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29,897 results for “activism”
Fig. 2 in Fallen leaves on the water-bed: diurnal camouflage of three night active fish species in an Amazonian streamlet
Fig. 2. Three leaf-shaped and cryptically colored fish species photographed underwater lying on their sides, and one escape response out of the root-tangle: Steatogenys duidae (top left), Helogenes marmoratus (top and bottom right), and Tetranematichthys quadrifilis (bottom left). Note body shape and color resemblance to dead leaves in the three species, as well as the fore body of H. marmoratus above the water surface in the bottom right picture (mauve asterisk).
Governing Activation in Ireland - Dataset for 2020 Survey of Contracted Employment Services
<p>This dataset relates to a survey of 77 JobPath frontline staff and 112 Local Employment Services staff, carried out by Dr Michael McGann over July and August 2020 as part of his study Governing Activation in Ireland. </p>
Active restoration fosters better recovery of tropical rainforest birds than natural regeneration in degraded forest fragments
<ol> <li>Ecological restoration has emerged as a key strategy for conserving tropical forests and habitat specialists, and monitoring faunal recovery using indicator taxa like birds can help assess restoration success. Few studies have examined, however, whether active restoration achieves better recovery of bird communities than natural regeneration, or how bird recovery relates to habitat affiliations of species in the community.</li> <li>In rainforests restored over the past two decades in a fragmented landscape (Western Ghats, India), we examined whether bird species richness and community composition recovery in 23 actively restored (AR) sites was significantly better than recovery in paired naturally regenerating (NR) sites, relative to 23 undisturbed benchmark (BM) rainforests. We measured 8 habitat variables and tested whether bird recovery tracked habitat recovery, whether rainforest and open-country birds showed contrasting patterns, and assessed species-level responses to restoration.</li> <li>We recorded 92 bird species in 460 point-count surveys. Rainforest bird species richness was highest in BM, intermediate in AR, and lowest in NR. Contrastingly, open-country bird species richness was least in BM, intermediate in AR, and highest in NR.</li> <li>Bird community composition varied significantly across treatment types with composition in AR in transition from NR to BM. Bird community dissimilarity between sites was positively related to dissimilarity in habitat structure and floristics, and geographic distance between sites. Variance partitioning indicated that structural and floristic dissimilarity explained 90% of the variation in community composition.</li> <li>Indicator species analysis revealed significant associations of 34 species with one or more treatment types. Species associated with BM and AR treatment types were all rainforest species, while only 38% of species associated with AR and NR treatment types were rainforest species.</li> <li> <em>Synthesis and applications</em>: We show that active restoration of degraded fragments benefits rainforest birds and reduces the infiltration of open-country birds, and highlight the importance of considering rainforest and open-country species separately. In human-modified tropical rainforest landscapes, active restoration of degraded fragments fosters partial recovery and complements protection of mature forests for bird conservation.</li> </ol>
Datasets and Software for publication "Distinct RPA domains promote recruitment and the helicase-nuclease activities of Dna2"
<p>- Magnetic tweezers and mass photometry datasets for the publication "Distinct RPA domains promote recruitment and the helicase-nuclease activities of Dna2, Nature Communications 2021"</p> <p>- costum written Matlab Software for Helicase Analysis</p>
Energetic return on investment determines overall soil microbial activity.
<p><strong>Data</strong> and <strong>R codes</strong> used for the manuscript entitled " <strong>Energetic return on investment determines overall soil microbial activity."</strong></p>
Active Regions with Multiple NOAA Numbers, 2011-2019
<p>This is a list of active regions which have been identified as lasting for multiple rotations, and their subsequent-rotation NOAA number designation. For more information on the creation of this dataset, please refer to (ApJ link coming).</p>
Top-down modulation of shape and roughness discrimination in active touch by covert attention
<p>Due to limitations in perceptual processing, information relevant to momentary task goals is selected from the vast amount of available sensory information by top-down mechanisms (e.g. attention) that can increase perceptual performance. We investigated how covert attention affects perception of 3D objects in active touch. In our experiment, participants simultaneously explored the shape and roughness of two objects in sequence, and were told afterwards to compare the two objects with regard to one of the two features. To direct the focus of covert attention to the different features we manipulated the expectation of a shape or roughness judgment by varying the frequency of trials for each task (20%, 50%, 80%), then we measured discrimination thresholds. We found higher discrimination thresholds for both shape and roughness perception when the task was unexpected, compared to the conditions in which the task was expected (or both tasks were expected equally). Our results suggest that active touch perception is modulated by expectations about the task. This implies that despite fundamental differences, active and passive touch are affected by feature selective covert attention in a similar way.</p> <p> </p> <p>There are zip files for the main experiment and the two pilot experiments, which contain all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file with the participant's answers for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) for each session in separate folders.</p> <p>Variables of the main experiment are described in the file VARIABLE_CODES_MainExp.txt and the variables of the pilot experiments are described in the files VARIABLE_CODES_PilotRoughness.txt and VARIABLE_CODES_PilotShape.txt.</p>
Dead or alive; or does it really matter? Level of congruency between trophic modes in total and active fungal communities in High Arctic soil.
<p>These are the rDNA and rRNA fragments of Internal transcribed spacer 2 (ITS2) extracted from snow fence experiment in Adventdalen, Svalbard. </p> <p>This is a dataset described in Wutkowska et al., (2019), 'Dead or alive; or does it really matter? Level of congruency between trophic modes in total and active fungal communities in High Arctic soil.', published in Frontiers in Microbiology</p> <p>All other corresponding data (for splitting libraries, environmental parameters etc.) can be found here: https://github.com/magdawutkowska/Dead_or_alive</p> <p> </p>
DCASE 2018, Task 5: Monitoring of domestic activities based on multi-channel acoustics - Evaluation dataset
<p>The dataset is a derivative of the SINS dataset and is meant to be used as an evaluation set for the <a href="http://dcase.community/challenge2018/task-monitoring-domestic-activities">DCASE2018 Task 5 challenge</a>. The development set to be used can be found <a href="https://zenodo.org/record/1247102#.WzIF_NUzZhE">here</a>. The dataset is a derivative of the SINS database.</p> <p>The SINS database contains a continuous recording of one person living in a vacation home over a period of one week. The recordings were manually annotated on daily activity level: "Cooking", "Dishwashing", "Eating", "Social activity (visit, phone call)", "Vacuum cleaning", "Watching TV", "Working", "Presence" and "Absence". More information can be found on (please cite this papers when using the dataset):</p> <p>G. Dekkers, S. Lauwereins, B. Thoen, M. W. Adhana, H. Brouckxon, T. van Waterschoot, B. Vanrumste, M. Verhelst, and P. Karsmakers, “The SINS database for detection of daily activities in a home environment using an acoustic<br> sensor network,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, November 2017, pp. 32–36.</p> <p>G. Dekkers, L. Vuegen, T. van Waterschoot, B. Vanrumste, and P. Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” KU Leuven, Tech. Rep., July 2018.</p> <p>The derivative of the SINS database, 'DCASE 2018 – Task 5 evaluation dataset' consists of data collected by 7 microphone arrays in the combined living room and kitchen area. The continuous recordings were split into audio segments of 10s. These audio segments are provided as individual files. In total 72972 segments are made available, leading to approximately 200 hours of data with annotations.</p> <p>More information about the challenge and the specific dataset can be found here. Information solely related to the content of the dataset is available in 'DCASE2018-task5-eval.doc.zip'.</p> <p>By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-eval.doc.zip).</p>
Active nematics director and flow field data
<p>We conduct our experiments on the microtubule-kinesin active nematic system pioneered in Sanchez et al. 2012. The long rod-like MTs are bundled together via depletion interactions and are driven out of equilibrium by the action of kinesin-streptavidin motor protein complexes, which are units that induce relative motion utilizing ATP<br> as the energy source. Depletion forces also aid in driving the MT bundles to form bundles to the oil-water<br> fluid interface, where they execute self-sustained bending and buckling instabilities. The system is extensile,<br> which means that active stresses cause the MT bundles to extend in length and contract in width.</p> <p> </p> <p>To investigate the dynamics of defects in 2D flat space, we prepare the active nematic in a flow-cell setup<br> where the entire pool of ingredients is confined in a 2D sealed cell roughly 10 cm2 in area and 100 μm in<br> thickness. The lower surface of the cell is subjected to hydrophobic treatment (using Aquapel) and the<br> upper surface to hydrophilic treatment (using polyacrylamide coating) to enhance wetting by the respective<br> fluid phases. A fluorinated oil (HFE-7500 with surfactant E2K0660) forms the oil-phase, and the active MT<br> suspension forms the water-phase. We obtained purified tubulin monomers and kinesin–streptavidin motor<br> protein complexes from the Dogic Group at Brandeis University. The polymerization of tubulin to<br> MTs is performed in our lab before mixing with other biomaterials as per the protocols described in previous<br> works. The final active mix has 20% MTs by volume aided with 144μM ATP. The entire flow<br> cell is sealed by epoxy resin and centrifuged at 1000 RPM to accelerate the depletion mechanism to the<br> interface.</p> <p><br> We use confocal fluorescence microscopy for visualization. The MTs are labeled with AlexaFlour 647 dye<br> and illuminated at 633 nm; the excitation and emission peaks are at 651 nm and 667 nm, respectively. After<br> sample preparation and centrifugation, we wait for 15-20 minutes to allow for uniform depletion, and then<br> image at a constant framerate till the activity ceases. Typically, the MTs stay active for 6+ hours. Imaging<br> is done using 10× and 20× objectives to focus on regions with area on the order of mm2, away from the<br> edges of the flow cell. The imaging process results in a time series of 8-bit grayscale images, which are stored<br> as the raw data.</p> <p> </p> <p>For more information, see "Physically-informed data-driven modeling of active nematics" by Golden et al.</p>
Data from: "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes"
<p>Dataset from the publication "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes". Full experimental details can be found in the related publication in ACS Applied Energy Materials: <a href="https://doi.org/10.1021/acsaem.2c02047">https://doi.org/10.1021/acsaem.2c02047</a></p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 2 SoC ranges [0-30, 0-100]%, with multiple cells tested under each condition. Cells were base-cooled at set temperatures using bespoke test rigs (see pubilcation for details). All electrochemical data were recorded using a Biologic BCS-815 battery cycler.</p> <p> </p> <p><strong>Break-in cycles:</strong></p> <p>Prior to any ageing or performance checks, all cells were subject to 5 full charge-discharge cycles as part of the break-in procedure. This consisted of a 0.2C charge to 4.2 V with CV-hold till C/100, and 0.2C discharge to 2.5 V (repeated for 5 cycles). Cells were rested under open circuit conditions for 2 hours after each charge and 4 hours after each discharge. These break-in cycles were performed at 25°C for all cells.</p> <p> </p> <p><strong>Ageing Conditions:</strong></p> <table align="center"> <caption>Ageing Conditions</caption> <thead> <tr> <th scope="col">Expt</th> <th scope="col">SoC Range</th> <th scope="col">C-rate</th> <th scope="col">Temperature</th> <th scope="col"># of cells</th> <th scope="col">Cell IDs</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, J</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>3</td> <td>D, E, F</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>K, L, M</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, C</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>2</td> <td>D, E</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>F, G, H</td> </tr> </tbody> </table> <p>For cells aged in the 0-30% SoC range, each ageing set consisted of 256 cycles over the 0-30% SoC range (discharge to 2.5 V, charge by passing 1500 mA h (== 0.3*nominal capacity)). C-rates were 0.3C for charge, and 1C for discharge.</p> <p>For cells aged in the 0-100% SoC range, each ageing set consisted of 78 cycles over the full SoC range (discharge to 2.5 V, charge to 4.2 V with CV hold till C/100). C-rates were 0.3C for charge, and 1C for discharge.</p> <p> </p> <p><strong>Reference Performance Tests (RPTs):</strong></p> <p>All cells were characterised at beginning of life (BoL) and after each ageing set using a reference performance test (RPT). The RPT was always performed at 25°C. Two different RPT procedures were used: a longer procedure which was performed after each even-numbered ageing set, and a shorter procedure which was used after each odd-numbered ageing set. Both procedures are detailed below. A CC-CV charge at 0.3C to 4.2 V, 4.2 V till C/100 was performed between each step of the procedures.</p> <p>Long RPT procedure:</p> <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>C/2 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>GITT discharge at 0.5C; 25 pulses with each pulse passing 200 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> <li>GITT discharge at 0.5C; 5 pulses with each pulse passing 1000 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> </ol> <p>Short RPT procedure:</p> <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of C/2. A baseline DC current of C/2 was applied with an HPPC-type profile superimposed on top. This was done for discharge and charge (with voltage limits of 2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of 1C. A baseline DC current of 1C was applied with an HPPC-type profile superimposed on top. This was done for discharge only (with a voltage limit of 2.5 V).</li> </ol> <p> </p> <p><strong>Extracted Data - Main </strong></p> <p>One csv file exists for each cell being tested, summarising the important data extracted from the ageing cycles and the RPTs. This includes:</p> <p>Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>Ageing Cycles: number of ageing cycles the cell has been subject to. *this is <strong>not </strong>equivalent full cycles.</p> <p>Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>Days of Degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>Age Set Average Temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps) using a K-type thermocouple. Units: °C.</p> <p>Charge Throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cummulative total since BoL (not including RPTs). Units: Ah.</p> <p>Energy Throughput: as with "charge throughput", but for energy. Units: Wh.</p> <p>C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT. Units: mAh.</p> <p>C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT. Units: mAh.</p> <p>0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition). Units: Ohms.</p> <p> </p> <p><strong>Extracted Data - Degradation Modes:</strong></p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge.</p> <p>The results of this analysis are saved in the DMA folder, with 4 csv files for each cell, which contain data for all RPTs. The 4 files contain:</p> <p>Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower lithitation fractions of each electrode and the capacity fraction of graphite in the negative electrode.</p> <p>Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>RMSE data: the root-mean-square error of the optimisation function calculated from the residual between the measured and calculated voltage vs capacity profiles.</p> <p> </p> <p><strong>Timeseries data from RPTs:</strong></p> <p>Timeseries datafiles from the Biologic battery cycler which have been exported to csv and sliced for each step of each RPT procedure to help with future use of the data. Files contain [time, voltage, current, charge, temperature] data.</p> <p> </p> <p><strong>Jupyter Notebook:</strong></p> <p>A jupyter notebook has been included to aid futher use of this data. The notebook shows how to load the data into pandas DataFrame objects and provides a couple of example plots to view the datasets.</p> <p> </p> <p><strong>Notes:</strong></p> <p>A faulty electrical connection to cell A of Expt 5 (i.e. one of the cells being aged at 0-100% SoC at 10°C) during RPT4 led to erroneous results for that performance check (as evidenced in the 0.1s resistance value). The faulty electrical connection was fixed prior to subsequent cycling but the RPT was not repeated. We have kept the data collected during this RPT as part of the dataset, so caution should be used when using this specific portion.</p>
Data for article "Assembling diuranium complexes in different states of charge with a bridging redox-active ligand"
<p>This upload contains raw data (NMR, X-Ray, Elemental Analysis, AC & DC SQUID, EPR) files for the article.</p>
Data and Analysis of Reading and Assessment Activities in Moodle
<p><strong>Interactions of reading and assessment activities in Moodle</strong></p> <p>Reading and assessment are elementary activities for knowledge acquisition in online learning. Assessments represented as quizzes can help learners to identify gaps in their knowledge and understanding, which they can then overcome by reading the corresponding text-based course material. Reversely, quizzes can be used to evaluate reading comprehension. In this paper, we ex- amine the interactions between reading and quiz activities using scroll and log data from an online undergraduate course (N=142). By analyzing processes and sequential patterns in user sessions, we identified six session clusters for characteristic reading and quiz patterns potentially relevant for adaptive learning support. Using these session clusters, we further clustered students by their reading and quiz behavior over six time periods within the semester. The results hypothesize a personalization for seven groups of learners characterized by their temporal activity and predominant quiz and reading behavior.</p> <p> </p> <p><strong>Pre-requisites and install instructions</strong></p> <p>1. Make sure Python v3.9 is installed on your system.</p> <p>2. conda env create -f environment.yml</p> <p>3. conda activate analysis</p> <p>4. To replicate the analysis open the file Ananlysis.ipynb and execute the code blocks one by one or all together.</p> <p> </p> <p>`jupyter nbconvert --to python Analysis.ipynb`</p> <p>`jupyter notebook Analysis.py`</p> <p> </p> <p><strong>Files and folders</strong></p> <ul> <li>(File) Analysis.ipynb: Python Notebook containing all applied code blocks applied for data analysis.</li> <li>(File) requirements.txt: List of python modules to be installed to fulfill the requirements of the Analysis.ipynb script.</li> <li>(Folder) data: The folder contains anonymized CSV files for each Moodle database table that was necessary for the data analysis. All data files are text files encoded in UTF-8. The columns are separated with a semicolon (";"), and rows are indicated by line breaks ("\n"). <ul> <li>m_assign.csv: ...</li> <li>m_course_modules.csv: ...</li> <li>m_quiz.csv: ...</li> <li>no_students.csv: ...</li> <li>user_acceptances.csv: ...</li> <li>m_assign_grades.csv: ...</li> <li>m_course_sections.csv: ...</li> <li>m_quiz_attempts.csv: ...</li> <li>scroll.csv: ...</li> </ul> </li> </ul> <p> </p> <p><strong>Publications and citation</strong></p> <p><strong>Publications</strong></p> <ul> <li>Seidel, N., & Menze, D. (2022). Interactions of reading and assessment activities. In S. Sosnovsky, P. Brusilovsky, & A. Lan (Eds.), 4th Workshop on Intelligent Textbooks, 2022 (pp. 64–76). CEUR-WS. http://ceur-ws.org/Vol-3192/</li> <li>Menze, D., Seidel, Ni., & Kasakowskij, R. (2022). Interaction of reading and assessment behavior. In P. A. Henning, M. Striewe, & M. Wölfel (Eds.), DELFI 2022 – Die 21. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. (pp. 27–38). Gesellschaft für Informatik. https://doi.org/10.18420/delfi2022-011</li> </ul> <p><strong>Citation of the dataset</strong></p> <ul> <li>Seidel, Niels, & Menze, Dennis. (2022). Data and Analysis of Reading and Assessment Activities in Moodle (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.730007</li> </ul> <p>The source code and data are maintained at GitHub: <a href="https://github.com/nise/delfi22">https://github.com/nise/delfi22</a></p> <p> </p> <p><strong>Acknowledgments </strong>This research was supported by <em>CATALPA - Center of Advanced Technology for Assisted Learning and Predictive Analytics</em> of the FernUniversität in Hagen, Germany.</p>
Girls in early childhood increase food returns of nursing women during subsistence activities of the BaYaka in the Republic of Congo
<p>Nursing mothers face an energetic trade-off between infant care and work. Under pooled energy budgets, this trade-off can be reduced by assistance in food acquisition and infant care tasks from non-maternal caregivers. Across cultures, children also often provide infant care. Yet the question of who helps nursing mothers during foraging has been understudied, especially the role of children. Using focal follow data from 140 subsistence expeditions by BaYaka women in the Republic of Congo, we investigated how potential support from caregivers increased mothers' foraging productivity. We found that the number of girls in early childhood (ages 4–7) in subsistence groups increased food returns of nursing women with infants (kcal collected per minute). This effect was stronger than that of other adult women, and older girls in middle childhood (ages 8–13) and adolescence (ages 14–19). Child helpers were not necessarily genetically related to nursing women. Our results suggest that it is young girls who provide infant care while nursing mothers are acquiring food – by holding, monitoring, and playing with infants– and, thus, that they also contribute to the energy pool of the community during women's subsistence activities. Our study highlights the critical role of children as caregivers from early childhood.</p>
Data and Source codes: Light alters activity but do not disturb tandem coordination of termite mating pairs
<p>This repository provides access to the tracking data and analysis code used for the manuscript</p> <p>Light alters activity but does not disturb tandem coordination of termite mating pairs</p> <p>by Nobuaki Mizumoto and Thomas Bourguignon</p> <p>Okinawa Institute of Science & Technology Graduate University, Onna-son, Okinawa, Japan</p> <p>published in the Ecological Entomology.<br> </p>
Analyzing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan
<p>#############################################################################################################<br> Datasets supporting the publication:<br> Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults:<br> a case study of Bishkek, Kyrgyzstan.<br> <a href="https://doi.org/10.3390/rs14225790">https://doi.org/10.3390/rs14225790</a></p> <p>-Please refer to the publication for details on the production of each dataset.<br> -Datasets are ordered following the publication figures.<br> -Please cite the publication and this dataset repository when using the data.<br> #############################################################################################################</p> <p>------------------------<br> Structure:<br> File ID<br> -[fields:] description<br> ------------------------</p> <p>KH9_1979_builtup.shp<br> -KH9 1979 built-up area classification</p> <p>S2_2021_builtup.tif<br> -Sentinel-2 2021 built-up area classification.</p> <p>S2_2021_corine_land_cover_class.tif<br> -Sentinel-2 2021 land cover classification in Corine 2018 land-cover classes.</p> <p>S2_KH9_DN_change_aggregated.shp<br> -Proportional DN change aggregated to a 1 km^2 grid for areas ≥50% built-up.</p> <p>building_characteristics.shp<br> -build_count: building count in 500 m square grid cell.<br> -mean_area: mean building size (m^2) in 500 m square grid cell.<br> -median_area: median building size(m^2) in 500 m square grid cell.<br> -cell_coverage: %building coverage of 500 m square grid cell.</p> <p>pleiades_buildings_all.shp<br> -All building detections from Pleiades data. Confidence values are output from the deep learning model.</p> <p>pleiades_buildings_heights.shp<br> -Building detections from the Pleiades data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>wv2_buildings_all.shp<br> -All building detections from WorldView-2 data. Confidence values are output from the deep learning model.</p> <p>wv2_buildings_heights.shp<br> -Building detections from the WorldView-2 data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>trained_rcnn.zip<br> -ArcGIS Pro deep learning model (DLPK) used to extract building footprints.</p>
The rapid and highly parallel identification of antibodies with defined biological activities by SLISY
<p>This is the sequencing data that accompanies the manuscript published in Nature Communications titled "The rapid and highly parallel identification of antibodies with defined biological activities by SLISY".</p>
Data from: Measurement of stress-induced sympathetic nervous activity using multi-wavelength photoplethysmography
<p>The onset of stress triggers sympathetic arousal (SA), which causes detectable changes to physiological parameters such as heart rate, blood pressure, dilation of the pupils and sweat release. The objective quantification of SA has tremendous potential to prevent and manage psychological disorders. Photoplethysmography (PPG), a non-invasive method to measure skin blood flow changes, has been used to estimate SA indirectly. However, the impact of various wavelengths of the PPG signal has not been investigated for estimating SA. In this study, we explore the feasibility of using various statistical and nonlinear features derived from peak-to-peak (AC) values of PPG signals of different wavelengths (green, blue, infrared and red) to estimate stress-induced changes in SA and compare their performances. The impact of two physical stressors, Cold Pressor and Hand Grip, is studied on 32 healthy individuals. The results show that the nonlinear features are the most promising in detecting stress-induced sympathetic activity. TotalSampEn feature was capable of detecting stress-induced changes in SA for all wavelengths, whereas other features (Petrosian, AvgSampEn) are significant (AUC≥0.8$) only for IR and Red wavelengths. The outcomes of this study can be used to make device design decisions as well as develop stress detection algorithms.</p>
NERC MSTRF Sky-Camera Time-Lapse Video for 2008-01-02 15:00 UTC (showing Altocumulus lenticularis clouds revealing Mountain Wave activity).
<p>A time-lapse video showing Altocumulus lenticularis clouds revealing Mountain Wave activity. Notice how the positions of the clouds remain fixed relative to the landscape. This contrasts with the lower-level (Cumulus) clouds, seen at the beginning of the sequence, which move downstream with the wind. This video has been created from images taken by the NERC MST Radar Facility's Sky-Camera, which is located near Aberystwyth in West Wales. The images are freely available, under an Open (UK) Government License, from http://tinyurl.com/nerc-mstrf-sky-camera/ . For an explanation of the atmospheric phenomena that can be seen, download the resource available at http://cedadocs.ceda.ac.uk/1259/ .</p>
Aligning Active Particles Simulation Data
<p>Simulation Data for Vicsek Model and related models, created by aappp software, see: https://github.org/kuersten/aappp/</p> <p>The example simulation data presented here show some of the features of the aappp software</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.