dataesr/scientific-paragraphs-categorization huggingface dataset:
Distribution maps WoRMS stores published species distributions by using location names. The status of the location name (including different spellings and languages), coordinates, shapefiles, and geographic hierarchy is provided by linking to the VLIZ Marine Gazetteer Hemichordata: Shenkar, N, Swalla, B. J. , van der Land, J. Xenoturbellida: Gofas, S. Hippoidea: Boyko, C. Zoanthidea: Reimer, J. D. , Sinnger F. Hirudinea marine: Kolb, J. doi:10. 1371/journal. pone. 0051629. t002 Table  2. Cont. Table  3. The (a) Regional Species Databases (RSD) and (b) Thematic Species Databases (TSD), hosted within WoRMS, and their editors. (VLIMAR, http://www. vliz. be/vmdcdata/vlimar). The coordinates and shapefiles can be used to build species distribution maps, as currently implemented on the sponge database  [82].
Photogallery The WoRMS image library is a user-controlled facility for the upload and display of images adjusted for online publication (i. e. 800 px, 72-96 DPI)  [83]. It is not necessary to create an account and log-in to do so, but account holders have edit privileges. It automatically resizes the image while storing the original size, to a 800 px wide 'thumbnail' image. If permitted, the original size can be provided online or made publicly available upon request. Video files can also be stored and displayed. The user must add some minimal metadata: including title, author, email, keywords; and terms of use (e. g. Creative Commons licence). It can automatically read embedded camera capture metadata (i. e. exif, gps) from uploaded pictures. The keywords are part of a controlled vocabulary and multiple entries are possible.
A drop-down list of taxonomic names avoids users entering misspellings. Editors can link images to specimens, which can have additional metadata (e. g. details on code number, storage, identification, locality, biology etc). Because many images are not uploaded by the taxonomic editors, the species they contain may not be correctly identified or the image may not be of sufficient quality for species recognition. Thus whether or not the image has been verified by an editor is indicated. Images can then be searched on species name, title, author and other keywords. There is an option to allow users to provide comments, which are moderated by the database administrator. Because they are associated with species names, the images are thus available to all Regional Species Databases in WoRMS, and can be accessed by external organisations, such as the Encyclopedia of Life.
Content In 2012, WoRMS contained almost 100 global, 12 regional and 4 thematic species databases overseen by 240 editors (Tables  2, 3 ). The editors are located in 133 institutions and 31 countries (Table  4 ). Of the GSDs, 22 have their own entry web page which provides scope for the editors to provide additional background and profile for their taxon (Table  2 ). Regional Species Databases (RSDs) cover less than half the oceans (Figure  2 ), but additional RSDs are planned. The RSD editors add distributional context to WoRMS, and work with the GSDs taxonomic editors to resolve nomenclatural discrepancies and omissions. Some editors are involved in a GSD, RSD and/or Thematic Species Database (TSD). One principle in setting up WoRMS was to not ask taxonomists to repeat their work. Thus WoRMS built on authoritative registers of all-taxon marine species lists that existed at regional levels (e.
g. Europe) and for particular taxa at global levels. Several GSDs were incorporated into WoRMS, including the world databases on all marine, freshwater and terrestrial Copepoda and Isopoda developed at the Smithsonian Institution, and world databases on Cumacea, Brachiopoda and Phoronida. Externally sourced content is from the collaborating databases: Biogeoinformatics of the Hexacorals (sea anemones and their relatives), World list of marine Fungi (from Index Fungorum), World list of Marine Pisces (from Catalog of Fishes via FishBase), World list of Algae (from AlgaeBase)  [84] , World list of free-living Nematodes -NeMys, World list of Marine Rotifers (from FADA), World list of marine reptiles (From Reptile Database), World list of Turbellaria (from Turbellarian Taxonomic Database), World list of Recent and Fossil Bryozoa, and the World list of Ctenophora.
WoRMS is updated by content from these scholarly resources, and, in turn WoRMS provides its content and/or services to other resources that might otherwise need to recreate it. At present, WoRMS contains 460,000 taxonomic names (from kingdom to species), and 368,000 species names. The latter include synonyms, nomina dubia, nomen nuda, misspellings, and old genus combinations. The species with the most synonyms is the breadcrumb sponge Halichondria panicea (Pallas, 1766), with 64. There are 215,000 accepted species names (Table  5 ). About 10% of the species names, entered by data assistants or editors, remain to be checked by editors. Some editors make their taxon complete across all environments, so there are 26,000 non-marine species also in the database (Table  5 ). Associated information includes about 150,000 literature sources, 20,000 pictures, and information on 44,000 specimens, of which over 5,000 are holotypes.
Specimen information in museum collections can be matched to species. For example, WoRMS has over 40,000 linked taxon records to the invertebrates deposited at the Smithsonian Institution, National Museum of Natural History. ERMS was moved to the present host institution in 2004 and once WoRMS was launched in 2008 significantly more content was entered (Figure  3 ). Since 2010 there have been fewer additional species to enter and thus effort has shifted to other content, notably vernacular names and distribution data (Figure  3 ). Table  5. The number of marine (a) taxa (infra-species and above), (b) species names (includes synonyms), (c) accepted species names (excluding synonyms); and (d) additional nonmarine species in the WoRMS database (note that there are additional non-marine species of many taxa not in WoRMS).
Matching taxa Determining the correct spelling of a scientific name is not always a trivial task (e. g. , which one is correct: Cirrhitichthys, Cirrhitychthys or Cirritichthys?) and it is very difficult for nontaxonomists to keep up with the status of species names. WoRMS has an online, semi-automated name validation tool called Taxon Match, to cross-check the spelling and taxonomic status of species against the WoRMS database. The tool is an implementation of the TaxaMatch algorithm which comprises a suite of custom filters and tests used in succession on genus, species epithet, plus authority where supplied  [85]. It also uses the Scientific Names Parser  [86]. The tool returns standard taxonomic information in a user-friendly format (e. g. , MS Excel or tab delimited text file). The user needs to upload a list of species names, match the columns with the fields in the database and the system will return the file with valid names.
Web services In contrast to the Taxon Match, where the user has to upload a species list, the portal also provides a platformindependent web service, that is it can run on PC, Mac and Linux operating systems. It uses the Web Services Description Language (WSDL) and Simple Object Access Protocol (SOAP) to enable data exchange. This web service allows users to dynamically link their own applications to the WoRMS database and will allow them to match a locally stored species list and add taxonomic and additional information derived from WoRMS (Table  6 ). WoRMS is also linking with other online data systems (link-out). They may use the web-services and/or use the WoRMS Taxon Match tool to cross-link names in their database andlink back to WoRMS (link-in) (Table  7 ).
Usage The WoRMS web service is being used by at least 28 organisations from 12 countries (Table  8 ). Permission is not required to use the Web service so there may be additional users we are not aware of. Copies of the database have been licensed out to 61 organisations in 21 countries (Table  9 ) with demand growing steadily (Figure  4 ). Since 2007, all website-use metrics show a steady increase in access (Figure  5 ). There were about 600,000 unique visitors in 2011, and 57,000 in December 2011 alone, and on average. 3,000 unique visitors per day (based on IP addresses). This represents over 3 million hits per month. Google scholar (24 April 2012) found over 800 citations for "World Register of Marine Species" and that it was cited in over 100 publications.
User feedback Typical benefits of WoRMS to users were that: (1) the process of reconciling names was automated; (2) the entry of names in a database could use a drop-down menu of existing names from WoRMS, so errors in manual entry could be avoided; (3) the names followed a standardized taxonomic hierarchy, thus aiding a user's classification of species in their own database and publications; (4) it was a single standard authoritative and time-saving resource to reference names and their classification;  (5)  it has an efficiently responding editorial system (Neil Holdsworth, personal communication,  25  November 2010); and (6) checking of names from collaborators and the literature could be automated. Including researching names not in WoRMS that would need to be checked from other sources, WoRMS saved users significant time compared to manually checking names using search engines and the literature (e.
Benefits Some of the most important benefits of WoRMS will be improved taxonomic efficiency, and greater quality control in the use of species names in the wider literature and environmental management. For example, EurOBIS corrected 28% of the names in its database by using WoRMS  [88]. By making a single inventory of all marine species names easily accessible on the internet it is anticipated that people will use it to correct spelling mistakes, use the currently accepted names rather than synonyms, and bring omissions, errors and anomalies to the attention of the taxonomic editors to address. The authors of popular species identification guides will find it easy to update the species names they use, and ecologists, conservationists and environmental managers will be using species names more consistently.
Use in research Although initially established to provide open-access information on marine species nomenclature, the aggregation of so much content is providing unanticipated benefits to researchers. ERMS provided the basis for (1) a review of taxonomic expertise and resources, including a list of species identification guides  [89, 90] , and (2) an analysis of trends in species discoveries and predictions of how many more species remain to be discovered  [17, 91]. This research stimulated the development of a new statistical approach, unusual in that it allowed calculation of confidence limits, to predict species richness from past rates of discovery  [92]. This work was then extended to WoRMS and CoL to predict global species richness  [22]. Other approaches to estimate species richness used the rate of discovery of higher taxa in WoRMS and other databases  [21] , and developed a software tool to provide a structured approach to using expert knowledge to estimate richness  [93].
WoRMS has also contributed to the annual reports of species discoveries  [94, 95]. Fisher et al. [96]  matched 2,380 species names from WoRMS to a bibliographic database so as to identify bias in research on coral, kelp, seagrass and mangrove habitats. Groups of WoRMS taxonomic editors have begun to synthesise knowledge on their taxon, including a major collaborative paper co-authored by over 100 editors  [91]. These studies form the basis for a special collection of papers in PLoS ONE. To date, they review the global diversity of several taxa: (1) Crustacea: Remipedia  [97] , Monstrilloida copepods  [98] , Tanadiacea  [99] , and non-asellote isopods  [100, 101] ; (2) Cnidaria: Stylasteridae corals  [102]  and Pennatulacea corals  [103] ; (3) Echinodermata: Ophiuroidea  [104]  and Asteroidea  [105] ; as well as (4) Porifera  [106]  Ascidacea  [107] ; Oligotrichea protists  [108] ; Reptilia  [109] ; and Placozoa  [110].
The present paper provides the introduction and context for this collection. It complements other PLoS ONE collections, notably those from the Census of Marine Life, e. g. ,  [111]  and one paper fits two collections  [112]. The study synthesises how many species are described, the number of accepted Table  9. Organisations with licensed copies of WoRMS for their research and/or data management. species names and synonyms, estimates of how many molecular cryptic species may exist, how many undescribed species are already in specimen collections, how many undescribed species have been found in field samples, and predictions of how many more species may yet be discovered  [91]. This study provides a baseline of current knowledge of marine biodiversity at the species level, summarises the rate of progress in discovering species, and should be reviewed every few years.
Future prospects Several initiatives are underway within WoRMS but not yet visible. These include new GSDs websites, and Thematic Species Databases on introduced species and parasite-host relationships. The major taxonomic gaps are amongst Mollusca, but no doubt there are omissions in other taxa and continuing updates needed. In the absence of alternative infrastructure and for taxonomic convenience, editors may add freshwater and terrestrial relatives to their marine GSDs, as already the case for Copepoda, Isopoda, Porifera, Gastrotricha, and Tardigrada. Users are encouraged to contact editors regarding possible omissions and errors in the database content. Continual improvements to content and database functionality are required. For example, about 5% of the literature sources are estimated to be duplicate entries and need to be manually rationalised.
We expect greater linkage with species distribution data in OBIS and GBIF. Several editors have developed online species identification resources. The future may see an online guide to all marine species. Some species' conservation status is indicated and WoRMS updates species names for the IUCN Red List. Thus, there is potential to create a thematic database on marine species of special conservation interest. New tools and online resources are materialising that provide opportunities for WoRMS to be more interoperable with online journals (e. g. using DOI or other identifiers), and other databases; such as the FilterPush (http://etaxonomy. org/mw/ FilteredPush) http://etaxonomy. org/mw/FilteredPush that networks species names. The classification of species by their biological (e. g. body size, parasites, dispersal), ecological (e.
languages. By June 2008, it was covered in 298 online stories, 23 newswires, 23 newspapers, and interviews on eight radio and two television stations. This was impressive for an online biodiversity database, and reflected the great media and popular interest in discoveries of marine biodiversity found by the Census of Marine Life [87]. The development of WoRMS has accelerated the availability of taxonomically authoritative GSDs for Species 2000, OBIS and GBIF. A growing number of GSDs are provided to Species 2000, sometimes replacing earlier GSDs that are no longer updated. In addition to WoRMS being directly provided to other organisations, many more use WoRMS GSDs via Species 2000 and its Catalogue of Life (CoL), and through the EOL website. Uniquely amongst species name systems, WoRMS provides environmental context (i.
Acknowledgments This paper is dedicated to the  late Jacob (Jaap) van der Land (Netherlands Centre for Biodiversity Naturalis, formerly National Museum of Natural History at Leiden)  who began a checklist of all marine species in the early 1990s, and was a contributor to ERMS, WoRMS, Species 2000 and CoL. He was employed as Head of the  Department of Invertebrates and Curator of ' worms' at the Leiden museum [113]. We thank  Karen Stocks  ( San Diego Supercomputer Center, La Jolla, USA ),  Neil Holdsworth  ( ICES Data Centre, Denmark ), and  Roy Lowry  ( British Oceanographic Data Centre, UK)  for helpful comments as database users. We acknowledge the important roles of past and present editors of WoRMS, and previous members of  WoRMS Steering Committee  for their collaboration:  Charles Griffiths  ( University of Cape Town, South Africa ),  Michael D.
(a) RSD European Register of  Marine Species  (ERMS): Costello, M. J. ; Bouchet, P. ; Boxshall, G. ; Arvantidis, C. ; Appeltans,  W. Canadian Register  of  Marine Species  (CaRMS): Kennedy, M. K. , L. Van Guelpen ,  G. Pohle ,  L. Bajona  The  SCAR-MarBIN Register of Antarctic Marine Species (RAMS ):  De Broyer , C. ; Clarke, A. ; Koubbi, P. ; Pakhomov, E. ; Scott, F. ;  Vanden Berghe , E. ; Danis, B. MArine Species Database for Eastern Africa (MASDEA):  Vanden Berghe ,  E. Black Sea  checklist for  Ocean-Ukraine & Sibema: Sergeyeva, O. The New Zealand Inventory of Biodiversity: Gordon, D. The Belgian Register of Marine Species (BeRMS) :  VLIZ Belgian Marine Species Consortium Gulf of Mexico Register  of marine species: Tunnel, W. ; Moretzsohn, F. The Arctic Register of  Marine Species  (ARMS) compiled by the  Arctic Ocean Diversity (ArcOD) : Sirenko, B.
Study set-up and design Construction of subject groups was based on large dataset examining HIV infection, the ANRS CO3 Aquitaine cohort. This investigation constitutes an open, prospective cohort of HIV-1 infected patients  [25, 26]. All adults 18 years or older who are inor out-patients of participating hospital wards with HIV-1 infection confirmed by Western blot testing and who have provided informed consent were eligible to be enrolled in the cohort. The study was approved by the ethics committee of the local institution (CPP Bordeaux) and written informed consent was obtained for all participants. A standardized questionnaire captures different types of data: epidemiologic information (age, gender, HIV transmission category), clinical events since last medical contact (whether or not HIV-related), laboratory (plasma HIV-RNA, CD4, haemoglobin, hepatitis B and C serological status, metabolic parameters) and therapeutic treatment (cART, prophylaxis treatment, other treatment).
e. toxoplasmosis), history of harmful alcohol or substance use, or absence of cART, were excluded from the present analysis (n = 46). The MRI data for each of the remaining 114 participants was inspected to discard major acquisition artifacts (n = 23), excessive head motion and technical failure during MRI post-treatment process (n = 12), or cerebral pathologies (n = 7). Using Fazekas scale  [29] , individuals presenting a high white matter lesion load (grade 2 and 3) were also excluded (n = 5). Finally, 86 participants were included in the present analysis, of which 40 were classified as 5STS-impaired, but none presented clinical signs of myopathy. Among the final participants, most had only small focal lesions (see Figure  1 ).
DTI analysis Microstructural analysis: Tract Based Spatial Statistics (TBSS). Data processing was performed using FSL 4. 1. 8 software (FMRIB's Software Library; www. fmrib. ox. ac. uk/fsl/). The data were first visually checked to ensure quality of acquisition. For each subject, raw DTI images were pre-processed using Eddy Current correction and a brain mask was created using BET (Brain Extraction Tool). FA, AD and RD maps were computed by fitting a tensor model to the raw diffusion data using FDT (FMRIB's Diffusion Toolbox). TBSS was then used to perform a voxel-wise between-groups analysis of DTI metrics  [30]. First, the FMRIB58 template was used as a target for all nonlinear registrations. The aligned FA maps were then averaged to create a mean FA image. Subsequently, classical TBSS processing was used (WM skeleton generation with a threshold FA value of 0.
Age, sex, laterality (and TIV when considering volumes) were entered into the models as covariates. We next conducted statistical analyses on extracted structural parameters of different ROIs using SPSS (16. 0. 1, SPSS Inc. ). ANCOVA was applied with age, sex and laterality as covariables. Pearson correlation analyses were also performed between 5STS scored and DTI indices. In these analyses, p-values below than 0. 05 were considered as significant.
Acknowledgments The authors wish to thank  Joel Swendsen  for the English revision of the manuscript. Composition of the  ANRS CO3 Aquitaine Cohort  and the  Groupe d'Epidemiologie Clinique du SIDA en Aquitaine (GECSA) -Coordination : F. Dabis 1*. Scientific committee: F. Bonnet 2 , F. Dabis 1 , M. Dupon 3 , G. Chene 1 , H. Fleury 2 , D. Lacoste 2 , D. Malvy 2 ,  P. Mercie2 ,  I. Pellegrin  2 , P. Morlat 2 , D. Neau 2 , JL. Pellegrin 2 ,  R. Thie  ´baut 2 ,  K. Titier 2. Epidemiology  and Methodology: M. Bruyand 1 , G. Chene 1 , F. Dabis 1 ,  S. Lawson-Ayayi 1  ,  R. Thie  ´baut 1 , L. Wittkop 1. Infectious Diseases and Internal Medicine: F. Bonnal 4 , F. Bonnet 2 , N. Bernard 2 , L. Caunegre 2 , C. Cazanave 2 ,  J. Ceccaldi  5 , D. Chambon 2 , I. Chossat 6 , K. Courtaud 7 , FA. Dauchy 2 ,  S.
4. Only few patches contained channels with two equally spaced conductance levels and common closures and conductances around 2 pS. Channels that did not show these characteristics were discarded. We usually observed background noise levels around 55-65 fA RMS in single-channel experiments before applying software filtering. Single-channel recordings were sampled at 50 kHz and filtered with a 1-kHz Bessel filter. Traces were digitally filtered at 200 Hz prior to dwell time analysis. Data were analyzed by PatchMaster (HEKA, Lambrecht/Pfalz, Germany), QuB (SUNY; Buffalo, NY; USA) and Origin 8 (OriginLab; Northampton, MA, USA). To obtain the voltage dependence of the relative open probability, instantaneous current amplitudes were determined 200 μs after a voltage step to 60 mV following prepulses to various voltages, normalized by their maximum value and plotted against the preceding potential.
Computational modeling of ClC-2 gating Modeling was based on Q-Matrix theories by Colquhoun and Hawkes (  [7] ) and implemented using custom-written Python scripts incorporating Q-Matrix implementations (C. S c h m i d t -H i e b e r ; v e r s i o n 2 0 1 0 -0 8 -2 3 ; h t t p : / / code. google. com/p/pyqmatrix), a directed evolution Algorithm (P. Sederberg; https://github. com/compmem/ desolver) running on ParallelPython (Vitalii Vanovschi; http://www. parallelpython. com). Rate constants (k) were constructed from Eq. 1, with k 1 or k -1 being the voltage-independent component, zδ the apparent gating charge, v the voltage, F the Faraday constant, R the gas constant, and T the assumed constant temperature of 296 k. k ¼ k 1 e zdvF RT = Starting values of rate constants were manually adjusted and compared with experimentally obtained data by eye.
Detailed values composing rate constants were obtained using directed evolution on a least-square deviation from 100-250 logarithmically spaced data points from normalized recorded currents. Time-dependent open probabilities for fast and slow gating were calculated from the rate constant Q-Matrix as described  [7]. Finally, open probabilities were calculated as product of probabilities of the fast gate being open and the sum of all open slow gate probabilities (Fig. 4b ). Rate constants from the Q-Matrix were used to simulate single-channel behavior of the common gating process in QuB Express (SUNY; Buffalo, NY, USA) to check for the occurrence of a long-lasting closed state. Fitting was repeated until the least-square deviation did not change by more than 0. 1 % over more than 50 cycles. Rate constants obtained by this algorithm were then used to plot the ATP concentrations and the voltage dependences of time constants (Fig.