dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Systematic digitalization and control of quality of the MG-CE are advantageous and would allow trained medical assistants to perform standardized examinations, allowing the physician to concentrate on patient questions and education instead of managing the logistics of the test. We also assessed our hardware-software "Inteleclinic" solution for telehealth consultation, which appears to be able to enhance data quality (described in a provisional patent; number 63305420; Garbey M and Joerger G, 2020). Our methods and technology would be particularly applicable to clinical trials, which are limited in requiring a large number of examiners who all perform assessments in slightly different manners. A trial could substitute present operations with a central telemedicine facility. We envision that our telehealth approach can be applied to other neuromuscular diseases beyond MG and will provide objective, reproducible, and quantitative health care assessments that go beyond the present capabilities.
Acknowledgments The work to gather clinical data was supported in part by the  MGNet , a member of the  Rare Disease Clinical Research Network Consortium (RDCRN)  ( NIH U54 NS115054 ). Funding support for the Data  Management Coordinating Center  is provided by the  National Center for Advancing Translational Sciences (NCATS)  and the  National Institute of Neurological Disorders and Stroke (NINDS).
PROJECT HISTORY Many archival institutions and projects around the world (like portals such as Archives Portal Europe and FranceArchives) use XML/EAD and XML/EAC-CPF files to describe their collections and the agents related to them. Based on ISAD(G) standard, XML/EAD (EAD) [3] DTD and schema, whose first version was released in 1998, facilitated its adoption and diffusion. EAD either is the production and storage format of finding aids or is the output format of data bases. It preserves finding aids and is an exchange format with external applications. Though it is now deprecated and no longer supported by the Technical Subcommittee on Encoded Archival Standards of the society of American Archivists, EAD 2002 remains the most used version of EAD in the world, much more than EAD 3, which was released in 2015 and has changed little since.
The ANF  6  have been interested in entity-relationships models and graph technologies since 2013, one of the reasons being that they already have authored a significant, and growing, number of authority records that were linked to each other and to the descriptions of the archives themselves. This in essence constitutes a very dense oriented graph, whose relations are not really displayed, and  3  RiC-O Converter source code: https://github. com/ArchivesNationalesFR/rico-converter. 4  The ANF use the 2010 version of EAC-CPF schema. They may decide to move to EAC-CPF 2. 0, which was released in August 2022. 5 The FAIR Guiding Principles for scientific data management and stewardship 6 are recommendations to improve the Findability, the Accessibility, the )nteroperability and the Reuse of digital assets , around which a community and various initiatives have developed.
The ANF also wanted to connect such metadata with other metadata sets created by other institutions. Linked Data technologies thus seemed to be a possible solution to meet these needs. RiC Ontology, an OWL domain ontology for archives, was at last available; it is also based on a recent entity-relationship conceptual model [7]; it is fully documented and rich. Which made it possible for the ANF to produce RDF datasets. The ANF first built a qualitative proof of concept (PIAAF)  7  , to show that converting existing archival metadata to RDF datasets conforming to RiC-O was possible, to interconnect datasets from different institutions, and to visualize and explore them in a new way. But the PIAAF prototype did not include a large quantity of metadata nor took into account the variety of their structure and content.
if the users are the staff of the archival institution. ) As concerns this parameters file, see also below, note 20. 12 Physdesc unit test: https://github. com/ArchivesNationalesFR/rico-converter/tree/master/ricoconverter/ricoconverterconvert/src/test/resources/ead2rico/_32_physdesc. One of the challenges of this conversion is the creation of entities that were implicit in the original format. This is typically the case for the )nstantiation RiC-O entity. An Instantiation in RiC-O is The inscription of information made by an Agent on a physical carrier in any persistent, recoverable form as a means of communicating information through time and space. 13 Each generated Record Resource will be associated to one Instantiation identifying its physical or digital primary instantiation. Then, a second )nstantiation is created based on the presence of a daogrp/daoloc element, considered to be a distinct instantiation (digital surrogate) of the record resource.
A TOOL FOR THE ARCHIVAL COMMUNITY The project team produced mappings between EAC and EAD to RiC-O. These mappings constituted the first stage of the specification work, before the preparation of the unit tests that implement them and define in detail the desired output. As in any conversion process from one format to another, the goal was to avoid information loss. This led, in particular, to include in the mappings the metadata of the EAD and EAC files, contained in the eadheader and control elements. As it involved producing RDF triples, another guiding principle was to achieve the finest granularity possible from the XML structures defined by EAD 2002 and EAC. This, without forgetting to express in the form of relations information hitherto represented by simple attributes e. g. the @level attribute of EAD archdesc and c elements, which is mapped to rdf:type or rdf:hasRecordSetType and one of the individuals defined in RiC-O, instances of rico:RecordSetType or rico:DocumentaryFormType , by some elements e.
15 We encountered no blocking problem during the mapping development phase. We chose not to use the RiC-O Date and Identifier classes, considering that the ANF information system does not provide unique identifiers for such data in the source files. We decided to use rico:identifier and rico:date datatype properties instead, the values of the rico:date property having W3C XML Shema date or gYear datatype. One of the difficulties was to decide how precisely, from each of the sub-elements of the EAD c units of description, to elaborate both the description of the intellectual content of the archival resource (instance of rico:RecordResource) and of its physical manifestation (instance of rico:Instantiation. ) Thus, for example, the call number of the archival resource, its physical description and its custodial history have been mapped to properties of the instantiation, while its title, its conditions of access and use, have been mapped to properties of both classes.
The mappings made for RiC-O Converter will serve as a basis for this work. The source code is open and freely accessible on GitHub. 18 The software is licensed under the terms of the CeCILL-B license,  19  which is equivalent to MIT license and close to a public domain license. The software is fully documented in English to reach the international community. The conversion process is therefore documented. The converter was developed to address first the needs of the ANF but we kept in mind its potential uses for any other archival institution; therefore the code is easily configurable. Typically, the root URI for the URIs to be generated is an option that can be easily changed. 20 However archival institutions would probably need to adapt the software to their own metadata sets and needs. The documentation explains how to customize the conversion, which requires XSLT development skills.
On these aspects, and on all the features of the tool, more information is available in French in [9]. 17 EAD to RiC-O and EAC-CPF to RiC-O mappings can be found in https://github. com/ArchivesNationalesFR/ricoconverter/tree/master/ricoconverter/ricoconverter-doc/src/main/resources (EAC_to_Ric-O_0. 1_documentation. xlsx and EAD_to_Ric-O_0. 1_documentation. xlsx files). 18 The GitHub repository also provides references to some presentations, including a short article published in April 2020 in the ICA Flash journal [8], and recordings of a webinar held in French in June 2020 (https://www. dailymotion. com/playlist/x6x1d0, last accessed 2022/12/30). 19 https://github. com/ArchivesNationalesFR/rico-converter/blob/master/license. txt. 20 The tool comes with two parameters files located in the parameters folder of the release: one for each of the input formats and conversion processes.
RESULTS: USING RIC-O CONVERTER AT THE ANF Using RiC-O Converter, the Lab of the ANF has converted the whole collection of archival metadata stored in XML produced and maintained by the institution, which is more than 31000 finding aids and 15400 authority records. These files can be converted again when needed, for example when major updates occur in this collection. The performance of the tool is very good: on a personal computer, the 15400 EAC files are processed in approximately 15 minutes, yielding 0. 7 million triples. The 31000 EAD files are processed in approximately 30 minutes, yielding approximately 160 million triples.
In such situations, the decision made was to ignore the source data, whenever it was possible to detect such problems in it -as is the case in the two examples we have just cited. 24 22  Other lists and vocabularies are used to index, either the content of the finding aids (for example, a vocabulary on record and record set types) or the content of the authority records (a vocabulary on corporate body types). On these vocabularies, see also notes 8 and 29. 23 See for example the unit test that concerns the EAD otherfindaid element: https://github. com/ArchivesNationalesFR/ricoconverter/tree/master/ricoconverter/ricoconverter-convert/src/test/resources/ead2rico/_41_otherfindaid. 24 See the unit tests that concern the EAC-CPF place element: https://github. com/ArchivesNationalesFR/ricoconverter/tree/master/ricoconverter/ricoconverter-convert/src/test/resources/eac2rico/_35placeInParis and https://github.
These tasks are now part of the institutional 2021-2025 strategy. 26 In order to fix some of the issues listed above, it will be necessary to revise and refine the local guidelines on using EAD, to extend the local EAD application profile and to better constrain its use (maybe using an application profile of the EAD 2002 schema, or even moving to EAD 3 schema and Schematron), as well as to improve the design of the authoring tool. Another essential task is to enrich the metadata with access points and authority data, which can partially be done using the linked data technologies themselves. The Lab of the ANF has started several projects of the kind, aiming, first, to build richer authority data (that would describe more concepts, agents and places, in a more accurate way). Let us, as an example, mention that the ANF descriptions of places are being aligned with other RDF datasets like those of the National Institute of Geographic and Forest Information (IGN), in order to georeference these places, among other benefits.
Several other projects aim to work on the description of agents connected to records, using for example the datasets of the French national Library (BnF) 27 or of Wikidata. 28 Since December 2021, these authority data have been versioned and publicly available on GitHub. 29 condly, the Lab also started a project which aims to develop an AI to recognize named entities which are mentioned in large numbers in the EAD files, without being indexed. This involves automatically annotating these entities, categorizing them (as persons, organizations, places, positions, events… and then reconciling them with entities described in Wikidata and in the ANF authority records (enriching these records along the way). This research project, carried out in partnership with the ALMAnaCH team at Inria, 30 should lead to an operational prototype in 2024.
Managing and using the RDF output: a quick overview of the first results of an ongoing project Working on the source metadata does not dispense with looking for ways to better control the quality of the resulting RDF data. We believe that such needs would be satisfied by using SHACL 32 rules to assess the conformance of the generated graph structure against some rules. These rules could be derived directly from the RiC-O ontology 33 (typically cardinality, domain and range check), or they can be hand-written to validate business oriented patterns in the graph. This is something we plan to do in the future; we will certainly publish the SHACL rules we have developed for the ANF purposes. In terms of challenges, the RDF datasets resulting from the conversion are not published or searchable yet because of the lack of infrastructure in the ANF information system.
This subset was chosen for the project because the records form a coherent whole, their description is rich and they are widely consulted by various audiences, from the general public to researchers. 28 About the Wikidata well-known knowledge base, see https://www. wikidata. org/ (last accessed 2022/12/30). 29 https://github. com/ArchivesNationalesFR/Referentiels (last accessed 2022/12/30). 30 https://www. inria. fr/en/almanach (last accessed 2022/12/30). 31 The project, whose name is NER4Archives, began in 2021. See a presentation dated March 2022 in [10]. 32 Shapes Constraint Language, https://www. w3. org/TR/shacl/ (last accessed 2022/12/30). 33 Using for example SHACL Play! See https://shacl-play. sparna. fr/play/rules-catalog (last accessed 2022/12/30). 34 See the project website: https://sparna-git.
github. io/sparnatural-demonstrateur-an/. The search interfaces and documentation are available in French and English for now. The EAD and EAC files were first converted to RDF/RiC-O using RiC-O Converter, that was slightly adapted to address needs of two kinds: generating datasets conforming to the latest official version of RiC-O (RiC-O 0. 2, which was published in February 2021), and enriching them in order to take into account some specific features of these metadata on notarial archives, either before importing the data in the RDF triplestore,  35  or after (using SPARQL in order to infer new triples). The changes made have increased the number of RDF triples (about 57. 9 million triples, including about 37 million inferred, which is very impressive) 36  compared to the number of triples that the software alone would have generated.
However, they do not affect the general statistics included in Table  1 , obtained by running SPARQL queries directly in the SPARQL endpoint of the RDF database. These statistics, on their own, show in our opinion the interest of the technologies used: it is currently impossible for an end-user to obtain them via the web interface of the ANF information system, and it is not easy to calculate them for a professional competent in the field of XML technologies. They show above all, if it is still necessary to prove it, the very richness of these archival holdings. 37 is dataset has thus become the first large-scale semantization operation (in RDF compliant with RiC-O. carried out in France as far as we know, of classic archival metadata. The content of the graph is being analyzed, in particular using the RiC-O Converter logs to which new error and warning messages have been added.
It uses a new version of Sparnatural, an open source SPARQL query visual editor, that was developed as the main part of the project. 40 Two intuitive, flexible and interactive interfaces have been configured, tested during several workshops by groups of end users, and published. Both enable to query the graph as a graph without knowing a word of SPARQL, using entry points of different types (thus, not only the records themselves, but also the contextual entities), showing the relation types available to connect the chosen category of entities with other categories, enabling the end user to build his own questionnaire, and to go back by undoing a step. These interfaces are generated from an OWL configuration file, somewhat a model for searching RiC-O datasets, whose components are mapped to the RiC-O classes and properties used.
Once this is done, the RiC-O Converter project team intends to develop a version 3 of RiC-O Converter, that will convert SEDA files to RDF files that conform to RiC-O and PREMIS ontologies. Then, if human and financial resources are available, a version 4 could also directly take into account sets of XML files conforming to PREMIS schema. 42 More details on this here: https://sparna-git. github. io/sparnatural-demonstrateur-an/presentation-en. html#research_device. 43 See the history note in the metadata of RiC-O 0. 2, https://www. ica. org/standards/RiC/RiC-O_v0-2. html#metadata (last accessed 2022/12/30). 44 The SEDA standard d échange de données pour l archivage models the various transactions that may occur between the agents involved in digital archiving. This French standard conforms to ISO 20614:2017 (Information and documentation -Data exchange protocol for interoperability and preservation).
On this key feature of RiC-CM and RiC-O, see, in the introduction of RiC-CM [7], sections 1. 5 (Record Description in Transition) and 1. 6 (Relationships between RiC-CM and Other Models and Standards for Describing Records). See also the Why use RiC-O page of the information web site on RiC-O (https://ica-egad. github. io/RiC-O/why-use-RiC-O. html, last accessed 2022/12/30), which says: As it is a domain ontology, its vocabulary and structure are intended to reflect long-established archival principles, terminology, and descriptive methods.
mapping language R2RML, to define mappings, and a RML processor to execute the mapping rules -in order to generate RiC-O datasets from various existing metadata, including XML files. The context and objectives of this project are different from those of the ANF s; however the chosen solution is based on free tools, generic and interesting. See, about the pipeline that is being build, a presentation by Tobias Wildi, Using RiC As Aggregation Format For Cultural (eritage Data in.
14 On this key feature of RiC-CM and RiC-O, see, in the introduction of RiC-CM [7], sections 1. 5 (Record Description in Transition) and 1. 6 (Relationships between RiC-CM and Other Models and Standards for Describing Records). See also the Why use RiC-O page of the information web site on RiC-O (https://ica-egad. github. io/RiC-O/why-use-RiC-O. html, last accessed 2022/12/30), which says: As it is a domain ontology, its vocabulary and structure are intended to reflect long-established archival principles, terminology, and descriptive methods.
Relations are grouped into large files, each corresponding to a high-level relation class in RiC-O: Agent Hierarchical Relations, Agent Origination Relations, Agent Temporal Relations, Agent To Agent Relations, Family Relations, Membership Relations, Work Relations. mapping language R2RML, to define mappings, and a RML processor to execute the mapping rules -in order to generate RiC-O datasets from various existing metadata, including XML files. The context and objectives of this project are different from those of the ANF s; however the chosen solution is based on free tools, generic and interesting. See, about the pipeline that is being build, a presentation by Tobias Wildi, Using RiC As Aggregation Format For Cultural (eritage Data in Switzerland , at the )CA Rome Conference Rome, September , : https://www.
ica. org/sites/default/files/4_praes_ric_20220922_wildi. pdf (last accessed 2022/12/30). 10 The unit tests are available at https://github. com/ArchivesNationalesFR/rico- converter/tree/master/ricoconverter/ricoconverter-convert/src/test/resources (in two subfolders named eac2rico and ead2rico). One input/output file encodes more than one test. standardized.
This first work therefore makes it possible to estimate the quantity of triples obtained from all the ANF metadata on notarial archives at around 170 million (using an instance of GraphDB Standard as a triplestore and RDFS-Plus inference rules). Even if simplistic extrapolations are to be avoided, especially since these EAD files are among the most indexed and precise in the ANF, it is now proved that semantizing all of the institution s XML metadata would produce an extremely massive dataset.
Impact overview The GPSMonitoring package has been developed to support Small scale fisheries monitoring development using GPS on board electronic device. This additional tool (in addition to landing survey data collection) will help fisheries science institution to provide spatial overview of fishing activities. Regular data collection toward small scale fisheries in a on developing countries context give information on the catches composition (What) and on effort (How) and GPSMonitoring package will support information development on fishing area (Where). Once we have where the catches occur (using GPS) and what catches are landed (using surveys) we still have to investigate the right way to mix the two information to know where the species are caught. Investigation was made during the DEMERSTEM project but still need to be strengthen in a real context, i.
Conclusion Fishing area of small-scale fisheries will become a key information for spatial/time regulation of fisheries activities. The first step of fishing regulation in on developing countries was designed for industrial fisheries (most of the time leaded by foreign counties). Now the second step has to consider small scale fisheries, too big to be ignored. For this consideration we need more information on small scale fisheries activities and especially where they occur to be able to support regulation for a sustainable fishing sector. The GPSMonitoring package could be part of the regular rata regulation toolbox. The package development was conduct under the DEMERSTEM EU project which aims to improve knowledge on demersal fisheries (2019-2023). Within this project the Work package 3 was dedicate to the pilot study on GPS logger integration in the regular data collection on small scale fisheries in West Africa.
Acknowledgments The work was supported by  European union and Ecowas  under the  FED/2018/402-604 - DEMERSTEM ,  DEMERsal ecosySTEMs  project. Authors would like to thanks  Marc Leopold  ( IRD ),  Marie Pierre Etienne  ( Institut Agro ) and  Faustinato Behivoke  (I. S. H. M. ) for feedback and development made during the similar experiment made in Madagascar. We are also grateful to  Nicolas Bez  ( IRD ) for his capabilities in rising right question to improve our analysis.
ENCRYPT is an EU funded research initiative, working towards the development of a scalable, practical, adaptable privacy-preserving framework, allowing researchers and developers to process data stored in federated cross-border data spaces in a GDPR-compliant way. ENCRYPT proposes an intelligent and user-centric platform for the confidential processing of privacy-sensitive data via configurable, optimizable, and verifiable privacy-preserving techniques. Hence, ENCRYPT builds on top of cutting-edge technologies such as Fully Homomorphic Encryption, Secure Multi-Party Computation, Differential Privacy, Trusted Execution Environment, GPU acceleration, knowledge graphs, and AI-based recommendation systems, making them configurable in terms of security and, most importantly, performance. The ENCRYPT framework is being designed taking into consideration the needs and preferences of relevant actors and will be validated in realistic use cases provided by consortium partners in three sectors, namely healthcare (oncology domain), fintech, and cyber threat intelligence domain.