dataesr/scientific-paragraphs-categorization huggingface dataset:
Challenge proposed to the participants SHREC, or 3D Shape Retrieval Challenges, are challenges primarily organized in order to evaluate the effectiveness of 3D-shape retrieval algorithms. A group organizes a challenge by building up a dataset, then proposes the challenge publicly to the community, and finally gathers, analyses and verifies the results. The theme of the challenge may vary from one to another, but all challenges take place in a limited time, which ranges from 1 to 1 1/2 months. In our contest, the participants were asked, given each of the query surfaces, to retrieve the molecular surfaces of proteins from the dataset that encompass the same domain as the query. Each query-todataset-surface distance was expected to be expressed as a dissimilarity score. The results were therefore 10 × 554 matrices of dissimilarity scores.
Participants and methods Among the seven groups that initially registered to this track, only were able to produce the results in time and returned a shape-only dissimilarity matrix. Only one method (3DZD, see 3. 1) returned a dissimilarity matrix for the shape + electrostatic dataset. The other groups were not able to produce a satisfying training dataset or willing to develop their algorithm to handle the electrostatics values. In the following subsections, each group describes their new, respective methods.
Summary of the 3DZD method Similar to SHREC ′ 20  [34] , our group trained two types of neural  network to output a score that measures the dissimilarity between a pair of protein shapes. Briefly, the first framework (the Extractor model) is structured into multiple layers: an encoder layer with 3 hidden units of size 250, 200 and 150, a feature comparator layer which computes the Euclidean distance, cosine distance, element-wise absolute difference and product, and a fully connected layer with 2 hidden units of size 100 and 50. There are multiple hidden units in each layer, and our group uses the ReLu activation function in all except the output of the fully connected layer where the sigmoid activation function is used to output the probability that the two proteins belong to the same protein-or species-level in the SCOPe dataset classification  [37, 38].
The second framework (the end-to-end model) is similar to the first except the feature comparator layer is removed and the output of the encoder is directly connected to the fully connected layer. The network is trained on the latest SCOPe dataset of 259,385 protein structures. 2500 protein structures were set aside for network validation. Proteins in Class I (Artifacts) were removed. Each of the two network frameworks is trained with two datasets. The first dataset is 3DZDs of the surface shape of proteins and the second one is feature vectors that concatenate 3DZD of shape and 3DZD of the electrostatic properties. Our group examined the performance of the networks on the validation dataset to determine which models to use. For the shape-only dataset, we submitted predictions generated by the Extractor model.
For the shape + electrostatic dataset, we submitted the average predictions between the Extractor model and the end-to-end model. For each protein in the provided dataset, our group performs a pre-processing step as follows: (1) the PLY mesh data file is converted to a volumetric skin representation (Situs file) where points within 1. 7 grid intervals are assigned with values that are interpolated from the mesh  [9]. For the electrostatic features, the interpolated values are the potentials at the mesh vertices. For the shape feature, a constant of 1 is assigned to grids that overlap with the surface. (  2 ) The resulting Situs file is fed into the EM-Surfer pipeline  [39]  to compute 3DZD.
Runtimes and computational cost It takes approximately 12-13 min to pre-process each file. Generating 3DZD took ~8. 00 s on average for each protein on an Intel® Xeon® CPU E5-2630 0 @ 2. 30 GHz. The training of each models took 12 h. Dissimilarity prediction between two proteins using the trained model took ~0. 22 s on average on a Nvidia® Titan X GPU. The averaging of the two matrices was almost instant and negligible. The code is available at the following url: https://github. com/kiharalab/shrec_2021_shape_ retrieval.
Data preparation and architecture training All the proteins of the dataset of the track have been sampled to 2048 points using a Poisson disk sampling technique  [41]  and then normalized into a zero-center unit sphere based on their respective minimum bounding spheres  [42]. The architecture has then been trained using a batch size of 16 on 80% of the dataset over 150 epochs and validated on the remaining 20% of the data. The training data were augmented on-the-fly (during the training process) by adding some geometric noise (e. g. random displacement of point coordinates in a limited interval).
Runtimes and computational cost This framework has been developed in Python 3. 7. 6 using different libraries, namely Open3D 0. 8. 0. 0, and Keras 2. 2. 4-tf on a TensorFlow-GPU 2. 1. 0 backend. The experiments have been conducted on an Intel Xeon® Gold® 5118 CPU@2. 30 GHz with 128 GB of memory and NVI-DIA® Tesla® T4 GPU with 16 GB of memory. The running times in s of each stage performed on CPU are reported in Table  1  for one protein. Table  2  shows the training times of the ProteinNet model trained on GPU. The code is available at the following url: https://github. com/ Benhabiles-JUNIA/ProteinNet.
In this work, we contribute a simpler approach, where each of the 6dimensional feature is binned into a 1-dimensional histogram with bins for each feature-dimension to produce a 210-dimensional local descriptor (APPFD) for every key point or local surface patch (LSP). Finally, the locally computed APPFDs are agglomerated into a compact code called the Fisher Vector (FV) with 4210 dimensions, which is L and power-normalized, and represents a single protein model, using the FK and GMM  [44]  framework. The goal of the APPFD-FK-GMM method/contribution is to provide a straight-forward, efficient, robust, and compact representation, describing the geometry of 3D protein surfaces, with a knowledge-based (i. e. non-learning) approach. While a single protein surface in this challenge contains an average of 120,000 vertices and 200,000 triangular faces, our implementation address this very high data-structure by reducing 3D protein surface representation to 3,500 points sample.
Runtimes and computational cost Our group submitted a dissimilarity matrix D = [10 × 554], where the entry D = [i, j] corresponds to the L 2 distance from ith FV in the query set to the jth FV in the collection set. While implementing the APPFD-FK-GMM for this task, K is specified by the parameter, vs = 0. 20, which is the voxel size for point cloud down-sampling, while the radius parameter, r = 0. 50 specifies the size of P. Regarding point cloud size, P = 3, 500 points are sampled. In conclusion, we present a pure Python 3. 60 implementation code that computes the APPFD-FK-GMM method. All experiments were carried out under Windows® 7 desktop PC with Intel® Core® i7-4790 CPU @ 3. 60 GHz, 32 GB RAM. It takes an average of 30 s to compute the APPFD-FK-GMM. The implementation code is available at the following url: https://tinyurl.
Runtimes and computational cost All the calculations were made on a computer based on a 64-bit OS with an Intel® Xeon® CPU @ 2. 30 GHz, a Nvidia® Quadro® k4200 GPU with 4 GB and 32 GB of RAM. Computing the WKS took on average 9 min and 31 s. It required on average 44 s to compute one PWKSM. The comparison of two surfaces (i. e 7 versus 7 PWKSM) takes on average 23 s. The code is available at the following url: https://gitlab. cnam. fr/gitlab/siruguej/PWKSM.
Runtimes and computational cost This method is implemented in Python 3. 8  [58] , using Pytorch  [59]  and Pytorch Geometric  [60]  libraries. The experiments were carried out a machine with an Intel® Core® i7-8700K 6-core CPU Processor @3. 70 GHz with 32 GB of RAM and an NVIDIA® TITAN V with 12 GB of VRAM. The training and test set's embedding extraction uses both the CPU and the GPU, while computation of distance matrix only uses the CPU. The detailed time report is represented in Table  3. The code is available at the following url: https://github. com/huunghia160799/SHREC-protei n-domains.
Evaluation metrics We use common evaluation metrics to assess the performance of the proposed methods, most of which are used in other SHREC tracks  [61]  or similar works evaluating the performance in retrieval  [62]. For each Fig. 6. Dynamic edge convolutional neural network. method, we compute the overall metrics (i. e the metrics averaged over all queries) and the individual metrics (i. e the metrics for each query) to provide a better understanding of the performance of each method for each query. Two composite classes are also presented: the SH3-like and the PDZ-like, which correspond to the grouping of the SH3 and SH3_2 classes, and of the PDZ and PDZ_6 classes, respectively. To set the dissimilarity values for these composite classes, for each entry of the dataset, we kept the minimal dissimilarity value from the SH3/SH3_2 queries and from the PDZ/PDZ_6 queries.
Confusion matrix A confusion matrix (CM) is a square matrix, whose columns represents the different classes of the dataset and rows the class of the query. For each row, each element CM(i, j) gives the number of objects from class i retrieved using the query j, considering the top k = C retrieved objects, C being the size of the class corresponding to query j. The elements CM(i, i) in the diagonal of the confusion matrix indicates the objects classified correctly, while the off-diagonal indicates mis--classified elements. To ease the comparison between classes of different sizes, the numbers were normalized over the class sizes C of the queries. Consequently, the sum ∑ j CM(i, j) of all elements of each row equals 1.
Shape-only challenge The results for the shape-only dataset are presented in Table  4  and in  Figs. 8  and 7. Table  4  summarizes the performances of all submitted matrices for the shape-only dataset. For each metric (Nearest Neighbor, First Tier, Second Tier and Mean Reciprocal Rank), the highest value is indicated in bold. Given the dataset structure and the selected query domains, the best method achieves an overall level of 0. 5 for the nearest neighbor metric, 0. 160 for the first tier, 0. 292 for the second tier and 0. 523 for the mean reciprocal rank. These results must be balanced by the fact that a few classes have only a small number of models (namely, the Stat-binding and m50 classes only have 6 and 4 members, respectively, see Fig. 2 ), and thus impact negatively the averaged results.
Shape + electrostatics challenge Similarly to the shape-only dataset, results for the shape + electrostatics dataset are presented in Table  5  and Figs. 9 and 10. Only one team returned a dissimilarity matrix for the shape + electrostatics dataset. The evaluation metrics are listed in Table  5. The results show similar trends compared to the shape-only dataset, with a nearest neighbor of 0. 5, a first tier value of 0. 16, a second tier value of 0. 321 and a mean reciprocal rank of 0. 454. These metrics are similar to the results obtained from the shape-only dataset for the 3DZD method (the second tier value increased while the mean reciprocal rank decreased). The perclass metrics are shown in Appendix D (Tables D11, D12, D13 and D14). The precision-recall curves (Fig. 9 ) show a similar overall behavior for the 3DZD method, whose performance improved significantly for the SH3 domain but decreased significantly for the PDZ domain (dark blue and green curves, respectively, left plot of Fig.
Shape-only dataset Overall, the 3DZD method obtained the best results, in line with the previous tracks on protein shapes, where this group similarly obtained overall good results  [33, 34]. This method relies on the use of 3D Zernike polynomials, which has been successfully used to retrieve proteins based on their shapes  [9]  or their Cα coordinates  [13]. It then uses a neural network trained on the SCOPe  [37, 38]  database, whose classification largely overlaps with the classification of the Pfam database  [16]. As instance, in the SCOPe databases, the SH3 domain and the SH3_2 domain are classified in two different SCOPe domains, similarly to the Pfam classification. The DGCNN used the data from another SHREC ′ 21 track, the Retrieval and classification of protein surfaces equipped with physical & chemical properties track  [35].
The organizers of this track, similarly to a previous track  [33] , proposed a set of shapes derived from NMR structures along their surficial physico-chemical properties to allow the participants to train their methods, and the resulting classification of the proteins was derived from the SCOPe database as well. The DGCNN and 3DZD methods were therefore trained on similar data, but   Another point to consider, is that the DGCNN method uses a sampling (down to ≈ 8, 000 points) of the initial point clouds (see 3. 5. 1) that potentially resulted in a loss of information that might explain the difference of performance between these two groups (DGCNN and 3DZD). However, the ProteinNet and APPFD-FK-GMM methods use more severe down-sampling steps as well to reduce the number of points down to 2,048 for ProteinNet (see 3.
2. 2) and to 3,500 for APPFD-FK-GMM (see 3. 3) with various outputs in terms of performance. These numbers should be compared to the initial meshes sizes, which range from 37,658 to 582,496 points. The APPFD-FK-GMM group, however, was able to better retrieve relevant results within the first hits, as evidenced by higher values of Nearest-neighbor and Mean Reciprocal Rank for the shape-only dataset (Table  4 ). While some methods were able to maintain medium precision levels at medium recall values (see section 5. 1), a few queries were difficult to handle for all methods. For the DNA-binding domain from the STAT protein family or the peptidase M50 domain, the low number of such surfaces (6 and 4, respectively) in the datasets explains the low performance observed for all methods. For the other queries, like the PDZ_6 domain or the SH3_2 domain, the explanation is the presence of closely related domains, the PDZ and SH3 domains, respectively.
Shape + electrostatic dataset While the shape of a protein is of tremendous importance, its surficial properties are important as well. Therefore, in this track, we generated the shape + electrostatic dataset which encompass both properties, in order to stimulate the development of such methods. However, only one groups returned a dissimilarity matrix for this dataset, namely the 3DZD group. Most groups that participated to this challenge come from the computer vision field. As such, most of the methods presented in this work are the result of methodological developments dedicated to the analysis of 3D point clouds. The development of new, specific methods to handle both shapes and electrostatics would require an amount of time far greater than the SHREC timeline. Nevertheless, we hope that this challenge along with the other challenge dedicated to protein shapes  [35]  would stimulate the development of such methods.
The overall results showed that the treatment of the electrostatics by the 3DZD marginally improved the results, compared to the shape-only results. Interestingly, the electrostatic potential impacted differently each class: it improved the ability of the 3DZD method to handle the SH3 or the Bromodomain classes, as evidenced by the precision-recall curves (Figs. 7  and 9 , Tables in Appendix C and Appendix D). The 3DZD method is derived from previous attempts from the same team to couple shape and electrostatics analysis to classify protein surfaces  [10]. As noted in this exploratory work, electrostatics may be suitable to compare closely related proteins, while our datasets was mainly composed of loosely related proteins (Fig. 1 ). The electrostatics potential feature is likely to be more beneficial for protein surface comparison of local features rather than global shapes.
Current machine learning-based methods: pitfalls and challenges Pitfalls of previously mentioned methods lies in the exploited protein datasets and their characteristics, notably the class imbalance as well as the high inter-class shape similarity. Indeed, the protein datasets are often highly imbalanced in terms of protein classes, which introduces a bias in the training process of these methods towards learning efficiently class representation. One common technique consists of using data augmentation to overcome this lack of original data. Hence, it is important to bear in mind that several protein classes cannot be considered as representative groups of protein families. Moreover, our problematic tackles a large quantity of classes composed of protein, which are e. g visually highly similar.
g both methods exceeding 0. 95 for the NN metric) on a problem similar to the shape-only problem (see Tables  6  and 7  of  [34] ). However, the adapted versions (3DZD and APPFD-FK-GMM) did not reach the same level of performance by exploiting this new, particular dataset of proteins. This decrease of performance (and low performances from the three other methods) reveals that this year dataset was particularly hard to analyze, and that there is still room for improvements. Among the proposed methods, we observed that 3 over 5 used a learning-based protocol at some point. This proportion is in line with last year track, and show that such approaches continue to be investigated as they usually improve the results. To this regard, the SHREC ′ 21 track on Retrieval and classification of protein surfaces equipped with physical & chemical properties might highlight some interesting points on the best architecture to learn protein surficial properties  [35].
showed that the electrostatic potential have the greatest impact of the physico-chemical features they computed. In our work, the electrostatic potential was used by the 3DZD team as an additional feature to help the retrieval task. The results reveals only slightly improved results compared to the results from the shape-only dataset. As noted in Ref. [10] , the electrostatic potential may be of better use to compare closely related proteins, rather than comparing loosely related proteins, as it is the case in our work. Alternatively, shapes and electrostatics may be used in hierarchical way, i. e using first the shape then the electrostatics to achieve a better result. This track reveals significantly lower results when compared to past tracks  [31] [32] [33] [34]. However, satisfactory solutions exist to distinguish between loosely related proteins, or to identify identical proteins with different conformations, based on their shapes only.
Our work also reveals some limits of the methods used by the participants for the challenge. Very closely related proteins (such as SH3 and SH3_2 protein domains), i. e proteins displaying a high topological similarity and limited variations of their amino-acid sequences, hence surfaces, are still difficult to separate in different classes, but some methods distinguish them from the other classes. Also, when we consider the DNA-binding domain from the STAT proteins, no method was able to produce satisfactory results. While the DNA-binding domain used as a query has a globular shape, the STAT proteins are significantly bigger, with a non-globular shape, and have 3 additional domains (1 of which is a SH2 domain, a domain included in the dataset), which means that only partial matches compared to the query may be achieved.
Funding Léa Sirugue ,  Matthieu Montès  and  Florent Langenfeld  are supported by the  European Research Council Executive Agency  under the research grant number  640 , 283. Daisuke Kihara  acknowledges supports from the  National Institutes of Health  ( R01GM133840 ,  R01GM123055 ) and the  National Science Foundation  ( DBI2003635 ,  CMMI1825941 , and  MCB1925643 ). Charles Christoffer is supported by  NIGMS -funded pre-doctoral fellowship  ( T32 GM132024 ). Huu-Nghia H. Nguyen ,  Tuan-Duy H. Nguyen ,  Vinh-Thuyen Nguyen-Truong ,  Danh Le ,  Hai-Dang Nguyen , and Minh-Triet Tran are supported by  National University Ho Chi Minh City (VNU-HCM)  ( DS2020-42-01 ).
The data presented here cover ∼4 decades of electrofishing surveys in mainland France rivers. Despite some changes in field protocols and sampling site locations over time, the ASPE database offers a unique set of 47,869 georeferenced and standardised sampling surveys. Fish individuals are identified to species, measured and weighted. Additional information encompasses habitat features as well as "river health" assessment by two fish-based indices. Though preferentially focused on fishes, the surveys also report crayfish data. Numerous studies have been already published using these data, mainly on the fields of macroecology, community and/or population ecology and bioassessment. However, we believe much more knowledge can be gained from the database in fields such as population dynamics or conservation practices in the context of global change.
, 2019)  and only 12. 5% of the freshwater fish species have a good conservation status according to the EU Habitats Directive  (PatriNat, 2019). The understanding of species responses to global change, and the assessment of species conservation status in order to establish efficient management plans, both require long-term monitoring. Such long-term monitoring data are globally rare  (Comte et al. , 2021). The ASPE ("Application de Saisie des données Piscicoles et Environnementales") database gathers together biological and environmental information on monitoring sites covering the French territory since the sixties. ASPE is unique in spatial extent (mainland France, ∼550,000 km 2 ), number of georeferenced sampling sites (∼6000) and time span (40þ years). The Office Français de la Biodiversité (OFB, https:// www.
ofb. gouv. fr/) currently administrates the database. ASPE was the main dataset used for the production of species distribution maps for the French atlas of freshwater fishes  (Keith et al. , 2020). The ASPE dataset also supported research in the fields of macroecology (e. g. Reyjol et al. , 2007; Hugueny et al. , 2011) , species ecology (e. g. Cattanéo et al. , 2002; Poulet et al. , 2011; Bret et al. , 2016 Bret et al. , , 2017;; Santos et al. , 2021) , community ecology (e. g. Oberdorff et al. , 1998 Oberdorff et al. , , 2001a;; Tales et al. , 2004) , bioassessment (e. g. Oberdorff et al. , 2001b Oberdorff et al. , , 2002;; Marzin et al. , 2012 Marzin et al. , , 2014;; Clavel et al. , 2013) , climate change (e. g. Buisson et al. , 2008; Buisson and Grenouillet, 2009; Comte and Grenouillet, 2013; Comte et al.
, 2014 Comte et al. , , 2016)) , biological invasions  (Manné and Poulet, 2008) , etc. Nonetheless, we believe much more research can be achieved due to the recent improvements in the quality of the data, the stability of the sampled sites network, the standardisation of the sampling schemes, and the overall amount of surveys reported. Here, we publish for the first time the ASPE database. Our workflow was designed to ensure reproducibility  (Peng, 2011). It includes a data processing step to produce the figures, maps and plots, followed by an authoring step to assemble these elements with the text with R Markdown  (Xie et al. , 2018). The R  (Ihaka and Gentleman, 1996)  and R Markdown  (Xie et al. , 2018)  files used for data preparation and manuscript production allow any reader wishing to reproduce or modify our analyses to do so.