diff --git a/marimo_eda/Individual_LWF_Deposition.py b/marimo_eda/Individual_LWF_Deposition.py
new file mode 100644
index 0000000..5f5f0b0
--- /dev/null
+++ b/marimo_eda/Individual_LWF_Deposition.py
@@ -0,0 +1,243 @@
+import marimo
+
+__generated_with = "0.24.0"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import pandas as pd
+    import marimo as mo
+    from pathlib import Path
+
+    return Path, mo, pd
+
+
+@app.cell
+def _(Path, pd):
+    foliage_path = Path("lwf_foliage_dw100_i_2026-07-30.csv")
+
+    foliage = pd.read_csv(
+        foliage_path,
+        sep=";",
+    )
+    return (foliage,)
+
+
+@app.cell
+def _(foliage):
+    foliage.head()
+    return
+
+
+@app.cell
+def _(foliage, pd):
+    foliage["survey_date"] = pd.to_datetime(
+        foliage["survey_date"],
+        errors="coerce",
+    )
+    return
+
+
+@app.cell
+def _(foliage, mo):
+    plot_selector = mo.ui.dropdown(
+        options=["All"] + sorted(
+            foliage["plot_id"]
+            .dropna()
+            .unique()
+            .tolist()
+        ),
+        value="All",
+        label="LWF site",
+    )
+
+    species_selector = mo.ui.dropdown(
+        options=["All"] + sorted(
+            foliage["species"]
+            .dropna()
+            .unique()
+            .tolist()
+        ),
+        value="All",
+        label="Species",
+    )
+
+    leaf_type_selector = mo.ui.dropdown(
+        options=["All"] + sorted(
+            foliage["leaf_type"]
+            .dropna()
+            .unique()
+            .tolist()
+        ),
+        value="All",
+        label="Leaf type",
+    )
+
+    age_selector = mo.ui.dropdown(
+        options=["All"] + sorted(
+            foliage["leaf_age_class"]
+            .dropna()
+            .unique()
+            .tolist()
+        ),
+        value="All",
+        label="Leaf age class",
+    )
+
+    mo.vstack([
+        mo.hstack([
+            plot_selector,
+            species_selector,
+        ]),
+        mo.hstack([
+            leaf_type_selector,
+            age_selector,
+        ]),
+    ])
+    return age_selector, leaf_type_selector, plot_selector, species_selector
+
+
+@app.cell
+def _(
+    age_selector,
+    foliage,
+    leaf_type_selector,
+    plot_selector,
+    species_selector,
+):
+    filtered_foliage = foliage.copy()
+
+    if plot_selector.value != "All":
+        filtered_foliage = filtered_foliage[
+            filtered_foliage["plot_id"] == plot_selector.value
+        ]
+
+    if species_selector.value != "All":
+        filtered_foliage = filtered_foliage[
+            filtered_foliage["species"] == species_selector.value
+        ]
+
+    if leaf_type_selector.value != "All":
+        filtered_foliage = filtered_foliage[
+            filtered_foliage["leaf_type"] == leaf_type_selector.value
+        ]
+
+    if age_selector.value != "All":
+        filtered_foliage = filtered_foliage[
+            filtered_foliage["leaf_age_class"] == age_selector.value
+        ]
+
+    filtered_foliage
+    return (filtered_foliage,)
+
+
+@app.cell
+def _(filtered_foliage):
+    filtered_foliage["gew100"].describe()
+    return
+
+
+@app.cell
+def _(filtered_foliage):
+    filtered_foliage["gew100"].isna().sum()
+    return
+
+
+@app.cell
+def _(filtered_foliage):
+    import plotly.graph_objects as go
+
+    plot_data = (
+        filtered_foliage[
+            ["survey_date", "gew100", "sample_id"]
+        ]
+        .dropna(subset=["survey_date", "gew100"])
+        .sort_values("survey_date")
+    )
+
+    fig = go.Figure()
+
+    fig.add_trace(
+        go.Scatter(
+            x=plot_data["survey_date"],
+            y=plot_data["gew100"],
+            mode="markers",
+            name="Individual measurement",
+            marker=dict(size=7),
+            customdata=plot_data["sample_id"],
+            hovertemplate=(
+                "<b>Date:</b> %{x|%d %B %Y}"
+                "<br><b>gew100:</b> %{y:.2f} g"
+                "<br><b>Sample:</b> %{customdata}"
+                "<extra></extra>"
+            ),
+        )
+    )
+
+    fig.update_layout(
+        title="Foliage dry weight over time",
+        xaxis_title="Survey date",
+        yaxis_title="gew100 (g)",
+        hovermode="closest",
+    )
+
+    fig
+    return (go,)
+
+
+@app.cell
+def _(foliage):
+    samples_per_group = (
+        foliage
+        .groupby(
+            [
+                "survey_date",
+                "species",
+                "leaf_type",
+                "leaf_age_class",
+            ],
+            dropna=False,
+        )
+        .agg(
+            n_samples=("sample_id", "nunique"),
+            mean_gew100=("gew100", "mean"),
+            median_gew100=("gew100", "median"),
+            std_gew100=("gew100", "std"),
+        )
+        .reset_index()
+        .sort_values("survey_date")
+    )
+
+    samples_per_group
+    return
+
+
+@app.cell
+def _(go, samples_per_date):
+    fig_1 = go.Figure()
+
+    fig_1.add_trace(
+        go.Bar(
+            x=samples_per_date["survey_date"],
+            y=samples_per_date["n_samples"],
+            hovertemplate=(
+                "<b>Date:</b> %{x|%d %B %Y}"
+                "<br><b>Samples:</b> %{y}"
+                "<extra></extra>"
+            ),
+        )
+    )
+
+    fig_1.update_layout(
+        title="Number of foliage samples per survey date",
+        xaxis_title="Survey date",
+        yaxis_title="Number of individual samples",
+    )
+
+    fig_1
+    return
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/marimo_eda/LAI_Licor.py b/marimo_eda/LAI_Licor.py
new file mode 100644
index 0000000..ae34d1c
--- /dev/null
+++ b/marimo_eda/LAI_Licor.py
@@ -0,0 +1,437 @@
+import marimo
+
+__generated_with = "0.24.0"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+
+    return (mo,)
+
+
+@app.cell
+def _(mo):
+    sites = [1,2,3]
+
+    mo.ui.dropdown(sites)
+    return
+
+
+@app.cell
+def _():
+    import pandas as pd
+
+    df = pd.read_excel("legend_deposition_2026-07-28.xlsx")
+    df2 = pd.read_csv("monthly_dep_lwf_2026-07-28.csv")
+    return (pd,)
+
+
+@app.cell
+def _():
+    from pathlib import Path
+
+    data_dir = Path("data")
+
+    files = sorted(data_dir.iterdir())
+
+    files
+    return Path, files
+
+
+@app.cell
+def _(files):
+    for file in files:
+        print(f"{file.name:60} {file.suffix}")
+    return
+
+
+@app.cell
+def _(Path, pd):
+    lai_path = Path("LAI_Licor_3_rings_all_years.xlsx")
+
+    raw_lai = pd.read_excel(
+        lai_path,
+        header=None,
+    )
+
+    raw_lai.head()
+    return (raw_lai,)
+
+
+@app.cell
+def _(pd, raw_lai):
+    lai = raw_lai.iloc[3:].copy()
+
+    lai.columns = [
+        "plot",
+        "subplot",
+        "date",
+        "plot_type",
+        "lai_miller",
+        "se_miller",
+        "lai_norman_campbell",
+        "se_norman_campbell",
+        "angle_miller",
+        "se_angle_miller",
+        "angle_norman_campbell",
+        "se_angle_norman_campbell",
+        "number_of_points",
+        "season",
+    ]
+
+    lai = lai.reset_index(drop=True)
+
+    # Strip whitespace from text fields
+    for column in ["plot", "subplot", "plot_type", "season"]:
+        lai[column] = lai[column].astype(str).str.strip()
+
+    # Convert "." to missing values
+    lai = lai.replace(".", pd.NA)
+
+    # Parse dates
+    lai["date"] = pd.to_datetime(lai["date"], errors="coerce")
+
+    # Numeric variables
+    numeric_columns = [
+        "lai_miller",
+        "se_miller",
+        "lai_norman_campbell",
+        "se_norman_campbell",
+        "angle_miller",
+        "se_angle_miller",
+        "angle_norman_campbell",
+        "se_angle_norman_campbell",
+        "number_of_points",
+    ]
+
+    for column in numeric_columns:
+        lai[column] = pd.to_numeric(lai[column], errors="coerce")
+
+    lai.head()
+    return (lai,)
+
+
+@app.cell
+def _(lai, mo):
+    plot_selector = mo.ui.dropdown(
+        options=["All"] + sorted(
+            lai["plot"].dropna().unique().tolist()
+        ),
+        value="All",
+        label="Plot",
+    )
+
+    subplot_selector = mo.ui.dropdown(
+        options=["All"] + sorted(
+            lai["subplot"].dropna().unique().tolist()
+        ),
+        value="All",
+        label="Subplot",
+    )
+
+    plot_type_selector = mo.ui.dropdown(
+        options=["All"] + sorted(
+            lai["plot_type"].dropna().unique().tolist()
+        ),
+        value="All",
+        label="Plot type",
+    )
+
+    season_selector = mo.ui.dropdown(
+        options=["All"] + sorted(
+            lai["season"].dropna().unique().tolist()
+        ),
+        value="All",
+        label="Season",
+    )
+
+    mo.hstack(
+        [
+            plot_selector,
+            subplot_selector,
+            plot_type_selector,
+            season_selector,
+        ]
+    )
+    return plot_selector, plot_type_selector, season_selector, subplot_selector
+
+
+@app.cell
+def _(
+    lai,
+    plot_selector,
+    plot_type_selector,
+    season_selector,
+    subplot_selector,
+):
+    filtered_lai = lai.copy()
+
+    if plot_selector.value != "All":
+        filtered_lai = filtered_lai[
+            filtered_lai["plot"] == plot_selector.value
+        ]
+
+    if subplot_selector.value != "All":
+        filtered_lai = filtered_lai[
+            filtered_lai["subplot"] == subplot_selector.value
+        ]
+
+    if plot_type_selector.value != "All":
+        filtered_lai = filtered_lai[
+            filtered_lai["plot_type"] == plot_type_selector.value
+        ]
+
+    if season_selector.value != "All":
+        filtered_lai = filtered_lai[
+            filtered_lai["season"] == season_selector.value
+        ]
+
+    filtered_lai
+    return (filtered_lai,)
+
+
+@app.cell
+def _(mo):
+    measurement_options = {
+        "LAI — Miller": "lai_miller",
+        "LAI — Norman & Campbell": "lai_norman_campbell",
+        "Mean angle — Miller": "angle_miller",
+        "Mean angle — Norman & Campbell": "angle_norman_campbell",
+    }
+
+    measurement_selector = mo.ui.dropdown(
+        options=list(measurement_options.keys()),
+        value="LAI — Miller",
+        label="Measurement",
+    )
+
+    measurement_selector
+    return measurement_options, measurement_selector
+
+
+@app.cell
+def _(measurement_options, measurement_selector):
+    selected_measurement = measurement_options[
+        measurement_selector.value
+    ]
+
+    selected_measurement
+    return (selected_measurement,)
+
+
+@app.cell
+def _():
+    import plotly.graph_objects as go
+    import numpy as np
+
+    return (go,)
+
+
+@app.cell
+def _(filtered_lai, go, measurement_selector, selected_measurement):
+    plot_data = filtered_lai[
+        ["date", selected_measurement]
+    ].copy()
+
+    plot_data = (
+        plot_data
+        .sort_values("date")
+        .reset_index(drop=True)
+    )
+
+    # ============================================================
+    # SETTINGS
+    # ============================================================
+
+    # Gaps longer than this are shown as dashed lines
+    max_gap_days = 365 * 2
+
+
+    # ============================================================
+    # FIGURE
+    # ============================================================
+
+    fig = go.Figure()
+
+
+    # ============================================================
+    # 1. MEASURED OBSERVATIONS
+    # ============================================================
+
+    measured = (
+        plot_data
+        .dropna(subset=[selected_measurement])
+        .sort_values("date")
+        .reset_index(drop=True)
+    )
+
+    fig.add_trace(
+        go.Scatter(
+            x=measured["date"],
+            y=measured[selected_measurement],
+            mode="markers",
+            name="Measured",
+            marker=dict(size=8),
+
+            # Full date shown when hovering
+            hovertemplate=(
+                "<b>Date:</b> %{x|%d %B %Y}"
+                "<br>"
+                f"<b>{measurement_selector.value}:</b> "
+                "%{y:.2f}"
+                "<extra></extra>"
+            ),
+
+            showlegend=True,
+        )
+    )
+
+
+    # ============================================================
+    # 2. CONNECT MEASUREMENTS
+    # ============================================================
+
+    for i in range(len(measured) - 1):
+
+        current = measured.iloc[i]
+        next_row = measured.iloc[i + 1]
+
+        gap_days = (
+            next_row["date"] - current["date"]
+        ).days
+
+        # --------------------------------------------------------
+        # Normal gap → solid line
+        # --------------------------------------------------------
+
+        if gap_days <= max_gap_days:
+
+            fig.add_trace(
+                go.Scatter(
+                    x=[
+                        current["date"],
+                        next_row["date"],
+                    ],
+                    y=[
+                        current[selected_measurement],
+                        next_row[selected_measurement],
+                    ],
+                    mode="lines",
+                    line=dict(width=2),
+                    showlegend=False,
+                    hoverinfo="skip",
+                )
+            )
+
+        # --------------------------------------------------------
+        # Long gap → dashed line
+        # --------------------------------------------------------
+
+        else:
+
+            fig.add_trace(
+                go.Scatter(
+                    x=[
+                        current["date"],
+                        next_row["date"],
+                    ],
+                    y=[
+                        current[selected_measurement],
+                        next_row[selected_measurement],
+                    ],
+                    mode="lines",
+                    line=dict(
+                        width=2,
+                        dash="dash",
+                    ),
+                    name="Long data gap",
+                    showlegend=(i == 0),
+                    hoverinfo="skip",
+                )
+            )
+
+
+    # ============================================================
+    # 3. HIGHLIGHT NaN DATES
+    # ============================================================
+
+    missing_dates = (
+        plot_data.loc[
+            plot_data[selected_measurement].isna(),
+            "date"
+        ]
+        .dropna()
+        .drop_duplicates()
+        .sort_values()
+    )
+
+    for date in missing_dates:
+
+        fig.add_vline(
+            x=date,
+            line_width=2,
+            line_dash="dot",
+            line_color="red",
+            showlegend=False,
+        )
+
+
+    # ============================================================
+    # 4. LEGEND ENTRY FOR MISSING OBSERVATIONS
+    # ============================================================
+
+    if len(missing_dates) > 0:
+
+        fig.add_trace(
+            go.Scatter(
+                x=[None],
+                y=[None],
+                mode="lines",
+                line=dict(
+                    width=2,
+                    dash="dot",
+                    color="red",
+                ),
+                name="Missing observation",
+                showlegend=True,
+                hoverinfo="skip",
+            )
+        )
+
+
+    # ============================================================
+    # 5. LAYOUT
+    # ============================================================
+
+    fig.update_layout(
+        title=f"{measurement_selector.value} over time",
+        xaxis_title="Date",
+        yaxis_title=measurement_selector.value,
+
+        # Important: inspect individual observations
+        hovermode="closest",
+    )
+
+    fig
+    return
+
+
+@app.cell
+def _():
+    return
+
+
+@app.cell
+def _():
+    return
+
+
+@app.cell
+def _():
+    return
+
+
+if __name__ == "__main__":
+    app.run()