Heliyon

This file relates to a study entitled "From sense of place to visualization of place: Examining people-place relationships for insight on developing geovisualizations" by Robert Newell and Rosaline Canessa. The research consisted of a survey-based study, where residents of the Capital Regional District of BC, Canada, were surveyed about their relationship with local coastal places, concerns for the coast, and how they mentally visualize these places. Factor analysis identified four sense of place dimensions - nature protection values, community and economic well-being values, place identity and place dependence, and four coastal concerns dimensions - ecological, private opportunities, public space and boating impacts. Mental visualization data were coded and treated as dependent variables in a series of logistic regressions that used sense of place and coastal concerns dimensions as predictors. The file features the survey questions and SPSS output from the analysis.

Data from Raman microspectrometry, LIBS, XRF, and particle sizer analysis supports the findings in the published article named Raman microspectroscopy and laser-induced breakdown spectroscopy to analyze polyethylene microplastics in human soft tissues. The aim of this research is to present the optimized protocol for the detection and analysis of microplastics in biological samples. The tonsil tissue is used for this experiment, and the workflow consists of a few steps: 1. digestion, 2. filtration, 3. analysis. The presented dataset includes the data for verifying the validity of this proposed protocol and the data from clinical experiments done on tonsils where the protocol is applied. We are focusing only on PE microplastics as they are one of the most frequent plastic types in the environment. Firstly, the data from the particle sizer show the size distribution before and after KOH treatment, which is necessary for digestion. We are testing if the particles are not affected by the KOH solution. The data are listed in an Excel sheet where the individual detected PE particle size [µm] and their frequencies [%] are annotated. The data are separated into 2 tables in one sheet - 1st represents data collected before KOH and the 2nd after KOH treatment. To test the limits of our selected systems for microplastic detection, we included the data from Raman and LIBS under the file ‘limitations.’ The different sizes of PE particles, from tens to 1 µm, were analyzed, and the spectra can be retrieved in the folders. The signal intensity can be observed to see the detection limits. For the Raman analysis, the particles were located on the filter. For LIBS, the particles were embedded in epoxy to enable the detection of PE particles in tens of microns. The Raman data are in .txt files and can be opened in any adequate software (Matlab, R, Python, etc.). LIBS data are in specific .libsdata format, which can be opened by LibsAnalyzer software by Lightigo. The clinical experiment was done on tonsil tissue. The tissue was disgusted and filtered. Then, the filters were analyzed. The dataset presents two sample groups: 1. control-native tissue and 2. test-spiked tissue with PE particles. The data from Raman analysis include both, with the aim to confirm the presence of PE particles in the test sample and to exclude the contamination in the control sample. The spectra are again in .txt files. In the case of control, spectra from unclassified particles are presented. For these reasons, the LIBS and XRF were run to exclude the possibility of the presence of polymeric material on the filter of the control sample. The analyzed chemical elements by LIBS for both samples are in the ASC file. Furthermore, individual PE particles were also analyzed on LIBS to obtain reference results for test samples with added PE microplastics. In the case of XRF, data from the empty filter, control, and test samples are included, each in a .txt file. Individual detected chemical elements and their intensities can be retrieved in the tables.

This dataset, FMPD (Freshwater Microscopy Phytoplankton Dataset), is released for non-comercial academic or research purposes only, subject to attribution through citation of the following papers - Figueroa, J. Rouco, J. Novo, "Phytoplankton detection and recognition in freshwater digital microscopy images using deep learning object detectors", Heliyon, 2023. - D. Rivas-Villar, J. Rouco, M. G. Penedo, R. Carballeira, J. Novo, "Fully automatic detection and classification of phytoplankton specimens in digital microscopy images", Computer Methods and Programs in Biomedicine, 200, 105923, 2021 Please also consider the citation of any of the other related papers from the dataset authors. Data: The FMPD dataset is a set of multi-specimen microscopy images of freshwater phytoplankton. These images have been captured with fixed settings, equal for each image, including illumination, focal point and magnification. The dataset contains 293 images from water sampled at lake of Doniños (Ferrol, Galicia, Spain) (UTM 555593 X, 4815672 Y; Datum ETRS89) on multiple visits throughout the year. This ensures seasonal representability. The phytoplankton sample was concentrated by filtering volume of 0.5 L through GF/F glass fiber filters and was then resuspended in 50 mL. Phytoplankton samples were preserved using 5% (v/v) glutaraldehyde, because it is efficient at preserving both cellular structures and pigment. The fixed sample was stored in the dark at constant temperature (10 oC) until analysis. The phytoplankton sample was homogenised for 2 min prior to microscopic examination. In addition, the sample was subjected to vacuum for one minute to break the vacuoles of some cyanobacterial taxa and prevent them from floating. Aliquots of the phytoplankton sample with a total volume of 1 mL were examined under light microscopy using a Nikon Eclipse E600 equipped with an E-Plan 10× objective (N.A. 0.25). Light microscopy images were taken with an AxioCam ICc5 Zeiss digital camera, maintaining the same illumination and focus throughout the image acquisition process and following regular transects until the entire surface of the sample was covered. The dataset contains 293 multi-specimen phytoplankton images. As mentioned, these images have fixed magnification, illumination and focal point. The produced images are saved in .tif format with a size of 2080x1540 pixels and are located in the dataset folder. The ground truth consists of bounding boxes that enclose the phytoplankton specimens, with an associated label identifying the species. Currently, this dataset has tags for: - Non-phytoplankton: particles, debris, zooplankton or any other object that could be mistaken as phytoplankton - Woronichinia naegeliana: Toxin-producing cyanobacteria - Anabaena Spiroides: Toxin-producing cyanobacteria - Dinobryon Sp.: Harmless but challenging as it can both appear solitary or in colonies - Other-phytoplankton: Other phytoplankton species. Annotations are provided in a .json file in the format typically used by the coco dataset, in the annotations.json file. Holdout train-test splits, as well as k-fold cross-validation splits, are provided in the splits folder, available in .json format. These splits correspond to those used in the previously mentioned papers to be cited, facilitating straightforward comparisons. Additionally, the annotations for each subset are included in separate files within the same folder for ease of use. It should be noted that the annotations.json contains all of these subsets of annotations.

This dataset contains all data and code required to clean the data, fit the models, and create the figures and tables for the laboratory experiment portion of the manuscript:Kannan, N., Q. D. Read, and W. Zhang. 2024. A natural polymer material as a pesticide adjuvant for mitigating off-target drift and protecting pollinator health. Heliyon, in press. https://doi.org/10.1016/j.heliyon.2024.e35510.In this dataset, we archive results from several laboratory and field trials testing different adjuvants (spray additives) that are intended to reduce particle drift, increase particle size, and slow down the particles from pesticide spray nozzles. We fit statistical models to the droplet size and speed distribution data and statistically compare different metrics between the adjuvants (sodium alginate, polyacrylamide [PAM], and control without any adjuvants). The following files are included:RawDataPAMsodAlgOxfLsr.xlsx: Raw data for primary analysesOrganizedDataPaperRevision20240614.xlsx: Raw data to produce density plots presented in Figs. 8 and 9raw_data_readme.md: Markdown file with description of the raw data filesR_code_supplement.R: All R code required to reproduce primary analysesR_code_supplement2.R: R code required to produce density plots presented in Figs. 8 and 9Intermediate R output files are also included so that tables and figures can be recreated without having to rerun the data preprocessing, model fitting, and posterior estimation steps:pam_cleaned.RData: Data combined into clean R data frames for analysisvelocityscaledlogdiamfit.rds: Fitted **brms** model object for velocitylnormfitreduced.rds: Fitted **brms** model object for diameter distributionemm_con_velo_diam_draws.RData: Posterior distributions of estimated marginal means for velocityemm_con_draws.RData: Posterior distributions of estimated marginal means for diameter distributionThe following software and package versions were used:R version 4.3.1CmdStan version 2.33.1R packages:brms version 2.20.5cmdstanr version 0.5.3fitdistrplus version 1.1-11tidybayes version 3.0.4emmeans version 1.8.9

This dataset contains all data and code required to clean the data, fit the models, and create the figures and tables for the laboratory experiment portion of the manuscript:Kannan, N., Q. D. Read, and W. Zhang. 2024. A natural polymer material as a pesticide adjuvant for mitigating off-target drift and protecting pollinator health. Heliyon, in press. https://doi.org/10.1016/j.heliyon.2024.e35510.In this dataset, we archive results from several laboratory and field trials testing different adjuvants (spray additives) that are intended to reduce particle drift, increase particle size, and slow down the particles from pesticide spray nozzles. We fit statistical models to the droplet size and speed distribution data and statistically compare different metrics between the adjuvants (sodium alginate, polyacrylamide [PAM], and control without any adjuvants). The following files are included:RawDataPAMsodAlgOxfLsr.xlsx: Raw data for primary analysesOrganizedDataPaperRevision20240614.xlsx: Raw data to produce density plots presented in Figs. 8 and 9raw_data_readme.md: Markdown file with description of the raw data filesR_code_supplement.R: All R code required to reproduce primary analysesR_code_supplement2.R: R code required to produce density plots presented in Figs. 8 and 9Intermediate R output files are also included so that tables and figures can be recreated without having to rerun the data preprocessing, model fitting, and posterior estimation steps:pam_cleaned.RData: Data combined into clean R data frames for analysisvelocityscaledlogdiamfit.rds: Fitted **brms** model object for velocitylnormfitreduced.rds: Fitted **brms** model object for diameter distributionemm_con_velo_diam_draws.RData: Posterior distributions of estimated marginal means for velocityemm_con_draws.RData: Posterior distributions of estimated marginal means for diameter distributionThe following software and package versions were used:R version 4.3.1CmdStan version 2.33.1R packages:brms version 2.20.5cmdstanr version 0.5.3fitdistrplus version 1.1-11tidybayes version 3.0.4emmeans version 1.8.9

TEM, FIIR and XPS datasets

Data and R scripts for statistical analyses for the article: Rhizobia-legume symbiosis mediates direct and indirect interactions between plants, herbivores and their parasitoids By: Carlos Bustos-Segura, Adrienne L. Godschalx, Lucas Malacari, Fanny Deiss, Sergio Rasmann, Daniel J. Ballhorn, Betty Benrey Abstract Microorganisms associated with plant roots significantly impact the quality and quantity of plant defences. However, the bottom-up effects of soil microbes on the aboveground multitrophic interactions remain largely under studied. To address this gap, we investigated the chemically-mediated effects of nitrogen-fixing rhizobia on legume-herbivore-parasitoid multitrophic interactions. To address this, we initially examined the cascading effects of the rhizobia bean association on herbivore caterpillars, their parasitoids, and subsequently investigated how rhizobia influence on plant volatiles and extrafloral nectar. Our goal was to understand how these plant-mediated effects can affect parasitoids. Lima bean plants (Phaseoulus lunatus) inoculated with rhizobia exhibited better growth, and the number of root nodules positively correlated with defensive cyanogenic compounds. Despite increase of these chemical defences, Spodoptera latifascia caterpillars preferred to feed and grew faster on rhizobia-inoculated plants. Moreover, the emission of plant volatiles after leaf damage showed distinct patterns between inoculation treatments, with inoculated plants producing more sesquiterpenes and benzyl nitrile than non-inoculated plants. Despite these differences, Euplectrus platyhypenae parasitoid wasps were similarly attracted to rhizobia- or no rhizobia-treated plants. Yet, the oviposition and offspring development of E. platyhypenae was better on caterpillars fed with rhizobia-inoculated plants. We additionally show that rhizobia-inoculated common bean plants (Phaseolus vulgaris) produced more extrafloral nectar, with higher hydrocarbon concentration, than non-inoculated plants. Consequently, parasitoids performed better when fed with extrafloral nectar from rhizobia-inoculated plants. While the overall effects of bean-rhizobia symbiosis on caterpillars were positive, rhizobia also indirectly benefited parasitoids through the caterpillar host, and directly through the improved production of high quality extrafloral nectar. This study underscores the importance of exploring diverse facets and chemical mechanisms that influence the dynamics between herbivores and predators. This knowledge is crucial for gaining a comprehensive understanding of the ecological implications of rhizobia symbiosis on these interactions.

General remarks This dataset was produced and used for the publication E. Sperber, U. Frey, V. Bertsch: Turn down your thermostats – a contribution to overcoming the European gas crisis? The example of Germany. Heliyon (2024), https://doi.org/10.1016/j.heliyon.2024.e23974 The dataset includes: Results for space heating demand at building archetype level Gas consumption results for space heating at building archetype level Parameters underlying the reduced-order building model ERA 5 weather data used in the model are publicly available at 10.24381/cds.adbb2d47. Data description Results for space heating demand at building archetype level The files titled "space_heating_demand_" followed by the relevant year show the calculated annual demand for space heating at the level of building archetypes, dependent on the temperature setpoint. The unit of measure is kWh/a. Results are available for the years 2010 and 2018 in relation to the weather in Frankfurt am Main, Germany. Gas consumption results for space heating at building archetype level The files titled "gas_consumption_" followed by the relevant year show the calculated annual gas consumption for space heating at the level of building archetypes, dependent on the temperature setpoint. The unit of measure is kWh/a. Results are available for the years 2010 and 2018 in relation to the weather in Frankfurt am Main, Germany. Parameters underlying the reduced-order building model The files start with "parameters", followed by the code of the building model (3R2C or 4R3C). The parameter files contain the following data: Ria: Thermal resistance between interior and the ambient in °C/kW Rie: Thermal resistance between interior and building envelope in °C/kW Rea: Thermal resistance between building envelope and the ambient in °C/kW Rhe: Thermal resistance between heaters and building envelope in °C/kW Rhi: Thermal resistance between heaters and interior in °C/kW Ci: Capacitance of interior in kWh/°C Ce: Capacitance of building envelope in kWh/°C Ch: Capacitance of heaters in kWh/°C Ai: Effective window area for absorption of solar gains on internal air in m² Tlimit: Heating limit temperature in °C Please refer to the publication for more details on the building models and their parameters: E. Sperber, U. Frey, V. Bertsch: Reduced-order models for assessing demand response with heat pumps – Insights from the German energy system. Energy and Buildings 223 (2020), https://doi.org/10.1016/j.enbuild.2020.110144