This webinar walks you through the basics of creating an OSF project, structuring it to fit your research needs, adding collaborators, and tying your favorite online tools into your project structure. OSF is a free, open source web application built by the Center for Open Science, a non-profit dedicated to improving the alignment between scientific values and scientific practices. OSF is part collaboration tool, part version control software, and part data archive. It is designed to connect to popular tools researchers already use, like Dropbox, Box, Github, and Mendeley, to streamline workflows and increase efficiency.

This video will introduce users of the Open Science Framework into some of the more advanced features.

Files for this webinar are available at: https://osf.io/ewhvq/ This webinar focuses on how to use the Open Science Framework (OSF) to tie together and organize multiple projects. We look at example structures appropriate for organizing classroom projects, a line of research, or a whole lab's activity. We discuss the OSF's capabilities for using projects as templates, linking projects, and forking projects as well as some considerations for using each of those capabilities when designing a structure for your own project. The OSF is a free, open source web application built to help researchers manage their workflows. The OSF is part collaboration tool, part version control software, and part data archive. The OSF connects to popular tools researchers already use, like Dropbox, Box, Github and Mendeley, to streamline workflows and increase efficiency.

This webinar will introduce how to use the Open Science Framework (OSF; https://osf.io) in a Classroom. The OSF is a free, open source web application built to help researchers manage their workflows. The OSF is part collaboration tool, part version control software, and part data archive. The OSF connects to popular tools researchers already use, like Dropbox, Box, Github and Mendeley, to streamline workflows and increase efficiency. This webinar will discuss how to introduce reproducible research practices to students, show ways of tracking student activity, and introduce the use of Templates and Forks on the OSF to allow students to easily make new class projects. The OSF is the flagship product of the Center for Open Science, a non-profit technology start-up dedicated to improving the alignment between scientific values and scientific practices. Learn more at cos.io and osf.io, or email contact@cos.io.

The Open Access Directory is an online compendium of factual lists about open access to science and scholarship, maintained by the community at large. It exists as a wiki hosted by the School of Library and Information Science at Simmons University in Boston, USA. The goal is for the open access community itself to enlarge and correct the lists with little intervention from the editors or editorial board. For quality control, editing privileges are granted to registered users. As far as possible, lists are limited to brief factual statements without narrative or opinion.

OATP is a crowd-sourced social-tagging project running on free software to capture new developments on open access to research. Its mission is (1) to create real-time alerts for OA-related news and comment, and (2) to organize knowledge of the field, by tag or subtopic, for easy searching and sharing.

The open-access.net platform provides comprehensive information on the subject of Open Access (OA) and offers practical advice on its implementation. Developed collaboratively by the Freie Universität Berlin and the Universities of Goettingen, Konstanz, and Bielefeld, open-access.net first went online at the beginning of May 2007. The platform's target groups include all relevant stakeholders in the science sector, especially the scientists and scholars themselves, university and research institution managers, infrastructure service providers such as libraries and data centres, and funding agencies and policy makers. open-access.net provides easy, one-stop access to comprehensive information on OA.

Aspects covered include OA concepts, legal, organisational and technical frameworks, concrete implementation experiences, initiatives, services, service providers, and position papers. The target-group-oriented and discipline-specific presentation of the content enables users to access relevant themes quickly and efficiently. Moreover, the platform offers practical implementation advice and answers to fundamental questions regarding OA.
In collaboration with cooperation partners in Austria (the University of Vienna) and Switzerland (the University of Zurich), country-specific web pages for these two countries have been integrated into the platform - especially in the Legal Issues section.

Each year since 2007, the information platform has organised the "Open Access Days" at alternating venues in collaboration with local partners. This event is the key conference on OA and Open Science in the German-speaking area.

With funding from the Ministry of Science, Research and the Arts (MWK) of the State of Baden-Württemberg, the platform underwent a complete technical and substantive overhaul in 2015.

Lesson on OpenRefine for social scientists. A part of the data workflow is preparing the data for analysis. Some of this involves data cleaning, where errors in the data are identifed 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.

About This Document: This manual was assembled and is being updated by Professor Benjamin Le (@benjaminle), who is on the faculty in the Department of Psychology at Haverford College. The primary goal of this text is to provide guidance to his senior thesis students on how to conduct research in his lab by working within general principles that promote research transparency using the specific open science practices described here. While it is aimed at undergraduate psychology students, hopefully it will be of use to other faculty/researchers/students who are interested in adopting open science practices in their labs.

“Open Science” has become a buzzword in academic circles. However, exactly what it means, why you should care about it, and – most importantly – how it can be put into practice is often not very clear to researchers. In this session of the SSDL, we will provide a brief tour d'horizon of Open Science in which we touch on all of these issues and by which we hope to equip you with a basic understanding of Open Science and a practical tool kit to help you make your research more open to other researchers and the larger interested public. Throughout the presentation, we will focus on giving you an overview of tools and services that can help you open up your research workflow and your publications, all the way from enhancing the reproducibility of your research and making it more collaborative to finding outlets which make the results of your work accessible to everyone. Absolutely no prior experience with open science is required to participate in this talk which should lead into an open conversation among us as a community about the best practices we can and should follow for a more open social science.

There is a vast body of helpful tools that can be used in order to foster Open Science practices. For reasons of clarity, this toolbox aims at providing only a selection of links to these resources and tools. Our goal is to give a short overview on possibilities of how to enhance your Open Science practices without consuming too much of your time.

Open Science, the movement to make scientific products and processes accessible to and reusable by all, is about culture and knowledge as much as it is about technologies and services. Convincing researchers of the benefits of changing their practices, and equipping them with the skills and knowledge needed to do so, is hence an important task.This book offers guidance and resources for Open Science instructors and trainers, as well as anyone interested in improving levels of transparency and participation in research practices. Supporting and connecting an emerging Open Science community that wishes to pass on its knowledge, the handbook suggests training activities that can be adapted to various settings and target audiences. The book equips trainers with methods, instructions, exemplary training outlines and inspiration for their own Open Science trainings. It provides Open Science advocates across the globe with practical know-how to deliver Open Science principles to researchers and support staff. What works, what doesn’t? How can you make the most of limited resources? Here you will find a wealth of resources to help you build your own training events.

Open Science is a collection of actions designed to make scientific processes more transparent and results more accessible. Its goal is to build a more replicable and robust science; it does so using new technologies, altering incentives, and changing attitudes. The current movement towards open science was spurred, in part, by a recent “series of unfortunate events” within psychology and other sciences. These events include the large number of studies that have failed to replicate and the prevalence of common research and publication procedures that could explain why. Many journals and funding agencies now encourage, require, or reward some open science practices, including pre-registration, providing full materials, posting data, distinguishing between exploratory and confirmatory analyses, and running replication studies. Individuals can practice and encourage open science in their many roles as researchers, authors, reviewers, editors, teachers, and members of hiring, tenure, promotion, and awards committees. A plethora of resources are available to help scientists, and science, achieve these goals.

Note: This webinar was presented in Spanish. The slides presented during this webinar can be found here:https://osf.io/6qnse/ The slides presented during this seminar can be found here: https://osf.io/6qnse/ Este seminario web se centrará en el estado de la ciencia abierta en América Latina, desde los esfuerzos de los investigadores individuales para abrir sus flujos de trabajo, herramientas para ayudar a los investigadores a ser abiertos y nuevas redes e iniciativas prometedoras en ciencia abierta. Ricardo Hartley (@ametodico) es profesor de metodología de la investigación de la Universidad Central de Chile, investigador en biología de la reproducción y en comunicación - valoración del conocimiento. Organizador de las OpenCon Santiago 2016 y 2017 y embajador COS. Erin McKiernan es profesora del Departamento de Física, Programa de Física Biomédica de la Universidad Nacional Autónoma de México. También es la fundadora del Why Open Research? proyecto, un sitio educativo para que los investigadores aprendan cómo compartir su trabajo, financiado en parte por la Fundación Shuttleworth. Fernan Federici Noe es profesor asistente e investigador de la Universidad Católica de Chile y fellow internacional del OpenPlant Synthetic Biology Center, University of Cambridge. Fernan es miembro del Global For Open Science Hardware (GOSH) y TECNOx (www.tecnox.org).

Open Source Guides (https://opensource.guide/) are a collection of resources for individuals, communities, and companies who want to learn how to run and contribute to an open source project.

Background: Open Source Guides were created and are curated by GitHub, along with input from outside community reviewers, but they are not exclusive to GitHub products. One reason we started this project is because we felt that there weren't enough resources for people creating open source projects.

Our goal is to aggregate community best practices, not what GitHub (or any other individual or entity) thinks is best. Therefore, we try to use examples and quotations from others to illustrate our points.

An ecosystem of free open source tools for improving the rigor and reproducibility of research is thriving. Information professionals at research institutions must stay informed about what tools are available and how they compare. Ideally, information professionals can also onboard researchers to kickstart adoption of these tools. However, developing quality curriculum to train researchers on new tools requires expertise in the tool itself, which leaves many researchers without training on tools that may benefit their research.
This course will train participants to run hands-on, quality modules designed to onboard researchers to four free open source tools. Participants will experience each module, practice the exercises, and explore the training material needed to run the module themselves. An instructor guide that includes the module outline, objectives, description, frequently asked questions, pre- and post-participant surveys, target audience, and instructions for running a successful module is provided for each tool taught.

This course will train participants to run modules on unique aspects of four free open source tools for researchers:

Binder: Share your computational environment, code, and research notebooks.
Renku: Document and share your analysis pipelines.
Open Science Framework: Create a centralized, structured workspace for your research materials.
KnitR: Knit your R code with your analysis narrative in one executable research notebook and capture your dependencies.

Many participants already run short-duration training events at their institutions. This course is ideal for those participants who wish to improve the quality and variety of the training they already offer to researchers. Participants who do not currently run short-duration training events at their institutions will benefit from the course by learning an accessible and efficient way of getting started with these four modules.

In this webinar, we demonstrate the OSF tools available for contributors, labs, centers, and institutions that support stronger collaborations. The demo includes useful practices like: contributor management, the OSF wiki as an electronic lab notebook, using OSF to manage online courses and syllabi, and more. Finally, we look at how OSF Institutions can provide discovery and intelligence gathering infrastructure so that you can focus on conducting and supporting exceptional research. The Center for Open Science’s ongoing mission is to provide community and technical resources to support your commitments to rigorous, transparent research practices. Visit cos.io/institutions to learn more.

This lesson is part of Software Carpentry workshops and teach an introduction to plotting and programming using python. This lesson is an introduction to programming in Python for people with little or no previous programming experience. It uses plotting as its motivating example, and is designed to be used in both Data Carpentry and Software Carpentry workshops. This lesson references JupyterLab, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2.

Computational analyses are playing an increasingly central role in research. Journals, funders, and researchers are calling for published research to include associated data and code. However, many involved in research have not received training in best practices and tools for sharing code and data. This course aims to address this gap in training while also providing those who support researchers with curated best practices guidance and tools.This course is unique compared to other reproducibility courses due to its practical, step-by-step design. It is comprised of hands-on exercises to prepare research code and data for computationally reproducible publication. Although the course starts with some brief introductory information about computational reproducibility, the bulk of the course is guided work with data and code. Participants move through preparing research for reuse, organization, documentation, automation, and submitting their code and data to share. Tools that support reproducibility will be introduced (Code Ocean), but all lessons will be platform agnostic.Level: IntermediateIntended audience: The course is targeted at researchers and research support staff who are involved in the preparation and publication of research materials. Anyone with an interest in reproducible publication is welcome. The course is especially useful for those looking to learn practical steps for improving the computational reproducibility of their own research.