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1,372 results for “engagement”
The Middle East in 2025: Stakeholder's Engagement Meeting Report
<p>On 10 and 11 January 2019, the Istituto Affari Internazionali (IAI), in collaboration with the Near East and South Asia (NESA) Center for Strategic Studies of the United States Department of Defense and the Qatar Armed Forces Strategic Studies Center (QAFSSC), organized the second MENARA Stakeholders’ Engagement Meeting with 32 participants, including representatives of Qatar’s government, academics and experts from Europe, the United States and the Persian Gulf region.</p> <p>The goal of the meeting was among others to discuss and test some of the Project's preliminary findings with regard to the regional order in the Middle East and North Africa (MENA) and the role of the European Union (EU) and other external players in the region with a group of authoritative stakeholders. This report summarizes the discussion during that meeting.</p> <p>This activity is part of the EU research project Middle East North Africa Regional Architecture (MENARA).</p>
HDAC11 target engagement assay development in cells - CETSA
<p><strong>Methods</strong></p> <p><strong>Permeabilized CETSA</strong></p> <p>HEK293T cells were seeded in 6-well plates (8 x 10<sup>5</sup> cells/well) in DMEM supplemented with 10% FBS , penicillin (100 U/mL) and streptomycin (100 µg/mL). 4 h after seeding cells were transfected with 0.2 µg of N-terminally NanoLuc-tagged HDAC11 and 1.8 µg of empty pcDNA3.1 vectors using X-tremeGENE™ HP transfection reagent, following manufacturer instructions. Next day cells were harvested using Trypsin-EDTA, and resuspended in pre-warmed Opti-MEM (no phenol-red) at a concentration of 1.2 x 10<sup>5</sup> cells/ml. A 10x stock solution of digitonin (500 µg/ml, Sigma) and 100x stock solution of protease inhibitor cocktail (cOmplete™, Mini, EDTA-free,<em> </em>Sigma<em>)</em> was prepared in Opti-MEM and DMSO, respectively. The protease inhibitor / DMSO stock solution was used as vehicle to prepare 100x stock solutions of all test compounds. The 10x digitonin stock solution and 100x test compound / vehicle stock solution were pre-dispensed into 96-well PCR reaction plates at 10 µl/well and 1 µl/well, respectively. Next, 89 µl/well of the cell suspension was added to the pre-loaded PCR plates. Plates were then covered with adhesive plate sealing tape and were incubated at 37°C for 1 h. Immediately prior to heating the PCR plates, a 5x substrate stock solution was prepared by diluting NanoBRET NanoGlo Substrate (Promega) 1:100 into Opti-MEM. The thermal cycler was allowed to reach the designated temperature prior to placement of the PCR plate into the heating block. For Tm determination experiments, duplicate plates containing a fixed concentration of test compounds (30 µM) as well as vehicle control were prepared to perform a 11 point temperature gradient spanning 37-68.5°C for 3 minutes. The plates were allowed to cool at room temperature for 3 minutes after the heat treatment followed by addition of 25 µl 5x substrate stock solution per well. To measure NanoLuc activity 100 µl of sample were transferred from the PCR plate to a white 96-well plate. Total luminescence was measured immediately after substrate addition with CLARIOstar microplate reader (BMG Labtech).</p> <p><strong>Non-permeabilized CETSA</strong></p> <p>HEK293T cells were seeded in 6 well plates (8 x 10<sup>5</sup> cells/well) in DMEM supplemented with 10% FBS , penicillin (100 U/mL) and streptomycin (100 µg/mL). 4 h after seeding cells were transfected with 0.2 µg of N-terminally NanoLuc tagged HDAC11 and 1.8 µg of empty pcDNA3.1 vectors using X-tremeGENE™ HP transfection reagent, following manufacturer instructions. Next day cells were tretaed with compounds/DMSO for 6 h. Cells were harvested using Trypsin-EDTA and resuspended in pre-warmed Opti-MEM (no phenol-red) at a concentration of 1 x 10<sup>5</sup> cells/ml. Next, 100 µl/well of the cell suspension was added to the pre-loaded PCR plates. Immediately prior to heating the PCR plates, a 5x substrate stock solution was prepared by diluting NanoBRET NanoGlo Substrate (Promega) 1:100 into Opti-MEM. The thermal cycler was allowed to reach the designated temperature prior to placement of the PCR plate into the heating block. For Tm determination experiments, duplicate plates containing a fixed concentration of test compounds (30 µM) as well as vehicle control were prepared to perform a 11 point temperature gradient spanning 37-72°C for 3 minutes. The plates were allowed to cool at room temperature for 3 minutes after the heat treatment followed by addition of 25 µl 5x substrate stock solution per well. To measure NanoLuc activity 100 µl of sample were transferred from the PCR plate to a white 96-well plate. Total luminescence was measured immediately after substrate addition with CLARIOstar microplate reader (BMG Labtech).</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>
Dog neuroimaging data from: Action observation reveals a network with divergent temporal and parietal cortex engagement in dogs compared to humans
<p>Action observation is a fundamental pillar of social cognition. Neuroimaging research has revealed a human and non-human primate action observation network (AON) encompassing fronto-temporo-parietal areas with links to the species’ imitation tendencies and relative lobe expansion. Dogs (Canis familiaris) have good action perception and imitation skills and a less expanded parietal than temporal cortex, but their AON remains unexplored. We conducted a functional MRI study with 28 dogs and 40 humans and found functionally analogous involvement of somatosensory and temporal brain areas of both species’ AONs and responses to transitive and intransitive action observation in line with their imitative skills. Employing a functional localizer, we also identified functionally analogous agent-responsive areas within both species’ AONs. However, activation and task-based functional connectivity measures suggested significantly less parietal cortex involvement in dogs than in humans. These findings advance our understanding of the neural bases of action understanding and the convergent evolution of social cognition, with analogies and differences resulting from similar social environments and divergent brain expansion, respectively.</p> <p>This data set contains:</p> <ul> <li>raw functional neuroimaging data of <em>N</em> = 28 dogs</li> <li>structural scans of the same dogs including brain masks & skull-stripped versions</li> <li>eventfiles containing the condition names, onsets and durations for each dog and task run</li> </ul> <p>Data of the comparative human neuroimaging sample will be made available by the first author upon reasonable request.</p> <p>Please also visit our Open Science Framework project site for group-level imaging data of both species (https://osf.io/z479k/).</p>
Engagement and Purchase Intention in storydoing and storytelling for Instagram ads
<p>This is a database with which we have worked on the engagement and purchase intention generated by storydoing and storytelling advertising.</p>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Public and Stakeholder Engagement
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
Science Communication Webinar Series: Engaging with Policymakers
<p>Blue-Action coordinated a series of webinars on climate science communication in 2020. These are aimed at early-career researchers, but anyone who wishes to engage people with their research at any stage will find them valuable.</p> <p>This webinar was delivered on 15th October 2020 by Jan-Stefan Fritz, Managing Director of the German Marine Research Consortium. Stefan has over fifteen years experience linking science with European policymakers in Brussels, and here shares his wealth of experience in how and why we should be engaging with policy. </p>
Facebook posts for analyzing Content Strategies for Digital Consumer Engagement: a curated dataset
<p>This database contains public data from publications made on Facebook by small and medium companies in the tourism sector operating in the Amazon region in Brazil. The collection was carried out from January to June 2018 using the public API provided by the platform.</p> <p>These data were processed and classified by different evaluators according to the content categories proposed by <a href="https://doi.org/10.1080/00913367.2017.1405751">Gavilanes</a>. Thus, there is a column in the dataset called "category", which contains the classification of each publication, with each number is associated with a category, as described below:</p> <ul> <li> <p>“0” - No category</p> </li> <li> <p>“1” - New product announcement</p> </li> <li> <p>“2” - Sweepstakes and contest</p> </li> <li> <p>“3” - Sales</p> </li> <li> <p>“4” - Consumer Feedback</p> </li> <li> <p>“5” - Infotainment</p> </li> <li> <p>“6” - Organization Branding</p> </li> <li> <p>“N\A” - Non-agreement between evaluators</p> </li> </ul> <p>Therefore, the columns with data present in the CSV file are:</p> <ul> <li> <p>“status_id” - Identification of each publication in the social network. String Textual content of each publication;</p> </li> <li> <p>“status_message” - The textual content of each publication;</p> </li> <li> <p>“link_name” - Which part of the profile the post are related;</p> </li> <li> <p>“status_type” - The type of the publication is a ’photo’, ’video’, ’status’ and/or ’link’;</p> </li> <li> <p>“status_link” - URL to the publications on the platform;</p> </li> <li> <p>“status_published” - Publication date in the social network;</p> </li> <li> <p>“num_comments” - Numbers of comments made by users;</p> </li> <li> <p>Reactions - Numbers of emoticons reactions from users on post – these numbers are presents in the columns: "num_reactions", "num_shares", "num_likes", "num_loves", "num_wows", "num_hahas", "num_sads", "num_angrys", "num_special";</p> </li> <li> <p>“category” - The category of content that we mentioned before.</p> </li> </ul>
School engagement, SRL and academic achievement
<p>This data set corresponds to the analyses carried out in the following article:</p> <p>Estévez, I., Rodríguez-Llorente, C., Piñeiro, I., González-Suárez, R., & Valle, A. School Engagement, Academic Achievement, and Self-Regulated Learning. <em>Sustainability, 13</em>, 3011. <a href="https://doi.org/10.3390/su13063011">https://doi.org/10.3390/su13063011</a></p> <p>Measures of school engagement, self-regulation and academic performance of a group of primary school students are included.</p>
Survey Data: Public Engagement with Science: A Survey for India Alliance Grantees
<p>Lately, the Indian research ecosystem has seen an upward trend in scientists showing interest in communicating their science and engaging with non-scientific audiences; however, the number and variety of science communication or public engagement activities undertaken formally by scientists remains low in the country. There could be many contributing factors for this trend. To explore this further, the science funding public charity in India, DBT/Wellcome Trust India Alliance (India Alliance), in a first of its kind of study by a funding agency in India, surveyed its 243 research grantees in November 2020 requesting their views on public engagement with science in India through an online survey. The survey included both quantitative as well as open-ended questions to assess the understanding of, participation in, and attitude of India Alliance Fellows/Grantees towards public engagement with research, identify the enablers, challenges, and barriers to public engagement for India Alliance Fellows/Grantees, understand the specific needs (training/capacity-building, funding, etc.) and develop recommendations for India Alliance as well as for the larger scientific ecosystem in the country. The survey showed that India Alliance grantees are largely motivated to engage with the public about science or their research but lack professional recognition and incentives, training and structural support to undertake public engagement activities.</p> <p>A shareable survey was designed using Microsoft Forms. The survey employed a mixed methods strategy and included 24 closed-ended questions (multiple choice and Likert scale) and nine qualitative questions, similar to open-ended questions. The questions were modelled on a similar survey carried out by Wellcome in 2016 to gather views of researchers on public engagement in Asia and Africa.</p> <p>The survey was shared by email with 243 Indian researchers in receipt of India Alliance fellowships or grants, out of which 137 (male – 78; female – 59) responded to the survey. Of these, 90 respondents were basic science researchers, 22 were clinical researchers, and 25 were public health researchers. A majority, i.e. 81% of the respondents indicated working as an independent researcher for more than 4 years (time post-PhD). Furthermore, 58% of the respondents were based at research institutions, 18% at higher education institutions, and 17% at central, state and private universities; these organisations geographically represent around 31 cities of India (9 respondents chose to not reveal their host institution in the survey). Periodic reminders were sent by email to the fellows to complete the survey. Three fellows who received funding from India Alliance for their public engagement projects were contacted via email after the survey for their views (in 50-150 words) on the public engagement funding programme of India Alliance. They were informed that their input is intended to be included in a report of the survey results, and they can choose to either stay anonymous or be credited. The data was collected from 27 November 2020 until 28 April 2021.</p> <p>Following the end of the survey, the data automatically mapped on Microsoft Excel was cleaned for duplication and errors. Microsoft Excel tools were used to analyse and visualise the data.</p> <p>The study did not undergo a formal ethical clearance process as the primary objective of the study was to gather insights of grantees to inform India Alliance’s public engagement support mechanisms. At the start of the survey, the objectives of the survey were clearly stated and the respondents were informed that the anonymized data of the survey may be communicated in the future. Participation in the survey was entirely voluntary.</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Graph Files
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding graph files of the extracted Zooniverse networks described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The graph files are in <code>.gexf</code> (graph exchange XML format) and <code>.gml</code> (graph modeling language) formats which can be used by common graph/network-analysis and visualisation tools such as Gephi.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations of the network structure, involving additional (not yet analysed) features such as the content of the comments etc.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains distinct graph files for each of the analysed projects. For each graph file, there are <em>nodes </em>and <em>edges</em> and their associated attributes (i.e., each edge can have an attribute). For the edges, apart from source and target, we have as attributes:</p> <ul> <li><code>weight</code></li> <li><code>project_title</code></li> <li><code>body </code>(i.e., text)</li> <li><code>created_at</code></li> <li><code>userRoles</code></li> <li><code>discussion_title</code></li> <li><code>discussion_id</code></li> <li><code>user_id</code></li> <li><code>board_title</code></li> <li><code>relation</code></li> <li><code>target_role</code></li> </ul> <p>For the nodes, the attributes are:</p> <ul> <li><code>user_id</code></li> <li><code>userRoles</code></li> <li><code>degree_reply </code>(i.e., degree for the reply relation)</li> <li><code>in_degree_reply</code></li> <li><code>out_degree_reply</code></li> <li><code>degree_comment</code></li> <li><code>in_degree_comment</code></li> <li><code>out_degree_comment</code></li> <li><code>degree_total</code></li> <li><code>in_degree_total</code></li> <li><code>out_degree_total</code></li> <li><code>target_role</code></li> </ul> <p><strong>Grouping:</strong></p> <p>Each graph file represents all the comments for the respective project across its lifespan irrespective of any time slices. Edges represent the comments and users represent the nodes. While the different boards are still contained within the data, all boards occur in the data.</p> <p> </p>
Master database for publication "Stakeholder engagement and knowledge co-production for better watershed management with the Freshwater Health Index"
<p> </p> <p>The spreadsheet is the master database used for the research with the title of "Stakeholder engagement and knowledge co-production for better watershed management with the Freshwater Health Index" to be published on <em>Current Research in Environmental Sustainability</em> in late 2022.</p> <p>The data include narratives for six principles on stakeholder engagement and knowledge co-production. These principles were derived by combining Talley et al’s five principles for effective stakeholder engagement (Talley et al. 2016) with Norström et al’s four principles for knowledge co-production (Norström et al. 2020). The six principles were aligned with the components of the freshwater health index (FHI) framework. Project leaders from nine FHI case studies completed a text table based on the description of the six principles and their FHI factors. This process generated the present master qualitative database with narrative responses. </p> <p>The socio-ecological context for each case study is provided for contextualization.</p>
Research Scholars Workshop: Research Posters that Engage
<p>In this session, we establish best practices for high-quality research posters, including design and presentation. We discuss internal opportunities and point you to resources for identifying external opportunities. We will have guidance and support no matter what stage you're at in disseminating your research as a poster, whether you're just curious or gearing up for a conference.</p>
TRANSFORM video - Deliberative engagement in Lombardy Region
<p>Within the realm of TRANSFORM, Lombardy Region commits to render S3 more inclusive and transparent, ensuring that citizens’ voices are heard, and opinions are taken into account in setting up key regional R&I policies (participatory research and innovation, agenda setting). This video describes the first stream of deliberative engagement conducted in Lombardy within the TRANSFORM project: sharing their experiences and opinions on personal and territorial needs, citizens contribute to shape regional R&I priorities. More than 1000 people leaving in the region are engaged thanks to a structured participatory agenda setting pilot exercise, which is held entirely online (due to the COVID-19 emergency) combining a mix of quantitative (a survey administered to a representative sample of the Lombardy population) and qualitative (a deliberative workshop) methods. The citizens' input collected from citizens has been included in the regional Three Years’ Strategic Plan for Research, Innovation, and Technology Transfer.</p> <p>This project has received funding from the European Union's Horizon2020 research and innovation programme under grant agreement No 872687. This video reflects only the authors' view. The European Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains.</p>
TRANSFORM video: Citizen engagement TRANSFORMing regional R&I policy making
<p>Over the past three years, the TRANSFORM project (funded under H2020) has explored and tested citizen engagement practices in the governance of regional research and innovation (R&I), in particular in the context of Smart Specialisation Strategies (S3) in three regional clusters: Lombardy (Italy), Catalonia (Spain), Brussels-Capital (Belgium). Under the common framework of Responsible Research and Innovation (RRI), each territory has implemented different methodologies and pilot actions with the common goal of better aligning regional R&I priorities with societal needs and aspirations.</p> <p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 872687. This video reflects only the authors' view. The European Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains.</p>
TRANSFORM final conference: RRI and citizen engagement to open up regional R&I policymaking
<p>Three regions, three different approaches testing citizen and stakeholder engagement in regional R&I policy making... TRANSFORM partners from three regions - Catalonia, Brussels-Capital and Lombardy on their experiences, activities and impact beyond the project. The video was filmed during the TRANSFORM Final Conference which took place on 1 and 2 December in a hybrid format in Miland and online via Zoom. An opportunity to share project results and outcomes, learn and exchange with the RRI community.</p> <p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 872687. This video reflects only the authors' view. The European Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains.</p>
Meta-analysis on Stakeholder Engagement in the Co-Production of Knowledge for Environmental Decision-making
<p>This dataset corresponds to a meta-analysis to examine the peer-reviewed scholarship on stakeholder engagement in co-production processes in environmental decision-making.</p> <p>The dataset was developed by searching keywords, abstracts, and titles in Scopus on three key themes: co-production, transdisciplinary, and community-based participatory research. A total of 709 publications were reviewed, from which 144 met the selection criterion. The 144 articles constitute this dataset. They pertain to articles published between January 2005 and June 2020.</p> <p>The data includes information on the following topics:</p> <ol> <li>Number of unique case studies reported</li> <li>The projects’ time span</li> <li>When stakeholder engagement activities occur during the project cycle</li> <li>Are stakeholders (non-university researchers) an author</li> <li>Geographic region of project</li> <li>Environmental issues addressed</li> <li>Geographic scale of project </li> <li>Types of stakeholders engaged </li> <li>How stakeholders were engaged</li> <li>What output resulted from stakeholder engagement</li> <li>Approach to stakeholder engagement</li> <li>Factors that enabled stakeholder engagement</li> <li>Barriers or challenges to engagement</li> <li>Societal impacts associated with stakeholder engagement</li> <li>Direct evidence of societal impacts of stakeholder engagement</li> </ol> <p>The methods and analysis are presented in "Stakeholder Engagement in the Co-Production of Knowledge for Environmental Decision-making," which was submitted for publication in 2022 to the Journal <em>World Development</em>. A draft of the manuscript can be provided upon request to authors.</p>
Dataset for "Exposure and environmental engagement: A pilot integrating wearable sensors, air quality and citizen science"
<p>The dataset contains anonymised readings of 7 citizens taking air quality measurements using PlumeLabs Flow 2 monitor. Data is for Falmouth/Penryn, and Bristol and it was collected between January 26, 2022 and March 9, 2022.</p> <p>CSV file:</p> <ul> <li>latitude: unit degrees, positive values indicate North hemisphere.</li> <li>longitude, unit degrees, positive values indicate East.</li> <li>AQI: PlumeLabs' Air Quality Index.</li> <li>site: A refers to Falmouth/Penryn(UK), B refers to Bristol (UK).</li> <li>count: auxiliary variable that indicates that the record was comprised of a single reading.</li> </ul> <p>Jupyter notebook: The air quality analysis was conducted with Python 3.9.16 alongside numpy 1.24.3, pandas 2.0.2, matplotlib 3.7.1, and cartopy 0.21.1 (background tiles by OpenStreetMaps).</p>
Scrub Typhus Community Engagement - Interviews with participants and other stakeholders
<p>Interviews with stakeholders from the scrub typhus community engagement project carried out by the Chaingrai team of the Mahidol Oxford Tropical Medicine Research Unit together with the Primary Care Department of Chiangrai Prachanukroh Hospital.</p>
AI-ENGAGEMENT's Gantt
<p>AI-ENGAGEMENT’s Gantt</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.