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This work identifies spatial–temporal patterns of marine species biodiversity in the Norwegian, Greenland and Barents Seas and provides specific information in Norway for Environmental Impact Assessments and Statements about area‐based indices for biodiversity. The opening of the Norwegian Extended Continental Shelf for deep‐sea mining is a currently relevant topic for environmental management, as strategies to minimize mining impacts and delimit key zones for ecological preservation have been widely advised. A quality control procedure covering temporal and spatial scales on open‐source biodiversity data was applied, including the compilation of marine species from the archives of the Norwegian North‐Atlantic Expedition 1876–1878. Here, we present biodiversity patterns for 10,505,496 marine occurrences from 1876 to 2025 (149 years). Data occurrences were classified into two main datasets (shallow, < 500 m and deep ≥ 500 m) and two sub‐datasets for each (planktonic and benthic). 97% of the total were classified in the first main and 3% in the second main. On map view and out of 122,955 grid cells, 32,274 and 15,528 encompass data from the shallow and deep datasets, respectively, with different degrees of coverage inside; most frequently, grid cells with 1 to 10 occurrences. Data is mainly planktonic (20,098 grid cells for shallow‐planktonic and 3127 grid cells for deep‐planktonic). Peaks of species richness occur from southern to northern latitudes, even with evidently reduced values for species occurrences and abundances at certain latitudes. We conclude that knowledge gaps of benthic biodiversity in the Norwegian deep‐sea mining opening area are huge. The cumulative curve of species richness reveals that species identities, included in deep‐sea data, are not sufficient to quantify area‐based biodiversity indices in the species pool. Our findings are congruent with the need to contemplate data from deeper areas for decision‐making at different spatial–temporal windows, especially considering the granting of deep‐sea mining licenses. To identify spatial–temporal patterns of marine species biodiversity and to provide specific information in Norway for Environmental Impact Assessments and Statements, we present biodiversity patterns for marine occurrences from 1876 to 2025 (149 years). The knowledge gaps of benthic biodiversity in the Norwegian deep‐sea mining opening area are huge, and species identities included in deep‐sea data are not sufficient to quantify area‐based biodiversity indices in the species pool.

Because transient receptor potential ankyrin 1 (TRPA1) is involved in various physiological functions, TRPA1-targeting drugs have been energetically developed. Although TRPA1 is considered a multimodal receptor, the structural diversity of TRPA1 agonists is not fully elucidated. We hypothesized that collecting a wider variation of TRPA1–compound interaction data would aid the understanding of its complex mechanism and aimed to challenge such data collection using an “image-based TRPA1 assay system combined with an in silico chemical space clustering concept.” Our library was clustered with 27 physicochemical molecular descriptors in silico, and structurally diverse compounds from each cluster were selected for a detailed kinetic assay to investigate variations of agonist structural rules. Through two sets of assays evaluating various compounds in parallel with validating effects of the previously established structural rules, we discovered that different chemical groups contribute to agonist activity, indicating that there are multiple agonist design concepts. A novel core structure for a TRPA1 agonist has been also proposed. Our new approach, “collection of TRPA1 activity data on compounds with physicochemical diversity,” will not only facilitate the understanding of the structural diversity of TRPA1 agonists but also contribute to the development of a new type of TRPA1-targeting drug.

1. Political commitment and policy instruments to halt biodiversity loss require robust data and a diverse indicator set to monitor and report on biodiversity trends. Gaps in data availability and narrow-based indicator sets are significant information barriers to fulfilling these needs. 2. In this paper, the reporting requirements of seven global or European biodiversity policy instruments were reviewed using the list of Essential Biodiversity Variables (EBVs) as an analytical framework. The reporting requirements for the most comprehensive policy instrument, the United Nation's Strategic Plan for Biodiversity 2011-2020, were compared with the indicator set actually used for its reporting, to identify current information gaps. To explore the extent to which identified gaps could be bridged, the potential contribution of data mobilization, modelling and further processing of existing data was assessed. 3. The information gaps identified demonstrate that decision-makers are currently constrained by the lack of data and indicators on changes in the EBV classes Genetic Composition and, to a lesser extent, Species Populations for which data is most often available. Furthermore, the results show that even when there is a requirement for specific information for reporting, the indicators used may not be able to provide all the information, for example current Convention of Biological Diversity indicators provide relatively little information on changes in the Ecosystem Function and Ecosystem Structure classes. This gap could be partly closed by using existing indicators as proxies, whereas additional indicators may be computed based on available data (e.g. for EBVs in the Ecosystem Structure class). However, for the EBV class Genetic Composition, no immediate improvement based on proxies or existing data seems possible. 4. Synthesis and applications. Using Essential Biodiversity Variables (EBVs) as a tool, theorydriven comparisons could be made between the biodiversity information gaps in reporting and indicator sets. Analytical properties, such as an identification of which data and indicator (s) are relevant per EBV, will need to be addressed before EBVs can actually become operational and facilitate the integration of data flows for monitoring and reporting. In the meantime, a first analysis shows that existing indicators and available data offer considerable potential for bridging the identified information gaps.

Citizen science is on the rise, with growing numbers of initiatives, participants and increasing interest from the broader scientific community. iNaturalist is an example of a successful citizen science platform that enables users to opportunistically capture and share biodiversity observations. Understanding how data from such opportunistic citizen science platforms compare with and complement data from structured surveys will improve their use in future biodiversity research. We compared the opportunistic fish photographs from iNaturalist to those obtained from structured surveys at eight study reefs in Sydney, Australia over twelve years. iNaturalist recorded 1.2 to 5.5 times more fish species than structured surveys resulting in significantly greater annual species richness at half of the reefs, with the remainder showing no significant difference. iNaturalist likely recorded more species due to having simple methods, which allowed for broad participation with substantially more iNaturalist observation events (e.g., dives) than structured surveys over the same period. These results demonstrate the value of opportunistic citizen science platforms for documenting fish species richness, particularly where access and use of the marine environment is common and communities have the time and resources for expensive recreational activities (i.e., underwater photography). The datasets also recorded different species composition with iNaturalist recording many rare, less abundant, or cryptic species while the structured surveys captured many common and abundant species. These results suggest that integrating data from both opportunistic and structured data sources is likely to have the best outcome for future biodiversity monitoring and conservation activities.

Ecologists and evolutionary biologists are increasingly using big-data approaches to tackle questions at large spatial, taxonomic, and temporal scales. However, despite recent efforts to gather two centuries of biodiversity inventories into comprehensive databases, many crucial research questions remain unanswered. Here, we update the concept of knowledge shortfalls and review the tradeoffs between generality and uncertainty. We present seven key shortfalls of current biodiversity data. Four previously proposed shortfalls pinpoint knowledge gaps for species taxonomy (Linnean), distribution (Wallacean), abundance (Prestonian), and evolutionary patterns (Darwinian). We also redefine the Hutchinsonian shortfall to apply to the abiotic tolerances of species and propose new shortfalls relating to limited knowledge of species traits (Raunkiæran) and biotic interactions (Eltonian). We conclude with a general framework for the combined impacts and consequences of shortfalls of large-scale biodiversity knowledge for evolutionary and ecological research and consider ways of overcoming the seven shortfalls and dealing with the uncertainty they generate.

Because of a range of constraints, the availability of biodiversity-related information varies considerably over space, time, taxa, and types of data, thereby causing gaps in knowledge. Despite growing awareness of this issue among scientists, it is still poorly known how—and whether— scientific efforts have contributed to overcoming these information gaps. Focusing on spatial gaps in global biodiversity data, we show that the accumulation rates of nonbird species occurrence records stored in the Global Biodiversity Information Facility have not improved—and have even potentially declined—over the past three decades in data-poor, often biodiversity-rich regions. Meanwhile, one citizen-science project, eBird, has been making a considerable contribution to the collection and sharing of bird records, even in the data-poorest countries, and is accelerating the accumulation of bird records globally. We discuss the potentials and limitations of citizen-science projects for tackling gaps in biodiversity information, particularly from the perspective of biodiversity conservation.

New developments in the funding requirements of biodiversity science as well as rapidly developing information technology warrant a sharper focus on the way in which biodiversity data are managed. We propose that an opportunity presents itself to develop a specific set of informatics skills among a new class of data analysts in the biodiversity science community. Our consideration of capacity development specifically emphasises the need for conceptual rigour, compliance with technical data standards and the culture of data publication or data sharing.

Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.

Large-scale biodiversity monitoring remains a challenge in science and policy. ‘Biodiversity Observation Networks’ provide an integrated infrastructure for monitoring biodiversity through timely discovery, access, and re-use of data, but their establishment relies on an in-depth understanding of existing monitoring effort. We performed a scoping review and network analysis to assess the scope of available data on amphibians and reptiles in the UK and catalogue the mobilisation of information across the data landscape, thereby highlighting existing gaps. The monitoring portfolio has grown rapidly in recent decades, with over three times as many data sources than there are amphibian and reptile species in the UK now available. We identified 45 active sources of ‘FAIR’ (‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’) data. The taxonomic, geographic and temporal coverage of datasets appears largely uneven and no single source is currently suitable for producing robust multispecies assessments on large scales. A dynamic and patchy exchange of data occurs between different recording projects, recording communities and digital data platforms. The National Biodiversity Network Atlas is a highly connected source but the scope of its data (re-)use is potentially limited by insufficient accompanying metadata. The emerging complexity and fragmented nature of this dynamic data landscape is likely to grow without a concerted effort to integrate existing activities. The factors driving this complexity extend beyond the UK and to other facets of biodiversity. We recommend integration and greater stakeholder collaboration behind a coordinated infrastructure for data collection, storage and analysis, capable of delivering comprehensive assessments for large-scale biodiversity monitoring.

Climate change is altering plant reproductive phenology; however, a scarcity of long‐term, systematic monitoring hinders our ability to quantify and predict these responses in many parts of the world. We addressed this gap by demonstrating how data integration can be used to produce a synthesised record of reproductive phenology observations (flowering and fruiting) that spans longer time periods, larger spatial scales, and includes more species than any single source alone. Using Australian subtropical rainforest trees as a case study, we integrated reproductive phenology observations from both common data sources – published datasets, herbarium specimens, and citizen science records – and previously untapped expert botanical knowledge, including private photographic collections, field notes, and seed collections. Data integration yielded 110 657 records of flowers or fruits from 915 species (representing half of all subtropical rainforest tree species) spanning 255 years (1770–2025). We found that different data sources provided unique information across temporal, spatial and taxonomic dimensions. Herbarium specimens provided the longest taxonomic coverage, while citizen science contributed the most recent observations. Critically, 197 species (21.5%) were represented from only a single source, including 154 species represented solely by herbarium specimens and 46 species in expert botanist collections. While 46.6% of species had fewer than 50 observations, for many species, these represent the only available historical phenology data. This integrated dataset may be the only available resource for establishing pre‐industrial baselines for the reproductive phenology of Australian subtropical rainforest trees. This would not have been possible without the engagement and contributions of the local botanical community, which greatly expanded the research capacity beyond conventional data sources.