dataesr/scientific-paragraphs-categorization huggingface dataset:
Uncertainty analysis We evaluated the uncertainty of our tree crown area mapping and carbon estimation in two ways. First, we quantified our tree crown mapping omission and commission errors by inspecting randomly selected areas from UTM zones 28-37, validating that our neural network generalized over UTM zones consistently (Extended Data Fig. 8 ). Second, we quantified the relative error of our tree crown area estimation. We consider the uncertainty Δ x of a quantity x and the corresponding relative uncertainty δ x defined by the absolute and relative error, respectively  45. To assess the relative error in crown area estimation resulting from errors by the neural network, we considered external validation data from ref. 1  , which were not used in the model-building process. We considered expert-labelled tree crowns as well as the predicted tree crowns from 78 plots of 256 × 256 pixels.
Allometric uncertainty estimation The prediction of tree carbon from the crown area for a single tree based on crown area alone is inherently uncertain  47, 48. As the allometric equations are based on three different datasets, we compute their uncertainties independently, combine them and put them in relation to the total carbon measured in the three datasets. The allometric equations were established using an optimal least-squares fit of an affine linear model predicting the logarithmic carbon from the logarithmic tree crown area  19. To estimate the uncertainty of the allometric equations, we repeated the fitting using random subsampling. The datasets were randomly split into training data (80%) for fitting the allometric equations and validation data (20%) for assessing the uncertainty.
Our viewer Visualizing our large tree-mapping dataset in an interactive format was essential for quality-control purposes, exploration of the data and hypothesis creation. Creating a web-based viewer serves the purpose of being the initial point of interaction with our dataset for fellow researchers, local stakeholders or the general public. The visualization of more than 10 billion trees in a web browser required maintaining performance, interactivity and individual metadata for each polygon. Users should be able to zoom in to any area within the dataset to view individual tree polygons and query their statistics while at the same time accurately depicting the overall trends of the dataset at lower zoom levels. The visualization also needed to clearly denote where data were missing or possibly affected by clouds or aerosols.
Finally, the extent and origin of the source imagery, its acquisition date and a preview of the imagery needed to be available. To accomplish these goals, a vector-tile-based approach was taken, with the data visualized in a Mapbox GL JS map within a React web application. To create vector tiles covering the entire study area, we developed a data-processing pipeline using high-performance computing resources to transform the data into compatible formats, as well as to package, optimize and combine the vector tiles themselves. We used two tracks to store and visualize the results of this study on the web: vector polygon data and generalized rasters representing tree crown density. At the native spatial resolution of 50 cm, the map shows the full-resolution tree polygon dataset. At lower-spatial-resolution zoom levels, rasterized representations of tree density are shown.
The rainfall data are found at https:// data. chc. ucsb. edu/products/CHIRPS-2. 0/africa_monthly/ (ref. 46 ). long-term precipitation zone (see ref. 19  ). The plot also shows the cumulative percentage up to 15 m 2 and 50 m 2 crown area (50% and 88. 4%, respectively) of the predicted trees in our study (95% of which had crown areas less than 78 m 2 ). b,c, Same as in a but for foliage and root mass. The foliage and root allometric equations were established from 900 trees of 26 species and 26 trees of five species, respectively. Extended Data Fig. 3  | Comparison between aboveground carbon-density maps and our data derived from 9,947,310,221 trees. a, Scatter plots showing the spatial agreement at the pixel level with all datasets aggregated to 1 × 1-km grids, with our data plotted on the x axes.
Extended Data Fig. 6 | Forest fractions of different TRENDY 23 models. The figure shows the percentage of forest areas assumed by each model along the rainfall gradient. Woody biomass in most models mainly comes from predefined forest areas and consequently results in a high degree of variation in the semi-arid area of our study. This is an example of the utility of our treemapping results for more accurate depiction of semi-arid trees for numerical simulation modelling. Extended Data Fig. 7  | Candidate and selected satellite images for our analysis. a, 326,523 Maxar images covering 90,097,073 km 2 from November to March were available for our study and were acquired from 2002 to 2020. b, The distribution by month of all satellite data in a. The selected imagery for processing (see Methods) by year totalled 94,502 images that covered 9,685,324 km 2 with 87% of the satellite data from 2010 to 2020 (c) and 94% of the selected images were from the early dry-season months of November, December and January (d).
We had a 9. 3:1 area ratio of available imagery to selected input data for analysis. See also Extended Data Table  1a. Extended Data Fig. 8  | Evaluation of the tree crown mapping omission and commission errors. The performance of the tree crown predictions was evaluated for 1,028 randomly selected 512 × 256-pixel areas from UTM zones 28-37, with approximately 100 areas for each UTM zone. The patches were extracted with an 80% bias towards precipitation  46  above 200 mm year -1 , as most tree identifications are above the 200 mm year -1 isoline. 178 areas were excluded from evaluation because the rainfall was >1,000 mm year -1 or the areas were devoid of trees. A total of 50,740 trees were evaluated. a, Class breakdown of our 3,765 omission and commission errors. b, Class numbers of all the trees evaluated.
In 78 plots of 256 × 256 pixels, the hand-labelled set contained 5,925 trees and the system delineated 5,915 trees. These crown areas were matched using inner spatial join. Multiple overlapping hand-labelled or predicted tree crown areas were merged into multi-polygons before calculating the RMSE. The crown areas of missed tress counted as errors. For calculating the corresponding RMSE of predicted carbon, we relied on the allometric equations given in Extended Data Fig. 2a-c. The abscissa has a logarithmic scale and 95% of our 9. 9 billion tree crowns had crown areas <78 m 2 (Fig. 3b ).
Acknowledgements We thank  Maxar, Inc. for providing commercial satellite data through the  NextView Imagery End User License Agreement. M. B. and F. R. were supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 947757 TOFDRY) and a  DFF Sapere Aude  grant (no. 9064-00049B ). A. K. , R. F. and C. I. acknowledge support by the  Villum Foundation  through the project  Deep Learning  and  Remote Sensing  for  Unlocking Global Ecosystem Resource Dynamics (DeReEco , grant no. 34306 ). C. I. acknowledges support by the  Pioneer Centre for Artificial Intelligence, DNRF  grant number  P1. NASA  supported this work through the.
Data availability The viewer can be accessed at https://trees. pgc. umn. edu/app. The Funk et al. rainfall data  46  are freely available at https://data. chc. ucsb. edu/products/CHIRPS-2. 0/africa_monthly/. Commercial very-high-resolution satellite images were acquired through NASA under the NextView Imagery End User License Agreement. The copyright remains at Maxar, Inc. and redistribution is not possible. However, the derived products produced by this study are publicly available at the Oak Ridge National Laboratory's Distributed Active Archive Center: https://doi. org/10. 3334/ORNLDAAC/2117. Please contact C. T. , M. B. or P. H. for more specific requests. A detailed description of our processed data for the 95,402 selected mosaic images, including output data, specific cutlines affected by aerosol optical depth and cloud cover; data distributions for year, month, solar azimuth angle and off-nadir angle for each UTM zone segment in our study, can also be found at https://doi.
Moreover, there is a lack of unanimity on the criteria that should be used to infer the targets of miRNAs. The main objective of this paper is thus to explore in finer detail some of the open problems that continue to haunt the in silico approaches for miRNA target identification, knowing that these continue to be important, as experimental approaches, on the other hand, remain expensive in terms of both cost and time. Experimental datasets do, however, exist, notably for miRNAs that have been studied for a longer time. Some studies try to detect regulation through a direct correlation of expression between miRNA and mRNA in diverse conditions  [9]. These results, however, do not indicate the exact sites of interaction. This is why the two that will be used in our case correspond to the CLASH (for Cross-linking, Ligation, And Sequencing of Hybrids) data presented in the paper of Helwak et al.
[10] , in which the authors identified human miRNAs along with their mRNA interaction sites within a single experiment, as well as the CLASH mouse dataset presented in the paper of Moore et al. [11]. A high number of features have been explored in the literature when trying to computationally infer the potential targets of miRNAs, as high as, e. g. ,  26  in one of the best-known methods TARGETSCAN  [12]. Among such features, some of the main ones that have been considered include: (1) a match with what is called the seed sequence in the miRNA, which is usually defined as starting at nucleotide 2 from the 5 end of the miRNA and is of a length between 6 and 8; (2) the stability of the duplex formed between the miRNA and the mRNA, which can be in the 3 UTR, the CDS, or the 5 UTR of the target mRNA;  (3)  the accessibility of such a site within the mRNA or of the close region around it; and (4) evolutionary conservation of the miRNA and mRNA sequences.
Datasets Among the published CLASH data, we chose to use those from two studies that provide information on the precise sites of interaction between the miRNAs and their mRNA targets. Experimental validation methods based on CLASH are the only ones that do not rely on in silico predictions to find the interacting miRNAs. Indeed, in such cases, the miRNAs are linked and sequenced along with their mRNA targets. In the present study, the input for the in silico methods used is always a set of miRNA-mRNA couples where, for each couple, at least one miRNA interaction site has been identified by CLASH in the mRNA (sometimes more than one). We then decided to proceed in two ways. In the first case, the complete mRNA sequences along with their interacting miRNAs were provided as input to the methods, and in the second one, only a window around the interaction site in the mRNA was considered.
We denoted by Seq C the dataset with complete mRNA sequences and by Seq W the dataset including the interaction site plus 200 nucleotides down-and upstream of said site. The first case may thus be considered as containing more noise, and the idea was to also see whether this influenced the behaviour of the different methods and the results obtained. Notice that by giving as input the exact miRNA-mRNA couples identified by the CLASH experiments, we never consider miRNA-mRNA couples where the miRNA is not known to interact. This means that there are never any true negatives in the results obtained. Moreover, any false positives are related not to the couple itself, but to the exact location of the interaction site on the mRNA. The miRNA-mRNA interactions provided by the CLASH experiments were supported by chimeric reads, which were composed of the merge of the sequences of an miRNA and of an mRNA.
Human CLASH Data The information provided by the data from this first study is detailed in  [10]. It describes the precise placement of 399 human miRNAs onto 7390 mRNA targets, representing a total of 18514 miRNA-mRNA interactions. The lengths of the mRNA subsequences as given by the authors to be involved in the interaction sites vary from 18 to 119, with an average of 52. 8 nucleotides. To generate the dataset Seq C , we proceeded as follows. From the version of the human genome used in the CLASH paper, namely GRCh37 release 60 downloaded from the Ensembl  [13]  database, we selected only the sequences of interest from the cDNA by matching the name of the mRNA from CLASH and then extracting the mRNA sequences that contain an interaction site situated exactly where CLASH placed it. If several candidates from the cDNA corresponded to one same mRNA in CLASH, we selected the one with the longest sequence.
In the process, two mRNAs from CLASH were thus not considered due to an inconsistency between the sequence provided in the paper and the sequence we retrieved from the database. Thus, we ended up with a dataset Seq C of 18,497 interactions from 399 miRNAs targeting 7388 mRNAs. The dataset Seq W , on the other hand, is slightly smaller as indeed, we eliminated from it those sequences in which, when extending the precise interaction sites as given by CLASH of 200 nucleotides down and upstream, the distance of the site from the 5 or the 3 end of the mRNA was smaller than 200. For this Seq W dataset, we obtained 15,754 interactions. The interaction sites were further classified by the authors into different groups, either based on the region of the mRNA where they are located or on the class of interaction to which they belong.
The proportion of targets in each class varies between approximately 17. 8% for the smallest class (Class II) and 25% for the biggest one (Class III); see Figure  1. We wish to call attention to the fact that while the numbers obtained for the classes add up, as expected, to the number of miRNA-mRNA interaction sites considered, this is not necessarily the case when we consider the regions instead of the classes. The reason is that some interaction sites may be located between two regions, namely 5 UTR and CDS or CDS and 3 UTR, or may be found in non-coding RNAs that arise from pseudogenes as indicated by the authors. When there exists a difference, it is a small one. The number of interaction sites for each of the two datasets above, in total and distributed in regions and classes, is given in Table  1.
The data from the second study were produced by Moore et al. [11]. The study made available 130,119 Mus musculus interactions involving 614 miRNAs and their 11,748 target sequences, for which only the chromosome and positions on them were given. The lengths of the mRNA subsequences involved in the interaction sites, as indicated by the authors, vary from 23 to 161, with an average of 45. 6 nucleotides. We first processed the target sequences in order to assign them to the 5 UTR, CDS, and 3 UTR regions. We retrieved the matching transcript names with annotation information that enabled us to associate such names with the positions on the chromosomes (from the mouse genome NCBIM37, Ensembl release 54  [13] ). We retrieved for each transcript name the corresponding cDNA sequences (from Biomart version 54  [13] ) and checked that the interaction sequence retrieved from the full chromosome could be identically aligned on the transcript sequence.
When several mRNAs matched with their names and sequences, we chose the longest one. The resulting dataset includes 13, 071 interactions between 378 miRNAs and 2462 mRNAs. The numbers of sequences for Seq W and Seq C , in total and by region and class, are given in Table  2. Notice that by focusing on the mRNA sequences, as in the human study, we eliminated a high number of interactions, such as miRNAs with introns and miRNAs with intergenic regions. Six different classes are distinguished by the authors in this dataset. Classes 1, 2, 3, and 4 correspond to, respectively, Classes I, II, III, and IV as determined in the human study  [10]. The other two classes, which involve the seed and a bi-or tripartite auxiliary pairing pattern, were defined as new ones, namely Class 5 and Class 6 (see Figure  1 ,  [11] ).
The third method, MIRANDA, is thus the only one that does not explicitly compute the accessibility of the mRNA. Moreover, we would like to observe that all methods, including the three that we chose to use, often include more general tools as modules within their algorithm; these tools are publicly available. Many belong to the ViennaRNA Package  [18]  and enable addressing some basic problems. These are related to the main features that are considered when trying to infer miRNA-mRNA interactions. These main features concern: • The seed match. We recall that this is the problem of finding the best binding site between two RNAs. It is addressed by adopting a sequence alignment method, albeit with some adaptation, the main of which is that sequence complementarity and not sequence identity is considered.
Performance Evaluation For each method, the performances were evaluated in terms of precision, recall and F-score metrics, considering the different numbers of chimeric reads at the interaction sites. On the other hand, we wanted to be very precise and thus decided to initially consider-in both cases of datasets-a position as a strong true positive, TP strong for short, when the interaction site predicted by the method is fully included within the site given by CLASH. It may also happen that the predicted site overlaps the site given by CLASH without being fully included in it. This may be seen as a weaker type of true positive, and we therefore chose to also consider it separately. We named it a weak true positive, TP weak for short. We then called false positives, FP for short, all predictions made that are not TP strong.
Exploring Intra-Species Conservation We used MEME  [24]  to explore intra-species conservation. We considered separately, for each miRNA in both CLASH datasets, the set of mRNAs it targets. In the first experiment, for each miRNA, we gave as input to MEME the exact interaction sites on its mRNA targets. This dataset is denoted by Seq E i. In the second experiment, for each miRNA, we gave as input to MEME the exact site plus 200 nucleotides down-and upstream of that site for all its mRNA targets. This dataset is denoted by Seq W i. In both cases, we selected only the motif with the lowest combined match p-value  [25] , which we considered as the best one found by MEME. We used the parameters by default from the tool as available on the website (memesuite. org; version accessed on 9 September 2022, see the commands used in Table  S1 ), except for the site distribution and the width of the motifs.
Prediction of Interactions from the CLASH Datasets Using Three Methods The results obtained for the human and mouse CLASH datasets in each of the two cases of input considered, namely Seq C or Seq W , are presented in Figure  2  for all regions and classes together and in the Supplementary Figures  S1  and S3  for each region and each class. We observe that the results obtained by any method on the miRNA-mRNA datasets are always worse in terms of precision or recall than those obtained when considering Seq W , and this occurs in a rather important way (see Figure  2 ). This is expected, as the amount of noise in the first case is, as mentioned, much higher. There indeed, we consider the whole sequence of the mRNAs for which at least one miRNA-mRNA interaction site has been validated by CLASH, while in the second, we consider only a small region around such interaction sites.
For the mouse dataset, the results improve as the number of reads increases. Considering classes and regions, the poorest results overall are, in general, obtained for Classes IV and V for the human data as well as Classes 4 and 5 for the mouse data (see Supplementary Figures  S1  and S3 ). This is expected as these classes are supposed to correspond to miRNAs that bind to the mRNA not through the seed, not even necessarily in a localised or strongly stable manner. For both datasets, the 3 UTR regions always exhibit the best results in terms of F-score. Interestingly, concerning accessibility, MIRANDA, which does not consider this as one of its features has, with a few exceptions, the worst behaviour in relation to both PITA and INTARNA, more substantially in terms of F-score and recall, whatever the type, than in terms of precision.
For both the human and the mouse datasets, the best results are obtained when considering the accessibility (Figure  3 ). There are rare exceptions in the case of the human data for Seq C (Precision-overlap for the 5 UTR region) and five for Seq W (Recall-strong for Total, 5 UTR, CDS, 3 UTR, and Class V; see the Supplementary Material, Figures  S2  and S4 ). At least in relation to INTARNA, this reinforces the idea that accessibility is an important feature and should be taken into account in the prediction of the interaction sites. While the worst results are obtained when not considering the accessibility, not considering the seed gives either the same results as INTARNA with default parameters or, in some cases, better results with some exceptions for Classes 1 and 6 on Seq W of the mouse data, where the results without the seed became the worst when considering interactions supported by at least 6 and 8 chimeric reads, respectively (see the Supplementary Figures  S2  and S4 ).
Refinement of the Accessibility Study Although the results of the plots presented in Figure  3  in Section 3. 2 reinforce the importance of considering the accessibility of an interaction site, we wished to further extend our exploration of this feature by using RNAFOLD and RNAPLFOLD. Given a window of length l, a given nucleotide in a given sequence may thus belong to a different structure at most l times, except for those at positions less than l or greater than Ll + 1 of the mRNA, where L is the latter s length. Considering all mRNAs of which there are N, a nucleotide may then be part of at most l times N structures. Following the literature, we fixed l at 150, which is the size of the window used in INTARNA. As concerns N, it is equal to 15,754 for the human dataset and 11,581 for the mouse dataset, as indicated in Tables  1  and 2.
The objective of this section is then to explore this idea in a more systematic way, still using the CLASH data. This exploration will involve first checking if one of the most, if not the most, universally used tool for inferring motifs, namely MEME and its suite  [24, 26] , is able to associate the exact interaction sites of an miRNA with a statistically significant motif. As concerns the number of miRNAs, which were 399 in human CLASH data, only 316 were thus taken into account, as the remaining 83 ones (approximately 21% of the miRNAs) have only one predicted interaction site. When considering miRNAs that have at least 10 predicted interaction sites, the number goes down to 162 (approximately 41% of the total). Similarly, in the mouse dataset, only 299 of the 378 miRNAs have 2 or more targets (approximately 79% of the miRNAs), and 147 (approximately 39% of the miRNAs) have at least 10 or more identified interaction sites (approximately 39% of the miRNAs).
Accessibility computed for all Seq W on mouse and human datasets. In red is the accessibility when considering only the optimal structure with a sliding window. In dark green and in olive green are the accessibility computed with RNAPLFOLD with an accessible window u of 1 and 8. When using the Seq E i datasets as input for MEME, we found a motif associated with each of the miRNAs, with an average width slightly larger than the expected seed length, as indicated in Table  3. On these particular datasets, we therefore always have a perfect score for both precision and recall. This is not surprising since it contains less noise besides the fact that we force the prediction of at least one occurrence per sequence. We also imposed the same constraint for the human and mouse Seq W i datasets. In a similar way to the comparison of methods carried out in Section 3.