251 to 260 of 10,428 Results
Plain Text - 8.1 KB -
MD5: 0809d4ace6d44efc717ceddd207e8b06
Readme file for Replication Data for: Note to first-year university students: Just do it! In the end, the fact that you study may be more important than how you study. |
Adobe PDF - 90.4 KB -
MD5: 7a2c04e800f7133b42e287e3d7698ed7
Consent form for participants |
Adobe PDF - 429.7 KB -
MD5: ba95ae3b4e4bbcef3c630429c8ce17f1
This is a brief assessment of whether the open publication of the dataset “Replication Data for: Note to first-year university students: Just do it! In the end, the fact that you study may be more important than how you study” in DataverseNO is aligned with applicable legal and research-ethical regulations and guidelines. |
Comma Separated Values - 24.6 KB -
MD5: 1d681db54eb5658dfeef53fe11fc1044
Dataset |
Sep 24, 2025 -
Supporting Data for: Assessing Ocean Surface Radial Current Uncertainties Derived from SAR via Atmospheric Ensemble Modeling
Plain Text - 6.3 KB -
MD5: 5a239403648a96fa6bc70aab57935aa8
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Sep 24, 2025 -
Supporting Data for: Assessing Ocean Surface Radial Current Uncertainties Derived from SAR via Atmospheric Ensemble Modeling
Jupyter Notebook - 561.3 KB -
MD5: 4b55e466ed0e0c741ae54e8e5fa0cfe2
Jupyter notebook for opening and plotting the data. |
Sep 24, 2025 -
Supporting Data for: Assessing Ocean Surface Radial Current Uncertainties Derived from SAR via Atmospheric Ensemble Modeling
Adobe PDF - 1.5 MB -
MD5: e6073b36fd34215933af700050e4adf8
Jupyter notebook for opening and plotting the data set, in .pdf format. |
Sep 24, 2025 -
Supporting Data for: Assessing Ocean Surface Radial Current Uncertainties Derived from SAR via Atmospheric Ensemble Modeling
ZIP Archive - 7.1 GB -
MD5: 28e5ddfa2327fcef56c4d752819c59b9
.zip file containing Doppler Centroid Anomaly, RVL, satellite information and collocated fields (wind and waves) for each scene. |
Plain Text - 6.4 KB -
MD5: be8f5aab88b22abc30bc3ddc85f464c6
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Aug 19, 2025 -
Appliance-Level Electricity Consumption Dataset
Plain Text - 9.8 KB -
MD5: f0153c3445e99e229d02b9e5d4863c7e
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