dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary and conclusions Galaxy NGC 6552, with an already identified Compton-thick AGN in its centre  (Ricci et al. 2015) , was observed with the MIRI Imager and Medium Resolution Spectrometer during the JWST commissioning to characterise the persistence of the MIRI detectors. We present the calibrated NGC 6552 MIRI image and MRS spectra, including an extensive and detailed explanation of the MIRI data and calibration process. We obtained the nuclear, circumnuclear, and central mid-IR spectra of NGC 6552. The nuclear and central spectra are dominated by the AGN emission, with steeply rising continuum and a large number of high-and low-excitation emission lines, warm molecular Hydrogen lines, and PAH features from the interplay between the star-formation and AGN components. The spectra of the circumnuclear region is mainly dominated by the starformation, with low excitation lines, molecular hydrogen lines, and PAH features.
Acknowledgements. JAM, AL, and LC acknowledge support by grant  PIB2021-127718NB-100  by the  Spanish Ministry of Science and Innovation/State Agency of Research   MCIN/AEI/10. 13039/501100011033  and by " ERDF A way of making Europe ". PJK acknowledges financial support from the  Science Foundation Ireland   / Irish Research Pathway programme  under Grant Number  21/PATH-S/9360. IA and DG thank the  European Space Agency (ESA)  and the  Belgian Federal Science Policy Office (BELSPO)  for their support in the framework of the  PRODEX Programme. The work presented is the effort of the entire MIRI team and the enthusiasm within the MIRI partnership is a significant factor in its success. MIRI draws on the scientific and technical expertise of the following organisations:  Ames Research Center, USA ;  Airbus Defence and Space, UK ;  CEA-Irfu ,  Saclay, France ;  Centre Spatial de Liége, Belgium ;  Consejo Superior de Investigaciones Científicas, Spain ;  Carl Zeiss Optronics ,  Germany ;  Chalmers University of Technology, Sweden ;  Danish Space Research Institute, Denmark ;  Dublin Institute for Advanced Studies, Ireland ;  European Space Agency, Netherlands ;  ETCA, Belgium ;  ETH Zurich, Switzerland ;  Goddard Space Flight Center, USA ;  Institute d'Astrophysique Spatiale, France ;  Instituto Nacional de Técnica Aeroespacial, Spain ;  Institute for Astronomy, Edinburgh, UK ;  Jet Propulsion Laboratory, USA ;  Laboratoire d'Astrophysique de Marseille (LAM), France ;  Leiden University, Netherlands ;  Lockheed Advanced Technology Center (USA) ;  NOVA Opt-IR group  at  Dwingeloo, Netherlands ;  Northrop Grumman ,  USA ;  Max-Planck Institut für Astronomie (MPIA) ,  Heidelberg, Germany ;  Laboratoire d'Etudes Spatiales et d'Instrumentation en Astrophysique (LESIA), France ;  Paul Scherrer Institut, Switzerland ;  Raytheon Vision Systems, USA ;  RUAG Aerospace, Switzerland ;  Rutherford Appleton Laboratory (RAL Space), UK ;  Space Telescope Science Institute, USA ;  Toegepast-Natuurwetenschappelijk Onderzoek (TNO-TPD) ,  Netherlands ;  UK Astronomy Technology Centre, UK ;  University College London, UK ;  University of Amsterdam, Netherlands ;  University of Arizona, USA ;  University of Bern, Switzerland ;  University of Cardiff, UK ;  University of Cologne, Germany ;  University of Ghent ;  University of Groningen, Netherlands ;  University of Leicester, UK ;  University of Leuven, Belgium ;  University of Stockholm, Sweden ;  Utah State University, USA.
Introduction While the Web of linked data makes more and more knowledge graphs available, their cross-use often remains a challenge. This study presents a dataset containing mappings between two knowledge graphs representing different points of view on the same objects. This mapping set should allow to query simultaneously these graphs to enrich object descriptions by combining these points of view. Agriculture offers a particular use case of mappings, linked to the modeling of cultivated plants. Several expertises are needed to describe a cultivated plant: farmer vs. agronomist, agronomist vs. ecologist. The scientific world (ecologists or agronomists) tends to use scientific names from taxonomic science to designate living organisms  (plants, insects). These scientific names are stored in biological taxonomies.
Vernacular names are often ambiguous and not consensual, which renders the matching activity particularly challenging. Our previous studies  (Michel et al. , 2022)  have implemented several automatic alignment methods based on vernacular names comparison. Those automatic methods reused existing reference sources such as EPPO global database and the official French catalog of species and varieties of cultivated plants GEVES. The results show that it is necessary to clean the automatically produced alignments due to the ambiguity of vernacular names. Therefore, a group of agricultural experts has produced a set of valid mappings. Those mappings are published as open data on the French Recherche Data Gouv repository. Thus, they could be used as a gold standard to validate any automatic alignment methods.
Manual alignment method Previously, we have tested some automatic alignment methods  (Michel et al. , 2022)  that reused existing reference sources such as EPPO global database (see text footnote ) and the official French catalog of species and varieties of cultivated plants GEVES (see text footnote ). The final automatic method computes a confidence score of the mapping between a taxon from TAXREF-LD and a The UML based CHOWLK language is used to present the knowledge graph. More information available on https://chowlk. linkeddata. es/notation. crop usage from FCU using EPPO database and GEVES catalog. The main problems come from name ambiguity: depending on the source (1) the scientific name may follow or not the botanical nomenclature code used in TAXREF-LD. For example, the scientific name of carrot may be "Daucus carota", "Daucus carota L", or "Daucus carota L, 1753".
CR_C PO_KB The Crop Planning and Production Process Ontology and Knowledge Base (C3PO KB) is a knowledge graph created by the Elzeard Enterprise  (Darnala et al. , 2021 (Darnala et al. , , 2022)). This KG is another reference source about vegetable. The knowledge graph is accessible as several TTL files on a git repository. To create a mapping between a crop usage and a taxon, the experts should search into the TTL file related to the plant module. Figure  13  presents an excerpt of this file. The vegetable description contains a crop usage indicated by the property c3poplant:hasFCUTaxon and a TAXREF-LD scientific name indicated by the property c3poplant:hasScientificName. The experts should find the corresponding taxon by searching in TAXREF-LD Web interface (following the link "is the reference name of ").
CR_Experts The experts can also use their own knowledge to state that a scientific name is linked to a given crop usage. In this case, the taxon name is searched in TAXREF-LD. If the scientific name is retrieved The ontology is accessible on AgroPortal repository https://agroportal. lirmm. fr/ontologies/C PO/?p=summary. https://gitlab. com/serre-des-savoirs/c po-kb The file is untitled "cultivatedplants-infos-result-triples. ttl" and is accessible at https://gitlab. com/serre-des-savoirs/c po-kb/-/blob/main/ plants/rdf-generated% triples/cultivatedplants-infos-result-triples. ttl.
The rootstock is the buried part of the vine and serves as a support for the graft. Here, we may want to align crop A with a combination of a taxon B (rootstock) and a taxon C (graft) and indicate their respective roles. The future extension of SSSOM to handle complex https://github. com/mapping-commons/sssom/issues mappings may include a property to assert the role of each component of a combination. If so, our RDF vocabulary would be extended to provide the types of roles in our specific context. • Negative mappings: the open world assumption holds in Semantic Web models. This means that the absence of mapping does not mean that this does not exist or is incorrect. Moreover, we want to declare that some mappings are false or irrelevant. The SSSOM community investigated two solutions: adding a modifier column or creating negative mapping properties.
Data availability statement The datasets presented in this study can be found in online repositories. TAXREF-LD: The version 15. 2 of TAXREF-LD graph used for this study can be found in the AgroPortal repository https://agroportal. lirmm. fr/ontologies/TAXREF-LD. The github repository is https://github. com/frmichel/taxref-ld. The SPARQL EndPoint is https://taxref. mnhn. fr/sparql. FCU: The version 3. 3 of the FCU thesaurus used for this study can.
Note that fitting a second-order logistic regression was impossible for factor combinations that almost always or never led to SP establishment in the 240 replicates. In such cases, a secondorder polynomial regression was fitted instead. Finally, for each combination of resistance deployment strategy, mutation probability, fitness cost, pathogen reproduction system and cropping ratio, we fitted local polynomial regressions to the temporal dynamics of the population of SP t f and HP t f. Statistical analyses were performed with R (v4. 0. 5, R Core Team 2021) software. The function rpart within the package rpart (v4. 1. 16, Therneau et al. 2022 ) was used to fit the classification and regression trees (we set a minimum number of values in any terminal node equal to 3% the total number of values).
The function glm within the package stats (v3. 6. 2, R Core Team 2022) was used to fit the logistic regression (glm(E SP ∼ |T SM1 -T SM2 | + strategy, family = "binomial"). The function geom smooth within the package ggplot2 (v3. 3. 6, Wickham et al. 2022 ) was used to fit second-order logistic (method = "glm", formula = y ∼ poly(x, 2), family = "binomial"), second-order polynomial (method = "lm", formula = y ∼ poly(x, 2)) and local polynomial (method = "loess", formula = y ∼ x) regressions.
Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a.
The first difficulty is the substantial number of response variables compared with predictor variables. There are hundreds of species represented in the metabarcoding dataset (after selection by their occurrence) but only 17 parameters of ocean color data acquired by multispectral sensors are available as predictor variables. The second difficulty is the small number of samples. In this study, we used the largest available compilation of eukaryotic metabarcoding data, complemented with novel sequence data from the Tara Oceans expeditions, but only a few hundred samples were available for analysis after appropriate filtering. Focusing on ecological networks alleviated these two difficulties by reducing the number of variables (dimensionality) in the metabarcoding data. Ecological networks tend to be structured and are non-randomly assembled  [18].
Satellite data Ocean color data acquired by the Moderate Resolution Imaging Spectroradiometer on board the Aqua and the Terra satellites were used in this study. Level-3 data, mapped to a 5ʹ (ca. 9 km on the Equator) square monthly grid, were downloaded from the Ocean Color Web operated by NASA (https://oceancolor. gsfc. nasa. gov/). The data included 17 parameters consisting of remote sensing reflectance (R rs (λ)) from 10 visible light wavelengths  (412, 443, 469, 488, 531, 547, 555, 645, 667 , and 678 nm); six environmental parameters derived from R rs (Chl a, diffuse attenuation coefficient for downwelling irradiance at 490 nm (K d (490)), particulate organic/inorganic carbon concentration (POC/PIC), photosynthetically available radiation (PAR), and normalized fluorescence line height (nFLH)); and another environmental parameter, SST, derived from infrared measurements.
Two-dimensional projection of satellite-derived parameters To capture the range of all possible satellite-derived parameter values, a two-dimensional (2-D) projection of randomly selected grid cells was performed. Twenty thousand grid cells were randomly selected from all the 5ʹ square grids with the probability proportional to the area of each grid. After removing grid cells on land or in coastal regions and those with missing data, 7019 grid cells remained (Fig. S2 ). A sampling month was randomly selected from 120 months (January 2009 to December 2018) for each grid cell. The satellite-derived parameters for these randomly selected grid cells and months were standardized by subtracting the mean and scaling to unit variance. Finally, the 7019 points with the 17 parameters were projected onto a 2-D map by Uniform Manifold Approximation and Projection (UMAP) using the Python package umap-learn  [20].
Metabarcoding data Raw sequencing data were downloaded from the EMBL/EBI-ENA EukBank umbrella project in their native format (accession numbers of all BioProjects under the EukBank umbrella project are listed in Data S1). When applicable, reads were merged and trimmed (using vsearch  [21]  and cutadapt  [22] ) to cover the 18 S V4 region, as defined by the primers TAReuk454FWD1 and TAReukREV3  [23] , resulting in 347,327,830 unique sequences, representing 1,672,099,024 reads. After clustering (swarm  [24] ), chimera detection (uchime  [25] ), quality-based filtering, and posttreatments based on occurrence patterns (swarm, lulu  [26] ; https:// github. com/frederic-mahe/mumu), representative sequences were compared with the 18 S rDNA database EukRibo  [27] , using a global pairwise alignment approach (usearch_global command in vsearch), and taxonomically assigned to their best hit (https://github.
com/frederic-mahe/ stampa/). The filtered occurrence table of EukBank contained 460,147 operational taxonomic units (OTUs) clustered by swarm, representing 1,403,019,176 reads, collected from 15,562 samples. The sequencing data from the EukBank umbrella project included the amplicon sequence data (837,127,965 reads) targeting 18 S V4 regions from 1011 samples (1191 datasets) collected through the Tara Oceans expeditions, which are newly released with this paper (accession numbers in Data S2). To use the filtered occurrence table of EukBank for the analysis, the raw number of reads was rarefied to 10,000 reads per sample. A total of 1715 samples from the ocean surface (depth < 10 m) with spatiotemporal metadata were retained. These came from several ocean sampling projects, including Tara Oceans  [1] , Malaspina  [28] , and Australian Microbiome  [29].
Spatial resampling A total of 653 metabarcoding samples from previous processing were further filtered using the following procedure. Samples with missing satellite data values owing to bad weather or other reasons were removed. Samples from locations where the sea floor was shallower than 200 m were detected using a global relief model  [30]. They were removed to keep only open ocean samples  [31]. Samples were thinned so that they were separated by a minimum of 200 km, using the R package spThin  [32]. This procedure resulted in 177 samples available for analysis (Fig. S5 ).
Network inference OTUs were selected by their occurrence to reduce the number of OTUs to those similar to previous studies that analyzed network structures  [33, 34]. Two hundred and eight OTUs with a minimum occurrence larger than 0. 2% (20 reads) in at least 10% of samples (18 samples) were retained (Fig. S6 ). OTU read counts were centered log-ratio-transformed  [35]. An ecological network was inferred based on co-occurrence patterns using the Julia package FlashWeave  [36]  with the settings "heterogeneous = False", "sensitive = True", and "alpha = 0. 05", as in previous studies  [36, 37]. FlashWeave is a package for detecting direct associations between OTU pairs based on the local-to-global learning framework for causal inference. The nodes in the obtained network were OTUs, and the edges were decided based on direct associations between OTU pairs.
Only positive associations (edges) were considered here because most module detection algorithms only allow non-negative networks. The module detection performances of eight algorithms (Fast Greedy, Infomap, Label Propagation, Leading Eigenvector, Leiden, Louvain, Spinglass, and Walktrap) were compared using the R package "igraph" (https://igraph. org/). To measure the structure of the detected module division, we used the modularity index Q as defined by the following equation: Q ¼ 1 2S X u;v σ u; v ð ÞÀ k u k v 2S δ M u ; M v ð Þ where u, v are nodes (OTUs), σ u; v ð Þ is an edge weight (association strength) between u and v, S is the sum of all edge weights, k u is a weighted degree of node u, M u is a module to which node u belongs, and δ x; y ð Þ is 1 if x = y and 0 otherwise  [38].
Machine learning and cross-validation Several machine learning algorithms were used to train predictive models of the community types from satellite-derived data. Spatial parameters (longitude and latitude) were also tested for their prediction ability. The sine and cosine of the longitude were used as independent parameters because longitude is circular (-180°and 180°are the same). K-nearest Neighbors, Naïve Bayes, Multilayer Perceptron, Random Forest, and Support Vector Machine (SVM) were applied using the Python package "scikit-learn" (https://scikit-learn. org/). In the training process for all the methods, except Random Forest, the satellite-derived and spatial parameters were standardized by subtracting the mean and scaling to unit variance. Both leave-one-out cross-validation and buffered crossvalidation  [39]  were used to measure the model accuracy.
Time series prediction The constructed model was used to predict the spatiotemporal distribution of each community type based on satellite data. Satellite data mapped to the 5ʹ square monthly grid from January 2003 to December 2021 were used for the prediction. Satellite data were downsized by choosing a grid cell at the center of each 12 × 12 grid to reduce the computational cost. In other words, a grid cell was chosen for every 1°square grid cell. The long-term trend in the areas of predicted community types was tested by the seasonal Mann-Kendall test and its slope was estimated by the seasonal Theil-Sen's slope estimator using the python package "pymannkendall"  [40].
Network inference and module detection The ecological network based on OTU co-occurrence patterns was inferred using the FlashWeave algorithm. OTUs were selected by their occurrence (see Materials & Methods). In the network, 560 positive edges (association strength > 0) between 208 OTUs were detected (Fig. 2A ). We applied several module detection algorithms to the network. The modules detected by the Leiden and Spinglass algorithms had the highest modularity index (0. 55) (Fig. S8 ). In the following analysis, the modules detected by the Leiden algorithm  [41]  were used because it captured the macrostructure better than the others (i. e. , there were no small modules) (Fig. S8 ). Among the six detected modules, module 1 was well separated from the other five modules, which formed one super module with a highly aggregated module structure (Fig.
DISCUSSION Here, six plankton community types were identified from a global co-occurrence network, and their distribution was successfully predicted from satellite data using a machine-learning approach. The predictive model outputs were plankton community types that were similar to the phytoplankton-dominated community output of the PHYSAT model  [15]  rather than a quantitative abundance output like the PhytoDOAS model  [12, 13]. However, our method has two advantages over these previous models. First, the output of our model was directly connected with the OTUs inferred from the metabarcoding data. We used a swarm for clustering sequences into OTUs, which was designed to maximize taxonomic resolution  [24] ; thus, the community types integrated high taxonomic resolution information. For example, dinoflagellates were treated as one group in the PhytoDOAS model  [12] , whereas they were represented by 136 OTUs that were classified into one of the six different modules in this study (Fig.
This may explain why the linear SVM was the best prediction algorithm for our problem. More complex and nonlinear algorithms such as Multilayer Perceptron, Random Forest, and kernel SVM overfitted the training dataset during model training (Fig. S18 ). The time series prediction of community types using the constructed model revealed the spatiotemporal distribution of each community type (Fig. 6  and Video S1). Generally, these community-type distributions were similar to previously obtained plankton provinces using the 18 S V4 rDNA dataset of Tara Oceans  [46, 47]. Those provinces were defined using species  compositional dissimilarity between samples, while the commutypes in our study relied on the species co-occurrence network. The consistent results obtained by different datasets and analytical approaches suggest the stability of plankton community partitioning and corroborate our approach using the newly edge satisfaction to capture community types.
Although the high taxonomic resolution of metabarcoding data is attractive for research, using a small number of samples imposed several limitations. After binning and thinning, only 177 samples were suitable for our study, although we accessed unprecedentedly large datasets. First, some taxonomic groups were lost in the process of pooling samples to make the analysis dataset. We used only four size fractions, mainly targeting piconano-plankton (0. 2-3 µm, >0. 2 µm, 0. 8-5 µm, and >0. 8 µm), to maximize the number of samples available for analysis. Through this procedure, however, taxonomies only observed in larger-size fractions (e. g. , diatoms) were lost in the network (Fig. S3A ). Second, the network inference algorithm used in this study was not entirely suitable for the dataset. The FlashWeave algorithm had two options: heterogeneous=False for data with a small (hundreds) number of samples from homogenous conditions, and heterogeneous=True for data with a large (thousands and more) number of samples from heterogeneous conditions  [36].
Gray points mean that no community type was predicted. OTUs (Fig. S6 ) and tuning the module algorithms to capture the microstructure of network can increase the number of detected modules, which will describe plankton diversity at a higher resolution. However, a machine learning model with a greater number of community is difficult to train because its prediction performance is dependent on the number of samples for each community type. In this study, we inferred the ecological network of OTUs using a global metabarcoding dataset and identified six distinct community types of plankton. We applied SVM to construct a predictive model of community types at each site based on satellite data and obtained an accuracy of 67% in cross-validation. The spatiotemporal distribution of community types was shown by applying the model to 19 years of global satellite data at monthly intervals.