1.
Dataset description:
DCAT and other
vocabularies
Valeria Pesce
Secretariat of Global Forum on Agricultural Research (GFAR)
and
Secretariat of the Global Open Data for Agriculture and Nutrition
(GODAN) initiative
DRTC/ISI-ICSU/CODATA
International Workshop on Open Data Repositories

2.
Data and datasets
Wikipedia
“Data are values of qualitative or quantitative variables, belonging to a set of
items.
Data as an abstract concept can be viewed as the lowest level of abstraction from
which information and then knowledge are derived.”
Wikipedia: A dataset (or data set) is a collection of data.
(Narrow definition) …the contents of a single database table, or a single
statistical data matrix, where each column of the table represents a particular
variable, and each row corresponds to a given member of the dataset in
question. […]
Nontabular datasets can take the form of marked up strings of characters, such
as an XML file.
W3C Government Linked Data Working Group (DCAT vocabulary):
http://www.w3.org/TR/vocab-dcat/#class--dataset
A collection of data, published or curated by a single source, and available for
access or download in one or more formats.
DATA
DATASETS

3.
Single dataset
• As long as you consider a dataset alone, you may not
need structured metadata about the dataset for further
re-use as long as the dataset uses sector standards and
is documented in some way
• When managing a single dataset or a few
homogeneous datasets between colleagues, it may be
enough to use sector-specific standards (e.g. Multi-crop
descriptors and Darwin Core for germplasm, INSPIRE
for soil/geo, “Minimum Set” recommendations for
observations, sector code lists / vocabularies) or
application-specific standards

4.
Datasets in repositories
But what happens when you have a big data repository with
heterogeneous datasets?
Or you have few datasets but you want to contribute them to a
huge data repository/catalog where many other datasets are?
Users will want to find your dataset among many
others, possibly together with datasets with similar
data using the same standards / measures / syntax
They will use tools to search for datasets
Then dataset metadata
(or a machine-readable dataset description)
becomes important.

5.
Why machine-readable descriptions
• Data will be re-used by applications
Others will search and make use of your data through tools
 Datasets have to be found by applications
 Datasets have to be understood by applications
• Datasets should be managed in data repositories / data catalogs
 Data catalogs have to provide enough dataset metadata to
applications to allow them to find and understand datasets
• Data catalogs themselves are implemented as applications, so
they need machine-readable dataset metadata

6.
Ref / ID / URI Dimension2 Dimension3 Dimension4
Value1.1 Value1.2 Value1.3 Value1.4
Value2.1 Value2.2 Value2.3 Value2.4
Value3.1 Value3.2 Value3.3 Value3.4
Value4.1 Value4.2 Value4.3 Value4.4
Dataset
Datum
Record,
“member”,
observation
File system
Data repository
DatasetRef Matadata1 Metadata2 Metadata3
Ref1 Address1 Value1.2 Value1.3
Ref2 Address2 Value2.2 Value2.3
Data catalog (also a dataset)
Tabular only for the sake of simplification, it could be triples or other data structures
CatalogID Value1 Value2 Value3
Search Export
Data type
We only focus on the data catalog level

7.
Dataset metadata
So, what metadata do applications
need to find in data catalogs?

8.
Dataset metadata for applications
1) General metadata about the dataset:
1) identifier(s)
2) who is responsible for it
3) when and where the data were collected
4) relations to organizations, persons, publications, software,
projects, funding…
5) the conditions for re-use (rights, licenses)
6) provenance, versions
7) the specific coverage of the dataset (type of data, thematic
coverage, geographic coverage)
8) time and space slices; subsets
9) the “dimensions” and “variables” covered by the dataset
10) the semantics of the dimensions (units of measure, time
granularity, syntax, reference taxonomies)

9.
WHY dimensions and semantics
Example of search by researcher:
I’m doing research on plant phenotypes: give me all
datasets of crop phenotypic data that include the
dimensions of time, geographic location and height,
plus units of measure used for time and height,
where geographic location is expressed as
coordinates (because my software only processes
coordinates)

10.
Data seralizations
• The metadata above refer to the collection as a whole,
but additional technical metadata refer to the different
“serializations” of the data…
• In many dataset description models, the metadata
about the data serialization is attached to the dataset
• In other dataset description models, information about
the data serializations is not considered inherent to the
nature and content of the data collection, so it is not
attached to the dataset but rather to related entities
called “distributions”

11.
Distribution metadata for applications
Applications have to find metadata about the actual
“serializations” or “distributions” of the dataset to understand:
1. Where to retrieve it: URL (data dump, service…)
2. the necessary technical specifications to retrieve and parse a
distribution of the dataset:
- format (file format, data format)
- protocol, API parameters…
And, if different for different distributions, again:
3. the conditions for re-use (rights, licenses)
4. the semantics of the dimensions (units of measure, time
granularity, syntax, reference taxonomies) if different
semantics for different distributions

12.
WHY protocols and API params
Example:
Some datasets are available behind a service: e.g.
RDF datasets are often retrieved as subsets of an RDF
store through SPARQL queries; or national research
institutes provide access to datasets behind a web
service, accepting parameters to filter the datasets
Use case: I have an application that can fetch from
several SOAP web services (protocol) at once
automatically if it knows the parameters required by
the SOAP service and the required syntax and type of
the parameters

13.
General issue with all metadata
Standardization of the values, e.g. for “thematic
coverage” or “dimensions” of datasets, “format” or
“protocol used” of distributions etc.
- The value should be standardized, possibly a URI
- The value should be part of an authority list / code
list
And… There is no authority “value vocabulary” or
code list for many of these values

14.
No out-of-the-box solution
• Do existing data catalog tools normally cover these metadata?
NO
• Do existing metadata vocabularies (RDF and not) cover all
these metadata? Or do they adopt the same model?
NO
BUT by using even basic metadata to describe datasets
in data catalogs “publishers increase discoverability and
enable applications to consume metadata from
multiple catalogs. This further enables decentralized
publishing of catalogs and facilitates federated dataset
search across sites.”
(from W3C page on “Best Practices for Publishing Linked Data”)

15.
Dataset description vocabularies
Let’s see if the main vocabularies
to describe datasets provide the
metadata we think are needed
for full interoperability

16.
Semantic interoperability
In this presentation we cover only RDF vocabularies
with special focus on semantic interoperability
Dataset metadata have been managed in several
ways before semantic technologies: see NetCDF or
HDF5 structures and various hierarchical array-based
structures used especially in observations datasets –
including dataset metadata at the top and data
arrays below.

17.
Dataset description vocabularies
• DCAT vocabulary
• RDF vocabulary for describing any dataset
• Datasets can be standalone or part of a “catalog”
• Metadata about dataset (collection) and related distributions
• DataCube vocabulary
• RDF vocabulary for describing statistical datasets
• Useful for attaching metadata about the “data structure” of a
dataset
• VOID vocabulary
• RDF vocabulary for expressing metadata about RDF datasets
• Useful especially for metadata related to RDF data services

18.
Definition of “dataset” in DCAT
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.

19.
c
DCAT model
1) identifier(s)
2) who is responsible for it
3) when and where the data were
collected
4) relations to organizations, persons,
publications, software, projects,
funding… NO
5) the conditions for re-use (rights,
licenses)
6) provenance, version NO
7) coverage of the dataset
8) dimensions and semantics NO
9) slices, subsets NO
10) URL
11) Format
12) Protocols, parameters NO

20.
DCAT and DCAT-AP
The DCAT Application profile for data portals in
Europe (DCAT-AP) is an extension of DCAT
It combines DCAT with the W3C Asset Description Metadata
Schema (ADMS) vocabulary, plus classes and properties from
Dublin Core, SKOS and Vcard, in an Application profile.
The elaboration of the DCAT-AP was a joint initiative of DG
CONNECT, the EU Publications Office and the ISA Programme.
A diagram, of the full DCAT-AP specification is on the next slide

21.
Full DCAT AP
versions
rights and
provenance
standards
rights
format
relation
1) who is responsible for it: MORE
2) relations to organizations, projects, publications, funding: Partly
3) the conditions for re-use (rights, licenses) MORE
4) provenance, version YES
5) protocols, parameters NO
6) dimensions and semantics NO
More than DCAT:

22.
Limitations of DCAT
It doesn’t cover:
• semantic relations to organisations, persons,
software, projects, funding…
• dimensions and variables and syntax / semantics of
dimensions and variables
• protocols and parameters for datasets available
through APIs
• time and space slices, subsets

23.
Combining DCAT with other vocabularies
“Other, complementary vocabularies may be used
together with DCAT to provide more detailed format-
specific information. For example, properties from
the VoID vocabulary can be used if that dataset is in
RDF format.”
(from the DCAT specification)

24.
DataCube: structure definition
A cube is organized according to a set of dimensions,
attributes and measures.
• The dimension components serve to identify the
observed dimensions (e.g. time, geographic region,
gender, elevation…).
• The measure components represent the
phenomenon being observed (e.g. life expectancy).
• The attribute components enable specification of
the units of measure, any scaling factors and
metadata such as the status of the observation
(e.g. estimated, provisional).

25.
DataCube model for dataset structure
This part of the
model could be re-
used for
describing the
dimensions of any
dataset, also non-
statistical
1) dimensions and semantics YES
2) slices, subsets YES
More than DCAT-AP:

26.
VOID model
c
dct:license
wv:norms, wv:waiver
3) URL MORE
4) the conditions for re-use (rights, licenses) MORE
5) Protocols, parameters Partly
6) dimensions and semantics of dimensions Partly
7) slices, subsets YES
More than DCAT-AP:

27.
Complementing DCAT
• For dimensions, semantics of dimensions, slicing:
• DataCube
• DDI
• For API aspects:
• VOID (linked data)
• Web services descriptions (Hydra, (WSDL, WADL))
• For relations to organizations, projects,
publications, funding…
• CERIF for datasets
• VIVO Datastar

28.
Many vocabularies
Vocabularies with relations to DCAT or same model
• DCAT-AP and other extensions
• W3C HCLS dataset descriptions; DataID extension with capabilities to
describe dataset hierarchies, fine-grained technical details of datasets,
dataset permissions, dataset distributions and machine-readable licensing
information; GeoDCAT-AP geospatial extension of DCAT
• DDI-RDF Discovery Vocabulary
(mapped to Data Cube, DCAT and XKOS; DDI XML exportable from Dataverse)
• Schema.org
Other vocabularies:
• DataCite and re3data
• CERIF for datasets
• VIVO Datastar
• INSPIRE

29.
Examples of application of DCAT
• CKAN data catalog tool
(more in your workshop)
• Data catalogs:
• data.gov, data.gov.uk, data.gov.au
• Africa Open Data, Indonesia Data Portal
• EU Data Portal
• More here: https://ckan.org/instances/
• CIARD RING federated data catalog
managed by GFAR

30.
CIARD RING

31.
Datasets in the RING dataset hub
• Datasets can be registered as standalone sources or as part of
a “collection” (DCAT model)
• A dataset is identified by: uniform type of content, uniform
data structure (dimensions / metadata set, encoding, reference
value lists)
• One dataset can be made available / accessible as different
“distributions” (format, protocol, URL)
• The RING uses a combination of the DCAT model + the VOID
vocabulary and the DataCube vocabulary
 a “RING DCAT profile” will be published

32.
Sample dataset record

33.
RDF of the record
<rdf:Description rdf:about="http://ring.ciard.net/node/19517">
<rdf:type rdf:resource="http://www.w3.org/ns/dcat#Dataset"/>
<rdf:type rdf:resource="http://rdfs.org/ns/void#Dataset"/>
<rdf:type rdf:resource="http://schema.org/Dataset"/>
<rdf:type rdf:resource="http://www.w3.org/ns/adms#Asset"/>
<dct:title>National Soil Database representative Soil Systems geography</ns1:title>
<schema:name>National Soil Database representative Soil Systems geography</ns2:name>
<dct:description>National Soil Database representative Soil Systems geography (1:500.000) ...</ns1:description>
<dcat:landingPage rdf:resource="http://soilmaps.entecra.it/ita/bancadati1.html"/>
<schema:url rdf:resource="http://soilmaps.entecra.it/ita/bancadati1.html"/>
<dct:publisher rdf:resource="http://ring.ciard.net/node/19510"/>
<schema:publisher rdf:resource="http://ring.ciard.net/node/19510"/>
<dct:issued rdf:datatype="xsd:gYear">1990</ns1:issued>
<dct:spatial rdf:resource="http://ring.ciard.net/taxonomy_term/326"/>
<dct:spatial>National</ns1:spatial>
<dcat:theme rdf:resource="http://ring.ciard.net/taxonomy_term/2052"/>
<schema:about rdf:resource="http://ring.ciard.net/taxonomy_term/2052"/>
<dct:conformsTo rdf:resource="http://ring.ciard.net/node/19239"/>
<dct:identifier>http://ring.ciard.net/node/19517.rdf</ns1:identifier>
<schema:contentLocation rdf:resource="http://ring.ciard.net/taxonomy_term/326"/>
<dct:type rdf:resource="http://ring.ciard.net/taxonomy_term/81"/>
<dcat:catalog rdf:resource="http://ring.ciard.net/node/19436"/>
<dcat:distribution rdf:resource="http://ring.ciard.net/field_collection_item/5055"/>
<schema:distribution rdf:resource="http://ring.ciard.net/field_collection_item/5055"/>
<void:dataDump rdf:resource="http://ring.ciard.net/field_collection_item/5055"/>
<dct:rights rdf:resource="http://ring.ciard.net/taxonomy_term/2053"/>
</rdf:Description>

34.
References
1. Issues and Recommendations Associated with Distributed Computation and Data
Management Systems for the Space Sciences
National Research Council (U.S.). Space Science Board
National Academies, 1986 - 111 pagine
https://books.google.co.uk/books?id=h4krAAAAYAAJ
2. Sacchi, S., Wickett, K. M., & Renear, A. H. (2010). Dataset definitions. Champaign,
IL: Center for Informatics Research in Science and Scholarship. (Rep. No.
CIRSS/DATACONS--2010/1/VER01+DCDC)
3. Alexander, K., Cyganiak, R., Hausenblas, M., & Zhao, J. (2009). Describing Linked
Datasets-On the Design and Usage of voiD, the'Vocabulary of Interlinked Datasets'.
In Linked Data on the Web Workshop (LDOW 09), in conjunction with 18th
International World Wide Web Conference (WWW 09).
4. Renear, A. H., Sacchi, S., Wickett, K. M. (2010). Definitions of Dataset in the
Scientific and Technical Literature.
http://mail.asist.org/asist2010/proceedings/proceedings/ASIST_AM10/submission
s/240_Final_Submission.pdf
5. W3C Government Linked Data Working Group
http://www.w3.org/2011/gld/wiki/Main_Page
6. UK Gov Linked Data Working Group: LD registry
https://github.com/der/ukl-registry-poc/wiki

35.
Vocabularies and catalogs
• DCAT: http://www.w3.org/TR/vocab-dcat/
• DCAT AP: https://joinup.ec.europa.eu/asset/dcat_application_profile/asset_release/dcat-application-
profile-data-portals-europe-final
• DataCube: http://purl.org/linked-data/cube#
• VOID: http://rdfs.org/ns/void-guide
• DDI-RDF Discovery Vocabulary: http://rdf-vocabulary.ddialliance.org/discovery.html
• VIVO Datastar: http://sourceforge.net/projects/vivo/files/Datastar%20ontology/
• CERIF for datasets: https://cerif4datasets.wordpress.com/c4d-deliverables/
• CKAN: http://ckan.org/
• Dataverse: http://dataverse.org/
• Datahub: http://datahub.io/
• DataCite: http://search.datacite.org/ui?q=subject%3Aagriculture
• Re3data: http://www.re3data.org
• OpenAIRE: https://www.openaire.eu/
• CIARD RING: http://ring.ciard.info

36.
Dataset description
and DCAT
DRTC/ISI-ICSU/CODATA
International Workshop on
Open Data Repositories
Thank you
Valeria Pesce
valeria.pesce@fao.org