Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “SCIENTISTS”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Position Papers for the ASCR Workshop on the Science of Scientific-Software Development and Use

Software is an increasingly important component in the pursuit of scientific discovery. Both its development and use are essential activities for many scientific teams. At the same time, very little scientific study has been conducted to understand, characterize, and improve the development and use of software for science. Computational science teams have diversified over time to include contributions from domain scientists who provide expertise in scientific and engineering disciplines, applied mathematicians and computer scientists who provide optimal algorithms and data structures, and software and data engineers who provide methodologies and tools adapted and adopted from other software domains. These diverse contributions have enabled tremendous advances in the pursuit of scientific discovery, even as models, computer architectures, and software environments have become more complicated. With this increasing diversity, we believe the next opportunity for qualitative improvement comes from applying the scientific method to understanding, characterizing, and improving how scientific software is developed and used. We believe that this pursuit requires expertise from computational scientists themselves, and from the cognitive and social sciences as well as the software engineering research community. As we look to increase the productivity and sustainability of the scientific-software-development-and-use cycle, a more systematic application of the scientific method to understand processes for software development and use will be a valuable tool to guide future work and result in more usable and sustainable software. This workshop will bring together computer scientists, software engineering researchers, computational scientists, applied mathematicians, social scientists, cognitive scientists, and others, to explore how we can conduct such systematic investigations, what can be learned, and how doing so will benefit the scientific enterprise. The workshop will be structured around a set of breakout sessions, with every attendee expected to participate actively in the discussions. Afterward, workshop attendees — from DOE, industry, and academia — will produce a report for ASCR that summarizes the findings of the workshop.

42 ENGINEERING↗
DOE OSTI2022

Report on DOE-Supported Participation in the 18th International Congress on Photosynthesis Research (IPC 2022)

Photosynthesis is the primary natural process for conversion of solar energy to chemical energy. Solar energy is the only clean, renewable source of energy that has sufficient capacity to replace fossil fuels and meet rising global energy needs. Meeting this challenge means understanding photosynthetic energy conversion at a molecular level, a task requiring perspectives from all disciplines of science. The 18th International Congress on Photosynthesis (IPC) is facilitating just that by providing a forum for scientists from all corners of the world to come together to share ideas and build new collaborations that will improve the lives of future generations. A vibrant knowledge of each of the diverse aspects of basic and applied photosynthesis research obviously depends upon a healthy scientific exchange among the members of the research community. While senior scientists are more likely able to make the long trip to this important meeting, the travel costs for students and postdocs would otherwise prohibit participation for talented junior US scientists. At the same time, this is precisely the kind of meeting that strengthens interest and careers in the important scientific directions represented by this conference. For all scientists this is an opportunity to learn firsthand about the very latest advances in the field and to meet and brainstorm with collaborators as well as make new collaborations. For junior scientists, this funding is giving an opportunity to do these things plus it is often the first tangible experience regarding the global, international significance of their own projects. Overall, the diversity of topics and technical approaches is providing an extremely stimulating multidisciplinary environment for junior and senior scientists performing research in a variety of areas that are within the scope to the US DOE mission. In collaboration with the executive members of the North American Photosynthesis Conference Association (NAPCA and the International Society of Photosynthesis research (ISPR), a competitive selection of junior participants is being made based upon the quality of the abstracts and materials submitted by self-identified candidates that have applied following a broadcast call for applications from junior US scientists. Financial support is consisting of waivers for the registration cost of the meeting and partial living expenses at local dormitory facilities at the conference site.

08 HYDROGEN↗
NASA NTRS2000

Supporting Scientific Analysis within Collaborative Problem Solving Environments

Collaborative problem solving environments for scientists should contain the analysis tools the scientists require in addition to the remote collaboration tools used for general communication. Unfortunately, most scientific analysis tools have been designed for a "stand-alone mode" and cannot be easily modified to work well in a collaborative environment. This paper addresses the questions, "What features are desired in a scientific analysis tool contained within a collaborative environment?", "What are the tool design criteria needed to provide these features?", and "What support is required from the architecture to support these design criteria?." First, the features of scientific analysis tools that are important for effective analysis in collaborative environments are listed. Next, several design criteria for developing analysis tools that will provide these features are presented. Then requirements for the architecture to support these design criteria are listed. Sonic proposed architectures for collaborative problem solving environments are reviewed and their capabilities to support the specified design criteria are discussed. A deficiency in the most popular architecture for remote application sharing, the ITU T. 120 architecture, prevents it from supporting highly interactive, dynamic, high resolution graphics. To illustrate that the specified design criteria can provide a highly effective analysis tool within a collaborative problem solving environment, a scientific analysis tool that contains the specified design criteria has been integrated into a collaborative environment and tested for effectiveness. The tests were conducted in collaborations between remote sites in the US and between remote sites on different continents. The tests showed that the tool (a tool for the visual analysis of computer simulations of physics) was highly effective for both synchronous and asynchronous collaborative analyses. The important features provided by the tool (and made possible by the specified design criteria) are: 1. The tool provides highly interactive, dynamic, high resolution, 3D graphics. 2. All remote scientists can view the same dynamic, high resolution, 3D scenes of the analysis as the analysis is being conducted. 3. The responsiveness of the tool is nearly identical to the responsiveness of the tool in a stand-alone mode. 4. The scientists can transfer control of the analysis between themselves. 5. Any analysis session or segment of an analysis session, whether done individually or collaboratively, can be recorded and posted on the Web for other scientists or students to download and play in either a collaborative or individual mode. 6. The scientist or student who downloaded the session can, individually or collaboratively, modify or extend the session with his/her own "what if" analysis of the data and post his/her version of the analysis back onto the Web. 7. The peak network bandwidth used in the collaborative sessions is only 1K bit/second even though the scientists at all sites are viewing high resolution (1280 x 1024 pixels), dynamic, 3D scenes of the analysis. The links between the specified design criteria and these performance features are presented.

Watson, Velvin R.↗
NASA NTRS2003

Laboratory for Atmospheres 2002 Technical Highlights

How can we improve our ability to predict the weather-tomorrow, next week, and into the future? How is the Earth's climate changing? What causes such change? And what are its costs? What can the atmospheres of distant planets teach us about our own planet and its evolution? The Laboratory for Atmospheres is helping to answer these and other scientific questions about our planet and its neighbors. The Laboratory conducts a broad theoretical and experimental research program studying all aspects of the atmospheres of the Earth and other planets, including their structural, dynamical, radiative, and chemical properties, with the overarching goal to provide better understanding and to improve prediction of the Earth's climate. Vigorous research is central to NASA's exploration of the frontiers of knowledge. NASA scientists play a key role in conceiving new space missions, providing mission requirements, and carrying out research to explore the behavior of planetary systems, including, notably, the Earth's. Our Laboratory's scientists also supply outside scientists with technical assistance and scientific data to further investigations not immediately addressed by NASA itself. Laboratory scientists submit competitive research proposals with diverse scientific or technological approaches to NASA and other Federal agencies to acquire research support. The Laboratory management strives to provide a working environment that promotes creativity, competition, and openness. The Laboratory for Atmospheres is a vital participant in NASA's research program. Our Laboratory often has relatively large programs, sizable satellite missions, or observational campaigns that require the cooperative and collaborative efforts of many scientists. We ensure an appropriate balance between our scientists' responsibility for these large collaborative projects and their need for an active individual research agenda. This balance allows members of the Laboratory to continuously improve their scientific credentials. The Laboratory places high importance on promoting and measuring quality in its scientific research. We strive to assure high quality through peer-review funding processes that support approximately 90% of the work in the Laboratory. The overall quality of our scientific efforts is evaluated periodically by committees of advisors from the external scientific community, as detailed in Appendix 2 of this document. Members of the Laboratory interact with the general public to support a wide range of interests in the atmospheric sciences. Among other activities, the Laboratory raises the public's awareness of atmospheric science by presenting public lectures and demonstrations, by making scientific data available to wide audiences, by teaching, and by mentoring students and teachers. Section 6 presents details of the Laboratory's outreach activities during 2002. The Laboratory is also committed to addressing the demographic imbalances that exist today in the atmospheric and space sciences. We must address these imbalances for our field to enjoy the full benefit of all of the Nation's talent. The Laboratory makes substantial efforts to attract new scientists to the fields of atmospheric and space sciences. We strongly encourage the establishment of partnerships with Federal and state agencies that have operational responsibilities to promote the societal application of Earth sciences.

Steven E. Platnick↗
NASA NTRSDate not supplied

Astrobee: Completed, Current, and Future Research using Free Flying Robots on the International Space Station

After four years on the International Space Station (ISS), the Astrobee Research Facility, has completed over 130 Test Sessions logging over 1000 hours of operations. Managed by the NASA ISS Program OZ office and supported by NASA Ames Research Center (ARC) in California, the Astrobee Team maintains three identical free-flying Astrobee robots for research on the ISS. As a technology demonstration platform, the Astrobee Robots are available for Guest Scientists to use for a spectrum of research capabilities. Astrobee, propelled by battery-operated fans, is designed to autonomously operate throughout most of the USOS (US Orbital Segment), with the objective of minimizing astronaut support. Astrobee carries a suite of six cameras, a two degree-of-freedom (DOF) arm with a gripper that can grasp ISS handrails and other objects, and three payload bays that provide power and data for guest science hardware. Astrobee can autonomously execute hours-long flight plans or be teleoperated from the ground or by astronauts. While the Astrobee Team continues to improve mapping and autonomous flight capabilities, one of the main goals of Astrobee Robots is to provide research opportunities for Guest Scientists. The Astrobee Robot Software (ARS) makes extensive use of the open-source Robot Operating System (ROS). The ARS can be used interchangeably with an Astrobee Simulator or as Astrobee’s onboard software. ARS features include autonomous docking and perching, real-time teleoperations from the ground, plan based autonomous tasks, multi Astrobee communication, among other capabilities. Through simulation software and ground testing laboratories, the Astrobee Team is available to support Guest Scientists during development and testing and lead real-time ISS operations. Guest Scientists can participate in this research opportunity following the Guest Science Lifecycle (GSL) shown in Figure 1: Guest Science Lifecycle below. The Astrobee Team and Guest Scientists have complete research including Gecko materials studies, RFID and sound sensing capabilities, student Robotics Programming Challenges, and Free Flyer formation flight investigations. Current science with the Astrobee Robots includes Free Flyer self-toss studies, new docking capabilities, advanced mapping resolution capabilities, and high resolution panoramic imagery. Future Guest scientists and Astrobee Team research will focus on robotics applications for future NASA missions such as Gateway and Artemis and potential experiments involving human-robot interactions. This presentation will focus on four main subjects, 1) completed, current, and future planned research using the Astrobee robots, 2) how Guest Scientist get from conception to the ISS, 3) Astrobee Facility resources available for Guest Science ground testing and real-time ISS operations support, and 4) lessons learned from four years of ISS operations.

Astrobee↗
DOE OSTI2022

The Evolution of the Peridynamics Co-Authorship Network

We report peridynamics is a relatively new field in continuum mechanics that has developed over the past 20 years. This paper studies the evolution of collaborations in the field of peridynamics since its inception using social network analysis. For this purpose, we construct a network for each year from 2000 to 2019 describing co-authorship between scientists in peridynamics. In these networks, each node represents a scientist and each link connects two co-authoring scientists with a link weight representing the frequency and strength of their collaboration; each network as a whole can be thought of as a graph representation of the peridynamics community for the given year. By constructing a network for each year, we are able to analyze the evolution of the network in time and discuss the implications of this evolution for the peridynamics community. Our study demonstrates that the peridynamics community has been growing exponentially in size in recent years. Centrality metrics are also used to identify the most collaborative scientists in the community. Moreover, we compute link recommendations based on both elevating a scientist’s position in the network with respect to certain centrality metrics or closing structural holes in the network identified with persistent homology. We further extend the analysis to higher-order networks whose nodes represent groups of scientists in the community and whose links connect collaborating groups. In some sense, our work studies the past, present, and future of the peridynamics community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community

Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG). This paper resulted from an initiative from 2018 through 2020 to convene LTG and data scientists in the U.S. to strategize future management of LTG data. Through webinars, a workshop, a preprint, a townhall, and a community survey, the group of U.S. scientists discussed the landscape of data management for LTG – the data-scape. Currently this data-scape includes a “street bazaar” of data repositories. This was deemed appropriate in the same way that LTG scientists publish articles in many journals. The variety of data repositories and journals reflect that LTG scientists target many different scientific questions, produce data with extremely different structures and volumes, and utilize copious and complex metadata. Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories, and, for sample-based data, registration of samples with globally unique persistent identifiers. LTG scientists should use certified data repositories that are either highly structured databases designed for specialized types of data, or unstructured generalized data systems. Recognizing the need for tools to enable search and cross-referencing across the proliferating data repositories, the group proposed that the overall data informatics paradigm in LTG should shift from “build data repository, data will come” to “publish data online, cybertools will find”. Funding agencies could also provide portals for LTG scientists to register funded projects and datasets, and forge approaches that cross national boundaries. Finally, the needed transformation of the LTG data culture requires emphasis in student education on science and management of data.

58 GEOSCIENCES↗
DOE OSTI2020

Publishing Environmental Assessment and Management Science: Crossing the Hurdles

Benefits accrue to scientists, resource managers, companies, and policymakers when environmental scientists publish in peer-reviewed journals. However, environmental scientists and practitioners face challenges, including the sometimes low value placed on journal articles, institutional vested interests in outcomes, and the changing priorities of employers and project sponsors. Confidentiality agreements can also lead scientists to assume publication is not an option. Case studies may be viewed by potential authors as too routine for peer-reviewed journals. On the basis of 30 years of experience, we suggest that publishing hurdles can be overcome and that environmental scientists have a range of options. The topics of manuscripts can include not only results from case studies and perspectives based on them but also byproducts of assessments, including definitions, plans, monitoring methods and models, and decision frameworks. Environmental scientists have unique opportunities to move science forward with their practical knowledge if they can move across the institutional, logistical, data-related, and content-related hurdles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows

Data driven science is becoming increasingly more common, complex, and is placing tremendous stresses on visualization and analysis frameworks. Data sources producing 10GB per second (and more) are becoming increasingly commonplace in both simulation, sensor and experimental sciences. These data sources, which are often distributed around the world, must be analyzed by teams of scientists that are also distributed. Enabling scientists to view, query and interact with such large volumes of data in near-real-time requires a rich fusion of visualization and analysis techniques, middleware and workflow systems. Here, this paper discusses initial research into visualization and analysis of distributed data workflows that enables scientists to make near-real-time decisions of large volumes of time varying data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Scalable data management, analysis and visualization (SDAV) Institute. Final Scientific/Technical Report

The purpose of the SDAV institute is to provide tools and expertise in scientific data management, analysis, and visualization to DOE’s application scientists. Our goal is to actively work with application teams to assist them in achieving breakthrough science, and to provide technical solutions in the data management, analysis, and visualization regimes that are broadly used by the computational science community. Over the last 5 years members of our institute worked directly with application scientists and DOE leadership-class facilities to assist them by applying the best tools and technologies at our disposal. We also enhanced our tools based on input from scientists on their needs. Many of the applications we have been working with are based on connections with scientists established in previous years. However, we contacted additional scientists though our outreach activities, as well as engaging application teams running on leading DOE computing systems. Our approach is to employ an evolutionary development and deployment process: first considering the application of existing tools, followed by the customization necessary for each particular application, and then the deployment in real frameworks and infrastructures. The institute is organized into three areas, each with area leaders, who keep track of progress, engagement of application scientists, and results. The areas are: (1) Data Management, (2) Data Analysis, and (3) Visualization. Kitware has been involved in the Visualization area. This report covers Kitware’s contributions over the last 5 years (February 2012 – February 2017). For details on the work performed by the SDAV institute as a whole, please see the SDAV final report.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Scalable Data Management, Analysis, and Visualization (SDAV) Institute (Final Report)

The purpose of the SDAV institute is to provide tools and expertise in scientific data management, analysis, and visualization to application scientists on leadership-class computing facilities. Our goal is to actively work with application teams to assist them in achieving breakthrough science, and to provide technical solutions in the data management, analysis, and visualization regimes that are broadly used by the computational science community. Over the last 5 years members of our institute worked directly with application scientists to assist them by applying the best tools and technologies at our disposal. We also enhanced our tools based on input from scientists on their needs. Many of the applications we have been working with are based on connections with scientists established in previous years. However, we contacted additional scientists though our outreach activities, as well as engaging application teams running on leading DOE computing systems. Our approach is to employ an evolutionary development and deployment process: first considering the application of existing tools, followed by the customization necessary for each particular application, and then the deployment in real frameworks and infrastructures. The institute is organized into three areas, each with area leaders, who keep track of progress, engagement of application scientists, and results. The areas are: (1) Data Management, (2) Data Analysis, and (3) Visualization. This report is organized along these areas. However, often there are multiple technologies from these areas that are applied to a single application need. These are described on a case-by-case basis in the appropriate sections. This final report for the SDAV Institute covers the 5-year period from February 2012 to February 2017. This report covers activities in all SDAV institutions listed next. Laboratories: Argonne National Laboratory (ANL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), Los Alamos National Laboratory (LLNL), Oak Ridge National Laboratory (ORNL), Sandia National Laboratory (SNL). Universities: Georgia Institute of Technology, North Carolina State University, Northwestern University, Ohio State University, Oregon State University, Rutgers University, University of California at Davis, University of Utah. Industry: Kitware.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

CmapTools: A Software Environment for Knowledge Modeling and Sharing

In an ongoing collaborative effort between a group of NASA Ames scientists and researchers at the Institute for Human and Machine Cognition (IHMC) of the University of West Florida, a new version of CmapTools has been developed that enable scientists to construct knowledge models of their domain of expertise, share them with other scientists, make them available to anybody on the Internet with access to a Web browser, and peer-review other scientists models. These software tools have been successfully used at NASA to build a large-scale multimedia on Mars and in knowledge model on Habitability Assessment. The new version of the software places emphasis on greater usability for experts constructing their own knowledge models, and support for the creation of large knowledge models with large number of supporting resources in the forms of images, videos, web pages, and other media. Additionally, the software currently allows scientists to cooperate with each other in the construction, sharing and criticizing of knowledge models. Scientists collaborating from remote distances, for example researchers at the Astrobiology Institute, can concurrently manipulate the knowledge models they are viewing without having to do this at a special videoconferencing facility.

Canas, Alberto J.↗
NASA NTRS2010

Spheres of Earth: An Introduction to Making Observations of Earth Using an Earth System's Science Approach. Student Guide

Scientists from the Image Science and Analysis Laboratory (ISAL) at NASA's Johnson Space Center (JSC) work with astronauts onboard the International Space Station (ISS) who take images of Earth. Astronaut photographs, sometimes referred to as Crew Earth Observations, are taken using hand-held digital cameras onboard the ISS. These digital images allow scientists to study our Earth from the unique perspective of space. Astronauts have taken images of Earth since the 1960s. There is a database of over 900,000 astronaut photographs available at http://eol.jsc.nasa.gov . Images are requested by ISAL scientists at JSC and astronauts in space personally frame and acquire them from the Destiny Laboratory or other windows in the ISS. By having astronauts take images, they can specifically frame them according to a given request and need. For example, they can choose to use different lenses to vary the amount of area (field of view) an image will cover. Images can be taken at different times of the day which allows different lighting conditions to bring out or highlight certain features. The viewing angle at which an image is acquired can also be varied to show the same area from different perspectives. Pointing the camera straight down gives you a nadir shot. Pointing the camera at an angle to get a view across an area would be considered an oblique shot. Being able to change these variables makes astronaut photographs a unique and useful data set. Astronaut photographs are taken from the ISS from altitudes of 300 - 400 km (~185 to 250 miles). One of the current cameras being used, the Nikon D3X digital camera, can take images using a 50, 100, 250, 400 or 800mm lens. These different lenses allow for a wider or narrower field of view. The higher the focal length (800mm for example) the narrower the field of view (less area will be covered). Higher focal lengths also show greater detail of the area on the surface being imaged. Scientists from the Image Science and Analysis Laboratory (ISAL) at NASA s Johnson Space Center (JSC) work with astronauts onboard the International Space Station (ISS) who take images of Earth. Astronaut photographs, sometimes referred to as Crew Earth Observations, are taken using hand-held digital cameras onboard the ISS. These digital images allow scientists to study our Earth from the unique perspective of space. Astronauts have taken images of Earth since the 1960s. There is a database of over 900,000 astronaut photographs available at http://eol.jsc.nasa.gov . Images are requested by ISAL scientists at JSC and astronauts in space personally frame and acquire them from the Destiny Laboratory or other windows in the ISS. By having astronauts take images, they can specifically frame them according to a given request and need. For example, they can choose to use different lenses to vary the amount of area (field of view) an image will cover. Images can be taken at different times of the day which allows different lighting conditions to bring out or highlight certain features. The viewing angle at which an image is acquired can also be varied to show the same area from different perspectives. Pointing the camera straight down gives you a nadir shot. Pointing the camera at an angle to get a view across an area would be considered an oblique shot. Being able to change these variables makes astronaut photographs a unique and useful data set. Astronaut photographs are taken from the ISS from altitudes of 300 - 400 km (approx.185 to 250 miles). One of the current cameras being used, the Nikon D3X digital camera, can take images using a 50, 100, 250, 400 or 800mm lens. These different lenses allow for a wider or narrower field of view. The higher the focal length (800mm for example) the narrower the field of view (less area will be covered). Higher focal lengths also show greater detail of the area on the surface being imaged. There are four major systems or spheres of Earth. They are: Atmosphere, Biosphere, Hydrosphe, and Litho/Geosphere.

Graff, Paige Valderrama↗
NASA NTRS2015

Goddard's Astrophysics Science Divsion Annual Report 2014

The Astrophysics Science Division (ASD, Code 660) is one of the world's largest and most diverse astronomical organizations. Space flight missions are conceived, built and launched to observe the entire range of the electromagnetic spectrum, from gamma rays to centimeter waves. In addition, experiments are flown to gather data on high-energy cosmic rays, and plans are being made to detect gravitational radiation from space-borne missions. To enable these missions, we have vigorous programs of instrument and detector development. Division scientists also carry out preparatory theoretical work and subsequent data analysis and modeling. In addition to space flight missions, we have a vibrant suborbital program with numerous sounding rocket and balloon payloads in development or operation. The ASD is organized into five labs: the Astroparticle Physics Lab, the X-ray Astrophysics Lab, the Gravitational Astrophysics Lab, the Observational Cosmology Lab, and the Exoplanets and Stellar Astrophysics Lab. The High Energy Astrophysics Science Archive Research Center (HEASARC) is an Office at the Division level. Approximately 400 scientists and engineers work in ASD. Of these, 80 are civil servant scientists, while the rest are resident university-based scientists, contractors, postdoctoral fellows, graduate students, and administrative staff. We currently operate the Swift Explorer mission and the Fermi Gamma-ray Space Telescope. In addition, we provide data archiving and operational support for the XMM mission (jointly with ESA) and the Suzaku mission (with JAXA). We are also a partner with Caltech on the NuSTAR mission. The Hubble Space Telescope Project is headquartered at Goddard, and ASD provides Project Scientists to oversee operations at the Space Telescope Science Institute. Projects in development include the Neutron Interior Composition Explorer (NICER) mission, an X-ray timing experiment for the International Space Station; the Transiting Exoplanet Sky Survey (TESS) Explorer mission, in collaboration with MIT (Ricker, PI); the Soft X-ray Spectrometer (SXS) for the Astro-H mission in collaboration with JAXA, and the James Webb Space Telescope (JWST). The Wide-Field Infrared Survey Telescope (WFIRST), the highest ranked mission in the 2010 decadal survey, is in a pre-phase A study, and we are supplying study scientists for that mission.

Caltech on the NuSTAR mission↗
NASA NTRS2018

Sixth Annual NASA Ames Space Science and Astrobiology Jamboree

Welcome to the Sixth Annual NASA Ames Research Center, Space Science and Astrobiology Jamboree at NASA Ames Research Center (ARC). The Space Science and Astrobiology Division consists of over 60 Civil Servants, with more than 120 Cooperative Agreement Research Scientists, Post-Doctoral Fellows, Science Support Contractors, Visiting Scientists, and many other Research Associates. Within the Division there is engagement in scientific investigations over a breadth of disciplines including Astrobiology, Astrophysics, Exobiology, Exoplanets, Planetary Systems Science, and many more. The Division's personnel support NASA spacecraft missions (current and planned), including SOFIA, K2, MSL, New Horizons, JWST, WFIRST, and others. Our top-notch science research staff is spread amongst three branches in five buildings at ARC. Naturally, it can thus be difficult to remain abreast of what fellow scientific researchers pursue actively, and then what may present and/or offer regarding inter-Branch, intra-Division future collaborative efforts. In organizing this annual jamboree, the goals are to offer a wholesome, one-venue opportunity to sense the active scientific research and spacecraft mission involvement within the Division; and to facilitate communication and collaboration amongst our research scientists. Annually, the Division honors one senior research scientist with a Pollack Lecture, and one early career research scientist with an Outstanding Early Career Space Scientist Lecture. For the Pollack Lecture, the honor is bestowed upon a senior researcher who has made significant contributions within any area of research aligned with space science and/or astrobiology. This year we are pleased to honor Linda Jahnke. With the Early Career Lecture, the honor is bestowed upon an early-career researcher who has substantially demonstrated great promise for significant contributions within space science, astrobiology, and/or, in support of spacecraft missions addressing such disciplines. This year we are pleased to honor Amanda Cook. We hope that you will make time to join us for the day in meeting fellow Division members, expanding knowledge of our activities, and creating new collaborations within the Space Science and Astrobiology Division.

Hollingsworth, Jeffery↗
DOE OSTI2020

Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

NSI conference support

One of the many services NSI provides as an extension of customer/user support is to attend major scientific conferences. The conference effort provides NASA/OSSA scientists with many benefits: (1) scientist get to see NSI in action; they utilize the network to read email, and have recently begun to demonstrate their scientific research to their colleagues; (2) scientist get an opportunity to meet and interact with NSI Staff, which gives scientists a chance to get status on their requirements, ask about network status, get acquainted with our procedures, and learn about services; and (3) scientists are exposed to networking in a larger sense; particularly by knowing about other NASA groups who provide valuable scientific resources over the Internet.

Aaron, Susan↗
NASA NTRS1994

A toolbox and record for scientific models

Computational science presents a host of challenges for the field of knowledge-based software design. Scientific computation models are difficult to construct. Models constructed by one scientist are easily misapplied by other scientists to problems for which they are not well-suited. Finally, models constructed by one scientist are difficult for others to modify or extend to handle new types of problems. Construction of scientific models actually involves much more than the mechanics of building a single computational model. In the course of developing a model, a scientist will often test a candidate model against experimental data or against a priori expectations. Test results often lead to revisions of the model and a consequent need for additional testing. During a single model development session, a scientist typically examines a whole series of alternative models, each using different simplifying assumptions or modeling techniques. A useful scientific software design tool must support these aspects of the model development process as well. In particular, it should propose and carry out tests of candidate models. It should analyze test results and identify models and parts of models that must be changed. It should determine what types of changes can potentially cure a given negative test result. It should organize candidate models, test data, and test results into a coherent record of the development process. Finally, it should exploit the development record for two purposes: (1) automatically determining the applicability of a scientific model to a given problem; (2) supporting revision of a scientific model to handle a new type of problem. Existing knowledge-based software design tools must be extended in order to provide these facilities.

Ellman, Thomas↗