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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Biomass Harmonization and SAR Analysis with the Multi-mission Algorithm and Analysis Platform (MAAP)

The Multi‐mission Algorithm and Analysis Platform (MAAP) is a collaborative effort between NASA and the European Space Agency (ESA) to support above ground biomass (AGB) research in an open science framework. MAAP brings together relevant data, algorithms, and computing capabilities in a common cloud environment to address the challenges of sharing and processing data from field, airborne and satellite measurements. MAAP was publicly released in October 2021, providing computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of data. MAAP has allowed scientists from both North America and Europe to collaborate on the generation and analysis/visualization of data derived from multiple, discipline-adjacent missions in an open, collaborative environment that has reached beyond traditional scientific investigation. MAAP has been used to support multiple scientific activities. To date, existing LiDAR data from multiple platforms has been calibrated with field measurements and combined for more comprehensive and accurate estimates of above ground biomass AGB; these LiDAR platforms include airborne (e.g. LVIS), the International Space Station (NASA’s Global Ecosystem Dynamics Investigation (GEDI), and satellites (e.g. ICESat-2). The current challenge is to effectively and seamlessly combine the aforementioned LiDAR-based data with new data sources such as P-band RADAR from ESA’s upcoming BIOMASS mission, existing ESA Sentinel-1 C-band SAR, and the 30 PB/yr of high cadence global coverage L-band SAR data from the upcoming NASA-ISRO SAR (NISAR) mission. Recent analysis using MAAP merged ICESat-2 and optical data (Harmonized Landsat Sentinel) produced the most comprehensively precise estimate of boreal-wide AGB to date. Another effort using MAAP is the production and open distribution of global comparisons of AGB map estimates, including from ICESat-2 and GEDI, to bolster stakeholder uptake for policy applications. These map estimates will feed into the Intergovernmental Panel on Climate Change (IPCC) database, likely aiding the next Global Carbon Stocktake of the UNFCCC. Furthermore, the biomass retrieval intercomparison exercise BRIX-2 could benefit from the MAAP providing standardized test cases (based on airborne campaign and spaceborne data) allowing the community to develop and apply retrieval algorithms based on these test cases, while forthcoming SAR data training curricula could also use the MAAP as a teaching and learning platform. The MAAP is meeting the challenges inherent in international, open science collaboration and large scale computing with a platform that is entirely open source and cloud native, using open standards for data access, manipulation, protocols, and formats. The MAAP data system consists of a dedicated data store whose data is indexed in an online catalog conforming to established metadata, application programmatic interfaces (APIs), and service interface standards, using an implementation of the open sourced NASA Common Metadata Repository. Federation of user identities allows users from either NASA or ESA to access and consume services from the other using a unified metadata catalog for the data utilized across the ESA and NASA MAAP platforms. Similarly, we are exploring how to increase interoperability to achieve a common approach to packaging, orchestrating and executing algorithms, with interoperable access to data for subsetting, fast browse, and cloud-optimized access, all using interoperable standards such as those from the Open Geospatial Consortium (OGC). Designed for interoperability, ESA and NASA utilize a common architecture for the software platform. It provides a cloud-based algorithm development environment (ADE) that enables scientists to develop algorithms collaboratively with access to the MAAP data catalog as well as other data archives. MAAP provides an Eclipse Che-based ADE supporting both Python and R languages, popular in this biomass community. Algorithms developed and containerized within the ADE can be deployed to run to thousands of computational nodes in the MAAP’s data processing system (DPS), dramatically speeding up processing and giving scientists a rapid, iterative turnaround of results. NASA’s implementation of the DPS is based on the Hybrid Science Data System (HySDS) framework, used by NASA flight projects to produce Earth science standard products.

cloud computing↗
NASA NTRSDate not supplied

Web-based Visualization and Analytics of Petascale Data: Equity as a Tide that Lifts All Boats

Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environmental variables is essential to predicting and establishing root causes of future undesirable scenarios and assessing possible mitigation strategies. While supercomputer centers provide a powerful infrastructure for generating petabytes of simulation output, accessing and analyzing these datasets interactively remains challenging on multiple fronts. This paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hid-ing the complexities of dealing with file systems or cloud services.We also optimize network utilization while streaming from petas-cale repositories through state-of-the-art progressive compression algorithms. Based on this abstraction, we provide customizable dashboards that can be accessed from any device with any inter-net connection, enabling interactive visual analysis of vast amounts of data to a wide range of users - from top scientists with access to leadership-class computing environments to undergraduate students of disadvantaged backgrounds from minority-serving institutions. We focus on NASA’s use of petascale climate datasets as an example of particular societal impact and, therefore, a case where achieving equity in science participation is critical. We validate our approach by improving the ability of climate scientists to visually explore their data via two fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution.These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Data visualization↗
NASA NTRS2024

Artificial Intelligence Workshop Report

The 4th NASA Science Mission Directorate (SMD) Artificial Intelligence (AI) Workshop, held during March 25-27, 2024, in Huntsville, AL, highlighted the significant potential of AI and machine learning (ML) in scientific research and processes. The workshop, supported by the NASA Office of Chief Science Data Officer (OCSDO), emphasized the critical role of foundation models (FMs) and large language models (LLMs) in advancing scientific disciplines. The event brought together domain scientists, computer scientists, AI experts, program managers, program scientists, and industry partners to address key challenges and explore opportunities in applying these advanced technologies.

Manil Maskey↗
DOE OSTI2014

Historic Manhattan Project Sites at Los Alamos

The Manhattan Project laboratory constructed at Los Alamos, New Mexico, beginning in 1943, was intended from the start to be temporary and to go up with amazing speed. Because most of those WWII-era facilities were built with minimal materials and so quickly, much of the original infrastructure was torn down in the late '40s and early '50s and replaced by more permanent facilities. However, a few key facilities remained, and are being preserved and maintained for historic significance. Four such sites are visited briefly in this video, taking viewers to V-Site, the buildings where the first nuclear explosive device was pre-assembled in preparation for the Trinity Test in Southern New Mexico. Included is another WWII area, Gun Site. So named because it was the area where scientists and engineers tested the so-called "gun method" of assembling nuclear materials -- the fundamental design of the Little Boy weapon that was eventually dropped on Hiroshima. The video also goes to Pajarito Site, home of the "Slotin Building" and "Pond Cabin." The Slotin Building is the place where scientist Louis Slotin conducted a criticality experiment that went awry in early 1946, leading to his unfortunate death, and the Pond Cabin served the team of eminent scientist Emilio Segre who did early chemistry work on plutonium that ultimately led to the Fat Man weapon.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

THE NEW ONLINE METADATA EDITOR FOR GENERATING STRUCTURED METADATA

Nobody is better suited to “describe” data than the scientist who created it. This “description” about a data is called Metadata. In general terms, Metadata represents the who, what, when, where, why and how of the dataset [1]. eXtensible Markup Language (XML) is the preferred output format for metadata, as it makes it portable and, more importantly, suitable for system discoverability. The newly developed ORNL Metadata Editor (OME) is a Web-based tool that allows users to create and maintain XML files containing key information, or metadata, about the research. Metadata include information about the specific projects, parameters, time periods, and locations associated with the data. Such information helps put the research findings in context. In addition, the metadata produced using OME will allow other researchers to find these data via Metadata clearinghouses like Mercury [2][4]. OME is part of ORNL’s Mercury software fleet [2][3]. It was jointly developed to support projects funded by the United States Geological Survey (USGS), U.S. Department of Energy (DOE), National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA). OME’s architecture provides a customizable interface to support project-specific requirements. Using this new architecture, the ORNL team developed OME instances for USGS’s Core Science Analytics, Synthesis, and Libraries (CSAS&L), DOE’s Next Generation Ecosystem Experiments (NGEE) and Atmospheric Radiation Measurement (ARM) Program, and the international Surface Ocean Carbon Dioxide ATlas (SOCAT).Researchers simply use the ORNL Metadata Editor to enter relevant metadata into a Web-based form. From the information on the form, the Metadata Editor can create an XML file on the server that the editor is installed or to the user’s personal computer. Researchers can also use the ORNL Metadata Editor to modify existing XML metadata files.As an example, an NGEE Arctic scientist use OME to register their datasets to the NGEE data archive and allows the NGEE archive to publish these datasets via a data search portal (http://ngee.ornl.gov/data). These highly descriptive metadata created using OME allows the Archive to enable advanced data search options using keyword, geo-spatial, temporal and ontology filters. Similarly, ARM OME allows scientists or principal investigators (PIs) to submit their data products to the ARM data archive.How would OME help Big Data Centers like the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)?The ORNL DAAC is one of NASA’s Earth Observing System Data and Information System (EOSDIS) data centers managed by the Earth Science Data and Information System (ESDIS) Project. The ORNL DAAC archives data produced by NASA's Terrestrial Ecology Program. The DAAC provides data and information relevant to biogeochemical dynamics, ecological data, and environmental processes, critical for understanding the dynamics relating to the biological, geological, and chemical components of the Earth's environment. Typically data produced, archived and analyzed is at a scale of multiple petabytes, which makes the discoverability of the data very challenging. Without proper metadata associated with the data, it is difficult to find the data you are looking for and equally difficult to use and understand the data.OME will allow data centers like the NGEE and ORNL DAAC to produce meaningful, high quality, standards-based, descriptive information about their data products in-turn helping with the data discoverability and interoperability. Useful Links:USGS OME: http://mercury.ornl.gov/OME/NGEE OME: http://ngee-arctic.ornl.gov/ngeemetadata/ARM OME: http://archive2.ornl.gov/armome/Contact: Ranjeet Devarakonda (devarakondar@ornl.gov)References:[1] Federal Geographic Data Committee. Content standard for digital geospatial metadata. Federal Geographic Data Committee, 1998.[2] Devarakonda, Ranjeet, et al. "Mercury: reusable metadata management, data discovery and access system." Earth Science Informatics 3.1-2 (2010): 87-94.[3] Wilson, B. E., Palanisamy, G., Devarakonda, R., Rhyne, B. T., Lindsley, C., & Green, J. (2010). Mercury Toolset for Spatiotemporal Metadata.[4] Pouchard, L. C., Branstetter, M. L., Cook, R. B., Devarakonda, R., Green, J., Palanisamy, G., ... & Noy, N. F. (2013). A Linked Science investigation: enhancing climate change data discovery with semantic technologies. Earth science informatics, 6(3), 175-185.

FGDC CSDGM↗
DOE OSTI2015

Complex Dark Matter

After a century of study, scientists have come to the realization that the ordinary matter made of atoms is a minority in the universe. In order to explain observations, it appears that there exists a new and undiscovered kind of matter, called dark matter, that is five times more prevalent than ordinary matter. The evidence for this new matter’s existence is very strong, but scientists know only a little about its nature. In today’s video, Fermilab’s Dr. Don Lincoln talks about an exciting and unconventional idea, specifically that dark matter might have a very complex set of structures and interactions. While this idea is entirely speculative, it is an interesting hypothesis and one that scientists are investigating.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Immersive Visualization for Materials Science Data Analysis using the Oculus Rift

In this paper, we propose strategies and objectives for immersive data visualization with applications in materials science using the Oculus Rift virtual reality headset. We provide background on currently available analysis tools for neutron scattering data and other large-scale materials science projects. In the context of the current challenges facing scientists, we discuss immersive virtual reality visualization as a potentially powerful solution. We introduce a prototype immersive visual- ization system, developed in conjunction with materials scientists at the Spallation Neutron Source, which we have used to explore large crystal structures and neutron scattering data. Finally, we offer our perspective on the greatest challenges that must be addressed to build effective and intuitive virtual reality analysis tools that will be useful for scientists in a wide range of fields.

analytics↗
DOE OSTI2014

ARM Data Integrator

The Atmospheric Radiation Measurement (ARM) Data Integrator (ADI) streamlines the development of scientific algorithms and analysis of time-series NetCDF data, and improves the content and consistency of the output data products produced by these algorithms. The framework automates the process of retrieving and preparing data for analysis, and allows users to design output data products through a graphical interface. It also provides a modular, flexible software development architecture that scientists can use to generate C, Python, and IDL source code templates that embed the pre and post processing logic allowing the scientist to focus on only their science. The input data, preprocessing, and output data specifications of algorithms are defined through a graphical interface and stored in a database. ADI implements workflow for data integration and supports user access to data through a library of software modules. Data preprocess capabilities supported include automated retrieval of data from input files, merging the retrieved data into appropriately sized chunks, and transformation of the data onto a common coordinate system grid. Through the graphical interface, users can view the details of both their data products and those in the ARM catalog and allows developers to use existing data product to build new data products. Views of the output data products include an overlay of how the design meets ARM archive's data standards providing the user with a visual cue indicating where their output violates an archive standard. The ADI libraries access the information provided through the GUI via a Postgres database. The ADI framework and its supporting components can significantly decrease the time and cost of implementing scientific algorithms while improving the ability of scientists to disseminate their results.

Source record↗
DOE OSTI2016

"FACILS 2014: Microbially-driven facilitation systems in environmental biotechnology" (hereafter "FACILS") presented here by the European Commission (EC)-United States (US) Task Force on Biotechnology Research

As we enter the 21st century, the sustainability of the biosphere is a global challenge that can best be met with a global response. This includes how we train and promote our next generation of research scientists in the emerging arenas of genome-enabled biology and a bio-based economy. It is this fundamental issue that formed the motivation for designing and conducting a shortcourse entitled “FACILIS 2014: Microbially-driven facilitation systems in environmental biotechnology” (hereafter “FACILIS”) presented here by the European Commission (EC)-United States (US) Task Force on Biotechnology Research. This WG was established in 1994 under the umbrella of the US-EC Task Force on Biotechnology Research, a transatlantic collaborative group overseen by the US Office of Science and Technology Policy (OSTP) and the EC. The Environmental Biotechnology Working Group maintains several goals, including establishing research links between scientists in EU countries and the US and fostering the careers of junior scientists from both sides of the Atlantic to the global nature of scientific cooperation. To that end, a shortcourse was held at the University of Milan in Italy on July 12-25 2014 organized around cross-cutting themes of genomic science and designed to attract a stellar group of interdisciplinary early carrier researchers. A total of 22 students, 10 from the US and 12 from the EU participated. The course provided them with hands-on experience with the latest scientific methods in genomics and bioinformatics; using a format that combines lectures, laboratory research and field work with the final goal to enable researchers to finally turn data into knowledge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

ISMB Conference Funding to Support Attendance of Early Researchers and Students

ISMB Conference Funding for Students and Young Scientists Historical Description The Intelligent Systems for Molecular Biology (ISMB) conference has provided a general forum for disseminating the latest developments in bioinformatics on an annual basis for the past 22 years. ISMB is a multidisciplinary conference that brings together scientists from computer science, molecular biology, mathematics and statistics. The goal of the ISMB meeting is to bring together biologists and computational scientists in a focus on actual biological problems, i.e., not simply theoretical calculations. The combined focus on “intelligent systems” and actual biological data makes ISMB a unique and highly important meeting. 21 years of experience in holding the conference has resulted in a consistently well-organized, well attended, and highly respected annual conference. "Intelligent systems" include any software which goes beyond straightforward, closed-form algorithms or standard database technologies, and encompasses those that view data in a symbolic fashion, learn from examples, consolidate multiple levels of abstraction, or synthesize results to be cognitively tractable to a human, including the development and application of advanced computational methods for biological problems. Relevant computational techniques include, but are not limited to: machine learning, pattern recognition, knowledge representation, databases, combinatorics, stochastic modeling, string and graph algorithms, linguistic methods, robotics, constraint satisfaction, and parallel computation. Biological areas of interest include molecular structure, genomics, molecular sequence analysis, evolution and phylogenetics, molecular interactions, metabolic pathways, regulatory networks, developmental control, and molecular biology generally. Emphasis is placed on the validation of methods using real data sets, on practical applications in the biological sciences, and on development of novel computational techniques. The ISMB conferences are distinguished from many other conferences in computational biology or artificial intelligence by an insistence that the researchers work with real molecular biology data, not theoretical or toy examples; and from many other biological conferences by providing a forum for technical advances as they occur, which otherwise may be shunned until a firm experimental result is published. The resulting intellectual richness and cross-disciplinary diversity provides an important opportunity for both students and senior researchers. ISMB has become the premier conference series in this field with refereed, published proceedings, establishing an infrastructure to promote the growing body of research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

OLED Fundamentals: Materials, Devices, and Processing of Organic Light-Emitting Diodes

What is an organic light emitting diode (OLED)? Why should we care? What are they made of? How are they made? What are the challenges in seeing these devices enter the marketplace in various applications? These are the questions we hope to answer in this book, at a level suitable for knowledgeable non-experts, graduate students and scientists and engineers working in the field who want to understand the broader context of their work. At the most basic level, an OLED is a promising new technology composed of some organic material sandwiched between two electrodes. When current is passed through the device, light is emitted. The stack of layers can be very thin and has many variations, including flexible and/or transparent. The organic material can be polymeric or composed small molecules, and may include inorganic components. The electrodes may consist of metals, metal oxides, carbon nanomaterials, or other species, though of course for light to be emitted, one electrode must be transparent. OLEDs may be fabricated on glass, metal foils, or polymer sheets (though polymeric substrates must be modified to protect the organic material from moisture or oxygen). In any event, the organic material must be protected from moisture during storage and operation. A control circuit, the exact nature of which depends on the application, drives the OLED. Nevertheless, the control circuit should have very stable current control to generate uniform light emission. OLEDs can be designed to emit a single color of light, white light, or even tunable colors. The devices can be switched on and off very rapidly, which makes them suitable for displays or for general lighting. Given the amazing complexity of the technical and design challenges for practical OLED applications, it is not surprising that applications are still somewhat limited. Although organic electroluminescence is more than 50 years old, the modern OLED field is really only about half that age – with the first high-efficiency OLED demonstrated in 1987. Thus, we expect to see exciting advances in the science, technology and commercialization in the coming years. We hope that this book helps to advance the field in some small way. Contributors to this monograph are experts from top academic institutions, industry and national laboratories who provide comprehensive and up-to-date coverage of the rapidly evolving field of OLEDs. Furthermore, this monograph collects in one place, for the first time, key topics across the field of OLEDs, from fundamental chemistry and physics, to practical materials science and engineering topics, to aspects of design and manufacturing. The monograph synthesizes and puts into context information scattered throughout the literature for easy review in one book. The scope of the monograph reflects the necessity to focus on new technological challenges brought about by the transition to manufacturing. In the Section 1, all materials of construction of the OLED device are covered, from substrate to encapsulation. In Section 2, for the first time, additional challenges in devices and processing are addressed. This book is geared towards a broad audience, including materials scientists, device physicists, synthetic chemists and electrical engineers. Furthermore, this book makes a great introduction to scientists in industry and academia, as well as graduate students interested in applied aspects of photophysics and electrochemistry in organic thin films. This book is a comprehensive source for OLED R&D professionals from all backgrounds and institutions.

OLED↗
DOE OSTI2016

The Strong Nuclear Force

Scientists are aware of four fundamental forces- gravity, electromagnetism, and the strong and weak nuclear forces. Most people have at least some familiarity with gravity and electromagnetism, but not the other two. How is it that scientists are so certain that two additional forces exist? In this video, Fermilab’s Dr. Don Lincoln explains why scientists are so certain that the strong force exists.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Automated metadata, provenance cataloging and navigable interfaces: ensuring the usefulness of extreme-scale data

The MPO (Metadata, Provenance, Ontology) Project successfully addressed the goal of improving the usefulness and traceability of scientific data by building a system that could capture and display all steps in the process of creating, analyzing and disseminating that data. Throughout history, scientists have generated handwritten logbooks to keep track of data, their hypotheses, assumptions, experimental setup, and computational processes as well as reflections on observations and issues encountered. Over the last several decades, with the growth of personal computers, handheld devices, and the World Wide Web, the handwritten logbook has begun to be replaced by electronic logbooks. This transition has brought increased capability such as supporting multi-media, hypertext, and fast searching. However, content creation and metadata (a set of data that describes and gives information about other data) capturing has for the most part remained a manual activity just as it was with handwritten logbooks. This has led to a fragmentation of data, processing, and annotation that has only accelerated as scientific workflows continue to increase in complexity. From a scientific perspective, it is very important to be able to understand the lineage of any piece of data: who, what, when, how, and why. This is typically referred to as data provenance. The fragmentation discussed previously often means that data provenance is lost. As scientific workflows move to powerful computers and become more complex, the ability to track all of the steps involved in creating a piece of data become even more difficult. It was the goal of the MPO (Metadata, Provenance, Ontology) Project to create a system (the MPO System) that allows for automatic provenance and metadata capturing in such a way to allow easy searching and browsing. This goal needed to be accomplished in a general way so that it may be used across a broad range of scientific domains, yet allow the addition of vocabulary (Ontology) that is domain specific as is required for intelligent searching and browsing in the scientific context. Through the creation and deployment of the MPO system, the goals of the project were achieved. An enhanced metadata, provenance, and ontology storage system was created. This was combined with innovative methodologies for navigating and exploring these data using a web browser for both experimental and simulation-based scientific research. In addition, a system to allow scientists to instrument their existing workflows for automatic metadata and provenance is part of the MPO system. In that way, a scientist can continue to use their existing methodology yet easily document their work. Workflows and data provenance can be displayed either graphically or in an electronic notebook format and support advanced search features including via ontology. The MPO system was successfully used in both Climate and Magnetic Fusion Energy Research. The software for the MPO system is located at https://github.com/MPO-Group/MPO and is open source distributed under the Revised BSD License. A demonstration site of the MPO system is open to the public and is available at https://mpo.psfc.mit.edu/. A Docker container release of the command line client is available for public download using the command docker pull jcwright/mpo-cli at https://hub.docker.com/r/jcwright/mpo-cli.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Virtual Walk: The Construction of the Long Baseline Neutrino Facility

This 2-minute animation shows a virtual walk through the large caverns of the Long-Baseline Neutrino Facility, which will house the Deep Underground Neutrino Experiment. To create the caverns for the huge DUNE particle detectors, construction crews will excavate more than 800,000 tons of rock a mile underground at the Sanford Underground Research Facility in South Dakota. Scientists and dignitaries broke ground for this project on July 21, 2017. When construction is complete, DUNE scientists will send an intense neutrino beam through 1,300 kilometers of rock from the Department of Energy’s Fermilab to the DUNE particle detectors to understand the role that neutrinos – the most abundant matter particles in the universe – play in our cosmos. About 1,000 scientists from more than 160 institutions in 30 countries work on the Deep Underground Neutrino Experiment.

42 ENGINEERING↗
DOE OSTI2017

ArrayBridge: Interweaving declarative array processing with high-performance computing

Scientists are increasingly turning to datacenter-scale computers to produce and analyze massive arrays. Despite decades of database research that extols the virtues of declarative query processing, scientists still write, debug and parallelize imperative HPC kernels even for the most mundane queries. This impedance mismatch has been partly attributed to the cumbersome data loading process; in response, the database community has proposed in situ mechanisms to access data in scientific file formats. Scientists, however, desire more than a passive access method that reads arrays from files. This paper describes ArrayBridge, a bi-directional array view mechanism for scientific file formats, that aims to make declarative array manipulations interoperable with imperative file-centric analyses. Our prototype implementation of ArrayBridge uses HDF5 as the underlying array storage library and seamlessly integrates into the SciDB open-source array database system. In addition to fast querying over external array objects, ArrayBridge produces arrays in the HDF5 file format just as easily as it can read from it. ArrayBridge also supports time travel queries from imperative kernels through the unmodified HDF5 API, and automatically deduplicates between array versions for space efficiency. Our extensive performance evaluation in NERSC, a large-scale scientific computing facility, shows that ArrayBridge exhibits statistically indistinguishable performance and I/O scalability to the native SciDB storage engine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Lake Arrowhead Microbial Genomics Conference

The Lake Arrowhead Microbial Genomics Conference (formerly the E. coli and Small Genomes Conference, and the Microbial Genomes Conference) was held September 16-20, 2018, at the UCLA Conference Center at Lake Arrowhead, California. This conference is part of a yearly meeting initiated in 1991 to bring together genome sequencers, bioinformatics specialists, biologists, and geneticists, to forge interactions that would result in meaningful functional genomics. The goal of the meeting was to translate the influx of new genome sequencing information into useful biological studies. The Lake Arrowhead 2018 meeting had a major focus on microbial communities, the human microbiome, pathogens, bioenergetics, and environmental microbiology. The field of genomics has reached the point where deriving the sequence of an organism’s entire genome is now seen as a beginning rather than an endpoint. Moreover, understanding the sequences of whole communities, and particularly those that make up different microbiomes, is now a central point of the field. The 2018 meeting covered microorganisms for which extensive analyses exist, and those for which new biological and technical strategies are being developed. The focus on biodiversity, the human microbiome, pathogenic organisms, and bioenergetics added special significance to this meeting. This meeting was designed to have a mix of 34 invited presentations and poster sessions with 80 posters, and had 160 participants. This meeting has become established as a major annual microbial genomics conference during the last 26 years and has assured status and quality. It offers a major opportunity for young investigators and students to attend, present posters, and give talks. In fact, 22 of the 34 invited speakers were early career stage scientists. An important impact of the conference was the forging of new collaborations between scientists and particularly young scientists with the array of multidisciplinary researchers at the meeting.

Miller, Jeffrey H.↗
DOE OSTI2018

Experts’ Familiarity Versus Optimality of Visualization Design: How Familiarity Affects Perceived and Objective Task Performance

Experts, cutting across domains like biology, climate science, cyber, energy, etc., frequently use visualizations as the principal medium for making analytical judgments or for presenting the results of their analysis. However, scientists are often skeptical about adopting new visualization methods over familiar ones, although the latter might be perceptually sub-optimal. This is due to the use of the familiarity heuristic, where the perceived cognitive ease in processing familiar representations of information leads scientists to undermine the effect of visualization best practices. Recent studies have shown that this often results in a discrepancy between scientists’ perceived and actual performance quality. It has also been shown that in some cases, participatory design sessions and qualitative and quantitative user studies are able to mitigate the effects of such bias. In this paper, we discuss the potential causes and effects of familiarity related biases with examples from recent studies and reflect on the related research questions.

visualization, climate science, cognitive bias, fa↗
DOE OSTI2018

Prototype detectors for the Deep Underground Neutrino Experiment

The international Deep Underground Neutrino Experiment, hosted by Fermilab, will investigate the role neutrinos play in the universe. To test the technologies used by DUNE, scientists are building two large prototype detectors at CERN. Filled with 800 tons of liquid argon, the ProtoDUNE detectors are the largest particle detectors of their kind. The actual DUNE detector modules, which will be assembled 1.5 kilometers underground at the Sanford Underground Research Facility, will be 20 times bigger than these test beds. More than 1,000 scientists from research institutions in 32 countries are working on DUNE. On Sept. 18, 2018, DUNE scientists announced the observation of the first particle tracks with the first of the two prototype detectors: https://news.fnal.gov/2018/09/first-tracks-in-prototype-for-neutrino-experiment/

42 ENGINEERING↗