dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, in replacing or extending this initial dataset, we incorporated new data with progressively lower priority given to data that was publicly available within the timeframe of the project, followed by voluntarily contributed datasets from the community encountered during a series of meetings, and ultimately by solicited local contributions where necessary. In handling the possible multiple contributions over the same areas, we prioritized newer data, national data when the dataset covered a Thus, these criteria were applied to collate the datasets, fill gaps, harmonize overlaps, and remove inconsistencies. Once the collated dataset was obtained, the performed data processing aimed to extract relevant information from the different datasets and convert it into the EFSM20 format, identify the possible duplicates, and assign the ID to each retained record.
The mapped features were resampled to obtain an evenly-spaced single-trace polyline with an average distance between consecutive nodes of ~5 km (Figure  2 ). Based on common fault scaling relationships  (Wells and Coppersmith, 1994; Leonard, 2010 Leonard, , 2014;; Allen and Hayes, 2017; Thingbaijam et al. , 2017) , this length enables us to capture with sufficient accuracy the smallest earthquake ruptures commonly modeled in most hazard analyses using fault sources. The even spacing also ensures that the fault-source total length is measured consistently, regardless of the subjective mapping strategy adopted in the original datasets. The fault trace nodes were sorted based on the average strike (or dip direction) to comply with the right-hand rule  (Aki and Richards, 1980). The strike values are then recalculated to reflect the variability of the re-mapped fault.
With this revised geometry, we determined the complexity index c, which is calculated as 𝑐 = (1 -𝐿 ′ 𝐿) ⁄ cos 𝛿 (1) where L' is the end-to-end fault length, L is the fault trace length, and δ is the reported dip angle (Figure  2 ). This complexity index tends to be close to zero when the fault tends to be nearly straight or vertical. It can contribute to better evaluating whether to adopt the simple-or complex-fault models when using the OpenQuake software  (Pagani et al. , 2014)  for modeling seismic hazards. We verified the depth datum (local ground surface or mean geoid/spheroid), then checked the possible intersections of the reported upper depth with the topo-bathymetry using the ETOPO1 Global Relief Model  (Amante, 2009; NOAA National Geophysical Data Center, 2009) , and the reported lower depth with the base of the crust using the European Moho  (Grad et al.
For the geographic distribution of the SCR, we started from the mapping made by  (Johnston, 1994)  and refined the INT and MAR based on the plate tectonic model by  (Kagan et al. , 2010)  and local geology where needed. Since several records of the original datasets only reported a single value for the dip angle, we extrapolated the dip angle variability from all the other records in the fault collection. We thus calculated the dip angle variation ratio of reported values and then applied it to extrapolate the dip uncertainty range around single-value dip angles, assuming the reported single value as the average dip. The dip variation ratio (∆𝛿) for each fault is calculated as ∆𝛿 = 𝛿 𝑚𝑎𝑥 -𝛿 𝑚𝑖𝑛 2 /𝛿 𝑎𝑣𝑔 (2) where 𝛿 is the dip angle value of all faults with 𝛿 𝑚𝑎𝑥 -𝛿 𝑚𝑖𝑛 > 0. We did this calculation separately for dip-slip and strikeslip faults.
Once the minimum and maximum depths and dip angles are determined, the fault width can be calculated using simple trigonometry. Also, in the case of the slip rate, several records of the original datasets only reported a single value, and thus we adopted the same approach. The slip-rate variation ratio (∆𝐷 ̇) for each fault is calculated as ∆𝐷 ̇= 𝐷 ̇𝑚𝑎𝑥 -𝐷 ̇𝑚𝑖𝑛 2 /𝐷 ̇𝑎𝑣𝑔 (3) where 𝐷 ̇ is the slip rate value of all faults with 𝐷 ̇𝑚𝑎𝑥 -𝐷 ̇𝑚𝑖𝑛 > 0 and 𝐷 ̇𝑚𝑖𝑛 > 1𝐸 -4 mm/yr (the latter assumed as a lower threshold for considering the fault activity. We did this calculation separately for the four different combinations of slip type (DS and SS) and tectonic setting (INT and SCR). Then we calculated the weighted average based on the number of faults in the four groups. The maximum earthquake magnitude of the crustal faults is estimated as the magnitude value, in the moment magnitude scale, that corresponds to the largest possible rupture that a fault can host based on its dimensions and the magnitude scaling relations by  (Leonard, 2010 (Leonard, , 2014)) , which also incorporate the rupture aspect ratio.
The applied rigidity is the global crustal average of 33 GPa  (Dziewonski and Anderson, 1981) , which is also coherent with the rigidity used for deriving the magnitude scaling relations  (Leonard, 2010 (Leonard, , 2014)). We use the term seismic efficiency to indicate a coefficient between 0 and 1 that quantifies how much of the total moment rate is to be converted into a seismic moment rate and ultimately into an earthquake rate forecast. This coefficient is often called seismic coupling, but we avoid using it because it also has various meanings  (Wang and Dixon, 2004). However, the seismic efficiency was not assigned to individual faults in this dataset. For the moment rate calculations, the seismic efficiency is conservatively assumed to be equal to 1, and it is thus left to the user to choose a value to apply in applications.
When multiple estimates from different data types were available, we assigned a higher weight to the Moho intersection and a lower weight to thermal and other models. We then obtained a weighted average of the minimum, intermediate, and maximum values retrieved from various literature sources. The convergence direction and rate were estimated by geodetic measurements (velocity vectors) and/or by modeling the relative motion of the upper and lower plates across the subduction interface, as available in the literature. The values from different sources were weight-averaged based on the length of the subduction interface sector over which the values were measured or estimated. The goal was to agnostically capture the plate convergence possible variability range, or its order of magnitude, rather than finding the best estimates.
The maximum earthquake magnitude of the seismic interface is estimated as the magnitude value, in the moment magnitude scale, that corresponds to the largest possible rupture that the seismic interface can host based on its area and magnitude scaling relations  (Allen and Hayes, 2017). Similarly to crustal faults, the seismic moment rate is estimated using the classic formulation reported in Eq. (  5 ). However, in the subduction case, Ḋ is the long-term convergence rate, and the rigidity (m) varies with depth within the upper and lower depth limits of the slab interface. The seismic efficiency was not assigned in this dataset; thus, it is left to the user to choose a value to apply. The distributed dataset thus reports the total moment rate.
EFSM20 data collation The EFSM20 final compilation covers an area spanning from the Mid-Atlantic Ridge to the Caucasus and from northern Africa to Iceland, counting 1,248 crustal faults -for a total length of ~95,101 km. Thanks to the continuous work on active faults in many regions, and the continual update of regional databases, we were able to obtain a much richer fault-source model with respect to EDSF13, which counted 1,128 crustal faults -for a total length of ~63,775 km, so that only 178 crustal faults (13,042 km) remain from the starting dataset. Of the cumulative EFSM20 crustal-fault length, 55,401 km are onshore. Of the 39,699 km offshore, 22,846 km are in the Atlantic Ocean and 16,853 km in the Mediterranean Sea, the Black Sea, and the Caspian Sea. EFSM20 also includes four subduction systemsfor a total length of the slab interface of ~2473 km.
Crustal faults Dataset #01: This is the original database EDSF13, compiled in the framework of the SHARE project  (Basili et al. , 2013) , which covered Europe and the Mediterranean region. This dataset was adopted as the starting point to build the new crustal fault-source model. The largest regions that remained unmodified are the Balkans and northern Africa. According to individual studies, most regions were entirely replaced by new datasets or partly revisited. Elements added in regions that EDSF13 did not cover are in Iceland, France, and the northern Mid-Atlantic plate boundary. The major regional updates are summarized below. Dataset #02: This dataset covers the Mid-Atlantic ridge and transforms. The initial geometry was derived from a global plate-boundary model  (Bird, 2003) , and the rest of the characterization was based on the oceanic crust age and spreading rate  (Müller et al.
, 2008). For the transform faults, the slip rate was directly derived from the spreading rate, aided by more local data for the Gloria fault  (Fadil et al. , 2006; Koulali et al. , 2011). For the normal faults, the slip rate was obtained by combining the spreading rate with local information about fault spacing and heave  (MacDonald and Luyendyk, 1977; Escartín et al. , 1999). Dataset #03: This dataset covers the French region and is derived from BDFA  (Jomard et al. , 2017). Due to the different strategies of fault mapping used in BDFA, we redrew the fault traces by interpolation and reassigned some parameters, particularly slip rates, based on recent regional works. Dataset #04: This dataset includes a few faults in the Gulf of Corinth. The initial geometry of the faults is based on GreDaSS  (Caputo and Pavlides, 2013) , and the slip rates were updated based on recent works not included in the GreDaSS compilation  (Bell et al.
, 2009; Fernández-Blanco et al. , 2019). and added data and considerations based on local studies  (Bergerat et al. , 1990; Forslund and Gudmundsson, 1991; Rögnvaldsson et al. , 1998; Garcia et al. , 2002; LaFemina et al. , 2005; Árnadóttir et al. , 2008; Rust and Whitworth, 2019). Dataset #10: This dataset deals with the Lower Rhine Graben. In this area, we started from EDSF13, already based on a local fault model  (Vanneste et al. , 2013) , and updated the slip rate of several faults based on more recent data  (Gold et al. , 2017). Dataset #11: This dataset covers the northwestern African region (Morocco, Algeria, and Tunisia). In this region, we mainly relied on EDSF13 with updates of a few faults in the Moroccan region based on the GEM Global Active Faults Database  (Styron and Pagani, 2020)  and various other works  (Gomez et al.
, 1996; Akoglu et al. , 2006; Rigby, 2008; van der Woerd et al. , 2014; Pastor et al. , 2015)  for refining several fault parameters. Dataset #12: The NOAFAULTS database  (Ganas, 2022)  was used to integrate the dataset in the Aegean region for faults not already included in GreDaSS. This dataset was built gradually since 2013  (Ganas et al. , 2013)  following a fault-trace (polyline) approach with significant upgrades whenever compiled fault maps were available, including faults activated during seismic sequences in the Aegean  (Ganas et al. , 2018). Dataset #13: This dataset covering Portugal and offshore regions was updated based on recent works in the Lower Tagus Valley Fault Zone (LTVFZ)  (Canora et al. , 2015)  and Algarve (Sanz de  Galdeano et al. , 2020). Dataset #14: This dataset covers most of the Iberian region, including the Pyrenees.
In this region, we relied on the Quaternary Faults Database of Iberia (QAFI) database  (García-Mayordomo et al. , 2012 , 2017; IGME, 2015). Due to the different strategies of fault mapping used in QAFI, we redrew the fault traces by interpolation. Dataset #15: This dataset deals with Slovenia and its surroundings. In this area, we relied on the recently published Database of Active Faults in Slovenia  (Atanackov et al. , 2021)  and the seismogenic fault source model  (Atanackov et al. , 2022)  prepared for the 2021 seismic hazard model for Slovenia  (Šket Motnikar et al. , 2022). This dataset provides the seismic component of the slip rates. Dataset #16: This dataset covers Anatolia and parts of the Middle East. In this region, we relied on recent data from the project EMME and data from the national update of the Turkish hazard model  (Danciu et al.
Subduction systems Dataset #01: This is the original database EDSF13, compiled in the framework of the SHARE project  (Basili et al. , 2013) , which covered the subduction systems in the eastern Mediterranean region. This dataset was adopted as the starting point to build the new subduction system models for the Hellenic and Cyprus Arcs. The geometry of both slabs were recently revisited in the framework of a tsunami hazard project  (Basili et al. , 2021). These datasets have also been re-examined in light of the SLAB 2 model  (Hayes et al. , 2018)  and several other slab geometry reconstructions  (Ganas and Parsons, 2009; Halpaap et al. , 2018 Halpaap et al. , , 2019;; Sachpazi et al. , 2016). et al. , 2021)  and considering typical values for the old Tethys oceanic crust in the range of 7-9 km  (Sallarès et al.
EFSM20 Harmonization and derived parameters This Section summarizes the results of filling in some missing information (e. g. , the variability range of some parameters), harmonizing the various datasets, and adding relevant parameters to complete the list of attributes established when designing the dataset structure. The crustal fault complexity index returned 174 faults with a value equal to zero. This value is due to a dip equal to 90 degrees for 62 cases and a rectilinear trace for the remaining 112. Only 6 faults have a complexity index larger than 0. 1. The classification per tectonic setting and slip type is summarized in Table  1. The dip harmonization procedure used 660 dip-slip faults to determine an average dip variation ratio of 0. 21 and 236 strikeslip faults to determine an average dip variation ratio of 0.
The rigidity (shear modulus) depth-dependence from different datasets  (Dziewonski and Anderson, 1981; Scala et al. , 2020; Bilek and Lay, 1999; Sallarès and Ranero, 2019)  are reported in Figure  7 , showing the rigidity variation within the common depth interval of the subduction interface of the four subduction systems. Combining the variability associated with the calculation (rigidity, area, and convergence rate) leads to 81 alternatives for each subduction interface. The resulting moment rates vary from a minimum of 4. 5E+17 Nm/yr in the Gibraltar Arc to a maximum of 1. 9E+20 Nm/yr in the Hellenic Arc. Each subduction system's largest moment rate value is 3-5 times larger than the smallest value.
EFSM20 data-products sharing portfolio The outcomes of collation, harmonization, and derived-parameter characterization of all fault sources form a portfolio of datasets publicly shared for download (GeoJSON files, ESRI shapefiles, MapInfo tables) and via web services (WFS and WMS) adopting the OGC standards. Crustal fault sources also include the geometric extrusion of the fault plane within the minimum and maximum depths in the direction normal to the fault trace. Key elements of the fault plane are the vertical projection onto the ground surface of the top and bottom traces, the midline trace, the polygon enclosing the fault plane, and depth isolines. These geometric features are provided in different files, including all the key parameters illustrated in the previous section as tabulated attributes.
The depth isolines have a 0. 5 km spacing interval and include the nominal depth as an attribute. The subduction systems include the geometric representation of the slab top surface by depth isolines and the slab by a cubic lattice. The depth isolines are at 1 km spacing between 0-40 km depth and 10 km spacing between 40-300 km depth. The side of the cubic lattice is 10 km, and each lattice node also provides the slab strike, dip-direction, and dip. The subduction systems, in addition to the geometry of the top surface of the slabs, include datasets for the slab interface parameters, the discretization, and the various realizations considered using the logic tree shown in Figure  5. Table  2  summarizes the content of these datasets, including a link to the attribute definitions of each file as given in Appendix A (Tables A1, A2, A3, A4, A5, A6, A7).
Color-coded depth isolines of the top surface of the slab. Spacing interval is 1 km between 0-40 km and 10 km between 40-300 km. Concerning crustal faults, we identified several regional datasets that vary in date of the latest release, geographical extent, level of fault characterization, and data formats. In addition to those listed in the final compilation, several other datasets were considered, such as, for example, those covering Romania and the northern Black Sea  (Diaconescu et al. , 2019a, b,  https://doi. org/10. 5194/nhess-2023-118 Preprint. Discussion started: 11 September 2023 c Author(s) 2023. CC BY 4. 0 License. 2021), Iceland  (Bayat et al. , 2022)  or the Caucasus  (Onur et al. , 2019 (Onur et al. , , 2020)). Although these datasets represented a significant advancement relative to EDSF13, they could not be used because we could not work out the compliance with the requirements above recalled within the project timeframe.
Studies that report accurate slip rate variations along strike are relatively rare, and those that report the slip variations with depth or along dip are even much rarer  (Finocchio et al. , 2016). Therefore, moment rate estimates in EFSM20 generally refer to the tectonic component, and the actual seismic moment rate to be converted in an earthquake rate forecast could be somewhat smaller depending on the seismic efficiency in Eq. 5. This parameter is not explored here. Users are thus cautioned about the possibility that some crustal fault slip rates, as in the case of Dataset #15 or others derived from the literature, could have already been "cleaned" by a predetermined or modeled seismic efficiency. This occurrence may not have been evident when the information was adopted in bulk from a large dataset of a regional compilation.
The seismic efficiency of the Calabrian Arc is also very variable. Based on geodetic observations and geodynamic modeling, the Calabrian Arc was hypothesized to be either locked or partly locked  (Carafa et al. , 2018)  or negligibly active  (Nijholt et al. , 2018). Also, different interpretations exist on the activity of the Gibraltar Arc subduction interface. For example, the QAFI dataset does not include the Gibraltar subduction system, and other studies on geodetic observations do not consider the subduction process active  (Stich et al. , 2006)  or consider it at all  (Palano et al. , 2015). The convergence rates reported in EFSM20 are thus meant to provide reasonable values in the hypothesis that the subduction interface is active, although EFSM20 remains neutral in this respect. The slab geometric reconstruction can still be useful for separating crustal seismicity from intraslab seismicity since the intraslab tectonic rates were not estimated.
Outlook EFSM20 was designed to fulfill the specific needs of a hazard application at the scale of a continent and has thus been one of the main input datasets used for the 2020 update of the European Seismic Hazard Model (ESHM20)  (Danciu et al. , 2021 (Danciu et al. , , 2022)). Its predecessor EDSF13  (Basili et al. , 2013)  was used for the 2013 European Seismic Hazard Model (ESHM13)  (Woessner et al. , 2015) , the first regional tsunami hazard model NEAMTHM18  (Basili et al. , 2021) , and several other hazard analyses at different scales, post-earthquake analyses, and tectonic modeling. Likewise, EFSM20 is aimed to serve the same scope. To this end, it is distributed as machine-readable files using open standards (OGC), which allow users to port the datasets on various platforms and use them programmatically.
Being a continent-scale compilation, however, EFSM20 implied data selections and simplifications, which may hinder its application at a more local scale. We thus recommend that the users resort to the original datasets and pertinent literature when performing analysis at a local scale, such as site-specific hazard applications or near-field seismic scenarios. In these regards, one first-order aspect is fault geometry. The down-dip planar simplification is known to generate bias when reconstructing the earthquake ruptures  (Dutta et al. , 2021)  or to underestimate the near-field ground motion  (Passone and Mai, 2017). Similarly, tsunami modeling requires full knowledge of the 3D geometry of faults (Gómez de la  Peña et al. , 2022; Serra et al. , 2021; Tonini et al. , 2020). The compilation of EFSM20 relied on the efforts made by many scientists in collecting and systematizing data about active faults with a region-wide perspective.
Acknowledgments This work started within the project Seismology and  Earthquake Engineering Research Infrastructure Alliance for Europe (SERA ) which received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No. 730900. Additional resources to this work were received from the Joint Research Unit, EPOS-ITALIA (https://www. epos-italia. it/) Executive Program 2021-2024, funded by the  Italian Ministry of University  and Research, and from the  Multi-Year Collaboration  Agreement on Data and Services Provision  2022-2023 between EPOS ERIC and EPOS TCS Seismology  and the associated  Work Program for 2022  " MYCA22/23DSP-WP22 " of the  European Facilities  for Earthquake Hazard  and Risk (EFEHR). We also thank the staff and colleagues who manage the  EPOS ICS-C  for their help in incorporating our data into the EPOS portal.
Dataset # 05 : 05 This dataset covers the offshore parts of the Gulf of Cadiz and the Alboran Sea. In this area, we updated the EDSF13 based on several recent works, providing updated geometries and/or slip rates (Koulali et al. , 2011; Martínez- Loriente et al. , 2018; Martínez-Loriente et al. , 2013;  Neres et al. , 2016; Perea et al. , 2018; Gómez de la Peña et al. , 2018). Dataset #06: This dataset covers the Italian territory and some surrounding regions. It is mainly based on the most recent version of the DISS (Basili et al. , 2008;  DISS Working Group, 2021). Dataset #07: This dataset includes a few faults in the Eastern Betic region. Such faults represent modifications of QAFI (see Dataset #14) according to recent works with substantial updates of fault geometries and slip rates(Borque et al.
, 2019;   Gómez-Novell et al. , 2020a, b; Herrero-Barbero et al. , 2020). Dataset #08: This dataset covers the Aegean region. It is mainly based on the most recent version of GreDaSS (Caputo and Pavlides, 2013). https://doi. org/10. 5194/nhess-2023-118 Preprint. Discussion started: 11 September 2023 c Author(s) 2023. CC BY 4. 0 License. Dataset #09: In Iceland, we started from the same approach as the Mid-Atlantic ridge and transform faults (see Dataset #02).
https://doi. org/10. 5194/nhess-2023-118 Preprint. Discussion started: 11 September 2023 c Author(s) 2023. CC BY 4. 0 License. Dataset #02: This dataset corresponds to the most recent version of the DISS (DISS Working Group, 2021), which includes an updated reconstruction of the Calabrian Arc slab geometry based on a rich dataset of seismic reflection profiles for the shallower part (<20 km depth) and the seismicity distribution for the deeper part (Maesano et al. , 2017). Dataset #03: This dataset is an original elaboration of the Gibraltar Arc based on published works. The geometry of the slab was reconstructed using different datasets at different depths. For the shallowest depths, we used data from bedrock markers based on interpreting multichannel seismic reflection profiles and wide-angle seismic surveys (Gutscher et al.