scholarly journals Rethinking phylogenetic comparative methods

2017 ◽  
Author(s):  
Josef C. Uyeda ◽  
Rosana Zenil-Ferguson ◽  
Matthew W. Pennell
Keyword(s):  
Graphical Models ◽  
Comparative Methods ◽  
Data Driven ◽  
Comparative Biology ◽  
Phylogenetic Comparative Methods ◽  
Common Theme ◽  
Closely Related Species ◽  
Way Of Thinking ◽  
The Impact

AbstractAs a result of the process of descent with modification, closely related species tend to be similar to one another in a myriad different ways. In statistical terms, this means that traits measured on one species will not be independent of traits measured on others. Since their introduction in the 1980s, phylogenetic comparative methods (PCMs) have been framed as a solution to this problem. In this paper, we argue that this way of thinking about PCMs is deeply misleading. Not only has this sowed widespread confusion in the literature about what PCMs are doing but has led us to develop methods that are susceptible to the very thing we sought to build defenses against — unreplicated evolutionary events. Through three Case Studies, we demonstrate that the susceptibility to singular events is indeed a recurring problem in comparative biology that links several seemingly unrelated controversies. In each Case Study we propose a potential solution to the problem. While the details of our proposed solutions differ, they share a common theme: unifying hypothesis testing with data-driven approaches (which we term “phylogenetic natural history”) to disentangle the impact of singular evolutionary events from that of the factors we are investigating. More broadly, we argue that our field has, at times, been sloppy when weighing evidence in support of causal hypotheses. We suggest that one way to refine our inferences is to re-imagine phylogenies as probabilistic graphical models; adopting this way of thinking will help clarify precisely what we are testing and what evidence supports our claims.

scholarly journals Vulnerability in a tracked society: Combining tracking and survey data to understand who gets targeted with what content

2020 ◽  
Vol 22 (11) ◽  
pp. 1996-2017
Author(s):  
Nadine Bol ◽  
Joanna Strycharz ◽  
Natali Helberger ◽  
Bob van de Velde ◽  
Claes H de Vreese
Keyword(s):  
Survey Data ◽  
Online Communication ◽  
Data Driven ◽  
Health Conditions ◽  
User Characteristics ◽  
Individual Perceptions ◽  
Health Related ◽  
Online Tracking ◽  
The Impact

While data-driven personalization strategies are permeating all areas of online communication, the impact for individuals and society as a whole is still not fully understood. Drawing on Facebook as a case study, we combine online tracking and self-reported survey data to assess who gets targeted with what content. We tested relationships between user characteristics (i.e. socio-demographic and individual perceptions) and exposure to branded content on Facebook. Findings suggest that social media use sophisticated algorithms to target specific groups of users, especially in the context of gender-stereotyping and health. Health-related content was predominantly targeted at older users, females, and at those with higher levels of trust in online companies, as well as those in poorer health conditions. This study provides a first indication of unfair targeting that reinforces stereotypes and creates inequalities, and suggests rethinking the impact of algorithmic targeting in creating new forms of individual and societal vulnerabilities.

scholarly journals Are Conference Posters Being Cited?

2021 ◽  
Vol 6 ◽  
Author(s):  
Nick Haupka ◽  
Cäcilia Schröer ◽  
Christian Hauschke
Keyword(s):  
Data Driven ◽  
Citation Data ◽  
Academic Publications ◽  
The Impact

We present a small case study on citations of conference posters using poster collections from both Figshare and Zenodo. The study takes into account the years 2016–2020 according to the dates of publication on the platforms. Citation data was taken from DataCite, Crossref and Dimensions. Primarily, we want to know to what extent scientific posters are being cited and thereby which impact posters potentially have on the scholarly landscape and especially on academic publications. Our data-driven analysis reveals that posters are rarely cited. Citations could only be found for 1% of the posters in our dataset. A limitation in this study however is that the impact of academic posters was not measured empirical but rather descriptive.

Data-driven Personalization of Physiotherapy Care Pathway: Case Posture Scanning (Preprint)

2020 ◽  
Author(s):  
Olli Korhonen ◽  
Karin Väyrynen ◽  
Tino Krautwald ◽  
Glenn Bilby ◽  
Anna Broers ◽  
...  
Keyword(s):  
Decision Making ◽  
Care Pathway ◽  
Human Movement ◽  
Digital Data ◽  
Data Driven ◽  
Care Process ◽  
Root Cause ◽  
Care Models ◽  
The Impact

BACKGROUND Advanced sensor, measurement and analytics technologies enable entirely new ways to deliver care. Increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making, or even automating some parts of decision making in relation to the care process. OBJECTIVE The aim of this study is to analyze how digital data acquired from posture scanning can enhance physiotherapy and enable more personalized delivery of physiotherapy. METHODS A Case study is conducted with a company that has designed a Posture Scan Recording System (PSRS), which is an Information System (IS) that can record, measure and report human movement digitally to be used in physiotherapy. Interviews are used to explore the viewpoints of different stakeholders involved in physiotherapy. The data is analyzed thematically. RESULTS As the result of our thematic analysis, we identified three different support types the posture scanning can provide to enable more personalized delivery of physiotherapy. The types are: (1) Modeling the condition, which is about the use of posture scanning data for detecting and understanding the healthcare user’s condition and the root cause of the possible pain. (2) Visualization for a shared understanding, which is about the use of posture scanning data to inform and involve the healthcare user in more collaborative decision-making regarding care. (3) Evaluating the impact of the intervention, which is about the use of posture scanning data to evaluate the care progress and impact of the intervention. CONCLUSIONS Current care models in healthcare emphasize the importance to put the healthcare user at the center of the care. However, physiotherapy has lacked data driven solutions to inform and involve the healthcare user in care in a person-centered manner. The present study analyzes how posture scanning can enhance physiotherapy and presents three different types of support that posture scanning can provide for data-driven personalization of physiotherapy.

Forecasting Technological Impacts on Customers’ Co-Consideration Behaviors: A Data-Driven Network Analysis Approach

2016 ◽  
Author(s):  
Mingxian Wang ◽  
Zhenghui Sha ◽  
Yun Huang ◽  
Noshir Contractor ◽  
Yan Fu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Product Design ◽  
Data Driven ◽  
Analysis Approach ◽  
Quadratic Assignment ◽  
Technological Changes ◽  
Technological Developments ◽  
Complex Relationships ◽  
The Impact

Forecasting customers’ responses and market competitions is essential before launching major technological changes in product design. In this research, we present a data-driven network analysis approach to understand the interactions among technologies, products, and customers. Such an approach provides a quantitative assessment of the impact of technological changes on customers’ co-consideration behaviors. The multiple regression quadratic assignment procedure (MRQAP) is employed to quantitatively predict product co-consideration relations as a function of various effect networks created by associations of product attributes and customer demographics. The uniqueness of the proposed approach is its capability of predicting complex relationships of product co-consideration as a network. Using vehicles as a case study, we forecast the impacts of two technological changes — adopting the fuel economy-boosting technology and the turbo engine technology by individual auto companies. The case study provides vehicle designers with insights into the change of market competitions brought by new technological developments. Our proposed approach links the market complexity to technology features and subsequently product design attributes to guide engineering design decisions in the complex customer-product systems.

scholarly journals Data Storytelling with Dashboard: Accelerating Understanding Through Data Visualization in Financial Technology Company Case Study

Jurnal METRIS ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ferdian Suprata
Keyword(s):  
Decision Making ◽  
Data Visualization ◽  
Rapid Development ◽  
Data Driven ◽  
Decision Making Process ◽  
Business Practices ◽  
Context Data ◽  
The Impact ◽  
Financial Technology

In the rapid development many organisation rely on context data to support as well as to assist its decision making process. Consequently, Business Intelligence (BI), Dashboard, and Data Visualization emerged as primary tools in early 1990s as a way to help practitioners, data analyst, and data scientist to present context data into an actionable information for decision making process. However, despite its robust and powerful tools, recent study done by Kaggle’s survey in 2017 resulted that in the last five years, many companies were not able to create effective data-driven dashboard due to complex dataset, poor dashboard design, and insufficient storytelling. Hence, understanding of who is going to use dashboard, choosing which data and metrics to visualize in the right context, knowing how to convey information, driving engagement, and persuading audiences are essential in current business practices. This study is aimed to help practitioners to understand the impact of effective dashboard can have on decision making process, to design leveraging dashboard, and to present the dashboard in storytelling. A literature study is performed to gather all relevant information resulted in guidelines for dashboard creator. Case study in financial technology company is applied to experiment and to test the guidelines for assisting dashboard creator to present data-driven insight to the stakeholder.

scholarly journals Data-Driven Personalization of a Physiotherapy Care Pathway: Case Study of Posture Scanning

10.2196/18508 ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. e18508
Author(s):  
Olli Korhonen ◽  
Karin Väyrynen ◽  
Tino Krautwald ◽  
Glenn Bilby ◽  
Hedwig Anna Theresia Broers ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Information System ◽  
Health Care Professionals ◽  
Digital Data ◽  
Digital Tools ◽  
Data Driven ◽  
Care Process ◽  
The Impact

Background Advanced sensor, measurement, and analytics technologies are enabling entirely new ways to deliver health care. The increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making or even by automating some parts of decision making in relation to the care process. Objective The aim of this study was to analyze how digital data acquired from posture scanning can enhance physiotherapy services and enable more personalized delivery of physiotherapy. Methods A case study was conducted with a company that designed a posture scan recording system (PSRS), which is an information system that can digitally record, measure, and report human movement for use in physiotherapy. Data were collected through interviews with different stakeholders, such as health care professionals, health care users, and the information system provider, and were analyzed thematically. Results Based on the results of our thematic analysis, we propose three different types of support that posture scanning data can provide to enhance and enable more personalized delivery of physiotherapy: 1) modeling the condition, in which the posture scanning data are used to detect and understand the health care user’s condition and the root cause of the possible pain; 2) visualization for shared understanding, in which the posture scanning data are used to provide information to the health care user and involve them in more collaborative decision-making regarding their care; and 3) evaluating the impact of the intervention, in which the posture scanning data are used to evaluate the care progress and impact of the intervention. Conclusions The adoption of digital tools in physiotherapy has remained low. Physiotherapy has also lacked digital tools and means to inform and involve the health care user in their care in a person-centered manner. In this study, we gathered insights from different stakeholders to provide understanding of how the availability of digital posture scanning data can enhance and enable personalized physiotherapy services.

scholarly journals Predicting product co-consideration and market competitions for technology-driven product design: a network-based approach

2018 ◽  
Vol 4 ◽  
Author(s):  
Mingxian Wang ◽  
Zhenghui Sha ◽  
Yun Huang ◽  
Noshir Contractor ◽  
Yan Fu ◽  
...  
Keyword(s):  
Data Driven ◽  
Quadratic Assignment ◽  
Vehicle Design ◽  
Technological Changes ◽  
Integrated Network ◽  
Market Structures ◽  
Qualitative Understanding ◽  
Technological Developments ◽  
The Impact

We propose a data-driven network-based approach to understand the interactions among technologies, products, and customers. Specifically, the approach enables both a qualitative understanding and a quantitative assessment of the impact of technological changes on customers’ co-consideration behaviors (decision of cross-shopping) and as a consequence the product competitions. The uniqueness of the proposed approach is its capability of predicting complex co-consideration relations of products as a network where both descriptive analyses (e.g., network statistics and joint correspondence analysis) and predictive models (e.g., multiple regressions quadratic assignment procedure) are employed. The integrated network analysis approach features three advantages: (1) It provides an effective visual representation of the underlying market structures; (2) It facilitates the evaluation of the correlation between customers’ consideration preferences and product attributes as well as customer demographics; (3) It enables the prediction of market competitions in response to potential technological changes. This paper demonstrates the proposed network-based approach in a vehicle design context. We investigate the impacts of the fuel economy-boosting technologies and the turbocharged engine technology on individual automakers as well as the entire auto industry. The case study provides vehicle engineers with insights into the change of market competitions brought by technological developments and thereby supports attribute decision-making in vehicle design.

scholarly journals Big(ger) data as better data in open distance learning

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Paul Prinsloo ◽  
Elizabeth Archer ◽  
Glen Barnes ◽  
Yuraisha Chetty ◽  
Dion Van Zyl
Keyword(s):  
Big Data ◽  
Data Sources ◽  
Data Driven ◽  
Descriptive Case Study ◽  
Student Data ◽  
Current State ◽  
The University ◽  
The Impact ◽  
Dominant Paradigm

<p>In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously considered in realising this potential.</p><p>The University of South Africa (Unisa) is one of the mega ODL institutions in the world with more than 360,000 students and a range of courses and programmes. Unisa already has access to a staggering amount of student data, hosted in disparate sources, and governed by different processes. As the university moves to mainstreaming online learning, the amount of and need for analyses of data are increasing, raising important questions regarding our assumptions, understanding, data sources, systems and processes.</p><p>This article presents a descriptive case study of the current state of student data at Unisa, as well as explores the impact of existing data sources and analytic approaches. From the analysis it is clear that in order for big(ger) data to be better data, a number of issues need to be addressed. The article concludes by presenting a number of theses that should form the basis for the imperative to optimise the harvesting, analysis and use of student data.</p>

Sign in / Sign up

Export Citation Format

Share Document