A summary of the results of the experiments

Routine to reproduce Figs 1D–1F, 2B, 2C, 3, 4 and 5 and S1–S6, panel B in S7, and S8 Figs. (ZIP)

Matlab scripts and functions and data used to build Poly3D models and create permeability potential layers for 1) St. Helens Shear Zone, 2) Wind River Valley, and 3) Mount Baker geothermal prospect areas located in Washington state.

Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluorescence, automated microscopy, and deep learning. Using the uncertainty of convolutional neural networks to cluster the phenotypes of 8 distinct immune cell subsets, we find that the resulting maps are influenced by donor age, gender, and blood pressure, revealing distinct polarization and activation-associated phenotypes across immune cell classes. We further associate T-cell morphology to transcriptional state based on their joint donor variability, and validate an inflammation-associated polarized T-cell morphology, and an age-associated loss of mitochondria in CD4+ T-cells. Taken together, we show that immune cell phenotypes reflect both molecular and personal health information, opening new perspectives into the deep immune phenotyping of individual people in health and disease. Methods This dataset accompanies the manuscript "Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes" by Yannik Severin et al., Science Advances, 2022. It includes: - knnlea.m: Matlab function for the presented Local Enrichment Analysis method - LEA_Example_Data.mat containing data from the manuscript to reproduce a LEA analysis - LEA_Example_Script.mat that runs through the analysis steps - README.txt

We contrast two theoretical positions on the relation between phenomenal and access consciousness. First, we discuss previous data supporting a mild Overflow position, according to which transient visual awareness can overflow report. These data are open to two interpretations: (i) observers transiently experience specific visual elements outside attentional focus without encoding them into working memory; (ii) no specific visual elements but only statistical summaries are experienced in such conditions. We present new data showing that under data-limited conditions observers cannot discriminate a simple relation (same versus different) without discriminating the elements themselves and, based on additional computational considerations, we argue that this supports the first interpretation: summary statistics (same/different) are grounded on the transient experience of elements. Second, we examine recent data from a variant of ‘inattention blindness’ and argue that contrary to widespread assumptions, it provides further support for Overflow by highlighting another factor, task relevance, which affects the ability to conceptualize and report (but not experience) visual elements.This article is part of the theme issue ‘Perceptual consciousness and cognitive access’.

These are the Matlab scripts to perform basic face rating summaries + auxiliary analyses. These files are associated with the following published study: Trujillo & Anderson (in press). Facial typicality and attractiveness reflect an ideal dimension of face structure. Cognitive Psychology. https://doi.org/10.1016/j.cogpsych.2022.101541. Please properly cite all usage of the files. All version changes to date (12/22/2022) reflect uploading of new files created for revised analyses as part of the peer review of the associated manuscript.

This dataset contains relevant MATLAB/SIMULINK(R) code and supporting data in relation to CHAPTER 2 of the dissertation 'Advances in Dynamic Inversion-based Flight Control Law Design: Multivariable Analysis and Synthesis of Robust and Multi-Objective Design Solutions' by T.S.C. Pollack (2024) [DOI: 10.4233/uuid:28617ba0-461d-48ef-8437-de2aa41034ea]. It concerns the impact of inversion strategy on the robustness of Incremental Dynamic Inversion (IDI) based control laws against mixed uncertainty. The MATLAB(R) Robust Control Toolbox serves as the main instrument for data generation. For more information, we refer to the respective thesis chapter / publication.

The following datasets are related:

1) MATLAB/SIMULINK(R) Code and Supporting Data for Multi-loop Robust Design of INDI-based Flight Control Laws (DOI: https://doi.org/10.4121/4a2afa5b-cc72-4ebe-adca-970e2fc0d988)

2) MATLAB/SIMULINK(R) Code and Supporting Data for Assessment of Commonalities between Hybrid INDI-based and PID-based Flight Control Design (DOI: https://doi.org/ 10.4121/1c425f5d-943c-4e9c-8c6b-4e026dba20ca)

3) MATLAB/SIMULINK(R) Code and Supporting Data for Design of Multi-objective Incremental Control Allocation-based Flight Control Laws (DOI: https://doi.org/10.4121/b265ae09-64ef-4faf-bd77-d18712c11239)

This folder contains the data and codes used in "Spatial and Temporal Analysis of Precipitation and Effective Rainfall using Gauge Observations, Satellite, and Gridded Climate Data for Agricultural Water Management in the Upper Colorado River Basin" paper.

This data set contains National Centers for Environmental Prediction (NCEP) re-analysis monthly mean data 2001-2004 for the SBI domain in Matlab format.

This document describes how to identify and extract ALM neurons from the MouseLight database of individual reconstructed neurons (https://ml-neuronbrowser.janelia.org/) to define ALM projection zones (relevant to Chen, Liu et al., Cell, 2023). All scripts are in Matlab R2022b./MouseLight_figshare/MouseLightComplete contains all reconstructed single neurons from the MouseLight data set, in .json and .swc formats.Use ‘ExtractMouseLightNeuronsFromJsonFiles.m’ to extract MouseLight neuron ID, soma coordinates and annotation, and axon coordinates from the above directory.Use ‘ExtractMouseLightALMneurons.m’ to identify and extract ALM neurons from the MouseLight data set. ALM neurons are defined based on functional maps of ALM (photoinhibition) in the CCF coordinate system, contained in ‘ALM_functionalData.nii’ (from Li, Daie, et al Nature, 2016).Use ‘ALMprojDensity.m’ to compute and generate an ALM projection map based on axonal density. The map is saved in ‘ALM_mask_150um3Dgauss_Bilateral.mat’ as smoothed (3D Gaussian, sigma = 150 um) axonal density in a 3D matrix: F_smooth.First axis: dorsal-ventral, second axis: medial-lateral, third axis: anterior-posterior.Use ‘medial_lateral_ALMprojDensities.m’ to compute and generate medial and lateral ALM projection maps separately.Medial ALM soma location < 1.5 mm from the midline; lateral ALM soma locations > 1.5 mm from the midline.The maps are saved in ‘medialALM_mask_150um3Dgauss_Bilateral.mat’ and ‘lateralALM_mask_150um3Dgauss_Bilateral.mat’ as smoothed (3D Gaussian, sigma = 150 um) axonal density in 3D matrices, respectively.Use ‘PlotALMinCCF.m’ to plot voxels of ALM in CCF, defined by the functional maps in ‘ALM_functionalData.nii’.Use ‘PlotMouseLightALMneurons.m’ to plot ALM neurons (all, medial, or lateral) in CCF; figures are saved in .tiff format.Other functions:‘loadTifFast.m’ is called to load CCF .tif file (Annotation_new_10_ds222_16bit.tif).‘plotCCFbrain.m’ is called to plot an isosurface of the CCF brain (Annotation_new_10_ds222_16bit.tif).

This data set contains National Centers for Environmental Prediction (NCEP) re-analysis 6-hourly data 2001-2004 for the SBI domain in Matlab format.

We contrast two theoretical positions on the relation between phenomenal and access consciousness. First, we discuss previous data supporting a mild Overflow position, according to which transient visual awareness can overflow report. These data are open to two interpretations: (i) observers transiently experience specific visual elements outside attentional focus without encoding them into working memory; (ii) no specific visual elements but only statistical summaries are experienced in such conditions. We present new data showing that under data-limited conditions observers cannot discriminate a simple relation (same versus different) without discriminating the elements themselves and, based on additional computational considerations, we argue that this supports the first interpretation: summary statistics (same/different) are grounded on the transient experience of elements. Second, we examine recent data from a variant of ‘inattention blindness’ and argue that contrary to widespread assumptions, it provides further support for Overflow by highlighting another factor, task relevance, which affects the ability to conceptualize and report (but not experience) visual elements.This article is part of the theme issue ‘Perceptual consciousness and cognitive access’.

MATLAB Scripts for SAR-OCT Analysis relating to Michael Gardner's dissertation (2018)

There are many MATLAB users in the Hydrology space. These scientists work as researchers and educators in academia and in agencies and institutes. Many of these institutions partner with CUAHSI and use their resources to share data and research. For data analysis and visualization, HydroShare provides integrations with Jupyter notebooks and other tools, via an ‘open with’ affordance.

MATLAB Online provides access to MATLAB from any standard web browser wherever you have internet access. It is ideal for teaching, learning and convenient, lightweight access. With MATLAB Online, you can share your scripts, live scripts, and other MATLAB files with others directly. Additionally, you can publish your scripts and live scripts to the web as PDFs or HTML and share the URL with anyone.

This web application enables the interactive exploration of MATLAB artifacts (such as Live Scripts) through a similar ‘open with’ affordance. When working with Live Scripts, users are presented with the option to open these artifacts in the Live Editor environment.

The zipped folder contains Matlab output. This output is processed (see related dataset containing R code) to produce figures and tables that appear in Genetics Early Online June 08, 2018 (https://doi.org/10.1534/genetics.118.300757)

These are the scripts to perform summary statistics and Markov modeling from the behavior data. Requires MATLAB and SPM12 toolbox (or higher version). Includes analysis results in XLS format.

This data set is uploaded as supporting information for the publication entitled:

A Comprehensive Tutorial on the SOM-RPM Toolbox for MATLAB

The attached file 'case_study' includes the following:

X : Data from a ToF-SIMS hyperspectral image. A stage raster containing 960 x800 pixels with 963 associated m/z peaks.

pk_lbls: The m/z label for each of the 963 m/z peaks.

mdl and mdl_masked: SOM-RPM models created using the SOM-RPM tutorial provided within the cited article.

Additional details about the datasets can be found in the published article.

V2 - contains modified peak lists to show intensity weighted m/z rather than peak midpoint.

If you use this data set in your work, please cite our work as follows:

[LINK TO BE ADDED TO PAPER ONCE DOI RECEIVED]

Here a matlab script was presented for lane tracking and band detection on the pulsed field gel electrophoresis (PFGE) images. it can also be used as a software tool for automatic analysis of PFGE images.

This zip file contains the data and the Matlab scripts for data analysis and generation of figures.

The open datasets provide the Matlab scripts for the calculation of Power Spectra Density and Rate Spectra in the manuscirpt 'How crystal surface reactivity controls the evolution of surface microtopography during dissolution'.