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The i2b2 tranSMART Foundation enables effective collaboration for precision medicine, through the sharing, integration, standardization and analysis of heterogenous data from healthcare and research; through engagement and mobilization of a life sciences focused open-source, open-data community. i2b2 provides access to the Big Mouth Dental Repository : https://bigmouth.uth.edu/
Faculty/department licensing of this commonly used tool for geospatial data is available through the UF GeoPlan Center. ArcGIS is available for free to students through UF Apps: http://info.apps.ufl.edu/
Electronic Lab Notebooks (also known as an ELNs) can be used to store, organize, share, and publish laboratory data. This link from the University of Utah Libraries includes a list of existing ELN products. The University of Cambridge also discusses ELN considerations: https://www.gurdon.cam.ac.uk/institute-life/computing/elnguidance
This open-source web application allows you to create and share documents that contain live code, equations, visualizations and narrative text. Notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer.
This web-based tool supports the scientist's workflow including components for documenting and archiving studies, sharing materials, increasing transparency, and registering hypotheses and other materials.
Open Refine (formerly Google Refine) is a freely-available tool for working with messy data. In a local, spreadsheet-like interface, Open Refine allows you to use filters and facets to clean messy data, to transform lists into tables, and to merge datasets.
A tool for mining data locked in .pdf fiiles. Tabula allows you to extract that data into a CSV or Microsoft Excel spreadsheet using a simple, easy-to-use interface works on Mac, Windows and Linux
Python of course is an excellent language for data manipulation. Add on the Pandas library, which includes its DataFrame object, and data scientists can quickly perform even more complex operations. For example, merging, joining, and transforming huge hunks of data with a single Python statement.
Multiple libraries have been created for wrangling messy data, including dlpr and tidyr. The link above includes a basic introduction to data wrangling with R.