Guides @ UF: Dental Data: Tools

i2b2: Informatics for Integrating Biology and Bedside

i2b2 : Provides access to the Big Mouth Dental Repository : https://bigmouth.uth.edu/
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/

Data Visualization Tools

Plotly
Plotly is a web-based tool for easily creating and sharing graphs and other data visualizations. Use their online spreadsheets or the following programming languages: MATLAB, Python, R, JSON, Julia, Javascript.
Tableau
This is a suite of tools for interactive data visualization. Tableau Public is free.
Sci2
Developed out of Indiana University, the Science of Science (Sci2) Tool is designed for temporal, geospatial, topical, and network analysis and visualization. Free registration is required.
Gephi
This tool is an interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs.
ArcGIS
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/
Visualization Tools Guide
This guide links to a number of additional data visualization tools and software, primarily for scientific applications.
Data Visualization Checklist
From Evergreen Data, this checklist covers text, arrangement, color, lines, and overall aspects of visualization.

General Data Management Tools

REDCap
Initially designed for clinical data capture, REDCap allows you to create a database and related forms quickly and output data into Excel and common statistical packages (SPSS, SAS, Stata, R).
Electronic Lab Notebooks (ELNs)
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
Jupyter Notebook
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.
Open Science Framework
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
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.
Trifacta Wrangler
Trifacta Wrangler allows users to clean and merge diverse data sources on their desktop in preparation for use in analytics or visualization .
NLM Scrubber
NLM-Scrubber is a new, freely available, HIPAA compliant, clinical text de-identification tool designed and developed at the National Library of Medicine.
DataCite
DataCite helps you cite research data by providing persistent identifiers and suggested citation styles. Also included are other tools to find, access, and re-use data.

Data Extraction & Conversion Tools

CSVKIT
csvkit is a suite of command-line tools for converting to and working with CSV.
Mr. Data Converter
Takes Excel data and transforms it to web-friendly formats like HTML, JSON and XML.
Tabula
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

Languages

Python
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.
R
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.