## 1 Introduction  
Research and development activity and decision making usually require a preliminary study of related literature to understand the up-to-date, state-of-the-art issues, techniques and methods in a given research field. For instance, Health Science researchers need to find out the scientific evidence that supports their clinical decisions. Analyses in bibliometrics [1], science mapping [2, 3] and logology [4] need to operate on data records that are usually retrieved from queries to a bibliographic database, such as Clarivate's Web of Science or Elsevier's SCOPUS, or a patent registry. A huge volume of data is published and stored in digital bibliographic repositories, which are often manually reviewed in order to select those related to the field and research purpose. Thus, it is important to be acquainted with the quality of the evidence provided in these studies. In this vein, tools such as GRADEpro [5] have emerged, to synthesize and evaluate the quality of evidence found in health science-related studies. Rooted in the Health Sciences, Evidence Synthesis (ES) methods are used to aggregate the global message of a set of studies [6]. The main goal of ES is to evaluate the included studies and select appropriate methods for integrating their information [7]. ES methods can be used to synthesize both qualitative and quantitative evidence [8], according to the type of research questions and forms of evidence analyzed. These methods are often specific or adapted to a given field. For example, scoping, thematic analysis, narrative synthesis, comparative analysis, meta-analysis, case survey and meta-ethography are ES methods in the Software Engineering field [9]. Evidence synthesis approaches are seamlessly linked to Systematic Reviews (SR) methods, which enable researchers to identify, evaluate and interpret the existing research that is relevant for a particular Research Question (RQ) or phenomenon of interest [10]. Some reasons for performing SRs include: to synthesize the existing evidence concerning a given topic, to identify gaps in the current research and to suggest areas for further investigation; and to provide a background for positioning new research lines [11]. Focused on the disciplines of the Health Sciences, the ES methods' steps are defined as the following [12]: aggregate information; explain or interpret processes, perceptions, beliefs and values; develop theory; identify gaps in the literature or the need for future research; explore methodological aspects of a method or topic; and develop or describe frameworks, guidelines, models, measures, scales or programmes. SR methods have also been used in domains such as Environmental Sciences [13] and Computer Science [14], which have benefited from the ES approach. In the latter field, a set of guidelines for performing Systematic Literature Reviews (SLR) has been published [14]. The guidelines define an SLR as a process consisting of three stages, namely, planning, conducting and reporting. The SLR method has become a popular research methodology for conducting literature reviews and evidence aggregation in Software Engineering. Similarly, Systematic Mapping Studies (SMS) and scope studies enable researchers to obtain a wide overview of a research area, providing them with a quantitative indication of the evidence found [15]. It is important to note that, regardless of the discipline in which ES is applied, a considerable number of studies must be processed and, eventually, selected as primary. Consequently, the information provided for those studies should be methodically synthesized. This process is a time-consuming task and is difficult to conduct manually. For this reason, using computer tools to support the process is essential in research and decision-making. The goal of this paper is to analyse and collect the essential features of ES methods in order to present a framework that can be used to improve cloud-based SR support tools, thus fostering comprehensive evidence-based systematic review processes.