dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS This work was made possible by the  RBG Kew Pilot Study Fund 2017  and the  QMUL  and  RBG Kew MSc  programme in Plant and Fungal Taxonomy, Diversity and Conservation on which the first author was enrolled during 2017-2018. The  Howard Lloyd Davies Legacy Fund  supported the RADseq work. We thank the staff of the  Kew Madagascar Conservation Centre ,  Stuart Cable  ( RBG Kew ),  Vololoniaina Jeannoda  ( University of Antananarivo ) and  Parc de Tsimbazaza  for their long-term collaboration and support for Madagascar Poaceae research. The  Direction Générale des Forêts ,  Madagascar National Parks , and  Ezemvelo KZN Wildlife  generously granted our research permits. Field collections in South Africa were made possible by a Newton grant awarded to  Caroline Lehmann  and  Gareth Hempson.
The microsatellite marker primers were designed with the help of  Méline Saubin  ( RBG Kew ). Jaume Pellicer Moscardó ,  Robyn Faye Powell  and  María Conejero  (RBG Kew) kindly performed the estimation of nuclear DNA contents. The curators of BM, K, PRE and TAN herbaria provided access to their collections; curators of TAN and PRE gave permission to sample herbarium specimens in their collections. Sarah Z. Ficinski  drew and edited the figures. JS, US and GB are members of the EDB lab that is supported by  LABEX TULIP  ( ANR-10-LABX-0041 ) and  CEBA  ( ANR-10-LABX-25-01 ) and  LIA BEEG-B (Laboratoire International Associé -Bioinformatics, Ecology, Evolution, Genomics and Behaviour, CNRS ). They were also funded by an  ERA-NET   BiodivERsA  project:  INFRAGECO (Inference, Fragmentation, Genomics , and  Conservation ,  ANR-16-EBI3-0014 ).
International collaboration contributes significantly to improving scientific performance in Vietnam, but it also results in Vietnamese researchers depending on foreign partners to get their work published. The current study is an initial effort to examine the domestic research capability of Vietnam's Social Science and Humanities (SSH) disciplines through scientific productivity. The research focuses on investigating various aspects, including leading Vietnamese authors, solo authors, and gender difference, as well as international and domestic collaboration networks. The study extracts the data of 2040 Vietnamese SSH authors, 1981 foreign authors, and 3160 publications during the period of 2008-2019, from the exclusive Social Sciences and Humanities Peer Awards (SSHPA) database. Findings show a steadily rising contribution from leading domestic authors in SSH research, with an annual growth rate of approximately 22.
Collaboration Patterns of Authors in Vietnam's SSH Regarding research output, Vietnam's research performance has achieved remarkable success in raising scientific productivity  [8, 26]. As an initiative to promote scientific works, the first national scientific funding agency, the National Foundation for Science and Technology (NAFOSTED), was established in 2008 by the Vietnamese government in order to provide financial support to both natural and social science fields. Together with the later issuing of Circular no. 23/2014/ND-CP, which included rigorous instructions for the organization and operation of the NAFOSTED, this has allowed progress towards making foundation-funded scientific projects compliant with international standards. Until 2019, there have been more than 10,000 participating scientists, over 2400 trained doctors, and more than 4000 Institute for Scientific Information (ISI)'s Web of Science-indexed articles published across nearly 2800 research projects, funded and supported by NAFOSTED sponsorship  [27].
Materials This article employs the Social Sciences and Humanities Peer Awards (SSHPA, accessible online at: https://sshpa. com). It is a semi-automatic system, operating since 2017 to collect the scientific output of Vietnamese authors in SSH from 2008. The system's creation process and logical structure can be found in detail in the article by  Vuong et al. (2018)   [29]  (The dataset is available in the Supplementary Materials). The SSHPA system collects papers from 31 fields in SSH. Detailed information about these disciplines is listed in Table  1. The data collection process of the SSHPA database was designed to: (i) collect data from every Vietnamese SSH researcher who has published in journals indexed in the Web of Science (WoS) or the Scopus database, or on the list of prestigious international and national sources approved by NAFOSTED  [33] , and (ii) ensure reliability and accuracy.
Variables With input from the SSHPA database, the study contains the following variables: • Author: the person who published the paper recorded into the SSHPA system. Author information includes general information (SSHPA ID, full name, year of birth, sex, nationality, Scopus Author ID), contact information, affiliation, and articles. In this article, sometimes the term 'Researcher' is used with the same meaning as 'Author'. Lead author: The first author of a paper. "New" author: Those who are recorded to have a publication for the first time in the SSHPA database. • Publication: includes journal articles, books, book chapters, and conference proceedings in the NAFOSTED list mentioned above. During the data entry process, each publication is assigned to a certain field. Interdisciplinary publications can be assigned to more than one field.
The Scientific Output of Authors in Vietnam's SSH in Terms of Lead Authors and Gender Differences In the 2008-2019 period (data as of 30 April 2020), the SSHPA system has recorded a total of 3160 publications written by 4021 authors, in which 2040 are Vietnamese authors and 1981 are foreign authors. Among 3160 publications, 2419 publications were led by Vietnamese authors (77. 26%) and 712 publications were led by foreign authors (22. 74%). As can be seen from Figure  1 , the number of Vietnamese lead authors increased steadily during the period from 2008-2019; the average annual growth rate of Vietnamese lead authors was approximately 22. 33%, while that of foreign authors was 10. 60%. In 2008, the ratio between the number of Vietnamese lead authors and foreign lead authors was 2. 07; the ratio became 6.
Discussion Through the data retrieved from the SSHPA database from 2008 to 2019, we examined the selfsustaining capacity of SSH research in Vietnam. Several main findings were observed. First, the internal research capacity in SSH fields in Vietnam has been increasing, as evidenced by a growing number of domestic lead authors and an expanding domestic research collaboration network. Second, even though the productivity and work quality of Vietnamese female researchers was undeniable, their participation in SSH research remained limited. Third, during the period, internationally collaborated research projects still dominated the scientific output of Vietnam's SSH; however, recent years have witnessed a strong rise in domestic authors and domestic networks. In 2019, the number of publications with exclusively Vietnamese authors even exceeded that figure of internationally collaborated ones.
Discussion Through the data retrieved from the SSHPA database 2008 to 2019, we examined the self-sustaining capacity of SSH research in Vietnam. Several main findings were observed. First, the internal research capacity in SSH fields in Vietnam has been increasing, as evidenced by a growing number of domestic lead authors and an expanding domestic research collaboration network. Second, even though the productivity and work quality of Vietnamese female researchers was undeniable, their participation in SSH research remained limited. Third, during the period, internationally collaborated research projects still dominated the scientific output of Vietnam's SSH; however, recent years have witnessed a strong rise in domestic authors and domestic networks. In 2019, the number of publications with exclusively Vietnamese authors even exceeded that figure of internationally collaborated ones.
Limitations Despite the contribution to the understanding of the internal capability of Vietnamese SSH, we would like to acknowledge several limitations of this article. Firstly, we approach the subject of internal research capacity from a sheer quantitative approach, focusing on ISI/Scopus-indexed journals. The journals, books or conference proceedings that are indexed in the ISI or Scopus database are potentially biased toward English language and Western-oriented publications. Even though the usage of these databases is common practice  [4, 5] , the exclusion of scientific publications in other languages and journals might hinder thorough evaluation of Vietnamese SSH researchers. Moreover, social sciences and humanities are qualitative and hermeneutic disciplines. Therefore, the usage of a quantitative approach and the act of counting publications only present an initial effort to reach a perspective on Vietnamese SSH.
Sample Collection, Processing and Metadata A detailed version of the following methods is provided in Supplementary Information. Naturally infected C. parvum hosts spanning the parasite ontogenetic development were searched among snails (Potamopyrgus antipodarum), amphipods (Paracalliope fluviatilis), and fish (Gobiomorphus cotidianus) collected from Lake Waihola, South Island, New Zealand during the 2019 austral summer. Samples were collected under an approved Animal Use Protocol from the University of Otago. Immediately prior to animal collection, two types of environmental samples were collected with sterile cotton swabs, i. e. , water (two samples) and lake sediment (two samples) Two controls for the swabs themselves were also taken by opening the swab and exposing it to natural air prior to saving it in a PowerBead Pro Tube.
Sequence Processing Data were received as demultiplexed paired-end raw sequences, and were processed and analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) 2 software package  (Bolyen et al. , 2019). Adapters and primers were removed from raw sequences using the plugin cutadapt (with 0 error-rate and minimum length of 240 bp)  (Martin, 2011) , and quality filtered using the dada2 plugin  (Callahan et al. , 2016)  after inspection of quality profile plots of forward and reverse reads. The resulting amplicon sequence variants (ASVs) table was filtered to exclude non-bacterial, mitochondrial, chloroplast and ASVs without a phylum assignment, contaminants, and samples with low sequencing depth (i. e. , frequency lower than 1,000, and/or with less than 8 ASVs) using the feature-table plugin (see Supplementary Information).
A 'reduced dataset' which did not include ASVs not shared by at least two samples (feature-table plugin) was also created. For analyses regarding the diversity of parasite-associated microbial communities, these two datasets were further filtered to include only those samples extracted from parasite tissues. For each dataset, different taxonomic levels were assigned to the ASVs using the plugin featureclassifier  (Bokulich et al. , 2018)  against the Greengenes 16S rRNA reference database (13_8 release) pre-trained on the 515F/806R region  (Pedregosa et al. , 2011). Sequenced data quality was evaluated based on the observed composition and sequence quality of the ZymoBIOMICS microbial community standards (MCS and MCS DNA), against the expected data of these mock communities, using qualitycontrol plugin.
Diversity Analyses Diversity analyses were performed primarily using QIIME 2, and the R packages vegan  (Oksanen et al. , 2019)  and phyloseq  (McMurdie and Holmes, 2013)  with default function settings unless otherwise noted. Prior to analyses, ASVs were aligned using the mafft program  (Katoh et al. , 2002)  and used to construct a phylogenetic tree using the fasttree2 program  (Price et al. , 2010)  with the phylogeny plugin. For analysis, the filtered ASVs and taxonomy tables, and the rooted tree were imported into R (R Core Team, 2018) with the qiime2R package  (Bisanz, 2018)  and together with the metadata combined into a phyloseq object. Given that one of the sources of potential 'noise' in metabarcoding analysis is the fine-scale data (here ASVs), analyses were also performed at the higher taxonomic ranks Phylum and Family using the agglomeration phyloseq function tax_glom.
Phyloseq objects were evenly subsampled using rarefy_even_depth(). We started by investigating the presence of a 'core' microbiome common to all parasite life stages, and specific to each life stage. First, Venn diagrams were created at the family level. We tested for the presence of a 'core' microbiome as defined by any taxon with a prevalence higher than 0. 95, 0. 75, or 0. 50 with the microbiome package  (Lahti and Shetty, 2012-2019)  at different taxonomic levels. To infer which families had a higher relative abundance among life stages, we created heatmaps using plot_ts_heatmap() of the mctoolsr package  (Leff, 2017). A tree plot was created over the full tree estimated from the alignment of parasite ASVs to visualize how microbiota components of the different life stages relate to each other, and how the life stages relate to each other.
Sources of Parasite Microbiome We tested whether the parasite's microbial composition differed from that of its different hosts and environment using the same diversity analyses as described above. Using Venn diagrams, we determined if there were any taxa unique to the parasite bacterial community, irrespective of their abundances and prevalence. To determine the likely main sources of each life stage microbiome, we used the Bayesian approach SourceTracker developed for R  (Knights et al. , 2011). For each parasite life stage (classified as 'sink'), we used SourceTracker to estimate the proportion of bacteria originating from potential 'sources': environmental samples (water and sediment, and laboratory environment), the host, the prior parasite life stage, or unknown sources (representing one or more sources absent from the training data) using the ASV data of the reduced dataset with a rarefaction of 1,000.
Samples were classified as sources (potential contributors to a given microbial community) or sinks (the community being investigated), and a total of four analyses were conducted (one per life stage). We then predicted for each sink and their respective sources training data, the proportional contribution of sources with predict(). Bar plots were created over the mean and standard deviation of the resulting proportion estimates of contributing sources for each parasite life stage, and also for the respective train data with ggplot2  (Wickham, 2016).
RESULTS About 5. 3 million demultiplexed paired-end raw sequences from 91 samples (including blank and control samples) were obtained. As outlined in the Methods, we analyzed two datasets: the main dataset, and a more conservative 'reduced' dataset which did not include ASVs not shared by at least two samples. After sequence quality control filtering (see Supplementary Information), the main and reduced datasets consisted of 60 samples with 2,648 ASVs, and 49 samples (since 11 had frequency below 1,000) with 937 ASVs, respectively. Microbial communities from parasite specimens included 30 samples with 360 ASVs, and 22 samples with 70 ASVs for the main and reduced datasets, respectively. Taxonomic assessment based on the Greengenes 16S rRNA reference database revealed biased classification below the family level (Supplementary Figure  S1b ).
5 and shared by all life stages. At the genus level, the two genera Geobacillus (Bacillaceae) and Ralstonia (Oxalobacteraceae) were detected at a prevalence threshold of 0. 5 and shared by all life stages. At the family level, two families occurred among all life stages at the higher prevalence threshold of 0. 75 (Comamonadaceae and Oxalobacteracea), and one additional family (Bacillaceae) could be included in the parasite family level 'core' with a prevalence threshold of 0. 50 (Figure  1B ) (see Supplementary Information for results pertaining to the reduced dataset). Life stage-specific 'core' analysis revealed higher prevalence values at a finer scale (i. e. , ASVs), between 0. 75 and 0. 5 for all life stages in the full dataset with the exception of cercariae [one for sporocysts (Thermaceae), one for metacercariae (Bacillaceae), and one for adult worms (Comamonadaceae)].
Community Ecology The annotated phylogenetic tree shows that only few ASVs are shared by many samples, but they are closely related within each life stage, without a clear distinction in diversity level among life stages (Figure  2A ). Microbial diversity did not differ significantly among life stages for any of the metrics used (in all cases p > 0. 1, Figure  2B  and Supplementary Table  S4 ), and for any of the taxonomic levels investigated. Post hoc pairwise comparisons show that, for the reduced dataset only, microbial communities of adult worms had higher Faith's phylogenetic diversity than those of metacercaria (Kuskal-Wallis pairwise H = 5. 357, df = 1; p = 0. 021, BH-FDR = 0. 124), and that metacercariae had higher evenness than sporocysts (Kuskal-Wallis pairwise H = 4. 167, df = 1; p = 0.
041, BH-FDR = 0. 247). Testing for changes in community composition along the parasite ontogenetic development with principal coordinate analysis on unweighted and weighted Unifrac distances showed very little clustering among life stage, with only a fraction of variation explained by the first two axes (Figure  2C ). Permutational ANOVA on the different distance metrics further supported these observations, and parasite life stage was not found to predict parasite microbial community structure at any taxonomic level and in either the full or reduced datasets (all metrics p > 0. 05, Supplementary Table  S5 ). However, post hoc tests identified cercariae and metacercariae as significantly different from each other based on unweighted unifrac (F = 1. 579, p = 0. 0313, but BH-FDR = 0. 188; similar for estimates at family level).
However, for the reduced dataset this was not the case and instead adults and metacercariae presented significantly different communities based on unweighted unifrac (F = 1. 751, p = 0. 038, BH-FDR = 0. 229; same for phylum and family level). Analysis of homogeneity of dispersion showed that all groups presented similar dispersions (all tests p > 0. 05). Knowing that there was no significant structure in microbial community of the parasite driven by life stage, we estimated whether any taxa were differentially abundant across the four life stages. The results revealed only very few taxa showing differential abundance from one life stage to the next (see Supplementary Information).
When considering only what is present in the community (unweighted unifrac), we uncovered distinct clusters among sample types; environmental samples, lab environment and amphipods seem to have a distinct microbiota from that of the parasite, but not snail and fish hosts. However, when considering relative abundance of bacterial taxa, only the environment seems to have a distinct microbial community (although for the reduced dataset, PCoA on weighted unifrac shows that all main sample types present distinct microbial communities; Supplementary Figure  S2 ). Permutational ANOVA indicated significant differences in microbial community among samples (unweighted unifrac: F = 1. 6256, p < 0. 001; weighted unifrac: F = 2. 787, p < 0. 001). However, in some cases, groups presented different dispersions (unweighted unifrac: F = 10.
429, p < 0. 001; unweighted unifrac: F = 1. 8311, p = 0. 0903; for reduced dataset both tests p > 0. 05). Post hoc pairwise analysis indicated that the microbial community of each parasite life stage differs significantly from that of the environment (p < 0. 05, and several cases BH-FDR < 0. 05, Table  1 ). With respect to their respective hosts (their direct environment), the composition of the parasite microbiota at each life stage did not differ significantly from   S3 ). The proportion of incorrect assignments for a source may be the main contributor of the 'unknown' source in (B), as opposed to an unsampled source excluded from the training data. their hosts (unweighted unifrac p > 0. 05, similar across taxonomic levels), the exception being metacercariae which differed from their amphipod host (F = 2.
986, p = 0. 000, BH-FDR = 0. 007, consistency across both datasets and taxonomic levels, Table  1  and Supplementary Table  S7 ). However, when relative abundance is considered in addition to taxonomic composition (weighted unifrac), all parasite life stages were found to harbor significantly different communities from their respective hosts, the only exception being for adult worms (Table  1 , but results depend on the taxonomic level considered). Using SourceTracker, we aimed to identify the main sources of microbiota of each parasite life stage. Results indicate that the previous life stage was the main known source of bacteria for all life stages, with the current host and the environment making much lower contributions (Figure  3B ). To determine if the unknown source was actually an artifact of rarefaction, we also analyzed the proportion of each source that was correctly identified during the training step of the analysis (Figure  3C ; see Supplementary Figure  S3  for full results).
Finally, we repeated the analyses on a 'reduced' dataset, which included only ASVs detected in at least two samples, and thus provided a more conservative test of our predictions based on more stringent criteria for data inclusion. Overall, we are confident that the patterns we observed are real ones. We expected the diversity of the trematode microbiome to peak at the adult stage, not simply because it has the largest body size, but because it is the only life cycle stage in the trematode studied here that actively feeds by ingesting host tissues, providing an invasion opportunity for host bacteria to colonize the parasite. In fact, our results reveal no consistent difference in microbiome diversity among life stages, across the different diversity metrics used, the different taxonomic levels analyzed, and the two different datasets (full and reduced).
DATA AVAILABILITY STATEMENT All microbial 16S rRNA sequences were deposited in the Sequence Read Archive (SRA, https://www. ncbi. nlm. nih. gov/ sra/) under accession number PRJNA623422. Metadata, raw data and scripts used in this study are available on the OSF repository (https:/ /osf. io/pmq7r/?view_only=bca6433734c246bab3da0dc24 3ff4706).
The purge gas was dry nitrogen. TGA/DSC experiments were repeated at least 3 times for 0% and 3% pAsp samples and twice for other samples. High Resolution X-Ray Powder Diffraction: High resolution X-ray powder diffraction (XRD) measurements were performed at the beamline ID22 of the European Synchrotron Radiation Facility (ESRF, Grenoble, France). The X-ray wavelength (0. 399952 Å) was calibrated with Si standard sample (NIST, Gaithersburg, MD, USA). The powder samples were put in glass capillaries and measured at room temperature. Rietveld refinement analysis was performed using the FULLPROF program version 2017,  [32]  with a 6 coefficients polynomial background fitting and a least squares refinement weighting model.
(9 of 10) © 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Adv. Funct. Mater. 2020, 2000003   In Situ Wide-Angle X-Ray Scattering: Wide-angle X-ray scattering experiments were carried out at the synchrotron µ-Spot beamline (BESSY II storage ring, Helmholtz-Zentrum Berlin) using a multilayer monochromator and spot size of 100 µm. A MarMosaic CCD detector (Rayonix L. L. C. , Evanston, IL, USA) was used to collected 2D scattering patterns in the transmission geometry. The rings of the pattern were circular and the intensity around each ring was uniform. Radial integration of the patterns with the software DADAK gave the spherically averaged scattering intensity as a function of the modulus of the scattering vector q. Here q = 4π sin(θ)/λ, 2θ is the scattering angle and λ is the wavelength.