findings conclusions and recommendations in research

Plasma Processing of Materials: Scientific Opportunities and Technological Challenges (1991)

Chapter: 1 summary, findings, conclusions, and recommendations, 1 summary, findings, conclusions, and recommendations.

This study focuses on the plasma processing of materials, a technology that impacts and is of vital importance to several of the largest manufacturing industries in the world. Foremost among these industries is the electronics industry, in which plasma-based processes are indispensable for the manufacture of very large-scale integrated (VLSI) microelectronic circuits (or chips). Plasma processing of materials is also a critical technology in the aerospace, automotive, steel, biomedical, and toxic waste management industries. Because plasma processing is an integral part of the infrastructure of so many American industries, it is important for both the economy and the national security that America maintain a strong leadership role in this technology.

A plasma is a partially or fully ionized gas containing electrons, ions, and neutral atoms or molecules. In Chapter 2 , the panel categorizes different kinds of plasmas and focuses on properties of man-made low-energy, highly collisional plasmas that are particularly useful in materials processing applications. The outstanding properties of most plasmas applied to processing of materials are associated with nonequilibrium conditions. These properties present a challenge to the plasma scientist and an opportunity to the technologist. The opportunities for materials processing stem from the ability of a plasma to provide a highly excited medium that has no chemical or physical counterpart in a natural, equilibrium environment. Plasmas alter the normal pathways through which chemical systems evolve from one stable state to another, thus providing the potential to produce materials with properties that are not attainable by any other means.

Applications of plasma-based systems used to process materials are diverse because of the broad range of plasma conditions, geometries, and excitation methods that may be used. The scientific underpinnings of plasma applications are multidisciplinary and include elements of electrodynamics, atomic science, surface science, computer science, and industrial process control. Because of the diversity of applications and the multidisciplinary nature of the science, scientific understanding lags technology. This report highlights this critical issue.

A summary of the many industrial applications of plasma-based systems for processing materials is included in Chapter 2 . Electronics and aerospace are the two major industries that are served by plasma processing technologies, although the automotive industry is likely to become a significant user of plasma-processed materials like those now in widespread use in the aerospace industry. The critical role of plasma processing technology in industry is illustrated in Chapter 2 .

For the electronics industry more than for any other considered by the panel, the impact of—and the critical and urgent need for—plasma-based materials processing is overwhelming. Thus Chapter 3 further elucidates plasma processing of electronic materials and, in particular, the use of plasmas in fabricating microelectronic components. The plasma equipment industry is an integral part of the electronics industry and has experienced dramatic growth in recent years because of the increasing use of plasma processes to meet the demands of fabricating devices with continually shrinking dimensions. In this country, the plasma equipment industry

is composed of many small companies loosely connected to integrated circuit manufacturers. In Japan, on the other hand, equipment vendors and device manufacturers are tightly linked and are often parts of the same company.

Plasma processes used today in fabricating microelectronic devices have been developed largely by time-consuming, costly, empirical exploration. The chemical and physical complexity of plasma-surface interactions has so far eluded the accurate numerical simulation that would enable process design. Similarly, plasma reactors have also been developed by trial and error. This is due, in part, to the fact that reactor design is intimately intertwined with the materials process for which it will be used. Nonetheless, fundamental studies of surface processes and plasma phenomena—both experimental and numerical—have contributed to process development by providing key insights that enable limitation of the broad process-variable operating space. The state of the science that underpins plasma processing technology in the United States is outlined in Chapter 4 . Although an impressive arsenal of both experimental and numerical tools has been developed, significant gaps in understanding and lack of instrumentation limit progress.

The broad interdisciplinary nature of plasma processing is highlighted in the discussion of education issues outlined in Chapter 5 , which addresses the challenges and opportunities associated with providing a science education in the area of plasma processing. For example, graduate programs specifically focused on plasma processing are rare because of insufficient funding of university research programs in this field. By contrast, both Japan and France have national initiatives that support education and research in plasma processing.

FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS

Finding and Conclusion : In recent years, the number of applications requiring plasmas in the processing of materials has increased dramatically. Plasma processing is now indispensable to the fabrication of electronic components and is widely used in the aerospace industry and other industries. However, the United States is seeing a serious decline in plasma reactor development that is critical to plasma processing steps in the manufacture of VLSI microelectronic circuits. In the interest of the U.S. economy and national defense, renewed support for low-energy plasma science is imperative.

Finding and Conclusion : The demand for technology development is outstripping scientific understanding of many low-energy plasma processes. The central scientific problem underlying plasma processing concerns the interaction of low-energy collisional plasmas with solid surfaces. Understanding this problem requires knowledge and expertise drawn from plasma physics, atomic physics, condensed matter physics, chemistry, chemical engineering, electrical engineering, materials science, computer science, and computer engineering. In the absence of a coordinated approach, the diversity of the applications and of the science tends to diffuse the focus of both.

Finding : Technically, U.S. laboratories have made many excellent contributions to plasma processing research—making fundamental discoveries, developing numerical algorithms, and inventing new diagnostic techniques. However, poor coordination and inefficient transfer of insights gained from this research have inhibited its use in the design of new plasma reactors and processes.

Finding : The Panel on Plasma Processing of Materials finds that plasma processing of materials is a critical technology that is necessary to implement key recommendations contained in the National Research Council report Materials Science and Engineering for the 1990s (National Academy Press, Washington, D.C., 1989) and to enhance the health of technologies as identified in Report of the National Critical Technologies Panel (U.S. Government Printing Office, Washington, D.C., 1991). Specifically, plasma processing is an essential element in the synthesis and processing arsenal for manufacturing electronic, photonic, ceramic, composite, high-performance metal, and alloy materials.

Accordingly, the panel recommends:

Plasma processing should be identified as a component program of the Federal Initiative on advanced materials synthesis and processing that currently is being developed by the Office of Science and Technology Policy.

Through such a Plasma Processing Program, federal funds should be allocated specifically to stimulate focused research in plasma processing, both basic and applied, consistent with the long-term economic and defense goals of the nation.

The Plasma Processing Program should not only provide focus on common goals and promote coordination of the research performed by the national laboratories, universities, and industrial laboratories, but also integrate plasma equipment suppliers into the program.

Finding and Conclusion : Currently, computer-based modeling and plasma simulation are inadequate for developing plasma reactors. As a result, the detailed descriptions required to guide the transfer of processes from one reactor to another or to scale processes from a small to a large reactor are not available. Until we understand how geometry, electromagnetic design, and plasma-surface interactions affect material properties, the choice of plasma reactor for a given process will not be obvious, and costly trial-and-error methods will continue to be used. Yet there is no fundamental obstacle to improved modeling and simulation nor to the eventual creation of computer-aided design (CAD) tools for designing plasma reactors. The key missing ingredients are the following:

A reliable and extensive plasma data base against which the accuracy of simulations of plasmas can be compared . Plasma measurement technologies are sophisticated, but at present experiments are performed on a large variety of different reactors under widely varying conditions. A coordinated effort to diagnose simple, reference reactors is necessary to generate the necessary data base for evaluation of simulation results and to test new and old experimental methodology.

A reliable and extensive input data base for calculating plasma generation, transport, and surface interaction . The dearth of basic data needed for simulation of plasma generation, transport, and surface reaction processes results directly from insufficient generation of data, insufficient data compilation, insufficient distribution of data, and insufficient funding of these activities. The critical basic data needed for simulations and experiments have not been prioritized. For plasma-surface interactions, in particular, lack of data has precluded the formation of mechanistic models on which simulation tools are based. Further experimental studies are needed to elucidate these mechanisms.

Efficient numerical algorithms and supercomputers for simulating magnetized plasmas in three dimensions . The advent of unprecedented supercomputer capability in the next 5 to 10 years will have a major impact in this area, provided that current simulation methods are expanded to account for multidimensional effects in magnetized plasmas.

The Plasma Processing Program should include a thrust toward development of computer-aided design tools for developing and designing new plasma reactors.

The Plasma Processing Program should emphasize a coordinated approach toward generating the diagnostic and basic data needed for improved plasma and plasma-surface simulation capability.

A program to extend current algorithms for plasma reactor simulation should be included among the activities funded under the umbrella of the federal High

Performance Computing and Communications program 1 developed by the Office of Science and Technology Policy and started in FY92.

Finding and Conclusion : In the coming decade, custom-designed and custom-manufactured chips, i.e ., application-specific integrated circuits (ASICs), will gain an increasing fraction of the world market in microelectronic components. This market, in turn, will belong to the flexible manufacturer who uses a common set of processes and equipment to fabricate many different circuit designs. Such flexibility in processing will result only from real understanding of processes and reactors. On the other hand, plasma processes in use today have been developed using a combination of intuition, empiricism, and statistical optimization. Although it is unlikely that detailed, quantitative, first-principles-based simulation tools will be available for process design in the near future, design aids such as expert systems, which can be used to guide engineers in selecting initial conditions from which the final process is derived, could be developed if gaps in our fundamental understanding of plasma chemistry were filled.

Finding and Conclusion : Three areas are recognized by the panel as needing concerted, coordinated experimental and theoretical research: surface processes, plasma generation and transport, and plasma-surface interactions. For surface processes, studies using well-controlled reactive beams impinging on well-characterized surfaces are essential for enhancing our understanding and developing mechanistic models. For plasma generation and transport, chemical kinetic data and diagnostic data are needed to augment the basic plasma reactor CAD tool. For studying plasma-surface interactions, there is an urgent need for in situ analytical tools that provide information on surface composition, electronic structure, and material properties.

Finding and Conclusion : Breakthroughs in understanding the science will be paced by development of tools for the characterization of the systems. To meet the coming demands for flexible device manufacturing, plasma processes will have to be actively and precisely controlled. But today no diagnostic techniques exist that can be used unambiguously to determine material properties related to device yield. Moreover, the parametric models needed to relate diagnostic data to process variables are also lacking.

According, the panel recommends:

The Plasma Processing Program should be dedicated in part to the development of plasma process expert systems.

A coordinated program should be supported to generate basic data and simulation of surface processes, plasma generation and transport, and plasma-surface interactions.

A program should be supported that focuses on development of new instrumentation for real-time, in situ monitoring for control and analysis.

Finding : Research resources in low-energy plasma science in the United States are eroding at an alarming rate. U.S. scientists trained in this area in the 1950s and early 1960s are retiring or are moving to other areas of science for which support is more forthcoming. When compared to those in Japan and France, the U.S. educational infrastructure in plasma processing lacks focus, coordination, and funding. As a result, the United States will not be prepared to maintain its leading market position in plasma processing, let alone capture more market share as the plasma process industry grows into the 21st century.

Finding : Graduate programs are not offering adequate educational opportunities in the science of weakly ionized, highly collisional plasmas. An informal survey by the panel indicated that only a few U.S. universities offer formal course work in this science and that there are

insufficient texts on collisional plasmas and plasma processing. These deficiencies are a direct result of low-level funding for graduate research in plasma processing and low-energy plasmas.

Finding and Conclusion : The most serious need in undergraduate education is adequate, modern teaching laboratories. Due to the largely empirical nature of many aspects of plasma processing, proper training in the traditional scientific method, as provided in laboratory classes, is a necessary component of undergraduate education. The Instrumentation and Laboratory Improvement Program sponsored by the National Science Foundation has been partly successful in fulfilling these needs, but it is not sufficient.

Finding and Conclusion : Research experiences for undergraduates made available through industrial cooperative programs or internships are essential for high-quality technical education. But teachers and professors themselves must first be educated in low-energy plasma science and plasma processing before they can be expected to educate students. Industrial-university links can also help to impart a much needed, longer-term view to industrial research efforts.

As part of the Plasma Processing Program, government and industry together should support cooperative programs specific to plasma processing with universities and national laboratories.

A program should be established to provide industrial internships for teachers and professors in the area of plasma processing.

Plasma processing of materials is a critical technology to several of the largest manufacturing industries in the world—electronics, aerospace, automotive, steel, biomedical, and toxic waste management. This book describes the relationship between plasma processes and the many industrial applications, examines in detail plasma processing in the electronics industry, highlights the scientific foundation underlying this technology, and discusses education issues in this multidisciplinary field.

The committee recommends a coordinated, focused, and well-funded research program in this area that involves the university, federal laboratory, and industrial sectors of the community. It also points out that because plasma processing is an integral part of the infrastructure of so many American industries, it is important for both the economy and the national security that America maintain a strong leadership role in this technology.

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• Differences between a finding, a conclusion, and a recommendation: examples
• Career Center

Table of Contents

• Defining the Terms: What Is a Finding, a Conclusion, and a Recommendation in M&E?
• Why It Matters: Understanding the Importance of Differentiating between Findings, Conclusions, and Recommendations in M&E
• How to Identify and Distinguish between Findings, Conclusions, and Recommendations in M&E
• How to Communicate Findings, Conclusions, and Recommendations Effectively in M&E Reports
• The Benefits of Clear and Accurate Reporting of Findings, Conclusions, and Recommendations in M&E

1. Defining the Terms: What Is a Finding, a Conclusion, and a Recommendation in M&E?

Monitoring and Evaluation (M&E) is a critical process for assessing the effectiveness of development programs and policies. During the M&E process, evaluators collect and analyze data to draw conclusions and make recommendations for program improvement. In M&E, it is essential to differentiate between findings, conclusions, and recommendations to ensure that the evaluation report accurately reflects the program’s strengths, weaknesses, and potential areas for improvement.

In an evaluation report, a finding, a conclusion, and a recommendation serve different purposes and convey different information. Here are the differences between these three elements:

1.1 Finding

A finding is a factual statement that is based on evidence collected during the evaluation . It describes what was observed, heard, or experienced during the evaluation process. A finding should be objective, unbiased, and supported by data. Findings are typically presented in the form of a summary or a list of key points, and they provide the basis for the evaluation’s conclusions and recommendations.

Findings are an important part of the evaluation process, as they provide objective and unbiased information about what was observed, heard, or experienced during the evaluation. Findings are based on the evidence collected during the evaluation, and they should be supported by data and other relevant information. They are typically presented in a summary or list format, and they serve as the basis for the evaluation’s conclusions and recommendations. By presenting clear and accurate findings, evaluators can help stakeholders understand the strengths and weaknesses of the program or initiative being evaluated, and identify opportunities for improvement.

1.2 Examples of Finding

Here are some examples of findings in M&E:

• “Program participants reported a high level of satisfaction with the quality of training provided, with 85% rating it as good or excellent.”
• “The program was successful in increasing the number of girls enrolled in secondary school, with a 25% increase observed in the target communities.”
• “Program beneficiaries reported improved access to healthcare services, with a 40% increase in the number of individuals accessing healthcare facilities in the program area.”
• “The program’s training curriculum was found to be outdated and ineffective, with only 30% of participants reporting that the training was useful.”
• “The program’s monitoring and evaluation system was found to be inadequate, with data quality issues and insufficient capacity among staff to carry out effective monitoring and evaluation activities.”

These findings represent objective, measurable results of the data collected during the M&E process, and can be used to inform program design and implementation, as well as to draw conclusions and make recommendations for improvement.

1.3 Conclusion

A conclusion is a judgment or interpretation of the findings based on the evidence collected during the evaluation. It is typically expressed in terms of what the findings mean or what can be inferred from them. Conclusions should be logical, evidence-based, and free from personal bias or opinion.

Conclusions often answer the evaluation questions or objectives, and they provide insights into the effectiveness or impact of the program, project, or intervention being evaluated. By synthesizing the findings into a cohesive narrative, evaluators can provide stakeholders with a clear and actionable understanding of the program or initiative being evaluated. Conclusions can also inform future planning and decision-making, by identifying areas for improvement and highlighting successful strategies or interventions. Overall, conclusions are a crucial component of the evaluation process, as they help stakeholders make informed decisions about the programs and initiatives they are involved in.

1.4 Examples of Conclusion

Here are some examples of conclusions in M&E:

• Based on the data collected, it can be concluded that the program was successful in achieving its objective of increasing access to clean water in the target communities.”
• “The data indicates that the program’s training curriculum is ineffective and in need of revision in order to better meet the needs of participants.”
• “It can be concluded that the program’s community mobilization efforts were successful in increasing community participation and ownership of the program.”
• “Based on the data collected, it is concluded that the program’s impact on improving maternal and child health outcomes is limited and further efforts are needed to address the underlying health system and infrastructure issues.”
• “The data collected indicates that the program’s impact on reducing poverty in the target area is modest, but still significant, and further investment in complementary programs may be needed to achieve more substantial reductions in poverty rates.”
• These conclusions are based on the evidence presented in the findings and represent the interpretation or explanation of the meaning of the findings. They help to provide insight into the impact and effectiveness of the program and can be used to make recommendations for improvement.

1.5 Recommendation

A recommendation is a specific action or set of actions proposed based on the findings and conclusions of the evaluation. Recommendations should be practical, feasible, and tailored to the needs of the stakeholders who will be implementing them. They should be supported by evidence and aligned with the goals of the program, project, or intervention being evaluated.

Recommendations often provide guidance on how to improve the effectiveness or efficiency of the program, project, or intervention, and they can help to inform decision-making and resource allocation. By presenting clear and actionable recommendations, evaluators can help stakeholders identify and prioritize areas for improvement, and develop strategies to address identified issues. Recommendations can also serve as a roadmap for future planning and implementation and can help to ensure that the program or initiative continues to achieve its intended outcomes over time.

Overall, recommendations are an essential component of the evaluation process, as they help to bridge the gap between evaluation findings and programmatic action. By proposing specific and evidence-based actions, evaluators can help to ensure that evaluation results are translated into meaningful improvements in program design, implementation, and outcomes.

1.6 Examples of Recommendation

Here are some examples of recommendations in M&E:

• “To improve the effectiveness of the program’s training, the curriculum should be revised to better meet the needs of participants, with a focus on practical, hands-on learning activities.”
• “To address the data quality issues identified in the monitoring and evaluation system, staff should receive additional training on data collection and management, and the system should be revised to incorporate additional quality control measures.”
• “To build on the success of the program’s community mobilization efforts, further investments should be made in strengthening community-based organizations and networks, and in promoting greater community participation in program planning and decision-making.”
• “To improve the program’s impact on maternal and child health outcomes, efforts should be made to address underlying health system and infrastructure issues, such as improving access to health facilities and training health workers.”
• “To achieve more substantial reductions in poverty rates in the target area, complementary programs should be implemented to address issues such as economic development, education, and social protection.”

These recommendations are specific actions that can be taken based on the findings and conclusions of the M&E process. They should be practical, feasible, and based on the evidence presented in the evaluation report. By implementing these recommendations, development practitioners can improve program effectiveness and impact, and better meet the needs of the target population.

2. Why It Matters: Understanding the Importance of Differentiating between Findings, Conclusions, and Recommendations in M&E

Differentiating between findings, conclusions, and recommendations is crucial in M&E for several reasons. First, it ensures accuracy and clarity in the evaluation report. Findings, conclusions, and recommendations are distinct components of an evaluation report, and they serve different purposes. By clearly defining and differentiating these components, evaluators can ensure that the report accurately reflects the program’s strengths and weaknesses, potential areas for improvement, and the evidence supporting the evaluation’s conclusions.

Second, differentiating between findings, conclusions, and recommendations helps to facilitate evidence-based decision-making. By clearly presenting the evidence supporting the evaluation’s findings and conclusions, and making recommendations based on that evidence, evaluators can help program managers and policymakers make informed decisions about program design, implementation, and resource allocation.

Finally, differentiating between findings, conclusions, and recommendations can help to increase the credibility and trustworthiness of the evaluation report. Clear and accurate reporting of findings, conclusions, and recommendations helps to ensure that stakeholders understand the evaluation’s results and recommendations, and can have confidence in the evaluation’s rigor and objectivity.

In summary, differentiating between findings, conclusions, and recommendations is essential in M&E to ensure accuracy and clarity in the evaluation report, facilitate evidence-based decision-making, and increase the credibility and trustworthiness of the evaluation.

3. How to Identify and Distinguish between Findings, Conclusions, and Recommendations in M&E

Identifying and distinguishing between findings, conclusions, and recommendations in M&E requires careful consideration of the evidence and the purpose of each component. Here are some tips for identifying and distinguishing between findings, conclusions, and recommendations in M&E:

• Findings: Findings are the results of the data analysis and should be objective and evidence-based. To identify findings, look for statements that summarize the data collected and analyzed during the evaluation. Findings should be specific, measurable, and clearly stated.
• Conclusions: Conclusions are interpretations of the findings and should be supported by the evidence. To distinguish conclusions from findings, look for statements that interpret or explain the meaning of the findings. Conclusions should be logical and clearly explained, and should take into account any limitations of the data or analysis.
• Recommendations: Recommendations are specific actions that can be taken based on the findings and conclusions. To distinguish recommendations from conclusions, look for statements that propose actions to address the issues identified in the evaluation. Recommendations should be practical, feasible, and clearly explained, and should be based on the evidence presented in the findings and conclusions.

It is also important to ensure that each component is clearly labeled and presented in a logical order in the evaluation report. Findings should be presented first, followed by conclusions and then recommendations.

In summary, identifying and distinguishing between findings, conclusions, and recommendations in M&E requires careful consideration of the evidence and the purpose of each component. By ensuring that each component is clearly labeled and presented in a logical order, evaluators can help to ensure that the evaluation report accurately reflects the program’s strengths, weaknesses, and potential areas for improvement, and facilitates evidence-based decision-making.

4. How to Communicate Findings, Conclusions, and Recommendations Effectively in M&E Reports

Communicating findings, conclusions, and recommendations effectively in M&E reports is critical to ensuring that stakeholders understand the evaluation’s results and recommendations and can use them to inform decision-making. Here are some tips for communicating findings, conclusions, and recommendations effectively in M&E reports:

• Use clear and concise language: Use clear, simple language to explain the findings, conclusions, and recommendations. Avoid technical jargon and use examples to illustrate key points.
• Present data visually: Use tables, graphs, and charts to present data visually, making it easier for stakeholders to understand and interpret the findings.
• Provide context: Provide context for the findings, conclusions, and recommendations by explaining the evaluation’s purpose, methodology, and limitations. This helps stakeholders understand the scope and significance of the evaluation’s results and recommendations.
• Highlight key points: Use headings, bullet points, and other formatting techniques to highlight key points, making it easier for stakeholders to identify and remember the most important findings, conclusions, and recommendations.
• Be objective: Present the findings, conclusions, and recommendations objectively and avoid bias. This helps to ensure that stakeholders have confidence in the evaluation’s rigor and objectivity.
• Tailor the report to the audience: Tailor the report to the audience by using language and examples that are relevant to their interests and needs. This helps to ensure that the report is accessible and useful to stakeholders.

In summary, communicating findings, conclusions, and recommendations effectively in M&E reports requires clear and concise language, visual presentation of data, contextualization, highlighting of key points, objectivity, and audience-tailoring. By following these tips, evaluators can help to ensure that stakeholders understand the evaluation’s results and recommendations and can use them to inform decision-making.

5. The Benefits of Clear and Accurate Reporting of Findings, Conclusions, and Recommendations in M&E

Clear and accurate reporting of M&E findings, conclusions, and recommendations has many benefits for development programs and policies. One of the most significant benefits is improved program design and implementation. By clearly identifying areas for improvement, program designers and implementers can make adjustments that lead to more effective and efficient programs that better meet the needs of the target population.

Another important benefit is evidence-based decision-making. When M&E findings, conclusions, and recommendations are reported accurately and clearly, decision-makers have access to reliable information on which to base their decisions. This can lead to more informed decisions about program design, implementation, and resource allocation.

Clear and accurate reporting of M&E findings, conclusions, and recommendations also supports accountability. By reporting transparently on program performance, development practitioners can build trust and support among stakeholders, including program beneficiaries, donors, and the general public.

M&E findings, conclusions, and recommendations also support continuous learning and improvement. By identifying best practices, lessons learned, and areas for improvement, development practitioners can use this information to improve future programming.

Finally, clear and accurate reporting of M&E findings, conclusions, and recommendations can increase program impact. By identifying areas for improvement and supporting evidence-based decision-making, development programs can have a greater positive impact on the communities they serve.

In summary, clear and accurate reporting of M&E findings, conclusions, and recommendations is critical for improving program design and implementation, supporting evidence-based decision-making, ensuring accountability, supporting continuous learning and improvement, and increasing program impact. By prioritizing clear and accurate reporting, development practitioners can ensure that their programs are effective, efficient, and have a positive impact on the communities they serve.

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• How to Write Recommendations in Research | Examples & Tips

How to Write Recommendations in Research | Examples & Tips

Published on September 15, 2022 by Tegan George . Revised on July 18, 2023.

Recommendations in research are a crucial component of your discussion section and the conclusion of your thesis , dissertation , or research paper .

As you conduct your research and analyze the data you collected , perhaps there are ideas or results that don’t quite fit the scope of your research topic. Or, maybe your results suggest that there are further implications of your results or the causal relationships between previously-studied variables than covered in extant research.

Table of contents

What should recommendations look like, building your research recommendation, how should your recommendations be written, recommendation in research example, other interesting articles, frequently asked questions about recommendations.

Recommendations for future research should be:

• Concrete and specific
• Supported with a clear rationale
• Directly connected to your research

Overall, strive to highlight ways other researchers can reproduce or replicate your results to draw further conclusions, and suggest different directions that future research can take, if applicable.

Relatedly, when making these recommendations, avoid:

• Undermining your own work, but rather offer suggestions on how future studies can build upon it
• Suggesting recommendations actually needed to complete your argument, but rather ensure that your research stands alone on its own merits
• Using recommendations as a place for self-criticism, but rather as a natural extension point for your work

Prevent plagiarism. Run a free check.

There are many different ways to frame recommendations, but the easiest is perhaps to follow the formula of research question   conclusion  recommendation. Here’s an example.

Conclusion An important condition for controlling many social skills is mastering language. If children have a better command of language, they can express themselves better and are better able to understand their peers. Opportunities to practice social skills are thus dependent on the development of language skills.

As a rule of thumb, try to limit yourself to only the most relevant future recommendations: ones that stem directly from your work. While you can have multiple recommendations for each research conclusion, it is also acceptable to have one recommendation that is connected to more than one conclusion.

These recommendations should be targeted at your audience, specifically toward peers or colleagues in your field that work on similar subjects to your paper or dissertation topic . They can flow directly from any limitations you found while conducting your work, offering concrete and actionable possibilities for how future research can build on anything that your own work was unable to address at the time of your writing.

See below for a full research recommendation example that you can use as a template to write your own.

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If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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While it may be tempting to present new arguments or evidence in your thesis or disseration conclusion , especially if you have a particularly striking argument you’d like to finish your analysis with, you shouldn’t. Theses and dissertations follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the discussion section and results section .) The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

The conclusion of your thesis or dissertation should include the following:

• A restatement of your research question
• A summary of your key arguments and/or results
• A short discussion of the implications of your research

For a stronger dissertation conclusion , avoid including:

• Important evidence or analysis that wasn’t mentioned in the discussion section and results section
• Generic concluding phrases (e.g. “In conclusion …”)
• Weak statements that undermine your argument (e.g., “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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National Research Council (US) Panel on Collecting, Storing, Accessing, and Protecting Biological Specimens and Biodata in Social Surveys; Hauser RM, Weinstein M, Pool R, et al., editors. Conducting Biosocial Surveys: Collecting, Storing, Accessing, and Protecting Biospecimens and Biodata. Washington (DC): National Academies Press (US); 2010.

Conducting Biosocial Surveys: Collecting, Storing, Accessing, and Protecting Biospecimens and Biodata.

• Hardcopy Version at National Academies Press

5 Findings, Conclusions, and Recommendations

As the preceding chapters have made clear, incorporating biological specimens into social science surveys holds great scientific potential, but also adds a variety of complications to the tasks of both individual researchers and institutions. These complications arise in a number of areas, including collecting, storing, using, and distributing biospecimens; sharing data while protecting privacy; obtaining informed consent from participants; and engaging with Institutional Review Boards (IRBs). Any effort to make such research easier and more effective will need to address the issues in these areas.

In considering its recommendations, the panel found it useful to think of two categories: (1) recommendations that apply to individual investigators, and (2) recommendations that are addressed to the National Institute on Aging ( NIA ) or other institutions, particularly funding agencies. Researchers who wish to collect biological specimens with social science data will need to develop new skills in a variety of areas, such as the logistics of specimen storage and management, the development of more diverse informed consent forms, and ways of dealing with the disclosure risks associated with sharing biogenetic data. At the same time, NIA and other funding agencies must provide researchers the tools they need to succeed. These tools include such things as biorepositories for maintaining and distributing specimens, better guidance on informed consent policies, and better ways to share data without risking confidentiality.

• TAKING ADVANTAGE OF EXISTING EXPERTISE

Although working with biological specimens will be new and unfamiliar to many social scientists, it is an area in which biomedical researchers have a great deal of expertise and experience. Many existing documents describe recommended procedures and laboratory practices for the handling of biospecimens. These documents provide an excellent starting point for any social scientist who is interested in adding biospecimens to survey research.

Recommendation 1: Social scientists who are planning to add biological specimens to their survey research should familiarize themselves with existing best practices for the collection, storage, use, and distribution of biospecimens. First and foremost, the design of the protocol for collection must ensure the safety of both participants and survey staff (data and specimen collectors and handlers).

Although existing best-practice documents were not developed with social science surveys in mind, their guidelines have been field-tested and approved by numerous IRBs and ethical oversight committees. The most useful best-practice documents are updated frequently to reflect growing knowledge and changing opinions about the best ways to collect, store, use, and distribute biological specimens. At the same time, however, many issues arising from the inclusion of biospecimens in social science surveys are not fully addressed in the best-practice documents intended for biomedical researchers. For guidance on these issues, it will be necessary to seek out information aimed more specifically at researchers at the intersection of social science and biomedicine.

• COLLECTING, STORING, USING, AND DISTRIBUTING BIOSPECIMENS

As described in Chapter 2 , the collection, storage, use, and distribution of biospecimens and biodata are tasks that are likely to be unfamiliar to many social scientists and that raise a number of issues with which even specialists are still grappling. For example, which biospecimens in a repository should be shared, given that in most cases the amount of each specimen is limited? And given that the available technology for cost-efficient analysis of biospecimens, particularly genetic analysis, is rapidly improving, how much of any specimen should be used for immediate research and analysis, and how much should be stored for analysis at a later date? Collecting, storing, using, and distributing biological specimens also present significant practical and financial challenges for social scientists. Many of the questions they must address, such as exactly what should be held, where it should be held, and what should be shared or distributed, have not yet been resolved.

Developing Data Sharing Plans

An important decision concerns who has access to any leftover biospecimens. This is a problem more for biospecimens than for biodata because in most cases, biospecimens can be exhausted. Should access be determined according to the principle of first funded, first served? Should there be a formal application process for reviewing the scientific merits of a particular investigation? For studies that involve international collaboration, should foreign investigators have access? And how exactly should these decisions be made? Recognizing that some proposed analyses may lie beyond the competence of the original investigators, as well as the possibility that principal investigators may have a conflict of interest in deciding how to use any remaining biospecimens, one option is for a principal investigator to assemble a small scientific committee to judge the merits of each application, including the relevance of the proposed study to the parent study and the capacities of the investigators. Such committees should publish their review criteria to help prospective applicants. A potential problem with such an approach, however, is that many projects may not have adequate funding to carry out such tasks.

Recommendation 2: Early in the planning process, principal investigators who will be collecting biospecimens as part of a social science survey should develop a complete data sharing plan.

This plan should spell out the criteria for allowing other researchers to use (and therefore deplete) the available stock of biospecimens, as well as to gain access to any data derived therefrom. To avoid any appearance of self-interest, a project might empower an external advisory board to make decisions about access to its data. The data sharing plan should also include provisions for the storage and retrieval of biospecimens and clarify how the succession of responsibility for and control of the biospecimens will be handled at the conclusion of the project.

Recommendation 3: NIA (or preferably the National Institutes of Health [ NIH ]) should publish guidelines for principal investigators containing a list of points that need to be considered for an acceptable data sharing plan. In addition to staff review, Scientific Review Panels should read and comment on all proposed data sharing plans. In much the same way as an unacceptable human subjects plan, an inadequate data sharing plan should hold up an otherwise acceptable proposal.

Supporting Social Scientists in the Storage of Biospecimens

The panel believes that many social scientists who decide to add the collection of biospecimens to their surveys may be ill equipped to provide for the storage and distribution of the specimens.

Conclusion: The issues related to the storage and distribution of biospecimens are too complex and involve too many hidden costs to assume that social scientists without suitable knowledge, experience, and resources can handle them without assistance.

Investigators should therefore have the option of delegating the storage and distribution of biospecimens collected as part of social science surveys to a centralized biorepository. Depending on the circumstances, a project might choose to utilize such a facility for immediate use, long-term or archival storage, or not at all.

Recommendation 4: NIA and other relevant funding agencies should support at least one central facility for the storage and distribution of biospecimens collected as part of the research they support.

• PROTECTING PRIVACY AND CONFIDENTIALITY: SHARING DIGITAL REPRESENTATIONS OF BIOLOGICAL AND SOCIAL DATA

Several different types of data must be kept confidential: survey data, data derived from biospecimens, and all administrative and operational data. In the discussion of protecting confidentiality and privacy, this report has focused on biodata, but the panel believes it is important to protect all the data collected from survey participants. For many participants, for example, data on wealth, earnings, or sexual behavior can be as or more sensitive than genetic data.

Conclusion: Although biodata tend to receive more attention in discussions of privacy and confidentiality, social science and operational data can be sensitive in their own right and deserve similar attention in such discussions.

Protecting the participants in a social science survey that collects biospecimens requires securing the data, but data are most valuable when they are made available to researchers as widely as possible. Thus there is an inherent tension between the desire to protect the privacy of the participants and the desire to derive as much scientific value from the data as possible, particularly since the costs of data collection and analysis are so high. The following recommendations regarding confidentiality are made in the spirit of balancing these equally important needs.

Genomic data present a particular challenge. Several researchers have demonstrated that it is possible to identify individuals with even modest amounts of such data. When combined with social science data, genomic data may pose an even greater risk to confidentiality. It is difficult to know how much or which genomic data, when combined with social science data, could become critical identifiers in the future. Although the problem is most significant with genomic data, similar challenges can arise with other kinds of data derived from biospecimens.

Conclusion: Unrestricted distribution of genetic and other biodata risks violating promises of confidentiality made to research participants.

There are two basic approaches to protecting confidentiality: restricting data and restricting access. Restricting data—for example, by stripping individual and spatial identifiers and modifying the data to make it difficult or impossible to trace them back to their source—usually makes it possible to release social science data widely. In the case of biodata, however, there is no answer to how little data is required to make a participant uniquely identifiable. Consequently, any release of biodata must be carefully managed to protect confidentiality.

Recommendation 5: No individual-level data containing uniquely identifying variables, such as genomic data, should be publicly released without explicit informed consent.

Recommendation 6: Genomic data and other individual-level data containing uniquely identifying variables that are stored or in active use by investigators on their institutional or personal computers should be encrypted at all times.

Even if specific identifying variables, such as names and addresses, are stripped from data, it is still often possible to identify the individuals associated with the data by other means, such as using the variables that remain (age, sex, marital status, family income, etc.) to zero in on possible candidates. In the case of biodata that do not uniquely identify individuals and can change with time, such as blood pressure and physical measurements, it may be possible to share the data with no more protection than stripping identifying variables. Even these data, however, if known to intruders, can increase identification disclosure risk when combined with enough other data. With sufficient characteristics to match, intruders can uniquely identify individuals in shared data if given access to another data source that contains the same information plus identifiers.

Conclusion: Even nonunique biodata, if combined with social science data, may pose a serious risk of reidentification.

In the case of high-dimensional genomic data, standard disclosure limitation techniques, such as data perturbation, are not effective with respect to preserving the utility of the data because they involve such extreme alterations that they would severely distort analyses aimed at determining gene—gene and gene—environment interactions. Standard disclosure limitation methods could be used to generate public-use data sets that would enable low-dimensional analyses involving genes, for example, one gene at a time. However, with several such public releases, it may be possible for a key match to be used to construct a data set with higher-dimensional genomic data.

Conclusion: At present, no data restriction strategy has been demonstrated to protect confidentiality while preserving the usefulness of the data for drawing inferences involving high-dimensional interactions among genomic and social science variables, which are increasingly the target of research. Providing public-use genomic data requires such intense data masking to protect confidentiality that it would distort the high-dimensional analyses that could result in ground-breaking research progress.

Recommendation 7: Both rich genomic data acquired for research and sensitive and potentially identifiable social science data that do not change (or change very little) with time should be shared only under restricted circumstances, such as licensing and (actual or virtual) data enclaves.

As discussed in Chapter 3 , the four basic ways to restrict access to data are licensing, remote execution centers, data enclaves, and virtual data enclaves. Each has its advantages and disadvantages. 1 Licensing, for example, is the least restrictive for a researcher in terms of access to the data, but the licensing process itself can be lengthy and burdensome. Thus it would be useful if the licensing process could be facilitated.

Recommendation 8: NIA (or preferably NIH ) should develop new standards and procedures for licensing confidential data in ways that will maximize timely access while maintaining security and that can be used by data repositories and by projects that distribute data.

Ways to improve the other approaches to restricted access are needed as well. For example, improving the convenience and availability of virtual data enclaves could increase the use of combined social science and biodata without a significant increase in risk to confidentiality. The panel notes that much of the discussion of the confidentiality risk posed by the various approaches is theoretical; no one has a clear idea of just what disclosure risks are associated with the various ways of sharing data. It is important to learn more about these disclosure risks for a variety of reasons—determining how to minimize the risks, for instance, or knowing which approaches to sharing data pose the least risk. It would also be useful to be able to describe disclosure risks more accurately to survey participants.

Recommendation 9: NIA and other funding agencies should assess the strength of confidentiality protections through periodic expert audits of confidentiality and computer security. Willingness to participate in such audits should be a condition for receipt of NIA support. Beyond enforcement, the purpose of such audits would be to identify challenges and solutions.

Evaluating risks and applying protection methods, whether they involve restricted access or restricted data, is a complex process requiring expertise in disclosure protection methods that exceeds what individual principal investigators and their institutions usually possess. Currently, not enough is known to be able to represent these risks either fully or accurately. The NIH requirement for data sharing necessitates a large investment of resources to anticipate which variables are potentially available to intruders and to alter data in ways that reduce disclosure risks while maintaining the utility of the data. Such resources are better spent by principal investigators on collecting and analyzing the data.

Recommendation 10: NIH should consider funding Centers of Excellence to explore new ways of protecting digital representations of data and to assist principal investigators wishing to share data with others. NIH should also support research on disclosure risks and limitations.

Principal investigators could send digital data to these centers, which would organize and manage any restricted access or restricted data policies or provide advisory services to investigators. NIH would maintain the authority to penalize those who violated any confidentiality agreements, for example, by denying them or their home institution NIH funding. Models for these centers include the Inter-university Consortium for Political and Social Research ( ICPSR ) and its projects supported by NIH and the Eunice Kennedy Shriver National Institute of Child Health and Human Development ( NICHD ) and the UK data sharing archive. The centers would alleviate the burden of data sharing as mandated of principal investigators by NIH and place it in expert hands. However, excellence in the design of data access and control systems is likely to require intimate knowledge of each specific data resource, so data producers should be involved in the systems’ development.

• INFORMED CONSENT

As described in Chapter 4 , informed consent is a complex subject involving many issues that are still being debated; the growing power of genetic analysis techniques and bioinformatics has only added to this complexity. Given the rapid pace of advances in scientific knowledge and in the technology used to analyze biological materials, it is impossible to predict what information might be gleaned from biological specimens just a few years hence; accordingly, it is impossible, even in theory, to talk about perfectly informed consent. The best one can hope for is relatively well-informed consent from a study’s participants, but knowing precisely what that means is difficult. Determining the scope of informed consent adds another layer of complexity. Will new analyses be covered under the existing consent, for example? There are no clear guidelines on such questions, yet specific details on the scope of consent will likely affect an IRB ’s reaction to a study proposal.

What Individual Researchers Need to Know and Do Regarding Informed Consent

To be sure, there is a wide range of views about the practicality of providing adequate protection to participants while proceeding with the scientific enterprise, from assertions that it is simply not possible to provide adequate protection to offers of numerous procedural safeguards but no iron-clad guarantees. This report takes the latter position—that investigators should do their best to communicate adequately and accurately with participants, to provide procedural safeguards to the extent possible, and not to promise what is not possible. 2 Social science researchers need to know that adding the collection of biospecimens to social science surveys changes the nature of informed consent. Informed consent for a traditional social science survey may entail little more than reading a short script over the phone and asking whether the participant is willing to continue; obtaining informed consent for the collection and use of biospecimens and biodata is generally a much more involved process.

Conclusion: Social scientists should be made aware that the process of obtaining informed consent for the use of biospecimens and biodata typically differs from social science norms.

If participants are to provide truly informed consent to taking part in any study, they must be given a certain minimum amount of information. They should be told, for example, what the purpose of the study is, how it is to be carried out, and what participants’ roles are. In addition, because of the unique risks associated with providing biospecimens, participants in a social science survey that involves the collection of such specimens should be provided with other types of information as well. In particular, they should be given detail on the storage and use of the specimens that relates to those risks and can assist them in determining whether to take part in the study.

Recommendation 11: In designing a consent form for the collection of biospecimens, in addition to those elements that are common to social science and biomedical research, investigators should ensure that certain other information is provided to participants: how long researchers intend to retain their biospecimens and the genomic and other biodata that may be derived from them; both the risks associated with genomic data and the limits of what they can reveal; which other researchers will have access to their specimens, to the data derived therefrom, and to information collected in a survey questionnaire; the limits on researchers’ ability to maintain confidentiality; any potential limits on participants’ ability to withdraw their specimens or data from the research; the penalties 3 that may be imposed on researchers for various types of breaches of confidentiality; and what plans have been put in place to return to them any medically relevant findings.

Researchers who fail to properly plan for and handle all of these issues before proceeding with a study are in essence compromising assurances under informed consent. The literature on informed consent emphasizes the importance of ensuring that participants understand reasonably well what they are consenting to. This understanding cannot be taken for granted, particularly as it pertains to the use of biological specimens and the data derived therefrom. While it is not possible to guarantee that participants have a complete understanding of the scientific uses of their specimens or all the possible risks of their participation, they should be able to make a relatively well-informed decision about whether to take part in the study. Thus the ability of various participants to understand the research and the informed consent process must be considered. Even impaired individuals may be able to participate in research if their interests are protected and they can do so only through proxy consent. 4

Recommendation 12: NIA should locate and publicize positive examples of the documentation of consent processes for the collection of biospecimens. In particular, these examples should take into account the special needs of certain individuals, such as those with sensory problems and the cognitively impaired.

Participants in a biosocial survey are likely to have different levels of comfort concerning how their biospecimens and data will be used. Some may be willing to provide only answers to questions, for example, while others may both answer questions and provide specimens. Among those who provide specimens, some may be willing for the specimens to be used only for the current study, while others may consent to their use in future studies. One effective way to deal with these different comfort levels is to offer a tiered approach to consent that allows participants to determine just how their specimens and data will be used. Tiers might include participating in the survey, providing specimens for genetic and/or nongenetic analysis in a particular study, and allowing the specimens and data to be stored for future uses (genetic and/or nongenetic). For those participants who are willing to have their specimens and data used in future studies, researchers should tell them what sort of approval will be obtained for such use. For example, an IRB may demand reconsent, in which case participants may have to be contacted again before their specimens and data can be used. Ideally, researchers should design their consent forms to avoid the possibility that an IRB will demand a costly or infeasible reconsent process.

Recommendation 13: Researchers should consider adopting a tiered approach to obtaining consent. Participants who are willing to have their specimens and data used in future studies should be informed about the process that will be used to obtain approval for such uses.

What Institutions Should Do Regarding Informed Consent

Because the details of informed consent vary from study to study, individual investigators must bear ultimate responsibility for determining the details of informed consent for any particular study. Thus researchers must understand the various issues and concerns surrounding informed consent and be prepared to make decisions about the appropriate approach for their research in consultation with staff of survey organizations. These decisions should be addressed in the training of survey interviewers. As noted above, however, the issues surrounding informed consent are complex and not completely resolved, and researchers have few options for learning about informed consent as it applies to social science studies that collect biospecimens. Thus it makes sense for agencies funding this research, the Office for Human Research Protection ( OHRP ), or other appropriate organizations (for example, Public Responsibility in Medicine and Research [PRIM&R]) to provide opportunities for such learning, taking into account the fact that the issues arising in biosocial research do not arise in the standard informed consent situations encountered in social science research. It should also be made clear that the researchers’ institution is usually deemed (e.g., in the courts) to bear much of the responsibility for informed consent.

Recommendation 14: NIA , OHRP , and other appropriate organizations should sponsor training programs, create training modules, and hold informational workshops on informed consent for investigators, staff of survey organizations, including field staff, administrators, and members of IRBs who oversee surveys that collect social science data and biospecimens.

The Return of Medically Relevant Information

An issue related to informed consent is how much information to provide to survey participants once their biological specimens have been analyzed and in particular, how to deal with medically relevant information that may arise from the analysis. What, for example, should a researcher do if a survey participant is found to have a genetic disease that does not appear until later in life? Should the participant be notified? Should participants be asked as part of the initial interview whether they wish to be notified about such a discovery? At this time, there are no generally agreed-upon answers to such questions, but researchers should expect to have to deal with these issues as they analyze the data derived from biological specimens.

Recommendation 15: NIH should direct investigators to formulate a plan in advance concerning the return of any medically relevant findings to survey participants and to implement that plan in the design and conduct of their informed consent procedures.

• INSTITUTIONAL REVIEW BOARDS

Investigators seeking IRB approval for biosocial research face a number of challenges. Few IRBs are familiar with both social and biological science; thus, investigators may find themselves trying to justify standard social science protocols to a biologically oriented IRB or explaining standard biological protocols to an IRB that is used to dealing with social science—or sometimes both. Researchers can expect these obstacles, which arise from the interdisciplinary nature of their work, to be exacerbated by a number of other factors that are characteristic of IRBs in general (see Chapter 4 ).

Recommendation 16: In institutions that have separate biomedical and social science IRBs, mechanisms should be created for sharing expertise during the review of biosocial protocols. 5

What Individual Researchers Need to Do Regarding IRBs

Because the collection of biospecimens as part of social science surveys is still relatively unfamiliar to many IRBs, researchers planning such a study can expect their interactions with the IRB overseeing the research to involve a certain learning curve. The IRB may need extra time to become familiar and comfortable with the proposed practices of the survey, and conversely, the researchers will need time to learn what the IRB will require. Thus it will be advantageous if researchers conducting such studies plan from the beginning to devote additional time to working with their IRBs.

Recommendation 17: Investigators considering collecting biospecimens as part of a social science survey should consult with their IRBs early and often.

What Research Agencies Should Do Regarding IRBs

One way to improve the IRB process would be to give members of IRBs an opportunity to learn more about biosocial research and the risks it entails. This could be done by individual institutions, but it would be more effective if a national funding agency took the lead (see Recommendation 14).

It is the panel’s hope that its recommendations will support the incorporation of social science and biological data into empirical models, allowing researchers to better document the linkages among social, behavioral, and biological processes that affect health and other measures of well-being while avoiding or minimizing many of the challenges that may arise. Implementing these recommendations will require the combined efforts of both individual investigators and the agencies that support them.

See the discussion on “Choosing a Data Sharing Strategy” in Chapter 3 .

In a few cases, it may be necessary to deceive participants about the purposes of a study—for example, in field tests of labor market discrimination—but these situations are unlikely to occur in biosocial studies. However, the Common Rule (45 CFR 46: 46.116.c.2, 46.116.d.3) explicitly permits such exceptions when they are scientifically necessary.

Penalties might include NIH eliminating researchers’ eligibility for funding and institutions eliminating research privileges of faculty.

Note that this report does not address the issue of obtaining informed consent from children.

Sharing expertise between biomedical and social science IRBs does not require a return to the days when there was only one IRB at each institution, a situation that still exists at many small institutions. For example, the Social and Behavioral Science IRB at the University of Wisconsin, Madison, has asked a geneticist to serve as an ex officio member of the IRB when it considers protocols that use genetic data.

• Cite this Page National Research Council (US) Panel on Collecting, Storing, Accessing, and Protecting Biological Specimens and Biodata in Social Surveys; Hauser RM, Weinstein M, Pool R, et al., editors. Conducting Biosocial Surveys: Collecting, Storing, Accessing, and Protecting Biospecimens and Biodata. Washington (DC): National Academies Press (US); 2010. 5, Findings, Conclusions, and Recommendations.
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