---
license: other
license_name: nist
license_link: https://github.com/mahynski/pychemauth/blob/main/LICENSE.md
language:
- en
tags:
- chemistry
size_categories:
- 1K<n<10K
---

# Summary

This is a subset of results from [NOAA's MusselWatch Program](https://coastalscience.noaa.gov/science-areas/pollution/mussel-watch/).  Only *[Crassostrea virginica](https://en.wikipedia.org/wiki/Eastern_oyster)* (American Oysters) have been selected from 1986-2021.  The figure below summarizes the number of measurements made in each year.  

![image/png](https://cdn-uploads.huggingface.co/production/uploads/664cd7f57c563d4d95dde3bf/AF-h0_0SYx7AkxbAP0Hte.png)

A "measurement" is an entry (row) in `raw.csv` and corresponds to a single analyte being tested in a single sample.  Each sample has a unique `Sample_ID` and it is possible for a given sample to have the same analyte tested for multiple times.  Thus, the data in `raw.csv` has been condensed by `Sample_ID` to form more user-friendly datasets.

This dataset contains 184,740 measurements spanning 1,879 unique `Sample_ID`s and 442 unique analytes.

# Metadata

The file `raw.csv` contains the following metadata on each measurement.

| Name | datatype | Description | 
| --- | --- | --- |
| Study | string | Name of study (all "Mussel Watch") |
| NST_Site | string | Short name for sampling site |
| General_Location | string | General location of sampling site |
| Specific_Location | string | More detailed location of sampling site |
| Sample_ID | string | Unique identifier for a sample |
| Latitude | float | Latitiude of sampling site |
| Longitude | float | Longitude of sampling site |
| Fiscal_Year | int | Year sample was collected | 
| Matrix | string | Description of what was sampled |
| Scientific_Name | string | Binomial nomenclature designation (all "C. virginica") |
| Collection_Date | date | YYYY-MM-DD of sampling |
| Method | string | Analytical chemistry method used |
| Laboratory | string | Name of laboratory that performed the analysis |

Each measurement is also accompanied by a description with the following fields:

| Name | datatype | Description | 
| --- | --- | --- |
| Parameter | string | Name of the analyte being measured |
| Value | float | Level or amount of analyte found |
| Unit | string | Units value is reported in |
| Qualifier | string | Comments about measurement (e.g., if below the limit of detection) |

# Units

Refer to to `units.json` for a list of units for each analyte. An analyte is reported in the same units across the entire dataset.

# Notes

When a `Qualifier` is `NaN` this means there were no issues with the measurement; anything potentially problematic with the measurement is reported here, if present.

# Duplicates 

The following `Sample_ID`s have duplicate measurements reported in `raw.csv`.  The processed datasets averaged the duplicate measurements.

1. 'MW2015CHBLCV'
2. 'MW2017AESPCV'
3. 'MW2017CBJBCV'
4. 'MW2017CKBPCV'
5. 'MW2017GBHRCV'
6. 'MW2017VBSPCV'

The file `condensed.csv` is formed by merging `raw.csv` by `Sample_ID` and averaging these duplicates.  This has N = 1,879 unique samples and P = 442 unique analytes.

# Sparsity

The majority of the analytes are not measured for all samples.  The figure below is a [missingno](https://github.com/ResidentMario/missingno) snapshot of the dataset.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/664cd7f57c563d4d95dde3bf/Yv5lOG0wHcwfjJ5-BxZ2S.png)

Observe that only a small number of analytes are missing less than 10% of the time.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/664cd7f57c563d4d95dde3bf/6byrVfG6eWPOKk4xr_JB3.png)

The user may create more advanced interpolation or data recovery / filling schemes starting with `condensed.csv`, but we avoided this for the training datasets provided here.  In these cases, we simply dropped all rows with any `NaN` (missing) values.  As a result, including many analytes as features (columns) increases the likelihood a row will be dropped.  Increasing the threshold, t, in the above figure ("Fraction of Samples where Analyte is Missing") tends to decrease the number of rows, N, and increase the number of columns, P.  The figures below show this tradeoff.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/664cd7f57c563d4d95dde3bf/sy9S3YU3q3jih7C1vtHMl.png)

We selected 2 thresholds (A) t = 0.14 and (B) t = 0.26 (shown above) as a fair balance, which resulted in the following analytes:

(A): t = 0.14 has 26 analytes: 2,4'-DDD, 2,4'-DDE, 2,4'-DDT, 4,4'-DDD, 4,4'-DDE, 4,4'-DDT, Aldrin, Alpha-Chlordane, Arsenic, Cadmium, Chromium, Copper, Dieldrin, Gamma-Hexachlorocyclohexane, Heptachlor, Heptachlor-Epoxide, Hexachlorobenzene, Iron, Lead, Mercury, Mirex, Nickel, Selenium, Silver, Tin, Trans-Nonachlor

(B): t = 0.26 has 76 analytes: 1,6,7-Trimethylnaphthalene, 1-Methylnaphthalene, 1-Methylphenanthrene, 2,4'-DDD, 2,4'-DDE, 2,4'-DDT, 2,6-Dimethylnaphthalene, 2-Methylnaphthalene, 4,4'-DDD, 4,4'-DDE, 4,4'-DDT, Acenaphthene, Acenaphthylene, Aldrin, Alpha-Chlordane, Anthracene, Arsenic, Benz[a]anthracene, Benzo[a]pyrene, Benzo[b]fluoranthene, Benzo[e]pyrene, Benzo[g,h,i]perylene, Benzo[k]fluoranthene, Biphenyl, C1-Chrysenes, C1-Dibenzothiophenes, C1-Fluoranthenes_Pyrenes, C1-Fluorenes, C1-Naphthalenes, C1-Phenanthrenes_Anthracenes, C2-Chrysenes, C2-Dibenzothiophenes, C2-Fluorenes, C2-Naphthalenes, C2-Phenanthrenes_Anthracenes, C3-Chrysenes, C3-Dibenzothiophenes, C3-Fluorenes, C3-Naphthalenes, C3-Phenanthrenes_Anthracenes, C4-Chrysenes, C4-Naphthalenes, C4-Phenanthrenes_Anthracenes, Cadmium, Chromium, Chrysene, Copper, Dibenzo[a,h]anthracene, Dibenzothiophene, Dibutyltin, Dieldrin, Endrin, Fluoranthene, Fluorene, Gamma-Hexachlorocyclohexane, Heptachlor, Heptachlor-Epoxide, Hexachlorobenzene, Indeno[1,2,3-c,d]pyrene, Iron, Lead, Manganese, Mercury, Mirex, Monobutyltin, Naphthalene, Nickel, Perylene, Phenanthrene, Pyrene, Selenium, Silver, Tin, Trans-Nonachlor, Tributyltin, Zinc

Once the rows with missing values (`NaN`) were dropped the remaining datasets had the following dimensions. 

(A): N = 1481 rows and P = 26 analytes.  
(B): N = 965 rows and P = 76 analytes.

# Training Datasets

The training sets reported here are reduced versions of (A) and (B) based on the minimum number of times a General Location must be observed to be included. The figure below shows the number of General Locations left in the dataset when a location is required to have a certain minimum number of observations, c.  As the minimum number increases, the number of General Locations decreases monotonically and tends to show a sharp decline around some critical minimum number.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/664cd7f57c563d4d95dde3bf/t7aq6-0dp_TvxHOx4kqKD.png)

We provide 4 different datasets for version (A) and version (B) based on different values of c.  If you wish to perform your own filtering, simply use the "v0" of each.  Note, however, for any simple (stratified) test/train splitting of this dataset a minimum number of 2 observations would be required.

| v | A | B |
| --- | --- | --- |
| v0 | c = 0, (1481, 26), 62 Locs. | c = 0, (965, 76), 61 Locs.|
| v1 | c = 10, (1466, 26), 57 Locs. | c = 6, (958, 76), 58 Locs. |
| v2 | c = 15, (1217, 26), 38 Locs. | c = 9 (823, 76), 40 Locs. |
| v3 | c = 20, (1013, 26), 25 Locs. | c = 15 (671, 76), 25 Locs. | 

Datasets are named, e.g., `train_A_v0.csv`, according to this table. To get an adequate statistical description of each site requires a minimum number of samples depending on the application and user tolerance.  We recommend using `v3`s for applications where a smaller number of well-sampled site is appropriate, while applications which require more sites but can tolerate fewer examples may benefit more from `v0` or `v1`.  

In the figure above we also report the performance of random forest trained on an 80:20 stratified train:test split of the data. The trends suggest that the more locations you have the harder it is to tell them apart from each other.

# Usage

One way to download the data is by using the [datasets](https://huggingface.co/docs/datasets/en/index) library.

~~~python
from datasets import load_dataset

df = load_dataset(
    "mahynski/crassostrea-virginica-chem",
    data_files="train_A_v0.csv",
)['train'].to_pandas()
~~~