diff --git a/Stoner/Core.py b/Stoner/Core.py
index 0a1801210..1d5189c38 100644
--- a/Stoner/Core.py
+++ b/Stoner/Core.py
@@ -1699,12 +1699,17 @@ def add_column(self, column_data, header=None, index=None, func_args=None, repla
         if not replace:
             colums = copy.copy(self.column_headers)
             old_setas = self.setas.clone
-            self.data = DataArray(
-                _np_.append(
-                    self.data[:, :index], _np_.append(_np_.zeros_like(_np__data), self.data[:, index:], axis=1), axis=1
-                ),
-                setas=self.setas.clone,
-            )
+            if index == self.data.shape[1]:  # appending column
+                self.data = DataArray(_np_.append(self.data, _np__data, axis=1), setas=self.setas.clone)
+            else:
+                self.data = DataArray(
+                    _np_.append(
+                        self.data[:, :index],
+                        _np_.append(_np_.zeros_like(_np__data), self.data[:, index:], axis=1),
+                        axis=1,
+                    ),
+                    setas=self.setas.clone,
+                )
             for ix in range(0, index):
                 self.column_headers[ix] = colums[ix]
                 self.setas[ix] = old_setas[ix]
diff --git a/Stoner/analysis/fitting/mixins.py b/Stoner/analysis/fitting/mixins.py
index 86d23b986..569ea9246 100644
--- a/Stoner/analysis/fitting/mixins.py
+++ b/Stoner/analysis/fitting/mixins.py
@@ -222,6 +222,18 @@ def name(self):
         """Name of the model fitted."""
         return self.func.__name__
 
+    @property
+    def dict(self):
+        """Optimal parameters and errors as a Dictionary."""
+        ret = {}
+        for p, v, e, p0 in zip(self.params, self.popt, self.perr):
+            ret[p] = v
+            ret["d_{}", format(p)] = e
+        ret["chi-square"] = self.chisqr
+        ret["red. chi-sqr"] = self.redchi
+        ret["nfev"] = self.nfev
+        return ret
+
     @property
     def full(self):
         """The same as :py:attr:`_curve_fit_result.row`"""
@@ -717,7 +729,22 @@ def __lmfit_one(self, model, data, params, prefix, columns, scale_covar, **kargs
                 ycol=columns.ycol,
             )
 
-            retval = {"fit": (row[::2], fit.covar), "report": fit, "row": row, "full": (fit, row), "data": self}
+            res_dict = {}
+            for ix, p in enumerate(fit.params):
+                res_dict[p] = fit.params[p].value
+                res_dict["d_{}".format(p)] = fit.params[p].stderr
+            res_dict["chi-square"] = fit.chisqr
+            res_dict["red. chi-sqr"] = fit.redchi
+            res_dict["nfeev"] = fit.nfev
+
+            retval = {
+                "fit": (row[::2], fit.covar),
+                "report": fit,
+                "row": row,
+                "full": (fit, row),
+                "data": self,
+                "dict": res_dict,
+            }
             if output not in retval:
                 raise RuntimeError("Failed to recognise output format:{}".format(output))
             else:
@@ -896,15 +923,21 @@ def _odr_one(self, data, model, prefix, _, **kargs):
         # Store our current mask, calculate new column's mask and turn off mask
 
         param_names = getattr(model, "param_names", None)
-        for i in range(len(param_names)):
+        ret_dict = {}
+        for i, p in enumerate(param_names):
             row.extend([fit_result.beta[i], fit_result.sd_beta[i]])
+            ret_dict[p], ret_dict["d_{}".format(p)] = fit_result.beta[i], fit_result.sd_beta[i]
         row.append(fit_result.redchi)
+        ret_dict["chi-square"] = fit_result.chisqr
+        ret_dict["red. chi-sqr"] = fit_result.redchi
+
         retval = {
             "fit": (row[::2], fit_result.cov_beta),
             "report": fit_result,
             "row": row,
             "full": (fit_result, row),
             "data": self,
+            "dict": ret_dict,
         }
         if output not in retval:
             raise RuntimeError("Failed to recognise output format:{}".format(output))
@@ -1221,8 +1254,22 @@ def differential_evolution(self, model, xcol=None, ycol=None, p0=None, sigma=Non
         row = self._record_curve_fit_result(
             model, fit, _.xcol, header, result, replace, residuals=residuals, prefix=prefix, ycol=_.ycol
         )
+        ret_dict = {}
+        for i, p in enumerate(model.param_names):
+            ret_dict[p], ret_dict["d_{}".format(p)] = row[2 * i], row[2 * i + 1]
+        ret_dict["chi-square"] = polish.chisqr
+        ret_dict["red. chi-sqr"] = polish.redchi
+        ret_dict["nfev"] = fit.nfev
+        fit.dict = ret_dict
 
-        retval = {"fit": (row[::2], fit.covar), "report": fit, "row": row, "full": (fit, row), "data": self}
+        retval = {
+            "fit": (row[::2], fit.covar),
+            "report": fit,
+            "row": row,
+            "full": (fit, row),
+            "data": self,
+            "dict": fit.dict,
+        }
         if output not in retval:
             raise RuntimeError("Failed to recognise output format:{}".format(output))
         else:
diff --git a/Stoner/core/utils.py b/Stoner/core/utils.py
index 2ba189d27..72b3dddf4 100644
--- a/Stoner/core/utils.py
+++ b/Stoner/core/utils.py
@@ -76,20 +76,24 @@ def add_core(other, newdata):
         ret = newdata
     elif isinstance(other, Mapping):
         # First check keys all in newdata
-        order = dict()
-        for k in other:
-            try:
-                order[k] = newdata.find_col(k)
-            except KeyError:
-                newdata.add_column(np.ones(len(newdata)) * np.NaN, header=k)
-                order[k] = newdata.shape[1] - 1
-        row = np.ones(newdata.shape[1]) * np.NaN
-        for k in order:
-            row[order[k]] = other[k]
-        newdata.data = np.append(newdata.data, np.atleast_2d(row), axis=0)
+        if len(newdata) == 0:
+            newdata.data = np.atleast_2d(list(other.values()))
+            newdata.column_headers = list(other.keys())
+        else:
+            order = dict()
+            for k in other:
+                try:
+                    order[k] = newdata.find_col(k)
+                except (KeyError, re.error):
+                    newdata.add_column(np.ones(len(newdata)) * np.NaN, header=k)
+                    order[k] = newdata.shape[1] - 1
+            row = np.ones(newdata.shape[1]) * np.NaN
+            for k in order:
+                row[order[k]] = other[k]
+            newdata.data = np.append(newdata.data, np.atleast_2d(row), axis=0)
         ret = newdata
     else:
-        ret = NotImplemented
+        return NotImplemented
     ret._data._setas.shape = ret.shape
     for attr in newdata.__dict__:
         if attr not in ("setas", "metadata", "data", "column_headers", "mask") and not attr.startswith("_"):
diff --git a/doc/samples/folder_fit.py b/doc/samples/folder_fit.py
new file mode 100755
index 000000000..b57aecbc4
--- /dev/null
+++ b/doc/samples/folder_fit.py
@@ -0,0 +1,46 @@
+"""Demo of Fitting a directory of files
+"""
+from os.path import join
+
+from Stoner import __home__, DataFolder, Data
+from Stoner.plot.formats import TexEngFormatter
+from Stoner.Fit import Quadratic
+
+# Set up the directory with our data
+datafiles = join(__home__, "..", "sample-data", "NLIV")
+
+# DataFolder of our data files
+fldr = DataFolder(datafiles, pattern="*.txt", setas="yx.")
+
+# Another Data object to keep the results in
+result = Data()
+
+# Loop over the files in the DataFolder
+for d in fldr:
+    # Fit, returning results as a dictionary
+    row = d.lmfit(Quadratic, output="dict")
+    # Add Extra information from metadata
+    row["Magnetic Field $\\mu_0 H (T)$"] = d["y"]
+    # Add the new row of data to the result.
+    result += row
+
+# Set columns and set the plot axis formatting to use SI precfixes
+result.setas(x="Field", y="b", e="d_b")
+result.template.xformatter = TexEngFormatter
+result.template.yformatter = TexEngFormatter
+
+# Plot
+result.plot(fmt="k.")
+
+# An alternative way to run the Analysis - this time with
+# an orthogonal didstance regression algorithm
+
+# Run the fitt for each file in the fldr. Set the outpout to "data" to
+# Have the amended results replace the existing data files
+fldr.each.odr(Quadratic, output="data")
+# Now take a slice through the metadata to get the files we want.
+result_2 = fldr.metadata.slice(["y", "Quadratic:b", "Quadratic:b err"], output="Data")
+
+# Set the columns assignments and plot
+result_2.setas = "xye"
+result_2.plot(fmt="r.", figure=result.fig)
diff --git a/tests/Stoner/test_Core.py b/tests/Stoner/test_Core.py
index 64f88625e..fe830e485 100644
--- a/tests/Stoner/test_Core.py
+++ b/tests/Stoner/test_Core.py
@@ -277,6 +277,16 @@ def test_operators(self):
         self.assertEqual(f1,self.d2.shape[0]+1,"Failed to add a row by providing a dictionary")
         self.assertEqual(f2,self.d2.shape[1]+1,"Failed to add an extra columns by adding a dictionary with a new key")
         self.assertTrue(np.isnan(f[-1,2]) and np.isnan(f[0,-1]),"Unset values when adding a dictionary not NaN")
+        d=Data()
+        d+=np.ones(5)
+        d+=np.zeros(5)
+        self.assertTrue(np.all(d//1==np.array([1,0])),"Adding 1D arrays should add new rows")
+        try:
+            d+=True
+        except TypeError:
+            pass
+        else:
+            self.assertTrue(False,"Failed to raise TypeError on adding unsupported type")
 
     def test_properties(self):
         self.little=Data()