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12,346 results for “derivatives”
A Precursor-derived Ultramicroporous Carbon for Printing Iontronic Logic Gates and Super-Varactors
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Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives
<p>In this study, we investigated the correlation between the electronic properties of anticholinesterase compounds and their biological activity. While the methodology of such correlation is well-established and has been effectively utilized in previous studies, we employed a more sophisticated approach: machine learning. Initially, we focused on a set of 22 molecules sharing a common chalcone skeleton and categorized them into two groups based on their IC50 indices: high activity and low activity. Utilizing the open-source software Orca, we conducted calculations to determine the geometries and electronic structures of these molecules. Over a hundred parameters were collected from these calculations, serving as the foundation for the features used in machine learning. These parameters included the Mulliken and Lowdin electronic populations of each atom within the skeleton, molecular orbital energies, and Mayer's free valences. Through our analysis, we developed numerous models and identified several successful candidates for effectively distinguishing between the two groups. Notably, the most informative descriptor for this separation relied solely on electronic populations and orbital energies. By understanding which computationally calculated properties are most relevant to specific biological activities, we can significantly enhance the efficiency of drug development processes, saving both time and resources.</p>
scRNA-seq dataset of iPSC-derived pancreatic islet cells
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IsoRSM simulation data for the Simulation of water isotopes in combustion-derived vapor emissions in winter
<p>The two files are the IsoRSM simulation results of the temperature, humidity and water vapor isotopes in Salt Lake City (January, 2017) and Beijing (January, 2007) with different emission rates of combustion-derived vapor. The data is used in the paper "Simulation of water isotopes in combustion-derived vapor emissions in winter".</p>
Dataset for Assessing Charge Transfer Characteristics of Triphenylamine Derivatives
<p>This repository comprises various essential components, including Cartesian coordinates, input files, properties files generated by Orca, and a Python script enabling the complete recreation of the results.</p> <p>The repository contains four main types of files:</p> <ol> <li><strong>Orca Inputs:</strong> These are utilized to compute properties of hole transporting materials associated to the power efficiency conversion of perovskite solar cells, such as reorganization energies, chemical reactivity parameters and transfer integrals.</li> <li><strong>DFTB+ Inputs:</strong> These files facilitate the optimization of molecular structures using the tight-binding method DFTB3-3ob-MBD.</li> <li><strong>Optimized Conformers: </strong> Each file contains a set of conformers (xyz format) obtained through the global optimization procedure conducted with CREST.</li> <li><strong>Python Script: </strong>This script calculates transfer integrals using the DIPRO method.</li> </ol> <p>Additionally, the repository includes r2scan-3c Orca gradient files (which encompass energy, gradient, and Cartesian coordinates in Bohr units), DFTB3-3ob-MBD optimized geometries, TD-DFT-M06/6-31G(d,p) Orca outputs, and properties files generated by Orca during the reorganization energy calculation step.</p> <p><strong>Requirements</strong></p> <p>To effectively utilize the contents of this repository, ensure you have the following:</p> <ul> <li>The latest release of Orca, which can be downloaded for free from the official site: https://orcaforum.kofo.mpg.de/</li> <li>Python, along with the cclib library, available at https://cclib.github.io. Ensure you have Python (version 3.7 or higher) and NumPy (version 1.15 or higher) installed.</li> <li>The latest releases of CREST and xtb, both accessible from the Grimme Lab GitHub page: https://github.com/grimme-lab</li> <li>The most recent version of DFTB+, which can be obtained from DFTB+ Official Site: https://dftbplus.org</li> </ul> <p>These requirements are necessary for replicating and utilizing the results and functionalities provided by the repository effectively.</p> <p><strong>Python script</strong></p> <p>The python code files extract the data from Orca file output and perform the matrix and vector products to calculate and display the transfer integral.</p> <p>The python script require three arguments: the momomer A and B and dimer orca output files:</p> <p> <em> python Jeff-hole.py A.out B.out dimer.out</em></p> <p><strong>Acknowledgments </strong></p> <p>Raul Flores acknowledges CONAHCYT for the postdoctoral fellowship (CVU: 365229). The authors gratefully acknowledge the computing time granted by LANCAD and CONAHCYT on the supercomputer Miztli at DGTIC UNAM.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Green derivative synchronous spectrofluorimetry for kinetic stability study of florfenicol
<p>This dataset contains data representing synchronous fluorescence spectra obtained upon utilizing spectrofluorometry as a highly sensitive analysis tool for the simultaneous determination of Florfenicol with its different degradation products obtained after stress testing. Synchronous fluorimetry was used followed by applying either first or second derivatization according to the type of degradation aiming to obtain highly resoluted spectra. Different degradation pathways according to published literature were used to interpret degradation kinetic parameters, which were in accordance with previously reported articles. The dataset also includes different validation parameters applied.</p>
Efficient oxidation attenuates porewater-derived methane fluxes in mangrove waters
<p>Data set used in the publication (<a href="https://doi.org/10.1002/lno.12639">https://doi.org/10.1002/lno.12639)</a></p> <ul> <li>timerseries.xlsx <ul> <li>This data includes continous data of ch4 concentration with other environmental parameters e.g. salinity, temperature, water depth, oxygen saturation and calculated water-air ch4 fluxes at two mangrove creeks in southeastern Brazil (Paraty-Mirim National Park, Florianópolis) </li> </ul> </li> <li>discrete data <ul> <li>This data includes discrete surface water sampling during spring and neap tide and porewater. The data includes ch4 concentration, d13C-CH4 and environmental parameters. </li> </ul> </li> </ul>
Annual probability of extreme heat and drought events, derived from Lange et al 2020
<p>The time series of extreme events given by Lange et al has been processed into an annual probability of occurrence by researchers at the University of Oxford, using the pipeline available online at <a href="https://github.com/nismod/isimip-exposure">https://github.com/nismod/isimip-exposure</a> - this is a draft dataset, used for visualisation in <a href="https://global.infrastructureresilience.org/">https://global.infrastructureresilience.org/</a> but not otherwise reviewed or published.<br><br>If you use this, please cite: Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI 10.1029/2020EF001616<br><br>This is shared under a CC0 1.0 Universal Public Domain Dedication (CC0 1.0) When using ISIMIP data for your research, please appropriately credit the data providers, e.g. either by citing the DOI for the dataset, or by appropriate acknowledgment.<br><br>Annual probability of drought (soil moisture below a baseline threshold) or extreme heat (temperature and humidity-based indicators over a threshold) events on a 0.5° grid. 8 hydrological models forced by 4 GCMs under baseline, RCP 2.6 & 6.0 emission scenarios. Current and future maps in 2030, 2050 and 2080.<br><br>The ISIMIP2b climate input data and impact model output data analyzed in this study are available in the ISIMIP data repository at ESGF, see <a href="https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=input">https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=input</a> and <a href="https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=output">https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=output</a>, respectively. More information about the GHM, GGCM, and GVM output data is provided by Gosling et al. (2020), Arneth et al. (2020), and Reyer et al. (2019), respectively.<br><br>Event definitions are given in Lange et al, table 1. Land area is exposed to drought if monthly soil moisture falls below the 2.5th percentile of the preindustrial baseline distribution for at least seven consecutive months. Land area is exposed to extreme heat if both a relative indicator based on temperature (Russo et al 2015, 2017) and an absolute indicator based on temperature and relative humidity (Masterton & Richardson, 1979) exceed their respective threshold value.</p> <p>Population exposure is calculated as annual expected population directly exposed to the occurrence of extreme heat or drought events, assuming any population directly within the footprint of an event is exposed, but not otherwise taking any other risk-mitigating or -propagating factors into account.</p> <p>Population is held constant at 2020 levels, using the JRC GHSL GHS-POP R2023A release, which can be cited as Schiavina M., Freire S., Carioli A., MacManus K. (2023) GHS-POP R2023A - GHS population grid multitemporal (1975-2030).European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe"> http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:<a href="https://doi.org/10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE">10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE</a> and for the concept and methodology, Freire S., MacManus K., Pesaresi M., Doxsey-Whitfield E., Mills J. (2016) Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE), AGILE 2016</p> <h3>Updates</h3> <p>2024-06-11: New draft with corrected aggregate population, created using https://github.com/nismod/isimip-exposure/tree/fcfbeff6778f697a5a470ac20dd912fd426a7f98</p>
Table presenting field data, isotopic data and 10Be exposure ages of cliffs and caprock-derived boulders sampled on the southern slope of the mesa of Szczeliniec Wielki (Central Sudetes, Stołowe Mountains, SW Poland)
<p><span>This research was funded by National Science Centre, Poland, research project no. 2020/39/D/ST10/00861.</span></p> <p><span>The data in the table presents the results of cosmogenic </span><sup><span>10</span></sup><span>Be exposure dating of cliffs and hillslope boulders resting on the southern slope of the sandstone mesa of Szczeliniec Wielki (Central Sudetes, Stołowe Mountains, SW Poland). The total of 20 samples was collected with growing distance from the present-day cliff lines. The samples were collected in October 2021, whereas their treatment and analyses were conducted in the following months at the University of Zurich (UZH) and ETH Zurich, Switzerland.</span></p> <p> </p> <p> </p>
Cartilage Responses to Inflammatory Stimuli and Adipose Stem/Stromal Cell-Derived Conditioned Medium: Results from an Ex Vivo Model
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Data for "Beyond theory driven discovery: hot random search and datum derived structures"
<p>Data associated with "Beyond theory driven discovery: hot random search and datum derived structures"</p> <p>contributed to: FD 2025: Data-driven discovery in the chemical sciences</p> <p><code>.</code><br><code>├── README</code><br><code>├── b</code><br><code>│ ├── collection</code><br><code>│ ├── eddp</code><br><code>│ ├── test-105-111</code><br><code>│ ├── test-192</code><br><code>│ └── test-56</code><br><code>├── c</code><br><code>│ ├── collection</code><br><code>│ ├── collection-0.5eV</code><br><code>│ ├── collection-1eV</code><br><code>│ ├── manifest-dia</code><br><code>│ ├── test-dia-25-48</code><br><code>│ ├── test-dia-8-24</code><br><code>│ └── test-dia-nosymm-8</code><br><code>└── pyrope</code><br><code> ├── airss</code><br><code> ├── collection-gamma-relax</code><br><code> ├── manifest</code><br><code> └── test</code></p> <p><br>C J Pickard, 2024<br>Cambridge</p>
Particle Size Distribution in the 3-100 micron size range derived from an Imaging FlowCytoboy for 4 transect cruises across the Pacific Ocean
<p>Surface, Underway Imaging FlowCytobot (IFCB) differential_particle_size_distribution (PSDn, images per L per micron for each size bin) or differential_particle_volume_concentration (VDn, microliters per L per micron for each size bin) for 50 log-spaced size bins in the ~3-100 micrometer size range. Size distribution data collected from surface waters using the ship"s flowthrough system (~ 7m) during SCOPE cruises Gradients 2, 3, 4, and 5 (http://scope.soest.hawaii.edu/data/gradients/gradients.html). The IFCB triggers on both scattering and fluorescence so both living and non-living particles are included in the binned estimates. Note that the particle abundances and/or concentrations in the first few and last size bins may be underestimated. Please use care when comparing results across cruises, since different settings per cruise may result in different sensitivity and detection levels. Particle size of individual particles was calculated from estimated biovolume using the distance-map algorithm of Moberg and Sosik (2012). Particles are assumed spherical and are binned over 50 logarithmically-spaced size classes centered between 3.11 micrometers and 98.15 micrometers. See metadata in each excel spreadsheet for information on binwidth of each size class as well as other relevant information. This dataset was not converted to Carbon units. Timestamp is in UTC.</p>
MUSES Leaf Area Index (LAI) Derived from MODIS Data Monthly Global 0.05º Geographic Grid Since 2000
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES LAI product was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). It is provided on Geographic grid and spans from 2000 to 2019 (continuously updated). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 2000 – 2019</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Leaf Area Index (LAI) Derived from MODIS Data 8-Day Global 0.05º Geographic Grid Since 2000
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>The MUSES LAI product at 0.05º spatial resolution and 8-day temporal resolution was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). It is provided on Geographic grid and spans from 2000 to 2023 (continuously updated). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 2000 – 2023</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Supplementary Data for "Molecular Docking, ADMET, Synthesis and Evaluation of New Indomethacin Hydrazide Derivatives as Antibacterial Agents"
<p>This supplementary data file contains additional information supporting the findings presented in the research article "<strong>Molecular Docking, ADMET, Synthesis and Evaluation of New Indomethacin Hydrazide Derivatives as Antibacterial Agents</strong>". The data includes:</p> <ul> <li>Infrared (IR) spectra of the synthesized compounds</li> <li>Proton nuclear magnetic resonance (H NMR) spectra of the synthesized compounds</li> <li>Minimum inhibitory concentration (MIC) and zone of inhibition (ZOI) data for the synthesized compounds against tested microorganisms</li> </ul>
Impact of atmospheric water-soluble iron on α-pinene-derived SOA formation and transformation in the presence of aqueous droplets
<p>The following data sets belong to the article "Impact of atmospheric water-soluble iron on α-pinene-derived SOA formation and transformation in the presence of aqueous droplets" The impact of water-soluble atmospheric iron on formation, growth and aging of secondary organic aerosol (SOA) is a controversial subject in the literature. Iron chemistry drives Fenton-like reactions in the aqueous phase which is dependent on pH. Flow reactor experiments in the dark and under humid conditions were conducted to investigate systematically the influence of ferrous iron in the aqueous phase on α-pinene SOA by online physical analysis and offline high-resolution mass spectrometry. In total 31 flow reactor experiments were conducted in four sets of experiments. All SMPS data and mass spectra in negative mode are uploaded sorted by set of experiments and figures in the paper. You can find the workflow of SMPS data analysis as well as the retention times of the target analysis in the paper and SI.</p>
Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>The data have been reprojected to EU LAEA.</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>EU LAEA (EPSG: 3035)</p> <p>Spatial extent:<br>north: 6874000<br>south: -485000<br>west: 869000<br>east: 8712000</p> <p>Spatial resolution:<br>1000 m</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="https://doi.org/10.5281/zenodo.6146383">https://doi.org/10.5281/zenodo.6146383</a></p>
Material of the paper "Deriving Domain Models from User Stories: Human vs. Machines"
<p>This folder represents the online appendix for the paper "Deriving Domain Models from User Stories: Human vs. Machines" It contains all relevant code, results, and data sets.</p>
DBpedia derived abstracts for text classification
<p>The derived datasets obtained after processing the raw texts from the DBpedia dataset.</p>
Data used in the study "Application of nanopore sequencing for accurate identification of bioaerosol-derived bacterial colonies"
<p>Data used in the study "Application of nanopore sequencing for accurate identification of bioaerosol-derived bacterial colonies." The datasets contain nanopore and Sanger sequencing data (including the electropherograms) as well as EPI2ME and NGSpeciesID analysis.</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.