dataesr/scientific-paragraphs-categorization huggingface dataset:
LiDAR monitoring protocol Acquisition surveys proposed by the OR2C are widely based on annual topo-bathymetric data carried out using airborne LiDAR technology. One of the first actions was to establish an initial LiDAR acquisition survey (t0) in 2016, using an airborne topo-bathymetric LiDAR (trademark Optech Titan DualWavelength) Titan DW600 acquired by a consortium, jointly founded in 2015 by the Universities of Nantes and Rennes. LiDAR data were collected on all sandy coasts of the Pays de la Loire region by GEOFIT Expert. The acquisition surveys take place every year from March to May and also at the end of summer (i. e. calm weather and reduced turbidity are required). Three LiDAR acquisition surveys have been conducted since 2016, including bathymetric acquisitions in shallow water areas in 2017-2018.
, 2018). Morphometric information is automatically extracted from LiDAR data for large areas (e. g. bases and crests of dunes, coastal dune morphometry, contact and transects of beach/coastal dune, zero-topography, etc. ) providing thus valuable information about beaches, coastal dune evolution, and shoreline mobility (i. e. erosion, accretion, stability). The technical committee members may request special flight plans in accordance with their needs. Several requests have already been processed since 2016, particularly in the context of mole monitoring to furnish coastal local authorities' GIS with high precision data or for the development of a PAPI.
Seismic, sedimentological and bathymetric surveys Because it is essential to consider total sediment flux (sand) from land to sea and from sea to land to understanding resilience possibilities of beaches, the OR2C carried out in 2018 seismic, sedimentological, and bathymetric surveys in the area between the island of Yeu and the mainland (see fig. 1 ). This offshore mission was accomplished along with a LiDAR airborne survey to gather land/sea data for an entire hydrosedimentological cell, and identify the main sand accumulations related to hydrodynamic conditions. The first step was to acquire high resolution seismic profiles and bathymetry datasets that are used to identify sediments (sand) partitioning. The offshore mission was carried out on the R/V Haliotis (Génavir, IFREMER), which is equipped with interferometric sonar (bathymetry and imagery) and a Chirp system (seismic profiles) fixed to the hull.
Bathymetry, imagery, and seismic profiles were simultaneously acquired on board. In order to achieve a centimetric spatial resolution for data positioning, an Aquarius Thales GPS coupled to an RTK beacon fixed in the study zone was used. Raw data were processed with CARAIBES software (IFREMER) to obtain bathymetric maps and sonar mosaics. The seismic profiles were processed with the SUBOP software (IFREMER). On the north of Yeu Island, a detailed bathymetric map was realized to delimitate the exact position, shape, and source of the sandy bank "La Sablaire" in 2018. Sedimentological analyses have not yet been completed, but the dune geometry indicated a main transit direction, along the shore, from the west and a potential secondary order from the east. New data acquisitions are planned for June 2019 with the aim to understand rhythmicity of sediment fluxes by seasons and storm events.
History of coastal hazards The history of risks has been studied with the help of local authorities and the Authority of Environment, Planning and Housing of the Pays de la Loire region (DREAL). Noirmoutier Island local authorities were consulted about building a dense historical storm database  (Athimon and Maanan, 2018). Its construction is based on historical archive data acquired during the PhD thesis of Athimon (2019) following the method described by  Athimon et al. (2016). The historical research was based on ancient documents preserved in libraries and archives (municipal, department and national). Over 19 888 documents were consulted, from which only 936 (around 5%) contained data on past storms and coastal floods since the 14 th century. For the Pays de la Loire region, especially Noirmoutier Island and Olonne, the first well-documented event was the winter storm of the year 1351-1352 with a memorable storm surge.
Second mission: organize and transmit the scientific knowledge The second OR2C mission is to organize and largely transmit the data concerning coastal hazards. All the data acquired by the OR2C progress including the datasets provided by the airborne LiDAR surveys are freely accessible on the OR2C website as DEM (i. e. Digital Elevation Model) or DSM (Digital Surface Model). In fact, these data are stored in a Spatial Data Infrastructure (SDI) created and led by the OSUNA (University of Nantes) with a special dedicated staff. However, data-sharing policy applies with an exchange agreement. Several agreements have been signed with the OR2C members to use the data in their own projects (e. g. coastal studies and research, coastal management and development of the PAPI, etc. ). Other agreements have been signed between the OR2C and structures outside the observatory with a general perspective of exchanging consists of a continuous and regular segmentation of the coast into polygons (i.
e. spatial units) positioned perpendicularly to the coastline direction. The units obtained are particularly adapted to integrate and aggregate various spatial information types such as geometric primitives (point, polyline and polygon), or raster datasets  (Juigner, 2017). Thus, the spatial unit segmentation is suitable to compute and combine multiple indicators extracted from different data sources. The module objective is not to create new dataset, but to exploit existing generic data (e. g. land occupation, topography i. e. LiDAR, orthophotography), and create new spatialized information computed inside each spatial unit. MAp-RISC is developed with an open-source database management system PostgreSQL and its spatial extension PostGIS in order to incorporate the tool to the web architecture.
In this sense, the OR2C acts to include all territories in terms of production of information. The OR2C also connects various operators for data acquisition and management on coastal risks, by promoting mediation through an exchange space aimed at defining a common strategy of data acquisition and common protocols for shoreline monitoring. Moreover, the OR2C promotes a dynamic of observation by encouraging the OR2C members to exchange their theoretical and technical skills as well as their methods. In early 2018, the observatory organized and supervised a 3-day training on shoreline monitoring, during which partners shared their knowledge. These collective actions are in accordance with the National integrated coastline management strategy. A second training session on the use of LiDAR data was proposed in January 2019.
Conclusion Regional coastal observatories like the OR2C are recognized as appropriate networks to fight against coastal risks because of their spatial dimension, data quality, and sustainability. Generally, regional coastal observatories aim to rationalize and mutualize expensively acquired and shared data in regards to coastal risks, in order to respond to public and private requirements for better management of coastal areas. Thus, the OR2C is set-up in response to the recent dramatic disaster (i. e. storm Xynthia) and to improve the national context for strengthening the tools that counteract coastal risks. Moreover, the decentralization to local authorities, of policy making against coastal risks in French coastal regions, can also be found in this set-up. This trend can be seen for the GEMAPI competence transfer from the French State to local authorities in 2018 (i.
e. the GEMAPI competence deals with managing aquatic environments and preventing floods including storm surges). This trend is also noticed in the context of two new French laws: the first decreed in 2014 called MAPTAM, "Modernisation de l'Action Publique Territoriale et d'Affirmation des Métropoles", and the second in 2015 called NOTRE, "Nouvelle Organisation Territoriale de la REpublique". The National network of regional shoreline observatories, led by the French Ministry of Environment, Ecological, and Solidary Transition ("Réseau National des Observatoires du Trait de Côte" or RNOTC), is largely based on regional observatories. Despite the fact the OR2C is recent, it provides in this context a new model of integrated regional collective actions against coastal hazards, strongly anchored at the local level, based on high-precision data used to strengthen the scientific knowledge, develop and strengthen strategic tools, and provide a new tool for coastal hazard management (MAp-RISC).
e. summary sheets, maps, papers, etc. ). These elements can also be downloaded in section 4 "data and resources" which is the website core. This section also includes all the thematic sheets as well as a bibliographic database of more than 600 references. Here, the user can access the spatial toolbox MAp-RISC. The OR2C website also offers to the user a diachronic analysis of the coastline based on postcards and old photographs from various sources dating to the beginning of the last century (e. g. scientific studies, department archives, private collections, etc. ). This application superimposes an old photograph on a recent one taken from the same spot, angle of view, and focal length. Comparison of images highlights the physical coastal dynamics (e. g. coastal erosion or accretion; dynamics of vegetation cover, etc.
). The diachronic application also shows human effects on the coastline (i. e. land use, urbanization development, etc. ). It is also participative by giving the opportunity to the users to share their own photographs. The successive stages of data acquisition process are shown in figure 6. The acquisition surveys are validated by the steering committee after consulting the technical committee. The data are processed and eventually stored in the Spatial Data Infrastructure of the OSUNA, then transmitted by the OR2C website. The technical committee members may also make available their own datasets. Discussions are currently taking place with OR2C members to insert their data in MAp-RISC.
Nursing home resident data Personal information and clinical information was collected by a practitioner from the hygiene team directly from the residents' health care records. Personal information was collected for all those present the first day of the outbreak. The collected information included: month and year of birth, sex, date of arrival at the nursing home and autonomy status. The autonomy status of residents in French nursing homes is assessed using the AGGIR scale (Autonomy Gerontology Groups Iso-Resources), which is the legal instrument for evaluating dependency in the elderly and whose primary purpose is the allocation of means and resources  [11]. With the AGGIR scale, autonomy is classified into 6 Iso-Resource Groups (GIR): GIR 1 (bedridden or armchair-bound persons, mental functions seriously altered and requiring continuous presence), GIR 2 (bedridden or armchair-bound persons, mental functions not totally altered and requiring assistance in most activities of daily living, or mental functions altered with preserved ability to get around), GIR 3 (preserved mental autonomy with partially preserved motor autonomy and assistance several times a day for physical autonomy), GIR 4 (moves around the home and sometimes assistance for washing, dressing, physical activities or eating), GIR 5 (only occasional assistance for washing, meal preparation, and housework), GIR 6 (autonomy for essential tasks of daily living).
Most testing targeted the norovirus (NoV) and influenza virus, but other tests were occasionally performed by the National Reference Centre for Influenza Viruses (rhinovirus, respiratory syncytial virus, human metapneumovirus, parainfluenza 1, 2, 3 and 4, and coronavirus) and the National Reference Centre for Enteric Viruses (rotavirus, astrovirus, and adenovirus). Because testing for the viruses was variable (from one institution/physician to another, not used in some outbreaks, types of virus sought) and considering the poor sensitivity of the rapid influenza tests, these two sources of data were used to define the epidemiological context of confirmed outbreaks. Consequently, individual cases were included consistently in all episodes according to clinical signs and medical evaluation. When virus testing was negative, the clinical signs were recorded and medical evaluation was used as previously to classify the included residents as infected or not infected.
Data analysis Sex, age, length of stay (LOS, in years) and autonomy status were described for all exposed residents. Influenza vaccination and oseltamivir administration rates were calculated for confirmed influenza outbreaks. Dichotomous or categorical variables were expressed as percentages. In univariate analysis, the categories were specific in order to obtain a precise description of the age and LOS variables. Class intervals were 5 years for age and one year for LOS. The residents classified as GIR 4 to 6 (sometimes, occasional and no assistance) were grouped together because they were few. For the multi-level analysis with 2x2 tables, a median value was used to define the two-level age categories. For LOS, assessing the longest stays was necessary to identify the effect of longer exposure in a nursing home.
Discussion In the present study, surveillance data obtained during GE and RTI outbreaks in nursing homes were used to construct stratified analyses and to identify specific infection and all-cause lethality rates according to the residents' individual characteristics. The infection rates observed here were similar to those found in previous studies of NoV and Influenza outbreaks (odds of being infected during a Flu+ outbreak were around 40% less than during a NoV+ outbreak). Reported infection rates were close to 30. 0% in influenza outbreaks and 40. 0% in NoV outbreaks  [18] [19] [20]. Older age appeared to increase the likelihood of GE and influenza infection, with increasing rates among older residents. Age is a well-known factor for influenza and norovirus severity in the elderly and in nursing homes  [21, 22].
Moreover, vaccination and oseltamivir prescriptions were recorded but not included because the influenza genotype was not determined and identification was limited. Secondly, the deaths of uninfected residents were not recorded in this protocol even though such data would have provided valuable information about the global epidemiological context. In conclusion, specific susceptibility patterns were observed among exposed residents. In this cohort of nursing homes, infection rates varied according to virus, sex, length of stay and age, and there were major differences in lethality depending on virus, age and autonomy score. The collected data were easy to record and could be used to improve the characterization of seasonal outbreaks in nursing homes, whose residents are particularly vulnerable.
Acknowledgments All the medical, nursing, management teams in the institutions for their commitment to this project,  National Influenza Center  ( Northern France ) and  Institute of Virology (Strasbourg)  for laboratory investigations,  Suzanne Rankin  ( Dijon University Hospital, France ) for the editorial assistance and  F. Barat ,  I Daviaud ,  HCWs  and the nursing homes for their unfailing commitment.
De novo Analysis: In silico Identification of Loci and Genotyping The quality of the raw demultiplexed and quality-filtered reads was verified by using FASTQC 0. 11. 5 1. Later, custom-made Python TM scripts were used for trimming the adaptors and filtering the reads for the presence of the IIB restriction enzyme (CspCI) recognition site, producing trimmed high-quality reads of uniform 32 bp-length. STACKS 1. 42  (Catchen et al. , 2013)  was used to carry out the assembly of loci and the individual genotyping (denovo_map. pl pipeline). Two technical replicates (one Mediterranean and one Black Sea specimen) were included for the optimization of the de novo assembly parameters in STACKS. Parameters were set as follows: m = 15 (minimum stack depth per allele in a sample), M = 4 (number of mismatches allowed between stacks to build a locus in an individual), n = 4 (maximum distance between loci from distinct individuals to be merged in the population catalog), SNP bound = 0-0.
1 (error rate to call SNP), alpha = 0. 05 (significance level to call a heterozygote or homozygote). The employment of SNPs as RAD-Seq markers in population genomics typically implicates a certain level of missing data in the dataset because not all the genotyped markers are shared by all the samples  (Chattopadhyay et al. , 2014). This implies the need to retain only those loci shared among a reasonable, yet arbitrary, fraction of individuals for downstream analysis. However, if the missing data are not evenly scattered among the samples, a dataset of many individuals collected from distinct areas could lose power in detecting a fine genetic differentiation within areas because of the decrease of the number of shared polymorphic markers usable for the analyses. For this reason, we performed three independent STACKS runs in order to achieve the best power of resolution according to the geographical scale considered: the wide scale (the entire study area) on one side, and the basin scale (Adriatic-Ionian basins or Black Sea) on the other side.
First, an overall catalog of loci for the exploration of the global population structure of the species throughout the entire study area was obtained by running together all 478 samples (with two additional technical replicates) from the 19 population samples. Later, the 207 samples (plus one technical replicate) from the 8 Adriatic-Ionian sites and the 232 samples (plus one technical replicate) from the 9 Black Sea's sites were run separately to obtain basin-specific datasets For each population samples, we report: area, nation, sampling location, acronym, coordinates (with latitude, N, and longitude, E), date of collection and number of samples (N). * Sampling site alternative to Othonoi Island, where the species was not found, following CoCoNET code. for the evaluation of the genetic structuring of the species at a small spatial scale.
For each of the three STACKS runs, we selected a final dataset consisting of all the polymorphic loci shared by at least 80% of the individuals with up to 3 SNPs and 6 alleles. When more than one SNP was found at a locus, only the SNP with the highest expected heterozygosity across each dataset was retained  (Phillips, 2005). The threshold of missing loci per individual was set to 30%. Since the parameter m (minimum read depth for allele) was set to 15, no additional filter for sequencing depth was applied  (Paterno et al. , 2017). At the end, we obtained 3 distinct datasets (i. e. , the overall dataset, the Adriatic-Ionian dataset, and the Black Sea one). The package Populations in STACKS was used to generate input file in GENEPOP format, and later CREATE  (Coombs et al. , 2008)  and PGDSPIDER 2.
Statistical Power of the Basin-Specific Datasets The statistical power to detect true levels of genetic differentiation was evaluated and compared for the Adriatic-Ionian and Black Sea's datasets by the simulation method of  Ryman and Palm (2006)  implemented in POWSIM 4. 1 (SNPs executable Powsim_b). POWSIM simulates sampling from populations at various levels of expected divergence under a classical Wright-Fisher model without migration or mutation. Simulations were run using default parameter values for dememorizations (1000), batches (100), and iterations per batch (1000) for a scenario involving 8 and 9 subpopulations, respectively, using a range of effective population size (N e = 1000-10,000) and of generations of drift (t = 10-100). The statistical power to detect an expected divergence as small as F ST = 0.
Population Genomics The genetic variability within population samples, population structure and genetic differentiation were evaluated separately for the three datasets (overall, Adriatic-Ionian, Black Sea). The observed heterozygosity (H o ), unbiased expected heterozygosity (H e ) and the inbreeding coefficient (F IS ) of each population sample were assessed on the polymorphic loci (GENETIX 4. 05. 2; Belkhir et al. , 2000) ; the allelic richness (A R ) was calculated on the smallest population sample size of each dataset (HP-RARE;  Kalinowski, 2005). Non-hierarchical and hierarchical analysis of molecular variance (AMOVA;  Excoffier et al. , 1992)  as well as the pairwise genetic distances between population samples (F ST ) were calculated (ARLEQUIN 3. 5;  Excoffier and Lischer, 2010) ; due to the presence of missing data, the locus by locus option was set.
Benjamini and Hochberg (1995)  correction for multiple tests was applied for adjusting the significance level in multiple comparisons. The Discriminant Analysis of Principal Components (DAPC; ADEGENET for R,  Jombart, 2008)  was used (1) to find the best number of clusters of genetically related individuals (based on the Bayesian Information Criterion, BIC) without the reference of the original populations and (2) for the visual assessment of the between-population differentiation of the original population samples. Bayesian clustering  (STRUCTURE 2. 3. 4; Pritchard et al. , 2000)  was also performed to infer the most likely genetic clusters. After some trials, a burn-in of 100,000 followed by one million steps, K = 1-8 (depending on the datasets) and 5 iterations per each K value was set; sampling locations as prior (model LOCPRIOR,  Hubisz et al.
Detection of Loci Under Selection (Outliers) For the identification of putative loci under directional selection, two neutrality tests were employed. The first test is the Bayesian method implemented in BAYESCAN 2. 01  (Foll and Gaggiotti, 2008) , and the second one is the F ST -outlier method implemented in LOSITAN  (Antao et al. , 2008). Both software were run separately with the three datasets. LOSITAN parameters were set as follows: 1 million simulations under neutral mean F ST , confidence interval of 0. 95% and a false discovery rate (FDR) of 0. 01; the infinite allele model was applied. For each run, three replicates were performed. BAYESCAN parameters were: burn in = 50,000; thinning interval = 30; sample size = 5000; numbers of pilot runs = 50; length of pilot runs = 5000 and the same false discovery rate (FDR) threshold set in LOSITAN (0.
Sequencing Results, Filtering and Selection of Loci for Genetic Analysis For all the collected samples (N = 478), high-quality RNA-free gDNA was successfully extracted from the gills. The Illumina sequencing of the three 2b-RAD pools (N = 480; 2 technical replicates included) produced 1,032,760,048 de-multiplexed and quality-filtered reads (mean quality score per base > 37) and more than 92% was retained after trimming and filtering for CspCI restriction site (about 1,990,000 reads for each sample). The three independent STACKS runs identified 113,494, 77,459, and 83,000 2b-RAD tags considering, respectively, all the 19 population samples, the 8 Adriatic-Ionian ones alone, or the 9 Black Sea's collections; about the 33% of loci were polymorphic in all 3 runs. After filtering and the exclusion of the two technical replicates and of those individuals showing more than 30% of missing loci, we obtained three datasets as follows: (1) the overall dataset: 461 individuals, 512 polymorphic loci; (2) the Adriatic-Ionian dataset: 201 individuals, 811 polymorphic loci; and (3) the Black Sea's dataset: 228 individuals, 998 polymorphic loci.