32 research outputs found
Workshop AI for Data Science 2023, PoF IV Subtopic 2.4
The Workshop is part of a yearly series within the AI/ML-Cluster in Subtopic 2.4 of the research program PoF IV: Changing Earth - Sustaining our Future
AI for Earth System Science - Ocean & Cryosphere in Climate, Workshop
The Workshop within the Research Program "Changing Earth - Sustain our Future" focuses on Artificial Intelligence topics and projects within the Data Science sub-cluster in Subtopic 2.4. Common directions and posible collaborations are discussed in four thematic sessions: AI Classification, Explainable AI, AI for Parameterization, and AI Enablement
Methods, Applications and Services 2024 Workshop-Seminar of the AI-Cluster in Subtopic 2.4
The yearly Wokshop is forum for data science research, developments and support services. The agenda is published regularly online
A visual analytics tool to validate simulation models against collected data. V. 1.0.0
The validation of a simulation model is a crucial task in model development. It involves the comparison of simulation data to observation data and the identification of suitable model parameters. SLIVISU is a Visual Analytics framework that enables geoscientists to perform these tasks for observation data that is sparse and uncertain. Primarily, SLIVISU was designed to evaluate sea level indicators, which are geological or archaeological samples supporting the reconstruction of former sea level over the last ten thousands of years and are compiled in a postgreSQL database system. At the same time, the software aims at supporting the validation of numerical sea-level reconstructions against this data by means of visual analytics
Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images
The analysis of natural disasters in a timely manner often suffers from limited sensor data. This limitation could be alleviated by leveraging information contained in images of the event posted on social media platforms, so-called “Volunteered Geographic Information (VGI)”. To save the analyst from manual inspection of all images posted online, we propose to use content-based image retrieval with the possibility of relevance feedback for retrieving only relevant images of the event. To evaluate this approach, we introduce a new dataset of 3,710 flood images, annotated by domain experts regarding their relevance with respect to three tasks (determining the flooded area, inundation depth, water pollution). We compare several image features and relevance feedback methods on that dataset, mixed with 97,085 distractor images, and are able to improve the precision among the top 100 results from 55% to 87% after 5 rounds of feedback
Towards a Quality Indicator for Research Data publications and Research Software publications -- A vision from the Helmholtz Association
Research data and software are widely accepted as an outcome of scientific
work. However, in comparison to text-based publications, there is not yet an
established process to assess and evaluate quality of research data and
research software publications. This paper presents an attempt to fill this
gap. Initiated by the Working Group Open Science of the Helmholtz Association
the Task Group Helmholtz Quality Indicators for Data and Software Publications
currently develops a quality indicator for research data and research software
publications to be used within the Association. This report summarizes the
vision of the group of what all contributes to such an indicator. The proposed
approach relies on generic well-established concepts for quality criteria, such
as the FAIR Principles and the COBIT Maturity Model. It does - on purpose - not
limit itself to technical implementation possibilities to avoid using an
existing metric for a new purpose. The intention of this paper is to share the
current state for further discussion with all stakeholders, particularly with
other groups also working on similar metrics but also with entities that use
the metrics.Comment: 21 pages, 1 figur
The Pilot Lab Exascale Earth System Modelling
The Pilot Lab Exascale Earth System Modelling (PL-ExaESM) is a “Helmholtz-Inkubator Information & Data Science” project and explores specific concepts to enable exascale readiness of Earth System models and associated work flows in Earth System science. PL-ExaESM provides a new platform for scientists of the Helmholtz Association to develop scientific and technological concepts for future generation Earth System models and data analysis systems