dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatial Data Infrastructures (SDI) established during the past two decades "unlocked" heterogeneous geospatial datasets. The European Union INSPIRE Directive laid down the foundation of a pan-European SDI where thousands of public sector data providers make their data, including sensor observations, available for cross-border and cross-domain reuse. At the same time, SDIs should inevitably adopt new technology and standards to remain fit for purpose and address in the best possible way the needs of different stakeholders (government, businesses and citizens). Some of the recurring technical requirements raised by SDI stakeholders include: (i) the need for adoption of RESTful architectures; together with (ii) alternative (to GML) data encodings, such as JavaScript Object Notation (JSON) and binary exchange formats; and (iii) adoption of asynchronous publish-subscribe-based messaging protocols.
Those developments, combined, provide access to thousands of spatial datasets. Currently, more that 100,000 datasets are made available in INSPIRE by roughly 7000 institutions, whereby many datasets are still provisionally provided in local non-aligned formats, awaiting harmonization by the 2020 deadline. As the geospatial data available within SDIs are an important contributor to the EU data economy, which is estimated at EUR 300 billion in 2016 (1. 99% of the EU GDP), unlocking the full potential of INSPIRE will provide an additional impulse to this already burgeoning industry. The estimated growth rate is also remarkable, as the contribution of data to the European economy is expected to increase to EUR 739 billion by 2020, representing thus 4. 00%  [2]. One of the challenges faced in the specification of the requirements to INSPIRE was the integration of measurement data with the spatial data that usually comprises an SDI.
?" workshop  [7]  include: (i) the need for adoption of RESTful architectures; together with (ii) alternative (to GML) data encodings, such as JavaScript Object Notation (JSON) and binary exchange formats; and (iii) adoption of asynchronous publish-subscribe-based messaging protocols. The research objective being addressed by this paper pertains to the challenge of the seamless integration of these new technologies into the emerging INSPIRE landscape, making best use of the potential provided while minimizing the disruptive effects, thus simplifying the data provision process, while making the existing data more available to all. Given the bounding conditions and overarching research objective outlined above, the newly established OGC standard SensorThings API  [8]  is of particular interest for investigation pertaining to INSPIRE, as it addresses all three topics together.
This feature type is integrated within the EF model, and is presented in Figure  3. -Package 4. Observation References. The Observation References section of the Observation Model provides the mechanisms required for linking between features and OM_Observation object. Thus, an Environmental Monitoring Facility can provide association references to observations stemming from this facility. In the same manner, an OM_Observation can provide a reference to the facility at which the observation was made. Download services shall provide a means for downloading of whole datasets (pre-defined access), or their subsets (direct access) through the use of a query. In addition, the specification of INSPIRE Download Services  [15]  requires functionality for downloading pre-defined data sets that are characterised by the following aspects:.
• A resolvable link (URL) must be provided, allowing that the dataset be immediately downloaded through a simple HTTP-protocol GET-request. Guidance for data providers is made available that covers several possible technological options (Table  1 ). All the solutions described in Table  1  might be used to serve spatio-temporal data with certain limitations. Most relevant to our work is the Sensor Observation Service, as it is to a large extent similar to the SensorThings API. The limitations of the SOS approach relate to: (i) the missing functionality of the SOS to filter observations based on their result values, although a corresponding extension has recently been proposed; (ii) lack of pagination; and (iii) the lack of an adopted REST/JSON binding of the SOS standard.
• FROST-Server FROST-Server (FRaunhofer Opensource SensorThings Server) is an open-source implementation of the standard, developed by the German research institute Fraunhofer IOSB, to cover their need for a standards-based, easy to use sensor management and sensor-data storage platform, for use in various research programs. It is written in the Java programming language and can be deployed on servlet containers such as Apache Tomcat or Wildfly. For data persistence, it currently has backends for the PostgreSQL database management system, with either numeric, string or UUID entity identifiers. Since it was developed to cover the wide range of use-cases that appear in research projects, the focus of development was on feature completeness and extendibility. The server implements the complete specification of the OGC SensorThings API Part 1: Sensing, including all extensions  [8].
• SensorUp SensorThings SensorUp, a Calgary-based startup, developed the first compliant SensorThings implementation. It is considered to be an OGC reference implementation. The implementation is Java-based and the current persistence system is a PostgreSQL database. The implementation is tuned for scalability without loss of performance. In addition to server development, SensorUp provides multiple clients and client libraries to make SensorThings even easier to use for client developers. Moreover, a wide variety of documentations, interactive SDKs, tutorials, and examples are provided by SensorUp for developers.
• GOST GOST is an open source implementation of SensorThings based on the GO programming language. This implementation was developed by Geodan, an Amsterdam based geo-ICT company. The implementation passed the SensorThings test suite and is OGC certified. It also has MQTT extension implemented. GOST is considered as alpha software and is not yet considered appropriate for customer use. GOST provides docker images as their easy deployment mechanism as well as binaries for users who do not prefer Docker.
Download Service Operations As clarified in Section 2. 1. 2, INSPIRE distinguished between: (i) pre-defined; and (ii) direct access download services. The implementation of the former requires functionality for downloading whole datasets. They shall be documented through metadata which is exposed online through a discovery service, following the specifications of the OGC Catalogue Service for the Web-CSW  [26]. A resolvable link (URL) shall be provided within each metadata record through which the dataset can be downloaded by sending a simple HTTP-protocol GET-request.
Get Download Service Metadata Provides all necessary information about the service, the available Spatial Data Sets, and describes the service capabilities. OpenAPI  [27]  document. This would require a mapping of the relevant (mostly static) contents of OGC GetCapabilities responses to OpenAPI. 2. Get Spatial Dataset Retrieval a dataset as a whole, as defined by the data provider The simplest option would be to map this operation to "Datastreams", i. e. , each datastream to deliver an INSPIRE dataset that is described separately through metadata. As a difference to the SOS offering concept  [11] , the datasets defined by the SensorThings API are restricted to one observed property. Thus, "MultiDatastreams" shall be used if the dataset contains more than one "ObservedProperty'. Alternatively, the powerful query mechanism of SensorThings API can be used to request information spread into multiple "Datastreams", including "Observations', "Observed Properties", "Thing", "Locations" and "FoI", all filtered by time interval, area, or other criteria.
• Capabilities for SensorThings API services. The standard provides a RESTful API for access to data. This is a convenient approach which allows easy uptake and immediate use by mainstream developers. The GetCapabilities operation included in the majority of OGC standards is not available. A solution for INSPIRE might be to use the OpenAPI standard (former Swagger)  [27]  to document the service, considering the required, mostly static, elements of the Get Download Service Metadata and Describe Spatial Dataset operations.
• Dataset in SensorThings API. The concept of a "dataset" does not exist in the SensorThings API standard. If following our proposal to equalise a dataset to a "Datastream", it should represent a logically consistent grouping of individual observations. • Support for request parameters. Request parameters are defined in  [15]  for the Get Spatial Dataset operation. Users should be able to request their datasets or spatial objects in any of the: (i) offered coordinate reference systems (CRS); and (ii) natural languages; and (iii) through their spatial data set identifier. The REST-like interface of SensorThings API does not support the first two parameters.
Direct Access In addition to the mandatory operations described in Section 3. 2. 1, non-mandatory "direct access" operations may be implemented to ensure that users can acquire the desired data in a flexible manner (i. e. , sub-setting the datasets by properties such as time periods and spatial extends). From our perspective they would be easy to implement through SensorThings API. Their mappings are provided in Table  4. Even though direct access operations are not mandatory, from our perspective, they are the ones that are, and would increasingly be, desired by users, as they really add value to the data provider infrastructure. Similar to the Get Spatial Dataset operation, the Get Spatial Object operation shall support requests for coordinate reference system and language that are not supported by the standard.
Discussion and Conclusions Within this manuscript we: (i) outline the challenges associated with the provision of spatio-temporal data in spatial data infrastructures; (ii) describe the contemporary technological scenery and emerging standardisation initiatives, and, most importantly, (iii) propose a solution that would allow the new SensorThings API to be considered as an INSPIRE solution. In summary, we consider that no major blocking factors exist for proposing SensorThings as an INSPIRE solution. As outlined in Section 2. 1, INSPIRE provides a transparent and straightforward means for encoding the location of environmental monitoring facilities and observation data. Nonetheless, there are several issues to be addressed. They include: However, the INSPIRE Directive mandates such an operation.
• Request for CRS and Language. As outlined in Section 3. 2, requesting datasets or individual observations in a particular natural language or coordinate reference system is not supported by SensorThings API. Satisfying this requirement would require a workaround that goes beyond the current version of the standard. When addressing the issue of CRS in particular, two aspects should be considered. Firstly, the provision of functionality for requesting CRS would add complexity to the service interface which might be conflicting with the overarching objective of the standard to be as developer friendly as possible. Secondly, the SensorThings API suggests GeoJSON for data encoding, and the conformance tests only test with it. This requirement poses a limitation, as all coordinates shall be provided in a geographic coordinate reference system, using the World Geodetic System 1984 datum  [28].
Definition of a dataset. Datasets in INSPIRE should include observations for a predefined period of time and for logically consistent geographical entities usually observed by the same procedure. Similar to SOS, the concept of a dataset is unknown to the SensorThings API. This we see as a broader issue that should be addressed within INSPIRE. • SensorThings API Extension Points. The data model of the SensorThings API has been concisely tailored to the requirements of sensors within the IoT while the INSPIRE data specifications require the provision of additional contextual information as required within an SDI. While some attributes required by INSPIRE can be directly aligned with attributes from the SensorThings API, additional attributes are required. For this purpose, we utilized the currently proposed extension (Appendix A) to the SensorThings API 1.
• Definition of a dataset. Datasets in INSPIRE should include observations for a predefined period of time and for logically consistent geographical entities usually observed by the same procedure. Similar to SOS, the concept of a dataset is unknown to the SensorThings API. This we see as a broader issue that should be addressed within INSPIRE. • SensorThings API Extension Points. The data model of the SensorThings API has been concisely tailored to the requirements of sensors within the IoT while the INSPIRE data specifications require the provision of additional contextual information as required within an SDI. While some attributes required by INSPIRE can be directly aligned with attributes from the SensorThings API, additional attributes are required. For this purpose, we utilized the currently proposed extension (Appendix A) to the SensorThings API 1.
Design and participants The Phase III, randomized, double-blind, multinational study was conducted to assess the safety and immunogenicity of IIV4 manufactured by an investigational process or by a licensed process in a cohort of adults (18-49 years), a cohort of children  (3-17 years) , and a cohort of infants  (6-35 months). The study was conducted during the 2014/15 influenza season in Bangladesh, Czech Republic, France, Germany, Poland, Spain and the United States (ClinicalTrials. gov: NCT02207413; trial registration date: August 4, 2014). To be eligible, participants in each age group had to be in stable health. Subjects were excluded if they had received 1) any non-registered drug or vaccine within 30 days or 2) any investigational or approved influenza vaccine (seasonal or pandemic) within 6 months of the first visit.
Enrolment and randomization The adults, children, and infants cohorts were enrolled sequentially. Adults were enrolled and vaccinated first, and a safety review was conducted by the sponsor's internal Safety Review Committee (iSRC) to review the 7 days postvaccination safety data. Upon the receipt of iSRC's endorsement based on the safety review in adults, enrolment of children aged 3-17 years began, and enrolment of infants aged 6-35 months started after the first 100 subjects in the 3-17 years cohort were vaccinated and no life-threatening related SAE(s) were reported within 2 days after dose 1. The randomization list and allocation of vaccines was generated using a software program developed for use in SAS (Cary, NC, USA) by GSK, Rixensart, Belgium. Vaccine allocation of the subjects at each study center was performed by investigators using an internet-based central randomization system.
5. The target sample size in the infant (6-35 months) cohort was based on 420 evaluable subjects needed in each group to detect non-inferiority of HI GMT ratio for IIV4-I compared with IIV4 with 92% global power using a 1-sided, 2-sample t-test to meet the UL of the 95% CI of the GMT ratio ≤1. 5. A total of 470 vaccinated infants provides 90% power to rule out the relative risk of fever (38. 0°C) >2 for IIV4-I/IIV4 using a 1-sided Score test  [31]. GMT ratios were derived using the Analysis of Covariance (ANCOVA) on the log10 transformed titers adjusted by age and pre-vaccination titer. Immunogenicity analyses of GMT, SPR, and SCR were described as point estimates with 95% CIs. All immunogenicity analyses were performed on the per-protocol immunogenicity cohort for each age group including eligible subjects without protocol deviation who had serological data available at a given time point.
Acknowledgements The authors are indebted to the participating study volunteers and their parents, clinicians, nurses and laboratory technicians at the study sites. In particular we thank  Jerzy Brzostek ,  Matthew Davis ,  Maria Mendez ,  Renata Ruzkova ,  Daniel Drazan ,  Richard Ohnmacht ,  Terry Poling  and  Stephan Sharp  who provided support and cared for study participants. The authors also thank the teams of GSK, in particular  Florence Bieswal (Study Delivery Lead ) and  Riju Ray  for his review. Finally, the authors thank  Annick Moon  ( Moon Medical Communications Ltd. , UK, on behalf of  GSK ) for providing medical writing services, and  Bruno Dumont  and  Véronique Gochet  ( Business and Decision Life Sciences , on behalf of  GSK ) for editorial assistance and manuscript coordination.
Availability of data and materials The results summary for this study (NCT02207413) is available on clinicaltrials. gov(trial registration date: August 4, 2014), the default register for GSK Human Subject Research. For interventional studies that evaluate our medicines, anonymized patient-level data will be made available to independent researchers, subject to review by an independent panel, at www. clinicalstudydatarequest. com within six months of publication. To protect the privacy of patients and individuals involved in our studies, GSK does not publically disclose patient-level data. The study adheres to Consolidated Standards of Reporting Trials (CONSORT) guidelines.
Genetic Characteristics of Strains In a first step, the strains with the private allele at microsatellite locus BC6 associated with the cryptic species B. pseudocinerea   Goudet, 1995)  was used to compute allelic richness (corrected for the smallest sample size) and unbiased gene diversity per locus. Unbiased gene diversity (Hnb) and allelic richness (average over the nine loci) were computed separately for the strains collected in the different reservoirs with the Genetix software  (Belkhir et al. , 1996 (Belkhir et al. , -2004)). The number of different multilocus haplotypes (MLH) was computed with GenClone 1. 0 software  (Arnaud-Haond and Belkhir, 2007). We used the index of haplotypic diversity (based on the number of individuals and the number of distinct MLH), which estimates the proportion of haplotypes present in a population and takes a value of 1 when a population is composed exclusively of unique haplotypes  (Arnaud-Haond et al.
Aggressiveness of Strains on Tomato Plants The aggressiveness of a subset of 128 environmental strains (Table  1 ) was assessed on 8-week old tomato plants cv. Monalbo (INRA). Tomato plants were grown in a greenhouse and watered daily with a nutrient solution as described previously  (Decognet et al. , 2009). Each strain was inoculated on three plants. On each plant, three leaves were removed, leaving 1 cm petiole stubs on the stems and the wounds were inoculated with 10 µL aliquots of spore suspension. The spore suspensions were prepared from two-week old cultures on PDA and were adjusted to 10 6 spores mL -1. All plants were incubated in a growth chamber with a photoperiod of 14 h, with a light intensity of 162 µmol m -2 s -1 , maintained at 21 • C with a relative humidity above 90%. The length of resulting stem lesions was monitored daily from the 3rd to the 7th day after inoculation and these data were used to compute the area under the disease progress curves (AUDPC).
L-citrulline Supplementation During the 12 weeks, participants in the HIIT+CIT group took a single daily dose of 10 grams of L-CIT (Citrage ©) containing 38 kcal per dose, while participants in the HIIT+PLA group took a single dose of a placebo powder (maltodextrin) equivalent in weight, appearance, taste and calories (see Table  1 ). Supplements were taken every day during lunch meal. The dose of CIT ingestion was based on the results of Moinard et al. [21]. An independent technician prepared and identified each individual's container containing PLA or CIT with a letter code. Each container had a sealed envelope containing the nature of the supplement that was securely stored in a locked container. Evaluators and experimentators were kept blind until the end of the protocol. Data on supplementation intake compliance was monitored by giving one box of the supplementation per month and by randomly assigning different quantities of powder packets per box to participants.
Energy Balance As previously described and validated in the elderly population, dietary intake was assessed before and after the intervention using the 3-day food record method (two weekdays and one weekend day)  [50]. Participants were asked keep their dietary habits during the intervention period. Analyzes of total intake as well as protein, lipids, or carbohydrate intake were performed using the software Nutrific© according to the standardized Canadian Food file (CNF2015). The number of steps were used to estimate the level of physical activity using a validated tri-axial accelerometer (SenseWear®Mini Armband) as previously described  [51, 52]. Participants had to wear the device on the left arm all the time during 7 consecutive days, except when taking a shower or swimming. Each participant had to wear the device at least 85% of the time to be included in the study.
Acknowledgments: This study was supported by the  Fonds de Recherche du Québec en Santé (FRQS)-Université du Québec à Montréal  fund and by the  Quebec Network for Research on Aging , a thematic network supported by the  FRQS (QNRA-FRQS)  and the  Canada Foundation for Innovation. MAL and GG are recipients of Chercheur Boursier Junior 1 (GG) and Junior 2 (MAL) salary awards from the  FRQS. The authors are grateful to all the volunteers for their participation in this study and to  Carole Roy  for her technical support. The citrulline supplementation was provided for free by Citrage©.
Bioinformatic analyses Gene sequences were obtained from the Genbank database  (38)  and homologue searches were performed with the BLAST tool  (39). Search of conserved protein domains and prediction of protein function were performed with the web-based PROSITE (40) and NCBI's Conserved Domain Database (41) tools. Comparative analyses of protein sequences was conducted in MEGA7: Molecular Evolutionary Genetics Analysis version 7. 0  (42).
Crystallization and structure determination of MouR Both native and SeMet-substituted MouR proteins were stored in 50 mM Tris-HCl pH 8. 0, 300 mM NaCl. Native crystals were grown at 20 • C from a 0. 1:0. 1 l mixture of a 10 mg/ml protein solution with a crystallization solution composed of 1. 5 M lithium sulfate, 100 mM sodium Hepes pH 7. 5. SeMet-MouR crystals were grown from a mixture of 6 mg/ml protein solution with a crystallization solution composed of 3 M NaCl, 100 mM Tris pH 8. 5. Crystals were flash-frozen directly in liquid nitrogen. Native and SeMet diffraction data were recorded on beamlines Proxima 2A (Soleil, Gif-sur-Yvette, France) and ID29 (ESRF, Grenoble, France). The structure was determined by the SAD (Single wavelength Anomalous Dispersion) method using the anomalous signal from the selenium element.
Data were processed with XDS (48) in space group P41, with two copies per asymmetric unit. All expected Se sites (eight by monomer) were found with SHELXD using reflections in the 50-3. 5 Å resolution range  (49). Refinement of Se atom positions, phasing and density modification were performed with Phenix-EP. The high quality of the experimental phases allowed automatic building (Autobuild program) of most of the protein model. The model was refined against the 2. 2 Å native data set using PHENIX  (50). The final model contains residues 3-217 from both monomers. In addition, 85 water molecules, 10 sulfate ions, one 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid and six ethylene-glycol could be modelled into the electron density maps. Statistics for data collection and refinement are summarized in Supplementary Table  S3.