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1. Implementing community best practice data and metadata capture into laboratory workflows using Sparrow

   Kuehn, S.C. (January 2022, EarthCube Annual Meeting 2022)

   An implementation of the Sparrow data system (https://sparrow-data.org) is currently being developed to support laboratory workflows for sample preparation, geochemical analysis, and SEM imaging in support of tephra research. Tephra, consisting of fragmental material ejected from volcanoes, has a multidisciplinary array of applications from volcanology to geochronology, archaeology, environmental change, and more. The international tephra research community has developed a comprehensive set of recommendations for data and metadata collection and reporting (https://doi.org/10.5281/zenodo.3866266) as part of a broader effort to adopt FAIR practices. Implementations of these recommendations now exist for field data via StraboSpot (https://strabospot.org/files/StraboSpotTephraHelp.pdf) and for samples, analytical methods, and geochemistry via SESAR and EarthChem (https://earthchem.org/communities/tephra/). Implementing these recommended practices in Sparrow helps to (1) cover laboratory workflows between field sample collection and project data archiving and (2) address a key researcher pain point. As re-emphasized by participants in the Tephra Fusion 2022 workshop earlier this year (Wallace et al., this meeting), the huge workload currently needed to capture and organize data and metadata in preparation for archiving in community data repositories is a major obstacle to achieving FAIR practices. By capturing this information on the fly during laboratory workflows and integrating it together in a single data system, this challenge may be overcome. We are implementing the tephra community recommendations as extensions to Sparrow’s core database schema. Data import pipelines and user interfaces to streamline metadata capture are also being developed. In the longer term, we aim to achieve interoperability with an ecosystem of tools and repositories like StraboSpot, SESAR, EarthChem, and Throughput. The results of these developments will be applicable not just to tephra but also to other research areas which utilize similar laboratory and analytical methods - e.g. sedimentology, mineralogy, and petrology. 

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2. Improving discoverability of tephra data through development of data upload templates and collection tools using community-driven best practices recommendations

   https://doi.org/10.7185/gold2021.3629  

   Wallace, Kristi; Bursik, Marcus; Goring, Simon; Kodama, Samuel; Kuehn, Steve; Kurbatov, Andrei; Lehnert, Kerstin; Profeta, Lucia; Ramdeen, Sarah; Walker, J. Douglas (July 2021, Goldschmidt2021)

   Tephra is a unique volcanic product that plays an unparalleled role in understanding past eruptions, the long-term behavior of volcanoes, and the effects of volcanism on climate and the environment. Tephra deposits also provide spatially widespread, extremely high-resolution time-stratigraphic markers across a range of sedimentary settings and are used by many disciplines (e.g. volcanology, seismotectonics, climate science, archaeology, ecology, public health and ash impact assessment). In the last two decades, tephra studies have become more interdisciplinary in nature but are challenged by a lack of standardization that often prevents comparison amongst various regions and across disciplines. To address this challenge, the global tephra community has come together through a series of workshops to establish best practice recommendations for tephra studies from sample collection through analysis and data reporting. This new standardized framework will facilitate consistent tephra documentation and parametrization, foster interdisciplinary communication, and improve effectiveness of data sharing among diverse communities of researchers. One specific goal is to use the best practice guidelines to inform digital tool and data repository development. Here we report on 1) a new set of templates for tephra sample documentation, geochemical method documentation and data reporting using recommended best- practice data and metadata fields, 2) a new tephra module added to StraboSpot, an open source geologic mapping and data- recording multi-platform software application, and 3) new implementations and cross-mapping of metadata requirements at SESAR (System for Earth Sample Registration) and EarthChem. Addition of tephra-specific fields to StraboSpot enables users to consistently collect and report essential tephra data in the field which is then automatically saved to an online data repository. A new tephra portal on the EarthChem website will allow users to follow simple workflows to register tephra samples at SESAR and submit microanalytical data to EarthChem. 

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3. Building Community Consensus for Scientific Metadata with YAMZ

   https://doi.org/10.1162/dint_a_00211  

   Greenberg, Jane; McClellan, Scott; Rauch, Christopher; Zhao, Xintong; Kelly, Mat; An, Yuan; Kunze, John; Orenstein, Rachel; Porter, Claire; Meschke, Vanessa; et al (January 2023, Data Intelligence)

   ABSTRACT This paper reports on a demonstration of YAMZ (Yet Another Metadata Zoo) as a mechanism for building community consensus around metadata terms. The demonstration is motivated by the complexity of the metadata standards environment and the need for more user-friendly approaches for researchers to achieve vocabulary consensus. The paper reviews a series of metadata standardization challenges, explores crowdsourcing factors that offer possible solutions, and introduces the YAMZ system. A YAMZ demonstration is presented with members of the Toberer materials science laboratory at the Colorado School of Mines, where there is a need to confirm and maintain a shared understanding for the vocabulary supporting research documentation, data management, and their larger metadata infrastructure. The demonstration involves three key steps: 1) Sampling terms for the demonstration, 2) Engaging graduate student researchers in the demonstration, and 3) Reflecting on the demonstration. The results of these steps, including examples of the dialog provenance among lab members and voting, show the ease with YAMZ can facilitate building metadata vocabulary consensus. The conclusion discusses implications and highlights next steps. 

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4. NEFFy: A Versatile Tool for Computing the Number of Effective Sequences

   https://doi.org/10.1093/bioinformatics/btaf222  

   Haghani, Maryam; Bhattacharya, Debswapna; Murali, T M (June 2025, Bioinformatics)

   Cheng, Jianlin (Ed.)

   Abstract MotivationA Multiple Sequence Alignment (MSA) contains fundamental evolutionary information that is useful in the prediction of structure and function of proteins and nucleic acids. The “Number of Effective Sequences” (NEFF) quantifies the diversity of sequences of an MSA. While several tools embed NEFF calculation with various options, none are standalone tools for this purpose, and they do not offer all the available options. ResultsWe developed NEFFy, the first software package to integrate all these options and calculate NEFF across diverse MSA formats for proteins, RNAs, and DNAs. It surpasses existing tools in functionality without compromising computational efficiency and scalability. NEFFy also offers per-residue NEFF calculation and supports NEFF computation for MSAs of multimeric proteins, with the capability to be extended to DNAs and RNAs. Availability and ImplementationNEFFy is released as open-source software under the GNU Public License v3.0. The source code in C ++ and a Python wrapper are available at https://github.com/Maryam-Haghani/NEFFy. To ensure users can fully leverage these capabilities, comprehensive documentation and examples are provided at https://Maryam-Haghani.github.io/NEFFy. Supplementary InformationSupplementary data are available at Bioinformatics online. 

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5. Detecting and correcting misclassified sequences in the large-scale public databases

   https://doi.org/10.1093/bioinformatics/btaa586  

   Bagheri, Hamid; Severin, Andrew; Rajan, Hridesh; Elofsson, Arne (June 2020, Bioinformatics)

   Abstract Motivation As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the non-redundant (NR) database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity. Results We found more than 2 million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases. Availability Source code, dataset, documentation, Jupyter notebooks, and Docker container are available at https://github.com/boalang/nr. Supplementary information Supplementary data are available at Bioinformatics online. 

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