dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion The pigments identified on the two Italian miniatures from the Marcadé collection correspond to materials commonly used in medieval times. The combination of methods based on imaging and point analysis has demonstrated its usefulness by providing an overall view which directed the choice of specific areas for point analyses. Generally, the pigments used for the medieval illuminations are natural minerals (lapis lazuli, cinnabar) and organic lakes from vegetable or animal sources, extracted from kermes or brazilwood. Other pigments were synthesized and include verdigris or verdets. According to their use, some compositions are difficult to study because of ternary mixtures, of superimposed layers of highlights to imitate relief, volumes, folds, or simply because the materials are not referenced in databases.
Acknowledgements This study is part of a project supported by the  Aquitaine region (France) ,  University of Bordeaux Montaigne  and  CNRS  and the  LaScArBx Cluster of excellence. This project has received support from the  French State , and was managed by the  National Research Agency  under the program Future Investments bearing the reference  ANR-10-LabX-52. We thank the cultural institutions of the  Direction Régionale des Affaires Culturelles  for their support and for providing access to the Marcadé collection of manuscripts and  Charlotte Denoël , curator at the  National Library of France.
Data organization is a difficult and essential component in cultural heritage applications. Over the years, a great amount of archaeological ceramic data have been created and processed by various methods and devices. Such ceramic data are stored in databases that concur to increase the amount of available information rapidly. However, such databases typically focus on one type of ceramic descriptors, e. g. , qualitative textual descriptions, petrographic or chemical analysis results, and do not interoperate. Thus, research involving archaeological ceramics cannot easily take advantage of combining all these types of information. In this application paper, we introduce an evolution of the Ceramom database that includes text descriptors of archaeological features, chemical analysis results, and various images, including petrographic and fabric images.
Using web-based databases, geographical coordinates, digital mapping, and digital photography is becoming popular. Scientific developments and statistical techniques have further contributed to the analysis of archaeological materials. Nowadays, digital systems are needed by archaeologists to study a variety of archaeological information and to share them. Digital systems also give an opportunity to study different aspects of the same objects or categories of objects. Archaeological ceramics 1  can be described in different ways, by archaeologists, museum curators or archaeological scientists, e. g. , through chemical, mineralogical, and petrographic analyses. In addition, ceramics can be used to determine contextual relationships, which help to highlight archaeologically meaningful data from the mass of individual data.
In other words, exploiting ceramic data allows to discover patterns that are only visible in larger and more distributed ceramic samples than can be collected about any single ceramic. In archaeology, core data are highly contextual. Thence, ceramics and their properties can help to obtain comprehensive knowledge about technological, cultural and geographical information. Such information may also contribute to understand the period and provenance of ceramics. However, information is globally very heterogeneous. Databases have different file formats, access protocols, and use various query languages. There is no standardized terminology, especially in terms of the description of ceramic materials and their properties. Moreover, databases have a strong focus by and large. For instance, in Lyon archaeometric studies carried out on ceramics  [1, 2]  led to the development of the Ceramom database  [3].
In Ceramom, whose development began in the late 1970's, ceramics were until recently mainly described by their chemical composition together with a text summary of archaeological information. Eventually, databases little interoperate, most being offline and the others only providing a web interface, but no API (Application Programming Interface). Thus, combining various information about archaeological objects, such as textual, numerical, and graphical documents, which would allow powerful computer analyses, is at best an intricate task as of today. Thus, in this application paper, we introduce the new Ceramom database, which models previously little-exploited textual descriptions of ceramic samples and includes image descriptions (technical drawings, photos, etc. ) as well. This new database aims to be the basis of powerful analyses, such as OLAP and data mining, which should integrate various points of view on ceramic objects, e.
g. , text descriptors, chemical analysis results, technical drawings, binocular images resulting from fabric analyses, and petrographic images resulting from petrographic analyses to be able to learn deep contextual information from all the bits and pieces of ceramic. To this aim, we use the new Ceramom database to source a data warehouse, which is original in its storage of data that are not only numerical. The remainder of this paper is organized as follows. Section 2 presents a selection of ceramic database projects, including Ceramom. Section 3 further details the new Ceramom database. Section 4 deals with remodeling this database as a multidimensional, data warehouse schema, presents a sample OLAP analysis scenario and discusses issues in ceramic data analysis. Finally, we conclude this paper and hint at future research in Section 5.
Ceramic Database Projects In recent years, several databases were created to highlight different perspectives in pottery research. These databases have different types of contents, depending on the aspects of ceramics studies they focus on. Moreover, specific formats may be implied based on different contents, e. g. , numbers for chemical analyses or text and/or images for petrographic analyses. Databases usually have a main type of contents, and may focus on specific categories of ceramics, time periods or regions. Databases can be either publicly available online or not. Additionally, interface features may also be available, such as interactive maps, interactive 2D or 3D views, or statistical tools, the latter being of particular interest in the context of our research. Table  1  lists a selection of databases that we consider representative of this diversity of contents, formats, statuses and features, with some indications on their specificity.
In Table  1 , primary content is indicated by X, secondary content by x, and occasional content by (x). The Levantine Ceramics Project (LCP), directed by Boston University, proposes an archaeological database focusing on ceramic wares produced in the Levant, from the Neolithic to the Ottoman periods  [4]. It mainly includes archaeological data (typological, chronological, and geographical), but also provides with fabric and petrographic data. The format of LCP data is either text or image. LCP is an open, interactive internet resource. Roman Amphorae: a digital resource, proposed by the University of Southampton, provides an online introductory resource for the study of Roman amphorae, based on a rich corpus of archaeological information together with petrographic and fabric data  [5]. POTSHERD is a collection regarding pottery from the Roman period (1 st cent.
BC -5 th cent. AD) in Britain and Western Europe, including distribution maps and links to complementary resources  [6]. The Worcestershire On-line Ceramic Database was designed to make available the complete pottery fabric and form type series for Worcestershire, from the Neolithic to the early post-medieval period  [7]. Other types of databases are more centered on either archaeological data or petrographic and fabric data. The National Roman Fabric Reference Collection (NRFRC) is the online version of a reference book providing detailed and standardized fabric descriptions of Roman wares found in Britain  [8]. FACEM focuses on fabric data of Greek, Punic, and Roman pottery in the Southern Central Mediterranean area  [9]. FACEM includes interactive maps and allows downloading detailed information.
Petrodatabase is a petrographic relational database X x x x x x x Roman Amphorae X X X x x x x POTSHERD X x x x x Worcestershire Ceramics X (x) X x x x x NRFRC (x) X X x x (x) FACEM x X x x x x Petrodatabase (x) X x x x (x) x ICERAMM X x x x x PECL X (x) (x) x x x x ASCSA X x x x x Sgraffito in 3D x x x x CeraDAT (x) X x x x x x MURR (x) X x x x downloadable Fribourg (x) X x x x Ceramom 2. 0 x X (x) x x x x Ceramom 3. 0 X X x x x x x x x x proposing interactive maps  [10]. ICERAMM focuses on medieval and modern ceramics in western and northern France, Belgium, and Switzerland  [11]. PECL is a project of encyclopedia for ceramics of the Mediterranean and sub-Saharian region of all periods, including detailed archaeological contexts information  [12]. ASCSA. net presents archaeological objects and contexts from the excavations of the American School of Classical Studies at Athens in the Athenian Agora and in Corinth  [13].
There are several image databases that are designed for a larger audience, using digital representations of ceramics. One of them, Sgraffito in 3D, proposes 3D reconstructions of late medieval pottery collection from the Museum Boijmans Van Beuningen (Rotterdam, the Netherlands)  [14]. Yet other databases focus on chemical data. The CeraDAT project, developed by the Demokritos National Centre of Science Research (Athens, Greece), is a prototype relational database including interactive maps and focusing on the Aegean and the wider Eastern Mediterranean Region  [15]. The Archaeometry Laboratory Database of the MURR laboratory (Missouri, USA) presents chemical analysis of ceramic artifacts from many regions, including Northern, Central and Southern America, and the Mediterranean  [16]. It also gives access to "historical" chemical databases, such as the Berkeley laboratory's.
Archaeological information of MURR data is presented as a bibliography. The Archaeometry Research group at the University of Fribourg (Switzerland) has established sev-eral reference groups with the chemical composition of ancient ceramics from Switzerland, Italy, France, and Germany  [17]. Archaeological information is also presented as a bibliography. Eventually, Lyon's Ceramom database used to be mainly a chemical database  [1, 2] , including only limited archaeological information. The new Ceramom 3. 0 model we detail in Section 3 has been recentered on ceramic objects, and enriched in archaeological and multimedia contents. It covers all periods. Ceramom is not available online yet, but it will be soon at the following address [3]. Table  1  clearly shows that Ceramom 3. 0 is one of the most comprehensive database model for ceramic data, including enriched archaeological (geographical and graphical descriptions) and archaeometric (numerical values of various analysis results) data.
3 The Ceramom Database Data may be qualified as complex if they are  [18] : multiformat, i. e. , represented in various formats (databases, texts, images, sounds, videos); and/or multistructure, i. e. , diversely structured (relational databases, XML documents repository); and/or multisource, i. e. , originating from several different sources (distributed databases, the Web); and/or multimodal, i. e. , described through several channels or points of view (radiographies and audio diagnosis of a physician, data expressed in different scales or languages); and/or multiversion, i. e. , changing in terms of definition or value (temporal databases, periodical surveys). The Ceramom database was designed from the requirements of ceramic specialists for recording, using, and analysing data generated by different techniques.
Thence, it stores complex data. Mineralogical and petrographical data with extensive definitions are indeed combined with rich location data. In addition to these data, graphical documents that are necessary to complement ceramic information are added in the new model, such as drawings and images of ceramic samples with variety of references. According to this model, the Ceramom database is centered on pottery samples, which are described by geographical features, several analyses, and various descriptions (Figure  1 ). Each of these packages is further detailed in the following subsections. All conceptual models are depicted as UML class diagrams  [19].
Status and Description The Status and Description package of the data model is depicted in Figure  3. The DESCRIPTION class bears textual descriptors of the object. It includes most of the information which enable us to identify the object archaeologically. The CATEGORY class helps categorize ceramics, e. g. , "COMM. " (common ware), "CARREAU" (tile). The PART OBJECT class collects data regarding parts of the form, e. g. , rim, base, body, etc. The WASTER class specifies whether the object is a waster. The FIRING MODE class contains data about firing mode, which are coded as mode A, mode B, mode C, etc. The STORAGE IN LABORATORY class bears location data if the object is stored in the laboratory. The LEGAL STATUS OF OBJECT class contains data about ownership of the object. The DIFFRACTION, DILATO, PETRO, CHEMISTRY, BINO, SEM, and additional analyses classes bear data regarding diffraction, dilatometry, petrography, chemistry, binocular microscopy, scanning electron microscopy, and additional, miscellaneous analyses, respectively.
Exploring Archaeological Ceramic Data Data warehouses are actually databases with a specific model tailored for efficient OLAP analyses. In a data warehouse, the observed data are called facts, e. g. , sales in a business context. They are characterized by measures that are usually numerical, e. g. , quantities sold and amounts of money. Facts are observed with respect to different analysis axes called dimensions, e. g. , sold products, store location and sale date. Thus, data warehouse schemas are called multidimensional schemas, or more casually star schemas, for facts are usually represented in the center of the model, with dimensions gravitating around. Star schemas help answer queries such as "total sales revenue of each product in Lyon in 2014", to go on with our business example. Moreover, dimensions may be organized in hierarchies, e.
g. , a time dimension could be subdivided into day, month, quarter, and year. Such a structure helps observing facts at different granularity levels, e. g. , "dezooming" from one quarter of a year to said year to have a more global (aggregate) view of sales, or "zooming" from one month to one day in this month to have a more detailed view of sale events. These operations actually correspond to OLAP's rollup and drill-down operators, respectively. Thus, to allow OLAP navigation in the Ceramom data, we must select facts to observe, axes of analysis (dimensions) and import data from Ceramom into the data warehouse. The result is called a cube (hypercube when the number of dimensions is greater than 3), where dimension values are coordinates that define a fact cell. There are a couple of interesting works done using OLAP analysis on archaeological data  [20, 21].
Multidimensional Model In this sample scenario, we choose to observe chemical dosages with respect to ceramic sample provenance, dating, description, and groups. Our data warehouse's star schema is provided in Figure  5 , again as a UML class diagram. Facts are modeled as a quaternary association-class connected to dimension classes. To make use of numerical values for analyses, the SAMPLE class from Figure  1  is combined with the Analysis package (Figure  4 ) into the SAMPLE ANALYSES class in Figure  5 , which models our analysis facts. In our case, aggregates (summaries) are number of sample, average number of sample, and number of analyses, etc. Dimension classes are PROVENANCE, GROUPS, DESCRIPTION, and DATING, which are the same as in the Ceramom database (Figures  2, 3 , and Figures 4).
OLAPing Archaeological Ceramic Data Once part of Ceramom data are multidimentionally remodeled, OLAP analyses can be done. OLAP actually helps interactively navigate the data warehouse, e. g. , to discover outliers or hidden patterns. We use Pentaho Business Analytics 2  , a suite of open source business intelligence, as our OLAP engine. Pentaho features a user console that is a web-based design environment. The console helps to visualize and navigate hypercubes, which are created from the data warehouse with the help of the schema-workbench tool. As an example of OLAP analysis, let us examine the contents of a specific chemical group coming from the GROUP class, and compare them to the initial typological classification from the DESCRIPTION class. Samples within a given chemical group belong to the same pottery production, i.
"Zeuxippus Ware" corresponds to a typological class that has 163 occurrences in the database. "Zeuxippus Ware" was found all over the Mediterranean and beyond, but was also largely imitated. In a second analysis, we slice on PROVENANCE, GROUPS, and DESCRIP-TION, and dice on "Zeuxippus Ware stricto sensu". A research program enabled to define several distinct chemical groups, including one corresponding to the "Zeuxippus Ware stricto sensu" (87 samples), which is the "prototype" of this ware. We identify the features of each production, including information on its geographic distribution, related to trade networks  [22]. Figure  6  compares data with the number of samples whose description includes the term "Zeuxippus", i. e. , including both "Zeuxippus Ware stricto sensu" and wares imitating it or related to it typologically.
In all countries, examples of both prototype and imitations were found. It is nonetheless noticeable that a larger proportion of imitations come from Greece, a new insight that may be significant. In histogram, "chemical classification"(in blue) is based on the actual diffusion of ceramic products, it is related to economic factors. It confirms the large distribution of this ware in countries of the Mediterranean and Black sea areas. Although the bias introduced by the initial sampling needs to be taken into account, the number of samples from each country gives an idea of the abundance of this ware, e. g. , only very few examples were found in France. "typological classification" (in red) refers to the diffusion of models and fashions, and is thus more related to cultural factors. This example somehow simulates the comparison of data obtained on the same categories of objects from two databases, focusing each on another aspect of these wares.
Issues in Archaeological Ceramic Data Analysis We have been confronted to a couple of major challenges before OLAP analyses and when performing OLAP analyses onto Ceramom data. First, we encountered a classical problem in databases, i. e. , missing values. In our example scenario, there is location information for provenance studies, but in practice, some information, i. e. , site, town, region or country might be missing in the database. For example, some samples relate to Sudak, Ukraine and Acre, Israel, with no archaeological site reference. This is why we complement the geographical hierarchy with direct associations from provenance to site, town, region, and country. Some of this missing information (town to region to country relationships) shall be found in external sources, though. Moreover, data about archaeological ceramics mostly consists of textual and numerical data.
Conclusion and Perspectives Designing comprehensive archaeological databases/tools is a challenge because of many reasons. Various dimensions should be integrated from distant databases that describe the same categories of objects in a complementary way. Thus, different point of views and parameters should be combined coming from different, heterogeneous databases. In this paper, we first survey representative ceramic databases and show there is no single comprehensive resource for studying ceramic materials. Then, we propose a new conceptual data model for archaeological ceramics. We believe this model could be a good starting point to help ceramic databases interoperate in the future. Moreover, we illustrate how ceramic data can source a data warehouse to perform OLAP. Such analyses help navigate and observe data from different perspectives, thus enabling archaeological researchers with better insights on their data.
Acknowledgements This project is supported by the  Rhône Alpes Region 's ARC 5: " Cultures, Sciences, Sociétés et Médiations " through a  PhD grant  to  A. Öztürk , who gratefully acknowledges this support. We also sincerely thank the  Archaeological Ceramics team of the "Archéométrie et Archéologie" Laboratory  ( CNRS UMR 5138 ), and especially  C. Brun , for its contribution to the design of the Ceramom model.
The proof of the untruth of this point of view, for the lychnological science, was set by the milestones built since the 1970s on huge museum collections, in primis by the researches published by Hayes 73 , Bailey 74 , Larese and Sgreva 75 , the same Bussière 76 , or Bémont and Chew 77 , which came to complete with harmony the missing information gathered in the excavation publications. Together with the other books mentioned above this volume can be framed in the top list of the most useful publications for scholars. This work succeeds to make from each of the 406 lamps -from Phoenicians to Medieval Arab times -a reference for scholars desiring to find an up-to-date database for their typology, chronology, area of production/ distribution, bibliography, etc. The structure of the book is a treaty of perfectlymastered methodology -Jean Bussière's 'trademark' -, by its structure, chapter descriptions, individual lamp records, chronology and list of analogies, perfected with one of the most complete and well-rendered illustrative apparatus we've seen recently.
" In particular, we have not yet broken the "sound barrier" by elaborating a "primitive cell" from a "prebiotic soup. " "The field is open, too open". And yet the author allows himself to dabble in "plausible" speculations. Once again, the "unknown" is in the conditional tense. Logical deduction makes up for what is lacking. When it strives for completeness, all scientific thought is an interpretation. It stirs the imagination. The arguments give order to reality to make it coherent. As a finite empirical dataset can be explained by many competing theories or doctrines, one cannot choose between origin models based on their inventiveness. Their history, as we have seen, is but a long string of quarrels that cannot be resolved without solid narrative preconceptions. All in all, everyone chooses his own narrative between science(s) and religion(s).
However, moderate improvement for patients who benefit from these therapies has been observed as described in Wykes et al. review (3). Next, neuropsychological models of schizophrenia are then presented. They highlight the critical role of the internally generated intentions in appropriate willful actions. The cognitive control mechanism deals with this ability. Interestingly, available cognitive remediation programs have not been influenced by these models. Hence, we propose another alternative to set up a specific cognitive remediation program for schizophrenia patients by targeting the cognitive control mechanism. We describe the PrACTice program that is in the process of being validated.