Matlab and compile cuda functions, for example:<br> mexcuda -R2018a RT3_1GPU.cu<br> The mexw64 cuda files attached in this folder are precompiled for windows, cuda version = 11.x</p> <p>If you use the code or the data, please cite our paper:<br> Pham, M., Yuan, Y., Rana, A. et al. Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Sci Rep 13, 5624 (2023). https://doi.org/10.1038/s41598-023-31124-7</p> <p>If you have question, feel free to email us at minhrose@ucla.edu</p>
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
LinearCoFold and LinearCoPartition: Linear-Time Algorithms for Secondary Structure Prediction of Interacting RNA molecules
<p>LinearCoFold and LinearCoPartition</p>
Algorithm and evaluation results of three-dimensional seismic resilience
<p>The algorithm is based on genetic algorithm and improved genetic algorithm. The calculation results are detailed in the Excel file</p>
EUROPT Clinical Trial to Study the Efficacy of One-Way Valve Implantation (New Treatment Algorithm) in Patients With Heterogeneous Emphysema
ClinicalTrials.gov study NCT00730301. IPD Sharing: Not stated. Countries: 2. Publications: 0.
FRAILTY Classification Algorithm for the Corsano CARDIOWATCH 287-2: a Development and Validation Study
ClinicalTrials.gov study NCT06930833. IPD Sharing: NO. Countries: 1. Publications: 0.
Pocket Colposcopy Using CARE Algorithm
ClinicalTrials.gov study NCT06458062. IPD Sharing: NO. Countries: 1. Publications: 0.
Reflectance Confocal Microscopy of Wounds During Moh's Surgery: Feasibility Testing of a Mosaicing Algorithm for Intraoperative Imaging of Cancer Margins
ClinicalTrials.gov study NCT01872130. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Diagnosis and Treatment Methods in Corrosive Esophagitis: A 26-Year Experience-Based Algorithm
ClinicalTrials.gov study NCT06816758. IPD Sharing: YES. Countries: 1. Publications: 0.
An AI Algorithm for Lymphocyte Focus Score of Minor Salivary Gland Biopsy Samples for Diagnosing Sjogren's Syndrome
ClinicalTrials.gov study NCT06437652. IPD Sharing: NO. Countries: 1. Publications: 0.
AI Algorithm-Informed Biopsy for Prostate Cancer Detection With Indeterminate and Low-Risk Prostate MRI Lesions
ClinicalTrials.gov study NCT07231627. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Evaluation of an Algorithm for the Identification of Post-traumatic Stress Disorder
ClinicalTrials.gov study NCT06796556. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Vocal Feature Analysis Algorithm for COVID-19 Detection
ClinicalTrials.gov study NCT04418544. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Validation of a Surgical Navigation Algorithm During Knee Replacements in Patients With Knee Osteoarthritis
ClinicalTrials.gov study NCT04912908. IPD Sharing: NO. Countries: 1. Publications: 0.
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.