Mouna Kamel

Amina Annane
email: amina.anane@geotrend.fr

Nathalie Aussenac-Gilles

Bao Long Nguyen

Christophe Baehr
email: christophe.baehr@meteo.fr

FAIRification of Multidimensional and Tabular Data by Instantiating a Core Semantic Model with Domain Knowledge: Case of Meteorology

Keywords: Meteorological data, FAIR principles, Semantic metadata

Open data is exposed in several formats, including tabular format. However, the meaning of columns, that can also be seen as dimensions, is not always explicit what makes difficult the reuse of this data for data consumers. This paper presents the FAIRification process of tabular and multidimensional datasets that relies on a (FAIR) core semantic model that is able to represent different kinds of metadata, including the data schema and the internal structure of a dataset. We describe how the instantiation of such a model offers in addition the possibility to describe the semantics of columns using domain ontologies. Once instantiated, this model forms a set of formal metadata that documents the dataset and facilitates understanding by data consumers. This process is then applied to three metereological datasets, for which the degree of improvement of the FAIRness ("I" and "R") has been evaluated.

Introduction

Large volumes of Open data, in particular, scientific data shared for an open science, or government and statistical data, are now available on the web. They can be accessed under open licenses from different portals, such as governmental portals for public data (e.g., data.gouv in France4 or data.gov5 in the US, European portals like the European Data Portal6 ), portals of public services (e.g., the French National Library7 ), or portals of scientific data (e.g. data-terra.org 8for Earth Sciences). This data is usually structured in tables, available in various formats, mainly CSV or JSON. Not only the schema of these tables is not always provided or made explicit, but it is also described with properties (in particular the meaning of columns) labelled in a relevant way for domain experts (data producers) but that are not properly understood and reusable by other scientific communities than the one of the authors. For the latter, one of the challenges is to find relevant data among the increasingly large amount of continuously generated data, by moving from the point of view of data producers to the point of view usages. One way to overcome these weaknesses is to guarantee compliance of data to the FAIR principles [START_REF] Wilkinson | The FAIR Guiding Principles for scientific data management and stewardship[END_REF]. These principles correspond to a set of 15 recommendations that aims to facilitate data reuse by humans and machines. The first step towards the fulfilment of FAIR principles is to define precise metadata schemes. Indeed, 12 out of the 15 FAIR principles refer to metadata [START_REF] Wilkinson | The FAIR Guiding Principles for scientific data management and stewardship[END_REF]. To go a step further in improving data FAIRness, several authors have shown that metadata schemes should be based on semantic models (i.e., ontologies) for a richer and more metadata representation [START_REF] Guizzardi | Ontology, Ontologies and the "I" of FAIR[END_REF]. Thanks to their ability to make data types explicit, in a format that can be processed by machines, ontologies are essential to make data FAIR [START_REF] Jacobsen | FAIR principles: Interpretations and implementation considerations[END_REF]. While most efforts in data FAIRification are limited to specific kinds of metadata, mainly those describing the overall features of datasets and data catalogues, this description is not enough to fully address all FAIR principles [START_REF] Koesten | Everything you always wanted to know about a dataset: Studies in data summarisation[END_REF], in particular for promoting data reuse by other scientific communities. This paper presents the FAIRification process of tabular and multidimensional datasets using a (FAIR) core semantic model. We describe how the instantiation of such a model additionally provides the ability to describe the semantics of columns using domain ontologies. Once instantiated, this model forms a set of formal metadata (including those describing the data schema and the internal structure of a dataset) that documents the dataset and facilitates understanding by data consumers. We illustrate this process in the meteorological domain. The contributions of the paper are the following: (1) an extension of the work in [START_REF] Trojahn | A FAIR Core Semantic Metadata Model for FAIR Multidimensional Tabular Datasets[END_REF] by describing the late stages of the FAIRification process, i.e. how the core model can be instantiated to generate a domain specific knowledge base (here meteorology) to used as a metadata schema. (2) an extension of the work in [START_REF] Annane | Towards the fairification of meteorological data: A meteorological semantic model[END_REF] by improving the FAIRness of three metereological datasets provided by Météo-France (the official French weather agency) that share the same features. Here, we use a new version of the semantic model [START_REF] Trojahn | A FAIR Core Semantic Metadata Model for FAIR Multidimensional Tabular Datasets[END_REF] that was generalised to accommodate any kind of tabular data together with new notions required to represent dataset collections. (3) an evaluation of the FAIRness degree of different datasets annotated either with existing metadata (most of which are not machine readable) or with semantic metadata using our semantic model, showing how the proposed model improves their interoperability and reusability.

The rest of this paper is organised as follows. Section 2 discusses the main related work. Section 3 shortly presents the used core semantic model and details its instantiation. We expose in Section 4 how this process is performed to describe three datasets in the domain of Meteorology. Section 5 reports the FAIRness evaluation of these datasets with or without the semantic metadata resulting from the instantiation process. Finally Section 6 concludes the paper.

Related work

(FAIR) metadata vocabularies A number of vocabularies has been proposed to represent metadata in general (Dublin core, VoID, Schema.org, DCAT, DCAT-AP) 9 , with extensions for accommodating specific kinds of data, such as geo-spatial data (GeoDCAT-AP) or statistical data (StatDCAT-AP). In [START_REF] Parekh | Ontology based semantic metadata for geoscience data[END_REF], the authors expose their own way of representing metadata on spatial and temporal data identification, content, distribution and presentation forms. In a different way, [START_REF] Frosterus | Datafinland -A semantic portal for open and linked datasets[END_REF] extend the existing VoID vocabulary to cover datasets that are not RDF ones. Another group of works and initiatives has addressed the problem of representing domain-specific metadata using domain vocabularies. For instance, in the context of social sciences and humanities, the Data Documentation Initiative 10 (DDI) proposes two XML schemes for metadata, reusing vocabularies like Prov-O 11 , DC-terms, Data Cube 12 or CSVW 13 . Targeting tabular data as we do, several proposals have combined the use of RDF Data Cube (qb) with other vocabularies to represent observational data, as in [START_REF] Lefort | A linked sensor data cube for a 100 year homogenised daily temperature dataset[END_REF] or [START_REF] Yacoubi | A Model for Meteorological Knowledge Graphs: Application to Météo-France Observational Data[END_REF]. Close to our goal, the Semantic Government Vocabulary is dedicated to the annotation of Open Government Data, notably CSV distributions [START_REF] Kremen | Improving discoverability of open government data with rich metadata descriptions using semantic government vocabulary[END_REF]. Thanks to these vocabularies, these authors annotate data in CSV format at different levels of detail and show how this improves the discovery of datasets [START_REF] Kremen | Improving discoverability of open government data with rich metadata descriptions using semantic government vocabulary[END_REF].

FAIR principles and FAIRness evaluation Several frameworks assess the degree of FAIRness of digital objects 14 . The reader can refer to [START_REF] Sun | A comprehensive comparison of automated fairness evaluation tools[END_REF] for a recent survey on the topic. In many of them, the evaluation is performed by answering a set of questions -also called metrics or indicators -or by filling a checklist such as the "FAIR Data Maturity Model" [START_REF]FAIR Data Maturity Model Working Group RDA. FAIR Data Maturity Model. Specification and Guidelines[END_REF] or "FAIRshake" [START_REF] Clarke | Fairshake: Toolkit to evaluate the fairness of research digital resources[END_REF]. This evaluation can be automated, as proposed by [START_REF] Wilkinson | Evaluating FAIR maturity through a scalable, automated, community-governed framework[END_REF][START_REF] Devaraju | FAIRsFAIR Data Object Assessment Metrics 0.5[END_REF], based on web applications that test digital resources against predefined metrics. Recently, in addition to the FAIRness degree of data, the FAIRness of vocabularies and ontologies used as metadata schemas was also evaluated [START_REF] Garijo | Best practices for implementing FAIR vocabularies and ontologies on the web[END_REF][START_REF] Cox | Ten simple rules for making a vocabulary fair[END_REF]. FOOPS! [START_REF] Poveda-Villalón | Coming to terms with FAIR ontologies[END_REF] and O'FAIRe (Ontology FAIRness Evaluator) [START_REF] Amdouni | FAIR or FAIRer? An integrated quantitative FAIRness assessment grid for semantic resources and ontologies[END_REF]) are some of the few tools automating this task.

FAIRification process

Making data FAIR (FAIRification) can be devided into several steps, such as those of the generic step-by-step FAIRification workflow in [START_REF] Jacobsen | A generic workflow for the data fairification process[END_REF]: 1) identify the FAIRification objective, 2) analyze data, 3) analyze metadata, 4) define semantic model for data (4a) and metadata (4b), 5) make data (5a) and metadata (5b) linkable, 6) host FAIR data, and 7) assess FAIR data. As part of a generic methodology, here we use the generic semantic model for metadata proposed in [START_REF] Trojahn | A FAIR Core Semantic Metadata Model for FAIR Multidimensional Tabular Datasets[END_REF]. For an easier understanding of the instantiation process, we briefly present in the following this model and how we instantiate it.

Core Dataset Metadata Ontology

We defined the Dataset Metadata Ontology Core (dmo-core, available at https: //w3id.org/dmo) to represent data schema and the internal structure of tabular datasets using several FAIR vocabularies. Here we briefly recall the main concepts and the reader can refer to [START_REF] Trojahn | A FAIR Core Semantic Metadata Model for FAIR Multidimensional Tabular Datasets[END_REF] for a detailed description. The notion of Catalog is a curated collection of metadata about Datasets, which in turn can be described with different types of metadata and may have associated Distributions. Distributions may be in a tabular format, where each Table is described by its Schema. A schema specifies the various Columns. Each column has an associated Measure or Dimension. While the model in [START_REF] Annane | Towards the fairification of meteorological data: A meteorological semantic model[END_REF] focused on the representation of spatio-temporal data (using GeoDCAT-AP and qb4st), dmo-core forms a more generic core semantic model for representing any kind of tabular data for any domain by adopting DCAT and qb. Improving the FAIRness of domain datasets requires to instantiate this model, as introduced in the following.

Model instantiation

The main idea behind our FAIRification process is to associate meaning to the data, in particular the columns of the tabular data. To that extent, domain ontology concepts are associated to that columns. We made the choice of instantiating the model instead of extending it, as there is no need for introducing new concepts or relations, as recommended when reusing the standard vocabularies (CSVW and qb), as detailed in the following. This process can be viewed in two stages: the instantiation of the core model, and the association of domain-specific concepts to the instances. The first stage concerns the description of the tabular dataset as a whole, and reference can be made to [START_REF] Trojahn | A FAIR Core Semantic Metadata Model for FAIR Multidimensional Tabular Datasets[END_REF]. The latter concerns the more specific description of table columns using relevant domain ontologies and is carried out in the following steps:

1. Selecting relevant domain ontologies Several ontology repositories can be queried, such as: Linked Open Vocabularies (LOV) (https://lov.linkeddata. es/dataset/lov/), vocab.org (http://purl.org/vocab/), ontologi.es (http: //ontologi.es/), SOCoP+OOR (https://ontohub.org/socop), AgroPortal (https://agroportal.lirmm.fr), BioPortal (https://bioportal.bioontology. org/), OntoHub (https://ontohub.org/), COLORE (http://stl.mie.utoronto. ca/colore/), OOR (Open Ontology Repository) (http://www.oor.net/), ONKI service (https://onki.fi/).

Choosing appropriate concepts in these ontologies

This step requires the intervention of domain experts. In fact, the core model instantiation must be carried out in collaboration between a domain expert and semantic web experts. This collaboration aims to present the domain to the semantic web expert, who is responsible for creating the instantiation files. At this stage, ontology editors such as Protégé15 can be useful. In the example below, to help understanding the meaning of column t (:t col) (Figure 2), from one of the meterological CSV files we evaluate here (Section 4), (:t col) is associated to the measure :t. This measure is then linked to the ENVO:ENVO 09200001 concept which represents the air temperature.

Metereological datasets FAIRification

Meteorological open data is essential in many applications, including weather forecast, climate change, environmental studies, agriculture, and risk management. Its production is based on mathematical models that assimilate different data from several sources including sensors located on weather stations, satellites and weather radars. While this data has been made available as open data, through different portals, its exploitation is rather limited. Not only the schema of these tables is not always provided or made explicit, but it is also described with properties (in particular the meaning of columns) labelled in a relevant way for meteorology data producers but that are not properly understood and reusable by other scientific communities. For the latter, one of the challenges is to find relevant data among the increasingly large amount of continuously generated data, by moving from the point of view of data producers to the point of view of data usages. Thus meteorological data is a good experimental ground to test the benefits brought by the addition of semantic metadata based on dmo-core to the dataset reusability by other scientific communities. To this end, we instantiate the dmocore model to describe three collections of tabular datasets (SYNOP, NIVO and SWI) provided by Météo-FranceThese datasets were chosen because, in the context of the Semantics4FAIR16 project, the biologist partners needed to access and reuse such (understandable) weather data for identifying the meteorological conditions that favor the germination and flowering of ragweed. Currently, on the Météo-France website, these datasets are presented with few metadata in natural language, which prevents dataset search engine crawlers from finding them, and hence minimises the dataset discoverability.

A search for domain ontologies on the above mentioned repositories led us to choose the following ones: SWEET (http://sweetontology.net/), ENVO (http://purl.obolibrary.org/obo/), QUDT (http://qudt.org/1.1/vocab/ unit), qb4st (http://www.w3.org/ns/qb4st/) and SOSA (http://www.w3. org/ns/sosa/). SWEET [START_REF] Raskin | Development of ontologies for earth system science[END_REF] is a collection of ontologies conceptualizing knowledge for the Earth sciences, a part of which models meteorological parameters such as humidity, wind speed, pressure at sea level or rainfall. ENVO [START_REF] Buttigieg | The environment ontology: contextualising biological and biomedical entities[END_REF] represents environmental entities. It is used in addition to SWEET to better describe environmental processes, for example by offering the possibility to specify the extremes of a temperature (minimum and maximum). QUDT defines the classes, properties, and restrictions for modelling physical quantities, units of measure, and their dimensions in various measurement systems. For our purpose, QUDT allows to specify units of measure for measurements. SOSA [START_REF] Janowicz | Sosa: A lightweight ontology for sensors, observations, samples, and actuators[END_REF] is a reference ontology to describe sensors (such as thermometer, barometer, etc.) and their observations (measures), the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. Finally, qb4st is a qb extension for spatio-temporal components.

SYNOP dataset

The SYNOP data archive consists of a set of monthly generated files (since January 1996) where each file covers only the observations made in one month. Generated files are freely available online 17 . These files share the same structure (1 table with 59 colums). Figure 2 shows an excerpt of one SYNOP file. DMO-SYNOP corresponds to the instantiation of dmo-core to describe the SYNOP dataset. Part of the instantiation is presented below and the whole DMO-SYNOP instantiation is available online 18 with the following prefix <https://www.irit.fr/recherches/MELODI/ontologies/DMO/dmo-c-synop#>. Representing metadata of SYNOP dataset. SYNOP dataset is represented by an instance of dmoc:Dataset. SYNOP is a collection of monthly files, that, in turn, can be considered as datasets themselves. Using the concepts linked to a dataset in DMO-core, :SYNOP dataset is given the following metadata values: dct:publisher is Météo-France; dct:provenance is made explicit with the label value "The measurements were provided by the meteo France stations"; dct:spatial points to France in Geonames (<https://www.geonames.org/countries/FR/>); etc. To represent the structure of the dataset, which is shared by all SYNOP files, we use :SYNOP dataset structure, an instance of qb:DataStructureDefinition. :SYNOP dataset structure is linked to an instance of qb:ComponentSpecification for each of the 59 columns, each measure unit (1) and each measuring method (1), i.e. 61 instances in total. For example, the instances :pmer Component and :month Component correspond respectively to "pmer" and "month" columns. Then each of these instance was linked to instances of qb:MeasureProperty, qb:DimensionProperty or qb:AttributeProperty depending on the nature of the component. Finally, these instances are also linked to concepts of domain ontologies (we mainly used SWEET) via the qb:concept property.For example, :pmer Component is linked to sweet:SeaLevelPressure (<http://sweetontology.net/propPressure/SeaLevelPressure>). The property dmo:requires makes explicit the dependency between :SYNOP dataset and :Meteo Station, the characteristics (longitude, latitude, etc.) of the weather station generating the measures being stored in the Weather Station file.

Representing metadata of SYNOP dataset (February, 2020). The dataset stored in each file of the SYNOP collection is represented as an instance of dmo:Slice. For instance, :SYNOP dataset Feb 20 is an instance of dmo:Slice linked to :SYNOP dataset via the property qb:slice. :SYNOP dataset Feb 20 is associated with several metadata (dct:created, dct:creator), including structural metadata via the qb:structure property (:SYNOP dataset structure). Representing the metadata of a qb:Slice also requires the definition of dimensions with fixed values, which are specified using the qb:SliceKey concept. In our case, the fixed dimensions for a monthly dataset are year and month, with values month:FEB and year:2020.

Representing metadata of a SYNOP dataset distribution (February 2020). The CSV file itself is represented as a distribution (dmo:TabularDistribution) of :SYNOP dataset Feb 20 with identifier :SYNOP distribution Feb 20. Several metadata associated with this distribution were specified: the format (CSV), the URL from which the CSV file can be downloaded, the kind of license (open license), the description, etc. The distribution schema is represented by :SYNOP Schema (to be reused across distributions), an instance of csvw:Schema. It includes all the columns of the CSV file (e.g., numer sta and pmer). For each column, we represent its name (csvw:name), its label (csvw:title), its data type (csvw:datatype), etc. The foreign key :SYNOP ForeignKey which connects the column "numer sta" of the SYNOP data, to the column "ID" of the station data (:Distribution Stations Météo) is represented by the instance :SYNOP Stations Table Reference of csvw:TableReference.

NIVO dataset This dataset refers to meteorological observation data from mountain stations operated by partners under agreement with Météo-France for monitoring the snowpack in winter. The generated files, containing data measured since January 1996, are available free of charge online 19 , in CSV format. Documentation is available on a PDF file. Each CSV file contains 45 columns (temperature, dew point, snow state, predominant type of surface grains, etc.). DMO-NIVO corresponds to the instantiation of dmo-core for describing the NIVO dataset. The instantiation rules are the same as those applied when instantiating DMO-SYMOP. ENVO [START_REF] Buttigieg | The environment ontology: contextualising biological and biomedical entities[END_REF], a knowledge representation of environmental entities, has been used as domain ontology. The whole DMO-NIVO instantiation is available online 20 .

SWI dataset

The (uniform) SWI dataset represents the Soil Wetness Index (SWI) calculated by the Safran-Isba-Modcou (SIM) model for measuring complex interactions between meteorological data. This kind of index is used by Météo-France in the reports to commission responsible for the management of natural disasters in France. Generated files are freely available online 21 , in CSV format. Each file contains 5 columns: grid cell number, geographic x and y coordinates (Lambert format), date, SWI value. Each monthly value integrates the current month and the two previous months: average of the three of daily SWI values. DMO-SWI corresponds to the instantiation of dmo-core for describing the SWI dataset. The instantiation rules and the domain ontology are the same as those applied when instantiating DMO-SYNOP. The whole DMO-SWI instantiation is available online 22 .

Evaluation

We evaluated the degree of FAIRness of the datasets before and after they are described with dmo-core, thanks to the framework FAIR data maturity model proposed by the Research Data Alliance (RDA) [START_REF]FAIR Data Maturity Model Working Group RDA. FAIR Data Maturity Model. Specification and Guidelines[END_REF]. This model is based on three components: i) 41 indicators measure the state or level of a digital resource according to a FAIR principle; ii) priorities (essential, important, useful ) are associated with the indicators; iii) two evaluation methods: the first assigns each indicator a maturity level between 0 and 4 so that data providers have indications about how to improve the FAIRness degree of their data; the second consists of verifying whether the criterion carried by the indicator is true or false. The indicators were applied first to the original dataset description, and then to the dataset described with metadata (MD) instantiating dmo-core. The evaluation was manually carried out and guided by the RDA Excel form.

We first evaluated the original description of the datasets (without semantic metadata). The datasets share the same conditions of access and lack of metadata. Their evaluation confirmed that they were not FAIR: i) level 0 for principles F, A and R, because at least one essential indicator was not satisfied for each of them; ii) level 1 for principle I, because no indicator is essential for this principle (Figure 3). The datasets were re-evaluated after generating the semantic metadata that describe them. These semantic metadata significantly contribute to improve their FAIRness level, especially for the I and R principles (Figure 4). In fact, one of the main concerns when proposing the dataset annotation with semantic metadata was to improve their exploitation by non-experts from other scientific communities which would consequently improve their interoperability. Indeed, the proposal meets the main I criteria: metadata and data schemes are expressed in standardised and machine-understandable format, using FAIR-compliant vocabularies; metadata and data refer to other (open) data (here, domain ontologies) and links with these files are made explicit. Although the re-evaluation of the F principle did not show any gain, the model does allow for the representation of "rich" indexing metadata that satisfy F2 principle. However, higher F and A degrees would require satisfying essential indicators that are beyond the capabilities of any semantic model e.g., the generation of persistent and unique identifiers (F1), persistent metadata (A2), publication of metadata on searchable resources (F4), which must be managed by the data publisher (Meteo-FR). We also observe that the FAIRness degree is preserved with the generic instantiation model with respect to the results obtained with the specific dmo model in [START_REF] Annane | Towards the fairification of meteorological data: A meteorological semantic model[END_REF].

Conclusion and future work

This paper presented the FAIRification process of tabular and multidimensional datasets. It detailed how we defined the metadata of each dataset as instances of the DMO-core ontology and domain-specific ontologies. Three meterological collections of datasets were annotated in that way. The paper finally reported the evaluation of the approach on these meteorological datasets. An evaluation of the FAIRness of the datasets with their semantic metadata proves the relevance of the proposal in the FAIRification process, improving in particular criteria I and R. Yet we have planned several improvements and additional evaluations in other domains than meteorology. A first goal is to extend dmo-core to tabular datasets with other format than CSV, such as XML or JSON (which can be done quite easily by integrating dedicated vocabularies as done for CSVW). In fact, the combination of RDF Data Cube and DCAT is suitable for describing any kind of general metadata. A second one is to write SHACL constraints for the DMO-core ontology and implement a form generated from the SHACL file to make it easier for domain expert to annotate their datasets. a third one could be to test our proposal in other domains (such as health) using other domain ontologies. Finally, we plan to complement our evaluation using other frameworks such as F-uji 23 and Fairshake 24 .

Fig. 1 .

 1 Fig. 1. Instantiation model.

3 .

 3 Associating dmo-core to domain concepts Tabular columns (instances of csvw:Column) are linked to a dimension, attribute or measure (represented as a qb:ComponentProperty). A qb:ComponentProperty has a qb:concept property whose default range is a skos:Concept to which is added a domain concept, as illustrated in Figure 1. More specifically, for each column COL i , the process consists in creating the following instances: (a) INST COL i of csvw:Column; (b) an anonymous instance of both skos:Concept and the domain concept; (c) COMPONENT PROPERTY COL i of qb:ComponentProperty (i.e., a dimension, attribute or measure). Then (d) COMPONENT PROPERTY COL i is linked to the blank node with the qb:concept property. Finally (e) the dmo-c:references property links INST COL i to COMPONENT PROPERTY COL i .

Fig. 2 .

 2 Fig. 2. Excerpt of SYNOP data.

Fig. 3 .

 3 Fig. 3. Dataset FAIRness progress per indicator (without semantic MD)

Fig. 4 .

 4 Fig. 4. Dataset FAIRness progress per indicator (with semantic MD)

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