dataesr/scientific-paragraphs-categorization huggingface dataset:
They are composed of a collection of terms as well as the relationships between them, which adds context and structure. Ontologies provide unique identifiers for concepts, making them machinereadable and retrievable. The standardized definitions for terms ensure that metadata tagged with ontology terms is interoperable between researchers. In addition to ontologies, metadata frameworks are important and widely-used data models for increasing the interoperability and shareability of data  (Sansone et al. , 2012). Metadata frameworks promote the structuring of data, ensuring it is in a consistent format which allows both data producers and consumers to effectively work with a diverse dataset. Well known examples of metadata frameworks include lightweight Bioschemas  (Michel and The Bioschemas Community, 2018)  and the ISA metadata framework (Investigation-Study-Assay)  (Sansone et al.
Git technology  (Chacon, 2014)  and GitHub (https://github. com/) have been transformative for the ontology community for facilitating open source work and version control. Researchers are able to interact with ontology providers to suggest terms and term improvements via the issues tab, and files are transparent and traceable, with changes and updates clearly marked and easily findable. Ontology structure and functions. (A) Ontology classes (blue circles) contain information such as a unique identifier, name, definition and synonyms for describing the class. Relations (arrow) connect classes, giving ontologies structure and context. (B) Ontologies can import and share terms from other ontologies to enable consistent representation of a term (concept) or domain, increasing the interoperability and standardization of ontologies and the terms they contain.
While the OBO Foundry includes a number of plant-related ontologies, topics more relating to the plant sciences, such as plant genomics, phenomics, or agronomy domains are not the focus and lack the same level of community involvement as the biomedical domain ontologies. For this reason, a number of ontology repositories specifically geared to different facets of the plant sciences have been developed. Among these is AgroPortal, a vocabulary and ontology repository founded by the Montpellier scientific community to facilitate open and collaborative science in agronomy (http:// agroportal. lirmm. fr/;  Jonquet et al. , 2018). AgroPortal was specifically designed for agronomy and related domains and reuses the openly available OntoPortal software (https://ontoportal. org/;  Jonquet et al. , 2023)  to build the repository and services platform.
The project aims to provide a reliable service involving hosting, searching and improving ontologies, allowing users to actively participate in the platform by uploading content and commenting on others' content  (Jonquet et al. , 2018). The original motivation for AgroPortal was guided by five agronomic use cases, which cover a range of agronomic topics from germplasm to livestock and contribute to the design and focus of the portal (https:// agroportal. lirmm. fr/about). These use cases include the Agronomic Linked Data knowledge base (http://agrold. southgreen. fr/aldp/;  Venkatesan et al. , 2018) , a knowledge-based database for plant molecular networks such as genes, proteins, metabolic pathways and plant traits, and the Crop Ontology (CO) Project (https:// cropontology. org/;  Arnaud et al.
, 2022)  of the Integrated Breeding Platform (https://www. integratedbreeding. net/), described in greater detail below. AgroPortal includes projects, vocabularies and ontologies which cover the entire range of agronomic research, from livestock and plant species to environmental conditions and land governance. The Planteome database is heavily based on ontologies and is an informative resource for scientists searching for terminologies applicable to plant research and describing plant traits and experiments (https://planteome. org/). The database contains a collection of general reference ontologies aimed at improving annotation of an array of plant-related research data, ranging from genes to phenotypes  (Cooper et al. , 2018). Planteome also actively maps the species-specific ontologies of the Crop Ontology against the species-neutral reference ontologies, allowing users to search for a trait without having to consider the specific species.
This is particularly useful for studies in comparative genomics or investigations of a family or clade  (Cooper et al. , 2018). As with the previously mentioned platforms, the Planteome ontologies are publicly available and openly maintained via GitHub repository (https://github. com/Planteome) to encourage sharing, tracking of revisions and new term requests  (Cooper et al. , 2018). A number of additional repositories for finding and querying ontologies are also available and include Ontobee (https://ontobee. org/;  Ong et al. , 2017) , BioPortal (https://www. bioontology. org/;  Whetzel et al. , 2011) , the Ontology Lookup Service (https://www. ebi. ac. uk/ols/ index;  Cotéet al. , 2006) , and the newly developed TIB Terminology service (https://terminology. tib. eu/ts;  Strömert et al.
, 2023). While their collections do not focus heavily on the plant sciences, all aim to facilitate data sharing, ontology visualization, querying, integration and analysis. As the plant sciences cover a wide range of different technologies, many of the ontologies collected in these repositories will contain terms relevant to experimental set up and analysis. For example, all repositories listed above contain the Chemical Methods Ontology (CHMO) (https://github. com/rsc-ontologies/rsc-cmo), an ontology developed to describe chemical methods applicable to experimental assays, such as electron microscopy, preparations of materials to be separated for further analysis, such as by electrophoresis, and the synthesis of materials. Terms included in CHMO are relevant to many common techniques used throughout plant-related experiments, making it crucial the annotation of metadata for plant-related research.
, 2015). COB is a basic and structured framework that serves as a foundational resource for the life sciences (https://github. com/OBOFoundry/COB). By capturing essential concepts and their relationships, it serves as a foundation for the development of advanced ontologies for biology and biomedicine  (Abdelmageed et al. , 2021). A general scientific ontology that defines all properties used by the OBO Foundry ontologies is the RO. It is a formal ontology that provides a framework for interoperability between different ontologies and can therefore be used for different contexts  (Smith et al. , 2005 ).
Developed by EMBL-EBI, ChEBI provides a classification of chemical entities  (Degtyarenko et al. , 2008). The ontology can be divided into three main branches: chemical entities, the role the chemical entity can have, and subatomic particles. Its importance to the wider scientific community is evident as ChEBI is widely incorporated into various databases, such as UniProt (The UniProt Consortium, 2015), and is heavily reused in well-established ontologies, such as GO, OBI, and EFO, as well as plant-specific ontologies. Within the plant sciences, ChEBI contains many terms for metabolites commonly found in plants, including primary metabolites such as glucose (CHEBI:17234) and fructose (CHEBI:287570), secondary metabolites such as pinitol (CHEBI:372080), and a wide range of terms for carotenoids (CHEBI:23044 and child terms).
For environmental entities, ENVO is a widely-used ontology for describing ecosystems, environmental processes or even entire planets. Originally designed to provide information regarding biomes and environmental features of genomic and microbiome samples for the Genomics Standards Consortium  (Field et al. , 2011) , ENVO has since evolved into a cross-discipline resource, spanning domains from biomedicine and omics to anthropogenic ecology and socioeconomic development  (Buttigieg et al. , 2016). As ENVO evolved, developers implemented changes to better align to OBO Foundry principles to ensure increased interoperability, such as incorporating terms from RO and BFO and moving the ontology to its own GitHub repository for better version control. Finally, there are a number of ontologies specifically relating relevant metadata important for describing the measurement of samples and subsequent data analysis.
For example, the Human Proteome Organization-Proteomics Standards Initiative (HUPO-PSI) developed the PSI-Mass Spectrometry (MS) controlled vocabulary to logically structure and capture all terms relating to an MS pipeline-from sample preparation (in-solution digestion, MS:1002986) to instrument models (LCMS-9030, MS:1002998), parameters (Mascot : SigThreshold, MS:1001316) and related software (Spectronaut, MS:1001327)  (Mayer et al. , 2013). Examples of ontologies for the annotation of software used during the analysis of data collected within the life sciences are the Software Ontology (SWO)  (Malone et al. , 2014)  and the Ontology of Bioscientific Data Analysis and Data Management (EDAM)  (Ison et al. , 2013). The need for such ontologies was realized as bioinformatic analysis became ever more prevalent.
Just as metadata annotation is crucial for the reproducibility of laboratory experiments, knowing what versions of what tools were used to analyze a dataset is necessary for the reproducibility of said analyses. The scope of SWO is broad as it incorporates tools and software versions not only used in bioinformatics analyses (SAMtools, SWO:1100143), but also tools (and their versions) used for the management, analysis and presentation of biological data (R software, SWO:1100075)  (Malone et al. , 2014). EDAM covers topics, operations, types of data and data identifiers (Database ID, EDAM:1048) and formats (RNA annotation format, EDAM:3824) relevant to data analysis and management in the life sciences  (Ison et al. , 2013). While a number of ontologies and their applications have been described above and in Table  1 , they are just a small subset of ontologies available to fundamental plant scientists.
The Planteome project reference ontologies The Planteome project (https://planteome. org/;  Cooper et al. , 2018)  develops and maintains a number of species-neutral reference ontologies, including the Plant Ontology (PO)  (Walls et al. , 2012; Cooper et al. , 2013) , the Plant Trait Ontology (TO)  (Cooper et al. , 2018)  and the Plant Experimental Conditions Ontology (PECO)  (Cooper et al. , 2018). The PO is crucial for the consistent annotation of anatomy, morphology and developmental stages of both plants and plant parts  (Walls et al. , 2012). Originally focused on Arabidopsis thaliana (mouse-ear cress) Zea mays (corn) and Oryza sativa (rice), it was broadened to cover all Viridiplantae (green plants). The primary aim of the PO is to bridge the diversity of data that can be collected during plant research-from genetics, molecular and cellular biology to taxonomy and botany research  (Walls et al.
, 2019). Two other plant reference ontologies used in the Planteome database are the TO and PECO. Both ontologies were conceived and developed with the aim of improving data interoperability for the advancement of plant research. One common issue within the fundamental plant sciences is the semantic inconsistencies that exist between species, especially for phenotypic descriptions, meaning data integration is often not possible without the manual identification of corresponding concepts  (Arnaud et al. , 2012). For example, what is referred to as a 'leaf' in some species is known as a 'frond' in others. It is therefore crucial to standardize trait terms between different species and projects in a way that allows for the easy integration of phenotypic and trait data from different sources. The TO was developed to address the discrepancies in trait descriptions and to increase interoperability of plant trait data between species  (Cooper et al.
, 2016). To facilitate the implementation of MIAPPE, the Plant Phenotyping Experiment Ontology (PPEO) was created. Whereas most of the plant-specific ontologies described above focus on defining terms for metadata description, PPEO represents the MIAPPE structure, incorporating the different sections of the framework as the primary backbone of the ontology, then adding the required attributes (https://github. com/MIAPPE/ MIAPPE-ontology). PPEO is therefore an important resource for organizing metadata collected during plant phenotyping experiments (rather than defining metadata terms), as all attributes have their own ontology id, making them easily searchable.
The DataPLANT Biology Ontology (DPBO) The NFDI funded DataPLANT consortium has developed its own ontology, the DataPLANT Biology Ontology (DPBO) (https:// github. com/nfdi4plants/nfdi4plants_ontology), to assist researchers with metadata annotation and help close the ontology gap. In conjunction with a collection of external ontologies, the DPBO helps users annotate their experimental metadata via DataPLANT's Swate tool (https://github. com/nfdi4plants/Swate;  Mühlhaus et al. , 2021). The DPBO contains terms from established ontologies needed by users as well as new terms not yet found in any ontology. Users suggest the terms they need via the GitHub issues tab and the DPBO curators provide quick feedback to term suggestions and incorporate them into the DPBO. For new terms, curators find the most fitting ontology and suggest the terms to be incorporated.
Implementation of metadata frameworks for ontology-enriched metadata annotation Challenges in data harmony and integration often arise when bringing together data from multiple sources to answer complex scientific questions. These challenges involve differences in terminology descriptions, a lack of sufficient context, or else the data is structured in a way being difficult to comprehend, thereby hindering its reuse. To address these challenges and facilitate the management and integration of experimental data across different research domains, a number of community-driven efforts have been founded that aim to develop metadata standards and frameworks for improved data sharing and handling. Some well-known examples are Bioschemas  (Michel and The Bioschemas Community, 2018)  and the ISA (Investigation-Study-Assay) framework  (Gonzaĺez-Beltrań et al.
For example, under the Assay entity is the 'material node', where researchers can describe materials consumed or produced during an experimental workflow. Within this node is the property "material type", which should be described using an ontology annotation (Figure  3B ). This makes the description of experimental metadata both precise and unambiguous, reducing the potential for errors and misinterpretation, enabling data to be more easily integrated and shared across different research projects, domains and platforms. Ontologies can also be used to map the relationships between different concepts and terms, which can facilitate potential connections and correlations between different datasets. There are a number of ways that plant-related ontologies can be incorporated into one of the ISA entities depending on the investigation that is being represented (Figure  4 ).
For example, OBI and EFO can be incorporated into the Study and Assay entities of the ISA framework as they contain terms relating to experimental setup, sample processing and analysis. ENVO is particularly relevant for annotating Study metadata as terms can be used when describing the habitat or environmental conditions of a location when a plant sample was collected (for example, ENVO_1000745: drought). One of the key benefits of using ontologies in the ISA data model is that it allows for more effective data integration and analysis, by controlling the values that a metadata element can take (for example Ho  Sui et al. , 2013; Peters et al. , 2019). Using ontologies to standardize the terms used to describe experimental variables allows researchers to more easily compare datasets and identify similarities and differences between them.
This can be especially important when analyzing large datasets, where manual inspection and interpretation of the data may be difficult or timeconsuming. In addition to providing a standardized vocabulary, ontologies also help to improve the accuracy and consistency of the data itself. By using an ontology to specify the units of measurement A B FIGURE 3 The ISA metadata framework  (Sansone et al. , 2016) , designed to capture experimental metadata. (A) The framework consists of three core entities: Investigation, Study and Assay. The structure allows for multiple studies to be described within one investigation and multiple assays to be described within one study. Each entity has metadata requirements that should be included to ensure the complete description of the entity. Within the Study and Assay entities, an ontology annotation is the required input for included metadata, such as design type in study and measurement type in assay (marked with blue boxes).
1) and either ontology can be selected for annotation. The reality is that there is no 'one size fits all' approach to what terms should be used to annotate metadata, and it is often a matter of familiarizing oneself with what ontologies and terms are available. Fortunately, there has been a push in recent years to provide more comprehensive overviews of ontologies that are available to the wider scientific community. A number of ontology repositories have been developed, all aiming to facilitate researchers in finding terms most fitting for annotating metadata. Ontology repositories and service platforms such as Planteome and AgroPortal are good resources for users trying to determine where to begin when annotating fundamental plant research data with ontology terms. Both are designed specifically for plant research, agronomy and related fields and encourage the active participation and collaboration of users for the improvement of included ontologies.
As the number of agricultural genetics, genomics and breeding databases increases, the need for better data and metadata sharing will too, and ontology use and promotion of data standards will grow  (Clarke et al. , 2023). Another potential challenge facing users is where to actually begin with when it comes to metadata annotation as it is not always clear what should be included when describing an experiment. This challenge has been recognized by the wider scientific community and a number of resources, tools, and projects have been developed in recent years to address these hurdles. Minimum information standards, such as MIAPPE  (C ́wiek-Kupczyńska et al. , 2016) , often provide comprehensive lists of required, recommended and optional attributes needed for the complete description of an experiment.
Tools such as DataPLANT's Swate (https:// github. com/nfdi4plants/Swate;  Mühlhaus et al. , 2021 ) and ISA's OntoMaton  (Maguire et al. , 2013 , https://github. com/ISA-tools/ OntoMaton) can be utilized to help users when beginning the process of annotating their spreadsheets with ontology terms. Swate has a number of templates available that can be used when submitting to repositories or if the researcher wants their data collection to comply with a minimum information standard. A number of recent collaborative projects have also recognized the need for better metadata management for the submission to repositories. For example, a data brokering prototype developed at the 2022 European Biohackathon provides a high-level alignment of ISA-JSON to the ENA (European Nucleotide Archive) XML submission template with the hope of streamlining the submission process  (D'Anna et al.
, 2023) , with future plans to expand to other repositories (https://github. com/elixir-europe/biohackathonprojects-2023/tree/main/27). One common theme apparent throughout the ontologies covered within this review is the importance of open and collaborative efforts for increasing the FAIRness of data. It is widely accepted by the greater scientific community that making data more FAIR will greatly benefit data reproducibility and data management practices  (Rocca-Serra et al. , 2023). However, the actual task of making data FAIR remains costly and time consuming, hindering progress. Ontology providers therefore rely on and encourage community engagement and interdisciplinary collaboration for the continuing expansion and improvement of concepts and their relations. Through technologies such as Git, coupled with open licensing and standardized ontology languages, ontologies such as EFO (https://github.
com/EBISPOT/ efo), OBI (https://github. com/obi-ontology/obi), the Planteome ontologies (https://github. com/Planteome) and many others encourage communication and collaboration with researchers to help fill ontology gaps. Fundamental plant research is diverse by nature, incorporating techniques and concepts from all corners of the life sciences, and benefits greatly from this community push for the standardization and improvement of ontologies for metadata annotation. The evolving nature of science requires that data can be reevaluated and benefits from a flexible data structure. For this reason, efforts such as NFDI (https://www. nfdi. de/;  Hartl et al. , 2021)  and Elixir (https://elixir-europe. org/;  Crosswell and Thornton, 2012)  have been established in recent decades. These efforts recognize the importance of well-curated and managed datasets for the increased management and archiving of research data.
One potential outcome of these efforts is the possibility of creating integrated repositories and enabling semantic searches upon those, facilitating the reuse of datasets to answer novel research questions. Similar work is already being done within the biomedical sciences with the development of resources such as GenoSurf (http://www. gmql. eu/genosurf/;  Canakoglu et al. , 2019) , specific to human genetics. Databases such as the European Bioinformatics Institute's BioSamples (https://www. ebi. ac. uk/ biosamples/;  Courtot et al. , 2019)  contains datasets from a wide range of different species, including many plant species. Also part of Elixir's infrastructure, BioSamples contains results from biological samples stored in archives such as ArrayExpress (https:// www. ebi. ac. uk/biostudies/arrayexpress;  Kolesnikov et al.
, 2015) , the European Nucleotide Archive (ENA) (https://www. ebi. ac. uk/ena/ browser/home;  Silvester et al. , 2018)  and the European Genomephenome Archive (EGA) (https://ega. crg. eu/;  Lappalainen et al. , 2015). Specifically for plant data, the NFDI funded consortiums FAIRagro (https://fairagro. net/en/,  Specka et al. , 2023)  and DataPLANT have developed their own infrastructure for data curation and archiving. The FAIRagro infrastructure contains a number of data repositories, including e!DAL-PGP (https://edalpgp. ipk-gatersleben. de/;  Arend et al. , 2016) , for the storage of data relating to agricultural research. DataPLANT has developed the PLANTdataHUB. Users have the option of submitting their datasets to the Annotated Research Context (ARC) registry, which provides an advanced search interface for locating relevant data via indexed metadata found within the datasets  (Weil et al.
, 2023). With the recent push for sustainable RDM and FAIR data practices, the potential that ontologies provide to the plant research community becomes evermore apparent. However, knowing where to begin when annotating experimental metadata with ontologies is sometimes a daunting task. Here we have outlined ontologies most relevant to the fundamental plant sciences and discussed resources available for finding ontology terms. This review is meant as a starting guide for plant researchers when considering the metadata annotation of their next datasets. The complexity of both the ontology landscape and the data produced within plant experiments means that there is no 'one size fits all' approach to metadata annotation and makes a comprehensive guide impossible. Instead, researchers are encouraged to explore the resources outlined here and familiarize themselves with ontology terms to help them decide on the terms best suited to their individual experiments.