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1,385 results for “liquid”
Dataset of "Fast carbon dioxide–epoxide cycloaddition catalyzed by metal and metal-free ionic liquids for designing non-isocyanate polyurethanes"
<p>The recycling of industrially produced greenhouse gases, such as CO2, into high-value-added chemicals is one of the most relevant strategies for reaching climate targets. A two-step strategy for designing non-isocyanate polyurethanes (NIPUs) from renewable carbon dioxide (CO2) using environmentally friendly conditions and catalysts is investigated. The first reaction step efficiently converts a mono-epoxidized monomer (phenyl glycidyl ether) into cyclic carbonates under mild reaction conditions and supercritical CO2, using imidazolium ionic liquids (ILs) as catalysts into cyclic carbonates. The DFT calculations suggested a comprehensive mechanistic pathway for the IL-catalyzed CO2-epoxy reaction showing a rate-determining step of the initial epoxide ring opening and the direct participation of IL-anions.</p>
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum - data
<p>Data set pertaining to the article "Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum", published in <em>Struct. Dyn.</em> 10, 034901 (2023), <a href="https://doi.org/10.1063/4.0000188" target="_blank" rel="noopener">https://doi.org/10.1063/4.0000188 </a>.</p> <p>The following data are provided:</p> <table> <tbody> <tr> <td>(zip-)file/Folder</td> <td>Description</td> <td>Format</td> <td>Extension</td> </tr> <tr> <td>IR_images/calibration_data/vacuum</td> <td> <p>Snapshots from a thermographic movie of our flat jet running in vacuum, at thirty different background temperature. (A snapshot shown in Fig. 3a, rhs.)</p> </td> <td> <p>temperature values per camera pixel (°C), 640 row * 480 columns, semicolon-separated ascii data</p> </td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/1atm</td> <td>As above, for our flat jet running in atmosphere. (Three snapshots shown in Fig. 2a.)</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/chipnozzle</td> <td>As above, for a flat jet produced from a chip nozzle, and running in atmosphere.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/raw_data</td> <td>As above, for various conditions of the flat jet environment as detailed in table exp_settings.csv.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_video</td> <td>Two thermographic movies recorded of our flat jet at varied conditions of the jet environment detailed in table chamber_pressure.pdf.</td> <td>Radiographic image stream, suitable for opening with free software Optris Pix Connect.</td> <td>.ravi</td> </tr> <tr> <td>FJ_cooling_2D.mph</td> <td>Input file for 2D finite element simulation of our flat jet.</td> <td>Input file suitable for Comsol software, proprietary format.</td> <td>.mph</td> </tr> <tr> <td>Y_Z_Temp_Comsol.txt</td> <td>Ascii representation of our simulated temperature profile (Fig. S7 (SI)).</td> <td>List of (y,z,T) tupels, with (y,z) in m and T in °C.</td> <td>.txt</td> </tr> </tbody> </table> <p> </p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Photoelectron Spectroscopy from a Liquid Flatjet - data
<p>Data set pertaining to the article "Photoelectron spectroscopy from a liquid flatjet", published in J. Chem. Phys. 158, 234202 (2023).</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2020.10, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)<br> Additionally, some properties of our liquid jet sample environment are described by extensions to standard NeXus explained in a notes-section in each file.</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br> 2. As-measured data ('raw').</p> <p>Files with extension .txt are tab-separated ascii-files.</p> <p>The following files are provided:</p> <p>Measured spectra underlying the articles' figures:<br> Figure2NEXUS.h5<br> Figure3NEXUS.h5<br> Figure4NEXUS.h5<br> Figure5NEXUS.h5<br> Figure6NEXUS.h5<br> Figure7NEXUS.h5</p> <p>Numeric representation of the results shown in graphical form:<br> 'Figure 6.txt'.</p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Liquid Flow and Control Without Solid Walls
<p>This repository contains additional data related to the publication: 10.26434/chemrxiv.7207001</p> <p>Contained in python_magneto_fluidics.zip are all the files needed to calculate magnetic fields of any assembly of cuboid permanent magnets such as used in this paper, along with the equilibrium diameters for each antitube-ferrofluid combination.</p> <p>data figures.zip contains all the experimental data plotted in the figures, consisting of data in figures:</p> <p>Main Text: 2, 3, 4<br> Extended Data: E2, E3, E4, E6, E8</p>
Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase (supplementary data)
<p>This a dataset of scanning transmission electron microscopy data showing Pt clusters nucleating in an ionic liquid. For each of the 4 movies there is the raw data (uncompressed .tif and compressed as .avi) and denoised versions (uncompressed .tif and compressed as .avi).</p> <p>This data is for the article "Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase" published in ChemNanoMat (2020), by Trond R. Henninen, Debora Keller and Rolf Erni. (https://onlinelibrary.wiley.com/doi/full/10.1002/cnma.202000503)</p> <p><strong>Movie 1:</strong> Homogeneous nucleations of two clusters in a suspended thin film of ionic liquid. </p> <p><strong>Movie 2: </strong>Heterogeneous nucleation of a ca 8-9 atom cluster near the edge of a nanodroplet supported on a carbon film.</p> <p><strong>Movie 3: </strong>Heterogeneous nucleation of multiple clusters in a nanodroplet. Shortly after nucleation, they coalesce to form disordered nanoclusters.</p> <p><strong>Movie 4:</strong> Heterogeneous nucleation and dissolution cycles of spherical particles in a nanodroplet.</p>
Dataset of "Liquid-Jet Photoemission Spectroscopy as a Structural Tool: Site-Specific Acid-Base Chemistry of Vitamin C"
<p>Liquid-jet photoemission spectroscopy (LJ-PES) directly probes the electronic structure of solutes<br>and solvents. It also emerges as a novel tool to explore chemical structure in aqueous solutions, yet<br>the scope of the approach has to be examined. Here, we present a pH-dependent liquid-jet photoelectron<br>spectroscopic investigation of ascorbic acid (vitamin C). We combine core-level photoelectron<br>spectroscopy and ab initio calculations, allowing us to site-specifically explore the acid-base chemistry<br>of the biomolecule. For the first time, we demonstrate the capability of the method to simultaneously<br>assign two deprotonation sites within the molecule. We show that a large change in chemical shift<br>appears even for atoms distant several bonds from the chemically modified group. Furthermore, we<br>present a highly efficient and accurate computational protocol based on a single structure using the<br>maximum overlap method for modeling core-level photoelectron spectra in aqueous environments.<br>This work poses a broader question: To what extent can LJ-PES complement established structural<br>techniques such as nuclear magnetic resonance? Answering this question is highly relevant in view<br>of the large number of incorrect molecular structures published.</p>
Controlling the Dewetting Morphologies of Thin Liquid Films by Switchable Substrates
<p>Data and scripts for the creation of the data used in the publication: "Controlling the dewetting morphologies of thin liquid films by switchable substrates" in Phys. Rev. Fluids.</p>
Photoelectron circular dichroism in angle-resolved photoemission from liquid fenchone - data
<p>Data set pertaining to the article "Photoelectron circular dichroism in angle-resolved photoemission from liquid fenchone" | Physical Chemistry Chemical Physics, rsc.org, doi: <a href="https://doi.org/10.1039/D1CP05748K">10.1039/D1CP05748K</a></p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.06, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>Files with extension .txt are ascii-files. Files with extension .csv are comma-separated ascii-files, designed to be opened with a spreadsheet programme.</p> <p><br> The following files are provided:</p> <p>Photoemission data from liquid (1R,4S)-Fenchone<br> 1R-fenchone_dset1.h5<br> 1R-fenchone_dset2.h5<br> 1R-fenchone_dset3.h5</p> <p>Photoemission data from liquid (1S,4R)-Fenchone<br> 1S-fenchone.h5</p> <p><br> Numeric representations of the traces shown in the following figures:<br> fig1.txt<br> fig2a_asymmetry.txt : Asymmetry data shown in Fig. 2a<br> fig2a_traces.txt : Photoemission curves shown in Fig. 2a<br> fig2b_expo_m.txt : Photoemission curve shown in Fig. 2b, (l-CPL)<br> fig2b_expo_p.txt : Photoemission curve shown in Fig. 2b, (r-CPL)<br> fig2c_sum_m.txt<br> fig2c_sum_p.txt</p> <p> </p> <p>Data points shown in Fig. 3 and Table 1.:<br> fig3.csv<br> This file uses the following conventions:<br> Values are given for <em>b</em><sub>1</sub>*100.<br> Rows are labelled:<br> b_ds_1_L_exp : (1R,4S)-Fenchone, dataset 1, exp-model<br> ...<br> b_ds_3_R_exp : (1S,4R)-Fenchone, exp-model<br> ...<br> b_average_L : Averaged data, for (1R,4S)-Fenchone<br> b,liq_average_L : Averaged data corrected for presence of gas phase and dependence on angular distribution parameter, for (1R,4S)-Fenchone<br> b_average_R : Averaged data, for (1S,4R)-Fenchone<br> ...</p> <p><br> Numeric representations of the traces shown in the following figures from the supplementary material:<br> sfig1_traces-p.txt<br> sfig2_totalcounts.txt<br> sfig3_ttrace.txt<br> sfig4_roi_301.txt<br> sfig5_asymm_301.txt</p> <p> </p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
How does Mg2+(aq.) interact with ATP(aq.)? Observations through the lens of liquid-jet photoelectron spectroscopy - data
<p>Dataset pertaining to the article "How does Mg2+(aq) interact with ATP(aq)? Biomolecular Structure through the Lens of Liquid-Jet Photoemission Spectroscopy", published in Journal of the American Chemical Society (<a href="https://doi.org/10.1021/jacs.4c03174" target="_blank" rel="noopener">doi: 10.1021/jacs.4c03174</a>). Here, we arrive at new information on the interaction of ATP with Mg under physiological conditions by interpreting photoelectron spectra and intermolecular Coulombic decay from a liquid microjet.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br>https://www.nexusformat.org/<br>https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br>1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data'). For ATP spectra, the ADP overview spectrum, and ADP/Mg2+ Mg 2s spectra, a binding energy correction shifting the liquid 1b1 feature to 11.33 eV is applied.<br>2. As-measured data ('raw').</p> <p>Files with extension .txt are comma-separated ascii-files.</p> <p><br>The following files are provided:</p> <p>Photoemission data pertaining to adenosine phosphate PES measurements:<br>atp-mg.h5 - ATP photoemission spectra in the presence of Mg2+ cations in varying concentration<br>adp-mg.h5 - ADP photoemission spectra in the presence of Mg2+ cations in varying concentration<br>amp-mg.h5 - AMP photoemission spectra in the presence of Mg2+ cations (a single concentration)<br>atp-adp-amp.h5 - ATP, ADP, AMP photoemission without Mg admixture<br>mg-only.h5 - Mg 2s core level spectra without ATP<br>tham-only.h5 - VB band measured with only THAM (tris(hydroxymethyl)aminomethane), used as buffer for pH stabilization<br>atp-icd.h5 - ATP photoemission spectra in the presence of Mg2+ cations, kinetic energy range of ICD features (publication is based on the last three entries).<br><br></p> <p>Numeric representations of the traces shown in the article's figures:<br>Figure_3-data.txt<br>Figure_5a-Mg2p.txt<br>Figure_5a-Mg2s.txt<br>Figure_5a-Mgonly.txt<br>Figure_5a-P2p.txt<br>Figure_5a-P2s.txt<br>Figure_5b.txt<br>Figure_5c.txt<br>Figure_6-ADP.txt<br>Figure_6-AMP.txt<br>Figure_6-ATP.txt<br>Figure_8a-data.txt<br>Figure_S2-Tris.txt<br>Figure_S2-Tris_with_Mg2+.txt<br>Figure_S4-data.txt.</p> <p>Version history<br>3: updated to reflect changes in Figure numbering between ArXiv-post and version published in JACS, additional Figure 5-data added<br>2: NeXus-data added<br>1: initial upload</p> <p>Contact person for questions regarding this data set: Uwe Hergenhahn, uhe@fhi.mpg.de . If you use these data for your scientific work we are curious to learn about it.</p>
Statistical analysis and dataset for: Three-dimensional body reconstruction enables quantification of liquid consumption in small invertebrates
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.14.599002).</p> <p><em><strong>Abstract</strong></em></p> <p>Quantifying feeding patterns provides valuable insights into animal behaviour. However, small invertebrates often consume incredibly small amounts of food. This renders traditional methods, such as weighing individuals before and after food acquisition, either inaccurate or prohibitively expensive. Here, we present a non-invasive method to quantify food consumption of small invertebrates whose body expands during feeding. Using the markerless pose estimation software DeepLabCut, we three-dimensionally track the body of Argentine ants, <em>Linepithema humile</em>. Using these extracted markers, we developed an algorithm which computationally reconstructs the ant’s body, directly measuring volumetric change over time. Moreover, we provide measures of accuracy and quantify the ant’s feeding response to a range of sucrose concentrations, as well as a gradient of caffeine-laced sucrose solutions. Small invertebrates are often prolific invasive species and disease vectors, causing significant ecological and economical damage. Understanding their feeding behaviour could be an important step towards effective control strategies.</p> <p> </p> <ul> <li><strong>VolEst_C1_volume_calculation_multiprocessing.py</strong>: Takes as input H5 3D DeepLabCut files, calculates the gaster volume at every frame using seven different methods and outputs these as CSV files.</li> <li><strong>VolEst_C2_interactive_GUI.py</strong>: Given a folder with Volume CSV files, interactively plots the volume over time, 3D coordinates tracked by DeepLabCut and the frame of interest for both cameras.</li> <li><strong>VolEst_C3_linear_regression.py</strong>: Applies a linear regression to each feeding event tracked and provides measures of interest such as crop load and consumption rate.</li> <li><strong>VolEst_C4_statistical_analysis</strong>: Complete statistical analysis and code for the manuscript.</li> <li><strong>VolEst_D1_sucrose_density.csv</strong>: Data obtained to quantify the density of sucrose solutions of varying molarity.</li> <li><strong>VolEst_D2_accuracy_weight_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D3_accuracy_weight.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D4_accuracy_nanoliter_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D5_accuracy_nanoliter.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D6_sucrose_caffeine_consumption_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_D7_sucrose_caffeine_consumption.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_Camera_A-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera A.</li> <li><strong>VolEst_Camera_B-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera B.</li> <li><strong>VolEst_base.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> <li><strong>VolEst_platform.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> </ul>
Liquid Resin Infusion (LRI) manufacturing and Spring_In monitoring by FBGs, DCs and 3D CMM meassurements
<p>ELADINE project is aiming to implement a numerical tool that can reduce reoccurring costs of low-volume production in composite manufacturing of primary structural elements and thus reducing overall manufacturing effort and carbon emissions. A<strong> primary goal of this project is to eliminate tolerance non-compliancy in the manufactured structures caused by natural and unavoidable post-manufacturing distortions, typical for composite materials</strong>. These distortions might render otherwise qualitative components unusable due to their final geometry.</p> <p>Objectives of the Numerical model validation are:</p> <ul> <li>To understand the dominant factors which affects the spring-in phenomenon.</li> <li>To provide the simulation tool with the required values of the properties that influence on spring-in.</li> <li>To verify the simulation tool ability to predict spring-in for a variety of conditions.</li> <li>To develop a procedure of adapting and embedding sensors (dielectric and fiber optic) to obtain proper, useful and accurate signals of the manufacturing parameters (T, degree of cure, strain).</li> <li>To develop interpretation procedures of the signal/curves of sensors to obtain on-line process monitoring information.</li> </ul> <p>To obtain the data to feed and develop the numerical tool able to estimate the component distortions after its manufacturing, a combination of Fiber Optic Sensors (FOS) based on Fiber Bragg Grating (FBG) technology, Dielectric Curing sensors (DC) and 3D scanning were used to monitor the composite coupon manufacturing and the distortions the days after being demoulded. During the manufacturing process embedded FBGs and DC sensors were used to monitor the coupon temperature and strain distribution and resin curing evolution. After the manufacturing and the demolding, the distortions evolution were monitored by the embedded FBGs and by 3D CMM measurements.</p> <p><strong>In the ELADINE project, the distortion monitoring was made to two Out-of-Autoclave manufacturing technologies: liquid resin infusion (LRI) and oven cured pre-preg</strong>. For both material systems, slightly curved coupons and C-shaped coupons were the geometries selected as representative for the Skin and spars of the wing box. The Skin coupon was curved panel with a 1475 mm radius (with edge rise of 7,65 mm) that was thought to best replicate the wing profile geometry. The C-spar coupon geometry selected for the study was a non-tapered spar section with two different angle with radius of curvature of 5mm and 12mm. This geometry was chosen to simplify measuring and comparisons with wing demo. Furthermore, three different thickness are studied for the Skin coupons and two for the C-spar coupons which were selected from different zones along the wing. Moreover, a C-spar coupon with variable thickness was studied, as a simulation of the transition between zones with different thickness in the wing.</p> <p><strong>In this dataset, the data from the FBGs, DCs and 3D CMM meassurements for the LRI manufacturing process and spring_in distortions monitoring is included.</strong></p>
Dataset for the published article "Quantum version of the integral equation theory based dielectric scheme for strongly coupled electron liquids"
<p>The data contained in the zip file constitute the main research data of the article entitled as "<em>Quantum version of the integral equation theory based dielectric scheme for strongly coupled electron liquids</em>", published in the Journal of Chemical Physics as a Communication. In this article, a novel dielectric scheme is proposed for strongly coupled electron liquids that handles quantum mechanical effects beyond the random phase approximation level and treats electronic correlations within the integral equation theory of classical liquids. This self-consistent scheme features a complicated dynamic local field correction functional and yields unprecedently accurate results for the static structure factor without featuring any adjustable or empirical parameters.</p> <p>In particular, the datasets contain the static structure factors of the paramagnetic electron liquid as computed by four schemes of the self-consistent dielectric formalism and as extracted from state-of-the-art path integral Monte Carlo (PIMC) simulations. The dielectric schemes of interest are all tailor-made for the strongly coupled regime of the finite temperature uniform electron fluid (UEF; also known as jellium or quantum one-component plasma). These are the newly proposed quantum version of the integral equation theory based scheme (qIET), the newly proposed quantum version of the hypernetted-chain based scheme (qHNC), the integral equation theory based scheme (IET) [1,2] and the hypernetted-chain based scheme (HNC) [3,4].</p> <p>The static structure factors are provided for 20 paramagnetic UEF state points defined by (r<sub>s</sub>,Θ)={(50,0.50),(60,0.50),(70,0.50),(80,0.50),(90,0.50),(100,0.50),(100,0.75),(100,1.00),(100,2.00),(100,4.00),(110,0.50),(125,0.50),(125,0.75),(125,1.00),(125,1.50),(125,2.00),(150,0.50),(150,1.00),(200,0.50),(200,1.00)} where r<sub>s</sub> is the quantum coupling parameter and Θ is the degeneracy parameter. </p> <p>In the qIET, qHNC, IET and HNC datasets; the first column corresponds to the wavenumber normalized to the Fermi wavenumber and the second column corresponds to the static structure factor value. In the PIMC datasets, the first column corresponds to the wavenumber multiplied by the first Bohr radius, the second column corresponds to the static structure factor value and the third column corresponds to the associated error bars.</p> <p>[1] P. Tolias, F. Lucco Castello and T. Dornheim, J. Chem. Phys. 155, 134115 (2021).<br> [2] F. Lucco Castello, P. Tolias and T. Dornheim, EPL 138, 44003 (2022).<br> [3] S. Tanaka, J. Chem. Phys. 145, 214104 (2016).<br> [4] T. Dornheim, T. Sjostrom, S. Tanaka and J. Vorberger, Phys. Rev. B 101, 045129 (2020).</p>
Quantitative electronic structure and work-function changes of liquid water induced by solute - data
<p>Data set pertaining to the article "Quantitative electronic structure and work-function changes of liquid water induced by solute" | Physical Chemistry Chemical Physics, 24, 1310 (2022).</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07 using the NXmpes user contributed format suggested by the Fairmat consortium, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> A few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file.<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br> 2. As-measured data ('raw').</p> <p>Files with extension .txt are comma-separated ascii-files.<br> The following files are provided:</p> <p>Photoemission data pertaining to solute measurements using the cut-off as energy reference:<br> NaI_data.h5<br> tbai_data.h5</p> <p>Biased spectra were typically recorded in the following order:<br> [cut-off (fine), cut-off (coarse), (valence band)*(N repeats)]*(M repeats)<br> To avoid the saving of overly complex hdf5-files, these data were saved in a different order, namely:<br> [cut-off (fine)*(M repeats), cut-off (coarse)*(M repeats), (valence band)*(N*M repeats)].</p> <p>Numeric representations of the traces shown in the article's figures:<br> Figure_1a-data.txt<br> Figure_1b-data.txt<br> Figure_2a-data.txt<br> Figure_2b-data.txt<br> Figure_2c-data.txt<br> Figure_3-data.txt<br> Figure_4-data.txt<br> Figure_5a-data.txt<br> Figure_5b-data.txt<br> Figure_6a-data.txt<br> Figure_6b-data.txt<br> Figure_6c-data.txt<br> Figure_7_diff_spectra-data.txt<br> Figure_8-data.txt</p> <p>Traces shown in several figures are included only in the data file pertaining to the figure in which they occur first.</p> <p> </p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Data for: Attractive solution of binary Bose mixtures: Liquid-vapor coexistence and critical point
<p>Path-integral Monte-Carlo results for a balanced Bose mixture with attractive interspecies interaction. In the files named "press_*" we provide the pressure data, presented in figures 1, 2, and S1, and then used to extract the coexistence regions. In the files named "coexistence_region_*" we provide the data extracted using the Maxwell construction (as well as the condensate fraction for a12=-1.2a) and presented in figures 3, 4, 5 and S2.</p>
Emissions of nitrous oxide and methane after field application of liquid organic fertilizers and biochar
<p>This dataset corresponds to the open access article "Emissions of nitrous oxide and methane after field application of liquid organic fertilizers and biochar" published in Agriculture, Ecosystems & Environment (<a href="https://doi.org/10.1016/j.agee.2023.108642">https://doi.org/10.1016/j.agee.2023.108642</a>) funded by the Swiss Federal Offices for the Environment (BAFU), Agriculture (BLW) and Energy (BFE).</p> <p> </p> <p> </p>
Minimum dataset for "Liquid-activated quantum emission from pristine hexagonal boron nitride for nanofluidic sensing"
<p>Frames and (linked) localization table used to produce Fig. 2 of the manuscript https://www.nature.com/articles/s41563-023-01658-2.</p> <p>Details are given in 'README.txt'.</p> <p>The rest of the data is provided with the paper at the publisher website.</p>
Photosynthetic pigments of water column samples analyzed using High Performance Liquid Chromatography (HPLC), sampled during the Palmer LTER field seasons at Palmer Station, Antarctica, 1991 – 2023.
Phytoplankton pigment sampling was led by Prezelin from the 1991-1992 season through the 1993-1994 season, and then by Vernet from the 1994-1995 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll’s (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods. Data is unavailable for the Palmer 2009-2010 season due to instrumentation problems and for the Palmer 2011-2012 season due to a freezer failure which resulted in the loss of samples. There is a temporary data gap for the Palmer 2015-2016, Palmer 2016-2017, Palmer 2019-2020, Palmer 2020-2021, and Palmer 2023-2024 seasons because those samples have not been analyzed yet.
Photosynthetic pigments of water column samples and analyzed with High Performance Liquid Chromatography (HPLC), collected aboard Palmer LTER annual cruises off the coast of the Western Antarctica Peninsula, 1991-2024.
Phytoplankton pigment sampling was led by Prezelin from 1991-1994, and then by Vernet from 1995-2008. Schofield is the third, and current lead, beginning in 2009. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll's (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods and the HPLC. Data is unavailable for the LMG10-01 cruise due to instrumentation problems and for the LMG12-01 cruise due to a freezer failure which resulted in the loss of samples. There is no data for 2021 because there was no LTER cruise.
In silico Database for Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1)
<p>Modern methods of mass spectrometry have emerged recently allowing reliable, fast and cost-effective identification of pathogenic microorganisms. For example, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has revolutionized the way pathogenic microorganisms are identified in today’s routine clinical microbiology. Furthermore, recent years have witnessed also substantial progress in the development of liquid chromatography-mass spectrometry (LC-MS) based proteomics for microbiological applications.</p> <p>In this context, we introduce a new concept for microbial identification by mass spectrometry. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS1 data are then extracted and systematically tested against <em>in silico</em> libraries of peptide mass data. The first version of such a database has been computed from UniProt Knowledgebase [Swiss-Prot and TrEMBL] and contains more than 12,000 strain-specific synthetic mass profiles. The database is stored in the pkf data format which is interpretable by the MicrobeMS software package (requires MicrobeMS version 0.82, or later).</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089.</em></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.