Trust is a critical enabler for collaboration and data sharing in data spaces, where diverse stakeholders must work together to create value while safeguarding data integrity and sovereignty. This study investigates how trust is conceptualized in the scientific literature on data spaces through a systematic literature review (SLR), addressing three core questions: How is trust conceptualized in data spaces? How is trust defined? And what mechanisms are discussed to establish and maintain trust? The findings reveal that trust is understood in three primary ways: as a foundational requirement for initiating collaboration, as a key element embedded in governance and operational mechanisms, and as an outcome that emerges through effective processes and stakeholder interactions. Its conceptualization spans technical, organizational, and legal dimensions, emphasizing the need for clear governance structures and robust technical safeguards. Key elements such as data sovereignty, privacy, and accountability highlight the interplay between governance and technology. Mechanisms like blockchain, access control, and certification frameworks play a crucial role in fostering trust, ensuring compliance, and instilling confidence among stakeholders. By synthesizing existing research, this paper provides insights for researchers and practitioners seeking to enhance trust-based collaboration in data spaces.