CyVerse: Cyberinfrastructure for Open Science

Swetnam, Tyson L.; Antin, Parker B.; Bartelme, Ryan; Bucksch, Alexander; Camhy, David; Chism, Greg; Choi, Illyoung; Cooksey, Amanda M.; Cosi, Michele; Cowen, Cindy; Culshaw-Maurer, Michael; Davey, Robert; Davey, Sean; Devisetty, Upendra; Edgin, Tony; Edmonds, Andy; Fedorov, Dmitry; Frady, Jeremy; Fonner, John; Gillan, Jeffrey K.; Hossain, Iqbal; Joyce, Blake; Lang, Konrad; Lee, Tina; Littin, Shelley; Mcewen, Ian; Merchant, Nirav; Micklos, David; Nelson, Andrew; Ramsey, Ashley; Roberts, Sarah; Sarando, Paul; Skidmore, Edwin; Song, Jawon; Sprinkle, Mary Margaret; Srinivasan, Sriram; Strootman, Jonathan D.; Stryeck, Sarah; Tuteja, Reetu; Vaughn, Matthew; Wali, Mojib; Wall, Mariah; Walls, Ramona; Wang, Liya; Wickizer, Todd; Williams, Jason; Wregglesworth, John; Lyons, Eric

doi:10.1101/2023.06.16.545223

Cited by 4 publications

(2 citation statements)

References 62 publications

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“…DIRT/μ is available on GitHub 44 and as a Docker container 45 . PlantIT and CyVerse are easily accessible to researchers without any programming knowledge 46 . Online workshops are available for end users for training through PhenomeForce 47 .…”

Section: Discussionmentioning

confidence: 99%

DIRT/μ – Automated extraction of root hair traits using combinatorial optimization

Pietrzyk,

Phan-Udom,

Chutoe

et al. 2024

Preprint

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Similar to any microscopic appendages, such as cilia or antennae, phenotyping of root hairs has been a challenge due to their complex intersecting arrangements in two-dimensional (2D) images and the technical limitations of automated measurements. Digital Imaging of Root Traits at Microscale (DIRT/μ) addresses this issue by computationally resolving intersections and extracting individual root hairs from 2D microscopy images. This solution enables automatic and precise trait measurements of individual root hairs. DIRT/μ rigorously defines a set of rules to resolve intersecting root hairs and minimizes a newly designed cost function to combinatorically identify each root hair in the microscopy image. As a result, DIRT/μ accurately measures traits such as root hair length (RHL) distribution and root hair density (RHD), which are impractical for manual assessment. We tested DIRT/μ on three datasets to validate its performance and showcase potential applications. By measuring root hair traits in a fraction of the time manual methods require, DIRT/μ eliminates subjective biases from manual measurements. Automating individual root hair extraction accelerates phenotyping and quantifies trait variability within and among plants, creating new possibilities to characterize root hair function and their underlying genetics.

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Section: Discussionmentioning

confidence: 99%

DIRT/μ – Automated extraction of root hair traits using combinatorial optimization

Pietrzyk,

Phan-Udom,

Chutoe

et al. 2024

Preprint

Self Cite

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“…This removes a great deal of burden on the user to figure out and troubleshoot environmental variables, installation of coding packages and other issues that typically increase the barrier to entry to coding. In our course, we utilized NSF-funded Cyverse Discovery Environment platform 26 , which has adopted Jupyterlab and offered free accounts to all the students in the Chemistry course.…”

Section: The Need For Computational Infrastructure That Accommodates ...mentioning

confidence: 99%

Data science for Chemists: Integrating and evaluating the use of interactive digital python notebooks in a large enrollment undergraduate biochemistry course

Brunk,

Shukla,

Hutson

et al. 2023

Preprint

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Genomic sequencing and other big biological data is unquestionably of paramount value, however the success in recruiting highly skilled individuals with diverse backgrounds has been limited. A main reason for this deficiency could be due to the lack of educational resources and early exposure to the field. With the steady increase in big biological data over the past decade1, we not only need to increase the number of skilled researchers in the field but also empower the next generation of students with skills that can apply data analysis skills to a variety of career trajectories23,4. Here, we share a successful example of integrating python-based interactive digital notebooks in a large-enrollment undergraduate chemistry course with more than 400 participants across various degree programs. The goal of this manuscript is to detail the teaching pedagogy, supply the teaching materials, and evaluate the outcomes of integrating coding in a large-enrollment undergraduate chemistry course. The benefit of integrating coding exercises in large-enrollment undergraduate classes is to provide earlier exposure of data science to undergraduate students. Gaining skills in big data analysis will be an asset to any chemist, biologist, physician or scientist, regardless of career path or academic trajectory.

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From Vision to Evaluation: A Metrics Framework for the ACCESS Allocations Service

Hart,

Deems,

Herriott

2024

SN COMPUT. SCI.

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The Allocations Service for the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program is charged with accepting, reviewing, and processing researchers’ requests to use resources that are integrated into the ACCESS ecosystem. We present as a case study the metrics framework used to evaluate the Allocations Service project, a metrics framework that aligns with the project’s goals and identifies key performance indicators (KPIs). Several of our top-level KPIs reflect complex concepts and are composite measures built from suites of metrics compiled from two primary sources: a well-instrumented allocations and accounting system and an annual survey of the ACCESS researcher community. This approach allows us to describe and measure complex concepts such as “democratization” and “ecosystem access time” in a quantitative manner and to target improvements to project activities. The metrics framework is augmented by metrics to measure the performance of the project team, to describe general ecosystem and allocations activity, and to capture publications from the researcher community. We used this framework to gather and present data as part of the ACCESS Allocations Service first annual NSF panel review. The metrics were largely successful at communicating our progress, but we also encountered a few unexpected technical issues with the data and calculations themselves, which we are continuing to refine. Presented here as a case study, this approach to a metrics framework for the Allocations Service has proved valuable in complementing more subjective descriptions of the project, its accomplishments, and progress toward our goals.

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CyVerse: Cyberinfrastructure for Open Science

Cited by 4 publications

References 62 publications

DIRT/μ – Automated extraction of root hair traits using combinatorial optimization

DIRT/μ – Automated extraction of root hair traits using combinatorial optimization

Data science for Chemists: Integrating and evaluating the use of interactive digital python notebooks in a large enrollment undergraduate biochemistry course

From Vision to Evaluation: A Metrics Framework for the ACCESS Allocations Service

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