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1. Urban Park Usage During the COVID-19 Pandemic

   https://doi.org/10.1142/S2345737621500081  

   Alizadehtazi, Bita ; Tangtrakul, Korin ; Woerdeman, Sloane ; Gussenhoven, Anna ; Mostafavi, Nariman ; Montalto, Franco A. ( December 2020 , Journal of Extreme Events)

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   Urban parks and green spaces provide a wide range of ecosystem services, including social interaction and stress reduction. When COVID-19 closed schools and businesses and restricted social gatherings, parks became one of the few places that urban residents were permitted to visit outside their homes. With a focus on Philadelphia, PA and New York City, NY, this paper presents a snapshot of the park usage during the early phases of the pandemic. Forty-three Civic Scientists were employed by the research team to observe usage in 22 different parks selected to represent low and high social vulnerability, and low, medium, and high population density. Despite speculation that parks could contribute to the spread of COVID-19, no strong correlation was found between the number of confirmed COVID-19 cases in adjacent zip codes and the number of park users. High social vulnerability neighborhoods were associated with a significantly higher number of COVID-19 cases ([Formula: see text]). In addition, no significant difference in the number of park users was detected between parks in high and low vulnerability neighborhoods. The number of park users did significantly increase with population density in both cities ([Formula: see text]), though usage varied greatly by park. Males were more frequently observed than females in parks in both high vulnerability and high-density neighborhoods. Although high vulnerability neighborhoods reported higher COVID-19 cases, residents of Philadelphia and New York City appear to have been undeterred from visiting parks during this phase of the pandemic. This snapshot study provides no evidence to support closing parks during the pandemic. To the contrary, people continued to visit parks throughout the study, underscoring their evident value as respite for urban residents during the early phases of the pandemic. 

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2. 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))

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   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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3. Building Bridges into Engineering and Computer Science: Outcomes, Impacts and Lessons Learned

   Espiritu, Doris ; Todorovic, Ruzica ; O'Connell, Bridget ( January 2022 , 2022 ASEE National Conference and Exposition)

   Wright College, an urban open-access community college, independently accredited within a larger community college system, is a federally recognized Hispanic-Serving Institution (HSI) with the largest community college enrollment of Hispanic students in its state. In 2018, Wright College received an inaugural National Science Foundation-Hispanic Serving Institution (NSF:HSI) research project grant “Building Capacity: Building Bridges into Engineering and Computer Science”. The project's overall goals are to increase underrepresented students pursuing an associate degree (AES) in engineering and computer science and streamline two transitions: high school to community college and 2-year to 4-year institutions. Through the grant, Wright College created a holistic and programmatic framework that examines and correlates engineering students' self-efficacy (the belief that students will succeed as engineers) and a sense of belonging with student success. The project focuses on Near-STEM ready students (students who need up to four semesters of math remediation before moving into Calculus 1). The project assesses qualitative and quantitative outcomes through surveys and case study interviews supplemented with retention, persistence, transfer, associate and bachelor's degree completion rates, and time for degree completion. The key research approach is to correlate student success data with self-efficacy and belonging measures. Outcomes and Impacts Three years into the project, Wright College Engineering and Computer Science Program was able to: • Develop and implement the Contextualized Summer Bridge with a total of 132 Near-STEM participants. One hundred twenty-seven (127) completed; 100% who completed the Bridge eliminated up to two years of math remediation, and 54% were directly placed in Calculus 1. All successful participants were placed in different engineering pathways, and 11 students completed Associate in Engineering Science (AES) and transferred after two years from the Bridge. • Increase enrollment by 940% (25 to 235 students) • Retain 93% of first-year students (Fall to fall retention). Seventy-five percent (75%) transferred after two years from initial enrollment. • Develop a holistic and programmatic approach for transfer model, thus increasing partnerships with 4-year transfer institutions resulting in the expansion of guaranteed/dual admissions programs with scholarships, paid research experience, dual advising, and students transferring as juniors. • Increase diversity at Wright College by bridging the academic gap for Near-STEM ready students. • Increase self-efficacy and belonging among all Program participants. • Increase institutionalized collaborations responsible for Wright College's new designation as the Center of Excellence for Engineering and Computer Science. • Increase enrollment, retention, and transfer of Hispanic students instrumental for Wright College Seal of Excelencia recognition. Lessons Learned The framework established during the first year of the grant overwhelmingly increased belonging and self-efficacy correlated with robust outcomes. However, the COVID-19 pandemic provided new challenges and opportunities in the second and third years of the grant. While adaptations were made to compensate for the negative impact of the pandemic, the face-to-face interactions were critical to support students’ entry into pathways and persistence within the Program. 

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4. Study on U.S. Parents' Divisions of Labor During COVID-19, Waves 1-2

   https://doi.org/10.3886/E183142V2  

   Carlson, Daniel L. ; Petts, Richard J. ( January 2022 , ICPSR - Interuniversity Consortium for Political and Social Research)

   The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally.
   
   The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality.
   
   Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in October 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through October 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study.
   
   SPDLC data will be released to the public two years after data is collected; Waves 1 and 2 are currently publicly available. Wave 3 will be publicly available in October 2023, with subsequent waves becoming available yearly. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available. Response Rates: Of the 1,157 parents who participated in Wave 1, 828 (72%) also participated in the Wave 2 study. Presence of Common Scales: The following established scales are included in the survey:
   

   • Self-Efficacy, adapted from Pearlin's mastery scale (Pearlin et al., 1981) and the Rosenberg self-esteem scale (Rosenberg, 2015) and taken from the American Changing Lives Survey
   • Communication with Partner, taken from the Marriage and Relationship Survey (Lichter & Carmalt, 2009)
     
   • Gender Attitudes, taken from the National Survey of Families and Households (Sweet & Bumpass, 1996)
     
   • Depressive Symptoms (CES-D-10)
   • Stress, measured using Cohen's Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983)
     

   Full details about these scales and all other items included in the survey can be found in the user guide and codebook
   The second wave of the SPDLC was fielded in November 2020 in two stages. In the first stage, all parents who participated in W1 of the SPDLC and who continued to reside in the United States were re-contacted and asked to participate in a follow-up survey. The W2 survey was posted on Prolific, and messages were sent via Prolific’s messaging system to all previous participants. Multiple follow-up messages were sent in an attempt to increase response rates to the follow-up survey. Of the 1,157 respondents who completed the W1 survey, 873 at least started the W2 survey. Data quality checks were employed in line with best practices for online surveys (e.g., removing respondents who did not complete most of the survey or who did not pass the attention filters). After data quality checks, 5.2% of respondents were removed from the sample, resulting in a final sample size of 828 parents (a response rate of 72%).
   
   In the second stage, a new sample of parents was recruited. New parents had to meet the same sampling criteria as in W1 (be at least 18 years old, reside in the United States, reside with a romantic partner, and be a parent living with at least one biological child). Also similar to the W1 procedures, we oversampled men, Black individuals, individuals who did not complete college, and individuals who identified as politically conservative to increase sample diversity. A total of 1,207 parents participated in the W2 survey. Data quality checks led to the removal of 5.7% of the respondents, resulting in a final sample size of new respondents at Wave 2 of 1,138 parents.
   
   In both stages, participants were informed that the survey would take approximately 20 minutes to complete. All panelists were provided monetary compensation in line with Prolific’s compensation guidelines, which require that all participants earn above minimum wage for their time participating in studies.
   To be included in SPDLC, respondents had to meet the following sampling criteria at the time they enter the study: (a) be at least 18 years old, (b) reside in the United States, (c) reside with a romantic partner (i.e., be married or cohabiting), and (d) be a parent living with at least one biological child. Follow-up respondents must be at least 18 years old and reside in the United States, but may experience changes in relationship and resident parent statuses. Smallest Geographic Unit: U.S. State

   This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. In accordance with this license, all users of these data must give appropriate credit to the authors in any papers, presentations, books, or other works that use the data. A suggested citation to provide attribution for these data is included below:            
   
   Carlson, Daniel L. and Richard J. Petts. 2022. Study on U.S. Parents’ Divisions of Labor During COVID-19 User Guide: Waves 1-2.  

   To help provide estimates that are more representative of U.S. partnered parents, the SPDLC includes sampling weights. Weights can be included in statistical analyses to make estimates from the SPDLC sample representative of U.S. parents who reside with a romantic partner (married or cohabiting) and a child aged 18 or younger based on age, race/ethnicity, and gender. National estimates for the age, racial/ethnic, and gender profile of U.S. partnered parents were obtained using data from the 2020 Current Population Survey (CPS). Weights were calculated using an iterative raking method, such that the full sample in each data file matches the nationally representative CPS data in regard to the gender, age, and racial/ethnic distributions within the data. This variable is labeled CPSweightW2 in the Wave 2 dataset, and CPSweightLW2 in the longitudinal dataset (which includes Waves 1 and 2). There is not a weight variable included in the W1-W2 repeated cross-section data file.
    

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5. A prospective cohort study of preconception COVID-19 vaccination and miscarriage

   https://doi.org/10.1093/humrep/dead211  

   Yland, Jennifer J. ; Wesselink, Amelia K. ; Regan, Annette K. ; Hatch, Elizabeth E. ; Rothman, Kenneth J. ; Savitz, David A. ; Wang, Tanran R. ; Huybrechts, Krista F. ; Hernández-Díaz, Sonia ; Eisenberg, Michael L. ; et al ( October 2023 , Human Reproduction)

   To what extent is preconception maternal or paternal coronavirus disease 2019 (COVID-19) vaccination associated with miscarriage incidence?

   COVID-19 vaccination in either partner at any time before conception is not associated with an increased rate of miscarriage.

   Several observational studies have evaluated the safety of COVID-19 vaccination during pregnancy and found no association with miscarriage, though no study prospectively evaluated the risk of early miscarriage (gestational weeks [GW] <8) in relation to COVID-19 vaccination. Moreover, no study has evaluated the role of preconception vaccination in both male and female partners.

   An Internet-based, prospective preconception cohort study of couples residing in the USA and Canada. We analyzed data from 1815 female participants who conceived during December 2020–November 2022, including 1570 couples with data on male partner vaccination.

   Eligible female participants were aged 21–45 years and were trying to conceive without use of fertility treatment at enrollment. Female participants completed questionnaires at baseline, every 8 weeks until pregnancy, and during early and late pregnancy; they could also invite their male partners to complete a baseline questionnaire. We collected data on COVID-19 vaccination (brand and date of doses), history of SARS-CoV-2 infection (yes/no and date of positive test), potential confounders (demographic, reproductive, and lifestyle characteristics), and pregnancy status on all questionnaires. Vaccination status was categorized as never (0 doses before conception), ever (≥1 dose before conception), having a full primary sequence before conception, and completing the full primary sequence ≤3 months before conception. These categories were not mutually exclusive. Participants were followed up from their first positive pregnancy test until miscarriage or a censoring event (induced abortion, ectopic pregnancy, loss to follow-up, 20 weeks’ gestation), whichever occurred first. We estimated incidence rate ratios (IRRs) for miscarriage and corresponding 95% CIs using Cox proportional hazards models with GW as the time scale. We used propensity score fine stratification weights to adjust for confounding.

   Among 1815 eligible female participants, 75% had received at least one dose of a COVID-19 vaccine by the time of conception. Almost one-quarter of pregnancies resulted in miscarriage, and 75% of miscarriages occurred <8 weeks’ gestation. The propensity score-weighted IRR comparing female participants who received at least one dose any time before conception versus those who had not been vaccinated was 0.85 (95% CI: 0.63, 1.14). COVID-19 vaccination was not associated with increased risk of either early miscarriage (GW: <8) or late miscarriage (GW: 8–19). There was no indication of an increased risk of miscarriage associated with male partner vaccination (IRR = 0.90; 95% CI: 0.56, 1.44).

   The present study relied on self-reported vaccination status and infection history. Thus, there may be some non-differential misclassification of exposure status. While misclassification of miscarriage is also possible, the preconception cohort design and high prevalence of home pregnancy testing in this cohort reduced the potential for under-ascertainment of miscarriage. As in all observational studies, residual or unmeasured confounding is possible.

   This is the first study to evaluate prospectively the relation between preconception COVID-19 vaccination in both partners and miscarriage, with more complete ascertainment of early miscarriages than earlier studies of vaccination. The findings are informative for individuals planning a pregnancy and their healthcare providers.

   This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Health [R01-HD086742 (PI: L.A.W.); R01-HD105863S1 (PI: L.A.W. and M.L.E.)], the National Institute of Allergy and Infectious Diseases (R03-AI154544; PI: A.K.R.), and the National Science Foundation (NSF-1914792; PI: L.A.W.). The funders had no role in the study design, data collection, analysis and interpretation of data, writing of the report, or the decision to submit the paper for publication. L.A.W. is a fibroid consultant for AbbVie, Inc. She also receives in-kind donations from Swiss Precision Diagnostics (Clearblue home pregnancy tests) and Kindara.com (fertility apps). M.L.E. received consulting fees from Ro, Hannah, Dadi, VSeat, and Underdog, holds stock in Ro, Hannah, Dadi, and Underdog, is a past president of SSMR, and is a board member of SMRU. K.F.H. reports being an investigator on grants to her institution from UCB and Takeda, unrelated to this study. S.H.-D. reports being an investigator on grants to her institution from Takeda, unrelated to this study, and a methods consultant for UCB and Roche for unrelated drugs. The authors report no other relationships or activities that could appear to have influenced the submitted work.

   N/A.

    

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