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1,923 results for “compounds”
Database for accurate EI-MS spectra for semi-volatile compounds identified in plastic from electronical waste
<p>Database of accurate EI-MS spectra, recorded using a GC-EI-TOF-MS instrument, containing more than 300 volatile and semi-volatile compounds tentatively identified in plastics of electrical waste (e-waste). Tentative identifications were based on normalized spectral matches above 60 (0-100 scale) with the low-resolution NIST17 database, linear retention index differences between 50 units compared to literature values for semipolar (HP5-type) columns, and mass errors below 5 mDa for a minimum of two relevant ions in the spectra of each compound.</p>
Hazard Atlas from "Quantifying the Compound Hazard of Freezing Rain and Wind Gusts Across CONUS"
<p>Summary data from the analysis of compound freezing rain-wind gust hazards using Automated Surface Observing Station (ASOS) network data for all hours from January 1, 2005 to December 31, 2022.</p> <p>Note (added 2024-03-06): The SPI index within the Atlas data refers to the SPIA index (https://spia-index.com/), which is copyrighted (https://spia-index.com/copyright.php). Special thanks to Sidney Sperry and Steven Piltz for permission to use the index in this work.</p>
Preliminary Compound Flooding Waikiki
Open the record for dataset details and reuse information.
Sustainable cultivation of melon landraces: effects of grafting on the accumulation of flavor-related compounds
<p>Melon landraces are highly appreciated by consumers who pay price premiums to compensate for lower yields, enabling thus supporting on-farm conservation. However, susceptibility to soilborne diseases poses a significant challengeHowever, they are highly susceptible to soilborne diseases. This study analyses the impact of Cucurbita and Cucumis rootstocks on the accumulation of flavor-related metabolites in several Spanish landraces of the Ibericus melon group, as a strategy to promote their sustainable cultivation. Scion genotype was the main factor conditioning the accumulation of sugars and acids both under standard and salineunder organic farming conditions in different environments. The effects of grafting on organic acid accumulation were negligible, while the effects on sugar profile content were limited butand volatile profile were significant, and. The latter effects were dependent on specific scion-rootstock combinations, though wild Cucumis (e.g. Fian) rootstocks represent an interesting alternative that should be furtherto be studied. The effect on the accumulation of volatiles was limited, and again depended on specific scion-rootstock combinations, and on the specific landrace population studied.. The rootstock effect even differed between populations of the same landrace.</p> <p>Additional data available thorugh contcts with correspondence author.</p> <p>Manuscript Published in Food chemistry.</p>
Figure 1 from: Youssef AMM, Maaty DAM, Al-Saraireh YM (2024) Qualitative chemical compounds analysis and in vitro estimation of antiproliferative, antidiabetic and anti-Alzheimer's disease effects of Ononis natrix (L.) family Fabaceae. Pharmacia 71: 1-11. https://doi.org/10.3897/pharmacia.71.e119755
Figure 1 The HPLC chromatograms. A. Standard chromatogram; B.O. natrix chromatogram.
Figure 8 from: Youssef AMM, Maaty DAM, Al-Saraireh YM (2024) Qualitative chemical compounds analysis and in vitro estimation of antiproliferative, antidiabetic and anti-Alzheimer's disease effects of Ononis natrix (L.) family Fabaceae. Pharmacia 71: 1-11. https://doi.org/10.3897/pharmacia.71.e119755
Figure 8 The inhibition effect of O. natrix and rivastigmine for Butylcholinesterase enzyme.
Figure 2 from: Marinov T, Kokanova-Nedialkova Z, Nedialkov P (2024) UHPLC-HRMS-based profiling and simultaneous quantification of the hydrophilic phenolic compounds from the aerial parts of Hypericum aucheri Jaub. & Spach (Hypericaceae). Pharmacia 71: 1-11. https://doi.org/10.3897/pharmacia.71.e122436
Figure 2 Chromatogram of the EtOH extract from the aerial parts of Hypericum aucheri.
Figure 5 from: Marinov T, Kokanova-Nedialkova Z, Nedialkov P (2024) UHPLC-HRMS-based profiling and simultaneous quantification of the hydrophilic phenolic compounds from the aerial parts of Hypericum aucheri Jaub. & Spach (Hypericaceae). Pharmacia 71: 1-11. https://doi.org/10.3897/pharmacia.71.e122436
Figure 5 A postulated fragmentation pathway of flavanols.
Figure 4 from: Marinov T, Kokanova-Nedialkova Z, Nedialkov P (2024) UHPLC-HRMS-based profiling and simultaneous quantification of the hydrophilic phenolic compounds from the aerial parts of Hypericum aucheri Jaub. & Spach (Hypericaceae). Pharmacia 71: 1-11. https://doi.org/10.3897/pharmacia.71.e122436
Figure 4 The fragmentation pattern of xanthone-C-glycosides.
Figure 3 from: Marinov T, Kokanova-Nedialkova Z, Nedialkov P (2024) UHPLC-HRMS-based profiling and simultaneous quantification of the hydrophilic phenolic compounds from the aerial parts of Hypericum aucheri Jaub. & Spach (Hypericaceae). Pharmacia 71: 1-11. https://doi.org/10.3897/pharmacia.71.e122436
Figure 3 A postulated fragmentation pathway of maclurin.
Compound Drought and Heatwave (CDHW) Event Indicators – Adige River Catchment, 1950–2023
<table> <tbody> <tr> <td> <p><strong>Science Case Name </strong></p> </td> <td> <p>Hot and dry compound events in the Adige River Catchment (Eastern Italian Alps)</p> </td> </tr> <tr> <td> <p><strong>Dataset Title </strong></p> </td> <td> <p>Compound Drought and Heatwave (CDHW) event indicators for the Adige River Catchment (1950-2023)</p> </td> </tr> <tr> <td> <p><strong>Dataset Description </strong></p> </td> <td> <p>Occurrence and severity gridded fields of CDHW events over the Adige River Catchment from 1950 to 2023. The dataset includes the event list with duration, extent, total severity, magnitude and ranking of each identified event.</p> </td> </tr> <tr> <td> <p><strong>Key Methodologies</strong></p> </td> <td> <p>A CDHW event is determined by the co-occurrence of drought and heatwave conditions over at least 60% of the Adige River Catchment. Drought and heatwave indicators were derived using daily temperature and precipitation data from the E-OBS gridded dataset.</p> <p>A drought period is identified as a sequence of consecutive months with a negative Standardized Precipitation Index (SPI), starting with the first month where the SPI-6 (6-month timescale) falls below -1. Heatwaves are defined as periods of at least three consecutive days where the daily maximum temperature (TX) exceeds the 90th percentile for that specific calendar day, determined using a 31-day running mean centred on the day under evaluation and considering all values from 1950 to 2023. When two or more periods of consecutive exceedances are separated by one day with TX below the threshold, they are considered as a single heatwave occurrence and the day below the threshold is included in the event duration. </p> <p>In the occurrence grids, for each day in a CDHW event, grid cells where both drought and heatwave conditions are detected are flagged as "1". If the compound condition is not met, the cell is flagged as "0". In the severity fields, the severity (dimensionless) for each day in a CDHW event is calculated as the product of the standardized daily TX over the days of the event and the absolute value of SPI-6 in the corresponding month. The calculation of the severity is similar to the one proposed by Mukherjee and Mishra (2021), but with the percentiles used in the standardization of TX varying with the day of the year. </p> <p>The list of CDHW events affecting the Adige River Catchment over 1950-2023 includes the start and end dates, the percentage of the area affected, the total severity (dimensionless), magnitude (dimensionless) and ranking of the event. The total severity of the event is defined as the average of the CDHW severities of all grid cells in the catchment experiencing the CDHW conditions. The total severity of the CDHW event at each grid cell is calculated as the sum of the daily severity values over days flagged as "1". The magnitude is the product of the total severity and the fraction of area affected. CDHW events are ranked based on their magnitude.</p> </td> </tr> <tr> <td> <p><strong>Temporal Domain</strong></p> </td> <td> <p>1950-2023</p> </td> </tr> <tr> <td> <p><strong>Spatial Domain</strong></p> </td> <td> <p>Extended region centred on the Adige River Catchment (7.10°-15.30°E, 44.10°-49.10°N); Spatial resolution: 0.1°x0.1° (EPGS:4326)</p> </td> </tr> <tr> <td> <p><strong>Key Indicators </strong></p> </td> <td> <p>CDHW occurrence and severity (grid) and CDHW duration, extent, total severity and magnitude (list)</p> </td> </tr> <tr> <td> <p><strong>Data Format</strong></p> </td> <td> <p>netCDF and CSV</p> </td> </tr> <tr> <td> <p><strong>Source Data</strong></p> </td> <td> <p>E-OBS dataset (v29.0e)</p> </td> </tr> <tr> <td> <p><strong>Accessibility</strong></p> </td> <td> <p>Zenodo, https://doi.org/10.5281/zenodo.13839122 </p> </td> </tr> <tr> <td> <p><strong>Stakeholder Relevance</strong></p> </td> <td> <p>The list of hot and dry events for the Adige River Catchment can support regional water managers to identify the most critical meteorological conditions over the past decades and link them to the observed local impacts. The details about event severity, magnitude and spatial and temporal extent provided in the list can be used directly to characterize the impactful hot and dry episodes. Moreover, the gridded fields offer a consistent description of the phenomena throughout the Adige River Catchment and can be used to localize the affected areas and identify the main spatial patterns of hot and dry conditions. </p> </td> </tr> <tr> <td> <p><strong>Limitations/Assumptions</strong></p> </td> <td> <p>Results accuracy can be influenced by potential biases in the original data source over the study area. The 0.1-grid of E-OBS limits a detailed representation of local conditions and spatial patterns, while the continuous temporal coverage enables historical event detection and analysis. Thresholds used in the proposed definitions are based on expert knowledge and other choices are also possible.</p> </td> </tr> <tr> <td> <p><strong>Contact Information</strong></p> </td> <td> <p>Elena Maines (Center for Climate Change and Transformation - Eurac Research) (data curator)</p> <p>Alice Crespi (Center for Climate Change and Transformation - Eurac Research) (data curator)</p> <p>Marc Lemus-Canovas (Center for Climate Change and Transformation - Eurac Research; Universidade de Santiago de Compostela) (data curator)</p> </td> </tr> </tbody> </table>
Figure 2 from: Fadhilah QG, Santoso I, Maryanto AE, Abdullah S, Yasman Y (2021) Evaluation of the antifungal activity of marine actinomycetes isolates against the phytopathogenic fungi Colletotrichum siamense KA: A preliminary study for new antifungal compound discovery. Pharmacia 68(4): 837-843. https://doi.org/10.3897/pharmacia.68.e72817
Figure 2 Graph of the antibiosis assay of the three marine actinomycetes isolates.
Molecular Characterization of Nitrogen-Containing Organic Compounds Emitted from Coal Combustion: 1N-PAHs Can Serve as Markers
<p>This is the data repository for <em>Geophysical Research Letters</em> manuscript "Molecular Characterization of Nitrogen-Containing Organic Compounds Emitted from Coal Combustion: 1N-PAHs Can Serve as Markers".</p>
Screening of 452 hit enrichment compounds for anti-SARS-CoV-2 activity meta data
<p>This short report describes the most relevant results of screening selected compounds from the Janssen Pharmaceutica compound collection in a VeroE6 cell-based anti-SARS-CoV-2 assay.</p>
Figure 2 from: Kirakosyan VG, Tsaturyan AH, Poghosyan LE, Minasyan EV, Petrosyan HR, Sahakyan LY, Sargsyan TH (2022) Detection and development of a quantitation method for undeclared compounds in antidiabetic biologically active additives and its validation by high performance liquid chromatography. Pharmacia 69(1): 45-50. https://doi.org/10.3897/pharmacia.69.e76247
Figure 2 Method selectivity.
Figure 3 from: Kirakosyan VG, Tsaturyan AH, Poghosyan LE, Minasyan EV, Petrosyan HR, Sahakyan LY, Sargsyan TH (2022) Detection and development of a quantitation method for undeclared compounds in antidiabetic biologically active additives and its validation by high performance liquid chromatography. Pharmacia 69(1): 45-50. https://doi.org/10.3897/pharmacia.69.e76247
Figure 3 Calibration curves of 1-Gliclazide, 2-Glibenclamide, 3-Glimepiride and 4-Metformin.
Figure 1 from: Kirakosyan VG, Tsaturyan AH, Poghosyan LE, Minasyan EV, Petrosyan HR, Sahakyan LY, Sargsyan TH (2022) Detection and development of a quantitation method for undeclared compounds in antidiabetic biologically active additives and its validation by high performance liquid chromatography. Pharmacia 69(1): 45-50. https://doi.org/10.3897/pharmacia.69.e76247
Figure 1 Chromatogram of a standard mixture.
Figure 5 from: Kirakosyan VG, Tsaturyan AH, Poghosyan LE, Minasyan EV, Petrosyan HR, Sahakyan LY, Sargsyan TH (2022) Detection and development of a quantitation method for undeclared compounds in antidiabetic biologically active additives and its validation by high performance liquid chromatography. Pharmacia 69(1): 45-50. https://doi.org/10.3897/pharmacia.69.e76247
Figure 5 "Sugar Balance" sample injection chromatogram.
Figure 4 from: Kirakosyan VG, Tsaturyan AH, Poghosyan LE, Minasyan EV, Petrosyan HR, Sahakyan LY, Sargsyan TH (2022) Detection and development of a quantitation method for undeclared compounds in antidiabetic biologically active additives and its validation by high performance liquid chromatography. Pharmacia 69(1): 45-50. https://doi.org/10.3897/pharmacia.69.e76247
Figure 4 "Dialevel" sample injection chromatogram.
Figure 4 from: Burda NYe, Zhuravel IO, Dababneh MF, Fedchenkova YA (2019) Analysis of diosgenin and phenol compounds in Tribulus terrestris L. Pharmacia 66(2): 41-44. https://doi.org/10.3897/pharmacia.66.e35023
Figure 4 Analog curve of the standard solution of diosgenin.
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.