Differences

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--- sift:documentation:knowledge_discovery_for_biomechanical_data [2024/08/28 18:40] – [Performing Analysis] Fixed typos. wikisysop
+++ sift:documentation:knowledge_discovery_for_biomechanical_data [2024/08/29 15:34] (current) – sgranger
@@ Line 3: / Line 3: @@
 Sift is designed to be a tool that helps you, the user, discover useful knowledge from your data. This process of knowledge discovery is iterative, requiring users to collect, clean, and shape their data before performing analysis and then communicating their results. Each of these steps requires experience to be done well, the aim of this article is to outline the goal of each step, how Sift lets you accomplish these goals, and to point you on to additional resources.
-==== Gather Data ====
+===== Gather Data =====
 The first step in learning from your data is bring it all together for analysis. Challenges can arise here if the data you are interested in has been collected by different researchers in different locations over many years. These challenges can be legal in nature - whether adequate permission was given by participants during data collection - or technical - how to transfer that many 1s and 0s. No matter how complex your study is, questions about how data is collected, how meta-data and data are linked, how data is shared or centralized, and how data is protected should always be answered before you start collecting.
@@ Line 13: / Line 13: @@
 Complete the [[Sift:Tutorials:Load_and_View_Data|tutorial]] for loading and viewing your data to see how you can get your data into Sift and ready for analysis.
-==== Cleaning Data ====
+===== Cleaning Data =====
 The second step in learning from your data is to clean your data. If we're honest with ourselves, no dataset from the real-world is perfect. Sometimes sensors fail, gait events are incorrectly identified, or something else just goes wrong. That's why it's important to clean your data and confirm that every piece of data that you put into analysis is a piece of data that you trust. This is also a chance for you to make sure that you have all of the data you expect. If you're missing something, then it's time to go back and make sure it gets collected.
@@ Line 23: / Line 23: @@
 Complete the [[Sift:Tutorials:Clean_your_Data|tutorial]] for cleaning your data to learn more about these processes.
-==== Shaping Data ====
+===== Shaping Data =====
 Now that you have a clean dataset in front of you, it's time to start analyzing the data! But wait, because a single study can contain multiple questions and each question might be concerned with a different portion of your dataset. Before we can jump into analysis, we have to shape our data to make sure that we are getting the right "view" into our data to answer the question we have in mind.
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 Beyond deciding which traces should be assigned to which group, we can also shape our data by deciding if and how these traces should be transformed. Sift's default behaviour is to time-normalize queried traces to 101 points, but users can also register their traces to intermediate events and points of interest using Sift's [[sift:curve_registration:curve_registration_for_biomechanical_waveforms|curve registration]] feature.
-==== Analysing Data ====
+===== Analysing Data =====
 Having queried and shaped your clean dataset to get exactly the traces and metrics that you wanted, now you're finally ready to start analyzing. We learn a lot about analytical techniques in our courses and throughout our formal training, but it's only one of the five steps here. Even though this is what we often think of as the difficult work of research, you've already put in a lot of effort to get through the first three steps and get to this point!
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 Complete the [[Sift:Tutorials:Perform_Principal_Component_Analysis|tutorial]] for performing PCA and the [[Sift:Tutorials:Perform_Statistical_Parametric_Mapping|tutorial]] for performing SPM to see some of the different ways you can analyze your data.
-==== Communicating Results ====
+===== Communicating Results =====
-Once you analysis has produced results, your last step is to communicate your findings to the wider world, Whether you're presenting to a collaborator, talking at a conference, or producing publication-ready figures, there is a lot to think about when communicating your results.
+Once you have some analysis results, your last step is to communicate your findings to the wider world, Whether you're sharing with a collaborator, talking at a conference, or producing publication-ready figures, there is a lot to think about when communicating your results.
 Sift's different visualization tools all let you control over the colours, line styles, and axis labels used to allow you to produce the figures you want. If more work is required, then you can export your analysis results to a number of different text formats including Visual3D ASCII, P2D, and SPSS.