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29,897 results for “activism”
Data from: Ethanol abolishes vigilance-dependent astroglia network activation in mice by inhibiting norepinephrine release
<p>This collection of data sets was obtained during a study of the effect of acute ethanol intoxication on vigilance-dependent astroglia network activation in mice. The main findings have been that ethanol inhibits astroglia activation by inhibiting vigilance-dependent norepinephrine release. This leads to a failure of activation of alpha<sub>1A</sub>-adrenergic receptors on astroglia. Further, this study has revealed that ethanol inhibition of cerebellar Bergmann glia Ca<sup>2+</sup> activation does not account for ataxic motor behavior, but may rather contribute to cognitive deficits.</p>
Dust source activation frequency dataset
<p>The data set contains information on the frequency of dust emission events, named dust source activation (DSA), on a regular longitude-latitude grid of 1°x1° resolution. The frequency is defined as number of observed DSA during the period March 2006 to February 2010. </p> <p>Dust source activation events are inferred from 15-minute MSG SEVIRI IR dust index images as described in Schepanski et al. (2007, 2009) by back-tracing dust plumes to their place of origin. </p> <p>As discussed in Schepanski et al. (2007, 2009, 2012), the DSA data provides information on the location of dust sources and their relevance.</p>
Learning Active Constraints to Efficiently Solve Bilevel Problems
<p>Datafiles for the test cases in the article <a href="https://arxiv.org/abs/2010.06344">"Learning Active Constraints to Efficiently Solve Bilevel Problems"</a>. The corresponding code is available on <a href="https://github.com/eleaprat/Bilevel---Active-Constraints">GitHub</a>.</p>
Time of activity is a better predictor of the distribution of a tropical lizard than pure environmental temperatures
<p>Environmental temperatures influence ectotherms' physiology and capacity to perform activities necessary for survival and reproduction. Time available to perform those activities is determined by thermal tolerances and environmental temperatures. Estimates of activity time might enhance our ability to predict suitable areas for species' persistence in face of climate warming, compared to the exclusive use of environmental temperatures, without considering thermal tolerances. We compare the ability of environmental temperatures and estimates of activity time to predict the geographic distribution of a tropical lizard, <i>Tropidurus torquatus</i>. We compared 105 estimates of activity time, resulting from the combination of four methodological decisions: (1) How to estimate daily environmental temperature variation (modeling a sinusoid wave ranging from monthly minimum to maximum temperature, extrapolating from operative temperatures measured in field or using biophysical projections of microclimate)? (2) In which temperature range are animals considered active? (3) Should these ranges be determined from body temperatures obtained in laboratory or in field? and (4) Should thermoregulation simulations be included in estimations? We show that models using estimates of activity time made with the sinusoid and biophysical methods had higher predictive accuracy than those using environmental temperatures alone. Estimates made using the central 90% of temperatures measured in a thermal gradient as the temperature range for activity also ranked higher than environmental temperatures. Thermoregulation simulations did not improve model accuracy. Precipitation ranked higher than thermally related predictors. Activity time adds important information to distribution modeling and should be considered as a predictor in studies of the distribution of ectotherms. The distribution of <i>T. torquatus</i> is restricted by precipitation and by the effect of lower temperatures on their time of activty and climate warming could lead to range expansion. We provide an R package "Mapinguari" with tools to generate spatial predictors based on the processes described herein.</p>
Does recent fire activity impact fire-related traits of Pinus halepensis Mill. and Pinus sylvestris L. in the French Mediterranean area?
<p>Data used for analyses in the paper by Romero B. and Ganteaume A., published in Annals of Forest Science in 2020.</p> <p>PS : <em>Pinus sylvestris</em></p> <p>PH : <em>Pinus halepensis</em></p>
Clean Sky 2. Active Cockpit Ground Demonstrator
<p>Promotional video describing Active Cockpit Ground Demonstrator developed under Clean Sky 2 initiative in Getafe (spain) by airbus DS. The video comprises the aircraft cokpit simulator developed by Airbus DS and ASCENT as well as the WL reduction technologies to be integrted into it (developed by Airbus DS and REACTOR)</p> <p>This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 807097. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.</p> <p>The results, opinions, conclusions, etc. presented in this work are those of the author(s) only and do not necessarily represent the position of the JU; the JU is not responsible for any use made of the information contained herein.</p>
Development and Optimization of an IQF USP5 Activity Assay
<p>Development and optimization of a fluorescence based deubiquitinase (DUB) activity assay for USP5 with internally quenched fluorophore pair (IQF) substrates: di-ubiquitin lysine linked K48 and K63 (Ub2K48, Ub2K63). The inhibitory capability of USP5 zinc-finger ubiquitin binding domain (ZnF-UBD) ligands was investigated. </p>
Migration variables and infection data for article "Effects of blood parasite infections on spatiotemporal migration patterns and activity budgets in a long-distance migratory passerine"
<p>Data related to the article "Effects of blood parasite infections on spatiotemporal migration patterns and activity budgets in a long-distance migratory passerine" in the format .csv. The 1st file (SupplTab_EcoEvo_data.csv) contains all variables used for the analyses and the 2nd file (SupplTab_EcoEvo_glossary.csv) contains explanations about the variables in the 1st file.</p>
Thiourea and urea in water drops and in the active site of TcDH
<p>Thiourea and urea in water drops and in the active site of TcDH.</p> <p>Equilibrium geometry configurations obtained at the QM(PBE0/6-31G**)/MM(AMBER) level of theory.</p> <p>Coordinates of QM subsystems have "qm" in names of PDB files.</p>
The Spring Festival Effect: the change of NO2 column concentration in China caused by the migration of human activities
<p>The Spring Festival is the most important holiday in China, human activity and population mobility may contribute greatly to air quality, especially in the megacities. According to the satellite-based tropospheric nitrogen dioxide (NO<sub>2</sub>) column and ground-based observational concentration of NO<sub>2</sub> in the megacities from 2013 to 2018 around the Spring Festival, we found that NO<sub>2</sub> concentration decreases obviously during the Spring Festival and rebounds after the Spring Festival in China, particularly in the megacities. The tropospheric NO<sub>2</sub> columns density around Beijing-Tianjin-Hebei region decreases about 40% than the period before the festival, and it in Beijing decreases by 41.6% and rebounds by 22.3%. While under the Coronavirus disease 2019 (COVID-19) pandemic progresses, the tropospheric NO<sub>2</sub> columns density in Beijing decreases by 56.2% and rebounds only by 6.8% in 2020.</p>
Supporting data for "Dynamics of RNA polymerase II and elongation factor Spt4/5 recruitment during activator-dependent transcription"
<p>Supporting data for</p> <p><strong>Dynamics of RNA polymerase II and elongation factor Spt4/5 recruitment</strong></p> <p><strong>during activator-dependent transcription </strong></p> <p>Grace A. Rosen<sup>a,1</sup>, Inwha Baek<sup>b,1</sup>, Larry J. Friedman<sup>a</sup>, Yoo Jin Joo<sup>b</sup>, Stephen Buratowski<sup>b,2</sup>, Jeff Gelles<sup>a,2</sup></p> <p><sup>a</sup>Department of Biochemistry, Brandeis University, Waltham, Massachusetts 02454, USA.</p> <p><sup>b</sup>Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, USA.</p> <p><sup>1</sup>Equal contributions</p> <p><sup>2</sup>Corresponding authors: <a href="mailto:steveb@hms.harvard.edu">steveb@hms.harvard.edu</a>; +1 (617) 432-0696 (S.B.) and <a href="mailto:gelles@brandeis.edu">gelles@brandeis.edu</a>; +1 (781) 736-2377 (J.G.)</p> <p>See <strong>Source data index.pdf</strong> for description of files.</p>
Host spatial structure and disperser activity determine mistletoe infection patterns
<p><span>What processes and factors are responsible for species distribution are long-standing questions in ecology and a key element for conservation and management. Mistletoes provide the opportunity to study a forest species whose occurrence is expected to be constrained by multiple factors as a consequence of their life form. We studied the mistletoe <i>Tristerix corymbosus</i> (Loranthaceae) on its most common hosts species in northwest Patagonia. The seeds of this mistletoe are almost exclusively dispersed by the small arboreal and endemic marsupial <i>Dromiciops gliroides</i> (Microbiotheridae). We assessed the underlying causes of plant spatial patterns through point pattern analysis and we used different variables that characterize the neighborhood around each host to analyze the relative effect of host availability, potential for disperser movement, and canopy light conditions. We found that potential hosts were strongly aggregated and that the three most common host species were distributed independent of each other. Considering all host species together, infected and non-infected host were individually aggregated but segregated from each other. The aggregated pattern of infected hosts could be explained in part by the template of potential hosts distribution, but was subsequently modulated by the activity of the mistletoe disperser. Potential for disperser movement, the proximity to reproductive mistletoes and habitat complexity, increased mistletoe infection probability. However, neighboring host availability decreased mistletoe infection probability, and tree DBH (used as surrogate for light conditions) had no detectable effect. Our results suggested that the distribution of mistletoe infection was determined by the structure of potential host populations and by the marsupial disperser activity. Compared to bird dispersed mistletoes, the scale of the infection was smaller and the proximity to reproductive mistletoes and habitat complexity were important for seed arrival and infection. The interplay between landscape structure and disperser activity determine the spatial structure of mistletoe future generations. </span></p>
A putative telomerase activator has tissue-specific effects on telomere length in a developing songbird
<p>There is good evidence that telomeres predict variation in health and longevity, yet it is unclear whether these patterns are causally derived from telomeres <i>per se</i>, in part because relatively little research directly manipulates telomere length during early life, when telomere shortening is most dynamic. Here, we test how the telomerase activator TA-65 (i.e., cycloastrogenol) affects telomere length in five tissues during the peak of growth in the wild tree swallow (<i>Tachycineta bicolor</i>). Following 8 days of oral TA-65 administration, chicks experienced telomere lengthening in the blood and accelerated feather growth, but no changes to mass over time. TA-65 did not affect telomere length in the brain or spleen and led to shorter telomeres in the liver and adrenals. This whole-organism experimental manipulation of telomere dynamics therefore reveals limitations to telomere protection and biological senescence. In doing so, this work advances our understanding of early-life telomere dynamics and their potential role in generating future variation in health and lifespan.</p>
Seismic Activity of South Asian Region from Jan 2018 to Jan 2020
<p>This data contains earthquake waveforms recorded for South Asian region between January 2018 to Jan 2020. The data has been saved in mseed format.</p>
Social partners and temperature jointly affect morning foraging activity of small birds in winter
Daily foraging activity of small wintering birds is classically thought to be driven by the need to gather enough energy reserves to survive each night. A separate line of research has shown that sociality is a major driver in winter foraging activities in many species. Here, we use wintering birds as a study system to move towards an integrative understanding of the influence of energy requirements and sociality on foraging ecology. We used RFID-enabled feeders in Lincoln, Nebraska, USA in January-March 2019 to measure foraging activity in two species (downy woodpecker, <i>Picoides pubescens</i>, and white-breasted nuthatches, <i>Sitta carolinensis</i>). We analyzed the relationship between overnight temperature and morning foraging activity and found that lowest overnight temperature was negatively correlated with morning visitation at feeders. We then used a network approach to ask if flock associations explain similarity in birds' foraging activity. In both species, individuals with stronger associations in a social network were more likely to share similar feeder activity, and an index of social partners' activity explained foraging activity better than overnight temperature. This brings forth new questions about the interplay between individual response to temperature and social factors in shaping how small animals cope with harsh winter conditions.
Cochlear activity in silent cue-target intervals shows a theta-rhythmic pattern and is correlated to attentional alpha and theta modulations
<p>Preprocessed data and analysis-scripts needed to reproduce the figures and statistics of the paper "Cochlear activity in silent cue-target intervals shows a theta-rhythmic pattern and is correlated to attentional alpha and theta modulations".</p>
Automated classification of avian vocal activity using acoustic indices in regional and heterogeneous datasets
<p>Acoustic indices combined with clustering and classification approaches have been increasingly used to automate identification of the presence of vocalizing taxa or acoustic events of interest. While most studies using this approach standardize data collection and study design parameters at the project or study level, recent trends in ecological research are to investigate patterns at regional or continental scales. Large-scale studies often require collaboration between research groups and integration of data from multiple sources to fulfill objectives, which can lead to variation in recording equipment and data collection protocols.</p> <p>Our objectives were to determine how analytical approaches and variation in data collection and processing that is typical of regional acoustic monitoring programs influences accuracy when identifying vocal activity in migratory breeding birds. We used data from three regional datasets in Northern Alberta, Northern British Columbia, and Southern and Central Yukon, Canada to investigate the effect of analytical framework, sample size, local species richness, and data collection variables on classification accuracy.</p> <p>We found supervised classification approaches to be the most effective, with boosted regression trees identifying vocal activity with a 92.0% accuracy and easily able to accommodate variation in data collection and processing parameters. We also provide recommendations on effectively processing large and heterogeneous datasets including sufficient sample size, accommodating nuisance variables, and selecting suitable model training data.</p> <p>The results presented in this study can help inform decisions in data collection, data processing, and study design and analysis, maximize performance and accuracy during analysis, and efficiently process large, heterogeneous datasets to answer questions at scales previously difficult to investigate.</p>
Supplementary Data for "Influence of First And Second Coordination Environment on Structural Fe(II) Sites in MIL-101 for C-H Bond Activation in Methane"
<p>Cartesian coordinates for all the optimized geometries reported in "Influence of First And Second Coordination Environment on Structural Fe(II) Sites in MIL-101 for C-H Bond Activation in Methane" (acscatal.0c03906)</p>
Active Learning with RESSPECT: Data Set
<p>This folder contains pre-processed simulated data first made available by Rick Kessler for the <br> <a href="https://arxiv.org/abs/1008.1024">Supernova Photometric Classification Challenge (SNPCC)</a>.</p> <p>All data were feature extracted using the <a href="https://arxiv.org/pdf/0904.1066.pdf">Bazin parametric function</a>.</p> <p>This version of the data set was used to obtain the results reported in <a href="https://arxiv.org/pdf/2010.05941.pdf">Kennamer et al., 2020 - <em>Active learning with RESSPECT: resource allocation for extragalactic astronomical transients</em>.</a> Published during the <a href="http://www.ieeessci2020.org/symposiums/ciastro.html">2020 IEEE Symposium Series on Computational Intelligence</a>. The code used to obtain the results shown in the paper is available in the <a href="https://github.com/COINtoolbox/RESSPECT">COINtoolbox</a> (github). <br> <br> This work was developed under the <a href="https://cosmostatistics-initiative.org/resspect/">RESSPECT project</a>, an inter-collaboration agreement established between the <a href="https://lsstdesc.org/">LSST Dark Energy Science Collaboration (LSST-DESC)</a> and the <a href="https://cosmostatistics-initiative.org/">Cosmostatistics Initiative (COIN)</a> in order to develop an active learning pipeline to advise the allocation of telescope resources.</p>
Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'
<p><strong>Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' along with the experimental results, and the methods used for comparison.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The data used in our experiments that we have the copyright of [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a>.<br> The licences valid for the elements of this repository are discussed under point "3. Licenses" below.</p> <p><strong>1. Structure</strong></p> <p>The repository contains the following items:</p> <ol> <li>"CODE_AND_RESULTS.zip" with the source codes and results of our method and the comparison methods,</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>We now focus on the structure of the file CODE_AND_RESULTS.zip.<br> It contains the following items:</p> <ol> <li>The directory "new_methods" contains the source code and results of the new methods proposed in our paper.</li> <li>The directory "comparison" contains the source code of the two approaches used for comparison: ACoL [<a href="https://doi.org/10.1109/CVPR.2018.00144">A</a>] and DANet [<a href="http://doi.org/10.1109/ICCV.2019.00669">B</a>].</li> <li>The folder "tools_and_metrics" holds additional libraries, software tools, and metrics using in our experiments. </li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>Inside the folder "new_methods," the following sub-folders are provided:</p> <ol> <li>"data" includes data loading code and code for how organizing the input data of the neural network.</li> <li>"expr" includes training code.</li> <li>"model" includes neural network model, basic network and additional modules, depending on the file name, including improved network, and comparison model.</li> <li>"utils" includes some used library functions and test codes when testing, including image segmentation, searching for the largest connected area and data visualization, etc. Verification on the WSADD dataset is done via test_airplane.py and on the DIOR dataset via val_model.py.</li> </ol> <p>In our experiments, we used two datasets:</p> <p>"WSADD" [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>], which is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a> under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.<br> The "<a href="https://doi.org/10.1109/CVPR.2018.00144">DIOR</a>" proposed in [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>].</p> <p><strong>2. References</strong></p> <p>[<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>] Z.-Z. Wu, T. Weise, Y. Wang, Y. Wang, Convolutional neural network based weakly supervised learning for aircraft detection from remote sensing image, <em>IEEE Access</em> 8 (2020) 158097-158106. doi:<a href="http://doi.org/10.1109/ACCESS.2020.3019956">10.1109/ACCESS.2020.3019956</a>. <br> [<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] Z.-Z. Wu. Weakly Supervised Airplane Detection Dataset: WSADD. May 2020. zenodo.org. doi:<a href="http://doi.org/10.5281/zenodo.3843229">10.5281/zenodo.3843229</a>.<br> [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>] K. Li, G. Wan, G. Cheng, L. Meng, J. Han, Object detection in optical remote sensing images: A survey and a new benchmark, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em> 159 (2020) 296-307. doi:<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">10.1016/j.isprsjprs.2019.11.023</a>. <br> [<a href="https://doi.org/10.1109/CVPR.2018.00144">D</a>] X. Zhang, Y. Wei, J. Feng, Y. Yang, T. S. Huang, Adversarial complementary learning for weakly supervised object localization, in: <em>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</em> (CVPR'18), Jun. 18-22, 2018, Salt Lake City, UT, USA, IEEE Computer Society, 2018, pp. 1325-1334. doi:<a href="https://doi.org/10.1109/CVPR.2018.00144">10.1109/CVPR.2018.00144</a>. <br> [<a href="http://doi.org/10.1109/ICCV.2019.00669">E</a>] H. Xue, C. Liu, F. Wan, J. Jiao, X. Ji, Q. Ye, DANet: Divergent activation for weakly supervised object localization, in: <em>Proceedings of the IEEE/CVF International Conference on Computer Vision</em> (ICCV'19), Oct. 27-Nov. 2, 2019, Seoul, Korea, IEEE, 2019, pp. 6588-6597. doi:<a href="http://doi.org/10.1109/ICCV.2019.00669">10.1109/ICCV.2019.00669</a>.</p> <p><strong>3. Licenses</strong></p> <p>The following licenses apply for the files and folders in the archive "CODE_AND_RESULTS.zip":</p> <ul> <li>The files in the folder `new_methods` are under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder `comparison/ACoL` have been obtained from https://github.com/xiaomengyc/ACoL, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>We put our code and data under the </li> <li>The files in the folder "comparison/DANet" have been obtained from <a href="https://github.com/xuehaolan/DANet">https://github.com/xuehaolan/DANet</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/xuehaolan/">https://github.com/xuehaolan/</a>.</li> <li>The files in the folder "tools_and_metrics/detections_DIOR" are related to the repository <a href="https://github.com/rafaelpadilla/Object-Detection-Metrics">https://github.com/rafaelpadilla/Object-Detection-Metrics</a>, which is under the <a href="https://mit-license.org/">MIT License</a>, and therefore are under the same license.</li> <li>The files in the folder "tools_and_metrics/Nest-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/Nest">https://github.com/ZhouYanzhao/Nest</a>, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder "tools_and_metrics/PRM-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/PRM">https://github.com/ZhouYanzhao/PRM</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/ZhouYanzhao/">https://github.com/ZhouYanzhao/</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><strong>4. Contact</strong></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, wuzz@hfuu.edu.cn<br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, tweise@hfuu.edu.cn, tweise@ustc.edu.cn</p> <p>Institute of Applied Optimization, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China<br> </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.