dataesr/scientific-paragraphs-categorization huggingface dataset:
Running time The running time of the KUPNetViz depends on two factors: i) the performance of the network layout algorithm and the visualization itself (e. g. supported network size) which depends on the Cytoscape Web library and ii) the querying performance of the application regarding the mapping of renal -omics datasets to the network. The latter depends on the number of queried molecules. Typical queries with a few molecule names complete in a few seconds. The only operations that are quite time consuming both in terms of querying and visualization time are the search for second level neighbors of selected nodes and the case of many queried molecules when working in multi-species mode. Finally, the speed of the network layout depends on the user's machine as the network rendering takes place in the user's browser.
This disease model was associated with, namely "Nakorchevsky, J Am Soc Nephrol, 2010", "Rodder, Am J Transplant, 2009" and "Scherer, Nephrol Dial Transplant, 2009". In addition to these datasets, we also selected "ECM-receptor interaction" pathway and "basement membrane" cellular component from the GO and KEGG panels respectively. The resulting network is presented in Figure  2. Overall, we observed that calreticulin was at the centre of a network where many genes and proteins were also modified in this pathology, and that most of these biomolecules were part of the basal membrane and involved with cell to extracellular matrix interactions (ECM). Specifically, the ECM-receptor interaction pathway is mostly upregulated, as observed from the orange to red colour scale of the nodes connected to calreticulin (Figure  2 ).
Furthermore, querying classic network visualization tools such as the tool provided in the STRING database  [23]  website lack background knowledge on kidney diseases. Case study 2: potential involvement of IL6 and the IL6 receptor in the progression of polycystic kidney disease IL6 is a pro-inflammatory cytokine that has been previously described as a pro-fibrotic mediator in liver  [30] , in lung and in skin  [31]  models of fibrosis. However, current evidence for a possible role of IL6 in the development of renal fibrosis is limited. The only available evidence of this link in the kidney was published recently in a study demonstrating that mice with genetic blockade of IL6 were protected against the development of renal fibrosis  [32]. In addition, the authors also observed increased IL6 expression levels in kidney biopsies of chronic kidney disease patients compared to agematched control biopsies.
In humans, the network associated with those two entities obtained by querying for and displaying their first level interacting neighbors was mostly up-regulated in patients with PKD (Figure  3A ). This outcome was derived after mapping three KUPKB datasets related to PKD, namely "Lai, Proteomics Clin Appl, 2008", "Mason, Proteomics Clin Appl, 2009" and "Song, Hum Mol Genet, 2009",  [35, 38, 39]  (disease filter: "autosomic dominant polycystic kidney disease" and "PKD1 autosomic dominant kidney disease"). More importantly, in mouse and rat models of PKD, upregulation of the network increased with the severity of the disease as levels of up-regulation increased (i. e. orange to red) and the p-values decreased with disease progression (thickening of node border). This outcome was derived after mapping the dataset "Chen, BMC Res Notes, 2008" for mouse  [40] , and the dataset "Koupedidou, BMC Nephrol, 2010" for rat  [36].
Discussion The wealth of biological information regarding kidney disease that can be extracted from the current version of the KUPKB is currently presented in tabular format. Given the possibly long returning lists of molecules, the nature of the knowledge base and the fact that it contains hierarchical and protein interaction data, we sought to visualize these interactions through a gene network visualization module. Prior to its development we tried to map experimental data to some of the numerous existing software packages that have been developed for biological network visualization and exploration and at the same time suited at least partially our needs (mapping of possible multi-omics data, integrated background databases, customizable data sources). These tools included EGAN,  [16]  and Biological Networks,  [41].
Leaving aside the domain-specific nature of KUPNetViz, most of the packages presented in the first table of  [14]  meet the criteria of combined views of interaction and abundance data (e. g. VANTED,  [42] , GENeVis,  [43] ) and are equipped with high-level functionalities (e. g. network clustering algorithms,  [44]  dimensionality reduction methods,  [45] , visualization of extremely large networks,  [46] , and network module detection,  [47] ). However, although the above tools excel in network-related functionalities and visualization quality, none of them comes with a complete and yet simple set of bundled background databases (protein-protein interaction, element annotation, functional and pathway associations) in one place, a fact which is essential for the researcher. Other solutions including Pajek,  [48] , and yEd (http://www.
yworks. com/en/products_yed_about. html) comprise also excellent network analysis tools but are very generic and not focused to biological problems which may also confuse the biologist. Another drawback with many current applications is that they are desktop-based while there is a current trend in software development to switch from desktop to web-based applications for a variety of reasons including flexibility, maintenance and platform independence. Thus, although there are certain web-based tools, based on Java TM webstart technology (VANTED,  [42] ) or related technologies (VisANT,  [13] ), they still have limitations similar to the ones described for certain desktop applications, and mainly the lack of a small but adequate set of bundled background databases and a relatively simplistic interface which does not require prior training.
The KUPNetViz application, although simplistic in its basic concepts, aspires to initiate a set of domain-specific integrative visualization tools that focus in comprehensive interfaces. These interfaces could be used to answer biological questions from the first usages, without demanding from the user to go through long manuals, time-consuming database and literature searches to annotate the various entities in the network and understand basic mathematical concepts of graph theory. Even though the developers of several biological network visualization and data integration packages have put substantial effort in the simplicity and clarity of the outcome, some of them can only be fully exploited by trained bioinformaticians while others require prior training in the form of workshops or seminars in order to be used at their full potential.
Although such an application is not alone in its kind, it manages to keep simplicity, visual complexity and speed at acceptable levels, as compared with the two aforementioned applications (  [16, 41] ). A schematic representation of the KUPNetViz modularization is depicted in Figure  4. The reasoning behind the construction of a MySQL backend database to support the application instead of using the KUPKB semantic web repository includes three main arguments: firstly, during the development we sought to maintain software modularity and re-usability even though in its current version, the visualization application is adjusted to kidney. Thus, we considered that an RDB model (especially regarding the background knowledge part) can be adjusted easier to other similar experimental data sources (e.
g. the highly detailed localization and hierarchization of cell types and kidney tissues) was required for the minimalistic approach promoted by KUPNetViz. Thus, we used an RDB model to reduce query complexities and running speeds. In addition to the latter, the backend database building time, including the download and parsing of the background knowledge files and the re-parsing of the experimental data requires less than three hours while the rebuild of the semantic web repository requires over a day (the times were measured on the same computer). This allows the easier maintenance of the KUPNetViz and the more frequent synchronization of the backend database with the latest releases of the incorporated background knowledge sources.
Conclusions and future work KUPNetViz is a user-friendly biological network visualization tool dedicated to renal research. This tool uses Figure  4  The modularization of KUPNetViz. A simple description of the architecture behind the KUPNetViz which consists of three simple layers, present in many similar applications: i) the background database, ii) the server PHP code layer handling database querying and parsing iii) the client side of the application where the interaction with the user is taking place. Client-server data exchange is performed using the widely used JavaScript Object Notation (JSON) format. The vertical dashed lines in the data stored in the backend database layer and the PHP layer mark the distinction of the different modules handling the background knowledge and network reconstruction and the KUPKB datasets and their mapping to the network.
Moreover, maybe its most important feature is the biomolecular interaction extrapolation across species which allows the researcher to quickly formulate and screen several hypotheses in a simple manner. We have demonstrated the value of KUPNetViz in two case studies, further investigating the role of calreticulin as a key player in a gene network mostly up-regulated in renal graft rejection and newly investigating the potential involvement of IL6 and the IL6 receptor in the progression of polycystic kidney disease. The value of the KUPNetViz in kidney research will be increased in parallel with the number of related public multi-omics datasets which will be also available in the KUPKB. The expertise of the KUPNetViz hosting group in renal biology guarantees its constant curation and addition of related datasets, as these are getting published.
This will potentially lead to increased confidence in the observed networks as additional biological evidence will eventually pile up from those constantly added datasets. The addition of datasets is easy and straightforward, since data are entered in ready-to-fill Excel sheets by biologists and incorporated by a single command line. We are aiming to the inclusion of the latest omics data sets in the KUPKB at least once a year from which the KUPNetViz will directly benefit from. Future extensions include the incorporation of additional biomolecule annotation and interaction resources (KEGG compounds) and the addition of a module that will identify highly enriched and over-represented biological functions and biochemical pathways using the StRAnGER algorithm  [53].
Figure1A snaphsot of KUPNetViz in action. Under the network view, the KUPKB data mapping tab is displayed, where the researcher can map onto the network several gene/protein/miRNA expression datasets and apply different criteria and combinations of kidney location(s), disease(s) and data from published studies. This snapshot focuses on the down-regulation of TGFβ1 in kidney in two renal disease states, namely in acute renal allograft rejection and interstitial fibrosis and tubular atrophy, and the up-regulation of the miRNA that targets TGFβ1 in interstitial fibrosis and tubular atrophy, as derived from two published studies.
Figure3The IL6/IL6R axis network for human and rat. The IL6/IL6R axis expression network created by querying the KUPNetViz for IL6 and IL6R and using the first level interaction neighbors of IL6. (A) The network associated with IL6/IL6R is mostly up-regulated in patients with PKD as observed after mapping the results of three relevant studies in human (namely "Lai, Proteomics Clin Appl, 2008", "Mason, Proteomics Clin Appl, 2009" and "Song, Hum Mol Genet, 2009"), (B-D) Progressive upregulation of the IL6/IL6R network in PKD in rat model as derived from the study of Koupepidou et al. [36]. The three figures depict three distinct stages of the disease, specifically (A) day 0, (B) day 6 and (D) day 24, after mapping the three experimental conditions in the dataset "Koupepidou, BMC Nephrol, 2010".
Acknowledgements The authors want to express their acknowledgements to  Dr Aristotle Chatziioannou  for useful input at the early stages of the application design. This work is funded by the  EU/FP7/ICT-2007. 4. 4  e- LICO  project (http://www. e-lico. eu)and by the  FP7-IAPP program  " Protoclin"  ( GA 251368 , http://www. protoclin. org).
Availability and requirements Home page: Accessible through the website of KUPKB and the iKUP browser (tab "KUPKB Network Visualizer") at http://www. kupkb. org or directly at http://www. kupkb. org/vis/index. php. Operating system: KUPNetViz is a web-based application thus it is platform independent. Requirements: KUPNetViz is best used and viewed under Internet Explorer 8 or higher, Mozilla Firefox, Google Chrome, Apple Safari and Opera. The use of Internet Explorer 7 and lower is not recommended. License: KUPNetViz is free for academic use but requires a license from the authors for any commer-cial purposes. The software is available without user registration. Further information: An analytical user's guide coupled with usage examples is available at the application's homepage and as supplementary material online (Additional file 2).
Bacterial infectious diseases are the result of multifactorial processes affected by the interplay between virulence factors and host targets. The host-Pseudomonas and Coxiella interaction database (HoPaCI-DB) is a publicly available manually curated integrative database (http://mips. helmholtzmuenchen. de/HoPaCI/) of host-pathogen interaction data from Pseudomonas aeruginosa and Coxiella burnetii. The resource provides structured information on 3585 experimentally validated interactions between molecules, bioprocesses and cellular structures extracted from the scientific literature. Systematic annotation and interactive graphical representation of disease networks make HoPaCI-DB a versatile knowledge base for biologists and network biology approaches.
Both players in the disease game have developed a variety of mechanisms to counter the others' defences. Existing information resources in the field of hostpathogen interactions cover a broad spectrum of organisms including virus-specific databases (2), bacteria-centric databases (3), fungi (4) and databases with a mixed spectrum of organisms  (5, 6). However, there is a lack of resources that collect experimentally verified results to annotate comprehensive biological networks on the level of cellular systems that are at the centre of the hostpathogen interactions. Here, we present HoPaCI-DB, a database with manually curated information from hostpathogen interactions. HoPaCI-DB has been developed to facilitate systems-level analyses for providing better insight into the complex networks of pathways and interactions that govern epidemiology in Pseudomonas aeruginosa and Coxiella burnetii.
Furthermore, generation of a resource that allows further processing of the data to perform systems biology analyses, bioinformatics analyses or graphical representation requires systematic presentation of the information and biocuration by using established biological vocabularies. Systematic transformation of complex information such as functional annotation from free text into biological vocabularies is a non-trivial task, and it has been demonstrated that current text mining methods are not yet able to produce satisfactory results for the extraction of biological information  (8). The term 'induction of endothelial cell gaps' (9), for example, cannot easily be transferred into the respective Gene Ontology term 'establishment of endothelial barrier' (GO:0061028) by automated methods. Comprehensive information extraction from the biomedical publications with high quality of the complete database content of HoPaCI-DB is obtained by experienced biocurators who manually annotate the complete articles from peer-reviewed scientific literature.
The increased virulence of PA14 is mainly due to ExoU, a type III secreted potent cytotoxin, absent in PAO1, and a mutation in the ladS gene that leads to an elevated T3SS activity and increased cytotoxicity towards mammalian cells  (14). Host-pathogen interaction information is enriched with data from external resources. Whenever possible, elements from HoPaCI-DB are hyperlinked to information-rich database entries from PubMed  (15) , EntrezGene  (11) , KEGG (  16 ), Gene Ontology (  17 ) and other resources. The complete database information or results from database searches can be downloaded as flat files or in Systems Biology Markup Language (SBML), a free and open interchange XML format  (18). Files in the SBML format can be visualized and analysed with network analysis tools such as Cytoscape  (19).
Other publicly available resources that provide hostpathogen interaction information are rather gene or genome oriented. PATRIC is a bacterial bioinformatics resource with a focus on human pathogenic species (3). PATRIC includes a built-in system for predicting genes, assigning gene functions and reconstructing metabolic pathways. The resource links the database information to a variety of external resources such as KEGG, Gene Ontology and PDB and offers various analysis tools. PHIbase has an even broader scope by providing manually curated information of pathogenicity, virulence and Figure  2. Graphical presentation of P. aeruginosa T1SS protein secretion system/complex focus topic. The graph shows the T1SS protein secretion system/complex with functional interactions between proteins/protein complexes (beige), chemical compounds (green) and biological processes (orange).
FUNDING Funding for open access charge:  The ''Pathomics'' ERAnet PATHO Grant  [ ANR-08-PATH-004-01 ] supported this work (in part). Helmholtz Zentrum Mu¨nchen -German Research Center for Environmental Health (GmbH) ; The  German Ministry of Education and Research (BMBF)  under contract No. [01KI1001] supported this work (in part).
The French program Coriolis, as part of the French operational oceanographic system, produces the COriolis dataset for Re-Analysis (CORA) on a yearly basis. This dataset contains in-situ temperature and salinity profiles from different data types. The latest release CORA3 covers the period 1990 to 2010. Several tests have been developed to ensure a homogeneous quality control of the dataset and to meet the requirements of the physical ocean reanalysis activities (assimilation and validation). Improved tests include some simple tests based on comparison with climatology and a model background check based on a global ocean reanalysis. Visual quality control is performed on all suspicious temperature and salinity profiles identified by the tests, and quality flags are modified in the dataset if necessary.
Introduction An ideal set of in-situ oceanographic data should cover the entire global ocean and be continuous in time, subject to regular quality control and calibration procedures, and encompass several spatial and temporal scales. This goal is not an easy one to achieve in reality, especially with insitu oceanographic data such as temperature and salinity. These data have as many origins as there are scientific initiatives to collect them. Efforts to produce such ideal global datasets have been made for many years, especially since  Levitus (1982). Among global datasets, the most comprehensive and widely used is the World Ocean Database produced by the National Oceanographic Data Centre (NODC) Ocean Climate Laboratory (OCL). This is a collection of both historical and modern ocean profiles and plankton data.
The latest release (World Ocean Database 2009 -WOD09) contains data from the 19th century to 2009. Temperature and salinity data are available at observed and standardized levels. More recently, researchers at the Met Office Hadley Centre have produced and maintained a quality-controlled subsurface ocean and salinity dataset  (Ingleby and Huddleston, 2007) , specially designed for ocean data assimilation systems. The latest version, called EN3, spans the period from 1950 until the present and is updated every month, with temperature and salinity data distributed on observed depths. Sporadically, other groups have also made global hydrographic datasets available, mainly dedicated to research purposes (e. g. Hydrobase, www. whoi. edu/hydrobase/, which provides profile data on observed levels -both raw and quality-controlled;  Lozier et al.
C. Cabanes et al. : The CORA dataset Quality control is a very important step both for reanalysis and research projects, although quality requirements can differ from one user to another. A wide variety of quality control methods exists for in-situ oceanographic datasets, ranging from fully automated methods (e. g. Ingleby and Huddleston, 2007)  to the manual checking of every profile. Depending on the dataset, bad data identified by the quality controls are either flagged (e. g. WOD09, EN3) or removed from the database (e. g. Hydrobase, although the raw data files are also provided). WOD09 includes quality control flags for each measurement and profile, although the data on observed levels went through less extensive tests than the standardized ones. Indeed, quality control of the data on observed levels is automatic and aims to detect gross errors or duplicates.
Additional checks are applied to the standardized data: density inversions and outliers are flagged (statistics used to find outliers are computed from observed data on each 5 • × 5 • square). Moreover, the results from objective mapping of the standardized data are closely examined to detect unrealistic features. Profiles or individual data points that create any unrealistic feature are then visually checked and flagged in both the observed and standard level datasets (see the WOD09 documentation for more details,  Johnson et al. , 2009). Quality controls applied to the EN3 dataset are based on fully automated methods and include checks against a gridded background and checks against nearby observations  (Ingleby and Huddleston, 2007). Data that fail the test are flagged as bad in the dataset.
However, with such kinds of fully automatic methods, it can be a challenge to find a compromise between rejecting all the bad observations and retaining all the good ones, particularly in regions with large gradients or small scale eddy variability. Correction of known instrumental biases should also be a priority in developing global datasets, as the impact of such biases can be serious for climate research studies. Indeed, different issues exist with eXpendable BathyThermograph (XBT) data and, if not corrected, can contribute to anomalous global oceanic heat content (OHC) variability (e. g. Gouretski and Koltermann, 2007; Wijffels et al. , 2008; Levitus et al. , 2009; Ishii and Kimoto, 2009; Gouretski and Resghetti, 2010; Lyman et al. , 2010). In the WOD09 database, the XBT data at observed levels retain the depths received from the data submitter, while the XBT data interpolated to standard levels use the appropriate corrected depth equation when possible and the appropriate time-varying bias correction from  Levitus et al.
(2009). The EN3 dataset uses the appropriate corrected depth equation for XBTs identified as uncorrected and for some of the XBTs of unknown type (reporting from less than 840 m and prior to the end of 1996). Moreover, versions of the EN3 dataset include XBT and mechanical bathythermograph (MBT) depth time-varying corrections computed by  Ishii and Kimoto (2009) ,  Levitus et al. (2009)  and  Wijffels et al. (2008). However, these time-varying XBT bias corrections have not been defined for the full time range covered by the EN3 dataset, and XBT corrections have hence been kept constant for the later years. Several widespread problems have also been discovered in the past few years in the Argo data and are known to impact estimates of the global OHC (e. g. Willis et al. , 2007; Barker et al.