Find research datasets worth reusing

opencc-by-4.0Aug 2020View details →

Reproduction packages for the paper "Spectral and Imaging properties of Sgr A∗ from High-Resolution 3 DGRMHD Simulations with Radiative Cooling"

This is a basic reproduction package for the paper"Spectral and Imaging properties of Sgr A&lowast; from High-Resolution 3D GRMHD Simulations with Radiative Cooling" by Yoon et al. (2020). It aims to provide the most important data products to check and reproduce the main results of the paper.

opencc-by-4.0Jun 2017View details →

Quantum Cascade Laser Spectral Histopathology: Breast Cancer Diagnostics Using High Throughput Chemical Imaging

Fourier transform infrared (FT-IR) microscopy, coupled with machine learning approaches, has been demonstrated to be a powerful technique for identifying abnormalities in human tissue. The ability to objectively identify the prediseased state, and diagnose cancer with high levels of accuracy, has the potential to revolutionise current histopathological practice. Despite recent technological advances in FT-IR microscopy, sample throughput and speed of acquisition are key barriers to clinical translation. Wide-field quantum cascade laser (QCL) infrared imaging systems with large focal plane array detectors utilising discrete frequency imaging, have demonstrated that large tissue microarrays (TMA) can be imaged in a matter of minutes. However this ground breaking technology is still in its infancy and its applicability for routine disease diagnosis is, as yet, unproven. In light of this we report on a large study utilising a breast cancer TMA comprised of 207 different patients. We show that by using QCL imaging with continuous spectra acquired between 912 and 1800 cm-1, we can accurately differentiate between 4 different histological classes. We demonstrate that we can discriminate between malignant and non-malignant stroma spectra with high sensitivity (93.56%) and specificity (85.64%) for an independent test set. Finally, we classify each core in the TMA and achieve high diagnostic accuracy on a patient basis with 100% sensitivity and 86.67% specificity. The absence of false negatives reported here opens up the possibility of utilising high throughput chemical imaging for cancer screening, thereby reducing pathologist workload and improving patient care.

opencc-by-4.0Apr 2024View details →

Europa spectral images of HCN, SO2, H2CO and CH3OH observed using ALMA

This dataset contains observations of Europa carried out using the Atacama Large Millimeter/submillimeter Array (ALMA) on four dates in 2021, using the Band 7 receiver. The targeted spectral lines included HCN ($J=4-3$; 354.505 GHz), H$_2$CO ($J_{K_a,K_c}=5_{1,5}-4_{1,4}$; 351.769 GHz), SO$_2$ ($J_{K_a,K_c}=5_{3,3}-4_{2,2}$; 351.257 GHz), observed at 122~kHz resolution, and CH$_3$OH ($J_K = 4_0 - 3_{-1}\ E$; 350.688 GHz), observed at 61 kHz resolution. The interferometric data were cleaned and imaged using CASA, with a pixel size of 0.02'' and a clean mask of 1.1''. The resulting spatial resolution is ~0.15 arcseconds. The data includes FITS image cubes and PDF spectral maps for each molecule, on each observing date. See FITS headers for further details. For each PDF, the ALMA beam FWHM is indicated lower left (dot-dashed ellipse); upper right axes show physical distances in the plane of the sky, while lower left axes show the spectral units for each sub-panel.

opencc-by-4.0Jul 2018View details →

The CAPA Apple Quality Grading Multi-Spectral Image Database

The CAPA Apple Quality Grading Multi-Spectral Image Database consists of multispectral (450nm, 500nm, 750nm, and 800nm) images of health and defected apples of bi-color, manual segmentations of defected regions, and expert evaluations of the apples into 4 quality categories. The defect types consist of bruise, rot, flesh damage, frost damage, russet, etc.  The database can be used for academic or research purposes with the aim of computer vision based apple quality inspection. The CAPA Apple Quality Grading Multi-Spectral Image Database is a propriety of ULG (Gembloux Agro-Bio Tech) - Belgium, and cannot be used without the consent of the ULG (Gembloux Agro-Bio Tech), Belgium.  For consent, contact Devrim Unay, İzmir University of Economics, Turkey: unaydevrim@gmail.com OR Marie-France Destain, Gembloux Agro-Bio Tech, Belgium: mfdestain@ulg.ac.be In disseminating results using this database,  1. the author should indicate in the manuscript that it was acquired by ULG (Gembloux Agro-Bio Tech), Belgium. 2. cite the following article Kleynen, O., Leemans, V., & Destain, M.-F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69(1), 41-49. Relevant publications: Kleynen et al., 2003 O. Kleynen, V. Leemans and M.F. Destain, Selection of the most efficient wavelength bands for ‘Jonagold’ apple sorting. Postharv. Biol. Technol.,  30  (2003), pp. 221–232. Leemans and Destain, 2004 V. Leemans and M.F. Destain, A real-time grading method of apples based on features extracted from defects. J. Food Eng.,  61  (2004), pp. 83–89. Leemans et al., 2002 V. Leemans, H. Magein and M.F. Destain, On-line fruit grading according to their external quality using machine vision. Biosyst. Eng.,  83  (2002), pp. 397–404. Unay and Gosselin, 2006 D. Unay and B. Gosselin, Automatic defect detection of ‘Jonagold’ apples on multi-spectral images: A comparative study. Postharv. Biol. Technol.,  42  (2006), pp. 271–279. Unay and Gosselin, 2007 D. Unay and B. Gosselin, Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition. J. Food Eng.,  78  (2007), pp. 597–605. Unay et al., 2011 Unay, D., Gosselin, B., Kleynen, O, Leemans, V., Destain, M.-F., Debeir, O, “Automatic Grading of Bi-Colored Apples by Multispectral Machine Vision”, Computers and Electronics in Agriculture, 75(1), 204-212, 2011.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.

opencc-by-4.0Jan 2020View details →

Datasets used in 'Streambed hydraulic conductivity estimated by spectral induced polarization imaging can help to improve groundwater modeling'

These datasets pertain to the manuscript entitled 'Streambed hydraulic conductivity estimated by spectral induced polarization imaging can help to improve groundwater modeling', which is currently submitted for revision in Water Resources Research. They comprise raw data from measurements taken in the field at 2 sites in terms of (1) pressure time series during the performed slug tests, (2) impedance from spectral induced polarization, (3) submersion levels of the used electrodes below the top of the water column, and (4) three-dimensional Cartesian coordinates relating the measurements spatially. The coordinates have been projected to a local coordinate system for each site, to comply with a non-disclosure agreement of the measurement locations. The projection of the coordinates still allows to fully reproduce the presented results, if using the methods described in the manuscript. The naming convention throughout the datasets is consistent with site labels used in the manuscript. All data is given as comma-separated values with intuitive file names and self-explanatory headers containing a list of field names. The slug test data includes multiple repetitions of the same measurement, and the impedance measurements contain normal as well as reciprocal readings – as described in the manuscript. The data is separated into two compressed file archives, named according to the site names given in the manuscript. Each file pertaining to slug tests at a certain location, in the subfolder “slugTestRecordings” has the following naming convention: “<locationTag>_<finalDepth>.csv”, where <locationTag> corresponds to the local coordinates given in “coordinates.txt”, and where <finalDepth> is an integer describing the largest depth in cm at which a slug test was performed according to the protocol described in the manuscript. Each file pertaining to impedance measurements at a certain profile, in the subfolder “SIPRecordings”, has the following naming convention: “<locationTag>_<frequency>.dat”, where <locationTag> corresponds to the local coordinates given in “coordinates.txt”, and where <frequency> is a zero-padded integer describing the measurement frequency in Hz at which the measurement was performed according to the protocol described in the manuscript. Submersion levels of the electrodes below the top of the stream’s water column are given in m in the file “submersionLevels.txt”, corresponding to the local coordinates given in “coordinates.txt”. The local coordinates given in m in “coordinates.txt” have the following convention for the column “locationTag”: “<profileTag>-<electrodeNumber>, where <profileTag> pertains to a name of the electrical array and <electrodeNumber”> is a continuous number for the electrode. Slug tests were exclusively perfomed at the location of electrodes and files are, thus, as described above, named accordingly.

opencc-by-4.0Dec 2021View details →

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery

Sample video showing the 'ischemia index' computed with a deep-learning model trained on multispectral images recorded during partial nephrectomy before a clamp is applied to the renal artery (perfused) and after clamping the artery (ischemic).

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Standard calibration

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin & Chlorophyll a

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Excel files include hyperspectral indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments. Scripts used for producing plots in the publication and supplementary material are available on Renku; see the Software section. Hyperspectral data are submitted separately due to their size; see the Related works.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin & Chlorophyll a

opencc-by-4.0Oct 2024View details →

Multi-view spectral images

Multi-view spectral images took on 07/11/2024 using Confocal 980.

dryad36/100

Active and low-cost hyperspectral imaging for spectral analysis in low lighting environment

Hyperspectral imaging can capture information beyond conventional RGB cameras; thus, it has many applications, such as material identification and spectral analysis. However, like many camera systems, most of the existing hyperspectral cameras are still passive imaging systems: they require external light sources to illuminate the objects to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting, and the imaging system cannot function in a dark or low-lighting environment. This work develops a prototype system for active hyperspectral imaging, which actively emits different single-wavelength lights at different frequencies when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is normalized to extract reflectance information; second, the system is capable of collecting information at the desired spectral range by tailoring the light sources; third, an active system is mechanically easier to make, since it does not require complex band filters as used in passive systems; last, such a system may work under low light or dark environments, which greatly facilitate underground/subsurface sensing applications such as borehole based mining exploration. This prototype is achieved by using an array of low-cost and single-wavelength LED (Light Emitting Diode) lights, a remote control module controlling the LED illuminator, and the shutter of a full spectrum camera. We demonstrate that such design is feasible and could yield informative hyperspectral images for spectral analysis and machine learning-based object identification in low light or dark environments, having great potential to benefit both the academic and industry such as in geochemistry, earth science, subsurface energy, and mining.

opencc-zeroDec 2022View details →

opencc-by-4.0Apr 2023View details →

Training dataset for spectral compressive imaging in DL4sSR

Training dataset should be placed as \DL4sSR\3SCI\26train_256_enhanced.h5 Benchmark: <a href="https://github.com/JiangHe96/DL4sSR">https://github.com/JiangHe96/DL4sSR</a> Reference: J. He, Q. Yuan, J. Li, Y. Xiao, D. Liu, H. Shen, and L. Zhang, "Spectral super-resolution meets deep learning: achievements and challenges," Information Fusion, 2023.