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29,897 results for “activism”
Fig.2 in On the daily activity of the Red Fox (Vulpes vulpes) in two village areas of Bulgaria: a case study
Fig.2. Number of all pictures of the red fox (Vulpes vulpes) made in 24 hours interval (number of images per hour) for NP "Sinite Kamani" (Georgiev et al., 2015).
Fig. 2 in Notes on the defensive behavior and activity of Ablepharus kitaibelii (Bibron & Bory de Saint-Vincent, 1833) in Bulgaria
Fig. 2. Spirally wound adult individual of A. kitaibelii from "Sinite Kamani" area, Sliven town. Photography: A. Dyugmedzhiev.
Fig. 1. Juvenile A in Notes on the defensive behavior and activity of Ablepharus kitaibelii (Bibron & Bory de Saint-Vincent, 1833) in Bulgaria
Fig. 1. Juvenile A. kitaibelii from Pancharevo, Sofia with orange-reddish tail. Photography: N. Tzankov.
Fig. 3 in On the daily activity of the Red Fox (Vulpes vulpes) in two village areas of Bulgaria: a case study
Fig. 3. Number of all pictures of the fox (Vulpes vulpes) made in 24 hours interval (number of images per hour) for the villages of Byaga and Isperihovo.
Suppression of Bend Instability in Microtubule-fd Virus Composite Active Nematics by External Magnetic Field
<p>The shear flow-aligned active nematic within a cuboidal channel is subjected to an external magnetic field (1.8T) in the aligned nematic direction. In the absence of the magnetic field, bend deformations proliferate, generating flows perpendicular to the initial alignment, as seen in the control case where no external field is applied (PIV vectors overlaid on the image for visual representation). Under the influence of the magnetic field, the bend instabilities are quelled, as there are no flows perpendicular to the applied field. Tracer beads are utilized for brightfield imaging in the magnetic field apparatus to distinguish between the conditions, while fluorescently labeled microtubules are observed in the control scenario.</p>
Suppression of Bend deformation in Active Nematics by Magnetic Field
<p>In the presence of an external magnetic field (1.8T) applied horizontally along the shear-aligned direction of the active nematic within a cuboidal channel, flows perpendicular to the field are hindered (time<6 min). This suppression arises because the active stress that distorts the nematic alignment encounters resistance from nematic elasticity and the externally applied magnetic field. When the magnetic field is switched off (time>6 min) (indicated by spikes in PIV vector magnitude due to sample holder movement), the nematic undergoes a bend deformation, initiating flows perpendicular to the field direction. This observation suggests that the magnetic field's influence lies in aligning the liquid crystal rather than disrupting the active stress through interference with motor proteins.</p>
Research Data for Research Activity "Virtual Climatization"
<p>Machining workpieces on machine tools is an inherently energy intensive process. To achieve ever increasing tolerance requirements, thermal disturbances on machine tool and workpiece and resulting thermo-elastic deformations need to be addressed. Instead of cooling whole shopfloor environments and machines, desensitizing the process against temperature changes has the potential to reduce the carbon footprint of machining processes.</p> <p>To develop models for machine tool error prediction due to temperature, a comprehensive and diverse data set is essential. It includes data sources that capture relevant information about the machine tool's thermal behavior and its corresponding error. Hence, an experiment moving a machine tool with a fixed path is run for several hundred times with the same path for each run. Simultaneously, the actual machine position is measured using a laser tracker and component temperature is tracked.</p> <p> </p> <p><a href="https://git-ce.rwth-aachen.de/mark.sanders/bzt-data">https://git-ce.rwth-aachen.de/mark.sanders/bzt-data</a></p>
Dataset for Climate-Active Building Enclosures
<p>The dataset represents a compilation of climate-active building enclosures gathered in the paper 'Climate-Active Building Enclosures: An Integrative Literature Review' (DOI).<br> The state-of-the-art represents the 23 most prominent studies in active building enclosures selected by Lucarelli, Menezes, and Carlo (2023). Further research is representative studies found through a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) workflow.</p>
Reinforcement-based processes actively regulate motor exploration along redundant solution manifolds
<p>From a baby's babbling to a songbird practicing a new tune, exploration is critical to motor learning. A hallmark of exploration is the emergence of random walk behaviour along solution manifolds, where successive motor actions are not independent but rather become serially dependent. Such exploratory random walk behaviour is ubiquitous across species, neural firing, gait patterns, and reaching behaviour. Past work has suggested that exploratory random walk behaviour arises from an accumulation of movement variability and a lack of error-based corrections. Here we test a fundamentally different idea—that reinforcement-based processes regulate random walk behaviour to promote continual motor exploration to maximize success. Across three human-reaching experiments, we manipulated the size of both the visually displayed target and an unseen reward zone, as well as the probability of reinforcement feedback. Our empirical and modelling results parsimoniously support the notion that exploratory random walk behaviour emerges by utilizing knowledge of movement variability to update intended reach aim towards recently reinforced motor actions. This mechanism leads to active and continuous exploration of the solution manifold, currently thought by prominent theories to arise passively. The ability to continually explore muscle, joint, and task redundant solution manifolds is beneficial while acting in uncertain environments, during motor development, or when recovering from a neurological disorder to discover and learn new motor actions.</p>
Dataset - Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements
<p>With the trend towards multinary materials and the associated increase in measurement time, there is a clear need for increasing the efficiency of measurement procedures. In systems requiring long materials characterization times, the implementation of active learning can help decreasing the measurement duration significantly. This dataset is part of the publication in Digital Discovery under the same title and holds the algorithm as well as the data used to test its performance. The algorithm leverages an active learning approach with a Gaussian process model capable of selecting the next measurement area of a library of materials based on the highest uncertainty. Ten materials libraries were manufactured by magnetron sputtering, the composition was measured with EDX and the electrical resistance was measured using the described test stand. The code can also be found on <a href="https://gitlab.ruhr-uni-bochum.de/fthelen/auto-resist-meas">Gitlab</a>.</p>
Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (wtChR2-mEos3.2)
<p>This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555)</p> <p>Zip file contains PALM measurements of HEK293 cells expressing ChR2<sub>WT</sub> fused with mEos3.2.<br>Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames.</p> <p>Detailed protocols for sample preparation and data acquisition are described in the article.<br>Some details are listed below.</p> <p><strong>SAMPLE PREPARATION</strong></p> <p>cell line: Flp-In™ T-REx™ 293<br>transfection method: modified calcium-phosphate transient transfection [1]<br>fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature<br>imaging buffer: PBS</p> <p><strong>PALM ACQUISITION</strong></p> <p>A custom-built setup for single-molecule localization microscopy is described in [2].<br>mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample).<br>PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera:<br>exposure time = 100 ms<br>frame size = 512x512 pixels<br>pixel size = 80 nm</p> <p> </p> <p>[1] Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52.<br>[2] Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073</p>
Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (ChR2(C34A/C36A)-mEos3.2)
<p>This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555)</p> <p>Zip file contains PALM measurements of HEK293 cells expressing ChR2<sub>C34A/C36A</sub> fused with mEos3.2.<br>Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames.</p> <p>Detailed protocols for sample preparation and data acquisition are described in the article.<br>Some details are listed below.</p> <p><strong>SAMPLE PREPARATION</strong></p> <p>cell line: Flp-In™ T-REx™ 293<br>transfection method: modified calcium-phosphate transient transfection [1]<br>fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature<br>imaging buffer: PBS</p> <p><strong>PALM ACQUISITION</strong></p> <p>A custom-built setup for single-molecule localization microscopy is described in [2].<br>mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample).<br>PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera:<br>exposure time = 100 ms<br>frame size = 512x512 pixels<br>pixel size = 80 nm</p> <p> </p> <p>[1] Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52.<br>[2] Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073</p>
Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (b1AR-mEos3.2)
<p>This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555)</p> <p>Zip file contains PALM measurements of HEK293 cells expressing ß<sub>1</sub>AR fused with mEos3.2.<br>Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames.</p> <p>Detailed protocols for sample preparation and data acquisition are described in the article.<br>Some details are listed below.</p> <p><strong>SAMPLE PREPARATION</strong></p> <p>cell line: Flp-In™ T-REx™ 293<br>transfection method: modified calcium-phosphate transient transfection [1]<br>fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature<br>imaging buffer: PBS</p> <p><strong>PALM ACQUISITION</strong></p> <p>A custom-built setup for single-molecule localization microscopy is described in [2].<br>mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample).<br>PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera:<br>exposure time = 100 ms<br>frame size = 512x512 pixels<br>pixel size = 80 nm</p> <p> </p> <ol> <li>Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52.</li> <li>Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073</li> </ol>
Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (CD28-mEos3.2)
<p>This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555)</p> <p>Zip file contains PALM measurements of HEK293 cells expressing CD28 fused with mEos3.2.<br>Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames.</p> <p>Detailed protocols for sample preparation and data acquisition are described in the article.<br>Some details are listed below.</p> <p><strong>SAMPLE PREPARATION</strong></p> <p>cell line: Flp-In™ T-REx™ 293<br>transfection method: modified calcium-phosphate transient transfection [1]<br>fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature<br>imaging buffer: PBS</p> <p><strong>PALM ACQUISITION</strong></p> <p>A custom-built setup for single-molecule localization microscopy is described in [2].<br>mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample).<br>PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera:<br>exposure time = 100 ms<br>frame size = 512x512 pixels<br>pixel size = 80 nm</p> <p> </p> <ol> <li>Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52.</li> <li>Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073</li> </ol>
Volitional activation of remote place representations with a hippocampal brain‐machine interface
<p><strong>Overview</strong></p> <p>This repository is associated with the following paper: <strong>Lai C, Tanaka S, Harris TD, Lee AK. Volitional activation of remote place representations with a hippocampal brain‐machine interface. Science, 2023 (in press).</strong></p> <p>This dataset demonstrates the ability of animals to activate remote place representations within the hippocampus when they aren't physically present at those locations. Such remote activations serve as a fundamental capability underpinning memory recall, mental simulation/planning, imagination, and reasoning. By employing a hippocampal map-based brain-machine interface (BMI), we designed two specific tasks to test whether animals can intentionally control their hippocampal activity in a flexible, goal-directed, and model-based manner. Our results show that animals can perform both tasks in real-time and in single trials. This dataset provides the neural and behavior data of these two tasks. The details of the tasks and results are described in the paper.</p> <p> </p> <p><strong>Dataset, pre-trained model and code access:</strong></p> <ul> <li> <p>Unzip the <code>data.7z</code> to get a <code>data</code> folder. The <code>data</code> folder contains three subfolders:</p> <ul> <li><strong>1. Running</strong>: This folder has two subfolders: <ul> <li><strong>run_before_jumper</strong>: Contains data files for the Running task performed before the Jumper task.</li> <li><strong>run_before_jedi</strong>: Contains data files for the Running task performed before the Jedi task.</li> </ul> </li> <li><strong>2. Jumper</strong>: Contains data files for the Jumper task.</li> <li><strong>2. Jedi</strong>: Contains data files for the Jedi task.</li> </ul> </li> <li> <p>Unzip the <code>model.7z</code> to get a <code>pretrained_model</code> folder, which contains all 6 pretrained models (<code>pth</code> files) trained using the data from the <code>Running</code> tasks, 3 used in <code>Jumper</code> tasks and 3 used in the <code>Jedi</code> tasks.</p> </li> <li> <p>Unzip the <code>code.7z</code></p> </li> </ul>
Fishing activity hotspots in the Mediterranean and Atlantic Seas at 0.5°
<p>Statistically significant hotspots of fishing activities in the Mediterranean and Atlanti Seas were identified by the application of the Getis-Ord Gi statistic (Getis and Ord 2010) though the statistical software R using the globalG.test function (spdep package). The function computes a global test for spatial autocorrelation using a Monte Carlo simulation approach. It tests the null hypothesis of no autocorrelation against the alternative hypothesis of positive spatial autocorrelation. Then the local spatial autocorrelation was tested calculating the Gi statistic, using the local_g_perm function (dfdep package), which indicates the strength of the clustering.</p> <p>Categorization of hotspots was performed, according to the Gi value and the p-value of a folded permutation test obtained for each grid cell, as follows:</p> <ul> <li>Gi>0 and p_value <=0.01 as Very hot</li> <li>Gi>0 and p_value <=0.05 as Hot</li> <li>Gi>0 and p_value <=0.1 as Somewhat hot</li> <li>Gi<0 and p_value <=0.1 as Somewhat cold</li> <li>Gi<0 and p_value <=0.05 as Cold</li> <li>Gi<0 and p_value <=0.01 à Very cold</li> </ul> <p>Grid cells with a p-value > 0.1 were categorized as Insignificant.</p> <p>The analyses were performed on cumulative fishing activity data at 0.5° resolution of seven different gears separately for the two macroareas.</p> <p>The dataset presented includes for each area maps of each gear hotspot and spatial layers of the gears hotspots (.shp; .csv)</p>
Fishing activity hotspots in case study areas of the Mediterranean, Black and Atlantic Seas at 0.1°
<p>Statistically significant hotspots of fishing activities in specific case study areas (Adriatic Sea, Aegean Sea, Balearic Sea, Baltic Sea, Bay of Biscay, Black Sea, Levantine Sea and North Sea) were identified by the application of the Getis-Ord Gi statistic (Getis and Ord 2010) though the statistical software R using the globalG.test function (spdep package). The function computes a global test for spatial autocorrelation using a Monte Carlo simulation approach. It tests the null hypothesis of no autocorrelation against the alternative hypothesis of positive spatial autocorrelation. Then the local spatial autocorrelation was tested calculating the Gi statistic, using the local_g_perm function (dfdep package), which indicates the strength of the clustering.</p> <p>Categorization of hotspots was performed, according to the Gi value and the p-value of a folded permutation test obtained for each grid cell, as follows:</p> <ul> <li>Gi>0 and p_value <=0.01 as Very hot</li> <li>Gi>0 and p_value <=0.05 as Hot</li> <li>Gi>0 and p_value <=0.1 as Somewhat hot</li> <li>Gi<0 and p_value <=0.1 as Somewhat cold</li> <li>Gi<0 and p_value <=0.05 as Cold</li> <li>Gi<0 and p_value <=0.01 à Very cold</li> </ul> <p>Grid cells with a p-value > 0.1 were categorized as Insignificant.</p> <p>The analyses were performed on cumulative fishing activity data at 0.1° resolution of nine different gears separately for the eight case study areas.</p> <p>Trawling hotspot cells categorized as “Very hot” (highly pressured) and “Hot” and “Somewhat hot” (medium pressured) were intersected with repositories of stocks and species-observation data (Coro et al., 2023) in order to retrieve information of species potentially caught by those fishing activities.</p> <p>The dataset presented includes for each case study area tables of species potentially caught within the trawling hotspots, maps of each gear hotspot, spatial layers of the gears hotspots (.shp; .csv)</p>
A large comprehensive curated dataset of small molecules and their activities covering three cardiac ion channels: hERG, Cav1.2, and Nav1.5
<p>The compressed data folder (dataset.rar) represents a data framework for researchers in the field of drug discovery to perform in depth analyses on a very large open-access unique and comprehensive hERG, Nav1.5, and Cav1.2 cardiotoxicity integrated database of small molecules and their activities. The database is organized as follows:</p> <ul> <li>Each sub-folder represents a cardiac ion channel target: hERG, Nav1.5, and Cav1.2</li> <li>Each target sub-folder consists of 3 files in CSV format: One file containing the development set (split into training and validation sets using an 80/20 ratio for hyperparameter tuning). The other 2 files contain external evaluation sets. The first test dataset consists of compounds with a structural similarity of no more than 60% (Tanimoto similarity ≤ 0.6) to the remaining development set, while the second test dataset comprises compounds with a structural similarity of no more than 70% (Tanimoto similarity ≤ 0.7) to the remaining development set.</li> <li>Each file contains data with 7 columns: "InChl Key" as a unique identifier of the chemical structure, "SMILES" as the string format of storage and exchange of the chemical structure, "Source" as the upstream data source from which the data was retrieved, "ChEMBL ID" as the ChEMBL identifier if the compound comes from ChEMBL database, "PubChem CID" as the PubChem compound identifier if the compound comes from PubChem database, "pIC50" as the negative logarithm of the half-maximal inhibitory concentration (IC50) to describe the potency of the compound, and "USED_AS" column specifying whether the compound was used for training or validation.</li> </ul> <p><strong>Upon usage, please cite this publication:</strong></p> <ul> <li>Issar Arab, Kristof Egghe, Kris Laukens, Ke Chen, Khaled Barakat, Wout Bittremieux, <strong>Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction</strong>, <em>Journal of Chemical Information and Modeling</em>, (2023). doi:<a href="https://doi.org/10.1021/acs.jcim.3c01301">10.1021/acs.jcim.3c01301</a></li> </ul> <p> </p>
WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32
<p><strong>WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32</strong></p> <p>This repository contains the WiFi CSI human presence detection and activity recognition datasets proposed in [1].</p> <p><strong>Datasets</strong></p> <ul> <li><strong>DP_LOS</strong> - Line-of-sight (LOS) presence detection dataset, comprised of 392 CSI amplitude spectrograms.</li> <li><strong>DP_NLOS </strong>- Non-line-of-sight (NLOS) presence detection dataset, comprised of 384 CSI amplitude spectrograms.</li> <li><strong>DA_LOS</strong> - LOS activity recognition dataset, comprised of 392 CSI amplitude spectrograms.</li> <li><strong>DA_NLOS</strong> - NLOS activity recognition dataset, comprised of 384 CSI amplitude spectrograms.</li> </ul> <p>Table 1: Characteristics of presence detection and activity recognition datasets. </p> <table> <tbody> <tr> <td><strong>Dataset</strong></td> <td><strong>Scenario</strong></td> <td><strong>#Rooms</strong></td> <td><strong>#Persons</strong></td> <td><strong>#Classes</strong></td> <td><strong>Packet Sending Rate</strong></td> <td><strong>Interval </strong></td> <td><strong>#Spectrograms</strong></td> </tr> <tr> <td>DP_LOS</td> <td>LOS</td> <td>1</td> <td>1</td> <td>6</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>392</td> </tr> <tr> <td>DP_NLOS</td> <td>NLOS</td> <td>5</td> <td>1</td> <td>6</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>384</td> </tr> <tr> <td>DA_LOS</td> <td>LOS</td> <td>1</td> <td>1</td> <td>3</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>392</td> </tr> <tr> <td>DA_NLOS</td> <td>NLOS</td> <td>5</td> <td>1</td> <td>3</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>384</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Format</strong></p> <p>Each dataset employs an 8:1:1 training-validation-test split, defined in the provided label files <em>trainLabels.csv</em>, <em>validationLabels.csv</em>, and <em>testLabels.csv</em>. Label files use the sample format [<em>i c</em>], with <em>i</em> corresponding to the spectrogram index (i.png) and <em>c </em>corresponding to the class. For presence detection datasets (DP_LOS <em>, </em>DP_NLOS), c in {0 = "no presence", 1 = "presence in room 1", ..., 5 = "presence in room 5"}. For activity recognition datasets (DA_LOS <em>, </em>DA_NLOS), c in {0="no activity", 1="walking", and 2="walking + arm-waving"}. Furthermore, the mean and standard deviation of a given dataset are provided in <em>meanStd.csv</em>.</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 our paper [1].</p> <p>[1] Strohmayer, Julian, and Martin Kampel. "WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32" <em>International Conference on Computer Vision Systems</em>. Cham: Springer Nature Switzerland, 2023. </p> <p>BibTeX citation:</p> <pre>@inproceedings{strohmayer2023wifi, title={WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={International Conference on Computer Vision Systems}, pages={41--50}, year={2023}, organization={Springer} }</pre>
Insights on the coupling between vibronically active molecular vibrations and lattice phonons in molecular nanomagnets
<p>Spin–lattice relaxation is a key open problem to understand the spin dynamics of single-molecule magnets and molecular spin qubits. While modelling the coupling between spin states and local vibrations allows to determine the more relevant molecular vibrations for spin relaxation, this is not sufficient to explain how energy is dissipated towards the thermal bath. Herein, we employ a simple and efficient model to examine the coupling of local vibrational modes with long-wavelength longitudinal and transverse phonons in the clock-like spin qubit [Ho(W<sub>5</sub>O<sub>18</sub>)<sub>2</sub>]<sup>9−</sup>. We find that in crystals of this polyoxometalate the vibrational mode previously found to be vibronically active at low temperature does not couple significantly to lattice phonons. This means that further intramolecular energy transfer <em>via</em> anharmonic vibrations is necessary for spin relaxation in this system. Finally, we discuss implications for the spin–phonon coupling of [Ho(W<sub>5</sub>O<sub>18</sub>)<sub>2</sub>]<sup>9−</sup> deposited on a MgO (001) substrate, offering a simple methodology that can be extrapolated to estimate the effects on spin relaxation of different surfaces, including 2D materials.</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.