dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Our main goal was to define seed transfer zones in Norway for the selected species. To do so, we needed to identify geographic structure and analyse the diversity for each taxon. To identify geographic structure, we used two approaches. First, we visualised the genetic variation using an ordination method, principal coordinate analysis (PCO) as we had binary matrices. The analyses were conducted using the software PAST  (Hammer et al. 2001 ) and Dice's similarity index  (Dice 1945). Second, we used a nonhierarchical clustering method that grouped the individuals to maximise linkage disequilibrium among groups, that is we assumed the same pattern in several markers across group barriers, whereas within groups, the patterns should be random. The groups were identified using the Bayesian program Structure v 2.
1  (Pritchard et al. 2000; Falush et al. 2003). Plots of likelihoods, similarity coefficients and DKs  (Evanno et al. 2005)  were made in the statistical package R (http://www. r-project. org/) using the script Structure-sum  (Ehrich 2006). To analyse the diversity patterns, we used analysis of molecular variance (AMOVA) in the program Arlequin v 3. 11  (Excoffier et al. 1992 (Excoffier et al. , 2005 ) that calculated the variation within and among prior defined populations. We also ran Mantel tests  (Mantel 1967)  for correlations between genetic and geographic distance matrices in Arlequin. To visualise patterns among geographical localities, we conducted a meta analysis where mean PCO scores for each population and each species (i. e. mean population values for the first two eigenvectors) were used as input in a principal component analysis (PCA) in PAST.
Acknowledgements The authors would like to acknowledge the contributions of several colleagues in the process of this study:  Øyvind Hammer  for statistical advice,  Reidar Elven  for taxonomical advice, all collectors of material, and  Thomas Marcussen ,  Marian Schubert ,  Ursula Brandes ,  Patricia Fuentes-Cross ,  Torbjørn Kornstad  and  Ben Trevaskis  for commenting on the manuscript. The study was funded by the  Norwegian Research Council  project no. 208024.
Acknowledgements This work was supported in part by the  "Investissements d'avenir, Labex ENIG-MASS" , the  ANR  project  DMASTROLHC  grant no. ANR-12-BS05-0006 , the  Theory-LHC-France initiative of the CNRS  ( INP/IN2P3 ) and the  Research Executive Agency (REA)  of the  European Union  under the Grant Agreement PITN- GA2012-316704  (HiggsTools). AG is supported by the " New Frontiers" program  of the  Austrian Academy of Sciences.
Morphobathymetry. The high-resolution bathymetry and seismic data of the slope failure sites along western slope of GBB were collected during two cruises. During the CARAMBAR research cruise on RV Le Suroît in 2010, a Kongsberg EM302 was used to collect the multibeam echosounder data and a grid of 2D high-resolution multichannel seismic reflection data were shot using a mini-GI 24/24 air gun, and a 96-traces/700-m-long streamer. The CARAMBAR cruise was part of the "Actions Marges" program by the French "Institut National des Sciences de l'Univers (INSU)", and the University of Bordeaux. Additional high-resolution subbottom profiles (Atlas Parasound ™ ) have been collected with a hull-mounted system on RV Maria S. Merian in 2012 (MARUM, Bremen); these were processed and displayed in Reflex ™ v.
6. The data for the margin collapse site in southern GBB was provided by the Bahamas Petroleum Company (BPC) and included bathymetry data collected with a Reson SeaBat 8160 59 kHz Multibeam Echosounder (MBES) system and high-resolution single-channel seismic survey acquired using a GeoPulse 5430A sub-bottom profiler system that had a dominant frequency of 3. 5 kHz. To analyze bathymetry, the processed multibeam data in ASCII xyz-format was used to produce a 30 × 30 m grid using standard awk (pattern-scanning processing language) and Generic Mapping Tools-GMT v. 4. 5. 9. The resultant digital elevation model (DEM) was visualized with Global Mapper ™ v. 12 and QPS Fledermaus ™ v. 7 software. From the DEM, we extracted the landslide parameters such as landslide height (b), length (T), width (w), and slope angle, which were used as input parameters for the landslides in the numerical model.
Landslide width is the extent of the scar parallel to the strike of the slope, whereas length is the extent orthogonal to slope strike. Height of the failure scar was measured along the different failure scars from the maximum slope break to bottom of the failure surface; ten profiles across the failure scar were measured and an average value was calculated. Water depth for the failure scar was measured from the top of the scar, but landslide location is implemented in the bathymetry with coordinates and does not have to be specified for the modeling. Sub-bottom profiles provided information about the height of the vertical incision along the incipient failure scar, while the seismic data provided the thickness of the landslide deposit  13. An overview of the parameters is given in Table  1.
Numerical models and setup. The submarine slope failure and resulting initial tsunami wave were modeled using the fully dispersive non-hydrostatic wave model NHWAVE 1. 1  [18] [19] [20]. NHWAVE solves either Euler or RANS equations in a surface-and terrain-following sigma coordinate system. We converted the 30 × 30 m resolution grid into UTM and re-gridded the data in MATLAB for implementation in NHWAVE in a 500 × 500 m grid. The initial tsunami wave was then simulated depending on identified source area, landslide scenario, and terminal landslide velocity. The failure body was idealized as smoothed streamlined Gaussian-shaped body. Terminal landslide volume and terminal velocity was calculated according to Enet and Grilli, (2007) using parameters in Table  1   24. For the landslide volume (V b ) we used the approximation of the Gaussian streamlined body: = ε ε - - ( ) V bwT b f 1 2 where b, w, and T are height, width, and length, respectively, and f , C, and ε are geometrical factors that are calculated according to = f C 2 a tan ε ε - + 1 1 , where ε ε = =.
The model domain includes 864 × 425 grid points in a horizontal Cartesian grid. Three sigma-layers were used in NHWAVE, while FUNWAVE-TVD describes a depth-integrated flow field. FUNWAVE-TVD and NHWAVE are written in Fortran with C-preprocessors, and use OpenMPI as the basis for parallelization. Both codes have been validated for landslide tsunami generation and propagation  [23] [24] [25] [26] [27] [28]. The code FUNWAVE-TVD is available from https://urldefense. proofpoint. com/v2/url?u=https-3A__github. com_feng-yanshi_FUNWAVE-2DTVD_&d=DQICaQ&c=y2w-uYmhgFWijp_IQN0DhA&r=BkBYOpHzQ0vATPH-8yaXkV FyY_4IXuJRBQOZ8IHZzYo&m=QOe25Qe8eevvEQDk8zGdfHORsAjOWLNqtSkbIpLoHzo&s=LgFKNvSEW8a2 z8kBlr1CycLca5c48768mdxdqhZhBgs&e=NHWAVE v1. 1. , used here, is available by request from kirby@udel. edu. The output files were prepared and visualized with MATLAB.
Acknowledgements Funding was provided by the industrial associates of the  CSL-Center for Carbonate Research at the University of Miami. We thank the  UMR 5805 EPOC, Université de Bordeaux , for providing the bathymetric data for this research. J. K. , F. S. and B. T. acknowledge support by the  NSF   Engineering for Natural Hazards program , grant  CMMI-1537568 , and NOAA NTHMP grants  NA14NWS4670041  and  NA15NWS4670029. D. H. and P. W. acknowledge support by the  Deutsche Forschungsgemeinschaft (DFG)  through grant  HE 3412/17-1  and through providing ship time on  R/V Maria S. Merian. The paper benefited from discussion with  Michael Strasser  ( ETH Zurich ). We want to thank  Gangfeng Ma  ( Old Dominion University ) for constructive reviews that improved our manuscript.
In Europe, EBB (www. eurobiobank. org) was the first biobanks network operating to collect DNA, cells and tissues for research in RDs. EBB network aims to identify and locate repositories, harmonize and disseminate quality banking practices, distribute quality materials and data and spread knowledge through training courses, conferences, articles and website [5]. In 2007, EBB became part of TREAT-NMD (partner of Fondazione Telethon) and in 2012 joined Fondazione Telethon. Actually, EBB network are composed by 25 members of which 21 are biobanks and 4 are not biobanks; some of which are non-European.
What is biobank? The term "biobank" refers to a biological specimens' collection like DNA, RNA, tissues and cells. The samples are linked, in an informatics network, to several information about donor like biobanks but are placed in Canada and Israel  [5]  (Table  1 ). As part of TREAT-NMD, EBB collaborated with BBMRI-ERIC. In this network, all biobanks in membership had the custodian of samples and EBB are a virtual biobank in which easily find desired sample for research  [5]. All biobanks in the world are encouraged to join to EBB Network. For join to network is sufficient to adhere to minimum entry criteria  [5] : • Presence of collections of RD biological samples and their availability to the scientific community; • A quality-control system for the management of the biobank; • Standard operating procedures (SOPs) regulating sample and data acquisition and sample processing, storage, and distribution.
The candidate biobanks should also adhere to Ethical, Legal and Social Implication (ELSI) principles and comply with the recommendations issued by the Oviedo Convention and the OECD Task Force on Biological Resource Centers and with the national and European laws and regulations  [5]. In order to create an EBB virtual catalogue, all partners agree the following data set  [5] : • Type of sample; • Classification of the disease based on ICD-10 identifier and name; • MIM number and name; • Number of families; • Number of patients; • Anatomic origin; • Biobank contact; • ORPHA code. In the EBB catalogue (http://www. eurobiobank. org/en/services/ CatalogueHome. html) you can search for biological samples by type of biological material and disease  [5]. In order to help pharmaceutic industries to discover disease biomarkers, new therapeutic approaches or new drugs, EBB Network has been involved in several pharmaceutic projects  [5].
EBB brought together several funding organizations, governments, academies, industries and patients' organizations that share common goals and principles into the International Rare Diseases Research Consortium (IRDiRC, http:// www. irdirc. org). IRDiRC aims to have a diagnostic test for most rare diseases and 200 new therapies by 2020  [6]. In 2012, European Union's Seventh Framework Programme under IRDiRC founded RD-Connect. RD-Connect is a global infrastructure that joins genomic data with registries, biobanks, and clinical bioinformatics tools  [7]. It aims to develop: • Clinical bioinformatics tools for analysis and integration of molecular and clinical data to discover new disease genes, pathways, and therapeutic targets; • Common infrastructures and data elements for rare disease patient registries; • Common standards and catalogue for rare disease biobanks; • Best ethical practices and a proposal for a regulatory framework for linking medical and personal data related to rare disease  [7].
BBMRI provides: • Biobanks of different formats (based on collections of DNA, tissue, cells, blood and other body fluids, together with pertinent medical, environmental, life-style and follow-up data); • Population cohorts; • Clinical case/control cohorts including disease-focused cohorts; • Biomolecular resources (comprising antibody and affinity binder collections, ORF (Open Reading Frame) clone collections, siRNA (small interference RNA) libraries, proteins, cellular resources, etc. ); • Enabling technologies, high-throughput analysis platforms and molecular tools to probe gene, protein and metabolite activities; • Harmonized standards for sample management; • Harmonized databases and biocomputing infrastructure; • Ethical, legal and societal guidance and platform  [4].
BBMRI gives European scientists and industry some advantages: • An unified catalogue information on biological samples and collected data; • Improved reliability and reduced ambiguity in comparing and interpreting results; • Aa setting to establish an open-source based federated database structure that can guarantee the same standard of data quality in annotation, while protecting donors' privacy; • Access to a Europe-wide data and sample set, thus providing data with better statistical power or permitting the investigation of rare or highly diverse diseases; • Capacity to develop prospective collections meeting the needs of particular research projects or clinical trials; • Compliance with ethical and legal requirements. • Sound governance system building on input by all stakeholders  [4].
Biobank's needs to ensure high quality A biobank needs adequate resources in terms of personnel, space, laboratories, instrumentation, computers and quality system. To ensure the specimens high quality, biobank needs qualified and trained personnel to adhere to SOPs and periodic update training  [13]. To ensure continuous biobanking service, two or three laboratory technicians are required at minimum  [14]. In biorepository, a director who is familiar with both clinical and research aspect of translational research should be appointed. The director prior responsibility is to ensure communication and collaboration between laboratory researchers and clinicians. An experienced research manager should be appoint to direct daily activities. He should be familiar with human-tissue handling, biosafety, shipping regulations, and site-specific health and safety issues; he also might collect, process, and ship specimens, maintain the database, order supplies, update the budget and revise the SOPs.
Long-term storage technics The facilities, personnel and expertise to the maintenance of biobanking have high costs. Process and scientific imperatives would be gained with centralized systems of recruitment, date collection, sample processing and follow-up. The centralized approach ensures increased efficiency promoting standardization. Opposite, distributed approach needs to establish and maintain dozens or hundreds centers. In order to guarantee the same high-quality level, each center must become expert in all study aspects  [16]. For example, UK DNA Banking Network (UDBN) built a network in which it aggregates and manages samples and data originated by others. The network comprises, on one hand investigator groups led by clinicians each with a distinct disease specialism and on the other hand, a research infrastructure to manage samples and data.
The importance of sharing is marked by RDs biobank network. In RDs field, there are few patients and a very little number of samples for each RD. Sharing materials and data is essential for identifying disease-causing genes, studying pathological mechanisms and developing treatment. The networks are built on pre-existing biobanks; in this context, to harmonize the collections and data annotations is essential. In order to ensure harmonization, for join new biobanks in the network, the biobanks have to adhere to minimum inclusion criteria. Building European-wide networks ensure: • Unified catalogue of samples and related date; • Reduced ambiguity of result interpretation; • An open-source database; • European-wide data to guarantee better statistical power; • Prospective collections; • Compliance with ethical and legal requirements.
Data availability The LOAC data from UAV are shared properties of the Fly N Sense Company and the LOAC team and are available on request, as well as the LOAC data obtained from the Vienna/Paris tethered balloons. The LOAC ChArMEx data are available on the ChArMEx database at http://mistrals. sedoo. fr/ChArMEx/. The user must register before having access to the data. The VOLTAIRE-LOAC balloon flight data are available at the ETHER database: http://www. pole-ether. fr/.
Acknowledgements. The  LOAC  project was funded by the  French National Research Agency's ANR ECOTECH. The instrument and the gondola are built by Environnement-SA and MeteoModem companies. The balloons flights of the MISTRALS/ChArMEx campaign were funded and performed by the  French Space Agency CNES. The various copies of LOAC used in the campaigns were funded with the support of  CNES, INSU-CNRS  and the  French VOLTAIRE-LOAC Labex  ( Laboratoire d'Excellence   ANR-10-LABX-100-01 ). The authors want to thank the  EGU Atmospheric Sciences Division, especially Division President Oksana Tarasova , for their involvement in the LOAC measurements during 2013  EGU General Assembly , and the  CNES  launching team for the support and dedication that ensured the success of the LOAC flights. OMP/SEDOO in Toulouse and ICARE thematic centre in Lille are gratefully acknowledged for their support to ChArMEx browse products and database.
Building RAD loci Raw reads were analyzed in Stacks 1. 19  (Catchen et al. 2011). Quality control, filtering for ambiguous barcodes and restriction sites, and demultiplexing took place using the script process_radtags (options -c -q -r). Due to the use of barcodes with different length, and limitations in this Stacks version to accommodate this design in a single step, process_radtags took place in four independent steps. First, the samples with seven-base-long barcodes in both ends were demultiplexed, second those with five at one end and seven bases at the other, third those with seven at one end and five at the other, and finally those with five-base barcodes at both ends. After each demultiplexing step, the unassigned reads (including the reads with the barcode combinations other than that in the ongoing step) were reconstructed in pairs using the script fastqCombinePairedEnd.
pl written by Eric Normandeau (https://github. com/enormandeau/ Scripts/blob/master/fastqCombinePairedEnd. py), and used for the subsequent round of demultiplexing. The files containing the paired forward and reverse reads of each sample were then concatenated and reads were trimmed to a length of 100 bases. Reads less than 100 bases long were discarded. Trimming and filtering took place with FASTX_toolkit (http:// hannonlab. cshl. edu/fastx_toolkit/). Stacks were built for each individual with the wrapper script denovo_map. pl included in Stacks using the default parameters (a minimum of three reads to form a stack, and a minimum of two mismatches allowed between loci when processing a single individual). Secondary reads were not used for genotype calling to reduce possible genotypic errors (option -H).
Linkage map construction For linkage mapping, we exported the haplotypes of each individual for each RAD locus using the Stacks script genotypes in the OneMap format (-r 80 -t CP -o onemap -c -s). Only parental loci mapped in more than 80 out of 97 progeny were kept. Finally, loci showing significant segregation distortion (chi-square p value , 0. 01) were excluded. The resultant genotypic data were input in OneMap  (Margarido et al. 2007 )-a software for genetic mapping in outbred populations. One-Map applies the methodology proposed by  Wu et al. (2002) , and leads to the construction of a linkage map combining information from markers showing different segregation patterns in both parents. Recombination fraction between all pairs of markers was estimated with two-point tests (function rf.
Following marker ordering, alternative orders were checked with the function ripple. seq. Map distances were calculated using the Kosambi map function (see File S1 for the R script including the commands implemented in OneMap). LGs were numbered based on the homology with European seabass, the species with the highest similarity as shown by the comparative genomic analyses presented in the Results section. Finally, the map was visualized with MapChart  (Voorrips 2002).
Comparative genomics The mapped RAD loci in common pandora were used in a comparative analysis with genomes from the following relatively closely related teleosts: medaka (Oryzias latipes, Ensembl 73), stickleback (Gasterosteus aculeatus, Ensembl 73), Nile tilapia (Oreochromis niloticus, ncbi GCA_000188235. 2) and European seabass (Dicentrarchus labrax, dicLab v1. 0c http://seabass. mpipz. de). Common pandora RAD loci sequences were extracted from the MySQL database built through Stacks pipeline, and were used in BLASTN sequence similarity searches against the genomes of the four other species (e-value threshold 10 29 ). Loci with more than 10 hits or more than 10 HSPs (highscoring segment pairs) within the first hit, were excluded to eliminate repetitive sequences. The top hit per sequence was retained and considered homologous to the RAD locus.
The chromosome of the reference species that corresponded to each common pandora LG was inferred based on the similarity search. If most loci of a common pandora LG were homologous to loci from a single chromosome in a reference species, we considered them homologous chromosomes. All links between common pandora LGs and the reference species chromosomes were displayed with Circos software  (Krzywinski et al. 2009). To investigate which loci are located within protein coding sequences, we conducted a second round of similarity searches against each of the four species cDNA sequence datasets, downloaded from http://seabass. mpipz. de for European seabass with corresponding annotations, and Ensembl 73 for the other three, and ran with the same parameters as above. The numbers of shared hits found in the four species were represented by Venn diagrams constructed using the online tool available at http://bioinformatics.
ddRAD-based phylogenetic analysis From the comparative genomic analysis we selected the loci showing hits in all four species after excluding those whose hits overlapped even partially in any of the four genomes. The homologous loci sequences from each species were extracted with a custom perl script. Then, all sequences were aligned for each locus independently using mafft v7. 050b (-auto option)  (Katoh and Standley 2013). The individual alignments were concatenated to a matrix with the perl script catfasta2phyml. pl (available at https://www. abc. se/~nylander/catfasta2phyml/). The matrix was filtered to eliminate divergent and ambiguously aligned regions with GBlocks v 0. 91b  (Castresana 2000)  (default parameters apart from setting the maximum number of contiguous nonconserved positions to five).
The resultant filtered alignment was used for phylogenetic analysis. First, the model of nucleotide substitution was chosen with jModelTest 2. 1. 7 (Guindon and Gascuel 2003; Darriba et al. 2012). Then, the best model returned through Akaike information criterion (AIC) and Bayesian information criterion (BIC) criteria was used to estimate the tree topology in PhyML 3. 1  (Guindon and Gascuel 2003). Branch support was based on 100 bootstrap (BS) datasets.
ACKNOWLEDGMENTS We thank  Dr. Jon Kristoffersen  for assistance with sequencing and fruitful discussions. We are thankful to four anonymous reviewers for providing valuable comments and input that greatly improved the article. Financial support was provided by the  Ministry of Education and Religious Affairs , under the call "ARISTEIA I" of the National Strategic Reference Framework 2007-2013 (SPARCOMP, # 36 ), cofunded by the  European Union  and the  Hellenic Republic  through the  European Social Fund. J. B. T. was supported by the  Marine Alliance for Science and Technology for Scotland (MASTS) pooling initiative  in the completion of this study. MASTS is funded by the  Scottish Funding Council  (grant reference  HR09011 ) and contributing institutions. The authors declare that they have no competing interests.