Data Inventories & Project Management 101
While there are many ways of evaluating whether data are useful, visualization is one of the quicker methods. That visualization can also be useful at virtually all other stages of your project is a happy accident of which you can take advantage. After discussing data visualization, we’ll introduce some tenets of how you can manage the many tasks that make up your goal of ‘doing this project.’ This will help you more precisely handle and delegate tasks going forward.
Making a Data Inventory
As you find data, it will become necessary to–somehow–record what data your group has on hand. A data inventory is an extremely useful tool in this effort! A data inventory can take many forms but at its simplest, it should be a table where each dataset you find gets one row and critical information about that dataset is recorded in each column. It can be difficult to know which columns to include but keep in mind that it is always easiest to record information that you don’t wind up using than it is to go hunt through all of your datasets for some piece of information you didn’t record in the first place.
We’ve grouped some recommendations for useful data inventory components below into some loose categories but these are guidelines and your team likely will come up with useful additions–or may not need some of these pieces of information!
First and foremost, you’ll want to record where you found the data and where you can find it again if needed.
- What is the URL to the data?
- When did you last download or otherwise receive the data?
- Who owns the data (either a person or an institution)?
- What is the contact info for the data owner?
- What is the name of the data file when you originally download it?
- Is there a paper or report that may provide context that isn’t included in the official metadata? If so, what’s the link/citation info for that document?
- Sometimes a peer-reviewed paper’s Methods section might include valuable context that isn’t actually included in the data!
- If the data have a license, how are you allowed to use the data? How must you cite the data owner/data maintainer?
Sometimes a data inventory can be a useful source of explanatory variables for your analysis!
- What are the coordinates (i.e., latitude & longitude) of the data?
- Remember to enter these in decimal degrees if you want to use them in analysis!
- What country/state/county/locality is the data from?
- Include all of these that might vary across your datasets!
- What habitat or ecosystem is the data from?
- What’s the focal taxonomic group?
- What’s the taxonomic granularity?
- I.e., is there information about different species or only at a higher level?
- Are the data experimental or observational?
- If experimental, what treatments were applied? When? How frequently?
- What is the time range of the data?
- What is the temporal granularity of the data?
- Relatedly, what are the first and last years/time points in the data?
- How big was the study area/experimental plot?
If you start relying on a data inventory, it can become a valuable place to assign or delegate tasks within your group. This can also be helpful at the end of the project when you’re trying to remember who deserves credit for finding data/metadata.
- Who–in your group–is responsible for filling out the other columns in the data?
- I.e., whose ‘homework’ is it to flesh out this row of the data inventory?
- Who is responsible for double-checking the metadata entered by the first team member?
- Who is the primary point of contact with the original data owner/maintainer?
One critical use of a data inventory that is often overlooked: are the data useful to your question?
If you found the data before, your later data searches will likely also turn it up even if it’s not useful! You can save other group members a headache by noting not applicable data so they can disregard it from their searches.
If a particular dataset is not useful, why is it not useful? If your questions or in/exclusion criteria evolve, maybe it will become useful later!
Data Inventory Value
Documenting potential datasets (and their metadata) thoroughly in a data inventory provides numerous benefits! These include:
- A well-documented data inventory makes it easier for researchers to find and access specific data for reproducible research
- Documentation will help researchers to quickly understand the context, scope, and limitations of the data, reducing the time spent on preliminary data assessment
- Detailed documentation will speed up the data publication process (e.g., data provenance, difference among methods)
What is Project Management?
There are dozens of formal project management frameworks/approaches, most of which come out of industries (such as software development, construction, and manufacturing) that require teams of people to work together with efficiency and accountability. You’ve probably heard the names tossed around: Agile, Scrum, Lean Sigma Six, Kanban, etc. There are whole industries devoted to training project managers and developing apps to support them.
The trick, for scientific research projects, is to identify the approaches that also allow questions and goals to evolve along the way – and that don’t require a full-time project manager to implement.
Some common elements of project management schemes include:
| Industry Outcome | Science Outcome |
|---|---|
| Ensure needed resources/people tools are available when needed | Know which skills are needed when; know what form the output of each script/analysis needs to take |
| Industry Outcome | Science Outcome |
|---|---|
| Avoid supply chain lags | Know what data you need and when you need them |
| Industry Outcome | Science Outcome |
|---|---|
| Document productivity and responsibility | Avoid duplication of effort; keep the project moving; support authorship/credit agreements |
| Industry Outcome | Science Outcome |
|---|---|
| Know when to abort or revise project goals | Assess threats to project success early and make a plan B |
How to Start
Like outlining a paper, there are a few basic steps to developing a solid project management plan. And it’s easy to think you can skip over the first steps because they seem so obvious. The trouble is, they may be obvious to each team member in different ways. Taking the time to make sure you all see them the same way can save worlds of headache down the road.
- Define the project
- Agree on the goal and the timeline
- If the timeline is determined externally (as for SSECR or for most grants), you’ll need to start with the timeline (possibly budget) and scope the project to fit the time and funds you have available.
- Develop a list of the steps needed to get from where you are to where you need to be.
- Place the steps in order and define the inputs and outputs of each step.
- Identify the people responsible for each step and an approximate timeline for its completion.
- Identify time points for check-in and re-evaluation
Honestly, you could do all of this in a spreadsheet, but there are a few types of visualizations that a lot of people find helpful, which we’ll discuss later today. First, though, we’ll remind ourselves of why the investment in planning time is so valuable.
Setting the Project Scope
For defining the project and agreeing on the goals, mind maps are useful individual or group exercises. Mind maps offer a lightly-structured way of exploring a focus area. The graphical approach helps to surface unexpected connections and reveal gaps in understanding. Tools for developing them can be as simple as pen and paper, basic drawing apps, a zoom whiteboard, or online collaboration and project planning tools such as Miro, Mural, and FigJam.
Start with a general topic or question, then expand to the precedents and implications. Then expand from there to the assumptions, modifying conditions, or follow-on implications related to each of your precedents or implications. Often, looking at a problem head-on reveals little new, but surfacing and testing the underlying assumptions can open up fresh territory.
The mind map will likely surface many relevant and un-answered questions, but time is limited. You’ll need to choose one on which to focus. One way to prioritize them is to place them on a two-by-two matrix of payoff v. feasibility. The most important payoff for a particular team may be scientific novelty, practical importance, or whether it’s a fun and engaging question for the group. Often, the most interesting questions will also require the most effort and the group will need to determine what trade-offs they are willing to accept. But sometimes, a question emerges that is both interesting and feasible–perhaps because a new source of data has just become available or simply because no one saw the question from that angle before.
