Skip to main content

biology

Mentors and Regional Facilitators
Name Region Skills Interests
diana Trotman CAREERS
Diana Toups Dugas RMACC, SWEETER, Campus Champions
Elie Alhajjar ACCESS CSSN
Jeffrey Weekley Campus Champions
Jason Wells ACCESS CSSN, Campus Champions
Amy Koshoffer Campus Champions
shuai liu ACCESS CSSN
Nicholas Danes Campus Champions, MINES
Nicholas Panchy Campus Champions
Rob Harbert Northeast
Xiaoqin Huang ACCESS CSSN
William Lai ACCESS CSSN
Blog Entries
There are no Blog Entries associated with this topic.

Organizations

"/themes/nect-theme/img/neutral-images/affinity-groups-default.png"

ERN


Website

https://www.ernrp.org

big-data data-access-protocols data-compliance data-management data-analysis

Affinity Groups

There are no Affinity Groups associated with this topic. View All Affinity Groups.

Announcements

Title Date
Ookami Webinar 02/14/24
Open Call: Minisymposia for PASC24 10/05/23

Upcoming Events & Trainings

No events or trainings are currently scheduled.

Topics from Ask.CI

Loading topics from Ask.CI ...

CI Links

Title Category Tags Skill Level
Awesome Jupyter Widgets (for building interactive scientific workflows or science gateway tools) Learning ai, computer-graphics, plotting, visualization, big-data, data-analysis, deep-learning, image-processing, machine-learning, monte-carlo, neural-networks, data-sharing, data-lifecycle, data-management, data-management-software, data-reproducibility, github, astrophysics, data-science, novel-accelerators, computational-chemistry, genomics, materials-science, gravitational-waves, oceanography, particle-physics, physiology, psychology, quantum-computing, quantum-mechanics, biology, ondemand, science-gateway, c++, jupyterhub, python Beginner, Intermediate, Advanced
How the Little Jupyter Notebook Became a Web App: Managing Increasing Complexity with nbdev Learning data-sharing, data-management-software, data-reproducibility, github, workflow, astrophysics, data-science, novel-accelerators, computational-chemistry, genomics, materials-science, gravitational-waves, oceanography, particle-physics, physiology, psychology, quantum-computing, quantum-mechanics, biology, science-gateway, software-carpentry, jupyterhub, programming, python Beginner, Intermediate, Advanced
Research Software Engineering Training Materials Website astrophysics, data-science, novel-accelerators, computational-chemistry, genomics, materials-science, gravitational-waves, oceanography, particle-physics, physiology, psychology, quantum-computing, quantum-mechanics, biology, git, training, workforce-development, programming, programming-best-practices, version-control Beginner, Intermediate, Advanced

Engagements

Run Markov Chain Monte Carlo (MCMC) in Parallel for Evolutionary Study
Texas Tech University

My ongoing project is focused on using species trait value (as data matrices) and its corresponding phylogenetic relationship (as a distance matrix) to reconstruct the evolutionary history of the smoke-induced seed germination trait. The results of this project are expected to increase the predictability of which untested species could benefit from smoke treatment, which could promote germination success of native species in ecological restoration. This computational resources allocated for this project pull from the high-memory partition of our Ivy cluster of HPCC (Centos 8, Slurm 20.11, 1.5 TB memory/node, 20 core /node, 4 node). However, given that I have over 1300 species to analyze, using the maximum amount of resources to speed up the data analysis is a challenge for two reasons: (1) the ancestral state reconstruction (the evolutionary history of plant traits) needs to use the Markov Chain Monte Carlo (MCMC) in Bayesian statistics, which runs more than 10 million steps and, according to experienced evolutionary biologists, could take a traditional single core simulation up 6 months to run; and (2) my data contain over 1300 native species, with about 500 polymorphic points (phylogenetic uncertainty), which would need a large scale of random simulation to give statistical strength. For instance, if I use 100 simulations for each 500 uncertainty points, I would have 50,000 simulated trees. Based on my previous experience with simulations, I could design codes to parallel analyze 50,000 simulated trees but even with this parallelization the long run MCMC will still require 50000 cores to run for up to 6 months. Given this computational and evolutionary research challenge, my current work is focused on discovering a suitable parallelization methods for the MCMC steps. I hope to have some computational experts to discuss my project.

Status: In Progress

People with Expertise

John Acheampong

Cuny SPS

Programs

CAREERS

Roles

student-facilitator

Expertise

Oriana Silva

University of North Texas

Programs

ACCESS CSSN

Roles

student-facilitator

Oriana Silva

Expertise

Timothy Meeker

Programs

ACCESS CSSN

Roles

cssn

Headshot of T.J. Meeker

Expertise

People with Interest

Lenore Martin

University of Rhoide Island

Programs

CAREERS

Roles

researcher/educator

Lenore in office

Interests

Kyle Randall

Programs

ACCESS CSSN

Roles

student-facilitator

NASA Langley Picture

Interests

Sami Muslmani

Programs

ACCESS CSSN

Roles

student-facilitator

Sami Muslmani standing with graduation regalia in his research lab.

Interests