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Data Science - Workflow Graphs
Raw
xholonWorkbook.xml
<?xml version="1.0" encoding="UTF-8"?>
<!--Xholon Workbook http://www.primordion.com/Xholon/gwt/ MIT License, Copyright (C) Ken Webb, Mon Feb 05 2018 16:56:07 GMT-0500 (EST)-->
<XholonWorkbook>
<Notes><![CDATA[
Xholon
------
Title: Data Science - Workflow Graphs
Description:
Url: http://www.primordion.com/Xholon/gwt/
InternalName: a4a73c9cfc09bd5e71533da4c6dffb6a
Keywords:
My Notes
--------
February 1, 2018
Data Science projects are typically organized as workflow graphs.
These graphs are implicit when using command-based tools such as R tidyverse [1,2].
Or they can be explicit with graphical tools such as Orange [3].
I've used Anaconda[5] to install and run Orange.
To repeat this Xholon Graphviz export:
$wnd.xh.xport("Graphviz", $wnd.xh.root().parent().xpath("Chameleon/PhysicalSystem/OrangeWorkflow"), '{"gvFileExt":".gv","gvGraph":"digraph","layout":"dot","edgeOp":"->","gvCluster":"","shouldShowStateMachineEntities":false,"filter":"--Behavior,Script","nameTemplateNodeId":"^^^^i^","nameTemplateNodeLabel":"R^^^^^","shouldQuoteLabels":true,"shouldShowLinks":true,"shouldShowLinkLabels":false,"shouldSpecifyLayout":true,"maxLabelLen":-1,"shouldColor":true,"defaultNodeColor":"#f0f8ff","bgGraphColor":"white","shouldSpecifyShape":true,"shape":"box","shouldSpecifySize":true,"size":"20","shouldSpecifyFontname":true,"fontname":"Courier","shouldSpecifyArrowhead":true,"arrowhead":"vee","shouldSpecifyStylesheet":true,"stylesheet":"Xholon.css","shouldSpecifyRankdir":true,"rankdir":"LR","shouldDisplayGraph":true,"outputFormat":"svg"}');
export d3cp with orange3 icons ("useIcons":true):
------------------------------
_d3,CirclePack
orangeWorkflow_69
{"shouldShowStateMachineEntities":false,"shouldIncludeDecorations":true,"sort":"default","filter":"--Behavior,Script","width":-1,"height":-1,"selection":"#xhgraph","mode":"new","labelContainers":false,"labelContainersOptions":"top","includeId":false,"includeClass":false,"shape":"circle","shapeParams":"5,5","maxSvg":50,"useIcons":true,"iconPos":"outside","useSymbols":false,"useAnno":false,"annoPos":"outside","maxChars":1,"fontSizeMultiplier":1.75,"marble":"","supportTouch":false,"_jsdata":false,"nonportPorts":true}
TODO
----
- do something similar with R tidyverse pipes
- Jen's pipelines
- think about nested pipelines
- workflows AND sets + category theory
- CWL, Taverna [12,13,14]
Try the following R process[11 p4-5] using Orange:
-------------------
original_books <- austen_books() %>%
group_by(book) %>%
mutate(linenumber = row_number(),
chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]", ignore_case = TRUE)))) %>%
ungroup()
February 5, 2018
----------------
Conclusion:
Workflow approaches:
- R tidyverse magritter pipes
- Python Orange3
- Common Workflow Language (CWL)
References
----------
(1) https://www.tidyverse.org/
(2) http://magrittr.tidyverse.org/
pipes %>%
(3) https://orange.biolab.si/
Open source machine learning and data visualization for novice and expert. Interactive data analysis workflows with a large toolbox.
(4) https://fri.uni-lj.si/en/laboratory/biolab
The laboratory carries out research in data mining, machine learning, data visualization, big data analysis and data fusion.
We apply computational methods to solve practical problems and focus on systems biology, biomedicine and natural sciences.
The laboratory is developing Orange, a comprehensive suite that joins machine learning and visual programming, and collaborates
in development of cool interactive web-based data exploration platforms like dictyExpress.
(5) https://www.anaconda.com/
The Most Popular Python Data Science Platform
(6) https://docs.anaconda.com/
(7) https://orange.biolab.si/toolbox/
list of all widgets in the Orangle toolbox
(8) https://codebeautify.org/remove-duplicate-lines
) https://codebeautify.org/base64-decode
(9) https://docs.python.org/3/library/pickle.html
the .ows files contain "pickle" code, in base64 + binary format
(10) search pickle javascript
) https://github.com/jlaine/node-jpickle
) https://github.com/sciyoshi/pickle-js
(11) Julia Silge and David Robinson, Text Mining with R
(12) https://taverna.incubator.apache.org/
Taverna is an open source and domain-independent Workflow Management System – a suite of tools used to design and execute scientific workflows and aid in silico experimentation.
Java, LGPL
(13) http://www.commonwl.org/
Common Workflow Language (CWL)
a specification for describing analysis workflows and tools in a way that makes them portable and scalable across a variety of software and hardware environments,
from workstations to cluster, cloud, and high performance computing (HPC) environments.
CWL is designed to meet the needs of data-intensive science, such as Bioinformatics, Medical Imaging, Astronomy, Physics, and Chemistry.
CWL is developed by an informal, multi-vendor working group consisting of organizations and individuals aiming to enable scientists to share data analysis workflows.
The CWL project is on Github and we follow the Open-Stand.org principles for collaborative open standards development.
CWL builds on technologies such as JSON-LD for data modeling and Docker for portable runtime environments.
(14) https://en.wikipedia.org/wiki/Common_Workflow_Language
The Common Workflow Language (CWL) is a specification for describing computational data-analysis workflows.
Development of CWL is focussed particularly on serving the data-intensive sciences, such as Bioinformatics, Medical Imaging, Astronomy, Physics, and Chemistry.
A key goal of the CWL is to allow the creation of a workflow that is portable and thus may be run reproducibly in different computational environments.
The CWL originated from discussions in 2014 between Peter Amstutz, John Chilton, Nebojsa Tijanic, and Michael Crusoe (of Galaxy, Arvados, and Seven Bridges) at the Open Bioinformatics Foundation BOSC 2014 codefest.
CWL is supported by multiple analysis environments such as Galaxy, Toil, Arvados, Rabix, and Apache Taverna and was identified in 2017 as one of the future trends for bioinformatics pipeline development.
(15) http://sysabee.com/index.html
Jen Schellinck
Jen's slide presentation and DS bootcamp
"My pragmatic definition of DS:
Data Science: Building a data pipeline"
"The Roles of the Data Scientist*
*Some of them, at least"
KSW
- final end product delivered to a client, vs the actual day-to-day back-and-forth as the data scientist works on a project
- a data pipeline is an agile process and not a waterfall process
- the goal is to deliver a reproducible pipeline that a customer can use to rerun their data and their updated data
- the delovered pipeline might be in R, python, Orange, other tool, some combination of these
(16) https://academic.oup.com/bib/article/18/3/530/2562749
A review of bioinformatic pipeline frameworks
Jeremy Leipzig
Briefings in Bioinformatics, Volume 18, Issue 3, 1 May 2017, Pages 530–536, https://doi.org/10.1093/bib/bbw020
Bioinformatic analyses invariably involve shepherding files through a series of transformations, called a pipeline or a workflow.
Typically, these transformations are done by third-party executable command line software written for Unix-compatible operating systems.
The advent of next-generation sequencing (NGS), in which millions of short DNA sequences are used as the source input for interpreting
a range of biological phenomena, has intensified the need for robust pipelines. NGS analyses tend to involve steps such as sequence alignment
and genomic annotation that are both time-intensive and parameter-heavy.
(17) http://www.commonwl.org/v1.0/
Common Workflow Language Specifications, v1.0.2
(18) https://json-ld.org/
JSON for Linking Data
(19) https://view.commonwl.org
) https://view.commonwl.org/workflows
Explore Workflows
View already parsed workflows here or click here to add your own
(20) http://rabix.io/
Rabix is an open-source development project for creating and running computational workflows.
It was founded by Seven Bridges with the goal of empowering researchers and developers to easily run reproducible analyses at scale.
The Rabix project is currently supporting two efforts:
The Rabix Executor
The Rabix Composer
a standalone integrated development environment with rich visual and text-based editors for rapidly describing tools and workflows in the Common Workflow Language (CWL). Use as a standalone application or
(21) https://github.com/rabix/composer
Visual and code editor for Common Workflow Language
(22) http://docs.rabix.io/rabix-composer-home
(23) https://github.com/Parsoa/Beatrice
Pipeline Assembler For The Common Workflow Language (CWL)
JavaScript
(24) https://bioexcel.eu/software/workflows/
Workflow blocks
As part of the BioExcel H2020 project we are creating a set of portable workflow building blocks to use the BioExcel-supported tools.
We will be curating their bio.tools descriptions with the EDAM ontology and then describing their execution as Common Workflow Language tools.
We create corresponding Python wrappers that provide a uniform interface across the tools and handle any input/output parameter adaptions.
These wrappers are configured using YAML/JSON, for example:
(25) https://github.com/bioexcel/pymdsetup/blob/3161ac56076893a73737d237f087999376422d50/cwl/schema_w.cwl
CWL YAML file
(26) https://github.com/common-workflow-language/cwltool
(27) http://www.commonwl.org/user_guide/
) http://www.commonwl.org/user_guide/setup/
(28) http://www.commonwl.org/user_guide/02-1st-example/
#!/usr/bin/env cwl-runner
cwlVersion: v1.0
class: CommandLineTool
baseCommand: echo
inputs:
message:
type: string
inputBinding:
position: 1
outputs: []
(29) https://github.com/common-workflow-language/common-workflow-language/wiki
(30) https://goo.gl/mp7WCb
CWL slides
(31) search: data science workflow
(32) https://cacm.acm.org/blogs/blog-cacm/169199-data-science-workflow-overview-and-challenges/fulltext
(33) https://github.com/rstudio/RStartHere
R packages organized into a Data Science Workflow
(34) https://cran.r-project.org/web/views/
CRAN Task Views
(35) https://www.civisanalytics.com/blog/workflows-in-python-getting-data-ready-to-build-models/
) https://www.civisanalytics.com/blog/workflows-in-python-curating-features-and-thinking-scientifically-about-algorithms/
) https://www.civisanalytics.com/blog/workflows-in-python-using-pipeline-and-gridsearchcv-for-more-compact-and-comprehensive-code/
(36) http://www.rebeccabarter.com/blog/2017-08-16-data-science-workflow/
A Basic Data Science Workflow
Developing a clean and easy analysis workflow takes a really, really long time. In this post, I outline the workflow that I have developed over the last few years.
R
(37) https://cran.r-project.org/web/packages/magrittr/magrittr.pdf
The magrittr package offers a set of operators which promote semantics that will improve your code by
• structuring sequences of data operations left-to-right (as opposed to from the inside and out),
• avoiding nested function calls,
• minimizing the need for local variables and function definitions, and
• making it easy to add steps anywhere in the sequence of operations.
The operators pipe their left-hand side values forward into expressions that appear on the right-hand
side, i.e. one can replace f(x) with x %>% f, where %>% is the (main) pipe-operator.
Consider the example below. Four operations are performed to arrive at the desired data set, and
they are written in a natural order: the same as the order of execution. Also, no temporary variables
are needed. If yet another operation is required, it is straight-forward to add to the sequence of
operations whereever it may be needed.
For a more detailed introduction see the vignette (vignette("magrittr")) or the documentation
pages for the available operators:
%>% forward-pipe operator.
%T>% tee operator.
%<>% compound assignment pipe-operator.
%$% exposition pipe-operator.
(38) https://en.wikipedia.org/wiki/Pipeline_(Unix)
(39) https://en.wikipedia.org/wiki/Pipeline_(software)
(40) http://r.analyticflow.com/en/
Analysis Workflow diagrams
R AnalyticFlow organizes data analysis processes in a workflow. Visualized processes can be reproduced easily and accurately by simply using a mouse.
Analysis workflows can be combined with related data and documents to form a project. With such features, R AnalyticFlow supports team-based sharing of analytical processes.
]]></Notes>
<_-.XholonClass>
<PhysicalSystem/>
<!-- Orange XML -->
<scheme/>
<nodes/> <!-- already used in gexf mechanism -->
<node/> <!-- already used in gexf mechanism -->
<links/>
<link/>
<annotations/>
<thumbnail/>
<node_properties/>
<properties superClass="Attribute_String"/>
<!-- Orange Widgets -->
<OrangeWidget>
<!-- Data -->
<File/>
<Datasets/>
<SQLTable/>
<SaveData/>
<DataInfo/>
<DataTable/>
<SelectColumns/>
<SelectRows/>
<DataSampler/>
<Transpose/>
<Discretize/>
<Continuize/>
<CreateClass/>
<Randomize/>
<Concatenate/>
<PaintData/>
<PythonScript/>
<FeatureConstructor/>
<EditDomain/>
<Impute/>
<MergeData/>
<Outliers/>
<Preprocess/>
<PurgeDomain/>
<Rank/>
<ColorO/>
<!-- Visualize -->
<BoxPlot/>
<Distributions/>
<HeatMap/>
<ScatterPlot/>
<VennDiagram/>
<LinearProjection/>
<ScatterMap/>
<SieveDiagram/>
<PythagoreanTree/>
<PythagoreanForest/>
<CN2RuleViewer/>
<MosaicDisplay/>
<SilhouettePlot/>
<TreeViewer/>
<Nomogram/>
<FreeViz/>
<Radviz/>
<!-- Model -->
<Constant/>
<CN2RuleInduction/>
<kNN/>
<Tree/>
<RandomForest/>
<SVM/>
<LinearRegression/>
<LogisticRegression/>
<NaiveBayes/>
<AdaBoost/>
<NeuralNetwork/>
<StochasticGradientDescent/>
<LoadModel/>
<SaveModel/>
<!-- Unsupervised -->
<PCA/>
<CorrespondenceAnalysis/>
<DistanceMap/>
<Distances/>
<DistanceMatrix/>
<DistanceTransformation/>
<DistanceFile/>
<SaveDistanceMatrix/>
<HierarchicalClustering/>
<k-Means/>
<MDS/>
<ManifoldLearning/>
<!-- Evaluation -->
<CalibrationPlot/>
<ConfusionMatrix/>
<LiftCurve/>
<Predictions/>
<ROCAnalysis/>
<TestAndScore/>
<!-- Data Fusion -->
<IMDbActors/>
<Chaining/>
<CompletionScoring/>
<FusionGraph/>
<LatentFactors/>
<MatrixSampler/>
<MeanFuser/>
<MovieGenres/>
<MovieRatings/>
<TabletoRelation/>
<!-- Educational -->
<Interactivek-Means/>
<PolynomialRegression/>
<GradientDescent/>
<PolynomialClassification/>
<!-- Text -->
<Corpus/>
<NYTimes/>
<Twitter/>
<Wikipedia/>
<PubMed/>
<CorpusViewer/>
<PreprocessText/>
<BagofWords/>
<TopicModelling/>
<WordEnrichment/>
<WordCloud/>
<GeoMap/>
<!-- Network -->
<NetworkFile/>
<NetworkExplorer/>
<NetworkGenerator/>
<NetworkAnalysis/>
<NetworkClustering/>
<NetworkfromDistances/>
<!-- Bioinformatics -->
<BioMart/>
<DatabasesUpdate/>
<DataProfiles/>
<DictyExpress/>
<DifferentialExpression/>
<ExpressionProfileDistances/>
<GeneInfo/>
<GenExpress/>
<GEODataSets/>
<GOBrowser/>
<KEGGPathways/>
<MAPlot/>
<PIPAx/>
<QualityControl/>
<SelectGenes/>
<SetEnrichment/>
<VolcanoPlot/>
<!-- Associate -->
<Associate/>
<AssociationRules/>
<FrequentItemsets/>
</OrangeWidget>
<OrangeWorkflow/>
<!-- Jen's slide presentation[15] -->
<!-- data pipeline blocks; these are processes (verbs) and not things (nouns) -->
<DataBlock>
<DataCollection/> <!-- the process of collecting data, rather than a collection of data (a thing) -->
<DataStorage/>
<DataPreparation/>
<DataAnalysis/>
<DataPresentation/>
</DataBlock>
<ProblemDomain/>
<DataPipeline/>
<!-- The Roles of the Data Scientist -->
<DataScientistRole/>
</_-.XholonClass>
<xholonClassDetails>
<Avatar><Color>rgba(255,215,0,0.8)</Color></Avatar>
<!-- allow all Orange Workflow nodes to have ports/links, and a color -->
<!-- Data -->
<File xhType="XhtypePureActiveObject"><Color>orange</Color><Icon>./images/orange3/data/File.svg</Icon></File> <!-- https://raw.githubusercontent.com/biolab/orange3/master/Orange/widgets/data/icons/File.svg -->
<Datasets xhType="XhtypePureActiveObject"><Color>orange</Color></Datasets>
<SQLTable xhType="XhtypePureActiveObject"><Color>orange</Color></SQLTable>
<SaveData xhType="XhtypePureActiveObject"><Color>orange</Color></SaveData>
<DataInfo xhType="XhtypePureActiveObject"><Color>orange</Color></DataInfo>
<DataTable xhType="XhtypePureActiveObject"><Color>orange</Color><Icon>./images/orange3/data/Table.svg</Icon></DataTable>
<SelectColumns xhType="XhtypePureActiveObject"><Color>orange</Color></SelectColumns>
<SelectRows xhType="XhtypePureActiveObject"><Color>orange</Color><Icon>./images/orange3/data/SelectRows.svg</Icon></SelectRows>
<DataSampler xhType="XhtypePureActiveObject"><Color>orange</Color><Icon>./images/orange3/data/DataSampler.svg</Icon></DataSampler>
<Transpose xhType="XhtypePureActiveObject"><Color>orange</Color></Transpose>
<Discretize xhType="XhtypePureActiveObject"><Color>orange</Color></Discretize>
<Continuize xhType="XhtypePureActiveObject"><Color>orange</Color></Continuize>
<CreateClass xhType="XhtypePureActiveObject"><Color>orange</Color></CreateClass>
<Randomize xhType="XhtypePureActiveObject"><Color>orange</Color></Randomize>
<Concatenate xhType="XhtypePureActiveObject"><Color>orange</Color></Concatenate>
<PaintData xhType="XhtypePureActiveObject"><Color>orange</Color></PaintData>
<PythonScript xhType="XhtypePureActiveObject"><Color>orange</Color></PythonScript>
<FeatureConstructor xhType="XhtypePureActiveObject"><Color>orange</Color></FeatureConstructor>
<EditDomain xhType="XhtypePureActiveObject"><Color>orange</Color></EditDomain>
<Impute xhType="XhtypePureActiveObject"><Color>orange</Color></Impute>
<MergeData xhType="XhtypePureActiveObject"><Color>orange</Color></MergeData>
<Outliers xhType="XhtypePureActiveObject"><Color>orange</Color></Outliers>
<Preprocess xhType="XhtypePureActiveObject"><Color>orange</Color></Preprocess>
<PurgeDomain xhType="XhtypePureActiveObject"><Color>orange</Color></PurgeDomain>
<Rank xhType="XhtypePureActiveObject"><Color>orange</Color></Rank>
<ColorO xhType="XhtypePureActiveObject"><Color>orange</Color></ColorO>
<!-- Visualize -->
<BoxPlot xhType="XhtypePureActiveObject"><Color>pink</Color><Icon>./images/orange3/visualize/BoxPlot.svg</Icon></BoxPlot>
<Distributions xhType="XhtypePureActiveObject"><Color>pink</Color><Icon>./images/orange3/visualize/Distribution.svg</Icon></Distributions>
<HeatMap xhType="XhtypePureActiveObject"><Color>pink</Color></HeatMap>
<ScatterPlot xhType="XhtypePureActiveObject"><Color>pink</Color><Icon>./images/orange3/visualize/ScatterPlot.svg</Icon></ScatterPlot>
<VennDiagram xhType="XhtypePureActiveObject"><Color>pink</Color></VennDiagram>
<LinearProjection xhType="XhtypePureActiveObject"><Color>pink</Color></LinearProjection>
<ScatterMap xhType="XhtypePureActiveObject"><Color>pink</Color></ScatterMap>
<SieveDiagram xhType="XhtypePureActiveObject"><Color>pink</Color></SieveDiagram>
<PythagoreanTree xhType="XhtypePureActiveObject"><Color>pink</Color></PythagoreanTree>
<PythagoreanForest xhType="XhtypePureActiveObject"><Color>pink</Color></PythagoreanForest>
<CN2RuleViewer xhType="XhtypePureActiveObject"><Color>pink</Color></CN2RuleViewer>
<MosaicDisplay xhType="XhtypePureActiveObject"><Color>pink</Color></MosaicDisplay>
<SilhouettePlot xhType="XhtypePureActiveObject"><Color>pink</Color></SilhouettePlot>
<TreeViewer xhType="XhtypePureActiveObject"><Color>pink</Color></TreeViewer>
<Nomogram xhType="XhtypePureActiveObject"><Color>pink</Color></Nomogram>
<FreeViz xhType="XhtypePureActiveObject"><Color>pink</Color></FreeViz>
<Radviz xhType="XhtypePureActiveObject"><Color>pink</Color></Radviz>
<!-- Model -->
<Constant xhType="XhtypePureActiveObject"><Color>purple</Color></Constant>
<CN2RuleInduction xhType="XhtypePureActiveObject"><Color>purple</Color></CN2RuleInduction>
<kNN xhType="XhtypePureActiveObject"><Color>purple</Color></kNN>
<Tree xhType="XhtypePureActiveObject"><Color>purple</Color></Tree>
<RandomForest xhType="XhtypePureActiveObject"><Color>purple</Color></RandomForest>
<SVM xhType="XhtypePureActiveObject"><Color>purple</Color></SVM>
<LinearRegression xhType="XhtypePureActiveObject"><Color>purple</Color></LinearRegression>
<LogisticRegression xhType="XhtypePureActiveObject"><Color>purple</Color></LogisticRegression>
<NaiveBayes xhType="XhtypePureActiveObject"><Color>purple</Color></NaiveBayes>
<AdaBoost xhType="XhtypePureActiveObject"><Color>purple</Color></AdaBoost>
<NeuralNetwork xhType="XhtypePureActiveObject"><Color>purple</Color></NeuralNetwork>
<StochasticGradientDescent xhType="XhtypePureActiveObject"><Color>purple</Color></StochasticGradientDescent>
<LoadModel xhType="XhtypePureActiveObject"><Color>purple</Color></LoadModel>
<SaveModel xhType="XhtypePureActiveObject"><Color>purple</Color></SaveModel>
<!-- Unsupervised -->
<PCA xhType="XhtypePureActiveObject"><Color>black</Color></PCA>
<CorrespondenceAnalysis xhType="XhtypePureActiveObject"/>
<DistanceMap xhType="XhtypePureActiveObject"/>
<Distances xhType="XhtypePureActiveObject"/>
<DistanceMatrix xhType="XhtypePureActiveObject"/>
<DistanceTransformation xhType="XhtypePureActiveObject"/>
<DistanceFile xhType="XhtypePureActiveObject"/>
<SaveDistanceMatrix xhType="XhtypePureActiveObject"/>
<HierarchicalClustering xhType="XhtypePureActiveObject"/>
<k-Means xhType="XhtypePureActiveObject"/>
<MDS xhType="XhtypePureActiveObject"/>
<ManifoldLearning xhType="XhtypePureActiveObject"/>
<!-- Evaluation -->
<CalibrationPlot xhType="XhtypePureActiveObject"><Color>black</Color></CalibrationPlot>
<ConfusionMatrix xhType="XhtypePureActiveObject"/>
<LiftCurve xhType="XhtypePureActiveObject"/>
<Predictions xhType="XhtypePureActiveObject"/>
<ROCAnalysis xhType="XhtypePureActiveObject"/>
<TestAndScore xhType="XhtypePureActiveObject"/>
<!-- Data Fusion -->
<IMDbActors xhType="XhtypePureActiveObject"><Color>black</Color></IMDbActors>
<Chaining xhType="XhtypePureActiveObject"/>
<CompletionScoring xhType="XhtypePureActiveObject"/>
<FusionGraph xhType="XhtypePureActiveObject"/>
<LatentFactors xhType="XhtypePureActiveObject"/>
<MatrixSampler xhType="XhtypePureActiveObject"/>
<MeanFuser xhType="XhtypePureActiveObject"/>
<MovieGenres xhType="XhtypePureActiveObject"/>
<MovieRatings xhType="XhtypePureActiveObject"/>
<TabletoRelation xhType="XhtypePureActiveObject"/>
<!-- Educational -->
<Interactivek-Means xhType="XhtypePureActiveObject"><Color>black</Color></Interactivek-Means>
<PolynomialRegression xhType="XhtypePureActiveObject"/>
<GradientDescent xhType="XhtypePureActiveObject"/>
<PolynomialClassification xhType="XhtypePureActiveObject"/>
<!-- Text -->
<Corpus xhType="XhtypePureActiveObject"><Color>black</Color></Corpus>
<NYTimes xhType="XhtypePureActiveObject"/>
<Twitter xhType="XhtypePureActiveObject"/>
<Wikipedia xhType="XhtypePureActiveObject"/>
<PubMed xhType="XhtypePureActiveObject"/>
<CorpusViewer xhType="XhtypePureActiveObject"/>
<PreprocessText xhType="XhtypePureActiveObject"/>
<BagofWords xhType="XhtypePureActiveObject"/>
<TopicModelling xhType="XhtypePureActiveObject"/>
<WordEnrichment xhType="XhtypePureActiveObject"/>
<WordCloud xhType="XhtypePureActiveObject"/>
<GeoMap xhType="XhtypePureActiveObject"/>
<!-- Network -->
<NetworkFile xhType="XhtypePureActiveObject"><Color>black</Color></NetworkFile>
<NetworkExplorer xhType="XhtypePureActiveObject"/>
<NetworkGenerator xhType="XhtypePureActiveObject"/>
<NetworkAnalysis xhType="XhtypePureActiveObject"/>
<NetworkClustering xhType="XhtypePureActiveObject"/>
<NetworkfromDistances xhType="XhtypePureActiveObject"/>
<!-- Bioinformatics -->
<BioMart xhType="XhtypePureActiveObject"><Color>black</Color></BioMart>
<DatabasesUpdate xhType="XhtypePureActiveObject"/>
<DataProfiles xhType="XhtypePureActiveObject"/>
<DictyExpress xhType="XhtypePureActiveObject"/>
<DifferentialExpression xhType="XhtypePureActiveObject"/>
<ExpressionProfileDistances xhType="XhtypePureActiveObject"/>
<GeneInfo xhType="XhtypePureActiveObject"/>
<GenExpress xhType="XhtypePureActiveObject"/>
<GEODataSets xhType="XhtypePureActiveObject"/>
<GOBrowser xhType="XhtypePureActiveObject"/>
<KEGGPathways xhType="XhtypePureActiveObject"/>
<MAPlot xhType="XhtypePureActiveObject"/>
<PIPAx xhType="XhtypePureActiveObject"/>
<QualityControl xhType="XhtypePureActiveObject"/>
<SelectGenes xhType="XhtypePureActiveObject"/>
<SetEnrichment xhType="XhtypePureActiveObject"/>
<VolcanoPlot xhType="XhtypePureActiveObject"/>
<!-- Associate -->
<Associate xhType="XhtypePureActiveObject"><Color>black</Color></Associate>
<AssociationRules xhType="XhtypePureActiveObject"/>
<FrequentItemsets xhType="XhtypePureActiveObject"/>
</xholonClassDetails>
<PhysicalSystem>
<!-- Orange3 .ows file -->
<scheme description="" title="" version="2.0">
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<node id="7" name="Distributions" position="(268.0, 202.0)" project_name="Orange3" qualified_name="Orange.widgets.visualize.owdistributions.OWDistributions" title="Distributions" version=""/>
<node id="8" name="Box Plot" position="(403.0, 179.0)" project_name="Orange3" qualified_name="Orange.widgets.visualize.owboxplot.OWBoxPlot" title="Box Plot" version=""/>
</nodes>
<links>
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<link enabled="true" id="2" sink_channel="Data" sink_node_id="3" source_channel="Data Sample" source_node_id="2"/>
<link enabled="true" id="3" sink_channel="Data" sink_node_id="4" source_channel="Data" source_node_id="0"/>
<link enabled="true" id="4" sink_channel="Data" sink_node_id="5" source_channel="Matching Data" source_node_id="4"/>
<link enabled="true" id="5" sink_channel="Data" sink_node_id="6" source_channel="Data" source_node_id="0"/>
<link enabled="true" id="6" sink_channel="Data" sink_node_id="7" source_channel="Data" source_node_id="0"/>
<link enabled="true" id="7" sink_channel="Data" sink_node_id="8" source_channel="Data" source_node_id="0"/>
</links>
<annotations/>
<thumbnail/>
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</scheme>
<OrangeWorkflow>
<!-- schemebehavior will create OrangeWidget nodes here -->
<!-- example -->
<!--<File qualified_name="Orange.widgets.data.owfile.OWFile">
<port name="port" index="0" connector="../*[@id='1']"/>
<port name="port" index="1" connector="../*[@id='2']"/>
</File>-->
</OrangeWorkflow>
<!-- The Roles of the Data Scientist; Jen's original slide is structured as a mindmap -->
<DataPipeline roleName="Areas of Expertise">
<ProblemDomain/>
<DataCollection>
<DataScientistRole roleName="API Wrangling"/>
<DataScientistRole roleName="Web Scraping"/>
<DataScientistRole roleName="Questionnaire Design + Use"/>
<DataScientistRole roleName="Sensor Design"/>
</DataCollection>
<DataStorage>
<DataScientistRole roleName="Database Design"/>
<DataScientistRole roleName="Streaming Data"/>
<DataScientistRole roleName="Big Data Wrangling"/>
<DataScientistRole roleName="Data Querying"/>
<DataScientistRole roleName="Database Implementation"/>
</DataStorage>
<DataPreparation>
<DataScientistRole roleName="Evaluating"/>
<DataScientistRole roleName="Cleaning"/>
<DataScientistRole roleName="Restructuring"/>
<DataScientistRole roleName="Condensing"/>
<DataScientistRole roleName="Transforming"/>
</DataPreparation>
<DataAnalysis>
<DataScientistRole roleName="Classification"/>
<DataScientistRole roleName="Clustering"/>
<DataScientistRole roleName="Conceptual Analysis"/>
<DataScientistRole roleName="Value Estimation"/>
<DataScientistRole roleName="Anomaly Detection"/>
<DataScientistRole roleName="Algorithm Performance"/>
<DataScientistRole roleName="Text Mining"/>
<DataScientistRole roleName="Sentiment Analysis"/>
</DataAnalysis>
<DataPresentation>
<DataScientistRole roleName="Web Apps"/>
<DataScientistRole roleName="Dashboard Design"/>
<DataScientistRole roleName="Data Visualization"/>
<DataScientistRole roleName="UX"/>
<DataScientistRole roleName="Infographics"/>
</DataPresentation>
</DataPipeline>
</PhysicalSystem>
<schemebehavior implName="org.primordion.xholon.base.Behavior_gwtjs"><![CDATA[
var me, beh = {
postConfigure: function() {
me = this.cnode.parent();
// nodes
var nodes = me.first();
var node = nodes.first();
var xmlStr = "<_-.widgets>\n";
while (node) {
var oname = node["name"];
oname = oname.replace(/[ ]/g, "");
xmlStr += ' <' + oname + ' id="' + node.id() + '" qualified_name="' + node["qualified_name"] + '"/>\n';
node = node.next();
}
xmlStr += "</_-.widgets>\n";
var owfNode = me.parent().xpath("OrangeWorkflow");
owfNode.append(xmlStr);
// links
var links = nodes.next();
var link = links.first();
while (link) {
var sourceNodeId = link["source_node_id"];
var sinkNodeId = link["sink_node_id"];
var sourceNode = null;
var sinkNode = null;
var owfCNode = owfNode.first();
while (owfCNode) {
if (owfCNode.id() == sourceNodeId) {
sourceNode = owfCNode;
}
else if (owfCNode.id() == sinkNodeId) {
sinkNode = owfCNode;
}
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