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+
+- [:material-clock-fast:{ .lg .middle } __Quick start__](quick-start.md)
+
+ ---
+
+ New to Zarr? Check out the quick start guide. It contains a brief
+ introduction to Zarr's main concepts and links to additional tutorials.
+
+- [:material-book-open:{ .lg .middle } __User guide__](user-guide/index.md)
+
+ ---
+
+ A detailed guide for how to use Zarr-Python.
+
+- [:material-api:{ .lg .middle } __API Reference__](api/zarr/index.md)
+
+ ---
+
+ The reference guide contains a detailed description of the functions, modules,
+ and objects included in Zarr. The reference describes how the methods work and
+ which parameters can be used. It assumes that you have an understanding of the
+ key concepts.
+
+- [:material-package-variant:{ .lg .middle } __Related projects__](subprojects.md)
+
+ ---
+
+ Companion packages developed in the zarr-python repository and released
+ independently, such as `zarr-metadata` and `zarr-indexing`, plus pointers to
+ the wider Zarr ecosystem.
+
+- [:material-account-group:{ .lg .middle } __Contributor's Guide__](contributing.md)
+
+ ---
+
+ Want to contribute to Zarr? We welcome contributions in the form of bug reports,
+ bug fixes, documentation, enhancement proposals and more. The contributing guidelines
+ will guide you through the process of improving Zarr.
+
+
+
+## Project Status
+
+More information about the Zarr format can be found on the [main website](https://zarr.dev).
+
+If you are using Zarr-Python, we would [love to hear about it](https://github.com/zarr-developers/community/issues/19).
+
+### Funding and Support
+
+The project is fiscally sponsored by [NumFOCUS](https://numfocus.org/), a US
+501(c)(3) public charity, and development has been supported by the
+[MRC Centre for Genomics and Global Health](https://github.com/cggh/)
+and the [Chan Zuckerberg Initiative](https://chanzuckerberg.com/).
+
+[Donate to Zarr](https://numfocus.org/donate-to-zarr) to support the project!
diff --git a/docs/index.rst b/docs/index.rst
deleted file mode 100644
index 5343dea0ed..0000000000
--- a/docs/index.rst
+++ /dev/null
@@ -1,108 +0,0 @@
-.. zarr documentation main file, created by
- sphinx-quickstart on Mon May 2 21:40:09 2016.
-
-Zarr
-====
-
-Zarr is a format for the storage of chunked, compressed, N-dimensional arrays.
-These documents describe the Zarr format and its Python implementation.
-
-Highlights
-----------
-
-* Create N-dimensional arrays with any NumPy dtype.
-* Chunk arrays along any dimension.
-* Compress and/or filter chunks using any NumCodecs_ codec.
-* Store arrays in memory, on disk, inside a Zip file, on S3, ...
-* Read an array concurrently from multiple threads or processes.
-* Write to an array concurrently from multiple threads or processes.
-* Organize arrays into hierarchies via groups.
-
-Status
-------
-
-Zarr is still a young project. Feedback and bug reports are very welcome, please get in touch via
-the `GitHub issue tracker
+
+- [:material-code-json:{ .lg .middle } __zarr-metadata__](https://zarr.readthedocs.io/projects/zarr-metadata/)
+
+ ---
+
+ Spec-defined metadata types, models, and validators for Zarr v2 and v3, with
+ minimal dependencies. Useful if your software reads or writes Zarr metadata
+ documents but does not need a full Zarr implementation.
+
+ ```bash
+ pip install zarr-metadata
+ ```
+
+- [:material-vector-polyline:{ .lg .middle } __zarr-indexing__](https://zarr.readthedocs.io/projects/zarr-indexing/)
+
+ ---
+
+ Composable, lazy coordinate transforms for Zarr array indexing. Makes the
+ mapping from requested coordinates to stored coordinates a first-class,
+ composable value, and resolves which chunks a selection touches.
+
+ ```bash
+ pip install zarr-indexing
+ ```
+
+- [:material-server:{ .lg .middle } __zarr-http-server__](https://zarr.readthedocs.io/projects/zarr-http-server/)
+
+ ---
+
+ HTTP server for Zarr stores, arrays, and groups.
+
+ ```bash
+ pip install zarr-http-server
+ ```
+
+
diff --git a/docs/talks/scipy2019/submission.rst b/docs/talks/scipy2019/submission.rst
deleted file mode 100644
index a7d531c91e..0000000000
--- a/docs/talks/scipy2019/submission.rst
+++ /dev/null
@@ -1,144 +0,0 @@
-Zarr - scalable storage of tensor data for use in parallel and distributed computing
-====================================================================================
-
-SciPy 2019 submission.
-
-
-Short summary
--------------
-
-Many scientific problems involve computing over large N-dimensional
-typed arrays of data, and reading or writing data is often the major
-bottleneck limiting speed or scalability. The Zarr project is
-developing a simple, scalable approach to storage of such data in a
-way that is compatible with a range of approaches to distributed and
-parallel computing. We describe the Zarr protocol and data storage
-format, and the current state of implementations for various
-programming languages including Python. We also describe current uses
-of Zarr in malaria genomics, the Human Cell Atlas, and the Pangeo
-project.
-
-
-Abstract
---------
-
-Background
-~~~~~~~~~~
-
-Across a broad range of scientific disciplines, data are naturally
-represented and stored as N-dimensional typed arrays, also known as
-tensors. The volume of data being generated is outstripping our
-ability to analyse it, and scientific communities are looking for ways
-to leverage modern multi-core CPUs and distributed computing
-platforms, including cloud computing. Retrieval and storage of data is
-often the major bottleneck, and new approaches to data storage are
-needed to accelerate distributed computations and enable them to scale
-on a variety of platforms.
-
-Methods
-~~~~~~~
-
-We have designed a new storage format and protocol for tensor data
-[1_], and have released an open source Python implementation [2_,
-3_]. Our approach builds on data storage concepts from HDF5 [4_],
-particularly chunking and compression, and hierarchical organisation
-of datasets. Key design goals include: a simple protocol and format
-that can be implemented in other programming languages; support for
-multiple concurrent readers or writers; support for a variety of
-parallel computing environments, from multi-threaded execution on a
-single CPU to multi-process execution across a multi-node cluster;
-pluggable storage subsystem with support for file systems, key-value
-databases and cloud object stores; pluggable encoding subsystem with
-support for a variety of modern compressors.
-
-Results
-~~~~~~~
-
-We illustrate the use of Zarr with examples from several scientific
-domains. Zarr is being used within the Pangeo project [5_], which is
-building a community platform for big data geoscience. The Pangeo
-community have converted a number of existing climate modelling and
-satellite observation datasets to Zarr [6_], and have demonstrated
-their use in computations using HPC and cloud computing
-environments. Within the MalariaGEN project [7_], Zarr is used to
-store genome variation data from next-generation sequencing of natural
-populations of malaria parasites and mosquitoes [8_] and these data
-are used as input to analyses of the evolution of these organisms in
-response to selective pressure from anti-malarial drugs and
-insecticides. Zarr is being used within the Human Cell Atlas (HCA)
-project [9_], which is building a reference atlas of healthy human
-cell types. This project hopes to leverage this information to better
-understand the dysregulation of cellular states that underly human
-disease. The Human Cell Atlas uses Zarr as the output data format
-because it enables the project to easily generate matrices containing
-user-selected subsets of cells.
-
-Conclusions
-~~~~~~~~~~~
-
-Zarr is generating interest across a range of scientific domains, and
-work is ongoing to establish a community process to support further
-development of the specifications and implementations in other
-programming languages [10_, 11_, 12_] and building interoperability
-with a similar project called N5 [13_]. Other packages within the
-PyData ecosystem, notably Dask [14_], Xarray [15_] and Intake [16_],
-have added capability to read and write Zarr, and together these
-packages provide a compelling solution for large scale data science
-using Python [17_]. Zarr has recently been presented in several
-venues, including a webinar for the ESIP Federation tech dive series
-[18_], and a talk at the AGU Fall Meeting 2018 [19_].
-
-
-References
-~~~~~~~~~~
-
-.. _1: https://zarr.readthedocs.io/en/stable/spec/v2.html
-.. _2: https://github.com/zarr-developers/zarr-python
-.. _3: https://github.com/zarr-developers/numcodecs
-.. _4: https://www.hdfgroup.org/solutions/hdf5/
-.. _5: https://pangeo.io/
-.. _6: https://pangeo.io/catalog.html
-.. _7: https://www.malariagen.net/
-.. _8: http://alimanfoo.github.io/2016/09/21/genotype-compression-benchmark.html
-.. _9: https://www.humancellatlas.org/
-.. _10: https://github.com/constantinpape/z5
-.. _11: https://github.com/lasersonlab/ndarray.scala
-.. _12: https://github.com/meggart/ZarrNative.jl
-.. _13: https://github.com/saalfeldlab/n5
-.. _14: http://docs.dask.org/en/latest/array-creation.html
-.. _15: http://xarray.pydata.org/en/stable/io.html
-.. _16: https://github.com/ContinuumIO/intake-xarray
-.. _17: http://matthewrocklin.com/blog/work/2018/01/22/pangeo-2
-.. _18: http://wiki.esipfed.org/index.php/Interoperability_and_Technology/Tech_Dive_Webinar_Series#8_March.2C_2018:_.22Zarr:_A_simple.2C_open.2C_scalable_solution_for_big_NetCDF.2FHDF_data_on_the_Cloud.22:_Alistair_Miles.2C_University_of_Oxford.
-.. _19: https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/390015
-
-
-Authors
--------
-
-Project contributors are listed in alphabetical order by surname.
-
-* `Ryan Abernathey