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/27705/?format=api
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "ob_id": 27705,
    "uuid": "891dc49a623c4d4a82c2ee17cd85d773",
    "title": "Qualitative Interviews with Women in Greater Manchester (UK) about their conceptualizations of air pollution and a city with clean air",
    "abstract": "This dataset contains semi-structure qualitative interview transcripts (n=30) with women in Greater Manchester, about how they conceptualize air pollution and envision a city with clean air. This data has been fully anonymized by the author. The data collection took place between 2018 and 2019. Interviews were designed to last approximately 1h. The questioning route received full ethical approval from Manchester Metropolitan University Academic Ethics Committee. Participants were recruited following a purposive sampling technique, assuring that the people being interviewed are relevant to the research question and that different segments of the population are interviewed. This was complemented with a snowball sampling technique to expand the sample size of relevant interviewees. Before the interviews started, the background of the researcher, the project, as well as the structure of the interviews were explained to the participants, and verbal consent was asked to proceed with the interview, to record, and to use the data in the form of papers or conference presentations, as well as to make the data publicly available. \r\n\r\nThe interviews were composed of two sections. The first part gathered data in relation to socio-demographic factors to make sure that women with different backgrounds were represented (i.e. age, ethnicity, nationality, number of children, occupation, co-habitation, responsibility for unpaid work, carbon footprint, and main form of transport). The second part contained questions about the present of air pollution and about the future of Greater Manchester and cleaner air. The questions were: (1) How do you feel when you hear the term air pollution? (2) How would you describe air pollution in your own words? (3) Which of your daily activities contribute to air pollution the most? (4) Is air pollution a concern for you? (5) Do you think Greater Manchester is polluted, and do you recognize any places as being more polluted than others? (6) When you decide on a form of transport, what do you value most? (7) How do you feel when you hear a city with clean air? (8) How would you describe a city with clean air? (9) If you could change anything in Greater Manchester, so that it becomes a city with clean air, what would you change? \r\n\r\nSample description: 30 women in Greater Manchester. \r\nAge: < 40 (14 participants), 40-60 (11 participants), > 60 (5 participants).\r\nNationality: British (23 participants), Other nationalities (7 participants).\r\nEthnicity: White (25 participants) Other ethnic groups (5 participants).\r\nOccupation: Policy-maker (3 participants), Teacher (4 participants), University lecturer or researcher (6 participants), Student (2 participants), Environmental manager (2 participants), Business professional (3 participants), Architect (1 participants), Service and sales worker (3 participants), Retired (6 participants). \r\nChildren: Yes (17 participants), No (13 participants).\r\nResponsibility for unpaid work: Myself (9 participants), 50% - 50% split (6 participants), Me > 50% (10 participants), Me < 50% (5 participants). \r\nCo-habitation: Alone (4 participants), Partner (16 participants), Partner and children (6 participants), Children (3 participants), Flatmate (1 participants). \r\nMain form of transport: Car (11 participants), Walk (9 participants), Public transport (6 participants), Bike (4 participants). \r\nCarbon footprint: High (23 participants), Low (7 participants). \",",
    "keywords": "Environmental, interview, attitudes",
    "publicationState": "citable",
    "dataPublishedTime": "2019-09-05T13:13:53",
    "doiPublishedTime": "2019-09-10T10:54:38",
    "updateFrequency": "notPlanned",
    "status": "completed",
    "result_field": {
        "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/27706/?format=api",
        "dataPath": "/badc/deposited2019/air-pollution-interviews",
        "oldDataPath": [],
        "storageLocation": "internal",
        "storageStatus": "online",
        "volume": 966436,
        "numberOfFiles": 2,
        "fileFormat": "Interviews are available as a pdf document."
    },
    "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/7418/?format=api",
    "geographicExtent": null,
    "nonGeographicFlag": true,
    "phenomena": [],
    "dataLineage": "These data have been fully anonymised by the author and delivered to the Centre for Environmental Data Analysis for archiving.",
    "removedDataTime": null,
    "removedDataReason": "",
    "language": "English",
    "identifier_set": [
        "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/10587/?format=api"
    ],
    "projects": [],
    "observationcollection_set": [],
    "responsiblepartyinfo_set": [
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115813/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115816/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115817/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115818/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115819/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115820/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115814/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115815/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/168890/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115821/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115822/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115823/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/115824/?format=api"
    ],
    "procedureAcquisition": null,
    "procedureCompositeProcess": null,
    "procedureComputation": null,
    "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": [
        {
            "ob_id": 1138,
            "name": "NDGO0003"
        }
    ]
}