Introducing the FAIR Principles for research software

Barker, Michelle; Hong, Neil Chue; Katz, Daniel S.; Lamprecht, Anna‐Lena; Martinez-Ortiz, Carlos; Psomopoulos, Fotis; Harrow, Jennifer; Castro, Leyla Jael; Gruenpeter, Morane; Martinez, Paula Andrea; Honeyman, Tom

doi:10.1038/s41597-022-01710-x

Cited by 196 publications

(137 citation statements)

References 19 publications

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“…As such, PyEt complies with many of the recommendations for good research software development as given in, for example, Hutton et al (2016) and the FAIR4RS principles (Barker et al, 2022). The scripts or the Jupyter notebooks used to apply PyEt provide full reproducibility and a transparent report of the entire calculation process (Kluyver et al, 2016).…”

Section: Software Designmentioning

confidence: 83%

Technical note: Improved handling of potential evapotranspiration in hydrological studies with PyEt

Vremec

Collenteur²,

Birk³

2023

Preprint

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Abstract. Evapotranspiration (ET) is a crucial flux of the hydrological water balance, commonly estimated using (semi-)empirical formulas. The estimated flux may strongly depend on the used formula, adding uncertainty to the outcomes of hydrological models using ET. Climate change may cause additional uncertainty as each formula may respond differently to changes in meteorological input data. To include the effects of model uncertainty and climate change, and facilitate the use of these formulas in a consistent, tested, and reproducible workflow, we present PyEt. PyEt is an open-source Python package for the estimation of daily potential evapotranspiration (PET) using available meteorological data. It allows the application of twenty different PET methods on both time series (Pandas) and gridded datasets (xarray). Most of the implemented methods are benchmarked against literature values and tested with continuous integration to ensure the correctness of the implementation. This article provides an overview of PyEt's capabilities, including the estimation of PET with twenty PET methods for station, and gridded data, a simple procedure for calibrating the empirical coefficients in the alternative PET methods, and estimation of PET under warming and elevated atmospheric CO2 concentration. Further discussion on the advantages of using PyEt estimates as input for hydrological models, sensitivity/uncertainty analyses, and hind/forecasting studies, especially in data-scarce regions, is provided.

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Section: Software Designmentioning

confidence: 83%

Technical note: Improved handling of potential evapotranspiration in hydrological studies with PyEt

Vremec

Collenteur²,

Birk³

2023

Preprint

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“…Image data that is findable (F), accessible (A), stored in interoperable (I) file formats in a way it can easily be reused (R). The FAIR principles were also recently re-formulated for research software [24]. It appears natural for this responsibility to be given into the hands of bio-image analysts as they profit from well-organized imaging data.…”

Section: Research Data Managementmentioning

confidence: 99%

Challenges and Opportunities for Bio-image Analysis Core-facilities

Müller¹,

Haase²

2023

Preprint

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Recent advances in microscopy imaging and image analysis motivate more and more institutes world-wide to establish dedicated core-facilities for bio-image analysis. To maximize the benefits research groups at these institutes gain from their core-facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core-facilities can offer. We also discuss potential conflicts of interests between the targeted missions and implementations of services to guide decision makers and core-facility founders to circumvent common pitfalls.

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“…More recently, ENNGene 39 was designed to create a simple graphical user interface (GUI) for non-computational users, but offers limited programmatic customizability for more advanced users. Generally, there is a need for a comprehensive toolkit in this space that follows FAIR data and software principles 41,42 and that is inherently designed to be simple and extensible .…”

Section: Introductionmentioning

confidence: 99%

EUGENe: A Python toolkit for predictive analyses of regulatory sequences

Klie

Stites²,

Jores

et al. 2022

Preprint

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Deep learning (DL) has become a popular tool to study cis-regulatory element function. Yet efforts to design software for DL analyses in genomics that are Findable, Accessible, Interoperable and Reusable (FAIR) have fallen short of fully meeting these criteria. Here we present EUGENe (Elucidating the Utility of Genomic Elements with Neural Nets), a FAIR toolkit for the analysis of labeled sets of nucleotide sequences with DL. EUGENe consists of a set of modules that empower users to execute the key functionality of a DL workflow: 1) extracting, transforming and loading sequence data from many common file formats, 2) instantiating, initializing and training diverse model architectures, and 3) evaluating and interpreting model behavior. We designed EUGENe to be simple; users can develop and deploy workflows on new or existing datasets with functions that act on two customizable Python objects, annotated sequence data (SeqData) and PyTorch models (BaseModel). The modularity and simplicity of EUGENe also make it highly extensible and we illustrate these principles through application of the toolkit to three predictive modeling tasks. First, we train and compare a set of built-in models along with a custom architecture for the accurate prediction of activities of plant promoters from STARR-seq data. Next, we apply EUGENe to an RNA binding prediction task and showcase how seminal model architectures can be retrained in EUGENe or imported from Kipoi. Finally, we train models to classify transcription factor binding by wrapping functionality from Janngu, which can efficiently extract sequences in BED file format from the human genome. We emphasize that the code used in each use case is simple, readable, and well documented (https://eugene-tools.readthedocs.io/en/latest/index.html). We believe that EUGENe represents a springboard toward a collaborative ecosystem for DL applications in genomics research. EUGENe is available for download on GitHub (https://github.com/cartercompbio/EUGENe) along with several introductory tutorials and for installation on PyPi (https://pypi.org/project/eugene-tools/).

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Introducing the FAIR Principles for research software

Cited by 196 publications

References 19 publications

Technical note: Improved handling of potential evapotranspiration in hydrological studies with PyEt

Technical note: Improved handling of potential evapotranspiration in hydrological studies with PyEt

Challenges and Opportunities for Bio-image Analysis Core-facilities

EUGENe: A Python toolkit for predictive analyses of regulatory sequences

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