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gCube Catalogue (gCat) Client is a library designed to interact with REST API exposed by the gCat Service. gCube is an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments, such as the D4Science Infrastructure, by favouring the realisation of reuse-oriented policies. gCube has been used to successfully build and operate infrastructures and virtual research environments for application domains ranging from biodiversity to environmental data management and cultural heritage. gCube offers components supporting typical data management workflows including data access, curation, processing, and visualisation on a large set of data typologies ranging from primary biodiversity data to geospatial and tabular data. D4Science is a Hybrid Data Infrastructure combining over 500 software components and integrating data from more than 50 different data providers into a coherent and managed system of hardware, software, and data resources. The D4Science infrastructure drastically reduces the cost of ownership, maintenance, and operation thanks to the exploitation of gCube. The source code of this software version is available at: https://code-repo.d4science.org/gCubeSystem/gcat-client/releases/tag/v2.4.0

gCube Catalogue (gCat) API is a library containing classes shared across gcat* components. gCube is an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments, such as the D4Science Infrastructure, by favouring the realisation of reuse-oriented policies. gCube has been used to successfully build and operate infrastructures and virtual research environments for application domains ranging from biodiversity to environmental data management and cultural heritage. gCube offers components supporting typical data management workflows including data access, curation, processing, and visualisation on a large set of data typologies ranging from primary biodiversity data to geospatial and tabular data. D4Science is a Hybrid Data Infrastructure combining over 500 software components and integrating data from more than 50 different data providers into a coherent and managed system of hardware, software, and data resources. The D4Science infrastructure drastically reduces the cost of ownership, maintenance, and operation thanks to the exploitation of gCube. The source code of this software version is available at: https://code-repo.d4science.org/gCubeSystem/gcat-api/releases/tag/v2.3.0

gCube Catalogue (gCat) Service allows any client to publish on the gCube Catalogue. gCube is an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments, such as the D4Science Infrastructure, by favouring the realisation of reuse-oriented policies. gCube has been used to successfully build and operate infrastructures and virtual research environments for application domains ranging from biodiversity to environmental data management and cultural heritage. gCube offers components supporting typical data management workflows including data access, curation, processing, and visualisation on a large set of data typologies ranging from primary biodiversity data to geospatial and tabular data. D4Science is a Hybrid Data Infrastructure combining over 500 software components and integrating data from more than 50 different data providers into a coherent and managed system of hardware, software, and data resources. The D4Science infrastructure drastically reduces the cost of ownership, maintenance, and operation thanks to the exploitation of gCube. The source code of this software version is available at: https://code-repo.d4science.org/gCubeSystem/gcat/releases/tag/v2.1.0

gCube SDI is the set of engines and logic responsible of dealing with GIS data management in a gCube Infrastructure. In the image above is presented the SDI from a logic point of view : Orange components are all gCube SDI logic meant to facilitate the management of the SDI nodes and content (see #gCube Logic below), in particular : SDI-Service manages instances credentials on IS and some configuration operation on the nodes GeoServer and GeoNetwork are equipped with connectors in order to manage http(s) requests Thredds node are provisioned with a Data_Transfer_2 equipped with a dedicated How_to_use_Data_Transfer_2#Thredds_Plugin_Suite. Third party engines are exploited for their core GIS features (i.e. store/access to GIS data/metadata) The generic rationale of gCube logic is to provide : Context-oriented credential management, in order to enforce access policies to published (meta) Metadata enrichment in publication phase GIS (meta)data publication utilities NB : It is important to note that while the use of gCube logic is recommended, various use cases may directly exploit provided third party engines for various reasons (e.g. non-Java algorithms) Third Party Engines In gCube SDI, third party engines are exploited for their GIS data/metadata management, more in details we use : GeoNetwork : Store GIS ISO Metadata (xml) Expose metadata with CSW Harvest metadata GeoServer : Store GIS DATA (e.g. shp, postgis, geopackage) Expose data with WMS, WFS, WCS.. Thredds : Store GIS DATA Expose data with WMS, WFS, WCS.. Managed Credentials GeoNetwork credentials are managed by gCube org.gcube.spatial.data.geonetwork library and org.gcube.spatial.data.sdi-service SmartGear service. These credentials are generated as needed, and configured in such a way that metadata visibility between gCube contexts reflects gCube policies (more information here). GeoServer and Thredds credentials are loosely managed, meaning that only administrator user is used in order to publish data to these engines. gCube Logic During SDI evolution, distributed gCube logic evolved according to emerging needs while trying to maintain retro-compatibility. For this reason, some components may still be used while being deprecated (e.g. ws-thredds) and there is still some overlap between logic. Following list aims to provide a generic overview of provided gCube logic features by component : org.gcube.spatial.data.geonetwork Library Client for GeoNetwork service Metadata publication utilities Metadata enhancement (NB uses IS Generic Resource, more information at GeoNetwork_library#Metadata_generation_facilities) GeoNetwork credentials management (generation and retrieval) org.gcube.spatial.data.gis-interface Library Data publication utilities with contextual metadata publishing (uses org.gcube.spatial.data.geonetwork for metadata) Currently supported data engine is only GeoServer org.gcube.spatial.data.sdi-service SmartGear Service Credentials management and retrieval Metadata enhancement via template application (used in Thredds publication, see ws-thredds below) SDI configuration management Thredds Plugin Suite Data Transfer Plugins Configure catalogs in local Thredds Publish transferred dataset's metadata in VRE's GeoNewtork org.gcube.spatial.data.ws-thredds Deprecated Library Synchronize a ws-folder with a Thredds catalog, generating and publishing metadata on GeoNetwork Connectors These components are library which implement a Web Application Filter in order to : Intercept incoming http(s) requests If gcube-token is declared, then retrieve credentials from SDI Service and set them in the current request This allows for authentication within third party engines unaware of gCube logic, by using gcube-token. NB : this approach is considered obsolete and should be changed in favor of oauth integration. Available connectors are : GeoNetwork : org.gcube.data.access.geonetwork.gcube-geonetwork-connector GeoServer : org.gcube.data.access.geoserver.gcube-geoserver-connector

The org.gcube.data.publishing.ckan2Zenodo gCube component is a Java library which translates CKAN items into Zenodo depositions, allowing to further editing and publication. The library is invoked by the client (in this case the gCube CKAN GUI), which relies on the gCat service for interacting with CKAN. After the ckan item is loaded, a default translation is then applied on the item's core fields generating in a Draft Zenodo Deposition. NB If the item has already a linked Zenodo DOI, the Draft Deposition overrides its specified informations. A XML Mapping associated with item's profile, is then loaded from the IS (token must be specified by the client). The XML Mapping extracts data from the CKAN item and applies them to the Draft Deposition, resulting in a Final Deposition. The Final Deposition is then returned to the client for further editing, which can eventually ask for its publication (the new Zenodo Doi will be then applied to the CKAN item through gCat).

Ce récit est le second de deux articles portant sur la naissance de la politique scientifique en Belgique. Il étudie la mutation du financement et de l'administration de la recherche dans ce pays, d'un modèle de financement privé et d'autogestion par les scientifiques à un modèle de 'Big Science' caractérisé par une forte augmentation du financement public et une direction de la science par les pouvoirs publics. Bestor

Pastaruoju metu neuroninių tinklų modeliai yra vis dažniau taikomi įvairiose mokslo srityse (mokslometrijoje, lingvistikoje, medicinoje). Tačiau socialiniuose moksluose šie modeliai ir giliojo mokymosi metodai apskritai kelią skinasi sunkiai. Pagrindinės menko jų populiarumo priežastys yra tai, kad netiesinių modelių rezultatus yra sunku interpretuoti ir jie nėra tinkami hipotezėms tikrinti. Nepaisant to, šie metodai gali būti labai naudingi socialiniuose moksluose, ypač automatizuojant darbui labai imlias tyrimo proceso dalis. Straipsnyje pristatomas pavyzdys, kaip šie modeliai yra naudojami atliekant didelio masto teksto kodavimą. Pateikiamas pavyzdys rodo, kad kai kuriais atvejais šie metodai leidžia išvengti kompromiso tarp analizės gylio ir imties dydžio, taip pat leidžia kombinuoti geriausias kokybinių ir kiekybinių metodų praktikas. Artificial Neural Networks (ANNs) are being increasingly used in various disciplines outside computer science, such as bibliometrics, linguistics, and medicine. However, their uptake in the social science community has been relatively slow, because these highly non-linear models are difficult to interpret and cannot be used for hypothesis testing. Despite the existing limitations, this paper argues that the social science community can benefit from using ANNs in a number of ways, especially by outsourcing laborious data coding and pre-processing tasks to machines in the early stages of analysis. Using ANNs would enable small teams of researchers to process larger quantities of data and undertake more ambitious projects. In fact, the complexity of the pre-processing tasks that ANNs are able to perform mean that researchers could obtain rich and complex data typically associated with qualitative research at a large scale, allowing to combine the best from both qualitative and quantitative approaches.