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1,956 results for “test data”
Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper
<p>Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper</p>
Training and test data set of thickness distribution of turbidites
<p>This is a data set of thickness distribution used for training and test of the inverse model of turbidites. Details were described in https://esurf.copernicus.org/preprints/esurf-2020-93/esurf-2020-93.pdf</p>
TEST DATA for Enhanced protein isoform characterization through long-read proteogenomics
<p>Test data for The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.</p> <p>Companion Repositories:</p> <ol> <li><a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow GitHub Repository Release</a></li> <li><a href="https://doi.org/10.5281/zenodo.5920847">Long-Read-Proteogenomics Analysis GitHub Repository Release</a></li> </ol> <p>Companion Datasets</p> <ol> <li><a href="https://zenodo.org/deposit/5703754">Jurkat Samples and Reference Data</a></li> <li><a href="http://10.5281/zenodo.5920920">Long-Read-Proteogenomics Workflow Results using Jurkat Sample data</a></li> </ol> <p>This Repository contains the test data, specifically:</p> <p><a href="https://doi.org/10.5281/zenodo.5234651">TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions</a></p>
CNN models and training, validation and test datasets for "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"
<p>Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript:</p> <p>Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"</p>
SPEA TESTBED TEST DATA FOR ML DEVELOPMENT
<p>The dataset files were generated at 3 different temperature with the improved ATE system developed in WP3 of the MET4FOF Project. </p> <p>The data are available for ML purposes and metrological investigation.</p>
Experimental investigation of composite materials for sliding friction dampers: data, plots, photos and videos of the tests
<p><strong>Folder DATA</strong></p> <p>This folder contains the data acquired by testing the friction pads M1, M2, M3, M4 and M5 under the following loading protocols:</p> <ul> <li>Linear static loading (M);</li> <li>Cyclic loading with constant amplitude (CA);</li> <li>Cyclic loading with decreasing amplitude at low rate (DA);</li> <li>Cyclic loading with increasing amplitude at low rate (IA);</li> <li>Cyclic loading with increasing amplitude at moderate rate (IA-H);</li> <li>Cyclic loading with increasing amplitude at high rate (IA-HH);</li> <li>Pulse-like loading protocol (PL);</li> <li>Mainshock-aftershock protocol (MS-AS): mainshock (MS), first aftershock (AS1) and second aftershock (AS2).</li> </ul> <p>The data include:</p> <ul> <li><em>Time</em>: time (unit: second);</li> <li><em>F</em>: axial force experienced by the sliding friction damper (unit: kN);</li> <li><em>N_bolt</em>: bolt preload (unit: kN);</li> <li><em>mu</em>: friction coefficient of the considered pad (unit: dimensionless);</li> <li><em>delta</em>: axial displacement experienced by the sliding friction damper (unit: mm);</li> <li><em>Cum. delta</em>: total cumulative displacement experienced by the sliding friction damper (unit: mm);</li> <li><em>Cum. E</em>: total cumulative energy dissipated by the sliding friction damper (unit: kJ);</li> <li><em>max Tin</em>: maximum temperature tracked close to the sliding interface (unit: Celsius);</li> <li><em>Tout</em>: temperature tracked at the surface of the inner slotted plate (unit: Celsius).</li> </ul> <p> The data are organized as follows:</p> <ul> <li>Folder <strong>T100</strong></li> </ul> <p>This folder contains the data acquired under the linear static loading protocol (M) for a tightening torque of 100 Nm. Each EXCEL file <strong>T100_M_Y</strong> saved in the folder <strong>T100</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol M.</p> <ul> <li>Folder <strong>T200</strong></li> </ul> <p>This folder contains the data acquired under the linear static loading protocol (M) for a tightening torque of 200 Nm. Each EXCEL file <strong>T200_M_Y</strong> saved in the folder <strong>T200</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol M.</p> <ul> <li>Folder <strong>Fs150</strong></li> </ul> <p>This folder contains the data acquired for an expected slip load of 150 kN. Each subfolder <strong>Fs150_X</strong> contains the data obtained under the loading protocol X (X = M, CA, DA, IA, IA-H). Each EXCEL file <strong>Fs150_X_Y</strong> saved in the subfolder <strong>Fs150_X</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol X.</p> <ul> <li>Folder <strong>Fs300</strong></li> </ul> <p>This folder contains the data acquired for an expected slip load of 300 kN. Each subfolder <strong>Fs300_X</strong> contains the data obtained under the loading protocol X (X = M, CA, DA, IA, IA-H, IA-HH, PL, MS, AS1, AS2). Each EXCEL file <strong>Fs300_X_Y</strong> saved in the subfolder <strong>Fs300_X </strong>contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol X.</p> <p><strong>Folder PHOTOS</strong></p> <p>This folder contains the following photos:</p> <ul> <li>Folder <strong>01_FrictionDamper</strong>: photos of the sliding friction damper and its components.</li> <li>Folder <strong>02_Instrumentation</strong>: photos of the instrumentation used for the data acquisition during the experimental campaign.</li> <li>Folder <strong>03_FrictionPads</strong>: <ul> <li>Subfolder <strong>BeforeTesting</strong>: photos of the friction pads before the experimental campaign.</li> <li>Subfolder <strong>AfterTesting</strong>: photos of the friction pads at the end of each loading protocol. The photo <strong>Fs150vs300_X_Y</strong> shows the condition of the pad Y (Y = M1, M2, M3, M4, M5) at the end of the loading protocol X (X = M, CA, DA, IA, IA-H, IA-HH, PL, MS, AS1, AS2) performed for an expected slip load of 150 kN and 300 kN (the pads shown at the top of each photo are those tested for an expected slip load of 150 kN). Similarly, the photo <strong>Fs300_X_Y</strong> shows the condition of the pad Y at the end of the loading protocol X performed for an expected slip load of 300 kN.</li> </ul> </li> <li>Folder <strong>04_Tests</strong>: photos taken from the east and north side of the sliding friction damper during the loading protocols that caused the fracture of the pads <ul> <li>Subfolder <strong>Fs150</strong>: photos taken during the tests conducted for an expected slip load of 150 kN. Each folder <strong>Fs150_X_Y</strong> contains the photos taken by testing the pad Y (Y = M1, M2, M4, M5) during the loading protocol X (X = CA, DA, IA, IA-H).</li> <li>Subfolder <strong>Fs300</strong>: photos taken during the tests conducted for an expected slip load of 300 kN. Each folder <strong>Fs300_X_Y</strong> contains the photos taken by testing the pad Y (Y = M1, M2, M4, M5) during the loading protocol X (X = CA, IA, IA-H). A video was recorded live during the loading protocols IA-HH, PL, MS, AS1 and AS2 (see folder <strong>VIDEOS</strong>).</li> </ul> </li> </ul> <p><strong>Folder PLOTS</strong></p> <p>This folder contains the following MATLAB plots:</p> <ul> <li><em>Force-Disp</em>: axial force – axial displacement response of the sliding friction damper;</li> <li><em>Preload-CumDisp</em>: bolt preload as a function of the total cumulative displacement experienced by the sliding friction damper;</li> <li><em>FrictionCoeff-CumDisp</em>: friction coefficient of the considered pad as a function of the total cumulative displacement experienced by the sliding friction damper;</li> <li><em>Temp-CumDisp</em>: rise in temperature as a function of the total cumulative displacement experienced by the sliding friction damper (the temperature values reported for the expected slip load of 150 kN correspond to “max Tin”, whereas those reported for the expected slip load of 300 kN correspond to “Tout”);</li> <li><em>FrictionCoeff-LoadingHistoryEffect</em>: friction coefficient of the considered pad as a function of the total cumulative displacement experienced by the sliding friction damper under different loading protocols;</li> <li><em>FrictionCoeff-RateEffect</em>: friction coefficient of the considered pad as a function of sliding velocity experienced by the sliding friction damper under different loading protocols;</li> <li><em>FrictionCoeff-TempEffect</em>: friction coefficient of the considered pad as a function of the rise in temperature tracked during different loading protocols;</li> <li><em>FrictionCoeff-PressureDependency</em>: mean and standard deviation of the friction coefficient of the considered pad obtained for different expected slip loads and loading protocols;</li> <li><em>FrictionCoeffStaticDynamic-PressureDependency</em>: mean of the static and dynamic friction coefficient of the considered pad obtained for different expected slip loads and loading protocols.</li> </ul> <p>The MATLAB plots are organized as follows:</p> <ul> <li>Folder <strong>T200</strong></li> </ul> <p>The MATLAB plots saved in this folder illustrate the data obtained by testing the friction pads M1, M2, M3, M4 and M5 under the linear static loading protocol (M) for a tightening torque of 200 Nm.</p> <ul> <li>Folder <strong>Fs150 and Fs300</strong></li> </ul> <p>The MATLAB plots saved in this folder illustrate the data obtained by testing the friction pads M1, M2, M3, M4 and M5 under the considered loading protocol (M, CA, DA, IA, IA-H, IA-HH, PL, MS-AS) for an expected slip load of 150 kN and 300 kN.</p> <p><strong>Folder VIDEOS</strong></p> <p>This folder contains the following videos:</p> <ul> <li>Folder <strong>T100</strong>: videos created from the photos taken during the tests conducted for a tightening torque of 100 Nm under the linear static loading protocol (M). The videos <strong>T100_M_Y_East</strong> and <strong>T100_M_Y_North</strong> show the test conducted on the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>T200</strong>: videos created from the photos taken during the tests conducted for a tightening torque of 200 Nm under the linear static loading protocol (M). The videos <strong>T200_M_Y_East</strong> and <strong>T200_M_Y_North</strong> show the test conducted on the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>Fs150</strong>: videos created from the photos taken during the tests conducted for an expected slip load of 150 kN. The videos <strong>Fs150_X_Y_East</strong> and <strong>Fs150_X_Y_North</strong> show the loading protocol X (X = M, CA, DA, IA, IA-H) applied to the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>Fs300</strong>: videos created from the photos taken during the tests conducted for an expected slip load of 300 kN. The videos <strong>Fs300_X_Y_East</strong> and <strong>Fs300_X_Y_North</strong> show the loading protocol X (X = M, CA, DA, IA, IA-H) applied to the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively. The videos obtained for the loading protocols IA-HH, PL, MS, AS1 and AS2 were recorded live during each test.</li> </ul>
Agronomic performance of cultivar mixtures of winter wheat varieties, obtained from mixture field trials at 5 locations in Switzerland from 2019 to 2020, together with yield data from the varieties in pure stand obtained from the national variety testing trial network
<p>This dataset contains agronomic parameters of 32 winter wheat variety mixtures tested during 2 growing seasons (2019-2020) at 5 locations in Switzerland, as well as yield data of these varieties in pure stands originating from the Swiss national variety testing network. The dataset has been used to investigate the links between asynchrony and yield stability, published in <a href="https://doi.org/10.1002/csc2.21151">https://doi.org/10.1002/csc2.21151</a>. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h2>Methods </h2> <p><em>Field trials </em></p> <p>The experiment took place in five sites across Switzerland, in 2019 and 2020. The sites were located in Nyon (1260), Delley (1567), Utzenstorf (3428), Zurich (8046), and Ellighausen (8566).</p> <p>Experimental communities consisted of 32 different two-variety mixtures grown in 7.1-m<sup>2</sup> plots (1.5 × 4.7 m). We replicated the mixture experiment three times per site with the exact same variety composition. We used a randomized block design, with plots being randomized at each site within each block. Density of sowing was 350 seeds/m<sup>2</sup>, and seeds were mixed beforehand at a 50:50 ratio in terms of mass. We used the 50:50 mass ratio as this is what is generally done in practice by farmers and seed suppliers. Plots were sown mechanically each autumn. The plots were mechanically fertilized according to the Principles of Agricultural Crop Fertilisation in Switzerland (Federal Office for Agriculture) with an average of 140 kg N/ha (ammonium nitrate), applied in three splits (40 at the tillering stage—60 at stem elongation stage—40 when the flag leaf is visible). The experimental trials were conducted following the extenso Swiss scheme, which means that there was no application of any fungicide, insecticide, or plant growth regulator. </p> <p>The performances of single varieties were obtained by going through the trials of the national variety testing program. We gathered the data for the years 2018/2019 and 2019/2020. The data regarding single varieties could be obtained for three out of the five sites used for the mixtures: 1260, 1567, and 8566. Because there were no national variety trials at the two other sites (8046, 3428), we could not get any data for single varieties in these sites. Thus, all further analyses including single variety data were only done for the three sites mentioned above. At each of these sites, the variety trials were located on the same plot as the mixture trials, even though a little further apart. Therefore, soil parameters and crop precedents were the same between the mixture and variety testing trials. Furthermore, we only selected the national variety testing trials that respected the <em>extenso</em> conditions, that is, no fungicide, pesticide, or growth regulator application, and that received the same amount of fertilization as the mixture trials. In 8566 and 1567, sowing and harvesting dates were identical between the two trials; in 1260, sowing and harvesting dates could vary but remained within a week of each other.</p> <p> </p> <p><em>Data collection </em></p> <p>For each plot, heading dates were monitored, and average height at BBCH 59–75 was measured.</p> <p>The prevalence of diseases was scored twice in the growing season. Specifically, the severity of brown rust, yellow rust, powdery mildew, and Septoria tritici blotch was assessed. This was performed by grading each individual plot from 1 to 9 for each disease, with 1 representing no disease and 9 a complete infection. The scoring scale follows a logistic progression based on the symptoms of the top three leaves. We used the data from the final scoring for statistical analysis, as the disease severity was usually more important then.</p> <p>At maturity, we harvested each plot with a combine harvester. The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured specific weight and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content was measured at the site level with a near-infrared instrument (ProxiMate; Büchi instruments).</p>
RADICLE_S355_test_data
<p>The RADICLE_S355_test_data dataset consists on the raw and treated data related to testing the laser welding process on S355 steel (a high-strength low-alloy structural grade), in a butt-weld configuration.</p> <p>S355 is a material commonly applied throughout the ‘heavy industry’ sectors, including transport (road, rail and marine), yellow goods (earth-moving and construction machinery), civil engineering and energy sectors. Hence, the H2020 RADICLE project consortium is providing open access to the data collected during the test trials, to allow for further analysis and re-use of this information.</p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
Data set. Testing Protest Paradigm in Femicide Protests in Mexican Press
<p>his Data set consist of the coding of 865 articles from national Mexican newspapers, from El Universal, Reforma, and Excélsior. It is corresponding to research that analyzes the representation of the protests against femicide in the Mexican press, and the opportunities reached by the social movement in the public discourse. Previous studies found a tendency for negative representations of social movements in the media in the so-called ‘protest paradigm’, this means that the attention obtained does not necessarily guarantee the reproduction of the movement’s discourse. We use the paradigm approach and test the degree of adherence. It registered media attention to the femicide issue for a period of 41 months in three national newspapers. The corpus was examined by using the qualitative content analysis technique, with variables that measure the stories’ emphasis, prominence, legitimation, and tone for social movements and authorities, to compare the two actors.</p>
Data from: Testing background matching and disruptive colouration in a sexually dichromatic grasshopper: a computer detection experiment.
<p>Cryptic colouration is an adaptative mechanism against predators. Colour patterns can become cryptic through background matching and disruptive colouration, which breaks up the outlines of an animal because the pattern does not coincide with the shape and outline of the animal’s body. Background matching could be advantageous in chromatically homogeneous microhabitats, whereas disruptive colouration can be favoured in visually heterogeneous microhabitats. Grasshoppers of the genus <em>Sphenarium</em> (Orthoptera: Pyrogomorphidae) inhabit very heterogeneous environments and exhibit both strategies. Adults show substantial continuous variation in colouration and longitudinal and transverse bands on the thorax and abdomen. However, males often exhibit considerably more variation in the number of longitudinal and transverse bands than females, which tend to have more uniform colouring (flatter patterns). In this study, we analysed the cryptic properties of the colour patterns of males and females of <em>Sphenarium </em><em>zapotecum</em><em> </em>Sanabria-Urbán, H. Song & Cueva del Castillo and tested the effectiveness of background matching and disruptive colouration using humans as ‘predators’ in a computer detection experiment. We found that the females and males are dichromatic and seem to follow different cryptic strategies in their colouration: males are more disruptive to the background than females, whereas females have a higher level of background matching. In addition, in visually heterogeneous areas, predators spent most time searching for striped male morphs with lower background matching and higher disruptive properties, as well as for female morphs with high background matching, potentially increasing prey survival. As background matching is associated with females and disruptive colouration with males, our results could help explain the evolution of sexual dichromatism in this and other species of grasshoppers of the genus <em>Sphenarium.</em></p> <p> </p>
Data for: Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes
<p><span>Proper differentiation of sperm from germline stem cells, essential for production of the next generation, requires dramatic changes in gene expression that drive remodeling of almost all cellular components, from chromatin to organelles to cell shape itself. Here we provide a single nucleus and single cell RNA-seq resource covering all of spermatogenesis in <em>Drosophila</em> starting from in-depth analysis of adult testis single nucleus RNA-seq (snRNA-seq) data from the Fly Cell Atlas (FCA) study (Li et al., 2022). With over 44,000 nuclei and 6,000 cells analyzed, the data provide identification of rare cell types, mapping of intermediate steps in differentiation, and the potential to identify new factors impacting fertility or controlling differentiation of germline and supporting somatic cells. We justify assignment of key germline and somatic cell types using combinations of known markers, <em>in situ</em> hybridization, and analysis of extant protein traps. Comparison of single cell and single nucleus datasets proved particularly revealing of dynamic developmental transitions in germline differentiation. To complement the web-based portals for data analysis hosted by the FCA, we provide datasets compatible with commonly used software such as Seurat and Monocle. The foundation provided here will enable communities studying spermatogenesis to interrogate the datasets to identify candidate genes to test for function <em>in vivo</em>.</span></p>
RDF version of the supplementary data from Shin, Hyun Kil and Seo et al. Meta-analysis of Daphnia magna nanotoxicity experiments in accordance with test guidelines. Environ. Sci.: Nano (2018)
<p>This is an RDF version of the dataset published by Shin, Hyun Kil and Seo et al. as a supplement of the study Meta-analysis of Daphnia magna nanotoxicity experiments in accordance with test guidelines. Environ. Sci.: Nano (2018).</p> <p>The original dataset is available online: <a href="https://ui.staging.kit.cloud.douglasconnect.com/dataexplorer?dataset=ab2bc1ee-99dc-4ddf-b1f9-9fdeb8a0f48c%3A1&q=%7B%7D">https://ui.staging.kit.cloud.douglasconnect.com/dataexplorer?dataset=ab2bc1ee-99dc-4ddf-b1f9-9fdeb8a0f48c%3A1&q=%7B%7D</a></p> <p>The original publication DOI: <a href="http://dx.doi.org/10.1039/C7EN01127J">http://dx.doi.org/10.1039/C7EN01127J</a></p> <p>GitHub repository of the datasets converted to RDF along with RML mappings: <a href="https://github.com/ammar257ammar/RDFied-datasets">https://github.com/ammar257ammar/RDFied-datasets</a></p>
Sample data for JRC_seeker test run
<p>This is sample data as part of the test run for JRC_seeker, a Snakemake pipeline for the genome-wide discovery of jointly regulated CpGs (JRCs) in pooled whole genome bisulfite sequencing (WGBS) data (https://github.com/BenjaminPlanterose/JRC_seeker).</p>
Sample data for JRC_sorter test run
<p>This is sample data as part of the test run for JRC_sorter, a classifier for Jointly Regulated CpGs (JRCs) (https://github.com/BenjaminPlanterose/JRC_sorter).</p>
Dataset test: Master Data Science (LEB Oro)
<p>Archivo de prueba para la asignatura de ciclo de vida de los datos. El dataset contiene las estadísticas principales de los equipos de la LEB Oro (2022/2023) de las primeras 20 jornadas.</p>
Research Data for System test results for the R744 systems
<p>This dataset provides the research data system test results of R-744 (propane) systems. In the TRI-HP project, a new heat pump system with R-744 was integrated into multiple renewable energy sources. There were two experimental campaigns. After analysing the results of the first experimental campaign, the prototype was further improved. The second experimental campaign was conducted with the improved prototype.</p> <p>The detailed information can be found in ZENODO:</p> <p><a href="https://zenodo.org/record/7324246">Refined heat pump design and results of final testing</a></p> <p><a href="https://zenodo.org/record/7285499">Critical review of heat pump prototype operation and required modications</a></p> <p><a href="https://zenodo.org/record/6538833">Performance investigation of an ejector-assisted transcritical CO2 heat pump with brazed plate tri-partite gas cooler for space heating and hot water production</a></p> <p><a href="https://zenodo.org/record/6405324">Experimental performance evaluation of supercritical CO2 in brazed plate heat exchangers of the tri-partite gas cooler</a></p> <p> </p>
Data for: Does the evolution of ontogenetic niche shifts favor species coexistence? An empirical test in Trinidadian streams
<p>A major question in ecology is how often competing species evolve to reduce competitive interactions and facilitate coexistence. One untested route for a reduction in competitive interactions is through ontogenetic changes in the trophic niche of one or more of the interacting species. In such cases, theory predicts that two species can coexist if the weaker competitor changes its resource niche to a greater degree with increased body size than the superior competitor. We tested this prediction using stable isotopes that yield information about the trophic position (δ15N) and carbon source (δ13C) of two coexisting fish species: Trinidadian guppies (Poecilia reticulata) and killifish (Rivulus hartii). We examined fish from locations representing three natural community types: 1) where killifish and guppies live with predators; 2) where killifish and guppies live without predators; and 3) where killifish are the only fish species. We also examined killifish from communities in which we had introduced guppies, providing a temporal sequence of the community changes following the transition from a killifish only to a killifish-guppy community. We found that killifish, which are the weaker competitor, had a much larger ontogenetic niche shift in trophic position than guppies in the community where competition is most intense (killifish-guppy only). This result is consistent with theory for size-structured populations, which predicts that these results should lead to stable coexistence of the two species. Comparisons with other communities containing guppies, killifish and predators and ones where killifish live by themselves revealed that these results are caused primarily by a loss of ontogenetic niche changes in guppies, even though they are the stronger competitor. Comparisons of these natural communities with communities in which guppies were translocated into sites containing only killifish showed that the experimental communities were intermediate between the natural killifish-guppy community and the killifish-guppy-predator community, suggesting contemporary evolution in these ontogenetic trophic differences. These results provide comparative evidence for ontogenetic niche shifts in contributing to species coexistence and comparative and experimental evidence for evolutionary or plastic changes in ontogenetic niche shifts following the formation of new communities. </p>
Data for: Predicting age and mass at maturity from feeding behavior and diet in M. sexta: An empirical test of a life history model
<p>Feeding for most animals involves bouts of active ingestion alternating with bouts of no ingestion. In insects, the temporal patterning of bouts varies widely with resource quality and is known to affect growth, development time, and fitness. However, the precise impacts of resource quality and feeding behavior on insect life history traits is poorly understood. To explore and better understand the connections between feeding behavior, resource quality and insect life history traits, we combined laboratory experiments with a recently proposed mechanistic model of insect growth and development for a larval herbivore, <em>Manduca sexta</em>. We ran feeding trials for 4<sup>th</sup> and 5<sup>th</sup> instar larvae across different diet types (two hostplants and artificial diet) and used these data to parameterize a joint model of age and mass at maturity that incorporates both insect feeding behavior and hormonal activity. We found that the estimated durations of both feeding and non-feeding bouts were significantly shorter on low- than on high-quality diets. We then explored how well the fitted model predicted historical out-of-sample data on age and mass of <em>M</em>.<em> sexta</em>. We found that the model accurately described qualitative outcomes for the out-of-sample data, notably that a low-quality diet results in reduced mass and later age at maturity compared to high-quality diets. Our results clearly demonstrate the importance of diet quality on multiple components of insect feeding behavior (feeding and non-feeding), and partially validate a joint model of insect life history. We discuss the implications of these findings with respect to insect herbivory and discuss ways in which our model could be improved or extended to other systems.</p>
RVFV data aligning only to M-Fragment to test the PARANOiD pipeline
<p>PARANOiD is a versatile software for fully automated analysis of iCLIP and iCLIP2 data. It contains all steps necessary for preprocessing, the determination of cross-link locations and several additional steps, which can be used to detect specific characteristics, e.g. definite distances between cross-link events or identify binding motifs. The cross-link sites are presented as WIG files that can be easily visualized e.g. using IGV, for which a config file is offered. Additionally, results are offered as statistical plots for a quick overview and as standardized bioinformatics file formats or TSV files, which can be used for further analysis steps.</p> <p>The data provided are used as a test case for PARANOiD.</p> <p>The data was extracted from RVFV MP-12 virions (virion-reads-M-fragment-only.fastq) and BHK cells infected with RVFV (BHK-reads-M-fragment-only.fastq) applying the iCLIP2 method for RVFV N iCLIP. Three independent biological replicates were performed for each sample. Sequencing was performed using the MiSeq Sequencer (Illumina) with MiSeq Reagent Kit v2 Micro (Illumina) for N-iCLIP from virus particles and MiSeq Reagent Kit v3 (Illumina) for N-iCLIP from infected BHK cells.</p> <p>The original reads have been aligned to the RVFV MP-12 reference genome and only reads aligning to the M-fragments were extracted. The whole dataset will be publish at a later date</p> <p>File description:</p> <p>virion-reads-M-fragment-only.fastq - Reads obtained from RVFV virions</p> <p>BHK-reads-M-fragment-only.fastq - Reads obtained from BHK cells infected with RVFV</p> <p>reference_RVFV.fasta - RVFV MP-12 reference genome</p> <p>barcodes-RVFV.tsv - Barcodes for virion-reads-M-fragment-only.fastq</p> <p>barcodes-RVFV-merge-all.tsv - Barcodes for merging all samples of virion-reads-M-fragment-only.fastq</p> <p>barcodes-BHK.tsv - Barcodes for BHK-reads-M-fragment-only.fastq</p> <p>barcodes-BHK-merge-all.tsv - Barcodes for merging all relevant samples of BHK-reads-M-fragment-only.fastq</p>
ScienceDex guides
Understand access before you commit
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.