dataesr/scientific-paragraphs-categorization huggingface dataset:
Improving User Experience in Selecting and Submitting Ontology Terms Used in Data Annotation Because of the urgency to release data generated annually that support agricultural research questions and technological innovation, best practices for quality data annotation are not always systematically applied. The CoP plays a key role in providing guidance and interacting with teams developing solutions that can facilitate the annotation process. Developing or completing ontologies, as well as recommending annotation support tools, are tasks for the well-defined Ontologies CoP of the CGIAR Platform for Big Data in Agriculture. Figure  1  illustrates the current user's generic experience for selecting ontology terms for data annotation and submitting new concepts.
Manual Ontology Term Searches In general, when annotating datasets, scientists and data managers first need to manually check if relevant ontology terms exist (Figure  1 , step 1). They also need to be familiar with the terms used in the original files because, for example, crop traits are included in a variety of nomenclatures, often decided by different groups of scientists without any coordination. To illustrate step 1, we provide a specific example of data annotation for the evaluation and adoption by farmers of floodtolerant rice varieties in Bangladesh. 34 Submergence tolerance is a target trait for rice breeders because flooding is a major abiotic stress causing important yield losses in rice production areas in South and South-East Asia,  35  and some parts of Africa. 36 This annotation exercise was performed by a scientist using survey data collected by the International Rice Research Institute (IRRI) with the support of the Ontologies CoP experts (Havard Dataverse: https://dataverse.
harvard. edu/dataset. xhtml?persistentId=doi:10. 7910/DVN/26165). It was simplified for this paper by only selecting a sample of key concepts that could annotate data files at the level of their metadata and their variables (Table  3 ). We did not include all concepts or the finer annotation of the value, describing measurements or observation methods and scales or units. Users who are familiar with the domain-specific ontologies can perform a search directly on the relevant ontology website where they can visualize, browse, and download the ontology, and access direct term submission forms or templates, when available. If the user does not know any domain-specific ontology, consulting the quality ontology selection criteria recommended by the CoP on its web page is always good practice. Then a term search using ontology look-up services of the main registries (e.
Thesauri have a simpler semantic structure than ontologies, called a Knowledge Organization System, that use broader narrower relationships between concepts. The most popular thesauri in Agriculture are: AGROVOC (http://agrovoc. uniroma2. it/agrovoc/agrovoc/en/) maintained by FAO, the Center for Agriculture and Biosciences International Thesaurus (https://www. cabi. org/), and the US National Agricultural Library Thesaurus (https://agclass. nal. usda. gov/). For the rice data annotation example, the concept of lowland as a landform was only found in AGROVOC. Once the term is identified, users can easily copy the URI and paste it in their file, ideally at the variable value level to increase the interoperability potential of the data. Table  3  summarizes the results of a manual ontology term search using the EBI OLS.
It shows only the key concepts that could be used to annotate the rice datasets relevant to the evaluation of flood-tolerant varieties. Datasets annotated with these ontological terms could then be retrieved through a query, such as: ''Rice varieties that are flood-tolerant and can grow in Bangladesh in rain-fed lowlands subject to recurrent devastating flooding. '' For example, annotated datasets on the rice strains with Sub1 gene disseminated in Bangladesh should systematically appear in a result list of such a search. Although over time users will gain experience and confidence in the term selection and insertion of URIs in their files, such a manual process remains laborious and time consuming, often discouraging scientists and data managers from finding adequate terms. Consequently, they will limit their annotations to a strict minimum, such as a few keywords in metadata, which is insufficient for the interoperability of the data.
In an Ontologies CoP survey, members identified the development of an online hub of ontologies recommended for agriculture, food, and environment research domains as a necessary resource to improve their annotations. Indeed, repositories, such as GARDIAN, and data discovery platforms, such as GEMS (the platform of the AgroInformatics Consortium; https://agroinformatics. org), combine multidisciplinary data from biophysical studies to socio-economic surveys, which implies the use of several domain-specific ontologies to fully describe the data. 23 Therefore scientists and data managers need direct access to the set of quality ontologies recommended for the specific domain to upload their data and metadata in such repositories. The ideal solution does not yet exist, but ontology look-up services and ontology registries will be a part of it.
The OLS (https://www. ebi. ac. uk/ols/index), developed and hosted by the EBI, is the closest tool to this requirement as it provides a simple search function for finding specific concepts across 251 ontologies comprising over 6. 1 million URIs representing concepts. The OLS API enables any database to access this wealth of ontologies. If users can restrict their term search to a single ontology, there is, however, no option for filtering the ones most used by agrifood domain experts. AgroPortal provides a complementary solution focused on agronomy that, aside from quality ontologies, includes draft and specific community ontologies, therefore acting as an ontology project discovery tool. AgroPortal offers a set of ontology descriptive metadata and statistics on the ontology files downloaded and should add information on all the criteria listed in this paper that would guide users toward quality and popular ontologies for agricultural data.
When further developed, COPO could fully describe the file's values drawing on terms from several ontologies. A feature that the CoP members proposed was for COPO to preferentially indicate, at the top of the list, the ontologies and the terms that were most used in previous data annotations. The CoP will continue conveying the members' needs to the developers of data annotation tools to ensure that they are fit for purpose and that developers understand users' priorities and requirements for ontology concept selection. Many agricultural databases enabling the production of electronic field books for homogeneous quality data collection provide direct assistance with ontology term selection and data annotation through an ontology manager (Figure  1 , step 3). Users simply need to select the ontology terms and variables directly in the database when designing their field books.
In general, an issue tracker enables subscribers of the open project management tool to directly insert their comments and suggestions, which will result in an email alert to all subscribers. For an ontologies project management tool, such as the Planteome GitHub (https://github. com/Planteome), any issue opened by a contributor will alert the ontology curators about new term submission or modification requests. Alternative options for submitting a term are the templates and forms proposed in the ontologies' websites. In this way, the new concepts are submitted to an established ontology and are correctly placed in the semantic graph by the ontology curator after its metadata is checked (synonyms, definition, context of use, reference) and is added with an URI. In the rice data annotation example, the terms weeding application date and fertilizer cost were not found by the scientist.
Upload of Annotated Files into Repositories and Databases Once the data file is described with appropriate metadata and ontologies, files can be uploaded into data repositories or a graph database (Figure  1 , step 5). Data repositories archive datasets with their metadata and annotations for long-term storage and access. COPO allows the annotated data to be directly deposited in a range of repositories, including DSpace (https:// duraspace. org/dspace/), CKAN (https://ckan. org/), and Dataverse (https://dataverse. org/), which are used by CGIAR. If the URIs of the selected ontology terms are ideally present for each variable, the file can be uploaded into a database, such as a graph database. A graph database has no predefined structure constraining the data and is based on a graph that represents the semantic relationships between data, showing how each individual entity connects with or is related to the other (https://neo4j.
Collaboration with the Agrifood Industry For over 10 years, the agrifood industry has shown a strong interest in using ontologies and semantic web technologies to improve their data science activities (e. g. , genomics data integration, data curation and annotation, responsible and ethical data management). The agrifood industry has progressed in the adoption of semantic tools and quality improvement of their data annotations faster than the public sector. Some success stories in industry and recurring challenges have been reported (https://f1000research. com/slides/5-348). 38 The rise in digitalized farming has created several open challenges related to the application of ontologies and semantic web technologies. The Ontologies CoP provide an adequate space for discussing the most prominent concerns about best practices and data reusability in this sector.
Therefore, sufficient resources should be allocated to developing those components when building a sustainable data management system. The next set of priority ontologies to be developed for CGIAR's Platform for Big Data in Agriculture will be related to livestock, fisheries and aquaculture, water management, food systems, and value chains. To create the semantic framework that will support the evidence of CGIAR's and partners' contributions to the SDGs, the CoP will continue integrating concepts on agriculture and food systems into the SDGiO. Based on emerging needs, the CoP will also create additional thematic working groups, for example to collaborate with the Geospatial Data CoP for the harmonization of data generated by remote sensors, such as drones. The CoP will stimulate collaboration on the development of knowledge graphs in agriculture that support graph databases, a domain in which the agrifood industry has made rapid progress.
Methods for one of the commercialized mPCR-1 for which undisclosed proprietary pre-processing of the data is performed. Using a VDJ rearrangement model (Methods), we computed 17 rearrangement parameters for TRA and TRB sequences from experiments A&B (Supplementary Fig. 3 ) and calculated Jensen-Shannon Divergence (JSD) distances between samples per parameter. Multi-Dimensional Scaling (MDS, Fig. 1d ) showed that, within each experiment, samples obtained with the same method clustered together, suggesting that each method imposed its methodological imprint on the repertoire profile. We further compared the different library methods' replicability (i. e. the similarity among data obtained with the same method) and reproducibility (i. e. the similarity among data obtained with different methods) using JSD as a measure of the distance between datasets  38.
For TRA (Fig. 5a -left), RACE-3 and RACE-5 datasets included up to 50% of the meta-repertoire clonotypes (MRC) compared to 10 to 20% for the other RACE method datasets. Similar results were found for TRB (Fig. 5a -right). We then computed for each method the fraction of MRC found in 0, 1, 2, 3 etc. up to 9 replicates. The dot-heatmaps (Fig. 5b ) showed that for TRA, RACE-3 and RACE-5 clearly outperformed the other methods, capturing up to 40% of the MRC in all 9 replicates (Fig. 5b -left; Replicate number=9) and missing (i. e. never captured in any of the 9 replicates) less than 1% of the MRC (Fig. 5b -left; Replicate number=0). The other RACE protocols detected only 1% of MRC in all 9 replicates and missed 15 to 20% of the MRC (Fig. 5b -left). In contrast, there was much less difference between the methods for TRB (Fig.
Such in silico standards may be of value in further comparative TCRseq method evaluation, although ideally synthetic repertoires recapitulating at least the extent of the TRAVJ and TRBVJ combinations and distributions may provide an even more robust alternative. [1]Two such approaches have been proposed for specific clone detection in Minimal Residual Diseases  45, 46  as well as for the BCR, but not TCR, repertoire  47  , still at a very low diversity level. The construction of such gold standard repertoires is currently very costly and remains a major challenge that the Adaptive Immune Receptor Repertoire Community (AIRR-C)  48  , engaged in AIRR-seq standardization  [49] [50] [51]  , may tackle in the future. Finally, in this study some data were pre-processed using proprietary (mPCR-1, mPCR-3) or published  30, 52  (RACE-1_U and RACE-2_U) tools and then aligned and error-corrected using MiXCR (v2.
1. 10)  37. To further optimize TCR data accuracy, it would also be interesting to benchmark available software analysis tools, especially regarding UMI analysis and sequence alignment. Our datasets generated using different methods should be a valuable complement to using datasets generated purely in vitro  53, 54. In conclusion, the take-home messages from this work are the following. Firstly, there are satisfactory TCRseq methods based on either DNA or RNA input, and in both cases the amount of material impacts both diversity and the detection of rare clones. Secondly, various methods are optimal for detecting maximal diversity, while others most accurately quantify the abundance of specific clonotypes. For the latter, UMI-based methods are potentially more accurate, although they could miss relevant but rare clones.
In contrast, non-UMI RACE methods are more sensitive in capturing rare clones, especially for TRA. Thirdly, the availability of raw data is crucial in allowing reliable and reproducible in-depth analyses of TCR repertoires; the mPCR-1 service provider does not provide access to raw sequence data, while mPCR-1 and mPCR-3 do not disclose the proprietary pre-processing filters. In contrast, the RACE-2 provider provides raw data and all preprocessing algorithms. We summarized our results as well as practical aspects in Table  1. Regarding the results, we calculated for each method a rank value for Replicability, reliability and sensitivity based on various measures (Table  1  and Supplementary file). We also summarized cost per sample, presence of controls or standards, format of the method and raw data availability.
Flow Cytometry Vβ identification was performed on enriched CD4 + effector T-cells from experiment A (see Blood effector T cell isolation for enrichment procedure) stained with the IOTest Beta Mark TR Repertoire Kit (Beckman Coulter®) according to the manufacturer's protocol as well as with CD4-APC, CD127-BV421, CD25-PECy7. Data acquisition was performed on a Cytoflex® (Beckman Coulter®) using CytExpert® software. FlowJo® was used for data analysis. Vb frequencies were calculated on CD4 + CD25 -CD127 + gated cells (Supplementary Fig. 4a-b ). Staining was performed at the Sorbonne Université laboratory. annotation using the MiXCR software (v2. 1. 10) with RNA-Seq parameters (-p rna-seq -s hsa)  50. MiXCR extracts TRA and TRB repertoire providing correction of PCR and sequencing errors.
Data analysis Statistical comparisons and multivariate analyses were performed using R software version 3. 5. 0 (www. r-project. org). FASTQ raw data files were obtained from each method, except for Multiplex-1 & 2, for which we obtained, respectively, FASTA file and FASTQ files following proprietary pre-processing. For RACE-1 and RACE-2, UMI pre-processing was performed following protocols published elsewhere  30, 31, 52. FASTQ and FASTA files were then processed for TRB and TRA sequence annotation using the MiXCR software  37  (v2. 1. 10) with RNA-Seq default parameters (-p rna-seq -s hsa) as available online. MiXCR extracts TRA and TRB repertoire providing correction of PCR and sequencing errors.
Comparing VDJ rearrangement statistics An empirical VDJ rearrangement model for each method was built as follows. We analyzed clonotype tables to obtain comprehensive statistics of VDJ rearrangements including the frequencies of V/D/J segment usage, number of added N Bases (namely "insert profile", i. e. the probability distribution of having A/T/G/C inserted in the N-region of CDR3 given that we observe a certain base inserted before it) and V/J segment trimming bases, with the IGoR package  56. This model is built in a 'greedy' way in the sense that it uses best alignments provided by MiXCR rather than running expectation maximization procedures as described in Murugan et al. 57. We utilized the Jensen-Shannon divergence (JSD) between distributions of VDJ usage to define the following two statistics that we use for comparative analysis of different TCRseq methods: 1) replicability measured as the distance between different samples produced by the same protocol and 2) reproducibility measured as the distance between samples produced by two different protocols.
Similarity analysis Pearson and Spearman correlations, the Morisita-Horn index  58  (MH) and the Jaccard similarity index  59  (JSI) were used to assess the similarity between samples. The MH index takes into account the relative abundance of species in the sample, while the JSI is a measure of the intersection between two populations relative to the size of their union, and is independent of relative abundances. Both indices vary between 0 (no overlap) and 1 (perfect overlap). JSI and MH were calculated using the DIVO package  60  on R. In order to discriminate indices represented by a heatmap with the pheatmap package  61  , we used a different set of colors. The Pearson and Spearman correlations are presented as yellow/white/orange (Fig. 2c  and Supplementary Fig. 4e ), MH is presented as blue/white/red (Fig.
Diversity profiling The diversity was analyzed using two indices. Rényi entropy 62 is a generalization of Shannon entropy, which increases when both species richness and evenness are high. Rényi entropy is a function of a parameter α spanning from (i) the species richness (α = 0), which corresponds to the number of clonotypes regardless of their abundance, to (ii) the clonal dominance (α→ + ∞), corresponding to the frequency of the most predominant clonotype. For α = 1, the Shannon diversity index is computed. The exponential of the Rényi entropy corresponds to the actual number of clonotypes in the datasets  63  and is used to build a diversity profile  64. It was computed using the entropy package  65  on R. ChaoE 66 index was calculated with the iNEXT package  67  as a measure of extrapolation of the possible number of clonotypes based on the observed clonotypes.
Meta-repertoire construction We generated an in silico meta-repertoire from the sequences obtained from the 108 replicates (45 for TRA and 63 for TRB). This meta-repertoire, for each chain, was designed to minimize biases by (i) pooling all clonotypes from the 9 datasets and removed singletons to avoid introducing noise due to PCR errors, (ii) Selecting non-reprocessed datasets, meaning before UMI, (iii) keeping only clonotypes found by at least 3 different methods to avoid bias toward one particular method. The threshold was defined to reach a dataset size as close as possible to the original datasets to avoid additional sampling, (iv) normalizing the size of each dataset to the lowest dataset to ensure the same weighting for each method. Completion of  (yellow, maximum dissimilarity) to 1 (orange, maximum similarity).
to  M. Barbie  for providing the human samples. This work benefited from equipment and services from the iGenSeq core facility, at  ICM. Funding: This work was supported the  ERC-Advanced TRiPoD  ( 322856 ),  LabEx Transimmunom  ( ANR-11-IDEX-0004-02 ) and  RHU iMAP  ( ANR-16-RHUS-0001 ) grants to DK. MS and DMC were supported by a grant from the  Ministry of Science and Higher Education of the Russian Federation  ( 075-15-2019-1789 ). This work was funded in part by the  intramural program  of the  National Institute of Allergy and Infectious Diseases (DCD). BC was supported by the  National Institute for Health Research UCL Hospitals Biomedical Research. AE was supported by  DFG CRTD  ( FZ 111 ). AND was supported by the  Ministry of Education, Youth and Sports of the Czech Republic  under the project  CEITEC 2020  ( LQ1601 ).
Data Availability All the fastq data obtained in this study, including the Jurkat Clone E6-1 (ATCC®TIB-152™) cell line TCR alpha and beta sequences, were deposited in the NCBI Sequence Read Archive repository following MiAIRR standard recommendations  47  under the BioProject ID PRJNA548335. Source data for TCRVb flow cytometry data are provided as Supplementary Fig. 4a-b. All other data are available from the corresponding author upon request. Code Availability All software packages and programs are publicly available and open source. Scripts used to analyze the data with MiXCR are available from https://mixcr. milaboratory. com ; Decombinator from https://github. com/innate2adaptive/Decombinator; MiGEC from https://github. com/mikessh/migec; detailed VDJ rearrangement statistics scripts are available from https://github.
A model and data toolbox is presented to assess risks from combined exposure to multiple chemicals using probabilistic methods. The Monte Carlo Risk Assessment (MCRA) toolbox, also known as the EuroMix toolbox, has more than 40 modules addressing all areas of risk assessment, and includes a data repository with data collected in the EuroMix project. This paper gives an introduction to the toolbox and illustrates its use with examples from the EuroMix project. The toolbox can be used for hazard identification, hazard characterisation, exposure assessment and risk characterisation. Examples for hazard identification are selection of substances relevant for a specific adverse outcome based on adverse outcome pathways and QSAR models. Examples for hazard characterisation are calculation of benchmark doses and relative potency factors with uncertainty from dose response data, and use of kinetic models to perform in vitro to in vivo extrapolation.
The data collected in the EuroMix project are summarised in section 2. 2. In sections 2. 3-2. 6 short descriptions are given of the methods implemented in the toolbox for the four areas of risk assessment, i. e. hazard identification, hazard characterisation, exposure assessment and risk characterisation, respectively, and the data for some simple examples are described. Sections 3. 1-3. 4 then show the results for the examples. The results and the intended use of the toolbox are discussed in section 4.
The data and calculation modules on the left side of The exposures module can aggregate non-dietary exposures (which then need to be provided as data) and dietary exposures. Dietary exposures are calculated from consumptions and concentrations, possibly incorporating many detailed aspects. Food consumptions may need to be redefined by conversion of food codes from the codes used in consumption surveys to the codes used in concentration monitoring data. Occurrence data (concentrations and substance authorisations) may need to be converted due to differences between active substances and measured substances (in the pesticide field known as complex residue definitions), and such data may be condensed in concentration models (e. g. a lognormal distribution with a spike of non-detects). Total diet study (TDS) concentration data require a conversion to appropriate food codes.