# data[].attributes.body
Patterns in data - the distribution, potential differences etc.

# data[].attributes.headline
Determining the underlying concepts for analysis

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Determining the underlying concepts for analysis

# data[].attributes.body
Understanding what fundamental questions to ask of the data, and then finding tools and methods to answer these questions

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They recall the same kind of thing that has happened before when troubleshooting a fault with equipment.

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They recall the same kind of thing that has happened before when troubleshooting a fault with equipment.

# data[].attributes.body
Novices can fail to see how things fit together (eg in cell signalling, the inter-connectiveness of the different pathways (but that are typically taught separately))

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Quality of scientific evidence. Have the data been collected in a robust manner, analysed appropriately and been interpreted accurately

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relationship with students and their motivation for the class

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relationship with students and their motivation for the class

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Incorporate scientific evidence in to their writing

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Break down a question into analysis steps

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Break down a question into analysis steps

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Working out what tools to apply, in what order and with what specific parameters, to find the answer to the required question

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Visualise and interpret data

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Ability to recognise specific signs in diagnosis or ability to bring lots of information together to reach a consensus decision

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Conduct a robust scientific experiment

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They know how best to adapt a scientific protocol to a new organism.

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They know how best to adapt a scientific protocol to a new organism.

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recognise common molecular structures and relate them to functions

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recognise common molecular structures and relate them to functions

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Microscopy - especially knowing what to look for, and what signs are not good (e.g. infection in cells)

# data[].attributes.body
Students in all years fail to make connections between taught theory (e.g. mechanistic discussions/outcomes) & the reactions they are carrying out - this then impacts their ability to think independently.

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They know what experimental controls are necessary.

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They know what experimental controls are necessary.

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Novices can find it hard to apply tools and analyses to a different context (question, data, etc.) to the one within which they first encountered/learned the tool

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perhaps a side point, but in general the ingrained idea in academia that "everyone can teach".- I.e. no perception that being good/ an expert at teaching is a thing!! I think this is a misconception/ pattern from academia (across different disciplines).

# data[].attributes.subject
perhaps a side point, but in general the ingrained idea in academia that "everyone can teach".- I.e. no perception that being good/ an expert at teaching is a thing!! I think this is a misconception/ pattern from academia (across different disciplines).

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They are better at recognising artefacts of preparation in a microscope image.

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They are better at recognising artefacts of preparation in a microscope image.

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The ability to take a step back and see the bigger picture 

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The ability to take a step back and see the bigger picture 

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They can link cause and effect more easily in science experiments.

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They can link cause and effect more easily in science experiments.

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They better understand the limitations of the experiments.

# data[].attributes.subject
They better understand the limitations of the experiments.