dataesr/scientific-paragraphs-categorization huggingface dataset:
In all, 801,900 plant occurrences are already present (without fossil specimens). The FLOTROP database will increase the GBIF base by 43% for the area as a whole. Several variables are presented for each occurrence: Regarding the way data are downloaded on the GBIF site (Source data or GBIF archive), some numbers presented here may vary (especially for the taxonomic description). The number here corresponds to the source data file. (1) Basis of record: All the surveys were made based on human observation. On some occasions, herbarium samples were collected and were conserved in different herbariums, such as the ALF herbarium  17  (http:// publish. plantnet-project. org/project/herbieralf). (2) The date of the observation (event Date): All the surveys are dated. However, for some surveys only the year was known.
In this case, the date of the survey is 1st January of that year. Similarly, for some surveys the month and the year of the survey were known but not the day, so in this case the date of the survey is the first of the month. Figure  1   1. Distribution between the different countries of plant occurrences in the FLOTROP and GBIF databases. The percentage due to the inclusion of FLOTROP in GBIF is calculated in the last column. for Egypt and 197% for Algeria. The contribution of FLOTROP is low for eastern Africa (an increase of only 3% of the GBIF data for Ethiopia) and for humid tropical western Africa. In tropical western Africa, the main ecosystem is tropical rainforest and FLOTROP only contains plant observations made in open ecosystems. (4) The coordinates of the observation (decimal Latitude and decimal Longitude): All the surveys are geotagged with an accuracy to the minute (around 1.
83 km in these regions). The coordinates are written in decimal for the latitude and the longitude. The climate within these African countries is highly variable from desert to tropical forest in Chad, for example. The North-South gradient in that country is a good indicator of the annual rainfall, especially in western Africa (dry climate in the North and wet climate in the South). Table  3  and Fig. 2  show the plant species observation number in FLOTROP and GBIF for each parallel. Between the 11 th and 25 th parallel, the inclusion of FLOTROP in GBIF will at least double the data (for the 11th parallel) or increase the data 19-fold (21st parallel). FLOTROP will thus amount to unique data on the drylands of sub-Saharan Africa. All the coordinates are in WGS 84. Figure  3  is a map of the density of occurrence of these species.
(5) The name of species (scientific Name and taxon Rank): In all, 4,372 species are presented in the FLOTROP database, of which 64 species have more than 1,000 occurrences in the base. Figure  4  shows the number of occurrences for the 19 species that have more than 2,000 occurrences in the FLOTROP base and the number of occurrences that were previously in the GBIF base for the same region. Most of the species are at species level some only at genus level. (  6     (8) Occurrence Remarks: We add the original survey methods in remarks. Survey protocols are classed in 8 different categories. These protocols are coded from 1 to 8. Most of the data come from method 2, Phytosociological Weighted Surveys (262,270 occurrences). In all, 40,768 occurrences were obtained with Phytosociological Unweighted Surveys (method 1).
technical Validation Most of these field observations were connected by herbarium specimens validated by the IEMVT botanist, J. P. Lebrun, and now accessible in the CIRAD herbarium in Montpellier 17 (about 50,000 specimens). Secondly, all the surveys included in the database were first assessed by Philippe Daget, who entered the surveys in the base according to their quality. For the data obtained for FLOTROP, we carried out several tests to eliminate some encoding errors (see methods). We removed 90,000 occurrences for the upload, which will be corrected at a later date, based on expert knowledge. The quantity of data is also a factor of quality: indeed, one species occurrence can be compared with the whole set of occurrences for the same species, to find out the distribution of any outliers.
Usage Notes The data presented here are a collection of multiple data sources, so they need to be analysed, using appropriate statistical tools. Users should remember that plant identification and taxonomy in Mauritania, Senegal, Mali and Niger are based on old Floras. Despite the large number of observations of this dataset and the updated nomenclature used, many species are only known by one or two specimens, sometimes without all the organs that are useful for good identification. New collections and observations are absolutely needed to confirm the identity or ecology of some complex taxa. The occurrence data can be used in at least two types of studies:  (1) Species distribution: Despite the fact that these data are not a full inventory of each country, for the main reason that they focus on herbaceous vegetation used for livestock or on potential economic species, they can be used to update national inventories or checklists, as in new Flora projects for Chad or Burkina Faso.
(2) Species ecology: These data can be used to understand the ecology of a species and its spatial and temporal dynamics. Many different models and statistical analysis can be carried out to study species  18, 19. For the spatial distribution of the species, users must be aware that the climate varied considerably in the West Africa over the period  7,8 (1950-2010)  and they must therefore include temporal changes in their spatial analysis model. (3) Biodiversity studies: These data can be used to assess some biodiversity indicators, such as alpha, beta and gamma diversity. Open ecosystems are composed of herbaceous and tree species. However, in the FLOTROP database some surveys were only carried out on herbaceous species, or on tree species. However, this information was not specified during the collection of the data.
The name of the observer (recorded By): The authors of these different surveys are specified in the metadata. Only the last names of the authors are indicated in the database. In a few surveys, the author was not identified and was coded by an x. The FLOTROP database contains data obtained from 222 known different authors. The two authors who contributed most to the base are Jean Valenza and Bernard Toutain, with almost 30,000 observations in the base. (7) Flotrop survey number (record Number): In the FLOTROP database, the observations have been regrouped per survey (observation of species together). The observations are in fact regrouped in 26,932 different surveys.
) The name of the observer (recorded By): The authors of these different surveys are specified in the metadata. Only the last names of the authors are indicated in the database. In a few surveys, the author was not identified and was coded by an x. The FLOTROP database contains data obtained from 222 known different authors. The two authors who contributed most to the base are Jean Valenza and Bernard Toutain, with almost 30,000 observations in the base. (7) Flotrop survey number (record Number): In the FLOTROP database, the observations have been regrouped per survey (observation of species together). The observations are in fact regrouped in 26,932 different surveys.
Regarding the distribution by country, in Table1, we have presented the number of occurrences in the FLOTROP database by country and by the data currently available in the GBIF database. The contribution of FLOTROP is quite consequent for Sahel countries (increasing by 1000% the data available for Chad, 600% for Mali and Mauritania, 500% for Niger, 300% for Senegal and 100% for Burkina Faso and Sudan (including South Sudan)) and for two small countries that are not well documented, Cape Verde and Djibouti. For the southern area of the Saharan countries, the contribution of FLOTROP is also substantial, increasing the GBIF data by 12%.
Assignment of transcriptomes to cell identities To generate a gene expression atlas of the developing mouse neural tube, we used droplet-based scRNAseq (10x Genomics Chromium) of microdissected and dissociated cervical and thoracic regions of the spinal cord from mouse embryos between e9. 5 and e13. 5  (Fig 1A). We generated two replicates per timepoint for e9. 5, e10. 5 and e11. 5. To compensate for the increase in size and cell number of the spinal 3 cord at later developmental stages, three replicates per timepoint were generated for e12. 5 and e13. 5  (Fig S1A). In total, 41,025 cells were sequenced. After applying quality filters, a dataset of 38976 cells was retained for further analysis (7476 cells at stage e9. 5, 6769 cells from stage e10. 5, 6634 cells from stage e11. 5, 8711 cells from stage e12.
5, 9386 cells from e13. 5). The average number of UMIs and detected genes in these cells was similar between all samples analysed (Fig S1B  , C ). To assign identities to the cells in the dataset, we first allocated cells to different tissues based on the combinatorial expression of a curated gene list  (Fig 1B). To this end, we used Sox2 to identify spinal cord neural progenitors; Tubb3 and Elavl3 as markers for spinal cord neurons; Fermt3, Klf1, Hemgn and Car2 for blood cells; Foxc1, Foxc2, Twist1, Twist2, Meox1, and Meox2 for early mesodermal cells; Myog1 for myoblasts; Sox10 and Sox2 for neural crest cells; Tubb3, Elavl3, Sox10, Six1 and Tlx2 for neural crest neurons; and Krt8 for skin cells. This allowed the classification of 33754 cells, 87% of the total cells. The remaining cells did not fall into any of the categories, potentially because of poorly resolved transcriptomes or because these cells were derived from other tissues of the embryo.
Further subclustering of the unclassified population did not reveal cell populations with spinal cord identity. We also estimated the rate at which the individual transcriptomes might represent more than one cell by assessing the proportion of transcriptomes displaying a gene expression signature of both neural and mesodermal tissue. This indicated that ~1% of the transcriptomes were from a mixture of neural and mesodermal cell types (Fig. S1D ). Visualizing the resulting dataset with tSNE dimensionality reduction revealed the separation of cells into multiple groupings that reflected the anticipated cell types (Fig  1C ). Cells from replicate embryos, whether of the same or different sex, tended to intermingle within the embedding, suggesting minimal batch variation between embryos (Fig S1E  , F ).
By contrast, there were obvious differences in the proportions of cell types at the different timepoints. Most neurons were contained in the datasets obtained at later developmental stages  (Figs 1C and S1E) , consistent with the proportion of neurons increasing in the spinal cord over time  (Kicheva et al. , 2014). Conversely, most of the neural crest and mesoderm cells originated from e9. 5 and e10. 5 embryos  (Figs 1C and S1E). This was due to the difficulty of cleanly dissecting the neural tube without accompanying adjacent tissue at these developmental stages. As it was our aim to construct a spatiotemporal gene expression atlas of the developing spinal cord, we focused on the cells identified as spinal cord neural progenitors and neurons. Extensive characterization of the different populations of neural progenitors and neurons in the spinal cord has revealed a variety of molecular markers that specifically or combinatorially define different domains of progenitors and classes of neurons  (Alaynick et al.
, 2011; Lai et al. , 2016). Taking advantage of this, we first generated a binary 'knowledge matrix' in which each progenitor domain and neuronal class is defined by the expression of a characteristic marker set (see Table  S1 ). Notably, a specific cell type can be defined by more than one combination of marker gene expression patterns, which helps to capture certain subpopulations, such as early neurons that still partially express progenitor markers and have not yet fully activated their neuronal gene expression programmes. We then binarized the expression profiles of the defined marker genes in the transcriptome data. Cells in the dataset were then assigned an identity from the knowledge matrix, using the minimal Euclidean distance between each transcriptome and cell type classes. Plotting the 4 resulting average levels of marker gene expression for the assigned cell identities revealed the well-known patterns of progenitor-and neuron-specific gene expression patterns.
To test if the proportions of cells recovered from the assignment of cell identities correctly recapitulated the growth dynamics of the spinal cord, we compared with the quantification in  Kicheva et al (2014). To this end we classified cells in our dataset as FP, p3, motor neuron (MN) progenitors (pMN), and the broader domains pI (composed of p0, p1 and p2) and pD (including pd1, pd2, pd3, pd4, pd5, pd6) (Fig. 2E ). This revealed a close match in the dynamics predicted by the two independent datasets. The correspondence between the data allowed us to extend the analysis beyond the E11. 5 end point of  Kicheva et al (2014). Between e11. 5 and e12. 5, the rate of neurogenesis in the spinal cord appears to slow, leading to a stabilization of the proportion of progenitors in all domains (Fig 2B  , E ); 24h later, at e13.
This raises the possibility of a molecular adhesion code that distinguishes inhibitory and excitatory interneurons in the spinal cord, which might contribute to synaptogenesis during circuit formation. The availability of transcriptome data from multiple time points highlighted the dynamics of gene expression in different cell types. To test whether the dataset could be used to correctly infer spatial and temporal gene expression patterns, we focussed on Cldn3. Cldn3 encodes for a tight junction component  (Morita et al. , 1999)  and was predicted to be specifically expressed in MNs before, but not after, e11. 5  (Fig 3B). We performed immunofluorescence assays for Cldn3 on transverse spinal cord sections from e10. 5-e12. 5 embryos. As predicted by the bioinformatic analysis, Cldn3 expression at e10.
Increasing the number of cells sampled, particularly at e12. 5 and e13. 5, and improving the sensitivity of the methods to increase the complexity of the analysed transcriptomes might reveal further cell type diversity. In addition, neuronal subtypes vary along the rostral caudal axis of the neural tube  (Hayashi et al. , 2018; Philippidou and Dasen, 2013; Sweeney et al. , 2018) , but to ensure the feasibility of sample collection and data analysis, we restricted our analysis to cervical and thoracic regions of the neural tube. Hence, our dataset will not represent cell types unique to lumbar and sacral spinal cord. Nevertheless, despite these limitations, we were able to detect all the progenitor populations and major neuronal subtypes described in cervical and thoracic regions of the spinal cord.
This provides a molecular basis for the further partitioning of V2a and V3 neurons into subpopulations as well as implicating these TFs in the generation of specific neuronal subtypes and providing genetic access to investigate their function. Importantly, experimental assays corroborated the transcriptome predictions, suggesting that the atlas is a generally faithful representation of gene expression in the spinal cord. Further mining the dataset will likely implicate additional TFs in the neural tube gene regulatory network and refine the molecular classification of cell types. Alongside information on the complement of TFs expressed by individual cells, the analysis also revealed the wider molecular repertoire of each cell type. This substantially extends our knowledge of the molecular profile and biological functions of neural tube cells.
We illustrated this by demonstrating that the neurotransmitter class of each neuronal subtype, implied by the transcriptome, matches the previously experimentally observed neurotransmitter type and we document the expression of a set of cell adhesion molecules that distinguishes excitatory and inhibitory interneurons (Fig  4A ). As anticipated for a complex developing tissue, gene expression profiles change rapidly over time. Cldn3, which encodes a component of tight junctions, is expressed in progenitors of MNs up to e10. 5 and then downregulated (Fig 3B  -D ). The functional significance of this remains to be determined. Considerable additional analysis will be necessary to explore the full potential of the dataset and advance our understanding of the molecular mechanisms of neural development and the allocation of cell type identity.
Analysis of the single cell transcriptome dataset revealed a set of gene expression modules activated synchronously at characteristic developmental timepoints in multiple neuronal classes. This raises the possibility of a coordinated temporal transcriptional programme operating in the neural tube that subdivides neuronal classes based on their time of generation. Such a mechanism would operate in parallel to the spatial patterning mechanisms and provide an opportunity to increase the molecular and functional diversity of cell types generated in the neural tube. In addition, coordinating the timing of the generation of sets of neurons might facilitate mechanisms that control the specific settling patterning and connectivity of different neuronal subtypes. Further analysis of the sets of neurons generated at different timepoints will be necessary.
Alignment and preparation of scRNA-seq data Demultiplexing, alignment, filtering, as well as barcode and UMI counting were performed using Cell Ranger (version 2. 2. 0, 10X Genomics, under default settings), which uses STAR aligner. The aggregated gene-barcode matrix was normalized by subsampling reads from higher-depth libraries until all samples had an equal number of confidently mapped reads per cell. Further analysis was performed using R.
Data analysis We partitioned the cells into 13 progenitor and 12 neuronal populations using the following two-step strategy. First, we determined the global cell identities by associating each cell to the closest target population state defined by a list of known marker genes (Figure  1B ). The binarized levels were obtained using a threshold of 2 UMI counts and cell-to-target distances were calculated by Euclidean distance. Second, each progenitor and neuronal population was further partitioned using the same approach using the list of marker genes shown on Figure  1D, E.
6. 4) differentialGeneTest function with gene level distributions modeled as negative binomial distributions with fixed variance (option expressionFamily=negbinomial. size(), as recommended for UMI datasets). Each gene was then associated with the population combination having obtained the highest likelihood. The gene list was trimmed using significance (P value < 10 -9 ) and fold-change (log2-fc > 2) cutoffs. We also ensured that the remaining genes had a minimal average level of expression (0. 2 UMI) and a ratio of expressed cells (10% in progenitors, 8% in neurons) in the positive samples. The gene sets highlighted in Figure  3A  were obtained by intersecting the differentially expressed genes with the following GO terms: GO:0098609 for cell-cell adhesion and GO:0006836 for neurotransmitter transport.
For each cell, we obtained an average expression level per module by averaging the z-scored log-transformed expression levels of all genes belonging to the module. These gene module averaged levels were then binarized independently by using a parameter-free adaptive thresholding method (R function binarize. array() from the ArrayBin package). A cell was considered to express a gene module if the associated Boolean value was true. Modules which were expressed in fewer than five cells were excluded. c. A second filtering criterion was then applied to test whether cells were expressing the gene module with consistently high levels over most of the genes in the module. We binarized the z-scored log-transformed expression levels of all the remaining genes independently. Then, for each module, we calculated the ratio of Boolean values in cells expressing the module (as defined in b.
Neuronal trajectories and Pseudotime reconstruction To project the whole neural dataset into a space that reveals cell state transitions during neurogenesis and progenitor maturation, we aimed at identifying a set of genes with similar dynamics in each dorsalventral domain. In order to reduce any bias toward overrepresented populations, we used a resampled dataset containing approximately the same number of cells per timepoint and dorsal-ventral domain. The unbiased gene module identification pipeline described above was used to identify 200 gene modules of concerted patterns of expression in the resampled dataset. Among them, 4 modules were retained as they comprised, respectively, the pan-progenitor marker Sox2, the pan-neuronal marker Tubb3, the early progenitor marker Lin28a and the late progenitor marker Fabp7 (114 genes).
An extra step was taken to ensure that no dorsal ventral bias occurred in the remaining genes by testing for differential expression in response to their DV position (Monocle 2. 6. 4, differentialGeneTest function, p-value < 5e-15). 14 genes were excluded and 100 retained (Fig. S6A ). After taking the log of the normalized UMI counts ("median ratio" normalization method), Principal Component Analysis was then applied to the 100-dimensional resampled dataset. The resulting PC1-PC2 plane was then populated by multiplying the whole (lognormalized) neural dataset by the eigenvector matrix. Neurogenic pseudotime orderings of each cell were mapped to the PC1 coordinates. Finally, we reconstructed, independently for each domain, smoothed profiles of gene expression along pseudotime by fitting spline curves (Monocle 2.
6. 4, genSmoothCurves with 3 degree of freedom). Each profile obtained with less than 20 expressed cells was set to zero. (A) Brachial (orange) and thoracic (blue) regions of the spinal cord of mouse embryos between stage e9. 5 to e13. 5 were dissected, dissociated and sequenced using the Chromium 10x Genomics system. (B) Partitioning of cells to specific tissue types based on the combinatorial expression of known markers. (C) t-distributed Stochastic Neighbor Embedding (tSNE) plot of the entire dataset based on transcriptional similarity using the same markers as (B), coloured by assigned cell type. Neural progenitors (yellow) and neurons (orange) were selected for further analysis. (D-E)  Bubble charts depicting the expression of markers used to identify different dorsal-ventral domains of progenitors  (D)  and neuronal classes  (E).
The size of the circles indicates normalized gene expression levels. Genes selected for cell categorisation are coloured; grey circles correspond to markers not used for the selection of a specific population. A, B ) The data are normalised to the sum of neurons and progenitors detected at each time point. (C,D)  show fractions within progenitors (B) or neurons  (D). (E)  Comparison of the ratio of progenitors from our dataset (solid lines) with those of  Kicheva et al. (2014; dashed lines). The broader domains pI and pD are composed of p0-p2 and pd1-pd6 progenitors, respectively. (D). Hierarchical clustering was performed using the indicated gene modules (Table  S2 ). A subset of genes included in the modules is indicated on the righthand side. (A) Two-dimensional PCA projection of all neural cells, shown on a hexagonal heatmap, from a 100dimensional space defined by genes expressed in all DV domains during progenitor maturation and neurogenesis.