Data Stewardship for Open Science

Barend Mons is a molecular biologist by training (PhD, Leiden University, 1986) and spent over 15 years in malaria research. After that he gained two decades of experience in computer-assisted knowledge discovery, which is still his research focus at the Leiden University Medical Centre.

• Introduction. Data cycle step 1: Design of experiment. Data cycle step 2: Data design and planning. Data cycle step 3: Data Capture (equipment phase). Data cycle step 4: Data Processing and Curation. Data cycle step 5 Data Linking and ‘Integration’. Data cycle step 6: Data Analysis, Interpretation. Data cycle step 7: Information and insight in publishing.

Data Stewardship for Open Science is especially recommended for corporate; college, and university library Computer Science and Engineering collections. It should be noted for the personal reading lists of students, academia, and non-specialist general readers that "Data Stewardship for Open Science" is also available in a paperback edition—James A. Cox, The Midwest Book Review, Science ShelfThe practice of science in the information age is changing in ways that have not been more pronounced since the advent of mathematics. Data Stewardship for Open Science provides a comprehensive inventory of what scientists need to know about putting data into public repositories in a manner that will allow their teams and those of other investigators to understand what experiments actually have been done, to explore online data in search of new discoveries, to build on the datasets of other scientists, and to prepare for a world in which the dissemination of research objects in digital form will become the primary means by which scientists communicate with one another. Mons does not offer a cookbook for managing experimental data, but rather a rich enumeration of what scientists need to do—and not do—to be successful in addressing the opportunities and the challenges provided by open science. This book provides essential advice for anyone who needs to generate, to access, or to manage scientific data for broad, public consumption—which is now just about anyone in the scientific community.—Mark A. Musen, Stanford UniversityProf. Barend Mons is a visionary of a new age of data-driven and machine-assisted science: the era of FAIR data, where the immensely valuable data is not lost any more, but it persists, and is reusable to enable currently imaginable avenues to increase human knowledge. This book is the first comprehensive publication containing the essential vision on a way forward, a book that sets a new standard of effective data stewardship and that should be on a shelf of every responsible data steward. We are honoured to be among the authors of a supporting software project: the Data Stewardship Web portal. [https://dmp.fairdata.solutions]—Dr. Robert Pergl and Dr. Jiri Vondrasek, Elixir Node Czech RepublicData stewardship, a concept that involves all those data management issues related to long-term data reusability and interoperability, requires careful planning and thought from the beginning of a research project. Producing research that complies with FAIR principles is an ethical responsibility for all scientists, and a plan for reuse should be an obligatory and fundamental part of study design, especially for those working with public funding. Beyond the ethical responsibility to produce transparent and reproducible research, young scientists today should view cultivation of data stewardship skills as an opportunity to participate in the exciting, innovative research of tomorrow. This book will greatly help early career researchers to get acquainted with the basics of modern data stewardship, as we described in our common paper: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1351-7—The collective students of the 2016 Summerschool of the League of European Research UniversitiesThis book is an excellent source to learn and practice exploration, explanation, and interpretation of meanings hidden in the data using what the author names ‘FAIR’ principles.-Ramalingam Shanmugam, Journal of Statistical Computation and Simulation