Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide.

Dams contribute to water security, energy supply, and flood protection but also fragment habitats of freshwater species. Yet, a global species-level assessment of dam-induced fragmentation is lacking. Here, we assessed the degree of fragmentation of the occurrence ranges of ∼10,000 lotic fish species worldwide due to ∼40,000 existing large dams and ∼3,700 additional future large hydropower dams. Per river basin, we quantified a connectivity index (CI) for each fish species by combining its occurrence range with a high-resolution hydrography and the locations of the dams. Ranges of nondiadromous fish species were more fragmented (less connected) (CI = 73 ± 28%; mean ± SD) than ranges of diadromous species (CI = 86 ± 19%). Current levels of fragmentation were highest in the United States, Europe, South Africa, India, and China. Increases in fragmentation due to future dams were especially high in the tropics, with declines in CI of ∼20 to 40 percentage points on average across the species in the Amazon, Niger, Congo, Salween, and Mekong basins. Our assessment can guide river management at multiple scales and in various domains, including strategic hydropower planning, identification of species and basins at risk, and prioritization of restoration measures, such as dam removal and construction of fish bypasses.

What Works Where and How for Uptake and Impact of Artificial Intelligence in Pathology: Review of Theories for a Realist Evaluation.

BACKGROUND: There is increasing interest in the use of artificial intelligence (AI) in pathology to increase accuracy and efficiency. To date, studies of clinicians' perceptions of AI have found only moderate acceptability, suggesting the need for further research regarding how to integrate it into clinical practice. OBJECTIVE: The aim of the study was to determine contextual factors that may support or constrain the uptake of AI in pathology. METHODS: To go beyond a simple listing of barriers and facilitators, we drew on the approach of realist evaluation and undertook a review of the literature to elicit stakeholders' theories of how, for whom, and in what circumstances AI can provide benefit in pathology. Searches were designed by an information specialist and peer-reviewed by a second information specialist. Searches were run on the arXiv.org repository, MEDLINE, and the Health Management Information Consortium, with additional searches undertaken on a range of websites to identify gray literature. In line with a realist approach, we also made use of relevant theory. Included documents were indexed in NVivo 12, using codes to capture different contexts, mechanisms, and outcomes that could affect the introduction of AI in pathology. Coded data were used to produce narrative summaries of each of the identified contexts, mechanisms, and outcomes, which were then translated into theories in the form of context-mechanism-outcome configurations. RESULTS: A total of 101 relevant documents were identified. Our analysis indicates that the benefits that can be achieved will vary according to the size and nature of the pathology department's workload and the extent to which pathologists work collaboratively; the major perceived benefit for specialist centers is in reducing workload. For uptake of AI, pathologists' trust is essential. Existing theories suggest that if pathologists are able to "make sense" of AI, engage in the adoption process, receive support in adapting their work processes, and can identify potential benefits to its introduction, it is more likely to be accepted. CONCLUSIONS: For uptake of AI in pathology, for all but the most simple quantitative tasks, measures will be required that either increase confidence in the system or provide users with an understanding of the performance of the system. For specialist centers, efforts should focus on reducing workload rather than increasing accuracy. Designers also need to give careful thought to usability and how AI is integrated into pathologists' workflow.

How to analyse the spatiotemporal tumour samples needed to investigate cancer evolution: A case study using paired primary and recurrent glioblastoma.

Many traits of cancer progression (e.g., development of metastases or resistance to therapy) are facilitated by tumour evolution: Darwinian selection of subclones with distinct genotypes or phenotypes that enable such progression. Characterising these subclones provide an opportunity to develop drugs to better target their specific properties but requires the accurate identification of somatic mutations shared across multiple spatiotemporal tumours from the same patient. Current best practices for calling somatic mutations are optimised for single samples, and risk being too conservative to identify shared mutations with low prevalence in some samples. We reasoned that datasets from multiple matched tumours can be used for mutual validation and thus propose an adapted two-stage approach: (1) low-stringency mutation calling to identify mutations shared across samples irrespective of the weight of evidence in a single sample; (2) high-stringency mutation calling to further characterise mutations present in a single sample. We applied our approach to three-independent cohorts of paired primary and recurrent glioblastoma tumours, two of which have previously been analysed using existing approaches, and found that it significantly increased the amount of biologically relevant shared somatic mutations identified. We also found that duplicate removal was detrimental when identifying shared somatic mutations. Our approach is also applicable when multiple datasets e.g. DNA and RNA are available for the same tumour.

Spatial patterns of agricultural expansion determine impacts on biodiversity and carbon storage.

The agricultural expansion and intensification required to meet growing food and agri-based product demand present important challenges to future levels and management of biodiversity and ecosystem services. Influential actors such as corporations, governments, and multilateral organizations have made commitments to meeting future agricultural demand sustainably and preserving critical ecosystems. Current approaches to predicting the impacts of agricultural expansion involve calculation of total land conversion and assessment of the impacts on biodiversity or ecosystem services on a per-area basis, generally assuming a linear relationship between impact and land area. However, the impacts of continuing land development are often not linear and can vary considerably with spatial configuration. We demonstrate what could be gained by spatially explicit analysis of agricultural expansion at a large scale compared with the simple measure of total area converted, with a focus on the impacts on biodiversity and carbon storage. Using simple modeling approaches for two regions of Brazil, we find that for the same amount of land conversion, the declines in biodiversity and carbon storage can vary two- to fourfold depending on the spatial pattern of conversion. Impacts increase most rapidly in the earliest stages of agricultural expansion and are more pronounced in scenarios where conversion occurs in forest interiors compared with expansion into forests from their edges. This study reveals the importance of spatially explicit information in the assessment of land-use change impacts and for future land management and conservation.

How, for whom, and in what contexts will artificial intelligence be adopted in pathology? A realist interview study.

OBJECTIVE: There is increasing interest in using artificial intelligence (AI) in pathology to improve accuracy and efficiency. Studies of clinicians' perceptions of AI have found only moderate acceptability, suggesting further research is needed regarding integration into clinical practice. This study aimed to explore stakeholders' theories concerning how and in what contexts AI is likely to become integrated into pathology. MATERIALS AND METHODS: A literature review provided tentative theories that were revised through a realist interview study with 20 pathologists and 5 pathology trainees. Questions sought to elicit whether, and in what ways, the tentative theories fitted with interviewees' perceptions and experiences. Analysis focused on identifying the contextual factors that may support or constrain uptake of AI in pathology. RESULTS: Interviews highlighted the importance of trust in AI, with interviewees emphasizing evaluation and the opportunity for pathologists to become familiar with AI as means for establishing trust. Interviewees expressed a desire to be involved in design and implementation of AI tools, to ensure such tools address pressing needs, but needs vary by subspecialty. Workflow integration is desired but whether AI tools should work automatically will vary according to the task and the context. CONCLUSIONS: It must not be assumed that AI tools that provide benefit in one subspecialty will provide benefit in others. Pathologists should be involved in the decision to introduce AI, with opportunity to assess strengths and weaknesses. Further research is needed concerning the evidence required to satisfy pathologists regarding the benefits of AI.

Implications of zero-deforestation palm oil for tropical grassy and dry forest biodiversity.

Many companies have made zero-deforestation commitments (ZDCs) to reduce carbon emissions and biodiversity losses linked to tropical commodities. However, ZDCs conserve areas primarily based on tree cover and aboveground carbon, potentially leading to the unintended consequence that agricultural expansion could be encouraged in biomes outside tropical rainforest, which also support important biodiversity. We examine locations suitable for zero-deforestation expansion of commercial oil palm, which is increasingly expanding outside the tropical rainforest biome, by generating empirical models of global suitability for rainfed and irrigated oil palm. We find that tropical grassy and dry forest biomes contain >50% of the total area of land climatically suitable for rainfed oil palm expansion in compliance with ZDCs (following the High Carbon Stock Approach; in locations outside urban areas and cropland), and that irrigation could double the area suitable for expansion in these biomes. Within these biomes, ZDCs fail to protect areas of high vertebrate richness from oil palm expansion. To prevent unintended consequences of ZDCs and minimize the environmental impacts of oil palm expansion, policies and governance for sustainable development and conservation must expand focus from rainforests to all tropical biomes.

Persuasion with Precision: Using Natural Language Processing to Improve Instrument Fidelity for Risk Communication Experimental Treatments.

Instrument fidelity in message testing research hinges upon how precisely messages operationalize treatment conditions. However, numerous message-testing studies have unmitigated threats to validity and reliability because no established procedures exist to guide construction of message treatments. Their construction typically occurs in a black box, resulting in suspect inferential conclusions about treatment effects. Because a mixed methods approach is needed to enhance instrument fidelity in message testing research, this article contributes to the field of mixed methods research by presenting an integrated multistage procedure for constructing precise message treatments using an exploratory sequential mixed methods design. This work harnesses the power of integration through crossover analysis to improve instrument fidelity in message testing research through the use of natural language processing.

Correction: Brüning-Richardson et al. GSK-3 Inhibition Is Cytotoxic in Glioma Stem Cells through Centrosome Destabilization and Enhances the Effect of Radiotherapy in Orthotopic Models. Cancers 2021, 13, 5939.

The authors wish to make the following corrections to this paper [1][...].

Appalachian regional model for organizing and sustaining county-level diabetes coalitions.

This article describes a model for developing diabetes coalitions in rural Appalachian counties and presents evidence of their sustainability. The rural Appalachian coalition model was developed through a partnership between two federal agencies and a regional university. Coalitions go through a competitive application process to apply for one-time $10,000 grants. The project has funded 7 to 9 coalitions annually since 2001, reaching 66 total coalitions in 2008. Sustainability of the coalitions is defined by the number of coalitions that voluntarily report on their programs and services. In 2008, 58 of 66 (87%) coalitions in the Appalachian region continue to function and voluntarily submit reports even after their grant funds have been depleted. The factors that may contribute to sustainability are discussed in the article. This model for organizing coalitions has demonstrated that it is possible for coalitions to be maintained over time in rural underserved areas in Appalachia.

Threats of global warming to the world's freshwater fishes.

Climate change poses a significant threat to global biodiversity, but freshwater fishes have been largely ignored in climate change assessments. Here, we assess threats of future flow and water temperature extremes to ~11,500 riverine fish species. In a 3.2 °C warmer world (no further emission cuts after current governments' pledges for 2030), 36% of the species have over half of their present-day geographic range exposed to climatic extremes beyond current levels. Threats are largest in tropical and sub-arid regions and increases in maximum water temperature are more threatening than changes in flow extremes. In comparison, 9% of the species are projected to have more than half of their present-day geographic range threatened in a 2 °C warmer world, which further reduces to 4% of the species if warming is limited to 1.5 °C. Our results highlight the need to intensify (inter)national commitments to limit global warming if freshwater biodiversity is to be safeguarded.

Effects of computerised clinical decision support systems (CDSS) on nursing and allied health professional performance and patient outcomes: a systematic review of experimental and observational studies.

OBJECTIVE: Computerised clinical decision support systems (CDSS) are an increasingly important part of nurse and allied health professional (AHP) roles in delivering healthcare. The impact of these technologies on these health professionals' performance and patient outcomes has not been systematically reviewed. We aimed to conduct a systematic review to investigate this. MATERIALS AND METHODS: The following bibliographic databases and grey literature sources were searched by an experienced Information Professional for published and unpublished research from inception to February 2021 without language restrictions: MEDLINE (Ovid), Embase Classic+Embase (Ovid), PsycINFO (Ovid), HMIC (Ovid), AMED (Allied and Complementary Medicine) (Ovid), CINAHL (EBSCO), Cochrane Central Register of Controlled Trials (Wiley), Cochrane Database of Systematic Reviews (Wiley), Social Sciences Citation Index Expanded (Clarivate), ProQuest Dissertations & Theses Abstracts & Index, ProQuest ASSIA (Applied Social Science Index and Abstract), Clinical Trials.gov, WHO International Clinical Trials Registry (ICTRP), Health Services Research Projects in Progress (HSRProj), OpenClinical(www.OpenClinical.org), OpenGrey (www.opengrey.eu), Health.IT.gov, Agency for Healthcare Research and Quality (www.ahrq.gov). Any comparative research studies comparing CDSS with usual care were eligible for inclusion. RESULTS: A total of 36 106 non-duplicate records were identified. Of 35 included studies: 28 were randomised trials, three controlled-before-and-after studies, three interrupted-time-series and one non-randomised trial. There were ~1318 health professionals and ~67 595 patient participants in the studies. Most studies focused on nurse decision-makers (71%) or paramedics (5.7%). CDSS as a standalone Personal Computer/LAPTOP-technology was a feature of 88.7% of the studies; only 8.6% of the studies involved 'smart' mobile/handheld-technology. DISCUSSION: CDSS impacted 38% of the outcome measures used positively. Care processes were better in 47% of the measures adopted; examples included, nurses' adherence to hand disinfection guidance, insulin dosing, on-time blood sampling and documenting care. Patient care outcomes in 40.7% of indicators were better; examples included, lower numbers of falls and pressure ulcers, better glycaemic control, screening of malnutrition and obesity and triaging appropriateness. CONCLUSION: CDSS may have a positive impact on selected aspects of nurses' and AHPs' performance and care outcomes. However, comparative research is generally low quality, with a wide range of heterogeneous outcomes. After more than 13 years of synthesised research into CDSS in healthcare professions other than medicine, the need for better quality evaluative research remains as pressing.

GSK-3 Inhibition Is Cytotoxic in Glioma Stem Cells through Centrosome Destabilization and Enhances the Effect of Radiotherapy in Orthotopic Models.

BACKGROUND: Previous data on glycogen synthase kinase 3 (GSK-3) inhibition in cancer models support a cytotoxic effect with selectivity for tumor cells compared to normal tissue but the effect of these inhibitors in glioma has not been widely studied. Here, we investigate their potential as cytotoxics in glioma. METHODS: We assessed the effect of pharmacologic GSK-3 inhibition on established (U87, U251) and patient-derived (GBM1, GBM4) glioblastoma (GBM) cell lines using cytotoxicity assays as well as undertaking a detailed investigation of the effect on cell cycle, mitosis, and centrosome biology. We also assessed drug uptake and efficacy of GSK-3 inhibition alone and in combination with radiation in xenograft models. RESULTS: Using the selective GSK-3 inhibitor AZD2858, we demonstrated single agent cytotoxicity in two patient-derived glioma cell lines (GBM1, GBM4) and two established cell lines (U251 and U87) with IC50 in the low micromolar range promoting centrosome disruption, failed mitosis, and S-phase arrest. Glioma xenografts exposed to AZD2858 also showed growth delay compared to untreated controls. Combined treatment with radiation increased the cytotoxic effect of clinical radiation doses in vitro and in orthotopic glioma xenografts. CONCLUSIONS: These data suggest that GSK-3 inhibition promotes cell death in glioma through disrupting centrosome function and promoting mitotic failure and that AZD2858 is an effective adjuvant to radiation at clinical doses.

A regression-based model to predict chemical migration from packaging to food.

Packaging materials can be a source of chemical contaminants in food. Process-based migration models (PMM) predict the chemical fraction transferred from packaging materials to food (FC) for application in prioritisation tools for human exposure. These models, however, have a relatively limited applicability domain and their predictive performance is typically low. To overcome these limitations, we developed a linear mixed-effects model (LMM) to statistically relate measured FC to properties of chemicals, food, packaging, and experimental conditions. We found a negative relationship between the molecular weight (MW) and FC, and a positive relationship with the fat content of the food depending on the octanol-water partitioning coefficient of the migrant. We also showed that large chemicals (MW > 400 g/mol) have a higher migration potential in packaging with low crystallinity compared with high crystallinity. The predictive performance of the LMM for chemicals not included in the database in contact with untested food items but known packaging material was higher (Coefficient of Efficiency (CoE) = 0.21) compared with a recently developed PMM (CoE = -5.24). We conclude that our empirical model is useful to predict chemical migration from packaging to food and prioritise chemicals in the absence of measurements.

A comprehensive planetary boundary-based method for the nitrogen cycle in life cycle assessment: Development and application to a tomato production case study.

Existing methods that apply the planetary boundary for the nitrogen cycle in life cycle assessment are spatially generic and use an indicator with limited environmental relevance. Here, we develop a spatially resolved method that can quantify the impact of nitrogen emissions to air, soil, freshwater or coastal water on "safe operating space" (SOS) for natural soil, freshwater and coastal water. The method can be used to identify potential "planetary boundary hotspots" in the life cycle of products and to inform appropriate interventions. The method is based on a coupling of existing environmental models and the identification of threshold and reference values in natural soil, freshwater and coastal water. The method is demonstrated for a case study on nitrogen emissions from open-field tomato production in 27 farming areas based on data for 199 farms in the year 2014. Nitrogen emissions were modelled from farm-level data on fertilizer application, fuel consumption and climate- and soil conditions. Two sharing principles, "status quo" and "gross value added", were tested for the assignment of SOS to 1 t of tomatoes. The coupling of models and identification of threshold and reference values resulted in spatially resolved characterization factors applicable to any nitrogen emission and estimations of SOS for each environmental compartment. In the case study, tomato production was found to range from not transgressing to transgressing its assigned SOS in each of the 27 farming areas, depending on the receiving compartment and sharing principle. A high nitrogen use efficiency scenario had the potential to reverse transgressions of assigned SOS for up to three farming locations. Despite of several sources of uncertainty, the developed method may be used in decision-support by stakeholders, ranging from individual producers to global governance institutions. To avoid sub-optimization, it should be applied with methods covering the other planetary boundaries.

Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations.

A reliable quantification of the potential effects of chemicals on freshwater ecosystems requires ecotoxicological response data for a large set of species which is typically not available in practice. In this study, we propose a method to estimate hazardous concentrations (HCs) of chemicals on freshwater ecosystems by combining two in silico approaches: quantitative structure activity relationships (QSARs) and interspecies correlation estimation (ICE) models. We illustrate the principle of our QSAR-ICE method by quantifying the HCs of 51 chemicals at which 50% and 5% of all species are exposed above the concentration causing acute effects. We assessed the bias of the HCs, defined as the ratio of the HC based on measured ecotoxicity data and the HC based on in silico data, as well as the statistical uncertainty, defined as the ratio of the 95th and 5th percentile of the HC. Our QSAR-ICE method resulted in a bias that was comparable to the use of measured data for three species, as commonly used in effect assessments: the average bias of the QSAR-ICE HC50 was 1.2 and of the HC5 2.3 compared to 1.2 when measured data for three species were used for both HCs. We also found that extreme statistical uncertainties (>105) are commonly avoided in the HCs derived with the QSAR-ICE method compared to the use of three measurements with statistical uncertainties up to 1012. We demonstrated the applicability of our QSAR-ICE approach by deriving HC50s for 1,223 out of the 3,077 organic chemicals of the USEtox database. We conclude that our QSAR-ICE method can be used to determine HCs without the need for additional in vivo testing to help prioritise which chemicals with no or few ecotoxicity data require more thorough assessment.

Decreasing formalin concentration improves quality of DNA extracted from formalin-fixed paraffin-embedded tissue specimens without compromising tissue morphology or immunohistochemical staining.

Genomic technologies are increasingly used clinically for both diagnosis and guiding cancer therapy. However, formalin fixation can compromise DNA quality. This study aimed to optimise tissue fixation using normal colon, liver and uterus (n=8 each) by varying neutral buffered formalin (NBF) concentration (1%-5% w/v) and fixation time (24-48 hours). Fixation using 4% NBF improved DNA quality (assessed by qPCR) compared with routine (4% unbuffered formal saline-fixed) specimens (p<0.01). Further improvements were achieved by reducing NBF concentration (p<0.00001), whereas fixation time had no effect (p=0.110). No adverse effects were detected by histopathological or QuPath morphometric analysis. Immunohistochemistry for multicytokeratin and α-smooth muscle actin revealed no changes in staining specificity or intensity in any tissue other than on liver multicytokeratin staining intensity, where the effect of fixation time was more significant (p=0.0004) than NBF concentration (p=0.048). Thus, reducing NBF concentration can maximise DNA quality without compromising tissue morphology or standard histopathological analyses.

Confronting variability with uncertainty in the ecotoxicological impact assessment of down-the-drain products.

The use of down-the-drain products and the resultant release of chemicals may lead to pressures on the freshwater environment. Ecotoxicological impact assessment is a commonly used approach to assess chemical products but is still influenced by several uncertainty and variability sources. As a result, the robustness and reliability of such assessments can be questioned. A comprehensive and systematic assessment of these sources is, therefore, needed to increase their utility and credibility. In this study, we present a method to systematically analyse the uncertainty and variability of the potential ecotoxicological impact (PEI) of chemicals using a portfolio of 54 shampoo products. We separately quantified the influence of the statistical uncertainty in the prediction of physicochemical properties and freshwater toxicity as predicted from Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) respectively, and of various sources of spatial and technological variability as well as variability in consumer habits via 2D Monte Carlo simulations. Overall, the variation in the PEIs of shampoo use was mainly the result of uncertainty due to the use of toxicity data from three species only. All uncertainty sources combined resulted in PEIs ranging on average over seven orders of magnitude (ratio of the 90th to the 10th percentile) so that an absolute quantification of the ecological risk would not be meaningful. In comparison, variation in shampoo composition was the most influential source of variability, although less than compared to uncertainty, leading to PEIs ranging over three orders of magnitude. Increasing the number of toxicity data by increasing the number of species, either through additional measurements or ecotoxicological modelling (e.g. using Interspecies Correlation Equations), should get priority to improve the reliability of PEIs. These conclusions are not limited to shampoos but are applicable more generally to the down-the-drain products since they all have similar data limitations and associated uncertainties relating to the availability of ecotoxicity data and variability in consumer habits and use.

Greenhouse gas footprints of palm oil production in Indonesia over space and time.

Palm oil, the most widely used vegetable oil, is one of the largest drivers of greenhouse gas (GHG) emissions from global land use and land cover change. Here, we provide fine-resolution (100 m × 100 m) estimates of GHG footprints of current (2015) and potential future scenarios (2030) of crude palm oil (CPO) production in Indonesia. The current estimated average GHG footprint excluding production on Java is 5.7 t CO2 eq t-1 CPO; ranging from 0.7 t CO2 eq t-1 CPO in Hulu Sungai Tengah, Kalimantan to 26.0 t CO2 eq t-1 CPO in Pontianak, Kalimantan, and these vast differences are only discernible at fine spatial scales. The future GHG footprint of Indonesian CPO could be reduced by 42% without compromising increased output by limiting expansion to non-forest and non-peat land. Our fine-scale analysis provides a spatial screening approach to inform new oil palm concessions and sourcing decisions, before more cost-intensive patch analysis and carbon stock assessments are conducted.

Characters matter: How narratives shape affective responses to risk communication.

INTRODUCTION: Whereas scientists depend on the language of probability to relay information about hazards, risk communication may be more effective when embedding scientific information in narratives. The persuasive power of narratives is theorized to reside, in part, in narrative transportation. PURPOSE: This study seeks to advance the science of stories in risk communication by measuring real-time affective responses as a proxy indicator for narrative transportation during science messages that present scientific information in the context of narrative. METHODS: This study employed a within-subjects design in which participants (n = 90) were exposed to eight science messages regarding flood risk. Conventional science messages using probability and certainty language represented two conditions. The remaining six conditions were narrative science messages that embedded the two conventional science messages within three story forms that manipulated the narrative mechanism of character selection. Informed by the Narrative Policy Framework, the characters portrayed in the narrative science messages were hero, victim, and victim-to-hero. Natural language processing techniques were applied to identify and rank hero and victim vocabularies from 45 resident interviews conducted in the study area; the resulting classified vocabulary was used to build each of the three story types. Affective response data were collected over 12 group sessions across three flood-prone communities in Montana. Dial response technology was used to capture continuous, second-by-second recording of participants' affective responses while listening to each of the eight science messages. Message order was randomized across sessions. ANOVA and three linear mixed-effects models were estimated to test our predictions. RESULTS: First, both probabilistic and certainty science language evoked negative affective responses with no statistical differences between them. Second, narrative science messages were associated with greater variance in affective responses than conventional science messages. Third, when characters are in action, variation in the narrative mechanism of character selection leads to significantly different affective responses. Hero and victim-to-hero characters elicit positive affective responses, while victim characters produce a slightly negative response. CONCLUSIONS: In risk communication, characters matter in audience experience of narrative transportation as measured by affective responses.

FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015.

Streamflow data is highly relevant for a variety of socio-economic as well as ecological analyses or applications, but a high-resolution global streamflow dataset is yet lacking. We created FLO1K, a consistent streamflow dataset at a resolution of 30 arc seconds (~1 km) and global coverage. FLO1K comprises mean, maximum and minimum annual flow for each year in the period 1960-2015, provided as spatially continuous gridded layers. We mapped streamflow by means of artificial neural networks (ANNs) regression. An ensemble of ANNs were fitted on monthly streamflow observations from 6600 monitoring stations worldwide, i.e., minimum and maximum annual flows represent the lowest and highest mean monthly flows for a given year. As covariates we used the upstream-catchment physiography (area, surface slope, elevation) and year-specific climatic variables (precipitation, temperature, potential evapotranspiration, aridity index and seasonality indices). Confronting the maps with independent data indicated good agreement (R2 values up to 91%). FLO1K delivers essential data for freshwater ecology and water resources analyses at a global scale and yet high spatial resolution.