Open science remains vital to the progress and functioning of the global research enterprise. Published in 2015, the Transparency and Openness Promotion Guidelines (TOP 2015) was developed as a policy framework to enhance the verifiability of empirical research claims in journal articles. It has been widely used and adopted by publishers and academic journals, but despite its uptake, concerns have been raised about aspects of the TOP 2015 framework and its implementation. In response to the above, the purpose of this manuscript is to introduce an official update to the TOP Guidelines. The final version—TOP 2025—provides updated guidelines for promoting the verifiability of published empirical research claims.

Learned societies, as professional bodies for scientists, are an integral part of the scientific system. However, their membership fees have the potential to be prohibitive to the most vulnerable members of the scientific community. To shed light on how membership fees are structured, we conducted a survey of 182 international learned societies relevant to researchers in ecology and evolution. We found that 83% of these societies offered fee concessions to students, but only 26% to postdoctoral researchers. An average regular membership fee-US$67.8, student fee-US$27.4 (42.7% of the regular fee) and postdoctoral fee-US$42.7 (52.9%). Other types of individual concessions, such as for emeritus, family or unemployed, were rare (2-20%). Of the surveyed societies, 43% had discounts for members from developing countries (Global South). Such discounts were more common among societies located in high-income countries. Societies with a publicly visible commitment to equity, diversity and inclusion were more likely to offer different types of concessions. Currently, fees may prevent researchers from vulnerable and underprivileged groups from accessing multiple professional benefits offered by learned societies in ecology and evolution. This includes postdoctoral researchers, who should receive more support. We recommend tangible actions towards making learned societies more affordable and accessible.

The production of chemical pesticides poses a critical threat to aquatic ecosystems worldwide, with sub-lethal impacts evident at even relatively low concentrations. Historically, ecotoxicologists have ignored an organism's social context when investigating the effects of pesticide exposure and, instead, have tended to focus on individual-level impacts. Recently, however, there has been a growing interest in understanding the impacts of pesticide exposure on social behaviour. Despite this shift, a holistic understanding of how pesticides impact conspecific interactions (i.e., social behaviour towards individuals of the same species) is lacking due to the multitude of behaviours, pesticides and species currently investigated. In this meta-analysis, we examine the effects of pesticide exposure on conspecific interactions in fish by using data collected from 37 studies on 31 pesticides and 11 species. Our results indicate that pesticide exposure generally reduces the expression of conspecific interactions, but it does not affect the variability of responses between individuals. Courtship behaviour was the most impaired, suggesting that pesticide exposure could weaken how matings are partitioned among individuals in a population. Triazoles and organochlorines were the most impactful pesticide classes for mean differences in behaviour, while triazoles and organophosphates had the greatest effects on response variability. These findings indicate that endocrine-disrupting and neurotoxic pesticides can impact fish conspecific interactions, regardless of their chemical class. Unfortunately, there is a large taxonomic bias in the literature, with most studies using zebrafish as a model, which, in turn, provides scope for studies using a broader range of fish species. We found little statistical evidence of publication biases in our dataset and our results were validated by sensitivity analyses. Overall, our synthesis suggests that pesticides broadly reduce the expression of social behaviours, though effects vary across behaviours, pesticide types, and fish species.

Meta-analyses, embedded in systematic reviews, are pivotal in today's scientific landscape for reconciling conflicting findings, increasing statistical power, and charting new research directions. However, poor reporting practices that conceal technical details and potential limitations often need to be revised to maintain their reliability. Despite existing reporting guidelines, a comprehensive tool has yet to be tailored to appraise the reporting quality of the quantitative aspects of meta-analysis in environmental sciences. To bridge this gap, we introduce the Meta-analysis Appraisal Tool for Environmental Sciences (MATES), a checklist of items to assess the reporting quality of meta-analyses. To develop MATES, we used an adapted Delphi process involving workshops (11-16 participants), a survey (193 participants), and validation (30 participants). This process resulted in a 14-item checklist, encompassing the environmental science communities view of important reporting elements. The validation, across 50 meta-analyses, indicated that the tool is repeatable (an average intra-class correlation of 88.97%) and time-efficient (17.00 ± 11.77 min) to implement. To enhance the accessibility and usability of MATES, we created an interactive web-based app that features training and implementation modules https://kylemorrisonisshiny99.shinyapps.io/MATES_shiny/. We also discuss how to interpret the MATES results, potential use cases of MATES and evaluate the development methodology. Overall, MATES provides authors, readers, reviewers, and editors with a reliable and user-friendly tool to assess the reporting quality of meta-analyses in the environmental sciences.

Meta-analyses are powerful synthesis tools that are popular in ecology and evolution owing to the rapidly growing literature of this field. Although the usefulness of meta-analyses depends on their reliability, such as the precision of individual and mean effect sizes, attempts to reproduce meta-analyses' results remain rare in ecology and evolution. Here, we assess the reliability of 41 meta-analyses on sexual signals by evaluating the reproducibility and replicability of their results. We attempted to: (i) reproduce meta-analyses' mean effect sizes using the datasets they provided; (ii) reproduce meta-analyses' effect sizes by re-extracting 5703 effect sizes from 246 primary studies they used as sources; (iii) assess the extent of relevant data missed by original meta-analyses; and (iv) replicate meta-analyses' mean effect sizes after incorporating re-extracted and relevant missing data. We found many discrepancies between meta-analyses' reported results and those generated by our analyses for all reproducibility and replicability attempts. Nonetheless, we argue that the meta-analyses we evaluated are largely reproducible and replicable because the differences we found were small in magnitude, leaving the original interpretation of these meta-analyses' results unchanged. Still, we highlight issues we observed in these meta-analyses that affected their reliability, providing recommendations to ameliorate them.

Rachel Carson's Silent Spring inspired a wave of research on the impacts of organochlorine pesticides, followed by a subsequent wave of meta-analyses. However, the methodological quality and content of these meta-analyses has not been evaluated. Here we systematically map and evaluate the methodological quality of 105 meta-analyses on organochlorine pesticides. We found that 83.4% of the evaluated methodological elements are low quality using the Collaboration for Environmental Evidence Synthesis Assessment Tool (CEESAT v2.1). We then reveal that 227 policy documents cited the included meta-analyses, and there is no difference in methodological quality between those that were cited in policy and those that were not. We also found a paucity of meta-analyses on wildlife despite ample primary evidence. Finally, we quantified the positive impact of using reporting guidelines and we provide recommendations for readily implementable methodological improvements.

Background Systematic reviews (SR), an evidence-synthesis tool, have been increasingly used in toxicology, for regulatory decision-making and allocation of research funding resources. A critical domain of methodological quality is publicly available in the SR protocol that describes the review methods prior to the review. Earlier work showed that only a small number of published SRs in environmental health had identifiable protocols, but did not evaluate their completeness of reporting. Thus, there is a critical need to assess the number of protocols published in peer-reviewed literature related to environmental/occupational toxicology and how much those SR protocols adhere to current reporting standards such as PRISMA-P.Objective The overarching objective of this review is to assess the reporting quality of SR protocols in environmental and occupational toxicology, published in peer-reviewed journals.Methods and analysis This protocol has been reported following PRISMA-P and PRISMA-S checklist and study selection, data extraction, and reporting quality assessment were piloted by independent reviewers. Four electronic databases (PubMed, Scopus, Web of Science, EMBASE) were selected and will be searched for SR protocols using pre-defined strings. Both title/abstract and full-text screening will follow detailed eligibility criteria based on the population concept context (PCC) framework. We will include peer-reviewed, self-identified SR protocols that aim to assess the adverse effects of environmental and occupational exposures. We will perform data extraction using the form that includes general bibliographic information, exposure, adverse effect information, evidence streams, and guidelines/checklists used in protocol reporting or development. A reporting assessment form consisting of 15 PRISMA-P-based items, of which eleven with identical binary responses (reported vs not reported) address critical elements of SR protocol. Assessments of the eleven quality items will provide data for the analysis of the reporting quality of SR protocols. We employ reporting quality as it provides a practical and suitable approach to evaluating the inclusion of essential SR elements, under the assumption that these elements would be reported if they had been appropriately planned rather than overlooked. The screening and data extraction will be conducted by two independent reviewers. Disagreements will be discussed if needed with the support of a third reviewer. The data analysis will describe the total number of SR protocols and trends over time, and absolute frequencies and/or proportion of bibliographic information items. It will summarize the reporting quality of all included protocols by a histogram and rank the quality criteria by their level of adherence across the included protocols.

Environmental enrichment has long been recognized as a non-pharmacological intervention to mitigate mental health issues, yet its efficacy, and heterogeneity of treatment effects across experimental contexts remain underexplored. Heterogeneity of treatment effects, which reflects variability in individual responses to interventions, is a critical factor in determining the generalizability and personalization needs of treatments. Here, we conducted a registered meta-analysis of 62 studies and 1,112 comparisons in rodent models to evaluate the impact of environmental enrichment on depressive and anxiety-like behaviours. We found that environmental enrichment reduced these behaviours of animal models by 16% on average and decreased inter-individual variability by 12%, indicating not only effectiveness but also low heterogeneity of treatment effects, which suggests consistent effects across individuals. Environmental enrichment further nullified the adverse effects of stressors, demonstrating a significant antagonistic interaction. These effects were robust across multiple sensitivity analyses, including model-based predictions, post-stratification, multi-model inference, publication bias correction, and critical appraisal of study quality. Moderator analyses highlighted the importance of exposure timing and the inclusion of social enrichment components. Taken together, our pre-clinical evidence on rodent models supports environmental enrichment as a low-cost, scalable, and biologically grounded intervention with translational relevance for developing equitable and accessible treatments for depression and anxiety. Given the importance of innovation and personalization in mental health care, the low heterogeneity of treatment effects of environmental enrichment positions it as a promising avenue for non-pharmacological therapeutic strategies that can be broadly applied without extensive tailoring. ### Competing Interest Statement The authors have declared no competing interest. Australian Research Council, DP210100812, DP230101248 Canada Excellence Research Chair, CERC-2022-00074

While psychologists have extensively discussed the notion of a “theory crisis” arising from vague and incorrect hypotheses, there has been no debate about such a crisis in biology. However, biologists have long discussed communication failures between theoreticians and empiricists. We argue such failure is one aspect of a theory crisis because misapplied and misunderstood theories lead to poor hypotheses and research waste. We review its solutions and compare them with methodology-focused solutions proposed for replication crises. We conclude by discussing how promoting inclusion, diversity, equity, and accessibility (IDEA) in theoretical biology could contribute to ameliorating breakdowns in the theory-empirical cycle.

Publishing preprints is quickly becoming commonplace in ecology and evolutionary biology. Preprints can facilitate the rapid sharing of scientific knowledge establishing precedence and enabling feedback from the research community before peer review. Yet, significant barriers to preprint use exist, including language barriers, a lack of understanding about the benefits of preprints and a lack of diversity in the types of research outputs accepted (e.g. reports). Community-driven preprint initiatives can allow a research community to come together to break down these barriers to improve equity and coverage of global knowledge. Here, we explore the first preprints uploaded to EcoEvoRxiv (n = 1216), a community-driven preprint server for ecologists and evolutionary biologists, to characterize preprint use in ecology, evolution and conservation. Our perspective piece highlights some of the unique initiatives that EcoEvoRxiv has taken to break down barriers to scientific publishing by exploring the composition of articles, how gender and career stage influence preprint use, whether preprints are associated with greater open science practices (e.g. code and data sharing) and tracking preprint publication outcomes. Our analysis identifies areas that we still need to improve upon but highlights how community-driven initiatives, such as EcoEvoRxiv, can play a crucial role in shaping publishing practices in biology.

Per- and polyfluoroalkyl substances (PFAS) threaten ecosystems worldwide due to their persistence, bioaccumulation, and toxicity. Through a global-scale meta-analysis of 122 aquatic and terrestrial food webs from 64 studies, we analyse 1,009 trophic magnification factors (TMFs) for 72 PFAS and identify key variability drivers. PFAS concentrations systematically doubled with each trophic level increase (mean TMF=2.00, 95% CI:1.64-2.45), confirming widespread biomagnification across ecosystems. Methodological disparities across studies emerged as the dominant source of TMF variability. Our models explained 84% of the variation in TMFs, underscoring predictive capacity. Notably, the industrial alternative F-53B exhibited the highest magnification (TMF=3.07, 95% CI:2.41-3.92), a critical finding given its expanding use and minimal regulatory scrutiny. This synthesis establishes PFAS as persistent trophic multipliers and provides a framework to prioritise high-risk compounds and harmonise biomagnification assessments. Our results call for consideration of stricter PFAS regulation to curb cascading ecological and health impacts.

Colourful body parts and bizarre displays that do not seem to contribute to the survival of individuals that express them have puzzled biologists for centuries. Sexual selection theory posits that these traits evolved because more conspicuous individuals attract more mates and experience greater fitness, yet evidence for this remains fragmented. Our augmented meta-meta-analysis of 41 meta-analyses, encompassing 375 animal species and 7428 individual effect sizes, shows that the conspicuousness of (putative) sexual signals is positively related to mate attractiveness, fitness benefits, individual condition, and other characteristics (e.g., body size) of signal bearers. Most of these patterns are consistent across both taxa and sexes, underscoring the generalisability of our results. Furthermore, the strength of pre-copulatory sexual selection on conspicuousness is positively associated with the relationship between (i) conspicuousness and fitness benefits and (ii) conspicuousness and individual condition. This suggests that the relationships we assessed regarding trait conspicuousness would be stronger if we could select only traits that are truly used for mate attraction. Our study unifies several decades of knowledge on conspicuous traits, confirms many predictions made by the theory of sexual selection, and lays a clear path for the future of research on this topic.

Bridging the divide between mathematical innovations and real-world applications is essential for addressing global challenges. By fostering interdisciplinary collaboration, diversity and inclusion, we can unlock the full potential of mathematical findings, driving innovation across applied disciplines and delivering mathematical solutions to society’s pressing problems.

Meta-analyses are powerful tools to synthesize the literature in several fields of study, including ecology and evolution. However, it remains uncertain whether ecologists and evolutionary biologists fully comprehend meta-analyses’ findings or effectively apply them when citing these studies in their own research. Here, we first discuss key meta-analytical concepts and provide a guide to researchers in ecology and evolution on how to harness meta-analyses’ insights. For instance, we clarify the meaning of effect sizes and heterogeneity to improve understanding of meta-analyses’ quantitative findings. In addition, we analysed articles published in 2023 in ecology and evolution to investigate how frequently and in what context meta-analyses were cited. We found that approximately 21% of articles cited at least one meta-analysis, and that the relative number of citations of meta-analyses (0.62% of all citations analysed) was greater than the publication frequency of meta-analytical articles (0.44% of all articles). Most importantly, we found that while the direction of mean effect sizes from cited meta-analyses was often mentioned, the magnitude of effect sizes and the limitations of the data analysed were frequently overlooked. These findings underscore the need for improved citation practices of meta-analyses in ecological and evolutionary research, which our recommendations seek to promote.

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.

Meta-analyses play an important role in empirically synthesising research and guiding future directions. The field of animal cognition is rapidly expanding, with both empirical and review papers increasing at a faster rate than those in the life sciences overall. However, the use of meta-analyses, their methodological rigour, and the geographic distribution of research activity remain unclear. We systematically reviewed 49 meta-analytical studies encompassing 1824 primary studies on animal cognition. Half of the meta-analytical studies focused on the evolution and diversity of non-human animal cognition, while the other half used animals as models to understand human cognition. Most studies addressed factors affecting cognitive abilities, focusing on mammals and birds. Although many studies aimed to examine evolutionary or diversity-related questions, few analysed cognitive variation across species or tested evolutionary hypotheses, and even fewer incorporated phylogenetic relationships. While some studies investigated sex differences, many reported that they could not due to unbalanced sex ratios in the primary studies, notably a predominance of males. Both primary and meta-analytical studies often lacked adequate methodological reporting and rarely shared raw data or analysis scripts. Our bibliometric analysis showed that research is geographically concentrated, with authorship and collaboration mostly in high-income countries. To address current gaps, we recommend greater adherence to open science practices, improved regional inclusivity, and broader taxonomic and individual-level coverage. Finally, we highlight the complementary roles of meta-analyses and Big Team Science in advancing the field by improving its transparency, inclusivity, and reliability.

Data visualization is crucial for effectively communicating knowledge in meta-analysis. However, existing visualization methods in meta-analysis have predominantly focused on quantitative aspects, such as forest plots and funnel plots, thereby neglecting qualitative information that is equally important for end-users in science, policy, and practice. We introduce a framework consisting of a series of visualization toolkits designed to enrich meta-analyses by borrowing approaches from other research synthesis methods, including systematic evidence mapping (scoping reviews), bibliometrics (bibliometric analysis), and alternative impact metric analysis. These “enrichment” toolkits aim to facilitate the synthesis of both quantitative and qualitative evidence, along with the assessment of the academic and nonacademic influences of the meta-analytic evidence base. While the meta-analysis yields quantitative insights, the enrichment analyses, and visualizations provide user-friendly summaries of qualitative information on the evidence base. For example, a systematic evidence map can visualize study characteristics, unraveling knowledge gaps and methodological differences. Bibliometric analysis offers a visual assessment of the nonindependent evidence, such as hyper-dominant authors and countries, and funding sources, potentially informing the risk of bias. Alternative impact metric analysis employs alternative metrics to gauge societal influence and research translation (e.g., policy and patent citations) of studies in the meta-analysis. We provide a dedicated webpage showcasing sample visualizations and providing step-by-step implementation in open-source software R ( https://yefeng0920.github.io/MA_Map_Bib/ ). Additionally, we offer a guide on leveraging three commercially free large language models (LLMs) to help adapt the sample script, enabling users with less R coding experience to visualize their own meta-analytic evidence base.

Phylogenetic comparative methods (PCMs) are fundamental tools for understanding trait evolution across species. While linear models are widely used for continuous traits in ecology and evolution, their application to discrete traits, particularly ordinal and nominal traits, remains limited. Researchers sometimes recategorise such traits into binary traits (0 or 1 data) to make them more manageable. However, this risks distorting the original data structure and meaning, potentially reducing the information it initially contained. This paper promotes the use of phylogenetic generalised linear mixed-effects models (PGLMMs) as a flexible framework for analysing the evolution of discrete traits. We introduce the theoretical foundations of PGLMMs and demonstrate how univariate and multivariate versions of binary PGLMMs, which might be more familiar to evolutionary biologists, can be conceptually extended to model ordinal and nominal traits. Specifically, we describe ordered and unordered multinomial PGLMMs for ordinal and nominal traits, respectively. We then explain how to interpret regression coefficients and (co)variance components, including associated statistics (e.g., phylogenetic heritability and correlation) from PGLMMs for discrete traits. Using real-world examples from avian datasets, we illustrate the practical implementation of PGLMMs to reveal evolutionary patterns in discrete traits. We also provide online tutorials to guide researchers through the application of these models using Bayesian implementations in R. By making complex models more accessible, we aim to facilitate a more precise and insightful understanding of the evolution and function of discrete traits, which have received relatively limited attention in evolutionary biology so far.

Systematic searches of published literature are a vital component of systematic reviews. When search strings are not “sensitive,” they may miss many relevant studies limiting, or even biasing, the range of evidence available for synthesis. Concerningly, conducting and reporting evaluations (validations) of the sensitivity of the used search strings is rare, according to our survey of published systematic reviews and protocols. Potential reasons may involve a lack of familiarity or inaccessibility of complex sensitivity evaluation approaches. We first clarify the main concepts and principles of search string evaluation. We then present a simple procedure for estimating a relative recall of a search string. It is based on a pre-defined set of “benchmark” publications. The relative recall, that is, the sensitivity of the search string, is the retrieval overlap between the evaluated search string and a search string that captures only the benchmark publications. If there is little overlap (i.e., low recall or sensitivity), the evaluated search string should be improved to ensure that most of the relevant literature can be captured. The presented benchmarking approach can be applied to one or more online databases or search platforms. It is illustrated by five accessible, hands-on tutorials for commonly used online literature sources. Overall, our work provides an assessment of the current state of search string evaluations in published systematic reviews and protocols. It also paves the way to improve evaluation and reporting practices to make evidence synthesis more transparent and robust.

BACKGROUND:Over the last decade, pharmaceutical pollution in aquatic ecosystems has emerged as a pressing environmental issue. Recent years have also seen a surge in scientific interest in the use of behavioural endpoints in chemical risk assessment and regulatory activities, underscoring their importance for fitness and survival. In this respect, data on how pharmaceuticals alter the behaviour of aquatic animals appears to have grown rapidly. Despite this, there has been a notable absence of systematic efforts to consolidate and summarise this field of study. To address this, our objectives were twofold: (1) to systematically identify, catalogue, and synthesise primary research articles on the effects of pharmaceuticals on aquatic animal behaviour; and (2) to organise this information into a comprehensive open-access database for scientists, policymakers, and environmental managers. METHODS:We systematically searched two electronic databases (Web of Science and Scopus) and supplemented these with additional article sources. The search string followed a Population-Exposure-Comparison-Outcome framework to capture articles that used an aquatic organism (population) to test the effects of a pharmaceutical (exposure) on behaviour (outcome). Articles were screened in two stages: title and abstract, followed by full-text screening alongside data extraction. Decision trees were designed a priori to appraise eligibility at both stages. Information on study validity was collected but not used as a basis for inclusion. Data synthesis focused on species, compounds, behaviour, and quality themes and was enhanced with additional sources of metadata from online databases (e.g. National Center for Biotechnology Information (NCBI) Taxonomy, PubChem, and IUCN Red List of Threatened Species). REVIEW FINDINGS:We screened 5,988 articles, of which 901 were included in the final database, representing 1,739 unique species-by-compound combinations. The database includes data collected over 48 years (1974-2022), with most articles having an environmental focus (510) and fewer relating to medical and basic research topics (233 and 158, respectively). The database includes 173 species (8 phyla and 21 classes). Ray-finned fishes were by far the most common clade (75% of the evidence base), and most studies focused on freshwater compared to marine species (80.4% versus 19.6%). The database includes 426 pharmaceutical compounds; the most common groups were antidepressants (28%), antiepileptics (11%), and anxiolytics (10%). Evidence for the impacts on locomotion and boldness/anxiety behaviours were most commonly assessed. Almost all behaviours were scored in a laboratory setting, with only 0.5% measured under field conditions. Generally, we detected poor reporting and/or compliance with several of our study validity criteria. CONCLUSIONS:Our systematic map revealed a rapid increase in this research area over the past 15 years. We highlight multiple areas now suitable for quantitative synthesis and areas where evidence is lacking. We also highlight some pitfalls in method reporting and practice. More detailed reporting would facilitate the use of behavioural endpoints in aquatic toxicology studies, chemical risk assessment, regulatory management activities, and improve replicability. The EIPAAB database can be used as a tool for closing these knowledge and methodological gaps in the future.