dataesr/scientific-paragraphs-categorization huggingface dataset:
Coverage. Similarly, as it is usually difficult for users to have an idea of how well a given motion dataset covers some important parameters that are required for a specific application, we provide users with the ability to generate coverage maps. In the case of locomotive motions explored in this paper, the coverage map is constructed in the  and  domains, and shows how sparse a given dataset is. This is done so that the evaluator can have a general insight over the linear and angular speed coverage of a dataset. In order to filter out noise, this map is constructed by computing the mean   and   over a 1-second sliding window through the dataset.
Procedure To compute the different metrics representative of a motion dataset, our framework relies on a reproducible procedure, currently tailored for locomotive motions. This procedure is comprised of three tasks: setting up a scenario, running simulations and data postprocessing. To evaluate how a locomotive dataset behaves across a spectrum of (   ,    ) pairs, we propose that each pair parameterizes a circular trajectory that the virtual character has to follow. Such a circular trajectory enables the evaluation of different datasets under the same conditions, standardizing the evaluation of the limitations of each dataset. To ensure that the virtual character reaches the circle at full speed, the first step is to make the virtual character move towards the tangent of the test circle and start turning with constant linear and angular velocities  and  once it reaches it.
Moreover, we also propose a second type of scenario to characterize locomotion datasets in terms of transitional capabilities, where the linear and angular velocities are changed over time during the simulation. In this second case, the scenarios are defined by a starting speed (   ), a switching speed (  ) and a time interval (  ). With these parameters, the simulation will switch the speed of the character between    and   every   seconds. By changing  over time, the user can therefore test the responsiveness of the virtual character and the time it takes for it to react when there is a change in speed, aiding the user in the evaluation of transitions. Figure  4  shows an example of such simulations. Since  and  will both be fixed during some part of each simulation, it is important to note that the radius of the circle will vary to allow for such speeds.
Technical details All simulations were driven by Unity 2019. 4. 35f, using a modified version of the Motion Matching for Unity (MxM) plugin as the implementation for motion matching. All experiments were run in a computer equipped with an Intel Core i7-9750H CPU, 32GB of RAM and an Nvidia 2070 Max-Q. Post-processing analyses of the simulation data were performed using Python 3. 10. Three motion datasets are used: the first one is comprised of 37 different motion files from Mixamo (total of 6108 frames at 30Hz), another one is comprised of 129 different motion files from Unity's Raw Mocap Data for Mecanim dataset (total of 78897 frames at 30Hz) and the last one is a whole 5 minutes file of motion data recorded at the authors' lab with an Xsens full-body suit (total of 37031 frames at 120Hz).
Results First, the framework's ability to interpret the accuracy of three different datasets, i. e. the ability to obtain motions at specific  and  pairs, is evaluated. For the evaluations presented in this paper, several simulations were performed by sampling (    ,     values from the following intervals:     ranging from 1 to 3 m/s by 0. 25 m/s step, and     ranging from -100 to 100 degrees/s by 25 degrees/s step. A single   of 4 m/s and a   of 5 seconds were also selected for simulations involving changes in speeds. Figure  5  summarizes the mean trajectory error obtained across the (    ,     ) pairs for each dataset and shows that the self-recorded dataset is the one generating a higher offset, followed by the Unity and Mixamo datasets, respectively. Figure  5  also shows that there are similarities among the lower  trials across all datasets: the  trials on -25 and 25 degrees/s are the ones with the highest offset, while the ones at 0 degrees/s score the lowest offsets across the board.
A further analysis of their offset errors also shows that each dataset has its own peculiarities, as seen in Figure  5. For instance, examples A and B have error margins ranging from 0 to 3. 5 meters and 0 to 2 meters, respectively. However, Example 3 had error margins ranging from 0 to 14 meters. This is expected, as example C represents a dataset with poor coverage, especially when considering the tested (, ) pairs. One can also see that by the horizontal variations in the graph,  has a greater influence over offset errors, meaning that matching  seems to be an easier task. Concurrently to the accuracy of a given dataset, evaluating its given coverage is also of interest, i. e. , how sparse or dense a given dataset is, for different (,) combinations. Figure  1  gives an idea of how coverage in one of the datasets tested in this work looks like.
Each motion is depicted as a different color and with a different type of marker. The motions with a low , thus around 0 degrees/s, are motions that mainly go straight. Instead, motions are constantly turning clockwise and counterclockwise when their  is negative or positive, respectively. Having that,  will be just how fast they do it. Figure  1  also shows that the space around a  of 2m/s is sparse when looking away from the  of 0 degrees/s. This means that any motion that falls into that area may have a hard time transitioning, incurring a high transitioning cost and potential  and  errors, characterizing poor coverage in those areas. Figure  6  shows how different  and  ranges are covered in the three datasets evaluated in this section. The brighter parts represent the most dense part of every dataset, showing where the areas with the most data are available.
In order to assess the quality of these maps, the symmetry of the points and their density has to be taken into account. From the figures, one can see that the Unity dataset is the most comprehensive one, followed by Mixamo dataset and the self-recorded dataset. Apart from the more general metrics, some metrics deal with individual simulations, such as the transition cost, the  and  errors over time, the use frequency from each motion frame and a graph structure showing the relationships between all motion clips  and frames. These metrics can help tracing an error when using an motion dataset and ease the recapturing process by giving a more specific idea of what needs to be corrected. As for the transition cost, one can see in Figure  7  the cost value changes according to the changes in.
In the example, the Mixamo dataset is used to switch between 1. 5m/s () and 4m/s (  ) with a   of 5 seconds and an  of -50 degrees/s. Considering the motion density for the dataset in Figure  6 , when looking at an  of -50 degrees/s, there are no motion examples when  is 4m/s, while there are multiple ones when  is 1. 5m/s. This means that during   periods, the algorithm will have a harder time searching for a matching pose. Considering an example using the Mixamo dataset with  set to 1m/s,   to 4m/s and  set to -100 degrees/s, the user can evaluate the  and  error through the error distributions shown in Figure  8. As two speeds are taken into account, the simulation accounts for errors around  and   , which results in a bimodal distribution. In this case, the  error will mostly roam around 0, although it also has a peak around 1.
5m/s to account for when it switches to. The  error is the same, roaming around -10 and 10 degrees/s. As a way to verify the general usage of a motion dataset, the use frequency of each animation is displayed in Figure  9 , showing how many times a frame from each animation was used. It also shows how many times a transition between different animations occurred through the "Transitions" bar in the graph. This helps in knowing which are the most relevant clips in the dataset. As a complement to the use frequency metric, one can also see how the clips relate to each other by visualizing the origins and destinations of the transitions. Figure  10  shows a dataset with two  animations transitioning between each other, which supports and gives more in-depth information to what is presented in Figure  9.
Completion Having a dataset that can withstand the different situations planned for a simulation is crucial in lowering transition costs and increasing accuracy. Considering the amount of information gathered from the simulations, especially when looking at the relevance metrics, an evaluator can judge if adding data to a dataset is needed. The first way an evaluator can make this decision is by looking at the coverage metrics presented in Section 3. 3. 3. These metrics will enable the evaluator to gauge how sparse some areas are and judge if more motion is needed. In case the evaluator decides to add data, then the metrics in Section 3. 3. 1 can show the impact of these modifications. At the time, these additions are done by either recording animation data empirically or gathering it from other datasets.
DISCUSSION Even though this work provides means to a more common grounded comparison of datasets, it is important to note that it is still a manual, subjective and iterative process. The main advantage, however, is that at least a decision can be made on interpretable metrics and reproducible simulations. As noted in Section 4. 2, datasets are divided in mainly three parts: motions that go straight and motions that turn clockwise or counterclockwise. Given that, if Figure  6a  is divided in the middle of its x axis, a symmetry can be seen between the left and right sides. Therefore, since the Unity and Mixamo datasets are mostly symmetrical, the results will also follow their symmetry. Figure  5a , for example, shows errors that are similar, but not entirely symmetrical. However, the self-recorded dataset has a lower symmetry than both other datasets and Figure  5  shows the asymmetry in offset error caused by it.
This can be explained by foot step desynchronization, which occurs when motion mirroring is not enforced: without mirroring, there is no guarantee that the same transitions will be available on both side of the  spectrum. If one looks at the offset error from the Unity and Mixamo datasets, it could be expected that the Unity dataset, by being the more complete one, would accrue less offset error, but this is not the case in the simulations. A dataset can seem complete when most of the  and  pairs are covered and every area is dense. However, speed is not the only factor to take into account when blending from one frame to the other. In fact, the most important feature when trying to blend motions is the similarity between one pose and the other. Therefore, in order to fully grasp the blending potential of a dataset, one can employ pose similarity as a metric that can tell how similar the poses are in an area.
Future work As mentioned above, one limitation of our current framework is related to the fact that coverage currently only accounts for linear and angular velocities. A first addition would therefore be to include additional metrics, in particular related to pose similarity, that would broaden the coverage analysis of the dataset. Coverage estimation is a useful metric to showcase the capabilities and limitations of a dataset, although it mostly focuses on highlighting sparse areas. However, little can be gathered from dense areas, apart from the fact that there may be enough motion samples to decrease transition costs in those cases. The main problem is that there is no way, at this moment, to know for sure that a dense area indeed has enough motions to support a low-cost transition. This stems from the basis that although there are different samples, these motions could have a wildly different pose: a character could run backwards at the same speed that it runs forwards, for example.
Hence, the system currently lacks a way to show how similar the poses are in each area which, when combined to the coverage map, could enable users to identifies areas of density and similarity. Such a feature would be valuable to add another dimension to the analysis and showing that even though there may be many motion examples in a dataset, those may be too diverse to enable low-cost transitions. Completing a dataset can sometimes be a challenging task, as users need to sort through a multitude of motions in databases to find one that works or have a whole rig to record it themselves. This means that if a new motion needs to be added, significant efforts and time will be employed. For these reasons, providing relevant search-based or similarity-based interfaces for intuitively identifying motions to include from external databases would also be a relevant direction of research in the future.
In the sense of optimizing a dataset, the system could also benefit from a more suggestive approach towards the modifications that can be done. As of now, the system shows the current popularity of every frame in the animations, but will not suggest which ones could be eliminated, for example. Another future addition would be an online evaluation mode for motion datasets. This means that a performer would be provided with some information on which areas to record more motions for, instead of having to post-process all this information and have to go back to recording. An enhancement of this addition would be a 3D guide for motion capturing. Using a head mounted display to guide a performer when recording motion could be a possibility to explore, as it could enable the performer to record motions more precisely.
Limitations Although the system is able to successfully compare and provide grounds for an evaluation of motion datasets, it does so only for the displacive aspect of any motion. In this sense, the motion of a virtual character stopped in front of a table picking up a cup is essentially the same as an avatar poking another one, since even though the poses will be different, there is no spatial displacement. Thus, the upper-body portion of any motion is disregarded for coverage terms. Considering the many applications that upper-body motions can have, especially when environment or inter-character interactions are required, including such elements for automatically evaluating motion datasets will therefore be an important direction to explore in the future.
Conclusion As noted in this paper, the analysis of motion datasets is subjective to the personal experience of the animator. Therefore, two animators may not rate a motion dataset the same as another, which not only introduces biases, but may also be subject to faults due to the sheer amount of data in these types of datasets. In order to provide a partial solution to this problem, we proposed in this paper a novel automatic and reproducible framework to evaluate motion datasets. Some of the metrics used in the paper are already known by the community, but they lack structured procedures to aid animators in analyzing their datasets. As a step towards the complete analysis of motion datasets, this work focuses on locomotion, relying on mechanical metrics, such as linear and angular velocities, to calculate accuracy and coverage, as well as statistical metrics to calculate motion relevance.
The library was sequenced using a Nanopore R9. 4. 1 revD flow cell and the GridION device with MinKNOW v. 4. 3. 4 and Guppy v. 5. 0. 11 1 2b6dbff software. A total of 338,403 reads were obtained with an N 50 value of 2. 66 kb. Two hybrid assemblies were launched using Unicycler v. 0. 4. 6 (one with default settings and one with the -sc option) and compared using Mummer v. 3. 23, to resolve the integrative and free circular copies of the plasmid. Overlapping sequences at the contig ends (obtained using the SPAdes process) were removed so that a circular contig was obtained. Default parameters were used for all software unless otherwise specified. The annotation was performed using the Microscope platform (4), and taxonomy analysis was conducted using GTDB-Tk v. 1. 3. 0. The completion and contamination percentages were calculated using CheckM v.
ACKNOWLEDGMENTS The  French National Research Institute supported this work for Sustainable Development (IRD) via the International Scientific Coordination Network-South Impact of Black Carbon in South-East Asia (SOOT-SEA ) and the  International Joint Laboratory Land-Ocean-Atmosphere Regional Coupled System Study Center (LOTUS). This work was also supported by the  IRD-MIO  collaborative research grant FORAG. We acknowledge the  France Génomique  and  French Bioinformatics Institute  national infrastructures (funded as part of the  Investissement d'Avenir program , managed by  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09  and  ANR-11-INBS-0013 ) for support with sequencing and the MicroScope annotation platform. For strain and DNA requests, please contact N. P.
Hundreds of palm libraries were sequenced through Illumina sequencing, providing a unique -high-coverage -genomic dataset in these non-model species. • We explored patterns of genomic diversity and differentiation within and across populations, bringing state-of-the-art knowledge about the roles of climate adaptation and pre-Columbian domestication. By documenting original cases of "incipient" domestication, these pioneer results open new avenues toward a better understanding of microevolution in Amazonia and of the impact of pre-Columbian societies on presentday biodiversity patterns.
Genetic differentiation. Pairwise genetic differentiation (FST) was estimated both between congeneric species and within each species. Weir and Cockerham's FST were estimated using the function genet. dist (package hierfstat) with argument method="WC84". Individual ancestry. Genome-wide genetic structure was assessed for each species by estimating individual ancestry  (Frichot et al. 2014)   These PODs correspond to a simulated set of SNP counts that take into account the genomewide (neutral) genetic structure empirically inferred from observed SNPs counts. The core model was run again using PODs as input under the same parameters to calibrate SNP-specific statistics (standardized allele frequencies α std , XtX and derived statistics) under neutral hypothesis. BayPass outputs were post-processed with R version 3.
5. 1. The robustness of the core model was assessed by evaluating the FMD distance  (Förstner and Moonen 2003)  between covariance matrices estimated on observed data (Ω OBS ) and on simulated data (Ω POD ) using fmd. dist function (BayPass miscellaneous R function). SNP-specific departures from genome-wide (i. e. neutral) differentiation were detected based on the joint distribution of: • SNP-specific XtXi (Günther and Coop 2013) estimated by BayPass (which describes the differentiation across all populations for each SNP i explicitly corrected for genomewide covariance, whatever their regional and local environmental conditions), and • two additional statistics ( and ∆ ) derived from estimated standardized allele frequencies ( , , which describes allele frequencies for each SNP i within each population j explicitly corrected for the genome-wide covariance matrix) and designed to quantify variations in driven by climate adaptation and pre-Columbian domestication, respectively.
Hardy Weinberg equilibrium was tested for each SNP through Chi-squared tests with the function hw. test (package pegas), and the proportion of SNPs showing significant departure from HW equilibrium at a 1% threshold was assessed. Selection test. We carried out a genome-wide scan for adaptive differentiation within each species using BayPass v. 2. 1. Genotype data were converted into allele counts and BayPass core model was run a first time using observed SNP counts as input. The number of populations in each dataset was specified with option -npop and MCMC parameters were specified with options -nthreads 8 -thin 15 -burnin 2500 -npilot 15pilotlength 5000. The estimated covariance matrix (Ω), which provides an accurate estimate of genome-wide allele frequency covariance across populations, was converted into a correlation matrix in R version 3.
∆ is the SNP-specific difference in allele frequency between local conditions of pre-Columbian occupation (+ versus -) estimated on observed data, and B (CD) (∆ -) the 95-quantile of the distribution (across all SNPs) of SNP-specific differences in allele frequency between local conditions of pre-Columbian occupation estimated on simulated data. Outlier blastn. Last, we retrieved the identity of genes containing precipitation and adaptation SNPs. For each species, we extracted the sequences of the genomic regions surrounding each outlier SNP (+/-200 bp) from consensus genomic sequences using bedtools getfasta (bedtools-2. 27. 1). Each SNP-surrounding region was then blasted against the NCBI nucleotide database using blastn (NCBI BLAST+ v. 2. 10. 1+), limiting blast search by taxonomy (Elaeis guineensis taxid = 51953 and Phoenix dactylifera taxid = 42345) and returning the first good hit to optimize computation time.
Acknowledgements This study has been conducted as part of the research program  PalmOmix  coordinated by  L. Brousseau. PalmOmix  ( ID 1702-01 ) was publicly funded by the  French National Research Agency (Agence Nationale de la Recherche, ANR)  under the " Investissement d'avenir" program  with reference  ANR-10-LABX-001-01   LabEx Agro  and coordinated by  Agropolis Foundation  under the frame of I- SITE MUSE  ( ANR-16-IDEX-0006 ). Sampling in the Nouragues research Station has been supported by the  LongTIme  project coordinated by  G. Odonne  and  J-F. Molino  and funded by an " Investissement d'avenir " grant from the  French National Research Agency  ( Agence Nationale de la Recherche, ANR ) with reference CEBA:  ANR-10-LABX-25-01. We are grateful to the  Nouragues Ecological Research Station (CNRS)  and the  Réserve Naturelle des Nouragues , to the  Paracou Research Station (CIRAD)  and to the National Forests We thank  Thierry Joët ,  Stéphane Dussert  and  James Tregear  for their help in pre-filtering candidate proteins ("bottom-up approach") to design the custom capture kit.
Díaz et al. 9  found that occupancy of the six-dimensional trait space is highly constrained, and is captured in a two-dimensional global spectrum of plant form and function, indicating strong correlation and trade-offs among traits. These results provide a foundation and baseline for studies of plant evolution, comparative plant and ecosystems ecology, and predictive modelling of future vegetation based on continuous variation in essential plant functional dimensions. Here we provide the trait dataset that served as basis for the analysis of the global spectrum of plant form and function presented in Díaz et al. 9  -the 'Global Spectrum of Plant Form and Function Dataset' (short here 'Global Spectrum Dataset'). The dataset is predominantly based on trait records compiled in the TRY database  10, 11  and provides trait values corresponding -to the extent possible-to mature and healthy plants grown under natural conditions within the species distribution range.
The dataset provides species mean values for the six plant traits mentioned above plus leaf dry matter content, used for the imputation of stem specific density. The dataset covers >46,000 of the approximately 391,000 vascular plant species known to science  12. Despite the rapid development of large plant trait datasets, the Global Spectrum Dataset stands out in terms of coverage and reliability. First, it provides quantitative information for a very high number of species, including about 5% of them with 'complete coverage' (all six traits). Second, it represents a unique combination of probabilistic outlier detection # A full list of authors and their affiliations appears at the end of the paper.