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29,897 results for “activism”
Spatiotemporal dynamics of grassland aboveground biomass in northern China and the alpine region: Impacts of climate change and human activities
<p>We employed CASA model to estimate grassland Net Primary Productivity and aboveground biomass A(AGB) from meteorological and GIMMS Normalized Difference Vegetation Index (NDVI) remote sensing data in northern China. We analyzed the dynamics of grassland AGB and impacts climate change and human activites.</p>
AlphaFold Structures for: A wheat tandem kinase activates an NLR to trigger immunity
<p>AlphaFold Predictions used for analysis in "A wheat tandem kinase activates an NLR to trigger immunity".</p>
Ferret mating (active thrust trial)
<p>X-ray trial taken on 23rd May 2019.</p> <p>Trial 5A and Trial 5B: biplanar x-ray motion of two ferrets copulating</p> <p>.csv file: Behaviour coding spreadsheet from the same trial </p>
Dataset: Redox-Activated Proton Transfer through a Redundant Network in the Qo Site of Cytochrome bc1
<p>This dataset contains initial molecular configurations and an example script used with the pDynamo3 library to obtain the results published in the paper "Redox-Activated Proton Transfer through a Redundant Network in the Qo Site of Cytochrome bc1" by Guilherme M. Arantes (USP, Brazil).</p>
NMR data for "Application of a Hydrophobic Polyglutamate Bearing a Triphenylphosphine Group for the Orientation of Pharmaceutically Active Compounds and the Measurement of Residual Dipolar Couplings"
<p>NMR raw data for the work titled:</p> <p>"Application of a Hydrophobic Polyglutamate Bearing a Triphenylphosphine Group for the Orientation of Pharmaceutically Active Compounds and the Measurement of Residual Dipolar Couplings"</p> <p>The archive consists of NMR spectra of all compounds synthesized and the NMR spectra used for the determination of RDCs (isotropic and anisotropic).</p>
A Prefrontal Cortex Map based on Single Neuron Activity
<p>Intermediate results files (after preprocessing) to recreate the figures from:</p> <p><em><strong>A Prefrontal Cortex Map based on Single Neuron Activity, </strong></em>Pierre Le Merre*, Katharina Heining*<em>, Marina Slashcheva, Felix Jung, Eleni Moysiadou, Nicolas Guyon, Ram Yahya, Hyunsoo Park, Fredrik Wernstal, and </em><em>Marie </em><em>Carlén.</em> Equal contribution.</p> <p>Article currently available at: https://www.biorxiv.org/content/10.1101/2024.11.06.622308v2</p> <p>The raw data can be found at: https://dandiarchive.org/dandiset/001260?pos=1</p> <p>Description: Brain-wide high-density extracellular recordings (Neuropixels) in head-fixed mice during distinct passive listening and auditory behavioral tasks. About half of the recordings are in the prefrontal cortex (PFC) and half from other brain regions (3 cortical and 10 subcortical). In PFC we include 11 subregions: secondary motor area (MOs), anterior cingulate area – dorsal and ventral part (ACAd, ACAv), prelimbic area (PL), infralimbic area (ILA), orbital area – medial, lateral, and ventrolateral part (ORBm, ORBvl, ORBl), agranular insular area – dorsal and ventral part (AId, AIv), and frontal pole (FRP).</p> <p>The code generating these intermediate files from the raw data can be found at: https://github.com/hejDMC/pfcmap</p> <p> </p>
Atomic coordinates for "Optimizing Surface Active Sites via Burying Single Atom in Subsurface Lattice for Boosted Alkaline Methanol Oxidation"
<p>Atomic coordinates of the optimized computational models in the manuscript of "Optimizing Surface Active Sites via Burying Single Atom in Subsurface Lattice for Boosted Alkaline Methanol Oxidation"</p>
MOVMUS-UJI Dataset & ERGOMOVMUS: EMG and kinematics data of the hand in activities of daily living with special interest for ergonomics
<p>A dataset of <strong>human hand kinematics</strong> and <strong>forearm muscle activation</strong> collected during the performance of a wide variety of activities of daily living (ADLs) is presented, with tagged characteristics of products and tasks. A total of <strong>26 participants</strong> performed <strong>161 ADLs</strong>, selected to be representative of common elementary tasks, grasp types, product orientations and performance heights. 105 products were used, being varied regarding shape, dimensions, weight and type (common products and assistive devices).</p> <p>The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each and seven surface EMG sensors per arm recording muscle activity. The products and their arrangement were the same across subjects, and tasks were performed in a guided way. Data of <strong>more than 4100 ADLs</strong> is presented in this dataset as <strong>Matlab structures</strong> with full continuous recordings, which may be used in applications such as machine learning or to characterize healthy human hand behaviour.</p> <p>The dataset is accompanied with a <strong>custom data visualization application (ERGOMOVMUS)</strong> as a tool for ergonomics applications, allowing visualization and calculation of aggregated data from specific task, product and/or subjects’ characteristics.</p> <p> </p> <p><strong>v3.0 includes the following updates:</strong></p> <p>- Statistical summary of the recordings both in .xlsx and .ods file format (v1.1 only included it in .xlsx file format)</p> <p>- Updated experiment details in "MOVMUS-UJI DATASET GUIDE.pdf".</p>
The yin and yang of hsa-miR-1244 expression levels during activation of the UPR control cell fate - Raw data for label-free holographic microscopy
<p>Regulation of endoplasmic reticulum (ER) homeostasis plays a critical role in maintaining cell survival. When ER stress occurs, a network of three pathways called the unfolded protein response (UPR) is activated to reestablish homeostasis. While it is known that there is cross-talk between these pathways, how this complex network is regulated is not entirely clear. Using human cancer and non-cancer cell lines, two different genome-wide approaches, and two different ER stress models, we searched for miRNAs that were decreased during the UPR and surprisingly found only one, miR-1244, that was found in all these conditions. The activation of both UPR adaptive and apoptotic signaling pathway was confirmed by parallel genome-wide mRNA expression arrays for Calu 3 cells and next generation sequencing for 16HBE14o- cells. We also verified that ER-stress related downregulation of miR-1244 expression occurred with 5 different ER stressors and was confirmed in another human cell line (HeLa S3). These analyses demonstrated that the outcome of this reduction during ER stress supported both IRE1 signaling and elevated BIP expression. Further analysis also revealed that this novel miRNA impacted all three pathways of the UPR using inhibitors specific for IRE1, ATF6, and PERK. This is the first example of a complex mechanism by which this miRNA serves as a regulatory check point for all 3 pathways that is switched off after UPR activation. In summary, the results indicate that ER stress reduction of <em>miR-1244 </em>expression contributes to the pro-survival arm of UPR.</p> <p>For real-time monitoring of cell viability, we applied real-time and label-free holographic microscopy-based monitoring of cell death and viability using HoloMonitor M4<strong>®</strong> time-lapse cytometer (Phase Holographic Imaging PHI AB, Lund, Sweden). Holographic microscopy was used to follow the optical thickness and irregularity of cells exposed for up to 24 h to Tm or Tg in the presence or absence of <em>miR-1244 </em>mimic or antagomiR. The images from up to 8 independent optical fields were collected and analyzed according to manufacture instructions with HoloMonitor® App Suite software. Healthy cells are irregular in shape and thin, whereas dying cells are round and thick . For all analysis, the same cells parameters qualification was applied.</p> <p> </p> <p> </p> <p><strong>Figure 4. Overexpression of </strong><strong>miR-1244 promotes cell death. </strong>The results of real-time monitoring of cell viability are shown with the real time and label free holographic microscopy using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later monitored up to 24 h. Images were collected every 15 min (from 8 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p><strong>Figure 5. </strong><strong>miR-1244 influences the fate of cells challenged with Tm induced ER stress. </strong>The results of real-time monitoring of cell viability with the real time and label free holographic microscopy are shown using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later treated with Tm (2.5 µg/ml) up to 24 h. Images were collected every 15 min (from 5 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. <a name="OLE_LINK3"></a>Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p><strong>Figure 6. Exogenous </strong><strong>miR-1244 influences the fate of cells challenged with Tg induced ER stress. </strong>The results of real-time monitoring of cell viability with the real time and label free holographic microscopy are shown using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later treated with Tg (500 nM) up to 24 h. Images were collected every 15 min (from 5 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p> </p> <p> </p>
Dataset: Survey for Identifying User Groups of Active Transport Modes in Urban Centers
<p>This dataset was derived from an online questionnaire conducted between June and July 2022. The survey aimed to identify groups of individuals based on patterns revealed through clustering methods, considering the importance assigned to various indicators associated with the dimensions of comfort, safety and security, and time and distance. <span>CSV file structure details are available at 'csv_structure.xlsx'. </span></p> <p>The questionnaire also included a set of items designed to capture the respondents' sociodemographic context, specifically:</p> <ul> <li>Gender </li> <li>Parental Status</li> <li>Age Group</li> <li>Educational Level</li> <li>Country of Residence </li> <li>Employment Situation </li> </ul>
A High-Quality Reprocessed MODIS Fraction of absorbed Photosynthetically Active Radiation Dataset (HiQ-FPAR)(Version 1)
<p>The High-Quality Fraction of absorbed Photosynthetically Active Radiation (HiQ-FPAR) is derived from reprocessed MODIS FPAR C6.1 product by SpatioTemporal Information Compositing Algorithm (STICA). This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and original observations, to improve the raw MODIS FPAR retrievals with poor quality. The HiQ-FPAR covers the period from 2000 to 2022, with spatial resolutions of 500m/5km for global vegetation area and temporal resolutions of 8 days.</p> <p>Ground-based verification results show that HiQ-FPAR performs better than the original MODIS product (MOD15A2H C6.1). Time series curves of the HiQ-FPAR exhibit reduced abnormal fluctuations and better alignment with expected phenological patterns. Additionally, the agreement with ground measurements increases gradually as raw data quality decreases. HiQ-FPAR was found to be more continuous and consistent than MODIS FPAR on a global scale from both spatial and temporal perspectives, especially in the equatorial regions where optical remote sensing usually cannot achieve good performance. Thus, We anticipate that HiQ-FAPR with better spatio-temporal continuity will better support varying global FPAR time series applications.</p> <p>Here, we offer a product version with a spatial resolution of 5km and a temporal resolution of 8 days. Another version has a spatial resolution of 500 meters and is available through Google Earth Engine (https://code.earthengine.google.com/?asset=projects/verselab-398313/assets/HiQ_FPAR/wgs_500m_8d).</p> <p>More details about HiQ-Fpar can be found at https://github.com/Gardenias-123/HiQ-FPAR</p>
HPLC Data associated with PI3KC3-C1 activation paper
<p>HPLC data of bound nucleotides and nucleotide standards</p>
APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19
<p><i><strong>Motivation:</strong></i> Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.</p><p><i><strong>Results:</strong></i> We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.</p><p><i><strong>Availability and Implementation:</strong></i></p><p> APNet's R, Python scripts and Cytoscape methodologies are available at </p><p><a href="https://github.com/BiodataAnalysisGroup/APNet">https://github.com/BiodataAnalysisGroup/APNet</a></p>
CARA: Benchmarking Compound Activity Prediction for Real-World Drug Discovery Applications
<p>Identifying active compounds for target proteins is fundamental in early drug discovery. Recently, data-driven computational methods have demonstrated promising potential in predicting compound activities. However, there lacks a well-designed benchmark to comprehensively evaluate these methods from a practical perspective. To fill this gap, we propose a benchmark, named CARA.Through carefully distinguishing assay types, designing train-test splitting schemes and selecting evaluation metrics, CARA can consider the biased distribution of current real-world compound activity data and avoid overestimation of model performances. We observed that current models can make successful predictions for certain proportions of assays, while the performances varied across different assays. In addition, evaluation of several few-shot training strategies demonstrated different performances related to task types. Overall, we provide a high-quality dataset for developing and evaluating compound activity prediction models, and the analyses in this work may inspire better applications of data-driven models in drug discovery.</p>
Precise and efficient modeling of stellar-activity-affected solar spectra using SOAP-GPU
<p>The simulated spectral time series generated using SOAP-GPU is based on the extraction of active regions from Solar Dynamics Observatory (SDO) data. The positions of sunspots and faculae on the solar disk at each time step are obtained from magnetogram and intensity maps, which are then input into SOAP-GPU to simulate the corresponding spectra. Comparison with HARPS-N solar spectra demonstrates that SOAP-GPU can accurately model the solar RV time series with a precision of 0.9 m/s.</p>
Wildlife Diversity and the Impact of Human Disturbance on Activity Patterns: A Case Study of the Labagoumen Nature Reserve, Beijing, China
<p>This version updates the dataset to reflect revised analyses in the resubmitted manuscript to Ecology and Evolution.</p> <p><strong>Title:</strong><br><strong>Data and Code for "Wildlife Diversity and the Impact of Human Disturbance on Activity Patterns: A Case Study of the Labagoumen Nature Reserve, Beijing, China"</strong></p> <p><strong>Description:</strong><br>This dataset contains the raw data and R scripts used to generate specific figures in the manuscript <em>"Wildlife Diversity and the Impact of Human Disturbance on Activity Patterns: A Case Study of the Labagoumen Nature Reserve, Beijing, China,"</em> submitted to <em>Ecology and Evolution</em>. The data were collected from infrared camera traps deployed in the Labagoumen Nature Reserve, Beijing, China, between 2019 and 2022. The dataset includes species detection records, timestamps, and information relevant to assessing the effects of human disturbance on wildlife activity patterns.</p> <p>The accompanying R scripts facilitate data processing, statistical analyses, and figure generation, enabling reproduction of the key results. This dataset may be useful for researchers interested in spatiotemporal wildlife monitoring, human-wildlife interactions, and disturbance ecology.</p> <p><strong>Contents:</strong></p> <ul> <li>raw data (Excel format, <code>.xlsx</code>)</li> <li>R scripts for data analysis and figure generation of <strong>Figures 3-5</strong> (from the raw data file "raw data.xlsx") (Text format, .doc).</li> <li> <p><strong>Figure 1</strong>: Created using <span>ArcMap 10.8 software</span> and Photoshop software. <span>ArcMap 10.8 software </span>was used for processing spatial data and map creation, while Photoshop was used for post-processing and image enhancement.</p> </li> <li> <p><strong>Figure 2 and Appendix Figure. A1</strong>: Designed and modified using Photoshop software.</p> </li> </ul>
Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona
<p>These are supplementary data for the paper "Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona". They are:</p> <ul> <li>Python code to forecast Kp index</li> <li><span>Code to construct a nerual network model</span></li> </ul>
Dataset supporting the manuscript "Comprehensive evaluation of phosphoproteomic-based kinase activity inference"
<p>Datasets involved in the benchmarking of kinase activity inference as presented in the manuscript "Comprehensive evaluation of phosphoproteomic-based kinase activity inference".</p>
Dataset and code for the study 'Heatwave effects on diel rhythms of insect activity: a comparison across habitats'
<p>Contained here are the csv dataset and R analysis code script underlying the report <em>Heatwave effects on diel rhythms of insect activity: a comparison across habitat</em>s. See <strong>metadata.pdf</strong> for details on the methodology, dataset and script.</p> <p> </p> <h3><strong>Version history</strong></h3> <table> <tbody> <tr> <td><strong>Version</strong></td> <td><strong>Date uploaded</strong></td> <td><strong>Changes made</strong></td> </tr> <tr> <td>v3</td> <td>9th March 2025</td> <td>Edited <strong>insect_script.R </strong>– for all plots with Wald 95% confidence intervals, added dashed lines to demarcate the edges of the shaded bands showing the confidence intervals, to make them more visible for colourblind readers.</td> </tr> <tr> <td>v2</td> <td>2nd March 2025</td> <td> <p>Edited <strong>metadata.pdf</strong> - added citation for key used for insect identification.</p> </td> </tr> <tr> <td>v1</td> <td>31st October 2024</td> <td>[Original upload]</td> </tr> </tbody> </table>
Cyber4OT: ICS network traces containing normal activity and full attack traffic
<p>The <em><strong>Cyber4OT</strong></em> dataset contains prepared in the test-bed environment packet traces from normal activity of OT network, as well as, full network attack. During recorded activity, the attacker performs full network reconnaissance, later disconnects legal Modbus TCP connection and performs PLC device hijacking.</p> <p>The dataset contains 96 files with more than 4,25 millions of packets.</p> <p><em><strong>ReadMe.txt</strong></em> file contains short description of each trace file content.</p> <p>Detailed description of the test bed, where data was prepared, is provided in the <em><strong>Cyber4OT_testbed_description.pdf</strong></em> file.</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.