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Background: Bacterial antimicrobial resistance (AMR) is a global threat to both humans and livestock. Despite this, there is limited global consensus on data-informed, priority areas for intervention in both sectors. We compare current livestock AMR data collection efforts with other variables pertinent to human and livestock AMR to identify critical data gaps and mutual priorities. Methods: We globally synthesized livestock AMR data from open-source surveillance reports and point prevalence surveys stratified for six pathogens (Escherichia coli, Staphylococcus aureus, non-typhoidal Salmonella, Campylobacter spp., Enterococcus faecalis, Enterococcus faecium) and eleven antimicrobial classes important in human and veterinary use, published between 2000 and 2020. We also included all livestock species represented in the data: cattle, chickens, pigs, sheep, turkeys, ducks, horses, buffaloes, and goats. We compared this data with intended priorities calculated from: disability-adjusted life years (DALYs), livestock antimicrobial usage (AMU), livestock biomass, and a global correlation exercise between livestock and human proportion of resistant isolates. Results: Resistance to fluoroquinolones and macrolides in Staphylococcus aureus were identified as priorities in many countries but, less than 10% of these reported livestock AMR data. Resistance data for Escherichia coli specific to cattle, chickens, and pigs, which we prioritized, were also well collected. AMR data collection on non-typhoidal Salmonella and other livestock species were often not prioritized. Of 232 categories prioritized by at least one country, data were only collected for 48% (n = 112). Conclusions: The lack of livestock AMR data globally for broad resistance in Staphylococcus aureus could underplay their zoonotic threat. Countries can bolster livestock AMR data collection, reporting, and intervention setting for Staphylococcus aureus as done for Escherichia coli. This framework can provide guidance on areas to strengthen AMR surveillance and decision-making for humans and livestock, and if done routinely, can adapt to resistance trends and priorities. [ABSTRACT FROM AUTHOR]

Over the last two decades, the causal relationship between climate change and migration has gained increasing prominence in international research and policy. Despite recent advances in conceptual frameworks and applied techniques, the empirical evidence does not provide clear‐cut conclusions, mainly due to the intrinsic complexity of the phenomenon of interest, the irreducible heterogeneity of the transmission mechanisms, some common misconceptions, and, in particular, the paucity of adequate data. In this work, we present the first data‐oriented review of the nexus between climate change and migration. Then, we discuss open issues and assess the main data gaps that currently prevent more robust quantifications. Finally, using a prominent survey collection produced by the World Bank as a case study, we highlight opportunities for exploiting and enhancing the potential of existing multi‐topic and multi‐purpose household survey datasets. [ABSTRACT FROM AUTHOR]

Abstract Background Bacterial antimicrobial resistance (AMR) is a global threat to both humans and livestock. Despite this, there is limited global consensus on data-informed, priority areas for intervention in both sectors. We compare current livestock AMR data collection efforts with other variables pertinent to human and livestock AMR to identify critical data gaps and mutual priorities. Methods We globally synthesized livestock AMR data from open-source surveillance reports and point prevalence surveys stratified for six pathogens (Escherichia coli, Staphylococcus aureus, non-typhoidal Salmonella, Campylobacter spp., Enterococcus faecalis, Enterococcus faecium) and eleven antimicrobial classes important in human and veterinary use, published between 2000 and 2020. We also included all livestock species represented in the data: cattle, chickens, pigs, sheep, turkeys, ducks, horses, buffaloes, and goats. We compared this data with intended priorities calculated from: disability-adjusted life years (DALYs), livestock antimicrobial usage (AMU), livestock biomass, and a global correlation exercise between livestock and human proportion of resistant isolates. Results Resistance to fluoroquinolones and macrolides in Staphylococcus aureus were identified as priorities in many countries but, less than 10% of these reported livestock AMR data. Resistance data for Escherichia coli specific to cattle, chickens, and pigs, which we prioritized, were also well collected. AMR data collection on non-typhoidal Salmonella and other livestock species were often not prioritized. Of 232 categories prioritized by at least one country, data were only collected for 48% (n = 112). Conclusions The lack of livestock AMR data globally for broad resistance in Staphylococcus aureus could underplay their zoonotic threat. Countries can bolster livestock AMR data collection, reporting, and intervention setting for Staphylococcus aureus as done for Escherichia coli. This framework can provide guidance on areas to strengthen AMR surveillance and decision-making for humans and livestock, and if done routinely, can adapt to resistance trends and priorities.

Copyright of Waste Forum is the property of Czech Environment Management Center (CEMC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Attention to public problems is increasingly being driven by data. Given that data is used as a communication tool, a lack of data may further exacerbate existing inequities when allocating public resources, especially around infrastructure. Using a co-creation and power-sharing approach to community involvement, this field report describes how a rural community and academic partners worked together to collect photo and video data to create a community-driven flood map that communicates flood issues. The results indicate community members felt like they belong, are valued, and their flooding challenges are visible. These are all important steps to achieving attention and support to address their flooding infrastructure challenges. We end by offering six concrete suggestions, ranging from centering community needs to interdisciplinary collaboration, for how this approach can be used in other applied communication projects. [ABSTRACT FROM AUTHOR]

NDVI (Normalized Difference Vegetation Index) is an essential tool for climate and environmental monitoring, but it is often contaminated by clouds and unfavorable atmospheric conditions. In this study, we designed a new, simple yet effective method for reconstructing data, which we call the Hybrid Filter. This study is the first to reconstruct missing NDVI time series data in cloud-prone areas by combining several data reconstruction techniques with a forecasting technique based on Exponential Moving Average (EMA). The study was conducted in Cilegon City and Batu City using NDVI time series data from the Landsat 8 satellite for the period 2014-2022. Experimental results show that the hybrid filter significantly outperforms the Spatio-Temporal Savitzky-Golay (STSG) filter, Gap Filling Savitzky-Golay (GFSG) filter, Savitzky-Golay (SG) filter, and Whittaker filter. The hybrid filter is capable of recovering missing data with high accuracy, stability, noise reduction, and maintaining the temporal integrity of NDVI data even under conditions of large data gaps and high missing data rates, making it a reliable solution for NDVI analysis in cloud-prone areas. These findings affirm the superiority of the hybrid filter in producing accurate and reliable NDVI data for vegetation and environmental monitoring.

Abstract Nonparametric estimation of the covariance function and the power spectral density of uniformly spaced data from stationary stochastic processes with missing samples is investigated. Several common methods are tested for their systematic and random errors under the condition of variations in the distribution of the missing samples. In addition to random and independent outliers, the influence of longer and hence correlated data gaps on the performance of the various estimators is also investigated. The aim is to construct a bias-free estimation routine for the covariance function and the power spectral density from stationary stochastic processes under the condition of missing samples with an optimum use of the available information in terms of low estimation variance and mean square error, and that independent of the spectral composition of the data gaps. The proposed procedure is a combination of three methods that allow bias-free estimation of the desired statistical functions with efficient use of the available information: weighted averaging over valid samples, derivation of the covariance estimate for the entire data set and restriction of the domain of the covariance function in a post-processing step, and appropriate correction of the covariance estimate after removal of the estimated mean value. The procedures abstain from interpolation of missing samples as well as block subdivision. Spectral estimates are obtained from covariance functions and vice versa using Wiener–Khinchin’s theorem.

Nonparametric estimation of the covariance function and the power spectral density of uniformly spaced data from stationary stochastic processes with missing samples is investigated. Several common methods are tested for their systematic and random errors under the condition of variations in the distribution of the missing samples. In addition to random and independent outliers, the influence of longer and hence correlated data gaps on the performance of the various estimators is also investigated. The aim is to construct a bias-free estimation routine for the covariance function and the power spectral density from stationary stochastic processes under the condition of missing samples with an optimum use of the available information in terms of low estimation variance and mean square error, and that independent of the spectral composition of the data gaps. The proposed procedure is a combination of three methods that allow bias-free estimation of the desired statistical functions with efficient use of the available information: weighted averaging over valid samples, derivation of the covariance estimate for the entire data set and restriction of the domain of the covariance function in a post-processing step, and appropriate correction of the covariance estimate after removal of the estimated mean value. The procedures abstain from interpolation of missing samples as well as block subdivision. Spectral estimates are obtained from covariance functions and vice versa using Wiener–Khinchin's theorem. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) poses an immediate danger to global health. If unaddressed, the current upsurge in AMR threatens to reverse the achievements in reducing the infectious disease–associated mortality and morbidity associated with antimicrobial treatment. Consequently, there is an urgent need for strategies to prevent or slow the progress of AMR. Vaccines potentially contribute both directly and indirectly to combating AMR. Modeling studies have indicated significant gains from vaccination in reducing AMR burdens for specific pathogens, reducing mortality/morbidity, and economic loss. However, quantifying the real impact of vaccines in these reductions is challenging because many of the study designs used to evaluate the contribution of vaccination programs are affected by significant background confounding, and potential selection and information bias. Here, we discuss challenges in assessing vaccine impact to reduce AMR burdens and suggest potential approaches for vaccine impact evaluation nested in vaccine trials. [ABSTRACT FROM AUTHOR]

In this article, we present a framework to assess the challenges in the climate-related data landscape. From our perspective, we describe challenges and opportunities for innovation to close existing gaps in selected data quality dimensions for central banks and statistical offices. We further examine the role of networks in promoting innovation and international collaboration, highlighting practical experiences with a case study of the Sustainable Finance Data Hub at the Deutsche Bundesbank. Finally, we discuss how these lessons can provide avenues to enhance data quality in central banks and official statistics and outline directions for future research. [ABSTRACT FROM AUTHOR]

Time series from fisheries often contain multiple missing data. This is a severe limitation that prevents using the data for research on population dynamics, stock assessment, forecasting, and, hence, decision-making around marine resources. Several methods have been proposed to impute missing data in univariate time series. Still, their performances depend not only on the amount of missing data but also on the data structure. This study compares the performance of twelve imputation methods on the time series of marine fishery landings for six species in the Colombian Pacific Ocean. Unlike other studies, we validate the precision of the imputations in the same target time series that include missing data, using the Known Sub-Sequence Algorithm (KSSA), a novelty validation approach that simulates missing data in known sub-sequences of the target time series. The results showed that the best methods for imputation are Seasonal Decomposition with Kalman filters and Structural Models with Kalman filters fitted by maximum likelihood. Results also show that validating the imputation methods with other time series different to the target time series, leads to wrong imputation methods choices. It is noteworthy that these methods and also the validation framework are mainly suited to time series with non-random distribution of missing data, this is, missing data produced systematically in chunks or clusters with predictable frequency, which are common in marine sciences.

Data gaps are a significant lack of data about marginalized groups existing due to unequal power relations (D’Ignazio and Klein, 2020). They both perpetuate and result in a dominance of male, white, hetero, and cis perspectives in how we make sense of and interact with the world. The most prominent data gap is the gender data gap notably described by Criado-Perez (2020). However, not only women, but all marginalized groups are affected by such gaps, as data about them are frequently not collected due to a disregard on behalf of those in power of the need to do so. LGBTIQ+ people, considered a ‘hidden population’ by demographers, are a case in point. The acronym is used to refer to lesbian, gay, bisexual, trans, intersex, and queer people, as well as all people with non-normative sexual or gender identities, including asexual and agender people, who do not consider themselves as falling under one of these labels. A first step towards identifying and closing data gaps is to take stock of data that already exist. In this paper we give an overview of LGBTIQ+ data in European social science archives. We researched all data archives of CESSDA ERIC, the Consortium of European Social Science Data Archives, and found 66 LGBTIQ+ datasets in 9 of the 34 member and associated archives and 1 former member archive. We discuss characteristics, coverages, and findability of the identified datasets and approach the question of potential data gaps by analyzing the keywords assigned to each dataset by the archive.

Today, the large cities of Colombia – especially Bogotá, due to the growth of its population (9.3 million with the arrival of immigrants) – demand the projection of intelligent public and private transport systems, as an achievement of the mobility policy of the Bogota Humana administration. Hence, this question arises: What are the challenges and areas of opportunity of adapting Big Data to project an Intelligent Transportation System for all citizens in Bogotá? Based on this question, our aim is to determine the contributions that Big Data offers as a collection center for the projection of an intelligent system for the city. Our research was proposed with a qualitative approach and a descriptive study. The review of some studies developed using Big Data techniques and content data analysis of their organized structure by the District Mobility Secretariat in Bogotá was included. The results allow guiding the contributions of Big Data after analyzing the structure of indicators offered by the data set. From these, we found gaps and voids that are concerning for the Intelligent Transportation System that is expected in the future for Bogotá.

A number of data governance policies have recently been introduced or revised by the Indian Government with the stated goal of unlocking the developmental and economic potential of data. The policies seek to implement standardized frameworks for public data management and establish platforms for data exchange. However, India has a longstanding history of record-keeping and information transparency practices, which are crucial in the context of data management. These connections have not been explicitly addressed in recent policies like the Draft National Data Governance Framework, 2022. To understand if record management has a role to play in modern public data governance, we analyze the key new data governance framework and the associated Indian Urban Data Exchange platform as a case study. The study examines the exchange where public records serve as a potential source of data. It evaluates the coverage and the actors involved in the creation of this data to understand the impact of records management on government departments’ ability to publish datasets. We conclude that while India recognizes the importance of data as a public good, it needs to integrate digital records management practices more effectively into its policies to ensure accurate, up-to-date, and accessible data for public benefit.

There are more than 3,500 active pharmaceutical ingredients (APIs) on the global market for human and veterinary use. Residues of these APIs eventually reach the aquatic environment. Although an environmental risk assessment (ERA) for marketing authorization applications of medicinal products is mandatory in the European Union since 2006, an ERA is lacking for most medicines approved prior to 2006 (legacy APIs). Since it is unfeasible to perform extensive ERA tests for all these legacy APIs, there is a need for prioritization of testing based on the limited data available. Prioritized APIs can then be further investigated to estimate their environmental risk in more detail. In this study, we prioritized more than 1,000 APIs used in Europe based on their predicted risk for aquatic freshwater ecosystems. We determined their risk by combining an exposure estimate (Measured or Predicted Environmental Concentration; MEC or PEC, respectively) with a Predicted No Effect Concentration (PNEC). We developed several procedures to combine the limited empirical data available with in silico data, resulting in multiple API rankings varying in data needs and level of conservativeness. In comparing empirical with in silico data, our analysis confirmed that the PEC estimated with the default parameters used by the European Medicines Agency often – but not always – represents a worst-case scenario. Comparing the ecotoxicological data for the three main taxonomic groups, we found that fish represents the most sensitive species group for most of the APIs in our list. We furthermore show that the use of in silico tools can result in a substantial underestimation of the ecotoxicity of APIs. After combining the different exposure and effect estimates into four risk rankings, the top-ranking APIs were further screened for availability of ecotoxicity data in data repositories. This ultimately resulted in the prioritization of 15 APIs for further ecotoxicological testing and/or exposure assessment.

This article is part of the Special Collection 'Global plant diversity and distribution'. See https://www.newphytologist.org/global-plant-diversity for more details. [ABSTRACT FROM AUTHOR]

Providing an accurate spatiotemporal distribution of rainfall and filling data gaps are pivotal for effective water resource management. This study focuses on the Asir region in the southwest of Saudi Arabia. Given the limited accuracy of satellite data in this arid/mountain-dominated study area, geospatial interpolation has emerged as a viable alternative approach for filling terrestrial records data gaps. Furthermore, the irregularity in rain gauge data and the yearly spatial variation in data gaps hinder the creation of a coherent distribution pattern. To address this, the Centered Root Mean Square Error (CRMSE) is employed as a criterion to select the most appropriate geospatial interpolation technique among 51 evaluated methods for maximum and total yearly precipitation data. This study produced gap-free maps of total and maximum yearly precipitation from 1966 to 2013. Beyond 2013, it is recommended to utilize ordinary Kriging with a J-Bessel semivariogram and simple Kriging with a K-Bessel semivariogram to estimate the spatial distribution of maximum and total yearly rainfall depth, respectively. Additionally, a proposed methodology for allocating additional rain gauges to improve the accuracy of rainfall spatial distribution is introduced based on a cross-validation error (CVE) assessment. Newly proposed gauges in the study area resulted in a significant 21% CVE reduction. [ABSTRACT FROM AUTHOR]

Time series from fisheries often contain multiple missing data. This is a severe limitation that prevents using the data for research on population dynamics, stock assessment, forecasting, and, hence, decision-making around marine resources. Several methods have been proposed to impute missing data in univariate time series. Still, their performances depend not only on the amount of missing data but also on the data structure. This study compares the performance of twelve imputation methods on the time series of marine fishery landings for six species in the Colombian Pacific Ocean. Unlike other studies, we validate the precision of the imputations in the same target time series that include missing data, using the Known Sub-Sequence Algorithm (KSSA), a novelty validation approach that simulates missing data in known sub-sequences of the target time series. The results showed that the best methods for imputation are Seasonal Decomposition with Kalman filters and Structural Models with Kalman filters fitted by maximum likelihood. Results also show that validating the imputation methods with other time series different to the target time series, leads to wrong imputation methods choices. It is noteworthy that these methods and also the validation framework are mainly suited to time series with nonrandom distribution of missing data, this is, missing data produced systematically in chunks or clusters with predictable frequency, which are common in marine sciences. [ABSTRACT FROM AUTHOR]

This report presents and reviews the available sources of data on public transit riders and ridership. We intend it to be a resource for those who manage or simply wish to understand U.S. transit. In conducting this review, we consider the advantages and disadvantages of publicly available data on transit from a variety of public and private sources. We consider as well the relatively scarcer and less available sources of data on other providers of shared mobility, like ride-hail services, that compete with and complement public transit, as well as pieces we see as missing from the transit analytics pie. We conclude by discussing how data gaps both align with existing inequities and enable them to continue, unmeasured, and how the COVID-19 pandemic has made closing these gaps all the more important.

This report presents and reviews the available sources of data on public transit riders and ridership. We intend it to be a resource for those who manage or simply wish to understand U.S. transit. In conducting this review, we consider the advantages and disadvantages of publicly available data on transit from a variety of public and private sources. We consider as well the relatively scarcer and less available sources of data on other providers of shared mobility, like ride-hail services, that compete with and complement public transit, as well as pieces we see as missing from the transit analytics pie. We conclude by discussing how data gaps both align with existing inequities and enable them to continue, unmeasured, and how the COVID-19 pandemic has made closing these gaps all the more important.

Available information and potential data gaps for non-fish marine organisms (cnidarians, crustaceans, echinoderms, molluscs, sponges, mammals, reptiles, and seabirds) covered by the global database SeaLifeBase were reviewed for eight marine ecosystems (Adriatic Sea, Aegean Sea, Baltic Sea, Bay of Biscay/Celtic Sea/Iberian Coast, Black Sea, North Sea, western Mediterranean Sea, Levantine Sea) across European Seas. The review of the SeaLifeBase dataset, which is based on published literature, analyzed information coverage for eight biological characteristics (diet, fecundity, maturity, length-weight relationships, spawning, growth, lifespan, and natural mortality). These characteristics are required for the development of ecosystem and ecological models to evaluate the status of marine resources and related fisheries. Our analyses revealed that information regarding these biological characteristics in the literature was far from complete across all studied areas. The level of available information was nonetheless reasonably good for sea turtles and moderate for marine mammals in some areas (Baltic Sea, Bay of Biscay/Celtic Sea/Iberian Coast, Black Sea, North Sea and western Mediterranean Sea). Further, seven of the areas have well-studied species in terms of information coverage for biological characteristics of some commercial species whereas threatened species are generally not well studied. Across areas, the most well-studied species are the cephalopod common cuttlefish (Sepia officinalis) and the crustacean Norway lobster (Nephrops norvegicus). Overall, the information gap is narrowest for length-weight relationships followed by growth and maturity, and widest for fecundity and natural mortality. Based on these insights, we provide recommendations to prioritize species with insufficient or missing biological data that are common across the studied marine ecosystems and to address data deficiencies.

Growing threats to biodiversity demand timely, detailed information on species occurrence, diversity and abundance at large scales. Camera traps (CTs), combined with computer vision models, provide an efficient method to survey species of certain taxa with high spatio-temporal resolution. We test the potential of CTs to close biodiversity knowledge gaps by comparing CT records of terrestrial mammals and birds from the recently released Wildlife Insights platform to publicly available occurrences from many observation types in the Global Biodiversity Information Facility. In locations with CTs, we found they sampled a greater number of days (mean = 133 versus 57 days) and documented additional species (mean increase of 1% of expected mammals). For species with CT data, we found CTs provided novel documentation of their ranges (93% of mammals and 48% of birds). Countries with the largest boost in data coverage were in the historically underrepresented southern hemisphere. Although embargoes increase data providers' willingness to share data, they cause a lag in data availability. Our work shows that the continued collection and mobilization of CT data, especially when combined with data sharing that supports attribution and privacy, has the potential to offer a critical lens into biodiversity. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'. [ABSTRACT FROM AUTHOR]

Copyright of Waste Forum is the property of Czech Environment Management Center (CEMC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Missing values in water level data is a persistent problem in data modelling and especially common in developing countries. Data imputation has received considerable research attention, to raise the quality of data in the study of extreme events such as flooding and droughts. This article evaluates single and multiple imputation methods used on monthly univariate and multivariate water level data from four water stations on the rivers Benue and Niger in Nigeria. The missing completely at random, missing at random and missing not at random data mechanisms were each considered. The best imputation method is identified using two error metrics: root mean square error and mean absolute percentage error. For the univariate case, the seasonal decomposition method is best for imputing missing values at various missingness levels for all three missing mechanisms, followed by Kalman smoothing, while random imputation is much poorer. For instance, for 5% missing data for the Kainji water station, missing completely at random, the Kalman smoothing, random and seasonal decomposition methods had average root mean square errors of 13.61, 102.60 and 10.46, respectively. For the multivariate case, missForest is best, closely followed by k nearest neighbour for the missing completely at random and missing at random mechanisms, and k nearest neighbour is best, followed by missForest, for the missing not at random mechanism. The random forest and predictive mean matching methods perform poorly in terms of the two metrics considered. For example, for 10% missing data missing completely at random for the Ibi water station, the average root mean square errors for random forest, k nearest neighbour, missForest and predictive mean matching were 22.51, 17.17, 14.60 and 25.98, respectively. The results indicate that the seasonal decomposition method, and missForest or k nearest neighbour methods, can impute univariate and multivariate water level missing data, respectively, with higher accuracy than the other methods considered. [ABSTRACT FROM AUTHOR]

Up-to-date reporting of atmospheric pollen contents is essential to assist doctors and allergy sufferers alike to undertake treatment or preventative measures. We have evaluated the extent of the digitally accessible knowledge (DAK) created by the Spanish network of pollen monitoring stations and analyzed the gaps in three main DAK factors: data completeness, data obsolescence, and data publication. Data from 118 distinct stations were discovered, of which one in four seemed to have discontinued publication of updated data either continuously or seasonally. While two-thirds of the sites published data through their own local portals, only about one-half also contributed daily data to the two main aggregators in the country (SEAIC and REA), which in turn were the only outlets available for one-tenth of the sites. The analysis revealed the probable existence of completely obscure sites recording, but not reporting, data. Recovering or surfacing dormant or silent sites may significantly improve the DAK about pollen in Spain. [ABSTRACT FROM AUTHOR]

Digital battery passports (DBPs) may help support the transition towards more sustainable and circular electric vehicle battery (EVB) value chains by providing respective value chain actors with valuable data in sustainable battery management. To ensure such support, DBPs have to fulfil EVB value chain actors' data needs and requirements. This work sets out to provide initial empirical insight into EVB value chain actors' data needs and requirements in the context of sustainable battery management. The analysis is based on a DBP concept comprising 54 data points and four information categories. The research design encompassed focus group expert workshops, expert interviews, follow-up expert consultation and subsequent qualitative content analyses. The results reveal diverging perspectives regarding data needs and availability. These are attributable to value chain actors' different positions and roles in the value chain, as well as to a lack of well-defined information flows along the EVB value chain. The work augments current DBP research by introducing the first systematic mapping of EVB value chain actors’ data needs in sustainability battery management. It further provides policy makers, and practitioners with guidance on the information content of DBPs, and on how their development and implementation may be supported.

Continuous meteorological variable time series are highly demanded for various climate related studies. Five statistical models were tested for application of temporal gaps filling in time series of surface air pressure, air temperature, relative air humidity, incoming solar radiation, net radiation, and soil temperature. A bilayer artificial neural network, linear regression, linear regression with interactions, and the Gaussian process regression models with exponential and rational quadratic kernel were used to fill the gaps. Models were driven by continuous time series of meteorological variables from the ECMWF (European Centre for Medium-range Weather Forecasts) ERA5-Land reanalysis. Raw ECMWF ERA5-Land reanalysis data are not applicable for characterization of specific local weather conditions. The linear correlation coefficients (CC) between ERA5-Land data and in situ observations vary from 0.61 (for wind direction) to 0.99 (for atmospheric pressure). The mean difference is high and estimated at 3.2 °C for air temperature and 3.5 hPa for atmospheric pressure. The normalized root-mean-square error (NRMSE) is 5–13%, except for wind direction (NRMSE = 49%). The linear bias correction of ERA5-Land data improves matching between the local and reanalysis data for all meteorological variables. The Gaussian process regression model with an exponential kernel based or bilayered artificial neural network trained on ERA5-Land data significantly shifts raw ERA5-Land data toward the observed values. The NRMSE values reduce to 2–11% for all variables, except wind direction (NRMSE = 22%). CC for the model is above 0.87, except for wind characteristics. The suggested model calibrated against in situ observations can be applied for gap-filling of time series of meteorological variables. [ABSTRACT FROM AUTHOR]

Copyright of RHS: Revista Humanismo y Sociedad is the property of Corporacion Universitaria Remington and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The transmission of antimicrobial resistance (AMR) between food‐producing animals (poultry, cattle and pigs) during short journeys (< 8 h) and long journeys (> 8 h) directed to other farms or to the slaughterhouse lairage (directly or with intermediate stops at assembly centres or control posts, mainly transported by road) was assessed. Among the identified risk factors contributing to the probability of transmission of antimicrobial‐resistant bacteria (ARB) and antimicrobial resistance genes (ARGs), the ones considered more important are the resistance status (presence of ARB/ARGs) of the animals pre‐transport, increased faecal shedding, hygiene of the areas and vehicles, exposure to other animals carrying and/or shedding ARB/ARGs (especially between animals of different AMR loads and/or ARB/ARG types), exposure to contaminated lairage areas and duration of transport. There are nevertheless no data whereby differences between journeys shorter or longer than 8 h can be assessed. Strategies that would reduce the probability of AMR transmission, for all animal categories include minimising the duration of transport, proper cleaning and disinfection, appropriate transport planning, organising the transport in relation to AMR criteria (transport logistics), improving animal health and welfare and/or biosecurity immediately prior to and during transport, ensuring the thermal comfort of the animals and animal segregation. Most of the aforementioned measures have similar validity if applied at lairage, assembly centres and control posts. Data gaps relating to the risk factors and the effectiveness of mitigation measures have been identified, with consequent research needs in both the short and longer term listed. Quantification of the impact of animal transportation compared to the contribution of other stages of the food‐production chain, and the interplay of duration with all risk factors on the transmission of ARB/ARGs during transport and journey breaks, were identified as urgent research needs. [ABSTRACT FROM AUTHOR]

Intergovernmental Panel on Climate change (IPCC) reports and other literature draw focus to disproportionate impacts of natural hazards on women and girls; as well as on the roles they play on climate change mitigation, adaptation, natural resource management, environmental conservation and degradation. The connections between climate change and gender are rarely measured and hence, fewer related policies are in place. This paper showcases three separate data collation and analysis exercises, that generated evidence on the gender and climate change nexus utilizing big data: i) geospatial data integrated with Demographic and Health Survey (DHS) data that provide evidence on the connections between climate change and gender outcomes; ii) search and social media data that show violence-related searches increasing at times of hazards; and iii) web document text data that find little mention of gender in climate speeches. Beyond shedding light on the clear connections between gender and climate change issues, it also highlights the important role that non-traditional sources can play in filling these data gaps. [ABSTRACT FROM AUTHOR]

A key goal of the Bank for International Settlements is to produce in-depth economic analysis drawing on its position at the intersection of research and policy, its financial markets savvy, and its unique statistical chest. Since the interwar financial crisis in the 1930s, the traditional objective of assessing international linkages has continuously guided BIS statistical offering. Important micro-level data sets have been collected to support global financial regulation in parallel. In addition, the BIS hosts the Irving Fisher Committee on Central Bank Statistics, a global network of central bank statisticians, economists and policymakers who discuss statistical issues of interest to central banks and develop related methodological work. It also actively supports various global statistical initiatives, especially to close economic and financial data gaps. These activities have proved instrumental for tracking and understanding developments in international finance, as was evident again on the occasion of the recent COVID-19 pandemic. Looking ahead, the BIS and the IFC can continue to contribute actively to the international statistical cooperation framework. [ABSTRACT FROM AUTHOR]

Data is an extremely important intangible good, but official data is not always available. It may be scarce for many reasons, among which: low statistical capacities, poor funding for data and statistics, weak data dissemination and use culture. A solution to fill data gaps needs to consider that there is data made available on the web, usually coming in an unstructured way, that can be combined with innovative methods to generate relevant information. National and international organisations need to engage with new data sources and methods considering the crisis of traditional data collection systems that causes data gaps. In this light, FAO created in 2019 the "Data Lab for statistical innovation" to fill such gaps by modernising the Organization's statistical business, which means improving the timeliness and granularity of data collection, providing automated analysis, and capturing early warning signals. It does so through the use of cutting-edge technologies (such as web scraping, text mining, geo-spatial data analysis and artificial intelligence) and by introducing nonconventional data sources (social media, online newspaper articles). This article summarises the experience of the FAO Data Lab and how it has been useful for the Organization to fulfil its mandate. [ABSTRACT FROM AUTHOR]

Intergovernmental Panel on Climate change (IPCC) reports and other literature draw focus to disproportionate impacts of natural hazards on women and girls; as well as on the roles they play on climate change mitigation, adaptation, natural resource management, environmental conservation and degradation. The connections between climate change and gender are rarely measured and hence, fewer related policies are in place. This paper showcases three separate data collation and analysis exercises, that generated evidence on the gender and climate change nexus utilizing big data: i) geospatial data integrated with Demographic and Health Survey (DHS) data that provide evidence on the connections between climate change and gender outcomes; ii) search and social media data that show violence-related searches increasing at times of hazards; and iii) web document text data that find little mention of gender in climate speeches. Beyond shedding light on the clear connections between gender and climate change issues, it also highlights the important role that non-traditional sources can play in filling these data gaps. [ABSTRACT FROM AUTHOR]

The European Commission has determined that cannabidiol (CBD) can be considered as a novel food (NF), and currently, 19 applications are under assessment at EFSA. While assessing these, it has become clear that there are knowledge gaps that need to be addressed before a conclusion on the safety of CBD can be reached. Consequently, EFSA has issued this statement, summarising the state of knowledge on the safety of CBD consumption and highlighting areas where more data are needed. Literature searches for both animal and human studies have been conducted to identify safety concerns. Many human studies have been carried out with Epidyolex®, a CBD drug authorised to treat refractory epilepsies. In the context of medical conditions, adverse effects are tolerated if the benefit outweighs the adverse effect. This is, however, not acceptable when considering CBD as a NF. Furthermore, most of the human data referred to in the CBD applications investigated the efficacy of Epidyolex (or CBD) at therapeutic doses. No NOAEL could be identified from these studies. Given the complexity and importance of CBD receptors and pathways, interactions need to be taken into account when considering CBD as a NF. The effects on drug metabolism need to be clarified. Toxicokinetics in different matrices, the half‐life and accumulation need to be examined. The effect of CBD on liver, gastrointestinal tract, endocrine system, nervous system and on psychological function needs to be clarified. Studies in animals show significant reproductive toxicity, and the extent to which this occurs in humans generally and in women of child‐bearing age specifically needs to be assessed. Considering the significant uncertainties and data gaps, the Panel concludes that the safety of CBD as a NF cannot currently be established. [ABSTRACT FROM AUTHOR]

Abstract The European Commission has determined that cannabidiol (CBD) can be considered as a novel food (NF), and currently, 19 applications are under assessment at EFSA. While assessing these, it has become clear that there are knowledge gaps that need to be addressed before a conclusion on the safety of CBD can be reached. Consequently, EFSA has issued this statement, summarising the state of knowledge on the safety of CBD consumption and highlighting areas where more data are needed. Literature searches for both animal and human studies have been conducted to identify safety concerns. Many human studies have been carried out with Epidyolex®, a CBD drug authorised to treat refractory epilepsies. In the context of medical conditions, adverse effects are tolerated if the benefit outweighs the adverse effect. This is, however, not acceptable when considering CBD as a NF. Furthermore, most of the human data referred to in the CBD applications investigated the efficacy of Epidyolex (or CBD) at therapeutic doses. No NOAEL could be identified from these studies. Given the complexity and importance of CBD receptors and pathways, interactions need to be taken into account when considering CBD as a NF. The effects on drug metabolism need to be clarified. Toxicokinetics in different matrices, the half‐life and accumulation need to be examined. The effect of CBD on liver, gastrointestinal tract, endocrine system, nervous system and on psychological function needs to be clarified. Studies in animals show significant reproductive toxicity, and the extent to which this occurs in humans generally and in women of child‐bearing age specifically needs to be assessed. Considering the significant uncertainties and data gaps, the Panel concludes that the safety of CBD as a NF cannot currently be established.

Missing values in water level data is a persistent problem in data modelling and especially common in developing countries. Data imputation has received considerable research attention, to raise the quality of data in the study of extreme events such as flooding and droughts. This article evaluates single and multiple imputation methods used on monthly univariate and multivariate water level data from four water stations on the rivers Benue and Niger in Nigeria. The missing completely at random, missing at random and missing not at random data mechanisms were each considered. The best imputation method is identified using two error metrics: root mean square error and mean absolute percentage error. For the univariate case, the seasonal decomposition method is best for imputing missing values at various missingness levels for all three missing mechanisms, followed by Kalman smoothing, while random imputation is much poorer. For instance, for 5% missing data for the Kainji water station, missing completely at random, the Kalman smoothing, random and seasonal decomposition methods had average root mean square errors of 13.61, 102.60 and 10.46, respectively. For the multivariate case, missForest is best, closely followed by k nearest neighbour for the missing completely at random and missing at random mechanisms, and k nearest neighbour is best, followed by missForest, for the missing not at random mechanism. The random forest and predictive mean matching methods perform poorly in terms of the two metrics considered. For example, for 10% missing data missing completely at random for the Ibi water station, the average root mean square errors for random forest, k nearest neighbour, missForest and predictive mean matching were 22.51, 17.17, 14.60 and 25.98, respectively. The results indicate that the seasonal decomposition method, and missForest or k nearest neighbour methods, can impute univariate and multivariate water level missing data, respectively, with higher accuracy than the other methods considered.

Motivation: Insects provide vital ecological functions and account for over half of all described species. An at least basic understanding of their geographical distributions is key for addressing a range of central ecological and evolutionary questions and to inform conservation. However, even for popular groups, such as butterflies, the knowledge of species' distributions at global scale remains highly incomplete. To address this information gap, we present a data product of comprehensive country‐level occurrences for the 19,327 accepted species of extant butterflies. This compilation is based on a quality‐controlled combination of 165 literature sources and publicly available occurrence records from Global Biodiversity Information Facility (GBIF), harmonized to a global master taxonomy, and constitutes 159,659 (87,506 unique) species–country combinations. We developed a protocol for the integration of country‐level information from literature into the process of cleaning/validating species point occurrence records that facilitates dynamic updates of these country‐level checklist data. Such occurrence records are available for less than 54% of the species, with an apparent bias towards temperate regions and taxa. We use this combined database for a global assessment of the geographical variation in the diversity of butterflies, including an analysis of latitudinal gradients in the species richness—the first undertaken at this higher resolution. Country‐level richness decreases from the equator to the poles, both with and without control for country sizes. The presented data and analyses highlight the potential of leveraging multiple types of distribution information, particularly for taxa with limited data and their incorporation in ecological and conservation analysis. Our database and associated workflows provide a basis for an improved biogeographical understanding and conservation of insect biodiversity. Main types of variables contained: Country‐level occurrences and their sources. Spatial location and grain: Global, 256 countries. Time period and grain: 1861 to 2021. Major taxa and level of measurement: 19,191 accepted species of extant butterflies. Software format:.csv,.shp. [ABSTRACT FROM AUTHOR]

The collection and use of empirical data for educational decision-making in the United States is required by the Every School Succeeds Act (ESSA) and the Results Driven Accountability mandate, but there is insufficient data available for one population, in particular: refugee, asylee, and humanitarian migrant (RAHM) students. As a result, the terms of these US federal programs are violated by all states, districts, and campuses who count refugee, asylee, or humanitarian migrant students among their populations. Evidence suggests that almost no standardized, publicly-available educational data is available for refugee, asylee, and humanitarian migrant students in the US, but what publicly-available and reported data is available is either so localized so that it can be neither replicated nor generalized or so aggregated that it cannot be disaggregated for analysis and decision-making. [ABSTRACT FROM AUTHOR]

Chemicals impact the environment. However, life cycle assessments (LCA) of products containing chemicals are often not possible due to a lack of available datasets. Existing methodologies to address this problem have several shortcomings. Therefore, a new approach to model chemicals is introduced to fill dataset gaps in inventory databases. Further data for 60 chemicals are provided. The approach consists of four steps: (i) general research on the chemical and the synthesis processes, (ii) setting up the reaction equations, (iii) researching the required thermal energy, and (iv) modeling of the dataset (RREM). Depending on the obtained data, calculations are carried out or assumptions are applied. The environmental impact of the chemicals is modeled in the LCA software linking to existing datasets. A case study of the chemical octocrylene illustrates the application of RREM. An overview is given of the environmental profile of 60 chemicals modeled based on RREM. The validity of the assumptions and their influences on the results are examined by a sensitivity analysis. By modeling chemicals with the RREM approach, previously unknown environmental impacts of chemicals and products containing them can be determined. [ABSTRACT FROM AUTHOR]

Abstract The prediction and correction of systematic errors in direct spectral estimation from irregularly sampled data taken from a stochastic process is investigated. Different sampling schemes are investigated, which lead to such an irregular sampling of the observed process. Both kinds of sampling schemes are considered, stochastic sampling with non-equidistant sampling intervals from a continuous distribution and, on the other hand, nominally equidistant sampling with missing individual samples yielding a discrete distribution of sampling intervals. For both distributions of sampling intervals, continuous and discrete, different sampling rules are investigated. On the one hand, purely random and independent sampling times are considered. This is given only in those cases, where the occurrence of one sample at a certain time has no influence on other samples in the sequence. This excludes any preferred delay intervals or external selection processes, which introduce correlations between the sampling instances. On the other hand, sampling schemes with interdependency and thus correlation between the individual sampling instances are investigated. This is given whenever the occurrence of one sample in any way influences further sampling instances, e.g., any recovery times after one instance, any preferences of sampling intervals including, e.g., sampling jitter or any external source with correlation influencing the validity of samples. A bias-free estimation of the spectral content of the observed random process from such irregularly sampled data is the goal of this investigation.

Pacific sand lance (Ammodytes personatus) and surf smelt (Hypomesus pretiosus) are ecologically important forage fish in the marine food webs within the Salish Sea, including British Columbia (BC). Although important, little information exists around the spatiotemporal distribution of these fishes’ beach spawning habitat in the BC Salish Sea. In 2017, the Mount Arrowsmith Biosphere Region Research Institute initiated spawning beach surveys within the Mount Arrowsmith Biosphere Region (MABR). Surveys have geographically expanded along the eastern Vancouver Island coastline between Bowser and Cowichan Bay, BC, including Gabriola Island and Thetis Island. Pacific sand lance embryos have been detected at 17 beaches, with six beaches located within the MABR. Pacific sand lance spawning events have been detected between November and mid-February, with the bulk of embryos detected in November and December. To date, surf smelt embryos have not been detected at the 56 different beaches. These data begin to fill existing data gaps surrounding Pacific sand lance and surf smelt in BC. Furthermore, longer-term data submissions to the Strait of Georgia Data Centre, an open-access database, will provide the necessary data needed to advocate for improved regulatory protections for these ecologically important fish and their spawning habitat.

Pacific sand lance (Ammodytes personatus) and surf smelt (Hypomesus pretiosus) are ecologically important forage fish in the marine food webs within the Salish Sea, including British Columbia (BC). Although important, little information exists around the spatiotemporal distribution of these fishes’ beach spawning habitat in the BC Salish Sea. In 2017, the Mount Arrowsmith Biosphere Region Research Institute initiated spawning beach surveys within the Mount Arrowsmith Biosphere Region (MABR). Surveys have geographically expanded along the eastern Vancouver Island coastline between Bowser and Cowichan Bay, BC, including Gabriola Island and Thetis Island. Pacific sand lance embryos have been detected at 17 beaches, with six beaches located within the MABR. Pacific sand lance spawning events have been detected between November and mid-February, with the bulk of embryos detected in November and December. To date, surf smelt embryos have not been detected at the 56 different beaches. These data begin to fill existing data gaps surrounding Pacific sand lance and surf smelt in BC. Furthermore, longer-term data submissions to the Strait of Georgia Data Centre, an open-access database, will provide the necessary data needed to advocate for improved regulatory protections for these ecologically important fish and their spawning habitat.

Abstract Background Tropical forests play a fundamental role in the provision of diverse ecosystem services, such as biodiversity, climate and air quality regulation, freshwater provision, carbon cycling, agricultural support and culture. To understand the role of forests in the carbon balance, aboveground biomass (AGB) estimates are needed. Given the importance of Brazilian tropical forests, there is an urgent need to improve AGB estimates to support the Brazilian commitments under the United Nations Framework Convention on Climate Change (UNFCCC). Many AGB maps and datasets exist, varying in availability, scale and coverage. Thus, stakeholders, policy makers and scientists must decide which AGB product, dataset or combination of data to use for their particular goals. In this study, we assessed the gaps in the spatial AGB data across the Brazilian Amazon forests not only to orient the decision makers about the data that are currently available but also to provide a guide for future initiatives. Results We obtained a map of the gaps in the forest AGB spatial data for the Brazilian Amazon using statistics and differences between AGB maps and a spatial multicriteria evaluation that considered the current AGB datasets. The AGB spatial data gap map represents areas with good coverage of AGB data and, consequently, the main gaps or priority areas where further biomass assessments should focus, including the northeast of Amazon State, Amapá and northeast of Pará. Additionally, by quantifying the variability in both the AGB maps and field data on multiple environmental factors, we provide valuable elements for understanding the current AGB data as a function of climate, soil, vegetation and geomorphology. Conclusions The map of AGB data gaps could become a useful tool for policy makers and different stakeholders working on National Communications, Reducing Emissions from Deforestation and Degradation (REDD+), or carbon emissions modeling to prioritize places to implement further AGB assessments. Only 0.2% of the Amazon biome forest is sampled, and extensive effort is necessary to improve what we know about the tropical forest.