Global Biodiversity Information Facility Data Research Articles

Predicting the distribution of Ixodes ricinus and Dermacentor reticulatus in Europe: a comparison of climate niche modelling approaches

BackgroundThe ticks Ixodes ricinus and Dermacentor reticulatus are two of the most important vectors in Europe. Climate niche modelling has been used in many studies to attempt to explain their distribution and to predict changes under a range of climate change scenarios. The aim of this study was to assess the ability of different climate niche modelling approaches to explain the known distribution of I. ricinus and D. reticulatus in Europe.MethodsA series of climate niche models, using different combinations of input data, were constructed and assessed. Species occurrence records obtained from systematic literature searches and Global Biodiversity Information Facility data were thinned to different degrees to remove sampling spatial bias. Four sources of climate data were used: bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived data. Eight different model training extents were examined and three modelling frameworks were used: maximum entropy, generalised additive models and random forest models. The results were validated through internal cross-validation, comparison with an external independent dataset and expert opinion.ResultsThe performance metrics and predictive ability of the different modelling approaches varied significantly within and between each species. Different combinations were better able to define the distribution of each of the two species. However, no single approach was considered fully able to capture the known distribution of the species. When considering the mean of the performance metrics of internal and external validation, 24 models for I. ricinus and 11 models for D. reticulatus of the 96 constructed were considered adequate according to the following criteria: area under the receiver-operating characteristic curve > 0.7; true skill statistic > 0.4; Miller’s calibration slope 0.25 above or below 1; Boyce index > 0.9; omission rate < 0.15.ConclusionsThis comprehensive analysis suggests that there is no single ‘best practice’ climate modelling approach to account for the distribution of these tick species. This has important implications for attempts to predict climate-mediated impacts on future tick distribution. It is suggested here that climate variables alone are not sufficient; habitat type, host availability and anthropogenic impacts, not included in current modelling approaches, could contribute to determining tick presence or absence at the local or regional scale.Graphical abstract

Elevating the Fitness of Use of GBIF Occurrence Datasets: A proposal for peer review

Biodiversity data plays a pivotal role in understanding and conserving our natural world. As the largest occurrence data aggregator, the Global Biodiversity Information Facility (GBIF) serves as a valuable platform for researchers and practitioners to access and analyze biodiversity information from across the globe (Ball-Damerow et al. 2019). However, ensuring the quality of GBIF datasets remains a critical challenge (Chapman 2005). The community emphasizes the importance of data quality and its direct impact on the fitness of use for biodiversity research and conservation efforts (Chapman et al. 2020). While GBIF continues to grow in terms of the quantity of data it provides, the quality of these datasets varies significantly (Zizka et al. 2020). The biodiversity informatics community has been working diligently to ensure data quality at every step of data creation, curation, publication (Waller et al. 2021), and end-use (Gueta et al. 2019) by employing automated tools and flagging systems to identify and address issues. However, there is still more work to be done to effectively address data quality problems and enhance the fitness of use for GBIF-mediated data. I highlight a missing component in GBIF's data publication process: the absence of formal peer reviews. Despite GBIF encompassing the essential elements of a data paper, including detailed metadata, data accessibility, and robust data citation mechanisms, the lack of peer review hinders the credibility and reliability of the datasets mobilized through GBIF. To bridge this gap, I propose the implementation of a comprehensive peer review system within GBIF. Peer reviews would involve subjecting GBIF datasets to rigorous evaluation by domain experts and data scientists, ensuring the accuracy, completeness, and consistency of the data. This process would enhance the trustworthiness and usability of datasets, enabling researchers and policymakers to make informed decisions based on reliable biodiversity information. Furthermore, the establishment of a peer review system within GBIF would foster collaboration and knowledge exchange among the biodiversity community, as experts provide constructive feedback to dataset authors. This iterative process would not only improve data quality but also encourage data contributors to adhere to best practices, thereby elevating the overall standards of biodiversity data mobilization through GBIF.

An Australian Model of Cooperative Data Publishing to OBIS and GBIF

The Australian Commonwealth Science and Industrial Research Organisation (CSIRO) hosts both the Australian Ocean Biodiversity Information System (OBIS) and Global Biodiversity Information Facility (GBIF) nodes within the National Collections and Marine Infrastructure (NCMI) business unit. OBIS-AU is led by the NCMI Information and Data Centre and publishes marine biodiversity data in the Darwin Core (DwC) standard via an Integrated Publishing Toolkit (IPT), with over 450 marine datasets at present. The Australian GBIF node is hosted by a separate team at the Atlas of Living Australia (ALA), a national-scale biodiversity analytical and knowledge delivery portal. The ALA aggregates and publishes over 800 terrestrial and marine datasets from a wide variety of research institutes, museums and collections, governments and citizen science agencies, including OBIS-AU. Many OBIS-AU published datasets are harvested and republished by ALA and vice-versa. OBIS-AU identifies, performs Quality Control and formats marine biodiversity and observation data, then publishes directly to the OBIS international data repository and portal, using GBIF IPT technology. The ALA data processing pipeline harvests, aggregates and enhances datasets from many sources with authoritative taxonomic and spatial reference data before passing the data on to GBIF. OBIS-AU and ALA are working together to ensure that the publication pathways for any datasets managed by both (with potential for duplication of records and incomplete metadata harvests) are rationalised and that a single collaborative workflow across both units is followed for publication to GBIF. Recently, the data management groups have established an agreement to cooperatively publish marine data and eDNA data. OBIS-AU have commenced publishing datasets directly to GBIF with ALA endorsement. We present the convergent evolution of OBIS and GBIF data publishing in Australia, adaptive data workflows to maintain data and metadata integrity, challenges encountered, how domain expertise ensures data quality and the benefits of sharing data skills and code, especially in publishing eDNA data types in DwC (using the DNA-derived data extension) and exploring the new CamTrap Data Package using Frictionless data. We also present the work that both data groups are doing toward adopting the GBIF new Unified Data model for publishing data. This Australian case study demonstrates the strengths of collaborative data publishing and offers a model that minimises replication of data in global aggregators through the development of regional integrated data publishing pipelines.

Improving Access to Collection Information in Asia and Africa through the Global Registry of Scientific Collections

Global Registry of Scientific Collections, or GRSciColl, is a comprehensive registry of natural history collections maintained by the Global Biodiversity Information Facility (GBIF), building upon previous efforts developed by the Consortium of the Barcode of Life (CBOL) and other organizations. Its primary purpose is to provide a centralized platform for accessing information on institutions, their collections and associated staff members. While both GBIF and GRSciColl aim to encompass data from all around the globe, Asian and African collections are often underrepresented. To address this disparity, initiatives like the Biodiversity Information for Development (BID) and the Biodiversity Information Fund for Asia (BIFA) have been established. These initiatives specifically focus on assisting institutions from underrepresented regions in sharing their data through GBIF. In addition to providing support in the framework of these initiatives, the GBIF Regional support teams are actively engaged in efforts to improve access to relevant data about Asian and African collections. This involves updating information on Asian and African institutions and their collections in GRSciColl. Furthermore, the teams assist towards publishing biodiversity data held by these institutions for open access. To support this objective, a Collection Mobilization repository has been established on GitHub, with more than 1,900 issues created at an institutional level and a systematic workflow is developed and followed. The overall aim of this workflow adheres to the goal of GRSciColl in providing an updated information on scientific collections, their host institutions, and relevant staff, also making the specimen data from those institutions open access when possible. Here are a few key points of this ongoing work: The process begins by compiling relevant information on institutions and their collections by browsing the institution’s web pages to gather accurate and up-to-date data. Based on the findings, necessary changes or additions to the existing data are made in the institution and collection page of the GRSciColl registry, and simultaneously noted in related GitHub issues in the collection mobilization repository, ensuring transparency and record keeping. When required, contact is established with the staff of the institution, to seek clarifications and ensure accuracy of the information in the registry. Appropriate GitHub labels are then assigned to the institutions based on their data mobilization or digitization status, to help prioritize the ongoing work. Institutions that are familiar with GBIF data infrastructure or those that have digitized records of the specimens are given priority for contact to gauge their interest in mobilizing their data for open access. Institutions interested in publishing their data are offered support in the process, and their datasets are added to the Data Mobilization Repository in GitHub as an issue, using the Suggest a Dataset tool on GBIF.org. Finally, progress on data preparation and then publication is tracked and relevant comments and labels are added in the GitHub issues. The process begins by compiling relevant information on institutions and their collections by browsing the institution’s web pages to gather accurate and up-to-date data. Based on the findings, necessary changes or additions to the existing data are made in the institution and collection page of the GRSciColl registry, and simultaneously noted in related GitHub issues in the collection mobilization repository, ensuring transparency and record keeping. When required, contact is established with the staff of the institution, to seek clarifications and ensure accuracy of the information in the registry. Appropriate GitHub labels are then assigned to the institutions based on their data mobilization or digitization status, to help prioritize the ongoing work. Institutions that are familiar with GBIF data infrastructure or those that have digitized records of the specimens are given priority for contact to gauge their interest in mobilizing their data for open access. Institutions interested in publishing their data are offered support in the process, and their datasets are added to the Data Mobilization Repository in GitHub as an issue, using the Suggest a Dataset tool on GBIF.org. Finally, progress on data preparation and then publication is tracked and relevant comments and labels are added in the GitHub issues. The ongoing work ensures continuous improvement and expansion of GRSciColl, while promoting open access to valuable scientific data. This presentation will highlight the work done so far, as well as the challenges and lessons learnt from this ongoing project.

Processing Country Centroids at the Global Biodiversity Information Facility

The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure that promotes open access to biodiversity data. GBIF serves as a global hub for aggregating and disseminating biodiversity information from diverse sources, including museums, research institutions, and citizen science projects. I will present recent additions to GBIF data quality measures, focusing on the introduction of the country centroid filtering feature. Additionally, I explore the functionality and significance of GBIF data quality flags, which aid in assessing the reliability and usability of the aggregated data. I will also discuss recent changes to the default filtering mechanism employed by GBIF to remove suspicious points. GBIF Data Quality Flags GBIF employs a large set of data quality flags to provide users with an assessment of the reliability and fitness for use of the aggregated data. These flags are assigned based on various quality-related criteria, such as geographic precision, taxonomic accuracy, and completeness of associated metadata. By analyzing these flags, users can make informed decisions regarding the suitability of data for their specific research or conservation needs. GBIF data quality flags serve as valuable indicators that facilitate data filtering and enhance data usability. These flags are assigned to individual occurrence records based on various quality-related criteria and are designed to help data users evaluate the trustworthiness and suitability of the data for their specific needs. Country Centroid filter Retrospective geocoding is a process in which historical occurrence data lacking precise geographic coordinates are assigned approximate mappable locations based on other available information, such as locality descriptions, gazetteers, or administrative boundaries. Retrospective geocoding introduces centroids into occurrence datasets wherever better information than a large named area is unknown. A Country centroid refers to the geographical center point of a country's geographic polygon. Recently, GBIF has implemented country centroid filtering as a feature. By utilizing this filtering technique, GBIF users can identify and exclude occurrences that are country or area centroids. This ensures that data users receive more accurate and reliable information, reducing potential errors resulting from georeferencing issues. The Catalogue of Centroids is a GitHub repository designed to collect and provide a comprehensive list of country centroids for the purpose of occurrence data filtering. The repository serves as a central resource for storing and sharing centroid coordinates associated with each country, area, or province. The main idea is to gather the from sources most often used during retrospective geo-referencing of museum collections. Fig. 1 Default Filtering and Treatment of Suspicious Points GBIF employs default filtering mechanisms to identify suspicious occurrences from its aggregated datasets. For example, the "Country Coordinate Mismatch" data quality flag in GBIF is a quality assessment indicator that highlights occurrences where there is a potential mismatch between the recorded coordinates and the associated country information. Similarly, zero coordinates are often encountered as placeholders or missing data points. GBIF's default filtering process identifies such occurrences from the dataset, as they can introduce inaccuracies when conducting spatial analyses or mapping exercises. This step ensures that only reliable and valid occurrences are available for further analyses and downstream applications. GBIF continues to improve data quality flagging, which significantly enables users to assess the fitness of data for a particular use and increases the utility of the mediated biodiversity information.

Biodiversity in cities: the impact of biodiversity data across spatial scales on diversity estimates

Abstract The assessment and monitoring of biodiversity in urban areas has been shown to have enormous potential to inform integrative urban planning in cities. In this context, digital biodiversity repositories such as the Global Biodiversity Information Facility (GBIF) has been promoted for its central role in gathering and harmonizing biodiversity data worldwide, thereby facilitating these assessments and monitoring efforts. While GBIF data has been investigated for its potential at a large scale and in natural ecosystems, the question remains as to what extent, and in which context, is GBIF data applicable to urban biodiversity assessment and monitoring? In this study, we assessed the spatial patterns of biodiversity, by exploring species richness patterns in relation to land use types for three taxonomic groups (birds, mammals and arthropods) in three cities in The Netherlands (Rotterdam, Amsterdam and Groningen) at multiple spatial scales. We found significant variation in the effect of land uses on the species richness patterns, in terms of taxonomic group, spatial configuration and land cover type, and across spatial scales. Our study demonstrates the potential of GBIF data while highlighting the importance of the careful selection of one or multiple spatial scales, especially in relation to the taxonomic group characteristics and ecology and the spatial configuration of the cities studied.

Contrasting biogeographical patterns of threatened vertebrates on islands emerge from disparities between expert‐derived maps and Global Biodiversity Information Facility data

AbstractAimThe most popular sources of information on species distributions are the expert‐derived maps and georeferenced occurrences, mainly those compiled by the Global Biodiversity Information Facility (GBIF). These sources have been constantly used with biogeographical and conservation goals. However, their degree of accuracy in representing geographical biodiversity patterns remains poorly understood. Here, we compared both sources of information on species distributions to estimate global patterns of richness and species composition of threatened vertebrates on marine islands.LocationGlobal.TaxonTerrestrial vertebrates.MethodsWe gathered distributional data of all threatened terrestrial vertebrate species inhabiting 22,471 marine islands worldwide from GBIF and expert derived maps. Then, to assess strengths and biases from each source, using geographical information systems, we calculated and compared: (a) species richness per island, (b) general patterns of richness and (c) the number of shared species from both sources per island.ResultsThere were dramatic differences between the information derived from both data sources. The species richness estimated with expert‐derived maps resulted in 10 more species per island, on average, than the estimations obtained from GBIF data. The mean proportion of shared species per island (between both data sources) was very low (3.1% of the species), and the general patterns of richness were markedly different. The most significant differences occurred in tropical areas and Europe.ConclusionDifferences between the two sources emerged from intrinsic biases: expert‐derived maps tend to overestimate species' counts, whereas GBIF occurrences tend to do the opposite, except for some well‐sampled regions where both data types converge. Our findings suggest that previous global assessments performed with these information sources on species distributions, especially those focusing on protected areas, should be carefully considered.

Plant-pollinator Interaction Data: A case study of the WorldFAIR project

Biodiversity is a data-intensive science and relies on data from a large number of disciplines in order to build up a coherent picture of the extent and trajectory of life on earth (Bowker 2000). The ability to integrate such data from different disciplines, geographic regions and scales is crucial for making better decisions towards sustainable development. As the Biodiversity Information Standards (TDWG) community tackles standards development and adoption beyond its initial emphases on taxonomy and species distributions, expanding its impact and engaging a wider audience becomes increasingly important. Biological interactions data (e.g., predator-prey, host-parasite, plant-pollinator) have been a topic of interest within TDWG for many years and a Biological Interaction Data Interest Group (IG) was established in 2016 to address that issue. The IG has been working on the complexity of representing interactions data and surveying how Darwin Core (DwC, Wieczorek 2012) is being used to represent them (Salim 2022). The importance of cross-disciplinary science and data inspired the recently funded WorldFAIR project—Global cooperation on FAIR data policy and practice—coordinated by the Committee on Data of the International Science Council (CODATA), with the Research Data Alliance (RDA) as a major partner. WorldFAIR will work with a set of case studies to advance implementation of the FAIR data principles (Fig. 1). The FAIR data principles promote good practices in data management, by making data and metadata Findable, Accessible, Interoperable, and Reusable (Wilkinson 2016). Interoperability will be a particular focus to facilitate cross-disciplinary research. A set of recommendations and a framework for FAIR assessment in a set of disciplines will be developed (Molloy 2022). One of WorldFAIR's case studies is related to plant-pollinator interactions data. Its starting point is the model and schema proposed by Salim (2022) based on the DwC standard, which adheres to the diversifying GBIF data model strategy and on the Plant-Pollinator vocabulary described by Salim (2021). The case study on plant-pollinator interactions originated in the TDWG Biological Interaction Data Interest Group (IG) and within the RDA Improving Global Agricultural Data (IGAD) Community of Practice. IGAD is a forum for sharing experiences and providing visibility to research and work in food and agricultural data and has become a space for networking and blending ideas related to data management and interoperability. This topic was chosen because interoperability of plant-pollinator data is needed for better monitoring of pollination services, understanding the impacts of cultivated plants on wild pollinators and quantifying the contribution of wild pollinators to cultivated crops, understanding the impact of domesticated bees on wild ecosystems, and understanding the behaviour of these organisms and how this influences their effectiveness as pollinators. In addition to the ecological importance of these data, pollination is economically important for food production. In Brazil, the economic value of the pollination service was estimated at US$ 12 billion in 2018 (Wolowski 2019). All eleven case studies within the WorldFAIR project are working on FAIR Implementation Profiles (FIPs), which capture comprehensive sets of FAIR principle implementation choices made by communities of practice and which can accelerate convergence and facilitate cross-collaboration between disciplines (Schultes 2020). The FIPs are published through the FIP Wizard, which allows the creation of FAIR Enabling Resources. The FIPs creation will be repeated by the end of the project and capture results obtained from each case study in order to advance data interoperability. In the first FIP, resources from the Global Biodiversity Information Facility (GBIF) and Global Biotic Interactions (GloBI) were catalogued by the Plant-Pollinator Case Study team, and we expect to expand the existing FAIR Enabling Resources by the end of the project and contribute to plant-pollinator data interoperability and reuse. To tackle the challenge of promoting FAIR data for plant-pollinator interactions within the broad scope of the several disciplines and subdisciplines that generate and use them, we will conduct a survey of existing initiatives handling plant-pollinator interactions data and summarise the current status of best practices in the community. Once the survey is concluded, we will choose at least five agriculture-specific plant-pollination initiatives from our partners, to serve as targets for standards adoption. For data to be interoperable and reusable, it is essential that standards and best practices are community-developed to ensure adoption by the tool builders and data scientists across the globe. TDWG plays an important role in this scenario and we expect to engage the IG and other interested parties in that discussion.

Taiwan Biodiversity Information Facility (TaiBIF)’s Role in Facilitating Open Biodiversity Data in Taiwan

Taiwan Biodiversity Information Facility (TaiBIF) was established in 2001 as one of the first Global Biodiversity Information Facility (GBIF) nodes. Funded by Taiwan's Ministry of Science and Technology and Forestry Bureau, TaiBIF’s missions are to: Develop infrastructures for biodiversity-related data, Promote the open data concept, Facilitate the publication and integration of biodiversity information, Support biodiversity-related research and policy-making, and Connect Taiwan and international biodiversity information. Develop infrastructures for biodiversity-related data, Promote the open data concept, Facilitate the publication and integration of biodiversity information, Support biodiversity-related research and policy-making, and Connect Taiwan and international biodiversity information. As part of the work to fulfill its missions, TaiBIF develops and maintains infrastructures such as a data portal, Taiwan Catalogue of Life (TaiCoL), and a tool for scientific name-checking - NomenMatch, for public and researchers to use as needed. Additionally, as the only GBIF data hosting centre currently available in Asia, TaiBIF offers all services mentioned in the GBIF’s data hosting criteria. Briefly, this includes a regularly maintained Integrated Publishing Toolkit (IPT) and help desk for supporting users in dataset and data paper publishing process. In the continuous effort to promote open biodiversity data, TaiBIF organises workshops annually for students, researchers, and any governmental or non-governmental organisations, providing hands-on training focusing on data standardising and the publishing process. As of June 2022, Taiwan has exposed more than 13 million occurrence records and 83 datasets through GBIF's data platform, contributed mostly by 19 data publishing units, making it the second-largest data contributor in Asia (after India). These data have been used in many peer-reviewed studies on various topics and for guiding conservation policies. TaiBIF has maintained close interactions and cooperation with GBIF, regularly assisting in the Biodiversity Information Fund for Asia (BIFA) projects, regional workshops and communications between members from Asian countries. TaiBIF’s current active tasks include allying with the Taiwan Biodiversity Information Alliance (TBIA), a national biodiversity network aimed at the integration of local biodiversity data from various governmental agencies and museums. Furthermore, a volunteer training system is being developed to support workloads at the TaiBIF office. Recognising the importance of data coverage, a data review study of open biodiversity data status in Taiwan is ongoing.

Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models.

Digital point‐occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time‐consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. Taking North American Ephedra as a model, we examined how different data cleaning pipelines (using, e.g., the GBIF web application, and four different R packages) affect downstream species distribution models (SDMs). We also assessed how data differed from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false positives, invalid coordinates, and duplicates, leading to datasets between 9484 (GBIF application) and 5196 records (manual‐guided filtering). The expert data consisted of 704 records, comparable to data from field studies. Although differences in the absolute numbers of records were relatively large, species richness models based on stacked SDMs (S‐SDM) from pipeline and expert data were strongly correlated (mean Pearson's r across the pipelines: .9986, vs. the expert data: .9173). Our results suggest that all R package‐based pipelines reliably identified invalid coordinates. In contrast, the GBIF‐filtered data still contained both spatial and taxonomic errors. Major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of taxonomic expert knowledge. We conclude that application‐filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high‐quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.

GBIF’s (Global Biodiversity Information Facility) New Strategic Framework to Address Biodiversity Data Needs

Every five years the Global Biodiversity Information Facility (GBIF) reevaluates its work in an updated Strategic Framework. The latest was recently approved and will be in effect 2023–2027. In responding to our Governing Board and network expectations to accelerate data mobilization and capacity enhancement to improve science for research and policy relevance, the new Framework specifically responds to the biodiversity crisis in its Vision, Mission and Values statements. In addition to the successful national data mobilization framework, GBIF is developing thematic data use cases to accelerate towards the goals set forth in the Framework's priority areas. The thematic data mobilization will identify critical data gaps in areas of research and policy importance and work with new communities to mobilize this data into the GBIF network. Additionally, we will work with user communities to facilitate and track data use. These thematic topics will include areas of interest to global policy initiatives, such as human health, invasive species, agricultural biodiversity, eDNA and others. Aligned with this work is the enhancement of the GBIF Data Model, based on DwC, to better integrate data from diverse sources. We expect that this advancement will encourage the publishing of more and diverse data types that can be used to improve science for research and policy relevance of GBIF-mediated data. In order to accomplish this ambitious Strategic Framework, GBIF will also need to evolve how we operate. The pandemic has taught us that virtual interactions are positive, but have limits. We are developing a more distributed workforce and importantly expanding collaborations, such as with the Atlas of Living Australia, Biodiversity Information Standards (TDWG), and the Catalogue of Life on joint infrastructure and models. These alliances build interoperability, enlarge and improve data mobilization and use, and quicken knowledge growth to strengthen the entire biodiversity knowledge network to meet our critical needs.

An occurrence data set for invasive and naturalized alien plants in India.

India has numerous invasive alien plant species (IAPSs), which seriously impact biodiversity, ecosystem services, and economic development. The availability of reliable occurrence records of IAPSs is of great importance for their successful management, prediction of distribution across time and space, and other research and development efforts. Global databases of occurrence data, like the Global Biodiversity Information Facility (GBIF), are often not exhaustive, especially for India, and poorly represent the actual distribution of IAPSs in the country. Our objective in creating this data set was threefold: (1) to compile occurrence data for the invasive and naturalized alien plant species of India, (2) to collect spatial and temporal information associated with occurrence records, and (3) to share the data in an accessible format so every record is traceable to its sources and allow users to submit data to increase the resolution of the data set. To achieve these objectives, we extracted data from 3137 literature records and 357 herbarium sheets. For each occurrence record, we curated information for 20 variables, which were arranged in a table with the Darwin Core (DwC) terms as column names. All data were subjected to technical validation before being included in the database. A total of 12,347 occurrence records were obtained for 362 species (195 invasive aliens and 167 naturalized aliens). The number of collected occurrence records was much higher for the invasive aliens (73.7%) than for the naturalized alien species (26.3%). Our data set will supplement the GBIF data by 60.39%, and occurrence records will be added for 64 invasive and naturalized alien plant species. The data set, as a part of the larger database of the Indian Alien Flora Information (ILORA) database, is made available without any restrictions on use as long as this data paper is properly cited. We have also made provisions for users to submit occurrence-related data following a data standard. The users are encouraged to cite the original reference when using a specific data record. The data set is expected to assist a wide range of stakeholders involved in India's scientific research, policy formulation, and decision-making related to IAPSs.

Reproduction phenology of a high conservation priority cracid – the Bare‑faced Curassow (Crax fasciolata; Aves, Galliformes, Cracidae)

Knowledge of the reproductive biology of Bare-faced Curassows (BFC) from their natural habitats is very limited. Our study covers a two-and-a-half year breeding phenology on BFC in the northern Pantanal (Mato Grosso State, Brazil) with the main objective of collecting information on reproduction biology to contribute to future conservation management strategies of this cracid, which received a recent status of "High Conservation Priority". The study was conducted at the SESC Pantanal, Baía das Pedras, Mato Grosso, Brazil (16°29′55″S, 56°24′46″W), a private protected area of approximately 4,200 ha. Between July 2015 and December 2017, 37 sampling locations were monitored with camera traps placed in a regular grid with a spacing of 1 km. Offspring were detected at least once at 8 locations, namely, in March, April, and May 2016 and in June, July, October, and November 2017, always together with parent(s). Territorial overlap between different family groups was detected. The camera trap dataset was supplemented by data from Citizen Science Projects (i.e., eBird) and the Global Biodiversity Information Facility (GBIF). Based on feather developmental stages and body size, offspring were classified into different age classes. Age determination indicates that breeding occurs year-round in the northern Pantanal region, supported by eBird and GBIF data. The use of a grid-based design for future camera-trapping studies of BFCs is strongly recommended. Our study is of biological relevance for conservation management projects since data were collected in an area with low anthropogenic disturbance and intact ecosystem services.

The “Our Planet Reviewed” Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys

Six years after the expedition “Our Planet Reviewed – French Guiana 2015” in the Mitaraka massif, we present a synthesis of the taxonomic and faunistic results obtained from the analysis of the 108 published articles and the 10 600 observations that were databased and disseminated. In this relatively short period of time, 218 new species for French Guiana including 127 species new to science, and also four genera new to science have been published based on the expedition material. These are primarily insects, notably Coleoptera (98), Diptera (60), Lepidoptera (16) and Orthoptera (17). For the 2015 to 2020 period, approximately 13% of the descriptions of new animal species in French Guiana use material from the expedition. The 3100 species identified in Mitaraka represent nearly 15% of the 21 042 animal species known from French Guiana. The observation data from the expedition that are open access via the Global Biodiversity Information Facility (GBIF) portal render the Mitaraka massif the area with the greatest documented and disclosed entomological richness on the Guiana Shield. Feedback has enabled us to draw up some recommendations for inventories aimed at taxonomic discovery: assign a person per taxonomic group to mobilize, coordinate, and motivate the taxonomic experts involved; deploy a variety of collecting strategies, from interception and attractive traps to active search; invest heavily in the sample processing phase; get a journal editor engaged to dedicate issues to thematic publications on the survey; and disclose the survey data through GBIF data sets and/or datapapers.

GBIF Data Processing and Validation

GBIF (Global Biodiversity Information Facility) is the largest data aggregator of biological occurrences in the world. GBIF was officially established in 2001 and has since aggregated 1.8 billion occurrence records from almost 2000 publishers. GBIF relies heavily on Darwin Core (DwC) for organising the data it receives. GBIF Data Processing Pipelines Every single occurrence record that gets published to GBIF goes through a series of three processing steps until it becomes available on GBIF.org. source downloading parsing into verbatim occurrences interpreting verbatim values source downloading parsing into verbatim occurrences interpreting verbatim values Once all records are available in the standard verbatim form, they go through a set of interpretations. In 2018, GBIF processing underwent a significant rewrite in order to improve speed and maintainablility. One of the main goals of this rewrite was to improve the consistency between GBIF's processing and that of the Living Atlases. In connection with this, GBIF's current data validator fell out of sync with GBIF pipelines processing. New GBIF Data Validator The current GBIF data validator is a service that allows anyone with a GBIF-relevant dataset to receive a report on the syntactical correctness and the validity of the content contained within the dataset. By submitting a dataset to the validator, users can go through the validation and interpretation procedures usually associated with publishing in GBIF and quickly determine potential issues in data, without having to publish it. GBIF is planning to rework the current validator because the current validator does not exactly match current GBIF pipelines processing. Planned Changes The new validator will match the processing of the GBIF pipelines project. Validations will be saved and show up on user pages similar to the way downloads and derived datasets appear now (no more bookmarking validations!) A downloadable report of issues found will be produced. Validations will be saved and show up on user pages similar to the way downloads and derived datasets appear now (no more bookmarking validations!) A downloadable report of issues found will be produced. Suggested Changes/Ideas One of the main guiding philosophies for the new validator user interface will be avoiding information overload. The current validator is often quite verbose in its feedback, highlighting data issues that may or may not be fixable or particularly important. The new validator will: generate a map of record geolocations; give users issues by order of importance; give "What", "Where", "When" flags priority; give some possible solutions or suggested fixes for flagged records. generate a map of record geolocations; give users issues by order of importance; give "What", "Where", "When" flags priority; give some possible solutions or suggested fixes for flagged records. We see the hosted portal environment as a way to quickly implement a pre-publication validation environment that is interactive and visual. Potential New Data Quality Flags The GBIF team has been compiling a list of new data quality flags. Not all of the suggested flags are easy to implement, so GBIF cannot promise the flags will get implemented, even if they are a great idea. The advantage of the new processing pipelines is that almost any new data quality flag or processing step in pipelines will be available for the data validator. Easy new potential flags: country centroid flag: Country/province centroids are a known data quality problem. any zero coordinate flag: Sometimes publishers leave either the latitude or longitude field as zero when it should have been left blank or NULL. default coordinate uncertainty in meters flag: Sometimes a default value or code is used for dwc:coordinateUncertaintyInMeters, which might indicate that it is incorrect. This is especially the case for values 301, 3036, 999, 9999. no higher taxonomy flag: Often publishers will leave out the higher taxonomy of a record. This can cause problems for matching to the GBIF backbone taxonomy.. null coordinate uncertainty in meters flag: There has been some discussion that GBIF should encourage publishers more to fill in dwc:coordinateUncertaintyInMeters. This is because every record, even ones taken from a Global Positioning System (GPS) reading, have an associated dwc:coordinateUncertaintyInMeters country centroid flag: Country/province centroids are a known data quality problem. any zero coordinate flag: Sometimes publishers leave either the latitude or longitude field as zero when it should have been left blank or NULL. default coordinate uncertainty in meters flag: Sometimes a default value or code is used for dwc:coordinateUncertaintyInMeters, which might indicate that it is incorrect. This is especially the case for values 301, 3036, 999, 9999. no higher taxonomy flag: Often publishers will leave out the higher taxonomy of a record. This can cause problems for matching to the GBIF backbone taxonomy.. null coordinate uncertainty in meters flag: There has been some discussion that GBIF should encourage publishers more to fill in dwc:coordinateUncertaintyInMeters. This is because every record, even ones taken from a Global Positioning System (GPS) reading, have an associated dwc:coordinateUncertaintyInMeters It is also nice when a data quality flag has an escape hatch, such that a data publisher can get rid of false positives or remove a flag through filling in a value. Batch-type validations that are doable for pipelines, but probably not in the validator include: outlier: Outliers are a known data quality problem. There are generally two types of outliers: environmental outliers and distance outliers. Currently GBIF does not flag either type of outlier. record is sensitive species: A sensitive species would be a record where the species is considered vulnerable in some way. Usually this is due to poaching threat or the species is only found in one area. gridded dataset: Rasterized or gridded datasets are common on GBIF. These are datasets where location information is pinned to a low-resolution grid. This is already available with an experimental API (Application Programming Interface). outlier: Outliers are a known data quality problem. There are generally two types of outliers: environmental outliers and distance outliers. Currently GBIF does not flag either type of outlier. record is sensitive species: A sensitive species would be a record where the species is considered vulnerable in some way. Usually this is due to poaching threat or the species is only found in one area. gridded dataset: Rasterized or gridded datasets are common on GBIF. These are datasets where location information is pinned to a low-resolution grid. This is already available with an experimental API (Application Programming Interface). Conclusion Data quality and data processing are moving targets. Variable source data will always be an issue when aggregating large amounts of data. With GBIF's new processing architecture, we hope that new features and data quality flags can be added more easily. Time and staffing resources are always in short supply, so we plan to prioritise the feedback we give to publishers, in order for them to work on correcting the most important and fixable issues. With new GBIF projects like the vocabulary server, we also hope that GBIF data processing can have more community participation.

Implementing GBIF Pipelines in the Atlas of Living Australia: The first step towards alignment and further collaboration

The Global Biodiversity Information Facility (GBIF) and the Atlas of Living Australia (ALA) are two leading infrastructures serving the biodiversity community. In 2020, the ALA’s occurrence records management systems reached end of life after more than 10 years of operation, and the ALA embarked on a project to replace them. Significant overlap exists in the function of the ALA and GBIF data ingestion pipeline systems. Instead of the ALA developing new systems from scratch, we initiated a project to better align the two infrastructures. The collaboration brings benefits such as the improved reuse of modules and an overall reduction in development and operation costs. The ALA recently replaced its occurrence ingestion system with GBIF pipelines infrastructure and shared code. This is the first milestone of the broader ALA’s Core Infrastructure Project and some of the benefits from it are a more reliable, performant and scalable system, proven by the ability to ingest more and larger datasets while at the same time reducing infrastructure operational costs by more than 40% compared to the previous system. The new system is a key building block for an improved ingestion framework that is being developed within the ALA. The collaboration between the ALA and GBIF development teams will result in more consistent outputs from their respective processing pipelines. It will also allow the broader collective expertise of both infrastructure communities to inform future development and direction. The ALA’s adoption of GBIF pipelines will pave the way for the Living Atlases community to adopt GBIF systems and also contribute to them. In this talk we will introduce the project, share insights on how both the teams from the GBIF and the ALA worked together and finally we will delve into details about the technical implementation and benefits.

Virtual Herbarium ALTB: collection of vascular plants of the Altai Mountain Country.

BackgroundThe herbarium of the South-Siberian Botanical Garden of Altai State University (ALTB) houses the largest collection of plants from the Altai Mountain Country (AMC), an area that extends across Russia, Kazakhstan, Mongolia and China. The collection of ALTB includes more than 450,00 specimens, making it the seventh largest in Russia and the fourth largest amongst Russian university herbaria. Altai State University (ASU), the home of ALTB, is one of the most important centres of academic education and research in Siberia and the Russian Far East. It is a sociocultural centre that provides a distinguished learning environment for undergraduate and graduate students in many scholarly and professional fields, meeting the needs of today's knowledge-based post-industrial society and contributing to regional development. It actively promotes international cooperation and strategic collaboration amongst countries of the AMC in the fields of science, education and culture. In particular, the activities of the South-Siberian Botanical Garden include: development of measures to protect rare and endangered plant species, research on the flora and vegetation of the AMC, preparation and publication of a multi-volume work "Flora Altaica", monographic study of individual plant groups, conducting laboratory classes, summer practicals and special courses. The main purpose of this article is to attract the attention of the scientific community to the botanical research of transboundary territory of the Altai Mountain Country (Russia, Kazakhstan, China and Mongolia) and to the future development of digital plant collections in partnership with Global Biodiversity Information Facility (GBIF).New informationThe Virtual Herbarium ALTB (Russian interface - altb.asu.ru) is the largest digital collection of plants from the transboundary territory of the Altai Mountain Country and the main source of primary material for the "Flora Altaica" project (http://altaiflora.asu.ru/en/). Since 2017, when Altai State University became a GBIF data publisher, data from the Virtual Herbarium ALTB has been exported to the dataset "Virtual Herbarium ALTB (South-Siberian Botanical Garden)" in GBIF. Currently, it includes images and data from 22,466 vascular plants, of which 67% have geographic coordinates (accessed on 30.03.2021). Most of the specimens have been collected since 1977, with the most intensive collecting years being 1995–2008. In 2019, the label-data table of the Virtual Herbarium ALTB was modified to bring it into conformity with the Darwin Core specification (http://altb.asu.ru/). This effectively solved the major impediment to sharing plant diversity data from the AMC and adjacent regions in a multilingual environment.

Measuring the impact of biodiversity datasets: data reuse, citations and altmetrics

Despite growing evidence of open biodiversity data reuse by scientists, information about how data is reused and cited is rarely openly accessible from research data repositories. This study explores data citation and reuse practices in biodiversity by using openly available metadata for 43,802 datasets indexed in the Global Biodiversity Information Facility (GBIF) and content analyses of articles citing GBIF data. Results from quantitative and content analyses suggest that even though the number of studies making use of openly available biodiversity data has been increasing steadily, best practice for data citation is not yet common. It is encouraging, however, that an increasing number of recent articles (16 out of 23 in 2019) in biodiversity cite datasets in a standard way. A content analysis of a random sample of unique citing articles (n = 100) found various types of background (n = 18) and foreground (n = 81) reuse cases for GBIF data, ranging from combining with other data sources to create species distribution modelling to software testing. This demonstrates some unique research opportunities created by open data. Among the citing articles, 27% mentioned the dataset in references and 13% in data access statements in addition to the methods section. Citation practice was inconsistent especially when a large number of subsets (12 ~ 50) were used. Even though many GBIF dataset records had altmetric scores, most posts only mentioned the articles linked to those datasets. Among the altmetric mentions of datasets, blogs can be the most informative, even though rare, and most tweets and Facebook posts were for promotional purposes.

The Possibilities of GBIF Data Use in Ecological Research

Abstract—The Global Biodiversity Information Facility (GBIF) is the largest source of open scientific data on the biodiversity of Russian: over 4.3 million species occurrences became available through GBIF as of May 2020; the most abundant among them are data on vascular plant occurrence (over 1.9 million) and bird occurrence (over 900 thousand). The representativeness of data for other taxonomic groups and most of the regions of the country still remains low, although the amount of available information continues to grow owing to the mobilization of data from scientific collections and personal archives of researchers, as well as from citizen science projects, iNaturalist, and eBird. Data available through the GBIF portal are of great interest to ecologists who work with methods for modeling the spatial distribution of species. However, the existing data gaps for the Russian territory limit the possibilities of their reuse, and it is necessary to address additional sources of information to achieve adequate results.

To clean or not to clean: Cleaning open‐source data improves extinction risk assessments for threatened plant species

AbstractPlants are under‐represented in conservation efforts, with only 9% of described species published on the IUCN Red List. Biodiversity aggregators including the Global Biodiversity Information Facility (GBIF) and the more recent Botanical Information and Ecology Network (BIEN) contain a wealth of potentially useful occurrence data. We investigate the influence of these data in accelerating plant extinction risk assessments for 225 endemic, near‐endemic, and socioeconomic Bolivian plant species. Geo‐referenced herbarium voucher specimens verified by taxonomic experts comprised our control data set. Open‐source data for 77 species was subjected to a two‐stage cleaning protocol (using an automated R package followed by a manual clean) and threat categories were computed based on extent of occurrence thresholds. Accuracy was the highest using cleaned GBIF data (76%) and uncleaned BIEN data (79%). Sensitivity was the highest for cleaned GBIF (73%) and BIEN (80%) data suggesting our cleaning protocol was essential to maximize sensitivity rates. Comparisons between the control, GBIF and BIEN data sets revealed a paucity of occurrence data for 148 species (66%), 72% of which qualified for a threatened category. Balancing data quantity and accuracy must be considered when using open‐source data. Filling data gaps for threatened species is a conservation priority to improve the coverage of threatened species within biodiversity aggregators.