Understanding IDS Design
The IDS captures as much available information from the RAW files as possible, such as:
- time - Time that the data set is related to
- system(s) - Equipment used to produce the result, software, and firmware
- user(s) - Person who performed the experiment
- sample(s) - Sample used in the experiment
- method(s) - Experimental recipe and input parameters
- run(s) - A particular execution of the experiment
- experiment(s) - Information about the experiment, id, name, and so on
- result(s) - Measurement results
- related_file(s) - Pointer(s) to related files (for example, raw experimental data sets in the vendor specific, and often proprietary, format)
- datacubes - Multi-dimensional data such as chromatogram, images, plate readings, and so on
IDS JSON Example
An example of an IDS JSON is below:
-
Three pieces of information are used to uniquely identify the IDS:
"@idsType": "cell-counter",
"@idsVersion": "v1.0.0",
"@idsNamespace": "common, -
Items such as the run, time, sample, method, user, and results are also included.
{
"@idsType": "cell-counter",
"@idsVersion": "v1.0.0",
"@idsNamespace": "common",
"system": {
"serial_number": "serial_number"
},
"run": {
"id": "413befdd-c7e2-4edd-9e9b-06cf1cb0283f"
},
"time": {
"measurement": "2015-09-24T03:47:13.0Z"
},
"sample": {
"id": "unknown-10",
"batch": {
"id": "batch-number"
}
},
"method": {
"instrument": {
"cell_type": "CHO",
"dilution_factor": 1
}
},
"user": {
"name": "operator-1"
},
"result": {
"cell": {
"viability": {
"value": 0.1,
"unit": "Percent"
},
"diameter": {
"average": {
"live": {
"value": 21.07,
"unit": "Micrometer"
}
}
},
"count": {
"total": {
"value": 1207,
"unit": "Cell"
},
"viable": {
"value": 1,
"unit": "Cell"
}
},
"density": {
"total": {
"value": 102.24,
"unit": "MillionCellsPerMilliliter"
},
"viable": {
"value": 0.1,
"unit": "MillionCellsPerMilliliter"
}
}
}
}
}
Updated 11 months ago