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7,195 results for “SET”
Data set for the journal article: Colloidal-ALD Grown Metal Oxide Shells Enable the Synthesis of Photoactive Ligand/ Nanocrystal Composite Materials
<p>The data for each figure of the main manuscript is included in this folder.</p> <p>Figure 1 is not included as it contains no data.</p> <p>The folder for Figure 2 contains a sub-folder for the EDX and NMR data of 9-ACA/PbS@AlOx. The NMR data was processed by Mestrenova.</p> <p>The folder for Figure 3 contains optical absorption spectrum data of 9-ACA/PbS@AlOx.</p> <p>The folder for Figure 4 contains NMR data which was processed by Mestrenova. It contains the data for 9-ACA/CuInS2@AlOx, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx.</p> <p>The folder for Figure 5 is made of three sub-folders for figure 5A, 5B and 5C. 5A and 5B contain optical absorption for the CuInS2 and CsPbBr3 datasets while 5C contain time resolved data for CsPbBr3.</p> <p>The folder for Figure 6 contains time resolved PL for the as synthesized CsPbBr3, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx. The 9-PTA/CsPbBr3@AlOx data contain two decays that span 200 ns (short) or 13.5 us (long).</p> <p>The folder for Figure 7 contains time resolved PL for the as synthesized 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx. For both samples the data contain two decays that span 200 ns (short) or 13.5 us (long). Also an NMR folder is present with the 1H spectrum for 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx.</p> <p> </p> <p> </p>
All centralising monoids on the set {0, 1, 2}, including their witnesses
<p>This dataset contains code and data to produce a list of all 192 centralising monoids on the set <span>\(\{0,1,2\}\)</span>, including a witness for each of them. This gives a computational verification of the classification of centralising monoids published by Machida and Rosenberg in <a href="https://doi.org/10.1109/ISMVL.2012.50">2012</a> and <a href="https://www.kurims.kyoto-u.ac.jp/~kyodo/kokyuroku/contents/pdf/1846-07.pdf">2013</a>. Furthermore, some of the cpp-files and the context files form supplements to the <a href="https://doi.org/10.5281/zenodo.7653085">bachelor dissertation</a> of the second-named author. The code and the computed data contained in this upload also supplement the <a href="https://dx.doi.org/10.1007/978-3-031-35949-1_8">conference article</a> that appeared in <a href="http://www.kde.cs.uni-kassel.de/icfca2023">ICFCA 2023</a>.</p> <p>A detailed description of the files in the dataset can be obtained from the file cent_mons3_witn.pdf (the code source for this file is given in cent_mons3_witn.tex).</p>
Bots in Software Development: A Systematic Literature Review [Data Set]
<p>This repository contains al the artifacts of the research: Bots in Software Development: A Systematic Literature Review </p>
Image sets used in the development of a connected auto-encoders based approach to separate mixed X-radiographs from double-sided paintings
<p>The following sets of images were used during the development of an algorithm (described in the publication detailed below) designed to separate the mixed X-radiographs from double-sided paintings into two hypothetical X-ray images corresponding to each side of the painting, when visible images of the two sides of the painting are available.</p> <p>The images sets are taken from a painting that is only painted on one side and were used to assess the regularization parameters associated with the separation approach. The details are taken from the visible image and the X-radiograph of Anthony van Dyck’s painting <em>Lady Elizabeth Thimbelby and Dorothy, Viscountess Andover</em> dated to about 1635 and now in the collection of the National Gallery in London (NG6437). See <a href="https://www.nationalgallery.org.uk/paintings/anthony-van-dyck-lady-elizabeth-thimbelby-and-her-sister">https://www.nationalgallery.org.uk/paintings/anthony-van-dyck-lady-elizabeth-thimbelby-and-her-sister</a> for further details of the painting.</p> <p>The code can be downloaded from: <a href="https://github.com/ART-ICT/Xray_Separation_2RGB">https://github.com/ART-ICT/Xray_Separation_2RGB</a> and the algorithm is described in W. Pu, B. Sober, N. Daly, C. Zhou, Z. Sabetsarvestani, C. Higgitt, I. Daubechies and M. Rodrigues, ‘Image Separation with Side Information: A Connected Auto-Encoders Based Approach’, <em>Transactions on Image Processing, </em>2023 </p> <p><strong>All images © The National Gallery, London</strong></p> <p> </p> <p><strong><em>Datasets available: </em></strong></p> <p><strong>NG6437_vis_800pixel_230502.tif</strong>: 800 pixel thumbnail visible image of the entire painting showing the location of the two details used for the algorithm development. This image is derived from a visible image of the whole painting acquired 25 November 2019 (Original file: N-6437-00-000041.tif; 6272 x 5940 pixels).</p> <p><strong>NG6437_xray_800pixel_230502.tif</strong>: 800 pixel thumbnail image of the X-radiograph of the entire painting showing the location of the two details used for the algorithm development. This image is derived from the composite X-radiography of the whole painting created by mosaicking digital scans of the individual sheets of film and then registering the resulting image to the high resolution visible image described above (Original file: N-6437-00-000049.tif; 36847 x 32516 pixels).</p> <p><strong>NG6437_vis_crop_01_230502.tif</strong>: 1543 x 2078 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_01_230502.tif</strong>: 1543 x 2078 pixel detail of the X-radiograph corresponding to NG6437_vis_crop_01_230502.tif. The X-ray images were acquired using sheets of film (27 November 2019) and 16-bit digital scans were then produced (original files: N-6437-00-000047-009 and -014 (each 9539 x 7199 pixels), processed 28 January 2020). This crop is an 8-bit composite image of 2 X-ray plates that had been manually registered to the high resolution visible image described above using Adobe Photoshop.</p> <p><strong>NG6437_vis_crop_02_230502.tif</strong>: 1562 x 2023 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_02_230502.tif</strong>: 1543 x 2078 pixel detail of the X-radiograph corresponding to NG6437_vis_crop_02_230502.tif. The X-ray images were acquired using sheets of film (27 November 2019) and 16-bit digital scans were then produced (original files: N-6437-00-000047-002 and -007 (each 9539 x 7199 pixels), processed 28 January 2020). This crop is an 8-bit composite image of 2 X-ray plates that had been manually registered to the high resolution visible image described above using Adobe Photoshop.</p> <p><strong>NG6437_vis_crop_03_230502.tif</strong>: 2088 x 2088 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_03_230502.tif</strong>: 2088 x 2088 pixel detail taken from the composite X-radiograph described above corresponding to NG6437_vis_crop_03_230502.tif. </p> <p><strong>NG6437_vis_crop_04_230502.tif</strong>: 2088 x 2088 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_04_230502.tif</strong>: 2088 x 2088 pixel detail taken from the composite X-radiograph described above corresponding to NG6437_vis_crop_04_230502.tif. </p> <p> </p>
Data set of two dual-task paradigms to measure listening effort in cochlear implant users
<p>This data set presents the data from the paper by Hendrikse, Dingemanse, & Goedegebure (2022). This study aimed to investigate the feasibility of using listening effort to measure relatively small differences in SNR, as would arise from different hearing-device settings. Listening effort was chosen, because there are indications in literature that listening effort may be more sensitive to differences between hearing-device settings than established speech intelligibility measures. Two behavioral listening effort tests were performed at two signal-to-noise ratios (SNRs) where the intelligibility was high. A sentence final word identification and recall test (SWIRT), and a sentence verification test (SVT) were compared with a group of 18 Dutch CI users. SWIRT measured the ability to recall the final words of sentences after a list of five or seven sentences was presented. The SVT measured the ability and reaction time to determine whether a sentence was true or false. Both tests were conducted in background noise at SNRs +4 dB and +8 dB above the 50% speech perception threshold. The structure of the data files is explained in the README file.</p>
MetTLM TLM waveform set 1
<p>“MetTLM waveform set 1” is a data set consisting of a set of simple mathematically generated waveforms indicative of typical behaviour of TLM measured in the field, it should not be considered representative in any strict sense.</p> <p>The waveforms contained in this data set consists of distinct points placed at equal distance on a time axis, where each point is defined independently.</p> <p><sup>- </sup>The duration is 180 seconds consisting of 3 600 000 points, interspaced by 5·10<sup>-5</sup> s.</p> <p>- The two first rows give the time interval and between each datapoint and the number of points, as suggested in CIE TN 012 (Thorseth et al., 2021)</p> <p>- The first column is the time stamp in seconds,</p> <p>- Each of the consecutive 20 next columns are representing the amplitude of the waveforms.</p>
QLKNN11D training set
<p><strong>QLKNN11D training set</strong></p> <p>This dataset contains a large-scale run of ~1 billion flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/">https://gitlab.com/qualikiz-group/QuaLiKiz/</a>. This dataset was generated with the 'QLKNN11D-hyper' tag of QuaLiKiz, equivalent to 2.8.1 apart from the negative magnetic shear filter being disabled. See <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/QLKNN11D-hyper">https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/QLKNN11D-hyper</a> for the in-repository tag.</p> <p>The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See <a href="https://doi.org/10.1063/1.5134126">https://doi.org/10.1063/1.5134126</a> for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on arXiv <a href="https://arxiv.org/abs/1911.05617">https://arxiv.org/abs/1911.05617</a> and the older dataset on Zenodo <a href="https://doi.org/10.5281/zenodo.3497066">https://doi.org/10.5281/zenodo.3497066</a>. For an application example, see Van Mulders et al 2021<a href="http://https://doi.org/10.1088/1741-4326/ac0d12"> https://doi.org/10.1088/1741-4326/ac0d12</a>, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. For any learned surrogates developed for QLKNN11D, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended.</p> <p>Related repositories:</p> <ul> <li>General QuaLiKiz documentation <a href="https://qualikiz.com">https://qualikiz.com</a></li> <li>QuaLiKiz/QLKNN input/output variables naming scheme <a href="https://qualikiz.com/QuaLiKiz/Input-and-output-variables">https://qualikiz.com/QuaLiKiz/Input-and-output-variables</a></li> <li>Training, plotting, filtering, and auxiliary tools <a href="https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop">https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop</a></li> <li>QuaLiKiz related tools <a href="https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools">https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools</a></li> <li>FORTRAN QLKNN implementation with wrapper for Python and MATLAB <a href="https://gitlab.com/qualikiz-group/QLKNN-fortran">https://gitlab.com/qualikiz-group/QLKNN-fortran</a></li> <li>Weights and biases of 'hyperrectangle style' QLKNN <a href="https://gitlab.com/qualikiz-group/qlknn-hype">https://gitlab.com/qualikiz-group/qlknn-hype</a></li> </ul> <p><strong>Data exploration</strong></p> <p>The data is provided in 43 netCDF files. We advise opening single datasets using <a href="https://xarray.dev/">xarray </a>or multiple datasets out-of-core using <a href="https://www.dask.org/">dask</a>. For reference, we give the load times and sizes of a single variable that just depends on the scan size `dimx` below. This was tested single-core on a Intel Xeon 8160 CPU at 2.1 GHz and 192 GB of DDR4 RAM. Note that during loading, more memory is needed than the final number.</p> <table align="center"> <caption>Timing of dataset loading</caption> <thead> <tr> <th scope="col">Amount of datasets</th> <th scope="col">Final in-RAM memory (GiB)</th> <th scope="col"> <p>Loading time single var</p> (M:SS)</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>10.3</td> <td>0:09</td> </tr> <tr> <td>5</td> <td>43.9</td> <td>1:00</td> </tr> <tr> <td>10</td> <td>63.2</td> <td>2:01</td> </tr> <tr> <td>16</td> <td>98.0</td> <td>3:25</td> </tr> <tr> <td>17</td> <td>Out Of Memory</td> <td>x:xx</td> </tr> </tbody> </table> <p><strong>Full dataset</strong></p> <p>The full dataset of QuaLiKiz in-and-output data is available on request. Note that this is 2.2 TiB of netCDF files!</p>
Data set for the journal article: Site-Specific Protein Ubiquitylation Using an Engineered, Chimeric E1 Activating Enzyme and E2 SUMO Conjugating Enzyme Ubc9
<p>Mutations observed in evolved chimeric E1 variants. Top row (1.X to 4.X) describes rounds of evolutions with respective variants in the round. </p> <p>Residues that appear to be enriched are highlighted with gray fill. Star (★) marks residues subjected to saturation mutagenesis in the round 4.</p>
CA-discharge data set, scripts and raw data
<p>Gauge locations of 295 gauges in Central Asia, including long-term norm discharge, basin outlines and basin characteristics compiled from 3rd-party data. Discharge time series for 135 gauge locations collected from the hydrological yearbooks of Hydrometeorological Organisations in Central Asia. </p> <p>Instructions of how to use the data can be found in the readme document in the folder CA-data-paper-scripts. </p> <p>! Important note: Please do not use the glacier thinning rates extracted from Hugonnet et al., 2021 (https://doi.org/10.1038/s41586-021-03436-z), i.e. features gl_dmdt_km3a and gl_dmdtda_mma. The ice density is not accounted for in our averages. </p>
Minimal data set for "Cohort profile: The ENTWINE iCohort Study, a multinational longitudinal web-based study of informal care"
<p><strong>Title:</strong></p> <p>Minimal Data Set for the Reproduction of Findings in "Elayan et al., Cohort Profile: The ENTWINE iCohort Study, a Multinational Longitudinal Web-Based Study of Informal Care".</p> <p> </p> <p><strong>Study Summary:</strong></p> <p>The data sets provided herein are derived from the ENTWINE iCohort Study, a multinational web-based cohort study employing an intensive longitudinal design. The study integrates a two-wave panel survey (baseline and 6-month follow-up) with optional weekly diary assessments. The cohort comprises caregivers and care recipients from nine countries: the United Kingdom, the Netherlands, Italy, Sweden, Israel, Germany, Greece, Poland, and Ireland. The study aimed to examine the influence of personal, psychological, social, economic, and geographic factors on caregiving experiences.</p> <p>Participants were eligible if they met the following criteria: 1) residency in a participating country; 2) capability to respond to surveys in English, Swedish, German, Dutch, Italian, Greek, Hebrew, or Polish; 3) access to the internet and ability to use it; 4) at least 18 years of age; 5) self-declared cognitive and physical capacity to complete the surveys; 6) either providing care to an adult (aged ≥ 18 years) with a chronic health condition, disability, or other care need, or receiving care from an adult due to similar conditions.</p> <p>The detailed methodology and results of the study can be found in the associated manuscript. For the complete survey questionnaires, please refer to: Morrison V, Zarzycki M, Vilchinsky N, Sanderman R, Lamura G, Fisher O, et al. A Multinational Longitudinal Study Incorporating Intensive Methods to Examine Caregiver Experiences in the Context of Chronic Health Conditions: Protocol of the ENTWINE-iCohort. Int J Environ Res Public Health. 2022;19. doi: <a href="https://doi.org/10.3390/ijerph19020821">10.3390/ijerph19020821</a></p> <p> </p> <p><strong>Data files:</strong></p> <p>The repository contains the following data files:</p> <ol> <li>"cg_minimal_dataset" (available in dta, sav, rds, and xlsx formats): This is a minimal data set containing de-identified and processed data derived from the ENTWINE iCohort Caregiver Baseline Survey. The variables present in this data set are detailed in the associated codebook, "cg_minimal_dataset_codebook".</li> <li>"cr_minimal_dataset" (available in dta, sav, rds, and xlsx formats): This is a minimal data set containing de-identified and processed data derived from the ENTWINE iCohort Care Recipient Baseline Survey. The variables present in this data set are detailed in the associated codebook, "cr_minimal_dataset_codebook".</li> </ol>
TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN temperature and humidity logger data set from Nafingalm
<p><strong>ABSTRACT</strong></p> <p>This data set was collected with a network of Onset HOBO temperature and humidity data loggers of <a href="http://acinn.uibk.ac.at/">ACINN</a> at the Nafingalm, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022).</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Location</strong></p> <p>The temperature and humidity data loggers were located at five different sites at the Nafingalm in the Weer Valley, Tyrol, Austria (see table below). Four sites were over land and one in a small lake, the so-called Nafingsee. At one of these sites an automatic weather station (AWS) was operated (see <a href="https://doi.org/10.5281/zenodo.8172308">DOI: 10.5281/zenodo.8172308</a>). Logger H06 to H32 measured air temperature and air humidity at three sites on two levels (2 and about 0.3 m above ground level) and at one site on one level (2 m above ground level). Logger T01 and T02 measured land surface temperature at two sites. Logger T03 and T04 measured lake water temperature at one site on two levels (0.3 and 1.0 m below lake level). Each logger was equipped with a single temperature/humidity probe. Hence, for each level a separate logger had to be used. Therefore, each data file provided here contains data from a single logger at a single site on a single level. For reasons of redundancy, two sites were equipped with two loggers at 2 m above ground level (main logger and backup logger) in order to fill data gaps in the event of a failure of the main logger. However, data gaps did not occur.</p> <table> <thead> <tr> <th scope="col">Location</th> <th scope="col">Latitude (°N)</th> <th scope="col">Longitude (°E)</th> <th scope="col">Altitude (m MSL)</th> <th scope="col">Parameters</th> <th scope="col">Logger names</th> </tr> </thead> <tbody> <tr> <td>north of the lake at the valley floor at the AWS</td> <td>47.215141</td> <td>11.712628</td> <td>1928</td> <td>air temperature and air humidity on two levels</td> <td>H32 (upper), H26 (lower)</td> </tr> <tr> <td>south of the lake at the valley floor</td> <td>47.212760</td> <td>11.713030</td> <td>1921</td> <td>air temperature and air humidity on two levels, surface temperature</td> <td>H06 (upper main), H07 (lower), H28 (upper backup), T01 (surface)</td> </tr> <tr> <td>in the lake at the valley floor</td> <td>47.213603</td> <td>11.712433</td> <td>1921</td> <td>lake water temperature on two levels</td> <td>T03 (upper), T04 (lower)</td> </tr> <tr> <td>on the slope</td> <td>47.208150</td> <td>11.721200</td> <td>2241</td> <td>air temperature and air humidity on two levels, surface temperature</td> <td>H08 (upper main), H09 (lower), H11 (upper backup), T02 (surface)</td> </tr> <tr> <td>at the peak</td> <td>47.202940</td> <td>11.730160</td> <td>2531</td> <td>air temperature and air humidity on one level</td> <td>H21</td> </tr> </tbody> </table> <p><strong>2. Period</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the temperature and humidity logger data set provided here contains the period from 16 June to 12 September 2022. The time series has a measurement interval of 5 minutes and, depending on the parameter, contains both mean values and instantaneous values.</p> <p><strong>3. Instrument details</strong></p> <p>Air temperature and air humidity were measured with Onset HOBO MX2302 temperature and humidity probes mounted on a pole above the surface in a RS3-B naturally aspirated multi-plate radiation shield. Land surface temperature was measured with Onset HOBO MX2201 temperature probes mounted on a pole at the surface under a home-made double-plate radiation shield. Lake water temperature was measured with Onset HOBO MX2201 temperature probes mounted on a rope under a buoy. Despite the double-plate radiation shield used to protect the land surface temperature measurements from radiation errors, such errors have to be expected, especially at low solar elevation angle in the morning and late afternoon. Therefore, use the land surface temperature data with caution. A detailed description of the sensors and parameters is provided as part of the netCDF file metadata as well as in a PDF file containing the netCDF header extracted with the Linux command ncdump -h.</p> <p><strong>4. Data file</strong></p> <p>The data set is provided in multiple netCDF files together with a data description in multiple PDF files, one for each data logger (teamx_pc22_aws_nafingalm_HOBOID.nc and teamx_pc22_aws_nafingalm_HOBOID_ncdump_output.pdf). Here, HOBOID represents the logger name (see table above).</p> <p><strong>5. Analysis</strong></p> <p>A first analysis of the data was performed in a Bachor thesis (Viebahn, 2023), which is available upon request from the author of this data set.</p> <p><strong>6. Contact</strong></p> <p>Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set.</p> <p><strong>7. References</strong></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubišic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J. Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, 2022: A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. <em>Bulletin of the American Meteorological Society</em>, <strong>103</strong>, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubišić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, 2020: <em>Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment</em>. Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Viebahn, T., 2023: <em>Windregime und Stabilität in einem alpinen Seitental im Sommer: Eine Standortcharakterisierung im Rahmen der TEAMx Vorkampagne 2022</em>. Bachlor thesis, University of Innsbruck, 75 pp.</p>
PANDEM-2 European COVID-19 training data set
<p>The PANDEM-2 COVID-19 European training dataset is a large collection of time series either of real or realistic synthetic (generated) data and indicators associated with the European pandemic response to the COVID-19 pandemic. It is intended to be used for training in pandemic management.</p> <p> </p> <p>This dataset is the result of a data gathering requirement process for pandemic management involving feedback and inputs from several public health and first responder professionals as well as researchers and military personnel directly involved in the European COVID-19 pandemic response. This work is part of the PANDEM-2 project funded by the <em>Horizon 2020 Secure Societies</em> program. To collect this data, an open source software was developed named PANDEM-Source allowing reproducibility and customisation of this dataset. </p> <p> </p> <p>The dataset includes indicators for cases, deaths, hospitalisation, testing and laboratory data including pathogen genomic information, vaccination, non-pharmaceutical interventions, participatory surveillance, social media, flights resources (human and material such as beds or vaccines), and contact tracing activities. When no open available data was found, realistic synthetic data and indicators were generated with the goal of producing a data set to be used for pandemic management training. </p> <p>The project received funding from the European Union’s Horizon 2020 Research and Innovation programme under the Grant Agreement No. 883285. The material presented and views expressed here are the responsibility of the author(s) only. The EU Commission takes no responsibility for any use made of the information set out.<br> References</p> <p> </p> <p> </p>
OneNet Portuguese demonstration - Open Data sets
<p>File containing the open data sets from the Portuguese demonstration of the OneNet project. The file includes the flexibility assets data used for the demonstration, as well as: 1) the data series for the estimation of the accumulated flexibility potential of MV customers (supermarkets) connected at the two substations considered; 2) the consumption and generation forecasts, with generation disaggregated by source; 3) short-circuit current values calculated at the EHV/HV interface level, including the TSO contribution, the DSO contribution and the joint TSO-DSO contribution. </p><p>Scope/objective of the demonstration: Test an optimized procedure for data exchange between the Portuguese DSO and TSO for flexibility and operational planning purposes.</p>
The NANOGrav 15-Year Data Set
<pre><strong>The NANOGrav 15-Year Data Set Public release "v2.1.0" 2025/07/17</strong> <strong>OVERVIEW</strong> -------- This file contains "narrowband" and "wideband" TOAs and timing solutions for the NANOGrav 15-year data set, covering data taken from 2004 to mid-2020 using Arecibo, the Green Bank Telescope (GBT), and the Very Large Array (VLA) with ASP/GASP and PUPPI/GUPPI/YUPPI backend instrumentation. The observations, data reduction, and analysis procedures used to produce these data are described in detail in the accompanying paper, "The NANOGrav 15-year Data Set: Observations and Timing of 68 Millisecond Pulsars" (Agazie et al., 2023, ApJL 951 L9, DOI 10.3847/2041-8213/acda9a, arXiv:2306.16217).<br> This release is available at Zenodo (DOI 10.5281/zenodo.16051178).<br>You can also reference all versions (DOI 10.5281/zenodo.7967584). All *.par and *.tim files are ASCII and are formatted for use with standard pulsar timing packages such as tempo2 and PINT, except for profile template files which are FITS format (narrowband) or python pickle files (wideband).<br><br> For our narrowband results, we have included ASCII space-separated <br>tables of our post-fit timing model residuals for all of our pulsars <br>(both un-whitened and whitened). These are available as both full <br>and epoch-averaged formats. Correlation matrix files are available in three formats (*.txt, *.npz, and *.hdf5). See "description.txt" in both the narrowband and wideband ./correlations subdirectories for more details about these files. Questions about the contents of this data set can be addressed to Joe Swiggum (swiggumj@gmail.com) or comments@nanograv.org. <strong>DIRECTORY AND FILE STRUCTURE (FURTHER DETAILS BELOW)</strong> --------------------------------------------------- ./README This file. ./clock Files for tracing observatory-measured TOAs to clock standards. ./narrowband Directory containing the narrowband data set. Details are provided in README.narrowband in that directory. ./wideband Directory containing the wideband data set. Details are provided in README.wideband in that directory. ./correlations Directory containing the correlation matrix files for both the narrowband and wideband data. Details are provided in the /wideband/ and /narrowband/ subdirectories' description.txt files. <strong>SOFTWARE</strong> -------- This data set requires up-to-date installations of PINT or tempo2. Our original analysis used PINT v0.9.1 and tempo2 v2022.01.1. Up to date versions of these packages, as well as usage information and documentation can be found at the following repositories: PINT https://github.com/nanograv/PINT tempo2 https://bitbucket.org/psrsoft/tempo2 Note that we do not guarantee complete/correct functionality of these timing models in the older original tempo software package. Please also ensure that the clock files you are using cover the full range of the data set. Using the provided clock files (see below) will ensure this. All models included here are based on a generalized least squares (GLS) fit that includes a noise model with covariance between TOAs (ECORR/jitter parameters, if narrowband; RNAMP/RNIDX red noise parameters, if significant), as well as "traditional" EQUAD and EFAC parameters. Additional EFAC parameters for the wideband DM measurements are also included. All noise model parameters are included in the par files. <strong>CLOCK FILES</strong> ----------- The clock files used for our analysis are provided in the clock/ subdirectory. While the standard files distributed with tempo and tempo2 should be consistent with the clock files provided in the current release at the time of writing, this may be a source of inconsistent results in the future. Please see ./clock/README.clock directory for installation instructions. <strong>PLANNED REVISIONS</strong> ----------------- The initial release of the data set contained all fundamental data<br>products needed for pulsar timing analysis: Times of arrival (.tim<br>files), timing models (.par files), standard template profiles,<br>clock correction files, and noise modeling MCMC chains. In v2,<br>parameter correlation matrices were added, as well as alternate versions<br>of timing model parameter files ("NoRedNoise" and "predictive"). In v2.1, <br>post-fit timing residuals for our narrowband data set were made available. <br>A future release will add a number of other useful derived products as <br>mentioned in the paper, including dispersion measure time series. <strong>CHANGE LOG</strong> ----------<br>2025/07/17<br> Addition of the Timing Model Residual files for the <br> narrowband dataset, including overview plots (v2.1.0). 2023/09/19 Addition of "NoRedNoise" and "predictive" par files, correlation matrices, noise modeling chains (v2). 2023/07/01 Correction to tar.gz directory structure (v1.0.1). 2023/06/28 Initial public release (v1).</pre>
General Lake Model-Aquatic EcoDynamics model parameter set for Falling Creek Reservoir, Vinton, Virginia, USA 2013-2019
The General Lake Model (GLM), an open-source, one-dimensional hydrodynamic model, was used to simulate physical, chemical, and biological variables in Falling Creek Reservoir, Vinton, Virginia, USA between 15 May 2013 and 31 December 2019. GLM (v.3.2.0a3) was coupled to the Aquatic EcoDynamics (AED) module library via the Framework for Aquatic Biogeochemical Modeling (FABM). GLM-AED requires three configuration files to run the model. First, the glm3.nml file configures lake metadata (including hypsometry), meteorological driver data, stream inflow and outflow driver data files, and physical response variables (mixing parameters and sediment heat zones). Second, the aed2_20220111_2DOCpools.nml file configures various biogeochemical modules for the simulation of oxygen, carbon, silica, nitrogen, phosphorus, organic matter, and phytoplankton. Third, the aed2_phyto_pars_4Jan2022.nml file configures all parameters pertaining to phytoplankton dynamics. Meteorological data, two surface stream inflow files, a submerged oxygenation inflow file, and outflow file used in this calibration are also included.
REU data set from summer of 2022. Project was designed to understand how crayfish (Faxonius rusticus) respond to chemical cues from largemouth bass predators under different shelter distributions.
Research into predator–prey interactions has focused on the landscape of fear and nonconsumptive effects that result from prey responses. Prey behavior is influenced by predator presence and the location and quality of foraging resources in habitats. These areas have been fruitful, but the role of prey refuges has lagged. We investigated how refuge spatial distribution and quality influence prey behavior. To determine the role of the landscape of safety (LOS) in prey decision-making, we altered spatial relationships between refuges, refuge quality, and predation threats in mesocosms. Mesocosms were constructed such that prey only received predatory chemical cues. We employed a behavioral assay including largemouth bass (Micropterus salmoides (Lacepède, 1802): predator) and virile crayfish (Faxonius rusticus (Girard, 1852): prey). Crayfish shelter use was significantly influenced by quality and spatial relationship of shelters to predatory threats, and the interaction of these two factors. Particularly, crayfish used high-quality shelters more often when located closer to predatory cues than farther away and did not use low-quality shelters more than controls. High-quality shelter usage decreased as threat level (measured by gape ratio) decreased. These results support the idea that prey utilize an LOS, and information contained in these two landscapes may alter behavioral decisions.
Global data set of long-term summertime vertical temperature profiles in 153 lakes
Climate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change.
Biogeochemistry data set for soil waters, streams, and lakes near Toolik on the North Slope of Alaska.
Data file describing the biogeochemistry of samples collected at various sites near Toolik Lake, North Slope of Alaska. Sample site descriptors include a unique assigned number (sortchem), site, date, time, depth, distance (downstream), elevation, treatment, date-time, category, and water type (lake, surface, soil). Physical measures collected in the field include temperature (water, soil, well water), conductivity, pH, average thaw depth, well height, discharge, stage height, and light (lakes). Chemical analysis for the sample include alkalinity; dissolved organic carbon (DOC); inorganic and total dissolved nutrients (NH4, PO4, NO3, TDN, TDP); particulate carbon, nitrogen, and phosphorus (PC, PN, and PP); cations (Ca, Mg, Na, K); anions (SO4 and Cl); silica and oxygen.
Stationary camera observations, set, and environmental data from Shark Bay Marine Park, Western Australia from July 2011 to June 2012
This dataset provides information on stationary video cameras set within the study area from 2011 to 2012, including animals viewed along with relevent environmental and camera data. These data provide insight into teleost communities that utilize various habitats within Shark Bay.
Fish trap catch, set, and environmental data from Shark Bay Marine Park, Western Australia from May 2010 to July 2012
This dataset provides information on fish traps set within the study area from 2010 to 2012, including animals caught, relevent environmental and trap data, animal specific measurements and logging of samples retained. Additionally the dataset contains stable isotope values for individuals that were retained for Stable Isotope Analysis in addition to stomach content data. These data provide insight into teleost communities that utilize various habitats within Shark Bay with further insights into their trophic relationships.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.