The Kälin and Kochenov Quality of Nationality Index (QNI) ranks the objective quality of nationalities worldwide. It explores three internal factors (economic strength, human development, and peace and stability) and four external factors (diversity and weight of travel freedom and diversity and weight of settlement freedom) which are used to measure the value of virtually all nationalities worldwide. Peace and stability counts for 10% of aggregate value, all other six factors count for 15% each. The QNI has been created by Dr. Christian H. Kälin, Chairman of Henley & Partners, and Prof. Dimitry Kochenov, Professor of European Constitutional Law and Citizenship at the University of Groningen. This dataset is the basis of the Kälin and Kochenov Quality of Nationality Index, edited by Dimitry Kochenov and Justin Lindeboom (Hart Publishing 2019).
Measurement and sources:
1) Economic Strength of the country conferring the nationality is measured by GDP, excluding NRR, with power purchasing parity (PPP). GDP with PPP and NRR have been collected from the World Bank. All figures are normalized to a 0-15% scale.
2) Peace and Stability of the country conferring the nationality is measured by reference to the Global Peace Index. All figures are normalized to a 0-10% scale.
3) Human Development of the country conferring the nationality is measured by reference to the UN Human Development Index. All figures are normalized to a 0-15% scale.
4) Diversity of Settlement Freedom refers to the number of foreign countries in which a nationality's holders can freely settle (including the right to work there) without having to obtain a visa or with visa-on-arrival. All figures are normalized to a 0-15% scale. Data is gathered through extensive research with the assistance of regional experts.
5) Weight of Settlement Freedom measures the qualitative value of the foreign countries in which a nationality's holder is allowed to settle freely. Each settlement destination is valued by reference to its Economic Strength and Human Development. The aggregate value of all settlement destinations determines a nationality's weight of settlement freedom. All figures are normalized to a 0-15% scale.
6) Diversity of Travel Freedom measures the number of destinations to which a nationality's holder can travel to visa-free or with visa-on-arrival. All figures are normalized to a 0-15% scale. This data is provided by the International Air Transport Association (IATA).
7) Weight of Travel Freedom measures the qualitative value of visa-free and visa-on-arrival travel destinations, and also relies on data provided by IATA. Each travel destination is valued by reference to its Economic strength and Human Development. The aggregate value of all travel destinations determines a nationality's weight of travel freedom. All figures are normalized to a 0-15% scale.
This dataset contains metadata collected for the purpose of the QNI from 2011 to 2018, as well as the resulting rankings.
Interaction area data in MOOCs
Contributors:Mohammad Hossein Rezvani
Identifying Factors Affecting E-customer Loyalty in Gamified Trusted Store Platforms: A Case Study Analysis in Iran
The micro x-ray CT images of two rock samples have been reconstructed.
The data type of images: uint8
Spatial Resolution: 1.2 micrometer/voxel
The reconstructed or "Original" images are suffering from existence of artifacts, roundoff errors, and different types of visual or mathematical noises in the reconstructed CT images.
Each image has been applied with a series of bandpass and bilateral filters.
Contributors:Bethan Davies, Christopher Darvill, Harold Lovell, Jacob Bendle, Julian Dowdeswell, Derek Fabel, Juan Luis Garcia, Alessa Geiger, Delia Gheorghiu, Neil Glasser, Monika Mendelova, Stephan Harrison, Andrew Hein, Mike Kaplan, Julian Martin, Adrian Palmer, Mauri Pelto, Angel Rodes, Esteban Sagredo, Rachel Smedley, John Smellie, Varyl Thorndycraft
Supplementary Data. Includes Excel data tables for ages and shapefiles for ages, geomorphology and ice-sheet reconstruction.
Contributors:Stephan Pfister, Laura Scherer
Non-marginal (average) AWARE CFs and WSI CFs:
We provide a shapefile, CSV file and KML file of the average AWARE characterization factors (CFs) based on the marginal AWARE CFs from Boulay et al. (2018). We also provide it together with average WSI factors from Pfister and Bayer (2014), since based on the UNEP SETAC recommendation, AWARE should be used together with an alternative scarcity method to test sensitivities (Jolliet et al. 2018).
The XLS version of the average AWARE CFs is available from the original publication:
Pfister S, Scherer L, Buxmann K (2020) Water scarcity footprint of hydropower based on a seasonal approach - Global assessment with sensitivities of model assumptions tested on specific cases. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2020.138188
The CSV files lists CFs for each month (01 to 12) and each methods: AWARE_01 stands for original marginal AWARE CFs of January, AWARE_a_01 represents the newly calculated average AWARE CFs for January, WSI_01 are the marginal WSI CFs for January and WSI_AVG_01 the average WSI CFs for January. The CSV file can be linked to WaterGAP watersheds based on the "BAS34S_ID" . The WaterGAP shapefile is e.g. available at http://www.wulca-waterlca.org/aware.html.
The Shapefile and KML file follows the same order but are already linked to the watershed shapefile.