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This report documents the implementation of drug discovery pipeline in the D4Science platform realised in the context of the EOSC-Pillar project. In particular, it documents the pipeline and its constituents. Moreover, it describes how this pipeline has been integrated into the D4Science platform and exploited to create a dedicated Virtual Research Environment facilitating its exploitation and promoting a collaborative oriented approach for screening activities.

InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2021 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, eScience, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2021 InfraScience members contributed to the publishing of 25 papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.

This deliverable D13.3 - "Software Release Final Activity Report - JRA2" documents the software packages produced by the project to implement the functionalities of the ARIADNEplus infrastructure, accessible from https://ariadne.d4science.org. These open- source software releases followed the procedures described in D13.1 Software Release Procedures and Tools - JRA2 governing the release of software, methods, and tools for the ARIADNEplus infrastructure. D13.3 reports on (i) the ARIADNE VRE software releases, where a total of 29 different release cycles were performed during the project period. Each release contained EUPL licensed software, whose source is accessible on the Code Versioning System (CVS) platform publicly available online at https://code-repo.d4science.org/gCubeCI/gCubeReleases, and reports on (ii) the ARIADNE Portal software releases whose source is accessible on the Code Versioning System (CVS) platform publicly available online https://github.com/ARIADNE- Infrastructure/portal. These activities have been carried out in the context of Task 13.4 Software integration and release (JRA2.4) as part of Work Package 13 (WP13). This task managed the process of software maintenance, enhancement, and provisioning in JRA work packages. Thus, it i) defines the release and provisioning procedures; ii) establishes the release plan; iii) coordinates the release process; iv) operates the tools required to support the release and provisioning activities; v) validates the software documentation; vi) takes care of the distribution of the software and its provisioning. This task benefits from the practices established and experience gained within the D4Science infrastructure.

Virtual Research Environments (VREs) are "systems" specifically conceived to provide their users with a web-based set of facilities (including services, data and computational facilities) to accomplish a set of tasks by dynamically relying on the underlying infrastructure. VREs are among the key products developed and delivered by the ARIADNEplus project to support the target communities and application scenarios in archaeology. The development of VREs is based on three main activities: (i) the development of software artifacts that realise a set of functions (including those needed for accessing certain datasets), (ii) the deployment of these artifacts in an operational infrastructure following the release procedures and tools presented in the deliverable D13.1 "Software Release Procedures and Tools JRA2", and (iii) the final deployment and operation of well-defined Virtual Research Environments by exploiting the facilities offered by the underlying D4Science infrastructure and its services [1]. This deliverable D13.4 - "VREs Operation Final Activity Report'' is the updated version of D13.2 - "VREs Operation Mid-term Activity Report ''. D13.4 documents the last of the above- mentioned three activities - i.e. the exploitation of the services and technologies offered by the underlying infrastructure to serve the needs of defined scenarios - as implemented in the second period, from January 2021 to November 2022 - of the ARIADNEplus project. Specifically, it focuses on how the components have been exploited and operated to support the development of the ARIADNEplus VRE gateway https://ariadne.d4science.org, its underlying infrastructure, and the VREs from M25 (January 2021) to M47 (November 2022). These activities have been carried out within Work Package 13. Specifically in Task 13.1 Infrastructure Operation (JRA2.1) and Task 13.3 VREs Operation (JRA2.3). In addition to the 5 VREs created and operated in the first period, 3 more VREs were created and operated in the second reporting period, for a total of 8 VREs. One VRE of the second reporting period, namely ARIADNEplus Lab (cf. Section 4.6), was created in July 2021 as the virtual laboratory to support developers, researchers, data managers, and data analysts belonging to the archaeological community worldwide. The "Geoportale Nazionale per l'Archeologia (GNA)" VRE (cf. Section 4.7) was created in January 2022, as the evolution of the existing Geoportal Prototype VRE (cf. Section 4.4), which was developed for the integration, validation, harmonization, visualization, and access of archaeological georeferenced datasets collected in Italy. Finally, the Esquiline VRE (cf. Section 4.8) was created in October 2022 for the integration and display of data originating from 19th century excavations and historical cartography in a spatio-temporal database, allowing the reconstruction of the transformation of an urban landscape through the centuries. As of November 2022, the VREs are serving the needs of more than 400 users in total spread across 21 countries and more than 10.000 user sessions. This required to deal with approximately 100 tickets (59 requests for support, 9 requests for incidents and bugs, 9 requests for Virtual Machine or Container creations).

D4Science is an IT infrastructure specifically conceived to support the development and operation of Virtual Research Environments by the as-a-Service provisioning mode. This report documents the activities performed in 2022 to develop this infrastructure and support several projects and exploitations.

InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2020 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e\-Sci\-ence, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, \ie D4Science and OpenAIRE. During 2020 InfraScience members contributed to the publishing of 30 papers, to the research and development activities of 12 research projects (11 funded by EU), to the organization of conferences and training events, to several working groups and task forces.

The enhancements in IT solutions and the open science movement are injecting changes in the practices dealing with data collection, collation, processing and analytics, and publishing in all the domains, including agri-food. However, in implementing these changes one of the major issues faced by the agri-food researchers is the fragmentation of the “assets” to be exploited when performing research tasks, e.g. data of interest are heterogeneous and scattered across several repositories, the tools modellers rely on are diverse and often make use of limited computing capacity, the publishing practices are various and rarely aim at making available the “whole story” with datasets, processes, workflows. This paper presents the AGINFRA PLUS endeavour to overcome these limitations by providing researchers in three designated communities with Virtual Research Environments facilitating the use of the “assets” of interest and promote collaboration.

This deliverable D13.2 - "VREs Operation Mid-term Activity Report" describes the activities carried out during the first 24 months of the ARIADNEplus project within Work Package 13. Specifically, in Task 13.1 Infrastructure Operation (JRA2.1) and Task 13.3 VREs Operation (JRA2.3). It reports the procedures governing the operation of the VREs as well as the status of the aggregated resources at mid-term in the ARIADNEplus infrastructure.

This deliverable describes the activities carried out during the first 18 months within Work Package 9 for the SoBigData e-Infrastructure operation activity since its deployment, including a detailed set of usage indicators (i.e. the number of users, accesses to resources, usage of resources from scientists, etc.). It also reports the deployment and procedures governing the operation of the Virtual Research Environments, the catalogue, and the ones for the services devoted to Data Analytics. A total of 15 Virtual Research Environments (VREs) have been created and are operational. In particular, the SoBigData gateway provide its users with: 6 Exploratories VREs paired with the use cases (Demography, Economy & Finance 2.0; Migration Studies; Societal Debates and Misinformation Analysis; Social Impacts of AI and Explainable Machine Learning; Sports Data Science; Sustainable Cities for Citizens); 2 Virtual Lab VREs - SoBigDataLab and OpenScienceGraphLab to exploit and experiment tools and solutions; 3 Applications VREs - TagME, SMAPH, M-Atlas; 2 Project Internal VREs - SoBigData.eu VRE for the communications and collaboration among project and initiative members and SBD-InfraCore VRE for supporting SoBigData-PlusPlus WP9; and 2 Literacy And Training VREs - the SoBigDataLiteracy, supporting Critical Data Literacy of task T.2.4, creating a curated collection of literature of interest for the SoBigData Community, and the e-Learning_Area VRE to host training materials developed within the SoBigData project. As of June '21, the 15 existing VREs served more than 8,000 users by a total of more than 30,000 working sessions, with an average of 1500 working sessions per month with increasing trend. This required to deal with approximately 40 issue tracker tickets (14 requests for support, 6 requests for incidents and bugs, 9 requests for new features, and 9 requests for Virtual Machine or Container creations).

This deliverable D5.2 "Virtual Research Environment Operation Report years 1-2" describes the activities carried out during the first 24 months of the DESIRA project within Work Package 5. Specifically in Task 5.1 "Knowledge Infrastructure: the DESIRA Virtual Research Environment" and Task 5.2 "Integration of Services and Tools, and Use Reporting". It reports the procedures governing the operation of the VREs as well as the status of the aggregated resources at mid-term in the DESIRA infrastructure. Virtual Research Environments (VREs) are "systems" specifically conceived to provide their users with a web-based set of facilities (including services, data and computational facilities) to accomplish a set of tasks by dynamically relying on the underlying infrastructure. VREs are among the key products to be developed and delivered by the DESIRA project to support Project coordination, Living Labs activities and Rural Digitization Forums activities. The development of VREs is based on three main activities: (i) the development of software artefacts that realise a set of functions (including those needed for accessing specific datasets), (ii) the deployment of these artefacts in an operational infrastructure following the release procedures and tools, and (iii) the final deployment and operation of well-defined Virtual Research Environments by exploiting the facilities offered by the underlying D4Science infrastructure and its services [1, 2]. This report documents the last of the above three activities - i.e. the exploitation of the services and technologies offered by the underlying infrastructure to serve the needs of defined scenarios - as implemented in the context of the DESIRA project from June 2019 to May 2021. The DESIRA Infrastructure Gateway actually offers end-user access to 14 VREs. As of June 2019, 1 VREs were created and operated. Specifically, the DESIRA Project VRE (cf. Sec 3.1.1) was created before the project kick-off. As of May 2021, these VREs have served the needs of more than 370 users and more than 7.000 user sessions. This required dealing with 177 tickets (103 related to the project management, 28 requests for support and enhancements, five requests for incidents and bugs, 14 requests for VRE creations).

The enhancements in IT solutions and the open science movement are injecting changes in the practices dealing with data collection, collation, processing, analytics, and publishing in all the domains, including agri-food. However, in implementing these changes one of the major issues faced by the agri-food researchers is the fragmentation of the “assets” to be exploited when performing research tasks, for example, data of interest are heterogeneous and scattered across several repositories, the tools modelers rely on are diverse and often make use of limited computing capacity, the publishing practices are various and rarely aim at making available the “whole story” including datasets, processes, and results. This paper presents the AGINFRA PLUS endeavor to overcome these limitations by providing researchers in three designated communities with Virtual Research Environments facilitating the use of the “assets” of interest and promote collaboration. © 2020 John Wiley & Sons Ltd

The Blue-Cloud project is piloting a cyber platform bringing together and providing access to multidisciplinary data from observations and models, analytical tools, and computing facilities essential to support research to understand better and manage the many aspects of ocean sustainability. This goal is realised by developing, deploying and operating the Blue-Cloud platform whose architecture consists of two major families of components: (a) the Blue Cloud Data Discovery and Access System to serve federated discovery and access to 'blue data' infrastructures; and (b) the Blue Cloud Virtual Research Environment (VRE) component to provide a Blue Cloud VRE as a federation of computing platforms and analytical services. This Deliverable D4.4 "Blue Cloud VRE Common Facilities (Release 2)" is the revised version of the D4.2 "Blue Cloud VRE Common Facilities (Release 1)". This revised version of the document covers the second period of the project, from M13 up to M27, including the up-to-date information of the services reported on D4.2 and the new services that have been developed and added to the VRE common facilities in the reporting period to serve the needs of the Blue Cloud community. The major changes and new services this deliverable introduces are: an Orchestrator (cf. Sec. 3.3), i.e. a software that allows for a declarative, technology agnostic definition of workflows to coordinate the execution of tasks across diverse services and systems; enhancements to the Workspace service to support tailored storage persistence and satisfy different application scenarios (cf. Sec. 4.1); enhancements in the Publishing Framework (cf. Sec. 6), namely the catalogue extension to deposit catalogue items to Zenodo and the facility to publish geospatial data from the workspace; the facility to interface with the Data Discovery & Access System (cf. Sec. 7.1) to transfer datasets of interest into the workspace for future uses; the notebook to facilitate the exploitation of the WEkEO Harmonised Data Access (HDA) API (cf. Sec. 7.2). This deliverable also updates the Identity and Access Management (cf. Sec. 3.1) and the Analytics Framework (cf. Sec. 5.1 and Sec 5.2) with minor changes reflecting the activities performed in the reporting period. A description of all the services previously documented in D4.2, not modified in the period, is preserved for this document to be self-contained and provide the reader with an overall description of the whole VRE Common Facilities offering. A total of 15 services and components are described in this deliverable by reporting their design principles, architecture and main features. These services and components contribute functionalities to the Blue Cloud VRE Enabling Framework (Identity and Access Management, VRE Management, Orchestrator), Collaborative framework (Workspace and Social Networking), Analytics Framework (Software and Algorithm Importer, Smart Executor), Publishing Framework (Catalogue Service) and improved support for RStudio, JupyterHub, ShinyProxy, and Docker Applications. Additionally, two new VRE services, aiming at bridging two VRE external systems such as the the WEkEO1 catalogue from Copernicus and the Data Discovery and Access from Blue-Cloud with the VRE tools are described. Services and components discussed in this deliverable have contributed to 14 gCube releases, from gCube 4.26 (November 2020) to gCube 5.6.0 (November 2021). They have been used to develop and operate the Virtual Laboratories of the Blue Cloud gateway https://blue-cloud.d4science.org and its underlying infrastructure. At the time of this deliverable the Blue-Cloud gateway and its services are serving more than 730 users with a total of 19000+ working sessions.

The Blue-Cloud project plans to pilot a cyber platform bringing together and providing access to multidisciplinary data from observations and models, analytical tools, and computing facilities essential to support research to understand better and manage the many aspects of ocean sustainability. To achieve this goal, Blue-Cloud is developing, deploying, and operating the Blue-Cloud platform whose architecture consists of two families of components: (a) the Blue Cloud Data Discovery and Access service component to serve federated discovery and access to 'blue data' infrastructures; and (b) the Blue Cloud Virtual Research Environment (VRE) component to provide a Blue Cloud VRE as a federation of computing platforms and analytical services. This deliverable presents the Blue Cloud Virtual Research Environment constituents by focusing on both new services and revised existing services that have been developed in the reporting period to serve the needs of the Blue Cloud community. In particular, this deliverable describes a total of 11 services and components. These services and components contribute functionalities to the Blue Cloud VRE Enabling Framework (Identity and Access Management, VRE Management), Collaborative framework (Workspace and Social Networking), Analytics Framework (Software and Algorithm Importer, Smart Executor), Publishing Framework (Catalogue Service) and improved support for RStudio, JupyterHub, ShinyProxy, and Docker Applications. The services are described below by reporting their design principles, architectures, and main features. The deliverable also describes the procedures and approaches governing services and components released by highlighting how Gitea (as Git hosting service), Jenkins (as automation server), and Maven (as project management and comprehension tool) are used to guarantee continuous integration processes. Services and components discussed in this deliverable contribute to 11 gCube open-source software system releases (from gCube 4.16 up to gCube 4.25.1) and are in the pipeline for the next ones. They have been used to develop and operate the Virtual Laboratories of the Blue Cloud gateway https://blue-cloud.d4science.org and its underlying infrastructure. At the time of this deliverable (November 2020), the gateway hosts a total of 8 VREs and VLabs, including five specifically conceived to support the co-development of some of the Blue-Cloud demonstrators (namely, the Aquaculture Atlas Generation for Demonstrator #5, the Blue-Cloud Lab for several demonstrators, the GRSF pre for Demonstrator #4, the Marine Environmental Indicators for Demonstrator #3, the Zoo-Phytoplankton EOV for Demonstrator #1). This gateway and its tools serve more than 400 users that (since January 2020) performed a total of more than 5000 working sessions, more than 1700 accesses to the Workspace, and more than 750 analytics tasks. These exploitation and uptake indicators are likely to grow in the coming months thanks to data updates and continued use, further development of existing VLabs, and finally, the creation of new ones.

This deliverable D13.1 - "Software Release Procedures and Tools JRA2" describes the activities carried out during the first six months of the ARIADNEplus project within Task 13.4 (T13.4) Software integration and release (JRA2.4) (Work Package 13 - WP13) and describes the procedures governing the release of software, methods, and tools for the ARIADNEplus infrastructure. This task is in charge of managing the process of the software maintenance, enhancement, and provisioning in JRA work packages. Thus, it i) defines release and provisioning procedures; ii) establishes the release plan; iii) coordinates the release process; iv) operates the tools required to support the release and provisioning activities; v) validates the software documentation; vi) takes care of the distribution of the software and its provisioning. This task benefits from the practices established and experience gained within the D4Science infrastructure. The procedures are documented through a set of documentation pages for single facilities hosted by the gCube wiki. In particular, this report provides the instructions and rules for three main patterns governing the provision of software and tools to the ARIADNEplus infrastructure. The first pattern is related to the provision of software to the gCube infrastructure enabling technology. This pattern describes the procedures for the storage of software and its management; the continuous integration of the software to build releasable software artefacts; and the generation of software distribution packages. The second pattern is related to the provision of software methods elaborated by the ARIADNEplus community requiring execution within the ARIADNEplus infrastructure. The third pattern is instead related to the provisions and integration of tools into the ARIADNEplus infrastructure.

This deliverable documents the implementation of the data processing solution defined in D7.1 "Interoperable data processing for environmental RIs projects: system design". The actual deliverable consists of the software realising the envisaged solution and the instances of it made available to the ENVRI community in the large. The distinguishing features of the proposed solution are (a) to be suitable for serving the needs of scientists involved in ENVRI RIs, (b) to be open and extensible both with respect to the algorithms and methods it enables and the computing platforms it relies on to execute those algorithms and methods, (c) to be open-science-friendly, i.e. it is capable of incorporating every algorithm and method integrated into the data processing framework as well as any computation resulting from the exploitation of integrated algorithms into a "research object" catering for citation, reproducibility, repeatability and provenance. The proposed solution is part of a larger software system named gCube and has been provisioned to ENVRI RIs via several Virtual Research Environments operated by D4Science.

Deliverable D3.1 "Open Science Data Analytics Technologies" is a deliverable of type Demonstrator meaning that it manifests in artefacts (software releases) other than reports. In particular, the deliverable is about the software realising the Data Analytics & Processing Layer of the AGINFRA+. This software is part of a large software system named gCube (www.gcube-system.org). The gCube system offers a large array of services supporting the entire lifecycle underlying a research activity (data management and collation, analytics, collaboration, sharing) and the possibility to combine these services in Virtual Research Environments1. In the context of AGINFRA PLUS the following gCube components have been primarily exploited, consolidated and enhanced to serve the analytics needs arising in the context of the project use cases. DataMiner, i.e. a service enacting its users to perform data analytics tasks by relying on an array of analytics methods and a distributed and heterogeneous computing infrastructure. This service is available by a web-based GUI as well as via a web-based API based on the OGC WPS standard. SAI (Statistical Algorithm Importer), i.e. a service enacting its users to make available their own analytics methods via the DataMiner service. In addition to that, the entire analytics solution made available for AGINFRA PLUS cases counts on (i) a shared workspace realising a cloud-based file manager for managing content of interest and sharing this content with co-workers, (ii) a social networking area enabling users to post messages and have discussions, (iii) a flexible catalogue enabling to publish and discover items of interest including "research objects" resulting from an analytics task. This technology is deployed in its latest version in every Virtual Research Environment supporting AGINFRA PLUS cases2. The major enhancements to the technology pertaining to AGINFRA PLUS have been included in three gCube major releases3 4.7 (October 2017), 4.8 (November 2017), and 4.9 (under production).In particular, with these releases a new"black-box" oriented approach (https://wiki.gcubesystem. org/gcube/Statistical_Algorithms_Importer:_Java_Project#Black_Box_Integration)has been envisaged and implemented to enact analytics method owners and developers to easily integrate theirsolutions into the DataMinerservice. Among the supported black-box typologies there is that for KNIME workflows, i.e. analytics methods implemented by a KNIME workflow. KNIME is among the key technologies supporting the Food Safety Risk Assessment cases. In order to enact the execution of KNIME-based black-boxes, the distributed computing part of the data analytics platform has been extended to integrate the KNIME execution engine. Other cases are counting on the same mechanism to integrate entire applications (WOFOST4) as well as Python-based methods.

New Science paradigms have recently evolved to promote open publication of scientific findings as well as multi-disciplinary collaborative approaches to scientific experimentation. These approaches can face modern scientific challenges but must deal with large quantities of data produced by industrial and scientific experiments. These data, so-called "Big Data", require to introduce new Computer Science systems to help scientists cooperate, extract information, and possibly produce new knowledge out of the data. E-Infrastructures are distributed computer systems that foster collaboration between users and can embed distributed and parallel processing systems to manage Big Data. However, in order to meet modern Science requirements, e-Infrastructures impose several requirements to computational systems in turn, e.g. being economically sustainable, managing community-provided processes, using standard representations for processes and data, managing Big Data size and heterogeneous representations, supporting reproducible Science, collaborative experimentation, and cooperative online environments, managing security and privacy for data and services. In this paper, we present a Cloud computing system (gCube DataMiner) that meets these requirements and operates in an e-Infrastructure, while sharing characteristics with state-of-the-art Cloud computing systems. To this aim, DataMiner uses the Web Processing Service standard of the Open Geospatial Consortium and introduces features like collaborative experimental spaces, automatic installation of processes and services on top of a flexible and sustainable Cloud computing architecture. We compare DataMiner with another mature Cloud computing system and highlight the benefits our system brings, the new paradigms requirements it satisfies, and the applications that can be developed based on this system.

Data processing is a very wide area or domain because of a series of characteristics including the contexts resulting from diverse application scenarios, the great variety of processes to be enabled, the large set of enabling technologies and solutions. One of the consequences of this large variety is that each software solution for data processing only manages to address parts, i.e. it is difficult to imagine a single solution that is equally suitable for any (or even most) application scenarios and contexts. This deliverable illustrates that scope and diversity by reporting detailed practices and requirements from seven of the ENVRI RIs. It then describes the design of a single data processing solution that will help meet a substantial range of requirements in a representative range of contexts. That approach is conceived to be (a) suitable for serving the needs of scientists involved in ENVRI RIs, (b) open and extensible both with respect to the algorithms and methods it enables and the computing platforms it relies on to execute those algorithms and methods, (c) open-science-friendly, i.e. it is capable of incorporating every algorithm and method integrated into the data processing framework as well as any computation resulting from the exploitation of integrated algorithms into a "research object" catering for citation, reproducibility, repeatability and provenance.

In this document we describe how to implement an algorithm that should run on the D4Science DataMiner service. DataMiner is a cross-usage service that provides users and services with tools for performing data mining operations. Specifically, it offers a unique access to perform data mining and statistical operations on heterogeneous data, which may reside either at client side, in the form of comma-separated values files, or be remotely hosted, possibly in a database. The DataMiner service is able to take inputs and execute the operation requested by a client or a user, by invoking the most suited computational facility from a set of available computational resources. Executions can run either on multi-core machines or on different computational platforms, such as D4Science and other different private and commercial Cloud providers.

In this document we describe the Statistical Algorithms Importer Web interface. The Statistical Algorithms Importer (SAI) is a tool to import algorithms in the D4Science e-Infrastructure. Currently, it supports R scripts integration. SAI separates R scripts development from its deployment in the infrastructure in a very flexible way. After the first deployment, made in collaboration with the e-Infrastructure team, script developers can modify and update their scripts by themselves, without the intervention of the e-Infrastructure team.

Prototype scripting is the base of most models in computational biology and environmental sciences. Scientists making prototype scripts (e.g. using R and Matlab) often need to share results and make their models used also by other scientists on new data. To this aim, one way is to publish scripts as-a-Service, possibly under a recognized standard (e.g. the Web Processing Service of the Open Geospatial Consortium). Unfortunately, prototype scripts are not generally meant to be transformed into Web services, which require managing multi-tenancy, concurrency etc. Often, porting prototype scripts to more efficient programming languages is not affordable, because this operation demands for time, competencies and money. For this reason, Web services are becoming smart enough to integrate prototype scripts directly and possibly make them run efficiently (e.g. WPS4R ). In this paper, we present an interface (Statistical Algorithms Importer, SAI) that allows scientists to easily and quickly import R scripts onto a distributed e-Infrastructure, which publishes the scripts as-a-Service and manages multi-tenancy and concurrency. Additionally, it allows scientists to update their scripts without following long software re-deploying procedures each time. SAI relies on the D4Science e-Infrastructure , a distributed computer system supporting large-scale resource sharing and Cloud computing, via the definition of Virtual Research Environments (VREs).

In this document we describe the DataMiner Manager Web interface that allows interacting with the gCube DataMiner service. DataMiner is a cross-usage service that provides users and services with tools for performing data mining operations. Specifically, it offers a unique access to perform data mining and statistical operations on heterogeneous data, which may reside either at client side, in the form of comma-separated values files, or be remotely hosted, possibly in a database. The DataMiner service is able to take inputs and execute the operation requested by a client or a user, by invoking the most suited computational facility from a set of available computational resources. Executions can run either on multi-core machines or on different computational platforms, such as D4Science and other different private and commercial Cloud providers.

A randomized controlled trial aimed at comparing the individual efficacy of cephalothin, cefoxitin, and metronidazole in the prevention of postoperative wound infection was performed among 74 colorectal surgical patients. Of 28 patients on cephalothin, seven (25 per cent) developed a postoperative infection, but among 23 patients in each of the other two groups, only one (4 per cent) in each group became infected. These results confirm the primary importance of anaerobes in the causation of postoperative sepsis after colorectal surgery.

A new technique is described for fermentation tests of anaerobes in which the fermentation reactions are carried out on cystine trypticase agar plates to which fermentable substances (Taxo discs) are added. This technique is simple, sensitive and reproducible.

This paper reviews the complications that arose after 68 276 percutaneous liver biopsies performed from 1973 to 1983. The complications are analyzed in relation to the underlying liver disease and to the type of needle used. Death was infrequent (9/100 000); it was always due to haemoperitoneum and occurred only in patients with malignant diseases or cirrhosis. Complications were less frequent in AVH (44/100 000) than in other liver diseases (from 125 to 278/100 000). Death, serious haemorrhagic complications, pneumothorax and biliary peritonitis were more frequent after biopsy with the Trucut needle than after biopsy with Menghini's needle (3/1000 against 1/1000). Sixty-one percent of complications were discovered within two hours of biopsy and 96% within one day. The data indicate a post biopsy observation period of at least 24 hours. The day-case procedure should be reserved for patients not presenting liver tumour or cirrhosis.