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- Creator:
- University of Michigan Museum of Zoology
- Description:
- Scan of specimen ummz:mammals:111984 (Handleyomys ALFAROI ALFAROI) - WholeBody. Raw Dataset includes 1601 TIF images (each 815 x 1310 x 1 voxel at 0.0421497172804555 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction. and Scan of specimen ummz:mammals:111984 (Handleyomys ALFAROI ALFAROI) - WholeBody. Reconstructed Dataset includes 2000 TIF images (each 815 x 1310 x 1 voxel at 0.042150 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction.
- Keyword:
- Animalia, Chordata, Mammalia, Rodentia, Cricetidae, Handleyomys ALFAROI ALFAROI, 1987286466, computed tomography, X-ray, and 3D
- Citation to related publication:
- For more information on the original UMMZ specimen, see: https://www.gbif.org/occurrence/1987286466
- Discipline:
- Science
-
- Creator:
- Lee, Shih Kuang, Tsai, Sun Ting, and Glotzer, Sharon C.
- Description:
- The trajectory data and codes were generated for our work "Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation" (amidst peer review process). The data sets contain trajectory data in GSD file format for 7 test systems, including cubic structures, two-dimensional and three-dimensional patchy particle shape systems, hexagonal bipyramids with two aspect ratios, and truncated shapes with two degrees of truncation. Besides, the corresponding Python code and Jupyter notebook used to perform data augmentation, MLP classifier training, and MLP classifier testing are included.
- Keyword:
- Machine Learning, Colloids Self-Assembly, Crystallization, and Order Parameter
- Citation to related publication:
- https://doi.org/10.48550/arXiv.2312.11822
- Discipline:
- Other, Science, and Engineering
-
- Creator:
- Stockbridge, Randy B. and Christian B. Macdonald
- Description:
- This data set includes text files (.csv files) for the bioinformatic annotation of SMR genes found in a dataset of phylogenetically diverse bacterial genomes. Bioinformatic analysis includes genome mining to identify SMR genes, prediction of the functional transporter subtype, and prediction of the direction of insertion in the bacterial membrane. Research overview: This bioinformatic dataset was prepared for a review on the structures, functions, and occurrence of Small Multidrug Resistance (SMR) Transporters. This dataset includes bioinformatic annotation of SMR genes identified in bacterial genomes from the Joint Genome Institute’s curated set of ~1000 Genomic Encyclopedia of Bacteria and Archaea (GEBA) genomes. The file GEBA_SMR_annotation.csv provides NCBI identification information (genome, species and chromosome information, locus tag, translation) and bioinformatic predictions of the SMR subtype and membrane insertion direction for each gene identified in the GEBA genome set. The file GEBA_SMR_species_table.csv has a separate entry for species in the GEBA genome set, along with the bioinformatic prediction of SMR subtype and membrane insertion direction for each SMR gene identified in the genome of that species. Dataset was generated by Christian B. Macdonald and Randy B. Stockbridge (Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI, 48019) Generation of this dataset was supported by National Institutes of Health grants R35-GM128768 to Randy B. Stockbridge. Use and access: This dataset is provided as a .csv file (comma separated values) and can be read using any text editor or spreadsheet software such as Microsoft Excel.
- Citation to related publication:
- Burata OE, Yeh TJ, Macdonald CB, Stockbridge RB. (2022). Still rocking in the structural era: a molecular overview of the Small Multidrug Resistance transporters. Journal of Biological Chemistry. In press.
- Discipline:
- Science
-
- Creator:
- Lin, Brian T. W.
- Description:
- This footage is an output of a USDOT-funded project titled "Development of Machine-Learning Models for Autonomous Vehicle Decisions on Weaving Sections of Freeway Ramps." It showcases an automated weaving maneuver within an augmented reality environment. During the demonstration, Mcity's automated vehicle navigates through a highway weaving section, making a lane change while interacting with a virtual vehicle. In this instance, Mcity's vehicle was operated by automated driving systems, which executed the lane change based on the detection for external environmental factors and parameter inputs received from the virtual vehicle.
- Discipline:
- Engineering
-
- Creator:
- Chung, Taewon , McClain, Taylor P. , Alonso-Mori, Roberto , Chollet, Matthieu , Deb, Aniruddha , Garcia-Esparza, Angel T. , Huang, Joel Ze En , Lamb, Ryan M. , Michocki, Lindsay B. , Reinhard, Marco , van Driel, Tim B. , Penner-Hahn, James E. , and Sension, Roseanne J.
- Description:
- UV-visible, X-ray absorption, and X-ray emission data used to characterize the dynamics of methyl cobalamin at low pH, so called "base off" configuration. Details of data collection and reduction are provided in the associated manuscript. Data files are all text files which contain tab-delimited columns of data corresponding to each figure in the manuscript
- Keyword:
- Ultrafast, X-ray, Transient absorption, cobalamin, vitamin B12, XAS, and XANES
- Citation to related publication:
- Chung, T., et al. (2024). "Ultrafast X-ray Absorption Spectroscopy Reveals Excited State Dynamics of B12 Coenzymes Controlled by the Axial Base". J. Phys. Chem. B. 2024, in press https://pubs.acs.org/doi/10.1021/acs.jpcb.3c07779
- Discipline:
- Science
-
- Creator:
- Xiantong Wang
- Description:
- Bursty bulk flows (BBFs) are identified as the fast earthward-propagating flows from magnetic reconnection in Earth's magnetotail. BBFs are related to particle energization process reported by satellite observations. For the first time, we use a novel numerical model that simulates kinetic physics directly in a global model. The energization of the electrons associated with BBF is demonstrated by the model. The electron velocity distribution functions (VDFs) extracted from multiple locations associated with BBF demonstrate good agreements with the observations. The energy-dependent electron pitch angle distribution at the leading part of the BBF can be explained by the enhancement of the local magnetic field.
- Discipline:
- Science
-
- Creator:
- Skinner, Katherine A., Vasudevan, Ram, Ramanagopal, Manikandasriram S., Ravi, Radhika, Carmichael, Spencer, and Buchan, Austin D.
- Description:
- This dataset is part of a collection created to facilitate research in the use of novel sensors for autonomous vehicle perception. , The dataset collection platform is a Ford Fusion vehicle with a roof-mounted novel sensing suite, which specifically consists of forward-facing stereo uncooled thermal cameras (FLIR 40640U050-6PAAX), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) time synchronized with ground truth poses from a high precision navigation system. , Further information and resources (such as software tools for converting, managing, and viewing data files) are available on the project website: https://umautobots.github.io/nsavp , and CHANGE NOTICE (January 2024): We identified an error in our timestamp post-processing procedure that caused all camera timestamps to be offset by the exposure time of one of the cameras. We corrected the error, applied the corrected post-processing, and reuploaded the corrected files. The change impacts all camera data files. Prior to the change, the timestamps between the cameras were synchronized with submillisecond accuracy, but the camera and ground truth pose timestamps were offset by up to 0.4 ms, 3 ms, and 15 ms in the afternoon, sunset, and night sequences, respectively. This amounted in up to ~0.25 meters of position error in the night sequences. For consistency, camera calibration was rerun with the corrected calibration sequence files. The camera calibration results have therefore been updated as well, although they have not changed significantly. Finally, we previously downsampled the frame data in the uploaded calibration seqeuence, but we decided to provide the full frame data in the reupload.
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://sites.google.com/umich.edu/novelsensors2023, https://github.com/umautobots/nsavp_tools, and https://umautobots.github.io/nsavp
- Discipline:
- Engineering
-
- Creator:
- Skinner, Katherine A., Vasudevan, Ram, Ramanagopal, Manikandasriram S., Ravi, Radhika, Carmichael, Spencer, and Buchan, Austin D.
- Description:
- This dataset is part of a collection created to facilitate research in the use of novel sensors for autonomous vehicle perception. , The dataset collection platform is a Ford Fusion vehicle with a roof-mounted novel sensing suite, which specifically consists of forward-facing stereo uncooled thermal cameras (FLIR 40640U050-6PAAX), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) time synchronized with ground truth poses from a high precision navigation system. , Further information and resources (such as software tools for converting, managing, and viewing data files) are available on the project website: https://umautobots.github.io/nsavp , and CHANGE NOTICE (January 2024): We identified an error in our timestamp post-processing procedure that caused all camera timestamps to be offset by the exposure time of one of the cameras. We corrected the error, applied the corrected post-processing, and reuploaded the corrected files. The change impacts all camera data files. Prior to the change, the timestamps between the cameras were synchronized with submillisecond accuracy, but the camera and ground truth pose timestamps were offset by up to 0.4 ms, 3 ms, and 15 ms in the afternoon, sunset, and night sequences, respectively. This amounted in up to ~0.25 meters of position error in the night sequences. For consistency, camera calibration was rerun with the corrected calibration sequence files. The camera calibration results have therefore been updated as well, although they have not changed significantly. Finally, we previously downsampled the frame data in the uploaded calibration seqeuence, but we decided to provide the full frame data in the reupload.
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://sites.google.com/umich.edu/novelsensors2023, https://github.com/umautobots/nsavp_tools, and https://umautobots.github.io/nsavp
- Discipline:
- Engineering
-
- Creator:
- Skinner, Katherine A., Vasudevan, Ram, Ramanagopal, Manikandasriram S., Ravi, Radhika, Carmichael, Spencer, and Buchan, Austin D.
- Description:
- This dataset is part of a collection created to facilitate research in the use of novel sensors for autonomous vehicle perception. , The dataset collection platform is a Ford Fusion vehicle with a roof-mounted novel sensing suite, which specifically consists of forward-facing stereo uncooled thermal cameras (FLIR 40640U050-6PAAX), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) time synchronized with ground truth poses from a high precision navigation system. , Further information and resources (such as software tools for converting, managing, and viewing data files) are available on the project website: https://umautobots.github.io/nsavp , and CHANGE NOTICE (January 2024): We identified an error in our timestamp post-processing procedure that caused all camera timestamps to be offset by the exposure time of one of the cameras. We corrected the error, applied the corrected post-processing, and reuploaded the corrected files. The change impacts all camera data files. Prior to the change, the timestamps between the cameras were synchronized with submillisecond accuracy, but the camera and ground truth pose timestamps were offset by up to 0.4 ms, 3 ms, and 15 ms in the afternoon, sunset, and night sequences, respectively. This amounted in up to ~0.25 meters of position error in the night sequences. For consistency, camera calibration was rerun with the corrected calibration sequence files. The camera calibration results have therefore been updated as well, although they have not changed significantly. Finally, we previously downsampled the frame data in the uploaded calibration seqeuence, but we decided to provide the full frame data in the reupload.
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://sites.google.com/umich.edu/novelsensors2023, https://github.com/umautobots/nsavp_tools, and https://umautobots.github.io/nsavp
- Discipline:
- Engineering
-
- Creator:
- Skinner, Katherine A., Vasudevan, Ram, Ramanagopal, Manikandasriram S., Ravi, Radhika, Carmichael, Spencer, and Buchan, Austin D.
- Description:
- This dataset is part of a collection created to facilitate research in the use of novel sensors for autonomous vehicle perception. , The dataset collection platform is a Ford Fusion vehicle with a roof-mounted novel sensing suite, which specifically consists of forward-facing stereo uncooled thermal cameras (FLIR 40640U050-6PAAX), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) time synchronized with ground truth poses from a high precision navigation system. , Further information and resources (such as software tools for converting, managing, and viewing data files) are available on the project website: https://umautobots.github.io/nsavp , and CHANGE NOTICE (January 2024): We identified an error in our timestamp post-processing procedure that caused all camera timestamps to be offset by the exposure time of one of the cameras. We corrected the error, applied the corrected post-processing, and reuploaded the corrected files. The change impacts all camera data files. Prior to the change, the timestamps between the cameras were synchronized with submillisecond accuracy, but the camera and ground truth pose timestamps were offset by up to 0.4 ms, 3 ms, and 15 ms in the afternoon, sunset, and night sequences, respectively. This amounted in up to ~0.25 meters of position error in the night sequences. For consistency, camera calibration was rerun with the corrected calibration sequence files. The camera calibration results have therefore been updated as well, although they have not changed significantly. Finally, we previously downsampled the frame data in the uploaded calibration seqeuence, but we decided to provide the full frame data in the reupload.
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://sites.google.com/umich.edu/novelsensors2023, https://github.com/umautobots/nsavp_tools, and https://umautobots.github.io/nsavp
- Discipline:
- Engineering