dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, if scientific output researchers in any country set out to analyse the performances of individuals or institutions in their country, the example above shows just how difficult it is to only rely on query data directly from the Scopus open database. In our process to construct the SSHPA database, we found that the names of Vietnamese authors in the Scopus database are often not consistent, which poses a significant cost to data verification. Solving this problem requires a system that allows comparison of all name versions for any two authors. The SSHPA system has a built-in name-generating tool: once the full name of an author is entered into our database, the software will automatically generate all possible versions of names for a Vietnamese author. For example, an author with the full name "Nguyen Ngoc Anh" could generate 12 different versions of name: [0]: "Nguyen, Ngoc Anh" [1]: "Nguyen, N.
A. " [2]: "Nguyen, Anh Ngoc" [3]: "Nguyen, A. N. " [4]: "Ngoc, Nguyen Anh" [5]: "Ngoc, N. A. " [6]: "Anh, Nguyen Ngoc" [7]: "Anh, N. N. " [8]: "Ngoc, Anh Nguyen" [9]: "Ngoc, A. N. " [10]: "Anh, Ngoc Nguyen" [11]: "Anh, N. N. " Searching in the Scopus database all these versions of this name gave us 32 results. As we filtered by author name and affiliation, the search result gave two Scopus profiles of a person named 'Nguyen Ngoc Anh' who worked at the Development and Policies Researcher Center, Hanoi, Vietnam. In our system, this mistake is eliminated and Mr Nguyen Ngoc Anh's profile is managed under one ID, the SSHPA-ID vm. 1 (Data Citation 1). This allows our system to track any changes in the public profiles as well as update our own changes. This is one of many similar examples where the Scopus system creates two or more Scopus EIDs for one person.
Diversifying datasets and data reports The SSHPA software's ability to generate many different kinds of datasets and data reports sets it apart from the time-consuming and rigid manual system (Computer Codes, Data Citation 1). Not only does this function increase the data readability for users but it also helps the system admins detect any potential anomalies, thereby, able to improve the data quality as a whole. The SSHPA software can generate four main kinds of report, as summarized in Table  3. Examples of the reports could be viewed in Data Citation 1's 'Extracted and Computed Data' folder, in which the number of articles according to fields, institutions, journals, publishers and years is shown. As SSHPA is expected to correct for the delay in data update present in many citation indexing databases, the system will inform users of any authors or articles that were not yet found on such services.
In the example of Mr Nguyen Ngoc Anh, thanks to cross-checking with other open resources such as Google Scholar, journal websites, institution websites, etc. , we were able to update four of his papers that were published in 2016 and 2017, indexed in Google Scholars but were not yet found in Scopus as of 1 st February 2018 (Data Citation 1). Given that Vietnamese social scientists are expected to corroborate their profiles in the SSHPA database, this information will be both accurate and up-to-date. Similarly, to improve data quality, the system's error report will list the missing information in the database, notifying the admins of any authors, articles, or affiliations that are duplicate or lacking details. The other two kinds of report give users a full picture of the system whenever necessary, one showing the statistical distribution of authors, articles according to gender, age, fields of study, etc.
, and the other providing the network data per specific requirements such as citation network or co-authorship network. An example of a general statistical report produced by SSHPA can be found in Table  4. Such reports are helpful in highlighting any abnormal data point, whether that be an incorrect count of authors, papers or affiliations. For further details, please visit Data Citation 1's folder 'Extracted and Computed Data', in which details of the number of articles according to fields, institutions, journals, publishers and years are recorded.
Visualization of incorrect data entry For the most part, given the rigorous data quality assurance and quality control, the maps of networks generated by the software are rarely prone to errors. However, in case of error, the system's ability to visualize selected scientific groups could flag the admins of any inconsistencies. For example, Fig. 5  shows a case of both correct and incorrect network visualization of the data extracted from the article in 2017 by Phan et al. 32. As the author Phan Van Phuc with SSHPA ID vm. 780 is the first author, the correct network visualization should have the arrows go from him to his co-authors (fm. 3086, fm. 3084, fm. 3085) as in Fig. 5b. When we incorrectly input the author fm. 3084 as the first author, the network changes as shown Fig. 5a ; the arrows now go from fm.
A map of Vietnamese social scientists' productivity The datasets produced by SSHPA, such as the one in Table  5 , could be used for cross-section and multivariate statistical analyses, allowing us to explore how networks of Vietnamese social scientists have grown over the years  17, 18. Table  5. Descriptive statistics of networks generated by SSHPA. Together with the numerical understanding of networks, SSHPA can also provide a visual representation of the collaboration patterns geographically, among Vietnamese provinces or between Vietnam and the world (See Fig. 6 ).
• 'NVSS_AllNodes_20180201. csv' is the attribute dataset containing the data on number of publications, nationality, sex of N = 1639 authors, Vietnamese and international. It was extracted from the SSHPA database on 1 st February 2018. • 'NVSS_DirectedLinks_20180201. csv' is a network dataset containing the data that represent the directed co-authorship connection among N = 1639 authors, Vietnamese and foreign, in the SSHPA database. It was extracted from the SSHPA database on 1 st February 2018. • 'NVSS_UnDirectedLinks_20180201. csv' is a network dataset containing the data that represent the directed co-authorship connection among N = 1639 scholars, Vietnamese and foreign, in the SSHPA database. It was extracted from this semi-automated database on 1 st February 2018. • 'Articles and Fields 20180201.
xlsx' contains N = 1289 unique articles and their respective fields. This dataset also contains the articles' SSHPA IDs, articles' titles, fields, journal name, year of publication, authors' names and their ids in the database. It was extracted from the database on 26 th April 2018 based on stored data until 1 st February 2018. • 'NVSS science profiles examples' is a folder that contains the two examples of authors' scientificprofiles generated by the SSHPA system and one example of the manual system.
Data Records The datasets are available from the Open Science Framework repository (Data Citation 1), under 'NVSS 2017-18 Scientific Productivity and Collaborative Networks of Vietnamese Researchers in the Social Sciences and Humanities. ' Below is the description of the main datasets that were produced by the SSHPA system, which can be found in Data Citation 1's Data files folder. • Network data of scholars in one or many institutions (with or without direction, weight of non-weighted).
The growth of scientific groups Another feature of the SSHPA database is allowing users to study how a group of researchers who have co-authored with each other evolves over time  17. Figure  7  is an example. First, we chose a researcher with SSHPA ID vm. 4 and set his level of connection to two (meaning the network data will be limited to researchers two walks away from vm. 4). Then, we picked the time periods for comparison: from 2008 to 2010 (Fig. 7a ), from 2008 to 2014 (Fig. 7b ), and from 2008 to 2018 (Fig. 7c ).
Network representation of all social scientists in the database Besides enabling the visual representation of any scientific group within the database, the built-in function of network generator could also produce the visualization of all Vietnamese scientists and 973 foreign scholars (Data Citation 1) over different periods. Figure  8  is the result of this function. Figures  7  and 8  have different ways of representing the researchers' geography and sex. This flexibility provides the data collectors with diverse angles in examining the data patterns. In Fig. 7 , colour represents sex while shape represents geography (Vietnamese or overseas); thus, both Vietnamese and foreign authors are represented equally. Figure  8  highlights the growth of research networks between Vietnamese authors and overseas, with three colours denoting the sex of Vietnamese scholars and the status of scholars who are foreign.
Usage Notes As the SSHPA system relies only on open and free recourses to collect and verify data related to scientific output, it opens the possibility of replicating the same system in other places throughout the world (Computer Codes, Data Citation 1). It is also applicable for collecting and verifying data in other fields such as biomedicine, math, biology, etc. This work, therefore, aims to reduce the cost of doing science  10, 33, 34. The datasets of this study are first and foremost suitable for analyses of the relationship between individuals' scientific output and demographic (such as sex, age, region) as well as their collaboration characteristics. Moreover, with the development of the SSHPA software, one can also get access to citation and co-authorship network data, which would allow for more in-depth analyses of scientific influence among the researchers in the datasets.
We are convinced that the practice of open data is one of many ways to help cure the crisis of reproducibility in social sciences  10, 34, 35  , and improve policy-making processes when it comes to the question of funding efficiency  36. We wish for scientists all over the world working on the issue of scientific productivity to replicate the design of the SSHPA system to verify the usefulness of the system in cleaning and eliminating data errors. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
2006) , and samples showing a 3′:5′ ratio higher than 7 were discarded. Primers were designed with Primer3 software (http:// bioinfo. ut. ee/primer3/) and in silico validated for specificity by electronic PCR (e-PCR; http://www. ncbi. nlm. nih. gov/ tools/epcr/) and by performing qPCR and inspecting the amplicon length via gel electrophoresis. Primers efficiency was calculated with LinRegPCR software (http://www. hartfaalcentrum. nl/index. php?main=files&sub=LinRegPCR). Primers with efficiency lower to 1. 8 were discarded and redesigned. If not differently specified, primers were designed in regions common to all the splicing variants described for that gene. For primers sequences, see Online Resource 2, Suppl. Table  S1. Quantitative PCR (qPCR) was performed on the Light Cycler LC480 (Roche) using the ABsolute Blue qPCR SYBR Green Mixes (Thermo Scientific AB-4167/B).
Each reaction was performed in triplicate, in a total reaction volume of 10 μl (primers 200 μM). The cycling program (45 cycles) consisted in 10 s at 95 °C (denaturation), 20 s at 64 °C (annealing), and 20 s at 72 °C (elongation). Relative quantification and normalization were performed with efficiency-corrected multiple reference genes model enabled by qBasePLUS software (http://www. qbaseplus. com). For each experience, the optimal number and choice of reference genes was determined with qBasePLUS within a panel of 7-9 putative reference genes. The selected reference genes were hprt1 and rpl13a for 6 h, 50-300 mM EtOH treatments on 1C11 cells, hprt1 and pgk1 for 0 h-24 h EtOH treatment on 1C11 cells, hmbs and pgk1 for H 2 O 2 treatment on 1C11 cells, and B2m and cyclophilin for EtOH treatments on MEFs.
Sistonen), anti-HSP70 (Enzo ADI-SPA-  810; 1:1000), anti-TET1 (Millipore 09-872,  1:1000), anti-TET2 (Abcam ab12497, 1:1000),  and anti-TET3 (Abcam 135033, 1:500 ). The intensity of the signals was quantified with ImageJ software (http://imagej. nih. gov/ ij/index. html).
Acknowledgements We thank  Dr. Slimane Ait-Si-Ali  and  Dr. Véronique Dubreuil  for helpful comments and discussion. We are grateful to  Professor Lea Sistonen  ( Turku University, Finland ) for kindly providing us with rabbit anti-HSF2 antibody # 57 , to  Professor Elisabeth Christians  ( UPMC Univ Paris 06, Villefranche-sur-mer, France ) for the Hsf1 tm1Ijb mouse strain, and to  Professor Shoji Tajima  ( Osaka University, Japan ) for anti-DNMT3L antibody. We thank  Anne Le Mouël  ( IR CNRS, UMR7216 ) for her advices in the RT-PCR experiments. We acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , member of the infrastructure  France BioImaging  (http:// imagoseine. ijm. fr/en/), for the use of the Leica SP5 confocal microscope. We acknowledge the  Functional Epigenomics Platform of the UMR7216 Epigenetics and Cell Fate.
VM laboratory  has been supported by the  Agence Nationale pour la Recherche  ( Program SAMENTA   ANR-13-SAMA-0008-01 ),  Fondation Jérôme Lejeune, IREB/FRA  ( 2014/18  and  2012/19 ). BS laboratory has been supported by the  Agence Nationale pour la Recherche  ( Program SAMENTA   ANR-13-SAMA-0001-01 ). FM has been supported by a PhD Fellowship from the  Centre National de Recherche Scientifique (CNRS)  and the  Fondation pour la Recherche Médicale ; FM and  ALS , by a postdoctoral fellowship by SAMENTA  ANR-13-SAMA-0008-01.
Pseudogenes are a paradigm of neutral evolution and their study has the potential to reveal intrinsic mutational biases. However, this potential is mitigated by the fact that pseudogenes are quickly purged from bacterial genomes. Here, we assembled a large set of pseudogenes from genomes experiencing reductive evolution as well as functional references for 15 which we could establish reliable phylogenetic relationships. Using this unique dataset, we identified 857 independent insertion and deletion mutations and discover a pervasive bias towards deletions, but not insertions, with sizes multiples of 3 nt. We further show that selective constraints for the preservation of gene frame are unlikely to account for the observed mutational bias and propose that a mechanistic bias in alternative end-joining repair, a recombination-independent double strand break DNA repair mechanism, is responsible for the accumulation of 3n deletions.
2013). Rather unusually, genome reduction is not a monophyletic trait in 15 Burkholderia  (Pinto-Carb o et al. 2018). A consequence of this is that many pseudogenes have functional orthologs in closely related free-living Burkholderia species. The relative long-term maintenance of pseudogenes in leaf nodule Burkholderia sp. and the sequence availability of homologs 20 from closely related genomes affords the assembly of a unique dataset to analyze background indel mutation bias. In this study, we take advantage of the abundance of pseudogenes in Ca. Burkholderia leaf nodule symbionts to assemble a large collection of pseudogenes with functional 25 orthologs in related species. Using this dataset, we investigate the nature and frequency of insertions and deletions in genomic regions evolving neutrally.
Data Collection and Pseudogene Prediction All genomes used for this study were downloaded from NCBI Genbank, with the accession numbers listed in supplementary table  S1 , Supplementary Material online. Ortholog computations were done with the Orthomcl v1. 4 software (Li et al. 40 2003), using Blastp cut-off values of 1. 0 Â 10 À6 (e-value), 50% identity over 50% of query length. Pseudogene prediction was done previously for Burkholderia leaf nodule symbionts  (Carlier and Eberl 2012; Carlier et al. 2013; Carlier et al. 2015; Pinto-Carb o et al. 2016 ). To avoid confusion in indel 45 calling caused by multiple copies of IS elements, repeats were masked in the genomes of leaf nodule symbiotic Burkholderia species using the RepeatScout and RepeatMasker software  (Price et al. 2005). In order to verify that repeat masking did not negatively impact the detection or introduced biases in 50 downstream analyses, we performed pseudogene prediction on unmasked Ca.
B. umbellata genome data. Of the candidate pseudogenes predicted only on unmasked data, none passed the criteria for downstream analysis as outlined below. For other genomes, intergenic regions (including flanking 100 bp) between functional CDS features were searched for homology with predicted functional proteins in ad-hoc databases consisting of predicted proteins of freeliving relatives using the NCBI blastx program  (Altschul et al. 1990). Only blastx hits with e-value < 10 À6 and average identity > 50% were considered. Finally, candidate pseudogenes were aligned to the top blastx hit using the tfasty program of the FASTA software suite  (Pearson 2000) , and bona fide pseudogenes identified when they contained frameshifts or premature stop codons that affected >20% of gene length. Finally, in order to exclude candidate pseudogenes corresponding to spurious hypothetical genes, pseudogene sequences were searched against the Conserved Domain Database  (Marchler-Bauer et al.
Alignment of Pseudogenes to Reference Genes The sequences of regions containing the target pseudogenes were extracted from unmasked genomes and flanking upstream and downstream genes were extracted using ad-hoc Python scripts. Corresponding syntenic regions were similarly extracted from the two reference genomes. The flanking genes were included in order to anchor the alignment. Sequences were aligned using the E-INS-i algorithm of the MAFFT v7 program with default parameters  (Katoh and Standley 2013). All alignments were visually inspected in CLC Main Workbench v7. 0 (Qiagen, Aarhus, Denmark) and alignments containing poorly aligned regions were discarded altogether. Indels for which we could not reliably infer the ancestral state were also discarded from our analysis. Alignments were then truncated to exclude regions flanking the reference genes and pseudogene, using the shortest functional ORF as a reference to avoid including mis-annotated 5 start codons, and thus 5 0 UTR, in the alignments.
Finally, to avoid including predicted pseudogenes resulting from sequencing errors, and because the first inactivating indel in a pseudogene may be subject to selection, we only included alignments with pseudogenes with at least three separate 10 indels in our final dataset. Alignments were analyzed using ad-hoc Python scripts. Deletions and insertions in pseudogenes were only counted if they were absent from both reference sequences. Directionality of the indel (i. e. , deletion or insertion) was also 15 inferred in relation to the consensus of the reference sequences. As a further control, and because individual genes may not follow species phylogeny, distance matrices were calculated for each alignment using the distmat tool of the EMBOSS package  (Rice et al. 2000)  with the Kimura two-parameter 20 multiple substitution correction algorithm and gaps ignored.
Alignments for which the computed minimal pairwise distance between the pseudogene sequence and either of the references was larger than the pairwise distance between the two reference genes may be due confounding factors such as 25 horizontal acquisition or gene conversion. Alternatively, some pseudogene sequences may experience accelerated substitution rates and should still be included in our analyses. To distinguish between these possibilities, we constructed rooted phylogenies for 40 suspicious alignments using appropriate 30 outgroup sequences selected as best nonself blastx hit in the NCBI refseq database (accessed January 2018). Our final dataset contained 151 curated alignments.
Repeats Direct repeat insertions in pseudogenes were detected using ad-hoc Python scripts. Upstream and downstream flanking regions surrounding insertion sites, of the exact size of the insertion, were extracted, allowing for 1 nt shift in either direction. The Hamming distance between the flanking strings and the string corresponding to the insertion was calculated. Direct repeats were called when the distance between at least one flanking string and the insertion string was 0 for insertions of sizes 2 or 3 bp, and 1 for insertions larger than 3 bp. The software Tandem Repeats Finder  (Benson 1999 ) was used to detect tandem repeats in pseudogene sequences and aligned functional homologs with the following parameters: Weights for match, mismatch and indels ¼ 2, 2, 7, respectively; Minimum alignment score ¼ 25; Detection parameters P m ¼ 0.
Annotation of Proteins Related to DNA Replication and Repair Predicted protein sets were downloaded from NCBI Genbank and annotated using the eggNOG-mapper 1. 0. 3 software and the EggNOG database v 4. 5  (Huerta-Cepas et al. 2016 , 2017). HMM searches were limited to the bactNOG database of prokaryotic HMM profiles. Annotations were filtered according to COG category L ("Replication, recombination and repair") and collated into supplementary table  S2 , Supplementary Material online.
Statistical Analyses The size distribution of indels was investigated using two methods Only indels with length 15 nt were used in the analysis. For non-Burkhorderia species, only indels 9 nt were used, as indels of size ! 10 nt were very rare in the dataset. Because indel size distribution has been shown to follow a power law in eukaryotic as well as prokaryotic genomes  (Zhang and Gerstein 2003; Cartwright 2009) , we fitted a linear model to the counts of indels according to their size using log-log-transformed data. In order to test whether indels of size 3n were enriched in our data, we added a categorical variable which takes the value of 1 if indel size is a multiple of 3 and 0 otherwise. Model fitting and statistical tests were performed in R, using the standard lm function. The second method used nonparametric bootstrapping on the nontransformed data.
Briefly, 10,000 bootstrap replications were performed, where each replication consists of creating a new indel population by sampling with replacement from the original population. For each indel length, a 95% confidence interval was estimated from the bootstrap distribution. Regression curves were calculated by fitting a power law function to the counts of non-3n indels and these were used to estimate expected counts for each indel size. Observed values were considered significantly different if the expected values fell outside of the 95% confidence interval estimated from the bootstrapped data. The bootstrapping analysis was performed in Python, using ad-hoc scripts and the "random" module for random sampling. The power-law was fitted to the data using the curve-fit function (using the trf method for least-squares), implemented in SciPy.
Genome-Wide Microhomology Distribution The distribution of interval lengths of k-mers was assessed using ad-hoc Python scripts. For every k-mer length (2 k 6), the start positions of all possible k-mers (e. g. , AA, AT, AC,. for 2-mers) were calculated from the genomes of two Ca. Burkholderia species (Ca. B. kirkii and Ca. B. brachyanthoides) and Lactobacillus johnsonii. The interval between two consecutive exact matches of that k-mer were computed. Frequency of intervals were pooled per k-55 mer length. Statistical overrepresentation of 3n interval lengths (up to 20 nt) was assessed by modelling the counts and the interval length and a factor considering if the interval length was a multiple of three or not. Negative binomial generalized linear models were found to best fit the 60 data (lowest Akaike information criteria) and fitted using the MASS library in R.