Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
124
datasets available to search
ShareScore release 0.9.0
Dataset results
124 results for “quantitative modeling”
Allele-specific quantitation of ATXN3 and HTT transcripts in polyQ disease models.
<p>Precise values obtained during the research that led to the publishing of scientific paper entitled 'Allele-specific quantitation of ATXN3 and HTT transcripts in polyQ disease models'.</p>
Mangrove terrestrial laser scanning (TLS) point clouds and quantitative structural models (QSMs)
<p>Datasets for a publication entitled, "Terrestrial laser scanning for the estimation of above ground biomass of mangrove roots by modelling them as inverted trees."</p> <p>See the file "Data dictionary for Mangrove terrestrial laser scanning.pdf" for a description of the datasets included in the zipped folder. </p>
The 2020 Comparison of Tools for the Analysis of Quantitative Formal Models: Results and Reproduction
<p>This archive contains detailed results from QComp 2020 as well as the necessary scripts and data to reproduce them.</p> <p>Visit http://qcomp.org for more information for QComp.</p> <p>Overview of Contents</p> <p>- `qcomp.org/` contains the state of our website from the timepoint of the competition. This includes:<br> - All benchmark files, browsable at `qcomp.org/benchmarks/index.html`<br> - Detailed competition results in a human-readable format, browsable at `https://qcomp.org/competition/2020/`<br> - `logs/` contains the raw logfiles and data gathered by our scripts<br> - `scripts/` contains scripts to replicate the whole competition<br> - `toolpackages/` contains a package for each participating tool which includes<br> - Instructions for obtaining and installing the tool<br> - a file `invocations.json` listing the commandlines used in QComp 2020<br> - a file `tool.py` providing functionalities to obtain the result from the tool output.</p>
Structure of Complement C3(H2O) Revealed By Quantitative Cross-Linking/Mass Spectrometry And Modeling
<p>The slow but spontaneous and ubiquitous formation of C3(H2O), the hydrolytic and conformationally rearranged product of C3, initiates antibody-independent activation of the complement system that is a key first line of antimicrobial defense. The structure of C3(H2O) has not been determined. Here we subjected C3(H2O) to quantitative cross-linking/mass spectrometry (QCLMS). This revealed details of the structural differences and similarities between C3(H2O) and C3, as well as between C3(H2O) and its pivotal proteolytic cleavage product, C3b, which shares functionally similarity with C3(H2O). Considered in combination with the crystal structures of C3 and C3b, the QCMLS data suggest that C3(H2O) generation is accompanied by the migration of the thioester-containing domain of C3 from one end of the molecule to the other. This creates a stable C3b-like platform able to bind the zymogen, factor B, or the regulator, factor H. Integration of available crystallographic and QCLMS data allowed the determination of a 3D model of the C3(H2O) domain architecture. The unique arrangement of domains thus observed in C3(H2O), which retains the anaphylatoxin domain (that is excised when C3 is enzymatically activated to C3b), can be used to rationalize observed differences between C3(H2O) and C3b in terms of complement activation and regulation.</p> <p><strong>For more information</strong> about how to reproduce this modeling, see the <a href="https://salilab.org/Complement/">Sali lab website</a> or the README file.</p>
Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis
<p>This repository contain datasets and results for the paper:</p> <p><strong>Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis</strong></p> <p> </p> <p><strong>Github repository for the code: </strong></p> <p><a href="https://github.com/siebeniris/QuantifyingLanguageConfusion/tree/main">Quantifying Language Confusion GitHub repo</a></p> <p> </p> <p><strong>DATA</strong> include the following datasets:</p> <p>i) raw language graphs and</p> <p>ii) the calculated language similarities from the language graphs,</p> <p>iii) <strong>MTEI</strong>: the files from the <a href="https://github.com/siebeniris/vec2text_exp/tree/aaai">experimental results of multilingual inversion attacks</a>, and calculated language confusion entropy from the data;</p> <p>iv) <strong>LCB</strong>: the files from the <a href="https://github.com/for-ai/language-confusion?tab=Apache-2.0-1-ov-file#readme">language confusion benchmark</a> and calculated language confusion entropy from the data </p> <p> </p> <p><strong>Results</strong> include aggregated results for further analysis:</p> <p>i) <strong>inversion_language_confusion</strong>: results from MTEI</p> <p>ii) <strong>prompting_language_confusion</strong>: results from LCB</p> <p> </p> <p> </p>
Main model fits and substitution rate predictions for: A quantitative genetic model of background selection in humans
<p>Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This can be thought of as a "linked selection signal" that reflects the impact of selection varying according to the placement of functional regions and recombination rates along the genome. Previous work has shown that negative selection against the steady influx of new deleterious mutations into conserved regions is the predominant mode of selection in humans. However, the theoretic model that underpins these results, classic Background Selection theory, is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of the linked selection, which models the effects of weak draft created according to how polygenic additive fitness variance is distributed along the genome. We use a recent model that jointly predicts the equilibrium fitness variance and substitution rates due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three human populations. While our model can accommodate weaker selection, we initially find evidence across three human populations of very strong selection against deleterious mutations consistent with previous work. However, the corollary predicted substitution rates for conserved regions are unreasonably low, and in disagreement with observed rates. We hypothesize this could be due to selected sites experiencing a further diminished population size due to selective interference. When we account for this in our method, we find evidence of weakly deleterious mutations in conserved regions which brings the predicted substitution rate into agreement with observations. However, these models lead to implausibly large mutation rate estimates. Overall, while our model of the genomic linked selection signal brings us a step towards uniting population and quantitative genetic selection models with the substitution process, our work suggests considerable uncertainty remains about the processes generating fitness variance in humans.</p>
A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects: dataset
<p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>The enhanced mechanical behavior of polymer nanocomposites with spherical filler particles is attributed to the formation of matrix-filler interphases. The nano-scale leads to particularly high interphase volume fractions while rendering experimental investigations extremely difficult. Previously, we introduced a molecular dynamics-based interphase model capturing the crucial spatial profiles of elastic and inelastic properties inside the interphase. This contribution demonstrates that our model captures polymer nanocomposites’ essential characteristics reported from experiments. To this end, we thoroughly verify and validate the model before discussing the resulting local plastic strain distribution. Furthermore, we obtain a reinforcement in terms of the overall stiffness for smaller particles and higher filler contents, while the influence of particle spacing seems negligible, matching experimental observations in the literature. This paper proposes a methodology to unravel the underlying complex mechanical behavior of polymer nanocomposites and to translate the findings into engineering quantities accessible to a broader audience and technical applications.</p> </blockquote> <p><br> <br> <strong>Contact:</strong><br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong><br> Abaqus version R2018</p> <p><strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context:</strong><br> Data set supplementing journal paper:<br> [1] Ries, M.; Weber, F.; Possart, G.; Steinmann, P. & Pfaller, S., “A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects”, Composites Part A: Applied Science and Manufacturing, 2022, 107094.<br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content:</strong></p> <p>simulation folder denotation (“-” used instead of decimal points):<br> distance_particles _ radius_particle _ thickness_ip _ num_ip _ length_box _ factor_el_length _ fraction_box_length _ switch_mat_ip</p> <p>with</p> <ul> <li> distance_particles: center distance of the nanoparticles in nm</li> <li> radius_particle: radius of the nanoparticles in nm</li> <li> thickness_ip: thickness of the interphase layers in nm</li> <li> num_ip: number of interphase layers</li> <li> length_box: box edge length in nm</li> <li> factor_el_length: factor scaling the element length on the arcs of the interphase layers (element length = factor_el_length * thickness_ip)</li> <li> fraction_box_length: matrix element length = length_box / fraction_box_length</li> <li> switch_mat_ip: if = 0: interphases are assigned their actual material properties, if = 1: interphases are assigned the material properties of the bulk</li> </ul> <p> <br> <br> each simulation folder contains the following file types:</p> <ul> <li> .cae: Abaqus model database, containing parts, meshes, loads, etc.</li> <li> .dat: Printed output from the analysis input file processor, as well as printed output of selected results written during the analysis</li> <li> .inp: Analysis input file</li> <li> .log: Log file, which contains start and end times for modules run by the current execution procedure</li> <li> .msg: Diagnostic or informative messages about the progress of the solution</li> <li> .odb: Output database containing all results data from an Abaqus analysis</li> <li> .sta: Status file with increment summaries</li> </ul> <p><strong>folder structure:</strong></p> <ul> <li>Standard_case:<br> simulation folders of the standard close (particle center distance: 5.1776 nm) and distant (particle center distance: 7.9481 nm) cases (particle radius: 2 nm, filler content 0.054 vol.%, number of interphase layers: 4, factor_el_length: 1.0) and further particle center distances</li> <li>Layers:<br> simulation folders with different numbers of interphase layers, i.e., different values for num_ip, based on the standard close and distant cases <ul> <li>Close_case</li> <li>Distant_case</li> </ul> </li> <li>Mesh:<br> simulation folders with different mesh qualities, i.e., different values for factor_el_length, based on the standard close and distant cases <ul> <li>Close_case</li> <li>Distant_case</li> </ul> </li> <li>Particle_size:<br> simulation folders with different particle sizes <ul> <li>2_nm: simulation folders with particle surface distance 2 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> <li>4_nm: simulation folders with particle surface distance 4 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> <li>8_nm: simulation folders with particle surface distance 8 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> </ul> </li> </ul>
A quantitative model of temperature-dependent diapause progression
<p>R-code and data for reproducing the results of von Schmalensee, Süess et al. 2024 <em>PNAS</em></p> <p>Scripts will run/load the models and save the raw figures in the figure folder.</p> <p>Remember to install the required packages (see 'functions_packages.R' in the functions folder).</p> <p>R version 4.3.3 and brms version 2.21.0 was used.</p>
Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Generation of tissue-like models of human bilayered tissues functionalized with olive oil components"
<p>This file contains the raw dataset generated in the work entitled "GENERATION OF NOVEL TISSUE-LIKE MODELS OF HUMAN BILAYERED TISSUES FUNCTIONALIZED WITH BIOACTIVE COMPONENTS OBTAINED FROM OLIVE OIL". These results correspond to the quantification of the histological results obtained in this work.</p>
Terrestrial laser scanning data Wytham Woods: individual trees and quantitative structure models (QSMs)
<p>This dataset was used for the analysis of the following publication:<br> <em>Laser scanning reveals potential underestimation of biomass carbon in temperate forest. Calders, K, Verbeeck, V, Burt, A, Origo, N, Nightingale, J, Malhi, Y, Wilkes, P, Raumonen, P, Bunce, R G H and Disney, M. Ecological Solutions and Evidence (accepted)</em></p> <p><strong>Any use of this dataset should cite the paper above </strong>(Creative Commons Attribution 4.0 International Public License).</p> <p>Contact: kim.calders@ugent.be</p> <p> </p> <p>================================================<br> Dataset<br> ================================================</p> <p><strong>General</strong>: <br> TLS data were collected in leaf-off conditions during late November 2015 - January 2016. Windy days were avoided to ensure data quality. We used a RIEGL VZ-400 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH). The instrument has a beam divergence of 0.35 mrad and operates in the infrared (wavelength 1550 nm) with a range up to 350 m. The pulse repetition rate for each scan was 300 kHz, the minimum range was 0.5 m and the angular sampling resolution was 0.04°. This resulted in 22,500,000 outgoing pulses for a single scan, resulting in a beam diameter of 2.45 cm and beam spacing of 3.5 cm at 50 m (for example). The azimuth angle range was 0-360° and the zenith angle range was 30-130°. Therefore an additional scan was acquired at each scan location with the scanner tilted at 90° from the vertical to complete sampling of the full hemisphere at each location. Scans were done in a larger 6 ha area using an approximate 20 m × 20 m grid, to ensure the best possible data quality within our 1.4 ha study area. Trees which had at least more than half of their stem at tree diameter 1.3 m inside the boundaries of the study area were included</p> <p>[ Note that this dataset contains 876 individual trees, but after applying the boundary conditions, 835 trees within the study area were used in the analysis of the paper >> see TLS_Inventory.ipynb]</p> <p>Full details of the methods to segment individual trees and generate the QSMs can be found in the paper <em>Calders et al. Ecological Solutions and Evidence.</em></p> <p><strong>Tree ID:</strong><br> Tree IDs can have numbers only or numbers + letters. A number only means this was a base with one stem. A number + letter means individual trees (split below 1.3m), that share a common tree base.</p> <p><strong>Datasets:</strong><br> 1) DATA_clouds_txt & DATA_clouds_ply: Individually segmented trees in *txt and *ply format. File naming is [tree_id].*txt or [tree_ply].*tx</p> <p>2) DATA_QSM_opt: optimised QSMs using TreeQSM v2.0 (https://github.com/InverseTampere/TreeQSM). File naming is [tree_id]-[dmin0]-[rcov0]-[nmin0]-[dmin]-[rcov]-[nmin]-[lcyl]-[NoGround]-[iteration].mat </p> <p>3) Raw scan data can be found here: http://dx.doi.org/10.5285/ed9156e1697343e4ad82e83ed550e345</p> <p> </p> <p>================================================<br> Paper analysis<br> ================================================</p> <p>We have provided all scripts (analysis_and_figures) that were used to:</p> <p>1 ) analyse the data (TLS_Inventory.ipynb):<br> ----- Analysis of point clouds and QSMs using TLS_Inventory.py.ipynb > tls_summary.csv (#876 trees)<br> ----- Link with census &1.4ha > trees_summary.csv (#835 trees)</p> <p>2) generate the paper figures:<br> ----- various *.R and *.ipynb scripts in the main folder and /allometriesTLS/</p> <p> </p> <p>================================================<br> Funding<br> ================================================</p> <p>The TLS fieldwork was funded through the Metrology for Earth Observation and Climate project (MetEOC-2), grant number ENV55 within the European Metrology Research Programme (EMRP). The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union. Funds for purchase of the UCL RIEGL VZ-400 instrument was provided by the UK NERC National Centre for Earth Observation (NCEO) and UCL Geography. The census of the forest plot was supported by an ERC Advanced Investigator Grant to Yadvinder Malhi (GEM-TRAIT, grant number 321131).</p>
Data and code for: A quantitative model for spatio-temporal dynamics of root gravitropism
<p>This repository contains the experimental data presented in "A quantitative model for spatio-temporal dynamics of root gravitropism" and Python scripts for the presented root model.</p>
Models and post-processing codes for paper "Quantitative stratigraphic analysis in a source-to-sink numerical framework"
<p>This package contains all the files required to reproduce the experiments in the manuscript: <strong>Quantitative stratigraphic analysis in a source-to-sink numerical framework</strong>.</p>
Main model fits and substitution rate predictions for: A quantitative genetic model of background selection in humans
Open the record for dataset details and reuse information.
Data from: Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modeling
<p>Molecular analysis of DNA left in the environment, known as environmental DNA (eDNA), has proven to be a powerful and cost-effective approach to infer occurrence of species. Nonetheless, relating measurements of eDNA concentration to population abundance remains difficult because detailed knowledge on the processes that govern spatial and temporal distribution of eDNA should be integrated to reconstruct the underlying distribution and abundance of a target species. In this study, we propose a general framework of abundance estimation for aquatic systems on the basis of spatially replicated measurements of eDNA. The proposed method explicitly accounts for production, transport, and degradation of eDNA by utilizing numerical hydrodynamic models that can simulate the distribution of eDNA concentrations within an aquatic area. It turns out that, under certain assumptions, population abundance can be estimated via a Bayesian inference of a generalized linear model. Application to a Japanese jack mackerel (<em>Trachurus japonicus</em>) population in Maizuru Bay revealed that the proposed method gives an estimate of population abundance comparable to that of a quantitative echo sounder method. Furthermore, the method successfully identified a source of exogenous input of eDNA (a fish market), which may render a quantitative application of eDNA difficult to interpret unless its effect is taken into account. These findings indicate the ability of eDNA to reliably reflect population abundance of aquatic macroorganisms; when the "ecology of eDNA" is adequately accounted for, population abundance can be quantified on the basis of measurements of eDNA concentration.</p>
A quantitative method to calibrate the SWAN wave model - pre-processed model outputs
<p>The pre-processed MAT files for the paper 'A quantitative method to calibrate the SWAN wave model'</p>
Data underpinning "Quantitative functional renormalization for three-dimensional quantum Heisenberg models"
<p>Data and script for creation of the plots.</p> <p>To install the software environment start julia in the directory of the files, type `] activate .` then `instantiate`</p>
Induced pluripotent stem cell-derived cardiomyocyte in vitro models: tissue fabrication protocols, assessment methods, and quantitative maturation metrics for benchmarking progress
<p>The advent of human induced pluripotent stem cells (hiPSCs) and techniques to differentiate cardiomyocytes from them has opened a viable path to creating <em>in vitro</em> models of normal and diseased hearts, accelerating more predictive drug screening and therapeutic strategies for cardiac pathologies. Currently, hiPSC-derived cardiomyocytes (hiPSC-CMs) are more similar to fetal than adult cardiomyocytes, leading many in the field to explore approaches to enhance cell and tissue maturation. There are over 2,000 studies utilizing hiPSC-CMs in models composed of various combinations of cell and extracellular matrix components, using a plethora of differentiation protocols, culture formats, and methods for quantifying cardiomyocyte function. To assess the current state of this rapidly growing area, we systematically analyzed 300 studies using hiPSC-CM models for their selection of hiPSC lines, hiPSC-CM differentiation protocols, types of <em>in vitro </em>models, maturation techniques, and metrics used to assess cardiomyocyte functionality and maturity. Here, we provide the data compiled from our analysis of these papers so others in the field can utilize it to inform their research.</p> <p>Based on this analysis, we highlight the diversity of, and current trends in, <em>in vitro</em> model designs and highlight the most common and promising practices for functional assessments. We further analyzed outputs spanning structural maturity, contractile function, electrophysiology, and gene expression and note field-wide improvements over time. Finally, we observe that a persistent lack of coordination amongst investigators is limiting the field's ability to benchmark and advance hiPSC-CM function against previous studies. We discuss opportunities to collectively pursue the common goal of hiPSC-CM model development, maturation, and assessment that we believe are critical to drive the entire community forward in engineering mature cardiac tissue.</p>
Quantitative assessment of the reservoir-induced and urbanization-induced impact on multivariate flood risk via a nonstationary vine Copula model
<p>Here we show the results of the characteristics of the floods at Huayuankou, Lanzhou and Toudaoguai gauges selected by AMS and POT mentod, respectively. Besides that, the inormation about the reservoirs and the imprevious layer in the control catchment of each station is also uploaded.</p>
Proccessed Data for the Pipelines of the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome"
<p><strong>Proccessed and Input Data for the Pipelines of the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome"</strong></p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Contents:</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Folder /ProcessedData/metabolomics/ contains processed metabolomics data from the project:</p> <p>/metabolomics/metabolites_allions_combined_norm_intensity.csv - file containing normalized intensities of ions detected across tissues with six measurement methods.<br> /metabolomics/metabolites_allions_combined_formulas_with_metabolite_filters_spatial100clusters_with_mean.csv - file containing metabolite attribution to spatial clusters and mean intensity values across tissues and conditions.</p> <p>Other files are described in README_ProcessedData.md.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Folder /ProcessedData/sequencing/ contains raw and normalized counts of metagenomics and metatransriptomics data mapped to bacterial genomes.</p> <p>Folder /ProccessedData/util/ contains files used for data preprocessing and attribution to chemical classes and pathways.</p> <p>Folder /ProcessedData/example_output/ contains example output of the pipelines:</p> <p>/output/model_results_SMOOTH_raw_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (intestinal flux and metabolic flux values) for the forward problem for metabolomics measurements in the GIT.<br> /output/model_results_SMOOTH_normbyabsmax_reciprocal_problem_allions.csv - file containing estimated model parameters for the reverse problem (metabolite intensities) for the parameters estimated with the forward problem.<br> /output/model_results_SMOOTH_normbyabsmax_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (intestinal flux and metabolic flux values) for the forward problem for metabolomics measurements in the GIT, normalized by absolute maximum value.<br> /output/model_results_SMOOTH_normbyabsmax_ONLYMETCOEF_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (only metabolic flux values) for the forward problem for metabolomics measurements in the GIT, normalized by absolute maximum value.<br> /output/table_hierarchical_clustering_groups.csv - file containing attribution of the annotated metabolites to groups according to hierarchical clustering of the normalized model parameters.<br> /output/cgo_clustergrams_of_model_coefficients.mat - matlab object containing clustergram of the normalized model parameters and manually derived sub-clustergrams corresponding to different largest parameter values.</p> <p>Description of other files is provided in the file README_ProcessedData.md.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Folder /InputData/ contains HMDB and KEGG tables used for metabolite annotations and chemical group analysis.</p> <p>Folder InputData_KEGGreaction_path contains matlab files with metabolite-metabolite paths calculated from KEGG reaction-pair information (Each matrix contains a subset of paths). These files are used by the script workflow_extract_keggECpathes_for_SPpairs_final.m.</p> <p>Folder InputData_metabolomics_data contains raw metabolomics data from six methods (three LC columns: C08, C18 and HILIC, and positive and negative acquisition modes) and file tissue_weights.txt with tissue weight information used for normalization.</p> <p>Folder InputData_sequencing_data contains folders ballgown_DNA and ballgown_RNA with results of metagenomic and metatranscriptomic data analysis (raw counts, GetMM normalized counts, EdgeR and DeSeq2 analysis). </p> <p>Description of folders is provided in the file readme_InputData.md.</p> <p>-----------------------------------------------------------------------------------------------------</p>
Quantitative modelling of nutrient-limited growth of bacterial colonies in microfluidic cultivation
<p>Data for "Quantitative modelling of nutrient-limited growth of bacterial colonies in microfluidic cultivation"</p> <p> </p> <p>GrowthChannelExperiments contains the data-folders of the following growth channel experiments:<br> ***********************************************************************************************</p> <p>Name Feeding Concentration [in units of 0.195mM PCA]<br> nd004_series1 0.5<br> nd004_series2 0.5<br> nd004_series3 0.5<br> nd004_series4 2.0<br> nd004_series5 2.0<br> nd004_series6 2.0<br> nd004_series7 3.0<br> nd004_series8 3.0<br> nd112_series2 0.25<br> nd112_series3 0.25<br> nd112_series7 3.0<br> nd112_series8 3.0</p> <p>Every folder contains:<br> - a tif-file with captured image series<br> - a PIV*-folder with four PIV-files for every frame pair. The four files belong to intermediate results of the multistep PIV. The final PIV-result is given in the file step2*.dat.nmt.<br> The PIV result will be stored in a plain text file. Each line in this file correspond to each PIV vector and comprised of 16 columns:<br> x y ux1 uy1 mag1 ang1 p1 ux2 uy2 mag2 ang2 p2 ux0 uy0 mag0 flag<br> -- (x,y) is the position of the vector (center of the interrogation window).<br> -- ux1, uy1 are the x and y component of the vector (displacement) obtained from the 1st correlation peak.<br> -- mag1 is the magnitude (norm) of the vector.<br> -- ang1, is the angle between the current vector and the vector interpolated from previous PIV iteration.<br> -- p1 is the correlation value of the 1st peak.<br> -- ux2,uy2,mag2,ang2,p2 are the values for the vector obtained from the 2nd correlation peak.<br> -- ux0, uy0, mag0 are the vector value at (x,y) interpolated from previous PIV iteration.<br> -- flag is a column used for mark whether this vector value is interpolated (marked as 999) or switched between 1st and 2nd peak (marked as 21), or invalid (-1). <br> According to the PIV-Fiji-plugin as provided by Qingzong Tseng, used also in : <br> Tseng, Q. et al. Spatial organization of the extracellular matrix regulates cell-cell junction positioning. Proc. Natl. Acad. Sci. 109, 1506–1511 (2012)<br> - two traj*.dat files, belonging to particle positions of the corresponding simulation with monod/teissier uptake. <br> Columns correspond to <br> 1 : time | 2 : cellID | 3 : rx | 4 : ry | 5 : rz | 6: species | 7 : vx | 8 : vy | 9 : vz | 10 : fx | 11 : fy | 12 : fz | 13 : B(g) |<br> -- rx,ry,rz 3D coordinates of particle<br> -- species is either 0 (living cell) or 1 (wall-particle)<br> -- vx,vy,vz 3D velocity of particle<br> -- fx,fy,fz 3D force of particle<br> -- B(g) growth force constant dependent on local g-concentration<br> Note that due to the simulation being 2D, rx=constant and vx=0=fx.<br> - two g*.dat files, belonging to nutrient concentrations of the corresponding simulation with monod/teissier uptake. <br> Columns correspond to <br> 1 : time | 2 : gridx | 3 : gridy | 4 : gridz | 5 : g-conc | 6: kcons | 7 : kprod | 8: Dlocal |<br> -- gridx,gridy,gridz coordinates of lattice side<br> -- kcons local nutrient consumption rate<br> -- kprod local nutrient production rate (always zero)<br> -- Dlocal local diffusion constant</p> <p> </p> <p>GrowthChamberExperiments contains the the data-folders of the following growth chamber experiments:<br> ***************************************************************************************************</p> <p>Name Feeding Concentration [in units of 0.195mM PCA]<br> nd143_xy009 1.0<br> nd143_xy013 1.0<br> nd143_xy025 1.0<br> nd143_xy032 1.0<br> nd143_xy059 1.0<br> nd143_xy060 1.0<br> nd143_xy061 1.0<br> nd143_xy165 0.1<br> nd143_xy184 0.1<br> nd143_xy214 0.1</p> <p>Every folder contains:<br> - a tif-file with captured image series<br> - five traj*.dat files, belonging to particle positions of the corresponding simulation with monod-uptake and five different ratios of the diffusion constants in- and outside the colony.<br> - five g*.dat files, belonging to nutrient concentrations of the corresponding simulation with monod-uptake and five different ratios of the diffusion constants in- and outside the colony.</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.