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.
1,640
datasets available to search
ShareScore release 0.9.0
Dataset results
1,640 results for “Magnetic Resonance”
Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment
<p>We uploaded the raw data related to extracted features of the manuscript "Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. Radiol Med. 2023 Nov;128(11):1310-1332. doi: 10.1007/s11547-023-01710-w. Epub 2023 Sep 11. PMID: 37697033."</p>
Ultrafast laser-induced magneto-optical changes in resonant magnetic x-ray reflectivity
<p>Datasets for the publication "Ultrafast laser-induced magneto-optical changes in resonant magnetic x-ray reflectivity", published in Physical Review B <strong>108</strong>, 054439 (2023).</p><p> </p>
Characterization of Functionalized Chromatographic Nanoporous Silica Materials by Coupling Water Adsorption and Intrusion with Nuclear Magnetic Resonance Relaxometry
<p>This data publication is based on the metadata and datasets underlying the manuscript "Characterization of Functionalized Chromatographic Nanoporous Silica Materials by Coupling Water Adsorption and Intrusion with Nuclear Magnetic Resonance Relaxometry" (<a href="https://doi.org/10.1021/acsanm.3c04330"><span>https://doi.org/10.1021/acsanm.3c04330</span></a>)</p> <p>Included are the datasets used, raw and processed data of Adsorption measurements (Water, Ar 87K, N2 77K), Water Intrusion measurements, NMR Relaxometry and solid state MAS NMR measurements. More information can be found in the Readme file.</p> <p> </p>
Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)
<p>This dataset is related to the paper <strong>“Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)”.</strong></p> <p>https://www.sciencedirect.com/science/article/pii/S2001037021003792?via%3Dihub</p> <p>The dataset comprises horizontal and vertical frequencies of 2D NMR TOCSY of breast cancer-tissue sample. 2D TOCSY was acquired by employing a broadband high resolution 600.13 MHz (B0 = 14.1 T) NMR Bruker spectrometer (AVANCE III 600 with the Bruker magnet ASCEND 600) supported with the room temperature probe (BBO model-Bruker) and Magic Angle Spinning (MAS) probehead. 1D and 2D NMR spectra acquisition and processing were achieved by using the TopSpin software package 3.6.</p> <p>There are two files:</p> <p><strong>BreastCancerMetabolites.csv:</strong></p> <p>First column: numerical labels of the metabolites. Each number represent a metabolite. In total, there are 27 metabolites with multiple multiplets per metabolite.</p> <p>Second and third column: Horizontal and vertical frequencies for each metabolite.</p> <p><strong>Labels.csv:</strong></p> <p>The corresponding metabolites names.</p> <p> </p>
Increased Cardiac Pi/PCr in the Diabetic Heart Observed Using Phosphorus Magnetic Resonance Spectroscopy at 7T
<p>The uploaded data relate to the work of Valkovič et al. entitled "Increased Cardiac Pi/PCr in the Diabetic Heart Observed Using Phosphorus Magnetic Resonance Spectroscopy at 7T." submitted in 2022<br> The data consist of the anonymised in vivo data of healthy volunteers and T2DM patients, in Siemens DICOM format, acquired as described in the manuscript. The uploaded data also include all analysed STEAM data results with visualised MR spectra and fitted values of PCr and Pi, used for Pi/PCr calculation.</p>
Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential
<p>This dataset contains all the raw source data and MATLAB analysis functions that comprise the study:</p> <p><strong>Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential</strong></p> <p>Canadian Journal of Forest Research | DOI: 10.1139/cjfr-2021-0273.</p> <p>Tuomainen, TV (1), Himanen, K (2), Helenius, P (2), Kettunen, MI (3), Nissi, MJ (1,4)*<br> 1. University of Eastern Finland, Department of Applied Physics, Kuopio, Finland<br> 2. Natural Resources Institute Finland, Suonenjoki Unit, Suonenjoki, Finland.<br> 3. University of Eastern Finland, Kuopio Biomedical Imaging Unit, A.I. Virtanen Institute for Molecular Sciences, Kuopio, Finland <br> 4. University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Oulu, Finland</p> <p>*Corresponding author:<br> Mikko J. Nissi<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627<br> FI-70211, Kuopio, Finland<br> mikko.nissi@uef.fi<br> +358-50-5955517</p> <p>Keywords: Pinus sylvestris, seed germination, MRI, radiography, relaxation time mapping</p> <p> </p> <p><strong>Study and data description</strong></p> <p>Altogether 90 Scots pine (Pinus sylvestris L.) seeds were MR imaged using RAREVTR, MSME, MGE and ZTE pulse sequences with reference radiograph from each seed.</p> <p>The data includes MR images and relaxation time data as well as individual X ray radiographs of Scots pine seeds. </p> <p>The data includes all data ('fid' and '2dseq' for MRI, and .jpeg/.png for radiographs), metadata (acquisition and reconstruction MRI parameters), figures of manuscript, and calculated relaxation time maps (in MATLAB MAT-file format).</p> <p> </p> <p>Included folders and files in the zenodo_repo_scotspine_MRI_zip_20012022 are:</p> <ul> <li><strong>additional_info_scotspine</strong>: Information on the seed batches, their germination and structure in .xlsx file format. Translated into English from Finnish on 06.10.2021.</li> <li><strong>manuscript_figures:</strong> Figures in .eps vector file format (fig1.eps-fig7.eps)</li> <li><strong>matlab_scripts:</strong> Contains MATLAB functions and scripts for data analysis of the MRI data, processed together with 'aedes' GUI (aedes.uef.fi/, redirects to github.com).</li> <li><strong>mri_scotspine</strong>: Contains the MRI data using 5 mm and 10 mm RF coils at 11.7 T (Bruker). The folders 'discard_folder/' contain ZTE data that are not processed with carbon_collector.m MATLAB script (i.e. processed separately).</li> <li><strong>radiography_scotspine: </strong>Contains radiographs of invidual seeds in two folders: old (lower resolution, Faxitron MX-20, Faxitron Bioptics LLC, <em>Tucson, Az, USA</em>) and new (higher resolution, Faxitron MultiFocus, Faxitron Bioptics LLC, <em>Tucson, Az, USA</em>).</li> <li><strong>readme.txt: </strong>More information on the file and folder structure and datatypes.</li> </ul> <p> </p> <p>Please see the included readme.txt for further details.</p> <p> </p> <p>(Teemu Tuomainen, Jan 25, 2022)</p>
Data on the detection of clinically significant prostate cancer by magnetic resonance imaging (MRI)-guided targeted and systematic biopsy
<p>This is a dataset from the original publication “Reasons for missing clinically significant prostate cancer by targeted magnetic resonance imaging/ultrasound fusion-guided biopsy”. From 01/2014 to 04/2019 a sample collective of 785 patients with 3T multiparametric magnetic resonance imaging (mp-MRI) of the prostate and subsequent combined systematic biopsy (SB) and magnetic resonance imaging/ultrasound (US) fusion-guided biopsy (TB) was retrospectively analyzed. Prostate carcinoma (PCa) detection by TB and/or additional SB was analyzed.</p>
Molecular Dynamics of Jelly Candies by Means of Nuclear Magnetic Resonance Relaxometry
<p><sup>1</sup>H spin-lattice Nuclear Magnetic Resonance relaxation studies have been performed for different kinds of Haribo jelly and Vidal jelly in a very broad frequency range from about 10 kHz to 10 MHz to obtain insight into the dynamic and structural properties of jelly candies on the molecular level. This extensive data set has been thoroughly analyzed revealing three dynamic processes, referred to as slow, intermediate and fast dynamics occurring on the timescale of 10<sup>−6</sup> s, 10<sup>−7</sup> s and 10<sup>−8</sup> s, respectively. The parameters have been compared for different kinds of jelly for the purpose of revealing their characteristic dynamic and structural properties as well as to enquire into how increasing temperature affects these properties. It has been shown that dynamic processes in different kinds of Haribo jelly are similar (this can be treated as a sign of their quality and authenticity) and that the fraction of confined water molecules is reduced with increasing temperature. Two groups of Vidal jelly have been identified. For the first one, the parameters (dipolar relaxation constants and correlation times) match those for Haribo jelly. For the second group including cherry jelly, considerable differences in the parameters characterizing their dynamic properties have been revealed.</p>
3D co-registration of ultra-low-field and high-field magnetic resonance images (data)
<p>Dataset used for "3D co-registration of ultra-low-field and high-field magnetic resonance images" submitted to PlosOne.</p>
A magnetic resonance multi-atlas for the neonatal rabbit brain - Dataset
<p>Dataset related to the Neuroimage paper https://doi.org/10.1016/j.neuroimage.2018.06.029. Download links, documentation and code for the manipulation are available from the software repository https://github.com/gift-surg/SPOT-A-NeonatalRabbit</p>
Reverberant Magnetic Resonance Elastographic Using a single Mechanical Driver
<p>Reverberant elastography provides fast and robust estimates of shear modulus. However, reverberant elastography uses multiple mechanical drivers, hampering clinical utility. In this work, we hypothesize that a single mechanical driver can generate reverberant shear fields in constrained organs such as the brain. To corroborate this hypothesis, we imaged the brain of a healthy volunteer; and two constrained phantoms containing spherical inclusions with diameters ranging from 4-18 mm. As a secondary goal, we assessed the feasibility of recovering shear modulus from a single component of the reverberant wave field. Viable reverberant and subzone elastograms were produced only when obtained at 50 and 100 Hz in phantoms. Different levels of reverberance were exhibited in different displacement components (70-82% for phantoms and 87-93% for the clinical case); however, wavefields obtained when imaging at 50 Hz and 100 Hz were not significantly different (p>0.05). Errors incurred in reverberant elastograms varied from 5% to 65% when imaging at 50 Hz and 2% to 55% when imaging at 100 Hz. Errors incurred in subzone elastograms ranged from 4% to 18% at 50 Hz and 5% and 50% at 100 Hz. The contrast-to-noise ratio of reverberant elastograms ranged from 20 dB to 44 dB compared to 25 dB to 31 dB in subzone elastograms. The accuracy of the elastograms acquired from the phantom containing internal shear wave reflectors did not differ noticeably. The global brain stiffness estimated from reverberant and subzone elastograms was 2.36 ± 0.95 kPa and 2.5 ± 1.1 kPa, respectively, when imaging at 50 Hz, and 2.56 ± 0.828 kPa and 2.89 ± 1.3 kPa respectively, when imaging at 70 Hz. The phantom study revealed that performance varied depending on the component of displacement used to compute reverberant elastograms; however, the clinical study demonstrated similar performance of reverberant and subzone elastograms</p>
Data for: Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations
<p>Magnetic Resonance Imaging measurement data used in our work about "Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p><br> Further information about the individual datasets:</p> <p>data_GSM_t1<br> Type: Gold-Standard T1 measurement<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: IR Single-Echo Spin-Echo<br> TR|TE [ms]: 8000|15<br> FOV [mm]: 200<br> T_INV [ms]: 30:250:2530</p> <p>data_GSM_t2<br> Type: Gold-Standard T2 measurement<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: Single-Echo Spin-Echo<br> TR|TE [ms]: 8000|(15:40:455)<br> FOV [mm]: 200</p> <p>data_05b_b1map<br> Type: B1 Map<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_05b_kspace<br> Type: Radial Single-Shot Dataset<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.88|2.44<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_06_irbssfp_long<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 10.8|5.4<br> FA [deg]: 45<br> T_RF [ms]: 2.5<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_irbssfp_short<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.88|2.44<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 4.1|2.58<br> FA [deg]: 6<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_s03_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_s03_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.75|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_2_5ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 6.14|3.07<br> FA [deg]: 35<br> T_RF [ms]: 2.5<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_2_1ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 5.5|2.75<br> FA [deg]: 35<br> T_RF [ms]: 2.1<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_1_6ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 5.0|2.5<br> FA [deg]: 35<br> T_RF [ms]: 1.6<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_1_2ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.6|2.3<br> FA [deg]: 35<br> T_RF [ms]: 1.2<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_0_6ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4|2<br> FA [deg]: 35<br> T_RF [ms]: 0.6<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_0_4ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 3.8|1.9<br> FA [deg]: 35<br> T_RF [ms]: 0.4<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p> </p>
Data for: Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging
<p>Magnetic Resonance Imaging measurement data used in the PhD thesis "Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).<br> </p> <p>Further information about the individual datasets:</p> <p>data_invivo_b0map<br> Type: B0 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence<br> TR|TE1|TE2 [ms]: 400|4.92|7.38<br> FA [deg]: 60<br> FOV [mm]: 240</p> <p>data_invivo_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 6830|2.19<br> FA [deg]: 8<br> FOV [mm]: 240</p> <p>data_invivo_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.8|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7</p> <p>data_invivo_irbssfp<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7</p> <p>data_invivo_irbssfp_shim<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7<br> missing shim break</p> <p>data_vfa_b0map<br> Type: B0 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence<br> TR|TE1|TE2 [ms]: 400|4.92|7.38<br> FA [deg]: 60<br> FOV [mm]: 240</p> <p>data_vfa_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 6830|2.19<br> FA [deg]: 8<br> FOV [mm]: 240</p> <p>data_vfa_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.8|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_20<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 20<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_40<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 40<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_45<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_50<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 50<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_60<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 60<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_70<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 70<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_77<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 77<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p>
Data Supplement: GIRFReco.jl: An Open-Source Pipeline for Spiral Magnetic Resonance Image (MRI) Reconstruction in Julia
<p><strong>Dataset for GIRFReco.jl Paper</strong><br> <br> Please download this and extract to an appropriate location prior to running the demonstration code in GIRFReco.jl. The extracted folder will serve as the root directory in the demo code.</p>
Understanding unconventional magnetic order in a candidate axion insulator by resonant elastic x-ray scattering - Data set
<p>This data set includes the resonant elastic xray scattering (REXS) data collected at the I16 (Diamond Light Source, United Kingdom) and P09 (DESY, Germany) beamline, along with the magnetization data.</p> <p>I16 Data<br> - Temperature dependence of the L=15 reflection<br> - Temperature dependence of the L=14.333 reflection<br> - 00L dependence (6K - 20K)<br> - Azimuthal dependence of L=15 reflection<br> - Azimuthal dependence of L=13.667 reflection</p> <p>P09 Data<br> - Field dependence of L=15 reflection (pi-pi channel)<br> - Field dependence of L=15 reflection (pi-sigma channel)<br> - Field dependence of L=14.333 reflection (pi-pi channel)<br> - Field dependence of L=14.333 reflection (pi-sigma channel)</p> <p>Magnetization Data<br> - Field Dependence of the Magnetization<br> </p>
Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo
<p>The idea that sensory stimulation to the embryo (in utero or in ovo) may be crucial for brain development is widespread. Unfortunately, up to now evidence was only indirect because mapping of embryonic brain activity in vivo is challenging. Here we applied for the first time Manganese Enhanced Magnetic Resonance Imaging (MEMRI), a functional imaging method, to the eggs of domestic chicks. We revealed both spontaneous and light-induced brain asymmetry by comparing embryonic brain activity in vivo of eggs that were stimulated by light or maintained in the darkness. Our protocol paves the way to investigation of the effects of a variety of sensory stimulations on brain activity in embryo.</p>
Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo
Open the record for dataset details and reuse information.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): nuclear magnetic resonance spectra of soils, 2009 and 2013
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes nuclear magnetic resonance spectra of surface organic, deep organic, and mineral layers of control and experimentally warmed soils sampled in 2009 and 2013.
Dataset for NMR quadrature echo and T1 saturation recovery pulse sequences underlying the publication 'On the quantification of solid phases in hydrated cement paste by 1H nuclear magnetic resonance relaxometry'
<p>This record comprises the datasets of combined 1H NMR quadrature echo and T1 saturation recovery pulse sequences underlying the publication “On the quantification of solid phases in hydrated cement paste by 1H nuclear magnetic resonance relaxometry” by Robert Schulte Holthausen & Peter J. McDonald, Cement and Concrete Research, https://doi.org/10.1016/j.cemconres.2020.106095.</p> <p><br> In this work different solid phases, important to cement paste hydration, are investigated with low-field bench top 1H nuclear magnetic resonance with a view to developing an alternate characterisation methodology that requires minimal invasive or destructive sample preparation.</p> <p><br> A combination of the well-established quadrature echo pulse sequence with variable pulse gap together with a T1 saturation recovery quadrature echo pulse sequence is used.</p>
Silicon-29 Magic-Angle Flipping Nuclear Magnetic Resonance dataset for Na2O•1.5SiO2 glass
<p>Raw and processed silicon-29 Magic-Angle Flipping Nuclear Magnetic Resonance dataset for Na2O•1.5SiO2 glass from the publication "Silicon site distributions in an alkali silicate glass derived by two-dimensional Si-29 nuclear magnetic resonance" in <a href="https://doi.org/10.1021/jp9700342">J. Non-Cryst. Solids, 204, 294 (1996)</a>, by P. Zhang, C. Dunlap, P. Florian, P. J. Grandinetti, I. Farnan, and J. F. Stebbins.</p> <p>Details of the csdf dataset format are given in <a href="https://doi.org/10.1371/journal.pone.0225953"><em>PLOS ONE,</em> 15(1): e0225953 (2020)</a>, "Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data," D. Srivastava, T. Vosegaard, D. Massiot, and P.J. Grandinetti. The data within csdf files can be accessed with the Python package <a href="https://csdmpy.readthedocs.io/en/stable">csdmpy</a>, or other CSDM-compliant software.</p>
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.