Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
380
datasets available to search
ShareScore release 0.9.0
Dataset results
380 results for “diamonds”
Long-Lived Ensembles of Shallow NV− Centers in Flat and Nanostructured Diamonds by Photoconversion
<p>Shallow, negatively charged nitrogen-vacancy centers (NV−) in diamond have been proposed for high-sensitivity magnetometry and spin-polarization transfer applications. However, surface effects tend to favor and stabilize the less useful neutral form, the NV0 centers. Here, we report the effects of green laser irradiation on ensembles of nanometer-shallow NV centers in flat and nanostructured diamond surfaces as a function of laser power in a range not previously explored (up to 150 mW/μm2). Fluorescence spectroscopy, optically detected magnetic resonance (ODMR), and charge-photoconversion detection are applied to characterize the properties and dynamics of NV− and NV0 centers. We demonstrate that high laser power strongly promotes photoconversion of NV0 to NV− centers. Surprisingly, the excess NV− population is stable over a timescale of 100 ms after switching off the laser, resulting in long-lived enrichment of shallow NV−. The beneficial effect of photoconversion is less marked in nanostructured samples. Our results are important to inform the design of samples and experimental procedures for applications relying on ensembles of shallow NV− centers in diamond.</p>
Analytical modeling of an hybrid power module based on diamond and SiC devices
<p>This dataset contains the raw data used for the publication (available here : <a href="https://doi.org/10.1016/j.diamond.2022.108936">10.1016/j.diamond.2022.108936</a> ).</p> <p><strong>Analytical modeling of an hybrid power module based on diamond and SiC devices</strong></p> <p>Marine Couret, Anne Castelan, Nazareno Donato, Florin Udrea, Julien Pernot, Nicolas Rouger</p> <p>Detailed descriptions for each file can be found in "Dataset_Description.docx".</p>
Boron-Doped-HPHT-Diamond CL Dataset
<p>Cathodoluminescence datasets and analysis notebooks accompanying the following publication:</p> <p>https://doi.org/10.1016/j.carbon.2022.01.030</p> <p>The analysis files rely on the development hyperspy version at the time of release (1.6.1). If compatibility issues arise with newer versions, please ask the curator of this repository for an update of the analysis notebooks.</p>
Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip
<p>Dataset accompanying "Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip". </p>
Data from article: "Wide‑field magnetometry using nitrogen‑vacancy color centers with randomly oriented micro‑diamonds"
<p>This repository contains the dataset obtained from the CW-ODMR magnetic imaging experiment with nitrogen-vacancy (NV) centers using a custom-built wide-field setup.</p> <p><strong>Related publication: </strong></p> <p>Sengottuvel, S., Mrózek, M., Sawczak, M. <em>et al.</em> Wide-field magnetometry using nitrogen-vacancy color centers with randomly oriented micro-diamonds. <em>Sci Rep</em> <strong>12</strong>, 17997 (2022). <a href="https://doi.org/10.1038/s41598-022-22610-5">https://doi.org/10.1038/s41598-022-22610-5</a>.</p> <p><strong>Authors:</strong></p> <ul> <li>Saravanan Sengottuvel, (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> <li>Mariusz Mrózek, (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> <li>Mirosław Sawczak, (Szewalski Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Poland)</li> <li>Maciej J. Głowacki, (Gdańsk University of Technology, Poland)</li> <li>Mateusz Ficek, (Gdańsk University of Technology, Poland)</li> <li>Wojciech Gawlik (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> <li>Adam M. Wojciechowski (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> </ul> <p><strong>Abstract: </strong></p> <p>Magnetometry with nitrogen-vacancy (NV) color centers in diamond has gained significant interest among researchers in recent years. Absolute knowledge of the three-dimensional orientation of the magnetic field is necessary for many applications. Conventional magnetometry measurements are usually performed with NV ensembles in a bulk diamond with a thin NV layer or a scanning probe in the form of a diamond tip, which requires a smooth sample surface and proximity of the probing device, often limiting the sensing capabilities. Our approach is to use micro- and nano-diamonds for wide-field detection and mapping of the magnetic field. In this study, we show that NV color centers in randomly oriented submicrometer-sized diamond powder deposited in a thin layer on a planar surface can be used to detect the magnetic field. Our work can be extended to irregular surfaces, which shows a promising path for nanodiamond-based photonic sensors.</p> <p><strong>Funding: </strong></p> <p>The research was carried out within the TEAM NET programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund, project POIR.04.04.00-00-1644/18. This research was funded in part by National Science Centre, Poland grant number 2020/39/I/ST3/02322<strong>. </strong></p> <p><strong>Description of the data: </strong></p> <p>The dataset consists of 24 individual data files labelled chronologically, starting from f0.fits to f24.fits. The data format is Flexible Image Transport System (FITS). Each FITS file consists of a header and 3-dimensional image data. The header contains the experimental parameters set during data acquisition, which may also be helpful for data analysis. The FITS file can be read using any software (e.g., MATLAB, Python) that supports the FITS file format.</p> <p><strong>An example header:</strong></p> <p> {'STARFREQ'} {[ 2700]} {' in MHz '}<br> {'STOPFREQ'} {[ 3000]} {' in MHz '}<br> {'STEPSIZE'} {[ 1.500000000000000]} {' Frequency interval '}<br> {'MWPOWER'} {[ 5]} {' in dBm '}<br> {'NSCANS'} {[ 5]} {' Total number of scan repetitions '}<br> {'EXPOSURE'} {[ 20]} {' in ms '}<br> {'FPS'} {[ 20]} {' no. of frames per second '}<br> {'LEDCURR'} {[ 0.990000000000000]} {' LED current in mA }<br> {'EXPTIME'} {[1.942112698000000e+02]} {' Measurement time in seconds '}<br> {'END' } {0×0 char } {0×0 char }</p> <p><strong>Table:</strong> Data file name and the associated current value set in the wire during the magnetic imaging measurement</p> <table> <thead> <tr> <th>File name</th> <th>Current value (mA)</th> <th>File name</th> <th>Current value (mA)</th> </tr> </thead> <tbody> <tr> <td>f0.fits</td> <td>0</td> <td>f13.fits</td> <td>-300</td> </tr> <tr> <td>f1.fits</td> <td>+50</td> <td>f14.fits</td> <td>-550</td> </tr> <tr> <td>f2.fits</td> <td>+100</td> <td>f15.fits</td> <td>-250</td> </tr> <tr> <td>f3.fits</td> <td>-50</td> <td>f16.fits</td> <td>+550</td> </tr> <tr> <td>f4.fits</td> <td>-100</td> <td>f17.fits</td> <td>+350</td> </tr> <tr> <td>f5.fits</td> <td>-150</td> <td>f18.fits</td> <td>+400</td> </tr> <tr> <td>f6.fits</td> <td>+250</td> <td>f19.fits</td> <td>-400</td> </tr> <tr> <td>f7.fits</td> <td>+450</td> <td>f20.fits</td> <td>-450</td> </tr> <tr> <td>f8.fits</td> <td>+600</td> <td>f21.fits</td> <td>-500</td> </tr> <tr> <td>f9.fits</td> <td>-200</td> <td>f22.fits</td> <td>+150</td> </tr> <tr> <td>f10.fits</td> <td>-350</td> <td>f23.fits</td> <td>+300</td> </tr> <tr> <td>f11.fits</td> <td>+200</td> <td>f24.fits</td> <td>-600</td> </tr> <tr> <td>f12.fits</td> <td>+500</td> <td> </td> <td> </td> </tr> </tbody> </table> <p>For more information on the data analysis methods and results, we recommend you to read the article.</p>
Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model
<p>The diamond is 58 times harder than any other mineral in the world, and its elegance as a jewel has long been appreciated. Forecasting diamond prices is challenging due to nonlinearity in important features such as carat, cut, clarity, table, and depth. Against this backdrop, the study conducted a comparative analysis of the performance of multiple supervised machine learning models (regressors and classifiers) in predicting diamond prices. Eight supervised machine learning algorithms were evaluated in this work including Multiple Linear Regression, Linear Discriminant Analysis, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosted Regression and Classification Trees, and Multi-Layer Perceptron. The analysis is based on data preprocessing, exploratory data analysis (EDA), training the aforementioned models, assessing their accuracy, and interpreting their results. Based on the performance metrics values and analysis, it was discovered that eXtreme Gradient Boosting was the most optimal algorithm in both classification and regression, with a R<sup>2</sup> score of 97.45% and an Accuracy value of 74.28%. As a result, eXtreme Gradient Boosting was recommended as the optimal regressor and classifier for forecasting the price of a diamond specimen.</p>
Free-standing n-type phosphorus-doped diamond
<p>Data from the article "Free-standing n-type phosphorus-doped diamond" submitted to Appl. Phys. Lett.</p>
Dataset for "Engineering defect clustering in diamond-based materials for technological applications via quantum mechanical descriptors"
<p>The unique set of extreme physical properties makes diamond an ideal candidate for applications in the energy industry such as in high-power and high-frequency electronics as well as in electrochemistry and photovoltaics. Furthermore, dopant-vacancy complexes in diamond can be exploited for further development of quantum computers, single-photon emitters, high-precision magnetic field sensing and nanophotonic devices. While certain dopant-vacancy complexes are well-studied, studies of other dopant/vacancy clusters are focused mostly on defect detection while investigations on how to tune their electronic and optical properties for specific applications is mostly omitted. To this aim, we attempted to reveal coupled structural-electronic features and their effect on the band gap of such defects through first principle calculations. We investigated four different defect types: a) dopant-vacancy complexes (X-V), b) two dopants as nearest neighbours (X-X), c) two dopants separated by one carbon atom (X-C-X) and d) two dopants separated by a vacancy (X-V-X). For each of these configurations, we considered Al, B, N, P and Si as dopant atoms. This dataset contains input files needed to reproduce every ground state geometry used in our study.</p>
Dataset: Diamond Hill Investment Group, Inc. (DHIL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Black Diamond Therapeutics, Inc. (BDTX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DIAMOND_Methyl_Haplo_table
<p>methylationLevels.csv</p> <p>Metadata by Klaus Von GRAFENSTEIN, Engineer</p> <p>24/06/2024</p> <p>Data from Michel, et al. « Non-Invasive Multi-Cancer Diagnosis Using DNA Hypomethylation of LINE-1 Retrotransposons ». Preprint. Oncology, 23 janvier 2024. <a href="https://doi.org/10.1101/2024.01.20.23288905" target="_blank" rel="noopener">doi:10.1101/2024.01.20.23288905</a></p> <p>Generated with code : <a href="https://github.com/ProudhonLab/DIAMOND">https://github.com/ProudhonLab/DIAMOND</a></p> <p><br><strong>Columns:</strong></p> <ul> <li> Sample_ID = Sample identifier composed of : the sequencing dataset code, followed by "S" and the sample number inside the sequencing batch*</li> <li> Cohorte = Cohort of the sample (Discovery or Validation)</li> <li> Disease_status = Biological class of the sample</li> <li> Metastasis_status = Factor of if the sample is from a metastatic patient or not (M+ or M0). NA when the sample is healthy plasma or Metastasis_status is unknown</li> <li> Stage = Cancer stage of the sample ( 1 to 4 ). NA when the sample is healthy plasma or Stage is unknown</li> <li> Age.range = Age between a 5-year range of the patient or donor</li> <li> Sex = Sex of the patient or donor (F or M). NA when the sex is unknown</li> <li> Haplotype proportions = columns with the name of the amplicon (1,2,4,5,6 or 7) and haplotype code (0 unmethylated, 1 methylated, for each CpG site of the amplicon). Value between 0 and 1</li> <li> Methylation rate = columns with the name of the amplicon (1,2,4,5,6 or 7) and CpG site number. Value between 0 and 1</li> </ul>
The Phonon-Modulated Jahn–Teller Distortion of the Nitrogen Vacancy Center in Diamond
<p>The data in this repository consists of three data sets that yielded transient absorption spectra, 2D electronic spectra, and 3D coherence spectra for the nitrogen vacancy (NV) center. These data supported the conclusions of the published report, namely that the LO Phonon of the diamond lattice modulates the Jahn-Teller distortion of the NV center and influences its excited-state dynamics. The data were taken between 2016 and 2020 and reflect the advancements made to the ultrafast instrumentation of te Turner Lab during this time period. The Matlab scripts and data are presented "as is", however we have attempted to include processed datasets legible to any open-source programming language when possible. Please consult the README.md file for further information.</p>
Dataset for What are key factors for detection of peptides using mass spec-trometry on boron-doped diamond surfaces?
<p>The data set to paper: </p> <p>What are key factors for detection of peptides using mass spec-trometry on boron-doped diamond surfaces?</p> <p>Juvissan Aguedo1, Marian Vojs2, Martin Vrška2, Marek Nemcovic3, Zuzana Pakanova3, Katerina Aubrechtova Dragounova4, Oleksandr Romanyuk4, Alexander Kromka4, Marian Varga5, Michal Hatala6, Marián Marton2 and Jan Tkac1,*</p> <p>1 Institute of Chemistry, Slovak Academy of Sciences, Bratislava, Slovakia<br>2 Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Bratislava, Slovakia<br>3 Centre of Excellence for Glycomic, Slovak Academy of Sciences, Bratislava, Slovakia<br>4 Institute of Physics, Czech Academy of Sciences, Prague 6, Czech Republic<br>5 Institute of Electrical Engineering, Slovak Academy of Sciences, Bratislava, Slovakia<br>6 Department of Graphic Arts Technology and Applied Photochemistry, Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovakia</p> <p>* corresponding author: jan.tkac@savba.sk</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 5. 2023 - 31. 5. 2024</p> <p>All the data shown in the pictures are provided with a described sample in X-Y or X-Y-Z format.<br>The respective figure to which the data belong is always provided in high resolution.<br>The data are in the following formats: <br>Scheme 1: pdf<br>Figure 1: pdf<br>Figure 2: pdf, csv<br>Figure 3: pdf<br>Figure 4: pdf, csv<br>Figure 5: pdf<br>Figure 6: pdf, csv<br>Figure 7: pdf, csv<br>Figure 8: pdf, csv<br>Figure 9: pdf, csv<br><br></p> <p>The comma separated values file (csv) always contain the description of the columns in the first row. In case of composed image the name of the file corresponds to the corresponding figure.</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI: 10.3390/nano14151241</p>
Real measurement of Coulomb diamonds
<p>Data used in the paper "Efficiently measuring a quantum device using machine learning".</p> <p>https://arxiv.org/abs/1810.10042</p> <p>It can be loaded directly from Numpy:</p> <p>import numpy as np</p> <p>data = np.load('data_real.npy')</p> <p> </p>
Single-crystal X-ray diffractometry data for a sample of NiCl₂-dppe collected on beamline I19-2 at Diamond Light Source
<p>Single-crystal X-ray diffractometry data for a sample of [1,2-Bis(diphenylphosphino)ethane]dichloronickel(II) (NiCl<sub>2</sub>-dppe, [(C<sub>6</sub>H<sub>5</sub>)<sub>2</sub>PCH<sub>2</sub>CH<sub>2</sub>P(C<sub>6</sub>H<sub>5</sub>)<sub>2</sub>]NiCl<sub>2</sub>).</p> <p>Data collected at Diamond Light Source I19-2 on 2015-05-18, publicly available for users to test data reduction routines. Data are known to produce good merging statistics and final refinements.</p> <p>The sample was prepared as follows:<br> Nickel chloride (II) hexahydrate (1 g, 2 mmol) was heated under vacuum to produce anhydrous nickel chloride (II) with a visible colour change from green to yellow. The resulting solid was taken up in ethanol (5 ml) and added to 1,2-bis(dimethylphosphine)ethane (dppe) (0.837 g, 2 mmol) in ethanol (10 ml). The solution was refluxed for 3 hour after which the solvent was evaporated. The small red crystals were purified by recrystallisation in acetone (70% yield).</p> <p>The sample was held at an approximate temperature of 150 K and the illuminating beam had a wavelength of 0.68890 Å (17.997 keV). The detector was held at 2θ = 25° throughout.</p> <p>Inventory of data:<br> <strong>010_Ni_dppe_Cl_2_150K01</strong> — 130° ω scan, 0.4° images, 0.4s per image, 325 images; κ = 45°, φ = 160°.<br> <strong>010_Ni_dppe_Cl_2_150K02</strong> — 130° ω scan, 0.4° images, 0.4s per image, 325 images; κ = 45°, φ = 40°.<br> <strong>010_Ni_dppe_Cl_2_150K03</strong> — 130° ω scan, 0.4° images, 0.4s per image, 325 images; κ = 45°, φ = -80°.<br> <strong>010_Ni_dppe_Cl_2_150K04</strong> — 198° ω scan, 0.4° images, 0.4s per image, 495 images; κ = 0°, φ = -80°.</p>
Tutorial Dataset from the I22 beamline at Diamond Light Source
<p>A tutorial dataset generated by the I22 beamline at Diamond Light Source to accompany the I22 beamline data reduction and analysis manual.</p>
In-situ data recorded from thaumatin crystals on Diamond Light Source VMXi
<p>Data collected as part of routine beamline commissioning, from samples of thaumatin grown in 0.1 M Sodium citrate, 0.75M Sodium / Potassium tartrate. Data were collected in unattended mode with positions identified <em>via</em> SynchWeb from photographs taken with a Formulatrix imaging system (picture included.) Each data set consists of 200 images taken with an Eiger 2X 4M detector at a distance of 186mm, with exposure time of 2 ms, wavelength 0.97950 angstroms and 2% transmission with a DMM beam. Automated processing combined data using the xia2 multiplex tool to give a sufficiently complete data set for structure solution and refinement.</p>
Multicrystal data of proteinase K collected on the VMXi beamline at the Diamond Light Source, UK
<p>These are a series of datasets which have been collected on the VMXi beamline at the Diamond Light Source, UK. They were collected <em>in-situ</em> at room temperature using a Dectris 2X 4M detector. The datasets were collected in unattended mode using samples preselected using the SynchWeb interface to ISPyB. Each dataset is a 60 degree wedge of data, collected using 1% DMM (double multilayer monochromator) beam (at a wavelength of 0.979A) with an exposure time of 0.002 secs/ frame. These data were merged and have been used in the deposition of a structure to the protein databank.</p>
A dataset on "Coating of self-sensing AFM cantilevers with boron-doped nanocrystalline diamond films at low temperatures"
<p>The data set to paper: </p> <p>Coating of self-sensing AFM cantilevers with boron-doped nanocrystalline diamond at low temperatures</p> <p>Štěpán Potocký1*, Jaroslav Kuliče1k, Egor Ukraintsev1, Ondřej Novotný2, Alexander Kromka3, and Bohuslav Rezek1</p> <p>1 Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 16627 Prague, Czech Republic<br>2 NenoVision s.r.o., Purkyňova 649, 61200 Brno, Czech Republic <br>3 Institute of Physics, Czech Academy of Sciences, Prague 6, Czech Republic<br>*corresponding author: potocky@fel.cvut.cz</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 10. 2023 - 31. 3. 2024</p> <p>All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective figure to which the data belong is provided in high resolution. <br>The data are in the following formats: <br>Figure 1: pdf<br>Figure 2: pdf<br>Figure 3: pdf, csv<br>Figure 4: pdf, csv, gwy<br>Figure 5: pdf, gwy<br>Figure S1: pdf<br>Figure S2: pdf</p> <p>The comma separated values file (csv) always contain the description of the columns in the first row. Gwy correspond to free Gwyddion SPM data analysis software (gwyddion.net). In case of composed image the name of the file corresponds to the corresponding figure.</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI:10.1002/pssa.202400553.</p>
Fig. 132 in Diamonds in the rough: Ibotyporanga (Araneae, Pholcidae) spiders in semi-arid Neotropical environments
Fig. 132. Ibotyporanga Mello-Leitão, 1944, male chromosome plates of I. naideae Mello-Leitão (A, C–I) and Ibotyporanga sp. (B). A. Spermatogonial metaphase, including Y chromosome; note metacentric morphology and slight positive heteropycnosis of this element. B. Spermatogonial metaphase (2n = 30), including Y microchromosome. C. Premeiotic interphase; note a heteropycnotic body formed by sex chromosomes and rod-shaped element exhibiting weak heteropycnosis. D–E. Diffuse stage; note cluster comprising four heteropycnotic sex chromosomes (D) or sex chromosome body (E) and a heteropycnotic bivalent. F. Late prophase I consisting of 13 bivalents and sex chromosomes, which show considerable decondensation. One bivalent is positively heteropycnotic except for chiasma region. G. Sex chromosome tetravalent from metaphase I (separated by dashed line from bivalents); note scheme of sex chromosome pairing (blue – X chromosomes, orange – Y chromosome). H. Two sister metaphases II separated by dashed line. Left plate contains 16 chromosomes, right plate 14 chromosomes including small Y chromosome. Note predominance of biarmed chromosomes. I. Metaphase II containing 14 chromosomes; note small heteropycnotic Y chromosome. Arrows without letters point at sex chromosome body/cluster; arrowheads point at heteropycnotic bivalent/element. Abbreviations: X = X chromosome; Y = Y chromosome. Scale lines: A–F, H–I = 10 µm; G = 5 µm.
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.