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1,146 results for “collaboration;”
Simulation of collaboration networks in software development
<p>Dataset resultant from Master Thesis 'Simulation of collaboration networks in software development' authored by José Miguel Gomes.</p>
The Smart City as a Field of Innovation: Effects of Public-Private Data Collaboration on the Innovative Performance of Small and Medium-Sized Enterprises in China
<p>The Smart City as a Field of Innovation: Effects of Public-Private Data Collaboration on the Innovative Performance of Small and Medium-Sized Enterprises</p> <p><strong>in China</strong></p> <p> </p> <p>Xiaohui Jiang<sup>1</sup>, Masaru Yarime<sup>1*</sup></p> <p><sup>1</sup> Division of Public Policy, The Hong Kong University of Science and Technology</p> <p>E-mail: <a href="mailto:yarime@ust.hk">yarime@ust.hk</a></p> <p> </p> <p><em>Keywords: Smart city, China, Data-driven innovation, Small and medium-sized enterprise, Public-private collaboration</em></p> <p> </p> <p>Extended Abstract</p> <p>Data is increasingly considered to be a key component in stimulating innovation (Cockburn, Henderson, and Stern, 2019). Numerous promising possibilities have been opened up by rapidly emerging, data-intensive technologies, including the Internet of Things (IoT) and artificial intelligence. The analysis and interpretation of big data are critical in the growth of technology firms in terms of AI training and computing capabilities (Allam and Dhunny, 2019). Small and medium-sized enterprises (SMEs), with their limited resources internally, particularly face a serious challenge of implementing innovation that depends upon data.</p> <p>The smart city provides an important opportunity for creating data-driven innovation. Significant amounts of data are increasingly available from various sources through sophisticated devices and equipment scattered in smart cities. Many smart city projects across the globe provide rich opportunities for SMEs to explore data-driven innovation (Bresciani, Ferraris, and Del Giudice, 2018). China, in particular, has recently been active in collecting and utilizing various kinds of data in smart cities. The availability of and access to data help to improve the software development of firms in China, where massive amounts of data resources are held by the government (Beraja, Yang, and Yuchtman, 2022).</p> <p>In the process of smart city development, there are also many tasks that are complementary to each other, including connecting databases, building online platforms that connect different data coming from different data sources, operating online platforms, and providing products and services to citizens. These diverse kinds of tasks involved in smart city projects initiated by local governments have brought about new business opportunities for innovative SMEs in China. To implement the policies of encouraging the development of SMEs by the central government, municipal governments have introduced policies that give priority to SMEs in participating in smart city projects (Ministry of Industry and Information Technology, 2016). Those companies that have access to the data held by government agencies are expected to benefit from utilizing the rich data for creating innovative products and services.</p> <p>There were few empirical studies conducted, however, to examine how data are actually managed and provided in smart cities and how they affect companies’ innovative activities. It remains unclear how public agencies and private enterprises collaborate on data and how that influences the innovation performance of SMEs in China. In smart cities, different types of public-private collaboration are involved, including hardware purchase, platform building, platform operation, and data analysis. It is not yet well-understood how these different types of collaboration influence the innovative performance of SMEs.</p> <p>In this study, we intend to address how data are managed through collaboration between the government and companies in smart cities and how the mode of collaboration influences firms’ performance on innovation. By focusing on the case of SMEs in China, this research aims to shed light on what kinds of data are available and used in smart cities and how the government and enterprises collaborate on data to facilitate innovation.</p> <p>The analysis of this study utilizes data on more than eight million contracts extracted from the official procurement database of the government. Data on companies are assembled with regard to the registered capital, industry, software products, and patents in 1990-2021 from the Tianyancha website. A panel data is established with key characteristics of SMEs, software and patents outputs, and their record on obtaining different government contracts annually. The government contracts are divided into three categories, namely, data analysis, platform building, and equipment supply, based on keyword identifications. To deal with the unbalance between the treatment group (the companies that obtained government contracts) and the control group, we use propensity score matching (one-to-one nearest neighbor matching) to narrow down the sample size of the control group to that of the treatment group. Then we apply the traditional difference-in-difference (DID) and DID with multiple time periods (Callaway & Sant’Anna, 2021) methods to examine whether there are significant differences in innovative outputs of software products and patents before and after the companies receive government contracts. We also compare how the innovation performance of companies differs based on the types of contracts these companies obtain. That makes it possible to identify what kinds of data collaboration would be effective in improving the innovative performance of SMEs.</p> <p>Our preliminary analysis of average treatment effect suggests that obtaining the government contracts, especially research contracts and platform building type of contracts, can effectively help improve the innovation performance when comparing between the control and treatment group. After checking the common support graph and doing a balance test, the outcome is solid. In general, based on the average treatment effect on the treated group, innovation performance, as measured by the number of patents and software productions, has shown a significant increase for those companies that obtain a research type of contract, whereas the increase is smaller for the platform building contractor and slight for the equipment purchase contractor. Government purchase of equipment that contains data-intensive technologies promotes the use of these products in smart cities. That, however, does not involve any substantive exchange or transfer of data possessed by the government and would not significantly contribute to stimulating innovation at firms.</p> <p>This study will have useful implications for establishing public-private collaboration on data for facilitating innovation in smart cities. Specifically, government procurement should also be viewed as a policy tool for improving China's technological innovation. According to our findings, research and platform development can successfully assist firms in improving their innovation performance. As a result, the government should grant enterprises access to additional data resources and data management related tasks if they want to see more industry structural transformation, as stated in the government documents.</p> <p> </p> <p>References</p> <p>Allam, Z. and Z. A. Dhunny (2019). "On big data, artificial intelligence and smart cities."</p> <p><em>Cities</em>, <strong>89</strong>, 80-91.</p> <p>Beraja, Martin, David Y. Yang, and Noam Yuchtman (2022). "Data-intensive Innovation and the State: Evidence from AI Firms in China." Working Paper, January 11.</p> <p>Bresciani, S., A. Ferraris, and M. Del Giudice (2018). "The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) smart city projects." <em>Technological Forecasting and Social Change</em>, <strong>136</strong>, 331-338.</p> <p>Callaway, B., & Sant’Anna, P. H. (2021). Difference-in-differences with multiple time. periods. Journal of Econometrics, 225(2), 200-230.</p> <p>Cockburn, Ian M., Rebecca Henderson, and Scott Stern (2019). "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," Agrawal, Ajay, Joshua Gans, and Avi Goldfarb, eds. <em>The Economics of Artificial Intelligence: An Agenda</em>. Chicago: The University of Chicago Press.</p> <p>Ministry of Industry and Information Technology (2016). "The "Plan for Promoting the Development of Small and Medium-sized Enterprises (2016-2020)" was officially released, and promoting entrepreneurship and innovation will become a key task." Ministry of Industry and Information Technology, Beijing, China, June 28.</p>
Material from a collaborative analysis with young people. Insights into the analysis process of R&I Action #2 Youth Employment of the CoAct project
<p>The collaborative data analysis presented in this document was undertaken in the context of the research project CoAct (Co-designing Citizen Social Science for Collective Action) as part of the R&I Action #2 on Youth Employment. In this R&I Action, using Citizen Social Science as a research approach, young people aged between 15 and 21 years in employment measures in Vienna/Austria participated in collaborative social research activities. The visualisation of data analysis in the form of posters, is a selection of the collaborative analysis of qualitative expert interviews using the the “Stop and Go” (Harrasser, 2017) method. Following this method, audio recording of interviews, done by the young co-researchers themselves, are listened to and summarized as well as connected to the co-researchers’ own experience.</p> <p>The Creative Commons license and a recommendation regarding the citation of the material are provided in the document.</p> <p>For more information please contact veronika.woehrer@univie.ac.at</p>
Players of the Innovation Ecosystem: A Quantitative Analysis of the Scientific Collaboration of a State-Run Research Institute in Brazil
<p>Análise de colaborações em artigos do Instituto Nacional de Tecnologia. </p>
Assessing the influence of organizational factors on knowledge sharing in inter-firm collaborations
<p><span>Collaborations between media organisations are becoming an increasingly common practice in the field of journalism. Academic research, so far, mostly focused on large-scale investigations and communities of digital outlets, such as fact-checkers networks. However, new types of inter-firm partnerships are emerging, namely between legacy media and tech startups, towards media innovation and digital transformation. The paper to which this database is connected aims to advance the theoretical understanding of the relationship between collaborations and media innovation. We formulate an original analytical model to assess the influence of organisational factors of collaborations on knowledge sharing, a key condition for explorative innovation. Based on the experience of the Stars4Media programme, we present an empirical application of the analytical model (based on this data-set) to a case study of thirty collaborative projects involving seventy-six European media companies.</span></p>
Enhancing teacher collaboration
<p><em><strong>Version Stata 17 of the dataset. The dataset files: full_dataset.dta and do file have been split.</strong></em></p>
Podcast: Digitization and Research Collaborations. Tobias Hodel, Universität Bern
<p>Wie definiert man die Methodik in den Digital Humanities? Ein Gespräch über interdisziplinären Austausch und digitale Kooperationen im Forschungsbereich.</p> <p>How to define methodology in Digital Humanities? A conversation about interdisciplinary exchange and digital collaborations in the research field.</p> <p>Come si definisce la metodologia nelle Digital Humanities? Una conversazione sullo scambio interdisciplinare e sulle collaborazioni digitali nel campo della ricerca.</p> <p>Comment définir la méthodologie dans les Digital Humanities? Une discussion sur l'échange interdisciplinaire et la coopération numérique dans le domaine de la recherche.</p>
Quality of Pediatric Resuscitation in a Multicenter Collaborative
ClinicalTrials.gov study NCT02708134. IPD Sharing: NO. Countries: 15. Publications: 9.
Improving Continuous Renal Replacement Therapy Outcomes in Neonates and Infants Through Interdisciplinary Collaboration
ClinicalTrials.gov study NCT05161078. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Pan European Collaboration on Antipsychotic Naive Schizophrenia (PECANS)
ClinicalTrials.gov study NCT01154829. IPD Sharing: NO. Countries: 1. Publications: 7.
Learning Collaborative Versus Technical Assistance in Delivering a Palliative Care Program to Patients With Advanced Cancer and Their Caregivers
ClinicalTrials.gov study NCT04062552. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of the Impact of a Collaboration Between Hospital and Community Pharmacists at Hospital Discharge
ClinicalTrials.gov study NCT06902779. IPD Sharing: YES. Countries: 1. Publications: 15.
Collaborative Care With Smart Health Management Program for Patients With Chronic Illness
ClinicalTrials.gov study NCT03294057. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Collaborative Care in Posttraumatic Epilepsy
ClinicalTrials.gov study NCT05353452. IPD Sharing: NO. Countries: 1. Publications: 1.
A Collaborative Approach in Diabetes Foot Education - A Pragmatic Randomised Control Trial
ClinicalTrials.gov study NCT04278742. IPD Sharing: NO. Countries: 1. Publications: 2.
Effectiveness of Telepsychiatry-based Culturally Sensitive Collaborative Treatment of Depressed Chinese Americans
ClinicalTrials.gov study NCT00854542. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Collabri Flex - Effect of Collaborative Care for People With Anxiety Disorders in General Practice
ClinicalTrials.gov study NCT03113175. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Multiple Sclerosis-Collaborative Approach to Rehabilitation Effectiveness Study
ClinicalTrials.gov study NCT02137044. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Collaborative Adolescent Research on Emotions and Suicide
ClinicalTrials.gov study NCT01528020. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Methadone Patient Access to Collaborative Treatment
ClinicalTrials.gov study NCT06556602. IPD Sharing: YES. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.