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237 results for “disking”
Lake Mendota Microbial Observatory Secchi Disk Measurements 2012-present
The Lake Mendota Microbial Observatory collects routine water clarity measurements alongside their microbial samples. This dataset includes measurements of water clarity collected at the central Deep Hole, collocated with a weather buoy (43°05'58.2"N 89°24'16.2"W). All measurements were collected with handheld Secchi discs. When multiple personnel performed the Secchi disc measurements, the average and standard deviation are reported. To take the Secchi depth, sunglasses are removed and the disc is lowered on the shaded side of the boat. The Secchi depth is the average between where the Secchi disc disappears while lowering it and where it reappears while raising it. Routine microbial observatory sampling continues into the present.
Lake Wingra Exclosure Experiment at North Temperate Lakes LTER: Secchi Disk Depth 2005 - 2008
Starting in late summer 2005, Wisconsin Dept of Natural Resources (WDNR), Dane County, Friends of Lake Wingra (FOLW), and NTL-LTER initiated a 3-year experiment in Lake Wingra to test the response of the native macrophyte community to clearer water produced from a major carp reduction program. This demonstration-scale experiment includes the construction of a 1.0-hectare rectangular carp exclosure with its solid vinyl walls extending from the lake shoreline to a water depth of 2.9 meters. NTL-LTER conducts the routine limnological monitoring of the lake and exclosure and is leading the science evaluation of potential lake restoration activities. The exclosure experiment was terminated in the fall of 2008. The exclosure was removed from Lake Wingra at that time. Sampling is done both within the exclosure and at a control site located nearby in the littoral zone. The sample location within the exclosure is equidistant from the side walls and approximately 75 meters from the shore in a water depth of approximately 2.5 meters. The control site sample location is approximately 75 meters west of the exclosure sample site at the same approximate distance from shore and water depth. Samples are taken at the same time and on the same schedule as the NTL-LTER limnological sampling on Lake Wingra, e.g., biweekly spring through summer, every 4 weeks in the fall, and once during the winter depending on ice conditions. Parameters measured within the exclosure and at the control site include water temperature, dissolved oxygen, secchi depth and chlorophyll-a. Additional parameters measured only within the exclosure include total Kjeldahl nitrogen, nitrate + nitrite nitrogen, ammonia nitrogen, total phosphorus, dissolved reactive phosphorus and dissolved reactive silica. Secchi disk depth is measured within the exclosure and at a nearby control site in the littoral zone . The disk is circular, 20 cm in diameter, and has alternating black and white quadrants. It is lowered using a c
Little Rock Lake Experiment at North Temperate Lakes LTER: Secchi Disk Depth; Other Auxiliary Sample Data 1983 - 2000
The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Secchi Disk data were collected from the treatment and reference basins of Little Rock Lake at one station in the deepest part of each basin. Auxiliary data associated with each sampling event include time of day, air temperature, wind direction and speed, wave height, and cloud cover at the time of sampling. Sampling Frequency: varies - Number of sites: 2
Satellite derived secchi disk depth and other lake and landscape characteristics in Wisconsin, USA, 1991 - 2012
This data supports the following publication: Rose, K.C., S.R. Greb, M. Diebel, and M.G. Turner. Annual precipitation as a regulator of spatial and temporal drivers of lake water clarity. Ecological Applications. The data uses satellite remotely sensed estimates of Secchi disk depth (Landsat imagery), landscape features, and lake characteristics to understand how and why lakes vary and respond to different drivers through time and space. The data were produced by the authors and their collaborators, as acknowledged in the manuscript. The Secchi disk depth data span the time period 1991-2012.
Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Secchi Disk Depth 2001 - 2004
Chemical Limnology data collected for Biocomplexity Project; Landscape Context - Coordinated Field Studies Replicate chemical samples were pumped from the surface water (0.5m depth) and secchi depth was recorded at each lake. Temperature/dissolved oxygen profiles were taken throughout the water column at one meter intervals on all lakes. For more detail see the Water Sampling Protocol. Sampling Frequency: During 2001, temperature/dissolved oxygen profiles and secchi depths were taken twice during the stratified summer period. Chemistry samples were only taken once during the 2001 stratified period. From 2002 through 2004, all chemical and physical water samples were taken once during June (or resampled during the stratified period if June samples were bad). All lakes in which color, DIC/DOC, and chlorophyll samples were taken in 2001 were resampled in 2002 due to error in collection and/or analysis. Number of sites: 62 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year).
FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment
<p>To assess the feasibility of producing FAIR data via the integration of a controlled vocabulary, an ontology, and an ELN, this dataset demonstrates the implementation of a tribological experiment while accounting for as many details as possible. The showcase experiment had a lubricated pin-on-disk arrangement, ran at 15 N normal load and a velocity range of 20 to 170 mm/s. With this dataset, we hope to provide a possible blueprint for FAIR data publication in experimental tribology.</p> <p><a href="http://www.nature.com/articles/s41597-022-01429-9">https://www.nature.com/articles/s41597-022-01429-9</a> - Garabedian, N.T., Schreiber, P.J., Brandt, N., Greiner, C., et al.</p> <p>Quick start with the dataset in README.txt (<em>included in the newest version of the dataset</em>)</p> <p>Abstract: Generating FAIR research data in experimental tribology. Sci Data 9, 315 (2022). Digital solutions for the generation of FAIR (Findable, Accessible, Interoperable and Reusable) data and metadata in experimental tribology are currently lacking, despite the looming challenge of integrating cutting-edge data science techniques – a promising scientific route for any field that often relies on phenomenology and empiricism. Additionally, the broad interdisciplinarity of tribology is probably a main contributing factor for the lack of community-wide data and metadata standards, and the heavy reliance on custom workflows and equipment. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability and reusability of experimental tribological data beyond typical publication practices.</p> <p><a href="http://youtu.be/xwCpRDnPFvs">https://youtu.be/xwCpRDnPFvs</a> - Generating FAIR Research Data in Experimental Tribology - Get Scientific Results Ready for ML</p> <p><a href="https://doi.org/10.5281/zenodo.5720626">https://doi.org/10.5281/zenodo.5720626</a> - FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment</p> <p><a href="https://doi.org/10.5281/zenodo.5720198">https://doi.org/10.5281/zenodo.5720198</a> or <a href="https://github.com/nick-garabedian/TriboDataFAIR-Ontology">https://github.com/nick-garabedian/TriboDataFAIR-Ontology</a> or <a href="https://fairsharing.org/3597">https://fairsharing.org/3597</a> - TriboDataFAIR Ontology</p> <p><a href="https://doi.org/10.5281/zenodo.5720218">https://doi.org/10.5281/zenodo.5720218</a> or <a href="https://github.com/nick-garabedian/SurfTheOWL">https://github.com/nick-garabedian/SurfTheOWL</a> - SurfTheOWL</p> <p><a href="https://kadi4mat.iam-cms.kit.edu/">https://kadi4mat.iam-cms.kit.edu/</a> - Kadi4Mat Virtual Research Environment and Electronic Lab Notebook </p>
Water in the terrestrial planet-forming zone of the PDS 70 disk
<p>This release includes the portion of the JWST-MIRI MRS spectrum of the PDS 70 disk analysed in the paper by Perotti et al. (2023). The original observational data are part of the Guaranteed Time Observation (GTO) program 1282 (PI: Th. Henning) with observation number 66 and will become public on 2 August, 2023 on the MAST database (https://archive.stsci.edu/). This release contains:<br> <br> 1) the full rebinned (4.9-22.5 μm) JWST-MIRI MRS spectrum of PDS 70 presented in Fig. 2 of Perotti et al. (2023);<br> 2) the JWST-MIRI MRS spectrum of PDS 70 in the 6.78-7.36 μm region used for the water line analysis shown in Fig. 3 of Perotti et al. (2023);<br> 3) the Spitzer-IRS low-resolution spectrum observed as part of the Spitzer-IRS GTO program 40679 (PI: G. Rieke) shown in Fig.1 of Perotti et al. (2023). </p> <p>The first dataset consists of one .csv file (1_PDS70_fig2_MIRI_Perotti23.csv) which contains the the 4.9-22.5 μm JWST-MIRI MRS spectrum of PDS 70 presented in Fig. 2 of Perotti et al. (2023). The spectrum is rebinned by averaging 15 spectral points and assign errors σ to the rebinned spectral points assuming a normal error distribution with equal weights for each individual spectral element.<br> <br> The second dataset consists of one .dat file and one python script. One .dat file contains the JWST-MIRI spectrum of PDS 70 in the 6.78-7.36 μm region shown in Fig. 3 of Perotti et al. (2023) where the brightest water emission lines are observed (2_PDS70_fig3_MIRI_Perotti23.dat). The JWST-MIRI continuum-subtracted spectrum and the best-fit water LTE slab model are included. The Python script used to reproduce Figure 3 of Perotti et al. (2023) is also provided (2_script_plot_fig3.py). <br> <br> The third dataset consists of one .dat file (3_PDS70_fig1_IRS_Perotti23.dat) which represents the Spitzer-IRS low-resolution spectrum of PDS 70 shown in Figure 1 of Perotti et al. (2023).</p>
PBG03 Disk pasture meter measurements to estimate plant standing biomass in the Patch-Burn Grazing experiment at Konza Prairie
"PBG" datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This dataset includes both the annual calibration data for the disk pasture meter measurements (PBG031) and the actual disk pasture meter measurements in the PBG experiment (PBG032). Measurements were taken at a total of 64 transects (8 watersheds x 4 sites per watershed x 2 pasture meter transects per site). Each cattle-grazed watershed (designated as of May 2011 C3A, C3B, C3C, and C1A) and the four Shane cattle-grazed watersheds (C1B, C3SA, C3SB, and C3SC) includes 4 plant composition sampling transects (A-D). Pasture meter measurements were taken alo
North Temperate Lakes LTER: Secchi Disk Depth; Other Auxiliary Base Crew Sample Data 1981 - current
Secchi disk depth is measured in the deepest part of each lake for the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout lakes, unnamed lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Fish, Mendota, Monona and Wingra). The disk is circular, 20 cm in diameter, and has alternating black and white quadrants. It is lowered using a calibrated Kevlar rope to minimize stretching. Readings are made on the shaded side of the boat both with (secview) and without (secnview) the aid of a plexiglass viewer. The points at which the disk disappears while being lowered and reappears while being raised are averaged to determine Secchi depth. Auxiliary data include time of day, air temperature, cloud cover, wave height, wind speed and direction and whether the lake was ice covered on the sampledate. Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season for the northern lakes. The southern lakes are similar except that sampling occurs monthly during the fall and typically only once during the winter (depending on ice conditions). Number of sites: 11 More information on NTL’s primary study lakes can be found at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-ntl&identifier=434
Data from "A multi-frequency ALMA characterization of substructures in the GM Aur protoplanetary disk"
<p>Image cubes, self-calibrated visibilities, and self-calibration and imaging scripts for data associated with Huang et al., 2020, "A multi-frequency ALMA characterization of substructures in the GM Aur protoplanetary disk," The Astrophysical Journal, 891, 48, arXiv:2001.11040</p> <p>The raw data are available on the ALMA archive under program IDs 2017.1.01151.S and 2018.1.01230.S (PI: Jane Huang). </p> <p><strong>Scripts:</strong></p> <p>reduction_utils.py: Set of helper functions for self-calibration scripts</p> <p>B4continuumreduction.py: Self-calibration and imaging script for Band 4 (2.1 mm) continuum (GMAurB4continuumfinal.image.fits) from GMAurB4continuum.final.ms.tgz</p> <p>B6continuumreduction.py: Self-calibration and imaging script for Band 6 (1.1 mm) continuum (GMAurB6continuumfinal.image.fits) from GMAurB6continuum.final.ms.tgz</p> <p>HCOp_imaging.py: Script producing GMAur_HCOp_continuumsubtracted.image.fits and GMAur_HCOp_withcontinuum.image.fits from GMAurB6_HCOp.ms.contsub.cvel and GMAurB6_HCOp.ms.cvel, respectively</p> <p><strong>Measurement sets:</strong></p> <p>GMAurB4continuumfinal.ms.tgz: Self-calibrated 2.1 mm continuum visibilities for GM Aur</p> <p>GMAurB6continuumfinal.ms.tgz: Self-calibrated 1.1 mm continuum visibilities for GM Aur</p> <p>GMAurB6_HCOp.ms.cvel.tgz: Self-calibrated HCO<sup>+ </sup>3-2 visibilities without continuum subtraction</p> <p>GMAurB6_HCOp.ms.contsub.cvel.tgz: Self-calibrated HCO<sup>+ </sup>3-2 visibilities with continuum subtraction</p> <p><strong>Images: </strong></p> <p>GMAurB6continuumfinal.image.fits: 1.1 mm continuum image of GM Aur</p> <p>GMAurB4continuumfinal.image.fits: 2.1 mm continuum image of GM Aur</p> <p>GMAur_HCOp_withcontinuum.image.fits: HCO<sup>+ </sup>3-2 image cube toward GM Aur without continuum subtraction</p> <p>GMAur_HCOp_continuumsubtracted.image.fits: HCO<sup>+ </sup>3-2 image cube toward GM Aur with continuum subtraction</p> <p> </p> <p> </p>
Far-infrared to millimeter data of protoplanetary disks: dust growth in the Taurus, Ophiuchus, and Chamaeleon I star-forming regions
<p>This repository contains the data set presented in the manuscript "Far-infrared to millimeter data of protoplanetary disks: dust growth in the Taurus, Ophiuchus, and Chamaeleon I star-forming regions" (Ribas et al. 2017), and includes a table with several sample properties (e.g. stellar properties, Herschel photometry, different spectral indices), spectral energy distributions, Spitzer/IRS and Herschel/SPIRE spectra, the median SEDs of Taurus, Ophiuchus and Chamaeleon I, and the Herschel maps used.</p> <p>ERRATUM: three Chamaeleon I sources (Hn 11, T45a, and WY Cha) were mislabeled in the original version of the manuscript, which resulted in their names, stellar parameters, extinction values, infrared slopes, and silicate feature properties being assigned to incorrect coordinates. Because the photometry and spectroscopy presented in the original article is coordinate- based, the provided SEDs and spectra were also missmatched: the data files labeled Hn 11 in the original manuscript correspond to T45a, those labeled T45a correspond to WY Cha, and those labeled WY Cha correspond to Hn 11. Additionally, due to a mislabeling issue in Manoj et al. 2011, the source formerly labeled UX Cha is actually CHSM 8284. Therefore, stellar parameters and photometry labeled UX Cha in our original manuscript correspond to CHSM 8284 The updated version of the repository fixes the issue both in the sample.csv file and in the individual SED and Spitzer/IRS spectra files. The published erratum is available here: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/abb66e">https://iopscience.iop.org/article/10.3847/1538-4357/abb66e</a>.</p>
Supporting data for "Measurable fractional spin for quantum Hall quasiparticles on the disk"
<p>Supporting data for the manuscript "Measurable fractional spin for quantum Hall quasiparticles on the disk", by T. Comparin, A. Opler, E. Macaluso, A. Biella, A. P. Polychronakos, L. Mazza.<br> If you use these numerical results in a scientific work, please cite the corresponding article [<a href="https://link.aps.org/doi/10.1103/PhysRevB.105.085125">Phys. Rev. B <strong>105</strong>, 085125 (2022)</a>].<br> For additional details, please contact Tommaso Comparin (tommaso.comparin@ens-lyon.fr).</p> <p>We computed the density profile rho(r) for the Laughlin state (with filling 1/m, for m=2,3,4) and for the Halperin 221 state, by means of Monte Carlo simulations. All data are in units of the magnetic length (that is, with lB=1).<br> When present, labels "QH0", "QH1" and "QH2" in the filenames correspond to the case with q=0, q=1 or q=2 quasiholes localized at the origin.</p> <p><br> Folders:</p> <ul> <li>Data_Laughlin contains the Laughlin density-profile data used to compute the spin values in Fig. 1.</li> <li>Data_Halperin221 contains the Halperin 221 density-profile data used to compute the spin values in Fig. 2. Filenames include a label for the type and number of quasiholes: "A" stands for Gamma=A and q=1; "AA" stands for Gamma=A and q=2; "AB" stands for Gamma=AB and q=1; "AABB" stands for Gamma=AB and q=2.</li> <li>Data_Laughlin_boundary contains the Laughlin density-profile data shown in Fig. 6.</li> <li>Data_Halperin221_boundary_A contains the Laughlin density-profile data shown in Fig. 7, for Gamma=A.</li> <li>Data_Halperin221_boundary_AB contains the Laughlin density-profile data shown in Fig. 7, for Gamma=AB.</li> </ul>
The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?
<p>This is a reproduction package for the paper "The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?" by Tazaki et al. (2021). In this repository, you will find the data files used to make figures in the paper. Source codes and scripts are included as well.</p>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
The eclipse of the V773 Tau B circumbinary disk
<p>Data related to the paper "The eclipse of the V773 Tau B circumbinary disk" to be published in A&A. Data includes high contrast imaging using the SPHERE instrument on the VLT, photometric time series data from several ground based observatories and from the TESS satellite, and orbital walker bundles from emcee and orbitize!</p>
Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models - Dataset
<p>Main data used in the following paper: Revaz, T.; Porté-Agel, F. Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3745. https://doi.org/10.3390/en14133745</p>
Radiative Transfer Edge-on Protoplanetary Disk Images
<p>Dataset used to train a Convolutional Autoencoder model to generate synthetic images of edge-on protoplanetary disks. The work is described in "A machine learning framework to predict images of edge-on protoplanetary disks", Telkamps et al. 2022, submitted to AAS. This image dataset was created using the radiative transfer (RT) modeling code MCFOST (Pinte et al. 2006; Pinte et al. 2009). </p>
Test dataset of VLT/SPHERE/IRDIS H2 observation Injected with simulated disks.
<p>Test data sets used in Juillard et. al. (2023) and , Juillard et. al. (2024), used to compare three different algorithms for processing data sets using ADI alone and to compare the three different strategies: RDI, ADI, and ARDI.</p> <p>This test pipeline consists of a total of 60 test data sets composed of five different disk morphologies, injected at three different contrast levels ($10^{-3}$, $10^{-4}$, and $10^{-5}$), into four different observing ADI sequences of stars without any known circumstellar signal, reflecting different observing conditions. </p> <div><strong><u>CONTENT </u></strong></div> <div> </div> <div>You will find in this folder the battery of test dataset in the compressed folder "test_cubes_sphere.zip". It contains 60 folders, one for each test dataset, with two files in each: "angle.fits" and "cube.fits"</div> <div> </div> <div>Additionaly there folders containg the empty cubes, injected disks, star flux, mask that reprensent the location of the apperture where the flux of the disk was integrated to compute contrast. The Jupyter notebook "asses_quality_of_disk_estimate.ipynb" shows how to compare a disk estimate. We also provided an example disk estimate (file 'X_3_2_0.fits') to try out the notebook.</div> <div> </div> <div>Finally, a folder named "Ref_lib_sphere" containes the different set of references frames library used in the publication Juillard et. al. (2024). In this folder, "ref_lib_X" corresponds to the optimal references for testing using empty cube X, "randref" corresponds to "shuffled" (same for every cube), and "randref_nooverlap_X" corresponds to "excluded." </div> <div>In the “Optimal” case, we used the most correlated frames, which is the same selection used in the first series, where we compare ADI, RDI, and ARDI. In the “Shuffled” case, we randomly selected a sample from the reference libraries dedicated to each of our four ADI test cubessequences into one common reference library. In this test, the random selection contains 25% of frames from the “Optimal” reference library. In the “Excluded” case, we selected for each ADI cube a sample only from references dedicated to the three other ADI cubes, creating a selection that excludes optimal references. The PCC computed for each of the three selections of references is presented in Fig. 5 of Juillard et. al. (2024).</div> <div> </div> <div><span><strong>ABOUT THE DATA</strong></span></div> <p>The data sets, obtained through the High-Contrast Data Center (HCDC), were acquired using the Infrared Dual-Band Imager and Spectrograph (IRDIS, Dohlen et al. 2008; Vigan et al. 2010) camera of the Spectro-Polarimetric High-contrast Exoplanet Research coronagraphic system on the Very Large Telescope (VLT/SPHERE, Beuzit et al. 2019). The test data sets all consist of the $H$2 channel from the dual-band $H$23 set. They were chosen to exhibit a diverse range of characteristics, including low Strehl ratio with a 26\degr\ rotation (ID #1), wind-driven halo (ID #2), an unstable speckle field (ID #3), and good Strehl ratio with an 80\degr\ field rotation (ID #4). The raw data processed with the data handling software (Pavlov et al. 2008) of the HCDC (Delorme et al. 2017), which performs dark, flat, and bad pixel correction on a coronagraphic sequence. <br>For future reference, we computed the mean and standard deviation of the Pearson correlation coefficients (PCC) between each unique pair of frames in the ADI cube. The mean PCC are as follows: Cube #1: $\mu = 0.99$; Cube #2: $\mu = 0.97$; Cube #3: $\mu = 0.93$; Cube #4: $\mu = 0.96$, with standard deviations below $0.001$ for all the cubes.</p> <p><br>The injected disks represent a range of scenarios for both debris and protoplanetary disks. As detailed in Juillard et.al 2023, this selection consists of two 75\degr\ inclined disks with varying sharpness levels (A and B), a 45\degr\ inclined disk with two concentric rings (C), a nearly face-on disk with azimuthal flux variation (D), and a hydrodynamical simulation of a disk with embedded spiral structures and a companion (E). <br>The contrast of the injected disks is determined by measuring the integrated flux within a full width at half-maximum (FWHM)-sized aperture, centered at the peak intensity of the disk, and then dividing this value by the integrated flux within an FWHM-sized aperture of the stellar point spread function. However, we made an exception for the synthetic disk E, where we measured the flux at the companion location.</p> <p>The reference frames were selected from a set of archival IRDIS observations taken with the same filter, coronagraph, and exposure time as the test data sets. These reference targets were observed between 2014 December 11 and 2021 June 1, and the raw data were calibrated through the same process as the test data sets. For each test data set, the PCC was calculated between the frames of the data set and the reference targets, excluding any observations of the data set star taken at different epochs. The PCC was calculated within a circular annulus between 0\farcs31 and 0\farcs67, which captures both the dominant speckle region and position of the waffle pattern, used for precise star centering of a coronagraphic sequence (Zurlo et al. 2014), if it was included in the observation. For each frame in the data set, the 300 best correlated reference frames were identified, and those that appeared in this selection for more than 30\% of the data set frames were selected for the final reference library. </p>
[NGC5084] SAUNAS II: Discovery of Cross-shaped X-ray Emission and a Rotating Circumnuclear Disk in the Supermassive S0 Galaxy NGC 5084
<p>The contained FITS files represent the processed Chandra/ACIS X-ray surface brightness maps of the NGC5084 galaxy, observed with Chandra/ACIS and analyzed with the SAUNAS pipeline as described in Borlaff et al. 2024b (https://ui.adsabs.harvard.edu/abs/2024arXiv240810449B/abstract). All the images have the photometric calibrations (in units of photons cm-2 s-1 pixel-1) and have been astrometrically aligned. </p> <div>Each file contains four FITS extensions as detailed below: </div> <div>----</div> <div>EXTENSION NAME TYPE SIZE DETAILS </div> <div>----</div> <div>0 INFO no-data 0 BLANK EXTENSION. <br>1 SB_FLUX float64 512x512 X-RAY SURFACE BRIGHTNESS MAP. [photons cm-2 s-1 pixel-1] <br>2 STD_SB_FLUX float64 512x512 X-RAY SURFACE BRIGHTNESS NOISE MAP [photons cm-2 s-1 pixel-1]<br>3 SNR float64 512x512 SIGNAL-TO-NOISE RATIO [ - ]</div> <div>-----------</div> <div> </div> <div>Use the SNR extension (extension #3) to determine if your source of interest in the SB_FLUX map (extension #1) is statistically significant over the background limit.</div> <div> </div> <div> </div> <div> </div> <div> </div>
AquaMatch Secchi Disk Depth Data from Water Quality Portal: ~1970-2024
This dataset, “AquaMatch Secchi Disk Depth Data from Water Quality Portal ~1970-2024”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“V2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat Secchi Disk Depth dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we can not verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data. This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 10, 1970, to July 1, 2024. The WQP is a data warehouse for water-related data measured or observed within the United States and US Territories managed by the Environmental Protection Agency, United States Geological Survey, and the National Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water Resources Res
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.