dataesr/scientific-paragraphs-categorization huggingface dataset:
A free version is available for personal, school, and nonprofit organization use. Highcharts offers a large panel of charts such as line, area, bubble, box plot, error bars, spline, areaspline, column, bar, and pie. The graphics are rendered using SVG, VML, or the Canvas tag from HTML5 standard which is supported by modern web browsers such as Chrome 38+, Firefox 33+, or Internet Explorer 10+. Highcharts graphics are also supported by mobile devices such as iPhone/iPad or android smartphones and tablets. Highcharts is widely used in several industry domains to display interactive graphics included into web dashboards. Highcharts is also used for Big Data visualization and the authors present an example dealing with 1. 7 million dots into a single figure including zooming capacities  [96]. In addition, in combination with Asynchronous JavaScript and XML (AJAX) technology, the graphics can be updated in real time with data from the server or users.
A second impressive professional JavaScript graphic library is D3 Data-Driven Documents [109] also called D3. js. This graphic web library takes advantage of the capacities of modern browsers to allow powerful interactive visualization. D3. js is freely available under the terms of the BSD 3 license  [75]. D3. js's growing popularity has led to numerous documented examples. To deal with large data, D3. js is usually combined with other tools such as Crossfilter  [89] , a JavaScript library that can explore large multivariate datasets in a web browser, or Zoomdata platform  [110] , a solution for fast analysis and visualization of Big Data. In addition, some BioJS components (Biojs. DNAContentViewer, Biojs. HeatmapViewer) include D3. js for advanced graphics and interactivity. Another use of D3.
Today, numerous teams, private or academic, are working on improving NGS data analysis. Biomedical research and NGS data management analysis follow a similar evolution as other industries before. Indeed, since the end of the 1990s, the difficulty of access, manipulation, and analysis of large amount of heterogeneous data has been addressed by different IT companies from the web and BI fields, developing NoSQL databases, high performance analysis tools and Big Data visualization solutions. That is the reason why all NGS research actors have to take these IT developments into account. They have proven their efficiency and demonstrated their efficacy in solving the problems that biomedical research is facing (integration of data, sharing of data, etc. ), since these problems have been already solved in other fields.
A next-generation sequencing solution to manage NGS data would have to meet the following requirements: (i) fast access to data through database systems such as NoSQL, (ii) a high performance analysis tool for instantaneous access to results, such as real-time data analysis solutions, and (iii) efficient and interactive Big Data-oriented visualization capacities through web interfaces, such as JavaScript graphic libraries recently developed for web and BI. An illustration of the interest that Life scientists should pay to solutions from major IT companies, is the recent proposal by Google to provide access to a beta version of an "API to store, process, explore, and share DNA sequence reads, " called Google Genomics  [112]. It seems obvious that, in future years, this type of solutions will be a core component for bioinformatics analysis of NGS and Life Science Big Data.
Glossary Data Warehouse. A data warehouse refers to a database used to integrate data from several sources. In a company, data warehouses are usually used to create reports for decision making process. Datamart. A data mart is a subset database of a data warehouse that is usually used for a specific need. Hashmap. In computing, a hash map (hash table) is a data structure used to implement an associative array, a structure that can map keys to values. NGS. Next-generation sequencing; this term has been used for the last 10 years to refer to the current methods of sequencing by synthesis: Solid, Illumina/Solexa, and Pyrosequencing. It is also used to refer to the methods in development: PacBio, Nanopore, and so forth. Relational Databases. A relational database is a structured database according a relational model  [113]  to establish relationships between different data it contains.
Acknowledgments The authors would like to thank our colleagues of the  ATRAGENE, ISOFT, University Paris Diderot, INTS, CNRS, University Paris Sud , and INSERM for their precious advice on this paper. This work was supported by grants from the  Ministry of Research (France) ,  University Paris Diderot, Sorbonne, Paris Cité (France) ,  National Institute for Blood Transfusion (INTS, France) ,  National Institute for Health and Medical Research (INSERM, France)  and labex GR-Ex to AdB,  University Paris Sud  and  CNRS  to CF,  INRA , and  AgroParisTech  to CE, ISOFT to  Jean-Philippe Meyniel , and  ATRAGENE  to  Alain Malpertuy. The labex  GR-Ex  reference  ANR-11-LABX-0051  is funded by the program " Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02.
[71]nted document. This category is based on the key/value paradigm. The value in this case, is a JSON or XML document type. The advantage is to be able to quickly recover via a single key, a set of hierarchically structured types of information. The most popular solutions are Apache CouchDB[71], RavenDB (Hibernating Rhinos Ltd, (Israel)), and MongoDB [72]. (iv) Graph database. This data representation model is based on graph theory. It is based on the notion of nodes, relationships, and properties attached to them. This model facilitates the representation of the real world, which makes it suitable for processing data in social networks. The main Graph NoSQL database solutions are InfiniteGraph (Objectivity, Inc. (USA)) and Neo4j (Neo Technology, Inc. (USA)). The best known solutions are Redis (Citrusbyte LLC (USA)), Riak (Basho Tech- nologies Inc.
(USA)), and Voldemort [68] created by LinkedIn. (ii) Column-oriented. This category can be viewed simply as a table in a relational database system but with a NoSQL column-oriented database, where the number of columns is dynamic. Indeed, in a relational table, the number of columns is fixed in the model and that number is the same for all records in the table. On the contrary, in a NoSQL database, the number of columns may vary from one record to another, which avoids columns with NULL values. The column- [70]nted NoSQL database solutions are HBase[69]an open Source implementation of the model published by Google BigTable[69]and Apache Cassandra[70], a project that respects the distributed architecture of Amazon DynamoDB (Amazon Inc. (USA)) and Google's BigTable model. (iii).
Introduction: The Third National Alzheimer Plan (2008)(2009)(2010)(2011)(2012) was a major public health initiative in France that included €200 million of funding for research in Alzheimer disease and related disorders (AD). The aim of this study was to document trends in French academic output in AD following the implementation of the plan. Methods: Academic output (i. e. , number of original articles) and scientific impact (i. e. , article citations) of French research in AD were obtained from the Web of Knowledge core collection database. Analyses compared the 5-year period immediately before (2004-2008) and after (2009-2013)  initiation of the plan. Comparisons were made with stroke, Parkinson disease, AIDS, and diabetes in the 14 leading countries worldwide and regionally within France.
Methods The Alzheimer Plan Foundation A non-profit private foundation was created in June 2008 by the French Ministry of High Education and Research to coordinate and implement the research measures of the Third National Alzheimer Plan. The Foundation connects multiple governmental funding sources, such as the ANR and the PHRC, to support research projects on basic, clinical, or social research and also plays the part of a think tank to encourage public and private financial support. Five major pharmaceutical companies operating in France (AstraZeneca, Merck Sharp & Dohme, Servier, Sanofi, and Ipsen) provided some financial support as a permanent guarantee. These private founders were members of the board of directors but had no input on the scientific options.
Bibliometric analyses To evaluate the international and national impact of the Third French Alzheimer Plan, bibliometric analyses were performed in July 2014 to examine the production of original scientific articles from France in comparison with other countries and diseases other than AD. The analyses compared the 5-year period immediately before the plan (2004-2008) with the 5 years after initiation of the plan (2009-2013) and were conducted using the Thomson Reuters Web of Knowledge core collection database. All analyses included only full articles in peer-reviewed journals; other forms of publication were excluded (i. e. , proceedings papers, reviews, abstracts, obituaries, corrections, editorials).
Volume of French scientific research output in Alzheimer disease To evaluate if the increase of scientific research output in AD in France was specific and not due to a global increase in research activity, we compared the change in scientific output between the periods 2004-2008 and 2009-2013 in the most productive countries and compared output in AD with that in stroke, Parkinson disease, HIV, and diabetes. Figure  2a  shows that global scientific output across all research fields in the Web of Science database increased in all countries, with an overall 39 % increase between the periods 2004-2008 and 2009-2013 (Table  2 ). The United States was by far the largest contributor of scientific articles in both periods, but its relative global contribution fell from 30 % to 27 % of total articles, reflecting the dramatic rise in the number of articles produced in countries such as China, India, South Korea, and Australia.
Introduction Data standardisation efforts can trigger ambivalent and often polarised reactions. Already when reading the normal scientific literature, experiments are described in a rather heterogeneous way with different levels of detail, or ambiguous and sometimes underspecified concepts such as ''replicate'', where the true meaning is often buried in traditions specific to human/plant or bacterial research disciplines. With biological assays increasingly represented in digital form, biology has become a data-intensive field of disparate methods, with images, sequence reads and spectra, to name only a few, all being acquired by the droves. Modern scientists and data managers are therefore faced with the tremendous challenge of handling, preserving and archiving large amounts of data. Metabolomics is no exception: PubMed returns 2460 hits for the search terms ''metabolomics or metabonomics'' from the year 2014 alone.
Yet, only a tiny fraction of the data from this scientific output has been made available to the scientific community, data-miners and so-called datawranglers through public repositories. In recent years, the notion of FAIR (Findable, Accessible, Interoperable and Reusable) research data objects has been endorsed by an increasing number of researchers and organisations, including the Dutch Techcenter for Life Sciences (DTL, http://www. dtls. nl/) and the FORCE11 (https://www. force11. org) or the Data FAIRport (http://datafairport. org/) initiatives. Data standards help to make data FAIR and contribute to the Open Access philosophy. Furthermore, in the wake of recent scientific malpractice scandals, see  (Fang et al. 2012) ,  (Obokata et al. 2014) ,  (Editorial 2014) ,  (Stern et al. 2014 ) and news on the consequences, 1  and in general the growing concern over the rise in paper retractions, 2 governments and funding agencies are increasingly mandating reproducible research and the release 3 and long-term archival of raw data with guaranteed rights to assess, review and appraise claims.
These in turn facilitate: (1) the broad support in Open Source research software and consequently (2) the adoption of mzML by major data repositories such as MetaboLights  (Haug et al. 2013 ) and PRIDE  (Jones et al. 2006) , which both encourage or even enforce data deposition in vendor independent (non-proprietary) formats. The mzML schema is generic enough to even support imaging mass spectrometry  (Schramm et al. 2012). The imzML format includes the required controlled vocabulary and optimised data layout, but can be interconverted to ''standard'' mzML without information loss  (Race et al. 2012). The optimized imzML is supported by both commercial and Open Source software, e. g. the Matlab-based MSiReader  (Robichaud et al. 2013)  or the R-Bioconductor based Cardinal package  (Bemis et al.
1. An early conversion is necessary if vendor agnostic or open source data analysis tools are to be used. The reason that only a few open tools support proprietary formats is the added development effort and time required to enable import of these formats and keep them up to date. Usually, the vendors provide software libraries to access their own formats. The downside is that these often have rather complex application programming interfaces (APIs), and worse, each vendor has their own proprietary API. Currently, most of these interfaces require Windows dynamic link libraries (DLLs) for the actual file access, which are not compatible with other operating systems such as MacOSX or Linux. The second reason to convert the vendor files is that the open formats can later be read by anyone, anywhere.
2012) , which is one of the reference implementations for mzML. It can convert to mzML from Sciex, Bruker, Thermo, Agilent, Shimadzu, Waters and also the earlier file formats like mzData or mzXML and is consequently widely used. As the developers do not have access to all available instruments, support for the latest might take a while to implement, and in some cases the vendor-provided DLLs do not allow access to all features of the instrument. Although Proteowizard was initially targeting LC/MS data, it can also readily convert GC/MS data for example from the Waters GCT Premier or Agilent instruments. The other main route to mzML formatted data is by using vendor supplied converters where available, such as the Bruker CompassXport, 10  AB SCIEX\MS Data Converter 11  or in case of GC/MS for example the LECO ChromaTOF-HRT software.
Only few vendor supplied converters are freely available and some require a commercial license. The wider community has to maintain constant pressure on all vendors to implement full access to our data in open formats. In the end, we are all their customers. An important aspect is that metabolomics studies might comprise many raw data files, so the conversion from the vendor formats should not involve expensive manual intervention to add information beyond what is already stored in the instrument software. Furthermore, command line converters are easier to incorporate into local data processing pipelines. For bioinformaticians developing either software or databases, it is highly recommended to use existing I/O parsing software and libraries. Several such mzML libraries have been developed for different programming languages and software frameworks, summarised in Table  1.
Formats for standardised metadata capture More structured (digital) schemata have been proposed, including elaborate XML schema definitions (XSDs) or database models like ArMet  (Jenkins et al. 2004)  or SetupX  (Scholz and Fiehn 2007) , but also lightweight spreadsheet templates  (Fernie et al. 2011). A nice summary of community accepted minimal information was presented in a recent editorial  (Goodacre 2014). Although the benefits of standard compliant reporting is undeniable, adoption is hampered by what is often viewed as a steep learning curve that can be time consuming for first time users. One remedy is to provide efficient software tools that integrate better with experimental workflows and provide configuration templates-sets of pre-defined attributes for different sample types used to capture metadata.
For identified spectral features, the metabolite information includes the name, external database identifiers, formula, and chemical structure as a SMILES or an InChI string. ISAcreator (Rocca-Serra et al. 2010) is a standalone, Java-based, platform-independent desktop application with a range of facilities to enable standards-compliant creation of ISA-Tab archives. The software enables ontology searches and term lookup with a great deal of flexibility for capturing metadata at various stages of the experimental workflow. Large portions of the data types, the actual Study layout, label descriptions, column names and recommended ontologies, are specified through a set of ISA configurations created with the ISAconfigurator. Several configurations exist for specific assay technologies, such as gene expression analysis, flow cytometry and different assay types in metabolomics.
With these configurations, it is also possible to validate the metadata to ensure whether it complies with available 'Metabolomics Standards Initiative' (MSI) reporting recommendations. The ISAcreator metabolomics plugin developed at the EMBL-EBI captures the metabolites measured, with their quantification as described above. As mentioned earlier, a factor that contributed to the widespread adoption of raw data standards was the support shown by vendors of MS instruments and the incorporation of the standards into their software. Similarly, incorporating the study design and experimental metadata standards into data processing and data management software promotes adoption of standards. The addition of standards into data management software, however, is not straightforward. This is because software such as Laboratory Information Management Systems (LIMS) and Electronic Lab Notebooks (ELN) are usually designed to be, and marketed as, generic products adaptable to a wide range of scenarios.
An example of such crosswalk in the case of metabolomics is the eXtensible Experiment Markup Language (XEML). The XEML-Lab  (Hannemann et al. 2009 ) (https://github. com/cbib/XEML-Lab) is an XML-based framework for designing and documenting experiments in an intuitive yet machine readable format, and to link experimental metadata with any type of data generated in the corresponding experiments, and ultimately, to make both metadata and data available for data mining. XEML descriptions are used in both the Golm Metabolome database  (Hummel et al. 2007; Kopka et al. 2005 ) (GMD, http://gmd. mpimp-golm. mpg. de) and the PLATO database (https://plato. codeplex. com) at INRA Bordeaux, which is a micro plate processing pipeline that supports enzyme activities and metabolite assays. The crosswalk is implemented in the XEML-Lab software, which can load experiments from these databases and export to ISA-Tab.
If required, information that is missing can be added from within the XEML-Lab software. Other academic efforts also demonstrated the feasibility to export experimental data via metadata conversion to the ISA-Tab format as shown by the MASTR-MS LIMS solution. 15,16 Another example for the export of metabolomics data into standard formats is the very positive interaction with software vendors such as Biocrates AG (PRS, personal communication), showing that standard compliance does not have to be taxing for the users. While such metadata crosswalks are essential, they are also labour intensive to develop and maintain. The mapping of schemes with fewer elements (less granularity) to those with more elements (more granularity) can be problematic.
Standards need not be a hindrance, but should be perceived and understood as vehicles to increased trust, secondary usage and higher visibility of scientific output. Reused data is useful data and is data that gets cited  (Piwowar et al. 2007 ). Standards compliance is just another standard operating procedure applied to the dissemination of the research output. This alternative approach is shown in Fig. 1b , where the whole experiment is driven by standards compliant results generation, here demonstrated using the ISA-Tab terms and concepts. While it may sound trivial, creating a crisp title and short description of the Investigation as part of the ISA-Tab metadata helps focus on the question at hand. It is also beneficial if the institute or laboratory has established short guidelines on naming and the directory hierarchy.
The ISA-Tab files now contain all information up to the data processing and analysis steps. Several data processing environments can take advantage of the annotation in ISA-Tab archives, for example the Galaxy workflow system  (Goecks et al. 2010 ) and the R/Bioconductor framework  (Gentleman et al. 2004 ). The R environment allows workflows to be written that combine the Risa (Gonzalez-Beltran et al. 2014) and xcms  (Smith et al. 2006 ) packages, and the creation for example, of routine Quality Control reports for the whole experiment, or after further processing statistics and visualisations. The MetaDB  (Franceschi et al. 2014 ) is a database and web application that provides a data processing workflow for untargeted MS-based metabolomics experiments with the incremental addition of ISA-Tab data as a core concept.
Grant applications should thus include data management costs just like laboratory consumables. On the bright side, publishers are playing an increasing role to reward scientists for their efforts in planning, producing and sharing datasets for the benefit of the scientific community. Datasets (and what are increasingly known as research objects) are being made citable and reusable, whose producers can be clearly identified, for instance by means of ORCID, which allows unambiguous tracking of persons and organisations. It has been shown that articles for which the data has been made available have increased citation rates  (Piwowar et al. 2007). Nature Publishing Group's Scientific Data and BiomedCentral's Gigascience are what is known as 'data journals'. These publications allow researchers to release their data and thereby provide the means for proper scholarly dissemination of their work via modern means, and without the need for a ground-breaking biological advance.
This also has the added benefit of countering publication bias, where only positive results are published. Both journals support ISA-Tab format for structuring and releasing experimental metadata and issue DOIs for the data sets. Other journals such as f1000Research publish ''Data Notes'', and more publishers are currently updating their data policies. Table  3  provides some examples for journal data deposition policies. A regularly updated list of journal research data policies is being compiled by the BioSharing Information Resource initiative 17 in collaboration with a JISC pilot initiative. 18 In BioSharing these will be cross-linked to the standards and databases, enabling access and crosssearch of the information, on which a variety of stakeholders can base their decisions. Specifically, journals, researchers and funders will be able to recommend or select mature and community endorsed databases and standards, and developers and curators of repositories and content standards will be aware of the requirements they need to meet to ensure their products are discoverable and well described so that they can be used by researchers or recommended by journals and funders.