This is a dataset generated as a part of a research project studying the changing support among European Union (EU) members for the war in Ukraine. The dataset contains a number of conditions (variables) used to conduct fuzzy-set qualitative comparative analysis (fsQCA) to test five critical conditions that have shaped the change in public opinion that include economic growth, democratic rule, distance from the front lines, level of energy dependence from Russia and trust in social media. These conditions (or variables) include:
Num: Case number in the row
MEMBR: EU member state two or three-letter abbreviation
WEALTH: GDP per capita in Euro (measured in purchasing power parties) as reported by Eurostat
GROWTH: GDP growth in volume based on seasonally adjusted data by Eurostat
DEMOCR: the overall score for each EU member’s democracy index for 2022. Data have been drawn from the Economist Intelligence Unit (EIU) 2022 report
DISTAN: an average distance (in thousand kilometers) from the geographic center point of the national capital of each EU member-state to the south-western and north-eastern tips of the frontline of the war in Ukraine. I have accepted that the western tip of the frontline is Kinburnsʹka Kosa National Park (Geographic Coordinates: 46°34’37”N 31°30’44”E) and the eastern tip of the frontline is at the village of Topoli in Kharkiv Oblast (Geographic Coordinates: 49°57’52″N, 37°54′31″E).
TRADE: volume of trade with Russia per capita in thousand of US Dollars.
ENERGO: EU energy dependence on Russia as estimated by the European Commission (from 0 to 100 percent) for 2020. Source: Eurostat.
GOVTR: Net trust in national government (difference between the sum of fully trust and partially trust responses and fully distrust and partially distrust responses).
MEDIATR: Net trust in social media (difference between the sum of fully trust and partially trust responses and fully distrust and partially distrust responses).
CNG_MIL -- change of net support for the financing of the purchase and supply of military equipment to Ukraine, Spring 2022-Spring 2025
CNG_FIN -- change of net support for the financing of support for Ukraine, Spring 2022-Spring 2025
CNG_HUM -- change of net support for providing humanitarian support for the people affected by the war, Spring 2022-Spring 2025
CNG_REF -- change of net support for welcoming in the EU people fleeing the war, Spring 2022-Spring 2025
AVCHNG: Difference in average change of the military, economic, humanitarian and refugee support for Ukraine Spring 2022-Spring 2025.
WEALTH1: Calibrated score for national wealth (see paper for details)
GROWT1: Calibrated score for economic growth (see paper for details)
DEMOCR1: Calibrated score for democracy (see paper for details)
DISTAN1: Calibrated score for distance (see paper for details)
TRADE1: Calibrated score for trade (see paper for details)
RENERGO1: Calibrated score for energy dependence (see paper for details)
GOVTR1: Calibrated score for trust in governance (see paper for details)
RMEDIATR1: Calibrated score for trust in social media (see paper for details)
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NB: File: "Dataset_UkrTime2yr.csv" contains data for the public support during the first two years (24 months) since the whole scale invasion of Ukraine, Spring 2022-Spring 2024
File"Dataset_UkrTime3yr.csv" contains data for the public support during the first three years (36 months) since the whole scale invasion of Ukraine, Spring 2022-Spring 2025
This capstone explores how a calming mobile living wall can improve the well-being and emotional health of individuals with ALS in palliative care, as well as those who care for them. This project examines a mobile green wall as an adaptable solution that introduces the benefits of green design into various spaces within a care facility. The goals of this project are (1) to better understand how mobile green walls enhance users’ senses, thus reducing anxiety and influencing mood and stress and (2) to gain insight into a mobile green wall’s overall impact in palliative care environments.
Varieties of International Cyber Strategies (VoICS): Text Analysis of National Cybersecurity Documents is a project that compares and contrasts the three main approaches to conceptualize national cybersecurity strategies (NSS): deterrence, norm-based approach (NBA) and cyber persistence engagement (CPE). Scholars and policymakers have initially conceptualized NSS in terms of deterrence or NBA. More recent academic research has demonstrated that these frameworks are inadequate for cyber space. As a result, Cyber Persistence Engagement (CPE) emerged as a third option.
The first version (1.0) of the VoICS database on National Cybersecurity Strategies focuses on nations in Europe and North America and includes a total of 77 NCS of the states in the North Atlantic Area—NATO allies, EU members and Switzerland—released from 2003 until the end of 2023. The current 1.2 version includes 83 strategies from 36 allies and partners. It consists of 27 variables, including country and strategy identifiers, EU and NATO membership, their respective accession dates, and total length of the documents. VoICS include eighteen variables representing different measures of relative and absolute weights of the three NSS types—deterrence, NBA and CPE.
The text analysis is based on official NSS documents provided by the NATO Cooperative Cyber Defence Centre of Excellence library (2024) and ENISA’s interactive map for National Cyber Security Strategies (2023). Both sources rely on voluntary submission from the member states. Unfortunately, some official documents were not available or accessible or were not listed at all. Authors have used various sources and contacts with a variety of cyber attachés in Brussels to determine if any additional strategies were released and to obtain the missing documents.
The 18 text analysis variables compare and contrast the extent to which different NCS are associated with a specific strategy. They represent different frequency scores based either on words, phrases, or words and phrases combined. These calculations are associated with either deterrence, NBA, or CPE in each strategy. The authors have generated respective vocabularies for the three strategic ideas through which each of these approaches are operationalized. We have conducted a text analysis using WordStat text analysis software by Provialis ( https://provalisresearch.com/products/content-analysis-software/). A detailed codebook for NSS Dataset 1.2 along with a NSS Dictionary 1.2 have been included in this collection/ repository.
The process of generating vocabulary associated with the three cybersecurity approaches involved several steps. First, upon reviewing the literature, the authors generated independently a list of words and phrases associated with each type of cybersecurity strategy. Second, the authors compared their lists to determine the degree of overlap in vocabulary. Those words and phrases that included in at least two different lists were reviewed and, if there was consensus, were incorporated in the dictionary. Finally, words and phrases which were identified in only one of lists were once again reviewed and, in case there was a consensus among the authors, these were also included in the dictionary. Third, the three vocabularies were updated on several instances when it was unanimously agreed that these words or phrases should be included in the analysis.
Data on orientation angle for fall migratory monarch butterflies exposed to experimental magnetic conditions north of, at, and south of their overwintering sites in Mexico. Additional orientation data for butterflies consistently exposed to either fall-like or colder temperatures.
This data set contains ultrasound image, tissue ablation. and temperature data measured in the study reported in the article:
Real-time control of radiofrequency ablation using three-dimensional ultrasound echo decorrelation imaging in normal and diseased ex vivo human liver
Elmira Ghahramani, Peter D Grimm, Benjamin E Weiss, Nicholas S Schoenleb, Alexander J Knapp, Jiang Wang, Syed A Ahmad, Shimul A Shah, Ralph C Quillin III, Sameer H Patel
Physics in Medicine and Biology, 2025
https://iopscience.iop.org/article/10.1088/1361-6560/adaacb
To best understand and use this data set, please refer to this article.
Files in the data set include:
Annotated Excel spreadsheet containing summary data for all 109 trials in this study (12 preliminary trials, followed by 97 primary trials; see spreadsheet for variable listing):
ExVivoHumanLiver_RFA_Decorrelation_SummaryData.xlsx
CSV spreadsheet containing the same summary data without annotations:
ExVivoHumanLiver_RFA_Decorrelation_SummaryData.csv
MATLAB data file (version 7) containing the same summary data, within the structure variable 'summary':
ExVivoHumanLiver_RFA_Decorrelation_SummaryData.mat
Example MATLAB script demonstrating reading and some analysis of results from all trials, with PDF of code and its output:
ExVivoHumanLiver_RFA_Decorrelation_ExampleScript.m
ExVivoHumanLiver_RFA_Decorrelation_ExampleScript.pdf
MATLAB data files (version 7) containing results from each trial:
ExVivoHumanLiver_RFA_Decorrelation_RawData_Trial1.mat through ExVivoHumanLiver_RFA_Decorrelation_RawData_Trial109.mat.
Each of these files contains the structure variable "rawdata", containing the following fields for each trial:
segmented_ablation_zone:
60x60x60 logical array (step size 1 mm) of segmented liver tissue map (1=ablated, 0=unablated). Dimensions are (z,y,x) as defined in Ghahramani et al. (2025), with z being range from the transducer surface (horizontal in room coordinates), y the azimuthal array direction (horizontal in room coordinates), and x the elevational array direction (vertical in room coordinates). The RFA probe was placed with its tip approximately at the center of this volume, with its needle parallel to the x axis.
IQ_echo_data:
60x60x60x2xN complex array containing beamformed, demodulated IQ (in-phase/quadrature) echo data interpolated onto the same grid. The array of echo volumes comprises N pairs (one pair acquired every 22 s), with the two volumes from each pair separated by the inter-frame time of 50 ms.
instantaneous_decorrelation:
60x60x60xN real array containing echo decorrelation per ms (linear scale) computed for each frame pair, using the equation and methods from section 2.3 in Ghahramani et al. (2025).
thermocouple_locations:
4x3 real array (same for each trial), with each row comprising the (z,y,x) coordinates (voxel indices within the 60x60x60 grid) of a corresponding thermocouple integrated into the RFA probe.
thermocouple_temperatures:
4xM real array, with each row comprising the measured temperatures (degrees C) measured during ablation, synchronous with the last M ultrasound IQ and decorrelation frames of each trial (not available for trials 30, 78, 91, and 102-109).
decorrelation_at_thermocouples:
4xM real array, with each row comprising the measured decorrelation per ms (linear scale) at each thermocouple location, synchronous with the last M ultrasound IQ and decorrelation frames of each trial.
