2021 Highschool Big Data Challenge: Paving the path to true equality and equal access in education

STEM Fellowship’s High School Big Data Challenge is an inquiry-driven experiential learning program that provides students an opportunity to learn and apply the fundamentals of data science – a crucial skill set for a young researcher in the digital age – through independent research projects. The COVID-19 pandemic disrupted high school education, at the same time creating a “fertile ground” for interdisciplinary, student-driven STEM education. This year, we invited students to explore issues of Affordable and Clean Energy at the Individual and Community Levels and to suggest their own evidence-based solutions, using Open Data and the principles of Open Science. Students explored many topics, ranging from Greenhouse Gas Emissions of School Buses to Legitimacy of Electric Vehicles to be the Greener Alternative We developed in-depth learning modules designed to bridge the gap between traditional high school courseware and digital reality and computational science. The students learnt a broad range of data analytics tools and programming languages which are useful for uncovering hidden patterns, trends in structured and unstructured data. Some of the tools the students learnt and used include Python, R, LaTeX, and machine learning. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, Digital Science, Infor, SCWST, CISCO Networking Academy, Canadian Science Publishing, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. It has been a privilege for us to witness the analytical capabilities of the data-native generation of students first hand, and we are certain all entrants will continue to demonstrate excellence in their respective academic and professional careers.

In the STEM Fellowship High School Big Data Challenge, students have the opportunity to engage in independent research projects and acquire fundamental data science skills – an essential skill set for a young researcher in the digital age. The program is inquiry-driven and experiential. This year, we invited students to explore issues of Fair Housing at the Individual and Community Levels and to suggest their own evidence-based solutions, using Open Data and the principles of Open Science. Students explored many topics, ranging from a New Framework for Public Rental Housing in Toronto to A Statistical Analysis on Thawing Permafrost and Its Effects on Housing. We designed in-depth learning modules for students as a means of bridging the gap between traditional high school courseware and digital reality and computational science. Students learned how to uncover hidden patterns and trends in structured and unstructured data using a range of data analytics tools and programming languages. Python, R, LaTeX, and machine learning were some of the tools the students learned and used. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, Digital Science, Infor, SCWST, CISCO Networking Academy, Canadian Science Publishing, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. We were privileged to witness first-hand the analytical capabilities of the data-native generation of students, and we are confident they will demonstrate excellence throughout their academic and professional careers.

The STEM Fellowship High School Big Data Challenge provides students with the unique opportunity of Open Data inquiry into one of the UN Sustainable Development Goals and experiential learning of fundamentals of data analysis – an essential skill set for a young researcher in the digital age. This year, students explore Generative AI and Data Cybersecurity to Conserve and Foster Local Biodiversity and to suggest their own evidence-based solutions following the principles of Open Science. They investigated different topics, ranging from Enhancing Forest Fire Predictions with Sequential Models for Ecosystem Preservation and Public Safety to Leveraging Semantic Segmentation to Perform Wildfire Prediction. We designed an interdisciplinary and agile educational environment, and in-depth learning modules for students as a means of bridging the gap between traditional high school courseware and digital reality and computational science. Students learned how to uncover hidden patterns and trends in structured and unstructured data using a range of data analytics tools and programming languages. Python, R, LaTeX, and machine learning were some of the tools the students learned and used. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, CISCO Networking Academy, Canadian Science Publishing, Schulich Foundation, SciNet at University of Toronto, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. We were privileged to witness first-hand the analytical capabilities of the data-native generation of students, and we are confident they will demonstrate excellence throughout their academic and professional careers.

The STEM Fellowship High School Big Data and AI Challenge provides students with a unique opportunity to utilize Open Data to investigate one of the UN Sustainable Development Goals while learning data science fundamentals in an experiential learning format – an essential skill set for a young researcher in the digital age. This year students tackled the challenge of Harnessing AI and Open Data Analytics to Combat Social Inequities Among Adolescents. Students suggested their own evidence-based solutions following the principles of Open Science. They investigated different topics, ranging from Optimizing Educational Equity to Advancing Equity for Disabled Youth. These future leaders were tasked with using Open Data to enhance our understanding of social inequities and explore areas for innovation to close inequity gaps and propagate the social pursuit of prosperity for all. Various topics were investigated, identifying different forms of socioeconomic determinants which impact inequity among adolescents on a global scale. By applying computational thinking, students explored the interplay between adolescent inequities and external factors, ultimately contributing to the development of new educational and social development approaches. STEM Fellowship has designed an interdisciplinary, agile educational environment with in-depth learning modules for students as a means to bridge the gap between traditional high school courseware and computational inquiry. Students learned how to uncover hidden patterns and trends in structured and unstructured data using a range of data analytics tools and programming languages. Python, R, LaTeX, and machine learning were some of the tools the students learned and used throughout the program. Additionally, all participants prepared a short slideshow and presented their research to a group of their peers. We are privileged to witness the analytical capabilities of this talented generation of students, and we are confident that they will demonstrate excellence throughout their academic and professional careers. The Western Canada and Eastern Canada finalist events were the culmination of the top participants’ trailblazing research, and were held at the Hunter Hub for Entrepreneurial Thinking at the University of Calgary in Calgary and at Microsoft Canadian Headquarters in Toronto respectively. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge and wish them the best for all of their future endeavours. We also want to express our appreciation to all of the STEM Fellowship volunteers who made this challenge possible. We greatly appreciate the patronage of the program by the Canadian Commission for UNESCO, as well as the Lieutenant Governor of Alberta and the Lieutenant Governor of Ontario. We want to thank Canadian Science Publishing, Environment and Climate Change Canada, Let’s Talk Science, National Research Council Canada, RBC Future Launch, SciNet at the University of Toronto, Hunter Hub at the University of Calgary, Microsoft Canada, Canadian Science Publishing, Overleaf, and Cisco Academy for their invaluable support.

Gender equality in Islamic education remains a significant discourse, especially when viewed through the perspectives of influential figures such as Ki Hajar Dewantara and KH. Ahmad Dahlan. This study aims to compare their thoughts on gender equality in education and analyze their relevance in the modern context. Using a qualitative approach with a comparative analysis method, this research examines primary sources, including writings, speeches, and educational practices. The results indicate that both figures emphasized inclusive education, yet their approaches differed: Ki Hajar Dewantara focused on humanist and nationalistic education, while KH. Ahmad Dahlan integrated religious values to promote gender equality. Ki Hajar Dewantara advocated equal educational access for all, emphasizing character development and independent thinking. Meanwhile, KH. Ahmad Dahlan emphasized the role of Islamic teachings in fostering balanced roles for men and women in society. The study concludes that their educational philosophies provide valuable insights for developing an inclusive and egalitarian Islamic education system today.

The National High School Big Data Challenge provides students with a unique opportunity to investigate one of the United Nation’s Sustainable Development Goals using open data, analytics, and AI. Through experiential learning, the program equips students with essential data science, critical thinking, and problem solving skills that are becoming increasingly more important in the digital age. The theme for the 2025-2026 program, “Leveraging Big Data and AI to Strengthen Food Security and Promote Sustainable Agriculture” invites students to explore how open data and AI can deepen our understanding of the systems that underpin equitable food production, distribution, and consumption. Students are encouraged to develop innovative data-driven insights and methodologies that contribute to more resilient agricultural practices and sustainable food systems, in alignment with global efforts to advance food security and environmental stewardship. Their investigations covered a wide array of topics, from the effects of climate change on food production to undernourishment and urbanization on a regional and international level. We are continually inspired by the intellectual curiosity, creativity, and analytical skills demonstrated by this next generation of researchers. Their work not only contributes to sustainable agricultural practices but also exemplifies the spirit of innovation that these programs seek to foster. To showcase and celebrate their achievements, two national conferences were held in February 2026: the Eastern Canada Conference at the University of Toronto (February 11, 2026) and the Western Canada Conference at the University of Calgary (February 20, 2026). On behalf of STEM Fellowship, we extend our heartfelt congratulations to all participants. We also thank our industry and academic reviewers and mentors, as well as our dedicated team of STEM Fellowship volunteers whose tireless support made this program possible. We would also like to gratefully acknowledge the invaluable partnership and contributions of RBC, Let’s Talk Science, Cisco Academy, National Research Council Canada, Government of Nunavut, Canadian Science Publishing, and the Hunter Hub for Entrepreneurial Thinking of the University of Calgary.

Today, one can observe shifts in the research landscape, which is formed by digitization and open science principles. The open science movement continues to gain momentum, attention and debate. In parallel with the principle of unity, open science gives rise to a taxonomy of several related ideas, guidelines and concepts, such as open access, open replicable research and open data. Over the past fifteen years, research institutions have focused on open access to publications. However, recently the focus of attention has shifted to research data as a “new currency” in research activities and their distribution in open access, and the guiding principles of data management are becoming crucial for the wide implementation of open science practices and the effective use of data in research, industry, business and other sectors of the economy. In this context, it is relevant to carry out a thorough study of primary scientific works on open science issues and to study the role of the concept of “open research data” in the paradigm of a holistic ecosystem of open science and business ecosystem. In this work, it is proposed to use the methods of quantitative and qualitative bibliometric analysis, which allows to identify the main trends and form the basis for further research. The information base for this work was the international scientometric database Scopus, which enables to analyze bibliographic data using built-in tools and import them for external use in the VOSviewer software. The study revealed an increasing trend in the number of publications on the subject under study, with the highest annual growth rate in 2017 (76%) and 2019 (66%). Qualitative bibliographic analysis made it possible to analyze the most cited and, therefore, trending works on the selected topic. In terms of the number of citations per year, the results show that the studies with such directions in open science as open program code (open source); data/research reproducibility, research data management; open access to publications (open access) are most popular. In addition, a cluster analysis of the co-prevalence of keywords was conducted. It formed clusters dedicated to both institutional and infrastructural problems of the development of open science and research data. Separately, the results of the analysis create a scientific basis for further research into the key determinants of the effectiveness of the implementation of a proper research data management system at the micro, meso, and macro levels. It will improve the effectiveness of the implementation of scientific developments from one field of knowledge to another, while achieving increased interdisciplinary research. In parallel with this, interested persons of the real sector of the economy get the opportunity to analyze scientific results, determining the possibility of their adoption in their own activities.

Today, one can observe shifts in the research landscape, which is formed by digitization and open science principles. The open science movement continues to gain momentum, attention and debate. In parallel with the principle of unity, open science gives rise to a taxonomy of several related ideas, guidelines and concepts, such as open access, open replicable research and open data. Over the past fifteen years, research institutions have focused on open access to publications. However, recently the focus of attention has shifted to research data as a “new currency” in research activities and their distribution in open access, and the guiding principles of data management are becoming crucial for the wide implementation of open science practices and the effective use of data in research, industry, business and other sectors of the economy. In this context, it is relevant to carry out a thorough study of primary scientific works on open science issues and to study the role of the concept of “open research data” in the paradigm of a holistic ecosystem of open science and business ecosystem. In this work, it is proposed to use the methods of quantitative and qualitative bibliometric analysis, which allows to identify the main trends and form the basis for further research. The information base for this work was the international scientometric database Scopus, which enables to analyze bibliographic data using built-in tools and import them for external use in the VOSviewer software. The study revealed an increasing trend in the number of publications on the subject under study, with the highest annual growth rate in 2017 (76%) and 2019 (66%). Qualitative bibliographic analysis made it possible to analyze the most cited and, therefore, trending works on the selected topic. In terms of the number of citations per year, the results show that the studies with such directions in open science as open program code (open source); data/research reproducibility, research data management; open access to publications (open access) are most popular. In addition, a cluster analysis of the co-prevalence of keywords was conducted. It formed clusters dedicated to both institutional and infrastructural problems of the development of open science and research data. Separately, the results of the analysis create a scientific basis for further research into the key determinants of the effectiveness of the implementation of a proper research data management system at the micro, meso, and macro levels. It will improve the effectiveness of the implementation of scientific developments from one field of knowledge to another, while achieving increased interdisciplinary research. In parallel with this, interested persons of the real sector of the economy get the opportunity to analyze scientific results, determining the possibility of their adoption in their own activities.

Education and Inclusive Growth Korean Experience

Open Access to scientific information, transparency of research processes and data is one of the most important conditions for the progress of science and scientific communication, the basis of international collaboration of researchers globally. The COVID-19 global pandemic has once again highlighted the need for open, efficient and equal access to scientific information for researchers, regardless of geographical, gender or any other constraints, promoting the exchange of scientific knowledge and data, scientific cooperation and scientific decision-making, knowledge and open data. The Internet has radically changed scientific communication, particularly on the model of peer-reviewed scientific journals and the way readers find and access the scientific information. Digital access is now the norm, thanks to the Open Access model. Although 20 years have passed since the announcement of the Budapest Open Access Initiative, and despite many achievements and advantages, there are still obstacles to the implementation of this model, there is some resistance from commercial publishers and other providers, and discussions continue in the academia world. The Open Access model is already supported by various strategies, policies, platforms, applications but is not yet established. Various business models for scientific journals are still being tested, a culture of preprints is being formed, and discussions are underway on the ethics of scientific publications, intellectual property, the need to finance the dissemination of research results, and so on. Various platforms and applications are being developed to help researchers “discover” research results. Nevertheless, this is not enough: it is important to “discover” not only the results but also the research data, allowing them be used for further research in the global world. Thus, the concepts and practices of Open Science, Open Data, development of research infrastructures, etc., are developing quite rapidly. The article considers the main stages of this 20-year path and outlines the main components and trends of the current stage. Emphasis is placed on the need to form a culture of Open Science and create incentives for its implementation, promoting innovative methods of Open Science at different stages of the scientific process, the needs of European integration of Ukrainian e-infrastructure development, the need for socio-cultural and technological change. The main international and domestic practices and projects in Open Access and Open Science, particularly the National Repository of Academic Texts and the National Plan of Open Science draft, are considered. The role of libraries and librarians in implementing the principles of Open Access and Open Science is emphasized.

Studies that compare programs from different substantive areas of policy are rare phenomena in the program implementation literature. Yet comparison is at the heart of the scientific method and is essential to the development of generalizations that might serve as guidance to the framers of policy. In part, this gap in the research literature can be attributed to the understandable reticence of scholars who have developed expertise in one substantive area of policy, such as education, to move into new and unfamiliar terrain. However, perhaps a more significant reason is the tendency of analysts to view programs in terms of their specific objectives, which are usually multiple and ambiguous, rather than in terms of the generic forms of government action-the strategies of interventionthey represent. Consider, for instance, the parallels between the development of federal policy in education and health care. Significant federal involvement in both delivery systems are post-World War II developments. The major turning point in political climate and constraints-the passage of the Elementary and Secondary Education Act (ESEA) and Medicare-came only a decade and a half ago (1965) as part of the initiatives of the Great Society. A major purpose of both ESEA and Medicare was to ensure that specific disadvantaged population groups had equal access to the existing delivery systems. Many of the subsequent programs in the two areas share the emphasis on equal access. Moreover, the constraints on federal involvement in education and health care are similar. The extensive diversity and structural decenANNE H. HASTINGS prepared this manuscript while a research fellow at the Brookings Institution, Washington, D.C. She is currently senior analyst, Advanced Technology Inc., Reston, Virginia.

STEM Fellowship’s Undergraduate Big Data Challenge (UnBDC) is an inquiry-driven and experiential learning program that invites students from across the country to strengthen their problem-solving and critical thinking skills while gaining familiarity with the fundamentals of data science. By allowing students to undertake independent research projects that tackle real-world public health and bioinformatics problems, the BDC fosters scientific inquiry and prompts new and innovative ideas. This year, we invited students to investigate the theme of Infodemiology for the Future of Digital and Public Health. It allowed students to explore practical applications and insights of infodemiology to discover breakthrough connections in Digital and Public Health using open social, demographic, and health data. Students explored many topics, ranging from using Twitter machine learning for fighting the COVID-19 infodemic, to the sentiment comparison between real and fake COVID-19 news articles. We developed in-depth learning modules designed to lead the student from zero-knowledge to an elementary working proficiency in data science. The students learn a broad range of data analytics tools, methods and programming languages which are useful for uncovering hidden patterns, trends in structured and unstructured data. Some of the skills the students learnt and used includes Data Visualization, Classification, Statistics and Data Handling, Overleaf etc. On behalf of STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. It has been a privilege for us to witness the analytical capabilities of the next generation of students firsthand, and we are certain all entrants will continue to demonstrate excellence in their respective careers.

Public science is critical to the economy and to society. However, much of the beneficial impact of scientific research only occurs when scientific knowledge is disseminated broadly and is used by others. This thesis examines the emerging policy, law, and practice of facilitating open access to scientific research data. One particular focus is to examine the open data policies recently introduced by research funders and publishers, and the potential in these for driving the practice of open scientific data into the future. This thesis identifies five major stumbling blocks to sustainable open scientific data. Firstly, the prevailing ‘mindset’ that facilitating open access to data is analogous to facilitating open access to publications and, therefore, research data can easily be shared, with research funders and librarians effectively leading the process. Secondly, the unclear meaning of the term ‘data’, which causes confusion among stakeholders. Thirdly, ‘misunderstood incentives’ for data sharing and the additional inputs required from researchers. Fourthly, ‘data privacy’—an issue that only applies to selected research datasets, and yet appears to dominate the discussion about open research data. Finally, there is ‘copyright law’, which poses challenges at different stages of data release and reuse. In this thesis, I argue that the above problems can be addressed using a staged model for open scientific data. I draw specifically on the practice with open scientific data at CERN (the European Organization for Nuclear Research) and the practice of sharing clinical trial data to argue that open data can be shared at various stages of processing and diversification. This model is supplemented by recommendations proposing changes to existing open data mandates and the introduction of a text and data mining exemption into Australian copyright law.

Equity in education refers to the facts that personal and social circumstances are not obstacles to achieving educational potential, and all individuals able to reach at least a basic minimum level of skills Equality in education, on the other hand, insinuates the important role in assisting deprived students and schools The main governing policy of education in Malaysia, the 1996’s Education Act 550, states that two main goals to be achieved in the nation’s educational system are equality and equitable provision. In order to be equal in education, the governance and the educators must provide the children with same educational opportunities regardless of their socio-economic background, genders, races, geographical location, and physical or mental disabilities. On the other hand, equity in education is highlighting that personal and social circumstances are not obstacles to achieving educational potential, and all individuals able to reach at least a basic minimum level of skills. This paper discusses the issues and challenges in providing both equal and equitable access of primary school education in Malaysia. Keywords: Education, equality, equity, primary school, Malaysia.

Data Scientist - The New ProfessionData scientist is probably the most trendy job in Information Technology (IT) nowadays. This new profession emerged with the Big Data wave. Even though there is no such thing like an exact job profile, we expect that the data scientist can handle all the Big Data challenges that are novel to us. Without being a magician she or he shall help to deliver us all the magic Big Data promises. The data scientist capitalizes on unstructured data without taking the roles of a programmer, database expert, statistician, or content manager. All these professions are around for decades. So, why invent a new one?More than anything, what data scientists do is make discoveries while swimming in data [...] they are able to bring structure to large quantities of formless data and make analysis possible. [3] They sketch, orchestrate, and control the discovery process. The leading paradigm in this process it to find information that meets a certain need or provides an answer for a certain problem. need to avoid the temptation of following a data-driven approach instead of a problem-driven one. [5] The data scientist has to develop an idea about the required information that meets our information need. We can expect that data scientists have a deep understanding of the foundations of both, the nature of information and the domain. They follow a mental model on the information demand that abstractly reflects the facts they expect to encounter in data and the way to detect and present them to us. We expect the data scientist to discover novel information that may provide us with new insights. And we want these insights to be true. It is thus part of the data scientist's responsibility to make sure that the discovered information is not only novel but also trustworthy. The data scientists cannot prove data analysis models. That exceeds their capabilities. We cannot hold them liable for information that eventually turns out to be wrong. Nevertheless their skills should include a sound sensation of plausibility that helps them to raise doubts and to prompt a closer look when the results of data analysis seem too questionable to them. However, separating questionable results from plausible ones is a task that is far from being trivial.Separating Data Science from Data FictionWhen driving a car we often encounter these nice roadside signs that sometimes make us realize that we're driving too fast. Furthermore, on some occasions there is official personnel not far from these signs measuring our speed, explaining to us our traffic infraction face-to-face and documenting it on a speeding ticket. Doesn't this sound a bit outdated? There are enough sensors in a car that measure location, speed, time, and more. Even if the car's sensors don't measure those, the driver's cellphone can do it. Combining this sensor information with the cartographic data about roads and speed limits we can easily imagine that, by the end of day, the car or the phone exactly knows every infraction we committed and can automatically trigger the issuing of speeding tickets. Aren't there just legal aspects that hamper this technically feasible scenario making its way into reality?One trait of Big Data is the availability of sensor data and their combination with already available data generating new information, like the detection of traffic infraction. We can extrapolate this scenario in many directions, including more types of infraction, driving for an irresponsibly long time without a substantial break, or detecting patterns of aggressive driving. We can extend it also towards future possibilities if we think about sensor information from smart watches indicating a possibly problematic pulse rate or from the car's air control sensor that the driver may drive under the influence of alcohol.Much like in Data Mining, the strength of Big Data originates from the combination of facts producing new information. …