dataesr/scientific-paragraphs-categorization huggingface dataset:
OCBSA-2021's Contribution to OCB Detection An initial comparison of OCBSA-2021 s contribution to band detection vs. that of OCBSA-2016 was carried on 165 CSF lanes from the POLAR database, using an enhanced version of our previously published automatic OCB detection method  [9]. The visual on-membrane expert consensus analysis was considered to be the ground truth. A profile with at least three OCBs was required to designate the profile as oligoclonal. The diagnosis was more precise with OCBSA-2021 (sensitivity: 0. 88; specificity: 0. 88) than with OCBSA-2016 (sensitivity: 0. 86; specificity: 0. 86). These improvements were probably due to the successful recovery of low-intensity bands with non-uniform deformation distributions. We intend to enhance our OCB detection method further.
Statistical Evaluation of the Algorithm's Performance with Real and Synthetic Data The bar charts in Figure 9A,B summarize the quantitative results for OCBSA-2021 and OCBSA-2016 for profiles with a least one band from the training and test datasets. The detailed results for each sample type (CSF, serum, tears and control) are also illustrated. The number of bands with a deformation degree greater than  = 2, 3, 4 or 5.
22, x FOR PEER REVIEW 17 of 20One can note the difference in the number of bands with SD(∆) > 5 pixels between the training dataset (58 bands) and the test dataset (25 bands). This difference is mainly due to two control lanes with, respectively, 11 and 7 highly distorted bands randomly assigned to the training database during database decomposition. Serum lanes contain rarely bands; however, in order to study band straightening on this sample type, the experts chose to also annotate several faint bands with no real clinical value. Tear lanes have less bands than CSF lanes, and so there were twice as many CSF bands (training: 109, test: 127) than tear bands (training: 59, test: 59). OCBSA-2021′s ability to decrease the number of deformed bands is obvious for largeangle, bent bands: in the training database, the number of strongly deformed band (SD > 5 pixels) was divided by  5 = 19, and the number of moderately-to-strongly deformed band (SD > 4 pixels) was divided by  4 = 12.
Sensors 2022 , 20 Figure 7. 1 3. 1. 2. 2022207112 Figure 7. Illustration of a band-straightening result for the CSF membrane in Figure 1 3. 1. 2. Results with the Synthetic Dataset Testing OCBSA-2021 on synthetic lanes exactly estimated the algorithm's effectiveness, since the results obtained (Figure 8d,h) can be directly compared with the original, synthetic lanes with non-deformed bands (Figure 8a,e). Figure 8d,h illustrate OCBSA-2021′s ability (in contrast to OCBSA-2016) to straighten bands with a non-uniform deformation distribution (Figure 5c,g).
3. 2. 2 Statistical Evaluation of the Algorithm's Performance with Real and Synthetic Data The bar charts in Figure 9A,B summarize the quantitative results for OCBSA-2021 and OCBSA-2016 for profiles with a least one band from the training and test datasets. The detailed results for each sample type (CSF, serum, tears and control) are also illustrated. The number of bands with a deformation degree greater than  = 2, 3, 4 or 5.
3. 1. 2. 20 Figure 7. 1 3. 1. 2. 12207112 Figure 7. Illustration of a band-straightening result for the CSF membrane in Figure 1 3. 1. 2. Results with the Synthetic Dataset Testing OCBSA-2021 on synthetic lanes exactly estimated the algorithm's effectiveness, since the results obtained (Figure 8d,h) can be directly compared with the original, synthetic lanes with non-deformed bands (Figure 8a,e). Figure 8d,h illustrate OCBSA-2021′s ability (in contrast to OCBSA-2016) to straighten bands with a non-uniform deformation distribution (Figure 5c,g).
3. 2. 20 Figure 9. 2209 Figure 9. Band-straightening results for the real IEF training (A) and test (B) datasets: the number of bands with different deformation thresholds (SD > 2 pixels, 3 pixels, 4 pixels, 5 pixels) before straightening, after OCBSA-2016 and after OCBSA-2021 are displayed for the whole dataset and for each sample type (control, tears, CSF, and serum). The   =       ratios with the different deformation thresholds are shown for the whole dataset (orange for OCBSA-2016, grey for OCBSA-2021).
Sensors 2022 , 2022 22, x FOR PEER REVIEW 17 of 20One can note the difference in the number of bands with SD(∆) > 5 pixels between the training dataset (58 bands) and the test dataset (25 bands). This difference is mainly due to two control lanes with, respectively, 11 and 7 highly distorted bands randomly assigned to the training database during database decomposition. Serum lanes contain rarely bands; however, in order to study band straightening on this sample type, the experts chose to also annotate several faint bands with no real clinical value. Tear lanes have less bands than CSF lanes, and so there were twice as many CSF bands (training: 109, test: 127) than tear bands (training: 59, test: 59). OCBSA-2021′s ability to decrease the number of deformed bands is obvious for largeangle, bent bands: in the training database, the number of strongly deformed band (SD > 5 pixels) was divided by  5 = 19, and the number of moderately-to-strongly deformed band (SD > 4 pixels) was divided by  4 = 12.
Stylized facts We built an annual dataset covering 2006-2017 for 119 cities in the Greater Paris Metropolis. Greater Paris notaries, in collaboration with the French Statistical and Economic Studies Institute (INSEE) provided prices per square meter for apartment dwellings, reported on a quarterly basis between 1996-2019, using hedonic models. 11 Fig. 1  shows the house price cycles in the Greater Paris Metropolis. We clearly identify five periods-three periods of housing booms and two periods of housing busts. The first boom period  (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008)  occurred because of financial liberalization and loose credit conditions. Between 2001 and 2007, mortgage rates decreased from 5. 7% to 3. 9%, and mortgage durations increased by 4.
Housing submarkets refer to homogeneous groups that are significantly heterogeneous with respect to other groups. Our approach to building housing submarkets was not predetermined by some prior view; it let the data determine the structure of the submarkets. A. Coën et al. To do so, we have a two-step approach. First, we implement a principal component analysis using a large dataset of nearly 30 variables, 14 including socio-eco-demographic and real estate characteristics. This approach can sum up and extract a small number of factors from a large dataset in which multicollinearity is a known problem. The procedure sum up the large dataset into five components. The second step implements a hierarchical agglomerative clustering on the score of the five components we drew from the principal component analysis.
4) Refer to other things using HTTP URI-based names when publishing data on the Web. The so-called "Web of Data" results from the adoption of these principles by data providers over the world. It is not intended to be a substitution to the already existing "Web of Documents", but rather to complement it to achieve the original vision of the Web  [Berners-Lee T. , 1989]. "Data on the Web" working group released its first public draft document in 2015 which became a W3C recommendation  [W3C, 2017a]. This set of recommendations aims that 'Data should be discoverable and understandable by humans and machines'. This was then complemented by a second document resulting from the OGC/W3C collaboration initiated in 2014: Spatial Data on the Web Best Practices  [W3C, 2017b]. These include the provision of metadata for the datasets, stable identifier in the form of HTTP URI, and linking data through those HTTP URIs.
Adopting them is essential for the geological community as it enhances the discoverability, reuse, and enrichment of datasets of public interest for education, research, knowledge creation, and support of economic activities. FAIR principles were defined in 2016 to improve the Findability, Accessibility, Interoperability, and Reuse of scientific data both for machines and humans  [Wilkinson et al. , 2016]. Presently, those principles extend to any other digital assets such as services and software tools  [Koers et al. , 2020].
Inter-Linked Data Architecture During the EPOS-IP project, geological data services of interest for the EPOS geoscientific scope were identified and prioritized in the TCS GIM roadmap. The first services delivered to the ICS-C were the European boreholes, 3D models, the geological map of Europe at 1:1,000,000 scale as well as the mineral occurrences and mines. To follow the path taken internationally by this very same community since 20 years, TCS GIM decided early in the EPOS-IP project that it would base its IT work solely on the ISO, OGC, IUGS/CGI, INSPIRE, W3C and RDA published identified standards and best-practices baseline. This philosophy has applied from this point in the following steps of the EPOS Operational Phase  [Cocco et al. , 2022; Saleh Contell et al. , 2022]. When needed, this work also contributed to the evolution of these standards.
Even though interoperability implementation frameworks are well described and used, it proved to be difficult for several data providers in several earlier pan-European projects to deploy the OGC services with full semantic data definition (OGC/ISO Complex Feature) for the discovery and view of millions of geological entities. TCS GIM decided to take a complementary route. Thus, the way the indices are served is unique compared to previous interoperability projects in which the TCS GIM community took part. Indeed, the entire data architecture revolve around the idea of serving SimpleFeature data. Fully Linked Data-oriented simple data payload provide the relevant link to semantically richer ones. The group reused and enriched specifications (e. g. , GeoSciML, EarthResourceML) and previous data collection projects when they were available (EGDI, Minerals4EU).
It also complemented the specification (e. g. , for Models) and data collection (borehole and 3D model data) landscapes where no previous standard or European data set existed. As such, the TCS GIM exposes at the central TCS level (i) a metadata description of the available data sets and services, (ii) a number of SimpleFeature "index services" towards the EPOS ICS-C, (iii) a code list registry and (iv) a Linked Data resolver. These allow EPOS end users to search and locate boreholes, geological maps and features, 3D models, etc. , based on the information referenced by the index services. Information indices have been set up for data types that require data compilation for EPOS (e. g. , Model Index) as well as for data types where such data were collected during previous European projects (e.
g. , GeologicalUnits by EGDI, GeoResources by Minerals4EU and Mintell4EU). In this Linked Data approach, the data instances are associated with URIs and point to other information resources also using URIs. The Linked Data principles ensure the best semantic description (e. g. , URIs to shared code list registries entries) and also enrich an initial "informa- As stated above, European geological data are exposed with a Linked Data approach. The borehole index gives access to the location and a limited number of descriptors whereas the comprehensive data include full information on the borehole and samples for scientific research. The full sample material (drill core) information is available by linked third party services as a reference with an International Geo Sample Number (IGSN)  [Conze et al.
Boreholes The data set exposed in EPOS includes onshore and offshore boreholes made available by the TCS GIM members (over 2. 2 million boreholes as on 31/12/2021). It includes both scientific and commercial boreholes. The regional and national geological surveys expose national data sets as part of their public service mission, often comprising several millions of boreholes. Most boreholes drilled for the purpose of scientific research are located in areas that are of particular interest for understanding the structure and composition of the subsurface and which are not sufficiently explored by commercial wells. Therefore, data from scientific drillings are of particular interest for the solid Earth science community despite their marginal number when compared to the data sets that contain millions of commercial Two services are offered to the users: -The Map View Service enables the user to discover and interact on a map with the available boreholes.
A click on a Borehole provides the user with a set of descriptors that are defined in EPOS GeoSciML-Lite profile (e. g. , depth, purpose, drilling method, availability of borehole logs, link to monitoring equipment, etc. ), -The Feature Index Discovery Service enables the user to search for boreholes matching a set of criteria such as location, and many other properties that are defined in the specification based on GeoSciML-Lite (e. g. , drilling method, core length, access to the physical core, to logs, to water level, etc. ). When code list registries were not available from INSPIRE and/or the Commission for the Management and Application of Geoscience Information of the International Union of Geological Sciences (IUGS/CGI) level, specific entries were added in the European Geoscience Registry.
The data providers expose OGC ComplexFeature services where such rich information is available. These services are directly interfaced from the index service as Linked Data, i. e. they are not routed through the EPOS TCS or ICS infrastructures. This is the perfect example of the benefits of a fully Linked Data approach. Those datasets/services are still integrated in EPOS in the sense that are described using the common metadata semantics agree on in EPOS while still being hosted as close as possible to the Data Provider which is important for data maintenance. Another benefit of such a fully Linked Data approach is the capacity to dynamically enrich the data content handled by a software when a user is using it without asking him to go back again in a data catalogue to search for the second dataset that will enrich the first.
Mineral resources This data set was compiled over more than 15 years in a series of European mineral inventory projects (e. g. , Pro-Mine, Minerals4EU, Mintell4EU). Most geological surveys host data on raw materials, however, data are typically organized in different ways from one country to another based on different geological traditions, legal frameworks, etc. The latest Mintell4EU project  [Flindt Jørgensen et al. , 2021]  built on previous projects to collect a selection of these national/regional raw material data, to store them in a central database, and finally to offer a visualization in a harmonized way at the European Geological Data Infrastructure (EGDI). This Minerals Inventory database  [Kumelj et al. , 2021]   Two services are offered to the users: -Map View Service: the European mineral occurrences and mines are displayed using a first set of descriptors (name, status, activities and associated products, citation), -Feature Index Discovery Service: in addition to the geographical search provided by the EPOS Data Portal interface, the user can interact with the mineral occurrences and mines with dedicated descriptors (e.
Vocabulary registry and resolver The TCS GIM utilizes the European Geoscience Registry, which was set up as the reference code list registry to consolidate and share controlled vocabularies throughout the various thematic projects and data infrastructures in which EuroGeoSurveys members participate. The TCS GIM activities aim at avoiding the single use of project code lists while building on the knowledge and enriching reusable European vocabulary registries on Boreholes, Models and associated Representations. Each registry and its entries are identified by a URI that can be addressed in data flows when needed. The system provides human and machine-readable representations of these vocabularies. Along with the resolver, each information element (e. g. , data, service, metadata…) served from TCS GIM to the EPOS ICS-C is assigned a URI under the same URI 16 base, which allows: -fine grained identification, -external systems to refer to them and dereference the content when needed, -guarantee access to those information resource to external systems without impacting when a resource provider changes within the TCS.
Serving multidisciplinary scientific drilling data In-situ subsurface information is the least accessible part of geoscience information. However, it is very valuable for interpreting complex data sets and building relevant models of the solid Earth. Although drillings can only penetrate the uppermost curst, this is the deepest in-situ information that is accessible, and it is from the part that is of most importance for society. A scientific borehole usually produces substantial sample material (drill core) supplemented by a geological description, image documentation and geophysical downhole logs. In special cases, time series data will be available from downhole sensors (e. g. , downhole seismometers installed after drilling an active fault zone; piezometers in aquifers). A wide range of research can benefit from such data, like modelling groundwater flow patterns, heat flow and geothermal potential; rock mechanical testing and modelling for subsurface construction; and estimating the risk for seismic hazards to a settlement, mine or water reservoir close to a drilled fault zone.
Data discovery for multidisciplinary data sets in the EPOS Portal provides an overview over the scientific (and other) boreholes of the area. Already at this stage-provided by the View-WMS of the European Borehole Index-information about the availability of data sets is available and the geological description can be dereferenced either directly or via the complex borehole service. This facilitates the scientists' selection of relevant objects. The complex service also provides information on whether the borehole is accessible for complimentary studies, its dimensions and last known conditions (where specified) and collected drill core samples. References to the International Geo Sample Numbers of the borehole and samples provide the opportunity to interface IGSN services from the EPOS GIM borehole service that allow scientists to screen the entire drill core virtually for further assessment of the borehole's relevance to the topic or require access to the physical samples in the core archive.
In this way, for example, seismologists and rock physicists can discover and access sensor data from a fault zone that was penetrated by the borehole, combine it with downhole logging data and compare results with images of the drill core (on an IGSN service) before they sample the optimal pieces of core for laboratory measurements and, eventually, add new research data to EPOS services. In the larger picture, data from the EPOS GIM borehole service would add an additional dimension to the regional model that is based on surface geology and seismological data, InSAR satellite and GNSS data for surface motion and tectonic analysis, and existing laboratory data on rock samples from the area -all discovered and provided by EPOS community services.
Groundwater monitoring From a Borehole Index entry, one can access the hydrogeological logs available along with the description of (i) the monitoring facility, e. g. , piezometer, (ii) the aquifer being monitored and (iii) the observations/measurements acquired by the monitoring facility (Fig. 3 ). All the elements in this data graph are described according to well-established international interoperability standards (such as OGC/ISO Observations & Measurement, OGC GeoSciML, INSPIRE Data Specifications, etc. ). Figure  3  exemplifies how such a fully Linked Data approach improves multidisciplinary science. Currently in many Geological Surveys legacy systems, each "box" shown on this figure is "siloed" in its own information system. In such a context, the end user must have an intimate knowledge of where are all the corresponding datasets/services, how to connect to them and search the data of interest within each database.
A fully Linked Data approach allows to go all across those siloes from one object description (e. g. , one borehole) to another (one geological log). Building on this approach that utilizes pre-existing open source libraries, interoperable client tools can be deployed in a generic way. For example, an open source desktop GIS software such as QGIS 17 can be used to ingest and interact with the interoperable data (Fig. 4 ). The Linked Data approach allows the end user to walk within and across data silos using features provided by a community (e. g. , a geologic fault, a borehole, etc. ) as a reference, without being the original data provider of that feature and without requiring additional data integration mechanics. This fosters multidisciplinary work as the access to relevant and useful data content is direct and immediate.
The subsequent steps are either data download for local analysis on the end user's system or triggering the borehole visualization tools in the EPOS Data Portal using TCS GIM dedicated libraries. This way, the user interacts with the data served by the EPOS platform, including the possibility of "bookmarking" the data URIs for later use or passing the data payload to a distributed processing system, the EPOS ICS-D  [Cocco et al. , 2022].
Geological data to foster multidisciplinary research Another prominent example is the on-demand and web-based modelling of geological structures on the basis of logging information from boreholes provided by web services. Figure  9  illustrates the web-based modelling (kriging) of a geological horizon based on stratigraphic information from borehole logs provided by web services. As a major objective of the EPOS research infrastructure, this data mining and fusion process joins the power of data integration and modelling in one tool and provides it to the end user.
Data integration using a notebook in Iceland The collaboration between largely heterogeneous geoscientific communities is facilitated by the adoption of interoperable services that provide standardized data. In return, these can be seamlessly integrated into scientific workflows or collaborative environments such as a Jupyter Notebook. Figure  9  illustrates a use case on how the temporal development of earthquakes can be related to geological structures (plate boundary, in this case) and outlined by geological modelling. The example shows a working notebook application 18 correlating information from different domains. All the required information is searched for and retrieved through the data assets discovered thanks to the EPOS ICS-C metadata catalogue. Although this is a simple example, it perfectly illustrates how scientists can collaborate in a common environment by interactively processing heterogeneous information in a cross-domain manner.
Geological information to address societal questions One of the main interests of a research infrastructure such as EPOS is the high-level disciplinary and cross-disciplinary interoperable services offered to scientists, the public and private stakeholders. Beyond a better understanding of the Earth system, also societal questions can be addressed such as the impact of anthropogenic hazards and the adaptation to climate change. A recently started research project (Geo-INQUIRE)  [Cotton et al. , 2022]  aims to complement multi-scale approaches of anthropogenic hazard episodes. This interdisciplinary research will take advantage of the synergy between TCS GIM and TCS AH (Anthropogenic Hazard)  [Orlecka-Sikora et al. , 2020]  data and product services through both the EPOS Data Portal and the EPISODES platform 19.