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43 results for “Smart cities”
Machine Learning-based Energy Optimisation in Smart City Internet of Things
<p>Dataset for the paper Machine Learning-based Energy Optimisation in Smart City Internet of Things accepted for publication at The First International Workshop on the Integration between Distributed Machine Learning and the Internet of Things, ACM MobiHoc 2023.</p> <p>The dataset is collected from a real-world deployment of environmental sensors in the city of Bern, Switzerland. Our proposed approach can be applied to determine the tradeoff between the accuracy of temperature measurements and reducing the energy consumption for a single sensor; hence, without loss of generality, the evaluation is conducted on a dataset from a single sensor. Overall, we acquired 3697 measurements, each long 138 seconds. To correct the measurements, we set the maximum ventilation duration of 138 seconds, during which the multivariate time series of humidity and temperature sensor values are recorded together with their corresponding timestamps. The sensor values are recorded at a fixed frequency.</p> <p>From this raw data, we created the training and test sets through data augmentation to simulate time series of different lengths. Namely, for each measurement, we generated 136 samples with the increasing length of measurement time-series, padding the residual time-series length with zeros until reaching a time-series length of 137.</p> <p>We released the source code and trained models on the following GitHub repository https://www.github.com/ricsamikwa/ml-iot-smartcitytemp</p>
Flick SMART multi-catch rodent station and bait station data sets: Council of the city of Sydney, October 2019 to July 2020
<p>Shortly after the enactment of preventative measures aimed at limiting the spread of COVID-19, local governments and public health authorities around the world reported an increased sighting of rats. We combined multi-catch rodent station data, rodent bait stations data, and rodent-related residents' complaints data to explore the effects that social distancing and lockdown measures might have had on the rodent population within the City of Sydney, Australia. We found that rodent captures, activity, and rodent related residents' complaints increased during the COVID-19 related lockdown period, followed by a steep decline post-lockdown. We found no changes in the geographical distribution of any of our indices of rodent abundance. We hypothesize that lockdown measures resulted in an increase in rodent activity driven by a reduction in human-derived food resources. This might have increased the mortality rate triggering a population crash. There is a high chance that the surviving individuals might be rodenticide resistant. It is possible that the onset of COVID-19 might have disrupted commensal rodent populations, with profound implications for the future management of these species. Here we make available multi-catch rodent station data and rodent bait stations data. We do not include rodent-related residents' complaints data due to potential identifier data that could be seen as a breach of private information sharing.</p>
SSH CENTRE - Mini-reports: Focus groups on "100 Climate-Neutral and Smart Cities by 2030"
<p>SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU's transition to carbon neutrality. </p><p>SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.</p><p>This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU's journey to a sustainable future.</p><p>The aim of the focus groups was to gather citizen's perspectives, their hopes, concerns and ideas related to the Horizon Mission of Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030. The focus group discussion topics while remaining close to the Mission, avoid specific technical references to allow citizens to contribute based on their differing levels of understanding. As part of the SSH CENTRE project, in total, four focus group series will be conducted relating to Adaptation to Climate Change; Restore our Ocean and Waters by 2030; 100 Climate-Neutral and Smart Cities by 2030; A Soil Deal for Europe. </p><p>Notes were taken during each focus groups and turned into mini-reports. These mini-reports sum up the essence of the discussion: the participants' main ideas and some interesting quotes. </p>
IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review
<p>Data collected from primary studies.</p> <p>We 1) identified the main approaches and services that are applied in the city and SGB-level SWM systems, 2) listed sensors and actuators and analyzed their application in various types of SWM systems, 3) listed the direct and indirect stakeholders of the SWM systems, 4) identified the types of data shared between the SWM systems and stakeholders, and 5) identified the main promising directions and research gaps in the field of SWM systems.</p>
PERCEIVE: WP4: Spatial determinants of policy performance and synergies: City smartness
<p>The dataset contains data on both smart cities projects and smart cities characteristics to be used for the computation of the composite indicators using the Stochastic Multi-criteria Acceptability Analysis (SMAA) methodology. The dataset reports the data used to analyze the concept of a ‘smart city’ along two main dimensions. First, it contains the data to operationalize the concept of smart city along the dimensions elaborated in the ongoing literature including proxies for networked infrastructure to improve economic and political efficiency and enable social and cultural development, the extent of business-led development, the social inclusion of various urban residents in public services, extent of high-tech and creative industries, social and relational capital, and social and environmental sustainability. Second, it reports the results of the analysis using the above dimensions to compute a new index of smartness and quality of life based on SMAA. </p> <p>PLEASE NOTE that the file ' PERCEIVE_WP4_T4-1_SmarCities_20190704_v01.csv' contains data from Eurostat. For those data re-use involves normalisation of the raw data according to the max-min procedure.</p>
Support data for article "The concept of network resource control of a 5G cluster focused on the smart city's critical infrastructure needs"
<p>Support data for article:</p> <div>V. Kovtun, K. Grochla, and K. Połys, “The concept of network resource control of a 5G cluster focused on the smart city’s critical infrastructure needs,” Alexandria Engineering Journal, vol. 94. Elsevier BV, pp. 248–256, May 2024. doi: 10.1016/j.aej.2024.03.038.</div> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
SPARCS_WP3_Espoo_City_SPARCS-WP3 participants in Smart Otaniemi events
<p>Number of participants in Smart Otaniemi, and other related, events completed during SPARCS Work Package 3 (WP3)</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>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>
Smart City Tracker: A living archive of smart city prevalence
<p>An interactive platform where users can track and explore local governments' smart city implementation in the United States.</p>
Flick SMART multi-catch rodent station and bait station data sets: Council of the city of Sydney, October 2019 to July 2020
Open the record for dataset details and reuse information.
LoRaWAN for smart city IoT deployments: A long term evaluation
<p>Data set to accompany paper titled LoRaWAN for smart city IoT deployments: A long term evaluation</p>
Application of Artificial Intelligence in the Development of Smart Cities: An Analysis of Trends and the Research Agenda
Open the record for dataset details and reuse information.
Research trends on sustainable development in smart cities
Open the record for dataset details and reuse information.
Framework and results of "Performance evaluation in the Inter-institutional collaboration context of hybrid smart cities"
<pre>Comparative study of the Lugano and Turin framework and policy and agenda elements.</pre>
Datasets for Digital tools and gamification to improve the consumers' waste recycling activity in smart cities
<p>In the data description you can find following:</p> <p>1 Methods of choosing participants</p> <p>2 Translations of survey questions<br>2.1 Survey A<br>2.2 Survey B<br>2.3 Survey C</p> <p>3 Demographic analysis<br>3.1 Respondents' age<br>3.2 Household sizes and dwelling types</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>
The Smart City Active Mobile Phone Intervention (SCAMPI)
ClinicalTrials.gov study NCT03086837. IPD Sharing: NO. Countries: 1. Publications: 3.
Cluster Data for creating smart city typology
<p>A data set used to create a typology of smart city apps</p>
5G-PICTURE_Smart_City_Demo_mmWave_60GHz_dataset
<p>In the context of 5G-PICTURE, three demos were performed, one being the Smart City demo. For this demonstration, three 60 GHz devices (one AP and two STA) were deployed to provide the high throughout Point-to-MultiPoint (P2MP) wireless connectivity for two use cases, the Virtual Reality and the Safety Camera. The two links were stationary with a distance of 60 metres and 80 metres, respectively, providing the wireless connectivity to the tents, specifically installed in the Millennium Square for the abovementioned use cases. In particular, for the mmWave (60GHz) backhaul links, the end-to-end throughput and latency have been measured in the field testing in days before the demo.</p> <p>For more details, interested reader should refer to the project website (https://www.5g-picture-project.eu) and deliverable D6.3.</p>
5G-PICTURE_Smart_City_Demo_UNIVBRIS
<p>Data Set collected during the 5G-PICTURE Smart City Demonstration in Bristol </p> <p>Before, during and after the Smart City demonstration several datasets were collected at Millennium Square in Bristol. Several data collection points has been captured as: i) data measured from probes within the network especially form the key network switches and main service router part of the test network ii) end to end data capture between UEs and the host server inside Smart Internet Lab Data Centre.</p> <p>For more information please refer to the project web site https://www.5g-picture-project.eu/ and deliverables D6.3 and D1.1 </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.