GitHub hosts Git repositories and provides issues-tracking services to provide a better collaboration environment for software developers. Issues and Pull-Requests are frequently used in GitHub to discuss and review the software requirements (new features, bugs, etc.) and software solutions (source code, test cases, etc.) respectively. The links between Issues and their corresponding Pull-Requests comprise valuable information to keep tracking current development as well as documenting knowledge for future development. Considering a large number of links, such information can be used to train machine learning models for several purposes such as feature location, bug prediction and localization, recommendationsystems and documentation generation. To the best of our knowledge, no dataset has been proposed asa ground-truth of links between Issues and Pull-Requests. In this paper, we propose, PI-Link, a newsignificant and reliable ground-truth dataset composed of 50369 links that explicitly connect 34732 Issueswith 50369 Pull-Requests. These links are automatically extracted from all (907,139) Android projects inGitHub created between January 1, 2011 and January 1, 2021. To better organize and store the collecteddata, we propose a metamodel based on the concepts of Issues and Pull Requests. Moreover, we analyzethe relationships between Issues and their linked Pull Requests based on four features related to their titles,bodies, labels and comments. The selected features are analyzed in terms of their lengths and similaritiesbased on three lexical and one semantic similarity metrics. The results showed promising similarities betweenIssues and their linked PRs at the lexical and semantic levels. In addition, some feature similarities aresensitive to the text length, whereas other feature similarities are sensitive to the term frequency.