8 datasets found
  1. H

    CES 2022, Team Module of University of California, Riverside (UCR)

    • dataverse.harvard.edu
    Updated Jul 31, 2024
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    Dan Biggers (2024). CES 2022, Team Module of University of California, Riverside (UCR) [Dataset]. http://doi.org/10.7910/DVN/LPFYET
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 31, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Dan Biggers
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    California, Riverside
    Description

    This dataverse contains the data and supporting documents for the CES 2022 Team Module of the University of California, Riverside. This project was supported by the National Science Foundation, Grant Number SES-2148907.

  2. H

    CES 2020, Team Module of University of California, Riverside (UCR)

    • dataverse.harvard.edu
    • search.dataone.org
    Updated Apr 25, 2022
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    Dan Biggers (2022). CES 2020, Team Module of University of California, Riverside (UCR) [Dataset]. http://doi.org/10.7910/DVN/SUTCWJ
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 25, 2022
    Dataset provided by
    Harvard Dataverse
    Authors
    Dan Biggers
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Riverside
    Description

    This dataverse contains the data and supporting documents for the CES 2020 Team Module of UC Riverside. This project was supported by the National Science Foundation, Grant Number SES-1948863.

  3. H

    CCES 2018, Team Module of UMBC / UCR

    • dataverse.harvard.edu
    Updated Jan 28, 2022
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    Tyson King-Meadows (2022). CCES 2018, Team Module of UMBC / UCR [Dataset]. http://doi.org/10.7910/DVN/E5LVIS
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 28, 2022
    Dataset provided by
    Harvard Dataverse
    Authors
    Tyson King-Meadows
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This dataverse contains the data and supporting documents for the CCES 2018 University of Maryland Baltimore County/UC Riverside team module. This project was supported by the National Science Foundation, Grant Number SES-1756447.

  4. d

    CCES 2016, Team Module of University of California, Riverside (UCR)

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 22, 2023
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    Biggers, Daniel; Merolla, Jenn (2023). CCES 2016, Team Module of University of California, Riverside (UCR) [Dataset]. http://doi.org/10.7910/DVN/IQVAHY
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Biggers, Daniel; Merolla, Jenn
    Description

    This dataverse contains the data and supporting documents for the CCES 2016 University of California, Riverside. This project was supported by the National Science Foundation, Grant Number SES-1559125.

  5. H

    Replication Data for: "Competitive dynamics between criminals and law...

    • dataverse.harvard.edu
    • search.dataone.org
    Updated Sep 15, 2015
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    Soumya Banerjee; Pascal Van Hentenryck; Manuel Cebrian (2015). Replication Data for: "Competitive dynamics between criminals and law enforcement explains the super-linear scaling of crime in cities" [Dataset]. http://doi.org/10.7910/DVN/ELSYXO
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 15, 2015
    Dataset provided by
    Harvard Dataverse
    Authors
    Soumya Banerjee; Pascal Van Hentenryck; Manuel Cebrian
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This submission has replication data for "Competitive dynamics between criminals and law enforcement explains the super-linear scaling of crime in cities". Has data from the UCI machine learning repository Communities and Crime Data Set - http://archive.ics.uci.edu/ml/datasets/Communities+and+Crime 1) filename: communities.data (actual data) 2) filename: communities.names (metadata) and the FBI uniform crime reports - 3) https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2011/crime-in-the-u.s.-2011/tables/table-79-1/view 4) https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2011/crime-in-the-u.s.-2011/tables/table-9/view

  6. H

    Replication Data for Aggarwal et al. 2022

    • dataverse.harvard.edu
    Updated Dec 10, 2022
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    Madison Coots (2022). Replication Data for Aggarwal et al. 2022 [Dataset]. http://doi.org/10.7910/DVN/UCRUNU
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 10, 2022
    Dataset provided by
    Harvard Dataverse
    Authors
    Madison Coots
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This repo contains the NHANES necessary to replicate Aggarwal et al. 2022, as well as some additional data files.

  7. H

    Data from: Core Transport Modeling and Characterization for Compact Tokamak...

    • dataverse.harvard.edu
    • search.dataone.org
    Updated Oct 20, 2024
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    C. Holland, E.M. Bass, D. Orlov, J. Mcclenaghan, B.C. Lyons, X. Jian, B.A. Grierson, N.T. Howard, P. Rodriguez-fernandez (2024). Core Transport Modeling and Characterization for Compact Tokamak Reactor Scenarios [Dataset]. http://doi.org/10.7910/DVN/QKDIFU
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 20, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    C. Holland, E.M. Bass, D. Orlov, J. Mcclenaghan, B.C. Lyons, X. Jian, B.A. Grierson, N.T. Howard, P. Rodriguez-fernandez
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Motivated by the current interest in compact, high-field approaches to fusion power plants, the OMFIT STEP integrated modeling workflow has been used to generate self-consistent core plasma transport solutions representative of potential compact tokamak reactor operating scenarios. In this study, solutions for an idealized Rmaj = 4 m, B0 = 8 T tokamak “use case reactor” (UCR) were developed, with the intention of providing starting parameters for more comprehensive future transport studies in the spirit of the CYCLONE base case. Both inductive pulsed (UCR-P) and steady-state (UCR-SS) solutions potentially capable of producing 1 GW of fusion power and 200 MW or more net electric power have been identified. A common feature of both scenarios is that the core confinement time is long enough for the plasmas to be well- coupled, even though core collisionality is low. This situation leads to significant core ion thermal transport, despite the heating being predominantly to the electrons, and a corresponding dominance of long-wavelength ion temperature gradient modes. A similar situation is found to hold for ITER and SPARC plasma scenarios, and is argued to be an inherent property of power plant-relevant burning plasmas. For both UCR scenarios, the EPED code predicts peeling-limited pedestals with extremely weak sensitivity to core pressure values, enabling use of a fixed boundary condition in core transport modeling. With this constraint, another key finding of this study is the extreme sensitivity of the results to the quantitative stiffness level of the transport model used as well as the predicted critical gradients, with outcomes ranging from runaway ignition to radiative collapse possible depending upon the choice of TGLF saturation rule. Given this uncertainty, new analysis presented in the paper details initial benchmarking of TGLF against linear and nonlinear gyrokinetic simulations. The gyrokinetic results are broadly consistent with the relevant TGLF predictions, but highlight the need to improve the accuracy of transport stiffness and particle flux predictions, especially at larger radii.

  8. H

    Replication Data for: Keeping One’s Seat: The Competiveness of MP...

    • dataverse.harvard.edu
    Updated Sep 29, 2016
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    Markus Baumann; Marc Debus; Tristan Klingelhöfer (2016). Replication Data for: Keeping One’s Seat: The Competiveness of MP Renomination in Mixed-Member Electoral Systems [Dataset]. http://doi.org/10.7910/DVN/UCRHWN
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 29, 2016
    Dataset provided by
    Harvard Dataverse
    Authors
    Markus Baumann; Marc Debus; Tristan Klingelhöfer
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Mixed-member electoral systems are supposed to simultaneously produce coherent parties and ensure the representation of local interests. We regard the competitiveness of MPs’ bids for renomination as a crucial indicator of the degree to which MPs are punished when deviating from their principals. Capitalizing on the opportunities offered by mixed-member electoral systems, we develop a theoretical account that makes the competitiveness of the candidate selection process conditional on the characteristics of the electoral system, the candidate selection regime, an MP’s parliamentary behavior, district characteristics and government status. Corroborating our expectations, the analysis of the candidate selection processes in the run-up to the 2013 Bundestag election shows that an increasing degree of ideological deviation from the party line—as expressed in parliamentary speeches—results in a worse position on the party list for opposition MPs, but does not affect the renomination chances of district candidates or of list candidates from the government camp.

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Dan Biggers (2024). CES 2022, Team Module of University of California, Riverside (UCR) [Dataset]. http://doi.org/10.7910/DVN/LPFYET

CES 2022, Team Module of University of California, Riverside (UCR)

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Jul 31, 2024
Dataset provided by
Harvard Dataverse
Authors
Dan Biggers
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Area covered
California, Riverside
Description

This dataverse contains the data and supporting documents for the CES 2022 Team Module of the University of California, Riverside. This project was supported by the National Science Foundation, Grant Number SES-2148907.

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