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3,172 results for “Publications”
Dataset for publication: An inter-laboratory study characterizes the impact of bioinformatic approaches on genome-based cluster detection for foodborne bacterial pathogens
<p>This dataset is part of a dry-lab interlaboratory study conducted across Germany, regarding bacterial outbreak detection based on NGS data, with a focus on bioinformatic analysis of four species to identify potential variability caused by different data analysis approaches and human interpretation. Participants were asked to follow their usual in-house protocols while adhering to the general guidelines. A quality assessment (with sample exclusion) was followed by 7-gene Multilocus-Sequence Typing (MLST), core genome Multilocus Sequencing Typing (cgMLST), and SNP calling. The participants were then asked to identify clusters. The study was not intended to resemble a standard proficiency test with a passing/failing grade, but rather to investigate and quantify obvious variability in the results and, where possible, the reasons for it. For this purpose, the datasets included borderline cases in terms of quality.</p>
European Education Provision on Public Procuement of Innovation: find your study course
<p>The growth of procurement and attracting future leaders can be enhanced through the visibility of procurement education. With the goal to map procurement education, PROCEDIN surveyed universities across Europe. The project partners compiled data on <a href="https://procedin.eu/database-of-european-education-provision/">European universities that provide master’s or bachelor’s level education in procurement</a>, sustainability, and entrepreneurship. In the present database, there are 114 different universities from 27 different countries, which together offer 1679 courses.</p> <p>In this dataset you can find all the opportunities for training in POI in European universities and beyond.</p> <p> </p>
Data for publication 'Detection of Artificial Seed-like Objects from UAV Imagery'
<p>This resource contains the datasets supporting the model development as published in the article 'Detection of Artificial Seed-like Objects from UAV Imagery' (https://doi.org/10.3390/rs15061637).</p> <p>In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.</p>
Public opinion poll "War, Peace, Victory and the Future" – National face-to-face opinion poll representative of the population in government-controlled territories of Ukraine on the war-related issues (June 2023)
The face-to-face survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Centre for Political Sociology from 5 to 15 June 2023. A total of 2,001 respondents aged 18 or older took part in the survey in Vinnytsia, Volyn, Dnipropetrovsk, Zhytomyr, Zakarpattia, Zaporizhzhia, Ivano-Frankivsk, Kyiv, Kirovohrad, Lviv, Mykolaiiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernihiv, and Chernivtsi regions, and the city of Kyiv (in Zaporizhzhia, Kharkiv, and Kherson regions – only in the territories controlled by Ukraine and not affected by hostilities). The sampling technique used in the survey is multi-stage, with a random selection of localities in the first stage and a quota-based selection of respondents in the final stage. The random selection is representative of the demographic structure of the adult population in the areas covered by the survey at the beginning of 2022. The maximum sampling error shall not exceed 2.3%. At the same time, it is necessary to take into account systematic deviations in the sample caused by the forced migration of millions of citizens due to the Russian-Ukrainian war. COMPOSITION OF MACRO-REGIONS: West – Volyn, Zakarpattia, Ivano-Frankivsk, Lviv, Rivne, Ternopil, and Chernivtsi regions; Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy, and Chernihiv regions, and the city of Kyiv; South – Zaporizhzhia, Mykolaiiv, Kherson, and Odesa regions; East – Dnipropetrovsk and Kharkiv regions. This dataset contains the original survey data. The SPSS file (.sav) is the original file. It has been exported to an Excel file. The content of the corresponding XLSX file should be identical to the original SAV file. The SAV file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation have also been included in this data collection as separate PDF files. In addition, the dataset includes a file of "selected findings", which documents some of the key findings of the survey in the form of analytical summaries and descriptive statistics. The report was prepared by the civil society organisation OPORA.
Samples used for MeStanG Technical Note Publication in Biology MDPI
<p>Samples for MeStanG analysis as a resource for High throughput sequencing standard datasets generation for bioinformatic methods evaluation and validation.</p> <ul> <li>Sample_101 - Metagenomic Bacterial-only Sample simulated using MeStanG</li> <li>Samples_2.tar - 15 Samples resembling Host/Pathogen wheat plant sequencing outputs simulated using MeStanG</li> <li>Sample_301 - Metagenomic Bacterial-only Sample simulated using NanoSim</li> <li>Samples_4.tar - 15 Samples resembling Host/Pathogen wheat plant sequencing outputs simulated using NanoSim</li> <li>Sample_501.tar - 20 replicates for Pathogen absolute abundance 500</li> <li>Sample_502.tar - 20 replicates for Pathogen absolute abundance 100</li> <li>Sample_503.tar - 20 replicates for Pathogen absolute abundance 50</li> <li>Sample_504.tar - 20 replicates for Pathogen absolute abundance 10</li> <li>Sample_505.tar - 20 replicates for Pathogen absolute abundance 5</li> </ul> <p>All files are compressed in gzip format and all sequences in fasta format</p>
Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river"
<h2>Summary</h2> <p>Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river" by Aki Vähä, Timo Vesala, Sofya Guseva, Anders Lindroth, Andreas Lorke, Sally MacIntyre, and Ivan Mammarella (2024), published in Biogeosciences.</p> <h2>Materials and Methods</h2> <h3>Measurement site</h3> <p>The experiment was conducted on a floating platform on the River Kitinen in northern Finland. The measurements took place from 1 June to 2 October, 2018.</p> <p>The River Kitinen is 235 km long and has a catchment area of 7672 km2. The catchment area consists mostly of managed boreal forest with Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) as the main tree species, wetlands of which a large portion is drained, small streams and rivers, some low mountains and a few small settlements. The experiment site (67.37◦ N, 26.62◦ E, 173 m above sea level) was located next to the Finnish Meteorological Institute’s research and weather station in Tähtelä. At the experiment location the river is 180 m wide and forms a straight section extending approximately 600 m upstream and 1000 m downstream from the site. The direction of the river at the site is roughly north-northwest–south-southeast and it flows towards the south. The mean annual discharge, measured at the closest power plant downstream, is 103 m3 s−1. The maximum depth at the site is 7 m. The River Kitinen’s Strahler stream order at the site is 5. The floating platform was located about 70 m from the eastern river bank where the water depth was 4.5 m.</p> <h3>Eddy covariance</h3> <p>The eddy covariance system measuring water-atmosphere turbulent fluxes was mounted on a mast on the southern side of the platform. This installation consisted of an ultrasonic anemometer (uSonic-3 Scientific, METEK Meteorologische Messtechnik GmbH, Elmshorn, Germany) for measuring the wind speed in three Cartesian coordinates and the sonic temperature, an enclosed-path gas analyser (LI-7200RS, LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) for measuring carbon dioxide and water vapour mole fractions, and a closed-path gas analyser (G1301-f, Picarro, Inc., Santa Clara, California, USA) for measuring methane and water vapour mole fractions. The centre of the sonic anemometer was 1.82 m above the water surface. An inclinometer (DOG2 micro-electro-mechanical system, Measurement Specialties, Inc., Hampton, Virginia, USA) was used for measuring the pitch and roll of the platform. Eddy covariance fluxes were calculated using the EddyUH software (Mammarella et al. 2016), following the state of art methodologies (Sabbatini et al. 2018, Nemitz et al. 2018).</p> <h3>Auxiliary measurements</h3> <p>Ambient air temperature and relative humidity were measured with a Rotronic HC2-S3C03 probe (Rotronic AG, Bassersdorf, Germany), mounted inside a Young model 41003 (R. M. Young Company, Traverse City, Michigan, USA) multi-plate radiation shield on the platform’s north-eastern corner. Air temperature and relative humidity were available only after 15th of June. Before that, the sonic temperature and humidity calculated from χH2O, measured with the LI-7200RS, were used instead. Atmospheric pressure and precipitation were measured at the Tähtelä weather station. Photosynthetically active radiation (PAR) in water was measured with two LI-192 sensors (LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) and one LI-193 sensor (LI-COR). The sensors were hanging from wires at 0.3 m, 0.65 m and 1.0 m depths on a beam on the southern side of the platform. Measurements of water side CO2 partial pressure (pCO2) were done by using an off-axis integrated cavity output spectrometer (Ultraportable Greenhouse Gas Analyzer – UGGA), Los Gatos Research, Inc., Santa Clara, California, USA) that was connected to the headspace of an equilibrator consisting of a floating Plexiglas chamber.</p> <p>A water temperature chain was set up 100 m upstream of the platform. It consisted of five temperature loggers of the type RBR Solo (RBR Ltd. Ottawa, Ontario, Canada). The loggers were placed on a taut line mooring at depths of 0.35 m, 1.35 m, 2.35 m, 3.35 m and 4.35 m (6 June to 17 June) and 0.07 m, 1.05 m, 2.05 m, 3.05 m and 4.05 m (17 June onwards). The topmost measurement was used as the surface temperature. The water flow velocity was measured with a acoustic Doppler velocimeter (Nortek Vector, Nortek AS, Rud, Norway) which was installed on a beam on the north-western corner of the platform, facing down (Guseva et al., 2021). The depth of the measurements was 0.4 m below the surface.</p>
Data for Publication: "Automated Investigation of Metal-Ligand Interactions by a Newly Established Robotic Workflow for Titrations"
<p>This dataset contains the whole primary and raw (original) data for the manuscript "Automated investigation of metal-ligand interactions by a newly established robotic workflow for titrations".</p>
Publication text: code, data, and new measures
<p>This Zenodo page describes data collection, processing, and different open access data files related to the text of scientific publications from OpenAlex. If you use the code or data, please cite the following paper: </p> <p>Sam Arts, Nicola Melluso, Reinhilde Veugelers; Beyond Citations: Measuring Novel Scientific Ideas and their Impact in Publication Text. <em>The Review of Economics and Statistics</em> 2025; 1–33 doi: <a href="https://doi.org/10.1162/rest_a_01561" target="_blank" rel="noopener">https://doi.org/10.1162/rest_a_01561</a></p> <p> </p>
Data supporting the publication "Many-body quantum sign structures as non-glassy Ising models"
<p>This repository contains all raw data that were used to draw conclusions and generate figures for the paper:</p> <p><strong>"Many-body quantum sign structures as non-glassy Ising models"</strong><br> by Westerhout, T., Katsnelson, M. I., & Bagrov, A. A.</p> <p><em>Abstract:</em> The non-trivial phase structure of the eigenstates of many-body quantum systems severely limits the applicability of quantum Monte Carlo, variational, and machine learning methods. Here, we study real-valued signful ground-state wave functions of frustrated quantum spin systems and, assuming that the tasks of finding wave function amplitudes and signs can be separated, show that the signs can be easily bootstrapped from the amplitudes. We map the problem of finding the sign structure to an auxiliary classical Ising model defined on a subset of the Hilbert space basis. We show that the Ising model does not exhibit significant frustrations even for highly frustrated parental quantum systems, and is solvable with a fully deterministic O(K log K)-time combinatorial algorithm (where K is the Ising model size). Given the ground state amplitudes, we reconstruct the signs of the ground states of several frustrated quantum models, thereby revealing the hidden simplicity of many-body sign structures.</p>
Deep learning models predicting gene functions and pathways using public DRKG knowledge graph and graph neural network
<p>The attached dataset contains pretrained link prediction models, as described in our paper 'Morphological Map of Under- and Over-Expression of Genes in Human Cells'.</p>
Dataset for publication: "Photosystem II supercomplexes lacking light-harvesting antenna protein LHCB5 and their organization in the thylakoid membrane"
<p>Data repository for "<strong>Photosystem II supercomplexes lacking light-harvesting antenna protein LHCB5 and their organization in the thylakoid membrane</strong>".</p> <p><strong>FIGURE </strong><strong>1</strong><strong><em> </em></strong><strong>Phenotype and photosynthetic characteristics of the <em>lhcb5</em> mutant. </strong>(A) Phenotype of <em>Arabidopsis thaliana</em> wild type (WT) and <em>lhcb5</em> mutant plants grown at controlled conditions for 6 weeks (8 h light/16 h dark cycle; 22/20°C; <br>110 µmol photons m<sup>-2</sup> s<sup>-1</sup>; 60% humidity). (B) Immunoblot analysis of thylakoid membranes of WT and <em>lhcb5</em> mutant plants with antibody directed against LHCB5. (C) Content of light-harvesting proteins LHCB1-6 evaluated relatively to the content of CP43 protein in the WT and the <em>lhcb5</em> mutant. The protein content was determined in isolated thylakoid membranes by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The columns represent means ± SD, individual points show technical replicates. All data passed the normality and equal variance tests and according to Student t-test the datasets of WT and <em>lhcb5</em> were not significantly different (α ≤ 0,05), except for the relative content of LHCB5/CP43. (D) Protein ratios of photosynthesis-related thylakoid membrane proteins of the WT and the <em>lhcb5</em> mutant. The protein content was determined by LC-MS/MS in isolated thylakoid membranes. PSII represents the sum of relative PG intensities of D1, D2, CP43, and CP47 proteins, LHCII - LHCB1–3 proteins, PSI - PSAA and PSAB proteins, LHCI - LHCA1–4 proteins, ATPS - α and β subunits of ATP synthase, and cyt f represents cytochrome f component of cytochrome b<sub>6</sub>f complex. The columns represent means ± SD, individual points show technical replicates. All data passed the normality and equal variance tests and according to Student t-test the datasets of WT and <em>lhcb5</em> are not significantly different (α ≤ 0,05).</p> <p><strong>FIGURE </strong><strong>2</strong><strong><em> </em></strong><strong>Separation and structural characterization of PSII supercomplexes from <em>lhcb5</em> mutant plants. </strong>(A) Separation of pigment–protein complexes from thylakoid membranes from <em>Arabidopsis thaliana</em> WT and <em>lhcb5</em> mutant plants by clear native polyacrylamide gel electrophoresis. Thylakoid membranes were solubilized by n-dodecyl α-D-maltopyranoside (detergent/chlorophyll mass ratio of 10). (B) Electron density maps of characteristic PSII supercomplexes from the separated green gel bands of the <em>lhcb5 </em>mutant designated as C<sub>2</sub>S<sub>2</sub>M<sub>2</sub>, C<sub>2</sub>S<sub>2</sub>M and C<sub>2</sub>SM. Projection maps are fitted by corresponding structural high-resolution models of PSII supercomplexes (Van Bezouwen et al., 2017) without LHCB5. Individual PSII subunits are color-coded according to (E). (C), (D) Comparison of structural models of the PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplexes from <em>Arabidopsis thaliana</em> WT and the <em>lhcb5</em> mutant. (C) Projection map of the PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplex from <em>Arabidopsis thaliana</em> wild type (Ilíková et al., 2021) fitted by the high-resolution structure from Van Bezouwen et al. (2017). (D) Overlay of structural models of the PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplex from <em>Arabidopsis thaliana</em> wild type (surface representation, partially transparent) and the <em>lhcb5</em> mutant shows a specific shift of the S and M LHCII trimers as well as the monomeric antenna LHCB6 (see arrows in the corresponding colors) due to the absence of LHCB5. Individual PSII subunits are color-coded according to (E). (E) Legend of individual PSII subunits, which are color-coded as follows: PSII core complex in green, S and M LHCII trimers in red and blue, respectively, and the monomeric antenna proteins, LHCB4, LHCB5, LHCB6, in yellow, cyan, and dark orange, respectively.</p> <p><strong>FIGURE </strong><strong>3</strong><strong> </strong><strong>Organization of photosystem II in thylakoid membranes of the <em>lhcb5</em> mutant. </strong>(A, B) Examples of electron micrographs of negatively stained thylakoid membrane isolated from the <em>lhcb5</em> mutant with densities corresponding to the PSII core complex. Representative picture of PSII supercomplexes “randomly” organized (A) and organized into 2D semi-crystalline array (B). (C, D, E) Projection maps of PSII megacomplexes obtained using image analysis of PSII particles in thylakoid membranes. Three specific associations of PSII supercomplexes are shown and fitted by the model of PSII supercomplex C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> without LHCB5 (see Figure 2B). Megacomplexes are averaged projections of 1 925 (C), 2 241 (D), and 2 305 (E) particles. (F) Isolated PSII particle from thylakoid membranes with “randomly” organized PSII as an average projection of 3 741 particles fitted by the model of PSII supercomplex C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> without LHCB5 (see Figure 2B). (G) PSII supercomplexes organized into 2D semi-crystalline array as an average projection of 418 sub-areas together with the fitted model of PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplexes (see Figure 2B). Projection maps of PSII supercomplexes show core complexes in green, S trimers in red, M trimers in blue, LHCB4 in yellow, and LHCB6 in dark orange color.</p> <p><strong>FIGURE </strong><strong>4</strong><strong><em> </em></strong><strong>Distribution of mutual distances between neighboring photosystem II particles in thylakoid membranes of <em>Arabidopsis thaliana</em> WT and the <em>lhcb5 </em>mutant. </strong>The distances between two closest neighboring PSII supercomplexes were analyzed using EM. Histograms are normalized to the maximum.</p> <p><strong>SUPPORTING FIGURE 1 Analysis of chosen photosystem I and II photosynthesis related parameters. </strong>(A) Quantum yield of photochemistry of PSI - Y(I). (B) Quantum yield of photochemistry of PSII - Y(II). (C) Non-photochemical quenching – NPQ. Parameters were measured during actinic light exposure (800 µmol photons m<sup>-2</sup> s<sup>-1</sup>) and dark relaxation using saturating light pulses (300 ms, 10 000 µmol photons m<sup>-2</sup> s<sup>-1</sup>) in WT and <em>lhcb5</em> mutant plants. Results represent mean values ± SD from 4 measurements. Plants were dark-adapted for 30 min before the measurement.</p> <p><strong>SUPPORTING FIGURE 2 Single-particle image analysis and classification of protein complexes from CN−PAGE C<sub>2</sub>S<sub>2</sub>M<sub>2 </sub>band from the Arabidopsis <em>lhcb5</em> mutant (Figure 2A). </strong>Number of averaged projections in given classes are indicated.</p> <p><strong>SUPPORTING FIGURE 3 Single-particle image analysis and classification of protein complexes from CN−PAGE C<sub>2</sub>S<sub>2</sub>M<sub> </sub>band from the Arabidopsis <em>lhcb5</em> mutant (Figure 2A). </strong>Number of averaged projections in given classes are indicated.</p> <p><strong>SUPPORTING FIGURE 4 Single-particle image analysis and classification of protein complexes from CN−PAGE C<sub>2</sub>SM band from the Arabidopsis <em>lhcb5</em> mutant (Figure 2A). </strong>Number of averaged projections in given classes are indicated.</p> <p><strong>SUPPORTING FIGURE 5<em> </em>A histogram of the relative abundance of PSII semi-crystalline arrays </strong><strong>in thylakoid membranes of Arabidopsis </strong><strong><em>lhcb5</em></strong><strong> mutant. </strong>The bars represent the number of electron micrographs where the 2D arrays cover the indicated percentage of the membrane. The histogram was obtained by evaluation of 50 randomly selected images.</p> <p><strong>SUPPORTING TABLE 1</strong> Physiological parameters of Arabidopsis WT and lhcb5 mutant plants.</p> <p><strong>SUPPORTING TABLE 2 </strong>Density of bands corresponding to LHCB5-less PSII supercomplexes evaluated relatively to WT.</p> <p><strong>Figure 1 C-D</strong> - source data for Figure 1. (panels C-D) Documentation of similar physiology of Arabidopsis thaliana wild type (WT), and its mutant with loss of LHCB5 protein subunit (lhcb5): (C) relative content of photosysthesis related proteins in thylakoid membranes of Arabidopsis thaliana lhcb5 genotype normalised to WT determined by LC-MS/MS; (D) relative protein ratios normalised to WT of photosynthesis related thylakoid membrane proteins of Arabidopsis thaliana lhcb5 genotype determined in isolated thylakoid membranes by LC-MS/MS.</p> <p><strong>Figure 4</strong> - source data for Figure 4. Relative distribution of photosystem II (PSII) distances in thylakoid grana membranes of Arabidopsis thaliana wild type (WT) and mutant with missing LHCB5 protein (lhcb5).</p> <p><strong>Supporting figure 1</strong> Source data for supporting figure 1 Photosynthesis related parametres describing PSI and PSII function. (A) quantum yield of photochemistry of PSI (Y(I)) in Arabidopsis thaliana WT and lhcb5 genotype leaves during red acitinic light exposure and dark relaxation; (B) quantum yield of photochemistry of PSII (Y(II)) in Arabidopsis thaliana WT and lhcb5 genotype leaves during red acitinic light exposure and dark relaxation; (C) non-photochemical quenching of Arabidopsis thaliana genotypes: The level of NPQ estimated during red acitinic light exposure and dark relaxation of WT and lhcb5 leaves.</p> <p><strong>Supporting figure 5</strong> Source data for Supplement figure 4. Relative abundance of 2D PSII arrays in the thylakoid membranes of Arabidopsis thaliana lhcb5 mutant from 50 randomly selected images.</p> <p><strong>Supporting table 1 - source data</strong> Source data for supporting table 1. Physiological parameters of witl type (WT) Arabidopsis thaliana and its mutant lacking LHCB5 protein (lhcb5): Repetitions of data measured for each genotypes.</p> <p><strong>Supporting table 2 - source data</strong> Source data for supporting table 2. Density of bands corresponding to LHCB5-less PSII supercomplexes evaluated relatively to WT: Repetitions of data measured for each genotypes.</p> <p><strong>Figure 1B source WB </strong>Source WB picture for FIGURE 1B.</p> <p><strong>Figure 1B source WB, marker </strong>Source WB picture with molecular marker for FIGURE 1B.</p>
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution; Supplementary Information"
<p>These files contains datasets from which the Figures appearing in the Supplementary Information have been calculated. </p> <p>Description of datasets:</p> <p>Datasets related to SupplFig1 contain two columns: Frequency in Hz and phase noise in rad^2/Hz</p> <p>Data_SupplFig1_stabilised_fringes: psd of the phase noise calculated from the interference fringes in a stabilised condition</p> <p>Data_SupplFig1_unstabilised_fringes: psd of the phase noise calculated from the interference fringes in an unstabilised condition</p> <p>Data_SupplFig1_roundtrip_sensing_laser: psd of the sensing laser signal after a round-trip in the interferometer, calculated from self-heterodyne beatnote</p> <p>Data_SupplFig1_differential_roundtrip_sensing_vs_reference_laser: psd of the difference between the round-trip self-heterodyne beatnotes at the sensing and reference laser wavelengths</p> <p>Datasets related to SupplFig2 contain two columns: time in seconds and normalised intensity (calculated as detailed in the main publication).</p> <p>Data_SupplFig2_High_power_PD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when no artificial phase drift was applied</p> <p>Data_SupplFig2_High_power_PD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_High_power_SPD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when no artificial phase drift was applied </p> <p>Data_SupplFig2_High_power_SPD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_Attenuated_SPD_free_evol: normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when no artificial phase drift was applied </p> <p>Data_SupplFig2_Attenuated_SPD_phase_drift: : normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when an artificial phase drift was applied (8pi/s)</p> <p> </p>
Data for Publication "Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1"
<p>The data are made available as part of the paper "Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1", submitted to Geoscientific Model Development. This data set incorporates selected postprocessed files needed to reproduce the results presented in the paper. </p> <p>The files including the monthly means of precipitation for domain 2 (5 km) are named following the same structure:</p> <p> RR-D02-EXPERIMENTNAME-25km3Doms-YYYY_monthly.nc</p> <p>These are the options available in each case:</p> <ul> <li>EXPERIMENTNAME: Europe, SouthAmerica, Kenya, Micro13 or NoCumulus. These are the names included in Table 1 <br> from the paper.</li> <li>YYYY: 2008 or 2012. This is only applicable to precipitation.</li> </ul> <p>The field means for the northeastern slopes follow this structure:</p> <p> VARIABLE-D02-ERA5-Peru-25-Present-EXPERIMENTNAME-2008.EastLow.fldmean.nc</p> <ul> <li>VARIABLE: CLOUDFRA, PW, RH2, RR, SMOIS or T2. This abbreviations represent the following variable from the model<br> respectively: cloud fraction, precipitable water, relative humidity at 2 meters, total precipitation, soil moisture<br> and temperature at 2 meters.</li> <li>EXPERIMENTNAME: Europe, SouthAmerica, Kenya, Micro13 or NoCumulus. These are the names included in Table 1<br> from the paper.</li> </ul> <p>These files contained hourly values so to obtain the monthly means or sums the user must perform the following command:</p> <p> cdo monmean/monsum in.nc out.nc</p> <p>Two .txt files including the information about the stations considered for the validation of the WRF experiments for year 2008 or 2012 are also included. The data is separated with white spaces, and the structure of the columns is the following one:</p> <p> STATION LATITUDE LONGITUDE ELEVATION COUNTRY PROVIDER REGION</p> <p>Finally, two .zip files are provided. Scripts.zip includes all the scripts used to read, process and plot the data from the model, and Namelist_files.zip includes all the namelist files used to run the WRF simulations.</p> <p> </p>
Data for publication "Statistical characteristics of extreme daily precipitation during 1501 BCE - 1849 CE in the Community Earth System Model".
<p>Here, the data used in Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). "Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model" in <em>Climate of the Past </em>(<a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-6</a>) are provided.</p> <p>Two simulations covering the period 1501 BCE - 2008 CE are performed with CESM 1.2.2: the orbital-only and the full-forcing simulations. The full-forcing transient simulation includes the new long record of volcanic eruptions (<a href="https://doi.org/10.1594/PANGAEA.928646">https://doi.org/10.1594/PANGAEA.928646</a>) that covers the last 3500 years. The output from the simulations is used to examine the long-term variability and characteristics of daily extreme precipitation during 1501BCE-1849 CE.</p> <p>The following files are provided:</p> <ul> <li> <strong>CESM122.transient.PRECT.anom.above99th.1501BCE-1849CE_I and II</strong>: Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the full forcing simulation. The file is split into two parts, with the first file containing the first 50% of extremes (I) and the second file containing the rest 50% (II).</li> <li><strong>CESM122.orbital.PRECT.anom.above99th.1501BCE-1849CE I and II:</strong> Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the orbital-only simulation.</li> <li> <strong>CESM122.transient.variables.mon.1979-2008CE:</strong> monthly precipitation, temperature, and geopotential height at 500 hPa for 1979-2008CE from the full-forcing simulation.</li> <li><strong>CESM122.trans.variable_names.years:</strong> Monthly variables from the full-forcing simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE. The variables are solar insolation (SOLIN), clear-sky net surface shortwave radiation (FSNSC), geopotential height at 500hPa (Z500), and surface temperature (TS).</li> <li><strong>CESM122.orbital.variable_names.years:</strong> Monthly variables from the orbital-only simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE.</li> <li><strong>CESM122.*.log-likelihood-GPDmodel-ExtForcing</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for external forcings.</li> <li> <strong>CESM122.*.log-likelihood-GPDmodel-ModesVar</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for modes of variability.</li> <li><strong>Evolk_EVA_distribution_1501BCE-2015CE</strong>: Distribution of volcanic aerosol for CAM5, produced based on Kim et al. (2021).</li> </ul> <p>If you use this dataset, please cite:</p> <p><em>Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model. Climate of the Past Discussions, 1-38. <a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-61</a></em></p>
Data and code for the publication "The effect of rainfall changes on economic production"
<p>The zipped file of this repository contains code and data to reproduce the results of the publication:</p> <p>Kotz et al, Nature, The effect of rainfall changes on economic production. (2021). <a href="http://doi.org/10.1038/s41586-021-04283-8">https://doi.org/10.1038/s41586-021-04283-8</a></p> <p> </p> <p>Economic data are derived from the DoSE database: <a href="http://doi.org/10.5281/zenodo.4681305">https://doi.org/10.5281/zenodo.4681305</a></p> <p>Climate data are derived from the ERA-5 reanalysis: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 </a></p> <p> </p> <p>Please see the README document for detailed:</p> <p>- Descriptions of the code and data provided</p> <p>- Lists of the required dependencies</p> <p>- Naming conventions for variables</p> <p> </p>
How does moving Public Engagement with Research Online Change Audience Diversity? Comparing Inclusion Indicators for 2019 & 2020 European Researchers' Night events
<p>Taking place annually in more than 400 cities, European Researchers’ Night is a pan- European synchronized event that aims to bring researchers closer to the public. In this paper audience profiles are compared from events in 2019 and 2020. In 2019, face-to-face events reached an estimated 1.6 million attendees, while in 2020, events shifted online due to the COVID-19 pandemic and reached an estimated 2.3 million attendees. Focusing on social inclusion metrics, survey data is analyzed across two national contexts (Ireland and Malta) in 2019 (n=656) and 2020 (n=506). The results from this exploratory, descriptive study shed light on how moving public engagement with research online shifted audience profiles. Based on prior research about the digital divide in access and use of online media, hypotheses were proposed that online European Researchers’ Night events would attract audiences with higher educational attainment levels and greater self-reported, subjective economic well-being. While changes were observed from 2019 to 2020, results for each hypothesis show a mixed picture. The first hypothesis was upheld for the highest education levels but failed for the lowest levels suggesting that the pivot to online events simultaneously attracted participants with no formal education and those with postgraduate qualifications, while attracting less of those with undergraduate or lower levels of education. The second hypothesis was not upheld, with online European Researchers’ Night events attracting audiences with slightly higher levels of economic well-being compared to face-to-face events. The findings of this study indicate that European Researchers’ Night events present a clear opportunity to measure the effects of the digital divide in relation to public engagement with research across Europe.</p>
Supplementary data for publication Global distribution of mcr gene variants in 214K metagenomic samples
<p># Supplementary data for the manuscript "Global distribution of mcr gene variants in 214,095 metagenomic samples"</p> <p>SD1_mapped_runids.csv : tab-separated file with columns of run_accessions downloaded from ENA and whether the metagenome were positive for at least one of the mcr genes.</p> <p>SD2_mcr_df.csv : compositional table of mcr-positive metagenomes with associated metadata (collection_year, country, and host) for each run_accession, as well as mapping results.</p> <p>SD3_mcr_contigs.fa : FASTA file with contigs carrying mcr genes. The header contains the run_accession ID.</p> <p>SD4_aldex2_results.csv: CSV file containing ALDEx2 results. The columns are as follows:<br> * group: metadata category (year, country or host). If the column contains more than one label, e.g., "Denmark - 2020 - Pigs", significance is tested within Danish pig samples from 2020.<br> * rab.all: median clr value for all samples in the feature<br> * rab.win.conditionA: median clr value for the condition A of samples<br> * rab.win.conditionB: median clr value for the condition B of samples<br> * diff.btw: median difference in clr values between A and B conditions<br> * diff.win: median of the largest difference in clr values within A and B conditions<br> * effect : median effect size: diff.btw / max(diff.win) for all instances<br> * overlap : proportion of effect size that overlaps 0 (i.e. no effect)<br> * we.ep: Expected P value of Welch’s t test<br> * we.eBH: Expected Benjamini-Hochberg corrected P value of Welch’s t test<br> * wi.ep: Expected P value of Wilcoxon rank test<br> * wi.eBH: Expected Benjamini-Hochberg corrected P value of Wilcoxon test<br> * parts: gene name<br> * conditionA: label of condition A that is compared against condition B<br> * conditionB: label of condition B that is compared against condition A<br> * conditions.A.vs.B: label to explain condition A compared against condition B<br> NOTE: see for more explanation of the output of ALDEx2 https://www.bioconductor.org/packages/release/bioc/vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html#5_ALDEx2_outputs</p> <p>SD5: Multi-VCF file containing SNP information on mcr alleles. Can be used to construct consensus sequences.</p> <p>SD6: FASTA file containing all unique consensus sequences reported in the manuscript.</p> <p>SD7: CSV file with an overview of which metagenome contains which unique consensus sequence.</p>
Saana-I/SoundScapeOfFear: February 2022 pre-publication dataset
<p>Data and code for our PNAS paper "Behavioral responses to predatory sounds predict sensitivity of cetaceans to anthropogenic noise within a soundscape of fear"</p> <p>Authors: Patrick J.O. Miller, Saana Isojunno, Eilidh Siegal, Frans-Peter A. Lam, Petter H. Kvadsheim, Charlotte Curé</p>
Datasets for publication titled "Chiral control of spin-crossover dynamics in Fe(II) complexes"
<p>Transient absorption (TA), transient absorption anisotropy (TAA), and time-resolved circular dichroism (TRCD) datasets analyzed and interpreted in the publication titled "Chiral control of spin-crossover dynamics in Fe(II) complexes" published in Nature Chemistry under the DOI 10.1038/s41557-022-00933-0.</p>
Data and ancillary data for publication: Natural infrastructure and water erosion mitigation in the Andes
<p>The data contain information on the effectiveness of natural infrastructure to mitigate soil erosion. Data were compiled from 118 case studies from the Andean region, whereby information on natural infrastructure interventions, soil erosion and soil quality were tabulated and analysed.</p> <p>The data contains the following documents:<br> -Database with data on soil erosion, soil quality for different types of natural infrastructure (118 case studies)<br> -Metadata<br> -Summary of terms used in the systematic review of the literature (in Spanish and English)<br> -List of bibliographic data sources that were searched with the search terms<br> -Full bibliographic references of all 118 case studies</p> <p><strong>Full reference </strong></p> <p><em>Vanacker V, Molina A, Rosas-Barturen M, Bonnesoeur V, Román-Dañobeytia F, Ochoa-Tocachi B, Buytaert W (2022). The effect of natural infrastructure on water erosion mitigation in the Andes. </em></p> <p> </p> <p> </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.