1,723
Views
15
CrossRef citations to date
0
Altmetric
Articles

Data Science Support at the Academic Library

, , &

REFERENCES

  • Aarts, A. A., Anderson, J. E., Anderson, C. J., Attridge, P. R., Attwood, A., Axt, J. …, Open Sci Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349, aa4716. doi:10.1126/science.aac4716
  • Allaire, J. J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., … Chang, W. (2018). Rmarkdown: Dynamic documents for R. Retrieved from https://CRAN.R-project.org/package=rmarkdown
  • Antell, K., Foote, J. B., Turner, J., & Shults, B. (2014). Dealing with data: Science librarians’ participation in data management at association of research libraries institutions. College and Research Libraries, 75, 557–574. doi:10.5860/crl.75.4.557
  • Baker, J., Moore, C., Priego, E., Alegre, R., Cope, J., Price, L., … Wilson, G. (2016). Library carpentry: Software skills training for library professionals. Liber Quarterly, 26, 141–162. doi:10.18352/lq.10176
  • Barba, L. A. (2018). Terminologies for reproducible research. CoRR. Retrieved from http://arxiv.org/abs/1802.03311
  • Barone, L., Williams, J., & Micklos, D. (2017). Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators. PLoS Computational Biology, 13, e1005755. doi:10.1371/journal.pcbi.1005755
  • Burton, M., Lyon, L., Erdmann, C., & Tijernia, B. (2018). Shifting to data savvy: The future of data science in libraries. University of Pittsburgh. Retrieved from http://d-scholarship.pitt.edu/id/eprint/33891
  • Camerer, C. F., Dreber, A., Forsell, E., Ho, T.-H., Huber, J., Johannesson, M., … Wu, H. (2016). Evaluating replicability of laboratory experiments in economics. Science, 351, 1433–1436. doi:10.1126/science.aaf0918
  • Carey, M. A., & Papin, J. A. (2018). Ten simple rules for biologists learning to program. Plos Computational Biology, 14, e1005871. doi:10.1371/journal.pcbi.1005871.
  • Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., … Böhner, J. (2015). System for automated geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8, 1991–2007. doi:10.5194/gmd-8-1991-2015
  • Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26, 745–766. doi:10.1080/10618600.2017.1384734
  • Environmental Systems Research Institute. (2018). ArcGIS. Retrieved from https://www.esri.com/en-us/store/arcgis-desktop
  • Galanek, J. D., & Brooks, D. C. (2018). Supporting faculty research with information technology. Research report. Educause Center for Analysis and Research.
  • Garcia-Milian, R., Hersey, D., Vukmirovic, M., & Duprilot, F. (2018). Data challenges of biomedical researchers in the age of omics. Peer Journal, 6, e5553. doi:10.7717/peerj.5553
  • GRASS Development Team. (2017). Geographic resources analysis support system (GRASS GIS) software, version 7.2, Open Source. Geospatial Foundation. Retrieved from http://grass.osgeo.org
  • Huppenkothen, D., Arendt, A., Hogg, D. W., Ram, K., VanderPlas, J. T., & Rokem, A. (2018). Hack weeks as a model for data science education and collaboration. Proceedings of the National Academy of Sciences, 115, 8872–8877. doi:10.1073/pnas.1717196115
  • Ioannidis, J. P. A. (2005). Why most published research findings are false. Plos Medicine, 2, 696–701. doi:10.1371/journal.pmed.0020124
  • Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2, 196–217. doi:10.1207/s15327957pspr0203_4
  • Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., … Willing, C. (2016). Jupyter notebooks – A publishing format for reproducible computational workflows. Paper presented at the Positioning and Power in Academic Publishing: Players, Agents and Agendas, 87. doi:10.3233/978-1-61499-649-1-87.Retrieved from http://ebooks.iospress.nl/publication/42900
  • Knuth, D. E. (1984). Literate programming. Computer Journal, 27, 97–111. doi:10.1093/comjnl/27.2.97
  • Kornfield, R., Sarma, P. K., Shah, V., Dhavan, McTavish, F., Landucci, G., Pe-Romashko, K., & Gustafson, D. H. (2018). Detecting recovery problems just in time: Application of automated linguistic analysis and supervised machine learning to an online substance abuse forum. Journal of Medical Internet Research, 20, e10136. doi:10.2196/10136
  • Lancaster, J., Lorenz, R., Leech, R., & Cole, J. H. (2018). Bayesian optimization for neuroimaging pre-processing in brain age classification and prediction. Frontiers in Aging Neuroscience, 10, 28. doi:10.3389/fnagi.2018.00028
  • Martin, E. R. (2016). The role of librarians in data science: A call to action. Journal of eScience Librarianship, 4, e1092. doi:10.7191/jeslib.2015.1092
  • Maxwell, D., Norton, H., & Wu, J. (2018). The data science opportunity: Crafting a holistic strategy. Journal of Library Administration, 58, 111–127. doi:10.1080/01930826.2017.1412704
  • Merchant, N., Lyons, E., Goff, S., Vaughn, M., Ware, D., Micklos, D., & Antin, P. (2016). The iPlant collaborative: Cyberinfrastructure for enabling data to discovery for the life sciences. PLoS Biology, 14, e1002342. doi:10.1371/journal.pbio.1002342
  • Moore-Sloan Data Science Environments. (2018). Creating institutional change in data science. Retrieved from http://msdse.org/creating_institutional_change.html
  • National Academies of Sciences, Engineering, and Medicine. (2018). Data science for undergraduates: Opportunities and options. Washington, DC: The National Academies Press. doi:10.17226/25104Retrieved from https://www.nap.edu/catalog/25104/data-science-for-undergraduates-opportunities-and-options
  • Nowviskie, B. (2013). Skunks in the library: A path to production for scholarly R&D. Journal of Library Administration, 53, 53–66. doi:10.1080/01930826.2013.756698
  • Oliver, J. C. (2017). Bioinformatic training needs at a health sciences campus. PLoS One, 12, e0179581. doi:10.1371/journal.pone.0179581.
  • Python Software Foundation. (2018). Python language reference. Retrieved from https://www.python.org
  • QGIS Development Team. (2018). QGIS geographic information system. Retrieved from http://qgis.osgeo.org
  • R Core Team. (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org
  • Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., … Wuetherick, A. B. (2015). Strategies and best practices for data literacy education: Knowledge synthesis report. Dalhousie University. Halifax, Nova Scotia, Canada. doi:10.13140/RG.2.1.1922.5044
  • Rubinstein, A., & Chor, B. (2014). Computational thinking in life science education. PLoS Computational Biology, 10, e1003897. doi:10.1371/journal.pcbi.1003897
  • Sayre, F., & Riegelman, A. (2018). The reproducibility crisis and academic libraries. College & Research Libraries, 79, 2–9. doi:10.5860/crl.79.1.2
  • Spies, J. R. (2013). The open science framework: Improving science by making it open and accessible. Retrieved from https://dl.acm.org/citation.cfm?id=2539283
  • Steeves, V. (2017). Reproducibility librarianship. Collaborative Librarianship, 9, 4. Retrieved from https://digitalcommons.du.edu/collaborativelibrarianship/vol9/iss2/4
  • Stingone, J. A., Pandey, O. P., Claudio, L., & Pandey, G. (2017). Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among US children. Environmental Pollution, 230, 730–740. doi:10.1016/j.envpol.2017.07.023
  • Tenopir, C., Allard, S., Frame, M., Birch, B., Baird, L., Sandusky, R., … Lundeen, A. (2015). Research data services in academic libraries: Data intensive roles for the future? Journal of eScience Librarianship, 4, e1085. doi:10.7191/jeslib.2015.1085
  • Towns, J., Andrews, P., Boisseau, J., Roskies, R., Brown, J., Boudwin, K., … Wallnau, K. (2010). Project summary: XSEDE: eXtreme science and engineering discovery environment. Retrieved from http://hdl.handle.net/2142/42546
  • Valle, D., & Berdanier, A. (2012). Computer programming skills for environmental sciences. Bulletin of the Ecological Society of America, 93, 373–389. doi:10.1890/0012-9623-93.4.373
  • Wang, M. (2013). Supporting the research process through expanded library data services. Program: Electronic Library and Information Systems, 47, 282–303. doi:10.1108/PROG-04-2012-0010
  • Wickham, H. (2016). Ggplot2: Elegant graphics for data analysis. New York: Springer-Verlag. Retrieved from http://ggplot2.org
  • Wilson, G. (2016). Software carpentry: Lessons learned. F1000Research, 3, 24. doi:10.12688/f1000research.3-62.v2
  • Xie, Y. (2014). Knitr: A comprehensive tool for reproducible research in R. In V. Stodden, F. Leisch & R. D. Peng (Eds.), Implementing reproducible computational research. Boca Raton, FL: Chapman and Hall/CRC. Retrieved from http://www.crcpress.com/product/isbn/9781466561595
  • Ye, H., Brown, M., & Harding, J. (2013). GIS for all: Exploring the barriers and opportunities for underexploited GIS applications. Paper presented at the Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings, Nottingham, UK (Vol. 13). doi:10.7275/R5W66J0K.Retrieved from https://scholarworks.umass.edu/foss4g/vol13/iss1/4

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.