Responsible Data Governance of Neuroscience Big Data

Open access article.Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of “responsible data governance,” applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP)

Rise of big data – issues and challenges

The recent rapid rise in the availability of big data due to Internet-based technologies such as social media platforms and mobile devices has left many market leaders unprepared for handling very large, random and high velocity data. Conventionally, technologies are initially developed and tested in labs and appear to the public through media such as press releases and advertisements. These technologies are then adopted by the general public. In the case of big data technology, fast development and ready acceptance of big data by the user community has left little time to be scrutinized by the academic community. Although many books and electronic media articles are published by professionals and authors for their work on big data, there is still a lack of fundamental work in academic literature. Through survey methods, this paper discusses challenges in different aspects of big data, such as data sources, content format, data staging, data processing, and prevalent data stores. Issues and challenges related to big data, specifically privacy attacks and counter-techniques such as k-anonymity, t-closeness, l-diversity and differential privacy are discussed. Tools and techniques adopted by various organizations to store different types of big data are also highlighted. This study identifies different research areas to address such as a lack of anonymization techniques for unstructured big data, data traffic pattern determination for developing scalable data storage solutions and controlling mechanisms for high velocity data

Big data science issues in distributed database

This paper reviewed on the issues that occurs by Big Data Science such as challenges, opportunities, good practices and tools required to run Big Data Science. The paper refers to different techniques used to define the requirements on data management, access control and security. The paper also explains on how Big Data helps in contributing its function towards the development of distributed database

Evolution to Big Data Analytics Techniques and Challenging Issues in Data Mining With Big Data

Big Data is another term used to recognize the datasets that because of their enormous size and multifaceted nature. Big Data are currently quickly growing in all science and engineering domains, including physical, natural and biomedical sciences. Big Data mining is the capacity of separating helpful information from these huge datasets or floods of data, that because of its volume, changeability, and velocity, it was impractical before to do it. The Big Data challenge is getting one of the most energizing open doors for the following years. In the present time of digitization, we take a shot at the variety of data. Colossal measure of data will be prepared by Google, Microsoft and Amazon. Regular routine these organization prepared huge measure of data. In such way we have to require some approach to adjust the innovation in with the end goal that every one of the data will be prepared adequately. Big Data is a developing concept that depicts imaginative systems and innovations to break down enormous volume of complex datasets that are exponentially produced from different sources and with different rates. Data mining procedures are giving extraordinary guide in the region of Big Data examination, since managing Big Data are big difficulties for the applications. Big Data examination is the capacity of removing valuable information from such colossal datasets. This paper exhibits a writing survey that incorporate the significance, difficulties and applications of Big Data in different fields and the various methodologies utilized for Big Data Analysis utilizing Data Mining procedures. The discoveries of this audit give important information to the analysts about the primary patterns in research and examination of Big Data utilizing diverse investigation domains. This examination paper incorporates the information about what is big data, Data mining, Data mining with big data, Challenging issues and its related work