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Archived high-energy physics datasets produce improved measurements when reanalyzed with modern methods, proving long-term preservation has lasting scientific value.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.5

2026-07-10 21:33 UTC pith:W4ZDDV2N

load-bearing objection Solid multi-experiment status report that documents real reuse of LEP/HERA/BaBar data and open-data progress; low novelty but high practical value for the community. the 2 major comments →

arxiv 2607.06775 v1 pith:W4ZDDV2N submitted 2026-07-07 hep-ex

Data Preservation in High Energy Physics: Global Report 2026

Matthew Bellis , Jamie Boyd , Daniel Britzger , Andy Buckley , Chris Burr , Micheal Buchar , Gang Chen , Andrew Chisholm
show 41 more authors
Jiri Chudoba Michael Davis Cristinel Diaconu David Dobrigkeit Chinellato Marcus Ebert Apranik Fatehi Dillon S. Fitzgerald Jose Benito Gonzalez Lopez Richard William Gran Giovanni Guerrieri Martin Habedank Hao Hu David Horvat Luka Lambrecht Clemens Lange Kati Lassila-Perini Jerome Lauret Jean-Yves Le Meur Dietrich Liko Panna Liptak Zach Marshall Thomas McCauley Cameron Duncan McClymont Wesley Middelbos Micha Moskovic Piet Nogga Ryunosuke O'Neil Stefano Piano Alan Price Tomasz Procter Fazhi Qi Diana Rand Hossein Rashidi Emily Rensch Pablo Saiz Ulrich Schwickerath Tibor \v{S}imko Andrii Verbytskyi Dirk Zerwas Zhengde Zhang Graham Wilson
This is my paper
classification hep-ex
keywords data preservationhigh energy physicsopen datalegacy data reanalysisFAIR principlesAI curationanalysis reproducibilityMonte Carlo generators
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This workshop report surveys the state of data preservation across high-energy physics experiments and sites. It establishes that impressive progress has been made since the previous cycle: legacy collision data from past facilities have been successfully revived and reanalyzed with contemporary software and machine-learning techniques, yielding clearer physics results than the original analyses. At the same time, open-data policies, FAIR principles, and shared tools are spreading, while automation is beginning to handle curation and metadata. A sympathetic reader cares because these unique datasets cannot be recreated; keeping them usable multiplies the scientific return of every experiment and supplies real-data testbeds for future machines. The report also flags that without sustained funding and institutional homes, aging infrastructure for closed experiments will fail.

Core claim

Data preservation in high-energy physics is delivering concrete scientific returns: archived datasets, once converted or reprocessed with modern analysis stacks, support new or improved measurements decades after data-taking ended, while common technologies and open-release policies are making that capability transferable across experiments.

What carries the argument

Legacy data revival via modern reanalysis: converting old formats and frozen software stacks into contemporary event models, then applying current reconstruction, tagging, and workflow tools so that archived collisions remain a living resource rather than a static archive.

Load-bearing premise

Long-term funding and institutional support will continue at the level needed to keep software stacks, storage media, and analysis environments operational once the original collaborations shrink or retire.

What would settle it

If, within the next decade, major archived datasets become unreadable or unanalyzable because supporting services and hardware fail, or if new reanalyses of those data fail to improve on the original published results, the claimed long-term scientific value would not hold.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Future collider design and algorithm development can be validated on real archived data rather than simulation alone.
  • Wider public releases will let theorists, educators, and outside researchers extract new measurements without reinventing experiment software.
  • AI-assisted curation and workflow tools will lower the expert-knowledge barrier for reusing complex datasets.
  • Shared preservation standards will reduce duplicated effort across experiments and host laboratories.
  • Without dedicated resources, unique legacy samples risk becoming permanently inaccessible once collaborations dissolve.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Early investment in open formats, containers, and documented workflows multiplies the lifetime scientific return of any experiment far beyond its active running period.
  • Host laboratories may need permanent archival mandates rather than experiment-by-experiment funding if aging hardware and shrinking collaborations are not to erase unique datasets.
  • Automated extraction of dataset usage from papers could create a feedback loop that decides which cold-storage samples stay hot, optimizing limited disk resources.
  • The same revival techniques that improve old measurements can serve as living testbeds for the software stacks planned for next-generation colliders.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 10 minor

Summary. This manuscript is the DPHEP Collaboration’s Global Report 2026, summarizing the 5th DPHEP workshop (CERN, 5–6 March 2026). It compiles status updates from legacy and running experiments (LEP, BaBar, H1, MINERvA, ATLAS, LHCb, CMS, ALICE, bubble-chamber film, IHEP facilities) and from transverse technology and policy efforts (k4GeneratorsConfig, CERN Open Data cold storage, EOSC EDEN, REANA, Preserve Platform, AI metadata extraction, DORA, ICFA recommendations). The central descriptive claim is that HEP data preservation has advanced substantially since 2024: legacy datasets have been successfully reanalysed with modern methods, open-data and FAIR practices have broadened, and common tooling and automation (including AI) are maturing, while long-term funding and institutional support remain critical risks for archival-mode experiments.

Significance. As a multi-experiment, multi-site status compilation, the report is a useful community reference rather than a single-result research paper. Its value lies in concrete, checkable demonstrations of scientific reuse of preserved data—most notably modern deep-learning jet flavour tagging on archived ALEPH data (§2.2), OPAL radiative-neutrino reanalysis after 26 years (§2.5), DELPHI conversion toward EDM4HEP (§2.3), ALICE AO2D migration and open-data releases (§2.12), MINERvA’s full open-data product (§2.8), and quantitative digitisation of CERN 2 m bubble-chamber film (§2.13)—together with operational details of cold storage, REANA, and policy implementation. These examples, plus explicit discussion of sustainability limits (BaBar LTDA hardware, LEP service dependencies), make the report a credible snapshot of the field’s progress and remaining gaps. Strengths include public DOIs/portals, named software stacks, and honest treatment of resource constraints.

major comments (2)
  1. §4.4 refers to “Figure TODO” for Rivet+LEP citation trends, while the actual multi-panel figure appears later as Figure 35. This is an incomplete cross-reference in a load-bearing illustration of long-term analysis reuse; the manuscript should replace “Figure TODO” with the correct figure number and ensure the caption and text agree before publication.
  2. §4.4 also contains an empty citation (“electron-positron alliance []”) and asserts complementarity between Rivet-style analysis preservation and full LEP/B-factory software preservation without a concrete reference. For a report whose claim of enduring scientific value rests partly on analysis-level reuse, incomplete bibliographic anchors should be filled or the claim rephrased to what is already documented elsewhere in the text.
minor comments (10)
  1. Throughout (title page, §2.6, §3.4, conclusions): the experiment name is rendered as “BABA R” / “BABA R’s” with a space; standardise to BaBar (or BABAR) consistently.
  2. Abstract and executive summary: “transferrable” → “transferable”; §2.10 “fundementally” → “fundamentally”; §2.1 “early 30th of the century” → “early 2030s” (or similar).
  3. §2.9 author line: “Zach Marschall” appears inconsistent with the front-matter “Zach Marshall”; align spelling.
  4. §2.4: “EOCS-CZ” is likely “EOSC-CZ”; confirm and correct. Also clarify that the Invenio prototype DOI/link is a test instance if that remains the case at publication.
  5. §3.1 / Fig. 18: “Y AML” and “key4HEP” capitalisation/spacing are inconsistent with “Key4hep” used elsewhere; standardise product names.
  6. §3.4 and §3.7 note that portions of the text were generated with Claude. For a formal journal version, either move this to a brief methods/acknowledgements note or ensure the journal’s AI-disclosure policy is met uniformly across sections.
  7. Table 2 (§4.2): “Expected (end of 2025)” vs manuscript date July 2026—clarify whether the baseline is end-2025 planning or update the column header to the reporting epoch used in the workshop.
  8. §2.13.3: “candiadate” → “candidate”; a few other local typos (e.g. “ditributions” in §4.1) should be caught in a full proofread.
  9. Figure 2 caption and related text: “3rd party publications” would read more cleanly as “third-party publications”; ensure the time window in the figure matches the prose (“since 2016”).
  10. Conclusions: the patrimonial-risk discussion for BaBar is important; a short cross-reference back to §2.6 would help readers who only skim the end matter.

Circularity Check

0 steps flagged

No circularity: multi-experiment status report with independent, publicly checkable demonstrations; no derivation that reduces to fitted inputs or self-referential definitions.

full rationale

This is a workshop summary (DPHEP-2026-01) aggregating independent contributions from LEP, BaBar, H1, MINERvA, ATLAS, LHCb, CMS, ALICE, bubble-chamber digitisation, and transverse projects (REANA, CERN Preserve, k4GeneratorsConfig, DORA, EOSC EDEN, etc.). Its central descriptive claims—“Impressive progress in HEP data preservation is observed” and that “Legacy data revival was showcased through successful reanalysis”—rest on concrete, externally verifiable examples (ALEPH modern b-tagging submitted to JHEP, OPAL radiative-neutrino reanalysis, DELPHI EDM4HEP conversion, CERN 2 m HBC film digitisation, open-data releases with DOIs on the CERN Open Data Portal, etc.). There is no mathematical derivation, no fitted parameter re-labelled as a prediction, no uniqueness theorem imported from the authors, and no ansatz smuggled via self-citation. Self-citations are to prior DPHEP reports, experiment policies, and public portals that are independently checkable. The sustainability risks (BaBar LTDA hardware aging, LEP CERN-service dependencies) are stated explicitly by the authors themselves and do not form a circular load-bearing step. Score 0 is therefore the correct, proportionate finding.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

As a status report the paper introduces essentially no free parameters or invented entities. It rests on standard HEP domain assumptions (value of long-term data reuse, FAIR principles, OAIS model) already established in prior DPHEP literature and CERN policies.

axioms (2)
  • domain assumption Long-term scientific value of preserved HEP datasets justifies continued investment in software, storage and documentation.
    Stated throughout the executive summary and conclusions; underpins every experiment section.
  • domain assumption FAIR principles and OAIS reference model are appropriate standards for HEP data preservation.
    Invoked in transverse projects (EOSC EDEN, CERN Preserve Platform) and ICFA recommendations section.

pith-pipeline@v1.1.0-grok45 · 47487 in / 1840 out tokens · 24039 ms · 2026-07-10T21:33:36.160424+00:00 · methodology

0 comments
read the original abstract

This document summarizes the contributions to the 5th DPHEP workshop March 5-6, 2026, CERN, and reflects the advancements since 2024, as well as future milestones and tendencies. Impressive progress in HEP data preservation is observed. Legacy data revival was showcased through successful reanalysis of archived data using contemporary methods, demonstrating the long-term scientific value of preservation. Sustainability challenges were noted, emphasizing the need for long-term funding and institutional support to maintain data preservation infrastructure, particularly for legacy experiments transitioning to archival modes. Innovative transverse projects display constant progress towards common technologies for a robust and transferrable DP. In particular, there is a clear shift toward automation, with increasing use of AI and machine learning for data curation, metadata extraction, and workflow optimization. Open science momentum is growing, with wider adoption of FAIR principles and open data policies, and experiments committing to public releases.

Figures

Figures reproduced from arXiv: 2607.06775 by Alan Price, Andrew Chisholm, Andrii Verbytskyi, Andy Buckley, Apranik Fatehi, Cameron Duncan McClymont, Chris Burr, Clemens Lange, Cristinel Diaconu, Daniel Britzger, David Dobrigkeit Chinellato, David Horvat, Diana Rand, Dietrich Liko, Dillon S. Fitzgerald, Dirk Zerwas, Emily Rensch, Fazhi Qi, Gang Chen, Giovanni Guerrieri, Graham Wilson, Hao Hu, Hossein Rashidi, Jamie Boyd, Jean-Yves Le Meur, Jerome Lauret, Jiri Chudoba, Jose Benito Gonzalez Lopez, Kati Lassila-Perini, Luka Lambrecht, Marcus Ebert, Martin Habedank, Matthew Bellis, Michael Davis, Micha Moskovic, Micheal Buchar, Pablo Saiz, Panna Liptak, Piet Nogga, Richard William Gran, Ryunosuke O'Neil, Stefano Piano, Thomas McCauley, Tibor \v{S}imko, Tomasz Procter, Ulrich Schwickerath, Wesley Middelbos, Zach Marshall, Zhengde Zhang.

Figure 1
Figure 1. Figure 1: Participation at the DPHEP workshops (left) and countries of participants at the 5th workshop [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: 3rd party publications mentioning LEP data since 2016. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: First W+W− event recorded at LEP. Original figure (left), recovered, reprocessed and re￾displayed in high resolution (right). 2.1.5 Risks DELPHI and OPAL data run on RHEL10 and compatible clones of this Linux flavor. This ensures continued access until the early 30th of the century. Nevertheless, the ever changing IT infrastructure continues to bear some risks which have to be detected and addressed in tim… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison between data and simulation for the dijet invariant mass (left) and the relative [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance parameters of the data repository prototype. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison between data (blue markers) and simulation (black histogram) for the number of [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Overview of the BABAR LTDA system; on top a general overview is shown, on the bottom the XRootD data access infrastructure is shown. While the LTDA system is in good use, the underlying hardware is old and starts to fail. Except for one new NFS server, all hardware is between 10 and 20 years old. To take the age and probability of failure into account, raid systems on the machines are used as well as redun… view at source ↗
Figure 8
Figure 8. Figure 8: Usage of the new BABAR LTDA system since mid 2022; on the left the total CPUh used over time is shown and on the right the total number of jobs that run on the system. • Data availability, • Documentation, • Software, and • the analysis environment in which the software can run. The CERN Open Data portal already provides a system that gives open access to experiment’s data together with some documentation … view at source ↗
Figure 9
Figure 9. Figure 9: Geolocation of visitors to the ATLAS Open Data website between June 2, 2025 and January [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Popularity of the ATLAS Open Data releases based on monitoring from the [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Number of samples per year produced with the Analysis Productions batch processing system, [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Schematic diagram of the LHCb Ntupling Service architecture. [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Timeline of CMS Open Data releases Analysis of the complex open data from an LHC experiment is not a trivial task, often requiring extensive physics, data analysis, and software knowledge. To help provide this knowledge CMS created the CMS Open Data Guide [47]. The main goal of this guide is to facilitate the usage of CMS open/legacy data. The sections guide one through the main topics necessary to learn … view at source ↗
Figure 14
Figure 14. Figure 14: Research publications citing CMS Open Data DOIs over time [ [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: The film digitisation system, showing the key components of the digital camera (red device [PITH_FULL_IMAGE:figures/full_fig_p026_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: An example of a digitised film frame from the T209 experiment (8 [PITH_FULL_IMAGE:figures/full_fig_p027_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Invariant mass distributions of K 0 s → π +π − candidates reconstructed from digitised film images (left) and simulated film images (right) of the T209 experiment at the CERN 2m HBC. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Workflow of the k4GeneratorsConfig [58] framework. be preserved for the long term. Simply expanding disk capacity is not a scalable solution given the pace at which new data releases are being made. To address this, the CERN Open Data team developed and deployed a cold storage system that leverages tape archives to preserve massive volumes of data while freeing up primary disk resources. A central design … view at source ↗
Figure 19
Figure 19. Figure 19: Cold storage infrastructure The cold storage infrastructure relies on three pillars: • EOS continues to serve as the primary hot storage for online data, as it did before the introduction of the cold storage layer. [68] • The CERN Tape Archive (CTA) serves as the cold storage layer, providing cost-effective long-term preservation on tape media. [69] • The File Transfer Service (FTS) manages the movement o… view at source ↗
Figure 20
Figure 20. Figure 20: Files volume transferred and number of transfers in April 2025 [ [PITH_FULL_IMAGE:figures/full_fig_p032_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Records availability [71] management frameworks (like Rucio [73]), to stage the data directly. However, this would represent not only a technical change but also a governance shift, as it would move the long-term responsibility for data preservation and access to the experiments themselves. This transition will require extensive discussion with the relevant collaborations. The deployment of a cold storage… view at source ↗
Figure 22
Figure 22. Figure 22: EOSC EDEN - Consortium 33 [PITH_FULL_IMAGE:figures/full_fig_p033_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: EOSC EDEN - Core Preservation Processes Building on top of the CPP framework and the discipline specific requirements [77], EOSC EDEN is specifying and developing shared digital preservation and automated curation services that can be reused across research domains. Aside from the services being aligned with the CPPs and discipline specific requirements, also applicable principles such as FAIR, TRUST and … view at source ↗
Figure 24
Figure 24. Figure 24: Plot from an intermediate stage in the BABAR search for B + → pΛ 0 , showing two kinematic variables used for signal and background discrimination. Monte Carlo is shown here. The plot is made with matplotlib [83], a modern python visualization library that was not widely used during BABAR ’s data taking, but which is taught to many undergraduate students. Several lessons emerge that are relevant to experi… view at source ↗
Figure 25
Figure 25. Figure 25: Information Package structure The major steps of the Preserve pipelines are covering the following actions: • Harvesting from the source repository • Creation of standardized Submission Information Packages (SIP) • Validation of the SIPs • Creation of AIPs • Notifying the source about the successful preservation • Push to CERN Tape Archives [69] (cold storage) • Registry update [PITH_FULL_IMAGE:figures/f… view at source ↗
Figure 26
Figure 26. Figure 26: CERN Preserve Platform Architecture The transformation of SIP into AIP includes the activation of multiple micro-services. The current pipelines are delegating this task to the Archivematica open-source software, mostly running key com￾ponents like: • file identification • virus scanning 37 [PITH_FULL_IMAGE:figures/full_fig_p037_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: REANA web interface with a running Dask-based analysis example. The Dask scheduler and [PITH_FULL_IMAGE:figures/full_fig_p039_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: An example of a REANA workflow specification using Dask resource hints to specify that [PITH_FULL_IMAGE:figures/full_fig_p039_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Results from text analysis of 15 journal articles using Claude and the Anthropic python API. [PITH_FULL_IMAGE:figures/full_fig_p041_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: The cognitive architecture of DORA—a continuous system loop comprising Sensor, LLM [PITH_FULL_IMAGE:figures/full_fig_p042_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: Data scale achieved by DORA at IHEP facilities—from 2.1 TB of manually curated data to [PITH_FULL_IMAGE:figures/full_fig_p043_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: RHIC preservation requires a holistic approach addressing four complementary challenges: [PITH_FULL_IMAGE:figures/full_fig_p047_32.png] view at source ↗
Figure 33
Figure 33. Figure 33: RHIC DAPP preservation infrastructure stack. Five integrated layers enable functional preser [PITH_FULL_IMAGE:figures/full_fig_p048_33.png] view at source ↗
Figure 34
Figure 34. Figure 34: SciBot reasoning pipeline: Documents are embedded and indexed in vector database (Chro [PITH_FULL_IMAGE:figures/full_fig_p048_34.png] view at source ↗
Figure 35
Figure 35. Figure 35: Papers on the Inspire database citing both Rivet and a LEP analysis with a public Rivet [PITH_FULL_IMAGE:figures/full_fig_p050_35.png] view at source ↗

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