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147 results for “sustainable development”
Semi-structured interviews about sustainable and equitable development in the USA
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Data from: Development of a sustainability assessment algorithm and its validation using case studies on cryogenic machining
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Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals (SDGs)"
<p><strong>This package contains data on five text analysis types (term extraction, contract analysis, topic modeling, network mapping), based on the survey data where researchers selected research output that are related to the 17 Sustainable Development Goals (SDGs). This is used as input to improve the current SDG classification model v4.0 to v5.0</strong></p> <p><a href="https://sustainabledevelopment.un.org/sdgs">Sustainable Development Goals</a> are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">Aurora SDG dashboard</a> is the initial outcome as <em>proof of practice</em>.)</p> <p>The initiative started from the Aurora Universities Network in 2017, in the working group "<a href="https://aurora-network.global/activity/societal-impact-and-relevance-of-research-sirr/">Societal Impact and Relevance of Research</a>", to investigate and to make visible 1. what research is done that are relevant to topics or challenges that live in society (for the proof of practice this has been scoped down to the SDGs), and 2. what the effect or impact is of implementing those research outcomes to those societal challenges (this also have been scoped down to research output being cited in policy documents from national and local governments an NGO's).</p> <p><strong>Context of this dataset | classification model improvement workflow</strong></p> <p>The classification model we have used are 17 different search queries on the Scopus database.</p> <ul> <li>SDG search queries version 4.0 (SQv4) have been created, Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3817443"><em>Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v4.0</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817443</a></li> </ul> </li> <li>A survey has been distributed to senior researchers to test the robustness of SQv4. Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3798385"><em>Survey data of "Mapping Research output to the Sustainable Development Goals SDGs"</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3798385</a></li> </ul> </li> <li>This text analysis has been made as one of the inputs to improve the classification model. Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3832090"><em>Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals SDGs"</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3832090</a></li> </ul> </li> <li>Improved SDG search queries version 5.0 (SQv5) have been created, Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3817445"><em>Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v5.0</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817445</a></li> </ul> </li> </ul> <p><strong>Methods used to do the text analysis</strong></p> <ol> <li><strong>Term Extraction</strong>: after text normalisation (stemming, etc) we extracted 2 terms in bigrams and trigrams that co-occurred the most per document, in the title, abstract and keyword</li> <li><strong>Contrast analysis</strong>: the co-occurring terms in publications (title, abstract, keywords), of the papers that respondents have indicated relate to this SDG (y-axis: True), and that have been rejected (x-axis: False). In the top left you'll see term co-occurrences that a clearly relate to this SDG. The bottom-right are terms that are appear in papers that have been rejected for this SDG. The top-right terms appear frequently in both and cannot be used to discriminate between the two groups.</li> <li><strong>Network map</strong>: This diagram shows the cluster-network of terms co-occurring in the publications related to this SDG, selected by the respondents (accepted publications only).</li> <li><strong>Topic model</strong>: This diagram shows the topics, and the related terms that make up that topic. The number of topics is related to the number of of targets of this SDG.</li> <li><strong>Contingency matrix</strong>: This diagram shows the top 10 of co-occurring terms that correlate the most.</li> </ol> <p><strong>Software used to do the text analyses</strong></p> <p>CorTexT: The <a href="https://www.cortext.net/">CorTexT Platform</a> is the digital platform of LISIS Unit and a project launched and sustained by IFRIS and INRAE. This platform aims at empowering open research and studies in humanities about the dynamic of science, technology, innovation and knowledge production.</p> <p><strong>Resource with interactive visualisations</strong></p> <p>Based on the text analysis data we have created a website that puts all the SDG interactive diagrams together. For you to scrall through. <a href="https://sites.google.com/vu.nl/sdg-survey-analysis-results/">https://sites.google.com/vu.nl/sdg-survey-analysis-results/</a></p> <p><strong>Data set content</strong></p> <p>In the dataset root you'll find the following folders and files:</p> <ul> <li><strong>/sdg01-17/</strong> <ul> <li>This contains the text analysis for all the individual SDG surveys.</li> </ul> </li> <li><strong>/methods/</strong> <ul> <li>This contains the step-by-step explanations of the text analysis methods using Cortext.</li> </ul> </li> <li><strong>/images/</strong> <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li><strong>LICENSE.md</strong> <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li><strong>README.md</strong> <ul> <li>description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.</li> </ul> </li> </ul> <p>Inside an <strong>/sdg01-17/</strong>-folder you'll find the following:</p> <ul> <li>This contains the step-by-step explanations of the text analysis methods using Cortext.</li> <li><strong>/sdg01-17/sdg04-sdg-survey-selected-publications-combined.db</strong> <ul> <li>his contains the title, abstract, keywords, fo the publications in the survey, including the and accept or rejection status and the number of respondents</li> </ul> </li> <li><strong>/sdg01-17/sdg04-sdg-survey-selected-publications-combined-accepted-accepted-custom-filtered.db</strong> <ul> <li>same as above, but only the accepted papers</li> </ul> </li> <li><strong>/sdg01-17/extracted-terms-list-top1000.csv</strong> <ul> <li>the aggregated list of co-occuring terms (bigrams and trigrams) extracted per paper.</li> </ul> </li> <li><strong>/sdg01-17/contrast-analysis/</strong> <ul> <li>This contains the data and visualisation of the terms appearing in papers that have been accepted (true) and rejected (false) to be relating to this SDG.</li> </ul> </li> <li><strong>/sdg01-17/topic-modelling/</strong> <ul> <li>This contains the data and visualisation of the terms clustered in the same number of topics as there are 'targets' within that SDG.</li> </ul> </li> <li><strong>/sdg01-17/network-mapping/</strong> <ul> <li>This contains the data and visualisation of the terms clustered in co-occuring proximation of appearance in papers</li> </ul> </li> <li><strong>/sdg01-17/contingency-matrix/</strong> <ul> <li>This contains the data and visualisation of the top 10 terms co-occuring</li> </ul> </li> </ul> <p>note: the .csv files are actually tab-separated.</p> <p><strong>Contribute and improve the SDG Search Queries</strong></p> <p>We welcome you to join the Github community and to fork, branch, improve and make a pull request to add your improvements to the new version of the SDG queries. <strong><a href="https://github.com/Aurora-Network-Global/sdg-queries">https://github.com/Aurora-Network-Global/sdg-queries</a></strong></p>
A controlled vocabulary defining the semantic perimeter of Sustainable Development Goals
<p>A set of controlled terms that define the scope and breadth of <a href="https://sustainabledevelopment.un.org/">Sustainable Development Goals (SDGs) as defined by the United Nations</a>. These terms may be used to tag and index textual records in accordance with SDGs.</p> <p>The vocabulary is constructed by means of the following steps:</p> <ol> <li>An initial set of terms per SDG target is built by extracting key terms from the UN official list of Goals, Targets and Indicators</li> <li>The list is manually enriched by performing a review of the literature produced around SDGs and by compiling lists of pertinent words per Target mentioned by the reviewed documents</li> <li>A reference textual corpus is downloaded by searching for the initial set terms defined at step 1. and 2. The corpus is used to train a Word2Vec word embedding model (a machine learning model based on neural networks).</li> <li>The terms’ list is then enriched by means of automatic methods, which are run in parallel: <ul> <li>The trained Word2Vec model is used to select, among the indexed keywords of the reference corpus, all terms “semantically close” to the initial set of words. This step is carried out to select terms that might not appear in the texts themselves, but that were deemed pertinent to label the textual records.</li> <li>Further terms that are mentioned in the texts of the reference corpus and that are valued by the trained Word2Vec model as “semantically close” to the initial set of words are also retained. This step is performed to include in the controlled vocabulary a series of terms that are related to the focus of the SDGs and which are used by practitioners.</li> <li>An automated algorithm is used to retrieve, from the APIs of WikiPedia a series of terms that have some categorical relationships (i.e. those that are indexed as “a broader concept of”, or “equivalent to” in DBpedia) with the initial set of words.</li> </ul> </li> <li>The final list produced by steps 1-4 s finally manually revised</li> </ol>
Data from: Assessing faculty professional development in STEM higher education: sustainability of outcomes
We tested the effectiveness of Faculty Institutes for Reforming Science Teaching IV (FIRST), a professional development program for postdoctoral scholars, by conducting a study of program alumni. Faculty professional development programs are critical components of efforts to improve teaching and learning in the STEM (Science, Technology, Engineering, and Mathematics) disciplines, but reliable evidence of the sustained impacts of these programs is lacking. We used a paired design in which we matched a FIRST alumnus employed in a tenure-track position with a non-FIRST faculty member at the same institution. The members of a pair taught courses that were of similar size and level. To determine whether teaching practices of FIRST participants were more learner-centered than those of non-FIRST faculty, we compared faculty perceptions of their teaching strategies, perceptions of environmental factors that influence teaching, and actual teaching practice. Non-FIRST and FIRST faculty reported similar perceptions of their teaching strategies and teaching environment. FIRST faculty reported using active learning and interactive engagement in lecture sessions more frequently compared with non-FIRST faculty. Ratings from external reviewers also documented that FIRST faculty taught class sessions that were learner-centered, contrasting with the teacher-centered class sessions of most non-FIRST faculty. Despite marked differences in teaching practice, FIRST and non-FIRST participants used assessments that targeted lower-level cognitive skills. Our study demonstrated the effectiveness of the FIRST program and the empirical utility of comparison groups, where groups are well matched and controlled for contextual variables (for example, departments), for evaluating the effectiveness of professional development for subsequent teaching practices.
The sustainable development of coal mines by new cutting roof technology
<p>China consumes more than 3.6 billion tons of coal every year, accounting for over 60% of the energy consumption. Therefore, the sustainable development of coal mine is a problem needed to be solved by the Chinese government. During the coal resources recovery, the protective coal pillars between the adjacent working faces cause the serious loss for coal resources. In order to solve the problem, it was put forward that the new technology of roof cutting with chain arm to retain roadway in the paper. Firstly, the process of retaining roadway, roof-cutting parameters and the damage ranges of roadway surrounding rock induced by roof cutting with chain arm were analyzed. Then, it was given that the working resistance of the temporary support equipment in technology of roof cutting with chain arm to retain roadway. Next, the roof-cutting height, the type of temporary support equipment, working resistance of portal support and support parameters of the bolt and anchor cables were optimized by the numerical calculation. Finally, the industrial experiment of retaining roadway by roof cutting with chain arm was carried out in a working face. The surrounding rock damage was minimal with the use of chain arm-roof cutting technology, and the variation range of the uniaxial compressive strength was only 5%, resulting in the roof damage rate to 82 mm. From the studies, it was concluded that this technology could be of a great asset to the coal mining community.</p>
Job Opportunities in NGOs for Sustainable Development in Myanmar
<p>During the Sabai Webinar Series 6, hosted by the Shwetaungthagathu Reform Initiative Centre (SRIc), Burmese Young Experts, including Ms May Wah Htwe, Capacity Building Specialist, Ms Yoon Aeindra Aung, Freelance Communication Consultant, engaged in a discussion on Job Opportunities NGOs for Sustainable Development. </p> <p>They highlighted the prerequisites and essential preparations required for individuals seeking to engage in meaningful work with NGOs and international Non-Governmental Organisations (INGOs) in Myanmar. </p> <p>This Sabai Webinar Series was conducted under the Edu4SD project. </p>
Youth-Led Sustainable Development
<p>During the Sabai Webinar Series 7, hosted by the Shwetaungthagathu Reform Initiative Centre (SRIc), Burmese Young Experts, including Ms. Ingyin Mhwe (YSEALI Alumnus: Environmental Issues and Natural Resources Management), Ms. Naychi Thel Kyaw Tun (YSEALI Alumnus: Women Leadership Academy: Women for E4) and Ms Hnin Thawdar Win ( YSEALI Alumnus: Civic Engagement) discussed the youth leadership in Sustainable Development. </p> <p>They highlighted the importance of Myanmar youths in implementing Sustainable Development in Myanmar. </p> <p>The Sabai webinar series was conducted under the Edu4SD project. </p>
Finance in Sustainable Development
<p>During the Sabai Webinar Series 8, hosted by the Shwetaungthagathu Reform Initiative Centre (SRIc), Burmese Young Experts, including Ms Zin Win Htet Aung, Financial Analyst, Ms Khin Thida Htwe, Finance Officer and Mr Tin Shine Aung, Sustainability Specialist, engaged in a discussion on Finance in Sustainable Development.</p> <p>They highlighted how sustainable finance is important for both business and non-profit sectors. </p> <p>This Sabai Webinar Series was conducted under the Edu4SD project.</p>
Research trends on sustainable development in smart cities
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Sustainable Development of Sorghum through the Promotion of Microbial Symbiosis and Disease Resistance: Supplemental Data
<p>Supplemental data for Chapter 3 of the dissertation titled: "Sustainable Development of Sorghum through the Promotion of Microbial Symbiosis and Disease Resistance"</p>
Open Data Package: Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science.
<p>Open Data Package for the paper: Stefanie Betz, Birgit Penzenstadler, Leticia Duboc, Ruzanna Chitchyan, Sedef Akinli Kocak, Ian Brooks, Shola Oyedeji, Jari Porras, Norbert Seyff, and Colin C. Venters. 2024. Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science. ACM Trans. Softw. Eng. Methodol. 24 00, JA, Article 00 (March 2024), 39 pages. https://doi.org/10.1145/3649597 25</p>
A Refined Supply-demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals
<p>This database relies on the article entitled "A Refined Supply-demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals " to be published in Earth's Future. The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang. The file named "Supplementary Data" contains the necessary Water Bulletin and Statistical Yearbook data.</p>
User Personas Improve Social Sustainability by Encouraging Software Developers to Deprioritize Antisocial Features
<p>This dataset contains the supplementary material for the paper "User Personas Improve Social Sustainability by Encouraging Software Developers to Deprioritize Antisocial Features".</p> <p>The dataset includes:</p> <ul> <li>Pilot study material and analysis document</li> <li>Task Materials(Instruction, prioritization worksheet, personas, stakeholder map)</li> <li>Instructions for Invigilators (experimental protocol)</li> <li>Consent form (de-identified)</li> <li>Data sheet</li> <li>Preliminary data analysis script and visualizations - Python Notebook</li> <li>Cummulative Link Mixed Model analysis script - R</li> <li>Propotional Odds analysis script - R</li> <li>Instructional videos<br><br>***This version includes written experimental protocol for invigilators, instructional videos for participants and Propotional Odds assumption analysis script.</li> </ul>
Supporting Information: Assessing the Social Dimension in Strategic Network Design for a Sustainable Development: The Case of Bioethanol Production in the EU
<p>Supporting Information S2 provides all parameters of the optimization model, both those associated with the social objective and hotspot functions of the study at hand, and those from the underlying model by Wietschel et al. (2021). To put the method of the study into perspective, it also gives an overview of referenced frameworks (GSLCAPO, SHDB, SDGs) and their categories, subcategories, and indicators. Furthermore, it includes detailed results for all social, environmental, and economic objective functions in all scenarios, and of the Pareto optimization. Lastly, the underlying data for all figures of the manuscript and Supporting Information S1 is provided.</p>
Eight archetypes of Sustainable Development Goal (SDG) synergies and trade-offs
<p>Data S1 - Details of system archetype application articles reviewed systematically.</p>
Figure 1 in Macroalgal diversity for sustainable biotechnological development in French tropical overseas territories
Figure 1: Distribution of French tropical overseas coral reef territories. Named and surrounding territories are those studied in this review: four in Pacific Ocean, three in Atlantic Ocean and three in Indian Ocean. Adapted from the website pinterest.com
Figure 2 in Macroalgal diversity for sustainable biotechnological development in French tropical overseas territories
Figure 2: (A) Phylogenetic tree showing the polyphyletic group of seaweeds positioned in several lineages. (B, C, D) Illustrations of species belonging to green, red and brown macroalgae that are commonly visible on French overseas territories: (B) Halimeda macroloba, Caulerpa chemnitzia, Boodlea composita; (C) Ganonema farinosum, Asparagopsis taxiformis, Lithophyllum kotschyanum; (D) Lobophora sp., Turbinaria ornata, Colpomenia sinuosa. Photo credits: V. Stiger-Pouvreau (T. ornata), M. Zubia (all others).
Figure 3 in Macroalgal diversity for sustainable biotechnological development in French tropical overseas territories
Figure 3: Structures of different types of molecules/natural compounds isolated from tropical macroalgae. MAAs: Mycosporine-like amino acids; DMSP: dimethylsulfoniopropionate.
Adhesive Antibacterial Moisturizing Nanostructured Skin Patch for Sustainable Development of Atopic Dermatitis Treatment in Humans [Swelling studies]
<p>Adhesive Antibacterial Moisturizing Nanostructured Skin Patch [Swelling studies]</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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