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51,102 results for “analysis”
mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis
<p>All the data for 'mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis'</p> <p>sim.zip is stimulated data for intensity cutoff 0.05. simxcms.csv is peak intensity profiles for their simulated peaks.</p> <p>sim3.zip are simulated data for normal/leading/tailing peaks with tailing factor of 1, 0.8, and 1.5, respectively.</p> <p>All the csv files begin with sim3 are extracted peaks list from the sim3.zip with corresponding data analysis software.</p> <p>csv.zip recorded the m/z, retention time, intensity, and compounds name for simulated compound for each condition (sim.zip and sim3.zip).</p> <p>sep1.mzML: simulation for 8 isomers with similar m/z while different retention times. 7 peaks are non baseline separation peaks. Peaks profile is saved in spe1.csv file.</p> <p>xcms.csv, mzmine.csv, openms.csv: peaks found in sep1.mzML by xcms, mzmine 4.5 and openms, respectively.</p> <p>R code: <a href="https://github.com/yufree/democode/blob/master/meta/simfin.R">https://github.com/yufree/democode/blob/master/meta/simfin.R</a></p> <p>Website of mzrtsim package: https://yufree.github.io/mzrtsim/</p>
Hail Event on 2022-06-28 in Locarno-Monti (TI), Switzerland: Drone Photogrammetry Imagery, Mask R-CNN Model and Analysis Data of Hailstones
<p>This hail data collection belongs to a drone hail survey performed on 2022-06-28 in Locarno-Monti (TI, Switzerland). The supercell reached the location around 07:50 UTC in the morning. Only one photogrammetry flight could be performed and thus no estimation of the hail melting process is available. The orthophoto is masked to ignore parts where detection of hail is unwanted.</p> <p> </p> <p>Expert 1 (lai, mlainer), Expert 2 (jtm), Expert 3 (por, jportmann)</p>
TAILVAR (Terminal codon Analysis and Improved prediction of Lengthened VARiants)
<p>This dataset includes relevant files for developing the TAILVAR score designed to assess the functional impact of <strong>stop-loss variants</strong> occurring at stop codons (TAA, TGA, TAG). <strong>TAILVAR</strong> is built using a Random Forest model that predicts the pathogenicity of <strong>stop-loss variants</strong>. By integrating a combination of in-silico prediction scores, transcript features, and protein context information, <strong>TAILVAR</strong> provides a score ranging from 0 to 1, indicating the probability of a variant being pathogenic.</p> <p>For more information, please visit <a href="https://github.com/dr-yoon/TAILVAR">https://github.com/dr-yoon/TAILVAR</a></p>
Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis
<p>This data repository is associated with the paper:</p> <p>Morris,J., A. Sokolov, J. Reilly, A. Libardoni, C. Forest, S. Paltsev, A Schlosser, R. Prinn and H. Jacoby (2025). Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis. <em>Nature Communications </em><strong>16</strong>, 2703. https://doi.org/10.1038/s41467-025-57897-1</p> <p>This paper quantifies key socio-economic and climate uncertainties using the MIT Integrated Global System Model. </p>
A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature
<p>A characterization of cloud data center logs, analyzing its workload, energy and thermal characteristics. For more details of the dataset, please read the following paper: <a href="http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf">http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf.</a></p><p> </p><p>If you use the dataset, please cite the following work:</p><p>Shashikant Ilager, Adel N. Toosi, Mayank Raj Jha, Ivona Brandic, Rajkumar Buyya, "A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature", In Proceedings of the 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC2023), Messina, Italy, December 4-7, 2023.</p>
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
Wollestraat 29, Bruges (BE): high-resolution images of dry wood cores taken form a medieval floor joists, for tree-ring analysis
<ul><li>Dry-wood cores taken from historical timbers of a floor joists in the medieval building 'De Oude Steen', Wollestraat 29, Bruges (Belgium).</li><li><a href="https://id.erfgoed.net/erfgoedobjecten/29956 ">https://id.erfgoed.net/erfgoedobjecten/29956 </a></li><li>The cores were sampled at 22/02/2023 with a dry-wood borer (internal diameter 12 mm, external diameter 19 mm).</li><li>The cores were surfaced with increasingly finer sanding papers, from P60 up to P4000.</li><li>The cores were photograpphed with a Sony alpha7R IV full frame camera and FE 90 mm F/2.8G macro lens.</li><li>The<a href="https://www.wsl.ch/en/services-produkte/skippy/"> Skippy</a> system served as the image capturing platform.</li><li>The individual digital macro-photos were stitched with PTGui into a mosaic image (.tiff).</li><li>The mosaic images have a resolution of ~4 µm.</li></ul>
Fault Analysis Database with Features (FADbF)
<p>This repository is also available in GitHub: <a href="https://github.com/leandroensina/FADbF">https://github.com/leandroensina/FADbF</a></p><p>The FADbF dataset companions the paper entitled "Fault Distance Estimation for Transmission Lines with Dynamic Regressor Selection", published in <i>Neural Computing and Applications</i>, <strong>doi</strong>: <a href="https://doi.org/10.1007/s00521-023-09155-y">10.1007/s00521-023-09155-y</a>. More information about the dataset can be found in this reference.</p><p><strong>Associated Tasks</strong>: classification and regression</p><p><strong>Instances</strong>: 168,000</p><p><strong>Attributes</strong>: 128, including the two possible targets</p><p><strong>Additional Information</strong>: this database comprises several attributes extracted from time series of fault simulations of a transmission line with 500 kV, 414 km, and 60 Hz. In total, we extracted 21 features separately for each of the three phases for both voltage and current waveforms along two post-fault cycles from a single terminal, resulting in 126 attributes (21 * 3 * 2 = 126) in addition to the two possible targets, i.e., fault type (classification task) and fault location (regression task). If desired, the fault type can also be used as a feature for the fault location task.</p>
Dataset of the manuscript "Are Serious Games an Alternative to Personality Questionnaires? Initial Analysis of a Gamified Assessment"
<p>The present database belongs to the manuscript titled "Are Serious Games an Alternative to Personality Questionnaires? Initial Analysis of a Gamified Assessment". The study has been peformed in English, but the research is conducted in Spanish.</p>
Data for: Thermal volume expansion as seen by Temperature-modulated optical refractometry, Oscillating dilatometry and Thermo-mechanical analysis
<p>The data is supplementary to the publication "Thermal volume expansion as seen by Temperature-modulated optical refractometry, Oscillating dilatometry and Thermo-mechanical analysis", DOI: <a href="https://doi.org/10.1016/j.polymertesting.2024.108340" target="_blank" rel="noopener">10.1016/j.polymertesting.2024.108340</a></p> <p>Key words: Thermal volume expansion, Temperature-modulated optical refractometry, Thermo-mechanical analysis, Dilatometry, Epoxy thermoset</p> <p>The data sets contain measured data on Thermo-mechanical analysis (TMA) and Temperature-modulated optical refractometry (TMOR) of a model epoxy polymer in the viscoelastic temperature range.</p> <p>Material details:</p> <ul> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) +pyridine</li> <li>n-tetradecane, C14H30</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> <li>(Austrian) Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Federal Ministry for Digital and Economic Affairs (COMET-Module project “Chemitecture”, project-no.: 21647048)</li> </ul>
Grapegenomics.com: a web portal with genomic data and analysis tools for wild and cultivated grapevines
<p><a href="https://grapegenomics.com">Grapegenomics.com</a> is a web portal that provides public access to genome references for grapevine cultivars (<em>Vitis vinifera</em> ssp. <em>vinifera</em>), wild grapevines (<em>Vitis vinifera</em> ssp. <em>sylvestris</em>), various wild grape species (<em>Vitis</em> spp. and <em>Muscadinia</em> spp.), and major fungal pathogens affecting grapes.</p> <p>All genomes are accessible through dedicated genome browsers, and published genomes are available for complete <a href="https://www.grapegenomics.com/download.php">download</a>.</p> <p>The site hosts all genomes produced by the laboratory of Dario Cantù in the Department of Viticulture and Enology at the University of California, Davis, along with published genome references generated by others, such as PN40024 and Pinot noir ENTAV115. Instructions for genome submission are provided <a href="https://www.grapegenomics.com/submit.php">here</a>. The portal is maintained by Noé Cochetel (ndcochetel[at]ucdavis.edu). In this version 2.0, all genome browsers utilize <a href="https://jbrowse.org/jb2/">jbrowse 2</a>. <br><br>Link to the website: <a href="https://www.grapegenomics.com">https://www.grapegenomics.com</a> </p>
GWAS Summary Statistics from "Sex and statin-related genetic associations at the PCSK9 gene locus – results of genome-wide association meta-analysis"
<p>GWAMA summary statistics of PCSK9 levels stratified by sex and statin useage in Europeans.</p> <p>When using this data, please cite:</p> <p>Pott, J., Kheirkhah, A., Gadin, J.R. <em>et al.</em> Sex and statin-related genetic associations at the <em>PCSK9</em> gene locus: results of genome-wide association meta-analysis. <em>Biol Sex Differ</em> <strong>15</strong>, 26 (2024). https://doi.org/10.1186/s13293-024-00602-6</p> <p>All txt files contain the following columns:</p> <ul> <li>markername (unique SNP ID)</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>EA (effect allele)</li> <li>OA (other allele)</li> <li>EAF (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (in case of double-stratified data: number of studies; in case of single-stratified data: 2, as it is a meta-analysis of the two double-stratified data sets)</li> <li>beta (effect estimate)</li> <li>SE (standard error)</li> <li>pval (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>invalidAssoc (TRUE/FALSE flag if this variant was excluded in our analysis)</li> <li>reason4exclusion (reason why this SNP was excluded)</li> <li>phenotype (phenotyp setting)</li> </ul>
Topological Data Analysis of Monopoles in U(1) Lattice Gauge Theory — Data Release
<div>This release contains data used to prepare the publication <a href="https://arxiv.org/abs/2403.07739">X. Crean, J. Giansiracusa and B. Lucini, Topological Data Analysis of Monopoles in U(1) Lattice Gauge Theory (2024)</a>. There exists an <a href="https://doi.org/10.5281/zenodo.10806185">accompanying software release</a> that explains in detail how to extract and use the compressed data files on a Linux distribution (or compatible environment).</div>
Problems with nanoparticle tracking analysis (NTA) of urine extracellular vesicles (uEVs)
<p>Urinary extracellular vesicle (uEV) proteins may be used as specific markers of kidney damage in various pathophysiological conditions. The nanoparticle-tracking analysis (NTA) appears to be the most useful method for the analysis of uEVs due to its ability to analyze particles below 300 nm. The NTA method has been used to measure the size and concentration of uEVs and also allows for a deeper analysis of uEVs based on their protein composition using fluorescence measurements. However, despite much interest in the clinical application of uEVs, their analysis using the NTA method is poorly described and requires meticulous sample preparation, experimental adjustment of instrument settings, and above all, an understanding of the limitations of the method. We present the problems encountered during analysis with possible solutions: the choice of sample dilution, the method of the presentation and comparison of results, photobleaching, and the adjustment of instrument settings for a specific analysis.</p> <p> </p> <p>Figure 1. Expressions of specific markers CD63 in protein-standardized samples detected with Western blot analysis; anti-CD 63 (HPA010088, Sigma-Aldrich, Saint Louis, MO, USA, 1:1000); secondary antibodies conjugated to horseradish peroxidase (554021, BD Pharmingen (BD Biosciences, San Jose, CA, USA) 1:10000).</p> <p> </p> <p>Nanoparticle-Tracking Analysis of uEVs. A NanoSight NS300 instrument (Malvern Panalytical, Malvern, UK) was used to determine the concentrations and sizes of the uEVs in the samples. The total number of extracellular vesicles was measured during the continuous flow of samples delivered from a syringe pump.</p> <p>Figure 2. Determination of the size and concentration of uEVs: dilution factor—1:100; laser—405 nm.</p> <p>Figure 3. Effect of dilution on total number of particles per milliliter and size of uEVs in nanoparticle tracking analysis: sample dilutions—1:100, 1:500, and 1:1000; laser—488 nm.</p> <p>Figure 5. Fluorescence-based nanoparticle-tracking analysis of CD 63 expression in uEVs: without 500 nm long-pass filter; with 500 nm long-pass filter; comparison of sizes and concentrations of uEVs without and with 500 nm long-pass filter; dilution factor—1:100; laser—488 nm; anti-CD 63 (HPA010088, Sigma-Aldrich); secondary antibodies conjugated to Alexa Fluor 488 fluorescent dye (ab150073-500, Abcam, Cambridge, MA, USA).</p> <p>Figure 6. Fluorescence-based nanoparticle-tracking analysis of podocin expression in uEVs: without 500 nm long-pass filter; with 500 nm long-pass filter; comparison of sizes and concentrations of uEVs without and with 500 nm long-pass filter; dilution factor—1:100; laser—488 nm; anti-podocin (P0372, Sigma-Aldrich); secondary antibodies conjugated to Alexa Fluor 488 fluorescent dye (ab150073-500, Abcam, Cambridge, MA, USA).</p>
Statistical analysis and dataset for: Acute exposure to caffeine improves foraging in an invasive ant
<p>Linked to the journal article published in iScience (https://doi.org/10.1016/j.isci.2024.109935).</p> <p><em><strong>Abstract</strong></em></p> <p>Argentine ants, <em>Linepithema humile</em>, are a particularly concerning invasive species. Control efforts often fall short likely due to a lack of sustained bait consumption. Using neuroactives, such as caffeine, to improve ant learning and navigation could increase recruitment and consumption of toxic baits. Here, we exposed <em>L. humile</em> to a range of caffeine concentrations and a complex ecologically relevant task: an open landscape foraging experiment. Without caffeine, we found no effect of consecutive foraging visits on the time the ants take to reach a reward, suggesting a failure to learn the reward’s location. However, under low to intermediate caffeine concentrations ants were 38% faster with each consecutive visit, implying that caffeine boosts learning. Interestingly, such improvements were lost at high doses. In contrast, caffeine had no impact on the ants’ homing behavior. Adding moderate levels of caffeine to baits could improve ant’s ability to learn its location, improving bait efficacy.</p> <p> </p> <ul> <li><strong>sample_videos.zip</strong>: A subset of the videos used for data extraction. The complete collection of videos is not publicly accessible primarily due to their considerable size (105.35GB). Requests for access to the entire video set are encouraged.</li> <li><strong>Preregistration.pdf</strong>: The preregistration created for data collection and analysis with justifications for deviations from it.</li> <li><strong>OpLan_D1_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments.</li> <li><strong>OpLan_D2_DLC_coordinates.zip</strong>: Cartesian coordinates obtained from DeepLabCut for each of the videos analysed.</li> <li><strong>OpLan_C1_reproject_coordinates.py</strong>: Python code used to standardise the ants' coordinates by ensuring the same corner of the A4 platform was used as the origin of the cartesian referential of all videos. The known dimensions of the A4 were further used to convert coordinates from pixels to millimetres.</li> <li><strong>OpLan_C2_remove_impossibilities.py</strong>: Python code used to account for DeepLabCut tracking errors, with any ant movement exceeding two millimetres per frame being considered implausible and subsequently removed.</li> <li><strong>OpLan_C3_find_changepoints.py</strong>: Python code used to automatically derive the times at which an ant reached and left the reward from the tracking data.</li> <li><strong>OpLan_C4_inward_outward_data.py</strong>: Python code used to calculate relevant measures for the foodward (inward) and nestward (outward) journey such as journey duration, mean instantaneous speed and path tortuosity.</li> <li><strong>OpLan_C5_Figure_2.R</strong>: R code used to produce the raw elements of Figure 2.</li> <li><strong>OpLan_C6_Figure_4.R</strong>: R code used to produce the raw elements of Figure 4.</li> <li><strong>OpLan_C7_Statistical_Analysis.html</strong>: Complete statistical analysis and code for the manuscript.</li> </ul>
Genome data from Effrenium voratum CCMP421, RCC1521, and rt-383 and their analysis
<p>This dataset represents secondary data generated from the genomic analysis of three isolates of <em>Effrenium voratum</em> (CCMP421, RCC1521, and rt-383), the early-diverging, free-living lineage of Symbiodiniaceae dinoflagellates. Theese data include <strong>(A)</strong> assembled genome sequences, predicted gene models and protein sequences, and <strong>(B)</strong> data and scripts associated with generation of graphs and figures presented in the key genome paper (Shah et al., 2024, Massive genome reduction predates the divergence of Symbiodiniaceae dinoflagellates, under review in <em>ISME Journal</em>). An earlier preprint of this manuscript is available at <em>bioRxiv</em>: <a href="https://doi.org/10.1101/2023.03.24.534093" target="_blank" rel="noopener">https://doi.org/10.1101/2023.03.24.534093</a>.</p> <p><strong>A. Genome assemblies, annotation and gene models. </strong>The dataset includes, for each taxon, (a) the <em>de novo</em> assembled genome sequences in FASTA format (<strong>*genome.fa.tgz</strong>), (b) structural annotation of the assembled genome in GFF3 format (<strong>*.genome.annotation.gff3.tgz</strong>), (c) the predicted protein-coding sequences of gene models in FASTA format (<strong>*genemodel.CDS.fa.tgz</strong>), (d) the predicted protein sequences of gene models in FASTA format (<strong>*genemodel.PROT.fa.tgz</strong>), and (c) the associated sequences and gene annotations of organellar genomic sequences (i.e. mitochondrial and plastid) (<strong>*organellar.tgz</strong>). Functional annotations of all gene models from the three genomes are available in the Excel spreadsheet (<strong>*GeneModels.xlsx</strong>).</p> <p><strong>B. Data and scripts associated with generation of graphs and figures in Shah et al. (2024). </strong>These files are organised based on key analyses specific to main figures and supplementary figures in the paper.</p> <p>See <strong>README.txt</strong> for a more-detailed description of the files.</p>
Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020
<p><strong>Overview</strong></p> <p>This dataset is a supplementary material to the paper "Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances". It provides detailed insights into land subsidence across Iran, derived from Sentinel-1 InSAR observations. This dataset is intended for use by researchers, policymakers, and practitioners interested in land subsidence, groundwater depletion, and related fields.</p> <p><strong>Dataset Contents</strong></p> <ol> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Annual rate of land subsidence in Iran over the six-year period, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.jpg</em><br>Subsidence map of Iran visualized as jpg</li> <li><em>Iran_subsidence_seasonal_amplitude_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Amplitude of seasonal ground deformation, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_mask_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Land subsidence mask, based on the annual rate of land subsidence.</li> </ol> <p><strong>Methodology</strong></p> <p>The data were derived using Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 satellite imagery. The original SAR data includes more than 6000 scenes of Sentinel-1 images collected across 10 descending tracks between 2014 and 2020. The details can be found in the original paper.</p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge the European Space Agency (ESA) for providing the Sentinel-1 satellite data used in this analysis.</p> <p><strong>License</strong></p> <p>This dataset is shared under CC BY 4.0 license, which allows for reuse and distribution, provided that the original authors and source are credited.</p> <p><strong>Citation</strong></p> <p>Please cite the following if you use this dataset:</p> <ol> <li>Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances.</li> <li>Haghighi and Motagh, 2024. Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020. Zenodo. doi:10.5281/zenodo.10815578</li> <li>The dataset contains modified Copernicus Sentinel data 2014-2020, processed by ESA.</li> </ol> <p><strong>Contact</strong></p> <p>Please contact Mahmud Haghighi for inquiries related to this dataset.<br>https://www.ipi.uni-hannover.de/en/haghighi</p>
Statistical analysis and dataset for: A high-throughput and sensitive method for food preference assays in walking insects
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.04.10.588882).</p> <p><em><strong>Abstract</strong></em></p> <p>Insects pose significant challenges in both pest management and ecological conservation. Often, the most effective strategy is employing toxicant-laced baits, which must also be designed to specifically attract and be preferred by the targeted species for optimal species-specific effectiveness. However, traditional methods for measuring bait preference are either non-comparative, meaning that most animals only ever taste one bait, or suffer from methodological or conceptual limitations. Here we demonstrate the value of direct comparison food preference assays using the invasive and pest ant <em>Linepithema humile </em>as a model. We compare the food preference sensitivity of non-comparative (one visit to a food source) and sequential comparative (visiting one type of food then another) assays at detecting low levels of aversive quinine in sucrose solution. We then introduce and test a novel dual-choice feeder method for simultaneous comparative evaluation of bait preferences, testing its effectiveness in discerning between foods with varying quinine or sucrose levels. While the non-sequential assay could not detect aversion to 1.25mM quinine in 1M sucrose, the sequential comparative approach detected aversion to quinine levels as low as 0.94mM. The novel dual feeder method approach could detect aversion to quinine levels as low as 0.31mM, and also preference for 1M sucrose over 0.75M sucrose. The dual-feeder method, combines the sensitivity of comparative evaluation with high throughput, ease of use, and avoidance of interpretational issues. This innovative approach offers a promising tool for rapid and effective testing of bait solutions, contributing to the development of targeted control strategies. Moreover, the method can be easily modified for application to a wide range of walking insects, such as cockroaches, crickets, and beetles.</p>
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></p>
The portrayal of underlings in Eastern Cālukya copper plates: textual analysis data with revised codebook
<p>This is a textual analysis dataset derived from Eastern Cālukya copperplate grants. The second version of 24 April 2024 contains a slightly revised codebook (in DOC and PDF formats with identical contents) in addition to the earlier dataset. This revision is essentially identical to that reflected in the "Revised tag" column of the dataset, except that many definitions have been made clearer and tidier, and a small number of intermediate-level categories have been added for better hierarchisation. The codes in use have not been altered.</p> <p><br>The data accompany the following forthcoming publications (title and date of publication subject to change):</p> <p>Balogh, Dániel (forthcoming 2024), 'The portrayal of underlings in Eastern Cālukya copper plates'. In: Self-Representation and Presentation of Others in Indic Epigraphical Writing, edited by Annette Schmiedchen and Dániel Balogh. Wiesbaden: Harrassowitz.</p> <div> <div>Balogh, Dániel (forthcoming 2024). ‘Textual Analysis Methodology and Royal Representation in Copperplate Grants’. In <em>Bhūtārthakathane ... Sarasvatī: Reading Poetry as a History Book</em>, edited by Marco Franceschini, Chiara Livio, and Lidia Wojtczak. Studies on the History of Śaivism. Naples: UniorPress.</div> </div> <p>The former publication introduces the method sketched out on the introductory page of this dataset and studies a particular topic through this methodology, while the latter discusses the method in more detail. An account of the technical details is in preparation by Balogh.</p> <p><br>This dataset, the underlying research and the relevant publications are results of the project DHARMA ‘The Domestication of “Hindu” Asceticism and the Religious Making of South and Southeast Asia’. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no 809994).</p>
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