Data preparation - Revision history

Maxim@tygron.com: /* Data publishing */

2026-07-07T12:22:21Z

Data publishing

← Older revision		Revision as of 12:22, 7 July 2026
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	===Data publishing===		===Data publishing===
	If possible, the best way to publish data for use in the {{software}} is as a [[WFS]]. A [[WFS]] is preferable because it allows users to import data using only a URL in the [[Geo_Data_Wizard\|Geo data wizard]]. This URL can be saved in a [[Template Project]] and makes it easier for users to import the data, since they only have to select the right URL in the [[Geo_Data_Wizard\|Geo data wizard]] and import the datasets they want to use.		If possible, the best way to publish data for use in the {{software}} is as a [[WFS]]. A [[WFS]] is preferable because it allows users to import data using only a URL in the [[Geo_Data_Wizard\|Geo data wizard]]. This URL can be saved in a [[Template Project]] and makes it easier for users to import the data, since they only have to select the right URL in the [[Geo_Data_Wizard\|Geo data wizard]] and import the datasets they want to use. In a WFS, the data provider can publish multiple datasets which are already prepared to use in the {{software}}.
	In a WFS, the data provider can publish multiple datasets which are already prepared to use in the {{software}}.

	If a WFS is not possible, use a ~~shared~~ folder accessible to all organizational {{software}} users. In the folder, the different prepared datasets, in [[GeoJSON]] format can be stored.		If a WFS is not possible, use a [[GeoShare]] folder accessible to all organizational {{software}} users. In the folder, the different prepared datasets, in [[GeoJSON]] format can be stored.

Maxim@tygron.com at 12:21, 7 July 2026

2026-07-07T12:21:17Z

← Older revision		Revision as of 12:21, 7 July 2026
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	===Why prepare data?===		===Why prepare data?===
	Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[MKP (Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicator\|indicators]] and [[Panel\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. ~~When spoken about data on~~ this page, [[Geo_Data#What_is_Geo_data\| vector data]] ~~is meant~~.		Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[MKP (Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicator\|indicators]] and [[Panel\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. On this page, 'data' refers to [[Geo_Data#What_is_Geo_data\|vector data]].

	===Attributes===		===Attributes===
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	====Naming of features====		====Naming of features====
	Text attributes in the data can only be used for naming features in the {{software}}. Naming of data is done in step 5 of the [[Geo_Data_Wizard#Name_the_features\|Geo data wizard]]. In this step it is possible to choose an attribute value for naming each polygon. It is important to give each area a fitting name, so if you have a lot of areas imported it is easy to distinguish between them. ~~Also, when creating an overlay of these areas and clicking on them, the name value can provide more information by choosing~~ an informative attribute for ~~the name~~.		Text attributes in the data can only be used for naming features in the {{software}}. Naming of data is done in step 5 of the [[Geo_Data_Wizard#Name_the_features\|Geo data wizard]]. In this step it is possible to choose an attribute value for naming each polygon. It is important to give each area a fitting name, so if you have a lot of areas imported it is easy to distinguish between them. Choosing an informative attribute for names provides more detail when users click on areas in an overlay.

	Consider the following example:		Consider the following example:
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	====Extra attributes====		====Extra attributes====
	It also might be convenient to add an extra attribute to your data if you want to create indicators or panels. Consider the following use case: you want to create a panel that pops up when you build a windmill in an area that is not allowed. These areas where a windmill is not allowed are for example Natura2000 areas, quiet areas (stiltegebieden), buffer zones around pipes, residential areas etc. These are all different datasets that can be imported into the {{software}}. In the panel, a [[TQL]] query to check if there are any gridcells where there is a windmill that overlaps with one of these imported areas, will look something like this: SELECT_LOTPOLYGONS_WHERE_FUNCTION_IS_880_AND_AREA_WITH_ATTRIBUTE_IS_.....		It also might be convenient to add an extra attribute to your data if you want to create indicators or panels. Consider the following use case: you want to create a panel that pops up when you build a windmill in an area that is not allowed. These areas where a windmill is not allowed are for example Natura2000 areas, quiet areas (stiltegebieden), buffer zones around pipes, residential areas etc. These are all different datasets that can be imported into the {{software}}. In the panel, a [[TQL]] query to check if there are any gridcells where there is a windmill that overlaps with one of these imported areas, will look something like this: SELECT_LOTPOLYGONS_WHERE_FUNCTION_IS_880_AND_AREA_WITH_ATTRIBUTE_IS_.....
	The dots indicate the name of a common attribute the different datasets have. ~~Therefore, adding to each of these dataset~~ a common attribute name~~, will ensure it~~ to ~~be easier to create~~ the panel and do the windmill building check.		The dots indicate the name of a common attribute the different datasets have. Adding a common attribute name to each dataset simplifies creating the panel and performing the windmill building check.
	The value of the attribute will not matter, as long as it is not 0, because that would translate in the {{software}} to the area not having this attribute. It is only about the attribute name.		The value of the attribute will not matter, as long as it is not 0, because that would translate in the {{software}} to the area not having this attribute. It is only about the attribute name.
	See the steps below for an example of adding an attribute to a dataset:		See the steps below for an example of adding an attribute to a dataset:
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	===Colors===		===Colors===
	In the {{software}} every polygon gets a random color. This will be visible in the legend. ~~It might be desirable for different use~~ cases ~~to have every polygon of~~ the same category ~~have~~ the same color.		In the {{software}} every polygon gets a random color. This will be visible in the legend. In some cases, you may want all polygons in the same category to share the same color.

	For example, if ~~you have~~ a dataset ~~with which houses are suitable for solar panels in where every house~~ is a polygon, ~~you might want~~ to ~~have the different house~~ polygons of the same suitability class ~~the same color so the map is better readable~~. It is possible, after importing the data to manually change the color of one or multiple areas. However, if you use the same dataset for multiple projects, it is more convenient to prepare the data first.		For example, if each house in a solar suitability dataset is a polygon, assigning the same color to polygons within the same suitability class improves readability. It is possible, after importing the data to manually change the color of one or multiple areas. However, if you use the same dataset for multiple projects, it is more convenient to prepare the data first.

	A solution could be to merge the polygons of the same classes so you will end up with one big multi polygon of suitable houses, one big multi polygon of less suitable houses etc. However, you will then not have access anymore to the individual attributes of each house polygon, such as the possible amount of solar panels on the house. In this situation (and numerous others), it might be helpful to add a column to the data with the color code to use by the {{software}}. Read below for the steps:		A solution could be to merge the polygons of the same classes so you will end up with one big multi polygon of suitable houses, one big multi polygon of less suitable houses etc. However, you will then not have access anymore to the individual attributes of each house polygon, such as the possible amount of solar panels on the house. In this situation (and numerous others), it might be helpful to add a column to the data with the color code to use by the {{software}}. Read below for the steps:
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	===Data publishing===		===Data publishing===
	If possible, the best way to publish data for use in the {{software}} is as a [[WFS]]. A [[WFS]] is ~~desirable,~~ because ~~you~~ only ~~have to provide an~~ URL in the [[Geo_Data_Wizard\|Geo data wizard]] ~~to import the data from~~. This URL can be saved in a [[Template Project]] and makes it easier for users to import the data, since they only have to select the right URL in the [[Geo_Data_Wizard\|Geo data wizard]] and import the datasets they want to use.		If possible, the best way to publish data for use in the {{software}} is as a [[WFS]]. A [[WFS]] is preferable because it allows users to import data using only a URL in the [[Geo_Data_Wizard\|Geo data wizard]]. This URL can be saved in a [[Template Project]] and makes it easier for users to import the data, since they only have to select the right URL in the [[Geo_Data_Wizard\|Geo data wizard]] and import the datasets they want to use.
	In a WFS, the data provider can publish multiple datasets which are already prepared to use in the {{software}}.		In a WFS, the data provider can publish multiple datasets which are already prepared to use in the {{software}}.

	If a WFS is not ~~achievable or~~ possible, ~~then consider~~ a shared folder ~~where users of your organization which use the~~ {{software}} ~~have access to~~. In the folder, the different prepared datasets, in [[GeoJSON]] format can be stored.		If a WFS is not possible, use a shared folder accessible to all organizational {{software}} users. In the folder, the different prepared datasets, in [[GeoJSON]] format can be stored.

	~~===Data representation===~~
	~~Vector geo data can consist of point, line and polygon features. In the {{software}} only polygons can be imported. Therefore, take into consideration to already convert point and lines to polygons.~~
	In step 2 of the [[Geo_Data_Wizard\|geo data wizard]], there is also the option to buffer the features by choosing a buffer distance. However, for consistency and ease of use it is much easier to already have the data buffered.

Maxim@tygron.com at 15:04, 2 July 2026

2026-07-02T15:04:26Z

← Older revision		Revision as of 15:04, 2 July 2026
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	===Why prepare data?===		===Why prepare data?===
	Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[MKP (Indicators)\|MKP template]] in the {{software}}. This module consists of [[~~Indicators~~\|indicators]] and [[~~Panels~~\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. When spoken about data on this page, [[Geo_Data#What_is_Geo_data\| vector data]] is meant.		Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[MKP (Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicator\|indicators]] and [[Panel\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. When spoken about data on this page, [[Geo_Data#What_is_Geo_data\| vector data]] is meant.

	===Attributes===		===Attributes===
Line 36:		Line 36:

	====Extra attributes====		====Extra attributes====
	It also might be convenient to add an extra attribute to your data if you want to create indicators or panels. Consider the following use case: you want to create a panel that pops up when you build a windmill in an area that is not allowed. These areas where a windmill is not allowed are for example Natura2000 areas, quiet areas (stiltegebieden), buffer zones around pipes, residential areas etc. These are all different datasets that can be imported ~~in to~~ the software. In the panel, a [[TQL]] query to check if there are any gridcells where there is a windmill that overlaps with one of these imported areas, will look something like this: SELECT_LOTPOLYGONS_WHERE_FUNCTION_IS_880_AND_AREA_WITH_ATTRIBUTE_IS_.....		It also might be convenient to add an extra attribute to your data if you want to create indicators or panels. Consider the following use case: you want to create a panel that pops up when you build a windmill in an area that is not allowed. These areas where a windmill is not allowed are for example Natura2000 areas, quiet areas (stiltegebieden), buffer zones around pipes, residential areas etc. These are all different datasets that can be imported into the {{software}}. In the panel, a [[TQL]] query to check if there are any gridcells where there is a windmill that overlaps with one of these imported areas, will look something like this: SELECT_LOTPOLYGONS_WHERE_FUNCTION_IS_880_AND_AREA_WITH_ATTRIBUTE_IS_.....
	The dots indicate the name of a common attribute the different datasets have. Therefore, adding to each of these dataset a common attribute name, will ensure it to be easier to create the panel and do the windmill building check.		The dots indicate the name of a common attribute the different datasets have. Therefore, adding to each of these dataset a common attribute name, will ensure it to be easier to create the panel and do the windmill building check.
	The value of the attribute will not matter, as long as it is not 0, because that would translate in the {{software}} to the area not having this attribute. It is only about the attribute name.		The value of the attribute will not matter, as long as it is not 0, because that would translate in the {{software}} to the area not having this attribute. It is only about the attribute name.
Line 59:		Line 59:
	In the {{software}} every polygon gets a random color. This will be visible in the legend. It might be desirable for different use cases to have every polygon of the same category have the same color.		In the {{software}} every polygon gets a random color. This will be visible in the legend. It might be desirable for different use cases to have every polygon of the same category have the same color.

	For example, if you have a dataset with which houses are suitable for solar panels in where ~~evere~~ house is a polygon, you might want to have the different house polygons of the same suitability class the same color so the map is better readable. It is possible, after importing the data to manually change the color of one or multiple areas. However, if you use the same dataset for multiple projects, it is more convenient to prepare the data first.		For example, if you have a dataset with which houses are suitable for solar panels in where every house is a polygon, you might want to have the different house polygons of the same suitability class the same color so the map is better readable. It is possible, after importing the data to manually change the color of one or multiple areas. However, if you use the same dataset for multiple projects, it is more convenient to prepare the data first.

	A solution could be to merge the polygons of the same classes so you will end up with one big multi polygon of suitable houses, one big multi polygon of less suitable houses etc. However, you will then not have access anymore to the individual attributes of each house polygon, such as the possible amount of solar panels on the house. In this situation (and numerous others), it might be helpful to add a column to the data with the color code to use by the {{software}}. Read below for the steps:		A solution could be to merge the polygons of the same classes so you will end up with one big multi polygon of suitable houses, one big multi polygon of less suitable houses etc. However, you will then not have access anymore to the individual attributes of each house polygon, such as the possible amount of solar panels on the house. In this situation (and numerous others), it might be helpful to add a column to the data with the color code to use by the {{software}}. Read below for the steps:
Line 82:		Line 82:

	===Data publishing===		===Data publishing===
	If possible, the best way to publish data for use in the {{software}} is as a [[WFS]]. A [[WFS]] is desirable, because you only have to provide an URL in the [[Geo_Data_Wizard\|Geo data wizard]] to import the data from. This URL can be saved in a [[Template ~~project~~]] and makes it easier for users to import the data, since they only have to select the right URL in the [[Geo_Data_Wizard\|Geo data wizard]] and import the datasets they want to use.		If possible, the best way to publish data for use in the {{software}} is as a [[WFS]]. A [[WFS]] is desirable, because you only have to provide an URL in the [[Geo_Data_Wizard\|Geo data wizard]] to import the data from. This URL can be saved in a [[Template Project]] and makes it easier for users to import the data, since they only have to select the right URL in the [[Geo_Data_Wizard\|Geo data wizard]] and import the datasets they want to use.
	In a WFS, the data provider can publish multiple datasets which are already prepared to use in the {{software}}.		In a WFS, the data provider can publish multiple datasets which are already prepared to use in the {{software}}.

Line 88:		Line 88:

	===Data representation===		===Data representation===
	Vector geo data can ~~consists~~ of point, line and polygon features. In the {{software}} only polygons can be imported. Therefore, take into consideration to already convert point and lines to polygons.		Vector geo data can consist of point, line and polygon features. In the {{software}} only polygons can be imported. Therefore, take into consideration to already convert point and lines to polygons.
	In step 2 of the [[Geo_Data_Wizard\|geo data wizard]], there is also the option to buffer the features by ~~chosing~~ a buffer distance. However, for consistency and ease of use it is much easier to already have the data buffered.		In step 2 of the [[Geo_Data_Wizard\|geo data wizard]], there is also the option to buffer the features by choosing a buffer distance. However, for consistency and ease of use it is much easier to already have the data buffered.

Rudolf@tygron.nl at 11:19, 2 November 2022

2022-11-02T11:19:37Z

← Older revision		Revision as of 11:19, 2 November 2022
Line 1:		Line 1:
	===Why prepare data?===		===Why prepare data?===
	Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[~~MKP_~~(Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicators\|indicators]] and [[Panels\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. When spoken about data on this page, [[Geo_Data#What_is_Geo_data\| vector data]] is meant.		Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[MKP (Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicators\|indicators]] and [[Panels\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. When spoken about data on this page, [[Geo_Data#What_is_Geo_data\| vector data]] is meant.

	===Attributes===		===Attributes===
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	If you would like to make use of these text attributes, do a mapping from text to numbers before importing the data.		If you would like to make use of these text attributes, do a mapping from text to numbers before importing the data.

	Consider the following example: you have a dataset with the suitability of buildings for solar panels. The suitability is described in a text attribute with values such as: suitable, not suitable, very suitable etc. We would like to create an indicator in ~~hte~~ {{software}} that calculates some statistics about this dataset per neighborhood. We therefore need to import this suitability attribute as a numerical value into the {{software}}.		Consider the following example: you have a dataset with the suitability of buildings for solar panels. The suitability is described in a text attribute with values such as: suitable, not suitable, very suitable etc. We would like to create an indicator in the {{software}} that calculates some statistics about this dataset per neighborhood. We therefore need to import this suitability attribute as a numerical value into the {{software}}.
	See below for the steps:		See below for the steps:

Line 82:		Line 82:

	===Data publishing===		===Data publishing===
	If possible, the best way to publish data for use in the {{software}} is as a [[WFS]]. A [[WFS]] is desirable, because you only have to provide an URL in the [[Geo_Data_Wizard\|Geo data wizard]] to import the data from. This URL can be saved in a [[~~Project_Template\|~~Template project]] and makes it easier for users to import the data, since they only have to select the right URL in the [[Geo_Data_Wizard\|Geo data wizard]] and import the datasets they want to use.		If possible, the best way to publish data for use in the {{software}} is as a [[WFS]]. A [[WFS]] is desirable, because you only have to provide an URL in the [[Geo_Data_Wizard\|Geo data wizard]] to import the data from. This URL can be saved in a [[Template project]] and makes it easier for users to import the data, since they only have to select the right URL in the [[Geo_Data_Wizard\|Geo data wizard]] and import the datasets they want to use.
	In a WFS, the data provider can publish multiple datasets which are already prepared to use in the {{software}}.		In a WFS, the data provider can publish multiple datasets which are already prepared to use in the {{software}}.

Maxim@tygron.com at 11:33, 7 October 2022

2022-10-07T11:33:44Z

← Older revision		Revision as of 11:33, 7 October 2022
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	~~{{learned\|why to prepare data\|some data preparation tips}}~~

	===Why prepare data?===		===Why prepare data?===
	Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[MKP_(Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicators\|indicators]] and [[Panels\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. When spoken about data on this page, [[Geo_Data#What_is_Geo_data\| vector data]] is meant.		Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[MKP_(Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicators\|indicators]] and [[Panels\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. When spoken about data on this page, [[Geo_Data#What_is_Geo_data\| vector data]] is meant.

Godelief@tygron.com: /* Data representation */

2020-04-24T09:14:26Z

Data representation

← Older revision		Revision as of 09:14, 24 April 2020
Line 91:		Line 91:
	===Data representation===		===Data representation===
	Vector geo data can consists of point, line and polygon features. In the {{software}} only polygons can be imported. Therefore, take into consideration to already convert point and lines to polygons.		Vector geo data can consists of point, line and polygon features. In the {{software}} only polygons can be imported. Therefore, take into consideration to already convert point and lines to polygons.
	In step 2 of the [[~~Geo_Data_wizard~~\|geo data wizard]], there is also the option to buffer the features by chosing a buffer distance. However, for consistency and ease of use it is much easier to already have the data buffered.		In step 2 of the [[Geo_Data_Wizard\|geo data wizard]], there is also the option to buffer the features by chosing a buffer distance. However, for consistency and ease of use it is much easier to already have the data buffered.

Godelief@tygron.com: /* Why preparing data? */

2020-04-24T09:03:01Z

Why preparing data?

← Older revision		Revision as of 09:03, 24 April 2020
Line 1:		Line 1:
	{{learned\|why to prepare data\|some data preparation tips}}		{{learned\|why to prepare data\|some data preparation tips}}

	===Why ~~preparing~~ data?===		===Why prepare data?===
	Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[MKP_(Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicators\|indicators]] and [[Panels\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. When spoken about data on this page, [[Geo_Data#What_is_Geo_data\| vector data]] is meant.		Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[MKP_(Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicators\|indicators]] and [[Panels\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. When spoken about data on this page, [[Geo_Data#What_is_Geo_data\| vector data]] is meant.

Godelief@tygron.nl at 12:41, 11 October 2019

2019-10-11T12:41:09Z

← Older revision		Revision as of 12:41, 11 October 2019
Line 1:		Line 1:
	{{learned\|why to prepare data\|some data preparation ~~examples~~}}		{{learned\|why to prepare data\|some data preparation tips}}

	===Why preparing data?===		===Why preparing data?===
	Preparing data is useful when you have datasets ~~available which~~ you ~~always want to~~ use in ~~your~~ {{software}} projects. For example, the Province of Utrecht has created the [[MKP_(Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicators\|indicators]] and [[Panels\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import into every new project they use the template ~~on, multiple datasets~~. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. When spoken about data on this page, [[Geo_Data#What_is_Geo_data\| vector data]] is meant.		Preparing data is useful when you have datasets you often use in {{software}} projects. For example, the Province of Utrecht has created the [[MKP_(Indicators)\|MKP template]] in the {{software}}. This module consists of [[Indicators\|indicators]] and [[Panels\|panels]] concerning different environmental topics. For their indicators and panels to work, they need to import multiple datasets into every new project they use the template. For such use cases, it can save a lot of time if you prepare these datasets once and then easily import them into every new project. Read below for some considerations on various topics and apply the ones useful for you. When spoken about data on this page, [[Geo_Data#What_is_Geo_data\| vector data]] is meant.

	===Attributes===		===Attributes===

Godelief@tygron.nl at 09:14, 26 August 2019

2019-08-26T09:14:48Z

← Older revision		Revision as of 09:14, 26 August 2019
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	{{learned\|why to prepare data\|~~why t~~}}		{{learned\|why to prepare data\|some data preparation examples}}

	===Why preparing data?===		===Why preparing data?===

Godelief@tygron.nl at 09:14, 26 August 2019

2019-08-26T09:14:34Z

← Older revision		Revision as of 09:14, 26 August 2019
Line 1:		Line 1:
	{{learned\|why to prepare data\|~~data preparation examples~~}}		{{learned\|why to prepare data\|why t}}

	===Why preparing data?===		===Why preparing data?===