TY - GEN A1 - Lisec, Jan T1 - ABID N2 - In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0. This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context. KW - Software KW - Antibody KW - MALDI PY - 2022 UR - https://github.com/BAMresearch/ABID PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-56192 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Lisec, Jan T1 - CorMID - Correct Mass Isotopologue Distribution Vectors N2 - The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer-based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN. KW - Mass Spectrometry KW - Software KW - R package KW - Mass Isotopomer Distribution KW - Flux Analysis PY - 2022 UR - https://github.com/cran/CorMID/ PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-56442 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Lisec, Jan T1 - IsoCor N2 - Despite numerous advantages offered by hyphenation of chromatography and electrokinetic separation methods with multicollector (MC) ICP-MS for isotope analysis, the main limitation of such systems is the decrease in precision and increase in uncertainty due to generation of short transient signals. To minimize this limitation, most authors compare several isotope ratio calculation methods and establish a multi-step data processing routine based on the precision and accuracy of the methods. However, to the best of our knowledge, there is no universal data processing tool available that incorporates all important steps of the treatment of the transient signals. Thus, we introduce a data processing application (App) IsoCor that facilitates automatic calculation of isotope ratios from transient signals and eases selection of the most suitable method. The IsoCor App performs baseline subtraction, peak detection, mass bias correction, isotope ratio calculation and delta calculation. The feasibility and reliability of the App was proven by reproducing the results from isotope analysis of three elements (neodymium, mercury and sulfur) measured on-line via hyphenated systems. The IsoCor App provides trackability of the results to ensure quality control of the analysis. KW - Shiny-App KW - ICP-MS KW - Software PY - 2022 UR - https://github.com/cran/IsoCor/ PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-56307 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Liehr, Sascha T1 - ANNforPAT - Artificial Neural Networks for Process Analytical Technology N2 - This code accompanies the paper "Artificial neural networks for quantitative online NMR spectroscopy" published in Analytical and Bioanalytical Chemistry (2020). KW - Artificial neural networks KW - Automation KW - Online NMR spectroscopy KW - Process industry KW - Real-time process monitoring PY - 2020 UR - https://github.com/BAMresearch/ANNforPAT PB - GitHub CY - San Francisco AN - OPUS4-54481 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Rühle, Bastian A1 - Krumrey, Julian Frederic A1 - Hodoroaba, Vasile-Dan T1 - Dataset accompanying the publication "Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks" N2 - This dataset accompanies the following publication, first published in Scientific Reports (www.nature.com/articles/s41598-021-84287-6): B. Ruehle, J. Krumrey, V.-D. Hodoroaba, Scientific Reports, Workflow towards Automated Segmentation of Agglomerated, Non-Spherical Particles from Electron Microscopy Images using Artificial Neural Networks, DOI: 10.1038/s41598-021-84287-6 It contains electron microscopy micrographs of TiO2 particles, the corresponding segmentation masks, and their classifications into different categories depending on their visibility/occlusion. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.com/BAMresearch/automatic-sem-image-segmentation KW - Electron microscopy KW - Neural networks KW - Image segmentation KW - Automated image analysis PY - 2021 U6 - https://doi.org/10.5281/zenodo.4563942 PB - Zenodo CY - Geneva AN - OPUS4-52246 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Kazlagic, Anera A1 - Rosner, M. A1 - Cipriani, A. A1 - Frick, D. A. A1 - Glodny, J. A1 - Hoffmann, E. J. A1 - Hora, J. M. A1 - Irrgeher, J. A1 - Lugli, F. A1 - Magna, T. A1 - Meisel, T. C. A1 - Meixner, A. A1 - Possolo, A. A1 - Pramann, A. A1 - Pribil, M. J. A1 - Prohaska, T. A1 - Retzmann, A. A1 - Rienitz, O. A1 - Rutherford, D. A1 - Paula-Santos, G. M. A1 - Tatzel, M. A1 - Widhalm, S. A1 - Willbold, M. A1 - Zuliani, T. A1 - Vogl, Jochen T1 - Data of the characterisation of conventional 87Sr/86Sr isotope ratios in cement, limestone and slate reference materials based on an interlaboratory comparison study N2 - This dataset represents the electronic supplementary material (ESM) of the publication entitled "Characterisation of conventional 87Sr/86Sr isotope ratios in cement, limestone and slate reference materials based on an interlaboratory comparison study", which is published in Geostandards and Geoanalytical Research under the DOI: 10.1111/GGR.12517. It consists of four files. 'ESM_Data.xlsx' contains all reported data of the participants, a description of the applied analytical procedures, basic calculations, the consensus values, and part of the uncertainty assessment. 'ESM_Figure-S1' displays a schematic on how measurements, sequences and replicates are treated for the uncertainty calculation carried out by PTB. 'ESM_Technical-protocol.pdf' is the technical protocol of the interlaboratory comparison, which has been provided to all participants together with the samples and which contains bedside others the definition of the measurand and guidelines for data assessment and calculations. 'ESM_Reporting-template.xlsx' is the Excel template which has been submitted to all participants for reporting their results within the interlaboratory comparison. Excel files with names of the the structure 'GeoReM_Material_Sr8786_Date.xlsx' represent the Rcon(87Sr/86Sr) data for a specific reference material downloaded from GeoReM at the specified date, e.g. 'GeoReM_IAPSO_Sr8786_20221115.xlsx' contains all Rcon(87Sr/86Sr) data for the IAPSO seawater standard listed in GeoReM until 15 November 2022. KW - Reference data KW - Strontium isotope ratio KW - Interlaboratory comparison KW - Reference material KW - Cement KW - Geological material KW - Value assignment KW - Measurement uncertainty KW - Conventional method PY - 2023 UR - https://doi.org/10.5281/zenodo.7804445 U6 - https://doi.org/10.5281/zenodo.7804444 PB - Zenodo CY - Geneva AN - OPUS4-57809 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Lisec, Jan T1 - CorrectOverloadedPeaks (R package to correct APCI-HR-MS peak data) N2 - Metabolomics, the analysis of potentially all small molecules within a biological system, has become a valuable tool for biomarker identification and the elucidation of biological processes. While metabolites are often present in complex mixtures at extremely different concentrations, the dynamic range of available analytical methods to capture this variance is generally limited. Here, we show that gas chromatography coupled to atmospheric pressure chemical ionization mass spectrometry (GC-APCI-MS), a state of the art analytical technology applied in metabolomics analyses, shows an average linear range (LR) of 2.39 orders of magnitude for a set of 62 metabolites from a representative compound mixture. We further developed a computational tool to extend this dynamic range on average by more than 1 order of magnitude, demonstrated with a dilution series of the compound mixture, using robust and automatic reconstruction of intensity values exceeding the detection limit. The tool is freely available as an R package (CorrectOverloadedPeaks) from CRAN (https://cran.r-project.org/) and can be incorporated in a metabolomics data processing pipeline facilitating large screening assays. KW - Software KW - Metabolomics KW - R package KW - Data processing PY - 2019 UR - https://github.com/cran/CorrectOverloadedPeaks/ PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-57853 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Lisec, Jan T1 - MetabolomicsBasics (R package to process and analyze metabolomics datasets) N2 - Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification. KW - Software KW - Mass Spectrometry KW - R package KW - Data processing PY - 2018 UR - https://github.com/cran/MetabolomicsBasics/ PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-57854 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Sommerfeld, Thomas A1 - Koch, Matthias A1 - Mauch, Tatjana A1 - Jung, Christian A1 - Riedel, Juliane A1 - Lisec, Jan T1 - CRM BAM-A001 eCerto data file N2 - Data file (RData) containing measurement data recorded during the production process of the Certified Reference Material BAM-A001 containing Polycyclic Aromatic Hydrocarbons (PAH) in olive oil. The data can be most conveniently openend using the Shiny-App eCerto which is accessible at https://www.bam.de/eCerto. KW - Certified reference material KW - Mass spectrometry KW - Olive oil KW - Aromatic hydrocarbons KW - Reference data PY - 2023 UR - https://doi.org/10.5281/zenodo.8380870 U6 - https://doi.org/10.5281/zenodo.8380869 PB - Zenodo CY - Geneva AN - OPUS4-58488 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Kern, Simon A1 - Wander, Lukas A1 - Meyer, Klas A1 - Guhl, Svetlana A1 - Gottu Mukkula, A. R. A1 - Holtkamp, M. A1 - Salge, M. A1 - Fleischer, C. A1 - Weber, N. A1 - Engell, S. A1 - Paul, Andrea A1 - King, R. A1 - Maiwald, Michael T1 - Raw data of pilot plant runs for CONSENS project (Case study 1) N2 - In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and o-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum. The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17. In the following the contents of the files are explained. KW - Process Analytical Technology KW - Multivariate Data Analysis KW - Nuclear Magnetic Resonance KW - Near Infrared Spectroscopy KW - Continuous Manufacturing KW - CONSENS PY - 2018 U6 - https://doi.org/10.5281/zenodo.1438233 PB - Zenodo CY - Geneva AN - OPUS4-48063 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -