dataesr/scientific-paragraphs-categorization huggingface dataset:
2012) , the biodiversity informatics data standard developed by the Biodiversity Information Standards (TDWG), which is the preferred format for publishing data to the GBIF network. Once converted, data relating to the treatments and specimens published in EJT is accessible via Treatment Bank and GBIF within a few days of publication, at no extra cost or effort for the author. The figures and captions, along with a full copy of the article, are also available via the Biodiversity Literature Repository. All data harvested from an article is explicitly linked to the original publication using full original citations and a DOI. This means that the sub-article elements are integrated into a much larger network of biodiversity data, which helps to ensure their preservation but also maximises the reach of the article (Fig.
Defining the EJT format The specimen citations contained within the test set of articles, as well as those found in further articles published by EJT and other taxonomic journals (Zoosystema, Adansonia, Geodiversitas, ZooKeys, Zootaxa, the Zoological Journal of the Linnean Society, the Botanical Journal of the Linnean Society, PhytoKeys, Phytotaxa), were analysed to establish the types of data most commonly presented. Once the specimen citations had been broken down into a list of highly recurrent fields, these fields were mapped to DWC terms (http://rs. tdwg. org/dwc/terms/). This permitted the creation of a flexible template for specimen citations, designed to fit a broad array of specimen data, which was tested on a wide sample of citations and refined according to feedback from Plazi, the EJT production staff and scientific editors, as well as several active EJT authors.
Material citations formatting guidelines In accordance with the European Journal of Taxonomy's FAIR Data & Open Science policy (available from https://europeanjournaloftaxonomy. eu), the formatting guide for zoological and botanical specimen citations is presented below. Authors are encouraged to prepare their manuscripts according to this model prior to submission, but they will also be given an opportunity to comply upon acceptance of the article. While EJT strongly recommends that authors adhere to the guidelines given below, the fine-grain formatting of the material citations is not compulsory; if an author decides not to comply or that the material is not appropriate, EJT will perform reduced formatting during production. In this case, the majority of the specimen data will not be tagged and converted into a machine-readable format; this means that the specimen-related information from the paper will not be included in major databases.
Order Each material citation is composed of diverse data fields (number of specimens, locality, date collected, etc. ) that EJT identifies using Darwin Core (DWC) terms. To efficiently perform this, it is important to ensure that the different fields of a material citation are consistently presented in the same order throughout the article or, at the very least, within a taxon treatment. The preferred order for data fields differs for zoology and botany, and are as follows: COUNTRY • specimen(s) (e. g. , "1 ♂"); geographic / locality data (from largest to smallest); geographic coordinates; altitude / elevation / depth (using alt. / elev. / m a. s. l. etc. ); date (format: dd Mmm. YYYY, e. g. , "16 Jan. 1998"); collector (followed by "leg. "); other collecting data (e. g. , micro habitat / host / method of collecting / "DNA voucher specimen"/ "vial with detached elements", etc.
); barcodes/identifiers (e. g. , "GenBank: MG779236"); institution code and specimen code (e. g. , "CBF 06023"). COUNTRY • locality data (from least to most specific); geographic coordinates; altitude / elevation / depth (using alt. / elev. / m a. s. l. etc. ); other collecting data (e. g. , habitat / substrate / method of collecting / "herbarium specimen" / "DNA voucher specimen"); date (format: dd Mmm. YYYY, e. g. , "16 Jan. 1998"); phenology (e. g. , fl / fr); collection number (collector's name + number in italics); determinator (followed by "det.
Additional data Ideally, the data fields identified above should be listed before any other collection data. If a different order is used, it is important to be as consistent as possible throughout the paper, or at least within a single treatment. Semicolons may be used to separate any additional data into appropriate fields, e. g. :  SWEDEN • ♂; Halland, Halmstad; Gårdshult, Buskastycket; 56. 41° N, 13. 91° E;   Additional data can also be given in the appropriate field between brackets, e. g. :.
Associated sequences Specimen accession numbers, barcodes and DNA accessions should be identified as such, e. g. , "GenBank: U34853. 1", "accession no. : 5587453". BRAZIL • ♂; Pernambuco, off Recife, approximately 20 nautical miles from the coast; REC I, dredge 4; 8°08′51. 5″ S, 34°34′08. 0″ W; 65 m depth; 7 Feb. 2018; agglomerations of sponges, rhodoliths and calcareous algae; GenBank 16S gene: MK918616; MOUFPE 19470.
First results By respecting the standardised format explained in this paper, the specimens cited in EJT publications are quickly and efficiently converted into DwC archives by Plazi using a semi-automatic mark-up workflow developed within GoldenGATE. Since the project was launched in early 2018, EJT and Plazi have contributed 2155 treatments and 7888 materials citations to major aggregators of biodiversity data, as well as 2288 figures and 7324 bibliographic references: all directly linked to the original publication. identifier in the repository Each identifier (barcode and/or other catalogue numbers) should be cited exactly as it is registered in the repository. Each individual code is presented within square brackets immediately after the herbarium acronym. E. g. :.
Repositories Acronyms of herbaria follow Index Herbariorum (http://sweetgum. nybg. org/science/ih/), except for the following repository: UANT = University of Antwerp, Belgium Botany repository Acronyms of herbaria must follow Index Herbariorum (http://sweetgum. nybg. org/science/ih) and a phrase to this effect will be included in the 'Materials and methods' section under the heading 'Repositories'. Any acronyms used for repositories that do not feature in the Index Herbariorum must also be given here, e. Spot checks carried out on the occurrence records obtained from the encoded specimen citations reveal that the use of the standard formatting greatly improves the precision and quantity of harvested data. Eventual discrepancies in the distributed data compared to the original data (stemming from misinterpretation by the parsing algorithms) can be easily reported ("contact" details available directly of the derived web pages) and corrected (updates performed by Plazi and automatically propagated throughout the network).
Workflow evaluation The most evident drawback of the GoldenGATE workflow is the approach of retro-conversion, whereby pattern recognition and natural language processing techniques are used to decipher the PDF postpublication. This method is seldom chosen by publishers setting up digital workflows owing to the difficulties connected to a computer interpreting the text correctly. However, for the EJT "born-digital" PDF publications, the text is recovered from the rendering instructions embedded in the PDF, which facilitates the task. In addition, a template has been built to automate the conversion process, right up to the fine granularity of material citations. Using Plazi's expertise and the EJT standardised formatting as much as possible, we have not only improved the quality and quantity of the data harvested from the articles, but also the speed with which Plazi can perform encoding.
2015; Nicolson et al. 2019). However, for as long as there is no standardised approach to the citation of biological specimens, any data obtained on a large scale will have a limited power. As an institutional publisher, a specific area of interest for EJT was the identification of institution codes within research articles, indicating where specimens are held. However, the lack of an agreed method for identifying a given institution, in fields other than botany, means that these results cannot currently be exploited. For example, within the test batch of converted EJT articles, the Natural History Museum of London was identified by several acronyms: NHM, NHMUK and BMNH. Without a recognised register of official acronyms for zoological collections, as is the case with botanical collections and the institutional register Index Herbariorum, it would be difficult to make any statements about the collections represented by the journal's publications.
Conclusion By contributing data to aggregators such as GBIF and BLR, the EJT-Plazi workflow not only improves access to and citation of taxonomic literature, but also means that the specimens studied and their linked taxonomic information are rapidly made available to the wider scientific community. Another huge benefit of this workflow is that the original data source is consistently cited and linked throughout all subsequent representations. The EJT-XML project was presented to the international community of biodiversity scientists at the Biodiversity Next meeting in Leiden, The Netherlands in October 2019  (Bénichou et al. 2019) , where it was received with enthusiasm. We hope that this paper will now serve as a reference for progress in the field of biodiversity informatics, as well as for EJT authors to prepare their manuscripts.
Acknowledgements The authors would like to thank  Daphné Duin  and  Jeremy Miller  ( Naturalis Biodiversity Centre ) for the initial project analysis carried out on behalf of EJT, as well as  Natacha Beau  ( Meise Botanic Garden ),  Alejandro Quintanar Sánchez  ( Real Jardín Botánico de Madrid ),  Emmanuel Côtez ,  Anne Mabille  and  Marc Pignal  from the  Muséum  national d'histoire naturelle, Paris, for their valuable help in defining aspects of the Material Citations Formatting Guidelines. We are also grateful for the support from the team of Scientific Editors and Desk Editors at the European Journal of Taxonomy regarding the implementation of the project described in this paper. All projects carried out by the European Journal of Taxonomy are made possible by the funding granted to the journal by the consortium of member institutions:  Muséum national d'histoire naturelle, Paris, France; Meise Botanic Garden, Belgium; Royal Museum for Central Africa, Tervuren, Belgium; Royal Belgian Institute of Natural Sciences, Brussels, Belgium; Natural History Museum of Denmark, Copenhagen, Denmark ;  Naturalis Biodiversity Center, Leiden , the  Netherlands ;  Museo Nacional de Ciencias Naturales-CSIC, Madrid, Spain ;  Real Jardín Botánico de Madrid CSIC, Spain ;  Zoological Research Museum Alexander Koenig, Bonn, Germany; National Museum, Prague, Czech Republic.
Bioluminescence is a prominent functional trait used for visual communication. A recent quantification showed that in pelagic ecosystems more than 75% of individual macro-planktonic organisms are categorized as able to emit light. In benthic ecosystems, only a few censuses have been done, and were based on a limited number of observations. In this study, our dataset is based on observations from remotely operated vehicle (ROV) dives conducted from 1991-2016, spanning 0-3,972 m depth. Data were collected in the greater Monterey Bay area in central California, USA and include 369,326 pelagic and 154,275 epibenthic observations at Davidson Seamount, Guide Seamount, Sur Ridge and Monterey Bay. Because direct observation of in situ bioluminescence remains a technical challenge, taxa from ROV observations were categorized based on knowledge gained from the literature to assess bioluminescence status.
Previous studies on the distribution of bioluminescence have shown differences between organisms' habitats  17, 20, 21. However, these studies have been based on a limited number of sampled organisms. Major challenges to sampling come from a restricted number of stations sampled, short duration of cruises, the escape behavior of animals, the limited capability of ROVs to catch and store organisms, and the lack of low-light cameras to document the physiological state of animals shipboard. For these reasons, the quantification of this capability based on direct observations remains challenging. In this work, we compiled pelagic and benthic deep-sea data, based on visual observations of organisms seen by the Monterey Bay Aquarium Research Institute (MBARI)'s ROVs in the Monterey Bay area. The dataset covers animals observed in the water column and on the deep seafloor in the Monterey Canyon, Guide and Davidson Seamounts as well as Sur Ridge, between 0-3,972 m depth.
We categorized this information with a database we created from the literature and direct scientific observations to assign bioluminescence ability to over 1,157 taxa. The objectives were to assess: (i) the extent of bioluminescence ability in deep-sea epibenthic organisms; (ii) whether bioluminescent organisms have a different distribution in pelagic vs. benthic ecosystems; or (iii) between various specific benthic ecosystems, (iv) depth-related spatial variability in bioluminescence use; (v) the gaps in our knowledge of bioluminescence capability in benthic organisms, highlighting the taxa that have not been tested in order to focus further investigations. This study takes trait-based approach to quantifying the occurrence of bioluminescence in the deep-sea benthos and documents the differences in the use of this trait at an ecosystem level.
Dataset and Methods Site location and dataset description. Observations of benthic animals were documented MBARI's remotely operated vehicles (ROV) on 621 dives between 1991-2016. Dives took place off the coast of California (Fig. 1 ), from nearshore waters to about 300 km offshore, (latitude from 34. 28° to 37. 04°N and longitude from 125. 02° to 121. 73°W). The sampled zone covers the continental shelf and slope, a major deep canyon (Monterey Canyon), and a fan valley down to 3,972 m depth (Fig. 1 ). Two regional offshore seamounts and one ridge were also included in the study. These three features rise above the deep-sea floor and are mainly composed of hard substrates. All sample locations except Guide Seamount are located within the Monterey Bay National Marine Sanctuary. Davidson Seamount (latitude 35.
71°N, longitude 121. 75°W) is located 121 km from the coast and measures about 42 km by 13. 5 km. Its shallowest point is at 1,246 m with a base depth of 3,656 m. Guide Seamount (latitude 37. 01°N, longitude 123. 21°W) rises about 1,440 m above the seafloor and the peak sits at depth of 1,682 m. Both Davidson and Guide Seamounts are oriented on a NE/SW axis. Sur Ridge (latitude 36. 21°N and longitude 122. 18°W) is located 45 km west of Point Sur; it lies on a N-S axis at a maximum depth of 1350 m rising to 790 m. Pelagic records reflect data from the water column over offshore of Monterey Canyon and are available in Martini & Haddock  18. Additional pelagic observations from the water column during benthic ROV dives descents and ascents were combined with the original dataset. Dives were conducted between 6:00 and 19:00 local time.
Over the period of this study, several ROVs were used (Tiburon, Doc Ricketts, Ventana) and four cameras were in use (Panasonic 3-chip, Sony 3-chip, Ikegami HDL40 and Sony HDTV). The focus distance was defined as 1. 5 m from the camera. The typical volume observed by the camera in the water column varied between 1. 2 and 3 m 3 during this period. For benthic surveys, the field of view varied from 1-4 m 2. All visible fauna in ROV videos (generally > 1 cm) were annotated by biologists using the MBARI's open source Video Annotation Reference System (VARS)  22  for database entry. Each observation of an animal was logged as a taxonomic entity, defined at its most specific taxonomic level observable from video, along with concurrent physical parameters (depth, latitude, longitude) and dive metadata (dive number, date).
Data treatment was performed using Python scripts for VARS database retrieval, and R version 3. 4. 3  23 for stats and plotting. In order to deal with the assignment of bioluminescent categories for organisms on various taxonomic levels and to highlight more general patterns, taxa were grouped to higher taxonomic levels based on functional groups in the ecosystem. For example, in this study Hydroidolina mainly represents benthic hydroids, while pelagic Hydromedusae and Siphonophora were treated separately. A total of 29 groups were described as follow: Appendicularia, Chaetognatha, Hydromedusae, Scyphozoa, Thaliacea, Ctenophora, Rhizaria, Platyhelminthes, Bryozoa, Brachiopoda, Siphonophora, Nemertea, Crustacea, other Arthropoda, Asterozoa, Crinoidea, Hemichordata, Hexacorallia, Holothuroidea, Hydroidolina, Octocorallia, Echinoidea, Pteropoda, Cephalopoda, other Mollusca, Porifera, Ascidiacea, Annelida and Fishes (elasmobranchs, bony fish, Actinopterygii).
Statistical test. A χ 2 -test (using function chisq. test() in R 3. 4) was performed on a contingency table of observations for benthic, pelagic, Davidson and Guide Seamounts, and Sur Ridge habitats, defined as independent, and with a number above 5. The Null Hypothesis was the similarity between two habitats (Table  2 ). Ethical statements. Field operations were conducted under permit SC-4029 issued to SHD Haddock by the California Department of Fish and Wildlife, and through institutional permit # MBNMS-2005-002 from the Monterey Bay National Marine Sanctuary. For this study, specimens were only observed and not collected. Species used are unprotected and unregulated, and no vertebrates or octopus were used, so the International ethics guidelines are not invoked. 523 dives) (Fig. 1 ). The proportion of bioluminescent capability of each of the 29 taxa observed in the dataset was described within each habitat (pelagic and 4 benthic subhabitats), (Fig.
Unlikely Organisms probably non-bioluminescent based on taxonomy assignment and direct observations. Undefined (1) Organisms that could not be categorized because they were equally likely to be bioluminescent or not, as well (2) organisms whose bioluminescent capability has not been examined or reported. Analysis for this dataset shows that benthic ecosystems support a significantly smaller percentage of bioluminescent animals compared to pelagic ecosystems (32. 4% vs. 75. 5%, p-value < 0. 01, Table  2 ). Comparisons between the benthic subhabitats (Fig. 4A ) shows that Sur Ridge (36. 1% bioluminescent), Guide Seamount (34. 0%), Davidson Seamount (40. 9%) and other substrates (30. 4%) are statistically different one from each other (Chi-squared test with p-values < 0. 01).
Discussion While quantification of bioluminescence between ecosystems has been previously investigated based on a limited number of in situ opportunities, this study proposes a trait-based methodology to quantify differences in the diversity of bioluminescence between pelagic and benthic ecosystems. Based on a large dataset and extensive knowledge from the literature, we show that the ability to bioluminesce may be strongly driven by habitat. We hypothesize that this is probably related to differences in optical properties between pelagic and benthic environments (Fig. 4 ). While more than 75% of individual pelagic taxa are known to emit light, in the various four benthic ecosystems we studied, only 30-41% of individuals have this ability.
Here, three specific locations (Guide Seamount, Davidson Seamount, Sur Ridge) and wider benthic observations have been differentiated to test spatial variability within these ecosystems, as well as variability in depth. Taxonomic diversity was expected between seamounts, but due to the relatively proximity of Davidson, Guide and Sur Ridge, it has previously been stated that only low endemism was observed within such distances  38. Moreover, a Chi-squared test shows that there are differences between almost all benthic sites (except benthic and Sur Ridge), supporting the need to study them independently, although the percentage of bioluminescent organisms in all those benthic stations was clearly different than the pelagic dataset (between 30-41% for benthic and 75% for pelagic). By taking into account those differences, we suggest that these observations are representative of benthic and pelagic fauna of greater Monterey deep water regions.
Acknowledgements The authors thank  Kyra Schlining ,  Susan Von Thun ,  Brian Schlining ,  Lonny Lundsten , and  Nancy Jacobsen Stout  for annotations and the implementation of the VARS database, as well as shipboard bioluminescence observations and reports. This work could not have been done without the ROV and ship crews and their expertise. The authors are grateful to  James P. Barry ,  David A. Clague  and  Robert C. Vrijenhoek  for providing benthic observations and to  Kim Fulton-Bennett  for helpful discussions. This work is supported by the  David and Lucile Packard Foundation  and  S. Martini  is supported in part by a grant from the  Bettencourt-Schueller Foundation. J. M. thanks the crew of RV Investigator and  Chief Scientist Dr. Tim O'Hara (Museums Victoria)  who led the cruise "Sampling the Abyss" supported by  CSIRO Marine National Facility  and  NESP Marine Biodiversity Hub as well as FNRS (Belgium)  for travel funding.
The chambers were opened twice daily at 07:00 h and 15:00 h for about 20 min for milking and subsequent feeding. The gas analyser was calibrated at the start of every CH 4 measurement period with pure N 2 and a certified standard gas mixture of CO 2 (1. 36 g/m 3 ) and CH 4 (0. 459 g/m 3 ). Real time gas emissions in a chamber were calculated by the difference between chamber and ambient gas concentrations multiplied by the airflow corrected for temperature, relative humidity, and pressure according to the Wexler equation  [19]. Calculations of CH 4 yield (g CH 4 /kg DMI) and intensity (g CH 4 /kg milk) were done using data on DMI and milk production when cows were in chambers.
Statistical analysis Data were averaged per period and per animal and analyzed using the Mixed procedure of SAS version 9. 4 (SAS Institute, 2004). Data from HSD and HFD were analyzed separately as comparison between diets was not the objective of the study. The following model was used: Y ijk = μ + T i + P j + C k + e ijk , where: Y ijk are observations for dependent variables; μ is the overall mean; T i is the fixed effect of DFM (control, Propionibacterium, L. pentosus and L. bulgaricus); P j , is the fixed effect of period (j = 1-4); C k is the random effect of cow; and e ijk is the random residual error. The effect of individual DFM supplementation was tested using Dunnett's test, whereas orthogonal contrasts were performed to evaluate the effect of CTL versus all DFM treatments. Data were considered significant at P < 0.
Acknowledgements We thank  D. Roux ,  J. Bourdassol ,  F. Rosa  and  S. Rudel  ( UE1414 Herbipôle ) and  P. G. Toral  ( UMR Herbivores ) for animal care and assistance in sample collection; and  Y. Rochette ,  D. Graviou  and  C. Delavaud  ( UMR Herbivores ) for their help in laboratory analysis.
This study has some limitations. Finding the DNA of human and veterinary pathogens in feeding ticks is only a marker of the community of tick-transmitted pathogens circulating in the target territory  [36]. Ticks were mostly analyzed in pooled samples and not individually. Working with pools can lead to underestimation of the infection rate, which must be interpreted with caution. The ticks were mostly collected in a municipal slaughterhouse in Northern Corsica. We have no data about the southern part of the island. Moreover, ticks from cattle, wild boars, and dogs were collected from several locations while the sheep were only from one farm.