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The document outlines the DataONE data life cycle, emphasizing its key stages: planning, collecting, assuring quality, describing metadata, preserving data, discovering relevant datasets, integrating from various sources, and analyzing the data. Each stage offers tools and best practices to support researchers in effective data management. Additionally, it references the provision of educational resources and best practices to enhance understanding and implementation of these processes.
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Michener workshop montpellier
- 1. DataONE Data Life Cycle: Toolsand Tips
- 2. The DataONE DataLife Cycle 2 Plan Collect Assure Describe Preserve Discover Integrate Analyze
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- 6. Develop Solutions forResearch 6 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 7. The DataONE DataLife Cycle 7 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 8. 1. Plan: Create andFollow 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 DataLife Cycle 26 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 27. 2. Collect andOrganize: Logically Structure the Data to Support Use 27 CCimagebyJustinSeeonFlickr Jones et al. 2007
- 28. 2. Collect andOrganize 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 andOrganize 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
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- 32. Data Entry Tools:Excel
- 33. Data Entry Tools:Excel
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- 37. The DataONE DataLife Cycle 37 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 38. 3. Assure: Incorporate QualityAssurance & Quality Control 38 0 10 20 30 40 50 60 0 10 20 30 40 Quality Engine MetaDIG DIBBs
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- 48. 3. Assure • JMP •R • MATLAB • many others 48
- 49. The DataONE DataLife 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 DataLife Cycle 52 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 53. 5. Preserve: Protect andPreserve Data for Long-term Use 53 Catalog of 1,500+ Data Repositories
- 54. Exercise • Search forrepositories 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 DataLife Cycle 55 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 56. 6. Discover Search aDomain Portal 56
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- 60. 60 Dryad links tojournals
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- 62. 6. Discover Search aData Aggregator 62
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- 67. Data Federations (DataONE, GBIF) carboncycling 67
- 68. Data Federations (DataONE, GBIF) carboncycling 68
- 69. Data Federations (DataONE, GBIF) carboncycling plant biomass 69
- 70. Data Federations (DataONE, GBIF) carboncycling plant biomass 70
- 71. Data Federations (DataONE, GBIF) carboncycling plant biomass ocean nitrogen avian distribution 71
- 72. Exercise • Search datadryad.orgfor plant trait • Search DataONE.org for plant trait 72
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- 79. 6. Discover: Support Discoveryof 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 DataLife Cycle 80 Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 81. 7. Integrate: Enable DataIntegration from Different Sources 81 Jones et al. 2007
- 82. 7. Integrate: DataONE ProvenanceTracking System 82
- 83. The DataONE DataLife Cycle 83 Plan Collect Assure Describe Preserve Discover Integrate Analyze
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- 89. Best PracticesWebinar seriesLessons and Exercises DataONE.org Education Resources 89
- 90. 90 DataONE Vision andMission
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Editor's Notes
- #3 The basic DLC
- #4 The basic DLC
- #6 Here is the DLC for a synthesis effort.
- #7 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
- #8 The basic DLC
- #9 DataONE has worked with several other institutions to pioneer the DMPTool and to promote training in the use of the DMPTool
- #27 The basic DLC
- #28 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.
- #31 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.
- #32 Data entered into a Googledoc form is stored in a spreadsheet. These data can be downloaded for further analysis.
- #33 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.
- #34 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.
- #35 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.
- #36 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.
- #37 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.
- #38 The basic DLC
- #39 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).
- #50 The basic DLC
- #51 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).
- #52 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).
- #53 The basic DLC
- #54 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.
- #55 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.
- #56 The basic DLC
- #67 Color of lines set by computer monitor
- #68 Color of lines set by computer monitor
- #69 Color of lines set by computer monitor
- #70 Color of lines set by computer monitor
- #71 Color of lines set by computer monitor
- #72 Color of lines set by computer monitor
- #73 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.
- #80 Color of lines set by computer monitor
- #81 The basic DLC
- #82 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.
- #83 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.
- #84 The basic DLC
- #85 DataONE has partnered with development teams from Kepler and VisTrails to develop and support use of scientfic workflows in research.
- #86 DataONE has partnered with development teams from Kepler and VisTrails to develop and support use of scientfic workflows in research.
- #87 DataONE has partnered with development teams from Kepler and VisTrails to develop and support use of scientfic workflows in research.
- #88 D
- #89 D
- #90 We do this through in-person, web-based, and resource based training activities.
- #91 Lastly, DataONE focuses significant effort of training in various elements of the Data Life Cycle.