Computing
Parent: Computing Sciences
eScholarship stats: Breakdown by Item for October, 2024 through January, 2025
| Item | Title | Total requests | Download | View-only | %Dnld |
|---|---|---|---|---|---|
| 4sf7n3pc | Nuclear Physics Network Requirements Review: One-Year Update | 373 | 159 | 214 | 42.6% |
| 00j3z3rd | AMReX: a framework for block-structured adaptive mesh refinement | 352 | 335 | 17 | 95.2% |
| 4mj3d1jb | The MyESnet Portal: Making the Network Visible | 276 | 126 | 150 | 45.7% |
| 3nx4d18c | Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data | 141 | 60 | 81 | 42.6% |
| 02x259pf | Randomized Algorithms for Scientific Computing (RASC) | 106 | 94 | 12 | 88.7% |
| 6j5521rr | Editorial | 80 | 3 | 77 | 3.8% |
| 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 | 73 | 59 | 14 | 80.8% |
| 2xf0f1dj | The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data | 62 | 47 | 15 | 75.8% |
| 6vb1r6c3 | PeleC: An adaptive mesh refinement solver for compressible reacting flows | 62 | 11 | 51 | 17.7% |
| 8md8q9kq | An Architecture For Edge Networking Services | 62 | 0 | 62 | 0.0% |
| 0t36p3hn | Lambda Architecture for Cost-Effective Batch and Speed Big Data Processing | 57 | 49 | 8 | 86.0% |
| 28b779q1 | The TOP500 List and Progress in High-Performance Computing | 51 | 22 | 29 | 43.1% |
| 0047q4b2 | High-Performance Computational Intelligence and Forecasting Technologies | 47 | 9 | 38 | 19.1% |
| 8pv153t1 | Adaptive dimension reduction for clustering high dimensional data | 42 | 14 | 28 | 33.3% |
| 81v261jh | PageRank, HITS and a unified framework for link analysis | 41 | 11 | 30 | 26.8% |
| 3vf1w91z | Report of the DOE Workshop on Management, Analysis, and Visualization of Experimental and Observational data – The Convergence of Data and Computing | 35 | 20 | 15 | 57.1% |
| 5vr0h58m | A conservative, thermodynamically consistent numerical approach for low Mach number combustion. Part I: Single-level integration | 35 | 25 | 10 | 71.4% |
| 7g6632bn | The NAS Parallel Benchmarks | 35 | 4 | 31 | 11.4% |
| 57t3r88k | A Parallel lanczos Method for Symmetric Generalized Eigenvalue Problems | 34 | 10 | 24 | 29.4% |
| 6km2b4fz | On the Equivalence of Nonnegative Matrix Factorization and K-means - Spectral Clustering | 34 | 4 | 30 | 11.8% |
| 61j6m742 | 2019 Computing Sciences Strategic Plan | 33 | 17 | 16 | 51.5% |
| 12r566sn | ExaSAT: An exascale co-design tool for performance modeling | 32 | 11 | 21 | 34.4% |
| 3b80v6zt | International Neuroscience Initiatives Through the Lens of High-Performance Computing | 30 | 9 | 21 | 30.0% |
| 59m140sn | Experiences with User-Centered Design for the Tigres Workflow API | 29 | 13 | 16 | 44.8% |
| 1ds6v8ww | Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data | 28 | 5 | 23 | 17.9% |
| 3q12x1gh | Perspectives for self-driving labs in synthetic biology | 28 | 20 | 8 | 71.4% |
| 5z934420 | Advances in Cross-Cutting Ideas for Computational Climate Science | 28 | 10 | 18 | 35.7% |
| 8nb5w553 | Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats | 28 | 7 | 21 | 25.0% |
| 9fg8k5xh | ESnet Requirements Review Program Through the IRI Lens: A Meta-Analysis of Workflow Patterns Across DOE Office of Science Programs (Final Report) | 28 | 12 | 16 | 42.9% |
| 9tg9045c | Beyond 100 Gb/s: capacity, flexibility, and network optimization [Invited] | 28 | 23 | 5 | 82.1% |
| 2g71q95s | Connected OFCity Challenge: Addressing the Digital Divide in the Developing World | 26 | 11 | 15 | 42.3% |
| 7v13c2dq | Network Hardware Virtualization for Application Provisioning in Core Networks | 26 | 8 | 18 | 30.8% |
| 1qs17737 | The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community | 25 | 8 | 17 | 32.0% |
| 2db7v922 | Enabling intent to configure scientific networks for high performance demands | 25 | 8 | 17 | 32.0% |
| 8kb0h1gb | Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis | 25 | 5 | 20 | 20.0% |
| 8mg0r153 | Low rank matrix approximation using the Lanczos Bidiagonalization Process | 25 | 2 | 23 | 8.0% |
| 3bd5p5j7 | Adaptive Projection Subspace Dimension for the Thick-Restart Lanczos Method | 24 | 6 | 18 | 25.0% |
| 3nw2w949 | An a priori evaluation of a principal component and artificial neural network based combustion model in diesel engine conditions | 24 | 11 | 13 | 45.8% |
| 87z3114q | Thick-Restart Lanczos Method for Symmetric Eigenvalue Problems | 24 | 23 | 1 | 95.8% |
| 8pj0n2v2 | FABRIC: A National-Scale Programmable Experimental Network Infrastructure | 24 | 18 | 6 | 75.0% |
| 9k855735 | A coupled discontinuous Galerkin-Finite Volume framework for solving gas dynamics over embedded geometries | 23 | 15 | 8 | 65.2% |
| 7298g7m9 | A Guide to Using GitHub for Developing and Versioning Data Standards and Reporting Formats | 22 | 4 | 18 | 18.2% |
| 7mv4p0m8 | A science data gateway for environmental management | 22 | 11 | 11 | 50.0% |
| 2sr9m6gk | Parameter Analysis of the VPIN (Volume synchronized Probability of Informed Trading) Metric | 21 | 11 | 10 | 52.4% |
| 6gk8b245 | Thick-Restart Lanczos Method for Electronic Structure Calculations | 21 | 6 | 15 | 28.6% |
| 1507s9df | Effectiveness and predictability of in-network storage cache for Scientific Workflows | 20 | 8 | 12 | 40.0% |
| 315614hd | Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization | 20 | 0 | 20 | 0.0% |
| 3t75f0qg | Performance characterization of scientific workflows for the optimal use of Burst Buffers | 20 | 6 | 14 | 30.0% |
| 611154fz | Iterative Importance Sampling Algorithms for Parameter Estimation | 20 | 14 | 6 | 70.0% |
| 8kf319cb | A taxonomy of constraints in black-box simulation-based optimization | 20 | 9 | 11 | 45.0% |
Note: Due to the evolving nature of web traffic, the data presented here should be considered approximate and subject to revision. Learn more.