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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Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision-and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper GE PENG

Under the auspices of the Earth Science Information Partners (ESIP) and with collaboration among the ESIP Information Quality Cluster (IQC), the Barcelona Supercomputing Center (BSC) Evaluation and Quality Control (EQC) team, and the Australia/New Zealand Data Quality Interest Group (AU/NZ DQIG), a community effort has been undertaken by international Earth Science domain experts. The objective of this effort is to develop global community guidelines with practical recommendations to promote sharing and reusing of quality information at the dataset level, leveraging the experiences and expertise of a team of interdisciplinary domain experts and community best practices. The community guidelines aim to help stakeholders such as science data centers, repositories, data producers and publishers, data managers and stewards, etc., i) to capture and represent quality information of their datasets in a way that is in line with the FAIR guiding principles; ii) to allow for the maximum trust, sharing, reuse and value of their datasets; and iii) to enable global access to and integration of dataset quality information. The vision of developing these guidelines is to promote the creation and use of freely and openly shared dataset quality information that is consistently described, readily available in community standardized formats, and capable of being integrated across commonly-used Earth science systems and tools for search and access with explicitly expressed usage licenses.

In order to help scientists in the above ground biomass community and to support the science behind the upcoming BIOMASS, NISAR, and GEDI satellite missions, ESA and NASA are collaborating on the Multi-Mission Algorithm and Analysis Platform (MAAP). The MAAP is a jointly developed and implemented platform that will include not only data (satellite, airborne, in situ data, and products), but also computing capabilities and sets of tools and algorithms developed to support this specific field of research. To best ensure that users are able to collaborate across the platform and to access needed resources, the MAAP requires all data, algorithms, and software to conform to open access and open source policies. In addition to aiding researchers, the MAAP exercise is establishing a collaboration framework between ESA and NASA that focuses on sharing data, science algorithms, and computable resources in order to foster and accelerate scientific research.