Foundational Practices of Research Data Management

[wavingtowaves]

Title: Include the manuscript's title

Study Link: https://doi.org/10.3897/rio.6.e56508
Citation: @doi:10.3897/rio.6.e56508

Suggested keywords that help identify the relevance of this paper to Ecology and Evolution:

• data management
• open science

Which areas of expertise are particularly relevant to the paper?

• ecology and/or evolution;
• biostatistics;
• informatics and computational research;
• open science and reproducibility

Summary

• Authors argue that there are lots of reasons that data management can help us reuse and recover data when we need it later, but most important reason for good data management is that actual research is more efficient--research worries less about administrative stuff and can focus on science.
• Data management grows from principles of project management.
• How to even begin with data mgmt? They suggest drawing up a workflow

10 data management tips:

1. keep sufficient documentation: details about the experiment needed for reproducibility, eNotebooks, readme, codebooks, data dictionary. Authors emphasize that documentation never ends.
2. define roles and responsibilities: Teams can be so distributed, its important to outline lab's practices during onboarding.
3. backup data: 3-2-1 rule, 3 copies of data, 2 geographically separate locations, more than 1 type of storage device
4. put data in repo: benefit is that you don't have to respond to every request, can write a data paper (e.g., plos, Nature Sci Data)

Suggested questions to answer about each paper:

• What is the general main finding or takeaway of the paper?
• What did they analyze and how did they do it?
• What does this paper suggest to improve the issue with reproducibility in science?
• Do you have any concerns about methodology or the interpretation of these results beyond this analysis?

Any comments or notes?