FAIR biodiversity data in Pensoft journals thanks to a routine data auditing workflow

 

Data audit workflow provided for data papers submitted to Pensoft journals.

To avoid publication of openly accessible, yet unusable datasets, fated to result in irreproducible and inoperable biological diversity research at some point down the road, Pensoft takes care for auditing data described in data paper manuscripts upon their submission to applicable journals in the publisher’s portfolio, including Biodiversity Data JournalZooKeysPhytoKeysMycoKeys and many others.

Once the dataset is clean and the paper is published, biodiversity data, such as taxa, occurrence records, observations, specimens and related information, become FAIR (findable, accessible, interoperable and reusable), so that they can be merged, reformatted and incorporated into novel and visionary projects, regardless of whether they are accessed by a human researcher or a data-mining computation.

As part of the pre-review technical evaluation of a data paper submitted to a Pensoft journal, the associated datasets are subjected to data audit meant to identify any issues that could make the data inoperable. This check is conducted regardless of whether the dataset are provided as supplementary material within the data paper manuscript or linked from the Global Biodiversity Information Facility (GBIF) or another external repository. The features that undergo the audit can be found in a data quality checklist made available from the website of each journal alongside key recommendations for submitting authors.

Once the check is complete, the submitting author receives an audit report providing improvement recommendations, similarly to the commentaries he/she would receive following the peer review stage of the data paper. In case there are major issues with the dataset, the data paper can be rejected prior to assignment to a subject editor, but resubmitted after the necessary corrections are applied. At this step, authors who have already published their data via an external repository are also reminded to correct those accordingly.

“It all started back in 2010, when we joined forces with GBIF on a quite advanced idea in the domain of biodiversity: a data paper workflow as a means to recognise both the scientific value of rich metadata and the efforts of the the data collectors and curators. Together we figured that those data could be published most efficiently as citable academic papers,” says Pensoft’s founder and Managing director Prof. Lyubomir Penev.
“From there, with the kind help and support of Dr Robert Mesibov, the concept evolved into a data audit workflow, meant to ‘proofread’ the data in those data papers the way a copy editor would go through the text,” he adds.
“The data auditing we do is not a check on whether a scientific name is properly spelled, or a bibliographic reference is correct, or a locality has the correct latitude and longitude”, explains Dr Mesibov. “Instead, we aim to ensure that there are no broken or duplicated records, disagreements between fields, misuses of the Darwin Core recommendations, or any of the many technical issues, such as character encoding errors, that can be an obstacle to data processing.”

At Pensoft, the publication of openly accessible, easy to access, find, re-use and archive data is seen as a crucial responsibility of researchers aiming to deliver high-quality and viable scientific output intended to stand the test of time and serve the public good.

To explain how and why biodiversity data should be published in full compliance with the best (open) science practices, the team behind Pensoft and long-year collaborators published a guidelines paper, titled “Strategies and guidelines for scholarly publishing of biodiversity data” in the open science journal Research Ideas and Outcomes (RIO Journal).

Audit finds biodiversity data aggregators ‘lose and confuse’ data

In an effort to improve the quality of biodiversity records, the Atlas of Living Australia (ALA) and the Global Biodiversity Information Facility (GBIF) use automated data processing to check individual data items. The records are provided to the ALA and GBIF by museums, herbaria and other biodiversity data sources.

However, an independent analysis of such records reports that ALA and GBIF data processing also leads to data loss and unjustified changes in scientific names.

The study was carried out by Dr Robert Mesibov, an Australian millipede specialist who also works as a data auditor. Dr Mesibov checked around 800,000 records retrieved from the Australian MuseumMuseums Victoria and the New Zealand Arthropod Collection. His results are published in the open access journal ZooKeys, and also archived in a public data repository.

“I was mainly interested in changes made by the aggregators to the genus and species names in the records,” said Dr Mesibov.

“I found that names in up to 1 in 5 records were changed, often because the aggregator couldn’t find the name in the look-up table it used.”

data_auditAnother worrying result concerned type specimens – the reference specimens upon which scientific names are based. On a number of occasions, the aggregators were found to have replaced the name of a type specimen with a name tied to an entirely different type specimen.

The biggest surprise, according to Dr Mesibov, was the major disagreement on names between aggregators.

“There was very little agreement,” he explained. “One aggregator would change a name and the other wouldn’t, or would change it in a different way.”

Furthermore, dates, names and locality information were sometimes lost from records, mainly due to programming errors in the software used by aggregators to check data items. In some data fields the loss reached 100%, with no original data items surviving the processing.

“The lesson from this audit is that biodiversity data aggregation isn’t harmless,” said Dr Mesibov. “It can lose and confuse perfectly good data.”

“Users of aggregated data should always download both original and processed data items, and should check for data loss or modification, and for replacement of names,” he concluded.

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Original source:

Mesibov R (2018) An audit of some filtering effects in aggregated occurrence records. ZooKeys 751: 129-146. https://doi.org/10.3897/zookeys.751.24791