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.
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Environmental Microbiology: Advanced Research and Multidisciplinary Applications focus on the current research on microorganisms in the environment. Contributions in the volume cover several aspects of applied microbial research, basic research on microbial ecology and molecular genetics. The reader will find a collection of topics with theoretical and practical value, allowing them to connect environmental microbiology to a variety of subjects in life sciences, ecology, and environmental science topics. Advanced topics including biogeochemical cycling, microbial biosensors, bioremediation, application of microbial biofilms in bioremediation, application of microbial surfactants, microbes for mining and metallurgical operations, valorization of waste, and biodegradation of aromatic waste, microbial communication, nutrient cycling and biotransformation are also covered. The content is designed for advanced undergraduate students, graduate students, and environmental professionals, with a comprehensive and up-to-date discussion of environmental microbiology as a discipline that has greatly expanded in scope and interest over the past several decades.
The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system.
This article offers a critical analysis of environmental science that develops the argument that science has itself become an obstacle for the transformations that are needed to ensure human-ecological well-being. Due to dominant norms and conceptualizations of what science is, how it should relate to policy and society, and what it is that science should contribute to, environmental science is set to continue to serve vested interests and seems unable to break free from this pattern. This deadlock situation is related to persistent patterns of inequality and marginalization in science that keep these dominant norms and conceptualizations in place and that marginalize alternative forms of knowledge, including critical social sciences and humanities, that are better equipped to support transformation. Inspired by feminist and anti-colonial scholarship, I suggest that transforming environmental science will require explicit refusal of dominant norms of science and conceptualizations of the environment, and a commitment to justice and pluralism.
Jasmine R. Kobayashi, Daniela Martin, Valmir P Moraes Filho
et al.
Labeling or classifying time series is a persistent challenge in the physical sciences, where expert annotations are scarce, costly, and often inconsistent. Yet robust labeling is essential to enable machine learning models for understanding, prediction, and forecasting. We present the \textit{Clustering and Indexation Pipeline with Human Evaluation for Recognition} (CIPHER), a framework designed to accelerate large-scale labeling of complex time series in physics. CIPHER integrates \textit{indexable Symbolic Aggregate approXimation} (iSAX) for interpretable compression and indexing, density-based clustering (HDBSCAN) to group recurring phenomena, and a human-in-the-loop step for efficient expert validation. Representative samples are labeled by domain scientists, and these annotations are propagated across clusters to yield systematic, scalable classifications. We evaluate CIPHER on the task of classifying solar wind phenomena in OMNI data, a central challenge in space weather research, showing that the framework recovers meaningful phenomena such as coronal mass ejections and stream interaction regions. Beyond this case study, CIPHER highlights a general strategy for combining symbolic representations, unsupervised learning, and expert knowledge to address label scarcity in time series across the physical sciences. The code and configuration files used in this study are publicly available to support reproducibility.
Elizabeth Bradley, Adilson E. Motter, Louis M. Pecora
Nonlinear science has evolved significantly over the 35 years since the launch of the journal Chaos. This Focus Issue, dedicated to the 80th Birthday of its founding editor-in-chief, David K. Campbell, brings together a selection of contributions on influential topics, many of which were advanced by Campbell's own research program and leadership role. The topics include new phenomena and method development in the realms of network dynamics, machine learning, quantum and material systems, chaos and fractals, localized states, and living systems, with a good balance of literature review, original contributions, and perspectives for future research.
Synthetic datasets are widely used in many applications, such as missing data imputation, examining non-stationary scenarios, in simulations, training data-driven models, and analyzing system robustness. Typically, synthetic data are based on historical data obtained from the observed system. The data needs to represent a specific behavior of the system, yet be new and diverse enough so that the system is challenged with a broad range of inputs. This paper presents a method, based on discrete Fourier transform, for generating synthetic time series with similar statistical moments for any given signal. The suggested method makes it possible to control the level of similarity between the given signal and the generated synthetic signals. Proof shows analytically that this method preserves the first two statistical moments of the input signal, and its autocorrelation function. The method is compared to known methods, ARMA, GAN, and CoSMoS. A large variety of environmental datasets with different temporal resolutions, and from different domains are used, testing the generality and flexibility of the method. A Python library implementing this method is made available as open-source software.
The technology industry offers exciting and diverse career opportunities, ranging from traditional software development to emerging fields such as artificial intelligence, cybersecurity, and data science. Career fairs play a crucial role in helping Computer Science (CS) students understand the various career pathways available to them in the industry. However, limited research exists on how CS students experience and benefit from these events. Through a survey of 86 students, we investigate their motivations for attending, preparation strategies, and learning outcomes, including exposure to new career paths and technologies. We envision our findings providing valuable insights for career services professionals, educators, and industry leaders in improving the career development processes of CS students.
Gavin Farrell, Eleni Adamidi, Rafael Andrade Buono
et al.
Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI implementation.
Per- and polyfluoroalkyl substances (PFAS) have drawn public concern recently due to their toxic properties and persistence in the environment, making it urgent to eliminate PFAS from contaminated water. Electrochemical oxidation (EO) has shown great promise for the destructive treatment of PFAS with direct electron transfer and hydroxyl radical (⋅OH)-mediated indirect reactions. One of the most popular electrodes is Magnéli phase titanium suboxides. However, the degradation mechanisms of PFAS are still unsure and are under investigation now. The main methodology is the first-principal density functional theory (DFT) computation, which is recently used to explore the degradation mechanisms and interpret by-product formation during PFAS mineralization. From the literature review, the main applications of DFT computation for studying PFAS degradation mechanisms by EO include bond dissociation energy, absorption energy, activation energy, and overpotential η for oxygen evolution reactions. The main degradation mechanisms and pathways of PFAS in the EO process include mass transfer, direct electron transfer, decarboxylation, peroxyl radical generation, hydroxylation, intramolecular rearrangement, and hydrolysis. In the recent 4 years, 11 papers performed DFT computation to explore the possible PFAS degradation mechanisms and pathways in the EO process. This paper’s objectives are to: 1) summarize the main degradation mechanisms of PFAS degradation in EO; 2) review the application of DFT computation for studying PFAS degradation mechanisms during EO; process; 3) review the possible degradation pathways of perfluorooctane sulfonoic acid (PFOS) and per-fluorooctanoic acid (PFOA) during EO process.
Tobias Heimann, Lara-Sophie Wähling, Tomke Honkomp
et al.
Bioenergy with carbon capture and storage (BECCS) is a crucial element in most modelling studies on emission pathways of the Intergovernmental Panel on Climate Change to limit global warming. BECCS can substitute fossil fuels in energy production and reduce CO _2 emissions, while using biomass for energy production can have feedback effects on land use, agricultural and forest products markets, as well as biodiversity and water resources. To assess the former pros and cons of BECCS deployment, interdisciplinary model approaches require detailed estimates of technological information related to BECCS production technologies. Current estimates of the cost structure and capture potential of BECCS vary widely due to the absence of large-scale production. To obtain more precise estimates, a global online expert survey ( N = 32) was conducted including questions on the regional development potential and biomass use of BECCS, as well as the future operating costs, capture potential, and scalability in different application sectors. In general, the experts consider the implementation of BECCS in Europe and North America to be very promising and regard BECCS application in the liquid biofuel industry and thermal power generation as very likely. The results show significant differences depending on whether the experts work in the Global North or the Global South. Thus, the findings underline the importance of including experts from the Global South in discussions on carbon dioxide removal methods. Regarding technical estimates, the operating costs of BECCS in thermal power generation were estimated in the range of 100–200 USD/tCO _2 , while the CO _2 capture potential was estimated to be 50–200 MtCO _2 yr ^−1 by 2030, with cost-efficiency gains of 20% by 2050 due to technological progress. Whereas the individuals’ experts provided more precise estimates, the overall distribution of estimates reflected the wide range of estimates found in the literature. For the cost shares within BECCS, it was difficult to obtain consistent estimates. However, due to very few current alternative estimates, the results are an important step for modelling the production sector of BECCS in interdisciplinary models that analyse cross-dimensional trade-offs and long-term sustainability.
Addressing the challenges posed by climate change, biodiversity loss, and environmental pollution requires comprehensive monitoring and effective data management strategies that are applicable across various scales in environmental system science. This paper introduces a versatile and transferable digital ecosystem for managing time series data, designed to adhere to the FAIR principles (Findable, Accessible, Interoperable, and Reusable). The system is highly adaptable, cloud-ready, and suitable for deployment in a wide range of settings, from small-scale projects to large-scale monitoring initiatives. The ecosystem comprises three core components: the Sensor Management System (SMS) for detailed metadata registration and management; time$.$IO, a platform for efficient time series data storage, transfer, and real-time visualization; and the System for Automated Quality Control (SaQC), which ensures data integrity through real-time analysis and quality assurance. The modular architecture, combined with standardized protocols and interfaces, ensures that the ecosystem can be easily transferred and deployed across different environments and institutions. This approach enhances data accessibility for a broad spectrum of stakeholders, including researchers, policymakers, and the public, while fostering collaboration and advancing scientific research in environmental monitoring.
Environmental data science for spatial extremes has traditionally relied heavily on max-stable processes. Even though the popularity of these models has perhaps peaked with statisticians, they are still perceived and considered as the `state-of-the-art' in many applied fields. However, while the asymptotic theory supporting the use of max-stable processes is mathematically rigorous and comprehensive, we think that it has also been overused, if not misused, in environmental applications, to the detriment of more purposeful and meticulously validated models. In this paper, we review the main limitations of max-stable process models, and strongly argue against their systematic use in environmental studies. Alternative solutions based on more flexible frameworks using the exceedances of variables above appropriately chosen high thresholds are discussed, and an outlook on future research is given, highlighting recommendations moving forward and the opportunities offered by hybridizing machine learning with extreme-value statistics.
Miguel Fuentes, Juan Pablo Cárdenas, Gastón Olivares
et al.
Resilience in social systems is crucial for mitigating the impacts of crises, such as climate change, which poses an existential threat to communities globally. As disasters become more frequent and severe, enhancing community resilience has become imperative. This study introduces a cutting-edge framework, quantitative network-based modeling called Complex Analysis for Socio-environmental Adaptation (CASA) to evaluate and strengthen social resilience. CASA transforms resilience models' linear and static structure into a complex network that integrates complexity and systems thinking, utilizing global scientific knowledge and complex network methodologies. The resulting resilience framework features rich interdependencies, and subsequent dimensionality reduction produces robust resilience indicators. This innovative application of network sciences is then demonstrated by quantitatively assessing what are known as "Sacrifice Zones," socio-environmentally sensitive areas. Results unveil the potential of this novel application of complex network methodologies as tools for systemic diagnostics, identifying vulnerabilities, and guiding policies and practices to enhance climate resilience and adaptation. The CASA framework represents a pioneering tool for assessing territorial resilience, leveraging network science applications, big data analytics, and artificial intelligence. CASA serves as a systemic diagnostic tool for urban resilience and a guide for policymakers, urban planners, and other professionals to promote sustainable, healthy cities in an era of climate change.