Dataset Information

A resource for automated search and collation of geochemical datasets from journal supplements.

ABSTRACT: This article presents a resource for automated search, extraction and collation of geochemical and geochronological data from the Figshare repository using web scraping code. To answer fundamental questions about the Earth's evolution, such as spatial and temporal evolution and interrelationships between the planet's solid and surficial reservoirs, researchers must utilize global geochemical datasets. Due to the volume of data being published, these datasets become quickly outdated. We present a resource that allows researchers to rapidly curate and update their own databases from existing published data. We use open-source Python code to web scrape the Figshare repository for journal supplementary files using the application programming interface, allowing for the collection and download of hundreds of supplementary files and metadata in minutes. Use of this web scraping tool is demonstrated here by collation of a zircon geochronology and chemistry database of >150,000 analyses. The database is consistent in reproducing trends in other published zircon compilations. Providing a resource for automated collection of Figshare data files will encourage data sharing and reuse.

SUBMITTER: Martin EL

PROVIDER: S-EPMC9700723 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A resource for automated search and collation of geochemical datasets from journal supplements.

Martin Erin L EL Barrote Vitor R VR Cawood Peter A PA

Scientific data 20221125 1

This article presents a resource for automated search, extraction and collation of geochemical and geochronological data from the Figshare repository using web scraping code. To answer fundamental questions about the Earth's evolution, such as spatial and temporal evolution and interrelationships between the planet's solid and surficial reservoirs, researchers must utilize global geochemical datasets. Due to the volume of data being published, these datasets become quickly outdated. We present a ...[more]

PMID: 36433993

Similar Datasets

Project description:BACKGROUND: We investigate automated and generic alphabet reduction techniques for protein structure prediction datasets. Reducing alphabet cardinality without losing key biochemical information opens the door to potentially faster machine learning, data mining and optimization applications in structural bioinformatics. Furthermore, reduced but informative alphabets often result in, e.g., more compact and human-friendly classification/clustering rules. In this paper we propose a robust and sophisticated alphabet reduction protocol based on mutual information and state-of-the-art optimization techniques. RESULTS: We applied this protocol to the prediction of two protein structural features: contact number and relative solvent accessibility. For both features we generated alphabets of two, three, four and five letters. The five-letter alphabets gave prediction accuracies statistically similar to that obtained using the full amino acid alphabet. Moreover, the automatically designed alphabets were compared against other reduced alphabets taken from the literature or human-designed, outperforming them. The differences between our alphabets and the alphabets taken from the literature were quantitatively analyzed. All the above process had been performed using a primary sequence representation of proteins. As a final experiment, we extrapolated the obtained five-letter alphabet to reduce a, much richer, protein representation based on evolutionary information for the prediction of the same two features. Again, the performance gap between the full representation and the reduced representation was small, showing that the results of our automated alphabet reduction protocol, even if they were obtained using a simple representation, are also able to capture the crucial information needed for state-of-the-art protein representations. CONCLUSION: Our automated alphabet reduction protocol generates competent reduced alphabets tailored specifically for a variety of protein datasets. This process is done without any domain knowledge, using information theory metrics instead. The reduced alphabets contain some unexpected (but sound) groups of amino acids, thus suggesting new ways of interpreting the data.

Project description:The Deutsche Apotheker Zeitung (DAZ, German Pharmacist Journal) is an independent pharmaceutical newspaper focusing on science and practice, mainly for the profession of pharmacist. In this study, drug advertising in the DAZ was analysed. To our knowledge, there is little scientific data available on drug advertising in professional journals. We assumed that professional journals provide particularly good background information on the advertised drugs because they are targeted to specialists. All non-prescription medicines and preparations that fall under the Medicines Advertising Law (Heilmittelwerbegesetz, HWG) were studied. The Medicines Advertising Law regulates the legal procedure for advertising medicinal products in Germany. The 167 product advertisements from the 52 issues of 2021 were analysed and checked for compliance with the Medicines Advertising Law. We identified significant deficiencies in compliance with the legislation. These included the lack of mandatory information required by the Medicines Advertising Law, for example the indication of adverse drug reactions and the listing of contraindications. There are very few peer-reviewed references on the efficacy of the advertised preparations. A scientific validation was carried out using the PubMed database, with the result that scientific information was available only for 1/3 of the advertisements. In addition, the appearance and target groups as well as social structures, images and feelings conveyed by the advertising were analysed. This study provides insights into the mechanisms of drug advertising in professional journals, which have not yet been researched to any great extent. Even in professional journals, pharmacological evidence plays a much smaller role than marketing, psychology and traditional social values. It seems that drug manufacturers deliberately ignore the German Medicines Advertising Law to advertise their products in the best possible way. Stricter legal controls should be put in place to prevent this practice and protect consumers from misinformation. This will increase drug safety.

Project description:Bibliometric indicators increasingly affect careers, funding, and reputation of individuals, their institutions and journals themselves. In contrast to author self-citations, little is known about kinetics of journal self-citations. Here we hypothesized that they may show a generalizable pattern within particular research fields or across multiple fields. We thus analyzed self-cites to 60 journals from three research fields (multidisciplinary sciences, parasitology, and information science). We also hypothesized that the kinetics of journal self-citations and citations received from other journals of the same publisher may differ from foreign citations. We analyzed the journals published the American Association for the Advancement of Science, Nature Publishing Group, and Editura Academiei Române. We found that although the kinetics of journal self-cites is generally faster compared to foreign cites, it shows some field-specific characteristics. Particularly in information science journals, the initial increase in a share of journal self-citations during post-publication year 0 was completely absent. Self-promoting journal self-citations of top-tier journals have rather indirect but negligible direct effects on bibliometric indicators, affecting just the immediacy index and marginally increasing the impact factor itself as long as the affected journals are well established in their fields. In contrast, other forms of journal self-citations and citation stacking may severely affect the impact factor, or other citation-based indices. We identified here a network consisting of three Romanian physics journals Proceedings of the Romanian Academy, Series A, Romanian Journal of Physics, and Romanian Reports in Physics, which displayed low to moderate ratio of journal self-citations, but which multiplied recently their impact factors, and were mutually responsible for 55.9%, 64.7% and 63.3% of citations within the impact factor calculation window to the three journals, respectively. They did not receive nearly any network self-cites prior impact factor calculation window, and their network self-cites decreased sharply after the impact factor calculation window. Journal self-citations and citation stacking requires increased attention and elimination from citation indices.

Dataset Information

A resource for automated search and collation of geochemical datasets from journal supplements.

Publications

A resource for automated search and collation of geochemical datasets from journal supplements.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets