Demo Data from EDBT 2026 / reviewers in the wild / expert
S. A. Leidel
dblp:223/7982
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › proteomics
mass spectrometry data analysis |
0.3 | 1 | 2018 | pymzML v2.0: introducing a highly compressed and seekable gzip format · Bioinform. 2018 |
Storage systems
data compression |
0.3 | 1 | 2018 | pymzML v2.0: introducing a highly compressed and seekable gzip format · Bioinform. 2018 |
Storage systems › data compression
random access compression |
0.3 | 1 | 2018 | pymzML v2.0: introducing a highly compressed and seekable gzip format · Bioinform. 2018 |
Methods — techniques the papers use, named apart from their topics
numerical computation libraries · 0.7gzip compression · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | pymzML v2.0: introducing a highly compressed and seekable gzip formatabstractMotivation: In the new release of pymzML (v2.0), we have optimized the speed of this established tool for mass spectrometry data analysis to adapt to increasing amounts of data in mass spectrometry. Thus, we integrated faster libraries for numerical calculations, improved data retrieving algorithms and have optimized the source code. Importantly, to adapt to rapidly growing file sizes, we developed a generalizable compression scheme for very fast random access and applied this concept to mzML files to retrieve spectral data. Results: pymzML performs at par with established C programs when it comes to processing times. However, it offers the versatility of a scripting language, while adding unprecedented fast random access to compressed files. Additionally, we designed our compression scheme in such a general way that it can be applied to any field where fast random access to large data blocks in compressed files is desired. Availability and implementation: pymzML is freely available on https://github.com/pymzML/pymzML under GPL license. pymzML requires Python3.4+ and optionally numpy. Documentation available on http://pymzml.readthedocs.io. M. Kösters, Johannes Leufken, Stefan Schulze, K. Sugimoto, Joshua Klein, R. P. Zahedi, Michael Hippler, S. A. Leidel, Christian Fufezan |
Bioinform. | 8 |