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Cornell Lab of Ornithology


Accept Data: How-to

HEAD poppies

Establish appropriate infrastructure:

  • determine technology needs for data collection, analysis, data visualization, archiving (backed up), and delivery
  • plan a strong user interface for entering, accessing, and viewing data
  • train volunteers and staff to use the technology
  • consider needs for integrating data management across projects
  • discus these needs during the design phase


Ensure the project receives the data:

  • minimize steps and avoiding multiple transfers of data (e.g., from paper to excel spreadsheet to internet...)
  • create an easy platform for data entry
  • provide data entry portals at collection sites (REEF example)
  • ensure the usability of data collection/data entry technology
  • provide tech support and customer service for users
  • train all staff in use of data entry platform


Provide data visualization/manipulation tools:

  • provide access  to data sets and interpretations for volunteers and scientists
  • recognize that data visualization allows participants to see trends (e.g, declines)
  • offer as much flexibility in output variables as possible
  • allow participants to ask questions by looking at data (both their own and previously collected data
  • provide an interface that lets people tell a story in the context of their data
  • explore technologies that allow volunteers to use data in presentations
  • convert data to common sharing format to improve liquidity
  • address up-front any potential challenges of data ownership and discovery (of published data)


Use technology carefully:

  • recognize that technology will allow users to view and analyze more data than you (or your project) can collect alone
  • be careful of presentation tools, as ease of use may lead to misleading or useless maps, graphs
  • make sure tools come with guidance and clearly stated assumptions
  • get tech and creative help from student volunteers  (e.g., power point slides)


Ensure collection of useful meta-data:

  • determine the metadata useful for your project and potential collaborators, e.g.:
    • observer name
    • geo-referenced
    • temporally referenced
  • use tech tools that allow automatic meta-data tagging, e.g.:
    • programmed hand-held devices
    • smart phones with GPS
    • digital cameras with date/time setting


Enhance robust data trends with technology:

  • build in quality assurance and quality control features (e.g., pop-up queries for seeming outliers)
  • tye data to observer
  • design tools/games for assessing volunteer skill levels
  • ensure the usability of all technology


Have any How-to ideas for this step?  Soon you will be able to share them through our discussion forum.


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