China 
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Semantic Interoperability of Long-Tail Geoscience Resources over the Web
Published in Ashok N. Srivastava, Ramakrishna Nemani, Karsten Steinhaeuser, Large-Scale Machine Learning in the Earth Sciences, 2017
Mostafa M. Elag, Praveen Kumar, Luigi Marini, Scott D. Peckham, Rui Liu
Scientists are now encouraged to share data and models (henceforth referred to as resources) over the Web, including an unstructured and uncurated collection of spreadsheets, documents, images, numerical models, etc. Unstructured collections is one of the major challenges in information technology because it is not straightforward to extract information out of these collections and transform it into actionable knowledge [2]. The file system model is considered an unstructured data model, which provides a chance to preserve resources that cannot be constrained by a schema such as curation of models in Web repositories. Large collections of distributed and heterogeneous Web resources are often referred to in scientific communities as long-tail resources [3]. Discoverability of these resources is defined as the ability to navigate within the unstructured content of resources and track their relationships.
Big Earth data: disruptive changes in Earth observation data management and analysis?
Published in International Journal of Digital Earth, 2020
Martin Sudmanns, Dirk Tiede, Stefan Lang, Helena Bergstedt, Georg Trost, Hannah Augustin, Andrea Baraldi, Thomas Blaschke
To deal with new, emerging and ever-changing systems regarding EO data, several norms, standards and guidelines have been set and agreed upon by the community. The Quality Assurance Framework for Earth Observation (QA4EO, endorsed by CEOS as a contribution to facilitate the GEO vision for a GEOSS) strives to promote synergistic use of data derived from a multitude of EO systems (satellite, airborne and in-situ measurements) as well as assure high quality of data and data products (Fox 2010). Within the GEO Strategic Plan 2016–2025 (GEO 2015) GEOSS Data Management Principles were formulated outlining five main key words: discoverability, accessibility, usability, preservation, and curation. The Research Data Alliance (RDA) provide ongoing collection and analyses on state-of-the-art data cubes and array databases in particular, including technical reports, standards, and implementations.
Big Earth Data science: an information framework for a sustainable planet
Published in International Journal of Digital Earth, 2020
Huadong Guo, Stefano Nativi, Dong Liang, Max Craglia, Lizhe Wang, Sven Schade, Christina Corban, Guojin He, Martino Pesaresi, Jianhui Li, Zeeshan Shirazi, Jie Liu, Alessandro Annoni
In addition to FAIRness and openness, other valuable requirements for the data framework include: Data uniqueness: to ensure data discoverability, findability and usability, each framework resource should be assigned with a globally unique persistent identifier (PID) during the publishing and archiving process (LIBER Europe 2017). PIDs help users to quickly locate and index unique datasets or metadata that correspond to them.Data standardization: To maximize data value and facilitate wide circulation of data among scientific and social communities, standardization should cover the whole process of the data life cycle, including trust and ethical aspects.Data comprehensibility: To enhance re-usability, data publishers should carefully describe each dataset with metadata as complete as possible. In particular, metadata elements should describe the generation process, including the behaviour of data producers, editors, publishers, etc., as well as the terms of use and reuse of data. Metadata and data sets can be managed separately, linked via the data PID.
Towards a knowledge base to support global change policy goals
Published in International Journal of Digital Earth, 2020
Stefano Nativi, Mattia Santoro, Gregory Giuliani, Paolo Mazzetti
A set of possible gaps were recognized dealing with the datasets processing level, discoverability, accessibility, and granularity level. The can be summarized as: GEOSS contains many ‘raw’ data that are instrumental to generate the searched EVs – however, users must know how to process them for that.Discovery and access metadata are often incomplete, imprecise (or unstructured), and the granularity level is unclear – spatial and temporal extent is a clear example, and it was addressed by using the metadata enrichment approach, where possible.Often there is no ‘direct’ access to EVs: the access is implemented via ‘landing page(s)’ and/or requires a (personal) account.