dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , content, time, study, article, input, and so on). We discard these kind of entities by applying a frequency-based filter which compares their frequency in three sets of documents: • the set of publications of the Semantic Web. • a set of the same size covering Computer Science domain, but not Semantic Web. • a set of the same size containing papers from various domains, but not about Semantic Web nor the Computer Science. For each entity e, we computed the number of times it appeared in the above datasets, so that we had three different counts c e , c e , c e. We normalized the counts by dividing them with the number of words of the set where they were computed. Then we computed the ratios r e =. If the ratio r e met a threshold t e = 2, and the ratio r e met a threshold t e = 10, the entity e was included in the graph.
Entities Merger Module We merge entities with the same meaning by using both a lemmatizer and the CSO ontology. Singular and plural forms are combined by using the Lemmatizer available in the SpaCy 26 library. Then we exploited the alternative labels described by CSO to merge entities that refer to the same research topic (e. g. , "ontology alignement" and "ontology matching"). More specifically, given an entity e ∈ E that is known by CSO, let A e = {e 0 ,. , e k-1 } be the set of alternatives of e in CSO. The module first finds the longest label e longest ∈ A e , then e is replaced by e longest. The same process is repeated for each entity f ∈ E.
Best Relation Finder Module Here, the set of triples R presents three different types of triples: those extracted by the Extractor Framework, let us say R EF , those coming from OpenIE, let us say R OIE , and those detected with the PoS tagger, called R PoS. We performed the following operations on these sets: • On the set of triples in R EF we acted as follows. Given a pair of entities (e p , e q ) in R EF , we merged into a list L r all relations' labels r i such that (e p , r i , e q ) ∈ R EF. Then we chose the most frequent relation r most f requent ∈ L r , and built a single triple (e p , r most f requent , e q ). Triples so built formed the set T EF. Clearly, the size of the set T EF is lower than the size of the set R EF. • On the set R OIE we performed a deeper merging operation. Similarly to the work performed on R EF , given a pair of entities (e p , e q ) in R OIE , we first merged into a list L r all relations' labels r i such that (e p , r i , e q ) ∈ R OIE.
In R OIE all triples have a verb as relation predicate. Hence, we assigned each r i to its word embedding w i from the word embeddings built on the MAG dataset, yielding the list L w. With the word embeddings in L w an averaged word embedding w avg was built. Then, the relation r i with the word embedding w i nearest to w avg according to the cosine similarity was chosen as final relation for the pair (e p , e q ), yielding the triple (e p , w i , e q ). The same procedure was also applied on R PoS. The execution of this procedure on R OIE and R PoS yielded the sets T OIE and T PoS , respectively. • Finally, for the sets T EF , T OIE , and T PoS , we saved for each triple (e p , r i , e q ) the number of papers where the pair of entities (e p , e q ) appeared. We refer to this number as the support of triples.
The Semantic Knowledge Graph Here we report the result of our framework, focusing on the Semantic Web domain. We used an input dataset composed by 26, 827 abstracts of scientific publications about this domain that was retrieved by selecting publications from the Microsoft Academic Graph dataset  29. It is a knowledge graph related to the scholarly domain that describes more than 200 million scientific publications through metadata such as title, abstract texts, authors, venue, field of study and so on. For our purpose we considered only abstracts that were classified under Semantic Web by the CSO Classifier  [30]. This dataset has also been used for exploring the relationship between Academia and Industry by Angioni et al. [35]. A few examples of retrieved triples as well as of triples that were discarded by our pipeline can be seen in Table  1.
Gold Standard Creation We first used several different approaches to generate triples from the 26, 827 abstracts described in the previous section. Specifically, we applied on this dataset: 1) T EF (i. e. , the Extractor Framework), 2) T OIE (i. e. , OpenIE), 3) T PoS (considering only the triples with support ≥ 10), and T PoS + Cons. Triples. The resulting set of 109, 105 triples would be unfeasible to manually annotate, since it is very large and includes terms related to very different areas of expertise. We thus focused only on 818 triples which contain (as subject or object) at least one of the 24 sub-topics  30  of Semantic Web and at least another topic in the CSO ontology. This set contains 401 triples from T EF , 102 from T OIE , 60 triples from T PoS and 110 relevant Cons. Triples.
Triples identified by • ontology evaluation uses natural language techniques. It suggests that there might be tools or methodologies that exploit textual resources written in natural language that have been involved in ontologies evaluation. • ontology evaluation is hyponym of the entity ontology construction indicating that a specialized task within ontology construction involves the evaluation of the produced ontologies. • ontology evaluation is hyponym of instance data evaluation which shows in which more general task the ontology evaluation falls. Finally, it is also interesting to consider an entity that is not so much represented in the input dataset. Figure  5  shows the subgraph of the entity supervised machine learning. This representation is useful to highlight which topics and kind of resources are employed by supervised machine learning within the Semantic Web domain.
As an example, it is easy to see that this entity uses both structured data model and rich semantics, and how these two entities are related as well. In the example, only two types of relations appear (i. e. , uses and includes). They seem too generic, in fact, it is not clear how supervised machine learning adopts the other linked entities. This can indicate that our taxonomy of predicates may be too general and we may have to adopt a more fine grained representation in future work. Overall, the knowledge graph seems to contain triples of good quality that well represent the main characteristics of research entities within the context of the input dataset. We thus believe that this version may already be used for enhancing the representation of research items and supporting users in understanding and navigating research outcomes.
Applicability in other domains In this section, we discuss which the current limitations of our pipeline to be used within other domains are, and suggest some developments that are required to capture other domain peculiarities. To start with, the current version of the proposed pipeline exploits both computer science-tuned modules (e. g. , the extractor framework and the CSO classifier) and others more general ones that do not depend on the domain (e. g. , OpenIE and the PoS Tagger extractors). Moreover, the handling of entities and relations does not depend on the target domain and, therefore, the pipeline is limited to the computer science field only by some tools employed in the extraction phase. More specifically, the extractor framework was trained on a corpus of computer science scientific papers, and the ontology employed only embraces computer science topics.
Related Work Many information extraction approaches for harvesting entities and relationships from textual resources can be found in literature. First, entities in textual resources have been detected by applying Part-Of-Speech (PoS) tags. An example is constituted by  [41] , where authors provided a graph based approach for Word Sense Disambiguation (WSD) and Entity Linking (EL) named Babelfly. Later, other approaches started to exploit various resources (e. g. , context information and existing knowledge graphs) for developing ensemble methodologies  [28]. Following this idea, we exploited an ensemble of tools to mine scientific publications and get information out of them. Then, we designed and implemented a software pipeline for the purpose of creating a scientific knowledge graph that organizes entities and their relations.
Relations extraction is not a novel task and has been already addressed in literature in order to connect information coming from different pieces of 34 https://allenai. github. io/scispacy/ 35 Medical Subject Headinghttps://www. ncbi. nlm. nih. gov/mesh 36 http://www. snomed. org/snomed-ct/five-step-briefing 37 Mathematics Subject Classificationhttps://mathscinet. ams. org/msc 38 Physics Subject Headingshttps://physh. aps. org/ 39 https://bioportal. bioontology. org/ontologies text. FRED  40  is a machine reader developed by  [27]  on top of Boxer  [42]. It links elements to various ontologies in order to represent the content of a text in a RDF representation. Among its features FRED extracts relations between frames, events, concepts and entities. However, integrating its extracted knowledge for specific domain applications still remains an open challenge due to the unpredictable and too generic type of knowledge that is extracted, making difficult the use of its entities and relations for modelling scholarly contents.
Another attempt to build scientific knowledge graphs from scholarly data was performed by Yi and colleagues  [29] , as an evolution of authors' work at SemEval 2018 Task 7. First, authors proposed a Deep Learning approach to extract entities and relations from scientific literature. Then, they used the retrieved triples for building a knowledge graph on a dataset of 110, 000 papers. Although our work takes inspiration from that, we propose different strategies to address open issues for combining entities and relations. For example, for solving ambiguity issues that regard the various representations of entities, Yi and colleagues  [29]  considered clusters of co-referenced entities to come up with a representative entity in the cluster. On the contrary, we adopted textual and statistics similarity to solve the ambiguity.
A fourth limitation regards the scalability of our pipeline. The current implementation presents a few bottlenecks that could make difficult to apply it on very large-scale datasets. First, the Extractor Framework requires a lot of hard disk space. This entails that data must be sampled to be processed. Second, the current pipeline only adopts the Stanford Core NLP server with just one thread, which requires a long time to mine textual resources sentence-by-sentence. However, this is not a big issue since it would be possible to run the Stanford Core NLP server in multi-thread mode, speeding up the extraction process. An important next step will also be to perform an extrinsic evaluation of the proposed knowledge base within different tasks. In particular, we would like to assess how AI tasks such as those tackled by recommender systems or graph embeddings creation strategies can benefit from it.
Acknowledgements Danilo Dessì  acknowledges  Sardinia Regional Government  for the financial support of his PhD scholarship (P. O. R. Sardegna F. S. E. Operational Programme  of the  Autonomous Region of Sardinia, European Social Fund   2014-2020 ). This work has also been partially supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the program " Investissements d'Avenir " (reference:  ANR-10-LABX-0083 ). It contributes to the  IdEx Université de Paris  - ANR-18-IDEX-0001. We gratefully acknowledge the support of  NVIDIA Corporation  with the donation of the  Titan X Pascal GPU  used for this research.
Mapping of supraglacial hydrology Mapping of Kongsfjord's supraglacial drainage system is achieved using TopoToolbox, a collection of scripts to analyse flow pattern in digital elevation models (DEMs)  (Schwanghart and Scherler 2014). TopoToolbox first seeks to determine surficial water movement within the DEM. Flow direction calculation is based on a multiple flow direction algorithm that examines elevation gradients between grid cells. The multiple flow direction algorithm allows for both convergence and divergence of flow, thus producing more realistic flow pathways than the single flow direction algorithm. Next, information on flow direction is used to derive: (1) flow accumulation, i. e. the number of cells draining in each grid cell, (2) the drainage network, i. e. the estimated flow paths in the DEM, and (3) drainage basins, i.
Experiment 3 Here we impose discrete water input at the same 13 moulins as in Experiment 2, with the difference that water recharge is calculated from the catchment area that is associated to each moulin (Fig. 2(c) ). Using the TopoToolbox program (described in Sect. 3. 1 above), we delineate the supraglacial watersheds and retrieve the meltwater volume in each of them. The moulins catchments are assumed to remain stationary over the study period. The total water amount involved here is slightly https://doi. org/10. 5194/tc-2020-319 Preprint. Discussion started: 20 November 2020 c Author(s) 2020. CC BY 4. 0 License. higher than in Experiments 1-2 because this scenario accounts for meltwater in watersheds that extend beyond the glacier boundaries.
Acknowledgements The authors would like to thank  Ward Van Pelt  for providing the water recharge time series used as input to the subglacial hydrology model. We are grateful to  Jack Kohler  and  Chris Nuth  for their support in obtaining and processing digital elevation models and satellite imagery. Chloé Scholzen would like to thank  Olivier Gagliardini  for fruitful discussions and assistance during her research stay at IGE, CNRS.
Data availability The digital elevation model can be accessed at https://doi. org/10. 21334/npolar. 2014. dce53a47 (Norwegian Polar Institute, 2014). The glacier and land masks were constructed from glacier outlines, which are available at https://doi. org/10. 21334/npolar. 2013. 89f430f8 (König et al. , 2013). The subglacial topography and bathymetry of Kongsfjorden is available at https://doi. org/10. 21334/npolar. 2017. 702ca4a7 (Lindbäck et al. 2018). The modelled water recharge dataset has been prepared and shared by Ward Van Pelt, and can also be accessed in the following repository: https://doi. org/10. 6084/m9. figshare. 7836530. v1 (Van Pelt et al. 2019). Crevasse zones and moulin locations have been mapped from commercially available, licenced satellite imagery (CNES (2014) and Airbus DS (2014), all rights reserved.
They were analysed using 2D-DIC to follow the deformation processes throughout each test. The software TomoWarp2 was used for the 2D-DIC  [13]. To increase the accuracy and precision of the DIC, the image texture was augmented by applying a 'speckle pattern' to the surfaces of the specimens. Speckle patterns are spots that, in this case, were sprayed using an aerosol spray-paint over the surface of the specimen. This speckletextured surface provided higher image contrast that could be easily identified in the acquired photographs at the scale of the measurement. The pixel size of the acquired images was 13 lm. The DIC procedure is based upon finding the optimal displacements to map small images, subsets, centred on nodal points distributed over the whole image. Strains were computed from the local gradient of the displacements within the nodal grid.
Acknowledgements The authors gratefully acknowledge M. Sc. students  Francesco Massaro  and  Alessandra Castellano  for their support in the experimental work. Some of the results were obtained during their research internships at  Lund University. Laboratoire 3SR is part of the  LabEx Tec21 (Investissements d'Avenir-Grant  Agreement n  ANR-11-LABX-0030 ). Funding Open access funding provided by  Lund University. Not applicable.
Fifty-six subjects were recruited (32 CD and 24 NCD). One participant from the CD group was excluded due to imaging artifacts, rendering a final sample of 31 CD and 24 NCD. Characterization of subjects was done in two (CD) or three (NCD) parts within a 3 h time-period: participants were first interviewed by a certified psychologist. This was followed by an MRI scanning session, and, in the case of the NCD, the first scanning session was followed by ingestion of coffee (Nespresso ® Ristretto, ~50 cc) before a rs-fMRI scan ~30 min thereafter. During the interview, the following data were gathered: demographic data; habits of coffee and other caffeinated products consumption; and assessment of depression, anxiety, and stress scores through the Depression, Anxiety and Stress Scales (DASS-21,  [31, 32] ).
Demographic and psychological data analysis CD and NCD groups were compared in terms of sociodemographic variables, frequency of consumption of caffeinated products, and psychological variables. Since the variables did not follow a normal distribution, nonparametric tests were applied (Wilcoxon test). Moreover, multiple regression analyses were performed, aiming to determine the association between daily consumption of caffeinated products such as coffee, tea, chocolate, etc. (0 = <1/day; 1 = 1/ day; 2 = 2/day; 3 = 3 or more/day) and the psychological data measured with the DASS-21 questionnaire (controlled for sex, age, and education), independently of the groups. These analyses were performed on Matlab2020a software (The Mathworks, Inc. ) and p < 0. 05 was considered the threshold for statistical significance.
Effects of acute coffee consumption and frequency of caffeine consumption The significant findings obtained with ICA, NBS, and LEiDA were further explored, aiming to assess the effects of acute coffee consumption in NCD and of frequency of consumption of caffeinated products in both groups. The first were assessed by comparing NCD after coffee consumption with data from CD (independent sample t-test) and NCD (before coffee consumption; paired sample t-test). The second were evaluated by performing multiple regression analyses following the same approach described for the DASS-21 questionnaire.
g. , stress). Our future work will also examine inter-individual differences in response to caffeine consumption, the subjective experience of coffee consumption, as well as the influence of additional factors as the consumption of alcohol and tobacco. Despite such gaps, the data presented here represent a contribution to the knowledge of the "caffeinated brain" and how these changes underlie the behavioral effects triggered by coffee intake, with implications for physiological and pathological conditions. MP-P, and ME were supported by post-doctoral grants from the project ISIC_2017_NS. PSM was supported by a fellowship grant from the Fundação para a Ciência e a Tecnologia (FCT; grant number PDE/ BDE/113601/2015) from the PhD-iHES program. was supported by a research fellowship of the project funded by FCT (UMINHO/BI/ 340/2018).
Funding This study was funded by the  Institute for the Scientific Information on Coffee (ISIC)  ( ISIC_2017_NS ); ISIC did not influence the experimental design or data analysis/interpretation. The laboratory was also supported by the project  NORTE-01-0145-FEDER000013  through the  Northern Portugal Regional Operational Programme  ( NORTE 2020 ), under the Portugal 2020 Partnership Agreement, through the  European Regional Development Fund (FEDER). RM,.
Later on, the World Ocean Atlas (WOA), the North Sea climatologies and the global ocean carbon climatology resulting from the GLODAP data product  (Key et al. , 2004; Olsen et al. , 2020; Lauvset et al. , 2021)  used the Cressman analysis  (1956)  with a modified Barnes scheme  (Barnes, 1964 (Barnes, , 1994)). In 1994, the first World Ocean Atlas (WOA94;  Conkright et al. , 1994)  was released, integrating temperature, salinity, oxygen, phosphate, nitrate and silicate observations. Every 4 years there is a new release of the WOA with an updated World Ocean Database (WOD). On the regional scale, the first salinity and temperature climatology of the Mediterranean Sea was produced by  Hecht et al. (1988)  for the Levantine Basin. Picco (1990)  was also among the first to describe the WMED between 1909 and 1987.
Data sources In total, 2253 in situ inorganic nutrient profiles are the base of the biogeochemical climatology of the WMED (Table  2 ) that is described here. These profiles cover the period 1981-2017 and come from the major data providers existing in the Mediterranean Sea, i. e. , the MEDAR/MEDATLAS  (1981 -1996; Fichaut et al. , 2003) , the recently published CNR_DIN_WMED_20042017 biogeochemical dataset  (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017)   (Belgacem et al. , 2020) , the MOOSE-GE cruises (Mediterranean Ocean Observing System for the Environment -Grande Échelle program)  (2011 -2016; Testor et al. , 2011 Testor et al. , , 2012 Testor et al. , , 2013 Testor et al. , , 2014 Testor et al. , , 2015;; Coppola, 2016)  stored in the SeaDataNet data  product (2001-2016)  and EMODnet, GLODAPv2 (https://www.
glodap. info/, last access: 2 November 2020) and CARIMED (http://hdl. handle. net/10508/11313, last access: 1 November 2020) data products, and other data collected during Med-SHIP programs  (Schroeder et al. , 2015). All datasets are from a selection of oceanographic cruises carried out within the framework of European projects such as the HYdrological cycle in the Mediterranean EXperiment (HyMeX) Special Observation Period 2  (Estournel et al. , 2016)  and the DEnse Water EXperiment (DEWEX; CNRS-INSU, https:// mistrals. sedoo. fr/MERMeX/, last access: 3 September 2021) project or by regional institutions having as objectives the investigation of the deep water convection and the biogeochemical properties of the WMED. Data were chosen to ensure high spatial coverage (Fig. 3 ).
The BGC-WMED gridded fields have been computed with the more advanced N-dimensional version of DIVA, DIVAnd v2. 5. 1  (Barth et al. , 2014)  (https://doi. org/10. 5281/zenodo. 3627113), using Julia as a programming language (https://julialang. org/, last access: 3 September 2019) under the Jupyter environment (https://jupyter. org/, last access: 3 September 2019). The code is freely available at https://github. com/gher-ulg/DIVAnd. jl (last access: 27 January 2020). DIVA is based on the variational inverse method (VIM)  (Brasseur et al. , 1996). It takes into account the errors associated with the measurements and takes account of the topography/bathymetry of the study area. The method is designed to estimate an approximated field ϕ close to the observations and find the field that minimizes the cost function J [ϕ].
For climatological studies, the four-dimensional extension was used on successive horizontal layers at different depths for the whole time period. Along with the gridded fields, DIVA yields error fields dependent on the data coverage and the noise in the measurements  (Brankart and Brasseur, 1998; Rixen et al. , 2000). Full details about the approach are provided extensively by  Barth et al. (2014)  and  Troupin et al. (2018)  in the "Diva User Guide" (https://doi. org/10. 5281/zenodo. 836723).
github. io/DIVAnd. jl/latest/#DIVAnd. diva3d (last access: 27 January 2021). Values of SNR provided by means of a generalized crossvalidation (GCV) technique  (Brankart and Brasseur, 1998)  gave a large estimate of the SNR (of the order of 22) showing a discontinuous analysis field and patterns around the cruise transects that do not represent properly the climatological fields.