dataesr/scientific-paragraphs-categorization huggingface dataset:
Article screening and study inclusion criteria Using the reference management software Zotero ® , all exported articles and documents will be organized into separate collections, one for each disease. Once the searches completed (one per disease and per database), references for each search will be archived in a unique database, and duplicates will be removed. Articles retrieved by the bibliographic search will be screened as detailed below to keep only those relevant for the map. An additional step of eligibility (detailed in the "Eligibility criteria" section of the "Systematic review" part of the protocol), will be performed to select the subset of articles included in the synthesis.
Data coding strategy Meta-data extraction for mapping will be performed by the 3 members of the review team. Meta-data will be extracted from all articles retained after the screening process. From the full-text of these articles, we will extract and store in an Excel database the following information: In the "Country" column, the name of the country/ countries in which field study was performed or data collected will be written. Studies performed at the global scale will be attributed the code "global". • Title • First author • Year of publication • Country • Continent • Disease • For the name of the disease, the coding will follow the list presented in the section Question components. We will code study designs as follow: PH for post hoc surveys, CS for cross-sectional studies, TS for timeseries, and BA for before-after studies.
Meta-data extraction and coding strategy Groups or articles studying the same disease and the same ecosystem component/function will be identified from the map, and submitted to critical appraisal. Then, meta-data extraction for synthesis will be performed by the 3 members of the review team. Meta-data will be extracted from articles with a low, medium/low and medium/high risk of bias. The same data extraction table will be used for all diseases, but reporting will be limited to variables that are relevant to each disease (for instance, not reporting vector-related information for non-vector-borne diseases). Below are listed the meta-data that will be extracted, related to the context of studies, and the PECO elements. This list will be implemented during a meeting of the expert panel that will take place prior to meta-data extraction.
Demonstrating procedural independence The screening process will be done by 3 members of the review team that have no particular expertise on any of the 14 diseases and are not author or co-author of any article potentially retrieved by the bibliographic search. Experts will join the review process once the screening is completed, to participate to the extraction of metadata and the critical appraisal. • fast, convenient online submission • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year.
Building the Tree of Life (ToL) is a major challenge of modern biology, requiring advances in cyberinfrastructure, data collection, theory, and more. Here, we argue that phylogenomics stands to benefit by embracing the many heterogeneous genomic signals emerging from the first decade of large-scale phylogenetic analysis spawned by high-throughput sequencing (HTS). Such signals include those most commonly encountered in phylogenomic datasets, such as incomplete lineage sorting, but also those reticulate processes emerging with greater frequency, such as recombination and introgression. Here we focus specifically on how phylogenetic methods can accommodate the heterogeneity incurred by such population genetic processes; we do not discuss phylogenetic methods that ignore such processes, such as concatenation or supermatrix approaches or supertrees.
INTRODUCTION Phylogenomics has been greatly enriched with the introduction of high-throughput sequencing (HTS) and increased breadth of phylogenomic sampling, which have allowed researchers interested in the Tree of Life (ToL) to scale up in several dimensions and placing both fields squarely in the era of 'big data'. Additionally, conceptual advances and improvements of statistical models used to analyze these data are helping bridge what some have perceived as a gap between phylogenetics and phylogeography (e. g. ,  Felsenstein, 1988; Huson & Bryant, 2006; Edwards et al. , 2016a). However, as datasets become larger, researchers are inevitably faced with a plethora of heterogeneous signals across different genomic regions that often depart from a dichotomously-branching phylogeny  (Kunin, Goldovsky & Darzentas, 2005; Jeffroy et al.
, 2006; Kumar et al. , 2012). Finding the balance between breadth, depth, and computational feasibility in project design and statistical analysis is crucial for the field today. Additionally, we discuss ways in which data archiving and integration can benefit access to phylogenomic data and the contributions of phylogenomics to society. Are the priorities that society places on the many disciplines feeding into scientific efforts toward the ToL-fieldwork, museum collections, databases-appropriate for this grand mission? Although we cannot possibly answer all of these questions within the scope of this perspective, we hope to at least spur discussion on the wide range of field, laboratory, conceptual, and societal issues that allow phylogenomics to move forward. We first describe the types of genomic data that researchers can generate to perform phylogenomic analyses and how those are more or less suitable for phylogenomic and phylogeographic analyses.
DATA GENERATION AND DATA TYPES IN PHYLOGENOMICS The need for large-scale phylogenomic data One of the fundamental challenges in evolutionary biology is to estimate a ToL for all species. The potential impact of such large phylogenies is reflected in their publication in the highest impact journals, but also in their broad contribution, which extends beyond big data, to methodological innovations, and downstream understanding of macroevolutionary processes (e. g. , coalescent methods of species tree inference; accounting for hybridization and unsampled species or localities in datasets; understanding community or genome evolution through large-scale phylogenetics). Hence, the phylogenomics community is now placing a high priority on very large-scale trees, whether in terms of number of taxa, number of genes, or both.
Data quality Genome-scale data in the form of multiple alignments and other homology statements are the foundation of phylogenomics. A major challenge is the difficulty of comprehensive quality checks of data, given that HTS datasets are so large. As researchers collect datasets consisting of thousands of alignments across scores of species, data quality is a serious concern that is left for detection and handling primarily by computer algorithms. In addition to inherent systematic errors in the data  (Kocot et al. , 2017) , several examples of errors in phylogenomic data sets have been reported in the literature, including the use of unintended paralogous sequences in alignments (e. g. ,  Struck, 2013) ; mistaking the genome sequence of one species for another  (Philippe et al. , 2011) ; and inclusion of genome sequence from parasites into the genome of the host  (Kumar et al.
, 2013). However, the incidence of smaller errors in alignments that are not easily discerned from natural allelic variation, such as base miscalls or misplaced indels, are probably much more widespread than has been reported in the literature. Combined with the sensitivity of some phylogenomic datasets to individual loci or single nucleotide polymorphisms (SNPs) within loci  (Shen, Hittinger & Rokas, 2017) , such errors could have damaging consequences for phylogenomic studies, for the inference of topology and branch lengths of both gene trees and species phylogenies  (Marcussen et al. , 2014; Bleidorn, 2017). Furthermore, as phylogenomic datasets increase in size, it is likely that the accumulation of errors due to the use of different sequencing chemistries and sequencing depths  (Quail et al.
, 2011)  that is beginning to be encouraged in peer review and in published papers  (Montague et al. , 2014; Liu et al. , 2017). Such checking is important not only for new data generated by a given study, but also for data downloaded from public repositories such as NCBI and OrthoMaM  (Ranwez et al. , 2007; Douzery et al. , 2014) , which are well known to contain errors  (Wesche, Gaffney & Keightley, 2009). Because several databases do not include the raw sequence data it is often impossible to evaluate whether oddities may derive from poor sequencing. Robust pipelines for flagging poorly aligned sites or non-homologous sequences, based on existing tools or novel scripts such as Gblocks  (Castresana, 2000; Talavera & Castresana, 2007)  or TrimAl  (Capella-Gutierrez, Silla-Martinez & Gabaldon, 2009)  are gradually being put into practice  (Marcussen et al.
, 2012) , and are informative markers for phylogeography and population genetics  (Brumfield et al. , 2003) , it remains to be seen how powerful they are at higher phylogenetic levels. Despite the diversity of marker types for phylogenomics, it remains unclear whether features specific to each marker type can ultimately result in phylogenomic datasets that can strongly mislead. For example, incongruence in the phylogeny of modern birds developed by  Jarvis et al. (2014; 48 whole genomes)  and  Prum et al. (2015; 259 anchored phylogenomics loci, 198 species)  has recently been attributed to differences in marker type rather than number of taxa  (Reddy et al. , 2017). Whereas  Jarvis et al. (2014)  used primarily noncoding loci because they observed gross incongruence when using coding regions, the loci used by  Prum et al.
Ultimately, these different kinds of data can be translated into species tree inferences (B-D). In the case of CNVs, only locus number 3 (orange) shows significant CNV. Because CNVs are measured as continuous characters (i. e. , copy depth), the orange shading represents a hypothetical evolutionary scenario of copy number variation of genomic region number 3 within the inferred species tree, which is incongruent with those based on sequence and SNP data from other loci in the genome. Full-size  DOI: 10. 7717/peerj. 6399/fig-  1  particularly useful to resolve problematic areas in the ToL (e. g. , sister group to animals;  King & Rokas, 2017). Despite methodological complications in the accurate estimation of population genetic parameters and computational limitations for MCMC convergence at deep times in coalescent-based analyses, recalcitrant nodes likely represent true complexities in the diversification history of these groups and not necessarily reflect failures of coalescent-based phylogenetics  (Lanier & Knowles, 2015).
Concept Definition The Tree of Life (ToL) This idea, originally articulated by Darwin and others, refers to the grand vision of understanding the branching pattern of all life on earth. Today the idea conveys the use of morphological and molecular data to reconstruct the phylogenetic relationships of all life forms. In some usages, the idea also includes reconstructing reticulate evolutionary events, such as introgression and hybridization, which are now thought to be common in many lineages. High-throughput sequencing (HTS) Also referred to as "next generation sequencing", this term refers to the plethora of new DNA and RNA sequencing technologies that in the last fifteen years have allowed biologists to dramatically increase the number of bases sequenced for a given species or clade. HTS technologies can be applied to sequencing whole genomes or transcriptomes and have been embraced by phylogeneticists interested in increasing the size of comparative molecular data sets.
Lateral gene transfer (LGT) This process occurs when genes jump taxonomic and phylogenetic boundaries, moving between unrelated species and therefore causing discordances between genetic and lineage history. LGT, along with ILS and GDL was among the three primary causes of discordance between gene and species trees identified by  Maddison (1997). LGT has been documented to occur between bacterial lineages, between bacteria and viruses, and between these two and eukaryotes, including plants and vertebrates. If not identified prior to phylogenetic analysis, LGT can cause many algorithms for phylogenetic inference to fail. Without prior identification, LGT essentially amounts to errors in data sets and sequence alignments. At the same time, LGT can be a source of adaptation and evolutionary novelty for recipient genomes and has had a major impact on the history of life.
See  Gogarten & Townsend (2005)  for a full review. (Hedtke, Townsend & Hillis, 2006). Nonetheless, we need further studies on the effects of different types of phylogenomic filters on the properties of large-scale phylogenomic datasets. High-throughput sequencing opens possibilities for new information and marker types Heterozygosity and intra-individual site polymorphisms Some of the prevalent occurrences in organisms with multiple ploidies are intra-individual polymorphisms and increased heterozygosity. However, due to issues such as lack of sufficient read coverage and connectivity, confident identification of such polymorphism continues to be challenging  (Garrick, Sunnucks & Dyer, 2010; Lischer, Excoffier & Heckel, 2014; Schrempf et al. , 2016)  and many data sets do not permit statistical approaches, such as PHASE  (Stephens, Smith & Donnelly, 2001) , to robustly determine haplotypes of different alleles  (Garrick, Sunnucks & Dyer, 2010).
Additionally, the adaptive nature of CNVs may or may not facilitate clear phylogenetic signals. For example, a study in Arabidopsis thaliana showed that adaptation to novel environments, or to varying temperatures, is associated with mutations in CNVs  (DeBolt, 2010). If CNVs are to become a useful tool in phylogenomics or phylogeography, we must understand their microevolutionary properties in greater detail. For example, the pattern of evolution of CNVs, wherein deletions of genetic material may not easily revert, resulting in a type of Dollo evolution, might help clarify the overall structure of the models applied to them  (Rogozin et al. , 2006; Gusfield, 2015). Recent advances in the generation of high-throughput sequence data and their impact on the reconstruction of the Tree of Life As sequencing technology rapidly moves forward (reviewed by  Goodwin, McPherson & McCombie, 2016) , our ability to accurately identify the aforementioned marker types increases considerably.
Bypassing full likelihood models by relying on summaries of the coalescent process Given the huge computational resources required for modelling all the complexities of evolutionary processes in a statistical framework, there is interest in methods that will accommodate genome-scale data for large numbers of species within a coalescent framework. The utility of such methods cannot be overstated: the rapid rise of large-scale genomic data sets has clearly outstripped theoretical and computational methods required to analyze them. For example, although progress is being made regarding scalability of full Bayesian methods of species phylogeny inference (e. g. ,  Ogilvie, Bouckaert & Drummond, 2017) , they are still unable to accommodate large phylogenomic datasets, which often consist of hundreds of species for thousands of loci (Table  S1 ).
g. ,  Lanier & Knowles, 2012). However, more analyses and studies are still needed to explore a wider range of factors and parameters that could affect phylogenetic inference when the assumption of recombination-free loci is violated (e. g. ,  Li et al. , 2018). For answering the second question, one approach involves combining the multispecies coalescent with hidden Markov models (e. g. ,  Hobolth et al. , 2007). These methods suffer from the "state explosion problem", where individual states are needed for the different coalescent histories, and they increase rapidly with the number of taxa in the dataset, making them infeasible except for very small (~4 taxa) datasets. New methods that scale to larger datasets are needed if such approaches are to be useful in practice. A different direction is to devise novel methods for inferring species phylogeny while assuming that the genealogies of the individual loci could take the form of an ancestral recombination graph (ARG:  Griffiths & Marjoram, 1996; Siepel, 2009; Rasmussen et al.
Mapping trait evolution on heterogeneous genomic datasets Mapping the genomic basis of phenotypic traits is a major trend in evolutionary biology today  (Elmer & Meyer, 2011; Hoban et al. , 2016). Such mapping can be conducted in the context of populations of a single species or, increasingly, via comparisons of species on a phylogeny (e. g. ,  Hiller et al. , 2012; Marcovitz, Jia & Bejerano, 2016). Phylogenetic genome-wide association ("PhyloGWAS") methods identify genomic features in coding or non-coding DNA that exhibit unusual patterns of evolution on branches concerned with repeated evolution of phenotypes, thereby drawing connections between the genomic and phenotypic levels  (Pease et al. , 2016). Such phylogenomic mapping usually assumes a single phylogeny, the species phylogeny, as a framework for analysis, and therefore ignores genomic heterogeneity.
Despite the great increase in the generation of genomic data across organisms, we are often limited to fast and efficient but simpler, less realistic phylogenetic methods and assumptions, such as IQ-Tree  (Nguyen et al. , 2015) , PhyloBayes  (Lartillot et al. , 2013) , and ExaML  (Kozlov, Aberer & Stamatakis, 2015) , to deal with large, heterogeneous datasets. The main downside of these options is that they are not a full coalescent framework and thus far rely on data concatenation. Fully coalescent methods, such as the popular phylogenetic software program *BEAST  (Heled & Drummond, 2010) , are not capable of dealing with more than a few hundred taxa and some dozen loci at a time for a common analysis, and only recently the release of StarBeast2 allows for the use of thousands of loci for tens of taxa  (Ogilvie, Bouckaert & Drummond, 2017).
To tackle this problem, we encourage the continuing development of methods that are fully scalable and ideally only increase analytical time linearly rather than exponentially with the number of taxa and loci. Phylogenetic methods should also be fully parallelizable (in order to run natively in computer clusters) and contain checkpoints, i. e. , be able to resume the analyses from the latest logged file in case an analysis crashes or the user wishes to evaluate partial results. Another point of possible improvement is in dealing with new sequences to be added to a previously large dataset: should the analysis start from scratch, or could there be substantial time gains by letting those sequences find their placement in the phylogeny 'on the fly' (e. g. ,  Siu-Ting et al. , 2014). Large scale phylogenies should ideally be based on the best (or most comprehensive) available datasets in terms of taxonomic and molecular sampling and be constructed from the data itself.
However, even supermatrix inference conducted under a single analysis can add bias on tree heights and coalescence times when performed across unbalanced sampled clades (a very common case for species-rich clades or understudied taxa), and therefore affect downstream analyses that rely on these parameters (e. g. , biogeography, trait evolution, diversification rates). Computing optimally populated datasets that combine the largest number of taxa and loci simultaneously is a complex mathematical problem, but recent approaches (e. g. ,  SUPERSMART-Antonelli et al. , 2017; PyPHLAWD-Smith & Brown, 2018)  attempt to overcome it objectively, such as applying the knapsack problem to phylogenetics by packing the optimal choice of species and suitable alignments into a minimally sparse supermatrix.
Community initiatives Building the ToL is a grand challenge in molecular phylogenetics, and one that cannot be accomplished by a single person or institution's efforts. Several initiatives have been developed in recent years to coordinate efforts and provide the research community with synthetic information. A prominent project is the Open Tree of Life (https://tree. opentreeoflife. org/; Hinchliff et al. , 2015). This project provides a synthesis of previously published phylogenies merged through supertree and other grafting methods. One issue faced by the initiative is that it relies on authors uploading their phylogenetic trees to open data repositories, such as Dryad Data Repository (http://datadryad. org/ pages/organization;  Vision, 2010)  or TreeBase  (Sanderson et al. , 1994; Piel et al.
, 2009) , which at least until recently only occurred in about 17% of cases  (Drew, 2013). Substantial curatorial efforts are also critical to facilitate reusability of deposited trees  (McTavish et al. , 2015). A different approach was taken by  Antonelli et al. (2017) , who developed a framework for continuously inferring time-calibrated large phylogenies from raw sequence data deposited in GenBank  (Clark et al. , 2016)  in a multi-step method. Similarly, various tools have been developed to make information contained in the ToL available for the general public (e. g. ,  Rosindell & Harmon, 2012; Harmon et al. , 2013).
Best practices for building the Tree of Life Data must be well curated in databases and publicly available As we are now in the era of big data in biological sciences, adequate reproducibility must be a fundamental endeavor of biodiversity research. Therefore, data publication in open access repositories represents a powerful tool that not only ensures long-term storage and public availability for future research, but also serves as a vehicle for clarifying intellectual rights and scientific merits  (Costello & Wieczorek, 2014). Biocuration, the activity of organizing, representing, and making biological information accessible to both biologists and bioinformaticians, has now become an important consideration in building, updating, and sustaining the ToL  (McTavish et al. , 2017). The exponential growth in the amount of genomic scale data and the increased dependence on the availability of each other's' data to answer complex biological questions means that there is a need for improved data management, analysis, and accessibility.
GenBank has been the main open access repository for annotated collections of publicly available molecular data. Although the data stored in this database usually lists information such as organism of origin and publication details, the utility of molecular data in this database to answer multiple biological questions, such as biogeographic patterns of biodiversity, is often hampered by lack of associated information such as collection locality  (Scotch et al. , 2011; Gratton et al. , 2017)  or attachment to a specific voucher specimen. Moreover, recent surveys have shown that fewer than 20% of phylogenetic studies provide access to phylogenetic data (i. e. , alignments and phylogenies) and when they do, critical biological information such as complete taxon names is missing  (Drew et al. , 2013; Magee, May & Moore, 2014).
We propose two urgent actions to advance this key field. First, authors should be encouraged to submit molecular data that is linked to voucher specimens deposited in recognized scientific collection and museums. Second, authors, journals, and curators should encourage all molecular data submitted to include information such as collection locality and details of voucher specimens. In this regard, other global initiatives such as the International Barcode of Life Project (iBOL; http://www. ibolproject. org) have had great success linking molecular data with morphological and distributional data. When all the data produced or published are curated to high standards and made accessible as soon as available, biological research will be able to process massive amounts of complex data much more quickly.