14234 research outputs found
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Data for: Optimizing Sensor Data Interpretation via Hybrid Parametric Bootstrapping
No full textThis dataset contains the measured concentrations of U-235 in granite samples from the eastern desert of Egypt [1], which were used to analyze and estimate the upper limits of U-235 concentrations. The data includes both the original dataset and modified versions used for statistical analysis, including nonparametric bootstrapping and hybrid parametric bootstrapping techniques. The repository also includes scripts and code used for analyzing the datasets, particularly in handling outliers and small sample sizes, as well as results from comparative analyses between different statistical approaches. This data is essential for further research on the safe management of nuclear legacy waste and is applicable to remediation and decommissioning efforts at nuclear sites such as Chalk River Laboratories.
[1] Harb, S., et al. "Concentration of U-238, U-235, Ra-226, Th-232 and K-40 for some granite samples in eastern desert of Egypt." (2008)
Global Flourishing Study Analytic Methodology
No full textThis repository contains the code relevant to the outcome-wide analyses, demographic variation analyses, childhood predictor analyses, and meta-analyses carried out are part of the coordinated efforts for the analyses of the Global Flourishing Study data. The code is openly available for those aiming to replicate analyses conducted as part of the collection and beyond for analyzing data from the Global Flourishing Study
Assessing coherence in L2 speech
No full textThis project seeks to operationalize perceived coherence in L2 speec
Children’s social connections, internet usage and mental health during the COVID-19 pandemic in the Global South: evidence from Disrupting Harm
No full textObjective:
To investigate the cross-sectional associations of digital technology use with mental health indicators during the COVID-19 pandemic in 12 countries in the Global South.
Methods:
We used data from the UNICEF Innocenti Disrupting Harm survey of 11,912 children aged 12–17 in Ethiopia, Kenya, Mozambique, Namibia, Tanzania, Uganda, Cambodia, Indonesia, Malaysia, Philippines, Thailand and Vietnam. We modelled the associations of social connection and internet use during lockdown with six different indicators of wellbeing in each country and in the overall sample using robust linear and logistic regression. We controlled for a range of putative socioeconomic and demographic confounders and handled missing data using multiple imputation.
Findings:
We did not find clear evidence for any general associations of social connection or internet use with mental health indicators during lockdown across countries. Rather, our results are complex and demonstrate that the relationship depends heavily on an individual’s context; not just on the country they are living in but also on their sex, urbanicity and other factors. Nonetheless, our results highlight key focus areas for further research.
Conclusion:
Extensive further research is needed to identify ways in which technology use, including internet use, might act as a risk or protective factor relevant to mental ill health among young people in the countries of the Global South
Transforming Math Coaching: Leveraging AI for Effective Feedback and Coaching Conversations
No full textThis study focuses on the development and evaluation of an AI-generated coach feedback tool for instructional coaching on math and its impact on coaching effectiveness. The Year 1 exploratory phase aims to analyze coaching conversations to inform the development of the AI tool. In Year 2, the tool will be evaluated using a randomized controlled trial (RCT), where the intervention involves using the AI tool to support coaching, while the control group follows business-as-usual coaching methods without the support of the AI tool
I still haven't found what I'm looking for: Predicting security-related incidents and conflict fatalities with Google Trends and Wikipedia data
No full textConflict forecasting has seen two recent developments: a shift to predicting continuous variables and a debate about the value of structural and procedural variables. This paper contributes to these efforts and proposes the category of salience variables in the form of Google Trends and Wikipedia data. Internet searches can be precursors of conflict intensity as a result of e.g. an increase in protests, violent behavior, or public announcements. Data are readily and openly available, updated in real time, and provide global coverage which makes it ideal for near-real time forecasting. Prediction targets are the number of security-related incidents and battle-related, non-state, and civilian casualties. I demonstrate the value of \textit{salience} variables using various out-of-sample windows and performance metrics on the country- and province-month level. I find evidence that \textit{salience} variables have considerable predictive power, outperform other commonly used variables, and are thus a valuable addition to the conflict forecasting toolkit
