Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
43
datasets available to search
ShareScore release 0.9.0
Dataset results
43 results for “Smart Cities”
Dataset: maturity of transparency of open data ecosystems in 22 smart cities
<p>This dataset contains data collected during a study "<a href="https://www.sciencedirect.com/science/article/pii/S2210670722002281?casa_token=8xHhtKug0xEAAAAA:POKIQswXhPdbwqgi5A8q98xitcUju_VS8T7oSP6YujXdABZlc5bNn4vEHzHGoxoW16mT6hA-HZ4#!">Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities</a>" (Sustainable Cities and Society (SCS), vol.82, 103906) conducted by Martin Lnenicka (University of Pardubice), Anastasija Nikiforova (University of Tartu), Mariusz Luterek (University of Warsaw), Otmane Azeroual (German Centre for Higher Education Research and Science Studies), Dandison Ukpabi (University of Jyväskylä), Visvaldis Valtenbergs (University of Latvia), Renata Machova (University of Pardubice).</p> <p>This study inspects smart cities’ data portals and assesses their compliance with transparency requirements for open (government) data by means of the expert assessment of 34 portals representing 22 smart cities, with 36 features.</p> <p>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities.</p> <p>***Purpose of the expert assessment***<br> The data in this dataset were collected in the result of the applying the developed benchmarking framework for assessing the compliance of open (government) data portals with the principles of transparency-by-design proposed by Lněnička and Nikiforova (2021)* to 34 portals that can be considered to be part of open data ecosystems in smart cities, thereby carrying out their assessment by experts in 36 features context, which allows to rank them and discuss their maturity levels and (4) based on the results of the assessment, defining the components and unique models that form the open data ecosystem in the smart city context.</p> <p>***Methodology***<br> Sample selection: the capitals of the Member States of the European Union and countries of the European Economic Area were selected to ensure a more coherent political and legal framework. They were mapped/cross-referenced with their rank in 5 smart city rankings: IESE Cities in Motion Index, Top 50 smart city governments (SCG), IMD smart city index (SCI), global cities index (GCI), and sustainable cities index (SCI). A purposive sampling method and systematic search for portals was then carried out to identify relevant websites for each city using two complementary techniques: browsing and searching.<br> To evaluate the transparency maturity of data ecosystems in smart cities, we have used the transparency-by-design framework (<a href="https://www.sciencedirect.com/science/article/pii/S0736585321000447?casa_token=7K8YGcYWbQcAAAAA:_HnV50rvxwQmDYyTjLYCmUkhDM2Qpsu8TPPBgOxajkV6ammJ1BBwgtQEnMMZdVk5ONxrGNY8hOw">Lněnička & Nikiforova, 2021</a>)*.<br> The benchmarking supposes the collection of quantitative data, which makes this task an acceptability task. A six-point Likert scale was applied for evaluating the portals. Each sub-dimension was supplied with its description to ensure the common understanding, a drop-down list to select the level at which the respondent (dis)agree, and a comment to be provided, which has not been mandatory. This formed a protocol to be fulfilled on every portal. Each sub-dimension/feature was assessed using a six-point Likert scale, where strong agreement is assessed with 6 points, while strong disagreement is represented by 1 point.<br> Each website (portal) was evaluated by experts, where a person is considered to be an expert if a person works with open (government) data and data portals daily, i.e., it is the key part of their job, which can be public officials, researchers, and independent organizations. In other words, compliance with the expert profile according to the International Certification of Digital Literacy (ICDL) and its derivation proposed in <a href="https://www.emerald.com/insight/content/doi/10.1108/OIR-05-2020-0204/full/html?casa_token=6Yd7zSiQMg0AAAAA:yT8d_thrh84stDSVbax8eXLm5vP9LkrwZZFMzC_vql9vZNoQP_iYBHCZ0NOndkvusIx9TZAvJLWBp6lqe9bymm-xHaZ93k2mYfoHXKdVq52A0a7MlwGa">Lněnička et al. (2021)</a>* is expected to be met.<br> When all individual protocols were collected, mean values and standard deviations (SD) were calculated, and if statistical contradictions/inconsistencies were found, reassessment took place to ensure individual consistency and interrater reliability among experts’ answers.<br> *<a href="https://www.sciencedirect.com/science/article/pii/S0736585321000447?casa_token=7K8YGcYWbQcAAAAA:_HnV50rvxwQmDYyTjLYCmUkhDM2Qpsu8TPPBgOxajkV6ammJ1BBwgtQEnMMZdVk5ONxrGNY8hOw">Lnenicka, M., & Nikiforova, A. (2021). Transparency-by-design: What is the role of open data portals?. Telematics and Informatics, 61, 101605</a><br> *<a href="https://www.emerald.com/insight/content/doi/10.1108/OIR-05-2020-0204/full/html?casa_token=6Yd7zSiQMg0AAAAA:yT8d_thrh84stDSVbax8eXLm5vP9LkrwZZFMzC_vql9vZNoQP_iYBHCZ0NOndkvusIx9TZAvJLWBp6lqe9bymm-xHaZ93k2mYfoHXKdVq52A0a7MlwGa">Lněnička, M., Machova, R., Volejníková, J., Linhartová, V., Knezackova, R., & Hub, M. (2021). Enhancing transparency through open government data: the case of data portals and their features and capabilities. Online Information Review.</a></p> <p>***Test procedure***<br> (1) perform an assessment of each dimension using sub-dimensions, mapping out the achievement of each indicator<br> (2) all sub-dimensions in one dimension are aggregated, and then the average value is calculated based on the number of sub-dimensions – the resulting average stands for a dimension value - eight values per portal<br> (3) the average value from all dimensions are calculated and then mapped to the maturity level – this value of each portal is also used to rank the portals.</p> <p>***Description of the data in this data set***<br> Sheet#1 "comparison_overall" provides results by portal<br> Sheet#2 "comparison_category" provides results by portal and category<br> Sheet#3 "category_subcategory" provides list of categories and its elements<br> </p> <p>***Format of the file***<br> .xls</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p>For more info, see README.txt</p>
Testing Smart City environmental monitoring technology using small scale temporary cities
<p>This is the data used for:</p> <blockquote> <p>S. J. Johnston <em>et al</em>., "Testing Smart City environmental monitoring technology using small scale temporary cities," <em>2019 IEEE 5th World Forum on Internet of Things (WF-IoT)</em>, 2019, pp. 578-583, doi: 10.1109/WF-IoT.2019.8767274.</p> </blockquote> <p> </p> <p><em><strong>Abstract:</strong></em></p> <p>Exposure to particulate matter has been identified as a major health problem worldwide. Established measurement<br> techniques require equipment costing many thousands of dollars and specialist expertise to maintain. Ongoing research<br> is investigating the use of low cost <$300 sensors to enable greater temporal-spatial density of readings to be taken. There<br> are questions about the suitability and reliability of these low-cost sensors, queries which can be addressed by deploying<br> and evaluating the sensors in a real world application. We propose festival site as small scale cities to enable a short term<br> deployments and evaluation of sensors. We present data from these devices and experiences gained from using a festival site as a substitute for a city.</p> <p><em><strong>Files:</strong></em></p> <p>1 - timeLapse: mp4 file presenting a time lapse of the measurements realised during the festival<br> 2 - sensor_data: csv file containing the 5 min averaged data from all the sensors deployed and their coordinates used to generate the graph and the maps in the paper</p> <p>3 - workshop.pdf: instructions to run the workshop and code used for the workshop</p>
Research data from the survey on Smart Cities professional profiles for the Article "Modelling and analyzing the availability of technical professional profiles for the success of Smart Cities projects in Europe"
<p>The file includes the complete version of data collected through the surrvey on recommended profile for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician. It complements the previous version focused on IoT implementation stired at <a href="../doi/10.5281/zenodo.7492254">https://zenodo.org/doi/10.5281/zenodo.7492254</a></p>
Guaranteed Publisher/Subscriber Anonymity with Interest Profiles for Smart Cities Applications: Trends and Gaps
<p>Studies carried out in the context of anonymous Publisher/Subscriber (MQTT protocol) and with a profile of interest, are topics that the literature and the IoT (Internet of Things) industry face to implement models, architecture and architectural patterns, mainly related to anonymity, and the profile of interest there is nothing in literature or industry. In this way, the creation of an architecture based on these themes can facilitate the communication of IoT devices in smart cities. Therefore, this article aims to identify in the literature/industry how to guarantee the anonymity of Publisher/Subscriber with interest profiles using the MQTT protocol, and as future works to create an architecture based on the theme so that it can be used by academia and industry. IoT. The search string returned 291 (Two hundred and Ninety-One) works, of which 4 (Four) were selected according to the criteria of the Systematic Literature Review.</p>
Fastprk2 video at Smart City World Congress 2017
<p>Video in booth during the Smart City World Congress 2017 (Barcelona, Spain)</p>
Figure 3. Screenshot of the MQTT broker, publisher, and two subscribers.-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology
<p>As it was mentioned above, IoT needs the appropriate lightweight protocols to transmit the<br> info because web-protocols (e.g. TCP) generate several times more traffic usually for IoT (e.g.<br> remote connection to the Arduino weather station). MQTT (Message Queuing Telemetry Transport)<br> and CoAP (Constrained Application Protocol) IoT protocols are mainly in use nowadays<br> (http://postscapes.com/internet-of-things-protocols). In this activity,Arduino Ethernet Shield and C#<br> console app are connected by MQTT Mosquitto open source software (http://mosquitto.org).Similar<br> work presented against https://iotguys.wordpress.com/2014/11/13/arduino-with-mqtt/.The activity<br> consists of the following steps:<br> 1. Download and installation of Mosquitto software gainst http://mosquitto.org/download/.<br> 2. Download and installation of the latest Arduino software against<br> http://arduino.cc/en/main/software.<br> 3. Development of the MQTT subscriber based on C programming language in Arduino<br> IDE.<br> 3. Development of the MQTT subscriber based on C# console app (laptop HP ProBook 650<br> G1 and Windows 10 are used) in Visual Studio.<br> Screen shot of the software is shown in Fig. 3.</p>
Figure 2. Screenshot of the Google Earth web-site's prototype on the visualization of heat/cold waves-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology-
<p>A non-anticipative analog method consists of four main steps:<br> 1. Generation of the prediction rules.<br> 2. Analysis of the prediction rules. The rules with time slots, which are not concentrated at<br> the same frame, are excluded.<br> 3. Generation of possible extremes.<br> 4. Analysis of the generated possible extremes. The extremes with time slots, which do not<br> correspond to the time slots of the appropriate rules, are excluded.<br> The results of the heat/cold waves’ prediction from 2011 to 2014 at different locations<br> (places are selected randomly) are presented in Table 2.</p>
Figure 1. Azure management portal and VM with two Delphi desktop apps-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology
<p><br> Nowadays, only D-Wave Systems Company produces commercially the 2nd generation<br> adiabatic quantum computer with up to 512 flux qubits (project code name ″Vesuvius″). They are<br> microscopic loops of niobium metal that are capable of quantum behavior at low temperatures.<br> Hence, electrical currents in the loops can flow in clockwise (+1) or counterclockwise (-1)<br> direction, or both, when in quantum superposition. Qubits are connected to neighbors according to<br> the topology of quantum processor. The hardware is controlled by a framework of Josephson<br> junctions that allow individual qubit values to be stored and read, and to influence the states of<br> neighboring qubits.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 2. Traditional growth model vs. adapted growth model Source: Adapted after Purdy & Daugherty, 2016
<p>The use of AI is not limited to smart buildings or transportation. It covers a wide range of application from medical diagnosis, to robot control and virtual assistance scientific tools. Nowadays, AI can be encountered in many services such as: cars speech recognitions, industrial robots, intelligent vacuum cleaners or fridges and so further. It can also be used in smart homes which permits by using hundreds or even thousands of sensors to provide services according to our preferences such as: ambient assisted living, energy saving etc. According to Skouby et al. (2014), AI also can be utilized in smart homes by adding personalized features in form of context awareness which allows AI to move beyond automation level. These authors designed a four-layer pyramid which encases the ICTs based infrastructure for future smart cites (Figure 3).</p>
Artificial Intelligence and the Future of Smart Cities-Table 1. Smart city complex system factors
<p>Figure 2 underlines the importance of traditional production factors such as capital and labour in achieving and driving growth which arises when either stock of capital or labour increase or they are more effectively used. Total factor productivity (TFP) represents the growth enhanced by the use of technology and innovation. Besides these traditional factors, Purdy and Daugherty (2016) consider that AI can be seen as a new production factor, a capital-labour hybrid that will lead to significant growth opportunities. This is due to the advancement made in AI, which allows nowadays to replicate some labour activities at a greater and faster scale than humans (e.g. virtual text assistance, self-learning machines) (Purdy and Daugherty, 2016).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 1. Smart people, smart ICTs and smart cities
<p>According to other authors “water, sewer, transportation, electricity, telecommunications, housing, healthcare, education — all of these functions—will have to be built from the ground up” (Glasmeier & Christopherson, 2015). This search is facilitated by the evolution of ICTs in general and of AI in particular. AI offers possibilities to replace the human being in complex and dangerous activities. But, smart cities start from smart human capital (Shapiro, 2006; Holland, 2008), because only smart people can create smart ICTs equipped with AI (Figure 1). These people and technologies will solve, by creativity and cooperation, problems associated with urban agglomerations, pollution, the depletion of some natural resources etc.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 9. AI influence on the environment
<p>The participants consider that AI development will increase the energy consumption and e- waste (M=3.60, SD=1.03), but will improve also the level of citizens’ information on the environmental changes (M=3.60, SD=.49). The contribution to CO2 emissions is on the fourth places (M=3.40, SD=.49), followed by the attracting of the community members to environmental actions (M=3.00, SD=.64). In the analysis of the statically significant differences by gender, female participants scored significantly higher (M=4.00, SD=.64) than male participants (M=3.63, SD=.77) in the case of the information of citizens on the environmental changes (M=3.80, SD=.75 vs. M=3.72, SD=.75) and the attracting of community members to environmental actions (M=3.00, SD=.90 vs. M=2.63, SD=.88). The analysis on age category, the results revealed that the 26-30 age group scored the highest at both questions (Figure 9).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 4. Influence of AI in the development of smart cities by respondents age
<p>Respondents were asked to indicate how they evaluate the influence of AI in the development of intelligent cities. In order to fully understand the concept of smart cities, the definition of smart city given by Caragliu (2009) was given to the respondents. It is presented in section 2. On question 6 two-way analysis was used to determine the difference by age and gender. There was no statistically significant interaction between groups as determined by two-way ANOVA F (3, 106) = 8.675, p value>0.05 (p=.387). The assumption of homogeneity of variance was tested using the Brown-Forsythe Test. There were statistically significant differences by gender (F=2.169, p<0.05) and by age (F=30.885, p<0.05). More than 9 in ten (almost 94%) consider AI to be important (50%) or very important (43.8%) while just a few (6.2%) recall a moderate importance for smart cities development. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=4.27, SD=.62) on question 6 „Generally speaking, how do you assess the influence of AI in the development of intelligent cities”. At the same question: the 41-50 age group scored the highest score followed by the 18-25 age group (M=4.40, SD=.49). The 26-30 age group scored lower than the 18-25 age group (M=4.37, SD=.48) and significantly higher than the 31-40 age group (Figure 4).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 5. Smart features as the main beneficiaries of AI in terms of the respondent's age (statistically significant differences only for 7.1 and 7.3)
<p>The majority of the respondents who found the smart features to be the main beneficiaries of AI facilities were ranging between 31-40 years old and +41 age old, followed by the 18-25 age group (M=3.80, SD =0.75), 26-30 (MD=4.0, SD =.75) (Figure 5).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 3. ICT-based infrastructure and its four layers Source: adapted after Skouby et al., 2014
<p>Respondents were asked to indicate how they evaluate the influence of AI in the development of intelligent cities. In order to fully understand the concept of smart cities, the definition of smart city given by Caragliu (2009) was given to the respondents. It is presented in section 2. On question 6 two-way analysis was used to determine the difference by age and gender. There was no statistically significant interaction between groups as determined by two-way ANOVA F (3, 106) = 8.675, p value>0.05 (p=.387). The assumption of homogeneity of variance was tested using the Brown-Forsythe Test. There were statistically significant differences by gender (F=2.169, p<0.05) and by age (F=30.885, p<0.05). More than 9 in ten (almost 94%) consider AI to be important (50%) or very important (43.8%) while just a few (6.2%) recall a moderate importance for smart cities development. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=4.27, SD=.62) on question 6 „Generally speaking, how do you assess the influence of AI in the development of intelligent cities”. At the same question: the 41-50 age group scored the highest score followed by the 18-25 age group (M=4.40, SD=.49). The 26-30 age group scored lower than the 18-25 age group (M=4.37, SD=.48) and significantly higher than the 31-40 age group (Figure 4).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 8. Influence of AI on individual safety by respondents age
<p>On question 11 respondents were asked to consider the influence of AI on individual safety using a 5 points Likert Scale ranging from 1 being “totally unimportant” and 5 being “very important”. The analysis of variance was used to determine the differences by age group and by gender. The analysis shows that no statistical interaction was found between age and gender F=3.081, p=0.08. We found statistically significant differences by gender F=7.639, p<0.05 and age F=6.318, p=.001). Female participants scored significantly higher (M=4.40, SD=.81) than male participants (M=4.00, SD=.60) on question 11 about the influence of AI on individual safety. At the same question: the 41-50 age group scored the highest (M=4.50, SD=.51), followed by the 18-25 age group (M=4.40, SD=.81); the 26-30 age group scored lower than the 18-25 age group and significantly higher than the 31-40 age group (M=3.87, SD=.60) (Figure 8).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 7. Q.9.Which of the following job functions will AI impact the most over the next 10 years? (Statistically significant differences by age for 9.3, 9.4, 9.5 and 9.6)
<p>The analysis reveals that people perceive that AI will have a greater impact over the next 10 years on marketing (for example, intelligent customer targeting, planning and executing marketing campaigns) scored significantly higher (M=4.37, SD=.69) than on finance (for example, robotic financial advisors, automated corporate financial analysis) (M=3.87, SD=.60) (Figure 7). For the same question customer services scored significantly higher (M=3.75, SD=.83) than health (e.g. consultation and diagnosis, surgery) (M=3.25, SD=.83). For the same question, the analyses by gender reveals that the majority of female participants scored significantly higher (M=3.40, SD=.81) than male participants (M=3.18, SD=.57) and those aged in the second group.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 10. Respondents' opinions about the use of robots in different activities (grouped by age)
<p>Question 14 was used to evaluate the respondents’ opinions about the use of robots in the following activities: performing medical surgeries, child care, supply of consumer goods, driving a car, assistance in performing tasks at work and cleaning (Figure 10). The respondents feel most confident and safe to use robots for cleaning (M=4.18, SD=1.07) and for assistance in performing tasks at work (M=4.12, SD=1.05) and less confident and safe to use robots for driving a car (M=3.87, SD=1.11), for supply of consumer goods (M=3.81, SD=.81) and performing medical surgeries (M=3.68, SD=.92) The child care obtained the lower score (M=2.00, SD=86).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 6. Importance of AI for respondents business or industry by respondent's age
<p>On question 8 respondents have to indicate on a scale of 1 to 5 (1 being “not important” and 5 being “critical for survival”), how important they think the AI will be for their business or industry (or for the one they are preparing for) in the next 10 years? No statistical interaction was found between age and gender F (3, 108) = .174, p>0.05). There were statistically significant differences by gender F (1, 108) = 50.261, p<0.05 and age, F (3, 108) = 9.298, p<0.05. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=3.36, SD=.88) on question 8 about the importance of AI for the fields of activity of the participants (or for those they are preparing for) in the next 10 years. At the same question: the 26-30 age group scored the highest followed by the 18-25 age group (M=3.80, SD=1.18); the 31-40 age group scored lower than the 18-25 age group (M=3.50, SD=.87); the 31-40 age group scored also lower than the 18-25 age group (M=3.50, SD=.51) (Figure 6).</p>
Public Emails from Brazilian Smart Cities
<p>18,850 public available emails from 673 Brazilian smart cities featured in the 2020 <a href="https://ranking.connectedsmartcities.com.br/">Connected Smart Cities Ranking</a>.</p> <p>The public pages were scraped using UNINOVE LabCidades' opensource email scraper, available in GitHub as <a href="https://github.com/LabCidades/email-scraper">LabCidades/email-scraper.</a></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.