The habitat of the intertidal flightless midge Telmatogeton magellanicus (Jacobs, 1900) is described for the first time from the northern coast of Navarino Island, Tierra del Fuego, Chile. Additionally, we report the first observations of adult behaviour in the wild. We delineate the species’ distribution across three tidal zones (high, mid and low), and identify substrate characteristics that favour the presence of the midge. The mid-tide zone was the key habitat utilized by T. magellanicus, with lower densities in the low-tide zone and no presence in the high-tide zone. There was a strong association between the presence of larvae and filamentous algae, especially Bostrychia spp. and, to a lesser extent, Ulva spp., as well as between larvae and the presence of larger, more stable boulders. As a result, the species’ overall distribution was widespread but patchy. We suggest that the main limiting factor is the relative humidity experienced in different habitats. One of the most striking features of the behavioural observations during data collection was the extremely active adults, which suggests high energy expenditure over a very short period of time. This may be due to the limited time available to find mates in a single low-tide period, when adults have about three hours after emerging from the pupa to complete mating and oviposition before inundation by the tide. The data presented here provide a baseline for future studies on this species’ ecology, phenology, physiology and general biology.

Plazi's TreatmentBank is a research infrastructure and partner of the recent European Union-funded Biodiversity Community Integrated Knowledge Library (BiCIKL) project to provide a single knowledge portal to open, interlinked and machine-readable, findable, accessible, interoperable and reusable (FAIR) data. Plazi is liberating published biodiversity data that is trapped in so-called flat formats, such as portable document format (PDF), to increase its FAIRness. This can pose a variety of challenges for both data mining and curation of the extracted data. The automation of such a complex process requires internal organization and a well established workflow of specific steps (e.g., decoding of the PDF, extraction of data) to handle the challenges that the immense variety of graphic layouts existing in the biodiversity publishing landscape can impose. These challenges may vary according to the origin of the document: scanned documents that were not initially digital, need optical character recognition in order to be processed. Processing a document can either be an individual, one-time-only process, or a batch process, in which a template for a specific document type must be produced. Templates consist of a set of parameters that tell Plazi-dedicated software how to read and where to find key pieces of information for the extraction process, such as the related metadata. These parameters aim to improve the outcome of the data extraction process, and lead to more consistent results than manual extraction. In order to produce such templates, a set of tests and accompanying statistics are evaluated, and these same statistics are constantly checked against ongoing processing tasks in order to assess the template performance in a continuous manner. In addition to these steps that are intrinsically associated with the automated process, different granularity levels (e.g., low granularity level might consist of a treatment and its subsections versus a high granularity level that includes material citations down to named entities such as collection codes, collector, collecting date) were defined to accommodate specific needs for particular projects and user requirements. The higher the granularity level, the more thoroughly checked the resulting data is expected to be. Additionally, steps related to the quality control (qc), such as the “pre-qc”, “qc” and “extended qc” were designed and implemented to ensure data quality and enhanced data accuracy. Data on all these different stages of the processing workflow are constantly being collected and assessed in order to improve these very same stages, aiming for a more reliable and efficient operation. This is also associated with a current Data Architecture plan to move this data assessment to a cloud provider to promote real-time assessment and constant analyses of template performance and processing stages as a whole. In this talk, the steps of this entire process are explained in detail, highlighting how data are being used to improve these steps towards a more efficient, accurate, and less costly operation.

Automatic data mining is not an easy task and its success in the biodiversity world is deeply tied to the standardization and consistency of scientific journals' layout structure. The various formatting styles found in the over 500 million pages of published biodiversity information (Kalfatovich 2010), pose a remarkable challenge towards the goal of automating the liberation of data currently trapped on the printed page. Regular expressions and other pattern-recognition strategies invariably fail to cope with this diverse landscape of academic publishing. Challenges such as incomplete data and taxonomic uncertainty add several additional layers of complexity. However, in the era of big data, the liberation of all the different facts contained in biodiversity literature is of crucial importance. Plazi tackles this daunting task by providing workflows and technology to automatically process biodiversity publications and annotate the information therein, all within the principles of FAIR (findable, accessible, interoperable, and reusable) data usage (Agosti and Egloff 2009). It uses the concept of taxonomic treatments (Catapano 2019) as the most fundamental unit in biodiversity literature, to provide a framework that reflects the reality of taxonomic data for linking the different pieces of information contained in these taxonomic treatments. Treatment citations, composed of a taxonomic name and a bibliographic reference, and material citations carrying all specimen-related information are additional conceptual cornerstones for this framework. The resulting enhanced data are added to TreatmentBank. Figures and treatments are made Findable, Accessible, Interoperable and Reuseable (FAIR) by depositing them including specific metadata to the Biodiversity Literature Repository community (BLR) at the European Organization for Nuclear Research (CERN) repository Zenodo, and pushed to GBIF. The automation, however, is error prone due to the constraints explained above. In order to cope with this remarkable task without compromising data quality, Plazi has established a quality control process, based on logical rules that check the components of the extracted document raising errors in four different levels of severity. These errors are also used in a data transit control mechanism, “the gatekeeper”, which blocks certain data transits to create deposits (e.g., BLR) or reuse of data (e.g., GBIF) in the presence of specific errors. Finally, a set of automatic notifications were included in the plazi/community Github repository, in order to provide a channel that empowers external users to report data issues directly to a dedicated team of data miners, which will in turn and in a timely manner, fix these issues, improving data quality on demand. In this talk, we aim to explain Plazi’s internal quality control process and phases, the data transits that are potentially affected, as well as statistics on the most common issues raised by this automated endeavor and how we use the generated data to continuously improve this important step in Plazi's workflow.

Here we present a descriptive analysis of the bibliographic production of the world-renowned heteropterist Dr. Jocélia Grazia and comments on her taxonomic reach based on extracted taxonomic treatments. We analyzed a total of 219 published documents, including scientific papers, scientific notes, and book chapters. Additionally, we applied the Plazi workflow to extract taxonomic treatments, images, tables, treatment citations and materials citations, and references from 75 different documents in accordance with the FAIR (Findability, Accessibility, Interoperability, and Reuse) principles and made them available on the Biodiversity Literature Repository (BLR), hosted on Zenodo, and on TreatmentBank. We found that Dr. Grazia published 200 new names, including species (183) and genera (17), and 1,444 taxonomic treatments in total. From these, 104 and 581, respectively, were extracted after applying the Plazi Workflow. A total of 544 figures, 50 tables, 2,242 references, 2,107 materials citations, and 1,101 treatment citations were also extracted. In order to make her publications properly citable and accessible, we assigned DOIs (Digital Object Identifiers) for all publications that lacked this persistent identifier, including those that were not processed (88 in total), therefore enhancing the open-access share of her publications.

One of the main challenges in biodiversity data reusability is finding ways to transform what is provided in research publications into different and reusable formats, following the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Agosti and Egloff 2009). Most often, data is restricted to text, figures and tables in the so-called “PDF prison” or other flat formats. Plazi's infrastructure and workflow (Guidoti et al. 2021) transform such data into reusable formats that can then be exported and linked across different platforms, such as the Global Biodiversity Information Facility (GBIF), Biodiversity Literature Repository, Zenodo, Synospecies, ChecklistBank, and OpenBiodiv among others. In order to liberate the many relevant pieces of information, such as taxonomic treatments (Catapano 2019), material citations (Darwin Core term MaterialCitation) or bibliographic references from the publication types mentioned above, one has to run a single document or a batch of documents through a series of extraction steps, which can be done manually or automatically, through the use of templates. The latter are a set of parameters that tell the Plazi-dedicated software (GoldenGATE suite) how to read and where to find key pieces of information; these parameters are established by examining publication standards and publisher-specific layouts, followed by a series of iterative tests, to ascertain the quality of the automation. However, even with a high number of tests to ensure a better extraction, human quality control is still needed (Simoes et al. 2021). To that end, Plazi has a quality control process, based on logical rules, which checks the components of the extracted document, flagging errors in four different levels of severity, which can then be checked and corrected (if needed) by a trained user. These errors are also used in a data transit control mechanism, internally dubbed “the gatekeeper”, which blocks certain data transits to create deposits or reuse of data in the presence of specific errors. In this presentation, we will go through the steps of the entire process, from publication to liberated data (and how it is presented in the linked platforms), highlighting the importance of accurate quality control, and explore some of the many challenges along the way.