1. Developing data literacy competenciesto enhance faculty collaborationsDon MacMillanLiaison Librarian, Biological Sciences, Physics,Astronomy & MathematicsUniversity of CalgaryCalgary, Canada

2. LIBER 2014-Riga 2 3. LIBER 2014-Riga 3 4. CollaborationReinvent information literacy (IL) program integrate genetics & biochemistry contentCourse-integrated library/lab project Biology311 (October) - Biochemistry 393 (February)Investigate the molecular & structural basis ofinherited diseases using disciplinary data550 students per class24 lab sections10 IL workshops in library classroomsLIBER 2014-Riga 4 5. Why Data Literacy?Data-intensive disciplines robustinfrastructure - unique research outcomesBioinformatics tools facilitates discovery &analysis of life sciences dataStudents conduct research using real-worldsolutions using domain repositoriesLibrarian re-skilling enhance our datacompetencies, domain expertise & relevanceLIBER 2014-Riga 3 6. BioinformaticsTools for collection & analysis of complex biological dataIntegrationLIBER 2014-Riga 6InformationLiteracyGeneticsBIOL311GenesBiochemistryBCEM393Proteins 7. Bioinformatics InteroperabilityGeneticsPubMed(Scholarly Literature)OMIM(Database of geneticdiseases)Gene (Nucleotidesequences)BiochemistryProtein Data Bank(Protein Structure)UniProt (ProteinSequence/Function)BLAST & ClustlW(Sequence similarity &Alignment)PyMOL(3D Visualization)LIBER 2014-Riga 7 8. Sample QuestionsGenetics(October)Which chromosomeis your selected genelocated?Obtain data on genestructure, mRNA andprotein lengthBiochemistry(February)Locate your Proteins3D coordinatesWhat effect does amutation have onprotein structure &functionLIBER 2014-Riga 8 9. Gene Data ExampleNCBIs Gene portal links to gene & protein data and PubMed(e.g. Cystic fibrosis = CFTR Gene)LIBER 2014-Riga 9 10. Protein Data ExampleProtein Data Bank (PDB) Repository of 3Dprotein structural data portal to PubMed andrelated data sourcesLIBER 2014-Riga 10 11. PyMOLThree-dimensional (3D) molecularvisualization systemStudents introduce mutations to study impact onprotein structure, function & related diseases(e.g. KRAS mutation causes certain types ofcancer)LIBER 2014-Riga 11 12. Implications for LibrariesData competencies inform domain expertiseandvice versaAlign library activities with faculty needs, datalife cycle, (Jaguszewski & Williams 2013)Librarians need to offer moreexpertisevalue (Kenney (Ithaka), 2014)Collaboration & soft skills critical toleveraging expertise and expanding servicesCreated a more rigorous & sustainable ILprogramLIBER 2014-Riga 12 13. Impact on student learningInnovative learning experience for students data integrates & informs contentStudents able to find real world solutions toresearch questionsStudents will be able to manage and analyzetheir data more efficientlyPeer-reviewed presentations demonstratedeeper understanding of subjectsLIBER 2014-Riga 13 14. Best PracticesMust be course-integrated worth %Develop sequential steps simple tocomplexStudents learn best by doing allowhands-on interactivity & flexibilityTailor relevant data sources to specificquestionsAdvantageous to have consistent deliveryto all lab sectionsLIBER 2014-Riga 14 15. Assessmentn Students prepare poster & presentation (mark and peers)n Peer-Review Marking Rubricn TooFast (Free Assessment Summary Tool)https://www.toofast.ca/LIBER 2014-Riga 15 16. Student feedbackn It was good that we were able to use things that we learned in BIOL311 to understand things in BCEM 393. I think that I learned morebecause I could see how labs and topics in the two courses related toeach othern I liked how the library and computer labs were hands-on. The TAsand library people were there to help us and not just tell us what todo. I think it was more fun and we got more out of it by doing thingson our own instead of following a step-by-step recipe in the labmanualn The presentations gave me the chance to learn many valuable skills.I liked picking the disease to study and doing the project with apartner. I also liked hearing about how other groups went aboutdoing their projects in different waysLIBER 2014-Riga 16 17. Thank You Paldies!Don MacMillanLiaison Librarian, Biological Sciences, Physics, Astronomy &MathematicsUniversity of CalgaryCalgary, Canadamacmilld@ucalgary.caLIBER 2014-Riga 17 18. Bibliographyn Jaguszewski, J.M., Williams, K. (2013). New Roles for New Times: TransformingLiaison Roles in Research Libraries. Association of Research Libraries. Retrievedfrom http://www.arl.org/storage/documents/publications/NRNT-Liaison-Roles-final.pdfn Kenney, A.R. (2014) Leveraging the Liaison model: From Defining 21st CenturyResearch Libraries to Implementing 21st Century Research Universities. Ithaka S+R.Retrieved fromhttp://www.sr.ithaka.org/sites/default/files/files/SR_BriefingPaper_Kenney_20140322.pdfLIBER 2014-Riga 18 19. Resourcesn NCBIhttp://www.ncbi.nlm.nih.gov/n OMIMhttp://www.ncbi.nlm.nih.gov/omimn Protein Data Bankhttp://www.rcsb.org/pdb/home/home.don UniProthttp://www.uniprot.org/n PyMOLhttp://www.pymol.org/LIBER 2014-Riga 19