CLARIN-D A Metadata-Based Research Infrastructure for the ... · Gerhard Heyer, Dirk Goldhahn...

Institut für Informatik

CLARIN-D A Metadata-Based Research Infrastructure for

the Humanities and Social Sciences

Gerhard Heyer, Dirk GoldhahnUniversität [email protected]

Gerhard Heyer, Dirk Goldhahn 30.10.18 2

Overview

• Concept of a Research Infrastructure

• CLARIN-D• Sample Applications


Tasks of Research Infrastructures

„Providing science and research with information and related services.“ [Rahmenkonzept für die Fachinformationsinfrastruktur in Deutschland, KII 2011]


Idea

Provide assistence to researchers for

– searching and finding research data

– analysing and annotating research data

– making own research results available to the respective scientific community

Accessing, Analysing, Archiving => AAA


Implementation in CLARIN

Distributed infrastructure with

– standardized interfaces (for metadata, data and tools)

– processing layers and

– communication protocols

A well-working paradigm just like the Internet

Users are both: providers and consumers


Layers and Components

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CLARIN-D

8 Resource Centres (B)3 Computing Centres (E)

Web: https://www.clarin-d.net/en/

funded by BMBFcurrent phase until 10/2020

Follow-up application in preparation

- Integration with DARIAH(CLARIAH) until 12/2021

- NFDI

10

AccessingAnalysingPreparation and Depositing

CLARIN: For users


DEMO

Virtual Language Observatory

Federated Content Search

WebLicht

Accessing, Analysing


FCS – Federated Content Search: Annotation layers

Accessing Data


DEMO

Use Cases in CLARIN-D

Accessing, Analysing

25

Abstraction of Data Management Plans in CLARIN● Similarities between DMPs → Reuse of parts

● Allows for (partly) system-generated DMPs

● Abstraction layer: Data Management Protocol

● Following Science Europe Guidance Document “Presenting a Framework for Discipline-specific Research Data Management” of January 2018

27 27

Usecases in CLARIN-D

28 28

Usecases in CLARIN-D

Gerhard Heyer 30.10.18 29

DEMO

Find a CLARIN-centre

Data Management Plans

Depositing


Ingestion and Integration of Resources

(Internal) standard workflow for ingesting a resource at the CLARIN centre Leipzig (shortend)



Why is this so complicated?

→ Minimal requirements:

● Resource is stored securely (consistency)

● Resource is available for a long-term period

● Resource can be referenced and fetched (e.g. via URL)

But: a successful life for the complete lifecycle of research data requires more



Long-lasting references (Persistent Identifiers)

● Link rot as a major problem for digital resources

● Need for long-lasting references that can be adapted if the need arises (e.g. when a resource or project is moved)

● Solution: use of Persistent Identifiers (DOI, Handle, ARK, ISLRN etc.)

● Automatic registration in PID registries as part of the ingestion process



Discoverability (Metadata)

● Researcher have to be able to find relevant resources

→ Creation and publication of structured metadata

● Task that is highly specific and depends on targeted communities and resource types

– What is mandatory or optional information about the resource?

– Consistency of metadata? Use of established vocabularies?– References to authority files? Which ones are relevant?– What metadata formats are relevant for the specific context?



Discoverability (Metadata)

● Publication of metadata via relevant interfaces

● depends on used standards in the concrete community

● Popular interfaces: OAI-PMH, SPARQL, ...

● Makes difference between "resource exists (somewhere)" and "resource can be actively found and used"

● Goal: ingestion leads automatically to visibility in relevant search and working environments



Community and resourcetype specific interfaces

● More relevant interfaces and access methods to the resource

● Integration/import in existing research and working environments, e.g.

– (Semi-)Automatic annotation (WebLicht, CLAMS, EXMARaLDA, WebMAUS, etc.)

– Manual annotation of textual resources (WebAnno, TextGrid, etc.)

– Federated resource aggregators (CLARIN FCS)

– Visualisation tools (DiaCollo, WordTies, CorpusDiff, etc.)

→Specific for every resourcetype (text, audio, video, multimodal)



Also to think about:

● Legal and licensing issues

● Adequate support (including technical support) from experienced users of the same or a comparable community

● Storage of intermediate/private results in a safe environment, still allowing collaborative work

● ...


Depositing Data: Standards of Quality

Standards of Quality are essential! Compliance with standardsneeds to be certified. CLARIN-centres are assessed by:

Core Trust Seal (CTS)

● Provides repositories with certification based on a universal requirement catalog

– Core features of trusted, persistent data repositories

– Organizational, technical, financial and legal aspects are examined

● Nonprofit organization committed to sustainable and trusted data infrastructures (under the umbrella of Research Data Alliance)

● Advantages for users of the infrastructure:

– Quality and sustainability of data management checked externally

– Guaranteed compliance with established standards

– Transparency of the (internal) processes in the repository

CLARIN-D A Metadata-Based Research Infrastructure for the ... · Gerhard Heyer, Dirk Goldhahn...

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