# ANNEX III: Survey details

The survey clustered questions in three thematic blocks detailed below:

## Some information about you

* Your country of residence? [droplist]

* List of all countries

* Which best describes your community?

* Medical and health sciences

* Biology and life sciences

* Metrology

* Earth sciences

* Astronomy

* Chemistry

* Engineering and technology

* Social sciences

* Law

* Humanities and the arts

* Library sciences

* Information and computer sciences

* Agriculture and veterinary sciences

* Theology

* Education

* Other (please specify)

* In what capacity are you responding? [multiple choices]

* Data(set) producer

* Data(set) steward or custodian or curator

* Data user

* Data quality practitioner

* Publisher

* Service or data(set) provider

* Policy/decision-maker

* Standard maker

* Funder

* Other (please specify)

* Your career stage?

* < 5 years of working experience

* >= 5 years and < 10 years of working experience

* >= 10 years and < 20 years of working experience

* >= 20 years of working experience

* Your organisation type?

* Academia / research

* Government / administration / public services
* Policy / funding agency

* Private IT consultancy / development

* Library

* Other private enterprises

## Evaluating and ensuring data quality

* What information do you consider most essential to properly use or select a dataset?
[Buttons with "mandatory," "truly relevant," "somewhat relevant," "not relevant," and "I do not know"]

* User guide (including a description of size, structure, abstract, typical usage, production methodology, and dictionary)

* Compliance of metadata with community standards

* Format according to community standards

* Scientifically accurate (e.g., validated against a reference, plausible)

* Technically correct / having passed sanity checks (e.g., no unexpected gaps in the time series, consistency between data and metadata)

* Details on strengths, limitations and known issues, including the availability of information about uncertainty

* Evidence of regard to ethical conduct, protection, and confidentiality of data

* Data are complete in space and time, with adequate resolution (if applicable)

* Up to date / currency, timeliness / novelty

* Provenance and traceability information

* Clarity about how to cite the dataset and the availability of its product locator, like URL or DOI

* Information about the data provider and point of contact

* The licence of use, including terms of use

* Archiving policy

* Version

* Evidence of data reuse

* Security

* Other (please specify)

* Have you tried to reuse someone else's data from a repository? Did you discover any quality issues? Y/N If yes, what actions did you take?

* Used as it is

* Cleaned/pre-processed data before use

* Seek further information

* Inform the provider before acting

* Do not use

* Other (please specify)

* If any correction of the data you provide is needed, how do you manage such issues
[multiple choices]

* Improve the data (new calculation)

* Describe weaknesses or errors in a transparent manner
* Keep such errors in mind and plan for the next generation of this dataset

* Provide feedback to the team (usually the data producer) in charge of rectification.
(It may be the case if I am a data service provider and not the original producer)

* Discard the data

* Other (please specify)

* What was your most significant concern/barrier to providing quality assessed data?
[buttons with "biggest," "very important," "somewhat important," "not important," and "I do not know"]

* Time-consuming

* Missing expertise

* Missing previous experience/guidance

* Missing tools for evaluation

* High complexity

* No access to standards

* No benefit

* The financial cost of certification/accreditation

* Other (please specify)

* Do you need to comply with any legal requirements (e.g., "General Data Protection Regulation" GDPR) when producing data? Y/N/I do not know, if Yes -> Which legal requirements does your discipline usually follow when producing data (e.g., GDPR compliance)?

* How do you prefer quality information to be supplied?

* In metadata within files

* Documents external to files

* Dynamic documents linked in the metadata of the files

* Dynamic documents are available on the website where the dataset files are available

* Other (please specify)

* Would you be happy for some ranking based on quality assessments, to be applied to your dataset? Please leave comments explaining the conditions which should apply for this to be suitable.

* Rate how much you agree with the following: "Information about valid ranges of plausibility is necessary to evaluate the scientific value of the datasets." [Button with "Highly agree," "agree," "not much," "not at all," "I do not know"] Could you list any physical quantity missing information about the valid ranges?

## Landscape and processes

* Which standardisation bodies are most active in your field (e.g., ISO, OGC, W3C)?

* Which organisation do you consider a reference or leader for quality directives in your field (e.g., WHO, WMO, EFSA)?

* Does your organisation have anybody responsible for data quality? Y/N/ I do not know. If Y -> Who or what department team is responsible for data quality in your organisation?
* Does your discipline have community-recognized metadata standards? Y/N/I do not know.
  If yes -> Which ones?

* Rate how much you agree with the following: "my community has a recognized structured vocabulary that is used to refer uniquely to specific quantities. In other words, there is a widely accepted definition of the most common variables." [Button with "Highly agree," "agree," "not much," "not at all," "not the case / not applicable," "I do not know"]

* My community lacks the following:

* Agreed controlled vocabularies and terminologies

* Agreed data formats

* Agreed metadata schemas

* None of the above

* I do not know

* Other (please specify)

* Are the data (excluding metadata) quality principles/practices/standards internal to your organisation, or are community-recognized?

* Internal to my organisation

* Community-recognized

* Both

* I do not use any principle/practice/standard

* Are there data (excluding metadata) of community-recognized standards in your field? No, yes one -> which one, Yes more than one -> Why do you think some are used, and some are not?

* What level of data quality management do you expect from EOSC? [Multiple choices]

* No need for a data quality function; content is distributed as deposited

* Provide a flag or star system to infer the level of data quality

* Allow (re)users to rate or leave comments on data quality

* Basic curation: e.g., control availability of basic metadata or documentation, basic metadata compliance checks

* Enhanced curation: e.g., enhancement of documentation, the performance of independent assessments

* What is the most critical need for data quality information from a data consumer perspective? [Ranking]

* Data quality information should better guide the user in selecting a dataset

* Data quality information must be available and consistent across multiple datasets and repositories

* Data quality information should be interoperable (e.g., by following standard machine-readable schemes to capture quality information)

* Dissemination of data quality information should be improved and made more understandable

* Other (please specify)

* What practices/tools should a discipline have to gauge its maturity in data quality management? [Button with "Highly agree," "agree," "not much," "not at all," "I do not know"]
* Existence of metadata community-recognized standards

* The existence of agreed definitions, for instance, when describing a physical quantity

* Availability of a standard quality management framework

* Availability of community-recognized metrics to quantify the quality

* Quality assessments are operational routine and funded

* Availability of checklists

* Availability of certifications

* Established data governance

* Existence of a ranking system

* Capacity building programme

* The feedback loop between supply and demand

* Availability of automatic tools for quality assessment

* Quality assessment is part of the data production and not a separate cumbersome process

* Other (please specify)

* Do you have any expectations or requirements regarding data quality as a service manager, funder, data provider, or user of EOSC? Some guiding questions: Which dataset lifecycle stage should EOSC consider? Which dataset characteristics are important to consider (e.g., archiving, DOI/PID, metadata) and prioritise? How do you want data quality to be measured (e.g., maturity models, checklists, statistical analyses, minimum requirements)? Any recommended standards to adopt? How shall quality be disseminated (e.g., KPIs)? Who improves dataset quality?

* Any other comments you wish to make? Y/N followed by free text