dataesr/scientific-paragraphs-categorization huggingface dataset:
The images of varying quality had been collected using different types of cameras, but their identifications had been doublechecked. Primary results showed that modern AI methods at that time gave an accurate identification for 67% of observations, which well illustrates the complexity of the dataset  (Van Horn et al. 2018). In 2018 most images of plants and animals from any part of the world were likely to recieve from the system an identification of species inhabiting North America. Over the course of 2019 and 2020, AI has almost stopped suggesting incorrect identifications for plants within European Russia  (Seregin et al. 2020). It still works somewhat worse with photos from Asian Russia and the Caucasus. Millions of new photos reviewed by the expert community and constantly added to the library of standard images allow AI to improve the performance.
Its capabilities are still inferior to expert assessments with regard to certain groups of organisms or certain geographic territories. Nevertheless, the system's general awareness of the world flora is many times larger than that of an individual botanist. In many ways this particular feature of iNaturalist attracts both amateurs and professionals. The success of iNaturalist has made possible the further use of AI for species recognition by photograph for millions of images in the GBIF database  (Robertson et al. 2019).
Sampling methods Sampling description: The standard procedure of sampling is described on iNaturalist in the form of 17 paragraphs in the "Observations" section of the help page (last revised Sep. 8, 2020 by Sam Kieschnick). Quality control: Data quality control is necessary for maintaining a high quality of records within a dataset. In the "Flora of Russia" project description, there is a well-structured, detailed and constantly improving section with recommendations for users in Russian. Apart from the general information (including short videos about iNaturalist and a description of available research tools of the portal), there are two particularly important sections, i. e. "Recommendations for new users" and "Recommendations for event curators". Both sections provide detailed instructions on what, how and where a user should do to create a good-quality observation on iNaturalist.
However, many users are not familiar with these guidelines. This imposes a certain responsibility on the identifiers and the project curators, who act as data stewards. The most important and/or frequently occurring issues are listed below. For each project on iNauralist, at least one or two curators should be assigned to review the uploaded observations and make comments, if necessary. The most frequent mistakes are: 1. low-quality or wrong-angle photos, 2. observations of cultivated plants without a relevant indication, 3. either unintentional or intentional duplication of the same observation, 4. unintentional merging of numerous observations into a single one, 5. lack of date or location of an observation, 6. lack of any original identification (at least a coarse one), 7. upload of copyrighted media.
Geographic coverage Description: Russia is a large country with an area of over 17 million km and an unevenly distributed human population. For instance, in Chukotka, the population density is only 0. 07 people per 1 km whereas in the City of Moscow it is 4,925 people per 1 km (Table  7 ). The geographic coverage of the dataset is characterized by significant spatial disparities in the presented data for all indexes, including the number of observations, species and observers (Fig. 5 ).
Number of observations. The key index of the "Flora of Russia" project is the number of uploaded observations (Fig. 1 ). The project reached 750,000 observations of "researchgrade" quality on Sep. 7, 2020, whereas ca. 135,000 unverified observations make the project's backlog, which is not included in the dataset. The City of Moscow topped the project by the number of observations from Aug. 18, 2019 to Jun. 15, 2020, when Moscow Oblast, the region with the largest community of observers, took the lead (Table  8 ). Other regions of Central Russia -Bryansk, Tula, Nizhny Novgorod, and Kursk Oblasts -hold the third to sixth places in the ranking. The top 10 regional projects contribute 45. 4% of observations of the entire project and this proportion is constantly decreasing due to the growth of the communities in other regions.
We actively promoted both events among the participants of the "Flora of Russia" project. It is worth mentioning that the COVID-19 restrictions of the spring of 2020 caused, for example, a low level of participation in CNC, which was made up for in the summer by offcampus student practices and events for high school students which all used iNaturalist this year. Summing up all Russian projects on student practices over the three summer months of 2020 (the common time for them in Russia) shows that 54,186 "research grade" observations by more than 750 observers meet the requirements of the "Flora of Russia" project. This makes a modest 17. 4% contribution to the summer observations of the project. In 2020, practices in the form of independent work of students supervised remotely by teachers were held in fourteen Russian universities: Another notable income of the summer data flow was the Herbarium 2.
Usage rights Use license: Other from the project's data and 587 observations from the backlog from the backup on this ground. The "research-grade" observations with free licenses (CC0, CC-BY, and CC-BY-NC) are fully available in GBIF within "iNaturalist Research-grade Observations" occurrence dataset (https://doi. org/10. 15468/ab3s5x). We added the last column "gbif_id" to all csv files of our dataset with URLs of GBIF records using GBIF Occurrence Download https: //doi. org/10. 15468/dl. msfxkn performed Sep. 28, 2020. Five amended csv-files with 750,146 observations from the project "Flora of Russia" ("research-grade" records) and 136,023 observations the project's backlog ("needs-id" records) represent the stable project backup (https://doi. org/10. 5281/zenodo. 4061848). The following abbreviations are used in column descriptions: • A -automatically generated data (usually from exif files of photos); • M -manually inserted data; • AM -both options are possible (automatically generated data which could be manually edited).
Productivity (number of observations per observer). This index clearly demonstrates the regions with a fairly modest community, where the data is received mainly from a few number of some most active participants ("mega-observers") (Table  16 ). Such active individuals greatly helped Omsk Oblast, Kursk Oblast, Kostroma Oblast, Bryansk Oblast, Chuvashia, Dagestan, Mordovia, Kamchatka, Sevastopol and Tyumen Oblast to rise high in the index. More than 10K people and about 4K identifiers have participated in the data collection and curation within this dataset. We would like to thank other active observers and identifiers from iNaturalist: afid, alchemillist, alexander_baransky, alina_kondratieva, almantas, arslan_tatamov, bobwardell, dakileno, davydovbotany, dryomys, evelina_ishmukhametova, evgeniy_benikhanov, fedor_kondrachuk, frontova, fylatra, fyodorkhomenko, gen_ok, ggularijants, hln_m_t, igor_olshanskyi, kai_schablewski, karavan-altay, kemper, korobkov, kosienkov_konstantin, laukines_pievos, lenatara, lilia_rakitianskaia, margosha, mariendistel, marina-privalova, max_carabus, mitchella_1, mobbini, natalya1406, naturalist19164, naturalist25051, naturalist31744, naturalist7664, nellysemenova, ocanire, olegdavydov, pavel_yevseyenkov, plrays, polemoniaceae, ppllaannttss_, roman_romanov, roman-evseev, sansan_94, schneider414, sergejseleznev, sergii_kudlaenko, sergilus, skuch, snsergeevna, sokolkov2002, stepangrebnev2005, stephen54, thilokrueger, tiggrx, Not peer-reviewed, not copy-edited manuscript posted on October 02, 2020.
43 Not peer-reviewed, not copy-edited manuscript posted on October 02, 2020. DOI: https://doi. org/10. 3897/arphapreprints. e59250 Figure  5. A map of 750K observations from the "Flora of Russia" project showing an extreme disproportion in data coverage (source: iNaturalist. org). 44 Verifiable observations from Russia on iNaturalist made in 2019 -tracheophytes (green) and all other groups (brown). A major event which contributed to data collection was the Russian Team Cup on photodocumentation of wild plants 2019 (from 1/32 to final). Figure  8. Verifiable observations from Russia on iNaturalist in 2020 -tracheophytes (green) and all other groups (brown). Major events contributed to data collection include the City Nature Challenge 2020 (CNC), the Russian Team Cup on photodocumentation of wild plants 2020 (from 1/32 to f inal) and the International Biodiversity Championship 2020 (IBC).
750K + 136K recordshttps://https://zenodo. org/record/4061848#. X3afWe1n1PZ, "Flora of Russia" backup (alternative Zenodo identifier); http://www. inaturalist. org/observations/gbifobservations-dwca. zip, complete iNaturalist dataset in GBIF (Ueda 2020); https:// www. gbif. org/dataset/50c9509d; https://doi. org/10. 15468/ab3s5x, alternative identifier of complete iNaturalist dataset in GBIF; https://doi. org/10. 13140/RGFlora of Russia on iNaturalist backup 2020 Sep 08 (750K + 136K recordshttps://zenodo. org/record/4061848/files/flora-of-russia%26d1%3D1970-09-01%26d2%3D2019-06-30. xlsx?download=1; https:// zenodo. org/record/4061848/files/flora-of-russia%26d1%3D2019-07-01%26d2%3D2020-05-20. xlsx?download=1; https:// zenodo. org/record/4061848/files/flora-of-russia%26d1%3D2020-05-21%26d2%3D2020-07-05.
PeerJ 6 https://doi. org/10. 7717/peerj. 4932 • Gubareva IY, Dedkov VP, Napreenko MG, Petrova NG, Sokolov AA (1999) Конспект сосудистых растений Калининградской области: справочное пособие. [Check-list of vascular plants of Kaliningrad Oblast: a reference book]. Publishing house of Kaliningrad University, Kaliningrad, 107 pp. [In Russian]. URL: http://herba. msu. ru/ shipunov/school/books/gubareva1999_konsp_fl_kaliningr_obl. pdf [ISBN 5-88874-140-Х] • Happel A (2019) A volunteer-populated online database provides evidence for a geographic pattern in symptoms of black spot infections. International Journal for Parasitology: Parasites and Wildlife 10: 156-163. https://doi. org/10. 1016/j. ijppaw. 2019. 08. 003 • Heberling JM, Isaac B (2018) iNaturalist as a tool to expand the research value of • Larson ER, Graham BM, Achury R, Coon JJ, Daniels MK, Gambrell DK, Jonasen KL, King GD, LaRacuente N, Perrin-Stowe TI, Reed EM, Rice CJ, Ruzi SA, Thairu MW, Wilson JC, Suarez AV (2020) Frome DNA to citizen science: emerging tools for the early detection of invasive species.
[In Russian]. URL: http://www. gbsad. ru/science/ doc/biulleten_gbs_2004_188. pdf • Reshetnikova NM (2016) Diss. ]. Main Botanical Garden, RAS, Moscow, 47 pp. [In Russian]. URL: https:// dlib. rsl. ru/viewer/01006653111 • Revushkin AS (Ed. ) (2014) Определитель растений Томской области. [Keys to plants of the Tomsk Oblast]. Publishing house of Tomsk Univ. , Tomsk, 464 pp. [In Russian]. • Robertson T, Belongie S, Adam H, Kaeser-Chen C, Zhang C, Chuan Tan K, Liu Y, Brulé D, Deltheil C, Loarie S, Van Horn G, Mac Aodha O, Beery S, Perona P, Copas K, Waller J (2019) Training machines to identify species using GBIF-mediated datasets. Biodiversity Information Science and Standards 3 https://doi. org/10. 3897/biss. 3. 37230 • Rosenberg MS (2018) New record and range extension of the fiddler crab Uca princeps (Smith, 1870) (Brachyura, Ocypodidae) from California, USA.
Genome sequencing, assembly and annotation We used 101 Dothideomycetes genomes and eight other Pezizomycotina genomes as outgroups representing the Pezizomycetes, Eurotiomycetes, Leotiomycetes and Sordariomycetes (Supplementary Table  1 ). The 55 newly sequenced Dothideomycetes in this study were assembled and annotated between January 2012 and November 2016. The rapid progress in sequencing and assembly techniques during this period is reflected in the various techniques used to assemble these genomes. Two primary sequencing technologies were used, namely Illumina for earlier genomes and Pacific Biosciences more recently. For the oldest, Cercospora zeae-maydis, the consensus assembly was from a mixture of Roche (454) pyrosequencing, Sanger fosmids, and Illumina data using Velvet  (Zerbino & Birney 2008).
The Illumina-based assemblies fall into two main classes: 1) minimal draft assemblies from single 270-bp insert size libraries using 2 × 150-bp paired-end reads assembled first with Velvet to create an in-silico long mate-pair library with insert sizes of 3 000 +/-300 bp, which was then assembled together with the original Illumina library using AllPaths-LG  (Gnerre et al. 2011) ; and 2) standard draft Illumina assembles where 2 × 100-bp paired-end reads from a 4-kb-long mate-pair library was used in addition to the reads from the 270-bp insert size libraries and assembled using AllPaths-LG. For PacBio sequencing, filtered subread data were assembled together with Falcon (https://github. com/ PacificBiosciences/FALCON), improved with finisherSC  (Lam et al. 2015) , and polished with Quiver (https://github.
Machine learning We used the Support Vector Machine method as implemented in Python's scikit-learn library (http://scikit-learn. org) to identify the most informative features (e. g. , OrthoMCL gene clusters, PFAM annotations) for differentiating between pathogen and saprobe genomes. The dataset was cleaned using the near-ZeroVar function in R to reduce noise in the analysis. We used a subset of known pathogens and saprobes (Supplementary Table  4 ) to train and identify features that were differentially distributed between the pathogens and saprobes. Features were ranked on ability to predict using C-Support Vector Classification within scikit-learn (sklearn. svm. SVC). Strength of prediction was validated using sklearn. cross_validation and jackknife, and best features were selected using sklearn.
001) in MFS and a 16-fold increase (p-value = 0. 0) in PF05922 (protease inhibitor). These differences are presumably due to the narrow ecological niche and relatively stable environment inhabited by these two fungi. An analysis of the genomes of Pseudovirgaria hyperparasitica and Ampelomyces quisqualis showed a two-fold increase (p-value = 0. 0) in PF01636 (Phosphotransferase enzyme family), a family of bacterial antibiotic resistance proteins. These mycoparasites may be using these genes to overcome antibiotics produced by their hosts during infection. However, this class of proteins also showed high numbers in insect-associated fungi like Tolypocladium inflatum  (Bushley et al. 2013)  and Metarhizium robertsii  (Gao et al. 2011) , and also some plant pathogens such as Neonectria ditissima  (G omez-Cortecero et al.
2015). In addition, several well-known mycoparasites like Trichoderma gamsii  (Baroncelli et al. 2016) , T. asperellum and T. harzianum  (Druzhinina et al. 2018)  do not show an increase in PF01636, suggesting that they either use alternative strategies or the lack of sufficient sampling in our study set introduced a statistical false positive in this case. Our analysis also showed PF01636 enrichment in aquatic and saprophytic lineages, suggesting that this family has diverse functions and testing individual genes using experimental approaches for roles in mycoparasitism might yield valuable insights.
We used a cutoff of p < 0. 005 and at least 100 genes in the test subset to identify 14 GO annotations and 28 PFAM annotations that showed different enrichment between the saprobes and pathogens. Only one PFAM (PF01328, Peroxidase) and one GO (0032440, 2-alkenal reductase activity) were enriched in pathogens compared to saprobes. While we observe a strong association between presence of these functional annotations and lifestyle, further experimental work is necessary to elucidate their potential roles in pathogenicity. Among the PFAM domains that were reduced in pathogens were several tetratricopeptide repeat (TPR) domains (Supplementary Fig. S6 ). The TPR structural motif was originally identified in the fission yeast Schizosaccharomyces pombe and appears in all branches of life in various unrelated protein families; it is linked to virulence in some human pathogens like Candida albicans  (Kaneko et al.
2006 ) and Porphyromonas gingivalis  (Kondo et al. 2010). The TPR motif has a role in protein-protein interactions and is found in proteins involved in the anaphase-promoting complex, NADPH oxidases, hsp90binding proteins, transcription factors, protein kinase inhibitors, and peroxisomal and mitochondrial import proteins, among others  (Das et al. 1998). TPR proteins are also part of the plant hormone signal-transduction pathway  (Schapire et al. 2006). Our dataset showed that fungal saprobes had more than double the number of TPR proteins compared to plant pathogens. Much of this expansion was driven by root-associated saprobes  (TPE 3, 4, 7, 8, 10 and 16 ), but TPR 12 was expanded (68 %) in the pathogens compared to saprobes (p-value = 0. 0). It is interesting to speculate whether this reduction is due to a decrease in signalling-related TPR proteins in pathogenic fungi, where they may be affected by plant hormone signalling induced by the fungal attack.
Other interesting groups of PFAMs depleted in plant pathogens are PF17111 (Fungal N-terminal domain of STAND proteins), PF05729 (NACHT domain) and PF14479 (involved in prion-inhibition and propagation). These domains have a role in heterokaryon incompatibility  (Paoletti & Saupe 2009 , Greenwald et al. 2010 , Daskalov et al. 2012). In Colletotrichum lindemuthianum, fusions between incompatible strains during colony initiation survived and formed heterokaryotic colonies from conidial anastomosis tubes  (Ishikawa et al. 2012). The authors hypothesized that such fusions allow asexual fungi to increase their genetic diversity and acquire new pathogenic traits. The reduction in PFAMs related to heterokaryon incompatibility suggests that other strategies to reduce heterokaryon incompatibility may exist in pathogenic fungi that may allow for an increase in fitness related to pathogenic traits.
Since FUNGuild results are based on database queries, the prediction accuracy is affected by the quality of data present in the database. To reconcile these discrepancies, we used machine-learning methods to identify hidden correlations between lifestyle and gene content. Large data sets are critical to machine learning, and only plant pathogens and saprobes had sufficient sampling depth in our dataset to be adequately separated using these methods. We selected a training set of 34 well known pathogens and 24 saprobes across the tree to identify genes that could discriminate between pathogens and saprobes in this study. Ten PFAM annotations provided accuracy >0. 8 in their ability to separate pathogens from saprobes in the machine learning analysis. The best PFAM discriminator was PF13350 (Protein tyrosine phosphatase), which showed an average of three versus four gene copies in pathogens versus saprobes.
Other PFAMs showing reduction between pathogens and saprobes included PF06544 (DUF1115), PF00017 (SH2 domain), PF11700 (Vacuole effluxer Atg22 like), PF07522 (DNA repair metallobeta-lactamase), PF05057 (DUF676), PF03537 (Glycoside hydrolase), PF01183 (Glycoside hydrolase family 25), PF00413 (Matrixin), and PF00076 (RNA recognition motif) (Supplementary data -Enrichment. xls). Of these, enrichment analysis using Fisher's Exact Test had identified PF05057 (p = 0. 03) and PF03537 (p = 0. 0) as reduced in pathogens. As seen in Fig. 4C , over a third of the genes in the Dothideomycetes genomes do not have functional annotation. On the other hand, OrthoMCL gene clusters (genes with orthologs) encompass 85 % of the gene space in these genomes (Fig. 5 ). Using the OrthoMCL gene clusters dataset (which ignores lineage-specific genes), 27 OrthoMCL gene clusters (presumed orthologs) produced accuracy scores >0.
ACKNOWLEDGEMENTS The work conducted by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. The U. S. Government retains, and the publisher, by accepting the article for publication, acknowledges, that the U. S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U. S. Government purposes. R. A. and I. V. G. were supported by the  NSF  grants  DEB-1354625  and  IOS-1456958. We thank  Daniele Armaleo  who provided materials for sequencing. Sequencing of Dothideomycetes genomes were conducted as part of the 1000 Fungal Genomes Project supported by the  JGI Community Sequencing  Project # 662.
, 2020). In this paper, we present a deep learning approach, using a Recurrent Convolutional Neural Network for the task of automatic CM sentiment classification of tweets. The rest of the paper is structured as follows. Section 2 provides background in brief. Section 3 provides the system overview and Section 4 describes our approach in detail. In Section 5, we discuss the analysis and evaluation results for our system. We conclude our work in Section 6. This work is licensed under a Creative Commons Attribution 4. 0 International Licence. Licence details: http:// creativecommons. org/licenses/by/4. 0/. The code of this work is available at https://github. com/somnath-banerjee/Code-Mixed_ SentimentAnalysis. 1 https://code-switching. github. io/2020/ Sentiment classification is the task of detecting whether a textual item (e.
g. , a product review, a blog post, an editorial, etc. ) expresses a POSITIVE or a NEGATIVE opinion in general or about a given entity, e. g. , a product, a person, a political party, or a policy  (Nakov et al. , 2016). Classifying tweets according to sentiment has many applications in political science, social sciences, market research, and many others  (Martínez-Cámara et al. , 2014; Mejova et al. , 2015). Although initially sentiment identification was focused on newswire text  (Baccianella et al. , 2010) , later research turned towards social media  (Rosenthal et al. , 2015). Since 2013, a sentiment classification task on Twitter data have been organized in SemEval campaigns. Most of the earlier approaches to this problem were based on hand crafted features and sentiment lexicons  (Pak and Paroubek, 2010; Mohammad et al.
, 2013). These features were then used as input to classifiers (e. g. , Support Vector Machines). However, such approaches required extensive domain knowledge, were laborious to define, and can lead to incomplete or over-specific features. Recently, researchers pay their attention to sentiment polarity detection on CM data. However, a few research work have been carried out in particular Hindi-English CM data with different approaches: lexicon lookup  (Sharma et al. , 2015) , sub-word with CNN-LSTM  (Joshi et al. , 2016) , Siamese networks  (Choudhary et al. , 2018) , dual Encoder Network with features  (Lal et al. , 2019). Lai et al. (2015)  proposed Recurrent Convolutional Network for text classification which is a foundational task in many NLP applications. We followed this model in our task.
Experimental setup Data: For sentiment analysis on Hindi-English CM tweets, we used the dataset provided by the organizers of Task 9 at SemEval-2020. The training dataset consists of 14 thousand tweets. Whereas, the validation dataset as well as the test dataset contain 3 thousand tweets each. The details of the dataset are given in  (Patwa et al. , 2020). For this task, we did not use any external dataset. Preprocessing: The CM Twitter data is not formal in nature. Also, often casing is not followed properly. As a result, it misleads the language processing task. Therefore, we converted the entire tweets into lower case. After observing the CM dataset, we found that the urls are not contributing any information. While writing, the writer of a tweet addresses a person by mentioning the person's name followed by @ or by twitter id which starts with @.