Publication Details |
| Category | Text Publication |
| Reference Category | Conference papers |
| DOI | 10.5194/egusphere-egu24-15782 |
Licence  |
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| Title (Primary) | Lambda-Miner: Enhancing reproducible natural organic matter data processing with a semi-automatic web application |
| Title (Secondary) | EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024 |
| Author | Wurz, J.
; Groß, A.; Franze, K.; Lechtenfeld, O.
|
| Source Titel | EGUsphere |
| Year | 2024 |
| Department | EAC |
| Page From | EGU24-15782 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Abstract | As the volume and complexity of data in
environmental sciences continue to grow, the need for data management
and reproducible processing methods becomes increasingly crucial. In the
specific research domain of natural organic matter (NOM), there is
currently no standardized tool for data processing, particularly for the
management of data and its respective metadata. We developed and
present the Lambda-Miner
- a semi-automatic web application for data processing of
ultrahigh-resolution mass spectrometry data of NOM. The platform
provides an end-to-end data processing pipeline and supersedes manual
steps via standardized data and metadata management. It empowers users
to execute interactive workflows for mass spectra calibration,
assignment of molecular formulas by specific rules to peak masses, and
validation of these formulas according to specific sets of rules. Peak
data as well as sample and measurement metadata are stored in a
relational database management system (RDBMS). The Lambda-Miner
thus facilitates reproducible, standardized data processing which
builds a common repository for mass data, metadata (such as sample type
and geolocation), intermediate, and final results in a format suitable
for subsequent analyses. The combination of this information in one
place enables meta-analyses such as long-term quality control studies
and software optimization assays. The Lambda-Miner
supports domain-specific requirements for research data management and
contributes to achieving FAIR data principles in the domain of NOM
analytics. The Lambda-Miner
allows researchers to process their ultrahigh-resolution mass
spectrometry data of NOM within minutes and linking it to features such
as extraction efficiency, accumulation time, and relation of total
assigned current to total ion current. Processed data can be downloaded
in an interoperable format, facilitating individual data processing or
visualization. The current implementation of the Lambda-Miner is
designed for studying NOM with Fourier transform ion cyclotron resonance
mass spectrometry (FT-ICR MS) allowing formula assignments with widely
used elemental compositions of NOM in the mass range from 0 to 1000 Da.
But its modular structure makes it easy to adjust and extend the
implementation for other kind of analyses or instrumentations. With its
adaptability and focus on reproducibility, the Lambda-Miner
introduces a valuable tool for advancing standardized data storage,
processing, and analysis in the study of Natural Organic Matter. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29498 |
| Wurz, J., Groß, A., Franze, K., Lechtenfeld, O. (2024): Lambda-Miner: Enhancing reproducible natural organic matter data processing with a semi-automatic web application EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024 EGUsphere Copernicus Publications, EGU24-15782 10.5194/egusphere-egu24-15782 |
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