SPNHC-TDWG2018 Symposium - Challenges Implementing Collections Data Quality Feedback
This is a half-day symposium at the SPNHC-TDWG2018 joint meeting in Dunedin, New Zealand at the Otago Museum and the University of Otago. Official Conference Website NOTE: session times provided in NZST and EDT. Please be aware the time differences result in our symposium being on Wednesday in the USA but Thursday in New Zealand.
Quick Links for SPNHC-TDWG2018 Symposium: Challenges Implementing Collections Data Quality Feedback | |
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Symposium Abstract | |
Symposium Schedule | |
Data Quality Resources | |
[SPNHC 2018 Blog Post for this Symposium] |
General Information
Full title: Challenges Implementing Collections Data Quality Feedback: synthesizing the community experience
When: Thursday, August 30, 2018 2 - 5 PM NZST (NOTE that's Wednesday August 29th 10PM - 1 AM EDT)
Where: SPNHC-TDWG 2018 first joint meeting in Dunedin, New Zealand
Organizers Deborah Paul (iDigBio) and Nicole Fisher(CSIRO)
Contact: dpaul AT fsu DOT edu or nicole DOT fisher AT csiro DOT au
Symposium Abstract
BISS Symposium Abstract (brief version shared here): Much data quality (DQ) feedback is now available to data providers from aggregators of collections specimen and related data. Similarly, transcription centres and crowdsourcing platforms also provide data that must be assessed and often manipulated before uploading to a local database and subsequently published with aggregators. In order to facilitate broader DQ information use aggregators (GBIF, ALA, iDigBio, VertNet) and others, through the TDWG BDQ Interest Group, are harmonizing the DQ information provided - transforming part of the DQ feedback process. But, collections sharing data face challenges when trying to evaluate and integrate the information changes offered (by aggregators) for given records in local collection management systems and collection databases. Sharing DQ integration experiences can help reveal risks and opportunities. Discovering others have the same conundrums helps develop a community of belonging and may assist in removing duplication of effort. It is important to leverage the knowledge and experience of those who are currently validating data to improve the efficiency and effectiveness of the process. Documenting and classifying these challenges also facilitates motivation and community building by informing those who would tackle these challenges. In this case, talks from aggregators and data providers give all of us a chance to learn from their stories about implementing and integrating DQ feedback.
Post Symposium Special Interest Group Meeting
After our symposium, on Friday at lunch, we're hosting a SPNHC Special Interest Group (SIG) Meeting to get the DQ feedback conversation started with all participants present and remote! You can find us in room: Burns 7 (88) if you are in Dunedin with us.
When: 12:30 - 2 PM NZST on Friday August 31th, which is 8:30 - 11:00 PM EDT Thursday August 29th
Symposium Schedule
Order | Time | Title | Presenter, Affiliation |
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Challenges Implementing Collections Data Quality Feedback | |||
1 | 14:00-14:20 NZST 30 Aug 22:00-22:20 EDT 29 Aug |
Challenges For Implementing Collections Data Quality Feedback: synthesizing the community experience
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Deborah Paul, Florida State University, Nicole Fisher, CSIRO |
2 | 14:20-14:40 NZST 30 Aug 22:20-22:40 EDT 29 Aug |
Data Quality – Whose responsibility is it?
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Arthur D Chapman, Australian Biodiversity Information Services, Ballan, Australia |
3 | 14:40-15:00 NZST 30 Aug 22:40-23:00 EDT 29 Aug |
Integrating Data Quality Feedback: a Data Provider’s Perspective
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Maire Nazaire, Rancho Santa Ana Botanic Gardens |
4 | 15:00-15:20 NZST 30 Aug 23:00-23:20 EDT 29 Aug |
Label Transcript is Done – Now what do we do with that Data?
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Robert W. N. Cubey, Elspeth Margaret Haston, Sally King, all at Royal Botanic Gardens Edinburgh (RBGE) |
15:30-16:00 NZST 30 Aug 23:30-24:00 EDT 29 Aug |
BREAK | ||
5 | 16:00-16:20 NZST 30 Aug midnight-0:20 EDT 30 Aug |
Practical use of aggregator data quality metrics in a collection scenario
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Andrew Bentley, University of Kansas Biodiversity Institute / Specify |
6 | 16:20-16:40 NZST 30 Aug 0:20 - 0:40 EDT 30 Aug |
Who Has Time for Biological Collections Data Quality Feedback? Maybe a Community Can Help
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Teresa Jegelewicz Mayfield, Museum of Southwestern Biology |
7 | 4:40-5:00 NZST 30 Aug 0:40 - 1:00 EDT 30 Aug |
Repatriation of Augmented Information to an Institutional Database
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Sharon Grant, Janeen Jones, Kate Webbink, Rob Zschernitz, all at the Field Museum |
LUNHC - Special Interest Group (SIG) Meeting: Challenges Implementing Collections Data Quality Feedback | |||
12:30-14:00 NZST 31 Aug 20:30 - 23:00 EDT 30 Aug |
SIG - Challenges For Implementing Collections Data Quality Feedback: Synthesizing the community experience
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Deborah Paul (iDigBio / Florida State University), Nicole Fisher (CSIRO) | |
Resources
- Belbin L, Daly J, Hirsch T, Hobern D, LaSalle J (2013) A specialist’s audit of aggregated occurrence records: An ‘aggregator’s’ perspective. ZooKeys 305: 67-76. https://doi.org/10.3897/zookeys.305.5438
- Belbin, L. and Chapman, A.D. (2017), A bite from the Core – testing for Data Quality. Darwin Core Hour Series Chapter 8. Vimeo: https://vimeo.com/239698443; Slides: https://docs.google.com/presentation/d/1GoCASj8BVawIhEFm-MgdAlpUWbSsCPFJr8XgiufOm_I/edit
- Chapman, A.D.(2005). Principles of Data Quality. Report for the Global Biodiversity Information Facility 2005. 61pp. Copenhagen: GBIF. http://www.gbif.org/resource/80509.
- Chapman, A.D. (2005). Principles and Methods of Data Cleaning Primary Species Occurrence Data. Report for the Global Biodiversity Information Facility 2005. 75pp. Copenhagen: GBIF. http://www.gbif.org/resource/80528.
- Mesibov R (2018) An audit of some processing effects in aggregated occurrence records. ZooKeys 751: 129–146. https://doi.org/10.3897/zookeys.751.24791
- Nicholls M (2011) ALA Guide to Data Quality. Atlas of Living Australia. pdf
- TDWG Data Quality Interest Group Git Hub https://github.com/tdwg/bdq
- Veiga, A.K, Saraiva, A.M., Chapman, A.D., Morris, P.J., Gendreau, C., Schigel, D. and Robertson, T.J. (2017). A conceptual framework for quality assessment and management of biodiversity data. PLOS ONE 12(6): e0178731. https://doi.org/10.1371/journal.pone.0178731