UNLOCK – Benchmarking Projects

Image: Scidraw

Published on 20.10.2025

TIMELY: Time-series Integration across Modalities for Evaluation of Latent DYnamics

TIMELY provides the first comprehensive benchmark for multimodal biological time-series data, addressing the lack of standardized, high-quality datasets for modeling complex dynamical systems. It fosters the development of statistical and foundation models tailored to the analytical needs of research in biomedicine and neuroscience.

Visual for UQOB; Creating a benchmark dataset for object-detection and Uncertainty Quantification (UQ) in a multi-rater setting, to address annotation variability and AI model evaluation.

Published on 14.10.2025

UQOB – Uncertainty Quantification in Object-detection Benchmark

Creating a benchmark dataset for object-detection and Uncertainty Quantification (UQ) in a multi-rater setting, to address annotation variability and AI model evaluation.

Visusal for SCHEMA;Metastases represent a significant exacerbation of tumor severity. If one could predict the likelihood of tumors metastasizing, this could inform treatment decisions to avoid or delay this outcome. SCHEMA develops a benchmark dataset of primary tumor samples and metadata on whether the tumor has metastasized at different time points after sampling. With this dataset, a challenge for machine learning scientists will be defined to build prognostic models for likelihood of tumors metastasizing, promoting innovation in prognostic modeling for a clinically relevant task.

Image: Hellmut Augustin, DKFZ (BSIC 2021 contribution)

Published on 14.10.2025

SCHEMA – profiling Spatial Cancer HEterogeneity across modalities to benchmark Metastasis risk prediction

SCHEMA creates a benchmark dataset linking tumor samples with metastasis outcomes to enable machine-learning models that predict metastasis risk and support clinical decision-making.

Published on 14.10.2025

RenewBench – A Global Benchmark for Renewable Energy Generation

Renewable energy’s variability makes grid management complex. RenewBench aims to provide standardized, high-quality data to advance trustworthy AI models and accelerate the transition to sustainable energy.

Visual for Pero; Addressing the lack of standardized, FAIR benchmark datasets in perovskite photovoltaics. Pero enables reproducible AI models for efficiency prediction, material classification, and defect detection, which are critical for industrial scaling of sustainable energy technologies.

Image: Photo: Markus Breig, KIT; illustration: Felix Laufer, KIT

Published on 14.10.2025

Pero – Unlocking ML Potential: Benchmark Datasets on Perovskite Thin Film Processing

Addressing the lack of standardized, FAIR benchmark datasets in perovskite photovoltaics. Pero enables reproducible AI models for efficiency prediction, material classification, and defect detection, which are critical for industrial scaling of sustainable energy technologies.

Visual for NeuroHarmonize; The benchmark addresses the lack of harmonized, reproducible, and privacy-preserving multimodal datasets for Alzheimer’s disease (AD). Current AI models struggle with fragmented and non-standardized data, which limits their generalizability and clinical deployment. NeuroHarmonize creates a FAIR-compliant, decentralized benchmarking framework to accelerate reliable, transparent, and collaborative AI for AD diagnosis, prognosis, and long-term monitoring.

Image: Georg Kislinger, Martina Schifferer, Christian Haass & Maryam Khojasteh-Farat, DZNE (BSIC 2021 contribution)

Published on 14.10.2025

NeuroHarmonize – A Benchmark Decentralized Data Harmonization Workflow for AI-Driven Alzheimer’s Disease Management

The benchmark addresses the lack of harmonized, reproducible, and privacy-preserving multimodal datasets for Alzheimer’s disease (AD). NeuroHarmonize creates a FAIR-compliant, decentralized benchmarking framework to accelerate reliable, transparent, and collaborative AI for AD diagnosis, prognosis, and long-term monitoring.

Visual for GRIDMARK; Transforming energy systems toward climate neutrality: Distribution grids have the potential to be catalysts for the energy transition. Unfortunately, most Distribution System Operators lack the resources to fully monitor their systems. Therefore, there is an urgent need for more high-quality data, particularly to develop and test machine learning models.

Published on 14.10.2025

GRIDMARK – Generating Reproducible Insights through Data Benchmarking for AI in Energy Systems

Transforming energy systems toward climate neutrality: Distribution grids have the potential to be catalysts for the energy transition. Unfortunately, most Distribution System Operators lack the resources to fully monitor their systems. Therefore, there is an urgent need for more high-quality data, particularly to develop and test machine learning models.

Visual for ForestUNLOCK; Building the first consistent multi-modal single tree benchmark for forest structure and carbon stock assessments of the northern boreal forest

Image: Open white spruce forest with glacier in background in the Chugach Mountains, Alaska, US ©Stefan Kruse, AWI

Published on 14.10.2025

ForestUNLOCK: A multi-modal Multiscale Benchmark Dataset for AI-Driven Boreal Forest Monitoring and Carbon Accounting

Building the first consistent multi-modal single tree benchmark for forest structure and carbon stock assessments of the northern boreal forest

Image: Ingmar Nitze, AWI (BSIC 2021 contribution)

Published on 14.10.2025

BASE: Benchmarking Agro-environmental database for Sustainable agriculture intensification

Building a BASE dataset enables robust predictions of yield potential, resource efficiency, and sustainability thresholds, driving climate resilience and sustainable agricultural intensification

Image: FZJ

Published on 14.10.2025

AMOEBE: lArge-scale Multi-mOdal Microbial livE-cell imaging BEnchmark

Building a large-scale, FAIR benchmark for AI-driven analysis of microbial communities using time-lapse microscopy to advance understanding of microbial dynamics, ecosystem stability, and their role in health and biotechnology.

Manual microscopic biodiversity monitoring is time consuming and expert requiring thus limits the potential for biodiversity monitoring and hence to recognize risks climate and environmental changes on biodiversity related to crucial ecosystem functions.

Image: AIMBIS

Published on 14.10.2025

AIMBIS – Artificial Intelligence for Microscopic Biodiversity Screening

Manual microscopic biodiversity monitoring is time-consuming and requires expert knowledge, limiting the potential for biodiversity monitoring, hence to recognize the impacts of climate and environmental change on crucial ecosystem functions.

Visual for ADD-ON; ADD-ON addresses the lack of reliable data for predicting how microbial enzymes assemble peptide-based natural products. By enabling accurate AI-driven structure prediction, it accelerates the discovery of new bioactive compounds and ultimately supports efforts to combat antimicrobial resistance.

Image: ADD-ON

Published on 14.10.2025

ADD-ON: Adenylation Domain Database and Online Benchmarking Platform

ADD-ON addresses the lack of reliable data for predicting how microbial enzymes assemble peptide-based natural products. By enabling accurate AI-driven structure prediction, it accelerates the discovery of new bioactive compounds and ultimately supports efforts to combat antimicrobial resistance.