dataesr/scientific-paragraphs-categorization huggingface dataset:
Construction of the database Laevilitorina records across the Southern Hemisphere were compiled from four main sources: 1) field sampling data; 2) published literature; 3) data already present in GBIF and 4) the data present in the repository of the Atlas of Living Australia (ALA)  (Belbin 2011). Duplicate records were removed to construct a unified database. In addition, the records available in GBIF and ALA were used to describe the distribution range of each species. To ensure the quality of the occurrence data, dubious records were excluded from the geospatial analysis. The criterion used to determine dubious records was records of species in geographic areas outside the distribution range described in the original descriptions and taxonomic revisions. Twelve marine biogeographical provinces in the Southern Hemisphere were considered for the purpose of our geospatial analyses, including the Magellan province (southern South America and Falkland / Malvinas Islands), West Antarctic, East Antarctica, Indian Ocean sub-Antarctic islands (Prince Edward Islands, Crozet Island, Kerguelen and Heard Islands), Macquarie Island, New Zealand sub-Antarctic islands, Southern New Zealand, Northern New Zealand, South-east Australian Shelf, Southwest Australian Shelf, West Central Australian Shelf and East Central Australian Shelf, as defined in  Spalding et al.
Recent sampling data New material was collected from multiple locations in southern South America between the Strait of Magellan (53°36'S, 70°55'W) and the Diego Ramirez archipelago (56°31. 345'S, 68°43. 622'W). In the Falkland/Malvinas Islands, specimens were collected from the intertidal zone of Hooker Point (51°42'S, 57°46'W). New Antarctic material was collected from the South Shetland Islands, Doumer Island, Palmer Land, and Avian Island under the framework of Antarctic Scientific Expeditions (ECA) 49, 53, 54 and 58 of the Chilean Antarctic Institute (INACH). Samples from the South Orkney Islands and South Georgia were obtained during British Antarctic Survey (BAS) and SAERI expeditions  (2016-2017, 2017-2018 and 2021). Samples from Kerguelen and Crozet archipelagos were obtained through the PROTEKER project under the framework of the French Polar Institute Paul Emile Victor (IPEV) summer campaign 2017.
Published literature To ensure maximum coverage of the generated dataset, information was gathered from all available scientific publications that have sampled or reviewed Laevilitorina species throughout the genus' distribution, from the description of the first species  (Gould 1849)  to the present. These records and their respective geographical positions were entered into a spreadsheet following the Darwin Core Standard structured procedure  (Wieczorek et al. 2012). Taxonomy used in these publications was updated following the most recent systematic revision  (Reid 1989; Waren and Hain 1996; Engl 2012; Bouchet et al. 2017; MolluscaBase 2022). We did not follow  González-Wevar et al. (2022)  for species names and databases, mainly because the lineages that would correspond to new species have not yet been formally described.
Digital database GBIF and ALA All georeferenced records of the genus Laevilitorina were retrieved from the GBIF and ALA database on 12 September 2022  (Rosenfeld et al. 2022 ). The point-radius method was used for georeferencing records lacking precise geographic location (coordinates), by identifying locality description included in the relevant metadata of the reported collection. This method considers the precision, datum and specificity of the locality description to determine the coordinates  (Wieczorek et al. 2004; Wieczorek and Wieczorek 2021). The species list was updated to exclude erroneous or suspect records, rule out possible synonymy and follow current taxonomy.
Dubious records Laevilitorina antarctica  (Smith, 1902) , originally described from Cape Adare in the Ross Sea, is also reported in GBIF from Macquarie Island (https://www. gbif. org/es/ occurrence/search?taxon_key=9810991). However, this species has historically been reported primarily from the biogeographic provinces of East Antarctica and West Antarctica  (Arnaud and Bandel 1976; Dell 1990 ). Therefore, the presence of L. antarctica on Macquarie Island requires confirmation and was not included in our database.
3 ). In Australia, four species, L. johnstoni  (Cotton, 1945) , L. mariae (Tenison Woods, 1876), L. bruniensis (Beddome, 1883), and L. kingensis  (May, 1924) , were recorded from mainland Australia. L. johnstoni would be the only species restricted to mainland Australia, while L. kingensis, L. mariae, and L. bruniensis are also present in Tasmania (Fig. 3 ). Based on our new sampling data only, we identified and reported seven Laevilitorina species in the Magellanic Province (L. caliginosa), Falkland/Malvinas Islands (L. caliginosa, L. latior), South Georgia (L. caliginosa, L. pygmaea, L. venusta; Fig. 1b, c ), Kerguelen and Crozet Islands (L. caliginosa), South Orkney Islands (Signy Island) (L. caliginosa), and Antarctic Peninsula (L. caliginosa, L. claviformis, L. umbilicata, L. wandelensis; Fig.
Discussion The increasing application of integrated taxonomy coupled with new modelling approaches, requires data to be Findable, Accessible, Interoperable, and Reusable in the long term  (Wilkinson et al. 2016). There is a need to revise the geographic distribution and taxonomic description of many taxa, as it can provide information about changes in the composition of communities in different environments, particularly in sensitive ecosystems  (Maturana et al. 2019). A number of studies have already discussed the importance of making an updated revision of the taxonomic status of several Laevilitorina species throughout their distribution  (Powell 1960; Reid 1989; Engl 2012). The compilation and unification of records of Laevilitorina in the Southern Hemisphere presented here contributes to improve our knowledge of the diversity and biogeography of the members of the genus in twelve biogeographic provinces of the Southern Hemisphere.
However, it is also important to note that, despite the unification and update of records of Laevilitorina, this study does not reflect the full systematic and biogeographic complexity of this genus. Distribution data are not currently available for many members of the genus, which have not been reported since their description. For example, among the five species of Laevilitorina described from South Georgia, three of them (L. pygmaea, L. venusta, and L. granum) have not been reported since their original description  (Castellanos 1989) , leaving doubts about the taxonomic validity of these species  (Castellanos 1989; Reid 1989; Engl 2012). In this study, the report of L. pygmaea is only the third record of the species, in addition to being the first record from shallow depths thereby extending our knowledge of its bathymetric range.
Acknowledgements This work was funded by,  ANID-Millennium Science Initiative Program   ICN2021_002  to SR, CM, PC, EP, and  CGW, Fondecyt Regular  Project  1210787  to CGW and SR, and  ANID/BASAL FB210018  to SR and CM. We also appreciate the support of the following projects:  Fondecyt Postdoctoral 3210063 to CM, IPEV program  PROTEK-ER (# 1044 ). We are grateful to the  Government of South Georgia & South Sandwich Islands (GSGSSI) ,  South Atlantic Environmental Research Institute (SAERI) ,  Natural History Museum (NHM) London , the  Shallow Marine Surveys Group (SMSG)  for organising the research cruise ( Operation Himantothallus -Darwin Plus Marine Biodiversity Research Cruise , Pharos  SG-11-2021 ), and  Tritonia Scientific Ltd  for diving operations and dive safety. The research cruise was funded by  GSGSSI  and a  Darwin Plus Grant  ( DPLUS122 ) to the NHM.
By extracting informative VOCs from the compiled database and implementing them into a classifier of which the performance was evaluated  [28] , we have already shown that a specific VOC profile in breath can discriminate healthy controls from patients suffering from a variety of lung diseases including chronic obstructive pulmonary disease, ventilated-associated pneumonia and asthma  [29] [30] [31]. However, it would clinically be more relevant to apply exhaled VOCs in differentiating between chronic lung diseases that (partly) share pathogenesis and symptoms yet display diverse outcomes and therefore require different treatment options. Therefore, the primary aim of the present study is to identify exhaled VOC profiles characteristic for IPF and CTD-ILD, in comparison to patients without lung disease as well as to each other.
CTD-ILD was defined by the combination of ILD and CTD. IPF was diagnosed according to 2018 ERS/ATS/JAS/ALAT criteria  [34]. IPF diagnoses were ascertained by reviewing medical charts established by the Referral Center for Rare Lung Diseases (Service de Pneumologie A, Paris, France) including information obtained after inclusion. All diagnoses were adjudicated by multidisciplinary discussion. Clinical and lung function data were obtained for patients in the two ILD groups according standard clinical protocols.
Clinical data Lung function and HRCT data were collected from ILD patients. Vital capacity (VC), forced expiratory volume in 1 s (FEV1), total lung capacity (TLC) and carbon monoxide diffusion capacity (TLCO) using the single breath method were obtained according to ATS/ERS guidelines  [35]  and expressed relative to ECCS1993 reference values  [36]. The 6-min walk test was performed according to American Thoracic Society guidelines  [37]  and the result was expressed as a fraction of the predicted value according to Enright  [38]. HRCT scans were reviewed and classified as showing either one the patterns described in the 2018 ATS/ERS/JRS/ALAT IPF diagnosis statement (usual interstitial pneumonia-UIP, probable UIP, indeterminate for UIP or non UIP pattern)  [34]  or as the non specific interstitial pneumonia (NSIP) pattern, with or without subpleural consolidation, in the presence of predominant ground-glass opacities and lack of honeycomb lesions.
Data analysis Selection of discriminatory VOCs In order to apply VOCs for the identification of IPF and CTD-ILD patients, multivariate statistical modelling has been performed for selection of discriminatory VOC profiles unique for the various groups (see Fig. 1  for a conceptual flow chart of the data analysis). More specifically, three comparisons were performed. First, IPF patients were compared to a group of controls sampled at the renal department. Second, CTD-ILD patients were compared to the group of controls from the renal department. Finally, IPF and CTD-ILD patients were compared. The selected multivariate statistical model was Random Forests (RF)  [43] , a machine learning algorithm that generates a large quantity of uncorrelated decision tree predictors to classify samples into the appropriate class.
1  Conceptual flowchart presenting the approach used for statistical analysis. In step 1, a database is build with all clinical data and the preprocessed VOCs data contain three main groups: IPF (n =53), CTD-ILD (n=51) and healthy controls (n=51). In step 2, the machine learning method Random Forests (RF) was used to find discriminatory VOCs. For that purpose three different discriminatory RF models were built. Each discriminatory RF model was constructed on a training set (containing 80% of samples of each group) and validated using an independent test set (containing 20% of samples of each group). Training and test sets were selected using Duplex method  (27). First RF algorithm was applied on VOCs data containing IPF and controls to find compounds linked to IPF. The second classification model was constructed on chromatograms belonging to CTD-ILD and healthy controls to allow selecting of VOCs related solely to CTD-ILD.
Correlation between VOCs and lung function parameters Canonical correlation analysis (CCA)  [46] , which can be considered an extension of the binary Pearson correlation analysis, was used to calculate a correlation between a set of compounds (in this case the whole set of discriminatory VOCs) and characteristics (in this case the corresponding lung function parameters of the same patients). A p-value of < 0. 05 was considered statistically significant. These lung function parameters include VC, TLC, FRC, FEV1, DLCO, PaO 2 , PaCO 2 and 6MWD. Before CCA analysis, both datasets were log-transformed. A subset selection of the lung function parameters was made based on the contribution of the VOCs to the CCA model to achieve the best possible correlation. A correlation coefficient and corresponding p-value were reported in combination with figures of the correlation.
VOC profiling for IPF patients versus controls The exhaled VOCs from the 53 IPF patients were compared with those present in the breath of the 51 controls. A total of 34 VOCs was selected that could discriminate the IPF patients from the controls. The IPF versus control profile had 84. 6% accuracy, sensitivity of 81. 1% and specificity of 88. 2%. The Receiver Operating Characteristic (ROC) curve of the IPF versus controls using the 34 discriminating VOCs had an area under the curve (AUC) of 91. 2% (Fig. 3A ). A PCA score plot was generated to display groupings in the data as a result of the selected subset of VOCs. This score plot is depicted in Fig. 3B  and shows clear separation between both groups. The chemical identity of the 5 most contributing VOCs is given in Table  2 , which shows that the concentration of all but benzaldehyde is lower in the IPF patients.
05 is considered significant. m-, p-, and o-xylene are hard to distinguish from ethylbenzene, leading to possible misidentification, thus their significances are also reported groups upon visual analysis of the data using untargeted PCA analysis. This promising observation indicates no patients had a VOC profile resembling the exhaled volatiles belonging to a diagnosis different from the one they were included for. The control subjects included in the present study could not be considered healthy since they were referred for recurrent urinary lithiasis. Additionally, some of the control subjects might already suffered from undiagnosed lung diseases as also ex-and current smokers were included to account for tobacco smoking being an important risk factor or developing ILD. Since they did not report any pulmonary symptoms at the time of inclusion, it was anticipated they did not suffer from clinically significant lung damage as observed in and related to ILD and would thus be a suitable control group to find discriminative VOCs related to ILD pathology instead of smoking.
2017]. A smaller number of intense rainfall events have also been mapped  [Marc et al. , 2018] , but unlike for earthquakes  [Schmitt et al. 2017]  https://doi. org/10. 5194/nhess-2021-250 Preprint. Discussion started: 2 September 2021 c Author(s) 2021. CC BY 4. 0 License. no centralized repository of these data exists at present. Location data for landslides triggered by intense rainfall is vitally important to calibrate and validate existing susceptibility models, since the datasets produced are generally considered to be nearly complete. It is also useful to characterize the rainfall required to trigger landslides, and thus help inform local and global hazard models  [Emberson et al. 2021 , Kirschbaum et al. 2018]. These can then be used to inform exposure and risk assessment estimates  [Emberson et al.
Assessing these landslide-event inventories both individually and in combination, we assess the local topographic characteristics that are most strongly related to where landslides initiate, as well as local forest loss that can be calculated from satellite data. We suggest that these inventories and the associated parameters can be used to calibrate and validate other models of susceptibility and hazard, and will provide valuable information to authors seeking landslide data with high spatial accuracy, as well as supporting characterization of rainfall thresholds for landslide impacts [e. g. Conrad et al. 2021]. These inventories will be available on the NASA Landslide Viewer app  (landslides. nasa. gov/viewer) for open access by other researchers.
[2010]. We contrast each of the inventories mapped here by comparing the size-frequency distributions of each dataset, shown in Figure  2. For each of the inventories, we show the probability of a landslide within a given area interval, as a way to assess the frequency of small and large landslides across the different datasets. Each of these landslide events was triggered by extreme rainfall, and although it is not our intention to examine the triggering rainfall in detail in this study it is useful to briefly discuss the characteristics of the rainfall events in question. A detailed analysis of the triggering rainfall associated with several of these inventories is described by  Marc et al. 2018, who used local  gauge data to characterize the rainfall intensities. We were unable to find consistent local gauge data for several of the more recent events that are published here for the first time (events in Zimbabwe, Burundi, Kenya, and the two events in the Philippines).
Topographic Analysis We have analyzed the characteristics of landslide locations for the event inventories with global satellite datasets to ensure consistency across each site. These datasets are also openly available, which supports replication of these methods and findings by other authors. In Table  3 , we show the datasets we have used. [Beven & Kirkby 1979 , Sorensen et al. 2006 ] Topographic Ruggedness Index, TRI  [Riley et al. 1999]  Topographic Position Index, TPI300 -300m wavelength  [Weiss, 2001]  Topographic Position Index, TPI2000 -2000m wavelength  [Weiss, 2001]  Forest Cover (1 arcsecond resolution) Global Forest Change 2000-2018  [Hansen et al. 2013]  Forest loss since 2000 Forest cover The DEM and forest loss data are both provided at approximately 1 arc-second resolution, which allows for direct comparison at this scale.
While this resolution is not as fine as some of the imagery used to map the landslides, which can be 3m or finer, it represents the finest resolution at which these two datasets can be analysed using non-commercial, open datasets at a global extent. We utilize forest loss data derived from Landsat imagery spanning the years 2000-2018. Cells where forest loss is observed on any year from 2000 until the year in which the landslide event occurred are considered as a binary 'True' value for forest loss. This does not consider regrowth of vegetation in places where forest loss was observed many years prior to the event, and as such it is a relatively blunt tool to assess the importance of vegetation to landslide location. Not all of the topographic parameters are universally used in landslide analysis.
This provides a binary raster of landslide presence. We then assess the parameter values for each of the pixels where landslides are present. This does mean that the largest landslides are most strongly represented in the distribution, but this is intentional as it permits us to focus on all of the areas affected in the landscape. It is important to note this approachcounting all pixelsis not appropriate for statistical susceptibility analysis, since it could lead to highly dependent datasets. For hazard analysis purposes, we feel it is appropriate to consider all pixels, since larger landslides are consistently more damaging, and we seek to capture the entire footprint. In Figure  3 , an example of the comparison is shown for the landslides occurring in Zimbabwe as a result of Cyclone Idai.
Discussion The primary intention of this study is to assess the critical topographic parameters associated with rainfall triggered landslides, using a large dataset of landslide inventories that includes 6 newly mapped events. Our results are comparable with existing studies [e. g. Marc et al. 2018 , Milledge et al. 2019 ] exploring landslide locations using inventories, while adding more detail and assessment of global variability, which we discuss below. First, however, we explore some of the limitations and assumptions that go into the mapping and analysis of the landslides.
One exception is the landslide inventory from Zimbabwe, which is in a semi-arid climatic region. Although our examples disproportionately sample tropical and subtropical areas, these areas generally experience the highest erosion rates  [Milliman & Syvitski 1991] , driven by increasingly intense rainfall (e. g. ,  Bookhagen & Strecker 2012). Similarly, while the inventories are drawn from places with diverse lithologies, we do not have datasets from a fully representative set of lithological locations  [Hartmann & Moosdorf 2012,    While we have attempted to ensure the consistency of landslide inventories by hand-correcting the events, some inconsistencies may still exist. One important consideration is that the datasets used here do not distinguish between different landslide types, or distinguish between scar and deposit.
However, without imagery to clarify it may be possible that parts of the previously published inventories are masked by cloud cover. In addition, the inventories mapped using coarser resolution satellite imagery, such as the part of Taiwan impacted by Typhoon Kalmaegi, may not capture the smallest landslides that resulted. If smaller landslides are preferentially found in certain parts of the landscape this may introduce systematic biases in observed likelihood ratios. While the imagery used to compile the different inventories varies in resolution, there do not seem to be consistent, systematic differences between likelihood ratios that can be explained as a result of small landslides that systematically bias the events. For example, landslides in the Dominica events have similar likelihood ratios for each parameter compared to the landslides from Typhoon Kalmaegi in Taiwan, despite these datasets having the largest difference in effective imagery resolution.
In these settings, we would expect that the influence of smaller landslides may be over-estimated at the 30m resolution of analysis, although in the majority of other inventories landslides below this cutoff represent less than 5-10% of the total landslide area (and therefore the analytical dataset of landslide pixels). To address the potential for bias due to over-sampling of small landslides with a coarse resolution DEM, we have resampled the DEM for the events in Hiroshima and Dominica to a resolution of 10m, at which nearly all landslides are captured without size exaggeration. We then recalculate the likelihood ratios for the landslides and compare the resampled DEM results with those from the original DEM [Supplementary Figure  S5 ]. For slope, average upstream angle, CTI, relief and TRI, only minor differences are observed between the results for a resampled DEM and the original DEM.
There are some differences between TPI at 300m resolution, but no consistent relationship seems to emerge. Thus we do not think our results are affected by the coarse rasterization process, although it is likely that accessing higher resolution DEM may alter the result depending on local variable, like slope, CTI or TRI. Other parameters are often incorporated into landslide susceptibility such as geological factors like soil characteristics or lithology, local land cover type or climatic metrics. Characterizing these parameters consistently across different inventories requires a global dataset, but the resolution of globally available, open data for rainfall, soil type, or geological parameters is too low to differentiate between landslide zones and non-landslide zones. As such, we have chosen to focus exclusively on topographic factors within our assessment.