dataesr/scientific-paragraphs-categorization huggingface dataset:
Reference ontologies and vocabularies for plant biology Ontologies provided by the Planteome can be used to annotate descriptions (for example, in experimental logs or trials), of the events, processes, conditions and observations for a broad range of entities. Such annotations range from the molecular function of a gene, its localization, and cell-, tissue-or organ-specific expression, to the broader role in response to growth environments and treatments at the whole plant or population level. In the current Planteome Release (2. 0), the Planteome database includes a collection of 51 874 ontology terms (excluding obsoletes) from a suite of reference ontologies for plants (Table  1 ). They include the Plant Ontology (PO) (2-7), Plant Trait Ontology (TO)  (8, 9)  and Plant Experimental Conditions Ontology (PECO)  (8) , which are developed in-house by the Planteome project.
Nucleic Acids Research, 2018, Vol. 46, Database issue D1171 Plant experimental conditions ontology. PECO describes the biotic and abiotic treatments, growing conditions, and study types used in various types of plant biology experiments  (8). For example, the conditions used to assess the response against a given type of treatment such as water deficit, red light, watering, photoperiod, soil type, fertilizer, nutrients, applications of growth hormones, and exposure to pest and pathogens.
Planteome annotations A key feature of the Planteome database is its use of the GO-derived strategy of annotations, described in detail by Hill et al. (22). An annotation is a link between an ontology term and a bioentity (also known as a data object). Annotations are created either manually by expert curators, or computationally and stored in Gene Association Format (GAF 2. 0) files (http://www. geneontology. org/page/ go-annotation-file-formats). The latter are essentially tabdelimited plain text files with the information organized into 17 columns. Each line in a GAF file corresponds to the assertion that some association exists between a bioentity and an ontology term. Each annotation includes a reference (usually to a PubMed ID) to support the assertion, and a conventional evidence code (http://planteome.
The curated data have been developed or sourced by Planteome curators and researchers at 20 other collaborating source databases (Table  4 ). The hyperlinked cross references in the browser connect the user to the original source for further information. Terms from both branches of the PO and the other reference ontologies (Table  1 ) have been used to annotate mutant phenotypes in six plant species to facilitate cross-species querying for phenologs (orthologous phenotypes) and semantic similarity analyses  (23). Classically, phenologs are defined as phenotypes related by the orthology of the associated genes in two species  (24). In the Planteome, annotations may include any characteristic from molecular, functional, to gross level anatomical and growth stage observations. Therefore, Planteome annotations and the ontologies may help in answering questions such as 'Do the gene family members preserve similar phenotypes?' or 'Are the phenologs also true gene homologs?' The annotation database is accessible online directly from the Annotation search page (see below) on the Planteome portal (http://browser.
Planteome ontology and data annotation browser Researchers interested in exploring the Planteome database can access the ontologies and annotated data in various ways. The Planteome home page (http://planteome. org/) features a search box where you can search directly for ontology terms or bioentities. The menu has links to documentation, issue trackers, information on the project and its ontologies, publications and a contact form. To access the ontology browser (http://browser. planteome. org/amigo), users can click on the 'Ontology Browser' link on the Planteome home page and then click on the "Browse' button on the menu bar at the top. On the browser page (http://browser. planteome. org/amigo/dd browse), users can explore the ontology hierarchy and associated annotation data using the 'drill down' browser (Figure  1A ).
If selected, it restricts the search results to the selected ontology. Other search options include filtering by subsets (terms that apply to a given taxa), ontology ancestors (moving up the tree) and whether or not the terms have been obsoleted. Users can go to an ontology term detail page by clicking on the hyperlinked term name in the results list. The second faceted search option is a search for bioentities (data objects) in the Planteome database (Figure  2B ). Options include filtering by source database, object type, taxon and direct and indirect (parent terms) annotated ontology terms. The third search option on the drop-down menu allows one to search for annotations between the D1174 Nucleic Acids Research, 2018, Vol. 46, Database issue bioentities and ontology terms (Figure  2C ).
Data downloads A custom download of up to 100 000 lines is allowed from any of the three faceted search pages shown in Figure  2. The interface allows selection of data fields for downloading in a tab-delimited, plain text file format. For advanced users, bulk annotations data files are freely available for download from the project SVN (http://planteome. org/svn) in the GAF format. The ontology files are accessible in OBO or OWL format from http://github. com/Planteome. The web services or API methods of data downloads and access are described later for advanced users.
Planteome community Since the inception of the GO in 1998  (25) , the global genomics community has come to recognize the importance of unified vocabularies to data interoperability. The plant genomics community has adopted the Planteome reference ontologies for use in a range of databases and genomics platforms (Table  5 ), from model organism sites such as TAIR and MaizeGDB to sites dealing with specific datasets such as protein domains (Superfamily), enzymes (BRENDA) or nuclear magnetic resonance spectra of metabolic profiles (MeRy-B). Several of the adopting projects (for example; Arapheno, BIP, Phenopsis DB, RARGEII and SGN) are using the Planteome ontologies to annotate and organize plant phenotyping data and a number of them, such as TAIR, MaizeGDB and SGN are contributing their annotated data to the Planteome Database.
This includes genes, diseases, phenotypes, and metadata such as ontology terms, and is also used by projects such as the Monarch Initiative (https://monarchinitiative. org/) to drive portions of their website. The implemented BioLink API server (https://github. com/biolink/biolink-api) provides a swagger I/O end-point (https://swagger. io/) that can be used to automatically generate code to extract data in a uniform manner from a script or as part of an alternate web site. Using the exposed API we were able to provide a tool for the Galaxy Workflow Tool to expose Planteome data (https://toolshed. g2. bx. psu. edu/view/ nathandunn/biolinkplanteome/66ece4fd024f), for example: http://biolink. planteome. org/api/bioentity/MaizeGDB% 3A9024907/associations/?rows=20. Ontology development and requests.
All Planteome ontologies are maintained on the GitHub site (https://github. com/Planteome). The files for each Planteome reference and community ontology such as the COs are maintained in separate repositories. The ontology releases are managed through the GitHub release process. It allows collaborative development of ontologies by multiple registered and trained curators in various parts of the world. We use the issue tracker for each respective ontology repository for requesting new terms, edits or offering comments. For example, one can submit requests for PO terms at https://github. com/Planteome/plant-ontology/issues. Gene annotation tool. Planteome Noctua (http://noctua. planteome. org/; Figure  3 ) is a web-based tool for collaborative curation and annotation of plant genes supported by empirical data and published literature sources.
It is a customized version of the one used by the GO consortium (http://noctua. berkeleybop. org/). Registered users login with their GitHub credentials and can either create new annotations or edit existing ones. Planteome Noctua utilizes the reference ontologies described here including the GO; thus providing the ability to create a knowledge graph or model to annotate genes, or gene products and associate them with anatomical parts of a plant, developmental stages and/or traits, phenotypes, experimental conditions and treatments. The core element of a statement in the model is called an 'individual' which can be an ontology term or a bioentity in the database. Individuals are linked together into units called 'annatons' by selecting the appropriate relationship and evidence type. For example, a curator may start by finding a bioentity (e.
g. flower development annotations from Arabidopsis were not projected to green algae. Duplicate annotations from the InterproScan and orthology-based annotations received higher confidence and were merged as unique. The Planteome is a unique an-notation resource for finding annotations for many of the 62 species (http://planteome. org/node/128). Germplasm annotations. A semi-automated pipeline was developed to create ontology-based annotations of plant germplasm  (31). Many plant breeding and germplasm repository databases such as the USDA Germplasm Resources Information Network (GRIN: https://www. ars-grin. gov/) and The International Rice Informatics Consortium (IRIC: http://iric. irri. org/home) evaluate germplasm for a limited set of traits on their sites and record them in their databases using trait descriptors in plain text, a species-specific CO vocabulary or a proprietary controlled list.
To improve interoperability of these data, a link between the individual trait descriptors and the reference ontology must be established. The native data format varies by source. To ensure proper data transformation and quality control before integration in the Planteome database, one of the first steps in annotating germplasm is mapping the source trait descriptors to the ontology terms from the reference TO. For example, 'pod color' trait evaluated in soybean/legume was mapped to the reference TO term fruit color (TO:0002617). This is followed by the data transformation step where a conversion script is run (script and examples available: https://github. com/Planteome/common-files-for-refontologies/tree/master/scripts/germplasm annotation) on  the source data file and the trait mapping data to format the data files in the standard GAF 2.
0 ontology annotation file format. The GAF formatted files are uploaded to the Planteome database at the time of the database build, and the resulting annotations provide hyperlinked cross references to the source. The original data must include three things: (i) a unique identifier for each germplasm entry, (ii) name of the evaluated trait and (iii) a phenotype score (observed qualitative/quantitative variables for the evaluated trait). It is important for the germplasm identifier to be unique in order to create a link back to the source database and avoid redundancies. When available, we encourage providing additional useful pieces of information, such as, germplasm name synonyms, geographic location anme and GIS coordinates identifying the place where the original seed or plant was collected, and where the phenotype was observed.
CONCLUSION AND FUTURE DIRECTIONS The Planteome is a unique resource for both basic plant biology researchers such as evolutionary or molecular biologists and geneticists, and also for plant breeders who are interested in selecting for various traits of interest. The novel aspect of the Planteome lies in the semantic strength of the integrated ontology network, which can be traversed computationally. Planteome allows plant scientists in various fields to identify traits of interest, and locate data, including germplasm, QTL and genes associated with a given trait, and can help in building hypotheses, confirming observations, data sharing and inter-and intra-specific comparisons. For example, plant biologists can use the annotation database to discover candidate genes from other species and compare the annotations to anatomy, growth stage and phenotypes supplemented with experimental evidence based on gene expression and analysis of mutants.
Plant breeders on the other hand, are limited by the number of crosses they can make in a season and need to plan quickly. Therefore, tools built around the OMICs data and ontology-based annotations can help accelerate the process of identifying potential breeding targets, genetic markers, or previously evaluated germplasm with potential genetic underpinnings of agronomic traits, can help accelerate genetic gain by reducing downtime between genetic crosses. The ability to perform semantic queries on traits of interest is vital to this task. Also facilitated by the Planteome, one can identify germplasm and associated characters that would otherwise be housed in an obscure or poorly crossreferenced database and only tagged with free text descriptions, or unlinked vocabularies that represent a barrier to interoperability.
The use of reference and species-specific ontologies for plants and the standardized annotations provided by the Planteome allows users the ability to leverage data from other studies and collaborate more efficiently. Future directions for the Planteome project include the development of a reference Plant Stress Ontology and the addition of more species-specific vocabularies. We will be launching a plant gene nomenclature and annotation portal where researchers would be able to add new genes and annotations and edit existing ones. The data collected from these efforts will be shared semantically with sequencing projects, sequence archives and publishers of scientific literature for useful integration and consistency. Database user interface enhancements will include refinement of evidence and evidence code/ECO-driven faceted searches.
ACKNOWLEDGEMENTS We would like to thank all the collaborating databases and projects (listed in Tables 4 and5) who contributed annotated data to the Planteome database and all the projects and genomics platforms who have adopted the Planteome ontologies. We also acknowledge support of the  NIH  funded  Gene Ontology  project and the researches who attended the 2017 Gene Ontology Consortium Meeting (https://sites. google. com/view/goc2017), held at  Oregon State University  and co-organized by the  Planteome  Project, the  Gene Ontology Consortium  (www. geneontology. org) and the  Gramene  Project (www. gramene. org). We acknowledge the computational infrastructure support provided by  The Center for Genome Research and Biocomputing (CGRB) at Oregon State University  for hosting the live and development sites and the databases and the  CyVerse  for hosting the project's mirror site.
The annotated data are freely accessible from the ontology browser (http: //browser. planteome. org/amigo) and our data repository. plant traits http://browser. planteome. org/amigohttps: //github. com/http://www. geneontology. org/ Phenotypic Qualities Ontology (PATO) qualities and attributes https://github. com/pato-ontology/pato Chemical https://www. ebi. ac. uk/chebihttp://www. evidenceontology. org/ Planteome NCBI Taxonomy* taxonomic hierarchy https://github. com/Planteome/planteome.
Database source URL Annotations total# Ensembl Plants http://plants. ensembl. org/index. html 13 436 306 Genetic Resources Information Management System (GRIMS) https://www. genesys-pgr. org/ 2 978 495 Maize Genetics and Genomics Database (MaizeGDB) https://www. maizegdb. org/ 1 520 941 Germplasm Resources Information Network (GRIN) https://www. ars-grin. gov/) 1 031 431 Planteome http://planteome. org/ 824 309 The Arabidopsis Information Resource (TAIR) https://www. arabidopsis. org/ 736 816 Gramene http://www. gramene. org/ 247 349 The Physcomitrella patens Resource (cosmoss) http://www. cosmoss. org/ 224 640 The Rice Annotation Project Database (RAP-DB) http://rapdb. dna. affrc. go. jp/ 73 952 The International Rice Informatics Consortium (IRIC) http://iric. irri. org/home 54 727 Genome Database for Rosaceae (GDR) https://www.
rosaceae. org/ 38 519 Sol Genomics Network (SGN) https://solgenomics. net/ 20 282 Jaiswal lab http://jaiswallab. cgrb. oregonstate. edu/ 9561 Grape Genome Database (CRIBI Vitis) http://genomes. cribi. unipd. it/grape/ 3410 SoyBase and the Soybean Breeders Toolbox https://www. soybase. org/ 2472 The European Arabidopsis Stock Centre (NASC) http://arabidopsis. info/ 1897 The Global Gateway to Genetic Resources (Genesys-pgr) https://www. genesys-pgr. org/ 389 AgBase http://www. agbase. msstate. edu/ 262 GenBank https: //www. ncbi. nlm. nih. gov/genbank/ AVAILABILITY All Planteome project ontologies and source code are available in the Planteome project repositories on the GitHub https://github. com/Planteome. The annotation data in the standardized GAF2 file format are available for download from the Planteome Subversion (SVN) Repository (http: //planteome.
Complete details about the MACCS method can be found in  [10]. The absorption by air molecules is modeled using the Simplified Model for Atmospheric Correction (SMAC)  [11]. Regarding the aerosol contribution, MAJA estimates the AOT using multi-temporal  [12]  and multi-spectral criteria as described in  [3] [4] [5] [6] 13, 14]. Based on this AOT estimate, and using a radiative transfer code  [15] , MAJA can convert top-of-atmosphere (TOA) reflectances into surface reflectances. Estimating the aerosol type using these techniques increases the noise in the surface reflectance time series because of the limited independent information. Thus, until now, MAJA has been using an aerosol type that is constant with time, which only depends on the type of site being processed. However, a good estimate of the dominant aerosol types present in the image is important because of the high temporal and spatial variability of absorption and optical properties of the various existing aerosol types  [16]  and their variable impact on the reflectance for a given AOT.
We chose to use the Copernicus Atmosphere Monitoring Service (CAMS) products  [26, 27] , mainly because we had direct access to accurate information from CAMS project members who are also part of this study, and because of the operational status of CAMS and the simple access to its products. The CAMS system uses the Composition-Integrating Forecasting System (C-IFS) model and provides global analysis and forecasts of AOT and optical properties for the five following aerosol types: Sulfate, Organic Matter, Black Carbon, Sea Salt, and Dust. The C-IFS model includes static or dynamical emissions and represents transport as well as sedimentation and wet and dry deposition. The sources of anthropogenic aerosols (Sulfate, Organic Matter and Black Carbon) are provided by external datasets such as the Global Fire Emission Database (GFED)  [28, 29] , Speciated Particulate Emission Wizard (SPEW)  [30]  and the Emission Database for Global Atmospheric Research (EDGAR)  [31].
On the other hand, the sources of natural aerosols (Sea Salt and Dust) come from C-IFS model parameters such as wind speed or soil moisture for instance. AOT from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument  [13, 27]  on board the Aqua and Terra satellites are assimilated. Using CAMS forecasts allows the determination of the dominant aerosols, and thus can help us choose the appropriate aerosol optical properties in our processor. Another possibility would have been to directly use the total AOT predicted by CAMS. However, because of the complexity of the processes involved in the C-IFS model, the incomplete information coming from MODIS (no data on the vertical distribution), and the absence of observations for cloudy scenes, it seemed relevant not to rely on CAMS for the AOT.
Besides, the AOT forecasts from CAMS were not reliable enough (root-mean-square error, RMSE = 0. 176) when compared to the Aerosol Robotic Network (AERONET) observations of summer 2016  [32]. Considering the high temporal variability of the AOT, the AOT estimated by the MAJA processor is potentially more accurate because it is processed on the current image, whereas the AOT from CAMS is a forecast. As a consequence, we decided to only use the information about the aerosol type coming from CAMS. The interest of using CAMS products was assessed using Sentinel-2A time series. Sentinel-2A is an optical high-resolution Earth observation satellite. It provides, with a 10-day repetitivity, global coverage of land surfaces and high quality data for operational land monitoring  [33]. The AOT calculated by MAJA (using CAMS products to constrain the AOT estimation) was compared to ground-based data from the Cimel sunphotometers comprising the AERONET network  [34, 35].
Direct validation of surface reflectances was performed on a particular site using ground-based observations. Finally, as surface reflectances are assumed to be relatively stable with time, a criterion was defined to assess if the noise in the surface reflectances time series after atmospheric correction was reduced when using better-suited aerosol optical properties (deduced from CAMS information). After a description of the methods and of the different datasets, this paper presents the results and evaluates the performances of the tool, either with or without using CAMS information.
About CAMS and Its Products Copernicus is the European Union's program for environment monitoring, with a strong satellite Earth observation component. The Copernicus Atmosphere Monitoring Service (CAMS) operates within the scope of this program and provides products describing air quality and atmospheric composition, climate forcing, ozone layer, solar radiation, emissions, and surface fluxes. It is operated operationally and the data is made freely available by the European Centre for Medium-Range Weather Forecasts (ECMWF). Among these products, analysis and forecasts of aerosol optical thickness at 550 nm for five different aerosol types are provided by the forward model C-IFS assimilating data from the MODIS instruments on the Aqua and Terra satellites  [27]. Polar Multi-Sensor Aerosol Product (PMAp) data from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) is also assimilated, but not for the period analyzed in this study.
The five aerosol types in C-IFS are Organic Matter, Sulfate, Sea Salt, Dust, and Black Carbon  [26, 36]. The data used in this study comes from the near-real-time dataset. For each day, the three-hour forecast based on the analysis (assimilation of MODIS data) at 00:00 UTC (Coordinated Universal Time) is used (i. e. , 03:00 UTC), as well as the three-hour forecast based on the analysis at 12:00 UTC (i. e. , 15:00 UTC, available from 21 June 2016). Finally, the data is downloaded and interpolated on a regular grid with a spatial resolution of 1. 25 • , from a Gaussian native grid with a resolution of 0. 4 • (0. 8 • before June 2016). In this study we used the aerosol optical thickness at 550 nm for the five aerosol types, the relative humidity at 23 different pressure levels, and the mass mixing ratios at 60 different model levels.
Using Five Aerosol Types Instead of One Instead of using the constant aerosol type, as in the original version of MAJA, our idea is to use the five aerosol types available within CAMS and to determine their contribution using their respective AOTs from CAMS. The AOTs of the five aerosol types at the coordinates of the image centre are computed by a linear interpolation using the four closest neighbours in the CAMS regular grid. All of this is done with the two CAMS dates surrounding the Sentinel-2 overpass time, within a 24-h window. These two datasets are used to compute a temporal linear interpolation, weighted according to the Sentinel-2 overpass time. We then compute a linear interpolation of the five look-up tables (previously computed for each one of the aerosol types), with the weight of each LUT being the proportion (based on the AOT at the image centre and at the Sentinel-2 overpass time) of the corresponding aerosol type.