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Calculation of Luminosity Profiles for a Sample of G...

Created: 2013-01-05 16:57:08

Credits: Jose Enrique Ruiz

License: Creative Commons Attribution-Share Alike 3.0 Unported License

File type: Adobe PDF

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Extraction of Properties from a Sample of Galaxies

Created: 2013-01-05 16:54:48 | Last updated: 2013-01-05 16:57:46

Credits: Jose Enrique Ruiz

License: Creative Commons Attribution-Share Alike 3.0 Unported License

File type: Adobe PDF

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Galaxies Sample Selection Research Object

Created: 2013-01-05 16:51:56 | Last updated: 2013-01-05 16:59:16

4 items in this pack

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D5.3v2 Calculation of Luminosity Profiles for a Samp...

Created: 2013-01-05 16:45:48 | Last updated: 2013-01-05 16:48:45

Credits: Jose Enrique Ruiz

License: Creative Commons Attribution-Share Alike 3.0 Unported License

File type: Adobe PDF

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Luminosity Profiles Research Object

Created: 2013-01-05 16:34:46 | Last updated: 2013-01-05 16:58:18

7 items in this pack

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Environment (2)

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From a previously selected cube of galaxies residing in a remote database, we provide extragalactic environment parameters for all galaxies sample. This workflow takes as input the path of the tabular *.pckl Python pickle dataset created in the previous workflow, as well as the database connection settings and several criteria on how to filter the potential companions of the target galaxies. It provides a file with the SDSS identifiers of each target galaxy of the sample, environmental estim...

Created: 2013-01-04 | Last updated: 2013-01-14

Credits: Juandesant Jsm

Intialize Sample (2)

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This workflow saves a tabular *.pckl Python pickle dataset in the local file system, containing values calculated on physical parameters associated to potential companions of a sample of target galaxies. These original physical parameters are extracted from a postgreSQL database, containing information of all galaxies covered by the SDSS spectroscopic survey. The workflow first access the external database located in the AMIGA server and selects the target galaxies from the sample (those havi...

Created: 2013-01-04 | Last updated: 2013-01-05

Credits: Juandesant Jsm

Non-redundant protein alignments (1)

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Execute BLASTP program to search protein databases using a protein query: Input file from iPlant Discovery Environment Save the data extract of similar sequences created by BLASTP Execute XMKNR to a compute a non-redundant set from a large collection of protein sequences. Input from the data extract created by BLASTP. Perform multiple sequence alignments using Clustal Omega and TCoffee Input from data extract created by XMKNR To execute this workflow, you need to be logged into the Bio...

Created: 2012-12-19 | Last updated: 2012-12-19

Credits: Carol Lushbough

Ingest.t2flow (1)

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 characterisation of NeXus files

Created: 2012-12-19

Credits: Arifshaon

AIT Matchbox Scenario Compare Image Pair b... (1)

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In this scenario matchbox will compare given image pair based on extracted profile information. User will get a histogram intersection distance value in result. Small value means high similarity, high value means different images. Matchbox in this scenario is installed on remote Linux VM. Digital collection is stored on Windows machine.

Created: 2012-11-24

Credits: Roman

AIT Matchbox Scenario Check Duplicate Pair... (1)

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In this scenario matchbox will check duplicate pair of previously found duplicates in passed digital collection if output information was lost. The pair check does not require the time consumpting whole analysis and is very fast. Matchbox in this scenario is installed on remote Linux VM. Digital collection is stored on Windows machine.

Created: 2012-11-24

Credits: Roman SCAPE

AIT Matchbox Scenario Find Duplicates usin... (1)

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In this scenario matchbox will find duplicates in passed digital collection. All matchbox workflow steps are defined separately using input parameter sequence: clean, extract, train, bowhist and compare. User will get a list of duplicates in result. Matchbox in this scenario is installed on remote Linux VM. Digital collection is stored on Windows machine.

Created: 2012-11-24

Credits: Roman SCAPE

AIT Matchbox Scenario Professional (3)

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In this scenario matchbox will find duplicates in passed digital collection. Each matchbox workflow step can be executed separately. User will get a list of duplicates in result. Matchbox in this scenario is installed on remote Linux VM. Digital collection is stored on Windows machine. This workflow starts duplicate finding process using the FindDuplicates python script of the matchbox tool. Matchbox tool support python in version 2.7. Execution starts from the directory where python scripts ...

Created: 2012-11-24 | Last updated: 2012-11-24

Credits: Roman SCAPE

AIT Matchbox Scenario All (1)

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In this scenario matchbox will find duplicates in passed digital collection. All matchbox workflow steps are executed automatically in one turn. User will get a list of duplicates in result. Matchbox in this scenario is installed on remote Linux VM. Digital collection is stored on Windows machine.

Created: 2012-11-24 | Last updated: 2012-11-24

Credits: Roman SCAPE

Finding the origins of Solar Wind events a... (6)

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This gives a comprenhensive overview of high speed solar wind events (CME or CIR) seen at Eart, by obtaining the maximum in-situ measure velocity from the data evaluation service (DES), propagating the even backwards to Earth using the HELIO processing service (HPS) and SHEBA propagation model, and search whether halo CMEs or CHs were observed on that time, and retrieves, besides all the previous information, a context solar wind plot (+/- 1 day) and a URL linking to a context movie for CMEs...

Created: 2012-11-16 | Last updated: 2013-03-21

Credits: David PS

Attributions: CME backwards propagation Co-rotating Interaction Regions backwards propagation HFC Synoptic map from date CDAW CME movie from date Solar Wind properties and plot Back CIR-CME propagation model LASCO_CME_QUERY

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Presentation - End-to-End Semantics

Created: 2012-11-12 15:28:38 | Last updated: 2012-11-13 00:35:30

4 items in this pack

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ImportRefSeqtoKegg (3)

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This workflow extracts a column of RefSeq gene IDs from a CSV file and then converts them to Kegg gene identifiers. This workflow uses the new KEGG REST service, which replaces the old SOAP interface from 31st December 2012

Created: 2012-11-12 | Last updated: 2013-01-08

Credits: Katy Wolstencroft

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Presentation - Knowledge Infrastructure for Global S...

Created: 2012-11-10 07:47:48 | Last updated: 2012-11-10 09:04:05

3 items in this pack

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Metamining: Create classifiers (1)

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 Uses mined patterns to learn rule model for metamining purposes.

Created: 2012-11-06 | Last updated: 2012-11-06

Split data for cross-validation (1)

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 Splits given dataset into 10 folds almost equal in size using stratified sampling.

Created: 2012-11-06 | Last updated: 2012-11-06