Data Science and Data Management

We, the Digital Information Research Labs of the Digital Information Research Foundation work in data organization and management. The current open data environment makes it useful billions of data pieces for the growth of science and technology. However, there are obstacles in the networking of data activities. Data sources are voluminous, heterogeneous, related to each other, and remain unprocessed in many cases.

Tracking multiple sources of data to identify useful variables is of critical importance for data users. Interestingly, these data sources can reflect several dimensions or points of view about a scientific phenomenon.

The data organization can help us to work on data science solutions with the opportunities to study, promote, and ensure access, inclusion, and reuse significantly and make an influence on research data management. We work and share the experiences of studying, analyzing, designing, and developing innovative methodologies of data management to derive insights, characterize data processing activities, and support researchers in promoting data use.

We work on implementing transparent data algorithms, design, and application of several data projects. We also work on developing models of data use in real-life applications and understand the fundamental characteristics of data types.

The abstraction, and the processing of big data, and process the complexity is a big challenge. To do so, data science requires understanding machine learning algorithms and natural language applications. This approach requires developing tools to handle and process big data. We make it known to the researchers that big data analyses will lead to acceptable scientific discoveries.

Industrial implementations of explainable/transparent data algorithm applications, design and deployment experience reports on various issues, raising data transparency projects, and avoiding bias and discrimination, are particularly welcome. We call for research and experience papers and demonstration proposals covering any aspect of data, algorithmic transparency, and accountability in real-life applications, fundamental properties to avoid bias and discrimination. These tasks warrant needs new methods, techniques, and solutions that take advantage of various disciplines including data integration, data security and provenance, data cleaning and data accuracy, graph processing, and a few more. We emphasize the data processing applications in various domains.