Computing
Parent: Computing Sciences
eScholarship stats: Breakdown by Item for September through December, 2024
| Item | Title | Total requests | Download | View-only | %Dnld |
|---|---|---|---|---|---|
| 00j3z3rd | AMReX: a framework for block-structured adaptive mesh refinement | 356 | 338 | 18 | 94.9% |
| 4sf7n3pc | Nuclear Physics Network Requirements Review: One-Year Update | 155 | 61 | 94 | 39.4% |
| 8qs8x847 | Accurate Projection Methods for the Incompressible Navier-Stokes Equations”, | 136 | 130 | 6 | 95.6% |
| 3nx4d18c | Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data | 115 | 39 | 76 | 33.9% |
| 02x259pf | Randomized Algorithms for Scientific Computing (RASC) | 104 | 92 | 12 | 88.5% |
| 4mj3d1jb | The MyESnet Portal: Making the Network Visible | 91 | 35 | 56 | 38.5% |
| 6j5521rr | Editorial | 72 | 3 | 69 | 4.2% |
| 9wp5j0dq | Nuclear Physics Exascale Requirements Review: An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Nuclear Physics, June 15 - 17, 2016, Gaithersburg, Maryland | 65 | 54 | 11 | 83.1% |
| 0t36p3hn | Lambda Architecture for Cost-Effective Batch and Speed Big Data Processing | 61 | 54 | 7 | 88.5% |
| 6vb1r6c3 | PeleC: An adaptive mesh refinement solver for compressible reacting flows | 57 | 11 | 46 | 19.3% |
| 8md8q9kq | An Architecture For Edge Networking Services | 51 | 0 | 51 | 0.0% |
| 28b779q1 | The TOP500 List and Progress in High-Performance Computing | 46 | 18 | 28 | 39.1% |
| 0047q4b2 | High-Performance Computational Intelligence and Forecasting Technologies | 41 | 7 | 34 | 17.1% |
| 8pv153t1 | Adaptive dimension reduction for clustering high dimensional data | 41 | 13 | 28 | 31.7% |
| 2xf0f1dj | The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data | 39 | 30 | 9 | 76.9% |
| 5vr0h58m | A conservative, thermodynamically consistent numerical approach for low Mach number combustion. Part I: Single-level integration | 39 | 25 | 14 | 64.1% |
| 81v261jh | PageRank, HITS and a unified framework for link analysis | 37 | 10 | 27 | 27.0% |
| 3q12x1gh | Perspectives for self-driving labs in synthetic biology | 36 | 27 | 9 | 75.0% |
| 7g6632bn | The NAS Parallel Benchmarks | 35 | 5 | 30 | 14.3% |
| 9fg8k5xh | ESnet Requirements Review Program Through the IRI Lens: A Meta-Analysis of Workflow Patterns Across DOE Office of Science Programs (Final Report) | 31 | 10 | 21 | 32.3% |
| 12r566sn | ExaSAT: An exascale co-design tool for performance modeling | 29 | 12 | 17 | 41.4% |
| 3vf1w91z | Report of the DOE Workshop on Management, Analysis, and Visualization of Experimental and Observational data – The Convergence of Data and Computing | 29 | 14 | 15 | 48.3% |
| 61j6m742 | 2019 Computing Sciences Strategic Plan | 28 | 12 | 16 | 42.9% |
| 9tg9045c | Beyond 100 Gb/s: capacity, flexibility, and network optimization [Invited] | 28 | 21 | 7 | 75.0% |
| 57t3r88k | A Parallel lanczos Method for Symmetric Generalized Eigenvalue Problems | 27 | 9 | 18 | 33.3% |
| 2db7v922 | Enabling intent to configure scientific networks for high performance demands | 26 | 11 | 15 | 42.3% |
| 2g71q95s | Connected OFCity Challenge: Addressing the Digital Divide in the Developing World | 26 | 11 | 15 | 42.3% |
| 8nb5w553 | Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats | 26 | 6 | 20 | 23.1% |
| 1qs17737 | The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community | 24 | 11 | 13 | 45.8% |
| 3b80v6zt | International Neuroscience Initiatives Through the Lens of High-Performance Computing | 24 | 9 | 15 | 37.5% |
| 59m140sn | Experiences with User-Centered Design for the Tigres Workflow API | 24 | 10 | 14 | 41.7% |
| 6km2b4fz | On the Equivalence of Nonnegative Matrix Factorization and K-means - Spectral Clustering | 24 | 1 | 23 | 4.2% |
| 5cg43163 | Extended Fayans energy density functional: optimization and analysis | 23 | 4 | 19 | 17.4% |
| 7v13c2dq | Network Hardware Virtualization for Application Provisioning in Core Networks | 22 | 7 | 15 | 31.8% |
| 8kb0h1gb | Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis | 22 | 4 | 18 | 18.2% |
| 611154fz | Iterative Importance Sampling Algorithms for Parameter Estimation | 21 | 14 | 7 | 66.7% |
| 7sk0f70s | libEnsemble: A complete Python toolkit for dynamic ensembles of calculations | 21 | 0 | 21 | 0.0% |
| 87z3114q | Thick-Restart Lanczos Method for Symmetric Eigenvalue Problems | 20 | 19 | 1 | 95.0% |
| 8kf319cb | A taxonomy of constraints in black-box simulation-based optimization | 20 | 5 | 15 | 25.0% |
| 9k855735 | A coupled discontinuous Galerkin-Finite Volume framework for solving gas dynamics over embedded geometries | 20 | 9 | 11 | 45.0% |
| 315614hd | Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization | 19 | 0 | 19 | 0.0% |
| 5ft99738 | Projecting Antarctica's contribution to future sea level rise from basal ice shelf melt using linear response functions of 16 ice sheet models (LARMIP-2) | 19 | 4 | 15 | 21.1% |
| 8mg0r153 | Low rank matrix approximation using the Lanczos Bidiagonalization Process | 19 | 0 | 19 | 0.0% |
| 8pj0n2v2 | FABRIC: A National-Scale Programmable Experimental Network Infrastructure | 19 | 14 | 5 | 73.7% |
| 3nw2w949 | An a priori evaluation of a principal component and artificial neural network based combustion model in diesel engine conditions | 18 | 7 | 11 | 38.9% |
| 4zg0g0zw | DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection | 18 | 0 | 18 | 0.0% |
| 67t402mh | ParMOO: A Python library for parallel multiobjectivesimulation optimization | 18 | 0 | 18 | 0.0% |
| 1ds6v8ww | Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data | 17 | 2 | 15 | 11.8% |
| 644743dd | Study of the e+e-→K+K- reaction in the energy range from 2.6 to 8.0 GeV | 17 | 2 | 15 | 11.8% |
| 7hk9g5d3 | QUANT-NET: A testbed for quantum networking research over deployed fiber | 17 | 2 | 15 | 11.8% |
Note: Due to the evolving nature of web traffic, the data presented here should be considered approximate and subject to revision. Learn more.