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1,287 results for “innovation”
Public Procurement of Innovation stakeholders map
<p>This interactive map contains a database of Public Procurement of Innovation (POI) stakeholders, including public buyers, procures, SMEs, start-ups, spin-offs, universities, incubators, technology centres, projects & other relevant actors.</p> <p>Go through the<a href="https://procedin.eu/stakeholder-map/"><strong> interactive stakeholders map</strong></a>, find the best contact and set up a call to enhance your businesses together.</p> <p><br>You can also explore F6S Platform or Tenderio, your place for match making.</p> <p> </p> <p>Additionally, you can find valuable information at: <br>– <a href="https://procurement-forum.eu/">Procurement Forum</a><br>– <a href="https://eafip.eu/">European Assistance for Innovation Procurement (EAFIP)</a> <br>– <a href="https://sustainable-procurement.org/sustainable-public-procurement/">Sustainable Procurement Platform</a></p>
ADAPTING SYSTEMS ENGINEERING TO EVALUATE TECH STARTUPS: AN INNOVATIVE FRAMEWORK BASED ON OMG ESSENCE
<p>The adaptation of system engineering to evaluate technical startups through an innovative framework based on the OMG Essence standard is a modern approach to simplify the process of analyzing and managing startups at various stages of their development. In this study, a universal framework was proposed that allows evaluating technical startups from the point of view of system engineering. This approach takes into account key aspects of startup development, such as requirements, stakeholders, technology, and team, which makes it an important tool for evaluating innovative projects.</p> <p>The main purpose of the proposed framework is to apply the principles of systems engineering to the evaluation of startups, focusing on technical aspects such as system architecture, integration capabilities, and the ability of the team to solve complex tasks. Traditional methods of evaluating startups, often focusing on business aspects, may not always take into account all the technical difficulties and risks that arise when developing software products. The OMG Essence-based framework fills this gap by providing a more comprehensive tool for analyzing and managing startup development.</p> <p>OMG Essence, as a standardized method, acts as a basis for formalizing practices and facilitating interaction between various project participants. The framework based on it offers the possibility of modular adaptation, which allows you to evaluate startups regardless of their complexity and current stage of development. An important feature of OMG Essence is the ability to integrate with other methodologies, which makes it universal for various fields. The application of this standard in the evaluation of startups allows you to obtain more objective results and improve the decision-making process.</p>
A controlled vocabulary for research and innovation in the field of Circular Bioeconomy
<p>We live in a world of limited resources. Facing global challenges such as climate change and degradation of natural capital, in addition to the increasing rate of resource consumption, we are compelled to look for new ways of producing and consuming that respect the ecological limits of our planet. A model based on the Circular Bioeconomy (<em>CBE</em>) is key to successfully tackling the complexity of this paradigm shift and addressing these challenges: CBE is a relatively new and fast evolving concept, which is still in the conceptualisation phase. It stems from the concepts of “<em>bioeconomy</em>” and “<em>circular economy</em>”, which have become progressively interlinked in recent years.</p> <p>Although the wider community of stakeholders has not reached yet a consensus on a single definition for CBE, we try to go beyond this limitation, by proposing a controlled vocabulary of keywords related to the field, built by eliciting domain knowledge from both experts and policy-makers. This effort responds to an explorative project launched by the Generalitat de Catalunya (the regional government of Catalonia, Spain - specifically, the Ministry of the Economy and Finance, Secretary for Economic Affairs and European Funds) in order to identify CBE research, development and innovation activities. The work, coordinated by SIRIS Academic, was carried out by consulting the advice of experts in the domain (see Acknowledgements, below), and it was ultimately applied to inform regional strategies on bioeconomy and the Research and Innovation Strategy for Smart Specialisation.</p> <p>After a qualitative analysis of numerous examples, the keywords extracted have been classified into three categories:</p> <ul> <li> <p><strong>Unequivocal</strong>: keywords positively associated with the CBE domain (e.g. <em>bioplastic conversion, biorefinery, or biomass</em>),</p> </li> <li> <p><strong>Bioeconomy</strong>: keywords in the bioeconomy domain but not necessarily within the circular paradigm (e.g. <em>agriculture, algae, or organic waste</em>),</p> </li> <li> <p><strong>Technologies and processes</strong>: keywords concerning circular processes and technologies (e.g. <em>valorisation, or bioconversion</em>).</p> </li> </ul> <p>This distinction allows one to apply the vocabulary to link a given text to the CBE domain. Indeed, a text may be considered in the CBE perimeter if it mentions an unequivocal keyword, or if it contains a concept concerning bioeconomy co-appearing with a concept concerning circular technologies and processes (e.g. food waste with composting, or vegetation with biosynthesis).</p> <p>The circular bioeconomy vocabulary, in its current form, contains 393 keywords (49 unequivocal keywords, 229 bioeconomy keywords, and 115 concerning circular processes and technologies). <br> </p> <p><strong>Datasets Provided</strong></p> <p><br> In this publication, we provide:</p> <p> </p> <ul> <li><strong>A document outlining the context that lead to the creation of the vocabulary</strong>, the methodology used to build it and the strategy to apply it</li> <li><strong>The full vocabulary of terms as a csv file</strong>. For each keyword of the vocabulary, the property “type” specifies whether the term is either an “Unequivocal” CBE keyword, a wider “Bioeconomy” term or a specification of “Technologies and processes”.</li> <li>In addition to the controlled vocabulary, we also provide <strong>a labelled dataset to foster the development of new text mining initiatives</strong> on the results obtained. The dataset consists of the description of 2000 R&D projects funded by the Horizon 2020 framework of the European Commission. Of these, 1000 have been associated with the field of CBE via the vocabulary and further manual validation, while further 1000 descriptions are unrelated to CBE. Each project entry has the following attributes: title, abstract, ecRef (project identifier in the EU information service, CORDIS), and label (where True are projects in the CBE domain)</li> </ul> <p><strong>Acknowledgements</strong></p> <p>The work described above, leading to the production of the datasets provided, was developed in collaboration with Generalitat de Catalunya officials, as well as CBE research and innovation experts and stakeholders that contributed essential input to the conceptual definition of the CBE, the review and improvement of the controlled vocabulary and the quality of the classification, as well as the the revision of the analytical results described in the section Applicability, in particular:</p> <ul> <li> <p>Tatiana Fernández (Direcció General de Promoció Econòmica, Competència i Regulació, de la Generalitat de Catalunya)</p> </li> <li> <p>Mercè Balcells i Ramón Canela (Universitat de Lleida)</p> </li> <li> <p>Teresa Botargues (Diputació de Lleida)</p> </li> <li> <p>Sergio Ponsà (Centre Tecnològic BETA)</p> </li> <li> <p>Ignacio Rodríguez and Jaume Sió (Departament d’Agricultura, Ramaderia, Pesca i Alimentació, de la Generalitat de Catalunya)</p> </li> </ul>
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>
Innovative forest products in the circular bioeconomy: questionnaire for semi-structured interview
<p>This upload includes the questionnaire for semi-structured interviews with stakeholders, as part of a case study done for the BioMonitor project. The data collected during the interviews are not publicly available as the dataset contains sensitive information of the organizations involved in the study.<br> The interview participants work in EU-based organizations involved in the development and manufacture of forest products, especially of the following categories: construction materials, textiles, chemicals, bioplastics, and wood-based composites.</p> <p> <br> <strong>About BioMonitor</strong><br> BioMonitor is an EU-funded project (biomonitor.eu) that aims to establish a sustainable and robust framework that different stakeholders can use to monitor and measure the bioeconomy and its various impacts in relation to the EU and its Member States. The BioMonitor consortium is composed of a team of universities, statistical and standardisation institutes as well as consultancies and data modelling experts.</p> <p><em>This work was supported by the BioMonitor project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement N° 773297.</em></p>
Managing innovation - how to evaluate costs/benefits and plan for the future
<p>A guest seminar offered by SCImPULSE Foundation CFO Stef Cuijpers for the University of Parma (IT) master "ARTE" https://www.masterarte-unipr.it/</p>
Results of EUA survey on universities and innovation
<p>This database refers to the data collected by the European University Association (EUA) through its survey on universities and innovation in 2021. Designed to gather evidence about the state of innovation at European universities, the EUA survey took stock of how these institutions pursue their third mission and help deliver the sustainable and digital transitions. As such, it continues EUA’s long-standing work showcasing universities’ key contributions to innovation ecosystems, in a context of multiplying societal challenges and the increasing relevance of knowledge to devising new solutions. The full report published by the Association is available at: <a href="https://www.eua.eu/resources/publications/1014:universities-as-key-drivers-of-sustainable-innovation-ecosystems.html">https://www.eua.eu/resources/publications/1014:universities-as-key-drivers-of-sustainable-innovation-ecosystems.html </a></p> <p>All information that could lead to the identification of individual universities and other higher education institutions was removed from the database. The following files are available:</p> <ul> <li>EUA survey on universities and innovation</li> <li>Database in the following formats: .xlsx (Microsoft Excel) and .sav (IBM SPSS)</li> <li>Survey Codebook: includes information on all the variables and their coding (.xlsx)</li> <li>Data Management Plan.</li> </ul>
i-SoMPE Inventory A: List and description of 58 innovative soil management practices
<p>List and description of 58 innovative soil management practices (inventory A)</p>
i-SoMPE Inventory A: Adoption rate of 58 innovative soil management practices
<p>Adoption rate of 58 innovative soil management practices (maps of inventory A)</p>
Innovative agrifood value chains
<p>This data-set has been produced in the frame of H2020 project CO-FRESH, in Deliverable 1.2 "List of value chains and a data-set on the characteristics of the identified sustainable, innovative and competitive agrifood value chains"</p> <p>Grand societal challenges, such as climate change, environmental degradation, food security, immigration, and digital transformation, urge value chains actors from farm to fork to interact for improving the system. In this context, innovation, that has long been considered as a factor of growth and competitiveness, becomes an increasingly important factor to create value while addressing sustainability challenges (both environmental, social and economic).</p> <p><strong>The CO-FRESH project</strong> aims to enhance collaboration for sustainability-oriented innovation in the agri-food sector, by proposing interventions for re-designing fruit and vegetables (F&V) value chains across Europe. To reach these objectives, Work Package 1 takes the lead in <em>Identification, Analysis and Design of Innovative and Sustainable Agri-food Value Chains</em>. The current report is the outcome of <em>Task 1.2. Identify innovative agri-food value chains and collect empirical data. </em></p> <p><strong>Deliverable 1.2.</strong> presents a list of value chains and a data-set on the characteristics of the identified sustainable, innovative and competitive agrifood value chains. This list is the results of a participatory inventory led by WUR and involving all the CO-FRESH consortium partners.</p> <p>This report contains three core elements. <strong>First,</strong> it presents the methodology that was used for the inventory creation, drawing on the concepts and selection criteria developed in <em>D1.1.Review state of the art</em>. <strong>Second,</strong> the report provides the list of the selected 100+ value chains and their general characteristics. <strong>Third,</strong> the report shows the different types of sustainability-oriented innovations implemented in these value chains, as well as the various modes of collaboration used to design and implement the innovations.</p> <p>The inventory has been presented to the CO-FRESH consortium during a workshop held 7 October 2021 (CO-FRESH Milestone 2). The inventory will be used in the next step of the project for further data collection and analysis (Task 1.3). For the next milestone, a survey will be addressed to a representative of each of these 100+ value chains in order to gain better understanding of the drivers of collaboration and to assess the impact of the sustainability-oriented innovation implemented. Second, the inventory will be used to select and investigate a portfolio of business models in the Task 1.4. Based on this inventory and the following analysis in Task 1.3 and Task 1.4, recommendations will be developed for interventions to redesign the value chains of the CO-FRESH pilot cases.</p>
Dataset from Experimental and Nonlinear Finite Element Modeling Investigating an Innovative Buckling Restrained Bracing System for Rehabilitation of Seismic Deficient Structures
<p>The data presented in this paper were collected experimentally and modeled using the finite element method. A total of six BRBs (i.e., duplicates of three types of BRB core bars) specimens were tested experimentally and verified numerically using the finite element method employing the commercial Software ABAQUS. Specific labeling was used to designate each BRB type. Three core bars were used in the tested BRBs: fully-threaded, threaded-notched, and smooth-shaved. The specimens are labeled according to their core bar type and diameter. i.e., BRB-12-Th stands for a full threaded core bar diameter of 12 mm, the threaded notched type was labeled BRB-12-Th-Nd, and the smooth shave one was labeled BRB-12-Sh.</p> <p>Further details of the tested BRBs are included in the excel file called dimensions and properties of BRBs. The worksheet provides details of the BRB components (i.e., core bar, restraining unit, and innovative end units). The dimensions and strength of the materials were obtained from coupon tests. The experimental data are presented in the second excel file labeled hysteresis with three embedded worksheets, one for each type of BRB. The excel sheets provide the cyclic loading data and plots showing the hysteresis behavior of tested BRBs. A sample of the loading protocol included in the second excel file is presented in Fig.1. The third excel file presents the analytical data extracted from experimental data that has two sheets: stiffness and energy dissipation. The sheet labeled stiffness has the secant stiffness versus deformation plot for the push-pull cycles (compression-tension). The second sheet labeled energy dissipation shows the cumulative energy dissipated.</p>
Dataset Bottom-up approach in the assessment of environmental impacts and costs of an innovative anammox-based process for nitrogen removal
<p>Dataset of</p> <p>Bottom-up approach in the assessment of environmental impacts and costs of an innovative anammox-based process for nitrogen removal</p> <p>J Environ Manage 2018 Nov 1;225:112-119.doi: 10.1016/j.jenvman.2018.07.070. Epub 2018 Jul 31.</p> <p> </p>
Collaborative Innovation Project funding launch - Dr Samantha Kanza (University of Reading, University of Southampton)
<p>This video is the eighth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Collaborative Innovation Project funding launch - Dr Samantha Kanza (University of Reading)</p> <p>Bio: Dr Samantha Kanza is a Senior Enterprise Fellow at the University of Southampton. She completed her MEng in Computer Science at the University of Southampton and then worked for BAE Systems Applied Intelligence for a year before returning to do an iPhD in Web Science (in Computer Science and Chemistry), which focused on Semantic Tagging of Scientific Documents and Electronic Lab Notebooks. She was awarded her PhD in April 2018. Samantha works in the interdisciplinary research area of applying computer science techniques to the scientific domain, specifically through the use of semantic web technologies and artificial intelligence. Her research includes looking at electronic lab notebooks and smart laboratories, to improve the digitization and knowledge management of the scientific record using semantic web technologies; and using IoT devices in the laboratory. She has also worked on a number of interdisciplinary Semantic Web projects in different domains, including agriculture, chemistry and the social sciences.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ </p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/PDWZkZBzfqw</p>
Story Map of the Council of Tampere Region on Responsible Regional Innovation Policy
<p>The Co-Change Lab at the Council of Tampere Region focuses on developing regional funding to improve responsibility and sustainability.</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>
The Why and What of AI Deployment and Innovation in Companies – Results and Learnings from a Systematic Literature Research
<p>These appendices are made available online as a critical part of the research paper accepted for publication at ISPIM 2024 in Tallinn on 10-12June 2024.</p> <p><strong>Abstract:</strong> Despite vast and growing investments in Artificial Intelligence, only a tiny fraction of companies report achieving tangible results. To fill this gap and establish a baseline for companies, we conducted Systematic Literature Research on the status of AI deployment in companies. We analyzed and synthesized 99 peer-reviewed articles for objectives, approaches, results, and learnings. This research confirms that AI is still at the pre-deployment stage in companies. Developing the capability to deploy AI is more challenging and takes more time, concerted effort, innovation, and learning than generally thought. Through the results and the conceptual framework, this research increases understanding of AI deployment and emphasizes the importance of the pre-deployment stages from theoretical and practical viewpoints. The Sensing stage of the dynamic capabilities theory aligns well with the developed pre-deployment stages. More real-life case research is needed, and existing theories verified to deploy AI knowledgeably for business and stakeholder benefits. </p>
Dataset: REX FANG & Innovation Equity Premium Income ETF (FEPI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: IDX Dynamic Innovation ETF (DYNI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Principal Healthcare Innovators ETF (BTEC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: BNY Mellon Innovators ETF (BKIV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.