Mary Ann Liebert, Inc., in partnership with the Rosalind Franklin Society has launched a prestigious annual award to recognize outstanding published peer-reviewed research by women and underrepresented minorities in science in each of the publisher’s peer-reviewed journals. The RFS / MAL Award in Science for Big Data has selected Maddalena Cavicchioli, PhD who co-authored Learning from Failure: Big Data Analysis for Detecting the Patterns of Failure in Innovative Startups.
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Aims & Scope
Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.
Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government.
Big Data coverage includes:
- Big data industry standards
- New technologies being developed specifically for big data
- Data acquisition, cleaning, distribution, and best practices
- Data protection, privacy, and policy
- Business interests from research to product
- The changing role of business intelligence
- Visualization and design principles of big data infrastructures
- Physical interfaces and robotics
- Social networking advantages for Facebook, Twitter, Amazon, Google, etc.
- Opportunities around big data and how companies can harness it to their advantage
Big Data is under the editorial leadership of Editor-in-Chief Zoran Obradovic, PhD, Temple University, and other leading investigators. View the entire editorial board.
Audience: Data miners, computer scientists, statisticians, researchers, chief data scientists, database engineers, software engineers, web developers, policy makers, CEOs of data startups, professors of computer science, mathematics, physics, statistics, and computational biology, among others.
- PubMed Central
- Web of Science: Science Citation Index Expanded™ (SCIE)
- Journal Citation Reports/Science Edition