A Beginner’s Guide to Conducting Reproducible Research

Alston, Jesse M.; Rick, Jessica A.

doi:10.32942/osf.io/h5r6n

Cited by 11 publications

(16 citation statements)

References 32 publications

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“…2020). In its most reproducible form, code repositories can combine with Docker (https://www.docker.com/) or Binder (https://mybinder.org/) ensuring a re-runnable workspace (workspace being the environment and files associated with the code) capable of displaying analysis and results independent of the original researchers' workspace (Alston & Rick, 2020).…”

Section: Open Datamentioning

confidence: 99%

“…Many data sharing best practices mirror those for data management, and easier to implement prior to data collection (Alston & Rick, 2020; see Supplementary Table 1). As ecological datasets grow in size and complexity, metadata generation and data management are becoming increasingly necessary skills (Hernandez et al, 2012;Lewis et al, 2018).…”

Section: Normative Peer-reviewmentioning

confidence: 99%

“…Open data benefits are further bolstered by open reproducible analyses. The rise of programmable/ code-based analyses has enabled entirely recreatable workflows, from data curation to publication (Alston & Rick, 2020); but providing open code is still lagging behind (Culina et al, 2020). A reproducible workflow is a phenomenal resource for reviewers and future researchers (Poisot, 2015), providing both transparency and guidance.…”

Section: Open Codementioning

confidence: 99%

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Make like a glass frog: In support of increased transparency in herpetology

Marshall¹

2021

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Across many scientific disciplines, direct replication efforts and meta-analyses have fuelled concerns on the replicability of findings. Ecology and evolution are similarly affected. Investigations into the causes of this lack of replicability have implicated a suite of research practices linked to incentives in the current publishing system. Other fields have taken great strides to counter incentives that can reward obfuscation –chiefly by championing transparency. But how prominent are protransparency (open science) policies in herpetology journals? We use the recently developed Transparency and Openness Promotion (TOP) Factor to assess the transparency promotion of 19 herpetology journals, and compare the TOP scores to broader science. We find promotion of transparent practices currently lacking in many herpetological journals; and encourage authors, students, editors, and publishers to redouble efforts to bring open science practices to herpetology by changing journal policy, peer-review, and personal practice. We promote an array of options –developed and tested in other fields– demonstrated to counter publication bias, boost research uptake, and enable more transparent science, to enrich herpetological research.

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Section: Open Datamentioning

confidence: 99%

Section: Normative Peer-reviewmentioning

confidence: 99%

Section: Open Codementioning

confidence: 99%

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Make like a glass frog: In support of increased transparency in herpetology

Marshall¹

2021

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“…Following EDA and the parameterisation of data cleaning steps, researchers must prioritise making their implementation of these steps reliable and reproducible (Fig. 1; see also Alston and Rick, 2021). Reproducing pre-processing steps can be challenging when using only written descriptions from published articles.…”

Section: Building Pre-processing Pipelines For Large Tracking Datamentioning

confidence: 99%

A Guide to Pre-Processing High-Throughput Animal Tracking Data

Gupte

Beardsworth

Spiegel

et al. 2020

Preprint

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Modern, high-throughput animal tracking studies collect increasingly large volumes of data at very fine temporal scales. At these scales, location error can exceed the animal step size, confounding inferences from tracking data. Cleaning the data to exclude positions with large location errors prior to analyses is one of the main ways movement ecologists deal with location errors. Cleaning data to reduce location error before making biological inferences is widely recommended, and ecologists routinely consider cleaned data to be the ground-truth. Nonetheless, uniform guidance on this crucial step is scarce. Cleaning high-throughput data must strike a balance between rejecting location errors without discarding valid animal movements. Additionally, users of high-throughput systems face challenges resulting from the high volume of data itself, since processing large data volumes is computationally intensive and difficult without a common set of efficient tools. Furthermore, many methods that cluster movement tracks for ecological inference are based on statistical phenomena, and may not be intuitive to understand in terms of the tracked animal biology. In this article we introduce a pipeline to pre-process high-throughput animal tracking data in order to prepare it for subsequent analysis. We demonstrate this pipeline on simulated movement data to which we have randomly added location errors. We further suggest how large volumes of cleaned data may be synthesized into biologically meaningful residence patches. We then use calibration data to show how the pipeline improves its quality, and to verify that the residence patch synthesis accurately captures animal space-use. Finally, turning to real tracking data from Egyptian fruit bats (Rousettus aegyptiacus), we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example. To help with fast implementations of our pipeline, and to help standardise methods, we developed the R package atlastools, which we introduce here. Our pre-processing pipeline and atlastools can be used with any high-throughput animal movement data in which the high data volume combined with knowledge of the tracked individuals biology can be used to reduce location errors. The use of common pre-processing steps that are simple yet robust promotes standardised methods in the field of movement ecology and better inferences from data.

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“…Online platforms can be used as teaching tools to introduce coding and noncoding approaches to explore datasets. Collectively, these approaches facilitate sharing of data and code and are therefore useful not only in field course projects, but also for reproducible research, remotely working with supervisors or collaborators, or when seeking advice, that is, from statisticians or online support groups (for a comprehensive list of approaches and tools, see Alston & Rick, 2020; Hampton et al., 2015). More broadly, these tools promote a culture of transparency and data stewardship, ensuring the integrity and reproducibility of scientific outputs (Powers & Hampton, 2019).…”

Section: A New Approach For Field Coursesmentioning

confidence: 99%