developing data literacy competencies to enhance faculty collaborations

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Developing data literacy competencies to enhance faculty collaborations Don MacMillan Liaison Librarian, Biological Sciences, Physics, Astronomy & Mathematics University of Calgary Calgary, Canada

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Developing Data Literacy Competencies to Enhance Faculty Collaborations (Don MacMillan, University of Calgary, Canada). This presentation was one of the 10 most highly ranked at LIBER's Annual Conference 2014 in Riga, Latvia. Learn more: www.libereurope.eu

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Page 1: Developing Data Literacy Competencies to Enhance Faculty Collaborations

Developing data literacy competencies to enhance faculty collaborations

Don MacMillanLiaison Librarian, Biological Sciences, Physics,

Astronomy & Mathematics

University of Calgary

Calgary, Canada

Page 2: Developing Data Literacy Competencies to Enhance Faculty Collaborations

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Page 4: Developing Data Literacy Competencies to Enhance Faculty Collaborations

Collaboration

Reinvent information literacy (IL) program –integrate genetics & biochemistry content

Course-integrated library/lab project Biology 311 (October) - Biochemistry 393 (February)

Investigate the molecular & structural basis of inherited diseases using disciplinary data

550 students per class 24 lab sections10 IL workshops in library classrooms

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Page 5: Developing Data Literacy Competencies to Enhance Faculty Collaborations

Why Data Literacy?

Data-intensive disciplines – robust infrastructure - unique research outcomes

Bioinformatics tools facilitates discovery & analysis of life sciences data

Students conduct research using “real-world”

solutions using domain repositories

Librarian re-skilling – enhance our data competencies, domain expertise & relevance

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Page 6: Developing Data Literacy Competencies to Enhance Faculty Collaborations

BioinformaticsTools for collection & analysis of complex biological data

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Integration

Information Literacy

Genetics BIOL311

Genes

Biochemistry BCEM393Proteins

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Bioinformatics – Interoperability

Genetics

PubMed (Scholarly Literature)

OMIM (Database of genetic diseases)

Gene (Nucleotide sequences)

Biochemistry

Protein Data Bank (Protein Structure)

UniProt (Protein Sequence/Function)

BLAST & ClustlW (Sequence similarity & Alignment)

PyMOL (3D Visualization)

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Page 8: Developing Data Literacy Competencies to Enhance Faculty Collaborations

Sample Questions

Genetics(October)

Which chromosome is your selected gene located?

Obtain data on gene structure, mRNA and protein length

Biochemistry (February)

Locate your Protein’s 3D coordinates

What effect does a mutation have on protein structure & function

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Page 9: Developing Data Literacy Competencies to Enhance Faculty Collaborations

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Gene Data ExampleNCBI’s Gene portal – links to gene & protein data and PubMed

(e.g. Cystic fibrosis = CFTR Gene)

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Protein Data ExampleProtein Data Bank (PDB) – Repository of 3D protein structural data – portal to PubMed and

related data sources

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PyMOLThree-dimensional (3D) molecular

visualization systemStudents introduce mutations to study impact on

protein structure, function & related diseases(e.g. KRAS mutation causes certain types of

cancer)

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Implications for LibrariesData competencies inform domain expertise and…vice versa

Align library activities with faculty needs, data life cycle, (Jaguszewski & Williams 2013)

Librarian’s need to offer more expertise…value (Kenney (Ithaka), 2014)

Collaboration & “soft skills” critical to leveraging expertise and expanding services

Created a more rigorous & sustainable IL program

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Page 13: Developing Data Literacy Competencies to Enhance Faculty Collaborations

Impact on student learning

Innovative learning experience for students –data integrates & informs content

Students able to find “real world” solutions to research questions

Students will be able to manage and analyze their data more efficiently

Peer-reviewed presentations demonstrate deeper understanding of subjects

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Best Practices

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Must be course-integrated – worth %

Develop sequential steps – simple to complex

Students learn best by doing – allow hands-on interactivity & flexibility

Tailor relevant data sources to specific questions

Advantageous to have consistent delivery to all lab sections

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Assessment

n Students prepare poster & presentation (mark and peers)

n Peer-Review “Marking Rubric”

n TooFast (Free Assessment Summary Tool)https://www.toofast.ca/

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Page 16: Developing Data Literacy Competencies to Enhance Faculty Collaborations

Student feedback

n “It was good that we were able to use things that we learned in BIOL 311 to understand things in BCEM 393. I think that I learned more because I could see how labs and topics in the two courses related to each other”

n “I liked how the library and computer labs were hands-on. The TAs and library people were there to help us and not just tell us what to do. I think it was more fun and we got more out of it by doing things on our own instead of following a step-by-step recipe in the lab manual”

n “The presentations gave me the chance to learn many valuable skills. I liked picking the disease to study and doing the project with a partner. I also liked hearing about how other groups went about doing their projects in different ways”

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Page 17: Developing Data Literacy Competencies to Enhance Faculty Collaborations

Thank You – Paldies!

Don MacMillanLiaison Librarian, Biological Sciences, Physics, Astronomy &

Mathematics

University of Calgary

Calgary, Canada

[email protected]

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Bibliography

n Jaguszewski, J.M., Williams, K. (2013). New Roles for New Times: Transforming Liaison Roles in Research Libraries. Association of Research Libraries. Retrieved from http://www.arl.org/storage/documents/publications/NRNT-Liaison-Roles-final.pdf

n Kenney, A.R. (2014) Leveraging the Liaison model: From Defining 21st Century Research Libraries to Implementing 21st Century Research Universities. Ithaka S+R. Retrieved from http://www.sr.ithaka.org/sites/default/files/files/SR_BriefingPaper_Kenney_20140322.pdf

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Resources

n NCBIhttp://www.ncbi.nlm.nih.gov/

n OMIMhttp://www.ncbi.nlm.nih.gov/omim

n Protein Data Bankhttp://www.rcsb.org/pdb/home/home.do

n UniProthttp://www.uniprot.org/

n PyMOLhttp://www.pymol.org/

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