Abstract (Broman and Woo, 2017)
Spreadsheets are widely used software tools for data entry, storage, analysis, and visualization. Focusing on the data entry and storage aspects, this paper offers practical recommendations for organizing spreadsheet data to reduce errors and ease later analyses. The basic principles are: be consistent, write dates like YYYY-MM-DD, don't leave any cells empty, put just one thing in a cell, organize the data as a single rectangle (with subjects as rows and variables as columns, and with a single header row), create a data dictionary, don't include calculations in the raw data files, don't use font color or highlighting as data, choose good names for things, make backups, use data validation to avoid data entry errors, and save the data in plain text file.
Rules of Thumb
This is a long overdue guide to the legal aspects of Open Science, which also covers research data specific questions in detail. Written in German, targeting the German legal situation, it will be applicable in large parts also for other jurisdictions.
Presentation: Managing Research Software Projects: How to set up, manage, and share your work in less time and with less pain. Greg Wilson, 2020.
A very concise overview that touches the most important points.
Online Book: Research Software Engineering with Python: Building software that makes research possible.
Targeting researchers, the book walks the reader through a worked example project and covers all required tools, techniques and best practices from the ground up. For example UNIX shell, Git, make, testing, continuous integration and python package creation are covered.
Science Europe represents the the national funding agencies of 28 European countries. "Research Data" is one of their top-priorities and their publications indicate where the community is moving towards. Of particular interest are the RDM-related evaluation criteria put forward by Science Europe.
The Practical Guide to Sustainable Research Data provides matrices that help to assess the maturity of research data management practices for
Other interesting publications can be found on their Research Data pages.
Book: Johnston, Lisa R. (2017). Curating Research Data Volume Two: A Handbook of Current Practice. Association of College & Research Libraries.
A very extensive checklist for people who establish an institutional research data repository.
Survey: TU Delft and EPFL have published an RDM-Survey among their researchers. n = 659 (both Institutions). Although it covers exclusively engineering + CS faculties, it is a good start for providing some sort of empirical status-quo.
It would be nice if other institutions used a compatible survey.
The Data Curation Network is a Sloan-funded project that aims to conceptualize and develop a “network of expertise” model for U.S. academic libraries to collectively provide data curation services to support digital research data deposit into repositories for open access and reuse.
Next to the down-to-earth conceptual model, they provide a wealth of recent empirical information, advice, checklists & workflows.
The first DCN report:
Data Curation Network: A Cross-Institutional Staffing Model for Curating Research Data
Download PDF (1.7 MB)
#activeDMPs is a pointer-webpage to the community that drives forward concepts and tools for active and machine-actionable data management plans.
The ELN-Wegweiser about Electronic Lab Notebooks (ELNs) (in German) gives practical advice for the establishment of ELNs in the life-sciences but should also provide a lot of help for similar projects in other felds.