dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , opinions or subjective content); factual but not interesting (consensual, general); factual and interesting (that is, check-worthy). To train the classifier, a database of 20,000 claims annotated with these categories has been built through crowdsourcing. Other work try to classify claims as either facts or opinions,  [69]  from the perspective of an opinion question answering system,  [64]  for research on deliberation and debate. The distinction is then made between an argument based on facts, from one appealing to feelings and emotions. An important problem to solve on the way to mostly automated fact-checking is understanding the context in which a given claim is made, that is: identifying the time to which the claim refers, possibly the location or geographical scope, identifying the speaker etc.
A final step is to combine the responses obtained from this evidence gathering module into a visual interface. In a separate study, the authors show a strong correlation between the output of their system, and that claims checked by professionals at CNN and PolitiFact. The Fast and Furious FactCheck Challenge 10  proposed to classify news articles (not claims) among the categories True, False, Somewhat True and Somewhat False using any combination of human and automated tools; the 2016 winner states he has used NLP and other AI technologies but not much more is known. We end this section discussing works which do not meet exactly our fact-checking definition yet come very close to it: Les Décodeurs, the fact checking team of Le Monde 11  have developed (and share as open data) a database of manual fact checks  12  , comprising for each claim a set of Web and social media sources having propagated it, the fact checking analysis, and the final levelof-truth classification.
Content organized according to a Schema. org vocabulary can be encoded in many formats, including JSON. LD (JSON linked data), microdata formats etc. The Claim-Review vocabulary comprises a claim which is of type text, and a claim review, which specializes the pre-existing Review concept from Schema. org. Further, a ClaimReview includes a review rating, and is about a CreativeWork, which can be for instance an Article, Blog, Movie etc. The integration of ClaimReview into the Schema. org vocabulary allows for interoperability with other structured (typically open) data sources. A database-oriented perspective is taken by the Structured Journalism 14  initiative, which encourages journalists to publish database items, that is, atomized pieces of structured information. Clearly, this can apply to raw data, used as input in the fact checking process, but also to its outputs.
Publishing content in such a structured format eases data reuse and allows building several stories by selecting different subsets of the data and focusing on different trends. As already pointed out, several human-powered fact checking platforms such as FactCheck. org and PolitFact already provide API access, and their output is already used by several other tools. Among automated systems ClaimBuster mentioned above, also provides access to their fact checking outputs. CJWorkbench, already mentioned, claims to enable the sharing of prior work for reuse in similar settings, for instance adapting a voter fraud analysis done on a local election in Texas to a different election in California.
Towards a fact check management system From our identified fact checking tasks, the analysis of existing works and techniques, as well as the needs and desiderata outlined above, we can derive the functionalities of an "ideal" fact check management system (FCMS), that is, a content management system (CMS) tailored to the needs and uses that fact checking entails: Data storage. The FCMS should be capable of storing data sources in various formats, such as they come in journalists' hands: PDF, structured text, JSON, CSV, relational databases etc. Automatically detecting the data type and relying on an appropriate system to store each kind of data (or simply a file system when nothing more is available) would simplify users' experience. Automatic back-up mechanisms e. g. toward a cloud-storage account would prevent accidental loss of data and allow data reuse.
Building highly accurate NLP tools for very specific tasks is and will probably stay out of reach for non specialists for a while. However, basic NLP functionalities such as named entity recognition from a knowledge base or structured database, transforming documents in bag of words, enhancing it through synonyms and close terms etc. are by now well understood and well supported by tools including open-source ones. They need to be made available and easy to integrate within the FCMS. Time management. Being able to trace the data and its evolution is important for accuracy, transparency, reproducibility. FCMS should be able to record and permanently store various pieces of time information such as: data creation time stamp (immutable publication date of an article or a dataset), acquisition times (can be repeated several times as the data evolves), statement date (when people made a certain statement etc.
) Any kind of data is concerned including results of fact check. Fact and validity time. Facts and events almost all have a limited period of validity. This information can be explicit in the dataset (e. g. a knowledge base gives the starting and ending dates of World War I, or the period where W. Churchill was Prime Minister of the U. K. [60] ); but it may need to be inferred from the different snapshots or versions of a same dataset, e. g. , a list of Twitter accounts of all ministers is not time-stamped, but evolves with time. In order to understand when a claim is or is not valid, and to ensure not to lose any information through time, all facts should be associated with a validity time, and all query languages and systems should be able to handle it. Data quality management. This point encompasses all the tools for managing the life cycle of reference sources and fact checks.
http://ropensci. github. io/reproducibility-guide/ checking effort so that one could "replay" it on the same inputs with the same tool, and get the same result. Modularity. Using today's technology, the processes involved in fact-checked are extremely time consuming. The FCMS should be designed from the start with modularity and reuse in mind, so that claim analysis pipelines can be easily built and their components re-used. This will offer a great advantage over "one-off" systems. Compliance with the standards. The FCMS should adopt all standards of the trade and be open in the export and sharing of its inputs and outputs.
We compiled a standardized dataset of old-growth tropical forest inventories of angiosperm trees (trunk diameter ≥10 cm) for 406 1°latitude/longitude grid cells (hereafter referred to as "locations") originally dominated by natural forests across the (sub)tropics (Table  S1 ). These locations represented all major tropical forest regions and had broad environmental amplitude, including low to high elevations and dry to wet forests (Fig. 1  and Fig. S1 ). To determine the phylogenetic distance between locations, we constructed a dated phylogenetic tree that was resolved to genus level and contained all taxa used for our classification analyses (Dataset S1). Location pairwise phylogenetic distance matrices were constructed using 20 randomly drawn tree taxa per location. We used 20 taxa, as this maximized the number of locations that could be included in the classification analyses while still providing a reliable classification result.
Materials and Methods Tree Inventory Dataset. Individual angiosperm trees (diameter at breast height ≥10 cm) from old-growth forest inventories throughout the (sub)tropics (between -35°S and 35°N latitudes) were pooled within their respective 1°latitude/longitude grid cells (henceforth called locations). These locations represented all major tropical forest regions and had broad environmental amplitude, including low to high elevations and dry to wet forests (Fig. S1 ). Monocots and Cactaceae were excluded because these were not consistently surveyed in all datasets. This dataset originally included 439 locations containing 925,009 individual trees belonging to 15,012 taxa. Species names were standardized using The Plant List (www. theplantlist. org), Taxonomic Name Resolution Service (tnrs.
iplantcollaborative. org/TNRSapp. html), and Asian Plant Synonym Lookup (phylodiversity. net/fslik/synonym_ lookup. htm). On average, 1. 4% of individual stems per location remained unidentified. These unidentified individuals were excluded from further analyses. Community Phylogenetic Tree. The APG-III classification (25) served as the family-level backbone of our community phylogenetic tree. Recent updates in APG-IV  (26)  are mostly of nomenclatural nature and did not affect our analyses. This tree was further resolved up to genus level using the specieslevel phylogeny (32,223 species included) published by Zanne et al. (27) , which covered most genera in our dataset (Dataset S1). Genera present in our dataset, but not in Zanne et al. (27) , were placed at the base of their respective families.
Both functions return a symmetrical matrix of locations versus locations with their pairwise phylogenetic distances. Principal coordinate (PCO) analyses (in MultiVariate Statistical Package v3. 13; Kovach Computing Services) on resulting location versus location matrices showed that the MPPD matrices had almost no explanatory power (generally the first five PCO axes explained less than 5% of data variance), meaning that detected patterns were mostly random. The MNTD matrices, however, explained considerable amounts of data variance in the first five axes of the PCO. Therefore, we used only MNTD for further analysis. Correcting for Taxon Richness Bias in MNTD. Taxon richness differed considerably between locations, varying between 4 and 1,466. MNTD may be sensitive to such differences in taxon richness because the chance of finding a close relative between two locations may increase when their taxon richness increases.
S5 ). Only in the 20-taxon analysis was one American location (location no. 165 from the Brazilian Atlantic Forest) placed in the Asian cluster. The amount of variance captured in the first five axes of a PCO analysis (using the same MNTD matrices) declined by only ∼20%, from 83. 3 to 60. 7%, between the 320-and 20-taxon analyses, respectively. We decided to use 20 taxa per location in the final analyses (Table  S1 ) because of this limited loss of information in the PCO and similarity of cluster results. In addition, we were able to use most of our locations (406 of the initial 439), including locations on remote islands and extreme habitats that would have been excluded if we had set the minimum number of taxa too high. Forest Classification Analyses. For the final analyses, we produced 20 locationby-taxon datasets.
In these datasets, each location was represented by 20 randomly drawn taxa (from that location). Random draws were irrespective of taxon abundance, as abundance is a spatially and temporally labile taxon trait that likely reflects contemporary environmental conditions rather than historical biogeographic signal. For each of these 20 location-by-taxon datasets, we calculated the corresponding symmetrical location-by-location matrices with their pairwise phylogenetic distances (MNTD). These matrices were then used as input for cluster analyses. Locations were grouped in clusters using Ward's minimum variance method (30), using MultiVariate Statistical Package v3. 13. This is a centroid-based clustering technique that identifies cluster centers (centroids) by minimizing the overall squared distances of the objects (in this case locations) to the centroids at each cluster level.
This clustering technique identified spatially clearly defined location groupings (Fig. S2 ). The optimal number of clusters for defining floristic regions across the tropics was determined by calculating the cophenetic correlation coefficient at each cluster level, starting at the first split (K2) in the dendrogram. The cophenetic correlation coefficient calculates the correlation between the distance of the clusters as calculated by the clustering algorithm and the distance based on observed MNTD values between clusters. The higher the cophenetic correlation, the better the cluster result reflects the patterns present in the original distance matrix. We applied this method to each of our 20 datasets, calculated the average cophenetic correlation coefficient for each cluster level, and found a steep increase in cophenetic correlation up to K5, after which it slowly declined (Fig.
ACKNOWLEDGMENTS. This study benefited greatly from data contributed by  Patricia Alvarez-Loayza ,  Ana Andrade ,  Peter Ashton ,  Julian Bayliss ,  Luis Bernacci ,  Lilian Blanc ,  J. Bogaert ,  Matt Bradford ,  Mireille Breuer Ndoundou Hockemba ,  C. De Cannière ,  Miguel Castillo ,  Eduardo Catharino ,  Connie Clark ,  David Clark ,  Deborah Clark ,  Gilles Dauby ,  Jean-Louis Doucet ,  Pedro Eisenlohr ,  Leandro Ferreira ,  Christine Fletcher ,  Geraldo Franco ,  Gabriella M. Fredriksson ,  Girirai ,  Nimal Gunatilleke ,  Terese Hart ,  Miriam van Heist ,  Zhila Hemati ,  M. A. Hernández-Ruedas ,  David Kenfack ,  Kanehiro Kitayama ,  Eileen Larney ,  Ieda Leao do Amaral ,  Jean-Remy Makana ,  Punchi Manage Saranga Amila Ruwan ,  Antti Marjokorpi ,  Olga Martha Montiel ,  Miguel Martínez-Ramos ,  Henrik Meilby ,  Jerome Millet ,  Cao Min ,  Kazuki Miyamoto ,  Xiaoxue Mo ,  Juan Carlos Montero ,  Badru Mugerwa ,  Pantaleo Munishi ,  Helen Murphy ,  Hidetoshi Nagamasu ,  David Newbery ,  Rueben Nilus ,  Meyner Nusalawo ,  Susana Ochoa-Gaona ,  Atila Oliveira ,  Navendu Page ,  Andrea Permana ,  Nigel Pitman ,  Jean Razafimahaimodison ,  Rocío Rojas ,  Hugo Romero ,  M.
For quantification of the peaks at m/z 177. 06 and 223. 10, diethyl phthalate (Sigma-Aldrich Chemie GmbH, Steinheim, Germany) was first solubilized in a 1:100 (v/v) ratio in distilled ethanol and then further diluted in a concentration series from 10 -3 to 10 -12 M in Milli-Q water. This series was injected into the ESI-MS instrument; pure diethyl phthalate (Sigma) was solubilized in 1 volume of ethanol and further diluted with 2 volumes of water to 1 μM, and concentration was used to check whether this fragmented in a manner similar to that of the m/z 223. 10 peak in the polymer-exposed samples (see Figure  S1  for the reference mass spectrogram). Data Analysis. Data were analyzed using IBM SPSS Statistics (version 24). A Shapiro-Wilk test was performed, and all groups were found to be normally distributed.
Design of Blocking Primers for Scleractinia A preliminary sequencing test was performed to study eukaryote diversity within a sample of P. damicornis using two primer sets targeting two differents regions of the 18S rRNA gene, 18SV1V2 and 18SV4 (Table  1 ). While primers for 18SV4 were designed previously to amplify all eukaryotic-specific 18S rDNA  (Stoeck et al. , 2010) , primers for 18SV1V2 were designed using the Protist Ribosomal Reference database (PR 2 )  (Guillou et al. , 2012)  in order to prevent amplification of metazoan 18S rRNA genes especially from Crassostrea gigas oysters. Both sequencing tests showed an excess of amplicons from P. damicornis, since they represented ∼99% of sequences (for a total of 3383 and 2460 cleaned sequences using 18SV1V2 and 18SV4, respectively; data not shown).
Thus we designed blocking primers for both primer sets in order to reduce the proportion of P. damicornis amplicons. First, we downloaded the non-redundant (99%) Silva SSU database (release 128, September 2016)  (Quast et al. , 2013; Yilmaz et al. , 2014). Then we only kept sequences that matched with either the primer set for 18SV1V2 or 18SV4. Based on annotations, metazoa were removed to produce a metazoa-free database and sequences of Scleractinia were used to create a host database. In order to design blocking primers that overlap the reverse primer and the 3 -region of Scleractinia amplicons, we aligned the last 40 nucleotides (corresponding to the 3 -region of amplicon and the reverse primer) of Scleractinia with metazoafree database using MUSCLE v3. 8. 31  (Edgar, 2004). Then, we analyzed the nucleotide polymorphism at each position of the alignment for Scleractinia and metazoa-free sequences using entropy decomposition (R package {otu2ot}, CalcEntropy.
seq) (Ramette and Buttigieg, 2014). According to previous studies and entropy values (Supplementary Figure  1 ), we designed blocking primers <30 bp with 10 bp overlapping the reverse primer, and having a Tm similar to the targeted primer set  (Vestheim and Jarman, 2008; Leray et al. , 2013). The best candidate for each primer set (targeting either 18SV1V2 or 18SV4 region) was identified using specificity tests against the Scleractinia and metazoa-free databases. Lastly, these primers were synthesized and modified at the 3 -end with a Spacer C3 CPG (3 hydrocarbons).
Sequence Analyses The FROGS pipeline (Find Rapidly OTU with Galaxy Solution) implemented into a galaxy instance was used to define Operational Taxonomic Units (OTU), and compute taxonomic annotations  (Escudié et al. , 2017). Briefly, paired reads were merged using FLASH  (Magoc and Salzberg, 2011). After denoising and primer/adapters removal with cutadapt  (Martin, 2011) , de novo clustering was done using SWARM that uses a local clustering threshold, with aggregation distance d = 3  (Mahé et al. , 2015). Chimera was removed using VSEARCH  (Rognes et al. , 2016). We filtered the dataset for singletons and performed affiliation using Blast+ against the Protist Ribosomal Reference database (PR 2 )  (Guillou et al. , 2012)  to produce an OTU and affiliation table in standard BIOM format. Because we were interested in studying low frequency OTUs, we used additional steps to remove most PCR and sequencing errors.
First, we removed OTUs having an annotation with a blast coverage <90%. Secondly, we computed a phylogenetic tree of the whole OTUs using MAFFT  (Katoh et al. , 2002)  and FastTree (GTR model)  (Price et al. , 2010). OTUs were removed from the dataset if they corresponded to very long branches on the phylogenetic tree (according to minimal branch length values). Lastly, OTUs were considered present in each sample if they had at least three sequences. Rarefaction curves of protist species richness were produced using the {phyloseq} R package, and the rarefy_even_depth and ggrare functions  (McMurdie and Holmes, 2013). We also used phyloseq to obtain abundances at the genus taxonomic rank (tax_glom function). Pielou's measure of evenness was computed using affiliated genera and the {vegan} R package.
First, Symbiodinium diversity was studied according to the annotation step of Symbiodinium OTUs. In particular, we used the output alignment of Mothur obtained at 95 and 97% similarity cutoffs for 18SV1V2 and 18SV4, respectively. These alignments contained representative OTUs of environmental Symbiodinium, the two outgroups (P. glacialis and P. beii), and the reference sequences of Symbiodinium clades. Secondly, sequences of protist genera, Symbiodinium clades, and P. damicornis (accession number: LT631138. 1 for both 18SV1V2 and 18SV4) were aligned using MUSCLE v3. 8. 31  (Edgar, 2004). Thirdly, we described the diversity of coral-associated coccidians using reference sequences of Apicomplexa  (Schrével et al. , 2016) , and best BLASTn hits of coccidian sequences of this study against the National Center for Biotechnology Information (NCBI).
Specificity of Blocking Primers In order to describe protist diversity associated with P. damicornis colonies from Djibouti and New Caledonia, we performed a preliminary sequencing test using one sample and two primer sets targeting different variable loops of the 18S rRNA gene (V1V2 and V4). Since most sequences corresponded to P. damicornis, we designed blocking primers using the Silva SSU database (see Methods for more details). These blocking primers targeted 10 bp of reverse primers and ∼15 bp of the amplicon 3end of Scleractinia (Table  1  and Supplementary Figure  1 ). Because their 3 -end had a spacer C3 CPG, elongation is expected to abort, whereas annealing properties are not modified  (Vestheim and Jarman, 2008). To estimate the specificity of these blocking primers, we identified sequences of the Silva database that matched with primer sets and blocking primers.
We found a very high in silico specificity, since 100 and 93. 8% of Scleractinia were removed by the 18SV1V2 and 18SV4 blocking primers, respectively (Table  2 ). In addition, they removed very low fractions of protists found in the Silva database. As a consequence, we used both primer sets and their blocking primers to study protists associated with the colonies of P. damicornis from Djibouti and New Caledonia. Because DNA extraction was done directly on coral tips, we expected to be able to describe the entire protist community (i. e. , microbes that were part of the holobiont, and possibly environmental microbes). On average, each sample had ∼29,566 Pocillopora sequences and ∼18,724 protist sequences representing ∼26 OTUs (Table  3  and Supplementary Table 4 ). While in silico analyses showed very high specificity of both blocking primers, PCR and MiSeq sequencing displayed higher host contamination (Figure  2A ).
Diversity of the Other Dominant Genera Although protist proportion was lower for 18SV1V2 (Figure  2A ), the proportion of protists other than Symbiodinium was lower for 18SV4 (0. 9% compared to 2. 9% for 18SV1V2) (Figure  2B ). The Symbiodinium genus was removed from the dataset to study the other protist genera of P. damicornis (Figure  2C  and Supplementary Table  5 ). Licnophora, unidentified coccidians and Dino-Group I-Clade 1 (Syndiniales) were the main taxa found in P. damicornis samples among the 17 genera found with both primer sets. Among them, Licnophora represented a high fraction for 18SV1V2 and 18SV4, whereas coccidians showed different proportions between these markers. In particular, 18SV1V2 showed a more even protist diversity at the genus level than 18SV4 (0. 05 > 0. 03, Pielou's measure of evenness).
Reference sequences of marine Apicomplexa and outgroups were selected according to a previous study  (Schrével et al. , 2016). The tree was rooted using Phytophthora strains as outgroups. Accession numbers and genus are indicated for each sequence (except for Symbiodinium, see Figure  3  and Supplementary Table  2 ). Numbers are bootstraps (%) of major nodes reflecting clade support. The dashed box indicates coccidian OTUs and known sequences of coccidian symbionts associated to corals and their corresponding references  (Toller et al. , 2002; Kirk et al. , 2013; Šlapeta and Linares, 2013). were present in several P. damicornis colonies. In particular, we observed different distribution between Djibouti and New Caledonia. In order to statistically test differences between both geographic regions, we computed Fisher's exact test for each protist genus and Symbiodinium clade (Figure  5 ).
Efficiency of Blocking Primers First, a very high specificity was obtained in silico for both blocking primers. In accordance with sequence entropy values (Supplementary Figure  1 ), all Scleractinia from the Silva database were expected to be blocked for 18SV1V2, and we found that ∼94% of them were targeted by the blocking primers of 18SV4. Although both blocking primers matched with Pocillopora sequences, we observed various efficiencies for the different samples from Djibouti and New Caledonia. On average Pocillopora still represented 70% (from 30 to 92%) and 39% (from 7 to 66%) of sequences for 18SV1V2 and 18SV4, respectively. Such variations were also described for artificial rDNA mixtures of algae and krill (between 26 and 42% of krill sequences were not blocked by blocking primers)  (Vestheim and Jarman, 2008) , and for gut content of fish (between 14 and 45% of sequences were not blocked by blocking primers)  (Leray et al.