203706 results
Contributors: marco mendoza, Maria B. Goncalves, Jonathan Corcoran
Date: 2019-09-12
... Differential gene expression analysis (RNA-sequencing) assessed in the SC from L5-SNL rats and non-injured rats, treated with vehicle or C286. Raw counts per gene (Raw counts.zip file) were normalized (quantile normalization; Normaliez counts.zip), then used for a differential gene expression analysis relative to the sample non-injured + vehicle (Diff_expression_DESeq2.zip). In addition, we have included herein a supplementary Table containing Gene Ontology (GO) terms per gene co-expression paths issued from the differential expression analysis. Notice that GO terms are described by their confidence of enrichment, expressend on "-10*log10(p-value).
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Contributors: Willem Roux, Imtiaz Gandikota, Guilian Yi
Date: 2019-09-11
... These are the computer models used for structural topology optimization from the paper "A spatial kernel approach for topology optimization" by Roux, Yi, and Gandikota.
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Contributors: Hashem Ezzati
Date: 2019-09-11
... These are some codes and data constructed in Matlab software.
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Contributors: Jeremy Diem
Date: 2019-09-10
... These data were generated for an analysis of trends in satellite-based seasonal rainfall in western Uganda from 1983-2017. Western Uganda was divided into five rainfall regions. Analyses of the data show the following: (1) wet days in most seasons have a disproportionately high frequency of westerly back-trajectories extending over the Congo Basin; (2) rainy seasons, especially the first rains, have gotten longer and wetter throughout western Uganda; (3) rainfall also has increased for climatological seasons, with the exception being December-February; and (4) increases in middle-troposphere specific humidity and vertical ascent over time provide support for the wetting trends derived from the satellite-derived rainfall data.
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Contributors: Ana Paula Favretto, Camila Grassi, Michele Marta Rossi, Jeffrey Thomas, Yifan Yang, Joseph DeCarolis, Soolyeon Cho, David Hill, Ranji Ranjithan, Karin Chvatal
Date: 2019-09-10
... Research objective: This study aimed to develop metamodels to assess the thermal discomfort in naturally ventilated Brazilian low-cost houses during early design as a decision-making support framework, and for educational purposes. Method overview: The method encompassed a large number of simulations of the software EnergyPlus [EP] 8.1 using the Monte Carlo method. These simulations were used to develop a set of regression-based mathematical relationships between the inputs and the outputs. The Monte Carlo method was selected to help sample the many independent input variables, each with its own range of values, and form equally likely random combinations of input conditions for the energy simulation. The EnergyPlus outputs were post processed to assess the thermal comfort by means of the degree hours of discomfort by heat and by cold. Files description: The following data items were made available. (a) Parameter_Domains: Curitiba_parameterdomains.csv; Manaus_parameterdomains.csv and Sao_Paulo_parametersdomains.csv: CSV files created for each location. They contain a list of the 24 key parameters and random combinations of their values to create the input data for the 10,000 simulations. (b) Performance_Metrics: Curitiba_performancemetrics.csv; Manaus_performancemetrics.csv and Sao_Paulo_performancemetrics.csv: CSV files created for each location. They contain output values (outdoor and indoor discomfort by heat and by cold) for 10,000 simulations. (c) Sandbox: Sandbox.xlsx: Excel file for the application of the metamodels. It enables a quick and easy assessment of discomfort by heat and by cold for specific combinations of parameters, for each location. (d) Python_Codes: Python_Codes.zip: Compressed file consisting of the codes used to 1) run the simulations randomly combining values for each selected parameter within their specified ranges, and 2) to calculate the hours of discomfort by heat and by cold for each of the parameters’ combinations (10,000 simulations for each studied climate). (e) IDFs_Base: Curitiba_idfbase.idf; Manaus_idfbase.idf; Sao_Paulo_idfbase.idf : Input Data File (IDF) created for each location. They contain the description of all input data considered in an annual building performance simulation.
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Contributors: Jonathan Long
Date: 2019-09-09
... Raw metabolomic data from acetonitrile/methanol-extracted livers transduced with the indicated AAV. LC-MS data were collected on an Agilent 6545 QTOF instrument in negative ionization mode.
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Contributors: Tianjin Xie
Date: 2019-09-09
... Purple rapeseed leaves dataset contains training set and test set, both of which included RGB images which were cropped using UAV orthoimage and the corresponding labels, with the size of 256 × 256 pixels. The codes for the U-Net model used in this experiment was also assigned to the folder.
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Contributors: Daniel Fitousi
Date: 2019-09-08
... The data are the result of an experiment with composite faces. This is a standard composite face task in which a study and test composite faces are presented one after the other. The participant's task is to indicate whether the top half of the test and study faces are 'same' or 'different'. Both RTs and accuracy are measured.
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Contributors: Bo Zhang, Feng Guo, Guoqing Wang, Xiaobing Zhang, Liang Zhao, Yangming Wu
Date: 2019-09-08
... Comprehensive
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Contributors: Michael Combi, Nicolas Fougere, Valeriy Tenishev, Yinsi Shou, Kenneth Hansen, Zhenguang Huang, David Marshall, Dominique Bockelee-Morvan, Martin Rubin, Kathrin Altwegg
Date: 2019-09-07
... The file MIRO_Columns.txt contains the column densities and their uncertainties corresponding to the results published previously in the paper by Marshall, D.W., Hartogh, P. Rezac, L. von Allmen, P., Biver, N., Bockelée-Morvan, D., Crovisier, J., Encrenaz, P. Bulkis, S. Hofstadter, M. Ip, W.-H., Jarchow, C. Lee, S., Lellouch, E. 2017. Spatially resolved evolution of the local H2O production rates of coet 67P/Churyumov,Gerasimenko, from the MIRO instrument of Rosetta. Astronomy & Astrophysics 603, A87. The format containes space delimited columns corresponding to Date, UT time, Heliocentric Distance (AU), column density (cm^-2), column density error (cm^-2).
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