dataesr/scientific-paragraphs-categorization huggingface dataset:
Search strategy, study characteristics, data extraction protocols, quality assess- ment, and analysis results (heterogeneity, publication bias, etc. ). Results: Supplementary results for umbrella review on menopausal hormone therapy and women's health. Table A: Search strategies used to retrieve papers from different databases. Table B: Characteristics of included systematic reviews and/or meta-analyses of randomized controlled trials on menopausal hormone therapy and multiple outcomes. Table C: Characteristics of included systematic reviews and/or meta-analyses of observational epidemiological studies on menopausal hormone therapy and multiple outcomes. Table D: Quality assessment of included systematic reviews and/or meta-analyses of randomized controlled trials on menopausal hormone therapy and multiple outcomes.
87 µm and were rescaled to a resolution of 5 µm such that the grain sizes were comparable with the Finney pack geometries. For the simulations, the CT images from  Molnar (2016)  were segmented in MATLAB using Otsu's method  (Otsu, 1979)  to generate a binary volume image. The average porosity of the middle section of the Finney pack was 48%, whereas the average porosity of the quartz pack was 33%. These values are on the higher and lower end of the values reported for the experimental columns (42%  9%), respectively. For our simulations, twenty two-dimensional (2D) domains were randomly selected from each 3D volume, as illustrated in Figure  1. The domain size was 1 by 1 mm (200 by 200 grid cells) for the Finney pack geometries (numbered as FGeo1-20) and was 1 by 1. 75 mm (200 by 350 grid cells) for the quartz pack geometries (numbered as QGeo1-20).
Department of Energy, Office of Fossil Energy , under Award Number  DE-AC02-05CH11231 , the  Agence Nationale de la Recherche (ANR)  under Contract No. ANR-18-CE05-0035-01 , a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" Program LabEx VOLTAIRE ,  10-LABX-0100 , and the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 847593.
Data Availability Statement All geometry data can be accessed through the digital rock portal (https://www. digitalrocksportal. org/). The fluid chemistry and thermodynamic and kinetic data are available in the cited references:  Chagneau et al. (2015) ,  Marty et al. (2015) ,  Nielsen & Söhnel, (1971) ,  Palandri (2004) , and  Poonoosamy et al. (2019). The code of CrunchTope is available at https://bitbucket. org/crunchflow/crunchtope-dev. Example input files and database for the CrunchTope simulation can be accessed at https://github. com/denghangPU/diffusion-dominated-pore-scale-precipitation. git.
AGAT is a Java library dedicated to the construction, handling and evaluation of binary partition trees, a hierarchical data structure providing multiscale partitioning of images and, more generally, of valued graphs. On the one hand, this library offers functionalities to build binary partition trees in the usual way, but also to define multifeature trees, a novel and richer paradigm of binary partition trees built from multiple images and / or several criteria. On the other hand, it also allows one to manipulate the binary partition trees, for instance by defining / computing tree cuts that can be interpreted in particular as segmentations when dealing with image modeling. In addition, some evaluation tools are also provided, which allow one to evaluate the quality of different binary partition trees for such segmentation tasks.
This expert / domain-based information is crucial in certain application fields. In other words, the binary partition tree is not only an image-oriented but also a knowledge-based data structure. As a consequence, it can be relevantly involved in image analysis tasks that require the embedding of expert-defined priors and knowledge. For instance, the binary partition tree is quite popular in remote sensing applications  [20, 21, 22, 23, 24, 25, 26] , where the way to decompose an image depends on its content (e. g. an urban area vs. a wild forest zone) but also on the purpose of the analysis (e. g. classifying the buildings vs. observing pollution effects). Various software programs and libraries are available for hierarchical image model handling. GraphBPT 1   [27]  is especially designed for building and using binary partition trees.
Software architecture AGAT is composed of three modules (Image, BinaryPartitionTree, TreeEvaluation) required for the construction of binary partition trees from images, their handling and their evaluation. The three modules are coded in pure Java 8, taking advantage of the latest language innovations. They also contain some external Java libraries, encapsulated in the projects in the form of. jar files (mainly for input / output). The Image module is independent. It contains basic tools for manipulating and processing raster images (e. g. equalization, conversion, channel management, inputs / outputs). Images are classically encoded via the BufferedImage class which is part of the Abstract Window Toolkit (AWT), a graphics library commonly used by the Java community. The BinaryPartitionTree module is dependent on the Image module.
It contains various functionalities for building binary partition trees in a usual way, but also to define multifeature trees from consensus of multiple images and / or multiple criteria. This module also allows to handle the trees, for instance by defining / computing tree cuts that can be interpreted in particular as segmentations when dealing with images. In AGAT, binary partition trees are modeled and analyzed through their hierarchical representations. They are encoded as trees, where each node is a region of the image support. The main data structures of the library are thus a graph class, implemented as an adjacency list (required for the construction step of the binary partition trees relying on an region adjacency graph), and a tree class, classically implemented with inheritance relationships.
To enable the multiple images and / or multiple criteria paradigm, data structures based on ordered lists of valued edges have been also implemented. These data structures are a bit specific because they should allow to choose efficiently the next edges to be selected during the construction of the trees. Multiple index systems and optimized iterators have thus been implemented to make it possible to speed up their scans. In addition, when the number of criteria to be considered is important, the lists are kept sorted sporadically, after a fixed number of modifications, which approximates the expected solution but enables better scaling. The TreeEvaluation module is dependent on the BinaryPartitionTree module. It provides various classes related to the quantitative evaluation of the quality of a binary partition tree (or its meta-parametrization) with respect to object segmentation purposes.
Both intrinsic and extrinsic analyses can be carried out. For a given binary partition tree (an object built from the BinaryPartitionTree module), the user can provide examples of groundtruth (defined as binary regions of interest in the image support) and quality metrics. The main data structures of this module make it possible to manage both information on the ground-truth segments provided by the user (e. g. coordinates of the bounding box, semantic labels, etc. ) but also sub-trees of interest on which the analysis is carried out (to make processing faster by restricting it spatially and hierarchically).
Software functionalities AGAT proposes a large amount of algorithms for the construction, the handling and the evaluation of binary partition trees (complementary technical details regarding the main data structure architecture can be found in [34, Appendix B]): • BPT construction: mono-image / mono-criterion  [11] : tree construction from pixels or flat zones or a given partition, various generic (e. g. color: RGB, LAB, geometric: elongation, smoothness) and thematic (e. g. NDVI, NDWI) criteria are available; multi-image / multi-criteria  [30] : efficient process to establish different kinds of consensus among the adjacency lists (e. g. majority vote, most frequent, etc. ), visualization of the conflict between metrics. The construction criteria and the consensus policy are provided by the user as parameters of the (mutltifeature) binary partition tree construction process (an example can be found in the code snippet of Listing 1).
Listing 1: Code snippet for building a binary partition tree using AGAT. These multispectral images are at a spatial resolution of 60 cm with 4 spectral bands (red, green, blue, near infrared). We considered four criteria for building the binary partition trees, each one modeling either radiometric or geometrical information: • C colour , defined as the increase of the range of the pixel intensity values for each radiometric band, induced by the putative fusion of incident regions; • C ndvi , that quantifies the difference of NDVI (Normalized Difference Vegetation Index, a standard indicator for the presence of green vegetation) between two adjacent regions; • C ndwi , that quantifies the difference of NDWI (Normalized Difference Water Index, a standard indicator for the presence of water) between two adjacent regions; • C elong , defined as the change of geometrical elongation, potentially induced by the fusion of two regions.
the associated ground-truth, with respect to the size of inside / outside markers generated from the ground-truth associated to the input image; • a framework adapted from  [36]  that aims at computing the best cuts composed of k nodes for each k in [0, p] (p > 0), allowing to reconstruct the targeted segmentation. In  [33] , extensive experiments were carried out with three commonly used datasets: Grabcut  [37] , Weizman  [38]  and VOC  [39]. Examples of partitioning of an image from the Grabcut dataset are illustrated in Figure  2. These partitions are obtained from three different binary partition trees, each one built with a given criterion, noted WSDM, Min-Max and MSE, respectively (see  [33]  for their formal definition). [35] , with respect to the size 2k + 1 of the structuring elements used for erosion (background and foreground) of the ground-truth.
Finally, it is worth mentioning that the binary partition trees, beyond their usefulness for image analysis task, are first of all a way of building partitions of graph-based structures, independently of their associated semantics. Based on this fact, AGAT may then be used for solving any graph partitioning problem provided that such partitionings are guided by one / many prior knowledge. This opens the way of the use of AGAT in a wide range of machine learning domains where the data are organized as graphs, i. e. with respect to usual binary relations.
Conclusions We presented AGAT, a library dedicated to the construction, the handling and the evaluation of binary partition trees, which are tree data structures providing hierarchical partitioning of images and, more generally, valued graphs. AGAT contains also many standard and state-of-the-art algorithms in this domain. AGAT is the first library allowing for the construction of multifeature binary partition trees, and it also gathers a large set of evaluation tools for traditional binary partition trees. From this point of view, it constitutes, to our knowledge, the most complete and up-to-date library dedicated to binary partition trees. It is composed of three self-contained Java modules. Code sources are available via GitHub and natively compatible for Linux, Mac, and Windows systems.
Hardware and Software for Image and Data Post-Processing Images are downloaded using the PlayMemories Home TM free software from SONY TM. The software also renames the videos with date and time information. However, other tools may be used. Two copies of each video are stored on external hard drives (capacity 1 or 2 To, format allowing for the transfer of large files). The typical size for a video is ∼2 Go. Image post-processing (extraction of features of interest from videos) is achieved using VLC media player  (VideoLan, 2006)  or an equivalent software, enabling zooming, speed control and production of snapshots. A large Full HD monitor (preferably 27 inches) is desirable, but the enhanced contrast of a smaller screen (e. g. , laptop) is sometimes useful. An additional monitor is needed to input the counts in a spreadsheet.
Software for EOV/EBV Production Quantitative data resulting from post-processing are analyzed in various ways. Our routine assessments use the R-based PAMPA User Interface (UI)  (Pelletier et al. , 2014; Pelletier, 2020a)  for producing and analyzing fish and habitat-related EBVs. Functionalities of the PAMPA UI include data import, computation of a wide range of ecological metrics based on species traits, versatile plotting of these metrics and their analysis through Generalized Linear Models (GLM). Metrics are exported to flat files for other analyses, e. g. , GIS-based or other statistical modeling. The UI also provides guidance for model selection. The UI does not require a connection to run and may be installed from an installer freely downloadable at https://github. com/yreecht/Plateforme_PAMPA/releases.
A video is deemed valid for image analysis when: (i) underwater visibility (estimated from reference images, see below) is at least 5 m; (ii) the field of view is not obstructed by any sea floor or benthos relief that would prevent image analysis within a 5 m radius around the lander; and (iii) three complete undisturbed rotations are recorded. If (i) and (ii) are met for at least a complete rotation, the video is only analyzed for habitat, or else it is used either for communication purposes only, or discarded. Information from the derushing and field metadata are input in a standardized Excel spreadsheet (Supplementary Material 3). These metadata are critically needed for the effective management and analysis of large numbers of observations. The tasks inherent to pre-and post-field work each day, respectively take 1-2 and 3 h with two of the crew.
Each analyst conducts joint annotations with an expert. For fish counts in coral reef ecosystems, training takes up to 1 month. Training is validated after successful joint analyses of a set of videos. In parallel, 5% of the videos are independently reviewed by an expert analyst. If identifications and counts differ by more than 10%, the video must be reanalyzed. Attention is paid to species that may be potentially confused with one another. Estimation of visibility and 5 m radius followed training of annotators with calibrated reference images comprising bright and dark fish silhouettes of several sizes filmed at a range of distances and in several visibility conditions. The template file for animal counts comprises several fields to record the time code and the position of the animal on the frame, in order to ease quality control and to anticipate the future making of annotated image databases for machine learning (ML) algorithms 5 https://www.
Habitatrelated metrics such as biotic covers may also be analyzed through the UI. Biotic cover per observation unit is defined as the mean percent cover of the biotic category (i. e. , macroalgae, sea grass, or live coral) averaged over the six frames of the analyzed rotation. Habitat data are moreover formatted in a data table to construct a habitat typology based on clustering and classification  (Pelletier et al. , 2020). This typology defines the local habitat to each observation unit as a covariate for spatial and temporal differences e. g. , in fish abundance and diversity. This is important because observation units are collected in various habitats, and the distribution of mobile macrofauna is strongly linked to habitat distribution. Metrics are efficiently computed, plotted and analyzed with GLMs using the PAMPA UI, and now with the Galaxy-E web platform (Supplementary Material 6).
Hard coral cover and composition Indicators are computed at each observation unit and their spatial distribution may be analyzed. anthropogenic pressures. Follow-up assessments involve testing both spatial and temporal variations of EBVs according to the same factors. The second application type deals with assessing ecosystem health or biodiversity status against conservation objectives at the scale of territories or wide areas. With numerous data collected in varied habitats subject to contrasted anthropogenic and environmental pressures, the distribution of EBVs is representative and may be mapped at the scale of the site, area or territory. EBVs may be scored and assigned color codes per observation unit; five scores are used from red (bad) to blue (excellent). For each EBV, scores are then averaged at the scale of each surveyed site and organized into aggregated radarplots.
Dissemination of Outcomes and Data Management The STAVIRO protocol generates spatially replicated EBV and a large number of observations. GIS-layers of EBVs are hosted on an institutional Open Access map serve 6. Quantitative data issued from image analysis are uploaded on institutional databases and/or shared to other initiatives for data sharing. Assessment reports are systematically posted on the Open archive https:// archimer. ifremer. fr/search. Image data are safeguarded through archives on institutional databases servers and duplicated on local hard drives. Two types of image-based outcomes are produced: (i) a video clip assembling short sequences recorded at a subset of representative stations, yielding a memory of the ecological status of the area at the time of the survey; (ii) a compilation of outstanding images that either depict the biodiversity assets inherent to the area, in order to provide end-users with a better knowledge of the values to be protected in the area; or display areas under critical anthropogenic pressure.
g. , in the Chesterfield and Bellona atolls and reefs (Supplementary Material 8). They also showed the exceptional health of Astrolabe's reefs, which are now a fully protected integral reserve. The presence of iconic and keystone species was quantified, in particular in the CSMP where frequent occurrences and large abundances of sharks were observed in the absence of any bait. During presentations to stakeholders, managers or to the public, screenshots and short clips illustrated scores and figures in a simple way. Lastly, the New Caledonian habitat data were part of the reference samples used in ML-based mapping of coral habitats for the Allen Coral Atlas 7. Many research opportunities are supported by the wealth of data provided by STAVIRO, in particular, statistical modeling requiring spatially distributed and replicated data, for example, species distribution modeling, spatial patterns of habitats  (Pelletier et al.
In addition to the two species lists for coral reef ecosystems (section "Image Post-processing"), a simpler list was devised based on the species groups considered in the participative Reef Check protocol 8. This list enables citizen involvement in image analysis, but was not used in our assessments. Web-based tools are also currently being developed for citizen-based image annotation  (Matabos et al. , 2016). The next improvement in our protocol lies in the use of annotation tools for direct annotation, and for constructing databases of images for ML algorithms. We have successfully used the EventMeasure software (seagis. com. au) and are investigating adapting BIIGLE  (Langenkämper et al. , 2017)  for video imagery. Our archived data enable to build training data sets to implement ML-based approaches in future applications.
EOV/EBV Data Products and Dissemination Through simultaneous observations of fishes, habitats and some other marine animals such as turtles and sea snakes, the STAVIRO protocol documents several EBVs: taxonomic diversity, population abundance (with additional information per size-class), habitat structure, ecosystem composition (and functional type) and phenology. Medium to large size mobile animals are well observed (section "Non-obtrusive Observation"). Relationships between habitat and macrofauna may be studied through paired information. However, the STAVIRO protocol is not the most appropriate protocol for counting small species and semi-cryptic species concealed in coral, crevasses or under rocks. It may thus be used in combination with a complementary monitoring protocol, in which case protocols should be intercalibrated.
For instance, participative UVC sampling schemes deployed over large areas such as Reef Life Survey  (Edgar et al. , 2020)  offer opportunities for spatial coverage. BRUVs are another avenue to reveal some cryptic species that may be attracted by bait. By collecting many deployments per day at sea with only two STAVIRO units, the protocol provides replicated data over large areas, thereby informing distributional EBVs such as species distribution, ecosystem extent and fragmentation. Standardization is indispensable not only for data quality and reproducibility, but also for effective management and analysis of these big datasets. The PAMPA UI was central for operationalizing the production and analysis of EBVs, and coding the PAMPA workflow on Galaxy-E (section "Software for EOV/EBV Production") facilitates data re-use.
CONCLUSION The standardized STAVIRO protocol and workflow have been fully operationalized through extensive and successful implementation in a variety of contexts, including at the scale of vast managed areas. The imagery, annotation and the derived EBV products and outcomes support assessments of coastal fish assemblages and habitats in a robust and effective way according to procedures that are evolving toward meeting FAIR principles. In future years, the protocol will support: (i) additional technology to optimize collection of imagery, (ii) software developments, especially machine learning, to facilitate image post-processing and annotation; and (iii) enhanced interoperability with other researchers and stakeholders. This paper aims to help using the protocol by sharing our extensive experience, the data collected and the savoir-faire gained since 2007.
ACKNOWLEDGMENTS We thanks to the numerous collaborators who helped with field work, image analysis, managing and analyzing the data collected since 2007 among which: in New Caledonia:  Kévin Leleu ,  Delphine Mallet ,  Charlotte Giraud-Carrier ,  Fanny Witkowski ,  Cyrielle Jac ; in la Réunion:  Johanna Herfaut  and  Paul Giannasi  from the  Mayotte Natural Marine Park ; in the Mediterranean Sea:  Gilles Hervé ,  Jérémy Pastor ,  Eric Charbonnel ; and in the Atlantic Ocean:  Claude Merrien  and  Aouregan Terre-Terrillon. We thank  Eric Charbonnel ,  Jérôme Payrot ,  Jérémy Pastor , and  Philippe Lenfant  for facilitating the survey in the  Banyuls and Côte Bleue MPAs. We thank  Eric Charbonnel ,  Jérôme Payrot ,  Jérémy Pastor  and  Philippe Lenfant  for their great help during the survey in the Banyuls and  Côte Bleue MPAs.
Sequence data processing Sequencing output reads (FASTQ files) were trimmed using Trimmomatic  (Bolger et al. , 2014)  to remove base pairs at the beginning of the reads as long as their Phred quality score remained below 30 and to trim the end of the reads once encountering a 4-bp window with quality below 30 (Trimmomatic settings LEADING: 30; TRAILING: 30; and SLIDING WINDOW:4:30). Trimmed reads shorter than 36 bp were then removed to decrease the risk of short reads that might not be uniquely positioned over sequences  (MINLEN: 36). Nuclear recovery-Paired reads and combined unpaired reads were used to recover target sequences using HybPiper v1. 3. 1  (Johnson et al. , 2016)  using the target file available at https://github. com/mossm atter s/Angio sperm s353 containing de-gapped medoid sequences  (Johnson et al.