*** Dataset_Dataset_Transparency_of_OD_ecosystems_Smart_Cities ***
Authors: Anastasija Nikiforova (1,2), Martin Lnenicka (3), Mariusz Luterek (4)
	(1) University of Tartu
	(2) European Open Science Cloud (EOSC) Task Force “FAIR Metrics and Data Quality”
	(3) University of Pardubice
	(4) University of Warsaw

Corresponding author: Martin Lnenicka, Anastasija Nikiforova
Contact Information: martin.lnenicka@upce.cz, nikiforova.anastasija@gmail.com

**General Introduction***
This dataset contains data collected during a study ("Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities") 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).
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.
It being made public both to act as supplementary data for "Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities" 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.
https://www.sciencedirect.com/science/article/pii/S2210670722002281?casa_token=8xHhtKug0xEAAAAA:POKIQswXhPdbwqgi5A8q98xitcUju_VS8T7oSP6YujXdABZlc5bNn4vEHzHGoxoW16mT6hA-HZ4#bib0045

***Purpose of the expert assessment***
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.

***Methodology***
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. 
To evaluate the transparency maturity of data ecosystems in smart cities, we have used the transparency-by-design framework (Lněnička & Nikiforova, 2021)*. 
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.
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 Lněnička et al. (2021)* is expected to be met. 
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. 
*Lnenicka, M., & Nikiforova, A. (2021). Transparency-by-design: What is the role of open data portals?. Telematics and Informatics, 61, 101605.
*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.

***Test procedure***
(1) perform an assessment of each dimension using sub-dimensions, mapping out the achievement of each indicator
(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
(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.

***Description of the data in this data set*** 
	Sheet#1 - "comparison_overall" (results by portal)
		column A - category of the portal {open data portal, geodata portal, smart data portal}
		column B - city - smart city to which the portal assessed belongs to (the category is provided in column A) 
		columns [C..L] - mean value by evaluator as a result of the assessment using the above-mentioned benchmarking framework using 6-point Likert scale, where 6 – strong agreement, while 1 – strong disagreement
		rows #23-24, #28-29, #41-42 provides mean value and standard deviation per data portal category (see column A)
		rows #43-44 provides mean value and standard deviation for all data portals
	
	Sheet#2 - "comparison_category" (results by portal and category)
		column A - category of the portal {open data portal; geodata portal; smart data portal}
		column B - city - smart city to which the portal assessed belongs to (the category is provided in column A)
		column C - category name - 1 of 8 categories as defined in the benchmarking framework {Data quantity, structure, and general features of the portal; Data quality; Data accessibility; Data findability; Data understandability; Data usefulness; Public engagement, collaboration and participation; Service quality}
		columns [D..M] - mean value by evaluator as a result of the assessment using the above-mentioned benchmarking framework using 6-point Likert scale, where 6 – strong agreement, while 1 – strong disagreement
	
	Sheet#3 - "category_subcategory" (list of categories and its elements)
		column A - category name - 1 of 8 categories as defined in the benchmarking framework {Data quantity, structure, and general features of the portal; Data quality; Data accessibility; Data findability; Data understandability; Data usefulness; Public engagement, collaboration and participation; Service quality}
		column B - subcategory name 
		column B - description of subcategory  
			for more details on the framework see *Lnenicka, M., & Nikiforova, A. (2021). Transparency-by-design: What is the role of open data portals?. Telematics and Informatics, 61, 101605.

***Format of the file***
.xls

***Licenses or restrictions***
CC-BY