data workflow management, data preservation and stewardship

1

Peter Fox

Data Science – ITEC/CSCI/ERTH-6961

Week 10, November 6, 2012

Data Workflow Management, Data Preservation and

Stewardship

Contents• Scientific Data Workflows

• Data Stewardship

• Summary

• Next class(es)

2

Scientific Data Workflow• What it is

• Why you would use it

• Some more detail in the context of Kepler– www.kepler-project.org

• Some pointer to other workflow systems

3

4

What is a workflow?• General definition: series of tasks performed

to produce a final outcome

• Scientific workflow – “data analysis pipeline”– Automate tedious jobs that scientists traditionally

performed by hand for each dataset– Process large volumes of data faster than

scientists could do by hand

5

Background: Business Workflows

• Example: planning a trip• Need to perform a series of tasks: book a

flight, reserve a hotel room, arrange for a rental car, etc.

• Each task may depend on outcome of previous task– Days you reserve the hotel depend on days of the

flight– If hotel has shuttle service, may not need to rent a

car

• E.g. tripit.com?

6

What about scientific workflows?

• Perform a set of transformations/ operations on a scientific dataset

• Examples– Generating images from raw data– Identifying areas of interest in a large dataset– Classifying set of objects– Querying a web service for more information

on a set of objects– Many others…

7

More on Scientific Workflows

• Formal models of the flow of data among processing components

• May be simple and linear or more complex

• Can process many data types:– Archived data– Streaming sensor data– Images (e.g., medical or satellite)– Simulation output– Observational data

8

Challenges • Questions:

– What are some challenges for scientists implementing scientific workflows?

– What are some challenges to executing these workflows?

– What are limitations of writing a program?

9

Challenges• Mastering a programming language

• Visualizing workflow

• Sharing/exchanging workflow

• Formatting issues

• Locating datasets, services, or functions

10

Kepler Scientific Workflow Management System

• Graphical interface for developing and executing scientific workflows

• Scientists can create workflows by dragging and dropping

• Automates low-level data processing tasks

• Provides access to data repositories, compute resources, workflow libraries

11

Benefits of Scientific Workflows

• Documentation of aspects of analysis

• Visual communication of analytical steps

• Ease of testing/debugging

• Reproducibility

• Reuse of part or all of workflow in a different project

12

Additional Benefits

• Integration of multiple computing environments

• Automated access to distributed resources via web services and Grid technologies

• System functionality to assist with integration of heterogeneous components

Why not just use a script?• Script does not specify low-level task

scheduling and communication

• May be platform-dependent

• Can’t be easily reused

• May not have sufficient documentation to be adapted for another purpose

13

Why is a GUI useful?• No need to learn a programming language

• Visual representation of what workflow does

• Allows you to monitor workflow execution

• Enables user interaction

• Facilitates sharing of workflows

14

The Kepler Project• Goals

– Produce an open-source scientific workflow system• enable scientists to design scientific workflows and execute them

– Support scientists in a variety of disciplines• e.g., biology, ecology, astronomy

– Important features• access to scientific data• flexible means for executing complex analyses• enable use of Grid-based approaches to distributed computation• semantic models of scientific tasks• effective UI for workflow design

Usage statistics• Source code access

– 154 people accessed source code– 30 members have write permission

–Projects using Kepler:•SEEK (ecology)

•SciDAC (molecular bio, ...)

•CPES (plasma simulation)

•GEON (geosciences)

•CiPRes (phylogenetics)

•CalIT2

•ROADnet (real-time data)

•LOOKING (oceanography)

•CAMERA (metagenomics)

•Resurgence (Computational

chemistry)

•NORIA (ocean observing CI)

•NEON (ecology observing CI)

•ChIP-chip (genomics)

•COMET (environmental science)

•Cheshire Digital Library

(archival)

•Digital preservation (DIGARCH)

•Cell Biology (Scripps)

•DART (X-Ray crystallography)

•Ocean Life

•Assembling theTree of Life

project

•Processing Phylodata (pPOD)

•FermiLab (particle physics)

Kepler downloadsTotal = 9204Beta = 6675

red=Windows

blue=Macintosh

Distributed execution• Opportunities for parallel execution

– Fine-grained parallelism– Coarse-grained parallelism

• Few or no cycles

• Limited dependencies among components

• ‘Trivially parallel’

• Many science problems fit this mold– parameter sweep, iteration of stochastic models

• Current ‘plumbing’ approaches to distributed execution– workflow acts as a controller

• stages data resources

• writes job description files

• controls execution of jobs on nodes

– requires expert understanding of the Grid system

• Scientists need to focus on just the computations– try to avoid plumbing as much as possible

– Higher-order component for executing a model on one or more remote nodes

– Master and slave controllers handle setup and communication among nodes, and establish data channels

– Extremely easy for scientist to utilize• requires no knowledge of grid computing systems

Distributed Kepler

OUT

IN

Master Slave

Controller Controller

Token

{1,5,2}

• Need for integrated management of external data– EarthGrid access is partial, need refactoring– Include other data sources, such as JDBC, OpeNDAP, etc.– Data needs to be a first class object in Kepler, not just represented

as an actor– Need support for data versioning to support provenance

• e.g., Need to pass data by reference– workflows contain large data tokens (100’s of megabytes)– intelligent handling of unique identifiers (e.g., LSID)

Token

ref-276

{1,5,2}

Data Management

A B

Science Environment for Ecological Knowledge

SEEK is an NSF-funded, multidisciplinary research project to facilitate …

Access to distributed ecological, environmental, and biodiversity data– Enable data sharing & reuse

– Enhance data discovery at global scales

Scalable analysis and synthesis – Taxonomic, spatial, temporal, conceptual

integration of data, addressing data heterogeneity issues

– Enable communication and collaboration for analysis

– Enable reuse of analytical components

– Support scientific workflow design and modeling

SEEK data access, analysis, mediation

Data Access (EcoGrid)– Distributed data network for environmental,

ecological, and systematics data– Interoperate diverse environmental data systems

Workflow Tools (Kepler)– Problem-solving environment for scientific data

analysis and visualization “scientific workflows”

Semantic Mediation (SMS)– Leverage ontologies for “smart”

data/component discovery and integration

Managing Data Heterogeneity• Data comes from

heterogeneous sources– Real-world observations– Spatial-temporal contexts– Collection/measurement

protocols and procedures– Many representations for the

same information (count, area, density)

– Data, Syntax, Schema, Semantic heterogeneity

• Discovery and “synthesis” (integration) performed manually– Discovery often based on intuitive notion of “what is out there”– Synthesis of data is very time consuming, and limits use

Scientific workflow systems support data analysis

KEPLER

Composite Component(Sub-workflow)

Loops often used in SWFs; e.g., in genomics and bioinformatics (collections of data, nested data, statistical regressions, ...)

A simple Kepler workflow

(T. McPhillips)

Workflow runs PhylipPars iteratively to discover all of the most parsimonious trees.

UniqueTrees discards redundant trees in each collection.

Lists Nexus filesto process (project) Reads text files Parses Nexus format

Draws phylogenetic trees

PhylipPars infers treesfrom discrete, multi-statecharacters.


(T. McPhillips)

An example workflow run, executed as a Dataflow Process Network


Smart Mediation Services (SMS) motivation

• Scientific Workflow Life-cycle – Resource Discovery

• discover relevant datasets

• discover relevant actors or workflow templates

– Workflow Design and Configuration• data actor (data binding)

• data data (data integration / merging / interlinking)

• actor actor (actor / workflow composition)

• Challenge: do all this in the presence of …– 100’s of workflows and templates– 1000’s of actors (e.g. actors for web services, data analytics, …)– 10,000’s of datasets– 1,000,000’s of data items– … highly complex, heterogeneous data

– price to pay for these resources: $$$ (lots) – scientist’s time wasted: priceless!

Approach & SMS capabilities

OntologiesOntologies

SemanticAnnotationSemanticAnnotation

IterativeDevelopmentIterativeDevelopment

Resource DiscoveryResource Discovery

• Annotations “connect” resources to ontologies– Conceptually describe a resource and/or its “data

schema” – Annotations provide the means for ontology-based

discovery, integration, …

Workflow ValidationWorkflow Validation

ResourceIntegrationResourceIntegration

WorkflowElaborationWorkflowElaboration

“Hybrid” types … Semantic + Structural Typing

Structural Types: Given a structural type language S– Datasets, inputs, and outputs can be assigned

structural types S S

Semantic Types: Given an ontology language O (e.g., OWL-DL)– Datasets, inputs, and outputs can be assigned

ontology types O O

SoutS

OoutO O : Observation obsProperty.SpeciesOccurrenceS : SpeciesData(site, day, spp, occ)

O : Observation obsProperty.SpeciesOccurrenceS : SpeciesData(site, day, spp, occ)S

O

Sout

Oout

Sin

Oin

Semantically compatiblebut structurally incompatible

A1A1 A2A2

Semantic & structural types can be combined using logic constraints

:= (site,day,sp,occ) SpeciesData(site, day, sp, occ) (y) Observation(y), obsProp(y, occ), SpeciesOccurrence(occ)

:= (site,day,sp,occ) SpeciesData(site, day, sp, occ) (y) Observation(y), obsProp(y, occ), SpeciesOccurrence(occ)

Semantic Type Annotation in Kepler

• Component input and output port annotation– Each port can be

annotated with multiple classes from multiple ontologies

– Annotations are stored within the component metadata

Component Annotation and Indexing

• Component Annotations– New components can be

annotated and indexed into the component library (e.g., specializing generic actors)

– Existing components can also be revised, annotated, and indexed (hiding previous versions)






• Ontology-based “smart” search– Find components by semantic types

– Find components by input/output semantic types

– Ontology-based query rewriting for discovery/integration• Joint work with GEON project (see SSDBM-04, SWDB-04)




Smart Search

Find a component (here: an actor) in different locations (“categories”)• … based on the semantic annotation of the component (or its ports)

Browse for Components Search for Component Name Search for Category / Keyword

Searching in context

• Search for components with compatible input/output semantic types– … searches over actor library

– … applies subsumption checking on port annotations






• Workflow validation and analysis– Check that workflows are semantically & structurally well-typed

– Infer semantic type annotations of derived data (ie, type inference)

• An initial approach and prototype based on mapping composition (see QLQP-05)

– User-oriented provenance

• Collect & query data-lineage of WF runs (see IPAW-06)




Workflow validation in Kepler

• Navigate errors and warnings within the workflow

– Search for and insert “adapters” to fix (structural and semantic) errors …

• Statically perform semantic and structural type checking






• Integrating and transforming data– Merge (“smart union”) datasets– Find mappings between data schemas for

transformation• data binding, component connections (see DILS-04)




Smart (Data) Integration: Merge

• Discover data of interest

• … connect to merge actor

• … “compute merge”– align attributes via annotations

– open dialog for user refinement

– store merge mapping in MOML

• … enjoy!– … your merged dataset – almost, can be much more complicated

a3

a6

a1

a8

a4

a1 a3 a4a 5.0 10b 6.0 11a 0.1c 0.2d 0.3

a1 a3 a4a 5.0 10b 6.0 11a 0.1c 0.2d 0.3

Merge Result

a1 a2 a3 a4a 5 10b 6 11

a1 a2 a3 a4a 5 10b 6 11

a5 a6 a7 a8 0.1 a 0.2 c 0.3 d

a5 a6 a7 a8 0.1 a 0.2 c 0.3 d

Merge

a1a8

a3a6

a4

BiomassBiomass

BiomassBiomassSiteSite

SiteSite

Under the hood of “Smart Merge” … • Exploits semantic type

annotations and ontology definitions to find mappings between sources

• Executing the merge actor results in an integrated data product (via “outer union”)






• Workflow design support– (Semi-) automatically combine resource

discovery, integration, and validation– Abstract Executable WF– … ongoing work!




Automated SWF Refinement

Initial Work on Provenance Framework • Provenance

– Track origin and derivation information about scientific workflows, their runs and derived information (datasets, metadata…)

• Need for Provenance– Association of process and results– reproduce results– “explain & debug” results (via lineage tracing, parameter settings, …)– optimize: “Smart Re-Runs”

• Types of Provenance Information:– Data provenance

• Intermediate and end results including files and db references– Process (=workflow instance) provenance

• Keep the wf definition with data and parameters used in the run– Error and execution logs

– Workflow design provenance (quite different)• WF design is a (little supported) process (art, magic, …)• for free via cvs: edit history• need more “structure” (e.g. templates) for individual & collaborative workflow design

Kepler Provenance Recording Utility• Parametric and customizable

– Different report formats– Variable levels of detail

• Verbose-all, verbose-some, medium, on error

– Multiple cache destinations• Saves information on

– User name, Date, Run, etc…

Provenance: Possible Next Steps• Provenance Meeting: Last week at SDSC

– Deciding on terms and definitions– .kar file generation, registration and search for

provenance information– Possible data/metadata formats– Automatic report generation from accumulated data– A GUI to keep track of the changes– Adding provenance repositories– A relational schema for the provenance info in addition to

the existing XML

Some other workflow systems• SCIRun• Sciflo• Triana• Taverna• Pegasus• Some commercial tools:

– Windows Workflow Foundation– Mac OS X Automator

• http://www.isi.edu/~gil/AAAI08TutorialSlides/5-Survey.pdf

• http://www.isi.edu/~gil/AAAI08TutorialSlides/ • See reading for this week

44

Data Stewardship• Putting a number of data life cycle,

management aspects together

• Keep the ideas in mind as you complete your assignments

• Why it is important

• Some examples

45

Why it is important• 1976 NASA Viking mission to Mars (A. Hesseldahl, Saving

Dying Data, Sep. 12, 2002, Forbes. [Online]. Available: http://www.forbes.com2002/09/12/0912data_print.html )

• 1986 BBC Digital Domesday (A. Jesdanun, “Digital memory threatened as file formats evolve,” Houston Chronicle, Jan. 16, 2003. [Online]. Available: http://www.chron.com/cs/CDA/story.hts/tech/1739675 )

• R. Duerr, M. A. Parsons, R. Weaver, and J. Beitler, “The international polar year: Making data available for the long-term,” in Proc. Fall AGU Conf., San Francisco, CA, Dec. 2004. [Online]. Available: ftp://sidads.colorado.edu/pub/ppp/conf_ppp/Duerr/The_International_Polar_Year:_Making_Data_and_Information_Available_for_the_Long_Term.ppt 46

At the heart of it

• Inability to read the underlying sources, e.g. the data formats, metadata formats, knowledge formats, etc.

• Inability to know the inter-relations, assumptions and missing information

• We’ll look at a (data) use case for this shortly

• But first we will look at what, how and who in terms of the full life cycle 47

What to collect?• Documentation

– Metadata– Provenance

• Ancillary Information

• Knowledge

48

Who does this?• Roles:

– Data creator– Data analyst– Data manager– Data curator

49

How it is done• Opening and examining Archive Information

Packages

• Reviewing data management plans and documentation

• Talking (!) to the people:– Data creator– Data analyst– Data manager– Data curator

• Sometimes, reading the data and code 50

Data-Information-Knowledge Ecosystem

51

Data Information Knowledge

Producers Consumers

Context

PresentationOrganization

IntegrationConversation

CreationGathering

Experience

Acquisition

• Learn / read what you can about the developer of the means of acquisition– Documents may not be easy to find

– Remember bias!!!

• Document things as you go

• Have a checklist (see the Data Management list) and review it often

52

20080602 Fox VSTO et al.

53

Curation (partial)• Consider the organization and presentation of

the data

• Document what has been (and has not been) done

• Consider and address the provenance of the data to date, you are now THE next person

• Be as technology-neutral as possible

• Look to add information and metainformation

54

Preservation• Usually refers to the full life cycle

• Archiving is a component

• Stewardship is one act of preservation

• Intent is that ‘you can open it any time in the future’ and that ‘it will be there’

• This involves steps that may not be conventionally thought of

• Think 10, 20, 50, 200 years…. looking historically gives some guide to future considerations 55

Some examples and experience

• NASA, NOAA• http://wiki.esipfed.org/index.php/

Preservation_and_Stewardship • Library community• Note:

– Mostly in relation to publications, books, etc but some for data

– Note that knowledge is in publications but the structure form is meant for humans not computers, despite advances in text analysis

– Very little for the type of knowledge we are considering: in machine accessible form

56

Back in the day... NASA

SEEDS Working Group on Data Lifecycle• Second Workshop Report

o http://esdswg.gsfc.nasa.gov/pdf/W2_lcbo_bothwell.pdf o Many LTA recommendations

• Earth Sciences Data Lifecycle Reporto http://esdswg.gsfc.nasa.gov/pdf/lta_prelim_rprt2.pdfo Many lessons learned from USGS experience, plus some

recommendations• SEEDS Final Report (2003) - Section 4

o http://esdswg.gsfc.nasa.gov/pdf/FinRec.pdfo Final recommendations vis a vis data lifecycle

MODIS Pilot Project• GES DISC, MODAPS, NOAA/CLASS, ESDIS effort• Transferred some MODIS Level 0 data to CLASS

Mostly Technical Issues

• Data Preservationo Bit-level integrityo Data readability

• Documentation• Metadata• Semantics• Persistent Identifiers• Virtual Data Products• Lineage Persistence• Required ancillary data• Applicable standards

Mostly Non-Technical Issues

• Policy (constrained by money…)• Front end of the lifecycle

o Long-term planning, data formats, documentation...• Governance and policy• Legal requirements• Archive to archive transitions

• Money (intertwined with policy)• Cost-benefit trades• Long-term needs of NASA Science Programs • User input

o Identifying likely users• Levels of service• Funding source and mechanism

HDF4 Format "Maps"for Long Term Readability

C. Lynnes, GES DISCR. Duerr and J. Crider, NSIDC

M. Yang and P. Cao, The HDF Group

Use case: a real live one; deals mostlywith structure and (some) content

HDF=Hierarchical Data FormatNSIDC=National Snow and Ice Data CenterGES=Goddard Earth ScienceDISC=Data and Information Service Center

In the year 2025...

A user of HDF-4 data will run into the following likely hurdles:• The HDF-4 API and utilities are no longer supported...

o ...now that we are at HDF-7• The archived API binary does not work on today's OS's

o ...like Android 9.1 • The source does not compile on the current OS

o ...or is it the compiler version, gcc v. 12.x?• The HDF spec is too complex to write a simple read

program...o ...without re-creating much of the API

What to do?

HDF Mapping Files

Concept: create text-based "maps" of the HDF-4 file layouts while we still have a viable HDF-4 API (i.e., now)• XML• Stored separately from, but close to the data files• Includes

o internal metadatao variable info o chunk-level info

byte offsets and length linked blocks compression information

Task funded by ESDIS project• The HDF Group, NSIDC and GES DISC

Map sample (extract)

<hdf4:SDS objName="TotalCounts_A" objPath="/ascending/Data Fields" objID="xid-DFTAG_NDG-5"> <hdf4:Attribute name="_FillValue" ntDesc="16-bit signed integer"> 0 0 </hdf4:Attribute> <hdf4:Datatype dtypeClass="INT" dtypeSize="2" byteOrder="BE" /> <hdf4:Dataspace ndims="2"> 180 360 </hdf4:Dataspace> <hdf4:Datablock nblocks="1"> <hdf4:Block offset="27266625" nbytes="20582" compression="coder_type=DEFLATE" /> </hdf4:Datablock> </hdf4:SDS>

Status and Future

Status • Map creation utility (part of HDF)• Prototype read programs

o Co Perl

• Paper in TGRS special issue• Inventory of HDF-4 data products within EOSDIS

Possible Future Steps• Revise XML schema• Revise map utility and add to HDF baseline• Implement map creation and storage operationally

o e.g., add to ECS or S4PA metadata files

7th Joint ESDSWG meeting, October 22, Philadelphia, PAData Lifecycle Workshop sponsored by the Technology Infusion Working Group

NASA/ MODIS Contextual Info

Presented by R. Duerr at the Summer Institute on Data Curation, June 2-5, 2008Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign

Instrument/sensor characteristics

66



Processing Algorithms & Scientific Basis

67



Ancillary Data

68



Processing History including Source Code

69



Quality Assessment Information

70



Validation Information

71



Other Factors that can Influence the Record

72



Bibliography

73



Contextual Information:

• Instrument/sensor characteristics including pre-flight or pre-operational performance measurements (e.g., spectral response, noise characteristics, etc.)

• Instrument/sensor calibration data and method• Processing algorithms and their scientific basis,

including complete description of any sampling or mapping algorithm used in creation of the product (e.g., contained in peer-reviewed papers, in some cases supplemented by thematic information introducing the data set or derived product)

• Complete information on any ancillary data or other data sets used in generation or calibration of the data set or derived product

74



Contextual Information (continued):

• Processing history including versions of processing source code corresponding to versions of the data set or derived product held in the archive

• Quality assessment information• Validation record, including identification of validation data sets• Data structure and format, with definition of all parameters and

fields• In the case of earth based data, station location and any

changes in location, instrumentation, controlling agency, surrounding land use and other factors which could influence the long-term record

• A bibliography of pertinent Technical Notes and articles, including refereed publications reporting on research using the data set

• Information received back from users of the data set or product

75


However…• Even groups like NASA do not have a

governance model for this work

• Governance: is the activity of governing. It relates to decisions that define expectations, grant power, or verify performance. It consists either of a separate process or of a specific part of management or leadership processes. Sometimes people set up a government to administer these processes and systems. (wikipedia)

76

Who cares…• Stakeholders:

– NASA for integrity of their data holdings (is it their responsibility?)

– Public for value for and return on investment– Scientists for future use (intended and un-

intended)– Historians

77

Library community• OAIS – Open Archival Information System,

http://en.wikipedia.org/wiki/Open_Archival_Information_System

• OAI (PMH and ORE) – Open Archives Initiative (Protocol for Metadata Harvesting and Object Reuse and Exchange), http://www.openarchives.org/

• Do some reading on your own for this

78


Metadata Standards - PREMIS

• Provide a core preservation metadata set with broad applicability across the digital preservation community

• Developed by an OCLC and RLG sponsored international working group– Representatives from libraries, museums,

archives, government, and the private sector.

• Based on the OAIS reference model

• Preservation Metadata Interchange Std.


Metadata Standards - PREMIS

• Maintained by the Library of Congress• Editorial board with international membership• User community consulted on changes

through the PREMIS Implementers Group • Version 1 was released in June 2005• Version 2 was just released


Rights

Events

Agents

“a coherent set of contentthat is reasonably

described as a unit”For example, a web site, data set or collection of data sets

“a coherent set of contentthat is reasonably

described as a unit”For example, a web site, data set or collection of data sets

“a discrete unit of information in digital form”

For example, a data file

“a discrete unit of information in digital form”

For example, a data file“assertions of one or more

rights or permissionspertaining to an object

or an agent”e.g., copywrite notice, legalstatute, deposit agreement

“assertions of one or more rights or permissions

pertaining to an objector an agent”

e.g., copywrite notice, legalstatute, deposit agreement

“an action that involves atleast one object or agentknown to the preservation

repository”e.g., created, archived,

migrated

“an action that involves atleast one object or agentknown to the preservation

repository”e.g., created, archived,

migrated

“a person, organization, orsoftware program associatedwith preservation events in

the life of an object”e.g., Dr. Spock donated it

“a person, organization, orsoftware program associatedwith preservation events in

the life of an object”e.g., Dr. Spock donated it

PREMIS - Entity-Relationship Diagram

IntellectualEntities

Objects


PREMIS - Types of Objects

• Representation - “the set of files needed for a complete and reasonable rendition of an Intellectual Entity”

• File • Bitstream - “contiguous or non-contiguous

data within a file that has meaningful common properties for preservation purposes”


Information from users• Data Errors found

• Quality updates

• Things that need further explanation

• Metadata updates/additions?

• Community contributed metadata????

Back to why you need to…• E-science uses data and it needs to be

around when what you create goes into service and you go on to something else

• That’s why someone on the team must address life-cycle (data, information and knowledge) and work with other team members to implement organizational, social and technical solutions to the requirements

84

(Digital) Object Identifiers• Object is used here so as not to pre-empt an

implementation, e.g. resource, sample, data, catalog– DOI = http://www.doi.org/, e.g. 10.1007/s12145-

008-0001-8 – visit crossref.org and see where this leads you.

– URI, http://en.wikipedia.org/wiki/Uniform_Resource_Identifier e.g. http://www.springerlink.com/content/0322621781338n85/fulltext.pdf

– XRI (from OAIS), http://www.oasis-open.org/committees/xri

85

Versioning• Is a key enabler of good

preservation

• Is a tricky trap for those that do not conform to written guidelines for versioning

• http://en.wikipedia.org/wiki/Revision_control

86

Summary• The progression toward more formal

encoding of science workflow, and in our context data-science workflow (dataflow) is substantially improving data management

• Awareness of preservation and stewardship for valuable data and information resources is receiving renewed attention in the digital age

• Workflows are a potential solution to the data stewardship challenge

• Which brings us to the final assignment87

Final assignment• See web (10% of grade).

88

What is next• Next week – Data quality, uncertainty, bias…

• Week after - Webs of Data and Data on the Web, the Deep Web, Data Discovery, Data Integration

• Reading for this week – see web site

• Last class is week 13, Nov. 27 – project presentations (and final assignment due)

89

data workflow management, data preservation and stewardship

Documents

semantic escience fall

scientific data workflowwhat

data workflow management

data repositories

data types

flow of data

data preservation

scientific workflowsscientists