| 15 Oct 2025
FAIR fission track analysis with geochron@home
Abstract. Fission track thermochronology is based on the visual analysis of optical images. This visual process is prone to observer bias. Fission track datasets are currently reported as small data tables. The interpretation of these tables requires a high degree of trust between the fission track analyst and the user of the data. geochron@home is software that removes this requirement of trust. It combines a browser-based ‘virtual microscope’ with an online database to provide FAIR (Findable, Accessible, Interoperable and Reproducible) access to fission track data.
geochron@home serves four different purposes. It can be used (1) to count fission tracks in 'private mode', i.e. hidden from other users on the internet; (2) to archive fission track images and counts for inspection by other users; (3) to create tutorials for new students of the fission track method; and (4) to serve randomly selected selections of images to citizen scientists. We illustrate these four applications with examples that demonstrate (1) geochron@home's ability to compare and combine fission track counts for multiple users within a lab group; (2) the value of the geochron@home archive in the peer review system; (3) the use of simple tutorials in teaching novice users how to count fission tracks; and (4) the opportunities and challenges of crowd-sourced fission track analysis.
geochron@home was written in Python and Javascript. Its code is freely available for inspection and modification, allowing users to set up their own geochron@home server. Alternatively, users who would like to upload data to the archive, but do not have the facilities to set up their own server, may use the server at University College London free of charge. The archive accepts image stacks acquired on any type of digital microscope, and accommodates fission track data (counts and length measurements) from external fission track analysis suites such as Fission Track Studio and TrackFlow.
We anticipate that the introduction of FAIR workflows will make fission track data more accurate and more future proof. Storing fission track data online will benefit future developments in fission track thermochronology. For example, archival datasets of peer reviewed fission track counts can be used to train and improve machine learning algorithms for automated fission track analysis. We invite other geochronological methods to follow the fission track community's lead in FAIR data processing. This would benefit all the Earth Science disciplines that depend on geochronological data.
Competing interests: PV is an Associate Editor of Geochronology
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
This manuscript is a timely (perhaps slightly overdue) contribution to the field of thermochronology and should definitely be published after minor revisions. This contribution has a high potential to be a highlight paper.
The manuscript introduces geochron@home, a novel public and open-source platform that provides the first web-based application for fission track (FT) analysis and archiving, promoting open science, data transparency, and sharing. The platform’s potential impact is immediate and multifaceted, and the manuscript convincingly demonstrates its value for the FT community. I also see considerable long-term benefits once the planned extensions (already mentioned in the paper) are implemented.
This is an important contribution. The platform represents a genuine step forward for transparent, FAIR thermochronology. Addressing the clarifications and expanding a few statements will strengthen an already excellent and impactful manuscript.
Line 2:
“Fission track datasets are currently reported as small data tables.”
You might consider modifying this sentence to acknowledge that FT datasets are sometimes accompanied by supplementary data files.
Line 35:
“…and accuracy of fission track data (Nachtergaele and De Grave, 2020).”
It would strengthen this section to include several more recent and relevant works on automated or AI-based fission-track identification and analysis.
Li et al. (2022); Boone et al. (2023); Ren et al. (2023); and Boone et al. (2025).
Lines 111–113:
“It is one of the reasons why fission track analysis is often done relative to age standards: observer bias does not have to be a problem provided that it is consistent between grains, and between samples.”
This sentence may oversimplify the challenge. In practice, unknown samples with a high proportion of non-track features (etch artefacts, scratches, or inclusions) can exhibit strong observer dependence. In such cases, even if a consistent bias exists for standards, it may not translate reliably to unknowns. The authors may wish to clarify that observer bias can be controlled by relative calibration but not necessarily eliminated, particularly in complex or low-quality samples.
Line 162:
“Everyone counted tracks in the same ROIs.”
A clearer phrasing could be:
“Everyone analyzed the features in the same ROIs.”
This distinction emphasizes that although the regions were identical, participants recognized and classified features differently.
Lines 167–169:
“Furthermore, the vast majority of students counted fewer tracks than PV did, often many fewer, and on two occasions someone counted no tracks at all. PV’s count is always above the students’ median and nearly always above the upper quartile (Figure 3).”
This section raises interesting questions: Why do the student results vary so widely? Were the students’ counts affected by differences in motivation or training?
It would help to specify whether this exercise was voluntary or part of an assessed class assignment. The distinction matters, since motivation strongly influences the reliability of such crowdsourcing results. The analogy to Galton’s experiment is apt, but unlike his participants, students under evaluation may behave differently if the task affects grades or workload.
Lines 178–179:
“Students near the bottom of Table 1 have undercounted the samples by such a large degree that their work can be qualified as vandalism.”
The term vandalism may be too strong or ambiguous for this (although I would have written exactly the same word). If this statement is retained, the authors might clarify what they mean (data of no interpretive value?).
An interesting, related (though off-topic) point concerns p-hacking and selective reporting. For example, in a recent review of Turkish FT literature by us, approximately 40% of published FT ages report chi-squared p-values near 1.0. This raises questions about data selection and reporting bias. It may be worth discussing how FAIR and transparent data repositories, such as geochron@home, could help identify and mitigate such practices by exposing raw data and full grain distributions.
Figure 4a:
For clarity, consider revising the axis labels to:
“Track counts in grain 23” and “Track counts in grain 25.”
General comment on transparency and data culture:
There is an inherent relationship between quantity and quality, or between “how many” and “which” grains are analyzed. This balance directly influences data integrity across all research fields, including thermochronology. The manuscript correctly notes that the interpretation of FT datasets requires a high degree of trust between the analyst and the data user.
The first author has previously discussed the number of grains required for meaningful statistics in a paper, and this concept has probably been wrongly adopted as a benchmark by many reviewers and editors. However, this has led some researchers to “squeeze” rocks for marginally suitable grains just to meet a numerical threshold, which may inadvertently degrade data quality.
Transparency mechanisms such as those implemented in geochron@home can alleviate this problem. By providing open access to raw images and full counting data, journals and reviewers can evaluate not only the quantity but also the quality of the analyses, reducing incentives for selective reporting or oversampling.
References (for suggested additions)
Boone, S. C., Faux, N., Nattala, U., Jiang, C., Church, T., Chung, L., McMillan, M., Jones, S., Jiang, H., Liu, D., Ehinger, K., Drummond, T., Kohn, B., and Gleadow, A. J. W. (2023). Towards Fully Automated Digital Fission-Track Analysis Through Artificial Intelligence. 18th International Conference of Thermochronology, Riva del Garda, Italy.
Boone, S. C., Chung, L., Faux, N., Nattala, U., Church, T., Jiang, C., McMillan, M., Jones, S., Liu, D., Jiang, H., Ehinger, K., Drummond, T., Kohn, B., and Gleadow, A. (2025). AI-based approach for constraining the thermal evolution of Earth’s crust through automated digital fission track analysis. Computers & Geosciences (under review). https://doi.org/10.22541/au.175856991.11941871/v1
Li, R., Xu, Z., Su, C., and Yang, R. (2022). Automatic identification of semi-tracks on apatite and mica using a deep learning method. Computers & Geosciences, 162, 105081. https://doi.org/10.1016/j.cageo.2022.105081
Ren, Z., Li, S., Xiao, P., Yang, X., & Wang, H. (2023). Artificial intelligent identification of apatite fission tracks based on machine learning. Machine Learning: Science and Technology, 4(4), 045039. https://doi.org/10.1088/2632-2153/ad0e17