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Many diverse types of data were collected as part of the SECARB Early Test. Very large amounts of data were collected and have been archived at BEG as reported in 9.1.1-h. This report overviews the main data collection, backup, distribution, and archival systems used.
We performed an image search for 'Comet Holmes', using the Yahoo{exclamation_point} Web search engine, on 2010 April 1. Thousands of images were returned. We astrometrically calibrated-and therefore vetted-the images using the Astrometry.net system. The calibrated image pointings form a set of data points to which we can fit a test-particle orbit in the solar system, marginalizing over image dates and detecting outliers. The approach is Bayesian and the model is, in essence, a model of how comet astrophotographers point their instruments. In this work, we do not measure the position of the comet within each image, but rather use the celestial position of the whole image to infer the orbit. We find very strong probabilistic constraints on the orbit, although slightly off the Jet Propulsion Lab ephemeris, probably due to limitations of our model. Hyperparameters of the model constrain the reliability of date meta-data and where in the image astrophotographers place the comet; we find that {approx}70% of the meta-data are correct and that the comet typically appears in the central third of the image footprint. This project demonstrates that discoveries and measurements can be made using data of extreme heterogeneity and unknown provenance. As the size and diversity of astronomical data sets continues to grow, approaches like ours will become more essential. This project also demonstrates that the Web is an enormous repository of astronomical information, and that if an object has been given a name and photographed thousands of times by observers who post their images on the Web, we can (re-)discover it and infer its dynamical properties.
Stakeholder participation is considered an integral part of HIA. However, the challenges that participation implies in a multi-disciplinary and multi-ethnic society are less studied. This paper presents the manifestations of the multiplicity of sectors and population groups in HIA and discusses the challenges that such diversity imposes. Specifically, there is no common ground between participants, as their positions entail contradictory knowledge regarding the current situation, reliance on distinct data and conflicting interests. This entails usage of multiple professional and ethnic languages, disagreements regarding the definition of health and prioritizing health issues in HIA, and divergent perceptions of risk. These differences between participants are embedded culturally, socially, individually and, maybe most importantly, professionally. This complex picture of diverse stakeholder attributes is grounded in a case study of stakeholder participation in HIA, regarding zoning of a hazardous industry site in Israel. The implication is that participatory HIAs should address the multiplicity of stakeholders and types of knowledge, data and interests in a more comprehensive way. - Highlights: Black-Right-Pointing-Pointer This paper analyses challenges in participation of diverse stakeholders in HIA. Black-Right-Pointing-Pointer The multiplicity of disciplines and population groups raises fundamental challenges. Black-Right-Pointing-Pointer Stakeholders possess distinct and often contradictory knowledge, data and interests. Black-Right-Pointing-Pointer They speak different languages, and differ on approaches to health and risk perceptions. Black-Right-Pointing-Pointer Substantial amendments to diverse participation are needed, in HIA and generally.
Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.
As global atmospheric CO 2 rapidly approaches a key tipping point, there is an urgent need to implement strategies to reverse this pattern. A generally accepted understanding of carbon (C) in agricultural fields includes: (H1) substantial C loss occurs when natural vegetation is converted to crops, (H2) soils typically reach a steady-state C concentration under contemporary practices, and (H3) improved management or crop selection can enhance soil C stocks over time. Significant variability exists, but studies consistently show large C losses from agricultural ecosystems, supporting H1. Although steady-state C levels (H2) are commonly assumed, measuring C gains or losses in mature agroecosystems is challenging. Efforts to increase soil C storage (H3) have limited data due to the diversity of potential practices, compounded by substantial variability in soil C measurements. Here, long-term (7–17 year) ecosystem C flux data from diverse cropping systems revealed that conventionally tilled annual row crops (maize and soybean) act as significant long-term atmospheric C sources, challenging H2. Furthermore, conservation tillage practices reduced C losses compared with conventional tillage but showed minimal evidence for long-term ecosystem C storage, even after 20+ years. This indicates that no-till practices reduce C losses but imply that no soil C is added, challenging H3. By contrast, perennial Miscanthus × giganteus, Panicum virgatum, and restored tallgrass prairie systems store C at the ecosystem scale more effectively than minimally tilled annual row crops. Analysis over multiple years demonstrates significant ecosystem C storage with perennial crops, varying by species, starting in the first year of transition. These findings, although focused on one region, suggest that the assumptions of steady-state C levels and increased storage from conservation practices do not universally apply and that significant changes to agroecosystems are required to increase C storage.
The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.
Shotgun metagenomic sequencing does not depend on gene-targeted primers or PCR amplification; thus, it is not affected by primer bias or chimeras. However, searching rRNA genes from large shotgun Illumina data sets is computationally expensive, and no approach exists for unsupervised community analysis of small-subunit (SSU) rRNA gene fragments retrieved from shotgun data. We present a pipeline, SSUsearch, to achieve the faster identification of short-subunit rRNA gene fragments and enabled unsupervised community analysis with shotgun data. It also includes classification and copy number correction, and the output can be used by traditional amplicon analysis platforms. Shotgun metagenome data using this pipeline yielded higher diversity estimates than amplicon data but retained the grouping of samples in ordination analyses. We applied this pipeline to soil samples with paired shotgun and amplicon data and confirmed bias against Verrucomicrobia in a commonly used V6-V8 primer set, as well as discovering likely bias against Actinobacteria and for Verrucomicrobia in a commonly used V4 primer set. In conclusion, this pipeline can utilize all variable regions in SSU rRNA and also can be applied to large-subunit (LSU) rRNA genes for confirmation of community structure.
Interview for Scientist 2 position in XCP-7: Radiation Transport Applications
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The Materials and Processes Technical Information System (MAPTIS) is a collection of materials data which was computerized and is available to engineers in the aerospace community involved in the design and development of spacecraft and related hardware. Consisting of various database segments, MAPTIS provides the user with information such as material properties, test data derived from tests specifically conducted for qualification of materials for use in space, verification and control, project management, material information, and various administrative requirements. A recent addition to the project management segment consists of materials data derived from the LDEF flight. This tremendous quantity of data consists of both pre-flight and post-flight data in such diverse areas as optical/thermal, mechanical and electrical properties, atomic concentration surface analysis data, as well as general data such as sample placement on the satellite, A-O flux, equivalent sun hours, etc. Each data point is referenced to the primary investigator(s) and the published paper from which the data was taken. The MAPTIS system is envisioned to become the central location for all LDEF materials data. This paper consists of multiple parts, comprising a general overview of the MAPTIS System and the types of data contained within, and the specific LDEF data element and the data contained in that segment.
Food and nutrient databases are populated with data obtained from a variety of sources including USDA Reference Tables, scientific journals, food manufacturers and foreign food tables. The food and nutrient database maintained by the Nutrition Coordinating Center (NCC) at the University of Minnesota is continually updated with current nutrient data and continues to be expanded with additional nutrient fields to meet diverse research endeavors. Data are strictly evaluated for reliability and relevance before incorporation into the database; however, the values are obtained from various sources and food samples rather than from direct chemical analysis of specific foods. Precise nutrient values for specific foods are essential to the nutrition program at the National Aeronautics and Space Administration (NASA). Specific foods to be included in the menus of astronauts are chemically analyzed at the Johnson Space Center for selected nutrients. A request from NASA for a method to enter the chemically analyzed nutrient values for these space flight food items into the Nutrition Data System for Research (NDS-R) software resulted in modification of the database and interview system for use by NASA, with further modification to extend the method for related uses by more typical research studies.
Beginning in 1997, the Merged Precipitation Group at NASA Goddard has distributed gridded global precipitation products built by combining satellite and surface gauge data. This started with the Global Precipitation Climatology Project (GPCP), then the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), and recently the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG). This 20+-year (and on-going) activity has yielded an important set of insights and lessons learned for making state-of-the-art precipitation data accessible to the diverse communities of users. Merged-data products critically depend on the input sensors and the retrieval algorithms providing accurate, reliable estimates, but it is also important to provide ancillary information that helps users determine suitability for their application. We typically provide fields of estimated random error, and recently reintroduced the quality index concept at user request. Also at user request we have added a (diagnostic) field of estimated precipitation phase. Over time, increasingly more ancillary fields have been introduced for intermediate products that give expert users insight into the detailed performance of the combination algorithm, such as individual merged microwave and microwave-calibrated infrared estimates, the contributing microwave sensor types, and the relative influence of the infrared estimate.
NASA's Earth Observing System Data and Information System (EOSDIS) has been a central component of the NASA Earth observation program since the 1990's. EOSDIS manages data covering a wide range of Earth science disciplines including cryosphere, land cover change, polar processes, field campaigns, ocean surface, digital elevation, atmosphere dynamics and composition, and inter-disciplinary research, and many others. One of the key components of EOSDIS is a set of twelve discipline-based Distributed Active Archive Centers (DAACs) distributed across the United States. Managed by NASA's Earth Science Data and Information System (ESDIS) Project at Goddard Space Flight Center, these DAACs serve over 3 million users globally. The ESDIS Project provides the infrastructure support for EOSDIS, which includes other components such as the Science Investigator-led Processing systems (SIPS), common metadata and metrics management systems, specialized network systems, standards management, and centralized support for use of commercial cloud capabilities. Given the long-term requirements, and the rapid pace of information technology and changing expectations of the user community, EOSDIS has evolved continually over the past three decades. However, many challenges remain. Challenges addressed in this paper include: growing volume and variety, achieving consistency across a diverse set of data producers, managing information about a large number of datasets, migration to a cloud computing environment, optimizing data discovery and access, incorporating user feedback from a diverse community, keeping metadata updated as data collections grow and age, and ensuring that all the content needed for understanding datasets by future users is identified and preserved.
Japan has re-emerged in 2013 as one of the world's fastest-growing and largest photovoltaic (PV) markets (Renewable Energy World 2013). Year-over-year growth in Japanese installed capacity in the first half of 2013 was about 270%. Japan's PV market was on pace to triple in 2013 compared with 2012, with approximately 2.7 GW installed during the first half of 2013 (compared to about 1.6 GW in the United States). This places Japan's market among the world's largest PV markets, along with China, Germany, and the United States. This report explores details of the rapidly changing Japanese market, and similarities and differences between the Japanese and U.S. markets. We collected data from a diverse group of Japanese PV installers, and we gathered additional Japanese and U.S. data from published sources as well as internal analyses.
We characterized the diverse and complex geology of the eastern limb region of the Moon using a trio of remote-sensing data sets: Clementine, Lunar Prospector, and Apollo. On the basis of Clementine-derived iron and titanium maps we classify the highlands into low-iron (3-6 wt % FeO) and high-iron (6-9 wt % FeO) units. The association of the latter with basalt deposits west of Smythii basin suggests that the highland chemical variation is the result of mixing between basalt and highland lithologies. Mare Smythii and Mare Marginis soils are compositionally similar, containing moderate iron (15-18 wt % FeO) and titanium (2.5-3.5 wt % TiO2). Smythii basin, in addition to the basalt deposits, contains an older, moderate-albedo plains unit. Our investigation reveals that the dark basin plains unit has a distinct albedo, chemistry, and surface texture and formed as a result of impact-mixing between highland and mare lithologies in approximately equal proportions. Clementine iron and maturity maps show that swirls along the northern margin of Mare Marginis have the same iron composition as the surrounding nonswirl material and indicate that the swirl material is bright because of its low agglutinate content. Gravity data for the eastern limb show high, positive Bouguer gravity anomalies for areas of thin basalt cover (e.g., Smythii basin and complex craters Joliot, Lomonosov, and Neper). We deduce that the uplift of dense mantle material is the primary (and mare basaltic fill the secondary) source for generating the concentration of mass beneath large craters and basins.
The Carbon Dioxide Information Analysis Center (CDIAC) at Oak Ridge National Laboratory (ORNL), USA has provided scientific data management support for the U.S. Department of Energy and international climate change science since 1982. Over this period, climate change science has expanded from research focusing on basic understanding of geochemical cycles, particularly the carbon cycle, to integrated research addressing climate change impacts, vulnerability, adaptation, and mitigation. Interests in climate change data and information worldwide have grown remarkably and, as a result, so have demands and expectations for CDIAC’s data systems. To meet the growing demands, CDIAC’s strategy has been to design flexible data systems using proven technologies blended with new, evolving technologies and standards. CDIAC development teams are multidisciplinary and include computer science and information technology expertise, but also scientific expertise necessary to address data quality and documentation issues and to identify data products and system capabilities needed by climate change scientists. CDIAC has learned there is rarely a single commercial tool or product readily available to satisfy long-term scientific data system requirements (i.e., one size does not fit all and the breadth and diversity of environmental data are often too complex for easy use with commercial products) and typically deploys a variety of tools and data products in an effort to provide credible data freely to users worldwide. Like many scientific data management applications, CDIAC’s data systems are highly customized to satisfy specific scientific usage requirements (e.g., developing data products specific for model use) but are also designed to be flexible and interoperable to take advantage of new software engineering techniques, standards (e.g., metadata standards) and tools and to support future Earth system data efforts (e.g., ocean acidification). CDIAC has provided data management support for numerous long-term measurement projects crucial to climate change science. One current example is the AmeriFlux measurement network. AmeriFlux provides continuous measurements from forests, grasslands, wetlands, and croplands in North, Central, and South America and offers important insight about carbon cycling in terrestrial ecosystems. We share our approaches in satisfying the challenges of delivering AmeriFlux data worldwide to benefit others with similar challenges handling climate change data, further heighten awareness and use of an outstanding ecological data resource, and highlight expanded software engineering applications being used for climate change measurement data.
An Earth Science Collaboratory is: A rich data analysis environment with: (1) Access to a wide spectrum of Earth Science data, (3) A diverse set of science analysis services and tools, (4) A means to collaborate on data, tools and analysis, and (5)Supports sharing of data, tools, results and knowledge
Analysis of the increasing wealth of metagenomic data collected from diverse environments can lead to the discovery of novel branches on the tree of life. Here we analyse 5.2 Tb of metagenomic data collected globally to discover a novel bacterial phylum (' Candidatus Kryptonia') found exclusively in higherature pH-neutral geothermal springs. This lineage had remained hidden as a taxonomic 'blind spot' because of mismatches in the primers commonly used for ribosomal gene surveys. Genome reconstruction from metagenomic data combined with single-cell genomics results in several high-quality genomes representing four genera from the new phylum. Metabolic reconstruction indicates a heterotrophic lifestyle with conspicuous nutritional deficiencies, suggesting the need for metabolic complementarity with other microbes. Co-occurrence patterns identifies a number of putative partners, including an uncultured Armatimonadetes lineage. The discovery of Kryptonia within previously studied geothermal springs underscores the importance of globally sampled metagenomic data in detection of microbial novelty, and highlights the extraordinary diversity of microbial life still awaiting discovery.