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The OBdrive mobile application, developed within the eCOMBINE framework, is a custom research tool designed to capture real-time occupant motivations during window and blind interactions. Installed on research-issued smartphones mounted near operable window handles, the app enables users to log their reasons for interaction at the moment of action. Prioritising usability and minimal intrusion, OBdrive facilitates structured, high-resolution data collection through a simple, context-sensitive interface. This design allows researchers to gather impulse-driven, multidimensional motivation data that reflects the complex, situational nature of human-building interactions.

Maximum Cumulative Water Deficit - MCWD: a R language script DOI: 10.5281/zenodo.2625772 (https://doi.org/10.5281/zenodo.2652630) Script for the calculation of MCWD (Maximum Cumulative Water Deficit) in rainfall raster data (e.g. CHIRPS and TRMM). Calculation based in Aragão et al. (2007, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2006GL028946). Please cite as: "Campanharo, W. A., and Silva Junior, C. H. L. (2019). Maximun Cumulative Water Deficit - MCWD: a R language script. doi:10.5281/zenodo.2652630." and "Silva Junior, et al. (2019). Fire responses to the 2010 and 2015/2016 Amazonian droughts. Frontiers in Earth Science. doi:10.3389/feart.2019.00097." Created by: Wesley A. Campanharo (https://www.researchgate.net/profile/Wesley_Campanharo) and Celso H. L. Silva Junior (https://www.researchgate.net/profile/Celso_Silva_Junior).

A planning and operation tool for Resilient Microgrids

What's Changed Include cplex.opt by @olejandro in https://github.com/MaREI-EPMG/times-ireland-model/pull/2 Update currency by @olejandro in https://github.com/MaREI-EPMG/times-ireland-model/pull/3 Add SingleRegion (default) and Counties region groups by @olejandro in https://github.com/MaREI-EPMG/times-ireland-model/pull/4 Add results tables for FEC of biofuels in Transportation by mode by @olejandro in https://github.com/MaREI-EPMG/times-ireland-model/pull/7 Clean-up result table definitions by @olejandro in https://github.com/MaREI-EPMG/times-ireland-model/pull/8 Various fixes by @olejandro in https://github.com/MaREI-EPMG/times-ireland-model/pull/10 Full Changelog: https://github.com/MaREI-EPMG/times-ireland-model/compare/v1.0...v1.0.1

Supplementary material of Neder C. 2023, Neder et al. 2024a & 2024b. R script for species distribution models for benthic antarctic species. A case study for Potter Cove. 

Source code for EC-CAS (Environment Canada Carbon Assimilation System) v1.0. This code uses GEM-MACH-GHG (https://zenodo.org/record/3246556) as the forward model. EC-CAS uses an Ensemble Kalman Filter (EnKF) as the data assimilation technique. Please see the header/comments in each file. The following files constitute the core of the EC-CAS. sekfeta.ftn90 and sekflib.ftn90 implement the EnKF. trlcma.ftn90 and observations.ftn90 implement the interpolation of model state (Hx). burp_read_mod.ftn90 and burp_functions.ftn90 convert the observations to BURP format. sortcma.ftn90 and regions_mod.ftn90 divide the observations into batches to be assimilated by the EnKF.

Python scripts and a Jupyter notebook for processing water demand and availability data, simulating three allocation methods (non-priority, sequential, and traffic light frameworks), and generating visualizations for the Almeria, Spain case study. These scripts support the analyses presented in the thesis “Exploring a Novel Modelling Framework for Water Management Purposes under Past and Future Conditions”, submitted in partial fulfillment of the requirements for the Master of Science in Water Resources Engineering.

GCM compareR GCM compareR is a web application developed to assist ecologists, conservationists and policy makers at understanding climate change scenarios and differences between Global Circulation Models (GCMs), and at assisting the triage of subsets of models in an objective and informed manner. GCM compareR is written in R and uses the web app development package shiny. The code of this app can be find in the project's github, https://github.com/marquetlab/GCM_compareR. The number of GCMs that are accessible to researchers and practitioners has grown large. Concretely, meteorological research centers worldwide have contributed more than 35 different GCMs for four distinct climate change scenarios as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5; (Taylor, Stouffer, and Meehl 2012)). All these models have shown good performance and skill in predicting historical climatic data, but present differences among them as a result of different sources of uncertainty (including model formulation, resolution and sensitivity to initial conditions, climate noise; (Flato et al. 2013)). GCMs could be ranked by their skill at specific geographic areas, but models that most accurately predict historic data are not necessarily the most useful for making future climate projections (Knutti 2008). In practice, best practices when conducting any evaluation advice for using multi-model approaches where differences in GCMs projections are adequantely understood and assessed as uncertainty (Pierce et al. 2009, Flato et al. (2013)). Also, and even though the ideal case would use all available GCMs, researchers are often forced to work with a few selected models for computational restrictions (Barsugli et al. 2013). However, the choice of some GCMs and not other has the potential to influence results (Synes and Osborne 2011), and thus it should be made following informed and replicable procedures (P. Mote et al. 2011, Snover et al. (2013), Vano et al. (2015)). GCM compareR has been design to serve the purpose of informing about differences and similarities between GCMs and climate change scenarios, and of assisting the triage of models that best suit every used needs.

Microplastics (MP) are recognized as a major pollutant in terrestrial environments, prompting concerns regarding their effects on plant-soil dynamics. Despite evidence of MP altering soil physicochemical properties, impacts on belowground root traits and arbuscular mycorrhizal (AM) fungi remains poorly explored. Existing research has mainly centered on a few model plant species, emphasizing root biomass, and often employs single polymer types and addition rates that surpass realistic scenarios. To investigate how environmentally relevant mixtures and concentrations of MPs impact plant growth, root trait expression and AM fungal colonization, we conducted a greenhouse experiment using six plant species chosen for their contrasting root life strategies; three species in the Amaryllidaceae family represented resource conservation root traits (Allium fistulosum (Onion), Allium tuberosum (Chive), Allium porrum (Leek)), and three from the Solanaceae family, represented plants with resource acquisitive root traits (Solanum lycopersicum (Tomato), Solanum melongena (Eggplant), Capsicum annuum (Pepper). MP treatments consisted of control (0% MP), low (0.1% w/w) and high (1% w/w) MP additions, using an environmentally relevant MP mixture of weathered polymer types and shapes. Above and belowground biomass, average root trait expression (specific root length (SRL), average root diameter (D) and root tissue density (RTD), AM fungal colonization, as well as intraspecific variability across MP addition treatments. We found that the addition of environmentally relevant additions of MPs was species specific and not determined by root life-strategy. MPs increased biomass in Leek, Eggplant and Tomato, while decreasing AM fungal colonization in Tomato. MP additions had no discernible impact on average root functional trait expression across species. However, the addition of MPs resulted in altered intraspecific variability in root traits and AM fungal colonization, indicating a mechanism for plant tolerance to MPs. To address the impacts of MP on plant functioning, our study highlights the need for future research to focus on environmentally relevant mixtures of MPs, considering various plant species' capacities to tolerate soil contamination and the potential for tipping points under real-world conditions. Funding provided by: Natural Sciences and Engineering Research CouncilROR ID: https://ror.org/01h531d29Award Number: