1 00:00:00,000 --> 00:00:03,630 In many scientific fields such as bioinformatics, medical imaging, and

2 00:00:03,629 --> 00:00:06,299 astronomy large quantities of data need to be

3 00:00:06,299 --> 00:00:10,769 analyzed. This can involve large-scale and repetitive processes in long

4 00:00:10,769 --> 00:00:15,239 pipelines of different tools — referred to as workflows. It can be very

5 00:00:15,240 --> 00:00:18,930 time-consuming to run data for all these different tools by hand and convert

6 00:00:18,930 --> 00:00:21,950 outputs to various formats to make them compatible with the next step.

7 00:00:21,949 --> 00:00:26,129 Workflow management systems are designed to alleviate this problem by allowing

8 00:00:26,130 --> 00:00:30,090 these workflows to be expressed formally and providing infrastructure to set up.

9 00:00:30,090 --> 00:00:34,290 execute, and monitor them. This formal expression of workflows allows for

10 00:00:34,290 --> 00:00:38,400 scientists to easily share and reuse them. Crucially they can also be used to

11 00:00:38,399 --> 00:00:43,109 verify results of computation for published work. However there are many

12 00:00:43,110 --> 00:00:46,580 competing [ways] for describing workflows which is a barrier to this aim.

13 00:00:46,579 --> 00:00:50,399 Currently there are over a hundred different data analysis workflow systems

14 00:00:50,399 --> 00:00:55,019 with no interoperability between them. The need has arisen to have a single

15 00:00:55,020 --> 00:00:58,470 common standard and so the "Common Workflow Language" project was created: an

16 00:00:58,469 --> 00:01:02,429 open standard designed to express workflows and their tooling in groups of

17 00:01:02,430 --> 00:01:05,540 YAML structured text files.