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1,041 results for “Spectroscopy”
Models for rapid estimates of leaf litter chemistry using reflectance spectroscopy
<p>Measuring the chemical traits of leaf litter is important for understanding plants' roles in nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression (PLSR) models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validation <em>R<sup>2</sup></em> = 0.543-0.941; %RMSE = 7.49-18.5) and ground-leaf litter spectra (validation <em>R<sup>2</sup></em> = 0.491-0.946; %RMSE = 7.00-19.5). Notably, intact-leaf spectra yielded better predictions of LMA. Our results support the feasibility of building models to estimate multiple chemical traits from leaf litter of a range of species. In particular, the success of intact-leaf spectral models allows non-destructive trait estimation in a matter of seconds, which could enable researchers to measure the same leaves over time in studies of nutrient resorption.</p>
AdaptFerm: Bioprocess Monitoring Using FTIR spectroscopy: Insights into Substrate Effects and Domain Adaptation
<h2> </h2> <h2><strong>1. Introduction</strong></h2> <p>The AdaptFerm dataset is designed to support the development of a monitoring framework for lactic acid production fermentation using Fourier Transform Infrared (FTIR) spectroscopy. Its primary goal is to facilitate the control strategies for continuous fermentation processes to maximize the lactic acid production. The AdaptFerm encompasses data from two distinct batch fermentation environments: one employing simple sugar (glucose) as the substrate and the other utilizing complex sugars derived from bio-waste. The study focuses on developing accurate predictive models for glucose and lactic acid concentrations, with an emphasis on applying classical machine learning techniques and enhancing domain generalization capabilities.</p> <h2><strong>2. Prediction Model for Different Substrate Environments</strong></h2> <p>The chemical composition of substrates are presented in <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Table 1 [1]</a>. The dataset is utilized to train and test models within the same substrate domain. For instance, data from a single fermentation environment (e.g., glucose substrate) is used for both training and testing phases. The applied machine learning models showed accurate prediction within the same domain <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">[1]</a>. For more details on the methods applied, please refer to the following link: <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">https://doi.org/10.1016/j.heliyon.2024.e38791</a>. In this study, the MIR results correspond to the AdaptFerm dataset. The spectra of the glucose and biowaste hydrolysate fermentation process are presented in <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Figure 3</a> and <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Figure 4</a>.</p> <h2><strong>3. Domain Adaptation</strong></h2> <p>The dataset was also used to address the challenge posed by shifts in FTIR data when substrates change. Transitioning from simple sugar (glucose) to complex sugar (bio-waste) causes significant variations in the FTIR spectra, making it difficult for models trained on glucose fermentation data to maintain prediction accuracy in the complex sugar fermentation environment. This results in reduced robustness and performance when applied to out-of-distribution data. To address these challenges, we explore methods that improve the generalization ability and robustness of models in such scenarios without using labels from complex sugar fermentation <a title="Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation" href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">[2]</a>. It shows the application of machine learning interpretation to find domain invariant features for glucose and lactic acid. For more details on the methods applied, please refer to the following link: <a title="Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation" href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">https://dx.doi.org/10.2139/ssrn.5012080</a>. The code is available at <a title="ShapFS" href="https://github.com/shl-shawn/ShapFS" target="_blank" rel="noopener">https://github.com/shl-shawn/ShapFS</a>.</p> <h2><strong>4. Real-World Use Cases</strong></h2> <h3><strong>4.1. Regression Task</strong></h3> <p>AdaptFerm serves as a benchmark for machine learning model applications in fermentation processes, specifically for predicting glucose and lactic acid concentrations, measured in g/L (grams per liter), while considering issues of out-of-distribution generalization.</p> <h3><strong>4.2. Domain Adaptation Regression Task</strong></h3> <p>The dataset is also suitable for evaluating different domain adaptation methods. In particular, the glucose substrate fermentation data can be used as the source domain, while the complex sugar fermentation data from bio-waste serves as the target domain. For semi-supervised domain adaptation approaches, it is recommended to use the initial data points (i.e., those collected at the beginning of the fermentation process) from the target domain, as the dataset is organized chronologically by collection day. These approaches aim to improve the robustness of models by transferring knowledge across domains and mitigating the effects of out-of-distribution data.</p> <h3><strong>4.3. Anomaly Detection</strong></h3> <p>The dataset can be used to train anomaly detection models to identify outliers or deviations from normal fermentation behavior. This could be valuable in industrial bioprocessing, where early detection of issues like contamination or process failure is crucial. Techniques like Isolation Forests, One-Class SVM, or Autoencoders could be applied to identify unusual patterns in FTIR spectra.</p> <h3><strong>4.4. Classification Task</strong></h3> <p>Although the main task is regression, the dataset could also be used in classification tasks by discretizing the concentrations of glucose and lactic acid into categories (e.g., low, medium, high). This would allow for the application of classification algorithms like Support Vector Machines (SVM), Random Forests, or Neural Networks for predicting the fermentation phase or identifying specific operational conditions.</p> <h3><strong>4.5. Transfer Learning</strong></h3> <p>Given the nature of the domain adaptation approach in this dataset, transfer learning models can be explored. Models pre-trained on glucose fermentation data can be fine-tuned on complex sugar fermentation data, enabling quicker model convergence and improved performance in data-scarce environments.</p> <h3><strong>4.6. Multi-Task Learning</strong></h3> <p>In a multi-task learning scenario, models could simultaneously predict both glucose and lactic acid concentrations from the same FTIR data. This could help in improving model accuracy by leveraging shared representations across the two tasks.</p> <h3><strong>4.7. Feature Selection</strong></h3> <p>The FTIR spectral data contains a large number of features (wavelengths), and feature selection techniques such as Recursive Feature Elimination (RFE), Lasso regression, or mutual information could be applied to identify the most relevant wavelengths for predicting glucose and lactic acid concentrations, improving model performance and interpretability.</p> <h2><strong>5. Dataset Structure and Meta Information</strong></h2> <p>The dataset is organized into four Excel files, corresponding to two main fermentation domains (different substrates) and two key process variables:</p> <p><strong>a) Simple Sugar Substrate</strong><br>This domain contains data for the fermentation process using glucose as the substrate to produce lactic acid. It includes two files—one for glucose concentrations and one for lactic acid concentrations. Both are measured in g/L.</p> <p><strong>b) Complex Sugar Substrate</strong><br>This doman contains data for the fermentation process using bio-waste as the substrate to produce lactic acid. Similar to the previous domain, it includes two files—one for glucose concentrations and one for lactic acid concentrations. Both are measured in g/L.</p> <p>Each file is structured as follows:</p> <ul> <li>The first column contains the<strong> </strong>sample ID, which serves as the timeline of measurements (Sample ID 1 represents the first measurement in the fermentation process).</li> <li>From the second column onwards, the FTIR data is provided, covering the spectral range from 549.6 cm-1 to 3999.6 cm-1 comprising 3,579 features.</li> <li>The final column contains the ground truth data, the chemical measurements of fermentation variables such as glucose and lactic acid concentrations, both measured in g/L.</li> </ul> <h2><strong>6. Conclusion</strong></h2> <p>The AdaptFerm features FTIR spectra data from two distinct fermentation environments: simple sugar (glucose) and complex sugar (bio-waste). The dataset is designed to be used in regression tasks, including domain adaptation, and can be applied in machine learning model development for fermentation process monitoring, with a focus on enhancing model robustness and handling out-of-distribution data. This dataset provides a valuable resource for exploring<strong> </strong>domain shift and improving the robustness of machine learning models in bioengineering and fermentation processes. It enables further research into domain generalization techniques and offers a wide range of possibilities for machine learning applications.</p> <h2>References</h2> <p> [1] Arman Arefi, Barbara Sturm, Majharulislam Babor, Michael Horf, Thomas Hoffmann, Marina Höhne, Kathleen Friedrich, Linda Schroedter, Joachim Venus, Agata Olszewska-Widdrat, Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates, Heliyon, Volume 10, Issue 19, 2024, e38791, ISSN 2405-8440, DOI: 10.1016/j.heliyon.2024.e38791, <a href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">https://doi.org/10.1016/j.heliyon.2024.e38791</a>.</p> <p>[2] Majharulislam Babor, Shanghua Liu, Arman Arefi, Agata Olszewska-Widdrat, Barbara Sturm, Joachim Venus, and Marina M.-C. Höhne, Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation. Available at <a href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">http://dx.doi.org/10.2139/ssrn.5012080.</a></p>
Data set from: Can laboratory-based XAFS compete with XRD and Mössbauer spectroscopy as a tool for quantitative species analysis?
<p><strong>Abstract:</strong> This work investigated the capability of quantitative laboratory X-ray Absorption Fine Structure Spectroscopy (lab-XAFS) via Linear Combination Fitting (LCF) of reference spectra in comparison with quantitative X-ray diffraction (XRD) and Mössbauer spectroscopy. While lab-XAFS already show good results when performing LCF with significant different spectra of the species to be identified, the method is challenging when the reference spectra and possibly species in the sample are very similar as it is the case for α-Fe<sub>2</sub>O<sub>3</sub>, γ- Fe<sub>2</sub>O<sub>3</sub> and Fe<sub>3</sub>O<sub>4</sub>. For this investigation an iron oxide mineral with origin from Mexico (here named Mexican Magnetite) with different iron oxide phases was used and measured using all three methods.</p> <p> </p> <p>This data set contains the raw data of the work “<em>Can laboratory-based XAFS compete with XRD and Mössbauer spectroscopy as a tool for quantitative species analysis? Critical evaluation using the example of a natural iron ore</em>” of XAFS, XRD and Mössbauer measurements. This includes XAFS, Mössbauer and XRD spectra of the reference materials α-Fe<sub>2</sub>O<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub> and the sample Mexican magnetite, the XAFS spectra of the reference material γ- Fe<sub>2</sub>O<sub>3</sub> and the XAFS, XRD and Mössbauer spectra of three different α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures.</p> <p> </p> <p><u>Sample information/sample list</u></p> <p><strong>sample/references:</strong> The sample and the corresponding short cut name used in the data files is listed. Furthermore the method the sample was measured with is also listed.</p> <table> <tbody> <tr> <td> <p><strong>Short cut name</strong></p> </td> <td> <p><strong> Sample/reference</strong></p> </td> <td> <p><strong>Measured with</strong></p> </td> </tr> <tr> <td> <p>MexicanMagnetite</p> </td> <td> <p> Iron oxide mineral with origin in Mexico</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe2O3</p> </td> <td> <p>Fe2O3-alpha / Hematite</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe3O4</p> </td> <td> <p>Fe3O4 / Magnetite</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe</p> </td> <td> <p>Iron powder</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>Fe2O3-alpha</p> </td> <td> <p>Fe2O3-alpha / Hematite</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>Fe2O3-gamma</p> </td> <td> <p>Fe2O3-gamma / Maghemite</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>30-70</p> </td> <td> <p>Mixture of 30 % Fe2O3-alpha/ 70 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>50-50</p> </td> <td> <p>Mixture of 50 % Fe2O3-alpha/ 50 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>70-30</p> </td> <td> <p>Mixture of 70 % Fe2O3-alpha/ 30 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Mixtures ratios:</strong> The prepared Fe2O3-Fe3O4 model mixtures with the weight-in ratios and the actual achieved mass percentage ratio between the two iron species, taken impurities of the used materials into account, are listed below. The short cut name is the name used in the data files (see table above).</p> <table> <tbody> <tr> <td> <p><strong>Short cut name</strong></p> </td> <td> <p><strong>Actual achieved weigh-in ratios</strong></p> <p><strong>m(Fe2O3)/m(Fe3O4)*</strong></p> </td> <td> <p><strong>Actual achieved mass percentage ratios ωrel(Fe2O3) / ωrel(Fe3O4)</strong></p> </td> </tr> <tr> <td> <p>30-70</p> </td> <td> <p>0.31380 g / 0.7059 g</p> </td> <td> <p>31.8 / 68.2</p> </td> </tr> <tr> <td> <p>50-50</p> </td> <td> <p>0.5140 g / 0.5174 g</p> </td> <td> <p>50.6 / 49.4</p> </td> </tr> <tr> <td> <p>70-30</p> </td> <td> <p>0.7037 g / 0.3041 g</p> </td> <td> <p>70.5 / 29.5</p> </td> </tr> </tbody> </table> <p>*the given masses here, ar the masses of the materials of the mixtures before sampel prepration. For the sample prepration the mass applied on the tape or mixed with wax is about 5-10 mg.</p> <p><u>Spectrometer Specifications</u></p> <p><strong>XAFS:</strong> The experimental setup for the laboratory XAFS measurement is based on the Highly Annealed Pyrolytic Graphite (HAPG) von Hámos spectrometer with the use of a cylindrically shaped crystal.</p> <p>As detector unit the pixelated X-ray hybrid-CMOS detector Dectris Eiger2 R 500k was used. The area of detection is 77.3 mm x 38.6 mm with a pixel size of 75 µm x 75 µm. The X-ray source was a water-cooled micro focus X-ray tube with molybdenum as anode material, a power of 30 Watt optimised at 15 kV and a spot size of 70 µm.</p> <p><strong>Sample preparation</strong>: α-Fe<sub>2</sub>O<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub>, the three α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures and the sample Mexican magnetite were applied on adhesive tape, sliced in 1cm x 1cm pieces characterized with XRF to determine the iron content as [<em>Q</em>] = mg/cm² and then stacked by taking the iron content of each slice into account to achieve an absorption of <em>µ*Q</em> of about 1 at the edge.</p> <p>The γ- Fe<sub>2</sub>O<sub>3</sub> and also the three α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures were prepared as Pellet. Here the sample material was mixed with Hoechst Wax C in a ratio of 1:6, mixed in a vortex shaker and then pressed with a hydraulic press with a Pellet diameter of 13 mm. The amount of the wax/sample powder material was weight before inserting in the press to the amount of <em>Q</em> to achieve a <em>µ*Q</em> of about 1 with a 13 mm Pellet.</p> <p>Shifts of the energy axis as well as a widening or compression of this axis could be present when comparing the data with other data sets of other spectrometer or synchrotron radiation facilities, since no precise energy calibration was carried out due to the reason that the samples were compared to the measured references and would have the same shift, widening or compression.</p> <p> </p> <p><strong>XRD:</strong> Two different commercial XRD set ups have been used. For the Mexican magnetite the Benchtop XRD spectrometer Bruker D2Phaser with a Cobalt X-ray source and a SSD160 detector (active length = 12 mm) was used. The measurement range was 10°- 90° 2theta with 0.014° step size and 4.8 s/step, resulting in a total measurement time of 8h. During the measurement the sample was rotated with 10 rpm. The sample was filled in PMMA-holders (Ø 2.5 mm) using the top-loading technique. The analysis was carried out using a 1-mm fixed divergence slit, a 2.5° primary and a 4° secondary soller collimator, a fixed knife edge (3 mm above the sample surface), and an Fe Kβ filter (2.5).</p> <p>For the X-ray diffraction measurements of the α-Fe2O3/Fe3O4 mixtures and the pure references a Panalytical X’Pert PRO diffractometer with a Bragg-Brentano setup was used. The diffractometer operates with a Cu anode and without a monochromator (Cu-Kalpha radiation) at 40 kV and 30 mA. The diffraction data were obtained over a measurement range of 10–120° 2theta. Samples were applied flat on a cut-off Si wafer attached to the sample holder.</p> <p><em> </em></p> <p><strong>Mössbauer:</strong> Mössbauer spectroscopy was performed at a MIMOS II type spectrometer with a <sup>57</sup>Co source (in rhodium matrix). For the analyses the <sup>57</sup>Fe-γ-line E = 14.4 keV was used and α-iron (α-Fe foil) was applied for the velocity calibration before the samples were analyzed. The samples were prepared in plastic powder sample holders and measured in transmission mode at room temperature. The measurement time varied between 12 h and 120 h depending on the sample.</p> <p> </p> <p><strong>Information on data sets</strong></p> <p>XAFS - this folder contains the XAFS spectra as intensity file with I0 (without the sample) and the It (transmission signal through the sample) for each sample. Multiple samples (It) share the same I0 and are therefore in the same data set. The Number in the filename between “XAFS“ and “data-set..” is the date of the measurement in the following format: YYYY_MM_DD. The first column in each file is the energy in unit eV. The abbreviation “WP” after each sample name in the header means “<strong>W</strong>ax <strong>P</strong>ellet” and indicates that the measurement was performed on a sample prepared as a wax pellet, the number (WP<strong>1</strong>) indicates the number of the pellet. Two pellets of each mixture were prepared to investigate the influence of the sample preparation. If the sample name is missing “WP#” the sample was prepared on adhesive tape as described above. The information on the contents of each data set as well as the measurement time (t = #h) for each It of the sample/reference can be found in data_dictionary_v2.txt.</p> <p>The intensity is normalized to counts per 1800 seconds in a 0.25 eV (for data-set-1) and 1 eV (for data-set-2, data-set-3 and data-set-4) energy interval with the indicated central bin energy.</p> <p> </p> <p>XRD - this folder contains the raw intensity files over 2theta (ASC-file). Each sample has its own file with the first column for the 2theta in unit degree and the second column for the measured intensity.</p> <p>The Number in the file name between XRD and sample name (e. g. Fe2O3, 30-70) is the date of the measurement in the following format: YYYY_MM_DD.</p> <p> </p> <p>MOESSBAUER - this folder contains the recoil Lorentz site analysis fit data of the samples. The files consist of the observed intensity (Iobs) over the velocity (v (mm/s)), including the calcucalted intensity (Icalc) and the fits of the subspectra (Sextet Site 1, etc. ). Each sample has it owns file. While the references substances <br>Fe2O3 and Fe3O4 were measured between 2016 and 2019, the MexicanMagnetite was measured 2020. An exact measurement date can’t be determined anymore.</p> <p> </p> <p>The corresponding sample to the short cut name (e. g. Fe2O3, 30-70,..) in the files can be found above and is listed in the <em>data_dictionary.txt</em> file as well.</p> <p> </p>
Dataset for the publication entitled: "Assessment of lithium ion battery ageing by combined impedance spectroscopy, functional microscopy and finite element modelling""
<p>Related to the publication: <a href="https://doi.org/10.1016/j.jpowsour.2021.230459">https://doi.org/10.1016/j.jpowsour.2021.230459</a></p> <p>Datasets for the following Figures:</p> <p>Figure 2.</p> <p>Figure 3.</p> <p>Figure 5.</p> <p>Figure 7.</p>
Use of infrared spectroscopy for a sampling study of waste wood samples in a panel board industry
<p>Oral presentation at the conference 'NIR Italia Online, waiting for Slovenia 2022' (24-25 February 2021). </p> <p>NIR Italia Symposium are biennial conferences on infrared spectroscopy. Due to the pandemic situation the Italian Society for Near Infrared Spectroscopy (SISNIR), in collaboration with the InnoRenew CoE and University of Primorska, has decided to organize an online National Symposium of NIR Spectroscopy, waiting for the opportunity to meet physically next year. </p> <p>The event is an important opportunity to virtually present works, and exchange ideas, opinions and future perspectives. This presentation perfectly fits with theme of NIR spectroscopy. Indeed, the presentation is about the use of NIR spectroscopy for assessing the best sampling procedure in order to describe a really heterogeneous material as waste wood is. </p>
Atrial Fibrillation Designation with Micro-Raman Spectroscopy and Scanning Acoustic Microscopy
<p>This repository was constructed tp provide the <strong>Raman Spectroscopy</strong> data and figure files related to the manuscript “Atrial Fibrillation Designation with Micro-Raman Spectroscopy and Scanning Acoustic Microscopy”. </p>
Quantification of salt stress in wheat leaves by Raman spectroscopy and machine learning
<p>Train and test datasets used in the manusicript "Quantification of salt stress in wheat leaves by Raman spectroscopy and machine learning". Trained models are included.</p>
Increased Cardiac Pi/PCr in the Diabetic Heart Observed Using Phosphorus Magnetic Resonance Spectroscopy at 7T
<p>The uploaded data relate to the work of Valkovič et al. entitled "Increased Cardiac Pi/PCr in the Diabetic Heart Observed Using Phosphorus Magnetic Resonance Spectroscopy at 7T." submitted in 2022<br> The data consist of the anonymised in vivo data of healthy volunteers and T2DM patients, in Siemens DICOM format, acquired as described in the manuscript. The uploaded data also include all analysed STEAM data results with visualised MR spectra and fitted values of PCr and Pi, used for Pi/PCr calculation.</p>
Dielectric-loss spectroscopy development and iridium photoredox catalyst ion pairing
<p>This data accompanies the publishing of the manuscript of reviving dielectric-loss spectroscopy and ion pair reorganization in an iridium photoredox catalyst. The data consists of microwave transients, microwave resonances, absorption/emission, and Stern-Volmer quenching data.</p>
First-Principles Core Spectroscopy of LiCoO2 and CoO2
<p>Input and results data for the paper "First-Principles Core Spectroscopy of LiCoO2 and CoO2"</p>
Data for: Nawrocka et al., Variable-temperature NMR spectroscopy for metabolite identification in biological materials, RSC Adv., 2021, 11, 35321, DOI: 10.1039/d1ra05626c
<p>Raw NMR spectrometer data (Varian/Agilent format) for the paper Nawrocka et al., Variable-temperature NMR spectroscopy for metabolite identification in biological materials, RSC Adv., 2021, 11, 35321, DOI: 10.1039/d1ra05626c</p> <p>Includes variable-temperature series for artificial and natural mixtures of metabolites and spectra of separate metabolites.</p>
Development of a Low-Cost Method for Quantifying Microplastics in Soils and Compost Using Near-Infrared Spectroscopy
<p>Datasets and scripts for data evaluation</p>
Correlation spectroscopy with multi-qubit-enhanced phase estimation: data
<p>This directory contains the data shown in Figures 2-8 of the paper "Correlation spectroscopy with multi-qubit-enhanced phase estimation" (https://doi.org/10.48550/arXiv.2203.12656). Each data set is stored as matlab .mat file containing variables described in the accompagnying .text document</p>
Data for analysis procedure for rectenna-based THz-spectroscopy as published in Lechelon et al., Sci. Adv. 8, eabl5855 (2022)
<p>Example of data for R-PE data processing </p>
Ultrafast Infrared Transient Absorption Spectroscopy of Gas-Phase Ni(CO)4 Photodissociation at 261 nm
<p>This is the data repository for the following publication:</p> <p>Neil C. Cole-Filipiak, Jan Tross, Paul Schrader, Laura M. McCaslin, and Krupa Ramasesha, "Ultrafast infrared transient absorption spectroscopy of gas-phase Ni(CO)4 photodissociation at 261 nm," J. Chem. Phys. 156, 144306 (2022), https://doi.org/10.1063/5.0080844.</p> <p><strong>Abstract:</strong></p> <p>We employ ultrafast mid-infrared transient absorption spectroscopy to probe the rapid loss of carbonyl ligands from gas-phase nickel tetracarbonyl following ultraviolet photoexcitation at 261 nm. Here, nickel tetracarbonyl undergoes prompt dissociation to produce nickel tricarbonyl in a singlet excited state; this electronically excited tricarbonyl loses another CO group over tens of picoseconds. Our results also suggest the presence of a parallel, concerted dissociation mechanism to produce nickel dicarbonyl in a triplet excited state, which likely dissociates to nickel monocarbonyl. Mechanisms for the formation of these photoproducts in multiple electronic excited states are theoretically predicted with one-dimensional cuts through the potential energy surfaces and computation of spin–orbit coupling constants using equation of motion coupled cluster methods (EOM-CC) and coupled cluster theory with single and double excitations (CCSD). Bond dissociation energies are calculated with CCSD, and anharmonic frequencies of ground and excited state species are computed using density functional theory (DFT) and time-dependent density functional theory (TD-DFT).</p> <p> </p> <p><strong>Experimental Data:</strong></p> <p>All data are saved as a .csv file. The first column contains frequencies (in cm<sup>-1</sup>) while the first row indexes each time delay (in ps). High-resolution transient spectra at select time delays are similarly structured. Each transient .csv file is labeled according to molecule, pump wavelength, file contents, pump laser power, pressure, and a date (<em>e.g.</em> NT261_trans_1mW_1.5torr_17Feb2021.csv).</p> <p> </p> <p><strong>Computational Data:</strong></p> <p>This data repository consists of 7 directories, which contain the data used in the main paper. Computational data published in the supplementary material may be requested from the corresponding authors.</p> <p>Anharmonic frequencies and DFT energies can be obtained in the directory "VPT2", where the files are labelled nicoX_*_anharm.out, where X=3,2,1 (the compound) and * corresponds to the electronic state for which the calculation was performed.</p> <p>EOM-CC calculations of the spin-orbit coupling constants at the geometries reported are found within the "SOCC" directory using the naming convention nicoX_[]_so_*.out, where X=3,2, []=an indication of the geometry, and * corresponds to the electronic state for which the calculation was performed.</p> <p>Calculations of the minimum energy crossing points (MECPs) can be found in the directory "MECP" using the naming convention nicoX_min*.out, where X=4,3,2,1, and * corresponds to the two electronic states for which the MECP is calculated (e.g. s0s1).</p> <p>The following 4 directories contain all the EOM-CC output files needed to reproduce the curves from Figure 2: 4to3, 3to2, 4to2, and 2to1, corresponding to panels a, b, c, and d, respectively. The naming conventions for the files within these directories are X_yz.out, where X=the name of the directory, y=the value of the reaction coordiante, and z=s (singlet) or t (triplet).</p>
Rapid assessment of lipidomics sample quality and quantity using attenuated total reflectance Fourier-transform infrared spectroscopy
<p>In this work, we aimed to develop a simple lipid quality and quantification method for biological lipid extracts, as a step in lipidomics workflows, with minimal sample requirement. We chose FTIR spectroscopy with an Attenuate Total Reflectance (ATR) sampling method as it requires just 1 microliter of MS-ready sample without additional sample preparation. We validated the proposed lipidomics sample quality control workflow using a set of plasma samples (n=107, with 3-4 technical replicates) with comparison to LC-MS-based lipidomics. The following file contains the resulting spectra acquired by ATR-FTIR spectrometry for these plasma samples, standard curves and contaminated samples used for method development. Spectrometer was ambient blanked and detector cleaned between each measurement. Lipid samples were extracted by butanol-methanol (3:1) precipitation, and dried directly onto the ATR-FTIR detector. Absorbance was measured between 4,000 and 650 cm-1 wavenumbers, at a resolution of 8cm-1. Each spectra has been baseline corrected (whole spectra).</p>
Quantitative spectroscopy of B-type supergiants [Dataset]
<p>This archive contains data used for the paper:</p> <p>Quantitative spectroscopy of B-type supergiants</p> <p>It contains reduced FOCES spectra - consult the paper for details on the reduction process.</p>
Supplementary files for paper: "Design and fabrication of an electrostatic precipitator for infrared spectroscopy" in Atmospheric Measurement Techniques, 2022.
<ol> <li>File of absorbance spectra and hypothetical thickness for each sample.</li> <li>MATLAB function to perform clean crystal spectrum subtraction and baseline correction (described in the paper).</li> </ol>
Excitation energy transfer and vibronic coherence in intact phycobilisomes — multidimensional electronic spectroscopy data set and MATLAB and Julia analysis code
<p>Data sets used in the article "Excitation energy transfer and vibronic coherence in intact phycobilisomes" by Sil et al. The phycobilisomes were isolated from the short-filament mutant (SF33) of <em>Fremyella diplosiphon</em> UTEX 481 (also known as <em>Tolypothrix</em> sp. PCC 7601). Multidimensional electronic spectroscopy was performed with 6.7 fs mid-visible pulses (520–700 nm) using a pump–probe optical configuration using adaptive pulse shaping techniques. In addition to the full set of two-dimensional spectra and analysis files generated using global and target modeling and analysis of coherences (3DES oscillation maps), we provide here a linear absorption spectrum with phycobiliprotein component analysis as well as a set of 2D excitation–emission fluorescence spectra of intact and broken phycobilisome preparations. </p> <p>Sil, S.; Tilluck, R. W.; Mohan TM, N.; Leslie, C. H.; Rose, J. B.; Domínguez-Martín, M. A.; Lou, W.; Kerfeld, C. A.; Beck, W. F. Excitation energy transfer and vibronic coherence in intact phycobilisomes. Nat. Chem. (2022), DOI: 10.1038/s41557-022-01026-8.</p> <p><a href="https://urldefense.com/v3/__https://www.nature.com/articles/s41557-022-01026-8__;!!HXCxUKc!yaVwTZFk8T-j3ROhygpOGW5Xy_E2wQvf-QgNGr9FZZbp4oNpfp_ZmhkdWYLdg2mKSDP8yYrNAZs$">https://www.nature.com/articles/s41557-022-01026-8</a></p> <p> </p> <p> </p>
Research data supporting "Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in human aorta"
<p>Research data supporting the publication:</p> <p>You, A. Y. F. <em>et al.</em>, 2017, "Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in human aorta", Science Advances, DOI: 10.1126/sciadv.1701156.</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.