Research Data Management, BExIS Hands-On Workshop

Research Data Management
BExIS Hands-On Workshop
Tayebeh Kiani, Javad Chamanara
February 2016
Tehran, Iran

Scientific Data
“The recorded information (regardless of the
form or the media in which they may exist)
necessary to support or validate a research
project’s observations, findings or outputs.”
-University of Oxford
2BExIS Hands-On Workshop, Feb. 2016, Tehran, Iran

A Usual Research Cycle

Data Intensive Research (DIR)

Considerations of DIR
• Data Acquisition
• Data Processing/Analysis
• Result reproduction
• Availability of data
• Teamwork and data sharing
• Digital rights
• Referencing and citation
Data Management is needed

Need for Data Management
“Data management refers to all aspects of
creating, housing, delivering, maintaining, and
archiving and preserving data. It is one of the
essential areas of responsible conduct of
research.”
-MANTRA 2013

DM is done through a Lifecycle
Boston University The University
Of Alabama
The University Of Virginia
DataONE The U.S. Geological Survey

In This Workshop

• Data and File Formats
• Data Standards
• Data Access Policies
• Data Management Plan
• Data Preservation Plan
• Data Retirement
• Quality Level
• Hardware
• Software
• Cost/ Funding
• Technical Staff
• Tools:
https://dmptool.org/
https://dmponline.dcc.
ac.uk

• By means of:
– Collecting new data
– Updating existing data
– Converting/Transforming existing data
– Purchasing/Obtaining data
• Either manually or automated
• In the laboratory, in the field, or by computation
• Following methodologies, standards,
recommendations
• Satisfying constraints such as access policies

• To prepare data for subsequent use
– Verify
– Organize
– Transform
– integrate, and extract
• Tools:
– OpenRefine/ GoogleRefine
– Statistical software: R, SAS
– Modeling Tools: ….

• describe facts
• detect patterns
• develop explanations
• test hypotheses.
• This includes
– data quality assurance
– statistical data analysis
– Modeling
– interpretation of analysis results.

• The need for:
– Supporting research publications by associated, accessible
datasets.
– re-usability by others
• actions and procedures to:
– keep data for some period of time
– set data aside for future use
– archiving in a data repository.
• Considering
– Discovering
– Identification
– Reproduction/ Presentation
– Policies

• Disseminate quality data to the public and to
other agencies
• Medium- and agent-independent
• Via non-/automated mechanisms
• Shared, but with controls
• Useful metadata

• What to publish:
– the research result citing
the data
– A data paper describing
the data
– The data itself
• Where to Publish:
– Catalogs
– Portals
– Repositories
– National Archives
• Considerations
– Licensing and rights
– Cost
– Sensitive data
– Anonymization

“Metadata is information about the context,
content, quality, provenance, and/or
accessibility of a set of data.”
-Digital Curation at the University of Wisconsin-Madison
But why it is needed?

DATADETAILS
Time of data development
Specific details about problems with individual items or specific dates
are lost relatively rapidly
General details about datasets are lost through
time
Accident or
technology
change may
make data
unusable
Retirement or career change makes
access to “mental storage” difficult or
unlikely
Loss of data developer
leads to loss of remaining
information
TIME (Michener et al 1997)

• Formally describes various key attributes of
each data element or collection of elements
• To maintain data quality.
• And make use of data possible/ easier

• QA focuses on building-in quality to prevent defects
– Setting the Quality Level
– Setting standards
– Proper protocols and methods for:
• Data collection
• Data processing and usage
• Maintenance
• QC focuses on testing for quality (Defect detection)
– Acceptance Criteria
– Automatic QC upon data manipulation
– Configuring/testing instruments
– Unit of measurement, accuracy, conversion errors, …

• Protect data from:
– Loss
– Corruption
– Unauthorized access
• Regular backups
• Regular restores
• Proper structure and naming

Feedback
The first part is over
Thank you

Note!
There are some suggestions for cooperation at
the end of the workshop

The Workshop
• BExIS
– Data Lifecycle
Management
– Generic
– Extensible
– Portable
– Scalable
• Flexible Data Structures
• Data Submission
• Validation
• Preserving
• Metadata Management
• Versioning

BExIS Core Concepts
DataMetadata
Data StructureMetadata Structure Semantics Geo
Administration Security
«use»
«use»
«use» «use»
«use»

BExIS Core Concepts cntd.
SearchPublishing
CMLand Use
Reservation
Data
Submission

Preparation
• URL: bx2train.inf-bb.uni-jena.de
• Demo: http://bexis2.vmguest.uni-jena.de/
• Source: http://fusion.cs.uni-jena.de/bexis

The Scenario
• Registration/ Logging in
• Seeing the data and metadata structures
• Downloading a template
• Filling in the Excel data (sample datasets)
• Uploading the datasets
• Providing metadata
• Checking validations
• Seeing the dataset in the system
• Searching, etc.

Registration

Logging In

Example Datasets
– Tectonic Stress Fields on BExIS website
– International Seismological Center
– DATA TYPE FOCAL MECHANISM

Creating a Data Structure

Downloading a Template

BExIS Dataset
Views
S.N. Tmp Time S.M. Depth Pos. Hu.
14 22 1 12 -10 A 46 78 Green
13 23 2 10 -10 B 45
16 21 3 12 -11 C 30 0.11
16 18 5 15 -10 A 25
18 14 6 17 -9 D 25 Yes 100 EP
Variable 1 Variable 2 Amendments
±0.10%Error
YesRounded
1 Sec.Interval
Tmp Time Hu.
22 1 46
23 2 45
21 3 30
18 5 25
14 6 25
S.N. S.M. Depth
14 12 -10
13 10 -10
16 15 -10
18 17 -9
Extended Properties ‫خاک‬ ‫رطوبت‬Persian
BodenfeuchteGerman
Soil MoistureEnglish
Globalization Info
Data Structure
Observation (Tuple)

Creating a Dataset

Choosing a Metadata Scheme

Entering Metadata

Submitting the Metadata

Adding Data to the Dataset

Choosing the Dataset’s File

Selecting the Online Dataset

Validating the Data

Submitting the Data

Searching for Datasets

Dataset Details

4949
Thank You:
 Workshop Participants
 Martin Hohmuth
 Nafiseh Navabpour
 Roman Gerlach
Contact:
javad.chamanara@uni-jena.de
http://bexis2.uni-jena.de
BEXIS Tech Talk #2: The Conceptual Model
Acknowledgment

Suggestions
• Data Lifecycle survey
– List of lifecycles
– Their features/domain of application
– Strengths/ weaknesses
• GSI data lifecycle
– Best of all
– Customizable
– …

Research Data Management, BExIS Hands-On Workshop

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