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1,287 results for “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>
ALL-READY Questionnare on potential drivers and barriers to the adoption of innovation management, open science, and Intellectual Property Rights (IPR) among the members of the Pilot Network
<p><strong>Background & Summary</strong>: </p><p>The ALL-READY project unites a diverse consortium of Research Infrastructures (RI) and Living Labs, instrumental in developing new methodologies and technologies in agroecology. The project focuses on effective management of innovation, adherence to open science principles, and strategic application of Intellectual Property Rights (IPR). Task 6.4 of the project, which concentrates on Innovation and IPR Management, seeks to understand the dynamics influencing the adoption of these practices among its members. Recognizing the need for end-to-end data management, the project emphasizes standardized data collection and management while adhering to FAIR principles.</p><p><strong>Methods</strong>: </p><p>The questionnaire was developed by LifeWatch ERIC to capture data reflecting current practices and perceptions in agroecology. It included 26 questions divided into four sections, focusing on existing practices, potential drivers, and barriers in innovation management, open science, and IPR. The survey was disseminated via an online platform to the ALLREADY Pilot Network, ensuring a representative sample from diverse organizations. The data collection process was closely monitored, and the responses were analyzed using a mixed-methods approach to extract meaningful insights.</p><p><strong>Data Records of the ALLREADY Project Questionnaire</strong>: </p><p>The dataset, collected through an online survey platform, underwent a meticulous process of data preparation, download, formatting, and anonymization. It consists of one text file containing metadata (Readme.txt) and a single CSV file encompassing all questionnaire responses. The dataset provides a comprehensive view of innovation management, open science adoption, and IPR handling within the agroecology sector, particularly among the network of RIs and Living Labs involved in the project.</p><p><strong>Technical Validation of the ALLREADY Project Questionnaire</strong>: </p><p>Several critical steps were taken to ensure the accuracy, reliability, and overall quality of the data collected. This included development and testing of the questionnaire, rigorous monitoring of the data collection process, and thorough checks for data quality and completeness. The representativeness of the sample was analyzed specifically with respect to the Pilot Network rather than the broader population involved in agroecology. Strategies were employed to counter survey fatigue and maintain respondent engagement.</p><p><strong>Usage Notes for the ALLREADY Project Questionnaire</strong>: </p><p>The dataset's proper usage is vital for ensuring the validity and reproducibility of research. Researchers are advised to consider the nature of the data, the representativeness of the dataset, and its generalizability. The dataset allows for comprehensive analysis and integration of different sections, and analysts have the flexibility to handle open and write-in responses according to their research needs. Additional information to facilitate analysis is provided in a separate documentation file.</p><p> </p>
Data for a publication "Exploring the microstructure, mechanical properties, and corrosion resistance of innovative bioabsorbable Zn-Mg-(Si) alloys fabricated via powder metallurgy techniques"
<p><span><span>These data are published as part of the paper: “</span><span>Exploring the microst</span><span>ructure, mechanical properties, </span><span>and corrosion resistance of innovative bioabsorbable Zn-Mg-(S</span><span>i) alloys fabricated via powder </span><span>metallurgy techniques</span><span>” published in journal: “</span><span>Journal of Materials Research and Technology</span><span>”.</span></span><span> </span></p>
DATASET OF RESPONSIBLE RESEARCH AND INNOVATION IN CITIZEN SCIENCE
<p><span>The research aim was to explore what aspects of citizen science (CS) make the involvement of researchers (the ones who implement CS projects) meaningful in terms of responsible research and innovation (RRI) principles. The following research questions were formulated:</span></p> <p><span>1) How does RRI contribute to the meaningfulness of CS projects and in which CS aspects?</span></p> <p><span>2) What motivates researchers to accommodate RRI principles in CS projects? </span></p> <p><span>3) What impedes researchers in accommodating RRI principles in CS projects?</span></p> <p><span>To answer these research questions, a qualitative research approach was employed using individual semi-structured interviews for data collection. Using a purposive criterion-based sample, inclusion criteria were the following:</span></p> <p><span>(i) European researchers (principal investigators/project managers) that are running (at least) one CS project; </span></p> <p><span>(ii) researchers who may represent different organisational settings with scientific orientation (e.g. academia, museums, and others) within Europe; </span></p> <p><span>(iii) the CS project, started before 2013 (year of introducing the concept of RRI into European Union Research and Innovation (EU R&I) policy) should be ongoing during the research conduct, or the CS project started in the period of 2014–2018 (the year 2014 was a starting point since it is the date of embedding RRI in the EU R&I policy as a mandatory component of all research activities) should be still ongoing; and </span></p> <p><span>(iv) the CS project covers any academic discipline.</span></p> <p><span>To identify potential informants, we used the list of CS projects publicised in Wikipedia (</span><span><a href="https://en.wikipedia.org/wiki/List_of_citizen_science_projects"><span>https://en.wikipedia.org/wiki/List_of_citizen_science_projects</span></a></span><span>) and added CS projects from authors’ home countries. In addition, we posted the invitation to participate in the study in a newsletter within the citizen science community (e.g. ECSA) and in social media targeting specific groups and using hashtags, namely on Facebook and Twitter.</span><span> </span><span>At the end, we identified 117 CS projects relevant to our research aim.</span><span> </span><span>20 CS projects (five females and fifteen males)</span><span> </span><span>consented to take part in the study.</span><span> </span><span>CS projects covered different academic disciplines, such as psychology, zoology, biology, ecology, linguistics, palaeontology, history and others.</span></p> <p><span>We constructed a questionnaire consisting of four items: self-identity and ties with CS, enablers of RRI in CS, limitations of RRI in CS and impact of RRI on CS. Interviews were conducted remotely. The interview language was English, except for one interview that was held in the participant’s first language and then translated into English. Though some interviews had minor language-specific flaws (for most informants English is not a native language), they did not interfere with understanding an informant.</span></p> <p><span>Each interview was audio-recorded, transcribed, and pseudonymized if such request was expressed in the informed consent. Average length of interview was 53 minutes. Non-pseudonymised full interviews contained an average of 6,409 words.</span></p> <p><span>Different strategies were used to validate all interview transcripts for purposes of data accuracy and clarifying inaudible responses (e.g. validation of half of transcripts involved two researchers, then validation of eleven transcripts involved interviewees). </span></p> <p><span>Nine informants allowed to publish pseudonymised transcripts while eight informants preferred to have non-pseudonymised transcripts published. Three informants disagreed to make publish a pseudonymised transcript as open research data.</span></p> <p><span> </span></p> <p><span>The complete research is published as Tauginienė, L., Butkevičienė, E., Heinisch, B., Massetti, L., Ugolini, F., Popov, S. (2024). Making Responsible Research and Innovation Meaningful in Citizen Science. </span><em><span>Science and Public Policy</span></em><span>. https://doi.org/10.1093/scipol/scae078 </span></p>
What does it take to generate new growth - Survey data on company perceptions on innovative behavior
<p>This data includes raw survey data, a codebook and the survey form for the survey <em>what does it take to generate new growth? </em>The survey focused on comprehensively mapping the Finnish companies growth outlooks and their underlying management practices and principles. The study creates an overview of top managers’ views on Finnish companies’ growth, innovativeness, and the ability for renewal. It allows us to identify what sets high-growing companies apart from others. The Codebook is associated with an SPSS and CSV file including the data.</p>
Survey on policies for innovation in the Global South, 2022.
<p>In July-August 2022, the <a href="acceleratorlabs.undp.org">UNDP Accelerator Labs</a> launched a survey to get a big picture view on the work that its global network of 91 labs was doing to support innovation ecosystems. The results were surprisingly clear-cut and coherent.</p> <p>First, <strong>we learned that a solid majority of Labs had partnered with governments to deploy interventions in support of national innovation ecosystems, or was planning to do so in the near future</strong>. We were looking at a surge of government investment in innovation across the Global South. Furthermore, these Global South governments were looking beyond the usual Global North example of policies to support innovation and learning from each other.</p> <p>Second, <strong>we learned that UNDP was widely recognized as the leading organization in supporting Global South governments in this journey</strong>. And third, <strong>we learned that collaboration with governments that have invested in innovation becomes smoother and more impactful</strong>.</p> <p>This Zenodo entry contains a file aggregating all the responses to that survey. </p>
Datasets from Analysis of the strategic management of science, research and innovation thesis
<p>Datasets were obtained from Czech R&D information system and used in my diploma thesis. The thesis (in Czech language) explores system of governance in R&D in the Czech republic and his impacts on the field of molecular biology in the period of 1995-2014.</p>
Enhancing Crowd Creativity as Innovation via Teamwork: Dataset
<p>These data files correspond to the results produced in the following paper currently accepted for publication.</p> <p>Pradeep K. Murukannaiah, Nirav Ajmeri, and Munindar P. Singh. 2022. Enhancing Creativity as Innovation via Asynchronous Crowdwork. In Proceedings of the 14th ACM Web Science Conference. Pages 1--9. To Appear.</p> <p>----------<br> Data files<br> ----------</p> <p>* all_scenarios.csv: 1,823 scenarios produced by MTurk workers</p> <p>* rated_scenarios.csv: 639 scenarios rated for creativity by three authors (700 scenarios were randomly selected for rating. 61 scenarios of these 700 scenarios were unclear or irrelevant and thus were discarded)</p> <p>* creativity.csv: data for RQ1 (Creativity)</p> <p>* personality-creativty.csv and team-composition-creativity.csv: data for RQ2 (Personality)</p> <p>* efficiency.csv: data for RQ3 (Efficiency)</p> <p>* emotions.csv: data for RQ4 (Emotions)</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>
European Education Provision on Public Procuement of Innovation: find your study course
<p>The growth of procurement and attracting future leaders can be enhanced through the visibility of procurement education. With the goal to map procurement education, PROCEDIN surveyed universities across Europe. The project partners compiled data on <a href="https://procedin.eu/database-of-european-education-provision/">European universities that provide master’s or bachelor’s level education in procurement</a>, sustainability, and entrepreneurship. In the present database, there are 114 different universities from 27 different countries, which together offer 1679 courses.</p> <p>In this dataset you can find all the opportunities for training in POI in European universities and beyond.</p> <p> </p>
Data for: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.
<p>Raw data set for Npj Regenerative Medicine article: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.</p>
Innovative forest products in the circular bioeconomy: online survey questionnaire and dataset
<p>This upload includes the dataset and questionnaire for an online survey with stakeholders, as part of a case study done for the BioMonitor project. The survey 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>
A controlled vocabulary for research and innovation in the field of Artificial Intelligence (AI)
<p><strong>A controlled vocabulary for research and innovation in the field of Artificial Intelligence (AI)</strong></p> <p>This controlled vocabulary of keywords related to the field of Artificial Intelligence (AI) was built by SIRIS Academic in collaboration with ART-ER (the R&I and sustainable development in-house agency of the Emilia-Romagna region in Italy) and the Generalitat de Catalunya (the regional government of Catalonia, Spain), in order to identify AI research, development and innovation activities. The work was carried out by consulting domain experts' advice and it was ultimately applied to inform regional strategies on AI and research and innovation policy.</p> <p>The aim of this vocabulary is to enable one to retrieve texts (e.g. R&D projects and scientific publications) featuring the concepts included in the present vocabulary in their titles and abstracts, assuming that these records have a certain contribution of applications, techniques and issues, in the domain of AI.</p> <p>The present effort was carried out because, despite the high number of contributions and technological developments in the field of AI, there is no closed or static vocabulary of concepts that allows to unequivocally define the boundaries of what should be considered “an Artificial Intelligence intellectual product” (or what should not). Indeed, the literature presents different definitions of the domain, with visions that could be contradictory. AI encompasses today a wide variety of subdomains, ranging from general purpose areas such as learning and perception to more specific ones such as autonomous vehicle driving, theorem proving, or industrial process monitoring. AI synthesises and automates intellectual tasks, and is therefore potentially relevant to any area of human intellectual activity. In this sense, it is a genuinely universal and multidisciplinary field. AI draws upon disciplines as diverse as cybernetics, mathematics, philosophy, sociology and economics.</p> <p>As a ground for the construction of the AI controlled vocabulary, an initial set of concepts was taken from different subdomains of the <em>ACM Computing Classification System 2012, </em> to define the boundaries of the AI domain. Notably, although some relevant AI subdomains have an independent category in the ACM taxonomy outside of AI, they have been included in the list of subdomains. In order to align the ACM taxonomical definition with the Catalan Strategy of AI, <em>CATALONIA.AI</em>, in <em>version 1 </em>of this resource the emerging area of AI Ethics was included in the vocabulary, while some other categories which are not relevant for the objectives were removed from the subdomains list. In the current <em>version 2</em>, the classification and the labels of the subdomains have been revised because of the evolution of the field. Some fields have been grouped in order to reduce the overlap between subdomains and to provide a taxonomy that makes more sense for the analysis of R&I ecosystems. </p> <p>The different subdomains in the versions are presented in the following table:</p> <table> <tbody> <tr> <td><strong>Version </strong></td> <td><strong>Subdomains</strong></td> </tr> <tr> <td> <p><em>Version 2</em></p> </td> <td> <p>(1) Machine learning and deep learning; (2) Computer Vision; (3) Natural Language Processing and speech recognition; (4) Intelligent agents, planning, scheduling, problem-solving, control methods, and search; (5) Expert Systems, Knowledge representation and reasoning; (6) AI Ethics.</p> </td> </tr> <tr> <td><em>Version 1</em></td> <td>(1) General, (2) Machine Learning, (3) Computer Vision, (4) Natural Language Processing, (5) Knowledge Representation and Reasoning, (6) Distributed Artificial Intelligence, (7) Expert Systems, Problem-Solving, Control Methods and Search and (8) AI Ethics.</td> </tr> </tbody> </table> <p>Although a keyword rule-based approach suffers from the major shortcomings of not capturing all the lexical and linguistic variants of specific concepts nor the context of the words - namely, keyword-based approaches would miss relevant texts if the specific pattern is not matched during the search - the present vocabulary allowed us to obtain fairly good results, due to the specificity of the concepts describing the AI domain. Furthermore, an understandable and transparent controlled vocabulary allows a better control of the final results and the final definition of the domain borders. Also, a plain list of terms allows a much easier and interactive engagement of interested stakeholders with different degrees of knowledge (such as, for instance, domain experts, policy-makers and potential users) who can make use of vocabulary to retrieve pertinent literature or to enrich the resource itself.</p> <p>The vocabulary has been built taking advantage of advanced language models and resources from knowledge datasets such as arXiv, DBpedia and Wikipedia. The resulting vocabulary comprises 833 keywords, and has been validated by experts from several universities in Emilia-Romagna and Catalonia.</p> <p>The <em>version 0.5</em> of this resource was developed by the SIRIS Academic in 2019 in collaboration with ART-ER, Emilia-Romagna (Quinquillá et <em>al.</em>, 2020), the <em>version 1 </em>was the result of an update done in 2020 in collaboration with the Generalitat de Catalunya, and the current version (<em>version 2</em>) has resulted in 2021 from the collaboration with ART-ER and the integration of an additional set of keywords provided by the <em>Artificial Intelligence and Intelligence Systems (AIIS)</em> Laboratory of the CINI (<em>Consorzio interuniversitario nazionale per l’informatica </em>based in Rome, Italy).</p> <p>The methodology for the construction of the controlled vocabulary is presented in the following steps:</p> <ol> <li> <p>An initial set of scientific publications was collected by retrieving the following records as a weakly-supervised (in the sense that records are linked to AI by their taxonomy and not by a manual label) dataset in the domain of Artificial Intelligence :</p> <ol> <li> <p>Publications from Scopus with the keyword “Artificial Intelligence”</p> </li> <li> <p>Publications from arXiv in the category “Artificial Intelligence”</p> </li> <li> <p>Publications in relevant journals in the scientific domain of “Artificial Intelligence”</p> </li> </ol> </li> <li> <p>An automated algorithm was used to retrieve, from the APIs of DBpedia, a series of terms that have some categorical relationships (i.e. those that are indexed as “sub-categories of”, “equivalent to”, among other relations in DBpedia) with the Artificial Intelligence concept and with the AI categories in the ACM taxonomy. The DBpedia tree has been exploited down to the level 3, and the relevant categories have been manually selected (for instance: <em>Classification algorithms</em>,<em> Machine learning</em> or <em>Evolutionary computation</em>) and others were ignored (for instance: <em>Artificial intelligence in fiction</em>, <em>Robots</em> or <em>History of artificial intelligence</em>) because they were not relevant, or not specifically in the domain.</p> </li> <li> <p>The keywords in publications in the dataset were extracted from the keyword sections and from the abstracts. The keywords with a higher <em>TF-IDF</em>, using an <em>IDF</em> matrix in the open domain, have been selected. The co-occurrence of keywords with categories in specific AI subdomain and a clusterization of the main keywords has been used for a categorization of the keywords at the thematic level.</p> </li> <li> <p>This list of keywords tagged by thematic category has been manually revised, removing the non-pertinent keywords and changing the wrong categorizations by fields.</p> </li> <li> <p>The weak-supervised dataset in the domain of Artificial Intelligence is used to train a Word2Vec (Mikolov <em>et al.</em>, 2013) word embedding model (a machine learning model based on neural networks).</p> </li> <li> <p>The terms’ list is then enriched by means of automatic methods, which are run in parallel: </p> <ol> <li> <p>The trained Word2Vec model is used to select, among the indexed keywords of the reference corpus, all terms “semantically close” to the initial set of words. This step is carried out to select terms that might not appear in the texts themselves, but that were deemed pertinent to label the textual records.</p> </li> <li> <p>Further, terms that are mentioned in the texts of the reference corpus and that are valued by the trained Word2Vec model as “semantically close” to the initial set of words are also retained. This step is performed to include in the controlled vocabulary a series of terms that are related to the focus of the SDGs and which are used by practitioners.</p> </li> </ol> </li> <li> <p>The final list produced by steps 2-6 is manually revised.</p> </li> </ol> <p> </p> <p>The definition of the vocabulary does not, per se, allow to identify STI contributions to AI: this activity in fact boils down to actually matching the terms in the controlled vocabulary to the content of the gathered STI textual records. To successfully carry out this task, a series of pattern matching rules must be defined to capture possible variants of the same concept, such as permutations of words within the concept and/or the presence of null words to be skipped. For this reason, we have carefully crafted matching rules that take into account permutations of words and that allow words within concept to be within a certain distance. Some relatively ambiguous keywords (which may match unwanted pieces of text), have a set of associated “extra” terms. These “extra” terms are defined as further terms that must co-appear, in the same sentence, together with their associated ambiguous keywords.</p> <p>Finally, each keyword in the vocabulary was assigned one or more AI subdomains, so that the vocabulary can also be used to tag collections of texts within narrower AI sub-domains. In order to complement the alignment between keywords and subdomains, a set of subdomain-specific keywords have been defined to better capture the scope of the subdomains. These allow better characterization of subdomains that are more difficult to define only by means of unambiguous specific concepts, or that overlap with the wide “machine learning” subdomain (example: machine learning applied to object recognition or text translation). The alignment between keywords and subdomains, and these keyword lists of each subdomain, have been applied to capture AI subdomains in research outputs. Through this classification process, we have identified projects and publications related to AI, with a focus on mapping the research competencies in the AI domain in Emilia-Romagna. The resulting research records have been reviewed by experts in the domain, given the occurrence of some false positives, which have been used to improve the approach.</p> <p>The final controlled vocabulary has been evaluated with an external test set, proposed by (Dunham <em>et al.,</em> 2020). The test set consists of the abstract of 10,606 papers published in the arXiv repository, of which 1,076 within the Artificial Intelligence subcategories and 9,530 in arXiv categories other than Artificial Intelligence. Evaluating the controlled vocabulary on this data set, we observe accuracy of .94. However, because the pertinence of these publications to the field of AI is based solely on their taxonomic classification (i.e., on whether they are classified in the arXiv within Artificial Intelligence and not on a manual labelling), this evaluation can only yield an orientative performance assessment.</p> <p>The version 2 includes new keywords extracted from the (1) re-training of the enrichment pipeline (steps 5-6 in the methodology) considering as initial set of terms the version 1 of the vocabulary on a reference corpus of new publications, and (2) from the flat keywords list provided by the <em>Artificial Intelligence and Intelligence Systems</em> <em>(AIIS)</em> Lab of CINI (Consorzio interuniversitario nazionale per l’informatica). The keywords in (2) have been cleaned by calculating precision and f-measure on the dataset (Dunham et al., 2020), selecting those keywords with the highest scores, and being manually validated a posteriori.</p> <p>The AI controlled vocabulary has been applied in two practical cases, which have the purpose of identifying skills, stakeholders and capabilities, of a specific research ecosystem at the regional level. See the following references:</p> <ul> <li> <p>Quinquillá, Arnau, Duran-Silva, Nicolau, Massucci, Francesco Alessandro, Fuster, Enric, Rondelli, Bernardo, Bologni, Leda, … Moretti, Giorgio. (2020). Text mining to identify skills, stakeholders and capabilities: the case of Artificial Intelligence in Emilia-Romagna. Zenodo. <a href="http://doi.org/10.5281/zenodo.3606342">http://doi.org/10.5281/zenodo.3606342</a>. Poster presented at: World Open Innovation Conference 2019 (WOIC); 11th december 2019, Rome, Italy.</p> </li> <li> <p>Bigas, E., Duran, N., Fuster, E., Parra, C., Fernández, T. (2021): “Anàlisi de l’especialització en intel·ligència artificial”. Col·lecció Monitoratge de la RIS3CAT, Generalitat de Catalunya <a href="http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf">http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf</a></p> </li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <ul> <li> <p>Tatiana Fernández (Direcció General de Promoció Econòmica, Competència i Regulació, de la Generalitat de Catalunya), </p> </li> <li> <p>Daniel Marco, Daniel Santanach and Eduard Balbuena (Departament de Polítiques Digitals i Administració Pública, de la Generalitat de Catalunya) </p> </li> <li> <p>Albert Sabater (Observatori d’Ètica en Intel·ligència Artificial i Universitat de Girona)</p> </li> <li> <p>Leda Bologni, Lucia Mazzoni and Giorgio Moretti (Art-ER)</p> </li> <li> <p>Prof. RIta Cucchiara and Dr. Lorenzo Baraldi (Università degli Studi di Modena e Reggio Emilia)</p> </li> <li> <p>Artificial Intelligence and Intelligence Systems (AIIS) Lab of CINI (Consorzio interuniversitario nazionale per l’informatica)</p> </li> </ul> <p> </p> <p><strong>Bibliography</strong></p> <p>Bigas, E., Duran, N., Fuster, E., Parra, C., Fernández, T. (2021): “Anàlisi de l’especialització en intel·ligència artificial”. Col·lecció Monitoratge de la RIS3CAT, Generalitat de Catalunya <a href="http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf">http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf</a></p> <p>Dunham, J.W., Melot, J., & Murdick, D. (2020). Identifying the Development and Application of Artificial Intelligence in Scientific Text. ArXiv, abs/2002.07143. Available at: <a href="https://arxiv.org/abs/2002.07143">https://arxiv.org/abs/2002.07143</a></p> <p>Mikolov, Tomas & Corrado, G.s & Chen, Kai & Dean, Jeffrey. (2013). Efficient Estimation of Word Representations in Vector Space. 1-12.</p> <p>Quinquillá, Arnau, Duran-Silva, Nicolau, Massucci, Francesco Alessandro, Fuster, Enric, Rondelli, Bernardo, Bologni, Leda, … Moretti, Giorgio. (2020). Text mining to identify skills, stakeholders and capabilities: the case of Artificial Intelligence in Emilia-Romagna. Zenodo. <a href="http://doi.org/10.5281/zenodo.3606342">http://doi.org/10.5281/zenodo.3606342</a>. Poster presented at: World Open Innovation Conference 2019 (WOIC); 11th december 2019, Rome, Italy.</p>
Online survey of needs and challenges of innovation ecosystems and intermediaries for taking up activity in the EU space sector
<p>The present dataset was generated as part of the "Needs and challenges of innovation ecosystems and intermediaries for taking up activity in the EU space sector" of the H2020 <a href="http://innorbit.eu">InnORBIT project</a>.</p> <p>The aim of this study was to identify and explore the available and missing skills of innovation intermediaries to provide business support services to innovators within their local ecosystems to develop commercial activity in space. The assessment of skills was based on a baseline framework encompassing a wide array of skills and competencies innovation intermediaries are supposed to possess in order to provide effective business support services to space innovators. The skills of the baseline framework are grouped into five broad categories: (i) space industry knowledge, (ii) business assessment knowledge, (iii) business support skills, (iv) organisational and digital skills and (v) soft skills. The baseline framework was originally developed by the InnORBIT consortium through research in related works of EU funded projects and publications and validated through a series of 15 interviews with top-level executives of organisations across the CEE and SEE area, belonging to the two target groups of the study (i.e., innovation intermediaries and innovators). An online survey was deployed from May 26th to June 18th using the EU Survey tool, to innovation intermediaries and innovators across the EU and CEE/SEE countries in particular. Two online questionnaires were developed building on the baseline skills framework - the first intended for innovation intermediaries asking them to perform a self-assessment of their skills in terms of providing business support services and the second targeting innovators, asking them to state their perception on how innovation intermediaries they have worked with, perform in each of the skills.</p> <p>The dataset contains four files:</p> <p>1. Zip file including the transcripts from 6 interviews with space innovators in Eastern Europe for the evaluation of the baseline framework of skills.</p> <p>2. Zip file including the transcripts from 9 interviews with innovation intermediaries in Eastern Europe for the evaluation of the baseline framework of skills.</p> <p>3. a pdf file of the digital questionnaires developed in EU Survey deployed to innovation intermediaries and innovators in the region</p> <p>4. An excel file with 104 valid responses collected from the online survey (56 innovation intermediaries and 48 innovators) across 16 EU countries / 21 countries total.</p> <p>The dataset contains only non-sensitive anonymised information and is in full compliance with the GDPR provisions. Any information leading to the identification of participants in activities (interviews, survey) is either modified or omitted and deonted with brackets.</p>
ROSEWOOD4.0 Best practices & innovations: CSV file
<p>ROSEWOOD4.0 harnesses digital solutions and knowledge transfer along the forest value chain to reinforce the sustainability of forest resilience and wood mobilisation in Europe. This CSV file includes the complete information of 279 Factsheets of <em>Best practices and Innovations</em> (BP&I) in forest management, wood supply and forest-based industries exploiting relevant digital technologies and industry 4.0 solutions. All these BP&I were jointly identified and validated by the project partners.</p> <p>The BP&I factsheets are published in a <em>Knowledge Platform for Regional Forest Innovation</em>, which is an open, multilingual repository (currently 13 European languages) created by the consortium to enable the widest possible dissemination of results. Spreading this knowledge in Europe will help practitioners and professionals to gain a better understanding of how the digital transformation in forestry can improve sustainable forest management and ecosystem resilience and thus benefit a more competitive forest-based sector in rural regions.</p> <p>The platform is accessible at: https://www.forestinnovationhubs.rosewood-network.eu</p> <p> </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>
Human resources for research and innovation in Italy
<p>Human resources play a crucial role in enabling research and innovation. Key players include university students, PhD and master graduates, researchers holding a European grant supporting excellent researchers in carrying out ground-breaking, high-risk, high-gain, frontier research projects, entrepreneurs engaged in spin-offs, startups or innovation project supported by Horizon 2020 <em>SME instrument</em> grants.</p> <p>Data is generally available, but often it is not easy to use due to different formats and vocabularies and the variety of geographical references (city names, province, region or zip codes).</p> <p>This file collection is part of ongoing research work carried out by the sustainability unit at Area Science Park. Data is collected from a variety of open sources, curated and prepared for further analysis. The focus is on Italy; geographical references use EUROSTAT NUTS-2 and NUTS-3 taxonomy.</p> <p>Data available: </p> <ul> <li>Maps of NUTS2 and NUTS2 regions in Italy</li> <li>NUTS2 and NUTS3 names in Italian</li> <li>Universities </li> <li>Phd and Masters graduates since 2010</li> <li>University spin-offs </li> <li>Innovative Startups</li> <li>Horizon 2020 grants for researchers: "<em>Marie Skłodowska Curie</em>" and "<em>European Research Council</em>"</li> <li>Horizon 2020 grants "<em>SME instrument</em>"</li> </ul> <p>Python scripts for data preparation are available in script.zip; development version is available on <a href="https://gitlab.com/area-science-park-sustainability/it_regional_innovation">this GitLab repository</a><br> Some examples of visual representation of the data are available in .pdf format and as <a href="https://app.powerbi.com/view?r=eyJrIjoiMWMyMjA1OWQtMzJmNi00NWJmLTk1OTctMzczZWUxYjYzYzFmIiwidCI6ImQ0YWFmY2E2LWJmMzUtNDUxNS1iMDZhLTQ5NzNjZGZiYmVkMyIsImMiOjh9&pageName=ReportSection3090d63ae7727ef701e8">online interactive visualization report.</a></p>
iGEM: a model system for team science and innovation
<p>This dataset is extracted from the <strong>international Genetically Engineered Machine (iGEM) competition </strong>between years 2008 and 2018, and can be used as a model system for studying team science and innovation. It is described at length in <a href="https://arxiv.org/abs/2310.19858">this article</a>.</p> <p>The dataset encompass detailed records from the iGEM competition, capturing various aspects of team participation and achievements. Specifically, the <strong>Team Information</strong> dataset (<strong>teams_table.csv</strong>) provides insights into team characteristics and achievements, including medal status and region of origin. <strong>User Information</strong> (<strong>users_table.csv</strong>) offers a look into individual participants, detailing their roles in the team. <strong>Awards Information</strong> (<strong>awards_table.csv</strong>) and <strong>Medal Criteria</strong> (<strong>medals_criteria.csv</strong>) lay out the awards teams have garnered and the standards for medal attainment. The <strong>BioBricks Information</strong> (<strong>biobricks_table.csv</strong>) corresponds to the BioBrick sequences associated with each team, while <strong>Wiki Edits</strong> (<strong>wikis_table.csv</strong>) tracks the changes made by users on their team's (wiki) lab notebook. Finally, the <strong>Collaboration Network</strong> (<strong>collaboration_network.csv</strong>) corresponds to the weighted directed inter-team collaboration network collected using team mentions across team wikis.</p> <p>In addition to the structured dataframes above, we provide in <strong>team_wikis_full_text.zip</strong> the full texts of the wiki pages from the digital laboratory notebooks collaboratively edited by iGEM teams in the forms of wiki instances. There is a folder for each year from 2008-2018 and within which there are individual folders for each team. Each team folder has a file denoting the pagelist and two files for each page. One file is the html content, and the other the text content, extracted using the "KeepEverythingExtractor" option in the <em>boilerpipe.extract</em> library for processing and removing boilerplate content after webscraping.</p> <p> </p>
Interviews for New Business Models for Pharmaceutical Innovation and Access to Medicines - Rare Diseases
<p>These supplementary materials represent the partial dataset in the form of semi-structured interviews, collected and analyzed in the research article "Alternative innovation models of pharmaceutical development for rare disease drugs: how (and) do they work?: A qualitative study". This article is one of the outcomes of the "New Business Models for Pharmaceutical Innovation and Global Access to Medicines" research project, conducted at the Global Health Center, within the Geneva Graduate Institute. The dataset contains 10/11 interviews collected and used in this article, which are published with the informed consent of the interviewees.</p> <p>Details about the research project can be found at: <a href="https://www.graduateinstitute.ch/NBM">https://www.graduateinstitute.ch/NBM</a></p>
The evolution of gender monitoring and its challenges in Research and Innovation in Europe: She Figures reports analysis dataset
<p><span>The article delves into the European Commission's flagship initiative on gender monitoring in science and innovation, offering a responsible metrics perspective and drawing on equality policy literature. Over two decades, the initiative has evolved from competitiveness-related justifications to more transformative objectives related to equality policy evaluation, with the measurement areas and policy focus also undergoing changes. While there has been notable progress, the article points out a logic of invisibility in how dimensions and indicators are conceptualised and their data sources and interpretation. However, it also highlights a significant improvement in the information available. The article suggests that the contextualisation of the process could be enhanced to better integrate it into the policy-making cycle, a crucial area for further research. It concludes with proposals for future gender monitoring science and innovation. The aim is to offer an encouraging vision of monitoring that counts more on who is monitored and in opening up the debates instead of closing them.</span></p>
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.