ESSI2 – Infrastructures across the Earth and Space Sciences
Metadata, Data Models, Semantics, and Collaboration
Earth systems science is fundamentally cross-disciplinary, and increasingly this requires sharing and exchange of geoscientific information across discipline boundaries. This information can be both rich and complex, and content is not always readily interpretable by either humans or machines. Difficulties arise through differing exchange formats, lack of common semantics, divergent access mechanisms, etc.
Recent developments in distributed, service-oriented, information systems using web-based (W3C, ISO, OGC) standards are leading to advances in data interoperability. At the same time, work is underway to understand how meaning may be represented using ontologies and other semantic mechanisms, and how this can be shared with other scientists.
This session aims to explore developments in interoperable data sharing, and the representation of semantic meaning to enable interpretation of geoscientific information. Topics may include, but are not limited to:
- standards-based information modelling
- interoperable data sharing
- use of metadata
- knowledge representation
- use of semantics in an interoperability context
- application of semantics to discovery and analysis
- metadata and collaboration
Data Cubes of Big Earth Data – a new paradigm for accessing and processing Earth Science Data
The term Data Cube as it relates to Big Earth Data has recently gained a lot of attention. The Data Cube concept promises to tackle some of the challenges associated with serving and consuming large volumes of environmental data. Data Cubes offer a more on-demand and analysis-ready access to n-dimensional data, that can be accessed along any dimension (space, time, spectrum), allowing for efficient trim or slice operations. The Data Cube concept makes large volumes of environmental and geospatial data more manageable and thus, increases the general uptake of Big Earth Data.
Even though the Data Cube concept is not new, the application to Big Earth Data entails quite a few challenges: interoperability between different data providers, combining data from different domains with domain-specific formats, different spatial and temporal resolutions and different coordinate systems. The success of Data Cubes for Big Earth Data relies on the cooperation of Data Cube technology providers, data users and large data organisations in the future. A better understanding of the challenges large data organisations face and the needs data users have is helpful for the adoption of existing technologies as well as for future development of Data Cube technologies.
This session aims to establish a dialogue between Data Cube technology providers, data users and large data organisations. A particular focus will be set on technical Data Cube solutions from current initiatives, on challenges large data organisations face and the requirements data users need in order to benefit most from Data Cube services.
This session aims to bring together researchers working with big data sets generated from monitoring networks, extensive observational campaigns and detailed modeling efforts across various fields of geosciences. Topics of this session will include the identification and handling of specific problems arising from the need to analyze such large-scale data sets, together with methodological approaches towards semi or fully automated inference of relevant patterns in time and space aided by computer science-inspired techniques. Among others, this session shall address approaches from the following fields:
• Dimensionality and complexity of big data sets
• Data mining in Earth sciences
• Machine learning, including deep learning and other advanced approaches
• Visualization and visual analytics of big data
• Informatics and data science
• Emerging big data paradigms, such as datacubes
Instrumentation and measurement technologies are currently playing a key role in the monitoring, assessment and protection of environmental resources. Climate study related experiments and observational stations are getting bigger and the number of sensors and instruments involved is growing very fast. This session deals with measurement techniques and sensing methods for the observation of environmental systems, focusing on climate and water. We welcome contributions about advancements on field measurement approaches, development of new sensing techniques, low cost sensor systems and whole environmental sensor networks, including remote observation techniques.
Studies about signal and data processing techniques targeted to event detection and the integration between sensor networks and large data systems are also very encouraged. This session is open for all works about an existing system, planning a completely new network, upgrading an existing system, improving streaming data management, and archiving data.
Communities, tools and policies for integrated Earth and Space Science (e)infrastructures
The last decade has seen rapid growth in the number of online data sets, tools, and research infrastructures many of which are coordinated by separate communities in the Earth, space, and environmental sciences. Notable efforts include GEO, EPOS, ENVRI, ESGF, ESIP, CUHASI, AuScope, EDI, EarthCube, OneGeology, ODIP, IGSN, DataOne and many more. In Europe, many of these activities are now connected to upcoming European Open Science Cloud (EOSC) initiative.
There are common technological, policy, and science challenges that each is trying solve, often in isolation. Although standards, vocabularies, formats, etc are cohesive within each community, there are sufficient differences that make it hard to integrate data across them and beyond to other disciplines. Similar barriers exists from different policies regarding licenses, access, citation and publishing practices. The time is ripe to synchronise efforts to create globally connected networks of Earth, Space and Environmental Science data, information systems, software and researchers to create a ‘cloud’ of networked infrastructures.
Papers are solicited from those building community specific systems or from those trying to resolve the challenges of internationally linking multiple communities to create networked environments for developing common global solutions for more efficient and effective use of limited, available funding.
Establishing a comprehensive Open and FAIR ecosystem for Solid Earth and Environmental researchers, repositories, publishers, policy makers and funders
Digital data, software and samples are key inputs that underpin research and ultimately scholarly publications, and there are increasing expectations from policy makers and funders that they will be Open and FAIR (Findable, Accessible, Interoperable, Reusable). Open, accessible, high-quality data, software and samples are critical to ensure the integrity of published research and to facilitate reuse of these inputs in future scientific efforts.
Currently, most research inputs have limited accessibility and persistence. Many journals accept data and software as part of supplementary information with little documentation; some journals accept contacting the author for access with few successful requests; and many samples underpinning key research papers are inaccessible and not well described. Given the diverse requirements of the Solid Earth and Environmental community, most repositories struggle to make artifacts of research and communications Open and FAIR.
An inclusive, integrated approach to Open and FAIR is now required from data/sample/software repositories, whilst publishers and funders need to provide consistent policies, standards and guidelines: the research community could also better share tools and techniques. This session solicits papers from repositories, publishers, funders, researchers, policy makers and anyone who is trying to establish components of an integrated, Open and FAIR research ecosystem.
Establishing Trustworthiness and Suitability of Data Products and Services with Content-Rich, Interoperable and Findable Quality Descriptive Information
A significant and daunting challenge for science data and service centers and repositories is to establish trustworthiness and fitness for purpose, i.e., suitability, at the level of individual data products and services. Having content-rich, interoperable and discoverable quality descriptive information will help organizations address this challenge. The scalability of curating such information, either as metadata records or as documents, has considerable challenges of its own. This session invites presentations on approaches, frameworks, workflows, best practices, tools, etc., that are under development or being implemented towards systematically evaluating quality attributes of individual data products and services, and automatically generating content-rich quality descriptive information that is interoperable and discoverable. All types of data, all perspectives of data and information quality, and all aspects of product and service quality attributes are welcome.
Recent advances in remote sensing and artificial intelligence in geosciences
The evolution of data acquisition systems has enabled increasing data quality and the volume of information to be analyzed, processed and interpreted. Nowadays, there has been a growing interest in the geophysical time series and image processing analysis in all most disciplines relates to Earth Sciences, such as Seismology, Geochemistry, Geodesy, Volcanology, Geology and Satellite Observations.
A major challenge that arises is how to structure and organize the huge amount of data and to determine the type of information that could help the scientific community for a deeper knowledge on the complex dynamics of geophysical and geochemical systems in our Planet.
The high volume of data recorded by those systems requires an appropriate framework that can enhance classical approaches by exploiting the latent knowledge embedded in the data. New rising methodologies have to tackle the long-term problems of data management, accessibility and deployment. Data Mining, Cloud Computing and Machine Learning are the most appropriate disciplines for the analysis of such high throughput data.
In this session, we welcome contributions focused on recent machine learning advances applied to Earth and planetary sciences, with a focus on remote sensing techniques and early warning systems. This is a highly interdisciplinary session, where artificial intelligence is combined with Earth sciences techniques in order to improve the knowledge of the complexity of our Planet.
This session is partially sponsored by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 798480, and by Government of Spain through the research project TEC2015-814 68752.
Validation of Satellite-Based Earth Observations and Earth System Models
Remote sensing techniques and earth system modelling have been widely used in earth science and environmental science. In particular, the world is suffering significant environmental changes such as hydro-climatic extremes, sea level rise, melting glaciers and ice caps and forest fires. The earth observations and earth system models provide valuable insight into climate variability and environmental change. Meanwhile, the question on how to derive and present uncertainties in earth observations and model simulations has gained enormous attention among communities in the earth sciences.
However, quantification of uncertainties in satellite-based data products and model simulations is still a challenging task. Various approaches have been proposed within the community to tackle the validation problem for satellite-based data products and model simulations. These progress include theory advancement, mathematics, methodologies, techniques, communication of uncertainty and traceability.
The aim of this session is to summarize current state-of-the-art in uncertainty quantification and utilization for satellite-based earth observations and earth system models.