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   https://doi.org/10.4081/jlimnol.2023.2116  

   Fresno Lopez, Zacarias; Cancellario, Tommaso; Fontaneto, Diego; Kamburska, Lyudmila; Karimullah, Karimullah; Wallace, Robert L.; Walsh, Elizabeth J.; Smolak, Radoslav (February 2023, Journal of Limnology)

   We report a dataset of all known and published occurrence records of animals of the phylum Rotifera, including Bdelloidea, Monogononta, and Seisonacea (with the exclusion of Acanthocephala) for Africa and surrounding islands and archipelagos. The dataset includes 24,704 records of 914 taxa (subspecies: 38; species: 783; genus: 76; family: 17), gathered from 610 published papers. The published literature spans from 1854 to 2022, with the highest number of records in the decades 1990-1999 and 2010-2019. The African countries with the highest number of taxa are Nigeria, Algeria, South Africa, and Democratic Republic of the Congo, whereas no records are yet available for a dozen countries. The number of species known from each country can be explained mostly by sampling efforts, measured as the number of papers published for each country up to now. The dataset is available through the Open Science Framework (OSF) and in the Global Biodiversity Information Facility (GBIF). 

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2. Scientific Tests and Continuous Integration Strategies to Enhance Reproducibility in the Scientific Software Context

   https://doi.org/https://doi.org/10.1145/3322790.3330595  

   Krafczyk, Matthew; Shi, August; Bhaskar, Adhithya; Marinov, Darko; Stodden, Victoria (June 2019, 2nd International Workshop on Practical Reproducible Evaluation of Computer Systems (P-RECS’19))

   Continuous integration (CI) is a well-established technique in commercial and open-source software projects, although not routinely used in scientific publishing. In the scientific software context, CI can serve two functions to increase reproducibility of scientific results: providing an established platform for testing the reproducibility of these results, and demonstrating to other scientists how the code and data generate the published results. We explore scientific software testing and CI strategies using two articles published in the areas of applied mathematics and computational physics. We discuss lessons learned from reproducing these articles as well as examine and discuss existing tests. We introduce the notion of a scientific test as one that produces computational results from a published article. We then consider full result reproduction within a CI environment. If authors find their work too time or resource intensive to easily adapt to a CI context, we recommend the inclusion of results from reduced versions of their work (e.g., run at lower resolution, with shorter time scales, with smaller data sets) alongside their primary results within their article. While these smaller versions may be less interesting scientifically, they can serve to verify that published code and data are working properly. We demonstrate such reduction tests on the two articles studied. 

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3. Planktic and benthic foraminiferal isotope and abundance records from the subpolar North Atlantic during the last 1,200 years

   https://doi.org/10.5281/zenodo.8429224  

   Lu, Wanyi; Oppo, Delia W; Gebbie, Geoffrey; Thornalley, David_J R (January 2023, Zenodo)

   Data published in Lu et al. 2023 and R script repository to reproduce published figures. Lu W., D. W. Oppo, G. Gebbie, D. J. R. Thornalley, Surface climate signals transmitted rapidly to deep North Atlantic throughout last millennium. Science, 382, 834-839 https://doi.org/10.1126/science.adf1646. 

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4. NMR and Metabolomics—A Roadmap for the Future

   https://doi.org/10.3390/metabo12080678  

   Wishart, David S.; Cheng, Leo L.; Copié, Valérie; Edison, Arthur S.; Eghbalnia, Hamid R.; Hoch, Jeffrey C.; Gouveia, Goncalo J.; Pathmasiri, Wimal; Powers, Robert; Schock, Tracey B.; et al (August 2022, Metabolites)

   Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021—the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements. 

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5. Gridded Alaska Sea Ice Program ice concentration data, 2007 - 2022

   https://doi.org/10.18739/A28G8FK4F  

   Pacini, Astrid; Steele, Michael; Schreck, Mary-Beth (January 2025, NSF Arctic Data Center)

   The National Weather Service Alaska Sea Ice Program (ASIP) produces manually-drawn, high-resolution sea ice maps for the Pacific Arctic. This is done by leveraging all available imagery and observations of sea ice conditions in the preceding 24 hours, prioritized by data quality and latency. These ice maps are published three times per week from 2007 to June 30, 2014, and then daily from July 1, 2014 to the present. The data follow World Meteorological Organization standard for ice charts, meaning the shapefiles are published in SIGRID-3 vector archive format and published charts are in standard color code. Within these shapefiles, the source data are expressed as a series of polygons, each with an ice concentration range. Here, we compute the average ice concentration within each polygon, as well as the range. These data are then projected onto a 0.05 degree grid in latitude and longitude. Ultimately, this results in gridded maps of sea ice concentration for each day of available data. 

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