dataesr/scientific-paragraphs-categorization huggingface dataset:
8 ). In order to limit detection errors, it is possible to filter out ambiguous data prior to detection. The filtering applied in this study uses an established threshold for both brightness and blur metrics to separate the good quality images from the darker and blurrier ones. For each camera, these metrics are calculated over an appropriate section of the image, in this case the sea, and objects with visible and contrasted edges respectively. Brightness is measured by adding the averaged red, green and blue channels of the image, and the blurriness index has been outlined by  Crété-Roffet et al. (2007). After calculating both metrics on the whole dataset, thresholds are empirically selected from their respective histogram for each camera in order to determine the combination that allows the best filtering possible.
DEM validation For each study site, a validation geodesy campaign was carried out using a differential Global Navigation Satellite System (dGNSS) in order to measure the ground truth topography. Measurements have been carried out with a non-gridded uniform spread of the sampling points over the video recorded area, covering the beach from the highest point of its top part to the low tide waterline, with numerous points ranging from 100 to 150 per site in total, measured on both slope breaks and centers. The intertidal topography was reconstructed at the geodesy campaign dates using the methodology described 2. 2. 4 and then compared to the dGNSS data for validation. Comparisons are evaluated using the Root Mean Squared Error (RMSE) calculated between the dGNSS points' elevation and the DEM's elevation at the same XY locations, thus corresponding to an average vertical offset.
Extracting the waterline from the images provided by coastal VMS can be a complicated and time-consuming task because of the many sources of noise specific to this type of data (e. g. weather, sun angle, brightness, sea state variabilities, presence of obstructing objects, people etc. ). In recent years, the development of convolutional neural network methods, including Mask R-CNN to process instance segmentations, made it possible to automatically extract detailed information from images and identify complex and abstract concepts in a wide variety of contexts. The methodology presented in this research proposes the use of Mask R-CNN as a tool to classify bodies of water, with the aim of extracting the waterline from a large dataset of VMS images. This approach provides a robust technique to automatically identify the waterline of the Normandy beaches on RGB images provided by VMS, as Mask R-CNN is able to perform satisfactory segmentation on 67%-97% of the provided datasets.
Acknowledgment This work is part of several projects:  EC2CO   INSU RGB (Response of Gravel Beaches-N • 12720 ) run by  INSU  ( National Institute of Sciences of the Universe ), SWOT 3MC run by  TOSCA/CNES (French National Center of Spatial Studies) ,  Rev-Cot  funded by  France Foundation , the  Raiv-Cot  project funded by the  Regional Council of Normandy , and  RICOCHET  ( ANR-16-CE03-0008 ) funded by  ANR (French National Research Agency ). The authors wish to thank the city services of Villers-sur-Mer, Etretat and  Hautot-sur-Mer , as well as the  Seine-Maritime Departmental Council  and the  Normandy Region  for supporting this study. Additionally, the authors would like to thank  Jacuelyn Steele  who helped and verified the English writing of this work.
The samples were wrapped in a tinfoil boat (Elementar) and pressed prior to analysis. Fumigated samples were measured as gas targets using MICADAS system at ETH-Zurich (Table  S4 ). Samples were normalized using oxalic acid II (NISTSRM4990C), and they were corrected for constant contamination introduced during fumigation and capsules contribution. The radiocarbon dates were calibrated with the calibration curve IntCal13  (Reimer et al. , 2013)  using the OxCal v. 4. 2 software  (Bronk Ramsey, 2009). For palaeo-hydraulic reconstructions, we used the palaeo-slope equation  (Trampush et al. , 2014) : log S ¼ a 0 þ a 1 log D 50 þ a 2 log H bf (1) where S is river slope, D 50 is median grain size, (H bf ) is channel depth, and a0, a1, and a2 are empirical coefficients with values of À2. 08 ± 0.
Acknowledgments This research was funded by a  Swiss Confederation Ph. D. grant (Nr:  2017. 1006 ) to A. S. Z. Travel, fieldwork, and laboratory analyses were sponsored by  Ernst et Lucie Schmidheiny Foundation and Augustin Lombard  fellowships to A. S. Z. We thank  Maxine Kleindienst  for enlightening discussions about the pottery industry in the region during the Holocene epoch. Also, we thank  Timoth ee Sassolas-Serrayet  for discussing Hack's law. The manuscript has benefited from the constructive reviews of  Giovanni Zanchetta ,  Martin Williams , and an anonymous reviewer.
The use of digital technologies and big data in the humanities and social sciences provided many opportunities for cultural heritage management and research, enabling data sharing and interdisciplinary collaborations. These developments increased the need for standardized data formats. General and domain-specific standards for describing and classifying cultural data, based on linked data principles, are developed to support increasingly numerous digital collections. However, the existing standards do not fully address the particular challenges concerning the standardized descriptions of images. Here we focus on ancient coins, an official image-bearing medium. We present current approaches to coin iconography, including the application of statistical measures to infer patterns in the use of images for communication.
Introduction In recent years, the scope and volume of research on human history have been expanded by the advancements in information technology, which allowed the creation of large online databases of cultural data, from digital libraries to interactive virtual museum collections, preserving cultural heritage and making it broadly available. A rapidly growing field of digital humanities has been developing computational approaches for data management and analysis. This digital revolution has created new possibilities for research, but it has also opened new questions and challenged traditional methods and practices. The existing systems of knowledge organization in humanities, such as terminologies and typologies, are not always consistent, nor commonly accepted, which complicates the process of digitization and the potential for cross-disciplinary collaboration.
Moving resources and research to the digital domain requires rethinking classification systems, and transforming cultural data to be suitable for computational approaches. In this article, we discuss the importance of data standardization for the advancement of research in the humanities. We first give a brief overview of the current trends in managing digital collections of cultural heritage, especially that of classical antiquity, and discuss the issues concerning the development of standards for describing iconography, focusing on ancient coins. Here we will present different approaches to coin images, including recent examples of using quantitative methods to study their meaning, and attempts at creating standards to describe and classify them. On the example of our recent study investigating the informational value of images on Ancient Greek coins, we discuss the challenges of choosing the appropriate level of detail to keep standards useful, but simple, and illustrate the importance of reliable, standardized data as a prerequisite for a successful implementation of statistical measures.
A popular tool for analysing mythological depictions in ancient art, the Lexicon Iconographicum Mythologiae Classicae, is available online as a database of records on ancient artefacts and the associated bibliography  (LIMC France, n. d. ) , but its potential as a semantic knowledge organization system has not been fully explored. Historical artefacts are often fragmented and their original context is not always known, which makes them challenging to study. The research on ancient art has thus been mostly oriented towards description and interpretation of individual examples or groups of artefacts, rather than conducting large-scale studies seeking general trends. However, the availability of digital art collections has renewed the interest in iconography, a discipline of art history focusing on the content of images (subject matter), aiming to classify them and interpret their meaning  (Panofsky, 1939; cf.
Mu ¨ller, 2011). Some systematically organized collections of iconographic material established since the beginning of the 20 th century are partially digitized and available online (e. g. , The Index of Medieval  Art, n. d. ; Warburg Institute, n. d. ). Iconclass, the first collection-independent classification system for iconographic data, developed by Henri van de Waal in the Netherlands (published in print 1973-85), has been recently converted to a semantic ontology format  (Iconclass, 2009). The Getty Research Institute developed several standardized vocabulary lists to support the linked databases of cultural data, including a thesaurus of iconographic terms (The Getty Research Institute, 2018). There were also initiatives to expand the CIDOC-Conceptual Reference Model ontology to include domain-specific concepts for annotating iconographic material  (Dentamaro et al.
Well-curated, standardized databases enabled such research on languages  (List et al. , 2017; Forkel et al. , 2018) , religion  (Slingerland and Sullivan, 2017; Watts et al. , 2018) , folktales  (Tehrani, 2013; Berezkin, 2015) , literature  (Hughes et al. , 2012; Yucesoy et al. , 2018) , paintings  (Sigaki et al. , 2018) , music  (Klimek et al. , 2019; Youngblood, 2019) , and films  (Sobchuk and Tinits, 2020). Quantitative methods are useful for modelling and statistical hypothesis testing using big, consistent, and systematically organized datasets, containing information in the form of measurements or discrete observations (on different types of quantitative research, see  Morin, 2015). These observations usually come from small-scale, qualitative studies, focusing on interpreting specific phenomena.
However, the designs composed of figurative motifs were soon present on both sides of the coin (obverse and reverse types), depicting deities, mythological figures and creatures, animals, plants, and objects, sometimes even visual puns alluding to the city's name  (Head, 1911, p. lv ff; Kraay, 1976, p. 2 ff. ). By the beginning of the 5 th century BCE, most of the coin-producing citystates started minted certain images consistently, thus allowing users to recognize the authority that guaranteed their authenticity and value. Despite some images being shared by multiple authorities, the existence of stable, 'diagnostic' design types, mainly associated with a single authority, has allowed numismatists to use these images to classify and date the coins  (Weir, 2010). Over the last couple of decades, numismatic research has been profiting from the creation of large online databases.
The Nomisma project, a collaborative international initiative hosted by the American Numismatic Society, developed a structured scheme for the digital representation of numismatic data, together with a general ontology built upon the existing classification systems, providing concepts to describe relevant numismatic categories  (Gruber, 2010; Gruber, 2019). Nomisma is envisioned as a mediating platform, connecting databases covering different periods and different kinds of numismatic data (single finds or coin hoards from archaeological contexts, museum collections, auction catalogues), and making them openly available  (Gruber et al. , 2013). Using linked data standards helps to create machine and human-readable data, allows integration of numismatic data with other material, and offers options for quantitative research using semantic queries  (Heath, 2018).
The Nomisma ontology strives to offer a sufficient number of key concepts to adequately describe numismatic data, identify and harmonize the inconsistencies between different data sources, while balancing the level of detail to fit the varied needs of its users  (Tolle et al. , 2018). At the moment, data standardization led by the Nomisma project is focused mainly on the technical aspects of coins, their condition, physical properties (size, weight, metal), denomination, authority, provenance, and date  (Nomisma. org, n. d. ). As of yet, standardized options for annotation of obverse and reverse design types are limited to marking the identity of the portrait head (object property 'nmo: hasPortrait', cf. Nomisma. org, n. d. ). However, this marker is not consistently applied to all entries in the dataset.
The images are usually described in freetext format, using traditional specialist terminology referring to the place of a motif on a coin (e. g. , 'to r. /l. of. ', 'in field', 'in exergue'), in a way that resembles the shorthand of heraldic blazons, although it is far less standardized. Despite the typology of ancient coins being relatively well-established though several generations of reference works, there is still a considerable lack of consensus on how to describe coin images and define individual motifs. In addition, most digital databases largely rely on human coders for data entry-often a heterogeneous group of collection curators, specialists, and interns, with a variable level of quality control and error management  (Gruber et al. , 2013; Tolle and Wigg-Wolf, 2015). These issues are not limited to numismatics, but shared with other disciplines working with material culture.
For their database of ancient and Byzantine weights, the Pondera Online team has replaced the error-prone and variable free-text descriptions of iconography with drop-down lists of predefined motifs nested in categories (e. g. , animal, deity, human), while also allowing for detailed annotation by introducing additional fields describing the technique, position of the motif, etc. (Pondera, n. d. ). For the most part, any confusion can be mitigated by accompanying the description with a photograph of the object itself and linking it to standard reference works and printed catalogues. However, due to the variable availability and format of data sources, the transformation of reference works into referential databases is for the most part still ongoing (Garcı ´a et al. , 2019). Despite the huge progress in making numismatic data available and partially standardized, which has increased the possibilities to conduct large-scale quantitative studies concerning coin production and circulation, any attempt to conduct similar research on images would likely be constrained by the raw format of descriptions and require a substantial amount of preparatory work.
A notable step towards the standardization of coin images has been made as a part of the ongoing Lexicon Iconographicum Numismaticae (LIN) project coordinated by Maria Caccamo Caltabiano. Their goal is to develop 'an objective and scientific method for interpreting coin types' based on a structural analysis of motifs, hoping to understand the visual 'language' of coins, considered to be analogous to spoken or written language  (Caccamo Caltabiano, 2018, pp. 77-8). An important contribution of this project is an online database of ancient coin iconography (DIANA), based on the previously published indices  (Caccamo Caltabiano, 2007). At present, the database includes 938 Greek and Roman coins, their motifs organized in a hierarchy of four macro-categories (person, animal/monster, flora, and object) with numerous subcategories (posture, sex, age, clothing, attributes).
The metadata give information on the provenance, value and date of each coin, allowing the users to explore the distribution of motifs and their combinations through time and space  (Caccamo Caltabiano et al. , 2013; Celesti et al. , 2017). Still under development, this database is mainly used by the researchers associated with the LIN project for case studies focused on specific motifs and their assumed semantic links, trying to interpret the observed diachronic patterns of use of these images by different ancient mints  (Puglisi, 2014; Sapienza, 2017). While undoubtedly useful for information organization, the explanatory power of their semantic approach has been criticized (de Callata€ y, 2018). The potential of comprehensive coding of iconographic elements pioneered by the DIANA database was further developed through the cooperation between the computer scientists from the Big Data Lab at the Goethe University in Frankfurt and numismatists working on the ongoing Corpus Nummorum Thracorum (CNT) project based in Berlin  (Corpus Nummorum Online, 2014-20).
In a recent article, Patricia Klinger and colleagues address the disadvantages of using unstandardized free-text descriptions to represent coin iconography in digital databases, which reduces the querying options to simple keyword searches  (Klinger et al. , 2018). Using the CNT Greek Coins data, they devised a natural language processing-based, semi-automatic process to convert obverse and reverse descriptions to a machine-readable format compatible with the linked data principles. Applying machine-learning algorithms makes this approach less reliant on manual interventions, while still allowing expert supervision. It has a lot of potential to be used on different numismatic collections and applied to other types of image-bearing objects such as pottery and seals, as an important step towards creating standardized typologies.
Images are instrumental for detecting individual types in a coin series (characterized by a unique combination of obverse and reverse dies), and estimating the total number of coins produced by an issuing authority in a given period. Quantitative methods have recently been used to study images themselves, in an attempt to understand the intentions behind the choice of certain motifs and their meaning  (Faucher, 2018; Iossif, 2018). Instead of discussing the characteristics of a small number of well-known examples, quantitative approaches provide an opportunity to study the images in the context of all known examples, thus avoiding giving undue importance to potentially exceptional objects  (Iossif, 2011; de Callata€ y, 2018). However, these studies are still limited by the affordances of the database queries, and the lack of reliable coin typologies.
In our recent study, we took a different approach to images on coins produced in the ancient Mediterranean states until the end of the Hellenistic period. We quantified the amount of information concerning the issuing authorities and the economic value present in the coin images ('designs') using measures based on information theory  (Pavlek et al. , 2019). This allowed us to investigate the influence of political and economic circumstances of the increasingly interconnected world of the ancient Mediterranean on the amount of information stored in coin designs. We were interested in Greek and other pre-Roman coins because they represent the first examples of coined money carrying graphic designs. Unfortunately, a comprehensive dataset of standardized coin types is not yet available for most Greek coins  (Wigg-Wolf and Duyrat, 2017).
d. ) and the MANTIS online database of the American Numismatic Society (2020), and prepared a corpus of unique coin types issued between c. 650 and c. 31 BCE, distributed approximately equally over eighteen time intervals. Time intervals were created using an unsupervised discretization algorithm (the discretize function from the infotheo package in R:  Meyer, 2014; R Core Team, 2017; cf. Pavlek et al. , 2019). For the purposes of the study, unique coin types were defined as coins issued in the same time interval by a certain authority, having a certain denomination, and marked with a unique design  (Pavlek et al. , 2019). Design types were constructed by combining obverse and reverse descriptions into alphabetically arranged strings of standardized motifs, following previously defined guidelines.
For example, the female portrait head on Syracusan silver tetradrachms is variously described as 'Arethusa', 'nymph' or '(female) head' (compare the following coins from the MANTIS database,  ANS 1997. 9. 20 and ANS 1984. 46. 12 dated 480-475 BCE; ANS 1997. 9. 39 and ANS 1997. 9. 42, dated 415-405 BCE)  1. (2) Addition, omission, and misidentification of motifs. In some cases, the descriptions either introduce motifs that are not present on the coin, omit motifs that are present, or confuse one motif for another. These issues can be a matter of subjective interpretation as much as a coding mistake. The raw text format of these descriptions makes it hard to spot such inconsistencies, which may hinder standardization efforts. Including more detail increases the number of unique motifs, and by extension, unique design types.
This, in consequence, influences the outcome of measures dependent on the frequency distribution of data types, such as the conditional entropy measure that we used in our study, by adding noise and obscuring or inflating the results of statistical analyses, especially when these analyses are performed on relatively small datasets. In the process of systematization and quantification of cultural data, it is necessary to apply a certain level of generalization and abstraction, which reduces the level of detail in the data-but the same is true of any classification system. Coding the data consistently throughout the dataset, according to a pre-established standard, helps to avoid ambiguity and eliminates potential biases. 4 The level of detail and statistical measures: the implications of coding decisions In order to illustrate the impact of coding decisions on the outcomes of statistical analyses, we used the dataset compiled for our previous study, and recoded obverse and reverse design descriptions adopting two extreme approaches to transform them into strings of individual motifs: a very fine-grained ('specific') and a very coarse-grained ('general') encoding.
The basic set of considerations for identifying individual motifs remained unchanged: we disregarded variants of the same motif, including motif repetitions on a single coin and spelling variants. Table  1  summarizes the differences between the 'general' and 'specific' coding approaches. We proceeded to explore the possible implications of these two coding variants on the conditional entropy measures, and on the diachronic trends of information about the authorities and denominations present in coin designs that we had identified in our original study  (Pavlek et al. , 2019). Each coding variant generated a different number of unique design types, resulting in two versions of the dataset characterized by a different number of unique coin types, sharing the same date, authority, denomination, and design.
)  in the general coding variant. In the specific coding, the persons that the experts identified based on the coin legends (inscriptions) or inferred from the context (the time and place of the coin's issue) were labelled by name. When the source databases omitted the person's name, as in the example of the Syracusan nymph Arethusa cited above, we assigned the names based on the analogous coin types, verifying the suspicious cases in the referent literature  (Head, 1911; Kraay, 1976). We strived to be systematic and consistent throughout the coding process, focusing primarily on resolving inconsistencies, and refrained from taking sides in complex numismatic debates on portrait attribution, which were beyond the topic of our study and our level of expertise. Figure  2  presents the number of unique design types over eighteen time intervals, highlighting the proportion of designs featuring persons.