How can BCO-DMO help with your oceanographic data?

BCO-DMO curates a database of research-ready data spanning the full range of marine ecosystem related measurements including in-situ and remotely sensed observations, experimental and model results, and synthesis products. We work closely with investigators to publish data and information from research projects supported by the National Science Foundation (NSF), as well as those supported by state, private, and other funding sources. BCO-DMO supports all phases of the data life cycle and ensures open access of well-curated project data and information. We employ F.A.I.R. Principles that comprise a set of values intended to guide data producers and publishers in establishing good data management practices that will enable effective reuse.

Data Security and Privacy-Preserving Framework Using Machine Learning and Blockchain in Big-Data to Data Middle Platform in the Era of IR 4.0

The modem data is collected by using IoT, stored in distributed cloud storage, and issued for data mining or training artificial intelligence. These new digital technologies integrate into the data middle platform have facilitated the progress of industry, promoted the fourth industrial revolution. And it also has caused challenges in security and privacy-preventing. The privacy data breach can happen in any phase of the Big-Data life cycle, and the Data Middle Platform also faces similar situations. How to make the privacy avoid leakage is exigency. The traditional privacy-preventing model is not enough, we need the help of Machine-Learning and the Blockchain. In this research, the researcher reviews the security and privacy-preventing in Big-Data, Machine Learning, Blockchain, and other related works at first. And then finding some gaps between the theory and the actual work. Based on these gaps, trying to create a suitable framework to guide the industry to protect their privacy when the organization contribute and operate their data middle platform. No only academicians, but also industry practitioners especially SMEs will get the benefit from this research.

From keyboard to cloud-base network revamped data lifecycle cybersecurity

This paper review seeks to identify the need for a revamped data life cycle security in the era of pervasive threat from skill cyber criminals at this time of internet of things. The motivation is to fill the knowledge gap by presenting some of the ways of data leakages and the likely protection in the organization. The aim is to present a good practice that encourages data confidentiality, acceptable use policy, knowledge of personnel and physical security policy. The building blocks of information security infrastructure across the entire organization is implemented by Enterprise Security Architecture. Rather than focus on individual functional and non-functional components in an individual application, it focuses on a strategic design for a set of security services that can be leveraged by multiple applications, systems, or business processes.

ODK-X: From A Classic Process To A Smart Data Collection Process

Data collection is one of the first and main phases of the data life cycle. It enables improvements to be made across all phases of the data lifecycle. In this sense, we have proposed a data collection process qualified as Smart. For our smart data collection process, we have adopted the principles of the smart data approach allowing less data to be transmitted to the analysis and storage processes, while maintaining better data quality. In addition, we also used Edge computing since it provides services with faster response and better quality, compared to cloud computing. To experiment this process on mobile data, we propose to extend a mobile data collection software solution and adopt one of the key data collection methods. In this paper, we tested our smart data collection process via the ODK-X software suite and were able to identify the added value of our process compared to the one used by default during collection.

Knowledge and Practice changes following a student data focused data management education program.

Introduction It is known that graduate students work with research data more intimately than their faculty mentors. Because of this, much data management education is geared toward this population. However, student learning has predominantly been assessed through measures of satisfaction and attendance rather than through evaluating knowledge and skills acquired. This study attempts to advance assessment efforts by asking students to report their knowledge and practice changes before, immediately after, and six months following education. Methods Graduate students in STEM and Health sciences disciplines self-enrolled in an eight-week data management program that used their research projects as the focus for learning. Three surveys were administered (pre, post, and six months following) to determine changes in students’ knowledge and practices regarding data management skills. The survey consisted of approximately 115 Likert-style questions and covered major aspects of the data life cycle. Results & discussion Overall students increased their data management knowledge and improved their skills in all areas of the data life cycle. Students readily adopted practices for straightforward tasks like determining storage and improving file naming. Students improved but struggled with tasks that were more involved like sharing data and documenting code. For most of these practices, students consistently implemented them through the six month follow up period. Conclusion Impact of data management education lasts significantly beyond immediate instruction. In depth assessment of student knowledge and practices indicates where this education is effective and where it needs further support. It is likely that this effect is due to the program length and focus on implementation.

A Research Data Infrastructure Component for the Automated Metadata and Data Quality Extraction to Foster the Provision of FAIR Data in Earth System Sciences

Metadata management is core to support discovery and reuse of data products, and to allow for reproducibility of the research data in Earth System Sciences (ESS). Thus, ensuring acquisition and provision of meaningful and quality assured metadata should become an integral part of data-driven ESS projects.We propose an open-source tool for the automated metadata and data quality extraction to foster the provision of FAIR data (Findable, Accessible, Interoperable Reusable). By enabling researchers to automatically extract and reuse structured and standardized ESS-specific metadata, in particular quality information, in several components of a research data infrastructure, we support researchers along the research data life cycle.