dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatial analysis applied to metadata This was done by extracting 4 coordinates from the bounding rectangles. This extraction was based on the retrieval of the XML tags corresponding to the bounding (<EX_GeographicBoundingBox>) in order to subsequently generate data in GeoJSON format. PostGIS and Python processing are then used to produce a geographical data density map. This makes it possible to highlight the spatial patterns in the composition of local and national institutional geographical data assets. A description of this processing chain is included in Appendix B and the script, as well as the analyses using this method, are available in free-access (see Supplementary Materials). To check the reproducibility of this processing chain beyond French SDIs, we successfully experimented on SDIs from other countries (GeoBolivia from Bolivia and INDIA from Brazil).
7% of their catalogue data are currently downloadable (Figure  4 ). A gap between the national and regional levels may be noted, with more than twice as high a percentage (19. 1%) of data tagged "open data" in regional compared to national catalogues (8. 5%). While our previous analyses provided a more nuanced picture than media announcements on the systematic opening of spatial databases associated with French SDIs, it is still true that data accessibility has increased considerably in recent years  [15]. However, the actual possibility of reusing the data depends on compliance with documentation rules. Indeed, the metadata are indicative of the data processing conditions, providing, for example, information on validity space and time scales. Despite the use of standardised tools compliant with international standards (ISO 19115), our analysis of metadata fields revealed that content was often very imprecise, which hampered rigorous reuse of the data.
Thus, we attempted to analyse several metadata fields to identify the sources and production methods of the spatial data presented. A textual analysis of the "genealogy" tag (supposed to provide a concise description of the history of the data set by presenting the resources, procedures, and processing implemented during their acquisition) revealed that this free-input metadata field was inoperable for systematic processing. The information was heterogeneous, referring to very different database or producer denominations, and was even, in many cases, non-existent. The study of time data tags provided another example of the limitations of metadata analysis. Thus, 95% of these data did not provide any information on the time depth of the data and the various "date" fields were completed in different formats (Figure  5 ).
This area of analysis is outside the scope of our study, which focused on a systematic exploration of metadata to visualise the contents of SDIs, without any distinction of quality. While our previous analyses provided a more nuanced picture than media announcements on the systematic opening of spatial databases associated with French SDIs, it is still true that data accessibility has increased considerably in recent years  [15]. However, the actual possibility of reusing the data depends on compliance with documentation rules. Indeed, the metadata are indicative of the data processing conditions, providing, for example, information on validity space and time scales. Despite the use of standardised tools compliant with international standards (ISO 19115), our analysis of metadata fields revealed that content was often very imprecise, which hampered rigorous reuse of the data.
What Do SDI Promoters Say about Interoperability? One of the recurring arguments in favour of implementing SDI concerns these platforms' technical capacities to connect databases stored on distributed servers. Thus, 40 out of the 45 SDI promoters surveyed reported that they used web services to access remote metadata (CSW) or data (WMS, WFS) potentially distributed on several servers. These interconnection issues are at the heart of national spatial data infrastructure implementation strategy. According to answers to our survey, the on-going reorganisation (2015-2018) of the geoportal/geocatalogue (known as "geoportal v4") is being implemented "in the context of the emergence of the platform-state" and is aimed, in particular, at reinforcing "the mechanisms for aggregating regionally-or locally-produced data on a national level", to develop "chained geoprocessing with heterogeneous sources" or even to "improve harvesting/caching mechanisms".
Are SDIs Interconnected to Accelerate Data Flows? Geocatalogue harvesting was analysed to assess the degree of interconnection of SDIs. Harvesting is a mechanism for collecting metadata from a remote catalogue and storing them on a local node to provide faster access. Harvesting is more than importing data, as the local and remote catalogue metadata are synchronised. For example, it is possible to access all the metadata in "departmental" catalogues and a selection of metadata from a national catalogue from a regional catalogue. Analysing the implementation of harvesting, therefore, makes it possible to identify links between SDIs. Furthermore, the harvesting process was studied by network analysis to identify the central (or isolated) nodes and any clusters (by displaying sub-networks). In addition to connections between SDIs, the survey obtained the first census (on a self-reporting basis) of connections between SDI and observatory geocatalogues, open data portals, infra-regional infrastructures, etc.
Supplementary Materials: The histograms and maps presented in Figures  3, 4 , 6, 7 and 9-12 will be available online on the research project website (http://geobs. cnrs. fr) when the article is published. It will thus be possible to explore the histograms interactively (choice of aggregated or detailed categories, displaying all the study material or the national and/or regional levels), as well as the graphs in Figures  6  and 7  (exploration of the neighbours of each node) and maps (choice of discretization and navigation) and the maps in Figures  11  and 12  (exploration of the spatial distribution of metadata). Once the article is published, this website will also provide access to the database, uploaded to http://data. gouv. fr under an Open Licence and the script, uploaded to http://github.
Sequencing, Mapping, and Variant Detection Pair-end sequencing was performed on Illumina HiSeq platforms, with 20 samples per lane, following the manufacturer's protocols for library preparations. The data downloaded from NCBI were generated from single-end libraries on the Illumina HiSeq 2000, and so all sequence data used in this study were produced using the same sequencing by synthesis technology. Sequencing reads were mapped to the reference genome (Amel 4. 5) using BWA-MEM v0. 7. 9a (Li H. 2013: Aligning sequence reads, clone sequences and assembly contigs with BWA MEM. ArXiv13033997 Q-Bio. http://arxiv. org/abs/1303. 3997; last accessed November 2017), and processed with regards to marking duplicates, local realignment, base quality score recalibration, and variant detection as described in  Wragg et al.
Mitochondrial Analyses Consensus mitochondrial whole-genome sequences were generated for each individual using SAMTOOLS and BCFTOOLS. Sequence reads aligned to the mitochondrial genome were extracted, and together with unmapped reads were aligned to the mitochondrial genome of A. m. scutellata (GenBank Acc. No. KJ601784). The best reference from which to generate a consensus sequence for each sample was determined as that with the least error rate for high quality aligned reads (mapping quality ! 20) as reported by Picard's CollectAlignmentSummaryMetrics tool (https://broadinstitute. github. io/picard; last accessed November 2017). Sequences were concatenated into a multiple fasta and aligned with ClustalW  (Larkin et al. 2007 ) to generate a nexus file for plotting a network in PopArt  (Leigh et al.
2015). As described in  Wragg et al. (2016) , to reconstruct tRNAleu-cox2 sequence mitotypes, sequencing reads were extracted from the BAM alignment for the region, aligned against 226 mitotype reference sequences downloaded from GenBank, and the reference mitotype to which the reads aligned best was then used as a reference for de novo assembly with MITObim v1. 8;  (Hahn et al. 2013). The resulting fasta files were manually aligned in AliView  (Larsson 2014 ). In the resulting alignment, at positions where insertions/deletions (indels) were present, "G" nucleotides were recoded to an otherwise absent nucleotide (e. g. , "C") and "missing" nucleotides at these positions recoded as "G" to facilitate their inclusion in network analysis with PopArt, which would otherwise exclude sites with 5% missing rate.
Individual mitochondrial gene trees were made using FastTree 2. 1. 8 (Price et al. 2010)  with default parameters and drawn with ggtree  (Yu et al. 2017). McDonald-Kreitman tests were performed for each of the 13 coding mitochondrial genes separately, and Tajima's D tests were performed on the whole mitochondrial genome of all Reunion bees having A. m. unicolor mitochondrial DNA using PopGenome  (Pfeifer et al. 2014). Mitotype sequences were verified for 42 SWIO honeybees by Sanger sequencing of PCR amplifications using primers E2 and H2  (Garnery et al. 1993). Sanger sequences were processed using FinchTV (https://digitalworldbiology. com/FinchTV; last accessed November 2017) and aligned manually in AliView against the de novo assembled mitotypes. Whole mitochondrial DNA sequences were assigned to groups using the partitioning around medoids clustering method as implemented in the fpc R package (Hennig C.
Autosomal Population Genetics Analyses To assess the general population structure of the data, principal components analysis (PCA) was conducted in R using the glPca function of the adegenet package  (Jombart 2008; Jombart and Ahmed 2011)  from a distance matrix, where distance is expressed as genomic proportions (1-identity by state) as generated in Plink. Using the same distance matrix, a complimentary neighbor-joining (NJ) tree was constructed using the NJ function of the phangorn package for R  (Schliep 2011) , which performs the NJ tree estimation of Saitou and Nei  (Saitou and Nei 1987). A more detailed assessment of population structure was performed using ADMIXTURE v1. 23  (Alexander et al. 2009) , assuming no prior knowledge of population origin, testing 2 K 16. The likelihood of the results was determined from the cross-validation (CV) error values, and Q estimates plotted in R.
Network analyses by k-nearest neighbor (kNN) were conducted using NetView  (Neuditschko et al. 2012)  for R (https://github. com/esteinig/netview; last accessed November 2017). kNN graphs were generated assuming 2 k 100, from which networks were constructed for each k where the number of communities (n) reached a temporary plateau, indicating the most visually informative network at k ¼ 41. This network was plotted incorporating the Q data from the K ¼ 7 ADMIXTURE analysis, which does not impact on the structure of the network but simply replaces the nodes with pie charts of the ADMIXTURE results for each sample. TreeMix  (Pickrell and Pritchard 2012)  analyses were conducted on stratified allele frequency data generated by Plink. This software is designed for the inference of patterns of population splitting and mixing from genome-wide allele frequency data.
Regions were considered significant if Bonferonni-corrected P < 0. 001 and where the mean posterior probability of ancestry inference across samples for the region >0. 75. Genes and their orthologs for Drosophila melanogaster were identified for significant regions using the A. mellifera Amel4_5 and D. melanogaster BDGP6 genes data sets of EnsemblMetazoa's BioMart. Unless elaborated upon further, gene functions are broadly summarized according to their gene ontologies, sourced through BioMart and flybase (flybase. org).
S7 , Supplementary Material online). There was little difference in median haplotype block size between the A (4. 5 cM) and C (4. 4 cM) lineages, or in the frequency of blocks irrespective of contiguity assigned to either the A (0. 51) or C (0. 49) lineage. Pearson's r as a measure of the linear correlation between the PCAdmix and ADMIXTURE ancestry inferences indicates strong correlations for both the A (r ¼ 0. 961) and C (r ¼ 0. 9) lineage backgrounds. The heterogeneity of local ancestry inference was tested using the CAnD method, which aims to test for systematic differences in ancestry of admixed populations which could for instance arise due to preferential selection of one or the other background. A. m. carnica and A. m. ligustica are the least divergent pair of subspecies in our data set (F ST ¼ 0.
2017). Given the high levels of mitochondrial DNA of A. m. unicolor origin in the Reunion population, we further looked at nuclear genes having annotations indicating that they interact with the mitochondria, none of which emerged as significant in the selection analysis at P < 0. 001. We searched for genes containing the Gene Ontology (GO) annotation keywords *mitoch* and *respir* (see complete list of keywords in supplementary table  S13 , Supplementary Material online) in the A. mellifera Amel4_5 Ensembl database. Of the 8,835 genes having GO annotations in the database, 165 (1. 86%) could be linked to the mitochondria or to the respiratory chains. Two of these, NDUFA5 and NDUFA10, are located in regions exhibiting a weak signal of selection toward the A. m. unicolor background, with P values of 0.
One current limitation of our study is that only 8,835 (57. 7%) out of the 15,314 A. mellifera genes reported in the Ensembl database have GO annotations. As a result, only 25 (58%) of the 43 genes identified in significant regions following our selection analyses are annotated. This greatly limits the extent of our analysis. However, as the reference genome assembly and annotation improves, it may be possible in the future to put into context some of the genes identified that currently lack annotation. Given the topography of the island, its diversity of microhabitats, and variations in rainfall and wind speed between the eastern and western halves, it might be interesting to consider that local adaptation can occur within its mosaic of different landscape patches. Such adaptation might be observed following the analyses presented here if clusters of samples shared haplotypes and their sampling habitats shared some if not all environmental variables.
Acknowledgments This work was supported by the SeqApiPop programme, funded by the  FranceAgriMer  grant  14-21-AT  and by a grant from the  INRA Departement de Genetique Animale  ( INRA Animal Genetics division ). Sequencing was performed in collaboration with the  GeT platform, Toulouse (France) , a partner of the National Infrastructure France Genomique, thanks to support by the  Commissariat aux Grands Invetissements  ( ANR-10-INBS-0009 ). Bioinformatics analyses were performed on the GenoToul Bioinfo computer cluster. Honeybee sampling in the Indian Ocean Islands was funded through the projects e-PRPV and EPIBIO-OI funded by the  European Union (ERDF) , the  Region Reunion and CIRAD. Drone samples were kindly provided by  Dr Cecilia Costa , from the  Council for Agricultura Research and Economics Honey Bee and Silkworm Unit, Bologna, Italy ;  Dr Ales Gregorc  from the  Kmetijski In stitut Slovenije  ( Agricultural Institute of Slovenia );  Kaspar Bienefeld  from the  Institute for Bee Research Hohen Neuendorf ;.
Image processing and analysis. Confocal stacks were analysed using Morpho-GraphX (ref. 40). Signal from layer one (L1) of the SAM was projected onto a 2. 5D surface representing the outer surface of the 3D meristem. Signal from cell membranes was used to segment the projected surface into individual cells. Since L1 thickness was consistent (Supplementary Fig. 1a, b ), outer surface area was found to be a good proxy of cell volume (Supplementary Fig. 1c ). Lineage information was manually annotated through pairwise comparisons of consecutive time points using lineage tracking tools, then compiled along with outer cell surface area in an SQLiteMan database. Developmental stages were identified according to morphological landmarks and timecourse information; the central zone was estimated as the area between outgrowing primordia, p1 was identified as the first primordia in which PIN1 veination was identified, primordia 2-10 were numbered according to the assumed order of phyllotaxy and size of organ, incipient primordia i1-i3 were predicted relative to the position of the outgrowing primordia.
Statistical analyses were performed in R. For cell size comparisons many cells were measured from each stem, therefore distributions were analysed using a generalized linear model, including stem as a random factor. To evaluate whether the data met the assumptions of the model, diagnostic plots were used to confirm that the residuals exhibited homogeneity, normality and independence. Cell cycle length and phase length were calculated as a stem average, and compared by T-test for mutant versus wild type analysis and by ANOVA for organ stage analysis. Sample sizes were determined based on the effect sizes observed in pilot experiments. Any stems that were damaged during the time course were excluded from analysis. Generation of pH4::DB-VENUS reporter. The DNA construct was built using a three-way Gateway (Invitrogen) reaction according to the manufacturer's instructions.
Immediately after imaging, stems were fixed by incubating in a solution of 3. 6% formaldehyde, 0. 01% Triton X100 in PBS (pH 7) for at least 1 h. Stems were rinsed three times in water before performing the Click-iT (ThermoFisher Scientific) detection reaction. For efficient detection  56  stems were incubated for one hour in detection solution one (10 mM Alexa 488 Azide, 100 mM Tris pH 8. 5) followed by 30 min in detection solution two (10 mM Alexa 488 Azide, 100 mM Tris pH 8. 5, 1 mM CuSO 4 , 100 mM Ascorbic Acid). One transition model implementation. Cell division models were run iteratively in MATLAB. Each time step (t), equating to 1 h, cell size is recalculated according to the growth rate, g (1), then CDK activity is produced according to the production rate pCDK (2). Size t n ð Þ ¼ Size tðn À 1Þ þ Size tðn À 1Þ ÂgÂðt ðnÞ À t ðn À 1Þ Þ À Á ð1Þ CDK t n ð Þ ¼ CDK tðn À 1Þ þ pCDKÂðt ðnÞ À t ðn À 1Þ Þ À Á ð2Þ Total CDK activity is compared to the CDK threshold value T Division.
Acknowledgements We would like to thank  Teva Vernoux ,  Christophe Godin  and  Meirion Jones  for advice and discussions,  Pradeep Das ,  Sophy Chamot  and  Geraldine Brunoud  for training in meristem dissection and imaging and  Angela Marchbank  and  Joanne Kilby  for expert technical support. The project was supported within the  ERA-NET in Systems Biology (ERA-SysBio  þ ; project iSAM) funded with support of the  European Commission , by the  BBSRC  (grants  BB/I004661/1 ;  BB/J009199/1 ) and by the  Cardiff University Synthetic Biology Initiative.
Nuclear localization signal prediction To identify NLS, we used NLStradamus (http://www. moseslab. csb. utoronto. ca/NLStradamus/) [74] , SeqNLS (http://mleg. cse. sc. edu/seqNLS/)  [75] , and NLS-Mapper (http://nls-mapper. iab. keio. ac. jp/cgi-bin/NLS_Mapper_form. cgi) [76]  predictors. For all predictors, we used default settings. We added a 60 amino-acids N-or Cterminal cut-off for the predictions by NLS-Mapper since it is the only one able to predict bipartite NLSs which are usually found in the 60 first or last amino acids of a sequence. We therefore only kept bipartite NLSs of aaRSs fitting this criteria This program is better suited for prediction purposes since instead of a simple ''nuclear or not nuclear" output, it ranks protein localization on a scale from exclusively nuclear (8-10), partially nuclear (7-8), dual-localized cytosol-nucleus  (3) (4) (5)  or exclusively cytosolic (1À2).
Mass spectrometry analysis and identification of cytosolic aaRSs in yeast mitochondrial extracts We performed yeast mitochondria purification as explained in the Section 4. 3. 1 and recovered pure mitochondria at the 32-60% interphase from a cushions gradient (Fig. 1A ). In order to identify mitochondrial proteins, mitochondria were disrupted (see Section 4. 3. 1) and soluble proteins were subjected to mass spectrometry (see protocol below). For analysis, we carried out 3 different injections, two of 90 min (one regular and one using restricted mass ranges of 400-800 m/z and 800-1250 m/z, termed Frac. ) and one of 120 min. These features allowed us to identify 905 (90 min. ), 982 (90 min. Frac. ) and 926 (120. min) mitochondrial proteins respectively (Fig. 1B ). We describe here the detailed procedure for protein digestion and mass spectrometry analysis as well as data analysis.
Database search and data analysis Raw data are first converted to Mascot Generic File format (. mgf) and searched against the yeast S. cerevisiae database supplemented by a decoy database (reverse sequences). The database search algorithm used is Mascot (version 2. 2, Matrix Science, London, UK) through the ProteinScape 3. 1 package (Bruker Daltonics, Leipzig, Germany). Peptide modifications allowed during the search are: N-acetyl (protein), carbamidomethylation(C) and oxidation (M). Mass tolerances in MS and MS/MS is set to 20 ppm and 0. 5 Da, respectively. Two trypsin missed cleavages sites is allowed. Peptide identifications obtained from Mascot is validated with a protein FDR < 1%, using the Protein Assessment tool from ProteinScape. Identified proteins are assessed by spectral count. The aaRSs identified in the mitochondrial extract analyzed by three different injections methods are presented in Fig.
Acknowledgements. 102 Appendix A. Supplementary data. 102 References. 103 Acknowledgements The work was supported by the  French National Program Investissement d'Avenir  administered by the '' Agence National de la Recherche'' (ANR-France), ''MitoCross'' Laboratory of Excellence (Labex ), funded as  ANR-10-IDEX-0002-02  (to H. D. B, J-O. D. C), the  University of Strasbourg  (H. D. B, E. M), the  CNRS (B. S, S. F) , the  INSERM  and the  Université de Strasbourg  through the  IDEX 2015 Attractivité  (to S. B), the  Agence Nationale de la Recherche  - ANR-13-BSV2-0004  to S. F and  AFM-Téléthon -AFM-SB/CP/2013-0133/16551  (to S. F), the  Minsitère de l'Education Nationale, de la Recherche et de l'Enseignement Supérieur (G. B, S. D) , the  Association pour la Recherche sur le Cancer  (D. L) and the  ANR  [ ANR-10-LABX-0036_NETRNA ] (L.
DAPI can be added to the secondary antibody solution at a concentration of 300 nM. -Finally, after two final washes in PBS, a drop of pre-warmed (50 °C) elvanol (Poly(vinyl alcohol) -341584 Sigma-Aldrich) is added to each spot and the slide sealed with a coverslip. Cells are then visualized using a confocal laser scanning microscope and fluorescence can be quantified with the ImageJ software. Compartment Protein marker * Antibody Nucleus Lamin B1 Abcam ab16048 Plasma membrane Cadherin Abcam ab6528 Endoplasmic Reticulum Calnexin Abcam ab22595 Calreticulin Abcam ab2907 GRP-78 Abcam ab21685 Golgi GM130 Abcam ab31561 Giantin Abcam ab80864 TGN46 Abcam ab2809 Mitochondria COX IV-1 Abcam ab14744 Cytochrome c Abcam ab13575 Endosomes Early endosome antigen 1 Abcam ab2900 Ras related protein Rab-5A Abcam ab18211 Ras related protein Rab-7a Abcam ab126712 Lysosomes LAMP-1 Abcam ab24170 LAMP-2 Abcam ab18529 Autophagosomes MAP1A/MAP1B LC3 A Abcam ab52768 Peroxisomes Catalase Abcam ab16771 * Protein marker names are recommended names or short names from the UniProt database.