The EuropaBON Stakeholder Dashboard: A dynamic web application to map Europe’s biodiversity community

PLOS One | https://doi.org/10.1371/journal.pone.0329390 August 13, 2025 4 / 16 Data processing Following a clean-up of the registration form’s input fields – examples of which are provided in S2 File – we grouped individual member entries by their affiliated institution (see S3 File) to ensure consistent institutional representation on the dashboard. If an institution has sub-institutions, these are not explicitly grouped together in the data. For example, GBIF and GBIF Spain are listed as separate entities. This distinction is maintained for two reasons: first, their differing geographical locations – GBIF is based in Denmark, while GBIF Spain operates under CSIC in Spain; and second, the dashboard reflects the information exactly as provided by individual members during registration. Modifying these entries could have distorted the actual output. Individual members’ information is not disclosed in the dashboard. To provide the data for the dashboard, we structured the grouped table into a JSON-based API (Application Programming Interface). JSON (JavaScript Object Notation) is an open standard file format that uses human-readable text to store and transmit data objects consisting of name-value pairs (e.g., “id”: “1”, where the name is “id” and the value is “1”). APIs facilitate communication via designated API endpoints, which are specific URLs for sending requests and receiv- ing responses. The API endpoints are available in the S4 File. The API endpoint https://europabon.org/dashboard/

api/nodes summarises the nodes and also contains information about the connections between the individual nodes, represented by the edges in the network graph. Each node is assigned an ID and corresponding attributes (e.g., label, country, scope, group, etc.). The S5 File shows an example of the attributes for ID 1. The attributes contain information about occupational sector (academia, NGO, governmental organisation, private sector, citizen science, other), realm (terrestrial, freshwater, marine, cross-realm) or geographic region (Northern Europe, Western Europe, Eastern Europe, Southern Europe, and non-European regions). If a node is connected to several realms (e.g., Terres- trial, Freshwater), it is referred to as “cross-realm”. The nodes also contain values for the activity level and the data exchange interactions, i.e., the position in the data flow (biodiversity data user, biodiversity data provider, or both). If multiple members from the same institution indicated different roles in the biodiversity data flow (e.g., one as a data user, another as a provider), their responses were combined to classify the institution accordingly (e.g., as both user and provider). Further details on centrality, activity level, and position in the data flow are provided in the following section. Node properties: Centrality, activity level and position in the data flow We used two functions to calculate network centrality: 1) degree centrality and 2) page rank. Both functions were

calculated with the JavaScript library Cytoscape.js [14]. Degree centrality is a measure utilised in network analysis to evaluate the significance of a node within a network. It relies on the concept of counting connections or edges. Put simply, the degree centrality of a node corresponds to the number of edges connected to it. Nodes exhibiting high degree centrality possess numerous connections, indicating their greater centrality or influence within the network. A highly central network member therefore has many connections to other members. Degree centrality is frequently employed to pinpoint key nodes in a network, such as hubs or highly connected individuals in social networks, crit- ical infrastructure nodes in transportation or communication networks, or highly cited papers or authors in citation networks. Page rank, which is used to evaluate the importance or relevance of web pages on the internet, can be regarded as equivalent to Eigenvector centrality in the context of network analysis. It measures the influence of a node in a con- nected network. This measure is based not only on the node’s direct connections, but also on the centrality of its neigh- bours. Nodes with higher page rank values are those that are connected to other highly central nodes and vice versa. An impactful network member therefore describes a member that has many connections with highly centralised members. Activity levels were determined based on the number of EuropaBON project events (n = 17) that a network member participated

in. These events included workshops, conferences, webinars, interviews, surveys, social media campaigns, and review processes (please see the EuropaBON website, https://europabon.org, for more details). Members were informed about these events via the