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24 results for “reaction monitoring”
Collection of UV/Vis spectra acquired while monitoring reaction progress of thymidine phosphorolysis with varying reactant concentrations
<p>This data set accompanies the publication "Dynamic modelling of phosphorolytic cleavage catalyzed by pyrimidine-nucleoside phosphorylase" in MDPI Processes, and is a collection of UV/vis spectra used for monitoring of reaction progress under various experimental conditions.</p> <p>The monitored reaction is thymidine phosphorolysis, catalyzed by an enzyme (EC 2.4.2.2). Phosphate and thymidine concentrations are in the range of 2–80 mM and 0.8–5 mM, respectively, and final enzyme concentration in the range of 12.5–50 µg/mL. The recorded data represents time courses over 24 hours for 48 reaction conditions.</p> <p>This collection also contains the documentation of all work done in the laboratory to achieve this data (provenance in the form of the complete lab journal).</p>
Development of a desorption electrospray ionization –multiple-reaction-monitoring mass spectrometry (DESI-MRM) workflow for spatially mapping oxylipins in pulmonary tissue
<p>Data from desorption electrospray ionization mass spectrometry – multiple-reaction-monitoring mass spectrometry (DESI-MRM) analysis of oxylipins in guinea pig lung tissue following<em> in vivo</em> exposure to house dust mite extract.</p> <p>Data are provided as Waters *.raw data folders, each incuding an 'Analyte .txt' file, which is generated from processing within MassLynx (Waters). The 'ion_library.txt' file includes details about the MRM transitions and is required for processing the data with quantMSImageR (<span><a href="https://github.com/targeted-lipidomics/quantMSImageR"><span>https://github.com/targeted-lipidomics/quantMSImageR</span></a></span><span>).</span></p>
Figure 5 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 5. KEGG pathways of the differentially expressed phosphorylated proteins. The abscissa indicates the first 10 significantly enriched KEGG pathways and the ordinate indicates the significance of enriched KEGG pathways, the more left, the more significant.
Figure 4 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 4. Gene ontology annotations of the differentially expressed phosphorylated proteins. The abscissa indicates the enriched GO functional classification, including biological process (A), cellular component (B), and molecular function (C). The ordinate indicates the size of the significance of corresponding to each entry, the more left, the more significant.
Figure 3 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 3. Clustering heatmap of different expression phosphorylated peptides. Each row represents a phosphorylated peptide segment, each column represents a group of samples. The logarithmic value (logarithmic transformation based on 2) of the significantly differentially expressed phosphorylated peptides in different samples is displayed in the clustering heatmap in different colors. Red represents significant upregulation of phosphorylated peptides; blue represents significant down-regulation of phosphorylated peptides.
Figure 2 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 2. Volcano plots from different group comparisons. The abscissa indicates difference multiple (logarithmic transformation based on 2), the ordinate indicates the significant of difference (logarithmic transformation based on 10). The red point is significantly upregulated phosphorylated peptide segment, the blue point is significantly downregulated phosphorylated peptide segment and the gray point is a phosphorylated peptide segment with no significant difference.
Application of spectral library prediction for parallel reaction monitoring of viral peptides_DDA_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_NCE_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Raw data accompanying: Ground reaction forces in monitor lizards (Varanidae) and the scaling of locomotion in sprawling tetrapods
<p>Geometric scaling predicts a major challenge for legged, terrestrial locomotion.<b> </b>Locomotor support requirements scale identically with body mass (α M<sup>1</sup>), while force generation capacity should scale α M<sup>2/3</sup> as it depends on muscle cross-sectional area. Mammals compensate with more upright limb postures at larger sizes, but it remains unknown how sprawling tetrapods deal with this challenge. Varanid lizards are an ideal group to address this question because they cover an enormous body size range while maintaining a similar bent-limb posture and body proportions. This study reports the scaling of ground reaction forces and duty factor for varanid lizards ranging from 7 g 37 kg. Impulses (force x time) scaled roughly as predicted by the inverted pendulum model (α M<sup>0.99-1.34</sup>) while peak forces (α M<sup>0.73-1.00</sup>) scaled higher than expected. Duty factor scaled α M<sup>0.04 </sup>and was higher for the hindlimb than the forelimb. The proportion of vertical impulse to total impulse increased with body size, and impulses decreased while peak forces increased with speed. These results provide valuable data into how locomotor forces vary with body size and suggest how other, extinct, sprawling tetrapods may have dealt with the biomechanical challenges associated with generating sufficient locomotor forces at larger body sizes.</p>
Figure 1 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 1. Proportion of serine, threonine, and tyrosine in phosphorylation sites.
Monitoring the evolution of relative product populations at early times during a photochemical reaction
<p class="MsoNormal"><span>Identifying multiple rival reaction products and transient species formed during ultrafast photochemical reactions and determining their time-evolving relative populations are key steps towards understanding and predicting photochemical outcomes. Yet, most contemporary ultrafast studies struggle with clearly identifying and quantifying competing molecular structures/species amongst the emerging reaction products. Here, we show that mega-electronvolt ultrafast electron diffraction in combination with <em>ab initio</em> molecular dynamics calculations offers a unique route to determine <em>time-resolved </em>populations of the various isomeric products formed after UV (266 nm) excitation of the five-membered heterocyclic molecule thiophenone. This strategy reveals an unexpectedly high (~50%) yield of an episulfide isomer containing a strained 3-membered ring within ~1 ps at early times and rapid interconversions between the rival photoproducts. </span></p>
Monitoring the evolution of relative product populations at early times during a photochemical reaction
Open the record for dataset details and reuse information.
Raw data accompanying: Ground reaction forces in monitor lizards (Varanidae) and the scaling of locomotion in sprawling tetrapods
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data for "A machine-learned approach to monitor chemical reaction via in-situ infrared spectroscopy"
<p>Source spectral and structural data of the AIMD trajectory and NEB calculation</p> <p>1. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/180-structure-IR.zip">180-structure-IR</a>.zip Source spectral and structural data of the AIMD trajectory for the selected 180 configurations</p> <p>2. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/md-pos-1.xyz?versionId=1a43760b-5cba-4548-85e6-6a45252403fc">md-pos-1.xyz</a> AIMD trajectories</p> <p>3. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/ML-0-5100.tar.gz?versionId=ed65c63f-aa96-4be4-abef-e7a5bb7bf22f">ML-0-5100.tar.gz</a> <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/ML-0-5100.tar.gz?versionId=ed65c63f-aa96-4be4-abef-e7a5bb7bf22f">ML-5101-7500.tar.gz</a> <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/ML-0-5100.tar.gz?versionId=ed65c63f-aa96-4be4-abef-e7a5bb7bf22f">ML-7501-9500.tar.gz</a> Source spectral and structural data of the AIMD trajectory for the extracted 9500 configurations</p> <p>4. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/split-neb-75.zip?versionId=a30f0f34-3ce2-4619-abd3-62e71174069b">split-neb-75.zip</a> Source spectral and structural data of the AIMD trajectory for the CO-CO dimerization reaction</p>
Immune CHeckpoint Inhibitors Monitoring of Adverse Drug ReAction
ClinicalTrials.gov study NCT03492242. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.
The Influence of Standardized Process Management of Laryngeal Mask Airway Placement Based on Pressure Monitoring on the Incidence of Adverse Reactions in Elderly Patients During the Perioperative Peri
ClinicalTrials.gov study NCT06954857. IPD Sharing: NO. Countries: 1. Publications: 0.
Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
NMR as a readout to monitor and restore the integrity of complex chemoenzymatic reactions
<p>The folder contains raw data used in the manuscript titled "NMR as a readout to monitor and restore the integrity of complex chemoenzymatic reactions". Data includes raw 1D NMR data, a new 1D isotope and diffusion filtered NMR pulse sequence, MALDI-TOF-MS data, and an SDS-PAGE gel image. </p>
A mid-infrared lab-on-a-chip for dynamic reaction monitoring
<p>This file contains the as measured source data of the above-mentioned manuscript. </p>
Monitoring Solid-Phase Reactions in Self-Assembled Monolayers by Surface-Enhanced Raman Spectroscopy
<p>Data underlying the figures in the publication “Monitoring Solid-Phase Reactions in Self-Assembled Monolayers by Surface-Enhanced Raman Spectroscopy”, published in <em>Angew. Chem. Int. Ed.,</em> <strong>2021</strong>, 60, 2–10<strong>.</strong></p> <p><a href="https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202102319">https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202102319</a></p> <p>Table of contents:</p> <p><strong>1. Figure 1C</strong>; Zip file containing the numerical data for <em>Figure 1C</em>.</p> <p>The data were obtained from optical numerical simulations using the software <em>Lumerical</em>. The parameters used for the simulations are described in the SI of the publication. The file “OCH04-015_0nm.txt” has been exported from the simulated solution. It includes the distribution of the electric field intensity (|E|^2) in x and y directions at the Au-air interface. The data were then plotted as the electromagnetic enhancement factor in log scale (log|E|^4) using the origin lab software (“OCH04-015.opju”.</p> <p><strong>2. Figure 1D, 1E, 1F</strong>; Zip file containing the numerical data for <em>Figures 1D, 1E</em> and <em>1F.</em></p> <p><strong>Figure 1D:</strong> 100 data files with the general file name:</p> <p>“OCH04-021_3_633nm_300lpermm_10perc_2x30s_300hole_100x_Yyy_Xxx.txt”</p> <p>The yy and xx are different numeric values for each file indicating the position in the 10 x 10 map. And:</p> <p>«OCH02-072_2_blankAu_2x30s_10perc_633nm_100x_01.txt” is the dataset of the orange dotted spectrum which was recorded on the planar Au surface.</p> <p>In all text files, there are two columns: The first one is the Raman shift in cm–1 and the second one the intensity in photon counts. The Raman spectroscopy data in the files starting with “OCH04-021…” were generated using the Horiba LabRAM Software and the baseline has already been subtracted using this software. The 100 spectra were plotted without further data smoothing (grey spectra) and the average spectrum (black) was generated by using the dedicated function in the Origin Lab software. The orange spectrum originates from «OCH02-072_2_blankAu_2x30s_10perc_633nm_100x_01.txt”. It was smoothed with 10 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted.</p> <p><strong>Figure 1E:</strong> The Box Plot was generated using the 100 grey spectra from 1D and applying a Gaussian fit to the three peaks indicated in the figure and extracting the peak positions. Using these peak position data, the box plot was generated using the Origin Lab software.</p> <p><strong>Figure 1F:</strong> The contour plot was generated using the 100 grey spectra from 1D and applying a gaussian fit to the peak indicated in the figure description and extracting the peak heights. Using these peak height data, the contour plot was generated using the Origin Lab software.</p> <p><strong>3. Figure 2</strong>; Zip file containing the numerical data for <em>Figure 2</em>.</p> <p>In all text files, there are two columns: The first one is the Raman shift in cm<sup>–1</sup> and the second one the intensity in photon counts. The spectra were smoothed with 10 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted. The y intensity was normalized so that the Si peak at approx. 950 cm<sup>–1</sup> had the same height. The Raman shift in x direction was shifted so that the Si peak at 300 cm<sup>–1</sup> was at the same position in each spectrum.</p> <p><strong>4. Figure 3A, 3C</strong>; Zip file containing the numerical data for <em>Figures 3A</em> and <em>3C</em>.</p> <p>In all text files, there are two columns: The first one is the Raman shift in cm–1 and the second one the intensity in photon counts. The spectra were smoothed with 8 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted. The average of three spectra was calculated for the spectra with the same y description for the plotted spectra in 3A. Figure 3C was generated by applying a gaussian fit to the three peaks indicated in the figure in the original 12 data sets and extracting the peak heights. The average and standard deviation of the peak height data from the spectra with the same y description was then calculated to generate Figure 3C.</p> <p><strong>5. Figure 4A, 4B</strong>; Zip file containing the numerical data for <em>Figures 4A</em> and <em>4B</em>.</p> <p><strong>4A:</strong> In all text files, there are two columns: The first one is the Raman shift in cm<sup>–1</sup> and the second one the intensity in photon counts. The spectra were smoothed with 10 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted. The y intensity was normalised so that the Si peak at approx. 950 cm<sup>–1</sup> had the same height. The Raman shift in x direction was shifted so that the Si peak at 300 cm<sup>–1</sup> was at the same position in each spectrum.</p> <p><strong>4B:</strong> The peak positions from <em>Figures 2</em> and <em>4A</em> were used to generate <em>Figure 4B</em>.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.