dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We have presented the experimental protocol for the APPO-SITE Arctic sea ice predictability multi-model intercomparison, and described the archive of model simulations which contributed to it. The mean state and variability of Arctic sea ice cover in the models were presented and compared to observed estimates. We utilise this database to assess the limit of initial-value Arctic sea ice extent and volume predictability from each of the models, updating the results of  Tietsche et al. (2014)  to include three more models. The results of this analysis of perfect model predictability can be summarised as follows. -The winter sea ice extent is predictable at interannual timescales (or possibly longer timescales) in all models. -There is significant intermodel spread in the timescale at which summer sea ice extent is predictable, with some models not showing any interannual or longer timescale predictability, and others showing significant predictability throughout all months of the 3-year simulations.
, 2015). APPOSITE requested a specific set of variables from participants focused on sea ice analysis, but many other variables have been archived besides. The file and directory naming convention, followed by the archived data set, is very similar to that followed by CMIP5 (http://cmip-pcmdi. llnl. gov/cmip5/output_req. html). APPOSITE required participants to prepare their data files so that they meet the following constraints. -Data files are in netCDF file format and ideally conform to the climate and forecast (CF) metadata convention (outlined on the website http://cf-pcmdi. llnl. gov). In instances where it was not possible to produce fully CF compliant netCDF files, participants were required to follow the CMOR variable naming convention. -There must be only one output variable per file.
Acknowledgements. We would like to thank both reviewers for their thorough and useful comments, which have helped improve the manuscript. This work was supported by the  Natural Environment Research Council  (grant  NE/I029447/1 ). Helge Goessling was supported by a fellowship of the  German Research Foundation  ( DFG grant   GO 2464/1-1 ). Data storage and processing capacity was kindly provided by the  British Atmospheric Data Centre (BADC). Thanks to  Yanjun Jiao  ( CCCma ) for his assistance with the CanCM4 simulations and to  Bill Merryfield  for his comments on a draft of the paper. Edited by:  W. Hazeleger.
Background: When modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks. Results: We present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database. Conclusion: STON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database.
It is widely used in Systems Biology and fills the previous gap of standardized visual representations for biological networks. SBGN is composed of a set of three complementary languages: Process Description (PD), Activity Flow (AF) and Entity Relationship (ER). PD shows "all the molecular processes and interactions taking place between biochemical entities, and their results"  [3]. It creates detailed SBGN maps by representing hierarchical structures and biological complexes. AF "shows only influences such as 'stimulation' and 'inhibition' between the activities displayed by the molecular entities"  [3]. It is the most elementary of the SBGN languages. Finally, ER shows all "influences of entities upon the behaviour of others", ignoring any temporal aspect  [4]. SBGN diagrams are drawn using three sets of standardized glyphs for the three SBGN languages.
SBGN maps are represented in an XML-based format: the SBGN-ML  [5] , which is both human-readable and machine-readable. They can be exported from software tools such as SBGN-ED  [6]  (a VANTED add-on  [7]  with SBGN support) or CellDesigner  [8]. In this work, we consider only the PD and AF languages and propose to store biological networks as graph-oriented models in a graph database. We take existing biological models represented in SBGN-ML files as input and convert them into a graph representation. The novelty of our work lies in the fact that, for the first time, SBGN maps are stored in a structured way and thus can be queried and compared to each other. Several studies show that graphs are realistic and wellsuited for the representation of biological networks  [9, 10]. Employing graph databases to store and explore biological models requires less effort and offers new insights into analyses  [11].
Our recent paper  [12]  describes the efficient storage of computational models in a graph database and informs on the improved interrogation of data relationships when represented as a graph. In that work, we show that it is more efficient to query SBML and CellML models based on their representation as a graph. One of the many interesting possibilities of applying the queries  [13]  is to highlight the important nodes in a network  [14]. The Neo4j graph database  [15]  was chosen for the representation of biological pathways. Neo4j is a freely available, labelled property graph database. The concepts and associations among them are represented as nodes connected by edges (denoted in Neo4j as relationships). The Neo4j nodes and relationships can be categorized using the "Label" and "Type" features, respectively.
Additional information about entities can be stored as attributes (denoted as properties). An example is given in Fig. 1. For access to the graph database, the Neo4j framework provides several APIs (for R, Java, Python programming languages) and integrates a web-based, intuitive graphical user interface. Integration and exploration of data within the database are realized using Cypher, a declarative language proposed by the Neo4j framework  [16]. Neo4j recently became a popular technology in different areas of computational biology  [17]. It is a key technology for model management tasks  [12]  and it was discussed as a mechanism to improve performances in network analysis  [18]. Further studies show that Neo4j performs well on distributed and heterogeneous data when compared to relational databases  [19, 20].
Implementation External libraries: LibSBGN and the Neo4j collection STON is a standalone, Java-based framework that uses two sets of external libraries: LibSBGN  [5]  and a collection of Neo4j libraries. The latest version of STON 1. 2 works Fig. 1  Workflow of STON software. This figure shows the workflow of the STON framework: an SBGN-ML file is provided as the input to the framework. It is parsed by STON and converted into a graph representation using the mapping rules described in the Additional file 1. The resulting data is then stored in a local directory as nodes, relationships and properties. Neo4j relies on this repository and, if run as a web server instance, offers a visualization of the data. The repository can be queried for biological entities, relations in the network, and similar nodes across networks, as described in the "Results" section.
The example is the IFNG receptor, a biological complex composed of four subunits: IFNGR1 and IFNGR2 that are dimerised, and JAK1 and JAK2 that are macromolecules. In Neo4J, all entities are connected to the complex node with the relationship belongs_to_complex with the LibSBGN milestone 2 and the Neo4j Community Edition version 2. 3. 1. The project and the source code are available on sourceforge  [21] , under the GNU General Public License version 2. 0 (GPLv2). LibSBGN provides access to SBGN through reading, writing and validation of SBGN-ML files and supports both, C++ and Java programming languages. The library offers several test files in the three SBGN languages. Test files contain small SBGN networks and are available for tool developers to check for SBGN compliance. The files are created by the developers of LibSBGN whenever new functionality is added to the languages.
The LibSBGN project also provides example pathways to showcase the features of the SBGN specifications  [22]. We use LibSBGN to read SBGN-ML files and for validation. The Neo4j libraries encode functionality for the Neo4j graph development (e. g. , create nodes, relationships and properties), and provide access to the Neo4j database environment, including a web interface to query the data. STON converts existing SBGN PD and SBGN AF files into a Neo4j representation. As input, it takes a SBGN PD or a SBGN AF file, and a path to the Neo4j database location. The STON framework then parses the SBGN-ML file and maps it on a graph structure (see Workflow section below). The graph representation, which is processable by Neo4j, is built according to the given SBGN-ML file; the location of the obtained graph has to be specified by the user (see Fig.
Workflow The representation of biological pathways in graph databases requires translation rules. The SBGN-ML format already describes a graph-like structure: Glyph nodes (representing entities) interact with each other by means of glyph arcs (representing relations). The translation process has been facilitated by the similarity of SBGN-ML and Neo4j. SBGN glyph entities are nodes in Neo4j, and SBGN arcs are relationships. Additional information (e. g. , ID, state variable, unit of information. ) is retrieved as properties for Neo4j nodes and relationships. Figure  1  exemplifies the translation of a small PD map: The SBGN diagram shows a receptor complex composed of four entities: a) the IFNGR1 macromolecule, b) the IFNGR2 macromolecule, c) the JAK1 macromolecule and d) the JAK2 macromolecule.
Data access Neo4j offers different ways to access the stored data. First, a visual interface is provided by the Neo4j web server for human interaction. Users can start with a Cypher query and then continue to traverse the resulting graph and explore the nodes' and relationships' properties. In addition, the web server offers a REST API for programmatic access (at http://$server_name:7474/db/data/ cypher). Using the exchange format JSON, one can send Cypher queries to the server's data endpoint and receive a JSON encoded response (please refer to Additional file 3 for an example). On the developer's side there are also possibilities to connect to the database using an implementation of the Neo4j Bolt driver in a language of choice  [25]. Once the driver established a connection one can use Cypher queries to traverse the database and map the results to objects.
Results STON supports modelers and researchers in their analysis of biological pathways. For the first time, pathways represented in the SBGN PD and AF languages are programmatically queryable. We present three biological applications of the STON framework to exemplify how networks can be represented in the Neo4j graph database and later on be queried using Cypher. The first application is the identification of specific entities and their neighbors in the network. Second, we present a method to combine data represented on PD and AF levels. Third, we describe an approach for identifying common processes found in different networks using the example of the INOS pathway. The map was developed by integrating data from several studies (listed in  [24] ). It is composed of 81 glyphs and 59 arcs and contains most of the graphical elements found in SBGN PD (e.
The query traverses through each node in the graph until it matches the targeted one. The system then retrieves the neighbours. The example network in Fig. 2  shows how IFNG binds the IFNG receptor to form a complex which is then phosphorylated. The Cypher language is flexible and permits to customize queries. For example, users may adjust the depth of the subnetwork they would like to retrieve to explore the neighborhood of an entity. Users may also search for a specific structure, e. g. , proteinreaction-protein structures, which is facilitated by the use of specific Cypher queries.
Linking levels of granularity The linking of different levels of granularity allows researchers to compare biological networks at different levels of detail. In Systems Biology, it is highly beneficial to have access to computational tools that can return detailed information about processes occurring in complex biological networks by connecting information from multiple layers. By using the Neo4j graph database, we are able to link PD and AF diagrams. The purpose is to compare both levels and to help addressing the difficulties related to incomplete knowledge representation. When studying a complex network, for example, we may first be interested in getting an overview of the influences between the biological entities involved (AF level). At a later time, we may want to study the processes occurring in a network in more detail (PD level).
The figure shows different levels of granularity of the iNOS pathway in PD (green background) and AF (blue background). The yellow relationships represent the linking between equivalent nodes. In the PD network, IFNG binds to the IFNG receptor complex. This complex will then activate the dimerisation of STAT1alpha. In the AF network, IFNG and elements of the receptor complex (seen in the PD level) are necessary to activate STAT1alpha. In order to create a link, the compared nodes should have different file names, but same name, nodetype, compartment and unit of information. In addition, one node should be represented in the SBGN PD language and another on SBGN AF complex, which is then phosphorylated and will stimulate the process of STAT1alpha dimerization. At the AF level, the activation of STAT1alpha is represented more simplistically by IFNG and the receptor elements only.
STON manages heterogeneous data at different levels of biological description by integrating i) various types of biological concepts including metabolites, proteins, complexes, genes, subcellular location, and ii) different types of processes such as metabolic reactions, signalling events and gene regulatory machinery. Once represented in a Neo4j database, the networks can be interrogated for different topics of interest using the Cypher query language. Cypher allows for structure-based queries that cannot be answered efficiently on the SBGN-ML file level, nor using SQL databases. We tested the capabilities of STON using a biological reference map from the KEGG pathway database  [27] : the large metabolic pathway (identifier number: ec01100) composed of 3814 nodes and 3633 relationships. The translation and storage of this network with STON takes approximately 10 minutes using an Intel ® Core™ i7-3930K computer at 3.
2GHz and 32GB of RAM (see Additional file 1: Table  S3 , and Additional file 4). The modular implementation of the Neo4j framework allows to further extend STON to support the management of additional data types such as tissue-specific expression levels or drug target information for proteins. Another reason for the easy implementation of these extensions is the adaptability of the Neo4j schema. Similarly, the existing mapping from SBGN-ML into Neo4j can easily be adapted and extended by adding new relationships and node types to the database schema. In the last few years there has been a growing interest in reconstruction of large disease networks. For example, the reconstruction of maps describing Alzheimer's disease (AlzPathway,  [28] ) contains 1070 reactions. The map of Parkinson's disease consists of 2045 entities  [29] , and the Atlas of Cancer Signalling Network has 4826 molecular processes  [30].
Conclusion STON is a framework that exploits the Neo4j graph database to store biological pathways. We mapped the SBGN standard (PD and AF levels) onto a Neo4j structure and we showed how STON enables i) the identification of subnetworks of interest, ii) the comparison of different layers of granularity in SBGN languages and iii) the merge of SBGN diagrams. STON provides new opportunities for managing and querying biological networks, as well as advanced manipulation of subnetworks. This will add to the infrastructure of tools for model management and exploration, which is necessary for efficient use of network approaches in Systems Biology and Systems Medicine.
Funding The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement number  IMI 115446  (eTRIKS), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. VT and DW were also supported by the  BMBF e:Bio SBGN-ED+  project ( FKZ 031 6181 ). RH received funding from the  German Federal Ministry of Education and Research (BMBF)  via grant number  FKZ 031 A540A (de. NBI ).
Optical imaging data The cluster is located in the CFHTLS W1 field, providing u*, g , r , i , and z data. We refer the reader to  Coupon et al. (2009)  for details. The limiting magnitude of these images is u * AB = 26. 3, g AB = 26. 0, r AB = 25. 6, i AB = 25. 4, z AB = 25. 0, and the pixel scale is 0. 187. In addition, we collected our own CFHT/WIRCAM K s data (subject of a future paper). Briefly, they were obtained as 2 × 525 s pointings with each pointing consisting of 21 dithers with 25 s of exposure per dither, for a total exposure time of 1050 s. Data were reduced by the standard TERAPIX pipeline and presented as 1 deg 2 tiles projected astrometrically onto CFHTLS optical images (same pixel scale). The depth is close to K s AB = 22 (5σ level) in a Kron-type aperture. Figure  3  shows the galaxy colour magnitude relation in a 5 square around the XLSSC 116 cluster centre.
The observations were centred on the XLSSC 116 BCG and executed in service mode during the nights of 25-26 June and 20-22 August 2014. We obtained four one-hour observing blocks, each consisting of four exposures separated by 90 • rotation, to average out the patterns of the slicers and channels seen by the detector. The data were reduced following the recipes of the MUSE pipeline, version 0. 18. 2. The June data were taken at a temperature below 7 • , which caused problems with the correct identification of the 10th slice of IFU No. 6. To obviate the problem, we followed the workaround recommended in the MUSE cookbook and used dedicated trace tables. The reduction steps for each individual exposure included bias, flat-field, wavelength and flux calibration, and correction for the telluric absorption lines using standard stars and geometric correction.
Spectroscopy of six other non-CFHTLS sources We scanned the MUSE data searching for localised regions exhibiting emission lines without any counterparts in the CFHTLS object list. We detected such emission lines in six distinct arcsecsize regions (seeing dominated, clearly smaller than the ELDL source). They all show [OII] emission lines at approximately the cluster redshift (see Table  6 ) without being identified with a detected CFHTLS object. They also do not exhibit strong Hβ lines. These sources are much smaller than the ELDL source. Two of these sources have a redshift slightly larger than 0. 54. The other four are more clustered along the line of sight (see Fig. 9 ) and have a velocity dispersion of 233 km s -1 (close to estimates of  Cui et al. 2014 , see their Fig. 10 ). These four sources are probably not all inside the halo of the BCG given their spatial distribution (see Figs.
Conclusions In this article, we studied a cluster detected in the Survey with an apparent very high level of diffuse light. -WIRCAM and MUSE data first allowed us to put the cluster characteristics on a firmer ground. It is a z ∼ 0. 534 cluster of galaxies with old stellar populations and consists of a main component with a velocity dispersion of the order of 600 km s -1 and an infalling low-mass group with a velocity dispersion of 170 km s   2015). -We also detect six other small-scale emission line sources that are not detected in the CFHTLS images, but are at the cluster redshift. Spatially, some of these sources are not located within the BCG halo and are therefore distributed in the cluster halo. -The diffuse light detected in CFHTLS and WIRCAM images near the BCG (region 1) exhibit some emission lines in addition to H&K lines, while the other region (region 2) contains only absorption lines.
Acknowledgements. XXL is an international project based around an XMM Very Large Programme surveying two 25 deg 2 extragalactic fields at a depth of ∼5 × 10 -15 erg/cm 2 /s in the [0. 5-2] keV band for point-like sources. The website is http://irfu. cea. fr/xxl. Multiband information and spectroscopic follow-up of the X-ray sources are obtained through a number of survey programmes, summarised at http://xxlmultiwave. pbworks. com/. Based on observations obtained with  MegaPrime/MegaCam , a joint project of  CFHT  and  CEA/IRFU , at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
This work is based in part on data products produced at TERAPIX available at the  Canadian Astronomy Data Centre  as part of the  Canada-France-Hawaii Telescope Legacy Survey , a collaborative project of  NRC  and  CNRS. This research has made use of the VizieR catalogue access tool,  CDS, Strasbourg, France. This research has also made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. Based on observations obtained with  XMM-Newton , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and  NASA. Based on observations made with the WHT telescope, operated on the island of  La Palma  by the  Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofsica de Canarias.
Controlling the Consistency of Your Encoding The preparation of a critical edition involves many sessions of meticulous proofreading, especially to check the accuracy of the apparatus. If the Critical Apparatus Toolbox cannot replace the careful eye of the editor, it oers an ecient way to control the consistency of the encoding by detecting small inevitable mistakes, like a typo in the list of sigla or the failure to record the reading of a particular witness in an apparatus entry. To perform those controls, the Critical Apparatus Toolbox will scan the <teiHeader> and <front> sections of the TEI le for a <listWit>, and nd all the sigla of the witnesses. Then, it will compare this list to the manuscripts appearing in the @wit attribute of <lem> and <rdg> elements. The nature of the controls will depend on the type of apparatus used in the edition.