dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Population This study is part of the CODAGLIO (ie, COmbining clinical trial DAtasets in GLIOma) project, in which a database was created including HRQoL data of individual glioma patients from 15 previously published phase II/III RCTs. We calculated the net clinical benefit in 2 RCTs, EORTC 26951 6 (radiotherapy alone vs radiotherapy plus PCV chemotherapy in anaplastic oligodendroglioma and oligoastrocytoma) and 22033  7  (radiotherapy alone vs temozolomide alone in lowgrade glioma), as a proof of concept for other trials, with 2 distinct methods.
2 μm, and additionally into one >0. 7-μm fraction. Further, HMW-DOM samples were gained by subsequent concentration using a 1-kilodalton (kDa) membrane. In the sunlit ocean, microalgae account for most of the POM in the fraction larger than 0. 7 μm. Smaller eukaryotic and prokaryotic species can be found in the <3-μm fraction (e. g. , the green algae Ostreococcus tauri) whereas diatoms contribute mainly to the biomass in the >3-μm fraction (e. g. , Thalassiosira pseudonana) and >10-μm fraction (e. g. , Coscinodiscus wailesii). Submicrometer particles are mostly derived from nonliving organic matter  (19). Sampling of small crustaceans (e. g. , copepods) was prevented either by additional metal meshes or by visual examination of the filter. The majority of samples were taken in surface waters between 0 and 40 m depth (detailed sample information is summarized in Dataset S1).
Only in the two Arctic and the North Atlantic datasets, samples were obtained from water depths of 350 m below the surface. The data for each of these cruises and the time series are presented in SI Appendix, Table  S1  and Dataset S2. The laminarin quantification method was based on specific enzyme hydrolysis and previously developed and validated  (17, 18). In the present study, it was further validated versus the classic acid hydrolysis method (SI Appendix, Fig. S3 ). A detailed description of these experiments and their results is found in the supplementary information (SI Appendix, Figs. S3-S6 ). Diatoms Drive Laminarin Production during Algal Spring Blooms in the North Sea. We monitored laminarin in the North Sea by sampling during spring in 2016 and 2017 (Fig. 2 ). This time series showed some of the highest laminarin concentrations of the entire dataset (∼2.
Intriguingly, the datasets from the North Sea and Peru upwelling zone, both of which had among the highest concentrations of POC and laminarin, were characterized by the lowest LamC:POC ratios of 21 ± 6% (SD) and significantly deviated from the overall LamC:POC median of 37 ± 17% (interquartile range [IQR]) (P < 0. 001 and P < 0. 05, Kruskal-Wallis test; n = 88) (Fig. 3B ). In these nutrient-rich regions, rapidly growing microalgae may reduce laminarin storage  (25). The Arctic 2017 and the Canary upwelling sites showed LamC:POC ratios of around 58 ± 13% (SD), exceeding the median significantly (P < 0. 001 and P < 0. 05, Kruskal-Wallis test). Our data suggest a number of reasons, including nutrient concentrations and time of day when samples were taken as important controllers of the LamC:POC ratio.
Together, laminarin, proteins and lipids accounted for about 90% total POC, leaving 10% of the POC to be composed of additional polysaccharides (e. g. , cell wall), DNA, RNA, and other organic molecules. We further tested the validity of the laminarin quantification by checking their consistency with measured C/N values for a subset of samples (SI Appendix, Fig. S14 ). The elemental composition of particulate organic matter is constrained by the content of the major macromolecules glycans, proteins, lipids, and DNA leading to a mean Redfield ratio of 6. 6  (32). Despite C/N values from 2. 4 and 24, the mean C/N ratio of the dataset (8 ± 4) is close to the canonical Redfield ratio. Assuming that POM is a two-component mixture of the major pools glycan and protein, we can estimate a maximum permitted N content for any given laminarin concentration using the equation C/N = 3.
6  (32) , which is close to the mean of our dataset 8 ± 4, yet previous studies showed that the range of C/N values in marine microalgae can be quite large  (33). Likewise, large differences in the C/N ratio were observed in different oceanic regions: In the North Pacific and Bering Sea, C/N values ranged from 3. 3 to 20 (median 8. 5) (50); in the North Pacific Central Gyre, from 6. 9 to 48. 1 (median 11. 4) (51); and, in the Mediterranean Sea, from 7. 8 to 40. 8 (mean 14. 7)  (52). Our dataset ranged between 2. 4 and 24 (mean 8. 2 ± 4. 0). North Sea samples from the Raunefjorden had the highest laminarin concentrations, with an average C/N ratio of 11. 9. The synthesis of proteins, DNA, RNA, and other nitrogen-bearing molecules can be constrained by low values of dissolved inorganic N, which limits the growth of microalgae during the stationary phase of an algal bloom.
Methods Sites and Sample Collection. The entire dataset comprises samples from six oceanic regions (Fig. 1 ). They were taken during eight separate cruises or campaigns (SI Appendix, Table  S1 ). The filtered volume was logged for every sample. Prior to sampling, the glass fiber filters were combusted for 4 h at 450 °C to remove carbon contamination. After sampling, the filters were wrapped in precombusted aluminum foil and kept frozen at -20 °C until further processing. Blank samples were taken by filtering 10 L of MilliQ-H 2 O. The North Sea was sampled during two campaigns in spring 2016 (from March to June) and 2017 (from January to May) at the long-term ecological research site (LTER) "Helgoland Roads" at the island of Helgoland in the German Bight (Fig. 1 ). The sea surface samples were collected in 50-L bottles (Nalgene), and the water was filtered sequentially through 10-μm, 3-μm polycarbonate (PC) and 0.
The total lipid weight was determined by five successive measurements on a microscale balance (Mettler-Toledo). The mean relative error was determined to be 3. 8%. A blank filter was extracted following the same procedure to check for potential nonlipid contamination. Total protein and lipid concentrations were converted to carbon equivalents, assuming conversion factors of 0. 50 and 0. 75, respectively  (67) (68) (69) (70) (71). Complementary Data. The Helgoland Roads LTER time series provides Chl-a and temperature data on a weekday basis  (72). Multiwavelength spectrofluorometric analysis (bbe Moldaenke) provided an estimate of the relative pigment contributions of diatoms, chlorophytes, cryptophytes, and cyanobacteria  (20)  during this campaign. Throughout both Arctic cruises, fluorescence measurements were conducted on the rosette water sampler that was deployed together with the in situ pumps  (73, 74).
The measurements were calibrated with HPLC-based chlorophyll data  (75). These data are partly already accessible via the public database Pangaea (https://www. pangaea. de) (76). The study uses also Chl-a and temperature data from Gavin Tilstone/Plymouth Marine Laboratory/Oceans 2025 project S01 Atlantic Meridional Transect, provided by the British Oceanographic Data Centre and funded by the Natural Environment Research Council (https://www. bodc. ac. uk/). Extracellular enzymatic activities (EEAs) of polymeric substrates, especially via endo-active enzymes, yield an estimate on the potential hydrolysis rates of laminarin and other polysaccharides by a bacterial community. This technique was developed by Arnosti and colleagues and makes use of fluorescently labeled polysaccharides  (77, 78).
4 nmol•L -1 •h -1. Statistical Analysis. Statistical analysis was carried out by using the R environment v. 3. 4. 3 (R Core Team 2017, https://www. r-project. org). Linear models were fitted to the investigated variables. Significant differences in the nonnormal distributions of values among groups were tested by the Kruskal-Wallis test. P values below 0. 05 were considered statistically significant. The confidence interval for the median of nonnormal data was calculated via bootstrapping with 1,000 replications  (79). Data Availability. Environmental data used in this manuscript will be made accessible via the public database Pangaea (https://www. pangaea. de).
ACKNOWLEDGMENTS. We thank all people who helped taking samples during cruises and campaigns. Especially, we thank  Alek Bolte  for his support during the analysis of the samples. We thank  Bernhard Fuchs  and  Laura Bristow  for providing samples from the cruises  AMT22  and  M138. We thank  Antje Boetius  for providing ship time for PS99. 2 and  PS107. We thank  Nadine Gerlach ,  Andreas Sichert ,  Lukas Mühlena , and  Jenny Wendt  for supporting sample acquisition and analysis. We thank  Rudolf Amann  for scientific discussions and for enabling our participation in the spring bloom campaigns. We thank  Carol Arnosti  and  Thorsten Dittmar  for scientific discussions; and  Helga van der Jagt  and  Pierre Luigi Buttigieg  for advice with statistical analysis. Moreover, we thank  Eva-Maria Nöthig  and  Wilken-Jon von Appen  for providing us with chlorophyll/fluorescence data.
We thank  Jorun Egge ,  Assaf Vardi , and the  AQUACOSM-Vims-Ehux  project for support and access to the  University of Bergen Mesocosm Centre at the Espegrend Marine Research Field Station. Furthermore , we thank the crews of RV Polarstern,  RV Meteor ,  RV Poseidon ,  RRS James Cook , and  RRS Discovery , and the  Alfred Wegener Institute Helgoland  for helping with sample acquisition. This research was supported by the  Deutsche Forschungsgemeinschaft (DFG)  through Project Grant  HE 7217/1-1  (to J. -H. H. ) and through the future concept of the  University of Bremen  ( ZUK66 ) funded by the  DFG excellence initiative , and by the  Max Planck Society. The work was also financially supported by the  DFG  in the framework of the research unit  FOR2406 "Proteogenomics of Marine Polysaccharide Utilization" (POMPU)  ( HE 7217/ 2-1 ) (to J.
Whole-Genome Sequencing and Variant Analysis In this study, whole-genome sequences of 193 strains were obtained from a HiSeq 2500 instrument (Illumina) at the Genoscreen sequencing platform (Genoscreen, Lille, France) using a Nextera XT DNA library prep kit (Illumina). For the remaining 157 strains, library preparations were performed using the same Nextera XT DNA library prep kit (Illumina), according to the manufacturer's recommendations, and wholegenome sequencing was performed with a MiSeq instrument at ANSES (Bacterial Zoonosis Unit and IdentyPath platform, Laboratory for Food Safety, Maisons-Alfort, France). All strains were subjected to paired-end sequencing (2 × 250 bp) and deep coverage ranged from 67 to 551 (Supplementary Table  S1 ), which allows for robust SNP detection. Data quality was assessed using FastQC v0.
11. 4 (Andrews 1  ), and quality trimming was performed with Trimmomatic v0. 32  (Bolger et al. , 2014). Genomes were assembled de novo using SPAdes v3. 12  (Bankevich et al. , 2012). Genomic metrics were checked using QUAST v4. 4  (Gurevich et al. , 2013). Annotation files were generated with Prokka v1. 13. 3  (Seemann, 2014)  using the Franco-Iberian Fth FTNF002-00 genome (NC_009749) as a reference. Abricate v0. 8. 10  (Seemann, 2019)  was used in order to detect genes associated with antibiotic resistance using ARG-ANNOT  (Gupta et al. , 2014) , CARD  (Jia et al. , 2017) , EcOH  (Ingle et al. , 2015) , NCBI Bacterial Antimicrobial resistance Reference Gene Database (Accession: PRJNA313047) and ResFinder  (Zankari et al. , 2012)  databases, with a minimum identity of 50%. CanSNPer v1. 0.
Whole-Genome Phylogenetic Analysis Based on the file of concatenated SNPs generated by iVarCall2, maximum likelihood (ML) phylogenetic trees were created with IQTree  (Nguyen et al. , 2015) , SH-aLRT  (Anisimova et al. , 2011)  and UFBoot2  (Hoang et al. , 2018)  supports, set to 1000 replicates), after using Modelfinder  (Kalyaanamoorthy et al. , 2017)  to determine the best model (TVM model in this study). The best-scoring ML tree was graphically represented with iTOL viewer (Supplementary Figure  S1 )  (Letunic and Bork, 2016)  and compared with the canSNP nomenclature to detect new subclades. The 'phyloFixedVar' script of FixedVarTools  (Felten et al. , 2017)  was then used with the annotated vcf file from the variant calling analysis, and the nwk file from the best-scoring ML tree, in order to detect specific variants of subclades associated with their corresponding annotations.
Only non-homoplastic SNPs (i. e. not found in any other strains of other clusters) were selected for the canSNP nomenclature. Newly identified canSNPs were named in agreement with this existing nomenclature  (Lärkeryd et al. , 2014). The text files containing the SNP positions and the tree topology were adjusted with the newly identified subtypes. CanSNPs identified in our study were arbitrarily classified from 1st to 8th order of discrimination (D1-D8), and their positions on genome FTNF002-00 are listed in Supplementary Table  S2. Genomes of the NCBI database (n = 248) that were available on the assembly summary file 2 (Supplementary Table  S3 ) were screened using the new canSNP nomenclature to validate the newly identified canSNPs.
Statistical Analysis The association between clade membership and epidemiological covariates (host species and geographical distribution) was analyzed for D4 clades containing at least 10 strains isolated in France  (B. 49, B. 50, B. 55, B. 87, B. 250, and B. 251). A spatiotemporal analysis was performed on the subset of strains isolated from hares in France between 1985 and 2018 (n = 204) since these animals are considered a relatively homogeneous host population. The association between geographic and genetic distances was assessed using Mantel's test (1000 repetitions), based on the SNP distance matrix, and on the geographic distance matrix computed by assigning each strain the coordinates of the centroid of the French 2 ftp://ftp. ncbi. nlm. nih. gov/genomes/genbank/bacteria/Anaplasma_ phagocytophilum/assembly_summary.
txt administrative area (department) of isolation (i. e. the 3rd level of NUTS [Nomenclature of Territorial Units for Statistics] in France). For each D4 clade containing at least 10 strains isolated from hares (to ensure the robustness of the statistical analysis), spatiotemporal clusters were identified using a Bernoulli model, with cases as the strains belonging to the considered clade, and controls those belonging to the other clades. The time and space resolution levels were the year and the department of isolation, respectively. Circular scanning windows were used. Purely spatial clusters were allowed, but purely temporal clusters were not, the maximum extent of a cluster being set to 50% of the strain population. R software (R Core Team, 2019) was used for statistical analysis and Mantel's tests were performed using the ade4 package  (Dray and Dufour, 2007).
ACKNOWLEDGMENTS This work benefited from input from the  Genoscreen (Lille)  and  Identypath  ( ANSES, Maisons-Alfort ) platforms for high-throughput sequencing. The authors would like to thank the  SAGIR surveillance network  and the  ONCFS in France  for the collection of animal samples (http://www. oncfs. gouv. fr/Reseau-SAGIR-ru105). The four Spanish strains were isolated thanks to the RESERTULA ( CLG2015-66962-C2-2-R ) project funded by  Ministerio de Economía y Competitividad, Government of Spain/FEDER. We are grateful to  K. Myrtennäs  from the  Swedish Defense Agency (FOI)  for providing us canSNP numbers for the CanSNPer nomenclature, and to  L. Michelet  and  M. L. Boschiroli  for their useful comments on this manuscript.
The Phoenix datosx2CT software was applied to gather radiographies in a reconstructed 3D volume, in which the voxel size ranged from 1 to 12 μm depending on the size of the specimen. Post-treatment of 3D volumes, including virtual sectioning as well as sub-volume extractions, was performed using VG-studio max software. Surface smoothing was done with Meshlab software. Incisors hole positions were detected using VG-studio max software and frontal sections were done at each hole using a rotation section plane to respect the natural curvature plane of the incisors. We studied 59 holes from ten incisors (6 individuals); the two remaining incisors were used for histology purposes. The centroid of the incisor section was drawn using ImageJ software and mesio-distal and linguo-vestibular axes were positioned in the incisor section.
Chronology of incisor development and replacement We have observed the establishment of the incisors on the upper and lower jaws. The dI1 and the dI2 incisors in the upper and lower jaws appear synchronous in their development. Rabbits have another incisor in the maxillary, named the dI 3 , which is replaced during ontogeny. We will focus here on the description of the upper incisors (Fig. 1 ), since we do not observe dental replacement in the lower ones. All our 3D reconstructions of the epithelial parts of the incisors are available in Morphomuseum (3D Dataset will remain locked until publication of the main article, Bertonnier-Brouty,  Viriot, Joly, & Charles, 2019). At 12 dpf, we observed no morphological structure of incisor development. Then, at 14 dpf, we detect the initiation of the placode formation at the upper and lower jaws (Fig.
Conclusion We provided here the description of the complete histo-morphological chronology of the tooth development and replacement in rabbit. It can be considered as a new dataset that allows a better understanding of the dental replacement among mammals. The 3Dreconstructions gave useful information about the geometric organization of the dental replacement and the precise knowledge of the dental development and replacement chronology in this species is a starting point that will allow further studies of gene functions at specific time windows. Development of the dI 1 , dI 2 , dI 3 and I 3 from 14 to 28 days post-fertilization (dpf). Indicated in blue, the vestigial incisor; in pink, the interdental lamina; in black (dotted line), the dental tissues; in black (arrow), the replacement tissues.
Acknowledgements We thank  Nicolas Goudemand  and all the members of his team for their help and support. We thank all the members of the " Evolution of vertebrate dentition " for the helpful advices and comments. We thank  Joanne Burden  for help in the final preparation of the manuscript. We thank  Cécile Callou , who is responsible for the collection of the lagomorphs at the  Museum National d'Histoire Naturelle (MNHN) of Paris , for giving us access to these collections and for her help during our visit. We are grateful to  Mathilde Bouchet-Combe , from the  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENSL ,  UCBL1 ), who helped for X-Ray microtomographic analyses. The authors declare no conflict of interest.
This is a setup especially designed for inelastic neutron scattering measurements on actinides and was suitable for detecting also the 14-keV γ ray. The data analysis of this experiment is currently ongoing. The latest measured iron isotope was 54 Fe. The experimental results obtained following the inelastic scattering of neutrons on 54 Fe were published in Ref. [12]  in comparison with previously reported results and with two talys theoretical calculations. The first calculation was done using the default input parameters. As the agreement between the experimental results and the default theoretical predictions was bad we performed a tuned calculation by modifying the optical model parameters. The modifications were done for the radius associated with the proton volume-central term and the diffuseness and the radius of the neutron surface-central component.
In 2010 and 2011, 395 research cluster proposals were submitted, out of which 170 were funded. The average allocation was 8. 8 million euros (approximately 10 millions USD) over 10 years. These clusters bring together researchers from different research units, not necessarily part of the same institution. Our research design exploits different key pieces of information. First, we obtained from the agency running the program the grades given by reviewers for all projects, both those accepted and those rejected. Second, we had access to all detailed proposals from which we extracted the list of associated research units and the names of authors cited in the project reference list. Third, we combined these data with a unique country-wide roster of tenured professors and researchers employed by all universities and research institutes to identify the members of each cluster.
Matching author names of each cluster reference list with the names of its members, allowed us to determine in a very precise way the core and periphery members of each proposal. Lastly, we extracted from the Web of Science bibliographic information of each cluster member. Our final dataset tracks from 2005 to 2017 nearly thirty thousand professors or researchers attached to at least one cluster proposal. Most of our analysis is conducted at the individual researcher level and includes time and researcher fixed effects. These fixed effects correct for differences in levels between members of financed and non-financed clusters, but cannot capture differential trends. We thus, in our preferred specification, restrict our analysis to researchers who were part of only one cluster proposal and that received similar grades on that unique proposal.
Wuchty, Jones and Uzzi (2007) , using several decades of data on publications, show that research is increasingly reliant on teamwork across fields and furthermore that the knowledge produced by teams are more likely to create very high impact research. Adams (2013)  shows that international teams are particularly productive. block for the organization of academic research. Some research units are attached to only one institution, but many are mixed in the sense that they are supported on a contractual basis by several institutions, most of the time one research institution (CNRS for example) and one university. Research units are evaluated every five years by a dedicated national agency. On the basis of those evaluations, units are maintained, closed or may be reorganized or merged. Each research unit is governed by its own statutes but typically has a director and an elected council which define and implement its strategy.
This unique dataset (described in more details in the Appendix) has been built using a variety of official sources thanks to the centralization of that information at the national level in France. As it offers information on the employing university or research institute and on the research unit, it was possible to attach each person to cluster proposals. Besides, we use data extracted from the project bibliography to further refine the membership definition. For each research cluster (financed or non financed), we identify two subgroups: • The Core members are members of the research cluster (i. e members of one of the research units listed as partners) who also appear as author of articles listed in the bibliography of the proposal. • The Periphery members who are members of the research cluster but whose work is not referenced in the bibliography.
Scientific Publications We then build bibliometric information on all cluster members. For that purpose, we first match all cluster members names to the authors of scientific articles (on the basis of surname and first name initials) in the Clarivate Web of Science (in house XML datafiles and online access), which gathers all the documents published in the main scientific journals. We retrieve more than ten million documents published until 2017 that need to be filtered out to keep only those that have been authored by professors and researchers in the research clusters we study and not by homonyms across the world. We do so using all available information we have on individual profiles through a "seed and expand" methodology  (Reijnhoudt et al. , 2014). 9 As all regressions below use individual fixed effects, we consistently restrict the data to those professors and researchers for whom we have retrieved at least one article over the period.