dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This work has benefited from funding provided by:  ANR (French National Research Agency ):  IHU Liryc, L'Institut de RYthmologie et modélisation Cardiaque , reference  10-IAHU-0004 ,  2011/2024. ANR: EQUIPEX MUSIC, Plateforme multi-modale d'exploration en cardiologie, reference  11-EQPX-0030 ,  2012/2021. Inria: ADT IDAM, engineering for the integration of cardiac electrophysiology images, data and models, 2015/2017. Inria: ADT EPICARD-VIZ, electrocardiographic imaging tool: simulation and visualization, 2016/2018. Inria: ADT CIESCARD, combining electrical and structural information to help cardiologists better target cardiac therapy, 2018/2020. ERC: Starting Grant ECSTATIC,  Electrostructural Tomography-Towards Multiparametric Imaging of Cardiac Electrical Disorders , reference 715093, 2017/2022 (https://www.
Semantic technologies have proven their relevance in facilitating the interpretation of Earth Observation (EO) data through formats such as RDF and reusable models, especially for the representation of space and time. While rasters are the usual data format for the results of image processing algorithms, a recurrent problem is transferring the pixel values of these rasters into features that make sense of the areas of interest on the Earth, thus facilitating the interpretation of their content. This paper addresses this issue through a semantic data integration process based on spatial and temporal properties. We propose (i) a modular and generic semantic model for the homogeneous representation of data qualifying a geographical area of interest thanks to territorial units (land parcels, administrative units, forest areas, etc.
) that we define as divisions of a larger territory according to a criteria in relation with human activities; and (ii) a semantic extraction, transformation and load (ETL) process that builds on the model and the data extracted from rasters and that maps aggregated data to the corresponding unit areas. We evaluate our approach in terms of the (i) adaptability of the proposed model and pipeline to accommodate different use cases (vineyard and urban expansion monitoring), (ii) added value of the generated datasets to assist in decision making, and (iii) scalability of the approach.
Introduction Earth Observation (EO) is a field that has evolved significantly in recent years thanks to large-scale Earth monitoring programs, such as the US Landsat Program (https:// www. usgs. gov/land-resources/nli/landsat, accessed date: 23 December 2021) and the EU Copernicus Program (http://www. copernicus. eu/en, accessed date: 23 December 2021). In particular, with the Copernicus program launched by the European Space Agency (ESA), EO satellites collect data that will be combined with observation data from sensor networks on the Earth's surface. Nowadays, two types of satellites are in production, Sentinel-1 and Sentinel-2, with several other types being expected by 2030. Since 2015, these satellites have provided high-quality Earth images (estimated at 8-10TB of data per day), providing users with free, reliable, and up-to-date Earth image data and metadata.
The main contributions of this paper can be summarized as follows: • A generic semantic model that allows the semantic and homogeneous description of spatio-temporal data to qualify predefined areas and to keep track of their provenance. This model is extendable to handle any kind of observed EO property and has been applied to several use cases. We evaluate our approach in terms of the adaptability of the proposed model to address different use cases (vineyard and urban expansion monitoring), the adaptability of the pipeline, and the added value of the generated datasets to assist in decision making. We also discuss the scalability of the approach and the relationship between the image resolution and the size of the reference territorial units. This paper extends  [2]  in two directions: (i) we detail the proposed semantic models and (ii) we extend the discussion on the use cases in which our models and pipeline have been applied.
This work was developed in the scope of the CANDELA (http://www. candela-h2020. eu/, accessed date: 23 December 2021) European project, which aims at creating a platform that provides building blocks and services that allow users to quickly use, manipulate, explore, and process Copernicus data as well as large open datasets. We contribute with this project by proposing a semantic-oriented data integration, as presented in this paper, and a semantic search module upon the integrated data. The remainder of this paper is organized as follows. Section 2 discusses key related work. Section 3 presents our semantic model for integrating geographical areas and data extracted from rasters through their spatial and temporal dimensions. Section 4 details the semantic data integration process. Section 5 describes the experimental evaluation of our approach.
Closer to us, the work from  [11]  defined an ETL process to integrate EO image and external data sources, such as Corine Land cover, Urban Atlas, and Geonames. The process is carried out based on their SAR ontology. Another close work in terms of datasets is from  [12] , where data are integrated and published as Linked Open Data (LOD) based on an ontology, which was called proDataMarket. Three data sources, the Spanish land parcel identification system, Sentinel-2 satellite, and LiDAR flights, are integrated. In  [13] , satellite images are classified and enriched with additional semantic data to enable queries about what can be found at a particular location. In our approach, an exploitation of our integrated data facilitates image search. Finally, while we do not fully address the scalability of our approach, several works are dealing with the management of large volumes of EO data.
In  [14] , a framework helps to integrate and process large-scale heterogeneous big data generated from multiple sources for decision support to prevent natural disasters. The semantic integration of EO and non-EO data is based on the MEMOn modular ontology that reuses the BFO (Basic Formal Ontology (https://basic-formal-ontology. org/, accessed date: 23 December 2021)), SSN, and ENVO (Environmental Ontology (https://sites. google. com/site/environmentontology, accessed date: 23 December 2021)) ontologies. While many approaches aim at producing open linked datasets, others focus on the methodological aspects of interconnecting heterogeneous data. For instance, to manage heterogeneous sensor data products, ref. [15]  uses several related ontologies that form the INSAT-3D satellite description vocabulary.
These entities are sets of raster elements (i. e. , sets of pixels) that meet a certain context-dependent definition. In  [19] , the approach integrates and processes scientific vector and raster data from LOD repositories using vectorization and mathematical tools for geo-processing. First, bounding boxes for input raster are used to query LOD endpoints for entities corresponding to a certain concept. The returned entities along with their geometry are next used to select raster pixels for supervised training based on content-based descriptors. Finally, the results can be vectorized and inserted back into the original repository. In  [11] , the approach proposes restructuring the images in patches that have a fixed size, on which external information is associated. Each patch is directly transformed into a feature based on the ontology.
In our case, we defined a set of requirements and searched for ontologies able to: (a) represent metadata concepts; (b) keep track of the raster files used as input of the ETL process; and (c) represent different types of data (territorial units, temporal and spatial data). As a result of this search, we identified and manually evaluated the following existing resources, which are well-known in the domain: OGC standards such as GeoSPARQL (for geospatial data representation) and W3C recommendations such as OWL-Time (for representing temporal data), SOSA (for earth observations), DCAT (https://www. w3. org/TR/vocab-dcat-2/, accessed date: 23 December 2021) (a metadata vocabulary for cataloguing datasets), and PROV-O (for the provenance of the data generated by the integration process). Hence, our model relies on these selected models.
Sentinel Images Metadata (eom) The Sentinel image metadata (eom) is the sub-model dedicated to the representation of Sentinel image metadata. It mainly describes the product (an image) as the result of a sosa:Observation. Each observation is associated with a temporal information, namely the capture date (sosa:phenomenomTime) and a spatial information, namely the captured area (either by the geometry property of the eom:Product or by the sosa:hasFeatureOfInterest relation). To reduce the cost of image indexing, we assume that each Sentinel-2 image is mapped to a tile. So, the model proposes a class to represent tiles as spatial entities (geo:Feature). The model has been designed to take into account both Sentinel-1 and Sentinel-2 image metadata Figure  2. The OWL file of this model is available at melodi.
EO Analysis Model (eoam) In the EO analysis model (eoam), Sentinel images are consumed in different kinds of analyses, such as machine learning algorithms that identify changes between two images. Eoam provides information about the results (raster datasets) of these activities, since they are or will be consumed by the semantic integration process. Thanks to the PROV-O vocabulary, it is possible to know which Sentinel images have been used (eom:Product) as the input of a process (eoam:EOAnalysis) and which agent (prov-o:wasAssociatedWith) realized it. DCAT is used to represent catalog metadata of both raster and vector datasets. Thus, both raster files (eoam:RasterFile) and vector files (eoam:GeoFeatureFile) are considered as distributions of these datasets. A raster distribution is described by properties coming from the DCAT vocabulary: temporal coverage (dct:temporal), spatial properties (dct:Location), and spatial resolution (dcat:spatialResolutionInMeter).
Data Extraction from Raster and Vector Files This step aims at extracting and structuring the data according to different objectives. The process requires two sources: a vector and a raster. First, metadata (e. g. , issue date, format, CRS, or spatial extent) are extracted. Next, the vector file is processed to extract information about territorial units of a given type, including their geometry (a polygon), which is projected to the WGS84 CRS coordinate (the default CRS in GeoSPARQL). They will populate the tom:GeoFeature class. The raster is also processed to qualify each territorial unit contained in the vector file. Currently, we distinguish two types of raster files, depending on the type of their pixel values:.
Data Transformation This step aims at transforming the processed data into the semantic one. Templates that define the mappings between the extracted data (in JSON) and the ontologies are used as a basis in this process. Templates are usually written by hand. Although different data translation tools exist, such as D2RQ (http://d2rq. org/, accessed date: 23 December 2021), Ultrawrap (https://capsenta. com/ultrawrap/, accessed date: 23 December 2021), Morph (http://mayor2. dia. fi. upm. es/oeg-upm/index. php/en/technologies/315morph-rdb/, accessed date: 23 December 2021), Ontop (http://ontop. inf. unibz. it/, accessed date: 23 December 2021), or GeoTriples (http://geotriples. di. uoa. gr/, accessed date: 23 December 2021), we chose to adapt the mapping template and processing mechanism described in  [29].
• A geospatial triplestore can be used as a warehouse to store semantic data to perform data enrichment and linking. • Different datasets may be offered by several endpoints requiring a federation mechanism. However, there is currently no query engine mature enough to answer GeoSPARQL queries over such a federation  [8]. Considering that we send a single GeoSPARQL query to examine the territorial units stored in different triplestores, spatial comparisons on the fly are not possible. In the following, we present an example SPARQL query on the resulting triplestore. This query retrieves all territorial units (their ID, type, and geometry) located in an area, defined by  [  FILTER((?dti1 <="[endDate]"^^xsd:dateTime && ?dti2>="[startDate]"^^xsd:dateTime))). ?geofeature tom:hasType/tom:name ?type_name.
Experimental Evaluation The evaluation was performed on two use cases describing different types of EO properties of a territorial unit. Since the integration process is performed by users based on their purpose, the integrated database is built according to these use cases. The semantic data generated for these use cases is stored in a database that can be exposed through a semantic search interface or accessed via a SPARQL endpoint (http: //melodi. irit. fr/tom/), accessed date: 23 December 2021. We evaluated the approach in terms of (i) adaptability of the proposed model to two use cases; (ii) adaptability of the pipeline to each use case; and (iii) added value of the generated datasets to assist in decision making. We also discuss the scalability of the approach.
• Land cover: The datasets provide information about the land cover of an area on Earth. The CESBIO land cover datasets (http://osr-CESBIO. ups-tlse. fr/~oso/, accessed date: 23 December 2021) are used for our use cases. They cover the French territory with a spatial resolution of 10 m 2. Regarding the territorial units (vector datasets), land register data are used for the first use case, while the second use case exploits administrative unit data.
• Vineyard use case: (i) We first obtain all the parcels of the Saint-Emilion village from the cadastre data and the CESBIO land cover raster for 2017. Then, semantic data integration is used to integrate the land cover and parcels information. As mentioned in Section 4. 2. 1, the semantic data integration is configurable by a set of four parameters. These parameters can be provided as raster metadata or as function parameters. Users can also provide custom thresholds for class classification since the results of image processing and analysis can be highly scenario dependent. For example, in the vineyard use case, custom thresholds can be set for the classification of change indicators. The values below 0. 1 could be translated as Very low change instead of below 0. 2. These types of parameters must be set explicitly at runtime.
Added Value of the Generated Datasets in Helping Decision Making We evaluate the benefits of using integrated data to assist in tasks such as land monitoring and data cross-checking, and we discuss them in what follows. 5. 4. 1. Improving Land Monitoring Thanks to Data Integration Figure  6  shows an example of the web interface used to retrieve observations for the vineyard use case. The application supports three filters, each corresponding to one dimension: temporal (the user must define a period of time), semantic (the user must select an ObservableProperty from the three-view), and spatial (the user must draw an area of interest on the map). GeoSPARQL queries are formulated based on the input parameters and are next sent to the endpoint. Territorial units satisfying the constraints are displayed on the map (in this example, the displayed polygons are parcels located around the Langon village having vineyard as land cover in April 2017).
Data Cross-Verification Thanks to Data Integration Another application is to check information from various sources, including the results provided by our partners. We used the land cover annotation dataset obtained by interactive data mining algorithms on satellite images  [30] , developed by DLR (German Aerospace Center). For example, it is possible to compare the CESBIO land cover information with the land cover annotated by DLR or to justify the change detection result based on land cover change. From Figure  6 , several remarks arise from the comparison of the two observations:.
• The parcel was not correctly annotated by the user of DLR tools, as he identified it as a 'Mixed urban area' (property LC_DM). • CESBIO (property LC_CESBIO17) and Corine (property LC_Corine) correctly identify land cover at the parcel level. In fact, the differences observed between the two land covers come from the spatial resolution of the rasters (which has an impact on the precision). While the best resolution for the French area is given by CESBIO, the DLR data mining labels are given by a user with limited domain knowledge.
Approach Scalability In terms of triplestore, we initially chose Strabon (http://strabon. di. uoa. gr/, accessed date: 23 December 2021) for its main advantages: (i) it extends the Sesame triplestore with the ability to store spatial RDF data in the PostgreSQL DBMS enhanced with PostGIS. It has a good overall performance due to special optimization techniques that allow spatial operations to take advantage of PostGIS functionality instead of relying on external libraries  [31]. For complex applications that include both spatial joins or spatial aggregations, Strabon is an RDF store that performs well  [32]. (ii) It provides a SPARQL endpoint that allows access to the content of the triplestore. The interface also provides additional capability to manage the knowledge base, for instance storage and update capabilities with SPARQL Update.
While Strabon performed well when considering a small number of triples in the data store, we faced scalability issues when the number of triples increased. Indeed, the same conclusion is reported in  [33]. Therefore, for these reasons, we looked at query federation approaches where the data are stored in different triplestores. In our case, Strabon is used only for spatial data and GraphDB (https://graphdb. ontotext. com/, accessed date: 23 December 2021) (a semantic graph database compliant to the W3C standards) for the other data types. Preliminary experiments have shown two main advantages of this option: (i) faster response time for non-spatial queries, and (ii) scalable and robust as it ensures the result regardless of the amount of integrated data. Regarding the size of the generated RDF datasets, we populated 0.
Conclusions In this paper, we presented an approach for the integration of data calculated from rasters as a way of qualifying territorial units (represented as vectors), based on their spatio-temporal features. A first contribution is a generic semantic model that allows the homogeneous description of spatio-temporal data to qualify predefined areas. We used this model to semantically describe data from different use cases. A second contribution is a configurable semantic Extract Transform and Load (ETL) process based on that generic vocabulary. We used this process to extract the data from rasters and to link the observations to territorial units through their spatio-temporal dimensions. Last but not least, we produced semantic databases that can be further exploited for new purposes. As future work, we plan to exploit big data scenarios with the management of Natura 2000 areas (https://www.
eea. europa. eu/data-and-maps/data/natura-11, accessed date: 23 December 2021) (land cover evolution and change detection on conservation areas). We also plan to extend the proposed approach to handle data from CSV files, in particular, weather observations from ECAD (https://www. ecad. eu/, accessed date: 23 December 2021) or Meteo France (http://www. meteofrance. com/, accessed date: 23 December 2021). We could apply the Location Index (Loc-I) approach presented in  [34]  to boost the performance of our application. Finally, considering data cubes as multidimensional data arrays frequently used to manage geospatial data, including rasters, we consider managing this type of structure in the ETL pipeline.
Satellite images processing and analysis: This task consists of processing and analyzing satellite images coming from a DIAS (Data and Information Access Services) (in our project, CreoDias (https://creodias. eu/, accessed date: 23 December 2021)). Possible analyses can range from simple ones such as NDVI calculation to more sophisticated analyses such as change detection on time series or land cover annotation. The task generates raster files as output. • Semantic data integration: The task extracts data from the raster files using vector files that contain the territorial units to be observed. Vector sources could come from open data repositories or from our semantic database through Semantic search. • Semantic search: The task aims to analyze the integrated data by querying the semantic database.
(ii) Vineyard parcels within villages are retrieved via semantic search. (iii) Appropriated Sentinel-2 images are used for NDVI calculation. (iv) These images are also used for change detection. (v) The generated rasters from the 3rd and 4th steps along with the vector from the 2nd step are integrated into the semantic database. (vi) Finally, semantic search can be used to analyze all integrated information related to the vineyard of interest. • Urban expansion use case: (i) We first select adapted Sentinel images and execute NDVI calculation. (ii) These images are also used for change detection. (iii) We obtain vector data of all the villages of the Gironde department (INSEE code: 33) from the open administrative unit datasets. Semantic data integration is next launched using the raster generated from the previous step and the obtained vector files.
• Satellite images processing and analysis: This task consists of processing and analyzing satellite images coming from a DIAS (Data and Information Access Services) (in our project, CreoDias (https://creodias. eu/, accessed date: 23 December 2021)). Possible analyses can range from simple ones such as NDVI calculation to more sophisticated analyses such as change detection on time series or land cover annotation. The task generates raster files as output. • Semantic data integration: The task extracts data from the raster files using vector files that contain the territorial units to be observed. Vector sources could come from open data repositories or from our semantic database through Semantic search. • Semantic search: The task aims to analyze the integrated data by querying the semantic database.
From these considerations, we propose three architectural key features to support FDC scenarios: • Data and Computation Placement. Frequently moving complete datasets between sites is inefficient due to constraints on network capacity and bandwidth costs. Alternatively, data can be processed locally and only partially transferred. Similarly, different sites may share computational resources to optimize computations. • Privacy and Security. Distributed biomedical analyses must ensure strong security and privacy properties, notably confidentiality and integrity properties throughout the complete processes. However, delegating security checks to third parties, e. g. , cloud providers, is frequently insufficient because of non-compliance with legal constraints and robust privacy requirements. Third parties provide other mechanisms to address security and confidentiality, such as confidentiality agreements, but these do not grant data privacy and confidentiality when data is moved to third-party facilities.
What are the technical features of the current tools and techniques used for scientific collaboration? Moreover, how do they fulfill the key features for fully-distributed collaborations? We were particularly interested in how these tools manage data and computation placement, privacy and security, and scalability and performance. To address the research questions, we first searched for scientific papers indexed in the following databases: ScienceDirect, PubMed, IEEE, Google Scholar, Scopus, and ACM. The literature search was limited to articles published during the last ten years. We then realized a cross-reference study and applied several filters to define the final set of research work. The search words used to address the first question were "biomedical applications" and "genomic analysis tools".