Replications are inevitably different from the original studies. How do we decide whether something is a replication? The answer shifts the conception of replication from a boring, uncreative, housekeeping activity to an exciting, generative, vital contributor to research progress.

This video is the first in a series of videos related to the basics of power analyses. All materials shown in the video, as well as content from the other videos in the power analysis series can be found here: https://osf.io/a4xhr/

Badges are a great way to signal that a journal values transparent research practices. Readers see the papers that have underlying data or methods available, colleagues see that norms are changing within a community and have ample opportunities to emulate better practices, and authors get recognition for taking a step into new techniques. In this webinar, Professor Stephen Lindsay of University of Victoria discusses the workflow of a badging program, eligibility for badge issuance, and the pitfalls to avoid in launching a badging program. Visit cos.io/badges to learn more.

A class covering the basics of writing a successful data management plan for federal funding agencies such as the NEH, NSF, NIH, NASA, and others.

Reproducibility is unquestionably at the heart of science. Scientists face numerous challenges in this context, not least the lack of concepts, tools, and workflows for reproducible research in today's curricula.This short course introduces established and powerful tools that enable reproducibility of computational geoscientific research, statistical analyses, and visualisation of results using R (http://www.r-project.org/) in two lessons:1. Reproducible Research with R MarkdownOpen Data, Open Source, Open Reviews and Open Science are important aspects of science today. In the first lesson, basic motivations and concepts for reproducible research touching on these topics are briefly introduced. During a hands-on session the course participants write R Markdown (http://rmarkdown.rstudio.com/) documents, which include text and code and can be compiled to static documents (e.g. HTML, PDF).R Markdown is equally well suited for day-to-day digital notebooks as it is for scientific publications when using publisher templates.2. GitLab and DockerIn the second lesson, the R Markdown files are published and enriched on an online collaboration platform. Participants learn how to save and version documents using GitLab (http://gitlab.com/) and compile them using Docker containers (https://docker.com/). These containers capture the full computational environment and can be transported, executed, examined, shared and archived. Furthermore, GitLab's collaboration features are explored as an environment for Open Science.Prerequisites: Participants should install required software (R, RStudio, a current browser) and register on GitLab (https://gitlab.com) before the course.This short course is especially relevant for early career scientists (ECS).Participants are welcome to bring their own data and R scripts to work with during the course.All material by the conveners will be shared publicly via OSF (https://osf.io/qd9nf/).

lab.js is a free, open, online study builder for the behavioral and cognitive sciences. (it works great in the lab, too)