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
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: 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.
Decision making in structural engineering problems under polymorphic uncertainty - A benchmark proposal
Contributors: Yuri Petryna, Martin Drieschner
... Benchmark_frame_det_2018_08.m: MATLAB file executing an exemplary deterministic calculation of the portal frame portalFrame_2018_08.m: MATLAB file calculating the limit state function value for material failure g_mat [/], the limit state function value for stability failure g_stab [/], the horizontal displacement of the girder V_4 [m] and the vertical displacement V_8 [m] at the load position L_V [m] measData.txt: text file with 5000 artificial measurements during operation of the crane (without occurrence of failure). 1st column: measurement set [/]. 2nd column: horizontal displacement of the girder V_4 [m]. 3rd column: vertical displacement V_8 [m] at L_V. 4th column: load position L_V [m]. 5th column: wind load F_H [N]. 6th column: brake load F_B [N]. 7th column: crane load F_V [N]. AdditionalInfo.pdf: pdf file with additional information concerning the contribution "Decision making in structural engineering problems under polymorphic uncertainty - A benchmark proposal"
Contributors: Ricardo Mariño-Pérez, Hojun Song
... Files for the different analyses described in Methods
Rearrangements within the U6 snRNA core during the transition between the two catalytic steps of splicing. Eysmont et al.
Contributors: Katarzyna Eysmont
... The RNA catalytic core of spliceosomes as visualized by cryo-EM remains unchanged at different stages of splicing. However, we demonstrate that mutations within the core of yeast U6 snRNA modulate conformational changes between the two catalytic steps. We propose that the intramolecular stem-loop (ISL) of U6 exists in two competing states, changing between a default, non-catalytic conformation and a transient, catalytic conformation. Whereas stable interactions in the catalytic triplex promote catalysis and their disruptions favor exit from the catalytic conformation, destabilization of the lower ISL stem promotes catalysis and its stabilization supports exit from the catalytic conformation. Thus, in addition to the catalytic triplex, U6-ISL acts as an important dynamic component of the catalytic center. The relative flexibility of the lower U6-ISL stem is conserved across eukaryotes. Similar features are found in U6atac and domain V of group II introns, arguing for the generality of the proposed mechanism.
Data for: Thermodynamics, electronic structure and vibrational properties of Sn(n)[S(1-x)Se(x)](m) solid solutions for energy applications
Contributors: Jonathan Skelton, David Gunn, Lee Burton, Sebastian Metz, Stephen Parker
... This repository provides additional data to accompany the paper: "Thermodynamics, Electronic Structure, and Vibrational Properties of Sn(n)[S(1–x)Se(x)](m) Solid Solutions for Energy Applications" D. S. D. Gunn, J. M. Skelton, L. A. Burton, S. Metz and S. C. Parker Chemistry of Materials 31 (10), 3672-3685 (2019), DOI: 10.1021/acs.chemmater.9b00362 This article examines the properties of four solid-solution models: Pnma and rocksalt Sn[S,Se], Sn[S,Se](2) and Sn(2)[S,Se](3). This repository makes available a full set of data for all of the ~5,000 symmetry-unique structures across the four sets of calculations, including: * Optimised structures; * Calculated total energies and degeneracies; * Calculated bandgaps and partial density of states (PDoS) curves; * Simulated dielectric functions; and * Data from lattice-dynamics calculations on selected structures. In addition, the thermodynamically averaged pair-distribution functions, PDoS curves, dielectric functions, and structural-similarity analyses presented in the paper, calculated based on a 900 K formation temperature, are also provided. Finally, the repository also contains sample input files for the Vienna Ab initio Simulation Package (VASP) code. For details of how this data was generated, viewers are referred to the published article and supporting information. Brief details of file formats and links to further documentation are given in the included README file.
Contributors: Szilárd Szabó, Boglárka Balázs, Zoltán Kovács, Balázs Deák, Ádám Kertész
... The dataset is derived from the Hungarian part of the CarpatClim database (https://doi.org/10.1002/joc.4059) and the MODIS MOD13Q1 16 days 250 m (https://doi.org/10.5067/MODIS/MOD13Q1.006) between 2000-2010, using bivariate linear regression on monthly data. The 1038 points represent 1038 R-squared (R2) values of the regressions. R2 values reflect the strength of relationship between aridity, precipitation, potential evapotranspiration, maximum temperature and the normalized vegetation index (NDVI). For spatial analysis, we provided the codes of Hungarian macro regions, land cover and topography data (terrain height, slope and aspect). Column name Description CC_ID: CarpatClim identifier Country: Country code of CarpatClim /1=Hungary/ UTM_X: X UTM Coordinate UTM_Y: Y UTM Coordinate ARIvsNDVI_R2: R2 of Aridification Index and NDVI 2000–2010 PRECvsNDVI_R2: R2 of Precipitation and NDVI 2000–2010 PETvsNDVI_R2: R2 of Potential Evapotranspiration and NDVI 2000–2010 TMAXvsNDVI_R2: R2 of Maximum Temperature and NDVI 2000–2010 DEM_slope: SRTM slope value (degree) DEM_aspect: SRTM aspect value (azimuth) DEM: SRTM elevation (m) CLC_code: CORINE Land Cover code /arable lands (211, 213,221,222, 242,243), grasslands (231, 321), forests (311, 312, 313, 324), wetlands (411, 412), water bodies (511, 512) and artificial surfaces (112, 121, 122, 131, 142) Macro_reg_code: Hunrarian Macro Region code /Great Hungarian Plain=1, Kisalföld=2, Alpokalja=3, Transdanubian Hills=4, Transdanubian Mountains=5, North-Hungarian Mountains=6/ Microregion_code: Hungarian Micro Region code (Dövényi, Z. 2010) Dövényi, Z. ed. 2010. Inventory of Natural Micro-regions of Hungary, Hungarian Academy of Sciences Geographical Institute, Budapest