TY - DATA AB - Data on Austrian open access publication output at Springer from 2013-2016 including data analysis. AU - Villányi, Márton ID - 5581 KW - Publication analysis KW - Bibliography KW - Open Access TI - Springer Austrian Publications 2013-2016 ER - TY - DATA AB - Data on Austrian open access publication output at SAGE from 2013-2017 including data analysis. AU - Villányi, Márton ID - 5580 KW - Publication analysis KW - Bibliography KW - Open Access TI - SAGE Austrian Publications 2013-2017 ER - TY - DATA AB - Data on Austrian open access publication output at RSC from 2013-2017 including data analysis. AU - Villányi, Márton ID - 5579 KW - Publication analysis KW - Bibliography KW - Open Access TI - RSC Austrian Publications 2013-2017 ER - TY - DATA AB - Comparison of Scopus' and FWF's data on Austrian publication output at T&F. AU - Villányi, Márton ID - 5576 KW - Publication analysis KW - Bibliography KW - Open Access TI - Data Check T&F Scopus vs. FWF ER - TY - DATA AB - Comparison of Scopus' and FWF's data on Austrian publication output at RSC. AU - Villányi, Márton ID - 5575 KW - Publication analysis KW - Bibliography KW - Open Access TI - Data Check RSC Scopus vs. FWF ER - TY - DATA AB - This package contains data for the publication "Nonlinear decoding of a complex movie from the mammalian retina" by Deny S. et al, PLOS Comput Biol (2018). The data consists of (i) 91 spike sorted, isolated rat retinal ganglion cells that pass stability and quality criteria, recorded on the multi-electrode array, in response to the presentation of the complex movie with many randomly moving dark discs. The responses are represented as 648000 x 91 binary matrix, where the first index indicates the timebin of duration 12.5 ms, and the second index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike in the particular time bin. (ii) README file and a graphical illustration of the structure of the experiment, specifying how the 648000 timebins are split into epochs where 1, 2, 4, or 10 discs were displayed, and which stimulus segments are exact repeats or unique ball trajectories. (iii) a 648000 x 400 matrix of luminance traces for each of the 20 x 20 positions ("sites") in the movie frame, with time that is locked to the recorded raster. The luminance traces are produced as described in the manuscript by filtering the raw disc movie with a small gaussian spatial kernel. AU - Deny, Stephane AU - Marre, Olivier AU - Botella-Soler, Vicente AU - Martius, Georg S AU - Tkacik, Gasper ID - 5584 KW - retina KW - decoding KW - regression KW - neural networks KW - complex stimulus TI - Nonlinear decoding of a complex movie from the mammalian retina ER - TY - DATA AB - Input files and scripts from "Evolution of gene dosage on the Z-chromosome of schistosome parasites" by Picard M.A.L., et al (2018). AU - Vicoso, Beatriz ID - 5586 KW - schistosoma KW - Z-chromosome KW - gene expression TI - Input files and scripts from "Evolution of gene dosage on the Z-chromosome of schistosome parasites" by Picard M.A.L., et al (2018) ER - TY - DATA AB - Data and scripts are provided in support of the manuscript "Efficient inference of paternity and sibship inference given known maternity via hierarchical clustering", and the associated Python package FAPS, available from www.github.com/ellisztamas/faps. Simulation scripts cover: 1. Performance under different mating scenarios. 2. Comparison with Colony2. 3. Effect of changing the number of Monte Carlo draws The final script covers the analysis of half-sib arrays from wild-pollinated seed in an Antirrhinum majus hybrid zone. AU - Ellis, Thomas ID - 5583 TI - Data and Python scripts supporting Python package FAPS ER - TY - DATA AB - Nela Nikolic, Tobias Bergmiller, Alexandra Vandervelde, Tanino G. Albanese, Lendert Gelens, and Isabella Moll (2018) “Autoregulation of mazEF expression underlies growth heterogeneity in bacterial populations” Nucleic Acids Research, doi: 10.15479/AT:ISTA:74; microscopy experiments by Tobias Bergmiller; image and data analysis by Nela Nikolic. AU - Bergmiller, Tobias AU - Nikolic, Nela ID - 5569 KW - microscopy KW - microfluidics TI - Time-lapse microscopy data ER - TY - DATA AB - Supporting material to the article STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH boundscoli.dat Flux Bounds of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium. polcoli.dat Matrix enconding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium, obtained from the soichiometric matrix by standard linear algebra (reduced row echelon form). ellis.dat Approximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium obtained with the Lovasz method. point0.dat Center of the approximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium obtained with the Lovasz method. lovasz.cpp This c++ code file receives in input the polytope of the feasible steady states of a metabolic network, (matrix and bounds), and it gives in output an approximate Lowner-John ellipsoid rounding the polytope with the Lovasz method NB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260. For further details we refer to PLoS ONE 10.4 e0122670 (2015). sampleHRnew.cpp This c++ code file receives in input the polytope of the feasible steady states of a metabolic network, (matrix and bounds), the ellipsoid rounding the polytope, a point inside and it gives in output a max entropy sampling at fixed average growth rate of the steady states by performing an Hit-and-Run Monte Carlo Markov chain. NB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260. For further details we refer to PLoS ONE 10.4 e0122670 (2015). AU - De Martino, Daniele AU - Tkacik, Gasper ID - 5587 KW - metabolic networks KW - e.coli core KW - maximum entropy KW - monte carlo markov chain sampling KW - ellipsoidal rounding TI - Supporting materials "STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH" ER -