Data for: Predicting missing links in directed networks based on local structure: An investment-profit index
Contributors: Jinsong Li, Shuxin Liu
... This dataset contains all necessary data, program files and raw results of our manuscript "Predicting missing links in directed networks based on local structure: An investment-profit index".
Contributors: Sergei Sitnov
... Raw data that served as the basis for the manuscript entitled "Exploring large-scale black-carbon air pollution over Northern Eurasia in summer 2016 using MERRA-2 reanalysis data" by Sitnov et al., submitted in Atmospheric Research
Code/Data for: Multi-omic Dissection of Oncogenically Active Epiproteomes Identifies Drivers of Proliferative and Invasive Breast Tumors
Contributors: John Wrobel
... R scripts and data files for analysis of TCGA and METABRIC datasets for Wrobel et al. (2019). Multi-omic Dissection of Oncogenically Active Epiproteomes Identifies Drivers of Proliferative and Invasive Breast Tumors. iScience. DOI: https://doi.org/10.1016/j.isci.2019.07.001
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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. The method is described by Laskar and Robutel (CMDA, 62, 193-217, 1995). We should note that our coefficients of secular Hamiltonian up to degree 4 coincide with those presented by Laskar and Robutel (see Section 7, CMDA, 62, 193-217, 1995). Therefore we hope that our expansions are error free. We plan to provide more thorough and complete comments on all of these data in the near future.
Contributors: Patricia Sawamura, Nicholas Pringle
... Internal testing - Mendeley data - fake data
Data for: Evaluation of implants placed in preserved sockets versus fresh sockets on tissue preservation and esthetics: Meta-analysis and systematic review
Contributors: Sijing Xie, Qingang Hu, Yongbin Mu, Li Wu, Xuna Tang, Weibin Sun, Jie Yang, Xin Zhou
... The files showed phased objectives of literature search and selection and statistical analysis, which could be of some help to understand our studies better.
Sensory-to-category transformation via dynamic reorganization of ensemble structures in mouse auditory cortex. Xin et al
Contributors: Ning-long Xu
... Dataset for the study by Xin et al., "Sensory-to-category transformation via dynamic reorganization of ensemble structures in mouse auditory cortex." 1. The folder "Behavior Dataset for Figure 1" contains the behavioral data that can be used to reproduce the results in Figure 1 2. The folder "Dataset for Figures 2, 4, 6, 7, 8" contains 21 sessions calcium imaging data acquired during behavioral task. All the raw data are included in the folder and each session was saved as a separate file. Please refer to the note before data loading. 3. The folder "Dataset for Figure 3" contains calcium imaging data that can be used to reproduce the results in Figure 3. 4. The folder "Dataset for Figure 5" contains calcium imaging data that can be used to reproduce the results in Figure 5, which shows results of the experiments with two different behavior contexts. The subfolder "4_16Sess" contains data for the 4-16kHz imaging sessions. The subfolder "7_28Sess" contains data for for 7-28kHz imaging session. Files within different folders but sharing same file names (e.g. "Sess1_data_save.mat" contains data from session 1) are for the same imaging field of view.
Contributors: Yang Hu, Xudong Li
... An integrated experiment is designed for assessing the effectiveness of applying three different levels of transfer learning into fault diagnostics, and the source codes and original dataset are uploaded here for open-public accessing. Thus the obtained results can be set as the benchmark for assessing the following new transfer learning variant algorithms in fault diagnostics field.
Contributors: Marsel Rabaev, Handy Pratama, Ka Ching Chan
... This data set was generated using Arena Simulation
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).