Materials Research Society Data Survey

1.Which of the following do you consider pain points in managing data? (check all that apply)

Quantity of data / volume on hard drive

Organizing and keeping track of data files

Linking data files to their metadata (e.g. details about how the data was collected)

Building data representations

Adjusting data processing to new types of data

Adding new data to existing databases

Other (please specify)

2.What software do you use to manage your data?

3.Is there any software related to data management or processing with which you wish you were more familiar?

Yes

4.If yes to Question 3, what software?

5.Have you ever shared data publicly?

Yes

6.If yes to Question 5, where is that data hosted?

8.What fraction of your time is spent managing data?

9.Have you had any notable research successes that have stemmed from FAIR data management?

Yes

10.If yes to Question 9, would you be willing to share this story in some form with the community? To indicate yes, enter your email address. Your email address will only be used to contact you about your story.

11.If you wanted to learn a new topic in machine learning related to materials research, where would you start? (check all that apply)

Ask a colleague

Internet search

Journal articles

Watch webinars or similar virtual talk

Watch live talks at conferences

Take a live course at a university

Take an open access course

Shared repositories of machine learning resources and materials data

Other (please specify)

12.How interested are you in shared repositories of machine learning resources and materials data?

Not very interested

I have not looked but would consider one that is readily available and clearly organized

I have looked but not found one that is readily available and clearly organized

I already draw resources from such repositories

I already contribute to such repositories

13.Note any resources that you are familiar with:

https://pages.nist.gov/remi/ (Catalog of a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data.)

https://materialsproject.org/ (Open web-based access to computed information on known and predicted materials as well as powerful analysis tools to inspire and design novel materials)

https://materialsdata.nist.gov/ (Materials science data repository as part of an effort in coordination with the Materials Genome Initiative (MGI) to establish data exchange protocols and mechanisms that will foster data sharing and reuse across a wide community of researchers, with the goal of enhancing the quality of materials data and models.)

https://materials.registry.nist.gov/ (This system allows for the registration of materials resources, bridging the gap between existing resources and the end users. The Registry functions as a centrally located service, making the registered information available for research to the materials community.)

https://nanohub.org/resources/tools (Many open access tools for ML in materials science, varying levels of expertise)

https://www.ccdc.cam.ac.uk/ (The Cambridge Crystallographic Data Centre (CCDC))

Other (please specify)