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INPACE
AI Ready Data Challenge
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Title
Mr.
Ms.
Prof.
Dr.
First name
*
Last name
*
Email
*
Organisational Information
Organisation name
*
Organisation type
*
University / Research institute
Industry (SME)
Industry (Large enterprise)
Government agency / Public body
Standardisation body
Other
Country
*
Afghanistan
Åland Islands
Albania
Algeria
American Samoa
Andorra
Angola
Anguilla
Antarctica
Antigua and Barbuda
Argentina
Armenia
Aruba
Australia
Austria
Azerbaijan
Bahamas
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin
Bermuda
Bhutan
Bolivia
Bonaire, Sint Eustatius and Saba
Bosnia and Herzegovina
Botswana
Bouvet Island
Brazil
British Indian Ocean Territory
Brunei Darussalam
Bulgaria
Burkina Faso
Burundi
Cabo Verde
Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Christmas Island
Cocos (Keeling) Islands
Colombia
Comoros
Congo
Congo, The Democratic Republic of the
Cook Islands
Costa Rica
Côte d'Ivoire
Croatia
Cuba
Curaçao
Cyprus
Czechia
Denmark
Djibouti
Dominica
Dominican Republic
Ecuador
Egypt
El Salvador
Equatorial Guinea
Eritrea
Estonia
Eswatini
Ethiopia
Falkland Islands (Malvinas)
Faroe Islands
Fiji
Finland
France
French Guiana
French Polynesia
French Southern Territories
Gabon
Gambia
Georgia
Germany
Ghana
Gibraltar
Greece
Greenland
Grenada
Guadeloupe
Guam
Guatemala
Guernsey
Guinea
Guinea-Bissau
Guyana
Haiti
Heard Island and McDonald Islands
Holy See (Vatican City State)
Honduras
Hong Kong
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Isle of Man
Israel
Italy
Jamaica
Japan
Jersey
Jordan
Kazakhstan
Kenya
Kiribati
Kuwait
Kyrgyzstan
Lao People's Democratic Republic
Latvia
Lebanon
Lesotho
Liberia
Libya
Liechtenstein
Lithuania
Luxembourg
Macao
Madagascar
Malawi
Malaysia
Maldives
Mali
Malta
Marshall Islands
Martinique
Mauritania
Mauritius
Mayotte
Mexico
Micronesia, Federated States of
Moldova
Monaco
Mongolia
Montenegro
Montserrat
Morocco
Mozambique
Myanmar
Namibia
Nauru
Nepal
Netherlands
New Caledonia
New Zealand
Nicaragua
Niger
Nigeria
Niue
Norfolk Island
North Korea
North Macedonia
Northern Mariana Islands
Norway
Oman
Pakistan
Palau
Palestine, State of
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Pitcairn
Poland
Portugal
Puerto Rico
Qatar
Réunion
Romania
Russian Federation
Rwanda
Saint Barthélemy
Saint Helena, Ascension and Tristan da Cunha
Saint Kitts and Nevis
Saint Lucia
Saint Martin (French part)
Saint Pierre and Miquelon
Saint Vincent and the Grenadines
Samoa
San Marino
Sao Tome and Principe
Saudi Arabia
Senegal
Serbia
Seychelles
Sierra Leone
Singapore
Sint Maarten (Dutch part)
Slovakia
Slovenia
Solomon Islands
Somalia
South Africa
South Georgia and the South Sandwich Islands
South Korea
South Sudan
Spain
Sri Lanka
Sudan
Suriname
Svalbard and Jan Mayen
Sweden
Switzerland
Syrian Arab Republic
Taiwan
Tajikistan
Tanzania
Thailand
Timor-Leste
Togo
Tokelau
Tonga
Trinidad and Tobago
Tunisia
Türkiye
Turkmenistan
Turks and Caicos Islands
Tuvalu
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
United States Minor Outlying Islands
Uruguay
Uzbekistan
Vanuatu
Venezuela
Vietnam
Virgin Islands, British
Virgin Islands, U.S.
Wallis and Futuna
Western Sahara
Yemen
Zambia
Zimbabwe
Participation mode for your organisation
*
Fully on-site (TTA premises, Pangyo)
Fully remote (online)
Mixed
Background & Motivation
How did you hear about the AI Ready Data Challenge?
*
Sejong University communication
INPACE mailing list / website
TTA communication
IITP communication
ETSI TC DATA mailing list
Colleague / referral
Social media / LinkedIn
Other
What is your main motivation for participating?
*
Test data quality metrics on our own datasets
Contribute to future standardisation
Evaluate the PoC validation tool for potential adoption
Explore EU-Korea data quality cooperation
Understand data quality requirements for AI/ML applications
Academic research on data quality
Other
What is your current level of familiarity with ETSI TR 104 180?
*
Not familiar at all — first encounter
Basic awareness — have read the executive summary
Moderate — have read the document; understand the main metrics
Advanced — have actively used or contributed to the TR
Have you attended the introductory webinar on TR 104 180?
*
Yes, attended live
Yes, watched the recording
No, but plan to before the event
No
Dataset Information
Dataset name / identifier
*
Brief description of the dataset
*
Application domain: (select the closest match)
*
Industrial IoT / Sensor data (manufacturing, energy, aerospace)
Demographics / Social data
Healthcare / Medical data
Smart city / Urban data
Environmental / Climate data
Financial / Economic data
AI/ML training dataset (labelled)
Data space / Data exchange platform
Physical AI / Robotics / Real-time systems
Telecommunications / Network data
Other
Intended use case(s)
AI/ML model training
AI/ML model validation / testing
Data exchange / interoperability (data space)
Real-time monitoring / control
Statistical analysis / reporting
Regulatory compliance
Physical AI / embedded intelligence
Other
Dataset size: describe for each datasept you aim to rpovide: - Approximate number of records / rows - Number of attributes / columns - File format (CSV, XLSX, JSON, etc.) - Approximate file size
Does the dataset contain time-series data?
Yes — with explicit timestamps
Yes — with implicit temporal order (sequence numbers, cycles)
No — snapshot / static dataset
Does the dataset contain labelled data for AI/ML?
Yes — fully labelled
Yes — partially labelled
Mix
No
Does the dataset contain personal or sensitive data?
Yes — fully anonymised prior to submission
Yes — pseudonymised
No personal data
Uncertain — seeking guidance
Datasets containing personal data must be fully anonymised or pseudonymised before submission. The organisers do not accept identifiable personal data. Participants are responsible for ensuring compliance with applicable data protection regulations (GDPR, PIPA, etc.).
Data submission format preference
I will upload the dataset directly in the PoC tool on-site (Day 1)
I will submit the dataset in advance (before September 10th) via the secure upload link
I would prefer to use a synthetic / anonymised version of the dataset — please advise
Is your organisation willing to share results publicly?
*
Yes — results may be included in the Challenge proceedings / ETSI TC DATA contribution
Yes — anonymised results only (organisation name not disclosed)
No — results for internal use only
Metrics & Technical Preferences
Which data quality metrics from ETSI TR 104 180 are you most interested in evaluating?
*
Completeness
Accuracy
Consistency
Timeliness
Reliability
Uniqueness
Redundancy
Precision
Availability
Coverage
Lineage
Anonymity
Label Quality
Traceability
Confidentiality
Integrity
Measurement Bias
Representation Bias
Does your organisation have existing business rules or thresholds defined for data quality?
*
Yes — fully documented
Yes — partially documented
No — we are looking to define them during the Challenge
Unsure
I also wish to join the 'INPACE' community
I have read and accepted the
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