Data Collection & Analysis

Data collection and analysis are often understood as the most technical aspects of research.  Due to this technical emphasis, the social context surrounding data is often muted, while the procedural aspects are emphasized.  The goal is that data collection and analysis is so standardized and transparent that it is valid and reliable regardless of who does it and in what context.  This goal is central to the efficacy of scientific inquiry.  This reliability and validity of data collection and analysis is one of the major factors that contributes to science’s aim to be a universal knowledge system.  The integrity of the data collection and analysis process must remain intact in order for a scientist to have confidence in the findings and the replicability of the findings regardless of context.  How might a better understanding of the social context of research assist in the goal of data integrity?  And, in turn, how does data integrity based on an understanding of social context help to produce findings that are relevant beyond the immediate context?  

The participants in the project illustrate that a greater understanding of context, cooperation, and communication can improve the reliability and validity of data and create opportunities for bidirectional exchange of skills from African and European partners.  In terms of context, researchers stress the importance of understanding the protocol and procedures of research institutions in the country in which research is being conducted as well as awareness of the cultural context of communities that neighbour conservation areas in which data may be collected.  Furthermore, relationships between members of data collection teams is also important as skills and techniques are exchanged and the scientist-community interface emerges in data collection.

Cooperation is essential for the collection of high-quality data because this is often a group activity.  The image of the lone foreign researcher out in the wilderness on an adventurous expedition is a romanticized notion of a fictionalized past.  In truth, data collection has often relied upon the assistance of African experts as collaborators although their contributions were not acknowledged.  Thus, data collection and analysis must acknowledge these contributions, erasures, and exclusions to validate the importance of data enumerators.  Through acknowledging that African people have historically contributed to scientific research, the place and knowledge of Africans in research about their conservation areas, in the past and present, should be valued.  This should go without saying but, unfortunately, it still needs to be said.  By acknowledging the erasure of the contributions, we can better understand the politics of knowledge in conservation science that often separate Africans and Europeans.  By understanding the exclusion of African people from the entire process of data collection and analysis, we can better address the skills that need to be ascertained so that African scholars can participate in every step of data collection and analysis as well as conduct the type of analyses that they see fit, given their research questions.  

An awareness of communication in data collection and analysis can also help to achieve the goals of reliability and validity in a project with many participants.  Good and continuous communication with people and institutions that neighbour conservation areas reduces the likelihood that respondents provide inaccurate responses.  Good and continuous communication among data collection teams ensures that each individual with knowledge to share is given the opportunity to articulate this knowledge.  In addition, each individual who has questions about the data collection and analysis process is given the opportunity to articulate their questions.  Communication ensures standardization through understanding and transparency.  Building a strong data collection team is essentially about getting everyone on focuses on the same process and outcome.  This requires meeting everyone where they are with the understanding that they have something to contribute to make the research process more thorough.  This also sets the stage for the synthesis of data, which is a complex and multi-dimensional task, and ensures that it is premised on a strong foundation.

The following quotations reflect the views of participants in the AfricanBioServices project when asked about the process of data collection and analysis.  Interviews were recorded and transcribed.  These raw data were analysed and synthesized, through which the three themes, the 3Cs—context, cooperation, and communication—emerged.  This three-part thematic approach is used to classify and present the quotations in an open and structured manner.  This presentation highlights the complexities of interpretation and consensus among a diverse group of scientists.

Though I am born in this country (Tanzania), I travelled to new areas for data collection.  I met people who didn’t speak our national language but only their local language.  I saw so many good things and some troubling.  Living in an urban center, you realize how precious certain resources are—water, food, the basics.  Going to villages opens up a different perspective and different appreciation for rural livelihoods.  They truly depend on the ecosystem.  Their lives depend on it, and it can be heart-breaking because it is difficult to imagine the making of a new future in rural conservation areas.
– Angela Hezekiah

We often take the context for granted.  Everyone thinks they know the Serengeti, but this must be described.  We must be on the same page or, at least, understand the picture in our respective minds before getting into fieldwork.
– Julius Nyahongo

The trick is to develop data collection protocols that are clear and straightforward and to do great training so that everybody collects data consistently in the same way. The frontier here is integrating local knowledge methodologies into scientific methodologies. I don’t see many scientists doing that very well.
– Robin Reid

Is it experimental, purposive, longitudinal?  Is it snowball, questionnaire, survey?  This depends on the aims and the context.
– Julius Nyahongo

For data collection, be prepared for all kinds of transportation, especially walking.  You will walk until you get blisters.  
– Angela Hezekiah

I lived in the village in which we worked.  It gave people the opportunity to see us learning from them.  I learned so much about consumption, time allocation, and sexual division of labor.  Living with a host family is not easy, it may not be for everyone. But push yourself to do it.    
– Flora Manyama

Working with communities, there are demands.  Many thought they would receive tangible benefits, especially those who provided responses to our questionnaires.  Many of these respondents were generous because we had to interview them repeatedly.  We interviewed them seasonally, sometimes as many as four or five times.  Lights in their home can be a nice gift.  Sometimes lunch or dinner.  How can you interview someone who has not eaten for two days?  You must go to the shop and provide something for them and their children in order to continue the interview.  
– Janemary Ntalwila

We must consider ethics.  Is it ethical to go and ask a person these kinds of questions?  
– Julius Nyahongo  

During data collection, particularly when you are discussing a sensitive topic, it is important that people feel comfortable talking with you.  My research focuses on illegal bushmeat hunting.  Any opportunity that I had to engage with respondents, I participated in it.  Even if it was to help someone else with their own work or have a conversation unrelated to research, I tried to be there.  I tried to be as present as possible.  I wanted people in the village to see me working to build trust and learn about them.  They should be able to see your efforts.
– Flora Manyama

Culture comes in.  If you want to interview Maasai ladies but you have assembled a group of men and women, it may fail because men will answer the questions.  If you have questions that are going to be asked among men and women, the context may dictate that you speak to men first.  In other cases, women are the ones who come first.  If you go to visit the household, especially in an area where there are illegal activities, a man will not come first to greet a visitor.   They are not sure if it is a representative of the government or park authority who is looking to catch them.  So the wife will come first and ask what are you trying to do.  Then, you should say we are here to see our friend.   If they are suspicious, they will say he is not around.  Then, you say, may I ask you a few things.  She may say ok but I am cooking so let me go and settle that.  She will go inside and talk to the husband.  They want to know that they have a project that is beneficial.  Then, the husband will come out.  We need to think about these things during data collection.  
– Julius Nyahongo

You go the first time and you think—this is not working.  You go the second time you think—Is this working? It might be working.  By the third time, respondents are beginning to open up and it is very interesting.  Then, by the fourth visit, I knew I would miss these conversations and this experience.  
– Angela Hezekia

It is an honour to be able to work in the Serengeti-Mara ecosystem.  When I step out the door (during fieldwork), I feel highly privileged to be in such a place of such beauty and naturalness.  
– Han Olff

Capacity building, especially in Africa, tends to be framed as degrees, computers, skills, teaching cost control, et cetera.  Once this is achieved, the capacity building process is seen as complete and work can commence. This technical side is important but don’t ignore the softer side, namely the social relationships between colleagues and their bosses.  If you don’t understand this, you won’t be able to understand why there is not always clear evidence of the capacity that has been built.  For example, if you meet a bright person and collaborate with them and then they are not demonstrating their capacities in a certain context as you expected, you should question the reason.  That person may remain quiet in meetings or out of fear that they will be fired by outshining their boss. Their capacities can be perceived as threats.  Or alternatively, that person may have issues outside of the workplace that influence their professional demeanor.  In the west, there is HR but in many African contexts there are not institutional resources to support workers.  Consider this when evaluating work performance.  
– James Odek

Establishing the objective and collecting a rich data set that can help build a strong and accurate narrative is crucial.  If your study requires field data, reliability and accuracy must be considered on the ground and in context.  In savannahs like the Serengeti-Mara Ecosystem, you need to take seasonality into account.  The system varies strongly according to season, and you need to capture that to understand how the system really works.  It is not sufficient to collect data in only one season.  
– Joseph Ogutu

Be very well prepared but don’t have expectations.  Go prepared to learn and be overwhelmed by everything.  Have an open mind.
– Angela Hezekiah

Data collection should stick to the approved data plan.  The most important aspect is adhering to the ethics for data collection.
– Hamza Kija

Qualitative methodology is key to understanding conservation.  It is important for sustainable management because through this research we can understand how to continue with communities in mind.
– Innocent Babili

For a long time, we (scientists) did most our research within protected areas, studying the basic ecology, studying the animals, what they eat, where they move, what they do, how they interact with predators. But, now I am expanding my views as my research also started to include work outside protected areas.  Also there, the landscapes are beautiful, and the people are interesting, exciting, and motivating.  They are curious about what we do, so it broadens my view as a researcher.
– Han Olff

In an ecosystem, there are a lot of changes as you move in space, you must design and collect data that reflects the variation in the landscape.  You must design and distribute the data collection in a way that it is representative of the landscape and specify the aspects of the system that your data explain.
– Joseph Ogutu

Seasonality is a logistical and scientific concern.  Our greatest findings are tied to the seasonal aspects of heavy metal accumulation in plants, but to determine this, we had to organize logistically.
– Masoud Masoud

Difference can be an asset.  Different people, backgrounds, and experiences help to understand what is really going in villages.
– Angela Hezekiah

Disciplines and cultural differences can have similar challenges with respect to interpretation, particularly the interpretation of the results to suit the local context.
– Ophery Ilomo

There are different types of protected areas and these different types provide different types of opportunities and challenges for scientists in terms of developing data protocols.  
– Philip Jacob

At the beginning, when writing the proposal, one has mostly questions.  Research is iterative, not fixed.  Before one undertakes the research, one must undertake the exploratory research.  The exploratory phase is important.  It is here that the literature and the reality meet.  This is how you can ask pertinent and relevant questions, particularly to the community.  Your first idea must be developed during exploratory research.  Here, you adapt your tool to the reality.
– Innocent Babili

Even when organized well, your research may encounter resistance.  People in villages have their experiences, and they may not compare with a researcher’s understanding.  A researcher must be polite and clever.  Use the appropriate channels.  
– Innocent Babili

There are differences between collecting ecological data inside the protected area and outside the protected area.
– Masoud Masoud

Local scientists contribute a lot and can take a lead in certain aspects.  They know the areas both inside and outside the protected areas and also know how to collect accurate and reliable data in such areas.  When it comes to collecting social data outside the protected areas, for instance, it is advised to use local scientists although they might be accompanied by foreign counterparts. The foreign counterparts may not be familiar with collecting this type of sensitive information from local people and most of the time, language acts as a barrier to foreigners.
– Flora Magige

Social scientists have different challenges than natural scientists when it comes to data collection.  They may often have different interests.  Social scientists are often aware of the concerns of communities and emphasize development.  Many natural scientists have been trained in conservation areas that are maintained using fines and fences.  Social scientists are often trained in conservation spaces as community-based.  Is livelihood a conservation activity?  This is a question that we must discuss extensively.
– Vedasto Ndibalema

Many data collection methods are similar across contexts.  Standard methods are used and variation, if any, might be small to accommodate differences in the environments.  
– Flora Magige

You can develop a similar metric to address different parts of the system.  There is a great deal of variation within a system, but similar measurements can be used across systems.  This comparison is important to understand the system holistically, though there is a great deal of variation.  
– Philip Jacob

Data collection has important logistical aspects.  In Tanzania, it can take three months for money to be released for use through the public university system.  This is a challenge.  
– John  Mgonja

If your host is unpopular, for instance a district game officer in an area where there is tension, then you’ll likely gather incorrect or biased data.
– Innocent Babili

Wildlife forage outside of protected areas, and people forage and domestic animals graze inside the park.  Understanding the similarities and differences can influence how questions are asked and the resulting data collection methods.
– Philip Jacob

Researchers often think about local communities as empty.  Communities are actually very informed.  
– George Kajembe

Time is a challenge with every aspect of the data collection and analysis process.
– John  Mgonja

A researcher should understand the context—the people, the culture, how they live.  They also need to understand the institutions involved in the management of wildlife resources, academic institutions, and research institutions.  The process matters, prior to data collection.  The design is the beginning of this process of interfacing, understanding, and inclusion.
– Rose Kicheleri

As an urban person from Tanzania who has spent time in rural areas, I still need to take time to contextualize myself.
– John Mgonja

Communities are very interesting.  They often know the answers or have observations that are relevant to scientific questions.  They will study a researcher.  They want to know your aims, evaluate your sincerity, and understand the benefits.  A researcher will have to establish and continue to build trust.  It takes time to understand the social and ecological surroundings.  
– Rose Kicheleri

As we become multi-disciplinary, we must not undervalue specialization. The practical aspects of science require disciplinary training, but we must also train in making links.
– Vedasto Ndibalema

From a professional point of view, I felt we made some huge progress with key questions.  I am interested in regarding the protection of ecosystems, such as what are the ecological effects of different management techniques, how do animals respond both behaviourally and physiologically to these interventions, and how do ecosystems change human behaviour (rather than the other way around).  The data sets generated by AfricanBioServices will continue to be mined for years to come and provide the foundation for continued collaboration among the partners even after the project has finished.
– Grant Hopcraft

Through data collection and analysis, we were able to build regional cohesion and coordination between East African institutions.  This will have a lasting impact.  National and regional connectivity around research is alongside international connectivity.
– Merceline Ojwala

It’s interesting that data analysis, at least statistical analysis, can be a rather solitary endeavor. Of course, once some analysis is done, a wider group can look at it. But in my experience, especially with male colleagues, this is a territorial part of science, where a scientist becomes known for being able to do a particular method of analysis. Personally, this behavior seems very foreign to me. With qualitative data, obviously coding of narrative can be done in a group and more easily compared. I look forward to the day when quantitative data analysis is more collaborative, but have not experienced that so much, except in a sequential way.
– Robin Reid

If you are from natural resource management or the natural sciences, a central concern is having enough quantitative data to run a valid statistical analysis.  Those who use qualitative data have other ways of confirming reliability and validity.  Often the two approaches don’t see the value in one another’s data and that is a problem.  There is some mind modulating that needs to happen.  I prefer to use both.  Questionnaires can have qualitative and quantitative responses.
– Bente Graae

It is difficult to do synthesis alone.  You need to interact with people who have different perspectives and originate from different disciplines so that your outcomes reflect a holistic way of thinking about the problem.  
– Martin Nielsen

Interpretation of data is where the very best of interdisciplinary and international work can occur. And in my experience, it is worthwhile sinking lots of time into joint work to interpret the data from different perspectives. That can be done around the graphics and in broad discussions about what the data really mean. Why is this important for policy? Why is this important for the community? What does this mean for what you would do to improve human welfare and the environment? This is true and can be done with both interdisciplinary and transdisciplinary teams.
– Robin Reid

Data collection in GSME is a challenging task for several reasons. Fortunately, a majority of the participating European researchers were experienced working in African ecosystems. It was a big advantage to have so many African research institutions and researchers involved, thus most of the data collection could be made as a mixed African/European team.  
– Kjetil Bevanger

Village monitors and rangers assisted with data collection.  Village monitors were teachers, self-employed entrepreneurs with wildlife, tourism, and agricultural experiences.  Many were recommended by the village leaders.  We compensated them for their work and the data was useful.  It also strengthened our relationships with the villages, which was helpful during the dissemination phase.  
– Stuart Smith

Patience is so important in data collection.  Sometimes a great deal of time will pass before one finds a specific species or individual.  It can take five visits, sometimes more.  Sometimes, it happens quickly but it will, inevitably, take time.
– Devolent Mtui

Remote sensing is a great tool to collect data.  The open source movement makes an enormous difference.  You can download the image, you can download the statistical package.  Stay motivated and persistent as you pursue this valuable skill.  It requires practices and will.  
– Lucy Njino

We try to train students in working in a multidisciplinary project, which involves being interested in each other’s disciplines, broadening one’s view, listening to each other, and trying to learn from each other.  But, my first lesson to students is to enhance their technical skills.  Being a researcher, it is less relevant what your personal opinion is about your study subject, and how strongly you are interested in other people.  Most important is what you technically can do to solve difficult and complex problems.  I compare it to building a bridge.  If I was an engineer and I was teaching, I could ask a student—what is your view on the importance of bridges.  They can have a beautiful story about how bridges can connect people and their ideas, but it all starts with the skills to build a bridge that doesn’t collapse.  And, protected areas and natural corridors between them are like bridges, they are bridges that shouldn’t collapse, and it starts with understanding how they really work, where they should be, and how they should be managed and protected.  So as a student, get the key data skills to be an excellent researching, by learning statistics, learning GIS, learning to write and structure a scientific paper. Once you know how to do these things, then you can develop opinions on whether management strategy A or management strategy B is better.  But, it begins with these skills, and your opinion should be based on the outcome of your scientific analyses, making it evidence-based.
– Han Olff

Data analysis is a great way to improve capacity building. Techniques, statistical software, and analysis training are important for data reliability and validity and capacity building.
– Philip Jacob

It is essential to focus on the quality of data and not necessarily the quantity.  Many researchers are very quantitative in nature and conduct large-scale surveys that are rushed through the use of local assistants who are not necessarily engaged in the entire process or the outcome.  As a result, the outcomes are not accurate portrayals of what is on the ground.  The focus should be on obtaining high-quality results.  Part of that process is sharing the questionnaire with people who can comment and provide suggestions about how to improve it. And select research assistants that are interested and have some stake in the outcome of the research to ensure that they remain truthful in the data collection process.
– Martin Nielsen

Different research institutions have different traditions when it comes to data collection procedures and this also extends to individual researchers. One of the problems turned out to be that due to a delay in money transfer, African institutions could not always be in the field at the set time – no money, no activity. Another obstacle was that research institution leaders prioritized other work for the researchers and projects compete for time. Sometimes fieldwork could not be carried out due to unfavorable weather conditions.  There are many kinds of obstacles.  
– Kjetil Bevanger

The synthesis needs to begin early enough and thought about well at the beginning of the study, and it needs to be a continuing process.  If you wait until the end to begin the synthesis, the team may already have moved on.  As soon as the data start becoming available, synthesis can begin.  The various participating partners can analyse and contribute in order to address a cross-cutting question.
– Joseph Ogutu

To collect and incorporate all the secondary data that was available before this project was an important task to which I contributed.  We processed it to ensure that they were entered validly before entering into the repository.  
– Wilfred Marealle

If a study is multidisciplinary, you may want your site or timing of data collection to overlap with your collaborators, and this will make it possible for you to do synthesis and comparison.  Think about time and spatial aspects of data collection.  Comparison becomes much more efficient and possible.
– Joseph Ogutu

There are people who work with existing data, and you have to get them from institutions.  When you are given data, you need to take steps to verify that data.  Our experience is that these data sets may have issues.  Your conclusions are only as good as the data that you use.  Original records that were collected in the field are helpful in establishing the quality of a data set.   Do not assume that because data is given to you by reputable institutions that it is reliable.  You need to interrogate, check if there are errors and outliers before a reliable analysis can be undertaken.
– Joseph Ogutu

Training is important on data collected, handled and presented systematically.  This is important, particularly for long-term projects.  Training should be ongoing throughout the project to ensure consistency over time and across individuals.  
– Wilfred Marealle

Partnering with botanists was very important for our research questions.  We increased the connectivity between botany and chemistry.  This bridge has been built within and across our institutions and can be used again.
– Masoud Masoud

Data collection with a diverse team is important, especially in terms of gender.  I went into the field with a fellow researcher, who was a woman.  In some cases, men don’t feel comfortable speaking to a woman researcher.  They can fear one another.  Because we were in the field together with one female and one male researcher, we could understand households better.
– Moses Kyando

First, do the analysis with which you are comfortable and build this into the design.  The tool you use and the analysis are tied.  The collection, analysis, and synthesis are connected.  Take the design and the methodology of analysing data that is consistent with your discipline.  If you want to go beyond your discipline, then you invite others and combine methodology.  A division of labour with analysis is ok.  Training and continuing education are also important.  
– Innocent Babili

Analysis is a challenge.  People’s skills differ, and it is important to seek advice early on in the design phase so that you are sure that you have the data that you need to conduct the analysis that you envision.  Very often people have collected a lot of data, and it turns out they miss important variables in constructing a model to make a prediction.  It is rarely possible to do anything about it at that stage.  It needs to be thought through very well at the onset and that requires that you are not alone but that you interact with people who can help you.
– Martin Nielsen

Even among hard sciences, it is not easy to connect fields.  Where do ecology and engineering connect?
– Vedasto Ndibalema

As students prepare for projects, there is a lot to learn.  They also bring some knowledge and experience.  But, they need short course training on how to write competitive projects as well as attending workshops in order to learn how to be engaged in large, interdisciplinary, and international projects.
– Flora Magige

Synthesis is challenging because differences in perspective and discipline can emerge.  Remaining open-minded and cooperative during synthesis is the only way to achieve an interdisciplinary interpretation of the data.  
– Joseph Ogutu

New ways of analysis are being developed.  Some people in the project are more advanced and more current in these developments hence frequent meetings enable new forms and tools for analysis to be shared.
– Flora Magige

Know your data collection tool well.  Spend time with it.  The tool is shared by many people, and everyone needs to have sufficient time to ask questions and standardize.  This way, everyone is using a similar approach and completing the entire process, and the interpretation of data can occur by different people with assurance.
– John  Mgonja

There should be opportunities for students to move abroad.  People learn to confront their perspectives and make connections. Exposure helps people see the intelligence of a person, not just the colour.  
– George Kajembe

In the past, data collection has had a different pace due to equipment.  With the digital data loggers, data collection was efficient, and we were able to generate preliminary analysis in the field.
– Shombe Hassan

Analysis should be rooted in the local communities.  It is a collaborative process.  In my case, our analysis team was international.  We considered various perspectives on what conservation institutions do and how they work.
– Rose Kicheleri

What I liked best was moving together to the site.  Everyone is doing their own thing—collecting water, looking at plants, but it is the same site, and we are looking for what is interesting on our terms.  In the evening, we are together again.  Eating, spending time by the bonfire.  Not talking about work necessarily but enjoying.  The next morning, everyone is back to work.  When a group of chemists work together in the field, the post-field conversation is only about chemistry.  In this case, after work, we discuss many things.  We explain our work, talk about context, and talk about experiences and life.
– Othman Chande

Without communication, things will go haywire.  
– Merceline Ojwala

In a project this large, there are overlaps and duplications in data collection.  Division of labour and delineation of work require time but can also save time during implementation.  As long as information is shared and transferred throughout the group, data collection can be assigned to specific groups and there needn’t be repeat field visits.
– Janemary Ntalwila

A data sharing protocol must be developed.  Data is dispersed and is not in one institution.  A group must be clear about how the data will be shared and disseminated.  
– Patrick Wargute

Patience.  People are not as understanding as you expect.  People are not forthcoming with strangers. You must be patient, polite, and persuasive in order to be let in and gather the info that you need.  
– Angela Hezekia

Local scientists should feel more comfortable asserting their knowledge and themselves.  Politeness and perceived hierarchies can stand in the way of constructive criticism and true collaboration.  As a foreign professor, I appreciate hearing criticism from people on the ground who have much knowledge and experience.  
– Bente Graae

You may put camera traps in a place or create an enclosure to study vegetation and later find that they have been removed.  You need to discuss your work with communities, and a community facilitator who can communicate the importance and relevance of what you are doing.  This is crucial.  The hope is that they will collaborate with you and support your work.
– Joseph Ogutu

We engaged communities to help with data collection and we also relayed preliminary results to the village monitors so they knew what we were collecting during the early phase.  This set the ground work for dissemination and the use of results to improve livelihoods.
– Stuart Smith

Every time the group gathers to discuss scientific results, project coordination must also be discussed.  Time should be dedicated to discussing coordination, expectations, and rules as a group so there is agreement or, at least, a common understanding.  The organizational structure of a project is fundamental and it must be regularly attended to and supported.
– Eivin Røskaft

Spatial data was very important for this project.  Household economics is my focus and learning to put this data into a spatial frame was important for the interdisciplinary objectives and synthesis.  Further developing this perspective was very important for data collection and analysis.
– Martin Nielsen

With the community, you need someone who can communicate effectively.  Translating data into a language that people can understand is important.  Sometimes it is not a national language but a local language, and you’ll need to be creative and understand the context so that you secure the right help and package the information in the appropriate media, language, and forum.  
– Joseph Ogutu

Synthesis is not an easy thing because it requires you to understand all the different aspects of a problem.  You have to learn to speak the language of the different sub-disciplines of a problem, and bridge and connect insights among them.  Students often try start too early with synthesis when they still insufficiently understand the ins and outs of the components of what they try to put together.  So begin by understanding these parts by reading a lot and talking to people who have specialized on these parts.  And, start to become one of these specialists yourself. After this, you should increasingly be able to put things together and see the big picture.  That is the beauty of scientific careers, they are like building a pyramid.  They start with key building blocks at the base that you can put down relatively independent from each other. But further on, the pyramid can become higher and higher when new building blocks you put down increasingly rest on your previous ones, increasing your view on the surroundings.  
– Han Olff

Continual two-way communication between junior scientists and senior scientists is important throughout the project to ensure that information is represented.  Continual communication about data can ensure that it is collected systematically.
– Wilfred Marealle

Talk to the people who know the study site so they can give insights about the area that are not necessarily available in published sources.  This will help you to understand, particularly if your study involves people.  You need to understand the cultural context.  The dos and do nots may not be written down or spoken about.  
– Joseph Ogutu

We had very efficient communications.  We used a variety of modes—emails, WhatsApp, phone—because we live in different areas and different countries, so getting messages on time is difficult.  This happens especially when there is a need for a quick response, especially related to field work and sampling, which samples should be prepared and in what ways.  There is difficulty communicating and understanding, but we made it work, we keep communicating and trying and using many methods.  The communication influenced the rigor of data collection and analysis.  
– John Bukombe

We must operate in the interface of knowledge.  There needs to be change of mindset.  Formal education must acknowledge indigenous knowledge.  This can be addressed through training.  
– George Kajembe

The line of questioning may make a researcher come across as a government agent who is in the process of land grabbing.  You have to explain your activity thoroughly.  You have to be open about the activities, objectives, data collection, and how you will use the data.  
– Innocent Babili

Translating data for policy and publishing are different processes, two very different lines of work.
– George Kajembe

Data collection in rural areas requires prior notice.  This is a way of communicating respect and ensuring the process goes as planned.  They need to be prepared to receive you.
– John Mgonja

Visualizing data is so important.  Software that deals with data visualization is an important skill to learn.  It is also important for dissemination.
– Shombe Hassan

Specialist knowledge is important, particularly in light of climate change and other threats facing wildlife.  Synthesizing this knowledge is key to the survival of wildlife.
– Rose Kicheleri

Synthesizing papers into briefs is a special skill.  The skew is on publications and data for policy needs greater emphasis.  
– George Kajembe

Data collection depends on having defined proper objectives for the study.  At the outset, one must specify to ensure the data are relevant to answer the outlined questions.  You need to define the variables and parameters to answer the question sufficiently so that you don’t complete the study with information that you don’t need and without information that you do need.  
– Joseph Ogutu

Data and statistical methods are highly exciting in their generality.  They allow connections between disciplines.  I was discussing with a social scientist about their household survey data and I think that I could contribute to the analysis from my (natural science) research because we use similar statistical approaches.  Therefore, developing strong quantitative skills enables you to work interdisciplinarily because you speak, at least, one common language.  If you don’t speak that language, you risk being locked up in the narrow boundaries of your own discipline.
– Han Olff