![MSU Libraries
Research Data Management
Positive reinforcement… (cont. w/
teeth!)
• AHRQ = “[unclassified digital data] should be
stored and publicly accessible to search,
retrieve, and analyze. For sharing of data in
digital format, all AHRQ-funded researchers
will be required to include a data
management plan for sharing final research
data in digital format, or state why data
sharing is not possible.
• NASA = This plan extends NASA’s culture of
open data access to all NASA-funded
research.”
• USDA = Phased approach beginning with](https://image.slidesharecdn.com/datamanagementforresearch20140820draft01-150507195505-lva1-app6891/85/Data-Management-for-Research-25-320.jpg)
![MSU Libraries
Research Data Management
Design a File Plan
Example documentation of Directory Hierarchy:
/[Project]/[Grant Number]/[Event]/[Investigator/Date]](https://image.slidesharecdn.com/datamanagementforresearch20140820draft01-150507195505-lva1-app6891/85/Data-Management-for-Research-49-320.jpg)
![MSU Libraries
Research Data Management
• To create consistent file names, specify a
template such as:
[investigator]_[descriptor]_[YYYYMMDD].[ex
t]
Use File Naming Conventions
This Not This
sharpeW_krillMicrograph_backscatter3_20110117.tif KrillData2011.tif
This Not This
borgesJ_collocation_20080414.xml Borges_Textbase.xml](https://image.slidesharecdn.com/datamanagementforresearch20140820draft01-150507195505-lva1-app6891/85/Data-Management-for-Research-53-320.jpg)
Data Management for Research
- 1. MSU Libraries Research Data Management Research Data Management Aaron Collie collie@msu.edu @aaroncollie
- 2. MSU Libraries Research Data Management Introductions • Please tell us your name and department • A brief description of your primary research area • What do you consider to be your research data • Experience and/or comfort level with managing research data? cc http://www.flickr.com/photos/quinnanya/
- 3. MSU Libraries Research Data Management • Introduction • Background • The Impetus: NSF Data Management Plan Mandate • The Effect: Policy to Practice • The Response: Changing Data Landscape • Fundamentals Practices • File Organization • Data Documentation • Reliable Backup • Data Publishing, Sharing, & Reuse • Protecting Data & Responsible Reuse • Data Lifecycle Resources Agenda
- 4. MSU Libraries Research Data Management Volunstrordinaries! Aaron Collie Hailey Mooney Devin Higgins Brandon Locke Ranti Junus Thomas Padilla Judy Matthews Tina Qin
- 5. MSU Libraries Research Data Management We teach people about RDM Librarianship Training Assessment Consultation Ad-hoc 6-12 new clients per semester 100% satisfied / 100% would use again 71% of new clients are referrals 60% requested additional services 15% through NFO, 14% through website
- 6. MSU Libraries Research Data Management RDM@MSU 101 • Who: You, as the designated steward • What: “the data” • When: Minimum 3 years after publ./degree • Where: Managed networked storage • Why: Legal, Ethical, Scholarly • How: With fidelity and documentation sufficient to reproduce the research
- 7. MSU Libraries Research Data Management http://retractionwatch.com/2014/01/07/doing-the-right-thing-authors-retract-brain-paper-with-systematic-human-error-in-coding/
- 8. MSU Libraries Research Data Management Jen Doty and Rob O'Reilly, “Learning to Curate @ Emory”. RDAP 2014
- 9. MSU Libraries Research Data Management Data Management. Isn’t that… trivial? • Not so much. Data is a primary output of research; it is very expensive to produce high quality data. Data may be collected in nanoseconds, but it takes the expert application of research protocol and design to generate data. CC-BY-SA-3.0 Rob Lavinsky CC-BY-SA-3.0 Rob
- 10. MSU Libraries Research Data Management Even more consequential, data is the input of a process that generates higher orders of understanding. Wisdom Knowledge Information Data Understanding is hierarchical! Russell Ackoff
- 11. MSU Libraries Research Data Management This is the engine of the academic industry…
- 12. MSU Libraries Research Data Management
- 13. MSU Libraries Research Data Management So, things can get a little messy.
- 14. MSU Libraries Research Data Management The scientific method “is often misrepresented as a fixed sequence of steps,” rather than being seen for what it truly is, “a highly variable and creative process” (AAAS 2000:18). Gauch, Hugh G. Scientific Method in Practice. New York: Cambridge University Press, 2010. Print. (Emphasis added)
- 15. MSU Libraries Research Data Management
- 16. MSU Libraries Research Data Management The Research Depth Chart Scientific Method Research Design Research Method Research Tasks MoreSpecificMoreGeneric
- 17. MSU Libraries Research Data Management Problem Identification Study Concept Literature Review Environmental Scan Funding & Proposal Research Design Research Methodology Research Workflow Hypothesis Formation Design Validation Research Activity Data Management Data Organization Data Storage Data Description Data Sharing Scholarly Communication Report Findings Publish Peer Review
- 18. MSU Libraries Research Data Management Problem Identification Study Concept Literature Review Environmental Scan Funding & Proposal Research Design Research Methodology Research Workflow Hypothesis Formation Design Validation Research Activity Data Management Data Organization Data Storage Data Description Data Sharing Scholarly Communication Report Findings Publish Peer Review
- 19. MSU Libraries Research Data Management • Introduction • Background • The Impetus: NSF Data Management Plan Mandate • The Effect: Policy to Practice • The Response: Changing Data Landscape • Fundamentals Practices • File Organization • Data Documentation • Reliable Backup • Data Publishing, Sharing, & Reuse • Protecting Data & Responsible Reuse • Data Lifecycle Resources Agenda
- 20. MSU Libraries Research Data Management Data Management • The process of planning for and implementing a system of care for your research data before, during, and after a research project in order to ensure a (re)usable resource.
- 21. MSU Libraries Research Data Management So why are we here? Good science! Government and Research Funder Mandates
- 22. MSU Libraries Research Data Management But why are we really here? • Impetus: NSF has mandated that all grant applications submitted after January 18th, 2011 must include a supplemental “Data Management Plan” • Effect: The original NSF mandate has had a domino effect, and many funders now require or state guidelines for data management of grant funded research • Response: Data management has not traditionally received a full treatment in (many) graduate and doctoral curricula; intervention is necessary
- 23. MSU Libraries Research Data Management Positive reinforcement…. • National Science Foundation Data Management Plan mandate (January 18, 2011) • Presidential Memorandum on Managing Government Records (August 24, 2012) – Managing Government Records Directive: All permanent electronic records in Federal agencies will be managed electronically to the fullest extent possible for eventual transfer and accessioning by NARA in an electronic format.
- 24. MSU Libraries Research Data Management Positive reinforcement… (cont.) • White House policy memo (February 22, 2013) – Increasing Access to the Results of Federally Funded Scientific Research: Federal agencies with more than $100M in R&D expenditures must develop plans to make the published results of federally funded research freely available to the public within one year of publication. • OSTP policy memo (March 20, 2014) – Improving the Management of and Access to Scientific Collections: directs each Federal agency that owns, maintains, or otherwise financially supports permanent scientific collections to develop a draft scientific-collections management and access policy within six months.
- 25. MSU Libraries Research Data Management Positive reinforcement… (cont. w/ teeth!) • AHRQ = “[unclassified digital data] should be stored and publicly accessible to search, retrieve, and analyze. For sharing of data in digital format, all AHRQ-funded researchers will be required to include a data management plan for sharing final research data in digital format, or state why data sharing is not possible. • NASA = This plan extends NASA’s culture of open data access to all NASA-funded research.” • USDA = Phased approach beginning with
- 26. MSU Libraries Research Data Management Funder Policies NASA “promotes the full and open sharing of all data” “requires that data…be submitted to and archived by designated national data centers.” “expects the timely release and sharing of final research data" "IMLS encourages sharing of research data." “…should describe how the project team will manage and disseminate data generated by the project”
- 27. MSU Libraries Research Data Management Policies for re-use, re-distribution, and creation of derivatives Plans for archiving data, samples, and other research outcomes, maintaining access Types of data, samples, physical collections, software generated • Standards for data and metadata format and content • Access and sharing policies, with stipulations for privacy, confidentiality, security, intellectual property, or other rights or requirements
- 28. MSU Libraries Research Data Management • NSF will not evaluate any proposal missing a DMP • PI may state that project will not generate data • DMP is reviewed as part of intellectual merit or broader impacts of application, or both • Costs to implement DMP may be included in proposal’s budget • May be up to two pages long
- 29. MSU Libraries Research Data Management • Investigators seeking $500,000 or more in direct costs in any year should include a description of how final research data will be shared, or explain why data sharing is not possible. • The precise content of the data-sharing plan will vary, depending on the data being collected and how the investigator is planning to share the data. • More stringent data management and sharing requirements may be required in specific NIH Funding Opportunity Announcements. Principal Investigators must discuss how these requirements will be met in their Data Sharing Plans.
- 30. MSU Libraries Research Data Management Roles and responsibilities Expected Data Period of data retention • Data formats and dissemination • Data storage and preservation of access
- 31. MSU Libraries Research Data Management Local Response: Policy University Research Council Best Practices: Research Data: Management, Control, and Access – To assure that research data are appropriately recorded, archived for a reasonable period of time, and available for review under the appropriate circumstances. • Ownership = MSU • “Stewardship” = You • Period of Retention = 3 years • Transfer of Responsibility = Written Request
- 32. MSU Libraries Research Data Management Broader Response: Changing Data Landscapes • Data Management Competencies – Standards & Best Practices – Discipline Specific Discourse • Data sharing and open data – Data sets as publications – Data journals – Citations for data (e.g., used in secondary analysis) – Data as supplementary materials to traditional articles – Data repositories and archives
- 33. MSU Libraries Research Data Management Curation responsibilities (Carlson, The Chronicle, 2006) “Data from Big Science is … easier to handle, understand and archive. Small Science is horribly heterogeneous and far more vast. In time Small Science will generate 2-3 times more data than Big Science.” big science data small science data institution? domain? MacColl, John (2010). The Role of libraries in data curation. RLG Partnership Annual Meeting, Chicago. June 2010
- 34. MSU Libraries Research Data Management What’s in it for me? • Better organization = less headaches – Course management – Bibliographic management – File management – Research • Career advancement – Publish datasets and list on your CV – Data management is an “unnamed practice” – name it for yourself and your students!
- 35. MSU Libraries Research Data Management Data Sharing Impacts • Reinforces open scientific inquiry • Encourages diversity of analysis and opinion • Promotes new research, testing of new or alternative hypotheses and methods of analysis • Supports studies on data collection methods and measurement Cc http://www.flickr.com/photos/pinchof_10/
- 36. MSU Libraries Research Data Management Data Sharing Impacts • Facilitates education of new researchers • Enables exploration of topics not envisioned by initial investigators • Permits creation of new datasets by combining data from multiple sources
- 37. MSU Libraries Research Data Management • Introduction • Background • The Impetus: NSF Data Management Plan Mandate • The Effect: Policy to Practice • The Response: Changing Data Landscape • Fundamentals Practices • File Organization • Data Documentation • Reliable Backup • Data Publishing, Sharing, & Reuse • Protecting Data & Responsible Reuse • Data Lifecycle Resources Agenda
- 38. MSU Libraries Research Data Management Research Data Management Fundamentals • Documentation • File Organization • Storage & Backup • Data Publishing, Sharing, & Reuse • Protecting Data & Responsible Reuse
- 39. MSU Libraries Research Data Management Documentation Practices: Overview • Researchers benefit from proper documentation to decipher or reuse their datasets – even prior to thinking about sharing • Think “downstream”
- 40. MSU Libraries Research Data Management Documentation Practices: Overview 1. At minimum create a README file that you can use to document your project 2. Utilize standards for describing data including Metadata Standards 3. If applicable, use in-line code commentary to explain code (cc) Will Scullin
- 41. MSU Libraries Research Data Management Create a README file • At minimum, store documentation in readme.txt file or equivalent, with data – What data consists of – How it was collected – Restrictions to distribution or use – Other descriptive information
- 42. MSU Libraries Research Data Management • “Data about data” • Standardized way of describing data • Explains who, what, where, when of data creation and methods of use • Data more easily found • Data more easily compared to other data sets Use Metadata Standards
- 43. MSU Libraries Research Data Management Use Metadata Standards Basic project metadata: • Title • Language • File Formats • Creator • Dates • File Structure • Identifier • Location • Variable List • Subject • Methodology • Code Lists • Funders • Data Processing • Versions • Rights • Sources • Checksums • Access Information • List of File Names
- 44. MSU Libraries Research Data Management Use Metadata Standards • Dublin Core: Commonly-used descriptive metadata format facilitates dataset discovery across the Web. • Data Documentation Initiative (DDI): Defines metadata content, presentation, transport, and preservation for the social and behavioral sciences. • ISO 19115:2003: Describes geographic data such as maps and charts. • More examples:http://www.lib.msu.edu/about/diginfo/coll ect.jsp
- 45. MSU Libraries Research Data Management Use In-Line Code Commentary Example of R code commentary # Cumulative normal density pnorm(c(-1.96,0,1.96)) • If applicable, in-line code commentary helps explain code
- 46. MSU Libraries Research Data Management File Organization Practices: Overview 1. Design a file plan for your research project 2. Use file naming conventions that work for your project 3. Choose file formats to maximize usefulness “When I was a freshmen I named my assignments Paper Paperr Paperrr Paperrrr” -Undergrad
- 47. MSU Libraries Research Data Management Design a File Plan • File structure is the framework • Classification system makes it easier to locate folders/files • Benefits: – Simple organization intuitive to team members and colleagues – Reduces duplicate copies in personal drives and e-mail attachments
- 48. MSU Libraries Research Data Management Design a File Plan Choose a sortable directory hierarchy • Example 1: Investigator, Process, Date Collie TEI_Encoding 20110117 • Example 2: Instrument, Date, Sample Usability Survey 2012043 sample_1
- 49. MSU Libraries Research Data Management Design a File Plan Example documentation of Directory Hierarchy: /[Project]/[Grant Number]/[Event]/[Investigator/Date]
- 50. MSU Libraries Research Data Management Use File Naming Conventions – Enable better access/retrieval of files – Create logical sequences for file sorting – More easily identify what you’re searching for
- 51. MSU Libraries Research Data Management • Meaningful but short—255 character limit • Use alphanumeric characters – Example: abc123 • Capital letters or underscores differentiate between words • Surname first followed by initials of first name Use File Naming Conventions
- 52. MSU Libraries Research Data Management • Year-month-day format for dates, with or without hyphens Example 1: 2006-03-13 Example 2: 20060313 • Decide on a simple versioning method Example: file_v001 Use File Naming Conventions
- 53. MSU Libraries Research Data Management • To create consistent file names, specify a template such as: [investigator]_[descriptor]_[YYYYMMDD].[ex t] Use File Naming Conventions This Not This sharpeW_krillMicrograph_backscatter3_20110117.tif KrillData2011.tif This Not This borgesJ_collocation_20080414.xml Borges_Textbase.xml
- 54. MSU Libraries Research Data Management Choose Appropriate File Formats • Non-proprietary • Open, documented standard • Common usage by research community • Standard representation (ASCII, Unicode) • Unencrypted • Uncompressed
- 55. MSU Libraries Research Data Management Choose Appropriate File Formats Format Genre Optimal Standards TEXT .txt; .odt; .xml; .html AUDIO .flac; .wav, VIDEO .mp2/.mp4; .mkv IMAGE .tif; .png; .svg; .jpg DATA .sql; .csv
- 56. MSU Libraries Research Data Management Storage & Backup Practices 1. Avoid single points of failure 2. Ensure data redundancy & replication 3. Understand common types of storage (cc) George Ornbo Data at significant risk of loss without storage and backup plan
- 57. MSU Libraries Research Data Management Avoid Single Points of Failure A single point of failure occurs when it would only take one event to destroy all data on a device • Use managed networked storage when possible • Move data off of portable media • Never rely on one copy of data • Do not rely on CD or DVD copies to be readable • Be wary of software lifespans
- 58. MSU Libraries Research Data Management Ensure Data Redundancy • Effective data storage plan provides for 3 copies: – Primary authoritative copy – Secondary local backup – Tertiary remote backup • Geographically distribute and secure – Local vs. remote, depending on needed recovery time • Personal computer, external hard drives, departmental, or university servers may be used
- 59. MSU Libraries Research Data Management Ensure Data Redundancy • Cloud storage – Amazon s3 – Google – MS Azure – DuraCloud – Rackspace – Glacier Note that many enterprise cloud storage services include a charge for in/out of data transfers $$$
- 60. MSU Libraries Research Data Management Understand Common Types of Storage • Optical Media • Portable Flash Media • Commercial Hard Drives • Commercial NAS • Cloud Storage • Enterprise Network Storage • Trusted Archival Storage
- 61. MSU Libraries Research Data Management Understand Common Types of Storage • Features of storage types: • Portable data transfers • Short-term storage • Project term storage • Networked data transfer • Long-term storage • Reliable backup option
- 62. MSU Libraries Research Data Management Understand Common Types of StoragePortable Data Transfer Short Term Storage Project Term Storage Networked Data Transfer Long Term Storage Reliable Backup Option Optical Media ✔ ✗ ✗ ✗ ✗ ✗ Portable Flash Media ✔ ✔ ✗ ✗ ✗ ✗ Commercial Hard Drives ✔ ✔ ✔ ✗ ✗ ✗ Commercial NAS ✗ ✔ ✔ ✔ ✗ ✗ Cloud Storage ✗ ✔ ✔ ✔ ✗ ✗ Enterprise Network Storage ✗ ✔ ✔ ✔ ✔ ✔ Trusted Archival Storage ✗ ✗ ✗ ✔ ✔ ✔
- 63. MSU Libraries Research Data Management Understand Common Types of Storage Media Storage @ MSU Optical Media MSU Computer Store—Sells Optical Media and hardware accessories UAHC Media Storage Service—Offers physical lock-box like storage for MSU Flash Media MSU Computer Store—Sells Optical Media and hardware accessories UAHC Media Storage Service—Offers physical lock-box like storage for MSU Commercial Hard Drives MSU Computer Store—Sells Optical Media and hardware accessories. UAHC Media Storage Service—Offers physical lock-box like storage for MSU Enterprise Cloud Storage Angel—Free. Ideal for collaboration; not storage space. Phase out 2015 Desire2Learn—Free. Ideal for collaboration; not storage space. Replaces Angel GoogleApps—Free. Ideal for collaboration; not intended as storage space Enterprise Network Storage AFS Space—Free to 1GB, add’l space can be purchased w/dept. account IT Services Individual, Mid-Tier and Enterprise Storage—Fee based HPCC Home or Research—Free up to 1TB. Fee based additions available Trusted Archival Storage Disciplinary Repositories – Disciplinary repositories offer archival services for pertinent research data.
- 64. MSU Libraries Research Data Management Data Publishing, Sharing, Reuse 1. Time-intensive, with potentially high return on investment 2. Publish data in several data publication venues to more broadly share results of research Research datasets on par with peer-reviewed journal articles as first-class scholarly contributions
- 65. MSU Libraries Research Data Management Sharing & Publishing Data • Data preparation for sharing and publication is a time-intensive process • Potential positive outcomes: • Increased research impact and citations • Enable additional scientific inquiry • Opportunities for co-authorship and collaboration • Enhance your grant proposal’s competitiveness
- 66. MSU Libraries Research Data Management Data Publication Venues • Multiple ways to publish research data • Faculty or project website • Journal supplementary materials • Disciplinary data repository (data archive) • Varying levels of support for indexing, access controls, and long-term curation
- 67. MSU Libraries Research Data Management Data Publication Venues • Disciplinary Data Repository • Securely share data, ensure long-term access • High visibility • Often offer persistent citations • Availability varies across domains • Databib.org directory
- 68. MSU Libraries Research Data Management Protecting Data & Responsible Reuse 1. Consider how to protect data and intellectual property rights while encouraging reuse 2. Keep in mind ethical concerns when sharing data (cc) Will Scullin
- 69. MSU Libraries Research Data Management Intellectual Property • IP refers to exclusive rights of creators of works • Individual data cannot be protected by US copyright • Organization of data such as database, creative work produced by data, and research instruments used may be protected ©
- 70. MSU Libraries Research Data Management Intellectual Property • Principal investigator’s institution holds IP rights • Provide clearly stated license for producing derivatives, reusing, and redistributing datasets • License under Creative Commons • State if any restrictions or embargos on use • Provide example of how work should be cited to encourage proper attribution on reuse • Document any IP / copyright issues
- 71. MSU Libraries Research Data Management Ethics & Data Sharing • Keep in mind the following ethical concerns when sharing your data: • Privacy • Confidentiality • Security and integrity of the data • For data involving human subjects, obtain written permission or consent stating how the data may be reused
- 72. MSU Libraries Research Data Management Best Practices = High Impact Data • File organization ensures easier access and retrieval of data • Documentation makes datasets accessible and intelligible to users • Storage and backup safeguards data • Data publishing and sharing encourages the most widespread reuse of data • Data protection ensures responsible reuse
- 73. MSU Libraries Research Data Management • Introduction • Background • The Impetus: NSF Data Management Plan Mandate • The Effect: Policy to Practice • The Response: Changing Data Landscape • Fundamentals Practices • File Organization • Data Documentation • Reliable Backup • Data Publishing, Sharing, & Reuse • Protecting Data & Responsible Reuse • Data Lifecycle Resources Agenda
- 74. MSU Libraries Research Data Management http://www.lib.msu.edu/rdmg
- 75. MSU Libraries Research Data Management Contact Aaron Collie collie@msu.edu @aaroncollie
Editor's Notes
- #3: Show of hands – how many here from the bench sciences, social sciences, humanities, medicine?
- #10: Data management is about more than just the lost back-pack. It is about expert application. Expert application in any industry is expensive.
- #11: In the academic industry data is the input to our final product. It takes years of training and experience to succeed in this field.
- #12: Research is a process, it is scientific, and we use an overarching model to describe the process at a high level. But this is a conceptual model, it is not a process model. But this is a pretty sterile model; and we know that because it is not prescriptive to all academic disciplines.
- #13: In practice, research is a complicated process. It is a creative process as well as a scientific process.
- #14: Research is hard, managing research is boring. So we want tips that make it easier.
- #15: This has been noticed.
- #16: You might think of the scientific method as a bit of an iceberg model. At the tip of the iceberg are these general activities, but research isn’t really conducted at this high of a level.
- #17: Research is a thing that happens at many levels simultaneously. The more experience you gain with research, the more of the depth chart you develop expertise within.
- #18: Data management is a subprocess of research. It is part of a holistic research method that includes a ton of other functions like funding, literature reviews, workflows and publication.
- #19: Today we are just going to focus on the one of these areas. Data management.
- #23: HANDOUT: DMP (blue)
- #27: National Oceanic and Atmospheric Administration (NOAA) IMLS encourages sharing of research data. Applications that develop digital products must fill out an additional form with ten questions focused on “Developing Data Management Plans for Research Projects. The federal government has the right to obtain, reproduce, publish or otherwise use the data first produced under an award and authorize others to do so for government purposes.” Ex: Digging Into Data
- #28: HANDOUT: DMP examples (white)
- #29: NSF’s data management plan requirement May be up to two pages long PI may state that project will not generate data or samples DMP is reviewed as part of intellectual merit or broader impacts of application, or both
- #30: HANDOUT: DMP examples (white)
- #31: HANDOUT: DMP examples (white)
- #32: (OMB Circular A-10, Sec. 53; 42CFR, Part 50, Subpart A)
- #33: Replication, transparency, re-use, mashups, repurposing, extending grant dollars and enabling more research…
- #39: Benefits include: Electronic documents maintained together in one place and easily accessible to project staff Data backed up and recoverable in the event of system failure Promote culture of sharing information as an institutional resource, rather than individual ownership Reduce duplicate copies in personal drives and email attachments
- #40: Starting point
- #42: Starting point
- #43: Descriptive documentation that accompanies a dataset
- #44: Better project transitions
- #47: Electronic documents maintained together in one place, easily accessible to project staff Reduces duplicate copies in personal drives and email attachments (Hierarchical/taxonomical/temporal)
- #48: Benefits include: Electronic documents maintained together in one place and easily accessible to project staff Data backed up and recoverable in the event of system failure Promote culture of sharing information as an institutional resource, rather than individual ownership Reduce duplicate copies in personal drives and email attachments
- #49: Benefits include: Electronic documents maintained together in one place and easily accessible to project staff Data backed up and recoverable in the event of system failure Promote culture of sharing information as an institutional resource, rather than individual ownership Reduce duplicate copies in personal drives and email attachments
- #50: Will know how to name future folders as your project grows.
- #52: Good practices
- #55: Good choices include… Consider later lifecycle activities Flexible What format used for analysis, preservation, etc.
- #56: Consider later lifecycle activities Flexible What format used for analysis, preservation, etc.
- #57: Data at significant risk of loss without storage and backup plan, including: Hardware / network failures Bit rot Human error Singular commercial grade hard drives Effective data storage plan provides for: Primary authoritative copy Secondary local backup Tertiary remote backup
- #58: One event might be a dropped hard drive Good practices Be wary of software lifespans, such as with course management software like ANGEL or Desire2Learn
- #59: Examples of 3 copies original + external/local + external/remote original + 2 formats on 2 drives in 2 locations Mention new Backup Media Storage service offered by the University Archives.
- #60: Mention new Backup Media Storage service offered by the University Archives. ANGEL, Desire2Learn, and Google Apps might be considered Cloud offerings from MSU. Good for collaboration and short term, don’t use for long-term storage.
- #62: In booklet For example….
- #63: Include description
- #64: Angel and Desire2Learn not intended as storage space For more information on disciplinary repositories, contact RDMG or peruse Databib.org
- #66: In booklet For example….
- #67: In booklet For example….
- #68: In booklet For example….
- #71: Principal investigator’s institution holds IP rights-- usually
- #73: File organization ensures easier access and retrieval of data during and after project Documentation make datasets accessible and intelligible to users Storage and backup safeguards data against technical failure, human error, and natural catastrophe Data publishing and sharing encourages the most widespread reuse of data Data protection ensures responsible reuse in light of intellectual property and ethical concerns Increase impact of data and promote new research opportunities
- #76: A Plus / Delta exercise focusing on extant infrastructure and services Weave known MSU resources Discussion starters: Describe your interaction with dept, college, university, external bodies? What makes managing research data difficult? What services/tools do you need/want? Advice Website Database designers Targeted seminar series Data storage and curation options