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61 results for “Cell parameters”
Database of optical parameters for the simulation of perovskite/silicon solar cells
<p>This dataset contains a set of representative optical parameters, i.e. the wavelength dependent complex refractive index (n+ik), for common materials used in perovskite-silicon tandems. In particular: SnO2, Spiro-MEOTAD, CH3NH3PbI2, a-Si:H, undoped c-Si, MgF.<br> The data are stored in the ASCII file “<em>nk_MaterialParameters.txt</em>”, where for each material, we report three data columns: wavelength (in µm), refractive index <em>n</em> and extinction coefficient <em>k</em>.</p>
1D cell trajectories as studied in "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference"
<p>Trajectories of motile cells represent a rich source of data that provide insights into the mechanisms of cell migration via mathematical modeling and statistical analysis. Here, we present trajectories of MDA-MB-231 breast cancer cells and MCF-10A breast epithelial cells. Cells were confined to 1D using fibronectin lanes and exposed to three different treatments, namely the actin polymerisation inhibitor Latrunculin A (LatA), the ROCK inhibitor Y-27632 (Y27) and a control. Each csv file contains a number of 24h long trajectories of cells corresponding to the name of the file. The column names are:</p> <p>`traject_id`: The trajectories are numbered, starting from 0 in each file.</p> <p>`time (h)`: Time in h, starting at 0h for each trajctory and ending at 24h with a temporal resolution of 2min.</p> <p>`x_front`: Position of the cell's front.</p> <p>`x_nucleus`: Position of the cell's nucleus, where x_nucleus=0 for the first time point of the trajectory</p> <p>`x_rear`: Position of the cell's rear.</p> <p>The data was analysed in our study "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference". Further information can be found there. </p>
Data for "Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis"
<p>The files named <code>df_scran.csv</code>, <code>df_seurat.csv</code>, <code>df_zinbwave.csv</code>, <code>df_dca.csv</code>, and <code>df_scvi.csv</code> contain one row per configuration that we ran successfully.</p> <p>The files named <code>DATASET.METHOD.h5ad</code> are encoded with anndata <code>v0.7.0</code> (be careful as they are not readable with previous versions) and contain 100 embeddings each. The embeddings are in the <code>obsm</code> attribute of the object. All the embeddings can be listed with the <code>obsm_keys()</code> method. The name of the embedding contains the parameters used to generate that embedding and are written like that <code>method=zinbwave.dims=10.epsilon=1000.features=300.gene_covariate=0</code>.</p> <p> </p> <p>For questions on this dataset please contact fraimundo@google.com</p>
Dataset for: "Parameter identifiability and model selection for partial differential equation models of cell invasion"
<p>This is the dataset accompanying the paper "Parameter identifiability and model selection for partial differential equation models of cell invasion" (https://arxiv.org/abs/2309.01476). It consists of a series of images taken of a barrier assay experiment to study tissue expansion of MDCK cells, along with cell density data in MATLAB format.</p> <p>File structure: the contents of the four zip files should be combined (they were split into four files for practical reasons regarding file size). The data corresponds to eight experiments, four with circular initial conditions, and four with triangular initial conditions, the associated data are located in 04-05-22 exp1/Circle and 04-05-22 exp1/Triangle respectively, each labeled "xy<n>", where <n> from 1 to 8 is an identifier for the experiment. The images under the xy<n>_Phase folders are the raw images taken of the experiment, those under the xy<n>_mask folder are processed images indicating the extend of the spread of the cell population. The DensityCellcyleFraction folder contain data files in MATLAB format. The most relevant is the "density" variable, which is a rank-3 tensor of size 150x150x77 such that density(i,j,k) corresponds to the cell density at location (x_i,y_j) and time t_k. The process for calculating the cell density is described in the paper.</p> <p>Alternatively, the density data is also provided in csv format. In the csv_data folder, xy<n>/t<k>.csv encodes a matrix representing cell density for experiment <n> at time t_k.</p> <p>The code for processing and analysing these data are provided in the "code" folder. It is also available at https://github.com/liuyue002/woundhealing .</p> <p>Abstract of the paper:</p> <p>When employing a mechanistic model to study biological systems, practical parameter identifiability is important for making predictions in a wide range of scenarios, as well as for understanding the mechanisms driving the system behaviour. We argue that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection. To demonstrate, we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher--KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and require more data to be practically identifiable. The results from identifiability analysis can inform model selection, as well as data collection and experimental design.</p>
Figure 1 in Blood cells and some hematological parameters of red drum (Linnaeus, 1766) in Vietnam
Figure 1. Erythrocytes of red drum. a - location L1, b - location L2, c - location L3. The red arrow indicates the deformed erythrocyte nuclear/uneven nuclear-matter distribution. The blue arrow indicates the slightly alkaline erythrocytes.
Tracking Data I/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p><strong>PLEASE NOTE: the folder compare_postprocessing_synth_bm is provided in the repository 10.5281/zenodo.5227610 due to size restrictions.</strong></p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [<strong>will be stored in 10.5281/zenodo.5227610 due to size restrictions</strong>]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p>
Data for "Influences of Glyphosate Contaminations and Concentrate Feed on Performance, Blood Parameters, Blood Cell Functionality and DNA Damage Properties in Fattening Bulls"
<p>The deposited data consist of three data tables and three tables containing legends for the data:</p> <p><a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/oneTimepoint.txt">oneTimepoint.txt</a> contains data from statistical tests within one timepoint; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/oneTimepoint_legend.txt">oneTimepoint_legend.txt</a> contains the corresponding legend.</p> <p><a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/threeTimepoints.txt">threeTimepoints.txt</a> contains data from statistical tests inclduing three distinct timepoints; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/threeTimepoints_legend.txt">threeTimepoints_legend.txt</a> contains the corresponding legend.</p> <p><a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/Performance.txt">Performance.txt</a> contains data from statistical tests inclduing two time periods in fattening; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/Performance_legend.txt">Performance_legend.txt</a> contains the corresponding legend.</p>
Figure 2. - Nesting architecture of Xylocopanasalis; Dissected nests of Xylocopanasalis revealing the nest structure inside the bamboo culm and its residents. Measurements of the nest parameters are shown in Table 1. The diameters of the nests (excluding the nest thickness) were measured at the nest entrance, followed by the vestibulum (antechamber) length, cell length, and the inner most cell length, respectively (2a). Cells containing larvae with pollen masses and their feces were collected and weighted (2b).
Figure 2. - Nesting architecture of Xylocopanasalis; Dissected nests of Xylocopanasalis revealing the nest structure inside the bamboo culm and its residents. Measurements of the nest parameters are shown in Table 1. The diameters of the nests (excluding the nest thickness) were measured at the nest entrance, followed by the vestibulum (antechamber) length, cell length, and the inner most cell length, respectively (2a). Cells containing larvae with pollen masses and their feces were collected and weighted (2b).
Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><strong>PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm </strong></p> <p><strong>All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions.</strong></p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p> </p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p> <p> </p> <p> </p>
Suppl. Table 2: P-values of statistical tests comparing morphometric parameters characterising Saccharina embryos and their cells.
<p>Samuel Boscq Thesis Chapter 2 Supplementary data</p>
Dataset related to the publication "Accelerated Electrochemical Investigation of Li Plating Efficiency as Key Parameter for Li Metal Batteries Utilizing a Scanning Droplet Cell"
<p>Related to the publication "Accelerated Electrochemical Investigation of Li Plating Efficiency as Key Parameter for Li Metal Batteries Utilizing a Scanning Droplet Cell" <a href="https://www.zenodo.org/record/5503915">https://www.zenodo.org/record/5503915</a></p> <p>The file contains raw data of the results shown in Figure 1, 2, 3, 4, 6, 7, 8, S1 and S2.</p>
Dataset with values of morphological parameters and phenotypes of cells and colonies from three human pluripotent stem cell lines
<p>The dataset is a part of the following manuscript submitted for publication in International Journal of Molecular Sciences (MDPI):</p> <p>"Prognostic Analysis of Human Pluripotent Stem Cells Based on their Morphological Portrait and Expression of Pluripotent Markers" by Olga A. Krasnova, Vitaly V. Gursky, Alina S. Chabina, K. A. Kulakova, L. L. Alekseenko, Alexandra V. Panova, Sergey L. Kiselev and Irina E. Neganova</p> <p>The files contain values of several morphological parameters and phenotypes obtained for cells and colonies from hESC line H9, hiPSC line AD3, and hiPSC line CaSR. The phenotypic information is presented in three forms: four possible classes of colonies according to the visually assessed phenotype ('bad', 'average', 'good', and 'excellent'), three possible classes (previous 'good' and 'excellent' combined in one 'good' class), and two possible classes (previous 'bad' and 'average' combined in one 'bad' class, and previous 'good' and 'excellent' combined in one 'good' class).</p>
The Effects of Crocin Supplementation on Metabolic Parameters, Oxidative Stress, AMP- Activated Protein Kinase and Inflammation-promoting Genes Expression in Peripheral Blood Mononuclear Cells in Pati
ClinicalTrials.gov study NCT04163757. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Red Blood Cell Transfusion Improves Perfusion Parameters in Septic Shock Patients With Hypoperfusion
ClinicalTrials.gov study NCT01611753. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Variation of Echocardiographic Parameters After Red Blood Cell Transfusion in Emergency Department Anemic Patients
ClinicalTrials.gov study NCT06060639. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Study on the Correlation Between the Quantitative Parameters of Mr Mean Cell Size Imaging and the Histopathological Characteristics of Breast Cancer
ClinicalTrials.gov study NCT05373628. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Arterial Function Parameters and Transcranial Doppler Velocity in Paediatric Patients With Sickle Cell Disease
ClinicalTrials.gov study NCT05748717. IPD Sharing: NO. Countries: 1. Publications: 7.
Morphological Parameters of Blood Cell's Activation in Characterization and Prognosis in a COPD Patients Cohort
ClinicalTrials.gov study NCT04923360. IPD Sharing: NO. Countries: 1. Publications: 22.
Study of Erythrocyte Parameters and Hypercoagulability in Sickle Cell Disease (SCD-TGA)
ClinicalTrials.gov study NCT05376046. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Exploration of the Parameters Influencing the Effort Limitation of Patients Suffering From Homozygous Sickle Cell Anemia
ClinicalTrials.gov study NCT06743139. IPD Sharing: NO. Countries: 1. Publications: 4.
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.