dataesr/scientific-paragraphs-categorization huggingface dataset:
The limitations of this study are potentially the random data collection and the relatively low model fit indices. Future research may aim at replicating the study in different cultural surroundings. According to the findings of our survey in Serbia, the average of overall assessment of service quality is 4. 72, on a scale from 1-strongly disagree to 7-strongly agree. Such results show the moderate satisfaction with the service quality. The lowest mean was in the case of Q07-"Knowledge of the clients on a personnel basis", while the highest mean was Q24-"Cleanliness of facilities". The dimensions on which banks in Serbia should put emphasis are Access and Price dimensions, for the reason of low mean scores. Banks should deliver high-quality service in all dimensions. Scales like this one may have practical ramifications and assist banks in identifying features that deserve more attention.
The availability of 3D Geospatial information is a key issue for many expanding sectors such as autonomous vehicles, business intelligence and urban planning. Its production is now possible thanks to the abundance of available data (Earth observation satellite constellations, insitu data, …) but manual interventions are still needed to guarantee a high level of quality, which prevents mass production. New artificial intelligence and big data technologies adapted to 3D imagery can help to remove these obstacles. The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information and new added-value services. This paper will first introduce AI4GEO initiative, context and overall objectives. It will then present the current status of the project and in particular it will focus on the innovative platform put in place to handle big 3D datasets for analytics needs and it will present the first results of 3D semantic segmentations and associated perspectives.
AI4GEO DATA ANALYTICS PLATFORM Considering the dramatic increase of data and processing needs for new Earth Observation program, CNES has developed since 2017, in collaboration with CS Group, a new Data Analytics platform based on big data and cloud technologies aimed at fostering user interaction and developing data analytics on large datasets  [2]   [3]. The main idea is to rely on a software ecosystem widely adopted by the scientific community and to build on top of it a complete and consistent thematic data analytics lab, called VRE (Virtual Research Environment) to access, manipulate, process and visualize at scale any kind of geospatial products (optical, radar, etc. ).
Application layer From the user point of view, the VRE consists mostly of a JupyterLab Interface, as shown in Fig. 3 , enhanced by powerful plugins among which: • A Virtual Desktop that allows to execute "desktopbind" application such as QGis in a web browser environment. • A complete Integrated Development Environment (IDE) based on Visual Studio Code. This is complementary to the notebook approach, mostly prototyping oriented. • EODAG  [4] , a Python framework with a JupyterLab plugin that acts as a catalog proxy. Thus, it simplifies data discover by providing a standard API through a single access point.
Platform layer At system level, the VRE relies on the Docker container technology. In addition to bringing substantial simplification of the deployment process, the Docker inheritance capability allows to provide each community with the right environment. The kernel VRE includes common packages of data science and tools whose versions are compatible with each other, on top of which multiple thematic overlays (Imagery, Hydrometry, etc. ) have been created. At last, a specific Project can also add its own overlay to embed dedicated libraries, specialized tools and so on. Finally, at infrastructure level, the VRE is based on Safescale platform developed by CS Group 1 that manages of security and interoperability. The VRE is able to seamlessly work on academic HPC cluster or on Public Cloud.
Variations of energetic particle fluxes after interplanetary shock arrivals and around significant geomagnetic storms observed by low altitude spacecraft We analyze variations of energetic particle fluxes measured by low altitude spacecraft after interplanetary shock arrivals and around the times of significant geomagnetic storms. Data from two different spacecraft and energetic particle detectors are used and compared. First, we use data measured by the energetic particle detector (IDP) onboard the Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions (DEMETER) spacecraft. The spacecraft operated between 2004 and 2010 on a circular Sun-synchronous orbit at an altitude of initially 710 km, which was decreased to 660 km in December 2005. The IDP instrument measured electron flux close to the loss cone at energies between about 70 keV and 2.
3 MeV (128 energy channels). Second, we use data measured by the Space Application of Timepix Radiation Monitor (SATRAM) onboard the Proba-V satellite operating since May 2013 on a circular Sun-synchronous orbit at an altitude of 820 km. The semi-conductor based pixelated radiation detector called Timepix is capable of detecting all charged particles and X-rays with sufficiently high energies. Electron energies higher than about 2 MeV and proton energies higher than about 20 MeV are detected. We identify the times of interplanetary shock arrivals and significant (Dst < -100 nT) geomagnetic storms during the mission durations. Then we perform a superposed epoch analysis to reveal characteristic particle flux variations around these times at different energies and L-shells. Although the used satellite missions do not overlap in time, we aim to compare the revealed flux variation signatures between these two independent data sets.
2017a). Third, larval competition experiments in a subset of plant species and abiotic conditions have evidenced hierarchical competition interactions among the generalist species  (Duyck et al. 2006b ). Here we confronted a long-term field dataset describing abundances of the eight fly species on 21 host plants with laboratory measures of fundamental host use obtained for seven of the fly species on the same plants. We first modelled joint species distributions using Poisson-LogNormal (PLN) modelling  (Chiquet et al. 2019)  and conducted model selection among various combinations of host plant species and ecological PEER COMMUNITY IN ECOLOGY 4 covariates (representing temperature, rainfall, elevation, land use and date). Residual correlations estimated under the selected model were further dissected to identify significant traces of unexplained co-occurences.
Consequently, the (logtransformed) total fruit weight of each sample was added as an offset to every tested model (fruit number was also tested as an offset yielding identical conclusions, results not shown). Model diagnostics were conducted using the R package DHARMa  (Hartig 2020). Covariations between species abundances unexplained by covariates were further investigated using the PLNnetwork() function, which adjusts the considered model under a sparsity constraint on the inverse of the variance-covariance matrix, i. e. , constraining the number of edges in the resulting estimated network. The stability of the resulting species associations was estimated as their selection frequency in bootstrap subsamples of the StARS model selection procedure (range 0-1;  Liu et al. 2010; Appendix S3).
We therefore compared all models with their diagonal counterpart (Models 1-2, 1-4 and 1-6). Third we estimated how well knowledge of the fundamental niche, here laboratorymeasured preferences and performances, explained field abundances. In its present form, PLNmodels does not allow accounting for species traits (covariates describe samples but not species within samples). To cope with this limitation, we considered that assuming that species distribute according to their fundamental niche implies that species interactions are negligible, and species distribute independently from one another. Using this assumption, we fitted models separately for each fly species, obtained their likelihoods and numbers of parameters, and computed the BIC of the seven-species dataset as: 𝐵𝐼𝐶 = 𝑘𝑙𝑛(𝑛) -2𝑙𝑛(𝐿), where k and L are the sums of the numbers of parameters and likelihoods over the seven single-species datasets and n the number of samples.
Following this principle, we built models where the host plant cofactor was replaced by either preference (Model 2-7 without ecological covariates and 2-8 with ecological covariates), or performance (Models 2-9 and 2-10), or both preference and performance (Models 2-11 and 2-12). In addition, all previous models were reevaluated on the datasets excluding the species lacking fundamental host use estimates (D. ciliatus; Models 2-0 to 2-6).
Results The variance-covariance structure of the complete dataset (8 fly x 21 plant species) was first inferred by fitting a PLN model without any covariate (Model 1-0). The obtained residual variance-covariance matrix (Figure  2A ) revealed a sharp distinction between three groups of flies: (i) the four generalists (Bactrocera zonata, Ceratitis capitata, C. catoirii, and C. quilicii), (ii) the three specialists of Cucurbitaceae (D. ciliatus, D. demmerezi and Zeugodacus cucurbitae), and the specialist of Solanaceae (Neoceratitis cyanescens). While N. cyanescens abundances showed very low covariances with other species, the two other groups showed positive within-group covariances and negative among-group covariances. This variance-covariance structure suggested strong separation of the realized niches of the three groups, likely mediated by host plants.
Considering model selection between models without species traits, i. e. , models including combinations of plant species and ecological covariates (Table  1 ), model ranking was equivalent on the eight-species (Models 1-0 to 1-6) and seven-species (Models 2-0 to 2-6) datasets, except that diagonal models tended to be slightly better than their full-matrix counterparts on the latter. This result was further shown robust to sample bootstrapping (Appendix S3). For both datasets, the selected model included both ecological variables and host plant species as covariates (Models 1-5 and 2-6), which strongly improved the BIC (ΔBIC = 17087. 4 and 18935. 7 respectively) as compared to the basic (no covariate) models 1-0 and 2-0. Models with host plant species ranked close to the selected model with a moderately inflated BIC relative to the selected model (Model 1-2: ΔBIC = 971.
The selected model had full residual variance-covariance matrix on the eight-species dataset and diagonal residual variance-covariance matrix on the seven-species dataset. On the eight-species dataset its diagonal version ranked second with ΔBIC = 428. 9. Congruently, the residual variance-covariance matrix inferred under the selected model (Figure  2D ) had very low covariance values for all pairs of fly species, all covariances being negative. This suggested possible, though weak, antagonist interactions. The largest residual correlations were observed between the three specialists D. demmerezi, D. ciliatus and Z. cucurbitae (residual correlations ranging from -0. 031 to -0. 112) and between the generalists B. zonata, C. quilicii and C. capitata (residual correlations from -0. 025 to -0.
3 vs. ΔBIC = 653. 2). This is congruent with the idea that plant species affect fly distributions more similarly within the Cucurbitaceae family than at the scale of the 21-plant dataset, so that the influence of plant identity is less important for species distributions among Cucurbitaceae than on a wider set of plants. For D. demmerezi and Z. cucurbitae, for which traits were available, models with species traits alone had poor performance, and displayed higher BIC than the basic model (Table  S5B ). However, the model with both PEER COMMUNITY IN ECOLOGY 12 species traits and ecological covariates ranked third just after the diagonal version of the selected model (Model 2-10, ΔBIC = 124. 9).
Solanaceae For Solanaceae and their associated fly species, N. cyanescens, C. capitata, and C. quilicii, the selected model included host plant species, ecological covariates and a diagonal residual variance-covariance matrix (Model 1-6, Table  S6 ). It improved the BIC of the basic model by 201. 8. It performed better than its full residual matrix version (ΔBIC = 15. 8) and congruently, applying network inference to the latter yielded no significant interaction between fly species. On this dataset, all models with a diagonal matrix performed better than their full residual matrix counterpart, further confirming the absence of detected interactions. Models with ecological covariates alone performed poorly (Model 1-3 ΔBIC = 176. 4 and Model 1-4 ΔBIC = 162. 0, respectively). Models with species traits were almost as good as their equivalent with host plant as a cofactor.
Other plant families The last dataset considers all families other than Cucurbitaceae or Solanaceae with B. zonata, C. capitata, C. catoirii, and C. quilicii (Table  S7 ). The selected model included host plant species and ecological covariates (Model 1-5, ΔBIC = 2639. 7 with the basic model 1-0). It assumed a full residual variance-covariance matrix and performed slightly better than its diagonal version (ΔBIC = 55. 8). All models with a full residual matrix performed better than their diagonal counterpart. Network inference yielded one significant interaction between B. zonata and C. quilicii (stability = 1. 0). The model with only ecological covariates performed well (Model 1-3, ΔBIC = 967. 3), and almost as good as the model with host plant alone (Model 1-1, ΔBIC = 684. 1), suggesting redundancy between ecological information and plant identity.
Obtaining long-term abundance data on all a priori relevant species of a given community is rarely possible. Here over the study period, one other fruit fly species (Carpomya vesuviana) was mentioned locally. It was considered very rare and only present on Indian jujube (Ziziphus mauritiana) in dry low-elevation areas  (Quilici & Jeuffrault 2001 , Franck et al. 2017). It was not detected in the 204 jujube samples of the full dataset. In addition, not all 108 plant species considered as potential fruit fly hosts were included. To evaluate the effect of omitted plants, we conducted the analysis on all plants with at least 10 samples with emerging flies (6434 samples, 36 plant species including the 21 studied in the laboratory) and obtained strikingly similar results (model ranking and estimates of regression coefficients, Appendix S3), comforting the idea that most relevant factors have been accounted for.
Detection of competitive interactions Some residual covariances remained non-negligible after accounting for fly species' response to host plants between some generalists and between some specialists of Cucurbitaceae. They were all negative, suggesting a possible minor role of antagonistic interactions within specialists and within generalists. Only one of these residual covariations resisted the network inference process on the whole dataset (D. ciliatus -Z. cucurbitae, the two most abundant specialists of Cucurbitaceae). Two more significant covariations were detected when focusing on Cucurbitaceae (Z. cucurbitae -D. demmerezi) and on other plant families (B. zonata -C. quilicii) suggesting possible dependence of species interactions on host plants. On Cucurbitaceae, although qualitatively congruent with other independent empirical measures of host range  (Vayssieres et al.
Acknowledgements This work is dedicated to  Serge Quilici  who has been leading prolific research on the Tephritids of Réunion his whole career. Cirad technicians  Jim Payet  and  Serge Glénac  made this study possible through their invaluable expertise with fly rearing and ecology. We also thank them as well as  Antoine Franck ,  Christophe Simiand , and  Patrick Turpin , for collecting field data over the years. Thomas Brequigny contributed to measuring larval traits during his internship. This work used images acquired within the framework of the  CNES Kalideos device (Réunion site ), which benefited from the " Programme Investissements d'Avenir" EQUIPEX of the French  " Agence Nationale de la Recherche " on project GEOSUD bearing the reference  ANR-10-EQPX-20. The images also required financial support the  French Ministry of Agriculture  and field data transmitted by the " Syndicat du Sucre de la Réunion " and the  "SAFER de la Réunion".
BF, FC, FM, JC, MD, SR , and VR received the financial support of the  French "Agence Nationale de la Recherche " project  NGB  ( ANR-17-CE32-011 ). EF, FC, PFD, and VR were funded by the  European Union  ( European Regional Development Fund ,  ERDF  contract  GURDT I2016-1731-0006632 ), the  Conseil Régional de La Réunion , and the  Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD). AH was funded by the  "Ministère de l'Enseignement supérieur et de la Recherche Scientifique de la Tunisie ". This study used the facilities provided by the  Plant Protection Platform  ( 3P , IBISA),  Saint-Pierre, Réunion, France. Version 4 of this preprint has been peer-reviewed and recommended by  Peer Community In Ecology  (https://doi. org/10. 24072/pci. ecology. 100080).
Because of their excellent fossil data, evolutionary events can be dated with real-time estimates making ostracods ideal models for evolutionary research  (Butlin and Menozzi 2000; Oakley and Cunningham 2002; Oakley et al. 2013; Scho ¨n and Martens 2016). Contrary to the extensive focus on this group for palaeontological research, there is a total lack of published ostracod genomes, and even isolated genomic data from ostracods in open access databases are still rare. Thus, the only resources available beyond individual gene sequences are four mitogenomes [the marine ostracods Vargula hilgendorfii  (Ogoh and Ohmiya 2004 ; GenBank accession number NC_005306) and Cypridina dentata  (Wang et al. 2019; NC_042792) ; and two unpublished mitogenomes from V. tsujii (NC_039175) and Cyprideis torosa (PRJNA302529)].
10. 1  (Bankevich et al. 2012). Scaffolding was performed using SSPACE v3. 0  (Boetzer et al. 2011). Scaffolds identified as contaminants were filtered out using Blobtools v1. 0  (Laetsch and Blaxter 2017). The completeness of genomes assemblies was assessed with BUSCO v3. 0. 2  (Seppey et al. 2019 ) against the arthropoda_odb9 dataset. More details of the assembly pipelines and the applied parameters can be found in Supplementary SM1.
Protein coding gene annotation Libraries were prepared using the Illumina TruSeq Stranded RNA kit, following the manufacturer's instructions. RNA reads were generated with the Illumina HiSeq 2500 system (Supplementary Table  S4 ). Reads were filtered with Trimmomatic v0. 36. All trimmed reads were mapped against the genomes with STAR v2. 5. 3a  (Dobin et al. 2013 ) and further assembled with Trinity v2. 5. 1  (Haas et al. 2013 ) under the "genome guided" mode to produce transcriptome assemblies. The obtained transcriptomes and protein evidence were used to train and predict protein coding genes using MAKER v2. 31. 8  (Holt and Yandell 2011). Predicted protein coding genes were functionally annotated with Blast2GO v5. 5. 1  (Conesa et al. 2005; Go ¨tz et al. 2008 ) against the NCBI non-redundant arthropods protein database (v 2018-10).
Data availability Raw sequence reads have been deposited in NCBI's sequence read archive under the following bioprojects: PRJNA515625 (reference genomes, Supplementary Table  S3 ) and PRJNA631617 (RNA-seq for annotations and resequenced individuals, Supplementary Tables  S4  and S5 ). Genome assemblies and annotations have been deposited in the European Nucleotide Archive (ENA) under the accession number PRJEB38362 (Supplementary Tables  S6  and S7 ). Codes for the analyses are available at: https://github. com/Asex GenomeEvol/Ostracoda_genomes. Additional supplementary material is available at the figshare collection page, including detailed descriptions of the methods (Supplementary Methods SM1-SM3), Tables  S1  and S2 , and S6 and S8, and the results of the GenomeScope analyses of D. stevensoni and N.
It allows optimizing the mechanical stability of entire capillaries while maintaining a large gaseous exchange surface. With a total surface area of opened membrane in the blood level of 0. 0026 m 2 , the effective gas exchange surface area after sealing the blood network with the air/O 2 network is reduced to 0. 00107 m 2. Since all the patterns have been designed with L-edit software, this gas exchange surface has been calculated very easily using the Boolean function that allows subtracting the area of the blood network from that of the gas network in the circular region of capillaries (Fig. 1a  and b ). It is of importance to note that the radial curved design for the blood network and the circular design for the air/O 2 network allows enhancing capillary density and thus mechanical stability.
2014; Liu et al. 2015; Wang et al. 2015a, b, c; Shen et al. 2018; Li et al. 2020; Lücking et al. 2020). Such taxonomic overlap requires careful consideration of history, practice, and taxonomic priority when dealing with names of fungi. Taxonomists of the International Commission on Yeasts (ICY), and editors and authors of the latest and past editions of the monographic book 'The Yeasts: A taxonomic study'  (Kurtzman et al. 2011 ) and the on-line database www. theyeasts. org (Boekhout et al. 2020 ) have established a modern framework that is used to provide descriptions of newly proposed yeast taxa. The number of currently accepted yeast species exceeds 1500  (Kurtzman et al. 2011) , increases by about 5% annually  (Yurkov 2017)  and is soon to reach > 2300 species according to www. theyeasts.
Collection practices One of the main purposes of culture collections is the preservation of microbial diversity. Culture collections generally preserve their material in a metabolically inactive state (WFCC guidelines, http://www. wfcc. info/ guidelines/) and they have increased their skills and quality standards following  ISO 9001, ISO 17025, and NF S 96-900 certifications (Boundy-Mills et al. 2016). Some European culture collections, such as BCCM, CBS, CECT, CIRM, and DSMZ, have acquired the status of Biological Resource Centers in the past decade. All certified and some accredited culture collections also consider biosafety and biosecurity issues by following published management guidelines  (Boundy-Mills et al. 2016). Many culture collections have adopted a professional management system, standard operational procedures, and databases to keep records, in order to ensure that their holdings will be properly preserved as viable and pure cultures and that the method for the identification of yeasts to the species level is sound, which is essential for safe and patent deposits  (Boundy-Mills et al.
8. 1 of the ICNafp Shenzhen Code  (Turland et al. 2018) : "Whenever practicable a living culture should be prepared from the holotype material of the name of a newly described taxon of fungi or algae and deposited in at least two institutional culture or genetic resource collections. " Following that recommendation, researchers have used subcultures of type strains (holotypes) preserved in culture collections to revise the taxonomy of yeasts, tracing them back to the material cited in original species descriptions. In addition to the literature, a collection certificate and a collection catalog (nowadays usually a dynamic online database) attests to the existence, authenticity and availability of the type material. A unique combination of a collection acronym and accession number allows searching of cultures (see ICNafp Shenzhen Code;  Turland et al.