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This dataset contains the PartMC-MOSAIC simulations used in the article “Plume-exit modeling to determine cloud condensation nuclei activity of aerosols from residential biofuel combustion”. The data is organized as a set of folders, each folder representing a different scenario modeled. Each folder contains a series of NetCDF files, which are the output of the PartMC-MOSAIC simulation. They contain information on particle and gas properties, both of the biofuel burning plume and background. Input files for PartMC-MOSAIC are also included. This dataset was used during the open review process at Atmospheric Chemistry and Physics (ACP) and supports both the discussion paper and final article.

This dataset contains ground motion data for Newmark Structural Engineering Laboratory (NSEL) Report Series 048, "Modification of ground motions for use in Central North America: Southern Illinois surface ground motions for structural analysis". The data are 20 individual ground motion time history records developed at each of the 10 sites (for a total of 200 ground motions). These accompanying ground motions are developed following the detailed procedure presented in Kozak et al. [2017].

This page provides the data for SuperFine, DACTAL, and BeeTLe publications. - Swenson, M. Shel, et al. "SuperFine: fast and accurate supertree estimation." Systematic biology 61.2 (2012): 214. - Nguyen, Nam, Siavash Mirarab, and Tandy Warnow. "MRL and SuperFine+ MRL: new supertree methods." Algorithms for Molecular Biology 7 (2012): 1-13. - Neves, Diogo Telmo, et al. "Parallelizing superfine." Proceedings of the 27th Annual ACM Symposium on Applied Computing. 2012. - Nelesen, Serita, et al. "DACTAL: divide-and-conquer trees (almost) without alignments." Bioinformatics 28.12 (2012): i274-i282. - Liu, Kevin, and Tandy Warnow. "Treelength optimization for phylogeny estimation." PLoS One 7.3 (2012): e33104.

If you use this dataset, please cite the IJRR data paper (bibtex is below). We present a dataset collected from a canoe along the Sangamon River in Illinois. The canoe was equipped with a stereo camera, an IMU, and a GPS device, which provide visual data suitable for stereo or monocular applications, inertial measurements, and position data for ground truth. We recorded a canoe trip up and down the river for 44 minutes covering 2.7 km round trip. The dataset adds to those previously recorded in unstructured environments and is unique in that it is recorded on a river, which provides its own set of challenges and constraints that are described in this paper. The data is divided into subsets, which can be downloaded individually. Video previews are available on Youtube: https://www.youtube.com/channel/UCOU9e7xxqmL_s4QX6jsGZSw The information below can also be found in the README files provided in the 527 dataset and each of its subsets. The purpose of this document is to assist researchers in using this dataset. Images ====== Raw --- The raw images are stored in the cam0 and cam1 directories in bmp format. They are bayered images that need to be debayered and undistorted before they are used. The camera parameters for these images can be found in camchain-imucam.yaml. Note that the camera intrinsics describe a 1600x1200 resolution image, so the focal length and center pixel coordinates must be scaled by 0.5 before they are used. The distortion coefficients remain the same even for the scaled images. The camera to imu tranformation matrix is also in this file. cam0/ refers to the left camera, and cam1/ refers to the right camera. Rectified --------- Stereo rectified, undistorted, row-aligned, debayered images are stored in the rectified/ directory in the same way as the raw images except that they are in png format. The params.yaml file contains the projection and rotation matrices necessary to use these images. The resolution of these parameters do not need to be scaled as is necessary for the raw images. params.yml ---------- The stereo rectification parameters. R0,R1,P0,P1, and Q correspond to the outputs of the OpenCV stereoRectify function except that 1s and 2s are replaced by 0s and 1s, respectively. R0: The rectifying rotation matrix of the left camera. R1: The rectifying rotation matrix of the right camera. P0: The projection matrix of the left camera. P1: The projection matrix of the right camera. Q: Disparity to depth mapping matrix T_cam_imu: Transformation matrix for a point in the IMU frame to the left camera frame. camchain-imucam.yaml -------------------- The camera intrinsic and extrinsic parameters and the camera to IMU transformation usable with the raw images. T_cam_imu: Transformation matrix for a point in the IMU frame to the camera frame. distortion_coeffs: lens distortion coefficients using the radial tangential model. intrinsics: focal length x, focal length y, principal point x, principal point y resolution: resolution of calibration. Scale the intrinsics for use with the raw 800x600 images. The distortion coefficients do not change when the image is scaled. T_cn_cnm1: Transformation matrix from the right camera to the left camera. Sensors ------- Here, each message in name.csv is described ###rawimus### time # GPS time in seconds message name # rawimus acceleration_z # m/s^2 IMU uses right-forward-up coordinates -acceleration_y # m/s^2 acceleration_x # m/s^2 angular_rate_z # rad/s IMU uses right-forward-up coordinates -angular_rate_y # rad/s angular_rate_x # rad/s ###IMG### time # GPS time in seconds message name # IMG left image filename right image filename ###inspvas### time # GPS time in seconds message name # inspvas latitude longitude altitude # ellipsoidal height WGS84 in meters north velocity # m/s east velocity # m/s up velocity # m/s roll # right hand rotation about y axis in degrees pitch # right hand rotation about x axis in degrees azimuth # left hand rotation about z axis in degrees clockwise from north ###inscovs### time # GPS time in seconds message name # inscovs position covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz m^2 attitude covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz deg^2 velocity covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz (m/s)^2 ###bestutm### time # GPS time in seconds message name # bestutm utm zone # numerical zone utm character # alphabetical zone northing # m easting # m height # m above mean sea level Camera logs ----------- The files name.cam0 and name.cam1 are text files that correspond to cameras 0 and 1, respectively. The columns are defined by: unused: The first column is all 1s and can be ignored. software frame number: This number increments at the end of every iteration of the software loop. camera frame number: This number is generated by the camera and increments each time the shutter is triggered. The software and camera frame numbers do not have to start at the same value, but if the difference between the initial and final values is not the same, it suggests that frames may have been dropped. camera timestamp: This is the cameras internal timestamp of the frame capture in units of 100 milliseconds. PC timestamp: This is the PC time of arrival of the image. name.kml -------- The kml file is a mapping file that can be read by software such as Google Earth. It contains the recorded GPS trajectory. name.unicsv ----------- This is a csv file of the GPS trajectory in UTM coordinates that can be read by gpsbabel, software for manipulating GPS paths. @article{doi:10.1177/0278364917751842, author = {Martin Miller and Soon-Jo Chung and Seth Hutchinson}, title ={The Visual–Inertial Canoe Dataset}, journal = {The International Journal of Robotics Research}, volume = {37}, number = {1}, pages = {13-20}, year = {2018}, doi = {10.1177/0278364917751842}, URL = {https://doi.org/10.1177/0278364917751842}, eprint = {https://doi.org/10.1177/0278364917751842} }

Raw MD simulation trajectory, input and configuration files, SEM current data, and experimental raw data accompanying the publication, "Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore". README.md contains a description of all associated files.

This dataset contains collected and aggregated network information from NCSA’s Blue Waters system, which is comprised of 27,648 nodes connected via Cray Gemini* 3D torus (dimension 24x24x24) interconnect, from Jan/01/2017 to May/31/2017. Network performance counters for links are exposed via Cray's gpcdr (<a href="https://github.com/ovis-hpc/ovis/wiki/gpcdr-kernel-module">https://github.com/ovis-hpc/ovis/wiki/gpcdr-kernel-module</a>) kernel module. Lightweight Distributed Metric Service ([LDMS](<a href="https://github.com/ovis-hpc/ovis">https://github.com/ovis-hpc/ovis</a>)) is used to sampled the performance counters at 60 second intervals. Please read "README.md" file. <b>Acknowledgement:</b> This dataset is collected as a part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.

#Overview These are reference packages for the TIPP3 software for abundance profiling and/or species detection from metagenomic reads (e.g., Illumina, PacBio, Nanopore, etc.). Different refpkg versions are listed. TIPP3 software: https://github.com/c5shen/TIPP3 #Changelog V1.2 (`tipp3-refpkg-1-2.zip`) >>Fixed old typos in the file mapping text. >>Added new files `taxonomy/species_to_marker.tsv` for new function `run_tipp3.py detection [...parameters]`. Please use the latest release of the TIPP3 software for this new function. V1 (`tipp3-refpkg.zip`) >>Initial release of the TIPP3 reference package. #Usage 1. unzip the file to a local directory (will get a folder named "tipp3-refpkg"). 2. use with TIPP3 software: `run_tipp3.py -r [path/to/tipp3-refpkg] [other parameters]`

This dataset contains hourly traffic estimates (speeds) for individual links of the New York City road network for the years 2010-2013, estimated from New York City Taxis.