DATA MANAGEMENTPLANNINGFEBRUARY 21, 2013 Lizzy Rolando, Research Data Librarian
Objectives2 Understand the current climate around data management and data sharing Learn about the basic elements of a data management plan Explore some of the best practices for data documentation, long-term preservation, and data sharing Work with the DMPTool to create a data management plan
Why Data Management?5 Good for You Photo taken by the U.S. Army Research Development and Engineering Command
Why Data Management?6 Good for Science Image from http://xnat.org/
Van Noorden, R. (2011). Science publishing: The trouble with retractions, Nature 478, 26-7 28. doi:10.1038/478026a
Why Data Management?8 Required by Funding Agencies
Funding Agency Requirements9 Funding Agency Requirement NSF* • Must include a 2-page DMP in proposal • Materials collected during research should be shared NIH • Papers must be submitted to PubMed • Projects with over $500,000 funding must share data and include Data Sharing Plan in proposal USDA • National Institute of Food and Agriculture requires all data to be submitted to public domain without restriction NOAA • Soon requiring that all grants include a data sharing plan, which must also be shared • All data should be made visible, accessible and independently understandable to users, within 2 years of end of grant NASA • Data should be made freely and widely available. • A data sharing plan and evidence of any past sharing activities should be included as part of the technical proposal CDC • All data should be released and/or shared as soon as feasible
Exciting News!10 Beginning January 14, 2013, the Biographical Sketch(es) for an NSF grant proposal will include a section on “Products,” and no longer “Publications.” This way, applicants can include not just publications, but also datasets, software, patents and copyrights.
Basic DMP Components11 The NSF requires a 2-page data management plan with every grant proposal. Data Description Data and metadata standards Data access and sharing policies Data re-use and re-distribution Data preservation and archiving Depending on the funding source and the directorate/division/program, data management plan requirements may differ.
Data Description12 What kinds of data will you produce? Numerical data, simulations, text sequences, etc. Experimental, observational, simulation Raw, derived How will you acquire the data? How will you process the data? How much data will you collect? Are you using any existing data? What QA/QC procedures will you use?
Recommendations13 A short description of your project helps to give context to why you are collecting the data. Survey existing data sources. Can be a narrative paragraph, table, or list. Keep all raw data separate from analyzed data, and maintain versions of data during analysis. Implement QA/QC procedures. Ex. Two people independently record data Ex. Tools to audit spreadsheets
Example (taken from Oceanography DMP)14 The project will collect and analyze the following data: Conductivity and temperature from moorings and shipboard CTD surveys Horizontal currents from Lowered ADCP and moorings. Horizontal currents from shipboard sonar Fine and micro-scale velocity from the WHOI High Resolution Profiler Fine and micro-scale temperature from fast-response thermistors (pods)
Data and Metadata Formats15 What metadata will you create/include with data? i.e. What does someone else need to know about your data in order to reuse them? Where will this be recorded? How? What format? Will you use a community metadata standard? Will you conform to community terminology?
Recommendations16 Use metadata standards common in your discipline. Include a “readme.txt” file that describes the who, what, where, when and why of the data, at a bare minimum. Make sure you have recorded the information that you would need if you were trying to use someone else’s data. Check with the data repository where you hope to store your data – sometimes they require a particular metadata standard. Use files names that are understandable to humans. Make sure you record units and have headers for rows and columns in your tables. Notes about the data should be recorded alongside the data by the data collectors. Thesauri
Example17From NEES (Network for Earthquake Engineering Simulation)
Example18 From NCAR (National Center for Atmospheric Research)
Example (from NASA SEAC4RS DMP)19Appendix A SEAC4RS data file naming convention:dataID_locationID_YYYYMMDD_R#.extensionThe only allowed characters are: a-z A-Z 0-9_.- (that is, upper case and lower case alphanumeric, underscore, period, and hyphen). Fields aredescribed as follows: dataID: an identifier of measured parameter/species, instrument, or model (e.g., O3; NxOy; and PTRMS). For DC3 and SEAC4RS data files, the PIs are required to use “DC3-” or “SEAC4RS-” as prefixes for their DataIDs, i.e., DC3-O3 and SEAC4RS-NxOy. locationID: an identifier of airborne platform or ground station, e.g., GV, DC8. Specific locationIDs for each deployment will be provided on the data website. R#: data revision number. For field data, revision number will start from letter “A”, e.g., RA, RB, … etc. Numerical values will be used for the preliminary and final data, e.g., R1, R2, R3 … etc. Extension: “ict” for ICARTT files, “h4” for HDF 4 files and “h5” for HDF 5 files. For example, the filename for the DC-8 Diode Laser Spectrometer H2O measurement made on June, 1, 2012 flight may be: DC3-DLH- H2O_DC8_20120601_RA.ict (for field data) or DC3-DLH-H2O_DC8_20120601_R1.ict (for final data)Appendix B Summary of ICARTT format metadata requirements (also required for HDF 5 files): Platform and associated location data: Geographic location and altitude will be embedded as part of the data file or provided via a link to the archival location of the aircraft navigational data. Data Source Contact Information: phone number, mailing information, and e-mail address shall be given for themeasurement Co-I and one alternate contact. Data Information: Clear definition of measured quantities will be given in plain English, avoiding the use of undefined acronyms, along with reporting units and limitation of data use if applicable. Measurement Description: A simple description of the measurement technique with reference to readme file and relevant journal publication. Measurement Uncertainty: Overall uncertainty will need to be given as a minimum. Ideally, precision and accuracy will be provided explicitly. The confidence level associated with the reported uncertainties will also need to be specified for the reported uncertainties if it is applicable. The measurement uncertainty can be reported as constants for entire flights or as separate variables. Measurement uncertainty is required by the ICARTT data file format. Data Quality Flags: definition of flag codes for missing data (not reported due to instrument malfunction or calibration) and detection limits. Data Revision Comments: Provide sufficient discussion about the rationale for data revision. The discussions should focus on highlighting issues, solutions, assumptions, and impact.
Policies for Access and Sharing20 Are your data sensitive, so access by others needs to be restricted? What license or publishing model will you use for your data? How will you make your data accessible to others? What data will you make available and at what stage of your research? Do you have protocols, such as IRB, that you need to comply with? If so, how will you do so?
Recommendations21 Apply an open license to data that you will share. Explain why you cannot share data, if that is the case. Forexample, the data used in your research are proprietary. Anonymize any sensitive data. Use a repository that can mediate data sharing if data cannot be sufficiently anonymized Comply with IRB restrictions. That should be obvious, but we’ll say it anyways Be aware of Georgia Tech Policy…
Policies and Provisions for Re-use23 Who do you expect will want to or can reuse your data? Should there be restrictions on who or how your data can be reused? How should others indicate that they have used your data? How long will your data be available to others for reuse? Does your institution have rules about data?
Recommendations24 Imagine the broadest possible audience for your data. Place as few restrictions on your data as you can. Link your published articles to the data underlying those data. Use a repository that can make your data available far into the future. Funding Agency Suggested Length of Time for Private Data Retention NIH No later than the acceptance for publication of main findings from final data set NOAA 2 years after data collection NSF-Engineering Directorate 3 years after the end of the project or public release, whichever comes first NSF-Earth Sciences Division 2 years after data collection NSF-Ocean Sciences Division 2 years after data collection
Archiving and Preservation26 What formats for your data will you use? Are they preservation friendly? What repository or data archive can take your data when you are finished? How do they preserve/share your data? What are their access policies? Is any extra work needed to prepare data for the repository? Who will be responsible for final preservation?
Recommendations27 Appraise your data, selecting those with long-term value, and document your choices. Use preservation friendly digital formats. Non-proprietary,commonly used You may need to transform data into new format. Find a repository that will take your data, and plan to comply with their policies early on. Look into using SMARTech! P.I.’s should ultimately be responsible for dealing with the final disposition of the data.
Example (from DataOne)28 Short Term: The data product will be updated monthly reflecting updates to the record, revisions due to recalibration of standard gases, and identification and flagging of any errors. The date of the update will be included in the data file and will be part of the data file name. Versions of the data product that have been revised due to errors/updates (other than new data) will be retained in an archive system. A revision history document will describe the revisions made. Daily and monthly backups of the data files will be retained at the Keeling Group Lab (http://scrippsco2.ucsd.edu, accessed 05/2011), at the Scripps Institution of Oceanography Computer Center, and at the Woods Hole Oceanographic Institution’s Computer Center. Long Term: Our intent is that the long term high quality final data product generated by this project will be available for use by the research and policy communities in perpetuity. The raw supporting data will be available in perpetuity as well, for use by researchers to confirm the quality of the Mauna Loa Record. The investigators have made arrangements for long term stewardship and curation at the Carbon Dioxide Information and Analysis Center (CDIAC), Oak Ridge National Laboratory (see letter of support). The standardized metadata record for the Mauna Loa CO2 data will be added to the metadata record database at CDIAC, so that interested users can discover the Mauna Loa CO2 record along with other related Earth science data. CDIAC has a standardized data product citation including DOI, that indicates the version of the Mauna Loa Data Product and how to obtain a copy of that product.
Step 2: Create a Plan33 Select a Funding Agency. Email is sent to Georgia Tech Library.
Creating and Naming your Plan34 Strongly Recommend Naming Plan “[Insert Proposal Title Here] Data Management Plan”.
Step 3: One Section at a Time35 Sections are different depending on funding source. Georgia Tech and DataONE Enter your have resources answers here. available for every section.
Some Sections Have Extra Advice36 Georgia Tech specific help text
Almost There37You shouldsave afterevery section,but definitely You’re so closesave at the to the end!very end.
Step 4: Export38 Now that you have the content, you can export your plan.
Step 5: Share plan39 Send your plan to the Research Data Librarian (Me!) to look over your plan. Have your colleagues look at your plan. Do you know your grant officer?
Step 6: Finish and Start Research!40 Add plan to proposal or distribute among research team Begin your newly funded research!
Other Data Management Plan Resources41 Digital Curation Centre - http://www.dcc.ac.uk/resources/data-management-plans ICPSR – while made for Social Science data, it has great resources for anyone: http://www.icpsr.umich.edu/icpsrweb/content/datamanage ment/dmp/plan.html UK Data Archive - http://www.data- archive.ac.uk/media/2894/managingsharing.pdf
Questions?42 Lizzy Rolando Research Data Librarian firstname.lastname@example.org 404.385.3706 http://libguides.gatech.edu/research-data