diff --git a/flamingo_tools/data_conversion.py b/flamingo_tools/data_conversion.py
index 9bc4723..d31dcca 100644
--- a/flamingo_tools/data_conversion.py
+++ b/flamingo_tools/data_conversion.py
@@ -10,7 +10,11 @@
 import numpy as np
 import pybdv
 
-from cluster_tools.utils.volume_utils import write_format_metadata
+try:
+    from cluster_tools.utils.volume_utils import write_format_metadata
+except ImportError:
+    write_format_metadata = None
+
 from elf.io import open_file
 from skimage.transform import rescale
 
diff --git a/reproducibility/templates_processing/REAMDE.md b/reproducibility/templates_processing/REAMDE.md
new file mode 100644
index 0000000..2de26aa
--- /dev/null
+++ b/reproducibility/templates_processing/REAMDE.md
@@ -0,0 +1,18 @@
+# Segmentation and detection workflows
+
+Implements workflows to segment SGNs or IHCs, and to detect ribbon synapses in slurm.
+
+For SGN segmentation run:
+- mean_std_SGN_template.sbatch
+- apply_unet_SGN_template.sbatch
+- segment_unet_SGN_template.sbatch
+
+For IHC segmentation run:
+- mean_std_IHC_template.sbatch
+- apply_unet_IHC_template.sbatch
+- segment_unet_IHC_template.sbatch
+
+For ribbon synapse detection without associated IHC segmentation run
+- detect_synapse_template.sbatch
+For ribbon synapse detection with associated IHC segmentation run
+- detect_synapse_marker_template.sbatch
diff --git a/reproducibility/templates_processing/apply_unet_IHC_template.sbatch b/reproducibility/templates_processing/apply_unet_IHC_template.sbatch
new file mode 100644
index 0000000..0d581ff
--- /dev/null
+++ b/reproducibility/templates_processing/apply_unet_IHC_template.sbatch
@@ -0,0 +1,60 @@
+#!/bin/bash
+#SBATCH --job-name=apply-unet-IHC
+#SBATCH -t 02:00:00             # for gerbil up to ~3 hours
+
+#SBATCH -p grete:shared         # the partition
+#SBATCH -G A100:1               # For requesting 1 A100 GPU.
+#SBATCH -c 1
+#SBATCH --mem 24G
+#SBATCH -a 0-9
+
+source ~/.bashrc
+# micromamba activate micro-sam_gpu
+micromamba activate sam
+
+# Print out some info.
+echo "Submitting job with sbatch from directory: ${SLURM_SUBMIT_DIR}"
+echo "Home directory: ${HOME}"
+echo "Working directory: $PWD"
+echo "Current node: ${SLURM_NODELIST}"
+
+# Run the script
+
+# SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+SCRIPT_REPO=/user/pape41/u12086/Work/my_projects/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# data in n5 format, e.g. GEK11L_PV_GFP_01_fused.n5
+DATA=$2
+# channel in n5 folder, e.g. 0, 1, or 2
+STAIN_CHANNEL=$3
+# segmentation name, as it appears in MoBIE, e.g. IHC_v4
+SEG_NAME=$4
+
+export INPUT=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/"$COCHLEA"/"$DATA"
+
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/"$SEG_NAME"
+
+# export MODEL=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/trained_models/IHC/v4_cochlea_distance_unet_IHC_supervised_2025-07-14
+export MODEL=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/trained_models/IHC/v5_cochlea_distance_unet_IHC_supervised_2025-08-20/
+export PREDICTION_INSTANCES=10
+export INPUT_KEY="setup$STAIN_CHANNEL/timepoint0/s0"
+
+echo "Input directory: ${INPUT}"
+echo "Output directory: ${OUTPUT_FOLDER}"
+echo "Model: ${MODEL}"
+
+cmd_array=(	'import sys,os;'
+	'sys.path.insert(0,os.environ["SCRIPT_DIR"]);'
+	'import unet_prediction;'
+	'unet_prediction.run_unet_prediction_slurm(input_path=os.environ["INPUT"],'
+	'output_folder=os.environ["OUTPUT_FOLDER"],model_path=os.environ["MODEL"],'
+	'input_key=os.environ["INPUT_KEY"],'
+	'prediction_instances=os.environ["PREDICTION_INSTANCES"])')
+cmd="${cmd_array[*]}"
+python -c "$cmd"
+
diff --git a/reproducibility/templates_processing/apply_unet_SGN_template.sbatch b/reproducibility/templates_processing/apply_unet_SGN_template.sbatch
new file mode 100644
index 0000000..fb3d1a9
--- /dev/null
+++ b/reproducibility/templates_processing/apply_unet_SGN_template.sbatch
@@ -0,0 +1,57 @@
+#!/bin/bash
+#SBATCH --job-name=apply-unet-SGN
+#SBATCH -t 02:00:00             # for gerbil up to ~3 hours
+
+#SBATCH -p grete:shared         # the partition
+#SBATCH -G A100:1               # For requesting 1 A100 GPU.
+#SBATCH -c 1
+#SBATCH --mem 24G
+#SBATCH -a 0-9
+
+source ~/.bashrc
+micromamba activate micro-sam_gpu
+
+# Print out some info.
+echo "Submitting job with sbatch from directory: ${SLURM_SUBMIT_DIR}"
+echo "Home directory: ${HOME}"
+echo "Working directory: $PWD"
+echo "Current node: ${SLURM_NODELIST}"
+
+# Run the script
+
+SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# data in n5 format, e.g. GEK11L_PV_GFP_01_fused.n5
+DATA=$2
+# channel in n5 folder, e.g. 0, 1, or 2
+STAIN_CHANNEL=$3
+# segmentation name, as it appears in MoBIE, e.g. SGN_v2 or Calb1_SGN_v2
+SEG_NAME=$4
+
+export INPUT=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/"$COCHLEA"/"$DATA"
+
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/"$SEG_NAME"
+
+export MODEL=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/trained_models/SGN/v2_cochlea_distance_unet_SGN_supervised_2025-05-27
+export PREDICTION_INSTANCES=10
+export INPUT_KEY="setup$STAIN_CHANNEL/timepoint0/s0"
+
+echo "Input directory: ${INPUT}"
+echo "Output directory: ${OUTPUT_FOLDER}"
+echo "Model: ${MODEL}"
+
+cmd_array=(	'import sys,os;'
+	'sys.path.insert(0,os.environ["SCRIPT_DIR"]);'
+	'import unet_prediction;'
+	'unet_prediction.run_unet_prediction_slurm(input_path=os.environ["INPUT"],'
+	'output_folder=os.environ["OUTPUT_FOLDER"],model_path=os.environ["MODEL"],'
+	'input_key=os.environ["INPUT_KEY"],'
+	'prediction_instances=os.environ["PREDICTION_INSTANCES"])')
+cmd="${cmd_array[*]}"
+python -c "$cmd"
+
diff --git a/reproducibility/templates_processing/detect_synapse_marker_template.sbatch b/reproducibility/templates_processing/detect_synapse_marker_template.sbatch
new file mode 100644
index 0000000..eb97e09
--- /dev/null
+++ b/reproducibility/templates_processing/detect_synapse_marker_template.sbatch
@@ -0,0 +1,57 @@
+#!/bin/bash
+#SBATCH --job-name=synapse-marker
+#SBATCH -t 08:00:00             # estimated time, adapt to your needs
+#SBATCH --mail-user=martin.schilling@med.uni-goettingen.de # change this to your mailaddress
+#SBATCH --mail-type=FAIL        # send mail when job begins and ends
+
+#SBATCH -p grete:shared         # the partition
+#SBATCH -G A100:1               # For requesting 1 A100 GPU.
+#SBATCH -A nim00007
+#SBATCH -c 2
+#SBATCH --mem 36G
+
+source ~/.bashrc
+micromamba activate micro-sam_gpu
+
+# Print out some info.
+echo "Submitting job with sbatch from directory: ${SLURM_SUBMIT_DIR}"
+echo "Home directory: ${HOME}"
+echo "Working directory: $PWD"
+echo "Current node: ${SLURM_NODELIST}"
+
+# Run the script
+#python myprogram.py $SLURM_ARRAY_TASK_ID
+
+SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# image channel, e.g. CTBP2 or RibA
+IMAGE_CHANNEL=$2
+# segmentation name, as it appears in MoBIE, e.g. synapses_v3
+IHC_SEG=$3
+
+export INPUT_PATH="$COCHLEA"/images/ome-zarr/"$IMAGE_CHANNEL".ome.zarr
+export MASK_PATH="$COCHLEA"/images/ome-zarr/"$IHC_SEG".ome.zarr
+
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/synapses_v3_"$IHC_SEG"
+
+export MODEL=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/trained_models/Synapses/synapse_detection_model_v3.pt
+export INPUT_KEY="s0"
+export MAX_DISTANCE=8
+
+echo "OUTPUT_FOLDER $OUTPUT_FOLDER"
+echo "MODEL $MODEL"
+
+python ~/flamingo-tools/scripts/synapse_marker_detection/marker_detection.py \
+	--input "$INPUT_PATH" \
+	--input_key $INPUT_KEY \
+	--output_folder "$OUTPUT_FOLDER" \
+	--mask "$MASK_PATH" \
+	--model $MODEL \
+	--max_distance $MAX_DISTANCE \
+	--s3
+
diff --git a/reproducibility/templates_processing/detect_synapse_template.sbatch b/reproducibility/templates_processing/detect_synapse_template.sbatch
new file mode 100644
index 0000000..4cc292b
--- /dev/null
+++ b/reproducibility/templates_processing/detect_synapse_template.sbatch
@@ -0,0 +1,61 @@
+#!/bin/bash
+#SBATCH --job-name=synapse-detect
+#SBATCH -t 42:00:00             # estimated time, adapt to your needs
+#SBATCH --mail-user=martin.schilling@med.uni-goettingen.de # change this to your mailaddress
+#SBATCH --mail-type=FAIL        # send mail when job begins and ends
+
+#SBATCH -p grete:shared         # the partition
+#SBATCH -G A100:1               # For requesting 1 A100 GPU.
+#SBATCH -A nim00007
+#SBATCH -c 2
+#SBATCH --mem 500G
+
+source ~/.bashrc
+micromamba activate micro-sam_gpu
+
+# Print out some info.
+echo "Submitting job with sbatch from directory: ${SLURM_SUBMIT_DIR}"
+echo "Home directory: ${HOME}"
+echo "Working directory: $PWD"
+echo "Current node: ${SLURM_NODELIST}"
+
+# Run the script
+#python myprogram.py $SLURM_ARRAY_TASK_ID
+
+SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# image channel, e.g. CTBP2 or RibA
+IMAGE_CHANNEL=$2
+# segmentation name, as it appears in MoBIE, e.g. synapses_v3
+IHC_SEG=$3
+
+export INPUT_PATH="$COCHLEA"/images/ome-zarr/"$IMAGE_CHANNEL".ome.zarr
+export MASK_PATH="$COCHLEA"/images/ome-zarr/"$IHC_SEG".ome.zarr
+
+# data on NHR
+# export INPUT_PATH=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/"$COCHLEA"/"$DATA"
+# export INPUT_KEY="setup$STAIN_CHANNEL/timepoint0/s0"
+
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/synapses_v3
+
+if ! [[ -f $OUTPUT_FOLDER ]] ; then
+	mkdir -p "$OUTPUT_FOLDER"
+fi
+
+export MODEL=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/trained_models/Synapses/synapse_detection_model_v3.pt
+
+echo "OUTPUT_FOLDER $OUTPUT_FOLDER"
+echo "MODEL $MODEL"
+
+python ~/flamingo-tools/scripts/synapse_marker_detection/run_prediction.py \
+	--input "$INPUT_PATH" \
+	--input_key "$INPUT_KEY" \
+	--output_folder "$OUTPUT_FOLDER" \
+	--model $MODEL \
+	--s3
+
diff --git a/reproducibility/templates_processing/mean_std_IHC_template.sbatch b/reproducibility/templates_processing/mean_std_IHC_template.sbatch
new file mode 100644
index 0000000..d11b91f
--- /dev/null
+++ b/reproducibility/templates_processing/mean_std_IHC_template.sbatch
@@ -0,0 +1,51 @@
+#!/bin/bash
+#SBATCH --job-name=mean-std-IHC
+#SBATCH -t 01:00:00             # usually takes ~20 min
+
+#SBATCH -p standard96s:shared         # the partition
+#SBATCH -A nim00007
+#SBATCH -c 3
+#SBATCH --mem 128G
+
+source ~/.bashrc
+# micromamba activate flamingo13
+micromamba activate sam
+
+# Run the script
+
+# SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+SCRIPT_REPO=/user/pape41/u12086/Work/my_projects/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# data in n5 format, e.g. GEK11L_PV_GFP_01_fused.n5
+DATA=$2
+# channel in n5 folder, e.g. 0, 1, or 2
+STAIN_CHANNEL=$3
+# segmentation name, as it appears in MoBIE, e.g. IHC_v4
+SEG_NAME=$4
+
+export INPUT=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/"$COCHLEA"/"$DATA"
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/"$SEG_NAME"
+export SEG_CLASS="ihc"
+export INPUT_KEY="setup$STAIN_CHANNEL/timepoint0/s0"
+
+if ! [[ -f $OUTPUT_FOLDER ]] ; then
+	mkdir -p "$OUTPUT_FOLDER"
+fi
+
+echo "Input directory: ${INPUT}"
+echo "Output directory: ${OUTPUT_FOLDER}"
+
+cmd_array=(	'import sys,os;'
+	'sys.path.insert(0,os.environ["SCRIPT_DIR"]);'
+	'import unet_prediction;'
+	'unet_prediction.run_unet_prediction_preprocess_slurm(input_path=os.environ["INPUT"],'
+	'input_key=os.environ["INPUT_KEY"],'
+	'output_folder=os.environ["OUTPUT_FOLDER"],seg_class=os.environ["SEG_CLASS"])')
+cmd="${cmd_array[*]}"
+python -c "$cmd"
+
diff --git a/reproducibility/templates_processing/mean_std_SGN_template.sbatch b/reproducibility/templates_processing/mean_std_SGN_template.sbatch
new file mode 100644
index 0000000..f9e3ee9
--- /dev/null
+++ b/reproducibility/templates_processing/mean_std_SGN_template.sbatch
@@ -0,0 +1,49 @@
+#!/bin/bash
+#SBATCH --job-name=mean-std-SGN
+#SBATCH -t 01:00:00             # usually takes ~20 min
+
+#SBATCH -p standard96s:shared         # the partition
+#SBATCH -A nim00007
+#SBATCH -c 3
+#SBATCH --mem 128G
+
+source ~/.bashrc
+micromamba activate flamingo13
+
+# Run the script
+
+SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# data in n5 format, e.g. GEK11L_PV_GFP_01_fused.n5
+DATA=$2
+# channel in n5 folder, e.g. 0, 1, or 2
+STAIN_CHANNEL=$3
+# segmentation name, as it appears in MoBIE, e.g. SGN_v2 or Calb1_SGN_v2
+SEG_NAME=$4
+
+export INPUT=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/"$COCHLEA"/"$DATA"
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/"$SEG_NAME"
+export SEG_CLASS="sgn"
+export INPUT_KEY="setup$STAIN_CHANNEL/timepoint0/s0"
+
+if ! [[ -f $OUTPUT_FOLDER ]] ; then
+	mkdir -p "$OUTPUT_FOLDER"
+fi
+
+echo "Input directory: ${INPUT}"
+echo "Output directory: ${OUTPUT_FOLDER}"
+
+cmd_array=(	'import sys,os;'
+	'sys.path.insert(0,os.environ["SCRIPT_DIR"]);'
+	'import unet_prediction;'
+	'unet_prediction.run_unet_prediction_preprocess_slurm(input_path=os.environ["INPUT"],'
+	'input_key=os.environ["INPUT_KEY"],'
+	'output_folder=os.environ["OUTPUT_FOLDER"],seg_class=os.environ["SEG_CLASS"])')
+cmd="${cmd_array[*]}"
+python -c "$cmd"
+
diff --git a/reproducibility/templates_processing/segment_unet_IHC_template.sbatch b/reproducibility/templates_processing/segment_unet_IHC_template.sbatch
new file mode 100644
index 0000000..8f37afb
--- /dev/null
+++ b/reproducibility/templates_processing/segment_unet_IHC_template.sbatch
@@ -0,0 +1,53 @@
+#!/bin/bash
+#SBATCH --job-name=segment-unet-SGN
+#SBATCH --time 35:00:00             # for gerbil up to ~30 hours
+
+#SBATCH -p standard96s:shared         # the partition
+#SBATCH -A nim00007
+#SBATCH -c 12
+#SBATCH --mem 400G
+
+source ~/.bashrc
+micromamba activate micro-sam_gpu
+
+# Print out some info.
+echo "Submitting job with sbatch from directory: ${SLURM_SUBMIT_DIR}"
+echo "Home directory: ${HOME}"
+echo "Working directory: $PWD"
+echo "Current node: ${SLURM_NODELIST}"
+
+# Run the script
+#python myprogram.py $SLURM_ARRAY_TASK_ID
+
+SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# segmentation name, as it appears in MoBIE, e.g. IHC_v4
+SEG_NAME=$2
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/"$SEG_NAME"
+export MIN_SIZE=1000
+
+# v4a
+# export CENTER_DISTANCE_THRESHOLD=0.4
+# export BOUNDARY_DISTANCE_THRESHOLD=0.5
+# export DISTANCE_SMOOTHING=0
+
+# v4b
+export CENTER_DISTANCE_THRESHOLD=0.5
+export BOUNDARY_DISTANCE_THRESHOLD=0.6
+export DISTANCE_SMOOTHING=0.6
+
+echo "Output directory: ${OUTPUT_FOLDER}"
+
+cmd_array=(	'import sys,os;'
+	'sys.path.insert(0,os.environ["SCRIPT_DIR"]);'
+	'import unet_prediction;'
+	'unet_prediction.run_unet_segmentation_slurm(output_folder=os.environ["OUTPUT_FOLDER"],'
+	'min_size=os.environ["MIN_SIZE"])')
+cmd="${cmd_array[*]}"
+python -c "$cmd"
+
diff --git a/reproducibility/templates_processing/segment_unet_SGN_template.sbatch b/reproducibility/templates_processing/segment_unet_SGN_template.sbatch
new file mode 100644
index 0000000..8ee9371
--- /dev/null
+++ b/reproducibility/templates_processing/segment_unet_SGN_template.sbatch
@@ -0,0 +1,44 @@
+#!/bin/bash
+#SBATCH --job-name=segment-unet-SGN
+#SBATCH --time 10:00:00             # for gerbil up to ~30 hours
+
+#SBATCH -p standard96s:shared         # the partition
+#SBATCH -A nim00007
+#SBATCH -c 8
+#SBATCH --mem 400G
+
+source ~/.bashrc
+micromamba activate micro-sam_gpu
+
+# Print out some info.
+echo "Submitting job with sbatch from directory: ${SLURM_SUBMIT_DIR}"
+echo "Home directory: ${HOME}"
+echo "Working directory: $PWD"
+echo "Current node: ${SLURM_NODELIST}"
+
+# Run the script
+#python myprogram.py $SLURM_ARRAY_TASK_ID
+
+SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# segmentation name, as it appears in MoBIE, e.g. SGN_v2 or Calb1_SGN_v2
+SEG_NAME=$2
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/"$SEG_NAME"
+export MIN_SIZE=1000
+export BOUNDARY_DISTANCE_THRESHOLD=0.5
+
+echo "Output directory: ${OUTPUT_FOLDER}"
+
+cmd_array=(	'import sys,os;'
+	'sys.path.insert(0,os.environ["SCRIPT_DIR"]);'
+	'import unet_prediction;'
+	'unet_prediction.run_unet_segmentation_slurm(output_folder=os.environ["OUTPUT_FOLDER"],'
+	'min_size=os.environ["MIN_SIZE"])')
+cmd="${cmd_array[*]}"
+python -c "$cmd"
+
diff --git a/reproducibility/templates_transfer/mobie_image_template.sbatch b/reproducibility/templates_transfer/mobie_image_template.sbatch
new file mode 100644
index 0000000..22334d4
--- /dev/null
+++ b/reproducibility/templates_transfer/mobie_image_template.sbatch
@@ -0,0 +1,37 @@
+#!/bin/bash
+#SBATCH --job-name=mobie_image
+#SBATCH -t 01:00:00             # estimated time, adapt to your needs
+#SBATCH --mail-user=martin.schilling@med.uni-goettingen.de # change this to your mailaddress
+#SBATCH --mail-type=FAIL        # send mail when job begins and ends
+
+#SBATCH -p standard96s:shared         # the partition
+#SBATCH -A nim00007
+#SBATCH -c 36
+#SBATCH --mem 180G
+
+source ~/.bashrc
+source ~/miniconda3/bin/activate
+source activate mobie
+
+# Run the script
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# data in n5 format, e.g. GEK11L_PV_GFP_01_fused.n5
+DATA=$2
+# segmentation name, as it appears in MoBIE, e.g. PV or Calb1
+CHANNEL_NAME=$3
+# channel in n5 folder, e.g. 0, 1, or 2
+STAIN_CHANNEL=$4
+
+MOBIE_PROJECT="/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/mobie_project/cochlea-lightsheet"
+INPUT_PATH=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/"$COCHLEA"/"$DATA"
+
+INPUT_KEY="setup$STAIN_CHANNEL/timepoint0/s0"
+RESOLUTION="[0.38,0.38,0.38]"
+SCALE_FACTORS="[[2,2,2],[2,2,2],[2,2,2],[2,2,2],[2,2,2],[2,2,2]]"
+CHUNKS="[64,64,64]"
+
+mobie.add_image --input_path "$INPUT_PATH" --input_key "$INPUT_KEY" --root "$MOBIE_PROJECT" \
+	--dataset_name "$COCHLEA" --name "$CHANNEL_NAME" --resolution "$RESOLUTION" \
+	--scale_factors "$SCALE_FACTORS" --chunks "$CHUNKS"
diff --git a/reproducibility/templates_transfer/mobie_segmentation_template.sbatch b/reproducibility/templates_transfer/mobie_segmentation_template.sbatch
new file mode 100644
index 0000000..eb8bbf3
--- /dev/null
+++ b/reproducibility/templates_transfer/mobie_segmentation_template.sbatch
@@ -0,0 +1,36 @@
+#!/bin/bash
+#SBATCH --job-name=mobie_segm
+#SBATCH -t 01:00:00             # estimated time, adapt to your needs
+#SBATCH --mail-user=martin.schilling@med.uni-goettingen.de # change this to your mailaddress
+#SBATCH --mail-type=FAIL        # send mail when job begins and ends
+
+#SBATCH -p standard96s:shared         # the partition
+#SBATCH -A nim00007
+#SBATCH -c 36
+#SBATCH --mem 180G
+
+source ~/.bashrc
+source ~/miniconda3/bin/activate
+source activate mobie
+
+# Run the script
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# data in n5 format, e.g. GEK11L_PV_GFP_01_fused.n5
+DATA=$2
+# segmentation name, as it appears in MoBIE, e.g. PV or Calb1
+CHANNEL_NAME=$3
+
+MOBIE_PROJECT="/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/mobie_project/cochlea-lightsheet"
+INPUT_PATH=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/"$COCHLEA"/"$DATA"
+
+SEGMENTATION_KEY="segmentation"
+
+RESOLUTION="[0.38,0.38,0.38]"
+SCALE_FACTORS="[[2,2,2],[2,2,2],[2,2,2],[2,2,2],[2,2,2],[2,2,2]]"
+CHUNKS="[64,64,64]"
+
+mobie.add_segmentation --input_path "$SEGMENTATION_PATH" --input_key "$SEGMENTATION_KEY" --root "$MOBIE_PROJECT" \
+	--dataset_name "$COCHLEA" --name "$CHANNEL_NAME" --resolution "$RESOLUTION" \
+	--scale_factors "$SCALE_FACTORS" --chunks "$CHUNKS"
diff --git a/reproducibility/templates_transfer/mobie_spots_template.sbatch b/reproducibility/templates_transfer/mobie_spots_template.sbatch
new file mode 100644
index 0000000..2cb4fba
--- /dev/null
+++ b/reproducibility/templates_transfer/mobie_spots_template.sbatch
@@ -0,0 +1,28 @@
+#!/bin/bash
+#SBATCH --job-name=mobie_spots
+#SBATCH -t 00:10:00             # estimated time, adapt to your needs
+#SBATCH --mail-user=martin.schilling@med.uni-goettingen.de # change this to your mailaddress
+#SBATCH --mail-type=FAIL        # send mail when job begins and ends
+
+#SBATCH -p standard96s:shared         # the partition
+#SBATCH -A nim00007
+#SBATCH -c 12
+#SBATCH --mem 16G
+
+source ~/.bashrc
+source ~/miniconda3/bin/activate
+source activate mobie
+
+# Run the script
+
+# name of cochlea, as it appears in MoBIE and the NHR
+COCHLEA=$1
+# segmentation name, as it appears in MoBIE, e.g. synapses_v3 or synapses_v3_ihc_v4
+SPOT_NAME=$2
+
+MOBIE_PROJECT="/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/mobie_project/cochlea-lightsheet"
+
+TABLE_PATH=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/"$SPOT_NAME"/synapse_detection.tsv # synapse_detection_filtered.tsv
+
+mobie.add_spots --input_table "$TABLE_PATH" --root "$MOBIE_PROJECT" \
+	--dataset_name "$COCHLEA" --name "$SPOT_NAME"
diff --git a/reproducibility/templates_transfer/s3_cochlea_template.sh b/reproducibility/templates_transfer/s3_cochlea_template.sh
new file mode 100644
index 0000000..30e4c13
--- /dev/null
+++ b/reproducibility/templates_transfer/s3_cochlea_template.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+MOBIE_DIR=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/mobie_project/cochlea-lightsheet
+# e.g. M_AMD_Runx1_L
+COCHLEA=$1
+
+export BUCKET_NAME="cochlea-lightsheet"
+export SERVICE_ENDPOINT="https://s3.fs.gwdg.de"
+mobie.add_remote_metadata -i $MOBIE_DIR -s $SERVICE_ENDPOINT -b $BUCKET_NAME
+
+rclone --progress copyto "$MOBIE_DIR"/"$COCHLEA" cochlea-lightsheet:cochlea-lightsheet/"$COCHLEA"
diff --git a/reproducibility/templates_transfer/s3_object_measures_template.sh b/reproducibility/templates_transfer/s3_object_measures_template.sh
new file mode 100644
index 0000000..45375f7
--- /dev/null
+++ b/reproducibility/templates_transfer/s3_object_measures_template.sh
@@ -0,0 +1,23 @@
+#!/bin/bash
+
+MOBIE_DIR=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/mobie_project/cochlea-lightsheet
+SEG_TABLE=PV-SGN-v2
+SEG_S3=PV_SGN_v2
+
+COCHLEA_TABLE=M-AMD-N62-L
+COCHLEA_S3=M_AMD_N62_L
+STAINS=("Calb1" "CR")
+
+#COCHLEA_TABLE=M-AMD-Runx1-L
+#COCHLEA_S3=M_AMD_Runx1_L
+#STAINS=("CR" "Ntng1")
+
+#COCHLEA_TABLE=M-LR-000214-L
+#COCHLEA_S3=M_LR_000214_L
+#STAINS=("CR" "Calb1")
+
+for stain in "${STAINS[@]}" ; do
+	# use --dry-run for testing
+	rclone  --progress copyto "$MOBIE_DIR"/tables/measurements2/"$COCHLEA_TABLE"_"$stain"_"$SEG_TABLE"_object-measures.tsv cochlea-lightsheet:cochlea-lightsheet/"$COCHLEA_S3"/tables/"$SEG_S3"/"$stain"_"$SEG_TABLE"_object-measures.tsv
+done
+
diff --git a/reproducibility/templates_transfer/s3_seg_template.sh b/reproducibility/templates_transfer/s3_seg_template.sh
new file mode 100644
index 0000000..2b4f100
--- /dev/null
+++ b/reproducibility/templates_transfer/s3_seg_template.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+MOBIE_DIR=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/mobie_project/cochlea-lightsheet
+# e.g. M_AMD_Runx1_L
+COCHLEA=$1
+# e.g. SGN_v2
+SEG_CHANNEL=$2
+
+export BUCKET_NAME="cochlea-lightsheet"
+export SERVICE_ENDPOINT="https://s3.fs.gwdg.de"
+
+mobie.add_remote_metadata -i $MOBIE_DIR -s $SERVICE_ENDPOINT -b $BUCKET_NAME
+
+rclone --progress copyto "$MOBIE_DIR"/"$COCHLEA"/dataset.json cochlea-lightsheet:cochlea-lightsheet/"$COCHLEA"/dataset.json
+rclone --progress copyto "$MOBIE_DIR"/"$COCHLEA"/images/ome-zarr cochlea-lightsheet:cochlea-lightsheet/"$COCHLEA"/images/ome-zarr
+# take care that segmentation tables containing evaluations (tonotopic mapping, marker labels, etc.) might be overwritten
+rclone --progress copyto "$MOBIE_DIR"/"$COCHLEA"/tables/"$SEG_CHANNEL" cochlea-lightsheet:cochlea-lightsheet/"$COCHLEA"/tables/"$SEG_CHANNEL"
+
diff --git a/reproducibility/templates_transfer/s3_synapse_template.sh b/reproducibility/templates_transfer/s3_synapse_template.sh
new file mode 100644
index 0000000..21634b2
--- /dev/null
+++ b/reproducibility/templates_transfer/s3_synapse_template.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+MOBIE_DIR=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/mobie_project/cochlea-lightsheet
+# e.g. G_EK_000233_L
+COCHLEA=$1
+# e.g. synapse_v3
+SYNAPSE=$2
+
+rclone --progress copyto "$MOBIE_DIR"/"$COCHLEA"/dataset.json cochlea-lightsheet:cochlea-lightsheet/"$COCHLEA"/dataset.json
+rclone --progress copyto "$MOBIE_DIR"/"$COCHLEA"/tables/"$SYNAPSE" cochlea-lightsheet:cochlea-lightsheet/"$COCHLEA"/tables/"$SYNAPSE"
+
diff --git a/reproducibility/tonotopic_mapping/transfer_mobie_ChReef.py b/reproducibility/tonotopic_mapping/transfer_mobie_ChReef.py
new file mode 100644
index 0000000..5fba275
--- /dev/null
+++ b/reproducibility/tonotopic_mapping/transfer_mobie_ChReef.py
@@ -0,0 +1,60 @@
+#!/usr/bin/python
+# -- coding: utf-8 --
+import os
+import subprocess
+
+
+mobie_dir = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/mobie_project/cochlea-lightsheet"
+
+COCHLEAE = [
+    "M_LR_000143_L",
+    "M_LR_000144_L",
+    "M_LR_000145_L",
+    "M_LR_000153_L",
+    "M_LR_000155_L",
+    "M_LR_000189_L",
+    "M_LR_000143_R",
+    "M_LR_000144_R",
+    "M_LR_000145_R",
+    "M_LR_000153_R",
+    "M_LR_000155_R",
+    "M_LR_000189_R",
+]
+seg = "SGN_v2"
+
+COCHLEAE = [
+    "M_LR_000226_L",
+    "M_LR_000227_L",
+    "M_LR_000226_R",
+    "M_LR_000227_R",
+]
+seg = "SGN_v2"
+# seg = "IHC_v4c"
+
+
+COCHLEAE = [
+    "M_AMD_N62_L",
+    "M_AMD_Runx1_L",
+    "M_LR_000099_L",
+    "M_LR_000214_L",
+]
+seg = "PV_SGN_v2"
+
+if "SGN" in seg:
+    tonotopic_dir = os.path.join(mobie_dir, "tables/tonotopic_sgn")
+elif "IHC" in seg:
+    tonotopic_dir = os.path.join(mobie_dir, "tables/tonotopic_ihc")
+else:
+    raise ValueError("Choose either a segmentation channel with 'SGN' or 'IHC'.")
+
+dry_run = False
+
+for cochlea in COCHLEAE:
+    cochlea_table = "-".join(cochlea.split("_"))
+    seg_table = "-".join(seg.split("_"))
+    in_path = os.path.join(tonotopic_dir, f"{cochlea_table}_{seg_table}.tsv")
+    out_path = f"cochlea-lightsheet:cochlea-lightsheet/{cochlea}/tables/{seg}/default.tsv"
+    print(out_path)
+
+    # "--dry-run"
+    subprocess.run(["rclone", "copyto", in_path, out_path], stdout=subprocess.PIPE).stdout.decode('utf-8')
diff --git a/scripts/synapse_marker_detection/add_synapse_per_ihc.py b/scripts/synapse_marker_detection/add_synapse_per_ihc.py
new file mode 100644
index 0000000..2ffa434
--- /dev/null
+++ b/scripts/synapse_marker_detection/add_synapse_per_ihc.py
@@ -0,0 +1,97 @@
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+
+from flamingo_tools.s3_utils import get_s3_path, BUCKET_NAME, SERVICE_ENDPOINT
+
+COCHLEAE = {
+    "M_LR_000226_L": {"seg_name": "IHC_v4c", "component_list": [1]},
+    "M_LR_000226_R": {"seg_name": "IHC_v4c", "component_list": [1]},
+    "M_LR_000227_L": {"seg_name": "IHC_v4c", "component_list": [1]},
+    "M_LR_000227_R": {"seg_name": "IHC_v4c", "component_list": [1]},
+    "M_AMD_OTOF1_L": {"seg_name": "IHC_v4b", "component_list": [3, 11]},
+}
+
+COCHLEA_DIR = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet"
+OUT_DIR = f"{COCHLEA_DIR}/mobie_project/cochlea-lightsheet/tables/syn_per_ihc"
+
+
+def add_syn_per_ihc(args):
+    syn_limit = 25
+
+    if args.output_folder is None:
+        out_dir = OUT_DIR
+    else:
+        out_dir = args.output_folder
+
+    for cochlea in args.cochlea:
+        if args.seg_version is None:
+            seg_version = COCHLEAE[cochlea]["seg_name"]
+        else:
+            seg_version = args.seg_version
+
+        print(f"Evaluating cochlea {cochlea}.")
+
+        ihc_version = seg_version.split("_")[1]
+        syn_per_ihc_dir = f"{COCHLEA_DIR}/predictions/synapses/ihc_counts_{ihc_version}"
+
+        if args.component_list is None:
+            component_list = COCHLEAE[cochlea]["component_list"]
+        else:
+            component_list = args.component_list
+
+        s3_path = os.path.join(f"{cochlea}", "tables", seg_version, "default.tsv")
+        tsv_path, fs = get_s3_path(s3_path, bucket_name=BUCKET_NAME,
+                                   service_endpoint=SERVICE_ENDPOINT)
+        with fs.open(tsv_path, 'r') as f:
+            ihc_table = pd.read_csv(f, sep="\t")
+
+        # synapse_table
+        syn_path = os.path.join(syn_per_ihc_dir, f"ihc_count_{cochlea}.tsv")
+        with open(syn_path, 'r') as f:
+            syn_table = pd.read_csv(f, sep="\t")
+
+        syn_per_IHC = [-1 for _ in range(len(ihc_table))]
+        ihc_table.loc[:, "syn_per_IHC"] = syn_per_IHC
+
+        ihc_table.loc[ihc_table['component_labels'].isin(component_list), 'syn_per_IHC'] = 0
+        zero_syn = ihc_table[ihc_table["syn_per_IHC"] == 0]
+        print(f"Total IHC in component: {len(zero_syn)}")
+
+        for label_id, syn_count in zip(list(syn_table["label_id"]), list(syn_table["synapse_count"])):
+            ihc_table.loc[ihc_table["label_id"] == label_id, "syn_per_IHC"] = syn_count
+        zero_syn = ihc_table[ihc_table["syn_per_IHC"] > syn_limit]
+        print(f"IHC in component with more than 25 synapses: {len(zero_syn)}")
+        zero_syn = ihc_table[ihc_table["syn_per_IHC"] == 0]
+        print(f"IHC in component without synapses: {len(zero_syn)}")
+
+        syn_per_IHC = list(ihc_table.loc[ihc_table['component_labels'].isin(component_list), 'syn_per_IHC'])
+
+        if args.ihc_syn:
+            syn_per_IHC = [s for s in syn_per_IHC if s != 0]
+
+        print(f"Mean syn_per_IHC: {round(sum(syn_per_IHC) / len(syn_per_IHC), 2)}")
+        print(f"Stdv syn_per_IHC: {round(np.std(syn_per_IHC), 2)}")
+        out_path = os.path.join(out_dir, cochlea + "_syn-per-ihc.tsv")
+        ihc_table.to_csv(out_path, sep="\t", index=False)
+
+
+def main():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-c", "--cochlea", type=str, nargs="+", default=COCHLEAE, help="Cochlea(e) to process.")
+    parser.add_argument("-o", "--output_folder", type=str, default=None, help="Path to output folder.")
+    parser.add_argument("-s", "--seg_version", type=str, default=None, help="Path to output folder.")
+    parser.add_argument("--ihc_syn", action="store_true", help="Consider only IHC with synapses.")
+    parser.add_argument("--component_list", type=int, nargs="+", default=None,
+                        help="List of IHC components.")
+
+    args = parser.parse_args()
+
+    add_syn_per_ihc(args)
+
+
+if __name__ == "__main__":
+    main()