dataesr/scientific-paragraphs-categorization huggingface dataset:
The need for a metadata standard was first recognised in the life sciences by the microarray community, who developed MIAME (Minimum Information About a Microarray Experiment)  (Brazma et al. , 2001). This was soon followed by similar standards for other domains (e. g. Field et al. , 2008; Bustin et al. , 2009; Lapatas et al. , 2015)  as can be seen on FAIRsharing  (Sansone et al. , 2019). In the plant phenotyping domain, the need for metadata to document experiments was initially addressed independently by the developers of phenotyping databases, such as BreedBase (BreedBase team, 2020), GnpIS  (Steinbach et al. , 2013) , PIPPA  (PIPPA team, 2020)  and Plant Hybrid Information System (PHIS)  (Neveu et al. , 2019) , which resulted in a multitude of implicit, often database-specific standards.
However, the need for an explicit consensus to enable interoperability between these databases brought this community together to develop MIAPPE (Minimum Information About a Plant Phenotyping Experiment), the first and so far only community metadata standard for the plant phenotyping domain  (Krajewski et al. , 2015). MIAPPE had three guiding principles: to minimise the chance of a researcher missing important information in the documentation of an experiment; to support the annotation of content with community-relevant vocabularies; and to promote a data format implementation. MIAPPE marked a critical step towards the FAIRness of plant phenotyping data, as concluded in a survey of c. 50 citations of this standard in publications and web portals  (Krajewski &  Cwiek-Kupczy nska, 2020). However, there were aspects to improve, such as the coverage, usability and clarity of the standard.
Currently all major databases, such as GenBank  (Benson et al. , 2013)  or UniProt (The UniProt Consortium, 2019), provide such access, but most smaller databases do not. This was the case for the plant phenotyping domain up until recently, with its numerous, independent and heterogeneous local databases. To address this problem and enable interoperability between databases, the plant community undertook the development of the Breeding API (BrAPI)  (Selby et al. , 2019) , a common API for data search and retrieval that can be implemented by plant breeding databases irrespective of their internal data model. Like the databases it aims to connect, BrAPI also has an implicit (meta)data model that aims to reconcile the metadata available in existing databases, spanning organisational metadata, plant phenotypic (meta)data and genotypic (meta)data.
BrAPI was initiated independently from MIAPPE, so while there is substantial overlap between the two resources, there are also a few key differences in their metadata fields, as well as differences in terminology. The way forward for enabling FAIR plant phenotyping data lies in bringing together all of the components described above. MIAPPE would be the cornerstone of such an architecture, specifying the metadata that is needed and connecting metadata fields to the ontologies recommended to fill them, as well as reconciling the several implicit metadata models of existing knowledge resources. BrAPI would serve as the means for federating the many independent plant phenotyping databases to enable findability and accessibility, and should enforce and validate the MIAPPE compliance of datasets.
The MIAPPE ISA-Tab implementation would support data publication and exchange. And potentially all of the ontologies listed above would play a role in describing the data and metadata of plant phenotyping experiments in a standardised and unambiguous way. However, it is clear that further development effort on these resources is needed to attain such a goal. In this paper, we detail the efforts of an international consortium to enhance the MIAPPE standard towards enabling FAIR plant phenotyping data. We describe the following refinements: (i) the extension of MIAPPE to accommodate a wider range of use cases (including those relevant to perennial and woody plants); (ii) the specification of a data model underlying the standard, to facilitate its interpretation and usage; (iii) the formalisation of MIAPPE in a computer-interpretable format (using the Web Ontology Language, OWL) to enable dataset validation and computational analysis; and (iv) the alignment of MIAPPE and BrAPI to enable the exposure of MIAPPE-compliant datasets via BrAPI endpoints.
Like its predecessor, MIAPPE 1. 1 is a metadata standard that formally organises the documenting of a phenotyping dataset, including environmental aspects. It primarily structures the metadata, imposing no constraints on the data itself (which may consist of images, other binary data, tabular files, etc. ). In comparison with MIAPPE 1. 0, MIAPPE 1. 1 introduces several new concepts while preserving most of those already present. The major change, however, is that it moved from a simple checklist to a fully formalised data model that makes explicit mandatory information, restrictions and expectations and thus represents a major improvement in clarity from MIAPPE 1. 0. The key changes in MIAPPE 1. 1 fall into one of three categories, which are detailed in the following subsections: scope extension, interoperability and data model specification.
Scope extension The scope of MIAPPE 1. 0, mostly restricted to field crops, was extended in MIAPPE 1. 1 to encompass woody plants, mainly by enabling the identification of plant materials by their geolocation coordinates, which are typically used to identify forest trees instead of plant identifiers (e. g. GenBank accession numbers) used in crop research. Two levels for plant material identification and description are available in MIAPPE 1. 1: (i) its identification within the experiment (biological material ); and (ii) its identification before the experiment (material source), allowing for individual plants, lots or progeny to be described and related to previously published or publicly accessible material. Furthermore, the preprocessing field (previously called pretreatments) can describe any type of action performed on the material source before it is used as the experimental biological material (for instance "tree transplantation" and "grafting").
Data model formalisation (PPEO) While the specification of the MIAPPE data model addresses the concern of improving the clarity of the standard for users, it does not address machine readability, which is important to enable validation and facilitate implementation at scale. The need for the latter led us to encode the MIAPPE standard in OWL as the PPEO  (Pommier et al. , 2020). In PPEO, each MIAPPE section is encoded as an ontology class, with additional classes declared to group linked MIAPPE fields (e. g. <method> groups the linked fields method description and method accession number). Each MIAPPE field is encoded as an ontology data property, which specifies the type of value expected (e. g. <has collection date> for class <sample>, which must take a date-time value). The relations between classes are formalised through object properties (e.
MIAPPE 1. 1 overview The MIAPPE sections, which correspond to objects in the data model, are the following: Investigationthe entry point of each MIAPPE dataset. It contains several general metadata fields (e. g. title, description, submission/publication dates), including some critical for FAIRness (unique identifier, licence, MIAPPE version). One or more publications may be associated with the investigation. Studycorresponds to one experiment and defines its location (which by definition must be single per study) and duration. It lists general fields documenting the experiment (e. g. experimental design, cultural practices, growth facility). Like the investigation, it contains a unique identifier field. An investigation must have one or more studies. Personcontains contact details for each contributor of an entire investigation or an individual study, including the role of the person.
Data filereferences a data file of the MIAPPE dataset (e. g. a tabular file containing the results of observations, an image file), which may be attached to the dataset (referenced by name) or available in an online repository (referenced by URL). A version and a description must be provided for each data file. A study may have any number of data files. Biological materialidentifies and describes the plant materials used in the studies. Plant materials must be identified through a biological material ID field, which can be institutionspecific or platform-specific (e. g. seed lot number for annual plants, clone number for perennials or an experimental plant ID), and is recommended to follow the MCPD convention of holding institute identifier (FAO WIEWS code) plus a unique identifier of the individual plant material provided by that institute.
These fields include the material source ID (which follows the same recommendations as the biological material ID), the material source DOI (for referencing material sources listed in repositories), four geographical coordinates fields (same as for biological material ), and finally a textual description. The biological material section thus covers a minimal subset of the MCPD standard used by gene banks, while also enabling interoperability and data linking through the use of identifiers (namely NCBI taxonomy identifiers) both between MIAPPE-compliant datasets and with external datasets. Moreover, through the provision of external identifiers to resources detailing their biological material (e. g. DOIs, accessions to gene banks or genome archives) researchers can encompass additional information, such as extended MCPD information (e.
It is characterised by a type or level, which can be a single "plant", a group of plants ("pot", "plot", "block"), or the whole "study". These types are hierarchical, meaning that we can have observation units and corresponding observations made from the study level down to the plant level. In some cases, an observation unit may contain no plant (e. g. raw plots after harvest or areas of a forest without tree), but can still be the object of environmental observations. Optionally, an observation unit can have a cross-reference to an external database, such as BioSamples  (Courtot et al. , 2019). Also optionally, it can have one or more spatial distribution key-value pairs that locate the observation unit in the experimental hierarchy (e. g. "block: 1") or globally (e. g. "latitude: +43. 619261").
Breeding API (BrAPI) The Breeding API (BrAPI)  (Selby et al. , 2019)  is a RESTful API developed by an international open-source community for querying plant breeding data, already implemented by several databases, and selected by the European biological data infrastructure ELIXIR as the cornerstone of its plant data search service. It is therefore critical that BrAPI and MIAPPE be compatible. The collaboration between the BrAPI and MIAPPE teams has aimed at ensuring the compatibility of the two schemas, and the latest BRAPI (v. 1. 3) covers most of MIAPPE. The main sections correspond to the same concepts and either share the same name or have direct correspondence, such as investigation (BrAPI Trial) and biological material (BrAPI Germplasm). Some MIAPPE sections are currently absent from BrAPI (e.
Spreadsheet template The spreadsheet template for MIAPPE (MIAPPE Contributors, 2020b) was developed mainly for training purposes, as a simpler alternative to ISA-Tab. It is an explicit representation of MIAPPE, where each section has been placed in a separate worksheet. This template facilitates the understanding of the connections between documentation, data model and actual data. For training, it is important that the data model and one-to-many relationships (Fig. 1 ) be explicitly presented and comprehensively explained to the users (e. g. biologists or data managers).
RDF based on the PPEO PPEO was conceived to enable the direct expression of MIAPPE datasets in RDF (W3C, 2020a), by instantiating the ontology. Moreover, because PPEO explicitly maps MIAPPE to its implementations, it should be straightforward to convert MIAPPE datasets expressed in any of them to RDF. MIAPPE and BrAPI are also connected through PPEO, which not only maps the two resources, but also includes classes exclusive to BrAPI, such as the <observation> class (which is outside of the scope of MIAPPE, as it pertains to data). A proof of concept has demonstrated the feasibility of producing linked data through BrAPI using the JSON-LD format (W3C, 2020b), with PPEO enabling the semantic mapping (see this dataset:  Oury et al. (2020) ). Having MIAPPE datasets in RDF enables the use of a wide range of available tools for reasoning and analysis, and facilitates data integration (by enabling data linking and cross-referencing at the semantic level) and validation.
Results To evaluate the applicability of the standard and the functionality of its implementations, plant scientists were asked to describe their phenotyping experiments using MIAPPE 1. 1. The datasets were provided by: the Instituto de Biologia Experimental e Tecnol ogica (iBET), Portugal; the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Germany; the Genetic and Genomic Information System (GnpIS) of the Institut National de la Recherche Agronomique, France; and the Vlaams Instituut voor Biotechnologie (VIB), Belgium. The datasets are summarised in Table  3 , and described in detail in Supporting Information Notes S1. All of them are listed under  Papoutsoglou et al. (2019) , and their files can be retrieved through the repositories listed there  (Baute et al. , 2019a (Baute et al.
, ,b,c, 2020;; Chaves et al, 2019a,b; Junker, 2020; Junker & Li, 2020; Michotey, 2019 Michotey, , 2020;; Oury et al. , 2019a,b; Pea et al. , 2019a,b). The datasets span model, crop and perennial plants in a variety of experimental settings, as well as various MIAPPE 1. 1 implementations. They demonstrate the ability of MIAPPE to handle diverse experimental designs, including automated glasshouses (IPK and VIB datasets), field networks for crops (GnpIS) and forest trees (iBET and GnpIS) with multiple scales and repetitions. Perennial plant use cases feature time series data, that is several observations across time for the same observed variable on the same plant. Field networks (GnpIS wheat) demonstrate the use case of a multilocal and multiannual dataset where each location represents one study over several years.
Several datasets demonstrate also the use of experimental factors such as cultural practices (nitrogen level in GnpIS wheat) or experimental questions (covered or uncovered plants in IPK Arabidopsis). The observed variables proved to be well suited for very diverse destructive and nondestructive measurements adapted to agronomic (e. g. yield, grain weight), morphological (e. g. plant height), stress (e. g. disease or game), molecular (e. g. protein content) and physiological (e. g. photosynthetic efficiency) data. The data types covered by the observed variables are mostly numeric or textual, but also include images (IPK barley). These observed variables were described using references to ontologies  (Michotey et al. , 2019; Pommier et al. , 2019a; Michotey & Chaves, 2020 ) whenever possible, but ad hoc variables were also used in specific cases not covered by ontologies.
One of the most challenging aspects addressed by MIAPPE and successfully demonstrated by the datasets is the documentation of the biological material. The datasets clearly demonstrate how to organise information for model plants (IPK Arabidopsis), mutants (IPK barley), recombinant inbred line and population (VIB maize), GenBank reference accessions (GnpIS wheat) and perennial plants including in situ material (iBET cork oak stands) or dedicated experimental locations acting as experimental tree fields with populations or crosses (GnpIS poplar). While the datasets showcase the applicability of MIAPPE to diverse experimental settings, they by no means represent the full extent of its coverage. Additional settings that were contemplated in the conception of MIAPPE but are not covered by the examples include: high-throughput phenotyping facilities with plants manipulated by conveyor belts, which produce large volumes of data with respect to the positions of plants and their development; precision agriculture field studies with drones and sensors capturing a wealth of data both about plant development and the environment; and cases where tracing the identity of plant materials is more complex.
Discussion A global metadata standard is a key component for enabling FAIR data in any research domain, by providing a common framework under which researchers can describe their datasets with the necessary information for their interpretation, thus promoting interoperability and reusability. MIAPPE aims at serving such a role for the plant phenotyping community, and the first version of the standard took ample strides in that direction. In this work, we summarise the steps taken to extend and improve the usability of the standard. The datasets presented in the Results demonstrate the broader applicability of MIAPPE 1. 1, which was one of the main goals behind the update. The datasets span a variety of settings (e. g. woody, crop and model plants; glasshouses, single fields and field networks; single-year and multiannual experiments) and include aspects that could not be modelled under MIAPPE 1.
We received evidence for the improved usability of MIAPPE from the community, during two open requests for feedback and in several training sessions. The specification of the data model and the enriched definitions and examples were highlighted as clear improvements. While MIAPPE promotes interoperability and reusability, the other two FAIR principles (findability and accessibility) rely mainly on BrAPI, which enables data search and retrieval through machine access. However, for these two resources to be part of a common scheme for enabling FAIR plant phenotyping data, it is necessary to ensure that BrAPI calls adequately cover MIAPPE and enable searches by all key MIAPPE fields. The process of reconciling MIAPPE and BrAPI was undertaken in parallel with the MIAPPE 1. 1 update, through a collaboration between the BrAPI team, the ELIXIR Interoperability platform and Plant Sciences community, the EMPHASIS Plant Phenotyping Infrastructure and the CGIAR.
BrAPI will be fully MIAPPE 1. 1 compliant once its (currently beta) 2. 0 release is finalised. This reconciliation and the interoperability between the various MIAPPE 1. 1 implementations is demonstrated in our Results, as most datasets are available in two different implementations (including BrAPI), in many cases through automatic conversion. This is critical, as MIAPPE aims to support a wide range of users and applications, from data submission by life scientists to data exchange, validation and even reasoning by machines. Formats supporting all these applications must not only be available but also be interconvertible. While, from a technical standpoint, we believe that the merits of MIAPPE 1. 1 speak for themselves, we are well aware of the many hurdles ahead of getting any standard widely adopted by the community it seeks to serve.
MIAPPE 1. 1 therefore gathers as close to a community-wide consensus as is possible to get and distil into a clear and well-organised standard, especially considering the heterogeneity and complexity of the plant phenotyping domain, and the difficulty in reconciling the perspectives of its different subdomains and experiment types. We will further foster its adoption through constant efforts of outreach and dissemination, to gather new communities and ensure the long-term usefulness of the standard. Also critical for adoption is demand: when funders and/or publishers require compliance with a standard or data publication practicesuch as depositing sequencing data in one of the public gene banksit tends to be widely adopted. In the case of MIAPPE, the demand consists of the increasing pressure from funding agencies towards compliance with the FAIR data principles.
Researchers working in plant phenotyping and seeking FAIR data solutions will be pointed towards MIAPPE thanks to its presence in the FAIRsharing portal (FAIRsharing. org: MIAPPE, 2020) and above all to the endorsement of ELIXIR, which led the MIAPPE 1. 1 update and is helping shape the policies and lay the foundations needed for enacting the FAIR principles. Equally critical is usability, as researchers tend to view the need for standardisation and reusability as a burden and often do as little effort as they can get away with when submitting a dataset, unless the process is virtually effortless. While MIAPPE's usability was improved with the 1. 1 update, it is still missing an easyto-use submission interface. For this reason, we are engaging with popular data management tools such as COPO (The COPO team, 2020) or FAIRDOM  (Wolstencroft et al.
, 2017)  to incorporate MIAPPE and thus enable user-friendly MIAPPE-compliant dataset submission. Last but not least, in order to persist, a standard must constantly evolve to keep up with technical and scientific advances. In this regard, the 1. 1 update demonstrates that MIAPPE is very much a living standard, and we are already starting the next phase of development of MIAPPE. It will concentrate on two main aspects: extending its coverage of environmental aspects (initiated by the EMPHASIS members of the MIAPPE community) and facilitating the recording of technical aspects of material and data processing (e. g. sensors, cameras, software, configurations, calibrations), which are becoming increasingly important. Within this scope, another possible improvement could be to establish a formal complementarity with the ICASA standard, which is used by the agronomic and modelling communities, and provides variables for agronomic management practices, treatments, environmental conditions and measurements of crop responses  (White et al.
MIAPPE-exclusive fields have been added as comments in the corresponding sections. The detailed mapping can be found in Supporting Information TableS1, and in the MIAPPE repository (https://github. com/MIAPPE/MIAPPE/tree/master/MIAPPE_Checklist-Data-Model-v1. 1/MIAPPE_mapping). Table 2The table lists the MIAPPE sections with the BrAPI objects holding their fields (in the current and future versions). The detailed mapping for each field can be found on the MIAPPE GitHub repository and in Supporting Information TableS1, and in the MIAPPE repository (https://github. com/MIAPPE/MIAPPE/tree/master/MIAPPE_Checklist-Data-Model-v1. 1/MIAPPE_mapping). New Phytologist (2020) 227: 260-273Ó 2020 The Authors New Phytologist Ó 2020 New Phytologist Trust www. newphytologist. com.
2019) , while 2D seismic data are generally affected by method-inherent imaging limitations due to side reflections from transported blocks or complex internal architectures. Therefore, detailed subsurface information, capable of highlighting complex internal deposit structures, erosional features, and spatially variable characteristics, may only be derived from 3D seismic datasets, as shown for volcanic landslide deposits offshore Montserrat  (Crutchley et al. 2013; Karstens et al. 2013 ) and Ritter Island  (Karstens et al. 2019). At Ritter Island, the combined analyses of a 3D seismic dataset with additional 2D seismic and sediment echosounder profiles, sediment samples, and seafloor imagery enabled the reconstruction of the 1888 collapse in great detail  (Micallef et al. 2017; Karstens et al.