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      TAME 2.0: expanding and improving online data science training for environmental health research

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          Abstract

          Introduction

          Data science training has the potential to propel environmental health research efforts into territories that remain untapped and holds immense promise to change our understanding of human health and the environment. Though data science training resources are expanding, they are still limited in terms of public accessibility, user friendliness, breadth of content, tangibility through real-world examples, and applicability to the field of environmental health science.

          Methods

          To fill this gap, we developed an environmental health data science training resource, the inTelligence And Machine lEarning (TAME) Toolkit, version 2.0 (TAME 2.0).

          Results

          TAME 2.0 is a publicly available website that includes training modules organized into seven chapters. Training topics were prioritized based upon ongoing engagement with trainees, professional colleague feedback, and emerging topics in the field of environmental health research (e.g., artificial intelligence and machine learning). TAME 2.0 is a significant expansion upon the original TAME training resource pilot. TAME 2.0 specifically includes training organized into the following chapters: (1) Data management to enable scientific collaborations; (2) Coding in R; (3) Basics of data analysis and visualizations; (4) Converting wet lab data into dry lab analyses; (5) Machine learning; (6) Applications in toxicology and exposure science; and (7) Environmental health database mining. Also new to TAME 2.0 are “Test Your Knowledge” activities at the end of each training module, in which participants are asked additional module-specific questions about the example datasets and apply skills introduced in the module to answer them. TAME 2.0 effectiveness was evaluated via participant surveys during graduate-level workshops and coursework, as well as undergraduate-level summer research training events, and suggested edits were incorporated while overall metrics of effectiveness were quantified.

          Discussion

          Collectively, TAME 2.0 now serves as a valuable resource to address the growing demand of increased data science training in environmental health research. TAME 2.0 is publicly available at: https://uncsrp.github.io/TAME2/.

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          Most cited references18

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            Comparative Toxicogenomics Database (CTD): update 2021

            Abstract The public Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is an innovative digital ecosystem that relates toxicological information for chemicals, genes, phenotypes, diseases, and exposures to advance understanding about human health. Literature-based, manually curated interactions are integrated to create a knowledgebase that harmonizes cross-species heterogeneous data for chemical exposures and their biological repercussions. In this biennial update, we report a 20% increase in CTD curated content and now provide 45 million toxicogenomic relationships for over 16 300 chemicals, 51 300 genes, 5500 phenotypes, 7200 diseases and 163 000 exposure events, from 600 comparative species. Furthermore, we increase the functionality of chemical–phenotype content with new data-tabs on CTD Disease pages (to help fill in knowledge gaps for environmental health) and new phenotype search parameters (for Batch Query and Venn analysis tools). As well, we introduce new CTD Anatomy pages that allow users to uniquely explore and analyze chemical–phenotype interactions from an anatomical perspective. Finally, we have enhanced CTD Chemical pages with new literature-based chemical synonyms (to improve querying) and added 1600 amino acid-based compounds (to increase chemical landscape). Together, these updates continue to augment CTD as a powerful resource for generating testable hypotheses about the etiologies and molecular mechanisms underlying environmentally influenced diseases.
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              The CompTox Chemistry Dashboard: a community data resource for environmental chemistry

              Despite an abundance of online databases providing access to chemical data, there is increasing demand for high-quality, structure-curated, open data to meet the various needs of the environmental sciences and computational toxicology communities. The U.S. Environmental Protection Agency’s (EPA) web-based CompTox Chemistry Dashboard is addressing these needs by integrating diverse types of relevant domain data through a cheminformatics layer, built upon a database of curated substances linked to chemical structures. These data include physicochemical, environmental fate and transport, exposure, usage, in vivo toxicity, and in vitro bioassay data, surfaced through an integration hub with link-outs to additional EPA data and public domain online resources. Batch searching allows for direct chemical identifier (ID) mapping and downloading of multiple data streams in several different formats. This facilitates fast access to available structure, property, toxicity, and bioassay data for collections of chemicals (hundreds to thousands at a time). Advanced search capabilities are available to support, for example, non-targeted analysis and identification of chemicals using mass spectrometry. The contents of the chemistry database, presently containing ~ 760,000 substances, are available as public domain data for download. The chemistry content underpinning the Dashboard has been aggregated over the past 15 years by both manual and auto-curation techniques within EPA’s DSSTox project. DSSTox chemical content is subject to strict quality controls to enforce consistency among chemical substance-structure identifiers, as well as list curation review to ensure accurate linkages of DSSTox substances to chemical lists and associated data. The Dashboard, publicly launched in April 2016, has expanded considerably in content and user traffic over the past year. It is continuously evolving with the growth of DSSTox into high-interest or data-rich domains of interest to EPA, such as chemicals on the Toxic Substances Control Act listing, while providing the user community with a flexible and dynamic web-based platform for integration, processing, visualization and delivery of data and resources. The Dashboard provides support for a broad array of research and regulatory programs across the worldwide community of toxicologists and environmental scientists. Electronic supplementary material The online version of this article (10.1186/s13321-017-0247-6) contains supplementary material, which is available to authorized users.
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                Author and article information

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                Journal
                Front Toxicol
                Front Toxicol
                Front. Toxicol.
                Frontiers in Toxicology
                Frontiers Media S.A.
                2673-3080
                12 February 2025
                2025
                : 7
                : 1535098
                Affiliations
                [1] 1 Department of Environmental Sciences and Engineering , Gillings School of Global Public Health , The University of North Carolina at Chapel Hill , Chapel Hill, NC, United States
                [2] 2 Center for Environmental Medicine, Asthma and Lung Biology , School of Medicine , University of North Carolina , Chapel Hill, NC, United States
                [3] 3 The Institute for Environmental Health Solutions , Gillings School of Global Public Health , The University of North Carolina at Chapel Hill , Chapel Hill, NC, United States
                [4] 4 Curriculum in Toxicology and Environmental Medicine , School of Medicine , University of North Carolina , Chapel Hill, NC, United States
                [5] 5 Renaissance Computing Institute , The University of North Carolina at Chapel Hill , Chapel Hill, NC, United States
                [6] 6 School of Data Science and Society , University of North Carolina , Chapel Hill, NC, United States
                [7] 7 Center for Computational Toxicology and Exposure , US Environmental Protection Agency , Durham, NC, United States
                [8] 8 Division of Translational Toxicology , National Institute of Environmental Health Sciences , Durham, NC, United States
                [9] 9 Center for Public Health and Environmental Assessment , US Environmental Protection Agency , Chapel Hill, NC, United States
                [10] 10 Department of Pediatrics , School of Medicine , University of North Carolina , Chapel Hill, NC, United States
                Author notes

                Edited by: Qiang Zhang, Emory University, United States

                Reviewed by: Dongying Li, National Center for Toxicological Research (FDA), United States

                Patra Volarath, United States Food and Drug Administration, United States

                Todd Everson, Emory University, United States

                *Correspondence: Julia E. Rager, jrager@ 123456unc.edu
                [ † ]

                These authors have contributed equally to this work and share first authorship

                Article
                1535098
                10.3389/ftox.2025.1535098
                11860945
                40012886
                f6a2fc32-a7fc-4a8e-9378-9bfa52782e3c
                Copyright © 2025 Payton, Hickman, Chappel, Roell, Koval, Eaves, Chou, Spring, Miller, Avenbuan, Boyles, Kruse, Rider, Patlewicz, Ring, Ward-Caviness, Reif, Jaspers, Fry and Rager.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 26 November 2024
                : 13 January 2025
                Funding
                Funded by: National Institute of Environmental Health Sciences , doi 10.13039/100000066;
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by grants from the National Institutes of Health (NIH) National Institute of Environmental Health Sciences (NIEHS) [T32 ES007126 (PI: IJ, Trainees: EH/SM); F32 ES036096 (EH); R01 ES035878 (JR); P42 ES031007 (RF); T32 ES007018 (Trainee: AS)].
                Categories
                Toxicology
                Original Research
                Custom metadata
                Computational Toxicology and Informatics

                coding,computational toxicology,data science,data visualizations,exposure science,health research,machine learning,training

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