http://lod.bco-dmo.org/id/dataset/1008652
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
2026-09-30
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Parent eastern oysters (Crassostrea virginica) from wild and hatchery populations along U.S. Atlantic and Gulf coasts (collected 2022), used as broodstock in a field experiment (2023–2025)
2026-09-30
publication
2026-09-30
revision
BCO-DMO Linked Data URI
2026-09-30
creation
http://lod.bco-dmo.org/id/dataset/1008652
Katie Lotterhos
Northeastern University
principalInvestigator
Madison Armstrong
Northeastern University
principalInvestigator
Ryan Carnegie
Virginia Institute of Marine Science
principalInvestigator
Madeline Eppley
Northeastern University
principalInvestigator
Camille Rumberger
Northeastern University
principalInvestigator
Jessica Small
The College of William & Mary
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: K. Eaton, A., Lotterhos, K., Rumberger, C., Eppley, M., Bajaj, K., Mongillo, N., Small, J., Carnegie, R., Segnitz, Z., Armstrong, M. (2026) Parent eastern oysters (Crassostrea virginica) from wild and hatchery populations along U.S. Atlantic and Gulf coasts (collected 2022), used as broodstock in a field experiment (2023–2025). Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2026-09-30 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/1008652 [access date]
Dataset Description: <p>Organism identifiers:<br />
Eastern oyster, Crassostrea virginica, urn:lsid:marinespecies.org:taxname:140657</p> Methods and Sampling: <p>Experimental Treatments</p>
<p>The monoculture treatments included six wild populations and two selectively bred populations (DEBY and LOLA) owned by the Aquaculture Genetics and Breeding Technology Center (ABC, Virginia Institute of Marine Science). Wild populations consisted of Maine, New Hampshire, Virginia, Florida, Louisiana, and Texas oysters. Sites will be referred to as acronyms in the remainder of this paper: ME, NH, VA, FL, LA and TX, respectively. Such locations span the native range of the eastern oyster, which is structured by two distinct genetic clusters (“Atlantic” and “Gulf”) separated by the Florida Peninsula (Puritz et al., 2022; Reeb and Avise, 1990). The LOLA selection line is considered local to Lewisetta and DEBY is considered local to the York River, for this study. Oysters for each monoculture were sourced September–November 2022 and conditioned at the Acuff Center for Aquaculture at VIMS until spawning in May 2023.</p>
<p>For each treatment, oysters were strip spawned and sperm from ten males per population were individually crossed with a pool of ten eggs from equally represented females to limit sperm competition (Levitan, 2004). Sperm from each male was then crossed with eggs from all females, producing new combinations and increasing genetic diversity in a polyculture treatment, “LARMIX”. The larvae were raised following standard hatchery (days 0–30) and nursery (days 30–77) methods. On day 77, a second polyculture treatment, “SEEDMIX”, was created by equally mixing seed from each monoculture to increase genetic diversity while maintaining original genotypes.<br />
<br />
Field Deployment</p>
<p>Seed from each treatment were split into six bags (approximately 1,200 per bag). Three replicate bags were deployed in bottom cages at east common garden site in August 2023 and assessed in November 2023 (time point 1), May 2024 (time point 2), November 2024 (time point 3), and May 2025 (final time point). At the first assessment, 40 oysters per bag were individually tagged. At each time point, tagged oysters were determined to be alive or dead. Note that not all tags were found at every assessment, some were missed due to debris on the oyster shell and others had become separated from the oyster in between assessments. For tagged oysters that could be identified, shell length (longest distance from the hinge to the bill) and width (longest distance perpendicular to the length) were measured. To reduce density–dependent effects, bags were thinned to 225 oysters at the second assessment (Marshall and Dunham, 2013).</p>
<p>Genetic data</p>
<p>Gill tissue was taken from each parent (<em>n</em> = 160, 83 dams and 77 sires) after spawning. At the second assessment, 267 tagged juvenile oysters were chosen at random for a nonlethal biopsy to collect gill tissue. All samples were stored in 95% ethanol at -80ºC. DNA was extracted using the Qiagen DNeasy Blood and Tissue Kit and shipped to Neogen Genomics in Lincoln, NE for genotyping on a 200K ThermoFisher Affymetrix Axiom SNP array derived from a 600K array assigned to the haplotig–masked <em>Crassostrea virginica</em> reference genome (Gómez-Chiarri et al., 2015; Guo et al., 2023; Modak et al., 2021; Puritz et al., 2024). The sequencing pipeline is outlined in Bajaj and Mongillo et al. (2026). Raw genotypes were processed and filtered in R v4.4.2 and a full, unimputed, SNP set was used for further analysis.</p>
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
Madison Armstrong
Northeastern University
m.armstrong@northeastern.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
Jessica Small
The College of William & Mary
jamoss@vims.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
Label_ind
Key_ind
clean_ID
Vial_Label
Sample_Filename
Pass_Fail
DQC
QC_call_rate
call_rate
QC_het_rate
het_rate
Average_call_rate_for_passing_samples
Key_box
Key_Vial
Tissue
Buffer
Extraction_plan_Key
PlateID_Key
ID_SiteDate
SiteDate_Key
Extraction_plan_Label
Sex
Shell_length_cm
Shell_width_cm
pops_Key
Alive
New_Growth
Sperm_Motility
theme
None, User defined
individual
sample description
file_name
No BCO-DMO term
sample type
sample identification
sex
length
width
mortality
growth
featureType
BCO-DMO Standard Parameters
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
-97.17
-69.53
28.1
44.02
2022-09-01
2025-05-01
East coast of North America
0
BCO-DMO catalogue of parameters from Parent eastern oysters (Crassostrea virginica) from wild and hatchery populations along U.S. Atlantic and Gulf coasts (collected 2022), used as broodstock in a field experiment (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
http://lod.bco-dmo.org/id/dataset-parameter/1009947.rdf
Name: Label_ind
Units: unitless
Description: <p>4 digit code for each individual oyster.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009948.rdf
Name: Key_ind
Units: unitless
Description: <p>Unique identifier key corresponding to each individual.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009949.rdf
Name: clean_ID
Units: unitless
Description: <p>Label corresponding to each individual with plate well_project label_plate_ number_individual label_individual key.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009950.rdf
Name: Vial_Label
Units: unitless
Description: <p>Label corresponding to each sample with project label_number identifier.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009951.rdf
Name: Sample_Filename
Units: unitless
Description: <p>Label corresponding to each individual with plate well_project label_plate_ number_individual label_individual key with additional information on plate number to match vcf file.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009952.rdf
Name: Pass_Fail
Units: unitless
Description: <p>Pass or Fail for the individual QC from the sequencing facility.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009953.rdf
Name: DQC
Units: unitless
Description: <p>DQC, or Dish QC, is a pre-genotyping quality control metric used to assess the quality of a hybridized Affymetrix SNP array before full genotype calling is performed.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009954.rdf
Name: QC_call_rate
Units: unitless
Description: <p>QC call rate is a sample-level quality control metric that measures the proportion of SNPs on the array that were successfully genotyped (i.e., assigned a genotype call) for a given sample on the Affymetrix SNP Array. A consistent benchmark across labs and studies and used to determine pass/fail.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009955.rdf
Name: call_rate
Units: unitless
Description: <p>This also measures the proportion of SNPs on the array that were successfully genotyped Affymetrix SNP Array, but is specific to the study.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009956.rdf
Name: QC_het_rate
Units: unitless
Description: <p>Heterozygosity rate measures the proportion of SNPs for a given sample that are called as heterozygous (AB) rather than homozygous (AA or BB) on the Affymetrix SNP Array.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009957.rdf
Name: het_rate
Units: unitless
Description: <p>Heterozygosity rate measures the proportion of SNPs for a given sample that are called as heterozygous (AB) rather than homozygous (AA or BB) on the Affymetrix SNP Array. This is specific to the study and used for later filtering steps.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009958.rdf
Name: Average_call_rate_for_passing_samples
Units: unitless
Description: <p>Average.call.rate.for.passing.samples is a batch-level (plate/run-level) QC metric rather than an individual sample metric. It summarizes the overall genotyping performance across all samples in a batch that passed individual QC filters.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009959.rdf
Name: Key_box
Units: unitless
Description: <p>Unique identifier key corresponding to each box the tissue samples were located in.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009960.rdf
Name: Key_Vial
Units: unitless
Description: <p>Unique identifier key corresponding to each vial of tissue sample.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009961.rdf
Name: Tissue
Units: unitless
Description: <p>Type of tissue taken for final tissue sample (gill or mantle).</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009962.rdf
Name: Buffer
Units: unitless
Description: <p>Preservative in the vial the final tissue sample was placed in.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009963.rdf
Name: Extraction_plan_Key
Units: unitless
Description: <p>Unqiue identifier for extraction sample.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009964.rdf
Name: PlateID_Key
Units: unitless
Description: <p>Unique identifier key corresponding to each plate of DNA.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009965.rdf
Name: ID_SiteDate
Units: unitless
Description: <p>Source population label.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009966.rdf
Name: SiteDate_Key
Units: NA
Description: <p>Unique identifier key corresponding to each source population.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009967.rdf
Name: Extraction_plan_Label
Units: unitless
Description: <p>Label corresponding to each extraction sample with plate well_project label_plate_ number_individual label_individual key.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009968.rdf
Name: Sex
Units: unitless
Description: <p>Sex of the oyster based on visual inspection of gametes for ripe oysters. If the eggs were visible in a clear beaker held up to the light, we labeled it as a female. If the beaker was cloudy we labeled it as a male.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009969.rdf
Name: Shell_length_cm
Units: centimeters (cm)
Description: <p>Length of each oyster (the longest distance from the hinge to the bill).</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009970.rdf
Name: Shell_width_cm
Units: centimeters (cm)
Description: <p>Width of each oyster (the longest distance perpendicular to the length measurement).</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009971.rdf
Name: pops_Key
Units: unitless
Description: <p>Unique identifier key corresponding to each source population.</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009972.rdf
Name: Alive
Units: unitless
Description: <p>Information on if the individual oyster was alive or dead (binary, TRUE or FALSE).</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009973.rdf
Name: New_Growth
Units: unitless
Description: <p>Information if shells had evidence of new growth for assessment of health (binary assignment, Yes or No).</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009974.rdf
Name: Sperm_Motility
Units: unitless
Description: <p>Information on how mobile the sperm of each individual was (range with 4 is fastest, 1 is slowest).</p>
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/1008652/data/download
download
onLine
dataset
<p>Experimental Treatments</p>
<p>The monoculture treatments included six wild populations and two selectively bred populations (DEBY and LOLA) owned by the Aquaculture Genetics and Breeding Technology Center (ABC, Virginia Institute of Marine Science). Wild populations consisted of Maine, New Hampshire, Virginia, Florida, Louisiana, and Texas oysters. Sites will be referred to as acronyms in the remainder of this paper: ME, NH, VA, FL, LA and TX, respectively. Such locations span the native range of the eastern oyster, which is structured by two distinct genetic clusters (“Atlantic” and “Gulf”) separated by the Florida Peninsula (Puritz et al., 2022; Reeb and Avise, 1990). The LOLA selection line is considered local to Lewisetta and DEBY is considered local to the York River, for this study. Oysters for each monoculture were sourced September–November 2022 and conditioned at the Acuff Center for Aquaculture at VIMS until spawning in May 2023.</p>
<p>For each treatment, oysters were strip spawned and sperm from ten males per population were individually crossed with a pool of ten eggs from equally represented females to limit sperm competition (Levitan, 2004). Sperm from each male was then crossed with eggs from all females, producing new combinations and increasing genetic diversity in a polyculture treatment, “LARMIX”. The larvae were raised following standard hatchery (days 0–30) and nursery (days 30–77) methods. On day 77, a second polyculture treatment, “SEEDMIX”, was created by equally mixing seed from each monoculture to increase genetic diversity while maintaining original genotypes.<br />
<br />
Field Deployment</p>
<p>Seed from each treatment were split into six bags (approximately 1,200 per bag). Three replicate bags were deployed in bottom cages at east common garden site in August 2023 and assessed in November 2023 (time point 1), May 2024 (time point 2), November 2024 (time point 3), and May 2025 (final time point). At the first assessment, 40 oysters per bag were individually tagged. At each time point, tagged oysters were determined to be alive or dead. Note that not all tags were found at every assessment, some were missed due to debris on the oyster shell and others had become separated from the oyster in between assessments. For tagged oysters that could be identified, shell length (longest distance from the hinge to the bill) and width (longest distance perpendicular to the length) were measured. To reduce density–dependent effects, bags were thinned to 225 oysters at the second assessment (Marshall and Dunham, 2013).</p>
<p>Genetic data</p>
<p>Gill tissue was taken from each parent (<em>n</em> = 160, 83 dams and 77 sires) after spawning. At the second assessment, 267 tagged juvenile oysters were chosen at random for a nonlethal biopsy to collect gill tissue. All samples were stored in 95% ethanol at -80ºC. DNA was extracted using the Qiagen DNeasy Blood and Tissue Kit and shipped to Neogen Genomics in Lincoln, NE for genotyping on a 200K ThermoFisher Affymetrix Axiom SNP array derived from a 600K array assigned to the haplotig–masked <em>Crassostrea virginica</em> reference genome (Gómez-Chiarri et al., 2015; Guo et al., 2023; Modak et al., 2021; Puritz et al., 2024). The sequencing pipeline is outlined in Bajaj and Mongillo et al. (2026). Raw genotypes were processed and filtered in R v4.4.2 and a full, unimputed, SNP set was used for further analysis.</p>
Specified by the Principal Investigator(s)
<p>NA</p>
Specified by the Principal Investigator(s)
Curation Actions Performed on Data
- Loaded parents.csv into table named 1008652_v1_cvimvp-parents, with header row 1, and "" and "NA" set as missing value identifiers.
- Deleted column Mortality_Date (no values in this column and it was not included in the column descriptions by the data submitter).
- Renamed columns Pass.Fail to Pass_Fail, QC.call_rate to QC_call_rate, Average.call.rate.for.passing.samples to Average_call_rate_for_passing_samples, Sample.Filename to Sample_Filename, and QC.het_rate to QC_het_rate, replacing periods with underscores to conform field names to naming convention
- Set data types for all columns: Alive as boolean; Average_call_rate_for_passing_samples, DQC, QC_call_rate, QC_het_rate, Shell_length_cm, Shell_width_cm, call_rate, and het_rate as number; Sperm_Motility as integer; remaining columns (Buffer, Extraction_plan_Key, Extraction_plan_Label, ID_SiteDate, Key_Vial, Key_box, Key_ind, Label_ind, New_Growth, Pass_Fail, PlateID_Key, Sample_Filename, Sex, SiteDate_Key, Tissue, Vial_Label, clean_ID, pops_Key) as string
- Updated column metadata with descriptions, standard parameter name IDs, and supplied units for all columns, including notes describing Alive, Average_call_rate_for_passing_samples, Buffer, DQC, Extraction_plan_Key, Extraction_plan_Label, ID_SiteDate, Key_Vial, Key_box, Key_ind, Label_ind, New_Growth, Pass_Fail, PlateID_Key, QC_call_rate, QC_het_rate, Sample_Filename, Sex, Shell_length_cm, Shell_width_cm, SiteDate_Key, Sperm_Motility, Tissue, Vial_Label, call_rate, clean_ID, and het_rate
- Updated package-level metadata with per-column summary statistics (counts, minimum, and maximum values) for all columns
- Saved table as 1008652_v1_cvimvp-parents.csv
Curation Actions Performed on Metadata
* Standard metadata entry and formatting of provided metadata.
* Data manager set the temporal and spatial bounds to the same lat lon and time range was used as the related Environmental dataset (See related datasets) which included the broodstock source population collection time and locations.
Known Issues [Potentially Impacting Reuse]
* Information for sites (lat,lons, etc) and information for source populations of parental broodstock genotyped were not included in this dataset directly. See Related Datasets where that information was provided. However, the exact way to associate that data with this dataset was not explicitly stated (shared keys not explained). However there is an implied association between site IDs and source populations/spawning treatment IDs due to the shared use of state abbreviations within the values (e.g. source population identified by "FL-KingPlan" may be from the FL site "W3_FL, 30.44003,-81.43638").
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