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This Python script queries the USGS StreamStats Service API for a list of available basin characteristics, and the values for those characteristics, for each input site. The script takes as input a matrix of site identifiers and location coordinates and returns 1) a matrix of values for available basin characteristics obtained from StreamStats for each input location and 2) a matrix of basin characteristic variable names and definitions. To run this script exactly as written, create 3 columns of data in comma-separated format: 1) 'Site,' which are the study site identifiers, 2) 'lonSS,' the longitudinal coordinates, and 3) 'latSS,' the latitudinal coordinates (in decimal degrees). Name the input file 'ssLocs.csv' and store it in a subfolder named 'Data.' Otherwise, the pathnames for input and output files can be modified within the script. The output files 'ssDats.csv' and 'Descriptions.csv' will also be saved to the subfolder 'Data'. Multiple code runs may be necessary to obtain information for all sites; as long as the output file 'ssDats.csv' remains in the 'Data' folder, the script will only query for sites with missing information. If the program returns an error or is unable to obtain data for a site after several attempts, it may be that the input coordinates do not point to a cell defined as water in the StreamStats application. A solution is to check the coordinates manually in the StreamStats web application (http://streamstats.usgs.gov). This script was developed as part of the analysis described in: URycki DR, Good SP, Crump BC, Chadwick J and Jones GD (2020) River Microbiome Composition Reflects Macroscale Climatic and Geomorphic Differences in Headwater Streams. Front. Water 2:574728. doi: 10.3389/frwa.2020.574728
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Mode I Fracture in Triangular Lattice
This dataset contains code to compute properties of Mode I cracks running in steady state in a triangular lattice. This version of the code was used to prepare graphs for a submission in 2019 to Philosophical Transactions of the Royal Society A in honor of Leonid Slepyan. There are two executable files here. One is 'mode1', which finds locations of atoms in time. The second is 'kelvin_integrate' which finds the relation between loading and crack speed.
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- Award ID(s):
- 1810196
- PAR ID:
- 10454963
- Publisher / Repository:
- Texas Data Repository Dataverse
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Dataset accompanying code and paper: AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs We present AircraftVerse, a publicly available aerial vehicle design dataset. AircraftVerse contains 27,714 diverse battery powered aircraft designs that have been evaluated using state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. This repository contains: A zip file "AircraftVerse.zip", where each design_X contains: design_tree.json: The design tree describes the design topology, choice of propulsion and energy subsystems. The tree also contains continuous parameters such as wing span, wing chord and arm length.design_seq.json: A preorder traversal of the design tree and store this as design_seq.json.design_low_level.json: The most low level representation of the design. This low level representation includes significant repetition that is avoided in the tree representation through the use of symmetry.Geom.stp: CAD design for the Aircraft in composition STP format (ISO 10303 standard).cadfile.stl: CAD design for the Aircraft in stereolithographic STL file,output.json: Summary containing the UAV's performance metrics such as maximum flight distance, maximum hover time, fight distance at maximum speed, maximum current draw, and mass.trims.npy: Contains the [Distance, Flight Time, Pitch, Control Input, Thrust, Lift, Drag, Current, Power] at each evaluated trim state (velocity).pointCloud.npy: Numpy array containing the corresponding point clouds for each design. corpus_dic: The corpus of components (e.g. batteries, propellers) that make up all aircraft designs. It is structured as a dictionary of dictionaries, with the high level components: ['Servo', 'GPS', 'ESC', 'Wing', 'Sensor', 'Propeller', 'Receiver', 'Motor', 'Battery', 'Autopilot'], containing a list of dictionaries corresponding to the component type. E.g. corpus_dic['Battery']['TurnigyGraphene2200mAh3S75C'] contains the detail of this particular battery. Corresponding code for this work is included at https://github.com/SRI-CSL/AircraftVerse. Acknowledgements: This material is based upon work supported by the United States Air Force and DARPA under Contract No. FA8750-20-C-0002. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force and DARPA.more » « less
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This dataset consists of weekly trajectory information of Gulf Stream Cold Eddies (CE) that existed between 2017 and 2023. The format of this Cold Eddy dataset is similar to the Warm Core Ring (WCR) Trajectory data from Porter et al. (2022, 2024) and Silver et al. (2022), and the following description is adapted from those datasets. This dataset is comprised of individual files containing each eddy’s weekly center location and its surface area for 181 CEs that existed and were tracked between January 1, 2017 and December 31, 2023 (28 CEs formed in 2017; 24 formed in 2018; 25 formed in 2019; 26 formed in 2020; 35 formed in 2021; 23 formed in 2022; and 20 formed in 2023). Each Cold Eddy is identified by a unique alphanumeric code 'CEyyyymmddX', where 'CE' represents a Cold Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'X' represents the sequential sighting (formation) of the eddy in that particular year. Continuity of an eddy which passes from one year to the next is maintained by the same character in the previous year and absorbed by the initial alphabets for the next year. For example, the first eddy formed in 2021 has a trailing alphabet of 'J', which signifies that a total of nine eddies were carried over from 2020 which were still present on January 1, 2021 and were assigned the initial nine alphabets (A, B, C, D, E, F, G, H, and I). Each eddy trajectory has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables every week, “Lon”- the eddy center’s longitude, “Lat”- the eddy center’s latitude, “Area” - the eddies size in km^2, and “Date” in days – which is the number of days since Jan 01, 0000. Note that in this dataset, which ended tracking all eddies up to 2023, there were six eddies that formed in 2023, and were carried over into 2024 were included with their full trajectories going into the year 2024. These eddies are: ‘CE20230515P’, ‘CE20230818U’, 'CE20230925V', 'CE20231030Y', 'CE20231103Z', and 'CE20231106a'. Findings from Jensen et al. (2024) suggest three different cyclonic eddy formation types: pinch-off cyclonic rings, hook-type cyclonic eddies, and Sargasso Sea cyclonic eddies. Pinch-off cyclonic rings form from a Gulf Stream meander trough amplifying, then encircling Slope Sea water and eventually detaching from the Gulf Stream as a cyclonic cold-core ring in the Sargasso Sea. Hook-type eddies form from a southward extending filament of the southern flank of the Gulf Stream establishing as a hook-like entity cyclonically encircling a body of Sargasso Sea water at its core. Sargasso Sea cyclonic eddies are isolated from the Gulf Stream and occur in the Sargasso Sea. A separate file is also created to help identify the cold eddy's formation type. Two files are provided here. These are: (1) The trajectories of all Gulf Stream Cold Eddies formed from 2017 to 2023. Filename – CE_2017_2023_ncfiles.zip (2) Information on the formation type of each Cold Eddy. Filename – CE_FormationTypes_2017to2023.doc The process of creating the CE weekly tracking dataset follows the same GIS-based methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts described in Gangopadhyay et al. (2019), and continued through 2023 were used to create this dataset and were available 2-3 times a week from 2017-2023. Thus, we used approximately 840+ Charts for the 7 years of analysis. All of these charts were reanalyzed between 75°W and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986). A single eddy trajectory is then obtained following an eddy through all of the available charts during the eddy's lifespan on a weekly basis. This process is repeated for every individual eddy.more » « less
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We are pleased to announce the release of ASPECT 2.5.0. ASPECT is the Advanced Solver for Problems in Earth's ConvecTion. It uses modern numerical methods such as adaptive mesh refinement, multigrid solvers, and a modular software design to provide a fast, flexible, and extensible mantle convection solver. ASPECT is available frommore » « less
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