Move sparse datasets to development

DEVELOPMENT.md

@@ -0,0 +1,32 @@
+# Development
+
+## Very sparse datasets
+
+Very sparse or small datasets can provide good explanation on interesting
+details of the plotting subcommands in this extension. Important details are
+for example the handling of prior knowledge and the computation of the recall
+prediction in case of random screening.
+
+The following plot shows the result of a collection of 4 records with 3
+relevant items (inclusions). The relevant items are found in the following
+order:
+
+```
+[1, 1, 0, 1, 0]
+```
+
+![Recall of small dataset example](https://github.com/asreview/asreview-insights/blob/master/figures/tests_small_dataset_recall.png)
+
+The black line is an estimate of the recall after every screened record in a
+naive manner (also refered to as 'random').
+
+The Work Saved over Sampling (WSS) is the difference between the recall of the
+simulation and the theoretical recall of random screening.
+
+![WSS for small dataset example](https://github.com/asreview/asreview-insights/blob/master/figures/tests_small_dataset_wss.png)
+
+The following graph shows the recall versus the WSS. This comparison is
+important because it is the fundamental of the `WSS@95%` metric used in the
+literature about Active Learning for systematic reviewing.
+
+![ERF for small dataset example](https://github.com/asreview/asreview-insights/blob/master/figures/tests_small_dataset_erf.png)

README.md

@@ -168,45 +168,6 @@ horizontal axis shows the proportion of  total number of records in the
 dataset. The steep increase of the ERF in the beginning of the process is
 related to the steep recall curve.
 
-### Very sparse datasets
-
-Very sparse or small datasets can provide good explanation on interesting
-details of the plotting subcommands in this extension. Important details are
-for example the handling of prior knowledge and the computation of the recall
-prediction in case of random screening.
-
-The following plot shows the result of a collection of 4 records with 3
-relevant items (inclusions). The relevant items are found in the following
-order:
-
-```
-[1, 1, 0, 1, 0]
-```
-
-![Recall of small dataset example](https://github.com/asreview/asreview-insights/blob/master/figures/tests_small_dataset_recall.png)
-
-The black line is an estimate of the recall after every screened record in a
-naive manner (also refered to as 'random').
-
-```
-Recall (est) when screening 1 = (3 relevant records / 4 records left)  / 3 = 0.25
-Recall (est) when screening 2 = (1/4) * (3 relevant records / 3 records left)  / 3 +
-                                (3/4) * (2 relevant records / 3 records left)  / 3 = (1/4 + 3/4*2/3) / 3 = 0.25
-
-```
-
-The Work Saved over Sampling (WSS) is the difference between the recall of the
-simulation and the theoretical recall of random screening.
-
-![WSS for small dataset example](https://github.com/asreview/asreview-insights/blob/master/figures/tests_small_dataset_wss.png)
-
-The following graph shows the recall versus the WSS. This comparison is
-important because it is the fundamental of the `WSS@95%` metric used in the
-literature about Active Learning for systematic reviewing.
-
-![ERF for small dataset example](https://github.com/asreview/asreview-insights/blob/master/figures/tests_small_dataset_erf.png)
-
-
 ### Plotting CLI
 
 See `asreview plot -h` for all command line arguments.