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Data for: Nonintrusive heat flux quantification using acoustic emissions during pool boiling
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A mesocosm comparison of laboratory‐based and on‐site eDNA solutions for detection and quantification of striped bass (Morone saxatilis) in marine ecosystems
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eDNA quantification to evaluate bullfrog management
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Data from: Digital PCR quantification of ultrahigh ERBB2 copy number identifies poor breast cancer survival after trastuzumab
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Datasets and scripts from: Sensory-based quantification of male colour patterns in Trinidadian guppies reveals no support for parallel phenotypic evolution in multivariate trait space
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Quantification of flagellar gait changes with combined shape mode analysis and swimming simulations
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Data from: High-throughput classification and quantification of skinning phenotype in sweet potatoes
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Data from: Bayesian quantification of ecological determinants of outcrossing in natural plant populations: computer simulations and the case study of biparental inbreeding in English yew
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Data from: Quantitative PCR primer design affects quantification of dsRNA-mediated gene knockdown
RNA interference (RNAi) is a powerful tool for studying functions of candidate genes in both model and non-model organisms and a promising technique for therapeutic applications. Successful application of this technique relies on the accuracy and reliability of methods used to quantify gene knockdown. With the limitation in the availability of antibodies for detecting proteins, quantitative PCR (qPCR) remains the preferred method for quantifying target gene knockdown after dsRNA treatment . We evaluated how qPCR primer binding site and target gene expression levels affect quantification of intact mRNA transcripts following dsRNA-mediated RNAi. The use of primer pairs targeting the mRNA sequence within the dsRNA target region failed to reveal a significant decrease in target mRNA transcripts for genes with low expression levels, but not for a highly expressed gene. By contrast, significant knockdown was detected in all cases with primer pairs targeting the mRNA sequence extending beyond the dsRNA target region, regardless of the expression levels of the target gene. Our results suggest that at least for genes with low expression levels, quantifying the efficiency of dsRNA-mediated RNAi with primers amplifying sequences completely contained in the dsRNA target region should be avoided due to the risk of false negative results. Instead, primer pairs extending beyond the dsRNA target region of the mRNA transcript sequences should be used for accurate and reliable quantification of silencing efficiency.
photogranulation-quantification
<p>This repository contains scripts and associated files to quantify the production of oxygenic photogranules cultivated under static batch conditions. The files are grouped in three folders: (1) for the preparation of a marker image used for photogranule detection (“marker_image”), (2) for the detection and quantification of particle size on the experimental images of a time series (“experimental_images”) and (3) for converting the image data into equivalent diameters and plotting (“photogranulation_quantification”).</p> <p>Note: Because of size limitations, we were unable to upload original experimental images acquired as uncompressed tagged image files (.tif) to GitHub. Instead, jpg files of the images were generated and are used for this demonstration. The use of jpg files leads to different results than the use of tif files.</p> <p>Production of oxygenic photogranules (OPGs) is quantified by time-lapse imaging coupled to automated image analysis. OPGs are produced in statically incubated vials. A scanning image using a desktop scanner is taken periodically through the bottom of the incubation vials over time. During successful photogranulation, the sludge bed that initially covers the entire vial bottom contracts, i.e., reduces in diameter, until it forms a well-contracted OPG. The surface area of particles in the vial, i.e., the contracting biomass, are measured using the image processing software ImageJ, version 1.52a (Schneider et al., 2012) and the MorphoLibJ plugin, version 1.4.0 (Legland et al., 2016). These data are subsequently entered into the software environment R, version 3.6.0 (R Core Team, 2019) to plot the decrease in biomass diameter over experimental time using packages plyr version 1.8.4 (Wickham, 2011) and ggplot2 version 3.2.0 (Wickham, 2016).</p> <p>(1) marker_image<br> The “marker_image” folder contains an ImageJ macro to generate a marker image from an image of the empty experimental setup (background_image.jpg). The marker image is used to remove the grid from the experimental images at (2) “experimental_images” and to identify measured particles from a vial at a specific physical location on each image at (3) “photogranulation_quantification”. The macro can be installed in ImageJ by clicking on ‘plugins’, ‘macros, ‘install’ and selecting the imagej_macro_marker_image.ijm. It can be run by clicking on ‘plugins’, ‘macros’, ‘imagej_macro_marker_image’. During execution of the macro, the output directory to save the created marker image and a background image (e.g., background_image.jpg) have to be manually selected. Thresholding has to be done manually (0,191 to obtain the same result files). The result files after successful execution of the macro should match the files marker_image.jpg, marker_overlay.jpg and results_marker.csv.<br> Note: the values in results_marker.csv does not match the values that will be entered into R in (3) “photogranulation_quantification”, because these were generated using the original background image in tif format.</p> <p>(2) experimental_images<br> The ImageJ macro to treat the experimental time-lapse images can be found in the “experimental_images” folder. This macro measures particle characteristics on an image (in our experiments 72 particles for 72 vials, or one photogranule per vial) on a time-series of images (in our experiment a total of 110 images, acquired every eight hours over six weeks). Three experimental images (first, middle and last image) are presented here as an example to run a demo. The macro can be installed in ImageJ by clicking on ‘plugins’, ‘macros, ‘install’ and selecting the imagej_macro_experimental_images.ijm. It can be run by clicking on ‘plugins’, ‘macros’, ‘imagej_macro_experimental_images’. During execution of the macro, the input directory with raw images (e.g., scanner_image_1/2/3.jpg), output directory to save created images and a marker image (e.g., marker_image.jpg) have to be manually selected. Thresholding has to be done manually (0,140; 0,72; 0,71 for respectively 1, 2, 3 to obtain the same result files). The results from executing this macro should match the scanner_image_1/2/3_final.jpg and scanner_image_1/2/3_results.csv.<br> Note: the values in the result files do not match the values that will be entered into R in (3) “photogranulation_quantification”, because the latter were generated using the original background image in tif format.</p> <p>(3) photogranulation_quantification<br> The “photogranulation_quantification” folder contains the R script to couple measured particle characteristics on experimental time-lapse images to unique samples and experimental conditions using the marker image. Subsequently, surface area of detected particles is converted to equivalent diameter and plotted over time. The plot plot_photogranulation_quantification.pdf can be reproduced by running the R script using results_marker.csv, results_scanner_images.csv, image_acquisition.txt and experimental_conditions.txt as input. The decrease in equivalent diameter per sample is a measure for the progression of photogranulation. Sludge 2 contracted better than sludge 1, resulting in a smaller average diameter. Biomass did not contract well in all 36 replicates per sludge source, hence the large standard deviations around the mean diameters. There is no data available between 354 and 472 hours due to a technical issue.</p> <p>References<br> Legland, D.; Arganda-Carreras, I. and Andrey, P. (2016). MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32(22): 3532-3534.<br> R Core Team (2019). R: A language and environment for statistical computing. R Found. Stat Comput, Vienna, Austria.<br> Schneider, C.A., Rasband, W.S.andEliceiri, K.W (2012). ImageJ. Fundam Digit Imaging Med 9(7), 185–188.<br> Wickham, H. (2011). The Split-Apply-Combine Strategy for Data Analysis. J Stat Software 40(1), 1-29.<br> Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.</p> <p>This work was supported by the French National Research Agency (ANR, grant ANR-16-CE04-0001).</p>
Photogranulation-quantification
<p>This repository contains scripts and associated files to quantify the production of oxygenic photogranules cultivated under static batch conditions. The files are grouped in three folders: (1) for the preparation of a marker image used for photogranule detection (“marker_image”), (2) for the detection and quantification of particle size on the experimental images of a time series (“experimental_images”) and (3) for converting the image data into equivalent diameters and plotting (“photogranulation_quantification”).</p> <p>Note: Because of size limitations, we were unable to upload original experimental images acquired as uncompressed tagged image files (.tif) to GitHub. Instead, jpg files of the images were generated and are used for this demonstration. The use of jpg files leads to different results than the use of tif files.</p> <p>Production of oxygenic photogranules (OPGs) is quantified by time-lapse imaging coupled to automated image analysis. OPGs are produced in statically incubated vials. A scanning image using a desktop scanner is taken periodically through the bottom of the incubation vials over time. During successful photogranulation, the sludge bed that initially covers the entire vial bottom contracts, i.e., reduces in diameter, until it forms a well-contracted OPG. The surface area of particles in the vial, i.e., the contracting biomass, are measured using the image processing software ImageJ, version 1.52a (Schneider et al., 2012) and the MorphoLibJ plugin, version 1.4.0 (Legland et al., 2016). These data are subsequently entered into the software environment R, version 3.6.0 (R Core Team, 2019) to plot the decrease in biomass diameter over experimental time using packages plyr version 1.8.4 (Wickham, 2011) and ggplot2 version 3.2.0 (Wickham, 2016).</p> <p>(1) marker_image<br> The “marker_image” folder contains an ImageJ macro to generate a marker image from an image of the empty experimental setup (background_image.jpg). The marker image is used to remove the grid from the experimental images at (2) “experimental_images” and to identify measured particles from a vial at a specific physical location on each image at (3) “photogranulation_quantification”. The macro can be installed in ImageJ by clicking on ‘plugins’, ‘macros, ‘install’ and selecting the imagej_macro_marker_image.ijm. It can be run by clicking on ‘plugins’, ‘macros’, ‘imagej_macro_marker_image’. During execution of the macro, the output directory to save the created marker image and a background image (e.g., background_image.jpg) have to be manually selected. Thresholding has to be done manually (0,191 to obtain the same result files). The result files after successful execution of the macro should match the files marker_image.jpg, marker_overlay.jpg and results_marker.csv.<br> Note: the values in results_marker.csv does not match the values that will be entered into R in (3) “photogranulation_quantification”, because these were generated using the original background image in tif format.</p> <p>(2) experimental_images<br> The ImageJ macro to treat the experimental time-lapse images can be found in the “experimental_images” folder. This macro measures particle characteristics on an image (in our experiments 72 particles for 72 vials, or one photogranule per vial) on a time-series of images (in our experiment a total of 110 images, acquired every eight hours over six weeks). Three experimental images (first, middle and last image) are presented here as an example to run a demo. The macro can be installed in ImageJ by clicking on ‘plugins’, ‘macros, ‘install’ and selecting the imagej_macro_experimental_images.ijm. It can be run by clicking on ‘plugins’, ‘macros’, ‘imagej_macro_experimental_images’. During execution of the macro, the input directory with raw images (e.g., scanner_image_1/2/3.jpg), output directory to save created images and a marker image (e.g., marker_image.jpg) have to be manually selected. Thresholding has to be done manually (0,140; 0,72; 0,71 for respectively 1, 2, 3 to obtain the same result files). The results from executing this macro should match the scanner_image_1/2/3_final.jpg and scanner_image_1/2/3_results.csv.<br> Note: the values in the result files do not match the values that will be entered into R in (3) “photogranulation_quantification”, because the latter were generated using the original background image in tif format.</p> <p>(3) photogranulation_quantification<br> The “photogranulation_quantification” folder contains the R script to couple measured particle characteristics on experimental time-lapse images to unique samples and experimental conditions using the marker image. Subsequently, surface area of detected particles is converted to equivalent diameter and plotted over time. The plot plot_photogranulation_quantification.pdf can be reproduced by running the R script using results_marker.csv, results_scanner_images.csv, image_acquisition.txt and experimental_conditions.txt as input. The decrease in equivalent diameter per sample is a measure for the progression of photogranulation. Sludge 2 contracted better than sludge 1, resulting in a smaller average diameter. Biomass did not contract well in all 36 replicates per sludge source, hence the large standard deviations around the mean diameters. There is no data available between 354 and 472 hours due to a technical issue.</p> <p>References<br> Legland, D.; Arganda-Carreras, I. and Andrey, P. (2016). MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32(22): 3532-3534.<br> R Core Team (2019). R: A language and environment for statistical computing. R Found. Stat Comput, Vienna, Austria.<br> Schneider, C.A., Rasband, W.S.andEliceiri, K.W (2012). ImageJ. Fundam Digit Imaging Med 9(7), 185–188.<br> Wickham, H. (2011). The Split-Apply-Combine Strategy for Data Analysis. J Stat Software 40(1), 1-29.<br> Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.</p>
GC-MS analysis of standard mix for geological biomarkers quantification
<p>Result from GC-MS analysis of standard mix prepared for geological biomarkers quantification.</p>
Example Data for 3D quantification of zebrafish cerebrovascular architecture
<p>Example data for:</p> <p>E. C. Kugler, J. Frost, V. Silva, K. Plant, K. Chhabria, T. J.A. Chico, P. A. Armitage</p> <p>3D quantification of zebrafish cerebrovascular architecture by automated image analysis of light sheet fluorescence microscopy datasets</p> <p>bioRxiv 2020.08.06.239905; doi: https://doi.org/10.1101/2020.08.06.239905</p> <p>Link: https://www.biorxiv.org/content/10.1101/2020.08.06.239905v2</p> <p><br> Code: https://github.com/ElisabethKugler/ZFVascularQuantification (doi:https://doi.org/10.5281/zenodo.3978278)</p> <p>Transgenic zebrafish 3dpf: Tg(kdrl:HRAS-mCherry)s916<br> Acquisition: Zeiss Z.1 light sheet microscope with a Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, sCMOS detection unit. <br> Activated pivot scan, dual-sided illumination and online fusion; <br> properties of acquired data are as follows: 0.7x zoom, 16bit image depth, 1920 x 1920px (approximately 0.33 x 0.33 µm) image size and minimum z-stack interval (approximately 0.5µm), <br> 561nm laser, LP560, and LP585.</p> <p>Data: <br> - tiff and MIPs<br> - TF: pre-processed with Sato enhancement<br> - TH: segmented with Otsu thresholding<br> - 512x512: downsampled<br> - Reg: intersample registration<br> - analysis: folder for quantification</p>
Datasets of the article "From Classification to Quantification in Tweet Sentiment Analysis"
<p>Datasets used for the following SNAM paper:<br> ---------------------------------------------------------------------------------------------------<br> Title: From Classification to Quantification in Tweet Sentiment Analysis<br> Authors: Wei Gao and Fabrizio Sebastiani<br> Organization: Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar<br> ---------------------------------------------------------------------------------------------------</p> <p>[Content]</p> <p>* SemEval2013, SemEval2014, SemEval2015 datasets:<br> - semeval.train.feature.txt: Training set for learning sentiment models at development stage<br> - semeval.dev.feature.txt: Held-out set for tuning parameters<br> - semeval.train+dev.feature.txt: Training set for learning the final sentiment model<br> - semeval13.test.feature.txt: SemEval2013 test set<br> - semeval14.test.feature.txt: SemEval2014 test set<br> - semeval15.test.feature.txt: SemEval2015 test set<br> <br> * Other datasets: semeval2016, sanders, sst, omd, hcr, gasp, wa, wb<br> - X.train.feature.txt: Training set for learning sentiment models at development stage<br> - X.dev.feature.txt: Held-out set for tuning parameters<br> - X.train+dev.feature.txt: Training set for learning the final sentiment model<br> - X.test.feature.txt (or X.dev-test.feature.txt for semeval2016 only): Test set<br> where X is one of semeval2016, sanders, sst, omd, hcr and gasp.</p> <p>* Training files are saved in ./data/train directory, and held-out and test files are in ./data/test directory</p> <p><br> For more details, please refer to the paper.</p> <p><br> [Citation]<br> You can cite the following paper when referring to the dataset:</p> <pre>@article{gao2016classification, title={From classification to quantification in tweet sentiment analysis}, author={Gao, Wei and Sebastiani, Fabrizio}, journal={Social Network Analysis and Mining}, volume={6}, number={1}, pages={19}, year={2016}, publisher={Springer} }</pre> <p> </p>
Data from: Real-time quantification of damage in structural materials during mechanical testing
A novel methodology is introduced for quantifying the severity and morphology of damage created during testing in composite components. The method utilises digital image correlation combined with image processing techniques to monitor the rate at which the strain field changes during mechanical tests. The methodology is demonstrated using two distinct experimental datasets, a ceramic matrix composite specimen loaded in tension at high temperature and nine polymer matrix composite specimens containing fibre-waviness defects loaded in bending. The changes in the strain field due to damage creation are shown to be a more effective indicator that the specimen has reached its proportional limit than using load-extension diagrams. The technique also introduces a new approach to using experimental data for creating maps showing the spatio-temporal distribution of damage in a component. These maps indicate where damage occurs in a component, its morphology and the time of occurrence. This presentation format is both easier and faster to interpret than the raw data which, for some tests, can consist of tens of thousands of images. This methodology has the potential to reduce the time taken to interpret large material test datasets whilst increasing the amount of knowledge that can be extracted from each test.
Data from: Quantification of avian hazards to military aircraft and implications for wildlife management
Collisions between birds and military aircraft are common and can have catastrophic effects. Knowledge of relative wildlife hazards to aircraft (the likelihood of aircraft damage when a species is struck) is needed before estimating wildlife strike risk (combined frequency and severity component) at military airfields. Despite annual reviews of wildlife strike trends with civil aviation since the 1990s, little is known about wildlife strike trends for military aircraft. We hypothesized that species relative hazard scores would correlate positively with aircraft type and avian body mass. Only strike records identified to species that occurred within the U.S. (n = 36,979) and involved United States Navy or United States Air Force aircraft were used to calculate relative hazard scores. The most hazardous species to military aircraft was the snow goose (Anser caerulescens), followed by the common loon (Gavia immer), and a tie between Canada goose (Branta canadensis) and black vulture (Coragyps atratus). We found an association between avian body mass and relative hazard score (r2 = 0.76) for all military airframes. In general, relative hazard scores per species were higher for military than civil airframes. An important consideration is that hazard scores can vary depending on aircraft type. We found that avian body mass affected the probability of damage differentially per airframe. In the development of an airfield wildlife management plan, and absent estimates of species strike risk, airport wildlife biologists should prioritize management of species with high relative hazard scores.
Data from: Technical note: rapid image-based field methods improve the quantification of termite mound structures and greenhouse-gas fluxes
Termite mounds (TMs) mediate biogeochemical processes with global relevance, such as turnover of the important greenhouse gas methane (CH4). However, the complex internal and external morphology of TMs impede an accurate quantitative description. Here we present two novel field methods, photogrammetry (PG) and cross-section image analysis, to quantify TM external and internal mound structure of 29 TMs of three termite species. Photogrammetry was used to measure epigeal volume (VE), surface area (AE) and mound basal area (AB) by reconstructing 3D models from digital photographs, and compared against a water-displacement method and the conventional approach of approximating TMs by simple geometric shapes. To describe TM internal structure, we introduce TM macro- and micro-porosity (θM and θµ), the volume fractions of macroscopic chambers, and microscopic pores in the wall material, respectively. Macro-porosity was estimated using image analysis of single TM cross-sections, and compared against full x-ray tomography (CT) scans of 17 TMs. For these TMs we present complete pore fractions to assess species-specific differences in internal structure. The PG method yielded VE nearly identical to a water-displacement method, while approximation of TMs by simple geometric shapes led to errors of 4–200 %. Likewise, using PG substantially improved the accuracy of CH4 emission estimates by 10–50 %. Comprehensive CT scanning revealed that investigated TMs have species-specific ranges of θM and θµ, but similar total porosity. Image analysis of single TM cross-sections produced good estimates of θM for species with thick walls and evenly distributed chambers. The new image-based methods allow rapid and accurate quantitative characterisation of TMs to answer ecological, physiological and biogeochemical questions. The PG method should be applied when measuring greenhouse-gas emissions from TMs to avoid large errors from inadequate shape approximations.
Data from: D quantification of tumor vasculature in lymphoma xenografts in NOD/SCID mice allows to detect differences among vascular-targeted therapies
Quantitative characterization of the in vivo effects of vascular-targeted therapies on tumor vessels is hampered by the absence of useful 3D vascular network descriptors aside from microvessel density. In this study, we extended the quantification of planar vessel distribution to the analysis of vascular volumes by studying the effects of antiangiogenic (sorafenib and sunitinib) or antivascular (combretastatin A4 phosphate) treatments on the quantity and spatial distributions of thin microvessels. These observations were restricted to perinecrotic areas of treated human multiple myeloma tumors xenografted in immunodeficient mice and to microvessels with an approximate cross-sectional area lower than 75 µm2. Finally, vessel skeletonization minimized artifacts due to possible differential wall staining and allowed a comparison of the various treatment effects. Antiangiogenic drug treatment reduced the number of vessels of every caliber (at least 2-fold fewer vessels vs. controls; p<0.001, n = 8) and caused a heterogeneous distribution of the remaining vessels. In contrast, the effects of combretastatin A4 phosphate mainly appeared to be restricted to a homogeneous reduction in the number of thin microvessels (not more than 2-fold less vs. controls; p<0.001, n = 8) with marginal effects on spatial distribution. Unexpectedly, these results also highlighted a strict relationship between microvessel quantity, distribution and cross-sectional area. Treatment-specific changes in the curves describing this relationship were consistent with the effects ascribed to the different drugs. This finding suggests that our results can highlight differences among vascular-targeted therapies, providing hints on the processes underlying sample vascularization together with the detailed characterization of a pathological vascular tree.
Data from: Quantification of correlational selection on thermal physiology, thermoregulatory behavior and energy metabolism in lizards
Phenotypic selection is widely accepted as the primary cause of adaptive evolution in natural populations, but selection on complex functional properties linking physiology, behavior, and morphology has been rarely quantified. In ectotherms, correlational selection on thermal physiology, thermoregulatory behavior, and energy metabolism is of special interest because of their potential coadaptation. We quantified phenotypic selection on thermal sensitivity of locomotor performance (sprint speed), thermal preferences, and resting metabolic rate in captive populations of an ectothermic vertebrate, the common lizard, Zootoca vivipara. No correlational selection between thermal sensitivity of performance, thermoregulatory behavior, and energy metabolism was found. A combination of high body mass and resting metabolic rate was positively correlated with survival and negatively correlated with fecundity. Thus, different mechanisms underlie selection on metabolism in lizards with small body mass than in lizards with high body mass. In addition, lizards that selected the near average preferred body temperature grew faster that their congeners. This is one of the few studies that quantifies significant correlational selection on a proxy of energy expenditure and stabilizing selection on thermoregulatory behavior.
Data from: Quantification of motor speech impairment and its anatomic basis in primary progressive aphasia
Objective: To evaluate whether a quantitative speech measure is effective in identifying and monitoring motor speech impairment (MSI) in patients with primary progressive aphasia (PPA), and to investigate the neuroanatomical basis of MSI in PPA. Methods: Sixty-four patients with PPA were evaluated at baseline, with a subset (N=39) evaluated longitudinally. Articulation rate (AR), a quantitative measure derived from spontaneous speech, was measured at each timepoint. MRI was collected at baseline. Differences in baseline AR were assessed across PPA subtypes, separated by severity level. Linear mixed-effects models were conducted to assess groups differences across PPA subtypes in rate of decline in AR over a one-year period. Cortical thickness measured from baseline MRIs was used to test hypotheses about the relationship between cortical atrophy and MSI. Results: Baseline AR was reduced for patients with non-fluent variant PPA (nfvPPA), as compared to other PPA subtypes and controls, even in mild stages of disease. Longitudinal results showed a greater rate of decline in AR for the nfvPPA group over one year, as compared to logopenic and semantic variant subgroups. Reduced baseline AR was associated with cortical atrophy in left-hemisphere premotor and supplementary motor cortices. Conclusions: The AR measure is an effective quantitative index of MSI that detects MSI in mild disease stages and tracks decline in MSI longitudinally. The AR measure additionally demonstrates anatomic localization to motor-speech specific cortical regions. Our findings suggest that this quantitative measure of MSI might have utility in diagnostic evaluation and monitoring of motor speech impairments in PPA.
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.