SlideShare ist ein Scribd-Unternehmen logo
1 von 21
Downloaden Sie, um offline zu lesen
The new CIARD RING
a machine-readable directory of
datasets for agriculture
Valeria Pesce
Global Forum on Agricultural Research (GFAR)
Research Data Alliance 4th Plenary Meeting
22-24 September 2014, Amsterdam
Agricultural Data Interoperability Interest Group
agINFRA project
EC 7th framework program INFRA-2011-1.2.2 - Grant agr. no: 283770
The CIARD RING
http://ring.ciard.net
The CIARD RING is a project implemented within the
CIARD initiative and is led by the Global Forum on
Agricultural Research (GFAR).
The CIARD RING is
a global directory of web-based information
services and datasets for agriculture
Why (1)
- Producers and managers of information /
data need a place where their information
products can be found
- Data consumers need to find suitable data
sources
- IT professionals need information on the level
and mode of interoperability of information
services and datasets for using data in their
applications
Numbers and map
• 468 data providers
• 1018 information services, of which
– 268 exposed datasets
Definition of “dataset” in the RING
The term “datasets” has been defined in several ways, all of which
further specify or extend the basic concept of “a collection of data”.
Definition given by the W3C Government Linked Data Working Group:
A dataset is “a collection of data, published or curated by a
single source, and available for access or download in one or
more formats”
The “instances” of the dataset “available for access or
download in one or more formats” are called
“distributions”. A dataset can have many distributions.
Examples of distributions include a downloadable CSV
file, an API or an RSS feed.
Direct submission + federation
• All datasets currently featured in the RING have
been manually submitted by their owners /
managers
• BUT, We don’t want to force data owners who already have a
dataset catalog to catalog and maintain their datasets in two
places
 We are working on procedures to federate
datasets from the most used dataset cataloguing
platforms (Dataverse, CKAN…)
First experiment started with the IFPRI Dataverse
dataset catalog
The RING user interface
Dataset record
The RING machine interface – Why (2)
• Datasets registered in the RING have to be found by
applications
• Applications have to be able to read all the metadata about
datasets and filter datasets according to their needs
• Applications have to find enough technical metadata in the
RING to:
– Identify datasets with a specific coverage (type of data, thematic
coverage, geographic coverage)
– Identify datasets that comply with certain technical specifications
(format, protocol etc.)
– Access the dataset and get the data
 This machine-readable layer can support the data
aggregation workflows of external services
The RING machine interface – SPARQL
An RDF store is a way of storing data using a machine-
readable "grammar" (the Resource Description Framework)
and documented semantics (RDF vocabularies).
URIs
The URI for each service / dataset is built as follows:
RING-domain/node/service-ID.
For example: http://ring.ciard.net/node/2417
The RING database is also an accessible RDF store.
SPARQL endpoint
http://ring.ciard.net/sparql1
SPARQL how to: vocabularies
The vocabularies used in the RDF store are:
• RDF: http://www.w3.org/1999/02/22-rdf-syntax-ns#
• RDFS: http://www.w3.org/2000/01/rdf-schema#
• DC: http://purl.org/dc/terms/
• DCAT: http://www.w3.org/ns/dcat#
• ADMS: http://www.w3.org/ns/adms#
• FOAF: http://xmlns.com/foaf/0.1/
• DOAP: http://usefulinc.com/ns/doap#
• SKOS: http://www.w3.org/2004/02/skos/core#
• VCARD: http://www.w3.org/2006/vcard/ns#
The data model chosen to describe datasets is the
W3C Data Catalog Vocabulary (DCAT)
designed to describe datasets
and the forms in which they are exposed, their "distributions"
SPARQL how to: sample query
To get all datasets available through the OAI-PMH protocol
Query:
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX
rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX dc:
<http://purl.org/dc/terms/> PREFIX dcat:
<http://www.w3.org/ns/dcat#> PREFIX adms:
<http://www.w3.org/ns/adms#> PREFIX doap:
<http://usefulinc.com/ns/doap#> PREFIX skos:
<http://www.w3.org/2004/02/skos/core#>
DESCRIBE ?dataset ?distro ?owner ?contact ?topic ?standard ?format
?protocol
WHERE { ?dataset rdf:type dcat:Dataset . ?dataset dc:title ?title .
?dataset dcat:distribution ?distro . ?dataset dc:publisher ?owner .
?distro dcat:accessURL ?url . ?distro adms:representationTechnique
<http://ring.ciard.net/taxonomy_term/108> . OPTIONAL {
?dataset doap:maintainer ?contact } OPTIONAL { ?dataset dcat:theme
?topic } OPTIONAL { ?distro dc:conformsTo ?standard } OPTIONAL {
?distro dc:format ?format } OPTIONAL { ?distro
adms:representationTechnique ?protocol } }
SPARQL how to: URIs?
All the URIs that you may need in queries are
listed on the RING web site
• A list of the URIs of all the RING
entities (services/datasets, organizations,
KOSs etc.)
• A list of the URIs of all RING
concepts (countries, topics, regions, protocols
etc.)
SPARQL how to: URIs of entities
SPARQL how to: exploit linked URIs
Example of use: AGRIS  RING
1. How AGRIS uses the RING Linked Data
AGRIS (http://agris.fao.org): database of more than 7
million bibliographic references on agricultural research
and technology and links to related data resources on
the Web.
AGRIS retrieves information on AGRIS centers through a
SPARQL query run against the RING.
<http://ring.ciard.net/node/10687> is the uRI of the
AGRIS network in the RING
------------------------------
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX dc:
<http://purl.org/dc/terms/> PREFIX dcat: <http://www.w3.org/ns/dcat#> DESCRIBE
?dataset WHERE { ?dataset rdf:type dcat:Dataset . ?dataset dc:partOf
<http://ring.ciard.net/node/10687> }
------------------------------
Example of use: AGRIS  RING
2. How to get AGRIS Linked Data bibliographic records for each AGRIS
center
In the AGRIS RDF store, all bibliographic records are
associated to the corresponding AGRIS center through
the dcterms:source property: the URI used to identify
the AGRIS center is the RING URI.
Any application can therefore retrieve all records
belonging to an AGRIS center by running a query
against the AGRIS SPARQL endpoint
(http://202.45.139.84:10035/catalogs/fao/repositories
/agris).
------------------------------------
PREFIX dcterms: <http://purl.org/terms> DESCRIBE ?rec WHERE { ?rec dcterms:source
<http://ring.ciard.net/node/2754> . }
-----------------------------------
Interoperability assessment in the RING
The technical metadata registered in the RING for
each dataset provide enough information to give a
good idea of the level of “interoperability” of that
dataset.
“Interoperability is a feature of datasets— and of information
services that give access to datasets— whereby data can easily
be retrieved, processed, re-used, and re-packaged (“operated”)
by other systems. The less pre-coordination required to achieve
this, the more “interoperable” the dataset.”
[from: Interim Proceedings of International Expert Consultation
on “Building the CIARD Framework for Data and Information
Sharing”, Beijing 20-23 June 2011. 2011.]
Metadata Type Interoperability points Tim Berner Lee’s stars
For the service/dataset in general
1
Global coverage Select list 4 if not empty
2
Regional coverage (FAO) Select list 4 if not empty
3
Regional coverage (GFAR) Select list 4 if not empty
4
National coverage Select list 4 if not empty
5
Specific topic (AGROVOC) Autocomplete multiple
(authority: AGROVOC)
8 if not empty
6
Type of content/data managed Autocomplete multiple 4 if not empty
7
KOSs used Select list multiple
(authority: VEST Registry)
10 for each KOS used 5 IF you already have 4
8
Special instructions for getting
data from this service
Text 3 if not empty
9
Examples Text multiple 2 for each example
For each distribution of the
dataset
10
URL / target / endpoint Text 30 if not empty 1
11
File upload Upload 10 if not empty 1
12
Access / licensing Autocomplete 4 if half-open; 6 if free / open; 8 if
formally open (OA, CC)
0.5 if half-open; 1 if open; 1.5 if
open and known license e.g. CC
13
License URL Text: URL 7 if not empty 0.5
14
Protocol Select list 10 ftp/download; 20 OAI-PMH or
web service; 30 if SPARQL
1 if ftp/download; 3 if OAI-PMH or
RSS; 4 if SPARQL
15
Format / serialization / notation Select list
(authority: subset of IANA
types)
5 Excel; 10 CSV, XML; 12 JSON; 15
RDFXML; 20 JsonLD, ntriples-n3-
turtle)
2 if Excel; 3 if CSV, XML, JSON; 4 if
JsonLD, RDFXML, ntriples-n3-turtle
16
Metadata set(s) used Select list
(authority: VEST Registry)
6 for each metadata set 2.5
17
Does the dataset use URIs? Yes/No 20 if yes; OR: multiply 15 by n. 10 4 (OR: 4 IF you already have 3)
18
Example of
interoperability
assessment in the
RING
Thank you
Thank you for your attention
Valeria Pesce
valeria.pesce@fao.org

Weitere ähnliche Inhalte

Was ist angesagt?

SPARQL Query Forms
SPARQL Query FormsSPARQL Query Forms
SPARQL Query FormsLeigh Dodds
 
LDP4j: A framework for the development of interoperable read-write Linked Da...
LDP4j: A framework for the development of interoperable read-write Linked Da...LDP4j: A framework for the development of interoperable read-write Linked Da...
LDP4j: A framework for the development of interoperable read-write Linked Da...Nandana Mihindukulasooriya
 
Enhancing Interoperability: The Implementation of OpenAIRE Guidelines and COA...
Enhancing Interoperability: The Implementation of OpenAIRE Guidelines and COA...Enhancing Interoperability: The Implementation of OpenAIRE Guidelines and COA...
Enhancing Interoperability: The Implementation of OpenAIRE Guidelines and COA...4Science
 
2009 0807 Lod Gmod
2009 0807 Lod Gmod2009 0807 Lod Gmod
2009 0807 Lod GmodJun Zhao
 
Introduction to Linked Data Platform (LDP)
Introduction to Linked Data Platform (LDP)Introduction to Linked Data Platform (LDP)
Introduction to Linked Data Platform (LDP)Hector Correa
 
Describing configurations of software experiments as Linked Data
Describing configurations of software experiments as Linked DataDescribing configurations of software experiments as Linked Data
Describing configurations of software experiments as Linked DataJoachim Van Herwegen
 
Describing LDP Applications with the Hydra Core Vocabulary
Describing LDP Applications with the Hydra Core VocabularyDescribing LDP Applications with the Hydra Core Vocabulary
Describing LDP Applications with the Hydra Core VocabularyNandana Mihindukulasooriya
 
FAIR Projector Builder
FAIR Projector BuilderFAIR Projector Builder
FAIR Projector BuilderMark Wilkinson
 
Semantic Technologies and Triplestores for Business Intelligence
Semantic Technologies and Triplestores for Business IntelligenceSemantic Technologies and Triplestores for Business Intelligence
Semantic Technologies and Triplestores for Business IntelligenceMarin Dimitrov
 
Extending DSpace 7: DSpace-CRIS and DSpace-GLAM for empowered repositories an...
Extending DSpace 7: DSpace-CRIS and DSpace-GLAM for empowered repositories an...Extending DSpace 7: DSpace-CRIS and DSpace-GLAM for empowered repositories an...
Extending DSpace 7: DSpace-CRIS and DSpace-GLAM for empowered repositories an...4Science
 
DSpace-CRIS ORCID Integration
DSpace-CRIS ORCID IntegrationDSpace-CRIS ORCID Integration
DSpace-CRIS ORCID Integration4Science
 
Data Integration And Visualization
Data Integration And VisualizationData Integration And Visualization
Data Integration And VisualizationIvan Ermilov
 
DSpace-CRIS & OpenAIRE
DSpace-CRIS & OpenAIREDSpace-CRIS & OpenAIRE
DSpace-CRIS & OpenAIRE4Science
 
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...semanticsconference
 
2015 09 rda-pre-meeting_jk
2015 09 rda-pre-meeting_jk2015 09 rda-pre-meeting_jk
2015 09 rda-pre-meeting_jkJohannes Keizer
 
Poster GraphQL-LD: Linked Data Querying with GraphQL
Poster GraphQL-LD: Linked Data Querying with GraphQLPoster GraphQL-LD: Linked Data Querying with GraphQL
Poster GraphQL-LD: Linked Data Querying with GraphQLRuben Taelman
 
Intro to metadata, RIF-CS, RDA records [ANU 2016]
Intro to metadata, RIF-CS, RDA records [ANU 2016]Intro to metadata, RIF-CS, RDA records [ANU 2016]
Intro to metadata, RIF-CS, RDA records [ANU 2016]Jane Frazier
 

Was ist angesagt? (20)

SPARQL Query Forms
SPARQL Query FormsSPARQL Query Forms
SPARQL Query Forms
 
Web of Data Usage Mining
Web of Data Usage MiningWeb of Data Usage Mining
Web of Data Usage Mining
 
LDP4j: A framework for the development of interoperable read-write Linked Da...
LDP4j: A framework for the development of interoperable read-write Linked Da...LDP4j: A framework for the development of interoperable read-write Linked Da...
LDP4j: A framework for the development of interoperable read-write Linked Da...
 
Enhancing Interoperability: The Implementation of OpenAIRE Guidelines and COA...
Enhancing Interoperability: The Implementation of OpenAIRE Guidelines and COA...Enhancing Interoperability: The Implementation of OpenAIRE Guidelines and COA...
Enhancing Interoperability: The Implementation of OpenAIRE Guidelines and COA...
 
2009 0807 Lod Gmod
2009 0807 Lod Gmod2009 0807 Lod Gmod
2009 0807 Lod Gmod
 
Introduction to Linked Data Platform (LDP)
Introduction to Linked Data Platform (LDP)Introduction to Linked Data Platform (LDP)
Introduction to Linked Data Platform (LDP)
 
Describing configurations of software experiments as Linked Data
Describing configurations of software experiments as Linked DataDescribing configurations of software experiments as Linked Data
Describing configurations of software experiments as Linked Data
 
Describing LDP Applications with the Hydra Core Vocabulary
Describing LDP Applications with the Hydra Core VocabularyDescribing LDP Applications with the Hydra Core Vocabulary
Describing LDP Applications with the Hydra Core Vocabulary
 
FAIR Projector Builder
FAIR Projector BuilderFAIR Projector Builder
FAIR Projector Builder
 
Semantic Technologies and Triplestores for Business Intelligence
Semantic Technologies and Triplestores for Business IntelligenceSemantic Technologies and Triplestores for Business Intelligence
Semantic Technologies and Triplestores for Business Intelligence
 
Extending DSpace 7: DSpace-CRIS and DSpace-GLAM for empowered repositories an...
Extending DSpace 7: DSpace-CRIS and DSpace-GLAM for empowered repositories an...Extending DSpace 7: DSpace-CRIS and DSpace-GLAM for empowered repositories an...
Extending DSpace 7: DSpace-CRIS and DSpace-GLAM for empowered repositories an...
 
DSpace-CRIS ORCID Integration
DSpace-CRIS ORCID IntegrationDSpace-CRIS ORCID Integration
DSpace-CRIS ORCID Integration
 
Data Integration And Visualization
Data Integration And VisualizationData Integration And Visualization
Data Integration And Visualization
 
DSpace-CRIS & OpenAIRE
DSpace-CRIS & OpenAIREDSpace-CRIS & OpenAIRE
DSpace-CRIS & OpenAIRE
 
Chlorine
ChlorineChlorine
Chlorine
 
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...
 
May 2013 HUG: HCatalog/Hive Data Out
May 2013 HUG: HCatalog/Hive Data OutMay 2013 HUG: HCatalog/Hive Data Out
May 2013 HUG: HCatalog/Hive Data Out
 
2015 09 rda-pre-meeting_jk
2015 09 rda-pre-meeting_jk2015 09 rda-pre-meeting_jk
2015 09 rda-pre-meeting_jk
 
Poster GraphQL-LD: Linked Data Querying with GraphQL
Poster GraphQL-LD: Linked Data Querying with GraphQLPoster GraphQL-LD: Linked Data Querying with GraphQL
Poster GraphQL-LD: Linked Data Querying with GraphQL
 
Intro to metadata, RIF-CS, RDA records [ANU 2016]
Intro to metadata, RIF-CS, RDA records [ANU 2016]Intro to metadata, RIF-CS, RDA records [ANU 2016]
Intro to metadata, RIF-CS, RDA records [ANU 2016]
 

Ähnlich wie The new CIARD RING , a machine-readable directory of datasets for agriculture

Presentation at the EMBL-EBI Industry RDF meeting
Presentation at the EMBL-EBI  Industry RDF meetingPresentation at the EMBL-EBI  Industry RDF meeting
Presentation at the EMBL-EBI Industry RDF meetingJohannes Keizer
 
Tim Pugh-SPEDDEXES 2014
Tim Pugh-SPEDDEXES 2014Tim Pugh-SPEDDEXES 2014
Tim Pugh-SPEDDEXES 2014aceas13tern
 
Virtuoso Sponger - RDFizer Middleware for creating RDF from non RDF Data Sources
Virtuoso Sponger - RDFizer Middleware for creating RDF from non RDF Data SourcesVirtuoso Sponger - RDFizer Middleware for creating RDF from non RDF Data Sources
Virtuoso Sponger - RDFizer Middleware for creating RDF from non RDF Data Sourcesrumito
 
Interoperability is the key: repositories networks promoting the quality and ...
Interoperability is the key: repositories networks promoting the quality and ...Interoperability is the key: repositories networks promoting the quality and ...
Interoperability is the key: repositories networks promoting the quality and ...Pedro Príncipe
 
EUDAT data architecture and interoperability aspects – Daan Broeder
EUDAT data architecture and interoperability aspects – Daan BroederEUDAT data architecture and interoperability aspects – Daan Broeder
EUDAT data architecture and interoperability aspects – Daan BroederOpenAIRE
 
OpenAIRE and the case of Irish Repositories, by Jochen Schirrwagen (RIAN Work...
OpenAIRE and the case of Irish Repositories, by Jochen Schirrwagen (RIAN Work...OpenAIRE and the case of Irish Repositories, by Jochen Schirrwagen (RIAN Work...
OpenAIRE and the case of Irish Repositories, by Jochen Schirrwagen (RIAN Work...OpenAIRE
 
Wed roman tut_open_datapub
Wed roman tut_open_datapubWed roman tut_open_datapub
Wed roman tut_open_datapubeswcsummerschool
 
The three investigators: OraChk, TFA and DBSAT
The three investigators: OraChk, TFA and DBSATThe three investigators: OraChk, TFA and DBSAT
The three investigators: OraChk, TFA and DBSATMarkus Flechtner
 
Dataset Descriptions in Open PHACTS and HCLS
Dataset Descriptions in Open PHACTS and HCLSDataset Descriptions in Open PHACTS and HCLS
Dataset Descriptions in Open PHACTS and HCLSAlasdair Gray
 
WarsawITDays_ ApacheNiFi202
WarsawITDays_ ApacheNiFi202WarsawITDays_ ApacheNiFi202
WarsawITDays_ ApacheNiFi202Timothy Spann
 
Opening and Integration of CASDD and Germplasm Data to AGRIS by Prof. Xuefu Z...
Opening and Integration of CASDD and Germplasm Data to AGRIS by Prof. Xuefu Z...Opening and Integration of CASDD and Germplasm Data to AGRIS by Prof. Xuefu Z...
Opening and Integration of CASDD and Germplasm Data to AGRIS by Prof. Xuefu Z...CIARD Movement
 
agINFRA EGI-APARSEN workshop, Amsterdam, 4-6 March 2014
agINFRA EGI-APARSEN workshop, Amsterdam, 4-6 March 2014agINFRA EGI-APARSEN workshop, Amsterdam, 4-6 March 2014
agINFRA EGI-APARSEN workshop, Amsterdam, 4-6 March 2014Andreas Drakos
 
RDFa Introductory Course Session 2/4 How RDFa
RDFa Introductory Course Session 2/4 How RDFaRDFa Introductory Course Session 2/4 How RDFa
RDFa Introductory Course Session 2/4 How RDFaPlatypus
 
Open for Business - Open Archives, OpenURL, RSS and the Dublin Core
Open for Business - Open Archives, OpenURL, RSS and the Dublin CoreOpen for Business - Open Archives, OpenURL, RSS and the Dublin Core
Open for Business - Open Archives, OpenURL, RSS and the Dublin CoreAndy Powell
 
05 SPARQL queries over Open Land Use, Open Transport Net and Smart Points Of ...
05 SPARQL queries over Open Land Use, Open Transport Net and Smart Points Of ...05 SPARQL queries over Open Land Use, Open Transport Net and Smart Points Of ...
05 SPARQL queries over Open Land Use, Open Transport Net and Smart Points Of ...plan4all
 

Ähnlich wie The new CIARD RING , a machine-readable directory of datasets for agriculture (20)

The CIARD RINGValeri
The CIARD RINGValeriThe CIARD RINGValeri
The CIARD RINGValeri
 
Presentation at the EMBL-EBI Industry RDF meeting
Presentation at the EMBL-EBI  Industry RDF meetingPresentation at the EMBL-EBI  Industry RDF meeting
Presentation at the EMBL-EBI Industry RDF meeting
 
AGROVOC, AGRIS and the CIARD RING, using RDF vocabularies and technologies f...
AGROVOC, AGRIS and the CIARD RING,  using RDF vocabularies and technologies f...AGROVOC, AGRIS and the CIARD RING,  using RDF vocabularies and technologies f...
AGROVOC, AGRIS and the CIARD RING, using RDF vocabularies and technologies f...
 
Tim Pugh-SPEDDEXES 2014
Tim Pugh-SPEDDEXES 2014Tim Pugh-SPEDDEXES 2014
Tim Pugh-SPEDDEXES 2014
 
Virtuoso Sponger - RDFizer Middleware for creating RDF from non RDF Data Sources
Virtuoso Sponger - RDFizer Middleware for creating RDF from non RDF Data SourcesVirtuoso Sponger - RDFizer Middleware for creating RDF from non RDF Data Sources
Virtuoso Sponger - RDFizer Middleware for creating RDF from non RDF Data Sources
 
Interoperability is the key: repositories networks promoting the quality and ...
Interoperability is the key: repositories networks promoting the quality and ...Interoperability is the key: repositories networks promoting the quality and ...
Interoperability is the key: repositories networks promoting the quality and ...
 
A Finnish perspective on FAIRsFAIR outputs
A Finnish perspective on FAIRsFAIR outputsA Finnish perspective on FAIRsFAIR outputs
A Finnish perspective on FAIRsFAIR outputs
 
EUDAT data architecture and interoperability aspects – Daan Broeder
EUDAT data architecture and interoperability aspects – Daan BroederEUDAT data architecture and interoperability aspects – Daan Broeder
EUDAT data architecture and interoperability aspects – Daan Broeder
 
OpenAIRE and the case of Irish Repositories, by Jochen Schirrwagen (RIAN Work...
OpenAIRE and the case of Irish Repositories, by Jochen Schirrwagen (RIAN Work...OpenAIRE and the case of Irish Repositories, by Jochen Schirrwagen (RIAN Work...
OpenAIRE and the case of Irish Repositories, by Jochen Schirrwagen (RIAN Work...
 
Wed roman tut_open_datapub
Wed roman tut_open_datapubWed roman tut_open_datapub
Wed roman tut_open_datapub
 
The three investigators: OraChk, TFA and DBSAT
The three investigators: OraChk, TFA and DBSATThe three investigators: OraChk, TFA and DBSAT
The three investigators: OraChk, TFA and DBSAT
 
Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"
Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"
Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"
 
Dataset Descriptions in Open PHACTS and HCLS
Dataset Descriptions in Open PHACTS and HCLSDataset Descriptions in Open PHACTS and HCLS
Dataset Descriptions in Open PHACTS and HCLS
 
WarsawITDays_ ApacheNiFi202
WarsawITDays_ ApacheNiFi202WarsawITDays_ ApacheNiFi202
WarsawITDays_ ApacheNiFi202
 
Opening and Integration of CASDD and Germplasm Data to AGRIS by Prof. Xuefu Z...
Opening and Integration of CASDD and Germplasm Data to AGRIS by Prof. Xuefu Z...Opening and Integration of CASDD and Germplasm Data to AGRIS by Prof. Xuefu Z...
Opening and Integration of CASDD and Germplasm Data to AGRIS by Prof. Xuefu Z...
 
agINFRA EGI-APARSEN workshop, Amsterdam, 4-6 March 2014
agINFRA EGI-APARSEN workshop, Amsterdam, 4-6 March 2014agINFRA EGI-APARSEN workshop, Amsterdam, 4-6 March 2014
agINFRA EGI-APARSEN workshop, Amsterdam, 4-6 March 2014
 
How RDFa works
How RDFa worksHow RDFa works
How RDFa works
 
RDFa Introductory Course Session 2/4 How RDFa
RDFa Introductory Course Session 2/4 How RDFaRDFa Introductory Course Session 2/4 How RDFa
RDFa Introductory Course Session 2/4 How RDFa
 
Open for Business - Open Archives, OpenURL, RSS and the Dublin Core
Open for Business - Open Archives, OpenURL, RSS and the Dublin CoreOpen for Business - Open Archives, OpenURL, RSS and the Dublin Core
Open for Business - Open Archives, OpenURL, RSS and the Dublin Core
 
05 SPARQL queries over Open Land Use, Open Transport Net and Smart Points Of ...
05 SPARQL queries over Open Land Use, Open Transport Net and Smart Points Of ...05 SPARQL queries over Open Land Use, Open Transport Net and Smart Points Of ...
05 SPARQL queries over Open Land Use, Open Transport Net and Smart Points Of ...
 

Mehr von Valeria Pesce

Codes of conduct for farm data sharing. Work done and ideas for a GODAN/CTA s...
Codes of conduct for farm data sharing. Work done and ideas for a GODAN/CTA s...Codes of conduct for farm data sharing. Work done and ideas for a GODAN/CTA s...
Codes of conduct for farm data sharing. Work done and ideas for a GODAN/CTA s...Valeria Pesce
 
Digital agriculture: ICT-amplified data asymmetries and power imbalances. Pol...
Digital agriculture: ICT-amplified data asymmetries and power imbalances. Pol...Digital agriculture: ICT-amplified data asymmetries and power imbalances. Pol...
Digital agriculture: ICT-amplified data asymmetries and power imbalances. Pol...Valeria Pesce
 
Farmers' data rights - Some findings
Farmers' data rights - Some findingsFarmers' data rights - Some findings
Farmers' data rights - Some findingsValeria Pesce
 
Semantic challenges in sharing dataset metadata and creating federated datase...
Semantic challenges in sharing dataset metadata and creating federated datase...Semantic challenges in sharing dataset metadata and creating federated datase...
Semantic challenges in sharing dataset metadata and creating federated datase...Valeria Pesce
 
Data discovery through federated dataset catalogs
Data discovery through federated dataset catalogsData discovery through federated dataset catalogs
Data discovery through federated dataset catalogsValeria Pesce
 
Inventory of data standards for food & agriculture
Inventory of data standards for food & agricultureInventory of data standards for food & agriculture
Inventory of data standards for food & agricultureValeria Pesce
 
Dataset description: DCAT and other vocabularies
Dataset description: DCAT and other vocabulariesDataset description: DCAT and other vocabularies
Dataset description: DCAT and other vocabulariesValeria Pesce
 
Semantics for food and agriculture: the GODAN Action map of data standards
Semantics for food and agriculture: the GODAN Action map of data standardsSemantics for food and agriculture: the GODAN Action map of data standards
Semantics for food and agriculture: the GODAN Action map of data standardsValeria Pesce
 
How to describe a dataset. Interoperability issues
How to describe a dataset. Interoperability issuesHow to describe a dataset. Interoperability issues
How to describe a dataset. Interoperability issuesValeria Pesce
 
A global linked and open data infrastructure for agricultural development
A global linked and open data infrastructure for agricultural developmentA global linked and open data infrastructure for agricultural development
A global linked and open data infrastructure for agricultural developmentValeria Pesce
 
The agINFRA Linked Data layer
The agINFRA Linked Data layerThe agINFRA Linked Data layer
The agINFRA Linked Data layerValeria Pesce
 
Publishing Germplasm Vocabularies as Linked Data
Publishing Germplasm Vocabularies as Linked DataPublishing Germplasm Vocabularies as Linked Data
Publishing Germplasm Vocabularies as Linked DataValeria Pesce
 
VIVOCamp slides: agenda and slides on the extension of the ontology
VIVOCamp slides: agenda and slides on the extension of the ontologyVIVOCamp slides: agenda and slides on the extension of the ontology
VIVOCamp slides: agenda and slides on the extension of the ontologyValeria Pesce
 
AgriVIVO: A Global Ontology-Driven RDF Store Based on a Distributed Architect...
AgriVIVO: A Global Ontology-Driven RDF Store Based on a Distributed Architect...AgriVIVO: A Global Ontology-Driven RDF Store Based on a Distributed Architect...
AgriVIVO: A Global Ontology-Driven RDF Store Based on a Distributed Architect...Valeria Pesce
 
AgriVIVO. Fostering better networking and collaboration among researchers, re...
AgriVIVO. Fostering better networking and collaboration among researchers, re...AgriVIVO. Fostering better networking and collaboration among researchers, re...
AgriVIVO. Fostering better networking and collaboration among researchers, re...Valeria Pesce
 
AgriDrupal: general presentation
AgriDrupal: general presentationAgriDrupal: general presentation
AgriDrupal: general presentationValeria Pesce
 
Developing Agricultural Research Information Systems. The experience of the G...
Developing Agricultural Research Information Systems. The experience of the G...Developing Agricultural Research Information Systems. The experience of the G...
Developing Agricultural Research Information Systems. The experience of the G...Valeria Pesce
 
Information / software architectures based on Content Management Systems (CMS)
Information / software architectures based on Content Management Systems (CMS)Information / software architectures based on Content Management Systems (CMS)
Information / software architectures based on Content Management Systems (CMS)Valeria Pesce
 
The CIARD RING, an infrastructure for interoperability of agricultural resear...
The CIARD RING, an infrastructure for interoperability of agricultural resear...The CIARD RING, an infrastructure for interoperability of agricultural resear...
The CIARD RING, an infrastructure for interoperability of agricultural resear...Valeria Pesce
 
Libraries 2.0 and RSS
Libraries 2.0 and RSSLibraries 2.0 and RSS
Libraries 2.0 and RSSValeria Pesce
 

Mehr von Valeria Pesce (20)

Codes of conduct for farm data sharing. Work done and ideas for a GODAN/CTA s...
Codes of conduct for farm data sharing. Work done and ideas for a GODAN/CTA s...Codes of conduct for farm data sharing. Work done and ideas for a GODAN/CTA s...
Codes of conduct for farm data sharing. Work done and ideas for a GODAN/CTA s...
 
Digital agriculture: ICT-amplified data asymmetries and power imbalances. Pol...
Digital agriculture: ICT-amplified data asymmetries and power imbalances. Pol...Digital agriculture: ICT-amplified data asymmetries and power imbalances. Pol...
Digital agriculture: ICT-amplified data asymmetries and power imbalances. Pol...
 
Farmers' data rights - Some findings
Farmers' data rights - Some findingsFarmers' data rights - Some findings
Farmers' data rights - Some findings
 
Semantic challenges in sharing dataset metadata and creating federated datase...
Semantic challenges in sharing dataset metadata and creating federated datase...Semantic challenges in sharing dataset metadata and creating federated datase...
Semantic challenges in sharing dataset metadata and creating federated datase...
 
Data discovery through federated dataset catalogs
Data discovery through federated dataset catalogsData discovery through federated dataset catalogs
Data discovery through federated dataset catalogs
 
Inventory of data standards for food & agriculture
Inventory of data standards for food & agricultureInventory of data standards for food & agriculture
Inventory of data standards for food & agriculture
 
Dataset description: DCAT and other vocabularies
Dataset description: DCAT and other vocabulariesDataset description: DCAT and other vocabularies
Dataset description: DCAT and other vocabularies
 
Semantics for food and agriculture: the GODAN Action map of data standards
Semantics for food and agriculture: the GODAN Action map of data standardsSemantics for food and agriculture: the GODAN Action map of data standards
Semantics for food and agriculture: the GODAN Action map of data standards
 
How to describe a dataset. Interoperability issues
How to describe a dataset. Interoperability issuesHow to describe a dataset. Interoperability issues
How to describe a dataset. Interoperability issues
 
A global linked and open data infrastructure for agricultural development
A global linked and open data infrastructure for agricultural developmentA global linked and open data infrastructure for agricultural development
A global linked and open data infrastructure for agricultural development
 
The agINFRA Linked Data layer
The agINFRA Linked Data layerThe agINFRA Linked Data layer
The agINFRA Linked Data layer
 
Publishing Germplasm Vocabularies as Linked Data
Publishing Germplasm Vocabularies as Linked DataPublishing Germplasm Vocabularies as Linked Data
Publishing Germplasm Vocabularies as Linked Data
 
VIVOCamp slides: agenda and slides on the extension of the ontology
VIVOCamp slides: agenda and slides on the extension of the ontologyVIVOCamp slides: agenda and slides on the extension of the ontology
VIVOCamp slides: agenda and slides on the extension of the ontology
 
AgriVIVO: A Global Ontology-Driven RDF Store Based on a Distributed Architect...
AgriVIVO: A Global Ontology-Driven RDF Store Based on a Distributed Architect...AgriVIVO: A Global Ontology-Driven RDF Store Based on a Distributed Architect...
AgriVIVO: A Global Ontology-Driven RDF Store Based on a Distributed Architect...
 
AgriVIVO. Fostering better networking and collaboration among researchers, re...
AgriVIVO. Fostering better networking and collaboration among researchers, re...AgriVIVO. Fostering better networking and collaboration among researchers, re...
AgriVIVO. Fostering better networking and collaboration among researchers, re...
 
AgriDrupal: general presentation
AgriDrupal: general presentationAgriDrupal: general presentation
AgriDrupal: general presentation
 
Developing Agricultural Research Information Systems. The experience of the G...
Developing Agricultural Research Information Systems. The experience of the G...Developing Agricultural Research Information Systems. The experience of the G...
Developing Agricultural Research Information Systems. The experience of the G...
 
Information / software architectures based on Content Management Systems (CMS)
Information / software architectures based on Content Management Systems (CMS)Information / software architectures based on Content Management Systems (CMS)
Information / software architectures based on Content Management Systems (CMS)
 
The CIARD RING, an infrastructure for interoperability of agricultural resear...
The CIARD RING, an infrastructure for interoperability of agricultural resear...The CIARD RING, an infrastructure for interoperability of agricultural resear...
The CIARD RING, an infrastructure for interoperability of agricultural resear...
 
Libraries 2.0 and RSS
Libraries 2.0 and RSSLibraries 2.0 and RSS
Libraries 2.0 and RSS
 

Kürzlich hochgeladen

Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024TopCSSGallery
 
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesMuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesManik S Magar
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...Wes McKinney
 
Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Kaya Weers
 
Kuma Meshes Part I - The basics - A tutorial
Kuma Meshes Part I - The basics - A tutorialKuma Meshes Part I - The basics - A tutorial
Kuma Meshes Part I - The basics - A tutorialJoão Esperancinha
 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityIES VE
 
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...Nikki Chapple
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality AssuranceInflectra
 
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsRavi Sanghani
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfIngrid Airi González
 
Microservices, Docker deploy and Microservices source code in C#
Microservices, Docker deploy and Microservices source code in C#Microservices, Docker deploy and Microservices source code in C#
Microservices, Docker deploy and Microservices source code in C#Karmanjay Verma
 
React JS; all concepts. Contains React Features, JSX, functional & Class comp...
React JS; all concepts. Contains React Features, JSX, functional & Class comp...React JS; all concepts. Contains React Features, JSX, functional & Class comp...
React JS; all concepts. Contains React Features, JSX, functional & Class comp...Karmanjay Verma
 
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...itnewsafrica
 
All These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFAll These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFMichael Gough
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integrationmarketing932765
 
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...panagenda
 
Generative AI - Gitex v1Generative AI - Gitex v1.pptx
Generative AI - Gitex v1Generative AI - Gitex v1.pptxGenerative AI - Gitex v1Generative AI - Gitex v1.pptx
Generative AI - Gitex v1Generative AI - Gitex v1.pptxfnnc6jmgwh
 
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...itnewsafrica
 
Assure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesAssure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesThousandEyes
 
Tampa BSides - The No BS SOC (slides from April 6, 2024 talk)
Tampa BSides - The No BS SOC (slides from April 6, 2024 talk)Tampa BSides - The No BS SOC (slides from April 6, 2024 talk)
Tampa BSides - The No BS SOC (slides from April 6, 2024 talk)Mark Simos
 

Kürzlich hochgeladen (20)

Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024
 
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesMuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
 
Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)
 
Kuma Meshes Part I - The basics - A tutorial
Kuma Meshes Part I - The basics - A tutorialKuma Meshes Part I - The basics - A tutorial
Kuma Meshes Part I - The basics - A tutorial
 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a reality
 
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
 
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and Insights
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdf
 
Microservices, Docker deploy and Microservices source code in C#
Microservices, Docker deploy and Microservices source code in C#Microservices, Docker deploy and Microservices source code in C#
Microservices, Docker deploy and Microservices source code in C#
 
React JS; all concepts. Contains React Features, JSX, functional & Class comp...
React JS; all concepts. Contains React Features, JSX, functional & Class comp...React JS; all concepts. Contains React Features, JSX, functional & Class comp...
React JS; all concepts. Contains React Features, JSX, functional & Class comp...
 
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
 
All These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFAll These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDF
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
 
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
 
Generative AI - Gitex v1Generative AI - Gitex v1.pptx
Generative AI - Gitex v1Generative AI - Gitex v1.pptxGenerative AI - Gitex v1Generative AI - Gitex v1.pptx
Generative AI - Gitex v1Generative AI - Gitex v1.pptx
 
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
 
Assure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesAssure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyes
 
Tampa BSides - The No BS SOC (slides from April 6, 2024 talk)
Tampa BSides - The No BS SOC (slides from April 6, 2024 talk)Tampa BSides - The No BS SOC (slides from April 6, 2024 talk)
Tampa BSides - The No BS SOC (slides from April 6, 2024 talk)
 

The new CIARD RING , a machine-readable directory of datasets for agriculture

  • 1. The new CIARD RING a machine-readable directory of datasets for agriculture Valeria Pesce Global Forum on Agricultural Research (GFAR) Research Data Alliance 4th Plenary Meeting 22-24 September 2014, Amsterdam Agricultural Data Interoperability Interest Group agINFRA project EC 7th framework program INFRA-2011-1.2.2 - Grant agr. no: 283770
  • 2. The CIARD RING http://ring.ciard.net The CIARD RING is a project implemented within the CIARD initiative and is led by the Global Forum on Agricultural Research (GFAR). The CIARD RING is a global directory of web-based information services and datasets for agriculture
  • 3. Why (1) - Producers and managers of information / data need a place where their information products can be found - Data consumers need to find suitable data sources - IT professionals need information on the level and mode of interoperability of information services and datasets for using data in their applications
  • 4. Numbers and map • 468 data providers • 1018 information services, of which – 268 exposed datasets
  • 5. Definition of “dataset” in the RING The term “datasets” has been defined in several ways, all of which further specify or extend the basic concept of “a collection of data”. Definition given by the W3C Government Linked Data Working Group: A dataset is “a collection of data, published or curated by a single source, and available for access or download in one or more formats” The “instances” of the dataset “available for access or download in one or more formats” are called “distributions”. A dataset can have many distributions. Examples of distributions include a downloadable CSV file, an API or an RSS feed.
  • 6. Direct submission + federation • All datasets currently featured in the RING have been manually submitted by their owners / managers • BUT, We don’t want to force data owners who already have a dataset catalog to catalog and maintain their datasets in two places  We are working on procedures to federate datasets from the most used dataset cataloguing platforms (Dataverse, CKAN…) First experiment started with the IFPRI Dataverse dataset catalog
  • 7. The RING user interface
  • 9. The RING machine interface – Why (2) • Datasets registered in the RING have to be found by applications • Applications have to be able to read all the metadata about datasets and filter datasets according to their needs • Applications have to find enough technical metadata in the RING to: – Identify datasets with a specific coverage (type of data, thematic coverage, geographic coverage) – Identify datasets that comply with certain technical specifications (format, protocol etc.) – Access the dataset and get the data  This machine-readable layer can support the data aggregation workflows of external services
  • 10. The RING machine interface – SPARQL An RDF store is a way of storing data using a machine- readable "grammar" (the Resource Description Framework) and documented semantics (RDF vocabularies). URIs The URI for each service / dataset is built as follows: RING-domain/node/service-ID. For example: http://ring.ciard.net/node/2417 The RING database is also an accessible RDF store. SPARQL endpoint http://ring.ciard.net/sparql1
  • 11. SPARQL how to: vocabularies The vocabularies used in the RDF store are: • RDF: http://www.w3.org/1999/02/22-rdf-syntax-ns# • RDFS: http://www.w3.org/2000/01/rdf-schema# • DC: http://purl.org/dc/terms/ • DCAT: http://www.w3.org/ns/dcat# • ADMS: http://www.w3.org/ns/adms# • FOAF: http://xmlns.com/foaf/0.1/ • DOAP: http://usefulinc.com/ns/doap# • SKOS: http://www.w3.org/2004/02/skos/core# • VCARD: http://www.w3.org/2006/vcard/ns# The data model chosen to describe datasets is the W3C Data Catalog Vocabulary (DCAT) designed to describe datasets and the forms in which they are exposed, their "distributions"
  • 12. SPARQL how to: sample query To get all datasets available through the OAI-PMH protocol Query: PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX dc: <http://purl.org/dc/terms/> PREFIX dcat: <http://www.w3.org/ns/dcat#> PREFIX adms: <http://www.w3.org/ns/adms#> PREFIX doap: <http://usefulinc.com/ns/doap#> PREFIX skos: <http://www.w3.org/2004/02/skos/core#> DESCRIBE ?dataset ?distro ?owner ?contact ?topic ?standard ?format ?protocol WHERE { ?dataset rdf:type dcat:Dataset . ?dataset dc:title ?title . ?dataset dcat:distribution ?distro . ?dataset dc:publisher ?owner . ?distro dcat:accessURL ?url . ?distro adms:representationTechnique <http://ring.ciard.net/taxonomy_term/108> . OPTIONAL { ?dataset doap:maintainer ?contact } OPTIONAL { ?dataset dcat:theme ?topic } OPTIONAL { ?distro dc:conformsTo ?standard } OPTIONAL { ?distro dc:format ?format } OPTIONAL { ?distro adms:representationTechnique ?protocol } }
  • 13. SPARQL how to: URIs? All the URIs that you may need in queries are listed on the RING web site • A list of the URIs of all the RING entities (services/datasets, organizations, KOSs etc.) • A list of the URIs of all RING concepts (countries, topics, regions, protocols etc.)
  • 14. SPARQL how to: URIs of entities
  • 15. SPARQL how to: exploit linked URIs
  • 16. Example of use: AGRIS  RING 1. How AGRIS uses the RING Linked Data AGRIS (http://agris.fao.org): database of more than 7 million bibliographic references on agricultural research and technology and links to related data resources on the Web. AGRIS retrieves information on AGRIS centers through a SPARQL query run against the RING. <http://ring.ciard.net/node/10687> is the uRI of the AGRIS network in the RING ------------------------------ PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX dc: <http://purl.org/dc/terms/> PREFIX dcat: <http://www.w3.org/ns/dcat#> DESCRIBE ?dataset WHERE { ?dataset rdf:type dcat:Dataset . ?dataset dc:partOf <http://ring.ciard.net/node/10687> } ------------------------------
  • 17. Example of use: AGRIS  RING 2. How to get AGRIS Linked Data bibliographic records for each AGRIS center In the AGRIS RDF store, all bibliographic records are associated to the corresponding AGRIS center through the dcterms:source property: the URI used to identify the AGRIS center is the RING URI. Any application can therefore retrieve all records belonging to an AGRIS center by running a query against the AGRIS SPARQL endpoint (http://202.45.139.84:10035/catalogs/fao/repositories /agris). ------------------------------------ PREFIX dcterms: <http://purl.org/terms> DESCRIBE ?rec WHERE { ?rec dcterms:source <http://ring.ciard.net/node/2754> . } -----------------------------------
  • 18. Interoperability assessment in the RING The technical metadata registered in the RING for each dataset provide enough information to give a good idea of the level of “interoperability” of that dataset. “Interoperability is a feature of datasets— and of information services that give access to datasets— whereby data can easily be retrieved, processed, re-used, and re-packaged (“operated”) by other systems. The less pre-coordination required to achieve this, the more “interoperable” the dataset.” [from: Interim Proceedings of International Expert Consultation on “Building the CIARD Framework for Data and Information Sharing”, Beijing 20-23 June 2011. 2011.]
  • 19. Metadata Type Interoperability points Tim Berner Lee’s stars For the service/dataset in general 1 Global coverage Select list 4 if not empty 2 Regional coverage (FAO) Select list 4 if not empty 3 Regional coverage (GFAR) Select list 4 if not empty 4 National coverage Select list 4 if not empty 5 Specific topic (AGROVOC) Autocomplete multiple (authority: AGROVOC) 8 if not empty 6 Type of content/data managed Autocomplete multiple 4 if not empty 7 KOSs used Select list multiple (authority: VEST Registry) 10 for each KOS used 5 IF you already have 4 8 Special instructions for getting data from this service Text 3 if not empty 9 Examples Text multiple 2 for each example For each distribution of the dataset 10 URL / target / endpoint Text 30 if not empty 1 11 File upload Upload 10 if not empty 1 12 Access / licensing Autocomplete 4 if half-open; 6 if free / open; 8 if formally open (OA, CC) 0.5 if half-open; 1 if open; 1.5 if open and known license e.g. CC 13 License URL Text: URL 7 if not empty 0.5 14 Protocol Select list 10 ftp/download; 20 OAI-PMH or web service; 30 if SPARQL 1 if ftp/download; 3 if OAI-PMH or RSS; 4 if SPARQL 15 Format / serialization / notation Select list (authority: subset of IANA types) 5 Excel; 10 CSV, XML; 12 JSON; 15 RDFXML; 20 JsonLD, ntriples-n3- turtle) 2 if Excel; 3 if CSV, XML, JSON; 4 if JsonLD, RDFXML, ntriples-n3-turtle 16 Metadata set(s) used Select list (authority: VEST Registry) 6 for each metadata set 2.5 17 Does the dataset use URIs? Yes/No 20 if yes; OR: multiply 15 by n. 10 4 (OR: 4 IF you already have 3) 18
  • 21. Thank you Thank you for your attention Valeria Pesce valeria.pesce@fao.org