Darwin Core Standards Research Articles

An integrated individual-level trait-based phytoplankton dataset from transitional waters

Functional trait-based approaches have undergone an extraordinary expansion in phytoplankton ecology. Morpho-functional traits have been shown to vary both within and between populations and species, potentially affecting individual fitness and the network of inter-individual relationships. Here we integrate six fully harmonized phytoplankton morpho-functional trait datasets, characterized by a fine data grain, reporting individual-level data over a large biogeographical area. Datasets refer to transitional water ecosystems, from five biogeographical areas: Northern Atlantic Ocean (Scotland), South-Western Atlantic Ocean (Brazil), South-Western Pacific Ocean (Australia), Indo Pacific Ocean (Maldives) and Mediterranean Sea (Greece and Turkey). The integrated dataset includes 127311 individual phytoplankton records with sampling locations, taxonomic and morphometric information according to Darwin Core standards and semantic annotations. The six FAIR datasets are openly available in the LifeWatch Italy data portal. The datasets have already been used for morpho-functional analyses and hypothesis testing on phytoplankton guilds at different levels of data aggregation and scale, from local to global.

Want to Describe and Share Biodiversity Inventory and Monitoring Data? The Humboldt Extension for Ecological Inventories Can Help!

Access to high-quality ecological data is critical to assessing and modeling biodiversity and its changes through space and time. The Darwin Core standard has proven to be immensely helpful in sharing species occurrence data (see Wieczorek et al. 2012, Global Biodiversity Information Facility, GBIF) and promoting biodiversity research following the FAIR principles of findability, accessibility, interoperability and reusability (Wilkinson et al. 2016). However, it is limited in its ability to fully accommodate inventory data (i.e., linked records of multiple taxa at a specific place and time). Information about the inventory processes is often either unreported or described in an unstructured manner, limiting its potential re-use for larger-scale analyses. Two key aspects that are not captured in a structured manner yet are: i) information about the species that were not detected during an inventory, and ii) ancillary information about sampling effort and completeness. Non-detections (i.e., reported counts of zero) potentially enable more accurate and precise estimates of distribution, abundance, and changes in abundance. This becomes possible when variation in effort is used to estimate the likelihood that a non-detection represents a true absence of that taxon during the inventory. Currently, ecological inventory data, when shared at all, are typically discoverable through dataset catalogs (e.g., governmental data repositories) and supplementary materials to publications. With few exceptions, indexing of such data with the detail and structure needed has not been attempted at broad temporal and spatial scales, despite the potentially high value resulting from making inventory data more readily accessible. To address these limitations in documenting inventory data using the Darwin Core, Guralnick et al. (2018) proposed the Humboldt Core. Subsequent discussions within the biodiversity standards community made it clear that greater integration could be achieved by creating an extension of the Darwin Core, rather than developing a new standard in isolation. Extension design work began in 2021 and progress has been reported by Brenton (2021) and Sica et al. (2022). Over the last year the Humboldt Extension Task Group has sought advice from data providers and aggregators and updated its vocabulary terms. A challenging aspect has been creating terminology for the parent-child relationships (see Properties of Hierarchical Events) needed to describe surveys that may be as simple as a collection of checklists (one level of hierarchy) or as complex as species records from traps within plots along transects across habitats over multiple years (at least four levels of hierarchy). The Task Group has committed to completing a User Guide for the Humboldt Extension. Group members who contributed to the Darwin Core (Darwin Core Task Group 2009) and the Vocabulary Maintenance Specification (Vocabulary Maintenance Specification Task Group 2017) have provided valuable expertise on term refinement and process. Through ratification of the Humboldt Extension as a Darwin Core Event extension, we expect to provide the community with a usable solution, tied to well-established data publication mechanisms, for sharing and using inventory data. This effort promises to overcome a key bottleneck in the sharing of critically important ecological data, enhancing data discoverability, interoperability and re-use while lowering reporting burden and data and metadata heterogeneity. Global data aggregation initiatives, such as GBIF, will benefit from this development as they develop their data models and the range of standards and extensions they support. We anticipate that the Humboldt Extension will be attractive both to data publishers and data users, by facilitating the representation and indexing of data in richer, more meaningful ways. Despite the data-intensive nature of fundamental ecological research and applied monitoring for management and policy, ecological data have remained as one of the FAIR data frontiers. We anticipate that the Humboldt Extension will address most data exchange needs of all professional communities involved.

Biotopes of the intertidal zone in Clarence Island (south of the Strait of Magellan).

The characteristics of the Strait of Magellan promote the formation of unique environments, with diverse habitats and marine organisms. This fragmentation of the landscape generates diverse little-explored ecological associations, especially in the zone of sub-Antarctic islands of the Tierra del Fuego archipelago. One way to address this lack of knowledge is through the biotope characterization methodology, with ecological units composed of the habitat and the communities associated with these environments, obtaining data and information on the dominant and incidental taxonomic groups. This is a good research model to conduct baseline studies in coastal benthic marine environments. A data set in Darwin Core standard is presented of the species that make up the intertidal biotopes of Clarence Island (Tierra del Fuego Archipelago, south of the Strait of Magellan). This includes 50 identified species and the specific coordinates for each sampled location, with a total of 1400 georeferenced records. Mollusks were the most diverse taxon with 21 species, followed by algae (14 species). Sessile organisms such as the barnacles Elminiuskingii and Austromegabalanuspsittacus predominate in these ecosystems, followed by bivalve mollusks such as Choromytiluschorus and Mytiluschilensis, which together with Nacellamagellanica and the alga Hildenbrandia sp. make up more than 50% of the total records. The inclusion of biotope patterns in this study complements the information on benthic marine flora and fauna in the intertidal zone, including new records for the coast in the Clarence Island area, which is within the boundary of the Kawésqar National Park.

Development of Malaysian Ethnobotanical Online Database

The existing Malaysian ethnobotanical database is not sufficiently comprehensive and may hinder sharing of ethnobotanical knowledge. The lack of interest and documentation especially in digitalizing prior knowledge is a worrying trend since Malaysia possess high ethnicity and abundance of biodiversity. It is crucial to preserve and digitalize the comprehensive ethnobotanical database as it gives benefits worldwide. This study aimed to identify online database features and data type for an ethnobotanical database, to collect and populate existing database via secondary data and to test the acceptance of the database by users by testing whether the database fulfil the requirements of a good ethnobotanical database. Rapid Prototyping method has been used in this study involving User Requirement Analysis and data acceptance test to construct and create the best ethnobotanical database. Malaysian Ethnobotanical Online Database (MYETHBO) is the database created by using the Omeka Classic management system with Darwin Core standard. There are 1,074 plants information are collected online and mostly referred GlobinMed and GBIF. In this study, 30 plant species has been setup as exemplar pages. This study successfully developed a comprehensive ethnobotanical database (MYETHBO) by being able to identify the database features and data type needed via the Rapid Prototyping method, collect and populate existing data via secondary data and test the acceptance of the database by distributing the questionnaire to lecturers. MYETHBO will be beneficial to researchers, students and public people to gain knowledge of ethnobotany. It is available at https://cercom.uthm.edu.my/myethbo/.

Implementing Nestedness in Darwin Core: An epidemiology case study

Wildlife diseases have an impact on biodiversity, the economy, and public health. Better knowledge of disease patterns would be to the benefit of conservation measures, livestock production and, thus, ultimately human health. Nevertheless, disease surveillance systems operate mostly at a national scale, with incompatible data structures, inhibiting effective data sharing and thus rapid transnational responses to disease outbreak. As the risk of disease to biodiversity, the economy and to public health increases with climate change, land-use change and trade, the necessity for a common data standard to improve data sharing of surveillance efforts is greater than ever to enable transnational proactive and reactive measures to be taken. To address these large issues, a consortium for the European Food and Safety Authority (EFSA; the Enetwild consortium) was formed to collect existing data on wildlife host abundance and distribution in Europe. Alongside data on their associated pathogens, the consortium is attempting to develop data models to aggregate the host data according to the Darwin Core*1 standard. However, the complexity of zoonotic disease and wildlife distribution data is not easily captured in the current version of the Darwin Core standard, often comprising complex data structures with species interactions and partial information. Firstly, zoonotic disease data consist of observations of both the host and the pathogen, whereby each positive case of a disease is associated with the observation of the host species. Secondly, wildlife host distribution data frequently contain detailed information for only some individuals of a group when multiple individuals of the host species are observed simultaneously, such as life stage and sex. In order to capture these types of interactions and subsetting, we need to be able to handle the hierarchical structuring of these data. In an attempt to resolve these issues, a data model in line with the Darwin Core standard was initially developed for wildlife host population data (Enetwild Consortium et al. 2020). Here, we propose a new data model for data on the hosts’ pathogens, and use both data models to demonstrate the model efficacy for complex hierarchical data structures. The epidemiology data model is structured around the existence of a primary occurrence of the host species observation, which may consist of one or many individuals. In order to associate the presence (or absence) of the pathogen to the host, or to provide details on the host group composition, the data model allows child occurrences to relate to the primary, or parent, occurrence at a particular event (Table 1). We propose the implementation of a hierarchical structure of the occurrence extension to allow these two phenomena to be effectively modelled. This hierarchical structuring relies on the adoption of a new term, the ‘parentOccurrenceID’, whereby each 'parent' observation of the host species can be associated with multiple 'child' observations. To this end, we propose the introduction of the parentOccurrenceID term (currently under discussion on GitHub). Using the Darwin Core standard, we propose a data model that would allow effective harmonisation of zoonotic disease data, demonstrating that harmonisation of disease surveillance data in Europe is possible. Finding solutions to capturing complex hierarchical biotic interactions in Darwin Core is currently underway at GBIF*2, 3, although relying on a separate relationship table. However, we advocate for the introduction of the new parentOccurrenceID thanks to its simplicity and very general applicability, being adaptable to any group occurrence where detailed or partial information is available, or to hierarchical interaction relationships such as between hosts and pathogens. We believe the employment of the new term would be complementary to the current GBIF developments, and of benefit to many Darwin Core users where details apply to differing levels of hierarchical occurrences.

Open Access to the Digital Biodiversity Database: A Comprehensive Functional Model of the Natural History Collections

The Natural History Collections of Adam Mickiewicz University (AMUNATCOLL) in Poznań contain over 2.2 million specimens. Until recently, access to the collections was limited to specialists and was challenging because of the analogue data files. Therefore, this paper presents a new approach to data sharing called the Scientific, Educational, Public, and Practical Use (SEPP) Model. Since the stakeholder group is broad, the SEPP Model assumes the following key points: full open access to the digitized collections, the structure of metadata in accordance with certain standards, and a versatile tool set for data mining or statistical and spatial analysis. The SEPP Model was implemented in the AMUNATCOLL IT system, which consists of a web portal equipped with a wide set of explorative functionalities tailored to different user groups: scientists, students, officials, and nature enthusiasts. An integral part of the system is a mobile application designed for field surveys, enabling users to conduct studies comparing their own field data and AMUNATCOLL data. The AMUNATCOLL IT database contains digital data on specimens, biological samples, bibliographic sources, and multimedia nature documents. The metadata structure was developed in accordance with ABCD 2.06 and Darwin Core standards.

Data standardization of plant-pollinator interactions.

BackgroundAnimal pollination is an important ecosystem function and service, ensuring both the integrity of natural systems and human well-being. Although many knowledge shortfalls remain, some high-quality data sets on biological interactions are now available. The development and adoption of standards for biodiversity data and metadata has promoted great advances in biological data sharing and aggregation, supporting large-scale studies and science-based public policies. However, these standards are currently not suitable to fully support interaction data sharing.ResultsHere we present a vocabulary of terms and a data model for sharing plant–pollinator interactions data based on the Darwin Core standard. The vocabulary introduces 48 new terms targeting several aspects of plant–pollinator interactions and can be used to capture information from different approaches and scales. Additionally, we provide solutions for data serialization using RDF, XML, and DwC-Archives and recommendations of existing controlled vocabularies for some of the terms. Our contribution supports open access to standardized data on plant–pollinator interactions.ConclusionsThe adoption of the vocabulary would facilitate data sharing to support studies ranging from the spatial and temporal distribution of interactions to the taxonomic, phenological, functional, and phylogenetic aspects of plant–pollinator interactions. We expect to fill data and knowledge gaps, thus further enabling scientific research on the ecology and evolution of plant–pollinator communities, biodiversity conservation, ecosystem services, and the development of public policies. The proposed data model is flexible and can be adapted for sharing other types of interactions data by developing discipline-specific vocabularies of terms.

Data on 30-year stand dynamics in an old-growth broad-leaved forest in the Kaluzhskie Zaseki State Nature Reserve, Russia

The article provides primary data on repeated tree measurements collected during two censuses on a permanent sampling plot (440 m × 200 m) established in the old-growth polydominant broad-leaved forest in the Kaluzhskie Zaseki State Nature Reserve (centre of European Russia). The time span between the inventories was 30 years, and a total of 11 578 individuals of ten tree, one shrub species, and several undefined tree species of three known genera were registered. During the surveys, tree identity, stem diameter at breast height (DBH) of 1.3 m, and life status (alive or dead) were recorded for every tree individual with DBH ≥ 5 cm. Additional attributes were determined for some individuals. Field data were digitised and compiled into the PostgreSQL database. An accurate data quality assessment, validation, and cleaning (with documentation of changes) have been performed before data standardisation according to the Darwin Core standard. Standardised data were published through the GBIF repository. From 1986 to 1988, 9811 individuals were recorded within the initial census, including 3920 Corylus avellana individual shrubs. Corylus avellana shrubs were recorded without measuring DBH. From 2016 to 2018, 7658 stems were recorded in the recensus, including 3090 living trees marked during the initial census, and 1641 other living trees reaching the DBH of at least 5 cm. Corylus avellana was not included in the recensus. Thus, over 30 years, about 65% of living tree individuals have survived, but the total number of living trees has not changed considerably. The mean diameter of shade-intolerant tree species (Quercus robur, Fraxinus excelsior, Populus tremula, and Betula spp.) has increased the most remarkably during 30 years. For these species, the increase in average diameter, along with the decrease in numbers, is associated with the death of young trees, presumably due to low illumination under the canopy. Contrastingly, shade-tolerant tree species (Ulmus glabra, Tilia cordata, Acer platanoides) increased in number, while their mean diameter increased slightly or even decreased, that evidences the successful regeneration of these species under the canopy. These data are relevant for investigating forest ecology questions at spatiotemporal scales as a model of natural succession.

Data Standards for the Phenology of Plant Specimens

Phenological data (i.e., data on growth and reproductive events of organisms) are increasingly being used to study the effects of climate change, and biodiversity specimens have arisen as important sources of phenological data. However, phenological data are not expressly treated by the Darwin Core standard (Wieczorek et al. 2012), and specimen-based phenological data have been codified and stored in various Darwin Core fields using different vocabularies, making phenological data difficult to access, aggregate, and therefore analyze at scale across data sources. The California Phenology Network, an herbarium digitization collaboration launched in 2018, has harvested phenological data from over 1.4 million angiosperm specimens from California herbaria (Yost et al. 2020). We developed interim standards by which to score and store these data, but further development is needed for adoption of ideal phenological data standards into the Darwin Core. To this end, we are forming a Plant Specimen Phenology Task Group to develop a phenology extension for the Darwin Core standard. We will create fields into which phenological data can be entered and recommend a standardized vocabulary for use in these fields using the Plant Phenology Ontology (Stucky et al. 2018, Brenskelle et al. 2019). We invite all interested parties to become part of this Task Group and thereby contribute to the accesibility and use of these valuable data. In this talk, we will describe the need for plant phenological data standards, current challenges to developing such standards, and outline the next steps of the Task Group toward providing this valuable resource to the data user community.

The Fungal Literature-based Occurrence Database in Southern West Siberia (Russia)

The Fungal Literature-based Occurrence Database in Southern West Siberia (Russia)

Fun and Easy Georeferencing with a New Online Tool from South Africa

A new online tool for georeferencing specimen records has been developed through the Natural Science Collections Facility (NSCF) in South Africa to address the need for rapid, high quality georeferencing of specimen collections in the region (Fig. 1). A dataset of specimen records with Darwin Core fields dwc:scientificName, dwc:country, optional dwc:stateProvince, dwc:locality or dwc:verbatimLocality, optional dwc:recordedBy, and a record identifier such as dwc:occurrenceID (see dwc.tdwg.org/terms for definitions) is first uploaded into the tool and a team of georeferencers then work to georeference the dataset. Fuzzy string matching is used to group similar locality strings and to search for potential matching georeferences from a georeference database. The tool aims to improve efficiency by storing georeferenced localities so that they can be reused when the same locality is encountered again in other datasets. Thus, a locality only needs to be georeferenced once, and that georeference is reused for any other permutations of that locality string. A georeference includes the most important metadata from the Darwin Core standard: a measure of uncertainty, dwc:georeferenceDate, dwc:georeferencedBy, dwc:georeferenceProtocol, dwc:georeferenceSources, and the all too often neglected dwc:geodeticDatum. dwc:georeferencedByID is included for recording the ORCID iD of the georeferencer to facilitate attribution further down the data publication pipeline. In theory, the process of georeferencing should become more efficient with time as the georeference database grows. The georeferencing process is gamified by showing each georeferencer their own numbers of georeferenced records as they work, and they can see activity of fellow georeferencers as the dataset statistics update in real time. Dataset owners can also see overall progress with the dataset and numbers of records georeferenced by each team member, which may be useful for management purposes. Once a dataset is completed, it is downloaded with the new georeferences so these can be incorporated back into the original source database. Within the landscape of currently available georeferencing tools the system presented here is specifically placed to facilitate the management of the georeferencing process for a dataset by a team of georeferencers. The georeferencing workflow still requires a full suite of tools for finding coordinates for localities, such as a GIS, gazetteers and online resources, as well as a specific georeferencing protocol. It essentially replaces the use of spreadsheets for doing georeferencing, or doing georeferences directly in a collection database, which can be inefficient. Related to this, it includes a quality assurance process whereby georeferences are checked for correctness and adherence to the protocol being used, and for identifying geographic and environmental outliers for each species within the dataset. In this way the tool supports current workflows and best practices for georeferencing (e.g. Chapman and Wieczorek (2020), Zermoglio et al. (2020)). The technology stack includes Firebase as the primary database, ElasticSearch for fuzzy string matching, and the user interface is built with the modern Javascript framework Svelte. The tool has been in use by the NSCF since April 2021 after being populated with approximately 300 000 existing georeferences for southern Africa from various sources, including the South African National Biodiversity Institute (SANBI) Gazetteer and several collections databases. While initial emphasis in developing the tool has focussed on southern Africa, the tool can be extended to other regions easily. Please contact data@nscf.org.za for further information.

Camtrap DP: A frictionless data exchange format for camera trapping data

Camera trapping is one of the most important technologies in conservation and ecological research and a well-established, non-invasive method of collecting field data on animal abundance, distribution, behaviour, temporal activity, and space use (Wearn and Glover-Kapfer 2019). Collectively, camera trapping projects are generating a massive and continuous flow of data, consisting of images and videos (with and without animal observations) and associated identifications (Scotson et al. 2017, Kays et al. 2020). In recent years, significant progress has been made by the global camera trapping community to resolve the challenges this brings, from the development of specialized data management tools and analytical packages, to the application of cloud computing and artificial intelligence to automate species recognition (Tabak et al. 2018). However, to effectively exchange camera trap data between infrastructures and to (automatically) harmonize data into large-scale wildlife datasets, there is a need for a common data exchange format—one that captures the essential information about a camera trap study, allows expression of different study and identification approaches, and aligns well with existing biodiversity standards such as Darwin Core (Wieczorek et al. 2012). Here we present Camera Trap Data Package (Camtrap DP), a data exchange format for camera trap data. It is managed by the Machine Observations Interest Group of Biodiversity Information Standards (TDWG) and developed publicly, soliciting community feedback for every change. Camtrap DP is built on Frictionless Standards, a set of generic specifications to describe and package (tabular) data and metadata. Camtrap DP extends these with specific requirements and constraints for camera trap data. By building on an existing framework, users can employ existing open source software to read and validate Camtrap DP formatted data. Validation especially is useful to automatically check if provided data meets the requirements set forth by Camtrap DP, before analysis or integration. Supported by the major camera trap data management systems e.g. Agouti, TRAPPER, eMammal, and Wildlife Insights, Camtrap DP is reaching its first stable version. The first Camtrap DP dataset was published on Zenodo (Cartuyvels et al. 2021b). This dataset was also published to the Global Biodiversity Information Facility (GBIF) (Cartuyvels et al. 2021a), demonstrating the ability and limitations of transforming the data to the Darwin Core standard.

Aggregated occurrence records of the invasive alien striped field mouse (Apodemus agrarius Pall.) in the former USSR

BackgroundOpen access to occurrence records of the most dangerous invasive species in a standardised format have important potential applications for ecological research and management, including the assessment of invasion risks, formulation of preventative and management plans in the context of global climate and land use changes in the short and long perspective. The striped field mouse (Apodemusagrarius Pallas, 1771) is a common species in the temperate latitudes of the Palaearctic. Due to land use and global climate changes, several waves of expansion of the range of this species have been observed or inferred. By intrusion into new regions, the striped field mouse has become an alien species there. Apodemusagrarius causes significant harm to agriculture and is one of the most important pests of grain crops. In tree nurseries, A.agrarius destroys seeds of valuable tree species and gnaws at the bark of saplings of broadleaf species and berry bushes. It is one of the most epidemiologically important rodents, involved in the circulation of the causative agents of haemorrhagic fever with renal syndrome (HFRS) and many other zoonotic infections. The foregoing allows us to classify the striped field mouse as a dangerous invasive alien species in the expanding part of the range. A lot of data accumulated for this species are of interest from both ecological and applied points of view. The accumulation and aggregation of data on the occurrence records of A.agrarius is relevant for the study of ecology, biogeography and construction of the spatial distribution and ecological niche models in the context of global climate change. We have created a dataset of 1603 occurrence records of this species, collected from 1936 to December 2020 by various zoologists, previously published or original. These records relate to a significant part of the striped field mouse’s range in Russia (1264 records) and neighbouring countries (339 records). The dataset shows the position of the northern and central parts of A.agrarius range, the disjunction of the range in Transbaikalia and isolated populations in the north of the range. The data were obtained in different formats from literature, indicating different degrees of accuracy of geographic coordinates and with several variations of the species' name. In the process of aggregating and fixing errors, we created a set of georeferenced occurrence records, adopted a controlled vocabulary, removed duplicates and standardised the format of records using unified data structure. We examined the dataset for inconsistencies with the taxonomic position of A.agrarius and removed the incorrect records. This paper presents the resulting dataset of A.agrarius occurrence records in the territory of Russia and neighbouring countries in a standardised format.New informationThis is a validated and comprehensive dataset of occurrence records of A.agrarius, including both our own observations and records from literature. This dataset is available for extension by other researchers using a standard format in accordance with Darwin Core standards. In different countries, there are a lot of occurrence records for the striped field mouse, but the overwhelming part of them is presented in separate literary sources, stored in the form of maps and in zoological collections. Prior to this project, such information was not available to a wide range of researchers and did not allow the use of these spatial data for further processing by modern methods of analysis, based on geographic information systems (GIS technologies). The created dataset combines species occurrence records of many Soviet zoologists who studied the distribution of the striped field mouse over a significant part of its recent range, in Russia and neighbouring countries (within the former USSR). The final set of records was created by combining the species occurrence records using a uniform data structure, checking geographic coordinates and removing duplicate and erroneous records. The dataset expands the available information on the spatial and temporal distribution of the dangerous invasive species in Russia and neighbouring countries of the former USSR (Estonia, Latvia, Lithuania, Belarus, Ukraine, Moldova, Georgia, Azerbaijan, Kazakhstan and Kyrgyzstan).

Building a transnational biodiversity geo-database of the protected areas in the Adriatic-Ionian Macro-Region: approaches and results from the IMPRECO Project.

BackgroundThe main objective of the project Common strategies and best practices to IMprove the transnational PRotection of ECOsystem integrity and services - IMPRECO is to enhance the safeguarding of ecosystems and ecosystem services. Additionally, the aim of this project is to tackle their environmental vulnerability by strengthening the potential of the Protected Areas in biodiversity, ecosystems and ecosystem services conservation. This is expected to be addressed by maintaining it through their transnational networking located in the European Adriatic-Ionian Macro-Region.New informationThe aim of this research is: 1) to characterise the habitats and ecosystems involved in the coastal-marine protected areas considered; 2) to set a biodiversity baseline; 3) to understand what current ecosystems’ conditions are; 4) to build up a transnational biomonitoring programme of target species and habitats and 5) to assess their response to pilot actions. To do so, a transnational inventory of species, habitats, ecosystems and ecosystem services was established, starting with the seven coastal-marine protected areas involved in the project. Data collection was carried out using different sources of information: scientific literature, officially available data from NATURA 2000 Standard Data Forms, checklists from local biomonitoring programmes, personal observations and citizen science, historical maps and data from new in-field analyses. Data were filled in the transnational biodiversity geo-databases according to the NATURA 2000 standards about habitat features, species protection level and species features. The presence of alien species (non-indigenous species, NIS) was also acknowledged and references about data collection were provided in the databases according to the Darwin Core standards.

A data management workflow of biodiversity data from the field to data users.

PremiseHeterogeneity of biodiversity data from the collections, research, and management communities presents challenges for data findability, accessibility, interoperability, and reusability. Workflows designed with data collection, standards, dissemination, and reuse in mind will generate better information across geopolitical, administrative, and institutional boundaries. Here, we present our data workflow as a case study of how we collected, shared, and used data from multiple sources.MethodsIn 2012, we initiated the collection of biodiversity data relating to Michigan prairie fens, including data on plant communities and the federally endangered Poweshiek skipperling (Oarisma poweshiek).ResultsOver 23,000 occurrence records were compiled in a database following Darwin Core standards. The records were linked with media and biological, chemical, and geometric measurements. We published the data as Global Biodiversity Information Facility data sets and in Symbiota SEINet portals.DiscussionWe highlight data collection techniques that optimized transcription time, including the use of predetermined and controlled vocabulary, Darwin Core terms, and data dictionaries. The validity and longevity of our data were supported by voucher specimens, metadata with measurement records, and published manuscripts detailing methods and data sets. Key to our data dissemination was cooperation among partners and the utilization of dynamic tools. To increase data interoperability, we need flexible and customizable data collection templates, coding, and enhanced communication among communities using biodiversity data.

The e-Flora of South Africa – restructuring data to comply with Darwin Core standards for inclusion into the World Flora Online

The e-Flora of South Africa project was initiated in 2013 by the South African National Biodiversity Institute (SANBI) in support of the Global Strategy for Plant Conservation (GSPC, 2011-2020). South Africa's flora consists of ca. 21,000 taxa of which more than half are endemic. South Africa will contribute a national Flora towards Target 1 of the GSPC ("To create an online flora of all known plants by 2020"). South Africa's contribution is ca. 6% of the world’s flora of which ca. 3% are endemic and therefore unique. South Africa’s electronic Flora is comprised of previously published descriptions. South Africa’s e-Flora data forms part of the Botanical Dataset of Southern Africa (BODATSA) that is currently managed through the Botanical Research And Herbarium Management System (BRAHMS). To date, South Africa’s e-Flora data (http://ipt.sanbi.org.za/iptsanbi/resource?r=flora_descriptions) represents 19,539 indigenous taxa, 79,139 descriptions of distribution, morphological, habitat and diagnostic data, and 27,799 bibliographic records. The e-Flora data was recently published online using the Integrated Publishing Toolkit and henceforth harvested by the World Flora Online (WFO) into the portal. A series of challenges were encountered while manipulating descriptive data from BRAHMS to be ingested by the WFO portal; from taxonomic issues to data quality issues not excluding compliance to data standards. To contribute to the WFO portal, the taxa in BODATSA has to match with the taxa in the WFO taxonomic backbone. Once there is a match, a unique WFO taxon identifier is assigned to the taxa in BODATSA. This process presented various challenges because the WFO taxonomic backbone and the taxonomic classification system that is used by South Africa (South African National Plant Checklist) does not fully correlate. The schema used to store taxonomic data also does not agree between BRAHMS and WFO and had to be addressed. To enable consistency for future, a detailed guideline document was created providing all the steps and actions that should be taken when publishing an e-Flora, managed in BRAHMS, to the WFO portal. The presentation will focus on matching taxonomic classifications between BRAHMS and WFO; dealing with character encoding issues and manipulating data to meet Darwin Core standards.

Bio-logging Data in Darwin Core: Use Cases

Animal-borne sensor data, along with other types of sensor-based observations, provide a growing volume and proportion of documentation about biodiversity. These data differ from the traditional specimen, sampling and human observation records for which the Taxonomic Database Working Group (TDWG) originally designed the Darwin Core standard. The original intention of the new TDWG Machine Observations Interest Group is to facilitate a body of work combining the informatics expertise of TDWG with that of subject matter experts to document best practice guidelines for applying Darwin Core to bio-logging datasets. This session offers the opportunity to walk through some of the use cases developed so far, including a terrestrial GPS tracking and acceleration dataset from Movebank and a marine acoustic telemetry dataset from the Ocean Tracking Network using stationary as well as mobile acoustic receivers. Through these examples, we will describe the strategy and rationale for the approaches taken to the application of Darwin Core using typical animal tracking scenarios laced with some of the common complexities in bio-logging and other types of machine-based biodiversity observations.

The Microbial Antarctic Resource System: Integrating discoverability and preservation of environmentally-annotated microbial 'omics data

Microbial organisms - including Archaea, Bacteria and unicellular Eukaryota - collectively dominate the Earth in terms of bio- and functional diversity. Their study, often constrained by technology, has strongly benefited from the recent advancements in high-throughput DNA sequencing techniques. The vast amounts of microbial data generated in the wake of these developments, however, remains severely underrepresented on open access biodiversity data repositories (e.g. the Global Biodiversity Information Facility; GBIF). Moreover, when sequencing data has been made publicly available, is often poorly annotated with metadata and environmental variables, making it difficult to find or query. Therefore, the microbial Antarctic Resource System (mARS) aims to fill this lacuna by documenting and geo-referencing microbial datasets and linking the sequence data in the International Nucleotide Sequence Database Collaboration (INSDC) repositories with the associated environmental measurements on mARS, which is aimed to be interoperable with both INSDC and GBIF. This way, mARS helps to preserve environmental data and the metadata that is crucial for the correct processing and interpretation of sequence data, while it also connects researchers via its webportal to the existing wealth of molecular information, and allows these datasets to be more effectively accessed. Given the general complexity of microbial ecological datasets, mARS needs to operate between different data archiving standards, such as MIxS (see https://press3.mcs.anl.gov/gensc/mixs/), which is oriented towards DNA sequence data, and the biodiversity-based DarwinCore standard. Currently, mARS tries to address the challenges of integrating microbial data with these existing systems as well as connecting with the communities behind them, by documenting the datasets on GBIF's extensions or investigate the feasibility of routinely processing raw sequence data into occurrence datasets using the open computing facilities offered by the European Molecular Biology Laboratory's (EMBL) MGnify resource.

A New Model for Efficient, Need-Driven Progress in Generating Primary Biodiversity Information Resources

The field of biodiversity informatics has developed rapidly in recent years with broad availability of large-scale information resources. However, online biodiversity information are biased (Boakes et al. 2010, Stropp et al. 2016) as a result of the relatively slow capture and digitization of existing data resources. The West African Plants (WAP) initiative approach to data capture is a prototype of a novel solution to the challenge of the traditional model, in which the institutional “owner” of the specimens is responsible for digital capture of associated data. The WAP Initiative is a consortium of West African researchers in botany, in coordination with six institutions across Europe and North America; its goal is to digitize and mobilize available, high-quality, primary biodiversity occurrence data resources for West African plant diversity (http://jrsbiodiversity.org/grants/university-of-ghana-herbaria/). Here, we developed customized workflows for data capture in formats directly and permanently useful to the “owner” herbarium, and digitized significant new biodiversity records adding to the information available for the plants of the region. Data records were captured strictly in accordance with DarwinCore standards, achieved either by (a) capturing data records from existing images (e.g., images supplied by Naturalis Bodiversity Centre), or (b) capturing data from images taken quickly and efficiently by project personnel in West African Herbaria. Digitization of images and data began in 2015 in West African partner institutions, and by middle of 2018 resulted in 190,953 records of species in 1965 genera and 331 families from 16 West African countries (Fig. 1). Our approach is cost-effective, allows development of information resources even for regions in which political situations make it impossible, and it provides a historical context against which to compare newer data as the latter become available (Peterson et al. 2016). Further measures of success of the initiative will center on whether the institutions “owning” the specimens follow through and put the new data records online. Already, several project institutions have put initial project data online as part of their GBIF data contributions, but—of course—success would consist of all project-generated data being completely available online. Note that this model is the reverse of the traditional model, in which the institutions holding the specimens create the information resources that are used by the rest of the world. This new paradigm in specimen digitization has considerable promise to accelerate and improve the process of generating biodiversity information, and can be replicated and applied in many biodiversity-rich, information-poor regions to remedy the oft-cited massive gaps in information availability.

The Semantic Field Book Annotator

Biodiversity research expeditions to the globe’s most biodiverse areas have been conducted for several hundred years. Natural history museums contain a wealth of historical materials from such expeditions, but they are stored in a fragmented way. As a consequence links between the various resources, e.g., specimens, illustrations and field notes, are often lost and are not easily re-established. Natural history museums have started to use persistent identifiers for physical collection objects, such as specimens, as well as associated information resources, such as web pages and multimedia. As a result, these resources can more easily be linked, using Linked Open Data (LOD), to information sources on the web. Specimens can be linked to taxonomic backbones of data providers, e.g., the Encyclopedia Of Life (EOL), the Global Biodiversity Information Facility (GBIF), or publications with Digital Object Identifiers (DOI). For the content of biodiversity expedition archives, (e.g. field notes), no such formalisations exist. However, linking the specimens to specific handwritten notes taken in the field can increase their scientific value. Specimens are generally accompanied by a label containing the location of the site where the specimen was collected, the collector’s name and the classification. Field notes often augment the basic metadata found with specimens with important details concerning, for instance, an organism’s habitat and morphology. Therefore, inter-collection interoperability of multimodal resources is just as important as intra-collection interoperability of unimodal resources. The linking of field notes and illustrations to specimens entails a number of challenges: historical handwritten content is generally difficult to read and interpret, especially due to changing taxonomic systems, nomenclature and collection practices. It is vital that: the content is structured in a similar way as the specimens, so that links can more easily be re-established either manually or in an automated way; for consolidation, the content is enriched with outgoing links to semantic resources, such as Geonames or Virtual International Authority File (VIAF); and this process is a transparent one: how links are established, why and by whom, should be stored to encourage scholarly discussions and to promote the attribution of efforts. the content is structured in a similar way as the specimens, so that links can more easily be re-established either manually or in an automated way; for consolidation, the content is enriched with outgoing links to semantic resources, such as Geonames or Virtual International Authority File (VIAF); and this process is a transparent one: how links are established, why and by whom, should be stored to encourage scholarly discussions and to promote the attribution of efforts. In order to address some of these issues, we have built a tool, the Semantic Field Book Annotator (SFB-A), that allows for the direct annotation of digitised (scanned) pages of field books and illustrations with Linked Open Data (LOD). The tool guides the user through the annotation process, so that semantic links are automatically generated in a formalised way. These annotations and links are subsequently stored in an RDF triplestore. As the use of the Darwin Core standard is considered best practice among collection managers for the digitisation of their specimens, our tool is equipped with an ontology based on Darwin Core terms, the NHC-Ontology, which extends the Darwin Semantic Web (DSW) ontology. The tool can annotate any image, be it an image of a specimen with a textual label, an illustration with a textual label or a handwritten species description. Interoperability of annotations between the various resources within a collection is therefore ensured. Terms in the ontology are structured using OWL web ontology language. This allows for more complex tasks such as OWL reasoning and semantic queries, and facilitates the creation of a richer knowledge base that is more amenable to research.