dataesr/scientific-paragraphs-categorization huggingface dataset:
Focusing only on haplogroups endemic to the region of interest minimizes the probability that the genetic diversity patterns observed in the study reflect processes that have occurred or occur outside the study area and, therefore, cannot be captured by our analyses (see, e. g. ,  Buckley, 2009; Buhay et al. , 2009). For example, if the Julian Alps (at the extreme northeast of Italy), received mtDNA from a Balkan refuge, this would increase the local genetic diversity because of the presence of a nearby stable area not included in the study area. Sequences were downloaded from GenBank (last accessed on 13/04/2023) and multiple sequence alignment was performed in ClustalW  (Larkin et al. , 2007)  to obtain, for each species, a final dataset of aligned and georeferenced mtDNA sequences. Preliminary bibliographic searches showed that, conversely to mtDNA, nuclear DNA data (e.
, 2019 and reference therein). Combining nucleotide diversity and phylogenetic originality might thus allow for a comprehensive and adequate evaluation of conservation priorities for both genetic diversity and differentiation  (Nielsen et al. , 2023). We described the geographical structures of genetic diversity using Voronoi tessellation, so to obtain "compact" polygons (spatial clusters of grid cells) with straight sides that can be used to identify the sampling sites (or grid cells, for environmental variables) contained within each of them. Voronoi diagram was built using the deldir R package  (Turner, 2021) ; to identify the set of coordinates to be triangulated, we performed a k-means clustering (number of clusters = 150) of all grid cell centroids from a 1 km resolution raster of the study area.
Environmental parameters We considered several variables describing environmental change between LGM and present: climate anomalies, temporal habitat (vegetation) stability and topographic heterogeneity. Additionally, we included human footprint to account for human-mediated effects on genetic diversity  (Miraldo et al. , 2016). Climate anomalies measure change in the temperature and precipitation regimes between the LGM and present (i. e. the temporal climate gradient). Large anomalies are thought to have negative effects on the local persistence of single species, and hence on the overall biodiversity  (Jansson, 2003). Current mean annual temperature and annual precipitation data were obtained at the 30 arcsec resolution from the CHELSA dataset  (Karger et al. , 2017) , and reprojected to UTM zone 32 N (EPSG:32632) with 1 km resolution.
Temporal habitat stability provides a measure of the time elapsed since each map cell acquired its current value (i. e. forested or not), under the assumption that the longer this time, the longer a given species should have been able to continuously occupy the cell, and hence, the higher the genetic diversity. To calculate temporal habitat stability, we relied on a millennial-scale (binary) model of suitability for temperate and warm-temperate forests at high resolution  (Marta et al. , 2013). We aggregated data from Marta and colleagues at 1 km resolution and identified the millennium in which each cell acquired its present-day habitat (forested or not). Consequently, the index ranged between 23 (completely stable during the period of interest -i. e. from the LGM to present days) and 1 (habitat conditions changed during the last millennium).
Topographic heterogeneity measures the spatial variability in topography and is usually expressed as the standard deviation in elevation within a given reference area. Large heterogeneity is usually thought to be positively related with biodiversity, as it potentially supports a larger number of potential niches and allows coping with environmental fluctuations through short-distance (altitudinal) movements  (Thuiller et al. , 2006; Ficetola et al. , 2013). The index was calculated as the standard deviation in elevation within each Voronoi cell; elevation data were retrieved from the Shuttle Radar topographic mission (SRTM, 90 m resolution). For the sake of mapping model predictions, we also generated a map at 5 km resolution where each cell represents the topographic heterogeneity within a 25 km buffer (the radius needed to obtain areas comparable with the mean Voronoi cell size -2124 km 2 ).
To evaluate the fit of mixed models, we calculated the amount of variation of dependent variables explained by the fitted models as a measure of pseudo-R 2 using the rsq R package  (Zhang, 2022). The percell number of individuals was highly variable across species. To assess the potential effect that differences in sample size may have on the correct estimation of the indices  (Goodall-Copestake et al. , 2012) , and hence on the robustness of the analyses, we performed 100 random samplings of 10 individuals from each sampling unit (species × cell). Genetic indices were then calculated in each replicate and models re-run for each dependent variable. The estimates of models including all the observations were finally compared with the ones obtained from the resampled datasets. To assess the sensitivity of model outputs to the spatial scale of analysis, two additional runs of the models were performed using the 50-and 450-cells clustering.
1  and Supplementary Table  1 ); range maps were rasterized to 1 km cell size (EPSG:32632) and then aggregated to 5 km using the maximum value. Species-specific maps of genetic diversity were then used as inputs to the spatial prioritization algorithm Zonation v. 4. 0  (Moilanen et al. , 2014) , as implemented in the rzonation R package  (Morris, 2022). Zonation is a commonly used tool that allows ranking areas based on their conservation values; the algorithm maximizes feature representation and the potential for conservation while limiting protection costs  (Moilanen et al. , 2005). The procedure starts by removing least valuable cells from the landscapes one by one, using minimization of marginal loss (i. e. under a "Basic core-area", removing the cell with the minimum value for the most valuable occurrence over all the species) to select the cells of lower conservation value.
Results We obtained sufficient data for 21 out of 45 amphibian species (10 urodeles and 11 anurans; 2596 individuals in 102 cells), representing 47 % of the whole amphibian diversity of the study area. The complete list of the species considered, with the criteria for the inclusion is provided in Supplementary Table  1. For five species (S. terdigitata, L. vulgaris, P. fuscus, B. balearicus and B. siculus), the number of cells containing ≥10 individuals was lower than 2, thus these species were excluded from analyses. The final dataset included data from 16 species and 1634 individuals (per-species mean = 102. 13; sd = 52. 84) in 77 cells (perspecies mean = 4. 94; sd = 2. 54; Table  1 ). Nucleotide diversity ranged between 0 and 0. 036 (mean = 0. 003; sd = 0. 004), with the maximum value recorded in E.
g. semi-artificial ponds, hedgerows, stone walls) that are the microhabitat of many amphibians  (Canessa et al. , 2013; Romano et al. , 2014; Guerra and Aráoz, 2015; Valdez et al. , 2021). Priority areas identified in our analyses might differ when implementing datasets based on different markers, and/or taxonomic groups with different characteristics (e. g. , dispersal ability or generation length), still amphibians stand out as one of the most endangered animal taxa  (Cox et al. , 2022) , thus their specificities should be prioritized during conservation planning. We also highlight that the conservation of areas identified by spatial prioritizations does not necessarily mean establishing new strictly protected areas; in fact, several other actions can be taken, from the restoration of natural habitats to allow the persistence of target species and/or enhance inter-population connectivity, to the control of invasive alien species (predators, competitors, vector of pathogens).
, 2018). Such issues could be solved by using statistically designed experiments. Response surface method (RSM) is a combination of statistical and mathematical approaches used for modeling and analyzing different processes in order to depict the main and interaction effects and estimate correlations between experimental factors and responses. It is a simple and economical optimization method with a few number of experiments, resulting in faster and more effective experiment performance  (Asfaram et al. , 2016; Mansouri et al. , 2018; Alhalili, 2022). In the same context, machine learning has also been widely used to solve complex adsorption problems and to provide a better understanding of the adsorption process  (Lakshmi et al. , 2021). It is a collection of techniques for generating predictive models from databases of training examples  (Schneider, 2020).
The models created for the independent variables were optimized for their influence on the dependent variables using the BBD. The initial concentration of methylene blue (400-700 mg/L), the adsorbent dosage (0. 6-1. 6 g/L), the contact time (30-540 min), the pH (7-11) and the temperature (20-50 • C) and response (MB uptake) were taken into account to find the best circumstances. Then, a Gaussian process regression model with Bagging (Bootstrap Aggregation_Bag) and Dragonfly (GPR_DA_Bootstrap) optimization algorithm was used to predict the outputs using the BBD database. After than, optimization by two techniques, dragonfly (DA) and particle swarm (PSO) was caried out to find the optimal for the two models. Finally, an application was created using the MATLAB guide to provide an easy way to implement optimization and prediction of MB uptake.
, 2013; Moussa et al. , 2022). In this work, the BBD technique, of the RSM approach, was used to optimize the studied process. Regarding the determination of the independent factors, the initial concentration of dye (X 1 ), the dose of PJAC (X 2 ), the contact time (X 3 ), the pH (X 4 ) and the temperature (X 5 ) were chosen as independent variables; while the MB uptake was chosen as the dependent variable. The JMP program (JMP® Pro 13. 0. 0) was used to.
In order to obtain a better model and better performance, the database was normalized from the beginning in the interval [-1. 1], and then it was divided into two sets: 75% for learning and 25% for validation. Then, ten Kernel functions were optimized (exponential, exponential squared, matern32, matern52, quadratic rational, exponential ard, exponential squared ard, ardmatern32, ardmatern52, and quadratic rational ard) with the basis function (constant, linear and zero). The functions of the ten used kernel were given in the supplementary materials section (Eqs. (S1) to (S10). The knowledge of each Kernel function was also optimized for their specific parameters (Kernel Scale [sigmaM, sigmaF] and sigma) (see Fig. 1 ). For this, the dragonfly algorithm was used to optimize the specific parameters of each Kernel function, since this optimization algorithm has been used to solve a wide variety of optimization problems including solving different machine learning optimization problems  (Moussa et al.
Interface for optimization and prediction To provide a simple way to implement optimal optimization and predict MB uptake, an interface was designed using the MATLAB guide for optimization and prediction (Fig. 15 ). This interface was converted into an executable application on Windows. This powerful application for direct use allows to predict the outputs by selecting the input values by BBD, as well asby GPR__DA_Bootstrap. Moreover, the application is also an optimization solution, allowing finding the optimal ones for each type of model (BBD and GPR_DA_Bootstrap) by two methods (DA and PSO).
These enable them to assess hazard and risk and make decisions in real-time (e. g. , CAL FIRE and electrical utilities are using Wildfire Analyst®). The urgent decision-making process involves multiple stages starting with the acquisition of the latest data related to the incident. Such data ranges from static topographic datasets, like digital elevation models (DEMs), to live sensor data describing the actual incident in physical units. An example is hotspot data provided trough satellites, describing the occurrence of above-normal thermal radiation when investigating wild fires. In order to evaluate the effects of the disaster on additional entities, such as infrastructure, more datasets are required. For critical infrastructure, these might be data about buildings or powerlines allowing for a detailed impact assessment.
Having the initial datasets acquired, domain experts then trigger the disaster simulation. The challenge then is to continuously update the simulation. This process not only involves a continuous stream of new input data, but also deals with forces in the field providing crucial updates on the current situation. The urgency in decision-making, the complexity of models, the uncertainty of nature, and the amount of data to be processed and analysed, often limit the use of such simulation approaches in real-time scenarios. High-Performance Computing (HPC) is the field of using supercomputers to solve large, computationally demanding problems. Today, HPC systems are reaching an ExaFLOP (10 18 operations per second) of performance, 1 and thus their capabilities allow for estimating risks for different natural hazards, the use of stochastic approaches to assess risks and 1 www.
This information is used to refine and steer the disaster simulation forecast. VESTEC supports several data analysis methods to distill out the most informative pieces of ensemble simulations, which can be visually explored and analyzed. In short, VESTEC aims to provide crisis management and to support operational disaster relief staff with the best possible knowledge of the evolving situation. Crisis management staff can also inject domain-specific knowledge into the VESTEC system to guide the evolving simulation, which can then be used to influence the mitigation actions prompted by the crisis manager. Since the source code is released on GitHub, 2  future extensions can come directly from the community. To demonstrate the scalability and effectiveness of the VESTEC system at supporting urgent decision-makers, we evaluate the system on three different use-cases: • Probabilistic forest fire forecasting and monitoring; • Mosquito-borne diseases spreading; • Space weather forecasting.
B. EXASCALE AND IN-SITU VISUALIZATION Producing accurate forecasts in case of an unfolding disaster, by combining real-time sensor data with numerical simulations, lead to enormous data amounts. The sheer volume of the raw data and the necessity to quickly provide insights in order to improve decision-making involves challenges for efficient data analysis and visualization. Especially in the era of exascale computing, data analysis and visualization as well as the corresponding algorithms have to be re-designed to accommodate highly parallel data processing and interactive user investigation. In order to deal with these massive scientific datasets produced by the urgent workloads, many use case specific software solutions have been developed. Two well known and generic applications in the field of scientific visualization are ParaView  [13]  and VisIt  [14].
This approach, known as in-situ processing, allows the analysis and visualizations steps to directly access the simulation data in main memory and therefore strongly benefit from the supercomputer processing power. An overview of in-situ processing techniques for large scale scientific visualization is given in  [16]. In-situ analysis and visualization is enabled in ParaView by using the Catalyst library  [17] ,  [18]. Each simulation code integrates a specific adaptor that passes its internal data to VTK in a 0-copy way, i. e. , by just sharing memory locations without allocating new memory. Then, the data is forwarded from the simulation model to the analysis and visualization pipelines in order to perform, for example, Topological Data Analysis (TDA) (cf. Section II-C), data reduction, or enabling an in-situ live visualization.
The developed data analysis pipelines running on top of ParaView rely on the Topology ToolKit (TTK)  [19] ,  [20]  in order to compute topological abstractions. The Topology ToolKit (TTK) is an open-source software library, available on GitHub, 3 for Topological Data Analysis. As soon as the data analysis step starts providing results, the ParaView (or ParaView Lite) graphical user interface is used to visually explore them. Besides classical rasterization with OpenGL, which usually requires a dedicated GPU for rendering, ParaView additionally provides a CPU based ray-tracing engine. This engine is implemented using the Intel OSPray open-source library. Intel OSPRay  [21] ,  [22]  is highly optimized for scientific visualization and supports the combined rendering of volumetric and geometric data.
Their implementation is publicly available in TTK  [19] ,  [20]. When dealing with time-varying data and ensembles, we have to use a methodology for comparing them, or technically, measuring their topological distance. The distance between two diagrams D(f ) and D(g) can be evaluated with an established metric, called the L 2 -Wasserstein distance  [23] ,  [63] ,  [64] ,  [65] , noted W 2 D(f ), D(g). It can be computed by solving an optimal assignment problem, a notoriously computationally expensive task, for which exact  [66]  and approximate  [67] ,  [68]  algorithms exist. Given this distance metric, datasets can then be compared by comparing their persistence diagrams. This paves the way for several advanced statistical analysis of ensemble data. In particular, given a set S D = {D(f 1 ),.
A. VESTEC ARCHITECTURE AND WORKFLOWS Urgent workloads typically comprise numerous repeatable steps, each of which depends upon specific conditions such as the completion of proceeding simulations or the arrival of input data. Throughout this research, we have found it is natural to describe these as workflows, which is provided by the workflow manager in blue of Figure  3. These workflows are built upon the Advanced Message Queuing Protocol (AMQP)  [69]  and the RabbitMQ framework  [70]. RabbitMQ provides an implementation of AMQP which is used as the substrate for communicating messages between workflow stages and marshalling their activation. Each urgent workload, defined per use-case, is provided to the system as a set of annotated Python functions that make up an overarching workflow, with triggers fired that will schedule other stages to run  [71].
The physical connection to HPC machines is handled by the Machine Interface (MI), providing a set of services to the simulation, machine status, and data managers, which are translated into appropriate commands depending upon the specifics of the machine. For instance, if an HPC machine is connected to the system via OpenSSH and is using the Simple Linux Utility for Resource Management (SLURM) 4  batch queue system, the machine interface handles this by using the OpenSSH adaptor with SLURM commands generated for gathering the status of the queue, and for submitting and cancelling jobs. All of this is abstracted from the workflow stage, with a unique identifier. This identifier can be used to retrieve further information for that job, e. g. the queue used, the number of compute nodes utilized, or sizes of memory allocated.
B. SUPERCOMPUTER SIDE ORCHESTRATION The VESTEC system federates over multiple HPC machines and manages submitted simulations. However, at the individual machine level, there is a significant complexity that must be managed. This includes how to physically configure different simulations, how to couple simulations, and how to undertake any required data movement. Ideally, all of this should be achievable in a machine-independent manner so that the VESTEC system can remain system agnostic. One might assume that a shell script approach could be used, however, this was quickly found to be brittle and lacking generality  [72]. Furthermore, there is also a trade-off between the flexibility provided by fine-grained HPC machine federation and the overhead that this entails. Therefore, the ability to couple simulation executions on an HPC machine, such that upon the completion of a simulation the next one will run automatically, is beneficial.
C. EDGE/IoT SENSORS AND DATA As described earlier in this section, data is ingested into the VESTEC system via the External Data Interface (EDI). It can operate in both a pull and push mode to best suit the data source. Endpoints in the EDI are registered by the use case. This is typically done during the initialization of workflow stages. The stage set up rules about which workflow stage should be activated when data arrives from a defined source. As described in Section IV, the use cases of VESTEC exploit different sets of input sensor data. At the beginning of the project, we initially defined the term sensor as the measuring tool being mounted on a remote sensing apparatus. In addition to that sensor, datasets store dynamic measurements that are highly dependent on space and time. Throughout the integration of the different use cases, we concluded that the initial sensor data, e.
1) IN-SITU COMPUTATION OF TOPOLOGICAL PROXIES In order to enable in-situ computation in VESTEC, we rely on the Topology ToolKit (TTK)  [19] ,  [20]. TTK leverages the VTK/ParaView ecosystem, that supports in-situ computation with Catalyst  [18]. Typically, an analysis pipeline using TTK modules is first modeled in the form of a Python script, which can be generated manually with a text editor or through the ParaView user interface. Next, Catalyst can be configured to run the above Python script at user-defined time steps of the simulation. Note that the simulation code must implement an interface with Catalyst, to make its internal data available in the form of VTK objects. The output of the analysis pipeline can be, itself, stored to disk if desired. In VESTEC, this strategy is particularly relevant for the in-situ computation of topological proxies.
It enables to compute and store these reduced data representations, without having to store the actual simulation data, which would not be feasible in an exascale context. The persistence diagram (section II-C) is used in VESTEC as the main topological proxy for a reduced representation of each scalar field. It is chosen for being concise, multiscale, stable and representative of the main features of the data. Additionally, it may be desirable for some applications to store selected scalar fields for key time steps at full resolution. To cope with data size, a topology-preserving compressor can also be used  [75]. The persistence diagrams and the topology-preserving compressor are computed in-situ for different time steps of the simulation. The results are then written into a CINEMA database  [76] , which is a SQL-type database for VTK files.
2) STATISTICAL ANALYSIS OF PERSISTENCE DIAGRAMS As soon as the CINEMA databases are made available, a statistical analysis step is performed to compare different simulation runs of an ensemble. To compare scalar fields and identify similarities or differences between the results of different simulation runs, TTK supports efficient approximations  [62] ,  [68]  of Wasserstein distances (section II-C) between persistence diagrams. Given the ensemble of persistence diagrams encoded in the database, a Wasserstein distance matrix is computed and used to embed the diagrams as a 3D point cloud. This is achieved by Multi-Dimensional Scaling (MDS)  [77] , a standard procedure for dimension reduction in data science. In short, MDS consists in generating a point cloud (in our case in 3 dimensions, where each point represents a persistence diagram), such that pairwise Euclidean distances in 3D are optimized to coincide (as much as possible) with the pairwise distances provided by the Wasserstein distance matrix.