Enhanced comment feature has been enabled for all readers including those not logged in. Click on the Discussion tab (top left) to add or reply to discussions.

Data Processing: Difference between revisions

From BIF Guidelines Wiki
(Created page with "=ID systems (link to Data Collection section)= ==Herd IDs== ==Tattoos== ==Breed Association Registration Numbers== ==International Registration Numbers== ===ICAR=== ==Breed co...")
 
No edit summary
 
(30 intermediate revisions by 3 users not shown)
Line 1: Line 1:
=ID systems (link to Data Collection section)=
[[Category:Data Collection]]
==Herd IDs==
The accurate processing and safe storage of cattle data require a professional approach to
==Tattoos==
[https://en.wikipedia.org/wiki/Data_quality data quality]. The goals of an organization or business that manages cattle data should include:
==Breed Association Registration Numbers==
* Accurate linking of related pieces of information
==International Registration Numbers==
* Timely retrieval of information
===ICAR===
* Easy-to-understand reporting
==Breed codes==
* Reliable storage and backup
===ICAR===
* Easy-to-use addition of new data
===NAAB===
* Consistent processing
=Whole Herd Reporting (link to Data Collection section)=
 
==WHR by Breed Association (add links to association pages)==
A key to reliably [[Data Collection | collecting]] and processing data is the development of a clear and reliable [[Identification Systems | animal identification method]].  Animal identification is used to link disparate pieces of performance, pedigree and [[Genomic Data | genomic data]] on an individual.  An important consideration for an animal identification method within a database is the transfer of the data to other organizations.  For example, most modern [[:Category:Genetic Evaluation | genetic evaluations]] include data from multiple organizations and animals' information may be included in multiple databases.
==Data Reporting Incentives==
 
===ASA Performance Advocate Program===
Most entities that process data either require [[Whole Herd Reporting]] or offer an option to their participants.  Advantages of Whole Herd Reporting include reduced reporting bias in [[Expected Progeny Difference | genetic predictions]] and the ability to produce [[Stayability | cow fertility predictions]].
===AAA Rebate Program===
=Data Transfer=
==Online Data Entry==
==Herd Management Software==
==File Transfer==
=Association Level Processing=
==Breed Association Member Handbooks (links)==
==Registration and Transfer==
==Data for Genetic Evaluation==
===Contemporary Groups (link to Data Collection page)===
===Adjustment Factors===
===Age Windows===
===ETs and Multiple Births===
===Data Quality===
==Association Reports==
===Pedigrees===
====Breed Composition====
===Performance Reports===
====Adjusted Measurements====
====Ratios====
====Cow Productivity====
=====Pathfinder Dams=====
=====Dams of Distinction=====
=Genomic Data=
==Overview of Genotyping==
==Sample Collection==
===Sample IDs===
===Strategies for Archiving Samples===
==Genomic Data for Genetic Evaluation==
===Quality Control===
===Imputation===
=Data Ownership=

Latest revision as of 17:51, 12 April 2021

The accurate processing and safe storage of cattle data require a professional approach to data quality. The goals of an organization or business that manages cattle data should include:

  • Accurate linking of related pieces of information
  • Timely retrieval of information
  • Easy-to-understand reporting
  • Reliable storage and backup
  • Easy-to-use addition of new data
  • Consistent processing

A key to reliably collecting and processing data is the development of a clear and reliable animal identification method. Animal identification is used to link disparate pieces of performance, pedigree and genomic data on an individual. An important consideration for an animal identification method within a database is the transfer of the data to other organizations. For example, most modern genetic evaluations include data from multiple organizations and animals' information may be included in multiple databases.

Most entities that process data either require Whole Herd Reporting or offer an option to their participants. Advantages of Whole Herd Reporting include reduced reporting bias in genetic predictions and the ability to produce cow fertility predictions.