Contributors: Miek Messerschmidt
... This dataset is related to the paper "On compact packings of the plane with circles of three radii". For each of the 248395 elements of the set K defined in Definition 5.1, the dataset contains a file (in JSON format). Each file contains all data required to apply Proposition 6.1, which allows for establishing the bound of 13617 of the number of pairs (r,s) satisfying 0<s<r<1 and which admits a compact packing of circles of radii r,s and 1.
Contributors: Silvester vankoten
... The data (as stata dta files) are the basis for a replication study, (re)testing the four hypothesis in Bessembinder & Lemmon (2002). The hypotheses are as following: The forward premium: H1: decreases in variance of prices H2: increasing in non-standardized skewness of price H3: initially decreases and then increases in demand standard deviation H4: increases in mean demand The results are that Hypotheses H1, H2 are not supported when the variable cost parameter is adaptive as specified in (2002, p.1360), but are supported when the variable cost parameter is fixed. Hypothesis H3 is supported for both cases and Hypothesis H4 for neither. The data have been generated using Bessembinder and Lemmon’s (2002) theoretical results (especially their Equation 3) to calculate outcomes (spot prices, forward prices, forward premia and optimal forward positions) for different demand distributions. As a robustness test, I also calculate forward positions, not by using Equation 3, but, as in Willems and Morbee (2010), by maximizing the joint utility of a producer and retailer, where the utility function is given by . The first welfare theorem implies that these solutions should be equal to the optimal solutions. Indeed the values I calculate in this manner are identical to the ones I calculate using Equation 3. I calculate the outcomes for eight configurations in total: four values of the cost convexity parameter (2, 3, 4, and 5) and two possible ways to determine the variable cost parameter (adaptive and fixed). For each configuration, outcomes are calculated for 195,891 different distributions (391 values for the demand standard deviation and 501 values for the mean demand). For each demand distribution in a configuration, I use a grid spanning 10 standard deviations with 1000 points per standard deviation. As another robustness test, I also run the same calculations using random samples of 1000 000 per distribution, programmed in Mathematica. All calculated outcomes are identical (except for minor rounding differences). To use the data, open the folder "Stata_analysis" > "Stata_analysis_home folder" > "stata_files" • "2. Analyse data_main.do" can be used to replicate all the figures in the paper. The data have been generated in Python. Run the full dofile. Before you run the dofile, make sure to unrar all the files in the folder "Stata_analysis_home folder\data_in_stata_format" with a rar-utility. • "3. Analyse data_robust.do" replicates the figures using the data generated by brute sampling in Mathematica. The results should be identical to those obtained with the dofile "2. Analyse data_main.do"
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
Top results from Data Repository sources. Show only results like these.
Contributors: Ping He, Charles Mader, Joaquim Martins, Kevin Maki
... This data set contains files and instructions to reproduce the results in the following paper: Ping He, Charles A. Mader, Joaquim R. R. A. Martins, and Kevin J. Maki. DAFoam: An open source adjoint framework for multidisciplinary design optimization with OpenFOAM. In this paper, we use an open source adjoint framework (DAFoam; see the link below) to perform optimizations that cover a wide range of disciplines (aerodynamics, heat transfer, structures, and radiation), configurations (wings, aircraft, turbine cooling channels, compressor blades), and conditions (incompressible, subsonic, and transonic). The following files are the meshes and flow and optimization configurations to reproduce the optimization results. - UAV_Aero_Opt.tar.gz: Multipoint aerodynamic optimization for a UAV wing at low speed. - DPW4_Aero_Opt.tar.gz: Trimmed aerodynamic optimization for a transonic aircraft configuration. - UBend_AeroTherm_Opt.tar.gz: Aerothermal optimization for a turbine internal cooling channel. - Rotor67_AeroStruct_Opt.tar.gz: Aerostructural optimization for an axial compressor rotor. The Code_Version.txt file contains the required codes to reproduce the results, including their links and GIT versions.
Data for: Dose response analysis program (DREAP): a user-friendly program for the analyses of radiation-induced biological responses utilizing established deterministic models at cell population and organ scales.
Contributors: Kyung-Nam Lee
... DREAP stands for dose response analysis program, a user-friendly program for analysis of radiation-induced biological responses such as survival fraction, relative biological effectiveness, normal tissue complication probability, and tumor control probability. 1. DREAP_source_code.zip: including m-files 2. How to execute DREAP: explaining how to use and some requirements of the DREAP at first 3. sample_input.zip: sample input files 4. sample_output.zip: sample output files from the DREAP 5. Validation study data: results of validation study on accuracy and speed
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.
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.
Data/Software for "Presynaptic Mitochondria Volumes and Complexity of Subsynaptic Distribution Increase During Development at a High-fidelity Synapse"
Contributors: Connon I. Thomas, Christian Keine, Satoko Okayama, Rachel Satterfield, Morgan Musgrove, Debbie Guerrero-Given, Naomi Kamasawa, Samuel M. Young, Jr.
... Contains data and software from the publication: "Presynaptic Mitochondria Volumes and Complexity of Subsynaptic Distribution Increase During Development at a High-fidelity Synapse" currently under review. The preprint to this data set has been published on bioRxiv (https://doi.org/10.1101/689653). In this study, we created a helper-dependent adenoviral vector (HdAd) to co-express cytoplasmic EGFP and a genetically encoded peroxidase marker (mito-APEX2) at the calyx of Held, an excellent model for deciphering regulatory mechanisms of presynaptic function. ABSTRACT: The calyx of Held, a large glutamatergic presynaptic terminal in the auditory brainstem undergoes developmental changes to support the high action-potential firing rates required for auditory information encoding. In addition, calyx terminals are morphologically diverse which impacts vesicle release properties and synaptic plasticity. Mitochondria influence synaptic plasticity through calcium buffering and are crucial for providing the energy required for synaptic transmission. Therefore, it has been postulated that mitochondrial levels increase during development and contribute to the morphological-functional diversity in the mature calyx. However, the developmental profile of mitochondrial volumes and subsynaptic distribution at the calyx of Held remains unclear. To provide insight on this, we developed a helper-dependent adenoviral vector (HdAd) that expresses the genetically encoded peroxidase marker for mitochondria, mito-APEX, at the mouse calyx of Held. We developed protocols to detect labeled mitochondria for use serial block face SEM (SBF-SEM) to carry out semi-automated segmentation of mitochondria, high-throughput whole terminal reconstruction and presynaptic ultrastructure in mice of either sex. Subsequently, we measured mitochondrial volumes and subsynaptic distributions at the immature postnatal day 7 (P7) and the mature (P21) calyx. We found an increase of mitochondria volumes in terminals and axons from P7 to P21 but did not observe differences between stalk and swelling subcompartments in the mature calyx. Based on these findings, we propose that mitochondrial volumes developmentally increase to support high firing rates but have limited contribution to morphological-functional diversity at the calyx. Data are sorted by the figures they appear in. Media (movies and 3D models) and custom-written software are located in separate folders.
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.