# The dataset > A public, CC0 dataset for Eternity II in two parts: fourteen benchmark instances to solve, and a corpus of 7,658 distinct strong boards to learn from. Every score is recomputed from the board itself, and the corpus is checked to be genuinely diverse rather than a thousand copies of one board. - Canonical page (with interactive figures/demos): https://eternity2.dev/research/build/dataset/ - Updated: 2026-07-16 - Topics: structure, learning - Source: The dataset (this repo): instances, the strong-board corpus zip, README, and the build script — https://github.com/raphael-anjou/eternity2/tree/main/research/datasets --- Two things recur when you work on Eternity II: you need instances to run a solver against, and, if you want to learn from strong boards, you need a corpus of them. This dataset packages both, released into the public domain under [CC0](https://creativecommons.org/publicdomain/zero/1.0/) so it can be reused for anything without attribution. It has two parts, and both live in the [dataset directory](https://github.com/raphael-anjou/eternity2/tree/main/research/datasets). ## Part A: benchmark instances Fourteen instances a solver runs on, each a self-contained JSON file with the piece set, any pinned hints, and the maximum reachable score. - **Ten 16×16 instances** (`e2-16x16-v00` … `v09`): the official Eternity II piece set with three corners pinned, in ten different corner arrangements. Pinning the corners gives ten distinct but equally hard instances off one piece set, which is what lets a study compare engines on a diversity axis rather than a single board. All three of this project's engine studies (the [DFS study](/research/lab/experiments/raphael-anjou/dfs-study), the [repair study](/research/lab/experiments/raphael-anjou/repair-study), and the [single-core benchmark](/research/lab/experiments/single-core-benchmark)) solve exactly these ten. Maximum score 480. - **Four clue sub-puzzles** (`e2-clue-clue1` … `clue4`): the smaller 6×6 and 12×6 boards from the puzzle's official clue set. Each is known to have a full solution, which makes them a fast correctness fixture: a solver that cannot close a clue board has a bug before it ever meets the full puzzle. Each cell is described in up, right, down, left order, colour 0 is the grey rim, and `pos` is the row-major cell index. The shape of an instance file: ```json { "id": "e2-16x16-v00", "family": "official-16x16-pin3corners", "width": 16, "height": 16, "numColors": 22, "pieces": [[up, right, down, left], ...], "hints": [{"pos": 0, "piece": 0, "rot": 3}, ...], "maxScore": 480 } ``` ## Part B: a corpus of strong boards **7,658 distinct boards** scoring 400 to 470, the raw material for methods that [learn from strong boards](/research/lab/experiments/raphael-anjou/learning): position priors, learned move-ordering, anti-pattern mining. It ships as a zip (`e2-strong-boards.zip`, about 16 MB) that unpacks to the same rows as CSV and as JSON-lines: ``` id,score,family,edges e2b-00001,469,f008,adcaaendadwe… (1024 lowercase letters) ``` The `edges` field is the board itself: 256 cells times four sides, row-major, in up-right-down-left order, `a` for the grey rim. It is the same string the [e2.bucas.name](https://e2.bucas.name) viewer reads, so any board pastes straight into the viewer. The `family` is the board's four corner pieces, a proxy for which basin it sits in; there are 28 distinct families. The score distribution spikes sharply in the low-450s, where a large single-core beam harvest concentrates, then thins to a long tail, with 38 boards at 460 or above and the best at 470. > **[Interactive: CorpusScoreDistribution]** Rendered on the canonical page (link above); not shown in this markdown export. ## Every score is recomputed, not trusted No score in this dataset is copied from a source field. Each one is recomputed from the board's own edge string by counting matched interior adjacencies. This is not a formality: in the source data one board carried a stale score, claiming 453 when its edges actually score 456, and recomputing from edges corrects it automatically. Re-derive the scores yourself from the edge strings and you will get the published numbers exactly. ## The corpus is diverse, not derivative A pile of several thousand boards is only worth publishing if the boards are actually different. Before releasing this one we checked that they are not just perturbations of a single board, and they are not: - Only 13 of the 7,671 source boards were exact duplicates; the rest are distinct, leaving 7,658. - The *median* board differs from its closest sibling by 235 of its 256 piece placements. Almost no board sits within 10 placements of another, and only 1% within 25. Even though most boards share a narrow score band, they are structurally almost entirely different. - The boards spread across 28 distinct corner families rather than piling into one. So the corpus is a genuinely varied sample of the strong-board landscape. That matters for anything that mines it: a learned prior built from thousands of copies of one board would encode that board, not the structure of good boards in general. ## Rebuilding it The [build script](https://github.com/raphael-anjou/eternity2/tree/main/research/datasets) regenerates the whole dataset. The instances are assembled from the public variant files; the corpus is deduplicated, rescored from edges, stripped of every working-store label (source paths, internal preset and seed names, filenames), and re-keyed with neutral ids, so what ships is board content and verified scores and nothing else. ## Related - [Known facts & numbers](https://eternity2.dev/research/build/known-facts) — The numbers every Eternity II researcher ends up re-deriving, collected in one place with their provenance: the puzzle definition, the clue placements, scoring conventions, the record table, search-space sizes and the structural counts. - [Reference numbers](https://eternity2.dev/research/reference) — Exact counts of how many valid ways a small block can be filled at a given position of the official Eternity II board, under increasingly constrained rules: known-good numbers to check your solver's edge-matching and constraint code against. - [Learning from strong boards](https://eternity2.dev/research/lab/experiments/raphael-anjou/learning) — A study in five experiments of one idea: instead of searching Eternity II from first principles, mine the corpus of strong boards already found for structure and feed it back into a search. A position prior, a learned move-vote, a scarce-demand compass, an anti-pattern miner, and a record decode, ordered from the simplest signal to the subtlest, and the one wall all five reach.