dataesr/scientific-paragraphs-categorization huggingface dataset:
Interactive engagement We also draw from  Oh et al. (2017)  to measure interactive engagement. The interactive engagement was measured as a composite first-order construct determined by Facebook talks, Twitter tweets, and YouTube comments. We also collected the data the day before the release date. Facebook talks refer to the "number of users who have created what could be called a 'story' about the movie profile"  (Oh et al. , 2017, p. 31 ) (i. e. , users who made at least one of the following actions: like a page, post on the page wall, like a post, comment on a post, share a post, or write a recommendation). Twitter tweets were specified as the "count of public tweets sent by other Twitter profiles about the movie profile"  (Oh et al. , 2017, p. 31). It includes the number of mentions to the Twitter profile and the number of movie hashtags.
Results of the empirical analysis "The proposed research model was tested empirically by using PLS-PM"  (Benitez et al. , 2020a, p. 137). We used the statistical software package Advanced Analysis for Composite (ADANCO) 2 Professional (https://www. composite-modeling. com/)  (Henseler and Dijkstra, 2015). ADANCO is a leading-edge software for variance-based structural equation modeling (SEM), which models composite constructs, among others, and it is mainly used for causal and predictive modeling  (Rueda et al. , 2017). This decision was statistically rational for the following reasons. PLS-PM is a fullfledged estimator to test empirical research, which is optimal to deal with composite models as our conceptual model  (Benitez et al. , 2020a). Moreover, PLS-PM is one of the most well-known estimation methods in IS research  (Benitez et al.
This stresses the need for novel spatio-temporal approaches adapted to the complexity of these large emerging datasets. Over recent years, spatial and temporal graphs have been the object of major developments in many fields, thanks to a sound theoretical framework and to computational efficiency when applied to the analysis of the properties of large interconnected data sets  [8]. Knowledge graphs (K-graphs)  [9]  offer a recent and promising paradigm that has emerged in the field of Semantic Web technologies. This approach promotes a graph-based data model that is both human and machine-readable and thus interoperable and dedicated to a semantically enriched knowledge representation by means of ontologies that offer a formal, and generally consensual, specification of the conceptualisation of a given domain.
Modelling the close relations between places, events and traffic patterns has been also explored using graph metrics and an experimental implementation within the Neo4j graph database  [26]. This is indeed a major development as graph databases certainly provide a much more logical and methodological connection between GIS and graph capabilities than the geo-relational principles available in most GISs. Overall, these recent advances and trends highlight the interest of different research communities in the potential of graphs, thus stressing the need for a clear picture of the underlying modelling principles and processing capabilities available and of the perspectives and challenges offered by a close integration of graphs and GISs.
Knowledge Graphs: Main Principles For about 10 years, the paradigm of K-graphs, which appeared in the field of the Semantic Web, has been the subject of great interest in many research domains, which have largely exceeded its original community and the field of Artificial Intelligence from which it originates. K-graphs are associated with Linked Data, an initiative of the W3C to publish and link data on the Web (Berners-Lee T. , Linked Data. Design Issues. W3C, 2006, https://www. w3. org/DesignIssues/LinkedData (accessed on 1 August 2021)). The RDF (Resource Description Framework) (RDF, RDF 1. 1 Concepts and Abstract Syntax, W3C, 2014, https://www. w3. org/TR/rdf11-concepts/ (accessed on 1 August 2021)) is the basic language of the Semantic Web (or Web 3. 0) and is made up of a set of languages and technologies dedicated to making the content of the Web interpretable by humans but also by machines.
In order to formally describe Web resources and their metadata, RDF is supported by a data model on top of a graph structure. RDF data are represented by a triple (subject, predicate, object), and an RDF graph is then a set of such triples. The subject of a triple can be a URI (Uniform Resource Identifier) or an empty node. A predicate represents a property always designated by a URI. The object corresponds to the value of the property (i. e. , predicate). This value can be a URI, an empty node or a literal. A literal denotes a string of characters or typed data. The vertices (which play the roles of subject or object) and the oriented edges (which carry the predicates) of an RDF graph are therefore labelled (see Figure  3 ). The challenge for humans, and especially for machines, is to understand these labels (especially when they are represented by URIs or by their generalisation, namely IRIs (Internationalised Resource Identifiers)).
OWL is also based on the RDF data model. An ontology in OWL is defined first by means of classes and properties that capture the concepts and relations between these concepts of the domain addressed by the ontology. Moreover, OWL proposes a set of axioms (equivalent classes, equivalent properties, equality of two resources, difference, opposite, symmetry, cardinality constraints. ) that gives it superior description capabilities. It is commonly acknowledged that SPARQL (SPARQL, SPARQL Query Language for RDF, W3C, 2008, https://www. w3. org/TR/rdf-sparql-query/ (accessed on 1 August 2021)) is to RDF what SQL is to relational databases: a query language allowing users to search, create, add, delete and modify data. SPARQL is therefore the language on which the triple stores, which are the RDF database management systems, are based  [53].
Triple stores rely on the SPARQL protocol to offer the possibility of creating access points, called SPARQL end points. These SPARQL end points allow a local RDF database to be accessed and queried via SPARQL queries from any client on the Web. Although the term "Knowledge Graph" was first introduced by Google (Singhal A. , Introducing the Knowledge Graph: Things, not Strings, May 2012. https://googleblog. blogspot. co (accessed on 1 August 2021)) to refer to the exploitation of semantics by its search engine-without however revealing all its secrets-it owes its success more to applications such as DBPedia, Freebase, Wikidata or Yago, which provide access to open and publicly accessible K-graphs  [54]. These multilingual K-graphs are all based on the linked data paradigm. The Wikidata knowledge base, for example, to date contains more than 92 million items (nodes).
Wikidata is a common source of data covering a wide variety of domains, topics and communities. All of these datasets, consisting of publicly accessible RDF triplets, are part of the Linked Open Data Cloud (LOD, https://lod-cloud. net/ (accessed on 1 August 2021)). Within the LOD Cloud, more than a thousand RDF datasets are mutually referenced through the URIs they contain, thus enabling navigation from dataset to dataset, from domain to domain, from subject to subject and from node to node. This global and semantic database greatly contributes to the strength and interest of the Linked Data Web. Figure  4  shows a simple example of a K-graph. Graph theory in support of K-graphs favours the manipulation of structural properties and metrics and the application of search-based algorithms for querying, browsing, digging (for a review, see  [9] ) and pattern extraction  [55].
As a K-graph is based on one-to-many ontologies, additional reasoning mechanisms can be applied from the Semantic Web. Immersed in the Linked Data Web, based on the same RDF model and reference vocabularies and serialisable, accessible and searchable via SPARQL, K-graphs are interoperable. They benefit from the standardisation operated by the W3C. They are easily navigable and composable. They make data from billions of linked triples and knowledge on multitudes of domains in most languages freely accessible on the Web. Finally, they are a natural support for the explicability, the provenance and therefore the traceability of facts. All this available knowledge can be used for different purposes, in different fields of application: natural language understanding, dialogue systems (questions/answers) and recommendation systems  [56]  in the field of tourism, energy or education  [57].
w3. org/TR/owl-time/ (accessed on 1 August 2021)). OWL-Time is an ontology dedicated to the representation of time, in which a temporal entity is either an instant or an interval (i. e. , a period of time whose duration is delimited by a beginning and an end instant). OWL-Time provides a description to express dates and durations, as well as properties to characterise and access temporal data, but also seven topological binary relationships and their inverses (e. g. , before, after, meets, met by, overlaps, overlapped, starts, started by, during, contains, finishes, finished by, equals), making it possible to locate, in the chronological order of a chosen reference time system, instants and intervals in relation to each other. As recommended by the OGC and W3C (SDWBP, Spatial Data on the Web Best Practices, W3C Working Group Note 28 September 2017, https://www.
w3. org/TR/sdw-bp/#spatial-relations (accessed on 1 August 2021)), the description of spatial data embedded in K-graphs can be based on the ontology of the OGC GeoSPARQL standard (GeoSPARQL, A Geographic Query Language for RDF Data, OGC, 2012. https://www. ogc. org/standards/geosparql (accessed on 1 August 2021)). GeoSPARQL is both a vocabulary for representing spatial or geospatial data in RDF and an extension of the SPARQL language to formulate queries on the spatial dimension of RDF data expressed in this vocabulary. GeoSPARQL integrates a number of standards, recommendations or norms that preceded it. The GeoSPARQL ontology is based on three main classes: one represents spatial objects in all their generality; the other two, which are distinct sub-classes, correspond respectively to features and geometries, so that, among the features of a spatial object, the characteristics specific to its geometry (spatial footprint) are distinguished.
With regard to the spatio-temporal aspect, the coverage of the temporal dimension in GeoSPARQL queries is limited to the functions available in the SPARQL language on dates and time. The topological temporal relations taken into account by OWL-Time are excluded. The storage of these STK-graphs is also carried out within triple stores. In  [61] , the authors propose a recent comparative study of the main triple stores that support GeoSPARQL. This study tests a substantial RDF dataset of more than 130 Gbytes against a benchmark composed of two series of queries (simple and complex). The Virtuoso, GraphDB and Strabon systems have proven to be very efficient here for processing queries on data sets of around 500 million RDF triplets. However, the OBDA approach implemented in the Ontop-spatial system  [62]  is superior due to its potential for scaling up.
Ontop-spatial is an extension of the Ontop system  [63]. The idea is to access geospatial data stored in relational databases from virtual geospatial RDF graphs, created using R2RML, which can be queried via GeoSPARQL queries translated into standard OpenGIS SQL (OGC, Simple Feature Access-Part 2: SQL Option, 2010, https://www. ogc. org/standards/sfs (accessed on 1 August 2021)). The design of an STK-graph from various sources has crystallised around the entity resolution problem and the link discovery problem. The former  [64, 65]  is tackled with non-spatial and/or spatial similarity measurements between entities and is established using the sameAs property defined in OWL. Link discovery echoes the fourth rule of the Data Web (linking data together to enrich their respective information context).
It is based on various measures of similarity  [66, 67]  adapted to the processed data. In the case of spatial data, it is possible to establish topological relationships [68], distances or orientations. Similarly, temporal topological relationships, where appropriate, can be generated. Entity resolution and link discovery are essential operations in that they make it possible, firstly, to link K-graphs (or RDF data sets) together and then to traverse them in a second stage dedicated to the exploration and exploitation of these graphs. The integration of heterogeneous data sources is currently facilitated by specific tools such as SILK  [69]. Spatial and temporal data lend themselves well to visualisation capabilities. In fact, there are several works that propose an interface to visualise, by means of maps, data extracted from STK-graphs.
com (accessed on 1 August 2021)) or RDF-star (RDF*) (https://w3c. github. io/rdf-star/cg-spec/ editors_draft. html (accessed on 1 August 2021)) are based on graph models that are capable to capture the evolution of information or knowledge through time, in the same manner as ST-graphs. A single dynamic K-graph is generally considered, but the nodes and edges of this dynamic K-graph are embedded or are labelled with some spatial and/or temporal information indicating when they are valid (in which space and/or during which period). It should be noted that labelled property graphs or RDF-star (RDF*) are generic approaches that are not specifically dedicated to the representation of knowledge evolution within a graph, but rather allow this representation through their model. Contextual K-graphs form a dual approach that expresses a set of temporalised (valid in the time interval associated with the K-graph) and/or spatialised (valid in the space or territory associated with the K-graph) knowledge.
As illustrated in Figure  6 , maritime datasets can also be integrated and aggregated using different data sources in order to provide additional semantics and processing capabilities  [80]. For instance, the global cargo trajectory data available in real-time can be associated with freight status (e. g. , type, length, width) and with additional maritime infrastructures such as ports and berths. This provides a sound support for the construction of a fine-grained global maritime network that can be further manipulated at either the local or the global level using graph functions and analytics, as well as GIS analysis and visualisations. From vessel departure and arrival data, origin and destination ports modelled as nodes, the underlying maritime routes are implicitly represented as links and then maritime routes.
The first category includes the work by Villa and Camossi  [112] , which introduces a Maritime Container Ontology (MCO) that represents ships, containers, their movements and their routes (see a excerpt in Figure  9 ) in order to identify and detect patterns of suspicious manoeuvres or movements using axioms described in OWL. Similarly, a semantic trajectory ontology is exploited for surveillance and control purposes in  [113]  in which spatial or aspatial rules, written in SWRL (the Semantic Web Rula Language (SWRL, SWRL: A Semantic Web Rule Language Combining OWL and RuleML, W3C, 2004, http://www. w3. org/Submission/SWRL/ (accessed on 1 August 2021))), allow abnormal ship behaviours to be described and alerts to be triggered. Lange et al. [114]  introduce an ontology to describe and simulate the behaviour of objects involved in the management of container loading and unloading terminals.
States are recognised and established by rules, and potential behaviours are inferred by a Dynamic Bayesian Network (DBN). Automatic Identification System (AIS (AIS, https://en. wikipedia. org/wiki/Automatic_identification_system (accessed on 1 August 2021))) data from maritime merchant traffic are used to establish typical profiles of ship behaviour in different phases (at berth, at anchor, underway, on a given route) through the DBN. In real time, ship trajectories can be annotated with the detected states and with alerts in case of the recognition of an abnormal behaviour. A visualisation of the annotated trajectories in real time is proposed. With the different states characterising the ships stored in the K-graphs, the SPARQL language can then be used to query the recorded trajectories.
The Linked Open Data (LOD) Cloud, which contains open and accessible K-graphs, linking together a multitude of domains, thus constitutes a global and RDF semantised database that can be browsed or traversed in search of data and knowledge but also extended by connecting one's own graphs by means of IRI referencing. The constant progress of RDF (triple) stores makes them capable today of competing on many points with graph database management systems or relational databases  [121] , while the complexity of SPARQL is proven to be similar to that of SQL  [122]. Moreover, these families of data management tools are not necessarily opposed. For example, graph DBMS can be used to store and analyse K-graphs. Neo4j is a popular graph DBMS which relies on a modelling based on labelled property graphs (i.
e. , LPG), which is both more flexible and more compact than the classic modelling by RDF triples, but also on a query language, called Cypher, adapted to these LPGs while ensuring compatibility with RDF graphs. Moreover, the debate around LPGs and K-graphs may well fade away with the recent proposal of RDF-star (RDF*), which allows metadata to be associated to any RDF triplet while still preserving the triplet unit format. The next sub-sections discuss the respective and mutual contribution of graphs and K-graphs and the emerging opportunities when the two are associated or closely connected.
From Knowledge Graphs to Spatio-Temporal Graphs Starting from a K-graph, one can extract an associated graph in order to analyse the structural properties and then manipulate it using specific graph operators and analytics. A promising objective is to combine the respective data representation and manipulation of both knowledge and structural graphs in order to take full advantage of the two approaches. Therefore, the approach can be implemented in a homogeneous, complementary and efficient way in which structural properties resulting from graph principles and metrics can be applied together with K-graph operations. Accordingly, this implies that an initial K-graph is available, as well as specific properties and constraints that can relate the structural and the K-graphs. In other words, this requires a domain-based ontological model, a structural-based model and indeed a relevant dataset.
e. , not already present in the initial K-graph/ST-graph). The processing carried out on these graphs thus enriched could be much more precise regarding the traversed semantics of the nodes and arcs. It would also be possible to show the potentiality of more complex queries which would not be considered without the combination of the ST-graph and K-graph. However, there will certainly be a need to further develop ST-graphs and K-graphs within integrated database frameworks that support conceptual design processes and query representation and manipulation interfaces that support a sound combination of the data representations and intuitive user-oriented functions. This is a still expected development that will imply the full involvement of the scientific community, standardisation bodies and the software industry  [123].
Not being able to predict the future accurately is not unique to epidemiology. Age and co-morbidities are important factors in determining how sick someone who gets infected will become and whether they are likely to die. As a rule, older people are more likely to become very sick, and also to die. Moreover, people who suffer from other health problems (also known as 'co-morbidities') are more likely to become severely sick and to die. Such health problems include: diabetes, obesity, respiratory disease, reduced immune system function, to name a few examples. These conditions can increase the chance of a bad outcome by a factor of up to 3 or more. A study published in the prestigious scientific journal Nature using data collected in the United Kingdom, explored how different factors increased or decreased the chance of dying from Covid-19.
Specialised chronic hospitals Long-term chronic psychiatric care has typically been offered in either public facilities or contracted private facilities. Although some private hospitals are specialised (e. g. maternity, psychiatric, day, etc. ), many are general hospitals with a mix of specialisations available. Data on private hospitals is, however, not routinely published by either the public or private sector, with information often available only from unverifiable proprietary data sources. However, data is available on request from various hospital groups and associations. Based on these sources, a dataset has been compiled by the author. The estimates for 2016 (and broadly consistent with 2020) by bed type are provided in Figure  9. 4.
As outlined in further detail in the following sections, decision making by the panel was facilitated by the executive team through: (a) a series of reviews of (i) the relevant literature in academic databases (Medline, Embase and PsychINFO [through Ovid], CINAHL [EBSCO host], and ProQuest) and grey literature; (ii) patient surveys by the WHO, the World Bank, the Organization for Economic Cooperation and Development (OECD), and the Commonwealth Fund; and (iii) existing ICHOM standard sets  [17]  (executive team, supplemented with input from panel members); (b) application of prespecified criteria for short-listing (executive team); (c) circulation of documentation to the panel members, subsequently discussed via.
Identification and Selection of Timepoints Recommendations regarding timing of measurement of different variables and instruments were based on literature review and extensive discussions among the Working Group members. There was consensus that the variables age and sex should be collected at baseline. The following variables should be measured at baseline and then annually: PROMIS-10, WHO-5, WHO-DAS-12, self-reported hearing and seeing, and all case-mix variables except age and sex (Fig. 2 ). This provides an adequate balance between administrative burden and sufficient frequency of measurement as to pick up relevant clinical changes. Timing of the collection of comorbid conditions should be synchronized with administration of other datasets.
Acknowledgements The authors thank all patients and professionals participating in the patient surveys. The  International Society for Quality-of-Life  Research provided support as part of a collaboration with ICHOM for supporting best methods in the identification, selection, and implementation of patient-reported outcomes. The views expressed herein are those of the author(s) and do not reflect the official policy or position of  Brooke Army Medical Center , the  US Army Medical Department , the  US Army Office of the Surgeon General , the  Department of the Army , the  Department of the Air Force , the  Department of Defense , or the  US Government.
Multi-Source Data Fusion Taxi GPS data include taxi ID, recorded time, geographical location (longitude and latitude), driving speed, driving direction, passenger load and other information. A large number of taxi trajectory data with high accuracy, wide coverage and rich content have become an important data source for studying the travel rules of urban residents  [16]. The common key taxi trajectory data structures are shown in Table  4. Table  5  shows some data fragments of common taxi trajectory data. Among them, T_Status means the taxi carrying passengers. As the value is 26,144, the taxi is carrying passengers, while the value is 0 means that the taxi is empty. The continuous track of taxis is discretized by the grid operation of the administrative map. The waiting time data, POI data, taxi track data, work and rest time data and geospatial data are mapped to each grid according to the underlying spatio-temporal data, so that each grid unit contains the fused multi-source data.