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1,413 results for “dispersal data”
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Raw data for "The role of conidia in the dispersal of *Ascochyta rabiei*"
<p>Raw data associated with the pre-print, “<em>The role of conidia in the dispersal of </em>Ascochyta rabiei”, <a href="https://doi.org/10.1101/2020.05.12.091827">https://doi.org/10.1101/2020.05.12.091827</a></p> <p>There are six data files associated with this manuscript. Five are raw data, one was generated as a course of the analysis, “weather_summary.csv”. All files are in .csv format. Details for each including the number of columns and column contents and units follow.</p> <p><strong>Files and Content Descriptions</strong></p> <ul> <li><strong>BCG_weather_data.csv</strong> – 15-minute interval weather data from the Birchip Ag Group automated weather station near Curyo, Victoria, Australia for the time period of 01/10/2019 to 31/10/2019</li> <li><strong>Curyo_SPA_2019_weather.csv </strong>– 10-minute interval weather data from AgVictoria’s automated weather station at Curyo, Victoria, Australia from 22/01/2019 to 06/12/2019 recorded with Measurement Engineering Australia, Adelaide, Australia equipment</li> <li><strong>Dispersal_experiment_dates.csv</strong> – Data detailing each spread event at each location including the time trap plants were put out and brought in and date assessed</li> <li><strong>Horsham_SPA_2019_weather.csv</strong> – 10-minute interval weather data from AgVictoria’s automated weather station at Horsham, Victoria, Australia from 01/01/2019 to 06/12/2019 recorded with Measurement Engineering Australia, Adelaide, Australia equipment</li> <li><strong>lesion_counts.csv </strong>– Data detailing lesion counts on trap plants for each location and spread event</li> <li><strong>weather_summary.csv</strong>– Summary weather data detailing for each location and spread event</li> </ul> <p><em><strong>BCG_weather_data.csv </strong></em>The file “BCG_weather_data.csv” contains six columns:</p> <ul> <li><strong>Reading Time </strong>– the time at which the data was recorded</li> <li><strong>Rainfall</strong> – the amount of rainfall (mm)</li> <li><strong>Humidity</strong> – relative humidity (%)</li> <li><strong>Temperature</strong> – air temperature (˚C)</li> <li><strong>Wind Speed</strong> – wind speed (km/p)</li> <li><strong>Wind Direction</strong> – direction in which the wind was blowing from (cardinal directions)</li> </ul> <p><em><strong>Curyo_SPA_2019_weather.csv</strong></em> and H<em><strong>orsham_SPA_2019_weather.csv </strong></em>The files “Curyo_SPA_2019_weather.csv” and “Horsham_SPA_2019_weather.csv” both contain 22 columns:</p> <ul> <li><strong>Time</strong></li> <li><strong>Air Temperature - average (ºC)</strong></li> <li><strong>Soil Temperature - average (ºC)</strong></li> <li><strong>Relative Humidity - average (%)</strong></li> <li><strong>Wind Speed - minimum (km/h)</strong></li> <li><strong>Wind Speed - average (km/h)</strong></li> <li><strong>Wind Speed - maximum (km/h)</strong></li> <li><strong>Wind Direction - average (º)</strong></li> <li><strong>Solar Radiation - average (W/m^2)</strong></li> <li><strong>Sigma - average (deg)</strong></li> <li><strong>Rainfall - (mm)</strong></li> <li><strong>Voltage - minimum (V)</strong></li> <li><strong>Voltage - average (V)</strong></li> <li><strong>Voltage - maximum (V)</strong></li> <li><strong>Apparent Temperature - average (ºC)</strong></li> <li><strong>Dew Point - average (ºC)</strong></li> <li><strong>Delta T - average (ºC)</strong></li> </ul> <p><em><strong>Dispersal_experiment_dates.csv </strong></em>The file “Dispersal_experiment_dates.csv” contains five columns:</p> <ul> <li><strong>site </strong>– The experiment location</li> <li><strong>rep </strong>– Spread event number for each location</li> <li><strong>time out </strong>– date and time on which the trap plants were deployed in the paddock for the spread event (rainfall)</li> <li><strong>time removed </strong>– date and time on which the trap plants were retrieved from the paddock after the spread event (rainfall)</li> <li><strong>assessment date</strong>– date on which trap plants were assessed for number of lesions</li> </ul> <p><em><strong>lesion_counts.csv </strong></em>The file "lesion_counts.csv” contains thirteen columns:</p> <ul> <li><strong>site </strong>– The experiment location</li> <li><strong>rep </strong>– Spread event number for each location</li> <li><strong>distance </strong>– Distance from infection source (m)</li> <li><strong>station – </strong>Trap plant units (number of trap plants at each point along transect)</li> <li><strong>transect – </strong>One of ten repeating lines along which the trap plant stations were deployed in a 90˚ arc downwind of the infection source</li> <li><strong>dist_stat – </strong>A concatenation of the ‘dist’ and ‘station’ columns</li> <li><strong>plant_no – </strong>Total number of plants at a given station</li> <li><strong>pot_no </strong>– Individually assigned pot number for each individual transect (1 – 56)</li> <li><strong>counts_p1 – </strong>Lesion counts for Pot 1</li> <li><strong>counts_p2 – </strong>Lesion counts for Pot 2</li> <li><strong>counts_p3 – </strong>Lesion counts for Pot 3</li> <li><strong>counts_p4 – </strong>Lesion counts for Pot 4</li> <li><strong>counts_p5 – </strong>Lesion counts for Pot 5</li> </ul> <p><em><strong>weather_summary.csv </strong></em>The file “weather_summary.csv” contains six columns that summarise the weather conditions during each of the six spread events at three experiment plot locations.</p> <ul> <li><strong>site </strong>– The experiment location</li> <li><strong>rep</strong> – Spread event number for each location</li> <li><strong>mws</strong> – Mean wind speed for the spread event (m/s)</li> <li><strong>ws_sd </strong>– Wind speed standard deviation for the spread event</li> <li><strong>mwd </strong>– Mean wind direction for the spread event (˚)</li> <li><strong>sum_</strong>rain – Total precipitation during spread event including both natural rainfall and overhead sprinkler irrigation (mm)</li> </ul>
Data from: Behavioural responses to potential dispersal cues in two economically important cereal-feeding eriophyoid mite species
<p>Variables:</p> <ol> <li>species (ABH = <em>Abacarus hystrix</em>, WCM = <em>Aceria tosichella</em> MT1 genetic lineage)</li> <li>variant - experimental treatment (type of dispersal cue): wind, an insect vector, presence of a fresh plant</li> <li>feeding - no. of feeding specimens</li> <li>walking - no. of walking specimens</li> <li>standing - no. of specimens standing vertically</li> <li>cha - no. of specimens forming chains</li> <li>mob - no. of specimens capable to move</li> <li>pop - no. of all specimens (including quiescent stages)</li> </ol>
Data from: Effects of dispersal and geomorphology on riparian seedbanks and vegetation in a boreal stream
<p>SiteData: information that describes 20 riparian zones along Svartån, a boreal free-flowing stream, indicated per LocationID (column A). Coordinates are given in SWEREF 99 TM (column B and C) and degrees of longitude and latitude (column D and E). RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Side of stream indicates plot placement when looking towards downstream. Data collection is described in the paper linked to below. </p> <p> </p> <p>LitterData: information that describes species lists of litter seedbanks from 20 riparian sites. Litter samples were taken in an unstandardised manner at each location. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing.</p> <p> </p> <p>SeedData: information that describes the soil seedbank composition from 20 riparian sites. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Layer refers to samples that are taken from from layer 0-1 cm in the soil, 1-5 cm or from 5-10 cm deep. Data collection is described in the paper linked to below. </p> <p> </p> <p>VegetationData: information that describes vegetation composition from 20 riparian sites. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Abundance is indicated following the categories in Table 1. Data collection is described in the paper linked to below. </p> <p> </p> <p>Table 1. Vegetation cover classes.</p> <table> <tbody> <tr> <td> <p><strong>Code</strong></p> </td> <td> <p><strong>Cover (%)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p><1</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>1-3</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>3-5</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>5-15</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>15-25</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>25-50</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>50-75</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>75-100</p> </td> </tr> </tbody> </table> <p> </p> <p>For more information, help or collaboration, please contact Jacqueline.Hoppenreijs@kau.se. If you use the data here in your work or research, please cite the publication appropriately.</p>
Data for article: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils
<p>Supplementary information for:</p> <p><strong>A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils</strong></p> <p>Torsten Hauffe, Mathias M. Pires, Tiago B. Quental, Thomas Wilke, and Daniele Silvestro</p> <p> </p><ul> <li> Simulations <ul> <li>Scripts <ul> <li>Scenario1_SamplingHeterogeneity.R: Script to simulate biogeographic histories with sampling heterogeneity</li> <li>Scenario3_SealevelInvasion.R: Script to simulate biogeographic histories where sea level facilitates dispersal and invasion induces extinction</li> <li>Scenario3_DiversityDependence.R: Script to simulate diversity-dependent biogeographic histories</li> <li>Scenario4_TraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> <li>Scenario5_CategoricalTraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> </ul> </li> <li>Results <ul> <li>Scenario1_SamplingHeterogenetiy_alpha05.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 0.5</li> <li>Scenario1_SamplingHeterogenetiy_alpha1.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 1</li> <li>Scenario1_SamplingHeterogenetiy_alpha2.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 2</li> <li>Scenario1_SamplingHeterogenetiy_alpha10.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 10</li> <li>Scenario2_Independent_dispersal_and_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_independent_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_independent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and invasion induced extinction</li> <li>Scenario3_Independent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-independent dispersal and extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_independent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Independent_dispersal_and_Diversity_dependent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and extinction</li> <li>Scenario4_Independent_dispersal_and_extinction.txt: Results of scenario 4 with trait-independent dispersal and extinction</li> <li>Scenario4_Trait_dependent_dispersal_and_independent_extinction.txt: Results of scenario 4 with trait-dependent dispersal and independent extinction</li> <li>Scenario4_Independent_dispersal_and_trait_dependent_extinction.txt: Results of scenario 4 with independent dispersal and trait-dependent extinction</li> <li>Scenario4_trait_dependent_dispersal_and_extinction.txt: Results of scenario 4 with trait-dependent dispersal and extinction</li> <li>Scenario5_CatTrait_dependent_dispersal_and_independent_extinction.txt: Results of model 2 with categorical traits (e.g family) influence dispersal but no influence of a category-specific continuous traits</li> </ul> </li> </ul> </li> <li>Carnivora <ul> <li>BinnedOccurrence: Folder with 100 replicates of binned occurrences of max. 330 carnivoran genera throughout the Neogene</li> <li>BodyMass: Folder with 100 replicates of body mass for 330 carnivoran genera</li> <li>Sealevel: Folder with sea level through the Neogene</li> <li>Temperature: Folder with the temperature record of the Neogene</li> <li>Families: Folder with families as taxonomic proxy for phylogeny. FamilyGeneraNumeric.txt is the numeric coding used for the Bayesian analyses of carnivoran biogeography</li> </ul> </li> </ul> <p></p>
Data from: Group size and dispersal ploys: An analysis of commuting behaviour of the pond bat (Myotis dasycneme)
<p>This study aimed to provide a description on how Pond bats (<em>Myotis dasycneme</em>) disperse, how to recognize a commuting route, and details about the effort needed to make a complete survey of one commuting route. The study area covered the provinces of Zuid-Holland, Overijssel, Friesland, Noord-Holland, and Utrecht. During 6 years of study between 2002 and 2009, researchers and bat volunteers studied pond bats along several waterways (all waterways wider than 10 m) between known roosts and their hunting areas. All the observations were made between April and September, starting 20 min before sunset. During the entire observation effort, the time (in hours and minutes) and direction of each bat was recorded. The time that each bat passed the observation location was later transformed to minutes after sunset. The number of animals on commuting route was related to the number of animals present in their respective roost.</p> <p> </p> <p>Data are organized in 3 files: <strong>commuting data 10 minutes.csv</strong>, <strong>commuting data.csv</strong> and <strong>observations waddinxveen.csv</strong>. The variables in these data files are explained here:</p> <p>Date: the observation date</p> <p>Location description: description of the location</p> <p>X Y: The coordinates of the location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p>Long Lat: The coordinates of the location in longitude and latitude.</p> <p>Distance over water: commuting distance over water. For each route, the distance (d) over water between roost and observation location was measured from a topographical map and expressed in kilometres.</p> <p>Moon cover: the amount of moon cover, expressed in percentages.</p> <p>Roost location: the assumed location of the roost of the bats passing on their commuting route</p> <p>Max N of bats in roost: the max number of bats observed emerging from a roost.</p> <p>Sum N of bats over 10-minute interval: the sum of all the observed bats passing in one direction within a 10-minute interval</p> <p>Time after sunset in 10 min: the begin time of each interval, measured in minutes after sunset</p> <p>Peak time after sunset: the time of the observed peak in numbers of bats, in minutes after sunset.</p> <p>Area: the municipality near the observation location.</p> <p>Total N of pond bats on route: the total number of pond bats observed on route, in the given observation time. Including foraging and returning bats.</p> <p>Total N of commuting pond bats: the total number of bats observed commuting (excluding all other behaviours).</p> <p>Time of first bat minutes after sunset: the time of the first bat, measured in minutes after sunset.</p> <p>Duration of commuting: the time in hours between the first and the last bat observed commuting.</p> <p>Observation time: the total duration (in minutes) of the observation period.</p> <p>Moon phases: a 1–3 scale, where c1 is the new moon, c2 is the first quarter, c3 half moon, c4 is the last quarter and c5 is the full moon.</p> <p>Cloud cover: estimation of the cover, using the following three categories: c1-0%–25% cover (clear night sky or some isolated clouds), c2-25%–75% cover (several scattered clouds but not covering more than 75% of the night sky), and c3- 75%–100% cover (scattered clouds covering more than 75% of the night sky to a completely overcast night sky</p> <p>Observation type: observation of either emerging bats from a roost (roost) or bats observed on commuting route (commuting).</p> <p> </p> <p>In addition, we also provide 2 pdf’s containing the observation protocols (in Dutch) for counting emerging bats (<strong>Handleiding tellen van een groep meervleermuizen.pdf</strong>) and bats along a commuting route (<strong>Handleiding vliegroute telling.pdf</strong>). The protocols are intended for professionals and citizen scientists.</p>
Dung removal and secondary seed dispersal data
<p>The datasets are described in the following data paper:<br> <br> Tanja Milotić, Christophe Baltzinger, Carsten Eichberg, Amy Eycott, Marco Heurich, Jörg Müller, Jorge Ari Noriega, Rosa Menendez, Jutta Stadler, Réka Ádám, Tessa Bahiga Bargmann, Isabelle Bilger, Jörn Buse, Joaquin Calatayud, Constantin Ciubuc, Gergely Boros, Marie Hauso, Pierre Jay-Robert, Märt Kruus, Enno Merivee, Geoffrey Miessen, Anne Must, Elham Omidzadeh Ardali, Elena Preda, Iraj Rahimi, Dirk Rohwedder, Eleanor M. Slade, László Somay, Pejman Tahmasebi, Stefano Ziani, Maurice Hoffmann (2018) Dung beetle assemblages and associated dung removal and secondary seed dispersal: data from a large-scaled multi-site experiment in the Western Palaearctic. Frontiers of Biogeography, Volume 10, Issue 1-2.</p>
Larval dispersal histogram data used for ATLAS deliverable D1.6: Biologically realistic Lagrangian dispersal and connectivity
<p>Larval dispersal histogram data for ATLAS deliverable D1.6 "Biologically realistic Lagrangian connectivity" (https://www.eu-atlas.org/resources/atlas-partners-document-area/atlas-deliverables/455-d1-6-biologically-realistic-lagrangian-connectivity/file). Tar archive files are ordered by ATLAS case study source region and with folders by larval behaviour type. The numbered behaviour types are described in deliverable D1.6. Each netcdf histogram file, e.g. hists_age_21.nc, contains the histogram for larvae of a single age in 5-day steps, from 00 (0 days) to 37 (185 days).</p> <p>Within each file histogram file, particle counts in each Viking20 model grid-cell are contained in a 4-d array with dimensions (launch month, lauch year, model gridsquare y index, model gridsquare x index). The Viking20 grid in the North Atlantic is the ORCA tripolar grid. Details of the model mesh are in the included file viking20_mesh_mask.tgz</p> <p>Histograms are in netcdf files:</p> <p>============================</p> <p>$ ncdump -h hists_age_00.nc<br> netcdf hists_age_00 {<br> dimensions:<br> coordinate = 4 ;<br> coordinate_1 = 50 ;<br> coordinate_2 = 1719 ;<br> coordinate_3 = 1784 ;<br> variables:<br> int64 coordinate(coordinate) ;<br> coordinate:units = "month" ;<br> coordinate:long_name = "Launch month" ;<br> int64 coordinate_1(coordinate_1) ;<br> coordinate_1:units = "year" ;<br> coordinate_1:long_name = "Launch year" ;<br> int64 coordinate_2(coordinate_2) ;<br> coordinate_2:units = "index" ;<br> coordinate_2:long_name = "J index" ;<br> int64 coordinate_3(coordinate_3) ;<br> coordinate_3:units = "index" ;<br> coordinate_3:long_name = "I index" ;<br> int64 data(coordinate, coordinate_1, coordinate_2, coordinate_3) ;<br> data :long_name = "particle count" ;</p> <p>// global attributes:<br> :Conventions = "CF-1.6" ;<br> }</p> <p>==========================================</p> <p> </p> <p> </p>
Data from: "Imprinted habitat selection varies across dispersal phases in a raptor species"
<p><span><span>Natal Habitat Preference Induction (NHPI) plays a significant role in shaping settlement decisions in dispersive animals. Despite its importance, limited research has explored how NHPI varies during natal dispersal phases and across different types of natal habitats. In this study, we examined NHPI in 77 GPS-tagged juvenile red kites <em>(Milvus milvus</em>) originating from different natal habitats along an elevational gradient in Switzerland. We applied individual-based step selection analysis to investigate habitat selection from independence to settlement. We found that during the prospecting phase, individuals predominantly selected habitats similar to their natal environments. However, this pattern changed in the settlement phase: individuals fledged from habitats at higher elevations or closer to urban areas mostly avoided similar habitats (negative NHPI), while those from areas with more farmlands or pastures (combined with forests) showed a preference for similar habitats (positive NHPI). Moreover, the magnitude and individual variation in NHPI differed depending on the natal habitat types from which individuals originated. These findings highlight that strength, direction, and individual variation in NHPI differ between natal habitat types and dispersal phases. Natal habitats therefore can have pervasive legacy effects on subsequent habitat selection, likely affecting population and range dynamics.</span></span></p>
Supporting data for "Dispersive sensing of charge states in a bilayer graphene quantum dot"
<p>Supporting data and analysis scripts for all figures in the article "Dispersive sensing of charge states in a bilayer graphene quantum dot", Appl. Phys. Lett. <strong>118</strong>, 093104 (2021); <a href="https://doi.org/10.1063/5.0040234">https://doi.org/10.1063/5.0040234</a></p> <p>The files are sorted according to the figures/panels in the publication with a "0-README.txt" file including further information. </p> <p>The following versions of Pyhton and the packages have been used:<br> python: 3.6.10<br> numpy: 1.18.1<br> matplotlib: 3.1.3<br> scipy: 1.4.1</p>
Micheluz et al energy dispersive X-ray-spectroscopy data
<p><strong># Micheluz et al energy dispersive X-ray-spectroscopy data</strong></p> <p><strong>## paper: https://doi.org/10.3390/pathogens11121462</strong></p> <p><strong>### Dataset: </strong></p> <p>Dataset1_Micheluz-et-al.csv</p> <p>This is a CSV file with 206 lines and 12 columns.</p> <p>Data are energy-dispersive X-ray spectroscopy data as weight%</p> <p>Explanation of the heading: Site, the Italian city where the samples of Eurotium halophilicum were collected;</p> <p>Structure, the features analysed (EPS, conidia, background); presence of crystal (TRUE/FALSE) indicates if in the areas/samples were detected biogenic crystals;</p> <p>C, carbon; O, oxygen; Na, sodium; P, phosphorus; S, sulphur; Cl, chlorine, K, potassium; Ca, calcium; Au, gold; Total, the sum of all the elements.</p> <p> </p> <p>Dataset1_Micheluz-et-al.csv</p> <p>This is a CSV file with 24 lines and 8 columns.</p> <p>Data are energy-dispersive X-ray spectroscopy data as atomic%</p> <p>Explanation of the heading: ID, is the biogenic crystal analysed; </p> <p>C, carbon; O, oxygen; Na, sodium; S, sulphur; Cl, chlorine, Ca, calcium; Total, the sum of all the elements.</p> <p><strong>### Sampling</strong></p> <p>The mycelium samples analysed with Energy-dispersive X-ray spectroscopy were obtained by pressing a carbon-based, impurity-free adhesive tape (FungiTapeTM, Scientific Device Lab., Inc Glenview, IL, USA) onto the fungal colonies found on the spines of books in five Italian libraries in Turin, Venice, Genova and Rome (2 libraries in Rome).</p> <p><strong>### Methods</strong></p> <p>Energy-dispersive X-ray spectroscopy was performed with an INCA Oxford 250 system, maintaining the electron beam at 20 keV, with a mean working distance to the sample of 12.5 mm. The electron beam could be focused on very small areas, in the order of nm2, allowing a surface resolution that readily resolves objects that are a few tens of nm in dimension. This way, a database was obtained with repeated observations of the composition of conidia and other fungus structures carried out on samples from the covers of different books. Some samples were analysed with energy-dispersive X-ray spectroscopy also after metallisation in order to be able to focus on peculiar structures like the (apparently biogenic) crystals. When this was the case, the spectra obtained contained gold, also present in the background.</p> <p>The calibration of the apparatus was based on the standards CaCO3, SiO2, albite, MgO, Al2O3, GaP, FeS2, wollastonite, feldspar MAD-10, Ti and Fe, supplied by Agar Scientific Ltd. (Stansted, UK) and the conventional ZAF correction (atomic number Z, absorption A, fluorescence F) from the Oxford INCA 250 software was applied to the measurements to convert apparent concentrations (raw peak intensity) into (semi-quantitative) concentrations corrected for inter-element matrix effects.</p> <p><strong>### Labels Used</strong></p> <p>Conidia = part of the fungal mycelium analysed</p> <p>EPS = Extracellular polymeric material present in the mycelium</p> <p>Background = the adhesive tape (made of carbon) used to collect the mycelium and prepare the samples for observation with scanning electron microscopy</p>
IMMERSE Horizon 2020 Project Downstream User Toolbox – data for tutorial on impact of wave coupling on surface particle dispersion simulations
<p>Exemplary data for tutorial on impact of wave coupling on surface particle dispersal simulations<br> <a href="https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU">https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU</a><br> created as part of the downstream user toolbox of the IMMERSE Horizon 2020 project (<a href="https://immerse-ocean.eu/">https://immerse-ocean.eu/</a>).</p> <p>In the tutorial the impact of new options for the representation of wave-current interactions in the NEMO ocean model (<a href="https://www.nemo-ocean.eu/">https://www.nemo-ocean.eu/</a>) on surface particle simulations are tested in a case study for the Mediterranean Sea. The tutorial consists of two jupyter notebooks: Parcels_CalcTraj.ipynb and CompTraj_uncoupledVScoupled.ipynb. Parcels_CalcTraj.ipynb calculates Lagrangian particle trajectories based on velocity output from ocean only as well as coupled ocean-wave model simulation by making use of the OceanParcels software (<a href="https://oceanparcels.org/">https://oceanparcels.org/</a>). CompTraj_uncoupledVScoupled.ipynb compares dispersal statistics of Lagrangian particle trajectories calculated from ocean-only vs coupled ocean-wave model simulations.</p> <p>This repository contains the surface velocity and ocean model grid data needed to run Parcels_CalcTraj.ipynb, as well as the trajectory data produced by Parcels_CalcTraj.ipynb, which is needed to run CompTraj_uncoupledVScoupled.ipynb. The surface velocity data stems from two simulations with a regional high-resolution (1/24° horizontal resolution) model configuration for the Mediterranean Sea: a coupled ocean-wave model simulation and a complimentary ocean-only simulation. These model simulations make use of the NEMO v4.2-RC ocean model, the Wave Watch 3 v.6.07 wave model, the OASIS3-MCT coupler, and ECMWF atmospheric fields; they are described in detail in IMMERSE deliverable D5.7 “Assessment of wave-current effects on the circulation in theMed-MFC system”<strong>.</strong></p>
Morphological cladogenesis and terminal dwarfing in extinct Late Miocene through Pliocene menardiform globorotalids: New complementary data to «Evolutionary prospection in the Neogene planktic foraminifer Globorotalia menardii and related forms from ODP Hole 925B (Céara Rise, western tropical Atlantic): evidence for gradual evolution superimposed by long distance dispersal ?, Swiss J. Palaeontology, 135:205-248»
<p>A complementary morphometric data set is provided to the study of Knappertsbusch (2016) about the shell evolution of menardiform globorotalids (Neogene planktic foraminifera) at ODP Hole 925B from Céara Rise in the the western tropical Atlantic. The new measurements confirm splitting of extinct <em>Globorotalia multicamerata</em> from the <em>G. menardii</em> stock via the intermediate form <em>G. limbata</em> between about 6 Ma to 5 Ma ago. After splitting both <em>G. limbata</em> and <em>G. multicamerata</em> show gradual divergence from <em>G. menardii</em> in several shell parameters illustrating morphological cladogenesis. Between 2.88 Ma and 2.59 Ma the same parameters show a concerted trend towards reduced values indicating pre-extinction dwarfing. A comparison with published literature data of Delta<sup>18</sup>O trends between species, that populated the mixed layer (<em>Globigerinoides sacculifer</em>) and the thermocline layer (<em>Neogloboquadrina dutertrei</em>) at this location during those times suggests, that both divergence and subsequent dwarfing trends were probably the results of changes in upper watermass stratification.</p> <p>The complementary data set is provided in six zipped archives APPENDIX A, B, C, D, E and F (zipped with free software 7-Zip 22.00 (x64), 2022-06-15 from 1999-2022 Igor Pawlow), together with a description of the data in file Report_925B_suppl_1.pdf.</p>
Seed dispersal data for Warneke et al "Habitat fragmentation alters the distance of abiotic seed dispersal through edge effects and direction of dispersal"
This csv file contains seed dispersal data for five species (Carphephorus bellidifolius, Aristida beyrichiana, Liatris squarrulosa, Sorghastrum secundum, and Anthenantia villosa). Data were collected at the Savannah River Site, near Aiken, South Carolina, United States. Data were collected between November 17, 2009, to January 22, 2010 and were collected using the methods outlined in this document.
Simulation data and scripts for CFD-DEM simulation of saturated bi-disperse granular flows
<ul> <li>Data set 1 - contains the raw data required to replicate and validate all plots in the main article. </li> <li>Sample case - a .zip file which includes codes which are needed to simulate a CFD-DEM case of a steady granular flow in water with cyclic boundaries in the stream wise direction. Also enclosed is a ReadMe.txt file detailing the implementation instructions for both Esys particle and OpenFOAM codes. Download links for Esys particle and OpenFOAM are also included</li> <li>Geo file generator - a .zip file which includes Esys particle codes that can be used to generate a .geo file specifying the initial position of the particles used in the test simulations. A ReadMe.txt file is enclosed with more detailed implementation instructions.</li> </ul>
Numerical code and data for: Suppressed Charge Dispersion via Resonant Tunneling in a Single-Channel Transmon
<p>The numerical code and data accompanying the analysis of Figs. 4 and 5 of Suppressed Charge Dispersion via Resonant Tunneling in a Single-Channel Transmon, Phys. Rev. Lett. (2020)</p>
Data and simulations files for the article "Quasinormal-mode perturbation theory for dissipative and dispersive optomechanics".
<p>Data and simulations files for the article "Quasinormal-mode perturbation theory for dissipative and dispersive optomechanics".</p>
Data from: Dispersal out of Wallacea spurs diversification of Pteropus flying foxes, the world's largest bats (Mammalia: Chiroptera)
<p><b>Aim: </b>Islands provide opportunities for isolation and speciation. Many landmasses in the Indo-Australian Archipelago (IAA) are oceanic islands, and founder-event speciation is expected to be the predominant form of speciation of volant taxa on these islands. We studied the biogeographic history of flying foxes, a group with many endemic species and a predilection for islands, to test this hypothesis and infer the biogeographic origin of the group.</p> <p><b>Location: </b>Australasia, Indo-Australian Archipelago, Madagascar, Pacific Islands</p> <p><b>Taxon: </b><i>Pteropus</i> (Pteropodidae)</p> <p><b>Methods: </b>To infer the biogeographic history of <i>Pteropus</i>, we sequenced up to 6169 bp of genetic data from 10 markers and reconstructed a multilocus species tree of 34 currently recognized <i>Pteropus</i> species and subspecies with 3 <i>Acerodon</i> outgroups using <span>BEAST</span> and subsequently estimated ancestral areas using models implemented in <span>BioGeoBEARS</span>.</p> <p><b>Results: </b>Species-level resolution was occasionally low because of slow rates of molecular evolution and/or recent divergences. Older divergences, however, were more strongly supported and allow the evolutionary history of the group to be inferred. The genus diverged in Wallacea from its common ancestor with <i>Acerodon</i>; founder-event speciation out of Wallacea was a common inference. <i>Pteropus </i>species in Micronesia and the western Indian Ocean were also inferred to result from founder-event speciation.</p> <p><b>Main conclusions: </b>Dispersal between regions of the IAA and the islands found therein fostered diversification of <i>Pteropus </i>throughout the IAA and beyond. Dispersal in <i>Pteropus</i> is far higher than in most other volant taxa studied to date, highlighting the importance of inter-island movement in the biogeographic history of this large clade of large bats.</p>
Ultrasonic guided-wave experiment data for manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textilecomposites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
Experimental data in support of manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
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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
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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.