dataesr/scientific-paragraphs-categorization huggingface dataset:
The graphical user interface of Galaxy means that no knowledge of code is needed, thus facilitating bioinformatics analyses by researches without computational expertise. We built a suite of federated Galaxy resources and tools, which we collectively named Rice Galaxy (Fig. 1 ). Rice Galaxy contains shared software tools and datasets tailored to the needs of rice researchers and breeders. A Rice Galaxy web server is also available, providing computing resources through an easy-to-use interface, and allowing reproducibility and publication of analytical methodology and results. The Rice Galaxy federated resources are available at: r Rice Galaxy reference web server, with working tools, built-in data, and shared datasets at IRRI  [18] ; r Rice Galaxy (common) Toolshed hosting the tool wrappers  [19] ; r Rice Galaxy code and built-in data for local/institutional de- ployment  [20].
Built-in/interoperable rice data The Rice Galaxy system is customized to provide rice-specific genomic and genotypic data. Of primary importance is the goldstandard japonica variety reference genome (Nipponbare International Rice Genome Sequencing Project [IRGSP] release 1. 0)  [21] , to which the reference gene models and most of the SNPs published have been anchored. In addition, 8 medium-to highquality published genomes from various sequencing projects and the respective genome annotations for each are installed as alternative genome builds and are available as drop-down menu choices in Rice Galaxy. These include 4 high-quality builds from indica-type varieties Minghui 63 and Zhengshan 97  [22] , IR 8 (GenBank: MPPV00000000. 1), Shuhui 498  [23] , as well as the aus-type variety N 22 (GenBank: LWDA00000000.
3K RG data access Rice Galaxy provides tools that can access the raw variant call format (VCF) files of each accession in the 3K RG project via connection (as data source in Rice Galaxy) to the 3,000 rice genomes at Amazon Web Services (AWS) Public Data  [30] , with tools allowing region-specific download. In Rice Galaxy, tools in the Get Data/FROM 3KRG section allow listing of the accessions in the 3K RG and retrieval of genotype data for a selected accession of interest from the 3K RG collection. The subset genome region of interest (chromosome name-base start-base end) can be specified and extracted from the VCF of the accession of interest stored in AWS Public Datasets. This functionality addresses a common use case for the 3K RG dataset, wherein a researcher has a gene or small genome region of interest mapped to the Nipponbare reference genome and wishes to determine the vari-ation of this gene/genome region in a particular accession of interest from the 3K RG.
As a result of the default limitations of the public Rice Galaxy server for user data storage space (∼6 GB), we recommend downloading subset regions instead of full VCF files (i. e. , on average ∼2 GB) from the 3K RG collection. Analyses that require full VCF data of multiple 3K RG accessions using the Rice Galaxy server are not recommended; these are best performed using a local deployment of Rice Galaxy. The details of local deployment are discussed in the Rice Galaxy architecture discussion section. In addition, we developed an original Rice Galaxy component called Rapid Allelic Variant extractor (RAVE), which allows simultaneous extraction of genotyping data from several accessions of the internal 3K RG resource. It relies on the PLINK software  [31] , which efficiently builds a user-adjusted genotyping submatrix from a compressed PLINK binary bi-allelic genotype table (bed file + bim, fam files).
Users can customize the genotyping dataset vertically by choosing a subpopulation (e. g. , indica, japonica, aromatic, aus, tropical, temperate) or setting a list of varieties, and horizontally by restricting variations with a list of genomic regions or a list of gene names. Additionally, users can filter the SNP positions by specifying thresholds for missing data or minor allele frequency. The extracted VCFs can be directly generated by Rice Galaxy, stored as output into the history pane of the Galaxy interface, and can be reformatted to Hapmap, a versatile file format for further analyses such as marker (SNP) design, GWAS analyses, or visualized in a JBrowse  [32]  genome browser (Vcf2jbrowse component). External SNP datasets can also be imported into Rice Galaxy and merged with 3K accessions in order to compare and look at the closest genotypes using the SNiPlay  [33]  workflow.
GWAS analysis using TASSEL The Rice Galaxy web server has sufficient storage and computing resources for GWAS, as long as the genotyping data are in matrix format (such as Hapmap), not as multi-sample VCF. Using this feature, it is relatively easy to construct a genotyping matrix for a subset of accessions from the 3K RG and connect associated phenotypic information to perform GWAS analyses online, with outputs being decorated with various graphical enhancements. For the 3K RG accessions, the subset 1 million GWAS and 440,000 Core SNPs that is usable for GWAS is already available as a shared dataset in Rice Galaxy (Fig. 2 ). Researchers working on the 3K RG panel can generate new phenotyping data from their respective experiments, upload the phenotype data into Rice Galaxy, and then perform GWAS using the TASSEL (Trait Analysis by Association, Evolution and Linkage) bioinformatics tool  [34].
Genomic selection using Oghma genome prediction tool The Rice Galaxy server allows the exploration of genomic selection methods. Genomic selection (GS) is a promising breeding technique with potential to improve the efficiency and speed of the breeding process in rice  [35]. With the intent of enabling the GS analysis process used on the 2 datasets in the Spindel et al. [35]  study (encoding data, filtering data to keep informative markers, creating a model from a training set, evaluating the model, and finally, performing the prediction itself), and to automate the analysis pipeline, the relevant packages (methods, fpc, cluster, vegan, pheatmap, pROC, randomForest, miscTools, pRF, e1076, rrBLUP, and glmnet) for the R Statistical language  [36]  were installed in Rice Galaxy and the tool suite was collectively named Oghma (Operators for Genome Deciphering by Machine Learning).
This output is used as input, along with the phenotypes and folds, by the "evaluation" tool, which outputs some performance indices (R 2 and correlation). Although it does give a real indication of performance, trying to predict the training set (i. e. , using the same genotype file in the pipeline described above), or at the least, showing whether the classifier is not under-fitting the data. We installed several classifiers in Oghma to allow users to test and determine the one(s) best suited for their dataset because our literature survey showed that no method seems to outperform the others on all genomic prediction tasks. It was noticed that random forest was the most accurate and the most stable classifier on the Spindel et al. dataset  [35] ; thus, we set this as the default in Oghma.
Diversity and population structure analysis of end-user datasets Resources in the Rice Galaxy server allow diversity and population structure analyses. SNP datasets-such as those extracted from the 3K RG resource after filtering by the RAVE module or custom sets directly uploaded in the Rice Galaxy environment (Fig. 7 )-can be processed for a complete exploration and largescale analysis thanks to the SNiPlay Rice Galaxy workflow (Fig. 8). The workflow is available through the instance, requiring a VCF file as input. This workflow allows various analyses: (i) SNP annotation by snpEff  [39]  wrapper preconfigured for Rice Genome Annotation Project release 7. 0  [40]  gene models, (ii) variant filtration using the PLINK wrapper, (iii) general statistics such as transition-transversion ratio, levels of heterozygosity, and missing data for each variety using VCFtools, (iv) SNP density analysis, (v) diversity index calculation in sliding windows along the genome using VCFtools (Pi, Tajima's D, fixation index if subpopulations provided), (vi) linkage disequilibrium, (vii) population structure by sNMF  [41] , (viii) principal component analysis and identity by state clustering of varieties by PLINK, and (ix) SNP-based distance phylogenetic tree by FastME  [42].
Most key steps are decorated with sophisticated visualizations using a dedicated plugin. Visualization can be displayed by clicking on the visualization icon. In practice, this workflow can be processed for many applications such as the identification of possible introgression events, the identification of putative genomic regions involved in the control of qualitative traits through a fixation index approach, the investigation for potential duplicates in the 3K RG accessions dataset and custom datasets, or the estimate of closest varieties of new sequenced accessions, by ranking a list of varieties from the database most closely matching the given sample. It can also be used for the close inspection of a genomic region of interest after a GWAS analysis, through a linkage disequilibrium focus or the haplotyping of candidate genes.
Uniqprimer module Uniqprimer is a workflow for comparative genomics-based diagnostic primer design, developed from a pipeline used inhouse at Colorado State University to develop novel species-and subspecies-level diagnostic tools for bacterial plant pathogens including pathovars of Xanthomonas translucens  [43] , geographical variants of rice-associated Xanthomonas spp. [44] [45] [46] , and the genetically diverse rice pathogen Pseudomonas fuscovaginae  [47]. Uniqprimer is now deployed in Rice Galaxy for user-friendly diagnostic primer design from draft or complete pathogen genomes. The user inputs multiple bacterial genomes from diagnostic target species as well as non-target species (i. e. , "include" and "exclude" genome files), and the tool performs comparative alignment, primer design, and primer validation to output a list of primers that are specific to the target genomes (Fig.
A researcher interacts with the OA-enhanced Rice Galaxy system as follows: (1) Researcher performs an analysis in Rice Galaxy. (2) Data objects (input data, output data, information such as configuration parameters) are extracted from Rice Galaxy OA into a PRAGMA Data Repository Database (MongoDB) (Fig. 10A ). (3) The data objects are assigned PIDs, the PID Kernel Information is assigned into the PID record at this time, and a landing page is created for each (Fig. 10B ). (  4 ) Data objects can be downloaded from the Data Identity server and re-loaded to the Rice Galaxy server for full faithful reproduction of the analysis. The resulting prototype system seems to be promising and addresses a number of the recommendations from CGIAR. The Rice Galaxy OA system is a user-transparent means of harvesting DOs from applications and assigning PIDs to scientific outcomes.
Rice Galaxy architecture and deployment We deployed the Rice Galaxy reference server (hosted by IRRI) into an AWS elastic computing cloud (EC2) instance (t2. large instance 2 vCPU, 4 GB RAM) with Linux Ubuntu release 12. 04. 2 LTS (GNU/Linux 3. 2. 0-40-virtual x86 64) operating system installed. We deployed Galaxy release 14 to this cloud server, following the method described in the Galaxy documentation. This Galaxy server has modest specifications and allows researchers to test the functionalities of the rice-specific tools installed, as well as conduct analyses on modestly sized datasets as allowed by the default memory and disk space allocation in the standard Galaxy deployment. External data from the 3K RG Project files stored in the 3K RG AWS Simple Storage Service (S3) Public Data resource hosted at [63] (or s3://3kricegenome/) is accessed using AWS S3 Command Line Interface, a command line tool utility in AWS that provides an interface to access AWS S3 objects (CLI  [64] ).
g. , full VCFs for 3 or more 3K RG accessions). We recommend the deployment of a local Rice Galaxy instance on a server that has more resources (RAM, disk space) and configuring Galaxy to provide access to the additional memory and disk space allocated to users. The general steps for local Rice Galaxy deployment are as follows: 1. Install Rice Galaxy and the required dependencies from the github repository mentioned in the Availability of source code and requirements section to your server. 2. Install the Rice Galaxy tools in your new Galaxy instance from the Rice Galaxy Toolshed; we are still developing these and will push the stable version(s) to the public Galaxy toolshed as soon as they are available. 3. Install the external tools from other projects that are installed in Rice Galaxy (but not in Rice Galaxy Toolshed, e.
g. , Uniqprimer, SNiPlay, RAVE, Oghma) to the local instance of Galaxy. Documentation on the availability and how to install these tools to local servers are available in their respective repositories and in the Rice Galaxy server Shared data →Pages section. 4. Download the shared 3K RG, test datasets, and tutorial pages from the Rice Galaxy server shared data library to your local Galaxy instance. We are in the process of developing a Docker container of the Rice Galaxy server with tools following the Galaxy Docker flavor initiative  [69]  so that local server deployment is easier. The limitation of this method is that we cannot include third-party software in the container. The link to the container will be provided in the Rice Galaxy server once it is available. The institutions collaborating to build the Rice Galaxy system are committed to providing the installer, tools, data, and computing resources (however limited), in order to enhance or even drive the rice research community's respective institutional genetic/genomic/breeding efforts.
Conclusion Rice Galaxy is a federated Galaxy resource specialized for rice genetics, genomics, and breeding. The resource empowers the rice research community to utilize publicly available datasets (3K RG), materials (seed/accessions), and their own data, allowing complex data analyses to be performed even without investment in their own computational infrastructure and software development team. Rice research-related tools are also hosted on the Rice Galaxy server (i. e. , Uniqprimer rice pathogen diagnostic design). The Rice Galaxy system is freely accessible to all, and we invite the rice research community to participate in enriching the tools hosted by the resource. It can serve as a repository for data, analysis results, and new bioinformatics tools coming from institutions that have used the publicly available rice diversity panels from 3K RG, or have developed rice genomic/genetic analysis tools that they wish to share to the community, and a modest computing infrastructure small institutes without in-house computing capability.
Funding Components of the project are supported by the following grants:  Taiwan Council of Agriculture  Grant to IRRI, International  Rice Informatics Consortium , and  CGIAR Excellence in Breeding Platform  for financial support to the  Rice Galaxy main server , the  Bill and Melinda Gates Foundation  through the  Genomic Open-source Breeding Informatics Initiative  project for applications development support, the  USA National Science Foundation  PRAGMA grant No. NSF OCI 1 , 234 , 983 , the  RDA/USsponsored adoption program  funded by the  MacArthur Foundation , and the  AIST ICT   International Collaboration Grant.
Rice Galaxy Project name: Rice Galaxy Project home page: https://github. com/InternationalRiceResear chInstitute/RiceGalaxy Operating system: Linux Ubuntu release 12. 04. 2 LTS Programming language: Python Other requirements: R release 3. 2. 3 and following packages: methods, fpc, cluster, vegan, pheatmap, pROC, randomForest, miscTools, pRF, e1076, rrBLUP, glmnet; TASSEL release 5. 2. 40; plink v1. 90b3k; JBrowse 1. 14. 1; snpEff 4. 3T; sNMF 1. 2 (and as R package LEA); FastME 2. 0 License: Rice Galaxy tools are released under GNU GPL. All software from external sources is bound by their respective licenses. Any restrictions to use by non-academics: Rice Galaxy tools are without restriction to non-academics. All software from external sources is bound by their respective non-academic restrictions.
Availability of supporting data and materials 3000 Rice Genomes Project data are available from the Gigascience GigaDB repository  [70]. Snapshots of the code and Docker images are also available from GigaDB  [71]. 3K RG BAM and VCF files are available from Amazon Public data and the ASTI-DOST IRODs site; see instructions at http://iric. irr i. org/resources/3000-genomes-project. SNP sets and morpho-agronomic characterization from 3K RG are available at the SNP-Seek download site (http://snp-seek. irr i. org/ download. zul).
Nevertheless, an important point about this is that though the past is evidently important in nearly all social affairs and without wanting to deny the merits of in vivo data of the kind alluded to, the desire for 'events in the past' can lead the eye away from consideration of the current -present time -circumstances in which this definitional work about the past is being done. The past that leads to homelessness is, in a sense, constructed through the sometimes 'prismatic' powers of language use in the present, in the here and now. Some of the properties of this prismatic work can be lost given the peculiar property of the category in question. We are noting that how this works tempts people to imagine how good it would be if they could have been there -in the past when the cause or event that led to homelessness occurred; it leads the eyes and the heart away from the here and now, the office in which some interview is being done in to a grief laden past of hurt and unintentional cruelty, a world in which ordinary kids leave home for all too understandable reasons only thereby to make themselves far from ordinary -kids without 'normal' mums and dad, kids without the normal given -that they have a home to go to, that they have a mum and dad that they can turn to and Skype to say, 'Can I come home?'.
A specific aim of the UniEuk project is to apply one taxonomic framework to all genetic data in the International Nucleotide Sequence Database Collaboration (INSDC) repositories, which includes  DDBJ (ddbj. nig. ac. jp), GenBank (ncbi. nlm. nih. gov)  and ENA (ebi. ac. uk/ena) databases. The system's broad use and preservation will be ensured by a direct implementation of the UniEuk taxonomic framework into the ENA (European Nucleotide Archive) at EMBL-EBI (http:// www. ebi. ac. uk/ena). The project will capture our collective knowledge on eukaryotic diversity, evolution, and ecology via three main modules (EukRef, EukBank and EukMap). EukRef (eukref. org; del  Campo et al. 2018 ) uses a standardized, open-source bioinformatics pipeline to generate homogenous, high-quality curation of sequences (primarily 18S rDNA) available in INSDC databases.
EukRef is fully operational; outputs include (on a lineage-by-lineage basis) taxonomically curated sequences, sequence alignments, phylogenetic trees and metadata. EukBank is a public repository of (primarily V4 18S rDNA) high-throughput metabarcoding data sets, centralized at ENA, with standardized protocols for submitting data sets and metadata. EukMap (eukmap. unieuk. org) is an editable, user-friendly representation of the UniEuk taxonomy in the form of a publicly navigable tree, where each node/taxon is associated with contextual data (taxonomic and ecological information,  links to representative images, etc. ). It will be operational by 2019 and will allow registered community members to directly interact with and inform the taxonomic framework, and to flag taxonomy issues requiring revision.
There are two parts to describing the feeding habit: what is eaten and how it is eaten. Species that release enzymes extracellularly to digest substrates in their habitat, are generally called saprotrophic or lysotrophic, and contribute to the decomposition of organic matter. One incredible resource is Fun-Guild  (Nguyen et al. 2016 , (https://github. com/UMNFuN/ FUNGuild) to determine substrate utilization for saprotrophic fungi. Probably all eukaryotes are capable of osmotrophy, the acquisition of soluble nutrients through the cell membrane. For example, plants obtain their carbon for photosynthesis from the air, as well as some oxygen-however, they rely on osmotrophy through the roots to obtain all the other elements they need. Osmotrophy occurs through the ciliary pit, by pinocytosis, by diffusion, and by various membrane transport proteins.
The least inclusive clade containing Ischnamoeba montana and Ischnamoeba sp. isolate F4 (Genbank: KP864094). Ischnamoeba. Darbyshirella Berney, Bass & Geisen 2015  Multinucleate, highly branching and reticulate amoebae with slender, pointed, sometimes branched and anastomosing pseudopodia; the whole cell body is strongly branching and narrow, especially in the most extended parts, while more condensed parts are wider; posterior end usually pointed with no or few pseudopodia and no branching; many contractile vacuoles present; movement very slow; ciliated stages unknown. The least inclusive clade containing Darbyshirella terrestris and Darbyshirella sp. (Genbank KP864088). Darbyshirella.
SAL Gentekaki et al. 2014 Group identified by phylogenomics. With a node-based definition: the clade stemming from the most recent common ancestor of the Spirotrichea (S) and Lamellicorticata (i. e. Armophorea (A) and Litostomatea (L)). Incertae sedis SAL: Cariacothrix  Orsi et al. 2012 [Cariacotrichea Orsi et al. 2011]  With archway-shaped kinety around oral opening and extending to posterior body end; with unique molecular signature 'GAAACAGUCGGGGGUAUCAGUA' (spanning nucleotide positions 283-305 in GenBank accession number GU819615); (continued).
In Arcellinida certain species of Cucurbitella, Difflugia, Heleopera, Hyalosphenia and Netzelia form symbiosis with Trebouxiophyta species and can be considered mixotrophic. Free-living basal genera of Holozoa and Nucletmycea are amoeboid, phagotrophic on bacteria. Parasitic ones, including Microsporidia, are intracellular parasites. The Choanoflagellata feed on bacteria by filtration through the collar. The Fungi are saprotrophic on external substrate, obtaining their nutrient by osmotrophy through a chitinous cell wall. Lichenous fungi are capable of photosynthesis. Mycorrhizal fungi receive most or all of their organic molecules from the host plant. Many Fungi are parasitic (see table  2 ). For details of substrate preference the reader is referred to FUNGuild  (Nguyen et al. , 2016 , https://github.
ACKNOWLEDGMENTS After the first author,  D. Bass ,  C. E Lane ,  J. Luke  s,  C. L. Schoch  and  A. Smirnov  have contributed equally and are We thank numerous colleagues who were consulted ad hoc throughout this process. In addition, we specifically thank  Alexander Ereskovsky  ( CNRS, Station marine d'Endoume, Marseille, France ) for help with the sponges; and  Iñaki Ruiz-Trillo  ( ICREA -Institut de Biologia Evolutiva, CSIC-Universitat Pompeu Fabra, Barcelona, Catalonia, Spain ) with the Holozoa;  David S. Hibbett  ( Biology Department, Clark University, Worcester, MA USA, and Radcliffe Institute for Advanced Study, Harvard University, Cambridge, MA ) with the Holomycota;  Isabelle Florent  ( Institut de Syst ematique, Evolution, Biodiversit e, Mus eum National d'Histoire Naturelle, Sorbonne Universit es, Paris, France ) with Apicomplexa;  Shauna Murray  ( Climate Change Cluster, University of Technology Sydney, Australia ),  Albert Reñ e (Dept.