Data Science News

NASA Goddard Workshop on Artificial Intelligence

October 27, 2024

Artificial Intelligence (AI) is a collection of advanced technologies that allows machines to think and act, both humanly and rationally, through sensing, comprehending, acting and learning. AI's foundations lie at the intersection of several traditional fields - Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Although the inception of AI started in the 1950's, it has recently made a strong comeback in all aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few.

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3rd SMD and ETD Workshop on A.I. and Data Science: Leaping Toward Our Future Goals

Oct. 21, 2024

The overarching goal of the meeting is to roll out NASA’s AI/ML strategy for science and engage the science community for feedback on how they can embrace new technologies to implement AI/ML in their workflows to advance their analyses for new scientific discoveries.

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Cancer Biomarkers AI and Bioinformatics Workshop

Aug. 13, 2024

Some of the major advances in the field of cancer over the last few years include a shift towards more data intensive and computational workflows through a variety of tools. This shift is highly relevant for cancer biomarkers. The increase of data and computation coupled with emerging capabilities in bioinformatics, machine learning (ML), and artificial intelligence (AI) is unlocking new opportunities to apply data-driven methods to scientific discovery and validation. Rapid advances in areas such as foundation models and generative AI are providing exciting new capabilities in areas such as image segmentation and identification. As research consortia—such as the Early Detection Research Network (EDRN)—continue to capture more data, there are numerous opportunities to leverage and apply shared tools, algorithms, and capabilities within the community. To leverage these capabilities, there is a need to ensure that diverse datasets are captured, structured, and made readily accessible for analysis. Similarly, there is a need to provide packaged

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NASA SMD AI Workshop 2024

March 25, 2024

This workshop is supported by the NASA Chief Science Data Office, emphasizing the transformative potential of artificial intelligence (AI) and machine learning (ML) for science. It focuses on exploring and applying foundation models (FMs) in science. Building on the momentum generated by recent results of FMs, we aim to delve deeper into how these powerful AI advances can be specifically harnessed within various scientific disciplines.

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SUDS Workshop

Aug. 19, 2023

Recent interdisciplinary collaborations between physical scientists and data scientists have yielded significant advancements in both fields. In order to define, prioritize, and unify our vision for these high-value collaborations across institutions, we invite SUDS leaders across institutions to participate in this first-of-kind workshop.

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NASA Turns to the Cloud for Help With Next-Generation Earth Missions

Oct. 13, 2021

Engineers and researchers face monumental challenges in the increasingly gargantuan datasets from more and more sophisticated instruments observing the Earth. Deploying a cloud-based system to process, store, and analyze the digital information has been the challenge of the Earth Science Data Systems group at NASA.

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Explainable AI: A Caltech Virtual Workshop, a part of the AI4Science Series

Sept. 23, 2021

As the size and complexity of data and software systems keep increasing, we are increasingly dependent on the Artificial Intelligence (AI) and Machine Learning (ML) in order to extract an actionable knowledge from the data.  In science, we are steadily moving towards a human-AI collaborative discovery, as we explore complex data landscapes.  However, the results or recommendations from the AI systems may be hard to understand or interpret, which is an essential component of the data-to-discovery process, whether in science, business, security, or any other data analytics domain.  Trust and credibility of AI in practical applications can have significant ethical, political or even life-or-death consequences.

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NASA JPL Building Models of Its Petabytes of Data with Artificial Intelligence

May 11, 2021

Artificial intelligence and machine learning are the technological tips of the spear towards extracting critical insights from the petabytes of data coming into the NASA Jet Propulsion Laboratory (JPL). Progarm Manager and Principal Computer Scientist Daniel Crichton is excited about these technologies and his mission: using the massive data to understand the Earth, the solar system, and beyond.

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A Machine-Learning Assist to Predicting Hurricane Intensity

Sept. 2, 2020

Hurricane Patricia in 2015 strengthened at an alarming pace from Category 1 to Category 5 in a mere 24 hour period. It wasn't the first nor the last to suddenly intensify. Accurate prediction of hurricane intensification is the goal of a machine learning effort at the Jet Propulsion Laboratory (JPL).

Read the entire article posted on the news section at JPL.
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Virtual Reality for Scientists

Oct. 4, 2019

Virtual Reality, or VR, is no longer the purview of action video games or deeply immersive tours of distant lands. Recently, scientists using VR goggles have begun using the technology to analyze and comprehend their data.

An article published at Caltech describes how VR tools are in development for just that—along with working prototypes for studying biological molecules, ocean waters, and even worms. This development comes powered by the joint efforts of JPL's and Caltech's data science efforts.

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Second AI and Data Science Workshop for Earth and Space Sciences

Oct. 1, 2019

Introduction

NASA’s mission of exploration requires leveraging new ways to utilize and learn from the unprecedented amount of data that space-based observation platforms generate.  New capabilities are needed, ranging from onboard autonomy for robotic spacecraft to techniques for understanding the world and universe where we live.  Artificial Intelligence (AI) and data science are rapidly becoming integral to NASA’s future to drive automation and interpretation.  

AI is a collection of advanced technologies that allow machines to think and act, through sensing, comprehending, interacting and learning. AI's foundations lie at the intersection of several traditional fields - Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Data Science provides a foundation for bringing software, data, and methodologies together to utilize and interpret data. The cross-disciplinary NASA challenges involving AI and data science require that teams of differing backgrounds come together and integrate capabilities to leverage data effectively.

The NSF, in its 10 Big Ideas, identifies deep integration of “knowledge, theories, methods, data, research communities and languages”, as critical to how research and analysis will be performed in the future, arguing that this multidisciplinary approach may yield entirely “new frameworks, paradigms or even disciplines.”  Furthermore, such frameworks must be rooted in data science methodology, following another NSF Big Idea, “Harnessing the Big Data Revolution.”

Although the inception of AI started in the 1950's, the field has recently made a strong comeback in many aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI and data science applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few.

Compared to Industry and Academia, NASA has specific challenges as well as resources that are particularly well-suited to the use of AI and data science:

• A wealth of data and information to leverage and "learn" from
• Many science- and mission-oriented applications that can benefit from learning on previous data and from domain and expert knowledge
• Extreme challenges unique to NASA’s exploration mission from onboard sensing in remote locations to retrospective and predictive analysis for monitoring our Earth’s state.

Previous Workshop at GSFC

In November 2018, Goddard Space Flight Center hosted the GSFC AI Workshop. This is a follow-on workshop that builds on the progress made in AI, data science, and within NASA in the application of these capabilities to NASA projects This workshop will focus on the following types of challenges:

• Discover events of interest and correlations in large amounts of science data
• Improve the outcomes of science modeling and data assimilation using improved data processing, integration, and analysis
• Design advisors for mission planning and operations, including anomaly detection and spacecraft health monitoring
• Enable new engineering systems and capabilities across NASA directorates
• Enable onboard autonomy using AI
• Develop tools for engineering support, including advanced manufacturing, orbit determination, new component design and system engineering
• Customize intelligent user interfaces, including visual analytics and natural language processing

Workshop Focus

The workshop will be organized as a combination of keynote addresses, invited speakers, short talks and posters centered around specific themes and topics of interest. These are as follows:

1. Data-Driven Science – Applications of AI and data science methodologies applied to enable science research at NASA. Support for working with observational data and model output. Data analytics support including i) quantification of uncertainty in inference from big data; ii) use of machine learning to support data mining and pattern recognition; iii) reproducibility in scientific inferences from AI and data science.
2. AI in Engineering – Applications of AI and data science methodologies applied to support NASA engineering applications across science, human exploration, and aeronautics. Use of AI methods for simulation, design, and operations.
3. Autonomy – Use of AI and data science to enable automated mission operations; onboard application of AI and data science to support autonomous missions including navigation, operations, and science.
4. Cross-cutting AI and Data Science activities at NASA – General AI methods and projects applicable to multiple NASA programs including machine learning, image analysis, natural language processing, etc.
5. Cross-Agency AI and Data Science activities – AI and data science collaborations across federal agencies
6. Emerging Research Topics in AI; Collaborations with Academia – AI and data science collaborations between NASA and academia. Training and educating next generation workforces in AI and data science. Research topics in AI and data science including trust, reproducibility, explainability, human AI interaction, etc.
7. Real-World Applications of AI and Data Science – NASA deployed applications in AI and data science.

Abstract Submission

Abstract submission is now closed. Abstracts will be accepted for oral and poster presentations. 

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JPL and National Cancer Institute Renew Big Data Partnership

Sept. 14, 2016

Every day, NASA spacecraft beam down hundreds of petabytes of data, all of which has to be codified, stored and distributed to scientists across the globe. Increasingly, artificial intelligence is helping to "read" this data as well, highlighting similarities between datasets that scientists might miss.

For the past 15 years, the big data techniques pioneered by NASA's Jet Propulsion Laboratory in Pasadena, California, have been revolutionizing biomedical research. On Sept. 6, 2016, JPL and the National Cancer Institute (NCI), part of the National Institutes of Health, renewed a research partnership through 2021, extending the development of data science that originated in space exploration and is now supporting new cancer discoveries.

Read entire article.

News Media Contact

Andrew Good
Jet Propulsion Laboratory, Pasadena, Calif.
+1 818-393-2433
andrew.c.good@jpl.nasa.gov

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Really, Really Big Data: NASA at the Forefront of Analytics

Oct. 1, 2015

NASA has led the US government's exploration of space since its founding, and its contributions have extended to advances that have benefited both government and private sector industries. Because it has unique requirements for data collection and analysis, NASA has been on the forefront of many innovations in data and computational science, and related IT.

Read the article.

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Information Technology and Bioinformatics Workshop for the Helmsley IBD Research Network

Sept. 29, 2015

A two-day workshop focusing on bioinformatics resources and capabilities, needs and challenges, and future priorities for Inflammatory Bowel Disease (IBD) research, sponsored by the Helmsley Charitable Trust, was held at the California Institute of Technology (Caltech) on September 29, 2015 – September 30, 2015.

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Big data and tools primary topics at 2nd Planetary Data Workshop in June in Flagstaff, Arizona

Aug. 11, 2015

2nd Planetary Data Workshop; Flagstaff, Arizona, 8–11 June 2015

Planetary science missions are returning data at an unprecedented rate. The NASA Planetary Data System (PDS) now provides nearly a petabyte of data, and these data are used daily by scientists worldwide for science research, mission planning, education, and outreach. New science questions, analysis methods, and data synthesis and visualization tools are continually being developed, necessitating regular consultation between users and tool developers.

By Lisa Gaddis and Trent Hare.

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Data and Computational Science Technologies for Earth Science Research - IEEE Big Data Conference

October 29, 2015 – November 1, 2015 Santa Clara, CA

Aug. 8, 2015

Currently, the analysis of large data collections from earth science research is executed through traditional computational and data analysis approaches, which require users to bring data to their desktops and perform local data analysis. Future earth science remote sensing missions, which historically assume that all data can be collected, transmitted, processed, and archived, may not scale as more capable instruments stress existing architectural approaches and systems. A new paradigm is needed in order to increase the productivity and effectiveness of scientific data analysis. This paradigm must recognize that architectural and analytical choices are interrelated, and must be carefully coordinated in any system that aims to allow efficient, interactive scientific exploration and discovery to exploit massive data collections, from point of collection (e.g., onboard) to analysis and decision support. Both future observational systems, including satellite and airborne experiments, and research in climate modeling will significantly increase the size of the data requiring new approaches across the entire data lifecycle from capture to generation, management, and analysis of the data.

This workshop seeks computational and data science experts to present on their research and discuss Big Data roadmaps, architectures, technologies, and methodologies for future Earth Science data challenges emerging from both observational systems and climate studies.
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New online tool, Mars Trek, brings the planet to a new generation

Aug. 5, 2015

Mars Trek provides high-quality, detailed visualizations of the planet using real data from 50 years of NASA exploration.

Data gathered from multiple instruments aboard multiple NASA Mars missions provide diverse views for Mars Trek’s user-friendly set of tools including interactive maps, 3-D printer-exportable topography, and standard keyboard gaming controls for flying high or low above Mars’ surface.

Mars Trek is already being used by a NASA team to aid in the selection of possible landing sites for the agency’s Mars 2020 rover, and the application will be used as part of NASA’s newly-announced process to examine and select candidate sites for the first human exploration mission to Mars in the 2030s.

See more from the NASA press release at Journey to Mars.

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$4.5 million NASA grant on big data given to University of California, Riverside to partner with NASA/JPL

July 1, 2015

The project, called “Fellowships and Internships in Extremely Large Data Sets” (FIELDS), will train qualifying students in science, technology, engineering and mathematics (STEM) fields to address a critical shortfall in the workforce essential for future NASA missions.

FIELDS will involve the following partner institutions besides UC Riverside: NASA’s Jet Propulsion Laboratory (JPL), the California State University system and the state’s two-year community colleges.

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Caltech's Center for Data-Driven Discovery (CD3) and JPL's Center for Data Science and Technology (CDST) combine forces.

June 17, 2015

Caltech's Center for Data-Driven Discovery (CD3) and JPL's Center for Data Science and Technology (CDST), have formally combined their complementary strengths to allow them to address the every growing need to manage, examine and analyze big data.

• Read the entire article posted on Caltech.
• Read the entire article posted on JPL News.

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DAWN

June 15, 2015

DAWN (Distributed Analytics, Workflows and Numerics) is a model developed at JPL to simulate Big Data processing in science. The model can be used to evaluate costs and tradeoffs of different system architectures based on several estimators such as the overall elapsed time, separate computation and data transfer time, uncertainty (if available) and monetary cost. Because of its generality, DAWN can be applied to the analysis of data processing use cases from virtually any scientific discipline: current applications include Astronomy, Climate Science, Hydrology, Medical Science, and generic processing on the Cloud. JPL is in the process of making DAWN open source, and hosting it on GitHub.

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2015 Planetary Data Workshop

June 8, 2015

The wealth of data now available for planetary research has created the need for new tools and capabilities for storing, delivering, and working with the data using cutting-edge methods. The goals of this workshop are to bring together planetary scientists, data providers from current and recent space exploration missions, data archivists, and software and technology experts to exchange ideas on current capabilities and needs for improved and new tools that can be used to address current needs in planetary research and data analysis. Encouraged topics include the availability of planetary data, including information on how the data are found, downloaded, processed, and used for cartography and scientific analysis; trends in data storage and rapid access; analysis and visualization tools using current and new algorithms and methods; hands-on training and how-to guides for acquiring, processing, and working with a variety of digital planetary data; and publicly available derived data products and services, including Geographic Information Systems, featuring data and tools customized for planetary data analysis.

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Memex initiative aides 'Deep Web Search' that may help scientists in the future

May 22, 2015

"We're developing next-generation search technologies that understand people, places, things and the connections between them," said Chris Mattmann, principal investigator for JPL's work on Memex.

Memex could one day benefit space missions that take photos, videos and other kinds of imaging data with instruments such as spectrometers. Searching visual information about a particular planetary body could greatly facilitate the work of scientists in analyzing geological features. Scientists analyzing imaging data from Earth-based missions that monitor phenomena such as snowfall and soil moisture could similarly benefit.

Read more in the JPL article 'Deep Web' Search May Help Scientists.

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$5 million NASA grant given to Cal State L.A. to partner with NASA/JPL

May 4, 2015

Cal State L.A. was one of 10 universities nationwide to receive NASA funding under NASA's Minority University Research and Education Project. The new Cal State center will partner with UC Irvine's Data Science Initiative and the Jet Propulsion Laboratory's Center for Data Science and Technology.

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JPL Team builds Data Science Visualization for Vesta

March 31, 2015

Vesta Trek compiled data from NASA’s Dawn spacecraft, which studies Vesta from July 2011 to September 2012 and now provides crowd-sourced scientists user-friendly data science tools to study and visualize the asteroid’s features.

• Read more about the tool release.

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NASA Scientist Tells CIOs to Set Realistic Expectations for Big Data

Feb. 3, 2015

“Any time you compute with data you collected, you are calculating sample statistics,” said Dr. Amy Braverman, principal statistician at the Jet Propulsion Laboratory. “It’s important to acknowledge there is uncertainty in the results.”

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SURF - Summer 2015 Announcement Posted

Feb. 2, 2015

Project: Visualizing Early Detection Cancer Data

Disciplines: Data Science, Visualization

Mentor: Ashish Mahabal, Senior Research Scientist (PMA), aam@astro.caltech.edu, phone: +16263954201

Co-Mentor: Thomas Fuchs

Health informatics is a rapidly growing field and squarely in the Big Data domain. Bioinformatics is a subfield which deals with data at the molecular level. But it has also been instrumental in bio-sciences in leading the field of data-handling. JPL is part of NCI's Early Detection Research Network (EDRN) for Cancer biomarkers. We have a wide collection of biomarkers from studies by collaborators across the US. The researchers have often done fundamental/basic research on their samples and published results and visualizations.

The aim of this SURF is to understand the connectivity of the datasets, reproduce the visualizations and results and using additional advanced techniques produce better or faster visualizations. Technically we aim for web-based visualization of biomedical data and research results. While the student will be free to experiment, various techniques from existing R packages will be suggested. Example datasets include those from the PLCO Ovarian Phase III Validation Study, which analyzes biomarker panels for the early detection of ovarian cancer.

The SURF will be carried out at Caltech and available to both Caltech and non-Caltech students.

More information and how to application details are available.

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Machines Teach Astronomers About Stars

Jan. 8, 2015

Astronomers are enlisting the help of machines to sort through thousands of stars in our galaxy and learn their sizes, compositions and other basic traits.

The research is part of the growing field of machine learning, in which computers learn from large data sets, finding patterns that humans might not otherwise see. Machine learning is in everything from media-streaming services that predict what you want to watch, to the post office, where computers automatically read handwritten addresses and direct mail to the correct zip codes.

Now astronomers are turning to machines to help them identify basic properties of stars based on sky survey images. Normally, these kinds of details require a spectrum, which is a detailed sifting of the starlight into different wavelengths. But with machine learning, computer algorithms can quickly flip through available stacks of images, identifying patterns that reveal a star's properties. The technique has the potential to gather information on billions of stars in a relatively short time and with less expense.

Read the entire story.

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New — Center for Data-Driven Discovery (CD3)

Jan. 1, 2015

With technology continuing to advance and allow researchers to collect enormous amounts of data. The new Center for Data-Driven Discovery (CD3), lead by professor of astronomy George Djorgovski, primary goal is to speed data-driven discoveries. This center will work together with resources at JPL’s Center for Data Science and Technology. More information is available.

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2014 Big Data from Space

Nov. 14, 2014

Research, Technology and Innovation (RT&I)
Jointly organised by ESA, SatCen, JRC
Dates: 12-14 November 2014, ESRIN, Frascati, Italy

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IEEE BigData 2014

Oct. 27, 2014

October 27 - 30, 2014, Washington D.C.

In recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself. The IEEE International Conference on Big Data 2014 (IEEE BigData 2014) provides a leading forum for disseminating the latest research in Big Data Research, Development, and Application.

We solicit high-quality original research papers (including significant work-in-progress) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity): big data science and foundations, big data infrastructure, big data management, big data searching and mining, big data privacy/security, and big data applications.

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NIST Megacities Carbon Project Named 'Project to Watch' by United Nations

Sept. 15, 2014

The Megacities Carbon Project was launched in 2012 to solve a pressing scientific problem: how to measure the greenhouse gases that cities produce. Urban areas generate at least 70 percent of the world’s fossil fuel carbon dioxide emissions, but gauging a city’s carbon footprint remains difficult due to the lack of effective measurement methods. The project aims to change that by developing and testing techniques for both monitoring urban areas’ emissions and determining their sources.

The large sensor networks that each city in the Megacities Carbon Project employs generate huge amounts of data that could reveal the details of the cities’ emissions patterns. It is the project’s use of this so-called “big data” that drew accolades in the Big Data Climate Challenge, hosted by U.N. Global Pulse and the U.N. Secretary General’s Climate Change Support Team. The ability to analyze big data—vast quantities of electronic information generated by many sources—has the potential to provide new insights into the workings of society, and Global Pulse is working to promote awareness of the opportunities big data presents across the U.N. system.

(Excerpt from NIST Tech Beat: September 15, 2014)

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JPL-Caltech Virtual Summer School

Sept. 2, 2014

JPL-Caltech Virtual Summer School Big Data Analytics Sept 2–12, 2014

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BiDS'14 Conference Proceedings

July 7, 2014

European Space Agency (esa) 2014 Conference on Big Data from Space conference proceedings.

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NSF EarthCube Conceptual Design Award

June 6, 2014

A two-year project, “EarthCube Conceptual Design: A Scalable Community-Driven Architecture”, has been awarded by the National Science Foundation (NSF). EarthCube is a NSF initiative to create a community-driven data and knowledge management system that will allow for unprecedented data sharing across the geosciences. The objective of this project is to develop a conceptual architecture that will serve as the blueprint for the definition, construction and deployment of a national infrastructure for EarthCube. This project is partnered with Caltech and Element 84 (a small business). George Djorgovski from Caltech serves as the Principal Investigator. Dan Crichton and Emily Law will be responsible for development of this architecture.

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LOPS 2014: Workshop on Long Term Preservation for Big Scientific Data

April 1, 2014

Scientific data collected with modern sensors or dedicated detectors exceed very often the perimeter of the initial scientific design in different application domains. These data including experiments and simulations are obtained more and more frequently with large material and human efforts. For instance high energy physics and astrophysics experiments involve multi-annual developments. Hence, the preservation of big data sets produced is of permanent concern and has been addressed in various disciplines at different levels. However, the challenge of digital preservation of scientific data lies in the need to preserve not only the dataset itself but also the ability it has to deliver knowledge to future user community. A real scientific research asset allows future users to reanalyze the data within new contexts. In fact, the data should be preserved long term such that the access and the re-use are made possible and lead to an enhancement of the initial investment. It is therefore of outmost importance to pursue coherent and vigorous approaches to preserve the scientific data at long term

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Megacities selected as a big data project to watch by the UN Big Data Climate Challenge

March 3, 2014

The United Nations today, September 2nd, announced the winners of the Big Data Climate Challenge at the UN Climate Summit on September 23rd at UN Headquarters in New York.

The Megacities Carbon Project, selected as a project to watch, is being established for the megacities of Los Angeles and Paris, and will leverage robust methods for assessing carbon emissions and rapid improvements in measurement technology to monitor the atmospheric trends of carbon attributed to the world’s largest cities.

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BRDI Announces Data and Information Challenge

Dec. 1, 2013

The National Academy of Sciences Board on Research Data and Information announces an open challenge to increase awareness of current issues and opportunities in research data and information. These issues include, but are not limited to, accessibility, integration, discoverability, reuse, sustainability, perceived versus real value, and reproducibility.

A Letter of Intent is requested by December 1, 2013 and the deadline for final entries is May 15, 2014.

Awardees will be invited to present their projects at the National Academy of Sciences in Washington DC as part of a symposium of the regularly scheduled Board of Research Data and Information meeting in the latter half of 2014.

More information is available. Please contact Cheryl Levey with any questions.

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Frontiers in Massive Data Analysis

Nov. 15, 2013

The National Research Council formed a committee in 2010 that included JPL's Dan Crichton as one of its members. The NRC has just released this report from the committee which surveys the current state and provides several recommendations for improving the analysis of massive data.

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Prolific NASA Mars Orbiter Passes Big Data Milestone

Nov. 8, 2013

NASA's Mars Reconnaissance Orbiter has returned three times more data through the Deep Space Network than all other missions combined. More information is available.

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Big Data is Too Big for Scientists to Handle Alone

Aug. 3, 2013

Quanta Magazine published this article on big data that includes JPL's David Schimmel and Chris Mattmann.

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Congratulations to Lew Allen Award winner David Thompson

Jan. 15, 2013

Dr. Thompson received the "2013 Lew Allen Award for Excellence" award for his extraordinary leadership and technical vision in the area of in-situ science autonomy. Dr. Thompson has made significant contributions in putting the analysis of data closer to the collection source in order to perform data triage on limited downlink channels as well as to react to dynamic events. Examples include the TextureCam smart instrument concept which advances JPL autonomy capabilities by enabling in-situ science understanding of images; the Agile Science project, which enables unique mission concepts, such as adaptive observations during asteroid fly-bys based on data collected, that would otherwise be impossible due to communication delays; hyperspectral data analysis for cloud screening that is in operations for airborne instruments; and the identification of transient events in radio astronomy data which is also now in operations.

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