dataesr/scientific-paragraphs-categorization huggingface dataset:
4. 1 The Domain Ontology As the domain ontology, we created a data model compatible with the CIDOC Conceptual Reference Model (CRM). 10 CIDOC-CRM is a high-level, eventcentric ontology (ISO standard 21127:2014)11 of human activity, things and events happening in spacetime, providing definitions and a formal structure for describing the implicit and explicit concepts and relationships used in cultural heritage documentation. 12 It is intended to be used as a common language for domain experts and implementers to formulate requirements for information systems, providing a way to integrate cultural heritage information of different sources. CIDOC-CRM has been used in a plethora of projects and data management activities related to (mainly) cultural heritage, history, and archaeology. 13 The data model, which we call "SeaLiT Ontology", is under constant evaluation as long as new archival sources are analysed.
Each of these classes allows describing additional information, like connected places, persons, or other activities. Fig. 17. 16 shows how we model information about a person. A "Person" is identified by an "Appellation" (like first name and last name), was born at a particular "Place" and within a particular "Time-Span", can be a current or former member of a "Legal Body", and has some attributes, like "Social Status", "Language Capacity", "Literacy Status", "Sex Type", and "Religion Status". Each of these attributes can receive values from a dedicated vocabulary or thesaurus of terms in an information management system (as we do in FAST CAT). Finally, a "Person" can be connected to a "Promotion" (i. e. , into a new social status or employment position) or "Punishment" (which is common for crew members during voyages).
4. 2 Data Transformation After defining the domain ontology to use for representing the data in the FAST CAT records as a semantic network, we need to create the schema mappings that associate equivalent concepts and relationships from the source schemata For this, we make use of the X3ML framework and the X3ML mapping definition language, a declarative, XML-based and human readable language that supports the cognitive process of schema mapping definition. 14 X3ML separates schema mappings from the generation of proper resource identifiers (URI s), so it distinguishes between activities carried out by the domain experts and data engineers, who know the data, from activities carried out by the IT experts who implement data transformation. The definition of the mappings is a time-consuming process that can last for several weeks and can require many revisions as long as the data engineer better understands the data or changes are made to the FAST CAT templates.
This process is supported by 3M Editor, an X3ML mapping management system suitable for creating and handling the mapping files. It offers a user interface and a variety of actions that help experts manage their schema mappings collaboratively. Fig. 17. 17 shows the home page of 3M Editor. The user can search for an existing mapping, create a new mapping, select a mapping from the list and view it, or edit one of the available mappings. When creating a new mapping, the user needs to upload a part of the source data (an XML file of the record data that can be obtained through FAST CAT) as well as the target schema (i. e. , the SeaLiT Ontology). Fig. 17. 18 shows a screen dump of a mapping definition for the FAST CAT template "Crew List (Ruoli di Equipaggio)". We see that the "ship name" field of the source schema (first column of the table "Ship Identity"; see Fig.
Semantic Network Exploration When the transformation process is completed, the derived semantic network is loaded to a semantic repository (database of RDF data) and is made available for analysis and exploration by other applications. Here, the user-friendly exploration of a semantic repository can be performed through two main general access methods: i) Keyword Search: the user submits a free text query and gets back a ranked list of results that are relevant to the query terms15 , 16 ii) Interactive Access: the user explores the data through intuitive interactions with a data access system, e. g. , through a data browsing system,17 a faceted search interface,18 or an assistive query building system19 In the context of the SeaLiT project, we make use of ResearchSpace,20 an open source platform built on top of the metaphactory platform.
For an organization, it shows its name and location. For a place, it shows the type (e. g. , region, port, etc. ) and its coordinates, while for crew employments and payments it shows: employee name, serial number, recruitment place, starting date, discharge place, ending date, profession, ship, pension fund, wage, duration, and data source name. For places, the user can also inspect a map of all places referred to in the archival sources, thereby allowing them to get an overview of the covered areas. In SeaLiT, for instance, almost all places are in Europe. The user can select one of the entity instances shown in the table and start exploring the associated data, taking advantage of the Linked Data concept. Fig. 17. 21 shows an example where the user has selected to browse the data of the voyage of the ship Mardocheo.
Conclusions We have presented a process and a set of Web-based tools for the holistic management of archival sources of maritime history. First, the historical sources can be transcribed and curated collaboratively by historians using the FAST CAT system. FAST CAT supports innovative features, like nested tabular structures for data entry, embedded instance matching and vocabulary maintenance processes, as well as provenance-aware data curation and enrichment. The transcribed and curated data are then transformed and integrated into a rich semantic network (RDF graph) using the X3ML data transformation framework and a specially-designed ontology, called "SeaLiT Ontology", which is compatible with existing standards for cultural heritage documentation, in particular CIDOC-CRM. Finally, the derived semantic network can be explored using the ResearchSpace platform.
Acknowledgements This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under: i) the  European Research Council (ERC)  grant agreement No. 714437  (Project " SeaLiT-Seafaring Lives in Transition. Mediterranean Maritime Labour and Shipping During Globalization ,  1850s-1920s "); and ii) the  Marie Sklodowska-Curie  grant agreement No. 890861  (Individual Fellowship, Project " ReKnow-Research Knowledge Documentation, Analysis and Exploration in Empirical and Descriptive Sciences ").
SulfAtlas (https://sulfatlas. sb-roscoff. fr/) is a knowledge-based resource dedicated to a sequencebased classification of sulfatases. Currently four sulfatase families exist (S1-S4) and the largest family (S1, formylglycine-dependent sulfatases) is divided into subfamilies by a phylogenetic approach, each subfamily corresponding to either a single characterized specificity (or few specificities in some cases) or to unknown substrates. Sequences are linked to their biochemical and structural information according to an expert scrutiny of the available literature. Database browsing was initially made possible both through a keyword search engine and a specific sequence similarity (BLAST) server. In this article, we will briefly summarize the experimental progresses in the sulfatase field in the last 6 years.
To improve and speed up the (sub)family assignment of sulfatases in (meta)genomic data, we have developed a new, freely-accessible search engine using Hidden Markov model (HMM) for each (sub)family. This new tool (SulfAtlas HMM) is also a key part of the internal pipeline used to regularly update the database. SulfAtlas resource has indeed significantly grown since its creation in 2016, from 4550 sequences to 162 430 sequences in August 2022.
1. 6. -, sulfamidases EC 3. 10. 1. -and dioxygenase EC 1. 14. 11. -)  (2, 3). Before 2016, the vast majority of characterized sulfatases were studied in human and animals in the context of severe metabolic disorders  (2). A few sulfatases were also characterized in bacteria with diverse substrate specificities  (1). However, this limited number of characterized sulfatases was far from reflecting the huge diversity of sulfated biomolecules. With the explosion of genomic data, the gap between new sequences and characterized enzymes was increasing and annotation of sulfatase sequences was prone to errors. Inspired by expert-curated databases such as the Carbohydrate-Active enZymes database (CAZY, http://www. cazy. org/) (4), we thus decided in 2016 to create the SulfAtlas database. Sul-fAtlas proposes a classification system of sulfatases based on sequence homology, allowing a better prediction of substrate specificity  (1).
D648 Nucleic Acids Research, 2023, Vol. 51, Database issue The S1 sulfatases contain a peculiar catalytic residue, the C␣-formylglycine (FGly) which is post-translationally generated from a conserved cysteine or serine  (7, 8). The S2 sulfatases are also fairly unusual enzymes, using an oxidative mechanism involving iron and alpha-ketoglutarate cofactors to desulfate akyl substrates  (3). Despite their extreme sequence divergence (<15% identity), the S3 and S4 sulfatases adopt the same fold and belong to the metallo-␤lactamase superfamily with a conserved cation-binding catalytic machinery  (9). Extensive details on the folds and catalytic machineries of the different types of sufatases have been reviewed in the SulfAtlas founding article  (1). The S1 family encompasses the vast majority of sulfatases and is the most diverse in term of substrate specificities.
For this reason, we have divided the S1 family into subfamilies, based on phylogenetic analyses. In 2016, we had thus defined 73 S1 subfamilies, each subfamily corresponding to either a known specificity or to uncharacterized substrates  (1). An additional S1 subfamily named S1 NC (for nonclassified) contains S1 sequences which cannot be reliably assigned to a phylogenetic subfamily. These sequences are thus considered orphans until close homologues are found. In SulfAtlas, each family or subfamily page provides (sub)family descriptors (known enzymatic activities, catalytic residues and available 3D structures) and a table with all the UniProt accession numbers of sulfatases belonging to this (sub)family with the protein or locus name, the organism taxon and the EC and PDB numbers when they exist.
All these fields are linked to corresponding databases (UniProt  (10) , ExplorEnz  (11) , the NCBI Taxonomy database  (12)  and the Protein Data Bank (  13 )). Different tools are provided to explore SulfAtlas: a search engine using keywords and a sequence similarity BLAST-based server (BLASTP and BLASTX,  (14) ) to query single or multiple sequences. Selected sulfatase sequences can be exported in FASTA format  (15). (Sub)family tables can be also downloaded as excel, CSV or PDF format. SulfAtlas content is regularly updated, both in terms of sequences and of experimental knowledge based on day-to-day inspection of new sulfatase characterizations in the literature. In the present article we outline the evolution of the field and the changes implemented in SulfAtlas since 2016. Notably we have developed new tools: (i) a search engine using Hidden Markov model (HMM) for each (sub)family allowing the query of single sequences or complete proteomes and (ii) an internal pipeline for the semi-automatic update of sequence content in order to cope with the ever-increasing flow of genomic sequences.
State of the art of characterized sulfatases In 2016, SulfAtlas contained 4,550 sequences (version 1. 0, July 2013 dataset; S1: 4,061; S2: 104; S3: 370; S4: 15, Table  1 ). Forty nine characterized sulfatases were initially known, although they only represented 16 distinct substrate specificities  (1). Only one sulfatase activity was known in the families S2 (alkylsulfatase), S3 (alkylsulfatase) and S4 (arylsulfatase) and all the other characterized enzymes were found in the S1 family (from S1 1 to S1 12 and in  S1 19). All of these S1 subfamilies were monospecific at the time of the SulfAtlas creation, with the exception of the sub-families S1 7 [iduronate 2-sulfatase  (16) , endo-4S-kappacarrageenan sulfatase  (17, 18) ], S1 11 [Mucin-desulfating sulfatase  (19) , heparan sulfate 6-O-sulfatase  (20) ] and S1 19 [endo-4S-iota-carrageenan sulfatase and endo-4S-kappacarrageenan sulfatase  (18, 21) ].
Database growth and creation of new S1 subfamilies A common challenge to all sequence databases is to keep the content up-to-date. In our initial article (1), SulfAtlas contained the sulfatase sequences identified in UniProt  (10)  in July 2013 (version 1. 0). In the first phase, we had updated the sequence content through a time-consuming, manual approach. New sulfatase sequences were searched in the subsequent releases of UniProt (10) using the SulfAtlas BLAST server. Attribution of a new sequence to an existing (sub)family was based on the conservation of the PROSITE signatures  (34)  previously defined (  1 ) and on a conservative threshold of identity with version 1. 0 sequences (at least 40% over a minimal length compatible with the size of characterized S1 sulfatases (∼400 residues).
In February 2019, we thus released a SulfAtlas version 1. 1 containing 31 327 sulfatase sequences (∼7 fold the initial size of SulfAtlas). In version 1. 1, the S1 subfamilies were still limited to the first described 73 subfamilies. However, unclassified sequences were accumulating in the 'storage' S1 NC subfamily, suggesting that version 1. 0 orphan sequences could have found close homologues in subsequent UniProt releases  (10). The need to create potential new S1 subfamilies was thus becoming obvious. Due to the huge number of S1 family sequences in the SulfAtlas version 1. 1, it was impossible to directly compute a reliable, global phylogenetic tree to determine whether S1 NC sequences could constitute new S1 subfamilies. Instead, we built an internal 'core' SulfAtlas dataset limited to 1000 sequences (named Seed S1 SulfAtlas) and composed of representative sequences of the first 73 S1 subfamilies.
To conserve a high sequence diversity, we selected one sequence per eukaryotic or prokaryotic phylum for each S1 subfamily. The Seed S1 SulfAtlas sequences were aligned with MAFFT  (35) , with the iterative refinement method L-INSi and the scoring matrix Blosum62. Each S1 NC sequence and its closest homologues were similarly aligned with MAFFT. Each S1 NC multiple alignment was added to the Seed S1 SulfAtlas multiple alignment using the Merge option of the MAFFT online version  (35). Each resulting multiple alignment was visualized and manually improved using Jalview  (36). Phylogenetic trees were derived from these refined alignments with RAxML (37) using Maxi-mum Likelihood method and the substitution model identified by the IQ-TREE web server  (38). The reliability of the trees was systematically tested by bootstrap analysis using 100 resamplings of the dataset.
Development of SulfAtlas HHM, an HHM server for Sul-fAtlas As an alternative strategy to detect new sulfatase sequences and assign them to their correct (sub)family, we have decided to develop Hidden Markov Models (HMM) specific for each SulfAtlas (sub)family. Beyond our internal need for updating SulfAtlas (see below), it was also an answer to regular external solicitations to analyze complete genomes or even large metagenomes. Indeed, the SulfAtlas BLAST server was well adapted for analyzing a limited number of sequences, but was inadequate for processing large datasets. In order to detect sulfatase modules, we used as a starting point domain definitions available in the Superfamily database  (40). We chose this classification because its HMM library is based on structural protein domains and we were confident to correctly cover the length of the different sulfatase modules.
We analyzed the sequences from SulfAtlas (version 1. 3) with the Superfamily HHM library using hmmscan (default parameters) from the HMMER package  (41)  and assigned one superfamily domain for each SulfAtlas family: S1 family = Alkaline phosphatase-like superfamily (NAME = 0042147); S2 family = Clavam- They can be copied-pasted or uploaded as a file (with a size limit of 50MB). The results can be obtained directly online or be sent by e-mail. As an example, here results are shown for the complete proteome of Zobellia galactanivorans Dsij T (4582 proteins). 72 hits were found (71 sulfatases and 1 pseudo-gene) which is consistent with previous genomic analyses  (46). Data processing took 2 minutes and 45 seconds. inate synthase-like superfamily (NAME = 0041871); S3 family = Metallo-hydrolase/oxidoreductase superfamily (NAME = 0053773); S4 family = Metallohydrolase/oxidoreductase (NAME = 0051194).
Based on these results, we determined the limits of each sulfatase module within the SulfAtlas full-length sequences. For each (sub)family, sulfatase module sequences were aligned using MAFFT  (35)  with the globalpair option and the maximum iteration set to 1000. These multiple alignments were manually checked for ensuring their consistency. Finally, we created an HHM for each multiple alignment using the HM-MER package  (41). The next step was the definition of rules to reliably assign a sequence to its correct SulfAtlas (sub)family. For this, we have compared all the full-length sequences from SulfAtlas (version 1. 3) to the generated HHM library using hmmscan  (41)  and manually determined in which conditions sequences were correctly assigned. Based on the results, we have defined the following rules: (i) the assignment is reliable when the score is superior to 300 and the coverage is superior to 80% of the HMM length; (ii) when the score is between 200 and 300 (with a coverage > 80%), the sulfatase sequence cannot be reliably automatically assigned to an existing (sub)family and additional manual analyses are needed to determine its status; (iii) a score between 100 and 300 and a coverage inferior to 80% is generally a sulfatase fragment coded by a pseudo-gene or due to an incorrect prediction of an open reading frame (ORF) in a genome.
On these bases, we have added a new interface to the SulfAtlas database (named 'SulfAtlas HMM') to query the SulfAtlas HMM library online using hmmscan (HMMER) on a SLURM-based cluster computing infrastructure (Figure  1 ). For each protein query, the interface will provide the assigned SulfAtlas (sub)family as well as additional information (e. g. the query and HMM lengths, the coverage, the score, the E-value, etc. ). Depending on the load of the underlying computing infrastructure, results can be obtained in a few seconds for individual sequences and in few minutes for complete proteomes (e. g. ∼2 min for a bacterial proteome of ∼4,000 proteins). Queries (protein sequences in FASTA format) can be made by copy-paste or by file upload. The results are obtained directly online or sent by email.
We have set a size limit of 50MB for the data submitted online. For larger datasets, users should contact the SulfAtlas team (projet. sulfatlas@sb-roscoff. fr). Importantly, personal data are not saved by SulfAtlas HMM. Scientific data are anonymized and are erased by the server after 7 days. It is noteworthy that the SulfAtlas HMM library has also been incorporated as a routine tool into Microscope (https://www. genoscope. cns. fr/agc/microscope), the microbial genome annotation & analysis platform of Genoscope  (43) , facilitating the correct annotation of sulfatases in new bacterial genomes.
Semi-automatic integration of new sulfatases in SulfAtlas The first releases of SulfAtlas relied only on expert curation to enrich the database with new sulfatases (1). To cope with the increasing flow of sequences, we have decided to develop a custom pipeline to semi-automatically update SulfAtlas, while still maintaining a high quality of information. The heart of this internal pipeline is the SulfAtlas HHM library. The pipeline includes the following steps: Extraction of new candidate sequences from the Uniprot database. In the first run of the pipeline (T0, done in May 2022), this step builds a local copy of all the sequences of a full Uniprot release (around 214. 4 million sequences for Uniprot release 2022 01)  (10). Subsequent update runs will query Uniprot to restrict sequence retrieval to entries added and sequences modified since the last Uniprot release.
HMM candidate selection. This step uses HMMER along with the SulfAtlas HMM profiles to compute an HMM score for each candidate. The output of this step is a file describing the HMM matches for each candidate sequence (family and/or subfamily, score, E-value, location and length of the matching fragment or fragments. ). Only candidates with an HMM-score of 100 or more are selected for the subsequent stages (189 938 candidates in T0). HMM full candidate information retrieval. In order to be included in the SulfAtlas database, a sulfatase sequence needs to be supplemented with extra information (taxonomy, NCBI identifier and accession  (12) , known PDB structures  (13) , known loci  (10) ). This step queries Uniprot  (10)  for each candidate selected in step 2 and completes the initial sequence information with the data retrieved from Uniprot.
BLAST-based candidate consistency checking. To enhance the quality of candidates that will be added to the SulfAtlas database, each of the candidates resulting from step 3 is used as query sequence in a BLAST against the database of sequences from the latest release of SulfAtlas. Information about each candidate is then enriched with the results of its best BLAST hit. Final candidate selection. The actual set of candidates considered for updating the SulfAtlas database is then built by extracting the candidates from the previous step which comply to two criteria: (i) their HMM-score is 300 or higher (with a coverage >80%) for a given SulfAtlas (sub)family, and (ii) their BLAST significant best hit matches a sequence belonging to the same (sub)family. This double condition is key to ensure the reliability of the (sub)family assignment.
The automatic assignment has clearly failed because we have chosen a quite conservative threshold in order to obtain a reliable automatic assignment. In most of these cases, a straightforward verification is sufficient to manually confirm the correct (sub)family assignment. (ii) Candidates with a coverage inferior to 80%. Generally, these sequences are sulfatase fragments corresponding to a pseudo-gene or an incorrectly predicted ORF. We have now decided to definitively reject those sequences from SulfAtlas; (iii) Candidates with a good coverage (>80%) for which the HMM and BLAST assignments are divergent or which have obtained good hits with several HMMs. Generally, these sequences are potential seeds for new subfamilies and we will temporarily assign them to the S1 NC subfamily. The pipeline, which is based on Python and Java programs and shell scripts, has been developed to leverage the power of a SLURM-based cluster computing infrastructure for steps 2 and 4.
It can be run stepwise or submitted as a single job. Execution time was around 7 days for the whole 2022 01 UniProt database (T0)  (10)  and it will be much shorter for the subsequent, incremental updates. The most recent SulfAtlas release (version 2. 3. 1) is the first including data generated by this pipeline and is significantly larger (162,430 sequences; Table  1 ).