dataesr/scientific-paragraphs-categorization huggingface dataset:
6) CONCLUSION To answer the question RQ3, the section above provides detailed information about the existing datasets that are intensively used for conducting rumors detection task. Figure  13  shows that, besides, the available publicly datasets (56%), some researchers prefer creating their own datasets by crawling Twitter or Weibo microblogs (22%). 26% of researchers use public PHEME dataset and LIAR datasets; however, these datasets are, generally, used as benchmarks for comparing their proposed approaches. We refer this to the high quality of annotation inter-agreement ratio of PHEME and LAIR datasets. In 13% of cases, researches propose to use news articles' datasets and in 9% use rumor tracking websites.
[72] , researchers tend to combine it with other architectures for rumor detection to empower the training process. The combination of RNN-CNN comes next in popularity (22%) and then CNN-RNN (9%). In contrast, we noted that AE, DBN and RBM architecture are missing in any research studies. As we can note, although applying LSTM model yields a satisfactory result in studies, the results are significantly improved when it is combined with CNN as presented in K5. It is better to word embedding on the dataset itself rather than using a pre-trained model especially when the domain is far away from the original domain of the pre-trained model. In K10 and K21, the accuracies of the DL models are the same. However, when the pre-trained word embedding model is applied in K6, we noted a significant difference in accuracies of the DL models.
A. RUMOR DETECTION CHALLENGE Many challenges face academic researchers when studying and analyzing the content of social media networks. Due to the accessibility of data, online citizens used Twitter as a primary space to publicly express their reactions to news/events Williams et al. [58]. Accordingly, Twitter has become the most preferred space where researchers can obtain data for research purposes. To address research question RQ6, we list some challenges next that are faced by the researchers when using social media data. -Ethical issues: Twitter provides developers as well as the scientific community with a set of Twitter streaming Application Programming Interface (APIs), which make the data more accessible to researchers. However, collecting and retrieving data without informed consent produces several ethical issues, especially when the research deals with tweets on sensitive topics.
-Legal issues: Twitter needs some requirements for producing and sharing tweets within a publication. In most cases, sharing datasets is prohibited since it requires informed consent from Twitter as well as from all of the participants. -Cost issue: Twitter provides three levels of data access Williams et al. : (1) a free random 1% with ∼5M tweets daily; (2) chargeable or free 10%; and (3) 100% to academic researchers upon request and approval. Twitter provides data within 7 days according to the topic of interest. Thus, if the research requires obtaining more historical data, researchers can purchase data at different costs depending on the query and time of retrieval. In addition to the aforementioned issues, there are some challenges regarding the dataset itself. Since building a reasonable dataset for detecting rumors through social media is a difficult task, we list below set of issues that researchers can face: -Dataset retrieval from the data source: using hashtags and keywords to obtain tweets related to a specific topic does not guarantee the retrieval of all related data.
In addition, using different keywords/hashtags to retrieve different data requires additional preparation and transformation of the resulting data such as conversion to the appropriate format and filtering non-relevant data after data retrieval. -Dataset bias: using keywords-based queries and hashtags allows obtaining data written only in a specific language. Thus, datasets are also likely to be limited by the language that is used to retrieve data. -Representativeness: user posts are not representative and the amount of posts depends mainly on the number of users who participate on social media. It can be the case that a rumor that spreads throughout a nation's offline population, but its impact on online social media is negligible due to the limited number of online users or due to political restriction.
-Dataset quality issue: the quality of a dataset is mainly affected by the dataset annotator (person who assesses the label classes of a dataset) or by enhancing the content of data. High dataset quality can be obtained by using two ways: (i) calculating inter-rater agreement where the κ statistic 12  can be a helpful technique  (Eugenio and Glass [18] ; Carletta et al. [10]  and (ii) examining properties of text and authors, where the goal is to detect wither the content is recognized as spam or not Agichtein et al. [2].
B. FUTURE PATH AND DIRECTIONS DL is a promising technology that can be used for rumor detection; however, researchers should be careful in selecting the appropriate DL architecture. DL models often require large datasets, which are not publicly available or need special permission from social media providers. Additionally, for training the DL, in many cases, there is not a clear methodology to understand how many layers should to used and which architecture is appropriate for the social analysis task. According to Zubiaga et al. [74] , there are four types of rumor classification tasks, namely, rumor detection, rumor tracking, stance detection, and veracity. The answer to the research question RQ7 is reported through the following recommendations: -When working with small datasets, the RNN technique is not recommended for the early detection task as it a bias towards the latest of the input sequence Cao et al.
We think that it is worthwhile to employ DL when dealing with visual formats such as videos and images. -Since there are a few publicly available datasets, it would be valuable for the research community if more researchers share their datasets. This, in particular, will enable researchers to perform further studies over different datasets and compare the performances of their works. -The researchers are encouraged to clearly specify tools used in their work as well as the performance obtained. This will enable the scientific community to reproduce the results and facilitate further extensions. -As we stated earlier, the dataset is collected based on keywords used to collect posts associated with rumors that are known a priori. In most cases, retrieved data is written only in the language of the executed queries.
al. (2016). The dataset is harvested from Twitter by crawling tweets using the Twitter streaming APIs. The dataset contains both rumors and non-rumors tweets posted during breaking news. According to Zubiaga et al. (2016), breaking news items are categorized into five groups based on the news event as follows: -Ottawa shooting, which occurred on Ottawa's Parliament Hill in Canada on October 22, 2014. -Charlie Hebdo shooting, which occurred on the offices of the French satirical weekly newspaper Charlie Hebdo in Paris on January 7, 2015. -Sydney siege, which occurred in Lindt chocolate cafe, located at Martin Place in Sydney, Australia, on December 15, 2014. -Ferguson unrest when citizens of Ferguson in Michigan, USA, protested on August 9, 2014. -Germanwings plane from Barcelona to Düsseldorf crashed in the French Alps on March 24, 2015.
In K13, Xu et al. (2018) proposed a model named MNRD that considers three aspects detect namely, original post content, diffusion of retweets, and user information. This model uses an attention mechanism to extract informative words and retweets in the process. -In K14, Qian et al. proposed a novel two-level convolutional neural network with a user response generator to develop an automated model to semantic information from the text at word and sentence levels. In order to generate a response to new articles, the authors proposed a generative model that is learned from the historical user responses. -In K17, a case study is presented to investigate the influence of the rumor ratio in the training dataset on the accuracy and performance of RNN based models. The rumor ratio in the training data must not be too low since a lack of rumors in the training data leads to a significant decrease in the performance of models.
Variant annotation and classification We annotated novel and previously reported variants based on the longest transcripts of DEPDC5 (Refseq NM_001242896; NP_001229825), NPRL2 (Refseq NM_006545; NP_006536) and NPRL3 (Refseq NM_001077350; NP_001070818), using the online version of Variant Effect Predictor (VEP) for human GRCh37 (http://grch37. ensembl. org/Homo_sapiens/ Tools/VEP). Population allele frequencies for each variant were extracted from the Genome Aggregation Database browser (gnomAD, http://gnomad. broadinstitute. org/; accessed in July 2017). 15 e used in silico prediction tools to assess the pathogenicity of variants: M-CAP (Mendelian Clinically Applicable Pathogenicity) for missense variants;  16  and HSF (Human Splice Finder v3. 0) for splice-region variants.
Comparison of GATOR1 variants in epilepsy and gnomAD cohorts Coding and splice altering variants in DEPDC5, NPRL2, and NPRL3 were extracted from the gnomAD database and annotated using VEP on the transcript of interest. The distribution of the different types of variants (missense, splicesite/region altering, nonsense, in-frame/frameshift indels, and CNVs) and the proportion of M-CAP predicted deleterious rare missense variants in the gnomAD cohort were then compared with those found in epilepsy subjects: 110 previously published and 73 from the present cohort.
Histological examinations confirmed FCD type II in six probands (five FCD IIa, one FCD IIb) and FCD type I in two probands. Cognitive impairment and/or psychiatric comorbidities were reported in 60% of the probands of this cohort, and in 76% (16/21) if considering only probands with age at onset ≤1 year. Neurocognitive deficits were reported in 44% (30/68) of the probands. Mild or moderate intellectual disability was reported in 20% (14/68) and severe in 6% (4/68); language learning delay was reported in 15% (10/68) and 9% (7/68) had other cognitive disturbances. In the subgroup of probands Because somatic variants in GATOR1-encoding genes (>400 in DEPDC5, >80 in NPRL2, and >100 in NPRL3 in the COSMIC database) have been mentioned in various cancers,  3, 17  we examined cancer occurrence in epilepsy probands carrying germline GATOR1 variants.
Gln54Pro), 118 (p. Ala452Val), 139 (p. Pro1031His), and 181 (c. 3265-3C>T) in gnomAD. Therefore, four previously reported variants are unlikely to be involved in the pathogenesis of epilepsy. In addition, we reclassified 12 variants as likely pathogenic and 17 as VUS. Among the 17 VUS, 7 had already been classified as VUS, while the remaining 10 had been initially classified as (likely) pathogenic, mainly because they were absent or rare in the public databases available at that time (ExAC, EVS, 1000 Genomes). Thanks to the recent high-throughput sequencing and data aggregation gnomAD initiative, which includes 123,136 exome sequences and 15,496 genome sequences, it now appears that most of these variants have a frequency above the pathogenic range, and consequently were reclassified as VUS.
Global GATOR1 mutational spectrum Overall, 140 distinct GATOR1 variants have been described in 183 epilepsy probands so far. Most variants are LoF (67%), followed by missense (27%), splice-region variants (4%), and in-frame indels (1%). Recurrent GATOR1 pathogenic LoF variants (n = 24) were reported in unrelated cases, indicating mutational hotspots or founder effects (represented in blue in Fig. 1  and Table  S2 ). Among the GATOR1 genes, DEPDC5 is the most frequently mutated gene accounting for 85% (155/ 183) of all cases (Fig. 4 ). This may be explained by the greater length of DEPDC5 transcript (5551 bp) compared with NPRL2 (1700 bp) and NPRL3 (2881 bp), and the fact that NPRL2 and NPRL3 have been tested in a lower number of individuals due to their more recent discovery. We then compared the type of variants among the 140 epilepsy-related GATOR1 variants with those listed in gnomAD.
ACKNOWLEDGEMENTS We thank individuals and families for their participation in the study, and clinicians for referring individuals. The authors would like to thank the Genome Aggregation Database (gnomAD) and the groups that provided exome and genome variant data to this resource. This work was supported by the  European Research Council  ( ERC 682345  to SB), the program " Investissements d'avenir "  ANR-10-IAIHU-06 , the  Fondation Française pour la Recherche sur les Epilepsies. Part of this work was undertaken at  University College London Hospitals , with funding from the  National Institute for Health Research (NIHR) Biomedical Research Centres funding scheme  and the  Muir Maxwell Trust and Epilepsy Society ,  United Kingdom.
Dataset and Analysis Strategy We generated whole-genome ancient DNA data from 523 previously unsampled ancient individuals and increased the quality of data from 19 previously sequenced individuals. The individuals derive from three broad geographical regions: 182 from Iran and the southern part of Central Asia that we call Turan (present-day Turkmenistan, Uzbekistan, Tajikistan, Afghanistan and Kyrgyzstan), 209 from the Steppe and northern forest zone mostly in present-day Kazakhstan and Russia, and 132 from northern Pakistan. The ancient individuals are from 1) Mesolithic, Copper, Bronze and Iron Age Iran and Turan (12000-1 BCE from 19 sites) including the Bactria Margiana Archaeological Complex (BMAC); 2) early ceramic-using hunter-gatherers from the western Siberian forest zone who we show represent a point along an early Holocene cline of North Eurasians and who emerge as a valuable source population for modeling the ancestry of Central and South Asians (6400-3900 BCE from 2 sites); 3) Copper Age and Bronze Age pastoralists from the central Steppe, including from Bronze Age Kazakhstan (3400-800 BCE from 56 sites); and 4) northernmost South Asia, specifically Late Bronze Age, Iron Age and historical settlements in the Swat and Chitral districts of present-day Pakistan (~1200 BCE -1700 CE from 12 sites) (Fig.
1 , Table  S1 ,  (1, 13) ). We prepared samples in dedicated clean rooms, extracted DNA  (14, 15) , and constructed libraries for Illumina sequencing  (16, 17). We enriched the libraries for DNA overlapping around 1. 2 million single nucleotide polymorphisms (SNPs)  (7, 18, 19) , sequenced the products on Illumina instruments, and performed quality control (Table  S2 )  (7, 19, 20). Our final dataset after merging with previously reported data  (7-9, 16, 18, 19, 21-31)  spans 837 ancient individuals that passed all our analysis filters, which included removing individuals determined genetically to be first-degree relatives of other higher coverage individuals (Table  S3 ), and restricting to the 92% of individuals (Table  S1 ) that were represented by at least 15,000 of the targeted SNPs which we found was the number at which we began to be able to reliably estimate proportions of the deeply divergent ancestry sources.
People of the BMAC Were Not a Major Source of Ancestry for South Asians From Bronze Age Iran and Turan, we obtained genome-wide data for 84 ancient individuals (3000-1400 BCE) who lived in four urban sites of the Bactria Margiana Archaeological Complex (BMAC) and its immediate successors. The great majority of these individuals fall in a cluster genetically similar to the preceding groups in Turan, consistent with the hypothesis that the BMAC coalesced from preceding pre-urban populations  (5). We infer three primary genetic sources: early Iranian farmer-related ancestry (~60-65%), and smaller proportions of Anatolian farmer-(~20-25%) and WSHG-related ancestry (~10%). Unlike preceding Copper Age individuals from Turan, people of the BMAC cluster also harbored an additional 2-5% ancestry related (deeply in time) to Andamanese Hunter-Gatherers (AHG).
Steppe Pastoralist-Derived Ancestry Arrived in Turan by 2100 BCE Our large sample sizes from Central Asia, including individuals from BMAC sites, are a particular strength of this study, allowing us to detect outlier individuals with ancestry different from those living at the same time and place, and revealing cultural contacts that would be otherwise difficult to appreciate (Fig. 2 ). Around ~2300 BCE, we observe three outliers in BMAC-associated sites carrying WSHG-related ancestry and we report data from the third millennium BCE from three sites in Kazakhstan and one in Kyrgyzstan that fit as sources for them (related ancestry has been found in ~3500 BCE Botai culture individuals (26)). Yamnaya-derived ancestry arrived by 2100 BCE, since from 2100-1700 BCE we observe outliers from three BMAC-associated sites carrying ancestry ultimately derived from Western_Steppe_EMBA pastoralists, in the distinctive admixed form typically carried by many Middle to Late Bronze Age Steppe groups (with roughly two thirds of the ancestry being of Western_Steppe_EMBA origin, and the rest consistent with deriving from European farmers).
3 ). We replicate previous findings that this group included people of the Corded Ware, Srubnaya, Petrovka, and Sintashta archaeological complexes spreading over a vast region from the border of eastern Europe to northwestern Kazakhstan  (8, 19, 21) , and our dataset adds more than one hundred individuals from this Western_Steppe_MLBA cluster. We also detect a further cluster, Central_Steppe_MLBA, which is differentiated from Western_Steppe_MLBA (p=7×10 -6 by qpAdm), due to carrying ~9% additional ancestry derived from Bronze Age pastoralists of the central Steppe of primarily of WSHG-related ancestry (Central_Steppe_EMBA). Thus, individuals with Western_Steppe_MLBA ancestry admixed with local populations as they integrated eastward and southward.
Thus, the archaeologically documented spreads of material culture and technology both north and south along the Inner Asian Mountain Corridor  (50, 51) , which began as early as the middle 3 rd millennium BCE, were associated with substantial movements of people (Fig. 2 ). Third, we observe individuals from Steppe sites (Krasnoyarsk) dated to ~1700-1500 BCE that derive up to ~25% ancestry from a source related to East Asians (well-modeled as ESHG), with the remainder best modeled as Western_Steppe_MLBA. By the Late Bronze Age, ESHG-related admixture became ubiquitous as documented by our time transect from Kazakhstan, and ancient DNA data from the Iron Age and from later periods in Turan and the central Steppe including Scythians, Sarmatians, Kushans, and Huns  (25, 52). Thus, these 1 st millennium BCE to 1 st millennium CE archaeological cultures with documented cultural and political impacts on South Asia cannot be important sources for the Steppe pastoralistrelated ancestry widespread in South Asia today (since present-day South Asians have too little East Asian-related ancestry to be consistent with deriving from these groups), providing an example of how genetic data can rule out scenarios that are plausible based on the archaeological and historical evidence alone ((13), Fig  S52 ).
In particular, the fact that they harbor substantial Iranian farmer-related ancestry (via the Indus Periphery Cline), disproves earlier suggestions that the ASI might not have any ancestry related to West Eurasians  (11). Using the DATES software, we estimate an average of 107 ± 11 generations since admixture of the Iranian farmer-related and AHG-related groups in one of these groups: Palliyar. This corresponds to a 95% confidence interval of 1700-400 BCE assuming 28 years per generation  (53). Thus, the ASI were note fully formed at the time of the IVC, and instead must have continued to form through mixture after its decline as material culture typical of the IVC spread eastward  (54)  and Indus Periphery Cline ancestry mixed with people of less West Eurasian relatedness. We also obtained additional evidence for a late (Bronze Age) formation of the ASI by building an admixture graph using qpGraph, co-modeling Palliyar and Juang (an Austroasiatic-speaking group in India with low West Eurasian-relatedness) (Fig.
An alternative is that this ancestry reflects movement into South Asia from the Iranian plateau of people accompanying the eastward spread of wheat and barley agriculture and goat and sheep herding as early as the 7 th millennium BCE and forming early farmer settlements such as those at Mehrgarh in the hills flanking the Indus Valley  (60, 61). However, this is in tension with the observation that the Indus Periphery Cline people had little if any Anatolian farmer-related ancestry, which is strongly correlated with the eastward spread of crop-based agriculture in our dataset. Thus, while our analysis supports the idea that eastward spread of Anatolian farmer-related ancestry was associated with the spread of farming to the Iranian plateau and Turan, our results do not support large-scale movements of ancestry from the Near East into South Asia following ~6000 BCE (the time after which all ancient individuals from Iran in our data have Anatolian farmer-related ancestry even though South Asians have very little).
Principal component analysis (PCA) We carried out PCA using the smartpca package of EIGENSOFT 7. 2. 1  (36). We used default parameters and added two options (lsqproject:YES and numoutlieriter:0) to project the ancient individuals onto the PCA space. We used two basis sets for the projection: the first based on 1,340 present-day Eurasians genotyped on the Affymetrix Human Origins array, and the second based on a subset of 991 present-day West Eurasians  (7, 32, 33). These projections are shown repeatedly in (13) and are used in the Online Data Visualizer. We also computed F ST between groups using the parameters inbreed:YES and fstonly:YES. We restricted these analyses to the dataset obtained by merging our ancient DNA data with the modern DNA data on the Human Origins array and restricting to 597,573 SNPs.
ADMIXTURE clustering Using PLINK2 (91), we first pruned our dataset using the --geno 0. 7 option to ensure that we only performed our analysis on sites where at least 70% of individuals were covered by at least one sequence. This resulted in 892,613 SNPs. Individuals without coverage on specific SNPs were assigned missing data at those sites. We ran ADMIXTURE (37) with 10 replicates, reporting the replicate with the highest likelihood. We show results for K=5 in (13), as we found that this provides good resolution for disambiguating the sources of pre-Copper Age ancestry in the ancient individuals.
Modeling admixture history We used qpAdm  (33)  in the ADMIXTOOLS software package to estimate the proportions of ancestry in a Test population deriving from a mixture of N 'reference' populations by leveraging (but not explicitly modeling) shared genetic drift with a set of 'Outgroup' populations. We set the details:YES parameter, which reports a normally distributed Z-score for the goodness of fit of the model (estimated with a Block Jackknife).
Method for dating admixture events. We implemented an algorithm, DATES, which leverages ancestry covariance patterns that can be measured in a single individual (instead of admixture LD that requires multiple individuals). Full details of the approach and simulations documenting its efficacy in modern as well as ancient data are presented in (13). The software implementing DATES is available at https://zenodo. org/record/3263997#. XRnebJNKj6A (DOI: 10. 5281/zenodo. 3263997). Foundation HOMINID grant BCS-1032255, by National Institutes of Health grant GM100233, by an Allen Discovery Center grant, and by grant 61220 from the John Templeton Foundation.