Data quality - Revision history

Eva Wiechers: /* Data Quality in the open energy database (oedb) */

2016-07-26T19:17:37Z

‎Data Quality in the open energy database (oedb)

Eva Wiechers: /* Data Quality in the open energy database (oedb) */

2016-07-26T18:59:14Z

‎Data Quality in the open energy database (oedb)

Eva Wiechers: /* Data Quality and Quality Criteria */

2016-07-26T18:37:43Z

‎Data Quality and Quality Criteria

Eva Wiechers: /* Data Quality in the open energy database (oedb) */

2016-07-26T18:25:39Z

‎Data Quality in the open energy database (oedb)

Eva Wiechers: /* Data Quality in the open energy database (oedb) */

2016-07-26T13:07:52Z

‎Data Quality in the open energy database (oedb)

Eva Wiechers: /* Data Quality in the open energy database (oedb) */

2016-07-26T13:07:07Z

‎Data Quality in the open energy database (oedb)

Eva Wiechers: /* Data Quality and Quality Criteria */

2016-07-26T13:04:30Z

‎Data Quality and Quality Criteria

Eva Wiechers: /* Data Quality in the open energy database (oedb) */

2016-07-26T13:04:13Z

‎Data Quality in the open energy database (oedb)

Eva Wiechers: /* Data Quality in the open energy database (oedb) */

2016-07-26T13:03:23Z

‎Data Quality in the open energy database (oedb)

Eva Wiechers: /* Data Quality in the open energy database (oedb) */

2016-07-26T13:03:07Z

‎Data Quality in the open energy database (oedb)

@@ Line 12: / Line 12: @@
 Data, datasets,&nbsp;descriptive data and metadata have to meet "quality criteria, such as accuracy, completeness, consistency, and actuality. [...] Additionally, Open Data credibility and transparency are crucial in order for Open Data to be trustworthy. Origin, originality, and data changes must be traceable and quality features testable." ([http://open-data.fokus.fraunhofer.de/en/projects/open-data-quality/ Fraunhofer FOKUS], see also [https://joinup.ec.europa.eu/sites/default/files/d2.1.2_training_module_2.2_open_data_quality_v1.00_en.pdf here])
@@ Line 19: / Line 20: @@
 The database set-up of the [[Database|oedb]] is designed to support users in achieving good data quality:<br/>
-#The interfaces (API and web interface) of the database structure the data and the metadata. In this way the database tries to ensure for instance the submission of all relevant metadata belonging to a dataset. Another example is the automatically generated primary key, which ensures that every row in a table has got an unique identifier. For further information see also [[DatabaseRules|DatabaseRules]].
+#The interfaces (API and web interface) of the database structure the data and their metadata. In this way the database tries to ensure for instance the submission of all relevant metadata belonging to a dataset. Another example is the automatically generated primary key, which ensures that every row in a table has got an unique identifier. For further information see also [[DatabaseRules|DatabaseRules]].
 #Plausibility and integration tests are applied to identify mistakes in the data, e.g. the geometrical correctness test which makes the manual testing with SQL-functions obsolete.
 #When the number of users becomes large enough, a third level of data testing and rating based on user evaluations might be implemented.

@@ Line 12: / Line 12: @@
 Data, datasets,&nbsp;descriptive data and metadata have to meet "quality criteria, such as accuracy, completeness, consistency, and actuality. [...] Additionally, Open Data credibility and transparency are crucial in order for Open Data to be trustworthy. Origin, originality, and data changes must be traceable and quality features testable." ([http://open-data.fokus.fraunhofer.de/en/projects/open-data-quality/ Fraunhofer FOKUS], see also [https://joinup.ec.europa.eu/sites/default/files/d2.1.2_training_module_2.2_open_data_quality_v1.00_en.pdf here])
 = Data Quality in the open energy database (oedb)<br/> =
-The database set-up is designed to support users in achieving good data quality:<br/>
+The database set-up of the [[Database|oedb]] is designed to support users in achieving good data quality:<br/>
 #The interfaces (API and web interface) of the database structure the data and the metadata. In this way the database tries to ensure for instance the submission of all relevant metadata belonging to a dataset. Another example is the automatically generated primary key, which ensures that every row in a table has got an unique identifier. For further information see also [[DatabaseRules|DatabaseRules]].
@@ Line 24: / Line 25: @@
 Users can contribute to data quality for instance by following the [[DatabaseRules|DatabaseRules]]. For further reading and guidelines with regard to data management and publication see for instance: [https://okfn.org/opendata/how-to-open-data Open Knowledge Foundation], [http://www.odaf.org/?lvl1=resources&lvl2=papers Open Data Foundation] and [http://software-carpentry.org/ Software Carpentry] (e.g. [https://github.com/swcarpentry/good-enough-practices-in-scientific-computing/blob/gh-pages/good-enough-practices-for-scientific-computing.pdf here]).
+<br/>
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@@ Line 6: / Line 6: @@
 |Sources=https://en.wikipedia.org/wiki/Data_quality; http://iaidq.org/main/glossary.shtml#I
 }}
 = Data Quality and Quality Criteria<br/> =
-Data, datasets,&nbsp;descriptive data and metadata have to meet "quality criteria, such as accuracy, completeness, consistency, and actuality. [...] Additionally, Open Data credibility and transparency are crucial in order for Open Data to be trustworthy. Origin, originality, and data changes must be traceable and quality features testable." ([http://open-data.fokus.fraunhofer.de/en/projects/open-data-quality/ Fraunhofer FOKUS])
+Data, datasets,&nbsp;descriptive data and metadata have to meet "quality criteria, such as accuracy, completeness, consistency, and actuality. [...] Additionally, Open Data credibility and transparency are crucial in order for Open Data to be trustworthy. Origin, originality, and data changes must be traceable and quality features testable." ([http://open-data.fokus.fraunhofer.de/en/projects/open-data-quality/ Fraunhofer FOKUS], see also [https://joinup.ec.europa.eu/sites/default/files/d2.1.2_training_module_2.2_open_data_quality_v1.00_en.pdf here])
-−
-−
-−

@@ Line 11: / Line 11: @@
 Data, datasets,&nbsp;descriptive data and metadata have to meet "quality criteria, such as accuracy, completeness, consistency, and actuality. [...] Additionally, Open Data credibility and transparency are crucial in order for Open Data to be trustworthy. Origin, originality, and data changes must be traceable and quality features testable." ([http://open-data.fokus.fraunhofer.de/en/projects/open-data-quality/ Fraunhofer FOKUS])
@@ Line 21: / Line 22: @@
 #The interfaces (API and web interface) of the database structure the data and the metadata. In this way the database tries to ensure for instance the submission of all relevant metadata belonging to a dataset. Another example is the automatically generated primary key, which ensures that every row in a table has got an unique identifier. For further information see also [[DatabaseRules|DatabaseRules]].
 #Plausibility and integration tests are applied to identify mistakes in the data, e.g. the geometrical correctness test which makes the manual testing with SQL-functions obsolete.
-Nontheless, the users need to contribute for instance by following the [[DatabaseRules|DatabaseRules]]. For further reading and guidelines with regard to data management and publication see for instance: [https://okfn.org/opendata/how-to-open-data Open Knowledge Foundation], [http://www.odaf.org/?lvl1=resources&lvl2=papers Open Data Foundation] and [http://software-carpentry.org/ Software Carpentry] (e.g. [https://github.com/swcarpentry/good-enough-practices-in-scientific-computing/blob/gh-pages/good-enough-practices-for-scientific-computing.pdf here]).
+Users can contribute to data quality for instance by following the [[DatabaseRules|DatabaseRules]]. For further reading and guidelines with regard to data management and publication see for instance: [https://okfn.org/opendata/how-to-open-data Open Knowledge Foundation], [http://www.odaf.org/?lvl1=resources&lvl2=papers Open Data Foundation] and [http://software-carpentry.org/ Software Carpentry] (e.g. [https://github.com/swcarpentry/good-enough-practices-in-scientific-computing/blob/gh-pages/good-enough-practices-for-scientific-computing.pdf here]).
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@@ Line 11: / Line 11: @@
 Data, datasets,&nbsp;descriptive data and metadata have to meet "quality criteria, such as accuracy, completeness, consistency, and actuality. [...] Additionally, Open Data credibility and transparency are crucial in order for Open Data to be trustworthy. Origin, originality, and data changes must be traceable and quality features testable." ([http://open-data.fokus.fraunhofer.de/en/projects/open-data-quality/ Fraunhofer FOKUS])
@@ Line 19: / Line 20: @@
 #The interfaces (API and web interface) of the database structure the data and the metadata. In this way the database tries to ensure for instance the submission of all relevant metadata belonging to a dataset. Another example is the automatically generated primary key, which ensures that every row in a table has got an unique identifier. For further information see also [[DatabaseRules|DatabaseRules]].
-#Plausibility and integration test are applied to identify mistakes in the data, e.g. the geometrical correctness test which makes the manual testing with SQL-functions obsolete.
+#Plausibility and integration tests are applied to identify mistakes in the data, e.g. the geometrical correctness test which makes the manual testing with SQL-functions obsolete.
 Nontheless, the users need to contribute for instance by following the [[DatabaseRules|DatabaseRules]]. For further reading and guidelines with regard to data management and publication see for instance: [https://okfn.org/opendata/how-to-open-data Open Knowledge Foundation], [http://www.odaf.org/?lvl1=resources&lvl2=papers Open Data Foundation] and [http://software-carpentry.org/ Software Carpentry] (e.g. [https://github.com/swcarpentry/good-enough-practices-in-scientific-computing/blob/gh-pages/good-enough-practices-for-scientific-computing.pdf here]).

@@ Line 12: / Line 12: @@
 For further reading and guidelines with regard to data management and publication see for instance: [https://okfn.org/opendata/how-to-open-data Open Knowledge Foundation], [http://www.odaf.org/?lvl1=resources&lvl2=papers Open Data Foundation] and [http://software-carpentry.org/ Software Carpentry] (e.g. [https://github.com/swcarpentry/good-enough-practices-in-scientific-computing/blob/gh-pages/good-enough-practices-for-scientific-computing.pdf here]).<br/>
@@ Line 23: / Line 24: @@
 #Plausibility and integration test are applied to identify mistakes in the data, e.g. the geometrical correctness test which makes the manual testing with SQL-functions obsolete.
-Nontheless, the users needs to contribute for instance by following the [[DatabaseRules|DatabaseRules]].
+Nontheless, the users needs to contribute for instance by following the [[DatabaseRules|DatabaseRules]]. For further reading and guidelines with regard to data management and publication see for instance: [https://okfn.org/opendata/how-to-open-data Open Knowledge Foundation], [http://www.odaf.org/?lvl1=resources&lvl2=papers Open Data Foundation] and [http://software-carpentry.org/ Software Carpentry] (e.g. [https://github.com/swcarpentry/good-enough-practices-in-scientific-computing/blob/gh-pages/good-enough-practices-for-scientific-computing.pdf here]).
 <br/>

@@ Line 12: / Line 12: @@
 For further reading and guidelines with regard to data management and publication see for instance: [https://okfn.org/opendata/how-to-open-data Open Knowledge Foundation], [http://www.odaf.org/?lvl1=resources&lvl2=papers Open Data Foundation] and [http://software-carpentry.org/ Software Carpentry] (e.g. [https://github.com/swcarpentry/good-enough-practices-in-scientific-computing/blob/gh-pages/good-enough-practices-for-scientific-computing.pdf here]).<br/>
 = Data Quality in the open energy database (oedb)<br/> =
-The quality of all datasets in the '''oedb''' is analysed and rated by defined criteria:<br/>
+The database set-up is designed to support users in achieving good data quality:<br/>
-#[[DatabaseRules|Data integrity]],
+When the number of users is large enough, a third level of data testing and rating based on user evaluations might be implemented
-#based on '''geometry correctness''' and
-#based on '''user evaluation'''.
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