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MetaboLights is a global database for metabolomics studies including the raw experimental data and the associated metadata. The database is cross-species and cross-technique and covers metabolite structures and their reference spectra as well as their biological roles and locations where available. MetaboLights is the recommended metabolomics repository for a number of leading journals and ELIXIR, the European infrastructure for life science information. In this article, we describe the continued growth and diversity of submissions and the significant developments in recent years. In particular, we highlight MetaboLights Labs, our new Galaxy Project instance with repository-scale standardized workflows, and how data public on MetaboLights are being reused by the community. Metabolomics resources and data are available under the EMBL-EBI's Terms of Use at https://www.ebi.ac.uk/metabolights and under Apache 2.0 at https://github.com/EBI-Metabolights.

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.

As the number and variety of cyber threats increase, it becomes more critical to share intelligence information in a fast and efficient manner. However, current cyber threat intelligence data do not contain sufficient information about how to specify countermeasures or how institutions should apply countermeasures automatically on their networks. A flexible and agile network architecture is required in order to determine and deploy countermeasures quickly. Software-defined networks facilitate timely application of cyber security measures thanks to their programmability. In this work, we propose a novel model for producing software-defined networking-based solutions against cyber threats and configuring networks automatically using risk analysis. We have developed a prototype implementation of the proposed model and demonstrated the applicability of the model. Furthermore, we have identified and presented future research directions in this area.