Observation Instance – Django REST framework

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/43733/?format=api

HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "ob_id": 43733,
    "uuid": "4cbd9c53ab07497ba42de5043d1f414b",
    "title": "Synthetic Hourly Air Pollution Prediction Averages for England (SynthHAPPE)",
    "abstract": "This dataset contains synthetic estimates of ambient air pollution concentrations across England, provided as hourly averages representing typical conditions. The data cover major pollutants, including Nitrogen Dioxide (NO2), Nitric Oxide (NO), Nitrogen Oxides (NOx), Ozone (O3), Particulate Matter smaller than 10 micrometres (PM10) and smaller than 2.5 micrometres (PM2.5), and Sulphur Dioxide (SO2). Each pollutant's concentrations are predicted not only as average (mean) values but also include estimates at lower (5th percentile), median (50th percentile), and upper (95th percentile) levels to highlight typical and potential extreme pollution scenarios.\r\n\r\nThe spatial coverage of the dataset includes the entire area of England, structured as an evenly spaced grid, with each grid square covering an area of 1 square kilometre (1 km^2). Data points correspond to the centre of these grid squares. Temporally, the dataset does not represent actual hourly measurements from specific dates; instead, it provides aggregated \"typical day\" profiles constructed by averaging observations collected from multiple years (2014-2018) for each month, weekday, and hour. This method offers representative insights into typical air pollution patterns, avoiding the complexity of handling large-scale raw datasets.\r\n\r\nThese pollution estimates were produced using a supervised machine learning method, which is a computational approach where algorithms are trained to identify patterns in historical data and apply these learned patterns to predict new data points. The predictions incorporated various environmental factors including weather conditions (e.g., temperature, wind, precipitation), human activities (traffic patterns), satellite measurements, land-use types (urban, rural, industrial areas), and emission inventories (datasets detailing pollutants released into the atmosphere). Additionally, the dataset provides uncertainty intervals through percentile-based estimates, giving users insights into the reliability of the predictions.\r\n\r\nThe dataset was developed to facilitate easier access to high-quality air pollution information for diverse stakeholders, such as researchers, policymakers, urban planners, and health professionals. By providing clear, simplified air quality scenarios, it helps users make informed decisions in urban planning, public health, environmental management, and policy development, as well as to assess potential impacts and interventions related to air pollution.\r\n\r\nThe dataset was created by Liam J. Berrisford at the University of Exeter during his PhD studies, supported by the UK Research and Innovation (UKRI) Centre for Doctoral Training in Environmental Intelligence. Full methodological details and data validation information are available in the associated open-access scientific publication. For more information about the data, see the README.md archived alongside this dataset.\r\n\r\nIn terms of completeness, this dataset intentionally provides representative hourly pollution estimates rather than exact historical measurements or specific pollution events. While it extensively covers typical pollution scenarios across England, direct measurements from specific air quality monitoring stations are not included. Users requiring detailed historical observations or data about specific events should refer to original monitoring station datasets.",
    "keywords": "Ambient Air Quality,England,Nitrogen Dioxide,Nitrogen Oxides,Ozone,Particulate Matter,Sulphur Dioxide,Machine Learning,Air Pollution Scenarios,AI",
    "publicationState": "citable",
    "dataPublishedTime": "2025-04-09T14:47:19",
    "doiPublishedTime": "2025-04-09T14:47:36",
    "updateFrequency": "notPlanned",
    "status": "completed",
    "result_field": {
        "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/43888/?format=api",
        "dataPath": "/badc/deposited2025/SynthHAPPE",
        "oldDataPath": [],
        "storageLocation": "internal",
        "storageStatus": "online",
        "volume": 296159688875,
        "numberOfFiles": 4034,
        "fileFormat": "NetCDF"
    },
    "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/12206/?format=api",
    "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/4726/?format=api",
    "nonGeographicFlag": false,
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    ],
    "dataLineage": "This dataset was produced through a research initiative at the University of Exeter. It was created using a supervised machine learning model-a type of artificial intelligence that learns patterns from existing data and applies those patterns to predict unknown values. Historical data from ambient air pollution monitoring stations across England were used to train the machine learning model. Input data included meteorological factors (such as wind speed and temperature), traffic activity levels, land-use information, satellite-based measurements, and emissions data. After the initial creation of hourly air pollution predictions, these data were aggregated to generate representative \"typical day\" hourly averages for each month and weekday. Predictions were validated for accuracy and reliability, with uncertainty intervals (5th, 50th, and 95th percentiles) calculated to indicate confidence in the estimates.",
    "removedDataTime": null,
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    "language": "English",
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    ],
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    ],
    "observationcollection_set": [],
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