IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0204519.html
   My bibliography  Save this article

IsoFishR: An application for reproducible data reduction and analysis of strontium isotope ratios (87Sr/86Sr) obtained via laser-ablation MC-ICP-MS

Author

Listed:
  • Malte Willmes
  • Katherine M Ransom
  • Levi S Lewis
  • Christian T Denney
  • Justin J G Glessner
  • James A Hobbs

Abstract

The IsoFishR application is a data reduction and analysis tool for laser-ablation strontium isotope data, following common best practices and providing reliable and reproducible results. Strontium isotope ratios (87Sr/86Sr) are a powerful geochemical tracer commonly applied in a wide range of scientific fields and laser-ablation inductively coupled mass spectrometry is considered the method of choice to obtain spatially resolved 87Sr/86Sr isotope ratios from a variety of sample materials. However, data reduction and analyses methods are variable between different research groups and research communities limiting reproducibility between studies. IsoFishR provides a platform to standardize these methods and can be used for both spot and time-resolved line transects. Furthermore, it provides advanced data analysis tools and filters for outlier removal, noise reduction, and visualization of time resolved data. The application can be downloaded from GitHub (https://github.com/MalteWillmes/IsoFishR) and the source code is available, encouraging future development and evolution of this software.

Suggested Citation

  • Malte Willmes & Katherine M Ransom & Levi S Lewis & Christian T Denney & Justin J G Glessner & James A Hobbs, 2018. "IsoFishR: An application for reproducible data reduction and analysis of strontium isotope ratios (87Sr/86Sr) obtained via laser-ablation MC-ICP-MS," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-15, September.
  • Handle: RePEc:plo:pone00:0204519
    DOI: 10.1371/journal.pone.0204519
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204519
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0204519&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0204519?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zeileis, Achim & Grothendieck, Gabor, 2005. "zoo: S3 Infrastructure for Regular and Irregular Time Series," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i06).
    2. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kuebart, Andreas & Stabler, Martin, 2023. "Waves in time, but not in space – an analysis of pandemic severity of COVID-19 in Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 47.
    2. Ben Teune & Carl Woods & Alice Sweeting & Mathew Inness & Sam Robertson, 2022. "A method to inform team sport training activity duration with change point analysis," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-11, March.
    3. Petter Arnesen & Odd A. Hjelkrem, 2018. "An Estimator for Traffic Breakdown Probability Based on Classification of Transitional Breakdown Events," Transportation Science, INFORMS, vol. 52(3), pages 593-602, June.
    4. Jacob Dice & Mallick Hossain & David Rodziewicz, 2024. "Flood Risk Exposures and Mortgage-Backed Security Asset Performance and Risk Sharing," Research Working Paper RWP 24-05, Federal Reserve Bank of Kansas City.
    5. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    6. Hui Zhang & Minna Väliranta & Graeme T. Swindles & Marco A. Aquino-López & Donal Mullan & Ning Tan & Matthew Amesbury & Kirill V. Babeshko & Kunshan Bao & Anatoly Bobrov & Viktor Chernyshov & Marissa , 2022. "Recent climate change has driven divergent hydrological shifts in high-latitude peatlands," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    7. Michael Berlemann & Julia Freese & Sven Knoth, 2020. "Dating the start of the US house price bubble: an application of statistical process control," Empirical Economics, Springer, vol. 58(5), pages 2287-2307, May.
    8. Subhashis Chatterjee & Ankur Shukla, 2016. "Change point–based software reliability model under imperfect debugging with revised concept of fault dependency," Journal of Risk and Reliability, , vol. 230(6), pages 579-597, December.
    9. Judith M. Ament & Robin Freeman & Chris Carbone & Anna Vassall & Charlotte Watts, 2020. "An Empirical Analysis of Synergies and Tradeoffs between Sustainable Development Goals," Sustainability, MDPI, vol. 12(20), pages 1-12, October.
    10. Percebois, Jacques & Pommeret, Stanislas, 2019. "Storage cost induced by a large substitution of nuclear by intermittent renewable energies: The French case," Energy Policy, Elsevier, vol. 135(C).
    11. Dehler-Holland, Joris & Okoh, Marvin & Keles, Dogan, 2022. "Assessing technology legitimacy with topic models and sentiment analysis – The case of wind power in Germany," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    12. Manfren, Massimiliano & Nastasi, Benedetto, 2023. "Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0," Energy, Elsevier, vol. 283(C).
    13. Li, Boyan & Diao, Xundi, 2023. "Structural break in different stock index markets in China," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    14. Golyandina, Nina & Korobeynikov, Anton & Shlemov, Alex & Usevich, Konstantin, 2015. "Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i02).
    15. Thelma Dede Baddoo & Zhijia Li & Yiqing Guan & Kenneth Rodolphe Chabi Boni & Isaac Kwesi Nooni, 2020. "Data-Driven Modeling and the Influence of Objective Function Selection on Model Performance in Limited Data Regions," IJERPH, MDPI, vol. 17(11), pages 1-26, June.
    16. Hansin Bilgili & Chwen Sheu, 2022. "A Bibliometric Review of the Mathematics Journal," Mathematics, MDPI, vol. 10(15), pages 1-17, July.
    17. Golyandina, Nina & Korobeynikov, Anton, 2014. "Basic Singular Spectrum Analysis and forecasting with R," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 934-954.
    18. Nikolaus Umlauf & Georg Mayr & Jakob Messner & Achim Zeileis, 2011. "Why Does It Always Rain on Me? A Spatio-Temporal Analysis of Precipitation in Austria," Working Papers 2011-25, Faculty of Economics and Statistics, Universität Innsbruck.
    19. Ibrar ul Hassan Akhtar, 2023. "Exploring Covid-19 Pandemic Initial 2020 Curve Based On Statistical Evaluation," Acta Scientifica Malaysia (ASM), Zibeline International Publishing, vol. 7(1), pages 08-16, February.
    20. Jaller, Miguel & Pineda, Leticia, 2017. "Warehousing and Distribution Center Facilities in Southern California: The Use of the Commodity Flow Survey Data to Identify Logistics Sprawl and Freight Generation Patterns," Institute of Transportation Studies, Working Paper Series qt5dz0j1gg, Institute of Transportation Studies, UC Davis.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0204519. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.