dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, in the STRING protein-protein interaction (PPI) database  [13] , evidence types from multiple sources are used to suggest functional associations between proteins. The evidence types include genome location, co-occurrence in the scientific literature and reported associations in other databases. Mechanistic insight into diseases can be obtained by mapping disease-associated genes (e. g. identified from omics data analyses) to such networks. The network neighbourhood of the disease genes provides a context and can suggest new candidates as well as common themes at the level of molecular processes and pathways that may be associated with disease pathogenesis. GeneMania  [14]  is a Cytoscape App that enables the construction of network neighbourhoods from a set of seed genes by integration of a number of constituent networks (e.
In some ways, they are similar to information overlay methods in data analytics. Points of correspondence between the different views (networks) need to be found, to get an integrated picture that will be useful for suggesting mechanism. To illustrate these contextualization strategies, 22 genes from the differential expression signature (derived from GSE43696 study as outlined in Supplementary Methods) were mapped to protein association network from STRING database. First, the results were explored using Web-based interactive visualization offered by STRING database (Figure  2 ). As proteins realize their function through complex sets of interactions, implications of gene expression changes can often be better understood by assessing its impact on the wider interactome. The top panel shows disease signature genes as well as 50 genes from the first shell identified as most relevant to this input.
Typically, such approaches involved identification of disease-related pathways by careful manual curation of the literature. AlzPathway  [40]  is a collection of signalling pathways in Alzheimer's disease. The map is represented in the Systems Biology Graphical Notation (SBGN)  [41]  process description (PD) language and was also made available in other formats. The resource also offers a Web interface that allows users to review and comment on annotations thus facilitating community curation. Another example of a pathway-based disease map is the Parkinson's disease map  [42] , which builds on an understanding of the hallmark metabolic, regulatory and signalling pathways of the disease  [39]. The Parkinson's disease map can be accessed using the Minerva Web server, which has advanced functionality for visualization (using the GoogleMaps API) that facilitates manual exploration  [43].
PathWhiz includes metabolic, signalling, as well as a number of diseaseassociated pathways. Recently, the PathWay Collage tool  [47]  has been developed that allows the visualization of fragments of pathways (defined by the user so as to reduce the complexity of full pathway representations) onto which omics data can be overlaid. At present, there are two leading exchange standards adopted for representing biochemical pathway networks. The first one is Systems Biology Markup Language (SBML)  [48] , which was primarily intended to support development and sharing of quantitative biochemical models. Therefore, SBML has been designed to be generic, with the main building blocks being species (quantifiable physical entities), processes (which define how entities are manipulated) and compartments (provide lowlevel context for processes and entities).
The PD language can represent all the steps in, for example, a reaction showing how a biological entity changes in a temporal manner. However, to fully exploit the PD representation for applications like numerical modelling would require additional data associated with component steps (such as rate constants), which often is not readily available. BLM CHEK1 PLXND1 TIPIN SEMA3E CLSPN ATR MAP3K6 HNMT KCNN4 MCM2 DPPA5 C MC M M M M M M M M M M M M M M M M M M M M M M M M TIMELESS PER1 ARNTL SYT13 CLOCK BMP5 CAMK2A GDF5 GD GD SMAD1 CRY2 CAMK2D SMAD4 CAMK2G BMPR2 MYO19 MPR2 ACVR2B SMAD5 CAMK2B GRIN2B FHOD1 SMAD9 GRIN2A D9 D9 Another prominent data exchange standard is BioPAX  [49] , which was primarily designed for unambiguous sharing of biochemical pathway data and therefore offers much wider selection of domain-specific terms to characterize and categorize both interactors and interactions.
As Ontology Web Language (OWL) forms the core of this standard, all of the terms are organized hierarchically and relationships between them are formally defined. BioPAX standard aims to facilitate data sharing and integration via Semantic Web technologies and Resource Description Format (RDF). If an RDF representation is used, the format can be easily extended to incorporate complex context information; however, BioPAX currently does not aim to offer the means of defining experimental or biomedical context for pathways, as, by design, other standards/ontologies can be brought in to model them. Both SMBL and BioPAX are predicated on the developments of the past decade, when several key technologies and design principles were introduced that greatly influenced how biomedical data-sharing standards are implemented.
One of these developments was increasing adoption of formal ontologies that allowed development of well-structured controlled vocabularies for different domains and definition of cross-mappings between them. For example, the SBML standard allows annotation of models with System Biology Ontology (SBO) terms, which are also used in SBGN and can be mapped on to BioPax via SBPAX  [50]. Therefore, in principle, it is possible to integrate models across all of these standards on a qualitative level. On a syntactic level, different formalisms can be integrated by introducing support for a low-level common language, like RDF, which is representation-agnostic and allows all types of data to be linked via common identifiers. Both of these solutions enable modern standards to be designed in a modular way, where different specialized representations can be developed by different communities of domain experts and then combined together-or even extended for the needs of a particular applied project.
Contextualization by knowledge graph representations There are many specialist sources of disease-related information such as databases of mutations and their consequences. In addition, there is vast information in unstructured formats in the scientific literature and much of it is contextual, for example statements that describe correlations between gene products or between small molecules and gene products that are observed in particular cell types. Information in the literature may also describe weak associations only seen in certain conditions, or for which there may be limited evidence that could nevertheless still be useful for hypothesis generation. The Biological Expression Language (BEL)  [55]  represents statements in biology, typically correlative and causative statements as well as the context in which these statements apply.
A biological statement is represented as a triple (subject, predicate and object) where the subject is a BEL term and the predicate is a biological relationship that connects the subject with object. A BEL term can be the abundance of a biological entity or it can be a biological process such as one described in the Gene Ontology (GO)  [56] , or even a given disease condition. Each entity is described in an existing namespace (e. g. from databases like ChEBI  [57] ) or in particular user-defined namespace(s). There is a fixed set of causal, correlative and other relationships that link these entities. The BEL complier checks that the statements are syntactically correct and carries out the integration and alignment of the data by establishing equivalences between identical concepts. The knowledge captured by BEL statements can be represented as a graph.
Using BEL, Hofmann-Apitius and co-workers have explored mechanistic aspects of Alzheimer's disease  [59] [60] [61] [62]. They used the BEL language to represent information from the literature together with relevant experimental data sets to build integrated evidence networks. This approach helps to mine what they refer to as the knowledge 'grey zone', which includes both established and emerging connections between entities. This approach can be powerful for linking mechanisms associated with biomarkers  [60]. Constructing such information-rich representations can involve considerable manual curation efforts. For this reason, TM will be increasingly important for supporting this process  [63]. Already existing examples include the BEL Information Extraction workFlow (BELIEF) system and BELSmile  [64].
Knowledge network representations of biological data are increasingly being leveraged in in integrative analysis platforms, which aim to combine information resources with common analysis and visualization tasks. One example of such platforms is Ingenuity Pathway Analysis (IPA V R ). IPA V R suite aims to facilitate interpretation of experimental data from high-throughput omics experiments and includes methods for identification of causal networks and visual exploration of biological graphs  [66]. The BioMax BioXM TM platform uses an object relational database architecture to store, query and envision disease information, including molecular, genetic, physiological and clinical data, as well as biological models. This platform has been used to create a resource for chronic obstructive pulmonary disease (COPD) called 'COPD knowledge base'  [67] [68] [69] [70].
The authors of COPD knowledge base do acknowledge that much diseaserelated information remains hidden in the literature and also recognize the difficulties of capturing quality and context specificity. The Malacards resource  [71]  uses a relational database for the semantic integration of multiple sources of information about diseases, and also provides network visualizations of disease-disease relationships. GeneAnalytics  [72]  enables contextualization of functional signatures (gene sets) in the context of organs and tissues and compartments, as well as other annotation sources, building on the GeneCards  [73]  and Malacards technologies. Recent advances in graph-based databases have opened up additional possibilities for organizing heterogeneous and interlinked data. Graph databases have several features that make them particularly attractive for management and exploratory analysis of complex biological data sets.
A particularly noteworthy feature is excellent performance of traversal-type queries across entities of diverse types they offer. Such queries could be challenging to realize in relational databases because of the cost of computing statements across different tables. Traversal queries can be an important way of identifying new relationships in integrated data, particularly in the cases where links only become apparent once multiple sources have been integrated, but may not be immediately obvious because of sheer amounts of data being involved. Modern graph databases offer sophisticated query languages and often also other types of framework-specific functionality to facilitate applied analysis, like an ability to extend the framework with user-defined functions  [74]  and integration with ontologies and reasoning systems.
One example of a graph database solution that has been already used in biomedical domain  [75] [76] [77] [78]  is Neo4J DBMS. Neo4J is a Java-based solution, which also offers its own graph query language (Cypher), which is conceptually similar to SQL. Semantic Web  [79] , RDF  [80]  and Linked Open Data  [81]  are emerging important standards for sharing biomedical data. At its core, Semantic Web is characterized by its use of globally unique, unambiguous identifiers for entities. The identifier used for this purpose is commonly a web link (URL). The RDF format is structured as a set of statements with three parts (subject, predicate and object), where a subject and object are entities with a unique identifier, and predicate defines the type of a relationship between them. Linked Open Data standard established a set of rules for providing meaningful RDF at the resolvable identifier URL, which generally is in the form of statements that provide meaningful information about that entity that can in turn link it to other URL-identified entities, and, finally, the entities and statements can be linked to specialized ontologies in OWL  [82]  that provide additional information about how to interpret them within a particular context.
From the perspective of biomedical data integration, RDF primarily plays the role of a low-level data exchange standard useful for addressing the syntactic heterogeneity issue and facilitating data integration. Although RDF representations can be interpreted as integrated knowledge networks, usually an additional set of standards will be necessary for meaningful interpretation of data from a biological perspective. Prominent resources that offer biological data on the Semantic Web include EBI RDF platform  [83]  (covers such important databases like UniProt  [84] , Reactome  [85] , Ensembl  [86] , ChEMBL  [87]  and Expression Atlas  [88] ), Bio2RDF (aims to mirror in RDF most of the prominent biological databases)  [89]  and OpenPhacts (uses Semantic Web technologies to represent the chemogenomics space)  [90].
Approaches for extraction of contextual knowledge Ultimately, all types of models in biomedical domain originate from data collected during relevant experiments, and such data would typically require both different levels of preprocessing and interpretation to yield such models. At present, relatively few types of raw experimental data can be straightforwardly converted into these representations without requiring human interpretation. Some possible examples are construction of coexpression networks  [98, 99]  from transcriptomic data and sequence homology graphs  [100, 101]. However, currently, manual interpretation and publishing of inferred models as free text in scientific papers remains the primary means of disseminating this knowledge, and all major biomedical databases discussed in this review expend considerable curation efforts to collect, verify and consolidate it in standardized formats from source literature.
Extraction of information from text is contingent on unambiguous identification of entities and relationship types from which model statements can be constructed. Therefore, TM technologies have a natural synergy with ontology-based data modelling, and to a lesser extent Semantic Web formats, like RDF  [102, 107]. The ontologies are used both as controlled vocabularies to resolve alternative naming conventions and as sources of common identifiers for recovered concepts and relationships. Some examples of specialized relationship types that can be recovered include protein-protein  [108] , drug-protein  [109]  and drug-drug  [110, 111]  interactions, and associations of genes with mutations  [112, 113]  and phenotypes  [114] [115] [116]. Data from IR and TM are increasingly incorporated alongside manually reviewed information by major databases, for example PPIs extracted from literature by fully automated approaches are offered by STRING  [13, 117].
During the competition, the best result was achieved by the BELMiner framework  [122] , which used a rule-based approach for extraction of BEL statements from text and got the highest F-measure of 20% for this task. After the challenge, BELSmile system was developed that reached an even better score of 28% on the same dataset  [64]. BELSmile implemented a semanticrole-labelling approach, which relies on recognition of verbs (predicates) and assignment of roles to associated subjects/objects relative to that verb. Notably, designs of both frameworks integrated multiple specialized IR and TM tools to handle specific low-level subtasks. It is clear that at present a substantial proportion of valuable biomedical knowledge is 'trapped' in scientific text, in a representation not readily suitable for automated computer-driven interpretation.
As outlined by Cohen in  [6] , modern biomedical data modelling formalisms can be categorized into 'curation' and 'mechanistic' types. Aim of the former is to primarily describe experimental results (e. g. Nanopublications, BioPAX), including observed associations between molecular signatures and diseases. The latter specify evaluable models used to simulate living systems and generate predictions based on a set of inputs (e. g. SBML, Kappa). It is obvious that although experimental observations are ultimately used to construct such models, the processing steps required to correctly generate the latter from the former would go beyond a simple format change. We believe that automation of this process is a highly promising emergent strategy for development of fully mechanistic models of disease.
Funding The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking eTRIKS Project  (Grant Number:  IMI 115446 ) resources of which are composed of a financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/ 2007-2013 ) and  European Federation of Pharmaceutical Industries and Associations (EFPIA)  companies' in kind contributions. The work was also partially supported by  Core Research for Evolutional Science and Technology (CREST)  Grant from the  Japan Science and Technology Agency  (Grant Number:  JPMJCR1412 ), and  Japan Society for the Promotion of Science KAKENHI  (Grant Numbers:  17H06307  and  17H06299 ).
Since 1999, the international Argo program has revolutionized modern oceanography by operating more than 4,000 autonomous profiling floats measuring temperature and salinity profiles across most of the global ocean. Argo is now the main source of CTD data for the world's oceans  (Roemmich, 2009). Thanks to the miniaturization of sensors, in particular optical devices, Argo floats have progressively acquired the capability to monitor additional seawater properties, relevant, in particular, for biogeochemical and bio-optical studies. The anticipated high spatio-temporal coverage resulting from such a program thus represents an opportunity for ocean color validation activities. In 2011, the International Ocean Color Coordinating Group (IOCCG) recommended the utilization of several types of profiling floats and provided recommendation for their use in synergy with ocean color  (IOCCG, 2011).
For the ProVal application, a new PROVOR float (NKE instrumentation) named "CTS5, " was developed as part of a collaboration between NKE and the Laboratoire d'Océanographe de Villefranche (LOV). As for any float of the PROVOR family, the large hull size ensures good stability (large separation of center of buoyancy from center of gravity), a substantial reserve of buoyancy, and a large battery capacity. The CTS5 offers extended capacities thanks to novel hardware and software (see Supplementary Material for details).
Platform shading issues are minimized by always having at least one sensor being outside of the float's shadow. This configuration also provides redundancy of data, which is helpful in monitoring the relative behavior of the instruments over time and for quality control. The characteristics of the two radiometers integrated on floats presented here are reported in Table  1. A channel for downward PAR (Photosynthetically Available Radiation) has been included to ensure compatibility with other floats deployed by the BGC-Argo program (Biogeochemical-Argo Planning Group, 2016), leaving six channels on each arm for irradiance measurements. As a result, irradiance at 380 and 412 nm are only measured on one arm (see Table  1. Note that space-borne measurements apply an irradiance model to derive reflectance while on the ProVal it is directly calculated from measurements).
Data Sampling Strategy Sampling rates of sensors onboard the float are too high for transmitting all data using iridium communication at a reasonable cost. The software on the acquisition board used on the ProVal float enables a flexible sampling strategy in order to optimize the resolution and volume of data. Several configurations have been tested to study the behavior of the platform. Here a description is given of the most appropriate configuration in which an initial dataset can be collected for data quality assessment (Table  2 ). No data are acquired during the descent of the float due to the variable speed during this phase (not shown). Some data are acquired during the parking phase at about 500 m depth to monitor the dark currents. Vertical profiles are acquired during the float's ascending phase with increasing resolution (decreasing depth intervals) from bottom to surface.
For example, radiometers (OCR507IR) are powered on at 250 m depth and data frames are recorded every 1 m up to 60 m then every 10 cm up to the surface. Finally, 1 min of radiometric data and tilt are acquired at the surface in "buoy" mode. This configuration can be modified remotely with a tradeoff between the amount of data to be transmitted and the available energy and data transmission costs. The configuration described here results in a data volume of nearly 140 kBytes per profile (average transmission time of 12 min) and provides a theoretical lifetime of more than 300 profiles.
Basic Processing Digital counts from the radiometers are converted into physical units using the manufacturer calibration coefficients. These coefficients include the dark counts which are, by nature, sensitive to temperature  (Kuusk, 2011). During a standard cruise operation with shipboard optical profilers, dark counts are measured just before and/or after the deployment and subtracted from measurements. Here, we use the median of data measured during a one-night profile (sampled at the beginning of the deployment) as an in situ dark correction. This in situ dark correction is typically <0. 05% of the signal at the surface and is therefore only significant at depths where signals are small. Depths in units of pressure are converted to meters by using the swDepth function of the oce R-package  (Kelley, 2017)  and the distance offsets between the pressure gauge and the optical collectors are taken into account.
The tilt and heading values associated with each radiometric measurement are calculated by interpolating measurements near the time of the radiometric measurement. On average, the time difference is 0. 5 s between tilt/heading and radiometric measurements. An offset is added to have heading values relative to North. This offset is determined Note that the two radiometers (OCR507IR_1 and OCR507IR_2) used here differ for the first E d band which is at 380 and 412 nm, respectively. The sampling frequency is the nominal output of the sensor and not the frequency of the data recorded by the float. E d (downward irradiance), Lu (upwelling nadir radiance), Chla (Chlorophyll-a), CDOM (Colored Dissolved Organic Matter), bb (backscattering coefficient). The CTD SBE41CP is the standard CTD of the Argo program.
To evaluate the performance of the design in terms of shading avoidance, a 3D Monte Carlo code, named SimulO, was used. This code has been previously used to evaluate the performance of optical instrumentations  (Leymarie et al. , 2010; Babin et al. , 2012; Doxaran et al. , 2016)  but can also serve to perform shading estimation  (Gerbi et al. , 2016)  based on a 3D backward Monte Carlo approach (see section E in the Supplementary Material for details). In this study, a direct sun in a black sky has been used to simulate sky radiances. This is a first acceptable approximation for evaluating shading  (Leathers et al. , 2004; Gerbi et al. , 2016)  even if this leads to an overestimation of the true shading effect when the float is shading the sensor and an underestimation on the opposite direction.