dataesr/scientific-paragraphs-categorization huggingface dataset:
REVEALS requires large sites (ideally >1 km 2 )  (Sugita, 2007)  or multiple small-sites  (Trondman et al. , 2015) ; we thus ran the model on the combined Aydat, Espinasse and Pavin datasets in order to reconstruct the macro-regional vegetation cover changes. This reconstruction is based on the same vegetation groups as those presented before and for which pollen productivity estimates (PPEs) of pollen taxa were available. PPEs and pollen fall speeds for the chosen taxa were derived from the work of  Mazier et al. (2012) , who derived datasets of average PPEs for major European plant taxa (Table  1 ). We selected PPEs from the Standard 2 dataset of  Mazier et al. (2012) , which minimises the effect of outliers in the PPEs calculation from different locations and was deemed to be the most objective dataset in order to obtain robust vegetation cover estimates in NW Europe  (Mazier et al.
, 2012; Trondman et al. , 2015). The REVEALS model was run for single sites using the DISQOVER package  (Theuerkauf et al. , 2016)  for R employing the Gaussian Plume Model with a wind speed set to default (3 m•s -1 ). The compilation of three sites was run using the R script available at https://github. com/ petrkunes/LRA. Rarefaction analysis was undertaken for the 3 case studies in order to assess temporal changes in pollen-assemblage richness or expected number of terrestrial pollen taxa (E(T n )), which is the number of pollen types in a pollen sample at a specific counting sum  (Berglund et al. , 2008). The mean pollen sums (hygrophytic plants excluded) are around 500 pollen grains for Pavin and Aydat sequences and around 300 pollen grains for the Espinasse sequence (because of the bad pollen preservation).
Comparison between pollen percentages and first quantitative reconstructions Figure  3  summarizes the pollen percentages obtained separately on the 3 study sites and, on the combined Aydat, Espinasse and Pavin datasets and REVEALS estimates for forest and non-forest taxa. These data allow us to characterize the regional vegetation cover of the Auvergne Mountains since c. 7000 cal BP. Pollen percentages of the tree taxa display mean values oscillating between 80% and 90% between approximately 7000 and 2000 cal BP. This high amount of tree pollen indicates that the landscape was largely dominated by forests, both at local and regional scales, during this earliest time-period. Nevertheless, the decreasing trend of the oak woodland values (from 50 to 25% approximately) is coeval with an increasing trend of the frequencies of the mountain woodland (from 5 to 55% approximately) between c.
Acknowledgements This research was supported by the  ERODE and DICENTIM  Projects both funded by the  INSU/CNRS  (respectively directed by  J. R. Disnar  and  A. C. Lahours ). M. Mariani  was supported by the  Australian Institute for Nuclear Science and Engineer (AINSE)  and the  John and Allan Gilmour Research Award (University of Melbourne ). We would like to give our warmest thanks to  F. Mazier  and  L. Marquer  for the organization of the  Summer School CNRS POLQUANT  (August 28 -September 2, 2016), where were initiated the attempts to do land cover estimates for the Auvergne Region. Thanks also to  S. Sugita  and  P. Kunes  for their teachings. Special thanks to  P. Kunes  for making the R script for multiple sites REVEALS available on github. Many thanks also to  A. Blaus ,  M. Caspers  and  A.
| Taxon and host range data selection We used all 105 Sclerotiniaceae species for which at least ITS marker sequence data were available in the GenBank database. As outgroups we selected 56 Rutstroemiaceae species and 39 representative species of Leotiomycetes and Sordariomycetes for a total of 200 species. Host range data were obtained from  (Boland & Hall, 1994; Melzer, Smith, & Boland, 1997) , Index Fungorum (http://www. indexfungorum. org), the SMML fungus-host distribution database  (Farr & Rossman, 2016)  and references therein. In total, we retrieved 7,101 fungus-host association records that did not show strong geographic or crop/wild species bias (Figure  S1 ). Fungal host range was not correlated to the number of database records (Figure  S1c ). Calibrated trees deciphering the relationships between the host families based on seven gene regions (18S rDNA, 26S rDNA, ITS, matK, rbcL, atpB and trnL-F) were extracted from  (Qian & Zhang, 2014)  and updated with  (Hedges, Marin, Suleski, Paymer, & Kumar, 2015).
Statistical branch support was inferred with the SH-like approximate likelihood ratio test (SH-aLRT)  (Anisimova, Gil, Dufayard, Dessimoz, & Gascuel, 2011 ) and bootstrap analysis with 100 replicates (Files 3 and 4). The topology of the tree was confirmed by three additional methods. First, we used a neighbour-joining approach in FastME 2. 0  (Lefort, Desper, & Gascuel, 2015)  with the LogDet substitution model and tree refinement by subtree pruning and regrafting (File S5). Second, we used the parsimony ratchet approach implemented in the PHANGORN package in R  (Schliep, 2011; File S6). Third, we used Bayesian analysis in MrBayes 3. 2. 6  (Ronquist et al. , 2012)  using the GTR substitution model with gamma-distributed rate variation across sites and a proportion of invariable sites in 100 million MCMC generations, sampling parameters every 1,000, removing 10% of the tree files as burn-in.
| Divergence dating analyses We used a Bayesian approach to construct a chronogram with absolute times with the program BEAST 1. 8. 2. As no fossil is known in this fungal group, we use Sordariomycetes-Leotiomycetes divergence time to calibrate the tree (~300 Mya,  Beimforde et al. , 2014). Using our initial phylogeny as a constraint, the partition file was prepared with the BEAUti application of the BEAST package. Considering that our data set includes distantly related taxa, subsequent analyses were carried out using an uncorrelated relaxed clock model with lognormal distribution of rates. We used a uniform birth-death model with incomplete species sampling as prior on node age, following  (Beimforde et al. , 2014)  to account for incomplete sampling in our phylogeny. Analyses were run three times for 100 million generations, sampling parameters every 5,000 generations, assessing convergence and sufficient chain mixing using Tracer 1.
| Diversification analysis We used the section containing all 105 Sclerotiniaceae species of the chronogram with absolute times generated by BEAST to perform diversification analyses with three different methods. We first used Bayesian Analysis of Macroevolutionary Mixtures (BAMM) version 2. 5  (Rabosky et al. , 2013 (Rabosky et al. , , 2014)) , an application designed to account for variation in evolutionary rates over time and among lineages. The priors were set using the setBAMMPriors command in the BAMMTOOLS R-package  (Rabosky et al. , 2014). We ran four parallel Markov chains for 5,000,000 generations and sampled for every 1,000 trees. The output and subsequent analyses were conducted with BAMMTOOLS. We discarded the first 10% of the results and checked for convergence and the effective sample size (ESS) using the coda R-package.
Lineage-through-time plots with extant and extinct lineages were computed using the PHYTOOLS package in R  (Revell, 2012)  with the drop. extinct=FALSE parameter. The exact number of Sclerotiniaceae species is currently unknown, and no intentional bias was introduced in data collection (Figure  S1 ). To detect shifts in diversification rates taking into account incomplete sampling of the Sclerotiniaceae diversity and tree uncertainty, we used MEDUSA, a stepwise approach based upon the Akaike information criterion (AIC)  (Alfaro et al. , 2009; Drummond, Eastwood, Miotto, & Hughes, 2012). For this analysis, we used the birth and death (bd) model, allowed rate shifts both at stem and nodes, used the AIC as a statistical criterion with initial r =. 05 and e = 0. 5. Sampling of the fungal diversity is typically estimated around 5%  (Hawksworth & Luecking, 2017).
| Two major diversification rate shifts in the evolution of the Sclerotiniaceae To test for a relationship between host range variation and biological diversification in the Sclerotiniaceae, we estimated divergence times for the fungal family Sclerotiniaceae using the ITS marker in a Bayesian framework. A calibrated maximum clade credibility chronogram from these analyses is shown in Figure  2a  (and Figure  S6 ). This showed that Sclerotiniaceae fungi shared a most recent common ancestor between 33. 9 and 103. 5 million years ago (Mya), with a mean age of 69. 7 Mya. The divergence of Botrytis pseudocinerea, estimated from the ITS data set, occurred ca. 3. 35-17. 8 Mya (mean age 9. 8 Mya) and is similar to a previous estimate of 7-18 Mya  (Walker et al. , 2011). Using this time-calibrated phylogeny, we calculated speciation and diversification rates using three different methods, to control for the limits of individual methods  (Rabosky, Mitchell, & Chang, 2017).
Indeed, Sclerotinia borealis, S. glacialis, S. subarctica and S. nivalis, that diverged during regime G2, are largely restricted to hemiboreal climates (circumboreal region) and can have a lower optimal growth temperature than their sister species  (Hoshino, Terami, Tkachenko, Tojo, & Matsumoto, 2010; Saito, 1997). Excellence project TULIP (ANR-10-LABX-41; ANR-11-IDEX-0002-02) and a "New Frontiers" grant of the Laboratory of Excellence project TULIP. We thank the TULIP international advisory board and the TULIP community for stimulating discussions.
DATA ACCESSIBI LITY • Species identifiers: GenBank accessions provided in Table  S1  • DNA sequences and alignments: provided as File S2 • Phylogenetic trees: provided as Files S1, S3, S4 and S5 • Host range data, DNA sequences and alignments and Phyloge- netic trees are available from the Dryad Digital Repository: https://doi. org/10. 5061/dryad. 7cs3g.
This study presents a description of an open database on scientific output of Vietnamese researchers in social sciences and humanities, one that corrects for the shortcomings in current research publication databases such as data duplication, slow update, and a substantial cost of doing science. Here, using scientists' self-reports, open online sources and cross-checking with Scopus database, we introduce a manual system and its semi-automated version of the database on the profiles of 657 Vietnamese researchers in social sciences and humanities who have published in Scopus-indexed journals from 2008 to 2018. The final system also records 973 foreign co-authors, 1,289 papers, and 789 affiliations. The data collection method, highly applicable for other sources, could be replicated in other developing countries while its content be used in cross-section, multivariate, and network data analyses.
To achieve the goals, the search covers only Vietnamese researchers in SS&H of Vietnamese nationality that meet at least one of the following criteria: • They are affiliated with an organization in Vietnam; OR • They have published at least one paper about Vietnam or use data collected in Vietnam related to SS&H topics. The search is further confined to Vietnamese authors who have published in Scopus-indexed scientific journals. It is important to note that the method could in principle cover publications indexed in the WoS, MathSciNet, PubMed and other reliable scientific databases. For comparison purpose, Scopus indexed about 22,600 titles  20  , which is almost twice as many as its counterpart WoS  21. Given the project aims to serve Vietnamese science policymakers, we take into account the fact that Scopus is one of many scholarly databases used by the Vietnamese government to judge academic credentials  22.
NVSS Manual System The manual process of data collection and verification was carried out from 1 st February 2017 to 15  th  July 2017, which resulted in the creation of the Network of Vietnamese Social Scientists (NVSS) dataset. NVSS contains 412 science profiles for 412 distinct Vietnamese researchers in social sciences and humanities who have published in Scopus-indexed journals. An example of these first science profiles could be found in Data Citation 1. Phase 3: Constructing Cross-section and Network Datasets from all the science profiles.
This process resulted in a clean, concise dataset of the most updated and complete profiles. We then contacted and invited the researchers to corroborate the profiles made by our team; the examples of some corroborated profiles could be found in Data Citation 1's Scientific Profiles (Examples) folder. A list of input names and explanations appears in Table  1  while their relationships are illustrated in Fig. 2. The third step involved summarizing all the profiles into a master file. The example of the master file resulted from the manual system could be found in Ho et al. 2, 9.
Shortcomings This manual method, albeit rigorous, faces two major shortcomings. First, the manual input of data is time-consuming and rigid, thus prone to human errors. The resulting dataset enables us to count how many publications each author has but lacks the capability for counting how many unique publications and journals exist in the entire database. This loophole excludes us from answering important questions such as how many new articles Vietnamese social scientists produce each year; or from generating data on international co-authorship network. Second, because the contribution-adjusted productivity ('cp') was computed manually, it would be immensely costly to switch to a different counting method such as the norm of all authors getting an equal share or the norm of first-last emphasis  29, 30.
Unique author au. uniq The sum of au. vn and au. fr. Unit: people Table  1. Input names and explanation. process. The purpose was to have a system capable of: (i) validating the quality of data previously collected, and (ii) making our database more flexible, less time-consuming to construct, and less prone to human errors. The semi-automated process also enables us to cover as close as possible to the actual number of eligible Vietnamese social scientists. For a brief overview of the distribution by sex, there were 262 female (39. 88%) and 391 male researchers (59. 51%), with four left unknown. Table  2  shows the descriptive statistics for continuous variables used in the SSHPA system. Other datasets related to these statistics can also be viewed in Data Citation 1's Extracted and Computed Data's table.
System architecture The SSHPA system, accessible online at https://sshpa. com/, is structured in MS SQL Server 2012 and is indexed to search Fulltext to centralize the management process. Its architecture is organized according to Client-server architecture. The software Server is built using Net Core which provides the APIs connections and functional modules such as Data Search & Filters, Data Validation, Network Builder and Reports. In addition, SSHPA Client software is built with C# that connects the database server through REST API Interface, this is intended to provide the users with complete data-input and data-check functions. Similar to the manual data collection process, the first step was to search for profiles of Vietnamese social scientists fitting our criteria. As shown in Fig.
3 , we collected the profiles provided by researchers and organizations then verified with other sources such as government websites, NAFOSTED's designated publications, journal websites, Scimagojr, Google Scholars, Scopus' freely accessible data, etc. The verified data were then entered into the SSHPA database and put through automated quality assurance and quality control steps. SSHPA was also designed with an authorization system with three levels: admins, supervisors, and collectors. Collectors could only input and edit unapproved data. Supervisors could approve a data entry, however, once the data entry is approved as most complete and accurate by the judgement of the supervisors, it cannot be changed or removed by either the supervisors or the collectors. Only the admins could remove a data entry or unlock the approved data for changes.
The datArticle box contains the relevant data on the articles or publications in the database: title, document type (proceedings or journal articles for example), publisher ID, journal ID, etc. The data are fed from other boxes which contain information on the publishers (lstPulisher), the sources (journals or proceedings of conferences or books) of the articles (lstSource), the citation information (lstCitation), or the document type (lstDocumentType). Similar principles are applied to Network data (datNetwork, datNeworkviz) and Affiliation data (datAffiliation, datAffiliationAuthor). The structure of the database may seem redundant, for example, the author's biographical information (datAuthor and datAuthorName) could have been merged into one file, but the separation serves a Figure 3. The system architecture of SSHPA.
The system consists essentially of three major steps: (i) collecting the profiles of social scientists and cross-verifying with five other sources, (ii) entering the verified data into the SSHPA database and getting checked by the automated quality assurance, after data are in the system, the quality control auto-checkers would screen the database again for consistency and accuracy, and (iii) authenticating and authorizing (through three levels of admins, supervisors, collectors) the final science profiles in the SSHPA database. function. This splitting enables the SSHPA system to filter out overlapping author names faster because: (i) a Vietnamese author might have his or her name written differently in different publications, and (ii) the names recorded in our database are in Vietnamese spelling which has some digraphs and the addition of nine accent marks or diacritics.
Data quality assurance and control The basic principle for building a good data verification process here is to ensure four intertwined layers of check are always carried out: (i) inter-data-sources check: different publicly accessible sources were used to cross-validate the accuracy of collected data; (ii) inter-data-types check: the different types of data collected were checked for coherence with one another; (iii) inter-data-collectors check: the data collectors involved in this study cross-checked the information collected by each other, especially contents that have raised doubts over accuracy; (iv) random and periodic check. In each step, every mistake would be classified either as a one-off or systematic type and corrected accordingly. In the SSHPA system, based on the above principle, the process is divided into quality assurance, which refers to the techniques implemented prior to entering data, and quality control, which indicates the techniques implemented after data is entered to check for errors.
Quality assurance The purpose of this step is to prevent bad data from ever being entered into the database in the first place. Several logic tests have been built into our semi-automatic system to help recognize suspicious authors or articles' data. For the authors' data, there are tests for:   -whether the name of an author already existed in the database -the name of an author must not be blank -if the author is Vietnamese, his/ her SSHPA ID must start with 'v'; 'f' if foreign author -if the author is female, her SSHPA ID must have the 'f' followed the initial 'v' or 'f'; 'm' if male author; '?' if sex is unknown -the correct format of SSHPA ID must be 'geography specifier + sex specifier + number'; for example: vm. 1 is a Vietnamese male researcher numbered 1 or ff. 1001 is a foreign female researcher numbered 1001.
For the articles' data, there are tests for: -whether the article of the same title already existed in the database -the title for the article must not be blank -the publisher and journal of the article must not be blank -the year of article publication must fall in the range 2008-now -fuzzy search article title for 90% similarity Failure to meet these requirements and the system will notify or even block the data collector from moving to the next data points in some cases. The data, when being entered, will also be changed to match the format designated by the system. For example, the paragraph break, the quotation mark (") ascii 147 code will be changed to (") ascii 34 in the title of the articles.
Quality control This step is about applying the data validation tools to control the quality of data. The data validation tools include data filter, the search function (for relative and unique subjects), and the automatic data check functions. Here are some examples of these data validation tools. Two authors with different SSHPA-IDs but same full names or middle names could easily be compared. And if they are suspected as being one person, the software can perform a three-step verification: -Through name: Check the author's name with all other authors with the same name in the system -Through affiliations: Check the author with all other authors with the same affiliation -Through publication: Check the author with all others with the same publication Furthermore, the software could filter out the low-quality data such as: -Authors with missing or invalid information: year of birth, sex, affiliation, article.
Automated construction of network data There are several kinds of network data being automatically recorded with SSHPA: co-authorship among authors (undirected network data), leading-author to non-leading author(s) connection (directed network data), co-authorship among affiliations, co-authorship among geographical locations, etc. The network data allows for different ways to representing the data visually as shown in Figs. 5 6 7 8. Here, each dot represents a researcher that has a connection with Phan Van Phuc, a researcher with SSHPA-ID vm. 780. Purple is coded for male, blue is coded for female; the square shape represents foreign researchers while the round shape is for the Vietnamese. Here, each dot represents a researcher that has a connection with vm. 4. Purple is coded for male, blue is coded for female; the square shape represents foreign researchers while the round shape is for the Vietnamese.
The size of the dot is the number of publications an author has within the designated period. The arrow shows the direction from key-author (first-author) to the others author in a paper. • 'NVSS_VietnameseNodes_20180201. csv' is a cross-section dataset containing information related to the productivity of N = 657 eligible Vietnamese authors in social sciences and humanities: id, name, year of birth, sex, number of publications in last 5 years, number of publications in leading role, number of solo publications, number of publications, contribution-adjusted productivity ('cp') calculated using three methods: sequence-determines-credit (cp. sdc), equal contribution for all (cp. eq) and first-last-author emphasis (cp. fl)  28, 31. The ('cp') results from the fact that we record the order of appearance of each author in the papers in our database; this allow us to calculate different variants of 'cp' with ease and flexibility.
Technical Validation Solving the problem of data duplication Thanks to cross-validating among various sources including research publication database Scopus, our SSHPA software found that of 34,629 articles indexed by Scopus open database, 463 articles have completely similar author names, which include (seemingly) Vietnamese full names and abbreviated names. The 5,414 authors associated with these articles turn out to have different Scopus EIDs. Many of those are of different nationalities such as Korean, Chinese, Taiwanese, etc. Moreover, given that any software would assign a unique ID to a unique name, any slightest variation in a name could yield a different ID. In this case, when looking at duplicated Scopus EIDs, we found that the most duplicated articles have up to five Scopus EIDs.