Crossing structures (e.g. culverts, wildlife underpasses and overpasses, canopy bridges) have been installed to provide safe passage for wildlife on linear infrastructure (e.g. roads, railroads, pipelines, canals), increasing ecological connectivity and reducing the risks of animal-vehicle accidents. Despite the increasing number of case studies worldwide evaluating the use of crossing structures by wildlife, it is only by a comprehensive dataset that important questions concerning mitigation effectiveness can be answered. An open-access database on the use of crossing structures by wildlife on linear infrastructures, with large geographical coverage, high landscape heterogeneity, and a diverse set of structures would allow researchers to explore questions like “Which factors are associated with the use of crossing structures by wildlife?” and “What type and design of a structure is better for use according to species traits?”. WILDCROSSDATA is a data paper initiative from the Road and Railroad Ecology Research Group of the Federal University of Rio Grande do Sul (NERF/UFRGS - Brazil), the Brazilian Network of Transport Ecology Specialists (REET Brazil), and the Latin American and Caribbean Transport Working Group (LACTWG - IUCN). We are currently inviting researchers and consultants that have worked monitoring crossing structures in Latin America to join us and share their data as co-authors of this data paper. We will include records of wild, feral, domestic and exotic animals, as the last three influence the occurrence of wildlife. We have distributed a form (https://docs.google.com/forms/d/e/1FAIpQLSeGTrR1MWub3Kit03qXhcCht53cPkqmKVpQ1s1WV6fOS_iLOQ/viewform) to identify possible collaborators, their contact info, as well as to have a glimpse of the kind of data they have available. By now we have 126 potential contributors from 13 countries (Brazil, Mexico, Colombia, Argentina, Costa Rica, Panama, Peru, Chile, Uruguay, Paraguay, Guiana, Ecuador and French Guiana – in decrescent order). The datasets are predominantly for roads but also include other infrastructures such as channels, pipelines, powerlines, and railways. The data are mainly from systematic surveys but occasional records will also be included (60% and 40% of the dataset, respectively). The most common method used in systematic surveys of crossing structures was camera traps (60% of the dataset), followed by animal tracks (19%). Our next step will be to collect and synthesize these contributions in a standardized way using a set of spreadsheets for different types of data, with information from each record of crossing structure use. The final dataset will allow researchers to evaluate which type of structure should be recommended for each target species or group, fostering more efficient use of the limited financial resources for mitigation of fatalities and/or reduced connectivity on linear infrastructures.