Housed worldwide, mostly in museums and herbaria, is a vast collection of biological specimens developed over centuries. These biological collections, and associated taxonomic and systematic research, have received considerable long-term public support. The work remaining in systematics has been expanding as the estimated total number of species of organisms on Earth has risen over recent decades, as have estimated numbers of undescribed species. Despite this increasing task, support for taxonomic and systematic research, and biological collections upon which such research is based, has declined over the last 30-40 years, while other areas of biological research have grown considerably, especially those that focus on environmental issues. Reflecting increases in research that deals with ecological questions (e.g. what determines species distribution and abundance) or environmental issues (e.g. toxic pollution), the level of research attempting to use biological collections in museums or herbaria in an ecological/environmental context has risen dramatically during about the last 20 years. The perceived relevance of biological collections, and hence the support they receive, should be enhanced if this trend continues and they are used prominently regarding such environmental issues as anthropogenic loss of biodiversity and associated ecosystem function, global climate change, and decay of the epidemiological environment. It is unclear, however, how best to use biological collections in the context of such ecological/environmental issues or how best to manage collections to facilitate such use. We demonstrate considerable and increasingly realized potential for research based on biological collections to contribute to ecological/environmental understanding. However, because biological collections were not originally intended for use regarding such issues and have inherent biases and limitations, they are proving more useful in some contexts than in others. Biological collections have, for example, been particularly useful as sources of information regarding variation in attributes of individuals (e.g. morphology, chemical composition) in relation to environmental variables, and provided important information in relation to species' distributions, but less useful in the contexts of habitat associations and population sizes. Changes to policies, strategies and procedures associated with biological collections could mitigate these biases and limitations, and hence make such collections more useful in the context of ecological/environmental issues. Haphazard and opportunistic collecting could be replaced with strategies for adding to existing collections that prioritize projects that use biological collections and include, besides taxonomy and systematics, a focus on significant environmental/ecological issues. Other potential changes include increased recording of the nature and extent of collecting effort and information associated with each specimen such as nearby habitat and other individuals observed but not collected. Such changes have begun to occur within some institutions. Institutions that house biological collections should, we think, pursue a mission of 'understanding the life of the planet to inform its stewardship' (Krishtalka & Humphrey, 2000), as such a mission would facilitate increased use of biological collections in an ecological/environmental context and hence lead to increased appreciation, encouragement and support from the public for these collections, their associated research, and the institutions that house them.
With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. Accurate geospatial predictions are vital for domain research based on ecosystem monitoring and quality assessment and for policy-making and action planning, considering effective management of natural resources. The accuracy and computation speed of ML has generally proved efficient. However, many questions have yet to be addressed to obtain precise and reproducible results suitable for further use in both research and practice. A better understanding of the ML concepts applicable to geospatial problems enhances the development of data science tools providing transparent information crucial for making decisions on global challenges such as biosphere degradation and climate change. This survey reviews common nuances in geospatial modelling, such as imbalanced data, spatial autocorrelation, prediction errors, model generalisation, domain specificity, and uncertainty estimation. We provide an overview of techniques and popular programming tools to overcome or account for the challenges. We also discuss pros
Using traditional Western research methods to explore Indigenous perspectives has often been felt by the Indigenous people themselves to be inappropriate and ineffective in gathering information and promoting discussion. On the contrary, using traditional storytelling as a research method links Indigenous worldviews, shaping the approach of the research; the theoretical and conceptual frameworks; and the epistemology, methodology, and ethics. The aims of this article are to (a) explore the essential elements and the value of traditional storytelling for culturally appropriate Indigenous research; (b) develop a model of a collaborative community and university research alliance, looking at how to address community concerns and gather data that will inform decision-making and help the community prepare for the future; (c) build up and strengthen research capacity among Indigenous communities in collaboration with Indigenous Elders and Knowledge-holders; and (d) discuss how to more fully engage Indigenous people in the research process. In two case studies with Indigenous and immigrant communities in Canada and Bangladesh that are grounded in the relational ways of participatory action research, the author found that traditional storytelling as a research method could lead to culturally appropriate research, build trust between participants and researcher, build a bridge between Western and Indigenous research, and deconstruct meanings of research. The article ends with a discussion of the implications of using traditional storytelling in empowering both research participants and researcher.
New multi-modal large language models (MLLMs) are continuously being trained and deployed, following rapid development cycles. This generative AI frenzy is driving steady increases in energy consumption, greenhouse gas emissions, and a plethora of other environmental impacts linked to datacenter construction and hardware manufacturing. Mitigating the environmental consequences of GenAI remains challenging due to an overall lack of transparency by the main actors in the field. Even when the environmental impacts of specific models are mentioned, they are typically restricted to the carbon footprint of the final training run, omitting the research and development stages. In this work, we explore the impact of GenAI research through a fine-grained analysis of the compute spent to create Moshi, a 7B-parameter speech-text foundation model for real-time dialogue developed by Kyutai, a leading privately funded open science AI lab. For the first time, our study dives into the anatomy of compute-intensive MLLM research, quantifying the GPU-time invested in specific model components and training phases, as well as early experimental stages, failed training runs, debugging, and ablation studi
Camera traps have long been used by wildlife researchers to monitor and study animal behavior, population dynamics, habitat use, and species diversity in a non-invasive and efficient manner. While data collection from the field has increased with new tools and capabilities, methods to develop, process, and manage the data, especially the adoption of ML/AI tools, remain challenging. These challenges include the sheer volume of data generated, the need for accurate labeling and annotation, variability in environmental conditions affecting data quality, and the integration of ML/AI tools into existing workflows that often require domain-specific customization and computational resources. This paper provides a guide to a low-resource pipeline to process camera trap data on-premise, incorporating ML/AI capabilities tailored for small research groups with limited resources and computational expertise. By focusing on practical solutions, the pipeline offers accessible approaches for data transmission, inference, and evaluation, enabling researchers to discover meaningful insights from their ever-increasing camera trap datasets.
During its annual conference in 2024, the French Society of Astronomy & Astrophysics (SF2A) hosted a special session dedicated to discussing the environmental transition within the scope of our occupation. Since 2021, thinking on this subject has progressed significantly, both quantitatively and qualitatively. This year was an opportunity to take stock of the main areas of reflection that we need to keep in mind in order to implement a fair, collective and effective environmental transition. This proceeding summarizes the key points from the plenary session related to the environmental transition special session. The purpose of the messages disseminated here is to suggest ideas for reflection and inspiration, so as to initiate, stimulate, and foster discussions within the A&A research community, towards the implementation of concrete measures to mitigate our environmental footprint.
Human activities degrade the Earth environment at an unprecedented scale and pace, threatening Earth-system stability, resilience and life-support functions. We can of course deny the facts, get angry about them, or try to bargain. Or we may overcome these stages of grief and move towards accepting that human activities need to change, including our own ones. The purpose of this paper is to support astronomers in this transition, by providing insights into the origins of environmental impacts in astronomical research and proposing changes that would make the field sustainable. The paper focuses on the environmental impacts of research infrastructures, since these are the dominant sources of greenhouse gas emissions in astronomy, acknowledging that impact reductions in other areas, for example professional air travelling, need also to be achieved.
Artificial Intelligence (AI) is changing the world, but its impacts on the environment and human well-being remain uncertain. We conducted a systematic literature review of 1,291 studies selected from 6,655 records, identifying the main impacts of AI and how they are assessed. The evidence reveals an uneven landscape: 72% of environmental studies focus narrowly on energy use and CO2 emissions, while only 11% consider systemic effects. Well-being research is largely conceptual and overlooks subjective dimensions. Strikingly, 83% of environmental studies portray AI's impacts as positive, while well-being analyses show a near-even split overall (44% positive; 46% negative). However, this split masks differences across well-being dimensions. While the impacts of AI on income and health are expected to be positive, its impacts on inequality, social cohesion, and employment are expected to be negative. Based on our findings, we suggest several areas for future research. Environmental assessments should incorporate water, material, and biodiversity impacts, and apply a full life-cycle perspective, while well-being research should prioritise empirical analyses. Evaluating AI's overall impa
Researchers are developing mobile sensing platforms to facilitate public awareness of environmental conditions. However, turning such awareness into practical community action and political change requires more than just collecting and presenting data. To inform research on mobile environmental sensing, we conducted design fieldwork with government, private, and public interest stakeholders. In parallel, we built an environmental air quality sensing system and deployed it on street sweeping vehicles in a major U.S. city; this served as a "research vehicle" by grounding our interviews and affording us status as environmental action researchers. In this paper, we present a qualitative analysis of the landscape of environmental action, focusing on insights that will help researchers frame meaningful technological interventions.
Software is at the core of most scientific discoveries today. Therefore, the quality of research results highly depends on the quality of the research software. Rigorous testing, as we know it from software engineering in the industry, could ensure the quality of the research software but it also requires a substantial effort that is often not rewarded in academia. Therefore, this research explores the effects of research software testing integrated into teaching on research software. In an in-vivo experiment, we integrated the engineering of a test suite for a large-scale network simulation as group projects into a course on software testing at the Blekinge Institute of Technology, Sweden, and qualitatively measured the effects of this integration on the research software. We found that the research software benefited from the integration through substantially improved documentation and fewer hardware and software dependencies. However, this integration was effortful and although the student teams developed elegant and thoughtful test suites, no code by students went directly into the research software since we were not able to make the integration back into the research software
The climate crisis and the degradation of the world's ecosystems require humanity to take immediate action. The international scientific community has a responsibility to limit the negative environmental impacts of basic research. The HECAP+ communities (High Energy Physics, Cosmology, Astroparticle Physics, and Hadron and Nuclear Physics) make use of common and similar experimental infrastructure, such as accelerators and observatories, and rely similarly on the processing of big data. Our communities therefore face similar challenges to improving the sustainability of our research. This document aims to reflect on the environmental impacts of our work practices and research infrastructure, to highlight best practice, to make recommendations for positive changes, and to identify the opportunities and challenges that such changes present for wider aspects of social responsibility.
This paper presents a scientometric analysis of research output from the University of Lagos, focusing on the two decades spanning 2004 to 2023. Using bibliometric data retrieved from the Web of Science, we examine trends in publication volume, collaboration patterns, citation impact, and the most prolific authors, departments, and research domains at the university. The study reveals a consistent increase in research productivity, with the highest publication output recorded in 2023. Health Sciences, Engineering, and Social Sciences are identified as dominant fields, reflecting the university's interdisciplinary research strengths. Collaborative efforts, both locally and internationally, show a positive correlation with higher citation impact, with the United States and the United Kingdom being the leading international collaborators. Notably, open-access publications account for a significant portion of the university's research output, enhancing visibility and citation rates. The findings offer valuable insights into the university's research performance over the past two decades, providing a foundation for strategic planning and policy formulation to foster research excellence
Purpose To investigate, analyse and critique contemporary research in social and environmental accounting. Design/methodology/approach An analysis and critique of the social and environmental accountability (SEA) research field since the late 1980s. The study revisits two key prior seminal papers on the field, examines the remit for SEA researchers' focus on practice and policy and offers an empirical analysis of the profile of SEA publication. Findings Theories are identified in two groups: augmentation and heartland theories. These have been more deductively than inductively generated, evidencing limited attention to field‐based engagement. An alternative to the elusive all‐embracing unitary SEA theory is presented. Researchers' concerns with capture of the SEA field is critiqued and an alternative researcher engagement orientation is offered. Environmental research dominates more recent SEA published output, the dominant methodological approach is literature‐based theorising, and national practices/comparisons and regulations are leading topic areas occupying researchers. Research limitations/implications Analysis of publishing patterns including the balance between social and environmental accountability research, research methodologies employed and SEA topics addressed is largely confined to four leading interdisciplinary accounting research journals. Practical implications The paper argues for greater SEA researcher engagement with SEA practice and involvement in SEA policy contributions. Originality/value The paper offers a contemporary assemblage and critique of the multiple theoretical perspectives applied to the SEA field and offers insights into the range and predominance of research methods and topics within the published SEA field.
Cybercrime is a complex phenomenon that spans both technical and human aspects. As such, two disjoint areas have been studying the problem from separate angles: the information security community and the environmental criminology one. Despite the large body of work produced by these communities in the past years, the two research efforts have largely remained disjoint, with researchers on one side not benefitting from the advancements proposed by the other. In this paper, we argue that it would be beneficial for the information security community to look at the theories and systematic frameworks developed in environmental criminology to develop better mitigations against cybercrime. To this end, we provide an overview of the research from environmental criminology and how it has been applied to cybercrime. We then survey some of the research proposed in the information security domain, drawing explicit parallels between the proposed mitigations and environmental criminology theories, and presenting some examples of new mitigations against cybercrime. Finally, we discuss the concept of cyberplaces and propose a framework in order to define them. We discuss this as a potential resear
This study investigates the interconnectivity of firms and Environmental Justice Organizations (EJOs) involved in socio-environmental conflicts worldwide, using data from the Environmental Justice Atlas (EJAtlas). By constructing a multilayer network that links firms, conflicts, and EJOs, the research applies social network analysis to evaluate the simultaneous involvement of these actors across multiple disputes. Both projected networks of firms and EJOs have been analysed by aggregating nodes by categories and countries to reveal structural differences. Findings reveal a stark contrast between the interconnectedness of firms and EJOs. Multinational corporations form a cohesive global network, enabling them to coordinate strategies and exert influence across regions. Conversely, EJOs are fragmented, often operating in isolated clusters with limited interconnection but forming a robust, decentralized and self-organized global network. Firms network present a strong dependence on pertaining conflict category while EJOs network does not depend on conflict category. This structural difference suggests a risk of systemic and structural coordination for firms towards exploitative expans
Potential societal and environmental effects such as the rapidly increasing resource use and the associated environmental impact, reproducibility issues, and exclusivity, the privatization of ML research leading to a public research brain-drain, a narrowing of the research effort caused by a focus on deep learning, and the introduction of biases through a lack of sociodemographic diversity in data and personnel caused by recent developments in machine learning are a current topic of discussion and scientific publications. However, these discussions and publications focus mainly on computer science-adjacent fields, including computer vision and natural language processing or basic ML research. Using bibliometric analysis of the complete and full-text analysis of the open-access literature, we show that the same observations can be made for applied machine learning in chemistry and biology. These developments can potentially affect basic and applied research, such as drug discovery and development, beyond the known issue of biased data sets.
The space-borne gravitational wave detectors such as TianQin offers a new window to test General Relativity by observing the early inspiral phase of stellar-mass binary black holes. A key concern arises if these stellar-mass binary black holes reside in gaseous environments such as active galactic nucleus accretion disks, where environmental effects imprint detectable modulations on the gravitational waveform. Using Bayesian inference on simulated signals containing both environmental and dipole deviation, we have assessed the extent to which the presence of environmental effects affects the detectability of dipole radiation. Our results demonstrate that even in the presence of strong environmental coupling, the dipole parameter can be recovered with high precision, and the evidence for dipole radiation remains distinguishable. Crucially, we find that the existence of environmental effects does not fundamentally impede the identification of dipole radiation, provided both effects are simultaneously modelled in the inference process. This study establishes that future tests of modified gravity with space-borne observatories can remain robust even for sources in astrophysical environ
Artificial intelligence (AI) is often presented as a key tool for addressing societal challenges, such as climate change. At the same time, AI's environmental footprint is expanding increasingly. This report describes the systemic environmental risks of artificial intelligence, in particular, moving beyond direct impacts such as energy and water usage. Systemic environmental risks of AI are emergent, cross-sector harms to climate, biodiversity, freshwater, and broader socioecological systems that arise primarily from AI's integration into social, economic, and physical infrastructures, rather than its direct resource use, and that propagate through feedbacks, yielding nonlinear, inequitable, and potentially irreversible impacts. While these risks are emergent and quantification is uncertain, this report aims to provide an overview of systemic environmental risks. Drawing on a narrative literature review, we propose a three-level framework that operationalizes systemic risk analysis. The framework identifies the structural conditions that shape AI development, the risk amplification mechanisms that propagate environmental harm, and the impacts that manifest as observable ecological
Biodiversity loss is accelerating at an unprecedented pace, threatening ecosystem stability, economic resilience, and human well-being, with billions required to reverse current trends. Against this backdrop, biodiversity finance has emerged as a rapidly expanding but highly fragmented field spanning ecology, economics, finance, accounting, and policy. However, it remains emerging and complex, with the majority of relevant knowledge being produced in non-finance journals. This study employs quantitative bibliometric analysis to examine a corpus of 189,456 references underlying 3,998 articles related to biodiversity and finance. The analysis identifies eight primary research streams within the field that concern (1) strategic and financial approaches in global biodiversity conservation, (2) the impact and implementation of payments for environmental services (PES) in developing countries, (3) neoliberal influences and implications in environmental conservation, (4) biodiversity offsets and conservation, (5) ecosystem services and biodiversity, (6) integrating conservation and community interests in biodiversity management, (7) balancing agricultural intensification with biodiversity