# The CHIPS Database: A Repository for Reference Data on Chemical Analysis in Archaeometallurgy of Iron

Alexandre Disser, Sylvain Bauvais, Huet Thomas, Laurent Costa, Thomas

Birch, Philippe Dillmann

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Alexandre Disser, Sylvain Bauvais, Huet Thomas, Laurent Costa, Thomas Birch, et al.. The CHIPS Database: A Repository for Reference Data on Chemical Analysis in Archaeometallurgy of Iron. 2025. hal-05070638

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# The CHIPS Database: A Repository for Reference Data on Chemical Analysis in Archaeometallurgy of Iron

Alexandre Disser*<fnref n="1" />, Sylvain Bauvais<fnref n="2" />, Thomas Huet<fnref n="1" />, Laurent Costa<fnref n="3" />, Thomas Birch<fnref n="4" />, Philippe Dillmann<fnref n="1" />

## ABSTRACT

Archaeometallurgists interested in iron do not currently have access to extensive chemical reference data repositories. Yet chemical data is of crucial importance both for reconstructing ancient technical systems and economic structures, by determining the operating conditions and the exchange networks. We are seeking to meet this need with the CHIPS database, developed on PostgreSQL. The goal is to provide the scientific community with global reference data in a standardised format. Chemical data can be selected using spatial, chronological or typological queries, thanks to the documentation of archaeological contexts and samples. Particular care has been taken to assess the quality of the chemical data, based on analytical metadata (types of analysis and technical characteristics of the analysers). In order to guarantee the interoperability of the database, the main tables are referenced by libraries, gazetteers and thesauri that are considered as standards by the archaeological community. The data can currently be consulted using a cartographic interface, and the development of a more advanced interface is envisaged, based on the implementation of graphic display tools for chemical data.

**Keywords:** chemical data ; archaeomaterials ; archaeometallurgy ; archaeometry

<footnote n="1">¹ Laboratoire Archéomatériaux et Prévision de l'Altération, IRAMAT UMR 7065 CNRS & NIMBE UMR 3685 CEA CNRS, Gif-sur-Yvette, France</footnote>

<footnote n="2">² Centre de Recherche Français de Jérusalem, UMIFRE 7 MEAE CNRS, Jérusalem, Israël</footnote>

<footnote n="3">³ Archéologie et Sciences de l'Antiquité, ArScAn UMR 7041, Nanterre, France</footnote>

<footnote n="4">⁴ Geosciences department, Aarhus University, Aarhus, Denmark</footnote>

<footnote>*Corresponding author</footnote>

<footnote>Correspondence: alexandre.disser@cea.fr</footnote>
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# 1. Introduction:

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Despite their being consensus for a shared repository for data associated with ironworking (Charlton, 2015), no initiative to establish a global source of reference data had been undertaken until now in the iron archaeometallurgy research field. However, a similar effort has been made by a closely related scientific community interested in amalgamating lead isotope data from non-ferrous metalworking (copper alloys or lead/silver metallurgy), along with metadata (i.e. geo-locations, dating, chemical data); known as TerraLID, the initiative was set up in 2009 and now exists on a web portal for common use (Klein et al., 2022). Such initiatives, when they come to fruition, can prove extremely beneficial for a community of researchers. The CHIPS database is intended to play such a role for archaeometallurgists of iron. Several members of the Institut de Recherche sur les Archéomatériaux (IRAMAT-CNRS) set about designing a database (DB) management system enabling researchers to select geolocated reference data easily. An interface is being developed in order to facilitate data queries either on categorical (chronology, typology, etc) or spatial data (https://tinyurl.com/CHIPSdatabase). Particular effort has been devoted to documenting analytical metadata (analytical conditions, analysis laboratories), which is essential for assessing data quality.

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The aim of this article is therefore to present in detail the choices made to design the database data model, collate heterogeneous datasets and provide queryable attribute descriptors. Some of the functions of information system are still under development, but the DB can already be used to make basic queries. It is being populated mainly from data that has already been published. A community of users is in the process of building up around this tool, which could rapidly become an essential reference for future research into iron archaeometallurgy.

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# 2. Background :

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The study of the *chaînes opératoires* and the circulation of materials (mainly ore and metal products) is one of the main thrusts of research into iron archaeometallurgy. These research topics rely heavily on the analysis of chemical data. The chemical composition of products and waste associated with iron- and steelmaking activities enables us, to some extent, to reconstruct the physico-chemical conditions in which metallurgical operations took place (ore preparation, direct smelting, primary and secondary smithing) and therefore to better understand the ways of the ancient metallurgists (Blakelock et al., 2009; Charlton et al., 2010; Dillmann and L'Héritier, 2007; Serneels, 1993; Serneels and Crew, 1997). These studies are necessary to understand the history of techniques, from both materials (physico-chemical processes) and socio-economic perspectives. In addition, several recent studies have shown that it is possible to establish a chemical link between the extracted ore, the workshop that reduced it and the metal products produced by this workshop. These studies have evaluated the potential of numerous chemical markers (Charlton et al., 2012; Dillmann et al., 2017; Disser et al., 2016; Milot et al., 2016; Pagès et al., 2022; Stepanov et al., 2020; Żabiński et al., 2020). These approaches make it possible to partially reconstruct economic systems that have now disappeared, by analysing the circulation of materials (routes taken, distances, etc.).

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For some forty years, researchers interested in ancient metallurgy have been accumulating chemical data to support their research. However, this documentation is widely dispersed due to the variety of media used to publish the results: printed sheets, tables in PDF or MS Word publications, spreadsheets, and now also raw data files with scripts (for reproducing analyses with statistical software). In addition, the analytical devices used to acquire these data use different physical phenomena (e.g. X-ray fluorescence spectroscopy, mass spectrometry, neutron activation) and have their own characteristics and performance (detection limits, measurement errors, and signal resolution).
Finally, archaeometallurgists have various disciplinary backgrounds: geochemistry, materials science and archaeology, which influences the way they represent data. The choices vary according to the authors: stoichiometric representations, quantification in elemental or oxide form, etc. This heterogeneity makes it difficult to collate data for collective use. Collecting, sifting through and merging various datasets to build reference data is a very time-consuming task for individual researchers. A single given project is unlikely to exploit the full potential of the data it generates (sometimes only appreciated with hindsight and in comparison with new datasets), and so the whole point of the semantic web, linked open data (LOD) and the FAIR (Easy to Find, Accessible, Interoperable, Reusable) guidelines is to help align data with standards (i.e. standardisation) to create possibilities for gaining new insights and new opportunities/methods for analysing data.

## 3. Design of the CHIPS database

The design of the CHIPS (CHImie en PaléoSidérurgie) database followed an initial attempt to create a repository in tabular form, but the need to structure the information in the form of a relational DB soon became obvious. It was therefore decided to develop a PostgreSQL DB. Among open-source database management system software, PostgreSQL is the closest to Oracle, the most popular system in the sector of information systems. In addition to the use of SQL, this choice was mainly motivated by the possibility of using its spatial extension PostGIS, which offers the functionalities required for the management of spatialised data, in particular the use of spatialised indexes and geoprocessing. The data is structured around four main tables, which respectively document the archaeological context of discovery ('sites'), the archaeological material ('samples'), the analysis carried out ('chips') and the associated measurement errors ('measurement errors'). These four tables will successively be described, along with the parent tables that document each of them. The simplified common data model (CDM) is shown in Figure 1.

<figure><img src="image_2.png" /><figcaption>Figure 1: Simplified MCD of the CHIPS DB</figcaption></figure>
## 3.1. 'Sites' table

It is composed of 12 fields (primary key: `site_id`) (Table 1). Its purpose is to identify and locate the archaeological contexts, link them to geoadministrative data, and define their chronology. The `locality_id` field acts as a foreign key to the `gadm` parent table. The Global Administrative Areas (GADM) dataset, published online by UC Davis, contains around 400,000 administrative entities worldwide, documented at five hierarchical levels, from country to municipality (Global administrative areas boundaries, 2022). Four fields provide spatial information. The `srid` field is documented by the `spatial_ref_sys` table associated with the PostGIS extension, and is used to interpret the coordinates in the `long` and `lat` fields (decimal degrees or projected coordinates). In the source documents, the geographic coordinates are expressed in a variety of projection systems, depending on the location of the study area or national administrative recommendations. For France, for example, coordinates are frequently published in the Lambert-93 system (EPSG:2154) (International Organization for Standardization, 2019). The Boolean `centroid` field indicates whether the coordinates are those of the site, or those of the administrative entity's centroid. Three fields are associated with chronology. We have chosen to represent the chronology by means of milestones, `start_date` and `end_date`, expressed in years of the Gregorian calendar. The Boolean `uncertain_date` indicates whether these milestones are reliable or not. The `periodo_id` field links the chronological data to the Periodo gazetteer to ease the definition of the activity periods. The last field, `points`, is made up of objects of type GEOGRAPHY, produced using the PostGIS ST_MakePoint geospatial function.

<table><thead><tr><th>site_id</th><th>site_name</th><th>locality_id</th><th>srid</th><th>long</th><th>lat</th><th>centroid</th></tr></thead><tbody><tr><td>integer<br>primary key</td><td>text</td><td>integer<br>foreign key</td><td>integer<br>foreign key</td><td>numeric</td><td>numeric</td><td>boolean</td></tr><tr><td>101</td><td>Fristingen</td><td>69449</td><td>4326</td><td>10.552</td><td>48.5564</td><td>True</td></tr><tr><td>102</td><td>Fruchteim</td><td>61792</td><td>4326</td><td>11.1978</td><td>48.7332</td><td>True</td></tr><tr><td>105</td><td>Grillheim</td><td>62761</td><td>4326</td><td>11.416</td><td>48.6756</td><td>True</td></tr><tr><td>108</td><td>Houdouard</td><td>68928</td><td>4326</td><td>0.1205</td><td>48.0648</td><td>True</td></tr><tr><td>109</td><td>Kicklingen</td><td>63611</td><td>4326</td><td>10.5933</td><td>48.5703</td><td>True</td></tr><tr><td>110</td><td>Kuhnbusch</td><td>61272</td><td>4326</td><td>8.651</td><td>48.8287</td><td>True</td></tr><tr><td>106</td><td>Grosseltal</td><td>61139</td><td>4326</td><td>8.6138</td><td>48.8327</td><td>True</td></tr><tr><td>107</td><td>Herrlenbusch</td><td>61139</td><td>4326</td><td>8.6138</td><td>48.8327</td><td>True</td></tr><tr><td>100</td><td>Foret<br>domaniale 427</td><td>62890</td><td>4326</td><td>6.0611</td><td>48.6414</td><td>False</td></tr><tr><td>103</td><td>Gaillefontaine</td><td>53374</td><td>4326</td><td>1.6152</td><td>49.6527</td><td>True</td></tr><tr><td>104</td><td>Glinet</td><td>53510</td><td>4326</td><td>1.5656</td><td>49.6725</td><td>False</td></tr></tbody></table>
<table><caption>Table 1: View created from the 'sites' table, showing 11 site examples</caption><thead><tr><th>start_date</th><th>end_date</th><th>uncertain_d<br>ate</th><th>periodo_id</th><th>points</th></tr></thead><tbody><tr><td>integer</td><td>integer</td><td>boolean</td><td>text</td><td>geometry</td></tr><tr><td>NULL</td><td>NULL</td><td>NULL</td><td>NULL</td><td>0101000020E6100000B4C<br>876BE9F1A254048BF7D1<br>D38474840</td></tr><tr><td>NULL</td><td>NULL</td><td>NULL</td><td>NULL</td><td>0101000020E6100000304<br>CA60A46652640AEB6627<br>FD95D4840</td></tr><tr><td>NULL</td><td>NULL</td><td>NULL</td><td>NULL</td><td>0101000020E6100000A24<br>5B6F3FDD42640BC96900<br>F7A564840</td></tr><tr><td>NULL</td><td>NULL</td><td>NULL</td><td>NULL</td><td>0101000020E61000000C0<br>22B8716D9BE3FF163CC5<br>D4B084840</td></tr><tr><td>NULL</td><td>NULL</td><td>NULL</td><td>NULL</td><td>0101000020E6100000006<br>F8104C52F254054742497<br>FF484840</td></tr><tr><td>NULL</td><td>NULL</td><td>NULL</td><td>NULL</td><td>0101000020E61000005A6<br>43BDF4F4D2140FC1873D<br>7126A4840</td></tr><tr><td>-460</td><td>-250</td><td>False</td><td>http://n2t.ne<br>t/ark:/99152/<br>p02chr44tnn</td><td>0101000020E6100000D19<br>15CFE433A21408AB0E1E<br>9956A4840</td></tr><tr><td>-460</td><td>-250</td><td>False</td><td>http://n2t.ne<br>t/ark:/99152/<br>p02chr44tnn</td><td>0101000020E6100000D19<br>15CFE433A21408AB0E1E<br>9956A4840</td></tr><tr><td>1400</td><td>1500</td><td>True</td><td>http://n2t.ne<br>t/ark:/99152/<br>p02chr49ctv</td><td>0101000020E6100000742<br>497FF903E1840C3D32B6<br>519524840</td></tr><tr><td>1400</td><td>1600</td><td>False</td><td>http://n2t.ne<br>t/ark:/99152/<br>p02chr49ctv</td><td>0101000020E6100000B9F<br>C87F4DBD7F93FB30C71<br>AC8BD34840</td></tr><tr><td>1400</td><td>1600</td><td>False</td><td>http://n2t.ne<br>t/ark:/99152/<br>p02chr49ctv</td><td>0101000020E6100000B1E<br>1E995B20CF93FAE47E17<br>A14D64840</td></tr></tbody></table>

## 3.2. 'Samples' table

It is composed of 5 fields (Table 2). Two fields identify the sample: 'sample_id' is the table's primary key and 'sample_name' field is the identifier given by the archaeologist during the post-excavation study of the material, or a code given when the sample was taken. 'site_id' is the foreign key referencing the context in which the artefacts were found. A 'comment' field allows a more precise yet less standardised description of the sample if needed. The 'id Typo' field is filled in by the 'typology' table, the aim of which is to obtain as precise information as possible on the artefacts, while ensuring the effectiveness of categorical queries.
The 'typology' table therefore has a hierarchical structure with five levels (Table 3). The first level 'process' corresponds to the ironmaking process in question, i.e. the direct or indirect process. Establishing this difference is fundamental, since the *chaînes opératoires* and physicochemical processes involved are radically different. The data associated with either process cannot therefore be compared directly. The 'context' field specifies whether the material was collected in a geological (i.e. extra-site, for ore or earth), archaeological (i.e. intra-site) or experimental context. The 'material' field indicates the nature of the artefact: slag, earth, ore, rock, etc. The 'category' field is the first level of qualification for the material. For the material slag, this qualifier could be ore smelting or metal forging, for example. The 'subcategory' field is the second level of qualification, which can give even more details about the material. For a smelting slag, this second level can be used to determine whether the slag is moulded at the bottom of the furnace, or whether it has flowed out of the furnace. This level of precision is necessary to accurately interpret the archaeological context and the associated chemical data. A last field 'ark_frantic' references each typological entity with an ARK permanent identifier linked to the Pactols thesaurus. This aspect will be further developed in section 4.1.

<table><caption>Table 2: View created from the 'samples' table, showing 11 sample examples</caption><thead><tr><th>sample_id</th><th>sample_name</th><th>site_id</th><th>typology_id</th><th>comments</th></tr></thead><tbody><tr><td>integer<br>primary key</td><td>text</td><td>integer<br>foreign key</td><td>integer<br>foreign key</td><td>text</td></tr><tr><td>6797</td><td>VG_1b</td><td>1744</td><td>24</td><td>Microlammellar hematite</td></tr><tr><td>6805</td><td>TN-2</td><td>1746</td><td>24</td><td>Lamellar/micaceous hematite with pyrite</td></tr><tr><td>6813</td><td>6997</td><td>1748</td><td>24</td><td>Massive goethite with calcite</td></tr><tr><td>6795</td><td>VIG_M</td><td>1742</td><td>24</td><td>Lamellar/micaceous hematite</td></tr><tr><td>6819</td><td>4526/b</td><td>1751</td><td>24</td><td>Hematite-bearing schist</td></tr><tr><td>6809</td><td>TN-9</td><td>1746</td><td>24</td><td>Micaceous hematite</td></tr><tr><td>6836</td><td>11007</td><td>1764</td><td>24</td><td>Micaceous hematite</td></tr><tr><td>6824</td><td>901</td><td>1756</td><td>24</td><td>hematite with calcite</td></tr><tr><td>6812</td><td>2914</td><td>1748</td><td>24</td><td>Brown-reddish massive goethite</td></tr><tr><td>6838</td><td>16259</td><td>1765</td><td>24</td><td>Fibrous-radiated hematite</td></tr><tr><td>6814</td><td>7231</td><td>1749</td><td>24</td><td>Micaceous hematite (Montomoli shaft)</td></tr></tbody></table>
<table><caption>Table 3: View created from the 'typology' table, showing 11 type examples</caption><thead><tr><th>typology_id</th><th>process</th><th>context</th><th>material</th><th>category</th><th>subcategory</th><th>ark_frantic_uri</th></tr></thead><tbody><tr><td>integer<br>primary key</td><td>text</td><td>text</td><td>text</td><td>text</td><td>text</td><td>text</td></tr><tr><td>161</td><td>Indirect</td><td>Experimental</td><td>Metal</td><td>Cast iron</td><td>NULL</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrtTc<br>vLDTNT86</td></tr><tr><td>190</td><td>Direct</td><td>Archaeological</td><td>Earth</td><td>Molten</td><td>NULL</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrt5b<br>IL1UtwcT</td></tr><tr><td>15</td><td>Direct</td><td>Archaeological</td><td>Slag</td><td>Smithing</td><td>Hearth<br>bottom</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrtfvl<br>QbnsSeS</td></tr><tr><td>16</td><td>Direct</td><td>Experimental</td><td>Slag</td><td>Smithing</td><td>Hearth<br>bottom</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrtfvl<br>QbnsSeS</td></tr><tr><td>3</td><td>Direct</td><td>Archaeological</td><td>Slag</td><td>Smelting</td><td>NULL</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrtzh<br>OWTynBny</td></tr><tr><td>24</td><td>Unknown</td><td>Geological</td><td>Ore</td><td>Smelting</td><td>NULL</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrt5b</td></tr><tr><td>5</td><td>Direct</td><td>Archaeological</td><td>Slag</td><td>Smelting</td><td>Tap slag</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrtzh<br>OWTynBny</td></tr><tr><td>10</td><td>Direct</td><td>Experimental</td><td>Slag</td><td>Smelting</td><td>Slag block</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrtzh<br>OWTynBny</td></tr><tr><td>63</td><td>Direct</td><td>Archaeological</td><td>Earth</td><td>Baked clay</td><td>Lining</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrtfx<br>4s0yvSLj</td></tr><tr><td>69</td><td>Indirect</td><td>Archaeological</td><td>Slag</td><td>Smelting</td><td>Blast furnace<br>slag</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrtzh<br>OWTynBny</td></tr><tr><td>62</td><td>Direct</td><td>Experimental</td><td>Earth</td><td>Baked clay</td><td>Tuyere</td><td>https://ark.frantiq.f<br>r/ark:/26678/pcrtwJ<br>getoWYz9</td></tr></tbody></table>

### 3.3. 'Chips' table

This table contains 77 fields. The 'sample_id' field is the foreign key to the 'samples' table. Three other fields are foreign keys to a table listing the analytical devices used to perform the analyses. We distinguish between 'major_setup_id', 'trace_setup_id' and 'isotope_setup_id', corresponding respectively to the equipment used to measure major elements, trace elements and isotopes (Table 4). These three categories of chemical data are often acquired by distinct instruments. Most analytical methods are documented either through scientific papers or through technical documents which make it possible to determine reference values for detection limits and measurement uncertainties. This aspect will be detailed in section 0. A 'bibreference' field is documented by a 'literature' table, which gives the database user
access to the publications associated with the analyses (Table 5). At present, the table includes 69 fields expressing chemical data, which form the core of the database. Of these, 65 are absolute concentrations of chemical elements. Fourteen fields relating to the contents of O, Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Mn and Fe, as well as the loss on ignition, are expressed by weight percent (wt%). The 'os_ppt' field is filled in with contents expressed in ng.kg⁻¹. The fifty other fields associated with contents are expressed in mg.kg⁻¹ (International Organization for Standardization, 2007). Four other fields document isotopic ratios. Depending on the type of analysis carried out, only some of the chemical elements are measured. Thus, for a given analysis, the absence of measurement is expressed by the constant NULL. The concentration of a given element may be too low to be reliably quantified. It is therefore considered as below the detection limit. We have decided to represent this data in the form of a single numerical value '-0.001'. Being negative makes this value easy to distinguish from elemental contents or isotopic ratios, whose values are systematically positive. It is common practice to replace values below detection limits in data analysis, so as not to exclude the values of other variables for a given individual. The CHIPS database user therefore has the choice of applying a replacement rule for the '-0.001' values in the exported dataset, or retaining this generic value. Because of its low amount, the associated observation will carry little weight in most statistical analyses.

<table><thead><tr><th>chips_id</th><th>sample_id</th><th>major_setup_id</th><th>trace_setup_id</th><th>isotope_setup_id</th><th>silicium</th></tr></thead><tbody><tr><td>integer<br>primary key</td><td>integer<br>foreign<br>key</td><td>integer<br>foreign key</td><td>integer<br>foreign key</td><td>integer<br>foreign key</td><td>numeric</td></tr><tr><td>152</td><td>152</td><td>10</td><td>11</td><td>NULL</td><td>8.87</td></tr><tr><td>655</td><td>655</td><td>8</td><td>9</td><td>NULL</td><td>9.42</td></tr><tr><td>664</td><td>664</td><td>8</td><td>9</td><td>NULL</td><td>11.25</td></tr><tr><td>665</td><td>665</td><td>8</td><td>9</td><td>NULL</td><td>8.3</td></tr><tr><td>672</td><td>672</td><td>8</td><td>9</td><td>NULL</td><td>9.06</td></tr><tr><td>725</td><td>725</td><td>8</td><td>9</td><td>NULL</td><td>8.5</td></tr><tr><td>884</td><td>884</td><td>8</td><td>9</td><td>NULL</td><td>7.7</td></tr><tr><td>1121</td><td>1121</td><td>4</td><td>7</td><td>NULL</td><td>11.71</td></tr><tr><td>1269</td><td>1269</td><td>4</td><td>7</td><td>NULL</td><td>8.27</td></tr><tr><td>1281</td><td>1281</td><td>4</td><td>7</td><td>NULL</td><td>16.57</td></tr></tbody></table>

<table><thead><tr><th>iron</th><th>arsenic</th><th>zirconium</th><th>bibreference</th></tr></thead><tbody><tr><td>numeric</td><td>numeric</td><td>numeric</td><td>integer<br>foreign key</td></tr><tr><td>41.58</td><td>10.3</td><td>171.5</td><td>1</td></tr><tr><td>29.52</td><td>5.76</td><td>246</td><td>1</td></tr><tr><td>25.14</td><td>-0.001</td><td>208.1</td><td>1</td></tr><tr><td>32.83</td><td>2.09</td><td>180.5</td><td>1</td></tr><tr><td>28.83</td><td>117.7</td><td>125.9</td><td>1</td></tr><tr><td>42.79</td><td>3.32</td><td>220</td><td>1</td></tr><tr><td>30.13</td><td>167</td><td>114</td><td>1</td></tr><tr><td>42.86</td><td>11.26</td><td>998.75</td><td>1</td></tr><tr><td>56.37</td><td>4.31</td><td>15.78</td><td>1</td></tr><tr><td>37.79</td><td>0.39</td><td>36.13</td><td>1</td></tr></tbody></table>

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<caption>Table 4: View created from the 'chips' table, showing 11 analyses examples. Only 4 out of the 69 fields related to chemical contents are displayed.</caption>

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<table><thead><tr><th>literature_id</th><th>authors</th><th>title</th><th>media</th><th>volume</th><th>issue</th></tr></thead><tbody><tr><td>Integer<br>primary key</td><td>text</td><td>text</td><td>text</td><td>integer</td><td>integer</td></tr><tr><td>6</td><td>Philippe Dillmann,<br>Roland Schwab, Sylvain<br>Bauvais, Michael<br>Brauns, Alexandre<br>Disser, Stéphanie Leroy,<br>Guntram Gassmann,<br>Philippe Fluzin</td><td>Circulation of iron products<br>in the North-Alpine area<br>during the end of the first<br>Iron Age (6th-5th c. BC): A<br>combination of chemical<br>and isotopic approaches</td><td>Journal of<br>Archaeologic<br>al Science</td><td>87</td><td>NULL</td></tr><tr><td>8</td><td>Anne-Marie Desaulty</td><td>Apport des analyses<br>chimiques multi technique<br>à la compréhension du<br>comportement des<br>éléments traces dans les<br>filières sidérurgiques<br>anciennes.</td><td>NULL</td><td>NULL</td><td>NULL</td></tr><tr><td>9</td><td>Jean Milot, Franck<br>Poitrasson, Sandrine<br>Baron, Marie-Pierre<br>Coustures</td><td>Iron isotopes as a potential<br>tool for ancient iron metals<br>tracing</td><td>Journal of<br>Archaeologic<br>al Science</td><td>76</td><td>NULL</td></tr></tbody></table>

<table><caption>Table 5 : View created from the 'literature' table, showing 3 reference examples.</caption><thead><tr><th>publication_year</th><th>pages</th><th>url</th><th>doi</th><th>publication_type</th></tr></thead><tbody><tr><td>integer</td><td>text</td><td>text</td><td>text</td><td>text</td></tr><tr><td>2017</td><td>108-124</td><td>https://www.sciencedirect.com/science/article/pii/S0305440317301486#fig4</td><td>https://doi.org/10.1016/j.jas.2017.10.002</td><td>Article</td></tr><tr><td>2008</td><td>NULL</td><td>https://theses.hal.sciencedel tel-00552060</td><td>NULL</td><td>Thesis</td></tr><tr><td>2016</td><td>NULL</td><td>https://www.sciencedirect.com/science/article/pii/S0305440316301480#bib61</td><td>https://doi.org/10.1016/j.jas.2016.10.003</td><td>Article</td></tr></tbody></table>

### 3.4. 'errors' table

This table consists of 71 fields. An 'error_id' field is the table's primary key (Table 6). The 'chips_id' field is a foreign key referenced in the 'chips' table. The 69 other fields contain values which correspond to the uncertainties associated with each measurement in the 'analyses' database, expressed as mass fractions (either percents, mg.kg⁻¹, or ng.kg⁻¹) (International Organization for Standardization, 2007). These values are either provided directly by the authors in the context of certain publications, or calculated according to a protocol which will be detailed in section 4.2.
<table><caption>Table 6: View created from the 'error' table, showing 10 series of measurement error values, associated to the measurements shown in Table 4.</caption><thead><tr><th>error_id</th><th>chips_id</th><th>silicon_error</th><th>iron_error</th><th>arsenic_error</th><th>zirconium_error</th></tr></thead><tbody><tr><td>integer<br>primary key</td><td>integer<br>foreign key</td><td>numeric</td><td>numeric</td><td>numeric</td><td>numeric</td></tr><tr><td>152</td><td>152</td><td>0.1774</td><td>4.158</td><td>NULL</td><td>NULL</td></tr><tr><td>655</td><td>655</td><td>0.942</td><td>0.5904</td><td>1.152</td><td>12.3</td></tr><tr><td>664</td><td>664</td><td>0.225</td><td>0.5028</td><td>NULL</td><td>10.405</td></tr><tr><td>665</td><td>665</td><td>0.83</td><td>0.6566</td><td>0.418</td><td>9.025</td></tr><tr><td>672</td><td>672</td><td>0.906</td><td>0.5766</td><td>5.885</td><td>6.295</td></tr><tr><td>725</td><td>725</td><td>0.85</td><td>0.8558</td><td>0.664</td><td>11</td></tr><tr><td>884</td><td>884</td><td>0.77</td><td>0.6026</td><td>8.35</td><td>5.7</td></tr><tr><td>1121</td><td>1121</td><td>0.2342</td><td>0.8572</td><td>NULL</td><td>NULL</td></tr><tr><td>1269</td><td>1269</td><td>0.1654</td><td>1.1274</td><td>NULL</td><td>NULL</td></tr><tr><td>1281</td><td>1281</td><td>0.3314</td><td>0.7558</td><td>NULL</td><td>NULL</td></tr></tbody></table>

# 4. Purposes and functionalities

Data interoperability is a central outcome of the DB. Users must be able to extract the data and use it directly, without having to convert it, which would require a very large amount of documentation and a considerable amount of time. We have also focused on assessing the quality of the data, so that users can easily check whether they may encounter any quality-related biases (missing data, measurement error, etc.) when analysing the data.

## 4.1. Data collation and standards

**Chemical data:** handling compositional data requires some precautions to be taken to ensure that no bias is introduced by the data format. Several disciplinary communities are interested in chemical data related to iron metallurgy. As a result of their developments, these communities have defined conventions for representing chemical information that are more or less specific to them.

In our case, geochemists will tend to represent the mass fractions of chemical elements in the form of oxides. This form is more adequate with rocks and minerals in which simple pure bodies are very rare. Silicon is therefore always represented by its most abundant compound form, silica $SiO_2$. In the materials sciences, the representations are more varied, and it is common to see the contents expressed in elementary mass fractions. Silica would then be represented by two contents, silicon and oxygen. In the documentation used to populate the CHIPS database, it is common to see the mass fraction of the same element represented by several oxides. Manganese, for example, is sometimes represented as $Mn_3O_4$, sometimes as MnO; iron as FeO and $Fe_2O_3$. Data expressed in two different forms are not directly comparable. 10 wt% $Fe_2O_3$ corresponds to 6.99 wt% Fe, while 10 wt% FeO corresponds to 7.77 wt% Fe. In order to avoid multiple alternative descriptors for the same chemical element, and to have comparable data sets, it was decided to represent the quantifications calculated as element weight percent. To achieve this, all the data expressed in terms of oxide content were converted by the corresponding stoichiometric factors before being included in the DB.

**Spatial and administrative data:** the administrative data of each archaeological context is documented by a top-down hierarchy of up to five levels, depending on the administrative complexity of the country in question. Queries can therefore be made at national, regional,
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provincial, etc. level for any data. This documentation is currently provided by version 4.1 of the
Global Administrative Areas (GADM) directory. Alignment with the GeoNames gazetteer, which
is increasingly becoming the reference standard, is planned. Spatial data is expressed in
accordance with the corresponding EPSG, enabling the correct calculation of GEOGRAPHY type
objects that are projected via the display interface.

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Categorical data (typo-functional description of the archaeological material): this is referenced
according to two systems. The first system is a hierarchical list of terms drawn up on the basis of
joint discussions among a board of archaeometallurgy specialists (Table 3). The entries are
currently translated into two languages, French and English. The second system is the Pactols
multilingual thesaurus (v2.0). It is based on the DARIAH-EU Backbone Thesaurus, which is
compatible with the CIDOC-CRM ontology. This choice was made because this controlled
vocabulary makes it possible to homogenise the data in line with the structure we have adopted
to describe the materials analysed. This facilitates the integration of the CHIPS database into
archaeological data (Katsianis et al., 2023). The description of individuals is less precise than that
of the first system, but the terms are translated into six different languages and allow easy
alignment with other thesauri such as the Getty Institute's Art & Architecture Thesaurus (AAT).

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## 4.2. Data quality and accuracy

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The quality or accuracy of three types of information is assessed by means of indicators in
the DB. The accuracy of the spatial data is expressed by a Boolean which indicates whether the
coordinates of each entity in the 'sites' table correspond to the actual location of the
archaeological context. If the precise coordinates are not documented, the entity is placed on the
centroid of the administrative entity concerned, generally the location of the building housing the
administrative authority (town hall, county, etc.). Knowing the accuracy of this data can be
important for spatial analyses, particularly on a micro-regional scale, for example when analysing
ore mining and transport strategies. The quality of the chronological data is also assessed by a
Boolean, which indicates whether the authors of the source documents consider the dating of the
archaeological context to be fuzzy. When absolute dates are given by the authors, they are put in
the database as such. If a period is mentioned, the commonly accepted intervals for the period
are entered in the database. For example, sites dated to phase C1 of the Roman era in Poland
are expressed in the interval [150 ; 250].

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Given that chemical information is at the heart of the DB, particular attention was paid to
qualifying data quality. This is expressed in the form of measurement errors. Each analytical
device has its own characteristics that influence the trueness and precision of the
measurements. To determine trueness, standard materials are used whose precise chemical
composition is known. The differences between the known value and the measured values
express the accuracy (International Organization for Standardization, 2023). Repeatability is
determined by carrying out a series of measurements on the same material, and studying the
dispersion of the measured values around the mean value of the series. Standard deviation is
usually used as indicator. The qualification of these factors have been widely discussed within
the archaeological community, e.g. (Hughes, 1998; Nazaroff et al., 2010; Frahm, 2012). In the case of
CHIPS, it has been decided to use a single factor to express the quality of the measurement: the
measurement error. This error is a function of the factors we have just mentioned. A large
number of methods can be used to calculate these values, a good summary of which has been
published by (Buonaccorsi, 2010). The value of the measurement error is also influenced by the
abundance of the element measured in the material. The closer is the measurement to the
instrument's detection limits, the greater the error. Most analytical laboratories use reference
values to express measurement errors. Measurement errors are generally expressed in relative
terms, as a fraction of the measured value (e.g. 5%). In some cases, these values are given in
concentration intervals, to take account of the influence of abundance that has just been
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mentioned. These values are communicated either through scientific articles (as in our case(Carignan et al., 2001)) or technical documentation.

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The following protocol has been established for recording measurement errors:

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- identify the analytical setups; each of them is recorded in the 'machines' table, specifying the type of chemical analysis, the laboratory and the model of device used.

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- identify their characteristics (i.e. measurement errors); for each analytical device listed in the 'machines' table, for each chemical element listed in the 'elements' table, and for a given range of values, the relative measurement uncertainty is expressed in the 'uncertainties' table. The query result in Figure 2 shows how these values are organised. Uncertainties 151 to 154 represent the relative errors of the Centre de Recherches Pétrographiques et Géochimiques (CRPG) laboratory's ICP-MS analyser for the element Yttrium. For a content of more than 50 mg.kg⁻¹, this error is 5%. It increases to 15% for a measured content of between 1 and 50 mg.kg⁻¹, and up to 100% for measured contents of less than 0.1 mg.kg⁻¹.

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<table><thead><tr><th>id_incertitude integer</th><th>laboratoire text</th><th>methode_analyse text</th><th>marque text</th><th> modele text</th><th>symbole text</th><th>int_min numeric</th><th>int_max numeric</th><th>val_incertitude numeric</th></tr></thead><tbody><tr><td>145</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Rb</td><td>1</td><td>10</td><td>0.2</td></tr><tr><td>146</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Rb</td><td>0</td><td>1</td><td>1</td></tr><tr><td>147</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Sr</td><td>50</td><td>1000000</td><td>0.05</td></tr><tr><td>148</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Sr</td><td>10</td><td>50</td><td>0.1</td></tr><tr><td>149</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Sr</td><td>1</td><td>10</td><td>0.2</td></tr><tr><td>150</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Sr</td><td>0</td><td>1</td><td>1</td></tr><tr><td>151</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Y</td><td>50</td><td>1000000</td><td>0.05</td></tr><tr><td>152</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Y</td><td>1</td><td>50</td><td>0.15</td></tr><tr><td>153</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Y</td><td>0.1</td><td>1</td><td>0.2</td></tr><tr><td>154</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Y</td><td>0</td><td>0.1</td><td>1</td></tr><tr><td>155</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Zr</td><td>50</td><td>1000000</td><td>0.05</td></tr><tr><td>156</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Zr</td><td>10</td><td>50</td><td>0.15</td></tr><tr><td>157</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Zr</td><td>0</td><td>10</td><td>1</td></tr><tr><td>158</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Nb</td><td>50</td><td>1000000</td><td>0.05</td></tr><tr><td>159</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Nb</td><td>1</td><td>50</td><td>0.1</td></tr><tr><td>160</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Nb</td><td>0.1</td><td>1</td><td>0.2</td></tr><tr><td>161</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Nb</td><td>0</td><td>0.1</td><td>1</td></tr><tr><td>162</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Mo</td><td>50</td><td>1000000</td><td>0.05</td></tr><tr><td>163</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Mo</td><td>10</td><td>50</td><td>0.15</td></tr><tr><td>164</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Mo</td><td>1</td><td>10</td><td>0.2</td></tr><tr><td>165</td><td>CRPG</td><td>ICP-MS</td><td>Science Perkin Elmer</td><td>ELAN 6000</td><td>Mo</td><td>0</td><td>1</td><td>1</td></tr></tbody></table>

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<caption>Figure 2: View built on the 'machines', 'elements' and 'uncertainties' tables, showing the relative measurement uncertainties depending on the analytical setup and the measured amount range</caption>

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- calculate the value of an error for a given measurement. The calculated value is entered into the 'measurement errors' table using a trigger function. The function is shown below. An UPDATE query is applied for each chemical element. To illustrate the calculation, only the example of Yttrium is shown. The absolute value (in ppm) of the measurement error, denoted y_err, is the product of the measured value, which forms the y attribute in the 'chemical data' table, and the associated relative measurement error in the 'uncertainties' table, under the val_incertitude attribute. This associated value is found by means of a query, the terms of which are to:

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- select the analytical device used with the clause uncertainties.id_machine = chips.trace_setup_id

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- select the analysis concerned with the clause measurement_errors.chips_id = chips.chips_id
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- determine the chemical element concerned using the `uncertainties.id_element=39` clause. Here, the value 39 is the identifier for the element Yttrium

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- determine the content range of the measured value y using the double clause `chips.y >= uncertainties.int_min` and `chips.y < uncertainties.int_max`

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Each time new data is filled in the 'chips' table, related with analytical setups for which uncertainties are known, a function is triggered to automatically calculate the associated measurement errors. This function is defined as follows:

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```sql
CREATE FUNCTION calculate_errors() RETURNS TRIGGER AS $calculate_error$
BEGIN
    DELETE FROM errors;
```

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```sql
ALTER SEQUENCE errors_id_errors_seq RESTART WITH 1;
```

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```sql
INSERT INTO errors (chips_id)
```

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```sql
SELECT chips_id
FROM chips;
```

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```sql
[...]
```

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```sql
UPDATE erreurs
```

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```sql
SET y_err = y*incertitudes.val_incertitude
```

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```sql
FROM uncertainties, chips
```

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```sql
WHERE uncertainties.id_machine = chips.trace_setup_id
```

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```sql
AND erreurs.id_chips = chips.chips_id
```

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```sql
AND uncertainties.id_element=39
```

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```sql
AND chips.y >= uncertainties.int_min
```

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```sql
AND chips.y < uncertainties.int_max
```

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```sql
;
```

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```sql
[...]
```

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```sql
RETURN NULL;
```

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```sql
END;
```

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```sql
$calcul_errour$ LANGUAGE plngsql;
```

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```sql
CREATE TRIGGER calcul_errours AFTER INSERT OR UPDATE ON chips
```

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```sql
EXECUTE FUNCTION calcul_errours();
```

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```sql
;
```

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Of the 33 analytical devices currently listed, only 8 have sufficiently precise documentation to determine measurement uncertainties. However, these 8 devices represent 1,583 of the 2,465 analyses in the open-access database, i.e. around 64%. Further documentation work will be carried out to extend this functionality to other analytical devices. In any case, this functionality is proving very useful to users in highlighting possible data processing biases due to analytical parameters.

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### 4.3. Online use of the database

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The DB is currently hosted on a shared web hosting solution granted by the Huma-Num research infrastructure (IR* Huma-Num), DARIAH-EU's national body (France), through a PostgreSQL 15 server running in SSL mode. This solution was chosen because it provides long-term hosting of the database, within an infrastructure whose hardware and technologies are kept up to date. The database has been integrated into the 'Fabrique Numérique du Passé' platform, dedicated to the open sharing of scientific research data and compliant with the FAIR principles

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(Costa, 2023). A data query interface was set up by the Huma-Num ‘Projets Time Machine’ consortium, using the GEO software developed by Business Geografic. A cartographic solution has been privileged, as the most appropriate to meet the needs of the users. It is useful to compare the spatial distribution of CHIPS data with other sources of information, such as geological maps. This makes it possible to consider the exploitation of nearby mineral resources, to facilitate data searches in the geochemical documentation, or even to plan field interventions (sampling, for example).

Each context is represented by a circular marker whose size depends on the number of analyses available. Cartographic representation can also be used to select entities according to spatial criteria, such as proximity. Spatial queries can be performed using predefined geometric shapes (ellipses, quadrilaterals) or free-form polygons. Queries based on administrative criteria have also been implemented. These can be performed on two hierarchical levels: country, and province/region/state. A search by sample name has also been implemented. At present, this search can only select a sample by its full name, but it is planned to include the Postgres extensions pg_trgm and fuzzystrmatch, which enable fuzzy matching on character strings, and to set up a LIKE query based on a search for values containing a given character string. Finally, a query based on chronological criteria is available. This works by defining a date interval, using two boxes, start date and end date. The query returns contexts whose start date is greater than or equal to the input value, and whose end date is less than or equal to the input value for end date. Queries based on the nature of the furniture being analysed are also planned. This will enable, for example, the selection of ore analyses only. Once the contexts selected, the data can be consulted using several nested forms. These show successively geo-administrative data for the context, data concerning the samples analyzed (sample name, typology), and chemical data divided into three subwindows (major elements, trace elements, isotopes) (Figure 3). A final sub-section lists the bibliographic references from which the analyses come.

<figure><img src="image_3.png" /><figcaption>Figure 3 : Example of chemical data vizualisation for a sample coming from the ‘Noires Terres’ archaeological context (blue marker highlighted on the cartographic view)</figcaption></figure>

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# 5. Perspectives

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**Core developments.** The CHIPS DB is now at an advanced stage of development. The conceptual model has been tried and tested, and allows the smooth running of functions already set up. The next stages of development will focus on improving the data query interface, in particular by proposing new queries. The database currently provides open access to 2465 chemical analyses relating to 477 archaeological contexts. Nevertheless, the database contains a total of 5719 analyses associated from 1077 different sites. Moderation work is underway to ensure that the dissemination of these analyses complies with legislation on the protection of intellectual property rights. The repository will also soon be enriched by the integration of structural data, such as the results of X-ray diffraction analyses. Data will be expressed in fractions of mineralogical species (e.g. hematite, siderite, quartz, etc.). These data will be particularly useful for describing ore samples, and enabling further interpretation of the geological contexts in which they were extracted.

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**User community.** We are looking to structure a community of users around the DB in a bottom-up organization with the aim of a collaborative research. The use of this tool will provide an opportunity to think collectively about the production of archaeometallurgical chemical data from different perspectives. In particular, we envisage a shared material reference set, i.e. materials containing the chemical elements of interest at certified levels, and whose content ranges are compatible with what is generally measured in materials related to iron metallurgy. CHIPS will actively pursue regular assessment and evaluation on the usage, growth and general development of the DB, with a view to optimising both usability/interfacing and data handling for research. One opportunity would be to use an analytical platform housed at the Moesgaard Museum and the University of Aarhus. This setup is run by members of the CHIPS user community, well aware of the petrographic and chemical features of materials linked to ancient metallurgical activities, and willing to contribute to a common effort aiming at providing extensive reference data to archaeometallurgists. The preparation and analytical processes could be optimised for the specific archaeometallurgical materials.

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The rules for contributions, the contributions themselves, the intellectual property of the data, as well as their reuse rules ("how-to-cite") will be made explicit in a transparent way on the GitHub of the DB. We envisage a CC-BY license for the data, CC0 for the metadata, ODbL for the BDD and MIT for the computer code.

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**Expanding the DB.** An international communication campaign is being conducted via a webinar and a mailing list. This will lead to the import of several new datasets in the near future, conducted by members from the user community. The data will be moderated prior to their integration in the DB.

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The initial communication campaigns around the CHIPS database have also aroused interest among researchers who are not strictly speaking interested in iron archaeometallurgy. For example, ore is a cultural term (to identify a rock/mineral from which metal can be extracted; the same rock/mineral may instead be used as a pigment material or colourant in another technological practice, and so its function/identification is contingent on its use and the contextMore generally, the conceptual data model of the CHIPS database can be used to create chemical reference systems for the study of other archaeological materials, such as glass or copper alloys.

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# Funding

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The authors declare that they have received no specific funding for this study
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Conflict of interest disclosure

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The authors declare that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.

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Acknowledgements

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The authors would like to thank Marion Berranger, Marc Leroy (Laboratoire Métallurgies et Cultures IRAMAT UMR 7065) and Gaspard Pagès (Institut Français du Proche-Orient, UMIFRE for the fruitful discussions having led to the conception of the typological structure. We are also grateful to Christophe de Traversay who provided useful advices on some of the SQL functions developed.

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6. References

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Blakelock, E., Martinon-Torres, M., Veldhuijzen, H., Young, T., 2009. Slag inclusions in iron objects and the quest for provenance: an experiment and a case study. J. Archaeol. Sci. 36, 1745-1757.

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Buonaccorsi, J.P., 2010. Measurement Error: Models, Methods, and Applications, Interdisciplinary Statistics Series. CRC Press.

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Carignan, J., Hild, P., Morel, J., Yeghicheyan, D., 2001. Routine Analyses of trace elements in geological samples using flow injection and low Pressure On-Line Liquid Chromatography couples to ICP-MS; study of geochemical Reference Materials BR, DR-N, AN-G and GH. Geostand. Newletter 25, 187-198.

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