Welcome! If you're reading this, then you've found the repository intended for contributors to The Data Neuroscientist project.
This project is intended to serve as an open-source compilation of and guide through open-access neuroscience publications with available datasets, By assembling these resources in an annotated and curated repository, we aim to provide a modular, data-driven way to develop an academic understanding of neurobiology and the fields of research associated with it to readers the user is a student seeking a general but nearly comprehensive overview of neuroscience or a new member of a specialized research team wanting experience with data from a specific sub-field.
How you use this syllabus and which sections are relevant are entirely up to you and based on what your needs are.
If you're relatively new to neuroscience and want the full survey of the field, I recommend acquiring the publications for each module in this syllabus, all of which should be open-access - please create an issue letting us know if a manuscript is inaccessible, if one doesn't already exist. If we've done our curation correctly, reading and understanding these manuscripts should provide a wide-breadth comprehension of neuroscientific research.
If you are seeking more technical and detailed understanding of a specific domain of neuroscience, e.g. for a new research assistantship or leadership position, then we recommend focusing only on the modules and manuscripts relevant to your purposes. Ideally, each manuscript should be accompanied by the dataset used to perform the analyses and conclusions. You should select which papers are most relevant to your use, read the manuscript thoroughly, especially paying attention to the methods for analyzing the data, and do your best to reproduce the results visualized in the manuscript using the computational tools and scripts at your disposal. In so doing, you'll gain a deeper understanding of how the researchers performed their analyses, what skills are involved in doing this type of research project, and if you're (un)lucky, what flaws and limitations there are to the stated results.
This curriculum is intended to be as accessible as possible to particpants, regardless of where your journey into neuroscience begins. That said, we will assume some experience and comfort with mathematics and software engineering. More specific recommendations will be added here as the final syllabus takes shape. If you feel unprepared for any material and would like guidance on where to find material, please do open an issue and let us know what help you need, after you've read the Code of Conduct.
As this project is in the early stages, there is nothing to install. However, we are trying to develop some simple scripts to acquire the relevant papers and datasets from your local computer. This section will be updated to explain how to use those tools at the appropriate time.
Please read CONTRIBUTING.md for details on the process for submitting pull requests to us.
Please read CODE_OF_CONDUCT.md before engaging with authors and other participants in the project, in order to help us make the project as inclusive and welcoming as possible.
See also the list of contributors who participated in this project.
All original web content is licensed under Creative Commons Attribution (CC-BY) 4.0 International License.
All original software of this project, of which there is currently none, will prospectively be licensed under the MIT License.
In short, I welcome anyone to extract and modify content from this repository and integrate it into a project of their own, as long as attribution is given to this project and, for web resources, a hyperlink or URL to this repository is included.
See the LICENSE.md file for details
- The Mozilla Science Lab, for admitting this project into Round 3 of their Open Project Leadership Program
- Alissa Nedossekina, who mentored me in creating this project
- Kirstie Whitaker, Ph.D., who provided critical feedback early in this project to set its direction
- Clare Corthell, Creator of The Open-Source Data Science Masters, which was one of the inspirations for the form of this project