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51,102 results for “analysis”
Survey Data and Analysis on Open Access Strategies (2022)
<p><strong>Description:</strong><br>This dataset includes the analysis, codebook, and raw survey data from a 2022 survey titled <em>"Which Open Access Strategies Are Relevant?"</em>. The survey targeted professionals in library and information sciences specializing in open access and scholarly publishing.</p> <p>The dataset is based on 100 adjusted responses (<em>N=100</em>) and aims to provide insights into the strategies and challenges associated with open access implementation in academic and professional environments.</p> <p><strong>Contents:</strong></p> <ol> <li><strong>analysis_oa-strategies-2024-01-14.xlsx</strong>: Processed data and key analyses, including summary tables and graphs.</li> <li><strong>codebook_oa-strategies_2024-01-14.xlsx</strong>: Comprehensive documentation of variables, codes, and their definitions for interpretation of the raw data.</li> <li><strong>survey_results_oa-strategies_2024-01-14.xlsx</strong>: Anonymized raw data from the survey, suitable for further analysis.</li> </ol> <p><strong>Methodology:</strong><br>The survey employed a structured questionnaire distributed in 2022 to professionals in library and information sciences. It focused on identifying key strategies, institutional policies, and perceived barriers to open access. The collected data were cleaned and anonymized to ensure privacy and compliance with ethical standards.</p> <p><strong>Purpose and Use:</strong><br>This dataset is designed for researchers, policymakers, and information science professionals. It is particularly valuable for studying open access adoption strategies, evaluating institutional policies, and conducting comparative research.</p>
Analysis of two Methods for Aircraft Fuel Requirement Calculations in the Context of a novel Methodological Framework for LCA of Sustainable Aviation
<p>This Microsoft Excel file contains equations to compare different approaches to calculate fuel efficiency ("energy use" in [MJ/t*km]) of aircraft over a specific distance at a specific payload. </p> <p>Two approaches are compared: A novel approach by <a href="10.1016/j.scitotenv.2023.163881" target="_blank" rel="noopener">Su-ungkavatin et al.</a> and the more established approach well documented by eg. <a href="https://www.fzt.haw-hamburg.de/pers/Scholz/arbeiten/TextBurzlaff.pdf" target="_blank" rel="noopener">Burzlaff</a> or <a href="http://www.aircraftmonitor.com/uploads/1/5/9/9/15993320/aircraft_payload_range_analysis_for_financiers___v2.pdf" target="_blank" rel="noopener">Ackert</a>.</p> <p>This work augments a Letter to the Editor we submitted to the journal <a href="https://www.sciencedirect.com/journal/science-of-the-total-environment" target="_blank" rel="noopener">Science of the Total Environment</a>.</p>
Rhyme analysis of Ukrainian Ballads: Towards a Computational Poetics
<p>This dataset is based on the folklore collection <em>Folk songs of Khmelnytsky region</em> (Iefremova & Dmytrenko, 2014). The text corpus includes 200 ballads in Ukrainian language. Ballads collected in the period between 1918 and 2010. There are 9125 ballad lines in the table; 40543 tokens.</p> <p>To analyze the rhyming and rhythmic elements of the text corpus of Khmelnytsky region ballads, the programming language R along with RStudio was used. Code written for text analysis in Estonian Literary Museum. </p> <p><br>This dataset consist of such files:</p> <ul> <li><strong>ballads_corpus_Khmelnytsky region</strong>: contains text data of Khmelnytsky region ballads in CSV;</li> <li><strong>stanza+syllables</strong>: R script to analyse the stanzas types and to calculate the number of syllables in each line;</li> <li><strong>finals in lines+POS analysis</strong>: R script to analyse the rhyme scheme by determine the final syllable in a line, and to do the PoS tags analysis of the rhyme;</li> <li><strong>rhyme_schemes:</strong> R script to analyze the distribution of rhyme schemes in Khmelnytsky region ballads;</li> <li><strong>ballads_corpus_POS:</strong> CSV file containing the text data of Khmelnytsky region ballads with part-of-speech (PoS) tags for the final word in each line;</li> <li><strong>rhyme_by_PoS:</strong> R script to analyze the distribution of rhymes by part of speech across the ballads;</li> <li><strong>ballads_fin_str</strong>: contains text data of Khmelnytsky region ballads with marked stress position in the last word in each line (in CSV);</li> <li><strong>rhyme_stressed_position</strong>: R script to analyse rhyme by stress position.</li> </ul>
Dataset and Analysis Code for an Experiment on Phosphorus Fertilizers
<p>Link to GitHub repository: <a href="https://github.com/jmalonso55/fosfatos">https://github.com/jmalonso55/fosfatos</a></p> <p>Link to analysis code and results: <a href="https://github.com/jmalonso55/fosfatos/blob/main/An%C3%A1lise_fosfatos_g.md">https://github.com/jmalonso55/fosfatos/blob/main/An%C3%A1lise_fosfatos_g.md</a></p> <p> </p> <h1><strong>About</strong></h1> <p>This repository contains the data and R code for the statistical analysis and results visualization of the paper: Ramos, J. F. K., Alves, B. J. R., Alonso, J. M., Teixeira, P. C., & Benites, V. D. M. (2025). Characterization and agronomic efficiency of natural and recovered phosphates in tropical soil with corrected acidity. <em>Rev. Bras. Ciênc. Solo</em>, <em>49</em>(spe1). Available in: <a href="https://dx.doi.org/10.36783/18069657rbcs20240099">https://dx.doi.org/10.36783/18069657rbcs20240099</a></p> <p>The study is part of the Master’s Dissertation of Ramos, J.F.K. (Ramos, J.F.K. (2023). Caracterização química, mineralógica e eficiência agronômica de diferentes fosfatos [Dissertation]. Universidade Federal Rural do Rio de Janeiro, Seropédica, Brasil). Available in: <a href="https://rima.ufrrj.br/jspui/handle/20.500.14407/18645">https://rima.ufrrj.br/jspui/handle/20.500.14407/18645</a>.</p> <h2>Methodological aspects</h2> <p>This study examined eleven phosphate fertilizer samples, encompassing Brazilian and imported products, as well as residue-recovered and soluble phosphates. The phosphate rocks of igneous origin were exclusively sourced from Brazil, specifically Catalão (Goiás) and Registro (São Paulo). Sedimentary sources included samples from Brazil (Arraias in Tocantins, Bonito in Mato Grosso do Sul, and Pratápolis in Minas Gerais) and from Morocco, Algeria, and Peru (Bayóvar). These phosphates are referred to as Catalão, Registro, Arraias, Bonito, Pratápolis, Morocco, Algeria, and Bayóvar, respectively.</p> <p>The experiment was carried out in a greenhouse, using plastic pots as experimental units, each containing 2 kg of a Ferralsol sample. The soil, initially identified as acidic (pH 4.68), was subjected to a correction process prior to the experiment. The study employed a completely randomized design with 12 treatments and four replicates, resulting in a total of 48 experimental units. The treatments included phosphate rocks (Catalão, Registro, Bonito, Pratápolis, Arraias, Morocco, Algeria, and Bayóvar), two animal-origin phosphates (Bonechar and ERCP), triple superphosphate (TSP) as a reference, and a control treatment without a phosphorus source.</p> <p>Each treatment received a single application of 320 mg P per pot (equivalent to 160 mg P per kg of soil), which was thoroughly incorporated into the soil before planting. The experiment spanned two successive cropping cycles, each lasting 45 days. At the end of each cycle, the aboveground parts of the plants were harvested, dried in a forced-air oven at 65°C until a constant weight was achieved, and their shoot dry mass (SDM) was recorded. The dried samples were finely ground in a Wiley mill and further processed in a ball mill for phosphorus content analysis. To evaluate the agronomic efficiency of the phosphate sources, the study calculated the Relative Agronomic Efficiency Index (RAE) and Phosphorus Efficiency (PE).</p>
Reference grids (vector) and their centroids for harmonization of analysis
<p>This dataset contains reference grids (vector) and their centroids for harmonized analysis. All the files are <em>geoparquet</em>, they are described below. Production procedure is available at projects GitHub repository (https://github.com/aavotins/HiQBioDiv/blob/main/Templates/TemplateGrids_Vector.R):</p> <ul> <li>"tikls100_sauzeme.parquet" contains terrestrial territory of Latvia divided in 100-by-100 m polygon grid cells. Contains fields: <ul> <li>"id" feature ID;</li> <li>"yes" a fields with values "1";</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"rinda300" with ID's matching file "tikls300_sauszeme.parquet";</li> <li>"ID1km" with ID's matching file "tikls1km_sauszeme.parquet";</li> <li>"rinda500" with ID's matching file "tikls500_sauszeme.parquet";</li> <li>"geom" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls300_sauzeme.parquet" contains terrestrial territory of Latvia divided in 300-by-300 m polygon grid cells. Contains fields: <ul> <li>"rinda300" feature ID;</li> <li>"x" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls500_sauzeme.parquet" contains terrestrial territory of Latvia divided in 500-by-500 m polygon grid cells. Contains fields: <ul> <li>"rinda500" feature ID;</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"x" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls1km_sauzeme.parquet" contains terrestrial territory of Latvia divided in 1000-by-1000 m polygon grid cells. Contains fields: <ul> <li>"ID1km" feature ID;</li> <li>"yes" a fields with values "1";</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"geometry" a {sf} geometry definition field.</li> </ul> </li> <li>"pts100_sauszeme.parquet" contains centroids of file "tikls100_sauszeme.parquet" with their attribute fields;</li> <li>"pts300_sauszeme.parquet" contains centroids of file "tikls300_sauszeme.parquet" with their attribute fields and additionally "tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"pts500_sauszeme.parquet" contains centroids of file "tikls500_sauszeme.parquet" with their attribute fields;</li> <li>"pts1000_sauszeme.parquet" contains centroids of file "tikls1km_sauszeme.parquet" with their attribute fields;</li> <li>"tks93_50km.parquet" contains topographic map of Latvia pages (TKS-93 M:50000). Contains fields: <ul> <li>"NOSAUKUMS" with a page name;</li> <li>"NUMURS" with a page number;</li> <li>"Shape_Length" an attribute from ESRI File Geodatabase;</li> <li>"Shape_Area" an attribute from ESRI File Geodatabase;</li> <li>"Shape" a {sf} geometry definition field;</li> </ul> </li> <li>All the above mentioned files are stored also as layers in geopakage file "vector_grids.gpkg" having the same names and attributes.</li> </ul>
Galápagos Archipelago Refined Analysis Validation data
<p>This dataset includes (near) surface data variables from the <a href="https://data.klima.tu-berlin.de/GAR/">GAR</a> dataset for the model validation period from 2022-04-01 to 2023-03-31.</p> <p>As this data is part of the GAR dataset, please find additional data at <a href="https://data.klima.tu-berlin.de/GAR/">https://data.klima.tu-berlin.de/GAR/</a></p> <p>The <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF</a> format is self-describing, so that all needed metadata are included within the files.</p> <p>The file names are composed with the following structure:</p> <p><model-setup>_<horizontal-resolution>_<time-resolution>_<variable-name>.nc</p> <p>The shorthands in the file names represent the following:</p> <p><strong>MM</strong> = Name of the model setup, described in Schmidt et al. (unpublished)</p> <p><strong>d02km</strong> = domain with a grid spacing of 2 km</p> <p><strong>2d</strong> = spatial dimensions (2d data, single level)</p> <p><strong>3d_press</strong> = spatial dimensions (3d data, pressure level)</p> <p><strong>d</strong> = time frequency of the data (daily)</p> <p><strong>m</strong> = time frequency of the data (monthly)</p> <p><strong>y</strong> = time frequency of the data (yearly)</p> <p><strong>psfc</strong> = surface (sfc) pressure</p> <p><strong>q2</strong> = water vapor mixing ratio (qv) st 2 m</p> <p><strong>q</strong> = mixing ratio</p> <p><strong>prcp</strong> = total precipitation (step-wise)</p> <p><strong>et</strong> = actual evapotranspiration (step-wise)</p> <p><strong>t2</strong> = temperature (temp) at 2 m</p> <p><strong>theta</strong> = potential temperature</p> <p><strong>sh2 </strong>= specific humidity at 2 m</p> <p><strong>rh2 </strong>= relative humidity at 2 m</p> <p><strong>u10</strong> = 10 m u-wind component</p> <p><strong>v10</strong> = 10 m v-wind component</p> <p><strong>ws10</strong> = 10 m wind speed</p> <p><strong>w</strong> = w-wind component</p> <p><strong>wd10</strong> = 10 m wind direction</p> <p><strong>hgt </strong>= surface height</p> <p><strong>landmask </strong>= landmask</p> <p> </p> <p>The data is in accordance with the <a href="https://cfconventions.org/">CF Conventions</a> CF-1.8</p>
MALDI MS data and metadata from "A biocodicological analysis of the medieval library and archive from Orval Abbey, Belgium"
<p>See <a href="https://doi.org/10.1098/rsos.210210">Ruffini-Ronzani et al</a>.</p>
Code Analysis Tables for Developers Interviews on Dependencies Paper
<p>Code Analysis Tables for the ACM CCS 2020 paper "A qualitative study of dependency management and its security implications"</p>
Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362
<p>Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362 from the the long term experiments belonging in some of the SoilCare project partners. </p>
XCT data of metallic feedstock powder with pore size analysis
<p><strong>X-Ray computed tomography (XCT) scan of 11 individual metallic powder particles, made of (Mn,Fe)<sub>2</sub>(P,Si) alloy</strong></p> <p>The data set consists of 4 single XCT scans which have been stitched together [3] after reconstruction.<br> The powder material is an (Mn,Fe)<sub>2</sub>(P,Si) alloy with an average density of 6.4 g/cm³. The particle size range is about 100 - 150 µm with equivalent pore diameters up to 75 µm. The powder and the metallic alloy are described in detail in [1, 2].</p> <p><strong>Data acquisition</strong></p> <p>The data was acquired using a Zeiss Xradia 620 Versa X-ray microscope which provides the opportunity of optical magnification.</p> <table> <caption><strong>Tomographic imaging parameters</strong></caption> <tbody> <tr> <td>XCT system</td> <td>Zeiss Xradia 620 Versa</td> </tr> <tr> <td>Voltage</td> <td>80</td> <td>kV</td> </tr> <tr> <td>Power</td> <td>10</td> <td>W</td> </tr> <tr> <td>Source filtering</td> <td>"<em>LE2</em>" (system specific)</td> <td>-</td> </tr> <tr> <td>Source-object distance</td> <td>10</td> <td>mm</td> </tr> <tr> <td>Object-detector distance</td> <td>10</td> <td>mm</td> </tr> <tr> <td>Geom. magnification</td> <td>2</td> <td>-</td> </tr> <tr> <td>Optical magnification</td> <td>20</td> <td>-</td> </tr> <tr> <td>Native pixel size</td> <td>13.5</td> <td>µm</td> </tr> <tr> <td>Binning</td> <td>2x2</td> <td>px</td> </tr> <tr> <td>Voxel size</td> <td>0.68</td> <td>µm</td> </tr> <tr> <td>No. of projections per scan</td> <td>801</td> <td>1</td> </tr> <tr> <td>No. of scans</td> <td>4</td> <td>-</td> </tr> <tr> <td>Exposure time per projection</td> <td>5</td> <td>s</td> </tr> </tbody> </table> <p> </p> <p><strong>Projection data</strong> (801 single TIFF-files each):</p> <ul> <li>proj_00</li> <li>proj_01</li> <li>proj_02</li> <li>proj_03</li> </ul> <p><strong>Reconstructed data</strong>:</p> <ul> <li>raw-volume (MnFePSi-Powder_80kV_10W_LE2_20x_5s_801_0p68_BHC=2_Stitch_U16_966x1020x2916.raw + header.txt)</li> <li>analyzed data as Volume Graphics Studio MAX 3.4.5 project</li> </ul> <p><strong>Stitched 2D data</strong> (images stitched with ImageJ-Plugin described in [3]<strong>:</strong></p> <ul> <li>Stitched_0deg_Projections.tif</li> <li>Pores+Particles_Analysis.tif</li> </ul> <p> </p> <p>[1] G.-R. Jaenisch, U. Ewert, A. Waske, and A. Funk, “Radiographic Visibility Limit of Pores in Metal Powder for Additive Manufacturing,” Metals, vol. 10, no. 12, p. 1634, Dec. 2020. https://doi.org/10.3390/met10121634</p> <p>[2] X. Miao et al., “Printing (Mn,Fe)2(P,Si) magnetocaloric alloys for magnetic refrigeration applications,” J. Mater. Sci., vol. 55, no. 15, pp. 6660–6668, May 2020. https://doi.org/10.1007/s10853-020-04488-8</p> <p>[3] S. Preibisch, S. Saalfeld, and P. Tomancak, “Globally optimal stitching of tiled 3D microscopic image acquisitions,” Bioinformatics, vol. 25, no. 11, pp. 1463–1465, Jun. 2009.</p>
MaxEnt analysis of Ochotona rufescens, Oumm Qatafa
<p>For pika, we collected the coordinates of 35 find spots of recent and sub-recent <em>O. rufescens</em> (Čermák et al. 2006; Khaki-Saneh 2014), a set of rasters representing current (1979 - 2013) climate based on standard 19 bioclim variables at 10-min resolution (Anthropocene, v1.2b: <a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). These data were used to construct a Maximum Entropy model for the current distribution of the Afghan pika using the ‘maxnet’ package (Phillips 2021) in R (version 4.0.2). Other libraries used include ‘terra’ (Hijman 2021) and ‘modEvA’ (Barbosa et al. 2013). The model provided a list of variables that parsimoniously predict suitable environments for the Afghan pika, and also a projection of the probability of finding suitable habitats, as defined by the bioclimatic variables, in geographical space under present-day conditions. The values of the selected model bioclimatic variables at the present find spots were compared with the same values for Oumm Qatafa to examine its present climatic suitability as a habitat for pikas.</p> <p>Barbosa, A.M., Real, R., Munoz, A.R. & Brown, J.A. (2013). New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity and Distributions, 19(10), 1333-1338. https://onlinelibrary.wiley.com/doi/full/10.1111/ddi.12100.</p> <p>Čermák, S., Obuch, S., Benda, P. (2006). Notes on the genus <em>Ochotona</em> in the Middle East (Lagomorpha: Ochotonidae). <em>Lynx</em> (Praha) 37, 51–66.</p> <p>Hijmans, R.J. (2021). terra: Spatial Data Analysis. R package version 1.5-8. https://rspatial.org/terra/</p> <p>Khaki Sahneh, S., Nouri, Z., Alizadeh Shabani, A., Dehdar Dargahi, M. (2014). A review on habitats selection by Afghan Pika (<em>Ochotona rufescens</em>), case study: the Lashgardar protected area in Hamadan Province. <em>Journal on New Biological Reports</em> 3(3), 186–199.</p> <p>Phillips, S. (2021). maxnet: Fitting 'Maxent' Species Distribution Models with 'glmnet'. R package version 0.1.4. https://CRAN.R-project.org/package=maxnet.</p>
dual scRNA-seq analysis of P. vivax infected hepatocytes
<p>Malaria-causing <em>Plasmodium vivax</em> parasites can linger in the human liver for weeks to years, and then reactivate to cause recurrent blood-stage infection. While an important target for malaria eradication, little is known about the molecular features of the replicative and non-replicative states of intracellular <em>P. vivax</em> parasites, or their human host-cell dependencies and the host responses to them. Here, we leverage a bioengineered human microliver platform to culture patient-derived <em>P. vivax</em> parasites in primary human hepatocytes and conduct transcriptional profiling. By coupling enrichment strategies with bulk and single-cell analyses, we captured both parasite and host transcripts in individual hepatocytes throughout the infection course. We define host- and state-dependent transcriptional signatures and identify previously unappreciated populations of replicative and non-replicative parasites, sharing features with sexual transmissive forms. We find that infection suppresses transcription of key hepatocyte function genes, and that <em>P. vivax</em> elicits an innate immune response that can be manipulated to control infection. Our work provides an extendible framework and resource for understanding host-parasite interactions and reveals new insights into the biology of <em>P. vivax</em> dormancy and transmission.</p>
Content analysis of occupational definitions
<p>The methodological process followed for the competence assignment has considered two main phases. In a first phase, a taxonomic analysis has been carried out, identifying and systematizing the tasks included in around the 1.300 definitions of the two main occupational classification systems SOC-2018 and ISCO-2008. Once systematized, it has been assigned to key competences from the European Reference Framework on Key Competences for Lifelong Learning established by the European Commission (European Council, 2018). In a second phase, and on the basis of the results obtained, an analysis of competences has been developed. To make it possible, it has been established two indexes that measure to what extent a competence is relevant to an occupation, quantifying if such competence is present in all the tasks performed for such occupation or just in some or even if it is not (extension). The second index, specialization, measures to what extent a given competence is the most important of the tasks developed by an occupation, or if on the contrary, it is only one of the required competences.</p>
An experimental data set for the analysis of the thermophysical behavior of a single-story naturally ventilated double-skin façade (DSF) under summer boundary conditions
<p>Double-skin facades (DSFs) are adaptive building envelope elements that offer the possibility to dynamically interact with the heat and mass flow between indoor and outdoor environments. Though designed to provide better performance compared to more conventional envelope solutions, these façade systems may, in some cases, underperform and lead to an increase in energy use or in thermal discomfort if not properly designed and operated. One of the known problems is the risk of overheating, in hot periods, in the ventilated cavity. In order to analyze this effect, we have systematically investigated the performance of a single-story, naturally ventilated DSF. The DSF is operated in the so-called outdoor air curtain mode and has venetian blinds installed in the 20 mm deep ventilated cavity. Tests were carried out under a steady-state regime corresponding to relevant summertime conditions. In an effort to enable the scientific community to access experimental data to analyze this problem further or for model validation purposes, we released together with the open-access paper entitled "<strong>Characterization of a naturally ventilated double-skin façade through the design of experiments (DOE) methodology in a controlled environment</strong>," the entire set of experimental data collected during the tests. The data set contains the results of a series of experimental runs where different configurations of the DSF, as detailed below, have been subjected to various boundary conditions through a climate simulator facility equipped with a solar simulator device. The database is supported by a guide ("Guide.pdf"), where further explanations about how to read data and schematic drawings of the sensor layout are provided. Additional information about the original aims of the experiments, the detailed methods, and other data processing procedures can be found in the article mentioned above. The collection of experimental tests in this data set covers:</p> <ul> <li>49 steady-state measurements where the following factors were changed using different experimental designs: solar irradiance (0, 350, and 700 Wm<sup>-2</sup>), outdoor chamber temperature (15, 25, and 35 ℃), opening size (7, 21, and 42 dm<sup>2</sup>), and venetian blinds angle (closed blinds θ=0 º, θ=45 º, and open blinds θ=90 º) [file name: "Complete_data.csv"],</li> </ul> <p>Any inquiries about the experimental data can be sent to: <a href="mailto:aleksandar.jankovic@ntnu.no">aleksandar.jankovic@ntnu.no</a></p> <p>The activities presented in this paper were carried out within the research project "REsponsive, INtegrated, VENTilated - REINVENT – windows," supported by the Research Council of Norway through the research grant 262198, and the partners SINTEF, Hydro Extruded Solutions, Politecnico di Torino and Aalto University.</p>
Data for "Traceable X-ray focal spot reconstruction by circular edge analysis: From sub-microfocus to mesofocus"
<p>Raw data used to create figures for the paper "Traceable X-ray focal spot reconstruction by circular edge analysis: From sub-microfocus to mesofocus" <a href="https://doi.org/10.1088/1361-6501/ac6225">https://doi.org/10.1088/1361-6501/ac6225</a></p>
The predator problem and PCR primers in molecular dietary analysis: swamped or silenced; depth or breadth? - Dataset
<p>Raw sequencing data and other metadata files are associated with Cuff et al. (2022), available at https://doi.org/10.5281/zenodo.4708418</p> <p>The associated code, files and description pertain to the non-metric multi-dimensional scaling plot presented in this review (Figure 4). The code and data required for the boxplot (Figure 3) are given at the Zenodo link above (for Cuff et al. 2022).</p> <p>Data were collected and processed according to Cuff et al., (2022) up to the point of aggregating the two primer pair datasets. Binary matrices for prey detections were combined for the two primer pairs, but each sample represented separately for each primer pair (i.e., not aggregated by sample). Instances where taxa were only identified to genus (or lower, e.g., family) level by only one of the primer pairs resulted in aggregation for the other primer pair at that taxonomic level, except for species within those groups that were reliably identified to species level by both primers. Samples for which only one primer pair generated prey data were removed. The non-metric multidimensional scaling spider plot was created using ‘metaMDS’ with a Jaccard distance matrix and 999 tries in the ‘vegan’ package (Oksanen et al., 2016). Outliers that obscured the overall patterns were removed, the final plot having a stress of 0.061. Point colours were assigned using the ‘set1’ palette of the ‘RColorBrewer’ package (Neuwirth, 2014) and the final plot created using ‘ggplot2’ (Wickham, 2016).</p>
FireCube: A Daily Datacube for the Modeling and Analysis of Wildfires in Greece
<p><strong>dataset_greece.nc</strong></p> <p><strong>This dataset is meant to be used to develop models for next-day fire hazard forecasting in Greece. It contains the following variables for the years 2009 to 2021 at a daily 1km x 1km grid.</strong><br> </p> <table> <thead> <tr> <th scope="col"><strong>Variable</strong></th> <th scope="col"><strong>Units</strong></th> <th scope="col"><strong>Long Name</strong></th> <th scope="col"><strong>Description</strong></th> </tr> </thead> <tbody> <tr> <td>avg_d2m</td> <td>K</td> <td>Avg 2 metre dewpoint temperature</td> <td>Daily Average 2 metre dewpoint temperature ERA5-Land</td> </tr> <tr> <td>avg_rh</td> <td>%</td> <td>Avg Relative humidity</td> <td>Daily Average Relative humidity calculated from t2m and d2m</td> </tr> <tr> <td>avg_sp</td> <td>Pa</td> <td>Avg Surface pressure</td> <td>Daily Average Surface pressure ERA5-Land</td> </tr> <tr> <td>avg_t2m</td> <td>K</td> <td>Avg 2 metre temperature</td> <td>Daily Average 2 metre temperature ERA5-Land</td> </tr> <tr> <td>avg_tp</td> <td>m</td> <td>Avg total precipitation</td> <td>Daily Average Total precipitation ERA5-Land</td> </tr> <tr> <td>avg_u10</td> <td>m/s</td> <td>Avg 10 metre U wind component</td> <td>Daily Average 10 metre U wind component ERA5-Land</td> </tr> <tr> <td>avg_v10</td> <td>m/s</td> <td>Avg 10 metre V wind component</td> <td>Daily Average 10 metre V wind component ERA5-Land</td> </tr> <tr> <td>burned_areas</td> <td>unitless</td> <td>Rasterized burned polygons</td> <td>EFFIS (https://effis.jrc.ec.europa.eu/) burned areas burned as raster (value 1). Starting date retrieved with intersection with MODIS active fires</td> </tr> <tr> <td>et</td> <td>kg/m^2/8day</td> <td>8-day Evapotranspiration</td> <td>Total Evapotranspiration - MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid (MOD16A2)</td> </tr> <tr> <td>evi</td> <td>unitless</td> <td>16-day EVI</td> <td>Enhanced vegetation index - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2)</td> </tr> <tr> <td>fapar</td> <td>%</td> <td>Fraction of Absorbed Photosynthetically Active Radiation</td> <td>FPAR - MOD15A2H MODIS/Terra Gridded 500M (8-day composite)</td> </tr> <tr> <td>fwi</td> <td>unitless</td> <td>Fire Weather Index</td> <td>Fire Weather Index 0.25 deg - https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview</td> </tr> <tr> <td>ignition_points</td> <td>unitless</td> <td>Rasterized ignition points</td> <td>Ignition points burned as raster (value 1) on the map calculated from intersection of MODIS active fires and EFFIS (https://effis.jrc.ec.europa.eu/) burned areas</td> </tr> <tr> <td>lai</td> <td>unitless</td> <td>Leaf Area Index</td> <td>Leaf Area Index (LAI) - MOD15A2H MODIS/Terra Gridded 500M Leaf Area Index LAI (8-day composite)</td> </tr> <tr> <td>lst_day</td> <td>K</td> <td>Day Land Surface Temperature</td> <td>Day Land Surface Temperature (LST) - MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid (MOD11A1)</td> </tr> <tr> <td>lst_night</td> <td>K</td> <td>Night Land Surface Temperature</td> <td>Night Land Surface Temperature (LST) - MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid (MOD11A1)</td> </tr> <tr> <td>max_d2m</td> <td>K</td> <td>Max 2 metre dewpoint temperature</td> <td>Daily Maximum 2 metre dewpoint temperature ERA5-Land</td> </tr> <tr> <td>max_rh</td> <td>%</td> <td>Max Relative humidity</td> <td>Daily Maximum Relative humidity calculated from t2m and d2m</td> </tr> <tr> <td>max_sp</td> <td>Pa</td> <td>Max Surface pressure</td> <td>Daily Maximum Surface pressure ERA5-Land</td> </tr> <tr> <td>max_t2m</td> <td>K</td> <td>Max 2 metre temperature</td> <td>Daily Maximum 2 metre temperature ERA5-Land</td> </tr> <tr> <td>max_tp</td> <td>m</td> <td>Max Total precipitation</td> <td>Daily Maximum Total precipitation ERA5-Land</td> </tr> <tr> <td>max_u10</td> <td>m/s</td> <td>Max 10 metre U wind component</td> <td>Daily Maximum 10 metre U wind component ERA5-Land</td> </tr> <tr> <td>max_v10</td> <td>m/s</td> <td>Max 10 metre V wind component</td> <td>Daily Maximum 10 metre V wind component ERA5-Land</td> </tr> <tr> <td>max_wind_direction</td> <td>degrees</td> <td>Wind direction of Max Wind</td> <td>Daily Maximum wind speed direction calculated from the U, V components</td> </tr> <tr> <td>max_wind_speed</td> <td>m/s</td> <td>Max wind speed norm</td> <td>Daily Maximum wind speed calculated from the U, V components</td> </tr> <tr> <td>max_wind_u10</td> <td>m/s</td> <td>10 metre U wind of Max Wind</td> <td>Daily 10 metre U wind component of Maximum Wind</td> </tr> <tr> <td>max_wind_v10</td> <td>m/s</td> <td>10 metre V wind of Max Wind</td> <td>Daily 10 metre V wind component of Maximum Wind</td> </tr> <tr> <td>min_d2m</td> <td>K</td> <td>Min 2 metre dewpoint temperature</td> <td>Daily Minimum 2 metre dewpoint temperature ERA5-Land</td> </tr> <tr> <td>min_rh</td> <td>%</td> <td>Min Relative humidity</td> <td>Daily Minimum Relative humidity calculated from t2m and d2m</td> </tr> <tr> <td>min_sp</td> <td>Pa</td> <td>Min Surface Pressure</td> <td>Daily Minimum Surface pressure ERA5-Land</td> </tr> <tr> <td>min_t2m</td> <td>K</td> <td>Min 2 metre temperature</td> <td>Daily Minimum 2 metre temperature ERA5-Land</td> </tr> <tr> <td>min_tp</td> <td>m</td> <td>Min Total precipitation</td> <td>Daily Minimum Total precipitation ERA5-Land</td> </tr> <tr> <td>min_u10</td> <td>m/s</td> <td>Min 10 metre U wind component</td> <td>Daily Minimum 10 metre U wind component ERA5-Land</td> </tr> <tr> <td>min_v10</td> <td>m/s</td> <td>Min 10 metre V wind component</td> <td>Daily Minimum 10 metre V wind component ERA5-Land</td> </tr> <tr> <td>ndvi</td> <td>unitless</td> <td>16-day NDVI</td> <td>Normalized Difference Vegetation Index - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid</td> </tr> <tr> <td>number_of_fires</td> <td>unitless</td> <td>Daily number of fires</td> <td>Daily number of fires</td> </tr> <tr> <td>smian</td> <td>unitless</td> <td>Soil moisture index anomaly</td> <td>Soil Moisture Index Anomaly 10day, 5km Europe - EDO https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2112</td> </tr> <tr> <td>sminx</td> <td>unitless</td> <td>Soil moisture index</td> <td>Soil Moisture Index 10day, 5km Europe - EDO https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2112</td> </tr> <tr> <td>ASPECT</td> <td>degrees</td> <td>Aspect</td> <td>Aspect calculated from Digital Elevation Model EU-DEM</td> </tr> <tr> <td>CLC_2006</td> <td>unitless</td> <td>Mode of Corine Land Cover 2006</td> <td>Mode of Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_0</td> <td>/100 %</td> <td>Fraction of lc class 0 (artificial_surfaces)</td> <td>Fraction of class 0 (artificial surfaces), Corine Class Codes [1, 3, 4, 5, 6, 7, 8, 9 , 10, 11], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_1</td> <td>/100 %</td> <td>Fraction of lc class 1 (discontinuous_urban)</td> <td>Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_2</td> <td>/100 %</td> <td>Fraction of lc class 2 (arable_land)</td> <td>Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_3</td> <td>/100 %</td> <td>Fraction of lc class 3 (permanent_crops)</td> <td>Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_4</td> <td>/100 %</td> <td>Fraction of lc class 4 (pastures)</td> <td>Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_5</td> <td>/100 %</td> <td>Fraction of lc class 5 (general_agricultural)</td> <td>Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_6</td> <td>/100 %</td> <td>Fraction of lc class 6 (forest)</td> <td>Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_7</td> <td>/100 %</td> <td>Fraction of lc class 7 (misc_vegetation)</td> <td>Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_8</td> <td>/100 %</td> <td>Fraction of lc class 8 (misc_no_vegetation)</td> <td>Fraction of class 8 (misc_no_vegetation), Corine Class Codes [30, 31, 32, 33, 34], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_9</td> <td>/100 %</td> <td>Fraction of lc class 9 (water)</td> <td>Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2012</td> <td>unitless</td> <td>Mode of Corine Land Cover 2012</td> <td>Mode of Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_0</td> <td>/100 %</td> <td>Fraction of lc class 0 (artificial_surfaces)</td> <td>Fraction of class 0 (artificial_surfaces), Corine Class Codes [1, 3, 4, 5, 6, 7, 8, 9 , 10, 11], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_1</td> <td>/100 %</td> <td>Fraction of lc class 1 (discontinuous_urban)</td> <td>Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_2</td> <td>/100 %</td> <td>Fraction of lc class 2 (arable_land)</td> <td>Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_3</td> <td>/100 %</td> <td>Fraction of lc class 3 (permanent_crops)</td> <td>Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_4</td> <td>/100 %</td> <td>Fraction of lc class 4 (pastures)</td> <td>Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_5</td> <td>/100 %</td> <td>Fraction of lc class 5 (general_agricultural)</td> <td>Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_6</td> <td>/100 %</td> <td>Fraction of lc class 6 (forest)</td> <td>Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_7</td> <td>/100 %</td> <td>Fraction of lc class 7 (misc_vegetation)</td> <td>Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_8</td> <td>/100 %</td> <td>Fraction of lc class 8 (misc_no_vegetation)</td> <td>Fraction of class 8 (misc_no_vegetation), Corine Class Codes [30, 31, 32, 33, 34], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_9</td> <td>/100 %</td> <td>Fraction of lc class 9 (water)</td> <td>Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2018</td> <td>unitless</td> <td>Mode of Corine Land Cover 2018</td> <td>Mode of Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_0</td> <td>/100 %</td> <td>Fraction of lc class 0 (artificial_surfaces)</td> <td>Fraction of class 0 (artificial_surfaces), Corine Class Codes [1, 3, 4, 5, 6, 7, 8, 9 , 10, 11], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_1</td> <td>/100 %</td> <td>Fraction of lc class 1 (discontinuous_urban)</td> <td>Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_2</td> <td>/100 %</td> <td>Fraction of lc class 2 (arable_land)</td> <td>Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_3</td> <td>/100 %</td> <td>Fraction of lc class 3 (permanent_crops)</td> <td>Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_4</td> <td>/100 %</td> <td>Fraction of lc class 4 (pastures)</td> <td>Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_5</td> <td>/100 %</td> <td>Fraction of lc class 5 (general_agricultural)</td> <td>Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_6</td> <td>/100 %</td> <td>Fraction of lc class 6 (forest)</td> <td>Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_7</td> <td>/100 %</td> <td>Fraction of lc class 7 (misc_vegetation)</td> <td>Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_8</td> <td>/100 %</td> <td>Fraction of lc class 8 (misc_no_vegetation)</td> <td>Fraction of class 8 (misc_no_vegetation), Corine Class Codes [30, 31, 32, 33, 34], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_9</td> <td>/100 %</td> <td>Fraction of lc class 9 (water)</td> <td>Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>DEM</td> <td>m</td> <td>Elevation</td> <td>Averaged Digital Elevation Model EU-DEM</td> </tr> <tr> <td>POP_DENS_2009</td> <td>humans/km^2</td> <td>Population density (2009)</td> <td>Population density for year 2009 from Worldpop</td> </tr> <tr> <td>POP_DENS_2010</td> <td>humans/km^2</td> <td>Population density (2010)</td> <td>Population density for year 2010 from Worldpop</td> </tr> <tr> <td>POP_DENS_2011</td> <td>humans/km^2</td> <td>Population density (2011)</td> <td>Population density for year 2011 from Worldpop</td> </tr> <tr> <td>POP_DENS_2012</td> <td>humans/km^2</td> <td>Population density (2012)</td> <td>Population density for year 2012 from Worldpop</td> </tr> <tr> <td>POP_DENS_2013</td> <td>humans/km^2</td> <td>Population density (2013)</td> <td>Population density for year 2013 from Worldpop</td> </tr> <tr> <td>POP_DENS_2014</td> <td>humans/km^2</td> <td>Population density (2014)</td> <td>Population density for year 2014 from Worldpop</td> </tr> <tr> <td>POP_DENS_2015</td> <td>humans/km^2</td> <td>Population density (2015)</td> <td>Population density for year 2015 from Worldpop</td> </tr> <tr> <td>POP_DENS_2016</td> <td>humans/km^2</td> <td>Population density (2016)</td> <td>Population density for year 2016 from Worldpop</td> </tr> <tr> <td>POP_DENS_2017</td> <td>humans/km^2</td> <td>Population density (2017)</td> <td>Population density for year 2017 from Worldpop</td> </tr> <tr> <td>POP_DENS_2018</td> <td>humans/km^2</td> <td>Population density (2018)</td> <td>Population density for year 2018 from Worldpop</td> </tr> <tr> <td>POP_DENS_2019</td> <td>humans/km^2</td> <td>Population density (2019)</td> <td>Population density for year 2019 from Worldpop</td> </tr> <tr> <td>POP_DENS_2020</td> <td>humans/km^2</td> <td>Population density (2020)</td> <td>Population density for year 2020 from Worldpop</td> </tr> <tr> <td>POP_DENS_2021</td> <td>humans/km^2</td> <td>Population density (2021)</td> <td>Population density for year 2021 from Worldpop</td> </tr> <tr> <td>ROAD_DISTANCE</td> <td>meters</td> <td>Distance from roads</td> <td>Distance from major roads from Worldpop</td> </tr> <tr> <td>ROUGHNESS</td> <td>unitless</td> <td>Roughness</td> <td>Roughness calculated from Digital Elevation Model EU-DEM</td> </tr> <tr> <td>SLOPE</td> <td>degrees</td> <td>Slope</td> <td>Slope calculated from Digital Elevation Model EU-DEM</td> </tr> <tr> <td>WATERWAY_DISTANCE</td> <td>meters</td> <td>Distance from waterways</td> <td>Distance from major waterways from Worldpop</td> </tr> </tbody> </table> <p>temporal_extent : (2009-03-06, 2021-08-29)</p> <p>spatial_extent : (18.7, 34.3, 28.9, 42.3)</p> <p>crs : EPSG:4326</p> <p>license : Creative Commons Attribution v4</p> <p>creators : Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis</p> <p>contact_person : Ioannis Prapas <iprapas (at) noa.gr></p> <p>citation : Ioannis Prapas, Spyros Kondylatos, & Ioannis Papoutsis. (2021). A Datacube for the analysis of wildfires in Greece (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4943354</p> <p>acknowledgements : This work has received funding from the European Union’s Horizon 2020 research and innovation project DeepCube, under grant agreement number 101004188.</p> <p>title : FireCube: A Daily Datacube for the Modeling and Analysis of Wildfires in Greece</p>
Prediction and analysis of phenotypes in the Arabidopsis clock mutant prr7prr9 using the Framework Model v2 (FMv2)
<p>This upload contains or links to the biological data, FMv2 model and simulations for the Chew et al. 2017 paper (bioRxiv <a href="https://doi.org/10.1101/105437">https://doi.org/10.1101/105437</a> ), updated 2022 as bioRxiv <a href="https://doi.org/10.1101/105437v2">https://doi.org/10.1101/105437v2</a>, mostly testing and simulating the effect of a slow circadian clock in the <em>prr7prr9 </em>double mutant compared to the Col wild type plants, with controls in <em>lsf1 </em>and <em>prr7 </em>single mutants. This is one of the outputs from the EU TiMet project, <a href="https://fairdomhub.org/projects/92">https://fairdomhub.org/projects/92</a>.</p> <p>Several data files contain results generated in the same studies, but not covered by the publication. For example, additional time points (18 or 21 days of growth), many additional metabolites, and additional genotypes including <em>pgm</em>, <em>lhy cca1, </em>and in one case, <em>toc1 </em>and <em>gi</em>.</p> <p>This data archive was updated during submisson to the journal _in Silico _Plants in 2022, and is formatted as a Research Object, generated by the Snapshot function of FairdomHub, based on <a href="https://fairdomhub.org/investigations/123">Investigation https://fairdomhub.org/investigations/123.</a> The same Snapshot is shared on FairdomHub and will be from the University of Edinburgh Datashare.</p> <p>We request that users gives appropriate credit to the authors of any data released here, as a norm of academic practice, including data released under CC-0 licence on the FairdomHub.</p>
Genetic association analysis of anti-VEGF treatment response in neovascular age-related macular degeneration
<p>Summary statisics of an association study of 6,908,005 genetic variants with anti-VEGF nAMD treatment response in 179 treatment-naïve nAMD probands. This dataset supplements the publication "Genetic Association Analysis of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration" (DOI: 10.3390/ijms23116094). Details regarding the methods and version numbers can be found in the corresponding manuscript.</p>
10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains 10-day backward trajectories along the ship track of the Antarctic Circumnavigation Expedition from Nov 2016 – April 2017 calculated with the Lagrangian analysis tool LAGRANTO using the 3D-wind fields from the European Centre for Medium Range Weather Forecasts (ECMWF) operational analysis data. The trajectories were started from up to 56 vertical levels between 0 and 500 hPa a.s.l. and various variables were interpolated along the trajectories.</p> <p><strong>Dataset contents</strong></p> <ul> <li>trajs_ACE.zip: lsl_${year}${month}${day}_${hour}, trajectory files (containing all trajectories starting at ${year}${month}${day} ${hour}UTC at the ACE track from different vertical levels), comma-separated values</li> <li>fig_map.zip: map_long10_${year}${month}${day}_${hour}.png, map plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by pressure, portable network graphics</li> <li>fig_cross.zip: cross10_q_${year}${month}${day}_${hour}.png, cross-section plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by specific humidity, portable network graphics</li> <li>data_file_header.txt, metadata for lsl-files, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This 10-day backward trajectory dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
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