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5,506 results for “variability”
Bright Southern Variable Stars in the bRing Survey
<p>The corresponding data and plots for the 353 variables in the comprehensive survey of bright stars from the bRing telescopes. The paper has been accepted to the Astrophysical Journal Supplemental Series (July 27, 2019). An arXiv pre-print article is now available.</p> <p>If these data are to be used in future works, we ask that a short list of the bRing team be included as co-authors. Please contact Samuel Mellon (smellon@ur.rochester.edu) or Matthew Kenworthy (kenworthy@strw.leidenuniv.nl) for details.</p> <p>Paper Abstract:</p> <p>Besides monitoring the bright star <em>β</em> Pic during the near transit event for its giant exoplanet, the <em>β</em> Pictoris b Ring (bRing) observatories at Siding Springs Observatory, Australia and Sutherland, South Africa have monitored the brightnesses of bright stars (<em>V</em> ≃ 4--8 mag) centered on the south celestial pole (<em>δ</em> ≤ -30∘) for approximately two years. Here we present a comprehensive study of the bRing time series photometry for bright southern stars monitored between 2017 June and 2019 January. Of the 16762 stars monitored by bRing, 353 of them were found to be variable. Of the variable stars, 80% had previously known variability and 20% were new variables. Each of the new variables was classified, including 3 new eclipsing binaries (HD 77669, HD 142049, HD 155781), 26 <em>δ</em> Scutis, 4 slowly pulsating B stars, and others. This survey also reclassified four stars based on their period of pulsation, light curve, spectral classification, and color-magnitude information. The survey data were searched for new examples of transiting circumsecondary disk systems, but no candidates were found.</p>
Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations
<p>This dataset contains the reduced Io spectra used in the paper "Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations" (doi: 10.1016/j.icarus.2024.116151). There are 150 spectra, spanning from 2001 to 2023. These spectra are described in Table 1 of the paper.</p> <p>The spectra in the data file are listed in date order. For each spectrum, we first provide the date (YYMMDD format) and the mean Io central longitude at the time of the observation. This is then followed by the spectrum. Column 1 is the wavelength, in units of microns. Column 2 is the Io spectrum, which has been divided by a Callisto spectrum, flattened in order to correct for any residual continuum slope, and then normalized such that the continuum level is 1. </p>
The Time Variable Ionospheric Electric Field (TiVIE) Model Outputs v 1.0
<p>These are the outputs for the TiVIE model v 1.0 produced by Maria-Theresia Walach, Lancaster University for the publication Walach, M.-T., and Grocott, A. (submitted 2024). </p>
Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet
<p>This dataset can be used to reproduce the figures created in Waling et al. 2024, "Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet." Each figure has its own script which can be executed.<br><br></p>
Supplemental data for: Mapping lifestyle factors in blood glucose variability in adolescents with Type 1 Diabetes Mellitus- A pilot study
<div> <p>The dataset was used in the paper “Mapping lifestyle factors in blood glucose variability in adolescents with Type 1 Diabetes Mellitus- A pilot study”. The article is currently under review for publication. DOI to be inserted.</p> </div> <div> <p>A data-in-brief article is to be published to give in-depth information about the data collected to improve reproducibility "Dataset for: Lifestyle Factors and Blood Glucose Variability in Adolescents with Type 1 Diabetes Mellitus". DOI to be inserted. </p> <p> </p> <p>The aim of the study was to assess whether adolescents with T1D in Ireland meet current nutrition and physical activity (PA) guidelines and to explore the impact of nutrition and PA on glycaemic variability (GV). The dataset includes continuous glucose monitoring (CGM) data, dietary intake records, and PA metrics, providing a comprehensive view of the participants' glucose levels and associated lifestyle behaviours.</p> </div>
Data for publication "The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zürich"
<p>Please see README.md for a description of this package. </p> <p>This work was funded by the European Union's Horizon 2020 research and innovation programme, grant agreement number 101037319, named Pilot Applications in Urban Landscapes - towards integrated city observatories for greenhouse gases (PAUL) and is known as ICOS Cities.</p> <p> </p>
The largest variable UCHII identified in GLOSTAR and CORNISH surveys
<p>The GLOSTAR and CORNISH 5 GHz images for the 38 variable sources identified in this work. The beam size is 1.5\arcsec for each image in the two surveys. Each figure is centered at the position of the identified \hii\ region. </p>
Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars
<p>Supporting data for peer-reviewed publication entitled: 'Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars', published in A&A. For the purpose of open access, the authors have applied a CC BY licence to the author accepted manuscript version and made it publicly available: <a href="https://arxiv.org/abs/2410.12726">https://arxiv.org/abs/2410.12726</a></p> <p>Evolutionary models and stability window calculations courtesy of Jermyn et al. 2022 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ac4e89">10.3847/1538-4357/ac4e89</a>) are publicly available via: <a href="https://github.com/adamjermyn/conv_trends">https://github.com/adamjermyn/conv_trends</a></p> <p>TESS full-frame image data are publicly available from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute (STScI): <a href="https://archive.stsci.edu/missions-and-data/tess">https://archive.stsci.edu/missions-and-data/tess</a></p> <p>TESS light curves (provided in this repository) were extracted using the publicly available tglc (Han & Brandt 2023; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/acaaa7">10.3847/1538-3881/acaaa7</a>) software package: <a href="https://github.com/TeHanHunter/TESS_Gaia_Light_Curve">https://github.com/TeHanHunter/TESS_Gaia_Light_Curve </a></p> <p>SLF variability parameters (provided in this repository; cf. Tables 1 and 2 of the paper) were obtained using GP regression with the publicly available celerite2 (Foreman-Mackey et al. 2017; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/aa9332">10.3847/1538-3881/aa9332</a>) software package: <a href="https://celerite2.readthedocs.io/en/latest/">https://celerite2.readthedocs.io/en/latest/</a> and confidence intervals were obtained using the publicly available pymc3 (Salvatier et al. 2016; <a href="https://doi.org/10.7717/peerj-cs.55">https://doi.org/10.7717/peerj-cs.55</a>) software package: <a href="https://github.com/pymc-devs/pymc">https://github.com/pymc-devs/pymc</a></p> <p>This research was supported in part by the National Science Foundation (NSF) under Grant Number NSF PHY-1748958; the Research Foundation Flanders (FWO) with grant agreement numbers 1286521N, 11F7120N, and V411621N; UK Research and Innovation (UKRI) in the form of a Frontier Research grant under the UK government's ERC Horizon Europe funding guarantee (SYMPHONY; grant number: EP/Y031059/1); a Royal Society University Research Fellowship (URF; grant number: URF\R1\231631); and the KU Leuven Research Council (grant number C16/18/005: PARADISE).</p>
Global 32-4 km variable-resolution mesh for the MPAS-Atmosphere model
<div> <div> <div>This mesh was created by a collaboration between the Department of Energy’s Water Cycle and Climate Extremes Modeling (WACCEM) project and the</div> <div>Mesoscale and Microscale Meteorology (MMM) Laboratory at the National Science Foundation National Center for Atmospheric Research (NSF/NCAR).</div> <div> </div> <div>The mesh contains 1,830,914 horizontal grid cells. The circular refinement region has a radius of approximately 20 degrees. The 4-32km grid-spacing range aims to achieve convection-permitting resolution in the high-resolution domain and resolution sufficient for the jet stream and mid-latitude wave activities (Lu et al., 2015) in the low-resolution domain.</div> <div> </div> <div>The netcdf file "x8.1830914.grid.nc" includes the variables defining the global unstructured grid for the MPAS model as described in the <a href="https://mpas-dev.github.io/files/documents/MPAS-MeshSpec.pdf">MPAS Mesh Specification</a>. Following the other MPAS mesh data, we provide graph.info.part.* files necessary for the Message Passing Interface (MPI) parallelism. For example, a simulation using 1024 MPI ranks will use graph.info.part.1024. For the number of MPI tasks not provided in this dataset, a user needs to create a new partitioning file using the METIS tool and the graph.info file as described in the MPAS user guide (available <a href="https://mpas-dev.github.io/atmosphere/atmosphere_download.html" target="_blank" rel="noopener">here</a>).</div> <div> </div> </div> </div> <div>This data will also be available from the <a href="https://mpas-dev.github.io/atmosphere/atmosphere_meshes.html">MPAS mesh website</a>.</div> <div> </div> <div>The mesh generation is supported by the U.S. Department of Energy Office of Science Biological and Environmental Research (BER) as part of the Regional and Global Model Analysis Program Area. We acknowledge the use of computational resources of the National Energy Research Scientific Computing Center (NERSC). The Pacific Northwest National Laboratory is operated for the Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830.</div>
Catalogue of XMM-SUSS variable sources
<p>Catalogue of variable XMM-Newton Optical Monitor (XMM-OM) Serendipitous Ultraviolet Sky Survey (XMM-SUSS) sources, identified as variable in one or more passbands from lightcurves derived from SUSS data. </p>
Alpine ice sheet erosion potential aggregated variables
<p>These data contain domain-integrated and time-integrated model output variables presented in the reference below or otherwise relevant to last glacial cycle glacier erosion in the Alps.</p> <p><strong>Reference:</strong></p> <ul> <li>J. Seguinot and I. Delanay. Last glacial cycle glacier erosion potential in the Alps, <em>submitted to Earth Surface Dynamics Discussions</em>, 2021.</li> </ul> <p><strong>File names:</strong></p> <pre><code>alpero.{1km|2km}.{epic|grip|md01}.{cp|pp}.agg.nc</code></pre> <ul> <li>Horizontal resolution: <ul> <li><em>1km</em>: 1 km horizontal resolution</li> <li><em>2km</em>: 2 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li><em>epic</em>: EPICA ice core temperature forcing</li> <li><em>grip</em>: GRIP ice core temperature forcing</li> <li><em>md01</em>: MD01-2444 core temperature forcing</li> </ul> </li> <li>Precipitation forcing: <ul> <li><em>cp</em>: constant precipitation</li> <li><em>pp</em>: palaeo-precipitation reduction</li> </ul> </li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Coordinate variables: <ul> <li><em>x</em>: X-coordinate in Cartesian system</li> <li><em>y</em>: Y-coordinate in Cartesian system</li> <li><em>lon</em>: longitude</li> <li><em>lat</em>: latitude</li> <li><em>time</em>: time</li> <li><em>age</em>: model age</li> <li><em>z</em>: elevation band midpoints</li> <li><em>d</em>: distance along transect</li> </ul> </li> <li>Glacier erosion variables: <ul> <li><em>coo2020_cumu</em>: Cook et al. (2020) cumulative glacial erosion potential</li> <li><em>coo2020_rate</em>: Cook et al. (2020) domain total volumic erosion rate</li> <li><em>coo2020_hyps</em>: Cook et al. (2020) erosion rate geometric mean</li> <li><em>coo2020_rhin</em>: Cook et al. (2020) rhine transect erosion rate</li> <li><em>her2015_cumu</em>: Herman et al. (2015) cumulative glacial erosion potential</li> <li><em>her2015_rate</em>: Herman et al. (2015) domain total volumic erosion rate</li> <li><em>her2015_hyps</em>: Herman et al. (2015) erosion rate geometric mean</li> <li><em>her2015_rhin</em>: Herman et al. (2015) rhine transect erosion rate</li> <li><em>hum1994_cumu</em>: Humphrey and Raymond (1994) cumulative glacial erosion potential</li> <li><em>hum1994_rate</em>: Humphrey and Raymond (1994) domain total volumic erosion rate</li> <li><em>hum1994_hyps</em>: Humphrey and Raymond (1994) erosion rate geometric mean</li> <li><em>hum1994_rhin</em>: Humphrey and Raymond (1994) rhine transect erosion rate</li> <li><em>kop2015_cumu</em>: Koppes et al. (2015) cumulative glacial erosion potential</li> <li><em>kop2015_rate</em>: Koppes et al. (2015) domain total volumic erosion rate</li> <li><em>kop2015_hyps</em>: Koppes et al. (2015) erosion rate geometric mean</li> <li><em>kop2015_rhin</em>: Koppes et al. (2015) rhine transect erosion rate</li> </ul> </li> <li>Other variables: <ul> <li><em>cumu_sliding</em>: cumulative basal motion</li> <li><em>glacier_time</em>: total ice cover duration</li> <li><em>warmbed_time</em>: temperate-based ice cover duration</li> <li><em>glacier_area</em>: glacierized area</li> <li><em>volumic_lift</em>: volumic bedrock uplift</li> <li><em>warmbed_area</em>: temperate-based ice cover area</li> </ul> </li> </ul> <p><strong>Data format:</strong></p> <p>The data use compressed netCDF format. For quick inspection I recommend ncview. Conversion to GeoTIFF (and other GIS formats) can be achieved with e.g. GDAL::</p> <pre><code>gdal_translate NETCDF:filename.nc:variable filename.variable.tif</code></pre> <p>The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. To convert all variables to separate files use:</p> <pre><code>gdalinfo $filename | grep NETCDF | cut -d '=' -f 2 | egrep -v '(lat|lon|time_bounds)' | while read sub do gdal_translate $sub ${filename%.nc}.${sub##*:}.tif done</code></pre> <p>Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata. Also see glacial cycle <a href="https://doi.org/10.5281/zenodo.1423160">aggregated</a> and <a href="https://doi.org/10.5281/zenodo.1423175">continuous</a> variables.</p> <p><strong>Changes:</strong></p> <ul> <li>Version 2: <ul> <li>Add variable for glacierized area within 100-m elevation band.</li> <li>Use 100-m instead of 10-m elevation bands for erosion rate.</li> </ul> </li> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>
Category Theory Framework for Variability Models with Non-functional Requirements @ CAiSE 21
<p><strong>Your can watch this video in my Youtube channel:</strong></p> <p><strong><a href="https://youtu.be/rX50Q3fpMZE">https://youtu.be/rX50Q3fpMZE</a></strong></p> <p><strong>This is a Live Conference Presentation, please access and cite the published version of the respective publication:</strong></p> <p><strong><a href="https://doi.org/10.1007/978-3-030-79382-1_24">https://doi.org/10.1007/978-3-030-79382-1_24</a></strong></p> <p>In Software Product Line (SPL) engineering one uses Variability Models (VMs) as input to automated reasoners to generate optimal products according to certain Quality Attributes (QAs). Variability models, however, and more specifically those including numerical features (i.e., NVMs), do not natively support QAs, and consequently, neither do automated reasoners commonly used for variability resolution. However, those satisfiability and optimisation problems have been covered and refined in other relational models such as databases. Category Theory (CT) is an abstract mathematical theory typically used to capture the common aspects of seemingly dissimilar algebraic structures. We propose a unified relational modelling framework subsuming the structured objects of VMs and QAs and their relationships into algebraic categories. This abstraction allows a combination of automated reasoners over different domains to analyse SPLs. The solutions’ optimisation can now be natively performed by a combination of automated theorem proving, hashing, balanced-trees and chasing algorithms. We validate this approach by means of the edge computing SPL tool HADAS.</p>
Data base of cycles 1 and 2 of biometric variables of fuzzy model for assessing the development of the radish crop
<p>This data represent the fuzzy model developed of a Rule-Based System (RBS) to evaluation the development of the radish crop in two production cycles, for the irrigation depth at 100% of evapotranspiration. This RBS represents the function <span class="math-tex">\(f:\mathbb{R}\rightarrow\mathbb{R}^{10}\)</span>, where the domain is represented by the Days After Sowing (DAS), and counterdomain is represented by the ten biometric variables, denominated: Number of Leaves (NL), Root Length (RL), Bulb Diameter (BD), Bulb Length (BL), Green Root Weight (GRW), Green Leaf Weight (GLW), Green Bulb Weight (GBW), Dry Root Weight (DRW), Dry Leaf Weight (DLW). </p>
Remote and Local Processes Controlling Decadal Sea Ice Variability in the Weddell Sea
<p>These datasets are based on the 270-yr simulation results of CTR and SAOWED experiments, which include annual average of atmospheric and ocean variables used to make figures in a paper by Morioka and Behera (2021).</p>
Synthetic Escherichia coli mixture samples with variable coverage
<p>This dataset contains the synthetic mixture samples and reference sequences - as well as the appropriate metadata - that were originally used in the 2021 revision of the mSWEEP manuscript.<br> <br> There are 87 samples in total, each containing 100bp paired-end Illumina sequencing reads from 10 different <em>Escherichia coli </em>strains from 10 different lineages. The number of reads is set so that the sequencing coverage of the individual strains varies between 50x and 0.10x and sums up to 100x.</p>
Database of the assessment of two instructional design variables in verbal reasoning and mathematical reasoning courses from the perspective of a Peruvian pre-university center students
<p>These are the data obtained from 4 evaluations made to a sample of 630 students of a Peruvian pre-university center. First, two study variables were evaluated: teaching sequence compliance and the student's educational need according to the perspective of 315 students of the verbal reasoning course. Second, the same study variables were assessed in the remaining 315 students of the mathematical reasoning course. This information is being used in research to obtain an academic degree and later to make a publication of a scientific article.</p> <p>For the treatment of these data, inferential statistics was used through the software R version 3.4.4 (2018) The R Foundation for Statistical Computing.</p>
Dataset of five years of in-situ and satellite derived chlorophyll a concentrations and its spatiotemporal variability in the Rotorua Lakes, New Zealand
<p><strong>rotorua_chl_fields_2015-2020.nc</strong> is a time series of 283 <em>Chl</em> fields of 13 of the lakes derived from Sentinel-2 MSI images with a regionalised parametrization of the C2RCC algorithm at 60 m pixel resolution. It also includes C2RCC and Idepix masks as well as a shoreline-and-shallow-water-buffer for flexible quality flagging.</p> <p><strong>rotorua_chl_spatial_variability.tif</strong> is a GeoTIFF that illustrates the representativeness of each grid cell for the <em>Chl</em> distribution in each lake and thus indicates recurring spatial patterns. The file contains three bands. Each band shows the relative frequency (in %) which <em>Chl</em> concentration was found near the median, or upper or lower quartile, respectively. The intervals around the median and quartiles are 5% to either side.</p> <p><strong>rotorua_insitu_chl_2015-2019.csv</strong> contains 831 in situ <em>Chl</em> measurements from 12 of the lakes collected between 2015 and 2019. The majority of these measurements (802) have been taken as part of the monthly Bay of Plenty lake water quality monitoring programme, in which 11 lakes are monitored. The data set also contains samples from field work under the <em>Eye on Lakes</em> project (University of Waikato) obtained by one of the authors (MKL). These 29 samples also include two measurements at Lake Rotokakahi, which is not part of the monthly monitoring program.</p> <p><strong>shoreline_shallow_water_buffer.zip</strong> contains a shapefile with polygons of the valid water pixels of all lakes to remove areas contaminated by bottom reflectance in remote sensing products. Each lake has a 120 m shoreline buffer to avoid mixed land-water pixels to reduce adjacency effects. It further excludes lake areas shallower than the 95%-quantile of all Secchi depth measurements of the Bay of Plenty lake water quality monitoring programme.</p>
Resarch data for common faults tested on a variable-speed propane-charged heat pump on heating mode
<p>Experimental data of common faults emulated on a 10 kW water-to-water variable-speed heat pump charged with propane. The faults emulated are evaporator fouling, compressor valve leakage, liquid line restriction and refrigerant overcharge. The faults are tested with 10 kW and 12 kW load demand.</p> <p>This data can be used to develop fault detection and diagnosis systems.</p>
Data for `Identifying New Pulsating Variables and Eclipsing Binaries Using TESS Data'
<p>This study presents a series of surveys using TESS data to identify new δ Scuti and γ Doradus stars, as well as eclipsing binaries with pulsating components. Preliminary catalogs of newly discovered variables are being made publicly available to encourage community use as the project progresses. Please visit this website for updates. </p> <p> 1. New δ Scuti stars, γ Doradus stars, and eclipsing binaries from a subset of 709,000 selected AF-type stars observed by TESS. Version 4.5 is an update to Version 4.0 (the first release for Part IV), while Part III was provided in Version 3.0.</p> <p> The attached CSV file <strong>NewVar_AF50w_R2.csv,</strong> contains the catalog of newly identified variables from Phases I and II of the survey (i.e. R2 includes R1 from Version 3.0). The accompanying file <strong>ms2RNAAS_2025AF50w_IV.pdf</strong> is the initial draft describing the Phase-II identifications. The published paper can be found in 2025 <em>Research Notes of the AAS</em>, <strong>Vol. 9, No. 1, 23</strong> (Zhou 2025, https://iopscience.iop.org/article/10.3847/2515-5172/adaf8c (ADS code: 2025RNAAS...9...23Z). These unpublished discoveries have been compiled into catalogs of δ Scuti and γ Doradus stars available at Zenodo https://zenodo.org/records/17096360 (DOI: <a href="https://doi.org/10.5281/zenodo.17096360">10.5281/zenodo.17096360</a>) -- which currently include <strong>118,410</strong> and <strong>41,622</strong> entries, respectively, as of the release date.</p> <p> 2. New Pulsating Variable Stars and Eclipsing Binaries around BL Cam (added in Version 2.0)</p> <p> The attached CSV file NewVar_BLCam_R2D.csv provides a catalog of the new variables with the following columns: TIC ID, Simbad main ID, Gaia DR3 ID, RA_deg Dec_deg(J2000), Tmag, Teff, SpType, Lum, logg, mass, VarType_Notes</p> <p> 3. New Pulsating Variable Stars and Eclipsing Binaries near NGC 6302 (Version 1.0)</p> <p> Check corresponding files containing the string "NGC 6302".</p>
R code and data for "Flake selection and scraper retouch probability: an alternative model for explaining Middle Paleolithic assemblage retouch variability"
<p>R code and data used for "Flake selection and scraper retouch probability: an alternative model for explaining Middle Paleolithic assemblage retouch variability" (Archaeological and Anthropological Sciences, Volume 10, Issue 7, pp 1791–1806)</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.