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Name:
EMEA Keynote - Dr. Caleb Kibet
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EMEA Keynote - Dr. Caleb Kibet
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Upload Date:
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Transcript:
Language: EN.
Segment:0 .
Well, good morning. Good afternoon, and good evening. We're very happy to be sponsoring ISO Plus 2023.
It's a great pleasure to introduce Dr. Caleb Corbett, the Europe, the Middle East and Africa keynote speaker. Dr. Caleb COVID is a bioinformatics researcher, a lecturer and open science advocate and a mentor. He has a PhD in bioinformatics from Rhodes university, South Africa. In addition to teaching bioinformatics at Pawnee university, he is a postdoc at ICP, the International Center of insect physiology and ecology in Nairobi.
As a 2019 2020 Mozilla open science fellow, he developed a research data management framework for research constrained regions. He is also a member of the dryad scientific advisory board and a board member of the open bioinformatics Foundation. Dr. COVID is a passionate about open science and reproducible bioinformatics. Research is a founder of open science k, an initiative that promotes open approaches to bioinformatics research in Kenya.
And he is involved in bioinformatics capacity building through the human heredity and health for Africa bioinformatics network and the Eastern African network for bioinformatics training. Dr. Cabot's talk is entitled unlocking open science in Africa mentorship and grassroot community building. Thank you so much. I think it's so much, Jessica, for your kind introduction.
So I do hope that you can see my slides and as introduced, I am based in Nairobi, Kenya, which is in East Africa. In Africa. And my interest is really around building communities and the aim of that is to unlock open science within various communities, open science within bioinformatics, open science in various research themes within Africa.
So this is a common picture, especially in for many African academic institutions and students who are undertaking research. And they would see some interesting abstract, which could really be the answer to a question that they're trying to explore in their research. But then I wonder what it means. And the answer being, it's a paywall.
The other aspect is they could come across, especially when they do not have access to funding to generate their own data. They might come across really interesting data, but or results, and they would be interested in combining that data with the data they may have been able to collect to address a research question. And what they would say as captured here is from the open Knowledge Foundation is all the coordinates show where the data is, but I can only detect for visualization.
So the aspect of data sharing. The other concept and I think this was from ISO Class 2021 and this is from Joi one and the general claim that Africa generates very little research or very little output of orally in various spaces. That's really a false claim because the reality is that lots of research from Africa is not discoverable and that 0.1%, it could be more given the time.
But there is general lack of discussion of discoverability, of research output from this region. So Africa scholars are inclined to, for example, provide the affiliation to be maybe their partners in Europe or being able to share a different affiliation or their information may be published in local journals that may not be indexed.
So the IDs may not actually be available. The other aspect, of course, is with open data, which is shown by this open data parameter in 2016, this was more or less the latter. The rest have not been as comprehensive as this one shows, really still low data sharing from most of the global South regions. So Africa is rich, rich in many spaces.
If you look at this map, this is just showing the various minerals that are being extracted from Africa. And it's really providing resources across the world in various places. And that extractive economy is not just for minerals and agriculture and some brain drain, but it's also on scientific resources. The concept of helicopter research, where research in poor countries is more focused on just extracting data, extracting samples without any respectful collaborations.
And what this does is that the questions and the research that could have been undertaken within these regions or the collaborations that could have been built through that are not in place. And really, the reality is that. It's not about the absence of researchers in Africa as captured by Elizabeth and Thomas kariuki from the African Academy of Sciences. They say that African science matters not only because African people matter, but also because people everywhere in the world will thrive only if science is driven by the best and best possible talent and initiative of all peoples of the world.
And therefore, this speaks to the need for science being done, and especially by people who bear the brunt of the diseases. The various research questions that are being addressed within. Within those regions. And several years back. You could say not much is happening in Africa, but right now there's so much research and there's so many initiatives that are in place that generate data, that generate scholarly output.
And one of the initiatives that I'm involved in is the esri Barnet, which is keen to develop bioinformatics capacity in Africa and specifically to enable genomic data analysis by African researchers across the continent. So the aspects that they deal with cut across Africa and not just data generation, but building the capacity to facilitate data generation and also data sharing.
So the training, publishing. And so forth. And as we go along on twilight that. The major research output, which is the publication, may not be the only research output that should be considered, but all the data, the core, the scholarly contributions across the board should be actually considered and the reward system should be able to factor that in.
So we are seeing increased funding and African driven publishing and this is really good news because initiatives like the other side of which is actually hosted within my institution is funding that comes directly from African governments. And it's being funding PhD and MSC research across Africa in applied sciences, engineering and technology. And this is actually a testament to a continuous change of culture and change of the landscape within Africa.
There is also open research African publishing platform, which is funded by the science for Africa Foundation. The sans for Africa Foundation is another initiative that is also facilitating and pushing for funding within Africa for African researchers to answer and address really pertinent questions that affect Africans. Therefore, we see all these initiatives the African archive, which is a preprint server that is really interested in, in addition to just publishing various research outputs, it's also interested in publishing indigenous knowledge, the knowledge that and if you look at it, Africa as traditionally and generally is an oral they have their way of disseminating this information is via oral narratives and stories and conversations.
And therefore most of the African output or information is always in the minds of our fathers and grandfathers. And it's really important to figure out ways to ensure that information, not only is it captured, but also those that are in archives and in papers that are in libraries and so forth, digitization of those information to, as we say, increase feasibility of African research, because it's not about the fact that nothing is going to on within Africa, but there is less visibility of this research output.
So it's really important to do that. And we see initiatives like the Training Center in communication. And I think joy wargo gave a talk on how they are increasing that visibility of African research. So we see several initiatives that are led by Africans within the continent to increase the visibility of African research and to increase the adoption of the tools that facilitate this.
We did a survey to just explore the research output and this may be biased because it's focused on Kenya, but you can see these are, as expected, a general increase in research output. But what's important there is to see this increase in open access publishing. Despite the barriers to open access publishing, which is the article publishing charge, which is always prohibitive to some researchers, we see an increase in openness as demonstrated by data analysis.
We the data mining of all this over the years and that shows a growth. But we still see certain technologies, newer technologies or newer approaches, for example, preprints. And if we go to open science, the adoption of open science tools is still a bit slow. And you can see that majority the global South global, not institutions or countries, but we see Kenya, Nigeria, Uganda and South Africa.
The data made has actually increased now, but the picture still remains the same, that there is still low adoption of some of these initiatives. And the question is, why do we still see a low adoption of such initiatives? Why do we still see some of the traditional research practices being really taking center stage within Africa and the traditional with the existence of the world of academia?
But if you look at it here, publishing is just one output. It's just a single output of the several things that a researcher would generate. And by the time the researcher gets to this point of publishing, it's a lot of time and work and resources that have gone into it. But then how can we ensure that there is visibility of this other aspect, the work that they. The data that they generate, the lab work that they do, the protocols that they develop, the interviews that they conduct, the community engagement practices that they do, the citizens sense that they do there, the initiatives that they are attempting to alleviate poverty, diseases within their communities.
A lot of this is happening, but then they are not considered to be the traditional research output, which is the publication. So how can that be facilitated? And here I'm really trying to make a case for open science. And in addition to that, how we can increase the adoption of this practice is through mentorship and grassroots community building, which would increase really the adoption of such practices.
So when we talk about open science, there are so many definitions of open science. So you could see a definition like open science means anyone can freely access, use and modify. Unesco's defines it as an inclusive construct that combines various movements and practices aiming to make multilingual scientific knowledge openly available. Multilingual but most of the publishing is done in English.
Most of the publishers actually accept only in english, so traditional or in local dialects. you look at the number of dialects just capturing our country in Kenya, we have more than 40 ethnic languages that are in existence. So we see so many definitions. But really, what does it mean within different contexts? What has open science mean and the most common depiction?
And when you talk about open science, what really always comes to mind is open access. So open access, which captures all those different routes of ensuring that publications are actually available for others to read. The green route, the gold route, and so forth. And then there is the aspect of data, open data. And when they talk about open data, most of the time is still a bit of a focus towards the government information and public information.
But then moving towards open research data, there's still a bit of a barrier to that, especially from resource constraints settings where data is really a valuable research outputs. And most of them would want to keep that data and derive as much value from that data before they get to share. And all of these other concepts, very open, reproducible research and so forth.
And in reality is that I would look at it as open access as just the open access, especially for publications, as just the final component of research as captured here. All these other components. And this quotation here by Beckett and Donahoe says that an article about computational or results and really most of the work these days which take some computational form in one way or the other, the analysis of the work is just an advertising.
It's not a Scholarship. The actual Scholarship is the full software environment, the code, the data that produced the results. So a published article is just the tip of the iceberg, and it's just one component of the many research outputs that a researcher would generate. So there would be data, the code, the version control and as I've already mentioned, samples by specimens and the rest.
So to really achieve 100% what we could consider to be science. Science is not really the publication, but science is everything else that a researcher does it collecting data, the interviews, the community outreach, the community engagement, the extraction, the protocols, the methodology, all of us. That's a Scholarship. That's the contribution to science from researchers.
And therefore, there is need to ensure that there is feasibility of all these various contributions, but not just the feasibility of these contributions, but also the recognition and incentives that facilitate or more or less that incentivize the sharing of all these other research outputs. So samples and specimens, I would say that really, as we talked about it, the raw materials, I see that as the raw scientific materials, and this should be documented and shared.
So if you look at the whole research lifecycle from designing the study all the way to really analysis what all this is science, the bulk of the work happens here, but what is always seen as a contribution is publication, publish or perish, which means there is very little sharing of these. And really this is a Scholarship of this information and someone who might have done all this but did not manage to publish for one reason or the other, might be seen as not being productive or contributing to the scholarly body of work.
But really all these are contributions. So data, the whole process of data management and just managing data. In the end, what would need to be to share this data? But in reality, that's the building blocks of the scholarly output and we glad to see these. That data is actually continually being shared. But then in the sharing and lots of talks have talked about metadata capturing the metadata standards.
And that's an area that's really important to see a greater adoption of that. The code is a Scholarship that's really good. And when someone writes software and they have written it in open source or share, it's open source. Then when someone needs to use their work, all they love to do is you can get it from gitlab, from GitHub. And all the other repositories. So the code is a scholarship, but then the time that it takes to prepare software or code for sharing documentation, that's a bulk and a huge amount of work spent there.
But then if there's no recognition of that as a contribution, then we would still run into the shortcut and someone not really being interested in sharing or sharing without their metadata or ensuring that this is fair, findable, accessible, interoperable and actually reusable with proper licenses attached to the data, the code, the methodology, the protocols and all those that are being shared by the various players.
So all great, really, all these beautiful things called the data management and data sharing, the Scholarship being the, the, the articles and the talk of ensuring that all the data, the code, the methods are actually shared. But as you said at the beginning, what does open science mean?
For resource constrained countries, for example, where we see a lack of policies, there's still very low government and institutional support. They are. The funding is not sufficient most of the time, and the academics from most of these regions, they are always overworked. And which means that they have less time to contribute to research, to write, to share.
Which means that adding more work, say, like ensuring that data is properly documented and shared and software is written and documented may be additional work to them. So what does it mean for them? But also, how can we ensure that they can adopt this open science practices? How can we facilitate the adoption? We look at students and early career researchers.
These now the push and the demand to publish or perish. They have to follow the advisors. And the advisors might, as we see in this other slide, is that they are much older researchers and they still stick to the walls of academia or traditional research practices, but they have the need for open science from the perspective of the tools that they are using, the scientific articles that they would need to read, as well as increasing the visibility of their own work.
They have that need for the established. This is a foreign concept and the tools are really hard to learn for them, but they do have the power to support those who can. And therefore the question is if we are to increase the adoption of open science practices and some of these newer practices within different communities where there is very little top down support, how can we build it from the bottom up and ensure that we understand and consider the context?
We understand the specific needs within these communities. What does it mean for them? What do they need? How can they be facilitated to ensure the adoption of these practices? And therefore the interventions that are being designed are owned and driven locally, which would ensure a greater adoption of these practices. For me, I've really seen that as a research I view are an open signed certificate or you really practice open science.
In the end, you cannot be an efficient researcher until you and your collaborators are aware, aware and equipped with the open science tools or with the tools that you are using with the new practices that you have in place. So really keeping that in mind, this my students that I've mentored and they've gone on to establish some of the communities that have been involved in grassroot community building, promoting open science, and within the bioinformatics space within Kenya.
And the work that they are doing is actually cutting across Africa and they receive even participants from all over the world. So this is the bioinformatics hub of Kenya, which is an initiative that really aims to bring up and raise a generation of globally competitive bioinformatics from Kenya and Africa as a whole for the good of the humanity. And one of their components is open science, which they also incorporate a group of scientists and researchers and students that seek to promote open science practices in bioinformatics.
And our reach has gone beyond just bioinformatics. It's open the life sciences all the way to the other, the other sciences. So we see such initiatives. We had a collaboration for our projects, for empowering researchers with skills and tools in open science and, and bioinformatics. So we see that growth. But what we really see and center and as I've been introducing is towards the fact that there is need to empower.
But really what it starts with is that it's really important to create, one, the awareness of these new initiatives open access, open science, the open tools, the open source, open educational resources, open data, introduce this concepts, the benefits of this to them as researchers and to the body of science as a whole. And then the second aspect is to empower them.
Yes having introduced that concept, then they need to ensure that they understand or they are empowered with the tools to be able to practice open science and then hack the hackathon and sustain aspect is to ensure that not just introducing the tools theoretically, but creating an environment in which they can practice the utility of such tools.
And through some of the collaborations that I've done before, starting from symposiums to workshops to hackathons were able to collaborate and publish a paper on the status of open science in Kenya. So it goes through the whole process. But the end of it is the need for community building communities through meetups, fireside chats and so forth. And this ensures sustainability of those initiatives. And what we are keen to say is the participants that go through our trainings and workshops can go forward and be the ambassadors, influence their supervisors to adopt the open practices in various spaces.
Because we see the younger generation have the ability to acquire this knowledge and to drive change from the bottom by influencing the instructors. So that's a model that centers on empowerment and community building and ensuring that there is growth within those groups. The within the net, we have the Open Lands learning cycles, which is create communities within. So if you looked at the initial they are nodes, all of Africa that are involved in bioinformatics.
And what we do is that we create various within the different nodes. We create learning cycles which go forward and continue creating the awareness and training within their local community. So this would be the grassroot communities or grassroots initiatives that facilitate the adoption of the practices within their communities. So this is really doing a great deal of work in increasing the adoption of these practices.
So we've talked about also mentorship and what I've been doing at a personal capacity is mentoring my students, mentoring my trainees to adopt these practices, but also build communities as highlighted by this informatics hub of Kenya, which was established by a number of my students and mentees. And we've also seen mentorship programs that are well structured, like the open life sciences, which have been involved in mentoring the leaders.
So what it does is create open leaders who would be involved in leading initiatives within their community. So things like the research software and systems engineers, which is based in South Africa, really covering the whole of Africa. The open sense community in Nigeria also mentored through the open life centers, those mentoring this team, the bioinformatics hub of Kenya.
So all of these groups are grassroot communities that understand the needs within their communities and they can be able to tailor initiatives or tailor programs and create frameworks that facilitate the adoption of the open practices within their communities. So Kevin was an amazing student when I just was just a PhD graduate at jomo Kenyatta University where I was teaching, and it was part of the training activities, introducing them to open practices from GitHub all the way to publish it openly and it captures it quite well.
Is that I can't overstate what GitHub training and also many other trainings have done. For me, it kind of changed my life. It just made everything seem so easy and even thought to be an advocate. And really what we ended up doing was create ambassadors is now an ambassador of open practices, is now a PhD student in South Africa and really continuing the practice.
So we see mentorship and creating ambassadors and you can be sure that it's students. Anyone who works with or collaborates with would be introduced to the European practices. So by creating ambassadors and creating mentors that become open leaders within their communities, then we can see we can end up with a critical mass that can ensure the adoption of these practices across the African continent.
And so many other. So many other regions. So through being involved in grassroots community building, being involved in various workshops and seminars and creating conferences and being a mentor in various initiatives. What are some of the lessons that I've learned? And one thing that may not have been I may not have captured in my is that I love hiking.
And within Canada we do organize hiking. We run a hiking group, we organize hikes. But also what we do is that we create and map trails within different regions with beautiful views and challenges and so forth. So and let's reflect about some of the lessons that we can learn from hiking and trail mapping, especially given that getting to the summit, which is always the ultimate aim, is, is a good thing.
Getting to the summit. Getting to the top of the hill. But how can one get to the top of the hill, but not just be the only one that gets to the top of the hill, but ensuring that others can also get to the top of the hill in regions where there is less adoption or there is still bushes that trails are and not yet mapped, they don't exist. How can they be trail mappers, creating trails for others to follow and facilitate others to enjoy the view that one can get by getting to the summit or getting the top of the mountain.
So how can we? Achieve or reap from the benefits of open science, from the benefits of sharing information, the Open Data and ensure that all these. Various research outputs and various contributions to research that come from different communities. Remember that we can really science is collaborative and science is highly and together thing.
So what I've come to realize is that there are so many communities. And I've highlighted this, all these communities, they are funding initiatives, there are all these programs that are in existence and sometimes what is missing could just be the awareness and someone might be there not being aware that some of the supports exist that they can adopt some of these.
For example, publishing open access. There are different routes that some one can achieve openness, awareness, capacity, ensuring that they are well trained and mentored. And it's not just about doing for them or showing them, but empowering them to be advocates in the end and take the lead within their communities, to be actually ambassadors within their own communities. And greater progress comes from community collaborations.
It comes from working together. It comes from as you move towards the mountain, trying to hold each other's hands, take advantage of the strengths of different initiatives. There's no need to reproduce what others are doing. If someone is really a good partner, they can do that very well. If someone is really good at leading, it's really good at providing the conversations and the cycles you try to climb the mountain.
Then it's important to ensure that we involve everyone within the team. So we see different initiatives. For example, as we run some of the training activities, there are those who are really good with the carpentry training materials. They open life sciences in project planning and management and mentorship, GDC Africa in communication, open science and open data and then open science.
Some are really good at sharing the African archives. They are the open life centers. All these communities have their strengths and there's so many other communities within the continent that are involved in the same things that. So many other communities outside the continent that are also participating within the continent in promoting open science, promoting open access, promoting open data, and providing funding for all these initiatives.
How can we involve all the various stakeholders within the local communities and building local and grassroot communities to ensure really we are fully aware of the intricacies within those regions we open by design and not by default. It's openness, texts, clarity and planning. And if you really think about getting to climb the hill or climb a mountain, you have to fully plan.
And when it comes to facilitating someone to climb the mountain, in the end, you have to ensure that you provide the hiking boots, the hiking sticks, the rain jackets and so forth to ensure that they are equipped to climb the mountain. They can be warm as they climb the mountain, and then they can also enjoy the view and the other side. So being inclusive and supportive and supportive, there are so many barriers and there are so many challenges that do exist and we see so many initiatives that are involved in fixing or dealing with some of these barriers.
But we can all reap the benefits of open science. We can all reap the benefits of open practices. If we get to work together, we get to support each other. If we become allies to the various communities that's now being an ally, create a pathway for others to follow. If you've gotten to the top of the mountain, then it would be nice if you can map a trail for someone else to follow. Make it easier for someone else to get to the mountain and enjoy the view that is at the top getting at the summit.
There's always a beautiful view, but ultimately, really, I see a change of culture as being one of the critical and important components and changing the culture. One, if there is the systems that create the representations, if they are the systems that reduce access to research or research output from a particular community, if they are systems that act as barriers to, say, publishing to actors, barriers to access to funding actors, barriers to collaboration and so forth, then we would have to change the culture towards openness, towards sharing, towards.
Breaking these barriers. And with that, then we would be able to see a greater adoption of these practices when those cultures have been changed, if their cultures of proper traditional research, what is causing that? Getting to understand that. And then creating now pathways towards change. And I see empowerment, allyship and really building grassroot communities and mentorship as a pathway towards that.
And my hope is that the future generations will look at a time of concerns as a tautology, a throwback from an era before science woke up. And this is really a beautiful quote from brand not second Christian, but and open science will simply become known as science. And at that point, the closed, secretive practices that define our current culture will seem as primitive to them as alchemy is to us.
When we get to see a greater adoption of all these practices, not just by a few, not just by the well-resourced, but by every everyone within the scientific and the research community, the knowledge community, then we would really have a greater focus within the scientific community. So thank you so much for the audience and the opportunity to speak to you about the work that we are doing in Kenya and in building grassroots communities and the challenges that we face and how we are seeing really a great direction towards the practice of openness and how really supporting these grassroot communities would facilitate a greater progress in these spaces.
So it's NISO. It's really a beautiful conference and great conversations that have been going on and all leading really towards ultimately openness in sharing and knowledge, visibility. So assignee Sana, thank you so much for your attention. I'll take any questions. So, Dr. Hibbert, thank you so much.
That was such fantastic talk and I truly appreciate it. I truly loved the metaphor that you'd provided about a hiker, and that is just so beautiful. I, I was at an ISO meeting in Kenya many years ago, and it was such a beautiful landscape. I just imagine, like going on a hike with you somewhere. Thank you. I encourage participants.
If you have any questions, comments, please add them to the Q&A. So that we have can organize them. Want to kick something off. I was really struck by this notion when you were reading definitions of open science, as, you know, as a sort of ideal by I think it was UNESCO in their definition that it needs to be multilingual. And yet scientific practice is overwhelmingly dominated by English.
And and your point being, you know, if you wanted to do multilingual science, you can't get it published in. X top journal because it has to be in English. I'm wondering from your perspective, are there ways that we, as a community, as an information community, can help change that narrative, can help provide greater visibility to content that is not in english?
Yeah Uh, and thank you so much. And that's the reality. And that's true. But I would, Uh, I would say that they are initiatives that are already facilitating publishing in, in local languages. And I think one, I think and I would say even I'm sure if someone tries to publish in their local dialects, it may not be rejected in all of them or a majority of them.
But many of them know that. The only way to publish this in English. So which means that the awareness and just being putting it out there that it is possible that you can share your research output in your local language to reach your own community. Because if you think about it, if I'm generating research output, the reason I am doing research is to benefit my community.
But if people within my community cannot read what I produce then or understand what I produce, then really am, I'm really already sidelining them from that. So creating that awareness, but also just supporting some of the initiatives that are already in place that facilitate the sharing of knowledge in indigenous languages would also be another alternative. So just create awareness and then support the initiatives that do that.
Another theme that came through for me is the importance of, as you said, kind of showing people the way and building recognition for things that are. You don't really get don't get a lot of added are important to disseminating work like documenting your code et cetera that don't get recognition.
Are be interested to hear from your perspective what are ways that we can build that recognition. Like, you know, having tried to understand other people's code that isn't well documented, that can be a real pain. But it's sort of like if you buy a house, you know, you expect it to have certain things, but you're like, oh, well, the basement doesn't leak. Well, OK, it's a problem if it's not there, but you don't really recognize it.
If it is, are there ways that we can build in that kind of recognition into this ecosystem? So in academia, the incentives are usually on the number of publications and the number of citations that they have. And when it comes to promotions, even if you've generated so much, so many software in academia, in most traditional academic institutions, that is not recognized as an incentive or as I would say, the reward system that is in existence do not consider that.
So the change of the reward system to include all research output within promotions or within scholarly recognition, not just a number of publications, but just the research of the number of mentorships, the community engagements and so forth that someone has been involved in. Then that would create an incentive for someone to put in the time and the resources to one, as you say, document they are because they know that one, the utility of this resource would be considered as a contribution to research and to knowledge in itself.
So the reward system is actually the major barrier to some of these open practices. The reward system doesn't recognize the effort and the time and the resources that go into generating results, documenting code and sharing them, and even preparing for sharing, capturing the metadata that's required and ensuring that it's reusable. There's lots of time and resources. That's involved in that, but the reward system doesn't reward.
If the reward system doesn't recognize that then or doesn't factor that in, then someone may not see the need to put in the time and the resources to do that, since they can still make progress in their academic life or they risk their career without sharing the data. Yeah, it kind of reminds me in the publishing world of, you know, a copy editing, copy editing will make something better, but do you really need it?
And you know, in the it kind of gets cut in the, in the, in the process of trying to save resources and time and oh, we got to get this out the door. We have a couple of questions coming in. Again, remind people, use the Q&A functionality, ask questions of Dr. Hibbert. One question here. In other regions, local language publishing is often the realm of the arts, humanities and social sciences.
Is this the case as well in Kenya, South Africa and in general in the region? And could STM learn from a. I'm not sure what a is. Arts humanities. Arts, humanities in. Social scientists. So yes, there is. For most of the cases where you see publishing in local languages, and especially when interviews are done in local languages and sharing, it's mostly within the arts and social sciences that that usually happens not as much as it should, I would say.
There's still a few barriers to that, but I think already. The initiatives or the practice is already there within the social sciences, and I would say, as you said, what they could learn is that. Especially community engagement and the. Return of feedback to the community should be done in local dialects, or even providing a summary or the abstract layman abstract in abstract in local dialects, especially when it benefits certain communities, it can start there.
It may not be a requirement to write the methodology in because they would not be interested in methodology, but they would be interested in the recommendation. They would be interested in the key findings. Those can be written up in the local languages and those can be published alongside the publications. So the lay abstract could just be in a local dialect. It kind of goes along with another question here about should all publications simply be created in two languages being local and english?
And then a follow up question is could technology play a role in facilitating that? So writing and my thought here, especially from the aspect of technology and really the various forms of sharing, is that it might even be easier to either create videos in local languages where someone just speaks about that research in local dialect as opposed to writing. Because many people we are oral communities, so we speak most of the time we speak written communication just came recently, which means it might be much, much easier to share this information as audio or as video in the local languages.
So using technology and all this in that way could increase such dissemination or access to information by different communities. So technology surely can play a role, and it might not be necessary that it must be written in English and the local languages, but it should be written in a language that is accessible to the community that would benefit from the research output.
Mm-hmm Yeah Yeah. I mean, that's a fantastic point. We don't necessarily need to distribute things in text, on paper or a digital form of paper. There are many ways that we could share content. Again, another question from our community. I'd like to hear your views on the misalignment between national evaluation systems based on international English based indexing databases which discover publications in universities.
In journals. And that that role there of how content the role of content in these assessment and evaluation processes and how say that's sort of dominated by Western European approaches to, you know, citation tracking and things like that. Yeah so, Uh, Thanks. Thanks, Anna, for that question.
Really fantastic question. So if you look at and this is the concept of impact factor, right, where publishing is in local journals may not gain as much value compared to publishing in nature, in science and some of the major public publishers. But that indigenous knowledge of local knowledge is actually most of the time shared within those platforms.
And that's why there are some initiatives that are trying to create the feasibility of such research outputs because they are of value. Yes, they may be valuable within a local community, but that doesn't discredit or just downplay the value of that output. So it's important for the indexing systems and their permanent identifiers and so forth to consider all research outputs.
And because in the end, what they end up doing is that they are sidelining these initiatives and considering it as less of an important output compared to the others. So there's no output that is not as important. Those are important, especially within the local context. So important how do we measure importance? How do you measure popularity? If you measure that, it's popular across the world.
Yet the research in itself is being done with an interest of a local community. Then that may not be the correct measure. So as I say, when the measure doesn't really consider the impact of the work, then it's no longer working as expected. Yeah, I think it's. At least from my perspective, my sense of academic assessment is.
There's some there's a lot of homogeneity. There's a lot of trying to do the same thing at every institution, which I don't think is appropriate. And this notion of, you know, what's right for your own institution, what's right for your own community, and building an assessment around that seems to make a lot more sense. Yeah is there a way that we can get University administrations and research funders to think along those lines?
in the end. In the end, we have to change how we measure impact. An impact to especially say someone is dealing with a very rare genetic disease that only affects a very small group. How can how can you measure the impact of that overall?
Yet it might have an impact on one person. So we have to localize even the incentives. We have to localize how we measure impact as required. Yes, there is a general high level of measuring of impact, but within the local institutions and universities and even just funders, encouraging all of us to publish in local journals and sponsoring them to publish in local journals, and not pushing them to publish in well known journals to ensure that these journals, the local journals, have greater contributions and they have also a wider reach.
And also just creating the visibility of some of these local journals across the board. So it's really important to just change how we measure impact. Yeah I mean, that's such a great point. And, you know, you we have this broad vision of impact. But if it's saving your family members life in some fashion, there could be nothing more important. Right I had a question.
I'm going to jump in because, you know, I can do that. He has the mic because I have a mic I can turn on. Dr. Corbett, you had there were some great metaphors in your talk, as well as just some examples of other arenas where tools have engendered sort of standard practices that make open the default. The big example, I think that I. Came that I felt strongly about when you mentioned it was, of course, you know, the sort of get lab, GitHub.
Default for development these days a computer coding it. That methodology, that tool chain is sort of the default at this point, honestly for code production. So you can you think of a tool or a tool chain, a system.
That would be the equivalent or that that's needed in this sort of scientific research space? That would be something that you could see acting in that same kind of way that would allow everyone to be more open by default, more collaborative by default, that sort of thing. Is there something that's missing that's needed there? OK so I'm from the computational biology space and analysis of genomic data.
Mostly benefits also from the same utilizes cells the same tools. Uh, and if you think about researchers, the lab protocols that they generate, so creating open lab notebooks that actually are shared. So that facilitates sharing not just of the application but the protocol. So the systems like the protocols that IoT which facilitate sharing of, of such or more or less laboratory information management system that also facilitate the capturing of all the metadata from the sample collection, sample storage to data sharing all the way, so which also facilitate collaboration.
So there are so many tools. And most of them are digital tools, but what they would do, they would facilitate the sharing. So sharing of what? When this publication is the journals, when it comes to code, these Git github, gitlab, but also GitHub in itself is for collaboration, for collaborative writing, documentation planning, project planning and so forth, which would actually cut across and are of benefit not just to the programmers and coders, but are of they can benefit all researchers across the board.
They can be used as collaborative tools as opposed to tools for sharing code. So the focus is on collaboration, creating collaborative platforms that can be used. And to facilitate the adoption of this. Now we have to empower and train researchers across the board because as a computational researcher, I would understand those tools. But I want to collaborate with the biologists.
If they don't understand or know how to use these collaborative tools, then we would end up resorting to what they know, right? So we have to empower across the board to share tools. So when we have shared tools, then we can work together more better. So like using if it's using cloud computing for analysis whereby researchers in the global node in the americas, the UK or Europe can collaborate with researchers in Africa.
If we understand, if these researchers know how to use the cloud tools, they know how to use high-performance compute clusters, then they can collaborate better in data analytics. Or it might be quicker to share data because now they are sharing the containers and so forth that contain that. So it's important across the board to facilitate collaboration through shared tools.
interestingly coming here interested to hear some more on this not just during the session yesterday Dr. mchunu from South Africa was talking about the collaborative research infrastructure. You're working in computational analysis. You know, you need data data storage tools, but you also need the kind of computational power and, you know, and.
You know, computer systems that have the resources to crunch the genomic data at scale. What's your experience in getting access to those computational resources in africa? Are there ways that, you know, environments are you able to do that kind of computational analysis in your own regional networks? Or are there ways in which you could, you know, dial into some other system somewhere and use some other, you know, compute resources located somewhere else and, you know, kind of live out that, you know, utilize those platforms.
Yeah so thank you so much. And in facilitating the adoption of open practices within our organization, we have a small, small HPC high performance compute cluster. And I've been strongly involved in one building this facility, but also installing tools that would make it accessible to the noncoding biologists so they can still be able to use these tools.
So having RStudio tools on the cloud or on the system, jupyter notebooks on the system, galaxy tools for genomic data analysis on the system to increase access by all researchers is something that I've been really keen about. And right now we're trying also to increase the adoption of cloud facilities and create that capacity. But we've seen in as much as it's still a challenge, we've seen so much support, especially within our country, the Kenya education research network, they support researchers to have access to virtual machines.
So for data analytics, access to the end runs the high speed internet networks to share data across Africa to different endpoints within Africa, facilitating access to high performance compute cluster in South Africa, which I also still have access to where it's a Federated resource that actually researchers across Africa can register as a research group and they can provide access to their students.
So we see local capacity building the extra bandwidth that I'm involved in. They're also building one, the infrastructure. They also building the containers for sharing data. And there was a there was a running joke that actually is much faster to move data by just shipping a hard drive from one institution to the other because of internet connectivity. And that's still the reality.
But we also see creation of Federated and shared data compute facilities, especially when it comes to human data, where there is a bit of privacy concerns, where the data cannot be transferred, but rather you can take the analysis to the data. So they'll be there. Also facilitating that, using the systems called beacons, where you can be able to access certain data points from wherever you are and just run your analysis.
And the only thing that you can get out is the output or the results. So we are seeing some growth, but more may still need to be done, but also just collaborating this bringing together these infrastructures instead of everyone setting them up. How can we Federated having an African open science platform that facilitate that? That would make it much better.
So but we're seeing some really good progress. Yes, that's fantastic to hear. I see a couple of comments in the chat wondering what the birds are. I'm not I'm not going to presume you're an ornithologist as well. Well, Dr. Corbett, this has been a fantastic talk.
One of the elements of this conference is trying to build global connections and trying to build and develop a better understanding of how science is taking place in other regions and how information distribution is taking place. I want to thank you so much for adding your voice to this, to the conferences, and providing us an insight into your experience and how the work you're doing with open science in Africa.
Thank you so much. Thank you for having me. It's been a pleasure.
Segment:0 .
Well, good morning. Good afternoon, and good evening. We're very happy to be sponsoring ISO Plus 2023.
It's a great pleasure to introduce Dr. Caleb Corbett, the Europe, the Middle East and Africa keynote speaker. Dr. Caleb COVID is a bioinformatics researcher, a lecturer and open science advocate and a mentor. He has a PhD in bioinformatics from Rhodes university, South Africa. In addition to teaching bioinformatics at Pawnee university, he is a postdoc at ICP, the International Center of insect physiology and ecology in Nairobi.
As a 2019 2020 Mozilla open science fellow, he developed a research data management framework for research constrained regions. He is also a member of the dryad scientific advisory board and a board member of the open bioinformatics Foundation. Dr. COVID is a passionate about open science and reproducible bioinformatics. Research is a founder of open science k, an initiative that promotes open approaches to bioinformatics research in Kenya.
And he is involved in bioinformatics capacity building through the human heredity and health for Africa bioinformatics network and the Eastern African network for bioinformatics training. Dr. Cabot's talk is entitled unlocking open science in Africa mentorship and grassroot community building. Thank you so much. I think it's so much, Jessica, for your kind introduction.
So I do hope that you can see my slides and as introduced, I am based in Nairobi, Kenya, which is in East Africa. In Africa. And my interest is really around building communities and the aim of that is to unlock open science within various communities, open science within bioinformatics, open science in various research themes within Africa.
So this is a common picture, especially in for many African academic institutions and students who are undertaking research. And they would see some interesting abstract, which could really be the answer to a question that they're trying to explore in their research. But then I wonder what it means. And the answer being, it's a paywall.
The other aspect is they could come across, especially when they do not have access to funding to generate their own data. They might come across really interesting data, but or results, and they would be interested in combining that data with the data they may have been able to collect to address a research question. And what they would say as captured here is from the open Knowledge Foundation is all the coordinates show where the data is, but I can only detect for visualization.
So the aspect of data sharing. The other concept and I think this was from ISO Class 2021 and this is from Joi one and the general claim that Africa generates very little research or very little output of orally in various spaces. That's really a false claim because the reality is that lots of research from Africa is not discoverable and that 0.1%, it could be more given the time.
But there is general lack of discussion of discoverability, of research output from this region. So Africa scholars are inclined to, for example, provide the affiliation to be maybe their partners in Europe or being able to share a different affiliation or their information may be published in local journals that may not be indexed.
So the IDs may not actually be available. The other aspect, of course, is with open data, which is shown by this open data parameter in 2016, this was more or less the latter. The rest have not been as comprehensive as this one shows, really still low data sharing from most of the global South regions. So Africa is rich, rich in many spaces.
If you look at this map, this is just showing the various minerals that are being extracted from Africa. And it's really providing resources across the world in various places. And that extractive economy is not just for minerals and agriculture and some brain drain, but it's also on scientific resources. The concept of helicopter research, where research in poor countries is more focused on just extracting data, extracting samples without any respectful collaborations.
And what this does is that the questions and the research that could have been undertaken within these regions or the collaborations that could have been built through that are not in place. And really, the reality is that. It's not about the absence of researchers in Africa as captured by Elizabeth and Thomas kariuki from the African Academy of Sciences. They say that African science matters not only because African people matter, but also because people everywhere in the world will thrive only if science is driven by the best and best possible talent and initiative of all peoples of the world.
And therefore, this speaks to the need for science being done, and especially by people who bear the brunt of the diseases. The various research questions that are being addressed within. Within those regions. And several years back. You could say not much is happening in Africa, but right now there's so much research and there's so many initiatives that are in place that generate data, that generate scholarly output.
And one of the initiatives that I'm involved in is the esri Barnet, which is keen to develop bioinformatics capacity in Africa and specifically to enable genomic data analysis by African researchers across the continent. So the aspects that they deal with cut across Africa and not just data generation, but building the capacity to facilitate data generation and also data sharing.
So the training, publishing. And so forth. And as we go along on twilight that. The major research output, which is the publication, may not be the only research output that should be considered, but all the data, the core, the scholarly contributions across the board should be actually considered and the reward system should be able to factor that in.
So we are seeing increased funding and African driven publishing and this is really good news because initiatives like the other side of which is actually hosted within my institution is funding that comes directly from African governments. And it's being funding PhD and MSC research across Africa in applied sciences, engineering and technology. And this is actually a testament to a continuous change of culture and change of the landscape within Africa.
There is also open research African publishing platform, which is funded by the science for Africa Foundation. The sans for Africa Foundation is another initiative that is also facilitating and pushing for funding within Africa for African researchers to answer and address really pertinent questions that affect Africans. Therefore, we see all these initiatives the African archive, which is a preprint server that is really interested in, in addition to just publishing various research outputs, it's also interested in publishing indigenous knowledge, the knowledge that and if you look at it, Africa as traditionally and generally is an oral they have their way of disseminating this information is via oral narratives and stories and conversations.
And therefore most of the African output or information is always in the minds of our fathers and grandfathers. And it's really important to figure out ways to ensure that information, not only is it captured, but also those that are in archives and in papers that are in libraries and so forth, digitization of those information to, as we say, increase feasibility of African research, because it's not about the fact that nothing is going to on within Africa, but there is less visibility of this research output.
So it's really important to do that. And we see initiatives like the Training Center in communication. And I think joy wargo gave a talk on how they are increasing that visibility of African research. So we see several initiatives that are led by Africans within the continent to increase the visibility of African research and to increase the adoption of the tools that facilitate this.
We did a survey to just explore the research output and this may be biased because it's focused on Kenya, but you can see these are, as expected, a general increase in research output. But what's important there is to see this increase in open access publishing. Despite the barriers to open access publishing, which is the article publishing charge, which is always prohibitive to some researchers, we see an increase in openness as demonstrated by data analysis.
We the data mining of all this over the years and that shows a growth. But we still see certain technologies, newer technologies or newer approaches, for example, preprints. And if we go to open science, the adoption of open science tools is still a bit slow. And you can see that majority the global South global, not institutions or countries, but we see Kenya, Nigeria, Uganda and South Africa.
The data made has actually increased now, but the picture still remains the same, that there is still low adoption of some of these initiatives. And the question is, why do we still see a low adoption of such initiatives? Why do we still see some of the traditional research practices being really taking center stage within Africa and the traditional with the existence of the world of academia?
But if you look at it here, publishing is just one output. It's just a single output of the several things that a researcher would generate. And by the time the researcher gets to this point of publishing, it's a lot of time and work and resources that have gone into it. But then how can we ensure that there is visibility of this other aspect, the work that they. The data that they generate, the lab work that they do, the protocols that they develop, the interviews that they conduct, the community engagement practices that they do, the citizens sense that they do there, the initiatives that they are attempting to alleviate poverty, diseases within their communities.
A lot of this is happening, but then they are not considered to be the traditional research output, which is the publication. So how can that be facilitated? And here I'm really trying to make a case for open science. And in addition to that, how we can increase the adoption of this practice is through mentorship and grassroots community building, which would increase really the adoption of such practices.
So when we talk about open science, there are so many definitions of open science. So you could see a definition like open science means anyone can freely access, use and modify. Unesco's defines it as an inclusive construct that combines various movements and practices aiming to make multilingual scientific knowledge openly available. Multilingual but most of the publishing is done in English.
Most of the publishers actually accept only in english, so traditional or in local dialects. you look at the number of dialects just capturing our country in Kenya, we have more than 40 ethnic languages that are in existence. So we see so many definitions. But really, what does it mean within different contexts? What has open science mean and the most common depiction?
And when you talk about open science, what really always comes to mind is open access. So open access, which captures all those different routes of ensuring that publications are actually available for others to read. The green route, the gold route, and so forth. And then there is the aspect of data, open data. And when they talk about open data, most of the time is still a bit of a focus towards the government information and public information.
But then moving towards open research data, there's still a bit of a barrier to that, especially from resource constraints settings where data is really a valuable research outputs. And most of them would want to keep that data and derive as much value from that data before they get to share. And all of these other concepts, very open, reproducible research and so forth.
And in reality is that I would look at it as open access as just the open access, especially for publications, as just the final component of research as captured here. All these other components. And this quotation here by Beckett and Donahoe says that an article about computational or results and really most of the work these days which take some computational form in one way or the other, the analysis of the work is just an advertising.
It's not a Scholarship. The actual Scholarship is the full software environment, the code, the data that produced the results. So a published article is just the tip of the iceberg, and it's just one component of the many research outputs that a researcher would generate. So there would be data, the code, the version control and as I've already mentioned, samples by specimens and the rest.
So to really achieve 100% what we could consider to be science. Science is not really the publication, but science is everything else that a researcher does it collecting data, the interviews, the community outreach, the community engagement, the extraction, the protocols, the methodology, all of us. That's a Scholarship. That's the contribution to science from researchers.
And therefore, there is need to ensure that there is feasibility of all these various contributions, but not just the feasibility of these contributions, but also the recognition and incentives that facilitate or more or less that incentivize the sharing of all these other research outputs. So samples and specimens, I would say that really, as we talked about it, the raw materials, I see that as the raw scientific materials, and this should be documented and shared.
So if you look at the whole research lifecycle from designing the study all the way to really analysis what all this is science, the bulk of the work happens here, but what is always seen as a contribution is publication, publish or perish, which means there is very little sharing of these. And really this is a Scholarship of this information and someone who might have done all this but did not manage to publish for one reason or the other, might be seen as not being productive or contributing to the scholarly body of work.
But really all these are contributions. So data, the whole process of data management and just managing data. In the end, what would need to be to share this data? But in reality, that's the building blocks of the scholarly output and we glad to see these. That data is actually continually being shared. But then in the sharing and lots of talks have talked about metadata capturing the metadata standards.
And that's an area that's really important to see a greater adoption of that. The code is a Scholarship that's really good. And when someone writes software and they have written it in open source or share, it's open source. Then when someone needs to use their work, all they love to do is you can get it from gitlab, from GitHub. And all the other repositories. So the code is a scholarship, but then the time that it takes to prepare software or code for sharing documentation, that's a bulk and a huge amount of work spent there.
But then if there's no recognition of that as a contribution, then we would still run into the shortcut and someone not really being interested in sharing or sharing without their metadata or ensuring that this is fair, findable, accessible, interoperable and actually reusable with proper licenses attached to the data, the code, the methodology, the protocols and all those that are being shared by the various players.
So all great, really, all these beautiful things called the data management and data sharing, the Scholarship being the, the, the articles and the talk of ensuring that all the data, the code, the methods are actually shared. But as you said at the beginning, what does open science mean?
For resource constrained countries, for example, where we see a lack of policies, there's still very low government and institutional support. They are. The funding is not sufficient most of the time, and the academics from most of these regions, they are always overworked. And which means that they have less time to contribute to research, to write, to share.
Which means that adding more work, say, like ensuring that data is properly documented and shared and software is written and documented may be additional work to them. So what does it mean for them? But also, how can we ensure that they can adopt this open science practices? How can we facilitate the adoption? We look at students and early career researchers.
These now the push and the demand to publish or perish. They have to follow the advisors. And the advisors might, as we see in this other slide, is that they are much older researchers and they still stick to the walls of academia or traditional research practices, but they have the need for open science from the perspective of the tools that they are using, the scientific articles that they would need to read, as well as increasing the visibility of their own work.
They have that need for the established. This is a foreign concept and the tools are really hard to learn for them, but they do have the power to support those who can. And therefore the question is if we are to increase the adoption of open science practices and some of these newer practices within different communities where there is very little top down support, how can we build it from the bottom up and ensure that we understand and consider the context?
We understand the specific needs within these communities. What does it mean for them? What do they need? How can they be facilitated to ensure the adoption of these practices? And therefore the interventions that are being designed are owned and driven locally, which would ensure a greater adoption of these practices. For me, I've really seen that as a research I view are an open signed certificate or you really practice open science.
In the end, you cannot be an efficient researcher until you and your collaborators are aware, aware and equipped with the open science tools or with the tools that you are using with the new practices that you have in place. So really keeping that in mind, this my students that I've mentored and they've gone on to establish some of the communities that have been involved in grassroot community building, promoting open science, and within the bioinformatics space within Kenya.
And the work that they are doing is actually cutting across Africa and they receive even participants from all over the world. So this is the bioinformatics hub of Kenya, which is an initiative that really aims to bring up and raise a generation of globally competitive bioinformatics from Kenya and Africa as a whole for the good of the humanity. And one of their components is open science, which they also incorporate a group of scientists and researchers and students that seek to promote open science practices in bioinformatics.
And our reach has gone beyond just bioinformatics. It's open the life sciences all the way to the other, the other sciences. So we see such initiatives. We had a collaboration for our projects, for empowering researchers with skills and tools in open science and, and bioinformatics. So we see that growth. But what we really see and center and as I've been introducing is towards the fact that there is need to empower.
But really what it starts with is that it's really important to create, one, the awareness of these new initiatives open access, open science, the open tools, the open source, open educational resources, open data, introduce this concepts, the benefits of this to them as researchers and to the body of science as a whole. And then the second aspect is to empower them.
Yes having introduced that concept, then they need to ensure that they understand or they are empowered with the tools to be able to practice open science and then hack the hackathon and sustain aspect is to ensure that not just introducing the tools theoretically, but creating an environment in which they can practice the utility of such tools.
And through some of the collaborations that I've done before, starting from symposiums to workshops to hackathons were able to collaborate and publish a paper on the status of open science in Kenya. So it goes through the whole process. But the end of it is the need for community building communities through meetups, fireside chats and so forth. And this ensures sustainability of those initiatives. And what we are keen to say is the participants that go through our trainings and workshops can go forward and be the ambassadors, influence their supervisors to adopt the open practices in various spaces.
Because we see the younger generation have the ability to acquire this knowledge and to drive change from the bottom by influencing the instructors. So that's a model that centers on empowerment and community building and ensuring that there is growth within those groups. The within the net, we have the Open Lands learning cycles, which is create communities within. So if you looked at the initial they are nodes, all of Africa that are involved in bioinformatics.
And what we do is that we create various within the different nodes. We create learning cycles which go forward and continue creating the awareness and training within their local community. So this would be the grassroot communities or grassroots initiatives that facilitate the adoption of the practices within their communities. So this is really doing a great deal of work in increasing the adoption of these practices.
So we've talked about also mentorship and what I've been doing at a personal capacity is mentoring my students, mentoring my trainees to adopt these practices, but also build communities as highlighted by this informatics hub of Kenya, which was established by a number of my students and mentees. And we've also seen mentorship programs that are well structured, like the open life sciences, which have been involved in mentoring the leaders.
So what it does is create open leaders who would be involved in leading initiatives within their community. So things like the research software and systems engineers, which is based in South Africa, really covering the whole of Africa. The open sense community in Nigeria also mentored through the open life centers, those mentoring this team, the bioinformatics hub of Kenya.
So all of these groups are grassroot communities that understand the needs within their communities and they can be able to tailor initiatives or tailor programs and create frameworks that facilitate the adoption of the open practices within their communities. So Kevin was an amazing student when I just was just a PhD graduate at jomo Kenyatta University where I was teaching, and it was part of the training activities, introducing them to open practices from GitHub all the way to publish it openly and it captures it quite well.
Is that I can't overstate what GitHub training and also many other trainings have done. For me, it kind of changed my life. It just made everything seem so easy and even thought to be an advocate. And really what we ended up doing was create ambassadors is now an ambassador of open practices, is now a PhD student in South Africa and really continuing the practice.
So we see mentorship and creating ambassadors and you can be sure that it's students. Anyone who works with or collaborates with would be introduced to the European practices. So by creating ambassadors and creating mentors that become open leaders within their communities, then we can see we can end up with a critical mass that can ensure the adoption of these practices across the African continent.
And so many other. So many other regions. So through being involved in grassroots community building, being involved in various workshops and seminars and creating conferences and being a mentor in various initiatives. What are some of the lessons that I've learned? And one thing that may not have been I may not have captured in my is that I love hiking.
And within Canada we do organize hiking. We run a hiking group, we organize hikes. But also what we do is that we create and map trails within different regions with beautiful views and challenges and so forth. So and let's reflect about some of the lessons that we can learn from hiking and trail mapping, especially given that getting to the summit, which is always the ultimate aim, is, is a good thing.
Getting to the summit. Getting to the top of the hill. But how can one get to the top of the hill, but not just be the only one that gets to the top of the hill, but ensuring that others can also get to the top of the hill in regions where there is less adoption or there is still bushes that trails are and not yet mapped, they don't exist. How can they be trail mappers, creating trails for others to follow and facilitate others to enjoy the view that one can get by getting to the summit or getting the top of the mountain.
So how can we? Achieve or reap from the benefits of open science, from the benefits of sharing information, the Open Data and ensure that all these. Various research outputs and various contributions to research that come from different communities. Remember that we can really science is collaborative and science is highly and together thing.
So what I've come to realize is that there are so many communities. And I've highlighted this, all these communities, they are funding initiatives, there are all these programs that are in existence and sometimes what is missing could just be the awareness and someone might be there not being aware that some of the supports exist that they can adopt some of these.
For example, publishing open access. There are different routes that some one can achieve openness, awareness, capacity, ensuring that they are well trained and mentored. And it's not just about doing for them or showing them, but empowering them to be advocates in the end and take the lead within their communities, to be actually ambassadors within their own communities. And greater progress comes from community collaborations.
It comes from working together. It comes from as you move towards the mountain, trying to hold each other's hands, take advantage of the strengths of different initiatives. There's no need to reproduce what others are doing. If someone is really a good partner, they can do that very well. If someone is really good at leading, it's really good at providing the conversations and the cycles you try to climb the mountain.
Then it's important to ensure that we involve everyone within the team. So we see different initiatives. For example, as we run some of the training activities, there are those who are really good with the carpentry training materials. They open life sciences in project planning and management and mentorship, GDC Africa in communication, open science and open data and then open science.
Some are really good at sharing the African archives. They are the open life centers. All these communities have their strengths and there's so many other communities within the continent that are involved in the same things that. So many other communities outside the continent that are also participating within the continent in promoting open science, promoting open access, promoting open data, and providing funding for all these initiatives.
How can we involve all the various stakeholders within the local communities and building local and grassroot communities to ensure really we are fully aware of the intricacies within those regions we open by design and not by default. It's openness, texts, clarity and planning. And if you really think about getting to climb the hill or climb a mountain, you have to fully plan.
And when it comes to facilitating someone to climb the mountain, in the end, you have to ensure that you provide the hiking boots, the hiking sticks, the rain jackets and so forth to ensure that they are equipped to climb the mountain. They can be warm as they climb the mountain, and then they can also enjoy the view and the other side. So being inclusive and supportive and supportive, there are so many barriers and there are so many challenges that do exist and we see so many initiatives that are involved in fixing or dealing with some of these barriers.
But we can all reap the benefits of open science. We can all reap the benefits of open practices. If we get to work together, we get to support each other. If we become allies to the various communities that's now being an ally, create a pathway for others to follow. If you've gotten to the top of the mountain, then it would be nice if you can map a trail for someone else to follow. Make it easier for someone else to get to the mountain and enjoy the view that is at the top getting at the summit.
There's always a beautiful view, but ultimately, really, I see a change of culture as being one of the critical and important components and changing the culture. One, if there is the systems that create the representations, if they are the systems that reduce access to research or research output from a particular community, if they are systems that act as barriers to, say, publishing to actors, barriers to access to funding actors, barriers to collaboration and so forth, then we would have to change the culture towards openness, towards sharing, towards.
Breaking these barriers. And with that, then we would be able to see a greater adoption of these practices when those cultures have been changed, if their cultures of proper traditional research, what is causing that? Getting to understand that. And then creating now pathways towards change. And I see empowerment, allyship and really building grassroot communities and mentorship as a pathway towards that.
And my hope is that the future generations will look at a time of concerns as a tautology, a throwback from an era before science woke up. And this is really a beautiful quote from brand not second Christian, but and open science will simply become known as science. And at that point, the closed, secretive practices that define our current culture will seem as primitive to them as alchemy is to us.
When we get to see a greater adoption of all these practices, not just by a few, not just by the well-resourced, but by every everyone within the scientific and the research community, the knowledge community, then we would really have a greater focus within the scientific community. So thank you so much for the audience and the opportunity to speak to you about the work that we are doing in Kenya and in building grassroots communities and the challenges that we face and how we are seeing really a great direction towards the practice of openness and how really supporting these grassroot communities would facilitate a greater progress in these spaces.
So it's NISO. It's really a beautiful conference and great conversations that have been going on and all leading really towards ultimately openness in sharing and knowledge, visibility. So assignee Sana, thank you so much for your attention. I'll take any questions. So, Dr. Hibbert, thank you so much.
That was such fantastic talk and I truly appreciate it. I truly loved the metaphor that you'd provided about a hiker, and that is just so beautiful. I, I was at an ISO meeting in Kenya many years ago, and it was such a beautiful landscape. I just imagine, like going on a hike with you somewhere. Thank you. I encourage participants.
If you have any questions, comments, please add them to the Q&A. So that we have can organize them. Want to kick something off. I was really struck by this notion when you were reading definitions of open science, as, you know, as a sort of ideal by I think it was UNESCO in their definition that it needs to be multilingual. And yet scientific practice is overwhelmingly dominated by English.
And and your point being, you know, if you wanted to do multilingual science, you can't get it published in. X top journal because it has to be in English. I'm wondering from your perspective, are there ways that we, as a community, as an information community, can help change that narrative, can help provide greater visibility to content that is not in english?
Yeah Uh, and thank you so much. And that's the reality. And that's true. But I would, Uh, I would say that they are initiatives that are already facilitating publishing in, in local languages. And I think one, I think and I would say even I'm sure if someone tries to publish in their local dialects, it may not be rejected in all of them or a majority of them.
But many of them know that. The only way to publish this in English. So which means that the awareness and just being putting it out there that it is possible that you can share your research output in your local language to reach your own community. Because if you think about it, if I'm generating research output, the reason I am doing research is to benefit my community.
But if people within my community cannot read what I produce then or understand what I produce, then really am, I'm really already sidelining them from that. So creating that awareness, but also just supporting some of the initiatives that are already in place that facilitate the sharing of knowledge in indigenous languages would also be another alternative. So just create awareness and then support the initiatives that do that.
Another theme that came through for me is the importance of, as you said, kind of showing people the way and building recognition for things that are. You don't really get don't get a lot of added are important to disseminating work like documenting your code et cetera that don't get recognition.
Are be interested to hear from your perspective what are ways that we can build that recognition. Like, you know, having tried to understand other people's code that isn't well documented, that can be a real pain. But it's sort of like if you buy a house, you know, you expect it to have certain things, but you're like, oh, well, the basement doesn't leak. Well, OK, it's a problem if it's not there, but you don't really recognize it.
If it is, are there ways that we can build in that kind of recognition into this ecosystem? So in academia, the incentives are usually on the number of publications and the number of citations that they have. And when it comes to promotions, even if you've generated so much, so many software in academia, in most traditional academic institutions, that is not recognized as an incentive or as I would say, the reward system that is in existence do not consider that.
So the change of the reward system to include all research output within promotions or within scholarly recognition, not just a number of publications, but just the research of the number of mentorships, the community engagements and so forth that someone has been involved in. Then that would create an incentive for someone to put in the time and the resources to one, as you say, document they are because they know that one, the utility of this resource would be considered as a contribution to research and to knowledge in itself.
So the reward system is actually the major barrier to some of these open practices. The reward system doesn't recognize the effort and the time and the resources that go into generating results, documenting code and sharing them, and even preparing for sharing, capturing the metadata that's required and ensuring that it's reusable. There's lots of time and resources. That's involved in that, but the reward system doesn't reward.
If the reward system doesn't recognize that then or doesn't factor that in, then someone may not see the need to put in the time and the resources to do that, since they can still make progress in their academic life or they risk their career without sharing the data. Yeah, it kind of reminds me in the publishing world of, you know, a copy editing, copy editing will make something better, but do you really need it?
And you know, in the it kind of gets cut in the, in the, in the process of trying to save resources and time and oh, we got to get this out the door. We have a couple of questions coming in. Again, remind people, use the Q&A functionality, ask questions of Dr. Hibbert. One question here. In other regions, local language publishing is often the realm of the arts, humanities and social sciences.
Is this the case as well in Kenya, South Africa and in general in the region? And could STM learn from a. I'm not sure what a is. Arts humanities. Arts, humanities in. Social scientists. So yes, there is. For most of the cases where you see publishing in local languages, and especially when interviews are done in local languages and sharing, it's mostly within the arts and social sciences that that usually happens not as much as it should, I would say.
There's still a few barriers to that, but I think already. The initiatives or the practice is already there within the social sciences, and I would say, as you said, what they could learn is that. Especially community engagement and the. Return of feedback to the community should be done in local dialects, or even providing a summary or the abstract layman abstract in abstract in local dialects, especially when it benefits certain communities, it can start there.
It may not be a requirement to write the methodology in because they would not be interested in methodology, but they would be interested in the recommendation. They would be interested in the key findings. Those can be written up in the local languages and those can be published alongside the publications. So the lay abstract could just be in a local dialect. It kind of goes along with another question here about should all publications simply be created in two languages being local and english?
And then a follow up question is could technology play a role in facilitating that? So writing and my thought here, especially from the aspect of technology and really the various forms of sharing, is that it might even be easier to either create videos in local languages where someone just speaks about that research in local dialect as opposed to writing. Because many people we are oral communities, so we speak most of the time we speak written communication just came recently, which means it might be much, much easier to share this information as audio or as video in the local languages.
So using technology and all this in that way could increase such dissemination or access to information by different communities. So technology surely can play a role, and it might not be necessary that it must be written in English and the local languages, but it should be written in a language that is accessible to the community that would benefit from the research output.
Mm-hmm Yeah Yeah. I mean, that's a fantastic point. We don't necessarily need to distribute things in text, on paper or a digital form of paper. There are many ways that we could share content. Again, another question from our community. I'd like to hear your views on the misalignment between national evaluation systems based on international English based indexing databases which discover publications in universities.
In journals. And that that role there of how content the role of content in these assessment and evaluation processes and how say that's sort of dominated by Western European approaches to, you know, citation tracking and things like that. Yeah so, Uh, Thanks. Thanks, Anna, for that question.
Really fantastic question. So if you look at and this is the concept of impact factor, right, where publishing is in local journals may not gain as much value compared to publishing in nature, in science and some of the major public publishers. But that indigenous knowledge of local knowledge is actually most of the time shared within those platforms.
And that's why there are some initiatives that are trying to create the feasibility of such research outputs because they are of value. Yes, they may be valuable within a local community, but that doesn't discredit or just downplay the value of that output. So it's important for the indexing systems and their permanent identifiers and so forth to consider all research outputs.
And because in the end, what they end up doing is that they are sidelining these initiatives and considering it as less of an important output compared to the others. So there's no output that is not as important. Those are important, especially within the local context. So important how do we measure importance? How do you measure popularity? If you measure that, it's popular across the world.
Yet the research in itself is being done with an interest of a local community. Then that may not be the correct measure. So as I say, when the measure doesn't really consider the impact of the work, then it's no longer working as expected. Yeah, I think it's. At least from my perspective, my sense of academic assessment is.
There's some there's a lot of homogeneity. There's a lot of trying to do the same thing at every institution, which I don't think is appropriate. And this notion of, you know, what's right for your own institution, what's right for your own community, and building an assessment around that seems to make a lot more sense. Yeah is there a way that we can get University administrations and research funders to think along those lines?
in the end. In the end, we have to change how we measure impact. An impact to especially say someone is dealing with a very rare genetic disease that only affects a very small group. How can how can you measure the impact of that overall?
Yet it might have an impact on one person. So we have to localize even the incentives. We have to localize how we measure impact as required. Yes, there is a general high level of measuring of impact, but within the local institutions and universities and even just funders, encouraging all of us to publish in local journals and sponsoring them to publish in local journals, and not pushing them to publish in well known journals to ensure that these journals, the local journals, have greater contributions and they have also a wider reach.
And also just creating the visibility of some of these local journals across the board. So it's really important to just change how we measure impact. Yeah I mean, that's such a great point. And, you know, you we have this broad vision of impact. But if it's saving your family members life in some fashion, there could be nothing more important. Right I had a question.
I'm going to jump in because, you know, I can do that. He has the mic because I have a mic I can turn on. Dr. Corbett, you had there were some great metaphors in your talk, as well as just some examples of other arenas where tools have engendered sort of standard practices that make open the default. The big example, I think that I. Came that I felt strongly about when you mentioned it was, of course, you know, the sort of get lab, GitHub.
Default for development these days a computer coding it. That methodology, that tool chain is sort of the default at this point, honestly for code production. So you can you think of a tool or a tool chain, a system.
That would be the equivalent or that that's needed in this sort of scientific research space? That would be something that you could see acting in that same kind of way that would allow everyone to be more open by default, more collaborative by default, that sort of thing. Is there something that's missing that's needed there? OK so I'm from the computational biology space and analysis of genomic data.
Mostly benefits also from the same utilizes cells the same tools. Uh, and if you think about researchers, the lab protocols that they generate, so creating open lab notebooks that actually are shared. So that facilitates sharing not just of the application but the protocol. So the systems like the protocols that IoT which facilitate sharing of, of such or more or less laboratory information management system that also facilitate the capturing of all the metadata from the sample collection, sample storage to data sharing all the way, so which also facilitate collaboration.
So there are so many tools. And most of them are digital tools, but what they would do, they would facilitate the sharing. So sharing of what? When this publication is the journals, when it comes to code, these Git github, gitlab, but also GitHub in itself is for collaboration, for collaborative writing, documentation planning, project planning and so forth, which would actually cut across and are of benefit not just to the programmers and coders, but are of they can benefit all researchers across the board.
They can be used as collaborative tools as opposed to tools for sharing code. So the focus is on collaboration, creating collaborative platforms that can be used. And to facilitate the adoption of this. Now we have to empower and train researchers across the board because as a computational researcher, I would understand those tools. But I want to collaborate with the biologists.
If they don't understand or know how to use these collaborative tools, then we would end up resorting to what they know, right? So we have to empower across the board to share tools. So when we have shared tools, then we can work together more better. So like using if it's using cloud computing for analysis whereby researchers in the global node in the americas, the UK or Europe can collaborate with researchers in Africa.
If we understand, if these researchers know how to use the cloud tools, they know how to use high-performance compute clusters, then they can collaborate better in data analytics. Or it might be quicker to share data because now they are sharing the containers and so forth that contain that. So it's important across the board to facilitate collaboration through shared tools.
interestingly coming here interested to hear some more on this not just during the session yesterday Dr. mchunu from South Africa was talking about the collaborative research infrastructure. You're working in computational analysis. You know, you need data data storage tools, but you also need the kind of computational power and, you know, and.
You know, computer systems that have the resources to crunch the genomic data at scale. What's your experience in getting access to those computational resources in africa? Are there ways that, you know, environments are you able to do that kind of computational analysis in your own regional networks? Or are there ways in which you could, you know, dial into some other system somewhere and use some other, you know, compute resources located somewhere else and, you know, kind of live out that, you know, utilize those platforms.
Yeah so thank you so much. And in facilitating the adoption of open practices within our organization, we have a small, small HPC high performance compute cluster. And I've been strongly involved in one building this facility, but also installing tools that would make it accessible to the noncoding biologists so they can still be able to use these tools.
So having RStudio tools on the cloud or on the system, jupyter notebooks on the system, galaxy tools for genomic data analysis on the system to increase access by all researchers is something that I've been really keen about. And right now we're trying also to increase the adoption of cloud facilities and create that capacity. But we've seen in as much as it's still a challenge, we've seen so much support, especially within our country, the Kenya education research network, they support researchers to have access to virtual machines.
So for data analytics, access to the end runs the high speed internet networks to share data across Africa to different endpoints within Africa, facilitating access to high performance compute cluster in South Africa, which I also still have access to where it's a Federated resource that actually researchers across Africa can register as a research group and they can provide access to their students.
So we see local capacity building the extra bandwidth that I'm involved in. They're also building one, the infrastructure. They also building the containers for sharing data. And there was a there was a running joke that actually is much faster to move data by just shipping a hard drive from one institution to the other because of internet connectivity. And that's still the reality.
But we also see creation of Federated and shared data compute facilities, especially when it comes to human data, where there is a bit of privacy concerns, where the data cannot be transferred, but rather you can take the analysis to the data. So they'll be there. Also facilitating that, using the systems called beacons, where you can be able to access certain data points from wherever you are and just run your analysis.
And the only thing that you can get out is the output or the results. So we are seeing some growth, but more may still need to be done, but also just collaborating this bringing together these infrastructures instead of everyone setting them up. How can we Federated having an African open science platform that facilitate that? That would make it much better.
So but we're seeing some really good progress. Yes, that's fantastic to hear. I see a couple of comments in the chat wondering what the birds are. I'm not I'm not going to presume you're an ornithologist as well. Well, Dr. Corbett, this has been a fantastic talk.
One of the elements of this conference is trying to build global connections and trying to build and develop a better understanding of how science is taking place in other regions and how information distribution is taking place. I want to thank you so much for adding your voice to this, to the conferences, and providing us an insight into your experience and how the work you're doing with open science in Africa.
Thank you so much. Thank you for having me. It's been a pleasure.
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