GET

Observation Instance

Get a list of Observation objects.

### Available end points:

- `/observations/` - Will list all Results in the database
- `/observations.json` - Will return all Results in json format. This can
also be achieved by using the accept header. `application/json`
- `/observations/<object_id>/` - Returns Results object with that id

### Available Methods:

- `GET`
- `HEAD`

### Available filters:

- `title`
- `uuid`
- `keywords`
- `status`
- `result_field`
- `discoveryKeywords`
- `updateFrequency`
- `nonGeographicFlag`
- `publicationState`
- `permissions`

### How to use filters:

These filters can be used like django query filters using __ for related model relationships.

- `/observations/?uuid=d594d53df2612bbd89c2e0e770b5c1a0`
- `/observations/?status=completed`
- `/observations/?results_field__dataPath__startswith=/neodc/esacci`
- `/observations/?discoveryKeywords__name=ESACCI`
- `/observations/?nonGeographicFlag=True`

Filters can be stacked to build an 'AND' relationship. E.g.

- `/observations/?publicationState__in=published,citable&nonGeographicFlag=True`
- `/observations/?publicationState__in=published,citable&discoveryKeyword__name=NDGO0003`

GET /api/v2/observations/26024/?format=api
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "ob_id": 26024,
    "uuid": "e58fdade3a6c46bbaae7c53e948dd6d0",
    "title": "Arctic Cloud Summer Expedition (ACSE): composite flux data for Icebreaker Oden",
    "abstract": "This dataset contains provides the final best estimates of fluxes, mean environmental variables and derived transfer coefficient estimates, along with asociated quality control flags, during the Icebreaker Oden voyage durning the Arctic Cloud Summer Expedition (ACSE) in summer 2014. These were calculated based on instrumentation data from the University of Leeds' Metek sonic anemometer, Licor LI-7500 gas analyzer and XSENS MTi-G-700 motion pack, plus mean surface meteorology data provided from the automatic weather station operated on board by the Department of Meteorology, Stockholm University (MISU).\r\n\r\nOther data from the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements.\r\n\r\nThe document \"ACSE_turbulent_fluxes_readme.txt\" in the archive contains fuller details of the flux calculations. The final data, prepared for archiving as NetCDF data at the Centre for Environmental Data Analysis (CEDA) by Ian Brooks, University of Leeds, contain:\r\n\r\n1) The final quality controlled best estimates of 20-min averaged dynamic fluxes, associated mean environmental variables (10m wind, etc), transfer coefficients, and quality control flags.\r\n\r\n2) The raw kinematic fluxes, etc that go into generating (1), along with the quality control variables used in generating the QC flags, and the QC flags.\r\n\r\n3) Other environmental variables (in some cases with duplicates from multiple different sensors) averaged onto the same time base as the flux estimates.\r\n\r\nThe authors note that in all cases a lot of work has been done on quality control and applying suitable corrections to raw measurements. In many cases other choices could have been made, and additional QC measures may need to be applied.\r\n\r\nMost of the work on the flux data processing has been done by John Prytherch, with additional input from Ian Brooks and Dominic Salisbury. Additional work on ancillary data was undertaken by other members of the ACSE science team.",
    "keywords": "turbulent fluxes, meteorology, winds, wind speed",
    "publicationState": "citable",
    "dataPublishedTime": "2018-04-14T12:59:34",
    "doiPublishedTime": null,
    "updateFrequency": "notPlanned",
    "status": "final",
    "result_field": {
        "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/26025/?format=api",
        "dataPath": "/badc/acse/data/composite-flux-data",
        "oldDataPath": [],
        "storageLocation": "internal",
        "storageStatus": "online",
        "volume": 4462682,
        "numberOfFiles": 3,
        "fileFormat": "Data are CF-compliant NetCDF formatted"
    },
    "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/7026/?format=api",
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    "nonGeographicFlag": false,
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    "dataLineage": "Data were collected, quality controlled and prepared for archiving by the instrument scientists before upload to the Centre for Environmental Data Analysis (CEDA) for long term archiving.",
    "removedDataTime": null,
    "removedDataReason": "",
    "language": "English",
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}