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88 results for “FTIR”
FTIR-ATR spectra of culinary grain legumes (pulse) flours
<p>FTIR-ATR spectra of 5 culinary grains: </p> <p><br> 1. chickpea (Cicer arietinum n=87)<br> 2. lentil (Lens culinaris n=93))<br> 3. grass pea (Lathyrus sativus n=116)<br> 4. pea (Pisum sativus n=119)<br> 5. faba bean (Vicia faba n=93)</p> <p>Grains were dried at 40 °C and milled using a miller Retsch cyclone mill with a particle size under 0.8 mm. The different flours were stored at -20 °C. For FTIR-ATR analysis there was no need for further sample preparation.</p> <p> </p> <p>Files: </p> <p><a href="https://zenodo.org/api/files/9c2c7a63-966d-4b4b-a1bf-5b8d8228ac48/FTIRATR_Pulse.mat">FTIRATR_Pulse.mat</a>: Matlab structure with, as fields:<br> <Data> : a 491x1734 matrix, each line corresponds to a spectra<br> <Wavelength>: a 1730 length vector, Wavelength of the incident light<br> <Tag> : a 491 length vector, labelling of the samples<br> <Label4Rag> : Names of the label.</p> <p><a href="https://zenodo.org/api/files/9c2c7a63-966d-4b4b-a1bf-5b8d8228ac48/FTIRATR_pulse_data.csv">FTIRATR_pulse_data.csv</a>: csv file with the data, wavelength and tag fields</p> <p> <a href="https://zenodo.org/api/files/9c2c7a63-966d-4b4b-a1bf-5b8d8228ac48/FTIRATR_pulse_labels.csv">FTIRATR_pulse_labels.csv</a>: Names of the label</p> <p> </p> <p><br> </p> <p> </p> <p> </p>
In situ FTIR, EXAFS and HR-STEM data for Pd/TiO2 samples under red-ox conditions
<p>Files Pd_photo-oxidation.xmu.dat and Pd_dep-oxidation.xmu.dat contain the sequence of X-ray absorption spectra during starting from the pre-reduced state (after reduction in H2) during heating in O2 from 50 to 400 for Pd_photo and Pd_dep samples, respectively (synthesized using photodeposition and deposition-precipitation methods). The last two columns in each file correspond to the as-synthesized state of the corresponding sample (before reduction in hydrogen) and reference palladium foil. </p> <p>Pd_photo.ftir.dat and Pd_dep.ftir.dat contain the sequence of the FTIR spectra for the same samples taken at room temperature after sending 35 mbar of CO on pre-oxidized samples.</p> <p>Video files show the evolution of the structure of Pd_dep and Pd_photo samples sample under different atmospheres and temperatures, visualized by in situ HR-STEM microscope.</p>
AMS and FTIR measurements and the corresponding codes for their statistical combination
<p>This dataset includes the post-processed FTIR and AMS data for the particulate phase obtained by Yazdani et al., https://doi.org/10.5194/amt-2021-186 form wood and coal burning experiments in the PSI environmental simulation chamber. It also contains the codes for the statistical combination of AMS and FTIR measurements to estimate the high-time-resolution functional group composition of organic aerosols. </p>
Nano-FTIR Investigation of the CM Chondrite Allan Hills 83100
<p>This is supporting data for the paper titled "Nano-FTIR Investigation of the CM Chondrite Allan Hills 83100." ALH 83100 nanoFTIR spectra.xlsx contains all of the nano-IR phase and amplitude spectra presented in the paper. ALH83100_172_0_Normalized.dat and ALH83100_172_0_Normalized.hdr are the ENVI-readable data and header files for the micro-FTIR hyperspectral image presented in Figure 1. ALH83100_xaxis.cxv contains the x axis in wavenumber units for the micro-FTIR hyperspectral image. Six .gsf files, readable with the free Gwyddion software, are the single wavelength O2A images used to create Figures 3, 4, and 5.</p>
Computational models for kaolinite nano-particles (Generations 1-3) and their comprehensive FTIR spectra
<p>The dataset contains a large number of computational models and detailed spectral comparison, fitting, and deconvolution of a large set of FTIR data for crystalline and exfoliation kaolinite, nano-kaolinite and halloysite, nano-halloysite samples.<br> The <strong>G1.xyz</strong>, <strong>G2.xyz</strong>, and <strong>G3.xyz</strong> files contain the initial structures for the first three generations of nano-kaolinite molecules.<br> The compressed folder <strong>SVP-def2TZVP.zip</strong> contains the structural information relevant for comparing and contrasting the performance a double-zeta (SVP) and triple-zeta (TZVP) basis sets.<br> The <strong>edge_protonation.zip</strong> folder guides the reader through the stepwise evaluation of various edge protonation models and shows the final converged results.<br> The <strong>full_optimization.zip</strong> folder summarizes the stationary structure calculations at various levels of theory carried out for the G2 model.<br> </p>
FTIR Breast Cancer
<p>Two breast cancer subtypes FTIR spectroscopy imaging</p> <p>A = LA breast cancer</p> <p>B = HER2 breast cancer</p>
FTIR measurements of formic acid (2010-2012)
<p>The ground-based Fourier Transform InfraRed (FTIR) total column measurements of formic acid (HCOOH) reported here have been derived from high-resolution (between 0.004 and 0.011 cm<sup>-1</sup>) IR solar absorption spectra recorded regularly, under clear-sky conditions, at a suite of sites located at various latitudes. Most of them are affiliated with the Network for the Detection of Atmospheric Composition Change (NDACC; <a href="http://www.ndacc.org">http://www.ndacc.org</a>).</p> <p>End users of this data set are invited to contact the authors to make sure they are using the data properly and check about the possible availability of more recent products.</p>
FTIR-ATR and VSC measurements on cremated archaeological bone from Aakre Kivivare tarand grave, Estonia
<p>This dataset is described in paper bt Lillak <em>et al. </em>in prep "FTIR spectroscopy and VSC-based colour assessment dataset for comparative analysis of cremated bones".</p> <p>This dataset comprises four excel files: </p> <p>Bone list of bones from Aakre Kivivare <em>tarand </em>cemetery. This list is in Estonian.</p> <p>Bone list of bones from Viimsi I <em>tarand </em>cemetery. This list is in English.</p> <p>Bone list template with a table heading proposal, both in Estonian and English.</p> <p>A table of excavated <em>tarand </em>cemeteries, when and by whom they were excavated and whether and where these bones are stored. </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>
DATASET: Biobased composites by photoinduced polymerization of car-danol methacrylate with microfibrillated cellulose - FTIR data
<p>Raw FTIR data for the article "Biobased composites by photoinduced polymerization of car-danol methacrylate with microfibrillated cellulose"</p>
FTIR Microscopy for Direct Observation of Conformational Changes on Immobilized ω-Transaminase: Effect of Water Activity and Organic Solvent on Biocatalyst Performance
<p>Enzyme immobilization is a key strategy to expand the scope of enzyme applications and to enable the recycling of biocatalysts, resulting in greener and more cost-efficient processes. The full exploitation of the technology advantages is strictly connected to the optimal selection of the carriers and the rational development of the immobilization protocol. The present study achieved such objectives by investigating the activity of a ω-transaminase in organic solvent (toluene) upon immobilization on commercially controlled porosity glass carriers (EziG™) with diverse porosity and surface functionalization. In addition to more conventional wet-chemistry approaches and confocal microscopy, infrared microspectroscopy and imaging were exploited to highlight the enzyme distribution in a label-free manner and provide details on the immobilized enzyme's conformation with respect to the native form. Contrary to what could be expected, the highest activity of the enzyme in organic solvent was achieved for the immobilization protocol on the most hydrophilic support that more severely affects the enzyme secondary structure, promoting a beta-sheet rich folding. Experimental data show that values of water activity above 0.90 in the reaction system had a positive effect on the efficiency of the transaminase reaction. The present study represents the first example of rational development of immobilization protocols relying on direct observation of the enzyme conformation upon immobilization, shedding light on the mutual interaction between the diverse process parameters and the carrier properties.</p>
FTIR dataset from the article "Resistance to Degradation of Silk Fibroin Hydrogels Exposed to Neuroinflammatory Environments"
<p>The attached archive encounters the FTIR data used for the calculation of the β-sheet content of the silk fibroin hydrogels in the in-vitro experiments. Such data is used in main Figures 2<strong>c</strong> and 2<strong>d</strong>, 4<strong>a </strong>and 7<strong>a</strong> as well as Supplementary Figure 2<strong>a</strong> of the associated article.</p> <p> </p> <p>The acquisition of the files has been performed as indicated below:</p> <ul> <li><strong>Equipment:</strong> Thermo Scientific™ Nicolet™ iS™ 5 Spectrometer (Thermofisher, United States)</li> <li><strong>Acquisition Software:</strong> OMNIC 9.2.86 (Thermofisher, United States)</li> <li><strong>Number of Scans:</strong> 64</li> <li><strong>Data Spacing: </strong>0.482 cm<sup> -1</sup></li> </ul> <p> </p> <p>The files have been reported as raw complete spectrum including the absorbances in the wavenumbers from 600 to 1500 cm<sup> -1</sup> in the format of “.SPA”. In the following lines, we provide examples of software to process and analyze the spectra files included in the database.</p> <ul> <li>Python version 3.8 to 3.10 upon the availability of the “SpectroChemPy” (Travert and Christian, 2023) function.</li> <li>Matlab R2016a and newer upon the availability of the “LoadSpectra” (Oldenburg, 2023) function.</li> <li>OMNIC 9 (Thermofisher, United States).</li> <li>Essential FTIR (Operant LLC, United States).</li> </ul> <p> </p> <p>Provision of the data in other formats is available upon request. Inquiries may be directed to <a href="mailto:daniel.gonzalez@ctb.upm.es?subject=FTIR%20Samples%20Format">Daniel González-Nieto</a> or <a href="mailto:mahdi.yonesi@ctb.upm.es?subject=FTIR%20Samples%20Format">Mahdi Yonesi</a>.</p> <p><strong>References:</strong></p> <p>OLDENBURG, K. 2023. LoadSpectra. MATLAB Central File Exchange.</p> <p>TRAVERT, A. & CHRISTIAN, F. 2023. SpectroChemPy, a framework for processing, analyzing and modeling spectroscopic data for chemistry with Python. Github.</p> <p> </p>
FTIR and NMR spectra of impurified and purified acrylonitrile (AN) (Original data)
<p>This dataset contains FTIR and NMR spectra for the successful purification of acrylonitrile (AN) monomer by a simple column technique which is related to our published paper with DOI 10.3390/polym13040660</p> <p>1. Fig. 2a FTIR non-Pure AN (httpswebbook.nist.govcgicbook.cgiID=C107131&Units=SI&Mask=80#IR-Spec)<br> 2. FTIR_ Pure AN_ Raw Data (Excel)<br> 3. SigmaPlot FTIR_ Pure AN_ Raw Data<br> 4. Pure AN_1H (1)<br> 5. Pure AN_1H (2)<br> 6. Pure AN_13C(1)<br> 7. Pure AN_13C (2)<br> 8. Non Pure AN_1H (1)<br> 9. Non Pure AN_1H (2)<br> 10. Non Pure AN_13C (1)<br> 11. Non Pure AN_13C (2)</p> <p>12. Figure (1) -A schematic diagram of a simple column's advantages versus the distillation technique's drawbacks</p> <p>13. Figure (6)- Experimental setup of monomer purification procedure</p> <p>14. Editing Figures (1) & (6)</p> <p>15. Fig. 3. Chemial Structure of AN, AA, and MeHQ</p> <p>16. Related research paper dataset</p> <p> </p> <p>In fact, As known, purification is of the utmost significance in any chemistry process. Besides, eliminating impurities enhances the quality and standard of the product. The primary method for monomer purification, like acrylonitrile (AN), is the distillation technique. However, this technique is unsafe and hard to set up or handle. A straightforward, risk-free, and low-cost method like the column technique resolves these issues. Therefore, the data will be helpful in numerous applications, especially in polymerization reactions that require the removal of inhibitors before the polymerization initiation process.</p>
Phylum level phytoplankton composition and FTIR spectra for body wash microplastics and plant-based scrub particles from a 7-day summer 2016 surface mesocosm experiment in Otsego Lake, NY, USA
We tested the effects of two types of microplastics, 50 µm polystyrene (PS) calibration beads and polylactic acid (PLA) plastic body wash scrub particles, and one type of plant-derived body wash scrub particle on a natural phytoplankton assemblage through a 7-day mesocosm incubation experiment in a temperate, mesotrophic lake (Otsego Lake, Otsego County, NY, USA) in summer 2016.
In-situ high temperature FTIR, Raman data and breakdown temperature for phlogopite
<p>This dataset contains all new data corresponding to figures in the manuscript, including in situ high temperature FTIR, Raman data, and breakdown temperature from previous studies and this study.</p>
Temperature-dependent Raman, FTIR data and breakdown temperature of phlogopite
<p>This dataset contains all new data corresponding to figures in the manuscript and the supporting information, including temperature-dependent FTIR, Raman data, and breakdown temperature from previous studies and this study.</p>
FTIR Dataset
<p>Fourier-Transform Infrared Spectroscopy (FTIR) analysis was performed using a High-throughput Screening eXTension (HTS-XT) unit coupled to a Vertex 70 FTIR spectrometer (both Bruker Optik, Germany) allowing to perform High-throughput Screening (HTS) transmission mode measurements. The spectra were recorded in the region between 4000 and 500 cm<sup>-1</sup> with a spectral resolution of 6 cm<sup>-1</sup> and an aperture of 5.0 mm. Dataset represent FTIR spectroscopy data of 45 fast-growing bacteria isolated from green snow (coastal area of Eastern part of Antarctica). </p>
A dataset of atmospheric ozone above the Mexico City basin retrieved from FTIR remote sensing observations made at two different ground altitudes
<p>This dataset of atmospheric ozone (O<sub>3</sub>) has been generated from solar absorption spectra measured in central Mexico using ground-based Fourier-Transform Infrared (FTIR) spectrometers. The FTIR experiments have been operated by the “Spectroscopy and Remote Sensing” Research Group of the Centro de Ciencias de la Atmósfera of the Universidad Nacional Autónoma de México (http://www.atmosfera.unam.mx/espectroscopia/index.html).</p> <p>The dataset covers measurements made between November 2012 and February 2014 applying two different FTIR spectrometers. The first instrument offers very high resolution spectra and contributes to NDACC (Network for the Detection of Atmospheric Composition Change). It is located at the mountain observatory of Altzomoni (ALTZ) about 1700m above the Mexico City basin. The second instrument has a medium spectral resolution and is located inside of Mexico City at the Universidad Nacional Autónoma de México (UNAM) at a horizontal distance of about 60km to the mountain observatory.</p> <p>The here provided dataset consists of two NETCDF data-files for each station and a MATLAB script for reading the NETCDF files. The files “ALTZ_IFS125_O3.nc” and “UNAM_IFS125_O3.nc” contain the retrieved O<sub>3</sub> state vectors, the O<sub>3</sub> averaging kernels and the O<sub>3</sub> a priori profiles, together with auxiliary data: observation time, observation geometry, instrumental settings, atmospheric temperature and humidity profiles. The data as well as the method for combining the two different observations are presented in Plaza-Medina et al. (2017), which should be consulted for more details.</p> <p>The files “ALTZ_IFS125_O3_Jac+Gain.nc” and “UNAM_IFS125_O3_Jac+Gain.nc” contain the Jacobians (for O<sub>3</sub> as well as for error sources) and the Gain matrix, together with the auxiliary data. The MATLAB script “readNETCDF_and_combine2FTIR.m” reads the NETCDF files and performs the operations needed for the generation of a combined product, thereby exploiting the synergetic effects of two observations made in coincidence but at different ground altitudes.</p> <p>A related dataset with Altzomoni O<sub>3</sub> profiles obtained by applying slightly different retrieval settings is available at the NDACC database (ftp://ftp.cpc.ncep.noaa.gov/ndacc/station/altzomoni/hdf/ftir/). Further datasets of atmospheric parameters as measured by different techniques are available at the webpage of the Red Universitario de Observaciones Atmosfericas (www.ruoa.unam.mx).</p>
NCAR/ACOM FTIR Atmospheric Composition Dataset
<p>The NCAR/ACOM Optical Techniques Project operates three high spectral resolution, solar viewing FTIR instruments at Thule, Greenland (76.5ºN, 291.3ºE, 225 masl), Boulder Colorado (40.03ºN, 254.7ºE, 1612 masl) and Mauna Loa Observatory Hawaii (19.5ºN, 204.4ºE, 3396 masl). These instruments operate within the framework of the Network for the Detection of Atmospheric Composition change (NDACC). The raw data are recorded autonomously and downloaded to ACOM servers daily. They are processed at ACOM and produce vertical profiles of several trace atmospheric constituents: O3, HNO3, HCl, HF, CO, N2O, CH4, HCN, C2H6, OCS, H2CO, H2O and ClONO2. These data constitute a long-term reference dataset for tropospheric and stratospheric chemistry investigations.</p> <p>The data, along with metadata and full error analysis are saved to GEOMS conforming HDF files. Per our agreement with NDACC and our sponsor NASA, they are uploaded bi-yearly to the NDACC data handling facility (DHF) at www.ndacc.org. For select species, HDF files are uploaded on a near-real-time basis for the CAMS27 project which use the data for realtime air quality forecasting. Once these data are uploaded they are publicly available. The NDACC has implemented Creative Commons licensing designations for data at the DHF. These data maintain a CC0 license.</p>
Fourier transformed infrared reflectance (FTIR) spectra of peat soils collected from the top and bottom of peatland erosion gullies
<p>Peat soil was randomly collected from gullies within two eroding blanket bogs. Balmoral (BAM) is on a large high-altitude plateau blanket bog in the eastern part of the Cairngorms National Park, Scotland, UK (56.93° N, − 3.16° E, 642 m asl) and Glensaugh (GSA) is an upland livestock farm with sections of and blanket bog peatland in the Grampian foothills (56.55° N, 2.33° E, 412 m asl). Both sites have undergone extensive degradation and peat erosion, and both have, in some parts, recently undergone restoration practices, including bunding and reprofiling.</p> <p>Peat samples were collected at Glensaugh and Balmoral as follows. At Glensaugh, peat at the top 1 cm of exposed gully sides (approximately 10-20cm from the vegetated surface) and at the gully bottom were taken, air dried and passed on for FTIR analysis. These gullies correspond to four erosion pin measurement areas and their corresponding peat sediment trap areas at Glensaugh. At Balmoral, the same approach was taken except six ‘gully top’ and ‘gully bottom’ sites were randomly selected and not geographically paired in the same way at Glensaugh.</p> <p>Samples were air dried and finely ball milled, prior to FTIR analysis. FTIR spectra were recorded using a Bruker Vertex 70 FTIR spectrometer (Bruker, Ettlingen, Germany) and OPUS 7.2 software. To record the FTIR spectra, each of the samples were placed, in turn, on a Diamond Attenuated Total Reflectance (DATR) sampling accessory, with a single reflectance system. Data points in the range of 4000-400 cm-1 were recorded with a resolution of 4 cm-1 and average of 64 scans. A spectrum of the empty sampling accessory, with the same resolution and number of scans, was recorded as the background spectrum before each measurement. </p> <p>Since the penetration depth for the DATR accessory is different for each wavelength and is directly proportional to the wavelength of the incident light (The higher the wavenumber the lower the penetration), an ATR correction was applied to the spectra to correct this effect, using the OPUS software. No correction was required for water vapour and CO2 as the spectrometer is continuously purged with dry air.</p> <p> </p> <p>In the dataset, columns correspond to the following:</p> <p>Site: Balmoral or Glensaugh</p> <p>Gully Position: Top or bottom</p> <p>Gully Number: Replicate gullies within the site</p> <p>Sample date: Date</p> <p>Sample ID: Unique identifier</p> <p>Remaining columns: Reflectance at a given wavelength</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.