Add/Update README and hero figure

README.md

@@ -1,3 +1,127 @@
----
-license: cc-by-4.0
----
+---
+configs:
+- config_name: default
+  data_dir: mmu_plasticc/dataset
+tags:
+- astronomy
+license: cc-by-4.0
+pretty_name: mmu_plasticc
+size_categories:
+- 1M<n<10M
+---
+
+<div align="center">
+<img src="example_figure.png" width="600">
+</div>
+
+# mmu_plasticc HATS Catalog Collection
+
+This is the collection of HATS catalogs representing mmu_plasticc.
+
+This dataset is part of the [Multimodal Universe](https://github.com/MultimodalUniverse/MultimodalUniverse),
+a large-scale collection of multimodal astronomical data. For full details, see the paper:
+[The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TBs of Astronomical Scientific Data](https://arxiv.org/abs/2412.02527).
+
+### Access the catalog
+
+We recommend the use of the [LSDB](https://lsdb.io) Python framework to access HATS catalogs.
+LSDB can be installed via `pip install lsdb` or `conda install conda-forge::lsdb`,
+see more details [in the docs](https://docs.lsdb.io/).
+The following code provides a minimal example of opening this catalog:
+
+```python
+import lsdb
+
+# Full sky coverage.
+catalog = lsdb.open_catalog("https://huggingface.co/datasets/UniverseTBD/mmu_plasticc")
+# One-degree cone.
+catalog = lsdb.open_catalog(
+    "https://huggingface.co/datasets/UniverseTBD/mmu_plasticc",
+    search_filter=lsdb.ConeSearch(ra=330.0, dec=-6.0, radius_arcsec=3600.0),
+)
+```
+
+Each catalog in this collection is represented as a separate [Apache Parquet dataset](https://arrow.apache.org/docs/python/dataset.html) and can be accessed with a variety of tools, including `pandas`, `pyarrow`, `dask`, `Spark`, `DuckDB`.
+
+### File structure
+
+This catalog is represented by the following files and directories:
+
+- [`collection.properties`](https://huggingface.co/datasets/UniverseTBD/mmu_plasticc/collection.properties) — textual metadata file describing the HATS collection of catalogs
+- [`mmu_plasticc`](https://huggingface.co/datasets/UniverseTBD/mmu_plasticc/mmu_plasticc) — main HATS catalog directory
+  - [`dataset/`](https://huggingface.co/datasets/UniverseTBD/mmu_plasticc/mmu_plasticc/dataset/) — Apache Parquet dataset directory for the main catalog
+    - ... parquet metadata and data files in sub directories ...
+  - [`hats.properties`](https://huggingface.co/datasets/UniverseTBD/mmu_plasticc/mmu_plasticc/hats.properties) — textual metadata file describing the main HATS catalog
+  - [`partition_info.csv`](https://huggingface.co/datasets/UniverseTBD/mmu_plasticc/mmu_plasticc/partition_info.csv) — CSV file with a list of catalog HEALPix tiles (catalog partitions)
+  - [`skymap.fits`](https://huggingface.co/datasets/UniverseTBD/mmu_plasticc/mmu_plasticc/skymap.fits) — HEALPix skymap FITS file with row-counts per HEALPix tile of fixed order 10
+- [`mmu_plasticc_10arcs/`](https://huggingface.co/datasets/UniverseTBD/mmu_plasticc/mmu_plasticc_10arcs) — default margin catalog to ensure data completeness in cross-matching, the margin threshold is 10.0 arcseconds
+  - ... margin catalog files and directories ...
+
+### Catalog metadata
+
+Metadata of the main HATS catalog, excluding margins and indexes:
+
+| **Number of rows** | **Number of columns** | **Number of partitions** | **Size on disk** | **HATS Builder** |
+| --- | --- | --- | --- | --- |
+| 7,001,476 | 8 | 1,509 | 3.9 GiB | hats-import v0.7.3, hats v0.7.3 |
+
+
+### Catalog columns
+
+The main HATS catalog contains the following columns:
+
+| **Name** |  **`_healpix_29`** | **`lightcurve.band`** | **`lightcurve.time`** | **`lightcurve.flux`** | **`lightcurve.flux_err`** | **`hostgal_photoz`** | **`hostgal_specz`** | **`redshift`** | **`ra`** | **`dec`** | **`obj_type`** | **`object_id`** |
+| --- |  --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| **Data Type** |  int64 | list[string] | list[float] | list[float] | list[float] | float | float | float | double | double | string | string |
+| **Nested?** |  — | lightcurve | lightcurve | lightcurve | lightcurve | — | — | — | — | — | — | — |
+| **Value count** |  7,001,476 | 5,849,936,616 | 5,849,936,616 | 5,849,936,616 | 5,849,936,616 | 7,001,476 | 7,001,476 | 7,001,476 | 7,001,476 | 7,001,476 | 7,001,476 | 7,001,476 |
+| **Example row** |  1310263451060905914 | [u, u, u, u, u, u, u, u, u, u, u, … (816 total)] | [5.979e+04, 5.982e+04, 5.982e+04, … (816 total)] | [-13.15, 7.379, 2.236, -2.3, … (816 total)] | [13.62, 8.448, 8.948, 13.9, 10.11, … (816 total)] | 0.545 | -9 | 0.551 | 330.5 | -6.129 | SNII | 75227491 |
+| **Minimum value** |  64871186081855 | Y | -0.0 | -8935484.0 | -0.0 | -0.0 | -9.0 | -0.0 | -0.0 | -64.7609 | AGN | 100000051 |
+| **Maximum value** |  3458741424076622916 | z | 60674.26171875 | 13675792.0 | 13791667.0 | 3.0 | 3.444999933242798 | 3.446000099182129 | 359.8242 | 4.1815 | TDE | 99999980 |
+
+"ra" and "dec" are random numbers and not real coordinates.
+
+
+"Nested" indicates whether the column is stored as a nested field inside another "struct" column.
+
+
+"Value count" may be different from the total number of rows for nested columns: each nested element is counted as a single value.
+
+
+
+
+### Crossmatch with another catalog
+
+HATS catalogs can be efficiently crossmatched using [LSDB](https://lsdb.io),
+which leverages the HEALPix partitioning to avoid loading the full datasets into memory:
+
+```python
+import lsdb
+
+mmu_plasticc = lsdb.open_catalog("https://huggingface.co/datasets/UniverseTBD/mmu_plasticc")
+other = lsdb.open_catalog("https://huggingface.co/datasets/<org>/<other_catalog>")
+
+crossmatched = mmu_plasticc.crossmatch(other, radius_arcsec=1.0)
+print(crossmatched)
+```
+
+See the [LSDB documentation](https://docs.lsdb.io/) for more details on crossmatching and other operations.
+
+### Dataset-specific context
+
+**Original survey**  
+The PLAsTiCC dataset is a collection of simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge, designed to match the expected characteristics of the Legacy Survey of Space and Time (LSST). 
+
+It includes 14 types of time-varying astronomical objects, broadly divided into two categories: transients (short-lived events such as supernovae) and variable sources (objects with fluctuating brightness such as pulsating stars).
+
+**Data modality**  
+The dataset consists of time-series data (light curves) including time of observation, flux, flux error, and filter information. Observations are provided in six filters (u, g, r, i, z, Y). Each time series is also associated with a class label and a redshift.
+
+**Typical use cases**  
+The dataset has been widely used to develop and test models for supernova classification.
+
+**Caveats**  
+The dataset consists of simulated light curves generated using the expected instrument characteristics and survey strategy of LSST.
+
+**Citation**  
+The dataset is released under the CC BY 4.0 license and can be cited using its DOI: [10.5281/zenodo.2539456](https://doi.org/10.5281/zenodo.2539456).