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DATADOI on multidistsiplinaarne avatud repositoorium teadusandmete jagamiseks ja avaldamiseks, mis põhineb avatud lähtekoodiga Dataverse tarkvaral.

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 for sharing and publishing research data, based on the open-source Dataverse software.

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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2,071 to 2,080 of 3,476 Results
HTML - 1.4 KB - MD5: 606866c229c7aebe526093ea2e4564ac
2026. aasta migratsiooni käigus varasemast DataDOI süsteemist üle kantud kasutusstatistika kajastab tegevust eelmises DSpace-põhises süsteemis ega näita Dataverse’i uusi kasutusandmeid. Usage statistics carried over from the previous DataDOI system as part of the 2026 migration reflect activity in the former DSpace-based system and do not represent...
Plain Text - 1.4 KB - MD5: 44a3ad420805a2995794340fa075c669
Dec 8, 2021 - Ühiskonnateaduste andmed
Janar, Pekarev, 2021, "The areas of concern of systematic literature review", https://doi.org/10.23673/RE-301, DATADOI, V1
The Weaponization of artificial intelligence (AI) is upending traditional warfare as the development of autonomous weapons systems (AWS) increases in pace and sophistication. As a result, the concept of the weaponized AI, where a weapon system that, once activated, can select and engage targets without human intervention, creates serious complicati...
HTML - 907 B - MD5: c07b6daef3dbee864bf87e6aa836cde2
2026. aasta migratsiooni käigus varasemast DataDOI süsteemist üle kantud kasutusstatistika kajastab tegevust eelmises DSpace-põhises süsteemis ega näita Dataverse’i uusi kasutusandmeid. Usage statistics carried over from the previous DataDOI system as part of the 2026 migration reflect activity in the former DSpace-based system and do not represent...
MS Excel Spreadsheet - 14.8 KB - MD5: e0479a9b04d63f1bf99d79e83d4ffe14
Dec 7, 2021 - Arvutiteaduse andmed
Dehury, Chinmaya Kumar; Chhetri, Tek Raj; Lind, Artjom; Srirama, Satish Narayana; Fensel, Anna, 2021, "HPC Cloud traces for better cloud service reliability", https://doi.org/10.23673/RE-300, DATADOI, V1
This data is in support of the research on "A combined system metrics approach to cloud service reliability using artificial intelligence" (doi: 10.20944/preprints202111.0548.v1)
ZIP Archive - 108.4 MB - MD5: c666a71f98376ba80b3a3112ecdd47ad
Contains the actual data in CSV and the code to anonymise the sesitive data.
HTML - 1.4 KB - MD5: 72348ee90b7662e869e642f00d89cc96
2026. aasta migratsiooni käigus varasemast DataDOI süsteemist üle kantud kasutusstatistika kajastab tegevust eelmises DSpace-põhises süsteemis ega näita Dataverse’i uusi kasutusandmeid. Usage statistics carried over from the previous DataDOI system as part of the 2026 migration reflect activity in the former DSpace-based system and do not represent...
Plain Text - 2.7 KB - MD5: f39cf0e1384e3bd39d931aefce86c262
Readme for the actual data present in "Anonymize_data_V2" zip file.
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