Nan Bernstein Ratner with the assistance of: Julianne Garbarino Veronica Builes

1. Kumbaya Science: Reflections from the front lines on the clinical and theoretical benefits of data sharing in speech/language research Nan Bernstein Ratner with the assistance of: Julianne Garbarino Veronica Builes Pamela Dominguez Courtney Overton University of Maryland, College Park

2. Acknowledgements Funding: NIHHD 9R01HD082736-11 (Brian MacWhinney, PI). Corpus-based assessment of child language; Consultant. 2014-2019, NIDCD: 1 R01 DC015494-01 (Brian MacWhinney, co-PI). A shared database for the study of the development of language fluency. , NSF BCS-1626300/1626294: The development of language fluency across childhood. N. Bernstein Ratner (PI) & B. MacWhinney, Co-I (Collaborative Research), 2016-2019. NIDCD: 1R01DC016076-01(Nan Bernstein Ratner, PI): 2018-2023) Validation and norming of children's expressive language sample analysis measures.

3. Overview: • I will discuss recent clinically-applied advances in language development/disorder and fluency that have emerged from data-sharing initiatives. • I will emphasize the value of cooperative initiatives that exploit existing resources to re-evaluate some basic tenets of child language assessment. • I will illustrate the benefits of amalgamating primary data to explore questions that by necessity require larger numbers of participants to inform critical questions, • such as predictors of stuttering persistence or recovery.

4. Data-sharing: like gravity, it’s more than a good idea – it’s the law, if you are applying to NIH

5. Origins of data-sharing in language science • History of language data – small N studies, over and over • 1985: Brian MacWhinney and Catherine Snow create Child Language Data Exchange System (CHILDES). • Over 30+ years, exponential expansion of speech/language data through increasing contributions to CHILDES, creation of new repositories • The endeavor, as we all know, is now called TALKBANK

6. CHILDES to TalkBank: the long and successful trip • CHILDES: the original initiative • Data repository • Cross-linguistic • Typical and clinical • Transcription conventions (CHAT) • Powerful, flexible computerized language analytics (CLAN) • TalkBank: the “umbrella” repository and project, has now spawned: • BilingBank • SLABank • CABank • CHILDES • PhonBank • Danish SamtaleBank • AphasiaBank • DementiaBank • TBIBank • RHD Bank

7. CHILDES, TalkBank, PhonBank

8. (One of) The newest (?) Bank is FluencyBank • Jointly funded by NSF/NIDCD (2016) – WHY? • Typical model in fluency research is 10-20 participants, often fewer • Only a few funded, longitudinal studies with larger N’s • Recruitment is difficult • Illinois, Illinois/Iowa, Purdue, Syracuse/Vanderbilt • Problems in reasoning from small N studies: • Children: 80% of children spontaneously recover • Low incidence phenomena can’t be found

9. FluencyBank work to date: • Published (some done as pilot for grant): • Bauman, Hall, Wagovich, Weber-Fox & Bernstein Ratner (JFD, 2012): evidence of low-frequency past tense problems in children who stutter, suggestive of more reliance on memorized rather than generated forms. • Enabled by CLAN utilities and data-sharing (N=62) • Leech, Bernstein Ratner, Brown & Weber (JSLHR, 2018): growth modeling of factors related to recovery and persistence in childhood stuttering. • Expressive language skill predicts recovery • Could not have been done without KidEval and data-sharing

10. FluencyBank: in progress • Luckman, Hall, Wagovitch, Weber, Choo, Johnson, Bernstein Ratner (ASHA, 2018; accepted pending revision, JFD). • Data from 99 PAIRS of CWS and typically fluent children – • CWS (grey) perform less well than CWNS on expressive vocabulary (p = 0.00057) • CWS (grey) perform less well even on receptive vocabulary (p < .05) • Converges on trends in Leech et al. for growth model

11. What FluencyBank also provides: • Provide teaching materials for graduate programs (similar to CHILDES, AphasiaBank, Phon) • Particularly important for clinical training in fluency disorders • Can now provide “virtual patients” for CSD courses • Half of all clinical programs world-wide do not have fluency specialist • We can assure that the “ABC’s” of stuttering are conveyed to future SLPs • Uniform codes for fluency transcription – not available earlier • Generate free clinical analysis “bundles” for assessment of patients/clients, who need both language and fluency appraisal: • Child Language (KidEval) • Fluency (FluCalc)

12. CHILDES enters the new millennium: CLASP • Child Language Assessment Project • Historically, most child language sample analysis (LSA) “norms” are quite weak • Even SALT has fewer than 90children under age 6 in its database. • Some evidence that LSA measures may not be appropriate for children at certain ages (Bernstein Ratner & MacWhinney, 2016) • Or at all.

13. MLU across historical studies (total N < 200)

14. CHILDES = > 1000 children under 6;0 and counting From the large cross-sectional corpora, only from North American English (typical), adult-child play These data permit real “norming” of behavior In pilot with > 650 children, MLU does well and looks similar to published values, with confidence intervals now possible Unfortunately, still used at older ages and as sole LSA measure in much clinical work.

15. Some measures do not fare well in large numbers – Compare TTR and VocD

16. Some measures require further evaluation: DSS andIPSyn

17. An important clinical concern:A growth trajectory ≠ clinical diagnosis • From Overton, Perry & Builes (this meeting): ability to distinguish Ellis Weismer children with LD from TD children • Success defined as significant differences between groups at p <.05

18. Our 5 year mission: • To create a free, easy to use, multi-measure, clinical analysis tool, requiring no transcription/coding skills, with on-line dynamic norming • AND evaluate which measures are sensitive to child language disorder at which ages • AND provide dialect-neutral alternatives (norms, algorithms) for fair assessment of minority dialect/language children.

19. The challenge of AAVE (African-American Vernacular Englishes) • Most clinical LSA measures discriminate against non-mainstream dialects of English: • Softening or “deletion” of word-final morphology • Unrealized aux’s and copular forms in contractible environments • Unique catenatives (e.g., fitna) • Dialect variation leads to BOTH over- and under-referral of children with language impairments

20. AAVE requires a three-pronged approach: Transcription, data-base, CLAN programming • Pronunciation variants are an important signal – • BUT CHAT despises pronunciation on the main tier • Possible dialect-specific lexicon (e.g., dese, dat, -in as final affix, etc.)? • What to do about “deleted”/unrealized free morphemes? • We don’t have many dialect-speaking kids in CHILDES!!! • Fewer kids are dialect speakers AND language-impaired • Need to recruit new data sets. • We need to write AAVE versions of DSS, IPSYN • We need to compute new norms by dialect and SES • We need to see if we can develop new and better measures that accurately discriminate language impairment in dialect-speaking kids. • In other words, no big deal; it should be easy!

21. Discussion: data in a changing world • OLD Science: small N’s, small teams, data go into file cabinets, and from file cabinets to dumpsters when people retire. • NEW Science: regularizing data formats, putting them in central repositories, combining them, letting new scientists test hypotheses on them (while giving credit to the original researchers) • TRANSLATIONAL Science: taking old grant data and using them to develop the equivalent of medical critical values (e.g., blood pressure, blood sugar levels) that can discriminate disorder and measure progress.

22. Kumbaya … singing around the campfire • Spreading the message that understanding the role of research data is a bit like Reba McEntire’s definition of love… • “Love isn’t love until you give it away” • Need to convince both old AND new researchers to… • “People, Get Together now”…

23. In sum, thank you, Brian (and Catherine), for inviting me to the campfire. It has been a prime determinant of my life as a researcher and clinician. A fast note about my strange history at UMD Rather than being a chair whose career was derailed… It was Brian’s work on TalkBank that made my career possible, through its archives and programs.

24. Additional thanks to: • Mary, for putting up with me and putting me up • LEONID! • Davida, Audrey, Margie, Yvan and other TalkBank front line soldiers • Julianne Garbarino • Past and present project managers: Mark Baer, Veronica Builes, Courtney Overton • All of my many students and past-students, who are really my colleagues in this effort • Our CLASP consultants: Jan Edwards, Barbara Pearson, Monique Mills