Hey everyone!
I’ve got a simple idea that could save us a lot of time. When you are gathering data for research, like clinical info or extracting data from papers, it can get messy if you are not organized. So, here’s my question:
- What methods do you use for this?
- Any handy tips about using Excel templates or Redcap that you could pass on?
- How do you stay organized when collecting and analyzing info?
I’ve learned a bunch from @danieltds on this, so maybe he could share his experience and throw in some basic Excel tips too. Let’s chat and make our research process smoother!
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Hi, Paula!
Probably this is not what you asked for, but we have found a common problem in our lab is that we have many data entry points (we are a big team with many sites and students), so we try to ensure data to be collected as homogeneously as possible with three key points:
a) Training and having available resources for later consultation (manuals, master files with many links to each Excel template, lab manual, tutorials, etc)
b) Collaborating only on environments with change control (Drive, RedCap, etc)
c) Trying that making errors is as hard as possible (we force fields to be numeric, alphabetic, to be in certain range, certain length) and provide examples when possible.
These are very simple tips but may help someone!
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Sorry for the very late response, but here goes my comments. First, thank you for mentioning me, @psaffie
I’m a clinician who loves trying to make subjective data (complaints, symptoms, signs etc) more objective, so I always liked standardizing data. When I got into data science, this evolved further, and today I follow these general guidelines on collection and standardizing data:
- Have a clear notion of the type of information I want to access. Although obvious, I think this is a major component of a good data collection and identification process. Having the correct question always lead to the most accurate answers when searching for information on PubMed, Google and others. Knowing how to have the correct questions takes time, but can be easier if, before collecting data, you are sure you have sufficiently studied the topic of interest.
- Focus specifically on the type of information I want to obtain. When reading articles, we often find ourselves dreaming about a lot of ideas and cool references, however, we can not deviate for our main goal. We went for that article for a specific purpose and must remain true to this (most times)!
- Write down important information in draft documents. Got and ongoing but yet not finished project but found a very interesting article that would fit your introduction, methodology and/or discussion? Put this in a word or excel document and explain why. Do not think of any excuses (like: “I will remember that article when I’m writting!”) or you will miss it.
- Do not aim to become and expert in the field, instead, just find what you need. To give you an example, in some cases, I do not need to have specific knowledge on laboratory techniques that a type of data I’m using needs. I can just take the result I’m interested in and cite the main paper that explains it.
- **Use standardized questionnaires with examples. ** If you want to collect information on a more formal way to make comparisons, I suggest following this method. I have previously organized two systematic reviews and am doing so in my third. Instead of using Excel spreadsheets and Google Docs or other kind of document to be filled, I often use some forms-like structure like Google Forms and I dedicated a considerable amount of time creating variables whose answer needs only clicking and having a lot of different options. I always want to provide less custom written text as possible to my forms
- Have realistical time expectations. Data collection and standardization can be quite tiresome and it is essential to know realistically how much time we will have available for that effort. If not, we can become overwhelmed and stressed with the amount of work we find ourselves doing, and so abandoning it.
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