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@Article{egusphere-2023-271,
AUTHOR = {Lombardozzi, D. L. and Wieder, W. R. and Sobhani, N. and Bonan, G. B. and Durden, D. and Lenz, D. and SanClements, M. and Weintraub-Leff, S. and Ayres, E. and Florian, C. R. and Dahlin, K. and Kumar, S. and Swann, A. L. S. and Zarakas, C. and Vardeman, C. and Pascucci, V.},
TITLE = {Overcoming barriers to enable convergence research by integrating ecological and climate sciences: The NCAR-NEON system Version 1},
JOURNAL = {EGUsphere},
VOLUME = {2023},
YEAR = {2023},
PAGES = {1--37},
URL = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-271/},
DOI = {10.5194/egusphere-2023-271}
}
@article{article,
author = {Taufer, Michela and Martinez, Heberth and Lüttgau, Jakob and Whitnah, Lauren and Scorzelli, Giorgio and Newell, Pania and Panta, Aashish and Bremer, Peer-Timo and Fils, Douglas and Kirkpatrick, Christine and Pascucci, Valerio},
year = {2023},
month = {09},
pages = {39-47},
title = {Enhancing Scientific Research with FAIR Digital Objects in the National Science Data Fabric},
volume = {25},
journal = {Computing in Science & Engineering},
doi = {10.1109/MCSE.2024.3363828}
}
@inproceedings{10.1145/3603166.3632136,
author = {Luettgau, Jakob and Martinez, Heberth and Olaya, Paula and Scorzelli, Giorgio and Tarcea, Glenn and Lofstead, Jay and Kirkpatrick, Christine and Pascucci, Valerio and Taufer, Michela},
title = {NSDF-Services: Integrating Networking, Storage, and Computing Services into a Testbed for Democratization of Data Delivery},
year = {2024},
isbn = {9798400702341},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3603166.3632136},
doi = {10.1145/3603166.3632136},
abstract = {The lack of a readily accessible, tightly integrated data fabric connecting high-speed networking, storage, and computing services remains a critical barrier to the democratization of scientific discovery. To address this challenge, we are building National Science Data Fabric (NSDF), a holistic ecosystem to facilitate domain scientists in their daily research. NSDF comprises networking, storage, and computing services, as well as outreach initiatives. In this paper, we present a testbed integrating three services (i.e., networking, storage, and computing). We evaluate their performance. Specifically, we study the networking services and their throughput and latency with a focus on academic cloud providers; the storage services and their performance with a focus on data movement using file system mappers for both academic and commercial clouds; and computing orchestration services focusing on commercial cloud providers. We discuss NSDF's potential to increase scalability and usability as it decreases time-to-discovery across scientific domains.},
booktitle = {Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing},
articleno = {11},
numpages = {10},
keywords = {high-performance computing, cloud computing, data democratization, perfsonar, XRootD},
location = {<conf-loc>, <city>Taormina (Messina)</city>, <country>Italy</country>, </conf-loc>},
series = {UCC '23}
}
@inproceedings{10.1145/3588195.3595948,
author = {Luettgau, Jakob and Martinez, Heberth and Tarcea, Glenn and Scorzelli, Giorgio and Pascucci, Valerio and Taufer, Michela},
title = {Studying Latency and Throughput Constraints for Geo-Distributed Data in the National Science Data Fabric},
year = {2023},
isbn = {9798400701559},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3588195.3595948},
doi = {10.1145/3588195.3595948},
abstract = {The National Science Data Fabric (NSDF) is our solution to the problem of addressing the data-sharing needs of the growing data science community. NSDF is designed to make sharing data across geographically distributed sites easier for users who lack technical expertise and infrastructure. By developing an easy-to-install software stack, we promote the FAIR data-sharing principles in NSDF while leveraging existing high-speed data transfer infrastructures such as Globus and XRootD. This work shows how we leverage latency and throughput information between geo-distributed NSDF sites with NSDF entry points to optimize the automatic coordination of data placement and transfer across the data fabric, which can further improve the efficiency of data sharing.},
booktitle = {Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing},
pages = {325–326},
numpages = {2},
keywords = {xrootd, perfsonar, high-performance computing, data democratization, cloud computing},
location = {Orlando, FL, USA},
series = {HPDC '23}
}
@inproceedings{10.1145/3588195.3595948,
author = {Luettgau, Jakob and Martinez, Heberth and Tarcea, Glenn and Scorzelli, Giorgio and Pascucci, Valerio and Taufer, Michela},
title = {Studying Latency and Throughput Constraints for Geo-Distributed Data in the National Science Data Fabric},
year = {2023},
isbn = {9798400701559},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3588195.3595948},
doi = {10.1145/3588195.3595948},
abstract = {The National Science Data Fabric (NSDF) is our solution to the problem of addressing the data-sharing needs of the growing data science community. NSDF is designed to make sharing data across geographically distributed sites easier for users who lack technical expertise and infrastructure. By developing an easy-to-install software stack, we promote the FAIR data-sharing principles in NSDF while leveraging existing high-speed data transfer infrastructures such as Globus and XRootD. This work shows how we leverage latency and throughput information between geo-distributed NSDF sites with NSDF entry points to optimize the automatic coordination of data placement and transfer across the data fabric, which can further improve the efficiency of data sharing.},
booktitle = {Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing},
pages = {325–326},
numpages = {2},
keywords = {xrootd, perfsonar, high-performance computing, data democratization, cloud computing},
location = {Orlando, FL, USA},
series = {HPDC '23}
}
@inproceedings{10.1145/3588195.3595948,
author = {Luettgau, Jakob and Martinez, Heberth and Tarcea, Glenn and Scorzelli, Giorgio and Pascucci, Valerio and Taufer, Michela},
title = {Studying Latency and Throughput Constraints for Geo-Distributed Data in the National Science Data Fabric},
year = {2023},
isbn = {9798400701559},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3588195.3595948},
doi = {10.1145/3588195.3595948},
abstract = {The National Science Data Fabric (NSDF) is our solution to the problem of addressing the data-sharing needs of the growing data science community. NSDF is designed to make sharing data across geographically distributed sites easier for users who lack technical expertise and infrastructure. By developing an easy-to-install software stack, we promote the FAIR data-sharing principles in NSDF while leveraging existing high-speed data transfer infrastructures such as Globus and XRootD. This work shows how we leverage latency and throughput information between geo-distributed NSDF sites with NSDF entry points to optimize the automatic coordination of data placement and transfer across the data fabric, which can further improve the efficiency of data sharing.},
booktitle = {Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing},
pages = {325–326},
numpages = {2},
keywords = {xrootd, perfsonar, high-performance computing, data democratization, cloud computing},
location = {Orlando, FL, USA},
series = {HPDC '23}
}
@INPROCEEDINGS{10222089,
author={Leventhal, Samuel and Gyulassy, Attila and Pascucci, Valerio and Heimann, Mark},
booktitle={2023 IEEE International Conference on Image Processing (ICIP)},
title={Modeling Hierarchical Topological Structure in Scientific Images with Graph Neural Networks},
year={2023},
volume={},
number={},
pages={2995-2999},
keywords={Training;Image segmentation;Message passing;Graph neural networks;Data models;Task analysis;graph neural networks;topological data analysis;image segmentation;persistence;Morse-Smale complex},
doi={10.1109/ICIP49359.2023.10222089}}
@inproceedings{10.1145/3502181.3533709,
author = {Olaya, Paula and Luettgau, Jakob and Zhou, Naweiluo and Lofstead, Jay and Scorzelli, Giorgio and Pascucci, Valerio and Taufer, Michela},
title = {NSDF-FUSE: A Testbed for Studying Object Storage via FUSE File Systems},
year = {2022},
isbn = {9781450391993},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3502181.3533709},
doi = {10.1145/3502181.3533709},
abstract = {This work presents NSDF-FUSE, a testbed for evaluating settings and performance of FUSE-based file systems on top of S3-compatible object storage; the testbed is part of a suite of services from the National Science Data Fabric (NSDF) project (an NSF-funded project that is delivering cyberinfrastructures for data scientists). We demonstrate how NSDF-FUSE can be deployed to evaluate eight different mapping packages that mount S3-compatible object storage to a file system, as well as six data patterns representing different I/O operations on two cloud platforms. NSDF-FUSE is open-source and can be easily extended to run with other software mapping packages and different cloud platforms.},
booktitle = {Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing},
pages = {277–278},
numpages = {2},
keywords = {cloud, file system, fuse, object storage, performance},
location = {Minneapolis, MN, USA},
series = {HPDC '22}
}
@INPROCEEDINGS{9973700,
author={Tarcea, Glenn and Puchala, Brian and Berman, Tracy and Scorzelli, Giorgio and Pascucci, Valerio and Taufer, Michela and Allison, John},
booktitle={2022 IEEE 18th International Conference on e-Science (e-Science)},
title={The Materials Commons Data Repository},
year={2022},
volume={},
number={},
pages={405-406},
keywords={Publishing;Genomics;Full stack;Fabrics;Bioinformatics;Standards;Next generation networking;Materials Science;FAIR data;Repository;MGI (Materials Genome Initiative);Open Research;Open Access},
doi={10.1109/eScience55777.2022.00060}}
@INPROCEEDINGS{9973700,
author={Tarcea, Glenn and Puchala, Brian and Berman, Tracy and Scorzelli, Giorgio and Pascucci, Valerio and Taufer, Michela and Allison, John},
booktitle={2022 IEEE 18th International Conference on e-Science (e-Science)},
title={The Materials Commons Data Repository},
year={2022},
volume={},
number={},
pages={405-406},
keywords={Publishing;Genomics;Full stack;Fabrics;Bioinformatics;Standards;Next generation networking;Materials Science;FAIR data;Repository;MGI (Materials Genome Initiative);Open Research;Open Access},
doi={10.1109/eScience55777.2022.00060}}
@INPROCEEDINGS{9973642,
author={Kennedy, Dominic and Olaya, Paula and Lofstead, Jay and Vargas, Rodrigo and Taufer, Michela},
booktitle={2022 IEEE 18th International Conference on e-Science (e-Science)},
title={Augmenting Singularity to Generate Fine-grained Workflows, Record Trails, and Data Provenance},
year={2022},
volume={},
number={},
pages={403-404},
keywords={Runtime;Instruction sets;High performance computing;Soil moisture;Geoscience;Containers;Metadata;Scientific workflows;containers;reproducibility},
doi={10.1109/eScience55777.2022.00059}}
@INPROCEEDINGS{9966406,
author={Venkat, Aniketh and Hoang, Duong and Gyulassy, Attila and Bremer, Peer-Timo and Federer, Frederick and Angelucci, Alessandra and Pascucci, Valerio},
booktitle={2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV)},
title={High-Quality Progressive Alignment of Large 3D Microscopy Data},
year={2022},
volume={},
number={},
pages={1-10},
keywords={Three-dimensional displays;Image resolution;Microscopy;Scalability;Instruction sets;Two dimensional displays;Random access memory;Alignment;Stitching;Normalized Cross-Correlation;Progressive Computations;Coarse-to-Fine;Microscopy;Terascale},
doi={10.1109/LDAV57265.2022.9966406}}
@INPROCEEDINGS{10061758,
author={Luettgau, Jakob and Scorzelli, Giorgio and Pascucci, Valerio and Tarcea, Glenn and Kirkpatrick, Christine R. and Taufer, Michela},
booktitle={2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)},
title={NSDF-Catalog: Lightweight Indexing Service for Democratizing Data Delivery},
year={2022},
volume={},
number={},
pages={1-10},
keywords={Cloud computing;Distribution strategy;Microservice architectures;Metadata;Fabrics;Optimization;Next generation networking;national science data fabric;scientific data;cloud;high performance computing},
doi={10.1109/UCC56403.2022.00011}}
@ARTICLE{10085988,
author={Hoang, Duong and Bhatia, Harsh and Lindstrom, Peter and Pascucci, Valerio},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Progressive Tree-Based Compression of Large-Scale Particle Data},
year={2023},
volume={},
number={},
pages={1-18},
keywords={Encoding;Decoding;Image reconstruction;Data models;Computational modeling;Compressors;Task analysis;particle datasets;compression (coding);data compaction and compression;hierarchical;progressive decompression;coarse approximation;tree traversal;multiresolution;visualization},
doi={10.1109/TVCG.2023.3260628}}
@InProceedings{10.1007/978-3-031-34668-2_25,
author="Luettgau, Jakob
and Scorzelli, Giorgio
and Pascucci, Valerio
and Taufer, Michela",
editor="Streitz, Norbert A.
and Konomi, Shin'ichi",
title="Development of Large-Scale Scientific Cyberinfrastructure and the Growing Opportunity to Democratize Access to Platforms and Data",
booktitle="Distributed, Ambient and Pervasive Interactions",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="378--389",
abstract="As researchers across scientific domains rapidly adopt advanced scientific computing methodologies, access to advanced cyberinfrastructure (CI) becomes a critical requirement in scientific discovery. Lowering the entry barriers to CI is a crucial challenge in interdisciplinary sciences requiring frictionless software integration, data sharing from many distributed sites, and access to heterogeneous computing platforms. In this paper, we explore how the challenge is not merely a factor of availability and affordability of computing, network, and storage technologies but rather the result of insufficient interfaces with an increasingly heterogeneous mix of computing technologies and data sources. With more distributed computation and data, scientists, educators, and students must invest their time and effort in coordinating data access and movements, often penalizing their scientific research. Investments in the interfaces' software stack are necessary to help scientists, educators, and students across domains take advantage of advanced computational methods. To this end, we propose developing a science data fabric as the standard scientific discovery interface that seamlessly manages data dependencies within scientific workflows and CI.",
isbn="978-3-031-34668-2"
}
@ARTICLE{10052758,
author={Leventhal, Samuel and Gyulassy, Attila and Heimann, Mark and Pascucci, Valerio},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Exploring Classification of Topological Priors with Machine Learning for Feature Extraction},
year={2023},
volume={},
number={},
pages={1-12},
keywords={Image segmentation;Machine learning;Semantics;Task analysis;Labeling;Training;Topology;Computational Topology;Topological Data Analysis;Machine Learning;Graph Learning;Graph Neural Networks;Morse-Smale Complex;Scientific Visualization;Segmentation;Feature Detection},
doi={10.1109/TVCG.2023.3248632}}
@article{10.1177/10943420231167800,
author = {Dongarra, Jack and Tourancheau, Bernard and Zhou, Naweiluo and Scorzelli, Giorgio and Luettgau, Jakob and Kancharla, Rahul R and Kane, Joshua J and Wheeler, Robert and Croom, Brendan P and Newell, Pania and Pascucci, Valerio and Taufer, Michela},
title = {Orchestration of materials science workflows for heterogeneous resources at large scale},
year = {2023},
issue_date = {Jul 2023},
publisher = {Sage Publications, Inc.},
address = {USA},
volume = {37},
number = {3–4},
issn = {1094-3420},
url = {https://doi.org/10.1177/10943420231167800},
doi = {10.1177/10943420231167800},
abstract = {In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.},
journal = {Int. J. High Perform. Comput. Appl.},
month = {jul},
pages = {260–271},
numpages = {12},
keywords = {cloud computing, materials science, job scheduling, resource management, orchestration, Scientific workflow}
}
@ARTICLE{9993758,
author={Li, Zhimin and Menon, Harshitha and Mohror, Kathryn and Liu, Shusen and Guo, Luanzheng and Bremer, Peer-Timo and Pascucci, Valerio},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={A Visual Comparison of Silent Error Propagation},
year={2022},
volume={},
number={},
pages={1-15},
keywords={Data visualization;Resilience;Time series analysis;Visualization;Task analysis;Fault tolerant systems;Fault tolerance;Error propagation;fault tolerance boundary;graph visualization;information visualization;silent data corruption},
doi={10.1109/TVCG.2022.3230636}}
@inproceedings{10.1145/3502181.3531468,
author = {Fan, Ke and Gilray, Thomas and Pascucci, Valerio and Huang, Xuan and Micinski, Kristopher and Kumar, Sidharth},
title = {Optimizing the Bruck Algorithm for Non-uniform All-to-all Communication},
year = {2022},
isbn = {9781450391993},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3502181.3531468},
doi = {10.1145/3502181.3531468},
abstract = {In MPI, collective routines MPI_Alltoall and MPI_Alltoallv play an important role in facilitating all-to-all inter-process data exchange. MPI_Alltoallv is a generalization of MPI_Alltoall, supporting the exchange of non-uniform distributions of data. Popular implementations of MPI, such as MPICH and OpenMPI, implement MPI_Alltoall using a combination of techniques such as the Spread-out algorithm and the Bruck algorithm. Spread-out has a linear complexity in P, compared to Bruck's logarithmic complexity (P: process count); a selection between these two techniques is made at runtime based on the data block size. However, MPI_Alltoallv is typically implemented using only variants of the spread-out algorithm, and therefore misses out on the performance benefits that the log-time Bruck algorithm offers (especially for smaller data loads).In this paper, we first implement and empirically evaluate all existing variants of the Bruck algorithm for uniform and non-uniform data loads-this forms the basis for our own Bruck-based non-uniform all-to-all algorithms. In particular, we developed two open-source implementations, padded Bruck and two-phase Bruck, that efficiently generalize Bruck algorithm to non-uniform all-to-all data exchange. We empirically validate the techniques on three supercomputers: Theta, Cori, and Stampede, using both microbenchmarks and two real-world applications: graph mining and program analysis. We perform weak and strong scaling studies for a range of average message sizes, degrees of imbalance, and distribution schemes, and demonstrate that our techniques outperform vendor-optimized Cray's MPI_Alltoallv by as much as 50\% for some workloads and scales.},
booktitle = {Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing},
pages = {172–184},
numpages = {13},
keywords = {mpi, collective communication, bruck algorithm, alltoallv},
location = {Minneapolis, MN, USA},
series = {HPDC '22}
}
@inproceedings{10.1145/3502181.3533710,
author = {Luettgau, Jakob and Olaya, Paula and Zhou, Naweiluo and Scorzelli, Giorgio and Pascucci, Valerio and Taufer, Michela},
title = {NSDF-Cloud: Enabling Ad-Hoc Compute Clusters Across Academic and Commercial Clouds},
year = {2022},
isbn = {9781450391993},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3502181.3533710},
doi = {10.1145/3502181.3533710},
abstract = {Computational resources are increasingly provisioned to users through cloud-like interfaces. Both academic and commercial cloud offerings exist, but no single standardized interface for common actions such as configuration, launching, and termination of virtual resources exists. This imposes huge technical burden on domain scientist that attempt to take advantage of these resources; even expert users spend considerable time to port their applications from one cloud platform to another.With this work, we make available to the community a unified API toolkit as well as five in-depth reports on challenges we encountered working with different academic and commercial cloud providers. Our toolkit implements automations for common tasks such as simultaneous launching and termination of large numbers of virtual machines (VM) across the cloud. We demonstrate that our toolkit brings down the time users need to spend launching and terminating these resources to mere minutes, thus enabling ad-hoc multi-cloud clusters.},
booktitle = {Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing},
pages = {279–280},
numpages = {2},
keywords = {virtual machine, data fabric, cyberinfrastructure},
location = {Minneapolis, MN, USA},
series = {HPDC '22}
}
@ARTICLE{9904457,
author={Morrical, Nate and Sahistan, Alper and Güdükbay, Uğur and Wald, Ingo and Pascucci, Valerio},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Quick Clusters: A GPU-Parallel Partitioning for Efficient Path Tracing of Unstructured Volumetric Grids},
year={2023},
volume={29},
number={1},
pages={537-547},
keywords={Rendering (computer graphics);Ray tracing;Memory management;Graphics processing units;Data visualization;NASA;Monte Carlo methods;Ray Tracing;Path Tracing;Volume Rendering;Scientific Visualization;Delta Tracking},
doi={10.1109/TVCG.2022.3209418}}
@article{https://doi.org/10.1111/cgf.14304,
author = {McDonald, T. and Shrestha, R. and Yi, X. and Bhatia, H. and Chen, D. and Goswami, D. and Pascucci, V. and Turbyville, T. and Bremer, P.-T.},
title = {Leveraging Topological Events in Tracking Graphs for Understanding Particle Diffusion},
journal = {Computer Graphics Forum},
volume = {40},
number = {3},
pages = {251-262},
keywords = {CCS Concepts, • Human-centered computing → Scientific visualization, • Applied computing → Computational biology},
doi = {https://doi.org/10.1111/cgf.14304},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14304},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14304},
abstract = {Abstract Single particle tracking (SPT) of fluorescent molecules provides significant insights into the diffusion and relative motion of tagged proteins and other structures of interest in biology. However, despite the latest advances in high-resolution microscopy, individual particles are typically not distinguished from clusters of particles. This lack of resolution obscures potential evidence for how merging and splitting of particles affect their diffusion and any implications on the biological environment. The particle tracks are typically decomposed into individual segments at observed merge and split events, and analysis is performed without knowing the true count of particles in the resulting segments. Here, we address the challenges in analyzing particle tracks in the context of cancer biology. In particular, we study the tracks of KRAS protein, which is implicated in nearly 20\% of all human cancers, and whose clustering and aggregation have been linked to the signaling pathway leading to uncontrolled cell growth. We present a new analysis approach for particle tracks by representing them as tracking graphs and using topological events – merging and splitting, to disambiguate the tracks. Using this analysis, we infer a lower bound on the count of particles as they cluster and create conditional distributions of diffusion speeds before and after merge and split events. Using thousands of time-steps of simulated and in-vitro SPT data, we demonstrate the efficacy of our method, as it offers the biologists a new, detailed look into the relationship between KRAS clustering and diffusion speeds.},
year = {2021}
}
@ARTICLE{9645370,
author={Venkat, Aniketh and Gyulassy, Attila and Kosiba, Graham and Maiti, Amitesh and Reinstein, Henry and Gee, Richard and Bremer, Peer-Timo and Pascucci, Valerio},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Towards replacing physical testing of granular materials with a Topology-based Model},
year={2022},
volume={28},
number={1},
pages={76-85},
keywords={Shape;Mathematical models;Surface treatment;Powders;Computational modeling;Area measurement;Particle measurements;Physical and Environmental Sciences;Computational Topology-based Techniques;Data Abstractions and Types;Scalar Field Data;Pore Network Model;Morse-Smale Complex},
doi={10.1109/TVCG.2021.3114819}}
@ARTICLE{9751449,
author={Bhatia, Harsh and Hoang, Duong and Morrical, Nate and Pascucci, Valerio and Bremer, Peer-Timo and Lindstrom, Peter},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={AMM: Adaptive Multilinear Meshes},
year={2022},
volume={28},
number={6},
pages={2350-2363},
keywords={Data visualization;Rendering (computer graphics);Spatial resolution;Open source software;Adaptive Meshes;Wavelets;Compression Techniques;Multiresolution Techniques;Streaming Data;Scalar Field Data},
doi={10.1109/TVCG.2022.3165392}}
@INPROCEEDINGS{9680353,
author={Fan, Ke and Hoang, Duong and Petruzza, Steve and Gilray, Thomas and Pascucci, Valerio and Kumar, Sidharth},
booktitle={2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC)},
title={Load-balancing Parallel I/O of Compressed Hierarchical Layouts},
year={2021},
volume={},
number={},
pages={343-353},
keywords={Layout;Pipelines;Data visualization;Distributed databases;Writing;Libraries;Supercomputers;Parallel I/O;load-balancing;multiresolution;precision;compression;data layout;aggregation},
doi={10.1109/HiPC53243.2021.00048}}
@inproceedings{10.1145/3502181.3533710,
author = {Luettgau, Jakob and Olaya, Paula and Zhou, Naweiluo and Scorzelli, Giorgio and Pascucci, Valerio and Taufer, Michela},
title = {NSDF-Cloud: Enabling Ad-Hoc Compute Clusters Across Academic and Commercial Clouds},
year = {2022},
isbn = {9781450391993},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3502181.3533710},
doi = {10.1145/3502181.3533710},
abstract = {Computational resources are increasingly provisioned to users through cloud-like interfaces. Both academic and commercial cloud offerings exist, but no single standardized interface for common actions such as configuration, launching, and termination of virtual resources exists. This imposes huge technical burden on domain scientist that attempt to take advantage of these resources; even expert users spend considerable time to port their applications from one cloud platform to another.With this work, we make available to the community a unified API toolkit as well as five in-depth reports on challenges we encountered working with different academic and commercial cloud providers. Our toolkit implements automations for common tasks such as simultaneous launching and termination of large numbers of virtual machines (VM) across the cloud. We demonstrate that our toolkit brings down the time users need to spend launching and terminating these resources to mere minutes, thus enabling ad-hoc multi-cloud clusters.},
booktitle = {Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing},
pages = {279–280},
numpages = {2},
keywords = {virtual machine, data fabric, cyberinfrastructure},
location = {Minneapolis, MN, USA},
series = {HPDC '22}
}
@inproceedings{10.1145/3502181.3533710,
author = {Luettgau, Jakob and Olaya, Paula and Zhou, Naweiluo and Scorzelli, Giorgio and Pascucci, Valerio and Taufer, Michela},
title = {NSDF-Cloud: Enabling Ad-Hoc Compute Clusters Across Academic and Commercial Clouds},
year = {2022},
isbn = {9781450391993},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3502181.3533710},
doi = {10.1145/3502181.3533710},
abstract = {Computational resources are increasingly provisioned to users through cloud-like interfaces. Both academic and commercial cloud offerings exist, but no single standardized interface for common actions such as configuration, launching, and termination of virtual resources exists. This imposes huge technical burden on domain scientist that attempt to take advantage of these resources; even expert users spend considerable time to port their applications from one cloud platform to another.With this work, we make available to the community a unified API toolkit as well as five in-depth reports on challenges we encountered working with different academic and commercial cloud providers. Our toolkit implements automations for common tasks such as simultaneous launching and termination of large numbers of virtual machines (VM) across the cloud. We demonstrate that our toolkit brings down the time users need to spend launching and terminating these resources to mere minutes, thus enabling ad-hoc multi-cloud clusters.},
booktitle = {Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing},
pages = {279–280},
numpages = {2},
keywords = {virtual machine, data fabric, cyberinfrastructure},
location = {Minneapolis, MN, USA},
series = {HPDC '22}
}
@INPROCEEDINGS{9787867,
author={Klacansky, Pavol and Gyulassy, Attila and Bremer, Peer-Timo and Pascucci, Valerio},
booktitle={2022 IEEE 15th Pacific Visualization Symposium (PacificVis)},
title={A Study of the Locality of Persistence-Based Queries and Its Implications for the Efficiency of Localized Data Structures},
year={2022},
volume={},
number={},
pages={121-130},
keywords={Human computer interaction;Temperature distribution;Histograms;Scalability;Forestry;Data structures;Minimization;Human-centered computing- Visualization- Visu-alization application domains-Scientific visualization},
doi={10.1109/PacificVis53943.2022.00021}}
@ARTICLE{9286513,
author={Morrical, Nate and Wald, Ingo and Usher, Will and Pascucci, Valerio},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Accelerating Unstructured Mesh Point Location With RT Cores},
year={2022},
volume={28},
number={8},
pages={2852-2866},
keywords={Ray tracing;Acceleration;Hardware;Graphics processing units;Rendering (computer graphics);Computer architecture;Tensors;Scientific ray tracing;unstructured scalar data;GPGPU;simulation;volume rendering},
doi={10.1109/TVCG.2020.3042930}}
@INPROCEEDINGS{9787915,
author={Klacansky, Pavol and Miao, Haichao and Gyulassy, Attila and Townsend, Andrew and Champley, Kyle and Tringe, Joseph and Pascucci, Valerio and Bremer, Peer-Timo},
booktitle={2022 IEEE 15th Pacific Visualization Symposium (PacificVis)},
title={Virtual Inspection of Additively Manufactured Parts},
year={2022},
volume={},
number={},
pages={81-90},
keywords={Solid modeling;Three-dimensional displays;Design automation;Computed tomography;Computational modeling;Volume measurement;Virtual reality;Human-centered computing—Visualization—Visualization systems and tools;Human-centered computing—Visualization—Visualization application domains—Scientific visualization;Human-centered computing—Visualization—Empirical studies in visualization},
doi={10.1109/PacificVis53943.2022.00017}}
@misc{valerio_pascucci_2022_7488466,
author = {Valerio Pascucci},
title = {NSDF AHM Feb 2022: The NSDF Vision and Mission},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488466},
url = {https://doi.org/10.5281/zenodo.7488466},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{glenn_tarcea_2022_7488485,
author = {Glenn Tarcea and
Brian Puchala and
Tracy Berman and
John Allison},
title = {The Materials Commons 2.0: A Collaboration Platform and Information Repository for the Global Materials Community},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488485},
url = {https://doi.org/10.5281/zenodo.7488485},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{steve_petruzza_2022_7488491,
author = {Steve Petruzza},
title = {Experimental data acquisition and analysis at Advanced Photon Source Argonne National Lab},
month = dec,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488491},
url = {https://doi.org/10.5281/zenodo.7488491},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{damian_clarke_2022_7488502,
author = {Damian Clarke},
title = {NSDF collaboration with Minority Serving Institutions (MSI). MS-CC overview and opportunities to collaborate with the NSDF},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488502},
url = {https://doi.org/10.5281/zenodo.7488502},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{brian_e_schuster_2022_7488508,
author = {Brian E. Schuster},
title = {UTEP class with support by the NSDF},
month = dec,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488508},
url = {https://doi.org/10.5281/zenodo.7488508},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{ivan_rodero_2022_7488519,
author = {Ivan Rodero},
title = {VDC Data Services Integration with NSDF discussion},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488519},
url = {https://doi.org/10.5281/zenodo.7488519},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{paula_olaya_2022_7488527,
author = {Paula Olaya and
Dominic Kennedy and
Ricardo Llamas and
Rodrigo Vargas and
Jay Lofstead and
Michela Taufer},
title = {Building trust in Earth Science Findings through Data Traceability and Results Explainability},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488527},
url = {https://doi.org/10.5281/zenodo.7488527},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{frank_wuerthwein_2022_7488537,
author = {Frank Wuerthwein},
title = {OSG use cases for NSDF: including XenonNT as an example},
month = dec,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488537},
url = {https://doi.org/10.5281/zenodo.7488537},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{kevin_coakley_2022_7488544,
author = {Kevin Coakley},
title = {The NSDF Federated infrastructure},
month = dec,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488544},
url = {https://doi.org/10.5281/zenodo.7488544},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{alexander_szalay_2022_7488550,
author = {Alexander Szalay},
title = {Integration of OSN-NSDF data movements},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488550},
url = {https://doi.org/10.5281/zenodo.7488550},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{chris_shelley_2022_7488556,
author = {Chris Shelley},
title = {Content Delivery Network – State of the Art},
month = dec,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488556},
url = {https://doi.org/10.5281/zenodo.7488556},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{jakob_luettgau_2022_7488562,
author = {Jakob Luettgau and
Daniel Balouek and
Kevin Coakley and
Paula Olaya and
Giorgio Scorzelli and
Glenn Tarcea and
Naweiluo Zhou},
title = {NSDF Software Development Life Cycle Procedures},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488562},
url = {https://doi.org/10.5281/zenodo.7488562},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{naweiluo_zhou_2022_7488564,
author = {Naweiluo Zhou and
Giorgio Scorzelli and
Valerio Pascucci and
Michela Taufer and
Paula Olaya Garcia and
Jakob Luettgau},
title = {Workflow Orchestration for Material Science},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488564},
url = {https://doi.org/10.5281/zenodo.7488564},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{paula_olaya_2022_7488576,
author = {Paula Olaya and
Jakob Luettgau and
Naweiluo Zhou and
Giorgio Scorzelli and
Jay Lofstead and
Sophia When and
I-Hsin Chung and
Seetharami Seelam and
Michela Taufer},
title = {Composition of a data-driven workflow in the cloud},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488576},
url = {https://doi.org/10.5281/zenodo.7488576},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{attila_gyulassy_2022_7488580,
author = {Attila Gyulassy and
Jakob Luettgau and
Steve Petruzza and
Giorgio Scorzelli and
Glenn Tarcea and
Michela Taufer and
Naweiluo Zhou},
title = {NSDF User Communities Workgroup Update},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488580},
url = {https://doi.org/10.5281/zenodo.7488580},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{giorgio_scorzelli_2022_7488588,
author = {Giorgio Scorzelli},
title = {They stole \$1,440 and ¢1 from NSDF},
month = dec,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488588},
url = {https://doi.org/10.5281/zenodo.7488588},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{michael_corn_2022_7488590,
author = {Michael Corn},
title = {Intersecting Cybersecurity And Research Infrastructure: Partnership Not Rules},
month = feb,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488590},
url = {https://doi.org/10.5281/zenodo.7488590},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Feb 2022}
}
@misc{valerio_pascucci_2022_7488663,
author = {Valerio Pascucci},
title = {The NSDF vision and current progress},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488663},
url = {https://doi.org/10.5281/zenodo.7488663},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{michela_taufer_2022_7488689,
author = {Michela Taufer and
Giorgio Scorzelli and
Glenn Tarcea and
Jakob Luettgau and
Attila Gyulassy and
Christine Kirkpatrick and
Kevin Coakley},
title = {Working Groups: the NSDF Spine and Driving Engine},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488689},
url = {https://doi.org/10.5281/zenodo.7488689},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{giorgio_scorzelli_2022_7488700,
author = {Giorgio Scorzelli},
title = {Don't be stupid: avoid the lock-ness},
month = dec,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488700},
url = {https://doi.org/10.5281/zenodo.7488700},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{sal_malik_2022_7488710,
author = {Sal Malik},
title = {Decentralized Cloud Storage Use Cases},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488710},
url = {https://doi.org/10.5281/zenodo.7488710},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{adam_jennings_2022_7488723,
author = {Adam Jennings},
title = {Managed Open-Source Data Stack for Sub-Second Analytics and Queries},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488723},
url = {https://doi.org/10.5281/zenodo.7488723},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{peer_timo_bremer_2022_7488727,
author = {Peer-Timo Bremer},
title = {Democratizing Data Access at Lawrence Livermore National Laboratory},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488727},
url = {https://doi.org/10.5281/zenodo.7488727},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{sara_k_yeo_2022_7488741,
author = {Sara K. Yeo},
title = {The Science of Science Communication},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488741},
url = {https://doi.org/10.5281/zenodo.7488741},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{attila_gyulassy_2022_7488747,
author = {Attila Gyulassy and
Jakob Luettgau and
Steve Petruzza and
Naweiluo Zhou},
title = {Feedback from the scientific community},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488747},
url = {https://doi.org/10.5281/zenodo.7488747},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{julie_christopher_2022_7488752,
author = {Julie Christopher},
title = {NSDF Communication Framework},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488752},
url = {https://doi.org/10.5281/zenodo.7488752},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{kevin_coakley_2022_7488768,
author = {Kevin Coakley and
Christine Kirkpatrick},
title = {Federated Infrastructure Updates and FAIR Digital Objects},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488768},
url = {https://doi.org/10.5281/zenodo.7488768}
}
@misc{frank_2022_7488772,
author = {Frank Wuerthwein},
title = {News from the Open Science Data Federation},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488772},
url = {https://doi.org/10.5281/zenodo.7488772},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{dan_milroy_2022_7488780,
author = {Dan Milroy},
title = {Cloud-Native HPC with Kubernetes and the Flux Framework},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488780},
url = {https://doi.org/10.5281/zenodo.7488780},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{paula_olaya_2022_7488788,
author = {Paula Olaya and
Jakob Luettgau and
Ricardo Llamas and
Rodrigo Vargas and
Jay Lofstead and
Sophia Wen and
I-Hsin Chung and
Seetharami Seelam and
Michela Taufer},
title = {Tuning Object Storage for Scientific Workflows on Cloud Computational Services},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488788},
url = {https://doi.org/10.5281/zenodo.7488788},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{jakob_luettgau_2022_7488796,
author = {Jakob Luettgau and
Giorgio Scorzelli and
Naweiluo Zhou and
Glenn Tarcea and
Jay Lofstead and
Christine Kirkpatrick and
Naweiluo Zhou and
Valerio Pascucci and
Michela Taufer},
title = {NSDF-Catalog: Toward a Lightweight Indexing Service for the National Science Data Fabric},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488796},
url = {https://doi.org/10.5281/zenodo.7488796},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{bin_fan_2022_7488800,
author = {Bin Fan and
Shouwei Chen},
title = {Architect a Heterogeneous Data Platform Across Clusters, Regions, and Clouds},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488800},
url = {https://doi.org/10.5281/zenodo.7488800},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{ivan_rodero_2022_7488806,
author = {Ivan Rodero},
title = {Leveraging NSF CI investments for NSDF},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488806},
url = {https://doi.org/10.5281/zenodo.7488806},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{glenn_tarcea_2022_7488808,
author = {Glenn Tarcea and
Brian Puchala and
Tracy Berman and
John Allison},
title = {The Materials Commons 2.0: A Collaboration Platform and Information Repository for the Global Materials Community},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488808},
url = {https://doi.org/10.5281/zenodo.7488808},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{owen_koppe_2022_7488814,
author = {owen Koppe},
title = {Unlocking Analysis Ready Cloud Optimized (ARCO) file format in the OpenVisus Framework and NSDF-Data-Portal Update},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488814},
url = {https://doi.org/10.5281/zenodo.7488814},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{bozo_vazic_2022_7488820,
author = {Bozo Vazic and
Pania Newell},
title = {Accelerating scientific discoveries by mapping 3D imaging data to computational models for physics-based simulations},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488820},
url = {https://doi.org/10.5281/zenodo.7488820},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{naweiluo_zhou_2022_7488827,
author = {Naweiluo Zhou and
Giorgio Scorzelli, and
Jakob Luettgau and
Rahul Reddy Kancharla and
Joshua Kane and
Robert Wheeler and
Brendan Croom and
Pania Newell and
Valerio Pascucci and
Michela Taufer},
title = {A Software Framework for the Orchestration of Materials Science Workflows at Large Scale},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488827},
url = {https://doi.org/10.5281/zenodo.7488827},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{duong_hoang_2022_7488835,
author = {Duong Hoang and
Aashish Panta and
Giorgio Scorzelli and
Philip Davis and
Manish Parashar and
Valerio Pascucci},
title = {Publishing NASA's Multi-Petabytes of Climate Datasets},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488835},
url = {https://doi.org/10.5281/zenodo.7488835},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{dominic_kennedy_2022_7488842,
author = {Dominic Kennedy and
Paula Olaya and
Jay Lofstead and
Rodrigo Vargas and
Michela Taufer},
title = {TRIC: Traceability and Reproducibility through Individual Containerization},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488842},
url = {https://doi.org/10.5281/zenodo.7488842},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{daniel_balouek_thomert_2022_7488778,
author = {Daniel Balouek-Thomert},
title = {Building data services for urgent applications},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488778},
url = {https://doi.org/10.5281/zenodo.7488778},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{steve_petruzza_2022_7488844,
author = {Steve Petruzza and
Glenn Tarcea and
Giorgio Scorzelli and
Valerio Pascucci},
title = {Interactive access and exploration of scientific data products},
month = oct,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.7488844},
url = {https://doi.org/10.5281/zenodo.7488844},
publisher = {National Science Data Fabric (NSDF) All Hands Meeting Oct 2022}
}
@misc{valerio_pascucci_2023_7829832,
author = {Valerio Pascucci},
title = {The state of NSDF},
month = apr,
year = 2023,
publisher = {{National Science Data Fabric (NSDF) All Hands
Meeting April 2023}},
doi = {10.5281/zenodo.7829832},
url = {https://doi.org/10.5281/zenodo.7829832}
}
@misc{frank_wurthwein_2023_7859290,
author = {Frank Würthwein},
title = {National Research Platform- A Status Update},
month = apr,
year = 2023,
publisher = {{National Science Data Fabric (NSDF) All Hands
Meeting April 2023}},
doi = {10.5281/zenodo.7859290},
url = {https://doi.org/10.5281/zenodo.7859290}
}
@misc{maria_elena_monzani_2023_7859311,
author = {Maria Elena Monzani},
title = {{Data Intensive Searches for Dark Matter with LUX-
ZEPLIN (LZ)}},
month = apr,
year = 2023,
publisher = {{National Science Data Fabric (NSDF) All Hands
Meeting April 2023}},
doi = {10.5281/zenodo.7859311},
url = {https://doi.org/10.5281/zenodo.7859311}
}
@misc{shouwei_chen_2023_7859331,
author = {Shouwei Chen},
title = {Data Locality for Large scale AI / ML training},
month = apr,
year = 2023,
publisher = {{National Science Data Fabric (NSDF) All Hands
Meeting April 2023}},
doi = {10.5281/zenodo.7859331},
url = {https://doi.org/10.5281/zenodo.7859331}