scholarly journals Critical Data Studies and Data Science in Higher Education

Seminar.net ◽  
2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Dan Verständig
Keyword(s):  
Higher Education ◽  
Data Science ◽  
Media Theory ◽  
Digital Data ◽  
Science Methods ◽  
Data Literacy ◽  
Critical Data ◽  
Data Practices ◽  
Using Data

This paper discusses an explorative approach on strengthening critical data literacy using data science methods and a theoretical framing intersecting educational science and media theory. The goal is to path a way from data-driven to data-discursive perspectives of data and datafication in higher education. Therefore, the paper focuses on a case study, a higher education course project in 2019 and 2020 on education and data science, based on problem-based learning. The paper closes with a discussion on the challenges on strengthening data literacy in higher education, offering insights into data practices and the pitfalls of working with and reflecting on digital data.

scholarly journals Closing the research-implementation gap using data science tools: a case study with pollinators of British Columbia

2020 ◽  
Author(s):  
Laura Melissa Guzman ◽  
Tyler Kelly ◽  
Lora Morandin ◽  
Leithen M’Gonigle ◽  
Elizabeth Elle
Keyword(s):  
British Columbia ◽  
Data Science ◽  
Academic Research ◽  
Science Methods ◽  
Research Implementation ◽  
Conservation Practice ◽  
Implementation Gap ◽  
Web App ◽  
Using Data

AbstractA challenge in conservation is the gap between knowledge generated by researchers and the information being used to inform conservation practice. This gap, widely known as the research-implementation gap, can limit the effectiveness of conservation practice. One way to address this is to design conservation tools that are easy for practitioners to use. Here, we implement data science methods to develop a tool to aid in conservation of pollinators in British Columbia. Specifically, in collaboration with Pollinator Partnership Canada, we jointly develop an interactive web app, the goal of which is two-fold: (i) to allow end users to easily find and interact with the data collected by researchers on pollinators in British Columbia (prior to development of this app, data were buried in supplements from individual research publications) and (ii) employ up to date statistical tools in order to analyse phenological coverage of a set of plants. Previously, these tools required high programming competency in order to access. Our app provides an example of one way that we can make the products of academic research more accessible to conservation practitioners. We also provide the source code to allow other developers to develop similar apps suitable for their data.

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

scholarly journals The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility

2015 ◽  
Vol 22 (6) ◽  
pp. 1120-1125 ◽  
Author(s):  
Joy P Ku ◽  
Jennifer L Hicks ◽  
Trevor Hastie ◽  
Jure Leskovec ◽  
Christopher Ré ◽  
...  
Keyword(s):  
Data Science ◽  
Human Mobility ◽  
Wearable Sensors ◽  
Human Movement ◽  
Science Research ◽  
Science Methods ◽  
Social Modeling ◽  
Mobility Data ◽  
Using Data ◽  
And Training

Abstract Regular physical activity helps prevent heart disease, stroke, diabetes, and other chronic diseases, yet a broad range of conditions impair mobility at great personal and societal cost. Vast amounts of data characterizing human movement are available from research labs, clinics, and millions of smartphones and wearable sensors, but integration and analysis of this large quantity of mobility data are extremely challenging. The authors have established the Mobilize Center ( http://mobilize.stanford.edu ) to harness these data to improve human mobility and help lay the foundation for using data science methods in biomedicine. The Center is organized around 4 data science research cores: biomechanical modeling, statistical learning, behavioral and social modeling, and integrative modeling. Important biomedical applications, such as osteoarthritis and weight management, will focus the development of new data science methods. By developing these new approaches, sharing data and validated software tools, and training thousands of researchers, the Mobilize Center will transform human movement research.

scholarly journals Big Data is Too Small: Research Implications of Class Inequality for Online Data Collection

2018 ◽  
Author(s):  
Jen Schradie
Keyword(s):  
Big Data ◽  
Data Science ◽  
Digital Data ◽  
The Internet ◽  
Sociological Research ◽  
Marginalized Populations ◽  
Online Data ◽  
Persistent Problem ◽  
Current State ◽  
Using Data

With a growing interest in data science and online analytics, researchers are increasingly using data derived from the Internet. Whether for qualitative or quantitative analysis, online data, including “Big Data,” can often exclude marginalized populations, especially those from the poor and working class, as the digital divide remains a persistent problem. This methodological commentary on the current state of digital data and methods disentangles the hype from the reality of digitally produced data for sociological research. In the process, it offers strategies to address the weaknesses of data that is derived from the Internet in order to represent marginalized populations.

scholarly journals Computational Design Map for Heterogeneous Experimental Studies

2021 ◽  
Author(s):  
Chhaya Kulkarni ◽  
Nuzhat Maisha ◽  
Leasha J Schaub ◽  
Jacob Glaser ◽  
Erin Lavik ◽  
...  
Keyword(s):  
Data Mining ◽  
Blood Loss ◽  
Decision Trees ◽  
Data Science ◽  
Computational Design ◽  
Science Methods ◽  
Frequent Patterns ◽  
Multiple Data ◽  
Mining Methods ◽  
Using Data

This paper focuses on the discovery of a computational design map of disparate heterogeneous outcomes from bioinformatics experiments in pig (porcine) studies to help identify key variables impacting the experiment outcomes. Specifically we aim to connect discoveries from disparate laboratory experimentation in the area of trauma, blood loss and blood clotting using data science methods in a collaborative ensemble setting. Trauma related grave injuries cause exsanguination and death, constituting up to 50% of deaths especially in the armed forces. Restricting blood loss in such scenarios usually requires the presence of first responders, which is not feasible in certain cases. Moreover, a traumatic event may lead to a cytokine storm, reflected in the cytokine variables. Hemostatic nanoparticles have been developed to tackle these kinds of situations of trauma and blood loss. This paper highlights a collaborative effort of using data science methods in evaluating the outcomes from a lab study to further understand the efficacy of the nanoparticles. An intravenous administration of hemostatic nanoparticles was executed in pigs that had to undergo hemorrhagic shock and blood loss and other immune response variables, cytokine response variables are measured. Thus, through various hemostatic nanoparticles used in the intervention, multiple data outcomes are produced and it becomes critical to understand which nanoparticles are critical and what variables are key to study further variations in the lab. We propose a collaborative data mining framework which combines the results from multiple data mining methods to discover impactful features. We used frequent patterns observed in the data from these experiments. We further validate the connections between these frequent rules by comparing the results with decision trees and feature ranking. Both the frequent patterns and the decision trees help us identify the critical variables that stand out in the lab studies and need further validation and follow up in future studies. The outcomes from the data mining methods help produce a computational design map of the experimental results. Our preliminary results from such a computational design map provided insights in determining which features can help in designing the most effective hemostatic nanoparticles.

Using Data Science To Drive Water Utility Decisions: A Case Study In A Medium-Sized Utility

2018 ◽  
Vol 2018 (1) ◽  
pp. 724-732
Author(s):  
Janani Mohanakrishnan ◽  
Christine Boyle ◽  
James G Poff
Keyword(s):  
Data Science ◽  
Water Utility ◽  
Using Data

A Case Study on Student Attrition Prediction in Higher Education Using Data Mining Techniques

2019 ◽  
pp. 181-192
Author(s):  
Syaidatus Syahira Ahmad Tarmizi ◽  
Sofianita Mutalib ◽  
Nurzeatul Hamimah Abdul Hamid ◽  
Shuzlina Abdul-Rahman ◽  
Ariff Md Ab Malik
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Student Attrition ◽  
Data Mining Techniques ◽  
Using Data

Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Haider Ilyas ◽  
Ahmed Anwar ◽  
Ussama Yaqub ◽  
Zamil Alzamil ◽  
Deniz Appelbaum
Keyword(s):  
Data Science ◽  
Supervised Machine Learning ◽  
Content Type ◽  
The Public ◽  
Public Sentiment ◽  
The People ◽  
The Usa ◽  
Using Data ◽  
The Uk

Purpose This paper aims to understand, examine and interpret the main concerns and emotions of the people regarding COVID-19 pandemic in the UK, the USA and India using Data Science measures. Design/methodology/approach This study implements unsupervised and supervised machine learning methods, i.e. topic modeling and sentiment analysis on Twitter data for extracting the topics of discussion and calculating public sentiment. Findings Governments and policymakers remained the focus of public discussion on Twitter during the first three months of the pandemic. Overall, public sentiment toward the pandemic remained neutral except for the USA. Originality/value This paper proposes a Data Science-based approach to better understand the public topics of concern during the COVID-19 pandemic.

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