# Welcome to AntADatabase

AntADatabase is Python-powered SQL database providing efficient access to Internal Reflecting Horizons (isochrones) data across Antarctica. Designed for ice sheet modelers, it offers fast, memory-efficient data structures to constrain models with published IRH data.

Explore the isochrones in the [3D viewer](https://antadatabase.com/viewer/), download the data or submit your own on [antadatabase.com](https://antadatabase.com).

![Overview Figure](figures/Overview_all.png)
*Example visualizations created using the anta_database plotting functions*

## Overview
The **AntADatabase** project consists of two distinct but complementary components:
### AntADatabase dataset

**A comprehensive compilation of Internal Reflecting Horizons (isochrones) across Antarctica**

The AntADatabase is a curated collection of all published isochrone datasets, standardized into a single, uniform format. This dataset is designed specifically for ice sheet modelers and researchers who need consistent, high-quality IRH data to constrain their models.

**Key characteristics:**
- **Comprehensive**: Aggregates data from multiple published sources into one cohesive dataset
- **Standardized**: All data converted to consistent HDF5 format with uniform variable naming
- **Curated**: Maintained by the AntArchitecture action group
- **Growing**: Continuously updated as new datasets are published

### anta_database Python Package

**A Python toolkit for accessing and analyzing the AntADatabase**

The `anta_database` an open-source Python package provides the tools to efficiently query, filter, visualize, and process the AntADatabase dataset. It's built for performance and ease of use, with SQLite indexing for fast data access.

**Key features:**
- **SQLite Indexing**: Fast querying by dataset, institute, project, age, region, basin, variable, or flight ID
- **Memory Efficient**: Lazy data generation to handle large datasets without loading everything into memory
- **Visualization Tools**: Built-in plotting functions for quick data exploration on Antarctic maps
- **Data Processing**: Tools to compile and index your own datasets

## Included Datasets

The AntADatabase currently includes the following published datasets:

- Ashmore et al. 2020, [10.1029/2019GL086663](https://doi.org/10.1029/2019GL086663)
- BEDMAP1: Lythe et al. (2022), [10.5285/f64815ec-4077-4432-9f55-0ce230f46029](https://doi.org/10.5285/f64815ec-4077-4432-9f55-0ce230f46029)
- BEDMAP2: Fretwell et al. (2022), [10.5285/2fd95199-365e-4da1-ae26-3b6d48b3e6ac](https://doi.org/10.5285/2fd95199-365e-4da1-ae26-3b6d48b3e6ac)
- BEDMAP3: Frémand et al. (2022), [10.5285/91523ff9-d621-46b3-87f7-ffb6efcd1847](https://doi.org/10.5285/91523ff9-d621-46b3-87f7-ffb6efcd1847)
- Beem et al. 2021, [10.15784/601437](https://doi.org/10.15784/601437)
- Bodart et al. 2021, [10.5285/F2DE31AF-9F83-44F8-9584-F0190A2CC3EB](https://doi.org/10.5285/F2DE31AF-9F83-44F8-9584-F0190A2CC3EB)
- Bodart and Sutter 2025a, [10.5281/zenodo.17348976](https://doi.org/10.5281/zenodo.17348976)
- Bodart and Sutter 2025b, [10.5281/zenodo.17348094](https://doi.org/10.5281/zenodo.17348094)
- Cavitte et al. 2020, [10.15784/601411](https://doi.org/10.15784/601411)
- Chung et al. 2023, [10.1594/PANGAEA.957176](https://doi.pangaea.de/10.1594/PANGAEA.957176)
- Franke et al. 2025, [10.1594/PANGAEA.973266](https://doi.org/10.1594/PANGAEA.973266)
- Jacobel and Welch 2005, [10.7265/N5R20Z9T](https://doi.org/10.7265/N5R20Z9T)
- Leysinger-Vieli et al. 2011, [10.5281/zenodo.15516203](https://doi.org/10.5281/zenodo.15516203)
- Muldoon et al. 2023, [10.15784/601673](https://doi.org/10.15784/601673)
- Mulvaney et al. 2023, [10.1594/PANGAEA.963470](https://doi.pangaea.de/10.1594/PANGAEA.963470)
- Napoleoni et al. 2026, [10.5194/tc-20-2793-2026](https://doi.org/10.5194/tc-20-2793-2026)
- Sanderson et al. 2024, [10.5285/cfafb639-991a-422f-9caa-7793c195d316](https://doi.org/10.5285/cfafb639-991a-422f-9caa-7793c195d316)
- Siegert and Payne 2024, [10.1002/esp.1238](https://onlinelibrary.wiley.com/doi/10.1002/esp.1238)
- Wang et al. 2023, [10.1594/PANGAEA.958462](https://doi.org/10.1594/PANGAEA.958462)
- Winter et al. 2018, [10.1594/PANGAEA.895528](https://doi.org/10.1594/PANGAEA.895528)
- Yan et al. 2025, [10.5281/zenodo.14962526](https://doi.org/10.5281/zenodo.14962526)

## Data Variables

The standardized dataset includes the following variables (when available in source data):

- **Coordinates**: `PSX`, `PSY` (Polar Stereographic coordinates)
- **Geometry**: `Distance` (along-track distance), `ICE_THK` (ice thickness), `SURF_ELEV` (surface elevation), `BED_ELEV` (bed elevation)
- **IRH Data**: `IRH_DEPTH` (Internal Reflecting Horizon depth), `IRH_NUM` (number of traced IRHs per point)

## Getting Started

To learn how to use the anta_database Python package, proceed to the [Quick Start](quick_start) guide.

For advanced usage, including database compilation and management, see the [Advanced](advanced) section.

## Tutorial

```{tableofcontents}
```

## Acknowledgments

This project is developed as part of a PhD project funded by the Swiss National Science Foundation (grant no. 211542, Project CHARIBDIS).

**Important**: Any data used through this database must be cited at source using the DOI provided in the metadata. If you use the anta_database package in your work, please cite this repository so others can discover and benefit from this tool.