Abstract
This paper considers recent attempts within archaeology to create, integrate and interpret digital data on an unprecedented scale—a movement that resonates with the much wider so-called big data phenomenon. Using the example of our work with a particularly large and complex dataset collated for the purpose of the English Landscape and Identities project (EngLaID), Oxford, UK, and drawing on insights from social scientists’ studies of information infrastructures much more broadly, we make the following key points. Firstly, alongside scrutinising and homogenising digital records for research purposes, it is vital that we continue to appreciate the broader interpretative value of ‘characterful’ archaeological data (those that have histories and flaws of various kinds). Secondly, given the intricate and pliable nature of archaeological data and the substantial challenges faced by researchers seeking to create a cyber-infrastructure for archaeology, it is essential that we develop interim measures that allow us to explore the parameters and potentials of working with archaeological evidence on an unprecedented scale. We also consider some of the practical and ethical consequences of working in this vein.
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Notes
The term ‘events’ is used throughout the text to mean archaeological fieldwork investigations.
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Acknowledgments
This study was carried out as part of a 5-year European Research Council funded research project. It also draws on the findings of work undertaken separately as part of an English Heritage-commissioned investigation aimed at developing a new information access strategy for England. The data upon which the study is based were provided by 75 separate HER Officers, English Heritage, the Archaeological Investigations Project and the Portable Antiquities Scheme. It goes without saying that our work would not have been possible without the support and expertise of the professionals involved in curating and extracting these data for us. We are particularly grateful to Sally Croft (Cambridgeshire HER), Simon Crutchley (English Heritage), Rebecca Loader (Isle of Wight HER), Dan Pett (PAS) and Emma Trevarthen (Cornwall HER) who kindly gave us permission to publish images of their data. Simon Crutchley, Martin Newman and Roger Thomas facilitated access to the NRHE data and have offered thoughtful guidance during our endeavour to get to grips with our various datasets. Ehren Milner gave us advice about accessing AIP data, and Dan Pett provided the PAS data. Letty ten Harkel, Zena Kamash and Laura Morley undertook the 100-km2 test exercise along with us. Miranda Creswell, Duncan Garrow, Chris Gosden, Letty ten Harkel, Zena Kamash and Dan Stansbie provided helpful comments on an earlier draft of the paper. The input of four anonymous reviewers improved substantially the version of this paper that was originally submitted for publication.
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Appendices
Appendices
Appendix 1: URLs for cited datasets
NRHE: http://www.pastscape.org.uk/
HERs: http://www.heritagegateway.org.uk/gateway/
AIP: http://csweb.bournemouth.ac.uk/aip/aipintro.htm
OASIS: http://archaeologydataservice.ac.uk/archsearch/
PAS: http://finds.org.uk/
EMC: http://www.fitzmuseum.cam.ac.uk/dept/coins/emc/
Appendix 2: Specificities of the datasets provided to the EngLaID project
HER data (including several UADs)
Seventy-five of the 84 HERs and UADs in England provided data for the EngLaID project. Most of these datasets use a monument and event structure, with events associated with a monument linked to it and, presumably, with events not linked to an existing monument generating a new monument upon their taking place. Nottinghamshire is an exception in having an intervening third layer, which can be thought of as a ‘feature’. Essentially, in that case, events are linked to features and then features are linked to monuments where a preexisting site is known of or once they become important enough to merit consideration as a ‘monument’. The majority, but not all, HERs also records sources/bibliography. Many records find details, especially amongst HBSMR users. However, such details tend to be added on an ad hoc basis.
NRHE
NRHE data were supplied by English Heritage as shapefiles and associated PDF documents that contained the most important attributes of each record. These PDF files had to be scanned using a script to extract the relevant attribute data. This process is slightly imperfect due to some monument types running across multiple lines, which makes it impossible for any automated (digital) process to tell when a term finishes. As such, the resulting output results in a ‘stream of consciousness’ list of monument types for each period, with one running into another, e.g. Roman: villa bathhouse barn round house. This makes some queries hard to perform, but is largely functional.
AIP
AIP data was downloaded by EngLaID from the AIP Web site. This is not entirely ideal due to a restriction in the database software used by the AIP, which means that it is not possible to download more than a certain (undetermined) number of records at one time. Some categories of data are, thus, not extractable due to exceeding this limit even when filtered down to the greatest level.
PAS
PAS data were supplied directly by the PAS in the form of a single large CSV spreadsheet. PAS data maps reasonably well onto HER finds recording formats, but contains a lot more detail.
Appendix 3: Additional methodological details for the 100-km2 exercise
Since, upon testing, a negligible degree of overlap between records held in the EMC and those within the PAS was identified, and since their structure and content is broadly compatible, these datasets are combined and treated as one.
One-hundred-square-kilometre test areas were selected so as to contain a reasonable, but not overly onerous number of records, so as to characterise as accurately as possible the nature of the dataset relationships. When tested specifically, almost all these squares included an above-average record density for the HER in question. If the HER area concerned included distinctly different landscape zones, which were likely to produce particular kinds of records (e.g. Norfolk, where the Fens produces very few PAS records, but the upland Breckland zone produces many) the 100-km2 area was positioned, as far as possible to include both/all such zones. In two cases, West Berkshire and North East Lincolnshire, it was not possible to fit a full set of 100 1 × 1-km cells within the case study area, so smaller areas were tested.
The findings of this exercise should be treated with some caution. All four EngLaID researchers involved in carrying out this exercise used the same broad methodology and at all times, we tried to maintain a consistent approach. However, the challenges involved in identifying links between records in different datasets varied considerably from test area to test area. Consequently, researchers necessarily developed their own ways of dealing with the particular ambiguities they faced in conducting the test.
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Cooper, A., Green, C. Embracing the Complexities of ‘Big Data’ in Archaeology: the Case of the English Landscape and Identities Project. J Archaeol Method Theory 23, 271–304 (2016). https://doi.org/10.1007/s10816-015-9240-4
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DOI: https://doi.org/10.1007/s10816-015-9240-4