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.
3,264
datasets available to search
ShareScore release 0.9.0
Dataset results
3,264 results for “fasting”
MRI data of 40 adult participants in response to a cue induced craving task following food fasting, social isolation and baseline (within-subject design)
Open the record for dataset details and reuse information.
CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes
Open the record for dataset details and reuse information.
Dataset of "Fast carbon dioxide–epoxide cycloaddition catalyzed by metal and metal-free ionic liquids for designing non-isocyanate polyurethanes"
<p>The recycling of industrially produced greenhouse gases, such as CO2, into high-value-added chemicals is one of the most relevant strategies for reaching climate targets. A two-step strategy for designing non-isocyanate polyurethanes (NIPUs) from renewable carbon dioxide (CO2) using environmentally friendly conditions and catalysts is investigated. The first reaction step efficiently converts a mono-epoxidized monomer (phenyl glycidyl ether) into cyclic carbonates under mild reaction conditions and supercritical CO2, using imidazolium ionic liquids (ILs) as catalysts into cyclic carbonates. The DFT calculations suggested a comprehensive mechanistic pathway for the IL-catalyzed CO2-epoxy reaction showing a rate-determining step of the initial epoxide ring opening and the direct participation of IL-anions.</p>
Dataset of "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"
<p>Novel simple and efficient method for synthesis of high entropy sulfides of iron group metals (Cr, Fe, Ni, Co, Zn) is describedThe created material was investigated as a catalyst for electrochemical water splitting in acidic, neutral and alkaline pH. Investigation of the electrocatalytic activity of the synthesized material shows its high efficiency for overall water splitting in alkaline media. </p>
Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network
<p>Datasets acquired and generated for the manuscript "Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network". The datasets include test, training and time series datasets each containing the raw data and the predicted data where it applies. </p>
Four lipidomics datasets (mouse liver, mouse pancreatic islets, mouse soleus muscle and mouse visceral adipose tissue), generated for the publication Mehl et al., "A multiorgan map of metabolic, signalling, and inflammatory pathways that coordinately control fasting glycemia in mice"
<p>Mehl, Thorens et al present a multiomics study aimiing to<span> identify the pathways that are coordinately regulated in pancreatic </span><span>b</span><span>-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis. </span></p>
Dataset for "Methodology for fast testing of carbon-based nanostructured 3D electrodes in vanadium redox flow battery"
<p>Here, we describe a technique for integrating carbon-based rod-like nanomaterials into a vanadium redox flow battery and a methodology for fast nanomaterial performance testing. The technique is based on creating a fixed nanomaterial bed sandwiched between two graphite felt electrodes, forming a 3D flow-through electrode in the battery. Performing various positive and negative control experiments, we show the beneficial effect of a nanostructured bed on the primary battery characteristics obtained from short-term electrochemical experiments. We then characterize carbon nanotubes exhibiting promising electrochemical behavior in vanadium electrolytes, as observed in our previous study. The load curves obtained from charge-discharge steps at various current densities and electrolyte flow rates revealed considerable differences in the performance of the tested materials, with few-walled carbon nanotubes reaching unsurpassable characteristics. Although developed for vanadium redox flow batteries, the method enables testing tube-like and rod-like (nano-)materials as electrodes for other flow battery systems. </p>
Robust framework and software implementation for fast speciation mapping
<p>R script and raw data to test the sparse excitation energy XAS procedure.</p>
Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals
<p>This repository contains Electrogastrography signals termed Electrogastrograms (<a href="https://en.wikipedia.org/wiki/Electrogastrogram">EGG</a>) recorded with surface Ag/AgCl electrodes placed over stomach and pre-processed in 20 healthy individuals (8 Females and 12 Males). The method for EGG recording and pre-processing together with subjects' data can be found in <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a>.</p> <p>For each subject, EGG was recorded from three locations before (fasting state) and after (postprandial state) a commercial oat meal (274 kcal). Two 20 minutes recordings (files) are obtained for each subject - fasting and postprandial.</p> <p>Naming convention for files: <strong>subjects ID _ type of recording (fasting / postprandial)</strong>.</p> <p>Sample rate was set at 2 Hz and <a href="https://en.wikipedia.org/wiki/Analog-to-digital_converter">A/D card</a> had 16 bits resolution. Gain of the amplifier was set at 1000. Overall, file size is 7200 samples (2400 samples for each channel). All signals were filtered with 3<sup>rd</sup> order band-pass <a href="https://en.wikipedia.org/wiki/Butterworth_filter">Butterworth filter</a> with cut-off frequencies of 0.03 Hz and 0.25 Hz. In order to avoid phase distortion, zero-phase digital filtering was performed in <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> R2013a by <a href="https://www.mathworks.com/help/signal/ref/filtfilt.html">filtfilt()</a> function. <a href="https://www.gnu.org/software/octave/">GNU Octave</a> code for analysis of EGG signals with statistical calculations presented in <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a> is also provided (<a href="https://zenodo.org/record/3878435/files/eggAnalysis.m?download=1">eggAnalysis.m</a>).</p> <p>For convenient test download and appropriate preview, we provided all signals in <a href="https://en.wikipedia.org/wiki/Zip_(file_format)">.zip</a> and sample signal for ID1 in <a href="https://en.wikipedia.org/wiki/Text_file">.txt</a> form.</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/EGG-database.zip?versionId=84315b6b-58da-4655-83f4-8f1d43c3b02c">EGG-database.zip</a>, data files, text format</li> <li><a href="https://zenodo.org/record/3878435/files/eggAnalysis.m?download=1">eggAnalysis.m</a>, GNU Octave code</li> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/README.txt">README.txt</a>, metadata for data files, text format</li> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/ID1_fasting.txt?versionId=47d0bd09-1a87-42f2-a3e5-ef0c4b4a18e2">ID1_fasting.txt</a> and <a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/ID1_postprandial.txt?versionId=c8936a32-2896-44d6-bf3d-2ee37887766c">ID1_postprandial.txt</a>, sample data files for subject ID1, text format</li> </ol> <p><strong>Data files contain numerical values with decimal point according to the following structure</strong></p> <ol> <li>column - CH1* (recorded samples from channel 1)</li> <li>column - CH2* (recorded samples from channel 2)</li> <li>column - CH3* (recorded samples from channel 3)</li> </ol> <p>* For exact anatomical locations for EGG channels CH1, CH2, and CH3, please refer to <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a>.</p> <p>If you find these signals useful for your own research or teaching class, please cite both relevant paper and dataset as:</p> <ol> <li> <p>Popović, N.B., Miljković, N. and Popović, M.B., 2019. Simple gastric motility assessment method with a single-channel electrogastrogram. <em>Biomedical Engineering/Biomedizinische Technik</em>, <em>64</em>(2), pp.177-185, doi: <a href="https://doi.org/10.1515/bmt-2017-0218">10.1515/bmt-2017-0218</a>.</p> </li> <li> <p>Popović, N.B., Miljković, N. and Popović, M.B., 2020. Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals [Data set]. <em>Zenodo</em>, doi: <a href="https://doi.org/10.5281/zenodo.3730617">10.5281/zenodo.3730617</a>.</p> </li> </ol> <p>DISCLAIMER: The GNU Octave code is provided without any guarantee and it is not intended for medical purposes.</p>
Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions
<p>This dataset provides various acquisitions for T2 mapping of the MnCl2 array of the NIST phantom at 1.5T. Data were acquired on a MAGNETOM Sola (Siemens Healthcare, Erlangen, Germany), with an 18-channel body coil and a 32-channel spine coil (12 elements used). It gathers original acquisitions from Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12.</p> <p>The dataset is composed of DICOM images from:</p> <p>i) Gold-standard single-echo spin echo (SE) sequences acquired at variable TE;</p> <p>ii) Alternative reference multi-echo spin echo (MESE) acquisitions;</p> <p>iii) Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images at variable TE in three orthogonal orientations.</p> <p>The acquisition parameters are further detailed in the ReadMe.txt file provided along with the images.</p> <p>These acquisitions were repeated independently on three different days during the month of January 2020.</p> <p>These data are made publicly available as a support for further reproducibility studies as well as for the validation of new T2 relaxometry strategies.</p> <p>Works using any of these data should cite the following two references:</p> <p>- Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12</p> <p>- Lajous, Hélène, Ledoux, Jean-Baptiste, Hilbert, Tom, van Heeswijk, Ruud B., & Bach Cuadra, Meritxell. (2020). Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3931812</p>
LMU Fast Decompression Experiment Data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This data is camera images and nozzle pressure gauge voltage traces from rapid decompression shots at the LMU shock tube facility.</p> <p>This data is discussed in the "Materials and Methods" section of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The prefixes of the filenames correspond to the shot dates and times listed in table S1 of the paper. </p> <p>The "_camera.zip" files contains tiff images of the camera frames. The ".ixc" file in each zip lists camera settings in plain text.</p> <p>The ".dat" file contains the voltage measurement of the nozzle pressure gauge. Row 1 is the header, row 2 is the time in seconds, and row 3 is the voltage of the pressure gauge in Volts. The peak pressure in the header can be used to relate the voltage to pressure.</p>
Dataset for "Fast and efficient demultiplexing of single photons from a GaAs quantum dot with resonantly enhanced electro-optic modulators"
<p><strong>Dataset for "Fast and efficient demultiplexing of single photons from a quantum dot with resonantly enhanced electro-optic modulators"</strong></p> <p>A description of the dataset is found in the <strong>readme.md</strong> file (markdown markup language).</p>
Dataset for "Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns"
<p>This archive contains the source code and data sets presented in the publication "Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns".</p> <p>Kabus, D., De Coster, T., de Vries, A. A., Pijnappels, D. A., & Dierckx, H. (2024). Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns. <em>Computers in Biology and Medicine</em>, 107949. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.compbiomed.2024.107949" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.compbiomed.2024.107949</span></a></p>
Datasets acquired using Dectris ARINA detector in paper "Using a fast hybrid pixel detector..." doi:10.1088/2515-7639/acf524
<p>Here are the raw data presented in the paper "Using a fast hybrid pixel detector for dose-efficient diffraction imaging beam-sensitive organic molecular thin films"<br> doi:10.1088/2515-7639/acf524</p> <p>It contains multiple datasets:</p> <p>SmB6: The sample is a monocrystalline domain of SmB6 oriented along the <110> zone axis, prepared with FIB by Elisabeth Mueller at PSI. Data was collected with a probe-corrected 200kV TEM microscope, by Daniel Stroppa (Dectris) supported by Mingjian Wu (FAU). Further experimental parameters are mentioned in the paper.</p> <p>Gd2O3 Ptycho: The sample is a quasi-2D poly-crystalline Gd2O3 supported on a carbon TEM grid provided by Baixu Zhu and Xingchen Ye (Indiana University Bloomington). Data was collected with a probe-corrected 200kV TEM microscope, by Philipp Pelz supported by Mingjian Wu. Raw data was a scan of 512x512; a crop of 256x256 was used for reconstruction as presented in the paper. Further experimental parameters are mentioned in the paper</p> <p>Gold thin film: poly-crystalline gold thin film (nominal thickness ~20 nm) deposited on SiN membrane, provided by Peter Denninger (FAU). Data was analyzed using ACOM in py4dstem (ver. 13.17). The analysis notebook is included in the zip. Further experimental parameters are mentioned in the paper.</p> <p>DRCNT_PCBM: bulk hetero-junction organic solar cell thin film provided by Christina Harreiss (FAU). Further experimental parameters (4D-SCED and NBD 4D-STEM) are mentioned in the paper. The same, but pre-processed version (in Gatan dm4 format) of the datasets are in the previous version of this publication.</p> <p>Data visualization and processing can be done with NOVENA software, freely available at DECTRIS website. Alternatively, the files can be opened using a HDF5 file reader.</p>
Software for processing data from a fast-responding RINKO EC oxygen/temperature sensor (JFE Advantech Co, Ltd)
This dataset describes how data from a fast-responding JFE Advantech RINKO EC ARO-EC-CM sensor connected to a Nortek Vector is processed to obtain accurate aquatic eddy covariance measurements. The code and documentation are stored in a .zip file. It consists of a manual, Fortran source code, a definition file and a complied executable suitable for running on Microsoft Windows. The software development was supported by NSF funding to PI Berg (OCE-1824144, OCE-2223204).
Fast MLE and Supervised Classification for the Beta-Liouville Multinomial -- Gold Standard Data
<p>Gold standard datasets used in the publication Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial. Datasets were prepared by Cardoso-Cachopo (2007).</p>
Mechanical data of rotary shear experiments and temperature measurements for the manuscript: "Fast and localized temperature measurements during simulated earthquakes in carbonate rocks"
<p>Mechanical data of rotary shear experiments and temperature measurements</p> <p>Each experiment is presented in a file with the experiment name (mechanical data of rotary shear experiment) and a file with the experiment name and _Temp (temperature measurement with the optical fiber).</p> <p>Mechanical data are presented in a tab-delimited file with calibrated measurements of:</p> <ul> <li>Time (milliseconds)</li> <li>Normal stress: Normal (MPa) </li> <li>Fault displacement: Slip (mm)</li> <li>Fault velocity: Velocity (mm/s)</li> <li>Shear stress: Shearstress (MPa)</li> <li>Axial shortening: Shortening (mm).</li> </ul> <p> In a separate file, temperature data are presented as tab-delimited file with calibrated measurements of:</p> <ul> <li>Time (milliseconds)</li> <li>Temperature from optical fiber in the channel at 1.5 µm : Temperature_1,5 (°C) </li> </ul>
Associated Data: RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features
<p>Additional digital data to "RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features" (ChemRxiv preprint:<a href="https://doi.org/10.26434/chemrxiv.12636704.v1">https://doi.org/10.26434/chemrxiv.12636704</a>).</p> <p>Associated code can be found at: <a href="https://github.com/HITS-MCM/RASPDplus">https://github.com/HITS-MCM/RASPDplus</a></p> <p>Files:</p> <ul> <li>weights.tar.gz: contains the model weights of one random dataset split and its associated crossvalidation folds. Used for standard RASPD+ evaluation.</li> <li>additional_model_replicates.tar.gz: contains the remaining models trained on the full set of descriptors.</li> <li>external_test_sets.tar.gz: contains the descriptor tables for all external test sets used</li> <li>dude.tar.gz: contains the descriptor tables for and several identifier lists for evaluation on the Directory of Useful Decoys - Enhanced (DUD-E)</li> <li>run_outputs.tar.gz: Performance metric data and predicted values created during the model training and evaluation runs. Basis for the figures and metrics in the manuscript.</li> </ul> <p> </p>
Dataset of 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks'
<p>Dataset of the article 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks' (https://doi.org/10.1016/j.cma.2023.116652). The codes processing data here are on https://github.com/AlvaroMS90/Complete-flow-characterization-from-snapshot-PIV-fast-probes-and-physics-informed-neural-networks.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085) and by MCIN/AEI /10.13039/501100011033 and the European Union ‘NextGenerationEU/PRTR’ as part of the grant FJC2020-044342-I.</p>
Data for Fast EMRI Waveforms
<p>This data is required for the Fast EMRI Waveform package (the code can be found <a href="https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms">here</a>). The user does not need to download this data from here. The data will automatically download from the <a href="https://download.bhptoolkit.org/few/">BHPT download server</a> when the code requires it.</p> <p>If you use this data, please follow citation guidance found at the <a href="https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms">code repository</a>. </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.