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22 results for “social innovation”
Catalogue of Diversity of Social Innovation
<p>The ‘Catalogue of Social Innovation Diversity in Rural Areas’ is the consolidated version of the research database of examples of social innovation in marginalised rural areas developed by the project SIMRA. The file contains a spreadsheet document that includes descriptive information of all the examples reviewed and recorded at some stage in the research database.</p> <p>The catalogue includes basic information for identifying and describing the examples and the characteristics of the social innovation. The total number of examples in the catalogue is 401. Of this number, 243 examples were positively validated using the SIMRA definition of social innovation. The information included in the catalogue for these examples is sufficient to meet the criteria of social innovation as defined by SIMRA. The remaining examples either demonstrate elements of social innovation without meeting all criteria, or include insufficient information to allow a positive validation. Examples can be filtered according to spatial scale, country, sector, topic, form and SIMRA validation.</p>
Database of Participatory Practices and Social Innovations in Wind Energy Developments
<p>Inês Campos was responsible for designing the database, collecting data, and analyzing data. Flávio Oliveira also collaborated in the design of the database and data collection. </p>
Social Innovations for Circularity in the Built Environment – a Scoping Review and Classification. Supplementary Data to the Bibliometric Review.
<p>To transition the built environment (BE) towards circularity, i.e., maximizing the time resources spend in the BE, thus minimizing negative environmental impacts of resource usage, social innovations (SI) – understood as new ways of doing, organizing, framing, and knowing – are just as important as technological advancements. This article provides an overview of the state of the knowledge regarding SI that contribute to circularity in the BE, and proposes a framework for coherently classifying such SI in terms of their main categories and effects.</p> <p>To identify and understand the current knowledge regarding social innovations (SI) that contribute to circularity in the built environment (BE), a bibliometric review of scientific literature is conducted. It shows that the term social innovation is not frequently used in this contexts although the buzzwords circularity and circular economy are themselves often framed as SI. To assess the characteristics and contribution of SI to circularity in the BE, a scoping review that includes grey literature into the context was conducted and a framework developed to classify predominant SI using concepts from transition studies as well as the systems thinking approach. The framework is designed to help assess the potential of SI and to identify research and/or action gaps. Key findings are (1) There is a broad diversity of SIs that contribute to circularity in the BE already in the focus of research, although they are not always identified as such; (2) Most SI focus on either the design or the demolition phase (i.e., market related phases), whereas user-centered SI are less frequently discussed; and (3) It is crucial to also consider potential sustainability goal conflicts in order to guide policies that address SI as a solution.</p> <p>These datasets are the basis to the bibliometric literature review.</p>
Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives (Supplemental material)
<p>This data upload provides a detailed account of indicators that can be used to measure RRI progress around the world against the UNESCO Recommendation for Science and Scientific Researchers at the level of individual researchers and public opinion. This data upload provides supplemental material for an article in Open Research Europe entitled, 'Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives'.</p> <p>Abstract for main article:</p> <p>As calls for more socially responsible research and innovation (RRI) policies and practices grow more insistent, the need for high quality indicators that can be used to evaluate progress is becoming increasingly important. Given the global nature of science, such indicators need to be relevant to countries across all world regions. Moreover, the methodological quality of indicators is critical to provide a strong foundation for long-term comparative measurement of the impacts of different kinds of policy intervention. There is a practical challenge in this effort relating to the uneven mechanisms for data collection and analysis available in different countries. There is also a geopolitical challenge in gaining buy-in from countries with very different, and sometimes competing, agendas. Here, the 2017 UNESCO-led Recommendation on Science and Scientific Researchers is highlighted as an existing vehicle that can enable cooperation on globally comparative measurement of socially responsible research and innovation. In particular, the quadrennial monitoring of the implementation of this wide-ranging global policy instrument that has been ratified by 195 countries affords a unique opportunity to add value for these countries by linking RRI to the 2017 Recommendation while establishing benchmark indicators for RRI more generally. As a practical and methodological contribution to the global community of science and innovation policymakers, researchers and research and innovation stakeholders committed to socially responsible research, this report contains specific, detailed survey questions and response options focusing on the public opinion and individual researchers’ level of measurement. It provides details of sources of benchmark survey data that have readily available open data that can be used to benchmark the development of socially responsible research and innovation over time from the vantage points of the public and researchers around the world. The aim of this kind of indicators framework is to enable evidence-based practice in socially responsible research and innovation. Robust and practical RRI measurement on a global scale can reduce the risk that well-intentioned but ill-conceived RRI policy and practice interventions create undetected, unchecked, and unreformed negative impacts in practice.</p>
Triggering Sustainable Biogas Energy Communities through Social Innovation- ISABEL ---- Social Innovation and Community energy best preactices, methods and tools across Europe ----Semi-structured interviews from communities
<p>Having identified through the literature review various success and failure factors for social innovation applied to community energy projects, ISABEL has further conducted 18 semi-structured interviews of a range of stakeholders. The interviewee sample was a convenience sample of participants in existing projects and thus, inevitably, they are able to speak more to successful than unsuccessful projects and they likely have had less exposure to obstacles to the success of their projects. T The interviews have focused on identifying answers to the questions: <em>What were the key success factors? What obstacles were overcome? How? Participants were also asked to specify the type of renewable energy and community energy model. </em></p>
Simulated results from an agent-based model examining inequality and innovation in social networks
<p>Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While population models have shown factors including population size, connectivity, and agent behavior as crucial for innovation, few have taken the individual-central approach seriously by examining the role individuals play within their groups. To explore how network structures influence not only population-level innovation but also performance among individuals, we studied an agent-based model of the Potions Task, a paradigm developed to test how structure affects a group's ability to solve a difficult exploration task. We explore how size, connectivity, and rates of information sharing in a network influence innovation and how these have an impact on the emergence of inequality in terms of agent contributions. We find, in line with prior work, population size has a positive effect on innovation, but that large and small populations perform similarly per capita; that many small groups outperform fewer large groups; that random changes to structure have few effects on innovation; and that the highest performing agents tend to occupy more central network positions. Moreover, we show that every network factor which facilitates innovation leads to a proportional increase in inequality of performance, creating "genius effects" among otherwise "dumb" agents in both idealized and real-world networks.</p>
Indirect social influence helps shaping the diffusion of innovations
<p>This dataset accompanies the paper "Indirect social influence helps shaping the diffusion of innovations".<br><br>It contains the participant choices between two colors when shown different information about the choices taken in their social network. The objective of the participants was to arrive to a consensus. One of the colors, which was the minority at the beggining, was given an extra incentive. Further details on the experimental setup are specified in the article.<br><br>The main file is "Unified_data", which contains all the data from the 21 sessions studied and whose fields are described below. The file "ParticipantsData" contains the gender and age distribution of the participants, while the rest of the files are the individual data for each session, in the format given by oTree.<br><br></p> <p>Explanation of "Unified_data.csv" fields:</p> <ul> <li>Experiment: Session number.</li> <li>participant.id_in_session: Identifier for each participant, constant for each session.</li> <li>config_name: Contains the order in which the different Setups were played. Its structure is of the form "egonetwork_1_a_b_c", where a,b,c are the numbers 2,3,4 in different order. These represent: 1-Setup AmAz (first neighbors), 2-Setup VerMag (first+second neighbors), 3-Setup RojNar (first+third neighbors), 4-Setup LilMor (first+fourth neighbors).</li> <li>Setup: Name of the Setup played (AmAz, VerMag, RojNar, LilMor and Instrucctions).</li> <li>node: Node assigned to the participant during that Setup.</li> <li>initialcolor: Initial color prescribed at the beginning of the Setup. Names are in spanish: Azul (blue) = #0072B2, Amarillo (yellow) = #FFC300, Verde (green) = #67E96E, Magenta = #C700AC, Rojo (red) = #E53950, Naranja (orange) = #FFA460, Lila (lilac) = #A792FC, Morado (purple) = #8000FF.</li> <li>InnovationAsInitialcolor: Binary variable showing if participant had the promoted color as initial color.</li> <li>threshold: Best threshold obtained in the model fitting.</li> <li>parameter: Best long-range parameter obtained in the model fitting.</li> <li>Time: Best time simulation obtained in the model fitting.</li> <li>AdoptersConsensus: First round in which all the participants chose the promoted color at least once.</li> <li>SimulationConsensus: Round in which the simulation of the fitted model arrived to consensus.</li> <li>Round: Round number.</li> <li>preg: Only in the Instructions Setup. Answer given to the control question.</li> <li>action: Color chosen by the participant in that round.</li> <li>Adopted_color: Previous varible translated into binary.</li> <li>Adopted_color_bin: Previous variable as logic variable.</li> <li>MajorityColor: Color most seen between the information given to the participant. In case of draw, one was chosen randomly.</li> <li>InnovationAsMajorityColorSeen: Binary variable showing if MajorityColor is equal to Promoted_color.</li> <li>bot: Binary variable showing if the participant was a bot in the current round (did not make a decision on time).</li> <li>color_neighbors_shown: List of the colors of the first neighbors shown to the participant in that round.</li> <li>color_neighbors_shown_color: Previous variable translated from hexadecimal to spanish.</li> <li>perc_color_neighbors: Percentage of neighbors shown with color equal to the promoted color.</li> <li>color_friends_shown: List of the colors of the long-distance friends shown to the participant in that round.</li> <li>color_friends_shown_color: Previous variable translated from hexadecimal to spanish.</li> <li>perc_color_friends: Percentage of long-distance friends shown with color equal to the promoted color.</li> <li>Adopted_innovation: Number of round since the participant first chose the promoted color.</li> <li>Promoted_color: Color with incentive. Setup AmAz - Azul, Setup VerMag - Magenta, Setup RojNar - Naranja, Setup LilMor - Morado.</li> <li>num_adopters: Number of adopters (people that have chosen the promoted color at least once) in the current round.</li> <li>total_participants: Constant showing the size of the network used (31).</li> <li>perc_adopters: Percentage of participants that are adopters in the current round.</li> </ul>
"Unlocking the Urban Code. Digital Social Innovation and City Spaces"
<p>DSI in the City is a project funded by MUR-PRIN 2022 led by Chiara Certomà (PI, University Sapienza Rome) and Venere Sanna (University of Siena). The video explore one of the main challenges for contemporary society, i.e. understand and govern the digital revolution and its sociopolitical consequences.The video identifies and deconstructs the principal issues determined by the digitalisation of urban reproduction processes, notably via the diffusion of DSI practices.</p> <p> </p> <p>The project has been funded by the PRIN2022 MUR program, Funded by the European Commission - Next Generation EU, code 2022KTEZPX to the University Sapienza Rome</p>
Exploring the needs of institutional stakeholders involved in the cultural heritage and social innovation sectors
<p>This data set was produced to gather information and lessons learnt from institutional stakeholders directly or indirectly involved in cultural approaches and/or promoting social integration of disadvantaged groups in order to understand what they need to facilitate participatory and inclusive initiatives. </p> <p>The data was gathered across Europe in early 2019 and consists of 90 surveys administered and 17 interviews. 90 surveys were administered to actors working at the intersection of cultural heritage and social innovation. The survey’s goal was to gather insights about the barriers these actors face in the implementation of participatory approaches and the measures adopted to overcome them. Semi-structured interviews (n=17) expanded upon the survey data and were carried out through an opportunistic sample of the survey respondents. The findings provided relevant insights in relation to the configuration of approaches for participatory projects, the main faced barriers, and the achieved level of impact.</p> <p>The original dataset has been processed by removing details and parts that might compromise the anonymity of the participant. </p> <p>This work is supported by the European Union’s Horizon2020 project CultureLabs: Recipes for Social Innovation, under Grant Agreement 770158. The sole responsibility of this work lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.</p> <p> </p>
Supplementary files: Social learning of innovations in dynamic predator-prey systems
<p>We investigate social transmission of behavioral innovations between predators in two classic predator-prey models. We assume that innovations increase predator attack rates or conversion efficiencies, or that innovations reduce predator mortality or prey handling time. We find that a common outcome of innovations is the destabilization of the system. Destabilizing effects include increasing oscillations or limit cycles. Particularly, in systems where prey are self-limiting and predators have a Type II functional response, destabilization occurs due to overexploitation of the prey. Whenever instability increases the risk of extinction, innovations that benefit individual predators may not have positive long-term effects on predator populations. An additional consequence of instability is the maintenance of behavioral variability among predators. Interestingly, when predator populations are low despite coexisting with prey populations near their carrying capacity, innovations that could help predators better exploit their prey are least likely to spread. Precisely how unlikely this is depends on whether or not naïve individuals need to observe an informed individual interact with prey to learn the innovation. Our results offer perspective on the potential role of innovation in biological invasions, urban colonization, and the maintenance of behavioral polymorphisms.</p>
Supplementary files: Social learning of innovations in dynamic predator-prey systems
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Simulated results from an agent-based model examining inequality and innovation in social networks
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Data from: Social and ecological factors associated with innovation in urban sulphur-crested cockatoos (<em>Cacatua galerita</em>)
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Database of social innovation in energy initiatives
<p>A database of n = 500 social innovation in energy initiatives.</p>
Data Leveraging Digital Innovation: Enhancing Tourist Intentions through Social Media, Websites, and Mobile App Experiences
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Personality predicts innovation and social learning in children; Implications for cultural evolution
<p>Innovation and social learning are the pillars of cultural evolution, allowing cultural behaviours to cumulatively advance over generations. Yet, little is known about individual differences in the use of social and asocial information. We examined whether personality influenced children's propensity to observe others or independently generate solutions to novel problems. Conscientiousness was associated with electing for no demonstrations, while agreeableness was associated with opting for demonstrations. For children receiving demonstrations, openness to experience consistently predicted deviation from observed methods. Children who opted for no demonstrations were also more likely than those opting for demonstrations to exhibit tool manufacture on an innovation challenge and displayed higher creativity. These results highlight how new cultural traditions emerge, establish and advance by identifying which individuals generate new cultural variants in populations and which are influential in the diffusion of these variants, and help reduce the apparent tension within the 'ratchet' of cumulative culture.</p>
Personality predicts innovation and social learning in children; Implications for cultural evolution
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Figure 2 from: Véron R, Fernando N, Narayanan N, Upreti B, Ambat B, Pallawala R, Rajbhandari S, Rao Dhananka S, Zurbrügg C (2018) Social processes in post-crisis municipal solid waste management innovations: A proposal for research and knowledge exchange in South Asia. Research Ideas and Outcomes 4: e31430. https://doi.org/10.3897/rio.4.e31430
Figure 2 Partly decentralized waste chains in Kerala.
Figure 1 from: Véron R, Fernando N, Narayanan N, Upreti B, Ambat B, Pallawala R, Rajbhandari S, Rao Dhananka S, Zurbrügg C (2018) Social processes in post-crisis municipal solid waste management innovations: A proposal for research and knowledge exchange in South Asia. Research Ideas and Outcomes 4: e31430. https://doi.org/10.3897/rio.4.e31430
Figure 1 Schematic representation of the institutional MSWM architecture.
An Innovative HIV Prevention Intervention Using Social Networking Technology
ClinicalTrials.gov study NCT01381653. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.