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The research data includes: the original spectrogram and visualized feature image; the denoised spectrogram; the cropped spectrogram;; results of deep-learning models; the result of this article(Result of experiment); core code; and the core code.
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All configuration files used in the analysis.
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MATLAB simulation code and scripts reproducing all figures in Tikhonov, Kachru, Fisher (2020), "A model for the interplay between tradeoff plasticity and evolution in changing environment". Optional pre-computed simulation data included to speed-up figure plotting; remove or rename any data file to rerun the relevant simulations from scratch.
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This dataset contains 26 sub-datasets of common distribution, including Beta, Exponential, Log-Normal, Normal, Triangular, Uniform, bi-modal. The size of datasets varying from 1000 points to 1 million points. The distributions are generated with various parameter settings.
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PDF in each folder shows un-cropped Western blots that were used to generate main and supplementary Figures. Raw microscopic images were also included in the data set.
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Using all stocks listed in the Johannesburg Stock Exchange and macroeconomic data for South Africa, the dataset comprises the following series: 1. Monthly returns for 6 size-asset growth portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 2. Monthly returns for 6 size-return on assets portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 3. Monthly returns for 10 size-price to cash flow portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 4. Monthly returns for 15 book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 5. Consumer Confidence Index (CCI) for South Africa. (Raw data source: OECD) 6. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for South Africa. (Raw data source: OECD) 7. Fama and French (1993) factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database) 8. Fama and French (2015) factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database) 9. Three-month interest rate of the Treasury Bill for South Africa. (Raw data source: OECD) We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) return on equity (WC08301 series), (v) total assets (WC02999 series), (vi) price-to-cash flow ratio (PC series), and (vii) tax rate (WC08346 series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. REFERENCES: Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.
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Bibliographic Data used for Scientometric Analysis
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Propionic acidemia (PA) is an autosomal recessive metabolic disorder caused by the deficiency of the mitochondrial protein propionyl-CoA carboxylase (PCC) and associated with pathogenic variants in either of the two genes, PCCA or PCCB. In the present study, three PA patients were diagnosed by using gas chromatography-mass spectrometry(GC-MS), tandem mass spectrometry (MS/MS) and Molecular diagnostic methods. All patients had onset in neonatal period. One patient died of infection and metabolic decompensation, and the other two had mild to moderate developmental delay/mental retardation. Mutation analysis of the PCCA gene identified compound heterozygous c.1288C>T(p.R430X) and c.2002G>A(p.G668R) in patient 1, homozygous c.1426C>T(p.R476X) in patient 2; mutation analysis of the PCCB gene identified compound heterozygousc.359_360del AT(p.Y120Cfs*40) and c.1398+1G>A in patient 3. Three novel mutations (c.1288C>T, c.359_360del AT and c.1398+1G>A were identified in PCCA and PCCB genes. Among them, in the PCCA gene, c.1288C>T(p.R430X) was a nonsense mutation, resulting in a truncated protein. c.359_360del AT was a frameshift mutation, leading to p.Y120Cfs*40 mutation of amino acid sequence in PCCB. c.1398+1G>A was a splicing mutation, causing skipping of the exons 13-14. In conclusion, the novel mutations of this study will expands the mutation spectrum of PA.
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This dataset contains sensor traces (multivariate time series) of six data acquisition campaigns performed by autonomous aquatic drones involved in water monitoring. A total of 5.6 hours of navigation are available, with data coming from both lakes and rivers, and from different locations in Italy and Spain. The monitored variables concern both the internal state of the drone (e.g., battery voltage, GPS position and signals to propellers) and the state of the water (e.g., temperature, dissolved oxygen and electrical conductivity). Data were collected in the context of the EU-funded Horizon 2020 project INTCATCH (http://www.intcatch.eu) which aims to develop a new paradigm in the monitoring of river and lake water quality. Both autonomous and manual drive is used in different parts of the navigation.
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The inherent heterogeneity of the prebiotic milieu is often overlooked when studying nonenzymatic reactions. However, it is important to note that the prebiotic soup of a putative ‘RNA World’ would have been replete with a plethora of molecules resulting from complex chemical syntheses, as well as exogeneous delivery. The presence of such background molecules could lead to pertinent phenomena such as molecular crowding, which can potentially affect how a reaction would advent in a crowded milieu. In the current study, we have analyzed the effect of crowding on the stacking ability of the RNA monomers, using Nuclear Magnetic Resonance (NMR) spectroscopy. In particular, DOSY and 13C-T1 relaxation studies are carried out to understand the formation of pseudo-oligomeric species at higher nucleotide concentrations and the effect of crowding agents on such oligomers. Our findings corroborate that the purine monomers possess better stacking efficiency than pyrimidine based monomers. Significantly, this competence is further enhanced in the presence of a crowding agent. Interestingly, this enhanced stacking could result in higher sequestration of the purine monomers, putting their ready availability for relevant nonenzymatic polymerization and replication reactions into question. Taken together, this study demonstrates the need for systematic biophysical characterization of molecular crowding in the context of prebiotically pertinent processes. Unraveling such phenomena is essential to gather a real understanding of how the transition from abiotic to biotic, would have happened during the origin of life.
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