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Replication Package: Software Architecture Assessment for Sustainability: A Case Study This repository contains the supplementary material to support the paper published at the International Conference on Software Architecture (ECSA) 2024 titled, "Software Architecture Assessment for Sustainability: A Case Study". This repository can be used to replicate the study and carry out a Software Architecture Evaluation of other software systems.The online version can be browsed on the linked Github Repository

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

This dataset and codebook correspond to the initial round of survey data gathered in Germany in 2022, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.

Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

This repository consists of two databases- CASE-ONSHORE and CASE-OFFSHORE, generated using OpenFAST v2.4 on NREL's 10-MW reference wind turbine for training data-driven probabilistic load surrogate models. The data is to be used for mapping 10-minute average environmental conditions to the corresponding 10-minute load statistics such as load average, fatigue and range at various locations on the tower and blades.

The repository contains the underlining data for "On the wake deflection of vertical axis wind turbines by pitched blades" This repository consists of experimental (PIV and force measurements) data in the wake of VAWTs with different blade pitch angles. The measured cases include an isolated VAWT with -10, 0, 10 degree pitch, respectively. Author contribution: Ming Huang: Conceptualization; Methodology; Validation; Experimental design; Carrying out the experiment; postprocessing Yugandhar Vijaykumar Patil: Design assistant; Carrying out the experiment; postprocessing Andrea Sciacchitano: Conceptualization; Experimental design; Methodology; Validation; Delphine De Tavernier: Conceptualization Carlos Simao Ferreira: Conceptualization; Methodology; Validation; Experimental design

To evaluate if Brivaracetam (BRV) influences the psychomotor and cognitive impairing effects of Ethanol

This repository contains additional data associated to Tedersoo et al. (2022) (https://doi.org/10.1111/gcb.16398).This repository contains those data necessary to rerun the superecoregion design and the endemicity analyses as well as vector maps on the endemicity and OTU richness of individual superecoregions. Further data associated with the same publication, regarding the vulnerability analysis, can be found here: 10.5281/zenodo.6983158. input.zip - Input data needed to run the analysis scripts provided [here](https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability/superecoregions_and_endemicity). The entire folder is to be copied in the working directory to run the pipeline. output.zip - Further data needed to run the analysis scripts provided [here](https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability/superecoregions_and_endemicity). The entire folder is to be copied in the working directory to run the pipeline. maps_otu_numbers_and_se_characteristics - vector maps of the superecoregions and the number of OTUs and endemic OTUs per region for different functional groups maps_traits_endemism - vector maps of endemicity indices per region f different functional groups 01_super_Ecoregions_numbers.pdf - vector map of the superecoregions used in the study numbered from 1 - 174. legend_01_super_ecoregions_numbers.csv - legend with superecoregion names for 01_super_Ecoregions_numbers.pdf. super_eoregions_results - Superecoregions with number of OTUs, number of endemic OTUs and endemicity indices in R data format, for further analyses and visualization super_ecoregions.shp - Superecoregions with number of OTUs, number of endemic OTUs and endemicity indices as shape file, for further analyses and visualization This repository contains additional data associated to Tedersoo et al. (2022) (https://doi.org/10.1111/gcb.16398).This repository contains those data necessary to rerun the superecoregion design and the endemicity analyses as well as vector maps on the endemicity and OTU richness of individual superecoregions. Further data associated with the same publication, regarding the vulnerability analysis, can be found here: 10.5281/zenodo.6983158.

The dataset covers biotic and abiotic data from the aquatic habitat of a population of the sediment-rooted macrophyte Myriophyllum spicatum in the temperate climate region (The Netherlands). The growth of M. spicatum was monitored in 0.2025 m2 plant baskets installed in an experimental ditch. Parameters monitored included biomass (fresh and dry weight), shoot length, seasonal short-term growth rates of shoots, relevant environmental parameters and weather data. This dataset includes the 2-year experimental biotic (macrophyte biomass and growth data) and environmental data (water quality data, sediment data). A second file includes the statistical data. A third file includes the weather data.