This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.
This video gives a short introduction to the EBRAINS data sharing platform, why it was developed, and how it contributes to open data sharing.
This video introduces the key principles for data organization and explains how you could make your data FAIR for data sharing on EBRAINS.
This video introduces the importance of writing a Data Descriptor to accompany your dataset on EBRAINS. It gives concrete examples on what information to include and highlights how this makes your data more FAIR.
This video demonstrates how to find, access, and download data on EBRAINS.
KnowledgeSpace (KS) is a data discoverability portal and neuroscience encyclopedia that was developed to make it easier for the neuroscience community to find publicly available datasets that adhere to the FAIR Principles and to provide an integrated view of neuroscience concepts found in Wikipedia and NeuroLex linked with PubMed and 17 of the world's leading neuroscience repositories. In short, KS provides a single point of entry where reseaerchers can search for a neuroscience concept of interest and receive results that include: i. a description of the term found in Wikipedia/NeuroLex, ii. links to publicly available datasets related to the concept of interest, and iii. up-to-date references that support the concept of interests found in PubMed. APIs are available so that developers of other neuroscience research infrastructures can integrate KS components in their infrastructures. If your repository or your favorite repository is not indexed in KS, please contact us.
This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics.
In this third and final hands-on tutorial from the Research Workflows for Collaborative Neuroscience workshop, you will learn about workflow orchestration using open source tools like DataJoint and Flyte.
In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks.
This video will document the process of uploading data into a brainlife project using ezBIDS.
This short video walks you through the steps of publishing a dataset on brainlife, an open-source, free and secure reproducible neuroscience analysis platform.
This video will document the process of visualizing the provenance of each step performed to generate a data object on brainlife.
This video will document the process of downloading and running the "reproduce.sh" script, which will automatically run all of the steps to generate a data object locally on a user's machine.
This video will document the process of creating a pipeline rule for batch processing on brainlife.
This short video shows how a brainlife.io publication can be opened from the Data Deposition page of the journal Nature Scientific Data.
In this tutorial, you will learn the basic features of uploading and versioning your data within OpenNeuro.org.
This tutorial shows how to share your data in OpenNeuro.org.
Following the previous two tutorials on uploading and sharing data with OpenNeuro.org, this tutorial briefly covers how to run various analyses on your datasets.
This video explains what metadata is, why it is important, and how you can organize your metadata to increase the FAIRness of your data on EBRAINS.
This lesson provides a hands-on tutorial for generating simulated brain data within the EBRAINS ecosystem.