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heiDATA is an institutional repository for Open Research Data from Heidelberg University. It is managed by Heidelberg University's Research Data Unit, a joint institution of the University Library and the Computing Centre. If you are interested in publishing your data here, please have a look at our user guide and get in touch with us. Detailed information on heiDATA can be found in our service description.
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Universitätsbibliothek Heidelberg(Heidelberg University - University Library)
Universitätsbibliothek Heidelberg logo
Dec 17, 2015
Forschungsdaten der Universitätsbibliothek Heidelberg
Dec 16, 2019 - PhD related material - Geosciences
Ritter, Simon, 2019, "Unravelling the formation of Hells Bells: underwater speleothems from the Yucatán Peninsula [Data]", https://doi.org/10.11588/data/TMYLWS, heiDATA, V2, UNF:6:WXDVvAQ9OEYqhDjPXPlhqg== [fileUNF]
Hells Bells are unique bell-shaped underwater speleothems recently discovered in deep meromictic sinkholes (cenotes) on the Yucatán Peninsula, Mexico. In order to unravel the underwater growth of Hells Bells and identify the mechanism of subaqueous calcite precipita-tion, several...
Plain Text - 134 B - MD5: deb636da0fc54baeccfc10ae0b2e69ed
Description
Documentation
Jun 17, 2022 - Division of Infectious Disease and Tropical Medicine
Herrmann, Yannis; Lainati, Federica; del Mar Castro, María; Mwamba, Chanda; Kumwenda, Moses; Muyoyeta, Monde; Broger, Tobias; Heinrich, Norbert; Olbrich, Lisa; Corbett, Elizabeth; McMahon, Shannon; Engel, Nora; Denkinger, Claudia, 2022, "User perspectives and preferences on a novel TB LAM diagnostic (Fujifilm SILVAMP TB LAM) – a qualitative study in Malawi and Zambia [data]", https://doi.org/10.11588/data/PLRQCZ, heiDATA, V1
This data set supports the findings of the FujiLAM qualitative study published in PLOS Global Public Health (Titel:"User perspectives and preferences on a novel TB LAM diagnostic (Fujifilm SILVAMP TB LAM) – a qualitative study in Malawi and Zambia"). The repository contains our d...
ZIP Archive - 158.2 KB - MD5: cd4bc35181a768beb6d889b6c73ad5d4
Python Source Code - 10.0 KB - MD5: 9b98e66e005b4ed8da5b9a39ac8a9638
C++ Source - 3.0 KB - MD5: 4d37220675e664e4abe9e7a2785dfbb3
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