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1. Tensions and synergies in arts‐integrated data literacy instruction: Reflections on four classroom implementations

   https://doi.org/10.1111/bjet.13257  

   Matuk, Camillia; DesPortes, Kayla; Amato, Anna; Vacca, Ralph; Silander, Megan; Woods, Peter_J; Tes, Marian (July 2022, British Journal of Educational Technology)

   Abstract Data‐art inquiry is an arts‐integrated approach to data literacy learning that reflects the multidisciplinary nature of data literacy not often taught in school contexts. By layering critical reflection over conventional data inquiry processes, and by supporting creative expression about data, data‐art inquiry can support students' informal inference‐making by revealing the role of context in shaping the meaning of data, and encouraging consideration of the personal and social relevance of data. Data‐art inquiry additionally creates alternative entry points into data literacy by building on learners' non‐STEM interests. Supported by technology, it can provide accessible tools for students to reflect on and communicate about data in ways that can impact broader audiences. However, data‐art inquiry instruction faces many barriers to classroom implementation, particularly given the tendency for schools to structure learning with disciplinary silos, and to unequally prioritize mathematics and the arts. To explore the potential of data‐art inquiry in classroom contexts, we partnered with arts and mathematics teachers to co‐design and implement data‐art inquiry units. We implemented the units in four school contexts that differed in terms of the student population served, their curriculum priorities, and their technology infrastructure. We reflect on participant interviews, written reflections, and classroom data, to identify synergies and tensions between data literacy, technology, and the arts. Our findings highlight how contexts of implementation shape the possibilities and limitations for data‐art inquiry learning. To take full advantage of the potential for data‐art inquiry, curriculum design should account for and build on the opportunities and constraints of classroom contexts. Practitioner notesWhat is already known about this topicArts‐integrated instruction has underexplored potential for promoting students' data literacy, including their appreciation for the role of context and real‐world implications of data and for the personal and social relevance of data.Arts‐integrated instruction is difficult to implement in school contexts that are constrained by disciplinary silos.What this paper addsDescriptions of four data‐art inquiry units, which take an arts‐integrated approach to data literacy.Examples of the synergies and tensions observed between data literacy, technology, and the arts during classroom implementation in four different schools.Reflections on the role of school contexts in shaping disciplinary synergies and tensions.Implications for practice and/or policyArts‐integration offers opportunities for data literacy learning.Consideration of the unique resources and constraints of classroom contexts is critical for fulfilling the promises of data‐art inquiry learning.There is a need to develop school support specific to arts‐integrated data literacy instruction. 

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2. IDTReeS 2020 Competition Data

   https://doi.org/10.5281/zenodo.3700196  

   Graves, Sarah; Marconi, Sergio (January 2020, Zenodo)

   {"Abstract":["Data provided by the Integrating Data science with Trees and Remote Sensing (IDTReeS) research group for use in the IDTReeS Competition.<\/p>\n\nGeospatial and tabular data to be used in two data science tasks focused on using remote sensing data to quantify the locations, sizes and species identities of millions of trees and on determining how these methods generalize to other forests.<\/p>\n\nVector data are the geographic extents of Individual Tree Crown boundaries that have been identified by researchers in the IDTReeS group. The data were generated primarily by Sarah Graves, Sergio Marconi, and Benjamin Weinstein, with support from Stephanie Bohlman, Ethan White, and members of the IDTReeS group.<\/p>\n\nRemote Sensing and Field data were generated by the National Ecological Observatory Network (NEON, Copyright © 2017 Battelle). Data were selected, downloaded, and packaged by Sergio Marconi. The most recent available data of the following products are provided:<\/p>\n\nNational Ecological Observatory Network. 2020. Data Product DP1.30010.001, High-resolution orthorectified camera imagery. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.<\/p>\n\nNational Ecological Observatory Network. 2020. Data Product DP1.30003.001, Discrete return LiDAR point cloud. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.<\/p>\n\nNational Ecological Observatory Network. 2020. Data Product DP1.10098.001, Woody plant vegetation structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.<\/p>\n\nNational Ecological Observatory Network. 2020. Data Product DP3.30015.001, Ecosystem structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.<\/p>\n\nNEON has the following data policy:<\/p>\n\n\u2018The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program.\u2019<\/p>\n\nTHE NEON DATA PRODUCTS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE NEON DATA PRODUCTS BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE NEON DATA PRODUCTS.<\/p>"],"Other":["This data is supported by the National Science Foundation through grant 1926542 and by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through grant GBMF4563 to E.P. White, and the NSF Dimension of Biodiversity program grant (DEB-1442280) and USDA/NIFA McIntire-Stennis program (FLA-FOR-005470)."]} 

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3. Functional data analysis using deep neural networks

   https://doi.org/10.1002/wics.70001  

   Wang, Shuoyang; Zhang, Wanyu; Cao, Guanqun; Huang, Yuan (July 2024, WIREs Computational Statistics)

   Abstract Functional data analysis is an evolving field focused on analyzing data that reveals insights into curves, surfaces, or entities within a continuous domain. This type of data is typically distinguished by the inherent dependence and smoothness observed within each data curve. Traditional functional data analysis approaches have predominantly relied on linear models, which, while foundational, often fall short in capturing the intricate, nonlinear relationships within the data. This paper seeks to bridge this gap by reviewing the integration of deep neural networks into functional data analysis. Deep neural networks present a transformative approach to navigating these complexities, excelling particularly in high‐dimensional spaces and demonstrating unparalleled flexibility in managing diverse data constructs. This review aims to advance functional data regression, classification, and representation by integrating deep neural networks with functional data analysis, fostering a harmonious and synergistic union between these two fields. The remarkable ability of deep neural networks to adeptly navigate the intricate functional data highlights a wealth of opportunities for ongoing exploration and research across various interdisciplinary areas. This article is categorized under:Data: Types and Structure > Time Series, Stochastic Processes, and Functional DataStatistical Learning and Exploratory Methods of the Data Sciences > Deep LearningStatistical Learning and Exploratory Methods of the Data Sciences > Neural Networks 

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4. Review on functional data classification

   https://doi.org/10.1002/wics.1638  

   Wang, Shuoyang; Huang, Yuan; Cao, Guanqun (January 2024, WIREs Computational Statistics)

   Abstract A fundamental problem in functional data analysis is to classify a functional observation based on training data. The application of functional data classification has gained immense popularity and utility across a wide array of disciplines, encompassing biology, engineering, environmental science, medical science, neurology, social science, and beyond. The phenomenal growth of the application of functional data classification indicates the urgent need for a systematic approach to develop efficient classification methods and scalable algorithmic implementations. Therefore, we here conduct a comprehensive review of classification methods for functional data. The review aims to bridge the gap between the functional data analysis community and the machine learning community, and to intrigue new principles for functional data classification. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and ClassificationStatistical Models > Classification ModelsData: Types and Structure > Time Series, Stochastic Processes, and Functional Data 

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5. Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study

   https://doi.org/10.2196/31633  

   Zhang, Dongsong; Lim, Jaewan; Zhou, Lina; Dahl, Alicia A (January 2021, JMIR Mental Health)

   BackgroundMobile mental health systems (MMHS) have been increasingly developed and deployed in support of monitoring, management, and intervention with regard to patients with mental disorders. However, many of these systems rely on patient data collected by smartphones or other wearable devices to infer patients’ mental status, which raises privacy concerns. Such a value-privacy paradox poses significant challenges to patients’ adoption and use of MMHS; yet, there has been limited understanding of it. ObjectiveTo address the significant literature gap, this research aims to investigate both the antecedents of patients’ privacy concerns and the effects of privacy concerns on their continuous usage intention with regard to MMHS. MethodsUsing a web-based survey, this research collected data from 170 participants with MMHS experience recruited from online mental health communities and a university community. The data analyses used both repeated analysis of variance and partial least squares regression. ResultsThe results showed that data type (P=.003), data stage (P<.001), privacy victimization experience (P=.01), and privacy awareness (P=.08) have positive effects on privacy concerns. Specifically, users report higher privacy concerns for social interaction data (P=.007) and self-reported data (P=.001) than for biometrics data; privacy concerns are higher for data transmission (P=.01) and data sharing (P<.001) than for data collection. Our results also reveal that privacy concerns have an effect on attitude toward privacy protection (P=.001), which in turn affects continuous usage intention with regard to MMHS. ConclusionsThis study contributes to the literature by deepening our understanding of the data value-privacy paradox in MMHS research. The findings offer practical guidelines for breaking the paradox through the design of user-centered and privacy-preserving MMHS. 

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