Data101 pmcb retreat_09-20-13_final

1. DATA MANAGEMENT 101 Nicole Vasilevsky, Jackie Wirz and Melissa Haendel PMCB New Student Orientation 20 September 2013

3. 1 | Data definitions 2 | Dealing with data 3 | How the OHSU Library can help

4. Nicole Vasilevsky, Ph D Project Manager, Ontolo gy Development Group Jackie Wirz, PhD Assistant Professor, Bioinformation Specialist Melissa Haendel, PhD Assistant Professor, Lead, Ontology Development Group

5. 1 | Data definitions

6. Data does not speak for itself…

7. YOU speak for YOUR data

8. But First, you need to manage it

9. But, even more fundamentally…

10. data means many things…

11. what does data mean to you?

15. What are data? Experimental data Social data School related data Personal data

17. Do you know what metadata is? a. Philosophy b. describes data c. dating site d. data

18. 2 | dealing with data

19. Do you get frustrated with any of the following? a. Storing data b. Backing up data c. Analyzing/manipulating data d. Finding data produced by other researchers/clinicians e. Ensuring data are secure f. Making data accessible to other researchers g. Controlling access to data h. Tracking updates to data (ie versioning) i. Creating metadata (ie describing the data to be more useful at a later time or by others) j. Protecting intellectual property rights k. Ensuring appropriate professional credit/citation is given to data sets/generated

20. Why? Personal organization Efficiency Credit where credit is due Accelerate scientific and clinical discovery Reproducibility of science and medicine

21. naming | metadata | tools | standards How?

22. naming

23. File naming

25. Naming conventions Project_instrument_location_YYYYMMDDhhm mss_extra.ext Index/grant conditions Leading zero! s/n, variable Retain order

26. Naming: Directory Structure

27. PCMB presentation Library presentation DMICE presentation Presentations PMCB Library DMICE

28. http://ftp.ihmc.us/

29. ReadMe

30. Version Control

31. Versioning • Save a copy of every version of a file • Follow a file naming convention Data101_PMCB_Retreat_09-20-13_v1 Data101_PMCB_Retreat_09-20-13_v2 Data101_PMCB_Retreat_09-20-13_Final

32. Versioning

33. Versioning

34. Versioning Version Control software: • GIT • SVN

35. Backups

36. Which of the following do you do? a. Save copies of data on a disk, USB drive, or computer hard drive b. Save copies of data on a local server c. Save copies of data on a central campus server d. Save copies of data on a web-based or cloud server e. Store data in a repository or archives f. Automatically backup files g. Manually generate backup h. Restrict access to files

37.  1 on your local workstation  1 local/removable, such as external hard drive  1 on central server  1 remote, such as on a cloud server* *Depending on the type of data, as cloud servers are not always secure Where can you backup your data?

38. Metadata

39. What is Metadata? Title Author Call number Publisher ISBN

41. - Anne Gilliland Your metadata should make your data understandable to others without your involvement Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata Metadata

42. Are you aware of data standards in your field?

43. data standards Data standards are the rules by which data are described and recorded. In order to share, exchange, and understand data, we must standardize the format as well as the meaning. http://www.usgs.gov/datamanagement/plan/datastandards.php

44. Controlled vocabularies

45. Structured data helps with searching Craigslist search: Chaise Craigslist matches on strings only Craigslist search: Fainting couch

46. Structured data helps with searching PubMed indexes articles with MeSH Terms

47. Structured data helps with searching

48. Why are CVs and Ontologies useful? • Can be used to structure your metadata • Are often used to structure information in databases Cell Ontology Linnean Taxonomy Order Genus Species Phylum Class Family Kingdom

49. tools

50. File renaming applications • Bulk Rename Utility (Windows) • Renamer (Mac) • PSRenamer

51. Data Management tools and repositories • Purpose: Software where you can organize, store and/or share data • Often contain metadata to assist with data entry and create structured data

52. Tools for data management

53. Repositories use Unique IDs • Document Object Identifier (DOI) • Example: DOIs for publications – doi: 10.1371/journal.pbio.1001339 • Unique resource identifier (URI) • A URI will resolve to a single location on the web • URIs for people

54. • Example: • John L Campbell, Research Ecologist, Oregon State University, Corvallis OR • John L Campbell, Research Ecologist, Center for Research on Ecosystem Change, Durham, NC

56. standards

57. nomenclature

58. antibodies Western Blot Immunohistochemstry ELISA Co-immunoprecipitation ChIP Radioimmunoassay

59. FACS analysis of T cells from LNs and tumors T cells were liberated from LNs by disruption between two frosted glass slides. Cells from LNs and tumors were stained with various combination of the following Abs: FITC- CD4, allophycocyanin-CD25, PE Cy7-CD8, APC-CD62L, PE- CD25, PE Cy7-CD25, and biotinylated-KJ-126 and in some experiments made permeable with fixation/permeablization buffers and stained with PE-FoxP3 (eBioscience). Harvested samples, isotype controls, and single stain controls were run on the FACSCalibur (BD Biosciences). Ruby and Weinberg (2009) J Immunol. 182(3):1481-9.

60. Which antibody did they use in the paper?

61. A Solution: Antibody Registry antibodyregistry.org

63. Meet the Urban Lab Meet the Urban Lab

64. A+ organization! The Urban lab antibodies

65. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Commerical Ab identifiable Catalog number reported Source organism reported Target uniquely identifiable Of 14 antibodies published in 45 articles, only 38% were identifiable Percentidentifiable

66. http://www.force11.org/node/4463 http://biosharing.org/bsg-000532

67. http://www.biosharing.org/standards/mibbi Minimum Information for Biological and Biomedical Investigations

68. data publication and sharing

69. Why share data? • Data sharing mandates • Further science and and medicine • Build collaborations • Enable new discoveries with your data • Can be required at time of publication

70. Distribution of 2004–2005 citation counts of 85 trials by data availability.

71. How?

72. Beyond the PDF: What can be published (and cited)? Raw Science Nanopublications Self-publishing

73. Beyond the PDF: What can be published (and cited)? Raw Science Nanopublications Self-publishing Datasets Code Experimental design Argument or passage Blogging Microblogging Comments on existing work Annotations on existing work Single figure publications

74. How? Data Journals and Repositories • FigShare • Dryad • DataVerse (social science) • Institutional repositories

75. www.impactstory.org

76. 3 | How the OHSU Library can help

77. 1 | Large Lecture: Data Management 101 2 | 10 –15 Small Groups: data playground • 1 researcher paired with 2 or 3 library staff • Tailored analysis of data reporting and instruction Save the date: 10/09/13 4-6pm 1k challenge award recipients

78. Thank you!

79. URLs to resources Go to: http://libguides.ohsu.edu/data

Data101 pmcb retreat_09-20-13_final

Data101 pmcb retreat_09-20-13_final

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