Greg Kiar, PhD
@gkiar.bsky.social
Research Scientist & Director of the Center for Data Analytics, Innovation, and Rigor @ The Child Mind Institute.
Computational medicine, uncertainty quantification, applied ML, software standardization, and some sports analytics for good measure.
Computational medicine, uncertainty quantification, applied ML, software standardization, and some sports analytics for good measure.
📚 Read the full perspective here: www.nature.com/articles/s41...
... For a deeper dive into how embracing variation can advance neuroimaging research.
#SciComm #NeuroscienceResearch
8/8
... For a deeper dive into how embracing variation can advance neuroimaging research.
#SciComm #NeuroscienceResearch
8/8
Why experimental variation in neuroimaging should be embraced - Nature Communications
Brain imaging analysis lacks accessible ground-truth approaches, leading to varied results across the field. Embracing analytical variability may allow researchers to enhance the generalizability of f...
www.nature.com
November 14, 2024 at 1:04 AM
📚 Read the full perspective here: www.nature.com/articles/s41...
... For a deeper dive into how embracing variation can advance neuroimaging research.
#SciComm #NeuroscienceResearch
8/8
... For a deeper dive into how embracing variation can advance neuroimaging research.
#SciComm #NeuroscienceResearch
8/8
🧠 Overall, we argue for a paradigm shift in neuroimaging research:
Moving beyond mere reproducibility towards replicability, generalizability, and robustness.
7/8
Moving beyond mere reproducibility towards replicability, generalizability, and robustness.
7/8
November 14, 2024 at 1:04 AM
🧠 Overall, we argue for a paradigm shift in neuroimaging research:
Moving beyond mere reproducibility towards replicability, generalizability, and robustness.
7/8
Moving beyond mere reproducibility towards replicability, generalizability, and robustness.
7/8
✅ We’ve made a checklist that we share through the #NMIND website to help researchers incorporate and report variability analysis in their studies.
This simple tool aims to standardize and encourage these practices.
nmind.org/variability-...
6/8
This simple tool aims to standardize and encourage these practices.
nmind.org/variability-...
6/8
Variability Capture Checklist
NMIND (this Neuroimaging Method Is Not Duplicated) is a collaborative dedicated to accelerating scientific discovery in neuroimaging research that was formed in 2020 as a grassroots initiative- resp...
nmind.org
November 14, 2024 at 1:04 AM
✅ We’ve made a checklist that we share through the #NMIND website to help researchers incorporate and report variability analysis in their studies.
This simple tool aims to standardize and encourage these practices.
nmind.org/variability-...
6/8
This simple tool aims to standardize and encourage these practices.
nmind.org/variability-...
6/8
While there are challenges like increased costs and complexity, these can be mitigated through open science practices and shared resources 🤝.
The long-term benefits outweigh the initial hurdles. #CollaborativeScience
5/8
The long-term benefits outweigh the initial hurdles. #CollaborativeScience
5/8
November 14, 2024 at 1:04 AM
While there are challenges like increased costs and complexity, these can be mitigated through open science practices and shared resources 🤝.
The long-term benefits outweigh the initial hurdles. #CollaborativeScience
5/8
The long-term benefits outweigh the initial hurdles. #CollaborativeScience
5/8
💡 Embracing variation offers several benefits:
It improves transparency, enhances generalizability, reduces the risk of p-hacking, and allows for quantification of result stability.
This leads to more robust scientific findings 🦾. #ReproducibleScience
4/8
It improves transparency, enhances generalizability, reduces the risk of p-hacking, and allows for quantification of result stability.
This leads to more robust scientific findings 🦾. #ReproducibleScience
4/8
November 14, 2024 at 1:04 AM
💡 Embracing variation offers several benefits:
It improves transparency, enhances generalizability, reduces the risk of p-hacking, and allows for quantification of result stability.
This leads to more robust scientific findings 🦾. #ReproducibleScience
4/8
It improves transparency, enhances generalizability, reduces the risk of p-hacking, and allows for quantification of result stability.
This leads to more robust scientific findings 🦾. #ReproducibleScience
4/8
🔍 Each of these can significantly impact results, so our perspective is this
As much as possible, we should avoid making these decisions, and instead explore the multiverse-of-methods.
3/8
As much as possible, we should avoid making these decisions, and instead explore the multiverse-of-methods.
3/8
November 14, 2024 at 1:04 AM
🔍 Each of these can significantly impact results, so our perspective is this
As much as possible, we should avoid making these decisions, and instead explore the multiverse-of-methods.
3/8
As much as possible, we should avoid making these decisions, and instead explore the multiverse-of-methods.
3/8
🛠️ When you’re constructing a study, there are many decisions you make:
What data to use, experts to include, analytic options to explore, tools to use, systems to run on, and perturbations/contrasts to introduce.
2/8
What data to use, experts to include, analytic options to explore, tools to use, systems to run on, and perturbations/contrasts to introduce.
2/8
November 14, 2024 at 1:04 AM
🛠️ When you’re constructing a study, there are many decisions you make:
What data to use, experts to include, analytic options to explore, tools to use, systems to run on, and perturbations/contrasts to introduce.
2/8
What data to use, experts to include, analytic options to explore, tools to use, systems to run on, and perturbations/contrasts to introduce.
2/8
🚨New paper alert!🚨
Does the sheer number of available analysis workflows (and their potential for conflicting results!!) keep you up at night?
Our new 📄🔽 in Nature Comms explores how embracing this variability may actually improve generalizability of results.
1/8 🧵
Does the sheer number of available analysis workflows (and their potential for conflicting results!!) keep you up at night?
Our new 📄🔽 in Nature Comms explores how embracing this variability may actually improve generalizability of results.
1/8 🧵
November 14, 2024 at 1:04 AM
🚨New paper alert!🚨
Does the sheer number of available analysis workflows (and their potential for conflicting results!!) keep you up at night?
Our new 📄🔽 in Nature Comms explores how embracing this variability may actually improve generalizability of results.
1/8 🧵
Does the sheer number of available analysis workflows (and their potential for conflicting results!!) keep you up at night?
Our new 📄🔽 in Nature Comms explores how embracing this variability may actually improve generalizability of results.
1/8 🧵