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At least 343 records · Page 19

Building a custom high-throughput platform at the Joint Genome Institute for DNA construct design and assembly—present and future challenges

Abstract The rapid design and assembly of synthetic DNA constructs have become a crucial component of biological engineering projects via iterative design–build–test–learn cycles. In this perspective, we provide an overview of the workflows used to generate the thousands of constructs and libraries produced each year at the U.S. Department of Energy Joint Genome Institute. Particular attention is paid to describing pipelines, tools used, types of scientific projects enabled by the platform and challenges faced in further scaling output.

36 MATERIALS SCIENCE↗

Updates to the ATLAS Data Carousel Project

The High Luminosity upgrade to the LHC (HL-LHC) is expected to deliver scientific data at the multi-exabyte scale. In order to address this unprecedented data storage challenge, the ATLAS experiment launched the Data Carousel project in 2018. Data Carousel is a tape-driven workflow whereby bulk production campaigns with input data resident on tape are executed by staging and promptly processing a sliding window to disk buffer such that only a small fraction of inputs are pinned on disk at any one time. Data Carousel is now in production for ATLAS in Run3. In this paper, we provide updates on recent Data Carousel R&D projects, including data-on-demand and tape smart writing. Data-on-demand removes from disk data that has not been accessed for a predefined period, when users request them, they will be either staged from tape or recreated by following the original production steps. Tape smart writing employs intelligent algorithms for file placement on tape in order to retrieve data back more efficiently, which is our long term strategy to achieve optimal tape usage in Data Carousel.

42 ENGINEERING↗

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗

dCache - Keeping up With the Evolution of Science

The dCache project provides open-source software deployed internationally to satisfy ever more demanding storage requirements of various scientific communities. Its multifaceted approach provides an integrated way of supporting different use-cases with the same storage, from high throughput data ingest, through wide access and easy integration with existing systems, including event driven workflow management. With this presentation, we will show some of the recent developments that optimize data management and access to maximise the gain from stored data.

Mkrtchyan, Tigran↗

Extended Application of State LiDAR Datasets in Locating Orphaned Wells in Appalachian Region

Location inaccuracies in historical and state oil and gas well databases present a major challenge in locating these orphaned wells. To address this, modern scientific methods such as Light Detection and Ranging (LiDAR), aerial magnetic remote sensing, and digital GIS products have been employed. LiDAR technology uses light to detect surface area changes, providing detailed surface views. This is a workflow to process LiDAR data for use in locating orphaned wells.

Gorantla, Vijaya [NETL Site Support Contractor, Na↗

Interactive Supercomputing With Jupyter

Rich user interfaces like Jupyter have the potential to make interacting with a supercomputer easier and more productive, consequently attracting new kinds of users and helping to expand the application of supercomputing to new science domains. For the scientist-user, the ideal rich user interface delivers a familiar, responsive, introspective, modular, and customizable platform upon which to build, run, capture, document, re-run, and share analysis workflows. From the provider or system administrator perspective, such a platform would also be easy to configure, deploy securely, update, customize, and support. Jupyter checks most if not all of these boxes. But from the perspective of leadership computing organizations that provide supercomputing power to users, such a platform should also make the unique features of a supercomputer center more accessible to users and more composable with high performance computing (HPC) workflows. Project Jupyter’s core design philosophy of extensibility, abstraction, and agnostic deployment, has allowed HPC centers like NERSC to bring in advanced supercomputing capabilities that can extend the interactive notebook environment. This has enabled a rich scientific discovery platform, particularly for experimental facility data analysis and machine learning problems.

97 MATHEMATICS AND COMPUTING↗

Extracted Radar Columns and In Situ Sensors (RadCLss) Value-Added Product Report

In order to validate precipitation, in 2010 the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility procured 3- and 5-cm wavelength radars for documenting the macrophysical, microphysical, and dynamical structure of precipitating systems. To maximize the scientific impact, ARM supported the development of an application chain to correct for various phenomena in order to retrieve the “point” values of moments of the radar spectrum and polarimetric measurements. In estimation from ARM radars, a workflow was created to directly compare radar “point” values with various in situ observations at the surface.

54 ENVIRONMENTAL SCIENCES↗

Extending the Publish/Subscribe Abstraction for High-Performance I/O and Data Management at Extreme Scale

The Adaptable I/O System (ADIOS) represents the culmination of substantial investment in Scientific Data Management, and it has demonstrated success for several important extreme-scale science cases. However, looking towards the exascale and beyond, we see the development of yet more stringent data management requirements that require new abstractions. Therefore, there is an opportunity to attempt to connect the traditional realms of HPC I/O optimization with the Database / Data Management community. As such, in this paper we offer some specific examples from our ongoing work in managing data structures, services, and performance at the extreme scale for scientific computing. Using the publish/subscribe model afforded by ADIOS, we demonstrate a set of services that connect data format, metadata, queries, data reduction, and high-performance delivery. The resulting publish/subscribe framework facilitates connection to on-line workflow systems to enable the dynamic capabilities that will be required for exascale science.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Data and scripts associated with the manuscript "Organic Molecules are Deterministically Assembled in River Sediments"

This data package is associated with the publication "Organic Molecules are Deterministically Assembled in River Sediments" submitted to Scientific Reports (Stegen et al., 2024). The study applies community ecology methods to dissolved organic matter (DOM) chemistry from variably inundated riverbed sediments to uncover principles governing DOM composition at a reach-scale. This data package documents the workflow used to process and generate the main findings in the manuscript. The R scripts reference the raw, unprocessed Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data from another data package, available on ESS-DIVE at https://data.ess-dive.lbl.gov/view/doi:10.15485/1834208. The scripts then process the raw FTICR-MS data and generate the findings and figures presented in the associated manuscript. In brief, this study demonstrates that DOM assemblages in variably inundated sediments are primarily governed by deterministic variable selection, including sediment moisture effecting the degree of deterministic assembly. See the manuscript for more details pertaining to interpretation and implications of the findings. This data package is associated with the GitHub repository found at https://github.com/WHONDRS-Hub/ECA_2020_Sed.This data package is comprised of 6 scripts and 7 folders. The file-level metadata file (file ending in "flmd.csv") lists all files contained in this data package and descriptions for each. The data dictionary (file ending in "dd.csv) describes all tabular data columns and their respective definitions and units. The FTICR_Processing_Scripts produce the outputs found in the "Processed_Data" folder. The remaining scripts (located in the parent directory) produce the outputs found in the following four folders: (1) "MCD_Dendrograms", "MCD_Randomizations", "MCD_bNTI_Outcomes", and "OM_Null_Modeling". The fifth script additionally takes the three comma-separated values (CSV) files found in the parent directory as input ("VGC_texture.csv", "merged_weights.csv", and "ECA2_FTICR_BetaDisp.csv"). The outputs of each of the five scripts serve as the input to the following script, with the final outputs stored in the folder "OM_Null_Modeling".

54 ENVIRONMENTAL SCIENCES↗

Predicting Execution Times for Disk-based and In-Situ Parallel Data Analytics (Final Technical Report)

In recent years, there has been a significant amount of interests in in-situ analytics on simulation programs. For a variety of reasons, it is desirable to be able to predict the execution time of an analytics program. At the same time, frameworks such as MapReduce have become popular for scientific data analytics. This paper focuses on developing performance models for predicting execution time of parallel data analytics, with a special emphasis on in-situ analytics. We take two distinct approach towards performance prediction. We first expand SKOPE (a SKeleton framewOrk for Performance Exploration) with performance models for disk data read, cache performance, and page fault penalty. Second, an analytical performance model is also developed. We have evaluated our performance prediction framework as well as the analytical model on three hardware setups with well-known data mining algorithms implemented in three programming paradigms, MapReduce, MATE (a MapReduce-like parallel system with an alternate API for multi-core environments) and Smart (a MapReduce-like framework for in-situ analytics). Results show that our performance prediction framework along with the incorporated performance models are capable of accurately predicting execution times for parallel scientific analytics on different hardware setups.

97 MATHEMATICS AND COMPUTING↗

TeMPI Shim

This is the manual for the TeMPI Shim library, whose goal is to facilitate scientific apps being loosely coupled through MPI. TeMPI Shim can be used to create and manage Message Passing Interface (MPI, see [MPI]) Communicators for Single Program, Multiple Data (SPMD, see [SPMD]) and Multiple Programs, Multiple Data (MPMD, see [MPMD]) workflows. MPI provides the MPI_APPNUM command number to each of the processes within its context. This number, starting at 0, is the application number being executed. In a case where 3 applications are being run in MPMD mode, there would be MPI_APPNUM values of 0, 1, and 2. TeMPI Shim creates intra- and inter-communicators between each pair of MPI_APPNUM values. In the aforementioned case, application 0 would have intra- and inter-communicators to speak to itself, application 1, and application 2. This is replicated for each of the applications. Additionally, TeMPI Shim creates intra- and inter-communicators for the first MPI rank of each application to directly communicate only with each other. Finally, TeMPI Shim creates its own copy of the default world communicator, i.e., MPI_COMM_WORLD. In the case where there is only a single application, it will have the communicators to only speak with itself. TeMPI Shim is useful in this case since it is considered good practice for MPI applications to copy the default world communicator and reference this copy (see [Duplicate World]_) anyways. Ultimately, it provides value independent of the number of applications present.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Arroyo Stream Processing Toolset (arroyopy) v0.1.0

Processing event or streaming data presents several technological challenges. A variety of technologies are often used by scientific user facilities. ZMQ is used to stream data and messages in a peer-to-peer fashion. Message brokers like Kafka, Redis Pubsub, EPICS PVA and RabbitMQ are often employed to route and pass messages from instruments to processing workflows. Arroyopy provides an API and structure to flexibly integrate with these tools and incorporate arbitrarily complex processing workflows, letting the hooks to the workflow code be independent of the connection code and hence reusable at a variety of instruments.

Chavez Esparza, Tanny Andrea [Lawrence Berkeley Na↗

DeepLynx Ecosystem 2025

Poor data integration and governance continue to plague complex engineering projects, resulting in missed cost, schedule, and performance targets. Departments operate in isolated systems with manual data exchange, creating fragmented information that compounds errors and leads to significant delays and cost overruns. The DeepLynx ecosystem addresses these challenges through an open-source, modular data management platform that transforms fragmented project data into an integrated digital thread. Built on a federated microservice architecture, the ecosystem comprises seven specialized tools centered around DeepLynx Nexus, a unified data catalog with hierarchical organization and graph-based navigation capabilities. The ecosystem includes: DeepLynx Stream for real-time timeseries data ingestion from industrial sources; DeepLynx Ingest for governed data uploads with formal review workflows; DeepLynx Lattice for ontology-based entity and relationship extraction; DeepLynx Run for workflow orchestration and secure AI/ML compute; DeepLynx Visualize for 3D digital twin visualization; and DeepLynx Insight for AI-assisted document analysis with traceable, grounded responses. Deployable in cloud, on-premise, or hybrid environments using containerized Docker applications and Helm charts, the DeepLynx ecosystem provides flexible infrastructure that adapts to organizational requirements. By consolidating project data into a unified data lake with role-based access controls and OAuth2 authentication, DeepLynx enables digital thread and digital twin capabilities that improve decision-making, reduce risk, and support complex engineering workflows throughout the project lifecycle.

42 - ENGINEERING↗

Abstraction hierarchy to define biofoundry workflows and operations for interoperable synthetic biology research and applications

Lack of standardization in biofoundries limits the scalability and efficiency of synthetic biology research. Here, we propose an abstraction hierarchy that organizes biofoundry activities into four interoperable levels: Project, Service/Capability, Workflow, and Unit Operation, effectively streamlining the Design‑Build‑Test‑Learn (DBTL) cycle. This framework enables more modular, flexible, and automated experimental workflows. It improves communication between researchers and systems, supports reproducibility, and facilitates better integration of software tools and artificial intelligence. Our approach lays the foundation for a globally interoperable biofoundry network, advancing collaborative synthetic biology and accelerating innovation in response to scientific and societal challenges.

Kim, Haseong↗

MISPR : an open-source package for high-throughput multiscale molecular simulations

Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure–Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure–property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

36 MATERIALS SCIENCE↗

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

59 BASIC BIOLOGICAL SCIENCES↗

AI-Ready Data Pilot Project Report

The proliferation of artificial intelligence in scientific research has created an urgent need to define "AI-ready data" for researchers and, more importantly, provide resources to help them produce AI-ready data. At Pacific Northwest National Laboratory, we conducted a pilot study with three data scientists evaluating three CSV datasets from different scientific domains, followed by semi-structured interviews capturing assessment practices. Our findings reveal that AI-readiness evaluation is intuition-based, with practitioners asking "How fast can I go from raw data to my machine learning pipeline?" Data scientists consistently prioritized workflow efficiency, human interpretability, and quality stewardship signals. From these insights, we developed a practical evaluation framework comprising data requirements, metadata standards, and validation tests that provides actionable criteria for producing and curating AI-ready datasets, addressing the gap between theoretical understanding and practical implementation.

97 MATHEMATICS AND COMPUTING↗