dataesr/scientific-paragraphs-categorization huggingface dataset:
e. from April 1 to September 30, over 100 years. To assess the realism of the simulated functional composition (research question 2) we compared model simulation outputs to observed data in the selected case study area. We extracted from Phytobase, a phytosociological database  (Gillet, 2015) , 68 floristic relevés of permanent grasslands sampled at 800-900 m a. s. l. elevation in the French Jura Mountains, i. e. under the climatic conditions close to those at Pontarlier. We classified the relevés into four management categories deduced from their phytosociological classification  (Ferrez et al. , 2011) : Gentiano luteae -Cynosuretum cristati was assigned to extensive grazing (9 relevés), Lolio perennis -Plantaginetum majoris, Lolio perennis -Cynosuretum cristati and Alchemillo monticolae -Cynosuretum cristati to intensive grazing (13 relevés), Euphorbio brittingeri -Trisetetum flavescentis to extensive mowing (18 relevés), and Alchemillo monticolae -Brometum hordeacei to intensive mowing (28 relevés).
Discussion With DynaGraM we introduce a relatively simple modeling solution to include the key notion of community dynamics in grassland models, by defining a generic structure in which interspecific interactions rely on resource competition and life strategies. Another approach has been proposed with the CoSMo module by  Confalonieri (2014) , CoSMo can coupled to any biogeochemical crop model, providing means for incorporating biodiversity in grassland models. CoSMo has been recently validated with experimental data  (Movedi et al. , 2019). However, the absence of state variables to describe the herbaceous species prevents the model to explicitly reproduce the competition within the assemblage, a point that we tackled with DynaGraM. The results support our strategy of choosing a common formulation of growth for all species, and indicate that the formulation of a potential growth reduced by environmental limitations, introduced by Schapendonk et al.
We examined the realism of the simulated community dynamics in terms of taxonomic and functional composition. Due to the difficulty to access repeated measurements of green biomass for individual species in semi-natural permanent grasslands, we relied on floristic relevés from a Fig. 7. Simulations of the model's sensitivity to (A) eight initial community structures (S1 to S8) after 10 years (B) and after 50 years (C). In (B) and (C), standing biomass values were averaged for each PFT over the vegetation period (between April 1 and September 30). phytosociological database  (Gillet, 2015)  and a phytosociological classification  (Ferrez et al. , 2011)  to define typical grassland compositions for management categories comparable to those defined in our four scenarios. It turns out that a fine tuning of some uncertain species-specific parameters was required to get simulation results close to observed vegetation structures.
Results showed the capacity of DynaGraM to simulate accurately the seasonal dynamics of herbage growth and standing biomass even after relaxing the assumptions of constant species composition and constant nutrient availability implicit in ModVege (Fig. D1 ). This implies that DynaGraM is able to capture key mechanisms of forage production defined by  (Jouven et al. , 2006a). ModVege has been validated with respect to field observations from Massif Central (France)  (Jouven et al. , 2006b) , the Jura Mountains and the Swiss central plateau (Switzerland)  (Calanca et al. , 2016). In addition, a modified version of ModVege, in which nitrogen and soil water components were included, was validated with experimental data in UK  (Ruelle et al. , 2018). Therefore, the model comparison presented in Appendix D (Online Supplementary Material) can also be seen as an indirect verification of the performance of DynaGraM.
Acknowledgements This research takes place in the  Long-Term Ecological Research (LTER) site Jurassian Arc  (http://zaaj. univ-fcomte. fr/?lang=en), which supports long-term interdisciplinary research on the dynamics of mountain social-ecological systems in the Jura Mountains. Our work will be integrated and will contribute to a wide range of activities coordinated by the  National centre for Climate Services ,  NCCS  (https: //www. nccs. admin. ch),aiming at establishing a web-based knowledge hub that supports climate-smart decision-making.
Bioinformatic Pipeline Bioinformatic analysis was performed using nextflow to monitor the run on the computing cluster. Fastq files from technical triplicates were pooled at the beginning of the analysis. TrimGalore was used for quality filtering and adapter removal, with a quality threshold of 25. Then, a second filtering of the non-target reads, in this case non-viral reads such as host or bacterial rRNA, was carried out by mapping clean reads using Bowtie2 (very-sensitive option)  (Langmead and Salzberg, 2012)  on the Silva database  (Nooij et al. , 2018). Finally, duplicate reads were binned using cd-hit-dup with a prefix length of 50 for each read  (Fu et al. , 2012) , in order to simplify the data set, facilitate the assembly step, and reduce PCR bias during abundance calculation. Taxonomic assignation of the reads was done using Kaiju with the nr_euk database (February, 2020), on the raw data and on the data post deduplication to assess the composition of our samples and impact of preprocess steps (filtering, rRNA removal, and deduplication).
Metagenomic Data The Illumina sequencing generated approximately 1. 9 billion raw reads ranging from 42 to 177 million per library. Of these, 20% were removed by quality filtering and rRNA removal (Table  2 ). To reduce the data size and optimize computing time for de novo assembly, the dataset was simplified by removing duplicate reads (deduplication), resulting in up to 85% of eliminated reads for some libraries. To verify the possible impact on reads distribution between the different life kingdoms, we performed a fast taxonomic assignation using Kaiju before and after the deduplication step (Figure  2 ). The kingdom that was most affected by deduplication was Bacteria with 88-96% of removed reads and Eukaryota with a fraction of removed reads between 77 and 95%. The number of reads for the viral fraction was reduced by 79-83% following deduplication with few variations between samples.
Picornaviridae Thirty-eight contigs gathering 69,517 reads, with sizes ranging from 353 to 4,631 bases, were identified as belonging to the Picornaviridae family. A short contig of 353 bases genetically close to the genogroup V of the simian hepatitis A virus was identified in a sample collected in Bol in October 2019 (Table  6 ). This sequence shared a high nucleotide identity (91. 7%) with the capsid gene of a simian strain detected in water samples in South Africa  (Rachida et al. , 2016). We previously sequenced a similar strain in a Sanaga clam sample collected in this area, albeit, with only 18% sequence overlap  (Bonny et al. , 2020). With the exception of one contig displaying 76% aminoacid identity with its best DIAMOND match, the other contigs were more distantly related (26-38% amino-acid identity) to Picornaviridae sequences from the database.
Preserving all viral genetic diversity presents in a sample is not possible yet since protocols used to date include steps that may favor the selection of certain types of viral particles. These can be, for example, (1) sampling bias, as collecting samples from the environment may be source of variations, (2) concentration methods that may eliminate some viruses of interest or increase the presence of inhibitors that may impact the efficacy of enzymes used for the molecular detection,  * This accession number corresponds to the sequence identified in this study and deposited in Genbank. Contig identification was done using Diamond except for some sequences noted with # for which BLASTn was used. Sequence identity is expressed as a percentage. E-value is from the BLAST tool. (3) nucleic acid extractions and subsequent library preparation including DNAse or RNAse treatments, or (4) at the last step, the bioinformatic pipeline.
, 2020). Generating technical or biological replicates help to increase virus diversity  (Kim et al. , 2017). Furthermore, we used an enrichment step based on capture of cDNA from vertebrate viruses using the VirCapSeq-VERT assay during libraries preparation  (Wylie et al. , 2015; Strubbia et al. , 2019). Finally, considering the low concentrations of norovirus and hepatitis A virus as measured by quantitative PCR, we selected a NovaSeq run to increase the depth per sample and thus increase the number of viral reads. As anticipated, we observed that viral reads remained a minority among the huge amount of reads that were obtained. These very large and diverse datasets resulted in long, difficult, and sometimes impossible de novo assembly. Read deduplication has been shown to allow a faster and easier assembly by removing repeated reads, mostly generated by the amplification required to reach minimal DNA amounts for sequencing  (Nieuwenhuijse et al.
, 2019). Characterization of the viral fraction is especially difficult as only a small portion of Earth's viral diversity has been identified and made available in reference sets  (Connor et al. , 2019; Plyusnin et al. , 2020; Sunagawa et al. , 2020). In our study, up to 87% of reads could not be identified, and 96% of RNA viral reads could not be assigned to any viral family. Such observations previously reported in other studies, confirmed that although advances in high-throughput sequencing expand the boundaries of viral sequence space, some developments are needed to increase viral annotations  (Bačnik et al. , 2020; Nieuwenhuijse et al. , 2020; Dance, 2021). In any case, it is important to continue improving bioinformatic pipelines and databases to be able to screen rapidly metagenomic data and fully identify the organisms in a sample  (Nieuwenhuijse et al.
, 2020; Plyusnin et al. , 2020; Dance, 2021). To reliably identify the very diverse sequences obtained through de novo assembly, each contig was identified by BLAST considering the length of overlap with the database sequence match, the numbers of reads and the BLAST E-value. The viral diversity in our study is described for all samples together rather than comparing the two sites or the season as the same sequences were detected in both sites and considering that the number of samples or the duration of the study may not be long enough to be able to detect some differences. Following a one-health approach, we focused on the identification of RNA mammalian viral sequences, and especially on the main viral families potentially related to human health and known to be present in human or animal excreta such as Astroviridae, Caliciviridae, Hepeviridae, Picobirnaviridae, and Picornaviridae.
Of note, since we rely on the sequence only, host assignation in our study is based on the host identified for the best BLAST match, which can be quite distantly related to our sequences. We grouped putative hosts into large families like humans, swine, or bats (Figure  3 ), but this information should be considered as indicative, and not definitive, as some sequences may have been detected in samples that are not their replicating host, and hosts may be misidentified in databases  (Cobbin et al. , 2021). Few sequences from human viruses were identified compared to the diversity of other sequences detected. This may be due to the low human population density and other environmental parameters such as currents or sewage disposal that were  not investigated here. As mentioned above, all but one of the samples selected for the metagenomic analysis were detected positive for norovirus by real-time RT-PCR.
* MW924373 MZ291981 MZ291982 MZ291983 MZ291984 MZ291985 MZ291986 MZ291987 MZ291988 MZ291989 MZ291990 Date October 19 October 19 January 20 May 18 August 18 April 19 November 19 April 19 February 20 October 19 December 19 Site Bol Bol Mou Bol Bol Mou Mou Mou Mou Bol Bol *This accession number corresponds to the sequence identified in this study and deposited in Genbank. Contig identification was done using Diamond except for some sequences noted with # for which BLASTn was used. Sequence identity is expressed as a percentage. E-value is from the BLAST tool.
Acknowledgements This manuscript reflects the author's perspective, informed by a review of literature conducted for the Global Assessment of the  Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services (IPBES). However, this work is not a formal product of IPBES. We thank  H. Ngo  and  M. Gue  `ze (IPBES) and  N. Crawhall  and  P. Bates  ( UNESCO ) for facilitating dialogues with representatives of Indigenous peoples and local communities. We also thank all the Indigenous peoples and local communities who over the years have shared their wisdom, struggles, and hopes with us. We also thank  AB. Junqueira ,  I. Dı ´az-Reviriego , and  S. Gallois  for comments on previous versions of this manuscript. VRG received funding from the  European Research Council  under grant agreement No  771056-LICCI-ERC-2017-COG.
Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system. The data are composed of two sets with synthetic data from the open source CARLA simulator (700 million points) and real data acquired in the city of Paris (60 million points), hence the name Paris-CARLA-3D. One of the advantages of this dataset is to have simulated the same LiDAR and camera platform in the open source CARLA simulator as the one used to produce the real data. In addition, manual annotation of the classes using the semantic tags of CARLA was performed on the real data, allowing the testing of transfer methods from the synthetic to the real data. The objective of this dataset is to provide a challenging dataset to evaluate and improve methods on difficult vision tasks for the 3D mapping of outdoor environments: semantic segmentation, instance segmentation, and scene completion.
Introduction Data in the form of a 3D point cloud are becoming increasingly popular. There are mainly three families of 3D data acquisition: photogrammetry (Structure from Motion and Multi-View Stereo from photos), RGB-D or structured light scanners (for small objects or indoor scenes), and static or mobile LiDARs (for outdoor scenes). The advantage of this last family (mobile LiDARs) is their ability to acquire large volumes of data. This results in many potential applications: city mapping, road infrastructure management, construction of HD maps for autonomous vehicles, etc. There are already many datasets published on the first two families, but few are available on outdoor mapping. However, there are still many challenges in the ability to analyze outdoor environments from mobile LiDARs.
Indeed, the data contain a lot of noise (due to the sensor but also to the mobile system) and have significant local anisotropy and also missing parts (due to occlusion of objects). The main contributions of this article are as follows: • the publication of a new dataset, called Paris-CARLA-3D (PC3D in short)-synthetic and real point clouds of outdoor environments; the dataset is available at the following URL: https://npm3d. fr/paris-carla-3d, accessed on 15 October 2021; • the protocol and experiments with baselines on three tasks (semantic segmentation, instance segmentation, and scene completion) based on this dataset.
Related Datasets With the democratization of 3D sensors, there are more and more point cloud datasets available. We can see in Table  1  a list of datasets based on 3D point clouds. We have listed only datasets available in the form of a point cloud. We have, therefore, not listed the datasets such as NYUv2  [1] , which do not contain the poses (trajectory of the RGB-D sensor) and thus do not allow for producing a dense point cloud of the environment. We are also only interested in terrestrial datasets, which is why we have not listed aerial datasets such as DALES  [2] , Campus3D  [3]  or SensatUrban  [4]. First, in Table  1 , we performed a separation according to the environment: the indoor datasets (mainly from RGB-D sensors) and the outdoor datasets (mainly from LiDAR sensors). For outdoor datasets, we also made the distinction between perception datasets (to improve perception tasks for the autonomous vehicle) and mapping datasets (to improve the mapping of the environment).
For example, the well-known SemanticKITTI  [5]  consists of a set of LiDAR scans from which it is possible to produce a dense point cloud of the environment with the poses provided by SLAM or GPS/IMU, but the associated tasks (such as semantic segmentation or scene completion) are only centered on a LiDAR scan for the perception of the vehicle. This is very different from the dense point clouds of mapping systems such as Toronto-3D  [6]  or our Paris-CARLA-3D dataset. For the semantic segmentation and scene completion tasks, SemanticKITTI  [7]  uses only one single LiDAR scan as input (one rotation of the LiDAR). In our dataset, we wish to find the semantic and seek to complete the "holes" on the dense point cloud after the accumulation of all LiDAR scans. Table  1  thus shows that Paris-CARLA-3D is the only dataset to offer annotations and protocols that allow for working on semantic, instance, and scene completion tasks on dense point clouds for outdoor mapping.
Paris (Real Data) To create the Paris-CARLA-3D (PC3D) dataset, we developed a prototype mobile mapping system equipped with a LiDAR (Velodyne HDL32) tilted at 45°to the horizon and a 360°poly-dioptric camera Ladybug5 (composed of 6 cameras). Figure  1  shows the rear of the vehicle with the platform containing the various sensors. The acquisition was made on a part of Saint-Michel Avenue and Soufflot Street in Paris (a very dense urban area with many static and dynamic objects, presenting challenges for 3D scene understanding). Unlike autonomous vehicle platforms such as KITTI  [25]  or nuScenes  [14] , the LiDAR is positioned at the rear and is tilted to allow scanning of the entire environment, thus allowing the buildings and the roads to be fully mapped. To create the dense point clouds, we aggregated the LiDAR scans using a precise SLAM LiDAR based on IMLS-SLAM  [26].
IMLS-SLAM only uses LiDAR data for the construction of the dataset. However, our platform is equipped with a high-precision IMU (LANDINS iXblue) and a GPS RTK. However, in a very dense environment (with tall buildings), an IMU + GPS-based localization (even with post-processing) achieves lower performance than a good LiDAR odometry (thanks to the buildings). The important hyperparameters of IMLS-SLAM used for Paris-CARLA are: n = 30 scans, s = 600 keypoints/scan, r = 0. 50 m for neighbor search (explanations of these parameters are given in  [26] ). The drift of the IMLS-SLAM odometry is less than 0. 40 % with no failure case (failure = no convergence of the algorithm). The quality of the odometry makes it possible to consider this localization as "ground truth". The 360°camera was synchronized and calibrated with the LiDAR.
The 3D data were colored by projecting on the image (with a timestamp as close as possible to the LiDAR timestamp) each 3D point of the LiDAR. The final data were split according to the timestamp of points in six files (in binary ply format) with 10 M points in each file. Each point has many attributes stored: x, y, z, x_lidar_position, y_lidar_position, z_lidar_position, intensity, timestamp, scan_index, scan_angle, vertical_laser_angle, laser_index, red, green, blue, semantic, instance. For the data annotation, this was done entirely manually with 3 people involved in 3 phases. In phase 1, the dataset was divided into two parts, with one person annotating each part (approximately 100 h of labeling per person). In phase 2, a verification of the annotations was performed by the other person on the part that he did not annotate with feedback and corrections.
In phase 3, a third person outside the annotation carried out the verification of the labels on the entire dataset and a consistency check with the annotation in CARLA. The software used for annotation and checks was CloudCompare. The total time in human effort was approximately 300 h to obtain very high quality, as visible in Figure  2. The annotation of the data consisted of adding the semantic information (23 classes) and instance information for the vehicle class. The classes are the same as those defined in the CARLA simulator, making it possible to test transfer methods from synthetic to real data.
Interest in Having Both Synthetic and Real Data One of the interests of the Paris-CARLA-3D dataset is to have both synthetic and real data. The synthetic data are built with a virtual platform as close as possible to the real platform, allowing us to reproduce certain classic acquisition system issues (such as the difference in point of view of LiDAR and cameras sensors, creating color artifacts on the point cloud). Synthetic data are relatively easy to produce in large quantities (here 700 M points) and with ground truth without additional work for various 3D vision tasks such as classes or instances. It is thus of increasing interest to develop new methods on synthetic data but there is no evidence that they work on real data. With Paris-CARLA-3D and therefore with particular attention to having the same annotations between synthetic and real data, a method can be learned on synthetic data and tested on real data (which we will do in Section 5.
Dataset Properties Paris-CARLA-3D has a linear distance of 550 m in Paris and approximately 5. 8 km in CARLA (the same order of magnitude as the number of points (×10) between synthetic and real). For the real part, this represents three streets in the center of Paris. The area coverage is not large but the number and variety of urban objects, pedestrian movements, and vehicles is important: it is precisely this type of dense urban environment that is challenging to analyze.
Transfer Learning Paris-CARLA-3D is the first mapping dataset that is based on both synthetic and real data (with the same "platform" and the same data annotation). Indeed, simulators are becoming more and more reliable, and the fact of being able to transfer a method from a synthetic dataset created by a simulator to a real dataset is a line of research that could be important in the future. We will now describe three 3D vision tasks using this new Paris-CARLA-3D dataset.
Task Protocol We introduce the task protocol to perform semantic segmentation in our dataset, allowing future work to build on the initial results presented here. We have many different objects belonging to the same class, as it the case in towns in the real world. This increases the complexity of the semantic segmentation task. The evaluation of the performance in semantic segmentation tasks relies on True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN) for each class c. These values are used to calculate the following metrics by class c: precision P c , recall R c , and Intersection over Union IoU c. To describe the performance of methods, we usually report mean IoU as mIoU Equation (1) and Overall Accuracy as OA. mIoU = 1 C C ∑ c=1 TP c TP c + FN c + FP c ( 1 ) where C is the number of classes.
Experiments: Setting a Baseline In this section, we present experiments performed under different configurations in order to demonstrate the relevance and high complexity of PC3D. We provide two baselines for all experiments with PointNet++  [29]  and KPConv  [30]  architectures, two models widely used in semantic segmentation and which have demonstrated good performance on different datasets  [4]. A recent survey with a detailed explanation of the different approaches to performing semantic segmentation on point clouds from urban scenes can be found at  [28, 31]. One of the challenges of dense outdoor point clouds is that they cannot be kept in memory, due to the high number of points. In both baselines, we used a subsampling strategy based on sphere selection. The spheres were selected using a weighted random, with the class rates of the dataset as probability distributions.