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DATADOI on multidistsiplinaarne Tartu Ülikoolile kuuluv avatud repositoorium teadusandmete jagamiseks ja avaldamiseks, mis põhineb avatud lähtekoodiga Dataverse tarkvaral ja mida haldab Tartu Ülikooli raamatukogu.

Andmed tuleks talletada selle asutuse kollektsiooni, millega vähemalt üks kaasautoritest on seotud. DATADOI sisaldab praegu institutsionaalseid kollektsioone Tartu Ülikoolile, Tallinna Ülikoolile, Estonian Business Schoolile ja Eesti Teadusagentuurile.

Kui sobivat kollektsiooni veel ei ole, võtke ühendust DATADOI meeskonnaga aadressil datadoi@datadoi.ee.

DATADOI is a multidisciplinary open repository owned by the University of Tartu for sharing and publishing research data, based on the open-source Dataverse software and managed by the University of Tartu Library.

Data should be deposited in the institutional collection assigned to the institution with which at least one of the contributors is affiliated. DATADOI currently includes institutional collections for the University of Tartu, Tallinn University, Estonian Business School, and the Estonian Research Council.

If a suitable collection does not yet exist, please contact the DATADOI team at datadoi@datadoi.ee.

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MODIS observations of cloud responses to ship emissions in precipitating marine clouds
Oct 9, 2026 - Füüsika andmed
Keernik, Hannes; Toll, Velle, 2026, "MODIS observations of cloud responses to ship emissions in precipitating marine clouds", https://doi.org/10.23673/NYBSIM, DATADOI, V1, UNF:6:VUiJeNWe/UhfVy1/n9Zemg== [fileUNF]
MODIS data used to study the temporal evolution of cloud fraction along ship tracks under precipitating conditions.
modis_polluted.tab
Oct 9, 2026 - MODIS observations of cloud responses to ship emissions in precipitating marine clouds
Tabular Data - 167.0 KB - 22 Variables, 1066 Observations - UNF:6:yHEY/BsmCL8TSjRrYFKgNA==
modis_track_geom.tab
Oct 9, 2026 - MODIS observations of cloud responses to ship emissions in precipitating marine clouds
Tabular Data - 180.4 KB - 6 Variables, 126 Observations - UNF:6:pZmZkDelvbikmzFthqC0kw==
modis_unpolluted_left.tab
Oct 9, 2026 - MODIS observations of cloud responses to ship emissions in precipitating marine clouds
Tabular Data - 166.8 KB - 22 Variables, 1066 Observations - UNF:6:ozG+IfwkVZszJ5lsxcB+3A==
modis_unpolluted_right.tab
Oct 9, 2026 - MODIS observations of cloud responses to ship emissions in precipitating marine clouds
Tabular Data - 166.7 KB - 22 Variables, 1066 Observations - UNF:6:fGmoDsIB0k6niPn4lgab8g==
polluted_unpolluted_classification.tab
Oct 9, 2026 - MODIS observations of cloud responses to ship emissions in precipitating marine clouds
Tabular Data - 2.7 MB - 8 Variables, 2243 Observations - UNF:6:fCmAEzC9x2zPUUNwfmHQhQ==
README.txt
Oct 9, 2026 - MODIS observations of cloud responses to ship emissions in precipitating marine clouds
Plain Text - 8.0 KB - MD5: 905e6b5d2c7b73a23e75d065bf4b6364
Python code for analysing the evolution of cloud properties downwind of strong aerosol point sources using MODIS satellite observations
Oct 9, 2026 - Füüsika andmed
Keernik, Hannes; Toll, Velle, 2026, "Python code for analysing the evolution of cloud properties downwind of strong aerosol point sources using MODIS satellite observations", https://doi.org/10.23673/SWZSAQ, DATADOI, V1, UNF:6:PXwu1GWbWC5Dvh3xFGcr5A== [fileUNF]
Python code for analysing the evolution of cloud properties downwind of strong aerosol point sources using MODIS satellite observations.
config.py
Oct 9, 2026 - Python code for analysing the evolution of cloud properties downwind of strong aerosol point sources using MODIS satellite observations
Python Source Code - 1.5 KB - MD5: 73254f23f38c0ae94ad4549d02855eb4
data_out.tab
Oct 9, 2026 - Python code for analysing the evolution of cloud properties downwind of strong aerosol point sources using MODIS satellite observations
Tabular Data - 26.0 KB - 34 Variables, 129 Observations - UNF:6:PXwu1GWbWC5Dvh3xFGcr5A==
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