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137 results for “mixing models”
MESA histories for "Characterizing Observed Extra Mixing Trends in Red Giants using the Reduced Density Ratio from Thermohaline Models"
<p>This repository provides MESA history files for each of the stellar models in the publication "Characterizing Observed Extra Mixing Trends in Red Giants using the Reduced Density Ratio from Thermohaline Models". The MESA version used was stable release version 21.12.21. Runs are organized into tarballs according to the thermohaline mixing prescription used:</p> <ul> <li>BGS13 = Brown, Garaud, Stellmach 2013</li> <li>Kipp1e-1 = Kippenhahn with alpha_th = 0.1</li> <li>Kipp2 = Kippenhahn with alpha_th = 2</li> <li>Kipp7e2 = Kippenhahn with alpha_th = 700</li> </ul> <p>and are additionally grouped according to the stellar mass (M = 0.9, 1.1, 1.3, 1.5, 1.7 in units of Msol). Within each tarball is a number of run directories which contain a LOGS/history.data file from the MESA run. The subdirectories in the tarball contain runs at various metallicities; conversion between Z (MESA input) and [Fe/H] (paper reported value) are found in Table 2 of the manuscript.<br> <br> Inlists and information for recreating these MESA simulations can be found online at the paper's github repository: <a href="https://github.com/afraser3/Empirical-Magnetic-Thermohaline">https://github.com/afraser3/Empirical-Magnetic-Thermohaline</a> (a copy of the code from this Github repository is located in this Zenodo repository in: Empirical-Magnetic-Thermohaline-main.zip)</p>
Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses
<p>1. Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect non-zero correlations has rarely been evaluated and these few studies have not focused on tag-recovery data, which is a common data type.</p> <p>2. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (<em>Anas</em> <em>platyrhychos</em>) tag-recovery dataset and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate more robust summary statistics for tag-recovery datasets than total individuals tagged.</p> <p>3. Different priors lead to substantially different estimates of correlation from the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations could not estimate strongly negative correlation with useful precision or accuracy. Many correlation estimates spanned the available parameter space (–1,1) and underestimated the magnitude of negative correlation. Only one prior combined with our most intensive monitoring scenario provided reliable results. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery.</p> <p>4. The inadequacy of prior distributions and sample size combinations previously assumed adequate for obtaining robust inference from tag-recovery data represents a concern in the application of Bayesian hierarchical models to tag-recovery data. Our analysis approach provides a means for examining prior influence and sample size on hierarchical models fit to capture-recapture data while emphasizing transferability of results between empirical and simulation studies.</p>
The Mixed Layer Depth in the Ocean Model Intercomparison Project (OMIP): Impact of Resolving Mesoscale Eddies: supporting data
<p>This file contains a jupyter notebook (python language) used to produce the figures of a manuscript submitted to the journal Geoscientific Model Development, and the data necessary to reproduce the figures.</p> <p>Abstract of the manuscript:</p> <p>The ocean mixed layer is the interface between the ocean interior and the atmosphere or sea ice, and plays a key role in climate variability. It is thus critical that numerical models used in climate studies are capable of a good representation of the mixed layer, especially its depth. Here we evaluate the mixed layer depth (MLD) in six pairs of non-eddying (1° resolution) and eddy-rich (up to 1/16°) models from the Ocean Model Intercomparison Project (OMIP), forced by a common atmospheric state. For model validation, we use an updated MLD dataset computed from observations using the OMIP protocol (a constant density threshold). In winter, low resolution models exhibit large biases in the deep water formation regions. These biases are reduced in eddy-rich models but not uniformly across models and regions. The improvement is most noticeable in the mode water formation regions of the northern hemisphere. Results in the Southern Ocean are more contrasted, with biases of either sign remaining at high resolution. In eddy-rich models, mesoscale eddies control the spatial variability of MLD in winter. Contrary to a hypothesis that the deepening of the mixed layer in anticyclones would make the MLD larger globally, eddy-rich models tend to have a shallower mixed layer at most latitudes than coarser models do. In addition, our study highlights the sensitivity of the MLD computation to the choice of a reference level and the spatio-temporal sampling, which motivates new recommendations for MLD computation in future model intercomparison projects.</p>
A Mixed-Flux-Based Nodal Discontinuous Galerkin Method for 3D Dynamic Rupture Modeling
<p>This repository contains data produced by a mixed-flux-based discontinuous Galerkin method for 3D dynamic rupture modeling, using the software DRDG3D (<a href="https://github.com/wqseis/drdg3d">https://github.com/wqseis/drdg3d</a>). Input scripts for the SCEC/USGS dynamic rupture benchmark validation problems (<a href="https://strike.scec.org/cvws">https://strike.scec.org/cvws</a>) and other cases are hosted on DRDG3D's GitHub page. The preprint is published at ESS Open Archive (DOI: <a href="http://doi.org/10.1002/essoar.10512657.1">10.1002/essoar.10512657.1</a>).</p>
Data, scripts, and figures of the article: Evaluation of oregano essential oil in broilers challenged with a mixed Eimeria spp. and high dietary protein model of subclinical coccidiosis
<p>Data, scripts, and figures of the article "Evaluation of oregano essential oil in broilers challenged with a mixed Eimeria spp. and high dietary protein model of subclinical coccidiosis" to be published in the journal Animal - Open Space. </p>
Model Output for Eddy Mediated Mixing in the Equatorial Pacific
<p>This repository contains processed model outputs used in the "Eddy Mediated Mixing in the Equatorial Pacific" submitted manuscript. </p>
Isotope mixing scenarios for: To what extent are the source mixing models accurate: evaluation of the model accuracy and guidelines for the site-specific model selection
<p><span>We selected 10 types of distinct isotope signatures that can be found in the samples of natural water. Every 3–10 types of hypothetical isotope signatures were conceptually grouped together. There would be 968 possible combinations based on combinatorics theory. However, we needed distinct mixing polygons to facilitate our determination of model capacity in dealing with uncertainties. Therefore, we </span><span>kept </span><span>only 240 such groups in </span><span>the </span><span>final</span><span> analysis</span><span>. Each group was designated with a </span><span>predefined</span><span> mixing ratio. After that, we ran all the examined models through these mixing scenarios to </span><span>obtain</span><span> the model estimation of the mixing ratios.</span></p>
Data for: Modeling the transition of death assemblages through the mixed layer predicts a downcore increase in time averaging
Open the record for dataset details and reuse information.
Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses
Open the record for dataset details and reuse information.
Isotope mixing scenarios and machine learning model in: To what extent are the source mixing models accurate: evaluation of the model accuracy and guidelines for the site-specific model selection
Open the record for dataset details and reuse information.
Enhanced reforming of mixed biomass tar model compounds using a hybrid gliding arc plasma catalytic process
<p>Datasheet for the paper; DOI: 10.1016/j.cattod.2019.05.046</p>
Plasma reforming of biomass gasification tars using mixed naphthalene and toluene as model compounds
<p>Dataset for the paper; DOI: 10.1016/j.enconman.2019.05.002</p>
Simulated data for paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4) with a rich (scenarios 1 and 3) and a sparse design (scenarios 2 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.</p>
Two-step mixed model approach to analyzing differential alternative RNA splicing: Datasets and R scripts for analysis of alternative splicing
<p>Changes in gene expression can correlate with poor disease outcomes in two ways: through changes in relative transcript levels or through alternative RNA splicing leading to changes in relative abundance of individual transcript isoforms. The objective of this research is to develop new statistical methods in detecting and analyzing both differentially expressed and spliced isoforms, which appropriately account for the dependence between isoforms and multiple testing corrections for the multi-dimensional structure of at both the gene- and isoform- level. We developed a linear mixed effects model-based approach for analyzing the complex alternative RNA splicing regulation patterns detected by whole-transcriptome RNA-sequencing technologies. This approach thoroughly characterizes and differentiates three types of genes related to alternative RNA splicing events with distinct differential expression/splicing patterns. We applied the concept of appropriately controlling for the gene-level overall false discovery rate (OFDR) in this multi-dimensional alternative RNA splicing analysis utilizing a two-step hierarchical hypothesis testing framework. In the initial screening test we identify genes that have differentially expressed or spliced isoforms; in the subsequent confirmatory testing stage we examine only the isoforms for genes that have passed the screening tests. Comparisons with other methods through application to a whole transcriptome RNA-Seq study of adenoid cystic carcinoma and extensive simulation studies have demonstrated the advantages and improved performances of our method. Our proposed method appropriately controls the gene-level OFDR, maintains statistical power, and is flexible to incorporate advanced experimental designs.</p>
Data from: Refining trophic dynamics through multi-factor Bayesian mixing models: a case study of subterranean beetles.
<p>Food web dynamics are vital in shaping the functional ecology of ecosystems. However, trophic ecology is still in its infancy in groundwater ecosystems due to the cryptic nature of these environments. To unravel trophic interactions between subterranean biota, we applied an interdisciplinary Bayesian mixing model design (multi-factor BMM) based on the integration of faunal C and N bulk tissue stable isotope data (δ<sup>13</sup>C and δ<sup>15</sup>N) with radiocarbon data (Δ<sup>14</sup>C), and prior information from metagenomic analyses. We further compared outcomes from multi-factor BMM with a conventional isotope double proxy mixing model (SIA BMM), triple proxy (δ<sup>13</sup>C, δ<sup>15</sup>N and Δ<sup>14</sup>C, multi-proxy BMM) and double proxy combined with DNA prior information (SIA+DNA BMM) designs. Three species of subterranean beetles (<i>Paroster macrosturtensis</i>, <i>Paroster mesosturtensis</i> and <i>Paroster microsturtensis</i>) and their main prey items Chiltoniidae<i> </i>amphipods (AM1: <i>Scutachiltonia axfordi</i> and AM2: <i>Yilgarniella sturtensis</i>), cyclopoids and harpacticoids from a calcrete in Western Australia were targeted. Diet estimations from stable isotope only models indicated homogeneous patterns with modest preferences for amphipods as prey items. Multi-proxy BMM suggested increased - and species-specific - predatory pressures on amphipods coupled with high rates of scavenging/predation on sister species. SIA+DNA BMM showed marked preferences for amphipods AM1 and AM2 and reduced interspecific scavenging/predation on <i>Paroster </i>species. Multi-factorial BMM revealed the most precise estimations (lower overall SD and very marginal beetles' interspecific interactions), indicating consistent preferences for amphipods AM1 in all the beetles' diets. Incorporation of genetic priors allowed crucial refining of the feeding preferences, while integration of more expensive radiocarbon data as a third proxy (when combined with genetic data) produced more precise outcomes but close dietary reconstruction to that from SIA+DNA BMM. Further multidisciplinary modelling from other groundwater environments will help elucidate the potential behind these designs and bring light to the feeding ecology of one the most vital ecosystems worldwide.</p>
Predicting subjective liking of bitter and sweet liquid solutions using facial electromyography: mixed model dataset
<p>Hedonic responses to foods are often measured using subjective liking ratings scales. This project investigated the potential use of electromyography as a means to predict subjective liking ratings using affective facial muscle activity recorded at different phases of oral processing while tasting liquids. Using linear mixed models, muscle activity recorded while emptying into the mouth, swirling, and thinking about the taste of bitter and sweet liquid solutions was used to predict subjective liking ratings. During different phases of the tasting, these mixed models demonstrate that zygomaticus major activity predicted increased liking and that corrugator supercilii and levator labii superioris predicted decreased liking. The change in liking ratings predicted by each muscle varied depending on whether participants were emptying, swirling, or thinking about the taste.</p>
The Sonora Substellar Atmosphere Models. IV. Elf Owl: Atmospheric Mixing and Chemical Disequilibrium with Varying Metallicity and C/O Ratios (Y- type Models)
<ul> <li><strong>Overview of V2: "The Sonora Substellar Atmosphere Models. V: A Correction to the Disequilibrium Abundance of CO2 for Sonora Elf Owl"</strong></li> </ul> <p> Version 2 of the Sonora Elf Owl Models updates the CO2 and PH3 abundances and spectra. As described in the Wogan et al. (2024) research note (URL OF NOTE GOES HERE), Version 1 of the models did not apply the CO2 quench approximation properly resulting in predicted CO2 abundances that were too small by several orders of magintude in some cases. Version 2 fixes this mistake, updating CO2 abundances and the emission spectra to reflect the new CO2 abundances. Version 2 also removes all spectra contributions of PH3 because Version 1 consistently contained too much PH3 absorption when compared to JWST data (Veiler et al. 2024, <a href="http://doi.org/10.3847/1538-4357/ad6759" target="_blank" rel="noopener noreferrer">http://doi.org/10.3847/1538-4357/ad6759</a>).</p> <ul> <li><strong>Overview of V1</strong></li> </ul> <p> The Sonora Elf Owl Models is a successor to the <a href="../records/5063476#:~:text=This%20particular%20set%20of%20model,g%20are%200.25%20or%200.5.">Sonora Bobcat</a> and <a href="../records/4450269">Sonora Cholla</a> models. The Sonora Elf Owl model grid includes cloud-free radiative-convective equilibrium model atmospheres with vertical mixing induced disequilibrium chemistry with sub-solar to super-solar atmospheric metallicities and Carbon-to-Oxygen ratio. The atmospheric models have been computed using the open-source radiative-convective equilibrium model <a href="https://natashabatalha.github.io/picaso/">PICASO</a>. The parameters included within this grid are effective temperature (<strong><em>Teff</em></strong>), gravity (<strong><em>log(g)</em></strong>), vertical eddy diffusion coefficient (<strong><em>log(Kzz)</em></strong>), atmospheric metallicity (<strong><em>[M/H]</em></strong>), and Carbon-to-Oxygen ratio (<strong><em>C/O</em></strong>).</p> <p>The ranges and increments of these parameters are described in the published paper.<br><br></p> <ul> <li><strong>Three grids available on three links</strong></li> </ul> <p><strong> The model grid has been presented using three Zenodo repositories. This repository has all the models between Teff of 275 to 550 K (applicable to Y-type objects). The models for Teff between 575 to 1200 K (applicable for T- type objects) are available in the Zenodo DOI :- <a href="../records/10385821">https://zenodo.org/records/10385821</a>. The models for Teff between 1300 to 2400 K (applicable for L- type objects) are available in the Zenodo DOI :- <a href="../records/10385987">https://zenodo.org/records/10385987</a>.</strong></p> <p> </p> <ul> <li><strong> File types and how to use them</strong></li> </ul> <p> The models have been presented in the Xarray format so that all the atmospheric properties including the T(P) profile, atmospheric chemistry, and thermal emission spectra can be accessed within the same files. A python based Jupyter notebook named "Reading and plotting Elf Owl Models.ipynb" has been also supplied which demonstrates how to open and use these files.</p> <ul> <li> <strong>Spectra</strong></li> </ul> <p> The emission spectra for each atmospheric model has been computed between 0.6 to 15 microns. The reported flux is in the units of erg/s/cm<sup>2</sup>/cm. Note that these fluxes need to be multiplied with R<sup>2</sup>/D<sup>2</sup> before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here.</p> <div> </div> <div> <ul> <li><strong>Note on CH4</strong></li> </ul> </div> <p> As stated in <a href="https://ui.adsabs.harvard.edu/abs/2023ApJ...942...71M/abstract">Mukherjee et al. 2023 </a>our CH4 opacity is derived using the <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab7a1a">Hargreaves et al. 2020</a> HITEMP line list and computed using the HAPI code (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0022407315302466">Kochanov et al. 2016</a>). HAPI automatically pre-weights the isotopologues according to earth abundances that are listed on the HITRAN website (<a href="https://hitran.org/lbl/2?6=on" target="_blank" rel="noopener noreferrer">see here for CH4</a>). Therefore, users should note that there will be minor features of CH3D included in the models. Given the general absence of deuterated molecules in brown dwarfs (Teff>~300) we will include a second posting of models which includes the Elf Owl grid with <strong>only</strong> the major CH4 isotopologue (12C-H4).</p> <div> <ul> <li><strong>Note on PH3</strong></li> </ul> </div> <p> PH3 abundance is treated separately from the general disequilibrium scheme. This is because of the current non-detection of PH3 in many brown dwarf atmospheres (see citations in paper). The current PH3 treatment uses the chemical equilibrium treatment described in Visscher et al. However, after publishing this grid and using the model for analysis of high precision JWST data, we noticed that even the simple chemical equilibrium treatment which reduces the abundance, introduces a noticeable PH3 feature. Therefore in our v2 of this model grid we will further diminish the abundance.</p>
Developing crown width model for mixed forests using soil, climate, and stand factors
<ol> <li>The tree crown is a useful measure of tree vigor and is highly relevant to a tree's environmental adaptability. Crown allometry depends on environmental and stand conditions. Several studies have focused on the effects of climate change and competitive intensity on the crown, but the regulatory role of soil resources and diversity on crown allometry and carbon allocation has been neglected.</li> <li>Data from 20,994 trees in 232 mixed forests collected between 2011 and 2019 was located near four major mountain ranges in northeast China. The proposed crown width model includes the stand developmental stage, soil, climate, competition intensity, species mixture, species diversity, structural diversity, and their interactions.</li> <li>We observed that the cross-species allometric scaling exponent does not conform to the universal scaling law. Our results showed that crown width increased with increasing soil bulk density, quadratic mean diameter, and coefficient of diameter variation but decreased with increasing de Martonne aridity index, basal area, Simpson index, and species mixture. The interaction of quadratic mean diameter and soil bulk density had a significant negative effect on crown width. The influence of a particular factor within the interaction term on crown width was modulated by the gradients of other factors. Furthermore, soil bulk density contributed more to crown width modeling than the aridity index, and structural diversity had a greater effect on crown width than species diversity.</li> <li> <em>Synthesis</em>. Our results provide new insights into the environmental variability of crown allometry in mixed forests under global change, which is critical for improving regional and global estimates of forest biomass and carbon stocks.</li> </ol>
Dataset: Testing for effects of growth rate on isotope trophic discrimination factors and evaluating the performance of Bayesian stable isotope mixing models experimentally: a moment of truth?
<p><span>Discerning assimilated diets of wild animals using stable isotopes is well established where potential dietary items in food webs are isotopically distinct. With the advent of mixing models, and Bayesian extensions of such models (Bayesian Stable Isotope Mixing Models, BSIMMs), statistical techniques available for these efforts have been rapidly increasing. The accuracy with which BSIMMs quantify diet, however, depends on several factors including uncertainty in tissue discrimination factors (TDFs; <em>Δ</em>) and identification of appropriate error structures. Whereas performance of BSIMMs has mostly been evaluated with simulations, here we test the efficacy of BSIMMs by raising domestic broiler chicks (<em>Gallus gallus domesticus</em>) on four isotopically distinct diets under controlled environmental conditions, ideal for evaluating factors that affect TDFs and testing how BSIMMs allocate individual birds to diets that vary in isotopic similarity. For both liver and feather tissues,<em> δ</em><sup>13</sup>C and <em>δ </em><sup>15</sup>N values differed among dietary groups. <em>Δ</em><sup>13</sup>C of liver, but not feather, was negatively related to the rate at which individuals gained body mass. For <em>Δ</em><sup>15</sup>N, we identified effects of dietary group, sex, and tissue type, as well as an interaction between sex and tissue type</span><span><span>, </span></span><span><span>with f</span></span><span>emales having higher liver <em>Δ</em><sup>15</sup>N relative to males. For both tissues, BSIMMs allocated most chicks to correct dietary groups, especially for models using combined TDFs rather than diet specific TDFs, and those applying a multiplicative error structure. These findings provide new information on how biological processes affect TDFs and confirm that adequately accounting for variability in consumer isotopes is necessary to optimize performance of BSIMMs. Moreover, they demonstrate experimentally that these types of models reliably characterize consumed diets when appropriately parameterized.<span> </span></span></p>
DeepBacs – Mixed segmentation dataset and StarDist model
<p>Mixed training and test images of <em>S. aureus</em>, <em>E. coli</em> and <em>B. subtilis </em>for cell segmentation using StarDist, as well as the trained StarDist model.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p> </p> <p><strong>Data type</strong>: Paired bright field / fluorescence and segmented mask images</p> <p><strong>Microscopy data type</strong>: 2D widefield images; DIC and fluorescence for <em>S. aureus</em>, bright field images for <em>E. coli</em>, and fluorescence images for <em>B. subtilis</em></p> <p><strong>Microscopes</strong>: </p> <p><em>S. aureus</em>: </p> <p>GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence)</p> <p><em>E.coli</em>:</p> <p>Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><em>B. subtilis</em>:</p> <p>Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics)</p> <p> </p> <p><strong>Cell types</strong>: <em>S. aureus</em> strain JE2, <em>E. coli </em>MG1655 (CGSC #6300) and <em>B. subtilis</em> strain SH130; all grown under agarose pads</p> <p><strong>File format</strong>: .tif (8-bit and 16-bit)</p> <p><strong>Image size</strong>: 512 x 512 px² @ 80 nm pixel size (S. aureus); 1024 x 1024 px² @ 79 nm pixel size (E. coli); 1024 x 1024 px² @ 65 nm pixel size (B. subtilis)</p> <p><strong>Image preprocessing</strong>: </p> <p><em>S. aureus:</em></p> <p>Raw images were manually annotated by drawing ellipses in the NR fluorescence image and segmented images were created using the LOCI plugin (“ROI Map”). For training, images and masks were quartered into four 256 x 256 px² patches.</p> <p><em>E. coli:</em></p> <p>Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). Images were upscaled with a factor of 2 (no interpolation) to enable generation of higher-quality segmentation masks.</p> <p><em>B. subtilis</em>:</p> <p>Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin</p> <p> </p> <p><strong>StarDist model:</strong></p> <p>The StarDist 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 200 epochs (120 steps/epoch) on 155 paired image patches (image dimensions: (1024, 1024), patch size: (256,256)) with a batch size of 4, 10% validation data, 64 rays on grid 2, a learning rate of 0.0003 and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1.12.2). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), csbdeep (v 0.6.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla P100GPU. The dataset was augmented by a factor of 3.</p> <p> </p> <p>The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook, the StarDist Fiji plugin or the TrackMate Fiji plugin (v7+).</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup>, Mia Conduit<sup>4</sup>, Séamus Holden<sup>4,5</sup>, Pedro Matos Pereira<sup>6,7</sup>, Mariana Pinho<sup>6,8</sup></p> <p><strong>Contact email</strong>: <a href="mailto:christoph.spahn@mpi-marburg.mpg.de">christoph.spahn@mpi-marburg.mpg.de</a>, <a href="mailto:Seamus.Holden@newcastle.ac.uk">Seamus.Holden@newcastle.ac.uk</a>, <a href="mailto:pmatos@itqb.unl.pt">pmatos@itqb.unl.pt</a> and <a href="mailto:mgpinho@itqb.unl.pt">mgpinho@itqb.unl.pt</a></p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p> <p>4) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK</p> <p>5) ORCID: 0000-0002-7169-907X</p> <p>6) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal</p> <p>7) ORCID: 0000-0002-1426-9540</p> <p>8) ORCID: 0000-0002-7132-8842</p>
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.