Results for “data management” — Page 48

DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Cyber Infrastructure for the Smart Electric Grid

As electric power systems undergo a transformative upgrade with the integration of advanced technologies to enable the smarter electric grid, professionals who work in the area require a new understanding of the evolving complexity of the grid. Cyber Infrastructure for the Smart Electric Grid delivers a comprehensive overview of the fundamental principles of smart grid operation and control, smart grid technologies, including sensors, communication networks, computation, data management, and cyber security, and the interdependencies between the component technologies on which a smart grid's security depends. The book offers readers the opportunity to critically analyze the smart grid infrastructure needed to sense, communicate, compute, and control in a secure way.

communication networks↗

NASA NTRS2000

Free-Flying Magnetometer Data System

The Free-Flying Magnetometer (FFM) is an autonomous "sensorcraft" developed at the Jet Propulsion Laboratory (JPL) for the Enstrophy sounding rocket mission. This mission was a collaborative project between the University of New Hampshire, Cornell University and JPL. The science goal of the mission was the study of current filamentation phenomena in the northern auroral region through multipoint measurements of magnetic field. The technical objective of the mission was the proof of concept of the JPL FFM design and the demonstration of an in-situ multipoint measurement technique employing many free-flying spacecraft. Four FFMs were successfully deployed from a sounding rocket launched from Poker Flats, Alaska on February 11, 1999. These hockey-puck-sized (80 mm diameter, 38 mm. height, 250 gram mass) free flyers each carry a miniature 3-axis flux-gate magnetometer that output +/- 2 V signals corresponding to a +/- 60,000 nT measurement range for each axis. The FFM uses a synchronized four-channel Sigma(Delta) Analog-to-Digital Converter (ADC) having a dynamic range of +/- 2.5V and converting at a rate of 279 samples/second/channel. Three channels are used to digitize the magnetometer signals to 17-bit (1.144 nT/bit) resolution. The fourth ADC channel is multiplexed for system monitoring of four temperature sensors and two battery voltages. The FFM also contains two sun sensors, a laser diode which emits a fan-shaped beam, a miniature S-band transmitter for direct communication to the ground station antennas, an ultra-stable Temperature Compensated Crystal Oscillator (TCXO) clock, an integrated data subsystem implemented in a Field-Programmable Gate Array (FPGA), a 4 Mbit Static Random Access Memory (SRAM) for data storage and Lithium Thionyl Chloride batteries for power. Communicating commands to the FFM prior to deployment is achieved with an infrared (IR) link. The FFM IR receiver responds to 9-bit pulse coded signals that are generated by an IR Light Emitting Diode (LED) in the payload for turning FFM power on or off and placing the FFM in a test mode or flight mode. The IR links are also used to synchronize (zero) the clocks onboard all the FFMs through a reset pulse originating from the payload GPS receiver that is issued when the FFMs are in flight mode. The FPGA based data subsystem manages continuous data collection from the four ADC channels and sun sensors, formatting and storing the data to SRAM, and controlling downlink transmission. The transmitter is powered only after a 2547 frame SRAM buffer has been filled (approx. 5 minutes of data). The data is Viterbi encoded and sent to the S-band transmitter via a First-In-First-Out (FIFO) buffer who's output is clocked at 100 bits/second. After the 26-second transmission, the transmitter is turned off to reduce noise coupling to the sensitive magnetometer. The data subsystem control consists of a master state machine that performs data flow management and is interfaced through a prioritized interrupt scheme to state machines that service the ADC, sun sensors and transmitter FIFO. Continuous data collection prevents the missing of data during transmission and provides implicit time tagging of the data acquired by the ADC because of synchronization with the TCXO clock.

Blaes, B.↗

NASA NTRS1979

Lunar laser ranging data identification and management

Activity under the subject grant during the first half of fiscal year 1979 at the University of Texas at Austin is reported. Raw lunar laser ranging data submitted by McDonald Observatory, Fort Davis, Texas and by the Australian Division of National Mapping at Orroral Valley, Australia were processed. This processing includes the filtering of signal events from noise photons, normal point formation, data archive management, and data distribution. System-wide program maintenance and up-grade carried out wherever and whenever necessary. Lunar laser ranging data is being transmitted from Austin to Paris for the extraction of earth rotation information during the EROLD campaign.

Source record↗

NASA NTRS1987

A Computer System for Mission Managers

Mission Managers' Workstation (MMW) is personal-computer-based system providing data management and reporting functions to assist Space Shuttle mission managers. Allows to relate events and stored data in timely and organized fashion. Using MMW, standard reports formatted, generated, edited, and electronically communicated with minimum clerical help. Written in PASCAL, BASIC, and assembler.

Tolchin, Robert↗

NASA NTRS1996

APMS: An Integrated Set of Tools for Measuring Safety

This is a report of work in progress. In it, I summarize the status of the research and development of the Aviation Performance Measuring System (APMS) for managing, processing, and analyzing digital flight-recorded data. The objectives of the NASA-FAA APMS research project are to establish a sound scientific and technological basis for flight-data analysis, to define an open and flexible architecture for flight-data-analysis systems, and to articulate guidelines for a standardized database structure on which to continue to build future flight-data-analysis extensions. APMS will offer to the air transport community an open, voluntary standard for flight-data-analysis software, a standard that will help to ensure suitable functionality, and data interchangeability, among competing software programs. APMS will develop and document the methodologies, algorithms, and procedures for data management and analyses to enable users to easily interpret the implications regarding safety and efficiency of operations. APMS does not entail the implementation of a nationwide flight-data-collection system. It is intended to provide technical tools to ease the large-scale implementation of flight-data analyses at both the air-carrier and the national-airspace levels in support of their Flight Operations and Quality Assurance (FOQA) Programs and Advanced Qualifications Programs (AQP). APMS cannot meet its objectives unless it develops tools that go substantially beyond the capabilities of the current commercially available software and supporting analytic methods that are mainly designed to count special events. These existing capabilities, while of proven value, were created primarily with the needs of air crews in mind. APMS tools must serve the needs of the government and air carriers, as well as air crews, to fully support the FOQA and AQP programs. They must be able to derive knowledge not only through the analysis of single flights (special-event detection), but through statistical evaluation of the performance of large groups of flights. This paper describes the integrated suite of tools that will assist analysts in evaluating the operational performance and safety of the national air transport system, the air carrier, and the air crew.

Statler, Irving C.↗

NASA NTRS1997

APMS: An Integrated Suite of Tools for Measuring Performance and Safety

This is a report of work in progress. In it, I summarize the status of the research and development of the Aviation Performance Measuring System (APMS) for managing, processing, and analyzing digital flight-recorded data. The objectives of the NASA-FAA APMS research project are to establish a sound scientific and technological basis for flight-data analysis, to define an open and flexible architecture for flight-data-analysis systems, and to articulate guidelines for a standardized database structure on which to continue to build future flight-data-analysis extensions . APMS will offer to the air transport community an open, voluntary standard for flight-data-analysis software, a standard that will help to ensure suitable functionality, and data interchangeability, among competing software programs. APMS will develop and document the methodologies, algorithms, and procedures for data management and analyses to enable users to easily interpret the implications regarding safety and efficiency of operations. APMS does not entail the implementation of a nationwide flight-data-collection system. It is intended to provide technical tools to ease the large-scale implementation of flight-data analyses at both the air-carrier and the national-airspace levels in support of their Flight Operations and Quality Assurance (FOQA) Programs and Advanced Qualifications Programs (AQP). APMS cannot meet its objectives unless it develops tools that go substantially beyond the capabilities of the current commercially available software and supporting analytic methods that are mainly designed to count special events. These existing capabilities, while of proven value, were created primarily with the needs of air crews in mind. APMS tools must serve the needs of the government and air carriers, as well as air crews, to fully support the FOQA and AQP programs. They must be able to derive knowledge not only through the analysis of single flights (special-event detection), but through statistical evaluation of the performance of large groups of flights. This paper describes the integrated suite of tools that will assist analysts in evaluating the operational performance and safety of the national air transport system, the air carrier, and the air crew.

Statler, Irving C.↗

NASA NTRS1997

APMS: An Integrated Suite of Tools for Measuring Performance and Safety

This is a report of work in progress. In it, I summarize the status of the research and development of the Aviation Performance Measuring System (APMS) for managing, processing, and analyzing digital flight-recorded data. The objectives of the NASA-FAA APMS research project are to establish a sound scientific and technological basis for flight-data analysis, to define an open and flexible architecture for flight-data-analysis systems, and to articulate guidelines for a standardized database structure on which to continue to build future flight-data-analysis extensions. APMS will offer to the air transport community an open, voluntary standard for flight-data-analysis software, a standard that will help to ensure suitable functionality, and data interchangeability, among competing software programs. APMS will develop and document the methodologies, algorithms, and procedures for data management and analyses to enable users to easily interpret the implications regarding safety and efficiency of operations. APMS does not entail the implementation of a nationwide flight-data-collection system. It is intended to provide technical tools to ease the large-scale implementation of flight-data analyses at both the air-carrier and the national-airspace levels in support of their Flight Operations and Quality Assurance (FOQA) Programs and Advanced Qualifications Programs (AQP). APMS cannot meet its objectives unless it develops tools that go substantially beyond the capabilities of the current commercially available software and supporting analytic methods that are mainly designed to count special events. These existing capabilities, while of proven value, were created primarily with the needs of air crews in mind. APMS tools must serve the needs of the government and air carriers, as well as air crews, to fully support the FOQA and AQP programs. They must be able to derive knowledge not only through the analysis of single flights (special-event detection), but through statistical evaluation of the performance of large groups of flights. This paper describes the integrated suite of tools that will assist analysts in evaluating the operational performance and safety of the national air transport system, the air carrier, and the air crew.

Statler, Irving C.↗

NASA NTRS1998

The Aviation Performance Measuring System (APMS): An Integrated Suite of Tools for Measuring Performance and Safety

This is a report of work in progress. In it, I summarize the status of the research and development of the Aviation Performance Measuring System (APMS) for managing, processing, and analyzing digital flight-recorded data, The objectives of the NASA-FAA APMS research project are to establish a sound scientific and technological basis for flight-data analysis, to define an open and flexible architecture for flight-data analysis systems, and to articulate guidelines for a standardized database structure on which to continue to build future flight-data-analysis extensions. APMS offers to the air transport community an open, voluntary standard for flight-data-analysis software; a standard that will help to ensure suitable functionality and data interchangeability among competing software programs. APMS will develop and document the methodologies, algorithms, and procedures for data management and analyses to enable users to easily interpret the implications regarding safety and efficiency of operations. APMS does not entail the implementation of a nationwide flight-data-collection system. It is intended to provide technical tools to ease the large-scale implementation of flight-data analyses at both the air-carrier and the national-airspace levels in support of their Flight Operations and Quality Assurance (FOQA) Programs and Advanced Qualifications Programs (AQP). APMS cannot meet its objectives unless it develops tools that go substantially beyond the capabilities of the current commercially available software and supporting analytic methods that are mainly designed to count special events. These existing capabilities, while of proven value, were created primarily with the needs-of aircrews in mind. APMS tools must serve the needs of the government and air carriers, as well as aircrews, to fully support the FOQA and AQP programs. They must be able to derive knowledge not only through the analysis of single flights (special-event detection), but also through statistical evaluation of the performance of large groups of flights. This paper describes the integrated suite of tools that will assist analysts in evaluating the operational performance and safety of the national air transport system, the air carrier, and the aircrew.

Statler, Irving C.↗

NASA NTRS2018

NASA's Earth Observing Data and Information System - Near-Term Challenges

NASA's Earth Observing System Data and Information System (EOSDIS) has been a central component of the NASA Earth observation program since the 1990's. EOSDIS manages data covering a wide range of Earth science disciplines including cryosphere, land cover change, polar processes, field campaigns, ocean surface, digital elevation, atmosphere dynamics and composition, and inter-disciplinary research, and many others. One of the key components of EOSDIS is a set of twelve discipline-based Distributed Active Archive Centers (DAACs) distributed across the United States. Managed by NASA's Earth Science Data and Information System (ESDIS) Project at Goddard Space Flight Center, these DAACs serve over 3 million users globally. The ESDIS Project provides the infrastructure support for EOSDIS, which includes other components such as the Science Investigator-led Processing systems (SIPS), common metadata and metrics management systems, specialized network systems, standards management, and centralized support for use of commercial cloud capabilities. Given the long-term requirements, and the rapid pace of information technology and changing expectations of the user community, EOSDIS has evolved continually over the past three decades. However, many challenges remain. Challenges addressed in this paper include: growing volume and variety, achieving consistency across a diverse set of data producers, managing information about a large number of datasets, migration to a cloud computing environment, optimizing data discovery and access, incorporating user feedback from a diverse community, keeping metadata updated as data collections grow and age, and ensuring that all the content needed for understanding datasets by future users is identified and preserved.

Remote Sensing↗

DOE OSTI2022

Echo: Data and Analysis Management [Slides]

Echo software capabilities are described. Echo provides a comprehensive suite of tools for data wrangling, management, and analysis. It is an object-oriented approach to data analysis designed for robust and scalable execution of complex analysis workflows written in MATLAB.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2017

Knowledge Base for Distributed Spacecraft Mission Design Using the Trade-Space Analysis Tool for Constellations (TAT-C)

Opportunities for multi-point measurements, greater revisit frequency, failure robustness, and improved cost effectiveness motivate consideration of Distributed Spacecraft Missions (DSMs) for future Earth science missions. However, careful analysis is required to assess the distributed sensing capabilities of a constellation compared to more mature monolithic spacecraft while also considering other important dimensions such as cost and risk. The large combinatorial DSM design space limits existing mission analysis tools and exploration methods which emphasize monolithic design variables. The Trade-space Analysis Tool for Constellations (TAT-C) under development at Goddard seeks to enumerate and evaluate alternative mission architectures to minimize cost and maximize scientific return for pre-defined goals during pre-phase A analysis.Similar to other model-centric engineering efforts, efficient data management is a significant challenge for DSM mission analysis. In TAT-C, a Knowledge Base (KB) is envisioned as a cumulative central repository of information and meta-information about DSMs. Initial KB concepts store related data for reuse within or across mission analyses; however, over time, the KB is envisioned to be an important layer to coordinate actions of both human analysts and automated design agents to search a large design space for desirable mission alternatives. Preliminary KB research builds on a modern web technology stack to provide the following functionality: 1) storage of trade-space search requests which set requirements and constraints for DSM concepts, 2) storage of analysis results which quantify performance metrics for evaluated DSM concepts, 3) a RESTful application programming interface (API) for scripted access to data from TAT-C modules, 4) web-based graphical user interface (GUI) for manual access to underlying data, and 5) access control and management restrictions relevant to data protection and security. These efforts have culminated in a prototype KB used by the research team during TAT-C development to assess opportunities for future work.

Pattern Recognition↗

NASA NTRS2017

Biosensor Integration Development ExMC/Canadian Space Agency Collaboration

In support of the NASA Human Research Program Exploration Medical Capability (ExMC) Element, NASA Ames Research Center (ARC) established a collaborative effort with the Canadian Space Agency (CSA). The collaboration focuses on leveraging CSA capability in the areas of biosensors and decision support that will augment future development of such components for Exploration Missions. The CSA advancement of biosensors enables NASA to focus on the integration and data management associated with these types of components through the system currently under development by the Medical Data Architecture (MDA) project. This approach has enabled the establishment of a successful collaborative working relationship between ExMC and CSA.Applying lessons learned from the fiscal year 2016 (FY16) Human Exploration Research Analog (HERA) campaign, CSA and NASA ARC developed a solution to provide real-time feedback to researchers who monitor the collection of vital signs data from a wearable Astroskin garment. The advances in the interfaces included the development of an iPad application (by CSA) to wirelessly forward the vital signs data to the MDA system, which collected the vital signs data through a receiver developed by NASA ARC. The development of these interfaces aims to provide communications between the Astroskin and the MDA system such that data may be seamlessly collected, stored and retrieved by the MDA. The first steps towards this goal were demonstrated in FY16. In FY17, ExMC will complete the first in a series of test beds that establishes a system to automate collection and management of vital sign data from the Astroskin, and other sources of data, to provide information for a crewmember to make medical decisions. In addition, the MDA Test Bed 1 will enable CSA to evaluate and optimize biosensor advancement and facilitate decision support algorithm development.

Vital signs↗

NASA NTRS2017

Biosensor Integration Development ExMC/Canadian Space Agency Collaboration

In support of the NASA Human Research Program Exploration Medical Capability (ExMC) Element, NASA Ames Research Center (ARC) established a collaborative effort with the Canadian Space Agency (CSA). The collaboration focuses on leveraging CSA capability in the areas of biosensors and decision support that will augment future development of such components for Exploration Missions. The CSA advancement of biosensors enables NASA to focus on the integration and data management associated with these types of components through the system currently under development by the Medical Data Architecture (MDA) project. This approach has enabled the establishment of a successful collaborative working relationship between ExMC and CSA.Applying lessons learned from the fiscal year 2016 (FY16) Human Exploration Research Analog (HERA) campaign, CSA and NASA ARC developed a solution to provide real-time feedback to researchers who monitor the collection of vital signs data from a wearable Astroskin garment. The advances in the interfaces included the development of an iPad application (by CSA) to wirelessly forward the vital signs data to the MDA system, which collected the vital signs data through a receiver developed by NASA ARC. The development of these interfaces aims to provide communications between the Astroskin and the MDA system such that data may be seamlessly collected, stored and retrieved by the MDA. The first steps towards this goal were demonstrated in FY16. In FY17, ExMC will complete the first in a series of test beds that establishes a system to automate collection and management of vital sign data from the Astroskin, and other sources of data, to provide information for a crewmember to make medical decisions. In addition, the MDA Test Bed 1 will enable CSA to evaluate and optimize biosensor advancement and facilitate decision support algorithm development.

Astroskin↗

NASA NTRS2013

An Offload NIC for NASA, NLR, and Grid Computing

This work addresses distributed data management and access dynamically configurable high-speed access to data distributed and shared over wide-area high-speed network environments. An offload engine NIC (network interface card) is proposed that scales at nX10-Gbps increments through 100-Gbps full duplex. The Globus de facto standard was used in projects requiring secure, robust, high-speed bulk data transport. Novel extension mechanisms were derived that will combine these technologies for use by GridFTP, bandwidth management resources, and host CPU (central processing unit) acceleration. The result will be wire-rate encrypted Globus grid data transactions through offload for splintering, encryption, and compression. As the need for greater network bandwidth increases, there is an inherent need for faster CPUs. The best way to accelerate CPUs is through a network acceleration engine. Grid computing data transfers for the Globus tool set did not have wire-rate encryption or compression. Existing technology cannot keep pace with the greater bandwidths of backplane and network connections. Present offload engines with ports to Ethernet are 32 to 40 Gbps f-d at best. The best of ultra-high-speed offload engines use expensive ASICs (application specific integrated circuits) or NPUs (network processing units). The present state of the art also includes bonding and the use of multiple NICs that are also in the planning stages for future portability to ASICs and software to accommodate data rates at 100 Gbps. The remaining industry solutions are for carrier-grade equipment manufacturers, with costly line cards having multiples of 10-Gbps ports, or 100-Gbps ports such as CFP modules that interface to costly ASICs and related circuitry. All of the existing solutions vary in configuration based on requirements of the host, motherboard, or carriergrade equipment. The purpose of the innovation is to eliminate data bottlenecks within cluster, grid, and cloud computing systems, and to add several more capabilities while reducing space consumption and cost. Provisions were designed for interoperability with systems used in the NASA HEC (High-End Computing) program. The new acceleration engine consists of state-ofthe- art FPGA (field-programmable gate array) core IP, C, and Verilog code; novel communication protocol; and extensions to the Globus structure. The engine provides the functions of network acceleration, encryption, compression, packet-ordering, and security added to Globus grid or for cloud data transfer. This system is scalable in nX10-Gbps increments through 100-Gbps f-d. It can be interfaced to industry-standard system-side or network-side devices or core IP in increments of 10 GigE, scaling to provide IEEE 40/100 GigE compliance.

Awrach, James↗

NASA NTRS1990

Using expert systems to implement a semantic data model of a large mass storage system

The successful development of large volume data storage systems will depend not only on the ability of the designers to store data, but on the ability to manage such data once it is in the system. The hypothesis is that mass storage data management can only be implemented successfully based on highly intelligent meta data management services. There now exists a proposed mass store system standard proposed by the IEEE that addresses many of the issues related to the storage of large volumes of data, however, the model does not consider a major technical issue, namely the high level management of stored data. However, if the model were expanded to include the semantics and pragmatics of the data domain using a Semantic Data Model (SDM) concept, the result would be data that is expressive of the Intelligent Information Fusion (IIF) concept and also organized and classified in context to its use and purpose. The results are presented of a demonstration prototype SDM implemented using the expert system development tool NEXPERT OBJECT. In the prototype, a simple instance of a SDM was created to support a hypothetical application for the Earth Observing System, Data Information System (EOSDIS). The massive amounts of data that EOSDIS will manage requires the definition and design of a powerful information management system in order to support even the most basic needs of the project. The application domain is characterized by a semantic like network that represents the data content and the relationships between the data based on user views and the more generalized domain architectural view of the information world. The data in the domain are represented by objects that define classes, types and instances of the data. In addition, data properties are selectively inherited between parent and daughter relationships in the domain. Based on the SDM a simple information system design is developed from the low level data storage media, through record management and meta data management to the user interface.

Roelofs, Larry H.↗

DOE OSTI2020

An open source fast fluid dynamics model for data center thermal management

Although computational fluid dynamics (CFD) has been widely adopted to improve data center thermal management, the high computational demand limits its applications, such as multivariate optimal design and operation. Fast fluid dynamics (FFD), which has been applied for fast airflow simulation, shows great potential. However, few research applied FFD for optimal design and operation of data center thermal management. This research improves the FFD model for data centers and conducts a comprehensive evaluation and demonstration. First, the FFD model is improved by solving the advection and diffusion equations together using an upwind scheme instead of a semi-Lagrangian advection solver in the conventional FFD model. Second, new features for data centers are added, such as a pressure correction method to simulate plenum airflow and dynamic boundary conditions for IT racks. The new FFD model is first validated with two indoor environment cases and the results show that the new FFD model has slightly better overall prediction accuracy and faster speed compared to the conventional FFD model. It is also observed that both FFD models achieve acceptable accuracy, except for a few localized disparities with experimental data, which might be due to simplified handling of turbulence viscosity near the boundaries. Furthermore, validation with a real data center shows that the FFD model achieves a similar level of accuracy as CFD when compared to the experimental measurements with some level of uncertainties. It is then demonstrated for data center optimal design and operation, which saves 53.4–58.8% of annual energy while still meeting the thermal requirements. In conclusion, with a much faster speed and comparable accuracy compared to CFD, the FFD model parallelized on a graphics processing unit is promising for practical model-based data center early design and operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗