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6,181 to 6,190 of 11,437 Results

Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues Jul 9, 2021 Ancin-Murguzur, Francisco Javier, 2021, "Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues", https://doi.org/10.18710/5PX1GJ, DataverseNO, V1 1. Stable isotopes analysis (SIA) of carbon and nitrogen provides valuable information about trophic interactions and animal feeding habits. 2. We used near-infrared reflectance spectroscopy (NIRS) and support vector machines (SVM) to develop a model for screening isotopic ratios of carbon and nitrogen (δ13C and δ15N) in samples from living animals...
00_README.txt Jul 9, 2021 - Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues Plain Text - 4.3 KB - MD5: e894a4c6a2a7a6c56193edad94b05e96 README file
Script.R Jul 9, 2021 - Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues R Syntax - 5.9 KB - MD5: aedacc09bfa6ffabbe0f9c029864a811 The "Script.R" file contains all the required R commands to develop the main model described in the related publication
Spectra.txt Jul 9, 2021 - Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues Plain Text - 30.0 MB - MD5: 7591534c20ec2d832f94a53bb7677f3c The "Spectra.txt" file contains the reference spectral dataset, with columns named with their corresponding wavelength number (X350 to X2450) and the sample identifier ("Sample")
Values.txt Jul 9, 2021 - Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues Plain Text - 10.6 KB - MD5: ed8103b3925e9bc9cfb0df4aa4d8794f The "Values.txt" file contains the reference chemical values for d13C, d15N, C, N and the C/N ratios (with equivalent column names) as well as the sample identifier
Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells Jun 25, 2021 Opstad, Ida S.; Godtliebsen, Gustav; Sekh, Arif Ahmed, 2021, "Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells", https://doi.org/10.18710/IDCUCI, DataverseNO, V1 Abstract: Segmenting sub-cellular structures in living cells from fluorescence microscopy images is a ground truth (GT) hard problem. The microscope’s 3-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution, and complex morphological manifestations of the structures, all contribute to GT-hardness...
0_ReadMe-1.txt Jun 25, 2021 - Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells Plain Text - 15.6 KB - MD5: e132db0a0f357ed56600fb8b42da3583
10-Regions-Imaged-every-5sec-10min-Z1-T1_50X_01.tif Jun 25, 2021 - Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells TIFF Image - 28.8 MB - MD5: 43524366000f47521f45da5437b0d04e
10-Regions-Imaged-every-5sec-10min-Z1-T1_50X_02.tif Jun 25, 2021 - Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells TIFF Image - 28.8 MB - MD5: b4ff893c4aab35bbe56a497c9ac58929
20160420_mcc13_bmr20ppc_mtdr500nM_T25_1520_convtl_005.tif Jun 25, 2021 - Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells TIFF Image - 2.2 GB - MD5: 029affcfecc6d103b8759f71573bb938 Microscopy image data for the dataset LiveEpi1Hu.

Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues

Jul 9, 2021

Ancin-Murguzur, Francisco Javier, 2021, "Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues", https://doi.org/10.18710/5PX1GJ, DataverseNO, V1

1. Stable isotopes analysis (SIA) of carbon and nitrogen provides valuable information about trophic interactions and animal feeding habits. 2. We used near-infrared reflectance spectroscopy (NIRS) and support vector machines (SVM) to develop a model for screening isotopic ratios of carbon and nitrogen (δ13C and δ15N) in samples from living animals...

00_README.txt

Jul 9, 2021 - Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues

Plain Text - 4.3 KB -

README file

Script.R

Jul 9, 2021 - Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues

R Syntax - 5.9 KB -

The "Script.R" file contains all the required R commands to develop the main model described in the related publication

Spectra.txt

Jul 9, 2021 - Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues

Plain Text - 30.0 MB -

The "Spectra.txt" file contains the reference spectral dataset, with columns named with their corresponding wavelength number (X350 to X2450) and the sample identifier ("Sample")

Values.txt

Jul 9, 2021 - Replication Data for: Using near-infrared reflectance spectroscopy (NIRS) to predict carbon and nitrogen stable isotope composition in animal tissues

Plain Text - 10.6 KB -

The "Values.txt" file contains the reference chemical values for d13C, d15N, C, N and the C/N ratios (with equivalent column names) as well as the sample identifier

Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells

Jun 25, 2021

Opstad, Ida S.; Godtliebsen, Gustav; Sekh, Arif Ahmed, 2021, "Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells", https://doi.org/10.18710/IDCUCI, DataverseNO, V1

Abstract: Segmenting sub-cellular structures in living cells from fluorescence microscopy images is a ground truth (GT) hard problem. The microscope’s 3-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution, and complex morphological manifestations of the structures, all contribute to GT-hardness...

0_ReadMe-1.txt

Jun 25, 2021 - Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells

Plain Text - 15.6 KB -

10-Regions-Imaged-every-5sec-10min-Z1-T1_50X_01.tif

Jun 25, 2021 - Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells

TIFF Image - 28.8 MB -

10-Regions-Imaged-every-5sec-10min-Z1-T1_50X_02.tif

Jun 25, 2021 - Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells

TIFF Image - 28.8 MB -

20160420_mcc13_bmr20ppc_mtdr500nM_T25_1520_convtl_005.tif

Jun 25, 2021 - Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells

TIFF Image - 2.2 GB -

Microscopy image data for the dataset LiveEpi1Hu.

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