dataesr/scientific-paragraphs-categorization huggingface dataset:
Results The final result consists of gridded fields of mapped climatological means of inorganic nutrients for the periods  1981-2004 and 2005-2017  and the whole period of 1981-2017, produced with the VIM described in Sect. 3, using data of Sect. 2. Together with the gridded fields, error maps have been generated to check the degree of reliability of the analysis. The resulting climatologies (Table  3 ) are aggregated in a 4D netCDF for each nutrient and each time period that contains the interpolated field of the variable and the related information: associated relative error and variable fields masked using two relative error thresholds (L1 and L2). The mapped climatology is available from PAN-GAEA (https://doi. org/10. 1594/PANGAEA. 930447, Belgacem et al. , 2021)  as one folder named BGC-WMED climatology.
Error fields The determination of the error field is important to gain insight into the confidence in the climatological results. Mostly, the error estimate depends on the spatial distribution of the observations and the measurement noise. In DIVAnd, there are different methods available to estimate the relative error associated with the analysis fields. A climatological field is computed at several depths (19 levels in this case), for different parameters (nitrate, phosphate and silicate in this case). Given these premises and following the approach of similar climatologies (GLO-DAPv2. 2016b, Lauvset et al. , 2016; SeaDataNet aggregated dataset products, Simoncelli et al. , 2015) , for the BGC-WMED the error fields were estimated using the default DI-VAnd method, i. e. , the "clever poor man's error approach", a less time-consuming but efficient computational approach.
Comparison with other biogeochemical data products In this section, a comparison of the BGC-WMED product with the most known global and/or regional climatologies that are frequently used as reference products for initializing numerical models is made. Specifically, the analyzed fields are compared to the reference data products of the WOA18  (Garcia et al. , 2019) , a large-scale illustration of nutrient distribution computed by objective analysis using the World Ocean Database 2018  (Boyer et al. , 2018). The new product is also compared to the reanalysis of the Mediterranean Sea biogeochemistry, MedBFM, a CMEMS product that assimilates satellite and Argo data and includes terrestrial inputs of nitrate and phosphate from 39 rivers  (Teruzzi et al. , 2019). Since the products used for inter-comparison were not originated from the same interpolation method or for the same time period and have different spatial resolutions, here the comparison is mostly targeted on the general patterns of nutrients in the region.
Data availability The climatologies of nitrate, phosphate and silicate are available as netCDF files from the data repository PAN-GAEA and can be accessed at https://doi. org/10. 1594/ PANGAEA. 930447 (Belgacem et al. , 2021). Ancillary information is in the readme in PANGAEA with the list of variables that are described in Table  3  of Sect. 4. The CNR_DIN_WMED_20042017 data are available from PANGAEA (https://doi. org/10. 1594/PANGAEA. 904172, Belagcem et al. , 2019). The MOOSE-GE data are available in the SISMER database (global https://doi. org/10. 18142/235,  Testor et al. , 2010).
Conclusions In this study, we investigated spatial variability in the inorganic nutrients in the WMED and presented a climatological field reconstruction of nitrate, phosphate and silicate using an important collection dataset spanning 1981 to 2017. The new BGC-WMED product is generated on 19 vertical levels on a grid with a 1/4 • spatial resolution. The new product represents very well the spatial patterns of nutrient distribution because of its higher spatial and temporal data coverage compared to the existing climatological https://doi. org/10. 5194/essd-13-5915-2021 Earth Syst. Sci. Data, 13, 5915-5949, 2021   (1981-2004, in violet)  and after (2005-2017, in green) the WMT. products (see Table  1 ); it contributes to the understanding of the spatial variability in nutrients in the WMED.
The novelty of the present work is the use of the variational analysis that takes into consideration physical and geographical boundaries, topography, and the resulting estimate of the associated error field. Comparison with previously reported studies indicates that the BGC-WMED reproduces common features and agrees with previous records. The reference products of the WOA18 and MedBFM biogeochemical reanalysis tend to underestimate nutrient distribution in the region with respect to the new product. The new product captures the strong east-west nutrient gradient and vertical features. The results obtained do not include seasonal or annual analysis fields. However, the aggregated dataset here does show improvements in describing the spatial distribution of inorganic nutrients in the WMED. We acknowledge that computing a climatological mean over a time period is not enough to estimate and detect the trend driven by the climate shift WMT change.
Acknowledgements. Data were provided through SeaDataNet pan-European infrastructure for ocean and marine data management (https://www. seadatanet. org,last access: 9 September 2019 ),  Mediterranean Ocean Observing System  for the Environment, MOOSE (http://www. moose-network. fr/,last access: 9 September 2019), thanks to the work of  Mireno Borghini  ( CNR-ISMAR ),  Patrick Raimbault  and  Nicole Garcia  ( MIO, Mediterranean Institute of Oceanography ) during the last 10 years. Malek Belgacem  acknowledges the WOA18 and CMEMS for the MedBFM data (https://www. cmcc. it/doi/mediterranean-seabiogeochemicalanalysis,last access: 11 September 2019). We wish to thank all colleagues who contributed to the data acquisition and the principal investigators (PIs) of the cruises involved. Malek Belgacem  thanks  Kanwal Shahzadi  from the  University of Bologna  for the discussions during our internship at  GHER, University of Liège.
Background & Summary In the cognitive neuroscience of language, there is a growing consensus that using more ecologically valid stimuli such as audiobooks might extend our understanding of language processing in the brain  [1] [2] [3]. Compared to traditional factorial designs with a large number of repetitive trials, naturalistic paradigms use stories and dialogues with a rich context and produce results that are generalizable to everyday language use  3, 4. However, prior naturalistic studies have typically been restricted to a single language, which limited neurobiological frameworks for language processing to small typological domains. Here we present Le Petit Prince fMRI Corpus (LPPC-fMRI), a multilingual fMRI dataset where English, Chinese and French speakers listened to the same audiobook Le Petit Prince (The Little Prince) in their native language (see Figure  1  for a Schematic overview of the LPPC-fMRI data collection, preprocessing, technical validation and annotation procedures).
This limited our understanding of the neural processes of language comprehension to small typological domains. To complement monolingual datasets such as the Narrative Brain Dataset (NBD)  14  , the Alice Dataset  15  and the Mother of Unification Studies  16  , we collected a multilingual fMRI dataset consisted of Antoine de Saint-Exupéry's The Little Prince in English, Chinese and French. A total of 112 subjects (49 English speakers, 35 Chinese speakers and 28 French speakers) listened to the whole audiobook for about 100 minutes in the scanner (see Table  1  and Table  2  for the demographics of the participants, data collection procedures, and stimuli information for the English, Chinese, and French datasets). This stimulus is considerably longer than other datasets (i. e. , 6 minutes on average for the NBD dataset and 12 minutes for the Alice dataset), allowing for testing linguistic phenomena that may not be sufficiently attested in smaller samples.
This dataset includes time-aligned speech segmentation, prosodic information and word-by-word predictors obtained using natural language processing tools, ranging from lexical semantics to syntax to discourse information (see Figure  2  for the annotations available for an example sentence from the English audiobook). The neuroimaging data, as well as the annotations and information about the experimental procedure are shared in a standardized format on OpenNeuro  17. The LPPC-fMRI facilitates cross-linguistic generalization and helps overcome current statistical and typological limitations in the neurobiology of language. We stress the importance of considering multiple languages when building and testing neurobiological models of language processing, assuming that the neural substrates and processes of language are shared among speakers of all languages.
As shown in previous work examining coreference resolution using the English and Chinese subset of this corpus, the computational model that best explains the neural signature for pronoun processing is generalizable for both English and Chinese  18. These data can be reused to address different research questions with a variety of analytical methods. Future work envisions an expanded LPPC, one that incorporates data from additional neuroimaging modalities, such as electrocorticography (EEG) and magnetoencephalography (MEG). For instance, LPPC-EEG dataset aspires to 26 languages 4. Our vision is for the LPPC to become an open infrastructure to which researchers from various communities can contribute by adding further modalities, languages and annotations.
Participants A total of 112 subjects listened to the whole audiobook for about 100 minutes in the scanner. Table  1  and Table  2  show the summary of the data collection procedure, the stimuli and participants information for the three datasets. English participants were 49 young adults (30 females, mean age=21. 3, SD=3. 6) with no history of psychiatric, neurological or other medical illness that might compromise cognitive functions. They self-identified as native English speakers, and strictly qualified as right-handed on the Edinburgh handedness inventory  19. All participants were paid, and gave written informed consent prior to participation, in accordance with the IRB guidelines of Cornell University. Chinese participants were 35 healthy, right-handed young adults (15 females, mean age=19.
Preprocessing MRI data files were converted from DICOM to NIfTI format and preprocessed using AFNI version 16  21. Anatomical. The anatomical/structural MRI scans were deskulled using 3dSkullStrip. The resulting anatomical images were nonlinearly aligned to the Montreal Neurological Institute (MNI) N27 template brain. Resulting anatomical images were used to create grey matter masks. Functional. The first 4 volumes in each run were excluded from analyses to allow for T1-equilibration effects. The fMRI timeseries were then corrected for slice-timing differences (3dTshift) and despiked (3dDespike). Next, volume registration was done by aligning each timepoint to the mean functional image of the centre timeseries (3dvolreg). Then the volume-registered and anatomically-aligned functional data were nonlinearly aligned to the MNI template brain.
Technical Validation Accuracy of participants' responses to the quizzes after each section was calculated to ensure adequate comprehension. To assess fMRI scan quality, we calculated framewise displacement (FD), temporal signal-to-noise ratio (tSNR) and inter-subject correlation (ISC). We also did two whole-brain functional analyses using pitch (f0) and word annotations. These serve to show data quality similar to past work and provide evidence for timing accuracy between fMRI timeseries for participants.
Analysis bottleneck Although GLM or encoding models have been commonly applied to fMRI data using long naturalistic stimuli like audiobooks  8, 9, 11, 18, [38] [39] [40]. There are no standardised approaches for analysing complex and high dimensional naturalistic fMRI data. Machine learning approaches are becoming an increasingly common way to analyze fMRI data, and we encourage the development of innovative analysis approaches by running machine learning competitions on the LPPC-fMRI corpus.
Acknowledgements This material is based upon work supported by the  National Science Foundation  under grant numbers  1903783  and  1607251 , the  French Agence Nationale pour la Recherche  under grant  NCM-NL ANR 16-NEUC-0005-02 , and the  Jeffrey Sean Lehman Fund for Scholarly Exchange  with China at  Cornell University. J. L. is supported by the  NYU Abu Dhabi Institute  under Grant  G1001.
Since 2012, numerous new records of the vector in several European countries have been published. We ourselves have acquired a large amount of data from sampling in France and other European countries. We have also carried out a thorough systematic literature review to find additional records, including all the original sources mentioning C. pruni (or its synonyms) since the first description by Scopoli in 1763. Our aim was to create an exhaustive georeferenced occurrence catalogue, in particular in countries that are occasionally mentioned in literature with little detail. Finally, for countries that seem suitable for the proliferation of C. pruni (USA, Canada, Japan, China etc. ), we dug deeper into literature and reliable sources (e. g. published checklists) to better substantiate its current absence from those regions.
New information We give free access to a unique file of 1975 records of all occurrence data in our possession concerning C. pruni, that we have gathered through more than twenty years of sampling efforts in Europe or through intensive text mining. We have made every effort to retrieve the source information for the records extracted from literature (1201 records). Thus, we always give the title of the original reference, together with the page(s) citing C. pruni and, if possible, the year of sampling. To make the results of this survey publicly available, we give a URL to access the literature sources. In most cases, this link allows free downloads of a PDF file. We also give access to information extracted from GBIF (162 exploitable data points on 245 occurrences found in the database), which we thoroughly checked and often supplemented to make the information more easily exploitable.
We give access to our own unpublished georeferenced and genotyped records from 612 samples taken over the last 20 years in several European countries (Switzerland, Belgium, Netherlands, Spain etc. ). These include two countries (Portugal and North Macedonia), for which the presence of C. pruni had not been reported before. As our specimens have been genotyped (74 sites with species A solely, 202 with species B solely and 310 with species A+B), our new data enable a better overview of the geographical distribution of the two cryptic species at the Palaearctic scale.
). Some of these samples have been used in publications, but the vast majority have not yet been released in a georeferenced format. We were also able to find unpublished and valuable information in GBIF (e. g. metadata from Natural History Museum of London). Recently, we conducted an extensive literature survey for the original sources mentioning C. pruni, as a mean of verification, but more importantly, to precisely locate the source of each specimen. This laborious work often resembled a treasure hunt with its typical pitfalls and puzzles, such as correctly translating Mongolian locality names from a text written in Russian and then georeferencing them (Fig. 1 ). Sparing others these obstacles was part of our motivation to make the results of this survey publicly available. Our objective is to give access, through a unique dataset, to all the data we have gathered on the two cryptic species of C.
General description Purpose: This dataset is a compilation that is meant to include all available information (literature, GBIF, INRAE unpublished data) on the geographical distribution of two cryptic species of the psyllid Cacopsylla pruni at the scale of the Palaearctic (Fig. 2 ). We aimed to publish third-party data that can be otherwise hard to access and first-party data that are not yet published and to ensure free, open access to that information.
Study extent: The data contained in this dataset have three different origins: a systematic literature review, the Global Biodiversity Information Facility [GBIF] network and field collections by researchers/students from INRAE-Montpellier. They cover several ecoregions of the Palaearctic (Fig. 2 ): the Euro-Siberian region, the Mediterranean Basin, the Western and East Asia (Northern parts). No data were found for Central Asia nor for the Nearctic, despite the known presence of trees of species of Prunus and conifers on which C. pruni could make its life cycle.
Sampling description: Literature data In order to extend upon the  Steffek et al. (2012)  review, we have undertaken a new systematic literature survey for articles/manuscripts/books using the keyword "Cacopsylla pruni", its previous combinations "Chermes pruni" and "Psylla pruni" or its synonym "Psylla fumipennis". To this end, we used the Google Scholar search engine (https:// scholar. google. com/) and we explored several scientific databases (AGRICOLA, Agris, The searches were not restricted by language and were traced back to the first description of C. pruni (1763). Each line of the dataset that we make available (see section 'Data resources') corresponds to a reference. For almost all of them, we have retrieved the PDF file of the orignal publication (including old books) which allowed us to verify the information.
The corresponding URL is given for each data in the dataset (DOI link or similar link generally giving direct access to the PDF). We systematically tried to specify the locality where the observation was made (see Quality control section). Whenever the Global map of the 1716 occurrence data available in the C. pruni dataset (map generated with QGIS 3. 14). The map shows the distribution of cryptic species A (green dots) and B (red dots) according to available data. However, most of the data from the literature (black dots), GBIF (orange dots) or the Psylloidea catalogue of the "Faune de France" (currently being published) do not allow a distinction to be made between cryptic species. information was available, we specified the cryptic species of C. pruni (A or B, according to  Peccoud et al.
GBIF data A search on the keywords "Cacopsylla pruni" returned 245 occurrences in GBIF. org (14 June 2021). The derived dataset with filtered export of GBIF occurrence data is available at this link: https://doi. org/10. 15468/dd. rm55g8. Amongst the 245 occurrences, we were able to extract the names of 45 localities with geographic coordinates. For 87 occurrences, for which only the name of the locality was given, we retrieved the geographic coordinates  from Google Earth. The database also provided images of scanned slides from the NHM collection (https://www. gbif. org/fr/occurrence/gallery?taxon_key=2012955) from which we retrieved precise information about the sampling (date, location, host plant, collector) (Fig. 4 ), sometimes redundant with our own information (e. g. data from Iran).
Sampling data For more than 20 years, researchers (Gérard Labonne, Gaël Thébaud, Jean We recorded the GPS coordinates of all collected samples in their wild habitat, geolocalising the bush, hedge or shrub sampled. For the few insects sampled in orchards, we attributed a unique GPS coordinate -corresponding to the centre of each plot -to all the corresponding samples. The name of the locality given in the dataset corresponds to the nearest locality to the sampled point. We sampled mainly France, without restriction to apricot-growing regions and focusing on southern regions where species A and B live in sympatry or in strict allopatry. We also collected samples in Spain, Switzerland and Italy. The addition of these 612 new occurrence data improves the picture of the geographical distribution of the two species, hence it should be valuable for risk assessment, phylogeography or population genetics studies (Fig.
2 , Fig. 6 , Fig. 7 ) Quality control: We have a strong expertise in the taxonomy of psyllids  (Ouvrard 2021). Over the last few years, we have accumulated a large number of references on these insects in an article database, including references that are old and/or difficult to trace. As we had all these articles in PDF or paper or other metadata (e. g. scanned images), we were able to retrieve and thoroughly verify all information concerning C. pruni or its synonyms and combinations. All the specimens that we collected in the field were first carefully visually examined and then genotyped according to  Peccoud et al. (2013) , which effectively eliminates all risks of misidentification. Wherever possible, geographic coordinates (in WGS-84 coordinate system) refer to specific localities. We used Google Earth to search and reference each locality name found in literature or GBIF, being careful about homonymy and translation of names and possible changes of country names.
We consider the precision of these geographical coordinates to be a few kilometres, as authors rarely give very precise coordinates of their sampling points. Conversely, whenever we found geographical coordinates in GBIF, we plotted them on a Google Earth map to identify the closest locality and to check consistency with other information provided (name of the region, country etc. ). When no locality name was given, precision may vary from city to province, region or country (e. g. "USSR: South European Part"). In this case, we specified that the "locality is not stated". For data points only specifying countries, we provided the GPS coordinates of the country centres extracted from Google Earth, for lack of a better option. We, therefore, included a column with the estimated precision for each record, stressing that some of these data should be used with caution depending on the level of precision required for analyses.
Conversely, GPS coordinates of our own collected samples (see previous section) have an accuracy of a few metres. Each point was first geolocalised with a portable GPS and then checked on Google Maps. Step description: Most field names of the dataset were chosen according to the Darwin Core format  (Wieczorek et al. 2012 ) and the latest version of the list of core terms as of 28-10-2020 (http://rs. tdwg. org/dwc/version/terms/2020-10-28. htm): "catalogNumber", "phylum", "order", "genus", "acceptedNameUsage", "Occurrence", "country", "countryCode", "locationRemarks", "locality", "coordinateUncertaintyInMetres", "decimalLatitude", "decimalLongitude", "ownerInstitutionCode", "locationAccordingTo", "dateIdentified", "eventDate", "associatedReferences". We have added 11 columns with names not defined by Darwin Core: "suborder", "superfamily", "family", "subfamily", "speciesA", "speciesB", "hostPlantFamily", "hostPlantLatinName", "hostPlantVernacularName", "sourceCategory", "page".
Geographic coverage Description: The database covers the entire known geographic range of the two cryptic species of the psyllid C. pruni, from Morocco to Norway and from Portugal to Mongolia. We have also extended our search to other countries where either species could potentially be found, in particular countries where different species of Prunus are described in wild or cultivated ecological compartments (e. g. Japan, China, USA, Canada) and where these psyllids could be phytoplasma vectors. Whenever possible, we relied on checklists from recognised taxonomists to ensure the veracity of the information before concluding as "absence" (e. g. Inoue 2010 , Maw et al. 2000). Coordinates:  33. 815458 and 65. 59623333 Latitude; -8. 383379 and 112. 52588611 Longitude.