In recent years, the world has been facing severe challenges from climate change and environmental issues, with carbon dioxide emissions being considered one of the main driving factors. Many studies have proven that activities in various industries and fields have a significant impact on carbon dioxide emissions. However, few studies have explored the impact of gender on carbon dioxide emissions. This study aims to explore the potential impact of gender diversity on carbon dioxide emissions in the boards of directors of developed and emerging market enterprises. In addition, we also analyzed how board cultural diversity affects carbon dioxide emissions. We searched two European indices provided by Morgan Stanley Capital International (MSCI) from the Bloomberg database and conducted empirical analysis. We selected the MSCI index and MSCI emerging market index from 2010 to 2019 as samples and thoroughly cleaned up the data by removing any observations containing missing information on any variables. Statistical methods such as t-test, ordinary least squares, panel data analysis, regression analysis, and robustness testing were used for statistical analysis. At the same time, differential testing was conducted on sensitive and non-sensitive sectors, and the average representation of female boards in sensitive industries was low. The research results show that the proportion of female members on a company’s board of directors is negatively correlated with carbon dioxide emissions. This discovery is consistent with the legitimacy theory advocating for gender equality and environmental sustainability, emphasizing the importance of gender diversity in reducing greenhouse gas emissions. However, agency theory suggests that diversity may lead to internal conflicts within a company, leading to agency costs and information asymmetry. The research results show a negative correlation between board cultural diversity and carbon dioxide emissions, indicating the potential challenge of board cultural diversity. This study provides important insights for decision-makers and managers, not only inspiring corporate social responsibility and environmental policy formulation, but also of great significance for academic research in the field of climate change. Our research findings help deepen our understanding of the factors that affect carbon dioxide emissions in different sectors and countries, while also expanding the research field between gender diversity, cultural diversity, and environmental sustainability. Although this study still needs to be further expanded and deepened, it provides useful insights into the relationship between board gender and cultural diversity and carbon dioxide emissions.

The continuous growth of the global human population results in increased use and change of landscapes, with infrastructures like transportation or energy facilities, being a particular risk to large carnivores. Environmental Impact Assessments were established to identify the probable environmental consequences of any new proposed project, find ways to reduce impacts, and provide evidence to inform decision making and mitigation. Portugal has a wolf population of around 300 individuals, designated as an endangered species with full legal protection. They occupy the northern mountainous areas of the country which has also been the focus of new human infrastructures over the last 20 years. Consequently, dozens of wolf monitoring programs have been established to evaluate wolf population status, to identify impacts, and to inform appropriate mitigation or compensation measures. We reviewed Portuguese wolf monitoring programs to answer four key questions: do wolf programs examine adequate biological parameters to meet monitoring objectives? is the study design suitable for measuring impacts? are data collection methods and effort sufficient for the stated inference objectives? and do statistical analyses of the data lead to robust conclusions? Overall, we found a mismatch between the stated aims of wolf monitoring and the results reported, and often neither aligns with the existing national wolf monitoring guidelines. Despite the vast effort expended and the diversity of methods used, data analysis makes almost exclusive use of relative indices or summary statistics, with little consideration of the potential biases that arise through the (imperfect) observational process. This makes comparisons of impacts across space and time difficult and is therefore unlikely to contribute to a general understanding of wolf responses to infrastructure-related disturbance. We recommend the development of standardized monitoring protocols and advocate for the use of statistical methods that account for imperfect detection to guarantee accuracy, reproducibility, and efficacy of the programs. Methods We reviewed all major wolf monitoring programs developed for environmental impact assessments in Portugal since 2002 (Table S1, Supplementary material). Given that the focus here is on the adequacy of targeted wolf monitoring for delivering conclusions about the effects of infrastructure development, we reviewed only monitoring programs that were specifically designed for wolves and not those concerned with general mammalian assessment. The starting point was a compilation from the 2019-2021 National Wolf Census (Pimenta et al., 2023), where every wolf monitoring program that occurred between 2014 and 2019 in Portugal was identified. The list was completed with projects that started before 2014 or after 2019 based on personal knowledge, inquires to principal scientific teams, governmental agencies, and EIA consultants. Depending on duration, wolf monitoring programs can produce several, usually annual, reports that are not peer-reviewed and do not appear on standard search engines (e.g., Web of Science or Google Schoolar) but are publicly available from the Portuguese Environmental Agency (APA – www.apambiente.pt). We conducted an online search on APA´s search engine (https://siaia.apambiente.pt/) and identified a total of 30 projects. For each of these projects, we were interested in the first and the last report to identify any methodological changes. If the last report was not present, we reviewed the most recent one. If no report was present, we requested it from the team responsible. Our investigation centred on characterizing and quantifying four components of wolf monitoring programs that are interlinked and that should be ideally determined by the initial objectives: (1) biological parameters, i.e., what wolf parameters were studied to assess impacts; (2) study design, i.e., what sampling schemes were followed to collect and analyse data; (3) data collection, i.e., which sampling methodology and how much effort was used to collect data; and (4) data analysis, i.e., how data were analysed to estimate relevant parameters and assess impact. Biological parameters were identified and classified under two categories: occurrence and demography, which broadly correspond to the necessary inputs to assess impacts like exclusion effect and changes in reproductive patterns. Occurrence-related parameters refer to variables used to measure the presence or absence of wolves, whereas demographic parameters refer to variables that intend to measure population-level effects such as abundance, density, survival, or reproduction. We also recorded whether any effort was made to quantify prey population distribution or abundance as recommended in the guidelines. For study design, we reviewed the sampling design of the project, with specific focus on the spatial and temporal aspect of the study such as total area surveyed, the definition of a sampling site within this region (i.e., resolution), the duration of the study and the number of sampling seasons. The goal here was to determine whether the sampling scheme used was appropriate for assessing infrastructure impacts on wolf distribution or demography, depending on what the focus was. For data collection, we identified the main data collection methodologies used and the corresponding sampling effort. By far the most frequent method used is sign surveys, and specifically scat surveys, and for these studies we recorded whether genetic identification of species or individuals based on faecal DNA was attempted. We compare how sampling effort varies by the various inference objectives and, as above, assess which, if any, project or data collection approach is most likely to produce evidence of impact. We divided the Analysis component into two groups: single-year and multi-year analyses. For single-year analysis we identified how monitoring projects used data to make inferences about the state biological parameters of interest and discuss the associated strengths and weaknesses. For multi-year analyses, we recorded how differences or trends were quantified and associated with infrastructure impacts, commenting on the statistical robustness of the analyses used across the projects.

SummaryThe repository includes the data and R script for performing an analysis of among- and within-individual differences in the timing of first nesting attempts of the year in natal and pre-breeding environmental conditions (see reference). The data come from a long-term study of the demography of Savannah sparrows (Passerculus sandwichensis) breeding on Kent Island, New Brunswick, Canada (44.58°N, 66.76°W). Climate data were taken from an Environment and Climate Change Canada weather station at the airport in Saint John, NB (45.32°N, 65.89°W; https://www.climate.weather.gc.ca)Datasets(1) SAVS_all_nests_samp.csv: contains summary information for all nest attempts observed for all females included in the analysis (i.e., including both first-of-year and subsequent lay dates).(2) SAVS_first_nest_per_year_samp.csv: contains detailed information on the first nesting attempt by each female Savannah sparrow monitored in the population over the course of the study (1987-2019, excluding the years 2005-2007; see Methods: Study site and field sampling in reference).(3) mean_daily_temperature.csv: contains mean daily temperature records from the ECCC weather station at Saint John, NB (see above). These mean daily temperatures were used in a climate sensitivity analysis to determine the optimum pre-breeding window on Kent Island.(4) SAVS_annual_summary.csv: contains annual summaries of average lay dates, breeding density, reproductive output, etc.Variables- female.id = factor; unique aluminum band number (USGS or Canadian Wildlife Service) assigned to each female- rain.categorical = binary (0 = low rainfall; 1 = high rainfall); groups females into low (81-171 mm) and high (172-378 mm) natal rainfall groups, based on the natal environmental conditions observed in each year (see Methods: Statistical analysis in reference)- year = integer (1987-2019); study year. The population on Savannah sparrows on Kent Island has been monitored since 1987 (excluding three years, 2005-2007)- nest.id = factor; an alpha-numeric code assigned to each nest; unique within years (the combination of year and nest.id would create a unique identifier for each nest)- fledglings = integer; number of offspring fledged from a nest- total.fledglings = integer; the total number of fledglings reared by a given female over the course of her lifetime- nest.attempts = integer; the total number of nest attempts per female (the number of nests over which the total number of fledglings is divided; includes both successful and unsuccessful clutches)hatch.yday = integer; day of the year on which the first egg hatched in a given nestlay.ydate = integer; day of the year on which the first egg was laid in a given nestlay.caldate = date (dd/mm/yyyy); calendar date on which the first egg in a given nest was laidnestling.year = integer; the year in which the female/mother of a given nest was born- nestling.density = integer; the density of adult breeders in the year in which a given female (associated with a particular nest) was born- total.nestling.rain = numeric; cumulative rainfall (in mm) experienced by a female during the nestling period in her natal year of life (01 June to 31 July; see Methods: Temperature and precipitation data in reference)- years.experience = integer; number of previous breeding years per female in a particular year- density.total = integer; total number of adult breeders in the study site in a particular year- MCfden = numeric; mean-centred female density- MCbfden = numeric; mean-centred between-female density- MCwfden = numeric; mean-centred within-female density- mean.t.window = numeric; mean temperature during the identified pre-breeding window (03 May to 26 May; see Methods: Climate sensitivity analysis in reference)- MCtemp = numeric; mean-centred temperature during the optimal pre-breeding window- MCbtemp = numeric; mean-centred between-female temperature during the optimal pre-breeding window- MCwtemp = numeric; mean-centred within-female temperature during the optimal pre-breeding window- female.age = integer; age (in years) of a given female in a given year- MCage = numeric; mean-centred female age- MCbage = numeric; mean-centred between-female age- MCwage = numeric; mean-centred within-female age- mean_temp_c = numeric; mean daily temperature in °C- meanLD = numeric; mean lay date (in days of the year) across all first nest attempts in a given year- sdLD = numeric; standard deviation in lay date (in days of the year) across all first nest attempts in a given year- seLD = numeric; standard error n lay date (in days of the year) across all first nest attempts in a given year- meanTEMP = numeric; mean temperature (in °C) during the breeding period in a given year- records = integer; number of first nest attempts from each year included in the analysis- total.nestling.precip = numeric; total rainfall (in mm) during the nestling period (01 June to 31 July) in a given year- total.breeding.precip = numeric; total rainfall (in mm) during the breeding period (15 April to 31 July) in a given year- density.total = integer; total density of adult breeders on the study site in a given year- total.fledglings = integer; total number of offspring fledged by all breeders in the study site on a given year- cohort.fecundity = numeric; average number of offspring per breeder in a given yearCodecode for Burant et al. - SAVS lay date plasticity analysis.RThe R script provided includes all the code required to import the data and perform the statistical analyses presented in the manuscript. These include:- t-tests investigating the effects of natal conditions (rain.categorical) on female age, nest attempts, and reproductive success- linear models of changes in temperature, precipitation, reproductive success, and population density over time, and lay dates in response to female age, density, etc.- a climate sensing analysis to identify the optimal pre-breeding window on Kent Island- mixed effects models investigating how lay dates respond to changes in within- and between-female age, density, and temperaturesee readme.rtf for a list of datasets and variables.

The relationships between trait plasticity and canalization, and between phenotypic integration and plasticity, have been under debate, largely because direct evidence is still scarce for their associations, especially in response to environments. To investigate the relationships between canalization, integration, and phenotypic plasticity in response to emergence time, we conducted a field experiment with an annual herbaceous species of Abutilon theophrasti, by subjecting plants to four treatments of emergence time (spring, late spring, summer, and late summer), to measure several morphological traits and analyze correlations of plasticity with canalization and integration in these traits, at two stages of plant growth. Results showed plants with delayed emergence had higher phenotypic integration and more positive correlations between integration and plasticity, but less negative correlations between decreased canalization and plasticity, compared to those that emerged in spring. Results suggested significant environmental changes that induce plastic responses, rather than environmental stress, can result in greater phenotypic integration in plants. Negative correlations between decreased canalization and plasticity occurred more frequently in plants emerging in spring and the least frequently in those emerging in summer, suggesting their relationship depends on specific environmental conditions and the degree of plasticity. Both increased phenotypic integration and decreased canalization might merely be the outcome of plastic responses, rather than mechanisms constraining or facilitating plasticity. Methods Experimental design We conducted the field experiment in 2007 at the Pasture Ecological Research Station of Northeast Normal University, Changling, Jilin Province, China (44°45’ N, 123°45’ E). The original soil of the experimental field (aeolian sandy soil, pH = 8.3) at the station had been used annually for many years, with nutrients availability of organic C 3.1 mg kg–1, available N 21.0 mg kg–1, and available P 1.1 mg kg–1 during the growth season of 2007. Seeds of A. theophrasti were collected from local wild populations near the research station in late August 2006 and dry stored at -4°C. We applied a randomized block design, with emergence timing (ET) as the main factor, and block as the sub-factor. The whole plot was divided into twelve 2 × 3 m sub-plots, which were randomly assigned with four ET treatments and three blocks. Plants of A. theophrasti were grown on June 7, June 27, July 17, and August 7, to make them emerge in different periods of the season, as four ET treatments of spring (ET1), late spring (ET2), summer (ET3) and late summer (ET4). The treatments of emergence timing accorded with the time range of emergence of A. theophrasti in its natural habitats in northeast China. Most of the seeds emerged four days after sowing. Seeds were sown at an inter-planting distance of 10 cm, and seedlings were thinned at the four-leaf stage. Plots were hand-weeded when necessary and watered regularly to prevent drought. Data collection For each ET treatment, we arranged two times sampling (at days 50 and 70 of growth; Appendix S1; see Supplemental Data with this article), according to the lengths of their life cycles, generally at the stages of vegetative growth, late vegetative or early reproductive growth, and middle to late reproductive growth respectively. For each sampling, five to six individual plants were randomly chosen from each plot, making a maximum total of 6 replicates × 3 blocks × 4 treatments × 2 samplings = 144 samplings. For each plant, the following traits were measured (if applicable): main root length, diameter at the basal of the main root, length and number of lateral roots (above or equal to 1 mm in diameter along the main root), the length of stem, diameter at the base of stem, petiole length and angle, leaf number, leaf size (lamina width), branch length and angle, and branch number (Appendix S1; see Supplemental Data with this article). Each plant individual was then separated into roots, stems, petioles, laminas, reproductive modules, and branches (if there were any), oven-dried at 75°C for two days, and weighed. Reproductive modules consisted of flowers and fruits produced along the main stem and branches, and branches included the stems and leaves on branches. Total mass and mass allocation traits were calculated. Statistical analysis Statistical analyses were conducted using IBM SPSS statistics 19. All measured and calculated traits were used for analysis (Appendix S2; see Supplemental Data with this article). To minimize variance heterogeneity, all data were log-transformed, except for petiole angles and branch angles (square root-transformed), before statistical analysis. For plant total mass, we applied two-way ANOVA to analyze the effects of emergence time, growth stage, and their interactions, and one-way ANOVA to analyze the effects of emergence time or growth stage within each or across all of the other treatments. Plant size (e. g. total mass) can have very significant effects on other traits, which may bias the effects of emergence time. Therefore, for all the other traits, we applied two-way ANCOVA to evaluate the overall effects of emergence time, growth stage, and their interactions, and one-way ANCOVAs for effects of emergence time or growth stage within each or across all of the other treatments, with total mass as a covariate. Multiple comparisons used the Least Significant Difference (LSD) method in the General Linear Model (GLM) program. For each trait, the percent number of significant correlations of it with other traits (NC; p < 0.05) was used as the index of phenotypic integration, and NC values were arcsine- and square-root-transformed before analyses; coefficient of variation (CV) was used to evaluate the level of canalization, which was calculated as the standard deviation divided by the mean value of the trait. Both the level and degree of plasticity (relative plasticity and absolute plasticity, PIrel and PIabs) for each trait were calculated with the revised simplified Relative Distance Plasticity Index (RDPIs, abbreviated as PI), for its strong statistical power in tests of differences in plasticity (Valladares et al., 2006), with the formula as: PIrel = (X – Y) / (X + Y) PIabs = |X – Y | / (X + Y) where X was the adjusted mean trait value in the treatment of earlier emergence (ET1, ET2, and ET3), and Y was the adjusted mean value in the treatment of delayed emergence (ET2, ET3, and ET4). The adjusted mean values and standard errors were produced in one-way ANCOVA, with emergence time as effect within each stage, and total mass as a covariate. Since there were four treatments of emergence timing, we calculated the PI in response to each treatment versus another one. Consequently, there were six kinds of plasticity in total, including the responses to ET4 (vs. ET1, ET2, ET3), ET3 (vs. ET1, ET2) and ET2 (vs. ET1). Correlations between PI and NC, and between PI and CV, were evaluated by Pearson Correlation Coefficients (PCC) produced by PROC CORR (Gianoli and Palacio-López 2009). We then applied regression analyses to quantify the relationships between PI and CV and between PI and NC for plants in different emergence treatments at both stages. Results of correlations and regressions were analyzed with two-way ANOVA for the effects of emergence time and growth stage and their interactions; and one-way ANOVA for the effects of emergence time on these relationships at each stage.

Factor analysis (FA) has the advantage of highlighting each semi-distinct cluster of samples in a data set with one axis at a time, as opposed to simply arranging samples across axes to represent gradients. However, in the case of presence-absence data it is confounded by absences when gradients are long. No statistical model can cope with this problem because the raw data simply do not present underlying information about the length of such gradients. Here I propose a simple way to tease out this information. It is a simple emendation of FA called stepping down, which involves giving an absence a negative value when the missing species nowhere co-occurs with the species found in the relevant sample. Specifically, a binary co-occurrence graph is created, and the magnitude of negative values is made a function of how far the graph must be traversed in order to link the missing species with each species that is present. Simulations show that standard FA yields inferior results to FA based on stepped-down matrices in terms of mapping clusters into axes one-by-one. Standard FA is also uninformative when applied to a global bat inventory data set. Step-down FA (SDFA) easily flags the main biogeographic groupings. Methods like correspondence analysis, non-metric multidimensional scaling, and Bayesian latent variable modelling are not commensurate with SDFA because they do not seek to find a one-to-one mapping of axes and clusters. Stepping down seems promising as a means of illustrating clusters of samples, especially when there are subtle or complex discontinuities in gradients.

Usage Notes bat referencesA list of references to publications yielding site-specific inventory data for bats from around the world. Raw data are also reposited in the Ecological Register.bat_references.txtbat registerSite-specific inventory data for bats from around the world. Each line includes a count of the individuals belonging to a species found at a site. Raw data are also reposited in the Ecological Register.bat_register.txt

Abstract from submitted PhD thesis: In the field of ecotoxicology, which studies the fate and effects of contaminants on biota, concentration-response experiments (toxicity tests) are conducted to determine the sensitivity of a single species to a toxicant. Critical Effect Concentrations (CECs) are estimated from the results of toxicity tests, to provide a measure of the tolerance threshold for that species. Once CECs have been generated for a sufficient number of taxa, the values are then used to establish a distribution of sensitivity estimates for the ecosystem, known as a species sensitivity distribution (SSD). It is from a SSD that environmental guidelines values (GVs) are frequently derived by estimating the Protective Concentration for x% of the community (PCx). Success in GV derivation requires the development and application of statistical approaches that improve the interpretation and application of ecotoxicological research. The methods we use to analyze ecotoxicological data to obtain CECs, together with the methods used to derive SSDs, impact the quality of the derived GVs. As such, reliable, user-friendly, and accurate statistical methods are critical to ensuring derived GVs are effective for environmental protection.
In this thesis, I focus on three different areas to improve the analysis and modeling of ecotoxicological data. First, I investigate how additional stressors, such as differing environmental conditions, can be incorporated into traditional dose-response modeling. Second, I investigate the use of alternate methods to calculate CECs to improve the analysis of data from tests with extended exposure durations. Lastly, I present three new approaches to constructing SSDs, the first approach integrates variation around each CEC estimate via the direct integration of raw toxicity test data. The second and third approaches are an extension of the presented integrated model with the use of a heavy-tailed distribution and the use of a truncated distribution.
Toxicity tests typically investigate the response of a single species to a single contaminant under standardized and optimized environmental conditions in the laboratory. However, organisms are rarely exposed to chemical or environmental stressors in isolation. Multiple stressor experiments provide a method to study how environment variability (i.e. temperature, pH, and salinity) can alter an organism's response to a contaminant. Yet, there is no standardized statistical method that allows you to easily incorporate these additional stressors into doseresponse regression, the most commonly used toxicity analysis method.
In Chapter 2, I present an extended dose-response regression method that simultaneously calculates Lethal Concentration estimate for x% of the population (LCx), with integrated handling of control mortality, for each stressor combination studied. The outcome of this model is a consistent framework to provide interpretable results that meaningfully deal with environmental variables and their possible impacts on the LCx estimates. To provide easy access to this model, it was incorporated into an R-package. We illustrate this method with data for a subantarctic marine invertebrate, to investigate its response to copper under levels of increasing temperature and decreasing salinity. These environmental conditions, intended to reflect future climate change scenarios, have the potential to impact the survival of individuals exposed to copper. The use of our model reveals that, while the additional stressors were not found to interact, a punctuated increase in temperature contributed to a significant decrease in the LCx estimate (indicating increased sensitivity).
While dose-response regression is the main methodology to analyze ecotoxicological data, its resulting metric of sensitivity, the EC/LCx, is criticized for its dependency on exposure duration. The No Effect Concentration (NEC) is widely suggested as an improvement to the EC/LCx, as it represents a concentration threshold below which no effect occurs, irrespective of the exposure duration. There are two currently proposed dose response analysis methods to calculate NECs. One method uses segmented regression to estimate an NEC in an empirical model, the other uses a mechanistic, toxicokinetic-toxicodynamic (TKTD) model to parameterize the time course of survival. To date, the use of either of these NEC models has been limited, due the increase in computational complexity and lack of user friendly software packages or code.
In Chapter 3, I compare NEC estimates from the two model types to LCx estimates from traditional dose-response regression. To do this, I use survival data through time for four Antarctic marine invertebrates in response to copper. For Antarctic biota, toxicity tests are conducted at low temperatures and typically require an extended exposure to illicit an acute response, with tested durations regularly extending up to 42 days. Without...

Fixed effects estimation with instrumental variable.

The dataset documents the relationship between changes in land use and occupation and the diversity and abundance of phlebotomines, the vectors of leishmaniasis, in a rural area of the municipality of Codó, Maranhão. It integrates spatial and entomological information collected between 2012 and 2023, providing a comprehensive basis for environmental, epidemiological and vector management analyses. The land use and occupation data was obtained from Sentinel-2 satellite images, processed in QGIS software (version 3.10) and classified using the Orfeo Toolbox Processing (OTB) tool. The images represent the years 2012, 2014, 2021 and 2023 and include variables such as the density and fragmentation of vegetation cover, as well as the expansion of built-up areas. This information was made available in geospatial formats, such as Geotiff and shapefiles, allowing detailed analysis of temporal changes in the landscape. Entomological data was collected bimonthly between August 2022 and June 2023. ..., Study area This study was conducted in the Santana IV rural settlement, Codó (04°27′12.8″S 43°53′01.7″W), Eastern Mesoregion of Maranhão State, Brazil (Figure 1). The municipality has an area of 4364.5 km2 and an estimated population of 114,275 people, with a population density of 26.20 people/km2 (IBGE, 2022). Vegetation cover varies according to relief characteristics, proximity to watercourses, and the extent of anthropic transformations. The predominant vegetation type is open forest/babassu forest, occupying the entire valley of the Itapecuru River. The main tree species are babassu palm (Attalea speciosa Mart. ex Spreng.) and carnauba [Copernicia prunifera (Miller) H.E.Moore]. Another common type of vegetation cover is campo cerrado, found mainly in the east, northwest, and southwest parts of the municipality (Correia Filho et al., 2011a). The climate is semi-humid, transitioning to semi-arid with precipitation. According to the Köppen classification, the climate is of the Aw..., , # Land use and cover changes and sand fly (Diptera: Psychodidae) assemblages in an emerging focus of leishmaniasis

https://doi.org/10.5061/dryad.jwstqjqm5

Description of the data and file structure

README: Sand fly survey data and statistical analysis

We sent the table with the data from the phlebotomine species survey (Sand_fly_survey_results_table.zip), the data used in the statistical analyses (raw_data_analyzes_statistics.zip), data used to generate graphs (Dados_para_gráficos.xlsx) and a summary of the data generated from the statistical analyses (summary_phlebotomine_results_statistical_analysis.zip).

Descriptions

Sand_fly_survey_results_table.zip

• Species: currently accepted scientific names of sand fly species;
• ♂: Male gender symbol;
• ♀: Female gender symbol;
• N: Number of specimens;

• Capture method: Capture methods used to obtain phlebotomine species;

  **raw_data_analyzes_statist...

Abstract Ecological factors and individual-specific traits affect parasite infestation in wild animals. Ixodid ticks are important ectoparasites of various vertebrate hosts, which include passerine bird species such as the great tit (Parus major). We studied various key ecological variables (breeding density, human disturbance) and phenotypic traits (exploratory behaviour, body condition) proposed to predict tick infestation probability and burden in great tits. Our study spanned 3 years and 12 nest box plots located in southern Germany. Breeding, adult great tits were assessed for exploration behaviour, body condition, and tick burden. Plots were open to human recreation; human disturbance was quantified in each plot as a recreation pressure index from biweekly nest box inspections. Infested individuals were repeatable in tick burden across years. These repeatable among-individual differences in tick burden were not attributable to exploration behaviour. However, faster explorers did have a higher infestation probability. Furthermore, body condition was negatively correlated to tick burden. Recreation pressure was correlated to increased infestation probability, although this relationship was just above the threshold of statistical significance. Our study implies that avian infestation probability and tick burden are each determined by distinct phenotypic traits and ecological factors. Our findings highlight the importance of animal behaviour and human disturbance in understanding variation in tick burden among avian hosts.

      Significance statement
      Various abiotic and biotic factors, including personality type, influence tick parasitism in birds, but exactly how all these factors interplay remains unclear. We studied a wild population of great tits over a 3-year period and assessed birds for their exploration behaviour and tick infestation. We found that more explorative great tits were more likely to be infested with ticks. By contrast, faster explorers did not have higher tick burdens. Tick burden was nevertheless moderately repeatable among individuals. Our results imply that animal personality influences the probability of parasite infestation, and that infestation likelihood versus intensity are determined by distinct mechanisms. Our work highlights the importance of animal behaviour to understand parasite infestation in wild populations.

Methods Material and Methods​​​​​​​

Study sites and data collection

Our study was performed in 12 nest box plots within a 10×15 km2 area in southern Munich (47° 57'N, 11° 21'E) established in autumn 2009. Each plot was fitted with 50 nest boxes in a regular grid covering approximately 9 hectares. Breeding parameters of great tits (detailed below) were monitored for three years (2017-2019) within all plots except one plot that was monitored only in 2017. Each nest box was inspected biweekly during the breeding season (April-July) for nesting activity. During plot inspections, the number of recreationists was counted as a measure of human disturbance (detailed by Hutfluss & Dingemanse, 2019).

When hatchlings were 10-12 days old, both parents were captured in the nest box using a spring trap. Birds were given a unique, numbered band if not previously banded. They were then tested for their exploration behaviour (detailed below), weighed, measured morphologically (Moiron et al. 2019), aged (based on plumage characteristic as first year breeder or older; Dingemanse et al. 2020), and screened for ticks (protocol detailed below). Most birds were captured in May and a few in June. These captures were used to calculate breeding density, defined as the number of breeding pairs producing first clutches per hectare.

  Exploration behaviour

Exploration behaviour was measured using a novel environment test adapted to the field using established protocols (see Stuber et al. 2013). Birds were transferred to a holding box attached to a cage (61L × 39W × 40 H cm) fitted with a mesh front and three perches, representing the novel environment. The holding box was covered with a cloth bag and the subject allowed to acclimatize for one minute. After acclimatization, the focal bird was released into the novel environment without handling and recorded for two minutes with the field observer located out of sight. Videos were subsequently scored by dividing the cage into six equal sections and three floor sections as described in Stuber et al. (2013). The exploration score was calculated as the sum of movements between all sections within the first two minutes of entering the novel environment (Abbey-Lee and Dingemanse 2019; Dingemanse et al. 2020).

  Tick burden and collection

Ticks tend to concentrate around the eyes and beak of great tits (Heylen and Matthysen 2008; Fracasso et al. 2019). A regional patch examination protocol (reviewed in Lydecker et al. 2019) was developed to standardize the screening: We screened 1) around the eyes/ears and along the margin of the beak on both sides of the bird, 2) underneath the beak, 3) along the top margin of the beak, and 4) on the top of the head. We subsequently calculated the total number of ticks carried by each captured bird. In 2018-2019, all captured birds were screened; in 2017 only a sample. In 2018 and 2019, a sample of ticks were collected using fine tweezers and stored in 99% ethanol as part of another study. Those were subsequently morphologically identified to life-stage and species according to published taxonomic keys (Filippova 1977; Hillyard 1996; Estrada-Peña et al. 2014).

  Recreation pressure index

During each plot inspection, all observers recorded each recreationist seen and their specific location (detailed by Hutfluss and Dingemanse 2019). Each recreationist was connected to the first observation to avoid double counts. The probability to observe a recreationist during a plot inspection is biased by various factors (Hutfluss and Dingemanse 2019). To obtain an unbiased index of recreation pressure, the binary probability to observe recreationists was calculated using all inspections conducted from 2010-2019 (n = 3724 inspections). This probability was calculated using a binomial generalized linear mixed effects model (GLMM) fitting fixed effects for plot inspection duration, the number of observers, and starting time (in hours from sunrise) (Hutfluss and Dingemanse 2019). We fitted random intercepts for each unique combination of plot and year (termed plot-year, see Abbey-Lee et al. 2016; Araya-Ajoy et al. 2016; Araya-Ajoy and Dingemanse 2017) to acquire an average value of recreation pressure for each plot in each year, and for date of observation to control for date-specific environmental effects (Supplemental Table 1). We extracted best linear unbiased predictors (BLUPs) for each plot-year between 2017 and 2019 (n = 34) and used them in subsequent models as a recreation pressure index. The usage of BLUPs has been criticised when associated uncertainty is not taken forward; some have proposed to estimate a posterior distribution of possible BLUP values and thereby take forward uncertainty in subsequent analyses (Hadfield et al. 2010; Houslay and Wilson 2017). However, taking forward uncertainty in BLUP-values can result in biased estimates, whereas utilizing average BLUPs as fixed effects result in less precise but unbiased estimates (Dingemanse et al. 2020). Therefore, we here present the estimated effect of the average BLUP values.

  Data Preparation

The scaled mass index (Peig and Green 2009) was used as our measure of body condition. Following Peig and Green (2009), this index was calculated separately for each sex according as follows:

1 SMi=Mi×L0LibSMA

where, Mi and Li are the mass and tarsus measurements of individual i respectively; L0 is the arithmetic mean of all tarsus lengths of either all females or all males (dependent on the sex of individual i); and bSMA is the scaling exponent calculated as:

2 bSMA=bOLSR

where bOLS is the slope of linear regression of log-transformed body mass as a function of log-transformed tarsus length of either all females or all males (dependent on the sex of individual i) and R is the Pearson’s correlation coefficient of the regression. We used all birds recorded from 2010-2019 (n = 4273 records) to calculate the scaling coefficient (including any repeated measures).

We calculated individual-mean exploration scores using a linear mixed effects model (LMM) fitting exploration behaviour as the response variable (Supplemental Table 2). We fitted test sequence as a fixed effect covariate to control for sequence effects ( Dingemanse et al. 2012, 2020), and included random intercepts for individual and plot-year. The model included data from all years (2010-2019) (n = 4251) and assumed a Gaussian error distribution. We extracted an average BLUP-value per individual, which we used in subsequent analyses as a measure of an individual’s average (experience-corrected) exploration score.

  Statistical Analysis

Tick infestation of great tits was analysed in two parts using GLMMs. First, infestation probability was estimated using all recorded captures from 2017 through 2019 (n = 784 records). Second, tick burden (i.e. the absolute number of ticks observed) was modelled using only infested individuals (n = 520 records). For both response variables, the model set up was the same. Fixed effects were fitted for recreation pressure, breeding density, body condition, sex, age, and individual-mean exploration score. Sex and age were fitted because females (versus males) and older (versus younger) birds often have higher tick burdens (Heylen and Matthysen 2008; Heylen et al. 2013). Random intercepts were fitted for year, plot, plot-year, field observer, nest box,

Many important ecological phenomena occur on large spatial scales and/or are unplanned and thus do not easily fit within analytical frameworks which rely on randomization, replication, and interspersed a priori controls for statistical comparison. Analyses of such large-scale, natural experiments are common in the health and econometrics literature, where techniques have been developed to derive insight from large, noisy observational datasets. Here, we apply a technique from this literature, synthetic control, to assess landscape change with remote sensing data. The basic data requirements for synthetic control include: (1) a discrete set of treated and un-treated units, (2) a known date of treatment intervention, and (3) timeseries response data that includes both pre- and post-treatment outcomes for all units. Synthetic control generates a response metric for treated units relative to a no-action alternative based on prior relationships between treated and unexposed groups. Using simulations and a case study involving a large-scale brush clearing management event, we show how synthetic control can intuitively infer treatment effect sizes from satellite data, even in the presence of confounding noise from climate anomalies, long-term vegetation dynamics, or sensor errors. We find that accuracy depends on the number and quality of potential control units, highlighting the importance of selecting appropriate control populations. Although we consider the synthetic control approach in the context of natural experiments with remote sensing data, we expect the methodology to have wider utility in ecology, particularly for systems with large, complex, and poorly replicated experimental units.

Abstract

Environmental statistics relating to households are an important instrument for making decisions, planning, and drawing up strategies for the environment. Due to the lack of data on this subject in Palestine, PCBS is building and developing a database on the environment in the household sector.

This survey is based on a household sample survey conducted during the period from 24th March 2015 to 31st May 2015. It provides basic statistics on various aspects of the environment, including water, solid waste, wastewater, noise, and air pollution. A special questionnaire was designed in accordance with United Nations standards and recommendations in the field of environmental statistics and adapted to Palestinian conditions.

This survey presents data on various environmental household indicators in Palestine and on water consumption for the household sector by water source, methods of solid waste disposal and their main components, the disposal of wastewater, and the existence of cesspits and water wells, in addition to exposure to noise and air pollution by source and time.

Geographic coverage

National

Analysis unit

Household

Universe

It consists of all Palestinian households who are staying normally in Palestine during 2015.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame

The sampling frame was based on the master sample which was updated in 2013-2014 for the Expenditure and Consumption Survey (PECS) and the Multiple Indicator Cluster Survey (MICS), and the frame consists from enumeration areas. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sampling Design: - Two stage stratified cluster sample as following:

• First stage: selection of a PPS random sample of 370 enumeration areas.

• Second stage: A systematic random sample of 20 households from each enumeration area selected in the first stage.

• Sample strata: The population was divided by:

• Governorate

• Locality type (urban, rural, camps)

Sample Size: The sample size is 7,690 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The environmental questionnaire was designed in accordance with similar international experiences and with international standards and recommendations for the most important indicators, taking into account the special situation of Palestine.

Cleaning operations

The data processing stage consisted of the following operations: Editing and coding prior to data entry: all questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: The household Environmental survey questionnaire was programmed and the data were entered into the computer in the offices in Nablus, Hebron, Ramallah and Gaza. At this stage, data were entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements: 1. To prevent the duplication of questionnaires during data entry. 2. To apply checks on the integrity and consistency of entered data. 3. To handle errors in a user friendly manner. 4. The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS.

Response rate

Response rate: 89.5%

7,690 households had been reached as a representative sample to Palestine, where the number of completed questionnaires amounted to 6,609 questionnaires of which 4,536 questionnaires were in West Bank and 2,073 questionnaires in Gaza Strip. Weights were amended at the level of design strata to modify effects of refusals rates and non response.

Sampling error estimates

The concept of data quality covers many aspects, starting from the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are seven dimensions of statistical quality: relevance, accuracy, timeliness and punctuality, accessibility and clarity, comparability, coherence and completeness.

Accuracy

This includes many aspects of the survey, mainly statistical errors due to the use of a sample, and also non-statistical errors from workers and survey tools. It also includes the response rates in this survey and their effect on the assumptions. This section includes:

Sampling Errors: Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators and the variance table is in the final report found in the related external resources under downloads. There is no problem with the dissemination of results on national and regional level (North, Middle, South West Bank, Gaza Strip) or by locality type.

Non Sampling Errors: The non-sampling errors are possible to occur at all phases of implementing the project, through data collection and entry which could be summarized as non-response errors, and responding errors (respondents), and interview errors (fieldworkers) and data-entry errors. To avoid errors and reduce the impact, it had been made great efforts through extensive training of fieldworkers on how to conduct interviews, things that ought to be followed during an interview, things that should be avoided, making some practical and theoretical exercises during training session, in addition to providing them with a manual booklet for fieldworkers which contained a private key questions of questionnaire, mechanism to fill questionnaire and methods of dealing with respondents to reduce refusal rates and providing correct and non-biased data. Also data entry staff were trained on the data entry program, which was tested before starting the data entry process.

As for office work, they had been trained for a special auditing of questionnaires and error detection, which greatly reduced rates of errors during field work. In order to reduce the percentage of errors during data entry, the program was designed to enter data so as not to allow any mistakes during the process and contained many of logical terms. This process led to disclosure of most of errors that had not been found in earlier phases of the work, where they were correcting all the errors that had been discovered.

After the completion of the aforesaid audits, data consistency was examined by computer using frequency and cross tables as turned out to be quite consistent. Errors impact was not detectable on data quality. This in turn gave a good impression of those in charge of the survey that we could rely on this data and extract reliable statistical and high significant indicators on the reality of corruption in Palestine.

A plant species' genetic population structure is the result of a complex combination of its life history, ecological preferences, position in the ecosystem, and historical factors. As a result, many different statistical methods exist that measure different aspects of species’ genetic structure. However, little is known about how these methods are interrelated and how they are related to a species’ ecology and life history. In this study, we used the IntraBioDiv AFLP-dataset from 27 high-alpine species to calculate eight genetic summary statistics that we jointly correlate to a set of six ecological and life-history traits. We found that there is a large amount of redundancy among the calculated summary statistics and that there is a significant association with the matrix of species traits. In a multivariate analysis, two main aspects of population structure were visible among the 27 species. The first aspect is related to the species’ dispersal capacities and the second is most likely related to the species’ postglacial recolonisation of the Alps. Furthermore, we found that some summary statistics, most importantly Mantel’s r and Jost’s D, show different behaviour than expected based on theory. We therefore advise caution in drawing too strong conclusions from these statistics.

In recent years, the world has been facing severe challenges from climate change and environmental issues, with carbon dioxide emissions being considered one of the main driving factors. Many studies have proven that activities in various industries and fields have a significant impact on carbon dioxide emissions. However, few studies have explored the impact of gender on carbon dioxide emissions. This study aims to explore the potential impact of gender diversity on carbon dioxide emissions in the boards of directors of developed and emerging market enterprises. In addition, we also analyzed how board cultural diversity affects carbon dioxide emissions. We searched two European indices provided by Morgan Stanley Capital International (MSCI) from the Bloomberg database and conducted empirical analysis. We selected the MSCI index and MSCI emerging market index from 2010 to 2019 as samples and thoroughly cleaned up the data by removing any observations containing missing information on any variables. Statistical methods such as t-test, ordinary least squares, panel data analysis, regression analysis, and robustness testing were used for statistical analysis. At the same time, differential testing was conducted on sensitive and non-sensitive sectors, and the average representation of female boards in sensitive industries was low. The research results show that the proportion of female members on a company’s board of directors is negatively correlated with carbon dioxide emissions. This discovery is consistent with the legitimacy theory advocating for gender equality and environmental sustainability, emphasizing the importance of gender diversity in reducing greenhouse gas emissions. However, agency theory suggests that diversity may lead to internal conflicts within a company, leading to agency costs and information asymmetry. The research results show a negative correlation between board cultural diversity and carbon dioxide emissions, indicating the potential challenge of board cultural diversity. This study provides important insights for decision-makers and managers, not only inspiring corporate social responsibility and environmental policy formulation, but also of great significance for academic research in the field of climate change. Our research findings help deepen our understanding of the factors that affect carbon dioxide emissions in different sectors and countries, while also expanding the research field between gender diversity, cultural diversity, and environmental sustainability. Although this study still needs to be further expanded and deepened, it provides useful insights into the relationship between board gender and cultural diversity and carbon dioxide emissions.

https://i.imgur.com/PcSDv8A.png" alt="Imgur">

Overview

The dataset provided here is a rich compilation of various data files gathered to support diverse analytical challenges and education in data science. It is especially curated to provide researchers, data enthusiasts, and students with real-world data across different domains, including biostatistics, travel, real estate, sports, media viewership, and more.

Files

Below is a brief overview of what each CSV file contains: - Addresses: Practical examples of string manipulation and address data formatting in CSV. - Air Travel: Historical dataset suitable for analyzing trends in air travel over a period of three years. - Biostats: A dataset of office workers' biometrics, ideal for introductory statistics and biology. - Cities: Geographic and administrative data for urban analysis or socio-demographic studies. - Car Crashes in Catalonia: Weekly traffic accident data from Catalonia, providing a base for public policy research. - De Niro's Film Ratings: Analyze trends in film ratings over time with this entertainment-focused dataset. - Ford Escort Sales: Pre-owned vehicle sales data, perfect for regression analysis or price prediction models. - Old Faithful Geyser: Geological data for pattern recognition and prediction in natural phenomena. - Freshman Year Weights and BMIs: Dataset depicting weight and BMI changes for health and lifestyle studies. - Grades: Education performance data which can be correlated with demographics or study patterns. - Home Sales: A dataset reflecting the housing market dynamics, useful for economic analysis or real estate appraisal. - Hooke's Law Demonstration: Physics data illustrating the classic principle of elasticity in springs. - Hurricanes and Storm Data: Climate data on hurricane and storm frequency for environmental risk assessments. - Height and Weight Measurements: Public health research dataset on anthropometric data. - Lead Shot Specs: Detailed engineering data for material sciences and manufacturing studies. - Alphabet Letter Frequency: Text analysis dataset for frequency distribution studies in large text samples. - MLB Player Statistics: Comprehensive athletic data set for analysis of performance metrics in sports. - MLB Teams' Seasonal Performance: A dataset combining financial and sports performance data from the 2012 MLB season. - TV News Viewership: Media consumption data which can be used to analyze viewing patterns and trends. - Historical Nile Flood Data: A unique environmental dataset for historical trend analysis in flood levels. - Oscar Winner Ages: A dataset to explore age trends among Oscar-winning actors and actresses. - Snakes and Ladders Statistics: Data from the game outcomes useful in studying probability and game theory. - Tallahassee Cab Fares: Price modeling data from the real-world pricing of taxi services. - Taxable Goods Data: A snapshot of economic data concerning taxation impact on prices. - Tree Measurements: Ecological and environmental science data related to tree growth and forest management. - Real Estate Prices from Zillow: Market analysis dataset for those interested in housing price determinants.

Format

The enclosed data respect the comma-separated values (CSV) file format standards, ensuring compatibility with most data processing libraries in Python, R, and other languages. The datasets are ready for import into Jupyter notebooks, RStudio, or any other integrated development environment (IDE) used for data science.

Quality Assurance

The data is pre-checked for common issues such as missing values, duplicate records, and inconsistent entries, offering a clean and reliable dataset for various analytical exercises. With initial header lines in some CSV files, users can easily identify dataset fields and start their analysis without additional data cleaning for headers.

Acknowledgements

The dataset adheres to the GNU LGPL license, making it freely available for modification and distribution, provided that the original source is cited. This opens up possibilities for educators to integrate real-world data into curricula, researchers to validate models against diverse datasets, and practitioners to refine their analytical skills with hands-on data.

This dataset has been compiled from https://people.sc.fsu.edu/~jburkardt/data/csv/csv.html, with gratitude to the authors and maintainers for their dedication to providing open data resources for educational and research purposes. https://i.imgur.com/HOtyghv.png" alt="Imgur">

In recent years, the world has been facing severe challenges from climate change and environmental issues, with carbon dioxide emissions being considered one of the main driving factors. Many studies have proven that activities in various industries and fields have a significant impact on carbon dioxide emissions. However, few studies have explored the impact of gender on carbon dioxide emissions. This study aims to explore the potential impact of gender diversity on carbon dioxide emissions in the boards of directors of developed and emerging market enterprises. In addition, we also analyzed how board cultural diversity affects carbon dioxide emissions. We searched two European indices provided by Morgan Stanley Capital International (MSCI) from the Bloomberg database and conducted empirical analysis. We selected the MSCI index and MSCI emerging market index from 2010 to 2019 as samples and thoroughly cleaned up the data by removing any observations containing missing information on any variables. Statistical methods such as t-test, ordinary least squares, panel data analysis, regression analysis, and robustness testing were used for statistical analysis. At the same time, differential testing was conducted on sensitive and non-sensitive sectors, and the average representation of female boards in sensitive industries was low. The research results show that the proportion of female members on a company’s board of directors is negatively correlated with carbon dioxide emissions. This discovery is consistent with the legitimacy theory advocating for gender equality and environmental sustainability, emphasizing the importance of gender diversity in reducing greenhouse gas emissions. However, agency theory suggests that diversity may lead to internal conflicts within a company, leading to agency costs and information asymmetry. The research results show a negative correlation between board cultural diversity and carbon dioxide emissions, indicating the potential challenge of board cultural diversity. This study provides important insights for decision-makers and managers, not only inspiring corporate social responsibility and environmental policy formulation, but also of great significance for academic research in the field of climate change. Our research findings help deepen our understanding of the factors that affect carbon dioxide emissions in different sectors and countries, while also expanding the research field between gender diversity, cultural diversity, and environmental sustainability. Although this study still needs to be further expanded and deepened, it provides useful insights into the relationship between board gender and cultural diversity and carbon dioxide emissions.

Aim: Despite mounting empirical evidence regarding the positive effects of forest structural diversity (STRDIV) on forest functioning, the underlying biotic mechanisms and controlling abiotic factors remain poorly understood. This study provides the first assessment of the interactive effects of STRDIV and diversity in species and functional traits on aboveground biomass (AGB) in natural forests in West and East Africa. Location: West and East Africa Time period: 2014-2020 Major taxa studied: Woody plants Methods: Using data from 276 plots and 7993 trees of 207 species distributed across various types of natural forests and major climatic zones of Africa, linear mixed-effects and structural equation models, we have evaluated how alternative causal relationships between STRDIV and taxonomic and functional diversity attributes influence AGB, while accounting for the effects of environmental covariates. We also assessed the consistency of these relationships across floristically and enviro..., The dataset results from forest inventories conducted in 276 plots, distributed across four West African countries (Benin, Burkina Faso, Togo, and Ghana) and one East African country (Ethiopia) from 5°40ʹ W to 48°23 ʹE longitude and 3°18ʹ N to 15°03ʹ N latitude. The plots spanned various climate types of the region, including arid, semi-arid, dry sub-humid, and humid, and covered a broad range of topographic and edaphic gradients. The forest inventories were conducted between 2014 and 2020. The plot size was on average 0.1 ha and ranged from 0.02 to 0.25 ha. Within each plot, several key dendrometric parameters including height and diameter at breast height (DBH) were systematically recorded for all living individual trees that met specific criteria. Environmental factors were also either recorded in the field or downloaded from publicly available databses., , # Both the selection and complementarity effects underpin the effect of structural diversity on aboveground biomass in tropical forests

https://doi.org/10.5061/dryad.dfn2z3582

Description of the data and file structure

The zipped file in Dryad contains the data necessary to reproduce the statistical analyses published in the manuscript "Both the selection and complementarity effects underpin the effect of structural diversity on aboveground biomass in tropical forests" in Global Ecology and Biogeography by Noulèkoun et al.

The file includes 3 files, whose content is described below.

1- Main database "dataall_Noulekoun_GEB" This is .csv document that contains all the variables used in the statistical analysis are displayed along with their values per plot. The names of the variables are abbreviated in this document and their description is provided in the second file entitled "Description_abbreviations_Noulekoun" (see also Table bel...

In Ecuador there have been two main projects that have collected national soil information. These projects are: a) “Generación de Geoinformación para la Gestión de territorio y valoración de tierras rurales de la Cuenca del Río Guayas, escala 1:25.000” (2007-2015) developed by the Instituto Espacial Ecuatoriano (IEE), and b) “Generación De Geoinformación Para La Gestión Del Territorio A Nivel Nacional" (2009-2012), developed by Sistema Nacional de Información de Tierras Rurales e Infraestructura Tecnológica (SIGTIERRAS). These projects followed a similar methodology to collect and analyze soil information. However, the resulting databases have different data structures, and they show differences in the way these projects store and present soil information. Only a portion of the original databases was digitized. Most of the available data was only available in PDF files. These PDF files need to be digitized into an easy-to-manage format (e.g., *csv). The difficulty is that each PDF represents one soil profile containing morphological and analytical soil information. Thus, given the volume of soil information available in hundreds of PDF files, manual extraction (e.g., capturing soil data one by one) was not feasible. Therefore, automatic extraction of soil information from each PDF file was developed using open-source programming for data management and statistical computing (in Python and R). This process was developed to optimize data extraction from PDF files. The soil information from both projects in PDF files has been digitized and unified into one harmonized database. We present a new database for Ecuador containing soil information from 13,542 soil profiles, 5 368 are from the IEE project and 8 174 profiles from the SIGTIERRAS project. The new database includes 5368 are from the IEE project and 8174 profiles from the SIGTIERRAS project. The new database includes data from 51,692 soil horizons and information of about 20 morphological and 46 analytical variables. Given the difficulty of designing a single structure for information linking soil profiles and soil horizons in the same database, the database was organized into two relational databases linked by a unique identifier. This relational database allows performing queries of information in a more efficient fashion. The unique (e.g., column ID_PER) identifier allows to couple soil horizon and soil profile information records. Both files (soil horizon database and soil profile database) are provided in *csv files, and they can easily be imported into statistical software (such as R) to perform analysis. The soil profile database (Table 1) contains information associated with soil profile site-level data. The variables Include geographic localization, taxonomic soil profile information (soil taxonomy, humidity, and temperature regimes), soil environmental characteristics associated with soil-forming factors (landscape attributes, land cover type, slope), land use, and soil conservation state. The horizon database (Table 2) includes extensive information from the soil profile description on a soil horizons basis. The variables include qualitative and quantitative information. We find morphological information in the soil horizon database (e.g., designation or depth of horizon, presence or absence of roots, the abundance of rock fragments). We also find over 40 analytical variables representing physical (textural properties of bulk density) and chemical soil properties, including the organic fraction, fertility, and salinity.

This data provides statistical information on sewage treatment plants in Gyeonggi-do, and includes information on the operating status and treatment performance of each sewage treatment plant. Key items include facility name, city/county name, start date of operation, operating entity name, contracted company name, treatment method, regional division, discharge area information (main stream, tributary, water system), physical/biological/advanced treatment facility capacity and actual treatment volume, discharge water disinfection method, contract scope, project cost, location address, and latitude/longitude coordinates. This data can be used to understand the operating status of regional sewage treatment infrastructure, establish environmental policies, manage river water quality, and analyze the efficiency of contracted operation. In particular, physical/biological/advanced treatment items are key indicators that can numerically determine the qualitative level of sewage treatment. It is provided as an open API and can be linked and utilized in various environmental data analysis and integration platforms.

Aim. Species distribution models (SDMs) have emerged as essential tools in the equipment of many ecologists, useful to explore species distributions in space and time and answering an assortment of questions related to biogeography, climate change biology and conservation biology. Historically, most SDM research concentrated on well-known organisms, especially vertebrates. In recent years, these tools are becoming increasingly important for predicting the distribution of understudied invertebrate taxa. Here, we reviewed the literature published on main terrestrial arthropod predators (ants, ground beetles and spiders) to explore some of the challenges and opportunities of species distribution modelling in mega-diverse arthropod groups. Location. Global. Methods. Systematic mapping of the literature and bibliometric analysis. Results. Most SDM studies of animals to date have focused either on broad samples of vertebrates or on arthropod species that are charismatic (e.g. butterflies) or economically important (e.g. vectors of disease, crop pests and pollinators). We show that the use of SDMs to map the geography of terrestrial arthropod predators is a nascent phenomenon, with a near-exponential growth in the number of studies over the past 10 years and still limited collaborative networks among researchers. There is a bias in studies towards charismatic species and geographical areas that hold lower levels of diversity but greater availability of data, such as Europe and North America. Conclusions. Arthropods pose particular modelling challenges that add to the ones already present for vertebrates, but they should also offer opportunities for future SDM research as data and new methods are made available. To overcome data limitations, we illustrate the potential of modern data sources and new modelling approaches. We discuss areas of research where SDMs may be combined with dispersal models and increasingly available phylogenetic and functional data to understand evolutionary changes in ranges and range-limiting traits over past and contemporary time scales.

Context and methodology

This dataset was created as part of the "Urban Climate Resilience" research project, conducted at the Department of Environmental Sciences, University of Example. The project aims to study temperature and humidity variations in metropolitan areas to better understand the impacts of urban heat islands.

The dataset serves as the primary data source for the statistical analysis of microclimate conditions across three city districts, collected over the summer of 2024. Data was gathered using IoT-based environmental sensors deployed at 30 locations. Each sensor recorded temperature, humidity, and air pressure at 5-minute intervals.

Technical details

The dataset is organized into three main folders, one for each district ("District_A", "District_B", and "District_C"). Each folder contains daily CSV files named in the format YYYY-MM-DD_sensorID.csv. A README file at the root level explains the folder structure, file naming convention, and column definitions.

The CSV files can be opened with any standard spreadsheet software (e.g., Excel, LibreOffice) or programmatically using tools such as Python (pandas) or R. A Jupyter Notebook is included to demonstrate basic data loading and visualization.

Additional documentation and source code for the data collection scripts and analysis pipeline are available on the project's GitHub repository: https://github.com/example/urban-climate-resilience

Further details

Please note that while sensor calibration was performed prior to deployment, occasional anomalies may occur due to weather interference or battery fluctuations. Users are advised to apply the provided quality control script (quality_check.py) before analysis.

We encourage reuse and welcome collaboration. If you use this dataset in your work, please cite it using the provided DOI.