dataesr/scientific-paragraphs-categorization huggingface dataset:
This study aims at investigating the capabilities of PolSAR parameters extracted from different polarimetric decompositions for forest height estimation by combining them with LiDAR-derived height using different machine learning algorithms and extracting the training samples from a subset of the scene. This allowed investigating the dependency of the model performance on the training data. Multiple machine learning algorithms including Random Forest (RF), Rotation Forest (RoF), Canonical Correlation Forest (CCF) and Support Vector Machine (SVM) are used to extrapolate LiDAR-derived height using PolSAR parameters. This allowed checking PolSAR parameters capabilities for forest height estimation independent of the type of machine learning algorithms. The specific objectives of this paper are to 1) quantify the accuracy of forest canopy height retrieval integrating PolSAR-decomposition-based components with LiDAR samples; 2) explore the potential of PolSAR components for estimation of forest height in the absence of interferometric SAR measurements; 3) investigate the dependency of model performance on the training data and 4) explore the sensitivity of the input features to forest canopy height estimation.
Radar acquisitions Fully polarimetric SAR data were acquired by the NASA's UAVSAR, which is an airborne instrument equipped with an L band SAR and operates at 1217. 5-1297. 5 MHz  (Lavalle and Hensley, 2012). The UAVSAR dataset for Lope was collected in February 2016. The nominal flight altitude was 12. 5 km, which allowed a data coverage of about 22 km wide. The incidence angles range from θ = 25 • to θ = 65 • from near to far range. The UAVSAR polarimetric single-look-complex (SLC) data has ground range and azimuth resolutions of 2. 5 m and 1 m, respectively  (Lavalle and Hensley, 2012, Hensley et al. , 2009).
LiDAR acquisitions LiDAR data were acquired by the Land Vegetation and Ice Sensor (LVIS) LiDAR sensor  (Anderson et al. , 2008). LVIS is an airborne mounted sensor on the NASA Langley KingAir B-200. LVIS nominal footprint diameter is 25 m with 9 m separation along track. The LVIS waveforms allow a retrieval of various metrics and relative height ranges. In this study we used the LVIS relative height 100 (RH100) which refers to forest top canopy height. RH100 was produced and distributed by the GSFC LVIS team. The raster image of RH100 datasets was provided in slant range geometry with a dimension of 600x1000 (pixels). LiDAR data was co-registered with UAVSAR image using location information stored in the LVIS metadata  (Pourshamsi et al. , 2018a). The original dimensions of UAVSAR SLC image were 3000 × 29000 pixels.
The UAVSAR image was multi-looked using a window of 5 × 29 to have it in the same geometry of the LiDAR image. The LVIS and UAVSAR images cover the same area (Fig. 1 ). Nevertheless, for the data integration, only 5000 samples (less than 1%) of the LiDAR collected across a small subset of the scene are used for training the algorithm; the remaining pixels are used for validating the results.
Desision tree ensmble DT ensemble consists of a series of T trees {ξ 1 (X), …, ξ T (X)} where {X, Y} = {(x 1 , y 1 ), …, (x n , y n )} are the total number of n training samples. × denotes D-dimensional features (F) obtained from PolSAR parameters and ancillary datasets and y is the forest top canopy height (RH100) acquired from LiDAR. In this work, D equals to 400 which is obtained by applying a 5 × 5 window to 16 input features. The final output is obtained by averaging all tree predictors. To construct the diverse decision trees, three ensemble methods, RF  (Breiman, 2001) , RoF  (Rodriguez et al. , 2006)  and CCF  (Rainforth and Wood, 2015)  are applied.
Support vector machine For SVM classifier, the following regression function g(x) is learned: g(x) = 〈W, ∅(x)〉 + b (4) where w is the weight vector in the kernel feature space, ∅(x) is the kernel feature mapping of x, and b is the bias. The solution is obtained by solving a convex optimization problem. Where LiDAR and PolSAR components are not linearly separable, a non-linear modeling can be achieved by means of the 'kernel trick', which maps the data into a higher dimensional space. We used a Radial Basis Function (RBF) kernel to model the non-linear relationship between the RH100 and the polarimetric SAR features. The kernel's bandwidth (h) and regularization parameter (λ) control the RBF. The possible overfitting issue is avoided by deciding optimal values for the parameters using a grid search method with a 10-fold cross validation approach  (Smola and Schölkopf, 2004, Pelckmans et al.
For box 3 (20-40 m), CCF and SVM performed slightly better in compare to RF and RoF. While CCF resulted in the scatterplot with the least dispersion, it is clear a saturation point for the upper lobe. This saturation is less evident in RF and RoF; and SVM seems to work better, generally. The reason for the saturation of RF, RoF and CCF, is because RF (and its derived algorithms) cannot predict values beyond the maximum and minimum values found in the training data, which is clearly seen in the scatterplots, whereas SVM can extrapolate values, although it is patent the large uncertainty of estimates outside the range of values of the training data  (Smola and Schölkopf, 2004, Pelckmans et al. , 2005). In general, different machine learning algorithms were applied to confirm the potential of this approach for estimating forest height from PolSAR parameters.
The best results were achieved when the training samples are selected from the heterogeneous part of the scene which includes vegetation with different height ranges and density. Therefore, for successful height estimation over a heterogeneous forest, the input features should convey a set of different features within the study site. Different machine learning algorithms were used for the experiment, RF, RoF, CCF and SVM. Whilst the results are pretty similar, SVM performed slightly better. The results confirm that it is possible to estimate forest height using PolSAR parameters together with a small portion of LiDAR samples in the absence of interferometric measurements. This presented way of combining PolSAR parameters and LiDAR for forest height estimation is beneficial for the upcoming and current data from spaceborne SAR and LiDAR missions (BIOMASS, NISAR and GEDI) which provide a wealth amount of polarimetric SAR and LiDAR data at global scale.
In recent years, our knowledge of the diversity of microbial communities has increased considerably, through the development of whole-community metagenomic sequencing methods, which have fuelled many studies on the microbial ecology of the phyllosphere  (Yang et al. , 2003) , rhizosphere  (Øvreås et al. , 1998; Zhang et al. , 2021) , and, more recently, the 'residue sphere'  (Kerdraon et al. , 2019b). However, these ecological studies cannot completely dispense with culture-dependent approaches. The isolation and culture of fungal and bacterial species are indispensable as they complement taxonomic databases through a single-strain approach (from isolation to molecular characterization), making it possible to test whether the interactions between micro-organisms predicted by metagenomic sequencing (e.
, 2007)  implemented in DADA2. ASVs derived from the 16S rRNA gene were classified with the Silva 132 taxonomic training data (silva_nr_v132_train_set. fa. gz). ASVs that were unclassified at phylum level or affiliated to chloroplasts and mitochondria were removed. Sequence contaminants were identified with decontam package version 1. 4  (Davis et al. , 2018) , using the "prevalence" method at a threshold of 0. 01. Finally, ASVs were filtered on the basis of their relative abundances per well. Only ASVs with a relative abundance greater than 1% per well were retained. A neighbor-joining phylogenetic tree was built with the phangorn R package v 2. 5. 5  (Schliep, 2011) , using a multiple sequence alignment obtained with the DECIPHER R package v. 2. 12. 0  (Wright, 2015).
The amplification parameters were as follows: initial denaturation at 95°C for 5 min, 35 cycles of 95°C for 60 s, 60°C for 30 s, and 72°C for 60 s, and a final elongation at 72°C for 10 min. We checked the quality of the PCR products by electrophoresis in 2% agarose gels. The PCR products were then subjected to single-strand sequencing by the Sanger method (Eurofins, France). Chromatograms were checked with DNASTAR lasergene sequence analysis software  (Burland, 2000)  to generate the sequences used for molecular identification. The taxonomic assignment of each sequence was performed with a naïve Bayes classifier on the UNITE7. 1 database, as for the culture-independent approach (metabarcoding). Phylogenetic tree for the ITS region of fungal strains -The ITS sequences of each of the 424 fungal strains were blasted against each other to prevent redundancy in subsequent analyses.
We obtained 131 unique sequences, which were considered to be 'representative ASVs' of the fungal collection. A phylogenetic tree of these ASVs was built with the "build" function of ETE3 v3. 1. 1  (Huerta-Cepas et al. , 2016) , as implemented on GenomeNet (https://www. genome. jp/tools/ete/). An alignment was generated with MAFFT v6. 861b, using the default options  (Katoh and Standley, 2013). The tree was constructed with FastTree v2. 1. 8, using the default parameters  (Price et al. , 2009). The values at the nodes were SH-like local support. Data were visualized with Interactive Tree Of Life (iTOL) v4  (Letunic and Bork, 2019).
Principal results for fungi Filtering of the collection data -The analysis of the cultivable fungal fraction led to the isolation and sequencing of 424 fungal strains; morphotypes were described from photographs for 171 of these strains (Fig. S1 ) and full ITS regions (ITS1-5. 8S-ITS2) were sequenced to assess the taxonomic diversity of the collection (Table  S2 ). A dataset of 131 unique ASVs was obtained by blasting the 424 sequences against each other. Description of the collection -The 131 ASVs were affiliated to 17 genera, with some left unclassified (Table  S2 , Fig. 5B ). The most prevalent strains isolated were affiliated to the genus Fusarium (50% of the strains, 37% of the ASVs). Comparison of metabarcoding and culture-dependent molecular identification -The metabarcoding analysis identified 894 ASVs belonging to 148 fungal genera  (Kerdraon et   ).
For technical reasons, the comparison of the results obtained by the two approaches was less straightforward than for bacteria, and fully comparable data would have been obtained had ITS1 been extracted alone from the long fragment. Indeed, the size of the genomic region amplified and sequenced for fungal strain identification in the culture-dependent approach (ITS1-5. 8S-ITS2 region) was larger and included the genomic region targeted in the culture-independent approach (ITS1 region only). Thus, the ITS1 region of one ASV obtained by metabarcoding could potentially display 100% identity with the IST1 of the sequenced ITS1-5. 8S-ITS2 region of several isolated strains. Overall, 33 of the 894 ASVs identified by metabarcoding were 100% identical to one or several ASVs from the dataset of the collection (Fig.
5B ). As an example of equivocal matches, one ASV detected by metabarcoding displayed 100% identity to eight ASVs of the genus Epicoccum from the collection. Conversely, several ASVs detected by metabarcoding identified displayed 100% identity to one ASV from one genus (e. g. Alternaria and Fusarium). The culture-dependent approach is complementary to metabarcoding, as three unidentified ASVs of the metabarcoding dataset were 100% identical to three ASVs corresponding to three strains from the collection.
This was the case for Lesptosphaeria (L. maculans), Plenodomus (P. biglobosus) and Oculimacula (O. yallundae), all of which are known to be pathogenic. However, a very large number of strains from different genera (e. g. Fusarium, Epicoccum, Boeremia, Botrytis, Mortierella, and Mucor) were frequently isolated, despite their low proportions in the metabarcoding analysis. Metabarcoding is, thus, not sufficient in itself, and isolation definitely provides added value for the accurate characterization of the fungal community in residues. Moreover, three ASVs from three strains from the culture collection matched three unidentified ASVs with 100% identity. The strains were not identified due to a lack of identity with sequences in databases, but it would be possible to characterize them phenotypically, by enzyme profiling or pathotyping, to obtain additional information about the residue-associated fungal community.
Some studies (e. g. Krehenwinkel et al. , 2019; Heeger et al. , 2018)  have suggested using long-read sequencing techniques for metabarcoding to improve taxonomic resolution, thereby providing a more accurate perception of the functional diversity of the residue microbial community. Indeed, although the ITS region generally performs well as a fungal barcoding marker, it is less efficient for some genera and it may be necessary to sequence several genes to identify a strain correctly  (Raja et al. , 2017). Even in this case, taxonomic identification is dependent on the presence of the corresponding sequences in international databases. For bacteria, the v4 region of 16S rRNA gene has poor discriminatory power at species level  (Callahan et al. , 2019) , but. CC-BY-NC-ND 4. 0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
Funding This study was supported by a grant from the  European Union Horizon   Framework 2020 Program (EMPHASIS program , agreement no. 634179 ) covering the 2015-2019 period, and was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr/),supported by  France Génomique National Infrastructure , funded as part of the  Investissement d'avenir program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). BIOGER benefits from the support of Saclay Plant Sciences-SPS supported by  Agence Nationale pour la Recherche  (contract  ANR-17-EUR-0007 ).
COVID-19 dataset We downloaded time series of confirmed COVID-19 cases (cumulative growth curves) from the Johns Hopkins University Center For Systems Science and Engineering (JHU-CSSE) GitHub repository (https://github. com/CSSEGISandData/COVID-19/) (Dong et al. , 2020). JHU-CSSE reports, for each day since January 22, 2020, confirmed COVID-19 cases at the country level or at the level of significant geographical units belonging to the same country, which we broadly defined here as 'regions' (e. g. US states, or China and Canada provinces; Supplement 1, Supplementary methods). Data referring to outbreaks occurring on cruise ships were not considered. The cumulative growth curves were carefully checked and obvious reporting errors (a few occurrences of temporary decreases in the cumulative number of cases) were corrected.
Our dataset included confirmed COVID-19 cases up to June 15, 2020. From this dataset, we selected data for all those regions in which local outbreaks were detected up to May 31, 2020 (see Local outbreaks and COVID-19 cases growth rates). Overall, we considered data from 159 countries. We considered sub-national level data for the all the countries of the world for which data were easily accessible from the original sources listed in the JHU-CSSE website (for a total of 17 countries; Table  S6 ). Our final dataset included information on 586 regions (Supplement 1, Fig. S2  and Supplementary methods).
Local outbreaks and COVID-19 cases growth rates To avoid the biases arising because of incomplete spread of the pathogen, our dataset included only those regions experiencing a local COVID-19 outbreak. Therefore, our results are unaffected by patterns occurring in regions where the pathogen showed a limited number of records (e. g. because of distributional disequilibrium, limited connections with other affected areas, or lack of reporting). The onset of a local COVID-19 outbreak event was defined as the day when at least 25 confirmed cases were reported in a given region. Visual inspection of growth curves showed that, in most cases, below this threshold the reporting of cases was irregular, or growth was extremely slow for prolonged periods. This approach also allowed us to exclude the first cases, often referring to individuals returning from foreign countries and not reflecting local transmission of the pathogen.
(2014). Lag and exponential phase duration, and rmax were computed through the R package growthrates  (Hall et al. , 2014). Mean and maximum daily growth rates were strongly positively correlated (Pearson's correlation coefficient, r = 0. 95, n = 586 regions), indicating that our growth rate estimates for a given region were highly consistent irrespective of the method used for calculations. By modelling the exponential phase, this approach allowed to focus on local transmission events occurring within the focal region. The average time interval between the first case and the onset of the exponential phase was 19. 5 days (SD = 11. 1 days), thus cases representing individuals returning from foreign countries likely have a negligible impact on our growth rate estimates.
Environmental variables We considered two climatic variables that are known to affect the spread of viral diseases: mean air temperature and specific humidity (water vapor pressure), which is a measure of absolute humidity. Previous studies showed that, for coronaviruses and influenza viruses, survival is generally higher at low temperature and low values of absolute humidity  (Lowen et al. , 2007; Shaman and Kohn, 2009; Tamerius et al. , 2013; Lowen and Steel, 2014; Kampf et al. , 2020; Yap et al. , 2020). For each region, we obtained the mean daily values for temperature (°C) and specific humidity (g/m 3 ) from the ERA5 hourly database (Supplement 1, Supplementary methods). The latency period of the infection, and the lag time between the onset of symptoms, PCR tests and publication of confirmed cases can be highly variable across patients and across areas of the world.
Stringency of containment measures For each region, we obtained an index of the overall stringency of COVID-19 containment measures adopted by local authorities in the corresponding country at the onset of a local outbreak (hereafter, stringency index). The stringency index was obtained by combining information for each country from two separate data sources (Supplement 1, Supplementary methods). This index simply record the number and strictness of government response measures, hence a higher stringency score does not necessarily imply that a country's response is more effective than that of other countries with lower scores  (Hale et al. , 2020). Nevertheless, the stringency index may be helpful to illustrate the timeline of interventions and to assess whether local governments' policy responses at outbreak onset had any impact on COVID-19 spread within a given region.
Non-linear relationships between climatic factors and ecological variables are frequent  (Legendre and Legendre, 2012) , and have also been suggested for relationships between SARS-CoV-2 occurrence and climate (e. g. Runkle et al. , 2020). As in exploratory plots we detected a clear non-linear relationship between r-values and climate variables, we included in models both linear and quadratic terms. Humidity, PM2. 5, population density, population size, health expenditure and region centrality were log10transformed to reduce skewness and improve normality of residuals. Regression models can be heavily affected by strong collinearity among predictors (|r| ~0. 70 or above)  (Dormann et al. , 2013). In our dataset, temperature and humidity showed a very strong positive correlation (Supplement 1, Fig.
S7  and Table  S1 ). We thus fitted separate models for temperature and humidity, and for different combinations of strongly correlated socio-economic predictors (Supplement 1, Supplementary methods and Table  S2 ). To assess temporal variation in the importance of different predictors on COVID-19 growth rates, we fitted LMMs considering regions experiencing outbreaks in different periods. Each LMM included data from regions experiencing outbreaks up to a given day. We started from regions experiencing local outbreaks up to February 27, the first day when local outbreaks occurred in at least 50 regions (n = 51 regions), and proceeded on a day-by-day basis until we included all regions experiencing outbreaks up to May 31, 2020 (n = 586 regions; see the cumulative curve in Supplement 1, Fig.
S4 ). The partial R 2 statistic (variance explained by each fixed effect, or semi-partial R 2 ) was taken as a measure of the importance of each fixed effect in each of these models. Furthermore, we assessed temporal variation of standardized regression coefficients for models fitted at different time points. Airport connections are expected to affect the first phases of the epidemic events, and we therefore tested the effect of region centrality in a model including data up to March 15, 2020 (Supplement 1, Supplementary results). To confirm the time lag period used for the calculation of temperature and humidity (30-days period vs. Δ14 days period) did not affect our results, we repeated analyses twice, first using the 30-days period data, and then using the Δ14 days period data. Climate variables calculated using the 30-days and the Δ14 days periods showed almost perfect correlation across regions (temperature, r = 0.
Acknowledgments The authors would like to acknowledge the funding of the  Agence Nationale de la Recherche (ANR)  under Contract No. ANR-18-CE05-0035-01 , the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 847593  and the Andra (the French waste management agency) under the  CEA/ANDRA  collaborative agreement n°CHR.
Data sources Most of the paintings we examined were obtained by searching the web using museum databases or grouped museums databases such as "Joconde" in France (Ministère de la Culture, France, http://www2. culture. gouv. fr/documentation/joconde/fr/pres. htm). The objective was to have the most reliable information on the artist behind each painting, the location, and the period of the work. The paintings (n = 73) were selected for their realism, and a sufficiently good quality image had to be available to enable species identification. Some museums were questioned directly to ensure the traceability of the painting and to obtain quality reproductions and copyrights.
Species selection, identification, and characterization The depicted species were identified by a panel of more than 10 specialists from three different institutions (see Acknowledgements). A protocol was determined by the panel in order to adopt the level of precision they deemed necessary. For each painting, every specimen or group of specimens of the same species was identified using a figure that corresponded to one line in a data frame that was filled in by each specialist to indicate presence (1)/absence (0). Finally, many of the presumed "species" identified were not determined at the species level and are identified in the text as taxon/taxa. They were all described using the appropriate taxonomic level following TAXREF nomenclature. Any representations of shells without their living organism inside, and any obviously mounted fishes such as pufferfish (Tetraodontidae), were excluded from identification because of their obvious provenance from worldwide collections (e.