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Description
DataStaR : Data Staging Repository
"_The purpose of DataStaR is to support collaboration and data sharing among researchers during the research process, and to promote publishing or archiving data and high-quality metadata to discipline-specific data centers, and/or to Cornell's own digital repository." (see DataStaR: An Institutional Approach to Research Data Curation)
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- Retangularity - this is very interactive which makes it a poor fit for Kepler
- Column headings - again, listing problem headings is not an issue, but "allow edits" is too interactive
- Data quality control - Kepler can certainly create the histograms or scatter plots of the data, but there wouldn't be the capability to select data values and correct them interactively.
Kepler Workflows
Kepler workflows were developed to illustrate how Kepler might be used as an accessioner's workbench.
FCRepoDateNormalizer
Retrieves a CSV datastream from an object in a Fedora Repository, processes date columns to standardize their format and saves the results to a local file in CSV format.
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FCRepoLatLongSplitter
Retrieves a CSV datastream from an object in a Fedora Repository, splits columns containing both latitude and longitude coordinates into two separate columns and saves the results to a local file in CSV format.
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FCRepoLatitudeNormalizer
Retrieves a CSV datastream from an object in a Fedora Repository, processes latitude columns to standardize their format and saves the results to a local file in CSV format.
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FCRepoLongitudeNormalizer
Retrieves a CSV datastream from an object in a Fedora Repository, processes longitude columns to standardize their format and saves the results to a local file in CSV format.
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Kepler Actors
A number of new actors were created that provide data access and accessioning functionality.
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This actor writes a log file with a summary of changes made during the latest run of the workflow.
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Source file :
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Input port :
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The "Change Log Writer" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
- FCRepoLatitudeNormalizer
- FCRepoLongitudeNormalizer
- FCRepoLatLongSplitter
CSV Datastream Dissemination Actor
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Screenshot :
Required Packages :
- JyFedoREST
- FCRepoKepler - Uses SimpleHTMLFormDialog to display and manage the form.
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Source file :
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CSVDatastreamDisseminationActor.py
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The "CSV Datastream Dissemination" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
- FCRepoLatitudeNormalizer
- FCRepoLongitudeNormalizer
- FCRepoLatLongSplitter
Error Log Writer Actor
This actor writes a log file with a summary of errors encountered during the latest run of the workflow.
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Input ports :
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The "Error Log Writer" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
- FCRepoLatitudeNormalizer
- FCRepoLongitudeNormalizer
- FCRepoLatLongSplitter
Normalize Date Actor
This actor looks at each column that contains a date value and removes extraneous time data when present. It .
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Required Packages :
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- FCRepoKepler - script is based on the RowAnalyzer class in fcrepo.kepler.RowAnalyzer.
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Source file :
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Input port :
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- input : ObjectToken containing a Python tuple or Java array with 2 items :
- the current row number as an integer.
- an ordered list of columns in the row.
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Other inputs :
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The "Normalize Date" script also needs a list of indexes for the columns that contain dates. This can be acquired in one of two ways:
A string parameter on the PythonActor named 'indexes'
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A port named 'indexes' containing a StringToken.
In either case, the string must contain either a comma-separated list of column numbers or a formula describing a regular sequence that can be used to generate the list. The format of the formula is START + INCREMENT * COUNT. For example, the formula 7+4*10 means there are 10 columns in the list, dates occur every 4 columns starting with column 7. This would generate the list 7,11,15,19,23,27,31,35,39,43.
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Output port :
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- output : ObjectToken containing a Python tuple or Java array with 4 items :
- the current row number as an integer.
- a tuple/array of values for each column in the row.
- a tuple/array of changes made.
- a tuple/array of errors encountered.
The "Normalize Date" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
Normalize Latitude Actor
This actor looks at each column that contains a latitude value and assures that all values are valid and in the same format.
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Required Packages :
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- FCRepoKepler - script is based on the RowAnalyzer class in fcrepo.kepler.RowAnalyzer.
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Source file :
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Input port :
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- input : ObjectToken containing a Python tuple or Java array with 2 items :
- the current row number as an integer.
- an ordered list of columns in the row.
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Other inputs :
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The "Normalize Latitude" script also needs a list of indexes for the columns that contain latitudes. This can be acquired in one of two ways:
A string parameter on the PythonActor named 'indexes'
OR
A port named 'indexes' containing a StringToken.
In either case, the string must contain either a comma-separated list of column numbers or a formula describing a regular sequence that can be used to generate the list. The format of the formula is START + INCREMENT * COUNT. For example, the formula 7+4*10 means there are 10 columns in the list, latitudes occur every 4 columns starting with column 7. This would generate the list 7,11,15,19,23,27,31,35,39,43.
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Output port :
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- output : ObjectToken containing a Python tuple or Java array with 4 items :
- the current row number as an integer.
- a tuple/array of values for each column in the row.
- a tuple/array of changes made.
- a tuple/array of errors encountered.
The "Normalize Latitude" PythonActor is used in the following workflows:
- FCRepoLatNormalizer
Normalize Longitude Actor
This actor looks at each column that contains a longitude value and assures that all values are valid and in the same format.
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Required Packages :
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- FCRepoKepler - script is based on the RowAnalyzer class in fcrepo.kepler.RowAnalyzer.
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Source file :
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NormalizeDateActorNormalizeLongitudeActor.py
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Input port :
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- input : ObjectToken containing a Python tuple or Java array with 2 items :
- the current row number as an integer.
- an ordered list of columns in the row.
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Other inputs :
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The "Normalize DateLongitude" script also needs a list of indexes for the columns that contain dateslongitudes. This can be acquired in one of two ways:
A string parameter on the PythonActor named 'indexes'
OR
A port named 'indexes' containing a StringToken.
In either case, the string must contain either a comma-separated list of column numbers or a formula describing a regular sequence that can be used to generate the list. The format of the formula is START + INCREMENT * COUNT. For example, the formula 7+4*10 means there are 10 columns in the list, dates longitudes occur every 4 columns starting with column 7. This would generate the list 7,11,15,19,23,27,31,35,39,43.
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Output port :
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- output : ObjectToken containing a Python tuple or Java array with 4 items :
- the current row number as an integer.
- a tuple/array of values for each column in the row.
- a tuple/array of changes made.
- a tuple/array of errors encountered.
The "Normalize DateLongitude" PythonActor is used in the following workflows:
- FCRepoDateNormalizerFCRepoLongNormalizer
Output Prep Actor
This actor sorts through the output created by a RowAnalysis script and routes the data to the proper output writer.
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Source file :
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Input port :
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- input : ObjectToken containing a Python tuple or Java array with 4 items :
- the current row number as an integer.
- a tuple/array of values for each column in the row.
- a tuple/array of changes made.
- a tuple/array of errors encountered.
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Other inputs :
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The "Normalize DateOutput Prep" script also needs the character to be used as a separator between columns in the output text string. This can be acquired in one of two ways:
A string parameter on the PythonActor named 'separator'
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A port named 'separator' containing a StringToken.
In either case, the string must contain a comma-separated list of column numbers.
Output ports :
- output : StringToken containg a string representing the ouput row in a CSV file. It is constructed by concatenating the values in the columns array using a separator character.
- changes : ObjectToken containing a tuple with 2 items :
- the current row number as an integer.
- the tuple/array of changes made received on the input port.
- errors : ObjectToken containing a tuple with 2 items :
- the current row number as an integer.
- the tuple/array of errors encountered received on the input port.
The "Output Prep" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
- FCRepoLatitudeNormalizer
- FCRepoLongitudeNormalizer
- FCRepoLatLongSplitter
Row To Columns Actor
This actor splits a text string representing a 'row' into 'columns' using a separator character such as ','.
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Source file :
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Input port :
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- row: StringToken containing a string representation of a single row in a spreadsheet or other data matrix.
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Output port :
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- columns - ObjectToken containing a Python tuple or Java array with 2 items :
- the current row number as an integer.
- a tuple containing an ordered list of values for each column in the row.
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The "Row To Columns" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
Kepler Workflows
Kepler workflows were developed to illustrate how Kepler might be used as an accessioner's workbench.
FCRepoDateNormalizer
Retrieves a CSV datastream from an object in a Fedora Repository, processes the date column to satandardize the format and saves the results to a local file in CSV format.
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Screenshot :
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Source file :
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- FCRepoLatitudeNormalizer
- FCRepoLongitudeNormalizer
- FCRepoLatLongSplitter
Split Lat/Long Actor
This actor looks for columns that contain both latitude and longitude coordinates and splits them into two separate columns. It leaves the latitude in the original column and moves the longitude to a new column immediately next to the latitude.
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Required Packages :
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- FCRepoKepler - script is based on the RowAnalyzer class in fcrepo.kepler.RowAnalyzer.
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Source file :
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Input port :
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- input : ObjectToken containing a Python tuple or Java array with 2 items :
- the current row number as an integer.
- an ordered list of columns in the row.
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Other inputs :
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The "Split Lat/Long" script also needs a list of indexes for the columns that contain lat/long coordinates. This can be acquired in one of two ways:
A string parameter on the PythonActor named 'indexes'
OR
A port named 'indexes' containing a StringToken.
In either case, the string must contain either a comma-separated list of column numbers or a formula describing a regular sequence that can be used to generate the list. The format of the formula is START + INCREMENT * COUNT. For example, the formula 7+4*10 means there are 10 columns in the list, coordinates occur every 4 columns starting with column 7. This would generate the list 7,11,15,19,23,27,31,35,39,43.
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Output port :
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- output : ObjectToken containing a Python tuple or Java array with 4 items :
- the current row number as an integer.
- a tuple/array of values for each column in the row.
- a tuple/array of changes made.
- a tuple/array of errors encountered.
The "Split Lat/Long" PythonActor is used in the following workflows:
- FCRepoLatLongSplitter
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Overview
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