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29,897 results for “activism”
Dataset of "Photoactivity Enhancement by Electrochemical Activation of Bifunctional V2C@S MXene: Desirable Approach in Single-architecture High-rate Zinc-ion Solar Batteries"
Single-architecture solar battery energy storage devices which are capable of storing energy from solar energy harvesting in only one package, are being developed as the upcoming and future off-grid energy storage systems. In this research, we explore the photo-responsive characteristics and application of vanadium-based MXene in pouch zinc-ion solar batteries. In addition to the pristine V2CTx MXene, a sulfur-incorporated nanocomposite of V2CTx@S was synthesized via a reaction between exfoliated V2CTx MXene and sulfur at a high benign temperature to evaluate the efficiency of both as photocathodes for such state-of-the-art devices. Through the initial electrochemical charging activation, photoactivity of the V2CTx@S photocathode was enhanced significantly because of the formation of nanoscale vanadium oxide on the surface of conductive V2CTx MXene and the presence of sulfur element without losing the MXene nanolayered characteristics. The results of the pouch zinc-ion solar battery made by V2CTx@S indicate a specific capacity of 197 mAh g–1 at 1 A g–1 under illumination, compared to the dark condition with the amount of 114 mAh g–1 at 1 A g–1 (72.8% enhancement). Additionally, the V2CTx@S zinc-ion solar battery carried out a photo-charging voltage response of 700 mV, and a remarkable energy density of 255 Wh kg–1 at 1 A g–1 under illumination compared to dark conditions with the energy density of 150 Wh kg–1 at 1 A g‒1. Furthermore, a photoconversion efficiency of ≈2.04% was obtained in the pouch zinc-ion solar battery. Density functional theory studies supported these experimental findings by revealing that sulfur modification and vanadium oxide formation improve the electronic properties and light absorption capabilities of the pristine V2C MXene, contributing to its superior photocathodic performance. These great achievements in photo-electrochemical behaviors of V2CTx@S are due to the catalytic synergy of bifunctional MXene decorated with sulfur element, the formation of outer high-valence vanadium oxide, and inner conductive of V2CTx. The findings provide crucial insights into designing of highly efficient photocathode materials for advanced solar batteries.
Dataset (active) - The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution
<p><strong>%%%% 26/09/2025 - NOTE: This entry only includes the active measurements conducted in the following work. </strong></p> <p><strong>The passive measurements can be found in https://doi.org/10.5281/zenodo.17208870</strong></p> <p> </p> <p>The present dataset is open-sourced with the paper "The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution".</p> <p>The paper is authored by Giuseppe Caso, Mohammad Rajiullah, Konstantinos Kousias, Usman Ali, Nadir Bouzar, Luca De Nardis, Anna Brunstrom, Ozgu Alay, Marco Neri, and Maria-Gabriella Di Benedetto</p> <p>If you use the dataset for your own research activities and publications, please consider citing the paper as follows: </p> <p><strong>G. Caso et al., "The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution," IEEE Open Journal of the Communications Society(IEEE OJ-COMS), pp. 1-21, 2024.</strong></p> <p><strong>@article{caso2024chronicles, <br>title={{The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution}}, <br>author={Caso, G. and Rajiullah, M. and Kousias, K. and Ali, U. and Bouzar, N. and De Nardis, L. and Brunstrom, A. and Alay, O. and Neri, M. and Di Benedetto, M.-G.}, <br></strong><strong>journal={IEEE Open Journal of the Communications Society},<br>pages={1--21}, <br></strong><strong>year={2024}, <br>publisher={IEEE}<br>}</strong></p> <p>A detailed description of the dataset is provided in the paper, and the README file provides additional details.</p> <p>In the paper, an in-depth performance analysis of 5G performance is carried out, also exploiting a dataset previously collected and open-sourced at https://zenodo.org/records/8224890, and fully described in the following publication:</p> <p><strong>@article{kousias2023large,<br> title={{A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements}},<br> author={Kousias, K. and Rajiullah, M. and Caso, G. and Ali, U. and Alay, O. and Brunstrom, A. and De Nardis, L. and Neri, M. and Di Benedetto, M.-G.},<br>journal={IEEE Communications Magazine},<br>volume = {62},<br>number = {5},<br>pages={44--49},<br>year={2024},<br>publisher={IEEE}<br>}</strong></p> <p>Contact Giuseppe Caso (giuseppe.caso@kau.se) for more information on the dataset(s) and potential access to additional data. </p>
Improving sperm quality of large South American catfish using trehalose-based activating solutions
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Autophagy regulator ATG5 maintains cerebellar function by preventing its excessive glycolytic activity.
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Phenotypically active ORF and CRISPR consensus profiles
This hosts the files on phenotypically active genes for JUMP, derived from https://zenodo.org/records/14025602 after filtering samples with no matches above a given threshold (see filenames for thresholds).
Physical Activity Levels on Functional Outcomes and Quality of Life in Musculoskeletal Rehabilitation.
<p><span>Physical Activity Levels on Functional Outcomes and Quality of Life in Musculoskeletal Rehabilitation.</span></p>
Predictive physiological anticipatory activity preceding seemingly unpredictable stimuli: An update of Mossbridge et al's meta-analysis
<p>The research that created this data is described in:</p> <ul> <li>Duggan M and Tressoldi P. Predictive physiological anticipatory activity preceding seemingly unpredictable stimuli: An update of Mossbridge et al’s meta-analysis [version 2; referees: 2 approved]. F1000Research 2018, 7:407 (https://doi.org/10.12688/f1000research.14330.2)</li> </ul> <p>Two files are included:</p> <ul> <li>MA_PPAA_Final.xlsx - Database</li> <li>Code.pdf - Code</li> </ul>
Dataset of use, acceptance, attitude and training needs in active method-ologies of Spanish teachers.
<p>Dataset collected for the purpose of analyzing various aspects related to the implementation of active methodologies (AM) in the educational field in Spain. This dataset includes data on: Use of active methodologies: information on the frequency and the way in which teachers use different active methodologies in their classes, such as cooperative learning, project-based learning, challenge-based learning, among others. Student acceptance: evaluation of how students perceive and respond to active methodologies, observing their willingness to participate and the benefits they consider derived from these methodologies. Teacher attitudes: Teachers' opinions and perceptions about active methodologies, including their effectiveness, advantages, and difficulties in implementing them in the classroom. Training needs: information on teachers' perceptions about the need for training in the use of active methodologies, particularly in emerging areas such as the use of digital technologies in the classroom, and the preparation to implement these strategies more effectively.</p>
Supporting for "Bivalve Resilience to Ocean Acidification: Active H+ Efflux as a Mechanism for Shell Growth Maintenance"
<p><span>Summary: </span></p> <p><span>This dataset encompasses a comprehensive collection of responses from the Manila clam <em>Ruditapes philippinarum</em> to ocean acidification. Key metrics included in this dataset are oxygen consumption rate, Ammonia-N excretion rate, intracellular pH, H⁺ flux, expression and activity of acid-base regulatory genes, and individual growth measurements.</span></p> <p><span> </span><span>The data was systematically compiled from both field mesocosm and laboratory experiments aimed at elucidating the physiological responses and underlying mechanisms by which bivalves adapt to the stressors associated with ocean acidification.</span></p> <p><span> </span><span>Data generation occurred primarily between 2020 and 2023. This dataset serves as a valuable resource for enhancing our mechanistic understanding of the responses and adaptive potential of marine bivalves in the context of escalating ocean acidification.</span></p>
Wave Activity Budget Dataset
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Dynamic Visualization of ResNet Layer Activations for Brain Health Classification
<p>This GIF file provides a dynamic visualization of the internal representations (activations) from the ResNet 18 model layers (2 to 69) during a brain health classification task. The sequence begins by showing the original input image, followed by successive activation maps visualized using the "jet" colormap. Each frame corresponds to the activations extracted from a specific layer in the ResNet, resized to match the input image dimensions for better interpretability. </p> <p>The dataset used for this visualization is from S. Bhuvaji, "Brain Tumor Classification MRI," published on Kaggle in 2023. This dataset contains MRI images of brain tumors and has been utilized to train and evaluate the ResNet model for the classification of brain health states. The input sample displayed in the GIF is one such MRI image from the dataset, highlighting the model's ability to extract and analyze features relevant to brain tumor diagnosis.</p> <p>The activations reveal how the ResNet processes the input image hierarchically. In the <strong>early layers (e.g., Layers 2-10)</strong>, the network preserves much of the spatial structure of the original image, focusing on edges and low-level features. Moving to the <strong>intermediate layers (e.g., Layers 11-40)</strong>, the network begins to emphasize localized patterns while filtering out irrelevant structures such as the skull, concentrating instead on regions associated with tumors or health-related features. Finally, the <strong>deep layers (e.g., Layers 41-69)</strong> extract highly abstract and classification-relevant patterns, concentrating on tumor-related features while discarding most of the background.</p> <p>The progression of the activations in the GIF demonstrates how the network transitions from general image features to highly specialized, diagnostic features that are critical for the classification task. This visualization helps provide an intuitive understanding of the hierarchical processing capabilities of convolutional neural networks (CNNs) in medical image analysis.</p> <h3>Key Features:</h3> <p>The visualization includes an input brain image and its corresponding activation maps, extracted from each ResNet layer. The activation maps are resized to match the original image for consistency, and the "jet" colormap is applied to enhance visual interpretation of activation intensities. Each frame in the GIF dynamically updates to show the activations of the next layer, offering an engaging representation of the network's internal behavior.</p> <h3>Use Cases:</h3> <p>This GIF is a valuable resource for education, research, and presentations. It can be used to illustrate how deep learning models process medical images, providing insights into the hierarchical feature extraction process. Researchers and educators can leverage this visualization to explain the concept of feature abstraction in CNNs. It is also ideal for inclusion in talks, posters, and papers to showcase the dynamic analysis of neural network activations.</p>
Exploiting the Redox Activity of MIL-100(Fe) Carrier Enables Prolonged Carvacrol Antimicrobial Activity
<p>Relevant data for publication with doi: https://doi.org/10.1021/acsami.1c21555</p>
ZIF-8 encapsulation improves the antifungal activity of benzaldehyde and methyl anthranilate in films
<p>Relevant Data for publication wit DOI <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3DT03229A">10.1039/D3DT03229A</a></p>
No bridge between us: EXAFS and computations confirm two distant iron ions comprise the active site of alkane monooxygenase (AlkB)
<p>This dataset contains computational data for the paper "No bridge between us: EXAFS and computations confirm two distant iron ions comprise the active site of alkane monooxygenase (AlkB)". All computations in this repository were performed by Clorice Reinhardt, a Arnold O. Beckman Postdoctoral Fellow in the Chemical Sciences in Prof. Heather Kulik's lab at MIT, and EXAFS data uploaded was analyzed and prepared for this repository by Christopher J. Pollock</p>
Data and code for "Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity"
<p>Data and code for "Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity"</p> <p>Nature Communications; 2024 Nov 24;15(1):10185. doi: 10.1038/s41467-024-54742-9.</p>
ASE Database of CO2 Electro Capture on Redox-Active Metal-Organic Frameworks
<p>ASE database containing simulated structures for 1D, 2D and 3D conductive metal-organic frameworks.</p>
Heterometallic palladium–iron metal–organic framework as a highly active catalyst for cross-coupling reactions
<p>Relevant data for the publication with DOI: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D2SC05192C">10.1039/D2SC05192C</a></p>
Carbon analysis_equipment based activity
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Appendix B and C of "The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning"
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Jupyter Notebook Activity Dataset (rsds-20241113)
<h2>List of data</h2> <ul> <li>rsds-20241113.zip: Collection of SQLite database files</li> <li>image.tar.gz: Docker image provided in our data collection experiment</li> <li>redspot-341ffa5.zip: Redspot source code (<a href="https://github.com/tomokinakamaru/redspot/tree/341ffa56cb941b6f1ad74bd50d23fcf0ee96b270" target="_blank" rel="noopener">redspot@341ffa5</a>)</li> </ul> <div> <h2>Extended version of Section 2D of our paper</h2> Redspot is a Jupyter extension (i.e., Python package) that records activity signals. However, it also offers interfaces to read recorded signals. The following shows the most basic usage of its command-line interface:<br> <div> </div> <div><code>redspot replay <path-to-db></code></div> <br> <div>This command generates snapshots (.ipynb files) restored from the signal records. Note that this command does not produce a snapshot for every signal. Since the change represented by a single signal is typically minimal (e.g., one keystroke), generating a snapshot for each signal results in a meaninglessly large number of snapshots. <em><strong>However, we want to obtain signal-level snapshots for some analyses. In such cases, one can analyze them using the application programming interfaces:</strong></em></div> <br> <div><code>from redspot import database</code></div> <div><code>from redspot.notebook import Notebook</code></div> <div><code>nbk = Notebook()</code></div> <div><code>for signal in database.get("path-to-db"):</code></div> <div><code> time, panel, kind, args = signal</code></div> <div><code> nbk.apply(kind, args) # apply change</code></div> <div><code> print(nbk) # print notebook</code></div> <br> <div>To record activities, one needs to run the Redspot command in the recording mode as follows:</div> <br> <div><code>redspot record</code></div> <br> <div>This command launches Jupyter Notebook with Redspot enabled. Activities made in the launched environment are stored in an SQLite file named ``redspot.db'' under the current path.</div> <br> <div>To launch the environment we provided to the participants, one first needs to download and import the image (image.tar.gz). One can then run the image with the following command:</div> <br> <div><code>docker run --rm -it -p8888:8888 <image-name></code></div> <br> <div>Note that the SQLite file is generated in the running container. The file can be downloaded into the host machine via the file viewer of Jupyter Notebook.</div> </div>
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.