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4 changes: 3 additions & 1 deletion assets/main.scss
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z-index: -1;
border-radius: inherit;
transform: scale(1.05);
transition: opacity 200ms ease-out, transform 100ms ease-out;
transition:
opacity 200ms ease-out,
transform 100ms ease-out;
}

&:hover::before {
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4 changes: 2 additions & 2 deletions content/about/mission/_index.md
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## Goals

We want to make analysis tools for omics data in the life sciences as accessible as possible. This means:
We as scverse® want to make analysis tools for omics data in the life sciences as accessible as possible. This means:

* Interoperability -- we focus on developing open, standardized, and efficient file formats to encourage data sharing and re-use.
* Diversity -- we believe in removing barriers to participation in research, including economic factors and institutional culture. Our tools will have the most impact if they are developed by and for a diverse community.
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The growing adoption of Python for single-cell omics data analysis has been catalyzed by [Scanpy](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1382-0) and [AnnData](https://anndata.readthedocs.io/en/latest/).
Around this infrastructure an ecosystem of packages has been created by various developers and institutions — extending single-cell analysis to different modalities and addressing challenges at the cutting-edge of single-cell research. We believe that progress in this field can't be silo'd to a few groups. To further nurture the growth of this ecosystem, we've formed a new organization -- **`scverse`**.

`scverse` is a consortium of tools with users and developers across the world. To sustain the utility and growth of the ecosystem, it’s essential that the core analytic tools are robust and well-maintained. This entails consistent support and improvement of tools beyond what's possible in the conventional single-lab academic setting.
`scverse®` is a consortium of tools with users and developers across the world. To sustain the utility and growth of the ecosystem, it’s essential that the core analytic tools are robust and well-maintained. This entails consistent support and improvement of tools beyond what's possible in the conventional single-lab academic setting.

`scverse` provides high quality infrastructure for analysis of single cell omics data. These *core* tools are well documented, tested, and provide broad functionality. They work with standardized data structures which use common Python numeric types and have interchange-friendly on-disk formats.
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10 changes: 6 additions & 4 deletions layouts/partials/main/cover.html
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<a class="btn btn-light btn-outline-dark btn-zulip" href="http://scverse.zulipchat.com" target="_blank">
Zulip
</a>
<a class="btn btn-light btn-outline-dark btn-twitter" href="https://x.com/scverse_team" target="_blank">
𝕏
</a>
<a class="btn btn-light btn-outline-dark btn-bluesky" href="https://bsky.app/profile/scverse.bsky.social" target="_blank">
<a class="btn btn-light btn-outline-dark btn-twitter" href="https://x.com/scverse_team" target="_blank"> 𝕏 </a>
<a
class="btn btn-light btn-outline-dark btn-bluesky"
href="https://bsky.app/profile/scverse.bsky.social"
target="_blank"
>
Bluesky
</a>
<a
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