Dataset Information

Strength in Numbers: The WWARN Case Study of Purpose-Driven Data Sharing.

ABSTRACT: Data are the basis for all scientific output. The sharing of data supporting that output is an important aspect of scientific communication, and is increasingly required by funders and publishers. Yet, academic advancement seldom recognizes or rewards data sharing. This article argues that although mandating data sharing will increase the amount of data available, this will not necessarily enable or encourage the secondary analyses needed to achieve its purported public good. We, therefore, need to build models that maximize the efficiency of processes for data collation and curation, and genuinely reward those engaged in data sharing and reuse. The WorldWide Antimalarial Resistance Network has 10 years of experience as a data platform, and its study group approach provides an example of how some of the challenges in equitable and impactful data-sharing and secondary use can be addressed, with a focus on the priorities of researchers in resource-limited settings.

SUBMITTER: Humphreys GS

PROVIDER: S-EPMC6335902 | biostudies-other | 2019 Jan

REPOSITORIES: biostudies-other

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Publications

Strength in Numbers: The WWARN Case Study of Purpose-Driven Data Sharing.

Humphreys Georgina S GS Tinto Halidou H Barnes Karen I KI

The American journal of tropical medicine and hygiene 20190101 1

Data are the basis for all scientific output. The sharing of data supporting that output is an important aspect of scientific communication, and is increasingly required by funders and publishers. Yet, academic advancement seldom recognizes or rewards data sharing. This article argues that although mandating data sharing will increase the amount of data available, this will not necessarily enable or encourage the secondary analyses needed to achieve its purported public good. We, therefore, need ...[more]

PMID: 30350772

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Project description:The analytic procedures incorporated to facilitate the delivery of projects are often referred to as project analytics. Existing techniques focus on retrospective reporting and understanding the underlying relationships to make informed decisions. Although machine learning algorithms have been widely used in addressing problems within various contexts (e.g., streamlining the design of construction projects), limited studies have evaluated pre-existing machine learning methods within the delivery of construction projects. Due to this, the current research aims to contribute further to this convergence between artificial intelligence and the execution construction project through the evaluation of a specific set of machine learning algorithms. This study proposes a machine learning-based data-driven research framework for addressing problems related to project analytics. It then illustrates an example of the application of this framework. In this illustration, existing data from an open-source data repository on construction projects and cost overrun frequencies was studied in which several machine learning models (Python's Scikit-learn package) were tested and evaluated. The data consisted of 44 independent variables (from materials to labour and contracting) and one dependent variable (project cost overrun frequency), which has been categorised for processing under several machine learning models. These models include support vector machine, logistic regression, k-nearest neighbour, random forest, stacking (ensemble) model and artificial neural network. Feature selection and evaluation methods, including the Univariate feature selection, Recursive feature elimination, SelectFromModel and confusion matrix, were applied to determine the most accurate prediction model. This study also discusses the generalisability of using the proposed research framework in other research contexts within the field of project management. The proposed framework, its illustration in the context of construction projects and its potential to be adopted in different contexts will significantly contribute to project practitioners, stakeholders and academics in addressing many project-related issues.

Dataset Information

Strength in Numbers: The WWARN Case Study of Purpose-Driven Data Sharing.

Publications

Strength in Numbers: The WWARN Case Study of Purpose-Driven Data Sharing.

Similar Datasets

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