dataesr/scientific-paragraphs-categorization huggingface dataset:
ncbi. nlm. nih. gov) and Barcode of Life Data System (BOLD, http://www. boldsystems. org) repositories using custom Perl scripts (https://osf. io/qsn5e/). Globally, from 207,686 known marine animal species, only 14. 5% (NCBI) and 13. 8% (BOLD) possessed COI barcodes. This represents 4. 3% and 5% increases (NCBI and BOLD respectively) (see Online Resource S2) since the estimate by  Bucklin et al. (2011). The five LMEs encompass a total of 34,286 nominal marine animal species, with 14,472 (42. 2%, NCBI) and 14,351 (41. 9%, BOLD) having at least one COI sequence (Fig. 1 ). Evidently, the proportion of barcoded species in these hotspots largely exceeds that at global level. Nevertheless, the current LMEs barcoding data are still not sufficient for comprehensive biodiversity assessments. Furthermore, this barcoding coverage and species richness varies across LMEs (Fig.
6% NCBI, 71. 5% BOLD). Most of these contributions originate from Vertebrata (70. 1% NCBI, 73. 1% BOLD), whereas non-vertebrate Chordata (Ascidians) are highly underrepresented (36. 3% NCBI, 35. 0% BOLD) (see Online Resources S4). Our results evidence a global lack of COI barcode availability for a large proportion of marine invertebrates, with the North, Caribbean, and Mediterranean Seas being highly underrepresented in barcode databases, thus requiring further efforts. The ongoing EU's key funding programme for research and innovation "Horizon 2030" (2021-2027) allocates 8,953 Bi€ for Pillar II, Cluster 6 "Food, Bioeconomy, Natural Resources, Agriculture & Environment", offering co-funded initiatives such as "Rescuing Biodiversity to Safeguard Life on Earth" (https://ec. europa. eu/info/horizon-europe_en) that could be pivotal for increasing the biodiversity knowledge at European level, including the marine realm.
To bridge the gaps in biodiversity assessment and monitoring worldwide, national and international collaborations among multidisciplinary teams (including taxonomists, molecular biologists, bioinformaticians, evolutionary biologists, ecologists, and environmental scientists) must be fostered. Such an integrated approach enables physical specimens, accessioned in permanent natural history museum collections, to be linked to molecular, morphological and ecological data, creating meaningful and long-lived reference data repositories. These efforts need to be accompanied by creating an interactive virtual platform able to interface with different database sources, showing upto-date species-specific ecological, morphological and molecular data. Configuring these public repository following a top-down layout linking other subset databases based on FAIR principles (Findable, Accessible, Interoperable and Reusable, http://wilkinsonlab.
info/node/FAIR) is mandatory to allow efficient data sharing. European initiatives such as DNAquanet (https://dnaqua. net), MBON (https://marinebon. org), SEAMoBB (https://seamobb. osupytheas. fr) are working to reduce morpho-genetic gaps and to expand our knowledge on marine biodiversity. Such initiatives should be further promoted worldwide. The BIOSCAN initiative (2019-2027, https://ibol. org/programs/bioscan/) from the iBOL Consortium aimed at "Revealing species, their interactions, and dynamics" is expected to promote new species descriptions and molecular characterization of terrestrial, freshwater, and marine species. Last but not least, in the present era of unprecedented marine biodiversity loss, society as a whole must be enthused about marine life in view to support measures that preserve marine biodiversity at a global scale.
Acknowledgements: This paper is a result of the CoMBoMed workshop ( Census of Marine Mediterranean Benthic Biodiversity : an integrative metabarcoding approach), held in Ravenna (Italy) on February 11-14, 2019 and organized by the  Department of Biological, Geological and Environmental Sciences (BiGeA) of the University of Bologna (UniBo). The CoMBoMed initiative was supported by the  European Marine Research Network (EUROMARINE Network) , the  Inter-Departmental Research Centre for Environmental Sciences (CIRSA -UniBo) , the  Cultural Heritage Department (DBC -UniBo , https://beniculturali. unibo. it/it), the  Fondazione Flaminia  and the  ERANet Mar-Tera  Project  SEAMoBB (Solutions for sEmi-Automated Monitoring of Benthic Biodiversity ).
General Discussion In this research, we studied whether presenting fact-checking tips and monetary incentives increases the correct evaluation of science-themed Facebook posts. In two experiments, participants rated the scientific validity of the content of one out of several posts, with some participants receiving a monetary reward when they responded correctly and other participants being shown a pop-up window (superimposed on the Facebook post itself) that contained a list of fact-checking techniques proposed in the literature (Civic Online Reasoning). Results showed that monetary incentives work as an accuracy booster. Moreover, data on search times and extremity of validity ratings corroborated the hypothesis that incentives operate by increasing motivation and, subsequently, attention on the content and other features of the post.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  870883. The information and opinions are those of the authors and do not necessarily reflect the opinion of the  European Commission. We would like to thank  Torbjørn Gundersen ,  Philipp Lorenz-Spreen ,  David J. Grüning , and the members of the  Prosocial Design Network  for their insightful comments and advice.
To identify new candidate genes involved in multiciliogenesis, we capitalized on the ever-growing quantity of biological data available to realize a large-scale screening of potential genes using several genomics techniques. We analyzed ten transcriptomics datasets of experiments dedicated to multiciliation to look for genes overexpressed in MCCs and combined our results with comparative genomics in an unprecedented study. We identified a specific evolutionary pattern of multiciliation involving a group of bony fish, the Otomorpha (D. rerio, Astyanax mexicanus, etc. ), that allowed us to prioritize target genes that were brought out in our transcriptomics analysis. The integration of both functional and comparative genomics allowed us to identify 11 new candidates that appear particularly promising.
Public Transcriptomic Datasets Eight datasets comprised of results from ten transcriptomics experiments specific to multiciliation (Table  1 ) were retrieved from the Gene Expression Omnibus (GEO) database of the NCBI  [37]  for our functional genomics analysis. The selected datasets are either microarray or RNAseq experiments conducted on X. laevis or Mus musculus and consist in the inactivation of genes involved in the currently known multiciliation pathway. GSE32452. In this experiment, Stubbs et al. compare gene expression between X. laevis embryo in which Notch intracellular domain (ICD) was injected and embryos in which both ICD and a glucocorticoid inducible multicilin were injected  [19]. GSE59309. In this experiment, authors compare gene expression in epithelial progenitors of Xenopus embryos in which multiciliation was induced by multicilin either with wild type E2F4 or a truncated form of E2F4 missing the last 140 amino acids  [23].
GSE89271. This dataset includes data from 3 experiments aimed at the characterization of Foxn4 and its role in multiciliation compared to Foxj1. Thus, gene expression of control embryos with glucocorticoid inducible multicilin was compared with three states: (1) embryos injected with glucocorticoid inducible multicilin and Foxn4 morpholino, (2) embryos injected with glucocorticoid inducible multicilin and CRISPR/Cas9 system aimed at Foxn4 or (3) embryos injected with glucocorticoid inducible multicilin and CRISPR/Cas9 aimed at Foxj1 [38]. GSE76342. Quigley and Kintner compare gene expression in epithelial progenitors of Xenopus at three timepoints (3, 6 and 9 h) in three pairs of conditions: (1) Notch blocking compared to injection of ICD, (2) injection of ICD compared to injection of ICD and multicilin and (3) Notch blocking compared to Notch blocking with inactive multicilin  [39].
GSE60365. In this experiment, authors compare gene expression in mouse tracheal cells when injected with control shRNA or with Myb blocking shRNA  [40]. GSE75715. Authors compare wild type individuals with p73 knockout mice [25]. GSE73331. Mori et al. compare wild type mice with E2F4 knockout mice  [41]. GSE116690. In this dataset, authors compare gene expression in lung cells of wild type mouse with cells of mice in which STK11 was deleted in lung progenitors using the Cre-lox system  [18].
Raw Data Analysis When available, the overexpression results provided by the authors were used directly (GSE89271, GSE76342, GSE78715 and GSE116690), otherwise, raw data were analyzed with the GEO2R tool of the NCBI  [37]  (GSE32452, GSE60365, GSE73331) and genes significantly overexpressed in a multiciliated state were retrieved. In the case of the GSE59309 dataset, the raw data were not directly analyzable with GEO2R, we thus followed the same method the authors describe in the manuscript and used the DESeq2 R package  [42]  to analyze each time point individually. For all datasets, the logFC threshold was set as ≥1 and as ≤0. 05 for the p-value.
Overexpressed Genes Clustering Before comparing overexpressed genes of the different datasets previously cited, we had to homogenize sequences identifiers, as the datasets come from different platforms and species. We thus used orthologous human gene names retrieved with the Mouse Genome Informatics database (http://www. informatics. jax. org/ (accessed on 26 November 2020)) and Xenbase  [43]  for mouse and xenopus sequences respectively. We generated a binary matrix with the results of the 10 experiments indicating the presence (1) or the absence (0) of overexpression for each gene (as rows) in each experiment (as columns). Pairwise distance between gene profiles was calculated using the dist function of the amap R package, using the 'binary' method (https://CRAN. R-project. org/package= amap (accessed on 2 December 2020)).
Hierarchical clustering was then carried out using the hclust R function with the 'ward. D2' method. Finally, gene clusters were defined through dynamic trimming of the resulting dendrogram using the cuttreeDynamic function of the dynamicTreeCut R package (https://CRAN. R-project. org/package=dynamicTreeCut (accessed on 4 December 2020)) using a depth of 2 and the 'hybrid' cut method.
Evolutionary Analysis of Multiciliated Genes For the evolutionary study of known multiciliated genes, we used the NCBI implementation of BLASTp  [44]  with default parameters to search for orthologous sequences, using the human sequence as a query. When necessary, gene absence was confirmed using tBLASTn on the latest release of the relevant species' genome, with default parameters and a word length of 3. Multiple sequence alignments of proteins were computed using the PipeAlign2 tool (http://www. lbgi. fr/pipealign (accessed on 12 January 2021))  [45] , using MAFFT as the main alignment software  [46]  and manually analyzed. Genomic context of selected genes was analyzed on the Ensembl platform  [47]  or the NCBI sequence viewer  [48].
In Depth Analysis of Target Genes 2. 3. 1. Generation of Interaction Networks We used the STRING protein interaction database  [50]  to construct our interaction networks and retrieved all genes that had interactions with at least 2 genes of our target list. We used a threshold score of 0. 7 for interactions between the genes of our list, and 0. 9 for interactions between genes of our list and external genes. We used Cytoscape  [51]  in combination with the GLay community clustering algorithm [52] implementation in the clusterMaker2 Cytoscape plugin  [53].
Computation and Clustering of Phylogenetic Profiles OrthoInspector 3. 0  [54]  was used to retrieve orthologs of our target genes in 711 eukaryotic species, with which we generated a matrix containing the presence or absence of each gene in those species. Once the phylogenetic profiles were constructed, we used the correlation method of the amap R-package. Hierarchical clustering was then carried out using the hclust R function with the 'ward. D2' method and trimming was done using the cuttreeDynamic function of the dynamicTreeCut R package with a depth of 2 and the 'hybrid' cut method (see Section 2. 1. 3).
Gene Overexpression To identify genes overexpressed in MCCs, we selected 8 transcriptomics datasets comprising 10 experimental designs aimed at comparing MCCs to cells in which multiciliation was altered by inactivating genes known to be involved in the generation of MCCs (Table  1 , see Section 2. 1. 1). These experiments can be grossly classified in two types depending on when the studied gene acts in the pathway: 'early' genes, responsible for the overall regulation of multiciliation (STK11, p73, MCIDAS and E2F4) and 'late' genes, responsible for the activation of multiple motile cilia generation (Myb, Foxn4 and Foxj1). For each dataset, we retrieved genes overexpressed in multiciliated condition using a threshold of 1 for logFC and 0. 05 for p-value, for a total of 4151 genes differentially expressed in at least one experiment.
Gene Clustering To further highlight genes of interest, we carried out a clustering of the various expression results using the Jaccard index applied to each gene profile to measure the distance between them, as well as the Ward algorithm  [55]  to perform the clustering (see Section 2. 1. 3). We obtained 28 clusters with 10 containing genes overexpressed in only one experiment, for a total of 2278 genes that will not be taken in consideration for the rest of our study (Figure  3 ). Circle size is proportional to the percentage of the cluster's genes found in each dataset. 1 GSE76342; 2 GSE116690; 3 GSE32452; 4 GSE75715; 5 GSE59309; 6 GSE73331; 7 GSE60365; 8 GSE89271. 'Early' genes (STK11, p73, MCIDAS and E2F4) are responsible for the overall regulation of multiciliation. 'Late' genes (Myb, Foxn4 and Foxj1) activate the generation of motile cilia.
Circle size is proportional to the percentage of the cluster's genes found in each dataset. 1 GSE76342; 2 GSE116690; 3 GSE32452; 4 GSE75715; 5 GSE59309; 6 GSE73331; 7 GSE60365; 8 GSE89271. 'Early' genes (STK11, p73, MCIDAS and E2F4) are responsible for the overall regulation of multiciliation. 'Late' genes (Myb, Foxn4 and Foxj1) activate the generation of motile cilia. The 18 remaining clusters can be regrouped according to the similarity of their expression profile and Panther  [56]  was used to perform Gene Ontology (GO)  [57]  term enrichments on each group of clusters to identify associated biological processes (Table  2 ). Clusters 3, 6, 7 and 14 contain 'late' genes that seem to be under the influence of Foxn4 and Foxj1 whose roles, as we mentioned before, are largely redundant. GO term enrichments reveal an overrepresentation of genes involved in the establishment of the localization of biological components, which may both be linked to the numerous roles played by the FOX protein family as well as the large need for vesicular transport mechanisms during ciliogenesis  [58].
Identification of Evolutionary Pattern To begin our search for a multiciliation-specific evolutionary pattern, we identified orthologs of the following proteins in Metazoa using BlastP and human sequences as a query on the RefSeq protein database  [59] : CEP63, DEUP1, MCIDAS, GEMC1, CCNO, CCDC78, E2F4, E2F5, CEP152 and CDC20B. We established the phylogenetic distribution of each gene in selected metazoan organisms to identify a phylogenetic profile linked to MCCs but most of the genes studied have different and seemingly complex evolutionary histories which did not enable us to construct a unique evolutionary signature (Figure  4 ). Some genes like CCDC78, CCNO, MCIDAS, GMNC and CDC20B seem to have appeared more recently and are mostly limited to Vertebrata, while the others seem to be older and present orthologs in most Metazoa.
Functional Interaction Networks To characterize our target genes, we used functional interaction networks with the hypothesis that proteins that interact together tend to be involved in the same biological process. With this in mind, we broadened our gene selection to build a sizeable network that allowed us to identify functional clusters. We used the STRING protein interaction database to select additional proteins that interacted with at least two of the proteins of our 122 targets, thus creating a network of 919 genes, including 57 from our list, divided into 10 clusters (Figure  7 ). Interaction network computed from the list of 114 potential multiciliated targets as well as genes presenting a functional interaction with at least two genes of that list. These clusters contain 57 of our genes (circled in bold) and a total of 919 genes.
Discussion Multiciliogenesis is a complex process involving numerous actors and our current knowledge about its mechanisms still leave a lot of gray areas that we need to shed light upon as shown by the existence of only eight genes associated with multiciliation in the GO database. Our goal was to identify new multiciliated genes to expand our understanding, using several genomics methods and combining them. In this study, we have done a large-scale screening of genes using 10 transcriptomics datasets dedicated to the analysis of multiciliation, which, to our knowledge, has never been done before. This allowed us to identify 1873 genes that are overexpressed in multiciliated conditions in at least two different experiments, including 148 with no GO annotations in humans, but also raised an important and frequent problem in the study of multiciliation, which is its tight link to ciliogenesis and the difficulty to separate both processes.
This led us to the development of a new and finer strategy. We used BLUR, a program specifically designed to highlight protein and sub-protein variations between two groups of species to compare Otomorpha and Euteleosteomorpha, another group of bony fishes, which showed that, besides multiciliation, several processes diverged between these two clades, such as immunity. This showed the relevance of combining the two approaches, which highlighted a list of 114 genes that were both functionally linked to multiciliation but also presented a specific evolutionary pattern. The functional characterization of these genes using the STRING database confirmed that a large part of our targets interacted with known ciliary genes, but also highlighted one common problem of in silico studies, which is the shortage of data and annotations in some cases, as shown by the lack of functional interactions detected for 65 of our target genes.
21 Figure 3. 213 Figure 3. Schematic representation of the clustering of the results from the transcriptomics datasets. Circle size is proportional to the percentage of the cluster's genes found in each dataset. 1 GSE76342; 2 GSE116690; 3 GSE32452; 4 GSE75715; 5 GSE59309; 6 GSE73331; 7 GSE60365; 8 GSE89271. 'Early' genes (STK11, p73, MCIDAS and E2F4) are responsible for the overall regulation of multiciliation. 'Late' genes (Myb, Foxn4 and Foxj1) activate the generation of motile cilia.
GEO Accession Overall Experimental Design (Multiciliation vs. No Multiciliation) Species Experiment Type GSE32452 [19] Notch intracellular domain (ICD) + glucocorticoid inducible Multicilin vs. ICD Xenopus laevis Microarray GSE59309 [23] Inducible Multicilin vs. inducible Multicilin + truncated E2F4 X. laevis RNASeq Inducible Multicilin vs. inducible Multicilin + Foxn4 morpholino X. laevis RNASeq GSE89271 [38] Inducible Multicilin vs. inducible Multicilin + CRISPR/Cas9 Foxn4 mutant X. laevis RNASeq Inducible Multicilin vs. inducible Multicilin + CRISPR/Cas9 Foxj1 mutant X. laevis RNASeq GSE76342 [39] Notch-vs. ICD; ICD vs. ICD + Multicilin; Notch-vs. Notch-+ Multicilin- X. laevis RNASeq GSE60365 [40] Non-targeted shRNA vs. Myb shRNA Mus musculus Microarray GSE75715 [25] Wild Type vs. p73 knockout M.
Providing long-term data about the evolution of railway networks in Europe may help us understand how European Union (EU) member states behave in the long-term, and how they can comply with present EU recommendations. This paper proposes a methodology for collecting data about railway stations, at the maximal extent of the French railway network, a century ago. The expected outcome is a geocoded dataset of French railway stations (gares), which: (a) links gares to each other, (b) links gares with French communes, the basic administrative level for statistical information. Present stations are well documented in public data, but thousands of past stations are sparsely recorded, not geocoded, and often ignored, except in volunteer geographic information (VGI), either collaboratively through Wikipedia or individually.
VGI is very valuable in keeping track of that heritage, and remote sensing, including aerial photography is often the last chance to obtain precise locations. The approach is a series of steps: (1) meta-analysis of the public datasets, (2) threesteps fusion: measure-decision-combination, between public datasets, (3) computer-assisted geocoding for 'gares' where fusion fails, (4) integration of additional gares gathered from VGI, (5) automated quality control, indicating where quality is questionable. These five families of methods, form a comprehensive computer-assisted reconstruction process (CARP), which constitutes the core of this paper. The outcome is a reliable dataset-in geojson format under open license-encompassing (by January 2021) more than 10,700 items linked to about 7500 of the 35,500 communes of France: that is 60% more than recorded before.
[6] , and Morillas-Torné  [7]  with a very similar goal, and uses several European public datasets, which record main rail lines (no secondary rail lines). This paper describes a method for building a digital representation of all the 'stations' (we use the French term 'gare') that have existed, between the maximum extent of the French railway network (1920s) and now, a century later. The goal is to deliver a dataset to answer questions such as: Does a commune possesses a gare? How far from the closest gare? Also, the aim is to give a comparison at different dates. For that we need: (a) to link gares to each other along their 'rail line' (we use French term 'ligne'), (b) to link every gare to a 'city or town': we use the French term 'commune', the smallest French administrative level at which most basic statistical data are collected.
There is no digital dataset providing all French gares at the maximum extent of the network, and even their number is unknown. There are two kinds of source: public records (open data from French public institutions: SNCF, Insee, IGN over 2014-2020), and a lot of volunteered geographic information (VGI), either collective through Wikipedia, or individual, providing sparse data about old gares (even simple stops) and lignes. Sections 2 and 3 present materials and methods, respectively for public and VGI sources, with the goal to "geocode" all the gares, in a reliable, controllable way; as "pointsof-interest" multi-source matching is presented in Li et al. [8]. Multi-source datasets are expected to show discrepancies between names (toponym changes), as investigated by Tian-Lan and Longley  [9] , and locations (existence and geocoding) of each individual gare.