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506 results for “assessment model”
Biophysical larval dispersal models of observed bonefish (Albula vulpes) spawning events in Abaco, The Bahamas: An assessment of population connectivity and ocean dynamics
<p>Biophysical models are a powerful tool for assessing population connectivity of marine organisms that broadcast spawn. <em>Albula</em> <em>vulpes</em> is a species of bonefish that is an economically and culturally important sportfish found throughout the Caribbean and that exhibits genetic connectivity among geographically distant populations. We created ontogenetically relevant biophysical models for bonefish larval dispersal based upon multiple observed spawning events in Abaco, The Bahamas in 2013, 2018, and 2019. Biological parameterizations were informed through active acoustic telemetry, CTD casts, captive larval rearing, and field collections of related albulids and anguillids. Ocean conditions were derived from the Regional Navy Coastal Ocean Model American Seas dataset. Each spawning event was simulated 100 times using the program Ichthyop. Ten thousand particles were released at observed and putative spawning locations and were allowed to disperse for the full 71-day pelagic larval duration for <em>A</em>. <em>vulpes</em>. Settlement densities in defined settlement zones were assessed along with interactions with oceanographic features. The prevailing Northern dispersal paradigm exhibited strong connectivity with Grand Bahama, the Berry Islands, Andros, and self-recruitment to lower and upper Abaco. Ephemeral gyres and flow direction within Northwest and Northeast Providence Channels were shown to have important roles in larval retention to the Bahamian Archipelago. Larval development environments for larvae settling upon different islands showed few differences and dispersal was closely associated with the thermocline. Settlement patterns informed the suggestion for expansion of conservation parks in Grand Bahama, Abaco, and Andros, and the creation of a park in Eleuthera and the Berry Islands to protect fisheries. Further observation of spawning events and the creation of biophysical models will help to maximize protection for bonefish spawning locations and nursery habitat, and may help to predict year-class strength for bonefish stocks throughout the Greater Caribbean.</p>
A comparative assessment of different adaptive spatial refinement strategies in phase-field fracture models for brittle fracture
<p><strong>Abstract:</strong></p> <p>(from [1])</p> <blockquote> <p>For the smeared approximation of a discrete crack, phase-field fracture simulations of brittle materials require suitable finite element meshes in regions where crack propagation is expected to get an accurate resolution of the phase-field function. The intuitive option is to pre-refine the mesh in regions of the expected crack paths. However, this could lead to very computationally intensive simulations due to the high number of elements. Alternatively, adaptive spatial refinement of the finite element mesh is utilized based on appropriate error indicators to obtain the required accuracy in the areas of crack propagation. Different error indicators can be used: the most common one for phase-field fracture simulations is the threshold-based approach, in which elements are refined depending on the value of the phase-field function. Alternatively, the Kelly error indicator can be used as a criterion for spatial adaptivity. It considers the jumps in the gradients of the phase-field function between the elements. We additionally introduce here an error indicator based on configurational forces, that depend on the Eshelby stress tensor. For mode I loading in linear elastic fracture mechanics, the configurational forces have a close connection to the <span class="math-tex">\(\mathscr{J}\)</span>-Integral and the critical fracture energy <span class="math-tex">\(\mathrm{G}_\mathrm{c}\)</span> , respectively. Therefore, a suitable norm of the configurational forces is introduced as an error indicator here. These three error indicators are introduced and compared to each other in terms of accuracy and efficiency by means of numerical examples for crack growth in the single edge notched shear test.</p> </blockquote> <p><strong>Contact:</strong></p> <p>Maurice Rohracker</p> <p>Institute of Applied Mechanics</p> <p>Friedrich-Alexander-Universität Erlangen-Nürnberg</p> <p>Egerlandstr. 5</p> <p>91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All phase-field fracture simulations were performed with <em>deal.II</em> [2], version 9.2.0, on the HPC cluster <em>Meggie</em> of NHR@FAU. The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The hardware is funded by the German Research Foundation (DFG).</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p><strong>Context:</strong></p> <p>Dataset supplementing preprint:</p> <p>[1] M.Rohracker, P.Kumar, J.Mergheim, "A comparative assessment of different adaptive spatial refinement strategies in phase-field fracture models for brittle fracture", Forces in Mechanics, 2022, <a href="https://doi.org/10.1016/j.finmec.2022.100157">10.1016/j.finmec.2022.100157</a>.</p> <p>This dataset contains the complete results presented in [1], which include global variables, field variables, and meshes.</p> <p><strong>File structure:</strong></p> <p>The file structure is explained in more detail in the shipped <em>README.md</em> in the dataset folder.</p> <p><strong>References:</strong></p> <p>[1] M.Rohracker, P.Kumar, J.Mergheim, "A comparative assessment of different adaptive spatial refinement strategies in phase-field fracture models for brittle fracture", Forces in Mechanics, 2022, <a href="https://doi.org/10.1016/j.finmec.2022.100157">10.1016/j.finmec.2022.100157</a>.</p> <p>[2] D. Arndt, W. Bangerth, B. Blais, T. C. Clevenger, M. Fehling, A. V. Grayver, T. Heister, L. Heltai, M. Kronbichler, M. Maier, P. Munch, J.-P. Pelteret, R. Rastak, I. Thomas, B. Turcksin, Z. Wang, D. Wells, <strong>The deal.II Library, Version 9.2</strong> Journal of Numerical Mathematics, vol. 28, p. 131-146, 2020.</p>
Dataset for "Assessing the aerosols, clouds and their relationship over the northern Bay of Bengal using a global climate model"
<p><strong>The dataset is organized into two ZIP folders as described below.</strong></p> <p><strong>1. Model simulation dataset:</strong> This folder (<strong>merged_data_files</strong>) contains 2 folders.</p> <ol> <li>Netcdf files of CESM experiments output for the study. A total of 8 experiments have been performed which can be identified by the filenames (CAM5, CAM5G, Q6, Q48, Q96, UV6, UV6Q48). Each experiment has a total of 360 time steps corresponding to daily output December-January-February (DJF) months of 2006-2010. It is to be noted that all the simulated cloud variables are from the MODIS COSP simulator output and have been restricted to low clouds (grids with cloud top pressure less than 680 hPa have been excluded). Following are the variables in each of the netcdf file of experiments which are analysed in the research article: <ol> <li> <p>AODVISEXT - Aerosol optical depth visible range</p> </li> <li> <p>LCODEXTLOW - MODIS Liquid Cloud Optical Thickness</p> </li> <li> <p>LCFEXTLOW - MODIS Liquid Cloud Fraction</p> </li> <li> <p>LCEREXTLOW - MODIS Liquid Cloud Particle Size</p> </li> <li> <p>LWPEXTLOW - MODIS Cloud Liquid Water Path</p> </li> <li> <p>CTPEXTLOW - MODIS Cloud Top Pressure</p> </li> <li> <p>CDNC - Cloud droplet number concentration</p> </li> </ol> </li> <li>CSV files of CTP-COT joint histogram output for each experiment averaged over the study region during DJF season for 2006-2010.</li> </ol> <p><strong>2. Study region shapefile:</strong> This folder (<strong>study_region_shapefile</strong>) contains the shapefile having the outline of the northern Bay of Bengal region which is our study region. The ACI sensitivities discussed in the article have been calculated using the spatial average of data points extracted for this region for the December-January-February months for the years 2006-2010 (360 time steps).</p>
Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model
<p>Dataset accompanying the publication "Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model"</p> <p> </p>
Data from: Integrating ecological niche and hydrological connectivity models to assess the impacts of hydropower plants on an endemic and imperiled freshwater turtle
<p>We built this dataset to assess the impacts of hydropower plants on the distribution of an endemic and imperiled freshwater turtle with very unique ecological requirements, the Williams' side-necked turtle (<em>Phrynops</em> <em>williamsi</em>). To prevent and mitigate impacts, we prioritized sites for species conservation by classifying planned HPP locations according to their predicted adverse effects on species distribution. The dataset has two files: i) species occurrence records and ii) hydropower plant data. The first dataset was fully built by the authors and the second was modified from the Brazilian Electricity Regulatory Agency (ANEEL) georeferenced data system.</p>
Supporting data for Assessing clouds using satellite observations through three generations of global atmosphere models
<p>Monthly data from CAM4, CAM5, and CAM6 that are needed to reproduce the analysis and figures in the manuscript entitled: Assessing clouds using satellite observations through three generations of global atmosphere models by Brian Medeiros, Jonah Shaw, Jennifer Kay, and Isaac Davis.</p>
Monitoring of postpartum body condition at the cow and herd levels: assessing explanatory and predictive power of disease risk models
<p>Objectives</p> <p>1- To define the herd threshold for cows with poor body condition based on its predictive capacity for disease risk at the herd level, and</p> <p>2- to estimate the impact measures on disease rates due to body condition indicators in transition period.</p> <p>Two commercial grazing dairy herds (Herd A=5.034 and herd B=7.965 lactations) from Argentinean Pampa region were used to perform a longitudinal retrospective study during a 4-year period (2014 –2017).Health, reproductive and body condition score (BCS) records were gathered. The BCS (5-point scale) was performed at calving and at the time of reproductive release. The difference between both measures of BCS was used to assess the body condition loss (∆BCS). All the cows not bred by 70 DIM were checked for anestrus.Calving cohorts of 21-day were defined at each herd and parity group through the entire study period. The frequency of cows with BCS<3 or ∆BC>-0.5 at each cohort were calculated and used to define quartiles through whole study period. Quartiles were used, one at a time, as threshold to dichotomize the cohorts to predict the risk that a cohort has a frequency of anestrus over the median.The higher AUC was used as selection criterium to determine the herd level threshold at each HERD and PARITY level. The population attributable fraction (AFP) of anestrus rate to body condition indicators at each cohort was calculated, for every HERD and PARITY level. </p>
data collection for NEMO BGC assessment in idealised models (GM in non-eddying models)
<p>Edit in ver 3, 17 Oct 2023:</p><ul><li>(<strong>important</strong>) there is a bug NEMO GEOMETRIC code (advection of parameterised eddy energy; see ldfeke.F90), fixed in the present version</li><li>new data files updated (everything is included for completeness, although only the ones using GEOMETRIC have been updated)</li><li>data files and software files have been split out into separate zip files to enable easier downloads</li></ul><p>=========================</p><p>Edit in ver 2, 06 Feb 2023:</p><ul><li>added calculation files with Treguier et al variant of GM, supplement data not explicitly leading to figure in paper;</li><li>fixes of masking for boundary values when computing averages (no change to figures, minor changes to numerical values);</li><li>fixing a bug in the calculation of surface vorticity (raw numerical value in units of s-1 correct, but incorrect in units of f_0 because of a missing sin(latitude) factor)</li></ul><p>=========================</p><p>Data archive for "Combined physical and biogeochemical assessment of mesoscale eddy parameterisations in ocean models: eddy induced advection at non-eddying resolutions". Provided are:</p><p>1) modification and configuration files for the GYRE_PISCES configuration in NEMO 4.0.5 (r14538), with sample restart and output files</p><ul><li>CONST restart file at year 2000 (end of spin up, denoted year -300 in the paper)</li><li>CONST, GEOM, R12 restart file at year 2300 (beginning of control/climate change split, denoted year 0 in the paper)</li><li>CONST, GEOM, R12 sample output files at year 2366 to 2370 (denoted year 66 to 70 in paper)</li></ul><p>2) processed time-averaged data for regenerating figures from the article<br>3) python scripts and notebooks for analysing the data</p>
Assessing the Efficacy of Enhanced Versus Standard 3D Training Models in Laparoscopic Skills Acquisition: A Randomised Controlled Trial
ClinicalTrials.gov study NCT06184854. IPD Sharing: YES. Countries: 1. Publications: 1.
Dentine Tubule Occlusion Assessment in a Modified in Situ Model
ClinicalTrials.gov study NCT02768194. IPD Sharing: Not stated. Countries: 1. Publications: 1.
One-Dimensional Mathematical Model-Based Automated Assessment of Fractional Flow Reserve
ClinicalTrials.gov study NCT03797118. IPD Sharing: NO. Countries: 1. Publications: 1.
Improving Access to Breast Cancer Screening and Treatment in Nigeria: The Triple Mobile Assessment and Patient Navigation Model
ClinicalTrials.gov study NCT05321823. IPD Sharing: YES. Countries: 1. Publications: 1.
Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence
ClinicalTrials.gov study NCT07328997. IPD Sharing: YES. Countries: 1. Publications: 30.
Flow virometry for water-quality assessment: Protocol optimization for a model virus and automation of data analysis
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Data from: Too much of a good thing? Supplementing current species observations with fossil data to assess climate change vulnerability via ecological niche models
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Data from: Integrating ecological niche and hydrological connectivity models to assess the impacts of hydropower plants on an endemic and imperiled freshwater turtle
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Assessing the adequacy of morphological models using Posterior Predictive Simulations
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Assessment of the stream invertebrate β-diversity along an elevation gradient using a bidimensional null model analysis
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Data from: Assessing the usefulness of Citizen Science Data for habitat suitability modelling: opportunistic reporting versus sampling based on a systematic protocol
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Data from: Assessing cetacean populations using integrated population models: an example with Cook Inlet beluga whales
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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.