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In this paper, we aimed to present a model to diagnose the occurrence of the workplace bullying and its consequences for worker health, in a credit union in Brazil. This union was reported to the Brazilian Ministry of Labour. We carried out an exploratory, quantitative and descriptive study. We applied to all 166 employees the questionnaires NAQ-R (Negative Acts Questionnaire), and an adaptation of the SRQ20 (Self Report Questionnaire-20). We analyzed data by means of univariate and bivariate statistics. The results showed an average of 1.34 negative acts per respondent, highlighting two to four cases of severe bullying. The relationship between workplace bullying and physical and psychological symptoms was confirmed. Therefore, we recommend a model to carry out the diagnosis of workplace bullying in a single organization, by using both questionnaires, combined with data and information about the organization and its work environment., Este artigo teve como objetivo apresentar um modelo para diagnosticar a ocorrência do assédio moral no trabalho e suas consequências para a saúde do trabalhador, em uma cooperativa de crédito no Brasil, denunciada ao Ministério Público do Trabalho por prática de assédio moral. Foi realizado um estudo exploratório, quantitativo e descritivo, sendo aplicado aos 166 empregados da cooperativa os questionários NAQ-R (Negative Acts Questionnaire) e SRQ20 adaptado (Self Report Questionaire-20). Os dados foram analisados por meio de estatística univariada e bivariada. Os resultados indicaram média de 1,37 atos negativos por respondente, destacando-se a ocorrência de dois a quatro casos graves de assédio. Foi confirmada a relação entre assédio moral e sintomas físicos e psíquicos. Portanto, recomenda-se um modelo para realizar o diagnóstico do assédio moral em uma única organização, no qual sejam utilizados os dois questionários, aliados a dados e informações sobre organização e seu ambiente de trabalho.

In response to the growing societal awareness of the critical role of healthy soils, there has been an increasing demand for accurate and high-resolution soil information to inform national policies and support sustainable land management decisions. Despite advancements in digital soil mapping and initiatives like GlobalSoilMap, quantifying soil variability and its uncertainty across space, depth and time remains a challenge. Therefore, maps of key soil properties are often still missing on a national scale, which is also the case in the Netherlands. To meet this challenge and fill this data gap, we introduce BIS-4D, a high-resolution soil modeling and mapping platform for the Netherlands. BIS-4D delivers maps of soil texture (clay, silt and sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity and their uncertainties at 25 m resolution between 0 and 2 m depth in 3D space. Additionally, it provides maps of soil organic matter and its uncertainty in 3D space and time between 1953 and 2023 at the same resolution and depth range. The statistical model uses machine learning informed by soil observations amounting to between 3815 and 855 950, depending on the soil property, and 366 environmental covariates. We assess the accuracy of mean and median predictions using design-based statistical inference of a probability sample and location-grouped 10-fold cross validation (CV) and prediction uncertainty using the prediction interval coverage probability. We found that the accuracy of clay, sand and pH maps was the highest, with the model efficiency coefficient (MEC) ranging between 0.6 and 0.92 depending on depth. Silt, bulk density, soil organic matter, total nitrogen and cation exchange capacity (MEC of 0.27 to 0.78), and especially oxalate-extractable phosphorus (MEC of − 0.11 to 0.38) were more difficult to predict. One of the main limitations of BIS-4D is that prediction maps cannot be used to quantify the uncertainty

Nature-inclusive scenarios of the future can help address numerous societal challenges related to soil health. As nature-inclusive scenarios imply sustainable management of natural systems and resources, land use and soil health are assumed to be mutually beneficial in such scenarios. However, the interplay between nature-inclusive land use scenarios and soil health has never been modelled using digital soil mapping. We predicted soil organic matter (SOM), an important indicator of soil health, in 2050, based on a recently developed nature-inclusive scenario and machine learning in 3D space and time in the Netherlands. By deriving dynamic covariates related to land use and the occurrence of peat for 2050, we predicted SOM and its uncertainty in 2050 and assessed SOM changes between 2022 and 2050 from 0 to 2 m depth at 25 m resolution. We found little changes in the majority of mineral soils. However, SOM decreases of up to 5% were predicted in grasslands used for animal-based production systems in 2022, which transitioned into croplands for plant-based production systems by 2050. Although increases up to 25% SOM were predicted between 0 and 40 cm depth in rewetted peatlands, even larger decreases, on reclaimed land even surpassing 25% SOM, were predicted on non-rewetted land in peat layers below 40 cm depth. There were several limitations to our approach, mostly due to predicting future trends based on historic data. Furthermore, nuanced nature-inclusive practices, such as the adoption of agroecological farming methods, were too complex to incorporate in the model and would likely affect SOM spatial variability. Nonetheless, 3D-mapping of SOM in 2050 created new insights and raised important questions related to soil health behind nature-inclusive scenarios. Using machine learning explicit in 3D space and time to predict the impact of future scenarios on soil health is a useful tool for facilitating societal discussion, aiding policy making and promoting transformat

This dataset contains 206 spatially explicit environmental variables, also termed covariates, at 25m resolution that cover the entire Netherlands (national scale). The raster data are comprised of covariates related to the soil-forming factors (climate, organism/land use/land cover, relief/topography, parent material/geology) for the purpose of using them for digital soil mapping. However, since the covariates cover a wide range of environmental variables, they can potentially be used for spatial modelling in the Netherlands also outside the field of soil science. All covariates can also be found from the original source, but the potential strength and practicality of this dataset lies in the broad range of readily available, collected, prepared and harmonized raster data. The metadata of all the covariates in this dataset can be found in the "00_covariates_metadata.csv" file, including information about the names, category, value types, specific value types, type of geospatial data, file type, whether its static or dynamic, temporal coverage, date/version, resolution (all 25m), origin, source, access/license, description, processing steps and comments. The dataset includes 3 different types of files: GeoTIFF (.tif): the covariates as raster data at 25m resolution in the EPSG:28992 (Amersfoort / RD New) spatial projection Text (.txt): README files for each covariate with additional metadata information (filename ending in "_readme.txt") Tabular data (.csv): Classification and re-classification table for categorical covariates (filename ending in "_reclassify.csv") Note that the reclassification tables contain potential ways to reclassify the data provided, but can be altered by the user. Reclassification may be useful for categorical covariates with a large number of classes/categories. Note that covariates with CC BY-ND 4.0 licenses, covariates that are not open data or for which the license was unknown are not shared in this dataset. More information about these c

This dataset is an asset of the scientific manuscript "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). It contains maps of soil properties and their uncertainties at 25m resolution in the Netherlands obtained using the BIS-4D soil modelling and mapping platform. BIS-4D is based on well-established digital soil mapping practices. This dataset includes maps of predictions of the mean, 0.05, 0.50 (median) and 0.95 quantiles and the 90th prediction interval width (PI90) of clay content [%], silt content [%], sand content [%], bulk density (BD) [g/cm3], soil organic matter (SOM) [%], pH [KCl], total N (Ntot) [mg/kg], oxalate-extractable P (Pox) [mmol/kg] and cation exchange capacity (CEC) [mmol(c)/kg]. Prediction maps are available for the standard depth layers specified by the GlobalSoilMap initiative (0-5, 5-15, 15-30, 30-60, 60-100 and 100-200cm). For SOM, these prediction maps are available for the years 1953, 1960, 1970, 1980, 1990, 2000, 2010, 2020 and 2023 based on changing land use, peat classes and peat occurrence over time. BIS-4D uses georeferenced soil point data (field estimates and laboratory measurements), spatially explicit environmental variables (covariates), and machine learning to predict in 3D space, and for SOM, in 3D space and time. More information about how these maps were created, the BIS-4D soil modelling and mapping platform, accuracy assessment, strengths, limitations, map assessment scale and specific user recommendations can be found in the scientific paper "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). The BIS-4D model code is available on GitLab. Please note that an earlier version of soil pH prediction maps were published. In comparison, this version contains several important updates. Firstly, covariates of peat classes, groundwater classes in agricultural areas

This dataset contains georeferenced point data of basic soil properties in the Netherlands from the "Bodemkundig Informatie Systeem" (BIS), the national soil database of the Netherlands. It includes soil point data of clay content [%], silt content [%], sand content [%], bulk density (BD) [g/cm3], soil organic matter (SOM) [%], pH [KCl], total N (Ntot) [mg/kg], oxalate-extractable P (Pox) [mmol/kg] and cation exchange capacity (CEC) [mmol(c)/kg]. All of the soil properties were measured with conventional laboratory methods. In addition to the laboratory measurements, there are also field estimates made by soil surveyors for clay, BD and SOM. All laboratory measurements are part of the "Profielbeschrijving" (PFB) dataset of BIS. Field estimates for clay, BD and SOM are mostly from the "Boring Bodemkundige paket" (BPK) dataset of BIS, as well as some from the PFB dataset. Field estimates from PFB for which there was a laboratory measurement available from the same location and depth were removed from the dataset. All soil laboratory measurements and field estimates were collected by taking soil samples in the field. Soil samples were taken by genetic soil horizons from depths between 0-2 m, excluding the humus/litter/O horizon layer between 1953-2023. Tabular datasets of each georeferenced soil property point data are provided as separate CSV files for each soil property. The soil point data cover the entire Netherlands (national scale) and the spatial projection of the provided X and Y coordinates is EPSG:28992 (Amersfoort / RD New), which is the national projection most commonly used in the Netherlands. All laboratory measurements of all 9 soil properties can be viewed at bodemdata.nl under "Profielbeschrijvingen met analyses". All field estimates can be viewed via the same link under "Profielbeschrijvingen". Here you can also find additional metadata and information of soil properties not included in this dataset. Part of the BIS is also part of the National Key re