CLARIAH Fellowship Call (2021)

CLARIAH launched a call for Research and Teaching Fellows on January 31, 2021.
Two kinds of fellowship are distinguished:

Research Fellowships
Teaching Fellowships.

In a Research Fellowship, a researcher (the CLARIAH Research Fellow) carries out a small research project based on an explicitly formulated research question by using data and/or software that are already part of the CLARIAH infrastructure.

In a Teaching Fellowship, a lecturer at a Dutch university (the CLARIAH Teaching Fellow) uses data and/or software that are part of the CLARIAH infrastructure in a BA, MA or RMA course for an explicitly identified category of students (e.g. defined in terms of presupposed background knowledge).

The call refers to an overview of the data and tools that qualify and is accompanied by introductory texts on these data and tools.

Deadline for submissions	April 4, 2021, 23:59hrs CET
Maximum funding per proposal	app. 12K€

Below you find (via the links) the call text, a template for a proposal for a Research Fellowship, a template for a proposal for a Teaching Fellowship, and a link to an overview of the CLARIAH data and tools that qualify for use in a Fellowship. Explanatory text on the data and tools can be found under the tabs below, grouped by CLARIAH Work Package.

The CLARIAH WPs have prepared a text to provide some context for the CLARIAH Data & Tools overview (which is just a list of each instance of data or tools plus soem properties). These texts can be found here:

WP2 (Technical Infrastructure)

The major WP2 functionality is ANANSI:

ANANSI is the central data connection between the three primary CLARIAH domains (Linguistics, Social & Economical History and Media Studies).
ANANSI facilitates the connectivity of structured (meta-)data served by the research infrastructures constructed in these domains. Furthermore, ANANSI integrates data with large scale existing data infrastructures outside CLARIAH - both within and outside the humanities.

For more information, see ANANSI .

WP3 (Linguistics)

CLARIAH Fellowship Call: WP3 Data and Tools

Introduction

CLARIAH has many data and tools on offer for doing linguistic research. We provide a brief introduction to these data and tools here. The full overview can be found here: Link

There are actually more data and tools than described here. This description focuses on tools and data that the CLARIAH-CORE and CLARIAH-PLUS projects actually worked or work on. These are the focus of the CLARIAH Fellowship Call 2021.

We will first briefly describe what kinds of research can be carried out with these data and tools, and what educational materials are needed for students and researchers to learn to select the right tools and to optimally use them. After that, we will describe which data and tools are offered, distributed over three sections: Data and tools for non-textual data, data and tools for text corpora, and data and tools for lexica.

Linguistic Research

Many different forms of linguistic research can be supported by the CLARIAH WP3 data and tools. These include theoretical linguistics research (e.g. a detailed study of a particular construction), corpus-linguistic studies based on the many large corpora made available by CLARIAH, research into language acquisition (e.g. using the CHILDES treebank in the PaQu application), research into language variation (e.g. by using the MIMORE application) , lexicological and lexicographic research (e.g. analysing collocations in SoNaR via the OpenSoNaR application), and undoubtedly many others. Many of the applications and data can also be used for other types of research in the humanities, e.g. for literary studies, history, religious studies, and philosophy.

We provide here several publications on (mostly linguistic)research using the CLARIAH tools and data. Full references can be found here: https://www.clariah.nl/nieuw/artikelen/overzicht-publicaties:

On the use of corpora in linguistics: Odijk 2020
Research on specific constructions: Hoeksema & Napoli 2019, Bouma 2018, Olthof et al. 2017, Arabic: Zwaan et al 2020
Research using PaQu: Bloem 2021 (PhD, to appear), Dros-Hendriks 2018 (PhD), Odijk et al. 2017, Bloem 2016, Odijk 2016, Odijk 2015
Wablieft corpus in PaQu: Vandeghinste et al 2019
Research using PaQu and GrETEL: Scholten 2018 (PhD), van der Wouden et al. 2017, 2016, Bouma et al 2015
Research using GrETEL: Odijk & Zwitserlood 2019, Augustinus et al 2017, Augustinus 2015
Research using PICCL: Betti et al 2017
More research: van Erp et al 2018, van Erp et al 2017 (2x)
Research and data on dialects: van Hout et al 2018

There are also many presentations. We list just a few, especially the ones for which there is no corresponding publication. Full references can be found here: https://www.clariah.nl/papers/Presentations_CLARIAH-CORE.pdf

Odijk, J. (2015) at Drongo, van Noord & Odijk 2016 (2x), Odijk 2017 Language Science Day, Odijk 2017 TIN-dag, Lange 2018 (2x), Odijk 2018 LREC, Odijk 2018 ESSLLI, Hoeksema et al 2019, Lange 2019 (2x), Odijk et al. 2019, Odijk 2019 Grote Taaldag,

Education and Training

Many of the tools are web applications so that one does not have to download software or data, to install software. Many have dedicated user interfaces that for many uses do not require any programming skills or even knowledge of query languages. Nevertheless, it requires some training and exercise to get to know the functionality offered by an application, and in order to get to know all of the options an application offers, so that it is used in the most effective and efficient way. Therefore, it is important that all kinds of educational material become available and get used in the regular curricula of the linguistics departments. One can think of tutorials, short explanations (in text, or video) of specific features offered by an application, short explanations of how a particular type of query can be formulated, as well as methodological considerations on the advantages, disadvantages and dangers of the use of corpora in general, automatically generated annotations, etc.

Data and Tools for Non-textual data

Audio data

If your data are audiodata or audiovisual data with recorded speech, you can use the speech recognition tools (Automatic Transcription of (Oral History) Interviews / KALDI Speech recogniser for Dutch, English Automatic Speech Recognition) to automatically transcribe the speech into text.
You can manually annotate such recordings with the Oral History Annotation Tool.

If you have a speech recording and a transcript of the speech, you can use Forced Alignment to align the speech with the transcript. This can be very useful for many purposes, e.g. for efficient searching in speech data.

Audio-Visual data

Audio-visual data can be annotated with ELAN. New extensions for dealing with audio-visual data are being developed in WP5, so take a look there as well.

Image data

If a picture (image) contains text (but as a picture), the PICCL pipeline can be used to turn the text in the form of a picture into a text as a sequence of characters (and even to enrich the resulting text with linguistic annotations).

Data and Tools for Text Corpora

If you have textual data, you can do various things with it in particular:

process the text, for example to enrich it with additional information, automatically, manually or semi-automatically
search in the (possibly enriched) textual data

Many textual corpora are made available via CLARIAH.

Textual Corpora

CLARIAH offers many text corpora, via a web application, a web service, and just as data. They include:

SoNar (via OpenSoNaR)
CGN (via OpenSoNaR)
Lassy-Small (via PaQu and GrETEL)
CGN Treebank (via PaQu and GrETEL)
CHILDES Treebank (via PaQu, partially via GrETEL)
BasiScript Treebank (via PaQu)
Delpher Data (via NederLab)
Other Dutch text corpora covering 900-2000 (via Nederlab)

Several corpora in the TextFabric format: NENA, Greek New Testament Tischendorf edition, Uruk, Old Babylonian Letters, Old Assyrian documents, Quran, Fusus Al Hikram, Generale Missieven, Fusus, Greek Literature, Dead Sea Scrolls, Biblia Hebraica Stuttgartensia Amstelodamensis, Peshitta.
And many other text corpora are made available.

Formats

Text corpora, their annotations and their metadata must be encoded in some way. An important de facto standard format for (mainly Dutch) annotated text corpora in the Netherlands is the Folia format, which comes with a lot of accompanying software, among them.
A more generally used format internationally is TEI, subsets of which are supported by many text corpus applications (e.g. there is a tei2folia converter, and Autosearch, PaQu and GrETEL support uploads of TEI-encoded text corpora.)

Another approach to text corpora is TextFabric: a Python3 package for Text plus Annotations. It provides a data model, a text file format, and a binary format for (ancient) text plus (linguistic) annotations.

Search Engines

Computers are too slow to efficiently search in text corpora when they are encoded as text. The text has to be restructured in a different way in order to make efficient search possible. Search engines make this possible. Usually existing open source search engines or indexing systems are used (e.g. Lucene, BerkelyDB (in PaQu), Xbase (in GrETEL))

Some institutions have built their own engine on top of such indexing systems.

For example,
IVDNT developed BlackLab, Meertens Institute MTAS (both build on top of Lucene), which is used in Nederlab.

Usually, a linguist does not have to know about these engines. The information about them is stated here just for completeness sake.

Processing Text

There are many tools for processing text. They are often used to enrich the text with additional annotations, but many of them can be used for other purposes as well. Enriching text with additional annotations can of course also be done manually.

Automatic Processing of Text

Orthography

There are several tools for orthographic normalisation, e.g. for correcting errors of optical character recognition (PICCL), or for detecting spelling errors and suggestions for correction Gecco (Generic Environment for Context-Aware Correction of Orthography) and Valkuil.

Tokenisation:

Tokenisation converts a sequence of characters into a sequence of tokens. A token is an occurrence of a word, symbol (e.g. interpunction) or symbol sequence.

The Ucto Engine is an engine for tokenisation and Ucto runs this engine with tokenisation models for a whole range of languages.
Most of the other applications for processing text have tokenisation built in.
Most also include sentence splitting functionality (converting a sequence of tokens into a sequence of sentences).

N-Gram analysis and more

A n-gram is a sequence of n tokens. Skip-grams and flex-grams are more sophisticated variations of n-grams. Colibri Core is software to extract and analyse patterns in the form of n-grams, skip-grams and flex-grams from large corpus data.

Parsing

Alpino is software to automatically assign a syntactic structure to a sentence (parsing). There is a web service and application of Alpino provided by CLST. Frog can be used to generate a dependency analysis of a sentence (the grammatical relations that hold between words)

Part of speech tagging, lemmatisation:

Part of speech tagging (assigning a part of speech tag to a token taking into account its context) and lemmatisation (assigning a lemma, i.e. the form of word as one finds it in a traditional dictionary) to a token can be done with UDPipe Frysk for Frisian, and with Frog for Dutch.
(one could also use Alpino for this, but that may be a bit overkill)

Part of speech taggers for new languages or language varieties can be created by tools such as Toad (trainer of all data) and Mbt (memory-based tagger) provided a sufficiently large corpus of training and test data is available.

Treebank search

A treebank is a text corpus in which each sentence has been assigned a syntactic structure.

PaQu and GrETEL 4.0 are treebank search applications. They offer several treebanks in which one can search (inter alia Lassy-SMALL, Spoken Dutch Corpus Treebank, parts of Lassy-LARGE, CHILDES treebank, and more).

Both PaQu and GrETEL also enable you to upload a text corpus. Each sentence in this text corpus will then be automatically assigned a syntactic structure (by Alpiono), resulting in a treebank, after which it is made available for search.

Syntactic Profiling

SPOD and T-Scan are tools to characterise a text or text corpus grammatically (syntactic profiling). Both make use of Alpino.

Pipelines

In many cases one does not want to use a single service or tool, but combine multiple of these in a pipeline. CLARIAH offers several predefined pipelines, e.g. PICCL, the AntiLoPe Pipeline, Nederlab Pipeline, and the VU-reading-machine.

The GM-Processor provides functionality for preprocessing of the VOC Generale Missiven

There are also services and software distributions to support the development of new pipelines, e.g. CLAM (for turning a tool into a web service) and LaMachine.

Text Classification:

The CLARIAH-CORE NewsGac project developed the Newsgac Text Genre Classification system, and one experiment with different

Conversion tools:

There are various conversion tools, in particular in OpenConvert (which is somewhat outdated), and especially in Piereling. CHAMD converts CHAT data into the PaQu Enhanced text format (which was developed in PaQu and is supported by PaQu and GreTEL4). GrETEL4 includes CHAMD, so one can upload files in CHAT-format there directly.

Manual Annotation of Text

One can use FLAT for manual annotation of text.

Search in Text

Token-annotated corpora search applications

Many corpora have the token as the most important annotation unit. We call these token-annotated corpora. There are many search applications for such corpora, among which Nederlab, AutoSearch, CHN, Corpus Gysseling, OpenSONAR, Bridge the Gap Arabic Corpora, and SHEBANQ.
MIMORE belongs to this class but has many features specific for searching in collections of dialect data.

Corpus search

There are also many corpus search applications that enable only searching for strings and metadata. These include Delpher Magazines, Delpher Newspapers, Delpher Books, KBNL Google Books, and DBNL.

Data and Tools for Lexical Data

Lexicons

CLARIAH offers many lexicons, among them: NAMES, GiGant, Open Dutch Wordnet & RBN, and Dutch FrameNet.

Lexicon search Applications

CLARIAH also offers many lexicon search applications, among them: MNW, ONW, VMNW, WNT, WebCELEX, DiaMaNT, Elektronisch Woordenboek van de Brabantse Dialecten (e-WBD).

Elektronisch Woordenboek van de Limburgse Dialecten (e-WLD), Elektronisch Woordenboek van de Gelderse Dialecten (e-WGD), and Elektronisch Woordenboek van de Achterhoekse en Liemerse Dialecten (e-WALD).

WP4 (Social-Economic History)

CLARIAH fellowship call: additional information on the datalegend tool suite

CLARIAH-dataLegend offers tools to expand research using tabular datasets (Excel, csv, etc.), in particular in the field of social, economic and demographic history. With our assistance, you will be able to convert your dataset to Linked Data and use SPARQL queries to publish your dataset, and share results and data visualisations live on the web. Besides providing advantages such as data curation, replicability and easy collaboration, Linked Data allows you to expand your research by connecting your data to other datasets. By doing so you can standardize variables, contextualize your data, and even add new variables to your own dataset with ease.

For example, a dataset with individuals from 19^th century Amsterdam can be connected to marriage certificates to retrieve biographical information, family relations, and occupations, to neighbourhood maps to plot where your individuals lived, and to historical coding schemes like HISCO and HISCAM. In a similar fashion, macro datasets can be linked to historical urban population figures (1300-2000), the Clio-Infra datasets with thousands of country-observations on a global scale (1500-2000), or to macro observations for Dutch municipalities (1800-1970).
As a result, you will be able to answer new questions with ‘old’ data, find new answers to old questions, and expand your research.

These are some examples of what has been done with the tools mentioned above:

Combine height observations from individuals across time and across the world with GPD observations (Clio-Infra), to examine the relation between average height in a country and its GPD at that time: https://druid.datalegend.net/dataLegend/-/queries/ESDG-gdp-heights-all-1/1
Link Amsterdam birth certificates from 1856 to Amsterdam polygons and HISCO standardization, to retrieve the mean HISCAM score of fathers of newborns on a neighbourhood level: https://druid.datalegend.net/RubenS/-/queries/births-status-districts/3
Use historical regions of the Low Countries to plot the origin of migrants arriving in 18^th century Haarlem: https://druid.datalegend.net/dataLegend/-/queries/migrants-by-hist-region/2
Use urban population figures to compute the share of inhabitants that migrated to Haarlem in the 18^th century: https://druid.datalegend.net/dataLegend/-/queries/originassharepopulation/5

WP5 (Media Studies)

CLARIAH fellowship call: additional information on the Media Suite

A link to the introduction video

What is the CLARIAH Media Suite?

The Media Suite is a Dutch digital research infrastructure that provides access to several major audio-visual collections in the Netherlands, and tools to work with these collections such as search, select/bookmark, view, annotate, and compare. Users with Python skills can work with the Media Suite’s Jupyter Notebooks. All researchers, lecturers and students at Dutch universities can log in to the Media Suite with their university credentials.

General information on the Media Suite: https://mediasuite.clariah.nl/documentation/faq/what-is-it.
Video introduction: https://vimeo.com/503507411.

What kind of data is available through the CLARIAH Media Suite?

The Media Suite provides access to, among others: (1) the collection of the Netherlands Institute for Sound and Vision with the meta-data of about 1.000.000 radio, television and other AV-items, for a vast majority also with access to streamed content; (2) the Jean Desmet film, poster and business archive of Eye; (3) oral history interviews made available through a.o. DANS; and (4) a variety of AV-collections held by other heritage institutes such as soundbites of the Meertens Institute. The majority of data collections are in the Dutch language.

In addition, the Media Suite provides subtitles, TT888 and ASR-files (automated speech recognition) of AV-items as a search function and as a possible tier for video-annotation in the Media Suite.

For copyright reasons, the data can not be exported or downloaded on your own computer. All users have a web-based Workspace within the Media Suite in which they can save their selected items, segmentations and annotations. It is not possible to import your own dataset within the Media Suite.

While the Media Suite provides access to some of the newspaper-collections of Delpher, we kindly refer to WP6 and the KB if you would like to apply for a fellowship with the newspaper-collection.

General information on datasets: https://mediasuite.clariah.nl/documentation/faq/what-data.
Full overview of the datasets: https://mediasuitedata.clariah.nl/
English language and other international collections: https://mediasuite.clariah.nl/learn/main/overview-international-collections-in-the-media-suite.
ASR in the Media Suite: https://mediasuite.clariah.nl/documentation/faq/is-data-enriched

What kind of research can be done with the CLARIAH Media Suite?

The Media Suite especially supports ‘close reading’: qualitative content analysis and annotation of audio-visual materials. For exploratory quantitative analysis and ‘distant reading’, we also provide histograms and line charts as visualization and filter options. All Media Suite tools (Search, Explore, Inspect, Compare, Resource Viewer, Workspace) are easy to use and do not require any knowledge of Python. All tools are built with the principle of tool and data criticism in mind; we offer substantial documentation and a metadata inspection tool (Collection Inspector). For more large-scale and advanced quantitative research, users with knowledge of Python can make use of experimental Jupyter Notebooks.

In previous years, researchers investigated, for instance, the debate on drug regulation in radio broadcasts, news reporting on pandemics on television and WW2 eyewitness testimonies across the media.

We also offer a platform for datastories within the Media Suite. Researchers can develop stories with data from the Media Suite, for which they can use the Media Suite tools and the Jupyter Notebooks. The datastories enable scholars to share the first results of their research with a wider audience in a multimedia format. We encourage Research Fellows to develop a data story or an educational tutorial as web text (deliverable 4).

Media Suite tools: https://mediasuite.clariah.nl/documentation/howtos/tools
Media Suite Jupyter Notebooks: https://mediasuite.clariah.nl/documentation/howtos/jupyter-notebooks
Example projects: https://mediasuite.clariah.nl/learn/example-projects
Datastories: https://mediasuitedatastories.clariah.nl/

What kind of teaching projects can be done with the CLARIAH Media Suite?

We welcome teaching projects that involve Media Suite’s available audio-visual collections and Media Suite tools. Since the Jupyter Notebooks are not yet fully integrated in the Media Suite and require a manual security check, it is at this moment not possible to use Jupyter Notebooks for teaching projects.

Educational materials can deal with a.o. the (politics of the) archive, audio-visual collections, digital methods, tool and data criticism. In terms of topics, we can think of a wide range a.o. film style, performance, language, representation, sound, music, gender, public debates and media histories.

For our Learn platform, we welcome a variety of educational formats, such as written tutorials, video tutorials, video lectures, video essays and desktop documentaries.

Media Suite Learn: https://mediasuite.clariah.nl/learn

Contact

For additional questions regarding the Media Suite, please contact us via . Please indicate in the subjectline whether your enquiry is for a teaching or a research fellowship

WP6: (Content of Text)

Organisation structure WP 6

Work package 6 "Text" focuses on data and tools for scholars working in literary studies, history, philosophy, theology and religion studies, and other researchers using textual data. Work Package 6 works towards an online environment in which researchers can process text in all possible phases of research. It aims to provide data, tools, and manuals for a wide arrange of tasks: handwritten text recognition, optical character recognition, part-of-speech tagging for historical Dutch and present-day Dutch, preparing a scholarly edition of texts, annotate text, analyze texts with computational stylistics tools, and much more.

Because Work Package 6 was added to CLARIAH in 2019, it does not yet have a lot of data and tools available yet. There is still a lot of work to be done before existing tools and data are part of CLAAS (CLARIAH As A Service), the digital infrastructure developed and delivered in the other work packages. The infrastructural-technical challenge for work package 6 is to offer existing resources (such as those available via Nederlab, to mention but one example) in a way that is suitable for reuse by arbitrary text analysis tools. Furthermore, dissemination through documentation and instruction are important in Work Package 6. Digital data and tools will have to be provided with sufficient documentation and training material to provide opportunities for researchers at every level of "digital literacy". Making clear what is and what is not possible with the available data and what computer tools can and cannot do, is a key topic.

To check whether your idea for a fellowship fits within the plans of Work Package 6, please contact Karina van Dalen-Oskam at .

Questions?

For questions about the call, contact

CLARIAH Fellowship Call (2021)

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