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+# Example Instance Datasets
+
+This folder contains three example datasets that would be available to the user for downstream model training. The derived data is calculated from
+log probabilities of each library candidate in an assay sample. An assay sample is defined by the library sequenced at different stages of the assay. The different stages are:
+
+- Bacterial Outgrowth
+- Phage Expression
+- 1 nM Dose of Target
+- 10 nM Dose of Target
+- 100 nM Dose of Target
+- 1000 nM Dose of Target
+- Blank Dose of Target
+
+The dosing stages are specific to a target molecule, so the dataset will include rows for each target that was tested for the library.
+
+The base dataset contains the log probabilities used to derive the relative dissociation constants and expression levels contained in the other datasets.
+
+## Base Log Probability Dataset
+
+This dataset contains metadata columns that are useful for filtering and grouping the data. The columns are:
+
+- `experiment_name`: The name of the experiment/library you ordered.
+- `target_name`: The name of the target molecule tested.
+- `candidate_name`: The name of the generated protein candidate in the library.
+- `candidate_id`: A unique identifier that is specific to the candidate sequence.
+- `experiment_replicate_num`: Any experiment/library can be replicated resulting in repeat samples that are run through the same assay workflow. This value is used to distinguish between experimental replicates.
+- `clone_num`: The index associated with a clone of a library candidate. This gets used to find means and variances for each library candidate.
+
+The dataset contains sequence information for each library candidate clone. The columns are:
+
+- `candidate_sequence`: The designed sequence of the library candidate from the order form.
+- `clone_sequence`: The consensus sequence of the library candidate clone validated by NGS.
+
+Finally, the dataset contains the log probabilities of each library candidate clone at each stage of the assay. The columns are:
+
+- `bacterial_outgrowth_log_prob`: The log probability of the library candidate clone being expressed after transformation and bacterial outgrowth.
+- `phage_expression_log_prob`: The log probability of the library candidate clone being expressed in phage.
+- `dose_1nM_log_prob`: The log probability of the library candidate clone being expressed at 1 nM.
+- `dose_10nM_log_prob`: The log probability of the library candidate clone being expressed at 10 nM.
+- `dose_100nM_log_prob`: The log probability of the library candidate clone being expressed at 100 nM.
+- `dose_1000nM_log_prob`: The log probability of the library candidate clone being expressed at 1000 nM.
+- `dose_blank_log_prob`: The log probability of the library candidate clone being expressed at a blank dose.
+
+We also know the standard deviation of each of the log probability estimates. The columns are:
+
+- `bacterial_outgrowth_log_prob_std`: The standard deviation of the log probability of the library candidate clone being expressed after transformation and bacterial outgrowth.
+- `phage_expression_log_prob_std`: The standard deviation of the log probability of the library candidate clone being expressed in phage.
+- `dose_1nM_log_prob_std`: The standard deviation of the log probability of the library candidate clone being expressed at 1 nM.
+- `dose_10nM_log_prob_std`: The standard deviation of the log probability of the library candidate clone being expressed at 10 nM.
+- `dose_100nM_log_prob_std`: The standard deviation of the log probability of the library candidate clone being expressed at 100 nM.
+- `dose_1000nM_log_prob_std`: The standard deviation of the log probability of the library candidate clone being expressed at 1000 nM.
+- `dose_blank_log_prob_std`: The standard deviation of the log probability of the library candidate clone being expressed at a blank dose.
+
+## pKD Dataset
+
+This dataset contains the relative dissociation constants for each library candidate clone. The dissociation constants are relative
+to the average dissociation constant of the entire library. Knowledge of absolute dissociation constants of any library candidate
+would set the scale and allow derivation of absolute dissociation constants for each library candidate. The relative dissociation
+constants are averaged over all clones of a library candidate producing a single value for each library candidate.
+
+The metadata columns are nearly the same as the base dataset. The `clone_num` column is removed as it was used to calculate the relative
+dissociation constants. The metadata columns columns are:
+
+- `experiment_name`
+- `target_name`
+- `candidate_name`
+- `candidate_id`
+- `experiment_replicate_num`
+
+The rest of the dataset would be values that are directly consumed by model training. The columns are:
+
+- `candidate_sequence`
+- `pK_D_relative`
+- `pK_D_relative_std`
+
+## Candidate Expression Dataset
+
+This dataset contains the expression levels for each library candidate. The metadata columns are the same as the pKD dataset. The columns are:
+
+- `experiment_name`
+- `target_name`
+- `candidate_name`
+- `candidate_id`
+- `experiment_replicate_num`
+
+The rest of the dataset would be values that are directly consumed by model training. The columns are:
+
+- `candidate_sequence`
+- `log_enrichment_expression`
+- `log_enrichment_expression_std`
+
+## Note on dataset size
+
+The base dataset is going to be larger than the pKD or candidate expression datasets because it contains rows for each clone of each library candidate. There are typically ~100 clones of each library candidate.
+So, the number of rows in the base dataset is N_candiates * N_targets * N_replicates * N_clones.
+
+The pKD and expression datasets are smaller because they average over the clones of each library candidate. So, the number of rows becoms N_candidates * N_targets * N_replicates.