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75 results for “sustainability assessment”
Software Architecture Assessment for Sustainability: A Case Study
<h3>Replication Package: Software Architecture Assessment for Sustainability: A Case Study</h3> <p><br>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.<br><br>The online version can be browsed on the linked <a href="https://github.com/S2-group/rep-pkg-ecsa-2024-SA-assessment-for-sustainability-a-case-study/tree/v1.0.0" target="_blank" rel="noopener">Github Repository</a></p> <p> </p> <p> </p>
Absolute environmental sustainability assessment of renewable dimethyl ether fuelled heavy-duty trucks
<p>Dataset associated with the publication "Absolute environmental sustainability assessment of renewable dimethyl ether fuelled heavy-duty trucks" by Margarita A. Charalambous, Victor Tulus, Morten W. Ryberg, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1039/D2SE01409B">https://doi.org/10.1039/D2SE01409B</a>. The dataset includes all the LCA inventories and numeric data required to plot all the figures embedded in the main manuscript.</p> <p>The dataset includes 4 Excel files. The content of each dataset is here elucidated:</p> <ul> <li><strong>LCA_data.xlsx:</strong> Inventory datasets used for life cycle assessment. Includes the inventory for the production of methanol from CO<sub>2</sub> and H<sub>2</sub> sources investigated in this work, carbon dioxide from direct air capture (DAC), and point source coal power plant and natural gas power plant, as well as, the production of hydrogen from biomass with CCS and polymer electrolyte water electrolysis powered with Wind power and BECCS. DME production from each methanol activity, and lastly, truck transport activities used in the study are also included. </li> <li><strong>LCA_relative_impact.xlsx</strong>: numerical values associated with the total share of safe operating space for all the assessed control variables of the seven planetary boundaries quantified in the study, for all the considered scenarios. These values represent the data used to create Figure 2 of the main manuscript.</li> <li><strong>LCA_breakdown.xlsx</strong>: numerical values associated with the breakdown of the environmental impacts for the studied scenarios, for all the control variables of the planetary boundaries. Each sheet includes data for one control variable. These values represent the data used to create Figure 3 in the main manuscript and Figures S4 and S5.</li> <li><strong>LCA_costs.xlsx: </strong>numerical values associated with the cost breakdown for each considered scenario. These values represent the data used to create Figure 4 of the main manuscript. </li> </ul>
Data and R Code from "A novel approach to sustainability assessment of food supply chains using networks of ecosystem services"
<p>Data and R code from this paper applying network analysis (iGraph) to two case studies pre and post agroecological transitions in Central America and Tanzania, Africa from the IPES-Food report. Further descriptions of this data and code can be found within the extended manuscript. R Code relies on the data from the scenarios (e.g., Nodes and Relations CSVs) and creates the output network metrics (e.g., Node Metric CSVs). </p>
Sustainable Aging in Aix-Marseille Metropolis: Assessment Indicators and Interactive Visualizations for Policy Making
<p>This upload contains a geopackage file, R code, Readme file and an HTML-based platform with interactive maps created using the geopackage and R code within the ForVie project (Forme et vieillissement au sein de la Métropole d'Aix-Marseille-Provence). This upload presents various data, indicators and interactive maps to help planners and policymakers to quickly identify patterns and trends related to aging within Aix-Marseille metropolis, France.</p> <p>Authors<br> Perez, J & Boyer, T. Université Côte d'Azur, UMR 7300 ESPACE-CNRS, Nice, France.</p> <p>Files<br> * `AGING_AMP_INDICATORS.gpkg`: The geopackage file contains spatial data used in the R code to create the maps.<br> * `code_R_platform_v1.0_ForVie.R`: The R code used to create the interactive maps.<br> * `ForVie_platform_v1.0.html`: The platform for visualizing the interactive maps.</p> <p>Instructions<br> To use this upload, follow these steps:<br> 1. Download the geopackage file, R code, and index.html file.<br> 2. Open the R code in RStudio or another R environment.<br> 3. Install any necessary packages as listed in the first section of the R code.<br> 4. Run the code in R to create the HTML-based platform with interactive maps<br> Alternatively, it is possible to directly open the ForVie_platform_v1.0.html file in a web browser to view the maps.</p> <p>Acknowledgment<br> This research was funded by a grant from Région Sud, Provence-Alpes-Côte d'Azur, France (ForVie).</p> <p>References<br> Perez et al., (2023) "Sustainable Aging in Aix-Marseille Metropolis: Assessment Indicators and Interactive Visualizations for Policy Making." Submitted (reference to be updated)</p> <p>Contact<br> If you have any questions about this upload, please contact Perez Joan at joan.perez@univ-cotedazur.fr</p>
A core ontology for modeling life cycle sustainability assessment on the Semantic Web with Accompanying Database
<p>To enable and support the uptake of semantic ontologies, we present a core ontology developed specifically to capture the data relevant for life cycle sustainability assessment. We further demonstrate the utility of the ontology by using it to integrate data relevant to sustainability assessments, such as EXIOBASE and the Yale Stocks and Flow Database to the Semantic Web. These datasets can be accessed by the machine-readable endpoint using SPARQL, a semantic query language.</p>
Environmental Sustainability Assessment of Hydrogen from Waste Polymers
<p>Dataset associated with the publication "Environmental Sustainability Assessment of Hydrogen from Waste Polymers" by Cecilia Salah, Selene Cobo, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1021/acssuschemeng.2c05729">https://doi.org/10.1021/acssuschemeng.2c05729</a>. The dataset includes the numeric data used both in figures and tables of the main text and the supporting information, in a machine-readable format.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>01_LCI</strong>: numerical values associated with the life cycle inventories of the two gasification processes presented in this work (wPG and wPG+CCS). These values are displayed as in Tables S1 and S2 in the Supporting Information.</li> <li><strong>02_GWI</strong>: numerical values associated with the global warming impact of the 13 technologies assessed in this study. These values are used to generate Figure 3 of the main text.</li> <li><strong>03_PB-LCIA</strong>: numerical values associated with the results from the planetary boundaries life cycle impact assessment of the 13 technologies assessed in this study. This sheet contains data on all three PB-LCIA assessments carried out in this work: transgression level (TL) of the technologies relative to SOS<sub>H2,wP,b</sub> following a utilitarian downscaling, the TL of the technologies relative to SOS<sub>GLO</sub>, and the TL of the technologies relative to SOS<sub>H2,wP,b</sub> downscaled following the grand-fathering downscaling. These values are used to generate respectively Figure 4 of the main text, and Figures S1 and S2 in the Supporting Information.</li> <li><strong>04_LCOH</strong>: numerical values associated with the levelized cost of hydrogen (LCOH) of the 13 technologies assessed in this work. The sheet contains the values used to generate Figure 5 of the main text, and the values as displayed in Tables S3, S4, and S5 in the Supporting Information, which are used to calculate the LCOH of PEM-BECCS, PEM-hydro and PEM-2018 grid mix.</li> <li><strong>05_TEA</strong>: numerical values associated with the cost breakdown of the wPG and wPG+CCS technologies. These values are used to generate Figure S3 in the Supporting Information, and are displayed as in Tables S6, S7 and S8 in the Supporting Information.</li> <li><strong>06_endpoints</strong>: numerical values associated with the total endpoint environmental impacts of the different technologies per impact category, broken down by process component. These values are used to generate Figure S4, and are displayed as in Table S9 in the Supporting Information.</li> <li><strong>07_TCH</strong>: numerical values associated with the total cost of hydrogen (TCH) of the 13 technologies assessed in this work. The sheet contains the values used to calculate the TCH of the different technologies, and the data used to generate Figure S5 in the Supporting Information.</li> <li><strong>08_LP</strong>: numerical values associated with the results of the optimization that minimizes the cost of meeting the global H<sub>2</sub> demand within planetary boundaries. These values are used to generate Figure 6 of the main text and Figure S6 in the Supporting Information.</li> </ul>
Description of tools for the sustainability assessment of farms and farming systems
<p><strong>The “summary” table shows the papers and names of the tools and the link to the main publication as well as the use of the paper in each part of the analysis. We presented the variables for all the methods in a table called “method” with the result of the classification. In the table “dimension”, we presented the extraction of the dimension of sustainability and classification of methods. We did the same for the “themes” with a colour that we used for providing the descriptive statistics on the number of themes per dimension. We used three tables for each indicator that present the name of the indicator as extracted in the publication.</strong></p>
Tool for the selection of indicators for agriculture sustainability assessment
<p>This is a preliminary version of the Excel-based tool that is being developed within the Working Group of the Crop diversification cluster on Multi-criteria Assessment and Sustainability Indicators (MCA & SI cluster WG). The tool has the aim to assist different end-users in the selection of sustainability indicators tailored to assess the impacts and effects of crop diversification. Currently this preliminary version contains only the indicators developed by the DiverIMPACTS and DiverFARMING projects (2 out 5 projects of Crop diversification cluster).</p> <p>The tool is composed by three sheets: 1) the “Selection” sheet for querying the database and obtaining the results; 2) the “Database” sheet where all indicators of the cluster projects are reported; 3) the “SAFA_HierachicalClassification” sheet with the description of the SAFA framework, and the items identified for the indicators of the cluster projects reported in the database.</p>
National SDG-7 performance assessment to support achieving sustainable energy for all within planetary limits
<p>Supplementary materials for publication Gebara et al. 2022, "National SDG-7 performance assessment to support achieving sustainable energy for all within planetary limits". </p>
Stream temperature data for Alaska Sustainable Salmon Fund project 53007 Assessing Thermal Habitat Variability to Identify Refugia in SE Alaska Subsistence Salmon Watersheds
<p>Hourly stream temperature data were collected in eight watersheds in southeast Alaska to better understand within-watershed thermal heterogeneity. Watersheds include: Chilkat, Chilkoot, Klag, Cowee, Peterson (Juneau road system), Saltery, Kadashan, and Klawock. Site latitude and longitude are recorded in the metadata file. Data were collected with HOBO Onset Pro V2 or HOBO TidbiT MX 400 temperature loggers following the protocols in <a href="https://doi.org/10.1016/j.ejrh.2015.07.008">Mauger et al, 2015</a>. Data collection dates range from January 1, 2020 to November 2, 2023, although not all sites cover this entire date range. </p>
Assessing the sustainability performance of sustainability management software - replication package
<p>This is the replication package for the article "Assessing the sustainability performance of sustainability management software", published in Technologies – SI „Advanced Green Information and Communication Technology“</p> <p><strong>Contents</strong><br> The repository contains the following files:</p> <ul> <li><em>Data_-_Scenario_Hardware_Measurement.csv:</em><br> measurement data acquired during the measurement of the standard usage scenario</li> <li><em>Data_-_Scenario_Log.txt:</em><br> timestamp log file for the measurements of the standard usage scenario</li> <li><em>Data_-_Baseline_Hardware_Measurement.csv:</em><br> baseline meaurements</li> <li><em>Data_-_Baseline_Log.txt:</em><br> baseline timestamp log file</li> <li><em>R_image.Rdata:</em><br> data is also available in an R image dump</li> <li><em>R_Analysis_Script.R:</em><br> analysis script, written in R</li> <li><em>Results_SCSS_WeSustain_ESM.pdf:</em><br> results of the analysis</li> <li><em>Results_and_calculation_for_Indicators_1.2.b_and_1.2.c.ods:</em><br> calculation for indicators 1.1.4.d) 1.2.b) and 1.2.c)</li> <li><em>Usage-scenario_WeSustain_ESM.pdf:</em><br> description of the standard usage scenario</li> </ul> <p><strong>Usage</strong><br> To recreate the analysis, run the R script and, if necessary, modify <em>lines 103 to 115</em> to fit the filenames where to find the data.<br> <em>Lines 193 to 217</em> need to be executed manually, to generate the desired plots and calculations.</p> <p>It is also possible to load the <em>R_image.RData</em> data dump into an R session, import the library `psych` (<em>line 2</em> in the script) and manually execute <em>lines 193 to 217</em> to generate the desired plots and calculations.</p>
Replication package: assessing the sustainability of software products - a method comparison
<p><strong>Assessing the Sustainability of Software Products - A Method Comparison - Replication Package</strong></p> <p>This is a replication package for the paper entitled "Assessing the Sustainability of Software Products - A Method Comparison". The paper was submitted to the 33. EnviroInfo conference "Environmental Informatics – Computational sustainability: ICT methods to achieve the UN Sustainable Development Goals", 23th – 26th September 2019 at the University of Kassel, Germany.</p> <p>For further information, please refer to the <a href="https://zenodo.org/record/3257517/files/README.md?download=1">README.md</a></p> <p>This replication package is licensed under <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons CC BY-NC 4.0</a>.</p>
nmatthews323/SustAssessR: An integrated codebase for sustainability assessment using R
<p>An integrated codebase for sustainability assessment using R</p> <p>This codebase was used for and is made available alongside the following publication: Matthews, N. E., Cizauskas, C. A., Layton, D. S., Stamford, L., & Shapira, P. (2019). Collaborating constructively for sustainable biotechnology. Scientific Reports. <a href="https://doi.org/10.1038/s41598-019-54331-7">https://doi.org/10.1038/s41598-019-54331-7</a></p> <p><strong>A note on input data</strong></p> <p>This repository is designed to demonstrate and make available the code used for the above publication. Due to the proprietary nature of some of the process flow modelling the data in the directory "Data/ProcessedFlows" has been averaged and rounded to three significant figures. Therefore, while this codebase will authentically replicate the data processing carried out in the published analysis, the outputs will not be identical due to the changes made to the input data. All output data from the original publication has been made available through the supplementary information of the original publication.</p> <p> </p>
Assessing Student Sustainable Learning Engagement in Mobile Learning through Social Cognitive Theory and Social Learning Theory
<p><span>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381)</em>.</span><span> the data was collected as part of ODDEA WP2.</span></p>
Data for 'A multi‐methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'
<p>Title: Data for 'A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'</p> <p>Recommended citation: Adhikari, B., Urbach, D., Chettri, N., Sharma, E., Breu, T., Geschke, J., Fischer, M. & Prescott, G. W. (2023) A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal. Sustainable Development https://doi.org/10.1002/sd.2582</p> <p>R code available at: https://github.com/biraj-ad/SDGIntearctions_Nepal_2023</p> <p>Principal Investigator: Graham W Prescott (graham.prescott.research@gmail.com)</p> <p>Authors:<br> Biraj Adhikari (biraj.adhikari@unibe.ch, ORCID: 0000-0002-4260-8706)<br> Davnah Urbach (davnah.payne@unibe.ch, ORCID: 0000-0001-9170-7834)<br> Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879)<br> Eklabya Sharma (eklabya.sharma11@gmail.com, ORCID:0000-0003-3089-8838)<br> Thomas Breu (thomas.breu@unibe.ch, ORCID: 0000-0003-2348-504X)<br> Jonas Geschke (jonas.geschke@unibe.ch, ORCID: 0000-0002-5654-9313)<br> Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)<br> Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X)</p> <p>Date of data collection: August 2021 - June 2022<br> Location of data collection: Kathmandu<br> Date of final release:</p> <p>Data Overview:<br> There are two excel files. Each excel sheet is also uploaded as a separate .csv file.</p> <p>1. 'Alldata.xls': This excel file contains data collected for three independent methods used to develop the study. There are four sheets in this excel workbook. The first three sheet pertains to data collected for the seven-point SDG interactions score, correlation, and expert elicitation method. The last sheet is a derivative of the first three methods, which contains synthesized information of data.<br> Sheet overview:<br> method1: This sheet relates to the data collected through online expert survey using Kobo Toolbox. Description of columns:<br> institution -> institutional affiliation of the participant<br> inst_cat -> categorization of the institution into Intergovernmental Organization (IGO), Non-governmental Organization (NGO), Academia, and Government.<br> exp_years -> experience (in years) of the participant in the conservation sector of Nepal<br> sdgout and sdgin -> The SDG that the participant was randomly assigned to rate its outgoing and incoming interaction with SDG 15 respectively<br> outscore -> the outgoing interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)<br> conf_out -> the degree of confidence of the participant in their answer to the outgoing interaction score<br> text_out -> (optional) a description of why the participant gave that outgoing interaction score<br> inscore -> the incoming interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)<br> conf_in -> the degree of confidence of the participant in their answer to the incoming interaction score<br> text_in -> (optional) a description of why the participant gave that incoming interaction score</p> <p>method2: This sheet relates to the data collected for correlation analysis. This includes time-series data of SDG indicators for Nepal obtained from the Global SDG Indicators Database (https://unstats.un.org/sdgs/indicators/database/). Description of columns:<br> Year -> Year when the indicator was measured<br> forest_cover -> Forest cover as a percent of total area (%)<br> kba_freshwater -> Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%)<br> kba_terrestiral -> Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%)<br> redlist -> Red List Index<br> undernourishment -> Prevalence of undernourishment (%)<br> food_insecurity -> Prevalence of moderate or severe food insecurity in the adult population (%)<br> death_chronic -> Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)<br> suicide -> Suicide mortality rate (deaths per 100,000 population)<br> uhc_index -> Universal health coverage (UHC) service coverage index<br> f_parliament -> Proportion of seats held by women in national parliaments (% of total number of seats)<br> safewater -> Proportion of population using safely managed drinking water services (%)<br> electricity -> Proportion of population with access to electricity, by urban/rural (%)<br> cleanfuel -> Proportion of population with primary reliance on clean fuels and technology (%)<br> renewable -> Renewable energy share in the total final energy consumption (%)<br> gdp_capita -> Annual growth rate of real GDP per capita (%)<br> manu_value -> Manufacturing value added as a proportion of GDP (%)<br> consumption_gdp -> Domestic material consumption per unit of GDP (kilograms per constant 2010 United States dollars)<br> consumption_all -> Domestic material consumption (tonnes)<br> consumption_cap -> Domestic material consumption per capita (tonnes)<br> death_missing -> Number of deaths, missing persons and persons affected by disaster per 100,000 people<br> local_drr -> Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)</p> <p>method3: This sheet relates to the data collected through key informant interviews. We obtained the count of interactions by coding interview responses as outgoing or incoming co-benefits or trade-offs in MaxQDA. The sheet summarizes the count for type of interaction (co-benefit or trade-off) for each SDG.</p> <p>allmethods: This sheet summarizes the proportion of co-benefits or trade-offs uncovered by each method for each SDG.</p> <p>2. 'ForSynthesistable.xlsx': This excel file synthesizes goal level interactions for the three methods in three separate sheets. We use this table to develop figure 6 of the Manuscript.</p> <p> </p>
A Study to Assess the Ability of Eltrombopag to Induce Sustained Response Off Treatment in Subjects With ITP
ClinicalTrials.gov study NCT03524612. IPD Sharing: YES. Countries: 15. Publications: 1.
Description of full chain Techno-Economic Sustainability Assessment (TESA) methodology
<p>The dataset provides a preliminary description for techno-economic sustainability principles and criteria in relation to SAT-ProBio blueprint.</p>
Data from: Development of a sustainability assessment algorithm and its validation using case studies on cryogenic machining
This work presents a comprehensive structure for evaluating the sustainability of machining processes. Industries can contribute towards developing a sustainable future by using algorithms that evaluate the sustainability of their processes. Inspired by the literature, the proposed model involves a set of metrics that are critical in evaluating the impact of a process on society, environment, and economy. The flexibility of this model allows decision-makers to use the available responses to identify the most favorable process. The entropy weight method was suggested for objectively calculating the weights of each indicator. A multi-criteria decision-making method i.e., Technique for Order Preference based on Similarity to Ideal Solution (TOPSIS), was used to rank processes in the decreasing order of their sustainability. The proposed algorithm was successfully validated with case studies from the published literature. A MATLAB code was also created so that industries may expeditiously apply this method to evaluate the sustainability of machining processes.
Datasets of the paper "Sustainable development pathways for Chinese cities: an assessment of the advanced inclusive wealth index"
<p>这是《中国城市可持续发展路径:先进包容性财富指数评估 》一文的数据集和代码,该论文涵盖了中国 10 个城市用于计算包容性财富的所有指标的数据。“calculatorADJUST.m”是matlab代码,“data_city_2010.xlsx”和“data_city_2019.xlsx”分别是2010年和2019年的普惠财富数据。</p>
Schematic and adapted figures from IPBES Sustainable Use of Wild Species Assessment - Chapter 3. Status of and trends in the use of wild species and its implications for wild species, the environment and people
<p>Schematic and adapted figures from Chapter 3 of the thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.