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2,163 results for “sustainability”
Sustaining Cities, Naturally Webinar: Education Session - Vigie-Nature école
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This pitch was made as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Audio files of synthetic sustained vowels for the study of vocal fry
<p>This is the dataset of stimuli used in the experiments reported in [1]. SinglePulsing.zip contains the stimuli of reported experiment 1, Transition.zip contains stimuli of experiment 2.</p> <p></p> <p>[1] V. Devaraj, F. Wendt, I. Roesner, J. Schoentgen, and P. Aichinger, “Auditory perception of impulsiveness and tonality in vocal fry,” <em>Appl. Sci. Basel</em>. (under review)</p>
Sustaining Cities, Naturally Webinar: Recording Day 1
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This recording of Day 1 was made as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Sustainable Expert Criteria Weights
<p>This data was undertaken within the framework of the EU-funded <a href="https://www.leadproject.eu/">LEAD project</a> aiming to create Digital Twins for urban logistics networks in six cities to support experimentation in decision-making on-demand logistics operations in a public-private urban setting. The questionnaire consists of identifying priorities among different sustainability criteria related to last-mile logistics using a pair-wise comparison method to determine the experts' weights for every criterion.</p> <p>Gonzalez, J. N., Sobrino, N., & Vassallo, J. M. (2023). Considering the city context in weighting sustainability criteria for last-mile logistics solutions. <em>International Journal of Logistics Research and Applications</em>, 1–21. <a href="https://www.tandfonline.com/doi/full/10.1080/13675567.2023.2264788">https://doi.org/10.1080/13675567.2023.2264788</a></p>
Green areas sustainability for Valladolid city
<p>Recreational (number of visitors, number of recreational activities) or cultural (number of cultural events, people involved, children in educational activities) value is an idicator calculated during the timespan of the UrbanGreenUP project. </p>
Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal'
<p>Title: Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal'</p> <p>Recommended Citation: Adhikari, B., Prescott, G., Urbach, D., Chettri, N., & Fischer, M. (2022). Nature’s contributions to people and the sustainable development goals in Nepal. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac8e1e</p> <p>Principal Investigator: Markus Fischer (markus.fischer@ips.unibe.ch)</p> <p>Authors:<br> Biraj Adhikari (biraj.adhikari@ips.unibe.ch, ORCID: 0000-0002-4260-8706)<br> Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X)<br> Davnah Urbach (davnah.payne@ips.unibe.ch, ORCID: 0000-0001-9170-7834)<br> Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879)<br> Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)</p> <p>Date of data collection: November 2020 - August 2021<br> Location of data collection: Kathmandu, Nepal<br> Article title: 'Nature's contributions to people and the Sustainable Development Goals in Nepal'</p> <p>R code available at: https://github.com/biraj-ad/r4dLiteratureReview_GithubRep</p> <p>Data Overview:</p> <p>1. 'Literature_References.csv'<br> List of 119 peer-reviewed journals and 21 grey literature documents used in the review. Each is assigned a unique identifier ("SN Ref") to link it to the other files.</p> <p><br> 2. 'Drivers_and_Trends.csv'<br> The "Quote" column is the text from papers which has information on (i) trends in ecosystems or NCPs, and if available (ii) direct and/or indirect drivers causing the trends.<br> The "Nature" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others, and Directly to NCP), the "NCP" column indicates which NCP (categorized into 18 categories based on the IPBES classification) the text refers to. The "NCP Category" column indicates whether the said NCP is a regulating, material or non-material NCP. <br> We also categorized direct and indirect drivers based on the IPBES classification (Direct Drivers: Land-use Change, Climate Change, Direct Exploitation, Invasive Alien Species, and Pollution; Indirect Drivers: Institutions and Governance, Demographic and Sociocultural, Economic and Technological). <br> If there were more than one drivers of change for a particular NCP, we have included them in additional columns. In order to avoid multiple counts for trends of a particular NCP, we introduced the "TrendCount" column. For example, columns 3, 4 and 5 refers to the same trend of decreasing WQN, but has 3 Direct Drivers. Therefore, each TrendCount is given a weight of 0.33 so that the total trend adds upto 1.</p> <p>3. 'NCP_to_SDG.csv'<br> The "Quote" Column is the text from papers which has information on which NCP is contributing towards which SDG. "Remarks from text" are the authors' own remarks based on the text and the overall context of the article.<br> The contribution of NCPs are classified as positive or negative, and indicated in the column "Effect (pos/neg)"<br> The "NCP" column indicates which NCP (categorized into 18 categories according to IPBES) the text refers to, while the "Contribution to SDG" column indicates which SDG the NCP is contributing towards.<br> The "Ecosystem" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others) the NCP is being supplied from.</p> <p>Codes for NCPS:<br> HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOI (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), ORG (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), INS (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), and OPT (Maintenance of Options).</p> <p>4. 'Data_consolidated.xlsx'<br> The above three data files combined into one xlsx document<br> </p> <p> </p> <p> </p>
Open Source and Open Science Sustainability Year-long Study - Pseudonymised
<p>Research software is often abandoned or shut down, for one reason or another. While some reasons may be straightforward, e.g. a sole maintainer has moved on, or grant funding has ceased - some projects are able to withstand these barriers and may remain active and maintained despite adversity.</p> <p>This study monitors open source projects over the period of a year, measuring common performance indicators, to see if any indicators are common to projects that remain sustainable and active.</p> <p>This study uses mixed methods:</p> <ol> <li>Initial survey gathers info about the project age, leadership, and GitHub (or other source control) URLs. Participants are asked to add <a href="https://sustainable-open-science-and-software.github.io/readme_notice">a short notice to their readme</a>.</li> <li>After the initial survey, we gathered information about the GitHub projects such as number of contributors, number of PRs, time taken to close/merge these PRs, and issues closed. Some of this info is gathered using scripts, and other parts are gathered manually. An example of a manual metric is the Code of Conduct - while we can programmatically check for the <em>existence</em> of CodeOfConduct.md, we can’t easily check for enforcement contacts without manual checks.</li> <li>6 months and 12 months after the initial survey, we send follow up surveys, and in month 12 we re-run the GitHub metrics to compare to month 0.</li> </ol> <p> </p> <p>For more study info see: <a href="https://sustainable-open-science-and-software.github.io/">https://sustainable-open-science-and-software.github.io/</a></p> <p> </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>
Dataset and code: The environmental sustainability of digital content consumption
Repository to share the code used in the scientific article Istrate et al., The role of digital content consumption in environmentally sustainable lifestyles. The repository contains data files and tailored notebooks to create the LCI database and reproduce the results presented in the article.
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>
EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 7: Aspects of ENCI III.: Towards social sustainability
<p>This document is Part 7 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship (ENCI) between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe.</p> <p>EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.</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>
Coastal SEES Collaborative Research: Coastal Sustainability: A cross-site comparison of salt marsh persistence in response to sea-level rise and feedbacks from social adaptations
Coastal ecosystems are often valued for decision-making purposes based on monetized market and non-market values of goods and services, and associated economic impacts. Examples include values of fishery landings, price changes for waterfront homes, and tourism revenues. Monetized quantities such as these do not provide a comprehensive characterization of the values provided by these ecosystems. Human reliance on the goods and services provided by ecosystems and the global decline in the health of many of these ecosystems suggests the need for ecosystem valuation to help inform decision-making and conservation policy. However, traditionally employed economic valuation methods are rarely able to capture the full scope of the benefits ecosystems provide, including benefits provided by "cultural" ecosystem services. Qualitative methods such as focus groups can provide insight on these values not available through quantitative methods alone. This research explores public perceptions of salt marsh value through the use of semi-structured focus groups in marsh-adjacent communities in Massachusetts, Virginia, and Georgia. The data include de-identified focus group transcripts from three 90-minute focus groups held in each state. Initial questions were drawn from the same semi-structured question list in each focus group, with exploratory follow-up questions based on participant responses. Results of text analysis suggest that in case study communities, outdoor experiences in salt marshes inspire serenity in Massachusetts, influence shore identities in Virginia, and promote stewardship cultivation in Georgia. Perceived threats to these benefits, such as the threat of residential development, industrial pollution, and increasing flood risk, together constitute the context for various community responses related to marsh protection. Results supplement information from extant economic valuations and show the importance of utilizing diverse methods to elicit information on soci
Spatial integration of transcription and splicing in a dedicated compartment sustains monogenic antigen expression in African trypanosomes
<p>This repository contains the data for the manuscript <a href="https://doi.org/10.1038/s41564-020-00833-4">https://doi.org/10.1038/s41564-020-00833-4</a>.</p> <p>The HiC analysis pipeline can be found at <a href="https://github.com/bgbrink/PRJEB35632">https://github.com/bgbrink/PRJEB35632</a>.</p> <p><strong>Abstract</strong></p> <p>Highly selective gene expression is a key requirement for antigenic variation in several pathogens, allowing evasion of host immune responses and maintenance of persistent infections. African trypanosomes — parasites that cause lethal diseases in humans and livestock — employ an antigenic variation mechanism that involves monogenic antigen expression from a pool of >2,600 antigen-coding genes. In other eukaryotes, the expression of individual genes can be enhanced by mechanisms involving the juxtaposition of otherwise distal chromosomal loci in the three-dimensional nuclear space. However, trypanosomes lack classical enhancer sequences or regulated transcription initiation. In this context, it has remained unclear how genome architecture contributes to monogenic transcription elongation and transcript processing. Here, we show that the single expressed antigen-coding gene displays a specific inter-chromosomal interaction with a major messenger RNA splicing locus. Chromosome conformation capture (Hi-C) revealed a dynamic reconfiguration of this inter-chromosomal interaction upon activation of another antigen. Super-resolution microscopy showed the interaction to be heritable and splicing dependent. We found a specific association of the two genomic loci with the antigen exclusion complex, whereby VSG exclusion 1 (VEX1) occupied the splicing locus and VEX2 occupied the antigen-coding locus. Following VEX2 depletion, loss of monogenic antigen expres- sion was accompanied by increased interactions between previously silent antigen genes and the splicing locus. Our results reveal a mechanism to ensure monogenic expression, where antigen transcription and messenger RNA splicing occur in a specific nuclear compartment. These findings suggest a new means of post-transcriptional gene regulation.</p>
Inventory of the sustainability methodologies, indicators and criteria of research projects funded by the European Union
<p>Based on a screening in the CORDIS database and the experience of project partners, 16 projects were selected for analyses of their contributions regarding sustainability criteria and indicators.</p>
Survey data of "Mapping Research Output to the Sustainable Development Goals (SDGs)"
<p><strong>This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs).</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 proof of practice.)</p> <p>In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to<strong> capture expert knowledge from senior researchers in their research domain related to the SDG</strong>. The survey was open to the world, but mainly distributed to researchers from the <a href="https://aurora-network.global/">Aurora Universities Network</a>. <strong>The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America.</strong></p> <p>17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey.<strong> The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs</strong>.</p> <p><strong>The survey contains the following questions (see inside dataset for exact wording):</strong></p> <ul> <li><strong>Are you familiar with this SDG?</strong> <ul> <li>Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.</li> </ul> </li> <li><strong>Suggest research papers that are relevant for this SDG (upload list)</strong> <ul> <li>This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)</li> </ul> </li> <li><strong>Select research papers that are relevant for this SDG (radio buttons: accept, reject)</strong> <ul> <li>A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.</li> </ul> </li> <li><strong>Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions)</strong> <ul> <li>The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Suggest SDG related glossaries with relevant keywords (text fields: url)</strong> <ul> <li>Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions)</strong> <ul> <li>The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Suggest improvements for the current queries (text field: suggestions per target)</strong> <ul> <li>We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.</li> </ul> </li> </ul> <p><strong>In the dataset root you'll find the following folders and files:</strong></p> <ul> <li><strong>/00-survey-input/</strong> <ul> <li>This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.</li> </ul> </li> <li><strong>/01-raw-data/</strong> <ul> <li>This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.</li> </ul> </li> <li><strong>/02-aggregated-data/</strong> <ul> <li>This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.</li> </ul> </li> <li><strong>/03-scripts/</strong> <ul> <li>This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.</li> </ul> </li> <li><strong>/04-processed-data/</strong> <ul> <li>This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.</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><strong>In the /04-processed-data/ you'll find in each SDG sub-folder the following files.:</strong></p> <ul> <li><strong>SDG-survey-questions.pdf</strong> <ul> <li>This file contains the survey questions</li> </ul> </li> <li><strong>SDG-survey-questions.doc</strong> <ul> <li>This file contains the survey questions</li> </ul> </li> <li><strong>SDG-survey-respondents-per-sdg.csv</strong> <ul> <li>Basic information about the survey and responses</li> </ul> </li> <li><strong>SDG-survey-city-heatmap.csv</strong> <ul> <li>Origin of the respondents per SDG survey</li> </ul> </li> <li><strong>SDG-survey-suggested-publications.txt</strong> <ul> <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-publications-with-eid-match.csv</strong> <ul> <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li> </ul> </li> <li><strong>SDG-survey-selected-publications-accepted.csv</strong> <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li> </ul> </li> <li><strong>SDG-survey-selected-publications-rejected.csv</strong> <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li> </ul> </li> <li><strong>SDG-survey-selected-keywords.csv</strong> <ul> <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-unselected-keywords.csv</strong> <ul> <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-keywords.csv</strong> <ul> <li>List of keywords researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-glossaries.csv</strong> <ul> <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-selected-journals.csv</strong> <ul> <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-unselected-journals.csv</strong> <ul> <li>As "selected-journals", this is the list of journals that respondents have not selected to represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-journals.csv</strong> <ul> <li>List of journals researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-suggested-query.csv</strong> <ul> <li>List of query improvements researchers suggest to use to find papers related to this SDG</li> </ul> </li> </ul> <p><strong>Cite as:</strong></p> <blockquote> <p><em>Survey data of "Mapping Research output to the SDGs"</em> by Aurora Universities Network (AUR) <a href="http://doi.org/10.5281/zenodo.3798385">doi:10.5281/zenodo.3798385</a></p> </blockquote> <p><strong>Attribute as:</strong></p> <blockquote> <p><em><strong>Survey data of "Mapping Research output to the SDGs</strong>"</em> by Aurora Universities Network (AUR); Alessandro Arienzo (UNA); Roberto Delle Donne (UNA); Ignasi Salvadó Estivill (URV); José Luis González Ugarte (URV); Didier Vercueil (UGA); Nykohla Strong (UAB); Eike Spielberg (UDE); Felix Schmidt (UDE); Linda Hasse (UDE); Ane Sesma (UEA); Baldvin Zarioh (UIC); Friedrich Gaigg (UIN); René Otten (VUA); Nicolien van der Grijp (VUA); Yasin Gunes (VUA); Peter van den Besselaar (VUA); Joeri Both (VUA); Maurice Vanderfeesten (VUA);<strong> is licensed under a Creative Commons Attribution 4.0 International License.</strong> <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/</a></p> </blockquote>
Towards Sustainable Energy - Live ITW with Antonín Vlček
<p>In the coming years, we will need to replace fossil fuels by sustainable energy sources. What should be the next steps towards this shift? Don't miss our live ITW with our partner Prof. Dr. Antonín Vlček from Queen Mary University of London & J. Heyrovsky Institute of Physical chemistry, during our consortium meeting in Brussels!</p>
Data from: Theta oscillations coincide with sustained hyperpolarization in CA3 pyramidal cells, underlying decreased firing
<p>Brain-state fluctuations modulate membrane potential dynamics of neurons, influencing the functional repertoire of the network. Pyramidal cells (PCs) in hippocampal CA3 are necessary for rapid memory encoding, preferentially occurring during exploratory behavior in the high-arousal theta state. However, the relationship between the membrane potential dynamics of CA3 PCs and theta has not been explored. Here, we characterize the changes in the membrane potential of PCs in relation to theta using electrophysiological recordings in awake mice. During theta, most PCs behave in a stereotypical manner, consistently hyperpolarizing time-locked to the duration of theta. Additionally, PCs display lower membrane potential variance and reduced firing rate. In contrast, during large irregular activity, a low-arousal state, PCs show heterogeneous changes in membrane potential. This suggests coordinated hyperpolarization of PCs during theta, possibly caused by increased inhibition. This could lead to higher signal-to-noise ratio in the small population of PCs active during theta as observed in ensemble recordings.</p>
Data, materials, methods and codes for publication Reis et al., "Understanding the stickiness of commodity supply chains is key to improving their sustainability", 2020, One Earth.
<p>Explanation of the data and code used for the article: "Understanding the stickiness of commodity supply chains is key to improving their sustainability", 2020, One Earth.</p> <p> The file: "StickinessAnalysisCompleteGeneric_CleanUpdate18-5-2020.R" is the R code/ script containing all the data preparation and the stickiness analysis.</p> <p>The file: "BRAZIL_SOY_V2.3_WITH_DOMESTIC_TRADERS.csv" contains the raw data of Brazil's soy exports and domestic consumption from trase.earth. This dataset can also be obtained from trase.earth in the latest version.</p> <p>The file: "NodesCiS.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: A. logistics hubs supplying traders, and D. traders supplying countries. They are put together in the same table because they are all "sending" relationships.</p> <p>The file: "NodesCiR.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: C. traders sourcing from logistics hubs, and E. countries sourcing from traders. They are put together in the same table because they are all "receiving" relationships.</p> <p>The file "NodesCiSMunCountry.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: B. logistics hubs supplying countries (directly not passing through traders). This is separate in another table because it is a direct sending relationship from LHs to countries.</p> <p>The file "NodesCiRMunCountry.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: F. countries sourcing from logistics hubs (directly not passing through traders). This is separate in another table because it is a direct receiving relationship from LHs to countries.</p> <p>The file: "NodesWPiS.csv" is a table with the WPi (stickiness on flows) measured for the types of supply chain relationships: A. logistics hubs supplying traders, and D. traders supplying countries. They are put together in the same table because they are all "sending" relationships.</p> <p>The file: "NodesWPiR.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: C. traders sourcing from logistics hubs, and E. countries sourcing from traders. They are put together in the same table because they are all "receiving" relationships.</p> <p>The file "NodesWPiSMunCountry.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: B. logistics hubs supplying countries (directly not passing through traders). This is separate in another table because it is a direct sending relationship from LHs to countries.</p> <p>The file "NodesWPiRMunCountry.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: F. countries sourcing from logistics hubs (directly not passing through traders). This is separate in another table because it is a direct receiving relationship from LHs to countries.</p>
SmartUpLab- Co-Creation in sustainable mobility research - Systematic Reviews and Case Studies
<p>The current dataset presents the results of systematic reviews and case studies about co-creation tools best practice for sustainable mobility, carried out in the context of the research project SmartUpLab (funded by EFRE).</p>
ScienceDex guides
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