diff --git a/query_example_v1.bkr b/query_example_v1_1.bkr
similarity index 68%
rename from query_example_v1.bkr
rename to query_example_v1_1.bkr
index bd73e36df5e3d1319270adcbd8787cf97156e635..da4ed650a841492ee8ef99ac1e805bf8a557a710 100644
--- a/query_example_v1.bkr
+++ b/query_example_v1_1.bkr
@@ -18,12 +18,35 @@
                     "mode": "javascript",
                     "background": "#FFE0F0"
                 }
+            },
+            "languageVersion": "ES2015"
+        },
+        {
+            "name": "IPython",
+            "plugin": "IPython",
+            "setup": "%matplotlib inline\nimport numpy\nimport matplotlib\nfrom matplotlib import pylab, mlab, pyplot\nnp = numpy\nplt = pyplot\nfrom IPython.display import display\nfrom IPython.core.pylabtools import figsize, getfigs\nfrom pylab import *\nfrom numpy import *\n",
+            "view": {
+                "cm": {
+                    "mode": "python"
+                }
             }
         }
     ],
     "cells": [
         {
-            "id": "sectionVN8BXw",
+            "id": "markdownT9rKmh",
+            "type": "markdown",
+            "body": [
+                "Warning: running the first cell of this notebook can take a long time(hours).",
+                "",
+                "One can run safely the second and third cell, that will analyze a precomputed result of the first cell (queryMgO.out).",
+                "",
+                "A more documented version of this notebook will be uploaded soon."
+            ],
+            "evaluatorReader": false
+        },
+        {
+            "id": "sectionQ3dZWh",
             "type": "section",
             "title": "Query Example",
             "level": 1,
@@ -31,7 +54,19 @@
             "collapsed": false
         },
         {
-            "id": "sectionWfNm4q",
+            "id": "markdownKIKNEj",
+            "type": "markdown",
+            "body": [
+                "*Warning*: running the first cell of this notebook can take a long time(hours), we are working to integrate the (much faster) flink query.",
+                "",
+                "One can run safely the second and third cell, that will analyze a precomputed result of the first cell (queryMgO.out).",
+                "",
+                "A more documented version of this notebook will be uploaded soon."
+            ],
+            "evaluatorReader": false
+        },
+        {
+            "id": "sectionzKgCDS",
             "type": "section",
             "title": "Scan the whole NOMAD Archive",
             "level": 2,
@@ -39,20 +74,14 @@
             "collapsed": false
         },
         {
-            "id": "markdownwxOAJP",
-            "type": "markdown",
-            "body": [
-                "Try to filter whole archives based on the summary data.",
-                "Extract not just the data you want to plot but also its identifiers  (provenance)"
-            ],
-            "evaluatorReader": false
-        },
-        {
-            "id": "codesNXl6E",
+            "id": "codecJWYiO",
             "type": "code",
             "evaluator": "IPython",
             "input": {
                 "body": [
+                    "from nomad_structure_tools import query",
+                    "percentageOfArchives = 5",
+                    "",
                     "def hasMgO(stats):",
                     "    els = set(stats.get(\"elements\",{}).keys())",
                     "    return stats and \"Mg\" in els and \"O\" in els and ((stats.get(\"nEigenvalues\", 0) > 0) or stats.get(\"nBands\", 0) > 0)",
@@ -93,7 +122,7 @@
                     "    else:",
                     "        return None",
                     "",
-                    "def methodToFile(outPath, archiveGroupIndexRange = query.archiveSplit(0,1), calculationGroupIndexRange = query.calculationSplit(0,1)):",
+                    "def methodToFile(outPath, archiveGroupIndexRange = query.archiveSplit(percentageOfArchives,101), calculationGroupIndexRange = query.calculationSplit(0,1)):",
                     "    \"\"\"perform query, stores result to file\"\"\"",
                     "    with open(outPath, \"w\", encoding=\"utf-8\") as outF:",
                     "        nomadArch.queryCalcs(",
@@ -114,10 +143,10 @@
                 "state": {}
             },
             "evaluatorReader": true,
-            "lineCount": 55
+            "lineCount": 58
         },
         {
-            "id": "sectionpPYVt5",
+            "id": "sectionLNKIwc",
             "type": "section",
             "title": "Put data in a table",
             "level": 2,
@@ -125,7 +154,7 @@
             "collapsed": false
         },
         {
-            "id": "markdownNWHQtR",
+            "id": "markdownqcviza",
             "type": "markdown",
             "body": [
                 "Organize results in a table, convert units (from SI)"
@@ -133,13 +162,12 @@
             "evaluatorReader": false
         },
         {
-            "id": "codeih6a8T",
+            "id": "codeCSQ8CP",
             "type": "code",
             "evaluator": "IPython",
             "input": {
                 "body": [
                     "import json",
-                    "from ipywidgets import *",
                     "import pandas as pd",
                     "#from nomadcore.unit_conversion.unit_conversion import convert_unit_function",
                     "def convert_unit_function(sourceUnits, targetUnits):",
@@ -177,7 +205,7 @@
                     "datatablestate": {
                         "pagination": {
                             "use": true,
-                            "rowsToDisplay": 50,
+                            "rowsToDisplay": 25,
                             "fixLeft": 0,
                             "fixRight": 0
                         },
@@ -187,6 +215,12 @@
                             "singleConfCalcGIndex",
                             "volumePerAtom"
                         ],
+                        "actualtype": [
+                            0,
+                            "4.4",
+                            2,
+                            "4.4"
+                        ],
                         "actualalign": [
                             "L",
                             "R",
@@ -205,7 +239,14 @@
                             true,
                             true,
                             true
-                        ]
+                        ],
+                        "barsOnColumn": {},
+                        "heatmapOnColumn": {},
+                        "tableFilter": "",
+                        "showFilter": false,
+                        "columnSearchActive": false,
+                        "columnFilter": [],
+                        "columnWidth": []
                     }
                 },
                 "result": {
@@ -1491,15 +1532,15 @@
                 },
                 "selectedType": "Table",
                 "pluginName": "IPython",
-                "shellId": "0CC47EED5B9E4D0B85D5B99C9E4DB0E4",
-                "elapsedTime": 353,
+                "shellId": "DD17159096204C748D91BB5FF8019664",
+                "elapsedTime": 346,
                 "height": 776
             },
             "evaluatorReader": true,
-            "lineCount": 32
+            "lineCount": 31
         },
         {
-            "id": "sectionFZY0gl",
+            "id": "sectionbRaG6F",
             "type": "section",
             "title": "Plot results",
             "level": 2,
@@ -1507,7 +1548,7 @@
             "collapsed": false
         },
         {
-            "id": "codeMcJ2gC",
+            "id": "codegThSLq",
             "type": "code",
             "evaluator": "IPython",
             "input": {
@@ -1525,19 +1566,26 @@
                             "value": "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n  if self._edgecolors == 'face':\n"
                         }
                     ],
-                    "payload": "<div class=\"output_subarea output_png\"><img 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+                    "payload": {
+                        "type": "OutputContainer",
+                        "psubtype": "OutputContainer",
+                        "items": [
+                            "<div class=\"output_subarea output_text\"><pre>&lt;matplotlib.collections.PathCollection at 0x7f2101fa7ed0&gt;</pre></div>",
+                            "<div class=\"output_subarea output_png\"><img 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             "body": [
                 "Having kept reference we can get extra features of the data whe have."