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1,742 results for “activity data”
Data from: GPCR genes as activators of surface colonization pathways in a model marine diatom
<p>Surface colonization allows diatoms, a dominant group of phytoplankton in oceans, to adapt to harsh marine environments while mediating biofoulings to human-made underwater facilities. The regulatory pathways underlying diatom surface colonization, which involves morphotype switching in some species, remain mostly unknown. Here, we describe the identifications of 61 signaling genes, including G-protein-coupled receptors (GPCRs) and protein kinases, that are differentially regulated during surface colonization in the model diatom species, <em>Phaeodactylum tricornutum</em>. We show that the transformation of <em>P. tricornutum</em> with constructs expressing individual GPCR genes induces cells to adopt the surface colonization morphology. <em>P. tricornutum</em> cells transformed to express GPCR1A display 30% more resistance to UV light exposure than their non-biofouling wild type counterparts, consistent with increased silicification of cell walls associated with the oval-biofouling morphotype. Our results provide a mechanistic definition of morphological shifts during surface colonization and identify candidate target proteins for the screening of eco-friendly, anti-biofouling molecules.</p>
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
Dataset for the paper: TaskTracker tool: a Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks (SIGCSE Technical Symposium 2021))
<pre>It is a dataset for the <em>TaskTracker-tool: a Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks</em> paper from <a href="https://sigcse2021.sigcse.org/">SIGCSE Technical Symposium 2021</a>. The dataset consists of code snapshots, IDE actions, and demographic information gathered by <a href="https://github.com/JetBrains-Research/codetracker">this</a> tool. We had 148 participants, aged 11 to 40 (mean age is 19 years), take part in the data gathering process. </pre> <pre>During data gathering, solutions were accepted in one of four languages: Python, Java, Kotlin, or C++. However, some of the students chose not to submit tasks or solved some tasks incorrectly. At the same time, some students solved some tasks many times in multiple languages. All submitted solutions are included in the final dataset.</pre> <pre>To get more information see the <em>README</em> file.</pre>
Supplementary data for calcium-vesicles perform active diffusion in the sea urchin embryo during larval biomineralization
<p><strong>Supplementary datasets for the paper Calcium-vesicles perform active diffusion in the sea urchin embryo during larval biomineralization.</strong></p> <p>Two deskewed and deconvolved lattice light-sheet datasets (100 frames each) from the live-cell experiments are available, a control embryo dataset (01-07-2016_TimeLapse4_DMSO_21hrs_Calcein_FM464) and a VEGFR inhibited dataset (24-06-2016_Timelapse1_Axtinib_150_19hrs_Calcein_FM464). These datasets were used for collecting size and motion statistics. The control embryo dataset is available in raw microscope output without deskew or deconvolution applied (Raw_01-07-2016_TimeLapse4_DMSO_21hrs_Calcein_FM464).</p> <p>Four confocal datasets from the cytoskeletal remodeling experiments are also included, phalloidin stained images, control (Phalloidin PMC DMSO 5 zoom4s) and VEGFR inhibited (Phalloidin PMC Axt 18 zoom4); and myosinIIP stained images, control (Myosin PMC 30h DMSO new slid 4a zoom4) and VEGFR inhibited (Phalloidin PMC Axt 18 zoom4).</p> <p>Source code and instructions for the analysis tools used for both the lattice light-sheet and confocal data is available at: <a href="https://git-bioimage.coe.drexel.edu/opensource/llsm-calcium-vesicles-lever">https://git-bioimage.coe.drexel.edu/opensource/llsm-calcium-vesicles-lever</a></p> <p>Code for the deconvolution and deskew algorithms is available from the Janelia research center at: <a href="https://www.janelia.org/open-science/lattice-light-deconvolution-software-cudadeconv">https://www.janelia.org/open-science/lattice-light-deconvolution-software-cudadeconv</a></p> <p> </p> <p> </p>
Physical activities data
<p>This upload contains anonymized open data for physical activities.</p>
Societies in balance: Monumentality and feasting activities among southern Naga communities, Northeast India (Data repository)
<p>The files provide supplementary information for the paper "Societies in balance: Monumentality and feasting activities among southern Naga communities, Northeast India".</p> <p>In accordance with the content and research questions of the article, this repository includes information on the megalithic monuments, as well as transcripts of the interviews conducted in the village of Rünguzu (Nagaland, India). Therefore, both the quantitative, and the qualitative results presented in the article could be reconstructed and reproduced on the basis of this repository.</p> <p>Information concerning the megalithic monuments of all the villages included in the analyses of the article are given in .csv format. The files include details of the monument type, the orientation of the monuments, the metric measures of the monuments, as well as the social affiliation of the monument builders (if available). These data are the basis for the comparative analyses of the megalithic monuments of the different villages, as well as the detailed analyses of the village Rünguzu. All box plots and bar charts presented in the article are completely based on the data made available here.</p> <p>Secondly, this repository includes transcripts of the interviews which were conducted in the village of Rünguzu. The qualitative descriptions of the village structure itself, the feasting activities, as well as the details of megalithic building activities are based on these interviews. Additionally, social anthropological literature and studies were vital as additional sources of information. The transcripts are, apart from the interviewers, completely anonymised in accordance to ethical standards in the publication of interviews.</p>
Bioactive compounds with no structural analogs (high-confidence activity data)
<p>A set of 52,815 unique bioactive compounds (human targets, high-confidence activity data) with no structural analogs with high-confidence activity data was extracted from ChEMBL. For each compound the ChEMBL compound ID (CHEMBLID_Compound) and high-confidence target annotation(s) (CHEMBLID_Targets) are provided. The data set was generated as a part of an analysis to be published in 'Medicinal Chemistry Communications'. </p>
Data from Evidence for elevated diversification rate associated with the evolution of active motility in diatoms
<p>These are data files for Evidence for elevated diversification rate associated with the evolution of active motility in diatoms</p>
Data for "Combining 13C, 15N, and 2H tracer to measure feeding and metabolic activity in marine, shallow-water sponges – A pilot study"
<p>This dataset includes raw data used in the paper "Combining <sup>13</sup>C, <sup>15</sup>N, and <sup>2</sup>H tracer to measure feeding and metabolic activity in marine, shallow-water sponges – A pilot study" (JEMBE).</p>
Crosswalks IUCLID 6 v9 EU PPP Microorganisms - active substance application (product) to Data Requirements
<p>The <strong>Excel file</strong> provides detailed crosswalks from the current Table of Content (ToC) for Microbial Plant Protection Product (PPP) dossier in <a href="https://iuclid6.echa.europa.eu/it/home">IUCLID 6 v.9</a> to Commission Regulation (EU) 283/2013 and Commission Regulation (EU) 284/2013 as amended by Commission Regulation (EU) 2022/1439 & Commission Regulation (EU) 2022/1440.</p> <p>There are two worksheets:</p> <ul> <li><strong>ACTIVE SUBSTANCE</strong> (283-2013): mapping between the current IUCLID working context "EU PPP Microorganisms - active substance information" to the Commission Regulation (EU) No 283/2013 as amended by Commission Regulation (EU) 2022/1439.</li> <li><strong>PRODUCT</strong> (284-2013): mapping between the the current IUCLID working context "EU PPP Microorganisms - active substance application (product)" to the Commission Regulation (EU) No 284/2013 as amended by Commission Regulation (EU) 2022/1440.</li> </ul> <p>The spreadsheets contain the following columns:</p> <ul> <li><strong>Data Requirements Section (Commission Reguation (EU) 2022/1439 or 2022/1440)</strong>: the name of the ToC section (in accordance with the new data requirements).</li> <li><strong>IUCLID section</strong>: the name of the ToC section in IUCLID.</li> <li><strong>Endpoint study record</strong>: name of the document template used to report individual studies of the section. These usually correspond to <a href="https://www.oecd.org/en/topics/sub-issues/assessment-of-chemicals/harmonised-templates.html">OECD Harmonised Templates (OHT)</a>. </li> <li><strong>Endpoint summary</strong>: name of the document template used to report the summary information for the section endpoints.</li> <li><strong>Other IUCLID document</strong>: name of any other document template in IUCLID used to report information of the section. </li> <li><strong>OHT</strong>: number of the OECD Harmonised Template used in the section.</li> <li><strong>Additional context</strong>: fulI IUCLID paths indicating the section of the respective document where information needs to be provided and/or specific values to be indicated. </li> </ul> <p>Note: <span>in cases where an IUCLID document is not included in the updated ToC this will be found in a specific section 'Documents applicable to the former data requirements' which can be found at the end of the dataset.</span></p> <p><strong>Version 5 </strong>includes changes in the table of contents of <a href="https://iuclid6.echa.europa.eu/it/home">IUCLID 6 v9</a>.</p> <p> </p> <p> </p>
Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix digital microfluidics
<p>Active-matrix digital microfluidics (AM-DMF), integrated with hundreds of thousands of active electrodes, can simultaneously realize multiple on-chip bio-chemical reactions at the single-cell level. An intelligent detection system is critical for fully automating manipulations of thousands of digitalized bio-samples and programming the subsequent experiments in real time. In this work, we developed a series of deep learning algorithms based on an AM-DMF system for sample detections. We used the U-net model to quantitatively evaluate different splitting methods on sample droplet generation uniformity. The results revealed that droplets generated using the "one-to-two" strategy exhibits optimal uniformity. We used the YOLOv5 model to monitor the droplet splitting success rates over 18 different AM-DMF chips, and a 97.7% splitting success rate was observed. The results indicated that the model precision was 99.980% and the model recall was 99.976% through manual verification. In addition, we used an improved YOLOv8 model to detect single cells in nanoliter droplets effectively. In comparison with manual verification, the results showed that the model achieved a precision of 99.260% and a recall of 99.193%. By leveraging an artificial intelligence enabled smart detection system, AM-DMF has shown great potential as a ubiquitous platform for true lab-on-a-chip.</p>
Raw absorbance data and R codes for analysing enzymatic activities
<p>Raw absorbance data and sample metadata for study by Prokkola et al. (submitted 2023). See README.</p><p>Statistical analysis of data available in another repository https://doi.org/10.5281/zenodo.8014314.</p><p> </p>
Training and test data, plus saved models for the upcoming paper `Top-down perceptual inference shaping the activity of early visual cortex'
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. These are 40px images that contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. These are 40px images that contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>The .zip file contains a saved model snapshot and various intermediate evaluative data. Details on these are coming soon.</p>
Code and Data for: Donor activity is associated with US legislators' attention to political issues
<p>Contains data, code, and annotations for:</p> <blockquote> <p>Goel P, Malkin N, Gaynor SW, Jojic N, Miler K, Resnik P (2023) Donor activity is associated with US legislators’ attention to political issues. PLoS ONE 18(9): e0291169. https://doi.org/10.1371/journal.pone.0291169</p> </blockquote>
Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article:</p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception".</p> <p>It contains the behavioral and neural experimental data (cuneate nucleus and somatosensory recordings from the Miller Lab, Northwestern University), the result dataframes for task-driven and untrained models, the activations and predictions for the *best models for all tasks* for active and passive movements and the predictions for linear models for active and passive movements. </p> <p>Note, the predictions of other models can be computed from the network weights that were deposited for all trained models. </p> <p>The overall structure of the data is:</p> <p>└── exp_analysis<br> ├── results - Contains the result dataframe of the predictions for all models, tasks and primates<br> ├── activations<br> │ ├── active - Contains activations related to active movements<br> │ └── passive - Contains activations related to passive movements<br> ├── predictions<br> │ ├── active - Contains predictions related to active movements<br> │ └── passive - Contains predictions related to passive movements<br> └── beh_exp_datasets<br> ├── matlab_data - Contains raw behavioral and neural data<br> ├── MonkeyAlignedDatasets_new - Contains padded test behavioral input for generating network activations<br> ├── MonkeyDatasets - Contains not aligned padded test behavioral input for generating network activations<br> ├── MonkeySpikeRegressDatasets - Contains datasets for training data-driven models<br> ├── MonkeySpikeRegressDatasets_new - Contains trial index for regression splits <br> └── new_beh_exp_dataframe - Contains pre-processed behavioral and neural data</p> <p>--------------------------------</p> <p>The activations and predictions for the best 3 models and for all tasks are stored in experiments folder (in .h5 format) that follows the same name convention of the checkpoints.</p> <p>The checkpoints are stored in experiment folders (experiment_***) that follow this scheme:<br>- Task: shallow exp id, deep TCNs exp id, LSTM id.</p> <p>Experiment IDs for each task:</p> <p>- Untrained: 15, 115, 45<br>- Classification: 4015, 5015, 4045</p> <p>- Torque: 8015, 8030, 8045</p> <p>- Regress joint pos: 17016, 17031, 17046<br>- Regress joint vel: 17216, 17231, 17246<br>- Regress joint pos & vel:: 17416, 17431, 17446<br>- Regress joint pos & vel & acc:: 20516, 20531, 20546</p> <p>- Regress hand pos: 4016, 5016, 4046<br>- Regress hand vel: 17316, 17331, 17346<br>- Regress hand pos & vel: 17516, 17531, 17546<br>- Regress hand pos & vel & acc: 20416, 17831, 17846</p> <p>- Regress hand and elbow pos: 20016, 20031, 20046<br>- Regress hand and elbow vel: 20916, 20931, 20946<br>- Regress hand and elbow pos & vel: 20616, 20631, 20646<br>- Regress hand and elbow pos & vel & acc: 20816, 20831, 20846</p> <p>- Redundancy reduction: 10020, 10035, 10050<br>- Autoencoder 20716 & 20717, 20731 & 20732, X</p> <p> </p> <p>The code to process the behavioral data is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing">https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing</a><br>The code to load and use the models to generate activations and predictions is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction">https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction</a></p> <p>To reproduce the results, it is possible to reproduce the main figures using the result dataframe. See our repository for more details. </p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Increase of active-power-based flexibility (data for KPI evaluation)
<p>There are stored data collected from new EV charging stations – this will be used as an aggregated source of active power-based flexibility procured for system operator (DSO) and managed through non frequency platform. Data originated from part of the CZ DEMO run directly by ČEZ distribuce called "e – fleet". At Zenodo there are data from all sites (EV charging poles while the first excel sheet contains calculation of the KPI “Increase of active-power-based flexibility”. Detailed explanation on KPI evaluation, data format and main findings from the tests are included in the <a title="link" href="https://www.onenet-project.eu/wp-content/uploads/2024/03/OneNet_D10.5_V1.0.pdf">Deliverable 10.5 (section 2.1.2). </a></p>
Image quantification data for: Activity-dependent mitochondrial ROS signaling regulates recruitment of glutamate receptors to synapses
<p>Our understanding of mitochondrial signaling in the nervous system has been limited by the technical challenge of analyzing mitochondrial function <em>in vivo</em>. In the transparent genetic model <em>Caenorhabditis elegans, </em>we were able to manipulate and measure mitochondrial ROS (reactive oxygen species) signaling of individual mitochondria as well as neuronal activity of single neurons <em>in vivo</em>. Using this approach, we provide evidence supporting a novel role for mitochondrial ROS signaling in dendrites of excitatory glutamatergic <em>C. elegans</em> interneurons. Specifically, we show that following neuronal activity, dendritic mitochondria take up calcium (Ca<sup>2+</sup>) via the mitochondrial Ca<sup>2+</sup> uniporter MCU-1 which results in an upregulation of mitochondrial ROS production. We also observed that mitochondria are positioned in close proximity to synaptic clusters of GLR-1, the <em>C. elegans</em> ortholog of the AMPA subtype of glutamate receptors that mediate neuronal excitation. We show that synaptic recruitment of GLR-1 is upregulated when MCU-1 function is pharmacologically or genetically impaired but is downregulated by mitoROS signaling. Thus, signaling from postsynaptic mitochondria may regulate excitatory synapse function to maintain neuronal homeostasis by preventing excitotoxicity and energy depletion.</p>
Data from: "Rare earth elements sediment analysis tracing anthropogenic activities in the stratigraphic sequence of Alagankulam (India)"
Open the record for dataset details and reuse information.
Data and ARRIVE 2.0 checklist for the original article "Lockbox enrichment facilitates manipulative and cognitive activities for mice"
<p>This repository contains data (XLSX file) related to the original article "Lockbox enrichment facilitates manipulative and cognitive activities for mice", which was submitted for publication to Open Research Europe. Moreover, the ARRIVE checklist including the ARRIVE Essential 10 and the Recommended Set is provided in Version v2.</p>
Data from: Insights from a 31-year study demonstrate an inverse correlation between recreational activities and red deer fecundity, with body weight as a mediator
<p>Human activity is omnipresent in our landscapes. Animals can perceive risk from humans similar to predation risk, which could affect their fitness. We assessed the influence of the relative intensity of recreational activities on body weight and pregnancy rates of red deer (<em>Cervus elaphus</em>) between 1985 and 2015. We hypothesized that stress, as a result of recreational activities, affects pregnancy rates of red deer directly and indirectly via a reduction in body weight. Furthermore, we expected non-motorized recreational activities to have a larger negative effect on both body weight and fecundity, compared to motorized recreational activities. The intensity of recreational activities was recorded through visual observations. We obtained pregnancy data from female red deer that were shot during the regular hunting season. Additionally, age and body weight were determined through post-mortem examination. We used two generalized linear mixed models (GLMM) to test the effect of different types of recreation on 1) pregnancy rates and 2) body weight of red deer. Recreation had a direct negative correlation with the fecundity of red deer, with body weight as a mediator as expected. Besides, we found a negative effect of non-motorized recreation on fecundity and body weight and no significant effect of motorized recreation. Our results support the concept of humans as an important stressor affecting wild animal populations at a population level and plead to regulate recreational activities in protected areas that are sensitive. The fear humans induce in large-bodied herbivores and its consequences for fitness may have strong implications for animal populations.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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.