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1,742 results for “activity data”
Raw data for Microwave-assisted Condensation Approach for Vanadium Silicate Microspheres and Their Catalytic Activity in Cyclohexene Epoxidation and Ethyl Lactate Oxidation
<p><strong>Specification of affiliations:</strong></p> <ul> <li>David Skoda - Centre of Polymer Systems</li> <li>Kamila Kuzelova - Centre of Polymer Systems</li> <li>Rajendran Blessy Pricilla - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Ales Styskalik - Department of Chemistry, Faculty of Science</li> <li>Tomas Pokorny - Department of Chemistry, Faculty of Science</li> <li>Iaroslav Doroshenko - Department of Chemistry, Faculty of Science</li> <li>Lucie Simonikova - Department of Chemistry, Faculty of Science</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript.</p>
Raw data for Figures in: LAP2alpha facilitates myogenic gene expression by preventing nucleoplasmic lamin A/C from spreading to active chromatin regions, Ferraioli et al., Nucleic Acids Res. 2024
<p>These datasets represent raw data for the preparation of Figures in:</p> <p><span>Ferraioli S, Sarigol F, Prakash C, Filipczak D, <strong>Foisner R</strong>, Naetar N. (2024) </span>LAP2alpha facilitates myogenic gene expression by preventing nucleoplasmic lamin A/C from spreading to active chromatin regions<span>. <em>Nucleic Acids Res.</em>2024 Sep 4:gkae752. doi: 10.1093/nar/gkae752.</span></p>
uncropped western blots for analysis of RPN13 ubiquitylation and NRF1 activation by protein aggregates, as well as source data for qPCR plots and flow cytometry gating and FCS files for agDD-GFP in HeLa or HEK cells
<p>This entry contains uncropped blots for Fig 4D and Fig S4C, Fig. 5B, Fig S5 and Fig S6, and the raw FCS files for Flow Cytometry data in doi.org/10.1101/2024.08.30.610524.</p>
Data Related to Osorio-Forero, Foustoukos, Cardis et al., "Noradrenergic locus coeruleus activity functionally partitions NREMS to gatekeep the NREM-REM cycle"
<p>This Zenodo Upload contains the Transparent Data Files for an updated version of the manuscript currently published in Nature Neuroscience</p> <p>and entitled </p> <p><em>'</em>Infraslow noradrenergic locus coeruleus activity fluctuations control are gatekeepers of the NREM–REM sleep cycle' </p> <p>published by the authors as indicated in the author list.</p>
Longitudinal structural MRI and behavioural data for mice prenatally exposed to maternal immune activation either early or late in gestation
<p>Prenatal maternal immune activation (MIA) is a risk factor for neurodevelopmental disorders. How the gestational timing of MIA-exposure differentially impacts downstream development remains unclear. The data presented here includes longitudinal structural magnetic resonance imaging (MRI) data from weaning to adulthood, and behavioural testing in adolescence and adulthood on C57BL/6 mice exposed to MIA induced by the viral mimetic, polyinosinic:polycytidylic acid (poly I:C) either early (gestational day [GD]9) or late (GD17) in gestation. </p> <p>The data published here was collected and analyzed for the following publication, where more details can be found (Guma et al., 2021 https://doi.org/10.1016/j.biopsych.2021.03.017). Briefly, we found that early MIA-exposure was associated with accelerated brain volume increases in adolescence/early-adulthood that normalized in later adulthood, in regions including the striatum, hippocampus, and cingulate cortex. Similarly, alterations in anxiety-like, stereotypic, and sensorimotor gating behaviours observed in adolescence normalized in adulthood. In contrast, MIA-exposure in late gestation had less impact on anatomical and behavioural profiles. </p> <p>In addition to the univariate analyses described above, we also undertook a multivariate analysis (partial least squares) to relate imaging and behavioural variables for the time of greatest alteration, i.e. adolescence/early adulthood. We further explored the molecular underpinnings of region-specific alterations in early MIA-exposed mice in adolescence using RNA sequencing (data for differentially expressed genes in the anterior cingulate cortex, dorsal hippocampus, and ventral hippocampus are available via the original publication https://doi.org/10.1016/j.biopsych.2021.03.017 for a separate cohort of adolescent mice prenatally exposed to MIA or vehicle at GD9). </p> <p>In this dataset, you will find a total of <strong>376 preprocessed structural MRIs</strong> (in MINC format) acquired at postnatal day ~21, ~38, ~60, and ~90 in mice exposed to poly I:C or vehicle control (0.9% sterile saline) at GD9 or 17. These are T1-weighted, manganese enhanced (50mg/kg 24 hours pre-scan), structural images at 100 micron isotropic resolution acquired on a 7 Tesla Bruker Biospec 70/30; matrix size of 180 x 160 x 90; 14.5 minutes, 2 averages, using 5% isoflurane for induction, 1.5% for maintenance of anesthesia during the scan. T1-weighted scans were preprocessed by stripping native coordinates, flipping left-right to maintain fidelity, denoising, correcting inhomogeneities in the bias field using the N4 algorithm, and registering in LSQ6 alignment (i.e. 6 degrees of freedom are allowed for imagine alignment: translations and rotations along x, y, and z dimensions). The demographics information for each animal is included in the <strong>demographics.csv</strong> file. </p> <p>Behavioural tests were performed following the postnatal day 38 and 90 scans in all animals with a 2 day rest period. These include: open field test, marble burying test, three chambered social approach, and prepulse inhibition. The attentional set shifting task was also performed following the final behavioural test in the postnatal day 90 wave of behaviours. The data for all of these tests is presented in its own individual .csv spreadsheet and includes data for both the timepoints evaluated.</p> <p>Included in this data set are the structural MRIs in MINC format, the behavioural .csv data, and a <strong>readme.txt</strong> file providing further detail on the data structure and content, and on how to interpret the data column titles. DICOMS are also available for the structural MRI data, as are the raw (not-preprocessed) MINC files, available upon request to the authors. </p> <p>Finally, the authors would like to acknowledge the funding bodies that supported the completion of this work including the Canadian Institute for Health Research, the Fonds de Recherche du Québec en Santé, and the Healthy Brains for Healthy Lives at McGill University.</p>
Fig. 1. Camera trap data was collected from 14 in Terrestrial Activity Patterns Of Wild Cats From Camera-Trapping
Fig. 1. Camera trap data was collected from 14 protected areas within Thailand. NP = national park; WS = wildlife sanctuary; NH = non-hunting area.
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>
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>
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>
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>
The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data
<p>In our work, we implemented the ABC-model and could show that one assay for measuring the openness of enhancers is sufficient. Further, we propose a generalised calculation of the ABC-score, which describes enhancer activity in a gene-specific manner, and which includes all TSS, without requiring any additional data. We combined our implementation of the ABC-score with an approach to quantify TF binding affinity into STARE: a framework to derive TF affinities to genes. STARE was also designed for potential application on single-cell data. You can find the code in our <a href="https://github.com/schulzlab/stare">GitHub repository</a> and more details in our <a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtad062">publication</a>.</p> <p>We provide the data for the validation of our ABC-implementation on two CRISPR-screens. We also provide the results of our analysis of single-cell data of the human heart with STARE. All data is in hg19.</p> <p>Content:</p> <ul> <li>CRISPRi_screens: One file for each CRISPRi-screen with interactions that were used to plot precision-recall curves, containing columns for different ABC scoring versions.</li> <li>Enformer: Similar to the CRISPRi_screens, but containing columns for different calculations for Enformer's predicted expression change upon in silico mutagenesis of the enhancer region.</li> <li>K562_CandidateEnhancer: K562 enhancer with the 4th column for enhancer activity, one file for each activity representation that was measured.</li> <li>K562_ABC_Predictions: Regular ABC-scores and generalised ABC-scores for each activity measurement. The files contain all scored interactions for a 10MB window, without any cut-off. We also included the results of the implementation of the ABC-score of Fulco et al. (2019).</li> <li>STARE_Hocker_*: Whole STARE output for human heart single-cell data, one for regular ABC, generalised ABC, generalised ABC with average Hi-C matrix and one based on co-accessibility analysis. All approaches were run with a 5 MB window (except for GeneralisedABC500kb), the ABC-based runs with a score cut-off of 0.02. Each folder contains two subdirectories, one for the ABC-scoring and one for the Gene-TF affinity matrices. The 'ABC_output' also contains a GeneInfo file for each cell type, summarising different attributes per gene.</li> <li>INVOKE_Hocker_*: Folder with the input and output of INVOKE (see https://github.com/schulzlab/tepic), based on the STARE runs. CS genes stands for cell type-specific genes, defined as genes with a z-score across cell types of ≥ 2 and TPM ≥ 0.5. The INVOKE commands were as follows: <ul> <li>Rscript INVOKE.R --dataDir=<TF-Gene matrix> --outDir=<out_path> --response=Expression --regularization=E --performance=TRUE --outerCV=10 --seed=1234</li> </ul> </li> </ul> <p>Importantly, the results are based on data from the following publications:</p> <ul> <li>CRISPRi-screens: <ul> <li>Gasperini, Molly, Andrew J. Hill, José L. McFaline-Figueroa, Beth Martin, Seungsoo Kim, Melissa D. Zhang, Dana Jackson, et al. “A Genome-Wide Framework for Mapping Gene Regulation via Cellular Genetic Screens.” <em>Cell</em> 176, no. 1–2 (January 2019): 377-390.e19. https://doi.org/10.1016/j.cell.2018.11.029.</li> <li> <p>Schraivogel, Daniel, Andreas R. Gschwind, Jennifer H. Milbank, Daniel R. Leonce, Petra Jakob, Lukas Mathur, Jan O. Korbel, Christoph A. Merten, Lars Velten, and Lars M. Steinmetz. “Targeted Perturb-Seq Enables Genome-Scale Genetic Screens in Single Cells.” <em>Nature Methods</em> 17, no. 6 (June 2020): 629–35. https://doi.org/10.1038/s41592-020-0837-5.</p> </li> <li> <p>Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” <em>Nature Genetics</em> 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</p> </li> </ul> </li> <li>Enformer model: Avsec, Žiga, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, and David R. Kelley. “Effective Gene Expression Prediction from Sequence by Integrating Long-Range Interactions.” <em>Nature Methods</em> 18, no. 10 (October 2021): 1196–1203. https://doi.org/10.1038/s41592-021-01252-x.</li> <li>K562 predictions and average Hi-C matrix: Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” <em>Nature Genetics</em> 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</li> <li>Hi-C matrix for K562 predictions: Rao, S. et al. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell, 159(7), 1665–1680</li> <li>STARE and INVOKE runs: Hocker, J. D. et al. (2021). Cardiac cell type–specific gene regulatory programs and disease risk association. Science Advances, 7(20), eabf1444</li> <li>H3K27ac HiChIP for STARE runs: Anene-Nzelu, C. G. et al. (2020). Assigning Distal Genomic Enhancers to Cardiac Disease–Causing Genes. Circulation, 142(9), 910–912</li> <li>INVOKE software: Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction Schmidt et al., Nucleic Acids Research 2016; doi: 10.1093/nar/gkw1061</li> </ul> <p> </p>
Data for: Skin bacterial microbiome diversity predicts lower activity levels in female, but not male, guppies, Poecilia reticulata
<p>While the link between the gut microbiome and host behaviour is well established, how the microbiomes of other organs correlate with behaviour remains unclear. Additionally, behaviour–microbiome correlations are likely sex-specific because of sex differences in behaviour and physiology, but this is rarely tested. Here, we tested whether the skin microbiome of the Trinidadian guppy, Poecilia reticulata , predicts fish activity level and shoaling tendency in a sex-specific manner. High-throughput sequencing revealed that the bacterial community richness on the skin (Faith's phylogenetic diversity) was correlated with both behaviours differently between males and females. Females with richer skin-associated bacterial communities spent less time actively swimming. Activity level was significantly correlated with community membership (unweighted UniFrac), with the relative abundances of 16 bacterial taxa significantly negatively correlated with activity level. We found no association between skin microbiome and behaviours among male fish. This sex-specific relationship between the skin microbiome and host behaviour may indicate sex-specific physiological interactions with the skin microbiome. More broadly, sex specificity in host–microbiome interactions could give insight into the forces shaping the microbiome and its role in the evolutionary ecology of the host.</p>
Data from: Relating pupil diameter and blinking to cortical activity and hemodynamics across arousal states
<p>Arousal state affects neural activity and vascular dynamics in the cortex, with sleep associated with large changes in the local field potential (LFP) and increases in cortical blood flow. We investigated the relationship between pupil diameter and blink rate with neural activity and blood volume in the somatosensory cortex in male and female unanesthetized, head-fixed mice. We monitored these variables while the mice were awake, during periods of rapid eye movement (REM), and during non-rapid eye movement (NREM) sleep. Pupil diameter was smaller during sleep than in the awake state. Changes in pupil diameter were coherent with both gamma-band power and blood volume in the somatosensory cortex, but the strength and sign of this relationship varied with arousal state. We observed a strong negative correlation between pupil diameter and both gamma-band power and blood volume during periods of awake rest and NREM sleep, though the correlations between pupil diameter and these signals became positive during periods of alertness, active whisking, and REM. Blinking was associated with increases in arousal and decreases in blood volume when the mouse was asleep. Bilateral coherence in gamma-band power and in blood volume dropped following awake blinking, indicating a 'reset' of neural and vascular activity. Using only eye metrics (pupil diameter and eye motion), we could determine the mouse's arousal state ('Awake', 'NREM', 'REM') with greater than 90% accuracy with a 5-second resolution. There is a strong relationship between pupil diameter and hemodynamics signals in mice, reflecting the pronounced effects of arousal on cerebrovascular dynamics.</p>
Data from: Non-breeding sites, loop migration and activity patterns over the annual cycle in the Lesser Grey Shrike Lanius minor from a western edge of its range
<p>Raw data from three tracked individuals. Two were tracked with light geolocators (22UL and an incomplete track of 22UH) and one (16KN) with GDL3-PAM multi-sensor logger. All produced by Swisss Ornithological Insitute.</p>
Data from: Beaver activity and red squirrel presence predict bird assemblages in boreal Canada
<p class="MsoNormal">Wetlands and predation in boreal ecosystems play essential roles throughout the breeding season for bird assemblages. We found a positive association of beaver activity and a negative influence of American red squirrels (<em>Tamiasciurus hudsonicus</em>) on bird assemblages. We used a multispecies hierarchical model to investigate whether bird communities differ between two major wetland habitats in boreal Canada: beaver ponds and peatland ponds. In addition to including variables such as forest cover and latitude, we adopted a structural equation model approach to estimate the occupancy of American red squirrels and its potential influence on bird communities. Using automated recording stations deployed at 50 ponds, we detected 96 bird species in 2018 and 2019. Bird species were grouped into four taxonomic guilds according to their habitat successional requirements: early successional species, late successional species, generalists, and wetland species. Beaver ponds harbored higher species richness, a pattern driven primarily by early successional species. The occupancy of almost a quarter of the species was lower in the presence of red squirrels. Late successional species responded positively to the cover of forest surrounding the pond. Our results highlight the value of considering acoustic data of red squirrels to quantify habitat quality in boreal forests. We conclude that beaver activity shapes bird assemblages through modification of their habitat, and that some bird guilds are associated negatively with the presence of American red squirrels.</p>
Predicting daily activity time through ecological niche modeling and microclimatic data
<p><span>1. </span><span>Climate temporality is a phenomenon that affects species' activity and distribution patterns across spatial and temporal scales. Despite the global availability of microclimatic data, their use to predict activity patterns and distributions remains scarce, particularly at fine temporal scales (e.g., < month). Predicting activity patterns based on climatic data may allow us to foresee some of the consequences of climate change, particularly for ectothermic vertebrates. </span></p> <p><span>2. </span><span>The Gila monster exhibits marked daily and seasonal activity patterns linked to physiology and reproduction. Here we evaluate if ecological niche models fitted using microclimate data can predict temporal activity patterns using the Gila monster (<em>Heloderma suspectum</em>) as a study system. Further, we identified if the activity patterns are related to physiological constraints.</span></p> <p><span>3. </span><span>We used dated occurrences from museum specimens and human observations to generate and test ecological niche models using minimum-volume ellipsoids. We generated hourly microclimatic data for each occurrence site for ten years using the NicheMapR package. For ecological niche modeling, we compared the traditional seasonal approach versus a daily activity pattern strategy for model construction. We tested both using the omission rate of independent observations (citizen science data). Finally, we tested if unimodal and bimodal activity patterns for each season could be recreated through ecological niche modeling and if these patterns followed known physiological constraints.</span></p> <p><span>4. </span><span>The unimodal and bimodal activity patterns previously reported directly from tracking individuals across the year were recovered by using niche modeling and microclimate across the species' geographical range. We found that upper thermal tolerances can explain the daily activity patterns of this species. </span></p> <p><span>5. </span><span>We conclude that ecological niche models trained with microclimatic data can be used to predict activity patterns at fine temporal scales, particularly on ectotherm species of arid zones coping with rapid climate modifications. Further, the use of fine temporal scale variables can lead to a better niche delimitation, enhancing the results of any research objective that uses correlative models.</span></p>
Simulation data for: "Unique Amphipathic a-helix Drives Membrane Insertion and Enzymatic Activity of ATG3"
<p>Simulation data from Nishimura et al. (2023), "Unique Amphipathic a-helix Drives Membrane Insertion and Enzymatic Activity of ATG3".</p> <p>The dataset contains the MD simulations executed for the Atg3/LC3/lipid membrane system, both in the WT and 5W-mutated variants.</p> <p>More information can be found in the README file and in Table 1 of the cited paper.</p> <p> </p>
ScienceDex guides
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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.