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1,079 results for “source data”
Data from: Having a lot of a good thing: multiple important group memberships as a source of self-esteem
Membership in important social groups can promote a positive identity. We propose and test an identity resource model in which personal self-esteem is boosted by membership in additional important social groups. Belonging to multiple important group memberships predicts personal self-esteem in children (Study 1a), older adults (Study 1b), and former residents of a homeless shelter (Study 1c). Study 2 shows that the effects of multiple important group memberships on personal self-esteem are not reducible to number of interpersonal ties. Studies 3a and 3b provide longitudinal evidence that multiple important group memberships predict personal self-esteem over time. Studies 4 and 5 show that collective self-esteem mediates this effect, suggesting that membership in multiple important groups boosts personal self-esteem because people take pride in, and derive meaning from, important group memberships. Discussion focuses on when and why important group memberships act as a social resource that fuels personal self-esteem.
Data from: Are traded forest tree seeds a potential source of non-native pests?
The international seed trade is considered relatively safe from a phytosanitary point of view and is therefore less regulated than trade in other plants for planting. However, the pests carried by traded seeds are not well known. We assessed insects and fungi in 58 traded seed lots of eleven gymnosperm and angiosperm tree species from North America, Europe and Asia. Insects were detected by x-raying and molecular methods. The fungal community was characterised using high-throughput sequencing (HTS) and by growing fungi on non-selective agar. About 30% of the seed lots contained insect larvae. Gymnosperms contained mostly hymenopteran (Megastigmus spp.) and dipteran (Cecidomyiidae) larvae, while angiosperms contained lepidopteran (Cydia latiferreana) and coleopteran (Curculio spp.) larvae. HTS indicated the presence of fungi in all seed lots and fungi grew on non-selective agar from 96% of the seed lots. Fungal abundance and diversity were much higher than insect diversity and abundance, especially in angiosperm seeds. Almost 50% of all fungal Exact Sequence Variants (ESVs) found in angiosperms were potential pathogens, in comparison with around 30% of potentially pathogenic ESVs found in gymnosperms. The results of this study indicate that seeds may pose a greater risk of pest introduction than previously believed or accounted for. A rapid risk assessment suggests that only a small number of species identified in this study is of phytosanitary concern. However, more research is needed to enable better risk assessment, especially to increase knowledge about the potential for transmission of fungi to seedlings and the host range and impact of identified species.
Data for "New circuits and an open source decoder for the colorcode"
<p>Code written, circuits generated, and statistics collected for the paper "New circuits and an open source decoder for the colorcode".</p>
Data from: Geographic sources of ozone air pollution and mortality burden in Europe
<p>This dataset contains all the maximum daily 8-hour mean O3 concentrations (MDA8 O3) presented in the paper entitled " Geographic sources of ozone air pollution and mortality burden in Europe". In this paper, the CMAQv5.0.2-ISAM was run to calculate the MDA8 O3 concentrations by country for the weeks 18-39 from 2015 to 2017 (approximately corresponding months from May to September). </p> <p>Within each tar file, you'll find the daily MDA8 O3 concentrations, presenting the contributions from 35 individual countries, contributions from seas and oceans (SEA), contributions from neighboring countries within the simulation domain (NOEU35), and contributions from the boundaries of the simulation domain (BCON) for each respective year. Each netcdf file follows the nomenclature "sconco3_Countrycode_Day.nc" , where day "1" corresponds to the first day of the week 18 of each year and the country codes are: Albania (AL), Austria (AT), Belgium (BE), Bulgaria (BG), Switzerland (CH), Cyprus (CY), Czechia (CZ), Germany (DE), Denmark (DK), Estonia (EE), Greece (EL), Spain (ES), Finland (FI), France (FR), Croatia (HR), Hungary (HU), Ireland (IE), Iceland (IS), Italy (IT), Liechtenstein (LI), Lithuania (LT), Luxembourg (LU), Latvia (LV), Montenegro (ME), Malta (MT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Sweden (SE), Slovenia (SI), Slovakia (SK), United Kingdom (UK).</p> <p> </p> <p>Acknowledgments</p> <p>BSC co-authors acknowledge support Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO) as part of the Plan Nacional del Ozono project (BOE-A-2021-20183), as well as through the VITALISE project (PID2019-108086RA-I00, MCIN/AEI/10.13039/501100011033) funded by the Agencia Estatal de Investigacion (AEI). We also acknowledge the AXA Research Fund and Red Temática ACTRIS España (CGL2017-90884-REDT), and H2020 ACTRIS IMP (No 871115), the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550).</p> <p> </p>
numerical source data for Figures 2A-F
Open the record for dataset details and reuse information.
Photizo: an open-source library for cross-sample analysis of FTIR spectroscopy data
<p>With continually improved instrumentation, Fourier transform infrared (FTIR) microspectroscopy can now be used to capture thousands of high-resolution spectra for chemical characterisation of a sample. The spatially resolved nature of this method lends itself well to histological characterisation of complex biological specimens. However, commercial software currently available can make joint analysis of multiple samples challenging and, for large datasets, computationally infeasible. In order to overcome these limitations, we have developed Photizo - an open-source Python library for spectral analysis which includes functions for pre-processing, visualisation and downstream analysis, including principal component analysis, clustering, macromolecular quantification and biochemical mapping. This library can be used for analysis of spectroscopy data without a spatial component, as well as spatially-resolved data, such as data obtained via infrared (IR) microspectroscopy in scanning mode and IR imaging by focal plane array (FPA) detector. The data set made available here was FTIR microspectroscopy spatially resolved data used for demonstrating cross-sample analysis using Photizo including example metadata. </p>
Source data: Transmission of SARS-CoV-2 from humans to animals and potential host adaptation
<p>All source data required for reproducing the results of the associated manuscript. Contains data inputs for all associated custom code hosted on Zenodo (https://doi.org/10.5281/zenodo.6528187).</p>
Figure 1 from: Roma-Marzio F, Peruzzi L, Bedini G (2017) Personal private herbaria: a valuable but neglected source of floristic data. The case of Italian collections today. Italian Botanist 3: 7-15. https://doi.org/10.3897/italianbotanist.3.12097
Figure 1 - Distribution of the 34 surveyed Italian private herbaria.
ProPhyle source data
<p>RefSeq reference genomes and NCBI taxonomic trees for building ProPhyle indexes.</p> <p>See http://github.com/karel-brinda/prophyle for more information about ProPhyle.</p>
Noise samples for Costantino et al., Seismic Source Characterization From GNSS Data Using Deep Learning (2023)
Open the record for dataset details and reuse information.
Source Data File for "Magnetocaloric Effect of Topological Excitations in Kitaev Magnets" published in Nat. Commun.
<table> <tbody> <tr> <td> <p><span>“Source Data.xlsx” for manuscript "</span>Magnetocaloric Effect of Topological Excitations in Kitaev Magnets"<span> is provided. Here is the title and a brief description for the data:</span></p> <p> </p> <p><span>Sheet 1: Fig1bcd(gh)</span></p> <p><span>Description: The data matrix in three-dimensional space for the landscape of isentropes as shown in Fig1.b-d. The data could also be used in generating Fig1.gh.</span></p> <p><span> </span></p> <p><span>Sheet 2: Fig1e</span></p> <p><span>Description: The data to generate Fig.1e. The rows starting with Gamma_B represent the Gruneisen parameter; while the rows starting with B indicate the magnetic fields. </span></p> <p><span> </span></p> <p><span>Sheet 3: Fig1f</span></p> <p><span>Description: The data to generate Fig.1f. The rows starting with T represent the temperature; while the rows starting with chi indicate the magnetic susceptibility. </span></p> <p><span> </span></p> <p><span>Sheet 4: Fig1gh</span></p> <p><span>Description: The data to generate Fig.1gh. The rows starting with T represent the temperature; while the rows starting with S/ln2 indicate the thermal entropy. Other relevant data could be found in Sheet 1.</span></p> <p><span> </span></p> <p><span>Sheet 5: Fig2ab</span></p> <p><span>Description: The data matrix in three-dimensional space for the landscape of isentropes as shown in Fig.2a. These data also include the portion used to generate Fig.2b.</span></p> <p><span> </span></p> <p><span>Sheet 6: Fig2cd</span></p> <p><span>Description: The data matrix in three-dimensional space for the landscape of isentropes as shown in Fig.2c. These data also include the portion used to generate Fig.2d.</span></p> <p><span> </span></p> <p><span>Sheet 7: Fig3ab</span></p> <p><span>Description: The data to generate Fig.3ab. The rows starting with T represent the temperature; rows starting with Cm represent the specific heat; rows starting with Wp represent the expectation value of flux operator; while the rows starting with S/ln2 indicate the thermal entropy. </span></p> <p><span> </span></p> <p><span>Sheet 8: Fig3c</span></p> <p><span>Description: The data to generate Fig.3c. The rows starting with T represent the temperature; while the rows starting with S1(omega=0) indicate the estimate of relaxation rate. </span></p> <p><span> </span></p> <p><span>Sheet 9: Fig3d</span></p> <p><span>Description: The data to generate the landscape of Fig.3d. The columns starting with kx/pi and ky/pi represent the q-points in momentum space; while the columns starting with S(q) and S_tr(q) indicate the data of spin structure factors. </span></p> <p><span> </span></p> <p><span>Sheet 10: Fig3e</span></p> <p><span>Description: The data matrix in three-dimensional space for the landscape of specific heat as shown in Fig.3e</span></p> <p><span> </span></p> <p><span>Sheet 11: Fig4a</span></p> <p><span>Description: The data to generate Fig.4a. The rows starting with T represent the temperature; while the rows starting with S/ln2 indicate the thermal entropy. Other relevant data could be found in Sheet 1.</span></p> <p><span> </span></p> <p><span>Sheet 12: Fig4c</span></p> <p><span>Description: The data to generate Fig.4c. The rows starting with Ti represent the initial temperature; while the rows starting with Tf indicate the final reached temperature. </span></p> <p><span> </span></p> <p><span>Sheet 13: Fig4d</span></p> <p><span>Description: The data to generate Fig.4d. The rows starting with T tilde represent the rescaled temperature; while the rows starting with S/ln(2s+1) indicate the renormalized entropy. Other relevant data could be found in Sheet 1.</span></p> <p><span> </span></p> <p><span> </span></p> <p><span> </span></p> </td> </tr> </tbody> </table>
Source data supporting the Article " Malaria transmission risk is projected to increase in the highlands of Western and Northern Rwanda"
<p>Average T<sub>min</sub>, T<sub>max</sub>, rainfall, malaria incedence in 30 districts were retrieved from the Rwanda Meteorological Agency and Rwanda’s Health Management Information System (HMIS) during 2010–2015. We applied an ensemble learning method, namely, Random Forest Model (RFM), to comprehensively estimate the effect of a changing climate on malaria incidence according to historical observations in Rwanda. Based on this forecasting model, we predict future malaria risk and its spatiotemporal changes under two distinct Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). </p>
Manual cross-validation data for the article: "Comparison of bibliographic data sources: Implications for the robustness of university rankings"
<p>These are sets of data collected from the manual cross-validation of DOIs (and related research outputs) that are sampled from Web of Science (WoS), Scopus and Microsoft Academic (MSA). For each of the 15 universities, we initially collect all DOIs indexed by each of the three bibliographic sources. Subsequently, we randomly sample 40, 30 and 30 DOIs from sets of DOIs that are exclusively indexed by WoS, Scopus and MSA, respectively, for each university. A manual cross-validation process is then followed to validate certain characteristics across the data sources. This cross-validation process was carried out by a data wrangler, on a part-time basis over a few months, for which online data was accessed from 18 December 2018 to 20 May 2019.</p>
Data from: Different food sources elicit fast changes to bacterial virulence
Environmentally transmitted, opportunistic bacterial pathogens have a life cycle that alternates between hosts and environmental reservoirs. Resources are often scarce and fluctuating in the outside-host environment, whereas overcoming the host immune system could allow pathogens to establish a new, resource abundant and stable niche within the host. We tested if shortterm exposure to different outside-host resource types and concentrations affect Serratia marcescens—(bacterium)'s virulence in Galleria mellonella (moth). As expected, virulence was mostly dictated by the bacterial dose, but we also found a clear increase in virulence when the bacterium had inhabited a low (versus high) resource concentration, or animal-based (versus plant-based) resources for 48 h prior to injection. The results suggest that temporal changes in pathogen's resource environment can induce very rapid changes in virulence and affect infection severity. Such changes could also play an important role in shifts from environmental lifestyle to pathogenicity or switches in host range and have implications for the management of opportunistic pathogens and disease outbreaks.
Supplementary Data and Scripts for the Paper "Synchronous Development in Open-Source Projects: A Higher-Level Perspective"
<p>Anonymized data and scripts used to produce the results of the paper "Synchronous Development in Open-Source Projects: A Higher-Level Perspective".</p>
Lightning and MMIA Source and Raw data
<p>This file contains the MMIA optical source and raw data from ASIM and the VLF/LF lightning raw data. The *xlsx is the source data for the plot of figures in the manuscript. The *lig is the lightning data. The *CDF is the MMIA data. </p>
Source Data
<p>The zipped folder contains 2 sub-folders with matfiles corresponding to panels in the Main and Supplementary figures. The .mat variables contain x and y coordinates of the plotted points. </p>
Source Data for the publication: Multi-omics and machine learning reveal context-specific gene regulatory activities of PML::RARA in Acute Promyelocytic Leukemia
<p>Source Data for the publication: Multi-omics and machine learning reveal context-specific gene regulatory activities of PML::RARA in Acute Promyelocytic Leukemia</p>
Supporting data to "Sediment source and pathway identification using Sentinel-2 imagery and (kayak-based) lagrangian river profiles on the Vjosa river"
<p>This release contains the supporting data files that are part of the manuscript "Sediment source and pathway identification using Sentinel-2 imagery and (kayak-based) lagrangian river profiles on the Vjosa river."</p>
Meterstick Benchmark: Source, Documentation and Data
<p>Contains the artifacts and data collected of Meterstick: a benchmark for performance variability in cloud-based and self-hosted modifiable virtual environments.</p> <p>Included is the source code and compiled artifacts of Meterstick, as well as associated documentation.</p> <p>Also included is the data collected using Meterstick during the experiments described in the related article, as well as plotting instruments for this data.</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.