International Love Data Week 2023

[Mathew Abrams]

Today is the first day of International Love Data Week. This year's theme, Data: Agent of change, aims to help new and seasoned data users find data training and other resources that can help move the needle on issues they care about. In honor of this year's theme, I will post community resources available to help push the needle forward in data management in neuroscience. As a first step, I would like to encourage you to listen to discussion that Letisha Wyatt, Jane Roskams, and Maryann Martone had on Dealing with Data (https://neuronline.sfn.org/scientific-research/dealing-with-data) of the Pathways to Rigor: A collection of conversations here on Neuronline where they discuss where we have been, where we are, and where we should aspire to go as a community in regards to data management, sharing, and storage. While this is an older post, I believe that it is worth resurrecting in light of NIH's new data sharing policy. I would love to hear your thoughts about the ideas presented in the conversation--are they feasible, have you used any of the methods presented, are you pro or against? 

[Mathew Abrams]

Happy Valentine's Day! What better way to celebrate than to show your data some LOVE by making it open, FAIR, and citable. Today's tips for data training resources are intended to provide you with a theoretical framework and practical solutions for implementing open and FAIR neuroscience in your research.

Today's tips:

• The Canadian Open Neuroscience Open Science Starter Kit developed by the Canadian Open Neuroscience Platform (CONP) was developed with the aim to provide guidance through the increasingly complex landscape of skills and tools required for open neuroscience research. It is intended for those new to open neuroscience, but I am certain that the seasoned practitioner will find some new nuggets of information as well. The Open Science Starter Kit is composed of 3 tracks: Data Science--tools of the trade, Statistics & Machine Learning, and Standards & Best Practices. Each lecture in the tracks contains a video and description of the content with learning objectives
• Introduction to FAIR Neuroscience developed by the International Neuroinformatics Coordinating Facility (INCF) was developed to help the neuroscience community gain a basic understanding of how to implement the FAIR Guiding Principles in their research. This content is intended for those familiar with the concepts of open neuroscience and is composed of 5 tracks which include: General perspectives on FAIR neuroscience, Standards & Best Practices, FAIR approaches for computational neuroscience, FAIR approaches for neuroimaging, and FAIR approaches for electrophysiology. Each lecture in the tracks contains a video and description of the content with learning objectives
• The BD2K Guide to the Fundamentals of Data Science developed by the Big Data 2 Knowledge (BD2K) Initiative to provides an introduction to the data science underlying biomedical research

Let me know your thoughts. Are there areas not covered that you would like to learn about? Please share any other resources that you may be aware of.

Edited February 14, 2023 by Mathew Abrams

[Mathew Abrams]

In today's installment, I will focus on resources where you can find data and metadata standards. As Maryann stated in the conversation, metadata is what makes data useful. Unfortunately, the standards landscape is a minefield filled with competing, overlapping, and incomplete standards making it difficult for both new and seasoned users to find the appropriate standard for their work. Today's tips attempt to create order in the chaos.  In your quest to find the right standard for your research, I recommend that you start your journey in the INCF resource since its content is neuroscience-specific and provides links to tools and tutorials to help with the implementation process; and if you can find what you are looking for there, move to FAIRsharing which contains a wide array of standards.

Today's tips: 

• INCF Standards and Best Practices Portfolio provides the community with an index of robust, well-validated standards and best practices for neuroscience that adhere and support the FAIR principles. The portfolio provides the community with descriptions of appropriate use cases, links to tools/infrastructures, and implementation tutorials for each standard and best practice indexed. In addition, all endorsed standards and best practices have been vetted by expert and community review
• FAIRsharing, a curated resource on data and metadata standards inter-related to databases and data policies that aims to guide consumers in discovering, selecting, and using its resources with confidence. FAIRsharing contains standards for a wide variety of disciplines including neuroscience.  

If you have any other resource tips, please let us know.

[Mathew Abrams]

Today's installment focuses on infrastructure, specifically on data repositories and scientific gateways for neuroscience research. FAIR requires that the necessary infrastructure in the form of web-accessible repositories is available for publishing research objects: data, code, and workflows; moreover, these repositories should support FAIR and implement basics such as persistent identifiers, programmatic access, and clear licenses. Instead of providing you with an exhaustive list of neuroscience-specific infrastructures that support FAIR, I thought that I would provide you with a list of resources to help you find and select suitable infrastructures for your research. A benefit of the resources listed below is that they all allow community contribution; so if you do not see your preferred infrastructure indexed, you can submit it!

Today's tips:

• Checklist for data repositories and scientific gateways developed by the INCF Infrastructure Committee. It is a living document that is intended to serve as both recommendations for infrastructure developers for setting-up and running a repository or scientific gateway and as a checklist of attributes that should be considered when selecting a repository of scientific gateway for your research  
• The FAIR Infrastructure Portfolio developed by the INCF provides you with guidance in selecting the best infrastructure for your data type, analysis, and sharing needs. The portfolio is intended to help you find the best infrastructure for your particular neuroscience data and offers more detailed neuroscience related metadata (modalities, file format, services offered, etc...) than what is typically available in general repository registries
• FAIRsharing. In addition to the database of standards and best practices, FAIRsharing also maintains a registry for databases that aims to guide users in discovering, selecting, and using its content with confidence. Similar to its database of standards and best practices, it registry of databases covers a wide variety of disciplines including neuroscience

 

Edited February 16, 2023 by Mathew Abrams

[Mathew Abrams]

My final post for this week addresses a need brought up in the conversation: forums where researchers can have conversations and share lessons learned about data management, sharing, and storage. Today's tip is a neuroscience called Neurostars, an open access question and answer site that serves as a forum for knowledge exchange between the neuroscience researchers at all levels of expertise and the community of standards, tools, and infrastructure developers and providers. The service is extremely popular with over 98,000 users in 2022; and importantly, all content is archived, searchable, and does not require a log-in. Importantly, the content is moderated by community moderators. I recommend that you check it out.

Today's Tip:

• Neurostars, an open access question and answer site that serves as a forum for knowledge exchange between the neuroscience researchers at all levels of expertise and the community of standards, tools, and infrastructure developers and providers.

Edited February 17, 2023 by Mathew Abrams