http://lod.bco-dmo.org/id/dataset/1007572 eng; USA utf8 dataset Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact 2026-09-10 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) Environmental Data from the common garden experiments run as part of CviMVP project from 2023-2025 2026-09-10 publication 2026-09-10 revision BCO-DMO Linked Data URI 2026-09-10 creation http://lod.bco-dmo.org/id/dataset/1007572 Katie Lotterhos Northeastern University principalInvestigator Jessica Small The College of William & Mary principalInvestigator Ryan Carnegie Virginia Institute of Marine Science principalInvestigator Madeline Eppley Northeastern University principalInvestigator Camille Rumberger Northeastern University principalInvestigator Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. publisher Cite this dataset as: Rumberger, C., Lotterhos, K., Small, J., Eppley, M., Carnegie, R., Mongillo, N., Bajaj, K., Segnitz, Z. (2026) Environmental Data from the common garden experiments run as part of CviMVP project from 2023-2025. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2026-09-10 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/1007572 [access date] Dataset Description: &lt;p&gt;Eastern oyster, Crassostrea virginica, urn:lsid:marinespecies.org:taxname:140657&lt;/p&gt; Methods and Sampling: Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2043905 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2043905 onGoing Katie Lotterhos Northeastern University 781-581-7370 X304 Northeastern University Marine Sciences Center 430 Nahant Rd. Nahant MA 01908 USA k.lotterhos@northeastern.edu pointOfContact Jessica Small The College of William & Mary jamoss@vims.edu pointOfContact Ryan Carnegie Virginia Institute of Marine Science carnegie@vims.edu pointOfContact Madeline Eppley Northeastern University eppley.m@northeastern.edu pointOfContact Camille Rumberger Northeastern University rumberger.c@northeastern.edu pointOfContact asNeeded Dataset Version: 1 Unknown otherRestrictions otherRestrictions Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use. CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP) https://osprey.bco-dmo.org/project/876610 CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP) <p><strong>NSF Award Abstract:</strong><br /> Environmental change can be rapid and involve multiple aspects of the environment changing at the same time, such as warming and increased disease pressure. Rapid environmental change threatens the productivity of aquaculture and crops on which humans depend. Predicting organisms' vulnerabilities to rapid and multifactor environmental change, however, is a major scientific challenge. A hurdle to addressing this challenge arises from the complex and non-intuitive ways that organisms adapt, through changes at the level of the DNA sequence, to many environmental stresses at the same time. Thus, there is a need for new approaches to understand and predict adaptation in multivariate environments. To address this need, this project integrates research and education with a Model Validation Program (MVP). The research is developing and evaluating Machine Learning Algorithms (MLAs) for understanding and predicting adaptation of organisms to multivariate environments from their DNA sequences. To evaluate MLAs, this research combines both data simulation and an empirical test in the field with the Eastern Oyster, which provide important ecosystem services and support a multi-million dollar industry. For oysters, this research is studying how temperature, disease pressure, and salinity interact with evolutionary history to determine fitness in the field. This research advances efforts toward addressing the major scientific challenge of predicting adaptation in complex environments by integrating concepts across the frontiers of marine, evolutionary, and statistical sciences in a new way. Machine learning and model validation are not traditionally taught in the marine and environmental sciences, but are becoming increasingly relevant to these fields. As part of a broader education program, this research is developing MVP Learning Modules for high school students and undergraduates, which help students build the foundational knowledge they need to critically evaluate and apply models. Modules are being disseminated to hundreds of students in the greater Boston area and are being made available online for widespread use. The MVP mentoring program is training graduate students, undergraduates, and high school students in marine evolutionary ecology, statistical genomics, and machine learning. This research addresses a pressing societal need to more informatively match genotypes to environments for restoration, farming, and assisted gene flow efforts. Results are being disseminated to stakeholders in the oyster industry.</p> <p>The goal of this research is to evaluate if MLAs, which can model non-linearities, can be used to understand and predict adaptation to multivariate environments under a wide range of scenarios. In Objective 1, the Principal Investigator (PI) is creating simulated datasets with different aspects of realism, and using them to evaluate and refine the MLAs. This novel set of simulations is studying genome evolution under high gene flow in complex, multivariate environments. In Objective 2, the PI is building on their expertise with the Eastern oyster to evaluate the MLAs in a field setting. The PI is first developing a comprehensive seascape genomic dataset and using it to train MLAs to predict an individual's multivariate environment based on a single nucleotide polymorphism genotype. Then, the PI is testing if the MLA prediction can predict the fitness of different genotypes from across the species range when raised in common garden field conditions. In Objective 3, the PI is integrating research and education by using the data obtained from Objs. 1 and 2 to develop a series of original "MVP Learning Modules" with interactive web apps for persons at different levels of understanding, using the relatable example of an oyster restoration project. This research lays the foundation for future studies by producing datasets that could become classical examples for developing and benchmarking innovative modeling approaches.</p> <p>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.</p> Model Validation Program largerWorkCitation project eng; USA oceans 2026-09-10 East coast of North America 0 BCO-DMO catalogue of parameters from Environmental Data from the common garden experiments run as part of CviMVP project from 2023-2025 Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact https://osprey.bco-dmo.org/dataset/1007572/data/download download onLine dataset Curation Actions Performed on Data * None - the data submitter is revising the submission. Curation Actions Performed on Metadata * None - the data submitter is revising the submission. Known Issues [Potentially Impacting Reuse] * N/A Specified by BCO-DMO Data Managers asNeeded 7.x-1.1 Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact