dataesr/scientific-paragraphs-categorization huggingface dataset:
) and physics (e. g. , temperature, heat, velocity, etc. ), as well as local anthropogenic impacts (e. g. , population density, land use of agricultural and urban areas). In comparison to existing repositories of marine environmental data (Bio-ORACLE: "Ocean Rasters for Analysis of Climate and Environment"  Assis et al. , 2018; Tyberghein et al. , 2012; MARSPEC:  "Ocean Climate Layers for Marine Spatial Ecology",  Sbrocco & Barber, 2013) , RECIFS has distinctive features to promote the study of coral reef ecosystem dynamics, and to facilitate the uptake of environmental information in reef conservation. These features are: 1. Reef specificity: RECIFS is the first database centred on the reef environment worldwide. Data provided through RECIFS characterize key environmental constraints of the reef ecosystem, and such characterization is missing in existing repositories of marine environmental data.
Among these environmental constraints are conditions that trigger coral bleaching events (e. g. , degree heating week) and that represent human-related disturbances (e. g. , boat traffic, coastal land use, human population density). 2. Spatial-temporal flexibility: RECIFS data are available under customizable spatial (i. e. , environmental measures from 5 to 100 km around a reef of interest) and temporal scales (i. e. , environmental measures for a given period of the year, and/ or for a given range of years). This is key, since understanding specific dynamics of the reef ecosystem requires environmental data at distinct spatial and temporal resolutions to be available  (Fernandez et al. , 2017; Leempoel et al. , 2017; Mannocci et al. , 2017; Melo-Merino et al. , 2020; Murray et al. , 2018).
For instance, the abundance of sessile taxa might be driven by finescale changes in water condition (e. g. , environmental variation measured at 5-km spatial resolution), whereas for mobile taxa (e. g. , sharks) large-scale changes might be more relevant (e. g. , 50-km resolution). Similarly, the reef community composition or adaptive processes might be shaped by long-term environmental constraints (e. g. , overall average temperature over past few decades), or by seasonal or episodic events (e. g. , heat waves during the hot seasons of the past 5 years). In existing repositories of marine environmental data such customization is not possible, since datasets are typically provided at a fixed spatial resolution, and as overall temporal trends (bioclimatic variables, climatologies) of contemporary seascape conditions.
3. Web application: RECIFS is available in open access through an intuitive interface requiring no prior knowledge on use of geographic information systems (GIS). While existing repositories of marine environmental data provide online tools for the visualization of the environmental datasets, none of them allows users to subset and process environmental data in real time, nor to customize, annotate and print the map visualization. These characteristics make remotely sensed environmental data accessible to non-specialists of GIS, including to practitioners of coral reef conservation  (Selmoni, Lecellier, Ainley, et al. , 2020). RECIFS can therefore provide a common syntax to describe the reef environment shared between scientists and conservation stakeholders. For instance, if a study reports that a given environmental variable is associated with a particular shift in a reef ecosystem dynamic (see 'Examples of use' for practical examples), any reef conservation stakeholder worldwide could then use the RECIFS interface to assess how the environmental variable is distributed in their reef system of interest.
| Data structure: Reef environment The RECIFS repository is based on three types of datasets describing the reef environment retrospectively. The first type describes data at monthly temporal resolution, the second at a yearly temporal resolution, while the third type is time-invariant (Table  1 ). These datasets were accessed at a global level as raw data, all sharing the same spatial projection (WGS 84, EPSG:4326), and were then processed and synthesized as outlined here below (and as summarized in Figure  1 ). Raw datasets with a monthly temporal resolution were sets of worldwide maps (called stacks of raster images), where every map (called a raster image) described a monthly statistic (usually the average) of a given oceanic environmental variable globally. RECIFS includes the raw monthly datasets for the following environmental variables: chlorophyll concentration, degree heating week, iron concentration, oxygen concentration, pH, nitrate concentration, phosphate concentration, sea current velocity, suspended matter concentration, salinity, and temperature.
These variables represent data at near-surface depth, with spatial resolutions ranging between 5-25 km,  Two time-invariant raw datasets were included in RECIFS, both derived from the Global Multi-Resolution Topography Data Synthesis (v. 4. 1, accessed in January 2021), which is a single raster image describing contemporary topography (on land) and bathymetry (on sea) worldwide at ~1-km spatial resolution  (Ryan et al. , 2009). The first derived dataset is a depth map where the pixels represent the topography of below sea surface level, and the second is a binary map where pixels were either assigned to land (topography > 0) or to sea (topography < 0). To process these raw environmental datasets, we extracted values from the raster stacks using custom R scripts (v. 4. 1; R Core Note: For each dataset, the table shows the name and description of the variable of interest, the temporal window covered by the dataset (with the temporal resolution in parenthesis), the spatial resolution of the dataset, the % of missing values (% NA) across all the reef cells, the source repository of the dataset (with the abbreviations defined at the bottom) and the identifier of the original product from the source repository.
Raw environmental data used in Reef Environment Centralized Information System (RECIFS) come from publicly available repositories, and have distinct temporal resolutions (monthly, annual or time invariant). Shapes of coral reefs worldwide were summarized into a regular grid (reef cell size: 5 by 5 km), and raw environmental values were extracted for each reef cell. These extractions were performed using four different buffer sizes (2. 5 km -on reef, 10, 25 or 50 km) around the centres of reef cells. The result is a set of tables ('Extended tables') describing variation across reef cells for each environmental variable, at different buffer sizes and through different time periods (months or years). These tables are synthesized into a 'Summary table', reporting overall temporal average and standard deviation of each environmental variable, for every reef cell.
, 2020; Lundgren et al. , 2013; Selmoni, Lecellier, Magalon, et al. , 2021 ; Selmoni, Rochat, et al. , 2020; Sherman et al. , 2020; Thomas et al. , 2017). In Boxes 1 and 2 we show possible implementations of the RECIFS data to (a) characterize coral diversity in the Caribbean, and (b) study local adaption of a reef fish population along the northwestern coast of Australia.
| Perspectives In the years to come, the development of RECIFS will focus on three domains: data, functionalities and integration. The datasets already available in the repository will be updated on an annual basis to provide the most recent records for the different environment variables. Furthermore, new variables that describe environmental factors shaping the dynamics of coral reef species will be added to the database as these variables become available and/or are required; we encourage coral reef researchers and conservation actors to contact us (the authors) to propose environmental descriptors to be added to RECIFS. With the future improvements of remote sensed data and environmental modelling techniques, we also aim to increase the spatial and temporal resolution of the reef cells, so that the environmental characterization can be more pertinent to finescale dynamics of the reef ecosystem  (Murray et al.
ACK N OWLED G M ENTS We thank the environmental data providers for sharing the datasets included in RECIFS. We thank the Catlin Seaview Project and Joseph DiBattista for sharing the data used for the case studies. We thank Annie Guillaume and the anonymous referees for the comments and We ran a redundancy analysis (RDA) to uncover which of the 302 environmental variables were associated with axes of outlier genomic variaƟon ("GT-environment model"). In Figure  c  , the tri-plot displays the redundancy between variaƟon of outlier-PCs (white boxes), sampling sites (blue dotes) and environmental condiƟons (red arrows). We found that more than half of the outlier-PCs variaƟon (R 2 =0. 52) could be explained by two environmental variables. The first variable was average Degree HeaƟng Week (DHW_010_OA me), which showed the strongest correlaƟon with the first axis of genomic variaƟon (outlier-PC1).
In the original work that produced the genomic dataset used in this case study, DiBaƫsta and colleagues highlighted a connecƟvity break between the reefs in Shark Bay and those located in northern areas  (DiBaƫsta et al. , 2017). For these reasons, stripey snapper in the Shark Bay area might be of parƟcular interest for conservaƟon efforts. The extended methods and results of this case study are available as supplementary material.
Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/geb. 13657 by IFREMER Centre Bretagne BLP, Wiley Online Library on [24/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License and across temporal windows generally covering the past 3-4 decades (see Table 1 for details). These raw monthly datasets are sourced from the repositories from the Coral Reef Watch of the National Oceanic and Atmospheric Administration (NOAA Coral Reef Watch, 2018) and the Copernicus Marine Service (EU Copernicus Marine Service, 2022). Raw datasets at yearly temporal resolution were also raster stacks, but here each image was a yearly measure of a given variable worldwide.
The datasets of this type included in RECIFS were: land cover of built-up and cropland areas (covering the 2015-2019 period, at 100-m resolution; Copernicus Global Land Service, 2022), boat detection (based on night-time light detection on sea; 2017-2020, at 500-m resolution; Elvidge et al. , 2015, 2018; Hsu et al. , 2019), and human population density (available for the years 2000, 2005, 2010, 2015, 2020, at 5-km resolution, Center for International Earth Science Information Network, 2022).
rents was sourced from the Copernicus Marine Service (dataset id: GLOBAL_REANALYSIS_PHY_001_030; EU Copernicus Marine Service, 2022). This dataset is obtained from a stack of raster images displaying global monthly averages of eastward and northward sea surface velocity, from 1993 to 2019. Using custom R scripts featuring the raster and the circular (v. 0. 4; Agostinelli & Lund, 2017) libraries, we combined eastward and northward velocities of each pixel into a vector of sea currents, and then computed (a) sea current direction (the angle of the vector) and (b) sea current velocity (the norm of the vector) for each monthly measure. Finally, we summarized trends of surface circulation by computing raster images representing the overall yearly average and 12 by-month averages for both sea current direction and velocities.
10. 13 ; 13 OpenJS Foundation). On the front-end, the server features an Openlayers (v. 5. 3; Open Source Geospatial Foundation) map interface displaying the reef cells, while the back-end of the server stores environmental data in a tabular format and the raster images summarizing sea surface currents. When the front-end user defines an 'Area of Interest' through the interactive map, the server queries the RECIFS database through R scripts (featuring the raster and the sp libraries; v. 1. 4; Bivand et al. , 2013)  processing, sub-setting and finally returning the data stored on the back-end. Three types of query are possible:1. Standard environmental query: The user selects an 'Area of Interest', and the server returns precomputed environmental values from the 'Summary table' for the reef cells from this area.
14668238, 2023, 5, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/geb. 13657 by IFREMER Centre Bretagne BLP, Wiley Online Library on [24/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License graph theory (van Etten, 2018) , and oceanographic models to estimate the probability of transition from one reef to another. Overall patterns of regional transitions could then be synthesized in connectivity indices, summarizing how each reef of a given region is interconnected to those located downstream or upstream via oceanic currents (Matz et al. , 2020; Selmoni, Rochat, Lecellier, et al. , 2020). Going forwards, it will also be important to integrate RECIFS with other projects and complementary datasets that characterize alternative facets of reef environments.
snapper adaptaƟon in NW Australia. For the second case study we used pre-exisƟng populaƟon genomics data from 1,016 stripey snapper individuals (Lutjanus carponotatus; shown in Figurea) from 51 reefs along the northwestern coast of Australia(DiBaƫsta et al. , 2017). Each sample in this dataset was genotyped at 17,007 single nucleoƟde polymorphisms (SNPs). Using genome scans, we idenƟfied 62 outlier SNPs that did not follow the neutral geneƟc structure of the populaƟon (i. e. , SNPs potenƟally involved in local adaptaƟon). We then ran a principal component analysis (PCA) to summarize the allelic frequencies of outlier SNPs into 12 axes of outlier genomic variaƟon (outlier-PCs). Using the analyƟcal pipeline shown in Figure b, we invesƟgated the associaƟon between the spaƟal distribuƟon of outlier-PCs and 302 environmental variables (ENV) from RECIFS.
TA B L E 1 Datasets included in Reef Environment Centralized Information System (RECIFS). Name Description Time window (resolution) Spatial res. % NA Source Original product id 14668238, 2023, 5, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/geb. 13657 by IFREMER Centre Bretagne BLP, Wiley Online Library on [24/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License OCEANCOLOUR_GLO_CHL_ L4_REP_OBSERVATIONS_009_082 ct5km_dhw-max_v3. 1 GLOBAL_MULTIYEAR_BGC_001_029 GLOBAL_MULTIYEAR_BGC_001_029 GLOBAL_MULTIYEAR_BGC_001_029 GLOBAL_MULTIYEAR_BGC_001_029 CMEMS NOAA CRW CMEMS CMEMS CMEMS CMEMS 4 km 12. 06 5 km 3. 14 0. 25°20. 62 0. 25°20.
I. INTRODUCTION T HE self-organizing map (SOM) is a special type of arti- ficial neural network (ANN) proposed by Kohonen  [1]. The SOM is an unsupervised learning algorithm performing a nonlinear mapping from a given high-dimensional input vector space to a low-dimensional map of neurons, usually a regular 2-D grid. It acts as a nonsupervised clustering algorithm as well as a powerful visualization tool, and it has been used to visualize, interpret, and classify large high-dimensional data in many application domains, such as economy, industry, management, sociology, geography, and text mining  [2] ,  [3]. Since its proposal, the implementation of the SOM algorithm has been the topic of many research works  [4] -  [41]. Indeed, different implementations of SOMs have been proposed so far: in software (SW) programmable platforms such as central processing unit (CPU)  [29] -  [33] ,  [35] -  [37]  and graphics processing unit (GPU)  [29] -  [35]  by using different programming languages (C/C++ w/wo OpenMP, Python, R, Manuscript received June 30, 2021; revised November 3, 2021 and January 15, 2022; accepted February 14, 2022.
This work was supported by JSPS KAKENHI under Grant JP20K11999. (Corresponding author: Slaviša Jovanović. ) Slaviša Jovanović is with CNRS, IJL, Université de Lorraine, 54000 Nancy, France (e-mail: slavisa. jovanovic@univ-lorraine. fr). Hiroomi Hikawa is with the Department of Science and Engineering, Kansai University, Osaka 564-8680, Japan (e-mail: hikawa@kansai-u. ac. jp). Color versions of one or more figures in this article are available at https://doi. org/10. 1109/TNNLS. 2022. 3152690. Digital Object Identifier 10. 1109/TNNLS. 2022. 3152690 CUDA, and so on) or in dedicated hardware (HW) implemented on field-programmable gate arrays (FPGAs) or in application-specific integrated circuit (ASICs) by proposing specific hardware architectures exploiting the inherent parallelism of the SOM algorithm for better performances  [4] -  [28].
Fig. 2. Illustration of a 2-D SOM structure: the black nodes represent the neurons, the grayed area represents the direct neighbors of the neuron K + 3, and the light gray lines represent the input vector delivery to all neurons. and optimization techniques that have been reported in the literature so far in the field of HW SOMs. The remainder of this article is organized as follows. Section II describes the original SOM algorithm, while Section III provides an overview of hardware adaptations of the original SOM algorithm in all phases (initialization, vector distance computation, BMU search operation, neighborhood function, and weight update). Section IV describes the types of architectures commonly found in hardware implementations of SOM. In Section V, the methods, tools, datasets, and application use cases for validation of hardware SOMs as well as an overview of their performance measurements are provided.
These operations are independent between neurons and can be performed simultaneously (see Algorithm 1). Consequently, this inherent parallelism of the SOM algorithm makes it suitable for hardware implementations. Neurons implemented in hardware can work in parallel, while the neurons in software are executed sequentially. Therefore, the hardware-implemented SOM can process its input vectors more efficiently than the software implemented one. This efficient computational power is especially desired when the size of the SOM or the input vector dimension is large. Due to the parallelism of the algorithm, the more neurons or vector processing elements (PEs) are implemented in hardware, the higher performance is obtained. In addition, the number of neurons implemented in hardware can be increased easily because they work independently of other neurons.
A. Initialization Even though, in the original SOM algorithm, it is suggested to use random initial values for demonstrations purposes, it turns out that this initialization policy does not necessarily provide the best performances in terms of convergence speed to the stationary values and quality of resulting maps  [1] ,  [43]. The initialization of SOMs has been widely studied in the literature  [1] ,  [43] -  [48]. Basically, two groups for initialization purposes can be found  [1] ,  [44] ,  [45] : random initialization and linear initialization (often called data-driven initialization). In the random initialization, the weights of the SOM neurons are initialized: either by randomly choosing data in the range of values observed in the input dataset, by randomly selecting the data from the input datasets, or by randomly choosing the perturbed values around the mean values observed in the input dataset.
In the data-driven or linear initialization, the input datasets are previously analyzed before initializing the SOM weights. Different methods can be found in the literature: based on the data analysis with k-means where input datasets are projected on the map of the same size in order to find the cluster centers that are then rearranged with some heuristics to fit the used SOM map  [44] ; on the statistical analysis such as principal component analysis (PCA) on regular grids  [47]  where largest eigenvectors of the projected input dataset are chosen; or the same PCA-based analysis with projection on irregular grids  [45] , by using connected graphs to rearrange the most distant elements in the input dataset of nonvectorial elements  [46] , or by placing the SOM neurons on the Hilbert self-similar curves  [47].
Akinduko et al. [48]  compared random and PCA-based initialization on quasilinear and nonlinear datasets. They concluded that there is no universal initialization policy giving the best performances in terms of convergence and quality of obtained results; the random initialization performs better on nonlinear datasets, whereas the PCA-based initialization gives better results for quasi-linear ones. When the SOMs are implemented in software, as it was the case in all initialization techniques previously presented, the weights can be easily programmed. However, the vector initialization is a burden for the hardware implementation since it requires additional circuits such as a linear feedback shift register (LFSR) that provides random values. If a single LFSR is employed, all vector elements must be initialized one by one, and thus, a communication link between the initialization circuit and all neurons is necessary.
Moreover, the sound performance of the SOM was also confirmed in the cases where the signal resolution of the neighborhood function was low. However, the triangular neighborhood function requires multipliers for its computation, leading to a higher hardware cost. The hardware SOM proposed by Pohl et al. [10]  is based on the modular array architecture where neurons exchange data via a shared bus. In this approach, during each iteration, the distance value to the winner in every neuron is decreased by one at each clock cycle. Then, the neuron whose distance reaches zero takes a value from the shared data bus, which represents the update coefficient for the neighborhood function. These values, provided by the bus to all the neurons of the architecture, are the neighborhood function values previously precalculated in software and stored in the memory.
The proposed accelerator was applied to three applications: chromaticity diagram learning, labeling of handwritten number image, and image vector quantization. The functionality of the proposed platform was proven by MATLAB simulations of these applications, whereas the hardware cost estimation was carried out through the FPGA synthesis. de Sousa et al. [72]  proposed an FPGA-based SOM architecture called SOMprocessor. The proposed architecture explores two different computational strategies to improve both the data flow through the processor and its flexibility to implement different network topologies. The first improvement is achieved by multiplexer components, which supports alternating processing of neuron sets by the arithmetic cir-cuits. This strategy provides a more flexible use of the chip, in which larger networks can be even processed in lowdensity FPGAs.
The generated color palette is afterward used to reconstruct the image (i. e. , for image compression purpose). Each pixel of the reconstructed image is replaced with one of the colors from the palette. This process allows to quantize the initial color of each pixel in the image and thus reduces the image data size without significantly degrading the image quality. In the 3-D SOM, the neurons are arranged as points on a cube with their red, green, and blue axes. The sequence of operations in the coding phase is controlled by the instructions generated and propagated by the control unit of the proposed SOM architecture. Another approach to build SOM PE is by using dedicated PE circuits made of hardwired logic circuit defined at the register transfer level (RTL). Since this approach does not have to decode the instructions or commands, the higher performance is yielded at the expense of the overall flexibility.