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- Creator:
- Revzen, Shai
- Description:
- This repository contains both the data and python3 code that reads this data and reproduces the relevant figures. The code depends on NumPy >1.17 and matplotlib >3.1 and was tested on python 3.8
- Keyword:
- locomotion, slipping, low Reynolds number, walking, and slithering
- Discipline:
- Science and Engineering
-
- Creator:
- Sun, Hu, Ren, Jiaen, Chen, Yang, Zou, Shasha, Chang, Yurui, Wang, Zihan, and Coster, Anthea
- Description:
- Our research focuses on providing a fully-imputed map of the worldwide total electron content with high resolution and spatial-temporal smoothness. We fill in the missing values of the original Madrigal TEC maps via estimating the latent feature of each latitude and local time along the 2-D grid and give initial guess of the missing regions based on pre-computed spherical harmonics map. The resulting TEC map has high imputation accuracy and the ease of reproducing. All data are in HDF5 format and are easy to read using the h5py package in Python. The TEC map is grouped in folders based on years and each file contains a single-day data of 5-min cadence. Each individual TEC map is of size 181*361. and WARNING: 2023-12-01 the data file for 2019-Jan-03 has badly fitted values. Please avoid using it. All other days' files are ready to use.
- Keyword:
- Total Electron Content, Matrix Completion, VISTA, Spherical Harmonics, and Spatial-Temporal Smoothing
- Citation to related publication:
- Sun, H., Hua, Z., Ren, J., Zou, S., Sun, Y., & Chen, Y. (2022). Matrix completion methods for the total electron content video reconstruction. The Annals of Applied Statistics, 16(3), 1333-1358., Sun, H., Chen, Y., Zou, S., Ren, J., Chang, Y., Wang, Z., & Coster, A. (2023). Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA. Scientific Data, in press., and Zou, S., Ren, J., Wang, Z., Sun, H., & Chen, Y. (2021). Impact of storm-enhanced density (SED) on ion upflow fluxes during geomagnetic storm. Frontiers in Astronomy and Space Sciences, 8, 746429.
- Discipline:
- Science and Engineering
-
- Creator:
- Towne, Aaron S. and Brès, Guillaume
- Description:
- This dataset contains data from a large eddy simulation of a turbulent jet at Mach number 0.9. The dataset contains 10000 time-resolved snapshots of three-dimensional velocity, density, and pressure fields spanning 2000 acoustic time units and also includes pre-processed azimuthal Fourier modes for each snapshot and the mean flow. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘jet_README.pdf’ file for more information. We recommend using the ‘jet_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides two examples of how these data can be used for reduced-complexity modeling. Users of these data should cite the two papers listed below.
- Keyword:
- fluid mechanics, jets, and turbulence
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892. and Brès, G. A., Jordan, P., Jaunet, V., Le Rallic, M., Cavalieri, A. V. G., Towne, A., Lele, S. K., Colonius, T., Schmidt, O. T. (2018) Importance of the nozzle-exit boundary-layer state in subsonic turbulent jets. J. Fluid Mech., 851:83–124.
- Discipline:
- Engineering and Science
-
- Creator:
- Towne, Aaron, Saxton-Fox, Theresa, and Parthasarthy, Aadhy
- Description:
- This dataset contains experimental measurements of a zero-pressure-gradient flat-plate boundary layer at five different Reynolds numbers collected using particle image velocimetry. For each Reynolds number, the dataset contains approximately 6000 snapshots of planar velocity fields as well as raw particle image pairs. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘BLexp_README.pdf’ file for more information. We recommend using the ‘BLexp_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the papers listed below.
- Keyword:
- fluid mechanics
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892.
- Discipline:
- Engineering and Science
-
- Creator:
- Towne, Aaron S. and Lozano-Durán, Adrián
- Description:
- This dataset contains data from two direct numerical simulations of a turbulent zero-pressure-gradient flat-plate boundary layer spanning friction Reynolds numbers from 292 to 728 (BL1) and 488 to 1024 (BL2). The dataset contains time-resolved snapshots of the three-dimensional velocity field for both cases: roughly 10,000 snapshots spanning 20 eddy-turnover times for BL1 and 7,500 snapshots spanning 7 eddy-turnover times for BL2 . Also included for both cases are pre-processed correlations at several wall-normal distances, mean and root-mean-squared velocity and vorticity profiles, several boundary-layer metrics, and time-resolved velocity data in the streamwise-wall-normal plane. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘BLdns_README.pdf’ file for more information. We recommend using the ‘BLdns_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the paper listed below.
- Keyword:
- fluid mechanics, boundary layer, and turbulence
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892.
- Discipline:
- Engineering and Science
-
- Creator:
- Towne, Aaron, Yeh, Chi-An., Patel, Het, and Taira, Kunihiko
- Description:
- This dataset contains data from a three-dimensional large eddy simulation of Mach 0.3 flow over a NACA 0012 airfoil at Reynolds number 23,000, which features a transitional boundary layer, separation over a recirculation bubble, and a turbulent wake. The dataset contains 16,000 time-resolved snapshots of the mid-span and spanwise-averaged velocity fields. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘airfoilLES_README.pdf’ file for more information. We recommend using the ‘airfoilLES_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the papers listed below.
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892. and Yeh, C.-A. and Taira, K. (2019) Resolvent-analysis-based design of airfoil separation control. Journal of Fluid Mechanics, 867:572–610.
- Discipline:
- Science and Engineering
-
- Creator:
- Malik, Hafiz and Khan, Muhammad Khurran, King Saud University
- Description:
- Details of the microphone used for data collection, acoustic environment in which data was collected, and naming convention used are provided here. 1 - Microphones Used: The microphones used to collect this dataset belong to 7 different trademarks. Table (1) illustrates the number of used Mics of different trademarks and models. Table 1: Trademarks and models of Mics Mic Trademark Mic Model # of Mics Shure SM-58 3 Electro-Voice RE-20 2 Sennheiser MD-421 3 AKG C 451 2 AKG C 3000 B 2 Neumann KM184 2 Coles 4038 2 The t.bone MB88U 6 Total 22 2- Environment Description: A brief description of the 6 environments in which the dataset was collected is presented here: (i) Soundproof room: a small room (nearly 1.5m × 1.5m × 2m), which is closed and completely isolated. With an exception of a small window in the front side of the room which is made of glass, all the walls of the room are made of wood and covered by a layer of sponge from the inner side, and the floor is covered by carpet. (ii) Class room: standard class room (6m × 5m × 3m). (iii) Lab: small lab (4m × 4m × 3m). All the walls are made of glasses and the floor is covered by carpet. The lab contains 9 computers. (iv) Stairs: is in the second floor. The place of recording is 3m × 5m (v) Parking: is the college parking. (vi) Garden: is an open space outside the buildings. 3- Naming Convention: This set of rules were followed as a naming convention to give each file in the dataset a unique name: (i) The file name is 19 characters long, and consists of 5 sections separated by underscores. (ii) The first section is of 3 characters indicates the Microphone trademark. (iii) The second section of 4 characters indicates the microphone model as in table (2). (iv) The third section of 2 characters indicates a specific microphone within a set of microphones of the same trademark and model, since we have more than one microphone of the same trademark and model. (v) The fourth section of 2 characters indicates the environment, where Soundproof room --> 01 Class room --> 02 Lab --> 03 Stairs --> 04 Parking --> 05 Garden --> 06 (vi) The fifth section of 2 characters indicates the language, where Arabic --> 01 English --> 02 Chinese --> 03 Indonesian --> 04 (vii) The sixth section of 2 characters indicates the speaker. Table 2: Microphones Naming Criteria Original Mic Trademark and model --> Naming Convenient Shure SM-58 --> SHU_0058 Electro-Voice RE-20 --> ELE_0020 Sennheiser MD-421 --> SEN_0421 AKG C 451 --> AKG_0451 AKG C 3000 B --> AKG_3000 Neumann KM184 --> NEU_0184 Coles 4038 --> COL_4038 The t.bone MB88U --> TBO_0088 For example: SEN_0421_02_01_02_03 is an English file recorded by speaker number 3 in the soundproof room using microphone number 2 of Sennheiser MD-421
- Keyword:
- audio forensic, multimedia forensics, microphone identification, tamper detection, splicing detection, and codec identification
- Citation to related publication:
- Muhammad Khurram Khan, Mohammed Zakariah, Hafiz Malik & Kim-Kwang Raymond Choo (2018). A novel audio forensic data-set for digital multimedia forensics, Australian Journal of Forensic Sciences, 50:5, 525-542, http://dx.doi.org/10.1080/00450618.2017.1296186
- Discipline:
- Engineering, Government, Politics and Law, and Science
-
- Creator:
- Gliske, Stephen V and Stacey, William C
- Description:
- This data is part of a large program to translate detection and interpretation of HFOs into clinical use. A zip file is included which contains hfo detections, metadata, and Matlab scripts. The matlab scripts analyze this input data and produce figures as in the referenced paper (note: the blind source separation method is stochastic, and so the figures may not be exactly the same). A file "README.txt" provides more detail about each individual file within the zip file.
- Keyword:
- hfo, high frequency oscillation, ripple, fast ripple, blind source separation, non-negative matrix factorization, and temporal variability
- Discipline:
- Science, Engineering, and Health Sciences
-
- Creator:
- Gliske, Stephen V and Stacey, William C
- Description:
- This data repository includes the quantitative features of high frequency, intracranial EEG along with all necessary scripts to reproduce the figures of the accompanying manuscript.
- Keyword:
- high frequency oscillation, HFO, high frequency activity, and epilepsy
- Citation to related publication:
- (under review)
- Discipline:
- Science, Engineering, and Health Sciences
-
- Creator:
- Stoev, Stilian and Hu, Weifeng
- Description:
- Many data sets come as point patterns of the form (longitude, latitude, time, magnitude). The examples of data sets in this format includes tornado events, origins/destination of internet flows, earthquakes, terrorist attacks and etc. It is difficult to visualize the data with simple plotting. This research project studies and implements non-parametric kernel smoothing in Python as a way of visualizing the intensity of point patterns in space and time. A two-dimensional grid M with size mx, my is used to store the calculation result for the kernel smoothing of each grid points. The heat-map in Python then uses the grid to plot the resulting images on a map where the resolution is determined by mx and my. The resulting images also depend on a spatial and a temporal smoothing parameters, which control the resolution (smoothness) of the figure. The Python code is applied to visualize over 56,000 tornado landings in the continental U.S. from the period 1950 - 2014. The magnitudes of the tornado are based on Fujita scale.
- Discipline:
- Engineering and Science