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1,923 results for “compounding”
Experimental assessment of the mixing quality in a circular cross-sectional t-shaped mixer for the precipitation of sparingly soluble compounds
<p>Precipitation processes have been successfully proved to be a feasible route to extract high-value products from wastes. However, reactive crystallization is characterized by fast kinetics, especially when highly concentrated solutions are used or sparingly soluble compounds precipitate, thus requiring a fast mixing of the reactants. In this context, T-shaped mixers have been extensively investigated both experimentally and numerically as a mean to achieve a rapid reactants homogenization. Many research efforts have been focused on the analysis of rectangular cross-sectional T-shaped mixers, while less attention has been devoted to the study of circular cross-sectional ones. In the present work, mixing times in a 3 mm circular cross-sectional T-shaped mixer have been optically estimated by exploiting the extremely fast neutralization reaction between sodium hydroxide and hydrochloric acid solutions. Reynolds numbers ranging between 1,000 and 6,000 were investigated. Mixing phenomena were (i) captured by detecting the colour change of a pH indicator by means of a high frame rate camera and (ii) quantified by a suitable image analysis technique. The assessed mixing times were found in accordance with available data in the literature.</p>
Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events
<p>Data, code and supplementary Figures for paper "Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events".</p>
Spectral sensitivity transition in the compound eyes of a twilight-swarming mayfly and its visual ecological implications
<p>Aquatic insect species that leave the water after larval development, such as mayflies, have to deal with extremely different visual environments in their different life stages. Measuring the spectral sensitivity of the compound eyes of the virgin mayfly (Ephoron virgo) resulted in differences between the sensitivity of adults and larvae. Larvae were primarily green-, while adults were mostly UV-sensitive. The sensitivity of adults and larvae were the same in the UV, but in the green spectral range, adults were 3.3 times less sensitive than larvae. Transmittance spectrum measurements of larval skins covering the eye showed that the removal of exuvium during emergence cannot explain the spectral sensitivity change of the eyes. Taking numerous sky spectra from the literature, the ratio of UV and green photons in the skylight was shown to be maximal for θ ≈ − 13° solar elevation, which is in the θmax = -14.7° and θmin = -7.1° typical range of swarming that was established from webcam images of real swarmings. We suggest that spectral sensitivity of both the larval and adult eyes are adapted to the optical environment of the corresponding life stages.</p>
Data from: Effect of altitude on volatile organic and phenolic compounds of artemisia brevifolia wall ex Dc. from the Western Himalayas
<p>Adaptation to changing environmental conditions is a driver of plant diversification. Elevational gradients offer a unique opportunity for investigating adaptation to a range of climatic conditions. The use of specialized metabolites as volatile and phenolic compounds is a major adaptation in plants, affecting their reproductive success and survival by attracting pollinators and protecting themselves from herbivores and other stressors. The wormseed <em>Artemisia brevifolia</em> can be found across multiple elevations in the Western Himalayas, a region that is considered a biodiversity hotspot and is highly impacted by climate change. This study aims at understanding the volatile and phenolic compounds produced by <em>A. brevifolia </em>in the high elevation cold deserts of the Western Himalayas with the view to understanding the survival strategies employed by plants under harsh conditions. Across four sampling sites with different elevations, polydimethylsiloxane (PDMS) sampling and subsequent GCMS analyses showed that the total number of volatile compounds in the plant headspace increased with elevation and that this trend was largely driven by an increase in compounds with low volatility, which might improve the plant's resilience to abiotic stress. HPLC analyses showed no effect of elevation on the total number of phenolic compounds detected in both young and mature leaves. However, the concentration of the majority of phenolic compounds decreased with elevation. As the production of phenolic defense compounds is a costly trait, plants at higher elevations might face a trade-off between energy expenditure and protecting themselves from herbivores. This study can therefore help us understand how plants adjust secondary metabolite production to cope with harsh environments and reveal the climate adaptability of such species in highly threatened regions of our planet such as the Himalayas.</p>
German Compounds and Paraphrases
<p>++++ english below ++++</p> <p>Beschreibung der enthaltenen Datensätze:</p> <p><br> all-phrases.csv:</p> <p>Was beinhalten die Spalten?</p> <p>| Kompositum | Erstes Kompositionsglied | Zweites Kompositionsglied | Frequenz des Kompositums im Korpus* | Paraphrasierung | Frequenz der Paraphrasierung im Korpus* |</p> <p>*Der Korpus, welcher für Extraktion der Phrasen und beider Frequenzwerte verwendet wurde, zu finden unter: https://corpora.uni-leipzig.de/de?corpusId=deu_newscrawl_2011</p> <p><br> labeled-phrases.csv:</p> <p>Hat den gleichen Aufbau wie all-phrases.csv mit einer zusatzlichen Spalte:</p> <p>|...| Label |</p> <p>Das Label hat die Werte 0 oder 1. Es ist eine Bewertung der zugehörigen Paraphrasierung, wobei 1 für 'richtig' und 1 für 'falsch' steht.</p> <p> </p> <p>++++ english ++++</p> <p>discription of datasets:</p> <p><br> all-phrases.csv:</p> <p>What do the columns contain?</p> <p>| compound word | first element of compound | second element of compound | frequency of compound in corpus* | phrase | frequency of phrase in corpus* |</p> <p>*Meaning the corpus used to obtain the phrases and both frequencies. Can be found here: https://corpora.uni-leipzig.de/de?corpusId=deu_newscrawl_2011</p> <p><br> labeled-phrases.csv:</p> <p>this one has the same columns as the previous dataset with one addition:</p> <p>|...| label |</p> <p>the labes has values of either 0 or 1. It is an evaluation of the correctness of the phrase with 1 meaning 'correct' and 0 meaning 'false'. </p>
Raw data of compounds extracted by GC-MS from each population replicate's of I. uriae ticks from Iceland.
<p>Raw data representing all the compounds extracted by GC-MS from each population replicate’s of <em>I. uriae</em> ticks from three sites in Iceland. Each replicate contain a pool of 10 living flat female ticks.</p> <p>Site: name of the site where ticks were collected.</p> <p>Host: name of the host bird.</p> <p>Replicate: number of the replicate (1 to 4).</p> <p>Peak: number of the detected peaks correponding to extracted compounds.</p> <p>Retention Time: time elapsed between sample introduction and the maximum signal of the given compound.</p> <p>Area: area under the curves of each detected coumpounds on the chromatogram.</p>
Compound Data for Robust Processes for Polymer Modification and Pharmaceutical Synthesis
<p>Compound structural (IUPAC name, InChI, InChI Key, SMILES, .mol, .sdf) and spectral (NMR, MS) data included for compounds reported in the associated doctoral thesis. NMR data collected on Bruker Avance 400, 500, or 600 MHz spectrometers. Compound structure data were generated by ChemDraw v.20 (PerkinElmer). More details about the preparation and characterization of these compounds can be found in the associated thesis.</p>
Dataset: Consistent release of volatile organic compounds across an actively degrading permafrost peatland
<p>Here, we conducted in situ measurements of soil and pond VOC emissions across an actively degrading permafrost peatland in subarctic Norway. We used a permafrost thaw gradient that covered bare soil and vegetated palsa plateaus, underlain by intact permafrost, and increasingly degraded permafrost landscapes: thaw slumps, thaw ponds, and vegetated thaw ponds.</p> <p>This dataset includes two excel files: 1) the first one "Finnmark_source_data" is the source data for figures in the publication <a href="https://doi.org/10.1016/j.geoderma.2023.116355">https://doi.org/10.1016/j.geoderma.2023.116355</a>. ii) the second one "Rawdata_of_emission_rate" is the emission rate of the 210 VOC species identified in this study.</p> <p>Results showed that every peatland landscape type was an important and consistent source of atmospheric VOCs, with a large variety species, such as methanol, acetone, monoterpenes, sesquiterpenes, isoprene, hydrocarbons, oxygenated VOCs, etc. VOC composition varied considerably across the measurement period and across the permafrost thaw gradient. We observed enhanced terpenoid emissions following thaw slump degradation, highlighting the potential atmospheric impact of permafrost thaw, due to the high chemical reactivities of terpenoid compounds. Overall, our study demonstrates that VOCs are being emitted in significant quantities and with largely similar composition upon permafrost thawing, inundation, and subsequent vegetation development, despite major differences in microclimate, hydrological regime, vegetation, and permafrost occurrence.</p> <p>Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or the PI of this project Prof. Rinnan at riikkar@bio.ku.dk</p>
Apertif drift scan based compound beam maps (1220-1520 MHz)
<p>Apertif (APERture Tile In Focus) is a Phased Array Feed (PAF) system on the Westerbork Synthesis Radio Telescope (WSRT) that conducted the Apertif legacy surveys between the 1st of July 2019 and the 28th of February 2022. Apertif carried out a two tiered imaging survey, with a shallow, wide-area and a medium-deep, small-area component, and a time domain survey. In the standard observing mode for imaging 40 partially overlapping compound beams (CBs) are formed simultaneously in a rectangular configuration. Each of these 40 CBs have a unique shape that differs substantially from the "old" WSRT beam shape.</p> <p>This data set contains Apertif drift scan based CB maps measured at the second Apertif imaging survey frequency setting (1220-1520 MHz). Apertif imaging observations were take at these frequencies between January 2021 and February 2022. The CB maps are generated from drift scan observations using the AperPB scrips (DOI: https://doi.org/10.5281/zenodo.6544109). These CB maps can be used to primary beam correct or mosaic images or data cubes from the Apertif imaging surveys.</p> <p>The dataset contains two types of fits files:</p> <p>1) Reconstructed CB maps from the drift scans. These maps are within directories called "drift_maps" and have the following naming scheme: <source>_<CB map ID>_<CB number>_<polarisation>.fits. The source refers to the continuum source used for the drift scan observations, this can be either CygA or CasA. The CB map ID is the identification of a set of CB maps based on the observing date in the format of yymmdd e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents (numbering from 00 to 39). Polarisation denotes which polarisation the map shows, these can be xx, yy, I and diff, where diff refers to xx-yy. These maps are 3.36 × 2.3 deg in size and are oriented upside down compared to the sky.</p> <p>2) Spline fitted CB maps. These are within directories called "beam_models" and have the following naming: <CB map ID>_<CB number>_<polarisation>_model.fits. The CB map ID is the identification of a set of CB maps based on the observing date e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents. Polarisation denotes which polarisation the map shows, these can be xx, yy, I. These maps are 1.1 × 1.1 deg in size and are subdivided into 18 frequency bins in directories called "channel_<num>". These maps are suitable for primary beam correcting or mosaicing Apertif images and data cubes. Since the CB shapes of Apertif change over time, we recommend to use the closest CB data set in time to the observation that the primary beam correction or mosaicing is going to be applied to. </p> <p>All CB data sets also contain per antenna CB maps and spline fitted CB maps within the directory "per_antenna_maps". The naming and organisation of the per antenna maps is the same as described above, with the exception that the spline fitted CB maps are not subdivided by frequency bins into separate directories, but are contained within a single fits file. The Apertif system uses 12 of the 14 WSRT antennas. In some cases one or two antennas were malfunctioning during the observations and there are only 10 or 11 per antenna CB map sets. </p> <table> <caption>Notes on the individual CB data sets</caption> <tbody> <tr> <td>CB map ID</td> <td>continuum source</td> <td>notes</td> </tr> <tr> <td> <p>210205</p> </td> <td>Cyg A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz)</p> </td> </tr> <tr> <td> <p>210402</p> </td> <td>Cas A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), no RTC and RTD, spline fit for CB01 failed for the per antenna maps</p> </td> </tr> <tr> <td> <p>211103</p> </td> <td>Cas A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), spline fit for CB01 failed for the per antenna maps</p> </td> </tr> <tr> <td> <p>211118</p> </td> <td>Cyg A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), spline fit for CB01 failed for the per antenna maps</p> </td> </tr> </tbody> </table> <p>For a detailed description of the characteristics of the Apertif CBs and how the CB maps are produced see the upcoming publication Dénes et al. 2022 submitted to A&A (ArXiv: https://arxiv.org/abs/2205.09662).<br> <br> For more information on the Apertif system see: van Cappellen et al. 2022, A&A, 658, 146 DOI: https://doi.org/10.1051/0004-6361/202141739</p>
Apertif drift scan based compound beam maps (1130-1430 MHz)
<p>Apertif (APERture Tile In Focus) is a Phased Array Feed (PAF) system on the Westerbork Synthesis Radio Telescope (WSRT) that conducted the Apertif legacy surveys between the 1st of July 2019 and the 28th of February 2022. Apertif carried out a two tiered imaging survey, with a shallow, wide-area and a medium-deep, small-area component, and a time domain survey. In the standard observing mode for imaging 40 partially overlapping compound beams (CBs) are formed simultaneously in a rectangular configuration. Each of these 40 CBs have a unique shape that differs substantially from the "old" WSRT beam shape.</p> <p>This data set contains Apertif drift scan based CB maps measured at the first Apertif imaging survey frequency setting (1130-1430 MHz). Apertif imaging observations were take at these frequencies between July 2019 and January 2021. The CB maps are generated from drift scan observations using the AperPB scrips (DOI: https://doi.org/10.5281/zenodo.6544109). These CB maps can be used to primary beam correct or mosaic images or data cubes from the Apertif imaging surveys.</p> <p>The dataset contains two types of fits files:</p> <p>1) Reconstructed CB maps from the drift scans. These maps are within directories called "drift_maps" and have the following naming scheme: <source>_<CB map ID>_<CB number>_<polarisation>.fits. The source refers to the continuum source used for the drift scan observations, this can be either CygA or CasA. The CB map ID is the identification of a set of CB maps based on the observing date in the format of yymmdd e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents (numbering from 00 to 39). Polarisation denotes which polarisation the map shows, these can be xx, yy, I and diff, where diff refers to xx-yy. These maps are 3.36 × 2.3 deg in size and are oriented upside down compared to the sky.</p> <p>2) Spline fitted CB maps. These are within directories called "beam_models" and have the following naming: <CB map ID>_<CB number>_<polarisation>_model.fits. The CB map ID is the identification of a set of CB maps based on the observing date e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents. Polarisation denotes which polarisation the map shows, these can be xx, yy, I. These maps are 1.1 × 1.1 deg in size and are subdivided into 18 frequency bins in directories called "channel_<num>". These maps are suitable for primary beam correcting or mosaicing Apertif images and data cubes. Since the CB shapes of Apertif change over time, we recommend to use the closest CB data set in time to the observation that the primary beam correction or mosaicing is going to be applied to. </p> <p>Wit the exception of two datasets (191008 and 191023), all CB data sets also contain per antenna CB maps and spline fitted CB maps within the directory "per_antenna_maps". The naming and organisation of the per antenna maps is the same as described above, with the exception that the spline fitted CB maps are not subdivided by frequency bins into separate directories, but are contained within a single fits file. The Apertif system uses 12 of the 14 WSRT antennas. In some cases one or two antennas were malfunctioning during the observations and there are only 10 or 11 per antenna CB map sets. </p> <p>Notes on the individual CB data sets:</p> <table> <tbody> <tr> <td>CB map ID</td> <td>continuum source</td> <td>notes</td> </tr> <tr> <td>190628</td> <td>Cyg A</td> <td>issue in channel 2 (1.31 GHz) with CBs 15-16,<br> issue in channels 5-6 (1.36 - 1.37 Ghz) and 8-9 (1.4 - 1.41 GHz), spline fit for CBs 01-07 failed for the per antenna maps</td> </tr> <tr> <td>190722</td> <td>Cyg A</td> <td>RFI in chan 6 (1.37 GHz) for CB 1</td> </tr> <tr> <td>190821</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190826</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190912</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190916</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>191008</td> <td>Cyg A</td> <td>CBs 14, 19, 20 and 26 are distorted, no per antenna maps</td> </tr> <tr> <td>191023</td> <td>Cyg A</td> <td>RFI in channel 6 ( 1.37 GHz) for CBs 0, 3-4, 9-10, 15-17, 22, no per antenna maps</td> </tr> <tr> <td>191120</td> <td>Cyg A</td> <td>RFI in channels 8-9 (1.4 - 1.41 GHz)</td> </tr> <tr> <td>200130</td> <td>Cyg A</td> <td>no RT5, RFI in channel 6 (1.37 GHz) for CBs 1, 6-8, 14, 21, 33</td> </tr> <tr> <td>200430</td> <td>Cyg A</td> <td>no RTC and RTD</td> </tr> <tr> <td>200710</td> <td>Cyg A</td> <td>no RTB, CBs 0, 20-39 are affected by RFI</td> </tr> <tr> <td>200819</td> <td>Cyg A</td> <td>issue with CBs 37-39, RFI in channel 6 (1.37 GHz), spline fit for CB01 failed for the per antenna maps</td> </tr> <tr> <td>201009</td> <td>Cyg A</td> <td>no RTB, antenna 9 (RTB) files are empty in the antenna based maps</td> </tr> <tr> <td>201028</td> <td>Cas A</td> <td>spline fit for CB01 failed for the per antenna maps</td> </tr> <tr> <td>201218</td> <td>Cyg A</td> <td> </td> </tr> </tbody> </table> <p>For a detailed description of the characteristics of the Apertif CBs and how the CB maps are produced see the upcoming publication Dénes et al. 2022 submitted to A&A (ArXiv: https://arxiv.org/abs/2205.09662).<br> <br> For more information on the Apertif system see: van Cappellen et al. 2022, A&A, 658, 146 DOI: https://doi.org/10.1051/0004-6361/202141739</p>
Functionalizing Aromatic Compounds with Optical Cycling Centers
<p>Molecular design principles provide guidelines for augmenting a molecule with a smaller group of atoms to realize a desired property or function. We demonstrate that these concepts can be used to create an optical cycling center that can be attached to a number of aromatic ligands, allowing the scattering of many photons from the resulting molecules without changing the molecular vibrational states. We provide further design principles that indicate the ability to expand this work. This represents a significant step towards a quantum functional group, which may serve as a generic qubit moiety that can be attached to a wide range of molecular structures and surfaces.</p>
Virtual screening on Nsp16: screening of 1084 compounds in VeroE6-eGFP cells
<p>This report describes the most relevant results of virtually screening the Janssen Pharmaceutica compound collection for potential activity against SARS-CoV-2 Nsp16 and confirmation of potential hits in a VeroE6 cell-based anti-SARS-CoV-2 assay.</p>
Annexes to the Extensive literature search report on N-nitroso compounds in food
<p>The overall purpose of this project was the identification and selection of relevant literature to gather information on chemical identification and characterisation, sources and occurrence in food as well as data on toxicokinetics and toxicity of N-nitrosocompounds in order to support the preparatory work for the human health risk assessment of this type of substances.</p>
Drought-heatwave compound events are stronger in drylands
<p>This data is supplementary data or code for the research article "Weather and Climate Extremes". The file name Global_CDHWs is the specific data of the six indicators of the global composite event calculated from 1961 to 2020, the granger_test file is the result data of the Granger causality test, and the lag_granger file is the lag result of the Granger causality test.</p> <p>Note: (Tsum, Tmax, Toccr, Tmean, AH, TAH,) correspond sequentially to (HDF, DHD, DHO, DHM, DHC, DHA). Inside variables correspond one to one.</p> <p>The Global CDHWs folder includes dry-heatwave compound events (state3) and wet heatwave compound events (state4), and each of the two folders contains six variables with annual and monthly scales, where the next level folder contains monthly scale data.</p>
Analog Series of Compounds with High Frequency of Activity in Screening Assays
<p>A set of 6941 analog series and associated data are provided. These series exclusively consist of compounds that are most frequently active across public screening assays. </p>
X-Ray Structures of Target-Ligand Complexes Containing Compounds with Assay Interference Potential
<p>A total of 2755 crystallographic complexes with ligands containing PAINS-defining substructures were extracted from the Protein Data Bank (PDB). PDB identifiers of these structures are made available together with the the corresponding PDB_PAINS (component identifier, aromatic nonstereo SMILES, PAINS class). </p>
Data and code for: Dihydrothiazolo ring-fused 2-pyridone antimicrobial compounds effectively treat Streptococcus pyogenes skin and soft tissue infection
<p>We have developed GmPcides from a peptidomimetic dihydrothiazolo ring-fused 2-pyridone scaffold that have antimicrobial activities against a broad-spectrum of Gram-positive pathogens. Here we examine the treatment efficacy of GmPcides using skin and soft tissue infection (SSTI) and biofilm formation models by <em>Streptococcus pyogenes</em>. Screening our compound library for minimal inhibitory (MIC) and minimal bactericidal (MBC) concentrations identified GmPcide PS757 as highly active against <em>S. pyogenes</em> . Treatment of <em>S. pyogenes</em> biofilm with PS757 revealed robust efficacy against all phases of biofilm formation by preventing initial biofilm development, ceasing biofilm maturation and eradicating mature biofilm. In a murine model of <em>S. pyogenes</em> SSTI, subcutaneous delivery of PS757 resulted in reduced levels of tissue damage, decreased bacterial burdens and accelerated rates of wound-healing, which were associated with down-regulation of key virulence factors, including M protein and the SpeB cysteine protease. These data demonstrate that GmPcides show considerable promise for treating <em>S. pyogenes</em> infections.</p>
Data for: Schaub et al., Impact of Organic Compounds on the Stability of Influenza A Virus in deposited 1-µl droplets
<p><strong>Experimental data</strong></p> <p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>Figure 1: inactivation data after 0 and 60 min for 1-μl droplet experiments at various RH in PBS, SLF and nasal mucus (infectivity titer and genomic copy enumeration).</p> <p>Figure 3: inactivation data after 0 and 60 min for 1-μl droplet experiments at 60% RH in SLF derivatives (infectivity titer and genomic copy enumeration).</p> <p>Figure 4: inactivation data after 0 and 60 min for 1-μl droplet experiments at 60% RH in various albumin:NaCl mass ratios (infectivity titer and genomic copy enumeration).</p> <p>Figure 5: inactivation data after 0 and 60 min for 1-μl droplet experiments at 60% RH with various proteins (infectivity titer and genomic copy enumeration).</p> <p>Figure S1: Control in bulk for data from Figure 1 (infectivity titer).</p> <p>Figure S2: Recovery fraction for data from Figure 1 (GC/GC<sub>0</sub>). </p> <p> </p> <p><strong>Abbrevations used:</strong></p> <p>GC = Genomic Copies</p> <p>LoQ = Limit of Quantification</p> <p>PFU = Plaque Forming Unit</p> <p>ul = microliter</p>
Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery
<blockquote> <p>Version 2: Added missing files to <code>sniffing_data.zip</code></p> </blockquote> <p>See GitHub repository for data processing functions and examples: <a href="https://github.com/soerenbrandt/sniffing-sensor">https://github.com/soerenbrandt/sniffing-sensor</a></p> <p><strong>Abstract</strong>:<br>Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.</p>
Datasets of DFT adsorption energies of H and for O and OH on different pure metals and binary intermetallic compounds considering the application of elastic strains and lists of candidates for screening
<p>This resource contains two datasets and two lists of candidates for screening in JSON format. Also It contains ZIP folders with all Quantum Espresso Inputs and outputs from which the JSON datasets were obtained. All Quantum Espresso outputs will be later added to Catalysis Hub (https://www.catalysis-hub.org/). The file "QuantumEspresso_versions" is a text file contaning the information of the Quantum Espresso versions employed for obtaining the dataset.</p> <p>The datasets contain the adsorption energies for surface slabs of a large number of binary intermetallic compounds with different compositions and lattices (for instance, A3B fcc, A3B hpc, AB bcc, etc.). Adsorption energies were computed for different adsorbates (H, O, and OH) on distinct adsorption sites (e.g., fcc AAB, fcc AAA, hcp AAA, hcp AAB, on-top A, and on-top B) and minimum energy surfaces. In addition, different elastic strains (biaxial tension, biaxial compression) were applied to assess their effect on adsorption energies. All calculations were carried out using DFT approximations as implemented in the Open-source software Quantum Espresso. Besides the adsorption energies, the datasets also contain relevant geometric and electronic descriptors (PSI, cell volume, weighted atomic radius, generalized coordination number, weighted electronegativity, weighted first ionization energy, outer electrons, and biaxial strain) calculated to feed them as features in the training of ML models. The datasets with the tag "scaled" on its name have the descriptors scaled following a MinMax scaling and are given in xlsx format.</p> <p>The lists for screening contain candidates not included in the dataset for which Random Forest predictions of the Eads were obtained. The lists contain the geometric and electronic descriptors of all screening candidates, as well as the predicted adsorption energy (Eads_RF).</p> <p>A GitHub repository is linked to this dataset (https://github.com/vvassilevg/HighHydrogenML). The repository contains two Python scripts:</p> <p>1) Script for creating a dataset from QuantumEspresso outputs, where all relevant descriptors are computed. It outputs a pickle and json files that can be later converted to any other desired format (like xlsx).</p> <p>2) Script for training a Random Forest model for the prediction of adsorption energies (the datasets with the "scaled" tag must be used for the script to work correctly).</p> <p> </p> <p>The dataset, ML model and screening have been accepted for publication in Catalysis Science & Technology DOI: DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D</a>. The accepted Manuscript and the Supplementary information are avilable within this repository.</p> <p> </p> <p>If you use this dataset or any of the files within this repository, please cite the original publication (<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D)</a> in your work.</p>
ScienceDex guides
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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.