Qualitative research has long suffered from a lack of free tools for analysis, leaving no options for researchers without significant funds for software licenses. This presents significant challenges for equity. This panel discussion will explore the first two free/libre open source qualitative analysis tools out there: qcoder (R package) and Taguette (desktop application). Drawing from the diverse backgrounds of the presenters (social science, library & information science, software engineering), we will discuss what openness and extensibility means for qualitative research, and how the two tools we've built facilitate equitable, open sharing.

The objectives of this course are as follows: Demonstrate an understanding of graphical representations of data and their interpretation; Demonstrate a competency in mathematical tools of decision making, including derivatives and analytical optimization; Demonstrate an understanding of descriptive statistics, hypothesis testing, and the theory of regression; Demonstrate competency in the use of software used in quantitative analysis, including Excel tools and statistical software. This textbook is organized to support you in these goals. The textbook is adapted from Contemporary Calculus, written by Dale Hoffman from Bellevue Community College and Business Calculus written by Shana Calaway from Shoreline Community College. New material is written by Margo Bergman from University of Washington Tacoma.

A survey in the United States revealed that an alarmingly large percentage of university psychologists admitted having used questionable research practices that can contaminate the research literature with false positive and biased findings. We conducted a replication of this study among Italian research psychologists to investigate whether these findings generalize to other countries. All the original materials were translated into Italian, and members of the Italian Association of Psychology were invited to participate via an online survey. The percentages of Italian psychologists who admitted to having used ten questionable research practices were similar to the results obtained in the United States although there were small but significant differences in self-admission rates for some QRPs. Nearly all researchers (88%) admitted using at least one of the practices, and researchers generally considered a practice possibly defensible if they admitted using it, but Italian researchers were much less likely than US researchers to consider a practice defensible. Participants’ estimates of the percentage of researchers who have used these practices were greater than the self-admission rates, and participants estimated that researchers would be unlikely to admit it. In written responses, participants argued that some of these practices are not questionable and they have used some practices because reviewers and journals demand it. The similarity of results obtained in the United States, this study, and a related study conducted in Germany suggest that adoption of these practices is an international phenomenon and is likely due to systemic features of the international research and publication processes.

We surveyed 807 researchers (494 ecologists and 313 evolutionary biologists) about their use of Questionable Research Practices (QRPs), including cherry picking statistically significant results, p hacking, and hypothesising after the results are known (HARKing). We also asked them to estimate the proportion of their colleagues that use each of these QRPs. Several of the QRPs were prevalent within the ecology and evolution research community. Across the two groups, we found 64% of surveyed researchers reported they had at least once failed to report results because they were not statistically significant (cherry picking); 42% had collected more data after inspecting whether results were statistically significant (a form of p hacking) and 51% had reported an unexpected finding as though it had been hypothesised from the start (HARKing). Such practices have been directly implicated in the low rates of reproducible results uncovered by recent large scale replication studies in psychology and other disciplines. The rates of QRPs found in this study are comparable with the rates seen in psychology, indicating that the reproducibility problems discovered in psychology are also likely to be present in ecology and evolution.

Hypothesizing after the results are known (HARK) has been disparaged as data dredging, and safeguards including hypothesis preregistration and statistically rigorous oversight have been recommended. Despite potential drawbacks, HARK has deepened thinking about complex causal processes. Some of the HARK precautions can conflict with the modern reality of researchers’ obligations to use big, ‘organic’ data sources—from high-throughput genomics to social media streams. We here propose a HARK-solid, reproducible inference framework suitable for big data, based on models that represent formalization of hypotheses. Reproducibility is attained by employing two levels of model validation: internal (relative to data collated around hypotheses) and external (independent to the hypotheses used to generate data or to the data used to generate hypotheses). With a model-centered paradigm, the reproducibility focus changes from the ability of others to reproduce both data and specific inferences from a study to the ability to evaluate models as representation of reality. Validation underpins ‘natural selection’ in a knowledge base maintained by the scientific community. The community itself is thereby supported to be more productive in generating and critically evaluating theories that integrate wider, complex systems.

This collection uses primary sources to explore Edgar Allan Poe's "The Raven." 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.

Various fields in the natural and social sciences face a ‘crisis of confidence’. Broadly, this crisis amounts to a pervasiveness of non-reproducible results in the published literature. For example, in the field of biomedicine, Amgen published findings that out of 53 landmark published results of pre-clinical studies, only 11% could be replicated successfully. This crisis is not confined to biomedicine. Areas that have recently received attention for non-reproducibility include biomedicine, economics, political science, psychology, as well as philosophy. Some scholars anticipate the expansion of this crisis to other disciplines.This course explores the state of reproducibility. After giving a brief historical perspective, case studies from different disciplines (biomedicine, psychology, and philosophy) are examined to understand the issues concretely. Subsequently, problems that lead to non-reproducibility are discussed as well as possible solutions and paths forward.

Modern scientific research takes advantage of programs such as Python and R that are open source. As such, they can be modified and shared by the wider community. Additionally, there is added functionality through additional programs and packages, such as IPython, Sweave, and Shiny. These packages can be used to not only execute data analyses, but also to present data and results consistently across platforms (e.g., blogs, websites, repositories and traditional publishing venues).

The goal of the course is to show how to implement analyses and share them using IPython for Python, Sweave and knitr for RStudio to create documents that are shareable and analyses that are reproducible.

Course outline is as follows:
1) Use of IPython notebooks to demonstrate and explain code, visualize data, and display analysis results
2) Applications of Python modules such as SymPy, NumPy, pandas, and SciPy
3) Use of Sweave to demonstrate and explain code, visualize data, display analysis results, and create documents and presentations
4) Integration and execution of IPython and R code and analyses using the IPython notebook

This is the website for the Autumn 2014 course “Reproducible Research Methods” taught by Eric C. Anderson at NOAA’s Southwest Fisheries Science Center. The course meets on Tuesdays and Thursdays from 3:30 to 4:30 PM in Room 188 of the Fisheries Ecology Division.
It runs from Oct 7 to December 18.

The goal of this course is for scientists, researchers, and students to learn:

to write programs in the R language to manipulate and analyze data,
to integrate data analysis with report generation and article preparation using knitr,
to work fluently within the Rstudio integrated development environment for R,
to use git version control software and GitHub to effectively manage source code, collaborate efficiently with other researchers, and neatly package their research.

Description

This hands-on tutorial will train reproducible research warriors on the practices and tools that make experimental verification possible with an end-to-end data analysis workflow. The tutorial will expose attendees to open science methods during data gathering, storage, analysis, up to publication into a reproducible article.

Attendees are expected to have basic familiarity with scientific Python and Git.

Workshop goals
- Why are we teaching this
- Why is this important
- For future and current you
- For research as a whole
- Lack of reproducibility in research is a real problem

Materials and how we'll use them
- Workshop landing page, with

- links to the Materials
- schedule

Structure oriented along the Four Facets of Reproducibility:

- Documentation
- Organization
- Automation
- Dissemination

Will be available after the Workshop

How this workshop is run
- This is a Carpentries Workshop
- that means friendly learning environment
- Code of Conduct
- active learning
- work with the people next to you
- ask for help

An introduction to managing, annotating, organizing, archiving, and publishing research data using the Open Science Framework.

This lesson in part of Software Carpentry workshop and teach novice programmers to write modular code and best practices for using R for data analysis. an introduction to R for non-programmers using gapminder data The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis. The lesson contains more material than can be taught in a day. The instructor notes page has some suggested lesson plans suitable for a one or half day workshop. A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.

From Data Carpentry: Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with social sciences data in R.This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting.

Data Carpentry lesson part of the Social Sciences curriculum. This lesson teaches how to analyse and visualise data used by social scientists. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with social sciences data in R. This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting.

This collection uses primary sources to explore the rise of conservatism in the US in the 1980s. 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.

This collection uses primary sources to explore the early history of Rock and Roll music. 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.

Una introducción a R utilizando los datos de Gapminder. El objetivo de esta lección es enseñar a las programadoras principiantes a escribir códigos modulares y adoptar buenas prácticas en el uso de R para el análisis de datos. R nos provee un conjunto de paquetes desarrollados por terceros que se usan comúnmente en diversas disciplinas científicas para el análisis estadístico. Encontramos que muchos científicos que asisten a los talleres de Software Carpentry utilizan R y quieren aprender más. Nuestros materiales son relevantes ya que proporcionan a los asistentes una base sólida en los fundamentos de R y enseñan las mejores prácticas del cómputo científico: desglose del análisis en módulos, automatización tareas y encapsulamiento. Ten en cuenta que este taller se enfoca en los fundamentos del lenguaje de programación R y no en el análisis estadístico. A lo largo de este taller se utilizan una variedad de paquetes desarrolados por terceros, los cuales no son necesariamente los mejores ni se encuentran explicadas todas sus funcionalidades, pero son paquetes que consideramos útiles y han sido elegidos principalmente por su facilidad de uso.

Dies ist eine kleine Ruby-Anleitung, für die man nicht mehr als 20 Minuten zum Lesen benötigen sollte. Voraussetzung ist, dass Du Ruby bereits installiert hast. (Falls nicht, solltest Du vor dem Weiterlesen Ruby herunterladen und installieren.)

Using the acronym STEAL, students will practice analyzing a character. This lesson includes an explanation of the STEAL strategy, a video demonstration, and two options for student practice or assessment.