dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We thank the  Service régional de l'Archéologie (SRA) Nouvelle-Aquitaine , the  Service départemental de l'Archéologie (SDA) of Charente-Maritime  and the  Conseil départemental of Charente-Maritime  for their financial support to the  Projet Collectif de Recherche (PCR) La Roche -à-Pierrot directed by I. C. We also want to thank the two anonymous reviewers for their suggestions and comments that improved the final version of the manuscript.
Manuscripts are a crucial form of evidence for research into all aspects of premodern European history and culture, and there are numerous databases devoted to describing them in detail. This descriptive information, however, is typically available only in separate data silos based on incompatible data models and user interfaces. As a result, it has been difficult to study manuscripts comprehensively across these various platforms. To address this challenge, a team of manuscript scholars and computer scientists worked to create "Mapping Manuscript Migrations" (MMM), a semantic portal, and a Linked Open Data service. MMM stands as a successful proof of concept for integrating distinct manuscript datasets into a shared platform for research and discovery with the potential for future expansion.
In 2017, the team received a Round Four Trans-Atlantic Platform Digging into Data Challenge grant to combine data from several disparate sources for premodern manuscripts, and to use the aggregated data to explore a range of research questions about manuscript history and provenance. MMM currently includes data from three harmonized heterogeneous databases of premodern manuscript metadata. Each database approaches manuscript description and metadata in markedly different ways. Bibale, a database produced by the Institut de recherche et d'histoire des textes (IRHT) in Paris, documents the history of transmission of manuscripts and their textual components from one collection to another across time  (Wijsman, 2017). The Schoenberg Database of Manuscripts (SDBM) 2  (Ransom et al. , 2018)  at the University of Pennsylvania Libraries collects data from over 14,000 auction and sale catalogs, institutional catalogs, and other published or non-published sources, including users' personal observations.
Data accuracy is dependent on these sources and may be highly variable. Although the SDBM captures detailed descriptive data about manuscripts, it is primarily used by researchers to track the ownership history of individual manuscripts. These two databases are built on bespoke and complex data models, and each delivers highly structured data dependent on external authorities for names and places. The third source, Medieval Manuscripts in Oxford Libraries, consists of XML documents encoded according to the Text Encoding Initiative (TEI) P5 guidelines for manuscript description. 3 These XML files are a digital transcription of historical manuscript catalogs, which have been annotated with TEI tags to mark up structured data elements. MMM stands as a successful proof of concept for harmonizing and aggregating three distinct datasets into a shared platform for research and discovery, with the potential to add more.
Both groups were interdisciplinary in their membership. The first group consisted of manuscript researchers, librarians working with manuscript collections, manuscript database managers, and digital humanities specialists. The second group contained SW expertise, digital humanities expertise, and manuscript metadata expertise. Overlapping membership helped to ensure regular communication between the two groups, which contributed more than 550 person-hours' work over almost 2 years. This iterative approach ensured that major project tasks (identifying research questions, developing and implementing the data model, and publishing the data) were the product of sustained collaboration between the wide range of specialists involved, and reflected the complexity of the data and the centrality of the modeling process.
Lessons learned from this collaboration will inform the practice of manuscript description in a LOD environment to enable deeper and more integrated research into the history, and histories, of premodern manuscripts. The paper builds on a previous dataset description article  (Burrows, Emery, et al. , 2020)  and a paper disseminating the external vocabularies  (Burrows, Brix, et al. , 2020). First results of building the semantic portal have been presented in  (Hyvönen, Ikkala, et al. , 2019).
| DEVELOPMENT OF THE RESEARCH QUESTIONS From the beginning, the goal of the MMM project was to create an online environment that would allow manuscript researchers to query data from multiple platforms. The challenge in building such an environment revolves around three key problems: 1. How to model manuscript metadata for interoperability, with a focus on provenance data? 2. How to extract, integrate, and reconcile heterogeneous manuscript metadata from distributed data sources? 3. How to publish Linked Data for digital manuscript research? Before these questions could be answered, the project team needed to come to a shared understanding of the content and nature of the data derived from the three sources. This extended beyond the contemporary formats of the constituent datasets from a purely technical perspective, to encompass an appreciation of their heritage-the sequence of historical motivations, actions, and constraints which led the catalogs to their current configurations (in some cases, going back over 150 years).
Having an understanding of the significance of the nuances and intricacies as well as the inconsistencies and ambiguities of the three datasets was essential for constructing a unified data model that was comprehensible to and useful for manuscript researchers. This was especially important given a requirement for the harmonized data service to supplement, rather than replace, the three data sources: each remains the canonical catalog for the records held within, and for their updating, with the Linked Data Service taking a layered approach  (Page et al. , 2017)  to provide additional discovery, analysis and visualization functions. The first stage of the collaboration therefore required manuscript scholars and metadata specialists to introduce the data modelers to the data and to identify the key data elements that would be most useful to intended users.
The source datasets were then analyzed together with the research questions to identify key data points in manuscript description that would provide the building blocks for a unified data model. This process identified the following elements: author, title, language, place of production, date of production, script, provenance agent, provenance event time range, and current location. Once these desired elements were established, the next step was to confirm that they existed across all three datasets and could be programmatically extracted. If all three datasets contained the same elements in a research question, then it was likely to make a viable contribution to the unified model. If only one or two of the datasets contained an element, then the potential viability of the question for the unified model needed to be reconsidered.
The set of research questions was also integral to testing the data harmonized with the unified data model. The "scholarship group" employed the questions to explore the user interface and provide feedback to the developer and designer. In several cases, the feedback also led to revisions to, and extensions of, the data model itself (e. g. , in such problematic areas as "last known location" of a manuscript). From July 2019, a group of researchers, librarians, manuscript metadata specialists, and computer scientists held weekly workshops in which they used the MMM SPARQL endpoint to query the data directly, once again taking the set of research questions as a starting-point for queries. These explorations also contributed to further refining of the data model.
| DATA MODELING Data from the three source databases (Bibale, SDBM, and Medieval Manuscripts in Oxford Libraries) was harmonized and published as LOD. For this purpose, a harmonizing data model was devised, based on CRM and FRBRoo. CRM is an event-based model for information integration in the field of cultural heritage. FRBRoo is a CRM compatible version of FRBR, a conceptual model for bibliographical information  (Le Boeuf, 2012). Building on earlier, related work, the MMM project nevertheless developed a new and unique data model which combines elements of CRM and FRBRoo with MMM-specific elements. Sub-properties of some CRM and FRBRoo properties are used for more specific relations, while also completely new properties are added for data that is out of the scope of CRM and FRBRoo, for example, mmms: last_known_location which is used to refer to the last known location of a manuscript, based on the available data.
| Related work Modeling rare and unique documents like manuscripts using CRM and FRBRoo has been studied in Le  Boeuf (2012) , the insights of which have guided our data modeling work. The suitability of CRM for representing manuscript metadata has also been noted in  Bellotto (2020). There are some existing SW approaches for harmonizing manuscript collections using CRM and FRBRoo, including the Biblissima project  (Frunzeanu et al. , 2016; Gehrke et al. , 2015)  and a catalog of historical Hebrew manuscripts  (Zhitomirsky-Geffet et al. , 2020) , The Biblissima project has developed a data model based on CRM and FRBRoo for the purpose of integrating and harmonizing a number of heterogeneous manuscript databases as LD. Although their data model is much broader in scope, the data model and data mapping templates were used as inspiration for the MMM data model.
• 3,750 collections (private or public libraries). • 17,000 books (almost all manuscripts, some printed books). • 750 bindings. • 13,500 provenance marks (ex-libris, ex-dono, heraldic arms, etc. ). • 500 external sources (ancient catalogs, inventories, lists, etc. ). • 1,250 texts or editions (the version of a text found in a specific manuscript). • 2000 works. Bibale contains references to external authorities, including VIAF for name authorities and textual place references based on GeoNames. The database contents were exported as CRM for the MMM project.
| Medieval Manuscripts in Oxford Libraries As of January 2021, the Medieval Manuscripts in Oxford Libraries dataset covers 10,272 manuscripts. Most of the descriptions are summary entries, digitally encoded from the Quarto and Summary Catalogs published between 1853 and 1924. Detailed modern descriptions are available for manuscripts acquired since 1916. The manuscript descriptions and authority files are encoded in XML according to a customization 8 of the Guidelines of the Text Encoding Initiative (TEI). Significant effort has been invested in the creation of local authority files for works, people and places, also using TEI. These have been, in turn, manually reconciled with uniform resource identifiers (URIs) of records in external authorities such as VIAF, Library of Congress, Bibliothèque nationale de France, Système Universitaire de Documentation, Gemeinsame Normdatei, and WikiData.
While the XML gives structure to the manuscript descriptions, some of the data that would become important to MMM, in particular the provenance data, is contained in narrative note fields; this could not be easily extracted and would require the team to find a workaround. The XML files, together with processing software, are stored in a publicly accessible GitHub repository. 9 The Bodleian's own catalog website 10 is generated from a version controlled check out of the XML repository. For the MMM project, the XML files were transformed into Resource Description Framework (RDF) based Linked Data  (Burrows et al. , 2021). The first step was to extract a selection of the XML elements from each file and combine them into a simpler, flatter XML structure using xQuery. A transform from the simplified XML into CRM compatible RDF was generated in the 3M (Mapping Memory Manager) software, 11 then scripted as a reproducible Docker file which upload the resultant data to a git repository.
| Schoenberg database of manuscripts Entries in the SDBM use 36 possible fields to record data from observations of manuscripts found in published and unpublished sources. The database contains over 250,000 records containing provenance-related observations of manuscript histories. The history of SDBM traces back to Lawrence J. Schoenberg, who started building the database in 1997 for private use. Currently the SDBM data is stored in a MySQL relational database. The current data model, launched in 2017, 12 is focused on the entries (manuscript observations) and their sources. Sources, such as auction catalogs and bookseller websites, describe manuscripts. Entries represent observations of manuscripts derived from a Source. A Manuscript Record links together entries that describe the same manuscript, gathering observations across time and place for easy reference and study.
SDBM uses Virtual International Authority File (VIAF) based Name and Place Authority Files to standardize spelling and naming conventions for people, organizations, and places. The place records are also linked to Getty Thesaurus of Geographic Names (TGN) and GeoNames identifiers. The SDBM also makes an RDF version 13 of the dataset available via a SPARQL endpoint, 14 which supplies the initial conversion for the MMM project. The SDBM data model presented two problems for MMM. First, multiple entries can represent a single manuscript. An entry typically corresponds to an auction lot or catalog entry. The same manuscript can be represented by multiple entries because manuscripts move through time and can be "observed" in different sale or collection catalogs. Many entries in the SDBM are linked together to form Manuscript records, but linkage among entries is not comprehensive.
| Modeling manuscript metadata as linked data Combining manuscript provenance metadata in an interoperable way from the heterogeneous data sources is required to depict a more complete view of the histories of the manuscripts. The semantic reconciliation needed for this task requires making the data semantically interoperable  (Hyvönen, Ahnert, et al. , 2019). This requires both the reconciliation of schemas and the reconciliation of entities in the data sources. Schemas can be reconciled by means of devising a unified data model encompassing the relevant entities and relations in the source datasets, to overcome their own data modeling conventions, and mapping the datasets into this schema. The MMM data model is based on CRM and FRBRoo, which support event-based modeling needed for provenance data that are essentially chains of events concerning the manuscript objects.
The production of a manuscript is expressed in FRBRoo differently depending on whether it is the production of an original manuscript, or the production of a copy of another manuscript (Le  Boeuf, 2012). The activity F28 Expression Creation corresponds to the former, resulting in new instances of each F2 Expression and F4 Manifestation Singleton. The production of a copy of a manuscript can also produce some difference from the original, and thus could be considered creating a new expression. However, the data available and the objectives in studying the data should dictate whether it makes sense to model the differences in expressions or consider copies to be exact copies  (Le Boeuf, 2012). In harmonizing the information in the source datasets, reinterpreting the source information in terms of the unifying data model is necessary.
| The MMM data model The MMM harmonizing data model uses the Erlangen versions 15 of CRM and FRBRoo. An overview of the data model is presented in Figure  1 , in which the nodes represent classes and the arrows represent commonly used properties between the instances of those classes. The MMM data model makes also use of subclasses and subproperties of those shown in the figure. The namespace prefixes used in this article are shown in Table  1. The scope of the MMM data model is restricted to the information available in the data sources, but the data model can easily be extended as needed, if new datasets are to be harmonized with the MMM data. Following the good practices of using CRM in RDF  (Doerr et al. , 2020) , we do not model names as appellations, but rather use literal values directly as names using SKOS properties.
Similarly, the CRM time primitives are not used, but instead temporal extents of time-spans are quantified by date references using four properties (P82a, P82b, P81a, P81b). Sources for individual pieces of information are given on the resource level as dct:source, indicating the source dataset of the resource. A URL link to the resource in the user interfaces of the source datasets is also added to the data where possible. Manuscripts are modeled as instances of frbroo:F4_Man-ifestation_Singleton. As there is no information available whether individual manuscripts are copies or original pieces of work, manuscripts are always assumed to be copies of other manuscripts and hence produced by a crm:E12_Production event. The databases contain three different notions of ownership of a manuscript.
(a) There is ownership that can be observed from visual signs in the manuscript itself, for example, ex-libris and seals. (b) Additionally, there is ownership that can be observed from a collection catalog. (c) In auction catalogs and some other sources, there can be information about an event in which a manuscript has been obtained or given away by someone. These are all modeled differently in the data model, with (a) expressed as a direct ownership of the manuscript resource, (b) expressed via a Collection resource, and (c) expressed as 1, with the addition of a Transfer of Custody event. The concept of a manuscript as a physical unit is inconsistently used, often even within catalogs. A manuscript in a database can be one of several different manuscript concepts: 1. Manuscript group: several physical things that are grouped together, probably because they are volumes of a single manifestation of a work.
2. Manuscript volume: a single physical object-often what people, especially in libraries, mean by manuscript. 3. Manuscript part: a constituent part of a manuscript volume which has at some point been physically separated from the other parts. If a manuscript volume has a part in it, then all of the volume should be described as parts. 4. Manuscript fragment: a physical thing that was at some point a constituent part of a larger manuscript volume and not an independent part. Of the source datasets, the Oxford catalog has separated these different levels in its data, whereas the SDBM and Bibale databases only address this to a limited degree. In the MMM data these are handled as part-of relations (crm:P46_is_composed_of ) between manuscripts when it is known that a manuscript is a part or a fragment of a manuscript group or volume.
| Places Geographical information has been expressed in each source dataset according to their individual conventions. The SDBM hosts a well-structured place authority 16  where external vocabularies are prioritized in the following order: (a) Getty TGN, (b) GeoNames, (c) VIAF. The Oxford catalog prioritizes Getty TGN, but additionally makes references to GeoNames and Historical Gazetteer of England. When populating Bibale database the place references (i. e. , Country-Region-Settlement) were taken from GeoNames, but the GeoNames ID was not stored in the database. Based on this initial state, it was decided that the Getty Thesaurus of Geographic Names 17 (TGN) would serve as the best shared place authority for all three databases. TGN was chosen because it was already used in most source datasets, and it is to our knowledge the only widely adopted digital gazetteer with a global coverage and support for the temporal dimension of geographic information.
| DATA HARMONIZATION AND TRANSFORMATION The data model is populated by reinterpreting the source datasets in terms of the data model. A repeatable automated data transformation pipeline 19 was developed to (re-)create the whole MMM KG from the source datasets when needed. To facilitate reproducibility, the pipeline is based on Docker. The pipeline enables updating the KG regularly with updated source datasets. The functionality of the pipeline is depicted in Figure  2. The three data sources in their original formats are depicted in red. The pipeline takes the RDF exports of the source datasets as input and first transforms these into the MMM data model using a total of 22 SPARQL Construct queries. The queries reinterpret the information in the source datasets in the terms of the MMM data model.
After the databases are transformed into using the MMM data model, various processes are used to disambiguate and reconcile a portion of the entities shared between them. Entity reconciliation processes merge entities both automatically and based on CSV files of matches created by domain experts. The matching entities are reconciled by merging the entities and their metadata, while also pointing all references to the new entity. To support data visualization, last known locations of manuscripts are inferred from the data and annotated to each manuscript. After the transformation and reconciliation tasks are completed, a set of 3 SPARQL queries are used to validate the data to find possible errors.
| Automatic entity reconciliation This step combines automated processes for matching and reconciling entities originating from different source datasets. This step aims to reconcile all places between the source datasets, but provides non-exhaustive results to the reconciliation of manuscripts and actors. The first step of this process was to identify the vocabularies used in the source datasets and how far the reconciliation is possible using these vocabularies. This revealed some gaps in the use of vocabularies, which were addressed to facilitate the automatic reconciliation process. Finally, automatic reconciliation processes were developed, which use the shared vocabularies.
F I G U R E 2 The MMM data harmonization and integration pipeline Duplicate actors from multiple data sources are recognized automatically based on their shared VIAF identifiers. Automatic processes match 589 actors between the databases based on their VIAF identifiers. Collection shelf-mark identifiers assigned to manuscripts and quoted in database descriptions can be used to automatically reconcile references to the same manuscript. Automatic linking can also be carried out based on the Phillipps numbers assigned to manuscripts formerly belonging to the huge collection of Sir Thomas Phillipps (1792-1872)  (Munby, 1960). There are a number of problems with this approach. A single Phillipps manuscript volume can be divided into multiple manuscript volumes in the datasets. For example, the manuscript with Phillipps no.
10584 corresponds to 38 items in the Bibale database. Conversely, there exists a single manuscript in Bibale that used to consist of two separate items in the Phillipps collection, and hence has two different Phillipps numbers. 3,525 matches between manuscripts are found by matching based on shelf-mark identifiers parsed from the manuscript metadata (3170) and Phillipps numbers (355). The textual place references of GeoNames places in Bibale are linked back to GeoNames. One issue with this is the temporal changes that have occurred in GeoNames since the textual references have been made, requiring to take into account the French administrative region reform in 2016. After linking to GeoNames, the places are linked to TGN. Then all place references from all datasets are harmonized by creating shared resources for the TGN places.