What are modern best strategies for finding geospatial data?  Despite the increase in volume and variety of spatial data even in the 8 years since we have been writing this column, I still receive …

RO-Crate is a community effort to establish a lightweight approach to packaging research data with their metadata. It is based on schema.org annotations in JSON-LD, and aims to make best-practice in formal metadata description accessible and practical for use in a wider variety of situations, from an individual researcher working with a folder of data, to large data-intensive computational research environments.

It’s not just the superbloom, either.

The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose...

Service suggesting publication outlets for data and null results. "File drawer data liberation effort."

NIH Funding Opportunities and Notices in the NIH Guide for Grants and Contracts: Final NIH Policy for Data Management and Sharing NOT-OD-21-013. NIH

Agency reportedly lost data after exceeding maximum rows for a spreadsheet.

Nature - Missing documentation and obsolete environments force participants in the Ten Years Reproducibility Challenge to get creative.

Microsoft Excel: 1 — Human Genetics: 0.

The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity. Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-consuming. By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free up more time for research. This paper provides an introduction to best practices for managing all types of data.

Last week I wrote about data ownership, and how focusing on “ownership” might drive you nuts without actually answering important questions about what can be done with data. In that context, I mentioned a couple of times that you (or your funder) might want data to be shared under CC0, but I didn’t clarify what CC0 actually means. This week, I’m back to dig into the topic of Creative Commons (CC) licenses and public domain tools — and how they work with data. People often refer to everything with a CC in front of it as a “Creative Commons license.” […]

Which of these is true? “The PI owns the data.” “The university owns the data.” “Nobody can own it; data isn’t copyrightable.” You’ve probably heard somebody say at least one of these things — confidently. Maybe you’ve heard all of them. Maybe about the same dataset (but in that case, hopefully not from the same person). So who really owns research data? Well, the short answer is “it depends.” A longer answer is that determining ownership (and whether there’s even anything to own) can be frustratingly complicated — and, even when obvious, ownership only determines some of what can be […]

Memorial is among the first universities in Canada to create and adopt a formal agreement that gives Indigenous groups the right to own, control, access and possess their research data.

This worksheet walks researchers through the process of creating a file naming convention for a group of files. This process includes: choosing metadata, encoding and ordering the metadata, adding version information, and properly formatting the file names. Two versions of the worksheet are available: a Caltech Library branded version and a generic editable version.