18 Works

Characteristics of an effective team

Dean Evasius, Karen Johnston, Sana Syed & Philip Bourne
Team science is a collaborative approach to addressing complex scientific questions. It engages the strengths of scientists from different fields, backgrounds, and expertise, often to solve the grand challenges of society. The iTHRIV Team Science Learning Shorts is a series of interviews with team science practitioners exploring the fundamentals of team science, team science participation, and the institutional supports. Learning Shorts are brief, digestible online educational offerings organized around a specific topic.

Data Science: Analytics

Don Brown
This learning shot in the Data Science Series will focus on the Analytics as a component of data science, having already been exposed to the data science framework (the 4+1 theory) in the 2nd episode of this series, the learner is ready to begin to understand more about the analytics domain. Analytics is defined as the procedures and methods used to turn data into useful information and includes everything from traditional statistical methods to advanced...

Resources on Research Integrity and Research Misconducts in the iTHRIV Portal

Medard Ng
This learning short will help you to identify the resources on Research Integrity and Research Misconducts in the iTHRIV Portal and guide you towards resources which you may find useful.

Data Science: Value Domain (Ethics)

Brian Wright
This learning shot in the Data Science Series will focus on the Value Domain, having already been exposed to the data science framework (the 4+1 theory) in the 2nd episode of this series, the learner is ready to begin to understand the details associated with this portion of the framework. This domain focuses more on the human-centered piece of data science and combines the traditional discipline of ethics with the professional activities of business planning,...

Miscataloging the Archive

Carmen Lamas
This 30 minute lecture discusses research in Latinx literature, and how conventional library practices in description may not fully represent the characters and themes found therein.

Data Science in Clinical Research: Injury Prevention

Thomas Hartka
This learning shot in the Data Science Series is an example of incorporating data science concepts into clinical research. In this learning shot, Thomas Hartka, MD explains the importance of developing practical prediction tools that can be implemented in clinical practice.

Data Science: Past, Present, Future

Don Brown
This Intro to Data Science Learning Shot is the first in a series of introductions to the field of data science. In this learning shot, Dr. Brian Wright discusses the history of data science, what is happening in today's environment and what the future may hold for data science

Creating a shared vision with a team

Karen Johnston, Sana Syed & Philip Bourne
Team science is a collaborative approach to addressing complex scientific questions. It engages the strengths of scientists from different fields, backgrounds, and expertise, often to solve the grand challenges of society. The iTHRIV Team Science Learning Shorts is a series of interviews with team science practitioners exploring the fundamentals of team science, team science participation, and the institutional supports. Learning Shorts are brief, digestible online educational offerings organized around a specific topic.

Recommendations for researchers new to team science

Dean Evasius, Karen Johnston, Sana Syed & Philip Bourne
Team science is a collaborative approach to addressing complex scientific questions. It engages the strengths of scientists from different fields, backgrounds, and expertise, often to solve the grand challenges of society. The iTHRIV Team Science Learning Shorts is a series of interviews with team science practitioners exploring the fundamentals of team science, team science participation, and the institutional supports. Learning Shorts are brief, digestible online educational offerings organized around a specific topic.

Incentives and Challenges

Dean Evasius, Karen Johnston, Sana Syed & Philip Bourne
Team science is a collaborative approach to addressing complex scientific questions. It engages the strengths of scientists from different fields, backgrounds, and expertise, often to solve the grand challenges of society. The iTHRIV Team Science Learning Shorts is a series of interviews with team science practitioners exploring the fundamentals of team science, team science participation, and the institutional supports. Learning Shorts are brief, digestible online educational offerings organized around a specific topic.

How to do Data Science feat. Dr. Donald Brown

Don Brown
The new era of data science has provided the means to collect, process and analyze large and small sets of data from diverse sources to produce solutions to complex problems in a variety of applications. The data range from large document collections, to streaming sensor data, to images and videos. This presentation describes the field of data science and gives an overview to how to conduct data science to support of organizational objectives. We then...

Best Practices in Data Visualization: Part 1

Marieke Jones
This Learning Short series, "How Humans See Data: Best Practices in Data Viz "(Part 1 and Part 2), explores best practices in data visualization, specifically how humans see data. This two part series will enable researchers to consider these principals when preparing their own data visualizations, helping to ensure clear and concise understanding of the data being presented.

Data Science: Framework Overview

Don Brown
This learning shot in the Data Science Series will focus on the complete Data Science Framework. This domain will define data science as a conceptual model of the field using the 4+1 model:

The NIH Genomic Data Sharing (GDS) Policy

Medard Ng
If your human subjects research includes any genetic research and/or specimen banking, this Learning Shot explains why you may wish to include the genomic data sharing language in the consent form from the beginning of your research. This helps ensure that you will avoid the possibility of re-consenting subjects in the future if your future research may be subject to the NIH Genomic Data Sharing Policy. This video also provides a brief overview of the...

What is team science?

Dean Evasius
Team science is a collaborative approach to addressing complex scientific questions. It engages the strengths of scientists from different fields, backgrounds, and expertise, often to solve the grand challenges of society. The iTHRIV Team Science Learning Shorts is a series of interviews with team science practitioners exploring the fundamentals of team science, team science participation, and the institutional supports. Learning Shorts are brief, digestible online educational offerings organized around a specific topic.

Updated NIH Biosketch and Other Support Requirements

Lauren Armstrong
As of May 31, 2021, the NIH updated the requirement for Biosketches and Other Support documentation. This Learning Shot details those changes and new requirements in a short 10 min video presentation. This Learning Shot was developed and presented by Lauren Armstrong, Assistant Director in Office of Grants & Contracts, University of Virginia School of Medicine.

Using online tools to support collaboration

Sana Syed, Karen Johnston & Philip Bourne
Team science is a collaborative approach to addressing complex scientific questions. It engages the strengths of scientists from different fields, backgrounds, and expertise, often to solve the grand challenges of society. The iTHRIV Team Science Learning Shorts is a series of interviews with team science practitioners exploring the fundamentals of team science, team science participation, and the institutional supports. Learning Shorts are brief, digestible online educational offerings organized around a specific topic.

Best Practices in Data Visualization: Part 2

Marieke Jones
This Learning Short series, "How Humans See Data: Best Practices in Data Viz "(Part 1 and Part 2), explores best practices in data visualization, specifically how humans see data. This two part series will enable researchers to consider these principals when preparing their own data visualizations, helping to ensure clear and concise understanding of the data being presented.

Registration Year

  • 2022
    18

Resource Types

  • Audiovisual
    18

Affiliations

  • University of Virginia
    18