dataesr/scientific-paragraphs-categorization huggingface dataset:
The latter is characterized by a 10-year resolution and a linear interpolation is applied to derive yearly emissions. The period spreads from August 1994 until December 2013, consistently with  Cohen et al. (2018). The first 14 years come from MOCAGE REF-C1SD simulation originally produced for CCMI project. For the years out of the period covered by the experiment, the MOCAGE REF-C1SD run has been extended until December 31, 2013 using the same code and inputs as in the original MOCAGE CCMI REF-C1SD simulation.
Methodology The objective of the proposed methodology is to make possible the comparison between the full IAGOS database and the 3D monthly mean volume mixing ratios from CTMs and CCMs simulations. Our approach consists in distributing the IAGOS observations, performed every 4 s, on a given model grid. A first application is proposed on MOCAGE REF-C1SD run, characterized by a ∼200 km horizontal resolution in the mid-latitudes, and a ∼800 m vertical resolution in the UTLS. In order to account for the eccentricity of the measurements inside one given cell, we chose a reverse linear interpolation at the first order, as described in Sect. 3. 1 and illustrated in Fig. 1. The subsequent gridded monthly means are derived using weighted averages, as described in Sect. 3. 2, and are directly comparable to the model monthly mean outputs.
In contrast, IAGOS-DM refers to the new product presented in this paper, i. e. the IAGOS data distributed on the model's grid, directly comparable to the simulation. Note that IAGOS-HR seasonal cycles were computed on the original regions' coordinates, but the changes induced by some 1 • difference are expected to be negligible, based on the geographical sensitivity tests mentioned in  Cohen et al. (2018). The comparison between the two IAGOS products in matter of seasonal cycles is proposed in Figs. 7  and 8 , respectively for O 3 and CO. They are shown with their corresponding interannual variability (IAV), defined as a year-to-year standard deviation. In Fig. 7 , both IAGOS versions show a summertime O 3 maximum in the UT and a springtime maximum in the LS. A lessened contrast between the UT and the LS is observed in IAGOS-DM.
In the UT, the O 3 volume mixing ratio and its interannual variability are higher in IAGOS-DM than in IAGOS-HR for winter and fall seasons (∼ 60 ± 20 ppb compared to ∼ 50 ± 10 ppb), whereas they are similar in spring and summer. In this layer, the most important differences between the two versions thus take place during lower-ozone seasons. In the LS, the O 3 amounts are lower in IAGOS-DM  (∼ 125-400 ppb)  than in IAGOS-HR (∼ 150-450 ppb) during the whole year. There are two main reasons that explain the lower O 3 amounts in the LS and the higher amounts in the UT in IAGOS-DM compared to IAGOS-HR. First, the projection of IAGOS observations with a very fine vertical resolution onto MOCAGE vertical grid with a ∼800 m vertical resolution. Second, the use of a monthly PV cannot provide the description of the day-to-day variations of the tropopause altitude, whereas the latter can be important to sort the data points between the two layers.
As for O 3 , the reason why the CO amounts in IAGOS-DM are higher in the LS and lower in the UT comes from the coarse vertical resolution in MOCAGE grid, and from the uncertainty when sorting the UT data from the LS data using a monthly mean modelled PV field. More generally, the comparison between IAGOS-HR and IAGOS-DM for O 3 and CO clearly shows that the processing applied for mapping IAGOS high resolution data set onto MOCAGE coarse grid slightly modifies IAGOS characteristics. This processing, which enables a meaningful comparison between IAGOS long-term measurements and the REF-C1SD simulation, acts as a numerical filter. It is important to note that the seasonal cycles in IAGOS-DM generally show values comprised between the MOCAGE-M and the IAGOS-HR cycles. It confirms that the representation derived from IAGOS-HR cannot be reached by a model with the typical REF-C1SD resolution, especially for CO in the LS, although some main characteristics mentioned above can still be used as criteria.
We now assess the MOCAGE-M seasonal cycles by comparing them to IAGOS-DM. As complements to Fig. 7 , statistical results are given in Table  3. Note that inter-regional averages have been computed only to synthesize the assessment and to provide quantifications that confirm some features seen in the figures. As they are similar with zonal averages, they are not meant to have a geophysical signification. A qualitative summary is also provided in Table  4. In the UT, MOCAGE-M shows a Table  3. Inter-regional averages derived from the seasonal cycles. Yearly mean values and interannual variabilities are shown for IAGOS-DM and MOCAGE-M, along with the correlation coefficient between these two data sets. strong in the northernmost regions (Western North America and Siberia) where the stratosphere is richer in O 3 , it is likely that the stratospheric influence on the UT is overestimated in the simulation.
In most regions, MOCAGE-M shows seasonal cycles in the LS in phase with the UT, thus contrasting with the observations and making the correlation drop from 0. 67 in the UT to 0. 22 in the LS. This suggests that the model simulation is affected in the LS by transport from the troposphere during springtime. In summer, the July maximum in Siberia is not represented in the simulation, showing an underestimation in the biomass burning inventory The present methodology could easily be applied to CCMI REF-C1SD simulations from other models, both for an intermodel comparison and for assessing CCMI products against IAGOS database, notably intermodel-averaged fields. To a greater extent, it can be used on a wide range of long-term simulations including CCMs free runs in order to perform climatological comparisons.
doi. org/10. 5194/gmd-2020-328 Preprint. Discussion started: 13 October 2020 c Author(s) 2020. CC BY 4. 0 License. or in convection in this region. Meanwhile, consistently with observations, MOCAGE-M shows a summertime maximum in Northeast Asia exclusively. Although part of this feature may originate from the positive bias in the UT, the fact that it only concerns the summer season, in contrast to the UT, suggests that summertime convection also plays a non-negligible role. 5 Summary and conclusionsWe developed a methodology that makes the IAGOS database ready to assess chemistry-climate long-term model simulations for recent decades, and particularly the REF-C1SD experiment produced in the frame of CCMI phase-I project. The current paper describes this methodology and its application on a chosen simulation (REF-C1SD simulation from MOCAGE-CTM), assessing modelled ozone and carbon monoxide monthly fields respectively duringAug.
Acknowledgements. The authors acknowledge the strong support of the  European Commission, Airbus , and the  Airlines (Lufthansa, Air-France ,  Austrian ,  Air Namibia ,  Cathay Pacific , Iberia and China Airlines so far) who carry the MOZAIC or IAGOS equipment and perform the maintenance since 1994. In its last 10 years of operation, MOZAIC has been funded by  INSU-CNRS (France) ,  Météo-France ,  Université Paul Sabatier (Toulouse, France)  and  Research Center Jülich (FZJ, Jülich, Germany ). IAGOS has been additionally funded by the  EU projects IAGOS-DS and IAGOS-ERI. We also wish to acknowledge our colleagues from the  IAGOS team in FZJ, Jülich  for useful discussions. The.
Code and data availability. The IAGOS ozone and carbon monoxide data are available at https://doi. org/10. 25326/20 and more precisely, the time series data are found at https://doi. org/10. 25326/06. The IAGOS-DM product adapted to the MOCAGE model grid (named IAGOS-DM-MOCAGE) is available at https://doi. org/10. 25326/80 and the software (named Interpol-IAGOS) at https://doi. org/10. 25326/81.
INTRODUCTION The increasing application to agrifood research data of the FAIR (findable, accessible, interoperable, and reusable) principles  1  has led to the research community's growing interest in using ontologies. FAIR principles indeed recommend that data must be described with commonly used, controlled vocabularies structured in thesauri and semantically rich ontologies. An ontology is a representation of a domain of knowledge where key concepts, as well as the relationships between those concepts, are defined. 2 By providing standardized definitions for the terms used by scientists along with defined logical relationships among these terms, ontologies compile information about the content of a dataset that can be explicitly used by computers. 3 Each concept has a Uniform Resource Identifier (URI) that uniquely identifies it as a web resource accessible by anyone for data labeling, to efficiently support consistent use of ontology terms within and across disciplines and domains.
cgiar. org/) in 2017. The aim is to increase the impact of agricultural research and development by turning FAIR data into a powerful tool for discovery, while integrating principles of responsible and ethical data use. Through the Platform on Big Data, CGIAR's primary objective is to annotate multidisciplinary research data with the appropriate ontologies for publishing on the GARDIAN platform (https://gardian. bigdata. cgiar. org/), CGIAR's metadata repository, and stimulate the ontology content gap filling rather than developing complete new ontologies. 6 The Ontologies Community of Practice (CoP) was created to harness in-house and external expertise in the development of ontologies and support the five other CGIAR Platform CoPs (Agronomy Data Crop Modeling, Geospatial Data, Livestock Data, and Socio-Economic Data) toward finding adequate ontologies for data description.
The Ontologies CoP, hereafter referred to as ''The CoP,'' was also developed as a means to include data generated by the latest technologies (e. g. , remote sensors) and expand beyond crops to encompass data on fisheries and aquaculture, livestock, socio-economics, water management, and agroecology (agroecology includes social, economic, and environmental aspects of the food production systems http://www. fao. org/agroecology/knowledge/ definitions/en/). The Ontologies CoP's thematic working groups currently develop ontologies, such as the Crop Ontology (CO) (http://www. cropontology. org), the Agronomy Ontology (ArgO) (https://bigdata. cgiar. org/resources/agronomy-ontology/), and the Socio-Economic Ontology (SEOnt) (https://github. com/ AgriculturalSemantics/SEOnt). The CoP provides the ideal forum for co-learning and knowledge exchange on ontologies and for guiding consistent data annotation, as well as the deployment of quality ontologies in databases and repositories.
The CoP stimulates exchanges between domain experts and experts in ontology design, knowledge modeling, ontology-driven applications, and semantic web technologies. While IGAD and the Ontologies CoP have members in common, only the Ontologies CoP aims to directly contribute to ontology development to ensure the quality of data mobilized by the CGIAR Platform for Big Data in Agriculture, its partners, as well as new players within the domains it covers. It includes researchers, modelers, information specialists, data managers, and ontology experts from the CGIAR research network, academia, and the private sector, thus creating a critical mass of expertise to tackle the major issues related to semantics for FAIR data in agrifood science. Currently, the Ontologies CoP newsletter has 353 subscribers and a LinkedIn group ''CGIAR Big Data-Ontologies CoP'' (https://www.
linkedin. com/groups/13707155/) with 144 active members: 35 from universities, 61 from public research institutes, and 48 from the private sector. We regularly organize webinars, which are recorded to build a public channel of online reference videos (https://www. youtube. com/c/OntologiesInAgriculture) and to which we have 118 subscribers. The CoP webpage (https://bigdata. cgiar. org/ communities-of-practice/ontologies/) provides access to its objectives and yearly workplan developed with members' input. In this paper we provide information on the ontology products that were developed by the CoP members, as well as the necessary perspectives to extend and cover all relevant domains for research on agriculture and food systems. We explain how the CoP supports and fosters the proper use of quality ontologies, the submission of missing terms by users, and collaboratively explore solutions to solving the complexity of data annotation.
Development of Ontologies for Agrifood Research Data CGIAR currently has eight agrifood research programs (https:// www. cgiar. org/research/research-portfolio/) focused on crop breeding, aimed at producing innovative technologies, such as improved crop varieties and advisory services to farmers. Producing FAIR data on plant genotypes and phenotypes, their environment, field management practices, and socio-economy is crucial to provide support information for the development and use of these technologies. For several years, CGIAR and its partners have contributed to ontology development for plant phenotype studies and field management practices. The ontologies developed by the CoP provide validated concepts and formatted variables for direct integration in the design of field or lab books, thus supporting data aggregation into multidisciplinary platforms or use by analytical and modeling tools.
The CoP provides wider communication and a formal framework for this work, stimulating new members' contributions, as in the case of PepsiCo Inc. and NIAB (a UK crop science organization) to the Oat Ontology development (https://www. cropontology. org/ontology/ CO_350/Oat; https://bigdata. cgiar. org/blog-post/agriculturalontologies-in-use-new-crops-and-traits-in-the-crop-ontology/) or interactions with other CoPs, such as the Data-driven Agronomy and the Socio-Economic Data (SED) CoPs.
Currently, the CO comprises 4,235 traits and 6,151 variables for 31 plant species. By providing descriptions of agronomic, morphological, physiological, quality, and stress traits along with a standard for composing the variables, the CO enables digital capture and aggregation of crop trait data, as well as comparison across projects and locations. 7 The CO was integrated into the Planteome's ontology project funded by the National Science Foundation, US (IOS:1340112 award; http://planteome. org) and was successfully adopted by the CGIAR Integrated Breeding Platform (https://www. integratedbreeding. net/) and by the Boyce Thompson Institute's Breedbase (https://breedbase. org/), both of which are comprehensive breeding management systems and analysis software, and by national databases, such as GnpIS (https://urgi.
versailles. inra. fr/Tools/GnpIS)  9 in France, or international projects, such as Emphasis (European Plant Phenotyping Infrastructures; https://emphasis. plantphenotyping. eu/). Both the Minimum Information About a Plant Phenotype Experiment (https://www. miappe. org/) metadata schema (MIAPPE),  10, 11  and the Breeding Application Programming Interface (BrAPI) (https://brapi. org/),  12 which enable the extraction of genotype and phenotype data across databases are compliant with the CO format. At the time CGIAR launched the CO, the Plant Trait Ontology (TO)  13  did not include traits and definitions required for breeding data on the CGIAR mandate crops. To remediate this situation and create the necessary upper-level connection between the species-specific ontologies, CO trait terms were mapped to terms, thus enabling searches of annotated data across species, using a single trait term.
14, 15 As a result, Planteome Release 3. 0 includes ten species-specific trait ontologies developed by the CO for the crops: cassava (Manihot esculenta), maize (Zea mays), pigeonpea (Cajanus cajan), rice (Oryza sativa), sweet potato (Ipomoea batatas), soybean (Glycine max), wheat (Triticum aestivum), lentil (Lens culinaris), sorghum (Sorghum bicolor), and yam (Dioscorea sp. ). These mappings can be automatically created but still require manual curation, making them difficult to maintain considering that ontologies evolve over time. 15 Planteome is developing a Plant Stress Ontology (https://github. com/Planteome/plant-stress-ontology) that will require support from the Ontologies CoP for content validation particularly on the described pest and disease symptoms. In 2014, CGIAR began developing the AgrO to support the new Agronomy Field Information System (AgroFIMS) (https://apps.
For instance, urea is a widely used fertilizer in agriculture, but the urea concept in ChEBI was not defined as having a fertilizer role. So, the missing link was requested by AgrO and added to ChEBI. More information about AgrO content can be found on the CGIAR Platform for Big Data in Agriculture Website (https://bigdata. cgiar. org/resources/agronomy-ontology/). Socio-economic Data: Starting with Agricultural Household Surveys CGIAR and its partners perform a large number of agricultural household surveys yielding important data and statistics on the socio-economic status, production and food systems, and environment of smallholders in the developing world. The SED CoP (https://bigdata. cgiar. org/communities-of-practice/ socio-economic-data/) created the ''100Q Working Group'' that developed 100 core questions to be included in household surveys to collect consistent information on key socio-economic indicators.
The set of questions consists of the following sections: household composition and characteristics, farm characteristics, land availability and use, livestock availability and use, income and assets, gender, food security and dietary diversity, and other aspects. 17 The Ontologies and SED CoPs are working together to identify concepts from the survey questions and results which will be used to form the new SEOnt. SEOnt will provide concepts and variables to the survey forms to annotate the data collected with the 100 questions, while taking into account the sensitive nature of the personal information. The first draft of SEOnt is available on GitHub (https:// github. com/AgriculturalSemantics/SEOnt). The use of ontologies in making data interoperable is also enhanced when metadata schemas are adopted, such as the metadata schema being developed by the SED CoP, which relies heavily on the work of the Ontologies CoP.
Expanding CoP Products to New Domains Relevant to Agriculture and Food Systems CGIAR research also aims to improve the sustainability, productivity, and resilience of fish agrifood systems and collects fish-related datasets, which include fish health, diseases, breeding, genetics, and catch data, among others. Harmonizing fish data annotation with an ontology will enable easier data aggregation and analysis. One available ontology, FISHO (https://bioportal. bioontology. org/ontologies/FISHO),  18 focuses on ichthyology, diversity, and adaptation. The Food and Agriculture Organization (FAO) of the United Nations initiated several fisheries ontologies, but the ontologies available remained drafts. 19 Therefore in May 2019, CGIAR and relevant partners formed the Fish Ontology Working Group to compile, update, and contribute fishery terms to existing ontologies.
The working group plans to collaborate with the other animal science partners toward developing and adopting animal ontologies within CGIAR. To enable the interoperability of data along the agricultural value chain, the Ontologies CoP members plan to foster a collaboration with the Food Ontology (https://foodon. org/) consortium, which aims at building a comprehensive global farm-to-fork ontology  20  by contributing concepts on tropical and subtropical production systems and food products. A specific value chain ontology will be developed indicating the actors and their roles in the chain. The CoP could use the terminology compiled by CGIAR's Research Program on Policies Institutions, and Markets for the Value Chains platform (http://tools4valuechains. org) as a source of concepts and invite social scientists and economists to contribute to this work.
Finally, CGIAR needs to demonstrate in a meaningful way the contribution of its research to the Sustainable Development Goals (SDGs). Integrating objectives, targets, and processes of the CGIAR Strategic Research Framework into the SDG Interface Ontology (SDGiO) (https://github. com/SDG-InterfaceOntology/ sdgio), which is developed with the support of the United Nations Environment Program, will provide a new set of concepts to annotate data about agrifood innovations and their impact on stakeholders. Identifying Criteria for the Adoption of Quality Ontologies by the Agrifood Research Community In general, an increasing number of controlled vocabularies, structured taxonomies, and semantically rich ontologies are developed ex novo in an ad hoc manner to support research projects, often without drawing on concepts and definitions from existing ontologies.
For example, the thematic repository AgroPortal (http://agroportal. lirmm. fr/),  21 developed by the Laboratoire d'Informatique de Robotique et de Microelectronique de Montpellier, currently compiles 121 ontologies and thesauri only for plants, agriculture, food, and biodiversity. This situation has led to a growing number of incompatible domain-specific ontologies impeding desirable data integration and interoperability. Consequently, scientists and data managers require guidance to unambiguously select the proper ontology terms in order to annotate data. Taking a step closer toward identifying and agreeing upon the criteria that make an ontology a quality resource for data annotation, the Ontologies CoP organized a webinar with an Expert Panel (https://www. youtube. com/c/OntologiesInAgriculture) involving Christopher J.
Mungall (Lawrence Berkeley National Laboratory) and Pier Luigi Buttigieg (Alfred Wegener Institute), who are both members of the OBO Foundry editorial board (http://www. obofoundry. org/docs/Membership. html), Pankaj Jaiswal, leader of the Planteome project (Oregon State University), and Alexandra Lafargue, Knowledge Manager (Syngenta). A list of 17 key criteria, inspired by the OBO Foundry principles (http://www. obofoundry. org/principles/fp-000-summary. html), was proposed by the Expert Panel (Table  1 ). CGIAR data managers and ontology curators were asked to rank the criteria to understand which were the most important to non-expert users and should therefore be documented as a priority to guide the selection for annotation. The five top-ranking criteria selected were: (1) the domainspecific coverage of the ontology; (2) the ontology must be widely used in annotation and data capture (to reduce the cost of external data integration, which is routine work for data scientists and so was ranked higher by data managers and curators than by the Expert Panel); (3) indicators used for quality assurance should be available; (4) ontology maintenance is centralized, while contributions and access are distributed among users; and lastly (5) the existence of sustainable funding to support the ontology, funding being a real challenge and clearly of primary importance in securing the human resources necessary to manage the ontology.
These five criteria are all part of the OBO Foundry principles of ontology design and format. There are several ontologies applicable to agrifood science, which comply with many of the above quality criteria, available for modeling crops, livestock, and other animal species (Table  2 ). The most used ontologies for plants,  22,,23  aside from the Gene Ontology (GO), are the: Plant Ontology,  24,,25  TO, CO,  7  Plant Experimental Conditions Ontology 13, -all included in the Planteome project (http://planteome. org/)-as well as the ENVO,  26,,27  AgrO,  8  and NCBI Taxon Ontology  28  (Table  2 ). The Sequence Ontology (SO)  29  and the Unit Ontology (UO)  30  are also widely used. Under the guidance of the Ontologies CoP several of these ontologies have been adopted within CGIAR, thus progressively increasing the quality of the data annotation.