¿Cómo se pueden desarrollar habilidades de investigación experimental en equipo?

It’s nice to have “yes” or “no” answers to the “clear and focused research question”, but my experience has been that Mother Nature usually provides either a “maybe” or something just plain uninterpretable. So, you need to have a backup plan and a backup to the backup plan. It’s REALLY important not to fall in love with your hypothesis; there’s a difference between tenacity and perseveration.

In my experience, when working on cutting-edge intersections like ours, where biomedical engineering meets real-world applications, the stakes are high, and precision is key. For instance, when designing an organ-on-chip, one must consider not only the biological and physiological facets but also the material properties, mechanical stimuli, and the interplay of all these elements. A question like "How does a specific biomaterial influence cell behavior in an organ-on-chip under dynamic flow conditions?" becomes multifaceted, requiring us to delve deep into material science, fluid dynamics, and cellular biology simultaneously.

It is crucial to clearly define your research question or hypothesis, as it is the foundation for your experiments, guiding the team's efforts and focus. Background Research: Conduct a thorough literature review to understand the existing knowledge in your field. Identify gaps, unresolved issues, or areas where further exploration is needed. Narrow Down Your Focus: Refine your research question to be specific and focused. Clearly outline what you aim to investigate and the expected outcomes. A precise question provides a clear direction for your experiments. This focus should help to develop a well-defined and qualitatively testable hypothesis. What else do you think?

It is helpful to document which research methods were evaluated and the pros and cons of each as well as the method finally chosen. Each method has its own strenghts and caveats, and it's vital to remember precisely what was done and what conclusions you can and cannot draw from the results. This enables stakeholders to use the research meaningfully to co-create actionable insights.

In biomaterials and organ-on-chip platforms, research design is very important. For studying new biomaterial effects on organ-on-chip systems, controlled experimental designs offer clear insights. Variables often include the biomaterial type (independent) and cell responses (dependent). Confounding variables, like flow rate in organ-on-chip studies, need attention. Measurement methods can vary, from fluorescence microscopy to transcriptomic analyses. Sampling often concerns cell line selection, with strategies like stratified approaches for specific cell types. Maintaining sample viability is paramount. Regular team discussions ensure holistic planning, and meticulous documentation in a research protocol aids reproducibility and clarity.

In molecular biology the output variables would be macromolecules that you can measure increase or decrease um those molecules can say a lot about the global changes happening at the cell level.

In my organ-on-chip and biomaterials research, executing the experiment is as critical as planning. Ethical considerations are paramount; even when working with cell lines, standards around sourcing and handling must be upheld. While informed consent may not be traditional, ensuring ethical acquisition of biological materials is vital. Data collection is meticulous: utilizing validated instruments guarantees reliability, and robust data management ensures integrity and security. Deviations, although rare, are documented for transparency. Collaboration is key; continuous dialogue with the team ensures alignment, while stakeholder engagement ensures our research remains pertinent and grounded.

Protocols. Protocols. Protocols. Using the best measurement tools will not help if the experiment setup is varied. You need to demonstrate reproducibility.

You must document and track everything. It is worth spending time planning for that. Invest in the tools needed to register the whole process in a structured, auditable, reliable way, in which data should be easy to recover.

The experiments should be performed with quality and with a statistical feasible number of experiments por group or treatments. The experiment also have to be done in a simple and clear way that can allow easy reproduction of the steps.

In our field, data analysis is the bridge between experimental work and meaningful outcomes. Depending on the complexity, I might employ descriptive statistics for preliminary insights or delve into inferential methods for deeper understanding. Tools like R or Python are my go-to for flexibility and precision. Interpreting results involves validating hypotheses, gauging significance, and discerning causal links. Equally crucial is recognizing limitations: ensuring the validity of our findings, acknowledging potential biases, and understanding the extent of generalizability. Regular team discussions facilitate a comprehensive analysis, while feedback from peers adds depth, ensuring our conclusions are both robust and insightful.

Indeed. It is also important to dedicate time and attention to "lessons learned" reports. This way, research teams and interested and affected stakeholders, could check what went right or wrong and what should serve as a reference in other projects, especially when it comes to costs, specifications and deadlines.

As part of the planning phase, think about the various audiences your research project has or may have, and deliberately design what/how to communicate to the different segments you want to impact (Science community? Research leaders inside an R&D operation? Regulatory bodies? Policymakers?). If you want to cause impact, you will need to address different audiences - each has a way of processing info and different interests/preoccupations. Don't wait for the experiment and analysis to be ready, plan upfront, and invest to deliver the best report to each stakeholder.

Typically, I adhere to the AIMRAD format for clarity and coherence in journals. Key insights from data analysis are distilled into concise points, emphasizing the implications for both academia and industry. Recommendations, based on the findings, provide actionable insights for further research or applications. Citations are rigorously done, giving due credit to the foundational work that bolstered our study. Team collaboration ensures the report's robustness, and adherence to journal or conference guidelines is paramount. Finalized after iterative reviews, our reports aim to contribute meaningfully to the ever-evolving landscape of biomedical engineering.

Reporting on your findings with the team and encourage the questions as to the 'how' it was found. Argumentation and discussion are a driver for both learning and development.

Reflecting on my biomaterials and organ-on-chip journey, the SFL framework captures it succinctly: Successes: Breakthrough insights, attributable to team synergy. Failures: Some hypotheses didn’t hold, highlighting research's unpredictable nature. Lessons: Flexibility is paramount, and setbacks are stepping stones.

While understanding the basic steps of the research design process, like defining the research question, planning, conducting experiments, and analyzing data, is a valuable approach to research, it may not necessarily promote the development of experimental research skills within a team. In my experience, nurturing these skills within a team requires effective communication among team members, adaptability to various situations and perspectives, and a willingness among team members to take risks in exploring new research domains. Additionally, staying updated with the latest knowledge, involving team members in experiment planning, and being open to feedback from peers and mentors are crucial steps for enhancing these skills within a team.

Yes! Another takeaway is the importance of interdisciplinary dialogue. A casual conversation with a colleague from a different specialty once reshaped an entire project, highlighting that diverse perspectives can be game-changers. Lastly, it's crucial to remember the end goal: improving health outcomes. Every microscale chip and biomaterial study is a step towards macro-scale impacts on human health and well-being.

It will all depend on your team and the individual experiences within the team. A big question should also be 'Do they want to learn it". It does not matter if the team is multi-disciplinary or similar in the way of working, you should be willing to learn the new or different methods.

Developing experimental research skills in a team requires a combination of individual effort, teamwork, and mentorship. Additional strategies include designing experiments, data collection, data analysis and troubleshooting. Team building activities, participation or presenting your research at meetings and collaborating with other research teams will surely help one to be a proficient researcher.

Here are some effective strategies that I have found in my experience: Define Clear Objectives: Clearly define the research objectives and goals of the experiment. Ensure everyone in the team understands the purpose of the research and what is expected to be achieved. Team Training: Provide training sessions or workshops on experimental techniques, data analysis, and safety protocols. Ensure everyone in the team has a solid foundation in these areas. Effective Communication: Encourage open communication within the team. Regular team meetings, brainstorming sessions, and discussions about experimental design can enhance collective knowledge and problem-solving skills.