1. Introduction

Chronology is the backbone of the archaeological record. As a necessary prerequisite to understanding the context of any past event or process (Lucas 2004), it is has unsurprisingly been at the forefront of methodological development in archaeology for as long as the discipline has existed: from putting finds and events in sequence (Flinders Petrie 1899;Ford 1962;Harris 1979;Thomsen 1836;Worsaae 1843) to an increasingly wide array of scientific methods that place them on an absolute timescale (Bada and Helfman 1975;Daniels, Boyd and Saunders 1953;Douglass 1929;Evernden et al. 1965;Libby 1955) and an increasingly sophisticated set of statistical tools to build them into chronologies (Buck et al. 1991;Crema 2024;Levy et al. 2021;Mischka 2004;Suess 1967). If archaeology is to be an open science (Lake 2012), it is therefore critical that effective open access to chronological information be placed front and centre.

Over the last two decades, archaeologists have answered this call by publishing an increasing number of compilations of dates from archaeological contexts as open data. These efforts have facilitated re-evaluations of chronologies themselves (e.g.,Higham et al. 2014;Katsianis et al. 2020;Loftus, Mitchell and Ramsey 2019;Prates, Politis and Perez 2020) but also the development of novel ways of using chronological data (e.g.,Crema 2022;Crema et al. 2024;Grove 2011;Marom and Wolkowski 2024;Riris et al. 2024;Silva and Steele 2014). The focus has been overwhelmingly on radiocarbon dating and most compilations focus on a single region and/or period. The profusion of open radiocarbon data in particular has prompted several initiatives towards a global synthesis (e.g.,Bird et al. 2022;Bronk Ramsey et al. 2019;Schmid, Seidensticker and Hinz 2019).

At the same time, the broad range of other types of chronological information used in archaeology — from other radiometric methods to dendrochronology to typological dating and epigraphy — remains relatively difficult to access as open data. Even when it comes to radiocarbon data, the coverage of available compilations is patchy both geographically and in time and of variable quality (see Section 2.1). The publication of many overlapping, non-standardised and mostly static open data resources means that it is still difficult to obtain reliable and up-to-date chronological datasets, especially for applications that crosscut conventional geographic and temporal domains of research. Initiatives towards synthesis have improved this situation, but the goal of a global dataset that is both comprehensive and up to date remains elusive.

XRONOS is a new open data infrastructure that aims to provide access to published radiocarbon dates and other chronometric data from any period, anywhere in the world. It is our attempt to move the state of the art in open archaeological chronology beyond the publication of static, one-off resources (‘uploading CSVs’,Batist 2023: 188–189), and towards a living digital infrastructure (Kintigh 2006) embedded in a transparent and sustainable collaborative network. The core of XRONOS is a server application that ingests chronological data from diverse sources, facilitates semi-automated and manual curation of this data, and makes it available via both a web-based graphical user interface (GUI) and machine-readable application programming interface (API). The web frontend can be accessed viahttps://xronos.ch and all components of the software are developed as free and open source software with source code available athttps://github.com/xronos-ch.

In the remainder of this paper, we describe the concept and implementation of XRONOS in relation to the state of the art in open chronometric data in archaeology and evaluate our progress in achieving these goals as of writing. Since we envisage both XRONOS as a dataset and XRONOS as software to be continually developing resources, the description here should be read as a ‘snapshot’ of the project as of version 1.1.0.

2. State of the Art

2.1 Compilations of radiocarbon dates

Though anexplicit emphasis on ‘open data’ is a relatively recent phenomenon in archaeology (Lake 2012), the open publication of compiled radiocarbon dates has a substantial prehistory. Arnold and Libby (1951) initiated the tradition of regularly publishing ‘data lists’, a practice that was subsequently continued by radiocarbon laboratories as supplements to journals such asRadiocarbon andArchaeometry. However, as the number of labs and volume of radiocarbon dates being produced grew, this paper-based format became impractical and mostly disappeared (Bronk Ramsey et al. 2019; cf. e.g.,Ndeye et al. 2022), without being replaced by another form of systematic data-sharing or dissemination. Additionally, because date lists were sourced from radiocarbon laboratories directly — not from those who collected the sample — they typically included only very limited contextual information. On the eve of the ‘accelerator mass spectrometry (AMS) revolution’ in radiocarbon dating, there was an effort to create a computerised ‘International Radiocarbon Database’ (Kra 1988) — already by 1989 described as a “much needed, long overdue enterprise” (Kra 1989: 1067) — but it never came to fruition.

Thus, even though radiocarbon data comes from a relatively limited number of sources (c. 172 active labs;Radiocarbon 2024) and has relatively standardised reporting conventions (Bayliss 2015;Millard 2014), in practice the only way to produce aggregated datasets in recent decades has been to manually search through relevant literature for dates reported secondarily by the submitter of the sample. This already laborious process is further hampered by a significant inconsistency in how much authors adhere to reporting conventions for measurements and sample metadata, a lack of conventions on the reporting ofcontextual information, weak or non-existent disciplinary norms regarding the responsibility to publish results openly in a timely fashion, and a range of other issues affecting data reuse (Moody et al. 2021).

Despite these inefficiencies, there has been a profusion of published radiocarbon compilations since the decline of the date list. Our review of the literature identified 61 published since 1994 (Table 1 and accompanying data, see section 8). This is almost certainly an undercount, because our firsthand knowledge of regional literature is limited to Europe and West Asia and many resources only ever existed in ‘grey’ formats (e.g., websites that were not indexed and no longer exist). We also restricted ourselves to structured datasets disseminated primarily in a digital format; ‘date lists’ in printed periodicals and gazetteers were excluded.

Table 1

Summary of published compilations of radiocarbon dates. For full data, see supplementary materials.

DATABASE	PUBLISHED	DATES	REFERENCES
Base de Données Archéologique	1994	7,000	Perrin (2019)
ANDES 14C	1994	5,800	Michczyński et al. (1995)
Archaeological Settlements ofTurkey	1998	1,600	Tanindi and Erdoğu (2005)
Canadian Archaeological Radiocarbon Database	1999	171,500	Gajewski et al. (2011); Kelly et al. (2022)
RADO.NB (incl. RADON and RADON-B)	1999	34,200	Raetzel-Fabian (1999); Hinz et al. (2012); Kneisel, Hinz, and Rinne (2014); Rinne et al. (2024)
New Zealand Radiocarbon Database	2000	2,000	McFadgen, Higham, and Sparks (2000)
International Central Anatolian Neolithic e-Workshop databases	2001	1,000	Reingruber and Thissen (2005); Reingruber and Thissen (2009)
Radiocarbon Palaeolithic Europe Database	2002	17,900	Vermeersch (2025)
CalPal database	2002	49,800	Weninger (2022)
CONTEXT	2002	2,900	Böhner and Schyle (2004)
Paleoindian Database of the Americas	2003	1,300	Anderson et al. (2010)
AustArch (1, 2, and 3)	2008	5,000	Williams et al. (2008); Williams and Smith (2012); Williams and Smith (2013)
Irish Radiocarbon & Dendrochronological Dates	2010	10,700	Chapple (2019)
Platformfor Neolithic Radiocarbon Dates (PPND)	2010	800	Benz (2010)
PACEA Geo-Referenced Radiocarbon Database	2011	6,000	d’Errico et al. (2011)
Peru archaeological radiocarbon database	2013	300	Rademaker, Bromley, and Sandweiss (2013)
EUBAR C14 database	2014	1,700	Capuzzo, Boaretto, and Barceló (2014)
Wang 2014	2014	4,700	Wang et al. (2014)
14SEA	2015	3,000	Reingruber and Thissen (2017)
EUROEVOL	2015	14,100	Manning et al. (2016)
Flohr et al. 2015	2015	3,000	Flohr et al. (2016)
South Central Andes Radiocarbon	2015	1,700	Gayo, Latorre, and Santoro (2015)
Archive des datations radiocarbones d’Afrique centrale	2016	1,900	Seidensticker and Schmid (2021)
KITE East Africa	2016	800	Courtney Mustaphi (2016)
Palmisano et al. 2017	2017	1,900	Palmisano, Bevan, and Shennan (2017)
Plateforme des datations archéologiques intertropicales	2017	1,300	de Saulieu et al. (2017)
Archivo de Dataciones Radiocarbónicas de la Prehistoria Recient (IDEARQ)	2017	—	Uriarte González et al. (2017)
Database of Radiocarbon Dates Published in JapaneseArchaeological Reports	2018	44,000	Kudo (2018);Kudo et al 2023
Southern African Radiocarbon Database	2019	2,700	Loftus, Mitchell, and Ramsey (2019)
Douglass et al. 2019	2019	200	Douglass et al. (2019)
MedAfriCarbon	2020	1,600	Lucarini et al. (2020)
A repository of radiocarbon data for Rapa Nui	2020	800	Lipo (2020)
rxpand	2020	2,800	Gregorio de Souza (2020)
Mesoamerican Radiocarbon Database (MesoRAD)	2020	1,800	Hoggarth, Ebert, and Castelazo-Calva (2021)
AgriChange	2020	3,700	Martínez-Grau, Morell-Rovira, and Antolín (2021)
Crema & Kobayashi2020	2020	2,100	Crema et al. (2016)
Katsianis et al. 2020	2020	3,200	Katsianis et al. (2020)
ArqueoData	2021	800	Alcántara (2021)
caribbean-14C	2021	2,100	Riris (2021)
Near East Radiocarbon Dates (NERD)	2021	11,000	Palmisano et al. (2022b)
NeoNet	2021	2,500	Huet et al. (2022); Huet et al. (2024)
Kim et al. 2021	2021	900	Kim, Lee, and Crema (2021)
Cochrane et al. 2021	2021	100	Cochrane, Rieth, and Filimoehala (2021)
Aotearoa New Zealand Radiocarbon Databas	2022	4,100	Petchey et al. (2022)
Archive of Italian Radiocarbon Dates (AIDA)	2022	4,000	Palmisano et al. (2022a)
p3k14c	2022	179,700	Bird et al. (2022)
Torres StraitRadiocarbon Database	2022	300	Linnenlucke et al. (2023)
14Canarias	2023	700	Pardo-Gordó et al. (2023)
Hoebe et al. 2023	2023	6,500	Hoebe, Peeters, and Arnoldussen (2023)
Bolivian Radiocarbon Database	2023	3,000	Capriles (2023)
An Annotated Compilation of Chronometric Dates for the Middle-UpperPalaeolithic Transition (45-30 ka BP) in Northern Iberia (Spain)	2023	200	Díaz-Rodríguez et al. (2023)
Großman et al. 2023	2023	3,400	Großmann, Weinelt, and Müller (2023)
Datations absolues, Inventaires archéologiques et Bibliographies en Afrique Centrale	2023	1,800	Clist, Denbow, and Lanfranchi (2023)
Updated Peru archaeological radiocarbon database	2024	500	Rademaker (2024)
Banque Nationale de Données Radiocarbone pour l’Europe et le Proche Orient	—	—	“Banque Nationale de Données Radiocarbone (BANADORA)” (n.d.)
Oxford Radicoarbon Accelerator Unit database	—	8,500	Gillespie et al. (1984); Bronk Ramsey et al. (2009)
Egyptian Radiocarbon Database	—	1,600	Bronk Ramsey et al. (2010); Dee et al. (2012); Dee et al. (2013)
NERC Radiocarbon Facility	—	6,100	Garnett et al. (2023)
Abu Dhabi Islands Archaeological Survey Radiocarbon Database	—	100	Al-Abyadh-Balghelam-Dalma-Jebel, Dhanna-Marawah-Marawah, and Yas (n.d.)
Royal Institute for Cultural Heritage web based Radiocarbon database	—	7,300	Van Strydonck and De Roock (n.d.)
La base de dades radiocarbòniques de Catalunya	—	—	Barceló Álvarez, Bogdanović, and Capuzzo (2013)

The number of available compilations has increased significantly since around 1995 (Figure 1). The first generation came around the turn of the century and consists mostly of online databases with a web frontend. These include some databases operated by radiocarbon labs, for example the Oxford Radiocarbon Lab (ORAU) and the Belgian Royal Institute for Cultural Heritage (KIK-IRPA) and essentially represent a continuation of their date lists in a digital format. The majority, however, were compiled from the literature by individual researchers interested in a particular region and/or period. Notable early examples include ANDES 14C in 1994 (Central Andes;Michczyński et al. 1995), CARD (Canada;Gajewski et al. 2011) and RADON (Europe;Raetzel-Fabian 1999) in 1999, and CANEW in 2001 (Near East;Reingruber and Thissen 2005). From 2010, coinciding with broader shifts in scientific publishing (Tenopir et al. 2011), it became more common to publish standalone ‘open data’ products in the form of journal supplements, archives in repositories and/or data papers; the Journal of Open Archaeology Data, launched in 2012, has been a prominent venue for this latter category. Most recently there has been a trend towards providing version-controlled plain text data via platforms such as GitHub, reflecting the broader adoption of these tools amongst computational archaeologists over the last decade (Batist and Roe 2024). The shift from online databases towards more static but more preservable open data products is welcome, given how many databases from the first generation have subsequently ceased to be accessible. Version-controlled repositories are particularly well-suited to data compilation projects because they allow for continued updates whilst still providing snapshot ‘releases’ that are citeable and can be archived in long-term repositories.

Figure 1 

Cumulative number of radiocarbon compilations published since 1995.

Although this body of work has greatly improved the accessibility of radiocarbon dates and supported significant methodological advances (Crema 2022;Crema et al. 2024), some limitations are apparent.

The geographic coverage of regional radiocarbon compilations is markedly uneven (Figure 2). Europe and, especially, North America are over-represented (Alcántara and Pedroza 2025;Chaput and Gajewski 2016). South America, West Asia, and East Asia are reasonably well-covered, but there are practically no systematically compiled dates from East or West Africa, Central or South Asia, or Mainland Southeast Asia. This is probably explained in part by a lower volume of archaeological research and access to radiocarbon dating in these regions, but a lack of attention in compilation work must also be a factor. For example, radiocarbon dating has been an established part of Indian archaeology since at least 1961 (Kusumgar, Lal and Sarna 1963), but we have not been able to locate a single systematic compilation of dates from South Asia.¹

Figure 2 

Geographic coverage of published regional radiocarbon compilations according to our survey (binned by UN M49 macroregion).

Datasets based on literature review also become out of date almost immediately upon publication, due the constant production of new dates. Unfortunately, this applies to many databases that are in theory continuously updated, as it is common to see them become unmaintained and or unexpectedly become unavailable. Of the 61 published datasets we identified, 33 were intended to be continuously updated, but only 13 have received updates in the last two years. The mean ‘lifespan’ of a dataset from its publication to its last update is around 4 years. Most radiocarbon datasets we reviewed were compiled with a specific goal in mind (e.g., a particular analysis) and, even where there is the intention to keep them updated afterwards, the exigencies of scientific production combined with the labour-intensive nature of the process make that difficult to achieve in practice.

Laboratory databases solve the problem of currency but tend to have more arbitrary coverage, since the inclusion of data is determined by who submits dates to that lab, not any form of principled curation. There are also comparatively few of them — most active labs no longer directly publish dates that they produce (if they ever did).

Other outstanding problems with existing compilations include various systematic biases in data collection (Clist, Denbow and Lanfranchi 2023) and a large degree of overlap and duplication between individual databases. For example, we identified 9 different resources covering Western Europe but none covering South Asia. The quality and accessibility of published compilations is also variable. Fifty of the 61 resources we reviewed are not ‘open’ according to the Open Knowledge Foundation’s definition of data openness (“Open data and content can be freely used, modified, and shared by anyone for any purpose”;Open Knowledge Foundation n.d.), which both limits the access to and reuse potential of these datasets. And even of these, many are not currently available in readily machine-readable formats (e.g., plain text or database files rather than PDFs or hypertext).

The fragmentation of the radiocarbon record into regional datasets also hinders analysis at larger scales. Although the core elements of a radiocarbon date — laboratory identifier, radiocarbon age, measurement error — are more or less standardised, there is no such consistency in contextual information on the sample or site. Such contextual information is important not just for the interpretation of dates, but for filtering out unreliable dates based on sample information (‘chronometric hygiene’ sensuPettitt et al. 2003) and for correcting for known systematic errors such as the marine reservoir effect (Alves et al. 2018). Most published datasets incorporate all or part of earlier compilations, meaning duplicate records are also very common, but deduplicating them is not a trivial problem due to format variations (see Section 4.2). These issues are by no means impossible to overcome but add a significant amount of data-cleaning effort to a process that would otherwise be very amenable to standardisation.

2.2 Global radiocarbon compilations

The profusion of radiocarbon compilations over the last decade has naturally prompted many to think globally. Three existing initiatives in particular share similar aims to XRONOS (at least as far as radiocarbon is concerned): c14bazAAR, IntChron, and p3k14c.

The first available synthetic radiocarbon database was c14bazAAR (Schmid, Seidensticker and Hinz 2019), an R package that provides an index of openly published radiocarbon databases and a common interface for retrieving them and performing basic data cleaning. Because c14bazAAR downloads data from its original source repositories, rather than mirroring it, it only includes resources that have been published in a fully open and machine-accessible format. Despite this limitation, it has global coverage and a large number of dates (Figure 3) and was therefore our starting point for data collection for XRONOS.

Figure 3 

Geographic (quantile kernel density) and temporal (sum calibration) of georeferenced dates in XRONOS and other global radiocarbon compilations.

Another indexical approach is taken by the IntChron project (Bronk Ramsey et al. 2019), which exposes data from multiple sources and exposes them with a common JSON-based web interface. The IntChron specification is open, meaning that radiocarbon labs or compilation projects can implement it independently and thereby allow end users to access their data through a common interface (though to our knowledge it has so far only been adopted by databases associated with the Oxford Radiocarbon Lab). The JSON format also lends itself to the implementation of wrapper libraries, for example the rIntChron package gives direct access to IntChron-indexed databases in R (Roe 2024).

The p3k14c database (Bird et al. 2022) instead compiles multiple source databases into a single flat file dataset, with a similar level of coverage to c14bazAAR. The major advantage of this approach is that the data is made internally consistent and has been manually cleaned to some extent, which makes it particularly well-suited to global analyses. The downside is that without the continuous link to the source databases present in the c14bazAAR and IntChron, it can only be kept up to date manually with periodic re-releases. An accompanying package (Bird, Bocinsky and Vander Linden 2024) provides direct access to the p3k14c dataset in R.

As of December 2024, c14bazAAR had 118,071 radiocarbon dates with unique laboratory identifiers (excluding those sourced from p3k14c and XRONOS), IntChron had 12,388 (excluding those from non-archaeological contexts), and p3k14c had 176,016. The geographic distribution of dates from each is similar (Figure 3), reflecting the large degree in overlap between the sources of each compilation. IntChron, which in practice is currently only used to publish dates associated with the Oxford Radiocarbon Lab, has dates from more diverse contexts, but is an order of magnitude smaller.

3. Concept

XRONOS inherits its basic structure from RADON (Hinz et al. 2012;Kneisel, Hinz and Rinne 2014;Raetzel-Fabian 1999;Rinne et al. 2024), with a database-backed web application and a data model that separates radiocarbon dates, contextual information, and sites. Our overall aims in developing XRONOS is to bring this model, which RADON has operated on for more than twenty years, up to date, to generalise it to other types of chronometric information, and to transform it from an online database to a data infrastructure that supports the continuous ingestion, curation, and open dissemination of archaeological chronologies from diverse sources.

3.2 Data model

At the base of the XRONOS data model (Figure 4) are sets of spatiotemporal coordinates or, as we call them,chrons. In an archaeological context, we conceptualise a chron as an assertion linking human activity with a particular point in space and time. Our data model currently encompasses three types of chron: radiocarbon dates, typological dates (e.g., ‘Early Neolithic’) and dendrochronological dates. However, we anticipate that the concept will accommodate other types of absolute and relative dating techniques, as the scope of the database expands.

Figure 4 

Simplified entity relationship diagram showing the XRONOS data model.

Chrons are conceptually useful because they emphasise that different types of archaeological ‘dates’, drawn from different sources, have essentially the same information content: the location of an event in space and time. We thereby avoid privileging certain sources of chronological data over others (as might be the case if, for example, we treated ‘period’ as a fixed attribute of a site) and can accommodate contradictory information (e.g., differences of opinion on typological classification). This is important given that XRONOS aspires to be an authoritative ‘backbone’ with a global scope, so we cannot realistically impose a single chronological scheme or resolve conflicting information provided by specialists. Chrons are useful practically because they expose a common interface for attributes that all types of chronological information share, such as aterminus post quem (TPQ),terminus ante quem (TAQ), and midpoint estimate. This allows applications to query chronological data from multiple sources, without necessarily having to be aware of the peculiarities of each type of dating.

In order to unify chronological information in the form of a chron, we need a common chronological ‘coordinate system’. The natural choice is acalendar probability distribution, which expresses the probability that an event occurred as a function of time on a calendric scale. Most archaeologists are familiar with working with this kind of representation in the form of calibrated radiocarbon dates, but it can be extended and generalised to essentially any kind of chronological information. For example, in aoristic analysis (Mischka 2004), a periodic time estimate (e.g., the event occurred in the Neolithic) is conceptualised as a uniform probability distribution over the timespan between the known start and end dates of that period. A similar model is used in OxCal (Bronk Ramsey 2009, a direct inspiration for our approach) to integrate prior chronological information from diverse sources. In practical terms, this model means that the canonical representation the time component of any chron in XRONOS, regardless of source, is a probability distribution over the set of calendar years (arbitrarily measured in years Before Present) in which it could have plausibly occurred. Further statistics, e.g., a midpoint estimate or TPQ/TAQ range, can be derived from this distribution using well-known methods. In this way, we can support many different types of dates and much of the implementation of XRONOS can be agnostic to the source of chronological information.

Chrons are located in space through association to asample — the physical object from which a chronological determination was made. The location of samples is represented with geographical coordinates and an associated coordinate reference system (CRS), though, since in practice the precise location of single samples is rarely available, this property is usually inherited from the site. We also record relevant metadata on the nature of the sample. For radiocarbon dates, for example, we follow established conventions (Millard 2014) in recording the type (e.g., charcoal, charred seed) and, where applicable, taxonomic designation (e.g.,Quercus, Triticum dicoccum) of the organic material used for dating. For typological dates, an ideal scenario would be for the sample to represent the specific object from which an inference was made (e.g., ‘Natufian’ might be inferred from ‘lunate-type microlith’). In practice, the best we can glean from most published datasets is the type of material used (e.g., ‘pottery’, ‘lithics’). The same sample can be associated with multiple chrons, including different types of chron. This is useful, for example, for representing replicate radiocarbon dates on the same sample, or radiocarbon dates and dendrochronological dating made on the same section of wood for wiggle-matching.

Further contextual information is associated withcontexts andsites. The site is the primary geographic container for chronological information. As already mentioned, we typically record the spatial location of chrons using this entity, though it is possible to modify this by providing specific coordinates at the sample level. Sites also have attributes describing their conventional name or names in different languages and are associated with a flexible ‘site type’ typology that combines information on their form and function.

A context represents the specific find-context of a sample, e.g., an architectural feature, stratigraphic unit, or phase. Since the units and conventions for recording such information vary greatly between different regions and archaeological traditions — and XRONOS is designed with global data in mind — we leave the question of what a context precisely represents open, and only record an unstandardised, free text label for it. Crucially, however, contexts can have a self-referential association to other contexts belonging to the same site. This allows it to encode arbitrary relational structures between contexts, whether they be hierarchical (e.g., phases and sub-phases) or graphical (e.g., stratigraphic). In this way, it can serve as a foundation for chronological modelling.

The series of relations(chron) > sample > context > site links the chronological and contextual sides of the XRONOS data model. Each step is a many-to-one association, meaning for example that it is possible to attach multiple chrons to the same sample (e.g., replicated radiocarbon dates on the same material), multipletypes of chrons to the same sample (e.g., radiocarbon dates on tree-rings for wiggle-matching). Since this kind of information is rarely systematically recorded in our source databases, there are currently few actual records that make use of this feature of the data model. However, we hope it will provide a foundation for more nuanced chronological modelling in the future.

Metadata is incorporated into XRONOS’ data model at the level of the individual records (e.g., all records store their data of creation and last modification) and through two additional types of record: bibliographicreferences andversions. Bibliographic references store information on the source of a record in BibTeX format and can be linked through many-to-many associations to sites or chrons. Versions are a special type of record that are associated with all other records (including bibliographic references) and store the previous versions of those records as a series of changesets. In this way all changes to data are recorded and can be reconstructed (or reversed) precisely. This ‘paper trail’ also stores contextual metadata, e.g., who made the change and why. It also means that records that are deleted can be reviewed or restored from their stored version history, which is never discarded. Together these two systems provide a transparent record where the data in XRONOS comes from and how it has been altered, which we view as essential in a scientific data infrastructure.

4. Implementation

Following a short pilot project in 2019, the first phase of development of XRONOS was completed in 2021–2024. The web interface (https://xronos.ch) has been publicly accessible since July 2021. Though we envisage XRONOS as a continuously developing open-source project and ‘living database’, the following offers a snapshot of progress at the end of our first grant-funded implementation phase. We do not aim to be comprehensive, but rather to describe some key elements of XRONOS’ current implementation that illustrate how our concept has been realised in practice.

4.3 User interfaces

The graphical user interface (GUI) to XRONOS, accessed through a web browser (e.g., athttps://xronos.ch), uses REST resources and actions as the building blocks for various interfaces through which users can browse, search, retrieve, and analyse chronometric data (Figure 5). Each action on each resource is represented by a page, though not all of these are publicly accessible. The most import of these are the ‘index’ views, which list and summaries all instances of a resource and support filtering and sorting; and ‘show’ views (e.g.,Figure 5b for a site), which give a more comprehensive information on an individual record along with visualisations, links to related records, external linked open data resources, and a log of changes made to the data since it was imported into XRONOS (Figure 5c). Pages representing REST resources directly are supplemented by a number of synthetic interfaces, for example the ‘data browser’ (https://xronos.ch/data,Figure 5a), which facilitates more complex filtering, or the search interface (https://xronos.ch/search). The GUI also includes several resources which are not part of XRONOS’ scientific data model, for example documentation pages, user profiles and news articles; these are not exposed in the API.

Figure 5 

Views in the XRONOS web graphical user interface.

Access to various ‘backstage’ interfaces for creating, editing, and deleting data, and monitoring data quality is managed using a user permissions system. Currently only authorised users affiliated with the XRONOS project can access these, but in the future, we intend to support open registration and expose editing interfaces to all authenticated users. For this reason, there is no sharp division between a ‘public’ and ‘private’ areas — viewing/querying data and editing/curating data share the same architecture and interface patterns.

The XRONOS API uses the same addresses as the web-based GUI (with the exception of some of the synthetic interfaces mentioned above) but responds with machine-readable data in JSON format, rather than a HTML page. This response can be triggered by appending.json to the address or by including a HTTPcontent-format header in the request. Though users can make such requests manually and parse the data with one of several off-the-shelf tools, the primary intended use of this interface is to provide access for 1) programmatic clients to XRONOS and 2) other web services. The XRONOS R package (Hinz and Roe 2024) is an example of a programmatic client; it uses the API to facilitate direct querying and retrieval of data from XRONOS in the R statistical programming language (R Core Team 1999–2025). Similar libraries could be developed in other programming environments used for scientific computing, such as Python (https://www.python.org/) or Haskell (https://www.haskell.org/). The API also provides the foundation for other web services to access XRONOS directly, to embed chronological information in other contexts or otherwise make use of its data resources.

An overarching principle of this software architecture is that all interaction with XRONOS’ data store, and as much of the data processing and ‘business logic’ of responding to REST requests as possible, is directed through the same server-side routines. First and foremost, this allows us to provide multiple interfaces (i.e. the GUI and API, perhaps more in the future) without duplicating these elements of our codebase. It also improves accessibility for users accessing XRONOS through devices with limited processing capability or through text-only browsers. More broadly, avoiding reliance on client-side processing, e.g., with Javascript (ECMAScript,https://ecma-international.org/publications-and-standards/standards/ecma-262/) or Web Assembly (WASM,https://webassembly.org/) — which would be the other option — allows us keep our client interfaces simple (in most cases plain, semantic HTML pages and self-contained stylesheets) and therefore, we hope, sustainable in the face of constantly-evolving client-side technologies and standards. It does have the weakness that, in practical terms, XRONOS is difficult to run and relies on the continued existence of a maintained external server. We have however tried to mitigate this by providing clearly documented source code and regular data dumps so that, if our instance of XRONOS disappears, or if one simply does not want to use it, it is possible for others to host a XRONOS server of their own. As the sustainability of scientific software and data infrastructures is a pressing problem, in the future it may be desirable to support further decentralisation through, for example, a federated server-server model.

5. Evaluation

In this paper we have outlined the conceptual and technical infrastructure developed during the initial, SNF-funded phases of development on XRONOS in 2019 and 2021–2024. These include a generalised data model for radiocarbon and typological dates, extendible to other chronometric information, and associated contextual information; an R- and Ruby-based pipeline for continuous ingestion of data from a variety of sources; continuous, semi-automated data cleaning protocols; a Ruby-on-Rails application providing a web-based frontend to the data and a REST API for programmatic access; and an R package for interfacing with the API. These systems are deployed and publicly accessible athttps://xronos.ch.

At the current stage of implementation, we argue that XRONOS provides a framework for access to chronometric data that is more open, more reliable, and more comprehensive than the previously available global radiocarbon compilations. XRONOS blends aspects of the three existing approaches (c14bazAAR, IntChron, and p3k14c; see Section 2.2) to achieve the same aim of providing access to the global radiocarbon data through a common interface. Like c14bazAAR and IntChron, it is a ‘metadatabase’ that draws from existing data resources and maintains an explicit link to them. But like p3k14c, it integrates these into a single database and applies data curation processes to harmonise them and improve the quality of the information. It has a wider scope than c14bazAAR or IntChron, as it mirrors rather than directly retrieves the source data (allowing us to use resources that aren’t openly published) and does not rely on the authors of these sources to implement a common specification. It also goes beyond the functionality of p3k14c by providing systems for the continuous ingestion and curation of new data as it is published. With that said, we see the approaches followed by as complementary rather than competing. A parser for XRONOS has been contributed back to c14bazAAR and we also aim to provide an IntChron interface to XRONOS’ data soon. New data and corrections from p3k14c are incorporated into XRONOS as they are released.

From 2025, development of XRONOS will continue within the framework of ‘ESTER’ (https://ester-project.org/), an ERC-funded research project on estimation of prehistoric population development from large, multiproxy datasets. Our immediate development goals include the incorporation of dendrochronology into the database, further refinement of our data curation pipelines, and the public release of the editing interfaces.

Looking beyond the near term, we have endeavoured to create a sustainable infrastructure that can be maintained by a wider scholarly commons — though we must acknowledge that this is a difficult problem, and one that is as much organisational than technical. The source code for all the software components of the system is available online (athttps://github.com/xronos-ch) under open licenses. The databases of the instance athttps://xronos.ch are also archived with Zenodo athttps://doi.org/10.5281/zenodo.14282598. With these two resources we reduce XRONOS’ ‘bus factor’: if we are not able to continue operating XRONOS, somebody else can fully recreate it. Equally importantly, we enable the ‘right to fork’ should others wish to take the software and/or database in another direction. But aside from these extreme scenarios, the long-term sustainability of XRONOS is contingent on the existence of a community of scholars that use and contribute back to it.

Data Accessibility Statement

The data and R code used to produce our analysis of the state of the art in radiocarbon compilation, including all the figures presented here, can be accessed via Zenodo athttps://doi.org/10.5281/zenodo.14282598.

The database and software described in this paper is open source and can be accessed athttps://github.com/xronos-ch.

Funding Statement

This work was funded by the Swiss National Science Foundation (SNSF Project #198152; SNSF Project #194326) and the University of Bern (UniBE Initiator Grant 2019).

Acknowledgements

The XRONOS project was carried under the direction of Albert Hafner. Individual contributors to the XRONOS database to date include Chiara Huwiler, Rivana Moser, Tomasz Chmielewski, and Stephanie Döppler. A complete and up-to-date list of acknowledgements for the project can be found athttps://xronos.ch/about/acknowledgements.

We are also grateful to the two anonymous reviewers of our manuscript, whose comments have improved the finished paper.

Competing Interests

The authors have no competing interests to declare.

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