dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of active modules The search for active modules has been performed with AMINE  (Pasquier et al, 2021 Preprint). The AMINE method was designed to identify the modules of genes that are triggered in a biological experiment. It uses as input the background knowledge on the interactions between genes and measurements representing, in the specific context of a given experiment, indicators of the involvement of genes in the studied process. Gene interactions data are retrieved from the String database  (Szklarczyk et al, 2017)  with a combined evidence score ≥0. 7 and included in the application. Measurements on the activity of genes must be supplied by the user. They are represented by the P-value, adjusted P-values, or FDR associated with the fold changes of the genes. At a minimum, the data are composed of a tabular file consisting of two columns: one containing the name of the gene and the other, the associated P-value.
The last phase of the method consists in combining the different clusters while ensuring that new merged clusters remain spatially cohesive  (Fig 8F). We say that two clusters M i and M j are spatially cohesive when M i \ M j ≠ ∅. Starting from the module with the higher z-score, the process consists in evaluating all possible clusters formed by M i [ }(M j/j≠i ) using the z-score, with }(M i ) denoting the powerset of M i and keeping the modules with the highest z-score. The workflow of the AMINE method is presented in Fig 8. The details of the method and the demonstration of its effectiveness are described in an article by  Pasquier et al (2021)  Preprint. The great advantage of AMINE is that it does not require any settings; it is not even necessary to indicate the number of modules to be identified or the size of the modules.
Acknowledgements This work was supported by the  French National Research Agency (ANR)  grant " Methylclonome "  ANR-12-BSV6-006-01  to  A Robichon  and  C Pasquier. This work was also supported by the  French National Research Agency  through the  LABEX SIGNALIFE program  (reference #  ANR-11-LABX-0028-01 ) and by the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  under reference number  ANR-15-IDEX-01. The authors are grateful to the  Observatoire Pluridisciplinaire des Alpes-Maritimes (OPAL) infrastructure from Universit é Côte d'Azur  and the  Universit é Côte d'Azur's Center for High-Performance Computing  for providing resources and support.
Data access The transcriptomic datasets were retrieved from the Sequence Read Archive: https://www. ncbi. nlm. nih. gov/sra with accession numbers SRR070436, SRR070437, SRR100281, SRR070430, SRR100278, SRR100282, SRR070388, SRR070419, SRR100275, SRR070434, SRR070435, SRR100279, SRR070414, SRR070415, SRR111882, SRR070420, SRR116383, and SRR100274.
Electron tomography is widely employed for the 3D morphological characterization at the nanoscale. In recent years, there has been a growing interest in analytical electron tomography (AET) as it is capable of providing 3D information about the elemental composition, chemical bonding and optical/electronic properties of nanomaterials. AET requires advanced reconstruction algorithms as the datasets often consist of a very limited number of projections. Total variation (TV)-based compressed sensing approaches were shown to provide highquality reconstructions from undersampled datasets, but staircasing artefacts can appear when the assumption about piecewise constancy does not hold. In this paper, we compare higher-order TV and wavelet-based approaches for AET applications and provide an open-source Python toolbox, Pyetomo, containing 2D and 3D implementations of both methods.
A highly sampled STEM-HAADF dataset of an Er-doped porous Si sample and a heavily undersampled STEM-EELS dataset of a Ge-rich GeSbTe (GST) thin film annealed at 450 • C are used to evaluate the performance of the different approaches. We show that polynomial annihilation with order 3 (HOTV 3 ) and the Bior4. 4 wavelet outperform the classical TV minimization and the related Haar wavelet.
Therefore, to minimize the total acquisition time and beam damage, AET datasets often consist of spectrum image datacubes acquired with a low signal-to-noise ratio and a relatively large tilt increment of 5 • to 10 •. Under these noisy and undersampled conditions, conventional reconstruction algorithms are not suitable as they yield poor image quality reconstructions with fanning artefacts. Various approaches have been proposed to overcome these intrinsic limitations: in  [9] , the convex hull of an object was estimated by geometric tomography from its binarised STEM-HAADF projections and used to mask the fanning artefacts present in the STEM-EDX reconstructions. Later, Zhong et al. introduced a bimodal HAADF-EDX tomography (HEBT) technique that combined STEM-HAADF and STEM-EDX data into one tomographic reconstruction process  [10].
HEBT is suitable for samples where the following assumptions are fulfilled: (1) both STEM-HAADF and STEM-EDX datasets satisfy the projection requirement, and (2) the STEM-HAADF datasets are the weighted sum of the elemental maps for all elements present in the sample. The first implementation of HEBT  [10]  used the simultaneous iterative reconstruction technique (SIRT) algorithm, yielding STEM-HAADF and STEM-EDX reconstructions that were still affected by fanning artefacts. Recent algorithmic developments using the compressed sensing (CS) framework  [11, 12]  have shown promising results for reconstructing volumes from heavily undersampled datasets  [13] [14] [15]  and are becoming increasingly popular for AET applications. These approaches exploit the object's sparsity in a chosen transform domain and employ the l1-norm minimization to recover a sparse solution under an l2-norm data consistency constraint.
Total Generalized Variation (TGV)  [21] [22]  and polynomial annihilation edge detection methods  [23] , referred to as HOTV (High Order TV), promote piecewise smooth solutions while preserving sharp edges. These approaches have been applied to STEM-HAADF  [24]  and STEM-EDX  [25]  datasets with improved performances compared to TV. Joint HAADF and spectroscopic reconstructions using TV and TGV have also been proposed to improve the reconstructions' quality, but this strategy is restricted to objects with boundaries recovered in both modalities  [25, 26]. Wavelet-based CS methods are widely used in MRI  [27] [28] [29]  and have been applied in TEM mainly for denoising  [30, 31]  and inpainting purposes  [32, 33]. They have recently gained interest in X-ray tomography  [34]  and ET  [13, 35, 36] , as they apply to a broader range of objects than gradient-based methods.
However, only gradient-based methods have thus far been made accessible to the community via open-source packages (e. g. [24, 25] ). In this work, we present an open-source toolbox, Pyetomo, where we implemented both gradient-based and wavelet-based methods for 2D slice-by-slice and 3D tomographic reconstructions. The current version of Pyetomo allows the testing of all the wavelet families built in the Pywavelet library, and makes use of cutting edge optimization algorithms initially developed for MRI and astrophysics applications  [28, 37]. Using this toolbox, we compare the performance of gradient-based and wavelet-based approaches on a test object and assess the quality of the reconstructions of an undersampled AET dataset. Instead of generating a tilt series from a simulated test object, we use a highly sampled STEM-HAADF dataset of an Er-doped porous Si sample.
The parameter λ controls the balance between the two terms. ‖ R(x) = x ‖ 2 2 corresponds to the minimal energy solution to the inverse problem. This regularisation is not sufficient for tomographic reconstruction with an undersampled dataset as it produces images with over-smoothed features. R(x) = ‖ Lx ‖ 0 promotes sparsity of x in the transform domain L (few non-zero elements in Lx). In the framework of CS, it was shown that under certain conditions (including sparsity of x in L and incoherence of L and P), the l 0 quasi-norm could be replaced by its convex envelope l 1 norm, leading to a convex optimization formulation: argmin x { 1 2 ‖ Px -y ‖ 2 2 + λ‖ Lx ‖ 1 } (4) In the Pyetomo toolbox, we implemented two types of transform domains: -Gradient-based regularisations: With L = ∇, the regularisation term in Eq.
Pyetomo toolbox The Python toolbox developed in this work is made available in this git repository: www. github. com/CEATomo/Pyetomo. It contains the 2D and 3D implementations of TV, HOTV, and wavelets using the PyWavelets library  [50]. For the data consistency term, we used the Non-Uniform Fast Fourier Transform (NUFFT) operator and its adjoint, implemented in the PyNUFFT package  [51]. Various algorithms have been proposed in the literature to solve the optimization problem in the CS reconstruction (Eq. (  4 ))  [52]. In this work, we used the Condat-Vu primal-dual splitting method, included in the ModOpt package  [37]. A non-negativity constraint was also added to the cost function. Fig. 2  summarises the different features of the toolbox. We point out in italic the options that were not tested in this work but are included in the toolbox or can easily be imported: the proximal gradient methods are included in ModOpt; the Radon-based projection operator can be imported from Astra toolbox  [53] ; Sparse2D package can be imported from https://github.
Experimental data acquisition and processing 2. 3. 1. STEM-HAADF tomography of Erbium-doped porous silicon As reported in  [38] , a needle-shaped sample was prepared by focused ion beam (FIB) and mounted onto an on-axis tomography holder (Fischione model 2050). A full 180 • tilt range tomographic dataset was acquired in a double-corrected Titan 80-300 kV STEM (Thermo Fisher Scientist) operated at 80kV, with a tilt increment of 1 • , a frame size of 1024*1024 pixels and a pixel size of 0. 41 nm. Fig. 3 (a) is the STEM-HAADF projection of the sample at 0 • tilt angle. The tilt series alignment was performed following the procedure described in  [39]. SIRT yielded a high-quality reconstruction with fine details about the pores and the Erbium clusters (Fig. 3(b-e )). In this paper, we compared the CS reconstructions of a selected slice (indicated in green in Fig.
STEM-EELS tomography of Ge-rich GST material annealed at 450 • C A needle-shaped sample of a Ge-rich GST thin film annealed at 450 • C was prepared by FIB and transferred onto an on-axis tomography holder. STEM-EELS acquisition was performed at 200kV in a probe-corrected Titan 80-200 kV STEM (Thermo Fisher Scientist) equipped with a Quantum Gatan imaging filter. 18 STEM-EELS datacubes were acquired over a tilt range of 180 • , with a pixel size of 2nm, a frame size of 100×75 pixels, and a dwell time of 0. 1 sec/pixel for the energy range of 250-2398 eV. The overlapping of Sb M 4,5 -edge (528 eV), O K-edge (532 eV) and Te M 4,5 -edge (572eV), and the presence of Ge in both phases make the use of classical elemental analysis difficult (see Figure  S2 ). We instead used vertex component analysis (VCA) to identify the different chemical components present in the hyperspectral dataset, including the SiN capping, the GST phase, the Ge phase, and the SiO 2 substrate.
Effect of undersampling A selected slice through the Er-doped Si needle was reconstructed with the full STEM-HAADF dataset (180 projections), using the 2D implementation of TV, Haar wavelet, HOTV 3 (HOTV with order 3) and Bior4. 4 sparse representations. The maximum number of iterations was set to 300, and the non-negativity constraint was added to the cost function. Different values of the regularisation parameter λ were tested. As shown in Fig. 5 , a small λ yielded reconstructions influenced mainly by the data fidelity term. By increasing λ, more weight was given to the regularization term, inducing artefacts specific to each sparse representation. Based on the visual quality assessment of the reconstructions, an optimal value of 4 for TV and HOTV 3 and 10 for Haar and Bior4. 4 was selected.
Reconstructions with datasets consisting of 90 (2 • increment), 45 (4 • increment), 36 (5 • increment) and 18 (10 • increment) projections were then performed and compared to the reconstructions with 180 Fig. 2. Projection operators, sparsity transforms, and optimization algorithms included in the toolbox Pyetomo. In italic are the features that are included but were not tested (FISTA, POGM), and those that can be easily incorporated in the future (TGV, the Radon operator and advanced wavelets from Sparse2D). projections. Fig. 6 (a) shows a selected region from the reconstructions. The normalized root mean square error (nRMSE) and structural similarity index (SSIM), as implemented in the module metrics from the scikit-image library, were used to assess the images' quality and evaluate the performance of the CS methods as a function of the angular increment (Fig.
6(b, c )). Based on these metrics, we see that Bior4. 4 yields improved results compared to TV and Haar regularisations, which exhibit the same behaviour and produce strongly degraded images as the angular increment increases. HOTV 3 lies between Bior4. 4 and TV/Haar, with a relatively high nRMSE at 10 • increment. Note that for each sparse representation, the corresponding fully sampled reconstruction was used as a reference image. As shown in Figure  S1 , selecting the same reference image for all CS methods (the Bior4. 4 reconstruction) yielded SSIM and nRMSE curves with the same trends as Fig. 6 (b, c ). These results are coherent with the visual assessment of the reconstructions: we notice that TV and Haar, which can be regarded as a multiscale version of TV, yield similar results and suffer from block artefacts when the dataset is heavily undersampled.
CS methods applied to STEM-EELS datasets The GST tilt series was used for the choice of the regularization parameter λ. Fig. 8  shows the reconstructions of a selected xy slice using the 2D implementations with 300 iterations and the non-negativity constraint. We see that as λ increases, both TV and Haar yield flat, blocky GST regions and over-regularised edges. HOTV 3 and Bior4. 4, on the other hand, preserve the surface roughness of the GST grains and recover fine details. HOTV 3 reconstruction shows fewer oscillations than in the STEM-HAADF reconstruction of the Er-doped Si sample. We can  attribute this to the difference in dimensions of the STEM-HAADF (1024×1024 pixels) and STEM-EELS chemical phase (100×75 pixels) projections. The correlation between the size of the images and the unwanted oscillations is discussed in more details in  [55].
Discussion In this work, we implemented gradient-based and wavelet-based reconstruction methods in the Pyetomo toolbox and applied them on STEM-HAADF and STEM-EELS datasets. TV and Haar yield very similar results and are mostly adapted for simple piecewise constant structures. TV and Haar are not suitable for more complex geometries and with heavily undersampled datasets. HOTV 3 approach outperforms these two methods as it eliminates the block artefacts but suffers from unwanted, noise-like oscillations, which makes the segmentation a challenging task. Our results confirm previous findings that the oscillations are related to the dimension of the reconstructed volume. Bior4. 4 yields the best results for STEM-HAADF and STEM-EELS datasets and is in excellent agreement with HOTV 3 for the 3D quantification of the STEM-EELS reconstructions.
Conclusion In this work, we have presented the Pyetomo toolbox and used it to investigate the performance of gradient-based and wavelet-based CS techniques for the reconstruction of heavily undersampled datasets, with a particular interest for AET applications. As an example, STEM-EELS tomography was performed on a Ge-rich GST material to understand better the phase separation process occurring at high annealing temperatures. We showed that polynomial annihilation with order 3 (HOTV 3 ) and the Bior4. 4 wavelet provide higher quality reconstructions than the classical TV minimization and the related Haar wavelet, making the segmentation and the quantification steps more reliable. Using an Er-doped Si structure as a test object, we also confirmed, as reported in  [19] , that the minimum number of projections required for a high-quality reconstruction strongly depends on the complexity of the object, regardless of the reconstruction algorithm.
Acknowledgements This work was carried out at the  Nanocharacterization Platform (PFNC) of Minatec , supported by the ' Recherche technologique de Base' program  of the  French Ministry of Research. We thank  Tony Printemps  for the acquisition of the porous Si dataset. We acknowledge the financial support of the  Cross-Disciplinary Program on Numerical Simulation of CEA , the  French Alternative Energies and Atomic Energy Commission.
Conclusions: The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, , biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology.
However, fine-grain data on the local co-occurrence of species in plant communities across continental or global spatial extents are required for macroecological studies that link diversity patterns and assembly processes  (Bruelheide et al. , 2019). Nevertheless, information on broad-scale, fine-grain plant distribution is still scattered, inconsistent, and often of uncertain data quality, especially for bryophytes and lichens  (Beck et al. , 2012). Vegetation plots stored in large databases (e. g. , European Vegetation Archive, EVA,  Chytrý et al. , 2016; sPlot, Bruelheide et al. , 2019)  are increasingly used to explore fine-grain plant diversity patterns and underlying assembly processes at continental to global extents  (Bruelheide et al. , 2018; Večeřa et al. , 2019; Axmanová et al. , 2021).
, 2020a). At fine spatial grains (< 100 m 2 ), they can even hold higher plant diversity than tropical forests  (Wilson et al. , 2012). After an early and rudimentary attempt of benchmarking grassland diversity globally (Faber-Langendoen & Josse, 2010),  Dengler et al. (2016a)  provided a first overview of benchmarking plant diversity of Palaearctic grasslands based on a relatively small dataset. Here, we use GrassPlot, the database of multi-scale plant diversity in Palaearctic grasslands m 2 , 10 m 2 , 100 m 2 and 1000 m 2. We aim to display hot-and cold-spots of fine-grain α-diversity (species richness) across biomes and vegetation types. Besides total plant richness (complete vegetation), we separately assess vascular plant, bryophyte and lichen richness, as it has been already demonstrated that the richness of these taxonomic groups should be assessed separately  (Dengler et al.
Data compilation We used plot-based data from the collaborative vegetation database GrassPlot  (Dengler et al. , 2018 ; https://edgg. org/databases/GrassPlot), affiliated to the Eurasian Dry Grassland Group (EDGG), and registered in the Global Index of Vegetation-Plot Databases (EU-00-003; GIVD;  Dengler et al. , 2011). 1 ) distributed across 49 countries (Appendix S2, Table  S2. 1), eight biomes throughout the Palaearctic realm and a wide range of open habitats, such as grasslands, dwarf shrublands, wetlands, and deserts (Figure  1 , Table  S2. 2). Biomes were assigned using the biome classification provided in  Bruelheide et al. (2019) , which is based on the nine ecozones of  Schultz (2005)  plus an additional alpine biome based on  Körner et al. (2017). Plots were also assigned to ten geographic regions following  Dengler et al.
Accepted Article This article is protected by copyright. All rights reserved categories: natural grasslands, secondary grasslands, azonal communities, dwarf shrublands, tallforb and ruderal communities and deserts and semi-deserts (more details in Appendix S2, Table  S2. 2 and Figure  S2. 1). Plots were assigned to vegetation types based on expert knowledge either individually by data owners or using general assignment rules of phytosociological syntaxa to vegetation types (see Table  S2. 3). 75% of plots in the dataset have a phytosociological assignment at least at the class level. GrassPlot includes plot data sampled following two alternative methods for recording the presence of vascular plant species: "rooted presence", which only records individuals as present in the plot if they root inside, and "shoot presence", which records individuals as present if any part of stems or leaves are inside the plot  (Dengler, 2008).
The majority of plots in the dataset were recorded using the "shoot presence" method, and 13. 4% of plots used "rooted presence", while only a small fraction (0. 1%) used a combined method, where shrubs were recorded using "rooted presence" and grasses and forbs using the "shoot presence", or the recording method was not known (0. 2%). For linguistic convenience, we include lichens under the generic term "plants". Thus, we considered four taxonomic groups: vascular plants, bryophytes, lichens and complete vegetation (i. e. , the sum of the former three groups).
Accepted Article This article is protected by copyright. All rights reserved (https://edgg. org/databases/GrasslandDiversityExplorer) for free download. We intend to update it at regular intervals while keeping former versions available to make any studies based on these data reproducible. Many nested series contain several subplots of the same size. Sometimes these are multiple contiguous subplots covering the entire surface of the largest plot. Because of a high degree of spatial pseudoreplication, using these richness values separately for calculating mean richness might bias the results. Thus, for all benchmarks, except for the maximum and minimum richness, we used the averaged values of each grain size in each nested series, i. e. , only the independent observations. The number of independent observations decreased from 126,524 to 48,449 plots (Table  1 ), 6,509 of them belonging to nested series with at least seven of our standard grain sizes, 16,499 belonging to nested series with less than seven standard sizes, and 25,441 individual plots.
In the dataset containing only independent observations, the percentage of plots using "rooted presence" rose from 13. 4 to 23. 4%. We also added two filtering options as they can have significant effects on resulting richness patterns. (1) We allow filtering for data that were sampled with "rooted presence" or "shoot presence". As has been shown theoretically  (Williamson, 2003)  and empirically  (Güler et al. , 2016; Cancellieri et al. , 2017; Zhang et al. , 2021) , species richness recorded with the rooted method deviates increasingly negatively from values recorded with the shoot-presence method as grain size decreases. (  2 ) Subsetting to only those plots belonging to nested series with at least the seven "EDGG standard grain sizes" (0. 0001 to 100 m²; see  Dengler et al. , 2016b ) is also possible.
Development of the GrassPlot Diversity Explorer The GrassPlot Diversity Explorer (https://edgg. org/databases/GrasslandDiversityExplorer) was developed to provide a dynamic version of the GrassPlot Diversity Benchmarks. We did this in R version 4. 0. 2 (R Core Team, 2020), using the 'shiny' package  (Chang et al. , 2020). We also used other R packages, including 'tidyr' and 'dplyr' for data preparation  (Wickham & Henry, 2020; Wickham et al. , 2020) , 'ggplot2', 'ggpubr' and 'sunburtsR' for visualisation of the outcomes  (Wickham, 2016; Bostock et al. , 2020; Kassambara, 2020) , 'summarytools' for generating summary statistics  (Comtois, 2020) , 'leaflet' for producing an interactive map  (Cheng et al. , 2019) , and 'shinyWidgets' and 'shinycssloaders' to increase the functionality of the shiny package  (Perrier et al.