What Is e-Humanities and How Do Language Data and Tools Support It?

e-Humanities (also written as digital humanities) is the practice of doing humanities research with digital resources, software tools and computational methods. Language data and tools support it by giving researchers searchable corpora, lexica and multi-modal collections, plus the software to process them. CLARIN ERIC is one infrastructure built for exactly this: it describes itself as "the Research Infrastructure for Language as Social and Cultural Data," offering data, tools and services for research based on language resources. It is most useful to you if your work involves text, speech or language-based cultural material and you want to move beyond reading individual documents toward querying and processing collections at scale.

What e-Humanities actually involves

The term covers a spectrum of activity, not a single method:

• Digitised source access — reading and searching scanned or transcribed texts, recordings and databases.
• Corpus-based analysis — asking questions across many texts at once (frequency, collocation, variation over time).
• Tool-assisted interpretation — using annotation, tagging or visualisation software to make patterns visible.
• Methodological reflection — treating the digital representation itself as an object of scholarly critique.

What ties these together is that the resource and the tool are part of the argument, not just convenience. A historian querying a newspaper corpus and a linguist annotating spoken recordings are both doing e-humanities when the computational step is load-bearing for their claim.

How language data and tools fit in

CLARIN's own description of its holdings is a useful map of the resource types you are likely to encounter:

Alongside the data, CLARIN lists "software tools and services to process the data." The practical point is that data and tool are paired: a corpus without an annotation or search tool is hard to interrogate, and a tool without a suitable corpus has nothing to work on. When planning a project, identify both at once.

The entry path: from finding resources to processing them

A workable first sequence, based on how CLARIN organises its services:

1. Clarify your research question and the language material it needs. Decide whether you need written, spoken or multi-modal data, and in which language(s).
2. Find the data. Use the Language Resources section to locate corpora, lexica and databases. Check the licence and access conditions for each item before building a workflow around it.
3. Find the tools. Look for software and services that process that data type — search, annotation, tagging, analysis.
4. Get guidance if the topic or language is unfamiliar. CLARIN's Knowledge Centres (K-centres) "share knowledge and expertise on language resources" and support researchers and educators across disciplines in using language data and technologies for research and teaching. This is the right place to ask before you commit to a method.
5. Build skills. The CLARIN Learning Hub offers online training modules, materials for designing university courses, and workshops — useful if you or a team need to learn a tool from scratch.
6. Check national support. The operations, services and centres are "provided and funded by the national consortia in the countries that have joined CLARIN ERIC, as well as by associated centres." Your country's consortium is often the fastest route to local help and language-specific collections.

A concrete example: a scholar studying political rhetoric in a single country might locate a national parliamentary corpus, use a search and annotation tool to tag rhetorical markers, and consult a K-centre when the annotation scheme needs validating — rather than reading speeches one by one.

Where newcomers get stuck

• Access and licensing. Resources vary in how they can be used and redistributed. Confirm the terms for each dataset before you design a pipeline; a tool that works perfectly on one corpus may be unusable on another for licensing reasons.
• Tool selection. With many tools available, the temptation is to pick the most powerful one. Start from the data type and the question instead; a simpler tool that fits your corpus beats a sophisticated one that does not.
• Coverage gaps. Language resources are uneven across languages and periods. If your material is under-represented, ask a K-centre or your national consortium early rather than after months of work.
• Skills gap. The Learning Hub exists precisely because tool use is a learnable skill; treat training as part of the project timeline.
• Knowing whom to ask. The distributed structure (national consortia, associated centres, K-centres) can be confusing. Route language- and topic-specific questions to K-centres, and infrastructure or access questions to your national consortium.

Deciding whether this route suits you

CLARIN-style e-humanities is a good fit when your research question benefits from scale, systematic comparison, or reproducible processing of language material, and when suitable data exists in your language. It is a weaker fit when your sources are not digitised, when the interpretive work is irreducibly close-reading of a handful of texts, or when licensing rules out the processing you need. In those cases, the infrastructure can still help — through training, expertise and pointers to related collections — but the computational step may not be central to your argument.