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datasets available to search
ShareScore release 0.9.0
Dataset results
24 results for “Quantitative biology”
Relaxation anisotropy of quantitative MRI parameters in biological tissues
<p>Dataset for the manuscript "Relaxation anisotropy of quantitative MRI parameters in biological tissues" published in Scientific Reports 2022</p>
Assessment of 3D MINFLUX data for quantitative structural biology in cells
<p>Reanalysed data for "Assessment of 3D MINFLUX data for quantitative structural biology in cells"</p> <p>https://www.biorxiv.org/content/10.1101/2021.08.10.455294v1</p> <p>Contact Hell lab for the raw data</p> <p>https://www.mpibpc.mpg.de/hell</p>
A quantitative autonomous bioluminescence reporter system with a wide dynamic range for Plant Synthetic Biology
<p>This data set includes: Luminescence (NeoLuc Luminescence), Fluorescence (eGFP) , NeoLuc/eGFP ratios, Area Under The Curve of NeoLuc/eGFP ratios, Normalized Area Under The Curve of Neoluc/eGFP (FBP-RTAs), Firefly Luciferase luminescence (FLuc), Renilla Luciferase luminescence (RLuc), FLuc/RLuc ratios and Normalized FLuc/RLuc values of all experiments in this work. </p>
Data from: A fluorometric assay for high-throughput phosphite quantitation in biological and environmental matrices
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Data from: A quantitative review of density-dependent growth and survival in salmonids: biological mechanisms, methodological biases, and management implications
<p>Understanding the complex variation in patterns of density-dependent individual growth and survival across populations is critical to adaptive fisheries management, but the extent to which this variation is caused by biological or methodological differences is unclear. Consequently, we conducted a correlational meta-analysis of published literature to investigate the relative importance of methodological and biological predictors on the shape and strength of density-dependent individual growth and survival in salmonids. We obtained 160 effect sizes from 75 studies of 12 species conducted between 1977-2019 that differed in experimental approach (sensu Hurlbert, 1984; 65 laboratory experiments, 60 observational field studies, and 35 field experiments). The experimental approach was the strongest factor influencing the strength of density-dependence across studies: density-dependent survival was stronger than growth in field observational studies, whereas laboratory experiments detected stronger density-dependent growth than survival. The difference between density-dependent growth and survival was minimal in field experiments, and between lotic and lentic habitats. The shape of density-dependence (logarithmic, linear, exponential, or density-independent) could be predicted with 66.7% accuracy based solely on the experimental approach and the density gradient (highest/lowest*100) of the study. Overall, the strength and shape of density-dependence were primarily influenced by methodological predictors, while biological factors (predator presence, food abundance, and species) had predictable but modest effects. For both empirical studies and adaptive fisheries management, we recommend using field experiments with a density gradient of at least 460% to detect the proper shape of the density-dependent response, or accounting for potential biases if observational or laboratory studies are conducted.</p>
Data from: A quantitative review of density-dependent growth and survival in salmonids: biological mechanisms, methodological biases, and management implications
Open the record for dataset details and reuse information.
Figure 5 from: Dadayan AS, Stepanyan LA, Sargsyan TH, Hovhannisyan AM, Dadayan SA (2021) Quantitative analysis of biologically active substances and the investigation of antioxidant and antimicrobial activities of some extracts of Osage orange fruits. Pharmacia 68(4): 731-739. https://doi.org/10.3897/pharmacia.68.e70180
Figure 5 Results of quantification and identification of flavonoids in ethanol extract of Osage Orange.
Figure 4 from: Dadayan AS, Stepanyan LA, Sargsyan TH, Hovhannisyan AM, Dadayan SA (2021) Quantitative analysis of biologically active substances and the investigation of antioxidant and antimicrobial activities of some extracts of Osage orange fruits. Pharmacia 68(4): 731-739. https://doi.org/10.3897/pharmacia.68.e70180
Figure 4 Results of quantification and identification of flavonoids in aqueous extract of Osage Orange.
Figure 10 from: Dadayan AS, Stepanyan LA, Sargsyan TH, Hovhannisyan AM, Dadayan SA (2021) Quantitative analysis of biologically active substances and the investigation of antioxidant and antimicrobial activities of some extracts of Osage orange fruits. Pharmacia 68(4): 731-739. https://doi.org/10.3897/pharmacia.68.e70180
Figure 10 Evaluation of the antimicrobial activity of Osage Orange extracts on strains of Bacillus subtilis 1820, E. Coli 5002, Serratia marcescens 5251 and Staphylococcus aureus ATCC-6538.
Figure 6 from: Dadayan AS, Stepanyan LA, Sargsyan TH, Hovhannisyan AM, Dadayan SA (2021) Quantitative analysis of biologically active substances and the investigation of antioxidant and antimicrobial activities of some extracts of Osage orange fruits. Pharmacia 68(4): 731-739. https://doi.org/10.3897/pharmacia.68.e70180
Figure 6 Results of quantification and identification of flavonoids in ethyl acetate extract of Osage Orange.
A quantitative autonomous bioluminescence reporter system with a wide dynamic range for Plant Synthetic Biology
<p>This data set includes: Luminescence (NeoLuc Luminescence), Fluorescence (eGFP) , NeoLuc/eGFP ratios, Area Under The Curve of NeoLuc/eGFP ratios, Normalized Area Under The Curve of Neoluc/eGFP (FBP-RTAs), Firefly Luciferase luminescence (FLuc), Renilla Luciferase luminescence (RLuc), FLuc/RLuc ratios and Normalized FLuc/RLuc values of all experiments in this work. </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 8 from: Dadayan AS, Stepanyan LA, Sargsyan TH, Hovhannisyan AM, Dadayan SA (2021) Quantitative analysis of biologically active substances and the investigation of antioxidant and antimicrobial activities of some extracts of Osage orange fruits. Pharmacia 68(4): 731-739. https://doi.org/10.3897/pharmacia.68.e70180
Figure 8 Quantitative content of vitamin C in ethanol extract of Osage Orange.
Figure 7 from: Dadayan AS, Stepanyan LA, Sargsyan TH, Hovhannisyan AM, Dadayan SA (2021) Quantitative analysis of biologically active substances and the investigation of antioxidant and antimicrobial activities of some extracts of Osage orange fruits. Pharmacia 68(4): 731-739. https://doi.org/10.3897/pharmacia.68.e70180
Figure 7 Quantitative content of vitamin C in aqueous extract of Osage Orange.
Figure 3 from: Dadayan AS, Stepanyan LA, Sargsyan TH, Hovhannisyan AM, Dadayan SA (2021) Quantitative analysis of biologically active substances and the investigation of antioxidant and antimicrobial activities of some extracts of Osage orange fruits. Pharmacia 68(4): 731-739. https://doi.org/10.3897/pharmacia.68.e70180
Figure 3 Results of chromatography of organic acids in ethyl acetate extract of Osage Orange.
Figure 1 from: Dadayan AS, Stepanyan LA, Sargsyan TH, Hovhannisyan AM, Dadayan SA (2021) Quantitative analysis of biologically active substances and the investigation of antioxidant and antimicrobial activities of some extracts of Osage orange fruits. Pharmacia 68(4): 731-739. https://doi.org/10.3897/pharmacia.68.e70180
Figure 1 Results of chromatography of organic acids in aqueous extract of Osage Orange.
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.