Table 3.

Strategies recommended for enhancing collaboration within research institutions, based on lessons learned and previous literature.

Recommendations		Discussion
1	Start by creating a clearly defined governance board	The defined governance board will be responsible for: 1. establishing policies and guidelines for data collection, documentation, storage, retention, and sharing, ensuring that the data is appropriately managed throughout the entire research lifecycle; 2. establishing policies to ensure compliance with ethical guidelines and with relevant data security regulations (safeguarding data against unauthorized access, breaches, and data loss); 3. establishing clearly defined protocols and mediating the sharing of resources, such as equipment, data, facilities, or funding, to support collaborative research; 4. mapping, monitoring, and supporting the efforts of research groups to produce research papers; 5. in the domain of health research, this board could take on the responsibility of identifying common/standardized measures for health conditions that would benefit a majority of, or all, the institution’s researchers; and any measures necessary for specific studies of higher interest at any given time. That could lead to the proposition of strategies to foster cross-disciplinary studies of institutional, national or global interest. The leadership positions could be assigned through institutional allocation or determined by votes from the research community.
2	Build infrastructure and resources	Institutions should allocate adequate funding to support research, investing in robust research organizational infrastructure, encompassing both the physical structures and systems as well as the underlying support personnel necessary for institutionally managed research collaborations.
3	Establish diverse multidisciplinary research teams	Complex research challenges require expertise from various disciplines. By creating diverse multidisciplinary research teams, institutions can leverage different perspectives and knowledge to address various research questions.
4	Establish clearly defined roles and responsibilities	Clearly define roles, responsibilities, and expectations for each collaborator involved in the research collaboration. Establishing a framework for decision-making, task allocation, and accountability helps prevent misunderstandings and ensures that everyone knows their contribution and commitment to the collaboration.
5	Create a team of professional data analytics and data science experts	To effectively handle large datasets and/or datasets that involve the integration of secondary data (such as data extracted from electronic health records), it is important to create a dedicated team of professional data analytics and data science experts to ensure that data is accurate, consistent, and reliable. The team should develop robust processes to prevent data errors, duplicates, and inconsistencies, resulting in improved data quality and integrity. Additionally, they should streamline data integration, standardization, and harmonization across various systems and departments. The team’s responsibilities also encompass managing data throughout its lifecycle, including identifying and mitigating security risks, ensuring data protection and compliance, and providing necessary technical assistance and support.
6	Establish a proficient hands-on research managing team	A hands-on research managing team, possessing a comprehensive understanding of the data and of the institutional research goals, will assist researchers in selecting relevant data fields and facilitate the application of data to their research questions, thereby avoiding redundant analyses, interpretation errors, and inconsistencies in reported data across various papers
7	Establish effective communication	Establish open lines of communication to facilitate information-sharing, exchange of ideas, and updates on progress. Clear and frequent communication helps build trust, resolve conflicts, and keep all collaborators engaged and informed. Innovative methods could be used; e.g. technology tools such as collaboration platforms or websites to present results and updates; or chatbots with 24/7 availability to provide quick and accurate responses to common queries, saving time for employees and reducing the burden on human resources by assisting with frequently asked questions, policy inquiries, or providing access to relevant documents and resources (complementing human communication rather than replacing it). It is also vital to establish feedback mechanisms by creating channels for employees to provide feedback, suggestions, and concerns. This can be through surveys, suggestion boxes, or regular feedback sessions.
8	Build trust and respect among collaborators, focusing on cooperation, equity, and transparency	Foster an environment of trust, respect, and integrity among collaborators. Encourage open and honest discussions, acknowledge diverse perspectives, and value each collaborator’s contributions. Trust is essential for sharing resources, data, and research findings.
9	Ensure mutual benefit for all parts involved, emphasizing meaningful outcomes	Ensure that all parties involved in the research collaboration can derive benefits from the partnership. Identify how each collaborator’s expertise, resources, or access to data can contribute to the collaborative effort, creating an advantageous situation for all. In the context of healthcare research, collaborative research must also always be carried out within a voluntary participation scenario governed by values of reciprocity with and non-exploitation of the patients and service of the public good.
10	Prioritize the timely sharing and dissemination of research findings	Request that collaborators swiftly publish their work in reputable scientific journals to make it accessible to the broader scientific community. Additionally, institutions can organize meetings, conferences, symposiums, and workshops to facilitate the exchange of knowledge and promote dialog among researchers.
11	Strengthen national and international research collaborations	Data from a single research center are less relevant than data collected from multiple centers; therefore, it is crucial to incorporate institutional data into a broader research network.