Raw statistical data underpinning the second two PhD research objectives for the thesis entitled "Money doesn’t grow on trees: How to increase funding for the delivery of urban forest ecosystem services?". These relate to the interviews with 30 Southampton businesses, and choice experiment survey with 415 Southampton citizens.

The KCI Linkage Information Service provides linkage information between major academic databases, including KCI, WOS, SCOPUS, and NDSL, for domestic academic papers. This allows users to view a variety of information, including citation status for each paper, related literature, and linkage dates. It also includes detailed information, such as citation relationships between papers, citation counts by database, and original text URLs, making it highly useful for research performance analysis, citation statistics, academic information linkage, and research trend monitoring. It can also be useful for researchers, institutions, and evaluation agencies in analyzing paper impact and managing research performance.

The German Library Statistics (DBS) is the national statistics of the German library system and contains statistical key figures. It includes public libraries, scientific libraries, as well as specialized scientific libraries. More information can be found at DBS. This dataset contains the following information on academic libraries in Bavaria 2019: Borrowings by total physical units, borrowings, of which: Extensions upon user request, reservations, attendance, requests for information, library visits, 1. ... Virtual visits (visits) input blocked, user training sessions (hours), participants in user training sessions, 1. Calls for e-learning offers from the library, 2. Accepted dissertations of the own university, 3. Accepted dissertations of your own university, of which: Online dissertations, 4. Open access green and gold publications provided on own repositories , searches in local online catalogues and discovery systems, searches in databases, access to journal titles, full advertisements of journal articles, full advertisements of individual digital documents, 1. Full display of individual digital documents, including: Full ads from commercially distributed e-books, 2. Full display of individual digital documents, including: Full display of individual documents on the institutional repository

The evolution of a software system can be studied in terms of how various properties as reflected by software metrics change over time. Current models of software evolution have allowed for inferences to be drawn about certain attributes of the software system, for instance, regarding the architecture, complexity and its impact on the development effort. However, an inherent limitation of these models is that they do not provide any direct insight into where growth takes place. In particular, we cannot assess the impact of evolution on the underlying distribution of size and complexity among the various classes. Such an analysis is needed in order to answer questions such as 'do developers tend to evenly distribute complexity as systems get bigger?', and 'do large and complex classes get bigger over time?'. These are questions of more than passing interest since by understanding what typical and successful software evolution looks like, we can identify anomalous situations and take action earlier than might otherwise be possible. Information gained from an analysis of the distribution of growth will also show if there are consistent boundaries within which a software design structure exists. In our study of metric distributions, we focused on 10 different measures that span a range of size and complexity measures. The raw metric data (4 .txt files and 1 .log file in a .zip file measuring ~0.5MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

The KCI Citation Information Service provides a variety of citation-related data, including citation status, self-citations, number of articles, and impact factor for domestic academic journals. Key indicators for each journal, including annual citation counts, number of articles, self-citations, impact factor, KOR FACTOR, ZIF, and immediacy index, enable a comprehensive analysis of a journal's influence and research performance. This service provides researchers, evaluation agencies, and journal operators with valuable information they can use to understand journal citation trends, evaluate research performance, and facilitate journal selection.

It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. In order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible, but these analysis methods can only be applied after a mature release of the code has been developed. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. We present a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

This file contains raw data base to replicate the statistical analysis conducted for Chapter 3 of my Ph.D dissertation.

This dataset contains responses from a survey conducted for a master's thesis at Erasmus University Rotterdam. The survey investigated how consumer perceptions of privacy and trust in interactions with centralized versus decentralized AI-powered chatbots influence customer satisfaction. The survey included a predetermined simulated conversation with an AI-powered chatbot.Purpose of the Study:The main research question addressed in this study is: "How do consumer perceptions of privacy and trust in interactions with centralized versus decentralized AI-powered chatbots influence customer satisfaction?" The study aims to compare the differences in customer satisfaction, privacy concerns, and trust between centralized and decentralized AI-powered chatbots.Data Description:This dataset includes responses from 175 participants after data cleaning and removal of incomplete and biased responses. Participants were randomly assigned to one of three groups:Unaware of the chatbot typeInformed they would interact with a centralized chatbotInformed they would interact with a decentralized chatbotVariables:Customer Satisfaction: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer Privacy Concerns: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer Trust in AI-Powered Chatbots: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer AI Familiarity: Measured with Likert scale questions regarding prior usage and understanding of AI technology on a 5-point scale from Strongly disagree to Strongly agree.Demographic Information: Age group, gender, highest education finished, nationality, and occupation.Chatbot Type: Categorical variable with values: 0 for not aware, 1 for aware of interacting with a centralized chatbot, and 2 for aware of interacting with a decentralized chatbot.Usage Notes:The dataset is provided in a XLSX file format and includes all necessary variables for analysis. The dataset can be used to conduct various statistical analyses, including descriptive statistics, hypothesis testing, and regression analysis.