google·May 1, 2026·3 min readCurated summary

Catalyzing scientific impact through global partnerships and open resources

Google Research argues that scientific breakthroughs have the greatest impact when their software, datasets, and methods are openly shared and responsibly maintained through global partnerships. Its open-science efforts span genomics, neuroscience, climate, biodiversity, and healthcare, reaching more than 250,000 researchers and developers. The post concludes that collaboration and open resources can turn individual discoveries into tools for broader scientific progress and real-world benefits. ## Partnerships Across the Scientific Ecosystem - Google Research works with organizations including UCSC’s Genomics Institute, Janelia Research Campus, ISTA, CSIRO, AIIMS, and the Centre for Population Genomics. - It supports major international initiatives such as: - The Human Pangenome Research Consortium - The Earth BioGenome Project - The NIH BRAIN Initiative - Google is also developing communities of practice for scientific developers, beginning in India, Korea, Japan, and Australia. ## Open-Source Tools and Datasets - **Genomics** - DeepVariant, DeepConsensus, and DeepPolisher support DNA analysis from sequencing through genome assembly. - These tools have helped process exomes and whole genomes from 2.5 million people. - **Neuroscience** - Flood-filling networks, Neuroglancer, and TensorStore enable analysis and visualization of petascale brain reconstructions. - The public H01 dataset contains 1.4 petabytes of human brain tissue data and has been accessed more than 200,000 times. - MICrONS provides a large wiring and functional map of the mouse visual cortex. - **Earth and Atmospheric Science** - Open Buildings contains 1.8 billion building detections across 58 million square kilometers. - Caravan supports large-scale hydrology and flood forecasting in 150 countries, covering roughly 2 billion people. - Groundsource includes 2.6 million historical urban flood events from more than 150 countries. - NeuralGCM is a differentiable hybrid atmospheric model, while FireBench supports wildfire research with high-resolution synthetic data. - **Biodiversity** - SpeciesNet classifies 2,498 animal categories in wildlife-camera images. - **Healthcare** - HAI-DEF provides open-weight medical foundation models, including MedGemma, with more than 4.8 million downloads. - Open Health Stack offers secure, offline-capable tools based on modern healthcare standards. - OHS-powered applications have reached more than 65 million people across over 10 countries. ## Scientific and Humanitarian Impact - **Genomics** - Work with UCSC improved pangenome references and reduced genetic-variant identification errors by 50%. - The research contributes to more representative genomic resources through the Human Pangenome Research Consortium. - **Weather and Agriculture** - The University of Chicago’s Human-Centered Weather Forecasts Initiative used NeuralGCM and ECMWF systems to predict India’s monsoon onset up to a month ahead. - Forecasts, including an unusual dry spell, were delivered by SMS to 38 million Indian farmers to support planting decisions. - **Disaster Response** - UNHCR and other organizations use Open Buildings to improve survey sampling for displaced populations. - The dataset also supports research into building vulnerability to sea-level rise in the Global South. - Sunbird AI uses the data to assess energy needs in urban and rural communities. - **Neuroscience and Medicine** - Johns Hopkins researchers used the H01 brain dataset to identify a possible new form of neuronal communication, suggesting that current models of brain organization may be incomplete. - The finding could have implications for understanding conditions such as Alzheimer’s disease. - Google also partnered with Stanford Medicine and UCSC to accelerate genome analysis in urgent cases of suspected genetic disease. ## Practical Conclusion The post presents open-source scientific infrastructure, accessible datasets, and cross-border partnerships as essential to accelerating discovery. Researchers and institutions can maximize impact by sharing reproducible tools, maintaining resources collaboratively, and applying them to urgent global challenges.

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NeuroscienceDatabase DesignDeep LearningGenomics+4

google·May 6, 2025Original article

A new light on neural connections (opens in new tab)

Google and the Institute of Science and Technology Austria (ISTA) have developed LICONN, the first light-microscopy-based method capable of comprehensively mapping neurons and their connections in brain tissue. This approach overcomes the traditional reliance on expensive electron microscopy by utilizing physical tissue expansion and advanced machine learning to achieve comparable resolution and accuracy. The researchers successfully validated the technique by reconstructing nearly one million cubic microns of mouse cortex, demonstrating that light microscopy can now achieve "dense" connectomics at scale. ## Overcoming Resolution and Cost Barriers * Connectomics has traditionally relied on electron microscopy (EM) because it offers nanometer-scale resolution, whereas standard light microscopy is limited by the diffraction limit of visible light. * Electron microscopes cost millions of dollars and require specialized training, restricting high-level neuroscience research to wealthy, large-scale institutions. * LICONN provides a more accessible alternative by utilizing standard light microscopy equipment already found in most life science laboratories. ## Advanced Tissue Expansion and Labeling * The project uses a specialized expansion microscopy protocol where brain tissue is embedded in hydrogels that absorb water and physically swell. * The technique employs three different hydrogels to create interweaving polymer networks that expand the tissue by 16 times in each dimension while preserving structural integrity. * A whole-protein labeling process is used to provide the necessary image contrast, allowing for the tracing of densely packed neurites and the detection of synapses. ## Automated Reconstruction and Validation * Google applied its established suite of machine learning and image analysis tools to automate the reconstruction of the expanded tissue samples. * The team verified the accuracy of the method by tracing approximately 0.5 meters of neurites within mouse hippocampus tissue, confirming results comparable to electron microscopy. * In a large-scale validation, the researchers provided an automated reconstruction of a volume of mouse cortex totaling nearly one million cubic microns. ## Integration of Molecular and Structural Data * One of LICONN’s primary advantages over electron microscopy is its ability to capture multiple light wavelengths simultaneously. * Researchers can use fluorescent markers to visualize specific proteins, neurotransmitters, and other molecules within the structural map. * This dual-layered approach allows scientists to align molecular information with physical neuronal pathways, offering new insights into how brain circuits drive behavior and cognition. LICONN represents a significant shift in neuroscience by democratizing high-resolution brain mapping. By replacing expensive hardware requirements with sophisticated chemical protocols and machine learning, this method enables a wider range of laboratories to contribute to the global effort of mapping the brain’s intricate wiring.

NeuroscienceAIMachine LearningConnectomics+3