Dataset Information

Dataset for WWW landing pages webobject retrieval performance evaluation.

ABSTRACT: This dataset describes data obtained from a multi-day World Wide Web (WWW) measurement campaign distributed internationally across multiple Amazon Web Service (AWS) datacentres. The Chrome web browser was controlled by the Selenium framework to make repetitive requests to several popular websites; the resulting webobjects were captured by a proxy server and details about them stored in the provided SQLite3 databases. A Python script is provided to evaluate the webobjects with respect to their configured as well as their actual expiration times, as part of our more detailed analysis that we provide in [1]. Researchers and practitioners can readily employ this dataset in their own research endeavours with little efforts for avenues of inquiry beyond webobject expiration times we described in [1], as we provide additional information about each webobject and each website visit during the measurement campaign time horizon.

SUBMITTER: Seeling P

PROVIDER: S-EPMC7118300 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Dataset for WWW landing pages webobject retrieval performance evaluation.

Seeling Patrick P

Data in brief 20200314

This dataset describes data obtained from a multi-day World Wide Web (WWW) measurement campaign distributed internationally across multiple Amazon Web Service (AWS) datacentres. The Chrome web browser was controlled by the Selenium framework to make repetitive requests to several popular websites; the resulting webobjects were captured by a proxy server and details about them stored in the provided SQLite3 databases. A Python script is provided to evaluate the webobjects with respect to their co ...[more]

PMID: 32258285

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Project description:AimEffective management strategies for conserving biodiversity and mitigating the impacts of global change rely on access to comprehensive and up-to-date biodiversity data. However, manual search, retrieval, evaluation, and integration of this information into databases present a significant challenge to keeping pace with the rapid influx of large amounts of data, hindering its utility in contemporary decision-making processes. Automating these tasks through advanced algorithms holds immense potential to revolutionize biodiversity monitoring.InnovationIn this study, we investigate the potential for automating the retrieval and evaluation of biodiversity data from Dryad and Zenodo repositories. We have designed an evaluation system based on various criteria, including the type of data provided and its spatio-temporal range, and applied it to manually assess the relevance for biodiversity monitoring of datasets retrieved through an application programming interface (API). We evaluated a supervised classification to identify potentially relevant datasets and investigate the feasibility of automatically ranking the relevance. Additionally, we applied the same appraoch on a scientific literature source, using data from Semantic Scholar for reference. Our evaluation centers on the database utilized by a national biodiversity monitoring system in Quebec, Canada.Main conclusionsWe retrieved 89 (55%) relevant datasets for our database, showing the value of automated dataset search in repositories. Additionally, we find that scientific publication sources offer broader temporal coverage and can serve as conduits guiding researchers toward other valuable data sources. Our automated classification system showed moderate performance in detecting relevant datasets (with an F-score up to 0.68) and signs of overfitting, emphasizing the need for further refinement. A key challenge identified in our manual evaluation is the scarcity and uneven distribution of metadata in the texts, especially pertaining to spatial and temporal extents. Our evaluative framework, based on predefined criteria, can be adopted by automated algorithms for streamlined prioritization, and we make our manually evaluated data publicly available, serving as a benchmark for improving classification techniques.

Dataset Information

Dataset for WWW landing pages webobject retrieval performance evaluation.

Publications

Dataset for WWW landing pages webobject retrieval performance evaluation.

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