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1,819 results for “Experimental data”

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zenodo40/100

Experimental data for "Exact inversion of partially coherent dynamical electron scattering for picometric structure retrieval"

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2023View details →
zenodo40/100

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article:</p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception".</p> <p>It contains the behavioral and neural experimental data (cuneate nucleus and somatosensory recordings from the Miller Lab, Northwestern University), the result dataframes for task-driven and untrained models, the activations and predictions for the *best models for all tasks* for active and passive movements and the predictions for linear models for active and passive movements.&nbsp;</p> <p>Note, the predictions of other models can be computed from the network weights that were deposited for all trained models.&nbsp;</p> <p>The overall structure of the data is:</p> <p>└── exp_analysis<br>&nbsp; &nbsp; ├── results &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - Contains the result dataframe of the predictions for all models, tasks and primates<br>&nbsp; &nbsp; ├── activations<br>&nbsp; &nbsp; │ &nbsp; ├── active &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; - Contains activations related to active movements<br>&nbsp; &nbsp; │ &nbsp; └── passive &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; - Contains activations related to passive movements<br>&nbsp; &nbsp; ├── predictions<br>&nbsp; &nbsp; │ &nbsp; ├── active &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - Contains predictions related to active movements<br>&nbsp; &nbsp; │ &nbsp; └── passive &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - Contains predictions related to passive movements<br>&nbsp; &nbsp; └── beh_exp_datasets<br>&nbsp; &nbsp; &nbsp; &nbsp; ├── matlab_data &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - Contains raw behavioral and neural data<br>&nbsp; &nbsp; &nbsp; &nbsp; ├── MonkeyAlignedDatasets_new &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; - Contains padded test behavioral input for generating network activations<br>&nbsp; &nbsp; &nbsp; &nbsp; ├── MonkeyDatasets&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - Contains not aligned padded test behavioral input for generating network activations<br>&nbsp; &nbsp; &nbsp; &nbsp; ├── MonkeySpikeRegressDatasets &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - Contains datasets for training data-driven models<br>&nbsp; &nbsp; &nbsp; &nbsp; ├── MonkeySpikeRegressDatasets_new &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - Contains trial index for regression splits&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp; └── new_beh_exp_dataframe &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp; - Contains pre-processed behavioral and neural data</p> <p>--------------------------------</p> <p>The activations and predictions for the best 3 models and for all tasks are stored in experiments folder (in .h5 format) that follows the same name convention of the checkpoints.</p> <p>The checkpoints are stored in experiment folders (experiment_***) that follow this scheme:<br>- Task: &nbsp; &nbsp; &nbsp; &nbsp; shallow exp id, &nbsp; &nbsp; deep TCNs exp id, &nbsp; &nbsp; LSTM id.</p> <p>Experiment IDs for each task:</p> <p>- Untrained: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; 15, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 115, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 45<br>- Classification: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp; 4015, &nbsp; 5015, &nbsp; 4045</p> <p>- Torque: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; 8015, &nbsp; 8030, &nbsp; 8045</p> <p>- Regress joint pos: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; 17016, &nbsp;17031, &nbsp;17046<br>- Regress joint vel: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; 17216, &nbsp;17231, &nbsp;17246<br>- Regress joint pos &amp; vel:: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; 17416, &nbsp;17431, &nbsp;17446<br>- Regress joint pos &amp; vel &amp; acc:: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; 20516, &nbsp;20531, &nbsp;20546</p> <p>- Regress hand pos: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4016, &nbsp; 5016, &nbsp; 4046<br>- Regress hand vel: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 17316, &nbsp;17331, &nbsp;17346<br>- Regress hand pos &amp; vel: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 17516, &nbsp;17531, &nbsp;17546<br>- Regress hand pos &amp; vel &amp; acc: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp;&nbsp; 20416, &nbsp;17831, &nbsp;17846</p> <p>- Regress hand and elbow pos: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 20016, &nbsp;20031, &nbsp;20046<br>- Regress hand and elbow vel: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 20916, &nbsp;20931, &nbsp;20946<br>- Regress hand and elbow pos &amp; vel: &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; 20616, &nbsp;20631, &nbsp;20646<br>- Regress hand and elbow pos &amp; vel &amp; acc:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 20816, &nbsp;20831, &nbsp;20846</p> <p>- Redundancy reduction: &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 10020, &nbsp;10035, &nbsp;10050<br>- Autoencoder &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; 20716 &amp; 20717, 20731 &amp; 20732, &nbsp; X</p> <p>&nbsp;</p> <p>The code to process the behavioral data is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing">https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing</a><br>The code to load and use the models to generate activations and predictions is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction">https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction</a></p> <p>To reproduce the results, it is possible to reproduce the main figures using the result dataframe. See our repository for more details.&nbsp;</p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br>&nbsp; title={Task-driven neural network models predict neural dynamics of proprioception},<br>&nbsp; author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br>&nbsp; journal={Cell},<br>&nbsp; year={2024},<br>&nbsp; publisher={Elsevier}<br>}</p>

opencc-by-4.0Jan 2024View details →
zenodo40/100

Experimental data from laboratory studies on the generation and evolution of internal tides under various Coriolis parameters

<p>The dataset includes experimental data from laboratory studies on the generation and evolution of internal tides under various Coriolis parameters.</p> <p>"uu" and "uh" are horizontal velocities. (unit: m/s)<br>"vv" and "vh" are vertical velocities. (unit: m/s)<br>"xx" and "yy" are the horizontal and vertical coordinates, respectively. (unit: m)</p> <p>The frequency of internal tide is 0.68 rad/s.<br>The Coriolis parameters are 0, 0.13, 0.17, 0.21, 0.25, 0.29, 0.335, 0.38, 0.42, 0.46, 0.54 rad/s for f00 to f26.<br>The time interval is 0.2s.</p>

opencc-by-4.0Mar 2024View details →
dryad40/100

Data from: Annual species' experimental germination responses to light and temperature do not correspond with their microhabitat associations in the field

<p>Annual species have evolved sets of germination cues that are thought to be predictive of the post-germination environment. In naturally patchy environments, germination microsites often vary considerably in the amount of light they receive and in the diurnal temperature fluctuations they experience. However, whether species' differential germination responses to light and temperature are associated with their spatial patterns of occurrence remains largely untested.</p> <p>We surveyed species' occurrences in annual plant communities in 150 quadrats across gradients of canopy cover and litter cover. Nineteen species recorded in this survey were then included in a germination experiment that manipulated (1) Light vs. Dark (12h light or continuous dark) approximating seeds near the soil surface versus those covered by litter and (2) Cold vs. Warm temperature regimes (7/18 °C and 7/24 °C) approximating diurnal fluctuations experienced in shaded versus sun-exposed microsites, respectively.</p> <p>In the germination experiment, six species had highest germination probabilities in the Light treatment (regardless of temperature), five in <em>Cold</em> + <em>Light</em>, one in <em>Warm</em> + <em>Light</em>, two were indifferent to the treatments, and four did not germinate at all. Binomial linear mixed-effects models showed that species' maximum responses to light and temperature did not explain their spatial distributions along canopy cover and litter cover gradients, contrary to theoretical expectations of germination being a strong driver of species' occurrences.</p> <p>Despite variation in species' responses to experimental treatments, no association was found with their field microsite associations. Germination strategies in our system were wider than expected for Mediterranean systems. Our results support that germination cues are not strong drivers of microhabitat associations in this system.</p>

opencc-zeroApr 2024View details →
zenodo40/100

Experimental data on "Sediment storage and fluvial sediment transport linkages across an experimental flood sequence"

<p>The repository contains data used in manuscript "Sediment storage and fluvial sediment transport linkages across an experimental flood sequence" by Hassan, Pierce, Chartrand.&nbsp;</p>

opencc-by-4.0Apr 2024View details →
zenodo40/100

Raw data for Figures 2-4 for journal article: "Experimental Evaluation of the Adhear, a Novel Transcutaneous Bone Conduction Hearing Aid""

<p>This is a data set containing the raw data for figures 2-4 from the journal article:</p> <p>"Experimental Evaluation of the Adhear, a Novel Transcutaneous Bone Conduction Hearing Aid"</p> <p>Original article DOI: 10.1055/a-1308-3888</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/33260222/</p> <p>&nbsp;</p> <p>The data is contained within MATLAB&nbsp; figure (.fig) files, all saved with MATLAB version R2020a.</p> <p>&nbsp;</p>

opencc-by-4.0Apr 2024View details →
zenodo40/100

Experimental Data for Wave Decay by Submerged Rigid Vegetation under Orthogonal Wave-Current Conditions

<p>This dataset includes wave amplitude decay data, force prediction and measurement data (organized in spreadsheets), and phase-averaged force measurement data stored in a MATLAB <code>.mat</code> file. The accompanying paper, titled <em>"Wave Decay by Submerged Rigid Vegetation under Orthogonal Wave-Current Conditions,"</em> will be published in <em>Geophysical Research Letters.</em> A detailed description of the variables is provided at the end of each spreadsheet. The detailed measurement methods are described in the paper. The data in the <code>.mat</code> file is arranged according to the experimental case order specified in the spreadsheet named <em>"force measurement."</em></p>

opencc-by-4.0Nov 2024View details →
zenodo40/100

Experimental X-ray Diffraction Data for "Cooling-Induced Order-Disorder Phase Transition in CsPbBr3 Nanocrystal Superlattices"

<p>Experimental X-ray diffraction data:&nbsp;</p> <p>-- temperature-dependent diffraction patterns (theta:2theta, rocking curves) for C18 and C8 CsPbBr3 nanocrystal superlattice samples;</p> <p>-- room temperature diffraction patterns (theta:2theta, rocking curves) for C6, C8, C10, C12, and C18 CsPbBr3 nanocrystal superlattices;</p> <p>in all files, first column is angle in degrees and the second column is intensity.</p>

opencc-by-4.0Nov 2024View details →
zenodo40/100

Supplementary data frames, AlphaFold models, Normal Mode Analysis (NMA) Data, and NMA of Corresponding NMR Ensembles in the S2RCI, MD, and S2 Datasets for "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics"

<h1><strong>Changes applied to V2</strong></h1> <p>In addition to the supplementary dataframes and AlphaFold models from each dataset in V1, V2 includes the additional data outlined below.</p> <p>The <strong>S2RCI</strong> and <strong>MD</strong>&nbsp;datasets include comprehensive analyses of AlphaFold2 models (both before and after truncation). These datasets feature: &nbsp;</p> <ul> <li><strong>AlphaFold2 Models</strong>: Both original and truncated structures. &nbsp;</li> <li><strong>WEBnma Modes</strong>: `modes.txt` files generated from WEBnma analysis, available for both non-truncated and truncated AF2 models. &nbsp;</li> <li><strong>Root-Mean-Square-Fluctuations (RMSF)</strong>: Profiles calculated before and after truncation of AF2 models. &nbsp;</li> <li><strong>NMR Data: Normal Mode Analysis (NMA)</strong>: Performed on corresponding NMR ensembles (see below). &nbsp;</li> </ul> <p>&nbsp;</p> <p>The&nbsp;<strong>NMR Data</strong> of NMA in these datasets includes: &nbsp;</p> <ul> <li>NMR ensembles &nbsp;</li> <li>Individual NMR models extracted from each ensemble &nbsp;</li> <li>STRIDE secondary structure calculations per-individual NMR models</li> <li>RMSF profiles per-individual NMR models</li> </ul> <p>For detailed information, please refer to the `Readme.txt` file within each corresponding folder. &nbsp;</p> <p>The <strong>S2 dataset</strong> includes all the features listed above, except for the NMR analysis.</p>

opencc-by-4.0Nov 2024View details →
zenodo40/100

Experimental data for: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits"

<p>This is the raw experimental data for the paper: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits"</p> <p>Now also including code to recreate figures, and data from alignment and multi-slice ptychography reconstructions.</p> <p>The data is in zarr format and can be read with the zarr python library. It also contains metadata in a dictionary.&nbsp;</p>

opencc-by-4.0Sep 2024View details →
zenodo40/100

Data, code, and outputs for the paper "Can Place-Based Crime Prevention Impacts be Sustained Over Long Durations? 11-Year Follow-Up of a Quasi-Experimental Evaluation of a CCTV Project"

<p>**********<br>Can Place-Based Crime Prevention Impacts be Sustained Over Long Durations?&nbsp;11-Year Follow-Up of a Quasi-Experimental Evaluation of a CCTV Project<br>**********</p> <p>The phase-specific datasets for the main and displacement analyses are contained in the &ldquo;1.datasets&rdquo; folder.&nbsp;</p> <p>The scripts used to conduct the microsynthetic control matching analyses are contained in the &ldquo;2.Rscripts&rdquo; folder. The subfolders &ldquo;3.outputs_main&rdquo; and "4.outputs_displacement" contains all graphs and tables generated by the microsynth package. Please note that running the analysis will take several hours on most computer systems.</p> <p>The Microsoft Excel file displays the results of all models conducted for this analysis. Yellow tabs contain the WDD results presented in the text of the article. &nbsp;Red tabs are the per-capita (quarter-year) microsynth results used to calculate WDD values. All other tabs contain the raw microsynth outputs used to calculate the per-capita results.&nbsp;</p>

opencc-by-4.0Nov 2024View details →
zenodo40/100

Experimental data for "Electric field drives Chern transition in Hofstadter bands of twisted double bilayer graphene"

<p>This experimental dataset was used in our study of &quot;Electric field drives Chern transition in Hofstadter bands of twisted double bilayer graphene&quot;.</p>

opencc-by-4.0Nov 2021View details →
zenodo40/100

Hyperbolic Shear Polaritons in Low-Symmetry Crystals - Experimental Data

<p>Data for Nature&nbsp;<strong>602</strong>,&nbsp;pages 595&ndash;600 (2022),&nbsp;https://doi.org/10.1038/s41586-021-04328-y</p> <p>preprint:&nbsp;https://doi.org/10.21203/rs.3.rs-558805/v1</p> <p>Contents:</p> <p>isotfrequency_surfaces.nb: Mathematica scripts generating Fig. 1c,d<br> raw_data.zip: contains all experimental raw data, lab book entries, and raw data preprocessing scripts.<br> SiO2_data.zip:&nbsp;experimental data analysis and simulations leading to Fig. 2b,c&nbsp;<br> bGO_data.zip: experimental data analysis and simulations leading to Fig. 2d,e<br> bGO_inplane_data.zip: experimental data analysis and simulations for inplane dispersion, Fig. 2f-j</p> <p>Please note that parts of the analysis&nbsp;code use the transfer matrix code developed in the Paarmann group (https://pc.fhi-berlin.mpg.de/latdyn/), see:&nbsp;https://doi.org/10.5281/zenodo.3648040. The code is included again here. However, the dielectric tensor&nbsp;of Gallium Oxide was not part of the previous code and only implemented here.&nbsp;</p> <p>Please contact Alex Paarmann (alexander.paarmann@fhi-berlin.mpg.de) if you have any questions.</p>

opencc-by-4.0Oct 2021View details →
dryad40/100

Data from: Rapid, parallel evolution of field mustard (Brassica rapa) under experimental drought

<p>Climate change is driving evolutionary and plastic responses in populations, but predicting these responses remains challenging. Studies that combine experimental evolution with ancestor-descendant comparisons allow assessment of the causes, parallelism, and adaptive nature of evolutionary responses, although such studies remain rare, particularly in a climate change context. Here, we created experimental populations of Brassica rapa derived from the same natural population and exposed these replicated populations to experimental drought or watered conditions for four generations. We then grew ancestors and descendants concurrently, following the resurrection approach. Experimental populations under drought showed rapid evolution of earlier flowering time and increased specific leaf area, consistent with a drought escape strategy and observations in natural populations. Evolutionary shifts followed the direction of selection and increased fitness under drought, indicative of adaptive evolution. Evolution to drought also occurred largely in parallel among replicate populations. Further, traits showed phenotypic plasticity to drought, but the direction and effect size of plasticity varied. Our results demonstrate parallel evolution to experimental drought, suggesting that evolution to strong, consistent selection may be predictable. Broadly, our study demonstrates the utility of combining experimental evolution with the resurrection approach to investigate responses to climate change.</p>

opencc-zeroNov 2021View details →
zenodo40/100

Experimental data for the manuscript "Magnetoelectric crankshaft"

<p>Here we provide the experimental data correcponding to Fig. 2 of the manuscript &quot;Magnetoelectric crankshaft&quot;.</p>

opencc-by-4.0Jan 2022View details →
zenodo40/100

Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.

<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in&nbsp;PNML-file)</li> </ol>

opencc-by-4.0May 2021View details →
zenodo40/100

Inner filter effect correction for fluorescence measurements in microplates (ZINFE and NINFE) - experimental data

<p>Experimental data for the paper entitled <em>Inner Filter Effect Correction for Fluorescence Measurements in Microplates Using Variable Vertical Axis Focus</em> (https://doi.org/10.1021/acs.analchem.2c01031).</p> <p>Separate datasets are provided for data with background correction, without background correction, and absorbance-corrected data.</p> <p>All results were obtained using the online calculator service written in Javascript: https://ninfe.science (version 15.9.2021.).</p> <p>For additional details please visit: https://glymech.pharma.hr//GlyMech.html.</p> <p>&nbsp;</p>

opencc-by-4.0Jan 2022View details →
zenodo40/100

Experimental and model data for "Nitrogen oxide production in laser-induced breakdown simulating impacts on the Hadean atmosphere"

<p>This is raw data and supporting figures associated with the publication: Heays, A. N., Kaiserov&aacute;, T., Rimmer, P. B., Kn&iacute;žek, A., Petera, L., Civi&scaron;, S., et al. (2022). Nitrogen oxide production in laser-induced breakdown simulating impacts on the Hadean atmosphere. <em>Journal of Geophysical Research: Planets</em>, 127, e2021JE006842. <a href="https://doi.org/10.1029/2021JE006842">https://doi.org/10.1029/2021JE006842</a></p> <p>Two data files contain model output of the ARGO atmospheric photochemistry code that was used to generate figures for Sec. 3 of the paper:</p> <ul> <li>ARGO_model_data_neutral_case.txt</li> <li>ARGO_model_data_reducing_case.txt</li> </ul> <p>The following data files contain a tabulation of laboratory-measured and modelled photoabsorption spectra as described in Sec. 2 of the paper.&nbsp; The A-G letter-encoding of these files follows Table 1 of the paper, and the spectral ranges correspond to the strongest bands of NO, N2O, and NO2. &nbsp;</p> <ul> <li>laboratory_spectrum_experiment_A_species_N2O.txt</li> <li>laboratory_spectrum_experiment_A_species_NO2.txt</li> <li>laboratory_spectrum_experiment_A_species_NO.txt</li> <li>laboratory_spectrum_experiment_B_species_N2O.txt</li> <li>laboratory_spectrum_experiment_B_species_NO2.txt</li> <li>laboratory_spectrum_experiment_B_species_NO.txt</li> <li>laboratory_spectrum_experiment_C_species_N2O.txt</li> <li>laboratory_spectrum_experiment_C_species_NO2.txt</li> <li>laboratory_spectrum_experiment_C_species_NO.txt</li> <li>laboratory_spectrum_experiment_D_species_N2O.txt</li> <li>laboratory_spectrum_experiment_D_species_NO2.txt</li> <li>laboratory_spectrum_experiment_D_species_NO.txt</li> <li>laboratory_spectrum_experiment_E_species_N2O.txt</li> <li>laboratory_spectrum_experiment_E_species_NO2.txt</li> <li>laboratory_spectrum_experiment_E_species_NO.txt</li> <li>laboratory_spectrum_experiment_F_species_N2O.txt</li> <li>laboratory_spectrum_experiment_F_species_NO2.txt</li> <li>laboratory_spectrum_experiment_F_species_NO.txt</li> <li>laboratory_spectrum_experiment_G_species_N2O.txt</li> <li>laboratory_spectrum_experiment_G_species_NO2.txt</li> <li>laboratory_spectrum_experiment_G_species_NO.txt</li> </ul> <p>The following file contains a tabulation of the full-spectral-range laboratory-measured photoabsorption spectrum of experiment A, along with a modelled spectrum.</p> <ul> <li><a href="https://zenodo.org/api/files/49f05962-9a34-4bbc-855c-1a0976f62531/laboratory_spectrum_experiment_A_full_spectrum.txt?versionId=79a70ade-63d1-4fd7-b423-176e27f8dc37">laboratory_spectrum_experiment_A_full_spectrum.txt </a></li> </ul> <p>The following file contains plots of the experimental spectra for all NxOy species in all measurements as well as the residual error of models fit to these spectra.&nbsp; Additional residual errors of model neglecting NxOy species indicates their contribution to the spectra.</p> <ul> <li>laboratory_spectrum_figures.pdf</li> </ul> <p>&nbsp;</p>

opencc-by-4.0Jan 2022View details →
zenodo40/100

Experimental data related to publication: "Dislocation Avalanches: Earthquakes on the MicronScale"

<p>Each .tar file corresponds to a single micropillar compression experiment. The nomenclature of the uploaded dia_<strong>{dia}</strong>_v_<strong>{v}</strong>_p_<strong>{p}</strong>.tar files is the following:</p> <ul> <li>{dia}: The diameter of the micropillar in &micro;m</li> <li>{v}: The compression platen velocity in&nbsp;&micro;m / s</li> <li>{p}: Unique identifier of the micropillar for a given diameter and compression platen velocity</li> </ul> <p>The tar files have the following content:</p> <ul> <li>A tar.gz file which contains the raw Acoustic Emission (AE) data in a compressed file format. Uncompressing it results in one or&nbsp;multiple wav files. In case of multiple ones the covered time range is included at the end of the file name like: 300to800 means the time interval from the compression experiment between 300 s and 800 s.&nbsp;The units of the AE measurement data is 0.1 mV and the sampling rate is 25 MHz.</li> <li>The raw&nbsp;data from the nanoindenter (time, force, displacement, etc.) is located in the file with .dat extension.</li> </ul>

opencc-by-4.0Jan 2022View details →
zenodo40/100

Experimental data of geoplate bedload signal of single pebbles

<p>Geophone raw data acquired during experiments carried out in the Hydraulics Laboratory of the Department of Civil, Environmental and Mechanical Engineering, University of Trento, Italy in dry and flow conditions.</p> <p>Experiments in dry conditions used a metal sphere hitting different locations on the plate. Signal was sampled with two different acquisition rates (10 and 30 kHz)</p> <p>Experiments in water flow conditions used 17 pebbles of different size and shape and 5 different flow conditions (F1 to F5).</p> <p>File &quot;Metadata.txt&quot; reports more information on how to read and use the dataset.</p> <p>These data have been used in the publication &quot;Analysis of the vibration modes of impact geoplates and implications for bedload flux and grainsize measurements&quot; by Portogallo et al., submitted to Water Resources Research.</p>

opencc-by-4.0Jan 2022View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record