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WYRED Project: Data Sharing Satellite Event
2nd August 2018
Data Sharing: Solutions
Vincent Hughes and Jessica Wormald
Re-cap
• Increasing focus on data sharing across sciences
(‘open science’)
• Especially useful for sociolinguistics…
oexpands scale of analyses (reveal subtle effects)
oreplication
• …and forensics
ofor empirical estimation of typicality/ validation
omove away from experience-based approaches
• Importance of collaboration
2
What is data?
• Anything is useful (and better than nothing):
oknowledge
oliterature
orecordings
oraw data
oplatforms/ scripts for data extraction
ocode (statistical modelling)
3
Solutions: Quantitative
General
• Recordings / raw data sharing
• Online platforms – transcriptions + recordings
Forensics
• Different types of features
• Different types of analysis
• Forensic-friendly data collection
4
Solutions: Qualitative
General + Forensic
• Specialist knowledge
• (Published) literature
• Speed of access
5
WikiDialects
What is it?
Wiki = repository for information which is
developed collaboratively by members of a
community
6
WikiDialects
How would it work?
7
WikiDialects
• How would it be used?
• Casework in forensics – assessing typicality
• Research by academics – useful resource for finding out
what is out there / sharing research
• But other beneficiaries / users too
• Speech and language therapy – assessing typicality
• L2 English studies – understanding variation
• Students – resource for studies
• Lay audience – general interest
9
What does this need to work?
Collaboration
10
What does this need to work?
Better communication
11
What does this need to work?
Ethics
12
What does this need to work?
Money
13

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Data sharing: solutions

  • 1. WYRED Project: Data Sharing Satellite Event 2nd August 2018 Data Sharing: Solutions Vincent Hughes and Jessica Wormald
  • 2. Re-cap • Increasing focus on data sharing across sciences (‘open science’) • Especially useful for sociolinguistics… oexpands scale of analyses (reveal subtle effects) oreplication • …and forensics ofor empirical estimation of typicality/ validation omove away from experience-based approaches • Importance of collaboration 2
  • 3. What is data? • Anything is useful (and better than nothing): oknowledge oliterature orecordings oraw data oplatforms/ scripts for data extraction ocode (statistical modelling) 3
  • 4. Solutions: Quantitative General • Recordings / raw data sharing • Online platforms – transcriptions + recordings Forensics • Different types of features • Different types of analysis • Forensic-friendly data collection 4
  • 5. Solutions: Qualitative General + Forensic • Specialist knowledge • (Published) literature • Speed of access 5
  • 6. WikiDialects What is it? Wiki = repository for information which is developed collaboratively by members of a community 6
  • 8.
  • 9. WikiDialects • How would it be used? • Casework in forensics – assessing typicality • Research by academics – useful resource for finding out what is out there / sharing research • But other beneficiaries / users too • Speech and language therapy – assessing typicality • L2 English studies – understanding variation • Students – resource for studies • Lay audience – general interest 9
  • 10. What does this need to work? Collaboration 10
  • 11. What does this need to work? Better communication 11
  • 12. What does this need to work? Ethics 12
  • 13. What does this need to work? Money 13

Editor's Notes

  1. Platforms: SLAAP, ONZE, FAVE suite, SPADE Forced alignment Searching for internal and external sources of variation Easy extraction of large amounts of data Continually updated (longitudinal resource) Forensic-specific features: e.g. MFCCs/ LTFDs from across an entire recording means of summarising data easily incorporating large-scale analyses into case reports Forensic-specific collection methods capture more real world variation forensically realistic conditions e.g. multiple recordings per speaker, technical factors…
  2. Platforms: SLAAP, ONZE, FAVE suite, SPADE Forced alignment Searching for internal and external sources of variation Easy extraction of large amounts of data Continually updated (longitudinal resource) Forensic-specific features: e.g. MFCCs/ LTFDs from across an entire recording means of summarising data easily incorporating large-scale analyses into case reports Forensic-specific collection methods capture more real world variation forensically realistic conditions e.g. multiple recordings per speaker, technical factors…
  3. Combined qualitative and quantitative resource – qualitative collation of research as a ‘first port of call’ Community in this case = researchers interested in accents (sociolinguists, forensic speech scientists, speech and language therapists) Not – lay individuals with a view on language patterns (‘the youth of today can’t say th anymore – they all think it’s f !)
  4. Academics from across disciplines (e.g. sociolinguistics, phonetics) could contribute descriptions of linguistic features for different regional and social groups Could use the repository to search about features in a given region to find out about what’s been done about that feature – e.g….
  5. Select feature and region Pulls up resources (and summaries) on that feature – resources could be project platforms, larger platforms (e.g. SPADE), individual publications, academic website / blog Would include metadata (how many speakers, how old were they?) Provides a quick starting point to find out info available (useful for all – caseworkers / researchers / students)
  6. Investment in the enterprise requires us to explain the importance both theoretical and practical benefits Becoming part of ‘standard practice’
  7. General Forensics
  8. Understandable concerns about how data is used (especially where the term forensic is used)
  9. Difficult to get funding for tools (unless some specific call from RCs) Especially if no direct research question associated Impact funding is one potential avenue Also need funding for long-term maintenance of resources certainly for data platforms maybe less so for a wiki (?)