Special Issue "Data Science for Environment and Health Applications"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Alireza Daneshkhah
E-Mail Website
Guest Editor
School of Computing, Electronics and Mathematics, Faculty of Engineering, Environment and Computing, Coventry University, Coventry, CV1 5FB, UK
Interests: Bayesian statistics; modelling big data using Bayesian networks; probabilistic sensitivity analysis; Bayesian uncertainty quantification; deep learning methods; health economic; expert elicitation; modelling and forecasting extreme climatic events; machine learning methods in health science applications
Dr. Amin Hosseinian-Far
E-Mail Website
Guest Editor
Centre for Sustainable Business Practices, University of Northampton, NN1 5PH, UK
Interests: Sustainable Development Goals (SDG); applied systems analysis; Sustainability Impact Assessment (SIA); systemic sustainability; resilience; causal modelling; AI policy
Prof. Dr. Vasile Palade
E-Mail Website
Guest Editor
Centre for Data Science, Coventry University, Coventry CV1 5FB, UK
Interests: machine learning and applications; deep learning; image processing; autonomous cars and smart cities
Special Issues, Collections and Topics in MDPI journals
Dr. Samer A. Kharroubi
E-Mail Website
Guest Editor
1. Department of Nutrition and Food Sciences, American University of Beirut, Beirut, Lebanon;
2. School of Health and Related Research, the University of Sheffield, Sheffield, UK
Interests: Bayesian statistics; health economics; statistical modelling; health related quality of life; Bayesian modeling of health state preferences

Special Issue Information

Dear Colleagues,

Data science and analytics is a growing academic discipline, and has applications in numerous fields, including environmental science and health-related research. The significant advances in data capture, storage and analytic technologies have given rise to immense data augmentation. Inadvertently, this has resulted in the requirement of low-cost and computationally efficient techniques which are needed to analyse data in order to provide pertinent and purposeful insights. These insights inform policy making, organisational practices, future research trajectories and, most importantly, the sustainability of our societies in terms of our health and environment. This Special Issue concentrates on state-of-the-art data science techniques, practices and applications within this field. Manuscripts are welcome for this Special Issue with the focus placed on the latest advances of data analytics methods that address the research challenges in the environmental science and public health fields. To date, plenty of research is conducted in this field and relates, for example, to the choice of the technique, methodological development, ability to capture specific healthcare informatics, etc. Particular emphasis is placed on complexity, spatial and temporal reasoning and managing uncertainty. The scope of this Special Issue includes but is not limited to the following key areas:

  • Data science application in public healthcare informatics;
  • Methods, techniques in data collecting for public healthcare;
  • Health economic case studies; health-related quality of life;
  • Medical and clinical data analysis case studies;
  • Machine learning methods in health science;
  • Deep learning methods with applications in health science;
  • Intelligent medical diagnosis;
  • Applications of AI in healthcare;
  • Medical information systems;
  • Smart healthcare systems;
  • Coastal flooding and erosion;
  • Quantifying and modelling wildfire risk;
  • Mangrove forest resilience;
  • Copula models in modelling extreme climatic events;
  • Approximation of the impacts of environmental changes on public health;
  • Sustainability and resilience modelling and simulation;
  • Estimations in food systems and environmental capacity;
  • Systematic review and meta-analysis studies in environmental science and public health;
  • Emerging data science techniques and technologies for environmental science and public health research;
  • Bootstrapping and Monte Carlo simulations for risk prediction;
  • Statistical/epidemiological modelling of disease risk;
  • Health hazards of environmental pollution and degradation.

Dr. Alireza Daneshkhah
Dr. Amin Hosseinian-Far
Prof. Dr. Vasile Palade
Dr. Samer A. Kharroubi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • health economic case studies
  • health-related quality of life
  • data science application in public healthcare informatics
  • medical and clinical data analysis case studies
  • machine learning methods in health science
  • intelligent medical diagnosis and smart healthcare systems
  • quantifying and modelling extreme climatic events
  • approximation of the impacts of environmental changes on public health
  • sustainability and resilience modelling and simulation
  • systematic review and meta-analysis studies in environmental science and public health
  • statistical/epidemiological modelling of disease risk

Published Papers (11 papers)

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Research

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Article
Validation of an Arabic Version of the Self-Efficacy for Appropriate Medication Use Scale
Int. J. Environ. Res. Public Health 2021, 18(22), 11983; https://doi.org/10.3390/ijerph182211983 - 15 Nov 2021
Viewed by 408
Abstract
Background: Medication adherence is essential for optimal treatment outcomes in patients with chronic diseases. Medication nonadherence compromises patient clinical outcomes and patient safety as well as leading to an increase in unnecessary direct and indirect medical costs. Therefore, early identification of non-adherence by [...] Read more.
Background: Medication adherence is essential for optimal treatment outcomes in patients with chronic diseases. Medication nonadherence compromises patient clinical outcomes and patient safety as well as leading to an increase in unnecessary direct and indirect medical costs. Therefore, early identification of non-adherence by healthcare professionals using medication adherence scales should help in preventing poor clinical outcomes among patients with chronic health conditions, such as diabetes and hypertension. Unfortunately, there are very few validated medication adherence assessment scales in Arabic. Thus, the aim of this study was to validate a newly translated Arabic version of the Self-Efficacy for Appropriate Medication Use Scale (SEAMS) among patients with chronic diseases. Methods: In this single-center cross-sectional study that was conducted between March 2019 and March 2021 at the primary care clinics of King Saud University Medical City (KSUMC) in Riyadh, Saudi Arabia, the English version of SEAMS was translated to Arabic using the forward–backward method and piloted among 22 adults (≥18 yrs.) with chronic diseases. The reliability of the newly translated scale was examined using the test–retest and Cronbach’s alpha methods. Exploratory and confirmatory factor analyses were conducted to examine the construct validity of the Arabic version of SEAMS. Results: The number of patients who consented to participate and filled out the questionnaire was 202. Most of the participants were males (69.9%), aged ≥50 years (65.2%), and had diabetes (96.53%). The 13-item Arabic-translated SEAMS mean score was 32.37 ± 5.31, and the scale showed acceptable internal consistency (Cronbach’s alpha = 0.886) and reliability (Intraclass correlation coefficient = 0.98). Total variance of the 13-item Arabic-SEAMS could be explained by two factors as confirmed by the factor analysis. Conclusion: The Arabic version of SEAMS should help in detecting poor self-efficacy for medication adherence among Arabic-speaking patient populations with chronic diseases, such as diabetes and hypertension. Future studies should examine its validity among more diverse patient populations in different Arabic-speaking countries. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
Article
Driver, Collision and Meteorological Characteristics of Motor Vehicle Collisions among Road Trauma Survivors
Int. J. Environ. Res. Public Health 2021, 18(21), 11380; https://doi.org/10.3390/ijerph182111380 - 29 Oct 2021
Viewed by 454
Abstract
Road trauma remains a significant public health problem. We aimed to identify sub-groups of motor vehicle collisions in Victoria, Australia, and the association between collision characteristics and outcomes up to 24 months post-injury. Data were extracted from the Victorian State Trauma Registry for [...] Read more.
Road trauma remains a significant public health problem. We aimed to identify sub-groups of motor vehicle collisions in Victoria, Australia, and the association between collision characteristics and outcomes up to 24 months post-injury. Data were extracted from the Victorian State Trauma Registry for injured drivers aged ≥16 years, from 2010 to 2016, with a compensation claim who survived ≥12 months post-injury. People with intentional or severe head injury were excluded, resulting in 2735 cases. Latent class analysis was used to identify collision classes for driver fault and blood alcohol concentration (BAC), day and time of collision, weather conditions, single vs. multi-vehicle and regional vs. metropolitan injury location. Five classes were identified: (1) daytime multi-vehicle collisions, no other at fault; (2) daytime single-vehicle predominantly weekday collisions; (3) evening single-vehicle collisions, no other at fault, 36% with BAC ≥ 0.05; (4) sunrise or sunset weekday collisions; and (5) dusk and evening multi-vehicle in metropolitan areas with BAC < 0.05. Mixed linear and logistic regression analyses examined associations between collision class and return to work, health (EQ-5D-3L summary score) and independent function Glasgow Outcome Scale - Extended at 6, 12 and 24 months. After adjusting for demographic, health and injury characteristics, collision class was not associated with outcomes. Rather, risk of poor outcomes was associated with age, sex and socioeconomic disadvantage, education, pre-injury health and injury severity. People at risk of poor recovery may be identified from factors available during the hospital admission and may benefit from clinical assessment and targeted referrals and treatments. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
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Article
COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare
Int. J. Environ. Res. Public Health 2021, 18(21), 11086; https://doi.org/10.3390/ijerph182111086 - 21 Oct 2021
Viewed by 672
Abstract
In the recent pandemic, accurate and rapid testing of patients remained a critical task in the diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of the sudden increase in cases, most countries have faced scarcity and a low rate [...] Read more.
In the recent pandemic, accurate and rapid testing of patients remained a critical task in the diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of the sudden increase in cases, most countries have faced scarcity and a low rate of testing. Chest X-rays have been shown in the literature to be a potential source of testing for COVID-19 patients, but manually checking X-ray reports is time-consuming and error-prone. Considering these limitations and the advancements in data science, we proposed a Vision Transformer-based deep learning pipeline for COVID-19 detection from chest X-ray-based imaging. Due to the lack of large data sets, we collected data from three open-source data sets of chest X-ray images and aggregated them to form a 30 K image data set, which is the largest publicly available collection of chest X-ray images in this domain to our knowledge. Our proposed transformer model effectively differentiates COVID-19 from normal chest X-rays with an accuracy of 98% along with an AUC score of 99% in the binary classification task. It distinguishes COVID-19, normal, and pneumonia patient’s X-rays with an accuracy of 92% and AUC score of 98% in the Multi-class classification task. For evaluation on our data set, we fine-tuned some of the widely used models in literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, and DenseNet-121, as baselines. Our proposed transformer model outperformed them in terms of all metrics. In addition, a Grad-CAM based visualization is created which makes our approach interpretable by radiologists and can be used to monitor the progression of the disease in the affected lungs, assisting healthcare. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
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Article
Item Analysis of the Czech Version of the WJ IV COG Battery from a Group of Romani Children
Int. J. Environ. Res. Public Health 2021, 18(19), 10518; https://doi.org/10.3390/ijerph181910518 - 07 Oct 2021
Viewed by 443
Abstract
The objective of the article is to present an item analysis of selected subtests of the Czech version of the WJ IV COG battery from a group of Romani children, ages 7–11. The research sample consisted of 400 school-aged Romani children from the [...] Read more.
The objective of the article is to present an item analysis of selected subtests of the Czech version of the WJ IV COG battery from a group of Romani children, ages 7–11. The research sample consisted of 400 school-aged Romani children from the Czech Republic who were selected by quota sampling. A partial comparative sample for the analysis was the Czech population collected as norms of the Czech edition of © Propsyco (n = 936). The Woodcock–Johnson IV COG was used as a research tool. Statistical analysis was performed in Winstep software using Differential Item Functioning; differences between groups were expressed in logits and tested via the Rasch–Welch T-test. It was discovered that higher item difficulty was noted in the verbal subtests, although variability in item difficulty was found across all subtests. The analysis of individual items makes it possible to discover which tasks are most culturally influenced. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
Article
Modeling SF-6D Health Utilities: Is Bayesian Approach Appropriate?
Int. J. Environ. Res. Public Health 2021, 18(16), 8409; https://doi.org/10.3390/ijerph18168409 - 09 Aug 2021
Viewed by 695
Abstract
Background: Valuation studies of preference-based health measures like SF6D have been conducted in many countries. However, the cost of conducting such studies in countries with small populations or low- and middle-income countries (LMICs) can be prohibitive. There is potential to use results from [...] Read more.
Background: Valuation studies of preference-based health measures like SF6D have been conducted in many countries. However, the cost of conducting such studies in countries with small populations or low- and middle-income countries (LMICs) can be prohibitive. There is potential to use results from readily available countries’ valuations to produce better valuation estimates. Methods: Data from Lebanon and UK SF-6D value sets were analyzed, where values for 49 and 249 health states were extracted from samples of Lebanon and UK populations, respectively, using standard gamble techniques. A nonparametric Bayesian model was used to estimate a Lebanon value set using the UK data as informative priors. The resulting estimates were then compared to a Lebanon value set obtained using Lebanon data by itself via various prediction criterions. Results: The findings permit the UK evidence to contribute potential prior information to the Lebanon analysis by producing more precise valuation estimates than analyzing Lebanon data only under all criterions used. Conclusions: The positive findings suggest that existing valuation studies can be merged with a small valuation set in another country to produce value sets, thereby making own country value sets more attainable for LMICs. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
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Article
Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks
Int. J. Environ. Res. Public Health 2021, 18(14), 7467; https://doi.org/10.3390/ijerph18147467 - 13 Jul 2021
Viewed by 877
Abstract
The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method [...] Read more.
The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
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Article
Systemic Lupus Erythematosus Research: A Bibliometric Analysis over a 50-Year Period
Int. J. Environ. Res. Public Health 2021, 18(13), 7095; https://doi.org/10.3390/ijerph18137095 - 02 Jul 2021
Viewed by 875
Abstract
Bibliometric analysis is a well-established approach to quantitatively assess scholarly productivity. However, there have been few assessments of research productivity on systemic lupus erythematosus (SLE) to date. The aim of this study was to analyze global research productivity through original articles published in [...] Read more.
Bibliometric analysis is a well-established approach to quantitatively assess scholarly productivity. However, there have been few assessments of research productivity on systemic lupus erythematosus (SLE) to date. The aim of this study was to analyze global research productivity through original articles published in journals indexed by the Web of Science from 1971 to 2020. Bibliometric data was obtained from the Science Citation Index Expanded in the Web of Science Core Collection database. Only original articles published between 1971 and 2020 on SLE were included in the analysis. Over the 50-year period, publication production in SLE research has steadily increased with a mean annual growth rate of 8.0%. A total of 44,967 articles published in 3435 different journals were identified. The journal Lupus published the largest number of articles (n = 3371; 8.0%). A total of 148 countries and regions contributed to the articles. The global productivity ranking was led by the United States (n = 11,244, 25.0%), followed by China (n = 4893, 10.9%). A three-field plot showed that the Oklahoma Medical Research Foundation and the Johns Hopkins University together contributed 18.5% of all articles from the United States. A co-occurrence network analysis revealed five highly connected clusters of SLE research. In conclusion, this bibliometric analysis provided a comprehensive overview of the status of SLE research, which could enable a better understanding of the development in this field in the past 50 years. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
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Article
Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients
Int. J. Environ. Res. Public Health 2021, 18(12), 6228; https://doi.org/10.3390/ijerph18126228 - 09 Jun 2021
Cited by 5 | Viewed by 1769
Abstract
Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive [...] Read more.
Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
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Article
Bibliometric Evaluation of Global Tai Chi Research from 1980–2020
Int. J. Environ. Res. Public Health 2021, 18(11), 6150; https://doi.org/10.3390/ijerph18116150 - 07 Jun 2021
Viewed by 1120
Abstract
While studies on the health benefits of Tai Chi have sprung up over the past four decades, few have engaged in collecting global data, estimating the developing trends, and conducting reviews from the perspective of visualization and bibliometric analysis. This study aimed to [...] Read more.
While studies on the health benefits of Tai Chi have sprung up over the past four decades, few have engaged in collecting global data, estimating the developing trends, and conducting reviews from the perspective of visualization and bibliometric analysis. This study aimed to provide a summary of the global scientific outputs on Tai Chi research from 1980 to 2020, explore the frontiers, identify cooperation networks, track research trends and highlight emerging hotspots. Relevant publications were downloaded from the Web of Science Core Collection (WoSCC) database between 1980 and 2020. Bibliometric visualization and comparative analysis of authors, cited authors, journals, co-cited journals, institutions, countries, references, and keywords were systematically conducted using CiteSpace software. A total of 1078 publications satisfied the search criteria, and the trend of annual related publications was generally in an upward trend, although with some fluctuations. China (503) and Harvard University (74) were the most prolific country and institution, respectively. Most of the related researches were published in the journals with a focus on sport sciences, alternative medicine, geriatrics gerontology, and rehabilitation. Our results indicated that the current concerns and difficulties of Tai Chi research are “Intervention method”, “Targeted therapy”, “Applicable population”, “Risk factors”, and “Research quality”. The frontiers and promising domains of Tai Chi exercise in the health science field are preventions and rehabilitations of “Fall risk”, “Cardiorespiratory related disease”, “Stroke”, “Parkinson’s disease”, and “Depression”, which should receive more attention in the future. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
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Article
Evaluating the Bovine Tuberculosis Eradication Mechanism and Its Risk Factors in England’s Cattle Farms
Int. J. Environ. Res. Public Health 2021, 18(7), 3451; https://doi.org/10.3390/ijerph18073451 - 26 Mar 2021
Cited by 1 | Viewed by 859
Abstract
Controlling bovine tuberculosis (bTB) disease in cattle farms in England is seen as a challenge for farmers, animal health, environment and policy-makers. The difficulty in diagnosis and controlling bTB comes from a variety of factors: the lack of an accurate diagnostic test which [...] Read more.
Controlling bovine tuberculosis (bTB) disease in cattle farms in England is seen as a challenge for farmers, animal health, environment and policy-makers. The difficulty in diagnosis and controlling bTB comes from a variety of factors: the lack of an accurate diagnostic test which is higher in specificity than the currently available skin test; isolation periods for purchased cattle; and the density of active badgers, especially in high-risk areas. In this paper, to enable the complex evaluation of bTB disease, a dynamic Bayesian network (DBN) is designed with the help of domain experts and available historical data. A significant advantage of this approach is that it represents bTB as a dynamic process that evolves periodically, capturing the actual experience of testing and infection over time. Moreover, the model demonstrates the influence of particular risk factors upon the risk of bTB breakdown in cattle farms. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
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Commentary
Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language
Int. J. Environ. Res. Public Health 2021, 18(17), 8985; https://doi.org/10.3390/ijerph18178985 - 26 Aug 2021
Viewed by 774
Abstract
Harmonized language is critical for helping researchers to find data, collecting scientific data to facilitate comparison, and performing pooled and meta-analyses. Using standard terms to link data to knowledge systems facilitates knowledge-driven analysis, allows for the use of biomedical knowledge bases for scientific [...] Read more.
Harmonized language is critical for helping researchers to find data, collecting scientific data to facilitate comparison, and performing pooled and meta-analyses. Using standard terms to link data to knowledge systems facilitates knowledge-driven analysis, allows for the use of biomedical knowledge bases for scientific interpretation and hypothesis generation, and increasingly supports artificial intelligence (AI) and machine learning. Due to the breadth of environmental health sciences (EHS) research and the continuous evolution in scientific methods, the gaps in standard terminologies, vocabularies, ontologies, and related tools hamper the capabilities to address large-scale, complex EHS research questions that require the integration of disparate data and knowledge sources. The results of prior workshops to advance a harmonized environmental health language demonstrate that future efforts should be sustained and grounded in scientific need. We describe a community initiative whose mission was to advance integrative environmental health sciences research via the development and adoption of a harmonized language. The products, outcomes, and recommendations developed and endorsed by this community are expected to enhance data collection and management efforts for NIEHS and the EHS community, making data more findable and interoperable. This initiative will provide a community of practice space to exchange information and expertise, be a coordination hub for identifying and prioritizing activities, and a collaboration platform for the development and adoption of semantic solutions. We encourage anyone interested in advancing this mission to engage in this community. Full article
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
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