{"ID":78043,"post_author":"2","post_date":"2018-12-14 13:14:46","post_date_gmt":"0000-00-00 00:00:00","post_content":"","post_title":"LIMSjournal - Summer 2017","post_excerpt":"","post_status":"draft","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"","to_ping":"","pinged":"","post_modified":"2018-12-14 13:14:46","post_modified_gmt":"2018-12-14 18:14:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.limsforum.com\/?post_type=ebook&p=78043","menu_order":0,"post_type":"ebook","post_mime_type":"","comment_count":"0","filter":"","_ebook_metadata":{"enabled":"on","private":"0","guid":"4D98F986-C5BB-422D-B305-9457835D59AB","title":"LIMSjournal - Summer 2017","subtitle":"Volume 3, Issue 2","cover_theme":"nico_21","cover_image":"https:\/\/www.limsforum.com\/wp-content\/plugins\/rdp-ebook-builder\/pl\/cover.php?cover_style=nico_21&subtitle=Volume+3%2C+Issue+2&editor=Shawn+Douglas&title=LIMSjournal+-+Summer+2017&title_image=https%3A%2F%2Fs3.limsforum.com%2Fwww.limsforum.com%2Fwp-content%2Fuploads%2FFig3_DAnca_NatHazEarth2017_17-2.png&publisher=LabLynx+Press","editor":"Shawn Douglas","publisher":"LabLynx Press","author_id":"26","image_url":"","items":{"bce85c098ea6958c92b6dcce94e42565_type":"article","bce85c098ea6958c92b6dcce94e42565_title":"Neuroimaging, genetics, and clinical data sharing in Python using the CubicWeb framework (Grigis et al. 2017)","bce85c098ea6958c92b6dcce94e42565_url":"https:\/\/www.limswiki.org\/index.php\/Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework","bce85c098ea6958c92b6dcce94e42565_plaintext":"\n\n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\n\t\t\t\tJournal:Neuroimaging, genetics, and clinical data sharing in Python using the CubicWeb framework\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\tFrom LIMSWiki\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tJump to: navigation, search\n\n\t\t\t\t\t\n\t\t\t\t\tFull article title\n \nNeuroimaging, genetics, and clinical data sharing in Python using the CubicWeb frameworkJournal\n \nFrontiers in NeuroinformaticsAuthor(s)\n \nGrigis, Antoine; Goyard, David; Cherbonnier, Robin; Gareau, Thomas; Papadopoulos Orfanos, Dimitri;\r\nChauvat, Nicolas; Di Mascio, Adrien; Schumann, Gunter; Spooren, Will; Murphy, Declan; Frouin, VincentAuthor affiliation(s)\n \nUniversit\u00e9 Paris-Saclay, Logilab, King\u2019s College London, F. Hoffmann-La Roche PharmaceuticalsPrimary contact\n \nEmail: antoine dot grigis at cea dot frEditors\n \nMarcus, DanielYear published\n \n2017Volume and issue\n \n11Page(s)\n \n18DOI\n \n10.3389\/fninf.2017.00018ISSN\n \n1662-5196Distribution license\n \nCreative Commons Attribution 4.0 InternationalWebsite\n \nhttp:\/\/journal.frontiersin.org\/article\/10.3389\/fninf.2017.00018\/fullDownload\n \nhttp:\/\/journal.frontiersin.org\/article\/10.3389\/fninf.2017.00018\/pdf (PDF)\n\nContents\n\n1 Abstract \n2 Introduction \n3 Materials and methods \n\n3.1 CubicWeb overview \n3.2 Structured data upload service \n3.3 Collaborative quality control service \n3.4 Publication service \n\n3.4.1 A dedicated structure for imaging genomics questionnaire data \n3.4.2 Efficient data selection and download tool: The data shopping cart mechanism \n3.4.3 The transfer of the shopping cart content: Data download \n3.4.4 Access rights mechanism \n3.4.5 The unified insertion procedure \n\n\n3.5 A transverse Python module to remotely connect a CubicWeb DSS \n\n\n4 Results \n5 Discussion and conclusion \n\n5.1 Lightweight solution for data sharing \n5.2 A PIx Swiss knife \n5.3 Future directions \n\n\n6 Author contributions \n7 Conflict of interest statement \n8 Acknowledgments \n9 Supplementary material \n10 References \n11 Notes \n\n\n\nAbstract \nIn neurosciences or psychiatry, the emergence of large multi-center population imaging studies raises numerous technological challenges. From distributed data collection, across different institutions and countries, to final data publication service, one must handle the massive, heterogeneous, and complex data from genetics, imaging, demographics, or clinical scores. These data must be both efficiently obtained and downloadable. We present a Python solution, based on the CubicWeb open-source semantic framework, aimed at building population imaging study repositories. In addition, we focus on the tools developed around this framework to overcome the challenges associated with data sharing and collaborative requirements. We describe a set of three highly adaptive web services that transform the CubicWeb framework into a (1) multi-center upload platform, (2) collaborative quality assessment platform, and (3) publication platform endowed with massive-download capabilities. Two major European projects, IMAGEN and EU-AIMS, are currently supported by the described framework. We also present a Python package that enables end users to remotely query neuroimaging, genetics, and clinical data from scripts.\nKeywords: web service, data sharing, database, neuroimaging, genetics, medical informatics, Python\n\nIntroduction \nHealth research strategies using neuroimaging have shifted in recent years: the focus has moved from patient care only, to a combination of patient care and prevention. In the case of neurodegenerative and psychiatric diseases, this drives the creation of increasingly numerous massive imaging studies, also known as population imaging (PI) surveys.[1][2] It should be noticed that PI studies no longer consist of image data only. The recent wide availability of high-throughput genomics has augmented the subject data with genetics, epigenetics, and functional genomics. Likewise, the standardization of personality, demographics, and deficit tests in psychiatry facilitates the acquisition of clinical\/behavioral records to enrich the subject data in large population studies. Moreover, PI studies now classically encompass more than one single imaging session per subject and cover multiple-time point heterogeneous experiments. Ultimately, these studies with complex imaging and extended data (PIx) require multi-center acquisitions to build a large target population.\nA regular PIx infrastructure has to cover the following three main topics: (1) data collection, (2) quality control (QC) with data processing, and (3) data indexing and publication with controlled data sharing mechanisms. Furthermore, PIx infrastructures must evolve during the life cycle of a population imaging project, and they must also be resilient to extreme evolutions of the data content and management. In the projects we manage, we experience several extreme evolutions. The first kind of evolution may affect the published dataset such as adding a new modality for all subjects, a new time point or a new subcohort. Second, the amount of data requested evolves dramatically as the project consortium gets enlarged.[3] Finally, internal ontologies have to evolve constantly in order to match the ongoing initiatives on interoperability.[4][5]\nSeveral existing open-source frameworks support one or several of the described topics, sometimes only for one specific data type. We propose in the following a brief overview of existing systems. Some of these systems have also been reviewed by Nichols and Pohl.[6] IDA[7] is a neuroimaging data repository and management system that supports data collection (topic one) and data sharing (topic three). With this system, the published datasets can be searched using automatically extracted metadata. The XNAT framework[8] is widely used for neuroimaging data and supports all the PIx infrastructure topics, focusing on tools to pipeline, and to audit the processing of image data (topic two). The LORIS[9] and NiDB[10] frameworks represent a significant effort to account for multimodal data involved in PIx studies. These frameworks, although addressing all the required topics, mainly support neuroimaging data. Openclinica[11] and REDCap[12] facilitate the collection of electronic data such as eCRF or questionnaires and are recognized in projects of various sizes that support data collection (topic one). Likewise, laboratory information management systems were developed for the collection of genomic measurements such as SIMBioMS.[13] Finally, the COINS framework brings essential tools for multimodal data support and, more interestingly, emphasizes the importance of providing sharing tools (topics one and three).[14]\nThe two European studies we manage require a tailored PIx infrastructure. Existing frameworks neither completely handle the diversity of our PIx requirements and project life cycle nor provide efficient tools to collect, check the quality of, and publish evolving data. Additional developments were required for building such complete infrastructure. We based these developments on a more general framework than the dedicated applications described above. In collaboration with Logilab company (Logilab SA, Paris, France), we developed three highly adaptive web services, based on the CubicWeb (CW) pure-Python framework, aimed at creating a (1) multi-center upload platform, (2) collaborative quality assessment platform, and (3) publication platform with massive-download features.[15] These developments were originally instituted for IMAGEN and EU-AIMS projects in order to host their data about mental health in adolescents[16] and autism[17], respectively. The corresponding studies require key features such as upload\/browse published data from the web, dynamic selection and filtering of displayed data, support for flexible download operations, high-level request language, multilevel access rights, remote data access, remote user access rights management, collaborative QC, and interoperability.\n\nMaterials and methods \nThe three services described in the introduction were handled in distinct developments. The next sub-section presents the CW framework capabilities, followed by introductions in the second and fourth sub-sections to the upload and publication web services through which the tailored requirements of PIx studies are satisfied. Furthermore, we describe in the third sub-section a collaborative rating web service that helps users to assess the data quality, and in the final sub-section a Python API that remotely queries these web services.\n\nCubicWeb overview \nAll the implemented services are based on the CW framework.[15] We choose a high level pure-Python framework that bridges web technologies and database engines. This choice was also based on the expertise and experience of people from our laboratory and a tight collaboration with Logilab.[18][19] CW distribution is organized in a core part and a set of basic Python modules, referred to as cubes, which can be used to efficiently generate web applications. The core of the CW framework, developed under the LGPL license, is constructed from well-established technologies (SQL, Python, web technologies such as HTML5 and Javascript). The main characteristics of the CW framework are given as follows:\n\n1. CW defines its data model with Python classes and automatically generates the underlying database structure.\n2. The queries are expressed with the RQL language, which is similar to W3C\u2019s SPARQL.[20] All the persistent data are retrieved and modified using this language.\n3. CW implements a mechanism that exposes information in several ways, referred to as views. This mechanism implements the classical model-view-controller software architecture pattern. Defined in Python, the views are applied to query results and can produce HTML pages and\/or trigger external processes. The separation of queries and views offers major advantages: first, the same data selection may have several web representations, and second, retrieved data can be exported in several other formats without modifying the underlying data storage.\n4. All the views and triggers are recorded in a registry and are automatically selected depending on the current context, which is inferred from the type of data returned by the RQL.\n5. Thanks to the semantic nature of CW, all developments inherit the possibility to follow existing or emerging ontologies, thereby facilitating sharing, access, and processing.\n6. CW has a security system that grants fine-grained access to the data. This system is similar to the row-level security and policies available in the most recent versions of PostgreSQL, and links access rights to entities\/relations in the schema. Each entity type has a set of attributes and relations, and permissions that define who can add, read, update, or delete such an entity and associated relations.\n7. CW may run either as a standalone application or behind an Apache front server. We refer to both settings as a data sharing service (DSS) (cf. Figure 1).\n8. CW can be configured to run with various database engines. For the best performance, PostgreSQL is recommended.\n\r\n\n\n\n\n\n\n\n\n\n\n Figure 1. Architecture of a CubicWeb data sharing service (DSS) integrated in an Apache platform with LDAP. The business logic cubes provide a schema that can be instantiated in the database management system (DBMS: red puzzle piece). The system cubes ensure low-level system interactions (green puzzle piece), and the application cube proposes a web user interface (blue puzzle piece). End users access the database content through a web browser, a Python API scripting the DSS or an FTP solution, where virtual folders (acting as filters on the central repository) are proposed for download.\n\n\n\nStarting from the basic CW distributions, our suite of services is composed of an assembly of Python modules, also referred to as cubes. The Python language is widely used in scientific communities and facilitates interfacing with major or emerging processing tools such as Nipype[21], Biopython[22], Nilearn[23], and Morphologist.[24] Application cubes, built over system cubes, and business logic cubes can be distinguished. The system cubes ensure interactions with the operating system and middleware. For example, they connect to LDAP for user credentials and information or invoke FUSE[25] as a module to construct virtual file systems in a user repository for downloading. The business logic cubes essentially provide the database schema and the application cubes define the access rights and the web interface.\nAmong the available Python-based frameworks, we chose CW. A major advantage of CW is the RQL language which brings end users a query interface adapted for PIx data sharing. It simplifies and improves the user experience in searching for custom datasets. RQL also avoids the use of a complicated object relational mapper (ORM), is focused on browsing relations, and allows requesting several DSS at once. The semantic nature of this request language requires the user to know only about the used data model defined as a graph (nothing about the underlying low-level relational model). This data model simplification and the expressiveness of RQL help users writing custom requests, while most of existing DSS do not expose a query language but offer a limited predefined number of operations that can be carefully designed to be efficient (e.g., RESTful APIs). Criticisms against systems exposing a query request language to the end users emphasize a risk of denial of service. To avoid this issue (i.e., overloading the server with arbitrary complex requests), CW allows limitation of usable resources (RAM per request, CPU per request, number of requests per user, CPU time per request). We believe that users should be able to select and download only what they specifically need using a query request language. This avoids filtering the data locally and saves the bandwidth.\n\nStructured data upload service \nIn PIx studies, massive and complex data are gathered from multiple data acquisition centers or devices (topic one). Each collected dataset must be mapped with definitions that follow consensus representation rules. Those definitions are grouped in data dictionaries that ideally follow standards[26], but they are mainly manufacturer- and\/or site-specific. Thus, an efficient and versatile tool is required for mapping the different data dictionaries during the collection process.\nLeveraging those ideas, we implemented a flexible upload mechanism, a system cube named rql_upload[27] and provided a web frontend by integrating this cube with the application cube named PIWS[28] (Population Imaging Web Service, cf. Figure 1). Based on a CW feature that allows database completion through online HTML forms, these two cubes were developed to collect, in a DSS, both raw data and metadata. CW also enables the customization of triggers that determine the integrity of the uploaded data: synchronous and asynchronous validation filters can be specified and applied to each upload dataset. The upload proceeds as follows (cf. Figure 2):\n\n1. Synchronous validations are applied to each form field (e.g., to check the extension of a file or the structure of an Excel table). If the validation filtering fails, then the web form is refreshed and an adapted feedback is displayed.\n2. After synchronous validation, all the uploaded raw data\/metadata are stored in generic entities and a \"Quarantine\" status is set. To avoid cluttering of the database and to ease file manipulation, files are stored in the central repository but remain accessible through the database. File hashes are automatically computed and indexed in order to assess data integrity.\n3. To update the upload status from \"Quarantine\" to \"Rejected\"\/\"Validated,\" automatic asynchronous validations can be configured in the service as looping tasks. Those validation filters are project and\/or data and\/or upload specific and generate adapted feedback for users and data managers.\n\r\n\n\n\n\n\n\n\n\n\n\n Figure 2. Illustration of the upload process. The (A) syntax of a form description JSON file, (B) corresponding web form as presented to users (here an error message returned by synchronous validation is displayed in the top red box), (C) \u201cQuarantine\u201d status, and (D) \u201cValidated\u201d status (obtained after asynchronous validation) as displayed to users: note that no feedback is shown here.\n\n\n\nMoreover, any entity or relation may be endowed with access permission rules.[15] Based on the CW security mechanisms, a customized security model was implemented for our upload DSS (it can be extended later). Only specific groups have the authorization to upload, and users can only access the uploads, which they are interested in. The customization of these core features allowed the creation of an upload web service that is completely described in a single JSON file. This file links the web form fields with customized or CW-internal controllers that manage the type of data to be collected.\n\nCollaborative quality control service \nOwing to the large amount of data gathered\/analyzed in PIx studies, we must consider more sophisticated operating procedures than simple quality controls (QCs), where datasets are usually only rated once by a handful of individuals. This issue can be addressed by implementing a web-based collaborative quality control process that will also remove the bias introduced by isolated raters (topic two). Moreover, for the studies we manage, we also added controlled vocabulary description to the ratings.\nWe achieve these goals by implementing a flexible collaborative rating mechanism, i.e., an application cube named zeijemol.[29] As in the previous sub-section, a collaborative quality control DSS is entirely described in a single JSON file. This file consists, on the one hand, of the list of elements that will be rated (e.g., a Nifti image, a FreeSurfer segmentation, or a motion curve in a diffusion sequence of an individual) and, on the other hand, related quality indicators (e.g., binary good\/bad, controlled vocabulary, scaled rating). Each element is displayed by one of the embedded viewers such as triplanar view or mesh rendering (cf. Figure 3). The QC results are stored directly in the database.\n\r\n\n\n\n\n\n\n\n\n\n\n Figure 3. The collaborative quality control web service of a FreeSurfer segmentation element of one subject. (A) the quality indicators (in this case, a controlled vocabulary with an accept\/prescribe manual edit\/reject decision and an optional check-box justification), (B) a triplanar view of the white and pial surfaces overlayed on the anatomical image, and (C) the white and pial meshes with statistical indicators.\n\n\n\nThe emergence of such DSS will allow machine learning techniques to learn new classifiers to automatized the quality control task. The QC scores may also be directly used as prior knowledge during the analysis stage.\n\nPublication service \nIn PIx studies, data collection and QC are followed by data anonymization, ordering, and analysis. Ultimately, data are made available to the acquisition partners or the scientific community (topic three). While browsing the database content through the web interface, users expect to be able to download the displayed files as well as the data description and rich links between the data, also referred to as metadata. An intuitive and reliable sharing mechanism is therefore crucial as large amounts of heterogeneous evolving data must be provided. Furthermore, for the studies we manage, access rights are split along time points, scan types, questionnaires, or questions to match the consortia multilevel access permissions.\nTherefore, we implemented a system cube named rql_download[30] and provided a web frontend by integrating this cube with PIWS[28] whenever it was used in a publication service (cf. Figure 1). The rql_download cube converts the result of any RQL query into files on a virtual file system that, in turn, can be accessed through a secured file transfer protocol (sFTP) (cf. Figure 1). The following five sub-sections introduce: (1) the business logic cubes used to describe the neuroimaging genetics data and metadata and the relationship between these data; (2) how users can save the content of their current search from the DSS web interface; (3) two approaches of rql_download, based on two basic softwares (FUSE or Twisted), that give users access to their saved searches, as well as the pros and cons of both; (4) a suitable strategy for setting user rights from the CW security system; and (5) a descriptive data insertion mechanism, as a set of Python scripts.\n\nA dedicated structure for imaging genomics questionnaire data \nThe database schema was developed for handling multi-time point\/multimodal datasets in the brainomics business logic cube.[31] This schema supports general information such as subject data and associated metadata (age, handedness, sex, etc.), acquisition center definitions, multimodal imaging datasets, clinical\/behavioral records, processed data, and some genomic concepts (including chromosomes, genes, SNPs, or genomic platforms). An excerpt of the produced schema is shown in Figure 4.\n\r\n\n\n\n\n\n\n\n\n\n\n Figure 4. A snippet of the schema used in a publication DSS. We see from the green boxes that all entities are related to an \u201cAssessment\u201d entity through an \u201cin_assessment\u201d relation. This behavior is inherited from the access rights described in the fourth sub-section.\n\n\n\nEfficient data selection and download tool: The data shopping cart mechanism \nWhen an RQL query result set is returned by the DSS, the most adapted view is automatically selected, and facets are attached to each web page, thereby providing filtering rules. Facets allow interactive and graphical search refinements in accordance with selected attributes (e.g., sex or handedness filter for a subject result set). The developed shopping cart mechanism serves to save the user searches that consist of data, possibly large files, and metadata. This mechanism and the facet filtering are smoothly integrated: activating a filter option from the web interface automatically updates the search query result set, and thus, the list of files that will be dropped for download (cf. Figure 5). The data added to one cart has an expiration date that can be configured in the service. Convenient access rights are set: users can only access their own searches. For the sake of the EU-AIMS project hosted in our laboratory, a video explaining the data shopping cart mechanism is available (.mp4 file).\n\r\n\n\n\n\n\n\n\n\n\n\n Figure 5. Illustration of the download process via the proposed shopping cart mechanism. (A) the facet filter bar when all the scans (\u201cScan\u201d entities) are requested (as highlighted in bold, the user has selected only the \u201cFU2\u201d time point and the diffusion MRI \u201cDTI\u201d scans), (B) the view corresponding to the filtered dataset, (C) add this new search to the cart (by activating these filtering options, the save RQL path search will be automatically updated), (D) a new search has been created, and (E) the download of the search and associated files as presented in FileZilla.\n\n\n\nThe transfer of the shopping cart content: Data download \nWhen saved, the cart content is made available as virtual files and folders. A major advantage of the developed solution is that data compression or duplication is avoided, and that in turn requires no extra load for the publication DSS. Data download operations are delegated to sFTP servers to ensure secure transfers. The sFTP is standard and supported by numerous client software on most systems.\nTwo approaches are implemented in the rql_download cube that can be selected by configuration settings:\n\n1. FUSE virtual folders: For each search, the system builds a list of files to be downloaded and subsequently creates a virtual FUSE directory acting as a filter on the central repository. The user can only see subsets of files\/directories corresponding to his queries built in accordance with his access rights. Finally, the system delegates the data transfers to the sFTP server. The major advantage of this approach is the use of the standard sFTP port. However, additional system level configurations are required during the installation of the DSS in order to set the user home directories and system accounts.\n2. Twisted server: This approach is characterized by a Python process that creates a Twisted[32] event-driven networking server, retrieves all the searches in the database, and exposes the files via sFTP through the created server. Again, this process acts as a filter on the central repository where a user only sees a subset of files\/directories. In this case, the authentication and file transfers are directly operated by CW. The major advantage of this strategy is that no system-level configuration is required. However, listening on a non-default sFTP port, which could lead to firewall issues, is sometimes required.\nAccess rights mechanism \nIn the CW security model, any entity or relation may be endowed with permission rules. To fulfill consortia's criteria, we propose an operational setup of the CW security model for our publication DSS. We built our security model around \"pivotal entities\" rather than specifying rights on all entities. Pivotal entities are those on which access rights are defined, and they are related to all entities that must be covered by the security model through a specific relation (the \"in_assessment\" relation in Figure 4). Each time an entity covered by the security model is requested, the system automatically requests its related pivotal entity and propagates the corresponding access rights.\n\nThe unified insertion procedure \nA unified insertion module is provided as a set of Python scripts to insert neuroimaging, genomic, and clinical data such as scans, genomic measures, questionnaires, and processing steps. These scripts were helpful in efficiently managing the large amount of evolving data in our projects. The indexed data are uniformly organized according to the schema structure and thus take advantage of all the aforementioned developments (e.g., shopping cart mechanism and security model of previous sub-sections, and common renderings cf. Figure 6). Generating such a DSS with these scripts can be performed without specific CW knowledge. Indeed, only a rich description of the data to be published is required as a set of Python dictionary objects.\n\r\n\n\n\n\n\n\n\n\n\n\n Figure 6. Summary views of the database status. Global information, for example the (A) gender or (B) handedness distributions, (C) acquisition status, and (D) age distribution, or longitudinal information, such as (E) the answers of subject2 to specific questions across the study time points.\n\n\n\nA transverse Python module to remotely connect a CubicWeb DSS \nWith the aforementioned capabilities of the DSS, a user manually selects and downloads data through graphical interfaces in order to analyze them locally (as discussed in the previous sub-section). In the case of an evolving DSS, the downloaded data must be regularly updated, and this manual process becomes time consuming and error prone when large and heterogeneous data are considered. Moreover, the metadata, such as quality scores, used to specify the dataset to download are also likely to change. Therefore, to achieve the analysis of up-to-date data stored in a DSS, direct programmatic interaction with the DSS is recommended. In the neuroimaging and neuroscience communities, data are typically analyzed by using Python scripts. Classically, the systems provide RESTful web services such as XNAT, with a Python API.[33] Inheriting from the RQL request language, our publication DSS (cf. previous sub-section) offers a rich interface to access the data.\nWe provide a regular Python module, named cwbrowser[34], that implements a Python API to connect and send RQL to a remote DSS based on the CW framework. This module is completely independent of CW (no CW installation required) and similar to the CW distribution cwclientlib cube. A publication DSS, as described in the previous sub-section, can be requested by the cwbrowser module that embeds the previously described data selection and shopping cart capabilities. It automatically fills and saves a shopping cart from a custom RQL request, downloads the associated virtual directories onto the local file system, and returns the complete requested dataset. The returned dataset contains metadata stored in the DSS such as subject sex or quality scores as well as the path to the downloaded directories. These resources are organized following the DSS layout of files and folders. The users will get the same local tree which will help in writing sharable analysis scripts.\n\nResults \nOur laboratory operates several DSS for the IMAGEN project about mental health in adolescents[16] and the EU-AIMS project about autism.[17] Other DSS are currently under development to support new and ongoing initiatives. Note that the access to both IMAGEN and EU-AIMS datasets is (to date) restricted.\nIn the IMAGEN project, 2,000 subjects are monitored on at least two visits (the third follow-up is underway). T1, T2, FLAIR, DWI, B0, task fMRI, and resting-state fMRI scans are acquired, as well as clinical\/behavioral records, genotyping, gene expression, and methylation. A publication DSS at https:\/\/imagen2.cea.fr\/database enables us to share more than 37,000 scans, 32,000 processing results, and 16 million distinct variables. In the near future, an upload DSS will allow us to collect a new time point.\nIn the EU-AIMS project, 1,500 subjects (from six months to 30 years old) are monitored on several visits through two distinct studies. T1, T2, FLAIR, DWI, B0, task fMRI, resting-state fMRI, and spectroscopy scans are acquired, as well as clinical\/behavioral records, EEG, eye-tracking, gene expression, and methylation. An upload DSS at https:\/\/eu-aims.cea.fr\/database provides the means for collecting this data from 10 centers across Europe. In addition, a collaborative quality check DSS at https:\/\/eu-aims.cea.fr\/qc allows us to assess the uploaded data quality, and a publication DSS at https:\/\/eu-aims.cea.fr\/data_repository enables us to share more than 13,000 scans, 12,000 processing results, and 15 million distinct variables.\n\nDiscussion and conclusion \nLightweight solution for data sharing \nWe developed a novel and lightweight PIx software infrastructure exclusively based on the CW framework. We offer a suite of CW tools that facilitates the creation of a DSS. The system delivers the data to users based on the principle of \"what you see is what you get\": users define their datasets of interest by browsing the database. Thanks to the RQL and the developed Python API, remote query of a DSS is easy and intuitive. In this environment, core features such as the schema definition, the web rendering of the database content, and the semantic request language are provided by a few lines of Python code at the heart of the CW framework. Our DSS can use any database engine, offers an access permission mechanism, and can be smoothly integrated with the standard Apache environment. Moreover the CW framework relies on a large community of developers led by Logilab.\n\nA PIx Swiss knife \nCW is well suited for all the scenarios one can face in a PIx project. For instance, in the projects we manage, we also provided a CW-based service to allow a collaborative moderation of user access to the different DSS. This service enables the consortium review boards to assign the relevant access rights to new or existing users. It is restricted to a few members and enables the user account administration of an upload, collaborative QC, and publication DSS.\n\nFuture directions \nOur developments inherit the web semantic capabilities embedded in the CW framework. Thanks to this key feature, numerous problems of interoperability can be efficiently tackled using emerging ontologies and standards in neuroinformatics, neurosciences, and bioinformatics, such as the NIDM standard for data exchange[35], the Cognitive Atlas Ontology[36], and OntoNeuroLOG[37] for data annotation, or the Bio2RDF for the federation of large datasets using open-source semantic web technologies.[38] The annotation of our datasets, with respect to these ontologies, is ongoing. Ultimately, should all DSS follow standard ontologies, RQL would provide new cross-project querying possibilities. Although the CW framework is already used successfully in several commercial applications, it would be interesting to evaluate the CW framework performances on our DSS with Logilab dedicated tools.\n\nAuthor contributions \nAG developed the cubes, performed its deployment, and maintained the online repositories. DG, DO, TG, and RC tested the proposed application and used it in two European projects (IMAGEN, EU-AIMS). NC and AM developed the CubicWeb framework. VF, GS, WS, and DM initiated and supervized the projects. All authors contributed to the manuscript.\n\nConflict of interest statement \nThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\n\nAcknowledgments \nThe research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115300 (EU-AIMS), resources of which are composed of financial contribution from the European Union\u2019s Seventh Framework Programme (FP7\/2007-2013) and EFPIA companies\u2019 in kind contribution.\n\nSupplementary material \nCodes are distributed under the terms of the CeCILL-B license, as published by the CEA-CNRS-INRIA. Refer to the license file or to http:\/\/www.cecill.info\/licences\/Licence_CeCILL-B_V1-en.html for details. Codes are freely accessible on github https:\/\/github.com\/neurospin. The DSS we are in charge of can be reached at https:\/\/imagen2.cea.fr and https:\/\/eu-aims.cea.fr.\n\nReferences \n\n\n\u2191 Hurko, O.; Black, S.E.; Doody, R. et al. (2012). \"The ADNI Publication Policy: Commensurate recognition of critical contributors who are not authors\". NeuroImage 59 (4): 4196\u20134200. doi:10.1016\/j.neuroimage.2011.10.085. PMC PMC3676932. PMID 22100665. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3676932 .   \n\n\u2191 Poldrack, R.A.; Gorgolewski, K.J. (2014). \"Making big data open: Data sharing in neuroimaging\". Nature Neuroscience 17 (11): 1510\u20137. doi:10.1038\/nn.3818. PMID 25349916.   \n\n\u2191 Gorgolewski, K.J.; Varoquaux, G.; Rivera, G. et al. (2015). \"NeuroVault.org: A web-based repository for collecting and sharing unthresholded statistical maps of the human brain\". Frontiers in Neuroinformatics 9: 8. doi:10.3389\/fninf.2015.00008. PMC PMC4392315. PMID 25914639. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC4392315 .   \n\n\u2191 Scheufele, E.; Aronzon, D.; Coopersmith, R. et al. (2014). \"tranSMART: An Open Source Knowledge Management and High Content Data Analytics Platform\". AMIA Joint Summits on Translational Science 2014: 96\u2013101. PMC PMC4333702. PMID 25717408. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC4333702 .   \n\n\u2191 Gorgolewski, K.J.; Auer, T.; Calhoun, V.D. et al. (2016). \"The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments\". Scientific Data 3: 160044. doi:10.1038\/sdata.2016.44. PMC PMC4978148. PMID 27326542. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC4978148 .   \n\n\u2191 Nichols, B.N.; Pohl, K.M. (2015). \"Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community\". Neuropsychology Review 25 (3): 356-68. doi:10.1007\/s11065-015-9293-x. PMC PMC5400666. PMID 26267019. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC5400666 .   \n\n\u2191 Van Horn, J.D.; Toga, A.W. (2009). \"Is it time to re-prioritize neuroimaging databases and digital repositories?\". NeuroImage 47 (4): 1720-34. doi:10.1016\/j.neuroimage.2009.03.086. PMC PMC2754579. PMID 19371790. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC2754579 .   \n\n\u2191 Marcus, D.S.; Harms, M.P.; Snyder, A.Z. et al. (2013). \"Human Connectome Project informatics: quality control, database services, and data visualization\". NeuroImage 80: 202-19. doi:10.1016\/j.neuroimage.2013.05.077. PMC PMC3845379. PMID 23707591. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3845379 .   \n\n\u2191 Das, S.; Zijdenbos, A.P.; Harlap, J. et al. (2012). \"LORIS: A web-based data management system for multi-center studies\". Frontiers in Neuroinformatics 5: 37. doi:10.3389\/fninf.2011.00037. PMC PMC3262165. PMID 22319489. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3262165 .   \n\n\u2191 Book, G.A.; Anderson, B.M.; Stevens, M.C. et al. (2013). \"Neuroinformatics Database (NiDB) - A modular, portable database for the storage, analysis, and sharing of neuroimaging data\". Neuroinformatics 11 (4): 495-505. doi:10.1007\/s12021-013-9194-1. PMC PMC3864015. PMID 23912507. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3864015 .   \n\n\u2191 \"OpenClinica User Documentation\". OpenClinica, LLC. 18 April 2016. https:\/\/docs.openclinica.com\/ .   \n\n\u2191 Harris, P.A.; Taylor, R.; Thielke, R. et al. (2009). \"Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support\". Journal of Biomedical Informatics 42 (2): 377\u201381. doi:10.1016\/j.jbi.2008.08.010. PMC PMC2700030. PMID 18929686. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC2700030 .   \n\n\u2191 Krestyaninova, M.; Zarins, A.; Viksna, J. et al. (2009). \"A system for information management in biomedical studies \u2013 SIMBioMS\". Bioinformatics 25 (20): 2768-2769. doi:10.1093\/bioinformatics\/btp420. PMC PMC2759553. PMID 19633095. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC2759553 .   \n\n\u2191 Scott, A.; Courtney, W.; Wood, D. et al. (2011). \"COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets\". Frontiers in Neuroinformatics 5: 33. doi:10.3389\/fninf.2011.00033. PMC PMC3250631. PMID 22275896. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3250631 .   \n\n\u2191 15.0 15.1 15.2 \"CubicWeb - The Semantic Web is a construction game!\". Logilab. 2016. https:\/\/www.cubicweb.org\/ .   \n\n\u2191 16.0 16.1 Schumann, G.; Loth, E.; Banaschewski, T. et al. (2010). \"The IMAGEN study: Reinforcement-related behaviour in normal brain function and psychopathology\". Molecular Psychiatry 15 (12): 1128-39. doi:10.1038\/mp.2010.4. PMID 21102431.   \n\n\u2191 17.0 17.1 Murphy, D.; Spooren, W. (2012). \"EU-AIMS: A boost to autism research\". Nature Reviews Drug Discovery 11 (11): 815-6. doi:10.1038\/nrd3881. PMID 23123927.   \n\n\u2191 Michel, V.; Schwartz, Y.; Pinel, P. et al. (2013). \"Brainomics: A management system for exploring and merging heterogeneous brain mapping data\". Proceedings from the 19th Annual Meeting of the Organization for Human Brain Mapping 2013. https:\/\/hal.inria.fr\/cea-00904768\/en .   \n\n\u2191 Papadopoulos Orfanos, D.; Michel, V.; Schwartz, Y. et al. (2017). \"The Brainomics\/Localizer database\". NeuroImage 144 (Pt B): 309-314. doi:10.1016\/j.neuroimage.2015.09.052. PMID 26455807.   \n\n\u2191 Prud'hommeaux, E.; Seaborne, A., ed. (15 January 2008). \"SPARQL Query Language for RDF\". World Wide Web Consortium. https:\/\/www.w3.org\/TR\/rdf-sparql-query\/ .   \n\n\u2191 Gorgolewski, K.; Burns, C.D.; Madison, C. et al. (2011). \"Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in Python\". Frontiers in Neuroinformatics 5 (Pt B): 13. doi:10.3389\/fninf.2011.00013. PMC PMC3159964. PMID 21897815. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3159964 .   \n\n\u2191 Chapman, B.; Chang, J. (2000). \"Biopython: Python tools for computational biology\". ACM SIGBIO Newsletter 20 (2): 15\u201319. doi:10.1145\/360262.360268.   \n\n\u2191 Abraham, A.; Pedregosa, F.; Eickenberg, M. et al. (2014). \"Machine learning for neuroimaging with Scikit-learn\". Frontiers in Neuroinformatics 8: 14. doi:10.3389\/fninf.2014.00014. PMC PMC3930868. PMID 24600388. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3930868 .   \n\n\u2191 Fischer, C.; Operto, G.; Laguitton, S. et al.. \"Morphologist 2012: The new morphological pipeline of BrainVisa\". Proceedings from the Human Brain Mapping Conference 2012 2012.   \n\n\u2191 \"libfuse\/libfuse\". GitHub, Inc. https:\/\/github.com\/libfuse\/libfuse .   \n\n\u2191 Rockhold, F.; Bishop, S. (2012). \"Extracting the value of standards: The role of CDISC in a pharmaceutical research strategy\". Clinical Evaluation 40: 91\u201396.   \n\n\u2191 NSAp developers. \"Rql Upload\". GitHub, Inc. http:\/\/neurospin.github.io\/rql_upload\/ .   \n\n\u2191 28.0 28.1 NSAp developers. \"Population Imaging Web Service: PIWS\". GitHub, Inc. http:\/\/neurospin.github.io\/piws\/ .   \n\n\u2191 \"neurospin\/zeijemol\". GitHub, Inc. https:\/\/github.com\/neurospin\/zeijemol .   \n\n\u2191 NSAp developers. \"Rql Download\". GitHub, Inc. http:\/\/neurospin.github.io\/rql_download\/ .   \n\n\u2191 \"neurospin\/brainomics2\". GitHub, Inc. https:\/\/github.com\/neurospin\/brainomics2 .   \n\n\u2191 \"What is Twisted?\". Software Freedom Conservancy. https:\/\/twistedmatrix.com\/trac\/ .   \n\n\u2191 Schwartz, Y.; Barbot, A.; Thyreau, B. et al. (2012). \"PyXNAT: XNAT in Python\". Frontiers in Neuroinformatics 6: 12. doi:10.3389\/fninf.2012.00012. PMC PMC3354345. PMID 22654752. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3354345 .   \n\n\u2191 NSAp developers. \"CWBROWSER\". GitHub, Inc. http:\/\/neurospin.github.io\/rql_download\/cwbrowser .   \n\n\u2191 Keator, D.B.; Helmer, K.; Steffener, J. et al. (2013). \"Towards structured sharing of raw and derived neuroimaging data across existing resources\". Neuroimage 82: 647-61. doi:10.1016\/j.neuroimage.2013.05.094. PMC PMC4028152. PMID 23727024. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC4028152 .   \n\n\u2191 Poldrack, R.A.; Kittur, A.; Kalar, D. et al. (2011). \"The cognitive atlas: Toward a knowledge foundation for cognitive neuroscience\". Frotiers in Neuroinformatics 5: 17. doi:10.3389\/fninf.2011.00017. PMC PMC3167196. PMID 21922006. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3167196 .   \n\n\u2191 Gibaud, B.; Kassel, G.; Dojat, M. et al. (2011). \"NeuroLOG: Sharing neuroimaging data using an ontology-based federated approach\". AMIA Annual Symposium Proceedings 2011: 472-80. PMC PMC3243145. PMID 22195101. http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3243145 .   \n\n\u2191 Dumontier, M.; Callahan, A.; Cruz-Toledo, J. et al. (2014). \"Bio2RDF release 3: A larger connected network of linked data for the life sciences\". Proceedings of the 2014 International Conference on Posters & Demonstrations Track 1272: 401\u201304. http:\/\/ceur-ws.org\/Vol-1272\/paper_121.pdf .   \n\n\nNotes \nThis presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. References are in order of appearance rather than alphabetical order (as the original was) due to the way this wiki works. Footnotes (URLs to projects) were turned into full citations. The URL to the zeijemol application cube was broken, and a direct GitHub URL was substituted instead.\n\n\n\n\n\n\nSource: <a rel=\"external_link\" class=\"external\" href=\"https:\/\/www.limswiki.org\/index.php\/Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\">https:\/\/www.limswiki.org\/index.php\/Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework<\/a>\n\t\t\t\t\tCategories: LIMSwiki journal articles (added in 2017)LIMSwiki journal articles (all)LIMSwiki journal articles on neuroinformatics\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\n\t\t\n\t\t\tNavigation menu\n\t\t\t\t\t\n\t\t\tViews\n\n\t\t\t\n\t\t\t\t\n\t\t\t\tJournal\n\t\t\t\tDiscussion\n\t\t\t\tView source\n\t\t\t\tHistory\n\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\n\t\t\t\t\n\t\t\t\tPersonal tools\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tLog in\n\t\t\t\t\t\t\t\t\t\t\t\t\tRequest account\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\t\n\t\tNavigation\n\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tMain page\n\t\t\t\t\t\t\t\t\t\t\tRecent changes\n\t\t\t\t\t\t\t\t\t\t\tRandom page\n\t\t\t\t\t\t\t\t\t\t\tHelp\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\n\t\t\t\n\t\t\tSearch\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t\t\t\n\t\t\t\t\n\n\t\t\t\t\t\t\t\n\t\t\n\t\t\t\n\t\t\tTools\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tWhat links here\n\t\t\t\t\t\t\t\t\t\t\tRelated changes\n\t\t\t\t\t\t\t\t\t\t\tSpecial pages\n\t\t\t\t\t\t\t\t\t\t\tPermanent link\n\t\t\t\t\t\t\t\t\t\t\tPage information\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\n\t\t\n\t\tPrint\/export\n\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tCreate a book\n\t\t\t\t\t\t\t\t\t\t\tDownload as PDF\n\t\t\t\t\t\t\t\t\t\t\tDownload as Plain text\n\t\t\t\t\t\t\t\t\t\t\tPrintable version\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\n\t\t\n\t\tSponsors\n\t\t\n\t\t\t \r\n\n\t\r\n\n\t\r\n\n\t\r\n\n\t\n\t\r\n\n \r\n\n\t\n\t\r\n\n \r\n\n\t\n\t\r\n\n\t\n\t\r\n\n\t\r\n\n\t\r\n\n\t\r\n\t\t\n\t\t\n\t\t\t\n\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t This page was last modified on 20 June 2017, at 16:58.\n\t\t\t\t\t\t\t\t\tThis page has been accessed 985 times.\n\t\t\t\t\t\t\t\t\tContent is available under a Creative Commons Attribution-ShareAlike 4.0 International License unless otherwise noted.\n\t\t\t\t\t\t\t\t\tPrivacy policy\n\t\t\t\t\t\t\t\t\tAbout LIMSWiki\n\t\t\t\t\t\t\t\t\tDisclaimers\n\t\t\t\t\t\t\t\n\t\t\n\t\t\n\t\t\n\n","bce85c098ea6958c92b6dcce94e42565_html":"<body class=\"mediawiki ltr sitedir-ltr ns-206 ns-subject page-Journal_Neuroimaging_genetics_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework skin-monobook action-view\">\n<div id=\"rdp-ebb-globalWrapper\">\n\t\t<div id=\"rdp-ebb-column-content\">\n\t\t\t<div id=\"rdp-ebb-content\" class=\"mw-body\" role=\"main\">\n\t\t\t\t<a id=\"rdp-ebb-top\"><\/a>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<h1 id=\"rdp-ebb-firstHeading\" class=\"firstHeading\" lang=\"en\">Journal:Neuroimaging, genetics, and clinical data sharing in Python using the CubicWeb framework<\/h1>\n\t\t\t\t\n\t\t\t\t<div id=\"rdp-ebb-bodyContent\" class=\"mw-body-content\">\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t<!-- start content -->\n\t\t\t\t\t<div id=\"rdp-ebb-mw-content-text\" lang=\"en\" dir=\"ltr\" class=\"mw-content-ltr\">\n\n\n<h2><span class=\"mw-headline\" id=\"Abstract\">Abstract<\/span><\/h2>\n<p>In neurosciences or psychiatry, the emergence of large multi-center population imaging studies raises numerous technological challenges. From distributed data collection, across different institutions and countries, to final data publication service, one must handle the massive, heterogeneous, and complex data from genetics, imaging, demographics, or clinical scores. These data must be both efficiently obtained and downloadable. We present a Python solution, based on the CubicWeb open-source semantic framework, aimed at building population imaging study repositories. In addition, we focus on the tools developed around this framework to overcome the challenges associated with data sharing and collaborative requirements. We describe a set of three highly adaptive web services that transform the CubicWeb framework into a (1) multi-center upload platform, (2) collaborative quality assessment platform, and (3) publication platform endowed with massive-download capabilities. Two major European projects, IMAGEN and EU-AIMS, are currently supported by the described framework. We also present a Python package that enables end users to remotely query neuroimaging, genetics, and clinical data from scripts.\n<\/p><p><b>Keywords<\/b>: web service, data sharing, database, neuroimaging, genetics, medical informatics, Python\n<\/p>\n<h2><span class=\"mw-headline\" id=\"Introduction\">Introduction<\/span><\/h2>\n<p>Health research strategies using neuroimaging have shifted in recent years: the focus has moved from patient care only, to a combination of patient care and prevention. In the case of neurodegenerative and psychiatric diseases, this drives the creation of increasingly numerous massive imaging studies, also known as population imaging (PI) surveys.<sup id=\"rdp-ebb-cite_ref-HurkoTheADNI12_1-0\" class=\"reference\"><a href=\"#cite_note-HurkoTheADNI12-1\" rel=\"external_link\">[1]<\/a><\/sup><sup id=\"rdp-ebb-cite_ref-PoldrackMaking14_2-0\" class=\"reference\"><a href=\"#cite_note-PoldrackMaking14-2\" rel=\"external_link\">[2]<\/a><\/sup> It should be noticed that PI studies no longer consist of image data only. The recent wide availability of high-throughput genomics has augmented the subject data with genetics, epigenetics, and functional genomics. Likewise, the standardization of personality, demographics, and deficit tests in psychiatry facilitates the acquisition of clinical\/behavioral records to enrich the subject data in large population studies. Moreover, PI studies now classically encompass more than one single imaging session per subject and cover multiple-time point heterogeneous experiments. Ultimately, these studies with complex imaging and extended data (PIx) require multi-center acquisitions to build a large target population.\n<\/p><p>A regular PIx infrastructure has to cover the following three main topics: (1) data collection, (2) quality control (QC) with data processing, and (3) data indexing and publication with controlled data sharing mechanisms. Furthermore, PIx infrastructures must evolve during the life cycle of a population imaging project, and they must also be resilient to extreme evolutions of the data content and management. In the projects we manage, we experience several extreme evolutions. The first kind of evolution may affect the published dataset such as adding a new modality for all subjects, a new time point or a new subcohort. Second, the amount of data requested evolves dramatically as the project consortium gets enlarged.<sup id=\"rdp-ebb-cite_ref-GorgolewskiFront15_3-0\" class=\"reference\"><a href=\"#cite_note-GorgolewskiFront15-3\" rel=\"external_link\">[3]<\/a><\/sup> Finally, internal ontologies have to evolve constantly in order to match the ongoing initiatives on interoperability.<sup id=\"rdp-ebb-cite_ref-Scheufele_tranSMART14_4-0\" class=\"reference\"><a href=\"#cite_note-Scheufele_tranSMART14-4\" rel=\"external_link\">[4]<\/a><\/sup><sup id=\"rdp-ebb-cite_ref-GorgolewskiTheBrain16_5-0\" class=\"reference\"><a href=\"#cite_note-GorgolewskiTheBrain16-5\" rel=\"external_link\">[5]<\/a><\/sup>\n<\/p><p>Several existing open-source frameworks support one or several of the described topics, sometimes only for one specific data type. We propose in the following a brief overview of existing systems. Some of these systems have also been reviewed by Nichols and Pohl.<sup id=\"rdp-ebb-cite_ref-NicholsNeuro15_6-0\" class=\"reference\"><a href=\"#cite_note-NicholsNeuro15-6\" rel=\"external_link\">[6]<\/a><\/sup> IDA<sup id=\"rdp-ebb-cite_ref-HornIsIt09_7-0\" class=\"reference\"><a href=\"#cite_note-HornIsIt09-7\" rel=\"external_link\">[7]<\/a><\/sup> is a neuroimaging data repository and management system that supports data collection (topic one) and data sharing (topic three). With this system, the published datasets can be searched using automatically extracted metadata. The XNAT framework<sup id=\"rdp-ebb-cite_ref-MarcusHuman13_8-0\" class=\"reference\"><a href=\"#cite_note-MarcusHuman13-8\" rel=\"external_link\">[8]<\/a><\/sup> is widely used for neuroimaging data and supports all the PIx infrastructure topics, focusing on tools to pipeline, and to audit the processing of image data (topic two). The LORIS<sup id=\"rdp-ebb-cite_ref-DasLORIS12_9-0\" class=\"reference\"><a href=\"#cite_note-DasLORIS12-9\" rel=\"external_link\">[9]<\/a><\/sup> and NiDB<sup id=\"rdp-ebb-cite_ref-BookNeuroinfo13_10-0\" class=\"reference\"><a href=\"#cite_note-BookNeuroinfo13-10\" rel=\"external_link\">[10]<\/a><\/sup> frameworks represent a significant effort to account for multimodal data involved in PIx studies. These frameworks, although addressing all the required topics, mainly support neuroimaging data. Openclinica<sup id=\"rdp-ebb-cite_ref-OpenClinicaUser16_11-0\" class=\"reference\"><a href=\"#cite_note-OpenClinicaUser16-11\" rel=\"external_link\">[11]<\/a><\/sup> and REDCap<sup id=\"rdp-ebb-cite_ref-HarrisResearch09_12-0\" class=\"reference\"><a href=\"#cite_note-HarrisResearch09-12\" rel=\"external_link\">[12]<\/a><\/sup> facilitate the collection of electronic data such as eCRF or questionnaires and are recognized in projects of various sizes that support data collection (topic one). Likewise, <a href=\"https:\/\/www.limswiki.org\/index.php\/Laboratory_information_management_system\" title=\"Laboratory information management system\" target=\"_blank\" class=\"wiki-link\" data-key=\"8ff56a51d34c9b1806fcebdcde634d00\">laboratory information management systems<\/a> were developed for the collection of genomic measurements such as SIMBioMS.<sup id=\"rdp-ebb-cite_ref-KrestyaninovaASys09_13-0\" class=\"reference\"><a href=\"#cite_note-KrestyaninovaASys09-13\" rel=\"external_link\">[13]<\/a><\/sup> Finally, the COINS framework brings essential tools for multimodal data support and, more interestingly, emphasizes the importance of providing sharing tools (topics one and three).<sup id=\"rdp-ebb-cite_ref-ScottCOINS11_14-0\" class=\"reference\"><a href=\"#cite_note-ScottCOINS11-14\" rel=\"external_link\">[14]<\/a><\/sup>\n<\/p><p>The two European studies we manage require a tailored PIx infrastructure. Existing frameworks neither completely handle the diversity of our PIx requirements and project life cycle nor provide efficient tools to collect, check the quality of, and publish evolving data. Additional developments were required for building such complete infrastructure. We based these developments on a more general framework than the dedicated applications described above. In collaboration with Logilab company (Logilab SA, Paris, France), we developed three highly adaptive web services, based on the CubicWeb (CW) pure-Python framework, aimed at creating a (1) multi-center upload platform, (2) collaborative quality assessment platform, and (3) publication platform with massive-download features.<sup id=\"rdp-ebb-cite_ref-LogilabCubicWeb_15-0\" class=\"reference\"><a href=\"#cite_note-LogilabCubicWeb-15\" rel=\"external_link\">[15]<\/a><\/sup> These developments were originally instituted for IMAGEN and EU-AIMS projects in order to host their data about mental health in adolescents<sup id=\"rdp-ebb-cite_ref-SchumannTheIMAGEN10_16-0\" class=\"reference\"><a href=\"#cite_note-SchumannTheIMAGEN10-16\" rel=\"external_link\">[16]<\/a><\/sup> and autism<sup id=\"rdp-ebb-cite_ref-MurphyEU-AIMS12_17-0\" class=\"reference\"><a href=\"#cite_note-MurphyEU-AIMS12-17\" rel=\"external_link\">[17]<\/a><\/sup>, respectively. The corresponding studies require key features such as upload\/browse published data from the web, dynamic selection and filtering of displayed data, support for flexible download operations, high-level request language, multilevel access rights, remote data access, remote user access rights management, collaborative QC, and interoperability.\n<\/p>\n<h2><span class=\"mw-headline\" id=\"Materials_and_methods\">Materials and methods<\/span><\/h2>\n<p>The three services described in the introduction were handled in distinct developments. The next sub-section presents the CW framework capabilities, followed by introductions in the second and fourth sub-sections to the upload and publication web services through which the tailored requirements of PIx studies are satisfied. Furthermore, we describe in the third sub-section a collaborative rating web service that helps users to assess the data quality, and in the final sub-section a Python API that remotely queries these web services.\n<\/p>\n<h3><span class=\"mw-headline\" id=\"CubicWeb_overview\">CubicWeb overview<\/span><\/h3>\n<p>All the implemented services are based on the CW framework.<sup id=\"rdp-ebb-cite_ref-LogilabCubicWeb_15-1\" class=\"reference\"><a href=\"#cite_note-LogilabCubicWeb-15\" rel=\"external_link\">[15]<\/a><\/sup> We choose a high level pure-Python framework that bridges web technologies and database engines. This choice was also based on the expertise and experience of people from our <a href=\"https:\/\/www.limswiki.org\/index.php\/Laboratory\" title=\"Laboratory\" target=\"_blank\" class=\"wiki-link\" data-key=\"c57fc5aac9e4abf31dccae81df664c33\">laboratory<\/a> and a tight collaboration with Logilab.<sup id=\"rdp-ebb-cite_ref-MichelBrainomics13_18-0\" class=\"reference\"><a href=\"#cite_note-MichelBrainomics13-18\" rel=\"external_link\">[18]<\/a><\/sup><sup id=\"rdp-ebb-cite_ref-PapadopoulosTheBrainomics17_19-0\" class=\"reference\"><a href=\"#cite_note-PapadopoulosTheBrainomics17-19\" rel=\"external_link\">[19]<\/a><\/sup> CW distribution is organized in a core part and a set of basic Python modules, referred to as cubes, which can be used to efficiently generate web applications. The core of the CW framework, developed under the LGPL license, is constructed from well-established technologies (SQL, Python, web technologies such as HTML5 and Javascript). The main characteristics of the CW framework are given as follows:\n<\/p>\n<dl><dd>1. CW defines its data model with Python classes and automatically generates the underlying database structure.<\/dd><\/dl>\n<dl><dd>2. The queries are expressed with the RQL language, which is similar to W3C\u2019s SPARQL.<sup id=\"rdp-ebb-cite_ref-Prud.27hommeauxSPARQL08_20-0\" class=\"reference\"><a href=\"#cite_note-Prud.27hommeauxSPARQL08-20\" rel=\"external_link\">[20]<\/a><\/sup> All the persistent data are retrieved and modified using this language.<\/dd><\/dl>\n<dl><dd>3. CW implements a mechanism that exposes <a href=\"https:\/\/www.limswiki.org\/index.php\/Information\" title=\"Information\" target=\"_blank\" class=\"wiki-link\" data-key=\"6300a14d9c2776dcca0999b5ed940e7d\">information<\/a> in several ways, referred to as views. This mechanism implements the classical model-view-controller software architecture pattern. Defined in Python, the views are applied to query results and can produce HTML pages and\/or trigger external processes. The separation of queries and views offers major advantages: first, the same data selection may have several web representations, and second, retrieved data can be exported in several other formats without modifying the underlying data storage.<\/dd><\/dl>\n<dl><dd>4. All the views and triggers are recorded in a registry and are automatically selected depending on the current context, which is inferred from the type of data returned by the RQL.<\/dd><\/dl>\n<dl><dd>5. Thanks to the semantic nature of CW, all developments inherit the possibility to follow existing or emerging ontologies, thereby facilitating sharing, access, and processing.<\/dd><\/dl>\n<dl><dd>6. CW has a security system that grants fine-grained access to the data. This system is similar to the row-level security and policies available in the most recent versions of <a href=\"https:\/\/www.limswiki.org\/index.php\/PostgreSQL\" title=\"PostgreSQL\" target=\"_blank\" class=\"wiki-link\" data-key=\"a5dd945cdcb63e2d8f7a5edb3a896d82\">PostgreSQL<\/a>, and links access rights to entities\/relations in the schema. Each entity type has a set of attributes and relations, and permissions that define who can add, read, update, or delete such an entity and associated relations.<\/dd><\/dl>\n<dl><dd>7. CW may run either as a standalone application or behind an Apache front server. We refer to both settings as a data sharing service (DSS) (cf. Figure 1).<\/dd><\/dl>\n<dl><dd>8. CW can be configured to run with various database engines. For the best performance, PostgreSQL is recommended.<\/dd><\/dl>\n<p><br \/>\n<a href=\"https:\/\/www.limswiki.org\/index.php\/File:Fig1_Grigis_FInNeuroinformatics2017_11.jpg\" class=\"image wiki-link\" target=\"_blank\" data-key=\"511bbebcfe35ad61ecff2048ecc63b64\"><img alt=\"Fig1 Grigis FInNeuroinformatics2017 11.jpg\" src=\"https:\/\/www.limswiki.org\/images\/6\/63\/Fig1_Grigis_FInNeuroinformatics2017_11.jpg\" style=\"width: 100%;max-width: 400px;height: auto;\" \/><\/a>\n<\/p>\n<div style=\"clear:both;\"><\/div>\n<table style=\"\">\n<tr>\n<td style=\"vertical-align:top;\">\n<table border=\"0\" cellpadding=\"5\" cellspacing=\"0\" style=\"\">\n\n<tr>\n<td style=\"background-color:white; padding-left:10px; padding-right:10px;\"> <blockquote><b>Figure 1.<\/b> Architecture of a CubicWeb data sharing service (DSS) integrated in an Apache platform with LDAP. The business logic cubes provide a schema that can be instantiated in the database management system (DBMS: red puzzle piece). The system cubes ensure low-level system interactions (green puzzle piece), and the application cube proposes a web user interface (blue puzzle piece). End users access the database content through a web browser, a Python API scripting the DSS or an FTP solution, where virtual folders (acting as filters on the central repository) are proposed for download.<\/blockquote>\n<\/td><\/tr>\n<\/table>\n<\/td><\/tr><\/table>\n<p>Starting from the basic CW distributions, our suite of services is composed of an assembly of Python modules, also referred to as cubes. The Python language is widely used in scientific communities and facilitates interfacing with major or emerging processing tools such as Nipype<sup id=\"rdp-ebb-cite_ref-GorgolewskiNipype11_21-0\" class=\"reference\"><a href=\"#cite_note-GorgolewskiNipype11-21\" rel=\"external_link\">[21]<\/a><\/sup>, Biopython<sup id=\"rdp-ebb-cite_ref-ChapmanBiopython00_22-0\" class=\"reference\"><a href=\"#cite_note-ChapmanBiopython00-22\" rel=\"external_link\">[22]<\/a><\/sup>, Nilearn<sup id=\"rdp-ebb-cite_ref-AbrahamMachine14_23-0\" class=\"reference\"><a href=\"#cite_note-AbrahamMachine14-23\" rel=\"external_link\">[23]<\/a><\/sup>, and Morphologist.<sup id=\"rdp-ebb-cite_ref-FischerMorph12_24-0\" class=\"reference\"><a href=\"#cite_note-FischerMorph12-24\" rel=\"external_link\">[24]<\/a><\/sup> Application cubes, built over system cubes, and business logic cubes can be distinguished. The system cubes ensure interactions with the operating system and middleware. For example, they connect to LDAP for user credentials and information or invoke FUSE<sup id=\"rdp-ebb-cite_ref-GitHubFUSE_25-0\" class=\"reference\"><a href=\"#cite_note-GitHubFUSE-25\" rel=\"external_link\">[25]<\/a><\/sup> as a module to construct virtual file systems in a user repository for downloading. The business logic cubes essentially provide the database schema and the application cubes define the access rights and the web interface.\n<\/p><p>Among the available Python-based frameworks, we chose CW. A major advantage of CW is the RQL language which brings end users a query interface adapted for PIx data sharing. It simplifies and improves the user experience in searching for custom datasets. RQL also avoids the use of a complicated object relational mapper (ORM), is focused on browsing relations, and allows requesting several DSS at once. The semantic nature of this request language requires the user to know only about the used data model defined as a graph (nothing about the underlying low-level relational model). This data model simplification and the expressiveness of RQL help users writing custom requests, while most of existing DSS do not expose a query language but offer a limited predefined number of operations that can be carefully designed to be efficient (e.g., RESTful APIs). Criticisms against systems exposing a query request language to the end users emphasize a risk of denial of service. To avoid this issue (i.e., overloading the server with arbitrary complex requests), CW allows limitation of usable resources (RAM per request, CPU per request, number of requests per user, CPU time per request). We believe that users should be able to select and download only what they specifically need using a query request language. This avoids filtering the data locally and saves the bandwidth.\n<\/p>\n<h3><span class=\"mw-headline\" id=\"Structured_data_upload_service\">Structured data upload service<\/span><\/h3>\n<p>In PIx studies, massive and complex data are gathered from multiple data acquisition centers or devices (topic one). Each collected dataset must be mapped with definitions that follow consensus representation rules. Those definitions are grouped in data dictionaries that ideally follow standards<sup id=\"rdp-ebb-cite_ref-RockholdExtract12_26-0\" class=\"reference\"><a href=\"#cite_note-RockholdExtract12-26\" rel=\"external_link\">[26]<\/a><\/sup>, but they are mainly manufacturer- and\/or site-specific. Thus, an efficient and versatile tool is required for mapping the different data dictionaries during the collection process.\n<\/p><p>Leveraging those ideas, we implemented a flexible upload mechanism, a system cube named <i>rql_upload<\/i><sup id=\"rdp-ebb-cite_ref-NSApRQLUpload_27-0\" class=\"reference\"><a href=\"#cite_note-NSApRQLUpload-27\" rel=\"external_link\">[27]<\/a><\/sup> and provided a web frontend by integrating this cube with the application cube named <i>PIWS<\/i><sup id=\"rdp-ebb-cite_ref-NSApPIWS_28-0\" class=\"reference\"><a href=\"#cite_note-NSApPIWS-28\" rel=\"external_link\">[28]<\/a><\/sup> (Population Imaging Web Service, cf. Figure 1). Based on a CW feature that allows database completion through online HTML forms, these two cubes were developed to collect, in a DSS, both raw data and metadata. CW also enables the customization of triggers that determine the integrity of the uploaded data: synchronous and asynchronous validation filters can be specified and applied to each upload dataset. The upload proceeds as follows (cf. Figure 2):\n<\/p>\n<dl><dd>1. Synchronous validations are applied to each form field (e.g., to check the extension of a file or the structure of an Excel table). If the validation filtering fails, then the web form is refreshed and an adapted feedback is displayed.<\/dd><\/dl>\n<dl><dd>2. After synchronous validation, all the uploaded raw data\/metadata are stored in generic entities and a \"Quarantine\" status is set. To avoid cluttering of the database and to ease file manipulation, files are stored in the central repository but remain accessible through the database. File hashes are automatically computed and indexed in order to assess data integrity.<\/dd><\/dl>\n<dl><dd>3. To update the upload status from \"Quarantine\" to \"Rejected\"\/\"Validated,\" automatic asynchronous validations can be configured in the service as looping tasks. Those validation filters are project and\/or data and\/or upload specific and generate adapted feedback for users and data managers.<\/dd><\/dl>\n<p><br \/>\n<a href=\"https:\/\/www.limswiki.org\/index.php\/File:Fig2_Grigis_FInNeuroinformatics2017_11.jpg\" class=\"image wiki-link\" target=\"_blank\" data-key=\"b2be30737501c6529bfc251cc9e19582\"><img alt=\"Fig2 Grigis FInNeuroinformatics2017 11.jpg\" src=\"https:\/\/www.limswiki.org\/images\/3\/33\/Fig2_Grigis_FInNeuroinformatics2017_11.jpg\" style=\"width: 100%;max-width: 400px;height: auto;\" \/><\/a>\n<\/p>\n<div style=\"clear:both;\"><\/div>\n<table style=\"\">\n<tr>\n<td style=\"vertical-align:top;\">\n<table border=\"0\" cellpadding=\"5\" cellspacing=\"0\" style=\"\">\n\n<tr>\n<td style=\"background-color:white; padding-left:10px; padding-right:10px;\"> <blockquote><b>Figure 2.<\/b> Illustration of the upload process. The <b>(A)<\/b> syntax of a form description JSON file, <b>(B)<\/b> corresponding web form as presented to users (here an error message returned by synchronous validation is displayed in the top red box), <b>(C)<\/b> \u201cQuarantine\u201d status, and <b>(D)<\/b> \u201cValidated\u201d status (obtained after asynchronous validation) as displayed to users: note that no feedback is shown here.<\/blockquote>\n<\/td><\/tr>\n<\/table>\n<\/td><\/tr><\/table>\n<p>Moreover, any entity or relation may be endowed with access permission rules.<sup id=\"rdp-ebb-cite_ref-LogilabCubicWeb_15-2\" class=\"reference\"><a href=\"#cite_note-LogilabCubicWeb-15\" rel=\"external_link\">[15]<\/a><\/sup> Based on the CW security mechanisms, a customized security model was implemented for our upload DSS (it can be extended later). Only specific groups have the authorization to upload, and users can only access the uploads, which they are interested in. The customization of these core features allowed the creation of an upload web service that is completely described in a single JSON file. This file links the web form fields with customized or CW-internal controllers that manage the type of data to be collected.\n<\/p>\n<h3><span class=\"mw-headline\" id=\"Collaborative_quality_control_service\">Collaborative quality control service<\/span><\/h3>\n<p>Owing to the large amount of data gathered\/analyzed in PIx studies, we must consider more sophisticated operating procedures than simple quality controls (QCs), where datasets are usually only rated once by a handful of individuals. This issue can be addressed by implementing a web-based collaborative quality control process that will also remove the bias introduced by isolated raters (topic two). Moreover, for the studies we manage, we also added controlled vocabulary description to the ratings.\n<\/p><p>We achieve these goals by implementing a flexible collaborative rating mechanism, i.e., an application cube named <i>zeijemol<\/i>.<sup id=\"rdp-ebb-cite_ref-GitHubZeijemol_29-0\" class=\"reference\"><a href=\"#cite_note-GitHubZeijemol-29\" rel=\"external_link\">[29]<\/a><\/sup> As in the previous sub-section, a collaborative quality control DSS is entirely described in a single JSON file. This file consists, on the one hand, of the list of elements that will be rated (e.g., a Nifti image, a FreeSurfer segmentation, or a motion curve in a diffusion sequence of an individual) and, on the other hand, related quality indicators (e.g., binary good\/bad, controlled vocabulary, scaled rating). Each element is displayed by one of the embedded viewers such as triplanar view or mesh rendering (cf. Figure 3). The QC results are stored directly in the database.\n<\/p><p><br \/>\n<a href=\"https:\/\/www.limswiki.org\/index.php\/File:Fig3_Grigis_FInNeuroinformatics2017_11.jpg\" class=\"image wiki-link\" target=\"_blank\" data-key=\"236ad6d64aecacbf31760b5244521230\"><img alt=\"Fig3 Grigis FInNeuroinformatics2017 11.jpg\" src=\"https:\/\/www.limswiki.org\/images\/6\/6a\/Fig3_Grigis_FInNeuroinformatics2017_11.jpg\" style=\"width: 100%;max-width: 400px;height: auto;\" \/><\/a>\n<\/p>\n<div style=\"clear:both;\"><\/div>\n<table style=\"\">\n<tr>\n<td style=\"vertical-align:top;\">\n<table border=\"0\" cellpadding=\"5\" cellspacing=\"0\" style=\"\">\n\n<tr>\n<td style=\"background-color:white; padding-left:10px; padding-right:10px;\"> <blockquote><b>Figure 3.<\/b> The collaborative quality control web service of a FreeSurfer segmentation element of one subject. <b>(A)<\/b> the quality indicators (in this case, a controlled vocabulary with an accept\/prescribe manual edit\/reject decision and an optional check-box justification), <b>(B)<\/b> a triplanar view of the white and pial surfaces overlayed on the anatomical image, and <b>(C)<\/b> the white and pial meshes with statistical indicators.<\/blockquote>\n<\/td><\/tr>\n<\/table>\n<\/td><\/tr><\/table>\n<p>The emergence of such DSS will allow machine learning techniques to learn new classifiers to automatized the quality control task. The QC scores may also be directly used as prior knowledge during the analysis stage.\n<\/p>\n<h3><span class=\"mw-headline\" id=\"Publication_service\">Publication service<\/span><\/h3>\n<p>In PIx studies, data collection and QC are followed by data anonymization, ordering, and analysis. Ultimately, data are made available to the acquisition partners or the scientific community (topic three). While browsing the database content through the web interface, users expect to be able to download the displayed files as well as the data description and rich links between the data, also referred to as metadata. An intuitive and reliable sharing mechanism is therefore crucial as large amounts of heterogeneous evolving data must be provided. Furthermore, for the studies we manage, access rights are split along time points, scan types, questionnaires, or questions to match the consortia multilevel access permissions.\n<\/p><p>Therefore, we implemented a system cube named <i>rql_download<\/i><sup id=\"rdp-ebb-cite_ref-NSApRQLDownload_30-0\" class=\"reference\"><a href=\"#cite_note-NSApRQLDownload-30\" rel=\"external_link\">[30]<\/a><\/sup> and provided a web frontend by integrating this cube with <i>PIWS<\/i><sup id=\"rdp-ebb-cite_ref-NSApPIWS_28-1\" class=\"reference\"><a href=\"#cite_note-NSApPIWS-28\" rel=\"external_link\">[28]<\/a><\/sup> whenever it was used in a publication service (cf. Figure 1). The <i>rql_download<\/i> cube converts the result of any RQL query into files on a virtual file system that, in turn, can be accessed through a secured file transfer protocol (sFTP) (cf. Figure 1). The following five sub-sections introduce: (1) the business logic cubes used to describe the neuroimaging genetics data and metadata and the relationship between these data; (2) how users can save the content of their current search from the DSS web interface; (3) two approaches of <i>rql_download<\/i>, based on two basic softwares (FUSE or Twisted), that give users access to their saved searches, as well as the pros and cons of both; (4) a suitable strategy for setting user rights from the CW security system; and (5) a descriptive data insertion mechanism, as a set of Python scripts.\n<\/p>\n<h4><span class=\"mw-headline\" id=\"A_dedicated_structure_for_imaging_genomics_questionnaire_data\">A dedicated structure for imaging genomics questionnaire data<\/span><\/h4>\n<p>The database schema was developed for handling multi-time point\/multimodal datasets in the brainomics business logic cube.<sup id=\"rdp-ebb-cite_ref-GitHubBrainomics2_31-0\" class=\"reference\"><a href=\"#cite_note-GitHubBrainomics2-31\" rel=\"external_link\">[31]<\/a><\/sup> This schema supports general information such as subject data and associated metadata (age, handedness, sex, etc.), acquisition center definitions, multimodal imaging datasets, clinical\/behavioral records, processed data, and some genomic concepts (including chromosomes, genes, SNPs, or genomic platforms). An excerpt of the produced schema is shown in Figure 4.\n<\/p><p><br \/>\n<a href=\"https:\/\/www.limswiki.org\/index.php\/File:Fig4_Grigis_FInNeuroinformatics2017_11.jpg\" class=\"image wiki-link\" target=\"_blank\" data-key=\"33d7be14b88c3684c75160ccc1a35bcb\"><img alt=\"Fig4 Grigis FInNeuroinformatics2017 11.jpg\" src=\"https:\/\/www.limswiki.org\/images\/f\/fa\/Fig4_Grigis_FInNeuroinformatics2017_11.jpg\" style=\"width: 100%;max-width: 400px;height: auto;\" \/><\/a>\n<\/p>\n<div style=\"clear:both;\"><\/div>\n<table style=\"\">\n<tr>\n<td style=\"vertical-align:top;\">\n<table border=\"0\" cellpadding=\"5\" cellspacing=\"0\" style=\"\">\n\n<tr>\n<td style=\"background-color:white; padding-left:10px; padding-right:10px;\"> <blockquote><b>Figure 4.<\/b> A snippet of the schema used in a publication DSS. We see from the green boxes that all entities are related to an \u201cAssessment\u201d entity through an \u201cin_assessment\u201d relation. This behavior is inherited from the access rights described in the fourth sub-section.<\/blockquote>\n<\/td><\/tr>\n<\/table>\n<\/td><\/tr><\/table>\n<h4><span class=\"mw-headline\" id=\"Efficient_data_selection_and_download_tool:_The_data_shopping_cart_mechanism\">Efficient data selection and download tool: The data shopping cart mechanism<\/span><\/h4>\n<p>When an RQL query result set is returned by the DSS, the most adapted view is automatically selected, and facets are attached to each web page, thereby providing filtering rules. Facets allow interactive and graphical search refinements in accordance with selected attributes (e.g., sex or handedness filter for a subject result set). The developed shopping cart mechanism serves to save the user searches that consist of data, possibly large files, and metadata. This mechanism and the facet filtering are smoothly integrated: activating a filter option from the web interface automatically updates the search query result set, and thus, the list of files that will be dropped for download (cf. Figure 5). The data added to one cart has an expiration date that can be configured in the service. Convenient access rights are set: users can only access their own searches. For the sake of the EU-AIMS project hosted in our laboratory, a video explaining the data shopping cart mechanism <a rel=\"external_link\" class=\"external text\" href=\"ftp:\/\/ftp.cea.fr\/pub\/unati\/euaims\/download_euaims_data.mp4\" target=\"_blank\">is available<\/a> (.mp4 file).\n<\/p><p><br \/>\n<a href=\"https:\/\/www.limswiki.org\/index.php\/File:Fig5_Grigis_FInNeuroinformatics2017_11.jpg\" class=\"image wiki-link\" target=\"_blank\" data-key=\"b7f5c486acacecb0374be5171038f9eb\"><img alt=\"Fig5 Grigis FInNeuroinformatics2017 11.jpg\" src=\"https:\/\/www.limswiki.org\/images\/1\/1b\/Fig5_Grigis_FInNeuroinformatics2017_11.jpg\" style=\"width: 100%;max-width: 400px;height: auto;\" \/><\/a>\n<\/p>\n<div style=\"clear:both;\"><\/div>\n<table style=\"\">\n<tr>\n<td style=\"vertical-align:top;\">\n<table border=\"0\" cellpadding=\"5\" cellspacing=\"0\" style=\"\">\n\n<tr>\n<td style=\"background-color:white; padding-left:10px; padding-right:10px;\"> <blockquote><b>Figure 5.<\/b> Illustration of the download process via the proposed shopping cart mechanism. <b>(A)<\/b> the facet filter bar when all the scans (\u201cScan\u201d entities) are requested (as highlighted in bold, the user has selected only the \u201cFU2\u201d time point and the diffusion MRI \u201cDTI\u201d scans), <b>(B)<\/b> the view corresponding to the filtered dataset, <b>(C)<\/b> add this new search to the cart (by activating these filtering options, the save RQL path search will be automatically updated), <b>(D)<\/b> a new search has been created, and <b>(E)<\/b> the download of the search and associated files as presented in FileZilla.<\/blockquote>\n<\/td><\/tr>\n<\/table>\n<\/td><\/tr><\/table>\n<h4><span class=\"mw-headline\" id=\"The_transfer_of_the_shopping_cart_content:_Data_download\">The transfer of the shopping cart content: Data download<\/span><\/h4>\n<p>When saved, the cart content is made available as virtual files and folders. A major advantage of the developed solution is that data compression or duplication is avoided, and that in turn requires no extra load for the publication DSS. Data download operations are delegated to sFTP servers to ensure secure transfers. The sFTP is standard and supported by numerous client software on most systems.\n<\/p><p>Two approaches are implemented in the <i>rql_download<\/i> cube that can be selected by configuration settings:\n<\/p>\n<dl><dd>1. FUSE virtual folders: For each search, the system builds a list of files to be downloaded and subsequently creates a virtual FUSE directory acting as a filter on the central repository. The user can only see subsets of files\/directories corresponding to his queries built in accordance with his access rights. Finally, the system delegates the data transfers to the sFTP server. The major advantage of this approach is the use of the standard sFTP port. However, additional system level configurations are required during the installation of the DSS in order to set the user home directories and system accounts.<\/dd><\/dl>\n<dl><dd>2. Twisted server: This approach is characterized by a Python process that creates a Twisted<sup id=\"rdp-ebb-cite_ref-SFCTwisted_32-0\" class=\"reference\"><a href=\"#cite_note-SFCTwisted-32\" rel=\"external_link\">[32]<\/a><\/sup> event-driven networking server, retrieves all the searches in the database, and exposes the files via sFTP through the created server. Again, this process acts as a filter on the central repository where a user only sees a subset of files\/directories. In this case, the authentication and file transfers are directly operated by CW. The major advantage of this strategy is that no system-level configuration is required. However, listening on a non-default sFTP port, which could lead to firewall issues, is sometimes required.<\/dd><\/dl>\n<h4><span class=\"mw-headline\" id=\"Access_rights_mechanism\">Access rights mechanism<\/span><\/h4>\n<p>In the CW security model, any entity or relation may be endowed with permission rules. To fulfill consortia's criteria, we propose an operational setup of the CW security model for our publication DSS. We built our security model around \"pivotal entities\" rather than specifying rights on all entities. Pivotal entities are those on which access rights are defined, and they are related to all entities that must be covered by the security model through a specific relation (the \"in_assessment\" relation in Figure 4). Each time an entity covered by the security model is requested, the system automatically requests its related pivotal entity and propagates the corresponding access rights.\n<\/p>\n<h4><span class=\"mw-headline\" id=\"The_unified_insertion_procedure\">The unified insertion procedure<\/span><\/h4>\n<p>A unified insertion module is provided as a set of Python scripts to insert neuroimaging, genomic, and clinical data such as scans, genomic measures, questionnaires, and processing steps. These scripts were helpful in efficiently managing the large amount of evolving data in our projects. The indexed data are uniformly organized according to the schema structure and thus take advantage of all the aforementioned developments (e.g., shopping cart mechanism and security model of previous sub-sections, and common renderings cf. Figure 6). Generating such a DSS with these scripts can be performed without specific CW knowledge. Indeed, only a rich description of the data to be published is required as a set of Python dictionary objects.\n<\/p><p><br \/>\n<a href=\"https:\/\/www.limswiki.org\/index.php\/File:Fig6_Grigis_FInNeuroinformatics2017_11.jpg\" class=\"image wiki-link\" target=\"_blank\" data-key=\"984b5bb8b1124582d39ff63825aafddc\"><img alt=\"Fig6 Grigis FInNeuroinformatics2017 11.jpg\" src=\"https:\/\/www.limswiki.org\/images\/c\/c9\/Fig6_Grigis_FInNeuroinformatics2017_11.jpg\" style=\"width: 100%;max-width: 400px;height: auto;\" \/><\/a>\n<\/p>\n<div style=\"clear:both;\"><\/div>\n<table style=\"\">\n<tr>\n<td style=\"vertical-align:top;\">\n<table border=\"0\" cellpadding=\"5\" cellspacing=\"0\" style=\"\">\n\n<tr>\n<td style=\"background-color:white; padding-left:10px; padding-right:10px;\"> <blockquote><b>Figure 6.<\/b> Summary views of the database status. Global information, for example the <b>(A)<\/b> gender or <b>(B)<\/b> handedness distributions, <b>(C)<\/b> acquisition status, and <b>(D)<\/b> age distribution, or longitudinal information, such as <b>(E)<\/b> the answers of subject2 to specific questions across the study time points.<\/blockquote>\n<\/td><\/tr>\n<\/table>\n<\/td><\/tr><\/table>\n<h3><span class=\"mw-headline\" id=\"A_transverse_Python_module_to_remotely_connect_a_CubicWeb_DSS\">A transverse Python module to remotely connect a CubicWeb DSS<\/span><\/h3>\n<p>With the aforementioned capabilities of the DSS, a user manually selects and downloads data through graphical interfaces in order to analyze them locally (as discussed in the previous sub-section). In the case of an evolving DSS, the downloaded data must be regularly updated, and this manual process becomes time consuming and error prone when large and heterogeneous data are considered. Moreover, the metadata, such as quality scores, used to specify the dataset to download are also likely to change. Therefore, to achieve the analysis of up-to-date data stored in a DSS, direct programmatic interaction with the DSS is recommended. In the neuroimaging and neuroscience communities, data are typically analyzed by using Python scripts. Classically, the systems provide RESTful web services such as XNAT, with a Python API.<sup id=\"rdp-ebb-cite_ref-SchwartzPyXNAT12_33-0\" class=\"reference\"><a href=\"#cite_note-SchwartzPyXNAT12-33\" rel=\"external_link\">[33]<\/a><\/sup> Inheriting from the RQL request language, our publication DSS (cf. previous sub-section) offers a rich interface to access the data.\n<\/p><p>We provide a regular Python module, named <i>cwbrowser<\/i><sup id=\"rdp-ebb-cite_ref-NSApCWBROWSER_34-0\" class=\"reference\"><a href=\"#cite_note-NSApCWBROWSER-34\" rel=\"external_link\">[34]<\/a><\/sup>, that implements a Python API to connect and send RQL to a remote DSS based on the CW framework. This module is completely independent of CW (no CW installation required) and similar to the CW distribution <i>cwclientlib<\/i> cube. A publication DSS, as described in the previous sub-section, can be requested by the <i>cwbrowser<\/i> module that embeds the previously described data selection and shopping cart capabilities. It automatically fills and saves a shopping cart from a custom RQL request, downloads the associated virtual directories onto the local file system, and returns the complete requested dataset. The returned dataset contains metadata stored in the DSS such as subject sex or quality scores as well as the path to the downloaded directories. These resources are organized following the DSS layout of files and folders. The users will get the same local tree which will help in writing sharable analysis scripts.\n<\/p>\n<h2><span class=\"mw-headline\" id=\"Results\">Results<\/span><\/h2>\n<p>Our laboratory operates several DSS for the IMAGEN project about mental health in adolescents<sup id=\"rdp-ebb-cite_ref-SchumannTheIMAGEN10_16-1\" class=\"reference\"><a href=\"#cite_note-SchumannTheIMAGEN10-16\" rel=\"external_link\">[16]<\/a><\/sup> and the EU-AIMS project about autism.<sup id=\"rdp-ebb-cite_ref-MurphyEU-AIMS12_17-1\" class=\"reference\"><a href=\"#cite_note-MurphyEU-AIMS12-17\" rel=\"external_link\">[17]<\/a><\/sup> Other DSS are currently under development to support new and ongoing initiatives. Note that the access to both IMAGEN and EU-AIMS datasets is (to date) restricted.\n<\/p><p>In the IMAGEN project, 2,000 subjects are monitored on at least two visits (the third follow-up is underway). T1, T2, FLAIR, DWI, B0, task fMRI, and resting-state fMRI scans are acquired, as well as clinical\/behavioral records, genotyping, gene expression, and methylation. A publication DSS at <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/imagen2.cea.fr\/database\" target=\"_blank\">https:\/\/imagen2.cea.fr\/database<\/a> enables us to share more than 37,000 scans, 32,000 processing results, and 16 million distinct variables. In the near future, an upload DSS will allow us to collect a new time point.\n<\/p><p>In the EU-AIMS project, 1,500 subjects (from six months to 30 years old) are monitored on several visits through two distinct studies. T1, T2, FLAIR, DWI, B0, task fMRI, resting-state fMRI, and spectroscopy scans are acquired, as well as clinical\/behavioral records, EEG, eye-tracking, gene expression, and methylation. An upload DSS at <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/eu-aims.cea.fr\/database\" target=\"_blank\">https:\/\/eu-aims.cea.fr\/database<\/a> provides the means for collecting this data from 10 centers across Europe. In addition, a collaborative quality check DSS at <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/eu-aims.cea.fr\/qc\" target=\"_blank\">https:\/\/eu-aims.cea.fr\/qc<\/a> allows us to assess the uploaded data quality, and a publication DSS at <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/eu-aims.cea.fr\/data_repository\" target=\"_blank\">https:\/\/eu-aims.cea.fr\/data_repository<\/a> enables us to share more than 13,000 scans, 12,000 processing results, and 15 million distinct variables.\n<\/p>\n<h2><span class=\"mw-headline\" id=\"Discussion_and_conclusion\">Discussion and conclusion<\/span><\/h2>\n<h3><span class=\"mw-headline\" id=\"Lightweight_solution_for_data_sharing\">Lightweight solution for data sharing<\/span><\/h3>\n<p>We developed a novel and lightweight PIx software infrastructure exclusively based on the CW framework. We offer a suite of CW tools that facilitates the creation of a DSS. The system delivers the data to users based on the principle of \"what you see is what you get\": users define their datasets of interest by browsing the database. Thanks to the RQL and the developed Python API, remote query of a DSS is easy and intuitive. In this environment, core features such as the schema definition, the web rendering of the database content, and the semantic request language are provided by a few lines of Python code at the heart of the CW framework. Our DSS can use any database engine, offers an access permission mechanism, and can be smoothly integrated with the standard Apache environment. Moreover the CW framework relies on a large community of developers led by Logilab.\n<\/p>\n<h3><span class=\"mw-headline\" id=\"A_PIx_Swiss_knife\">A PIx Swiss knife<\/span><\/h3>\n<p>CW is well suited for all the scenarios one can face in a PIx project. For instance, in the projects we manage, we also provided a CW-based service to allow a collaborative moderation of user access to the different DSS. This service enables the consortium review boards to assign the relevant access rights to new or existing users. It is restricted to a few members and enables the user account administration of an upload, collaborative QC, and publication DSS.\n<\/p>\n<h3><span class=\"mw-headline\" id=\"Future_directions\">Future directions<\/span><\/h3>\n<p>Our developments inherit the web semantic capabilities embedded in the CW framework. Thanks to this key feature, numerous problems of interoperability can be efficiently tackled using emerging ontologies and standards in <a href=\"https:\/\/www.limswiki.org\/index.php\/Neuroinformatics\" title=\"Neuroinformatics\" target=\"_blank\" class=\"wiki-link\" data-key=\"4944aa70b984d0a743d4e9de46416311\">neuroinformatics<\/a>, neurosciences, and <a href=\"https:\/\/www.limswiki.org\/index.php\/Bioinformatics\" title=\"Bioinformatics\" target=\"_blank\" class=\"wiki-link\" data-key=\"8f506695fdbb26e3f314da308f8c053b\">bioinformatics<\/a>, such as the NIDM standard for data exchange<sup id=\"rdp-ebb-cite_ref-KeatorTowards13_35-0\" class=\"reference\"><a href=\"#cite_note-KeatorTowards13-35\" rel=\"external_link\">[35]<\/a><\/sup>, the Cognitive Atlas Ontology<sup id=\"rdp-ebb-cite_ref-PoldrackTheCog11_36-0\" class=\"reference\"><a href=\"#cite_note-PoldrackTheCog11-36\" rel=\"external_link\">[36]<\/a><\/sup>, and OntoNeuroLOG<sup id=\"rdp-ebb-cite_ref-GibaudNeuroLOG11_37-0\" class=\"reference\"><a href=\"#cite_note-GibaudNeuroLOG11-37\" rel=\"external_link\">[37]<\/a><\/sup> for data annotation, or the Bio2RDF for the federation of large datasets using open-source semantic web technologies.<sup id=\"rdp-ebb-cite_ref-DumontierBio2RDF14_38-0\" class=\"reference\"><a href=\"#cite_note-DumontierBio2RDF14-38\" rel=\"external_link\">[38]<\/a><\/sup> The annotation of our datasets, with respect to these ontologies, is ongoing. Ultimately, should all DSS follow standard ontologies, RQL would provide new cross-project querying possibilities. Although the CW framework is already used successfully in several commercial applications, it would be interesting to evaluate the CW framework performances on our DSS with Logilab dedicated tools.\n<\/p>\n<h2><span class=\"mw-headline\" id=\"Author_contributions\">Author contributions<\/span><\/h2>\n<p>AG developed the cubes, performed its deployment, and maintained the online repositories. DG, DO, TG, and RC tested the proposed application and used it in two European projects (IMAGEN, EU-AIMS). NC and AM developed the CubicWeb framework. VF, GS, WS, and DM initiated and supervized the projects. All authors contributed to the manuscript.\n<\/p>\n<h2><span class=\"mw-headline\" id=\"Conflict_of_interest_statement\">Conflict of interest statement<\/span><\/h2>\n<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\n<\/p>\n<h2><span class=\"mw-headline\" id=\"Acknowledgments\">Acknowledgments<\/span><\/h2>\n<p>The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115300 (EU-AIMS), resources of which are composed of financial contribution from the European Union\u2019s Seventh Framework Programme (FP7\/2007-2013) and EFPIA companies\u2019 in kind contribution.\n<\/p>\n<h2><span class=\"mw-headline\" id=\"Supplementary_material\">Supplementary material<\/span><\/h2>\n<p>Codes are distributed under the terms of the CeCILL-B license, as published by the CEA-CNRS-INRIA. Refer to the license file or to <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/www.cecill.info\/licences\/Licence_CeCILL-B_V1-en.html\" target=\"_blank\">http:\/\/www.cecill.info\/licences\/Licence_CeCILL-B_V1-en.html<\/a> for details. Codes are freely accessible on github <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/github.com\/neurospin\" target=\"_blank\">https:\/\/github.com\/neurospin<\/a>. The DSS we are in charge of can be reached at <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/imagen2.cea.fr\" target=\"_blank\">https:\/\/imagen2.cea.fr<\/a> and <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/eu-aims.cea.fr\" target=\"_blank\">https:\/\/eu-aims.cea.fr<\/a>.\n<\/p>\n<h2><span class=\"mw-headline\" id=\"References\">References<\/span><\/h2>\n<div class=\"reflist references-column-width\" style=\"-moz-column-width: 30em; -webkit-column-width: 30em; column-width: 30em; list-style-type: decimal;\">\n<ol class=\"references\">\n<li id=\"cite_note-HurkoTheADNI12-1\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-HurkoTheADNI12_1-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Hurko, O.; Black, S.E.; Doody, R. et al. 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(2016). <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC4978148\" target=\"_blank\">\"The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments\"<\/a>. <i>Scientific Data<\/i> <b>3<\/b>: 160044. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/Digital_object_identifier\" target=\"_blank\">doi<\/a>:<a rel=\"external_link\" class=\"external text\" href=\"http:\/\/dx.doi.org\/10.1038%2Fsdata.2016.44\" target=\"_blank\">10.1038\/sdata.2016.44<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Central\" target=\"_blank\">PMC<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4978148\/\" target=\"_blank\">PMC4978148<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Identifier\" target=\"_blank\">PMID<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/27326542\" target=\"_blank\">27326542<\/a><span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC4978148\" target=\"_blank\">http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC4978148<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+brain+imaging+data+structure%2C+a+format+for+organizing+and+describing+outputs+of+neuroimaging+experiments&rft.jtitle=Scientific+Data&rft.aulast=Gorgolewski%2C+K.J.%3B+Auer%2C+T.%3B+Calhoun%2C+V.D.+et+al.&rft.au=Gorgolewski%2C+K.J.%3B+Auer%2C+T.%3B+Calhoun%2C+V.D.+et+al.&rft.date=2016&rft.volume=3&rft.pages=160044&rft_id=info:doi\/10.1038%2Fsdata.2016.44&rft_id=info:pmc\/PMC4978148&rft_id=info:pmid\/27326542&rft_id=http%3A%2F%2Fwww.pubmedcentral.nih.gov%2Farticlerender.fcgi%3Ftool%3Dpmcentrez%26artid%3DPMC4978148&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-NicholsNeuro15-6\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-NicholsNeuro15_6-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Nichols, B.N.; Pohl, K.M. (2015). <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC5400666\" target=\"_blank\">\"Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community\"<\/a>. <i>Neuropsychology Review<\/i> <b>25<\/b> (3): 356-68. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/Digital_object_identifier\" target=\"_blank\">doi<\/a>:<a rel=\"external_link\" class=\"external text\" href=\"http:\/\/dx.doi.org\/10.1007%2Fs11065-015-9293-x\" target=\"_blank\">10.1007\/s11065-015-9293-x<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Central\" target=\"_blank\">PMC<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5400666\/\" target=\"_blank\">PMC5400666<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Identifier\" target=\"_blank\">PMID<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26267019\" target=\"_blank\">26267019<\/a><span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC5400666\" target=\"_blank\">http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC5400666<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Neuroinformatics+Software+Applications+Supporting+Electronic+Data+Capture%2C+Management%2C+and+Sharing+for+the+Neuroimaging+Community&rft.jtitle=Neuropsychology+Review&rft.aulast=Nichols%2C+B.N.%3B+Pohl%2C+K.M.&rft.au=Nichols%2C+B.N.%3B+Pohl%2C+K.M.&rft.date=2015&rft.volume=25&rft.issue=3&rft.pages=356-68&rft_id=info:doi\/10.1007%2Fs11065-015-9293-x&rft_id=info:pmc\/PMC5400666&rft_id=info:pmid\/26267019&rft_id=http%3A%2F%2Fwww.pubmedcentral.nih.gov%2Farticlerender.fcgi%3Ftool%3Dpmcentrez%26artid%3DPMC5400666&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-HornIsIt09-7\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-HornIsIt09_7-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Van Horn, J.D.; Toga, A.W. (2009). <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC2754579\" target=\"_blank\">\"Is it time to re-prioritize neuroimaging databases and digital repositories?\"<\/a>. <i>NeuroImage<\/i> <b>47<\/b> (4): 1720-34. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/Digital_object_identifier\" target=\"_blank\">doi<\/a>:<a rel=\"external_link\" class=\"external text\" href=\"http:\/\/dx.doi.org\/10.1016%2Fj.neuroimage.2009.03.086\" target=\"_blank\">10.1016\/j.neuroimage.2009.03.086<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Central\" target=\"_blank\">PMC<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC2754579\/\" target=\"_blank\">PMC2754579<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Identifier\" target=\"_blank\">PMID<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/19371790\" target=\"_blank\">19371790<\/a><span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC2754579\" target=\"_blank\">http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC2754579<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Is+it+time+to+re-prioritize+neuroimaging+databases+and+digital+repositories%3F&rft.jtitle=NeuroImage&rft.aulast=Van+Horn%2C+J.D.%3B+Toga%2C+A.W.&rft.au=Van+Horn%2C+J.D.%3B+Toga%2C+A.W.&rft.date=2009&rft.volume=47&rft.issue=4&rft.pages=1720-34&rft_id=info:doi\/10.1016%2Fj.neuroimage.2009.03.086&rft_id=info:pmc\/PMC2754579&rft_id=info:pmid\/19371790&rft_id=http%3A%2F%2Fwww.pubmedcentral.nih.gov%2Farticlerender.fcgi%3Ftool%3Dpmcentrez%26artid%3DPMC2754579&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-MarcusHuman13-8\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-MarcusHuman13_8-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Marcus, D.S.; Harms, M.P.; Snyder, A.Z. et al. 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(15 January 2008). <a rel=\"external_link\" class=\"external text\" href=\"https:\/\/www.w3.org\/TR\/rdf-sparql-query\/\" target=\"_blank\">\"SPARQL Query Language for RDF\"<\/a>. World Wide Web Consortium<span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/www.w3.org\/TR\/rdf-sparql-query\/\" target=\"_blank\">https:\/\/www.w3.org\/TR\/rdf-sparql-query\/<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.btitle=SPARQL+Query+Language+for+RDF&rft.atitle=&rft.date=15+January+2008&rft.pub=World+Wide+Web+Consortium&rft_id=https%3A%2F%2Fwww.w3.org%2FTR%2Frdf-sparql-query%2F&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-GorgolewskiNipype11-21\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-GorgolewskiNipype11_21-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Gorgolewski, K.; Burns, C.D.; Madison, C. et al. 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(2014). <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3930868\" target=\"_blank\">\"Machine learning for neuroimaging with Scikit-learn\"<\/a>. <i>Frontiers in Neuroinformatics<\/i> <b>8<\/b>: 14. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/Digital_object_identifier\" target=\"_blank\">doi<\/a>:<a rel=\"external_link\" class=\"external text\" href=\"http:\/\/dx.doi.org\/10.3389%2Ffninf.2014.00014\" target=\"_blank\">10.3389\/fninf.2014.00014<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Central\" target=\"_blank\">PMC<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3930868\/\" target=\"_blank\">PMC3930868<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Identifier\" target=\"_blank\">PMID<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24600388\" target=\"_blank\">24600388<\/a><span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3930868\" target=\"_blank\">http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3930868<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine+learning+for+neuroimaging+with+Scikit-learn&rft.jtitle=Frontiers+in+Neuroinformatics&rft.aulast=Abraham%2C+A.%3B+Pedregosa%2C+F.%3B+Eickenberg%2C+M.+et+al.&rft.au=Abraham%2C+A.%3B+Pedregosa%2C+F.%3B+Eickenberg%2C+M.+et+al.&rft.date=2014&rft.volume=8&rft.pages=14&rft_id=info:doi\/10.3389%2Ffninf.2014.00014&rft_id=info:pmc\/PMC3930868&rft_id=info:pmid\/24600388&rft_id=http%3A%2F%2Fwww.pubmedcentral.nih.gov%2Farticlerender.fcgi%3Ftool%3Dpmcentrez%26artid%3DPMC3930868&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-FischerMorph12-24\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-FischerMorph12_24-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Fischer, C.; Operto, G.; Laguitton, S. et al.. \"Morphologist 2012: The new morphological pipeline of BrainVisa\". <i>Proceedings from the Human Brain Mapping Conference 2012<\/i> <b>2012<\/b>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Morphologist+2012%3A+The+new+morphological+pipeline+of+BrainVisa&rft.jtitle=Proceedings+from+the+Human+Brain+Mapping+Conference+2012&rft.aulast=Fischer%2C+C.%3B+Operto%2C+G.%3B+Laguitton%2C+S.+et+al.&rft.au=Fischer%2C+C.%3B+Operto%2C+G.%3B+Laguitton%2C+S.+et+al.&rft.volume=2012&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-GitHubFUSE-25\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-GitHubFUSE_25-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation web\"><a rel=\"external_link\" class=\"external text\" href=\"https:\/\/github.com\/libfuse\/libfuse\" target=\"_blank\">\"libfuse\/libfuse\"<\/a>. GitHub, Inc<span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/github.com\/libfuse\/libfuse\" target=\"_blank\">https:\/\/github.com\/libfuse\/libfuse<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.btitle=libfuse%2Flibfuse&rft.atitle=&rft.pub=GitHub%2C+Inc&rft_id=https%3A%2F%2Fgithub.com%2Flibfuse%2Flibfuse&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-RockholdExtract12-26\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-RockholdExtract12_26-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Rockhold, F.; Bishop, S. 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GitHub, Inc<span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/neurospin.github.io\/rql_upload\/\" target=\"_blank\">http:\/\/neurospin.github.io\/rql_upload\/<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.btitle=Rql+Upload&rft.atitle=&rft.aulast=NSAp+developers&rft.au=NSAp+developers&rft.pub=GitHub%2C+Inc&rft_id=http%3A%2F%2Fneurospin.github.io%2Frql_upload%2F&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-NSApPIWS-28\"><span class=\"mw-cite-backlink\">\u2191 <sup><a href=\"#cite_ref-NSApPIWS_28-0\" rel=\"external_link\">28.0<\/a><\/sup> <sup><a href=\"#cite_ref-NSApPIWS_28-1\" rel=\"external_link\">28.1<\/a><\/sup><\/span> <span class=\"reference-text\"><span class=\"citation web\">NSAp developers. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/neurospin.github.io\/piws\/\" target=\"_blank\">\"Population Imaging Web Service: PIWS\"<\/a>. GitHub, Inc<span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/neurospin.github.io\/piws\/\" target=\"_blank\">http:\/\/neurospin.github.io\/piws\/<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.btitle=Population+Imaging+Web+Service%3A+PIWS&rft.atitle=&rft.aulast=NSAp+developers&rft.au=NSAp+developers&rft.pub=GitHub%2C+Inc&rft_id=http%3A%2F%2Fneurospin.github.io%2Fpiws%2F&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-GitHubZeijemol-29\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-GitHubZeijemol_29-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation web\"><a rel=\"external_link\" class=\"external text\" href=\"https:\/\/github.com\/neurospin\/zeijemol\" target=\"_blank\">\"neurospin\/zeijemol\"<\/a>. GitHub, Inc<span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/github.com\/neurospin\/zeijemol\" target=\"_blank\">https:\/\/github.com\/neurospin\/zeijemol<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.btitle=neurospin%2Fzeijemol&rft.atitle=&rft.pub=GitHub%2C+Inc&rft_id=https%3A%2F%2Fgithub.com%2Fneurospin%2Fzeijemol&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-NSApRQLDownload-30\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-NSApRQLDownload_30-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation web\">NSAp developers. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/neurospin.github.io\/rql_download\/\" target=\"_blank\">\"Rql Download\"<\/a>. GitHub, Inc<span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/neurospin.github.io\/rql_download\/\" target=\"_blank\">http:\/\/neurospin.github.io\/rql_download\/<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.btitle=Rql+Download&rft.atitle=&rft.aulast=NSAp+developers&rft.au=NSAp+developers&rft.pub=GitHub%2C+Inc&rft_id=http%3A%2F%2Fneurospin.github.io%2Frql_download%2F&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-GitHubBrainomics2-31\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-GitHubBrainomics2_31-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation web\"><a rel=\"external_link\" class=\"external text\" href=\"https:\/\/github.com\/neurospin\/brainomics2\" target=\"_blank\">\"neurospin\/brainomics2\"<\/a>. GitHub, Inc<span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/github.com\/neurospin\/brainomics2\" target=\"_blank\">https:\/\/github.com\/neurospin\/brainomics2<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.btitle=neurospin%2Fbrainomics2&rft.atitle=&rft.pub=GitHub%2C+Inc&rft_id=https%3A%2F%2Fgithub.com%2Fneurospin%2Fbrainomics2&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-SFCTwisted-32\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-SFCTwisted_32-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation web\"><a rel=\"external_link\" class=\"external text\" href=\"https:\/\/twistedmatrix.com\/trac\/\" target=\"_blank\">\"What is Twisted?\"<\/a>. Software Freedom Conservancy<span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"https:\/\/twistedmatrix.com\/trac\/\" target=\"_blank\">https:\/\/twistedmatrix.com\/trac\/<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.btitle=What+is+Twisted%3F&rft.atitle=&rft.pub=Software+Freedom+Conservancy&rft_id=https%3A%2F%2Ftwistedmatrix.com%2Ftrac%2F&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-SchwartzPyXNAT12-33\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-SchwartzPyXNAT12_33-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Schwartz, Y.; Barbot, A.; Thyreau, B. et al. (2012). <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3354345\" target=\"_blank\">\"PyXNAT: XNAT in Python\"<\/a>. <i>Frontiers in Neuroinformatics<\/i> <b>6<\/b>: 12. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/Digital_object_identifier\" target=\"_blank\">doi<\/a>:<a rel=\"external_link\" class=\"external text\" href=\"http:\/\/dx.doi.org\/10.3389%2Ffninf.2012.00012\" target=\"_blank\">10.3389\/fninf.2012.00012<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Central\" target=\"_blank\">PMC<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3354345\/\" target=\"_blank\">PMC3354345<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Identifier\" target=\"_blank\">PMID<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/22654752\" target=\"_blank\">22654752<\/a><span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3354345\" target=\"_blank\">http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3354345<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=PyXNAT%3A+XNAT+in+Python&rft.jtitle=Frontiers+in+Neuroinformatics&rft.aulast=Schwartz%2C+Y.%3B+Barbot%2C+A.%3B+Thyreau%2C+B.+et+al.&rft.au=Schwartz%2C+Y.%3B+Barbot%2C+A.%3B+Thyreau%2C+B.+et+al.&rft.date=2012&rft.volume=6&rft.pages=12&rft_id=info:doi\/10.3389%2Ffninf.2012.00012&rft_id=info:pmc\/PMC3354345&rft_id=info:pmid\/22654752&rft_id=http%3A%2F%2Fwww.pubmedcentral.nih.gov%2Farticlerender.fcgi%3Ftool%3Dpmcentrez%26artid%3DPMC3354345&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-NSApCWBROWSER-34\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-NSApCWBROWSER_34-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation web\">NSAp developers. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/neurospin.github.io\/rql_download\/cwbrowser\" target=\"_blank\">\"CWBROWSER\"<\/a>. GitHub, Inc<span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/neurospin.github.io\/rql_download\/cwbrowser\" target=\"_blank\">http:\/\/neurospin.github.io\/rql_download\/cwbrowser<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.btitle=CWBROWSER&rft.atitle=&rft.aulast=NSAp+developers&rft.au=NSAp+developers&rft.pub=GitHub%2C+Inc&rft_id=http%3A%2F%2Fneurospin.github.io%2Frql_download%2Fcwbrowser&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-KeatorTowards13-35\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-KeatorTowards13_35-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Keator, D.B.; Helmer, K.; Steffener, J. et al. (2013). <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC4028152\" target=\"_blank\">\"Towards structured sharing of raw and derived neuroimaging data across existing resources\"<\/a>. <i>Neuroimage<\/i> <b>82<\/b>: 647-61. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/Digital_object_identifier\" target=\"_blank\">doi<\/a>:<a rel=\"external_link\" class=\"external text\" href=\"http:\/\/dx.doi.org\/10.1016%2Fj.neuroimage.2013.05.094\" target=\"_blank\">10.1016\/j.neuroimage.2013.05.094<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Central\" target=\"_blank\">PMC<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4028152\/\" target=\"_blank\">PMC4028152<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Identifier\" target=\"_blank\">PMID<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/23727024\" target=\"_blank\">23727024<\/a><span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC4028152\" target=\"_blank\">http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC4028152<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Towards+structured+sharing+of+raw+and+derived+neuroimaging+data+across+existing+resources&rft.jtitle=Neuroimage&rft.aulast=Keator%2C+D.B.%3B+Helmer%2C+K.%3B+Steffener%2C+J.+et+al.&rft.au=Keator%2C+D.B.%3B+Helmer%2C+K.%3B+Steffener%2C+J.+et+al.&rft.date=2013&rft.volume=82&rft.pages=647-61&rft_id=info:doi\/10.1016%2Fj.neuroimage.2013.05.094&rft_id=info:pmc\/PMC4028152&rft_id=info:pmid\/23727024&rft_id=http%3A%2F%2Fwww.pubmedcentral.nih.gov%2Farticlerender.fcgi%3Ftool%3Dpmcentrez%26artid%3DPMC4028152&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-PoldrackTheCog11-36\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-PoldrackTheCog11_36-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Poldrack, R.A.; Kittur, A.; Kalar, D. et al. (2011). <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3167196\" target=\"_blank\">\"The cognitive atlas: Toward a knowledge foundation for cognitive neuroscience\"<\/a>. <i>Frotiers in Neuroinformatics<\/i> <b>5<\/b>: 17. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/Digital_object_identifier\" target=\"_blank\">doi<\/a>:<a rel=\"external_link\" class=\"external text\" href=\"http:\/\/dx.doi.org\/10.3389%2Ffninf.2011.00017\" target=\"_blank\">10.3389\/fninf.2011.00017<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Central\" target=\"_blank\">PMC<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3167196\/\" target=\"_blank\">PMC3167196<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Identifier\" target=\"_blank\">PMID<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/21922006\" target=\"_blank\">21922006<\/a><span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3167196\" target=\"_blank\">http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3167196<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+cognitive+atlas%3A+Toward+a+knowledge+foundation+for+cognitive+neuroscience&rft.jtitle=Frotiers+in+Neuroinformatics&rft.aulast=Poldrack%2C+R.A.%3B+Kittur%2C+A.%3B+Kalar%2C+D.+et+al.&rft.au=Poldrack%2C+R.A.%3B+Kittur%2C+A.%3B+Kalar%2C+D.+et+al.&rft.date=2011&rft.volume=5&rft.pages=17&rft_id=info:doi\/10.3389%2Ffninf.2011.00017&rft_id=info:pmc\/PMC3167196&rft_id=info:pmid\/21922006&rft_id=http%3A%2F%2Fwww.pubmedcentral.nih.gov%2Farticlerender.fcgi%3Ftool%3Dpmcentrez%26artid%3DPMC3167196&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-GibaudNeuroLOG11-37\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-GibaudNeuroLOG11_37-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Gibaud, B.; Kassel, G.; Dojat, M. et al. (2011). <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3243145\" target=\"_blank\">\"NeuroLOG: Sharing neuroimaging data using an ontology-based federated approach\"<\/a>. <i>AMIA Annual Symposium Proceedings<\/i> <b>2011<\/b>: 472-80. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Central\" target=\"_blank\">PMC<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3243145\/\" target=\"_blank\">PMC3243145<\/a>. <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/en.wikipedia.org\/wiki\/PubMed_Identifier\" target=\"_blank\">PMID<\/a> <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/22195101\" target=\"_blank\">22195101<\/a><span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3243145\" target=\"_blank\">http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?tool=pmcentrez&artid=PMC3243145<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=NeuroLOG%3A+Sharing+neuroimaging+data+using+an+ontology-based+federated+approach&rft.jtitle=AMIA+Annual+Symposium+Proceedings&rft.aulast=Gibaud%2C+B.%3B+Kassel%2C+G.%3B+Dojat%2C+M.+et+al.&rft.au=Gibaud%2C+B.%3B+Kassel%2C+G.%3B+Dojat%2C+M.+et+al.&rft.date=2011&rft.volume=2011&rft.pages=472-80&rft_id=info:pmc\/PMC3243145&rft_id=info:pmid\/22195101&rft_id=http%3A%2F%2Fwww.pubmedcentral.nih.gov%2Farticlerender.fcgi%3Ftool%3Dpmcentrez%26artid%3DPMC3243145&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<li id=\"cite_note-DumontierBio2RDF14-38\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-DumontierBio2RDF14_38-0\" rel=\"external_link\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation Journal\">Dumontier, M.; Callahan, A.; Cruz-Toledo, J. et al. (2014). <a rel=\"external_link\" class=\"external text\" href=\"http:\/\/ceur-ws.org\/Vol-1272\/paper_121.pdf\" target=\"_blank\">\"Bio2RDF release 3: A larger connected network of linked data for the life sciences\"<\/a>. <i>Proceedings of the 2014 International Conference on Posters & Demonstrations Track<\/i> <b>1272<\/b>: 401\u201304<span class=\"printonly\">. <a rel=\"external_link\" class=\"external free\" href=\"http:\/\/ceur-ws.org\/Vol-1272\/paper_121.pdf\" target=\"_blank\">http:\/\/ceur-ws.org\/Vol-1272\/paper_121.pdf<\/a><\/span>.<\/span><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Bio2RDF+release+3%3A+A+larger+connected+network+of+linked+data+for+the+life+sciences&rft.jtitle=Proceedings+of+the+2014+International+Conference+on+Posters+%26+Demonstrations+Track&rft.aulast=Dumontier%2C+M.%3B+Callahan%2C+A.%3B+Cruz-Toledo%2C+J.+et+al.&rft.au=Dumontier%2C+M.%3B+Callahan%2C+A.%3B+Cruz-Toledo%2C+J.+et+al.&rft.date=2014&rft.volume=1272&rft.pages=401%E2%80%9304&rft_id=http%3A%2F%2Fceur-ws.org%2FVol-1272%2Fpaper_121.pdf&rfr_id=info:sid\/en.wikipedia.org:Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\"><span style=\"display: none;\"> <\/span><\/span><\/span>\n<\/li>\n<\/ol><\/div>\n<h2><span class=\"mw-headline\" id=\"Notes\">Notes<\/span><\/h2>\n<p>This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. References are in order of appearance rather than alphabetical order (as the original was) due to the way this wiki works. Footnotes (URLs to projects) were turned into full citations. The URL to the <i>zeijemol<\/i> application cube was broken, and a direct GitHub URL was substituted instead.\n<\/p>\n<!-- \nNewPP limit report\nCached time: 20181214181500\nCache expiry: 86400\nDynamic content: false\nCPU time usage: 0.841 seconds\nReal time usage: 0.869 seconds\nPreprocessor visited node count: 29459\/1000000\nPreprocessor generated node count: 35265\/1000000\nPost\u2010expand include size: 239646\/2097152 bytes\nTemplate argument size: 77434\/2097152 bytes\nHighest expansion depth: 18\/40\nExpensive parser function count: 0\/100\n-->\n\n<!-- \nTransclusion expansion time report (%,ms,calls,template)\n100.00% 824.291 1 - -total\n 87.09% 717.883 1 - Template:Reflist\n 77.31% 637.261 38 - Template:Citation\/core\n 63.20% 520.929 27 - Template:Cite_journal\n 17.92% 147.736 11 - Template:Cite_web\n 8.97% 73.930 61 - Template:Citation\/identifier\n 8.23% 67.824 1 - Template:Infobox_journal_article\n 7.95% 65.567 1 - Template:Infobox\n 4.85% 39.978 80 - Template:Infobox\/row\n 4.47% 36.819 38 - Template:Citation\/make_link\n-->\n\n<!-- Saved in parser cache with key limswiki:pcache:idhash:10148-0!*!0!!en!5!* and timestamp 20181214181459 and revision id 30787\n -->\n<\/div><div class=\"printfooter\">Source: <a rel=\"external_link\" class=\"external\" href=\"https:\/\/www.limswiki.org\/index.php\/Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework\">https:\/\/www.limswiki.org\/index.php\/Journal:Neuroimaging,_genetics,_and_clinical_data_sharing_in_Python_using_the_CubicWeb_framework<\/a><\/div>\n\t\t\t\t\t\t\t\t\t\t<!-- end content -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"visualClear\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t<!-- end of the left (by default at least) column -->\n\t\t<div class=\"visualClear\"><\/div>\n\t\t\t\t\t\n\t\t<\/div>\n\t\t\n\n<\/body>","bce85c098ea6958c92b6dcce94e42565_images":["https:\/\/www.limswiki.org\/images\/6\/63\/Fig1_Grigis_FInNeuroinformatics2017_11.jpg","https:\/\/www.limswiki.org\/images\/3\/33\/Fig2_Grigis_FInNeuroinformatics2017_11.jpg","https:\/\/www.limswiki.org\/images\/6\/6a\/Fig3_Grigis_FInNeuroinformatics2017_11.jpg","https:\/\/www.limswiki.org\/images\/f\/fa\/Fig4_Grigis_FInNeuroinformatics2017_11.jpg","https:\/\/www.limswiki.org\/images\/1\/1b\/Fig5_Grigis_FInNeuroinformatics2017_11.jpg","https:\/\/www.limswiki.org\/images\/c\/c9\/Fig6_Grigis_FInNeuroinformatics2017_11.jpg"],"bce85c098ea6958c92b6dcce94e42565_timestamp":1544811299,"e86f9fcd9da316970f965a883c42e462_type":"article","e86f9fcd9da316970f965a883c42e462_title":"A multi-service data management platform for scientific oceanographic products (D\u2019Anca et al. 2017)","e86f9fcd9da316970f965a883c42e462_url":"https:\/\/www.limswiki.org\/index.php\/Journal:A_multi-service_data_management_platform_for_scientific_oceanographic_products","e86f9fcd9da316970f965a883c42e462_plaintext":"\n\n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\n\t\t\t\tJournal:A multi-service data management platform for scientific oceanographic products\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\tFrom LIMSWiki\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tJump to: navigation, search\n\n\t\t\t\t\t\n\t\t\t\t\tFull article title\n \nA multi-service data management platform for scientific oceanographic productsJournal\n \nNatural Hazards and Earth System SciencesAuthor(s)\n \nD'Anca, Alessandro; Conte, Laura; Nassisi, Paola; Palazzo, Cosimo; Lecci, Rita; Cret\u00ec, Sergio; Mancini, Marco;\r\nNuzzo, Alessandra; Mirto, Maria; Mannarini, Gianandrea; Coppini, Giovanni; Fiore, Sandro; Aloisio, GiovanniAuthor affiliation(s)\n \nCentro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), University of SalentoPrimary contact\n \nEmail: alessandro dot danca at cmcc dot itEditors\n \nMarra, P.Year published\n \n2017Volume and issue\n \n17(2)Page(s)\n \n171-184DOI\n \n10.5194\/nhess-17-171-2017ISSN\n \n1684-9981Distribution license\n \nCreative Commons Attribution 3.0Website\n \nhttp:\/\/www.nat-hazards-earth-syst-sci.net\/17\/171\/2017\/nhess-17-171-2017.htmlDownload\n \nhttp:\/\/www.nat-hazards-earth-syst-sci.net\/17\/171\/2017\/nhess-17-171-2017.pdf (PDF)\n\nContents\n\n1 Abstract \n2 Introduction \n3 Related work \n4 The data platform architecture \n5 The Data Access Service \n\n5.1 The dataset repository: The TESSA data archive \n5.2 Implementation of the DAS-DTS modules \n\n\n6 The Metadata Service and the metadata profile \n\n6.1 Implementation of the Metadata Service \n\n\n7 The Complex Data Analysis Module \n\n7.1 The CDAM gateway \n7.2 The CDAM launcher \n\n\n8 Operational activity: TESSA data platform use cases \n\n8.1 Automatic publishing procedure on the ESGF data node \n8.2 DTS automatic procedures for DSS and numerical models transformations \n8.3 SeaConditions operational chain \n\n\n9 Conclusions \n10 Data availability \n11 Acknowledgements \n12 Competing interests \n13 References \n14 Notes \n\n\n\nAbstract \nAn efficient, secure and interoperable data platform solution has been developed in the TESSA project to provide fast navigation and access to the data stored in the data archive, as well as a standard-based metadata management support. The platform mainly targets scientific users and the situational sea awareness high-level services such as the decision support systems (DSS). These datasets are accessible through the following three main components: the Data Access Service (DAS), the Metadata Service and the Complex Data Analysis Module (CDAM). The DAS allows access to data stored in the archive by providing interfaces for different protocols and services for downloading, variable selection, data subsetting or map generation. Metadata Service is the heart of the information system of TESSA products and completes the overall infrastructure for data and metadata management. This component enables data search and discovery and addresses interoperability by exploiting widely adopted standards for geospatial data. Finally, the CDAM represents the back end of the TESSA DSS by performing on-demand complex data analysis tasks.\n\nIntroduction \nTESSA (Development of TEchnology for Situational Sea Awareness) is a research project born from the collaboration between operational oceanography research and scientific computing groups in order to strengthen operational oceanography capabilities in Southern Italy for use by end users in the maritime, tourism and environmental protection sectors. This project has been very innovative as it has provided the integration of marine and ocean forecasts and analyses with advanced technological platforms. Specifically, an efficient, secure and interoperable data platform solution has been developed to provide fast navigation and access to the data stored in the data archive, as well as standard-based metadata management support. \nThis platform mainly targets scientific users and contains a set of high-level services such as the decision support systems (DSS) for supporting end users in managing emergency situations due to natural hazards in the Mediterranean Sea. For example, the DSS WITOIL[1][2] is crucial for oil spill accidents which could have severe impacts on the Mediterranean Basin and contribute effectively to the reduction of natural disaster risks. Moreover, the DSS Ocean-SAR[3] supports the search-and-rescue (SAR) operations following accidents, and EarlyWarning manages alerts in cases of extreme events by providing near-real-time information on weather and oceanographic conditions. Finally, VISIR[4][5] is able to compute the optimal ship route with the aim of increasing safety and efficiency of navigation. It relies on various forecast environmental fields which affect the vessel's stability in order to ensure that the computed route does not result in an exposure to dynamical hazards. In this context, the developed platform faces the lack of an efficient dissemination of marine environmental data in order to support situational sea awareness (SSA), which is strategically important for maritime safety and security.[6] In fact, an updated situation awareness requires an advanced technological system to make data available for decision makers, improving the capacity of intervention and avoiding potential damages.\nIn a \"data-centric\" perspective \u2014 in which different services, applications or users make use of the outputs of regional or global numerical models \u2014 the TESSA data platform meets the request of near-real-time access to heterogeneous data with different accuracy, resolution or degrees of aggregation. The design phase has been driven by multiple needs that the developed solution had to satisfy. First of all, nowadays each data management system should satisfy the FAIR guiding principles for scientific data management and stewardship[7]: they face the lack of a shared, comprehensive, broadly applicable and articulated guideline concerning the publication of scientific data. Specifically, they identify findability, accessibility, interoperability and reusability as the four principles which need to be addressed by data (and associated metadata) to be considered a good publication. As explained later, the employment of well-known standards concerning protocols, services and output formats satisfies these requirements. In addition, the need for a service that provides information about sea conditions 24\/7 at high and very high spatial and temporal resolution has been addressed by exploiting high-performance and high-availability hardware and software solutions. The developed platform must be able to support the requests of intermediate and common users. To this end, data must be available in the native and standard format (NetCDF)[8][9] as output of the oceanographic models, through a simple and intuitive platform suitable for machine-based interactions, in order to feed user-friendly services for displaying clear maps and graphs. At the end, the system has to provide on-demand services to support decisions; the users must be able to interact with the datasets produced by the models in near-real time. As such, the platform has to provide services and datasets suitable for on-demand processing while minimizing the downloading time and the related input file size.\nThis paper is organized as follows: The next section presents a survey on related work, and in the following section \"The data platform architecture\" an architectural overview of the main data platform components is provided. The focus is on providing easier and unified access to the heterogeneous data produced in the framework of the TESSA project. The implemented data archive and the modules that make the datasets available to the entire set of services are presented in \"The Data Access Service.\" In \"The Metadata Service and the metadata profile,\" the Metadata Service features are detailed from a methodological and technical point-of-view. The module developed in order to serve remote DSS submission requests is described in \"The Complex Data Analysis Module.\" Finally in the final section on operational activity, a few use cases are presented, highlighting the operational chains which exploit the data platform services.\n\nRelated work \nThe need for marine and oceanic data management supporting the SSA and the operational oceanography has led to the definition and development of various platforms that provide different types of data and services. In this context, the MyOcean project[10][11] can be mentioned, as this project implementation was in line with the best practices of the GMES\/Copernicus framework.[12]\nSpecifically, regarding data access, MyOcean provides the scientific community with a unified interface designed to take into account various international standards (ISO 19115[13], ISO 19139[14] and INSPIRE[15][16]). Concerning data management, MyOcean relies on OPeNDAP\/THREDDS for tasks like map subsetting and FTP for direct download. In addition, a valid solution is represented by the Earth System Grid Federation (ESGF)[17][18], a federated system used as metadata service with advanced features, that will be described later. EMODNET MEDSEA Checkpoint[19][20] is another solution supporting data collection and data search and discovery: it exploits a checkpoint browser and a checkpoint dashboard, which presents indicators automatically produce
