dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, accessing the historical literature and assessing the validity of species with a controversial taxonomic history are challenging tasks, even for experts  [26]. Some rapid and reliable species identification tools such as DNA barcoding have been developed to facilitate species identification  [26] [27] [28] [29] [30]. Given the estimated $US200 billion annual value of fisheries worldwide, the Fish Barcode of Life campaign (FISH-BOL) initiative, as a part of the International Barcode of Life Project (iBOL; http://www. ibolproject. org), is addressing socially relevant questions concerning market substitution and quota management of commercial fisheries (http://www. fishbol. org), with a special focus on developing countries  [31]. However, species identification tools require complete and reliable databases.
Indeed, DNA databases play a key role for the species identification of groupers, and more generally, for seafood, as non-specialists use essentially those databases to identify species for which they often have access to tissue samples only. The Epinephelidae comprise about 163 species  [32]  among which 106 are recorded in BOLD (942 public sequences in February 2014). The incompleteness of the reference datasets is a wellidentified problem for species identification  [33] , which can be slightly alleviated if the marker used for identification is also relevant for phylogeny. In such a case, and if the taxonomic framework is accurate, species not represented in the database might still be assigned to clades or higher rank groups, like genera. Completing the largest molecular identification dataset (the cytochrome oxidase 1 of the Barcode of Life project), combined with an accurate study of the relationships of groupers, will help the management of grouper diversity through easier and more accurate identifications.
Taxonomic sampling Sequencing fresh material collection. Fishes collected from different localities (Table  1 ) in 2009-2011 were dead at the time we acquired them for study, having been commercially caught, and available for purchase at the Noumea fish market. Each individual was morphologically identified according to Heemstra & Randall  [18] , measured, weighed, and photographed, and a tissue sample was collected and preserved in absolute ethanol until DNA extraction. Several specimens per species were sequenced to evaluate intraspecific variation and to corroborate identification (data not shown). Additional fish tissues were obtained from colleagues (see acknowledgements) or bought at fish markets, and also preserved in absolute ethanol. For these tissues, no photograph was available; the identification of these tissues was checked by a BLAST search in BOLD  [38]  followed by a thorough evaluation of the results.
Samples and results not corresponding to a higher sequence and identification quality standard were discarded. Publicly available sequences. All available COI, 16S and TMO-4C4 sequences of Epinephelidae and some outgroup sequences were downloaded from public sequence databases (Barcode of life Database, GenBank Nucleotide). All sequences were controlled for contamination, indels, and stop-codons indicating possible pseudogenes  [39]. We followed the classification of Craig and Hastings  [37]. When necessary, we amended the species name to agree with the genus gender (Cephalopholis rogaa, Mycteroperca canina and M. marginata) as per the recommendations of the International Code of Zoological Nomenclature (Fourth Edition). Problems were identified within both GenBank and BOLD sequences, and some sequences available in those databases were therefore not integrated in our dataset.
Database sequences presented either: (i) taxonomic problems, such as high genetic divergence within species or erroneous species identification and (ii) nomenclatural problems like the use of invalid species names. The specimen identification for some sequences was problematic. While there were often several specimens attributed to a species in the databases, high intraspecific variability for COI between specimens was observed for some of them. For instance, the species E. macrospilos and E. tauvina have a COI genetic divergence within species of 7. 4 and 6. 2% respectively, values very largely above what is known of fish intraspecific diversity for this marker  [40]. These species, and some others, were represented by multiple, disjoint clusters. For example, for Epinephelus tauvina one cluster of database sequences included our seven E.
1%), we selected the specimen collected closest to the type-locality to minimize taxonomic misidentifications and errors linked to possible cryptic species. Second, the databases use some invalid names. While spelling errors are relatively straightforward to identify, like the use of Cephalopholis miniatus instead of C. miniata or Pseudogramma polyacanthum instead of P. polyacantha, nomenclaturally invalid names were also present in the databases. For instance, E. 'fario' (Thunberg, 1793), represented by eight specimens in GenBank including two COI sequences, has been pointed out by Heemstra and Randall  [18]  as a synonym of E. longispinis (Kner, 1864) and as a nomen dubium. Randall and Heemstra  [41]  also regarded this species as unidentifiable. We did not include these sequences in our dataset.
The final extension was conducted at 72uC for 109. Purification and cycle-sequencing reactions were performed at the Genoscope (E ´vry, France), using the BigDye Terminator version 3 sequencing kit, the GeneAmp PCR System 9700 and a capillary ABI3730 DNA Analyser, all from Applied Biosystems. Sequences were edited and assembled using Sequencher 4. 9 (Gene Codes Corporation, Ann Arbor, MI, USA). Sequences for this study were deposited in GenBank (Table  1 ).
Phylogenetic analyses of the Epinephelidae All markers were first analysed separately, tested for incongruence and, since none was detected, concatenated in two different datasets. The combined and separate analyses have three different aims (i) obtaining the most robust phylogenetic reconstruction while including a maximal species representation, i. e. including downloaded sequences (dataset 1), (ii) maximising the number of markers, even if it includes less species (dataset 2), and (iii) best representing the diversity of the Epinephelidae with the COI gene alone (dataset 3). Dataset 1 included the concatenated COI, 16S and TMO-4C4 sequences. Dataset 2 included all five concatenated markers, i. e. COI, 16S, TMO-4C4, Rhodopsin and Pkd1 sequences. The best-fitting models of nucleotide evolution for each gene and for the concatenation were determined based on the Akaike Information Criterion (AIC) implemented in ModelTest 2.
3  [43]  in conjunction with PAUP 4. 0b10  [44]. The GTR + I + C model was selected for each marker and for the two datasets. Trees were inferred using two probabilistic approaches: maximum likelihood with a non-parametric bootstrap (BP) using RAxML 7. 2. 8  [45, 46]  and Bayesian Inference  [47]. Maximum likelihood (ML) analyses were carried out online on the CIPRES Science Gateway (The CIPRES Portals. URL: http://www. phylo. org/sub_sections/portal) with RAxML-HPC BlackBox (7. 2. 7)  [45]. Datasets were partitioned by codon position for each marker (except 16S) and by marker and by codon position for datasets 2 and 3. BI analyses were performed with MRBAYES version 3. 2. 1  [47]  using 75,000,000, 75,000,000, and 30,000,000 generations for datasets 1, 2 and 3 respectively, with sampling every 1,000 generations and four Metropolis-coupled Markov chains Monte Carlo (MCMCMC).
Mapping of potential ciguatera poisonous species Potentially ciguatera affected species were mapped onto the phylogenetic tree inferred from all the available reliable sequences for the COI fragment. Although several relationships were not resolved compared to the combined analyses, the COI topology is congruent with the combined analyses topologies, and moreover best represents the diversity of the Epinephelidae. A large number of studies on ciguatera were reviewed to establish a list of ciguatera affected species  [7, 11, 12, 19, 20, [50] [51] [52] [53] [54] [55] [56] [57] [58] [59]. However, there were very few publications where species names were precisely indicated. We ended up by using five  [11, 20, 53, 54, 58] , as well as the available ciguatera data in Fishbase database  [17]. To detect a potential pattern in the evolution of the occurrence of ciguatera in Epinephelinae, we quantified the strength of phylogeny-trait association.
The MrBayes phylogeny from the dataset 3 was used as an input in the BaTS software  [60] , with the occurrence of the ciguatera coded as absent/present. 10. 000 trees from the posterior set of trees from MrBayes analysis were randomly selected after removing the first 7. 5 million generations as a burnin according to Tracer  [48] , and re-rooted using the outgroups and a custommade R script  [61]  (script available on request). BaTS estimates several statistics: the parsimony score (PS), the association index (AI) and the monophyletic clade (MC) and tests their significance against a null distribution (obtained by reshuffling 100 times the ciguatera states on the tips). A strong phylogeny-trait association is identified by low PS and AI scores and a high MC score.
Evolution of the ciguatera fish poisoning (CFP) Ancestral character state reconstructions for ciguatera fish poisoning were conducted using the maximum-likelihood method implemented in Mesquite 2. 75  [62]. Recognized species were assigned different states (following a bibliographic survey, see above): absence (0) and presence (1) of CFP. Ancestral states were reconstructed for all Bayesian trees retained from the analysis of the combined data set and their mean likelihood was then plotted on the maximum clade credibility tree.
Phylogenetic relationships within Epinephelidae Dataset length and number of variable sites are reported in Table  3. 47 and 58 sequences were obtained for the mitochondrial markers COI and 16S, respectively. 54 sequences were obtained for TMO-4C4, 50 for Rhodopsin and 50 for Pkd1. The concatenated phylogeny (Fig. 1A ) based on the three markers with the largest sampling (COI, 16S and TMO-4C4) recovered the monophyly of Epinephelidae comprising the four subfamilies, Diploprioninae, Epinephelinae, Grammistinae, and Liopropominae with a weak support. The Grammistinae was monophyletic. The three species of the genus Liopropoma of the Lioprominae were grouped. The Diploprioninae and the Epinephelinae were together not monophyletic because of the inclusion of Belonoperca chabanaudi within Epinephelinae.
The concatenated phylogeny (Fig. 1B ) based on all five markers (COI, 16S, TMO-4C4, Rhodopsin and Pkd1) confirmed the monophyly of Epinephelinae as well as the three subfamilies Epinephelinae, Grammistinae and Liopropominae. No specimen from the subfamily Diploprioninae was available for the Rhodopsin and Pkd1 markers; the monophyly of this subfamily cannot be evaluated with this second dataset. Within Epinephelinae and as suggested by dataset 1, the monophyly of Mycteroperca, Plectropomus and Variola were well supported. Cephalopholis also constituted a robust monophylum with two clades, one containing C. argus and C. boenak and the other the rest of the species (as observed with the previous tree). Epinephelus was monophyletic with a weak support. D. dermatolepis was sister-group to a clade including Epinephelus and Mycteroperca.
The recent changes in classification based on the reanalyses of the relationships within Epinephelidae (  [37] , this study) have an impact on the interpretation of the ciguatera distribution in the genera. Multiple species from the genus Epinephelus that were not reported to be ciguateric belong in fact to other genera that have very few ciguateric species (Hyporthodus and Mycteroperca). As for the genera Plectropomus and Variola, the species included in our datasets are all considered potentially ciguateric (4 and 2 species respectively). In the three other species contained in the genus Plectropomus, only one, P. oligacanthus, is ciguateric according to FishBase. In our study, even if the COI gene gives little support for deeper nodes in phylogenetic analyses, it performs well in Epinephelidae for interspecific relationships, as already observed for other Teleosts and taxonomic groups  [66, 67].
Better phylogenetic performance means that even if an identical sequence is not available for identification in the database, the position of the unknown sequence in a phylogenetic analysis can provide information about the genus it belongs to, and which species it is most closely related to. Consequently, samples from species that cannot be readily identified, but fall within these groups in an analysis might best be considered potentially ciguateric when tested for ciguatera case suspicions. This, added to the large dataset already available, and the more stringent guidelines of the Barcode of Life project compared to other sequence databases, makes it a very good choice for identification in the group. Many questions remain to be answered pertaining to the production and accumulation of ciguatoxins and the subsequent occurrence of ciguatera fish poisoning.
The key role of the public databases For DNA barcoding to be a useful tool for identifying species, it needs complete (or at least large) and reliable reference sequences available in public databases. For an example of both the usefulness of barcoding methods and the limits of the available databases, a sample of fish tissue collected at the fish market in Noumea (New Caledonia) showed 100% identity in the databases with those of two distinct species. Both are deep-sea species, as was the grouper from the market according to the fishmongers. H. ergastularius is not recorded from New Caledonia, but H. octofasciatus was recorded, sometimes under other names  [71]. Unfortunately, identical sequences for H. ergastularius and H. octofasciatus in databases preclude an identification of this sample.
Currently, available grouper sequences contain several cases of misidentification both in GenBank and in BOLD. The numerous grey-brown species with dots (E. tauvina, E. akaara, E. diacanthus, E. amblycephalus, E. longispinis, E. sexfasciatus and E. macrospilos) are particularly affected, with the additional problem that young specimens of some species resemble adults of smaller species. This is not an isolated case. According to Vilgalys  [72]  up to 20% of the named sequences in public databases may be misidentified. Hassanin et al. [73]  suggested to annotate database sequences through an additional ''external expertise'' field, and there is indeed a possibility to add comments to data in BOLD. However, a lot remains to be done before all available identifications can be trusted, even with the additional geographical and voucher information required by BOLD.
Acknowledgments Anne-Claire Lautredou (MNHN, Paris),  Paolo Galli  ( University of Milano, Italy: material from Red Sea ),  Philippe Be  ´arez (MNHN, Paris: Ecuador, Philippines),  Serges Planes  ( Moorea Biocode Project, French Polynesia ),  Monica Mwale  ( SAIAB, South Africa ),  Dominique Ponton  ( IRD, Noumea, New Caledonia ),  Jean-Dominique Durand  ( IRD, Dakar, Senegal ),  Rishen Liang  ( Guangzhou University, China), Lassad Neifar (Sfax University, Tunisia ) provided tissues samples and/or sequences. The Australian Node of the  CReefs Global Research Initiative ,  Thomas Cribb ,  Terrence Miller , and  Holly Heiniger  helped in obtaining tissue samples during a mission in Heron Island. Martine Desoutter  ( MNHN, Paris ) and  Philippe Bouchet  ( MNHN, Paris ) discussed nomenclatural questions and  Gae ¨l Denys, Frederic Busson and Gae ¨l Lancelot  contributed to the analyses, the data collection and text corrections respectively.
This study was funded by  MNHN ATM Barcode  (2010Barcode ( , 2011)),  MNHN ATM BiodiversiteActuelle et Fossile  (2010) and MNHN BQR (2011), awarded to CS and JLJ. This work was supported by the  "Consortium National de Recherche en Genomique", and the "Service de Systematique Mole  ´culaire" of the  Museum National d'Histoire Naturelle  ( CNRS UMS 2700 ). It is part of the agreement nu2005/67 between the Genoscope and the  Museum National d'Histoire Naturelle  on the project " Macrophylogeny of life " directed by  Guillaume Lecointre. This work is part of the project @  SPEED-ID ''Accurate SPEciEs Delimitation and IDentification of eukaryotic biodiversity using DNA markers '' proposed by  F-BoL , the  French Barcode of life initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Accurate foetal electrocardiogram (FECG) morphology extraction from noninvasive sensors remains an open problem. This is partly due to the paucity of available public databases. Even when gold standard information (i. e derived from the scalp electrode) is present, the collection of FECG can be problematic, particularly during stressful or clinically important events. In order to address this problem we have introduced an FECG simulator based on earlier work on foetal and adult ECG modelling. The open source foetal ECG synthetic simulator, fecgsyn, is able to generate maternal-foetal ECG mixtures with realistic amplitudes, morphology, beat-to-beat variability, heart rate changes and noise. Positional (rotation and translation-related) movements in the foetal and maternal heart due to respiration, foetal activity and uterine contractions were also added to the simulator.
Introduction Despite significant advances in the clinical analysis of adult electrocardiogram (ECG), signal processing techniques and the computational power of digital processors, the analysis of noninvasive foetal ECGs (NI-FECG) is still a challenge. The PhysioNet/Computing in Cardiology Challenge 2013 ('the Challenge')  (Silva et al 2013 , Clifford et al 2014)  partially addressed these limitations by making a set of NI-FECG data publicly available to the scientific community for evaluation of signal processing techniques. This database consists of 447, one-minute abdominal signals, making it the largest publicly available dataset for NI-FECG signal processing to date. Despite the advancements in the field of NI-FECG signal processing that the Challenge facilitated, there were a number of limitations concerning the available data.
One of the main limitations (aside from the limited number of channels of maternal and fetal data available) was the absence of pathological cases (pregnancy with adverse outcome) or rare cases such as similar foetal (FHR) and maternal heart rates (MHR) at times of abrupt heart rate changes. This motivated the development of an artificial NI-FECG simulator and inclusion of a limited number of artificially generated signals into the challenge database (20 in total) in order to model some of these events. An artificial FECG-maternal ECG (MECG) mixture simulator can be used for creating realistic abdominal recordings that model specific physiological phenomena. The simulator presented in this work, fecgsyn, is an extension of the ECG simulator introduced by  McSharry et al (2003) , later adapted by  Sameni et al (2007)  to reflect a single dipole per cardiac source.
The velocity of the circular trajectory of the heart dipole t d( ) is ω = 2 π f, with f being the beat-to-beat heart rate (in Hz). The model parameters for the FECG-MECG mixture were derived from a set of vectorcardiograms (VCGs) 4  from the PTB Diagnostic ECG Database  (Bousseljot et al 1995)  records (i. e. α i j , b i j , ξ i j parameters were derived for all three channels of the VCGs). For each cardiac signal, heart rate variability (HRV) is introduced by making ω time variant. This allows the production of maternal and foetal signals in both physiological and pathological scenarios; important for testing robustness of NI-FECG extraction techniques. Moreover, since each dipole is independent from the others, more than one foetal heart dipole can be generated to simulate multiple pregnancies.
Modelling noise Real noise signals from the PhysioNet Noise Stress Test Database  (Goldberger et al 2000 , Moody et al 1984)  (NSTDB) were used. The NSTDB contains 30 min, two-channel recordings of three noise sources: motion artifact (MA), electrode motion (EM) and baseline wander (BW). However there are three main limitations when using these records: (1) their length is limited to 30 min (thus sub-segments of these records would be re-used if the modelled signal has a greater length), (2) there are only two channels available (if we want to model a noise dipole we need three orthogonal components) and (3) this is noise from one individual, thus not accounting for inter-individuals variability. In order to tackle these limitations we propose to use an auto-regressive (AR) model to simulate the non-stationarity of the noise process.
Discussion The simulator introduced in this paper is not intended to be a substitute for clinical experimentation. In fact, it is meant to account for the inherent incompleteness of the current NI-FECG databases and simulators. Its purpose is to facilitate the rapid stress-testing of NI-FECG algorithms under highly varied conditions, and with a complete gold standard. Essentially, the main purpose is to provide a common open-source tool for benchmarking extraction/detection algorithms. This tool allows the comparison between methods in the literature to extract the NI-FECG and FQRS complexes, which are a pre-requisite for clinical studies. We propose that the tool is used in order to provide a preliminary hurdle for stress testing algorithms and not to replace a data driven approach. To date there is no study known to the authors that assessed the performance of the NI-FECG extraction algorithms with respect to electrode placement.
Acknowledgments JB is supported by the  UK Engineering and Physical Sciences Research Council , the  Balliol French Anderson Scholarship Fund and MindChild Medical Inc. North Andover, MA. FA is financially supported by the  Conselho Nacional de Desenvolvimento Tecnologico (CNPq -Brazil). JO is supported by the  Royal Society  under a  Newton Fellowship , grant number  793/914/N/K/ EST/DD PF/tkg/4004642  and the  Wellcome Trust  under grant number  098461/Z/12/Z  for The University of Oxford Sleep and  Circadian Neuroscience Institute (SCNi).
The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology.
Existing ontologies and standards (described in more detail in the following sections) were not designed to describe and integrate data across these processes, and the need to do so motivated the creation of the Biological Collections Ontology (BCO), a semantic resource representing the central notions of sampling, specimen collection, and observations. Herein, we present and describe the BCO, including its relationships to other biological ontologies -in particular the Environment Ontology (ENVO), a common framework for describing environmental information  [18] ,  [19] , and the Population and Community Ontology (PCO), which models collections of biological entities and their interactions. Finally, we discuss how this set of ontologies can be applied to real-life biodiversity use cases and argue for their adoption by the biodiversity community.