diff --git a/5.data_packaging/constants.py b/5.data_packaging/constants.py
index e458443..19f39e9 100644
--- a/5.data_packaging/constants.py
+++ b/5.data_packaging/constants.py
@@ -31,7 +31,7 @@
     ]
 ]
 
-PACKAGING_FILES = ["5.data_packaging/location_and_ch5_frame_image_data.parquet"]
+PACKAGING_DATASETS = ["5.data_packaging/location_and_ch5_frame_image_data"]
 
 # FTP resources for accessing IDR
 # See here for more:
diff --git a/5.data_packaging/create_lancedb.py b/5.data_packaging/create_lancedb.py
index 2cdd9a4..5d4c7d7 100644
--- a/5.data_packaging/create_lancedb.py
+++ b/5.data_packaging/create_lancedb.py
@@ -8,7 +8,7 @@
 import lancedb
 import pandas as pd
 import pyarrow as pa
-from constants import DATA_FILES_W_COLNAMES, PACKAGING_FILES
+from constants import DATA_FILES_W_COLNAMES, PACKAGING_DATASETS
 from pyarrow import parquet
 
 # specify a dir where the lancedb database may go and create lancedb client
@@ -59,10 +59,10 @@ def get_arrow_tbl_from_csv(filename_read: str) -> str:
         mode="overwrite",
     )
 
-for filename in PACKAGING_FILES:
-    table = parquet.read_table(source=filename)
+for dir in PACKAGING_DATASETS:
+    table = parquet.ParquetDataset(path_or_paths=dir).read()
     ldb.create_table(
-        name=f"{filename.replace('/','.')}",
+        name=f"{dir.replace('/','.')}",
         data=table,
         schema=table.schema,
         mode="overwrite",
diff --git a/5.data_packaging/gather_images.py b/5.data_packaging/gather_images.py
index cd5e9b2..3c7c601 100644
--- a/5.data_packaging/gather_images.py
+++ b/5.data_packaging/gather_images.py
@@ -4,14 +4,18 @@
 
 import os
 import pathlib
+import warnings
 from ftplib import FTP
 from typing import List
 
-import awkward as ak
 import docker
 import duckdb
+import h5py
+import numpy as np
 import pyarrow as pa
-import tifffile
+import pyarrow.compute as pc
+import pybasic
+import skimage
 from constants import (
     DOCKER_PLATFORM,
     FTP_IDR_MITOCHECK_CH5_DIR,
@@ -48,25 +52,27 @@ def retrieve_ftp_file(
             A string indicating the path to the downloaded file.
     """
 
-    try:
-        # Connect to the FTP server
-        with FTP(ftp_url) as ftp:
-            # Log in to the FTP server
-            ftp.login(user=ftp_user, passwd=ftp_pass)
+    # Specify the file to download
+    download_filepath = f"{download_dir}/{pathlib.Path(ftp_file).name}"
 
-            # Specify the file to download
-            download_filepath = f"{download_dir}/{pathlib.Path(ftp_file).name}"
+    # if we don't already have the file, download it
+    if not pathlib.Path(download_filepath).is_file():
+        try:
+            # Connect to the FTP server
+            with FTP(ftp_url) as ftp:
+                # Log in to the FTP server
+                ftp.login(user=ftp_user, passwd=ftp_pass)
 
-            # Open a local file for writing in binary mode
-            with open(download_filepath, "wb") as local_file:
-                # Download the file from the FTP server
-                ftp.retrbinary(f"RETR {ftp_file}", local_file.write)
+                # Open a local file for writing in binary mode
+                with open(download_filepath, "wb") as local_file:
+                    # Download the file from the FTP server
+                    ftp.retrbinary(f"RETR {ftp_file}", local_file.write)
 
-        # return the download filepath
-        return download_filepath
+        except Exception as e:
+            print("An error occurred:", e)
 
-    except Exception as e:
-        print("An error occurred:", e)
+    # return the download filepath
+    return download_filepath
 
 
 def get_image_union_table() -> pa.Table:
@@ -112,7 +118,9 @@ def get_image_union_table() -> pa.Table:
                     '/hdf5/00',
                     format('{{:03d}}', locations_union."Well Number"),
                     '_01.ch5'
-                ) AS IDR_FTP_ch5_location
+                ) AS IDR_FTP_ch5_location,
+                'TARGET_FRAME' as Frame_type,
+                ''::BLOB as Frame_tiff
             FROM locations_union
             LEFT JOIN read_csv('1.idr_streams/stream_files/idr0013-screenA-plates-w-colnames.tsv') as plates ON
                     plates.Plate = locations_union.Plate
@@ -176,7 +184,61 @@ def run_dockerfile_container(
         print(line.decode("utf-8").strip())
 
 
-def get_frame_tiff_from_idr_ch5(frame: str, ftp_file: str, local_frame_tif: str) -> str:
+def find_frame_len(ch5_file: str):
+    """
+    Find the length of the 'time_lapse' dataset within an HDF5 file.
+
+    This function recursively searches for a dataset named 'time_lapse' within
+    the provided HDF5 (or HDF5-like) file. Once found, it returns the size of
+    the dataset, which represents the number of frames.
+
+    Args:
+        ch5_file (str): The path to the HDF5 file to be searched.
+
+    Returns:
+        int: The length of the 'time_lapse' dataset (number of frames).
+
+    Example:
+        >>> find_frame_len('example.h5')
+        100
+
+    Note:
+        This function returns None if the 'time_lapse' dataset is not found
+        within the HDF5 file.
+    """
+
+    def find_time_lapse_len(name, obj):
+        """
+        Recursively search for a dataset named "time_lapse"
+        within an HDF5 (or HDF5-like) file.
+
+        Return the dataset object when found.
+        """
+
+        if isinstance(obj, h5py.Dataset):
+            if "sample" in name and "time_lapse" in name:
+                print("Found 'time_lapse' dataset at:", obj.name)
+                # Return the dataset object
+                return obj.size
+
+        elif isinstance(obj, h5py.Group):
+            # Traverse the group's children recursively
+            for key, value in obj.items():
+                # Recursively search within the group
+                result = find_time_lapse_len(name, value)
+                if result is not None:
+                    # If "time_lapse" is found in a subgroup, return the result
+                    return result
+
+    # Open the HDF5 file in read-only mode
+    with h5py.File(ch5_file, "r") as f:
+        # Start the search from the root group
+        return f.visititems(find_time_lapse_len)
+
+
+def get_frame_tiff_from_idr_ch5(
+    frame: str, local_ch5_file: str, local_frame_tif: str
+) -> str:
     """
     Gather IDR ch5 file and extract a frame as a TIFF, returning the local filepath
     and cleaning up the ch5 afterwards.
@@ -184,8 +246,8 @@ def get_frame_tiff_from_idr_ch5(frame: str, ftp_file: str, local_frame_tif: str)
     Args:
         frame (str):
             The frame number to extract from the IDR ch5 file.
-        ftp_file (str):
-            The name of the IDR ch5 file to retrieve.
+        local_ch5_file (str):
+            The local filepath where the ch5 file may be referenced.
         local_frame_tif (str):
             The local filepath where the extracted TIFF will be saved.
 
@@ -195,61 +257,268 @@ def get_frame_tiff_from_idr_ch5(frame: str, ftp_file: str, local_frame_tif: str)
     """
 
     print(
-        "Working on ",
+        "Working on",
         f"frame: {frame}",
-        f"ftp file: {ftp_file}",
         f"tiff: {local_frame_tif}",
     )
-    # download the ch5 file
-    local_ch5_file = retrieve_ftp_file(
-        ftp_file=str(ftp_file), download_dir=image_download_dir
-    )
 
-    # extract a frame from the ch5 file using bfconvert through a docker container
-    run_dockerfile_container(
-        dockerfile="./5.data_packaging/Dockerfile.bfconvert",
-        image_name="ome_bfconvert",
-        volumes=[f"{os.getcwd()}/5.data_packaging/images/extracted_frame:/app"],
-        command=f"-timepoint {frame} {pathlib.Path(local_ch5_file).name} {str(local_frame_tif)}",
-    )
+    # if we don't already have a file, create it
+    if not pathlib.Path(local_frame_tif).is_file():
+        # extract a frame from the ch5 file using bfconvert through a docker container
+        run_dockerfile_container(
+            dockerfile="./5.data_packaging/Dockerfile.bfconvert",
+            image_name="ome_bfconvert",
+            volumes=[f"{os.getcwd()}:/app"],
+            command=(
+                f"-timepoint {frame} {local_ch5_file} {str(local_frame_tif)}"
+                " -overwrite"
+            ),
+        )
+
+    return local_frame_tif
+
+
+# modified from:
+# https://github.com/WayScience/IDR_stream/blob/main/idrstream/preprocess.py#L194C1-L227C76
+def get_ic_context_frames(target_frame: int, movie_len: int) -> List[int]:
+    """
+    Gather additional non-target frames for use with PyBasic IC.
 
-    # remove the ch5 file as we no longer need it
-    pathlib.Path(local_ch5_file).unlink()
+    This function returns a list of three frames: one frame before the target frame,
+    the target frame itself, and one frame after the target frame. The frames are
+    0-indexed, while the movie length is not. If the target frame is the first frame
+    (0), it returns the target frame and the next two frames. If the target frame is
+    the last frame, it returns the target frame and the two preceding frames.
+
+    Args:
+        target_frame (int):
+            The index of the target frame (0-indexed).
+        movie_len (int):
+            The length of the movie (1-indexed).
+
+    Returns:
+        List[int]:
+            A list of three frame indices for context.
+
+    Example:
+        >>> get_ic_context_frames(2, 5)
+        [1, 2, 3]
+
+        >>> get_ic_context_frames(0, 5)
+        [0, 1, 2]
+
+        >>> get_ic_context_frames(4, 5)
+        [2, 3, 4]
+    """
+
+    # "sandwich" the frames using one frame before and one frame after
+    # the target frame provided from frame_num.
+    if target_frame + 1 <= movie_len:
+        return [target_frame - 1, target_frame, target_frame + 1]
 
-    return f"{image_download_dir}/{str(local_frame_tif)}"
+    # else if we have the first frame, use two frames after
+    elif target_frame == 0:
+        return [target_frame, target_frame + 1, target_frame + 2]
+
+    # otherwise we have the last frame, so use two frames prior
+    else:
+        return [target_frame - 2, target_frame - 1, target_frame]
+
+
+# referenced with modifications
+# from: https://github.com/WayScience/IDR_stream/blob/main/idrstream/preprocess.py#L114
+def pybasic_IC_target_frame_to_tiff(
+    frames_as_arrays: List[np.ndarray], target_frame: int, destination_filename: str
+):
+    """
+    PyBaSiC Illumination correction as described in http://www.nature.com/articles/ncomms14836
+
+    Parameters
+    ----------
+    frames_as_arrays : List[np.ndarray]
+        array of frames to perform illumination correction on
+    target_frame : int
+        target frame within the context of the frames_as_arrays
+        to return.
+    destination_filename : str
+        export the target_frame to a destination filepath specified
+        through this parameter.
+
+    Returns
+    -------
+    str
+        filepath with the IC image as tiff
+    """
+    # capture pybasic warnings
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        flatfield, darkfield = pybasic.basic(
+            frames_as_arrays, darkfield=True, verbosity=False
+        )
+        baseflour = pybasic.background_timelapse(
+            images_list=frames_as_arrays,
+            flatfield=flatfield,
+            darkfield=darkfield,
+            verbosity=False,
+        )
+        brightfield_images_corrected_original = pybasic.correct_illumination(
+            images_list=frames_as_arrays,
+            flatfield=flatfield,
+            darkfield=darkfield,
+            background_timelapse=baseflour,
+        )
+
+        # convert corrected images to numpy array, normalize, and convert to uint8
+        brightfield_images_corrected = np.array(brightfield_images_corrected_original)
+        brightfield_images_corrected[brightfield_images_corrected < 0] = (
+            0  # make negatives 0
+        )
+        brightfield_images_corrected = brightfield_images_corrected / np.max(
+            brightfield_images_corrected
+        )  # normalize the data to 0 - 1
+        brightfield_images_corrected = (
+            255 * brightfield_images_corrected
+        )  # Now scale by 255
+        corrected_movie = brightfield_images_corrected.astype(np.uint8)
+
+        # export the target frame to file
+        skimage.io.imsave(fname=destination_filename, arr=corrected_movie[target_frame])
+
+        # return the filepath
+        return destination_filename
+
+
+def read_image_as_binary(image_path: str) -> bytes:
+    """
+    Reads an image file and returns its content as binary data.
+    We read image data files as bytearrays in order to retain their
+    full representation within the data package without data loss or
+    transformation.
+
+    Args:
+        image_path (str): The path to the image file to be read.
+
+    Returns:
+        bytes: The binary content of the image file.
+
+    Example:
+        binary_data = read_image_as_binary("path/to/image.jpg")
+        print(binary_data)
+    """
+    with open(image_path, "rb") as f:
+        return f.read()
 
 
 # specify an image download dir and create it
 image_download_dir = "./5.data_packaging/images/extracted_frame"
 pathlib.Path(image_download_dir).mkdir(parents=True, exist_ok=True)
 
+# specify an export dir and create it
+export_dir = "./5.data_packaging/location_and_ch5_frame_image_data"
+pathlib.Path(export_dir).mkdir(parents=True, exist_ok=True)
+
 # get a table of image-relevant data
 table = get_image_union_table()
 
-# create a new table from awkward array to help join the multi-dim image data
-table = ak.to_arrow_table(
-    # join a pyarrow table as an awkward array with a new field for multi-dim tiff data
-    ak.with_field(
-        array=ak.from_arrow(array=table),
-        what=[
-            # read the tiff as a numpy array
-            tifffile.imread(
-                # gather tiff frame from ch5 for every row in location union data
-                get_frame_tiff_from_idr_ch5(
-                    frame=frame, ftp_file=ftp_file, local_frame_tif=local_frame_tif
+# iterate through location union data
+for unique_file in pc.unique(table["IDR_FTP_ch5_location"]).to_pylist():
+
+    # download the ch5 file
+    local_ch5_file = retrieve_ftp_file(
+        ftp_file=unique_file, download_dir=image_download_dir
+    )
+
+    # find the movie length
+    movie_length = find_frame_len(ch5_file=local_ch5_file)
+
+    # reference rows with the same ch5 file
+    for batch in table.filter(
+        pc.equal(table["IDR_FTP_ch5_location"], unique_file)
+    ).to_batches(max_chunksize=1):
+
+        # convert to a dictionary for the row, where the dictionary
+        # elements include the column name as key and
+        # value as the value from a single row in the table.
+        row = batch.to_pydict()
+
+        # reference a target frame as an integer
+        target_frame = int(row["Frames"][0])
+
+        # loop through frames to extract them
+        frames_to_tiffs = {
+            # for each frame, extract a tiff from the ch5
+            str(frame): get_frame_tiff_from_idr_ch5(
+                frame=frame,
+                local_ch5_file=local_ch5_file,
+                local_frame_tif=(
+                    f"{image_download_dir}/"
+                    + row["DNA_dotted_notation"][0].replace(
+                        f"_{target_frame}.tif", f"_{frame}.tif"
+                    )
+                ),
+            )
+            # gather all frames based on a target frame
+            for frame in get_ic_context_frames(
+                target_frame=target_frame, movie_len=movie_length
+            )
+        }
+
+        # read the tiffs as arrays for use with pybasic
+        # and then add the IC image filepath as a new
+        # element along with the others
+        frames_to_tiffs[f"{target_frame}_IC"] = pybasic_IC_target_frame_to_tiff(
+            frames_as_arrays=[
+                skimage.io.imread(fname=tiff_file)
+                for tiff_file in frames_to_tiffs.values()
+            ],
+            target_frame=list(
+                idx
+                for idx, frame in enumerate(frames_to_tiffs.keys())
+                if frame == str(target_frame)
+            )[0],
+            destination_filename=(
+                f"{image_download_dir}/"
+                + row["DNA_dotted_notation"][0].replace(
+                    f"_{target_frame}.tif", f"_{target_frame}_IC.tif"
                 )
+            ),
+        )
+
+        # create record batches from the frames_to_tiffs
+        rows = [
+            pa.RecordBatch.from_pydict(
+                # retain data from original row if our frame matches the original
+                {
+                    **row,
+                    "Frame_tiff": [read_image_as_binary(image_path=frame_tiff)],
+                }
+                if row["Frames"][0] == str(frame_number)
+                # otherwise, create new rows with relevant IC-focused frame data
+                else {
+                    **row,
+                    "Frames": [str(frame_number)],
+                    "DNA_dotted_notation": [frame_tiff],
+                    "Frame_type": (
+                        ["IC_FRAME"]
+                        if "_IC" not in frame_number
+                        else ["IC_TARGET_FRAME"]
+                    ),
+                    "Frame_tiff": [read_image_as_binary(image_path=frame_tiff)],
+                }
             )
-            # iterate through location union data
-            for (frame,), (ftp_file,), (local_frame_tif,) in table.select(
-                ["Frames", "IDR_FTP_ch5_location", "DNA_dotted_notation"]
-            ).to_batches(max_chunksize=1)
-        ],
-        # name the new field / column
-        where="ch5_frame_tiff_unaltered",
-    )
-)
+            for frame_number, frame_tiff in frames_to_tiffs.items()
+        ]
 
-# write the table to parquet file
-parquet.write_table(
-    table=table, where="5.data_packaging/location_and_ch5_frame_image_data.parquet"
-)
+        # write a table with the row baches
+        parquet.write_table(
+            # create a table from the row batches
+            table=pa.Table.from_batches(rows),
+            where=f"{export_dir}/{pathlib.Path(local_ch5_file).stem}.frame_{row['Frames'][0]}.parquet",
+        )
+
+        # remove the tiff files as we no longer need them
+        """for tiff in frames_to_tiffs.values():
+            pathlib.Path(tiff).unlink()"""
+
+    # remove the ch5 file as we no longer need it
+    # pathlib.Path(local_ch5_file).unlink()
diff --git a/poetry.lock b/poetry.lock
index c4b9d36..4453f0e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -30,80 +30,6 @@ tests = ["attrs[tests-no-zope]", "zope-interface"]
 tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
 tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
-[[package]]
-name = "awkward"
-version = "2.6.3"
-description = "Manipulate JSON-like data with NumPy-like idioms."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "awkward-2.6.3-py3-none-any.whl", hash = "sha256:2a87dccc13de843600f09b16561e689a3cdcdb5d13ebcbb69c80bfa525179cbb"},
-    {file = "awkward-2.6.3.tar.gz", hash = "sha256:481a1640760aa174cd34bf60d45920f70cfe8b555403962c4e5864929b0548e8"},
-]
-
-[package.dependencies]
-awkward-cpp = "32"
-fsspec = ">=2022.11.0"
-importlib-metadata = {version = ">=4.13.0", markers = "python_version < \"3.12\""}
-numpy = ">=1.18.0"
-packaging = "*"
-
-[[package]]
-name = "awkward-cpp"
-version = "32"
-description = "CPU kernels and compiled extensions for Awkward Array"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "awkward-cpp-32.tar.gz", hash = "sha256:ad8c5af86206fb0f434b16749cc5cca69a15a88e16ca43e91e56c5cca77421f0"},
-    {file = "awkward_cpp-32-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:478182d275d020f76e85c461be9b297075267ce78fa7395db1206d1e69b46366"},
-    {file = "awkward_cpp-32-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:cb75345214f1c773b0c61eb02a850bbf560390bdd8c973827119432b971ec2b0"},
-    {file = "awkward_cpp-32-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:06e666e8ebe5a0d6769a5995366306073647b17446b28062807f70a7904dd3b9"},
-    {file = "awkward_cpp-32-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c452da573de52b92f4f66819aa74521225543bf028b2f6d7b1477cbac52063f"},
-    {file = "awkward_cpp-32-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:7b347e5283703b188fde4baea32408cf5c5d9569173d7a7f4e8183d9fe070153"},
-    {file = "awkward_cpp-32-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:2a083bb1cc458b2cfb2bcf371bab47ac07054c211fa38b633815e3748e9c9832"},
-    {file = "awkward_cpp-32-cp310-cp310-win_amd64.whl", hash = "sha256:0c80213069e4c80c568dcae9c29b8c4f973ae59bb9bc2b09af372f1e6929cc28"},
-    {file = "awkward_cpp-32-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:cf43a6a90adb96c366ac6234320e2c6117a8b662e12b01b0e11441c188eeb121"},
-    {file = "awkward_cpp-32-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:91b583980e947a397358b4de484204a2d7e8558296cf8be3b321a8b19abb8ec0"},
-    {file = "awkward_cpp-32-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3686d45c3c7775679fad8f3de25ce298f8dca36ab025f41dfd68c24d7cdb5178"},
-    {file = "awkward_cpp-32-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:37decc9a65e81a9e71ce5d90090b669a2aed2c25d689c5d7547f84979cc08cbb"},
-    {file = "awkward_cpp-32-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:7fdd9b91433f4c1f8a3dd92a19646e305632df6982f49a48b7e3cfaeeadfa690"},
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-
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-numpy = ">=1.18.0"
-
 [[package]]
 name = "cachetools"
 version = "5.3.3"
@@ -353,39 +279,37 @@ files = [
 ]
 
 [[package]]
-name = "fsspec"
-version = "2024.3.1"
-description = "File-system specification"
+name = "h5py"
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+description = "Read and write HDF5 files from Python"
 optional = false
 python-versions = ">=3.8"
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-[package.extras]
-abfs = ["adlfs"]
-adl = ["adlfs"]
-arrow = ["pyarrow (>=1)"]
-dask = ["dask", "distributed"]
-devel = ["pytest", "pytest-cov"]
-dropbox = ["dropbox", "dropboxdrivefs", "requests"]
-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
-fuse = ["fusepy"]
-gcs = ["gcsfs"]
-git = ["pygit2"]
-github = ["requests"]
-gs = ["gcsfs"]
-gui = ["panel"]
-hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
-libarchive = ["libarchive-c"]
-oci = ["ocifs"]
-s3 = ["s3fs"]
-sftp = ["paramiko"]
-smb = ["smbprotocol"]
-ssh = ["paramiko"]
-tqdm = ["tqdm"]
+[package.dependencies]
+numpy = ">=1.17.3"
 
 [[package]]
 name = "idna"
@@ -399,23 +323,36 @@ files = [
 ]
 
 [[package]]
-name = "importlib-metadata"
-version = "7.1.0"
-description = "Read metadata from Python packages"
+name = "imageio"
+version = "2.34.1"
+description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-zipp = ">=0.5"
+numpy = "*"
+pillow = ">=8.3.2"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-perf = ["ipython"]
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+all-plugins = ["astropy", "av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
+all-plugins-pypy = ["av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
+build = ["wheel"]
+dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
+docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
+ffmpeg = ["imageio-ffmpeg", "psutil"]
+fits = ["astropy"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
+gdal = ["gdal"]
+itk = ["itk"]
+linting = ["black", "flake8"]
+pillow-heif = ["pillow-heif"]
+pyav = ["av"]
+test = ["fsspec[github]", "pytest", "pytest-cov"]
+tifffile = ["tifffile"]
 
 [[package]]
 name = "lancedb"
@@ -455,6 +392,43 @@ docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocs-ultralytics-plugi
 embeddings = ["awscli (>=1.29.57)", "boto3 (>=1.28.57)", "botocore (>=1.31.57)", "cohere", "google-generativeai", "huggingface-hub", "instructorembedding", "open-clip-torch", "openai (>=1.6.1)", "pillow", "sentence-transformers", "torch"]
 tests = ["aiohttp", "duckdb", "pandas (>=1.4)", "polars (>=0.19)", "pytest", "pytest-asyncio", "pytest-mock", "pytz"]
 
+[[package]]
+name = "lazy-loader"
+version = "0.4"
+description = "Makes it easy to load subpackages and functions on demand."
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+packaging = "*"
+
+[package.extras]
+dev = ["changelist (==0.5)"]
+lint = ["pre-commit (==3.7.0)"]
+test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
+
+[[package]]
+name = "networkx"
+version = "3.3"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.10"
+files = [
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+]
+
+[package.extras]
+default = ["matplotlib (>=3.6)", "numpy (>=1.23)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["myst-nb (>=1.0)", "numpydoc (>=1.7)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
+extra = ["lxml (>=4.6)", "pydot (>=2.0)", "pygraphviz (>=1.12)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -605,6 +579,92 @@ files = [
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 ]
 
+[[package]]
+name = "pillow"
+version = "10.3.0"
+description = "Python Imaging Library (Fork)"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "scipy-1.13.1.tar.gz", hash = "sha256:095a87a0312b08dfd6a6155cbbd310a8c51800fc931b8c0b84003014b874ed3c"},
+]
+
+[package.dependencies]
+numpy = ">=1.22.4,<2.3"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
+test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
 [[package]]
 name = "semver"
 version = "3.0.2"
@@ -1084,22 +1248,7 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
-[[package]]
-name = "zipp"
-version = "3.18.1"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
-    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
-
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "37885f4cc48dbd59ecdc84c2f46bc48b189ff1e71171f842454846005037fd43"
+content-hash = "482f381c6a0f8bbc8db7d697233961cb14cbebd027cefe067f7bbcecad60f6bb"
diff --git a/pyproject.toml b/pyproject.toml
index e585918..acc39f9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,8 +15,9 @@ pyarrow = ">=12,<15.0.1"
 lancedb = "^0.6.7"
 pandas = "^2.2.1"
 docker = "^7.0.0"
-tifffile = "^2024.2.12"
-awkward = "^2.6.3"
+h5py = "^3.11.0"
+pybasic = { git = "https://github.com/WayScience/BaSiCPy.git", branch="mitocheck_data" }
+scikit-image = "^0.23.2"
 
 [tool.poe.tasks.package_data]
 # run a task to package data