dataesr/scientific-paragraphs-categorization huggingface dataset:
We scanned retractions that these sources may have missed, then stored the data in a database. We built this database using SQL Server 2016 (Microsoft, Seattle, WA, USA) and employed a web crawler tool to scan the data (see the file retractionCrawler (code). pdf at https://osf. io/7ahsn/ in  [9]  for the code for the web crawler tool). Then, articles collected by the web crawler tool were cleaned and assessed for duplication using the DOI and PubMed databases. Additionally, a fuzzy matching Levenshtein distance algorithm was used to find articles that had titles with a similarity of more than 90% (see file and validData (code). pdf at https:// osf. io/c2zvj/ in  [9]  for the code for data validation). A code snippet is provided in Fig. 1. After we eliminated 430 duplicate and incorrect records, the dataset contained 18,603 retractions, covering 127 research fields, from 1753 (when the first retracted paper was.
Results Retractions were found in 4,289 journals belonging to 753 publishers (or publishing organizations). From the analysis of data through February 2019, 18,603 retractions were found. In the past, this phenomenon was rare. Table  1  presents information regarding the 10 oldest retracted articles, with the oldest dating back to 1756. The next recorded retraction occurred in 1927; following that, retractions were typically recorded as taking place every several years. The first five articles on this list are not accessible because no digitized document is available. The increasing number of retractions in recent years  [12]  may also reflect trends in time to retraction (the time from the publication of the article to the publication of the retraction note)  [4]. We measured the time to retraction for the 10 articles with the longest time to retraction, and the longest interval before retraction was 80 years (Table  2 ).
Discussion Key results: RetractionWatch is among the few databases tracking retractions exclusively on the global scale; hence, making the best use of this resource can greatly benefit the scientific community. Recognizing this fact, we have collected a comprehensive database on scientific retractions from 1753 to February 2019 using SQL Server 2016 and homemade arti-  ficial intelligence tools. This database enabled us to answer the questions posed in this paper. We found that although retraction is an old phenomenon, with the first retraction of a paper dating back to 1756 (Table  1 ); it became a common practice in 1999, and the most retractions were issued in 2010. Moreover, the longest duration that a retracted paper stayed in the literature was 80 years (Table  2 ). Most notably, the IEEE, Elsevier, and Springer together accounted for nearly 60% of all retracted papers, with the IEEE accounting for the most.
Of the reasons for retraction, "fake peer review" was the most common. Additionally, our database noted a sharp rise in the number of retracted papers from China (Table  5 ). These insights suggest that future studies can continue to explore various aspects of retractions. Interpretation: This rise of retraction that began in 1999 (as shown in Fig. 2 ) is nearly consistent with the findings of Brembs et al. , which concluded that the retraction rate of articles had remained stable since the 1970s and began to increase rapidly in the early 2000s. They also saw the creation and popularization of a website dedicated to monitoring retractions in 2010  [13]. However, this increase may be a sign that journal editors are becoming more skillful at identifying and removing flawed publications  [14].
[9]]list of the ten oldest retracted articles Raw data for the dataset of 18,603 retractions covering 127 research fields from 1753 until February 2019 are available in both. csvand. xlsxformat in the files named retraction_18603. csv(https://osf. io/2kymw/)[10]andretraction_18603. xlsx(https://osf. io/a2w8h/),respectively[11]. The dataset, code examples, and all figures are stored and publicly available in the OSF system[9].
Data Availability Raw data for the dataset of 18,603 retractions covering 127 research fields from 1753 until February 2019 are available in both. csv and. xlsx format under the files named retrac-tion_18603. csv (https://osf. io/2kymw/) and retraction_18603. xlsx (https://osf. io/a2w8h/), respectively. The dataset, code examples, all figures, and other files are deposited and publicly available in OSF (https://osf. io/pbwv3/).
Revue de Chirurgie Orthopedique et Reparatrice de L'appareil Moteur, URL: https://www. ncbi. nlm. nih. gov/pubmed/4261570 (Elsevier) February 24, 1977 November 1, 1975 Effects of cholinergic agents and sodium ions on the levels of guanosine and adenosine 3':5'-cyclic monophosphates in neuroblastoma and neuroblastoma X glioma hybrid cells. FEBS Letters, DOI: 10. 1016/0014-5793(75)80344-9 (Wiley) February 24, 1977 September 1, 1976 The effects of noradrenaline, acetylcholine, cyclic AMP, cyclic GMP, and other agents on the concentration of unesterified fatty acids in synaptosomes and synaptic membranes. FEBS Letters, DOI: 10. 1016/0014-5793(76)80541-8 (Wiley) Statistical methods:Having organized the dataset, we then.
Background: The complex network of interactions occurring between gastrointestinal (GI) and extra-intestinal (EI) parasitic helminths of humans and animals and the resident gut microbial flora is attracting increasing attention from biomedical researchers, because of the likely implications for the pathophysiology of helminth infection and disease. Nevertheless, the vast heterogeneity of study designs and microbial community profiling strategies, and of bioinformatic and biostatistical approaches for analyses of metagenomic sequence datasets hinder the identification of bacterial targets for follow-up experimental investigations of helminth-microbiota cross-talk. Furthermore, comparative analyses of published datasets are made difficult by the unavailability of a unique repository for metagenomic sequence data and associated metadata linked to studies aimed to explore potential changes in the composition of the vertebrate gut microbiota in response to GI and/or EI helminth infections.
Results: Here, we undertake a meta-analysis of available metagenomic sequence data linked to published studies on helminth-microbiota cross-talk in humans and veterinary species using a single bioinformatic pipeline, and introduce the 'MICrobiome HELminth INteractions database' (MICHELINdb), an online resource for mining of published sequence datasets, and corresponding metadata, generated in these investigations. Conclusions: By increasing data accessibility, we aim to provide the scientific community with a platform to identify gut microbial populations with potential roles in the pathophysiology of helminth disease and parasite-mediated suppression of host inflammatory responses, and facilitate the design of experiments aimed to disentangle the cause(s) and effect(s) of helminth-microbiota relationships.
Nevertheless, whilst a small number of qualitative and/or quantitative changes in the composition of the host gut microbiota have been repeatedly observed in helminth-infected humans or animals and irrespective of infecting helminth species, the vast majority of published investigations are characterised by inconsistent and seldom contradictory findings  [20, 29]. These apparent discrepancies may be linked to the fundamentally diverse biology of the parasites under investigation, that might result in microbiota alterations that are specific to the colonising helminth species. However, one possible explanation is technical and linked to the vast heterogeneity of experimental designs and metagenomic sequence analysis techniques that characterise currently available studies and that may have contributed to the observed differences between published datasets  [29].
Similarly, the availability of a range of bioinformatic pipelines and reference databases for metagenomic data analysis and annotation, each with its pros and cons, may substantially impact the final outputs, thus making findings from studies utilising different workflows not directly comparable. Whilst a complete standardisation of study designs and experimental protocols is difficult to achieve, bioinformatic (re)analyses of sequence data using a single workflow and up-to-date databases for sequence annotation may assist the detection of common sets of findings across studies  [29]. Such knowledge is indeed crucial to evaluate the real impact (if any) of helminth infections on the composition and the metabolic functions of the host gut microbiota, and thus to develop strategies to minimise such effects.
Thus far, the vast majority of published studies on helminth-microbiota interactions have relied on highthroughput sequencing of the bacterial 16S rRNA gene fragment for determination of host gut microbial profiles prior to and following helminth infections  [20] ; these studies will represent the focus of the present article. Amongst the bioinformatic tools for sequence analysis and annotation, the Quantitative Insights Into Microbial Ecology (QIIME)  [30]  and Mothur  [31]  pipelines have been the most widely used platforms for taxonomic analyses of metagenomic sequence data generated in these experiments; nevertheless, recent benchmarking tests have shown that other open source software, such as Multiplexed Analysis of Projections by Sequencing (MAPseq)  [32]  and QIIME 2 (an updated version of QIIME)  [33]  provide a faster and more accurate read classification, better sensitivity and specificity at different taxonomic levels and miscall rates of < 2%  [34].
Furthermore, the accurate annotation of bacterial 16S rRNA sequence data relies on the availability of continuously updated and curated reference databases; thus far, the Greengenes database (http://greengenes. secondgenome. com/) has been widely used in bioinformatic analyses of sequence data generated in studies of helminthmicrobiota interactions  [28, 35, 36]. However, the last update of this database dates back to 2013, and therefore it is likely that sequence information available from this repository are incomplete and/or outdated. Conversely, the SILVA database (https://www. arb-silva. de/) [37]  is frequently updated (latest release: December 2017) and comprehensive, as it also includes annotated datasets of aligned rRNA sequences for Bacteria, Archaea and Eukarya  [37, 38]. In this study, we undertake a meta-analysis of highthroughput amplicon 16S rRNA sequence datasets linked to studies of helminth-microbiota interactions in humans and veterinary species, using consistent data reprocessing; in addition, in order to facilitate the retrieval of information generated in published investigations conducted in different host-parasite pairs, under varying conditions of helminth infection (i.
e. natural vs. experimental and acute vs. chronic), and analysing different biological specimens (i. e. faeces vs. host GI tissue samples), we created the 'MICrobiome HELminth INteractions database' (MICHELINdb; www. michelindb. com). In this database, information generated from individual studies of helminth-microbiota interactions are stored together with associated metadata on host and parasite species, infection site, sample type and metagenomic sequencing strategy (amongst others), and links to raw sequence data and published papers. By increasing the accessibility of these datasets and by presenting the results of a meta-analysis of available data using a single bioinformatic pipeline, we aim to provide the scientific community with a platform to identify gut microbial signatures that occur across several host-helminth systems, and facilitate the design of experiments aimed to disentangle the cause(s) and effect(s) of helminth-microbiota relationships.
Study selection and data acquisition Published studies on the composition of the gut microbiota of vertebrate hosts acutely and/or chronically infected by parasitic helminths (nematodes, cestodes and trematodes) were retrieved by mining search engines (i. e. NCBI PubMed, ISI Web of Knowledge, Google Scholar and ScienceDirect) using the terms 'gut' AND/ OR 'microbiome' AND/OR 'microbiota' AND/OR 'parasite' AND/OR 'helminth' and by following references in published reviews and related studies. All sequence datasets retrieved using this method, as well as associated metadata, were included in MICHELINdb, together with study findings (see below), whilst only studies that satisfied the following criteria were included in the metaanalysis: (a) Available paired-end high-throughput bacterial 16S rRNA sequence data in.
fastq format deposited in publicly accessible databases (e. g. the NCBI Sequence Read Archive [SRA] or EBI European Nucleotide Archive [ENA]) (b) Available metadata on matching helminth-infected and -uninfected biological specimens and specimen origin (e. g. small or large intestine, faeces, etc. ) Sequence datasets linked to studies that required additional ethics committee approval or institutional authorization for data access were not included in the meta-analysis; however, these datasets and corresponding findings, as presented in the original publications, are available for mining in MICHELINdb. For studies investigating the impact of anthelmintic administration on host gut microbiota composition, only sequence data obtained from biological specimens collected from helminthinfected and -uninfected human or animal controls were included in the meta-analysis; nevertheless, any available information on observed differences in microbial composition between specimens collected prior to and following anthelmintic treatment (as per original publication) is included in MICHELINdb.
Data processing Raw sequence data representing the hypervariable regions of the bacterial 16S rRNA gene and linked to the studies outlined above were obtained from public sequence data repositories using accession numbers provided in the corresponding publications or following direct email communication with the corresponding authors (Table  1 ). When needed, provided barcodes were matched to sequences for demultiplexing. Then, sequence data were (re)processed (individually for each study) using the European Bioinformatics Institute (EBI) MGnify service pipeline, that integrates MAPseq v 1. 2  [32]  for operational taxonomic unit (OTU) assignment and SILVA (https://www. arb-silva. de/) [37]  as the reference database for taxonomic classification. For details on the full properties of the MGnify pipeline, its performance compared to other open source software for highthroughput 16S rRNA sequence data analysis, as well as on its validation for metagenomic studies, we refer to the original publication by Mitchell et al.
Statistical analyses All statistical analyses were executed using both the Calypso software (cgenome. net/calypso/) [51]  and a custommade code in R statistical software version 3. 5. 2, packages 'phyloseq', 'vegan', 'meta', 'metaphor' and 'ggplot2'. For microbial diversity indices calculation, sequence data were rarefied to the highest sequencing depth required to retain all study samples. Then, differences in bacterial alpha diversity (Shannon index), and evenness between the microbiota sequence data generated from helminth-infected and corresponding uninfected specimens were calculated. To produce a robust and conservative estimation of alpha diversity indexes, whilst accounting for the heterogeneity of data across studies, a random-effects meta-analysis model with DerSimonian-Laird estimator, adjusted with the Hartung-Knapp-Sidik-Jonkman method, was used to pool effect sizes and corresponding standard deviations from all of the studies included in our meta-analysis.
Results Selected studies on helminth-microbiota interactions and corresponding bacterial 16S rRNA sequence datasets Literature database searches using the keywords listed above yielded 54 unique studies, published between 2010 and 2019, on the interactions between GI and EI helminth parasites and the gut microbiota of their vertebrate hosts (Additional file 1: Table  S1 ). Of these, 15 matched the criteria outlined above and were retained in our meta-analysis (Table  1 ). These studies spanned ten helminth species, including nematodes (e. g. N. americanus and Haemonchus contortus) and trematodes (e. g. Opisthorchis viverrini and S. mansoni), of both public health and veterinary significance, and seven vertebrate hosts (i. e. human, cat, dog, goat, hamster, mouse and wild-mouse); of 15 studies included in our meta-analysis, 14 targeted the V3-V4 hypervariable region of the bacterial 16S rRNA gene (Table  1 ).
Clustering of microbial communities In order to assess the variability of the microbial community profiles included in our meta-analysis, individual sequence datasets generated from helminth-infected and -uninfected specimens were clustered at OTU level using PCoA, based on Bray-Curtis dissimilarity matrix and with 'host' as explanatory variable (Fig. 2 ). The two axes accounted for 44% of the total variance, i. e. 24% and 20% for PCoA1 and PCoA2, respectively (Fig. 2 ). In particular, the microbial profiles of specimens from humans, cats, dogs and goats clustered closely together, with minimal overlap with those from hamsters, mice and wild-mice (that were spread across the two variables) (Fig. 2 ). Therefore, given the largely distinct gut microbial profiles that characterised these two host groups, downstream analyses were conducted by categorising microbial profiles into 'Non-rodents' (i.
Database properties and usage A total of 54 published studies, spanning 33 parasites species and 14 vertebrate hosts, are currently stored in MICHELINdb (latest update October 2019). publication is assigned a unique 'Mdb ID' and an individual webpage including key study features (e. g. host and parasite species), sample metadata, microbiota profiling method, a summary of salient findings (as reported in the original publication) and details of microbial diversity indexes, as well as of specific taxonomic assignments of bacterial 16S rRNA sequences positively or negatively associated to helminth infection in the original publication (ranked according to phylum, order, class, family, genus and species). Links to the corresponding published article and to public sequence repositories hosting raw datafiles, as well as information on Open Access availability are also provided (see, for example, http://www.
The random-effects meta-analysis model was performed using the DerSimonian-Laird estimator and adjusted using the Hartung-Knapp-Sidik-Jonkman method Fig. 5  Microbial composition (at phylum and genus level) of helminth-infected and -uninfected samples from Non-rodents (a, c) and Rodents (b, d), ordinated according to infection status. Taxa making up < 1% of the overall microbiota are grouped under 'unclassified' by means of drop-down menus located at http://www. michelindb. com/a/as. In addition, metadata from individual studies can be exported by the user in. xml format. MICHELINdb will be subjected to regular updates (every 6 months) to ensure inclusion of new data and corresponding metadata from published investigations of helminth-microbiota relationships that were unavailable at the time of manuscript submission and peer-review, as well as post-website launch.
Discussion As a first step towards the identification of gut microbial taxa with potential roles in host-parasite crosstalk, we conducted a meta-analysis of 15 bacterial 16S rRNA sequence datasets obtained from seven host and ten parasite species, respectively, using a single bioinformatic pipeline. Such an approach allowed us to detect differences between the microbial profiles of rodent hosts (mainly laboratory models of helminth infections) and non-rodents (spanning experimentally and naturally helminth-infected humans and animals), which were therefore considered separately in downstream bioinformatic and biostatistical analyses of sequence data. The gut of rodent hosts has been reported to harbour a significantly different microbiome to that of humans and other mammalians  [52, 53] , which might explain the dissimilarities between the microbial profiles of these two groups observed in our meta-analysis.
Indeed, subtle changes in the composition of the mucosally associated and/or luminal microbiota can be missed in stool samples  [55] ; similarly, variations in the abundances of bacterial taxa inhabiting regions of the GI tract downstream of the site of helminth localisation (mediated, for instance, by systemic immune responses mounted against the invading parasites) are unlikely to be detected in mucosal and/or luminal samples collected from the region of parasite establishment. For each Rodents and Non-rodents, gut microbial communities formed two separate clusters according to helminth-colonisation status, thus providing support to a role of GI and EI parasite infections in shaping the gut bacterial make-up of infected hosts  [20] [21] [22]. Of note, the pooling of 16S rRNA datasets generated from multiple studies allowed to detect statistically significant differences between the gut microbial profiles of helminth-infected and uninfected hosts that were previously unreported due to limitations in the numbers of specimens analysed in individual experiments  [28, 56].
Conclusion The (re)analysis of microbial 16S rRNA sequence data generated from a range of helminth-infected and -uninfected hosts and biological samples using a single bioinformatic pipeline allowed us to identify 'common signatures' characterising the microbial profiles of parasite-colonised hosts that may be targeted in future studies aimed at discovering and developing novel strategies of parasite treatment and control via the manipulation of resident populations of gut bacteria. Furthermore, we constructed MICHELINdb, a publicly available database including bacterial sequence data and sample metadata from published studies comparing the gut microbiota composition of several host species infected by one or multiple GI and/or EI helminth parasites with that of corresponding uninfected hosts.
MICHELINdb allows easy data access, and enables time-efficient comparisons of quantitative and qualitative alterations in gut microbial composition upon GI and EI helminth infections detected in studies conducted in a range of host-parasite systems and using different experimental protocols and microbiota profiling techniques. Data and metadata linked to studies stored in MICHELINdb can be readily exported, to allow users to collect and store information, undertake further analyses and expand the meta-analysis dataset. Users are encouraged to submit newly generated data and metadata to MICHELINdb using the link provided, in order to enhance the exhaustiveness of the database and facilitate meaningful biological interpretations of study findings. the MICrobiome HELminth INteractions database (MICHELINdb).
Studies from which datasets included in the meta-analysis were retrieved are indicated in green. Additional file 2: Table  S2. Metadata associated with individual helminth-infected and -uninfected samples (n = 732) included in the meta-analysis. Information provided was derived from the original publication or following personal communication with the corresponding author. Additional file 3: Figure  S1. Gut microbial profiles of helminth-infected and -uninfected samples. Samples from Non-rodents (i. e. human, cat, dog and goat) (a) and Rodents (i. e. mouse, hamster and wild yellow-necked mouse) (b), are clustered at operational taxonomic unit (OTU) level by supervised canonical correspondence analysis (CCA), using 'type/site of sampling' as explanatory variable.