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1,742 results for “activity data”
BIDS Data for "A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation"
<p>Base data package for the “"A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation” article, formatted corresponding to the Brain Imaging Data Structure.</p>
Supplementary data for "Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily"
<p>Supplementary data for "Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily"</p>
Supplementary data for the manuscript "Technical note: Estimating aqueous solubilities and activity coefficients of mono- and α,ω-dicarboxylic acids using COSMO-RS-DARE"
<p>.cosmo files (BP-TZVPD-FINE) of dicarboxylic acids (C2-C8), dimers and monohydrates of mono- (C1-C6) and dicarboxylic acids, and water dimer.</p>
Data supplement for "Bifurcations of front motion in passive and active Allen-Cahn-type equations"
<p>This dataset contains the data and source files for figures 5 and 7-10 in the following publication: </p> <p>F. Stegemerten, S.V. Gurevich, U. Thiele</p> <p><em>'Bifurcations of front motion in passive and active Allen–Cahn-type equations' </em></p> <p>published in 2020 in CHAOS.</p> <p>Please follow the instructions given in 'Readme.txt'.</p>
Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity
<p>The deposited files contain balanced data sets of multi-target (MT) and single-target (ST) compounds (CPDs) used for machine learning studies (https://dx.doi.org/10.1021/acs.molpharmaceut.0c00901). The first file (st_mt_data.tsv) contains 15,142 MT- and 15,081 ST-CPDs and the second (st_dt_data.tsv) 1828 DT- and 1776 ST-CPDs. For each CPD, a nonstereo_aromatic_SMILES representation, the original ChEMBL_cid, UniProt (target) IDs, and CPD category (CPD_CAT) (i.e. DT/MT/ST) is provided. DT stands for 'diverse-target' and denotes a subset of MT-CPDs (as detailed in the publication). In addition, a CPD is tagged “Y” if it continued to be present in the data set after removal of 50% randomly selected CPDs or 50% CPD nearest neighbors (NN), respectively.</p>
Phenology data set of plants and birds and other taxonomic groups, as well as agrarian activities and abiotic phenomena from Latvia, 1970-2018
<p>A data set of phenological observations of plants, birds, as well as agrarian activities and abiotic phenomena from Latvia, 1970-2018 is presented. The data include limited number of observations of insects, amphibians, mammals, mushrooms, mollusks and fishes as well. The data was collected by voluntary observers (citizen scientists) and published as paper based yearly bulletins. It includes almost 48 000 individual observations of 159 different phenological phases from 103 locations in Latvia. Each entry is comprised of following fields:</p> <ol> <li>Station: name of the observation station</li> <li>Year: year of observation</li> <li>Season: season of observation as indicated in the primary publication</li> <li>Species: English name of the species observed or description of phenomena observed in case of abiotic occurrences</li> <li>Species Latin: Latine name of the species observed</li> <li>Taxonomic_group: taxonomic group of the species observed or grouping of non-biological phases (“Abiotic” for meteorological phenomena and “Agrarian” for agrarian activities)</li> <li>Phenophase: description of phenological phase observed</li> <li>BBCH: attributed BBCH code for phenological phase observed, where applicable</li> <li>Date: date of the first observation of the phase</li> <li>DoY: day of the year of the first observation of the phase</li> <li>Implausible: flag indicating of the reported date of phenological phase is highly implausible (TRUE) or realistic (FALSE)</li> <li>Wrong_order: flag indicating if the order of the reported phases at a given station and year is not realistic (TRUE) or realistic (FALSE)</li> </ol>
Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"
<p>Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron.</p> <p>2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘Functions’ contains functions called by the main codes.</p> <p>The main folder contains the following codes:</p> <p><em>Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1.</em></p> <p><em>Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2.</em></p> <p><em>Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3.</em></p> <p><em>Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4.</em></p> <p><em>Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1.</em></p> <p><em>Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.</em></p> <p> </p> <p>The subfolder ‘Data’ contains the data structures used for the different figures:</p> <p><em>data_figure1.mat</em></p> <p><em>data_figure2.mat</em></p> <p><em>data_figure3.mat</em></p> <p><em>data_figure4_MECA.mat</em></p> <p><em>data_figure4_Activation.mat</em></p> <p><em>data_figure4_Inactivation.mat</em></p> <p><em>data_figureS2_Activation.mat</em></p> <p><em>data_figureS2_Inactivation.mat</em></p> <p><em>data_Axon.mat</em></p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>Mouse_Name</em> : name of the mouse.</p> <p><em>Mouse_DateOfBirth</em>: date of birth of the mouse (YMD).</p> <p><em>Mouse_Sex</em>: sex of the mouse (F or M).</p> <p><em>Mouse_Genotype</em>: genotype of the mouse.</p> <p><em>Mouse_Drug</em>: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’).</p> <p><em>Mouse_Virus</em>: virus injected if any.</p> <p><em>Cell_Counter</em>; cell recorded in a given mouse.</p> <p><em>Cell_Type</em>: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP).</p> <p><em>Cell_Depth</em>: depth of the recorded cell relative to pia (µm).</p> <p><em>Cell_TargetedBrainArea</em>: cortical area targeted (C2 column of the barrel cortex = C2).</p> <p><em>Cell_Fluorescence</em>: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence</em>=TRUE is considered as a VIP neuron (cf <em>Cell_Type</em>).</p> <p><em>Sweep_Counter</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s).</p> <p><em>Sweep_Type</em>: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’; Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’; ).</p> <p><em>Sweep_Start_Time</em>: time at the beginning of the sweep recording (YMDHms).</p> <p><em>Sweep_WhiskerAngle</em>: C2 whisker angular position extracted from simultaneous high-speed video filming (deg).</p> <p><em>Sweep_WhiskerAngle_SamplingRate</em>: sampling rate of the whisker angle trace.</p> <p><em>Sweep_WhiskingOnset_Time</em>: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s).</p> <p><em>Sweep_MembranePotential</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>Sweep_MembranePotential_SamplingRate</em>: sampling rate of the membrane potential signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_CurrentInjected</em>: current injected into the cell (pA).</p> <p><em>Sweep_CurrentInjected_SamplingRate</em>: sampling rate of current signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_WhiskerStim_Name</em>: whisker to which the magnetic stimulus was applied to (C2 or B2&C2).</p> <p><em>Sweep_WhiskerStim_Time</em>: onset times of the whisker stimulus (s).</p> <p><em>Sweep_OptoStim_Power</em>: light power applied for optogenetic manipulations (% of the max power).</p> <p><em>Sweep_OptoStim_Time</em>: onset times of the light pulses for optogenetic manipulations (s).</p> <p><em>Cell_ID</em>: unique cell identifier (= <em>Mouse_Name</em>+<em>Cell_Counter</em>).</p> <p><em>SpikeThreshold</em>: spike threshold used to detect APs (mV).</p> <p><em>Trial_WhiskingOnset</em>: data structure containing the cut signals used to compute averaged responses around whisking onset times.</p> <p><em>Trial_WhiskerStim</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times.</p> <p><em>Trial_WhiskerStim_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements.</p> <p><em>Trial_WhiskerStim_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements.</p> <p><em>Trial_Opto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times.</p> <p><em>Trial_OptoAndWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements.</p> <p><em>Trial_OptoAndWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements.</p> <p><em>Trial_OnlyWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyOpto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.</p>
Data from: Chronic Rapamycin administration via drinking water mitigates the pathological phenotype in a Krabbe disease mouse model through autophagy activation.
<p>ABSTRACT </p><p>Krabbe disease (KD) is a rare disorder caused by a deficiency of the lysosomal enzyme galactosylceramidase (GALC), resulting in the accumulation of the cytotoxic metabolite psychosine (PSY) in the nervous system. This accumulation triggers demyelination and neurodegeneration. Despite ongoing research, the underlying pathogenic mechanisms remain incompletely understood, and there is currently no cure available.</p><p>Previous studies from our lab revealed the presence of autophagy dysfunctions in KD pathogenesis, as evidenced by the presence of p62-tagged protein aggregates in the brains of KD mice and increased p62 levels in the KD sciatic nerve. We also demonstrated that the autophagy inducer Rapamycin (RAPA) can partially restore the wild-type (WT) phenotype in KD primary cells by reducing the number of p62 aggregates.</p><p>In this study, we tested RAPA in the Twitcher (TWI) mouse, a spontaneous KD mouse model. We administered the drug ad libitum via drinking water (15 mg/L) starting from post-natal day (PND) 21-23. We longitudinally monitored the motor performance of the mice through grip strength and rotarod tests, along with various biochemical parameters related to KD pathogenesis (i.e. autophagy markers expression, myelination, astrogliosis, and PSY accumulation).</p><p>Our findings demonstrate that RAPA significantly enhances motor functions at specific treatment time points and reduces astrogliosis in TWI brain, spinal cord, and sciatic nerves. Using western blot and immunohistochemistry, we observed a decrease in p62 aggregates in TWI nervous tissues, which corroborates our earlier in-vitro results. Furthermore, RAPA treatment partially reduces PSY levels in the spinal cord.</p><p>In conclusion, our results support the consideration of RAPA as a supportive therapy for KD. Importantly, as RAPA is already available in pharmaceutical formulations for clinical use, its potential for KD treatment can be promptly evaluated in clinical trials.</p>
Dataset and data analysis of activities of Paris between 1829 and 1907
<p>Dataset construction and data analysis of 'A typology of activities over a century of urban growth', <em>Nature Cities</em>, DOI: <a href="https://www.doi.org/10.1038/s44284-024-00108-7">10.1038/s44284-024-00108-7</a></p>
Data underpinning "Engineering unsteerable quantum states with active feedback"
<div> <p>We provide the raw data used to produce plots shown in our paper "Engineering unsteerable quantum states with active feedback". The data is structured by: entangled state - number of qubits - target fidelity F*. For each parameter configuration 10 simulation runs were performed.</p> <p> </p> </div> <h2>Abstract</h2> <p>We propose active steering protocols for quantum state preparation in quantum circuits where each ancilla qubit (detector) is connected to a single system qubit, employing a simple coupling selected from a small set of steering operators. The decision is made such that the expected cost function gain in one time step is maximized. We apply these protocols to several many-qubit models. Our results are underlined by three remarkable insights. First, we show that the standard fidelity does not give a useful cost function; instead, successful steering is achieved by including local fidelity terms. Second, although the steering dynamics acts on each system qubit separately, entanglement in the generated target state is introduced, and can be tuned at will, by performing Bell measurements on ancilla qubit pairs after every time step. This implements a weak-measurement variant of entanglement swapping. Third, numerical simulations suggest that the active steering protocol can reach arbitrarily designated target states, including passively unsteerable states such as the N-qubit W state.</p>
Biased ensembles of pulsating active matter: figure data
<p>The following is a zip file containing figure data for all figures published in the manuscript tittled 'Biased ensembles of pulsating active matter', available in archive: https://arxiv.org/abs/2403.16961</p> <p>v2 includes the updated data for figure 2. Otherwise all remain as before.</p>
Wallhack1.8k Dataset | Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition
<p>This repository contains the <strong>Wallhack1.8k dataset</strong> for WiFi-based long-range activity recognition in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)/Through-Wall scenarios, as proposed in [1,2], as well as the <strong>CAD models</strong> (of 3D-printable parts) of the WiFi systems proposed in [2].</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the Wallhack1.8k dataset is provided at: <a href="https://github.com/StrohmayerJ/wallhack1.8k" target="_blank" rel="noopener">https://github.com/StrohmayerJ/wallhack1.8k</a></p> <p><strong>Dataset Description</strong></p> <p>The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets).</p> <p>To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly:</p> <ul> <li>LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system</li> <li>LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system</li> <li>NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system</li> <li>NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system</li> </ul> <p>These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector <em>H</em> being stored in the "data" field and the class label being stored in the "class" field. <em>H </em>is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of <em>H</em> (e.g., via <em>numpy.abs(H)</em>) yields the subcarrier amplitudes <em>A</em>.</p> <p>To extract the 52 L-LTF subcarriers used in [1], the following indices of <em>A</em> are to be selected:</p> <pre><code># 52 L-LTF subcarriers csi_valid_subcarrier_index = [] csi_valid_subcarrier_index += [i for i in range(6, 32)] csi_valid_subcarrier_index += [i for i in range(33, 59)]</code></pre> <p>Additional 56 HT-LTF subcarriers can be selected via:</p> <pre><code># 56 HT-LTF subcarriers csi_valid_subcarrier_index += [i for i in range(66, 94)] csi_valid_subcarrier_index += [i for i in range(95, 123)]</code></pre> <p>For more details on subcarrier selection, see <a href="https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html">ESP-IDF</a> (Section Wi-Fi Channel State Information) and <a href="https://github.com/espressif/esp-csi">esp-csi</a>.</p> <p>Extracted amplitude spectrograms with the corresponding label files of the train/validation/test split: "trainLabels.csv," "validationLabels.csv," and "testLabels.csv," can be found in the <em>spectrograms/</em> directory.</p> <p>The columns in the label files correspond to the following: [Spectrogram index, Class label, Room label]</p> <ul> <li>Spectrogram index: [0, ..., n]</li> <li>Class label: [0,1,2], where 0 = "no presence", 1 = "walking", and 2 = "walking + arm-waving."</li> <li>Room label: [0,1,2,3,4,5], where labels 1-5 correspond to the room number in the NLoS scenario (see Fig. 3 in [1]). The label 0 corresponds to no room and is used for the "no presence" class.</li> </ul> <p><strong>Dataset Overview:</strong></p> <p>Table 1: Raw WiFi packet sequences.</p> <table> <tbody> <tr> <td><strong>Scenario</strong></td> <td><strong>System</strong></td> <td><em>"no presence" / label 0</em></td> <td><em>"walking" / label 1</em></td> <td><em>"walking + arm-waving" / label 2</em></td> <td><strong>Total</strong></td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4</td> <td>20</td> <td>20</td> <td><strong>44</strong></td> </tr> </tbody> </table> <p>Table 2: Sample/Spectrogram distribution across activity classes in Wallhack1.8k.</p> <table> <tbody> <tr> <td><strong>Scenario</strong></td> <td><strong>System</strong></td> <td> <p><em>"no presence" / </em> label 0</p> </td> <td> <p><em>"walking"</em> / label 1</p> </td> <td><em>"walking + arm-waving" / </em>label 2</td> <td><strong>Total</strong></td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>149</td> <td>154</td> <td>155</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>149</td> <td>160</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>148</td> <td>150</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>143</td> <td>147</td> <td>147</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>589</td> <td>611</td> <td>606</td> <td><strong>1,806</strong></td> </tr> </tbody> </table> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to one of our papers [1,2].</p> <p>[1] Strohmayer, Julian, and Martin Kampel. (2024). “Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition”, <em>In IFIP International Conference on Artificial Intelligence Applications and Innovations</em> (pp. 42-56). Cham: Springer Nature Switzerland<em>,</em> doi: <a href="https://doi.org/10.1007/978-3-031-63211-2_4" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-63211-2_4</a>.</p> <p>[2] Strohmayer, Julian, and Martin Kampel., “Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition,” <em>2024 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2024, pp. 3594-3599, doi: <a href="https://doi.org/10.1109/ICIP51287.2024.10647666" target="_blank" rel="noopener">https://doi.org/10.1109/ICIP51287.2024.10647666</a>.</p> <p>BibTeX citations:</p> <pre>@inproceedings{strohmayer2024data, title={Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages={42--56}, year={2024}, organization={Springer}}<br><br>@INPROCEEDINGS{10647666,<br> author={Strohmayer, Julian and Kampel, Martin},<br> booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, <br> title={Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition}, <br> year={2024},<br> volume={},<br> number={},<br> pages={3594-3599},<br> keywords={Visualization;Accuracy;System performance;Directional antennas;Directive antennas;Reflector antennas;Sensors;Human Activity Recognition;WiFi;Channel State Information;Through-Wall Sensing;ESP32},<br> doi={10.1109/ICIP51287.2024.10647666}}<br><br><br></pre>
Data for: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.
<p>Raw data set for Npj Regenerative Medicine article: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.</p>
Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022)
<p>Output data of the different models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022).</p> <p>Output data is included for 50 nm particles containing malonic acid (mna), succinic acid (sca) and glutaric acid (glutarica), mixed with ammonium sulphate (AS) in different organic mass fractions. </p> <p>A plotter that allows the user to plot the Köhler curves, surface tensions and organic<br> partitioning factors during droplet growth from the model output data provided is included. </p>
Data set for "On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials"
<p>This excel file contains the raw data gathered with the measurements performed under different conditions of illumination for the different sensors. These data have been exploited in the paper "On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials" DOI: 10.3390/s21113736</p>
Biological data science courses at UMONS, Belgium: student's activity for 2019-2020
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2019-2020.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2020-2021
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2020-2021.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2018-2019
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2018-2019.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Data for the publication "Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations"
<p>This repository contains the data for the paper:</p> <p>Wieder, J., Ihn, N., Mignani, C., Haarig, M., Bühl, J., Seifert, P., Engelmann, R., Ramelli, F., Kanji, Z. A., Lohmann, U., and Henneberger, J.: Retrieving ice nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-67, in review, 2022.</p> <p>More information can be found in the README files.</p> <p>Note that the scripts to reproduce the figures of the publication are available on request.</p>
GFDL hurricane model track data associated with "Dynamical downscaling projections of late 21st century U.S. landfalling hurricane activity"
<p>These data include North Atlantic tropical cyclone track and intensity for control and projected late 21st century simulation from the GFDL hurricane model used in a <em>Climatic</em> <em>Change</em> manuscript: </p> <p>Knutson, T., J. Sirutis, M. Bender, R. Tuleya, and B. Schenkel, 2022: Dynamical downscaling projections of late 21st century U.S. landfalling hurricane activity. <em>Clim. Change</em>, <strong>171</strong>, 1–23.<br> <br> A readme file included below describes the variables and format of the tropical cyclone track data. Questions about the dataset may be directed to Ben Schenkel (<a href="mailto:benschenkel@gmail.com">benschenkel@gmail.com</a>) and Tom Knutson (<a href="mailto:tom.knutson@noaa.gov">tom.knutson@noaa.gov</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.