KPLEX Deliverable D3.1
H2020-ICT-2016-1-732340Deliverable Title: Deliverable D3.1 Report on Historical Data as SourcesDeliverable Date: 30/03/2018Version: 1.0Project Acronym:K-PLEXProject Title:Knowledge ComplexityFunding Scheme:H2020-ICT-2016-1Grant Agreement number:732340Project Coordinator:Dr. Jennifer Edmond (edmondj@tcd.ie)Project ManagementContact:Michelle Doran (doranm1@tcd.ie)Project Start Date:01 January 2017Project End Date:31 March 2018WP No.:WP3WP Leader:KNAWAuthors and Contributors(Name and email address):Nicola HorsleyMike Priddy (mike.priddy@dans.knaw.nl)Dissemination Level:PUNature of Deliverable:R report1
H2020-ICT-2016-1-732340Abstract:What do cultural heritage institutions and theirpractitioners do and how is this changing in the big dataera? Through a series of interviews with culturalheritage practitioners and an online survey, this reportpresents the investigations into the handling ofknowledge complexity, changes in archival practice anddata use, how data is shared, and also hidden, as partof the historical record. Furthermore, the barriers to theapplication of big data computational method to thehistorical record are considered, conclusions andrecommendation are drawn.2
H2020-ICT-2016-1-732340Deliverable D3.1 - Report on HistoricalData as SourcesTable of ContentsTable of Contents . 3Table of Figures . 4Work Package Objectives . 5Introductory Literature Review . 5Methodology . 9Survey . 10Interviews . 11Analysis . 11Survey data used to validate and guide interview themes . 11Introduction . 18Conceptualising Data . 19What do Cultural Heritage Practitioners do and how is this Changing in the Big Data Era? . 19Creating Cultural Heritage . 19Handling Knowledge Complexity – Acquisition . 19Handling Knowledge Complexity – Description . 21Handling Knowledge Complexity – Helping researchers with research problems andmethods . 25Handling Knowledge Complexity – Serving ‘non-research’ users . 28Changing Practice . 30How is Archival Data Use Changing? – Getting a grip on what data is used and how . 30How is Archival Data Use Changing? – The changing uses of archival data . 35How is Archival Data Use Changing? – Decision making about alternative ways oforganising material and their impact . 36How is Archival Data Use Changing? – The Importance of context of items indescriptions of archival holdings . 39How is Archival Data Use Changing? – Moving from analogue ways of working to digitalsystems . 42How is Archival Data Use Changing? – The Consequences of Digital Discoverability . 45How is Archival Data Use Changing? – How technical expertise is used and integratedwithin the archive . 50Common Knowledge: how does Cultural Heritage Practice Affect the Kinds of Data thatare Shared Through, and Hidden from, the Historical Record? . 53Why do Cultural Heritage Institutions Share? . 53Cultural Barriers to Sharing . 57The Challenges of Standardising Practice . 62Hidden Knowledge – How data that is not shared is at risk of disuse . 64Technical Barriers . 68The Politics of Sharing . 70Jumping Google . 723
H2020-ICT-2016-1-732340Translation of Data Needs . 75The Digital Future of the Historical Record . 78Conclusion – Hidden Data and the Historical Record . 85Recommendations . 87Bibliography . 89Appendices . 93Appendix 1: Interview Questions for CHI and Infrastructure Practitioners . 93Appendix 2: Survey for Cultural Heritage Institution (CHI) Practitioners . 98Table of FiguresFigure 1: Job roles of survey respondents . 12Figure 2: Experience of survey respondents . 12Figure 3: Types of cultural heritage institution of survey respondents . 13Figure 4: Types of holdings worked with by survey respondents . 13Figure 5: Survey respondents’ descriptions of their institutions’ user community . 14Figure 6: The beneficiaries of the work of survey respondents . 14Figure 7: Responsibilities of survey respondents and their institutions . 15Figure 8: Engagement with data sharing infrastructures identified by survey respondents . 15Figure 9: Elements of practitioners’ roles survey respondents identified as unknown to theirbeneficiaries . 30Figure 10: Survey respondents' institutional monitoring of holdings usage . 31Figure 11: Survey respondents’ perception of the percentage of their institutions’ collectionsthat are used, by type of holdings . 31Figure 12: How survey respondents’ institutions handle user access requests . 43Figure 13: Methods of communicating information about collections to researchers reportedby survey respondents . 46Figure 14: Survey respondents’ percentage of the information (metadata) describing theircollections is available online to the general user . 46Figure 15: Survey respondents’ perceptions of the significance of specialised skills versusroutinisation in adopting new practices . 47Figure 16: Continuing support with new developments in practice as part of surveyrespondents’ training . 51Figure 17: How survey respondents accessed training provision . 52Figure 18: The extent to which survey respondents felt engaged in a public duty to sharedata . 53Figure 19: The relevance of aggregation projects to cultural heritage institutions’ currentoperation and future goals, according to survey respondents . 54Figure 20: survey respondents’ institutions’ involvement with aggregation projects . 54Figure 21: The greatest challenges that prevent survey respondents’ institutions fromsharing more information . 57Figure 22: Survey respondents’ feelings of common goals amongst cultural heritageinstitutions at local, national and international levels . 58Figure 23: Types of unforeseen challenges in participating in aggregation projects, accordingto survey respondents . 684
H2020-ICT-2016-1-732340Work Package ObjectivesThe objectives of this work package were: To evaluate the issues and challenges surrounding the aggregation of historical dataas knowledge, and in particular for those institutions which are not active participantsof large national or international aggregations. To further define a model of cultural heritage holdings as data (digital and otherwise)and investigate cultural and ethical barriers to big data approaches to historical andcultural sources, through interaction with cultural heritage institutions. To synthesise and communicate the findings as a white paper for policy/generalaudiences and a journal article.This deliverable presents the results and findings of the investigation (through, literaturereview, interviews, and an online survey) of the issues, challenges, and barriers towards thepossible application big data approaches in analysis of the historical record. Furthermore,the work presented here lays the groundwork towards a white paper.Introductory Literature ReviewIn order to navigate an information environment experiencing a ‘data deluge’, we seek waysto reduce noise and enhance signal, most obviously through the use of metadata. Clearlythis practice involves judgements of value to determine what is worthy of the mantle of‘signal’ and what is labelled ‘noise’. Archival science navigates the blurred contours of thislandscape, which has always been shaped by cultural and temporal perceptions and theaffordances of technology. The technologies that become part of standard practice in anarchive then favour the creation of certain narratives over others. If data complexity issuppressed or left unaccounted for by those technologies, it will occupy a blind spot withinthe archive but if its description is too bound up with its complexity, its diverse potential useswill not be discovered. Either extreme represents a dilution of the richness of knowledgecreation.In discussing big data in relation to archives, K-PLEX is interested in approaches thatsupport the potential for data to be re-used and re-analysed in conjunction with other datathat may have been collected by unrelated researchers. Such research is facilitated throughthe use of descriptive metadata, appropriate preservation systems, informed institutionalpractice, and architecture for sharing across institutions to enable discovery by diffuseresearchers. Mirroring wider society, academic research is currently in thrall to big data.Funding calls offer large grants to researchers who can apply the least datafied (Schafer andvan Es, 2017) research interests to data-rich areas, ‘consigning research questions for whichit is difficult to generate big data to a funding desert and a marginal position within andoutside the academy’ (Kitchin, 2014a). Researchers taking on this challenge must thengrapple with the socially constructed nature of datasets containing knowledge complexitythat must nevertheless exemplify the gold standard of a five-star (re-)usability rating, ahallmark of epistemic authority that can only be achieved by containing some of thatcomplexity in a black box (Latour, 1999). Such flattening of nuance is described as thedefining characteristic of data engineering, which leads to what McPherson (2012) calls alenticular view of knowledge.5
H2020-ICT-2016-1-732340To understand what such a turn really means for archival practice, it has been argued (Bolinand Schwarz, 2015; Kitchin, 2014b that we must clarify whether big data is genuinely beingadopted as a heuristic by academic, governmental and associated actors, or if the ‘myth’ of‘Big Data’ (boyd and Crawford, 2012) is merely a useful discourse for those whose interestsare served by the promulgation of an evangelical ‘dataism’ (van Dijck, 2014). Thisphenomenon has parallels across society. For example, Williamson (2016) analyses how theHour of Code and Year of Code initiatives saw ‘a computational style of thinking’ infiltrateschools in the US and UK, which he describes as a style of thinking that ‘apprehends theworld as a set of computable phenomena’. Williamson draws attention to a deficit ofreflexivity amongst advocates of computational approaches to social problems, whichobfuscates the ‘worldviews, ideologies and assumptions’ of the creators of systems forprocessing data, black-boxing the processes that delimit data use. Berry (2011) draws onFuller (2010) in pointing out that the potential for new technologies to produce and reproduceinequalities in society is not simply a matter of a ‘digital divide’ but is significantly influencedby the commercial roots and market values of much of this techno-solutionist innovation.Archivists are uniquely placed within this discourse, with everyday practices and systems formanaging collections, and the confluence of traditions of working with cultural heritageholdings and adaptation to emerging technologies, all in their purview. As such, culturalheritage practitioners are more than a vital link in the chain through which historical data aremaintained and transmitted. Engaging with practitioners’ perspectives is fundamental tounderstanding the drivers behind data use and non-use and viewing the knowledgelandscape from their position of archival thinking offers insight into how the computationalturn is experienced in practice and how this may render new forms of research engagementwith the historical record.A shift towards big data approaches necessarily poses questions of how the contemporarylandscape is characterised and what the custodianship of cultural heritage looks like atpresent moment of the computational turn, on the cusp of big data’s installation as thedominant discourse across research disciplines. Perhaps because of the nature of theacademic research lifecycle, academic publications have not kept up with the challenges KPLEX is exploring. The most illuminating literature has come from major European digitalresearch infrastructure projects federating cultural heritage data for use by researchers.These projects (including CENDARI1, EHRI2, DARIAH-EU3, DASISH4, PARTHENOS5,ARIADNE6 and HaS7) have all faced and to some degree addressed challenges associatedwith the sharing of cultural heritage data. The EHRI and CENDARI projects exposed threatsto the sustainability of sharing knowledge from previously ‘hidden archives’, including a lackof consistency in the information that could be viewed across institutions and projects that, inone example, resulted in thousands of digitised sources that could not be displayed on theinstitution’s website being hidden from potential users as well as from the institution itself1Collaborative European Digital Archival Research Infrastructure http://www.cendari.euEuropean Holocaust Research Infrastructure https://www.ehri-project.eu3Digital Research Infrastructure for the Arts and Humanities https://www.dariah.eu4Data Service Infrastructure for the Social Sciences and Humanities http://dasish.eu5Pooling Activities, Resources and Tools for Heritage E-research Networking, Optimization and w.ariadne-infrastructure.eu7Humanities at Scale http://has.dariah.eu26
H2020-ICT-2016-1-732340(Vanden Daelen et al., 2015). The CENDARI and EHRI projects also found evidence of aneed for archival workflows to be more transparent and reproducible as engaging with aresearch infrastructure currently involved ‘talking with at least three people: the one whocould tell you “what” (was described and relevant for the portal), the one who could tell you“how” (software and standards or mapping) and the one who could tell you “yes” (theauthority to give permission to integrate data from this archive into the portal) (VandenDaelen et al., 2015: 8). With core elements of practice obscured, there was a danger thatdata could end up hidden between the cracks of the institution. This supports Star’s (2007)observations of the hiddenness of much of the ‘work, practice, and membership’ of sociotechnical networks.It has been suggested that, rather than seeking to maintain or arrive at a finished model,practitioners’ ideas of completeness may be more akin to ‘equilibrium in flow’ (vonBertalanffy, 1949) In which case, the historical record should be seen as a process, not aproduct. There are many lines of enquiry about the compatibility of archival thinking andpractice with the computational turn and scholarly literature has only recently begun to turnits gaze in their direction. This early analysis has tended to privilege the most sensationalhypotheses. For instance, Kitchin’s (2014a) reporting of the humanities’ parallels with anincreasing marginalisation of deductive approaches in scientific fields is intriguing. While itwould seem unlikely that humanists and social scientists would reject deductive methods infavour of purely inductive methods, the extent to which data-driven approaches aresupported by archivists may be revealing. The practitioner view of the opportunities andchallenges for broader use of data that big data approaches offer has been conspicuouslyabsent from a discourse that largely represents them as passive actors, resistant to changeDuderstadt et al. (2002; Edwards et. al, 2013).Ribes and Jackson’s (2013) investigation of the workings of the data archive describes how‘the work of sustaining massive repositories reveals only a thin slice in the long chain ofcoordinated action that stretches back directly to a multitude of local sites and operationsthrough which data in their "raw" form get mined, minted, and produced. What remain atrepositories are the distilled products of these field sites; behind these ce
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