dataesr/scientific-paragraphs-categorization huggingface dataset:
Fire 2023,  6, 215  National maps were used to generate a point file with residential house locations, in order to accurately identify all individual residential structures. The road network was obtained from the OpenStreetMap dataset (https://wiki. openstreetmap. org/wiki/Downloading_data, accessed on 13 March 2023), and the Natura 2000 protected sites was downloaded from the European Environment Agency (https://www. eea. europa. eu/data-and-maps/data/natura-14, accessed on 20 November 2022). The GISCO database was used to obtain the spatial distribution of the population in the study region (https://ec. europa. eu/eurostat/web/ gisco/geodata/reference-data/population-distribution-demography/geostat, accessed on 20 November 2022). Its database contains core geographic data (e. g, administrative boundaries, and population grid data) covering the whole of Europe and is based on the official statistics from National Statistical Institutes.
Ecological Values The EV included in the first version of the FirEUrisk integrated scheme took into account four components: soils, vegetation, fauna and the conservation value through natural protected areas. The ecological values of soils were first assessed as the main resource for vegetation growth, regeneration and survival. Soils provide the water storage and nutrient supply for plant growth. The subsoil and topsoil Available Water Content (AWC) based on soil texture, soil depth and rock fragment content provided by the JRC (https://esdac. jrc. ec. europa. eu/content/european-soil-database-derived-data, accessed on 10 March 2023) was used. Soil N and P content was also available from the JRC referring to top soil N and P concentration in soils and it was used as the standard fertility status without disturbance (https://esdac.
jrc. ec. europa. eu/themes/npk-european-soils, accessed on 10 March 2023). Ecological values related to vegetation were assessed through forest biomass [362], maximum tree height as a proxy of forest diversity  [363]  taken from [364] (https://glad. umd. edu/dataset/gedi, accessed on 10 March 2023), landscape habitat diversity based on tree cover from  Hansen et al. [365]  (https://data. globalforestwatch. org/documents/treecover-loss/explore, accessed on 15 March 2023), and tree height coefficient of variation within a 1km grid cell from the 30m resolution raw data. Animal species diversity was obtained from the potential habitat data for each species of mammals, birds, reptiles, and amphibians available in the IUCN repository (https: //www. iucnredlist. org/resources/spatial-data-download, accessed on 20 January 2023).
Bird biodiversity analysis was taken from  Fusco et al. [366]  (http://datazone. birdlife. org/ home, accessed on 10 March 2023). For reptiles and amphibians, the data were produced by  Sillero et al. [367]  (https://www. seh-herpetology. org/distribution-atlas, accessed on 15 March 2023) and for mammals the input data came from the European atlas for mammals (https://discovermammals. org/projects/the-2nd-european-mammal-atlas/, accessed on 15 March 2023). From these databases, the presence of each species was resampled at 1 km 2 , following the target resolution of the FirEUrisk project. The same source was used to estimate the potential habitat data for each species belonging to the category of "critically endangered", "endangered" and "vulnerable" of mammals, birds, reptiles, amphibians and vascular plants.
Finally, places of special conservation were used to weigh each ecological value according to human perception and strategies of conservation priorities. Data were downloaded from the World Database on Protected Areas (WDPA) (https://www. protectedplanet. net/en/thematic-areas/wdpa?tab=WDPA, accessed on 15 March 2023). Only the terrestrial protected areas classified under the International Union for Conservation of Nature's categories I-IV were considered, since they have reliable data, verified on the ground, and they are managed in a similar way, thus enabling us to assume that they all have the same biodiversity conservation values. The vector data layer was converted to raster binary data by assigning the value 1 where a WDPA was located and 0 where it was not. Objective valuations for conservation areas were estimated from the Key Biodiversity Areas (KBA) maps  [368].
KBAs have outstanding biodiversity value due to their extraordinary ecological integrity, globally significant ecosystems, or significant populations of animals, fungi, and plants. The Intact Forest Landscapes (IFL) dataset  [368]  was used to obtain the extent of the forests and terrestrial ecosystems that remain unaltered by humans, with a minimum mapping unit of 500 km 2. Human pressure was also used as a surrogate of landscape fragmentation produced by human infrastructures, road and railways, with the highest percentages corresponding to highly degraded or heterogeneous landscapes and the lowest to areas that are unfragmented or homogeneous. The database was collected from the United Nations Environment Program (www. unep. org%2Fdata-resources&usg=AOvVaw3tEdJJmd7wlzixU-1TIMNN, accessed on 25 January 2023).
Old growth undisturbed forests were derived from forest cover change dataset of  Hansen et al. [365]. All the variables were normalized between 0 and 1 through a linear function for an equal weight assemblage in a Principal Components Analysis (PCA)  [369]. This technique reduces the complexity of a multivariate dataset by extracting the most explicative axis that summarizes the input variables by minimizing the correlation among the new components. PCA has been previously used for spatial assessment of ecosystem multifunctionality  [370]. In the context of ecological values, PCA can be used to identify the most important factors that contribute to biodiversity, ecosystem health, or other ecological measures. For example, a PCA analysis of ecological data might identify factors such as habitat diversity, soil quality, water quality, and nutrient availability as the primary drivers of ecosystem health in a particular area (Figure  13 ).
Resilience (RES) was finally evaluated as the area under the curve; meanwhile, the regeneration process takes place. Based on the dominant tree species map from the European Atlas (https://forest. jrc. ec. europa. eu/en/european-atlas/atlas-data-and-metadata/, accessed on 30 March 2023), the information on resistance and recovery of vegetation to fire for each of these species was obtained from the BROT 2 and TRY databases. The BROT 2. 0 is a database of functional traits for vascular plant species of the Mediterranean Basin (https://www. uv. es/jgpausas/ brot. htm, accessed on 30 March 2023). The database includes 25,764 individual records of 44 traits from 2457 plant taxa distributed in 119 taxonomic families. Trait data were obtained from a comprehensive literature review, plus some field and experimental observations.
All records are fully referenced and, in many cases, include geographic coordinates. The TRY (TRY Plant Trait Database) data is a global database of plant traits that provides standardized information on the characteristics of plant species from around the world (https://www. try-db. org/TryWeb/Home. php, accessed on 30 March 2023). The TRY database contains information on a wide range of plant traits, including morphological, physiological, biochemical, and ecological traits. The data were completed for temperate and boreal biomes from a collaborative expert knowledge. Resistance was assessed through species traits on bark thickness, wood density, and specific wood composition as cork (as in the Quercus suber species). The ecosystem resistance at 1 km 2 resolution was calculated as the weighted mean of traits according to species cover.
The daily burned date information at the pixel level was used to deduce the time since the last fire (in days), which was combined with fire size as a modifier for regeneration time, since seeder species take longer time to colonize a large fire than a small fire. The pixel aggregation from  Laurent et al. [332]  was used for the fire patch event database. The recovery rate RCR locally depends on climate and edaphic conditions. ERA5land mean annual temperature, solar radiation and precipitation were assembled at 5 km resolution, locally modified by slope, aspect and altitude derived from Aster-gDEM at 30 m and resampled at 1 km 2 resolution. Post fire recovery rate (RCR) was also modified by soil AWC, soil fertility based on the standard soil N/P content (see  Section 4. 3. 7) , affected by fires from volatilization and wind/water post fire erosion potentials.
Post-fire soil loss and recovery was assessed by estimating nutrient depletion as the amount of leaf N and P volatilized during combustion, as the product of Leaf Area Index (LAI), specific leaf area (SLA) (mm 2 mg -1 ), leaf nitrogen content (LNC) (mg g -1 ) and leaf phosphorus content (LPC) (mg g -1 ). These datasets were obtained from. Post fire N/P stock recovery was based on N Atmospheric deposition map (https://www. syke. fi/en-US/Current/Sciencepolicy_cooperation_has_improved_ a%2859833%29, accessed on 20 March 2023). Since water and wind erosion can affect soil AWC and N/P deposition from ashes through erosion processes, both factors were considered taken the information from  Borrelli et al. [374]. To estimate post-fire soil erosion potential, the Soil Erosion map for Europe (https://data.
jrc. ec. europa. eu/dataset, accessed on 18 December 2022) was used as an input to the Revised Universal Soil Loss Equation (RUSLE). The values were then transformed into a categorical variable, 1 for lowest values and 5 to highest values according to the criterion for soil erosion due to water, proposed by the FAO (FAO/UNEP/UNESCO, 1979): 1 for values between 0-20, 2 for values between  3  for values between  50-200 and 4  for values over to 200, which is also applicable to fire erosion processes  [211]. Finally, humans might play a significant role in post-fire forest recovery by stimulating planting. For this variable, we used different management maps  [204, 375]  which affect the regeneration time (RGT) as a local modifier of regeneration. From this mechanistic framework, all identified resilience variables were combined using again PCA to generate the final ecological resilience map.
FirEUrisk Information System The FirEUrisk project has built a dedicated platform to store all spatial data sets generated and geodatabases developed in the context of FirEUrisk by the consortium partners for the ET and PS areas, which will be the primary target for integration. In addition, the system hosts all the risk components and variables considered in FirEUrisk for the ET and PSs. Access to this information is available through a dedicated web mapping service. Some layers are also accessible through a dedicated mobile app, developed as a citizen-science application that allows ordinary citizens to report and gather fire risk information, such as fuel information or fire alerts. Wildfire risk communication and citizen science applications can work together to improve the understanding and management of wildfires.
FirEUrisk Information System The FirEUrisk project has built a dedicated platform to store all spatial data sets generated and geodatabases developed in the context of FirEUrisk by the consortium partners for the ET and PS areas, which will be the primary target for integration. In addition, the system hosts all the risk components and variables considered in FirEUrisk for the ET and End users of wildfire risk information will test the relevant FirEUrisk component in six Demonstration Events. In these events, the consortium will engage with stakeholders to show them preliminary results of the project. Representatives of the target audiences addressed in the different PS and ET area will use web tools to provide relevant feedback via validation forms. The contributions collected from members of the scientific community, first responders, landowners/managers, policy and regulation makers, industrial stakeholders, civil society organizations, risk prevention communities, and the general public will help improve the FirEUrisk information system in the following two years.
Using the land use results from CLUMondo, which cover the decades from 2020 to 2050, the DGVMs then simulate vegetation patterns, highlighting specific areas that may present greater fire risk. The DGVMs, however, also require historic land use data that is consistent in terms of spatial characteristics with the future land used scenarios. Thus, the CLUMondo land use scenario results were linked to representations of historical land use extending back to the year 1960 provided via the HILDA+ dataset  [378] , which combines several open data streams to estimate land use change. The resulting dataset is the first long term Europespecific land use scenario set that is aligned with a historic reconstruction, improving the ability of the DGVMs to forecast future fire regimes [155] at a fine spatial resolution.
While the final time series is based on a 1-km 2 resolution, the results were converted to a 9-km 2 resolution to match the resolution that is used natively in the DGVMs and harmonized the land cover class legend to the fuel map generated within the project  [343]. Data are available at DataverseNL (https://dataverse. nl/dataset. xhtml?persistentId=doi:10. 34894/ALZTYS, accessed on 20 April 2023). FirEUrisk also aims to create stylized fuel management scenarios that explore the potential to manage future fire risk using land management strategies that link land use scenarios from CLUMondo and associated vegetation patterns simulated in the DGVMs. Together, the findings will be synthesized into a series of stylized fuel management scenarios representing low and high adoption of land management strategies and potential trade-offs between them and other desired outcomes, such as biodiversity and ecosystem services.
Fire 2023, 6, x FOR PEER REVIEW 26 of 62[224]. High risk may be expected where high wildfire danger affects the most critical areas for people, and secondarily for the other ecological and socioeconomic aspects (Figure2). Next steps of the EWRA development will include testing and validating the current and future versions in close collaboration with the EGFF. At the same time, future research will focus on deriving enhanced datasets and methods for fuel classification, socio-economic components, and live fuel moisture content.
To have a better outlook on CALRm, we have proposed a specific database named CALR-ETdb (https://www. dsimb. inserm. fr/CALR-ET, accessed 20 February 2023)  [47]  that aggregates all CALRms from the COSMIC database  [48]  and bibliographic searches. Originally composed of 155 CALRms, it has now been updated to 169. CALR-ETdb is searchable and provides information not only in terms of references and types but also of structural properties with secondary structures and three-dimensional structural models. Thanks to this large number of CALRms, we propose a new classification of CALRm sequences defining five different classes and some surprising underlying properties. Hence, class A aggregates 96% of the former type 1 (and those that are 1-like), while class B comprises only 64% of the former type 2 (and those that are 2-like), i.
Dataset The CALR sequence was retrieved from UniProt (UniProt ID: P27797)  [54]. For clarity, the wild-type CALR was named CALRwt. ET impacts the C-terminus of the CALR protein, i. e. , the C-domain corresponding to the third (and last) domain; it is the ninth exon. The C-domain extends from alanine 352 to leucine 417 of CALRwt. The 5 other sequences of CALRms were taken from CALR-ETdb. Each one corresponds to one of the five classes described in  [47]  CALR. The results clearly show that (i) the protocol was correct, (ii) the dynamics of classes A, B, and C are slightly different and are stable as dimers, and (iii) that classes D and E cannot be stable as dimers. These last two seem to not be associated directly with MPL binding. Therefore, they also may not be directly linked to ET and platelet proliferation.
Dataset The CALR sequence was retrieved from UniProt (UniProt ID: P27797)  [54]. For clarity, the wild-type CALR was named CALRwt. ET impacts the C-terminus of the CALR protein, i. e. , the C-domain corresponding to the third (and last) domain; it is the ninth exon. The C-domain extends from alanine 352 to leucine 417 of CALRwt. The 5 other sequences of CALRms were taken from CALR-ETdb. Each one corresponds to one of the five classes described in  [47].
Structural Models The structural templates to model CALRwt were selected thanks to PSI-BLAST software  [55]  used with the Protein Data Bank  [56]. It makes it possible to detect proteins with sequences close to the query sequence and with structures that can be used for modeling, namely, structural modeling. The mining of the different databases was performed with PSI-BLAST with an e-value greater than 0. 005. CALRwt was based on the calreticulin partial structure (PDB ID 6ENY chain E structure  [35]  from the Protein Data Bank  [56] ) and completed (please notice that the C-domain is identical to the one from the PDB, ID 5LK5  [57] ). The CALRm structural models were taken from our database CALR-ETdb (https://www. dsimb. inserm. fr/CALR-ET accessed on 17 December 2022)  [47]. The modeling of the 3D models was performed with MOD-ELLER v.
10. 0  [58]. The selection of the best structural model was mainly based on the renowned DOPE approach [59]. The three-dimensional structure and structural models were visualized using PyMOL (the PyMOL Molecular Graphics System, Version 1. 7. 2. 2 Schrödinger, LLC)  [60]. Other modeling approaches were also used, i. e. , I-TASSER  [61] , Robetta  [62] , AlphaFold2  [63] , and Phyre2  [64] , but without satisfying results compared with a human-supervised approach using MODELLER (see the Discussion section). Figure  2  shows all the modeled systems (they are all provided in Data S1). CALRwt was based on the calreticulin partial structure (PDB ID 6ENY chain E structure  [35]  from the Protein Data Bank  [56] ) and completed (please notice that the C-domain is identical to the one from the PDB, ID 5LK5  [57] ).
The CALRm structural models were taken from our database CALR-ETdb (https://www. dsimb. inserm. fr/CALR-ET accessed on 17 December 2022)  [47]. The modeling of the 3D models was performed with MODELLER v. 10. 0  [58]. The selection of the best structural model was mainly based on the renowned DOPE approach [59]. The three-dimensional structure and structural models were visualized using PyMOL (the PyMOL Molecular Graphics System, Version 1. 7. 2. 2 Schrödinger, LLC)  [60]. Other modeling approaches were also used, i. e. , I-TASSER  [61] , Robetta  [62] , AlphaFold2  [63] , and Phyre2  [64] , but without satisfying results compared with a human-supervised approach using MODELLER (see the Discussion section). Figure  2  shows all the modeled systems (they are all provided in Data S1).
Molecular Dynamics Molecular Dynamic (MD) simulations were performed using GROMACS 2020. 4 software  [65]  with the CHARMM-36 forcefield  [66]. Before starting any simulation experiments, each structural variant was energy-minimized for 500 steps of the steepest descent and 500 steps of the conjugate gradient optimized with the SHAKE algorithm, using the GRO-MACS suite. CALRwt and 5 different CALRms, both in monomeric and dimeric forms, were soaked in a rhombic dodecahedral simulation box with TIP3P water molecules. A total of 13 calcium ions (Ca 2+ ) were added as positive counter-ions into the system, in order to neutralize it, for CALRwt and CALRm class E because these two systems are negatively charged. The other variants (classes A, B, and C) had chloride ions (Cl -) as counter-ions because they are positively charged.
Molecular Dynamics Analysis The analysis of MD was carried out using classic tools, for example, the RMSD and RMSF, and other more innovative ones such as PBxplore  [69] , a tool developed within the team. PBxplore makes it possible to analyze protein blocks throughout the MD. MD analysis  [70] , a Python library, was used to compute distance calculations between Ca 2+ ions and their binding sites during the simulations. The RMSD (root-mean-square deviation) makes it possible to see how a structure varies during the dynamics by comparing it with the starting structure (reference). For each frame, an average of the differences between the reference positions and the positions of the current frame is performed in order to obtain an RMSD value per unit of time. The RMSF (root-mean-square fluctuation) is similar to the RMSD.
It makes it possible to determine the fluctuation of each residue following the same principle as for the RMSD, i. e. , a comparison with a reference that is the average position of each residue and, thus, measure the difference between the current position and the average position in order to have a flexibility value per position. Protein Blocks (PBs) are a structural alphabet composed of 16 local prototypes  [71]. Each specific PB is characterized by the ϕ, ψ dihedral angles of five consecutive residues, with each PB assignment focused on the central residue. Obtained through an unsupervised training approach and performed on a representative non-redundant databank, PBs give a reasonable approximation of all local protein 3D structures  [72]. PBs are very efficient in tasks such as protein superimpositions  [73]  and MD analyses  [74].
Analysis of CALRwt Monomer Dynamics CALRwt is a calcium-binding chaperone residing in the endoplasmic reticulum (ER), thanks to the KDEL motif localized at the end of its C-terminus, which functions as an endoplasmic reticulum retention signal  [75]. As the CALR C-domain is incomplete in the PDB structure (PDB ID 6ENY chain E structure  [35]  (see Figure  2a )), it was previously completed with comparative modeling, with MODELLER [58] and PSIPRED  [76]  predictions used as constraints (see Materials and Methods section and Figure  2b ). The electrostatics shows highly negative values in the C-terminus region. This first part consists of using the wild-type reference form of CALR to assess whether it is possible via MD to reproduce the physiological behavior of the C-domain of CALR, i. e. , to capture Ca 2+ ions.
Unexpected Similarities between CALRm Classes D and E and CALRwt Dynamics This third part focuses on classes D and E. The former corresponds to a short form of the C-terminal of CALRwt, while the latter has only a point mutation with CALRwt. It was carried out using SCWRL software  [78]. They are, therefore, very close to CALRwt, i. e. , they are cysteine-free and therefore cannot be dimerized by the covalent bond of cystine. Ca 2+ ions were, therefore, added, as with CALRwt, and all the parameters were identical to the previous simulations. CALRm class D is extremely short (see Figure  5  and Video S7). It is mainly composed of one long α-helix ranging from residue 353 to 386 (see Figure  5c ). This helix remains stable during 400 nsec (with N eq values of one), but its extremities are flexible with an N eq value of around five for the end of the helix (see Figure  5b ).
Predictions of secondary structures were made with PSI-PRED  [76, 86] ; their confidence indices are excellent. In the first step, CALRwt and CALRm types I and II were built using MODELLER  [58]. The other CALRms were derived from these, sometimes with other constraints to avoid having too-long helices. We mainly focused on CALRwt, CALRm class A (we took the classical type I as the reference for this), and CALRm class B (we took the classical type II as the reference for this class). CALRm class D corresponds well to the X-ray structure because it is very short. CALRm class E is simply CALRwt with a point mutation obtained using SCWRL  [78]. For CALRm class C, we used class B as a support (see the alignment in Figure  1 ). With the increase in Protein Data Bank structural data [87] and new Deep Learning approaches, we wanted to test (or re-test) these approaches.