http://kulibrarians.g.hatena.ne.jp/kulibrarians/20170222
Presentation by Cuna Ekmekcioglu (The University of Edinburgh)
- Creating and Managing Digital Research Data in Creative Arts: An overview (2016)
CC BY-NC-SA 4.0
1. Creating and Managing
Digital Research Data in Creative Arts:
An overview
Looking After Your Digital Research Data: Now, later, and long-term
2. 1. Introduction
Defining research data
2. The Theory
Data lifecycle and management plans – an overview
3. The Carrot
Practical stuff File structure, naming and formats, etc.
Useful stuff Intellectual Property Rights and research data
Essential stuff Archiving digital data
4. The Stick!
Writing Data Management Plans
Outline
3. Key points
• Understand the bigger issues relating to the use and archiving of data.
• Know of the data management requirements and resources of the University.
• Be aware of national online resources in research data management.
• Be prepared for data management in the real world.
• Think of your data early in project planning.
• Have a Post-Graduate Data Management Plan in place.
• Data management goes hand-in-hand with research outputs.
• Make your research data understandable to others.
• Be proficient in looking after your own digital research data.
• Have started to make plans for archiving the digital data from your PhD.
4. Research Data
In the 'hard' sciences
Research data often equates to 'datasets' of primary or secondary research
(i.e. textual data).
Research outputs usually mean a peer reviewed & published journal article.
In the creative arts
Research data might be an event, exhibition, performance, physical object, an
image, a video recording, an audio recording, a script, or a score (plus datasets
of primary or secondary research).
Research outputs may be any of the above (or a peer reviewed & published
journal article).
5. Why keep research data?
Q. Once completed, what do people use arts research data for?
(From Cairo User Needs Analysis 2010)
• To help indirectly in a research process
• To help directly in a research process (e.g. re-staging)
• As discussed exemplars for teaching
• To help teaching in a general way (e.g. for illustration)
• For personal interest
6. Why keep research data?
Q. Top three reasons to manage arts data?
(From Cairo User Needs Analysis 2010)
1/ To maximise the impact and reuse potential of publicly funded research.
2/ To facilitate the personal re-use or re-exhibition of work at a later date.
3/ To improve the chances of further project funding*.
*i.e. because the AHRC tell us to
7. However…
“Digital objects break. They are bound to the specific
application packages (or hardware) used to create them.
They are prone to corruption. They are easily misidentified.
They are generally poorly described.”
(Seamus Ross, Digital Preservation, Archival Science and
Methodological Foundations for Digital Libraries ECDL, 2007)
8. However…
• Some collections are not represented digitally.
• Some digital collections are offline.
• Copyright may not be cleared for reuse.
• File formats used can limit sharing/reuse.
• Lack of/insufficient accompanying information
(metadata) can limit sharing/reuse.
9. But wait…
There's research data management…
“A series of actions undertaken to ensure evidence
of research survives in a useful form and achieves
maximum impact.”
10. Post-Graduate Teaching in
Managing Research Data in Arts
• Good data underpins high quality research.
• Credible and verifiable interpretations – long term preservation.
• Academic and professional recognition and reputation.
– Funding body requirements, legal, and ethical codes of conduct.
• To help you finish your thesis on time with the least stress.
Why are we teaching data management?
Why is it important and how is it useful?
11. Managing Research Data:
The wider context
• The exponential growth of digital data.
• Researchers’ and institutional responsibilities.
• Institutional policies on Research Data Management.
• Funding body requirements: Data produced through
publically funded research should be made public and
open access.
12. Defining your Post-Graduate Research Data
Questions:
• Define research topic and research ‘location’.
• List physical data you will work with, e.g. published reports, existing/own field
docs, artefacts, bones, etc.
• Data origin, e.g. published material, physical archive held in museum, samples
from…….now in….. Museum in the UK, etc.
• Types of digital data you will derive from the physical data.
e.g. text docs, scans, spreadsheets, etc.
• What types of data will you create digitally?
• Where will your data end up after the project?
• How do you look after your data?
• Any other issues for management and curation of your digital data?
– Risks? Ownership? Sharing?
13. 4.
Deposit
PhD & Data
5.
Preservation
& Re-Use
1.
Create
2.
Active Use
3.
Selection &
Evaluation
1. What data will I produce?
2. How will I organise the data?
3. Is my data management working well?
What data will I keep?
4. What data will be deposited and where?
5. Who will be interested in re-using the
data?
Data Lifecycles & Data Management Plans
14. 4.
Deposit
PhD & Data
5.
Preservation
& Re-Use
1.
Create
2.
Active Use
3.
Selection &
Evaluation
1. What data will I produce?
• Text documents
• Artefact analyses
• Sample analyses
• Survey data
• Drawings
• Photographs
• Recorded interviews, etc.
Plan early for issues of:
• Original ownership of data
• Intellectual Property Rights
• Sensitive and personal data
• What data will be deposited?
• Where might the data be deposited?
Data Lifecycles & Data Management Plans
15. 4.
Deposit
PhD & Data
5.
Preservation
& Re-Use
1.
Create
2.
Active Use
3.
Selection &
Evaluation
2. How will I look after my data?
• File structure
• File naming
• What file formats will I use?
• Which software will I use?
• Roughly how many files?
• How will I describe and document
my data?
• Where will I store my data?
Data Lifecycles & Data Management Plans
16. 4.
Deposit
PhD & Data
5.
Preservation
& Re-Use
1.
Create
2.
Active Use
3.
Selection &
Evaluation
3. Evaluating data management:
• Is the file structure / naming
understandable to others?
• Are further data required?
• Are new data types required?
• Which data will be kept?
• Which data can be discarded?
Data Lifecycles & Data Management Plans
17. 4. What data will be deposited and where?
• Define the core data set of the
project
• Which data will be included in the
thesis?
• Which data are supplementary?
• Will I produce an E-Thesis?
• Where will I deposit my E-Thesis?
• Will I deposit supplementary data?
Data Lifecycles & Data Management Plans
4.
Deposit
PhD & Data
5.
Preservation
& Re-Use
1.
Create
2.
Active Use
3.
Selection &
Evaluation
Talk to the digital repository early
18. 5. Preservation and Re-Use
• Who will be interested in re-using the
data?
• Is there sufficient information to allow
easy re-use of the data?
Data Lifecycles & Data Management Plans
4.
Deposit
PhD & Data
5.
Preservation
& Re-Use
1.
Create
2.
Active Use
3.
Selection &
Evaluation
The best way to help preserve data is to plan for its re-use […in 10, 50, 100 or even 500 years time…]
19. 4.
Publish &
Deposit Data
5.
Preservation
& Re-Use
1.
Create
2.
Active Use
3.
Selection &
Evaluation
Who owns the original data?
• Are the data covered by Intellectual
Property Rights?
• Are there sensitive data in the project?
• Are there personal data as part of the
project archive?
• Will I have authority to archive these
data?
• How do I get permission to archive
these data?
Back to the Future…
1.
Create
22. Working with Digital Data
Practical stuff
• File structures - where to put stuff so you won’t lose it.
• File naming - what to call stuff so you know what it is.
• Version control - keeping track of stuff.
• File formats - what to save stuff in so it’s still readable in
future (Text, Images, Spreadsheets/databases, CAD/GIS,
Audio/Video).
• Documenting data - letting others understand your data.
• Storing data and regular backups – minimising the risk
of data loss.
• Selection - chucking stuff away!
23. File Structure
Where to put stuff so you won’t lose it
• Understanding the structure of your own data.
• Logical to you and allows others to understand your data.
• Ease of sharing / exchange of data.
• Establishes good practice early by helping form working
habits.
• Print out and stick on the wall above your desk!
24. Which primary data defines your research?
Material Type
e.g. Pottery
Site A Site B Site C
Geographical
Location
Material A Material B Material C
Material or Location (site based)
• Distinguish between projects.
• Distinguish between sub-folders.
• Define ‘end-product’ of research – and keep clean of temporary folder and files.
• Research designs change and so must file structure.
• Avoid overuse of folders – easier said than done though.
25. File Naming
What to call stuff so you know what it is
1. Names tell us what a file is: Contextual information.
2. Names order files: Making stuff easy to find.
3. Define your system: And stick to it.
26. File Naming
What to call stuff so you know what it is
In retrospect I am not very happy with the method I used
for naming files. The biggest problem was with the
newspaper articles I downloaded… I named the files only
based on the topic of the article, without mentioning the
name of the periodical and the year of publication, which
would have been very useful later, when I began writing
the thesis.
– Doctoral student researching communication history
27. First: Define the types of data and file formats for the research.
• Different data may require different naming conventions:
– Should different data/file formats be identified as part of same project?
• Examples of contextual information in file names:
– Date, Author or Initials, Site or Project, Material.
• Capitals in file names affect ordering – be consistent.
• Numbers order files only if zeros are used before units and tens:
– 001, 002, 003, etc will order files up to 999.
• Dates are useful for version control and ordering files.
– YY-MM-DD (11-03-02) at end of name orders files of same name by year.
– Year first is good for ordering files, e.g. publication pdfs
• Avoid special characters (“£$%!”¬&*^()+=[]{}~@:;#,.<>).
• Spaces between file names cause havoc in some systems.
use_underscores
• / Forwardslashes / in file names can cause problems too.
File Naming
28. Version Control
Keeping track of stuff
It’s surprisingly easy to lose track of the current version of a file.
Especially:
• Word file drafts of thesis chapters.
• Word files commented on by others.
• Multiple-author files sent back and forth by e-mail.
• Graphics and AutoCAD files.
• Be consistent with up-dating file names: version number, initials, date.
E.g. filename.v1
• Put old versions in separate “Drafts” folder.
• Possibly delete old drafts when final version is finished.
29. File Formats
What to save stuff in so it’s safe and
readable in future
• Ensures your data are still readable in future
• Facilitate exchange of data
• Ease of working on different computers / software packages
• Preserve data for re-use in the future
30. File Formats - Key Issues
Proprietary vs Open
Non-Standard vs ISO Standard
Binary vs XML Extensible Mark-up Language
(non-human readable) (human readable)
Compressed vs Uncompressed
31. File Formats – online information
National Digital Repositories
United Kingdom Data Archive (UKDA)
Social Sciences and Humanities + Ethical and consent guidelines
Institutional Repositories – University specific Data Management Guidelines
Deposit guides – summarise key information of what repositories want, e.g.
DataShare guide: http://www.ed.ac.uk/information-services/research-support/data-
library/data-repository/checklist
Other useful advisory bodies
Digital image, audio, and video format information www.jiscdigitalmedia.ac.uk
Museum collections (incl. digital material) www.collectionslink.org.uk
32. Text Files
• Manuscripts produced on computers: word files.
– conference notes, articles, theses, books, etc.
• Scanned printed material often made into a PDF
file.
– Conversion into editable text files using Optical
Character Recognition (OCR) software.
• Marked-up formats for viewing as web-pages:
HTML.
33. Format Description / Properties Usage and Archival Recommendations
.txt
Text file.
Simple plain text document.
Compatible across software packages.
Supports very little formatting.
Good for extremely simple files.
Commonly used for introductory “Read me” files containing basic
information on project archives.
.doc
Microsoft Word document ( - 2003)
Proprietary binary format.
Can be read by OpenOffice.
Easily converted into PDF format.
Accepted for archiving because it is so widely used.
However, will soon become obsolete.
.docx
Microsoft Word document (2007)
Human readable XML format.
Stored along with embedded content as zipped file.
Good for dissemination and preservation.
Conversion to .doc file to open with earlier versions of MS Word.
.rtf
Rich Text Format (Microsoft)
Tagged plain text format.
Formatting issues when using opened in different software.
Large file sizes mean that .docx or .odt file formats are preferred.
.odt
Open Document Text (OpenOffice)
ISO standard, human readable XML format.
Open source format good for use, dissemination and archiving.
Archive files in uncompressed form.
Can open .doc files.
Might not open correctly in other word processing programs.
.pdf
Portable Document Format (Adobe)
Proprietary binary format.
Aims to retain document formatting.
Can store embedded data: raster and vector images
(e.g. Adobe Illustrator files)
Highly suitable for dissemination.
PDF creators and readers freely and widely available.
Retain original text document and embedded objects.
(e.g. images, tabular data, etc).
PDF/A
Portable Document Format / Archive (Adobe)
Open ISO standard format for long term archiving.
Formatting data self-contained in file.
Widely accepted as viable format for long-term archiving.
Retain original text file and embedded objects separately.
(e.g. images, tabular data, etc).
Common Text File Formats
34. Archiving Text Files
• Complete, self explanatory and self contained files.
• Retain embedded data (images, tables) and save in
suitable format in a parallel folder.
• No external links to material outside of document.
Significant Properties of Text Files
• Words and word order.
• Correct script for non-English words.
• Hierarchical structure: headings and sub-headings.
• Formatting: italicised and bold text (but not font type).
• Page numbering.
• Non-text content: images, tabular data stored separately.
35. Routes to Rasters
• Scanned images of paper illustration or photograph.
• Digitally captured or created: cameras or digitally created
illustrations.
• Output product of other digital applications: vector, CAD, or GIS
work, or geophysical survey data, etc.
• Think of the purpose of image when making it:
– screen, print or reference image.
• Formats have different qualities which affect their output use
and preservation.
36. Resolution / Level of detail in image:
• Pixels per inch (PPI) or Dots per inch (DPI) or Samples per inch (SPI)
• Bigger the physical size of the picture + increased resolution = bigger file size
• min. 600 dpi for photographs and 300 for illustrations.
Bit (Colour) Depth / Level of colour information:
2 Bit = Black/White (line drawings with only black and white needed)
8 Bit = Greyscale
24 Bit = Standard colour
32 Bit = High colour
Colour Space / Type of colour
• Bi-tonal = black/white
• Greyscale
• RGB (Red/Green/Blue) used for screen presentations. Cameras generally capture images in RGB.
• CMYK (Cyan/ Magenta/Yellow /Key [Black]) used for printing colour images.
Compression
• Non-compressed (Lossless): GIF, PNG, TIFF.
• Compressed: JPG
Some formats (TIFF, PNG) allow files to be saved as non-compressed.
Important to be aware of when compression is occurring and at what level.
Image layering
Layering is NOT supported in final raster image and layers will be merged into a single layer from top down.
Raster Files: Technical Stuff
37. Raster File Formats
Format Description / Properties Recommendations
.bmp
Bit-Mapped Graphics Format
Microsoft proprietary format in older MS applications for simple graphics.
Limited embedding of metadata.
Not recommended for either working files or long-term file storage.
.gif
Graphics Interchange Format Compuserve proprietary format.
Lossless compression with 8-bit colour depth (256 colours).
Limited embedding of metadata.
Superseded by PNG format, but still widely used for still and animated Web graphics.
.png
Portable Network Graphics.
ISO standard.
Lossless compression with 32-bit colour depth,
and Alpha channel (transparency), with few ‘visible artefacts’ (cf. jpegs).
Does not support EXIF metadata.
Designed for internet and uses RGB colour space.
Standard format for lossless presentation. Use instead of GIF format.
ADS do NOT recommended for PNG long term storage (use TIFF).
NOT recommended for digital photographs, as only supports RGB colour.
.jpg /
.jpeg
Joint Photographic Expert Group
ISO standard.
32-but colour depth with extremely efficient lossy compression of image.
Compression creates ‘visible artefacts’ around complex high-contrast
image areas (e.g. text).
Supports EXIF and IPTC metadata.
Designed for photographic or painted images with smooth varying tones that do not have
sharp contrast.
Much smaller file sizes than PNG or TIFF.
While unsuitable for long-term storage, accepted format for archiving digital photographs.
Superseded by lossless compression JPEG2000.
.jp2 /
.jpx
JPEG2000
ISO standard intended to replace .jpeg.
Higher performance and lossless compression.
JPX format use XML to store metadata, and supports IPTC and
Dublin Core metadata, but not EXIF.
JPEG2000 will probably become popular format use and long term preservation.
However, not yet supported by internet browsers,
nor taken up by digital camera manufacturers.
.tif /
.tiff
Tagged Image File Format (Adobe)
Uncompressed image format.
Can support range of metadata: EXIF, GeoTIFF for georeferencing.
Uncompressed Baseline TIFF Version 6 standard format for long term preservation of
digital figures.
.psd
Photoshop Document (Adobe)
Proprietary format and can be used with open Photoshop Elements software.
Supports variety of features: image layering, transparency, text.
Supports IPTC, EXIF and XMP metadata.
‘Industry standard’ for image creation.
Proprietary nature means limited third-party support for PSD format.
Limited compression results in large file size.
Unsuitable for long term preservation. (TIFF for figures or DNG for photographic images.)
.cpt
Corel Photo-Paint (Corel)
Proprietary format for Corel Draw software. Main competitor to Adobe.
Commonly used for creating or editing figures.
Highly specific to Corel software.
Files should be stored in uncompressed TIFF format.
.dng
Digital Negative format (Adobe)
Open and archival format for storing raw uncompressed digital photographs.
Can read all tagged metadata from original raw format and store in DNG file.
Supports input of other XMP metadata.
Suitable for long-term storage of image data.
Store copy DNG files in parallel project archive folders.
Free Adobe downloadable convert to DNG files from RAW files.
raw
Unprocessed bitmap files created by digital cameras and some scanners.
Proprietary and require specific software.
No standardisation in file formats.
If possible, convert raw files to DNG format for long-term preservation.
38. Archiving Raster Images
Image Capture Format options Archive Recommendations
Cameras Dependent on model of camera.
1. Raw DNG (or TIFF) file if possible.
2. Original JPEG: save archive copy on download and
for presentation images always work on copies of file.
Scanners Wide range once scanned
Save uncompressed/lossless format (TIFF) as archive
copy regardless of intended format.
Graphics Images
Wide choice of formats under
‘save as’ command.
Alongside software package files (e.g Photoshop [.psd],
Corel Draw [.cpt]), save draft images in uncompressed
TIFF format if possible, and replace with archive TIFF of
end product image.
39. Raster Files – key points
• Think of the purpose of image.
• Document rationale of image creation.
• Maintain image documentation:
– Image properties, file naming and image
description file.
• If working with JPEGs, save original as archive and
work on copies.
• Save working copies of raster outputs of vector files
and replace with final version.
40. Vector File Formats
• Variety of proprietary and open-source software for producing vector
images, none of which is recommended for long-term archiving:
– Coreldraw (.cdr); Adobe Illustrator (.ai); OpenOffice Draw (.odg).
• Think of the purpose of vector files and the output.
Illustrations:
• Save output in high quality TIFF or PNG format.
Files with important vector information:
• Document layer conventions
• Export as SVG file (Scalable Vector Graphics)
• PDF files also holds vector data.
41. CAD & GIS
• Used to make figures of real world entities: site plans, maps, building
plans, etc.
• Files comprise layers – turned on or off depending what is required.
CAD Computer Aided Design AutoCAD
• Developed as technical drafting tool for precise geometric objects.
• Layers connected to data tables – but can not be analysed.
GIS Geographical Information System ArcGIS
• Links graphic objects (points, areas on maps, etc) to associated data
tables.
• Geographical analyses can be performed on data tables.
42. CAD & GIS
Common Data Management Issues
• Document methods of data caption or collection.
• Document terms and conventions used for the layers.
• Record processes carried out on the data in work log:
– Date; Process [history function in GIS]; Purpose; Output
Common Archiving Issues
• Proprietary software that is not backwardly compatible. Migrate!
• Retain raw survey data.
• Digital exchange formats (DXF, SVG).
43. Spreadsheets and Databases: Overview
Spreadsheets:
Designed on accounting worksheets, primarily for ordering
numerical data, performing calculations, and produce charts and
figures from data and calculations.
Databases:
Designed to store a wide variety of data (numerical, text, images)
and provide complex search and reporting on these data.
What is important?
• Data values themselves
• Structure of the tables/sheets used to store
• Structure of relationships between tables in database
44. Spreadsheet and Database: Data Management
Data consistency
• Standardised data entry is essential.
• Methods for controlled data entry recommended.
• File and field name and codes need to be documented in separate file.
• Document relationships of database tables (screen shot as jpeg)
Embedded objects
• Embedded objects (images, charts, figures) stored separately.
• Document analysis/search procedures from which figures are produced.
• Embedded objects removed from final archived file.
Non-data content (presentation formatting)
• Document formatting of tabular data (fonts, colours, cell borders, etc).
• Document data input forms and search query results (‘reports’).
45. Audio and Video Files
Format Notes
.wav
Waveform Audio (Mircosoft)
Uncompressed file. Recommended for long term preservation.
.aif
Audio Interchange File (Apple)
Uncompressed file. Recommended for long term preservation.
.mp3
MPEG1, 2 Audio Layer 3
(Moving Picture Expert Group: Audio group).
Patented ISO standard compressed format.
.rm / .ram ReadAudio file format used for streaming radio over the internet
.wma
Windows Media Audio
Compressed file used by Windows
.ogg Open standard format for compressed audio files.
.avi Audio Video Interleave (Microsoft)
.wmv
Windows Media Video (Microsoft)
Proprietary compression format for hard media delivery (DVD, Blu-Ray)
.mov QuickTime File Format (Apple)
.mp4
MPEG4 – Digital Video File Format
ISO standard. Recommended by some repositories for long term storage.
46. Documenting Audio and Video Files
Technical Information
• Software and hardware used to make recordings, incl. KHz, sample bits, frames per sec.
• Length of recording (min, sec)
Contextual Information
• Date
• Location
• Creator
• Brief description of recording (people, site tour, etc)
• Copyright holder and clearance status
• Transcripts of audio content (Y/N)
Can some of this information be included in the file name?
47. Documentation and Metadata
Letting others understand your data
• Project Documentation
– Methodology Chapter of Thesis: general information, standards used, etc.
– Introduction / Guide to Appendices: detailed technical information, e.g.
explanation of file names and formats used, methods and standards of digital
data capture (scanning settings etc).
• Individual File Documentation: embedded or stored separately.
– Descriptive data on images, audio-visual files, etc.
– Explanation of headings, codes, structure and format of spreadsheets
and databases.
– Explanation of vector file layers.
• CAD and GIS Documentation
– Keep a log of changes to file data and procedures carried out in GIS.
48. Storage and backup
Minimising the risk of data loss
• Use managed network services whenever possible to ensure
regular back-up, data security and accessibility: DataStore.
• Avoid using portable HD’s, USB memory sticks, CD’s, or DVD’s for
your master copy to avoid:
– Data loss due to damage, failure, or theft
– Quality control issues due to version confusion
– Unnecessary security risks
• Make at least 3 copies of the data, keep storage devices in
separate locations, check they work regularly.
• Ensure you can keep track of different versions of data, especially
when backing-up to multiple devices.
• Ensure PC’s, laptops, and portable data storage devices are
stored securely and encrypted if necessary to protect sensitive or
valuable data.
49. Selection
Chucking stuff away!
• Should you keep everything?
• Define the core data which will form the project archive.
• Keep the core data clean.
• Can we keep hold of data that other people send us?
• Chuck stuff away during the project.
– Try not to hoard multiple versions of the same file.
• Store earlier drafts in separate folder as back-up.
– Delete draft documents when file is finalised.
– Draft research proposals may be useful to refer to later.
• What to do with e-mails?
50. Selection
Chucking stuff away!
• Should you keep everything?
• Define the core data which will form the project archive.
• Keep the core data clean.
• Can you keep hold of data that other people send you?
• Chuck stuff away during the project.
– Try not to hoard multiple versions of the same file.
• Store earlier drafts in separate folder as back-up.
– Delete draft documents when file is finalised.
– Draft research proposals may be useful to refer to later.
• What to do with e-mails?
51. Personal Data
• Data relating to living individuals which identifies them: name, age, sex, address, etc.
Sensitive Personal Data
• Data that may incriminate a person:
– Race, ethnic origin, political opinion, religious beliefs, physical/mental health,
sexual orientation, criminal proceedings or convictions.
Personal & Sensitive Personal Data
Data Protection Act (UK) 1998
Personal Data that may be considered confidential
• Data connected to a person providing them.
• Data which identifies a person (name, addresses, occupation, photographs).
• Data given in confidence, or agreed to be kept confidential (i.e. not released into
public domain).
• Data covered by ethical guidelines, legal requirements, or research consent forms.
Familiarise yourself with the Data Protection Act!
http://www.ed.ac.uk/records-management/data-protection/guidance-policies/research/act
52. Intellectual Property Rights and Research Data
If somebody says they know that they understand IPR and Copyright,
don’t believe them, they are probably wrong!
• Important disclaimer – what follows is a very basic introduction.
• These issues are important in regard to research data.
• Think how they may affect your research and research data.
• Consult further information – ERI: https://www.ed.ac.uk/edinburgh-research-
innovation/inventions-intellectual-property/understanding-ip
Also check digital repository websites, publisher copyright policies, contract of
employment etc., always read the small print!
53. “Intellectual property rights, very broadly, are rights granted to creators and owners of
works that are the result of human intellectual creativity” (jisclegal.ac.uk)
• Copyright
– Creative works fixed in material form.
• Designs
– Appearance and shape of product.
• Patents
– Inventions – things that make things work.
• Trade marks
– Signs that distinguish goods and services.
Intellectual Property Rights and Research Data
• Moral Rights
– Right to be attributed for your work.
– Right to object to derogatory treatment of your work.
54. Copyright Quiz
• Intellectual Property Rights can be bought, sold, rented, gifted and bequeath?
• Copyright requires registration?
• Copyright protection lasts forever?
• Most web content can be re-used freely?
• The ownership of copyright is the same for creators of work regardless of their
academic status (e.g. students or lecturers), employment status (e.g. employed or
self employed)?
• The onus of responsibility lies with the user of a work to get permission, even if the
rights holder is unknown or cannot be traced?
55. Copyright & Research Data
• Copyright protects the expression of an idea
– not the idea itself.
• Where data are structured within a database as a result of substantial intellection
investment, an additional ‘database right’ can also sit alongside the copyright
attaching to the data contents.
• Copyright does not need to be registered.
– It is automatically assigned when a creative work is produced.
• Different forms of creative work are copyrighted for different lengths of time.
• Different institutions have different copyright clauses in their employment
contracts.
• Different countries have different copyright law.
57. Copyright - Online Guidelines
• University Guidelines
– Each country and each institution (employer) has different copyright regulations.
https://www.ed.ac.uk/edinburgh-research-innovation/inventions-intellectual-
property/understanding-ip
– Students who are not employed by an institution own the copyright of the work they
produce.
– Students who part of a larger research project should check the terms and conditions of
their contract.
• JISC Legal (www.jisclegal.ac.uk)
– Legal guidance for information communication technology use in education, research,
and external engagement
• Intellectual Property Office (http://www.ipo.gov.uk)
– Official governmental copyright summary
60. Research data support
• Introductory sessions on RDM: contact Cuna Ekmekcioglu at
cuna.ekmekcioglu@ed.ac.uk for a session for your School or
subject group.
• Support for writing data management plans.
• RDM website: http://www.ed.ac.uk/is/data-management
• RDM blog: http://datablog.is.ed.ac.uk
• Training sessions and workshops:
http://www.ed.ac.uk/schools-departments/information-
services/research-support/data-management/rdm-training
61. RDM training
RDM training at the University of Edinburgh:
http://www.ed.ac.uk/information-services/research-support/data-
management/rdm-training
• Managing your research data: why is it important and what should
you do?
• Creating a data management plan (DMP) for your grant application
• Working with sensitive and personal data
• Good Practice in Research Data Management
• Handling data with SPSS
• MANTRA online course
http://datalib.edina.ac.uk/mantra
• Research Data Management & Sharing MOOC
https://www.coursera.org/learn/data-management
63. Acknowledgements
Slides are taken or based on material created by:
• The JISC-funded DataTrain Project based at the
Cambridge University Library|:
http://www.lib.cam.ac.uk/preservation/datatrain/
• Project Cairo:
http://www.webarchive.org.uk/wayback/archive/20111001
142018/http://www.projectcairo.org/node/9