- Data and artificial intelligence
- Environmental science
- Biomedical science
- Computer science and mathematics
Library Carpentry features (1) a guide presenting the application of FAIR principles to nanotechnology. This booksprint is addressed to students, researchers, and research assistance personnel 10 steps or chapters with information, best practices, and videos of activities in the form of exercises.
The goal of PaNOSC, Photon and Neutron Open Science Cloud (2) is to share a common history of data for neutron and photon science by providing services and tools for data storage, analysis, and simulation. The exchange of skills and experience as well as the development of data culture are essential. To that end, PaNOSC has created tutorials and teaching aids on data simulation, data analysis and archiving.
Data and artificial intelligence
Deep Learn Physics Open Data: a group of high-energy particle physicists offers samples of data and tutorials for applying artificial intelligence methods.
Two self-training resources particularly suitable for environmental science:
Data Tree is a free on-line course on research data management, with 8 modules (context, practices, NERC, analysis, visualization, etc.) each between 15 and 20 hours. The course includes videos, quizzes, practical case-studies and expert advice. Issues such as data sharing with the general public, decision-makers, industry and the media are also discussed. The course is particularly appropriate for graduate students, PhD students and early career researchers, or to those who wish to acquire skills in data management. Digital certificates are available per module. Data Tree is supported by the Natural Environment Research Council (NERC), by the Institute for Environmental Analytics and Stats4SD and by the Institute of Physics.
The Data Management Training (DMT) Clearinghouse is a directory of around a hundred learning resources on research data management. The directory was created by the U.S. Geological Survey’s Community for Data Integration, the Earth Sciences Information Partnership (ESIP) Federation and DataONE. Resources (videos, presentations, etc.) can be found in the search engine using keywords and filters.
The medical libraries of the University of Massachusetts have developed a research data management curriculum: New England Collaborative Data Management Curriculum with courses, case studies and scenarios organized in various modules. The curriculum focuses on medical science and engineering data. It also features examples of DMPs and case studies for chemistry and engineering science.
Guidelines for Responsible Data Management in Scientific Research (Clinical Tools Inc., Office of Research Integrity, US Department of Health and Human Services) is a course suited to research in biomedical science.
NEW ! Harvard Medical School offers a MOOC free-of-charge for learning about data management in the medical sciences (best practices, available tools, description of data, data storage, etc.). MOOC managers estimate the course requires seven hours of work per week.
Computer science and mathematics
The Journées Calcul Données (JCAD) [Data Calculation Days]: the GIS FRANCE GRILLES and GRID’5000 the the CNRS Groupe Calcul, the GDR RSD, GENCIand partners of the Equipex EQUIP@MESO organize the Journées Calcul Données, a scientific and technical event on calculation and data. Watch presentation videos of JCAD 2019 here and that of JCAD 2018 here.