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Dataset Title:	[CviMVP Experiment - Parents genotypes] - SNP genotypes of 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) (CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP))
Institution:	BCO-DMO   (Dataset ID: bcodmo_dataset_998738_v1)
Information:	Summary | License | Metadata | Background | Data Access Form | Files

 

Graph Type:  lines linesAndMarkers markers X Axis:  Chromosome Position FST He pvalues pvaluesRightTail qvalues Tile_Std Tile_max Tile_v3 Replicates Rank chrom_position Y Axis:  Chromosome Position FST He pvalues pvaluesRightTail qvalues Tile_Std Tile_max Tile_v3 Replicates Rank chrom_position Color:  Chromosome Position FST He pvalues pvaluesRightTail qvalues Tile_Std Tile_max Tile_v3 Replicates Rank chrom_position   Constraints Optional Constraint #1 Optional Constraint #2 Affx_ID AX_ID Chromosome Position FST He pvalues pvaluesRightTail qvalues OutlierFlag gene_id gene_description go_ids go_terms SNPType Tile_Std Tile_max Tile_v3 Replicates Rank Group organism cust_id on_oystercv mutID chrom_position scaffold Sequence thinned_dataset     != =~ <= >= = < >     != =~ <= >= = < > Affx_ID AX_ID Chromosome Position FST He pvalues pvaluesRightTail qvalues OutlierFlag gene_id gene_description go_ids go_terms SNPType Tile_Std Tile_max Tile_v3 Replicates Rank Group organism cust_id on_oystercv mutID chrom_position scaffold Sequence thinned_dataset     != =~ <= >= = < >     != =~ <= >= = < > Affx_ID AX_ID Chromosome Position FST He pvalues pvaluesRightTail qvalues OutlierFlag gene_id gene_description go_ids go_terms SNPType Tile_Std Tile_max Tile_v3 Replicates Rank Group organism cust_id on_oystercv mutID chrom_position scaffold Sequence thinned_dataset     != =~ <= >= = < >     != =~ <= >= = < > Affx_ID AX_ID Chromosome Position FST He pvalues pvaluesRightTail qvalues OutlierFlag gene_id gene_description go_ids go_terms SNPType Tile_Std Tile_max Tile_v3 Replicates Rank Group organism cust_id on_oystercv mutID chrom_position scaffold Sequence thinned_dataset     != =~ <= >= = < >     != =~ <= >= = < > Affx_ID AX_ID Chromosome Position FST He pvalues pvaluesRightTail qvalues OutlierFlag gene_id gene_description go_ids go_terms SNPType Tile_Std Tile_max Tile_v3 Replicates Rank Group organism cust_id on_oystercv mutID chrom_position scaffold Sequence thinned_dataset     != =~ <= >= = < >     != =~ <= >= = < >   Server-side Functions  distinct() orderBy orderByDescending orderByClosest orderByCount orderByLimit orderByMax orderByMin orderByMinMax orderByMean orderBySum (" Affx_ID AX_ID Chromosome Position FST He pvalues pvaluesRightTail qvalues OutlierFlag gene_id gene_description go_ids go_terms SNPType Tile_Std Tile_max Tile_v3 Replicates Rank Group organism cust_id on_oystercv mutID chrom_position scaffold Sequence thinned_dataset Affx_ID AX_ID Chromosome Position FST He pvalues pvaluesRightTail qvalues OutlierFlag gene_id gene_description go_ids go_terms SNPType Tile_Std Tile_max Tile_v3 Replicates Rank Group organism cust_id on_oystercv mutID chrom_position scaffold Sequence thinned_dataset Affx_ID AX_ID Chromosome Position FST He pvalues pvaluesRightTail qvalues OutlierFlag gene_id gene_description go_ids go_terms SNPType Tile_Std Tile_max Tile_v3 Replicates Rank Group organism cust_id on_oystercv mutID chrom_position scaffold Sequence thinned_dataset Affx_ID AX_ID Chromosome Position FST He pvalues pvaluesRightTail qvalues OutlierFlag gene_id gene_description go_ids go_terms SNPType Tile_Std Tile_max Tile_v3 Replicates Rank Group organism cust_id on_oystercv mutID chrom_position scaffold Sequence thinned_dataset ")   Graph Settings Marker Type:  None Plus X Dot Square Filled Square Circle Filled Circle Up Triangle Filled Up Triangle Borderless Filled Square Borderless Filled Circle Borderless Filled Up Triangle  Size:  3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Color:  Color Bar:  BlackBlueWhite BlackGreenWhite BlackRedWhite BlackWhite BlueWhiteRed BlueWideWhiteRed LightRainbow Ocean OceanDepth Rainbow Rainbow2 Rainfall ReverseRainbow RedWhiteBlue RedWhiteBlue2 RedWideWhiteBlue Spectrum Topography TopographyDepth WhiteBlueBlack WhiteGreenBlack WhiteRedBlack WhiteBlack YellowRed KT_algae KT_amp KT_balance KT_curl KT_deep KT_delta KT_dense KT_gray KT_haline KT_ice KT_matter KT_oxy KT_phase KT_solar KT_speed KT_tempo KT_thermal KT_turbid  Continuity:  Continuous Discrete  Scale:  Linear Log    Minimum:   Maximum:   N Sections:  2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Y Axis Minimum:   Maximum:    Ascending Descending Linear Log   Redraw the Graph (Please be patient. It may take a while to get the data.)   Optional: Then set the File Type: .asc .csv .csvp .csv0 .dataTable .das .dds .dods .esriCsv .fgdc .geoJson .graph .help .html .htmlTable .iso19115 .itx .json .jsonlCSV1 .jsonlCSV .jsonlKVP .mat .nc .ncHeader .ncCF .ncCFHeader .ncCFMA .ncCFMAHeader .nccsv .nccsvMetadata .ncoJson .odvTxt .subset .tsv .tsvp .tsv0 .wav .xhtml .kml .smallPdf .pdf .largePdf .smallPng .png .largePng .transparentPng (File Type information) and or view the URL: (Documentation / Bypass this form )

 

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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

• Web page authors can embed a graph of the latest data in a web page using HTML <img> tags.
• Anyone can use ERDDAPs Slide Sorter to build a personal web page that displays graphs with the latest data (or other images or HTML content), each in its own, draggable slide.
   

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Affx_ID {
    String long_name "Affx_id";
    String units "unitless";
  }
  AX_ID {
    String long_name "Ax_id";
    String units "unitless";
  }
  Chromosome {
    Int32 actual_range 1, 10;
    String long_name "Chromosome";
    String units "unitless";
  }
  Position {
    Int32 actual_range 591, 104128119;
    String long_name "Position";
    String units "unitless";
  }
  FST {
    Float32 actual_range -0.0409, 0.9754;
    String long_name "Fst";
    String units "unitless";
  }
  He {
    Float32 actual_range 0.0894, 0.5;
    String long_name "He";
    String units "unitless";
  }
  pvalues {
    Float32 actual_range 9.375e-7, 0.9999;
    String long_name "Pvalues";
    String units "unitless";
  }
  pvaluesRightTail {
    Float32 actual_range 4.688e-7, 0.9999;
    String long_name "Pvaluesrighttail";
    String units "unitless";
  }
  qvalues {
    Float32 actual_range 9.345e-4, 0.6295;
    String long_name "Qvalues";
    String units "unitless";
  }
  OutlierFlag {
    String long_name "Outlierflag";
    String units "unitless";
  }
  gene_id {
    String long_name "Gene_id";
    String units "unitless";
  }
  gene_description {
    String long_name "Gene_description";
    String units "unitless";
  }
  go_ids {
    String long_name "Go_ids";
    String units "unitless";
  }
  go_terms {
    String long_name "Go_terms";
    String units "unitless";
  }
  SNPType {
    String long_name "Snptype";
    String units "unitless";
  }
  Tile_Std {
    Int32 actual_range 0, 1;
    String long_name "Tile_std";
    String units "unitless";
  }
  Tile_max {
    Int32 actual_range 0, 1;
    String long_name "Tile_max";
    String units "unitless";
  }
  Tile_v3 {
    Int32 actual_range 1, 1;
    String long_name "Tile_v3";
    String units "unitless";
  }
  Replicates {
    Int32 actual_range 2, 4;
    String long_name "Replicates";
    String units "unitless";
  }
  Rank {
    Int32 actual_range 2000, 31611;
    String long_name "Rank";
    String units "unitless";
  }
  Group {
    String long_name "Group";
    String units "unitless";
  }
  organism {
    String long_name "Organism";
    String units "unitless";
  }
  cust_id {
    String long_name "Cust_id";
    String units "unitless";
  }
  on_oystercv {
    String long_name "On_oystercv";
    String units "unitless";
  }
  mutID {
    String long_name "Mutid";
    String units "unitless";
  }
  chrom_position {
    Float32 actual_range 1.000068, 10.32639;
    String long_name "Chrom_position";
    String units "unitless";
  }
  scaffold {
    String long_name "Scaffold";
    String units "unitless";
  }
  Sequence {
    String long_name "Sequence";
    String units "unitless";
  }
  thinned_dataset {
    String long_name "Thinned_dataset";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String cdm_data_type "Other";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_url "https://www.bco-dmo.org/";
    String doi "10.26008/1912/bco-dmo.998738.1";
    String history 
"2026-11-07T12:04:01Z (local files)
2026-11-07T12:04:01Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_998738_v1.das";
    String infoUrl "https://osprey.bco-dmo.org/dataset/998738";
    String institution "BCO-DMO";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    String sourceUrl "(local files)";
    String summary "This dataset contains SNP genotype and metadata for broodstock (parent) Eastern oysters (Crassostrea virginica) used in a multi-year common garden field experiment (2023–2025) as part of the project \"CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP)\" (NSF award #2043905 to Dr. Katie Lotterhos). The goal of the broader project is to assess the ability of machine learning algorithms (MLAs) to predict adaptation of organisms from their DNA sequences. Broodstock were sourced from six wild populations along the U.S. Atlantic and Gulf coasts and two hatchery selection lines. These parents were used in spawning treatments that included monocultures of each population or line, as well as two polyculture (mixed) treatments. The resulting juvenile oysters were deployed at two common garden sites in the Chesapeake Bay (Lewisetta, VA and York River, VA) and monitored over two years. Mortality and phenotype data were collected at six-month intervals to assess fitness across spawn treatments and garden sites, providing a field-based validation of MLA predictions. Broodstock were genotyped on a 200K ThermoFisher Affymetrix Axiom SNP array derived from a 600K array (Gómez-Chiarri et al., 2015; Guo et al., 2023; Modak et al., 2021; Puritz et al., 2024) and aligned to the haplotig-masked C. virginica reference genome (C_virginica-3.0, GCA_002022765.4). The data are archived as two files: (1) a SNP genotype matrix with processed, imputed genotype calls per individual in 0/1/2 integer format (homozygous reference, heterozygous, homozygous alternate), and (2) a SNP metadata file with per-locus information including SNP identifiers, genomic positions, gene annotations, Gene Ontology terms, and population genetic statistics from OutFLANK outlier detection (FST, expected heterozygosity, p-values, q-values, and outlier flags).";
    String title "[CviMVP Experiment - Parents genotypes] - SNP genotypes of 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) (CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP))";
  }
}

 

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Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP Data Access Protocol (DAP) and its selection constraints.

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

• (easy) You can get data by using the dataset's Data Access Form or Subset form. They make the URL for you.
• (easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
• (not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.

 

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