How we set goals

January 30, 2020

Our overarching mission is to empower pediatric cancer researchers poised for the next big discoveries with the methods, data, and knowledge to reach them. This mission is very broad-scale and translating from this to annual goals is important to making meaningful contributions. Our particular process is designed to source opportunities from our team members and external stakeholders, convert those opportunities into a set of potential goals, and then select the goals that we expect will most advance our mission.

We have a ticket system (we use GitHub issues, but any such system could work in principle) that members of the team can use throughout the year to identify opportunities in one of our core areas (methods, data, and knowledge) as well as opportunities to build capabilities within the team. In November, we put out a reminder for folks on the team to get these opportunities into our ticket system. We also engage with stakeholders who are external to the Childhood Cancer Data Lab (CCDL) team. This includes other units within Alex’s Lemonade Stand Foundation as well as those working on complementary efforts in pediatric cancer research (e.g., the UCSC Treehouse team).

We take the opportunities that are identified and write them on post-it notes which we post in a conference room. We provide team members with dot stickers in four colors (one each for methods, data, knowledge, and team capabilities) and ask the team to score opportunities by the extent to which they would advance our mission. This provides us with a rough assessment of the perceived importance of each potential effort. For example, we can infer from the voting on the post-it note for “R package for refine.bio” that many members of the team felt that realizing this opportunity would advance multiple missions (Figure 1). We filter out opportunities with the goal of triaging those that are not central to our mission, those that are too diffuse to convert to goals, and those that are already being addressed elsewhere.

Example of how the Data Lab uses post-it notes for goal setting
Figure 1

For the next stage, we need to convert selected opportunities to goals. In research, we would say that a good hypothesis is one that is falsifiable. We have a similar mantra for our goals: an ideal annual goal is one that can be accomplished (or not). We ask one or more members of the team to determine what a meaningful goal or goals would be towards the identified opportunity. Discussion occurs within GitHub issues. The aim of each discussion is to end with the goal. In the case of the R package for refine.bio, the ticket got converted to the goal: “Make API package available for users to download datasets from refine.bio via command line, python, and R.” We then create a GitHub issue for our annual goals with a checkbox that links to the tickets hosting the underlying discussions.

In December, we shared highlights from last year. In this post, we’re sharing our some of our 2020 goals. Most of our goals relate to our central mission around data availability and knowledge dissemination. For example, here are four of our goals related to these areas.

This goal is centered on making it easier to use refine.bio data in concert with existing, public resources with pediatric cancer data.

Create quantile normalized childhood cancer datasets using the refine.bio QN target to make it easy for investigators to compare pediatric cancer data with refine.bio data.

This goal is centered on making the data in refine.bio easier to use in computational workflows.

Make API package available for users to download datasets from refine.bio via command line, python, and R.

  • R Package
  • Python Package
  • Command Line Package  

This goal is centered on building capacity for reproducible -omics analyses across the pediatric cancer field, and builds on workshops that we hosted in 2019 in Houston, Chicago, the Bay Area, and Philadelphia.

Host 4 training workshops

  • Develop new chromatin material and deliver it at least twice
  • Revise scRNA-seq module to reflect new-in-2019 literature and best practices
  • Train >= 60 researchers  

This goal is centered on fostering a community of practice in pediatric cancer data science that can support ongoing skills development outside of the setting of a workshop.

  • 15% of Participants in 2020 workshops remain "Active" (logged in on at least one device) in Cancer Data Science Slack in Dec 2020  

In total, we have roughly thirty goals. Some are larger and others are smaller. Hopefully this gives insight into how we identify and prioritize our goals to ensure that we continue making meaningful contributions to our core mission.

Our overarching mission is to empower pediatric cancer researchers poised for the next big discoveries with the methods, data, and knowledge to reach them. This mission is very broad-scale and translating from this to annual goals is important to making meaningful contributions. Our particular process is designed to source opportunities from our team members and external stakeholders, convert those opportunities into a set of potential goals, and then select the goals that we expect will most advance our mission.

We have a ticket system (we use GitHub issues, but any such system could work in principle) that members of the team can use throughout the year to identify opportunities in one of our core areas (methods, data, and knowledge) as well as opportunities to build capabilities within the team. In November, we put out a reminder for folks on the team to get these opportunities into our ticket system. We also engage with stakeholders who are external to the Childhood Cancer Data Lab (CCDL) team. This includes other units within Alex’s Lemonade Stand Foundation as well as those working on complementary efforts in pediatric cancer research (e.g., the UCSC Treehouse team).

We take the opportunities that are identified and write them on post-it notes which we post in a conference room. We provide team members with dot stickers in four colors (one each for methods, data, knowledge, and team capabilities) and ask the team to score opportunities by the extent to which they would advance our mission. This provides us with a rough assessment of the perceived importance of each potential effort. For example, we can infer from the voting on the post-it note for “R package for refine.bio” that many members of the team felt that realizing this opportunity would advance multiple missions (Figure 1). We filter out opportunities with the goal of triaging those that are not central to our mission, those that are too diffuse to convert to goals, and those that are already being addressed elsewhere.

Example of how the Data Lab uses post-it notes for goal setting
Figure 1

For the next stage, we need to convert selected opportunities to goals. In research, we would say that a good hypothesis is one that is falsifiable. We have a similar mantra for our goals: an ideal annual goal is one that can be accomplished (or not). We ask one or more members of the team to determine what a meaningful goal or goals would be towards the identified opportunity. Discussion occurs within GitHub issues. The aim of each discussion is to end with the goal. In the case of the R package for refine.bio, the ticket got converted to the goal: “Make API package available for users to download datasets from refine.bio via command line, python, and R.” We then create a GitHub issue for our annual goals with a checkbox that links to the tickets hosting the underlying discussions.

In December, we shared highlights from last year. In this post, we’re sharing our some of our 2020 goals. Most of our goals relate to our central mission around data availability and knowledge dissemination. For example, here are four of our goals related to these areas.

This goal is centered on making it easier to use refine.bio data in concert with existing, public resources with pediatric cancer data.

Create quantile normalized childhood cancer datasets using the refine.bio QN target to make it easy for investigators to compare pediatric cancer data with refine.bio data.

This goal is centered on making the data in refine.bio easier to use in computational workflows.

Make API package available for users to download datasets from refine.bio via command line, python, and R.

  • R Package
  • Python Package
  • Command Line Package  

This goal is centered on building capacity for reproducible -omics analyses across the pediatric cancer field, and builds on workshops that we hosted in 2019 in Houston, Chicago, the Bay Area, and Philadelphia.

Host 4 training workshops

  • Develop new chromatin material and deliver it at least twice
  • Revise scRNA-seq module to reflect new-in-2019 literature and best practices
  • Train >= 60 researchers  

This goal is centered on fostering a community of practice in pediatric cancer data science that can support ongoing skills development outside of the setting of a workshop.

  • 15% of Participants in 2020 workshops remain "Active" (logged in on at least one device) in Cancer Data Science Slack in Dec 2020  

In total, we have roughly thirty goals. Some are larger and others are smaller. Hopefully this gives insight into how we identify and prioritize our goals to ensure that we continue making meaningful contributions to our core mission.

Back To Blog