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2,163 results for “sustainability”
Bioplastics and Carbon-Based Sustainable Materials, Components, and Devices: Toward Green Electronics
<p>This dataset contains the measurement data for figures (graphs) published in journal article:</p> <p>Bioplastics and Carbon-Based Sustainable Materials, Components, and Devices: Toward Green Electronics</p> <p>by Éva Bozó, Henri Ervasti, Niina Halonen, Seyed Hossein Hosseini Shokouh, Jarkko Tolvanen, Olli Pitkänen, Topias Järvinen, Petra S. Pálvölgyi, Ákos Szamosvölgyi, András Sápi, Zoltan Konya, Marta Zaccone, Luana Montalbano, Laurens De Brauwer, Rakesh Nair, Vanesa Martínez-Nogués, Leire San Vicente Laurent, Thomas Dietrich, Laura Fernández de Castro, and Krisztian Kordas </p> <p>https://doi.org/10.1021/acsami.1c13787</p>
Supporting material: Prospective life-cycle assessment of sustainable alternatives for road freight transport
<p><span>This study investigates decarbonization pathways for the road freight transport sector</span><span> </span><span>by evaluating three alternatives to conventional</span><span> </span><span>diesel trucks:</span><span> </span><span>trucks powered by biofuels, battery electric trucks, and fuel cell trucks with hydrogen</span><span>. A prospective life cycle assessment of these options is conducted under two policy scenarios for decarbonization across 12 distinct regions over the century. Employing a cradle-to-grave approach, the assessment covers activities from fuel and electricity production to the end-of-life of truck components. Findings reveal that, in eight of the 12 regions examined, an early transition to battery electric trucks could increase life-cycle greenhouse gas emissions by up to 70% by 2030 compared to the continued use of conventional diesel trucks, underscoring the significance of liquid fuels for short to medium-term decarbonization. However, in the long term, as electricity mixes and hydrogen production are decarbonized, battery electric trucks and hydrogen fuel cell trucks emerge as superior alternatives in all regions, emitting, at least, 29% less greenhouse gases than trucks powered by biofuels, and 45% less than diesel trucks.</span><span> </span><span>The optimal transition from conventional diesel trucks to trucks powered by biofuels and, subsequently, to battery electric trucks and/or hydrogen could avoid 134-204 Gt CO<sub>2-eq</sub> worldwide and prevent a temperature rise of 0.22-0.33°C compared to the diesel-based scenario. This emphasizes the crucial role of appropriate policies for the timely transformation of the road freight transport sector. </span></p>
Sustainable Aquaculture
<p>During the Sabai Webinar Series 15, hosted by the Shwetaungthagathu Reform Initiative Centre (SRIc), Burmese Experts, including Mr Hsu Htoo, Marine Biologist, Mrs Moe Kyi Phyu, Aquaculture Specialist and Mr Tin Shine Aung: Sustainability Consultant and Researcher engaged in a discussion on Sustainable Aquaculture practices, what Myanmar need to consider to transform Myanmar Aquaculture industry to be more sustainable and new. Technology such as eDNA for ecological impact assessment. They highlighted the consequences of wrongdoing in aquaculture and how sustainable aquaculture practices, such as an ecosystem approach to aquaculture management, can help farmers and all the stakeholders in this industry in terms of long-term economic impact and social impacts.</p> <p> </p> <p> </p>
Dataset Bibliometric - Smart Tourism and Sustainability Tourism
<p>The dataset contains a collection of articles related to smart tourism and sustainable tourism in the Scopus database from 2010-2024.</p>
Sustainability-3225848 IBM SPSS survey raw data
<p>The dataset represents 183 validated survey answers analysed in the paper Sustainability-3225848<br><br>The data was analysed using IBM SPSS Statistics vs 29</p>
Data from: Sustainable aerogels based on biobased poly (itaconic acid) for adsorption of cationic dyes
<p>The upload contains data associated with the publication, including raw data in the original file format whenever possible.</p> <p>This work was financially supported by the Lead Agency bilateral a Czech-Polish project provided by the Czech Science Foundation (21-07004K) and National Science Center Poland (CEUS-UNISONO project grant no. 2020/02/Y/ST5/00021).</p>
System Stability and Sustainability Data for 295 Agile Systems
<div><strong>Purpose</strong>: This data set is a collection of 295 "Agile Systems" performed by an European mid-tier software development consulting firm between June 2008 and August 2023. The systems were a mixture of Scrum, Kanban, LeSS, SAFe and other Agile-type approaches to work, including Agile In Name Only (AINO). The data set is the output from a script that analysed the raw data extracted from Jira with each Agile System corresponding to a Jira "Project".</div> <div> </div> <div>The script performed the following actions:</div> <div>1. Read the raw data for each system. (this is not included in this dataset)</div> <div>2. Remove Epics and Sub-tasks so all PBIs are approximately the same size.</div> <div>3. Remove any cancelled (and similar) PBIs so only PBIs analysed were done by a team.</div> <div>4A. Identify the Arrival rate (average rate at which PBIs were created over the duration of the system, Lambda) and the Service rate (average rate at which PBIs were moved to Done over the duration of the system, Mu). Also calculate the system size (total arrivals versus total services) at the end of the duration of the system. </div> <div>4B. Calcuate the Stability Metric (Mu / Lambda) and Inventory Days (Total System Size / Mu). </div> <div>4C. Classify the system to a Strategy:</div> <div>If Stability Metric < 1 and Inventory Days < 30, Strategy : Start-Up </div> <div>If Stability Metric >= 1 and Inventory Days < 30, Strategy : Plan-Up</div> <div>If Stability Metric >= 1 and Inventory Days >= 30, Strategy : Catch-Up</div> <div>If Stability Metric < 1 and Inventory Days >= 30, Strategy : Scale-Up</div> <div>4D. Identify when the first arrival, first service, last arrival and last service.</div> <div>4E. Calculate t0 - the time required for 5 PBIs to be completed. </div> <div>4F. Calculate the number of PBIs that occurred outside 6am to 6pm Mon-Fri. These arrivals and services were for our research purposes "Unsustainable".</div> <div>4G. This data is under the Timeset = "All" for each system.</div> <div>5. Repeat step 4 but for just for the time period leading up to t0. This data is under the Timeset = "0" for each system.</div> <div>6. Recursively repeat step 4 by incrementing the period under investigation after t0 by 7 days until the higher of last arrival and last service is reached. Each period is given an incremented timestep, 1,2,3 and so on. This data is under a numbered Timeset each system. </div> <div> </div> <div><strong>To use the data set: </strong></div> <div>We recommend importing this data into a tool that faciliates pivot tables.</div> <div>It is possible to compare across systems at each timepoint or review time histories across systems. </div>
Software sustainability of global impact models (Dataset and analysis script)
<p><strong><em>slocount.py</em></strong>: This script calculates the number of comment lines, total lines of code (TLOC) and source lines of code (SLOC). It uses a code line counter developed by Ben Boyter, which must be installed (https://github.com/boyter/scc.). The source code links to the global impact models (GIMs) can be found in the 'ISIMIP_models.xlsx' file.</p> <p><strong><em>active_dev.py</em></strong>: This script plots the number of active developers for each GIM across 10 sectors. It utilizes data from the 'active_dev.csv' file, which lists the GIMs and their respective number of developers.</p> <p><strong><em>cocomo.py</em></strong>: This script estimates the effort required for software development using the methodology proposed by Sachan et al. 2016 (https://doi.org/10.1016/j.procs.2016.06.107). It also generates plots for these estimates.</p> <p><strong><em>comment_density_modularity.py</em></strong>: This script calculates the comment density and evaluates the modularity of the modules. It also produces plots for these metrics.</p> <p><strong><em>code_standard.py</em></strong>: This script uses Pylint (<a href="https://pylint.readthedocs.io/en/latest/user_guide/usage/output.html">https://pylint.readthedocs.io/en/latest/user_guide/usage/output.html</a>) to check if the source code, either in part or in its entirety, adheres to the PEP8 coding standard. It also generates lint scores for the source code.</p> <p><strong><em>line_count.zip</em></strong>: This file contains the results of counting the number of comment lines, TLOC and SLOC for each GIM.</p> <p><strong><em>lint_score.zip</em></strong>: This file contains the results of running pylint on GIMs that include Python in their source code. Results also include lint score per GIM</p>
Synthetic Fleet Generation and Vehicle Assignment to Synthetic Households for Regional and Sub-regional Sustainability Analysis
<p>This dataset provides the MOVES-Matrix emission and energy use rates for the NCST project "Synthetic Fleet Generation and Vehicle Assignment to Synthetic Households for Regional and Sub-regional Sustainability Analysis" by the Georgia Tech research team.</p> <p> </p> <p>The abstract of the project is as follows.</p> <p><span>In this study, a modeling framework was developed to generate high-resolution synthetic fleets, for use with synthetic household modeling in activity-based travel models, by integrating various data sources. The synthetic households were generated by pairing household locations and demographic attributes, and synthetic fleets were assigned to the households so that travel demand model outputs would have vehicles associated with each model-predicted tour for energy and emissions analysis. The CO emissions were modeled for each vehicle and each link traversed by vehicles as predicted by the travel demand model, and the results of the synthetic fleet (by employing Monte Carlo simulations and Bootstrap techniques) were compared with those from standard regional and sub-regional fleet configurations. The results demonstrated that using a traditional sub-regional fleet scenario produced 30% higher predicted emissions than when the synthetic fleet was employed with predicted vehicle trips, and that using a regional average fleet (applied throughout the region) produced emissions that were more than 50% higher than synthetic fleet emissions. Lowest household emissions were associated with low-income and non-working households, and highest emissions were associated with moderate-income households and one-person high-income household groups. The results presented in the research are not necessarily conclusive, because the licensed vehicle data procured for Atlanta appear to be biased toward older vehicles. Model year penetration rates are accounted for in these analyses, but the authors believe that the variability in the registration mix for newer vehicles is likely underestimated in the data procured for these analyses. The authors conclude that access to statewide registration data will be required to remove potential biases that exist in licensed private data sets. Nevertheless, the study does demonstrate that properly pairing vehicle model years with the most active households (and their daily trips) significantly impacts energy and emissions analysis.</span></p> <p> </p>
Raw images, video, and data file for the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials"
<p>This dataset includes the raw images and data file in the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials", specifically:</p> <ul> <li>Raw images for the optimized print with the PEGDA-glycerol-water resin (Figure 2 & Figure S2)</li> <li>Raw images for the optimized print with the PEGDA-glycerol-LB resin (Figure 2)</li> <li>Raw images for the optimized print with the BSA-PEGDA-water resin (Figure 3)</li> <li>Raw images and video for the spontaneous capillary flow of LB media in a PEGDA-glycerol-LB microfluidic chip (Figure 4)</li> <li>Raw data for the UV-vis spectrum of LB media (Figure S4)</li> </ul>
Data from 'Sustainability in the laboratory: evaluating the reuseability of microtiter plates for PCR and fragment detection'
<p>This repository contains the data for the manuscript: Sustainability in the laboratory: evaluating the reuseability of microtiter plates for PCR and fragment detection.</p> <p>The script can be found here: https://github.com/AneLivB/SGP</p> <p><strong>Files: </strong></p> <p><strong>R15*_R*.csv</strong></p> <p>The processed data used for the script. All other files of the same name format contain the same elements!</p> <ul> <li> <p>Columns: Rack_location, ID, all loci</p> <ul> <li> <p>Rack_location: the location on the microwell plate the individual had in the wet lab</p> </li> <li> <p>ID: individual identification tag of the animal the DNA sample stems from</p> </li> <li> <p>_a and _b denotes the first and second allele of all loci</p> </li> </ul> </li> </ul> <p><strong>Mismatches</strong></p> <p>A dataframe created by the script and later used to calcualte per treatment single-locus genotype error rates.</p> <ul> <li> <p>Columns: Rack.1, Rack.2, Treatment, No..of.mistyped.alleles, No..of.mistyped.reactions, No..of.reactions, Allelic.error.rate, Genotype.error.rate</p> <ul> <li> <p>Rack.1: One of three racks (R154, R155, R156) from the standard protocol</p> </li> <li> <p>Rack.2: One of three racks (R154, R155, R156) from one of the other treatments (e.g. R154_R2, R154_R3, R154_R4)</p> </li> <li> <p>Treatment: One of three treatments (Internal control, Reused detection plate, Reused PCR plate)</p> </li> <li> <p>No..of.mistyped.alleles: Number of mismatched alleles within one treatment group, within a DNA plate</p> </li> <li> <p>No..of.mistyped.reactions: Number of mismatched single-locus genotypes within one treatment group, within a DNA plate</p> </li> <li> <p>No..of.reactions: Number of single-locus genotypes within one treatment group, within a DNA plate</p> </li> <li> <p>Allelic.error.rate: Error rate per allele</p> </li> <li> <p>Genotype.error.rate: Error rate per single-locus genotypes</p> </li> </ul> </li> </ul> <p><strong>model.mismatch.2.Rdata</strong></p> <p>RData file containing the model output.</p> <p> </p> <p><strong>Manuscript abstract: </strong></p> <p>Single-use plastics (SUPs) are indispensable in laboratory research, but their disposal contributes substantially to environmental pollution. Consequently, reusing common SUP items such as microtiter plates represents a promising strategy for improving laboratory sustainability. However, the key challenge lies in determining whether SUP reuse can be implemented without sacrificing data quality. To investigate this, we conducted a simple experiment to assess the impact of reusing microtiter plates on microsatellite genotyping accuracy. Plates previously used for PCR and fragment detection were cleaned using an environmentally friendly method and then reused. Our results indicate that, while reusing PCR plates significantly increases genotyping error rates due to residual DNA contamination, detection plates can be reused without compromising data quality. Our approach offers laboratories a practical and sustainable option for reducing SUP waste and costs while maintaining research integrity.</p>
Properties of selected alkali-activated materials for sustainable development
<p>The presented research focuses on three selected variants of alkali-activated materials, where the goal is to compare key properties from the point of view of material engineering and structural design. Tests of the mechanical properties of the examined materials are carried out and their durability is compared, namely frost resistance, resistance to chemical and de-icing substances and resistance to elevated temperature.</p>
The dataset of "Evaluation of Digital Supply Chain Technology's Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"
<p>This dataset involves the data from the questionnaire, which come from the project "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain". It comprises three dimensions questions, technology, sustainability and supply chain dynamism. The datas come from two Chinese energy firms, <span>China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd.</span></p>
Raw data for optimization and calibration of Micellar liquid chromatography as a sustainable tool to quantify three statins in oral solid dosage forms
<p>A method based on micellar liquid chromatography has been developed to determine rosuvastatin, lovas- tatin and simvastatin in oral solid dosage forms. Samples were solved in mobile phase up to the target concentration, filtered and directly injected. The three statins were resolved in 30 min, using an aqueous solution of 0.10 M sodium dodecyl sulfate –7.0% 1-butanol, buffered at pH 3 with 0.01 M phosphate salt as mobile phase, running under isocratic mode at 1 mL/min through a C 18 column. Detection was at 240 nm. The effect of sodium dodecyl sulfate on elution strength was more important than that of the organic solvent. The procedure was successfully validated by the guidelines of the International Coun- cil for Harmonization in terms of: specificity, linearity ( r 2 > 0.990), calibration range (1.5 - 15 mg/L for rosuvastatin, 0.5–10 mg/L for lovastatin and simvastatin), limit of detection (0.4, 0.2 and 0.15 mg/L for ro- suvastatin, lovastatin and simvastatin, respectively), trueness (98.8–101.7%), precision ( < 2.7%), carry-over effect, robustness, and stability. Values were inside the acceptance criteria of the Methods, Method Veri- fication and Validation, Food and Drug Administration-Office of Regulatory Affairs, thus ensuring the re- liability of the results. The main feature was the low proportion of organic solvent used, thus making the procedure sustainable and green. Besides, it was easy-to-conduct and with high sample-throughput, and then useful for routine analysis in pharmaceutical quality control. Finally, it was applied to commercial pharmaceutical preparations.</p>
2 PREHEALING: Design of concrete precast elements incorporating sustainable strategies for self-healing to increase their service life. Concrete analysis
<div>This project addresses the analysis of the performance of concrete with internal curing aggregates (ICA), low-clinker cementitious materials, and steel fibres for use in real applications in the precast industry. A total of eight mixes were designed: 100C, 60C25BA15M (where BA denotes forestry biomass and M denotes metakaolin), 60C25LF15M (with LF as limestone filler and M as metakaolin), 100C-30CBA (where CBA denotes porous aggregate from coal ash), 60C25BA15M-30CBA, 60C25LF15M-30CBA, 60C25BA15M-30CBA-F (where F denotes fibres), and 60C25LF15M-30CBA-F. Two different curing conditions were analysed (standard water curing and humidity/drying cycles), assessing the recovery of mechanical properties and four curing conditions for impermeability recovery: i) carbonated water (CW), ii) immersion/drying cycles in carbonated water (CW wet-dry), iii) tap water (TW), and iv) immersion/drying cycles in tap water (TW wet-dry).</div> <div> </div> <div>This section includes the results of all tests conducted during the experimental campaign, divided into two files:</div> <div> </div> <div> <ul> <li>01_Permeability Test.zip: <br>The attached files contain the results of the permeability tests conducted on cracks opened through the indirect tensile test on cylindrical discs. Permeability tests were initially performed after the crack was opened and then following a self-healing process under four curing conditions: i) carbonated water (CW), ii) immersion/drying cycles in carbonated water (CW wet-dry), iii) tap water (TW), and iv) immersion/drying cycles in tap water (TW wet-dry), at two exposure times: 28 and 90 days.</li> </ul> </div> <div> <ul> <li>02_Mechanical Recovery.zip:<br>The attached files contain the results of the three-point bending test, including crack opening measurements and the force applied at each interval. The cracks were reopened after a curing period in continuous tap water and in immersion/drying cycles. After a period of 28 and 90 days, the cracks were reopened to calculate the mechanical recovery during the self-healing period. The attached files contain all test results conducted during both phases.</li> </ul> </div>
IA2030 Movement - Sustainability Survey 2023
<p><span><span>Data set from online survey. Distributed in English and French. Sent on 11th May 2023 and closed on 18th of May 2023.</span></span></p>
Software Sustainability Evaluation Data
<p>The dataset contains the examples used for evaluation in our journal paper "Software Sustainability using MDE"</p>
Increasing production efficiency and coping with climate change, while ensuring sustainability and resilience
<p>This experiment aims to test two of the most performing Tomres used as rootstocks in the commercial variety (Elpida F1) cultivated in the region. More specifically, 2 tomato Tomres lines (TOMRES- 149, Bil-6191 and TOMRES 162, M82) x 2 water/nutritional regimens (standard water/nutrient supply vs 20% irrigation reduction/no nutrient supply). Greenhouse will also have non grafted plants (Elpida F1) cultivated under standard water/nutrient supply and 20% irrigation reduction/no nutrient supply</p>
Increasing production efficiency and coping with climate change, while ensuring sustainability and resilience
<p>Screening experiment aiming a first evaluation of the five PGPR that have been isolated in AUA, Laboratory go General & Agricultural Microbiology in a previous research project. To minimize interference of the treatments with soil fertility and soil heterogeneity, this first experiment will be conducted in a soilless cultivation system</p>
BIOPLAT-EU: Target Area Base Layer providing statistical information on demography, employment and land use for sustainability assessment
<p>This dataset provides information on demographic variables related to population and employment as well as land use/land cover share on the basis of local administrative units (LAU). Within the BIOPLAT-EU project, this information is integrated into the webGIS sustainability assessment tool.<br> Main source of the administrative unit geometries is the spatial data set of local administrative units (LAU) (2016) provided by the European Commission (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/lau#lau19). The data set was extended by Level 2 administrative boundaries of Albania (https://data.humdata.org/dataset/albania-administrative-level-0-3-boundaries) and Level 3 administrative boundaries of Ukraine as of 2017 (https://data.humdata.org/m/dataset/ukraine-administrative-boundaries-as-of-q2-2017?force_layout=light)</p> <p>Data on demography (2016) for LAU + Albania was acquired using the Eurostat statistical database (https://ec.europa.eu/eurostat/web/main/data/database). To calculate land use share for LAU and Albania, Corine Land cover (CLC) data from 2018 was used (https://land.copernicus.eu/pan-european/corine-land-cover). CLC classes were summarized into the following classes: urban areas (UrAr), forest (Fo), permanent crops (PeCr), annual crops (AnCr), permanent meadows and pastures (PeMaPa), industrial sites (InSi), water and wetlands (We), others (Ot).</p> <p>For Ukraine data on demography provided by the State Statistics Survey of Ukraine (http://www.ukrstat.gov.ua/) . To calculate land use share per administrative unit, the land use map produced by Myroniuk et al. 2020 (https://doi.org/10.3390/rs12010187) was used. Based to this map shares of the following land use classes are calculated: urban areas (UrAr), forest (Fo), annual and permanent cropland (AnPeCr), grassland (Gra), water and wetlands (We), others (Ot).</p> <p> </p> <p><em><strong>Terms of use:</strong></em> These data are provided "as is". <em>The authors make <strong>no </strong></em><strong><em>warranty</em></strong><em>, representation, or guaranty of any type as to the completeness, accuracy, content or fitness for any particular purpose or use of any </em><strong><em>open data</em></strong><em> set made available here</em><em>, nor shall any </em><strong><em>warranties</em></strong><em> be implied with respec</em><em>t to the data provided.</em></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.