6th International workshop on Data Analytics solutions for Real-LIfe APplications
                        (DARLI-AP 2022)
              November 29th - December 3rd 2021 Online
              website:  https://adageo.github.io/summit-2021/
              Registration: https://forms.gle/4Xz4fQ4ajqJbgEHx7
                             Let us dig into the geophysics of data!


The ADAGEO Summit and Thematic School aim at promoting scientific discussion and practical actions and projects that can address geosciences problems through data science solutions. Thereby, the activities and interaction in this event will build a scientific and practitioners’ transdisciplinary community the will develop a novel way of doing geosciences.

Massive data production is a critical aspect of experimental sciences. We have experienced exponential growth in data availability in the past decade, and it has not been different for geoscience. Examples of geoscientific data include any physical observable related to the energy industry, mining, monitoring hazardous areas (e.g., effects of mining in populated areas), etc. Nowadays, with the relative facility and lowering the cost to acquire data – even in continuous mode – the data processing to exploit their value is a challenge. Also, it requires expertise in data maintenance and processing, data analysis, and the design of experiments of target domains for which data will provide insight and knowledge.

Traditional analysis in the area using these computing and data services revolves around the creation of physics-based models. These are developed and executed by geoscience professionals with a deep understanding of geology, geophysics, engineering, reservoir dynamics, production technology, and economics, among other things. Data managers support these geoscience professionals to ensure high-quality data that powers the decision-making process. In contexts with a high data density, significant actions are now being invested in advanced analytical techniques such as machine learning to augment decisions traditionally made exclusively by geoscientists and engineers.

Many scientists and companies believe that they can generate fresh insight, reduce decision cycle times, and steal a march on their competition by automating the search for patterns and relationships in their data. Therefore, geophysics and data science, including algorithms, mathematical models, and computing, must converge for developing experiments for obtaining insight and foresight about the observations contained in data collections. Furthermore, experiments represent best practices for addressing problems and questions on geophysics that must be treated as data and knowledge to be shared and reused by scientists and practitioners.

* Statistical methods to investigate & unveil new geophysical data patterns
answer open problems lead to further research questions

* Unveil new geophysical data patterns, answer open problems, lead to new research questions

* Parallelizing algorithms for processing geophysical data in reasonable times

* Scalable Machine Learning
* Machine Learning Automation
* Geophysics for dummies
* Data Science Platforms: how to set data science experiments at different scales
* Experiments reproducibility
* The what, where and when of the space-time cubes
* Data Science Current Status and Trends
* Real-time detection & nucleation of small earthquakes in Stable South America

* Get together, trivia and working time with couches (every day)
* Data Science for Geophysics: demo fest
* Awards to the best challenges solutions!

Free event for logistic reasons register: https://forms.gle/4Xz4fQ4ajqJbgEHx7

Diversity and Inclusion Statement
We support Diversity & Inclusion in the ADAGEO community therefore we kindly ask authors to adopt inclusive language in their papers and presentations.

++ Inclusion and diversity in writing https://dbdni.github.io/pages/inclusivewriting.html
tips for designing inclusive presentations https://dbdni.github.io/pages/inclusivetalks.html
++ We also encourage women and underrepresented communities to attend and actively participate in the challenges session.

Genoveva Vargas-Solar, CNRS, LIRIS, France
Aderson Nascimento, UFRN, Brazil
Martin Musicante, UFRN, Brazil

Notícia cadastrada em: 12/11/2021 14:00
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