| Feb. 13, 2024, 4:19 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 62149, "fields": {"project": 10342, "organisation": 11073, "amount": 136319, "start_date": "2014-04-01", "end_date": "2015-03-31", "raw_data": 176194}}]
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| Jan. 30, 2024, 4:24 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 54993, "fields": {"project": 10342, "organisation": 11073, "amount": 136319, "start_date": "2014-04-01", "end_date": "2015-03-31", "raw_data": 151311}}]
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| Jan. 2, 2024, 4:15 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 47792, "fields": {"project": 10342, "organisation": 11073, "amount": 136319, "start_date": "2014-04-01", "end_date": "2015-03-31", "raw_data": 131304}}]
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| Dec. 5, 2023, 4:23 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 40544, "fields": {"project": 10342, "organisation": 11073, "amount": 136319, "start_date": "2014-03-31", "end_date": "2015-03-30", "raw_data": 96417}}]
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| Nov. 27, 2023, 2:14 p.m. |
Added
35
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{"external_links": []}
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| Nov. 21, 2023, 4:37 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 33248, "fields": {"project": 10342, "organisation": 11073, "amount": 136319, "start_date": "2014-03-31", "end_date": "2015-03-30", "raw_data": 55222}}]
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| Nov. 21, 2023, 4:37 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 96056, "fields": {"project": 10342, "organisation": 13041, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:37 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 96055, "fields": {"project": 10342, "organisation": 13040, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:37 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 96054, "fields": {"project": 10342, "organisation": 13041, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:37 p.m. |
Created
40
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[{"model": "core.projectperson", "pk": 60291, "fields": {"project": 10342, "person": 15061, "role": "PM_PER"}}]
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| Nov. 20, 2023, 2:04 p.m. |
Updated
35
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{"title": ["", "Intelligent Prediction for Cargo Traffic Routing (iPrediCTOR)"], "description": ["", "\nThe Intelligent Prediction for Cargo Traffic Optimised Routing (iPrediCTOR) project is led by Sea Level Research and aims to produce locally optimised sea level predictions to reduce costs in the port, shipping and offshore energy industries. The project combines cutting edge Machine Learning techniques with observations from tide gauges and marine weather data and cloud computing infrastructure to generate precise sea level predictions from weather forecasts. These predictions, which account for the surge on top of the tide, will deliver a number of major benefits: Improved scheduling for ships, leading to reduced fuel and operating costs; optimisation of port logistics and cargo loading, leading to a more efficient supply chain; better scheduling of maintenance visits for offshore energy companies with less aborted trips, leading to reduced operating costs for wind farms and tidal energy production; and greater navigational safety since the amount of water under keel will be more accurately known. With containerisation continuing to grow, ships becoming ever bigger and the vast expansion of offshore renewable energy the development of an accurate prediction system will facilitate optimal decision making and reduced costs.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Closed"]}
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| Nov. 20, 2023, 2:04 p.m. |
Added
35
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{"external_links": [42815]}
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| Nov. 20, 2023, 2:04 p.m. |
Created
35
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[{"model": "core.project", "pk": 10342, "fields": {"owner": null, "is_locked": false, "coped_id": "d7cbdb98-17da-4492-9527-801ec127a10c", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 55205, "created": "2023-11-20T13:38:31.549Z", "modified": "2023-11-20T13:38:31.549Z", "external_links": []}}]
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