Data management study, volume 5. Appendix B - Contractor data package Planetary Quarantine /PQ/ Final report
Contractor data management package for Voyager spacecraft sterilization project
PROGRAM MANAGEMENT↗
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Laying The Foundations for FAIR-ER Science: ISA And LSDA Data Submission Process in NASA’s Evolving Data Management Environment
The Life Sciences Data Archive (LSDA) archives data resulting from research on the effects of spaceflight on humans and the development of countermeasures to mitigate spaceflight hazards. Archivists work with researchers to ensure that unique and high value data products and their metadata are preserved and managed to support current and future research. Currently, LSDA is updating its procedures and data submission requirements in response to the evolving data preservation environment at NASA. LSDA is implementing best practices for research data management through the establishment of clear data submission guidelines, integration of the FAIR (Findability, Accessibility, Interoperability, Reusability) principles, and use of the ISA (Investigation, Study, Assay) research metadata framework for data discoverability and transparency into the data management processes. These changes directly impact LSDA’s requirements for research data submissions. The newly revised Research Data Submission Agreement (RDSA), formerly the Data Submission Agreement (DSA), introduces ISA-compatible metadata collection standards to LSDA’s process. Adherence to LSDA’s data submission guidelines enhances the FAIR-ness of the repository’s collections for future users. This presentation will discuss (1) how submission of research data and associated metadata are impacted by current data management policies, (2) benefits of the adoption of FAIR principles and the ISA metadata framework for retrospective studies utilizing existing LSDA datasets and historic data collections, and (3) the support LSDA will provide to researchers during this transition.
Data submission↗
Laying the Foundations for FAIR-er Science: ISA and the LSDA Data Submission Process in NASA's Evolving Data Management Environment
The Life Sciences Data Archive (LSDA) archives data resulting from research on the effects of spaceflight on humans and the development of countermeasures to mitigate spaceflight hazards. Archivists work with researchers to ensure that unique and high value data products and their metadata are preserved and managed to support current and future research. Currently, LSDA is updating its procedures and data submission requirements in response to the evolving data preservation environment at NASA. LSDA is implementing best practices for research data management through the establishment of clear data submission guidelines, integration of the FAIR (Findability, Accessibility, Interoperability, Reusability) principles, and use of the ISA (Investigation, Study, Assay) research metadata framework for data discoverability and transparency into the data management processes. These changes directly impact LSDA’s requirements for research data submissions. The newly revised Research Data Submission Agreement (RDSA), formerly the Data Submission Agreement (DSA), introduces ISA-compatible metadata collection standards to LSDA’s process. Adherence to LSDA’s data submission guidelines enhances the FAIR-ness of the repository’s collections for future users. This presentation will discuss (1) how submission of research data and associated metadata are impacted by current data management policies, (2) benefits of the adoption of FAIR principles and the ISA metadata framework for retrospective studies utilizing existing LSDA datasets and historic data collections, and (3) the support LSDA will provide to researchers during this transition.
LSDA↗
A data management system for engineering and scientific computing
Data elements and relationship definition capabilities for this data management system are explicitly tailored to the needs of engineering and scientific computing. System design was based upon studies of data management problems currently being handled through explicit programming. The system-defined data element types include real scalar numbers, vectors, arrays and special classes of arrays such as sparse arrays and triangular arrays. The data model is hierarchical (tree structured). Multiple views of data are provided at two levels. Subschemas provide multiple structural views of the total data base and multiple mappings for individual record types are supported through the use of a REDEFINES capability. The data definition language and the data manipulation language are designed as extensions to FORTRAN. Examples of the coding of real problems taken from existing practice in the data definition language and the data manipulation language are given.
Elliot, L.↗
Transitioning CMS to Rucio Data Managment
Following a thorough review in 2018, the CMS experiment at the CERN LHC decided to adopt Rucio as its new data management system. Rucio is emerging as a community software project and will replace an aging CMSonly system before the start-up of LHC Run 3 in 2021. Rucio was chosen after an evaluation determined that Rucio could meet the technical and scale needs of CMS. The data management system for CMS needs to manage the current data sample of approximately 200 PB of data with 1 PB of transfers per day. The data management system must also have a development path suitable for LHC Run 4 (2026) data rates, which are expected to be 50 times larger.This contribution details the ongoing CMS adoption process as we replace our legacy system with Rucio, focusing on the challenges of integrating Rucio into an established set of experimental tools and procedures. This will include the migration of metadata, the construction of interfaces to the rest of the CMS computing tools, scale tests, operations, monitoring, and the plan to gradually turn over primary responsibility for the management of data to Rucio. A description of CMS user data management with Rucio will also be provided.
Vaandering, Eric↗
Application of data management to thermal/structural analysis of space trusses
A relational data base management system has been used for managing engineering data in a prototype computerized thermal/structural integrated design system. The resulting system has been applied to the analysis of a tetrahedral space truss to demonstrate and evaluate the ability to store, retrieve, query, modify, and manipulate data. Key software used includes relational data management software, several applications programs, and selected integrated software developed during the study. Results discussed include system development, system use and performance, and advantages of an integrated data management system.
Rogers, V. A.↗
The NBS data management technology program
The National Bureau of Standards (NBS) Data Management Technology Program is discussed. The NBS Data Management Technology Program addresses major problems encountered during the following stages of an application's lifetime: requirements analysis and data base design, system selection and implementation, operations management and conversion. Products developed include standard software specifications, guides to best practice, standard data elements and representations, and reports documenting the experiences of other organizations as they attempt to improve the management of their computing resources. Data base Laboratory facilities are maintained for the investigation and analysis of state of the art data base technology. These facilities support collaborative testing with researchers, vendors, users, and standards developers.
Wood, H. M.↗
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↗
Engineering data management: Experience and projections
Experiences in developing a large engineering data management system are described. Problems which were encountered are presented and projected to future systems. Business applications involving similar types of data bases are described. A data base management system architecture proposed by the business community is described and its applicability to engineering data management is discussed. It is concluded that the most difficult problems faced in engineering and business data management can best be solved by cooperative efforts.
Jefferson, D. K.↗
Self-Test Techniques for Redundant Data Management Systems
Characteristics of Space shuttle data management system for data transmission and service for avionics equipment and other subsystems
Kayton, M.↗
An Autonomous MCP Bridge to Rucio: Enhancing Data Management Accessibility for High Energy Physics
The Rucio Data Management System [1] is an important tool used by High Energy Physics experiments, including those at Fermi National Accelerator Laboratory, to store and manage exabyte-scale scientific datasets. Despite its central role in coordinating data across globally distributed storage sites, Rucio's command line interface (CLI) presents a steep learning curve, and makes it difficult for scientists to navigate through. To solve this issue, a containerized Model Context Protocol (MCP) [2] server was built that connects Large Language Models directly to Rucio, allowing AI agents to handle data tasks by using simple, natural language rather than memorized terminal commands. The core engineering focus of this project was moving the server away from slow terminal commands that require text parsing and replacing them with a native Python Client API toolset and a planned REST API framework. Moving to the Python API handles data operations directly in memory, which helps clear up formatting errors, provides the AI with clean, structured JSON data and speeds up tool execution. To prove that the system actually works, a benchmarking pipeline was also built with various questions to test the AI across four different model configurations. The questions included finding data scopes, tracking down specific datasets, and checking replication rules. Through benchmarking, early runs showed that with raw terminal text, the model would get confused and stuck, whereas switching to the Python API to feed the AI clean, structured data yielded massive improvement. By creating an intelligent and autonomous bridge to a storage network, this project shows how AI can be implemented in scientific data management, which ultimately helps scientists at Fermilab spend less time sorting through data and more time focusing on their experiments and analysis.
Akella, Kashyap [William Rainey Harper Coll.]↗
The role of data management in discipline-independent data visualization
The common data format (CDF) is described in terms of its support applications for the database management of visualization systems. The CDF is a self-describing data abstraction technique for the storage and manipulation of multidimensional data that are based on block structures. The discipline-independent approach is designed to manage, manipulate, archive, display, and analyze data, and can be applied to heterogeneous equipment communicating different data structures over networks. An improved CDF version incorporates a hyperplane access allowing random aggregate access to subdimensional blocks within a multidimensional variable. The visualization pipeline is also discussed, which controls the flow of data and permits the visualization of different classes of data representation techniques. The system is found to accommodate a large variety of scientific data structures and large disk-based data sets.
Treinish, Lloyd A.↗
Data Management as a Cluster Middleware Centerpiece
Through earth and space modeling and the ongoing launches of satellites to gather data, NASA has become one of the largest producers of data in the world. These large data sets necessitated the creation of a Data Management System (DMS) to assist both the users and the administrators of the data. Halcyon Systems Inc. was contracted by the NASA Center for Computational Sciences (NCCS) to produce a Data Management System. The prototype of the DMS was produced by Halcyon Systems Inc. (Halcyon) for the Global Modeling and Assimilation Office (GMAO). The system, which was implemented and deployed within a relatively short period of time, has proven to be highly reliable and deployable. Following the prototype deployment, Halcyon was contacted by the NCCS to produce a production DMS version for their user community. The system is composed of several existing open source or government-sponsored components such as the San Diego Supercomputer Center s (SDSC) Storage Resource Broker (SRB), the Distributed Oceanographic Data System (DODS), and other components. Since Data Management is one of the foremost problems in cluster computing, the final package not only extends its capabilities as a Data Management System, but also to a cluster management system. This Cluster/Data Management System (CDMS) can be envisioned as the integration of existing packages.
Zero, Jose↗
Software Framework for Peer Data-Management Services
Object Oriented Data Technology (OODT) is a software framework for creating a Web-based system for exchange of scientific data that are stored in diverse formats on computers at different sites under the management of scientific peers. OODT software consists of a set of cooperating, distributed peer components that provide distributed peer-to-peer (P2P) services that enable one peer to search and retrieve data managed by another peer. In effect, computers running OODT software at different locations become parts of an integrated data-management system.
Hughes, John↗
Data management in pattern recognition and image processing systems
Data management considerations are important to any system which handles large volumes of data or where the manipulation of data is technically sophisticated. A particular problem is the introduction of image-formatted files into the mainstream of data processing application. This report describes a comprehensive system for the manipulation of image, tabular, and graphical data sets which involve conversions between the various data types. A key characteristic is the use of image processing technology to accomplish data management tasks. Because of this, the term 'image-based information system' has been adopted.
Zobrist, A. L.↗
A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community
Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.
36 MATERIALS SCIENCE↗
Data-Management Software For PiFEx
UNIX Based Data Management System is collection of computer programs for use in Pilot Field Experiment (PiFEx), which attempts to mimic mobile/satellite-communications (MSAT) scenario. Major purposes to define mobile-communications channels and test workability of new concepts used to design various components of receiver system. Results, large amounts of raw data that must be retrieved according to researcher's needs. Intended to manage PiFEx data in interactive way. Written in either FORTRAN-77 or UNIX shell-scripts.
Kantak, Anil V.↗
Microbiome data management in action workshop: Atlanta, GA, USA, June 12–13, 2024
Microbiome research is revolutionizing human and environmental health, but the value and reuse of microbiome data are significantly hampered by the limited development and adoption of data standards. While several ongoing efforts are aimed at improving microbiome data management, significant gaps still remain in terms of defining and promoting adoption of consensus standards for these datasets. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines for human microbiome research have been endorsed and successfully utilized by many research organizations, publishers, and funding agencies, and have been recognized as a consensus community standard. No equivalent effort has occurred for environmental, synthetic, and non-human host-associated microbiomes. To address this growing need within the microbiome research community, we convened the Microbiome Data Management in Action Workshop (June 12–13, 2024, in Atlanta, GA, USA), to bring together key decision makers in microbiome science including researchers, publishers, funders, and data repositories. The 50 attendees, representing the diverse and interdisciplinary nature of microbiome research, discussed recent progress and challenges, and brainstormed actionable recommendations and paths forward for coordinated environmental microbiome data management and the modifications necessary for the STORMS guidelines to be applied to environmental, non-human host, and synthetic microbiomes. The outcomes of this workshop will form the basis of a formalized data management roadmap to be implemented across the field. These best practices will drive scientific innovation now and in years to come as these data continue to be used not only in targeted reanalyses but in large-scale models and machine learning efforts.
54 ENVIRONMENTAL SCIENCES↗