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Enabling Open and Interoperable Science: Multi-omics Data Processing with Standardized Bioinformatics Workflows for Earth and Space Research
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LoVoCCS. I. Survey Introduction, Data Processing Pipeline, and Early Science Results
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Ka-band High-Rate Downlink System for the NISAR Mission
This paper provides a description and analyses of the high-rate Ka-band telecom system for the upcoming NISAR (NASA-ISRO Synthetic Aperture Radar) mission. NISAR is a collaborative Earth-Science mission between National Aeronautics and Space Administration (NASA) and Indian Space Research Organization (ISRO), which features an L-band SAR instrument and an S-band SAR instrument. The simultaneous dual-frequency radar system at peak rates will produce data at gigabit-per-second speeds, which drives the data-volume requirements. The key driving requirement for the payload communication subsystem is to provide a minimum of 26 Terabits per day of radar science data to the ground. The high-rate transmitter on the flight system is a software-defined radio developed at the Jet Propulsion Laboratory (JPL), based on the Universal Space Transponder platform, providing an offset quadrature phase shift key modulated waveform with Low-Density Parity-Check encoding of the data transfer frames. Two transmitters used in a dual-polarization configuration with each transmitter providing two giga-symbols per second (Gsps) of coded data provides an aggregate rate of four Gsps. In this system, only one watt of signal power is necessary on each polarization to overcome propagation losses and achieve a successful RF link. Several Near Earth Network (NEN) ground station sites (Alaska in the United States, Svalbard in Norway, and Punta Arenas in Chile) are baselined for the space-to-Earth communications link. Each ground station will also feature multiple upgrades to support NISAR’s transmission starting with new Ka-band antennas, wideband downconverters and high-rate receivers. In addition, a baseband data processor called Data Acquisition Processor and Handling Network Environment (DAPHNE), newly developed by the NEN, provides data storage and connectivity to backhaul networks. With NISAR’s large quantities of data (over 3.5 Petabytes over the mission), the processing of science data will be primarily performed on a cloud system to reduce the overall cost to the mission. The system described herein will be the first operational use of Gsps-class downlink rates on an Earth-Science mission.
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.
The design of a petabyte archive and distribution system for the NASA ECS project
The NASA EOS Data and Information System (EOSDIS) Core System (ECS) will contain one of the largest data management systems ever built - the ECS Science and Data Processing System (SDPS). SDPS is designed to support long term Global Change Research by acquiring, producing, and storing earth science data, and by providing efficient means for accessing and manipulating that data. The first two releases of SDPS, Release A and Release B, will be operational in 1997 and 1998, respectively. Release B will be deployed at eight Distributed Active Archiving Centers (DAAC's). Individual DAAC's will archive different collections of earth science data, and will vary in archive capacity. The storage and management of these data collections is the responsibility of the SDPS Data Server subsystem. It is anticipated that by the year 2001, the Data Server subsystem at the Goddard DAAC must support a near-line data storage capacity of one petabyte. The development of SDPS is a system integration effort in which COTS products will be used in favor of custom components in very possible way. Some software and hardware capabilities required to meet ECS data volume and storage management requirements beyond 1999 are not yet supported by available COTS products. The ECS project will not undertake major custom development efforts to provide these capabilities. Instead, SDPS and its Data Server subsystem are designed to support initial implementations with current products, and provide an evolutionary framework that facilitates the introduction of advanced COTS products as they become available. This paper provides a high-level description of the Data Server subsystem design from a COTS integration standpoint, and discussed some of the major issues driving the design. The paper focuses on features of the design that will make the system scalable and adaptable to changing technologies.
A Multi-mission Event-Driven Component-Based System for Support of Flight Software Development, ATLO, and Operations first used by the Mars Science Laboratory (MSL) Project
This paper details an architectural description of the Mission Data Processing and Control System (MPCS), an event-driven, multi-mission ground data processing components providing uplink, downlink, and data management capabilities which will support the Mars Science Laboratory (MSL) project as its first target mission. MPCS is developed based on a set of small reusable components, implemented in Java, each designed with a specific function and well-defined interfaces. An industry standard messaging bus is used to transfer information among system components. Components generate standard messages which are used to capture system information, as well as triggers to support the event-driven architecture of the system. Event-driven systems are highly desirable for processing high-rate telemetry (science and engineering) data, and for supporting automation for many mission operations processes.
Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS)
Opportunities exist for realizing transformative advances in productivity and reductions in energy footprint through ubiquitous sensing in manufacturing environments. Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS) is a 21-month (4 academic semesters, plus one summer) experience for graduate students that focuses on scaling the knowledge, understanding and leadership skills in the cyber manufacturing area. Masters students (8/year, 32 total) complete 2-year projects on industrially-driven project topics, rotating to internships in summer semester to work on scoping and implementation at project partners. Students complete academic training in embedded systems, process modeling, data science, and cloud-based systems design. Their projects are targeted toward sensor retrofit, process monitoring, root cause analysis, and sensor fusion.
An interfaces approach to TES ground data system processing design with the Science Investigator-led Processing System (SIPS)
Developing production-quality software to process the large volumes of scientific data is the responsibility of the TES Ground Data System, which is being developed at the Jet Propulsion Laboratory together with support contractor Raytheon/ITSS. The large data volume and processing requirements of the TES pose significant challenges to the design.
Conserving Land–Atmosphere Synthesis Suite (CLASS)
Accurate estimates of terrestrial water and energy cycle components are needed to better understand climate processes and improve models’ ability to simulate future change. Various observational estimates are available for the individual budget terms; however, these typically show inconsistencies when combined in a budget. In this work, a Conserving Land–Atmosphere Synthesis Suite (CLASS) of estimates of simultaneously balanced surface water and energy budget components is developed. Individual CLASS variable datasets, where possible, 1) combine a range of existing variable product estimates, and hence overcome the limitations of estimates from a single source; 2) are observationally constrained with in situ measurements; 3) have uncertainty estimates that are consistent with their agreement with in situ observations; and 4) are consistent with each other by being able to solve the water and energy budgets simultaneously. First, available datasets of a budget variable are merged by implementing a weighting method that accounts both for the ability of datasets to match in situ measurements and the error covariance between datasets. Then, the budget terms are adjusted by applying an objective variational data assimilation technique (DAT) that enforces the simultaneous closure of the surface water and energy budgets linked through the equivalence of evapotranspiration and latent heat. Comparing component estimates before and after applying the DAT against in situ measurements of energy fluxes and streamflow showed that modified estimates agree better with in situ observations across various metrics, but also revealed some inconsistencies between water budget terms in June over the higher latitudes.
Advancing User Supports with a Structured How-To Knowledge Base for Earth Science Data
It is a challenge to access and process fast growing Earth science data from satellites and numerical models, which may be archived in very different data format and structures. NASA data centers, managed by the Earth Observing System Data and Information System (EOSDIS), have developed a rich and diverse set of data services and tools with features intended to simplify finding, downloading, and working with these data. Although most data services and tools have user guides, many users still experience difficulties with accessing or reading data due to varying levels of familiarity with data services, tools, and/or formats. A type of structured online document, data recipe, were created in beginning 2013 by Goddard Earth Science Data and Information Services Center (GES DISC). A data recipe is the How-To document created by using the fixed template, containing step-by-step instructions with screenshots and examples of accessing and working with real data. The recipes has been found to be very helpful, especially to first-time-users of particular data services, tools, or data products. Online traffic to the data recipe pages is significant to some recipes. In 2014, the NASA Earth Science Data System Working Group (ESDSWG) for data recipes was established, aimed to initiate an EOSDIS-wide campaign for leveraging the distributed knowledge within EOSDIS and its user communities regarding their respective services and tools. The ESDSWG data recipe group started with inventory and analysis of existing EOSDIS-wide online help documents, and provided recommendations and guidelines and for writing and grouping data recipes. This presentation will overview activities of creating How-To documents at GES DISC and ESDSWG. We encourage feedback and contribution from users for improving the data How-To knowledge base.
GES DISC Data Recipes in Jupyter Notebooks
The Earth Science Data and Information System (ESDIS) Project manages twelve Distributed Active Archive Centers (DAACs) which are geographically dispersed across the United States. The DAACs are responsible for ingesting, processing, archiving, and distributing Earth science data produced from various sources (satellites, aircraft, field measurements, etc.). In response to projections of an exponential increase in data production, there has been a recent effort to prototype various DAAC activities in the cloud computing environment. This, in turn, led to the creation of an initiative, called the Cloud Analysis Toolkit to Enable Earth Science (CATEES), to develop a Python software package in order to transition Earth science data processing to the cloud. This project, in particular, supports CATEES and has two primary goals. One, to transition data recipes created by the Goddard Earth Science Data and Information Service Center (GES DISC) into an interactive and educational environment using JupyterNotebooks. Two, to acclimate Earth scientists to cloud computing. To accomplish these goals, we create JupyterNotebooks to compartmentalize the different steps of data analysis and help users obtain and parse data from the command line. We also develop a Docker container, comprised of Jupyter Notebooks, Python dependencies, and command line tools, and configure it into an easy-to-deploy package. The end result is an end-to-end product that simulates the use case of end users working in the cloud computing environment.
Autonomous Sciencecraft Experiment (ASE) Test Operations in 2003
NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The Autonomous Sciencecraft Experiment (ASE) has been selected for flight demonstration by NASA s New Millennium Program (NMP) as part of the Space Technology 6 (ST6) mission. ASE is scheduled to fly on the US Air Force Research Laboratory (AFRL) Techsat-21 constellation in 2006. Tech- Sat-21 consists of three satellites flying in a variable-geometry formation in Earth orbit. Each satellite is equipped with X-band Synthetic Aperture Radar, yielding high spatial resolution images (approx. 3 m) of the Earth s surface. The constellation will fly at an altitude of 550 km, in a 35.4 inclination circular orbit, yielding exact repeat-track observations every 13 days. Prior to full deployment, elements of the versatile ASE spacecraft command and control software, image formation software and science processing software will be utilized and tested on two very different platforms in 2003: AirSAR and EO-1 (described below). Advantages of Autonomous Operations: ASE will demonstrate advanced autonomous science data acquisition, processing, and product downlink prioritization, as well as autonomous spacecraft command and control, and fault detection. The advantages of spacecraft autonomy are to future missions include: (a) making the best use of reduced downlink; (b) the overcoming of communication delays through decisionmaking in situ, enabling fast reaction to dynamic events; (c) an increase of science content per byte of returned data; and (d) an avoidance of return of null (no-change/no feature) datasets: if there is no change detectable between two scenes of the same target, there is no need to return the second dataset.
NASA's Earth Science Flight Program Meets the Challenges of Today and Tomorrow
NASA's Earth science flight program is a dynamic undertaking that consists of a large fleet of operating satellites, an array of satellite and instrument projects in various stages of development, a robust airborne science program, and a massive data archiving and distribution system. Each element of the flight program is complex and present unique challenges. NASA builds upon its successes and learns from its setbacks to manage this evolving portfolio to meet NASA's Earth science objectives. NASA fleet of 16 operating missions provide a wide range of scientific measurements made from dedicated Earth science satellites and from instruments mounted to the International Space Station. For operational missions, the program must address issues such as an aging satellites operating well beyond their prime mission, constellation flying, and collision avoidance with other spacecraft and orbital debris. Projects in development are divided into two broad categories: systematic missions and pathfinders. The Earth Systematic Missions (ESM) include a broad range of multi-disciplinary Earth-observing research satellite missions aimed at understanding the Earth system and its response to natural and human-induced forces and changes. Understanding these forces will help determine how to predict future changes, and how to mitigate or adapt to these changes. The Earth System Science Pathfinder (ESSP) program provides frequent, regular, competitively selected Earth science research opportunities that accommodate new and emerging scientific priorities and measurement capabilities. This results in a series of relatively low-cost, small-sized investigations and missions. Principal investigators whose scientific objectives support a variety of studies lead these missions, including studies of the atmosphere, oceans, land surface, polar ice regions, or solid Earth. This portfolio of missions and investigations provides opportunity for investment in innovative Earth science that enhances NASA's capability for better understanding the current state of the Earth system. ESM and ESSP projects often involve partnerships with other US agencies and/or international organizations. This adds to the complexity of mission development, but allows for a greater scientific return on NASA's investments. The Earth Science Airborne Science Program provides manned and unmanned aircraft systems that further science and advance the use of satellite data. NASA uses these assets worldwide in campaigns to investigate extreme weather events, observe Earth system processes, obtain data for Earth science modeling activities, and calibrate instruments flying aboard Earth science spacecraft. The Airborne Science Program has six dedicated aircraft and access to many other platforms. The Earth Science Multi-Mission Operations program acquires, preserves, and distributes observational data from operating spacecraft to support Earth Science research focus areas. The Earth Observing System Data and Information System (EOSDIS), which has been in operations since 1994, primarily accomplishes this. EOSDIS acquires, processes, archives, and distributes Earth Science data and information products. The archiving of NASA Earth Science information happens at eight Distributed Active Archive Centers (DAACs) and four disciplinary data centers located across the United States. The DAACs specialize by topic area, and make their data available to researchers around the world. The DAACs currently house over 9 petabytes of data, growing at a rate of 6.4 terabytes per day. NASA's current Earth Science portfolio is responsive to the National Research Council (NRC) 2007 Earth Science Decadal Survey and well as the 2010 NASA Response to President Obama's Climate Plan. As the program evolves into the future it will leverage the lessons learned from the current missions in operations and development, and plan for adjustments to future objectives in response to the anticipated 2017 NRC Decadal Survey.
Process mining for healthcare: Characteristics and challenges
Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.
Spacecraft computers for scientific information systems.
Data processing and control by spaceborne computer on instrumentation satellite used for analyzing space scientific properties, noting factors influencing system design
OCI Geolocation Evaluation and Refinement Using Landsat Control Points
The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission is NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer, Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. The geolocation processing is performed for OCI using spacecraft navigation data and an instrument geometry model. To evaluate the geolocation accuracy for OCI and develop refinements to the processing methods, control point matching using Landsat data has been implemented as a step in the operational processing of OCI data at the Science Data Segment. This processing provides between 200 and 300 high-quality matchups per day with good global and geometric distribution, allowing rapid evaluation of the OCI geolocation accuracy. The results provided an early indication of the overall quality of the geolocation processing and of specific aspects needing improvement. A standard set of granules was identified to support rapid implementation and testing of geolocation refinements, and this approach has been highly successful in improving the geolocation processing accuracy to meet the science requirements. The evaluation will continue throughout the mission to ensure the ongoing accuracy of geolocation. This paper describes the control point matching methodology, the approach to development of the geolocation processing refinements, and the recent results.
OCI Geolocation Evaluation and Refinement Using Landsat Control Points
The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission is NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer, Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. The geolocation processing is performed for OCI using spacecraft navigation data and an instrument geometry model. To evaluate the geolocation accuracy for OCI and develop refinements to the processing methods, control point matching using Landsat data has been implemented as a step in the operational processing of OCI data at the Science Data Segment. This processing provides between 200 and 300 high-quality matchups per day with good global and geometric distribution, allowing rapid evaluation of the OCI geolocation accuracy. The results provided an early indication of the overall quality of the geolocation processing and of specific aspects needing improvement. A standard set of granules was identified to support rapid implementation and testing of geolocation refinements, and this approach has been highly successful in improving the geolocation processing accuracy to meet the science requirements. The evaluation will continue throughout the mission to ensure the ongoing accuracy of geolocation. This paper describes the control point matching methodology, the approach to development of the geolocation processing refinements, and the recent results.