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Digital data sharing: the opportunities and challenges of opening research panel discussion DH research data: identification and challenges Rebecca Grant, Digital Archivist, Digital Repository of Ireland Doctoral student, Archivistics Department, UCD

Rebecca Grant - DH research data: identification and challenges (DH2016)

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Page 1: Rebecca Grant - DH research data: identification and challenges (DH2016)

Digital data sharing: the opportunities and challenges of opening research panel discussion

DH research data: identification and challenges

Rebecca Grant, Digital Archivist, Digital Repository of Ireland Doctoral student, Archivistics

Department, UCD

Page 2: Rebecca Grant - DH research data: identification and challenges (DH2016)

Research data publication and Open Access

Image: "Digital Preservation Business Case Toolkit http://wiki.dpconline.org/".

• A global initiative which has gained traction since the 2000s and rise of the Internet

• Promoting the concept that the outputs of publicly funded research should be accessible publicly for consultation and reuse

• Influences policy for higher educationinstitutions, journal publishers, and funding agencies

Page 3: Rebecca Grant - DH research data: identification and challenges (DH2016)

Drivers in research data publication

• Universities – enhancing reputation and researcher profile through data publication and citation.

• Funders – preventing duplication of effort in research, contributing to the public good.

• Journal publishers - reproducibility, rigorous research methodologies. Journals published by Taylor and Francis, such as World Archaeology and The Journal of Visual Art Practice suggest the deposit of accompanying research data with submitted publications.

Page 4: Rebecca Grant - DH research data: identification and challenges (DH2016)

What is research data?

“A record, if it is to be useful to science, must be continually extended, it must be stored, and above all it must be consulted”. Vannever Bush, Atlantic Monthly, 1945.

[f]aced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete. Chris Anderson, Wired, 2008.

“Raw and abstracted material created as part of research processes and which may be used again as the input to further research”, An Introduction to Humanities Data Curation

Page 5: Rebecca Grant - DH research data: identification and challenges (DH2016)

What does Digital Humanities research data include?

Images, sound, AV, text, performances, exhibitions, archives, publications, logbooks, journals, linguistic corpora, musical scores, geospatial datasets, virtual reality and CAD files, mixed media installations; digital models, algorithms, scripts; contents of an application (input, output, log files for analysis software, simulation software, schemas)..

The subject OR the product of research

“The first step... is achieving consensus over what research data comprises, and the forms it takes. In the sciences this first step is relatively straightforward and reasonably well described; however this is not always the case in the creative disciplines” Pinning it Down

Page 6: Rebecca Grant - DH research data: identification and challenges (DH2016)

Why is humanities data different?

Text corpora: markup representing linguistic, grammatical, vocal, or semantic categories; documenting the principles of corpus construction

Text with markup: XML languages (eg TEI) representing document structure, editorial annotations, and also aspects of content such as named entities, intertextual references, and thematic or interpretive information.

Data with accompanying analysis or annotation “primary” digital object (such as an image, a map, a virtual 3-D reconstruction) which has been enhanced with annotations or analysis.

Finding aids, bibliographies with formal structures

https://guide.dhcuration.org/contents/intro/

Specialised data aggregations which have significance in and of themselves

Page 7: Rebecca Grant - DH research data: identification and challenges (DH2016)

Why is humanities data different?

• The significance of interpretive layering - interpretation as the primary object of interest

• The importance of information about how the data is captured and prepared – documentation of decisions affecting usability and meaning

• The importance of capturing responsibility, editorial voice, and debate - These layers represent scholarly agency and as a result are subject to debate

https://guide.dhcuration.org/contents/intro/

Important to acknowledge when treating the data:

Page 8: Rebecca Grant - DH research data: identification and challenges (DH2016)

DANS – “Dutch Love Emblems” website

https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:33856

Page 9: Rebecca Grant - DH research data: identification and challenges (DH2016)

VADS - Royal College of Art Record of Student Work Collection

http://www.vads.ac.uk/large.php?uid=161245&sos=0

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Oxford Text Archive – Corpora collection

http://ota.ox.ac.uk/catalogue/index.html

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Digital Repository of Ireland – Teresa Deevy Archive

https://dx.doi.org/10.7486/DRI.95944c09k

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Bibliography

Budapest Open Access Initiative, http://www.budapestopenaccessinitiative.org/, accessed 8 July 2016

Garrett, Leight and Marie-Therese Gramstadt. ‘KAPTUR: exploring the nature of visual arts research data and its effective management’. EVA LONDON 2012 Electronic Workshops in Computing (2012), pp 88-96.

Guy, Marieke, Martin Donnelly and Laura Molloy. ‘Pinning it Down: Towards a Practical Definition of ‘Research Data’ for Creative Arts Institutions’. International Journal of Digital Curation 8, no. 2 (2013), pp 99-110.

Flanders , Julia and Trevor Muñoz . ‘An Introduction to Humanities Data Curation’, https://guide.dhcuration.org/contents/intro/, accessed 8 July 2016

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@beck_grant

[email protected]@gmail.com

www.dri.ie

The content of this presentation is licensed as CC-BY unless otherwise stated. Please attribute to Rebecca Grant, Digital Archivist, Digital Repository of Ireland, 2015.