Can you please clarify what you mean with "sample resolution"? Do you mean some kind of uncertainty? Or do you mean that data is available at a specific interval?
To clarify what I meant by sample resolution: I'm referring to the number of samples taken over a given period of time. E.g. a hydrologist monitors river chemistry between 2 Jan 2022 to 22 Jan 2022 (that would be the temporal coverage input into the calendar field). But the other interesting piece of information is how often they took a sample, i.e. the sample resolution. E.g., one sample a day, 1 sample an hour or 1 sample every 10 minutes. It's also useful information for people searching for publications/data.
In geology, an example could be an ice core, which dates to 100 000 years ago at the bottom of the core, and 10 000 years ago at the top of the core. The scientists took a sample of ice every 1 cm, which represents 1000 years of ice formation. So in the published paper, their data covers 100 000 to 10 000 years ago (often written as 100–10 ka) at a sample resolution of 1000 years.
Does that make sense? Perhaps it is irrelevant for this plugin, at least at this stage. But maybe something to think about in the future?
Yes, that absolutely makes sense! I understand how this can be an interesting piece of information for a reader.
I would probable call this sampling interval or sampling period... the OGC standards probably have a well though out and complex naming scheme for this.
Are you aware of any data repositories who already collect/provide this kind of metadata? It would be great to follow an established approach here. I've sketched out some top-of-my-mind steps below how this could be approached. I cannot make any promises as to when this feature might be worked on. I do think it's an interesting (BSc) thesis topic though: research options for data modeling and visualisation, implement, evaluate with small group of experts.
Collect a reasonable size of example papers (20-50?) with different kinds of sample resolution
Do some research about data repositories who might support something like this (Pangaea?)
Involve @CathJex for requirements gathering
How can the sample resolution be exposed in a machine readable way (using a standard?)
How can the sample resolution be visualised?
How can the sample resolution be integrated into searching for articles?
From #121 by @CathJex:
To clarify what I meant by sample resolution: I'm referring to the number of samples taken over a given period of time. E.g. a hydrologist monitors river chemistry between 2 Jan 2022 to 22 Jan 2022 (that would be the temporal coverage input into the calendar field). But the other interesting piece of information is how often they took a sample, i.e. the sample resolution. E.g., one sample a day, 1 sample an hour or 1 sample every 10 minutes. It's also useful information for people searching for publications/data.
In geology, an example could be an ice core, which dates to 100 000 years ago at the bottom of the core, and 10 000 years ago at the top of the core. The scientists took a sample of ice every 1 cm, which represents 1000 years of ice formation. So in the published paper, their data covers 100 000 to 10 000 years ago (often written as 100–10 ka) at a sample resolution of 1000 years.
Does that make sense? Perhaps it is irrelevant for this plugin, at least at this stage. But maybe something to think about in the future?
Yes, that absolutely makes sense! I understand how this can be an interesting piece of information for a reader.
I would probable call this sampling interval or sampling period... the OGC standards probably have a well though out and complex naming scheme for this.
Are you aware of any data repositories who already collect/provide this kind of metadata? It would be great to follow an established approach here. I've sketched out some top-of-my-mind steps below how this could be approached. I cannot make any promises as to when this feature might be worked on. I do think it's an interesting (BSc) thesis topic though: research options for data modeling and visualisation, implement, evaluate with small group of experts.