Connecting the data landscape of long‐term ecological studies: The SPI‐Birds data hub
Authors: Culina Antica , Adriaensen Frank , Bailey Liam D. , Burgess Malcolm D. , Charmantier Anne , Cole Ella F. , Eeva Tapio , Matthysen Erik , Nater Chloé R. , Sheldon Ben C. , Sæther Bernt‐Erik , Vriend Stefan J. G. , Zajkova Zuzana , Adamík Peter , Aplin Lucy M. , Angulo Elena , Artemyev Alexandr , Barba Emilio , Barišić Sanja , Belda Eduardo , Bilgin Cemal Can , Bleu Josefa , Both Christiaan , Bouwhuis Sandra , Branston Claire J. , Broggi Juli , Burke Terry , Bushuev Andrey , Camacho Carlos , Campobello Daniela , Canal David , Cantarero Alejandro , Caro Samuel P. , Cauchoix Maxime , Chaine Alexis , Cichoń Mariusz , Ćiković Davor , Cusimano Camillo A. , Deimel Caroline , Dhondt André A. , Dingemanse Niels J. , Doligez Blandine , Dominoni Davide M. , Doutrelant Claire , Drobniak Szymon M. , Dubiec Anna , Eens Marcel , Einar Erikstad Kjell , Espín Silvia , Farine Damien R. , Figuerola Jordi , Kavak Gülbeyaz Pınar , Grégoire Arnaud , Hartley Ian R. , Hau Michaela , Hegyi Gergely , Hille Sabine , Hinde Camilla A. , Holtmann Benedikt , Ilyina Tatyana , Isaksson Caroline , Iserbyt Arne , Ivankina Elena , Kania Wojciech , Kempenaers Bart , Kerimov Anvar , Komdeur Jan , Korsten Peter , Král Miroslav , Krist Miloš , Lambrechts Marcel , Lara Carlos E. , Leivits Agu , Liker András , Lodjak Jaanis , Mägi Marko , Mainwaring Mark C. , Mänd Raivo , Massa Bruno , Massemin Sylvie , Martínez‐Padilla Jesús , Mazgajski Tomasz D. , Mennerat Adèle , Moreno Juan , Mouchet Alexia , Nakagawa Shinichi , Nilsson Jan‐Åke , Nilsson Johan F. , Cláudia Norte Ana , van Oers Kees , Orell Markku , Potti Jaime , Quinn John L. , Réale Denis , Kristin Reiertsen Tone , Rosivall Balázs , Russell Andrew F , Rytkönen Seppo , Sánchez‐Virosta Pablo , Santos Eduardo S. A. , Schroeder Julia , Senar Juan Carlos , Seress Gábor , Slagsvold Tore , Szulkin Marta , Teplitsky Céline , Tilgar Vallo , Tolstoguzov Andrey , Török János , Valcu Mihai , Vatka Emma , Verhulst Simon , Watson Hannah , Yuta Teru , Zamora‐Marín José M. , Visser Marcel E. 
Published: November 2020

The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long‐term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad‐scale global issues (e.g. climate change).

To solve data integration issues and enable a new scale of ecological and evolutionary research based on long‐term studies of birds, we have created the SPI‐Birds Network and Database (www.spibirds.org)—a large‐scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI‐Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2,000 cumulative years, and counting.

SPI‐Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community‐derived data and meta‐data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta‐data language).

The encouraging community involvement stems from SPI‐Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI‐Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community‐specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much‐needed large‐scale ecological data integration.