Advancing Open Science Through Innovative Data System Solutions: The Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)'s Data Ecosystem

Bugbee, Kaylin; Ramachandran, Rahul; Maskey, Manil; Barciauskas, Aimee; Kaulfus, Aaron; That, Dai Hai Ton; Virts, Katrina S.; Markert, Kel; Lynnes, C.

doi:10.1109/igarss39084.2020.9323731

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…A conceptual cloud-native data system framework that supports such new cyberinfrastructures is depicted in Figure 2 below (Bugbee et al, 2020). The data lake serves as a central repository of different data.…”

Section: From Data Systems To Enabling Collaborative Infrastructuresmentioning

confidence: 99%

“…The Joint ESA-NASA multimission algorithm and analysis platform (MAAP) is one such example. The MAAP is a collaborative, cloud-based, open science platform dedicated to addressing the biomass community's unique research needs (Bugbee et al, 2020). The MAAP provides a new approach to accessing, sharing, analyzing, and processing data.…”

Section: From Data Systems To Enabling Collaborative Infrastructuresmentioning

confidence: 99%

“…Data programs should also invest in collaborative, open-source data stewardship tools that support open science principles. Tools such as the Algorithm Publication Tool (APT) make it easier for scientists to collaboratively write Algorithm Theoretical Basis Documents (ATBDs) (Bugbee et al, 2020). ATBDs describe the physical theory, mathematical procedures, and assumptions made for the algorithms that create higher-level data products.…”

Section: From Data Systems To Enabling Collaborative Infrastructuresmentioning

confidence: 99%

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From Open Data to Open Science

Ramachandran

Bugbee

Murphy³

2021

Earth and Space Science

Self Cite

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Open science, as both a concept and a term, is increasing in popularity and usage. However, definitions, interpretations, and perceptions as to what the term "open science" means varies. Some definitions are fairly narrow and only focus on providing more open access to science as a body of knowledge. These narrow definitions place an emphasis on openly sharing scientific knowledge as early as possible in the research process (University of Cambridge, 2020). On the other hand, broader definitions of open science acknowledge that science is both a body of knowledge and a systematic method for thinking. Broad definitions place an emphasis on encouraging a culture of openness (Bartling & Friesike, 2014) that includes the entire process of conducting science (National Academies of Sciences, Engineering, & Medicine, 2018a, 2018b) and encourages open collaboration and access to knowledge (Vicente-Saez & Martinez-Fuentes, 2018). In its broadest definition, the term "open science" refers to a paradigm shift in how the methods of science are conducted. This expansive vision of open science acknowledges that rapid technology changes, primarily driven by the Internet, may enable a second scientific revolution that fundamentally changes research methods and standards across science. To complicate matters, the term "open science" is sometimes used interchangeably to represent various principles that support the broader idea of open science itself. These principles include ideas such as open data, open source software, open journal access, and reproducibility. For example, reproducibility, or the ability to verify another scientist's results, is enabled by the principles of open data, open code, and transparent methodologies, yet reproducibility itself is not equivalent to open science.While open science definitions are variable and ambiguous, the value of open science as both a concept and a paradigm change is accepted by the majority of the scientific community. Open science not only benefits the scientific endeavor itself but has also been shown to benefit individual researchers through increased citations and media attention, a larger collaborative network, and exposure to new career and funding opportunities (

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Section: From Data Systems To Enabling Collaborative Infrastructuresmentioning

confidence: 99%

Section: From Data Systems To Enabling Collaborative Infrastructuresmentioning

confidence: 99%

Section: From Data Systems To Enabling Collaborative Infrastructuresmentioning

confidence: 99%

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From Open Data to Open Science

Ramachandran

Bugbee

Murphy³

2021

Earth and Space Science

Self Cite

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“…Hence, cloud computing has also emerged as the analysis and processing platform for many applications [65]. In RS, there are many examples of data processing frameworks developed in cloud computing [66]- [70]. In fact, many new RS data products are being generated using cloud computing (https://earthdata.nasa.…”

Section: Cloud Computingmentioning

confidence: 99%

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

Cavallaro

Heras

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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T he High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article.

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“…The data lake is a big data analytics solution that addresses the wide variety and volume of data. The data lake has become popular in research data management projects that mix several communities (EOSC with ESCAPE [7], Data Terra with Gaia data project 3 , ESA / NASA with MAAP [4], European Commission with Destination Earth [8]). However, Open Big Data is a specific context that brings many additional constraints.…”

Section: Introductionmentioning

confidence: 99%

Multi-disciplinary Research: Open Science Data Lake

Dang,

Aussenac-Gilles,

Ravat

2023

Communications in Computer and Information Science

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Open Science aims to establish an interdisciplinary exchange between researchers through knowledge sharing and open data. However, this interdisciplinary exchange requires exchanges between different research domains and there is currently no simple computerized solution to this problem. Although the data lake adapts well to the constraints of variety and volume offered by the Open Science context, it is necessary to adapt this solution to (1) the accompaniment of data with metadata having a specific metadata model depending on the domain and community of origin, (2) the cohabitation of open and closed data within the same open data management platform, and (3) a wide diversity of preexisting research data management platforms to deal with. We propose to define the Open Science Data Lake (OSDL) by adapting the Data Lake to this particular context and allowing interoperability with preexisting research data management platforms. We propose a functional architecture that integrates multi-model metadata management, virtual integration of externally stored (meta)data and security mechanisms to manage the openness of the platforms and data. We propose an opensource and plug-and-play technical architecture that makes adoption as easy as possible. We set up a proof-of-concept experiment to evaluate our solution with different users from the research community and show that OSDL can meet the needs of transparent multidisciplinary data research.

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Advancing Open Science Through Innovative Data System Solutions: The Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)'s Data Ecosystem

Cited by 6 publications

References 4 publications

From Open Data to Open Science

From Open Data to Open Science

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

Multi-disciplinary Research: Open Science Data Lake

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