diff --git a/.DS_Store b/.DS_Store
index c297da6a..f6092697 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/4j2b_horCR.yaml b/4j2b_horCR.yaml
deleted file mode 100644
index 9e6cb989..00000000
--- a/4j2b_horCR.yaml
+++ /dev/null
@@ -1,231 +0,0 @@
-independent_variables:
-  - header:
-      name: sample
-    values:
-      - value: 4j2b CR bin 1
-      - value: 4j2b CR bin 2
-      - value: 4j2b CR bin 3
-      - value: 4j2b CR bin 4
-      - value: 4j2b CR bin 5
-      - value: 4j2b CR bin 6
-      - value: 4j2b CR bin 7
-      - value: 4j2b CR bin 8
-      - value: 4j2b CR bin 9
-      - value: 4j2b CR bin 10
-      - value: 4j2b CR bin 11
-
-dependent_variables:
-  - header:
-      name: W+jets
-    values:
-      - value: 167.53
-        errors:
-          - symerror: 71.38
-      - value: 83.77
-        errors:
-          - symerror: 107.65
-      - value: 502.60
-        errors:
-          - symerror: 180.89
-      - value: 125.65
-        errors:
-          - symerror: 168.99
-      - value: 209.42
-        errors:
-          - symerror: 72.83
-      - value: 125.65
-        errors:
-          - symerror: 68.64
-      - value: 125.65
-        errors: 
-          - symerror: 98.56
-      - value: 0.00
-      - value: 125.65
-        errors: 
-          - symerror: 87.34
-      - value: 83.77
-        errors:
-          - symerror: 65.34
-      - value: 0.00
-        errors: 
-          - symerror: 38.74 
-  - header:
-      name: single top, s-channel
-    values:
-      - value: 2.43
-        errors:
-          - symerror: 0.58
-      - value: 2.43
-        errors:
-          - symerror: 0.65
-      - value: 2.68
-        errors:
-          - symerror: 0.74
-      - value: 2.10
-        errors:
-          - symerror: 0.72
-      - value: 1.92
-        errors:
-          - symerror: 0.64
-      - value: 1.48
-        errors:
-          - symerror: 0.56
-      - value: 0.90
-        errors:
-          - symerror: 0.39
-      - value: 0.69
-        errors:
-          - symerror: 0.33
-      - value: 0.58
-        errors:
-          - symerror: 0.24
-      - value: 0.51
-        errors:
-          - symerror: 0.21
-      - value: 0.35
-        errors:
-          - symerror: 0.16
-  - header:
-      name: single top, t-channel
-    values:
-      - value: 93.54
-        errors:
-          - symerror: 22.49
-      - value: 130.55
-        errors:
-          - symerror: 30.51 
-      - value: 125.16
-        errors:
-          - symerror: 34.37
-      - value: 113.72
-        errors:
-          - symerror: 31.08
-      - value: 76.04
-        errors:
-          - symerror: 23.60
-      - value: 80.08
-        errors:
-          - symerror: 26.88
-      - value: 53.16
-        errors:
-          - symerror: 17.97
-      - value: 45.76
-        errors:
-          - symerror: 16.91
-      - value: 34.99
-        errors:
-          - symerror: 14.34
-      - value: 30.95
-        errors:
-          - symerror: 11.01
-      - value: 35.66
-        errors:
-          - symerror: 14.05
-  - header:
-      name: tW
-    values:
-      - value: 305.89
-        errors:
-          - symerror: 66.61
-      - value: 453.69
-        errors:
-          - symerror: 118.41
-      - value: 321.00
-        errors:
-          - symerror: 88.65
-      - value: 256.92
-        errors:
-          - symerror: 77.19
-      - value: 180.45
-        errors:
-          - symerror: 61.59
-      - value: 143.27
-        errors:
-          - symerror: 50.40
-      - value: 117.28
-        errors:
-          - symerror: 44.33
-      - value: 80.10
-        errors:
-          - symerror: 33.23
-      - value: 64.99
-        errors:
-          - symerror: 26.78
-      - value: 58.34
-        errors:
-          - symerror: 24.93
-      - value: 43.83
-        errors:
-          - symerror: 21.73
-  - header:
-      name: ttbar
-    values:
-      - value: 8025.68
-        errors:
-          - symerror: 2228.73
-      - value: 14588.12
-        errors:
-          - symerror: 4091.35
-      - value: 7870.49
-        errors: 
-          - symerror: 2729.98
-      - value: 4968.02
-        errors:
-          - symerror: 1916.02
-      - value: 3508.46
-        errors:
-          - symerror: 1512.30
-      - value: 2597.63
-        errors:
-          - symerror: 1246.67
-      - value: 1965.77
-        errors:
-          - symerror: 920.95
-      - value: 1320.99
-        errors:
-          - symerror: 819.50
-      - value: 1090.04
-        errors:
-          - symerror: 614.33
-      - value: 829.54
-        errors:
-          - symerror: 465.73
-      - value: 666.96
-        errors:
-          - symerror: 405.96
-  - header:
-      name: total
-    values:
-      - value: 8595.07
-        errors:
-          - symerror: 2331.90
-      - value: 15258.55
-        errors:
-          - symerror: 4264.57
-      - value: 8821.93
-        errors:
-          - symerror: 2914.95
-      - value: 5466.41
-        errors:
-          - symerror: 2060.48
-      - value: 3976.29
-        errors:
-          - symerror: 1627.32
-      - value: 2948.11
-        errors:
-          - symerror: 1326.71
-      - value: 2262.76
-        errors:
-          - symerror: 1002.38
-      - value: 1447.53
-        errors:
-          - symerror: 850.66
-      - value: 1316.25
-        errors:
-          - symerror: 691.95
-      - value: 1003.11
-        errors:
-          - symerror: 512.32
-      - value: 746.80
-        errors:
-          - symerror: 435.96
\ No newline at end of file
diff --git a/analyses/cms-open-data-ttbar/ttbar_analysis_pipeline.ipynb b/analyses/cms-open-data-ttbar/ttbar_analysis_pipeline.ipynb
index 44f6458b..8b4f8bd9 100644
--- a/analyses/cms-open-data-ttbar/ttbar_analysis_pipeline.ipynb
+++ b/analyses/cms-open-data-ttbar/ttbar_analysis_pipeline.ipynb
@@ -901,31 +901,6 @@
     "utils.file_output.save_histograms(all_histograms['hist_dict'], \"histograms.root\")\n",
     "\n",
     "if USE_INFERENCE:\n",
-    "    utils.file_output.save_histograms(all_histograms['ml_hist_dict'],\n",
-    "                                      fileset,\n",
-    "                                      \"histograms_ml.root\",\n",
-    "                                      utils.config[\"ml\"][\"FEATURE_NAMES\"],\n",
-    "                                      add_offset=True)\n",
-    "    #Ok, here supposely needs to be the  \n",
-    "    # REANA platform runs each workflow in an independent workspace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7a53282f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Merging step\n",
-    "import uproot\n",
-    "#LIST_OF_FILES_PER_SAMPLE is a list of 9 files containing histograms per sample \n",
-    "\n",
-    "with uproot.recreate(\"histograms_merged.root\") as f_out:\n",
-    "    for h_file in LIST_OF_FILES_PER_SAMPLE:\n",
-    "        with uproot.open(\"h_file\") as f_per_sample:\n",
-    "            for key, value in f_per_sample.items():\n",
-    "                f_out[key] = value "
     "    utils.file_output.save_histograms(all_histograms['ml_hist_dict'], \"histograms_ml.root\", add_offset=True)"
    ]
   },
@@ -974,7 +949,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "code",
    "execution_count": 16,
    "id": "09a83712",
@@ -1190,7 +1164,7 @@
     "We have a channel for each ML observable:"
    ]
   },
-{
+  {
    "cell_type": "code",
    "execution_count": 22,
    "id": "d8e36bac",
@@ -1332,7 +1306,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "if utils.config[\"preservation\"][\"HEP_DATA\"] == False:\n",
+    "if utils.config[\"preservation\"][\"HEP_DATA\"] == True:\n",
     "    import utils.hepdata\n",
     "    #Submission of model prediction\n",
     "    utils.hepdata.submission_hep_data(model, model_prediction, \"hepdata_model\")\n",
@@ -1341,8 +1315,6 @@
    ]
   },
   {
-   "attachments": {},
-
    "cell_type": "markdown",
    "id": "a2ce2d14",
    "metadata": {},
diff --git a/analyses/cms-open-data-ttbar/ttbar_analysis_pipeline.py b/analyses/cms-open-data-ttbar/ttbar_analysis_pipeline.py
index 7a8b85c9..58cb3846 100644
--- a/analyses/cms-open-data-ttbar/ttbar_analysis_pipeline.py
+++ b/analyses/cms-open-data-ttbar/ttbar_analysis_pipeline.py
@@ -25,7 +25,7 @@
 
 # ### Imports: setting up our environment
 
-# In[1]:
+
 
 
 import logging
@@ -70,7 +70,7 @@
 # 
 # The input files are all in the 1–3 GB range.
 
-# In[2]:
+
 
 
 ### GLOBAL CONFIGURATION
@@ -103,7 +103,7 @@
 # 
 # During the processing step, machine learning is used to calculate one of the variables used for this analysis. The models used are trained separately in the `jetassignment_training.ipynb` notebook. Jets in the events are assigned to labels corresponding with their parent partons using a boosted decision tree (BDT). More information about the model and training can be found within that notebook.
 
-# In[3]:
+
 
 
 class TtbarAnalysis(processor.ProcessorABC):
@@ -353,7 +353,7 @@ def postprocess(self, accumulator):
 # 
 # Here, we gather all the required information about the files we want to process: paths to the files and asociated metadata.
 
-# In[4]:
+
 
 
 fileset = utils.file_input.construct_fileset(
@@ -372,7 +372,7 @@ def postprocess(self, accumulator):
 # 
 # Define the func_adl query to be used for the purpose of extracting columns and filtering.
 
-# In[5]:
+
 
 
 def get_query(source):
@@ -458,7 +458,7 @@ def get_query(source):
 # 
 # Using the queries created with `func_adl`, we are using `ServiceX` to read the CMS Open Data files to build cached files with only the specific event information as dictated by the query.
 
-# In[6]:
+
 
 
 if USE_SERVICEX:
@@ -495,7 +495,7 @@ def get_query(source):
 # 
 # When `USE_SERVICEX` is false, the input files need to be processed during this step as well.
 
-# In[7]:
+
 
 
 NanoAODSchema.warn_missing_crossrefs = False # silences warnings about branches we will not use here
@@ -535,7 +535,7 @@ def get_query(source):
 
 print(f"\nexecution took {exec_time:.2f} seconds")
 
-# In[8]:
+
 
 
 # track metrics
@@ -546,7 +546,7 @@ def get_query(source):
 # Let's have a look at the data we obtained.
 # We built histograms in two phase space regions, for multiple physics processes and systematic variations.
 
-# In[9]:
+
 
 
 import utils.plotting  # noqa: E402
@@ -558,7 +558,7 @@ def get_query(source):
 plt.title("$\geq$ 4 jets, 1 b-tag")
 plt.xlabel("$H_T$ [GeV]");
 
-# In[10]:
+
 
 
 all_histograms["hist_dict"]["4j2b"][:, :, "nominal"].stack("process")[::-1].plot(stack=True, histtype="fill", linewidth=1,edgecolor="grey")
@@ -574,7 +574,7 @@ def get_query(source):
 # 
 # We are making of [UHI](https://uhi.readthedocs.io/) here to re-bin.
 
-# In[11]:
+
 
 
 # b-tagging variations
@@ -587,7 +587,7 @@ def get_query(source):
 plt.xlabel("$H_T$ [GeV]")
 plt.title("b-tagging variations");
 
-# In[12]:
+
 
 
 # jet energy scale variations
@@ -598,7 +598,7 @@ def get_query(source):
 plt.xlabel("$m_{bjj}$ [Gev]")
 plt.title("Jet energy variations");
 
-# In[13]:
+
 
 
 # ML inference variables
@@ -626,7 +626,7 @@ def get_query(source):
 # We'll save everything to disk for subsequent usage.
 # This also builds pseudo-data by combining events from the various simulation setups we have processed.
 
-# In[14]:
+
 
 
 utils.file_output.save_histograms(all_histograms['hist_dict'], "histograms.root")
@@ -643,7 +643,7 @@ def get_query(source):
 # A statistical model has been defined in `config.yml`, ready to be used with our output.
 # We will use `cabinetry` to combine all histograms into a `pyhf` workspace and fit the resulting statistical model to the pseudodata we built.
 
-# In[15]:
+
 
 
 import utils.rebinning  # noqa: E402
@@ -659,14 +659,14 @@ def get_query(source):
 
 # We can inspect the workspace with `pyhf`, or use `pyhf` to perform inference.
 
-# In[16]:
+
 
 
 !pyhf inspect workspace.json | head -n 20
 
 # Let's try out what we built: the next cell will perform a maximum likelihood fit of our statistical model to the pseudodata we built.
 
-# In[17]:
+
 
 
 model, data = cabinetry.model_utils.model_and_data(ws)
@@ -678,7 +678,7 @@ def get_query(source):
 
 # For this pseudodata, what is the resulting ttbar cross-section divided by the Standard Model prediction?
 
-# In[18]:
+
 
 
 poi_index = model.config.poi_index
@@ -687,7 +687,7 @@ def get_query(source):
 # Let's also visualize the model before and after the fit, in both the regions we are using.
 # The binning here corresponds to the binning used for the fit.
 
-# In[19]:
+
 
 
 model_prediction = cabinetry.model_utils.prediction(model)
@@ -699,7 +699,7 @@ def get_query(source):
 # ### ML Validation
 # We can further validate our results by applying the above fit to different ML observables and checking for good agreement.
 
-# In[20]:
+
 
 
 # load the ml workspace (uses the ml observable instead of previous method)
@@ -717,7 +717,7 @@ def get_query(source):
 
     cabinetry.workspace.save(ws_pruned, "workspace_ml.json")
 
-# In[21]:
+
 
 
 if USE_INFERENCE:
@@ -725,27 +725,26 @@ def get_query(source):
 
 # We have a channel for each ML observable:
 
-# In[22]:
 
 
 !pyhf inspect workspace_ml.json | head -n 20
 
-# In[23]:
+
 
 
 # obtain model prediction before and after fit
 if USE_INFERENCE:
-    model_prediction_ml = cabinetry.model_utils.prediction(model_ml)
+    model_prediction = cabinetry.model_utils.prediction(model_ml)
     fit_results_mod = cabinetry.model_utils.match_fit_results(model_ml, fit_results)
     model_prediction_postfit = cabinetry.model_utils.prediction(model_ml, fit_results=fit_results_mod)
 
-# In[24]:
+
 
 
 if USE_INFERENCE:
-    utils.plotting.plot_data_mc(model_prediction_ml, model_prediction_postfit, data_ml, config_ml)
+    utils.plotting.plot_data_mc(model_prediction, model_prediction_postfit, data_ml, config_ml)
+
 
-# In[ ]:
 
 
 if utils.config["preservation"]["HEP_DATA"] == True:
@@ -754,7 +753,6 @@ def get_query(source):
     utils.hepdata.submission_hep_data(model, model_prediction, "hepdata_model")
     #Submission of model_ml prediction
     utils.hepdata.submission_hep_data(model_ml, model_prediction_ml,"hepdata_model_ml")
-    
 
 # ### What is next?
 #