{"action":"created","issue":{"url":"https://api.github.com/repos/aodn/imos-toolbox/issues/516","repository_url":"https://api.github.com/repos/aodn/imos-toolbox","labels_url":"https://api.github.com/repos/aodn/imos-toolbox/issues/516/labels{/name}","comments_url":"https://api.github.com/repos/aodn/imos-toolbox/issues/516/comments","events_url":"https://api.github.com/repos/aodn/imos-toolbox/issues/516/events","html_url":"https://github.com/aodn/imos-toolbox/issues/516","id":359346156,"node_id":"MDU6SXNzdWUzNTkzNDYxNTY=","number":516,"title":"QC for uncalibrated sensor to be flagged with QCflag3","user":{"login":"windyana","id":11793450,"node_id":"MDQ6VXNlcjExNzkzNDUw","avatar_url":"https://avatars2.githubusercontent.com/u/11793450?v=4","gravatar_id":"","url":"https://api.github.com/users/windyana","html_url":"https://github.com/windyana","followers_url":"https://api.github.com/users/windyana/followers","following_url":"https://api.github.com/users/windyana/following{/other_user}","gists_url":"https://api.github.com/users/windyana/gists{/gist_id}","starred_url":"https://api.github.com/users/windyana/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/windyana/subscriptions","organizations_url":"https://api.github.com/users/windyana/orgs","repos_url":"https://api.github.com/users/windyana/repos","events_url":"https://api.github.com/users/windyana/events{/privacy}","received_events_url":"https://api.github.com/users/windyana/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2018-09-12T07:18:19Z","updated_at":"2018-09-13T02:41:52Z","closed_at":null,"author_association":"NONE","body":"Hi @ggalibert \r\nAny possibility of being able to have an option somewhere or a post-processing routine that would flag all the data for a given instrument/sensor that were not calibrated (so for instance, following the automatic QC routine, flagging all the data points that are 'good' between the in/out of water times of a time series, and changing them to being as potentially correctable due to lack of calibration -  then,  being later on able to change this back to 'good' data after calibration?) Maybe having a drop-down option button to select for eg;\r\n1) 'uncalibrated instrument or sensor' ;  which should then automatically flag all good data that are between the in/out of water times or between the times of first and last good data.\r\n2) 'apply calibration' ;  which would then ask to enter the calibration formula', then correct the data and flag all the previously 'potentially correctable' data points as good data.\r\n\r\nFor instance, I have gone through Manual QC for the temperature data of the RDI which needed to be flagged as 'potentially correctable data' (flag#3) - since the temperature sensor of this RDI was not yet calibrated. Following the QC automatic routine, I found it is not easy to select all the data that needs to be manually QC/flagged without having to be careful not to select the already QC data (e.g. bad data from in/out of water test), especially at the start and end of the deployment. I have to zoom in, perfom the manual QC, then zoom out, etc.. all along that time series, which takes time... so just some thoughts here for improvements.\r\n\r\nCheers,\r\nV"},"comment":{"url":"https://api.github.com/repos/aodn/imos-toolbox/issues/comments/420864405","html_url":"https://github.com/aodn/imos-toolbox/issues/516#issuecomment-420864405","issue_url":"https://api.github.com/repos/aodn/imos-toolbox/issues/516","id":420864405,"node_id":"MDEyOklzc3VlQ29tbWVudDQyMDg2NDQwNQ==","user":{"login":"ocehugo","id":1202691,"node_id":"MDQ6VXNlcjEyMDI2OTE=","avatar_url":"https://avatars1.githubusercontent.com/u/1202691?v=4","gravatar_id":"","url":"https://api.github.com/users/ocehugo","html_url":"https://github.com/ocehugo","followers_url":"https://api.github.com/users/ocehugo/followers","following_url":"https://api.github.com/users/ocehugo/following{/other_user}","gists_url":"https://api.github.com/users/ocehugo/gists{/gist_id}","starred_url":"https://api.github.com/users/ocehugo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ocehugo/subscriptions","organizations_url":"https://api.github.com/users/ocehugo/orgs","repos_url":"https://api.github.com/users/ocehugo/repos","events_url":"https://api.github.com/users/ocehugo/events{/privacy}","received_events_url":"https://api.github.com/users/ocehugo/received_events","type":"User","site_admin":false},"created_at":"2018-09-13T02:41:52Z","updated_at":"2018-09-13T02:41:52Z","author_association":"NONE","body":"As a user of ADCP temperature variables:\r\n\r\n1. Shouldn't the manufacture uncertainty be added as an attribute (or as variable if applicable) to all ANMN (and AODN) data?\r\n\r\nFinal users would be happy to avoid digging that info in a per sensor/instrument basis. \r\nAlso, they can take this into account in their analysis if wanted.\r\n\r\nAlthough the end variable uncertainty for a lot of instruments is less than the actual instrument one (say variable are burst average in the instrument itself), some info regarding the uncertainty is *VERY* useful.\r\n\r\nMaybe something like \"nominal_uncertainty\" attribute, or even like precision and/or accuracy would be useful.\r\n\r\nStepping outside of my fence:\r\n\r\nI wonder, shouldn't the ADCP temperature sensors be also calibrated !? Although the uncertainty is higher compared to aqualoggers (Nominal 0.05C vs say 0.4C), time averaging reduces the uncertainty a lot (say hourly or daily values). In those scales, this uncertainty difference is far surpassed. Even more, if we considering adcps are lying at the bottom most of the time (T variance is much lower than at mid-surface).\r\nFinally, I think they are way more reliable (broken less/less problematic) than dealing with aqualoggers for example.\r\nSo I don't see why we are trowing out a change of providing a better quality temperature logger closer to the bottom in all moorings by not calibrating them.\r\n"}}
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