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The ENVRIplus Service portfolio contains the services the ENVRIplus offers to the ENVRI community and beyond.

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Overview

Service name
찾은 내용 없음.

Service NameAccess policiesAction requiredContact (internal) *Contact (public)Cost *

Customer group

FeaturesFinances & resourcesOther agreementsPayment model(s)PricingRequest workflow *Revenue stream(s) *SLA(s)Service architectureService areaService componentsService descriptionService management informationService nameService optionsService overviewService owner *Service phaseService request listSupport unitTaglineTerms of useUser groupUser manualValue
A3. ENVRI Knowledge Base  

Zhiming Zhao (z.zhao@uva.nl)

  
  • RI architects/developers,
  • Investigators into RI design or current RI assets and technologies.
  • Uses Open Information Linking for environmental science research infrastructures (OIL-E).
  • Captures information about research infrastructure characteristics and design (“RI design wisdom”), structured according to ENVRI RM.
  • Captures information about technologies and standards used by RIs for key services.
  • Provided as an (RDF) knowledge graph, accessible via SPARQL requests over HTTP.
  • Permits analysis and comparison of RI characteristics.

 free 

The ENVRI Knowledge Base should be maintained as part of the ENVRI community portal.
At end of project, the usefulness of aggregating design wisdom and technology landscape for RI should be evaluated and, if positively received, a recipe for provisioning new knowledge bases for similar cluster initiatives should be compiled and published.

 
 

TRL 6, live demonstrator

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ENVRI Knowledge Base
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University of Amsterdam

Beta

 
  • email support.
  • open for new case studies.
   online accessible documentation via http://oil-e.vlan400.uvalight.net/ 
C3. Data / metadata generation from semantic annotations     
  • RI metadata and data managers and publishers
  • e-Infrastructure semantic operators
  • A-Generation of ISO19139 metadata records from rdf triples.
    • Step 1) convertion of OBOE-based triples to DCAT-AP
    • Step 2) from DCAT-AP to ISO. This second step can be re-used alone.
  • B- Generation/identification of datasets from raw data OBOE-based RDF triples.
    • Step 1) data perimeter delimitation (from metadata),
    • Step 2) identification of dataset dimensionalities
    • Step 3) Data file (NETCDF) generation and
    • Step 4) DOI generation

   

  
 

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Data / metadata generation from semantic annotations
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  • Development stage
  • Open Source
      
A4. Architecture Design  

Keith Jeffery: NievadelaHidalgaA@cardiff.ac.uk

  
  • RI data service operators (provider)
  • e-Infrastructure operators (provider)
  • RI researchers (users)
  • Recommendations to RIs for reference architecture
  • Derived from D5.1 (requirements and State of the Art)
  • Assumes RI have local e-I capability and access to European e-Is

   

As a reference for implementation by RIs

  • Overall architectural intent
  • Components: catalog, common and cross-cutting services
  
consultancy

Architectural components expected to be TRL6-8

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Architecture Design
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     ENVRIplus Deliverable D5.5: http://www.envriplus.eu/wp-content/uploads/2015/08/D5.5.pdf 
B1. Linked open data ingestion and metadata service
  • GPL v3 license
 

Oleg Mirzov (oleg.mirzov@nateko.lu.se), Jonathan Thiry

  
  • RI data service operators,
  • data application developers,
  • e-Infrastructure operators.
  • Machine to machine ingestion of data objects based on simple metadata profile
  • Minting of ePIC PIDs, DOIs
  • Streaming to trusted repository (iRods, B2SAFE)
  • Creates dynamic landing pages based on ontology

   

  
data
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Linked open data ingestion and metadata service

TRL 7，operational in ICOS Carbon Portal

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ICOS



  • email support,
  • open for implementation at other portals
   

online accessible documentation https://github.com/ICOS-Carbon-Portal/meta

 
D1. ENVRIplus Service Testbed based on EGI Cloud Compute  Yin Chen, yin.chen@egi.eusupport@egi.eu 
  • ENVRIplus RI research communities
  • ENVRIplus individual researchers
  • ENVRIplusService Providers
  • ENVRIplus related SME/Industry
  • Execute compute- and data-intensive workloads (both batch and interactive)
  • Host long-running services (e.g. web servers, databases or applications servers)
  • Create disposable testing and development environments
  • Configure Virtual Machines (VMs)according to requirements
    • Resources: CPU, memory, disk
    • Applicationenvironments
  • Scaleinfrastructure andmanageresourcesin a flexible way
  • Integrated monitoring and accounting capabilities

   

  
Compute, storage

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The provision of ENVRIplus service testbed corresponds to Reference Model Technology Viewpoint, that provide a real-world configuration to support testing and validation of ENVRIplus services
ENVRIplus Service Testbed based on EGI Cloud Compute

TRL 9

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EGI Foundation

Production: service available in the live environment meeting security/performance requirements

fedcoud.egi.eu VO    
C1. Data Subscription Service
 Chris.Ariyo@csc.fi  Researchers 
   

  

Discovery of data set changes and user notification

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Discover changed data through notifications that are triggered by data or metadata updates.
Data Subscription Service
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Data storage providers and applications representing individual users subscribe to data through a well-defined interface
Data owner must be able to mark data as subscribe-able
Subscriptions are activated by matching notifications
EUDAT operated by CSC - IT Center for Science

Working Proof of Concept

  Discover change data sets through data or metadata changes Researchers Enlarge the number of users than can use data services without wasting expensive research times checking for changed data sets
C4. Dynamic ecological Information Management System (DEIMS)  johannes.peterseil@umweltbundesamt.atjohannes.peterseil@umweltbundesamt.at Research Infrastructuressite registry, data set registry, range of metadata formats, support of standardised ogc-services
   

  
Discovery of (meta)data of RIs

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Standardised documentation of research sites, datasets, data products and sensors exposing data through standardised Services (CSW, WFS, WMS, ...)
Dynamic Ecological Information Management System - Sites and Dataset Registry (DEIMS-SDR)
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LTER Europe

Production

  Comprehensive database of environmental research sites environmental scientists, RI data managers, data application developers, decision makers Standardised access to a comprehensive database of environmental research sites
B5. Flagship product cataloguefree access thomas loubrieu (thomas.loubrieu@ifremer.fr)
 ENVRIplus project managers. RI managers. Eventually EU DG Research.portal
   access will be free and open  
discovery of data and acquisition services

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a portal is provided and machine to machine link for harvesting or semantic linking.demonstration for envriplus project.RI flagship product catalogue
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Service where the main outcomes or products provided by the Envrionment Research Infrastructure involved in ENVRIplus provide. This includes data services and acquisition services (observatories). The purpose is to enable users to discover this resources at transdisciplinary level.ENVRIplus project; Operated by EUDAT/DKRZ

under development

  discover what data or experiment hosting support you can get from the European envrionment Research Infrastructures. researchers enlarge the target of users who can use the data services or acquisition services provided by the RI beyond their traditionnal communities (atmosphere, biodiversity, biodiversity, solid earth, marine).
B3. Dynamic Real-time Infrastructure Planner (DRIP)Policy-based. 

Spiros Koulouzis: S.Koulouzis@uva.nl

Zhiming Zhao: Z.Zhao@uva.nl Any

Partial critical path based VM infrastructure planning; Automated networked VM provisioning with support for multiple sites; Remote deployment of containerised application components onto provisioned VMs.


See: https://github.com/QCAPI-DRIP/DRIP-integration/wikiFree. 

See: https://github.com/QCAPI-DRIP/DRIP-integration/wiki

Project funding (H2020 SWITCH, ENVRIplus, VRE4EIC),No SLA at present.

compute

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The Dynamic Real-time infrastructure planner (DRIP) allows application developers to plan a customized virtual infrastructure based on application level deadline constraints and resource budgets, provisioning the virtual infrastructure using standardized interfaces (TOSCA and OCCI), deploy application components onto the virtual infrastructure, and start execution on demand.

Dynamic Real-time Infrastructure Planner (DRIP)

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UvA (University of Amsterdam)

alpha

See: https://github.com/QCAPI-DRIP/DRIP-integration/wiki Optimised infrastructure planning and provisioning for time-critical applications.https://github.com/QCAPI-DRIP/DRIP-integradation/wikiDistributed application developershttps://github.com/QCAPI-DRIP/DRIP-integration/wikiAutomated selection of optimal VMs and VM topology for an application workflow with deadline constraints; automatic provisioning of VMs according to the planned topology; automatic installation of application containers onto provisioned VMs from remote repositories.
B2. gCube / D4Science DataMinerPolicy-based and Wide-use. D4Science operates a number of instances of this info@d4science.orginfo@d4science.org Any Research Performing Organization willing to provide its scientists with an Open Science compliant data analytics platform. 
 Free 

  
Data processing and analytics

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Enabling


DataMiner Manager

DataMiner is a Web Service running in a Tomcat container equipped with gCube SmartGear. The Web Service exposes an interface compliant with the Web Processing Service (WPS) standard. DataMiner is able to exploit the heterogeneous resources offered by the D4Science e-Infrastructure to both retrieve and store data. The service allows users to execute community developed algorithms, written in several programming languages (e.g. Fortran, R, Java). Through the WPS standard, DataMiner provides three main access servlets:

• GetCapabilities: Allows a client to request and receive service metadata (Capabilities), which describe the algorithms supplied by the service. Such operation provides the name and the general description of each process;

• DescribeProcess: Allows a client to request and receive detailed information about the processes that can run on the service instances. Information includes the input and the expected output.

8 (at least)
2Enabling

DataMiner SmartExecutor

The DataMiner SmartExecutor executes tasks, i.e. functionally unconstrained pieces of code that lack a network interface but can be deployed into the service and executed through its interface. Example of tasks are R scripts and Java programs. An instance of SmartExecutor publishes descriptive information about the running environment. It can either execute tasks upon request or schedule the execution at configurable intervals on behalf of clients. Clients may interact with the Executor service through a Java library of high-level facilities that subsumes standard service stubs to simplify the discovery of available tasks in those instances. Each client can request to execute a Task or gathering information about the state of their execution. The client library simplifies the following activities: a) discover service instances that can execute the target task; b) launch the execution of the task with one the discovered instances; c) monitor the execution of the running task.

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This service offers a web-based workbench for data analytics compliant with Open Science practices. From the end user perspective, it offers a collaborative-oriented working environment where users:

  • can easily execute and monitor data analytics tasks by relying on a rich and open set of available methods either by using a dynamically generated web-based user- friendly GUI or by using a RESTful protocol based on the OGC WPS Standard;

  • can easily share & publish their analytics methods (e.g. implemented in R, Java, Python, etc.) to the workbench and make them exploitable by an automatically generated web- based GUI and by the OGC WPS protocol;

  • are provided with a “research object” describing every analytics task executed by the workbench enabling for repeatability, computational reproducibility, reuse, citation and provenance. The research object contains every input & output, an executable reference to the method as well as rich metadata including a PROV-O provenance record;

The data analytics framework is integrated with a shared workspace where the research objects resulting from the analytics tasks are automatically stored together with rich metadata. Objects in the workspace can be shared with coworkers as well as published by a catalogue with a license governing their uses. Moreover, the framework is conceived to operate in the context of one or more Virtual Research Environments, i.e. it is actually made available by a dedicated working environment offering (besides the framework and the workspace) additional services including those for managing users, creating communities, and supporting communication and collaboration among VRE members.

The data analytics framework is conceived to give access to two typologies of resource:
• a distributed, open & heterogeneous computing infrastructure for the real execution of the analytics tasks. This distributed computing infrastructure is capable to exploit resources from the EGI infrastructure.

• the pool of methods integrated in the platform, i.e. each method integrated in the framework is made available as-a- Service to other users according to the specific policy;

More details on this framework is available at https://wiki.gcube-system.org/gcube/DataMiner_Manager


gCube / D4Science DataMiner
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D4Science.org

Production

Ask to join one of the existing instances hosted by VRE, e.g. the ENVRIplus VRE https://services.d4science.org/group/envriplus/

For having dedicated instances, please contact info@d4science.org

http://support.d4science.orgOpen, user friendly and extensible data analytics platform ready for Open Science and VREs.

https://services.d4science.org/terms-of-use

The service is not tailored to serve the needs of a specific community. Rather, it is community agnostic and highly and easily customizable thus to serve the needs of a given community. Customization is achieved by configuring the instance serving a certain Virtual Research Environment with (a) the set of methods to be made available as-a-Service and (b) the resources forming the distributed computing infrastructure dedicated to execute the analytics tasks.

Up to now it has been and is successfully used by a quite rich array of diverse communities, namely those associated with the supported projects,, e.g., i-Marine (fisheries and marine biodiversity scientists), BlueBRIDGE (fisheries and aquaculture scientists, educators & SMEs), SoBigData.eu (social mining scientists), ENVRI+ (environmental scientists), AGINFRA+ (agriculture scientists), EGIP (geothermal scientists).

 

The analytics platform is conceived to serve the needs of scientists (in particular, those belonging to the so called long-tail of science) by providing them with an easy to use working environment (nothing need to be installed on users’ machine). It is conceived to hide the technicalities related with the execution of tasks by relying on distributed computing infrastructures. Moreover, it is conceived to be exploitable by third-party software/applications, e.g. R-Studio, Q-GIS, or any workflow management system or application capable to interface with a RESTful service.

Worth highlighting that the platform is Open Science “compliant” (e.g., every method is “published” and citable, every task leads to a research object) and Virtual Research Environment friendly, i.e. it is conceived to be customizable with respect to the methods to offer in a given application context as well as it is conceived to benefit from a collaborative environment for sharing artefacts and comments.

Its characteristics make it particularly suitable to serve typical scientific contexts of the long tail of science.

A2. Open Information Linking for Environmental science research infrastructuresWide access p.w.martin@uva.nl 

The sustainability of the ENVRI RM ontology in OIL-E is partially tied to the sustainability of ENVRI RM itself.
Application of OIL-E, e.g. for the ENVRI Knowledge Base, creates a community of use for ontologies.
Publication of OIL-E in ontology repositories also increases exposure and provides limited curation tied to lifespan of repository.

RI data engineers/architects
  • Captures ENVRI RM as a multi-viewpoint OWL ontology for RI architecture.
  • Permits analysis and comparison of RI characteristics as the foundation for an ENVRI Knowledge Base.
  • Provides a linking framework for describing the different metadata schemes and technologies used by RIs as well as to identify any semantic mappings available to convert between schemes.

 Free 

  
data

TRL3–4，development on-going.

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OIL-E is a developing framework for addressing the semantic linking requirements of environmental science e-RIs. Specifically, it is intended to provide a machine-readable bridge between the ENVRI Reference Model and other concept models related to research infrastructure, architecture and scientific (meta)data.
Open Information Linking for Environmental science research infrastructures (OIL-E)
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Paul Martin

alpha

email support: Paul Martin (p.w.martin@uva.nl), Zhiming Zhao (z.zhao@uva.nl)

open for new test data and test queries

Open linking between environmental science RI reference modelling and controlled vocabularies. Data-driven researchersonline accessible documentation via http://www.oil-e.net/.OIL-E is intended to provide technical and methodological guidance for the linking of different controlled vocabularies in order to help the construction of cross-cutting services.
A1. RM Training: Practical Introduction to the ENVRI RM

Open Content, Licensed under a Creative Commons Attribution - NonCommercial-ShareAlike 2.0 Licence

 Abraham Nieva, Alex Hardisty, Aurora Constantin, Malcolm Atkinson Abraham Nieva/Alex Hardisty (NievadelaHidalgaA@cardiff.ac.uk)  Environmental Research Infrastructure personnel who have little or no experience designing and modelling complex distributed information systems
  • Provides practical examples of the use of the Reference Model
  • Based on a real life modelling case (DASSH use case)
  • 9 Lessons covering the science, information and technology viewpoints

 Free 

in-kind 
Reference model Training

TRL 7. prototype demonstration in operational environment

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This training module provides a structured introduction to the main concepts of the ENVRI Reference Model in its 3 logical viewpoints. Nine lessons, starting with a use case description and the research data lifecycle, incrementally introducing more details about the Science, Information and Computational Viewpoints.
RM Training: Practical Introduction to the ENVRI RM
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Cardiff University

beta: service being developed while available for testing publicly

https://training.envri.eu/course/search.php?search=ENVRI+RM

Abraham Nieva, Alex Hardisty, Aurora Constantin, Malcolm Atkinson

Support type:

   https://training.envri.eu/course/search.php?search=ENVRI+RM

Covers all phases of the data lifecycle; Provide Common Vocabulary for Data for Science; Architecture Design RIs ICT and crosscutting mechanisms

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