dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion started: 17 August 2022 c Author(s) 2022. CC BY 4. 0 License. ://doi. org/10. 5194/essd-2022-172 Preprint. Discussion started: 17 August 2022 c Author(s) 2022. CC BY 4. 0 License. ://doi. org/10. 5194/essd-2022-172 Preprint. Discussion started: 17 August 2022 c Author(s) 2022. CC BY 4. 0 License. ://doi. org/10. 5194/essd-2022-172 Preprint. Discussion started: 17 August 2022 c Author(s) 2022. CC BY 4. 0 License. ://doi. org/10. 5194/essd-2022-172 Preprint. Discussion started: 17 August 2022 c Author(s) 2022. CC BY 4. 0 License. ://doi. org/10. 5194/essd-2022-172 Preprint. Discussion started: 17 August 2022 c Author(s) 2022. CC BY 4. 0 License. HRS, TF, KMC, and NJA directed the CoralHydro2k Project. RMW built and managed the CoralHydro2k database 465 DR00NBB01 KI14PAR01 XU15BVI03 CO03PAL03 DR99ABR01 KA17RYU01 MO20WOA01 RE19GBR03 TU01SIA01 AB20MEN08 KU99HOU01 ZI15MER01 7 d18O d18O, Sr/Ca (Kilbourne et al.
234 Urvina Bay, Isabela Island, Sibuti Coral Reefs National (Lombok Strait) (Great Barrier Reef) Kiribati (Gilbert Islands) Strait) Kenya Mauritius (Totor Reef) AL16PUR01 MO06PED01 CO03PAL05 NU09FAN01 SA16CLA01 WE09ARR01 CH97BVB01 PF04PBA01 ZI16ROD02 6 Sr/Ca d18O d18O d18O, Sr/Ca (Nurhati et al. , 2009) (Alpert et al. , 2017) Poore, 2013; DeLong (Moses et al. , 2006) (Cobb et al. , 2003a, d18O (Sanchez et al. , 2016) d18O, Sr/Ca (Wei et al. , 2009) d18O (Charles et al. , 1997) d18O (Pfeiffer et al. , Sr/Ca (Zinke et al. , 2016) 18. 1153 16. 76 5. 87 3. 85 18. 4 -16. 72 -4. 6162 -5. 43 -19. 667 -67. 9374 Mona Island, Puerto Rico Keys) -22. 89 Pedra de Lume, Sal Island Levu, Fiji -162. 13 Palmyra Island, United Ecuador (Galapagos Park) -159. 35 Tabuaeran (Fanning -114. 7 Clarion Island, Mexico 146.
Material and methods Three broad-band seismometers RMA1, RMA2 and RMA3 were installed along the riverbed of the Rivière du Mât (De la Torre, 2008) respectively at 500, 350 and 20 m from the riverbed (Fig. 2b ). RMA1 and RMA2 stations were equipped with Guralp CMG40 seismometers and RMA3 with a Guralp CMG3-ESPC seismometer. The Guralp CMG40 sensor is characterized by a flat response between 1/40 and 50 Hz whereas the response of the Guralp CMG3-ESPC sensor is flat between 1/ 120 and 50 Hz. Data were acquired continuously by Nanometrics Taurus digitizers at 100 samples/s which limited our investigations to the Nyquist frequency of 50 Hz (see waveforms on Appendix A1). Rainfall data (6 min resolution) were extracted from a tipping bucket rain gauge (each tip corresponds to 0. 1 mm of rainfall) deployed 800 m downstream from the RMA1 station, in Salazie village (Fig.
2b ) and maintained by Météo-France Réunion (https://publitheque. meteo. fr/). Water level data (blue curve, Fig. 3 ) were recorded by a pressure sensor installed in a mobile-bed hydrometric station with a time resolution of 5 min. Interestingly, this sensor was quasi-colocated with the RMA3 station (6. 4 km and 7. 7 km downstream from RMA1 and RMA2, respectively). The data collected were made available from the "Office de l'Eau de La Réunion" (https://www. eaureunion. fr/bdd/). We computed the power spectral density (PSD, Fig. 3 ) with a timefrequency analysis using the TiSKit package developed by W. Crawford (http://www. ipgp. fr/~crawford/Homepage/Software. html). The seismic records were first corrected for the full instrument response. This procedure was applied to the seismometer vertical component and with a 60 s moving window and a 50 % overlap.
Results are provided in decibel (dB) relative to velocity (10 × log 10 [(m/s) 2 /Hz]). We also calculated the average Power Spectral Density (PSD) with the SACPSD software  (Herrmann, 2013) , over the frequency band 0. 01 to 25 Hz before the cyclone on March 3, 2018 (in blue, Fig. 4a, b  and c ) and during the cyclone, from March 4 to 8 (in red, Fig. 4a, b  and c ). To compare hydrological and seismic data, we calculated the root mean square (RMS) of the seismic vertical component expressed in velocity (m/s). The RMS provides a smoothed measure of the seismic signal amplitude over time and over a given frequency range. We also chose to use the RMS because PSD lowers the highest values of energy due to its unit in dB, issued from the logarithm of the ratio between the energy and the discrete length of the time series.
-The spectrograms in Fig. 3  do not show a clear correlation between the seismic signal and rain for the two closest seismic stations (RMA1 and RMA2) located at large distance to the river, whereas the seismic signal appears to be strongly correlated to the river water level, particularly at the station RMA3 located close to the river, as discussed below. This map was created with the GMT software  (Wessel and Smith, 1991)  and the digital elevation model of the IGN (http://professionnels. ign. fr/, 5-m resolution). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) Air pressure changes can load or unload the mass at the Earth's surface inducing near surface variation of seismic velocity  (Kramer et al. , 2023).
In order to discriminate and locate possible seismic sources during the cyclone, we also performed polarization measurements based on principal component analyses (PCA) of ground motion measured by the three-component seismometers, using MatLab codes (Ppol package) (e. g. ,  Fontaine et al. , 2009; Davy et al. , 2014). This allows retrieving the apparent back-azimuth of the sources in the horizontal plane (the backazimuth being the direction of the source determined at the recording station) provided at ±180 •. The interpretation of the polarization depends on the type of seismic phase considered. Determining all types of seismic waves generated during the period of the cyclone is beyond the scope of this paper and specific numerical models could help to decipher the various types of seismic phases generated at high-frequency.
u 3 corresponds to the coordinate relative to Z the vertical component. In order to investigate the possible frequency dependence of the polarization and to separate any possible water-induced signals from sediment-induced signals, data were first filtered in various frequency bands (Table  1 ). For each band, we used a 1000 seismic cycle-long moving window and a 50 % overlap to bring out the preferential orientation (for example a 222 s time window for the range 2-7 Hz). As shown in Appendix B for CpH = 0 the orientation is not resolved. Therefore, we considered significant polarization measurements when CpH > 0. 8 in order to well constrain the orientations and to decrease the uncertainty. The polarization analysis was performed on continuous data spanning March 3 to 7. This window covers the whole Dumazile flood period and includes one day of record before the flood with a water level characterized as low-flow (10 cm at RMA3 station).
Acknowledgments This study benefited from support from the  Research Federation OMNCG (Observations des Milieux Naturels et des Changements Globaux)  and  OSU-R (Observatoire des Sciences de l'Univers -de La Réunion) at University of La Réunion , the  Région Réunion  and the  CNRS-INSU (Institut National des Sciences de l'Univers ) funding from the  TelluS-SYSTER program. We would like to acknowledge the  INSU-RESIF/SISMOB  for providing the seismic station deployed in the " Rivière des Pluies" network https://doi. org/10. 15778/RESIF. ZF2015. Seismic data are available at the French RESIF archive center (http:// seismology. resif. fr)under the ZF experiment code (Fontaine et al. , 2015). This study has been also supported by the  ReNovRisk-Erosion FEDER  project funded by the  Région Réunion and European Community.
These mismatches further encourage a joint inversion of the three data sets. In this work, we analyze the ellipticity and phase shift of the Rayleigh waves related to the large seismic events S1000a, S1094b and especially S1222a, to obtain further constraints on the structure of the Martian crust. In particular, we perform a joint inversion of Rayleigh wave ellipticity, P-to-s RFs and T a , aiming to obtain a consolidated local crustal model.
The predicted ellipticity curves are obtained via the gpell toolbox included in the Geopsy package  (Wathelet, 2005). Lastly, the T a are computed assuming two-way vertical travel paths of the P waves. The inversion scheme utilized is the Neighborhood Algorithm (NA), first introduced by Sambridge (1999) and widely used for multiple geophysical applications, including subsurface characterization (e. g. ,  Carrasco et al. , 2022; Ferreira et al. , 2020; Hobiger et al. , 2013). We applied a modified version of the NA, where the parameters space can be constrained by physical conditions and prior information  (Wathelet, 2008). The parameter space is configured as 3-uniform-layers over a half-space (3LOH), where the depths of the discontinuities are constrained by  Joshi et al. (2023). We inverted for shear-wave velocity V s , P-wave velocity V p and bottom depth z of each layer.
Data Availability Statement The event information of the Martian seismic events can be found in the InSight seismic event catalog version 13 (InSight Marsquake Service, 2023). The waveform data and station metadata are available from IPGP's MSDC as well as from IRIS MDC (InSight Mars SEIS Data Service, 2019). Seismic data for station DREG  (BQ network, 2016)  are publicly available via EIDA (http://eida. gfz-potsdam. de/webdc3/). The GCMT catalog is available here https://www. globalcmt. org/CMTsearch. html, whereas the events analyzed for the Earth case and the codes utilized for the processing of the events can be found under https://github. com/scarrascom/Rellipy. Observed data used for the joint inversion, a Python implementation and preferred crustal models shown in Figure  3  can be found at https://doi.
org/10. 5281/zenodo. 8051337 (Carrasco, 2023). The NA was implemented via the dinverext plugin, which can be obtained from http://www. geopsy. org/. operations team at JPL, SISMOC, MSDS, IRIS-DMC and PDS for providing SEED SEIS data. Thanks to Dr. J. Li for his thoughtful comments about the manuscript. French co-authors acknowledge the French Space Agency CNES and ANR (ANR-19-CE31-0008 and ANR-18-IDEX-0001). PL, ES, ZX acknowledge Idex Paris Cité (ANR-18-IDEX-0001). The research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). Open access funding enabled and organized by Projekt DEAL. This is InSight contribution ICN 299.
Although soil ecology has benefited from recent advances in describing the functional and trophic traits of soil organisms, large scale reconstruction of soil food webs is still hampered by the issues that (1) most of the data about trophic interactions and diets are distributed across heterogeneous repositories, (2) no well-established terminology exists for describing trophic groups, feeding processes,  and resource types,  and (3 ) large heterogeneity exists in the trophic classification of different soil organisms, or such classifications are lacking. Here, we propose the Soil Food Web Ontology as a new formal framework for representing knowledge on trophic ecology of soil organisms. This ontology captures the semantics of trophic concepts, including consumer-resource interactions, diets and feeding processes, and provides a formalisation of trophic group definitions.
This ontology can be used to add semantic annotations to trophic data, thus facilitating the integration of heterogeneous datasets. It also provides lexical resources that can support the development of information extraction tools for the creation of literature-based datasets. Finally, it enables automatic and consistent classification of soil organisms based on their trophic relationships. We argue that, by harmonising the terminology and underlying concepts of soil trophic ecology, our ontology allows a better use of available information on the feeding habits of soil organisms and sounder trophic classifications, thus facilitating the reconstruction of soil food webs and making food web research more accessible, reusable and reproducible.
g. textual definitions, and (4) logical axioms and definitions that enable specialised softwares (reasoners) to automatically access and interpret the knowledge encoded in the ontology  [10]  (Figure  1 ). These features make ontologies a powerful tool that facilitates the harmonisation, interpretation, sharing and reuse of domain knowledge, eases the process of integrating heterogeneous data from multiple sources (semantic data integration), enables inference of new knowledge using automated reasoning, and supports literature-based database creation and curation using information extraction. definitions for a hierarchy of concepts related to the food preferences, food resources, feeding processes, and trophic groups of soil-associated consumers. In this paper, we build on previous efforts to harmonise and standardise terms related to the trophic behaviour of soil-associated consumer  [2, 6]  to develop the Soil Food Web Ontology (SFWO), a formal representation of domain knowledge in the field of soil trophic ecology.
The development of SFWO pursues several objectives (Figure  2 ). First, it aims to standardise the terminology in use in soil trophic ecology, e. g. by proposing a reference vocabulary to describe information about diet and feeding processes and harmonising the definitions of trophic groups across taxonomic groups. This will allow to:  (1)  facilitate knowledge sharing between experts on different taxonomic groups and from different disciplines, (2) facilitate the integration of heterogeneous trophic datasets, (3) support information extraction applications by providing a comprehensive list of terms related to soil trophic ecology, and ultimately to address more complex food-web related research questions in large synthetic studies. Secondly, SFWO aims to provide a logical formalisation of the domain of soil trophic ecology, which will facilitate the creation of food webs through the use of automated reasoning.
Diet terms refer to an organism preference for one or several food resources, e. g. , a saproxylophage  3 https://github. com/oborel/obo-relations 4 Actually, similarly to textual definitions, logical definitions should preferably adopt the genusdifferentia form, so that an equivalence axiom consists of a genus, i. e. the broader class to which the term belongs, and one or more differentia, i. e. the property/ies that distinguish the term from other members of the same class. According to this best practice, the logical definition of nematophage should be equivalentTo(nematophage, predator and eats some Nematoda), where predator is the genus and eats some Nematoda is the differentia. In SFWO, we deviate from this best practice for diet classes as it makes inferring an organism diet from the resources it consumes more difficult.
g. Nematoda, and a (possibly empty) list of traits, including feeding-related traits such as fungivore, herbivore, bacterivore, etc. For illustration, the hierarchy of nematode trophic groups from Potapov et al. is represented in Figure  5. Fungal trophic groups are taken from the FungalTraits database  [13]. Among other functional traits, FungalTraits provides data about the trophic modes (saprotroph, symbiotroph, pathotroph) and guilds (e. g. litter saprotroph, arbuscular mycorrhizal, animal pathogen) for fungi and fungus-like organisms. Adopting the same approach as for animal and protist trophic groups, we turned FungalTraits trophic modes and feeding guilds into a hierarchy of fungal trophic groups, a subset of which is also shown in Figure  5. SFWO also provides logical definitions for most of these trophic group classes.
This could be useful for text mining applications (e. g. extracting mentions of food resources from the literature as part of a trophic interaction extraction pipeline) and for trophic database exploration. Note that taxonomic classes are not included in this food resource branch, as taxonomic entities all come from the same external ontology (NCBITaxon) and already have their own branch in SFWO. Trophic processes. Mirroring the hierarchy of diet classes, SFWO provides a hierarchy of terms referring to trophic processes under the root term trophic process [ECOCORE:00000008] (see Figure  3 ). For the sake of completeness, all the nutrition-related classes have their processual counterparts, e. g. coprophagy [SFWO:0000043] (a process) is the trophic process involving a coprophage [ECOCORE:00000176] (a diet) feeding on faeces [UBERON:0001988] (a resource).
Terminological issues The development of SFWO has highlighted a number of terminological issues currently present in soil ecology that were, at least partly, addressed here (see terminological discussions on SFWO's GitHub issue tracker 6 ). Below we describe a few of these issues as examples of the challenges in harmonising the terminology in a scientific domain and modelling these concepts as part of a formal 6 https://github. com/nleguillarme/soil_food_web_ontology/issues representation of the domain. In our decisions we tried to adhere to the most widely accepted use of terms, rather than creating a de-novo (perfectly consistent) system. Among the most evident issues was the terminological diversity of diets/trophic processes in soil food webs. For example, organisms feeding on fungi are named in different literature sources fungivores (e.
SFWO's development and maintenance The ontology design process started with a small team of experts in soil invertebrate ecology who provided a list of over 100 terms related to primary food groups, diets and food resources of soilassociated consumers, their definitions and relationships between diets and food resources, e. g. entomophage → Insecta or saproxylophage → dead wood. A subset of these core terms was manually aligned with classes from existing ontologies using Ontobee's search engine  [20]. For instance, the term "bacterivore" matched the class bacterivore [ECOCORE:00000098] defined in the ECOCORE ontology. These classes were used as "seeds'' for module extraction, a popular strategy for ontology reuse that avoids the overheads involved in importing complete ontologies  [21]. Modules were merged to form the backbone of SFWO.
Standardisation of trophic trait datasets The lack of standardisation for trait data, including feeding-related trait data, hampers the efficient reuse and synthesis of published datasets  [24]. A major challenge in trait-data standardisation is the lack of widely accepted and unambiguous trait definitions. The T-SITA thesaurus  [6]  is an effort to provide such definitions for soil invertebrates. Out of many non-trophic traits, T-SITA defines a hierarchy of 20+ diet-related terms. SFWO goes one step further, with an ever-increasing vocabulary of over 500 terms (including synonyms) for describing trophic interactions, diets, food resources and trophic processes. SFWO has the potential to become the consensus terminology for describing, storing and publishing standardised soil trophic data.
This assures data providers that their data is unambiguous, interoperable with other datasets using the same standard vocabulary, and more easily comparable/reusable as part of future data aggregation initiatives and synthetic studies. We demonstrate the applicability of SFWO to standardisation of trophic datasets by adding semantic annotations to two open datasets, namely the BETSI database of soil invertebrate traits  [25]  and the Hexapoda dietary trait dataset from Rainford and Mayhew  [26]. Table  2   Table  3. Samples of the Hexapoda dietary trait dataset from  [26]. These samples show information about the diet of three families of beetles at the larval and adult stage. Dietary information is encoded using a numerical encoding scheme: 1=Fungivory, 2=Detritivory, 4=Predation. Data in the Larval diet and Adult diet columns are matched with the corresponding concepts in SFWO.
SFWO is a major contribution for trophic data integration, as it provides the semantic foundations for representing knowledge in the domain of soil trophic ecology, across taxonomic groups and trophic levels. This makes SFWO a prime choice as a conceptual model to reconcile schematic and semantic heterogeneities between data sources and build a unified knowledge base of trophic data for all soil biota  [27]  (Listing 1). Such a knowledge base would be a valuable resource for multitrophic studies of soil ecosystems and soil food web reconstruction, as it would provide a unified access to multigroup, multitrophic information. Listing 1. A semantic data integration approach such as the one described in  [27]  can transform data from multiple heterogeneous data sources into a common format -here, the Resource Description Framework (RDF) data model.
The atomic unit of data in RDF is called a triple. A RDF triple expresses a relationship between two entities in the form of a subject-predicate-object expression. In this example, the annotated data in Table  2  and Table  3  have been transformed to RDF triples and combined into a single RDF dataset. This provides a unified view over the initial data sources that can be queried using the SPARQL Query Language 10 for RDF to retrieve information across sources.
Soil food web reconstruction Several aspects of SFWO can support multitrophic studies and soil food web reconstruction from community composition data. First, SFWO contributes to fill the "Eltonian shortfall"  [28]  in soil ecology, i. e. a lack of knowledge about the inter-and intra-specific relationships within soil communities, by facilitating the integration of trophic data for different taxonomic groups from multiple data sources, making trophic knowledge easier to access, and making implicit knowledge explicit using automated reasoning. Secondly, with over 160 trophic group classes (most of which axiomatized) covering a 10 https://www. w3. org/TR/rdf-sparql-query/ broad spectrum of taxonomic groups, SFWO provides a common, logically-sound framework for soilassociated consumers classification.
Trophic information extraction The above applications of SFWO rely on our ability to compile large databases on the trophic ecology of soil consumers. Although much data is available from previously published research, the creation of literature-based datasets requires significant manual investment for literature searching, acquisition, screening, data extraction, and harmonisation of entities, such as species or trait names  [29]. An alternative is to use information extraction approaches to automatically turn unstructured text into structured data, an approach currently taken by the Specialised Information Service Biodiversity Research 11 (BIOfid), which aims at extracting structured data from legacy Central European literature through semantic role labelling. With its large and ever-increasing terminology related to soil trophic ecology, SFWO is a valuable lexical resource for trophic information extraction.
The most straightforward way to use SFWO as part of an information extraction pipeline is to assemble all or some of the terms defined in the ontology into a gazetteer, i. e. a list containing names of entities of interest, and use this gazetteer to find mentions of these entities in text (Figure  8 ). SFWO is particularly suitable to detect mentions of trophic processes, food resources and diets in text. In combination with a taxonomic entity recognition system such as the one described in Le Guillarme and Thuiller  [30] , SFWO could support the automatic creation, curation and updating of literaturebased trophic trait datasets. Figure 8. SFWO can be used as a dictionary of terms (or gazetteer) for the automatic retrieval of mentions of feeding behaviours in the literature. In this example, all of the terms in SFWO terms are grouped into a single entity class (DIET).