| Item | Title | Total requests | 2025-06 | 2025-05 | 2025-04 | 2025-03 |
|---|---|---|---|---|---|---|
| 00j3z3rd | AMReX: a framework for block-structured adaptive mesh refinement | 393 | 99 | 106 | 87 | 101 |
| 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 | 166 | 60 | 30 | 57 | 19 |
| 3vf1w91z | Report of the DOE Workshop on Management, Analysis, and Visualization of Experimental and Observational data – The Convergence of Data and Computing | 154 | 85 | 41 | 10 | 18 |
| 611154fz | Iterative Importance Sampling Algorithms for Parameter Estimation | 150 | 55 | 22 | 65 | 8 |
| 61j6m742 | 2019 Computing Sciences Strategic Plan | 134 | 68 | 16 | 33 | 17 |
| 02x259pf | Randomized Algorithms for Scientific Computing (RASC) | 123 | 51 | 29 | 27 | 16 |
| 7298g7m9 | A Guide to Using GitHub for Developing and Versioning Data Standards and Reporting Formats | 117 | 39 | 32 | 20 | 26 |
| 3nx4d18c | Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data | 111 | 61 | 15 | 13 | 22 |
| 81v261jh | PageRank, HITS and a unified framework for link analysis | 111 | 34 | 27 | 33 | 17 |
| 0t36p3hn | Lambda Architecture for Cost-Effective Batch and Speed Big Data Processing | 107 | 42 | 19 | 29 | 17 |
| 2xf0f1dj | The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data | 100 | 39 | 32 | 18 | 11 |
| 8pv153t1 | Adaptive dimension reduction for clustering high dimensional data | 95 | 30 | 42 | 13 | 10 |
| 6vb1r6c3 | PeleC: An adaptive mesh refinement solver for compressible reacting flows | 93 | 39 | 26 | 20 | 8 |
| 12z7x7hj | A Class of Sparse Johnson–Lindenstrauss Transforms and Analysis of their Extreme Singular Values | 92 | 47 | 14 | 17 | 14 |
| 5z934420 | Advances in Cross-Cutting Ideas for Computational Climate Science | 87 | 37 | 20 | 13 | 17 |
| 97d284ps | Machine learning-based Analysis of COVID-19 Pandemic Impact on US Research Networks | 87 | 44 | 14 | 21 | 8 |
| 6j5521rr | Editorial | 86 | 47 | 13 | 9 | 17 |
| 3q12x1gh | Perspectives for self-driving labs in synthetic biology | 85 | 39 | 22 | 15 | 9 |
| 57t3r88k | A Parallel lanczos Method for Symmetric Generalized Eigenvalue Problems | 79 | 27 | 16 | 23 | 13 |
| 5vr0h58m | A conservative, thermodynamically consistent numerical approach for low Mach number combustion. Part I: Single-level integration | 79 | 26 | 21 | 14 | 18 |
| 7hk9g5d3 | QUANT-NET: A testbed for quantum networking research over deployed fiber | 79 | 40 | 21 | 13 | 5 |
| 8kf319cb | A taxonomy of constraints in black-box simulation-based optimization | 75 | 36 | 16 | 14 | 9 |
| 12r566sn | ExaSAT: An exascale co-design tool for performance modeling | 74 | 40 | 20 | 10 | 4 |
| 6nf470xc | Bandwidth Enables Generalization in Quantum Kernel Models | 73 | 23 | 39 | 11 | |
| 8qb0p7j7 | Distributed Acoustic Sensing Using Dark Fiber for Near-Surface Characterization and Broadband Seismic Event Detection | 72 | 31 | 16 | 15 | 10 |
| 9fg8k5xh | ESnet Requirements Review Program Through the IRI Lens: A Meta-Analysis of Workflow Patterns Across DOE Office of Science Programs (Final Report) | 72 | 38 | 17 | 9 | 8 |
| 1ds6v8ww | Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data | 71 | 31 | 17 | 15 | 8 |
| 63f2185k | Long-term missing value imputation for time series data using deep neural networks | 71 | 39 | 17 | 9 | 6 |
| 7876x5ks | Search for mixing-induced CP violation using partial reconstruction of B¯0→D*+Xℓ-ν¯ℓ and kaon tagging | 71 | 30 | 26 | 8 | 7 |
| 8md8q9kq | An Architecture For Edge Networking Services | 71 | 16 | 21 | 23 | 11 |
| 2db7v922 | Enabling intent to configure scientific networks for high performance demands | 70 | 28 | 23 | 14 | 5 |
| 4xn2z9gq | Big-Data Science: Infrastructure Impact | 70 | 29 | 13 | 16 | 12 |
| 67p4c44z | Time-dependent analysis of B0→KS0π-π+γ decays and studies of the K+π-π+ system in B+→K+π-π+γ decays | 70 | 34 | 18 | 12 | 6 |
| 3gr340t0 | Designing a Framework for Solving Multiobjective Simulation Optimization Problems | 66 | 34 | 22 | 10 | |
| 7k9351p3 | Fluctuating hydrodynamics of electrolytes at electroneutral scales | 66 | 37 | 19 | 4 | 6 |
| 08w3f6zf | Software-Defined Network for End-to-end Networked Science at the Exascale | 65 | 43 | 15 | 4 | 3 |
| 5239p7pp | A Low Mach Number Model for Moist Atmospheric Flows | 65 | 40 | 15 | 6 | 4 |
| 0047q4b2 | High-Performance Computational Intelligence and Forecasting Technologies | 63 | 36 | 15 | 10 | 2 |
| 5658p36f | Meeting the Challenges of Modeling Astrophysical Thermonuclear Explosions: Castro, Maestro, and the AMReX Astrophysics Suite | 62 | 32 | 16 | 8 | 6 |
| 2d43x0c7 | Randomized Algorithms for Scientific Computing (RASC) | 61 | 36 | 14 | 8 | 3 |
| 3b80v6zt | International Neuroscience Initiatives Through the Lens of High-Performance Computing | 61 | 24 | 16 | 12 | 9 |
| 0x7138n5 | Unmatched: 50 Years of Supercomputing, A Personal Journey Accompanying the Evolution of a Powerful Tool | 59 | 26 | 16 | 12 | 5 |
| 3t75f0qg | Performance characterization of scientific workflows for the optimal use of Burst Buffers | 59 | 27 | 15 | 5 | 12 |
| 2sr9m6gk | Parameter Analysis of the VPIN (Volume synchronized Probability of Informed Trading) Metric | 58 | 29 | 9 | 6 | 14 |
| 3bd5p5j7 | Adaptive Projection Subspace Dimension for the Thick-Restart Lanczos Method | 58 | 25 | 14 | 12 | 7 |
| 3nw2w949 | An a priori evaluation of a principal component and artificial neural network based combustion model in diesel engine conditions | 58 | 38 | 9 | 9 | 2 |
| 7v13c2dq | Network Hardware Virtualization for Application Provisioning in Core Networks | 58 | 30 | 15 | 6 | 7 |
| 28b779q1 | The TOP500 List and Progress in High-Performance Computing | 57 | 23 | 13 | 14 | 7 |
| 8nb5w553 | Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats | 57 | 27 | 12 | 11 | 7 |
| 6km2b4fz | On the Equivalence of Nonnegative Matrix Factorization and K-means - Spectral Clustering | 56 | 22 | 17 | 8 | 9 |
Note: Due to the evolving nature of web traffic, the data presented here should be considered approximate and subject to revision. Learn more.