Selecting, storing and handling lab (meta)data: a materials case

Transcription

1 Selecting, storing and handling lab (meta)data: a materials case Nick Schryvers Electron Microscopy for Materials Science (EMAT), University of Antwerp, Belgium After NSF Data workshop on Rise of Data in Materials Research, June 29-30, 2015, Washington, USA

2 NSF workshop topics 2 Materials Cyberinfrastructure Enabling Data Infrastructure Creation Data Handling and Management Knowledge from Data Education Support from the Top

3 3 Why do we need to go for better data handling? - Lots of existing (meta)data will never make it to a publication, while it might be relevant to someone other than the original researcher - If you are not aware of existing data, you ll be reproducing the same stuff - As a result, funds are not adequately used - Also negative results are hardly ever published, yet would be very useful for others to know - Available (meta)data is not always used optimally, i.e. many new opportunities for data mining, AI, exist -

4 What holds us back to share our data? 4 - It takes time and money to store your (meta)data in a proper way, while the direct benefit is unclear - We do not have a direct interest when someone else uses our dataset (or at least we think so) - Someone can see our dataset is less reliable than I want them to know - I m afraid someone else will publish results with my data faster (or better) than I can - My data is IP critical - In materials science, data is often complex rather than big - Companies use different standards (often on purpose) -

5 Historic examples sequential, reachable, readable, limited content, 5

6 uncertainty provenance Example of current metadata from a TEM picture all technical data instrument no sample info no relation with earlier or following images not connected to subsequent data treatment looks different for each manufacturer 6

7 As for data, today, it s a mess 7 Original data - is often deleted - stored in various places - stored with various names - duplicated under various names - types of data are mixed - metadata is hidden and unused -

8 8 and sometimes even ridiculous programs exist that scan published spectra and yield the data points as columns in excel, while the original dataset is obviously out there somewhere (and should include a lot more info)

9 and I didn t even mention sample preparation! 9 No labeling protocols Different labeling protocols used in different labs Part of the treatment not mentioned

10 Typical MS issues 10 Existing cases often focus on single length scale In material images acquired at a selected length scale it is typically not known a priori which of the image features are important, i.e. responsible for a material's performance characteristics Matched by diversity in techniques and protocols No broadly adopted or accepted standards Inconsistent reporting, storage, preservation, both for data & metadata Judicious fusing is needed, dedicated to the MS community h5ebsd: an archival data format for electron back-scatter diffraction data sets, M.A. Jackson et al., Integrating Materials and Manufacturing Innovation, 2014, 3:4

11 What can be done about this: 11 - First of all, scientists need to realize that, bottom line, sharing data is proper conduct - This can already be introduced in the education system so coming generations are less reluctant to it - The field of materials science deals with a large variety of complex data rather than with really big data sets, which can trigger colleagues from computer science to search for innovative solutions - Examples should be provided by fields with more expertise in this matter, such as astronomy, particle physics, bioinformatics, pharma, social sciences, - Proper formats need to be selected for easy mining or not

12 In practice, things can and should be optimized and incentives build in, such as 12 - A dataset should have a unique identifier so everyone can refer to it (and this should be added to the indexing systems) - Each dataset should come with its metadata so people know how this dataset was obtained - Each dataset should come with a quality identifier so people know how strict they can rely on this dataset (and at the same time appreciate the work of others) - Government and Funding agencies should provide central storage facilities (or at least fund those explicitly) and request & support data storage of funded projects - Funding for research on MS data handling should be available -

13 13 always available to provide sample data Materials Data Science: Current Status and Future Outlook, Surya R. Kalidindi and Marc De Graef, Annual Review of Materials Research, 45: (2015)