This R package provided a programmatic interface to the Bacterial Diversity Metadatabase of the DSMZ (German Collection of Microorganisms and Cell Cultures).
As of June 2021, BacDive's "redesign" has rendered this R package inoperable. Apparently, they want you to use either of these clients.
BacDiveR helps you improve your research on bacteria and archaea by providing access to "structured information on [...] their taxonomy, morphology, physiology, cultivation, geographic origin, application, interaction" and more (Söhngen et al. 2016). Specifically, you can:
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download the BacDive data you need for offline investigation, and
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document your searches and downloads in
.Rscripts,.Rmdfiles, etc.
Thus, BacDiveR can be the basis for a reproducible data analysis pipeline. See TIBHannover.GitHub.io/BacDiveR for more details, /news there for the changelog, and GitHub.com/TIBHannover/BacDiveR for the latest source code.
It was also built to serve as a demonstration object during TIB's "FAIR Data & Software" workshop.
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Because the BacDive Web Service requires registration please do that first and wait for DSMZ staff to grant you access.
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Once you have your login credentials, install the latest BacDiveR release from GitHub with:
if(!require('devtools')) install.packages('devtools'); devtools::install_github('TIBHannover/BacDiveR'). -
After installing, follow the instructions on the console to save your login credentials locally and restart R(Studio) or run
usethis::edit_r_environ()and ensure it contains the following:
[email protected]
BacDive_password=YOUR_20_char_password
In the examples and vignettes, the data retrieval will only work if your login credentials are correct in themselves (no typos) and were correctly saved. Console output like "{\"detail\": \"Invalid username/password\"}", or Error: $ operator is invalid for atomic vectors indicates that either the login credentials are incorrect, or the .Renviron file.
There are two main functions: retrieve_data() and retrieve_search_results().
Please click on their names to read their docu, and find real-life examples in
the vignettes "BacDive-ing in" and "Pre-Configuring Advanced Searches".
How to cite: See Cite as & Export on Zenodo
You can also run citation('BacDiveR') in the R console and use its output because that
ensures you are citing exactly the installed version.
If you want to import this repo's metadata into a reference manager directly, I recommend Zotero and its GitHub translator. Please double-check, that the citation refers to the same version number that you ran your analysis with.
When using BibTeX, you may want to try changing the item type from to @Software
;-) Support for that is being worked on.
Don't forget to also cite BacDive itself whenever you used their data, regardless of access method.
How to contribute: See CONTRIBUTING.md file.
These seem to scrape all data, instead of retrieving specific datasets.
- @cjm007's
BacDive_&BacDivePy(Python) - @zorino's
microbe-dbs(Python & Shell) - @EngqvistLab's Python script
- or generally: GitHub.com/topics/bacdive
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Söhngen, Bunk, Podstawka, Gleim, Overmann. 2014. “BacDive — the Bacterial Diversity Metadatabase.” Nucleic Acids Research 42 (D1): D592–D599. doi:10.1093/nar/gkt1058.
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Söhngen, Podstawka, Bunk, Gleim, Vetcininova, Reimer, Ebeling, Pendarovski, Overmann. 2016. “BacDive – the Bacterial Diversity Metadatabase in 2016.” Nucleic Acids Research 44 (D1): D581–D585. doi:10.1093/nar/gkv983.
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Reimer, Vetcininova, Carbasse, Söhngen, Gleim, Ebeling, Overmann. 2018. “BacDive in 2019: Bacterial Phenotypic Data for High-Throughput Biodiversity Analysis” Nucleic Acids Research doi:10.1093/nar/gky879.
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