- Apache (1)
- api (2)
- arcus (8)
- bias (4)
- census (1)
- clinical data (5)
- coding (1)
- cohorts (1)
- consent (1)
- crowdsourcing (1)
- cTAKES (2)
- data (3)
- data catalog (2)
- data collection (10)
- data combining (1)
- data discovery (1)
- data integration (1)
- data management (1)
- data munging (3)
- data privacy (2)
- data reshaping (1)
- data security (1)
- data storage (2)
- data types (3)
- data visualization (11)
- demographics (1)
- descriptive statistics (1)
- EHR (3)
- electronic health records (3)
- environmental data (1)
- Excel (2)
- geographic data (1)
- geojson (1)
- ggplot (1)
- ggplot2 (2)
- git (2)
- Google Sheets (1)
- graphs / networks (1)
- high performance computing (1)
- history (1)
- human subjects (4)
- library science (1)
- linear algebra (1)
- linux (1)
- literate statistical programming (4)
- machine learning (5)
- mean (1)
- measures of central tendency (1)
- measures of dispersion (1)
- mechanical turk (1)
- median (1)
- metadata (3)
- missing data (1)
- missingness (1)
- mode (1)
- natural language processing (3)
- networkx (1)
- NIMH (2)
- NLP (3)
- nlp (2)
- nltk (2)
- omics (1)
- ontology (1)
- privacy (4)
- python (11)
- r (30)
- RDoC (2)
- redcap (9)
- REDCap (1)
- regex (1)
- regression (1)
- reliability (1)
- reproducibility (2)
- reproducible research (6)
- responsible conduct of research (7)
- social determinants of health (1)
- spreadsheets (1)
- sql (2)
- ssh (1)
- standard deviation (1)
- statistics (12)
- Sublime Text (1)
- swirl (1)
- terminal emulation (1)
- text processing (1)
- tidy data (1)
- validity (1)
- variable types (1)
- variables (2)
- variance (1)
- version control (1)

## Apache

## api

## arcus

- Arcus Data Repository: A Fast Track to Research
- Arcus Annotations and cTAKES
- Arcus Annotations and RDoC
- Arcus Annotations: Harvesting Data from Text Notes
- Feasibility Analysis Using Arcus Cohort Discovery
- Meet the Arcus Library Science Team
- Why Archivists and Librarians?
- Arcus's Virtual Biobank

## bias

- FIPs and the Belmont Report: Divergence
- FIPs and the Belmont Report: Similarities
- FIPs and the Belmont Report: Principles
- Social Justice and Data Science

## census

## clinical data

## coding

## cohorts

## consent

## crowdsourcing

## cTAKES

## data

## data catalog

## data collection

- Data Preparation
- Best Practices for REDCap Variables and Instruments
- Collecting Sex and Gender Data
- REDCap Race and Ethnicity Data Collection
- REDCap: PHI and Permissions
- REDCap Data Collection Overview
- REDCap Free Text Collection
- REDCap Field Types
- REDCap Free Text Collection
- Cartesian Result Sets

## data combining

## data discovery

## data integration

## data management

## data munging

## data privacy

## data reshaping

## data security

## data storage

## data types

## data visualization

- Descriptive Statistics: The Bullet
- Python Lab for Beginners
- Customizing ggplot2 Visualizations With ggThemeAssist
- ggplot overview
- Intro to Machine Learning: Trees
- Understanding Pearson's r
- Statistical Intervals and Visualizations: Difference Between Means
- R Lab for Beginners
- Base R Plotting
- Sparklines in ggplot2
- Jupyter 101

## demographics

## descriptive statistics

## EHR

- Arcus Annotations and cTAKES
- Arcus Annotations and RDoC
- Arcus Annotations: Harvesting Data from Text Notes

## electronic health records

- Arcus Annotations and cTAKES
- Arcus Annotations and RDoC
- Arcus Annotations: Harvesting Data from Text Notes

## environmental data

## Excel

## geographic data

## geojson

## ggplot

## ggplot2

## git

## Google Sheets

## graphs / networks

## high performance computing

## history

## human subjects

- FIPs and the Belmont Report: Divergence
- FIPs and the Belmont Report: Similarities
- FIPs and the Belmont Report: Principles
- Social Justice and Data Science

## library science

## linear algebra

## linux

## literate statistical programming

- Statistical Programming Languages
- Why Use Literate Statistical Programming?
- R Markdown 101
- Literate Statistical Programming

## machine learning

- What Type of Machine Learning Should I Use?
- Arcus Annotations and cTAKES
- Arcus Annotations and RDoC
- Arcus Annotations: Harvesting Data from Text Notes
- Intro to Machine Learning: Trees

## mean

## measures of central tendency

## measures of dispersion

## mechanical turk

## median

## metadata

## missing data

## missingness

## mode

## natural language processing

- Arcus Annotations and cTAKES
- Arcus Annotations and RDoC
- Arcus Annotations: Harvesting Data from Text Notes

## networkx

## NIMH

## NLP

- Arcus Annotations and cTAKES
- Arcus Annotations and RDoC
- Arcus Annotations: Harvesting Data from Text Notes

## nlp

## nltk

## omics

## ontology

## privacy

- Data Sharing and Privacy: A Very Cursory Overview
- FIPs and the Belmont Report: Divergence
- FIPs and the Belmont Report: Similarities
- FIPs and the Belmont Report: Principles

## python

- What Type of Machine Learning Should I Use?
- User Groups at CHOP
- Cloud Tools for the Unconvinced
- Using the REDCap API
- Comparing Parts of Speech with NLTK
- My File is Over There: File Paths for Data Scientists
- Python Lab for Beginners
- Natural Language Processing with NLTK
- Statistical Programming Languages
- Intro to NetworkX
- Jupyter 101

## r

- What Type of Machine Learning Should I Use?
- The REDCap API and Windows
- User Groups at CHOP
- Cloud Tools for the Unconvinced
- Swirl: Learn R in R
- Variable Types
- Do Patterns in Missing Data Matter?
- Tiny Munge
- Using the REDCap API
- Date Pairing in R
- Data Preparation
- Clinical Data in R
- My File is Over There: File Paths for Data Scientists
- Ordinary Linear Regression in R
- Customizing ggplot2 Visualizations With ggThemeAssist
- Mapping Environmental Exposures
- Data Combining in R
- ggplot overview
- Intro to Machine Learning: Trees
- Understanding Pearson's r
- Statistical Programming Languages
- R Markdown 101
- R Lab for Beginners
- Scripted Analysis for Reproducibility
- Base R Plotting
- Literate Statistical Programming
- Sparklines in ggplot2
- Welcome to the Tidyverse!
- Writing Functions in R
- When R Gets Too Helpful

## RDoC

## redcap

- The REDCap API and Windows
- Best Practices for REDCap Variables and Instruments
- Collecting Sex and Gender Data
- REDCap Race and Ethnicity Data Collection
- REDCap: PHI and Permissions
- REDCap Data Collection Overview
- REDCap Free Text Collection
- REDCap Field Types
- REDCap Free Text Collection

## REDCap

## regex

## regression

## reliability

## reproducibility

## reproducible research

- Why Use Literate Statistical Programming?
- Distributed Humaning
- Interrogating the Data Until it Confesses
- Statistical Intervals and Visualizations: Difference Between Means
- Data Dictionaries
- Scripted Analysis for Reproducibility

## responsible conduct of research

- FIPs and the Belmont Report: Divergence
- FIPs and the Belmont Report: Similarities
- The p Value Controversy
- FIPs and the Belmont Report: Principles
- Social Justice and Data Science
- Clinical Data at CHOP
- Recording Consent

## social determinants of health

## spreadsheets

## sql

## ssh

## standard deviation

## statistics

- What Type of Machine Learning Should I Use?
- The Argument Against Aggregation
- Statistics Chapter 1: Measures of Central Tendency and Dispersion
- Variable Types
- Do Patterns in Missing Data Matter?
- Descriptive Statistics: The Bullet
- Ordinary Linear Regression in R
- Null Hypothesis Statistical Testing (NHST)
- The p Value Controversy
- Understanding Pearson's r
- Interrogating the Data Until it Confesses
- Statistical Intervals and Visualizations: Difference Between Means