Let go of your data

Noy, Natalya F.; Noy, Aleksandr

doi:10.1038/s41563-019-0539-5

Cited by 5 publications

(3 citation statements)

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“…Future studies should capitalise on the increasing availability of spatial telemetry and biologging technology, and associated analytical capacity (Long et al 2014;Kays et al 2015;Williams et al 2020) to more frequently record, analyse, and share spatial data in disease ecology (Kirby et al 2017;Albery et al 2019). This practice and the associated calls to "let go of your data" (Noy & Noy 2020) will facilitate testing of related hypotheses.…”

Section: Discussionmentioning

confidence: 99%

Fine-scale spatial patterns of wildlife disease are common and understudied

Albery

Sweeny

Becker

et al. 2020

Preprint

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All pathogens are heterogeneous in space, yet little is known about the prevalence and scale of this spatial variation, particularly in wild animal systems. To address this question, we conducted a broad literature search to identify datasets involving diseases of wild mammals in spatially distributed contexts. Across 31 such final datasets featuring 89 replicates and 71 host-parasite combinations, only 51% had previously been used to test spatial hypotheses. We analysed these datasets for spatial dependence within a standardised modelling framework using Bayesian linear models. We detected spatial autocorrelation in 44/89 model replicates (54%) across 21/31 datasets (68%), spread across parasites of all groups and transmission modes. Surprisingly, although larger sampling areas more easily detected spatial patterns, even some very small study areas (under 0.01km2) exhibited substantial spatial heterogeneity. Parasites of all transmission modes had easily detectable spatial patterns, implying that structured contact networks and susceptibility effects are likely as important in spatially structuring disease as are environmental drivers of transmission efficiency. Our findings imply that fine-scale spatial patterns of infection often manifest in wild animal systems, whether or not the aim of the study is to examine environmentally varying processes. Given the widespread nature of these findings, studies should more frequently record and analyse spatial data, facilitating development and testing of spatial hypotheses in disease ecology.

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Section: Discussionmentioning

confidence: 99%

Fine-scale spatial patterns of wildlife disease are common and understudied

Albery

Sweeny

Becker

et al. 2020

Preprint

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“…Likewise, we can infer that data formats tied to a small number of (outdated) programmes runs the risk that data becomes inaccessible. Another aspect on the data side considered by Noy and Noy (2020) who complained that common open-data surrogate statements in articles such as 'data being available upon request' may equate to no data access. Indeed, a pivotal aspect of the FAIR guidelines is their emphasis on principles fostering automated data processing or enabling such processing in the first place.…”

mentioning

confidence: 99%

“…Indeed, a pivotal aspect of the FAIR guidelines is their emphasis on principles fostering automated data processing or enabling such processing in the first place. The requirement to actively contact the study authors to request access to the data, e.g., e-mail requests, therefore inherently undermines the principles of open data (Noy and Noy, 2020). On the other hand, authors perhaps refrained from direct sharing because of unclear reporting guidelines or the effort required to document data of presumed low demand.…”

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confidence: 99%

XLUM: an open data format for exchange and long-term data preservation of luminescence data

Kreutzer

Grehl²,

Höhne³

et al. 2022

Preprint

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Abstract. Open data has become the modern science meme, and major funding bodies and publishers support open data. On a daily basis, however, the open data mandate frequently encounters technical obstacles, such as a lack of a suitable data format for data sharing and long-term data preservation. Such issue is often community-specific and best addressed through community-tailored solutions. In Quaternary sciences, luminescence dating is widely used for constraining the timing of event-based processes (e.g., sediment transport). Every luminescence-dating study produces a vast body of primary data that usually remains inaccessible and incompatible with future studies or adjacent scientific disciplines. To facilitate data exchange, long-term data preservation, in short, open data, in luminescence dating studies, we propose a new XML-based structured data format called XLUM. The format applies a hierarchical data storage concept consisting of a root node (node 0), a sample (node 1), a sequence (node 2), a record (node 3) and a curve (node 4). The curve level holds information on the technical component (e.g., photomultiplier, thermocouple). A finite number of curves represent a record (e.g., an optically stimulated luminescence curve). Records are part of a sequence measured for a particular sample. This design concept allows the user to retain information on a technical component level from the measurement process. The additional storage of related metadata fosters future data mining projects on large datasets. The XML-based format is less memory efficient than binary formats, however, in focus is data exchange, preservation and hence XLUM long-term format stability by design. XLUM is inherently stable to future updates and backwards compatible. We support XLUM through a new R package, `xlum', facilitating the conversion of different formats into the new XLUM format. XLUM is licensed under the MIT licence and hence available for free to be used in open and closed-source commercial and non-commercial software and research projects.

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