research data management, bexis hands-on workshop

Research Data Management

BExIS Hands-On Workshop

Tayebeh Kiani, Javad ChamanaraFebruary 2016

Tehran, Iran

2BExIS Hands-On Workshop, Feb. 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


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

Javad Chamanara

Who is this?


DM is done through a Lifecycle

Boston University The UniversityOf 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

https://dmptool.org/

https://dmponline.dcc.ac.uk/

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?


DATA

DET

AILS

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 overThank 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

ReservationData 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

http://bexis2.vmguest.uni-jena.de/DDM/Data/ShowData/2

http://isc-mirror.iris.washington.edu/iscbulletin/search/fmechanisms/

http://isc-mirror.iris.washington.edu/iscbulletin/search/fmechanisms/

http://isc-mirror.iris.washington.edu/cgi-bin/web-db-v4?request=COMPREHENSIVE&out_format=FMCSV&searchshape=RECT&bot_lat=25&top_lat=40&left_lon=44&right_lon=63&ctr_lat=&ctr_lon=&radius=&max_dist_units=deg&srn=&grn=&start_year=2013&start_month=1&start_day=01

Javad Chamanara

Why data is on BExIS website?What are the others, Do we have the data structures ready for all the examples?


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

YesRounded1 Sec.Interval

Tmp Time Hu.22 1 4623 2 4521 3 3018 5 2514 6 25

S.N. S.M. Depth14 12 -1013 10 -1016 15 -1018 17 -9

Extended Properties خاک PersianرطوبتBodenfeuchteGermanSoil 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:[email protected]://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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