Centralized Open-Access Rehabilitation Database for Stroke (SCOAR) is a tool that stroke therapy researchers can use to understand the relationships among variables better, efficiently share data, generate hypotheses, and streamline clinical trial design. To prepare SCOAR, 2,892 titles and 514 manuscripts were screened by full text, leaving 215 RCTs in the database. There is 489 independent group representing 12,847 patients. The collected data contains papers from 1981 to early 2016. The goal is to organize a database to allow researchers to add their studies to the database to have constant information architecture. SCOAR is a living database, however, and data are continuously extracted from publications and uploaded into the database. The SCOAR dataset is extracted by Dr. Lohse and his colleagues [1]. 

 

In this research, using stroke rehabilitation as an example, we demonstrate that organizing RCTs around core experimental data advances scientific understanding beyond common bibliometric methods. This organization highlights sources of systematic variability in the outcomes of RCTs, which in turn provides insights for practitioners and identifies opportunities for future research. In this regard, we developed a website with interactive data visualizations. 

 

The SCOAR website (https://keithlohse.github.io/SCOAR_data_viz/index.html) allows users to visualize some of the complex relationships among the variables in the SCOAR dataset. Furthermore, these visualizations are interactive, allowing users to filter the data in each visualization, allowing them to ask their own unique questions. The website is a powerful platform that enables researchers/ practitioners to review and monitor the trends in various aspects of stroke rehabilitation.

The visualizations were arranged around three main research questions that are broadly applicable to scientific research:

 

    1. Is there substantial bias in a field?

By collating summary statistics from different experimental studies, which can be further filtered by study characteristics, it is easy to see publication bias across a field (or sub-field).

 

    2. What are the common experimental parameters and how do they vary?

Cross-indexing common study parameters with experimental outcomes allow researchers to quickly and easily visualize relationships in a field. These visualizations are useful for summarizing past research and thus inform future research (i.e., better planning, power analyses, etc.).

 

    3. What interventions are most effective and for whom?

Cross-indexing common study parameters with outcome measures enable knowledge translation on a large scale. In stroke rehabilitation, for instance, the SCOAR database allows users to plot dose-response curves for therapy broadly (using all outcomes), for specific outcomes (e.g., upper extremity versus lower extremity) or for specific groups of participants (e.g., younger to older, severely impaired to minimally impaired).

 

 

The purpose of this website is to bring people as close to the data as possible through effective and efficient visualizations. To create the interactive visualizations on the SCOAR website, several web design tools are used such as HTML, JavaScript, CSS, Bootstrap, and dc.js/d3.js libraries. The following pictures show some of the visualizations. To interact with data, filter and explore the results please refer to the website.