dataesr/scientific-paragraphs-categorization huggingface dataset:
| THE ConR PACKAGE ConR allows users to estimate the above parameters automatically for any list of taxa and then assigns each taxon to a preliminary IUCN threat category according to Criterion B. These preliminary assessments are Under Criterion, B, the assessment of a taxon is based on the calculation of its EOO (B1) and/or AOO (B2). In addition, at least two of the following conditions must be taken into consideration: (1) the number of locations; (2) continuing decline of different aspects of its distribution (EOO, AOO, number of locations, etc. ); and (3) extreme fluctuation of certain aspects of the taxon's distribution (Table  1 ). Calculation of the two key range parameters, EOO and AOO, can be easily automated either using a taxon-by-taxon approach, as provided for by the web service GeoCAT  (Bachman, Moat, Hill, de la Torre, & Scott, 2011) , or in batch mode, for example in other R packages such as speciesgeocodeR  (Töpel et al.
First, a grid with cells of a chosen size is overlaid on taxa occurrences and the number of locations is estimated by the number of occupied cells. The grid cell size must be defined by the user and should represent the scale at which subpopulations are equally affected by a given threat. For example, a cell size of 10 km² may be considered a good estimate of the scale at which a particular serious threat event such as mining could equally affect individuals of a given taxon  (Durán et al. 2013). The user can choose a fixed cell size across the whole multispecies dataset (e. g. 10 km²) or can use a species-specific sliding scale approach  (Rivers et al. , 2010). In the latter approach, cell size is defined as 1/x of the maximum interoccurrence distance, where x is the maximum distance between two occurrences (e.
| ConR FEATURES ConR includes four functions, two sample occurrence datasets, and two sample shapefile datasets. All functions operate on a mandatory single data frame providing taxon occurrences and on optional user-provided shapefiles of land/sea and protected area limits. Occurrence data and shapefiles must be provided using the WGS84 reference coordinate system. The input data frame requires three mandatory fields: latitude and longitude (in decimal degrees), and taxon name. The collection year can also be added, thereby allowing graphic visualization of a taxon's collecting history (Figure  1 ). Additional information, such as higher taxonomic rank, can also be provided. By default, ConR saves all results in the user's R working directory. A step by step tutorial (R vignette) describing all options is provided as supplementary material and on the CRAN website.
| CASE STUDY In order to illustrate the usefulness and limits of ConR, we tested the package on a high-quality dataset of continental African palm distributions for 60 species (of the 68 currently known;  Stauffer et al. , 2017). A large part of the data were extracted from the RAINBIO database  (Dauby et al. , 2016) , which contains nearly all herbarium collections for African palms. Additional recent collections were added when available, resulting in a dataset of 4,234 unique occurrence records. The dataset was first used for the preparation and submission (as of April 2017) of full, species by species IUCN Red Listing assessments, mainly under Criterion B. Second, using ConR (with default parameters) and the same data for all 60 species, but excluding any "nonherbarium" occurrences (such as those based on satellite imagery or population censuses), we performed preliminary assessments as a batch operation.
We also ran the dataset with and without PA information (downloaded and filtered from https://www. protectedplanet. net) using the "protect. areas" default option. ConR analyzed the dataset in less than 5 minutes using a standard laptop. The results of the full IUCN assessments and those generated by ConR (with and without PA information), summarized in Table  3 , are quite congruent. Factoring in PAs did not alter the outcomes, F G U R E 1 Continued expect for a single species: Oncocalamus wrightianus was assessed as EN in the full assessment and by ConR when no PA information was included, but as VU with PA information. Regarding whether a species was assessed as threatened (i. e. , CR, EN or VU) or not (NT or LC), we see that for 43 species (71%), the results from ConR and the full assessments agreed.
For seven species, the ConR assessment indicated a threatened status, whereas the full assessment was LC. Finally, for eight species, the full assessment yielded a status of Data Deficient (DD) while ConR suggested either EN (5 species) or CR (3 species). In addition to this case study, we also undertook a preliminary conservation assessment of amphibians of Madagascar using a dataset that contained 7,657 georeferenced records representing 201 species, downloaded on February 9, 2016, from www. gbif. org (https://doi. org/10. 15468/dl. 2tkoae). This analysis was performed mainly to demonstrate the graphical outputs of ConR (Figures  1  and 2 ). This dataset is available within ConR as an example data frame (Malagasy_amphibian).
| DISCUSSION ConR provides for the first time a dedicated, multispecies conservation assessment package based specifically on IUCN criterion B and using only species geographic distribution. It provides an efficient tool to help accelerate the work of the conservation community by enabling practitioners to conduct preliminary assessments that are both reliable and informative. We stress that ConR does not (and is not intended too) replace the full IUCN Red Listing process; it can, however, assist and facilitate this process. ConR uses a number of assumptions in order to automate category assignment, especially the estimation of the number of locations sensu IUCN. Notwithstanding these assumptions, detailed above, which must be understood and acknowledged by the user, ConR is flexible in their implementation, allowing the user to explore various approaches and methodologies and to customize values for each option.
, 2017) , which also triggers subcriterion "a" (Table  1 ), which was used along with continuing decline (subcriterion "b"), and thus the number of locations was not used for the full assessment. In contrast, for some species, ConR indicated a status of CR, EN or VU, whereas the full assessment was LC (e. g. , Raphia gentiliana and R. monbutturom; Table  3 ). These mismatches occurred for species with broad geographic distributions but for which there were few collections thus fewer than 10 locations were inferred by while field data provided no clear of highly fragmented populations. case study clearly illustrates that ConR (1) can be used to generate fairly reliable preliminary conservation assessments on large datasets, but (2) has limitations when, for example, a species widespread and common but poorly collected or is widespread but severely fragmented.
It is important to stress that the accuracy of the georeferencing in such datasets is crucial for estimating risk. Two recently released R packages, Biogeo  (Robertson, Visser, & Hui, 2016)  and speciesgeocodeR  (Töpel et al. , 2017) , are designed to help curate and clean large datasets, and are thus complementary to ConR. F I G U R E 2 ConR will be useful for a variety of applications. First, as distributional data for taxa become increasingly available via large multitaxon databases  (Dauby et al. , 2016; Marshall, Wieringa, & Hawthorne, 2016;   Second, ConR will also be of value to taxonomists, who are increasingly expected to provide preliminary conservation assessments when describing new species or publishing revisions or monographs. By generating key parameters (EOO, AOO and an estimate of the number of locations), ConR will greatly facilitate this process.
Finally, rapid preliminary assessments of IUCN conservation status based on large, multitaxon sets will support studies on a wide range of subjects such as the evolution of extinction risk within and among clades  (Forest, Crandall, Chase, & Faith, 2015; Jetz et al. , 2014)  and the phylogenetic component of extinction risk within regional floras  (Leão, Fonseca, Peres, & Tabarelli, 2014)  or faunas. The ConR package has already been used by the authors to facilitate full assessments (e. g. , of palms) and to prepare IUCN Red List workshops. Also, ConR has been successfully used as part of an IUCN "Green Listing" of Protected and Conserved Areas  (IUCN, 2012)  for private sector players in order to identify potentially threatened species occurring in their concessions that, after verification, will be the subject of specific conservation management plans (unpublished results).
Two examples of map outputs generated by the IUCN. eval function of ConR for two species of Malagasy amphibians that were chosen from the example dataset: (a) Aglyptodactylus madagascariensis and (b) Blommersia sarotra. The top main inset shows a map of the region concerned with the occurrences of the species shown as black dots (records situated outside protected areas) and as blue dots (within protected areas). The delimitation of locations is shown by pink squares and of subpopulations by circles. For the species in (a), the convex hull used for calculating the EOO is shown for the first species as a gray polygon. For the species in (b), the EOO was not calculated because it has a single known occurrence. The bottom left gray inset summarizes all the information calculated by the IUCN. eval function.
ConR to estimate the number of locations (Cell_size_locations and method_protected_area) are customizable by the user, who can decide what cell size to use, whether or not to include PAs, and if so, how to take them into account. Also, if alternative shapefiles are available for the PAs, the user can select which one to use, such as the World Database on Protected Areas (WDPA, https:// www. protectedplanet. net).
see also Botanical Information and Ecology Network http://bien. nceas. ucsb. edu/bien/) and online repositories (Global Biodiversity Information Facility, http://www. gbif. org/; Atlas of Living Australia; www. ala. org. au), ConR makes it possible to calculate/estimate key parameters for conducting preliminary assessments of conservation status based on selected IUCN criteria and to provide data on the threat multiple species for a region, a specific clade, a functional group, etc. The application of ConR to large datasets could also contribute to meeting Target 2 of the Global Strategy for Plant Conservation (GSPC).
ACKNOWLEDGMENTS This work was supported by the  French Foundation for Research on Biodiversity (FRB)  through its  Center for Synthesis and Analysis of Biodiversity data (CESAB) program , as part of the  RAINBIO  research project, the  RAPHIA  project (grant number  1403-026 ) funded by  Agropolis Fondation (France)  and the  Agence Nationale de la Recherche  (grant number  ANR-15-CE02-0002-01 ) to TLPC. GD was supported by the  Belgian Fund for Scientific Research (F. R. S. -FNRS). We are also grateful to the office of the Chair of  IUCN Species Survival Commission (SSC, IUCN ), which made valuable resources available via the  Environment Agency of Abu Dhabi (United Arab Emirates)  for the African palm assessments. The authors also wish to thank  Alexander Zizka  and an anonymous reviewer for suggestions that helped improve an earlier version of this MS.
1 Two examples of map outputs generated by the IUCN. eval function of ConR for two species of Malagasy amphibians that were chosen from the example dataset: (a) Aglyptodactylus madagascariensis and (b) Blommersia sarotra. The top main inset shows a map of the region concerned with the occurrences of the species shown as black dots (records situated outside protected areas) and as blue dots (within protected areas). The delimitation of locations is shown by pink squares and of subpopulations by circles. For the species in (a), the convex hull used for calculating the EOO is shown for the first species as a gray polygon. For the species in (b), the EOO was not calculated because it has a single known occurrence. The bottom left gray inset summarizes all the information calculated by the IUCN.
ConR to estimate the number of locations (Cell_size_locations and method_protected_area) are customizable by the user, who can decide what cell size to use, whether or not to include PAs, and if so, how to take them into account. Also, if alternative shapefiles are available for the PAs, the user can select which one to use, such as the World Database on Protected Areas (WDPA, https:// www. protectedplanet. net). Both approaches used by Definition (IUCN Standards and Petitions Features Subcommittee, 2016) GeoCAT speciesgeocodeR RED ConR Batch or multispecies No Yes Yes Yes estimates Extent of occurrence Intended to "measure the degree to which risks from Yes Yes Yes Yes (EOO) calculation threatening factors are spread spatially across the taxon's geographical distribution" Area of occupancy (AOO) Area within a taxon's EOO that is occupied by the Yes Yes Yes Yes calculation taxon and RED (https://CRAN.
Wendl. Sunderl. 18°S Eremospatha dransfieldii EN B2ab(iii) VU B2a 60 NT or LC VU VU B2a NT or LC VU 20°S De Wild. Eremospatha haullevilleana LC B2 LC B1a + B2a 40 NT or LC NT or LC LC B1a + B2a NT or LC NT or LC Eremospatha hookeri LC B2 LC B1a + B2a NT or LC NT or LC LC B1a + B2a NT or LC NT or LC 22°S (G. Mann & H. Wendl. ) H. Wendl. 20 24°S Eremospatha laurentii De LC B2 LC B1a + B2a NT or LC NT or LC LC B1a + B2a NT or LC NT or LC Wild 0 40°E Eremospatha macrocarpa 4 5°E LC B2 5 0°E 5 5°E LC B1a + B2a NT or LC NT or LC LC B1a + B2a NT or LC NT or LC (G. Mann & H. Wendl. ) H. Wendl. res function. The dataset contains distribution information for 201 Eremospatha quinquecostu-LC B2 VU B2a Malagasy amphibian species and 7,657 occurrence records (available NT or LC lata Becc. from GBIF (https://doi.
Pattern registration in the case of chaetal sacs does not allow a perfect one-cell resolution analysis because cells do not always display perfectly constant positions within the sacs. It is sufficient however to accurately visualize expression domains in threedimensional views, to establish gross overlap of expression domains and to corroborate hypotheses of coexpression. Expression patterns of all Notch components expressed in chaetal sac cells (Pdu-Notch, Pdu-Delta, Pdu-SuH and Pdu-Nrarp) were thus scanned, aligned and averaged. We have shown previously  [53]  the expressions of two Hes (Hairy/enhancer of Split) genes ( potential target genes of the Notch pathway in eumetazoans) in the chaetal sacs. Pdu-Hes2 and Pdu-Hes12 that are found in 12 patches corresponding to, respectively, a large proportion and a few internal cells of the chaetal sac, were also included in the database.
Using the IMARIS software, we counted on these averages the number of positive cells for each gene in both the VNC and brain. This automatized approach for cell counting is highly linked to the in situ hybridization experiment efficiency and gives only a rough estimate of number of neurons. For all three genes, the number of neurons in the brain is almost equivalent between the control and treated larvae, while a few more interneurons are found after Notch inhibition (figure  9d-f ). The number of interneurons is nevertheless constant in the VNC, while the cholinergic neurons and the overall differentiated neurons in general appear to be slightly more numerous in treated larvae. However, these numbers may be biased by the quality of the in situ hybridization and are in no way comparable to the drastic phenotypes observed in other models (in different contexts, see  [57] [58] [59] [60] ).
Phylogenetic analyses The predicted amino acid sequences of the identified Platynereis gene fragments were aligned with their presumptive orthologues from 13 metazoan species. These genes were either annotated as such in public databases, or found as predicted genes in whole-genome BLAST screening. Monosiga brevicolis sequences were selected as outgroup in order to root the tree, when possible. For Notch and Delta/Jagged proteins, the number of EGF domains is variable, preventing from adequate alignments; they were thus excluded. The Notch alignment includes a partial sequence that begins at the first LNR domain until the end of the protein. The Delta/Jagged alignment includes partial protein sequence from the signal peptide to the end of the DSL domain. Multiple alignments were then performed with MUSCLE 3.
Whole-body gene expression registration Image registration of three-dimensional gene pattern scans based on the DAPI nuclear labelling were performed using the open-source software package ITK4. 0 and a collection of specific scripts developed in the Jekely Lab  [52]. All patterns were registered to average anatomical reference templates (48 and 72 hpf). Both of these reference templates were calculated using a total of 40 DAPI-labelled larva three-dimensional stacks. The complete protocol for unbiased anatomical reference calculation and the reference files themselves are available upon request. Average patterns for a given gene were calculated using at least five individual larvae and gene pattern comparisons were performed using IMAGEJ and IMARIS.
For cell counting of EdU experiments, confocal stacks were obtained for five control embryos and five embryos treated with each of the three different drugs. To enumerate precisely all cells in a given larval region of interest (ROI), all embryos were registered with ITK4. 0 as described above to the 48 hpf anatomic reference template. ROIs corresponding to the neurectoderm, episphere, chaetal sacs and stomodeum were delimitated using the 48 hpf template and the three-dimensional surface tool of IMARIS software (Bitplane). DAPI-and EdU-positive nuclei were then counted for each registered sample stack and each ROI using IMARIS.
Imaging Bright field images were taken on a Leica microscope. Classical confocal images were taken with a Leica SP5 confocal microscope. Adjustments of brightness, contrast and Z projections were performed using the IMAGEJ software. Confocal images for registration procedure were taken in a Zeiss LSM 710 confocal microscope. Three-dimensional surface of expression patterns and associated videos were performed using, respectively, the surface and animation tools of IMARIS. Co-localization stacks of expression patterns were obtained with the Coloc tool of IMARIS and co-localization images were then edited on IMAGEJ. Live imaging of the 24 hpf FM4-64 labelled embryos was performed on a Zeiss LSM 780 confocal microscope with a 63Â water immersion objective. Z-stacks (z ¼ 0. 53 mM) of several whole embryos were performed every hour (using rsob.
A Platynereis Notch gene had already been identified from a BAC sequence ([28], Genbank: AM114766). For Delta and Jagged family genes, a combination of the specific MNLL and DSL domains found in both type of proteins was used. Complete coding sequences for all genes were assembled from EST fragments using CODONCODE ALIGNER (CodonCode Corporation, USA). Putative exons positions, for the Platynereis Delta gene, were mapped on genomic DNA by comparison with ESTs using ARTEMIS[83]. lowcut-offE-valuethreshold of 0. 1, against 15 genome datasets. Lists of BLAST hits were then reciprocally BLASTed against the human proteins dataset of the NCBI database (reciprocal best hits[69]). Those genomes correspond to 15 species representatives of the main lineages of animals: Porifera, Ctenophora, Cnidaria, Lophotrochozoa, Ecdysozoa and Deuterostomia, with a lophotrochozoan focus ( plus one choanoflagellate).
Acknowledgements. We are grateful to the Genoscope and the  EMBL  for giving access to unpublished  Platynereis ESTs  and genomic sequences. We also acknowledge the platform  ImagoSeine  staff for help in confocal imagery and Imaris software use. The authors wish to especially thank  Dr G. Je  ´kely and  Dr A. Asadulina  for their help in adapting the registration tools in our laboratory. Dr G. Je  ´kely is also thanked for sharing neuropeptide antibodies. We thank  Dr P. Steinmetz  and  Dr N. Dray  for sharing preliminary data. We also thank  A. Guillou  for his help with gene cloning at the beginning of this study,  O. Tournie  `re for her help with ISH of Hes2 and Vasa genes,  Dr S. Schneider  for the Chitin synthase clone,  Dr A. Zakrzewski  for her expertise on chaetogenesis and  Dr D.
Funding. This work was funded by the  CNRS , the  Agence Nationale de la Recherche (France)  ( ANR  grant no. BLAN-0294  and  ANR BLAN-METAMERE) , the  Institut Universitaire de France  and the  Who am I? laboratory of excellence  (No. ANR-11-LABX-0071 ) funded by the  French Government  through its  'Investments for the Future' program  operated by  ANR  under grant No. ANR-11-IDEX-0005-01.
For each species, we screened the genome assembly, the predicted protein sequence dataset and transcriptomes when available. The presence of the specific protein domains (available upon request) was systematically checked by scanning sequences with both NCBI Conserved Domain search option V3. 10 [84]and INTERPROS- 5. Material and methods 5. 1. Survey of Notch signalling pathway core components in Platynereis dumerilii: identification and cloning Large gene fragments (1-3 kb) were subsequently cloned by PCR using sequence-specific primers using cDNAs from mixed larval stages as templates (primer sequences and PCR conditions are available upon request). PCR products were TA cloned into the PCR2. 1 vector following the manufacturer's instructions (Invitrogen) and sequenced. Partial cDNAs obtained were then used as templates to produce RNA anti- sense probes for WMISH using Roche reagents.
Orthology relationships were defined using as criteria sequence similarities, presence of specific domains and phylogenetic ana- lyses (see below). The newly identified Platynereis gene sequences were deposited in Genbank with the following acces- sion numbers: Pdu-Delta, KP293866; Pdu-Jagged, KP293873; Pdu- Su(H), KP293861; Pdu-psen, KP293862; Pdu-Nrarp, KP293864; Pdu-fng, KP293863; Pdu-numb, KP293865; Pdu-dl-like1, KP293867; Pdu-dl-like2, KP293868; Pdu-dl-like3, KP293869; Pdu- DSL-like1, KP293870; Pdu-DSL-like2, KP293871; Pdu-DSL-like3, KP293872. 5. 2. Survey of Notch signalling pathway core components in a panel of metazoans and phylogenetic analyses 5. 2. 1. Data sources, sequence retrieving and domains composition Notch pathway core component searches were carried out using the tblastn or blastp algorithms implemented in nGKLAST (Korilog V 4.
Introduction In the last 20 years, e-infrastructures have become ever more important for the conduct and progress of research in all branches of scientific enterprise. Increasingly collaborative, distributed and data-intensive research requires the sharing of resources (data, tools, computing facilities) via e-infrastructure as well as support for effective co-operation among research groups  (ESF 2011; ESFRI 2016). Moreover there is the expectation that with large datasets ('big data'), e-infrastructure and advanced computing techniques, new scientific questions can be tackled. The archaeological research community has been an early adopter of various digital methods and tools for data acquisition, organisation, analysis and presentation of research results of individual projects. The provision of e-infrastructure and services for data sharing, discovery, access and re-use for the heritage sector is, however, lagging behind other research fields, such as the natural and life sciences.