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Data Analysis and Visualization with Python for Social Scientists
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CC BY
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Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Geoffrey Boushey
Stephen Childs
Date Added:
08/07/2020
Data Analysis and Workflows
Unrestricted Use
Public Domain
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Understanding the types, processes, and frameworks of workflows and analyses is helpful for researchers seeking to understand more about research, how it was created, and what it may be used for. This lesson uses a subset of data analysis types to introduce reproducibility, iterative analysis, documentation, provenance and different types of processes. Described in more detail are the benefits of documenting and establishing informal (conceptual) and formal (executable) workflows.

Subject:
Applied Science
Education
Higher Education
Information Science
Material Type:
Lesson
Provider:
DataONE
Author:
DataONE Community Engagement & Outreach Working Group
Date Added:
11/21/2020
Data Carpentry for Biologists
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CC BY
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The Biology Semester-long Course was developed and piloted at the University of Florida in Fall 2015. Course materials include readings, lectures, exercises, and assignments that expand on the material presented at workshops focusing on SQL and R.

Subject:
Applied Science
Biology
Computer Science
Information Science
Life Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Ethan White
Zachary Brym
Date Added:
08/07/2020
Data Cleaning with OpenRefine for Ecologists
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CC BY
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A part of the data workflow is preparing the data for analysis. Some of this involves data cleaning, where errors in the data are identified and corrected or formatting made consistent. This step must be taken with the same care and attention to reproducibility as the analysis. OpenRefine (formerly Google Refine) is a powerful free and open source tool for working with messy data: cleaning it and transforming it from one format into another. This lesson will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you make. Many people comment that this tool saves them literally months of work trying to make these edits by hand.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Cam Macdonell
Deborah Paul
Phillip Doehle
Rachel Lombardi
Date Added:
03/20/2017
Data Intro for Archivists
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CC BY
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This Library Carpentry lesson introduces archivists to working with data. At the conclusion of the lesson you will: be able to explain terms, phrases, and concepts in code or software development; identify and use best practice in data structures; use regular expressions in searches.

Subject:
Applied Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
James Baker
Jeanine Finn
Jenny Bunn
Katherine Koziar
Noah Geraci
Scott Peterson
Date Added:
08/07/2020
Data Management with SQL for Ecologists
Unrestricted Use
CC BY
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Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Christina Koch
Donal Heidenblad
Katy Felkner
Rémi Rampin
Timothée Poisot
Date Added:
03/20/2017
Data Management with SQL for Social Scientists
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CC BY
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This is an alpha lesson to teach Data Management with SQL for Social Scientists, We welcome and criticism, or error; and will take your feedback into account to improve both the presentation and the content. Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Social Science
Material Type:
Module
Provider:
The Carpentries
Author:
Peter Smyth
Date Added:
08/07/2020
Data, Math, Visualization, and Interpretation of Networks: An Introduction
Read the Fine Print
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Network analysis is one of the four pillars of computational humanities, along with geographic, text, and image analysis. Participants in this course will receive a broad overview of networks as they’re applied to humanities problems.

Subject:
Applied Science
Arts and Humanities
Computer Science
Material Type:
Reading
Provider:
Scott B. Weingart
Author:
Scott B. Weingart
Date Added:
07/14/2015
Data Organization in Spreadsheets for Ecologists
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CC BY
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Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place that many research projects start. We organize data in spreadsheets in the ways that we as humans want to work with the data, but computers require that data be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: Good data entry practices - formatting data tables in spreadsheets How to avoid common formatting mistakes Approaches for handling dates in spreadsheets Basic quality control and data manipulation in spreadsheets Exporting data from spreadsheets In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Christie Bahlai
Peter R. Hoyt
Tracy Teal
Date Added:
03/20/2017
Data Organization in Spreadsheets for Social Scientists
Unrestricted Use
CC BY
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Lesson on spreadsheets for social scientists. Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place that many research projects start. Typically we organize data in spreadsheets in ways that we as humans want to work with the data. However computers require data to be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: Good data entry practices - formatting data tables in spreadsheets How to avoid common formatting mistakes Approaches for handling dates in spreadsheets Basic quality control and data manipulation in spreadsheets Exporting data from spreadsheets In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed.

Subject:
Applied Science
Information Science
Mathematics
Measurement and Data
Social Science
Material Type:
Module
Provider:
The Carpentries
Author:
David Mawdsley
Erin Becker
François Michonneau
Karen Word
Lachlan Deer
Peter Smyth
Date Added:
08/07/2020
Data Wrangling and Processing for Genomics
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CC BY
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Data Carpentry lesson to learn how to use command-line tools to perform quality control, align reads to a reference genome, and identify and visualize between-sample variation. A lot of genomics analysis is done using command-line tools for three reasons: 1) you will often be working with a large number of files, and working through the command-line rather than through a graphical user interface (GUI) allows you to automate repetitive tasks, 2) you will often need more compute power than is available on your personal computer, and connecting to and interacting with remote computers requires a command-line interface, and 3) you will often need to customize your analyses, and command-line tools often enable more customization than the corresponding GUI tools (if in fact a GUI tool even exists). In a previous lesson, you learned how to use the bash shell to interact with your computer through a command line interface. In this lesson, you will be applying this new knowledge to carry out a common genomics workflow - identifying variants among sequencing samples taken from multiple individuals within a population. We will be starting with a set of sequenced reads (.fastq files), performing some quality control steps, aligning those reads to a reference genome, and ending by identifying and visualizing variations among these samples. As you progress through this lesson, keep in mind that, even if you aren’t going to be doing this same workflow in your research, you will be learning some very important lessons about using command-line bioinformatic tools. What you learn here will enable you to use a variety of bioinformatic tools with confidence and greatly enhance your research efficiency and productivity.

Subject:
Applied Science
Computer Science
Genetics
Information Science
Life Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Thomas
Ahmed R. Hasan
Aniello Infante
Anita Schürch
Dev Paudel
Erin Alison Becker
Fotis Psomopoulos
François Michonneau
Gaius Augustus
Gregg TeHennepe
Jason Williams
Jessica Elizabeth Mizzi
Karen Cranston
Kari L Jordan
Kate Crosby
Kevin Weitemier
Lex Nederbragt
Luis Avila
Peter R. Hoyt
Rayna Michelle Harris
Ryan Peek
Sheldon John McKay
Sheldon McKay
Taylor Reiter
Tessa Pierce
Toby Hodges
Tracy Teal
Vasilis Lenis
Winni Kretzschmar
dbmarchant
Date Added:
08/07/2020
Databases and SQL
Unrestricted Use
CC BY
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Software Carpentry lesson that teaches how to use databases and SQL In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and Valentina Roerich led expeditions to the Pole of Inaccessibility in the South Pacific, and then onward to Antarctica. Two years ago, their expeditions were found in a storage locker at Miskatonic University. We have scanned and OCR the data they contain, and we now want to store that information in a way that will make search and analysis easy. Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets. These lessons will show how to use a database to explore the expeditions’ data.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Amy Brown
Andrew Boughton
Andrew Kubiak
Avishek Kumar
Ben Waugh
Bill Mills
Brian Ballsun-Stanton
Chris Tomlinson
Colleen Fallaw
Dan Michael Heggø
Daniel Suess
Dave Welch
David W Wright
Deborah Gertrude Digges
Donny Winston
Doug Latornell
Erin Alison Becker
Ethan Nelson
Ethan P White
François Michonneau
George Graham
Gerard Capes
Gideon Juve
Greg Wilson
Ioan Vancea
Jake Lever
James Mickley
John Blischak
JohnRMoreau@gmail.com
Jonah Duckles
Jonathan Guyer
Joshua Nahum
Kate Hertweck
Kevin Dyke
Louis Vernon
Luc Small
Luke William Johnston
Maneesha Sane
Mark Stacy
Matthew Collins
Matty Jones
Mike Jackson
Morgan Taschuk
Patrick McCann
Paula Andrea Martinez
Pauline Barmby
Piotr Banaszkiewicz
Raniere Silva
Ray Bell
Rayna Michelle Harris
Rémi Emonet
Rémi Rampin
Seda Arat
Sheldon John McKay
Sheldon McKay
Stephen Davison
Thomas Guignard
Trevor Bekolay
lorra
slimlime
Date Added:
03/20/2017
Declaration of the Rights of Man and of the Citizen
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CC BY
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This collection uses primary sources to explore the Declaration of the Rights of Man and of the Citizen. Digital Public Library of America Primary Source Sets are designed to help students develop their critical thinking skills and draw diverse material from libraries, archives, and museums across the United States. Each set includes an overview, ten to fifteen primary sources, links to related resources, and a teaching guide. These sets were created and reviewed by the teachers on the DPLA's Education Advisory Committee.

Subject:
History
World History
Material Type:
Primary Source
Provider:
Digital Public Library of America
Provider Set:
Primary Source Sets
Author:
Albert Robertson
Date Added:
04/11/2016
Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking
Unrestricted Use
CC BY
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The designing, collecting, analyzing, and reporting of psychological studies entail many choices that are often arbitrary. The opportunistic use of these so-called researcher degrees of freedom aimed at obtaining statistically significant results is problematic because it enhances the chances of false positive results and may inflate effect size estimates. In this review article, we present an extensive list of 34 degrees of freedom that researchers have in formulating hypotheses, and in designing, running, analyzing, and reporting of psychological research. The list can be used in research methods education, and as a checklist to assess the quality of preregistrations and to determine the potential for bias due to (arbitrary) choices in unregistered studies.

Subject:
Psychology
Social Science
Material Type:
Reading
Provider:
Frontiers in Psychology
Author:
Coosje L. S. Veldkamp
Hilde E. M. Augusteijn
Jelte M. Wicherts
Marcel A. L. M. van Assen
Marjan Bakker
Robbie C. M. van Aert
Date Added:
08/07/2020
Design Step 4: Engineering Analysis
Read the Fine Print
Educational Use
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Engineering analysis distinguishes true engineering design from "tinkering." In this activity, students are guided through an example engineering analysis scenario for a scooter. Then they perform a similar analysis on the design solutions they brainstormed in the previous activity in this unit. At activity conclusion, students should be able to defend one most-promising possible solution to their design challenge. (Note: Conduct this activity in the context of a design project that students are working on; this activity is Step 4 in a series of six that guide students through the engineering design loop.)

Subject:
Applied Science
Engineering
Material Type:
Activity/Lab
Provider:
TeachEngineering
Provider Set:
TeachEngineering
Author:
Denise W. Carlson
Lauren Cooper
Malinda Schaefer Zarske
Date Added:
09/18/2014
Design and Graphics Communications
Conditional Remix & Share Permitted
CC BY-SA
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The Design Process is a modern approach to the teaching of practical skills in schools, colleges and universities. It is sometimes called Product Design. In this course learners will learn how to define the Design Process and explain the framework of design. This course discusses the advantages and disadvantages of the design process and it illustrates the design process diagrammatically. It explains problem identification techniques and discusses ways of analysing products to be designed. In addition, this course discusses the importance of investigating into problems before designing and making.

Subject:
Arts and Humanities
Material Type:
Full Course
Homework/Assignment
Reading
Provider:
WikiEducator
Date Added:
02/16/2011
Design and Manufacturing I
Conditional Remix & Share Permitted
CC BY-NC-SA
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Welcome to 2.007! This course is a first subject in engineering design. With your help, this course will be a great learning experience exposing you to interesting material, challenging you to think deeply, and providing skills useful in professional practice. A major element of the course is design of a robot to participate in a challenge that changes from year to year. This year, the theme is cleaning up the planet as inspired by the movie Wall-E.
From its beginnings in 1970, the 2.007 final project competition has grown into an Olympics of engineering.  See this MIT News story for more background, a photo gallery, and videos about this course.

Subject:
Applied Science
Career and Technical Education
Electronic Technology
Engineering
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Frey, Daniel
Gossard, David
Date Added:
02/01/2009
Details from Steve Jobs's Speech
Conditional Remix & Share Permitted
CC BY-NC
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On Monday, you scanned Steve Jobs' commencement speech from Stanford and on Tuesday in class we close-read paragraphs 6 through 8. In this discussion, you will post one detail from the speech and provide your thinking about the detail.

Subject:
Arts and Humanities
Material Type:
Lesson Plan
Author:
Tim Batiuk
Date Added:
01/02/2018