The super-resolution microscopy (SRM) hub website shares SRM resources and facilitates benchmarking competitions, primarily focused on single-molecule localization microscopy (SMLM). To revive user participation, we plan to expand the site as an inclusive platform for SRM education, dataset sharing, and software resources. We will keep content current with modern techniques and provide user-friendly access through Jupyter notebooks and instructional videos. This will empower multidisciplinary researchers. Educational presentations and workshops will increase engagement. Standardized benchmarks will aid algorithm selection. User feedback will improve resource quality and foster collaborations among creators and users of shared datasets and software on the SRM hub.

In a recent WSL research project (deepT - internal grant no. 202011N2099), a machine learning model was developed for gap-filling multi-channel time series data. The goal is to incorporate this model into the automated near real-time TreeNet data acquisition infrastructure. This addition will allow TreeNet dendrometer data users to fill time series gaps automatically using artificial intelligence. The existing model needs refinement with new data, programming in R (the native language of TreeNet software), integration into the pipeline, and adaptation of data to the model's input and output requirements.

Particle physics analyses face reusability challenges due to manual, undocumented steps by analysis teams. Typically, reusable datasets are processed with individually developed scripts and frameworks. This project addresses these issues for PSI Laboratory for Particle Physics (CMS, Mu3e, n2EDM) experiments by encoding missing information in the code repository and integrating it into the execution environment. It provides templates for automatic code testing and packages it in portable software containers. These containers are distributed on compute clusters for parallel processing. With a focus on user experience and adoption, this setup builds on PSI's existing infrastructure, aiming for large-scale deployment. It includes documentation, training, and sharing results at an international computing workshop.

The power and pace of climate research are being greatly increased by maximizing the volume and quality of openly accessible data. Vital sources of climate data are paleoclimate archives such as speleothems. Robust infrastructures are crucial for global analysis across time scales. Community-curated data resources have proven to be the most successful solution, establishing science-driven data standards and wide community support. The SpeleoFAIR project integrates SISAL with Neotoma and LiPD, strengthening data accessibility and governance. This aligns with broader goals of enhancing data sustainability and accelerating multi-proxy climate research analysis.

Geodetic Very Long Baseline Interferometry (VLBI) is vital for precise Earth rotation and orientation measurements. The International VLBI Service for Geodesy and Astrometry (IVS) collaboratively collects VLBI-related files from global institutions. This project consolidates performance metrics from IVS submissions into a common database with session-, station-, and source-based tables. It offers open access through a web interface and API, enabling quick program adjustments with online plotting tools. These databases optimize observing plans by excluding underperforming elements. This enhances data accuracy and fosters VLBI community collaboration, advancing open research data initiatives in Earth sciences and geodesy, particularly for the IVS community.

This project aligns with the development of a high-resolution airborne image spectrometer (AIS) by NASA-JPL for Swiss research institutions. It aims to create tools for precise georeferencing of hyperspectral data, vital for measuring Earth system processes regionally. Outcomes include: a) Introducing an open sensor stabilization format in collaboration with ISPRS. b) Developing a tool for annotating known data points. c) Expanding EPFL's public service for sensor-motion estimation and calibration. d) Releasing reference-processed datasets through peer-reviewed publication and webinars.

The project's main goal is to develop a visualization platform for the pNEUMA dataset, a significant urban traffic data collection from drones. This dataset benefits global researchers, offering both macroscopic and microscopic traffic insights. Due to its vast scale, users struggle to identify relevant portions. Our platform aims to provide an open tool with maps and traffic metrics like vehicle counts, speed, and flows. An initial prototype exists, with plans to expand and improve user interactivity, data filters, and visualization for better traffic understanding. These enhancements empower users to quickly grasp traffic conditions, benefiting traffic engineering and sustainable transportation research.

Brain-Score, a rapidly expanding platform, compiles a wide array of neural and behavioral data from non-human primate neuroscience experiments. It facilitates the evaluation of computational models of the brain's visual system. By translating machine learning models into neuroscientific hypotheses and converting neuroscience data into quantitative benchmarks, Brain-Score offers both computational and experimental advancements to the broader community synergistically. This proposal aims to expand Brain-Score to include data from human experiments, making it accessible to the cognitive neuroscience community and enhancing the applicability of computational models. The objectives are as follows:

• Develop software for human data integration.
• Curate the Natural Scenes Dataset (NSD) for reproducible model evaluations.
• Include the THINGS-similarity dataset for accessible and reproducible model assessments.

In the past decade, numerous initiatives have provided annotated scalp EEG datasets of individuals with epilepsy for global researchers. However, data format discrepancies and varying validation methods impede algorithm comparisons, rendering ranking and research progress challenging. This proposal extends ORD datasets, community standards, and guidelines to introduce a unified framework for validating seizure detection algorithms. The primary goal is to create tools and standards streamlining the algorithm validation process, aligning data with Findable, Accessible, Interpretable, and Reusable (FAIR) principles. To establish this framework, existing public annotated datasets will be curated, and their data format standardized. Software tools will be developed to convert original datasets into this standardized format. Methodologies for algorithm evaluation will also be standardized to create a benchmark for cutting-edge algorithms. All this knowledge, datasets, standards, tools, and benchmarks will be centralized on a website, expediting seizure detection algorithm development.

Every year, building collapses due to earthquakes cause thousands of deaths. Due to the lack of design to resist seismic forces, unreinforced masonry buildings are very prone to collapse. By developing an open-access web-based platform that will contain the first-ever consolidated database for shake-table tests on complete unreinforced masonry buildings, this project aims to contribute to addressing this critical issue. The platform will make comprehensive data from over seventy experimental campaigns conducted over the last 30 years easily accessible to the earthquake engineering community, including researchers and practitioners. As a result, by facilitating data exchange and collaboration, this platform will play a key role in advancing our understanding of the seismic behavior of unreinforced masonry structures. The platform may be used by researchers and practitioners as a valuable reference to benchmark their models, enhance predictive capabilities, and encourage improved design and retrofitting practices. Overall, the development of this platform has enormous potential to reduce seismic risk and increase the resilience of unreinforced masonry buildings, hence increasing public safety in earthquake-prone areas.