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677 results for “time of use”
Use of Time Dependent Data in Bayesian Global 21cm Foreground and Signal Modelling (supplementary data)
<p>These are the posterior files, foreground simulation data sets and chromaticity factor values used to produce the results for <a href="https://arxiv.org/abs/2210.04707">arXiv:2210.04707</a>.</p> <p> </p> <p>A plotting script to reproduce key figures is included.</p> <p>Software used:</p> <ul> <li><a href="https://github.com/PolyChord/PolyChordLite/tree/839292290a7747dbee82933bb9f7f955ac45c3ca">PolyChord</a></li> <li><a href="https://github.com/williamjameshandley/fgivenx">fgivenx</a></li> </ul>
REFERENCES DATASET: A SYSTEMATIC REVIEW OF THE EDUCATIONAL USE OF MOBILE PHONES IN TIMES OF COVID-19
<p>The article "A systematic review of the educational use of mobile phones in times of COVID-19" aims to review what research has delved into the educational use of mobile phones during the COVID-19 pandemic. To do this, 38 papers indexed in the Journal Citation Reports database between 2020 and 2021 were analyzed. These works were categorized into the following categories: the mobile phone as part of educational innovation, improvement of results and academic performance, positive attitude towards mobile phone use in education, and risks and/or barriers to mobile phone use. The conclusions show that most teaching innovation experiences focus more on the device than on the student. Beyond its innovative nature, the mobile phone became a tool to allow access and continuity of training during the pandemic, especially in post-compulsory and higher education.</p> <p>This data set is composed of the table with the references used for the review.</p> <p> </p>
Code and data used for the study: 'BioDeepTime: a database of biodiversity time series for modern and fossil assemblages'
<p>The repository includes code and data to reproduce the results in the manuscript ‘BioDeepTime: a database of biodiversity time series for modern and fossil assemblages' by Smith et al. (<code>analysis_biodeeptime.zip</code>).</p>
Data from: The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition
<p>With phenotypic heterogeneity in whole cell populations widely recognised, the demand for quantitative and temporal analysis approaches to characterise single cell morphology and dynamics has increased. We present CellPhe, a pattern recognition toolkit for the unbiased characterisation of cellular phenotypes within time-lapse videos. CellPhe imports tracking information from multiple segmentation and tracking algorithms to provide automated cell phenotyping from different imaging modalities, including fluorescence. To maximise data quality for downstream analysis, our toolkit includes automated recognition and removal of erroneous cell boundaries induced by inaccurate tracking and segmentation. We provide an extensive list of features extracted from individual cell time series, with custom feature selection to identify variables that provide the greatest discrimination for the analysis in question. Using ensemble classification for accurate prediction of cellular phenotype and clustering algorithms for the characterisation of heterogeneous subsets, we validate and prove adaptability using different cell types and experimental conditions.</p>
PB preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Preprocessed PB data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of solar power data collected in Alabama by <a href="https://www.nrel.gov/grid/solar-power-data.html">C</a><a href="https://pems.dot.ca.gov/">alTrans</a>.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=325 representing 325 traffic detectors ordered like in <a href="https://www.nrel.gov/grid/solar-power-data.html">C</a><a href="https://pems.dot.ca.gov/">alTrans</a>).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :<br> Type: .npz<br> Size: 114.23MB<br> *** Key: 'data_TR_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325]</p> <p>*** Key: 'data_VAL_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325]</p> <p>*** Key: 'data_TE_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325]</p> <p>*** Key: 'data_TR'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325]</p> <p>*** Key: 'data_VAL'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325]</p> <p>*** Key: 'data_TE'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325]</p> <p>*** Key: 'position_TR_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TR'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p>
AL preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Preprocessed AL data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of solar power data collected in Alabama by <a href="https://www.nrel.gov/grid/solar-power-data.html">NREL</a>.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=137 representing 137 solar plants ordered like in <a href="https://www.nrel.gov/grid/solar-power-data.html">NREL</a>).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :<br> Type: .npz<br> Size: 34.48MB<br> *** Key: 'data_TR_AL'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_VAL_AL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TE_AL'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TR'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_VAL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TE'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'position_TR_AL'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL_AL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE_AL'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TR'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p>
Dataset of "Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network"
<p>Dataset containing the jupyter notebook used to construct the database of image, to model and train ANN and to analyze the experimental data. Furthermore there are also a reduced database of 100 images that can be utilized to test the ANN, the h5 file containing the ANN weigths and other supporting files.</p>
Simulation data and code used for the publication in Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes"
<p>Simulation data used in the publication Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes". The simulation data set, and the code developed to generate such data, are included. Details in the published paper </p>
Sound stimuli: Considerations for the perceptual evaluation of steady-state and time-varying sounds using psychoacoustic metrics
<p>The current dataset provides all the sound stimuli (wav files) to recreate the figures from the study with the title "Considerations for the perceptual evaluation of steady-state and time-varying sounds using psychoacoustic metrics" by the same authors. There are three datasets: <strong>dataset 1</strong> for fly-by aircraft sounds (directory <em>1-Aircraft-fly-by</em>), <strong>dataset 2</strong> for pass-by train sounds (directory <em>2-Train-pass-by</em>), and <strong>dataset 3</strong> for sounds from a resonating tube called hummer (directory <em>3-Hummer-resonances</em>). A brief description of the datasets is included. The reproduction of figures requires the installation of the sound quality analysis toolbox (SQAT) for MATLAB.</p> <p><strong>Use these data</strong>:<br> Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the <strong>SQAT toolbox</strong> (open access at: <a href="https://github.com/ggrecow/SQAT">https://github.com/ggrecow/SQAT</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_SQAT;', without quotation marks in MATLAB), run the script <strong>copy_sounds_for_pub_Osses2023c_to_SQAT.m</strong>. This last step is not required but recommended. Subsequently, you can run the toolbox script pub_Osses2023c_Forum_Acusticum_SQAT.m and follow the instructions on screen.</p>
Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-worn Inertial Sensors
<p>In this paper we present a benchmark dataset for evaluation of physical human activity recognition from wrist-worn sensors, for the specific setting of basketball training, drills, and games.<br> Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications toward game analysis, guided training, and personal physical activity tracking.<br> The dataset was recorded for two teams from separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist and spanned both repetitive basketball training sessions and full games.<br> Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels, since the participants were heterogeneous in terms of prior basketball experience.<br> We illustrate the datasets' features in several time-series analyses and report on a baseline classification performance study with a state-of-the-art deep learning architecture.</p>
3-D T1 relaxation time measurements in an equine model of subtle post-traumatic osteoarthritis using MB-SWIFT
<p>This dataset contains Key analysis and plotting scripts, data, and sample images.</p> <p>3-D T1 relaxation time measurements in equine model of post-traumatic osteoarthritis using MB-SWIFT</p> <p>Journal of Orthopaedic Research | DOI: 10.1002/jor.25629</p> <p>Swetha Pala (1), Nina Hänninen (1,2), Ali Mohammadi(1), Mohammadhossein Ebrahimi (1,2), Nikae C.R. te Moller(3), Harold Brommer(3), P. René van Weeren (3), Janne T.A. Mäkelä (1), Rami K. Korhonen (1), Isaac O. Afara(1), Juha Töyräs (1,4,5), Santtu Mikkonen (1), Mikko J. Nissi (1*), Olli Nykänen (1,2)</p> <p> 1Department of Applied Physics, University of Eastern Finland <br> 2Research Unit of Medical Imaging, Physics and Technology, University of Oulu<br> 3Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University<br> 4Science Service Center, Kuopio University Hospital, Kuopio, Finland<br> 5School of Information Technology and Electrical Engineering, The University of Queensland </p> <p><br> *Corresponding author<br> Mikko J. Nissi<br> Department of Technical Physics<br> University of Eastern Finland, Kuopio Finland<br> POB 1627<br> 70211 Kuopio<br> mikko.nissi@uef.fi<br> +358-50-5955517<br> Running title: ‘3D T1 of mild PTOA using MB-SWIFT’</p> <p><br> Keywords: Quantitative MRI, T1 relaxation, equine model, post-traumatic osteoarthritis, proteoglycan content.</p> <p>Included folders and files are:<br> - Article_figures: all figures published in the manuscript (.svg format)<br> - Data: Raw MRI data files per flip angle (phase & magnitude images) from 28 samples and corresponding fitted T1 maps within respective folders. SPSS structured data files used for statistical analysis.<br> - Matlab scripts: Matlab functions used for data processing and T1 computation, aedes plugins, and data analysis with subfolders and files:<br> - aedes_plugins: plugins for aedes (http://aedes.uef.fi) and scripts for calculation of surface visualisations from relaxation time maps and auto-segmented mesh. <br> - Data processing and T1 computation: Scripts for non-linear 3D T1 fitting. <br> - Analysis: Key scripts used for analysis and plotting.</p> <p>- README.txt: this file describing the contents of the dataset.</p> <p><br> See more info in separate readme files included in sub-folders.</p> <p><br> (Swetha Pala, 31 May 2023)</p>
Data for "ChickenSense: A low-cost deep learning-based solution for real-time poultry feed intake monitoring using sound technology"
<p>The dataset includes two types of data: scale and audio. The scale data is recorded using a hanging scale and measures the feeders' weight with a 5 Hz frequency. The audio data contains 1824 hours of recorded audio using a piezo-electric sensor attached to a bucket feeder to capture vibrations as audio signals with a sampling rate of 48 kHz. Each .wav file contains 15 minutes of audio recording.<br> Along with the raw data, a part of the dataset containing 36 30-second audio recordings was sampled and labeled in four classes, including pecking, singing, anomaly, and silence, resulting in 3319 labeled events. </p>
Data from: Using adversarial networks to extend brain computer interface decoding accuracy over time
<p>Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the "decoder" at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder's mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called "Adversarial Domain Adaptation Network" (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by factor analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time.</p>
Anisotropic Gold Nanomaterial Synthesis Using Peptide Facet Specificity and Timed Intervention: Experiment and simulation data, figures, and notebooks for recreating figures
<p><strong>/Data</strong></p> <p>Contains all experimental and simulation data used to generate figures in the manuscript. Each folder is labelled with the relevant technique: atomic force microscopy (AFM), dynamic light scattering (DLS), molecular dynamics (MD), small-angle X-ray scattering (SAXS), scanning electron microscopy (SEM), transmission electron microscopy and selected area electron diffraction (TEM-SAED), and ultraviolet-visible light spectroscopy (UV-Vis). Folders may contain subfolders from different experiments and should be unchanged for the notebooks to point to the correct path when loading data. Any file which contains SI in the filename indicates sample information, which includes sample concentration, delay associated with creating the sample (Delay4 is the relevant column name), unique identification (UID), etc.</p> <p><strong>/Data/AFM</strong></p> <p>Each filename is labelled with the number of delay associated with the sample or No Z2 Control in the case of the control sample. Each sample contains 3 files, the raw AFM data, a processed image, and an excel file with line profile measurements.</p> <p><strong>/Data/DLS</strong></p> <p>Contains exported data from Malvern Nano ZS DLS instrument.</p> <p><strong>/Data/Images</strong></p> <p>Images from the colloidal stability experiment.</p> <p><strong>/Data/MD</strong></p> <p>Molecular dynamics data and some notebooks for processing peptide and facet combinations.</p> <p><strong>/Data/SAXS</strong></p> <p>Reduced SAXS data from time-resolved study of nanoplatelet growth.</p> <p><strong>/Data/SEM</strong></p> <p>SEM images of samples prepared with and without dynamic intervention (control).</p> <p><strong>/Data/TEM_SAED</strong></p> <p>TEM images of samples prepared with and without dynamic intervention (control), and SAED image of a nanoplatelet.</p> <p><strong>/Data/UV-Vis</strong></p> <p>Spectroscopy data from different Z2 variants, the microplate based stability assay, cuvette based stability assay, and the time resolved (kinetics) measurements of particle growth as a result of manual dynamic intervention.</p> <p><strong>/Notebooks</strong></p> <p>This folder contains all of the Jupyter notebooks used to plot data and fit SAXS scattering profiles.</p> <p>See <strong>/Notebooks/environment.yml</strong> for packages necessary to execute the notebooks here and in <strong>/Synthesis_Protocol</strong>. We recommend installing this environment by using:</p> <p> </p> <p>Refer to <a href="https://github.com/SasView/sasmodels">https://github.com/SasView/sasmodels</a> and the first cell of <strong>/Notebooks/SAXS_Fitting.ipynb</strong> for specific instructions on how to complete installation of the sasmodels module (sasmodels will be installed by Pip if you correctly use the shared environment.yml file).</p> <p><strong>/Figures</strong></p> <p>Figures presented in the publication</p>
Data from: A user-friendly guide to using distance measures to compare time series in ecology
<p>Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g. assigning species to individual bird calls); clustering (e.g. clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g. accuracy of model predictions to original time series data); and anomaly detection (e.g. detecting possible catastrophic events from population time series). These common tasks in ecological research rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored.</p> <p>Here we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology.</p> <p>Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends.</p> <p>The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series, and help us answer new ecological questions.</p>
Predicting time of failure of Internet of Things devices using Bayesian workflow
<p>Repository includes the environment details in ```energies_iot_env.yml``` file.</p> <p>All code for model analysis is included in the ```iot_tests_refactor.ipynb``` notebook.</p> <p>Code for computing simulation based calibration is in the ```compute_sbc.py``` file, and can be run by ```just_csv.ipynb``` notebook.</p> <p> </p> <p>The data set was created in the project NCN OPUS "Process Fault Prediction and Detection" (UMO-2021/41/B/ST7/03851)</p>
Interpretation results used in the paper "Real-Time Dual-Para FWI-Inv of GPR Data Based on Robust Deep Learning"
<p>We provide the updated synthetic data and inversion results used for all interpretations presented in the paper "Real-Time Dual-Parameter FWI Inversion of GPR Data Based on Robust Deep Learning."</p>
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19
"The sound comes from a meadow in the Sierra Nevada Mountains in California. The meadow is at an elevation of 2400 meters near a mountain named Olancha Peak, which is 3700 meters in altitude. Ihave a group of friends with which Ibackpack (trek) into the mountains. Our goal was to spend some time in the mountains and hike to the top of Olancha Peak (…) By the time we reached the meadow, we were in a forest and there was still snow on the ground in some places. We took the trip in June of 2006. The Sierra Nevada Mountains are a large mountain range. Much of the range is protected by national parks or preserved areas we call 'wilderness areas' (…) Ihave been backpacking for nearly 40 years and Iwill hopefully continue with this challenging activity for 40 years more! Many of my friends are much younger than Iam and it gives me much satisfaction to be able to have as much or more stamina for this activity than they have! When we are on these trips, we hike up peaks, catch fish, drink some whiskey around campfires and enjoy our time in the beautiful solitude. My memories of this trip were of the steep, hot hike from the desert to the cool meadow; the overall beauty of the nature, the absolute solitude of our campsite near the meadow; the strenuous hike to the top of Olancha Peak; the camaraderie of my friends; and, of course the sound of the frogs in the meadow. The frog sounds were astounding to me and Iwould listen in awe of the creature's instinctual desire to reproduce and continue the existence of their kind. Surely there were different species in the meadow for some of the frog sounds were different than others. The sounds only occurred after the Sun went down for the evening. Istood next to the creek in the meadow and recorded the sounds using my digital camera." [Peter/plentz1960]16 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"The sound comes from a meadow in the Sierra Nevada Mountains in California. The meadow is at an elevation of 2400 meters near a mountain named Olancha Peak, which is 3700 meters in altitude. Ihave a group of friends with which Ibackpack (trek) into the mountains. Our goal was to spend some time in the mountains and hike to the top of Olancha Peak (…) By the time we reached the meadow, we were in a forest and there was still snow on the ground in some places. We took the trip in June of 2006. The Sierra Nevada Mountains are a large mountain range. Much of the range is protected by national parks or preserved areas we call 'wilderness areas' (…) Ihave been backpacking for nearly 40 years and Iwill hopefully continue with this challenging activity for 40 years more! Many of my friends are much younger than Iam and it gives me much satisfaction to be able to have as much or more stamina for this activity than they have! When we are on these trips, we hike up peaks, catch fish, drink some whiskey around campfires and enjoy our time in the beautiful solitude. My memories of this trip were of the steep, hot hike from the desert to the cool meadow; the overall beauty of the nature, the absolute solitude of our campsite near the meadow; the strenuous hike to the top of Olancha Peak; the camaraderie of my friends; and, of course the sound of the frogs in the meadow. The frog sounds were astounding to me and Iwould listen in awe of the creature's instinctual desire to reproduce and continue the existence of their kind. Surely there were different species in the meadow for some of the frog sounds were different than others. The sounds only occurred after the Sun went down for the evening. Istood next to the creek in the meadow and recorded the sounds using my digital camera." [Peter/plentz1960]16
Analysis of division and replication cycles in E. coli using time-lapse microscopy, microfluidics and the MoMA software
<p>Dataset used in the publication "Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism" by Guillaume Witz, Erik van Nimwegen and Thomas Julou (<a href="https://doi.org/10.1101/593590">https://doi.org/10.1101/593590</a>).</p> <p>The zip folder contains four subfolders each containing data corresponding to a given growth condition. Each subfolder contains one folder with images of time-lapses of E. coli cells growing in single microfluidics growth lanes and one folder with data obtained by analysing those images with the software MoMA (<a href="https://github.com/fjug/MoMA">https://github.com/fjug/MoMA</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.