Update README.md

README.md

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 ---
 license: mit
+tags:
+- time series
+- astrophysics
+- pretraining
+- connect-later
+size_categories:
+- 100K<n<1M
 ---
+# AstroClassification and Redshifts Datasets
+
+<!-- Provide a quick summary of the dataset. -->
+
+This dataset was used for the AstroClassification and Redshifts introduced in [Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations](). This is a dataset of simulated astronomical time-series (e.g., supernovae, active galactic nuclei), and the task is to classify the object type (AstroClassification) or predict the object's redshift (Redshifts).
+
+- **Repository:** https://github.com/helenqu/connect-later
+- **Paper:** will be updated
+- **Point of Contact: Helen Qu (<helenqu@sas.upenn.edu>)** 
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+- **object_id**: unique object identifier
+- **times_wv**: 2D array of shape (N, 2) containing the observation times (modified Julian days, MJD) and filter (wavelength in nm) for each observation, N=number of observations
+- **lightcurve**: 2D array of shape (N, 2) containing the flux (arbitrary units) and flux error for each observation
+- **label**: integer representing the class of the object (see below for details)
+- **redshift**: redshift of the object
+
+## Dataset Creation
+
+### Source Data
+
+This is a modified version of the dataset from the 2018 Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) Kaggle competition
+The original Kaggle competition can be found [here](https://www.kaggle.com/c/PLAsTiCC-2018). [This note](https://arxiv.org/abs/1810.00001) from the competition describes the dataset in detail. Astronomers may be interested in [this paper](https://arxiv.org/abs/1903.11756) describing the simulations used to generate the data.
+
+- **Train**: 80% of the original PLAsTiCC training set augmented using the redshifting targeted augmentation described in the Connect Later paper
+- **Validation**: Remaining 20% of the original PLAsTiCC training set, *not* augmented or modified
+- **Test**: Subset of 10,000 objects randomly selected from the PLAsTiCC test set
+
+### Object Types
+```
+ 0: microlens-single
+ 1: tidal disruption event (TDE)
+ 2: eclipsing binary (EB)
+ 3: type II supernova (SNII)
+ 4: peculiar type Ia supernova (SNIax)
+ 5: Mira variable
+ 6: type Ibc supernova(SNIbc)
+ 7: kilonova (KN)
+ 8: M-dwarf
+ 9: peculiar type Ia supernova (SNIa-91bg)
+ 10: active galactic nuclei (AGN)
+ 11: type Ia supernova (SNIa)
+ 12: RR-Lyrae (RRL)
+ 13: superluminous supernova (SLSN-I)
+ 14: 5 "anomalous" types that are not present in training set: microlens-binary, intermediate luminosity optical transient (ILOT), calcium-rich transient (CaRT), pair instability supernova (PISN), microlens-string
+```
+
+## Citation
+will be updated