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621 results for “methodologies”
FIGURE 2 in Application of shell spiral deviation methodology to fossil brachiopods: Implications for obtaining specimen ontogenetic ages
FIGURE 2. Example of a shell sample (Platystrophia ponderosa) sectioning through the plane of symmetry, producing two identical halves. Both halves were analyzed for their spiral deviations.
FIGURE 4 in Application of shell spiral deviation methodology to fossil brachiopods: Implications for obtaining specimen ontogenetic ages
FIGURE 4. Spiral deviations from the dorsal and ventral valves of specimens one (1), two (2), and three (3) of the species Laqueus rubellus.
FIGURE 1 in Application of shell spiral deviation methodology to fossil brachiopods: Implications for obtaining specimen ontogenetic ages
FIGURE 1. Ventral, dorsal, posterior, and longitudinal-sectional views of the fossil brachiopods Laqueus rubellus, Terebratula terebratula, Pseudoatrypa sp., and Platystrophia ponderosa. Scale bars equal 1 cm.
FIGURE 9 in Application of shell spiral deviation methodology to fossil brachiopods: Implications for obtaining specimen ontogenetic ages
FIGURE 9. Comparison of spiral deviation graphs when the dorsal valve of Terebratula terebratula specimen is fit to one (left) and two (right) spirals. A and B represent locations on the graphs that change drastically when two spirals are fit to the biological outline instead of one. Minimum A becomes more distinct when two spirals are used whereas the opposite occurs for minimum B.
FIGURE 3 in Application of shell spiral deviation methodology to fossil brachiopods: Implications for obtaining specimen ontogenetic ages
FIGURE 3. Example of shell (Laqueus rubellus) digitization and spiral fitting. 1, The brachiopod shell was digitized by clicking the outline in an anterior-ward direction, starting at the umbo. 2, The digitized outline was saved as (x,y) coordinates, which were used to plot a biological outline in R. 3, R code was used to fit a perfect logarithmic spiral to the shell outline. 4, The deviations from a perfect spiral were represented in a spiral deviation graph, with green points representing spiral maxima and red points representing spiral minima.
Fig. 1 in Automated size measurements of Halyomorpha halys (Stål) (Heteroptera: Pentatomidae) with simple imagebased methodology
Fig. 1. Illustration of the image-based measurement method. (A) Image of brown marmorated stink bug used for automated size estimation, shown afer CAmera aS Scanner processing. (B) Image of segmented insect, whereby the body is coded as white and the antennae and legs are shown as gray. Gray sec- tions are excluded from analysis. The red line represents the pronotal width measurement, the purple line denotes the ventral length measurement.
Data sets accompanying "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning"
<p>This data sets accompany the publication "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning" in One Earth.</p> <ul> <li>lur-db-output-zenodo.xlsx: contains all land use requirement estimates for renewable energies reviewed in the publication. Meta-data is reported in one sheet, the other sheet contains the original data.</li> <li>authorship-tree-zenodo.xlsx: contains the information necessary to derive the authorship tree shown in the supplementary information. Meta-data is reported in one sheet, the other sheet contains the original data.<br><br><br></li> </ul>
Micro-urban environment experimental dataset to validate performance of different Computational Fluid Dynamics methodologies.
<p><span><span>This dataset enclosed wind 3D geolocated wind flow and air concentrations </span><span>5-minutal </span><span>data </span><span>collected in </span><span>El Prat del Llobregat (Spain) </span><span>between January and August 2022 in the context of the experiment 1012-ibam of the FF4EuroHPC European project. The intention of this dataset is to provide a </span><span>resource to do performance benchmark of micro-urban chemical – dispersion models to assess their performance</span><span>. To do so, we enclose experimental data collected by </span><span>Bettair</span><span> Mk2 Series Air quality monitors, 2 Air Quality Monitoring stations equipped with reference instruments f</span><span>rom “La </span><span>Xarxa</span><span> de </span><span>Vigilància</span> <span>i</span> <span>Previsió</span><span> de la </span><span>Contaminació</span> <span>Atmosfèrica</span><span> (XVPCA)”</span><span>, and different data from the repository of the ECMWF Era-5 land and CAMS. We also provide the </span><span>3D watertight geometry model of the </span><span>el</span><span> Prat de Llobregat (Spain) in step file format</span><span> (layout from 2020)</span><span>.<br></span></span></p>
Ice Anatomy: A Benchmark Dataset and Methodology for Automatic Ice Boundary Extraction from Radio-Echo Sounding Data
<p>The measurement of ice thickness is of great importance for the accurate estimation of glacier volume and the delineation of their bedrock topography. In particular, this is a crucial factor in forecasting the future evolution of glaciers in the context of a changing climate. In order to derive the ice thickness, the travel time of electromagnetic waves in radargrams acquired by radio-echo sounding (RES) systems is analyzed. This can only be achieved by identifying the ice surface and underlying ice bottom in corresponding radargrams. Manually identifying these two reflection horizons in RES data is a laborious and time-consuming process. Consequently, scientists are attempting to automate this task through the use of techniques such as deep learning. Such automation can significantly reduce the time between a field campaign and the calculation of the glacier's ice thickness distribution. In this paper, we present the first benchmark dataset for delineating the ice surface and bottom boundaries in RES data, to facilitate straightforward comparisons of deep learning models in the future. The ``IceAnatomy'' dataset comprises radargrams and the corresponding manual picks, amounting to a total of over 45,000km of observations. The RES data originates from three sources: FAU, CReSIS, and AWI. The dataset comprises different RES systems as well as different pre-processing methods. In addition, the data was acquired over a large range of geographical and glaciological settings, featuring different thermal regimes present in Antarctica and the Southern Patagonian Icefield. This diversity ensures that the models' behaviors can be analyzed in different scenarios. We define a standardized train-test split for each source in the dataset. This allows us to introduce not only a baseline model trained on the entire training set (the ``omni'' model), but also three source-specific baseline models. The source-specific models are trained exclusively on the subset of the training data acquired by the specified source. The baseline models provide an initial benchmark against which subsequent models can be compared. The source-specific models demonstrate more accurate results than the omni model. For the FAU, CReSIS, and AWI test sets, the source-specific models achieve Mean Meter Errors of 2.1m, 23.1m, and 4.9m for the ice surface and 9.1m, 78.2m, and 29.3m for the ice bottom. In relation to the mean measured ice thickness, these errors equate to 1.2%, 3.1%, and 0.3% for the ice surface and 4.9%, 10.4%, and 1.5% for the ice bottom.</p> <p> </p> <p> For more information, please read the following paper:</p> <p>[Coming soon. Currently under review.]</p> <p>Please also cite this paper if you plan on using the dataset.</p> <p> </p> <p>For the implementation of a baseline model please visit:</p> <p>[Coming soon]</p> <p> </p>
Wind and Precipitation Extremes in Great Britain (1979-2019) to apply the methodology for Spatiotemporal Identification of Compound Hazards
<p>The data used in this study is extracted from ERA5. ERA5 is a climate reanalysis product which was released in 2019 by ECMWF and benefits from the latest improvements in the field (Hersbach et al., 2020). ERA5 data (ECMWF, 2020) is available 1979 to present (we use up to September 2019), with a spatial resolution of 0.25deg x 0.25deg and an hourly temporal resolution. The data resolves the atmosphere using 137 levels from the surface up to a height of 80 km (ECMWF, 2020). ERA5 data are generated with a short forecast of 18 h twice a day (06:00 and 18:00 UTC) and assimilated with observed data (ECMWF, 2020). more information about ERA5 can be found <a href="https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation">here</a>.</p> <p>The two following variables are extracted from the product:</p> <ul> <li> <p>Extreme precipitation (p): accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth’s in one hour (mm). This value is averaged over a grid cell.</p> </li> <li> <p>Extreme wind (w): hourly maximum wind gust at a height of 10 m above the surface of the Earth (m s-1). The WMO (2021) defines a wind gust as the maximum of the wind averaged over 3 s intervals. As this duration is shorter than a model time step, this value is deduced from other parameters such as surface stress, surface friction, wind shear and stability. This value is averaged over a grid cell.</p> </li> </ul> <p>Importation of the raw data</p> <p>Input data is divided into 4 files for each variables representing 4 periods:</p> <ol> <li>1979-1986</li> <li>1987-1997</li> <li>1998-2008</li> <li>2009-2019</li> </ol> <pre>library(ncdf4) filer=c(paste0(getwd(),"/data/in/raindat_7986.nc"), paste0(getwd(),"/data/in/raindat_8797.nc"), paste0(getwd(),"/data/in/raindat_9808.nc"), paste0(getwd(),"/data/in/raindat_0919.nc")) filew=c(paste0(getwd(),"/data/in/windat_7986.nc"), paste0(getwd(),"/data/in/windat_8797.nc"), paste0(getwd(),"/data/in/windat_9808.nc"), paste0(getwd(),"/data/in/windat_0919.nc")) Startdate=as.POSIXct("1979-01-01 10:00:00") Enddate=as.POSIXct("1986-12-31 23:00:00") # ncr = nc_open(filer) # ncw = nc_open(filew)</pre> <p>Intermediary data</p> <p>Intermediary data are stored in the “data/interdat” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "allraininclusters1.Rdata" "allraininclusters2.Rdata" ## [3] "allraininclusters3.Rdata" "allraininclusters4.Rdata" ## [5] "extremEventsWind.Rdata" "interclustRain.Rdata" ## [7] "interclustWind.Rdata" "metaclustRain.Rdata" ## [9] "metaclustWind.Rdata" "Rain_99_AllP.Rdata" ## [11] "rainP1.Rdata" "rainP2.Rdata" ## [13] "rainP3.Rdata" "rainP4.Rdata" ## [15] "rawclustRain.Rdata" "rawclustWind.Rdata" ## [17] "timeP1.Rdata" "timeP2.Rdata" ## [19] "timeP3.Rdata" "timeP4.Rdata" ## [21] "windP1.Rdata" "windP2.Rdata" ## [23] "windP3.Rdata" "windP4.Rdata" ## [25] "Wnd_99_AllP.Rdata" </code></pre> <ul> <li> <p>allraininclustersX: [data.frame] files are used to assess more accurately the accumulated precipitation during events by collecting precipitations from timesteps in which precipitation is above and below the threshold for every grid cell and the whole duration of the cluster.</p> </li> <li> <p>99_allp: [matrix] value of extreme precipitation and extreme wind gust threshold over the whole domain (one value per grid cell)</p> </li> <li> <p>interclust: [list] files contain a list of data from wind and precipitation clusters divided in the 4 periods aggregated over space and clusters (1 value per grid cell per cluster). These files are uses to create the files “RainEv_ldat” and “Windev_ldat”.</p> </li> <li> <p>metaclust: [list] files contain a list of metadata from wind and precipitation clusters divided in the 4 periods . These files are uses to create the files “RainEv_meta” and “Windev_meta”.</p> </li> <li> <p>rainPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing precipitation data for the period X.</p> </li> <li> <p>rawclust: [list] files contain a list of data.frame from wind and precipitation clusters divided in the 4 periods. These files are uses to create the files “RainEv_hdat” and “Windev_hdat”.</p> </li> <li> <p>timePX: [vector] contain vectors of time for the 4 periods.</p> </li> <li> <p>windPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing wind gust data for the period X.</p> </li> </ul> <p>Output data</p> <p>Output data contains metadata and raw data of single and compound hazard clusters are stored in the “data/out” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "compoundclusters.csv" "CompoundRW_79-19.v3x.Rdata" ## [3] "extremEvents_Rain.Rdata" "extremEvents_Wind.Rdata" ## [5] "Rain_stfprint.Rdata" "rainclusters.csv" ## [7] "RainEv_hdat_1979-2019.Rdata" "RainEv_ldat_1979-2019.Rdata" ## [9] "Rainev_ldatp_1979-2019.Rdata" "RainEv_meta_1979-2019.Rdata" ## [11] "RainEv_metap_1979-2019.Rdata" "Wind_stfprint.Rdata" ## [13] "windcluster.csv" "WindEv_hdat_1979-2019.Rdata" ## [15] "WindEv_ldat_1979-2019.Rdata" "WindEv_meta_1979-2019.Rdata" </code></pre> <ul> <li> <p>CompoundRW: [data.frame] contains metadata for the compound hazard clusters identified</p> </li> <li> <p>_hdat: [data.frame] hourly data of precipitation and wind gust clusters.</p> </li> <li> <p>_ldat: [data.frame] aggregated data over space and clusters (1 value per grid cell per cluster) for wind gust and precipitation clusters.Rain_ldatp contains aggregated values including non-extreme timesteps. Created from allraininclustersX.</p> </li> <li> <p>_meta:[data.frame] metadata for wind gust and precipitation clusters</p> </li> <li> <p>stfprint: [data.frame] files containing duration*footprint of each hazard clusters during all clusters</p> </li> <li> <p>sptdf: [data.frame] data.frame containing spatial, temporal, cluster and intensity information</p> </li> </ul> <p>Codes assiciated to the method are availaible here: https://github.com/Alowis/SI-CH</p>
Data supporting: Methodological overview and data-merging approaches in the study of plant-frugivore interactions
<p>Recording species interactions is one of the main challenges in ecological studies. Frugivory has received much attention for decades as a model for mutualisms among free-living species, and a variety of methods have been designed and developed for sampling and monitoring plant–frugivore interactions. The diversity of techniques poses an important challenge when comparing, combining or replicating results from different sources with different methodologies. With the emergence of modern techniques, such as molecular analysis or multimedia remote recorders, issues when combining data from different sources have become especially relevant. We provide an overview of all the techniques used for monitoring endozoochorous primary seed dispersal, focusing on a critical appraisal of the advantages and limitations, as well as the context-dependency nature, of the different methods. We propose five data merging approaches potentially useful to combine frugivory interactions data from different methodologies. Additionally, we provide two case studies where we combine empirical data from plant–animal interactions in Mediterranean shrublands using different methodologies. Data merging resulted in a net increase in the number of distinct pairwise interactions recorded and compensated biases inherent to different methods, resulting in a more robust estimation of network topological descriptors. These case studies clarify the context-dependent character of the merging approaches, highlighting the value of collecting detailed information on the sampling effort in terms of reliable results and reproducibility. Finally, we discuss the trends with different methodological approaches used in the last decades and future perspectives in this field.</p>
An operational methodology for validating satellite-based snow albedo measurements using a UAV
<p>This dataset contains all data supporting the conclusions of the manuscript entitled "An operational methodology for validating satellite-based snow albedo measurements using a UAV", submitted to Frontiers in Remote Sensing on August 30, 2021.</p>
Liriomyza bioassay methodology database
<p>ABSTRACT – <em>Liriomyza sativae</em> infestations cause economic losses of up to 30% in horticulture like melons and tomatoes, usually controlled with insecticides. Despite the high risk of evolution of resistance, rare are the works seeking to establish a standardization in the bioassay methodology that detects <em>L. sativae</em> resistance to insecticides. In this way, this work improved existing bioassay methodologies for larvae of <em>L. sativae</em>, seeking to standardize and make it possible to obtain more consistent data on susceptibility. Two populations of <em>L. sativae</em> were evaluated with insecticides by immersing leaf discs (2 cm in diameter) of melon and pork beans. Treated leaf discs containing a larva were transferred to trays (<em>Bioassay tray</em>) and acrylic plates. Bioassays were evaluated after 48 and 72 hours of exposure. The susceptibility parameters varied with exposure, type of arena and host, as well as the mode of action of the insecticides. Bioassays in the <em>Bioassay tray</em> provided greater homogeneity of response compared to acrylic plates. The response concentration curves were more accurate and homogeneous with melon leaves compared to pig bean leaves, presenting LC values well below these latter. Although the two methodologies can be used for toxicological bioassays, however, the use of <em>Bioassay tray</em> showed less economic cost and greater agility in the elaboration. Therefore, the enhancement toxicological bioassay methodology is suggested for <em>Liriomyza</em> species that provides more reproducible and less heterogeneous results, enabling research on susceptibility and monitoring of resistance to insecticides.</p> <p> </p>
Numerical methodology for design and evaluation of natural circulation systems for MSR applications - Dataset
<p>Dataset associated to the 2022 ANS Annual meeting conference paper "Numerical methodology for design and evaluation of natural circulation systems for MSR applications".</p>
Supplementary materials of the active learning methodology for online engineering education
<p>These are the supplementary materials for the active learning methodology for engineering education integrating online and mobile learning. The methodology is focused on teaching electronics, physical computing, basic robotics, and programming. The purpose of the methodology was to provide active learning, experimentation, and reflection in online classes to the students increasing their motivation and self-efficacy.</p> <p>The file contains the following elements:</p> <ol> <li>Rubric employed to evaluate the student-created videos and blogs.</li> <li>The survey's questions of the different courses that employ online and mobile learning modalities.</li> <li>A set of URLs with examples of the blogs and videos constructed by the students.</li> </ol>
Modeling Flocculation Processes of Cohesive Sediment Through Moment Methodology
<p>The data were used in the paper "Modeling Flocculation Processes of Cohesive Sediment Through Moment Methodology" which was submitted to "<em>Water Resources Research</em>". In this paper, a new mechanistic Lagrangian-type flocculation dynamic model was proposed by solving the population balance equation with moment methodology and similarity transformation, and the influence of particles' surface heterogeneities (<em>F</em><sub>2a</sub>) was incorporated. Furthermore, the roles of shear rate, sediment concentration, particle size and surface heterogeneity were investigated theoretically.</p>
Revisiting the historical scenario of a disease dissemination using genetic data and Approximate Bayesian Computation methodology: the case of Pseudocercospora fijiensis invasion in Africa
<p class="MsoNormal"><span>The reconstruction of geographic and demographic scenarios of dissemination for invasive pathogens of crops is a key step towards improving the management of emerging infectious diseases. Nowadays, the reconstruction of biological invasions typically uses the information of both genetic and historical information to test for different hypotheses of colonization. The Approximate Bayesian Computation framework and its recent Random Forest development (ABC-RF) have been successfully used in evolutionary biology to decipher multiple histories of biological invasions. Yet, for some organisms, typically plant pathogens, historical data may not be reliable notably because of the difficulty to identify the organism and the delay between the introduction and the first mention. We investigated the history of the invasion of Africa by the fungal pathogen of banana, <em>Pseudocercospora fijiensis</em>, by testing the historical hypothesis against other plausible hypotheses. We analysed the genetic structure of eight populations from six eastern and western African countries, using 20 microsatellite markers, and tested competing scenarios of population foundation using the ABC-RF methodology. We do find evidence for an invasion front consistent with the historical hypothesis, but also for the existence of another front never mentioned in historical records. We question the historical introduction point of the disease on the continent. Crucially, our results illustrate that even if ABC-RF inferences may sometimes fail to infer a single, well-supported scenario of invasion, they can be helpful in rejecting unlikely scenarios, which can prove much useful to shed light on disease dissemination routes.</span></p>
Supporting data and tool, for the paper "A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe"
<p>This 'Zenodo' contains supporting data and tools, for the paper 'A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe'.</p> <p>The repository includes the source code (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/DELTA_7.2.zip">DELTA_7.2.zip</a>) and the dataset (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/GMD_Vitali_et_all_data_20230516.zip">GMD_Vitali_et_all_data_20230516.zip</a>).</p>
A global LC-MS2-based methodology to identify and quantify anionic phospholipids in plant samples
<p>This table contains peaks aera values from LC-MS used to develop a method for identification and quantification of anionic lipid in plant sample: Genva et al. 2023: “A global LC-MS2-based methodology to identify and quantify anionic phospholipids in plant samples”</p>
Accounting for behaviour in fine-scale habitat selection: a case study highlighting methodological intricacies
<p>Data for the article “Accounting for behaviour in fine-scale habitat selection: a case study highlighting methodological intricacies”</p> <p>By LT Beumer, NMS Schmidt, J Pohle, J Signer, M Chimienti, JP Desforges, LH Hansen, SH Pedersen, DA Rudd, M Stelvig, FM van Beest</p> <p>The data set includes five files: A readme file describing the data files, and four data files accompanying the above publication.</p> <p>For further queries please contact Larissa T. Beumer: ltbeumer@gmail.com</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.