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1. K. Le, T. Butterfield, K.E. Kelly, Citizen scientist engagement in air quality monitoring, Proc. ASEE Annual Meeting, June 23 – 27, Salt Lake City, UT.

   Le, K. ; Butterfield, T. ; Kelly, K.E. ( July 2018 , Review & directory - American Society for Engineering Education)

   Citizen scientist efforts, wherein members of the public who are not professional scientists participate in active research, have been shown to effectively engage the public in STEM fields and result in valuable data, essential to answering pressing research questions. However, most citizen scientist efforts have been centered in colleges of science, and a limited number have crossed into research areas important to chemical engineering fields. In this work we report on the results of a project to recruit high school and middle school students across Utah’s Salt Lake Valley as citizen scientists and potential engineering students who work in partnership with chemical engineering researchers in an effort to create a distributed online network of air quality sensors. Middle and high school students were trained by undergraduate mentors to monitor and maintain their own outdoor air quality sensor with the help of teaching materials that were co-developed with Breathe Utah, a local community group concerned with air quality. With the help of these tailored teaching modules, students learned about the science behind air quality research and the difficulties common to physical measurements to better prepare them to analyze their data. Once trained, students are expected to become semi-independent researchers in charge of monitoring and maintaining their piece of a larger air quality map. We describe in this work the hurdles inherent in citizen science engagement within a chemical engineering research program and the means to address them. We describe successful means of engaging classrooms, training citizen scientists, obtaining faculty buy-in within the confines of state curricular demands, and addressing school administration concerns. With this model, we have directly engaged over 1,000 high school and over 3,000 middle school students. The project has resulted in a growing network of citizen-maintained sensors that contributes to a real-time air quality map. Student scientists may also use the sensors to participate in active research or conduct science fair projects. Student response to this citizen scientist project, where it may be measured, has been enthusiastic and almost wholly positive. 

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2. Open science in the classroom: Students designing and peer reviewing studies in human brain and behavior research

   https://doi.org/10.1007/s11251-023-09633-9  

   Matuk, Camillia ; Yetman-Michaelson, Lucy ; Martin, Rebecca ; Vasudevan, Veena ; Burgas, Kim ; Davidesco, Ido ; Shevchenko, Yury ; Chaloner, Kim ; Dikker, Suzanne ( January 2023 , Instructional science)

   Citizen science programs offer opportunities for K-12 students to engage in authentic science inquiry. However, these programs often fall short of including learners as agents in the entire process, and thus contrast with the growing open science movement within scientific communities. Notably, study ideation and peer review, which are central to the making of science, are typically reserved for professional scientists. This study describes the implementation of an open science curriculum that engages high school students in a full cycle of scientific inquiry. We explored the focus and quality of students’ study designs and peer reviews, and their perceptions of open science based on their participation in the program. Specifically, we implemented a human brain and behavior citizen science unit in 6 classrooms across 3 high schools. After learning about open science and citizen science, students (N = 104) participated in scientist-initiated research studies, and then collaboratively proposed their own studies to investigate personally interesting questions about human behavior and the brain. Students then peer reviewed proposals of students from other schools. Based on a qualitative and quantitative analysis of students’ artifacts created in-unit and on a pre and posttest, we describe their interests, abilities, and self-reported experiences with study design and peer review. Our findings suggest that participation in open science in a human brain and behavior research context can engage students with critical aspects of experiment design, as well as with issues that are unique to human subjects research, such as research ethics. Meanwhile, the quality of students’ study designs and reviews changed in notable, but mixed, ways: While students improved in justifying the importance of research studies, they did not improve in their abilities to align methods to their research questions. In terms of peer review, students generally reported that their peers' feedback was helpful, but our analysis showed that student reviewers struggled to articulate concrete recommendations for improvement. In light of these findings, we discuss the need for curricula that support the development of research and review abilities by building on students’ interests, while also guiding students in transferring these abilities across a range of research foci. 

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3. Same soup, different bowl: Understanding the mentoring attitudes of STEM doctoral faculty at HBCUs

   Merriweather, Lisa ; Douglas, Niesha ; Howell, Cathy ; Sanczyk, Anna ( August 2022 , ASEE Annual Conference proceedings)

   ackground: Historically Black College and Universities (HBCUs) have for decades played a pivotal role in producing Black scientists. Research found that HBCUs, despite being under funded and resourced, were responsible for over 10% of Black scientists with doctorates. Even though most earn their doctorates at Historically White Institutions (HWIS), understanding the experience of Black STEM doctoral students at HBCUs is of paramount importance to impacting opportunity for success for underrepresented population groups. HBCUs are recognized for approaches to learning and learning environments that are more relational, encouraging peer to peer and student to faculty relationships, particularly in the form of same-race and same sex mentorships, resulting in less negative racialized gendered experiences and less competitive atmospheres. In spite of what appears to be accepted truths, such as HBCUs offering more culturally affirming experiences, some researchers suggests that little empirical research exists on the quality of support structures available for graduate students at HBCUS in STEM academic fields, particularly mentoring. Increased understanding would provide essential framing necessary for developing more effective mentors at HBCUs, especially given that there are limited numbers of Black faculty in STEM, even at HBCUs. Theoretical Framework: Anti-racism and critical capital theory are employed as theoretical frameworks. Both are well suited for questioning taken-for-granted assumptions about the lived experiences of racialized others and for deconstructing systemic issues influencing common faculty practices. These frameworks highlight the contextual experiences of STEM doctoral learning. Research Design: The researchers were interested in understanding how STEM doctoral faculty at HBCUs perceive their role as mentors. An NSF AGEP sponsored social science research project explored the dispositions, skills, and knowledge of eight STEM faculty at a HBCU. Attitudes towards culturally liberative mentoring were explored through a qualitative case study. The participating faculty were involved in an institutional change program and were interviewed for an average of 60 minutes. Constant comparative data analysis method was used. Additionally, STEM faculty from participating departments completed two mentoring competency and attitude inventories. This case was drawn from a larger multiple embedded case study. Research Findings: The research findings indicate that STEM doctoral faculty mentors at HBCUs express attitudes about mentoring that are not all that different from their PWIS counterparts. They have a tendency to hold deficit views of domestic Black students and have minimal awareness of how culture inhibits or facilitates a positive learning experience for Black students. Further the culture of science tended to blind them from the culture of people. Research Implications: In order to enhance the learning experiences of Black STEM doctoral students at HBCUs, the Black student experience at HBCUs must be deromanticized. Understanding the impact of anti-Black racism even within an environment historically and predominantly Black is imperative. Recognizing the ways in which anti-Black attitudes are insidiously present in faculty attitudes and practices and in environments perceived as friendly and supportive for Black students highlights opportunities for STEM faculty development that can move toward a more culturally liberative framework. 

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4. At the Intersection of Science and Education: The Process of Scientists and Educators Co‐developing Authentic Learning Experiences for K‐16 Students

   https://doi.org/10.1002/lob.10627  

   Granville, Kayleigh ; Baird, Cora ; Carlson, Charles ; Berg, Peter ( February 2024 , Limnology and Oceanography Bulletin)

   Students lose interest in science as they progress from elementary to high school. There is a need for authentic, place‐based science learning experiences that can increase students' interest in science. Scientists have unique skillsets that can complement the work of educators to create exciting experiences that are grounded in pedagogy and science practices. As scientists and educators, we co‐developed a lesson plan for high school students on the Eastern Shore of Virginia, a historically underserved coastal area, that demonstrated realistic scientific practices in students' local estuaries. After implementation of the lesson plan, we observed that students had a deeper understanding of ecosystem processes compared to their peers who had not been involved, were enthusiastic about sharing their experiences, and had a more well‐rounded ability to think like a scientist than before the lesson plan. We share our experiences and five best practices that can serve as a framework for scientists and educators who are motivated to do similar work. Through collaboration, scientists and educators have the potential to bolster student science identities and increase student participation in future scientific endeavors.

    

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5. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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