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/44622/?format=api
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "ob_id": 44622,
    "uuid": "7f91b1326a324caa9e436b8fdef4a0d8",
    "title": "Machine Learning for Hourly Air Pollution Prediction – Global (ML-HAPPG)",
    "abstract": "This dataset contains estimates of air pollution levels across the globe for every hour of the year 2022. It covers five major air pollutants that can affect human health and the environment. The data cover major air pollutants, including Nitrogen Dioxide (NO2), Ozone (O3), Particulate Matter smaller than 10 micrometres (PM10) and smaller than 2.5 micrometres (PM2.5), and Sulphur Dioxide (SO2). Each air 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. The spatial coverage of the dataset includes the entire globe, structured as an evenly spaced grid, with each grid square covering an area of 0.25 degrees (0.25 degrees x 0.25 degrees). Data points correspond to the centre of these grid squares. There is also training data used for the model from real-world monitoring stations.",
    "keywords": "Ambient Air Quality,Global,Nitrogen Dioxide,Ozone,Particulate Matter,Sulphur Dioxide,Machine Learning,AI",
    "publicationState": "citable",
    "dataPublishedTime": "2025-08-13T09:15:33",
    "doiPublishedTime": "2025-08-13T10:36:50.108412",
    "updateFrequency": "notPlanned",
    "status": "completed",
    "result_field": {
        "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/44770/?format=api",
        "dataPath": "/badc/deposited2025/ML-HAPPG/",
        "oldDataPath": [],
        "storageLocation": "internal",
        "storageStatus": "online",
        "volume": 383201486318,
        "numberOfFiles": 26397,
        "fileFormat": "NetCDF, txt, json"
    },
    "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/12543/?format=api",
    "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/4813/?format=api",
    "nonGeographicFlag": false,
    "phenomena": [
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/70270/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/70271/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84233/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84237/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84239/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84250/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84253/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84266/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84276/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84290/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84301/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84325/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84333/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84336/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84345/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84358/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84359/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84360/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84361/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84364/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84365/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84366/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84368/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84370/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84371/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84372/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84374/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84375/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84377/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84378/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84379/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84380/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84381/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84383/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84384/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84386/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86815/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86816/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86817/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86818/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86819/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86820/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86821/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86822/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86823/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86824/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86825/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86826/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86827/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86828/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86829/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86830/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86831/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86832/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86833/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86834/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86835/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86836/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86837/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86838/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86839/?format=api"
    ],
    "dataLineage": "These 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), satellite measurements, 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.",
    "removedDataTime": null,
    "removedDataReason": "",
    "language": "English",
    "identifier_set": [
        "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/13493/?format=api"
    ],
    "projects": [
        "https://api.catalogue.ceda.ac.uk/api/v2/projects/43886/?format=api"
    ],
    "observationcollection_set": [],
    "responsiblepartyinfo_set": [
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213647/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213648/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213649/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213650/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213651/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213652/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213653/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/214361/?format=api"
    ],
    "procedureAcquisition": null,
    "procedureCompositeProcess": null,
    "procedureComputation": "https://api.catalogue.ceda.ac.uk/api/v2/computations/44638/?format=api",
    "permissions": [
        {
            "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/2526/?format=api",
            "useLimitation": null,
            "accessConstraints": null,
            "accessCategory": "public",
            "accessRoles": null,
            "label": "public: None group",
            "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/",
            "licenceClassifications": "any"
        }
    ],
    "discoveryKeywords": []
}