The end of the reading room? Simulating the impact of digitisation on the physical access of archival collections

Digitisation has become an essential part of archival and library strategies to enhance access to collections. As the digital content is increasing due to large-scale digitisation projects, it is expected that providing digital access to the analogue collections will eventually reduce the number of archival records accessed in the reading room. In this paper, we investigate this issue using two approaches: system dynamics and agent-based modelling. We first analyse real data in order to identify the dynamic hypothesis of the model. Then, a sensitivity analysis is conducted on two baseline models to identify scenarios that match the real dataset. Although the two approaches suceed to simulate the number of requests in the reading room, the experimental results show that a better fit is obtained in the agent-based model when not only the number of records that have been accessed and digitised is taken into account, but also the number of times that such records have been accessed before digitisation. The proposed model can be used to explore the impact of different digitisation strategies on the decrease in access requests in the archival and library reading rooms

Automated corrosion detection in Oddy test coupons using convolutional neural networks

The Oddy test is an accelerated ageing test used to determine whether a material is appropriate for the storage, transport, or display of museum objects. The levels of corrosion seen on coupons of silver, copper, and lead indicate the material’s safety for use. Although the Oddy test is conducted in heritage institutions around the world, it is often critiqued for a lack of repeatability. Determining the level of corrosion is a manual and subjective process, in which outcomes are affected by differences in individuals’ perceptions and practices. This paper proposes that a more objective evaluation can be obtained by utilising a convolutional neural network (CNN) to locate the metal coupons and classify their corrosion levels. Images provided by the Metropolitan Museum of Art (the Met) were labelled for object detection and used to train a CNN. The CNN correctly identified the metal type and corrosion level of 98% of the coupons in a test set of the Met’s images. Images were also collected from the American Institute for Conservation’s Oddy test wiki page. These images suffered from low image quality and were missing the classification information needed to train the CNN. Experts from cultural heritage institutions evaluated the coupons in the images, but there was a high level of disagreement between expert classifications. Therefore, these images were not used to train the CNN. However, the images proved useful in testing the limitations of the CNN trained on the Met’s data when applied to images of coupons from different Oddy test protocols and photo documentation procedures. This paper presents the effectiveness of the CNN trained on the Met’s data to classify Met and non-Met Oddy test coupons. Finally, this paper proposes the next steps needed to produce a universal CNN-based classification tool. Graphic Abstrac

Semantic segmentation and photogrammetry of crowdsourced images to monitor historic facades

Crowdsourced images hold information could potentially be used to remotely monitor heritage sites, and reduce human and capital resources devoted to on-site inspections. This article proposes a combination of semantic image segmentation and photogrammetry to monitor changes in built heritage sites. In particular, this article focuses on segmenting potentially damaging plants from the surrounding stone masonry and other image elements. The method compares different backend models and two model architectures: (i) a one-stage model that segments seven classes within the image, and (ii) a two-stage model that uses the results from the first stage to refine a binary segmentation for the plant class. The final selected model can achieve an overall IoU of 66.9% for seven classes (54.6% for one-stage plant, 56.2% for two-stage plant). Further, the segmentation output is combined with photogrammetry to build a 3D segmented model to measure the area of biological growth. Lastly, the main findings from this paper are: (i) With the help of transfer learning and proper choice of model architecture, image segmentation can be easily applied to analyze crowdsourcing data. (ii) Photogrammetry can be combined with image segmentation to alleviate image distortions for monitoring purpose. (iii) Beyond the measurement of plant area, this method has the potential to be easily transferred into other tasks, such as monitoring cracks and erosion, or as a masking tool in the photogrammetry workflow

Classifying degraded modern polymeric museum artefacts by their smell

Volatile organic compound (VOC) analysis is a successful method for diagnosing medical conditions such as Alzheimer’s disease. However, despite its relevance to heritage, it has found little application in museums. We report the first use of VOC analysis to ‘diagnose’ degradation in modern polymeric museum artefacts. Modern polymers are increasingly found in museum collections but pose serious conservation difficulties due to unstable and widely varying formulations. Solid-phase microextraction gas chromatography/mass spectrometry and linear discriminant analysis were used to classify samples according to the length of time they had been artificially degraded. Classification accuracies of 50-83% were obtained after validation with separate test sets. The method was applied to three artefacts from collections at Tate to detect evidence of degradation. This novel approach could be used for any material in heritage collections and more widely in the field of polymer degradation

Beyond Heritage Science: A Review

Heritage science is an established and thriving field of enquiry. Initially considered as inherently cross-disciplinary, encompassing both the needs of conservators and practitioners and the high-quality evidence produced by scientists, heritage science has, through its expansion in recent years, formed a discipline in its own right. Here, we examine how heritage science can, and to an extent has, moved beyond the straightforward scientific analysis of historical materials and artefacts through an exploration of heritage science’s interactions with four key themes: (i) historical and archival research, (ii) conservation practice, (iii) policy at governmental, organisational and institutional levels, and (iv) a view to how new technologies, such as machine learning and artificial intelligence, can shape the future of heritage science. Much of the review narrative is framed via the analysis of UK-based case studies; however, they deal with issues that are international in nature (universal) and therefore transcend the UK context. Taken together, we demonstrate that heritage science as a discipline is capable of directly instigating or (re-)framing new areas or avenues of research, as well as enhancing and feeding into existing research questions, and has adapted and evolved along with emerging technologies and funding opportunities

Martí i Franquès: the man who watched air

Antoni de Martí i Franquès laid down his pen, got up from his desk and leant out of the open window. It was spring and a pleasant breeze was blowing. Although he spent most of the year at his home in Tarragona, he still liked to come to Altafulla when he needed to concentrate. From the window of the office in his house, which was near the castle, there was a view of the sea and a few boats quietly floating there. One of them belonged to Martí and was sailing out of the port of Tarragona loaded with goods. But this was not what he was looking at. The window also looked out onto the terraces of other houses, the fields between the village and the sea, and the track along which the farmers toiled to and from their crops. Some of these men worked on their land that was spread all over the Camp de Tarragona. But this was not what Martí was looking at either

Accumulation of wear and tear in archival and library collections. Part I: exploring the concepts of reliability and epidemiology

Abstract Wear and tear is the outcome of degradation most frequently reported in assessments of archival and library collections. It is also problematic to study in controlled experiments, due to the difficulty in reproducing the conditions in which original objects are kept and used in archives and libraries. Hence, data collected from actual collections, for instance during surveys, could provide the evidence on how wear and tear occurs. However, to be useful, such data need to be purposely collected and analysed: unlike the usual collection surveys, the aim is not to quantify the prevalence of a certain damage type but to provide evidence on how such damage occurs. In this paper we explore whether two approaches used in other disciplines could be useful: reliability engineering, the method that deals with failure in complex systems, and epidemiology, which explores diseases in defined populations. We show that based on reliability engineering we can decide which data related to the causes of mechanical failure should be collected during collection surveys, while using epidemiology we can develop the study design and the data analysis needed to study the relationship between mechanical failure, and the factors that might affect the degree of failure. The results of epidemiological studies in heritage collections could provide quantitative evidence of patterns of decay in collections, and corroborate the qualitative analysis provided by reliability. The results can directly support collection management decisions or can be used in mathematical models in which the effect of preservation measures is explored

Fluid simulations in heritage science

Abstract This review addresses the use of computational fluid dynamics for the interpretation and preservation of heritage. Fluid dynamic simulations in the heritage field focus mostly on slow air movement in indoor spaces and they usually involve temperature and humidity. Simulations have different roles: they may be exploratory, they may be used to support preventive conservation and occasionally they aid historical or archaeological interpretation. The research questions rarely involve testing or development of new mathematical formulations; instead, existing computational models are used as a means to help solve practical issues. Computationally, the simulations are typically steady-state and they always use a turbulence model. Experimental validations against measured data are uncommon and there is a need for the production of benchmarking cases and the publication of experimental data. Further research is needed in order to explore suitable approximations to the simulation of change in the time-scale of months or years, low turbulence flows for which current mainstream turbulence models are ill-suited, and new mathematical formulations for near-wall transport phenomena

Accumulation of wear and tear in archival and library collections. Part II: an epidemiological study

Abstract This paper proposes a new approach to collection surveying based on epidemiology, the discipline that describes and explains disease patterns in populations. In epidemiology the focus of attention lies not only on the occurrence of a disease but also on the characteristics of the individuals which might play a role in the occurrence of the disease. To explore the applicability of epidemiology to heritage collections, we take as example the study of the occurrence and accumulation of wear and tear in archive collections, which so far has only been studied in controlled experimental studies. We designed an observational study (survey) in which the assessment of mechanical failure is understood as the outcome variable, and the factors that might affect the degree of failure are defined as exposure variables. To evaluate the relevance of the assessed factors in relation to the observed mechanical failure, exploratory data analyses were conducted by comparing groups of objects that differ regarding their level of exposure to different factors. Although highly scattered data is not unusual in this type of studies and confounding has to be taken into account during the data analysis, this paper shows that through an epidemiological approach to surveys, the factors that have a greater effect on mechanical failure can be identified. Moreover, the rate of failure can also be determined for certain groups of objects. Also patterns of decay emerge which show the vulnerability of certain groups of objects. In this paper the practical aspects of the design and analysis of observational epidemiological studies for heritage collections are discussed. As a final note, the applicability and relevance of this approach to support collection management is briefly discussed