dataesr/scientific-paragraphs-categorization huggingface dataset:
This knowledge could vastly improve current models of global climate-vegetation dynamics under warming scenarios given the central role of this biome in regulating Earth's climate and biogeochemical cycles  (Brown & Lugo 1982; Melillo et al. 1993; Cleveland et al. 1999; Bonan 2008; Fleischer et al. 2019). While our findings highlight the great value of palaeoecological data for advancing our understanding of the impacts of climate and nutrient availability on Dipterocarp forests, additional records are needed to test the generality of the findings reported here across larger temporal and spatial scales in order to improve the forecasts of tropical rainforest dynamics in a warming world. Fig. 2  The temporal trends in mean and variance for the key paleo-records from Bulusan Lake according to the best fitted generalised additive models.
Acknowledgments General: We thank the  Philippine Bureau of Fisheries and Aquatic Resources  and the  Philippine Department of Environment and Natural Resources , as well as the  Bulusan Volcano Natural Park  staff, for permitting and facilitating our research at the Bulusan Lake. We also thank  Professor Keith Bennett  ( University of St Andrews ) and  Philip Bartilet  ( Bulusan Volcano Natural Park ) for fieldwork support, and  Iris Van Der Veen  ( University of Potsdam ) and  Bernd Meese  ( Fraunhofer Institute  for Manufacturing Engineering and Automation) and  Anne Thoisen  ( University of Aarhus ) for laboratory assistance. For assistance with the figures we thank  Marwan Butrous. Funding: This work was supported by  Royal Geographical Society  ( Monica Cole Research Grant and Paddy Coker Postgraduate Research Award ),  Quaternary Research Association (New Research Worker's Award ),  Department of Zoology at the University of Oxford  ( Postgraduate Research and Training Grant ), and  Merton College  (Graduate Research Expenses and Supplementary Travel Grants).
Radiocarbon dating was funded by the  Natural Environment Research Council. A. P. was supported by the  Clarendon Fund  and the  Leverhulme Trust Research Project  grant ( RPG-2016-235 ). D. S. was supported by an  Emmy-Noether  grant of the  DFG  ( SA-1889/1 ) and an  ERC   Consolidator grant  ( STEEPclim, Grant  agreement no. :  647035 ).
, 2018; Cheutin et al. , 2021). Data from wild fish species from freshwater water bodies, for example, include some works on cichlids from Lake Tanganyika, and crater lakes in Nicaragua (e. g. ,  Baldo et al. , 2019). In this study, we investigated the effect of host versus environmental factors on the structure of gut-associated bacterial communities in four fish species collected from the Aghien lagoon (Ivory Coast). Aghien lagoon is a eutrophic freshwater lagoon and harbors high biomasses of microbial primary producers including potentially toxic cyanobacteria  (Mitroi et al. , 2020; Ahoutou et al. , 2021). In the context of an international project addressing the sustainable monitoring of surface water resources used for the production of drinking water, specimens of commonly consumed fish species were collected over a year.
Gut was dissected immediately and frozen. As fish are mobile, water was sampled monthly from the central area of the lagoon. Water was sampled over the first meter depth using an integrative sampler (see  Ahoutou et al. , 2021) , filtered (0. 22 µm nitrocellulose filters, 11 samples, Table  1 ), and filters were frozen. Average values of various environmental data collected from six sampling stations along a west-east transect, are available in  Ahoutou et al. (2021). Based on these previous results, turbidity, rainfall and Chlorophyll-a (Chla) concentration were the most variable parameters and were thus used in this study. Notably, water temperatures over the sampling period oscillated between 26-31 • C, and eutrophication levels were with high phytoplankton biomasses expressed as Chlorophyll-a (Chl a) concentration (26-187 µg.
, 2016). PCR products were sequenced on an Illumina MiSeq platform (2 × 300 bp, paired-end sequencing, GenoToul, Toulouse, France) and paired-ends were assembled. Blank extractions and a community of known composition were used as internal controls for the sequencing procedure. Raw reads were deposited into the GENBANK Sequence Read Archive (SRA, Bioproject PRJNA758258) database under accession numbers SAMN20081138-SAMN20081229.
Gene sequence analysis Gene sequence datasets were analyzed using QIIME2  (Bolyen et al. , 2019). Amplicon Sequence Variants (ASVs) were identified using DADA2 after quality filtering (using default parameters of the qiime2 dada2 denoise-single method) and chimera removal  (Callahan et al. , 2017). Taxonomic affiliations were obtained by the sklearn-based classifier (SILVA 132-99 release)  (Quast et al. , 2013). Sequences matching "Eukaryota, " "Archaea, " "Unassigned, " "Mitochondria, " and "Chloroplast" were discarded. Datasets were normalized by rarefaction to 4,133 reads, following recommendation by  Weiss et al. (2017) , while two T. guineensis and one S. melanotheron samples with lower counts were discarded.
Comparison of bacterial communities Diversity indices (ASV richness, evenness) were calculated for water and fish gut samples using the package vegan  (Oksanen et al. , 2020)  in R 4. 1. 0 (R Core Team, 2021). The comparison of species richness among the five sample types was performed with a Kruskal-Wallis rank sum test and pairwise comparisons, using the package rstatix  (Kassambara, 2021). Bar plots were created using the packages ggplot2 (Wickham, 2016) and microshades  (Dahl et al. , 2022). Principal coordinates analysis (unweighted and weighted UniFrac distances), PERMANOVA, pairwise comparisons, and bar charts were produced. Packages phyloseq and RVAideMemoire were employed  (McMurdie and Holmes, 2013; Hervé, 2021). PERMANOVA comparisons involving S. melanotheron and H. fasciatus were conducted versus water, T.
Fish guts are reservoirs of rare bacteria in the Aghien lagoon Several recent studies have emphasized the importance of animals as reservoirs and dispersal agents for rare bacteria. In a recent census of the literature, macro-organism-associated bacteria were found to represent a significant fraction of the total bacterial diversity in the marine realm  (Troussellier et al. , 2017). Another study found that 90% of the prokaryotic diversity associated with metazoans tissues was not recovered in the surrounding plankton on a coral reef around the Mayotte island, and extrapolated that coral reef animalassociated microbiota could represent up to 2. 5% of the total prokaryotic diversity on Earth  (Chiarello et al. , 2020). A similar trend is observed in our dataset. Indeed, the vast majority (77.
Funding This study was supported by the  French ANR program  (project  COM2LIFE ,  ANR-20-CE32-0006 ), by the  French Facility for Global Environment (Fonds Français pour l'Environnement Mondial, FFEM , research program WaSAf). AG was funded through a Ph. D. grant from  Ecole Doctorale 227 "Sciences de la Nature et de l'Homme, " MNHN.
INTRODUCTION In processes that rely on motion datasets to perform animations, a relationship can be frequently found between the quality of a dataset and the output of the process. This relationship is important in many contexts, such as motion capture-based animation techniques, which is the one that interests us in this paper. However, quantifying the quality of a motion dataset is not a straightforward process. The typical process when trying to evaluate whether one has the correct motion dataset for a given scenario is to simulate the scenario, judge its perceptual quality and search through the whole dataset, trying to figure out if the gathered motions are adequate candidates. This is in general manually performed by animators, resulting in a subjective analysis. Another major concern is, after analysing the dataset, what happens if the user adds or removes data.
There is no objective way to quantify that the modifications made to the dataset had an impact, be it positive or negative. For example, a motion dataset might contain motions that are not used or might be lacking other motions: if the character needs to perform sharp turns and the dataset is tailored to slower turning velocities, then the character might not be able to turn as effectively. Similarly, if a character tries to run faster than what the currently available motions are capable of delivering, the dataset would need to be improved with faster running motions. Without having a bigger perspective related to the motion dataset, any modification becomes a cumbersome trial-and-error process. This work therefore investigates how to optimize locomotive motion datasets used for animating virtual characters.
To tackle this problem, an approach that considers motion matching, while also being independent of a specific animation technique, is proposed. The approach is also based on a number of novel metrics that quantify accuracy, coverage and relevance of a whole locomotive motion dataset. It also aids the user in performing more accurate optimizations, saving on memory footprint and making sure that every planned scenario is covered. To achieve this, this approach relies on motion matching to simulate different animation conditions while gathering evaluation data. This is later processed and compiled into an evaluation of the resulting animations. The main contributions of this paper are: • a more robust and quicker way to make informed choices when analyzing locomotive motion datasets; • a formal analysis cycle to evaluate the impact of the removal or addition of locomotive motions; • reproducible tests to allow the evaluation and reversibility of changes.
RELATED WORKS Motion datasets used to animate virtual characters are generally built using motions captured on real actors or manually created by animators. To efficiently control the locomotion of virtual characters, it is recommended to plan motion capture session, e. g. using dance cards as proposed by  Zadziuk [2016]. If one does not have the ability to record motions, public datasets are also available, such as Adobe's Mixamo 1  and Carnegie Melon University's motion database 2. No matter the way a dataset is established, modifying it, judging when a specific motion should be used or how to sequentially combine multiple motions can all be cumbersome jobs, as it requires manual visual analysis of the motions. To solve this issue,  Kovar and Gleicher [2002]  and  Lee et al. [2002]  proposed a structure called motion graphs, with the main objective of giving a wide view of how similar some motions are to each other and how they can be interlinked (see Figure  2.
a). The structure is laid out as a strongly connected graph, where each node is a frame and each edge a potential transition to another frame of the dataset. By contrast, on  Lee et al's work [2010] , this assumption is taken as the opposite, where the edges are motions and the nodes are transitions. This means that the graph can be set either way. By the layout of the graph, some assumptions can be made, such as needing more motion clips when you have dead ends or to connect other weakly connected components, to avoid a logical motion discontinuity. One of the main advantages of approaching motion data using motion graphs is that the animator is able to create new motion from motion fragments, as in  [van Basten and Egges 2009] , by connecting similar poses to other parts of the dataset. Various extensions of the motion graphs approach were proposed by the community, e.
The core aspect is to search in the database of poses the one best fitting a set of features specified by the user, and to transit to this pose (while blending to accommodate for slight differences) if this pose has a lower cost than the next pose in the current clip. Several authors have then aimed to improve the motion matching approach, to address some of the core limitations in terms of memory usage and computation  [Buttner 2019; Holden 2018; Holden et al. 2020 Holden et al. , 2017 Holden et al. , 2016]]. In particular,  Holden et al [2018; 2017]  have shown that "motion matching is a special case of machine learning for animation where a Nearest Neighbor Regression is used to map from user controls to animation data".
Quality measurement While the main works analyzed in the previous section  [Buttner 2019; Clavet and Buttner 2015; Holden 2018; Holden et al. 2020 Holden et al. , 2017 Holden et al. , 2016;; Lee et al. 2010; Reitsma and Pollard 2007]  all conclude that their methods are limited by the quality of the input data, there is still not a clear way to evaluate the quality of such input motion dataset, as it is often a subjective process and many motion datasets can perform a given task with varying degrees of success and realism. As it is difficult to predict the quality of animations resulting from motion graphs,  Reitsma and Pollard [2007]  proposed a number of task-based metrics evaluating a motion graph capacity to produce required animations. To achieve this, one of the steps is comprised of embedding all possible directions a character could take into a grid, which overlaid the navigable surface and took obstacles into account.
Interestingly, they report that Euler angles are more efficient for analyzing raw data, while quaternion representations are more efficient when considering velocities and accelerations. Motion analysis and classification has also been explored using deep learning and long short-term memory (LSTM) neural network  [da Silva et al. 2019] , providing the ability to automatically label motion capture data with up to 95% accuracy. However, the principal objective of this work was to provide solutions for gathering motion capture data from other projects, in order to cut costs and increase productivity, and not to evaluate the overall quality of the resulting datasets. Dirafzoon et al. [2016]  propose a set of low dimensional features to classify motion data through the use of topological data analysis.
Their approach uses the extraction of point clouds, and their reasoning is that by reconstructing the phase space of a system through the analysis of their time series, they can focus on persistence diagrams and use it as features for motion classification. By processing the point cloud, they are able to extract its maximum persistence, which is representative of the range of a joint motion. With that, they feed many instances of the maximum persistence metric to a k-NN classifier and are able to classify each window of motion into a set of individual activities (cycling, golfing, walking, waving and sitting) with over 97% accuracy. While tracking systems provide information about the specifics of their equipments, most vendors do not provide much about the recognition, post-processing or the final motion data quality.
Manns et al. [2016]  try to shed some light over this matter by proposing a set of metrics that show the amount of information present in a motion dataset. Through the use of the Principal Component Analysis (PCA) technique, in conjunction with a Shannon entropybased measurement, the system is able to calculate the uncertainty contained in the motion data, which translates into the amount of diversity in the input datasets. Even though they conclude that more work needs to be done in this area to prove the robustness of these metrics, they are able to classify different sets of motion capturing equipment and tell how well they adapt to different data-driven motion synthesis approaches. van  Basten and Egges [2009]  evaluate three distance metrics for concatenating and blending animations: the difference between joint-angle values, point-clouds and PCA.
Optimizing motion datasets When working with datasets, it is common practice to optimize it in order not only to reduce resource costs and processing time, but also to give a result that is not tainted by bad data. Such examples can be widely seen in the machine learning community with methods such as data pruning: in an evaluation performed by  Saseendran et al. [2019] , they list the impact on a machine learning algorithm's performance by removing certain samples from a dataset. There is also a technique called data wrangling, explored in  Furche et al. [2016] , which tries to better understand a dataset to deliver the cleanest and most useful part of a bigger dataset to an application that requests it. As can be demonstrated by the needs of the machine learning community, optimizing datasets is one of the core principles of working with data.
Specifically, when working with motion data, selecting the correct set of motions has also been a long investigated subject  [Holden 2018; Holden et al. 2020 Holden et al. , 2017 Holden et al. , 2016;; Kovar et al. 2002; Lee et al. 2010; Reitsma and Pollard 2007 ] that has shifted considerably, for a over two decades, in respect to interpretability, goals and performance. The main issue, however, is that all these methods assume the quality of the dataset and there is no clear method to evaluate, visualize and optimize a whole motion dataset. This means that no matter how good a technique is, if the culprit is the data, there is no easy way of knowing this without shoveling through a huge amount of information.
Structure of a motion dataset As one can see in the wild [Adobe 2022;  Krebs et al. 2021; Lab 2000] , motion datasets can be quite different in their gathering methods and purposes. Establishing a unique structure, consequently, is a difficult process, as there is a multitude of capturing equipment from different manufacturers, such as Vicon, Qualisys, Xsens, Nokov and OptiTrack. Not only will their capturing methods be different (some use cameras and others use inertial measurement units), the characteristics of the data generated by each of those systems also will. This means that there is no unique structure, but more so of a general guideline and the adaptations that go with it for the different purposes that data can have. Thus, the usual capturing strategy, as noted on the above-cited datasets, is to use the joints in the human body as capturing points: since bones will not bend or move, the only moving parts in the human body are the joints.
Situation Although a number of approaches addressed some of the limitations inherent to using motion datasets, such as responsiveness  [Lee et al. 2010] , performance  [Clavet and Buttner 2015]  or memory footprint  [Holden et al. 2020 ], being able to interpret results obtainable using such motion datasets is still an open question. In particular, early work on motion fields  [Lee et al. 2010 ] was difficult to interpret, as it was not a graph structure, which is well understood and extensively researched. They specifically noted that new tools should be built to aid analyzing, editing and authoring capabilities for new motion fields animation controllers. Subsequently, motion matching reconciled motion fields and graph structures, which are easier to interpret, unlike learned approaches  [Fan et al.
2021; Holden et al. 2020]. The objective of this work is therefore to facilitate interpretation of such datasets by proposing a novel framework to analyze and identify the problem of lack of quality in motion data. It proposes new metrics to qualify a dataset and quantify whether motions are missing and, if so, how much and which type of it. By giving the user a better understanding of the limitations, possibilities and redundancies of a motion dataset, this work tries to endow the user with a tool to better evaluate a motion dataset on both macroscopic and microscopic perspectives.
A NOVEL ANALYSIS FRAMEWORK 3. 1 Overview This work proposes a framework to formalize the qualitative analysis of a locomotive motion dataset in three ways: accuracy, coverage and relevance (detailed in Section 3. 3). As for the first one, the main objective is to find how much positional and velocity errors are introduced by the dataset. As for coverage, the aim is telling how sparse/dense a dataset is, thus showing where more motion is needed or can be removed. As for the latter, the relevance metric shows the usage frequency of each motion frame and a graph structure showing the relationships between all motion clips and frames. To operate, the framework relies on a reproducible procedure (detailed in Section 3. 4), currently tailored for locomotive motions. This procedure is based on a parameterization across a spectrum of linear and angular velocities, leading to a number of circular trajectories that the virtual character has to follow and which enable the evaluation of different datasets under the same conditions.
Virtual characters are animated using the Motion Matching approach (introduced in Section 2), which works under the principle that every pose in the dataset is a potential transition at every frame. In the implementation of Motion Matching that we use, the criteria for transition is called transition cost and the lowest value is always selected. This transition cost is computed as the cumulative distance over a selection of joints between the current pose and the candidate pose. While our approach could be used with any animation method, Motion Matching was chosen for its responsiveness properties, and for the fact that it is now established as an industrial state-of-the-art standard in character animation.
Data collection and generation To analyse a dataset, our approach takes a set of animation files and a few parameters to evaluate as input, such as the reference linear and angular speed intervals, represented by    (/) and    (/), respectively. The    and    values are taken from two intervals, which are defined according to the sets {   ∈ ℜ| 1 ≤    ≤   } and {   ∈ ℜ| 1 ≤    ≤   }. At each trial, one (    ,     ) pair is taken from the intervals as starting variables for a simulation. Then, some variables are recorded or computed as follows: 3. 2. 1 Positional data. Any trajectory in the system will be composed of 2-dimensional positions (on the X-Z horizontal plane). Considering that, when starting a simulation, the system will have a reference (   ,    ) pair, which will be used to generate a reference trajectory, with constant (or piecewise constant) speed profile.
Accuracy. The accuracy of a dataset can be evaluated using a couple of metrics, which account for the behavior of linear and angular speeds. The first set of metrics is the two static errors   and   , which correspond to the difference of the mean   and   to the reference    and    over the whole duration of the simulation, defined as 𝑆𝐸 𝑣 = 1 𝑚 𝑚 ∑︁ 𝑖=0 𝑣 𝑟 -𝑣 𝑟𝑒 𝑓 (1) 𝑆𝐸 𝜔 = 1 𝑚 𝑚 ∑︁ 𝑖=0 𝜔 𝑖 -𝜔 𝑟𝑒 𝑓 (2) The second set of metrics is the dynamic speed errors   and   , which represent how spread away from the mean the speeds are: 𝐷𝑆𝐸 𝑣 = (𝑣 𝑟 -𝑆𝐸 𝑣 + 𝑣 𝑟𝑒 𝑓 ) 2 𝑐𝑎𝑟𝑑 ({𝑣 𝑘 𝑟𝑒 𝑓 ] ∈ ℜ|𝑣 1 ≤ 𝑣 𝑟𝑒 𝑓 ≤ 𝑣 𝑛 }) (3) 𝐷𝑆𝐸 𝜔 = √︄ (𝜔 𝑟 -𝑆𝐸 𝜔 + 𝜔 𝑟𝑒 𝑓 ) 2 𝑐𝑎𝑟𝑑 ({𝜔 ∈ ℜ|𝜔 1 ≤ 𝜔 𝑟𝑒 𝑓 ≤ 𝜔 𝑛 }) (4) The third metric is the offset error, measured in meters, which is calculated as the absolute euclidean distance from the final trajectory ( ) to the reference trajectory (   ).
Relevance. Considering that a dataset can also be optimized by removing frames or entire clips that are not used or redundant, calculating metrics to measure the relevance of said data is important. This work does that by calculating three metrics: transition cost, frame popularity and animation trajectory. First, a transition cost metric, based on joint distance, is introduced. It stores the cost involved in transitioning to and using the next frame in an animation. Considering that in the Motion Matching approach there is a pose transition at every frame and that the pose most similar to the current one, in all the dataset, is always chosen for it, then the higher the distance in the joints from the chosen pose to the current one, the higher the transition cost. Concurrently, we also propose to visualize the relevance of animation frames using a directed graph structure where both frame popularity and animation trajectory are presented.