Origin 2016

Manufactured by OriginLab

Sourced in United States

Origin 2016 is a data analysis and graphing software developed by OriginLab. It offers a range of features for data processing, visualization, and statistical analysis. The software is designed to provide users with a versatile platform for managing, analyzing, and presenting scientific and engineering data.

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Lab products found in correlation

Prism 7, by GraphPad (17 mentions) Imagej, by OriginLab (14 mentions) Ms excel, by OriginLab (11 mentions) Prism 8, by GraphPad (4 mentions) Pclamp 10, by Molecular Devices (4 mentions) Spss 22, by IBM (2 mentions) Matlab, by MathWorks (2 mentions) Pls toolbox, by Eigenvector Research (2 mentions) Axopatch 200b amplifier, by Molecular Devices (2 mentions) Spri plex 2, by Horiba (2 mentions) Thermo genesys 10s, by Thermo Fisher Scientific (2 mentions) Visionlite, by Thermo Fisher Scientific (2 mentions) Infinite 200 pro spectrophotometer, by Tecan (1 mentions) Fluoview 1200, by Olympus (1 mentions) Spss 20, by IBM (1 mentions) Digidata 1440a, by Molecular Devices (1 mentions) Clampfit, by Molecular Devices (1 mentions) P 97 puller, by Sutter Instruments (1 mentions) Phosphoenolpyruvate, by Merck Group (1 mentions) Pyruvate kinase, by Merck Group (1 mentions)

Close spelling variants of this product

Origin Pro 2016 64-bit (3 citations) Origin 2016 Software suite (2 citations) MS Origin 2016 (1 citations)

112 protocols using origin 2016

Analysis of Experimental Parameters

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All the data were analyzed according to the experimental design, using SPSS 22 software (SPSS Statistics 22.0, IBM, United States). Pictures were drawn by Origin 2016 (Origin 2016, OriginLab, United States) and ImageJ (ImageJ 1.8.0, National Institutes of Health, United States). The means of these parameters were compared using Duncan’s multiple range test at P < 0.05.

Zhang P., Zhang H., Wu G., Chen X., Gruda N., Li X., Dong J, & Duan Z. (2021). Dose-Dependent Application of Straw-Derived Fulvic Acid on Yield and Quality of Tomato Plants Grown in a Greenhouse. Frontiers in Plant Science, 12, 736613.

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Whitefly Abundance Analysis via ANOVA

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Data for analysis of variance were transformed to log₁₀(x), or log₁₀(x+1) if any zeroes were present, to homogenise the variance. Residuals were checked for normality. Multiple comparisons were made using Tukey’s test. A general linear model was used for regression analysis, allowing for blocks. The data for the regression were transformed to log₁₀(x+1) and the residuals were checked for normality. Regression on increasing shades of grey used a dummy variable where plain = 0, light grey = 1, mid grey = 2, and black = 3. All tables and figures show untransformed means and standard errors for adult whiteflies per trap, whereas all the statistical analyses used log-transformed data and allowed for blocks. Statistical analysis was carried out with Minitab 16 (Minitab Inc., USA) and measurement of the areas under curves was carried out with Origin 2016 (OriginLab Corp., USA).

Sampson C., Covaci A.D., Hamilton J.G., Hassan N., Al-Zaidi S, & Kirk W.D. (2018). Reduced translucency and the addition of black patterns increase the catch of the greenhouse whitefly, Trialeurodes vaporariorum, on yellow sticky traps. PLoS ONE, 13(2), e0193064.

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Assessing Lactobacillus rhamnosus Growth

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Lactobacillus rhamnosus strains were grown statically for 16 h in MRS broth at 37°C. Freshly harvested cells were washed with MRS without glucose and diluted 1:100 in MRS (pH 6.6) containing either bile (0, 0.1, 0.3, 0.5, and 1.0% w/v oxgall), or adjusted to pH 4 (HCl) or pH 8 (NaOH). 200 μl of bacterial suspensions were placed into each well of a 96 well plate, sealed and placed into Tecan Infinite 200 Pro spectrophotometer (Tecan, Switzerland), where they were grown for 24 h at 37°C. Measurements of optical density at 600 nm (OD_600nm) were taken every 15 min during this 24-h period. Maximum specific growth rates (μ_max) were then calculated for each treatment type and plotted in Origin2016 (OriginLab, Northampton, MA).

Arnold J.W., Simpson J.B., Roach J., Kwintkiewicz J, & Azcarate-Peril M.A. (2018). Intra-species Genomic and Physiological Variability Impact Stress Resistance in Strains of Probiotic Potential. Frontiers in Microbiology, 9, 242.

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Imaging Striatal Astrocyte Calcium Dynamics

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Striatal slice preparation was performed as described above.
Striatal slices were maintained in oxygenated ACSF (124 mM NaCl, 4.5 mM KCl,
1 mM MgCl₂, 1.2 mM NaH₂PO₄, 26 mM
NaHCO₃, 10 mM D-glucose, and 2.0 mM CaCl₂) through
a perfusion system. Astrocytes for all the experiments were imaged using a
confocal microscope (Fluoview 1200; Olympus) with a 40×
water-immersion objective lens with a numerical aperture (NA) of 0.8 and at
a digital zoom of two to three. We used the 488 nm line of an Argon laser,
with the intensity adjusted to 9% of the maximum output of 10 mW. Astrocytes
were chosen typically ~20 to ~30 μm below the slice
surface and scanned at 1 frame per second for imaging sessions.
Analyses of time-lapse image series were performed using ImageJ
(NIH). XY drift was corrected using a custom plugin in ImageJ. Time traces
of fluorescence intensity were extracted from the ROIs and converted to dF/F
values. For analyzing spontaneous Ca²⁺ signaling, ROIs were
defined in normal aCSF (control). Using Origin 2016 (Origin Lab Corp),
Ca²⁺ events were manually marked. Event amplitudes, half
width, event frequency per ROI per min, the integrated area-under-the-curve
(AUC) of dF/F traces were measured. Events were identified based on
amplitudes that were at least 2-fold above the baseline noise of the dF/F
trace.

Yu X., Nagai J., Marti-Solano M., Soto J.S., Coppola G., Babu M.M, & Khakh B.S. (2020). Context-specific striatal astrocyte molecular responses are phenotypically exploitable. Neuron, 108(6), 1146-1162.e10.

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Luminescence-Based Assay Optimization

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S/B ratio was calculated
by μ_max/μ_min and coefficient of
variation (CV%)
as (σ/μ) × 100. μ_max and μ_min are the mean values of three triplicates of luminescence
intensity signals. The Z′ factor was calculated
as . In all calculations,
σ refers as
standard deviations (SD) and μ as mean values. The half-maximal
inhibitor concentration (IC₅₀) values and all other curves
were fitted using standard sigmoidal fitting functions with Origin
2016 (OriginLab, Northampton, MA).

Eskonen V., Tong-Ochoa N., Valtonen S., Kopra K, & Härmä H. (2019). Thermal Dissociation Assay for Time-Resolved Fluorescence Detection of Protein Post-Translational Modifications. ACS Omega, 4(15), 16501-16507.

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Comparative Analysis of Leaf and Sink Traits

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The experiments were conducted as randomized block design using three biological replicates. One-way analysis of variance (ANOVA) was performed with the whole dataset to confirm the variability of data and validity of results, and Duncan’s multiple range test (DMRT) was performed to determine the significant difference between treatments. Different letters indicate significantly different values (DMRT, p ≤ 0.05). Principal component analysis (PCA) was performed with datasets of the source leaf and developing sink, using Origin 2016 (Origin Lab, Northampton, MA, USA), and the first two components (PC1 and PC2) explaining the maximum variance in the datasets were used to make biplots.

Pandey M., Paladi R.K., Srivastava A.K, & Suprasanna P. (2021). Thiourea and hydrogen peroxide priming improved K+ retention and source-sink relationship for mitigating salt stress in rice. Scientific Reports, 11, 3000.

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Quantitative Influenza Virus Analysis

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The signal-to-background (S/B) ratios were calculated as µ_max/µ_min, specific signals as µ_max-µ_min, and coefficient of variations (CV%) as (σ/µ) × 100. The limit of detection (LOD) was calculated as µ_min + 3SD (zero concentration), and the lower limit of detection (LLD) as 3SD. In these formulas, µ is the mean value and σ is the standard deviation (SD). The TEM picture analysis and virus particle calculations were performed using Trainable Weka Segmentation for training a random forest classifier to detect viruses from the TEM images. The plug-in was then used to count the objects that fit the size range and circularity typical for influenza viruses from the segmented binary images [42 (link)]. All data was analyzed and figures were drawn with Origin 2016 (OriginLab, Northampton, MA, USA) using standard fitting functions.

Kopra K., Hassan N., Vuorinen E., Valtonen S., Mahran R., Habib H., Jalkanen P., Susi P., Hytönen V., Hankaniemi M., Ylä-Herttuala S., Kakkola L., Peurla M, & Härmä H. (2022). Rapid high-throughput compatible label-free virus particle quantification method based on time-resolved luminescence. Analytical and Bioanalytical Chemistry, 414(15), 4509-4518.

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Multivariate Analysis of Optical Metrics

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The statistical difference between multiple parameters was tested by Tukey-Kramer's multiple comparisons of means. Linear regressions were fitted using the ordinary least squares method with log10 transformation of variables when needed. Correlations among Fmax/DOC values of the identified PARAFAC components were evaluated using Spearman's rank correlation coefficient (ρ).
A significance level of 0.05 was selected for multiple comparisons to test the null hypothesis. Linear correlations were considered statistically significant when P < 0.01. All analyses were conducted with Origin 2016 (OriginLab Corporation, MA, USA).

Kida M., Kojima T., Tanabe Y., Hayashi K., Kudoh S., Maie N, & Fujitake N. (2019). Origin, distributions, and environmental significance of ubiquitous humic-like fluorophores in Antarctic lakes and streams. Water research, 163.

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Multivariate Analysis of Wine Volatiles

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Statistical data processing was performed using the software Statistical Product and Service Solutions (SPSS 20.0) for Windows (IBM, Armonk, NY, USA). Statistical analyses of the data were performed using one-way analysis of variance (ANOVA) and Duncan’s test at the p < 0.05 level. To obtain an overview of the different wine samples, the data of volatile compounds and phenols were subjected to principle component analysis (PCA) to visualize all information in the data set. All plots were prepared using Origin 2016 (OriginLab Corporation, Northampton, MA, USA).

Zhao T., Wu J., Meng J., Shi P., Fang Y., Zhang Z, & Sun X. (2019). Harvesting at the Right Time: Maturity and its Effects on the Aromatic Characteristics of Cabernet Sauvignon Wine. Molecules, 24(15), 2777.

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Spectral Analysis with Multivariate PLS-DA

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Spectra were analyzed and plotted using Origin 2016 (Origin Lab Corporation, MA, USA). Multivariate analysis was carried out using the MATLAB (2016, Math Works, Inc., MA) version of PLS-Toolbox (v8.0 Eigenvector Research Inc., WA). First derivative and smoothing was done for all the spectra by the Savitzky-Golay (SG) method using a 2^nd order polynomial with a 9 point data window. All spectra were normalized using the standard normal variate method and mean centered before applying multivariate analysis. Partial Least Squares-Discriminant Analysis (PLS-DA) was performed on the preprocessed data and the robustness of the model was verified by cross-validation using Venetian blinds algorithm with 10 data splits, which calibrates the classification model based on 90% of the data, tests the remaining 10% against that model, and repeats the process through ten iterations to assess its predictive power.

Gautam R., Oh J.Y., Patel R.P, & Dluhy R.A. (2018). Non-Invasive Analysis of Stored Red Blood Cells Using Diffuse Resonance Raman Spectroscopy. The Analyst, 143(24), 5950-5958.

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