Get a list of Instrument objects. Instruments have a 1:1 mapping with Observations.

GET /api/v3/onlineresources/?format=api&offset=85300
HTTP 200 OK
Allow: GET, POST, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
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    "next": "https://api.catalogue.ceda.ac.uk/api/v3/onlineresources/?format=api&limit=100&offset=85400",
    "previous": "https://api.catalogue.ceda.ac.uk/api/v3/onlineresources/?format=api&limit=100&offset=85200",
    "results": [
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            "linkage": "https://cordis.europa.eu/project/id/835398",
            "name": "Project website",
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                "uuid": "0b0c5a2c0a7b466ab4286a7fc8bda179",
                "short_code": "proj"
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        },
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            "linkage": "https://www.gov.uk/government/publications/natural-capital-and-ecosystem-assessment-programme",
            "name": "Natural Capital and Ecosystem Assessment (NCEA) programme",
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                "short_code": "ob"
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        },
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            "ob_id": 93216,
            "function": "documentation",
            "linkage": "https://www.rdocumentation.org/packages/raster/versions/3.4-5/topics/calc",
            "name": "Raster:calc function",
            "relatedTo": {
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                "uuid": "0a058f0b3eb940d09b5a7834fc9163b9",
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        },
        {
            "ob_id": 93217,
            "function": "documentation",
            "linkage": "https://cran.r-project.org/package=raster",
            "name": "Hijmans, R. (2023). raster: Geographic Data Analysis and Modeling. R package version 3.6-26",
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                "uuid": "bf9ac3f9051d4988a45fb7acf9aa944c",
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            "ob_id": 93218,
            "function": "documentation",
            "linkage": "https://www.r-project.org/",
            "name": "R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.",
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            "name": "Hijmans, R. (2023). raster: Geographic Data Analysis and Modeling. R package version 3.6-26",
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            "function": "documentation",
            "linkage": "https://www.r-project.org/",
            "name": "R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.",
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            "name": "R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.",
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            "name": "R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.",
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            "name": "R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.",
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            "linkage": "https://cran.r-project.org/package=raster",
            "name": "Hijmans, R. (2023). raster: Geographic Data Analysis and Modeling. R package version 3.6-26",
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            "name": "R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.",
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            "name": "Hijmans, R. (2023). raster: Geographic Data Analysis and Modeling. R package version 3.6-26",
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            "name": "R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.",
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            "function": "documentation",
            "linkage": "https://zenodo.org/records/5236762",
            "name": "Dataset originally published on Zenodo but copy archived here as it was partially the result of a NERC projects",
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            "function": null,
            "linkage": "https://gtr.ukri.org/projects?ref=NE%2FT007648%2F1",
            "name": "The multi-trophic impact of ash dieback, NERC grant webpage",
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        {
            "ob_id": 93239,
            "function": "documentation",
            "linkage": "https://doi.org/10.1016/j.rse.2017.04.030",
            "name": "Wilkes, P., Lau, A., Disney, M. I., Calders, K., Burt, A., Gonzalez de Tanago Menaca, J., … Herold, M. (2017). Data Acquisition Considerations for Terrestrial Laser Scanning of Forest Plots. Remote Sensing of Environment, 196, 140–153.",
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            "linkage": "https://doi.org/10.34726/wim.1862",
            "name": "Wilkes, P., Disney, M., Dahlsjö, C. A. L., & Malhi, Y. (2021). Characterising understorey Plant Area Index with TLS. In Proceedings of the SilviLaser Conference 2021 (pp. 7–9).",
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            "name": "Environmental Insights: Democratizing access to ambient air pollution data and predictive analytics with an open-source Python package",
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            "linkage": "https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6",
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