Cosmic-Ray Database Update: Ultra-High Energy, Ultra-Heavy, and Antinuclei Cosmic-Ray Data (CRDB v4.0)

Abstract

We present an update on CRDB, the cosmic-ray database for charged species. CRDB is based on MySQL, queried and sorted by jquery and table-sorter libraries, and displayed via PHP web pages through the AJAX protocol. We review the modifications made on the structure and outputs of the database since the first release (Maurin et al., 2014). For this update, the most important feature is the inclusion of ultra-heavy nuclei ($Z>30$), ultra-high energy nuclei (from ${10}^{15}$ to ${10}^{20}$ eV), and limits on antinuclei fluxes ($Z\le -1$ for $A>1$); more than 100 experiments, 350 publications, and 40,000 data points are now available in CRDB. We also revisited and simplified how users can retrieve data and submit new ones. For questions and requests, please contact [email protected].

1. Introduction

Cosmic-ray (CR) physics has been established more than a century ago and progress in the measurements has been performed through this entire period. Besides preserving the measurements for their historical value, the motivations for a CR database are numerous.

Firstly, GeV CRs vary with the Solar cycle, and past data are still of interest—for an illustration of the usage of CR data going back to the 1950s, see Ghelfi et al. [1]. Secondly, it is infeasible to design and build CR experiments that measure all species at all energies, so that multi-species studies over different energy ranges have to rely on many datasets from as many experiments—for an illustration of the usage of data samples of H to Ni elements over 45 years, see Shen et al. [2]. Thirdly, new synergies with other fields of astrophysics make the collection of even the rarest CR data desired. For instance, ultra-heavy elements (UHCRs, $Z>30$) have very small fluxes and are difficult to measure. They require dedicated experiments and so far the data are very sparse (e.g., Donnelly et al. [3], Binns et al. [4]). UHCRs have not been a very active topic in the last decade, but the situation is likely to change: the first detection of gravitational waves from a binary neutron star inspiral [5]—and in particular optical follow-ups—indicated that neutron star mergers could be a major contributor to r-process nucleosynthesis for UHCRs [6,7]. Fourthly, in the last two decades, a growing number of high-precision experiments were developed to measure leptons, antimatter, and $Z<30$ nuclei in tens of MeV to hundreds of TeV range (AMS-02, PAMELA, TRACER, etc.). These balloon-borne or space-based experiments allow us to address many astrophysics questions (e.g., [8]) including searches for dark matter (e.g., [9]). In the context of these searches, upper limits on antinuclei were improved [10] and efforts are growing towards the first detection of CR antideuterons [11]. A few puzzling 40 GeV events compatible with ${}^3\overline{\mathrm{He}}$ were reported by the AMS-02 collaboration [12]. Adding previously reported upper limits on antinuclei (on various energy ranges) in a database is thus also timely.

A central motivation for a comprehensive CR database is the energy coverage. CR experiments based on direct detection have recently shown a spectral feature in several species at a few hundreds of GV [13,14,15]. Other spectral features have been observed at higher energies: the knee (at a few PeV), the ankle (around 5 EeV), and the toe (flux suppression above 100 EeV); a fainter second knee around 200 PeV was also observed more recently [16]. These features were observed with ground-based indirect CR experiments (KASCADE-Grande, Pierre Auger Observatory, Telescope Array, IceCube, TUNKA, and many more), which do not resolve CR species individually. Weak spectral features can be confirmed and enhanced by combining data from multiple experiments. Gathering data that cover the full CR spectrum can help to shed light on the Galactic to extragalactic transition (e.g., Globus et al. [17], Thoudam et al. [18]) and on the possible sources for the most energetic CR events ever observed (e.g., [19]).

A central shared database offers multiple advantages for research. It enables studies, which would otherwise be too time-consuming or error-prone, by assuring data quality, completeness, and traceability. In principle, it can also allow for a pooling of human resources (from the CR community) to fill and maintain it. Electronic resources to gather and disseminate data became available in the 1990s, but CR databases appeared much later. For galactic CR (GCR) data, the momentum was provided by teams involved in the development of GCR propagation codes. The GALPROP1 team started to distribute CR data as a simple ASCII file [20]. As part of the development of the USINE2 propagation code [21], our team independently gathered these data and set up the first CR database, called CRDB (Maurin et al. [22]); our database comes along with a contextualised presentation of the data and experiments, ASCII file exports (compliant to GALPROP and USINE formats), and online plot generation. Shortly after, the Italian Space Agency (ASI) developed a CR database to host originally ASI-supported experimental data [23]. The design and functionality of the CRDB-ASI3 was strongly influenced by the CRDB, following discussions between our teams. Since then, the CRDB and CRDB-ASI have been independently developed further. The CRDB-ASI is not as comprehensive as the current version of the CRDB, but it offers specialised datasets (trapped events and Solar Energetic Events) absent in our database. For ultra high-energy CRs (UHECRs, above a PeV), the first initiative to build a common database came from the KCDC project4 [24]. The primary goal of KCDC is to offer researchers open access to raw data of the KASCADE and KASCADE-Grande experiments. It also contains a collection of high-energy CR flux measurements from more than 20 experiments published between 1984 and 2020 (contrarily to CRDB, an account is required to access KCDC data).

This paper discusses CRDB v4.0, a major upgrade of the database in terms of its data content, user interfaces, and data submission forms. The paper is organised as follows: Section 2 recalls important definitions and the database structure; Section 3 highlights the specifics of the data added in this version; Section 4 briefly goes over the web interfaces and outputs, focusing on the new and simpler data submission form and the updated REST and Solar modulation time series interfaces. We conclude and discuss possible CRDB extensions in Section 5. We gather in Appendix A useful tips and caveats (on extracted data) for CRDB users, and we provide in Appendix B sorted lists of all experiments and publications in CRDB v4.0.

2. Database Structure and Updates

Throughout the paper, we separate the data in two broad categories, data and meta-data (data about data). Clearly separating these two categories is important to define the format to submit new data (see Section 3). The categories comprise the following items:

• Data: CR data points, including energy range and data uncertainties;
• Meta-data: CR data-related informations that include data taking periods, experiments that provide the data, publication from which the data were taken, etc.

The first CRDB publication [22] focused on the rationale behind the design choices made for the database structure and organisation (in MySQL). In this update, we follow a complementary approach. We start at the data level, moving up to the experiments and then publications, underlining some salient aspects of the data themselves and how they are tied to the other meta-data tables. Along the way, we highlight the changes made between CRDB v2.1 [22] and CRDB v4.0.

The database structure is shown in Figure 1. By design, each entry has a unique ID in all tables, so that all tables can be accessed by other tables; this is particularly useful to build bridge tables (i.e., building associations between table entries). The different tables, their contents, and the associations between data and meta-data tables are further detailed in the following subsections.

2.1. DATA Table

A data point corresponds to a quantity measurement (with uncertainties) at a given energy. We list below the keys required for a full data entry description in the DATA table (see Figure 1). The only change made since CRDB v2.1 is enabling upper limits data (new key IS_UPPER_LIMIT).

• ID: unique identifier for a data.
• NUM and DEN: a data quantity is a single item (NUM) or a ratio of two items (NUM and DEN). The former usually corresponds to a measured flux (in unit of $E_{\mathrm{unit}}^{-1}{\mathrm{m}}^{-2}{\mathrm{s}}^{-1}{\mathrm{sr}}^{-1}$, with $E_{\mathrm{unit}}$ in GeV, GeV/n, or GV in CRDB) and the latter to a ratio of fluxes (no unit). Depending on the detector charge and isotopic resolution, the measured quantities are isotopes (e.g., ${}^{12}$C), elements (e.g., C), or even groups of elements; the latter case mostly applies for UHCR and UHECR data (see next sections). Indirect detection CR experiments also provide shower-related quantities (with their specific units) that we plan to add in the future. To allow for these various possibilities, we define a CR_QUANTITY table which contains a list of names including CR isotope names, element names, and names for relevant groups of elements or other significant measurable quantities (e.g., AllParticles, see

  Table A2. List of new CRDB names for CR_QUANTITY (see Section 2.1), along with their proxy (i.e., isotope) used to perform approximate energy axis conversions (n/a indicates that no energy conversion at all is enabled).

  CRDB Name	Description	Proxy for E Conversion
  For UHCR data (Section 3.2)
  HS-group	Heavy secondary ($70\le Z\le 73$)	n/a
  LS-group	Light secondary ($62\le Z\le 69$)	n/a
  Pt-group	Platinum group of element ($74\le Z\le 80$)	n/a
  Pb-group	Lead group of elements ($81\le Z\le 87$)	n/a
  Subactinides	Pt+Pb groups ($74\le Z\le 87$)	n/a
  Actinides	All the heaviest CRs ($Z\ge 88$)	n/a
  Zgeq70	CRs heavier than charge 70 ($Z\ge 70$)	n/a
  For upper limits on antinuclei (Section 3.3)
  Zgeq1	Flux of all CR nuclei $Z\ge 1$	${}^1\mathrm{H}$
  Zgeq1-bar	Flux of all CR antinuclei $Z\le -1$	$\overline{{}^1\mathrm{H}}$
  Zgeq2	Flux of all CR nuclei $Z\ge 2$	${}^4\mathrm{He}$
  Zgeq2-bar	Flux of all CR antinuclei $Z\le -2$	$\overline{{}^4\mathrm{He}}$
  H-BAR	Antihydrogen flux ($\overline{\mathrm{H}}$)	$\overline{{}^1\mathrm{H}}$
  1H-BAR	Antiproton flux ($\overline{p}$)	n/a
  2H-BAR	Antideuteron flux ($\overline{{}^2\mathrm{H}}$)	n/a
  He-BAR	Antihelium flux ($\overline{\mathrm{He}}$)	$\overline{{}^4\mathrm{He}}$
  …	(down to isotopes and elements $Z=-8$)
  For UHECR data (Section 3.4)
  H-He-group	Flux inferred from mixture of H and He in air-shower simulations	n/a
  C-Fe-group	Flux inferred from mixture of C to Fe in air-shower simulations	n/a
  N-group	Flux inferred from N in air-shower simulations	${}^{14}$N
  O-group	Flux inferred from O in air-shower simulations	${}^{16}$O
  Al-group	Flux inferred from Al in air-shower simulations	${}^{27}$Al
  Si-group	Flux inferred from Si in air-shower simulations	${}^{28}$Si
  Fe-group	Flux inferred from Fe in air-shower simulations	${}^{56}$Fe
  O-Fe-group	Flux inferred from a mixture of O to Fe in air-shower simulations	n/a
  AllParticles	Flux of all particles (sum over all isotopes and species)	n/a

  for new names in CRDB v4.0).
• E-AXIS: the database energy types are ETOT, EK, R, EKN, and ETOTN. In CRDB, they correspond to the total $\left(E_{\mathrm{tot}}\right)$ and kinetic energy $(E_{\mathrm{k}}=E_{\mathrm{tot}}-m)$ set in GeV, rigidity $(\mathcal{R}=p/(Ze\left)\right)$ in GV, kinetic energy per nucleon $(E_{\mathrm{k}/\mathrm{n}}=E_k/A)$ in GeV/n, and total energy per nucleon $(E_{\mathrm{tot}/\mathrm{n}}=E_{\mathrm{tot}}/A)$ in GeV/A. Depending on the detector principle (calorimeter, spectrometer, etc.), different energy types are measured in experiments and provided in publications, and in CRDB, the data are always stored as published (see Appendix A.4 for conversion).
• E_MEAN, E_BIN_L, and E_BIN_U: fluxes are measured by counting events in energy bins. In CRDB, we include the energy interval range $[E_{\mathrm{lo}},E_{\mathrm{up}}]$ when it is published. The measured flux obtained from a finite bin size neither exactly corresponds to the differential flux at the bin centre or at the mean energy $\langle E\rangle$, unless a correction is applied [25]; when comparing a model to the data, it is thus recommended to integrate the flux model over the bin width instead of using the value at the mean energy [26]. If only a single value $\langle E\rangle$ is provided in the publication, we set $E_{\mathrm{lo}}=E_{\mathrm{hi}}=\langle E\rangle$ in CRDB; conversely, if only the bin range is provided, we set $\langle E\rangle =\sqrt{E_{\mathrm{lo}}{E}_{\mathrm{hi}}}$.
• VALUE, VALUE_ERRSYST_L,…, IS_UPPER_LIMIT: a measurement has a value and statistical and systematic uncertainties. In CRDB, we store the value and the possibly asymmetric statistical and systematic uncertainties—see Appendix A.2 for subtleties and caveats concerning the gathering of uncertainties from publications. In CRDB v4.0, upper limits with the flag IS_UPPER_LIMIT set to 1.
• SUBEXP_PUBLI_ID: a datum is always attached to a unique publication and a sub-experiment (see next sections). This key enables to bridge the data ID (this table) to the subexp-publi ID (SUBEXP_PUBLI table, see Section 2.2.2).

2.2. EXP and SUBEXP Tables and Meta-Data

CR data are collections of data points at different energies, from an experiment measuring one or several quantities. In CRDB, for flexibility, we make the distinction between an experiment and sub-experiment (hereafter sub-exp for short). The experiment is defined by its name and starting date. The sub-exp is linked to the experiment, but is more closely related to the data in order to uniquely tag the dataset released by this experiment. The tag is here to inform us on (i) analyses from different data taking periods, (ii) data from the same period but from different analysis techniques or different sub-detectors, (iii) data relying on third-party model assumptions (e.g., using different Monte Carlo generators for air showers in UHECRs).

Experiment and sub-experiment data are meta-data (attached to CR data) stored in the EXP and SUBEXP tables respectively (see Figure 1); they are briefly described below.

2.2.1. EXP Table

A single change has been made for CRDB v4.0, related to the experience type (key TYPE, see below).

• ID: unique identifier for an experiment.
• EXPNAME: unique name associated with the main instrument (AMS-01, KASCADE, etc.). Most of the data before the 1990s come from balloons, and to ensure a unique identifier CRDB uses the syntax: Balloon (YYYY) for a single flight, Balloon (1966,1967,…) for balloons flown several times, and Balloon (1967+1968+…) for experiments that report only the combined analysis of several flights.
• TYPE: in CRDB v4.0, we allow for three experiment types (balloon, ground, or space) that represent three broad categories of measurement conditions with different caveats and systematic uncertainties. The experiments in the ‘Experiments/Data’ tab (see Section 4.1.2) on the CRDB website can be sorted according to date, name, and this type.
• DATE and HTML: we also keep track of the starting year of the experiment and a link to the official experiment website is provided (if it exists).

2.2.2. SUBEXP Table

The SUBEXP table contains detailed information on possible sub-detector(s) from which the data were obtained; this includes the data taking conditions and, in some cases, the analysis technique. In CRDB v4.0, a new key was added to track the energy-scale uncertainty (i.e., a calibration uncertainty on the energy measurement), which is especially important for ground-based experiments (≳10 %). We list all keys in the SUBEXP table (see Figure 1) and then comment on the effect of Solar modulation on multi-GeV GCR data [27]. In particular, we stress how the last three keys listed below are necessary inputs for Solar modulation calculations.

• ID: unique identifier for a sub-experiment.
• EXP_ID: ID of the experiment in the EXP table to which the sub-exp is attached.
• NAME: concatenation of the experiment (or sub-experiment) name with the sub-detector/specific detection technique used (PAMELA-CALO…), or with the air shower MC generator used (IceTop SIBYLL-2.1…), and always with the data taking period (e.g., IMP7 (1973/05-1973/08)).
• DESCRIPTION: rough description of the detection techniques and salient features of the detector (in the sub-experiment). If available from the collaboration, an image of the detector is displayed in the user interface (see Section 4).
• INFO: additional information relevant for the sub-exp. This can be the location of the balloon flight (e.g., one flight McMurdo, Antarctica), some details about the reconstructed data (e.g., Fractions of H,He,O,Fe-like showers in 4-component fit to $X_{max}$ data), indication of times series (e.g., Monthly average 2007/03), etc.
• ESCALE_RELERR: if provided in the publication, CRDB v4.0 stores the relative energy-scale uncertainty, ${(\Delta E/E)}_{\mathrm{scale}}$, which applies to all energies measured by the sub-experiment5. We refer the reader to Appendix A.3 for more details on the origin of this uncertainty.
• DATES: list of data taking periods for the instrument taken from the publication or, if not reported in old balloon flights, retrieved from the StratoCat database6.
• DISTANCE: most of the experiments are located on Earth, or are orbiting Earth. However, a few satellites (e.g., Ulysses, Voyager, etc.) took data at various locations in the Solar system. We provide in CRDB the average distance to the Sun in a.u. (an astronomical unit is the distance Sun–Earth) during the data taking period, as provided in the publications.
• SMALL_PHI: Accurate Solar modulation models are complex and depend on many parameters. The simplest model, the Force-Field approximation [28,29,30], depends on a single parameter $\varphi \left(t\right)$, and remains useful for many applications despite its limitations [31]. Therefore we provide the mean modulation level $\langle {\varphi }_{\mathrm{FF}}\rangle$ calculated externally from $\varphi$ time series (see below) over the sub-experiment data taking periods.

From interstellar (IS) to top-of-atmosphere (TOA) fluxes

GCR fluxes are Solar modulated, the modulation level depending on the Solar cycle [32] and the position in the Solar cavity. Interstellar (IS) fluxes refer to fluxes outside of the solar cavity while top-of-atmosphere (TOA) fluxes correspond to modulated fluxes at Earth. The impact of the Solar modulation decreases with energy and can only be disregarded when it becomes much smaller than the data precision7. Most balloon and space experiments in CRDB are TOA data from experiments inside the solar cavity. To be compared to them, IS fluxes in GCR propagation models must therefore be Solar modulated. This makes the bookkeeping of the experiment data taking periods and position in the Solar cavity mandatory. These dates and positions can then be fed to dedicated Solar propagation codes—for instance SolarProp8 [33] or HelMod9 [34]. Alternatively, if CRDB users wish to use the simpler Force-Field approximation [31], they can directly retrieve $\langle {\varphi }_{\mathrm{FF}}\rangle$ values provided in CRDB.

Average Force-Field levels $\langle {\varphi }_{\mathrm{FF}}\rangle$ from $\varphi \left(t\right)$ time series

Modulation time series (in the Force-Field approximation) can be obtained from the analysis [1,35,36] of neutron monitor data10. These analyses were pioneered by Usoskin et al. [35] in 2005 and updated in Usoskin et al. [36] in 2017. Following a similar approach, and based on improved propagation of error [37] and interstellar spectra [38], our team independently reconstructed its own $\varphi$ time series in Ghelfi et al. [1], roughly finding

$${\varphi }_{\left[\mathrm{G}\mathrm{h}\mathrm{e}17\right]}\left(t\right)\approx {\varphi }_{\left[\mathrm{U}\mathrm{s}\mathrm{o}17\right]}\left(t\right)\approx {\varphi }_{\left[\mathrm{U}\mathrm{s}\mathrm{o}05\right]}\left(t\right)+100\mathrm{MV}.$$

In CRDB v4.0, we provide these three values in the SMALL_PHI keys (of the SUBEXP table), tagged as [Uso05], [Uso17], and [Ghe17]. We stress that whereas [Uso05] and [Uso17] are based on monthly-averaged distributed values11, [Ghe17] relies on daily averaged values. Independently of the respective merits of these three sets, daily average $\varphi$ values are better suited than monthly ones for short duration experiments. Our values [Ghe17] cover all years from 1950 till now and are automatically updated. These values can also be retrieved from the website (see Section 4.2).

2.3. PUBLI and SUBEXP_PUBLI Tables and Meta-Data

CR data are published by experimental teams and collaborations in scientific publications. To save time and avoid confusion, we have a strong preference to include only final data published in peer-reviewed journals, and most data in CRDB come from refereed journals. An important exception is the proceedings of the biyearly International Cosmic-Ray Conference (ICRC, started in 1947): until the 1990s, many balloon flight results were published in the ICRC proceedings only, and even nowadays these proceedings still often contain the latest preliminary results and updates of previously published results.

2.3.1. PUBLI Table

To store what a publication is, the following keys are used.

• ID: unique identifier for a publication.
• HTML: unique ID for the reference, taken from the Astrophysics Data System (ADS)12, e.g., 2014A&A...569A..32M.
• REF and BIBTEX: these two keys provide a full publication reference and BibTEX13 entry, automatically retrieved from the ADS ID thanks to the ADS application programming interface (API)14.
• SUPERSEDED_BY: in some cases, the same data (same quantity from the same sub-exp) are re-analysed and updated in a subsequent publication. This key enables to store the ID (in the PUBLI table) of the newer publication analysis, keeping track at the same time of deprecated analyses and references. A single level of superseded data can always be seen from the ‘Experiments/Data’ tab (see Section 4.1).

Deprecated keys inCRDB v4.0

One major modification in the PUBLI table in this release was to abandon book-keeping Solar modulation informations from the publication, i.e., Solar modulation levels calculated by the authors for their data. This feature was introduced in CRDB v2.1—requiring many extra keys in the PUBLI table, see Maurin et al. [22]—in order to highlight the various assumptions behind the modulation levels provided. The motivation was to ease comparisons between modulation levels for data taken at similar periods but provided by different experimental teams. However, picking this information from the publication was time consuming for very little practical use in retrospect15. Moreover, as stressed in the previous section, modern CR propagation model studies either make use of ${\varphi }_{\mathrm{FF}}$ or rely on public Solar modulation codes, and the informations stores in the SUBEXP table allow both these practices (see Section 2.2.2). A major benefit of not considering the modulation from the publication is that it simplifies the structure of the database, the extraction of data, and the submission of new data.

2.3.2. SUBEXP_PUBLI Bridge Table

To avoid duplicates, for cases where different data refer to the same publication or the same sub-experiment, a bridge table (SUBEXP_PUBLI, see Figure 1) enables to link entries from the SUBEXP and PUBLI tables. Its keys are as follows.

• ID: unique identifier for a subexp-publi association.
• QUANTITIES: lists all the quantities (fluxes, ratios, etc.) provided by a given sub-exp/publi association.
• DATA_UPLOAD: stores the date at which the corresponding data were uploaded.
• SUBEXP_ID: bridges a sub-experiment/publication result to a ‘sub-exp’ ID (SUBEXP table).
• PUBLI_ID: bridges a sub-experiment/publication result to a ‘publi’ ID (PUBLI table).

3. New Data in CRDB v4.0

We now turn to the database content. As much as possible, data are added in CRDB on a continuous basis (by one of us); new data can also be submitted (see Section 4.4). Thanks to the feedback of many users, typos and mismatches in the data and meta-data are regularly corrected—the list of corrections and people who helped us (see acknowledgements) are available online (see CRDB log file).

In this section, we highlight the main datasets added since the first release [22], in particular in the two most recent releases CRDB v3.1 (Section 3.1) and v4.0; for the latter, we detail the motivation and specifics of UHCR data (Section 3.2), antinuclei upper limits (Section 3.3), and UHECR data (Section 3.4). The complete list of experiments and publications (presently in CRDB) is gathered in Appendix B.

3.1. Relevant Data Updates Before CRDB v4.0

Since the first release of CRDB seven years ago, two major data updates are worth highlighting. The first, tagged as CRDB v3.0, added H and He data from a large number of balloon flights going back to the 1950s. These data sets were used to derive Solar modulation levels over sixty years and compared to values derived from neutron monitor data in Ghelfi et al. [1].

The second major upload, tagged CRDB v3.1, was made a few weeks before preparing this release. The update is the result of a tremendous concentrated effort to include a wealth of data released in the last two years from various experiments (AMS-02, CALET, NUCLEON, TRACER, Voyager, and a few others); they correspond to some of the most interesting and high-precision CR data ever recorded. In addition, time-dependent fluxes and ratios from AMS-02 [47,48] and PAMELA [49] were added.

We emphasise at this point that the CRDB is open for data submissions from experiments since its first release, and contributions are very welcome. We further simplified the submission interface, presented in Section 4.4, to encourage more data submissions in the future.

3.2. Very Heavy and Ultra-Heavy CRs

The theory of stellar nucleosynthesis to explain Solar system abundances goes back to the fifties [50]. In the following decade, $Z\ge 30$ [51,52] and even a few $Z>90$ (actinides) [53,54,55] CRs were detected. The abundance of these UHCRs are driven by three processes, corresponding to three categories of stable heavy nuclides: the p-, r-, and s-nuclides, respectively located at the neutron-deficient side, neutron-rich side, and bottom of the valley of nuclear stability [56]. How much p- [56,57], r-, and s-processes [58,59] contribute to the various UHCRs, and which astrophysical sites are involved remain debated. Moreover, the abundance of UHCRs slightly differs from the Solar system ones, and these differences are likely to be related to specific acceleration (e.g., [60]) or transport processes (e.g., [61]).

Measuring UHCR data is challenging because the fluxes are low. From $Z=28$ (Fe) to $Z=30$ (Zn) and then $Z=40$ (Zr), the flux is suppressed by a factor ∼${10}^3$ and ∼${10}^5$ [4]. The rarest CRs, the actinides $Z\ge 90$ (Th and U), are about seven orders of magnitude less abundant than Fe [62]. Standard techniques used for measurements of $Z<30$ CRs can still be pushed to cover the $30\le Z\le 60$ region [4], but come slightly short for the heaviest ones [63,64]. An alternative consists of passive detectors exposed for long durations (several years). The detection principle is based on the chemical modification made in a solid state nuclear track detector when ionising particles go through it. After long exposure, the detector is etched in a chemical agent and each track left is analysed and its properties can be related to the charge and velocity of the CR.

We added in CRDB v4.0 most of the CR data found for $Z>30$. Most measurements do not resolve elements, and we needed to create new name holders for groups of charges, for instance Pt-group ($74\le Z\le 80$), Pb-group ($81\le Z\le 87$), Subactinides ($74\le Z\le 87$), and Actinides ($Z\ge 88$)—see Table A2. We note that in the literature different groups sometimes used slightly different charge range in their definition, but we stick to this one for all implemented data—data uncertainties are larger than the error made from using slightly enlarged or reduced charge groups. The data themselves mostly consist of ratios of elements (or of groups of elements) in an unspecified energy band (related to the detector capabilities). In practice, CR fluxes are expected to be maximal at a few GeV/n, and several experiments reported no energy evolution (within the uncertainties) of their measured ratios. For this reason, and for simplification, we set all UHCR data $Z>50$ to a generic energy bin around 1.5 GeV/n.

More than sixty years after their discovery, only very few UHCR data are available and almost no new experiment is running or planned. Except for the relatively recent superTIGER experiment in the $Z=$ 30–40 range [65], the experiments in the high charge range date back to the end of the seventies (Ariel6 [63], HEAO3-HNE [66,67]), the early eighties (UHCRE-LDEF [3,68,69]), or at best the nineties (Trek [70,71]). The most recent results on subactinides and actinides come from indirect measurements of the CRs from their impact on meteorite olivines [72,73,74], namely the Olimpiya Experiment (not implemented yet in CRDB). The solution to the origin of these elements may come from yet another route: recently, optical follow-up observations of a gravitational wave event have shown that binary star mergers could be a major r-process contributor to UHCRs [75]. More such events are likely to be detected in the future, and they will provide a complementary view to that of CR data.

3.3. Antimatter CRs

So far, antiprotons are the only antinuclei found in CRs. They were first detected at the end of the seventies [76,77] and soon interpreted by Silk and Srednicki [78] as possible dark matter signatures. The pioneering theoretical aspects and subsequent efforts were acknowledged by the 2019 Cosmology Gruber prize: “Silk recognised dark matter’s indirect signatures such as antiprotons in cosmic rays and high energy neutrinos from the Sun”. This exploration continues with modern experiments, which have detected several tens of thousands of antiprotons [79].

The quest for $Z<-1$ CRs is also very much alive but the expected fluxes are very low. Indeed, most if not all CRs antiprotons can be attributed to nuclear production (H and He CRs on the interstellar gas) during CR propagation in the Galaxy. The cost to create and fuse together an extra antiproton or antineutron from colliding protons is a roughly ${10}^{-4}$ suppression factor (e.g., [80]). As a consequence, the ratio of astrophysical CR antiprotons to protons is at most ∼${10}^{-4}$ (e.g., [79]), and the ratio of astrophysical CR antinuclei (of atomic number A) to protons is expected to go very roughly as ${10}^{-4A}$. So far, only upper limits have been derived on the latter [81]. CR antideuterons are particularly difficult to detect because they must be isotopically separated from the $\overline{p}$ tail, and also because charge confusion rejection from p should be at the level of ${10}^8$. The AMS-02 experiment on the ISS or the GAPS experiment based on a novel detection technique could detect a few events in the near future [11]. Although at a much lower level, antihelium events may have been seen in AMS-02 very preliminary analyses [12]. Whether these events are real, and whether this is compatible with no detection of antideuterons and can be accounted for physics scenario is still at the exploratory stage [82,83,84]; a confirmation of these events would have huge consequences.

To tackle upper limits, a new key in the CR database table was added (see Section 2.1). Limits on antielements are also usually derived with respect to elements, and we added in CRDB v4.0 new names to cover all associated data (see Table A2). The best limits so far come from the BESS balloon flights [10] and PAMELA mission [85], as shown in Figure 2 with results from other experiments derived over the years (all included in CRDB v4.0).

3.4. Ultra-High Energy CRs

A major extension of the already comprehensive CR database is the inclusion of data from air shower experiments. High-energy CRs are measured indirectly with ground-based experiments. They detect the particle cascades initiated by CRs in Earth’s atmosphere (air showers). Since the CR flux drops rapidly with increasing energy, the air-shower technique is the only feasible way to obtain CR data above PeV energies. The air-showers allow for large aperture experiments due to their extensive footprint on the ground in charged particles and Cherenkov light, and their visibility from a distance in ultra-violet light.

The energy and direction of a CR can be well inferred from air showers, but the identity of each individual CR cannot be determined accurately. The identity, more specifically the mass A of the CR, has to be inferred from air shower properties that naturally fluctuate, like the slant depth $X_{\max }$ of shower maximum measured from the top of the atmosphere or the total number $N_{\mu }$ of muons produced in the shower. These natural fluctuations make it impossible to distinguish species on an event-by-event basis. Therefore, fluxes of individual elements or isotopes cannot be determined.

Experiments most commonly report the all-particle flux. In principle, this also includes stable electromagnetically and weakly interacting particles, but the flux of these components at the PeV scale and above is negligible compared to nuclei [104,105]. In addition, some experiments report the fluxes of mass groups by splitting the observed mass range into two groups (proton-helium group and the rest), four groups (proton, helium, oxygen-, and iron-group), or five (as before, but adding a silicon group). These groups span over roughly equal intervals in $lnA$, since the mass sensitivity is roughly constant in this variable. Beside the usual individual element or isotope names, the list of CR quantity names in CRDB v4.0 was expanded to handle these groups (e.g., H-He-group, Fe-group, AllParticle, see Table A2).

We note that these results are obtained by simulating air showers initiated by the leading element in each group only, and by fitting the sum of the air shower response distributions to the observed distribution [106,107]. Representing a whole mass-group by a single element is a great simplification, but unavoidable since the relative fluxes of species within a mass group are unknown. The analysis approach works in practice because air shower fluctuations are large compared to the small differences in the response to individual elements within a group.

Another peculiarity of high-energy data is that one raw data set often has several interpretations, in which the fluxes of mass groups and, to a lesser degree, also the all-particle flux vary. These parallel interpretations are reported by the experiments due to significant theoretical uncertainties in simulated air showers. Detailed air shower simulations are required to infer the mass A from air shower properties like $X_{\max }$ and $N_{\mu }$, and these simulations use hadronic interaction models that extrapolate fixed-target and collider measurements of particle interactions using theory and phenomenology. Air showers are dominated by soft QCD (Quantum Chromo Dynamics) interactions, which so far cannot be predicted accurately from first principles. Therefore the interpreted measurements depend on the hadronic interaction model used in air shower simulations, which is listed with the interpreted data.

For this update, we focus on adding flux measurements to the CRDB, but we also consider to include measurements of air shower properties like the average of $X_{\max }$ or $N_{\mu }$ as a function of the shower energy in the future. In the current update, we include air-shower based flux data from the Pierre Auger Observatory, Telescope Array, the IceCube Neutrino Observatory, the KASCADE-Grande experiment, the TUNKA-133 Array, and the H.E.S.S. observatory. An illustration of the evolution with energy of the ratio of two groups of elements is shown in Figure 3.

4. User-Interface Updates and New Submission Form

CRDB is hosted by the LPSC laboratory website. It runs on a LAMP solution, i.e., a stack of free open source softwares: Linux (operating system), Apache HTTP (server), MySQL (database), and PHP (hypertext pre-processor language). The web interface relies on several third-party libraries (jquery, jquery-ui, jquery.cluetip, and table-sorter) that are used to sort and display the database content, as briefly presented in the various sections below. For efficiency and speed, all web pages make use of AJAX (Asynchronous JavaScript and XML) web development techniques.

Accessing the data in CRDB can be achieved in two ways, either by accessing the resources via CRDB web pages, or via a REST (representational state transfer) interface. While web pages allow to access the fully contextualised data and meta-data (thanks to several sorting, selections, display, and download of data options), the REST interface is useful for users who want to access and retrieve data from command lines (i.e., from their terminal or codes, without going through the CRDB web pages).

Below, we discuss separately the various web interfaces. We start with the description of the improvements brought on those already available in the first release (Section 4.1). We then describe the interface for extracting ${\varphi }_{\mathrm{FF}}$ time series (Section 4.2), the new help page for the REST interface (Section 4.3), and the simplified procedure (and format) to submit new data (Section 4.4).

4.1. Web User Interface

The address https://lpsc.in2p3.fr/crdb leads the user to a choice of various tabs, as illustrated in Figure 4. Among them, two tabs provide differently contextualised informations on CR experiments and data, while other tabs provide general information on CRDB, external CR resources, etc.

We give below a brief description of these tabs, highlighting the most salient features and novelties of this version, and providing a few snapshots as illustrations.

4.1.1. ‘Welcome’ Tab

This is the unique entry point of the website (see Figure 4). It contains a brief description of the database content and structure, and informations on CRDB including versions, log, logo, and publications. In particular, the log file tracks the changes made in the various releases (data corrections and past and ongoing developments).

4.1.2. ‘Experiments/Data’ Tab

This is the tab to go for users looking for a specific experiment, its meta-data, and the data it gathered; experiments can be sorted according to their name, starting date, or type. This tab lists the quantities they measured. It also provides links to the experiment web page, the associated publications, and pictures of the sub-detectors (by clicking on the ‘magnifying glass’ icon); see Figure 5 for an illustration. Clicking on ‘[data]’ for any given sub-exp gives further information on the CR data origin: data taking period, how data were retrieved (numbers from tables, extracted from figures, or from personal communications), details on the detector, etc.

We stress that the data presented in this tab are ‘native’ data only (see Appendix A.1), i.e., data as provided in publications and uploaded in the database without modification (which is not the case in the ‘Data extraction’ tab). Accordingly, the energy axes for the data are as provided in the publications.

4.1.3. ‘Data Extraction’ Tab

This is the main interface to select, extract and plot data, displaying also some meta-data (sub-exp names, links and BibTEX references for publications matching the selection), with the possibility to save the outputs in different formats (images, ASCII files, etc.). The web page consists of a selection box allowing to choose a CR quantity (numerator and denominator) and to display all native and combined data (see Appendix A.1)16—for caution about the usage of overlapping data taking periods for data from the same experiment, we refer the reader to Appendix A.5. More selection criteria allow to ask for matching sub-exp (partial or full) names, specific dates and energy range, and flux rescaling with energy. In the context of long time series provided by both the AMS-02 [47,48,111] and PAMELA [49] experiments, CRDB v4.0 allows to select only (or discard) data from time series, in order not to overcrowd the display of data. This is illustrated on a ‘time-series only’ selection in Figure 6, where we show Carrington rotation-averaged PAMELA H data from 2006 to 2010 [112]. This list triggers on the presence of the word ‘average’ (in yearly, monthly, daily, Bartels rotation…averages) in the ‘subexp-info’ key in the SUBEXP table (see Section 2.2.2).

A critical choice is that of the energy axis (among $E_{k/n}$, R, $E_{\mathrm{tot}}$, etc.): if no native data exist for this energy axis, conversion are performed to rescale the data from its native axis to the queried one. However, this conversion is only possible for fluxes (not ratios), and is exact for CR isotopes and leptons (whose A, Z, and m are identified) but approximate for elements and group of elements (see Appendix A.4).

Hitting the ‘Extract Selection’ button pops-up a new window displaying: (i) a plot of the data, (ii) list of meta-data (per sub-exp) associated with the data, (iii) a summary of the extraction process (i.e., whether combinations and approximations were made), and (iv) a full listing of all retrieved data points (energy, values, and uncertainties). In CRDB v4.0, we improved the browsability between these informations. From the plot, we can export the data in a format compliant with the USINE and GALPROP propagation code, or as a cvs or tarball of ASCII files. We can also directly retrieve images or ROOT17-compliant macros (.root, .C). A ‘replot’ button allows to further trim the data and modify the plot axes, look, and error bars shown, with the same options (as in the original plot) to export and save the results.

To conclude on this tab, we are also pleased to provide in CRDB v4.0 an additional selection option, that allows to display a comma-separated list of quantities: we stress that all selection criteria (specific energy range, dates, sub-experiments, etc.) are enforced to all quantities in the list. An illustration is provided Figure 7, where we show the full CR spectrum from MeV to EeV energies from a selection of recent experiments (top panel), and a selection of all nuclear fluxes from the AMS-02 experiment (bottom panel).

4.1.4. ‘Admin’ Tabs

This tab can be accessed by authenticated users only (CRDB maintainers). In this page, various scripts provide internal checks of the database content (missing images for the detectors, orphan ID in the various tables, list of superseded publications, etc.). This page used to handle validation forms for submitted data, but they were removed thanks to the new submission form in CRDB v4.0 (see Section 4.4). We can also monitor from this page the traffic on CRDB, stored in a specific LOG_QUERIES table (connection date, IP address, and page visited, see Figure 1). This traffic since August 2013 is shown in Figure 8. CRDB received more than a quarter million queries, from ∼20,000 different IP addresses in over a hundred countries—most queries originate from Germany, USA, Italy, France, China, Switzerland, and Japan, i.e., countries strongly involved in recent experiments and phenomenology.

4.1.5. ‘Useful Links’ Tab

This tab gathers links to many online CR resources. These links include propagation codes for Solar, Galactic and extra-galactic CRs, other useful CR databases, and CR-related websites. If you feel that some important resources are missing, please contact us ([email protected]), we will be happy to add them.

4.2. ‘Solar Modulation’ Tab

This tab was added in CRDB v3.0 and thus not described in Maurin et al. [22]. Its purpose is to provide Solar modulation level ${\varphi }_{\mathrm{FF}}$ in the Force-Field approximation (e.g., [31]) for any date between the 1950s and today. The calculation is based on neutron monitor (NM) data and the reconstruction algorithm of Ghelfi et al. [1].

Neutron monitors are devices developed in the 1950s to monitor Solar activity [126]. They combine a very good time resolution (a few minutes) and a good stability over decades. For this reason, they are well-suited to get time series over large time periods [37]. These values are for instance used to fill ${\varphi }_{FF}$ from any CRDB sub-experiment as discussed in Section 2.2.2. Averaged $\varphi \left(t\right)$ time series (bottom panel) can also be retrieved, as illustrated in Figure 9, from a selection (top panel) of NM stations, a time period, and a time resolution (from the finer-grain 10 min to monthly averaged time series).

In practice, scripts on LPSC servers retrieve and process NM data from NMDB. They produce, behind the scene, ASCII files for all available stations at a 10 min time resolution. Then for each CRDB query, another layer of scripts reads these files to calculate and display the user selection. To ensure a continuous service and update, a cron18 time-based job scheduler is used on a daily basis.

4.3. ‘REST Access’ Tab

The database offers a REST19 interface, which makes it easy to download data sets according to selection criteria from another program. The interface has been available since version CRDB v1.3, but the new help page in CRDB v4.0 makes this feature more prominent.

Data sets can be selected by quantity (element, isotope, electron or positron, or mass group), experiment, energy range, and time range. Users must specify the energy type in their query. As mentioned in previous sections, data sets are stored in their native energy type, which can be kinetic energy, kinetic energy per nucleon, total energy, rigidity. The native type is converted to match the request, and details on the conversion are given in Appendix A.4. Other parameters of the interface control the output format, and whether ratios should be synthesised from individual flux measurements.

The available parameters are documented in

Table 1. REST interface parameters, description, and examples values. Only the first three parameters are mandatory. See text for how to use REST in a command line.

Parameter	Description	Allowed Values (Examples) [Default]
Native data selection (mandatory)
num${}^a$	Numerator of quantity to retrieve (element, isotope, lepton, mass group)	CRDB name (H, 1H-BAR, e-, O-group)
den${}^a$	Denominator of quantity to retrieve (only for ratio, leave empty for flux)	CRDB name (He, 1H, e-+e+, AllParticles)
energy_type	Energy axis for returned quantities: kinetic energy per nucleon (GeV/n), kinetic energy (GeV), rigidity (GV), total energy (GeV), or total energy per nucleon (GeV/A)	CRDB keyword (EKN, EK, R, ETOT, ETOTN)
Data combinations and/or modifications (optional)
combo_level${}^b$	Add combinations via ratio or product of native data (from the same sub-exp at the same energy) that match quantities in list (e.g., compute B/C from native B and C). Three levels of combos are enabled: 0 (native data only, no combo), 1 (exact combos), or 2 (exact and approximate combos): in level 1, the mean energy (or energy bin) of the too quantities must be within 5%, whereas for level 2, it must be within 20%	Integer in 0–2 (0, 1, 2) [1]
energy_convert_level${}^c$	Add data obtained from an exact or approximate energy_type conversion (from native to queried). Three levels of conversion are enabled: 0 (native data only, no conversion), 1 (exact conversion only, which applies to isotopic and leptonic fluxes), and 2 (exact and approximate conversions, the latter applying to flux of elements and of groups of elements)	Integer in 0–2 (0, 1, 2) [1]
flux_rescaling	Multiply fluxes by ${\langle E\rangle }^{\mathtt{flux}\_\mathtt{rescaling}}$	Decimal number (1, 2.5) [0.]
Trimming the selection (optional)
energy_start	Lower limit for energy_type	Floating point number (2e-1, 1.2e2) [1.e-40]
energy_stop	Upper limit for energy_type	Floating point number (1e5, 5.3e7) [1.e40]
time_start	Lower limit for interval selection	Date YYYY or YYYY/MM (2014, 2010/06) [1950]
time_stop	Upper limit for interval selection	Date YYYY or YYYY/MM (2020, 2019/06) [2050]
time_series	Whether to discard, select only, or keep time series data in query	CRDB keyword (no, only, all) [no]
exp_dates	Comma-separated list (optional time intervals) of sub-experiment partial or full names	Partial name (AMS, ACE), full name (BESSPolarI), or combinations of names and time intervals (PAMELA(2006:2008), BESS(2002/01:2004/12)) [n/a]
Outputs (optional)
format	File output format	CRDB keyword (csv, usine, galprop) [csv]
modulation	Source of Solar modulation values	CRDB keyword (USO05, USO17, GHE17) [GHE17]

^a Url syntax requires e%2B instead of e+ for a positron. ^b See Appendix A.1 for a discussion on how to form combos and propagate errors from the native data. ^c Energy type conversions are exact for isotopic and leptonic fluxes, but not for elements, since an elemental flux in general is the sum of several isotope fluxes in often unknown fractions. Usually, isotope fractions have to be assumed (see Section A.4).

(also reproduced on the website). To give an example, the boron-to-carbon flux ratio as a function of the kinetic energy per nucleon can be queried with the command-line utility curl as follows:

curl -L ‘http://lpsc.in2p3.fr/crdb/rest.php?num=B&den=C&energy_type=EKN’ > db.dat

The output in USINE format (table with columns separated by spaces) is stored in the file db.dat. Queries can be made from any general purpose programming language. For Python users, we provide a simple module to run queries from Python and return the result as a numpy array. The code and a tutorial with example usage are available on GitHub20 and linked to from the CRDB website.

4.4. ‘Submit Data’ Tab: New Submission Form

In CRDB v2.1, the submit data interface was based on a sequential online 4-step procedure. The user had to fill meta-data first (experiment, sub-experiment, and then publication) and the data. Each step had to be completed and validated by one of us (via the admin interface) before being able to go to the next step. In practice, because of the too many steps, probably not documented enough procedure, and delay between the validation steps, this was almost never used.

In CRDB v4.0, we decided to completely change the approach and only ask for a single csv file, whose expected columns are described in

Table 2. Description of the 23 expected columns in the csv file prepared by the user to submit data. The table shows the column ID, the associated keyword in the database structure (see Figure 1), the expected content of the column, and an example (and associated unit) for this column—entries corresponding to starred keywords $\star$ can be left empty (i.e., “ ”) if the user is unsure. For more details on CRDB data and meta-data definitions, see Section 2.1 (data points), Section 2.2.1 (experiments), Section 2.2.2 (sub-experiments), and Section 2.3.1 (publications).

Col.	(Meta-)Data	Description	“’Example” [Unit]
1	EXP-NAME	Experiment name	“AMS02”
2	EXP-TYPE	balloon, ground, or space	“space”
3	EXP-HTML$\star$	Experiment official website	“http://www.ams02.org”
4	EXP-STARTYEAR$\star$	Experiment starting year	“2011”
5	SUBEXP-NAME$\star$	Name specific to analysis (Section 2.2.2)	“AMS02 (2011/05-2012/12)”
6	SUBEXP-DESCRIPTION$\star$	Detector information	“9 planes of silicon tracker, TRD, etc”
7	SUBEXP-ESCALE_RELERR	Energy scale relative error (fraction)	“0.15”
8	SUBEXP-INFO$\star$	Summary or additional info	“18 months data 2011/05-2012/12”
9	SUBEXP-DISTANCE	Sub-exp. average distance to Sun	“1” [AU]
10	SUBEXP-DATES	Start-stop data taking period	“2011/05/19-000000:2012/12/10-000000”
11	PUBLI-HTML	Publication ADS ID or doi name	“2013PhRvL.110n1102A”
12	PUBLI-DATAORIGIN$\star$	Source for the data in the publication	“Table 1, Figure 2 and Figure 3”
13	DATA-QTY${}^{†}$	Measured quantity (CRDB names)	“B/C”
14	DATA-EAXIS	R [GV], EK [GeV], ETOT [GeV],	“R” [GV]
		EKN [GeV/n], or ETOTN [GeV/A]
15	DATA-E_MEAN	$\langle E\rangle$ for bin (empty if no value)	“ ”
16	DATA-E_BIN_L	$E_{\mathrm{lo}}$ in unit of [DATA-EAXIS]	“4.88” [GV]
17	DATA-E_BIN_U	$E_{\mathrm{up}}$ in unit of [DATA-EAXIS]	“5.37” [GV]
18	DATA-VAL	y, measured value	“0.3214” [m${}^{-2}$ s${}^{-1}$ sr${}^{-1}$ GV${}^{-1}$]
19	DATA-VAL_ERRSTAT_L	$\Delta y_{\mathrm{lo}}$, lower stat. err.	“ − 0.0011” [m${}^{-2}$ s${}^{-1}$ sr${}^{-1}$ GV${}^{-1}$]
20	DATA-VAL_ERRSTAT_U	$\Delta y_{\mathrm{up}}$, upper stat. err.	“+0.0011” [m${}^{-2}$ s${}^{-1}$ sr${}^{-1}$ GV${}^{-1}$]
21	DATA-VAL_ERRSYST_L	$\Delta y_{\mathrm{lo}}$, lower syst. err.	“ − 0.075” [m${}^{-2}$ s${}^{-1}$ sr${}^{-1}$ GV${}^{-1}$]
22	DATA-VAL_ERRSYST_U	$\Delta y_{\mathrm{up}}$, upper syst. err.	“+0.0089” [m${}^{-2}$ s${}^{-1}$ sr${}^{-1}$ GV${}^{-1}$]
23	DATA-ISUPPERLIM	“0” (measured) or “1” (upper limit)	“0”

$\star$ Columns that can be left empty (but not omitted) if the user is unsure about what he should write. ${}^{†}$ To help pick the correct denomination for the measured quantities, we list in the webpage all currently defined names. If necessary, quantities not defined yet will be added along with the submitted data.

(and also reported on the ‘Submit data’ tab); files must be sent to [email protected]. One of us then uses dedicated scripts on these files to finalise the upload on CRDB. In this new format, informations on the data and meta-data are still required, but to simplify the submitter task, many entries can be left empty (and will be filled by us), as indicated by stars in Table 2. We hope that this simplified submission procedure and lessened number of meta-data entry to provide will convince experimentalist to make the extra step of submitting the data to CRDB after they submit their publication to a journal.

5. Summary and Future Developments

In this article, we have presented CRDB v4.0, a new version of the CR database for charged species, highlighting several improvements on the database structure and content, and on the user interfaces. We summarise the changes made below:

• Database structure: a few tables were simplified and three became deprecated (Solar modulation table from the publication, users and validation tables for the old data submission interface). The pre-defined key values were also modified for the experiment type (now balloon, ground, or space) and energy type (now also includes ETOTN). Two new keys were added in the in the SUBEX_PUBLI and PUBLI tables respectively, in order to keep track of the data origin (retrieved from from a table or figure in the publication, etc.), and to store a possible energy scale uncertainty in the data (single number for a given sub-experiment).
• Database content: this version is associated with a massive upload of CR data. For CRDB v3.1, released a couple of weeks before this version, we filled important GCR high-precision data published in the last two years (AMS-02, DAMPE, NUCLEON, TRACER, Voyager, etc.). For CRDB v4.0, we extended the CR data content towards higher energies (KASCADE-GRANDE, Pierre Auger Observatory, etc.), higher charges $Z>30$ (LDEF UHCRE, TREK, etc.), and lower charges $Z<-1$ (upper limits from AMS-01, BESS, PAMELA, etc.). The database now contains more than 100 experiments and 350 publications, covering 14 decades in energy (from ${10}^6$ to ${10}^{20}$ eV).
• User interfaces: the many improvements we implemented provide users with more options to retrieve data, along with a clearer presentation of the various interfaces. These changes include: (i) more data selection options (e.g., on times series) and plotting/export options (.pdf, .csv), including the possibility to display several CR quantities on the same plot; (ii) new help page and an example python notebook script to retrieve data via the REST interface; (iii) updated interface to retrieve Solar modulation level ${\varphi }_{\mathrm{FF}}$ time series (or average) from any time period since 1950 with its dedicated REST interface; (iv) simplified file format to submit new data; (v) extended list of links for online CR resources. We hope in particular that the new submission format, with clearer explanations about the data and meta-data to fill, will help us always keeping CRDB up-to-date in the future.

Final thoughts on CRDBEvolution

CRDB was thought from the start as a service to the CR community. In an ideal world, we would like experimentalists to submit their data to CRDB as soon as their results appear on the arXiv preprint server21 or in a peer-review journal.

There are several possible directions along which CRDB could be improved. On a technical aspect, we are planning to make the website responsive for a better rendering on diverse electronic devices. On the data content aspect, we will obviously continue to upload new data as they appear, as quickly as we can. We would also be grateful for any help to extend the datasets to new quantities. Indeed, without any change in the database structure, we could quite easily include (i) more relevant quantities for UHECRs, i.e., the mean logarithmic mass $\langle lnA\rangle$, the mean depth of shower maximum $\langle X_{\max }\rangle$, etc.; (ii) dipole anisotropy spectral data as presented in Ahlers and Mertsch [127], (iii) upper limits or data on the neutrino CR spectrum discovered by the IceCube Collaboration [128]. As a first step in this direction, near the completion of this article, a preliminary agreement was reached with the KCDC team [24] to include their data in a forthcoming CRDB release. Finally, we could also imagine CRDB as a portal to gather more CR-related data, for instance hosting the many nuclear cross-section data presented in Génolini et al. [129]. These future developments will depend on the workforce available and feedback from the CR community.

To conclude, any help and feedback to further expand the database is welcome. Comments, questions, suggestions, and corrections are to be sent at [email protected].