Introduction to Research Data Management
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1 Introduction to Research Data Management Erik Brisson
2 Why data management? Increase likelihood of successful research More understandable view of process Avoid catastrophic loss Increase trust Reproducibility Verification Reuse Recent requirements by funding agencies Data management Data sharing / reuse
3 What is research data? Physical data Tissue samples, soil samples, etc Printed matter Surveys, books, lab notebooks, photographs, drawings Digital data Numerical data, images/movies, programs, documentation
4 What is digital data? Numerical data Documentation Notes Text Images Movies Software Scripts Metadata Etc.
5 The Data Lifecycle
6 E.g., Scientific Visualization Math Proc Scientific visualization Computer Graphics
7 The Sci Vis Pipeline - Hardware Field instruments Scientists/programmers Supercomputers Graphics machines Publications, movies/dvds, web Demos, educational software
8 The Sci Vis Pipeline - Software Data acquisition and preparation Sci-vis tools Geometric Representation Rendering Post-processing
9 Software Tools and the Pipeline Data Matlab VTK OSG Maya IDL Paraview Performer OpenGL Gnuplot DAFFIE Display Wall Xmgrace Web Excel Photoshop Premier
10 Data flow for specific project
11 Data flow for another project Interviewer gives survey Data typed in and fixed Data uploaded Data analyzed Data graphed and published Paper surveys Excel files Files on mainframe Statistical results Graphs for publication
12 The gritty details
13 Bottom-line Questions Do you know where your data is? If you lost your lap top, could you recreate it? If something happened to you, could anyone else? Can you trace provenance of final results? In five years, can you reconstruct the process? If applies - will your plan satisfy funders? Will your data be ready to share?
14 Topics Processes and Provenance Backups Storage Software, scripts, etc Archival storage Dissemination
15 Processes and Provenance Keeping track of the process / workflow Examples of data process diagrams (above) Processes that change or move data Data movement Keeping a journal / log notebook Probably online Others should know it s location Metadata
16 Processes and Provenance Workflow systems SCIRun Kepler scientific workflow system KNIME, the Konstanz Information Miner Taverna workbench Sumatra Provenance at the O.S. level
17 Metadata Data documentation Provenance or context. Ensures useful long-term Descriptive metadata Facilitates searching, retrieval, management Structural metadata Internal structure. Technical Metadata Layout or format specifications. Administrative metadata Facilitate management, tracking, preservation, rights
18 Backups Automatic / incremental Disaster recovery (disk crash) Mistake recovery (accidentally delete files) Checkpoint Save known good scenario Process reconstruction (provenance) Before and after of manual edits (e.g., Excel) Scripts, programs Notes, documentation Save for later (storage management) Know won t need data for a while
19 Backup or not? Back up Original / raw data (impossible to recreate) Hard-to reproduce intermediate data Processes, notes, documentation Before and after (for manual changes) Don t back up Scratch files (intermediate files from experimental runs)
20 Backup how? to where? External drives Cheap, convenient, and failure-prone BU service Automatic, nightly. Inexpensive, secure. Cloud Often convenient. May be unpredictable, insecure.
21 Storage Files / disk available for use Backed-up, or not backed up Local or remote Sharing
22 Software, Scripts, etc. Equal importance with data For reuse, reconstruction, provenance Brings us back to process and provenance In a perfect world, tracking all processes would be automatic And we could push a button to rerun entire experiment Recommend keeping clear, dated copies of run scripts and output for every run Something similar for manual processes Running notes; before and after copies.
23 Software, Scripts, etc. Can be stored and managed like any other data Good to keep revision history and comments Crucial to track what version used in each run Good to use version control system Can use version control for data, in general
24 Archival Storage Difference from backing up? Intended for long term; permanence; worth Not expected to change Intended for some level of reuse / dissemination Provide means of discovery Implement access control Different technology for implementation
25 Dissemination May happen in any/all stages of research May be informal (on personal or lab web site) May be institutional May be domain-specific repository May be as simple as a page of data May be a complicated, interactive simulation or database interface
26 Security and Privacy Physical safety of data Backups, redundant storage, etc. Who has access General management of access control Protection from malicious agents Intellectual property / digtal rights Protection of Privacy Legal issues Important through entire lifecycle
27 Summary Predict processes and data for your project Keep desired end product in mind from beginning Identify compute and storage resources Start with clear, simple method for journaling process Checkpoint and document when at good points Regularly stop and ask if you know what and where everything is Good habits most important when deadlines loom Plan for change!
28 The End Contacts: Erik Brisson, Jenna Ryan, BU Data Management page: Tutorial presentations and examples online: Online evaluation:
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