# ICE-ID: A Novel Historical Census Dataset for Longitudinal Identity Resolution

arXiv: https://arxiv.org/abs/2506.13792 · HTML: https://mlsanity.com/paper/2506.13792/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://mlsanity.com/paper/2306.11698/) (2023)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models](https://mlsanity.com/paper/2404.16019/) (2024)
- [Benchmarking Distribution Shift in Tabular Data with TableShift](https://mlsanity.com/paper/2312.07577/) (2023)
- [TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks](https://mlsanity.com/paper/2406.19380/) (2024)
- [A Survey of Blocking and Filtering Techniques for Entity Resolution](https://mlsanity.com/paper/1905.06167/) (2019)
- [A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms](https://mlsanity.com/paper/2307.01231/) (2023)

## Abstract (arXiv)

We introduce ICE-ID, a benchmark dataset comprising 984,028 records from 16 Icelandic census waves spanning 220 years (1703--1920), with 226,864 expert-curated person identifiers. ICE-ID combines hierarchical geography (farm→parish→district→county), patronymic naming conventions, sparse kinship links (partner, father, mother), and multi-decadal temporal drift -- challenges not captured by standard product-matching or citation datasets. This paper presents an artifact-backed analysis of temporal coverage, missingness, identifier ambiguity, candidate-generation efficiency, and cluster distributions, and situates ICE-ID against classical ER benchmarks (Abt--Buy, Amazon--Google, DBLP--ACM, DBLP--Scholar, Walmart--Amazon, iTunes--Amazon, Beer, Fodors--Zagats). We also define a deployment-faithful temporal OOD protocol and release the dataset, splits, regeneration scripts, analysis artifacts, and a dashboard for interactive exploration. Baseline model comparisons and end-to-end ER results are reported in the companion methods paper.