A Validated, Population-Scale Dataset of 2.7 Million IRONMAN® Triathlon Records with Separated Transition Times (2002–2026)

Companion repository — data-collection pipeline, analysis scripts, and figures — for the IRONMAN® triathlon dataset described in the published article:

Seffrin, A.; Nikolaidis, P.T.; Andrade, M.S.; Villiger, E.; Rosemann, T.; Weiss, K.; Ferreira, D.; Knechtle, B. A Validated, Population-Scale Dataset of 2.7 Million IRONMAN® Triathlon Records with Separated Transition Times (2002–2026). Sci 2026, 8(9), 265. https://doi.org/10.3390/sci8090265

Open access (CC BY 4.0), published 19 September 2026 under Open Peer Review: the review reports and the authors' responses are available with the article.

Authors

Aldo Seffrin¹ (ORCID 0000-0001-8229-8565), Pantelis Theodoros Nikolaidis² (0000-0001-8030-7122), Marilia Santos Andrade³ (0000-0002-7004-4565), Elias Villiger⁴ (0000-0001-8371-1390), Thomas Rosemann⁴ (0000-0002-6436-6306), Katja Weiss⁴ (0000-0003-1247-6754), Daniel Ferreira¹ (0000-0002-8958-9442), Beat Knechtle⁴ˌ⁵ (0000-0002-2412-9103)*

¹ Nova O2 Sports Science, São Paulo 12246-020, Brazil ² School of Health and Caring Sciences, University of West Attica, 12243 Athens, Greece ³ Department of Physiology, Federal University of São Paulo (UNIFESP), São Paulo 04023-062, Brazil ⁴ Institute of Primary Care, University of Zurich, 8091 Zurich, Switzerland ⁵ Medbase St. Gallen Am Vadianplatz, Vadianstrasse 26, 9001 St. Gallen, Switzerland

* Corresponding author

Dataset

Metric	Value
Records described	2,706,922
Records deposited	2,706,867
Race-years	1,554
Years	2002–2026 (2026 partial; collected 27–28 March 2026)
Full-distance (IRONMAN®)	1,340,799 (49.5%)
Half-distance (IRONMAN® 70.3)	1,366,123 (50.5%)
Source: official	2,041,743 (75.4%)
Source: supplement	665,179 (24.6%)
T1 / T2 coverage	84.2% / 84.1%
Cross-source agreement	98.76% of 4,732,776 athlete-discipline pairs, over all 559 race-years present in both sources

The described and deposited record counts differ by 55. Those records carried an explicit redaction applied by the supplementary source; that suppression is propagated into the deposit rather than reversed.

Data availability

The de-identified dataset (athlete names removed) is openly deposited on Zenodo under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence: https://doi.org/10.5281/zenodo.19284673 — a concept DOI that always resolves to the current version. The data derive from publicly available race results: the official IRONMAN® results platform and the CoachCox aggregator. The full dataset can also be reproduced from source with the collection scripts below.

Reproduction

Collection scripts are in data/collection/; see data/README.md for step-by-step instructions.

pip install -r requirements.txt

cd data/collection
python scrape_official.py      # ~2-4h, official IRONMAN® platform
python combine_official.py     # JSON to CSV

python scrape_coachcox.py      # ~30min, supplementary source
python combine_coachcox.py     # JSON to CSV

python merge_sources.py        # Merge into unified dataset

Analysis scripts are in notebooks/. Each writes a CSV to notebooks/results/ carrying a provenance header — when it ran, against which input file, at what size, and in which environment. Every quantitative claim in the manuscript is checked against those files by notebooks/r1_manuscript_number_audit.py, which exits non-zero on any mismatch.

These scripts read the merged dataset. Download the deposited file from Zenodo and place it at data/collection/ironman_merged.csv, or rebuild it with the collection scripts above; note that the deposited file has athlete names removed and 55 fewer records, so analyses that depend on either will differ.

python notebooks/r1_manuscript_number_audit.py   # verifies 100 claims
python notebooks/generate_tables.py              # rebuilds Tables 1 and 2
python notebooks/generate_figures.py             # rebuilds Figures 1-5

Structure

data/
├── collection/
│   ├── scrape_official.py       # Official IRONMAN® platform scraper
│   ├── scrape_coachcox.py       # Supplementary source scraper
│   ├── combine_official.py      # Official JSON to CSV consolidation
│   ├── combine_coachcox.py      # Supplementary JSON to CSV consolidation
│   ├── merge_sources.py         # Deterministic merge procedure
│   ├── event_uuids_full.csv     # 128 event series (official platform)
│   ├── all_subevents.csv        # 1,235 race editions
│   └── race_metadata.csv        # Supplementary race metadata
└── README.md                    # Reproduction instructions

notebooks/
├── _common.py                   # Paths, dtypes, colours, result writing
├── generate_figures.py          # Figures 1-5
├── generate_tables.py           # Tables 1 and 2
├── r1_manuscript_number_audit.py# Verifies every manuscript number
├── r1_*.py                      # One analysis per script
└── results/                     # Analysis outputs, with provenance headers

figures/
├── Figure1.tiff                 # Dataset composition by source and race type
├── Figure2.tiff                 # Temporal trends in participation and performance
├── Figure3.tiff                 # Split time distributions
├── Figure4.tiff                 # Participation by sex over time
└── Figure5.tiff                 # Transition time coverage, T1 and T2

Notes on the revision history

Script docstrings reference the review comments they answer, using the identifiers from the peer-review rounds (R1-Rev1-*, R1-Rev2-* for reviewer comments, R1-Int-* for issues the authors found themselves). The article was reviewed under Open Review, so the reports and the point-by-point responses are published with it.

The figures were renumbered in the second round to follow their order of first citation (R1 → R2: 1→1, 4→2, 2→3, 5→4, 3→5); a figure number quoted in the first-round response letter refers to the old numbering. At proofreading the axis ticks and panel labels of Figures 1–3 were reformatted to the journal's number style (thousands separator from five digits up); the data drawn did not change.

The two exploratory notebooks previously in notebooks/ have been removed rather than updated. Both computed field coverage with a null check, which overstates it by roughly ten percentage points because the split-time columns store zero as well as missing values, and the figure notebook assigned category labels positionally, which transposed the two data sources in one figure. The scripts above replace them and are checked by the numeric audit.

Licence

MIT — Copyright (c) 2026 Nova O2 Sports Science