scholarly journals HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards

2019 ◽  
Author(s):  
Andrew J. Tritt ◽  
Oliver Rubel ◽  
Benjamin Dichter ◽  
Ryan Ly ◽  
Donghe Kang ◽  
...  
Keyword(s):  
Modern Science ◽  
Data Modeling ◽  
Hierarchical Data ◽  
Data Standards ◽  
Science Data ◽  
Modeling Framework

SOLSTICE: Space Weather Modeling Meets Machine Learning

2020 ◽  
Author(s):  
Tamas Gombosi ◽  
Keyword(s):  
Machine Learning ◽  
Space Weather ◽  
High Performance ◽  
Numerical Models ◽  
Machine Learning Techniques ◽  
Grand Challenge ◽  
Science Data ◽  
Modeling Framework ◽  
Weather Modeling ◽  
Solar Storms

<p>The last decade has truly witnessed the rise of the machine age. The enormous expansion of technology that can generate and manipulate massive amounts of information has transformed all aspects of society. Missions such as SDO and MMS, and numerical models such as the Space Weather Modeling Framework (SWMF) are now routinely generating terabytes of science data, far beyond what can be analyzed directly by humans. Fortunately, concurrent with this explosion in information has come the development of powerful capabilities, such as machine learning (ML) and artificial intelligence (AI), that can retrieve revolutionary new understanding and utility from the massive data sets.<span> </span></p><p><span>SOLSTICE (Solar Storms and Terrestrial Impacts Center) is a recently selected NASA/NSF DRIVE Center. It</span> will serve as the vanguard for developing and applying ML methods, which will then raise the capabilities of the entire community. We will combine next generation ML technology with our world-leading numerical models and the exquisite data from the space missions to make breakthrough advances in Heliophysics understanding and space weather capabilities, and then transition our technology to the CCMC for the benefit of all.</p><p>We use ML to attack Grand Challenge Problems that cover the major aspects of space weather science: (i) use interpretable deep learning models, archived solar observations and high-performance physics-based simulations to identify the onset mechanism of solar flares and coronal mass ejections; and (ii) use high-cadence observations and physics-based feature learning to predict solar storms many hours before eruption, training time-to-event models to predict event times and flare magnitudes using innovative machine learning techniques.</p>

scholarly journals Tackling the Challenges of Earth Science Data Synthesis: Insights from (meta)data standards approaches

2021 ◽  
Author(s):  
Valerie C Hendrix ◽  
Danielle S Christianson ◽  
Charuleka Varadharajan ◽  
Madison Burrus ◽  
Shreyas Cholia ◽  
...  
Keyword(s):  
Earth Science ◽  
Data Standards ◽  
Science Data ◽  
Data Synthesis ◽  
Meta Data ◽  
Earth Science Data

An XML Framework for Simulation and Analysis of Buildings

2003 ◽  
Vol 1 (2) ◽  
pp. 191-204
Author(s):  
Filiz Ozel ◽  
Robert Pahle ◽  
Manu Juyal
Keyword(s):  
Data Storage ◽  
Data Modeling ◽  
Modeling Framework ◽  
Building Model ◽  
Java Application ◽  
Building Information ◽  
Extensible Markup ◽  
Language Data ◽  
Data Files ◽  
Initial Results

This study focuses on the problem of how to structure spatial and component based building data with the intention to use it in the simulation and analysis of the performance of buildings. Special attention was paid to the interoperability and optimization of the resulting data files. The study builds its investigation onto XML (Extensible Markup Language) data modeling framework. The authors have studied different ways of arranging building information in XML format for effective data storage and have developed a data modeling framework called bmXML for buildings. Initial results are two-fold: a VBA application was developed to create the appropriate building model in AutoCAD with the intention to write building data in bmXML format, and a JAVA application to view the file thus created. This paper primarily focuses on the former, i.e. the AutoCAD application and the bmXML format.

scholarly journals Digitalization in the system of methods of scientific knowledge

2021 ◽  
Vol 125 ◽  
pp. 05008
Author(s):  
Anatoly Vasilyevich Denikin ◽  
Aleksandr Valentinovich Sablukov ◽  
Viacheslav Leonidovich Primakov ◽  
Valery Alexandrovich Lapshov ◽  
Pirmagomed Shikhmagomedovich Shikhgafizov
Keyword(s):  
Scientific Knowledge ◽  
Inductive Logic ◽  
Modern Science ◽  
Science Data ◽  
Systems Methodology ◽  
Digital World ◽  
Nature Of Information ◽  
Methodological Status ◽  
Logical Functions ◽  
Paradigm Analysis

The study aims to clarify the methodological status of digitalization in the framework of the philosophical theory of knowledge. The subject of the study: explication of the instrumental and methodological nature of digitalization. The problem is solved by employing the categorical and conceptual apparatus of philosophy of science, particularly in paradigm analysis. A comparative analysis of the concepts of “information” and “digitalization” is carried out. Information within the framework of classical scientific rationality coincides with the phenomenon of absolute-relative knowledge. In non-classical rationality, it represents a movement of meaning and expresses the value-valued nature of information technology. In postnonclassical science, the phenomenon of information is associated with intrasystemic communications. Digitalization as a mode of data transmission is incorporated into the deductive and inductive logic of classical science and leads to an essential limitation of the source base. In modern science, data selection entails the creation of systematic factual meaning, and digitalization coincides with analytical activity. In addition to general logical functions, digitalization points to new perspectives on such types of systems methodology as modeling and design. In relation to the information as a methodology, digitalization acts as a meta-methodology. That is, the digital world is defined as an observing reality. In the framework of digitalization, information is identical to knowledge in its postnonclassical meaning. In digitalization, the process of cognition is incorporated into reality itself, forming an intersubjective field of meaning. In this context, digitalization serves as an inter-paradigmatic method of scientific knowledge. Digitalization refers to the rational side of cognition and is a type of systematic methodology and general logical methods.

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