dataesr/scientific-paragraphs-categorization huggingface dataset:
Trace elements measurements came from the RMQS, ensuring a perfect homogeneity in sampling, analytical procedures and geographical coverage. The data capturing for the BDAT came from contracting with private laboratories. From 1999 to 2014, the number of samples increased regularly at a rate of about 120000 per year, and reached about 2600000 in 2014. The analyses, however, mainly concern agricultural soils and routine agronomic measurements from topsoil (e. g. pH, SOC, CEC, clay content, available P and K). Even with such a number of measurements, they remain irregularly spread, and some temporal trends are not yet detectable over the entire French territory. This advocates for pursuing data capturing in order to confirm observed trends or to detect new ones.
Such topics will certainly have a large impact on the general public though their dissemination in national and international media. Additionally, all samples from the RMQS were used to build a national spectral library including visible, near-infrared, and mid-infrared spectra (e. g. Caria et al. , 2011; Goge et al. , 2014). This library opens the door to low-cost mapping of new elements that were not initially included in the analytical menu of the RMQS (e. g. emerging contaminants such as antibiotics and microplastics and human exposure to some contaminants). Finally, the efforts made by InfoSol to assemble national covariates will be beneficial to all people performing DSM at a more local scale. As an example,  Loiseau et al. (2019)  recently produced a multidate mosaic of bare soil data reflectance using the new Sentinel 2 satellite products, and this product is already employed for large-scale monitoring of the SOC in a cost-efficient way in small representative areas  (Vaudour et al.
, 2019)  using high-resolution Earth Observation (EO) data for generating DSM maps. Internationally, there is a large amount of interest in global soil monitoring using EO data (e. g. Chabrillat et al. , 2019, and  see http://worldsoils2019. esa. int/). Finally, the new partnership GLADSOILMAP (https://www6. inra. fr/gladsoilmap-consortium/) is a recently established consortium that was established to scientifically support many of these international initiatives and advance methods for realising urgently needed products.
national and local authorities. INRA, FEDER, the Region Centre Val de Loire, and the DRTT. The microbiological study was granted by ADEME (French Environment and Energy Management Agency) and by "France Génomique" through involvement of the technical facilities of Genoscope (project number ANR-10-INBS-09-08). Due to the involvement of technical facilities at the GenoSol platform of the infrastructure ANAEE France, it also received a grant from the French state through the National Agency for Research under the program "Investments for the Future" (reference ANR-11-INBS-0001). Songchao Chen received the support of China Scholarship Council for three years' Ph. D. study in INRA and Agrocampus Ouest (under grant agreement no. 201606320211). Thomas Loiseau received the support of CNES for one year work (160102 agreement).
Acknowledgments The GlobalSoilMap activities of the  INRA InfoSol Unit  are partly funded by the  Environment & Agronomy department of the National Institute for Agronomic Research (INRA). Most of the French point soil data has been collected or rescued thanks to funding from the national ' Groupement d'Intérêt Scientifique Sol ' involving the  French Ministry of Ecology , the  French Ministry of Agriculture , the  French Environment and Energy Management Agency (ADEME) , the  French Office for Biodiversity (OFB) , the  French Institute for Research and Development (IRD) , the  Institute for National geographic and Forest Information (IGN)  and the  INRA. In most cases, these national funding were complemented by French Regions or more local agencies and organizations. We thank all the people involved in sampling the sites and populating the database.
It is a real-time ultrasound-guided HIFU device, and is also equipped with ultrasound-magnetic resonance imaging (MRI)-fusion software. Several studies have reported the efficacy of HIFU for treating gynecological disorders such as uterine leiomyomas, adenomyosis, endometriosis parietal nodules and ectopic pregnancies on Cesarean scars  [13] [14] [15] [16] [17]. Our primary objective in this Phase-I clinical trial was to evaluate the ability of the Focal One HIFU device to detect and target posterior DIE lesions with intestinal involvement. Our secondary objectives were to apply HIFU thermal ablation, evaluate morphological modifications of the endometriotic lesion in post-therapeutic imaging scans, assess evolution of gynecological symptoms, intestinal symptoms and the patients' quality of life after treatment by HIFU, and to record potential postoperative complications.
After the first five cases, in which no major complications were experienced, l'Agence Nationale de Securité du Medicament (ANSM, the French National Drug Safety Agency) allowed us to expand the inclusion criteria to include age over 25 years and no plans to conceive in the 6-month period following treatment (i. e. until after postoperative MRI). The clinical trial was approved by the regional ethics committee (CPP SUD EST II, reference number 2014-045-2) under EudraCT 2014-A01728-39 and by ANSM. All patients provided written informed consent to participate.
Focal One Focal One (EDAP-TMS, Vaulx-en-Velin, France) is a robotic computer-controlled medical device used for TR-HIFU ablation of prostate cancer. Available in Europe since June 2013 and in the USA since June 2018, it is a real-time ultrasound-guided HIFU device, equipped with ultrasound-MRI-fusion software. The endorectal probe includes a HIFU transducer that delivers the therapeutic ultrasound and an ultrasound imaging transducer to visualize and locate areas to be treated (Figure  2a ). The HIFU transducer is an annular array composed of 16 concentric rings. It is coupled to a multichannel amplifier, which, by shifting the phase of exciting electrical signals, moves the focal point of the ultrasonic beam electronically, without mechanical movement. This technology, designated 'dynamic focusing', allows the focal spot to be located between 32 and 67 mm from the center of the probe.
The Focal One software automatically dispensed the burst of the HIFU beam in order to cover the target area entirely. The same procedure was carried out for each transverse section, in order to cover the entire volume of the endometriotic lesion (Figure  2b ). Once all slices had been defined, HIFU treatment could begin. The software controlled electronically the focal distance along the acoustic axis, while the device controlled the position of the probe, moving it backwards along its longitudinal axis and rotating it on its transverse axis. The sagittal axis of the probe remained fixed during the whole procedure. The distribution and number of beams per section depended directly on the size and form of the endometriotic nodule. In order to prevent the risk of rectovaginal fistulae, a safety margin of 3 mm from the internal wall of the rectum was respected, as is recommended in the treatment of prostate cancer (Figure  2b ).
Statistical analysis All parameters collected were analyzed descriptively using Microsoft Excel software (Microsoft Office for Mac version 16. 9, Microsoft Corp. , Redmond, WA, USA). All patient data were collected and anonymized in an Excel spreadsheet. Statistical analyses and graphs were created with IBM SPSS v 24 software (IBM, Armonk, NY, USA). Means, SDs and 95% CIs were used to estimate the reliability of descriptive data. The Mann-Whitney-Wilcoxon test was used to compare pre-and postoperative paired data (including lesion size, and patients' VAS scores and SF-36 questionnaire results) and to assess the influence on feasibility of whether the lesion location was rectal or sigmoid and whether the treatment was complete or partial (treatment categorized as: no treatment performed (failure), treatment of entire volume (complete) or treatment of part of the volume due to technical issues (partial)).
Related Work Due to its importance in the automatic and semi-automatic building and maintenance of Knowledge Bases, the area of Information Extraction (IE) comprises a large body of work, which includes a variety of methods for harvesting entities and relationships from text. In many of the proposed solutions, IE relies on Part-Of-Speech (PoS) tagging and various type of patterns, morphological or syntactical  [28, 22] , often complementing themselves to compensate for reduced coverage. The most recent approaches exploit various resources to develop ensemble methodologies  [18]. If we consider IE as the combination of two main tasks, extracting entities and identifying relations from text, the latter has proven without doubt the most challenging. The most successful models for relation extraction are either based on knowledge or supervised and, therefore, depend on large annotated datasets, which are rare and costly to produce.
Since then, extraction methodologies for the purpose of building knowledge graphs from scientific papers started to spread in the literature  [15]. For example, authors in  [1]  employed syntactical patterns to detect entities, and defined two types of relations that may exist between two entities (i. e. , hyponymy and attributes) by defining rules on noun phrases. Another attempt to build scientific knowledge graphs from scholarly data was performed by  [16] , as an evolution of the authors' work at SemEval 2018 Task 7. First, authors proposed a Deep Learning approach to extract entities and relations from the scientific literature; then, they used the retrieved triples for building a knowledge graph on a dataset of 110, 000 papers. However, they only used a set of six predefined relations, which might be too generic for the purpose of yielding insights from the research landscape.
AI-KG Overview The Artificial Intelligence Knowledge Graph (AI-KG) includes about 14M RDF triples and describes a set of 1. 2M statements and 820K entities extracted from a collection of 333,609 publications in Artificial Intelligence (AI) in the period 1989-2018. In order to interlink AI-KG with other well-known knowledge bases, we also generated 19,704 owl:sameAs relationships with Wikidata and 6,481 with CSO. The current version of AI-KG was generated and will be regularly updated through an automatic pipeline that integrates and enriches data from Microsoft Academic Graph, the Computer Science Ontology (CSO), and Wikidata. The AI-KG ontology is available online  17  and builds on SKOS  18  , PROV-O  19  , and OWL  20. Each statement in AI-KG is associated with a triple describing the relationship between two entities and a number of relevant metadata.
-The CSO Classifier 21   [23] , is a tool built on top of the Computer Science Ontology, an automatically generated ontology of research areas in the field of Computer Science  [25]. It identifies topics by means of two different components, the syntactic module and the semantic module. The syntactic module adopts syntactical rules in order to detect topics in the text. In particular, on unigrams, bigrams, and trigrams computed on text, it applies the Levenshtein similarity with the labels of the topics in CSO. From each abstract a i of the input AI papers, the pipeline extracted entities E i and relations R i. More specifically, these sets were firstly extracted by using the DyGIE++ tool  23. Then, E i was expanded by using all research topics that were found by the CSO classifier. Subsequently, OpenIE was applied to parse the text, and all triples in the form < subject, verb, object > with both subject and object that overlap research entities in E i were added to R i.
AI-KG Generation In this section, we discuss the generation of AI-KG from the triples of the set T valid and describe how it is driven by the AI-KG ontology introduced in Section 3. 1. We also report how we materialized several additional statements entailed by the AI-KG schema using inverse and transitive relations. Finally, we describe how we mapped AI-KG to Wikidata and CSO. Ontology-driven Knowledge Graph Generation As discussed in Section 3. 1, the most frequent predicates of the set set T valid were given to four domain experts associated with several examples of triples. After several iteration, the domain experts produced a final set of 27 relations and defined their range, domain, and transitivity. We mapped the relations in T valid to those 27 relations whenever was possible and discarded the inconsistent triples.
The latter included both the triples whose predicate was different from the nine predicates adopted for the AI-KG ontology or their synonymous and the ones that did not respect the domain of the relations. For instance, the domain of the relation "includesTask" does not include the class "Material", since materials cannot include tasks. Therefore, all triples stating that a matarial includes a task, such as <jaffe face database, includesTask, face detection>, were filtered out. This step generated 1,075,655 statements from the 1,493,757 triples in T valid. These statements were then reified using the RDF reification vocabulary  25. Statement Materialization. In order to support users querying AI-KG via SPARQL and allowing them to quickly retrieve all information about a specific entity, we decided to also materialize some of the statements that could be inferred using transitive and inverse relations.
Since we wanted for the resulting statements to have a minimum consensus, we computed the transitive closure of all the statements extracted by at least two research articles. This resulted in additional 84,510 inferred statements. We also materialized the inverse of each statement, e. g. , given the statement <sentiment analysis, usesMaterial, twitter> we materialized the statement <twitter, materialUsedBy, sentiment analysis>. The final version of the KG, including all the inverse statements, counts 27,142,873 RDF triples and 2,235,820 reified statements. Integration with other Knowledge Graphs. We mapped the entities in AI-KG to Wikidata, a well-known knowledge base containing more than 85M of data items, and to CSO. In particular, each entity in AI-KG was searched in Wikidata and, when there was just one corresponding valid Wikidata entry, we generated a owl:sameAs relation between the two entities.
Generation of New Versions The pipeline described in this section will be employed on novel research outcomes in order to keep the AI-KG updated with the latest developments in the AI community. Specifically, we plan to run it every 6 months on a recent corpus of articles and release a new version. We are currently working on ingesting a larger set research papers in the AI domain and further improving our characterization of research entities. As next step, we plan to release a more granular categorization of the materials by identifying entities such as knowledge bases, textual datasets, image datasets, and others.
To our knowledge, EMA has hence only been used with school-aged children (e. g. ,  Terband, Maassen, Van Lieshout, & Nijland, 2011). Electropalatography (EPG) was more readily adapted to child speech research (e. g. ,  Gibbon, Hardcastle, & Dent, 1995; Wood, Timmins, Wishart, Hardcastle, Cleland, 2019; Gibbon & Lee, 2017; Gibbon, 1999). Contrary to EMA, EPG estimates places of contact between the tongue and the hard palate (for a comparison between EMA and EPG methods across a single speech dataset produced by adults, see  Kochetov, 2020). Because it only requires that an artificial palate be positioned in children's mouths, the method is easy to use a priori. However, EPG is relatively expensive because most systems require a custom-fit palate for each child (and at each visit in the case of longitudinal studies) and even for each adult participant because they do not have uniform palate shapes (e.
The desk experimenter (DE) instead operates all devices, which are hidden behind screens to avoid distracting participants during testing. She/he also controls for the quality of the data collected (e. g. , participant position, video, and ultrasound image quality). Before the data collection starts, the PE engages with the child familiarizing him/her with the ultrasound device in a playful way, explaining the space mission's goals with excitement, asking what they know about planets and how they feel about the space adventure. Because the child needs to wear goggles for subsequent pixels to mm conversion (see Section 3. 4) and apply blue markers on their face to correct for possible head movement during postprocessing (see Section 3. 2), the PE may also wear goggles and applies markers on his/her own face to connect with the child and create an empathetic atmosphere.
Alternatively, we have also used two separate webcams positioned in the front and at the side of the participant to get simultaneous face and profile views without the mirror (see Figure  2 ). Video is digitized using an AverMedia GameBroadcaster HD video capture card, which combines the ultrasound video stream with the audio signal from the microphone into a single video recording using the Open Broadcaster Software Studio (OBS, http://obsproject. com). The camcorder video is captured by a Blackmagic Design Intensity Shuttle video interface and also recorded using OBS. In the dual-webcam set-up, the two video streams are combined in a split-view image, and we use the audio stream from the frontal camera for synchronization of the two video streams. The UTI and video recording streams are synchronized offline after the recording, by maximizing the crosscorrelation of their respective audio signals.
General description Once all of the different types of raw data are recorded, they need to be synchronized and processed to correct for head movement, extract tongue surface contours, and so on. These steps can be time-consuming. To address this, the SollarSuite package of data processing and analysis tools was developed in MATLAB (see Figure  3  for a flowchart showing its main components). SollarSync synchronizes the different raw data streams by cross-correlating the audio streams, thus creating a common timecode and building a frame-by-frame table incorporating keyframes identified in the acoustic labeling. Tongue contours are traced in SollarContours and stored in the integrated data structure. In a second step, head motion tracking is performed using the video data. For this, a reference frame is defined for each participant and the configuration of blue markers on the head and probe in both the frontal and profile views are taken as tracking templates.
Cannot be missing; • wav contains high-quality voice recordings of the participants. These files are usually the basis for any acoustic labelling done in PRAAT  (Boersma & Weenink, 2016)  and the synchronized timeline is created from them. If no corresponding wave files are found, SollarSync extracts the audio stream from the US videos as a fallback; • praat TextGrid files are optional. If found, all available tiers are imported to the data structure and are available for keyframe selection in further processing; • cam contains video files from an external camera. In the SOLLAR setup, these recordings combine frontal and profile views of the participant's head with tracking markers applied to the face to allow for head motion tracking. If no such video data is recorded, a version of SollarSuite that does not attempt motion tracking is available separately.
SollarSync will run on such a structured data folder without further user interaction and with status information displayed in MATLAB's Command Window (release R2019a). A matrix of all data sources found will be shown first and the synchronization process will proceed through this list with the following steps: • us and cam video files are analyzed for duration, picture size, and frame rate; • The audio streams from wav, us, and cam sources are cross-correlated to estimate the lag between each source. The estimated lag reflects the fact that different streams are recorded with slightly different starting times. The voice audio recording is taken as the reference stream for synchronization and lag values for other sources are computed relative to the audio recording starting point. These lag values are then added to each frame's individual timestamp, resulting in a timecode table for each data source where a certain time point in the audio recording can be associated with the corresponding frame in each video stream; • Lastly, available praat TextGrid files are parsed for SollarSync to import all available intervals or point tiers into its data structure.
Tongue surface contour detection (SollarContours) Analysis and synchronization results are stored in a structured data array and saved as an. sllr file within the participant folder. For tongue contour tracing, SollarSuite builds upon GetContours, (https://github. com/mktiede/GetContours), a Matlab-based program for fitting discretized tongue surface contours to ultrasound imaging data  (Tiede & Whalen, 2015). The program supports image preprocessing, sequence playback, and frame selection. Click-and-drag positioning of reference points control a cubic spline fit to the currently displayed image frame, which can then be refined using an integrated active contour model ('snake'). SollarContours. m contains an extended GUI that ties in with the SollarSuite by making use of the. sllr data structure (Figure  4 ).
slurp is a publicly available MATLAB-based software tool for automatically tracing tongue contours in ultrasound video data. Given a small number of anchor points manually positioned on any single frame of the video, slurp uses a particle filtering method to robustly track an active contour  (Li, Kambhamettu, & Stone, 2005)  across the video in a compact space parameterized by contour location, length, and a small number of shape characteristics. Automatic slurp tracking is performed outside SollarSuite and the resulting. mat file can be selected for import from a menu item. Any contour data found in this file is transformed into the SollarContours format and merged into the data structure, including the energy map as a measure of contour quality. In the process of importing slurp data, the contour coordinates are transformed into an anchor-based representation.
Second, a 5 cm segment has to be selected on the scales attached to the goggles. This measure is used to compute the conversion factor from pixels to mm. For continuous tracking in a recorded video, each of the four tracking templates in the starting frame of the video are matched to their locations in the reference frame. The rigid transformation between the two frames is estimated 1  and stored within the tracking data structure and its corresponding. dtrk file. Frame-by-frame tracking is then performed on probe templates in a two-step process of tracking marker positions first, then computing the rigid transformation for each frame. Working from the binary image, SollarTrack takes all pixels that fall within the vicinity of the selected blue markers. These pixel coordinates are fed into a MATLAB PointTracker object and SollarTrack proceeds stepwise through the frames.
Separated by color are the three consonants /b, d, g/ in various vocalic contexts. Each point in this scatter plot therefore shows how the front-back value of the tongue body relates between consonant and vowel midpoints across all consonant-vowels trials. This visualization can be further restricted by selecting factor variables to either plot data with different factor values as separate colors within one plot and/or to plot them in separate axes. The result will be displayed in a scatter plot including a regression line, with the r 2 value indicated in the plot's legend. The details of the selection are also printed to MATLAB's Command Window as well as a plain-text log file in the current working directory, along with the parameters of the linear regression such as n, degrees of freedom, r 2 , and slope.