Contributors: Marsel Rabaev, Handy Pratama, Ka Ching Chan
... This data set was generated using Arena Simulation
Contributors: Damian Matuszewski, Carolina Wählby, Jordi Carreras-Puigvert, Ida-Maria Sintorn
... PopulationProfiler – is light-weight cross-platform open-source tool for data analysis in image-based screening experiments. The main idea is to reduce per-cell measurements to per-well distributions, each represented by a histogram. These can be optionally further reduced to sub-type counts based on gating (setting bin ranges) of known control distributions and local adjustments to histogram shape. Such analysis is necessary in a wide variety of applications, e.g. DNA damage assessment using foci intensity distributions, assessment of cell type specific markers, and cell cycle analysis. The software imports measurements from a simple text file, visualizes population distributions in a compact and comprehensive way, and can create gates for subpopulation classes based on control samples. The simple graphical user interface (GUI) allows selection of multiple csv files with image-based screening data. Each file is treated as a separate plate (i.e. independent experiment) with rows representing cell measurements. One measurement is processed at a time and cells are grouped based on well labels. The measurement is selected by the user from a drop-down list created from the csv file header (first row). The GUI also allows selection of control wells based on the treatment labels. If such labels are not available, the user can select control wells manually. The corresponding data is pooled and stored as a separate record in the output csv file. PopulationProfiler thereafter calculates and displays the distribution of the selected measurement as a histogram for each well. A vector representation of each well’s histogram is saved in the output file, and can be used as input for e.g., cluster analysis, elsewhere. The cell count for each well is also saved as a measure of statistical relevance of population effects. Cite this software as: Matuszewski, D.J., Wählby, C., Puigvert, J.C., Sintorn, I.-M. (2016) PopulationProfiler: A Tool for Population Analysis and Visualization of Image-Based Cell Screening Data. PloS one, 11(3), e0151554. The Python source code, sample data and user manual are available free of charge.
Contributors: andre chevrier
... fMRI data from "Disrupted reinforcement learning during post-error slowing in ADHD" All files are in NIFTI (.nii) format. All maps are in talairach (+tlrc) space. All directories contain an average anatomic file for display purposes. Directories and contents: maps-main study: Single subject activation maps used as input to ANOVA and correlation analyses (sub-brik#(0-13) = subject#(0-13)). go = response-phase maps (i.e. (1/2)(left+right); det = error detection maps; pes = post-error slowing maps e.g. "det-adhd" refers to error detection maps for all ADHD subjects - sub-brik #0 = subject #1 ... sub-brik #13 = subject #14. maps-replication: Single subject activation maps (as above) for replication study. Table 1 ANOVAS: Raw ANOVA output corresponding to Table 1 (and Supplementary Figure 1) for TD, ADHD, and group difference analyses (t* and estimate maps for Detect and Post-error slowing), and cluster-thresholded ANOVA outputs. Files (sub-briks): TD 0, 1: Full model estimate, F-stat(4,65) 2, 3: Fixate estimate, t*(13) 4, 5: Go estimate, t*(13) 6, 7: Stop-Go estimate, t*(13) 8, 9: Detect estimate, t*(13) ADHD 0-9: same as above TD-thresholded 0-4: Thresholded maps of %BOLD estimates during Fixate, Go, Stop-Go, Detect, and PES ADHD-thresholded 0-4: same as above diffs 0, 1: Group difference estimate and t*(26) during Detect 2, 3: Group difference estimate and t*(26) during PES diffs-thresholded 0: Cluster thresholded group difference in %BOLD during Detect 1: Cluster thresholded group difference in %BOLD during PES Table 2 ... Table 7: Raw correlation outputs (e.g. "SN-Det-go-td" refers to SN seed activity during error detection correlated with response-phase (i.e.go) activity in TD group) corresponding to Tables 2-7 (and Supplementary Figures 2-5). Extra sub-briks are appended to correlation output files, one at the beginning and one at the end. The first appended sub-brik contains a cluster-thresholded map of B1 estimates, and the last appended sub-brik contains the raw, signed correlation coefficient 'r' correlation file sub-briks 0: Thresholded map of B1 (slope term) estimates 1: Baseline offset (B0) estimate 2: Baseline offset t*(12) 3: Slope (B1) estimate 4: Slope t*(12) 5: Full F-stat (1,12) 6: Squared correlation (r^2) 7: Unsigned correlation coefficient (r) Data corresponding to confirmatory analyses portrayed in Supplementary Figures 6 and 7 are identical to the thresholded corelation maps in substantia nigra (Table 3) and raphe nucleus (Table 4).
Contributors: Sreetam Bhaduri, Pranab Bhattacharyya
... MATLAB program and APDL Program
Data for: An approach to fabricate high performance cooler with nearly ideal emissive spectrum for above ambient radiative cooling
Contributors: YuXiang Zheng
... These are the research data files of "An approach to fabricate high performance cooler with nearly ideal emissive spectrum for above ambient radiative cooling".
Contributors: Paul Pecorino, Mark Van Boening
... These files both contain the same data. One is a text file and one is a PDF. They are the data used in the paper. "An Empirical Analysis of Litigation with Discovery: The Role of Fairness", by Paul Pecorino and Mark Van Boening.
Contributors: Bin Ren
... This dataset is created to report the captured human subjects' motion and a servo control mechanism that aims at minimizing trajectory tracking error.
Contributors: Denis Mikryukov
... Here we store the coefficients of the planetary disturbing function in computer-readable form. These were obtained by Maxima computer algebra system and are supposed to be used/read with the help of that system. We plan to provide more thorough and complete comments on these data in the nearest future.
Contributors: akhmad habibi, Lantip Diat Prasojo, Amirul Mukmini, Mohd Faiz Mohd Yaakob
... This dataset aims at developing and validating the proposed survey instrument and elaborating how Indonesian English as Foreign Language (EFL) in-service teachers perceive their TPACK. A total number of 573 in-service EFL teachers completed a 28-item survey instrument.
Contributors: Jacqueline Zadelaar
... Are Individual Differences Quantitative Or Qualitative? An Integrated Behavioral And Fmri Mimic Approach. Authors: Jacqueline N. Zadelaar, Wouter D. Weeda, Lourens J. Waldorp, Anna C. K. Van Duijvenvoordee, N. E. Blankenstein, Hilde M. Huizenga In cognitive neuroscience there is a growing interest in individual differences. We propose the Multiple Indicators Multiple Causes (MIMIC) model of combined behavioral and fMRI data to determine whether such differences are quantitative or qualitative in nature. A simulation study revealed the MIMIC model to have adequate power for this goal, and parameter recovery to be satisfactory. The MIMIC model was illustrated with a re-analysis of Van Duijvenvoorde et al. (2016) and Blankenstein et al. (2018) decision making data. This showed individual differences in Van Duijvenvoorde et al. (2016) to originate in qualitative differences in decision strategies. Parameters indicated some individuals to use an expected value decision strategy, while others used a loss minimizing strategy, distinguished by individual differences in vmPFC activity. Individual differences in Blankenstein et al. (2018) were explained by quantitative differences in risk aversion. Parameters showed that more risk averse individuals preferred safe over risky choices, as predicted by heightened vmPFC activity. We advocate using the MIMIC model to empirically determine, rather than assume, the nature of individual differences in combined behavioral and fMRI datasets.