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650 results for “Workflow”
Supplementary Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists
<p>This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It replicates the main analysis in the article <em>Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists</em> and accompanies the main research data set (<a href="https://doi.org/10.5281/zenodo.10878070" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10878070</a>).</p> <p>In this replication study, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to pulse density variation and how uncertainties and biases propagate to commonly used forest structure metrics.</p> <p>The main data source for this study are ALS point clouds from nine U.S. sites, acquired by the National Ecological Observatory Network (NEON, 6 sites, 3 km x 3 km) and by the United States Geological Survey's 3DEP program (3 sites, also 3 km x 3 km). The underlying data can be found here: https://data.neonscience.org/data-products/DP3.30024.001 (NEON) and here: https://apps.nationalmap.gov/lidar-explorer (3DEP)</p> <p>The different data layers are:</p> <p><strong>replicate.US.R</strong> </p> <p> is a single R script that contains all the code necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data as well as the overall robustness analysis. To replicate the processing of the raw point clouds step by step, this script should be located in a folder called "rscripts".</p> <p><strong>pointclouds_original.zip</strong></p> <p> is the set of original point clouds (3 km x 3 km in extent) used for the replication test, separated into 9 subfolders/sites. Can be used to reproduce the original workflow by placing them in a folder called "data/original". The script will then automatically produce derived point clouds at pulse densities of 2 and 16 per squaremetre and process them into digital terrain models (DTMs), digital surface models (DSMs) and CHMs. Note that, for convienence, these derived products are also included in a separate .zip file (cf. below).</p> <p><strong>processed_foranalysis.zip</strong></p> <p> is the set of derived products (DTMs, DSMs, CHMs), separated into 9 subfolders/sites, i.e. the result of processing the original point clouds. To use these layers directly with the provided script, they should be put into a folder called "processed_foranalysis".</p> <p><strong>summaries.zip</strong></p> <p><strong> </strong>is a set of summary statistics (as .csv files) that were used to generate the main analysis tables in the replication study. To use these summary statistics directly with the provided script, they should be put into a folder called "summaries".</p> <p><strong>figures.zip</strong></p> <p> is a set of figures displayed in the Supplementary Material of the paper <em>Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists</em>.</p>
Workflow Trace Archive askalon-new_ee55 trace
Wien2k uses a full-potential Linearized Augmented Plane Wave (LAPW) approach for the computation of crystalline solids.
Data from: Defining host–pathogen interactions employing an artificial intelligence workflow
For image-based infection biology, accurate unbiased quantification of host–pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentation. Host protein recruitment to pathogens is often refractory to accurate automated assessment due to its heterogeneous nature. An intuitive intelligent image analysis program to assess host protein recruitment within general cellular pathogen defense is lacking. We present HRMAn (Host Response to Microbe Analysis), an open-source image analysis platform based on machine learning algorithms and deep learning. We show that HRMAn has the capacity to learn phenotypes from the data, without relying on researcher-based assumptions. Using Toxoplasma gondii and Salmonella enterica Typhimurium we demonstrate HRMAn's capacity to recognize, classify and quantify pathogen killing, replication and cellular defense responses. HRMAn thus presents the only intelligent solution operating at human capacity suitable for both single image and high content image analysis.
Figure 1 from: Tulig M, Tarnowsky N, Bevans M, Kirchgessner A, Thiers B (2012) Increasing the efficiency of digitization workflows for herbarium specimens. ZooKeys 209: 103-113. https://doi.org/10.3897/zookeys.209.3125
Figure 1 - Digitization workflows at The New York Botanical Garden over the past 17 years.
Figure 8 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 8 - Excursion to Sierra Nevada. Missing: three meters snow (credits: Katrin Vohland).
Figure 4 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 4 - First day of the Stakeholder Roundtable (credits: Dirk Schmeller).
Figure 3 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 3 - Thematic pillars of GEO BON (Gary Geller, GEO Secretariat, 2015)
Figure 7 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 7 - Discussion at the second day of the roundtable (credits: Dirk Schmeller)
Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach: Workflow and Data
<p>This repository contains input files, workflow scripts, and output datasets and interatomic potentials for a diverse set of metal-organic frameworks, as discussed in this <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6363dbf718a8ccae675d2ac8">preprint paper</a>. In addition, we provide the scripts to compute the extended Hessian and subsequently the elastic constants using automatic differentiation.</p>
Workflow Optimization During Pulse Field Ablation for Atrial Fibrillation
ClinicalTrials.gov study NCT07354217. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Patient Satisfaction in Occlusal Splints Fabricated Using Fully Digital Versus Conventional Workflow
ClinicalTrials.gov study NCT06985173. IPD Sharing: NO. Countries: 0. Publications: 0.
Implant Retained Rehabilitation With Surgical and Prosthetic Digital Workflow
ClinicalTrials.gov study NCT04066309. IPD Sharing: NO. Countries: 1. Publications: 0.
Assessment of Digital and Clinical Workflow Using Patient Specific Sticky Bone /Implant Housing PEEK Shell in Anterior Atrophic Maxilla: A Case Series
ClinicalTrials.gov study NCT05624697. IPD Sharing: Not stated. Countries: 1. Publications: 0.
AR Ruler to Improve Safety and Clinical Workflow During PICC Placement
ClinicalTrials.gov study NCT05399875. IPD Sharing: NO. Countries: 1. Publications: 0.
Human-AI Collaborative INSIGHT Diagnostic Workflow for in Breast Cancer With Extensive Intraductal Component
ClinicalTrials.gov study NCT07060599. IPD Sharing: NO. Countries: 1. Publications: 0.
Assessment of the Accuracy of Full Digital Workflow in Patient Specific Submerged Subperiosteal Implants: A Case Series Study
ClinicalTrials.gov study NCT07014527. IPD Sharing: YES. Countries: 1. Publications: 0.
Systematic Workflow for Pentaspline Pulsed-field Ablation Optimization: Real-world Performance of the 12 COmmandments (12-O) Strategy
ClinicalTrials.gov study NCT06706518. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Daily Imaging, Target Identification, and Simulated Computed Tomography-Based Stereotactic Adaptive Radiotherapy Workflow in a Novel Ring Gantry Radiotherapy Device
ClinicalTrials.gov study NCT04008537. IPD Sharing: NO. Countries: 1. Publications: 0.
Clinical Workflow Optimization Using Artificial Intelligence for Dermatological Conditions
ClinicalTrials.gov study NCT06263413. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Safety and Efficacy of Workflows of High Volume Single Operators in a LAAO Device Implant Procedural Day
ClinicalTrials.gov study NCT06436924. IPD Sharing: Not stated. 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
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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.