Brief lightning talk for UofM THL, repeated for MLA Research Caucus on January 27, 2021. On the subject of using systematic review search skills in combination with non-systematic review research methodologies.
9. “Tech Mining extracts useful intelligence
from electronic text sources.”
“Here’s a 10-step approach to Tech Mining.
1. Spell out the focal intelligence questions and decide how to answer them
2. Get suitable data
3. Search (iterate) & retrieve ~abstract records
4. Import into text mining software [e.g., Thomson Data Analyzer (TDA), VantagePoint]
5. Clean and consolidate the data - Clumping
6. Analyze
7. Visualize
8. Integrate with internet analyses and expert opinion
9. Interpret and summarize findings; communicate those (possibly multiple ways)
10. Standardize and semi-automate where possible”
Porter et al, 2015.
<https://www.thevantagepoint.com/data/documents/FRM%203.0%20chapter%2020-Tech%20Mining-3-7-15yz.pdf>
10. Tech Mining Sources
Databases
Grey literature
People
Porter et al, 2015.
<https://www.thevantagepoint.com/data/documents/FRM%203.0%20chapter%2020-Tech%20Mining-3-7-15yz.pdf>
11. Tech Mining Sources Compared
Anderson et al, 2018.
<https://deepblue.lib.umich.
edu/bitstream/handle/2027.
42/147169/jrsm1318.pdf?s
equence=2>
12.
13. Text Mining of Tech Mining Results
Anderson et al, 2017
<https://www2.slideshare.net/um
healthscienceslibraries/textmining
-pubmed-search-results-to-identi
fy-emerging-technologies-releva
nt-to-medical-librarians>