diff --git a/README.md b/README.md
index f48966edacea1a0099600b9ae06fbbbf3b14236e..cc208718576d924e4308a1a3cbb14e2f150d16e4 100644
--- a/README.md
+++ b/README.md
@@ -11,13 +11,20 @@ tags:
 size_categories:
 - 1M<n<10M
 viewer: true
+configs:
+- config_name: jsonl
+  data_files: "cold.jsonl/*"
+- config_name: parquet
+  data_files: "cold.parquet/*"
+  default: true
 ---
 
-<img src="https://huggingface.co/datasets/harvard-lil/cold-cases/resolve/main/coldcases-banner.webp"/>
+<a href="https://huggingface.co/datasets/harvard-lil/cold-cases/resolve/main/coldcases.png"><img src="https://huggingface.co/datasets/harvard-lil/cold-cases/resolve/main/coldcases-banner.webp"/></a>
 
 # Collaborative Open Legal Data (COLD) - Cases
 
-COLD Cases is a dataset of 8.3 million United States legal decisions with text and metadata, formatted as compressed parquet files. If you'd like to view a sample of the dataset formatted as JSON Lines, you can view one [here](https://raw.githubusercontent.com/harvard-lil/cold-cases-export/main/sample.jsonl)
+COLD Cases is a dataset of 8.3 million United States legal decisions with text and metadata, formatted as one JSON object per decision.
+The total dataset size is approximately 104GB of uncompressed JSON.
 
 This dataset exists to support the open legal movement exemplified by projects like
 [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law) and 
@@ -27,7 +34,7 @@ United States caselaw is collected and published as open data by [CourtListener]
 a wide range of public sources.
 
 COLD Cases reformats CourtListener's [bulk data](https://www.courtlistener.com/help/api/bulk-data) so that all of the semantic information about each legal decision 
-(the authors and text of majority and dissenting opinions; head matter; and substantive metadata) is encoded in a single record per decision, 
+(the authors and text of majority and dissenting opinions; head matter; and substantive metadata) is encoded in a single JSON object per decision, 
 with extraneous data removed. Serving in the traditional role of libraries as a standardization steward, the Harvard Library Innovation Lab is maintaining
 this [open source](https://github.com/harvard-lil/cold-cases-export) pipeline to consolidate the data engineering for preprocessing caselaw so downstream machine
 learning and natural language processing projects can use consistent, high quality representations of cases for legal understanding tasks.
@@ -43,15 +50,44 @@ Prepared by the [Harvard Library Innovation Lab](https://lil.law.harvard.edu) in
 ---
 
 ## Summary
-- [Format](#format)
+- [Formats](#formats)
+- [File structure](#file-structure)
 - [Data dictionary](#data-dictionary)
-- [Notes on appropriate use](#notes-on-appropriate-use)
+- [Notes on appropriate use](#appropriate-use)
 
 ---
 
-## Format
+## Formats
 
-[Apache Parquet](https://parquet.apache.org/) is binary format that makes filtering and retrieving the data quicker because it lays out the data in columns, which means columns that are unnecessary to satisfy a given query or workflow don't need to be read. Hugging Face's [Datasets](https://huggingface.co/docs/datasets/index) library is an easy way to get started working with the entire dataset, and has features for loading and streaming the data, so you don't need to store it all locally or pay attention to how it's formatted on disk.
+We've released this data in two different formats:
+
+### JSON-L or JSON Lines
+
+This format consists of a JSON document for every row in the dataset, one per line. This makes it easy to sample a selection of the data or split it out into multiple files for parallel processing using ordinary command line tools such as `head`, `split` and `jq`. 
+
+Just about any language you can think of has a ready way  to parse JSON data, which makes this version of the dataset more compatible. 
+
+See: https://jsonlines.org/
+
+### Apache Parquet
+
+Parquet is binary format that makes filtering and retrieving the data quicker because it lays out the data in columns, which means columns that are unnecessary to satisfy a given query or workflow don't need to be read. 
+
+Parquet has more limited support outside the Python and JVM ecosystems, however.
+
+See: https://parquet.apache.org/
+
+[☝️ Go back to Summary](#summary)
+
+---
+
+## File structure
+
+Both of these datasets were exported by the same system based on [Apache Spark](https://spark.apache.org/), so within each subdirectory, you'll find a similar list of files:
+
+- **_SUCCESS**: This indicates that the job that built the dataset ran successfully and therefore this is a complete dataset.
+- **.json.gz or .gz.parquet**: Each of these is a slice of the full dataset, encoded in JSON-L or Parquet, and compressed with [GZip](https://www.gnu.org/software/gzip/).
+- **Hidden `.crc` files**: These can be used to verify that the data transferred correctly and otherwise ignored.
 
 [☝️ Go back to Summary](#summary)
 
@@ -92,7 +128,7 @@ Partial glossary of the fields in the data.
 | `opinions.opinion_text` | Actual full text of the opinion. |
 | `opinions.ocr` | Whether the opinion was captured via optical character recognition or born-digital text. |
 
-### court_type field values
+### court_jurisdiction field values
 | Value | Description |
 | --- | --- |
 | F | Federal Appellate |
@@ -121,8 +157,6 @@ Partial glossary of the fields in the data.
 
 [☝️ Go back to Summary](#summary)
 
----
-
 ## Notes on appropriate use
 
 When using this data, please keep in mind:
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