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1. medna-metadata: an open-source data management system for tracking environmental DNA samples and metadata

   https://doi.org/10.1093/bioinformatics/btac556  

   Kimble, M. ; Allers, S. ; Campbell, K. ; Chen, C. ; Jackson, L. M. ; King, B. L. ; Silverbrand, S. ; York, G. ; Beard, K. ; Robinson, ed., Peter ( August 2022 , Bioinformatics)

   Environmental DNA (eDNA), as a rapidly expanding research field, stands to benefit from shared resources including sampling protocols, study designs, discovered sequences, and taxonomic assignments to sequences. High-quality community shareable eDNA resources rely heavily on comprehensive metadata documentation that captures the complex workflows covering field sampling, molecular biology lab work, and bioinformatic analyses. There are limited sources that provide documentation of database development on comprehensive metadata for eDNA and these workflows and no open-source software.

   We present medna-metadata, an open-source, modular system that aligns with Findable, Accessible, Interoperable, and Reusable guiding principles that support scholarly data reuse and the database and application development of a standardized metadata collection structure that encapsulates critical aspects of field data collection, wet lab processing, and bioinformatic analysis. Medna-metadata is showcased with metabarcoding data from the Gulf of Maine (Polinski et al., 2019).

   The source code of the medna-metadata web application is hosted on GitHub (https://github.com/Maine-eDNA/medna-metadata). Medna-metadata is a docker-compose installable package. Documentation can be found at https://medna-metadata.readthedocs.io/en/latest/?badge=latest. The application is implemented in Python, PostgreSQL and PostGIS, RabbitMQ, and NGINX, with all major browsers supported. A demo can be found at https://demo.metadata.maine-edna.org/.

   Supplementary data are available at Bioinformatics online.

    

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2. Vivarium: an interface and engine for integrative multiscale modeling in computational biology

   https://doi.org/10.1093/bioinformatics/btac049  

   Agmon, Eran ; Spangler, Ryan K. ; Skalnik, Christopher J. ; Poole, William ; Peirce, Shayn M. ; Morrison, Jerry H. ; Covert, Markus W. ; Valencia, ed., Alfonso ( February 2022 , Bioinformatics)

   This article introduces Vivarium—software born of the idea that it should be as easy as possible for computational biologists to define any imaginable mechanistic model, combine it with existing models and execute them together as an integrated multiscale model. Integrative multiscale modeling confronts the complexity of biology by combining heterogeneous datasets and diverse modeling strategies into unified representations. These integrated models are then run to simulate how the hypothesized mechanisms operate as a whole. But building such models has been a labor-intensive process that requires many contributors, and they are still primarily developed on a case-by-case basis with each project starting anew. New software tools that streamline the integrative modeling effort and facilitate collaboration are therefore essential for future computational biologists.

   Vivarium is a software tool for building integrative multiscale models. It provides an interface that makes individual models into modules that can be wired together in large composite models, parallelized across multiple CPUs and run with Vivarium’s discrete-event simulation engine. Vivarium’s utility is demonstrated by building composite models that combine several modeling frameworks: agent-based models, ordinary differential equations, stochastic reaction systems, constraint-based models, solid-body physics and spatial diffusion. This demonstrates just the beginning of what is possible—Vivarium will be able to support future efforts that integrate many more types of models and at many more biological scales.

   The specific models, simulation pipelines and notebooks developed for this article are all available at the vivarium-notebooks repository: https://github.com/vivarium-collective/vivarium-notebooks. Vivarium-core is available at https://github.com/vivarium-collective/vivarium-core, and has been released on Python Package Index. The Vivarium Collective (https://vivarium-collective.github.io) is a repository of freely available Vivarium processes and composites, including the processes used in Section 3. Supplementary Materials provide with an extensive methodology section, with several code listings that demonstrate the basic interfaces.

   Supplementary data are available at Bioinformatics online.

    

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3. Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning–driven data analysis

   https://doi.org/10.1093/gigascience/giad111  

   Akshay, Akshay ; Katoch, Mitali ; Shekarchizadeh, Navid ; Abedi, Masoud ; Sharma, Ankush ; Burkhard, Fiona C. ; Adam, Rosalyn M. ; Monastyrskaya, Katia ; Gheinani, Ali Hashemi ( January 2024 , GigaScience)

   Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance.

   To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating 4 essential functionalities—namely, Data Exploration, AutoML, CustomML, and Visualization—MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on 6 distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme’s feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations.

   MLme serves as a valuable resource for leveraging ML to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

    

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4. Opportunities for enhancing MLCommons efforts while leveraging insights from educational MLCommons earthquake benchmarks efforts

   https://doi.org/10.3389/fhpcp.2023.1233877  

   von Laszewski, Gregor ; Fleischer, J. P. ; Knuuti, Robert ; Fox, Geoffrey C. ; Kolessar, Jake ; Butler, Thomas S. ; Fox, Judy ( October 2023 , Frontiers in High Performance Computing)

   MLCommons is an effort to develop and improve the artificial intelligence (AI) ecosystem through benchmarks, public data sets, and research. It consists of members from start-ups, leading companies, academics, and non-profits from around the world. The goal is to make machine learning better for everyone. In order to increase participation by others, educational institutions provide valuable opportunities for engagement. In this article, we identify numerous insights obtained from different viewpoints as part of efforts to utilize high-performance computing (HPC) big data systems in existing education while developing and conducting science benchmarks for earthquake prediction. As this activity was conducted across multiple educational efforts, we project if and how it is possible to make such efforts available on a wider scale. This includes the integration of sophisticated benchmarks into courses and research activities at universities, exposing the students and researchers to topics that are otherwise typically not sufficiently covered in current course curricula as we witnessed from our practical experience across multiple organizations. As such, we have outlined the many lessons we learned throughout these efforts, culminating in the need forbenchmark carpentryfor scientists using advanced computational resources. The article also presents the analysis of an earthquake prediction code benchmark while focusing on the accuracy of the results and not only on the runtime; notedly, this benchmark was created as a result of our lessons learned. Energy traces were produced throughout these benchmarks, which are vital to analyzing the power expenditure within HPC environments. Additionally, one of the insights is that in the short time of the project with limited student availability, the activity was only possible by utilizing a benchmark runtime pipeline while developing and using software to generate jobs from the permutation of hyperparameters automatically. It integrates a templated job management framework for executing tasks and experiments based on hyperparameters while leveraging hybrid compute resources available at different institutions. The software is part of a collection calledcloudmeshwith its newly developed components, cloudmesh-ee (experiment executor) and cloudmesh-cc (compute coordinator).

    

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5. Workshops for Building the Mechatronics and Robotics Engineering Education Community

   Gennert, M. ; Lotfi, N. ; Mynderse, J. ; Jethwani, M. ; Kapila, V. ( June 2020 , ASEE annual conference exposition proceedings)

   Intelligent Autonomous Systems, including Intelligent Manufacturing & Automation and Industry 4.0, have immense potential to improve human health, safety, and welfare. Engineering these systems requires an interdisciplinary knowledge of mechanical, electrical, computer, software, and systems engineering throughout the design and development process. Mechatronics and Robotics Engineering (MRE) is emerging as a discipline that can provide the broad inter-disciplinary technical and professional skill sets that are critical to fulfill the research and development needs for these advanced systems. Despite experiencing tremendous, dynamic growth, MRE lacks a settled-on and agreed-upon body-of-knowledge, leading to unmet needs for standardized curricula, courses, laboratory platforms, and accreditation criteria, resulting in missed career opportunities for individuals and missed economic opportunities for industry. There have been many educational efforts around MRE, including courses, minors, and degree programs, but they have not been well integrated or widely adopted, especially in USA. To enable MRE to coalesce as a distinct and identifiable engineering field, the authors conducted four workshops on the Future of Mechatronics and Robotics Engineering (FoMRE) education at the bachelor’s degree level. The overall goal of the workshops was to improve the quality of undergraduate MRE education and to ease the adoption of teaching materials to prepare graduates with a blend of theoretical knowledge and practical hands-on skills. To realize this goal, the specific objectives were to generate enthusiasm and a sense of community among current and future MRE educators, promote diversity and inclusivity within the MRE community, identify thought leaders, and seek feedback from the community to serve as a foundation for future activities. The workshops were intended to benefit a wide range of participants including educators currently teaching or developing programs in MRE, PhD students seeking academic careers in MRE, and industry professionals desiring to shape the future workforce. Workshop activities included short presentations on sample MRE programs, breakout sessions on specific topics, and open discussion sessions. As a result of these workshops, the MRE educational community has been enlarged and engaged, with members actively contributing to the scholarship of teaching and learning. This paper presents the workshops’ formats, outcomes, results of participant surveys, and their analyses. A major outcome was identifying concept, skill, and experience inventories organized around the dimensions of foundational/practical/applications and student preparation/MRE knowledgebase. Particular attention is given to the extent to which the workshops realized the project goals, including attendee demographics, changes in participant attitudes, and development of the MRE community. The paper concludes with a summary of lessons learned and a call for future activities to shape the field. 

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