FAIR fission track analysis with geochron@home

Vermeesch, Pieter; Band, Tim; He, Jiangping; Galbraith, Rex; Carter, Andrew

doi:10.5194/egusphere-2025-4948

Preprints

https://doi.org/10.5194/egusphere-2025-4948

Preprints

15 Oct 2025

| 15 Oct 2025

FAIR fission track analysis with geochron@home

Pieter Vermeesch, Tim Band, Jiangping He, Rex Galbraith, and Andrew Carter

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.

Received: 06 Oct 2025 – Discussion started: 15 Oct 2025

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.

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Pieter Vermeesch, Tim Band, Jiangping He, Rex Galbraith, and Andrew Carter

Status: final response (author comments only)

RC1: 'Comment on egusphere-2025-4948', Murat Taner Tamer, 16 Oct 2025

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

Citation: https://doi.org/10.5194/egusphere-2025-4948-RC1
RC2: 'Comment on egusphere-2025-4948', Ling Chung, 26 Nov 2025

This is a clearly structured, well-written work presenting a timely and much-needed contribution to the fission-track (FT) community. The authors introduce what is the first online platform aimed at making raw FT data transparent, reviewable, and aligned with FAIR principles. Given the inherently interpretive nature of grain selection and track identification, the ability to examine imagery alongside reported measurements is a significant step toward improving reproducibility and strengthening community-wide standards
An additional strength of this contribution is the cultural shift it may encourage. By providing a venue where raw data can be openly inspected, the platform promotes confidence in one’s analytical procedures and creates an environment in which training, documentation, and quality assurance are taken more seriously at the laboratory level. Over time, this could foster an atmosphere in which FT work is routinely assessed from its raw components upward, a development that would benefit both individual laboratories and the broader field.
It is also worth acknowledging the broader questions that may arise once such a framework is adopted. For example, to what extent can a reviewer be expected to interrogate the raw imagery, and how differing interpretations should be handled if a reviewer disagrees with elements of the dataset. These issues lie beyond the scope of the current manuscript but will inevitably require community-level discussion as FAIR-style reporting and review become more widely adopted.
Overall, the manuscript provides a solid framework for FAIR-oriented FT workflows. I recommend acceptance with minor revisions and clarifications.

Specific comments:
Line 2: “Fission track datasets are currently reported as small data tables.”
To maintain neutrality, I recommend removing “small.” Current guidelines (e.g., Kohn et al., 2024) encourage submission of comprehensive raw data tables as supplementary material.

Line 34: “Ongoing developments in artificial intelligence generate further opportunities to improve the throughput and accuracy of fission track data (Nachtergaele and De Grave, 2020).”
Recommend adding a reference “Boone et al. (2025)” (under review)

Line 35: “But despite the richness of the digital datasets produced by these novel tools, fission track data are still reported as small summary tables.
As above, recommend removing “small”.

Line 61: Note that the raw microscope images are generally not stored as .jpeg files but in uncompressed czi (for Zen Blue), .tif (for Fission Track Studio) or nd2 (for Nikon/TrackFlow) formats.
Regarding raw microscope images and file formats: Fission Track Studio (both V3 and V4) can generate .jpg images by default, and these can be imported, viewed and analysed in FastTracks.
Line 64~65: For example, if a user has already counted fission tracks in Fission Track Studio, they can then store those results in a .json file at the ‘sample’ level directory. Because Fission Track Studio stores results in an .xml format, a second conversion script was created to translate those results into an equivalent .json format.
Since FTS .xml files contain a wide range of metadata (e.g., ROIs, auto/manual counts, c-axis, Dpar, uncertain feature flags, grain notes), it would be useful to clarify whether the json format implemented here can also accommodate this information. Although this is not the main focus of the manuscript, a brief outline of what metadata geochron@home can store would help potential users understand the platform’s capacity for full transparent reporting.

Line 58~69 (section 2-2)
This section provides important information for users preparing imagery to upload. I recommend adding a figure illustrating the directory structure and file layering. If available, providing suggested or minimum numbers of reflected-light and transmitted-light slices would also be helpful. Alternatively, presenting the settings used at University College London would offer practical guidance.

Line 80: Depending on the permissions granted to the user by the administrator, … In contrast, ‘superusers’ are allowed to define their own regions of interest.
A brief explanation of what qualifies a user to become a ‘superuser’ would add clarity.
Also note a typo: “microsocope” →“microscope”

Line 91: Fig. 1
The ROIs differ in size between GR, MR and GT, MT (smaller in mica images MR and MT), yet the scale bars are identical across four images. Recommend revising and re-exporting the figure to avoid potential confusion.

Line 106:
Check the expression ˆρ1/ˆρ1 = 0.94.

Line 133: Given the right permissions (assigned by an administrator), users can build tutorial pages by annotating features in fission track images.
Similar to the comment for Line 80, outlining the levels of user permissions (e.g., entry-level, intermediate, superuser) and their associated capabilities would be beneficial. This could be incorporated into the proposed structural diagram for Section 2.2.

Line 178-179 (Section 6 Crowd-sourcing fission track data)
The authors present an interesting experiment reflecting the strength of geochron@home. The decision to include the full suite of student dataset is appreciated. The severe undercounting observed in the lower third is indeed striking. It may also be helpful to interview the top-performing students (for example, the top third) which parts of the tutorial helped them identify features correctly. Their feedback could provide useful guidance for improving the tutorial.

Line 209-210: First, a ‘quiz’ will be added to the tutorial pages to ensure that novice users do not count the tails but the etch pits of fission tracks.
Recommend adding an exercise that trains users to click the "centre" of each etch pit. This would reinforce good counting practice and would also provide cleaner, more consistent data for future machine-learning development, especially for high-density grains.

Line 241-242: Using geochron@home, a single microscope can serve multiple users and make fission-track analysis more affordable.
It is not entirely clear how this conclusion follows from the information provided. It would be helpful if the authors could outline a practical example or workflow that illustrates this point.

General Comments on geochron@home
I completed the training section and reviewed all PV and AC counts in Section 3. The issue of distinguishing multiple track openings located very close to one another remains challenging in the current counting interface. While I agreed with nearly all of the authors’ counts, I would have identified an additional 1–3 tracks in several grains where openings appear adjacent to those already marked. This is not a flaw of the manuscript but highlights a broader point: as FAIR-based reviewing becomes more common, the community will need to define how deeply a reviewer is expected to re-examine primary imagery and what threshold constitutes grounds for rejecting or disputing raw counts. These are future considerations rather than criticisms, but acknowledging them may further strengthen the discussion.

Recommend additional references
Kohn, B. P., Ketcham, R. A., Vermeesch, P., Boone, S. C., Hasebe, N., Chew, D., Bernet, M., Chung, L., Danišík, M., Gleadow, A. J. W., & Sobel, E. R. (2024). Interpreting and reporting fission-track chronological data. GSA Bulletin, 136(9–10), 3891–3920. https://doi.org/10.1130/B37245.1

Boone, S. C., Chung, L., Faux, N., et al. (2025). AI-based approach for constraining the thermal evolution of Earth’s upper crust through automated digital fission-track analysis (preprint). Authorea, September 22, 2025. https://doi.org/10.22541/au.175856991.11941871/v1
Note: This work has been reviewed and resubmitted with a new title “Raising the Bar: Deep Learning on Comprehensive Database Sets New Benchmark for Automated Fission-Track Detection”

Citation: https://doi.org/10.5194/egusphere-2025-4948-RC2

Pieter Vermeesch, Tim Band, Jiangping He, Rex Galbraith, and Andrew Carter

Data sets

Geochron@home archive Pieter Vermeesch https://github.com/pvermees/GaHa/blob/main/Vermeesch2025.md

Pieter Vermeesch, Tim Band, Jiangping He, Rex Galbraith, and Andrew Carter

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Short summary

geochron@home is an open-source platform that makes fission track dating more transparent and reliable. It combines a virtual microscope with an online database to share images and data openly, following FAIR principles. Researchers can analyse tracks privately, archive data for peer review, teach students, or involve citizen scientists. By improving data access and reproducibility, geochron@home helps build trust and supports future advances in Earth science.


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