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- Creator:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- Reconstructed CT slices for navicular of Cantius trigonodus (University of Michigan Museum of Paleontology catalog number UMMP 87973) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
- Keyword:
- Paleontology, Fossil, CT, Notharctidae, UMMP, University of Michigan Museum of Paleontology, Eocene, and a537f0d8-6185-9562-9b9a-a233468bf8e1
- Discipline:
- Science
-
- Creator:
- Umaña, Maria, Swenson, Nathan G, and Arellano, Gabriel
- Description:
- Identifying the functional traits that enable recovery after extreme events is necessary for assessing forest persistence and functioning, yet this is a difficult task because the traits mediating the responses to disturbance may vary depending on the disturbance type and over time. This study investigates the effects of traits on tree growth –for short and longer terms– in response to two vastly different extreme climatic events, droughts, and hurricanes, in a Puerto Rican forest.
- Keyword:
- Tropical tree, relative growth rates , and Puerto Rico
- Citation to related publication:
- Umaña, M. N. In review. The interplay of drought and hurricanes on tree recovery: insights from dynamic and weak functional responses. Forthcoming and Umana, M. (2023). Functional trait data across an elevational gradient of six tree species in El Yunque National Park, Puerto Rico in 2015 [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/7962-4t98
- Discipline:
- Science
-
- Creator:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- Reconstructed CT slices for navicular of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP 86543) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
- Keyword:
- Paleontology, Fossil, CT, Notharctidae, UMMP, University of Michigan Museum of Paleontology, Eocene, and 00827513-d7c4-2cf2-9bc7-ad510d0e4886
- Discipline:
- Science
-
- Creator:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- Reconstructed CT slices for L cuboid of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP 81824) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
- Keyword:
- Paleontology, Fossil, CT, Notharctidae, UMMP, University of Michigan Museum of Paleontology, Eocene, and e763ae30-4a86-9d02-0b8a-9297ff48cf58
- Discipline:
- Science
-
- Creator:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- Reconstructed CT slices for navicular of Cantius trigonodus (University of Michigan Museum of Paleontology catalog number UMMP 73318) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
- Keyword:
- Paleontology, Fossil, CT, Notharctidae, Eocene, and 0d607d85-8d27-6be2-dbc5-9cb73f1324ae
- Discipline:
- Science
-
- Creator:
- Penner-Hahn, James, Sension, Roseanne, McClain, Taylor, Lamb, Ryan, Alonso-Mori, Roberto, Lima, Frederico, Ardana-Lamas, Fernando, Biednov, Mykola, Chollet, Matthieu, Chung, Taewon, Deb, Aniruddha, Dewan, Paul, Gee, Leland, Huang, Joel, Yifeng, Khakhulin, Dmitry, Li, Jianhao, Michocki, Lindsay, Miller, Nicholas, Otte, Florian, Uemura, Yohei, and van Driel, Tim
- Description:
- UV-visible, X-ray absorption, and X-ray emission data used to characterize the dynamics of aquo and hydroxo cobalamin. 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, XES, and XANES
- Citation to related publication:
- Sension, R.J., et al. (2023). Watching Excited State Dynamics with Optical and X-ray Probes: The Excited State Dynamics of Aquocobalamin and Hydroxocobalamin. J. Am. Chem. Soc. in press. and https://doi.org/10.1021/jacs.3c04099
- 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 released in support of an IROS 2023 workshop publication, with a supporting website ( https://sites.google.com/umich.edu/novelsensors2023). To enable new research in the area of novel sensors for autonomous vehicles, these datasets are designed for the task of place recognition with novel sensors. To our knowledge, this new dataset is the first to include stereo thermal cameras together with stereo event cameras and stereo monochrome cameras, which perform better in low-light than RGB cameras., The dataset collection platform is a Ford Fusion vehicle with roof-mounted sensing suite, which consists of forward-facing stereo uncooled thermal cameras (FLIR Boson 640+ ADK), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) aligned with ground truth position from a high precision navigation system. Sequences include ~10 km routes, which may be driven repeatedly under varying lighting conditions and feature instances of direct sunlight and low-light that challenge conventional cameras., and A software toolkit to facilitate efficient use of the dataset including dataset download, application of calibration parameters, and evaluation of place recognition results based on standard metrics (e.g., maximum recall at 100% precision). These software tools for converting, managing, and viewing datafiles can be found at the associated GitHub repository ( https://github.com/umautobots/nsavp_tools).
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://sites.google.com/umich.edu/novelsensors2023 and https://github.com/umautobots/nsavp_tools
- Discipline:
- Engineering
-
- Creator:
- Wu, Chen/ University of Michigan, Ridley, Aaron/ University of Michigan, and DeJong, Anna/ Howard Community College
- Description:
- 1.5 years of Polar UVI data was used to construct the Feature Tracking empirical model of Auroral Precipitation (FTA). A cumulative energy grid was tracked with the energy flux and the latitude position in each MLT bin for individual images. The auroral characteristics show linear relationships with the AE index depending on the MLT region. Thus, the FTA model was constructed to describe the global energy flux and the averaged energy as a function of the AE index based on the LBHl and LBHs emissions. Compared with two other empirical models, FTA predicted more consistent aurora with the observations on 17 March 2013 at higher activity levels.
- Keyword:
- Aurora, Polar UVI, precipitation model
- Citation to related publication:
- Wu, C., Ridley, A. J., DeJong, A. D., & Paxton, L. J. (2021). FTA: A Feature Tracking Empirical Model Of Auroral Precipitation. Space Weather, 19, e2020SW002629. https://doi.org/10.1029/2020SW002629
- 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 released in support of an IROS 2023 workshop publication, with a supporting website ( https://sites.google.com/umich.edu/novelsensors2023). To enable new research in the area of novel sensors for autonomous vehicles, these datasets are designed for the task of place recognition with novel sensors. To our knowledge, this new dataset is the first to include stereo thermal cameras together with stereo event cameras and stereo monochrome cameras, which perform better in low-light than RGB cameras., The dataset collection platform is a Ford Fusion vehicle with roof-mounted sensing suite, which consists of forward-facing stereo uncooled thermal cameras (FLIR Boson 640+ ADK), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) aligned with ground truth position from a high precision navigation system. Sequences include ~10 km routes, which may be driven repeatedly under varying lighting conditions and feature instances of direct sunlight and low-light that challenge conventional cameras., and A software toolkit to facilitate efficient use of the dataset including dataset download, application of calibration parameters, and evaluation of place recognition results based on standard metrics (e.g., maximum recall at 100% precision). These software tools for converting, managing, and viewing datafiles can be found at the associated GitHub repository ( https://github.com/umautobots/nsavp_tools).
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://sites.google.com/umich.edu/novelsensors2023 and https://github.com/umautobots/nsavp_tools
- Discipline:
- Engineering
-
- Creator:
- Wu, Ziyou and Revzen, Shai
- Description:
- The data in this repository is a nearly unique dataset at the time of its making -- precise measurements of all contact forces of a 6-legged robot during multi-legged slipping motions and regular walking. These data were collected to establish the validity of the observation presented in this article: Zhao et al. Walking is like slithering: A unifying, data-driven view of locomotion. (2022) PNAS 119(37): e113222119. DOI: https://doi.org/10.1073/pnas.2113222119
- Keyword:
- robot, locomotion, and multilegged
- Citation to related publication:
- Science Robotics paper being submitted
- Discipline:
- Engineering