# Data Preservation in High Energy Physics: A more than ten years perspective

Cristinel Diaconu, Gerardo Ganis, Ulrich Schwickerath

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Cristinel Diaconu, Gerardo Ganis, Ulrich Schwickerath. Data Preservation in High Energy Physics: A more than ten years perspective. 27th International Conference on Computing in High Energy and Nuclear Physics, Oct 2024, Krakow, Poland. pp.01033, 10.1051/epjconf/202533701033. hal-05306486

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# Data Preservation in High Energy Physics: a more than ten years perspective

Cristinel Diaconu<fnref n="1" />,<fnref n="3" />,*, Gerardo Ganis<fnref n="2" />,<fnref n="3" />,**, and Ulrich Schwickerath<fnref n="2" />,<fnref n="3" />,***

¹CPPM, Aix-Marseille Université, CNRS/IN2P3

²CERN

³on behalf of the DPHEP Collaboration

**Abstract.**

Data Preservation (DP) is a mandatory specification for any present and future experimental facility and it is a cost-effective way of doing fundamental research by exploiting unique data sets in the light of the ever increasing theoretical understanding. When the technical and organisational aspects are adequately taken into account, DP leads to a significant increase in the scientific output (10% typically) for a minimal investment overhead (0.1%). DP relies on and stimulates cutting-edge technology developments and is strongly linked to Open Science and FAIR data paradigms.

## 1 Introduction

In 2008, an inter-experimental study group on HEP data preservation was convened; the issue became prominent with the end of large collider experiments at the eve of the LHC start. The study group produced an initial report [1] that defined the concepts of a transverse and coordinated initiative in HEP and further developed the concepts in a "blueprint" document [2] where the first dedicated data preservation projects were explored and the collaborative framework consolidated. In 2013, the group evolved into the DPHEP collaboration<fnref n="1" />, initiated by about ten national funding agencies and CERN. DPHEP, in collaboration with ICFA<fnref n="2" />, aims to serve as a central forum for discussion, consensus, and knowledge transfer in HEP data preservation and governance. It coordinates R&D efforts to develop shared preservation tools, aligns projects across partners and related fields, and plans a sustainable, cost-effective long-term preservation strategy. Additionally, it advocates for data preservation within the HEP community and key stakeholders. Also, DPHEP acts at present as a sub-panel of the ICFA Panel on the Data Lifecycle<fnref n="3" />.

Recently, the collaboration organized its 4th workshop at CERN [5], where significant progress was reported in various areas of data preservation and open data. The workshop followed the publication of an decennial report [4], containing a more than ten year perspective of data preservation in HEP. The present documents reports on the most recent aspects discussed during the workshop and detailed in the publication.

<footnote>*e-mail: diaconu@cppm.in2p3.fr</footnote>

<footnote>**e-mail: Gerardo.Ganis@cern.ch</footnote>

<footnote>***e-mail: Ulrich.Schwickerath@cern.ch</footnote>

<footnote n="1">¹http://dphep.org</footnote>

<footnote n="2">²International Committee for Future Accelerators https://icfa.hep.net/</footnote>

<footnote n="3">³https://icfa.hep.net/icfa-panel-on-the-data-lifecycle/</footnote>
# 2 Data Preservation in a nutshell

## 2.1 Definitions and context

It is important to note that the term *data* designs not only "files on disk", but comprises every digitally encoded information which has been created as a result of the planning, running and exploiting of an experiment (software, documentation, meta-data, databases etc.). Similarly, *preservation* is not restricted to securely storing data, but comprises all it takes for keeping the data alive, accessible and usable, across the ever changing IT infrastructure and technologies.

It is therefore important to note that the term "Data Preservation" cover all necessary ingredients to ensure extraction of original results from experimental data well beyond the period of data taking. In that sense, multiplying the denominations upfront "preservation" (software preservation, analysis preservation etc.) is pleonastic, since any of those (data in the simplistic meaning, as well as software, analysis etc.) would be only of a limited use as an isolated tentative. DP covers therefore whatever it takes to dig out new results from data in the long term.

While data preservation (DP) and Open Data (OD) are often mentioned together, they do not superpose and should not be confused. Preserved data is not necessarily open nor findable, and an open approach for the whole DP system (in the sense described above), may not be practical nor possible within the limited computing resources usually available for HEP experiments. Thinking for the long term though, opening access to partially unexploited and valuable data is an excellent way to ensure its future usability. Both DP and OP require clear plans and long term organization and funding, within each collaboration and at international level. While multiple successful examples of DP are presented below, the "DP" function of "OD" approach is yet to be proven. With the increasing OD samples and vigorous Open Science policy adopted at CERN and elsewhere, is is likely that OD approach constitute a very promising opportunity to revise, improve and consolidate the DP methodologies for future projects.

## 2.2 HEP Data structure for preservation

Across the scientific data landscape, HEP data acquisition and processing presents several outstanding features: size, complexity, distributed and custom-made computing. It is structured by "events", that are time stamped and offer to HEP data sets "atomic" structure. One of the central computing optimization goals is to collect as much information as possible from that provided by the detector within an "event". To make it meaningful, the information need to be reconstructed and interpreted. Those enhanced "events" are selected, then grouped in statistical ensembles (histograms, likelihoods, etc.), compared to the simulation and theory, and finally parameters and physics measures are extracted. In the end, not only a very small fraction of events are used for a given physics paper, but only a fraction (that is very small also, in general) of each "event" is effectively used for specific analyses during the final analyses steps.

Therefore, the ability to do investigate new phenomena using a (preserved) data set is related to the amount of information that is readily available to perform the analysis. This line of thinking is used to define "the preservation levels", presented in Table 2. The first level essentially corresponds to the publication of results in papers which will then be preserved, while level 3 grants full access to the data, with the possibility to re-make all published analyses in all details. The last level (4) allows in addition to rerun the reconstruction and simulation (for instance to qualify fully new models). It is globally agreed that only level 4 enables the exploitation of the full potential of the data; consequently, level 4 is adopted by a majority of DP projects.
<table><caption>**Table 1.** Different levels of data preservation</caption><thead><tr><td>Level</td><td>Preservation model</td><td>Use case</td><td></td></tr></thead><tbody><tr><td>1</td><td>Provide additional documentation</td><td>Publication related info search</td><td>Documentation</td></tr><tr><td>2</td><td>Preserve the data in a simplified format</td><td>Outreach, simple analyses</td><td>Outreach, reanalysis</td></tr><tr><td>3</td><td>Preserve the analysis level software and data format</td><td>Full scientific analysis, based on the existing reconstruction</td><td>Technical Preservation Projects</td></tr><tr><td>4</td><td>Preserve the reconstruction and simulation software as well as the basic level data</td><td>Retain the full potential of the experimental data</td><td>Technical Preservation Projects</td></tr></tbody></table>

The DP levels are not absolute definitions, but simple guidelines towards an effective long term strategy. They open the way of various technological solution envisaged within the DP projects, that have to take into account all aspects of the data preservation described above.

## 2.3 Organizational forms for a successful DP

One of the most important (though not technical) aspect for a successful DP project is the organization. Indeed, no (DP or other) project can be viable without a human organization that accounts for a continuous supervision and management. It is understood (by definition), that the effort in DP mode is significantly lower than the effort invested in HEP computing overall. Though it cannot be infinitely small, and it has to be well defined and properly identified. From this argument it becomes clear that data preservation is a collaborative effort, to be prepared as part of the experiment design and implemented during the experiment "online" time. In parallel with the DP technical levels defined above, various organisation levels (OL) can be defined (table2)

<table><caption>**Table 2.** The different organisational levels (OL) of the HEP collaborations, relevant for the DP</caption><thead><tr><td>OL</td><td>Timeline</td><td>Target and use case</td></tr></thead><tbody><tr><td>0</td><td>Organisation during experiment proposal</td><td>Proto-collaborations; the DP aspects impact the analysis and computing design of the future experiment</td></tr><tr><td>1</td><td>Organisation during data taking</td><td>Focused on running the experiment, taking and exploiting data towards the most efficient publication process; DP can impact the way analyses are documented and stored; the overall data and software management</td></tr><tr><td>2</td><td>Organisation after the data taking</td><td>Active during analysis-only mode, with no specific difference to OL 1, but needs to account for the progressive decrease in the human resources; specific technical preservation projects should be active during this phase already.</td></tr><tr><td>3</td><td>DP Organisation after the collaboration funding scheme</td><td>Light but well identified organisation, the paper review as well as the technical frameworks should be well defined.</td></tr><tr><td>4</td><td>Rescue organisation</td><td>This organisation scheme is to be activated when:<ul><li>the host laboratory stops support and announce no long-term commitment.</li><li>the official collaboration/data stewardship is stopped with no further plans (no step 3 is clearly defined).</li></ul></td></tr></tbody></table>
It should be clear that if no specific action is taken to organize the DP at technical and organizational levels, leaving the system in an undefined state<fnref n="4" />, the data must be considered lost and can be deleted. Figure 1 gives an overview over the status of participating experi-

<table><thead><tr><th>Laboratory/ Collider</th><th>Experiment</th><th>Data taking period</th><th>DP Level</th><th>Data Volume</th><th>Present status</th><th>OL</th></tr></thead><tbody><tr><td>DESY/PETRA</td><td>JADE</td><td>1979–1986</td><td>4</td><td>1 TB</td><td>Analysis running on preserved data; migrated from DESY to MPP</td><td>4</td></tr><tr><td rowspan="2">CERN/LEP</td><td>ALEPH, L3, DELPHI, OPAL</td><td>1989-2000</td><td>4</td><td>0,5 PB</td><td>Analysis running on preserved data</td><td>4</td></tr><tr><td></td><td></td><td></td><td>0.5 PB</td><td></td><td></td></tr><tr><td>DESY/HERA</td><td>H1, ZEUS</td><td>1992–2007</td><td>3/4</td><td>0.2 PB</td><td>Analysis running on preserved data</td><td>3</td></tr><tr><td>SLAC/PEP II</td><td>BABAR</td><td>1999–2008</td><td>4</td><td>2 PB</td><td>Analysis running on preserved data; migrated from home lab to different centers</td><td>4</td></tr><tr><td>KEK/KEKB</td><td>Belle I</td><td>1999-2010</td><td>4</td><td>4 PB</td><td>Analysis running on preserved data; Compatible with Belle II computing</td><td>2</td></tr><tr><td rowspan="2">FNAL/Tevatron</td><td>DØ</td><td></td><td>4</td><td>8.5 PB</td><td rowspan="2">Archived on tapes</td><td rowspan="2">4</td></tr><tr><td>CDF</td><td>1983–2011</td><td>4</td><td>9 PB</td></tr><tr><td>BNL/RHIC</td><td>PHENIX</td><td>2000–2016</td><td>3</td><td>25 PB</td><td>Analysis running on preserved data</td><td>3</td></tr><tr><td>FNAL/v-beam</td><td>Minerva</td><td>2010–2019</td><td>3</td><td>10 TB</td><td>Analysis running</td><td>2</td></tr><tr><td>IHEP/BEPCII</td><td>BESIII</td><td>2009–2030</td><td>4</td><td>6 PB</td><td>Collecting and analyzing data</td><td>1</td></tr><tr><td>CERN/LHC</td><td>ALICE,CMS ATLAS,LHCb</td><td>2010–2041</td><td>4</td><td>O(1EB)</td><td>Collecting and analyzing data</td><td>1</td></tr></tbody></table>

**Figure 1.** The preservation and organisations levels by experiment in 2024.

It is noticeable that a majority of experiments have chosen the data preservation level at 4. Concerning the form of organization, the panel of participating experiments include "running" experiment (such as LHC or Belle I/II), where the organizational form is **1** as well as "very long term" experiments (LEP, Babar, Tevatron...) where is organizational form is **4**. The latter face a new challenge in DP that is: when/if the DP should stop explicitly and is there a "very long term" DP form possible?

## 2.4 Data preservation costs and benefits

The main justification for a DP project is not sentimental, but scientific. The benefits of DP were predicted in the initial DPHEP document [1]. The long-term continuation and extension of scientific programs maximize the potential of existing data, even with reduced collaboration. These additional analyses, though fewer, are often the most sophisticated and benefit from the full statistical power of the data. Cross-collaboration analyses offer unique opportunities to reduce uncertainties and enable new discoveries. Reusing past data can also lead to significant advancements through new theories or analytical techniques. Additionally, preserving data opens avenues for education, training, and outreach, allowing students worldwide to engage in high-energy physics research and attracting new talent to the field.

After about 15 years, several (now historical) experiments are able to look back and make the inventory costs/benefits of their DP projects. The scientific output is illustrated in figure 2. As expected, the publications generally do not stop after the end of data taking, they rather continue for many years. Recent examples are from Zeus [7][6], Babar[8] and H1[9]. In some cases, a significant fraction of the publication happen after the end of data taking. The case of Babar is striking, since the collaboration has run longer after the end of the data taking than the data taking itself. Moreover, one third of Babar and about 15% of HERA publications have been produced using dedicated DP projects, thereby proving the important role of developing structured and innovative DP initiatives. The overall costs of those projects, dominated by human resources, do not exceed a few per-mille of the overall project costs (in fact a few FTE/years to design and maintain the DP systems, while the hardware related costs

<footnote n="4">⁴The so called "freezer illusion", i.e. the hope that saving the data and some computing environment on an unsupervised media (tapes) is sufficient to find it back up and running at a later stage.</footnote>
<figure><img src="image_2.png" /><figcaption>**Figure 2.** Examples of experiments scientific output. The period after the data taking is indicated with a blue background and the periods where the collaborations relied on computing projects newly designed for DP is shown by an arrow (figure updated in [12]).</figcaption></figure>

are mostly negligible and accounted for as part of the laboratories running costs). These estimates confirm the initial 2009 assumptions and point to a high-quality and low-cost science enabled by DP in HEP.

# 3 Highlights from the 4th DPHEP workshop

The 4th DPHEP workshop in autumn 2024 marked significant progress in both data preservation and open data.

## 3.1 Previous generation collider experiments

LEP data is (mostly) back and some progress have been registered in recovering essential functionalities. CERNLIB, a crucial element to access this data, has been resurrected, and various experiments have already switched to the new version. ALEPH reported about progress on the migration of their data to EDM4HEP format [10], making it usable for the future circular collider (FCC\_ee) studies. Both DELPHI and OPAL have their original software stack running on recent operating systems, including their resurrected event displays which were thought to be gone because of a dependency on commercial software. This issue has been resolved by extending and moving to an openly available prototype implementation named OpenPHIGS. This library has been republished in the mean time, and is now freely available. In addition, the DELPHI collaboration has decided to open their data. It is now available via the open data portal at CERN. JADE data can be expected to be published there soon as well.

The HERA experiments ZEUS and H1 both continue to attract collaborators and publish new results, based on preserved experiment data. The ZEUS data remains accessible in the form of ROOT ntuples. This work, completed many years ago, has been remarkably successful, enduring through transitions from SLC5 to the current RHEL9-like operating systems. Notably, 30% of ZEUS papers were published after data collection ended, and past projections about the number of papers have proven to be remarkably accurate. The system's ease of use has enabled even theorists with no ZEUS background to utilize it effectively. The H1 software, on the other hand, is built on a Fortran core with C++ and ROOT serving as
user-facing interfaces. While the transition to RHEL9 is still in progress, the software stack can be seamlessly used within containers. H1 remains active and has already published three papers this year. It is remarkable that both collaborations have actually increased the number of institutes in the past few years, in connection with the use of HERA data for pre-studies of the future EIC physics potential and possible synergies.

JADE experiment was a pioneering actor for the DP in high energy physics [11]. The JADE software stack, revived over nearly a decade, is set to be published on CERN's open data portal. Originally stored on 6,500 tapes, the data includes 3,500 pages of scanned log entries from the data collection period. The software runs in containers using the 32-bit community CERNLIB. A 64-bit version has not been explored due to other priorities.

The BABAR experiment was situated originally at SLAC. The collaboration was one of the first to propose a dedicated and innovative DP system based on virtual machines. The host support ended in early 2021, a decision that shows the fragility of even well defined and successful DP initiatives. Nevertheless, the collaboration remains active and initiate new analyses, with an impressive record of publications and conferences contributions (as indicated above), some of which are at the core of hot topics. The collaboration decided to move its 1.5 PB of data to GridKA in Karlsruhe, with copies at CERN and IN2P3. The software is hosted at UVIC, and an Oracle database at SLAC still needs to be dumped. The hardware hosting the software stack is old and out of warranty, with no funds for replacement. The analysis framework does not compile on 64-bit systems and relies on older software, complicating migration to recent operating systems. Those technical issues hint to the need of continuous support for the long term, and possibly encourages a transition to a centrally supported open data system. This transition would require a dedicated project by itself.

BESIII experiment is actively collecting data and is considering data preservation since the early stages of the experiment. The Collaboration aim for a Level 4 preservation mode, which includes preserving raw data. The plan features an AI-based data ecosystem designed to help users quickly begin data analysis, which constitute an innovative approach, thereby illustrating the technological challenges related to DP.

The PHENIX Collaboration has actively pursued a Data and Analysis Preservation program since 2019, the first such effort at RHIC. The collaboration successfully leveraged the Zenodo platform at CERN for knowledge management and the HEPData portal to preserve numerical data from PHENIX publications. Preserving complex physics analyses is particularly challenging, so the experiment focused on two impact-full PHENIX results: the joint study of direct photons and neutral pions in d+Au collisions, and the study of $J/\Psi$ production via the di-muon decay channel. To ensure reproducibility, the analyses are partitioned into self-contained tasks, using containerization, code management, and robust documentation. REANA is one of the preferred tools for running the required software.

The Belle experiment, which finished data collection in 2010, passed its data to the Belle II experiment. Belle II is currently accumulating data, and Belle data will be used exclusively by Belle II collaborators until Belle II's data exceeds that of Belle. Belle II has developed tools to handle Belle data within its software framework and continues to produce physics results based on Belle data. However, some software libraries used by Belle are no longer compatible with new operating systems and computing environments. The collaborations points out the challenges and opportunities for the use of Belle I data together with Belle II data, and for the longer term potential of the b-factories.

## 3.2 Transverse projects for open and preserved data

The CERN Open Data Portal has seen a fourfold increase in records, with new experiments like JADE and DELPHI expected to hit production soon. Accessible at https://opendata.cern, the portal undergoes about seven updates annually. Efforts are underway to support cold
data storage, where large datasets are partially kept on disk and the rest on tape, recalled
only upon request. Challenges include notifying users when requested data is available. The
portal follows CERN IT best practices and utilizes services like the monitoring system and
OpenSearch.

REANA is an open-source platform for reproducible containerized data analyses, avail-
able as a service at CERN and other institutions. It features command-line and web interfaces
for managing analysis workflows. A new feature allows users to share workflows with col-
leagues. REANA integrates with GitLab to promote reproducible workflow practices from
the start. A notable use case is "continuous reuse," enabling data curators to periodically
execute data usage examples to ensure future re-usability.

The CERN Analysis Preservation (CAP) platform manages and safeguards research outputs by capturing knowledge, data, code, and documentation. It offers advanced search functionality and new tools for preservation administrators, enhancing collaboration within the CERN community. Access currently requires a CERN account.

PUNCH4NFDI is a German DFG-funded initiative promoting FAIR and open data man-
agement and tool development for particle physics, astrophysics, astro-particle physics, nu-
clear and hadron physics, and lattice QCD. Following the FAIR principles, it aims to enable
cross-community access to data wherever possible. A series of PUNCH use cases are being
released to demonstrate its capabilities. One notable example is a HEP demonstrator using
open data from both CMS and ATLAS, covering LHC Run 1 and Run 2. The workflow, ex-
emplified by an analysis of Higgs-to-four-lepton production, is publicly documented on the
PUNCH4NFDI portal. It runs within a few hours on initially unfiltered open data and MC
samples and can be adapted by users for their own physics cases. While the input files have
somewhat condensed information, this workflow can cover approximately 50% of all poten-
tial LHC physics topics. Following the endorsement of the LHC policy file, the implemen-
tation phase is underway, extending to smaller LHC and non-LHC experiments. Some ex-
periments require tailored policies due to community differences. The working group meets
annually, focusing on level 3 preservation as storing raw data is resource-intensive. Currently,
5.5 PB of data is stored in the portal, with resources secured until 2025. Discussions for the
next period are ongoing.

3.3 LHC Experiments

ALICE has deployed a new software framework featuring the AO2D format, significantly
reducing data size. A dataset initially taking 1.2PB now requires only 52 TB. However, the
total size of ALICE data, driven by increasing trigger rates, will require several PB per year
by 2030, necessitating a rethink of storage strategies.

ATLAS released its first Open Data for Research in July, including 2015 and 2016 proton-proton collision datasets and accompanying simulations, totaling 65 TB. Documentation is ongoing to address systematic uncertainties and the limitations of the PHYSLITE format. ATLAS is considering an Open Data workshop in 2025 and discussing the long-term preservation of Run 1-3 data.

LHCb uses GitLab continuous integration for analysis preservation. While Run 1 open data has been published, this model is unsustainable due to storage requirements. LHCb is developing an Ntuple Wizard to create ntuples using standard experiment resources, in collaboration with the CERN IT Open Data team.

CMS has pioneered open data, trackable via DOIs. Their nanoAOD format, based on flat
ROOT trees, does not require CMS-specific software. Open data is also used for educational
purposes, with examples available on the portal. CMS is interested in using cold storage for a
fraction of their open data storage needs and organizes annual workshops on open data usage.
## 3.4 Astroparticles example

ANTARES, a neutrino experiment in the Mediterranean Sea, took data from 2000 to 2022 and has been succeeded by KM3NET. ANTARES plans to hand over its 600 TB of data to KM3NET, with legacy software preservation being a key concern. Long-term funding remains an issue.

## 3.5 Data Lifecycle

A presentation by the ICFA panel highlighted that preservation requires long-term resources. The panel aims to bridge past and future practices, emphasizing the importance of both in-reach and outreach to raise awareness within collaborations. The goal is to recommend future directions based on concrete experiences, following a bottom-up approach. A survey is available on the conference page for contributions.

# 4 Conclusions

Dedicated data preservation projects have a significant and measurable impact, producing high-quality scientific results at a low cost. Moreover, DP projects have a significant potential to improve the running experiments and enhance their ability to share the HEP data through Open Data and Open Science initiatives. Furthermore, it is observed that DP projects, consolidated on both technical and organizational aspects, are able to initiate new collaborations and build bridges towards future experiments via novel initiatives: HERA data can be used to prepare the EIC, or LEP data as a testbed for FCC\_ee. The case for long-term data preservation is therefore very strong. However, the maintenance of older data sets from the previous experiments like CDF, D0, BaBar, LEP, and Jade need permanent attention and dedicated resources in order to remain usable. The DPHEP collaboration will continue to encourage the cross collaboration, inter- and intra-communities exchanges, innovative technical solutions and consolidated and sustainable organizational frameworks that preserved data for future scientific results [12]. These efforts also extend to education, training, and outreach, leveraging the availability of open data to attract and train new talent in the field. Data preservation is therefore crucial for enabling new interpretations and discoveries through advanced techniques, ensuring transparency and third-party validation, expanding research access and education, integrating data across present and next experiments, and safeguarding unique, irreplaceable datasets as treasures for future generations.

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