Research Dataset Catalog and ShareScore Ratings | ScienceDex

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Key dataset used in the paper of "Emergent constraints reveal lower estimates of global river flow"

This dataset includes key data used in the emergent constraint approach for refined partitioning of global water cycle components.

opencc-by-4.0Apr 2024View details →

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Data from: Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA

Novel methods for sampling and characterizing biodiversity hold great promise for re-evaluating patterns of life across the planet. The sampling of airborne spores with a cyclone sampler, and the sequencing of their DNA, have been suggested as an efficient and well-calibrated tool for surveying fungal diversity across various environments. Here we present data originating from the Global Spore Sampling Project, comprising 2,768 samples collected during two years at 47 outdoor locations across the world. Each sample represents fungal DNA extracted from 24 m3 of air. We applied a conservative bioinformatics pipeline that filtered out sequences that did not show strong evidence of representing a fungal species. The pipeline yielded 27,954 species-level operational taxonomic units (OTUs). Each OTU is accompanied by a probabilistic taxonomic classification, validated through comparison with expert evaluations. To examine the potential of the data for ecological analyses, we partitioned the variation in species distributions into spatial and seasonal components, showing a strong effect of the annual mean temperature on community composition. The database is organized in five datasets in a csv format (columns separated by commas): (1) metadata providing the location, date, and time for each sample, along with sequencing depth and other essential information (metadata.csv); (2) species-level OTU tables per sample describing the number of sequences assigned to each species (otu.table.csv 3); (3) taxonomic classification of each species-level OTU (taxonomy.csv); (4) closest matching sequences and their taxonomy for ASVs in putatively fungal pseudophyla, which are included in (2) and (3) (fungi_pseudophyla.csv); and (5) closest matching sequences and their taxonomy for ASVs in putatively non-fungal pseudophyla, which are not included in the other datasets (nonfungi_pseudophyla.csv). The first four datasets can be linked to each other using the unique sample codes and the unique identifiers for species-level OTUs. The three first datafiles are also provided in allData.RData which can be read into R as load("allData.RData").

opencc-by-4.0May 2024View details →

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Global Health Security (GHS) Tracking dataset

<div> The Global Health Security Tracking (GHS) dataset is a research effort led by Dr. Rebecca Katz and Dr. Ellie Graeden at the Georgetown University Center for Global Health Science and Security. The dataset was developed to provide a shared resource to map the flow of disbursed funds for global health security. GHS Tracking allows both funders and recipients to identify gaps and prioritize future investments, and helps to highlight the ways in which funds can be allocated most effectively to have the greatest impact. The dataset and platform serve as a basis for mutual accountability within the global health security community, promoting public accounting, and providing an opportunity for countries, organizations, and other funders to showcase their successes and identify priorities for future investments. GHS Tracking aggregates publicly reported financial disbursements from funders to recipients for activities related to global health security, from January 2014 and December 2022, reported in nominal U.S. dollars. This effort draws from a large number of <a href="https://tracking.ghscosting.org/about/data-sources/">data sources</a>, each with varying reporting standards, methods of summarizing, and levels of metadata quality. To learn more, visit our <a href="https://tracking.ghscosting.org/about/methods/">methods documentation</a> or read the attached Technical Appendix. To see how our team and other researchers have used the data, check out the publications <a href="https://tracking.ghscosting.org/about/publications/">here</a>. The GHS Tracking dataset can be explored, visualized, and downloaded from <a href="tracking.ghscosting.org">tracking.ghscosting.org</a>. Data can also be accessed in CSV format on this page. In addition to the Technical Appendix, there are two data files available for download: <ul> <li>Funding distributed from 2014 – 2022 for each of the Joint External Evaluation (JEE) core capacities</li> <li>Funding distributed from 2014 – 2022 for each of the WHO public health emergencies of international concern (PHEIC)</li> </ul> Funding is reported as the total amount received and disbursed by each stakeholder entity per year in nominal U.S. dollars. The data should not be used without special attention to the <a href="https://tracking.ghscosting.org/about/data-limitations/">limitations</a>. Please use the citation on this page to cite these data and contact us at <a href="mailto:globalhealthsecurity@georgetown.edu">globalhealthsecurity@georgetown.edu</a> so that we can showcase your work on the site! </div>

openmit-licenseMay 2024View details →

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Characterisation and comparison of Mycoplasma bovis strain types from Irish and Scottish bovine isolates in a global context - code and datasets.

The objectives of this paper were to firstly, characterise the strains and genetic diversity within isolates of Mycoplasma bovis collected from clinical samples of bovine respiratory disease in Ireland and Scotland, and secondly, to provide a global phylogenetic context to these isolates.  This archive contains associated Jupyter notebooks and metadata used in the analysis for the study.

opencc-by-4.0Jun 2024View details →

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Dataset: ATA Creativity Global (AACG) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.