Ensuring Scientific Reproducibility within the Earth Observation Community: Standardized Algorithm Documentation for Improved Scientific Data Understanding
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Aaron Kaulfus↗
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Laboratory on the Moon: Equipping and Testing of a Habitat Laboratory for the Scientific Exploration of the Moon by Humans
A human habitat on the Moon is predestined to house a laboratory, especially if it is built for scientific missions on the Moon. This laboratory could be used for (1) conducting experiments utilizing the lunar gravity and prepare experiments to be placed outside the laboratory in the lunar environment, (2) conducting analyses of lunar rock and regolith in high volume, and (3) performing preliminary analyses and screening of samples to be sent to Earth for more detailed, specialized analysis. In the past, spaceflight missions have often been implemented by adding scientific instruments after most of the engineering work is already finished, limiting scientific studies to relatively scattered, insular topics. However, if prepared appropriately, a research laboratory on the Moon can help address scientific questions thoroughly and at a fundamental level. Moreover, the challenge of creating a viable habitat is not only an engineering one, but one that requires input from architects, designers, and psychologists. After all, the crew not only works inside the habitat laboratory, but they spend (close to) their entire time on the Moon inside the habitat and under the isolation and confinement that comes with it. We combine science, engineering, and architecture to create a habitat laboratory that meets their conflicting requirements: The laboratory of the Moon and Mars Base Analog (MaMBA) has been designed with inputs from (1) scientists of selected disciplines, particularly geology, materials science, chemistry, biology, and medicine, (2) space architects specializing on extraterrestrial bases, and (3) engineers for the constraints imposed by the lunar environment. The MaMBA laboratory was built in 2019 into the mock-up of the first MaMBA module (out of six). Subsequently, the laboratory was tested by volunteer scientists for its usability. The scientists used the laboratory for work deemed representative of lunar scientific analyses; the complete test runs lasted one week each, with one test run in June 2019 and the second test run in late September 2019 following some modifications suggested by the scientists. Here, we will present an overview of the scientific topics that we think should be addressed on the Moon, together with a suggestion of scientific instrumentation that would be helpful for such investigations. Moreover, we present the MaMBA laboratory, equipped with the proposed instrumentation, and the outcomes of the test runs with a particular focus on the lessons learned regarding the equipping of the laboratory.
Christiane Heinicke↗
Harnessing Large Language Models for Scientific Endeavors
The rapid proliferation of Large Language Models (LLMs) such as GPT, Bard, and Llama has revolutionized various sectors, including the scientific community. These models, with their potential to automate and augment tasks, are increasingly being recognized as both a valuable asset and a potential challenge in the realm of scientific research and data management. However, the current LLMs, primarily trained on general corpora, exhibit a limited understanding of scientific concepts and terminologies due to the lack of scientific corpus in their training data. Recognizing this gap, several groups are now advocating for the development of LLMs specifically tailored for scientific applications. A notable initiative in this direction is the Large Language Model effort initiated by NASA's CSDO. This endeavor aims to align LLM efforts across NASA’s Science Mission Directorate, develop a science-specific corpus and validation test set for model training, and create an encoder-only model for various downstream tasks. Moreover, the initiative also plans to develop a decoder-only model to explore the potential benefits and risks associated with a generative LLM for science. Lastly, the project aims to create a science evaluation suite, encompassing various categories of downstream scientific tasks, to serve as a benchmark for assessing the value of any LLM for future use. This presentation will provide an overview and current status of this ongoing initiative, highlighting its potential to reshape the use of LLMs in the scientific domain.
Rahul Ramachandran↗
Building the Scientific Modeling Assistant: An interactive environment for specialized software design
The construction of scientific software models is an integral part of doing science, both within NASA and within the scientific community at large. Typically, model-building is a time-intensive and painstaking process, involving the design of very large, complex computer programs. Despite the considerable expenditure of resources involved, completed scientific models cannot easily be distributed and shared with the larger scientific community due to the low-level, idiosyncratic nature of the implemented code. To address this problem, we have initiated a research project aimed at constructing a software tool called the Scientific Modeling Assistant. This tool provides automated assistance to the scientist in developing, using, and sharing software models. We describe the Scientific Modeling Assistant, and also touch on some human-machine interaction issues relevant to building a successful tool of this type.
Keller, Richard M.↗
Issues in knowledge representation to support maintainability: A case study in scientific data preparation
Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and runtime estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks. Because the scientific data processing modules (called fittings) evolve to match scientists' needs, issues regarding maintainability are of prime importance in PIPE. This paper describes the PIPE system and describes how issues in maintainability affected the knowledge representation used in PIPE to capture knowledge about the behavior of fittings.
Chien, Steve↗
A scientific assessment of a new technology orbital telescope
As part of a program designed to test the Alpha chemical laser weapons system in space, the Ballistic Missile Defense Organization (BMDO) developed components of an agile, lightweight, 4-meter telescope, equipped with an advanced active-optics system. BMDO had proposed to make space available in the telescope's focal plane for instrumentation optimized for scientific applications in astrophysics and planetary astronomy for a potential flight mission. Such a flight mission could be undertaken if new or additional sponsorship can be found. Despite this uncertainty, BMDO requested assistance in defining the instrumentation and other design aspects necessary to enhance the scientific value of a pointing and tracking mission. In response to this request, the Space Studies Board established the Task Group on BMDO New Technology Orbital Observatory (TGBNTOO) and charged it to: (1) provide instrumentation, data management, and science-operations advice to BMDO to optimize the scientific value of a 4-meter mission; and (2) support a space studies board assessment of the relative scientific merit of the program. This report deals with the first of these tasks, assisting the Advanced Technology Demonstrator's (ATD's) program scientific potential. Given the potential scientific aspects of the 4-meter telescope, this project is referred to as the New Technology Orbital Telescope (NTOT), or as the ATD/NTOT, to emphasize its dual-use character. The task group's basic conclusion is that the ATD/NTOT mission does have the potential for contributing in a major way to astronomical goals.
Source record↗
Secure Peer-to-Peer Networks for Scientific Information Sharing
The most common means of remote scientific collaboration today includes the trio of e-mail for electronic communication, FTP for file sharing, and personalized Web sites for dissemination of papers and research results. With the growth of broadband Internet, there has been a desire to share large files (movies, files, scientific data files) over the Internet. Email has limits on the size of files that can be attached and transmitted. FTP is often used to share large files, but this requires the user to set up an FTP site for which it is hard to set group privileges, it is not straightforward for everyone, and the content is not searchable. Peer-to-peer technology (P2P), which has been overwhelmingly successful in popular content distribution, is the basis for development of a scientific collaboratory called Scientific Peer Network (SciPerNet). This technology combines social networking with P2P file sharing. SciPerNet will be a standalone application, written in Java and Swing, thus insuring portability to a number of different platforms. Some of the features include user authentication, search capability, seamless integration with a data center, the ability to create groups and social networks, and on-line chat. In contrast to P2P networks such as Gnutella, Bit Torrent, and others, SciPerNet incorporates three design elements that are critical to application of P2P for scientific purposes: User authentication, Data integrity validation, Reliable searching SciPerNet also provides a complementary solution to virtual observatories by enabling distributed collaboration and sharing of downloaded and/or processed data among scientists. This will, in turn, increase scientific returns from NASA missions. As such, SciPerNet can serve a two-fold purpose for NASA: a cost-savings software as well as a productivity tool for scientists working with data from NASA missions.
Karimabadi, Homa↗
A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics
In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.
Temporal Queries↗
Exploring the Potential of Hydrogen as a Lift Gas for Nasa’s Scientific Balloon Program
The United States National Aeronautics and Space Administration (NASA) Balloon Program has a rich history of conducting scientific research in the stratosphere, offering a unique platform for experiments in the near-space environment. One critical component of these high-altitude balloons is the choice of lift gas, which is pivotal to achieving the desired altitude and, thus, mission objectives. Traditionally, helium has been the preferred lift gas due to its inert properties and safety considerations. However, the increasing demand and limited global helium reserves have prompted NASA to explore alternative lift gases, with hydrogen emerging as the most logical choice. Hydrogen boasts several advantages, including its abundant availability, cost-effectiveness, and enhanced lift capability compared to helium. Moreover, it can support a wide range of scientific payloads, enabling extended mission durations and increased payload capacities, ultimately strengthening the program’s scientific potential. However, adopting hydrogen as a lift gas is not without challenges. Safety concerns, especially during the launch and recovery phases, are paramount. NASA and the other major players in the scientific ballooning community have been developing stringent safety protocols and engineering solutions to mitigate these risks. This development includes specialized support systems, leak detection mechanisms, and enhanced launch and recovery procedures. Additionally, environmental considerations must be carefully evaluated. Hydrogen release has the potential to increase concentrations of various greenhouse gases, and its usage must be balanced against the benefits it offers to scientific research and exploration. This paper delves into the ongoing research and development efforts to ensure balloon-based research’s long-term sustainability and its ability to continue pushing the boundaries of scientific discovery. This research demonstrates NASA’s commitment to innovation, safety, and environmental responsibility while maintaining its mission to advance our understanding of the Earth and the cosmos.
balloon↗
Connecting Community Scientific Hypotheses to Mars Sample Science
The proposed Mars Sample Return (MSR) program seeks to bring back to Earth a set of specially curated samples from the surface of Mars. The first segment of the MSR program is presently under-way; the Mars 2020 rover Perseverance has, to date, collected 24 samples of martian rock, regolith, and at-mosphere within Jezero crater. Its geologically diverse rock sample set can provide insight into the time evolu-tion of the Jezero environment, including the possible preservation of ancient biosignatures. Presently, a cache of 10 samples has been deposited in the Three Forks region on the floor of Jezero crater. These sam-ples, deemed scientifically return worthy by the Mars science community [1], are intended as a contingency sample set, as Perseverance has subsequently obtained more samples, and is therefore storing a more compre-hensive and scientifically valuable set of samples that will grow as the rover ascends to the rim of Jezero crater. The return of samples from Mars has been identi-fied as the highest priority planetary mission in the past three National Academies Decadal Surveys [2-4]. Sub-stantial work has gone into establishing science objec-tives for an MSR program, an overall framework for sample retrieval and analysis, and the expected impact on future Mars science and exploration (e.g., [5]). These objectives have been established at a relatively high level, e.g., “Interpret the primary geologic pro-cesses and history that formed the martian geologic rec-ord, with an emphasis on the role of water.” (Objective 1 in [5]) and are designed to encapsulate the broad sci-entific priorities of, and lay the groundwork for, inves-tigation strategies and measurements to be conducted by a future MSR program. It is from this overarching framework that the Connecting Community Scientific Hypotheses to Mars Sample Science workshop (‘The Workshop’) was conceived, with the purpose of distil-ling these high-level scientific objectives into discrete, testable hypotheses across a range of scientific disci-plines. The output of the workshop is meant to be a ‘hy-pothesis document’ serving as a starting point for cata-loging and condensing specific hypotheses for consid-eration by the scientific community. The template for this approach is based on the “driving hypotheses” de-veloped by the OSIRIS-REx team as part of their Sam-ple Analysis Plan [6].
Michael A Mischna↗
NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility
NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship. 1. Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. 2. National Academies of Sciences, E. and Medicine, Open Science by Design: Realizing a Vision for 21st Century Research. 2018, Washington, DC: The National Academies Press. 232. 3. Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5.
Life Sciences data↗
Intelligent assistance in scientific data preparation
Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and run time estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks.
Chien, Steve↗
Artificial intelligence support for scientific model-building
Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.
Keller, Richard M.↗
A toolbox and a record for scientific model development
Scientific computation can benefit from software tools that facilitate construction of computational models, control the application of models, and aid in revising models to handle new situations. Existing environments for scientific programming provide only limited means of handling these tasks. This paper describes a two pronged approach for handling these tasks: (1) designing a 'Model Development Toolbox' that includes a basic set of model constructing operations; and (2) designing a 'Model Development Record' that is automatically generated during model construction. The record is subsequently exploited by tools that control the application of scientific models and revise models to handle new situations. Our two pronged approach is motivated by our belief that the model development toolbox and record should be highly interdependent. In particular, a suitable model development record can be constructed only when models are developed using a well defined set of operations. We expect this research to facilitate rapid development of new scientific computational models, to help ensure appropriate use of such models and to facilitate sharing of such models among working computational scientists. We are testing this approach by extending SIGMA, and existing knowledge-based scientific software design tool.
Ellman, Thomas↗
Hubble Space Telescope servicing mission scientific instrument protective enclosure design requirements and contamination controls
The Scientific Instrument Protective Enclosures were designed for the Hubble Space Telescope Servicing Missions to provide a beginning environment to a Scientific Instrument during ground and on orbit activities. The Scientific Instruments required very stringent surface cleanliness and molecular outgassing levels to maintain ultraviolet performance. Data from the First Servicing Mission verified that both the Scientific Instruments and Scientific Instrument Protective Enclosures met surface cleanliness level requirements during ground and on-orbit activities.
Hansen, Patricia A.↗
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↗
Parallel processing for scientific computations
The scope of this project dealt with the investigation of the requirements to support distributed computing of scientific computations over a cluster of cooperative workstations. Various experiments on computations for the solution of simultaneous linear equations were performed in the early phase of the project to gain experience in the general nature and requirements of scientific applications. A specification of a distributed integrated computing environment, DICE, based on a distributed shared memory communication paradigm has been developed and evaluated. The distributed shared memory model facilitates porting existing parallel algorithms that have been designed for shared memory multiprocessor systems to the new environment. The potential of this new environment is to provide supercomputing capability through the utilization of the aggregate power of workstations cooperating in a cluster interconnected via a local area network. Workstations, generally, do not have the computing power to tackle complex scientific applications, making them primarily useful for visualization, data reduction, and filtering as far as complex scientific applications are concerned. There is a tremendous amount of computing power that is left unused in a network of workstations. Very often a workstation is simply sitting idle on a desk. A set of tools can be developed to take advantage of this potential computing power to create a platform suitable for large scientific computations. The integration of several workstations into a logical cluster of distributed, cooperative, computing stations presents an alternative to shared memory multiprocessor systems. In this project we designed and evaluated such a system.
Alkhatib, Hasan S.↗
XML Based Scientific Data Management Facility
The World Wide Web consortium has developed an Extensible Markup Language (XML) to support the building of better information management infrastructures. The scientific computing community realizing the benefits of HTML has designed markup languages for scientific data. In this paper, we propose a XML based scientific data management facility, XDMF. The project is motivated by the fact that even though a lot of scientific data is being generated, it is not being shared because of lack of standards and infrastructure support for discovering and transforming the data. The proposed data management facility can be used to discover the scientific data itself, the transformation functions, and also for applying the required transformations. We have built a prototype system of the proposed data management facility that can work on different platforms. We have implemented the system using Java, and Apache XSLT engine Xalan. To support remote data and transformation functions, we had to extend the XSLT specification and the Xalan package.
Mehrotra, Piyush↗