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7,195 results for “SET”
Hyperspectral dataset: Ceramic tiles in dry and underwater setting
<p>The objective of this experiment was to observe the changes in spectral information of a calibration tile when placed underwater. For this purpose, the SPECIM IQ hyperspectral camera was used which operates in Visible and near infra-red regions (400-1000nm) with a spectral resolution FWHM of 7nm resulting in 204 bands in a data cube.</p> <p>To establish a baseline for comparison, data was acquired before submerging the calibration tile into water. Then the tile was immersed in water and data was recorded under two settings i.e., outdoor and indoor. The latter provided a controlled environment where a halogen lamp was used as the light source. Lastly, the tile was removed and data was captured both outside and inside once it had dried off. The data was annotated to enable comparison of similar data points under different conditions.</p> <p> </p>
Performance Analysis of LoRa in Indoor Settings: A Data Descriptor
<p>This work is a description of the experiment conducted to understand the reception<br> of LoRa in closed environments, such as a building.<br> The experiment was carried out on 04/05/2023, in the NW1 building of University of<br> Bremen. The data’s primary goal is to provide researchers with the understanding of<br> factors such as distance, obstacles, interference with other wireless devices that<br> dictates LoRa’s performance.</p>
Data Set: Promoting Open Science in Test-Driven Software Experiments
<p>Data set for reproduction purposes (tabular data is stored using Apache's Parqet format).</p>
Data set for "Neural mechanisms underlying uninstructed orofacial movements during reward-based learning tasks"
<p>Data set for Li, Nakano, et al. (2023) Neural mechanisms underlying uninstructed orofacial movements during reward-based learning behaviors. Current Biology, https://doi.org/10.1016/j.cub.2023.07.013</p> <p><br> The file "Li_Nakano_et_al_detaset.zip" (~45.4 GB) is a compressed version of the "Li_Nakano_et_al_detaset" folder. The uncompressed folder (~76.4 GB) houses the data and some analytical codes used in the study. Upon extraction, the folder reveals two subfolders, "Raw data" and "Source data," and a 'README' text file providing further instructions or information.</p>
Supporting information for: Integrating morphological, molecular, and cytogenetic data for F2 sea turtle hybrids diagnosis revealed balanced chromosomal sets
<p><span>Hybridization could be considered part of the evolutionary history of many species. The hybridization among sea turtle species on the Brazilian coast is atypical and occurs where nesting areas and reproductive seasons overlap. Integrated analysis of morphology and genetics is still scarce, and there is no evidence of the parental chromosome set distribution in sea turtle interspecific hybrids. In this study, chromosome markers previously established for pure sea turtle species were combined with morphological and molecular analyses aiming to recognize genetic composition and chromosome sets in possible interspecific hybrids initially identified by mixed morphology. The data showed that one hybrid could be an F<sub>2</sub> individual among <em>Caretta caretta </em>× <em>Eretmochelys</em> <em>imbricata</em> × <em>Chelonia</em> <em>mydas</em>, and another is resulting from backcross between <em>C. caretta </em>× <em>Lepidochelys</em> <em>olivacea</em>. Native alleles of different parental lineages were reported in the hybrids, and, despite this, it was verified that the hybrid chromosome sets were still balanced. Thus, how sea turtle hybridism can affect genetic features in the long term is a concern, as the implications of the crossing-over in hybrid chromosomal sets and the effects on genetic function are still unpredictable. </span></p>
300 K time-resolved photoluminescence on sample set- Buffer temperature series-Sample C3206
<p>Raw data of time-resolved photoluminescence measurements on the sample C3206 (buffer grown at 870 °C) in the buffer temperature sample set</p>
Correction of 4sU induced quantification bias of Spt6 data set
<p>This data set contains the GRAND-SLAM output of the Spt6 data set (https://zenodo.org/record/4275956) after correcting the 4sU induced quantification bias using the correction approach described <a href="https://www.biorxiv.org/content/10.1101/2023.04.21.537786v1">here</a> and implemented in the <a href="https://www.nature.com/articles/s41467-023-39163-4">grandR package</a>.</p> <p>The zip file contains the full GRAND-SLAM output, the *.tsv.gz file is the GRAND-SLAM output table. The RData file contains the grandR object as analyzed in the original original Spt6 data set (see respective zenodo repository).</p> <p> </p> <p> </p> <p> </p>
Data Set Accompanying "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding Thermodynamics of Mixed Surfactant Systems"
<p>This data set accompanies "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding<br> Thermodynamics of Mixed Surfactant Systems" by Colin K. Egan and Ali Hassanali. It includes example GROMACS<br> input files for all simulations analyzed in the paper, as well as example data sets and analysis scripts. See<br> https://doi.org/10.26434/chemrxiv-2023-h11k5 for the preprint manuscript.</p>
Ibn ʿAsākir and His History of Damascus (Data Set)
<p>This release pertains to a series of seven blog posts that investigate the working methods of ʿAlī Ibn ʿAsākir (d. 571/1176) in his <em>The History of Damascus</em> (<em>Tārīkh madīnat Dimashq</em>, hereafter <em>TMD</em>). The data set is oriented to the following questions:</p> <ol> <li> <p>The <em>TMD</em>’s <em>isnād</em>s name individuals who, in one way or another, transferred information to Ibn ʿAsākir. When we read the <em>isnād</em>s en masse, what can we learn about his reliance on the people he cites as his direct informants? From approximately how many people did Ibn ʿAsākir obtain information directly? How vast was his source base? What can we learn about his reliance on different people?</p> </li> <li> <p>How does Ibn ʿAsākir cite his sources within <em>isnād</em>s? What vocabulary does he use and what might it mean? Did he acquire the information on his own, or as part of a group; through oral communication, in writing or via a mix of the two? </p> </li> <li> <p>Previous historians have written about a ‘library’ used by Ibn ʿAsākir and listed books and book titles that it might have contained. When author names appear within <em>isnād</em>s, what do the names signify for Ibn ʿAsākir? What can <em>isnād</em>s reveal about his reliance on books? How does Ibn ʿAsākir cite books themselves? </p> </li> <li>Outside of <em>isnād</em>s, how does Ibn ʿAsākir cite books and other written materials</li> </ol> <p>To generate our data, we relied on the version of Ibn ʿAsākir’s text contained in the <a href="https://zenodo.org/record/6808108">2022.1.6</a> release of the OpenITI corpus through Zenodo. This same release provided the basis for the text reuse alignments provided here.</p> <p>We used the <a href="https://zenodo.org/record/7687795">2022.2.7</a> version of the corpus in just one case: the ‘IsnadFractions_ML’ data.</p>
Lineage And Outgroup Reference Set
<p>This is a reference set that includes genomes of specific lineages as well as outgroups for the building of the phylogenetic trees that are produced by the MAGMA pipeline</p>
Date set for the manuscript: Exploring the temporal dynamics of methane ebullition in a subtropical freshwater reservoir
<p>The dataset supports the findings of the manuscript entitled ‘Exploring the Temporal Dynamics of Methane Ebullition in a Subtropical Freshwater Reservoir’. The results of the manuscript are based on continuous in-situ measurements conducted at Passaúna Reservoir, located in the southern part of Brazil (South America). The monitoring was carried out from 2017 to 2020, during which a comprehensive set of environmental variables was obtained from various studies. The primary objective of the manuscript was to comprehend the temporal dynamics of ebullition flux in the reservoir. Therefore, time series data of ebullition and relevant environmental variables were analyzed across different time scales, ranging from minutes to daily resolutions. Statistical and data driven models were tested to predict ebullition at varying time scales, considering the influence of environmental variables. The findings are discussed in the manuscript.</p> <p>Here, xlsx files and Matlab scripts are provided. The xlsx files contain time series data for environmental variables ('Environmental_Variables_TimeSeries') and ebullition flux ('Ebullition_Flux_TimeSeries'). Separate sheets were utilized for different time intervals, namely 5 minutes (dt=5min), 10 minutes (dt=10min), 1 hour (dt=1hr), and 1 day (dt=1d). Explanations and units are provided within the column labels, while 'NaN' denotes missing data.</p> <p>Matlab scripts for all the empirical models that were tested, as outlined in the manuscript's supporting information (Tables S1 and S2), have been made available (detailed in the following table). These scripts were developed using MatLab R2023a.</p> <table> <tbody> <tr> <td> <p>File (.m)</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p><strong>Main scripts</strong></p> </td> </tr> <tr> <td> <p>TableS1_Empirical_Models_Literature</p> </td> <td> <p>Empirical models from the literature tested. (Table S1 in the manuscript)</p> </td> </tr> <tr> <td> <p>TableS2_FirstPart_Reffited_Models</p> </td> <td> <p>Empirical models from the literature refitted. (Table S2 in the manuscript)</p> </td> </tr> <tr> <td> <p>TableS2_SecondPart_New_Models</p> </td> <td> <p>New empirical models implemented. (Table S2 in the manuscript)</p> </td> </tr> <tr> <td> <p>TableS2_SecondPart_New_Models_GAM_Timescales</p> </td> <td> <p>Generalized additive models (GAM) applied to predict ebullition at different timescales. (Table S2 in the manuscript).</p> </td> </tr> <tr> <td> <p>PredictedR2_GAM_models</p> </td> <td> <p>Calculate the predicted R-squared for GAM.</p> </td> </tr> <tr> <td> <p><strong>Functions (need for the main scripts)</strong></p> </td> </tr> <tr> <td> <p>load_TimeSeries</p> </td> <td> <p>Import time series of environmental variables from excel sheets into MatLab.</p> </td> </tr> <tr> <td> <p>load_EbullitionTS</p> </td> <td> <p>Import time series of ebullition from excel sheets into MatLab.</p> </td> </tr> <tr> <td> <p>units_description</p> </td> <td> <p>Description containing units of the variables.</p> </td> </tr> <tr> <td> <p>Bin_xdata</p> </td> <td> <p>Creates data binning of X and Y based on X data.</p> </td> </tr> <tr> <td> <p>RelativeError</p> </td> <td> <p>Calculate the relative error of accumulated flux between measured and simulated.</p> </td> </tr> <tr> <td> <p>ANN5Neurons_Retrained_Deshmukh2014</p> </td> <td> <p>Trained Artificial Neural Network based on the ANN proposed by Deshmukh et al. (2014).</p> </td> </tr> <tr> <td> <p>ANN_Passauna_20Neurons</p> </td> <td> <p>Trained Artificial Neural Network with the addition of more environmental variables.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Financial support</strong></p> <p>The field measurements were financed by the German Federal Ministry of Education and Research (BMBF, Grant 02WGR1431A), in the framework of the research project MuDak-WRM (https://www.mudak-wrm.kit.edu). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. Tobias Bleninger received productivity stipend from the National Council for Scientific and Technological Development (CNPq, grant no. 312211/2020-1, call no. 09/2020). Michael Männich received productivity stipend from the National Council for Scientific and Technological Development (CNPq, grant no. 308744/2021-7, call no. 04/2021). Andreas Lorke received financial support from the German Research Foundation (DFG, grant number LO1150/16-1).</p>
Data set for "Electrical detection of the flat band dispersion in van der Waals field-effect structures"
<p>Data set for "Electrical detection of the flat band dispersion in van der Waals field-effect structures" paper published in doi:10.1038/s41565-023-01489-x</p>
Agriculture Futures and Climate Data Set
<p>This is the second release.</p> <p>The agriculturefuturedata.mat includes the realized volatility series of corn, cotton, palm, wheat, and soybean futures.</p> <p><br> The climatedata.mat includes the average air pollution variables, weighted average air pollution variables, weighted average weather variables, attention to climate change variables, and attention to extreme weather variables.</p>
Data set for the publication " Evaluation of the Accuracy and Frequency Response of Medium-Voltage Instrument Transformers under the Combined Influence Factors of Temperature and Vibration"
<p>This is dataset for paper published:</p> <p>Agazar, M.; Istrate, D.; Pradayrol, P. Evaluation of the Accuracy and Frequency Response of Medium-Voltage Instrument Transformers under the Combined Influence Factors of Temperature and Vibration. <em>Energies</em> <strong>2023</strong>, <em>16</em>, 5012. https://doi.org/10.3390/en16135012</p>
MYRIAD – Hazard Event Sets (MYRIAD-HES)
<p>A global multi-hazard event set database, spanning from 2004 to 2017, which includes eleven hazards (coldwaves, heatwaves, droughts, earthquakes, extreme wind events, floods, landslides, tropical cyclones, tsunamis, volcanic eruptions, and wildfires).</p> <p>These multi-hazards event sets have been compiled using the MYRIAD – Hazard Event Sets Algorithm (MYRIAD-HESA).</p> <p>If you have used MYRIAD-HES in your work, please cite our paper:</p> <p><strong>Claassen, J.N., Ward, P.J., Daniell, J. <em>et al.</em> A new method to compile global multi-hazard event sets. <em>Sci Rep</em> 13, 13808 (2023). https://doi.org/10.1038/s41598-023-40400-5</strong></p> <p><strong>Data Description </strong></p> <p>The multi-hazard event sets are provided in a <em>.csv</em> file, where the events are defined in the ‘Event’ column, and each row corresponds to a single hazard in an event. The following table describes the columns present in MYRIAD-HES:</p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Format</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Event</p> </td> <td> <p>"Event" + #</p> </td> <td> <p>The event number of a unique event. Rows with the same event number contain the hazard information of the individual hazards in that event</p> </td> </tr> <tr> <td> <p>Hazard</p> </td> <td> <p>name</p> </td> <td> <p>The hazard type</p> </td> </tr> <tr> <td> <p>code</p> </td> <td> <p>"Acronym" + #</p> </td> <td> <p>The unique hazard code</p> </td> </tr> <tr> <td> <p>starttime</p> </td> <td> <p>yyyy-mm-dd</p> </td> <td> <p>The start time of the hazard</p> </td> </tr> <tr> <td> <p>endtime</p> </td> <td> <p>yyyy-mm-dd</p> </td> <td> <p>The end time of the hazard</p> </td> </tr> <tr> <td> <p>Intensity</p> </td> <td> <p>varies, NaN if not Intensity information is known </p> </td> <td> <p>The intensity of the hazard if applicable</p> </td> </tr> <tr> <td> <p>Unit</p> </td> <td> <p>varies, NaN if not Intensity information is known </p> </td> <td> <p>The unit of the intensity</p> </td> </tr> <tr> <td> <p>Geometry</p> </td> <td> <p>Well-Known Text (WKT)</p> </td> <td> <p>The geometry of the hazard footprint</p> </td> </tr> </tbody> </table> <p> </p>
Development Settings
<p>The development settings used in the publication " Imaged Based Clinical Evaluation of Suction Blister Wounds".</p> <p>Instructions for use:</p> <p>1. Open Adobe Lightroom Classic</p> <p>2. Click on File -> Import Profiles and Presets in the head menu of lightroom.</p> <p>3. Choose the files downloaded from here.</p> <p>4. Apply the development setting to images using the preset sidebar.</p>
Cognitive development among children in a low-income setting: cost-effectiveness analysis of a maternal nutrition education intervention in rural Uganda
<p><span>Inadequate nutrition and insufficient stimulation in early childhood can lead to long-term deficits in cognitive and social development. Evidence for policy and decision-making regarding the cost of delivering nutrition education is lacking in low and middle-income countries (LMIC). In rural Uganda, we conducted a cluster-randomized controlled trial (RCT) examining the effect of a maternal nutrition education intervention on developmental outcomes among children aged 6–8 months. This intervention led to significantly improved cognitive scores when the children reached the age of 20–24 months. When considering the potential for this intervention's future implementation, the desired effects should be weighed against the increased costs.</span><span> This study therefore aimed to assess the cost-effectiveness of this education intervention compared with current practice.</span><span> Health outcome data were based on the RCT. Cost data were collected via interviews with researchers involved in processing the intervention. This study considered a healthcare provider perspective for an 18-month time horizon.</span><span> The control group was considered as the current practice for the future large-scale implementation of this intervention. </span><span>A cost-effectiveness analysis was performed, including calculations of incremental cost-effectiveness ratios (ICERs). In addition, uncertainty in the results was characterized using one-way and probabilistic sensitivity analyses. The ICER for the education intervention compared with current practice was USD ($) 16.50 per cognitive composite score gained, with an incremental cost of </span><span>$265.79 and an incremental cognitive composite score of 16.11. The sensitivity analyses indicated the robustness of these results. The ICER was sensitive to changes in cognitive composite score and the cost of personnel, but the education intervention can be considered cost-effective compared with the current practice. The outcome of this study, including the cost analysis, health outcome, cost-effectiveness, and sensitivity analysis, can be useful to inform policymakers and stakeholders about effective resource allocation processes in Uganda and possibly other LMIC.</span></p>
Chroma training, test, and validation sets.
<p>Column <strong>pdb</strong> contains the PDB codes for entries used in training Chroma, while column <strong>split</strong> designates whether the corresponding entry was part of the training set (value <strong>train</strong>), the test set (value <strong>test</strong>), or the validation set (value <strong>validation</strong>).</p>
Data set used in article: On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach
<p>Step-wise pitch simulation of two wind turbines interacting using SOWFA. More information in the paper.</p>
(Data Set) Shielding Factors for a Fission-Powered Mars Exploration Rover
<p>Data Set to Accompany Technical Paper: </p> <p>A. BENDOYRO et al., Shielding Factors for a Fission-Powered Mars Exploration Rover, 2023</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.