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Wikidata offers a centralized, accessible platform for working collaboratively to disambiguate entities, e.g., people associated with biodiversity collections, and to mobilize information about them. This shared information can be used to improve connections across institutions and individuals, to augment local data records, and to encourage expertise-sharing. Over the past year, the Paleo Data Working Group has organized several events to bring the Wikidata movement to paleontological collections. This talk will share key products and findings generated by these efforts, and note parallels to disciplines beyond paleontology. In the global collections community, momentum has been building to leverage linked data principles and facilitate data discovery using the Wikidata platform. An example of this is the increasing adoption of Wikidata specifically for the purpose of storing biographical information about people associated with collections, because doing so facilitates discovery about who is doing what work across multiple institutions, and with what impact (Groom et al. 2020, Güntsch et al. 2021). In March 2022, thirty participants gathered virtually for a participatory workshop, Using Wikidata to capture and share information about people in paleontology. This workshop was an introduction to finding, editing, and using data in Wikidata, focusing on people associated with paleontology collections (e.g., collectors, researchers, collections staff) as subjects. Together, workshop participants created or enhanced Wikidata records for around 100 individuals from a shared list, including a dozen female collectors previously known only by their husbands’ names, e.g., “Mrs. Paul E. Drez,” who we now know is Nancy Sue Drez. At the conclusion of the workshop, participants collaborated on an open-access document to share the findings. The goal of Guidelines for Using Wikidata to Mobilize Information about People in Collections: A Paleontology Perspective (see Bauer et al. 2022 for repository) is to further encourage uptake in the paleontological and collections communities. The first workshop was focused almost exclusively on the people connected to paleontological collections. A follow-up workshop in October 2022 entitled, Using Wikidata to capture and share information about paleontological collecting sites, significantly broadens this scope by exploring how Wikidata can be used to mobilize community knowledge about specimen collecting events, such as sites or expeditions. Paleontological collecting sites pose an information management challenge because they tend to be detail rich, attached to local but stable identifier schemes, and associated with specimens curated in multiple institutions. Participants in this workshop established a defined scope of information associated with a paleontological collection site–including flagging aspects of the data that may be problematic to share publicly–and subsequently were able to assess how these information needs might, or might not, fit into a linked data model on Wikidata. These findings are particularly relevant to the global collections community where we see a trend towards sharing specimen data from an event-based perspective (Schindel and Cook 2018, Robertson et al. 2019), as has long been the norm for paleontological specimens.

Paleontology collections face many similar challenges mobilizing taxonomic data as extant collections disciplines: from complex nomenclatural histories to incomplete taxonomic authorities to questionable specimen identifications. These challenges exist within the information management of an individual institution and are amplified at the level of data aggregation, where a minor misspelling of authorship or a historic name can completely erase the discoverability of a specimen. In addition to those challenges shared with extant taxonomy, fossil taxonomy must also address uncertainty in far more detail and at many levels. It may be impossible to assign a name into the traditional Linnaean hierarchy or to provide classification at higher ranks due to uncertainty in our understanding of how the specimen is related to other known taxa. Moreover, some branches of paleontology prefer not to use Linnaean ranks at all, making it even more difficult to map concepts and/or clade names to terms in the Linnaean-based Darwin Core standard (DwC, Darwin Core Task Group 2009). Incomplete preservation of fossil specimens also makes it difficult or impossible to identify many specimens to the extant gold standard rank of species. While these specimens are often recorded as precisely as possible (e.g., to a secondary Linnaean rank like subfamily) in local collection management systems, the data are obscured when shared with global aggregators since these ranks do not exist in the DwC framework. The paleontology collections community has been facilitating efforts to address these challenges for many years in an attempt to improve the quality of our data and the treatment of taxonomic information for fossil specimens by aggregators. Most recently, the Paleo Data Working Group has been focusing on the practical matters of digitizing, mobilizing, and promoting the use of specimen data from our collections, and further assessing possible community-sourced solutions that could increase the discoverability of our specimens from a taxonomic entry point. In this talk, we share our explorations into the past and current state of fossil taxonomy in the global landscape (including notable gaps) and its use in paleontology collections, and we also report on possible paths forward. Understanding some of the historical issues with fossil taxonomy in the global landscape has led to a better understanding of root differences between extant and fossil taxonomic data models. The Paleo Data Working Group (Krimmel et al. 2021) has reviewed fossil taxonomic data as it is published through aggregators like the Global Biodiversity Information Facility (GBIF), and identified concrete issues in order to classify systematic pain points. We have discussed existing sources of taxonomic data and the agency of collections professionals to take an active role in working toward new solutions for enhancing and evolving these systems. Opportunities for intentional action have been identified across a spectrum of local to global efforts, and from individual capacity to collaboration across stakeholder groups, connecting with those who can more directly address a particular gap or combine expertise. On the individual scale, we are promoting the adoption of best practices for recording taxonomic data in collections information systems and subsequent mapping to DwC. Through coordination with stakeholder groups, we are hoping to enable paleontologists to provide taxonomic authority information to Catalogue of Life for certain fossil groups in new ways. Working within this space, we have tested new systems for collaborative taxonomic data curation and produced updated community data guidelines for use in paleontology collections, to improve the consistency and quality of mobilized data. Accurately representing extinct taxa in an extant world is a moving target, affected by constantly evolving data standards and technical capacity. For instance, GBIF is moving to an operational model where Catalogue of Life provides the taxonomic backbone, but fossil names are problematic in Catalogue of Life because they cannot always integrate into the single tree of life perspective. Seeking solutions that are inclusive of fossil taxonomy will enable our global corpus of biodiversity data to extend back into deep time and promote new opportunities for synergy and cooperation.

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.

Citizen science projects that use sensors (such as camera traps) to collect data can collect large-scale data without compromising information quality. However, project management challenges are increased when data collection is scaled up. Here, we provide an overview of our efforts to conduct a large-scale citizen science project using camera traps—North Carolina’s Candid Critters. We worked with 63 public libraries to distribute camera traps to volunteers in all 100 counties in North Carolina, USA. Candid Critters engaged 580 volunteers to deploy cameras at 4,295 locations across private and public lands, collecting 120,671 wildlife records and 2.2 million photographs. We provide eight key suggestions for overcoming challenges in study design, volunteer recruitment and management, equipment distribution, outreach, training, and data management. We found that citizen science was a successful and economical method for collecting large-scale wildlife records, and the use of sensors allowed for inspectable quality and streamlined acquisition. In three years, we collected roughly five times the number of verified mammal records than were previously available in North Carolina, and completed the work for less than the typical cost of collecting data with field assistants. The project also yielded many positive outcomes for adult and youth volunteers. Although citizen science presents many challenges, we hope that sharing our experiences will provide useful insight for those hoping to use sensors for citizen science over large scales.

Web APIs (Application Programming Interfaces) facilitate the exchange of resources (data) between two functionally independent entities across a common programmatic interface. In more general terms, Web APIs can connect almost anything to the world wide web. Unlike traditional software, APIs are not compiled, installed, or run. Instead, data are read (or consumed in API speak) through a web-based transaction, where a client makes a request and a server responds. Web APIs can be loosely grouped into two categories within the scope of biodiversity informatics, based on purpose. First, Product APIs deliver data products to end-users. Examples include the Global Biodiversity Information Facility (GBIF) and iNaturalist APIs. Designed and built to solve specific problems, web-based Service APIs are the second type and the focus of this presentation (referred to as Service APIs). Their primary function is to provide on-demand support to existing programmatic processes. Examples of this type include Elasticsearch Suggester API and geolocation, a service that delivers geographic locations from spatial input (latitude and longitude coordinates) (Pejic et al. 2010). Many challenges lie ahead for biodiversity informatics and the sharing of global biodiversity data (e.g., Blair et al. 2020). Service-driven, standardized web-based Service APIs that adhere to best practices within the scope of biodiversity informatics can provide the transformational change needed to address many of these issues. This presentation will highlight several critical areas of interest in the biodiversity data community, describing how Service APIs can address each individually. The main topics include: standardized vocabularies, interoperability of heterogeneous data sources and data quality assessment and remediation. standardized vocabularies, interoperability of heterogeneous data sources and data quality assessment and remediation. Fundamentally, the value of any innovative technical solution can be measured by the extent of community adoption. In the context of Service APIs, adoption takes two primary forms: financial and temporal investment in the construction of clients that utilize Service APIs and willingness of the community to integrate Service APIs into their own systems and workflows. financial and temporal investment in the construction of clients that utilize Service APIs and willingness of the community to integrate Service APIs into their own systems and workflows. To achieve this, Service APIs must be simple, easy to use, pragmatic, and designed with all major stakeholder groups in mind, including users, providers, aggregators, and architects (Anderson et al. 2020Anderson et al. 2020; this study). Unfortunately, many innovative and promising technical solutions have fallen short not because of an inability to solve problems (Verner et al. 2008), rather, they were difficult to use, built in isolation, and/or designed without effective communication with stakeholders. Fortunately, projects such as Darwin Core (Wieczorek et al. 2012), the Integrated Publishing Toolkit (Robertson et al. 2014), and Megadetector (Microsoft 2021) provide the blueprint for successful community adoption of a technological solution within the biodiversity community. The final section of this presentation will examine the often overlooked non-technical aspects of this technical endeavor. Within this context, specifically how following these models can broaden community engagement and bridge the knowledge gap between the major stakeholders, resulting in the successful implementation of Service APIs.

This paper reports on the development of a smartphone app designed to give drivers and managers in a charity organisation greater visibility of transport, donation bank and shop stock in time and space. Trials of the app with samples of drivers and shop managers across three counties in the UK showed that users' understanding of vehicle activity and how time was utilised in the business was enhanced. The app also informed their decision-making, aided some collaboration and helped in their understanding of donation bank and shop performance, with one region altering their collection schedules. The quality of 3G signal was an issue in certain areas which impeded performance and the rules by which the messaging platform should be used in such a tool need careful consideration.