The recent fossil record provides some of our best available evidence about how species and ecosystems have adapted to large climate changes and the intensifying impact of human societies.  Collecting these data is laborious and expensive, yet these datasets are highly reusable, for many individual sites can be gathered together and reanalyzed to study biodiversity dynamics at local to global scales.  The Neotoma Paleoecology Database supports global-change research by providing an open, ethical, sustainable, high-quality, and community-curated repository for multiple kinds of paleoecological data. With sustained support from NSF, Neotoma has become one of the top international data resources for paleoecological data, widely used by global change scientists, biogeographers, paleoecologists, and other geoscientists as a trusted resource for their research, teaching, and outreach. Project outcomes are grouped into three areas: 1) Data growth and stewardship, 2) Enhancement of software services, and 3) Training and empowering the next generation of scientists.

1) Data Growth:  Over the last four years, data volumes increased by 30 to 45%, increasing from 7.3 to 10.5 million observations and from 39,329 to 49,745 datasets.  These datasets have an estimated replacement cost of over $1.4 billion dollars, and so are effectively irreplaceable.  Neotoma achieves high data quality via expert curation, working closely with community experts who serve as data contributors and data stewards. The most sustained growth has been in the data types with expert stewards directly supported by this award:  vertebrates, pollen, diatoms, ostracodes, and testate amoebae.  New data types directly supported by this award include isotopic data (an indicator of past vertebrate diet and behavior) and lead-210 data (an estimate of sediment age).  As Neotoma’s reputation has grown, other scientific groups have asked to join Neotoma as constituent databases and add their data.  New data types now being supported or in the process of being added include ancient environmental DNA, marine micropaleontological data, paleofire data, and speleothem (cave) records. Using one data system to support many different kinds of paleoenvironmental data helps streamline costs and enhance data sustainability while offering scientists a high-quality data resource that houses a wide variety of paleodata types

2) Enhanced Services:  A) Because the work involved in data upload and curation is the major rate-limiting step, a new system, called DataBUS, has been built to support large-volume uploads of data to Neotoma, with multiple automated validation checks.  DataBUS is now being tested with research groups.  The Neotoma website was fully redesigned and relaunched, with highly positive feedback from users.  New versions were released of the Application Programming Interfaces (2.0) and R package (neotoma2), which are used by scientists and developers building their own scientific workflows to access and use Neotoma data.  A usability and usefulness survey of Neotoma Explorer, an interactive map-based interface for finding and exploring Neotoma data, has both showcased the popularity of Explorer and identified areas where future improvements can be made.  Neotoma sustainability has been enhanced by moving the entire software stack to the Amazon Web Services Cloud, with support from the NSF Cloudbank program.  

3) Empowering Scientists:  The popularity of Neotoma is demonstrated by its high level of scientific usage.  Over 1,600 scientific papers have cited Neotoma or its constituent databases, with a combined H-factor of 139.  Neotoma data are routinely incorporated into scientific projects studying past ecological and environmental change at local to global scales.  Over the last four years, Neotoma stewards and developers have led dozens of workshops to train other scientists in how to use Neotoma services and join Neotoma as data contributors and stewards.  The enhanced data access provided by Neotoma is essential for early career scientists, for whom data access can be a critical barrier to scientific progress, and who need advanced skills in data science for successful careers in academia, government, and the private sector. 

Last Modified: 02/24/2025
Modified by: Robert K Booth