Add organization card with featured Our Science video

.gitattributes

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README.md

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 ---
 title: README
-emoji: 💻
-colorFrom: blue
-colorTo: indigo
+emoji: 🔺
+colorFrom: indigo
+colorTo: purple
 sdk: static
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<video autoplay muted loop playsinline width="100%"
+       poster="https://huggingface.co/spaces/PrismScienceOrg/README/resolve/main/assets/our-science.jpg">
+  <source src="https://huggingface.co/spaces/PrismScienceOrg/README/resolve/main/assets/our-science.mp4" type="video/mp4">
+  <img src="https://huggingface.co/spaces/PrismScienceOrg/README/resolve/main/assets/our-science.jpg" width="100%" alt="Prism: samples, data collection, and data processing flow into modeling, encoding, and interpretation">
+</video>
+
+# Who We Are
+
+Prism is making protein motion measurable, predictable, and actionable. We are rebuilding the foundation of structural biology to transform how we discover drugs, engineer biology, and understand disease.
+
+Proteins are always moving. They wiggle, they jiggle, they interact with their surroundings, and that movement drives their function. Yet this motion mostly remains invisible. Nearly all of our structural data, and the insights drawn from it, rests on static structures that hide the motion. Static structural biology has delivered extraordinary breakthroughs, including AlphaFold. But AlphaFold predicts form, not function. That gap is why we can predict a protein's fold but not whether a mutation will break it, why we can design a binder but not an enzyme, and why drug specificity remains so much harder than affinity.
+
+Three shifts make now the moment to treat proteins as dynamic ensembles rather than static structures. AI-driven structure prediction proved that biological insight invisible to us can emerge from quality data at scale. Experimental methods for collecting ensembles are becoming faster, richer, and more routine. And machine learning algorithms are maturing enough to learn from noisy, heterogeneous structural data. What is missing is the rest of the stack: much of the technology and infrastructure needed to measure, represent, and model these ensembles still does not exist.
+
+We build it as a coordinated whole, because progress is lost at the interfaces. A better method that the next tool cannot read is a method that stops there. A model trained on one group's data cannot be tested against another's. Fixing one stage in isolation produces a better stage and little else. Standards, formats, benchmarks, and infrastructure have to move together, because each one is what makes the next one usable.
+
+Prism is a distributed team, combining full-time scientists at the Astera Institute with researchers in academic groups. Biological questions define our research areas; capabilities define our technology areas. Prism is a project within Radial, a division of Astera.
+
+**Prism is fully open, in line with [Astera's Open Science Policy](https://zenodo.org/records/17795285).**
+
+# What We Build
+
+Four focus areas that advance each other, linking measurement, modeling, infrastructure, and biology.
+
+- **Accelerating dynamic data collection** — diffuse scattering and cryoEM. Developing methods to democratize the techniques for capturing dynamic conformational ensembles.
+- **Modeling heterogeneity latent in experimental data** — the hidden PDB and generative sampling. Using AI to model the ensembles already latent in experimental data.
+- **Building supportive infrastructure** — live databases, encodings that are both human- and machine-readable, and metrics designed for ensembles rather than single structures.
+- **Understanding the biological impact** — connecting dynamic structural data to drugs, disease variants, and protein design.
+
+# Open Code
+
+Our software is developed in the open at [github.com/prism-science](https://github.com/prism-science).
+
+| Repository | What it does |
+| --- | --- |
+| [sampleworks](https://github.com/prism-science/sampleworks) | Framework for modified sampling from biomolecular generative models |
+| [WaterFlow](https://github.com/prism-science/WaterFlow) | Predicting water placement on protein surfaces with flow matching conditioned on learned structure embeddings |
+| [qFitLatent](https://github.com/prism-science/qFitLatent) | Learning qFit multi-conformer dynamics with an invariant backbone |
+| [mdx2](https://github.com/prism-science/mdx2) | Diffuse scattering data reduction in Python |
+| [goodvibes](https://github.com/prism-science/goodvibes) | Lattice vibration model for diffuse scattering |
+| [macromolecular-observables](https://github.com/prism-science/macromolecular-observables) | Conveying and transforming information about proteins: ensembles, structures, and measurements |
+| [pdbx_hierarchy](https://github.com/prism-science/pdbx_hierarchy) | Tools for working with hierarchical heterogeneity in mmCIF models |
+
+# Join Us
+
+Build the dynamic era of structural biology with us.
+
+- Reach out at [prism-core@astera.org](mailto:prism-core@astera.org)
+- Read the science at [prismscience.org](https://prismscience.org)
+- See [open roles](https://prismscience.org/careers)
+- Follow us on [Bluesky](https://bsky.app/profile/prismscienceorg.bsky.social), [LinkedIn](https://www.linkedin.com/company/prismscienceorg/), and [X](https://x.com/PrismScienceOrg)

assets/our-science.mp4

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