Michener workshop montpellier
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A workshop presented by Bill Michener (University of New Mexico, DataONE) at CNRS Montpellier. Co-sponsored by FRB-CESAB and CEFE (CNRS)
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Michener workshop montpellier
- 1. DataONE Data Life Cycle: Tools and Tips
- 2. The DataONE Data Life Cycle 2 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 6. Develop Solutions for Research 6 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 7. The DataONE Data Life Cycle 7 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 8. 1. Plan: Create and Follow a Data Management Plan 8 Michener WK (2015) Ten Simple Rules for Creating a Good Data Management Plan. PLoS Comput Biol 11(10): e1004525. doi:10.1371/journal.pcbi.1004525
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- 26. The DataONE Data Life Cycle 26 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 27. 2. Collect and Organize: Logically Structure the Data to Support Use 27 CCimagebyJustinSeeonFlickr Jones et al. 2007
- 28. 2. Collect and Organize 28 • Columns of data are consistent: only numbers, dates, or text • Consistent Names, Codes, Formats (date) used in each column • Data are all in one table, which is much easier for a statistical program to work with than multiple small tables which each require human intervention
- 29. 2. Collect and Organize 29 • Columns of data are consistent: only numbers, dates, or text • Consistent Names, Codes, Formats (date) used in each column • Data are all in one table, which is much easier for a statistical program to work with than multiple small tables which each require human intervention
- 30. Googledocs Forms
- 31. Googledocs Forms
- 32. Data Entry Tools: Excel
- 33. Data Entry Tools: Excel
- 37. The DataONE Data Life Cycle 37 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 38. 3. Assure: Incorporate Quality Assurance & Quality Control 38 0 10 20 30 40 50 60 0 10 20 30 40 Quality Engine MetaDIG DIBBs
- 39. 3. Assure 39
- 40. 3. Assure 40
- 41. 3. Assure 41
- 42. 3. Assure 42
- 43. 3. Assure 43
- 44. 3. Assure 44
- 45. 3. Assure 45
- 46. 3. Assure 46
- 47. 3. Assure 47
- 48. 3. Assure • JMP • R • MATLAB • many others 48
- 49. The DataONE Data Life Cycle 49 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 50. 4. Describe: Develop Comprehensive, Standardized Metadata 50 Darwin Core – species and biodiversity collections EML – Ecological Metadata Language ISO 19115 – geospatial data http://rs.tdwg.org/dwc/
- 51. 4. Describe 51 Tools Specify Morpho https://knb.ecoinformatics.org/#tools/morpho http://specifyx.specifysoftware.org
- 52. The DataONE Data Life Cycle 52 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 53. 5. Preserve: Protect and Preserve Data for Long-term Use 53 Catalog of 1,500+ Data Repositories
- 54. Exercise • Search for repositories that host particular types of data (e.g., biodoversity, trait) • Visit one of the repositories and identify the services that they offer 54
- 55. The DataONE Data Life Cycle 55 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 56. 6. Discover Search a Domain Portal 56
- 57. 57
- 58. 58
- 59. 59
- 60. 60 Dryad links to journals
- 62. 6. Discover Search a Data Aggregator 62
- 63. 63
- 64. 64
- 65. 65
- 67. Data Federations (DataONE, GBIF) carbon cycling 67
- 68. Data Federations (DataONE, GBIF) carbon cycling 68
- 69. Data Federations (DataONE, GBIF) carbon cycling plant biomass 69
- 70. Data Federations (DataONE, GBIF) carbon cycling plant biomass 70
- 71. Data Federations (DataONE, GBIF) carbon cycling plant biomass ocean nitrogen avian distribution 71
- 72. Exercise • Search datadryad.org for plant trait • Search DataONE.org for plant trait 72
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- 79. 6. Discover: Support Discovery of Relevant Data 79 Dryad DataONE google plant trait 2,137 26,300,000 plant trait datadryad 803 1,908 17,400 • Differential content searched • Automated annotation via ontologies and other approaches • Differential filtering • Different definitions of data sets (e.g., entire package vs individual data sets)
- 80. The DataONE Data Life Cycle 80 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 81. 7. Integrate: Enable Data Integration from Different Sources 81 Jones et al. 2007
- 82. 7. Integrate: DataONE Provenance Tracking System 82
- 83. The DataONE Data Life Cycle 83 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 89. Best PracticesWebinar series Lessons and Exercises DataONE.org Education Resources 89
- 90. 90 DataONE Vision and Mission
- 91. 91
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- 93. dataone.org
Editor's Notes
- The basic DLC
- The basic DLC
- Here is the DLC for a synthesis effort.
- We use the DLC concept for 2 primary activities: Identifying and developing (needed) tools that facilitate different components of the data life cycle; and Training, which you will hear more about later
- The basic DLC
- DataONE has worked with several other institutions to pioneer the DMPTool and to promote training in the use of the DMPTool
- The basic DLC
- DataONE has developed connectors with R and Matlab that make it easy to take data from DataONE repositories, analyze the data and deposit derived products back in DataONE repositories. We also encourage best practices in organizing data using common tools such as ArcGIS and MySQL.
- This is an example of a data entry form created in Googledocs. Such forms are easy to create, and free. Here, a form field is being created that will allow the user to select from three locations where data were collected. In practice, GoogleDocs work best for entering survey data, or entering lots of text data. The advantages to using a data entry form, as opposed to entering data directly in to a spreadsheet, is that the form can enforce data entry rules – that is, you can create a pick-list of items for a user to select from. That way, you have consistent info being entered – a user will always enter Deep Well, instead of DW.
- Data entered into a Googledoc form is stored in a spreadsheet. These data can be downloaded for further analysis.
- Excel is a very popular data entry tool. It also allows you to enforce data validation rules. Here, a dropdown list has been generated that allows the user to only select entries from this list. In this way, only defined species codes get entered, and the data is consistent.
- Excel is a very popular data entry tool. It also allows you to enforce data validation rules. Here, a dropdown list has been generated that allows the user to only select entries from this list. In this way, only defined species codes get entered, and the data is consistent.
- Here is another example of data validation using Excel. Height has been defined to contain values between 11 and 15. When 20 is entered, the user is told that they have entered an illegal value.
- Here is another example of data validation using Excel. Height has been defined to contain values between 11 and 15. When 20 is entered, the user is told that they have entered an illegal value.
- Here is another example of data validation using Excel. Height has been defined to contain values between 11 and 15. When 20 is entered, the user is told that they have entered an illegal value.
- The basic DLC
- Through the connectors with R and MATLAB, QA/QC is facilitated. We will also soon be releasing a new tool (on the right) that analyzes metadata and provides guidance wrt meeting community standards (such as LTER best practices).
- The basic DLC
- DataONE’s affiliates have developed user friendly metadata management tools like “Morpho” and will soon be releasing a new web-based metadata entry tool that will further ease metadata management (initially through the Arctic Data Center).
- DataONE’s affiliates have developed user friendly metadata management tools like “Morpho” and will soon be releasing a new web-based metadata entry tool that will further ease metadata management (initially through the Arctic Data Center).
- The basic DLC
- DataONE encourages good practices in data preservation via our 30+ affiliated repositories as well as other repositories listed in re3data.org. We also encourage preservation of code, workflows, and other data resources.
- DataONE encourages good practices in data preservation via our 30+ affiliated repositories as well as other repositories listed in re3data.org. We also encourage preservation of code, workflows, and other data resources.
- The basic DLC
- Color of lines set by computer monitor
- Color of lines set by computer monitor
- Color of lines set by computer monitor
- Color of lines set by computer monitor
- Color of lines set by computer monitor
- Color of lines set by computer monitor
- DataONE encourages good practices in data preservation via our 30+ affiliated repositories as well as other repositories listed in re3data.org. We also encourage preservation of code, workflows, and other data resources.
- Color of lines set by computer monitor
- The basic DLC
- DataONE is doing a number of things related to data integration. First, connections with R and MATLAB simply many data processing setps. Second, manual and automated semantic annotation services make it easy to find and interpret the data one is looking for, Third, the DataONE provenance management system is being release to allow researcher to readily see how data were derived and where they were subsequently used.
- DataONE is doing a number of things related to data integration. First, connections with R and MATLAB simply many data processing setps. Second, manual and automated semantic annotation services make it easy to find and interpret the data one is looking for, Third, the DataONE provenance management system is being release to allow researcher to readily see how data were derived and where they were subsequently used.
- The basic DLC
- DataONE has partnered with development teams from Kepler and VisTrails to develop and support use of scientfic workflows in research.
- DataONE has partnered with development teams from Kepler and VisTrails to develop and support use of scientfic workflows in research.
- DataONE has partnered with development teams from Kepler and VisTrails to develop and support use of scientfic workflows in research.
- D
- D
- We do this through in-person, web-based, and resource based training activities.
- Lastly, DataONE focuses significant effort of training in various elements of the Data Life Cycle.