Methodology – Data Sharing Framework

   The Data Sharing Framework (DSF) is based on needs from automation pilots and requirements from the General Data Protection Regulation (GDPR, REGULATION (EU) 2016/679). Updates based on the last years’ of experience in implementing the DSF were included in the updated version (v1.1) of the framework.

The FOT-Net Data Sharing Framework consists of:

  • project agreement content, including guidelines and checklists to incorporate the pre-requisites for data sharing in the agreements, which together with legal and ethical constraints form the conditions for data sharing. The project agreements include the grant agreement (together with the description of the work), the consortium agreement, the participant agreement and external data provider agreements;
  • data and metadata description recommendations to facilitate the understanding of the context in which the data was collected and the validity of the data. These include a suggested standard for the documentation of the data and metadata, divided into 5 categories: FOT/NDS study design and execution documentation, descriptive metadata (e.g., how the data is calculated), data (e.g., sampling frequency), structural metadata (e.g., how the data is organised) and administrative metadata (e.g., access procedures);
  • data protection recommendations, focusing on FOT/NDS personal and confidential data issues. It consists of security procedures and requirements at both the data provider and analysis sites, including detailed implementation guidelines;
  • security and human subject protection training for all involved personnel. The guidelines consists of 4 parts: who should be trained and when, what content should be part of the training (including detailed suggestions), how to do the training ,and how to document it;
  • support and research services, proposing functions such as providing information/training to facilitate the start-up of projects, offering (for example) processed data for researchers less familiar with FOT/NDS data, making analysis tools available or performing complete research tasks;
  • financial models to provide funding for the data to be maintained and available, and data access services. Eight financial models are discussed and a list of data management costs is provided;
  • application procedures which provide detailed content lists to address when developing application procedures and data application forms.

Another way of describing the common data sharing framework is by the contents of its documents, as in the table below.

Data sharing framework documents and content

Document typeContent
ProceduresApplication and approval, support/research functions, data extraction and download
TemplatesApplication form, data description, consent form, data-sharing agreements, data-sharing text for consortium agreements, data security presentation, approved training certificate, financial models, data-protection implementation, data-extraction request, non-disclosure agreement (NDA) for analysts/visitors, application to ethical review board, description of content to be funded
StandardsData protection—data provider/analysis site, data extraction format, data and metadata description, training

Generally, the data can be managed either by the project itself or by an external data provider. An external data provider could also just provide test samples of the different datasets and guide the interested researchers to the organisation hosting the complete dataset. The current recommendation, however, is to let one or more project partner(s) from the original project maintain the data, possibly with test samples. Analysis of the datasets and research support services in most cases require a deep knowledge of the data and the way they were collected.


Data sharing in general project documents

The initial process of setting up a project is crucial in order to be able to share data during and after the project. Agreements can of course always be renegotiated, but the time and money consumed could be substantial, especially in large consortia; the partners have entered the consortium on the conditions stated in the agreements, and alterations could lead to reconsiderations.

The DSF focuses on the four different agreements:

  • agreement with the funding body
  • consortium agreement
  • participant agreement
  • agreement with an external data provider.

The recommendations are developed to provide guidance what to include in the agreements to facilitate data sharing within and after the project collecting the data. These recommendations also apply to automation pilots, there are no significant differences regarding this topic between former FOT and current automation pilots.

Funding agreement including the description of the work

In the funding agreement and the description of the work, the result of the project and the funding are agreed upon. It is important to be aware of the topics and issues to be discussed in relation to data sharing and re-use of data, and to focus on them during the project application and also during a possible negotiation phase. It is especially important to pay attention to the possibilities of providing open data after the project, based on the scope of the project and the data to be collected.

The requirements in a funding agreement are based on general requirements for projects collecting valuable datasets. As an example, in the European Commission´s Open Research Data pilot, projects which are voluntarily part of the pilot are required to upload datasets to an archive of their selection before the project ends. It includes exceptions for projects collecting personal data.

The description of the work should include most of the topics listed in Table 2, at least on a high level. During the project application phase, it is especially important to address the possibility of post-project funding and other conditions which will keep the data available for sharing after the project—especially if there is such a requirement in the project funding conditions. For the following questions, it is beneficial for the project partners to develop common answers as early as the application phase:

  • Which categories of data will be handled and who will own the data?
  • May third parties access the data? To what extent? Under which conditions?
  • Where will the data be stored during and after the project? Who is responsible for maintaining the data?
  • How will the data be accessed? Who makes decisions on data provision?
  • Who owns the analysis tools and who will have access to them, if they are not generally available?
  • Are there legal (e.g. GDPR for personal data) and ethical or post-project funding constraints to be considered?
Consortium Agreement

The consortium agreement is an important document for setting the required conditions for data sharing and re-use of the data. Numerous topics need to be discussed and resolved in order to establish a legal platform for the handling of the data during and after the project. In Table 2, the topics to be included in the consortium agreement are listed. The questions should be seen as providing guidance in identifying the issues that would need to be solved for a specific project: they are all related to the possibility of sharing and re-using the data after the project. The text of the consortium agreement is fairly general; any details are developed during the project. Before the project ends, it is important to have a comprehensive written agreement for how the data should be handled after the project.

Table Data-sharing topics within the consortium agreement

TopicComments
Ownership and access to data and data tools
  • Who owns the data?
  • Is it necessary to add a specific ownership clause for the collected data?
  • How could the data be used and on which conditions?
  • Will all partners have access to all/part of the data?
  • May the data be licensed to third parties?
  • May third parties have access to the data and on what conditions?
  • Are there constraints related to personal data, especially video and tracked location data or location data in general?
  • Are there future agreements with data providers to take into account?
  • Who will own the analysis tools and on what conditions are they licensed during and after the project?
  • Has a partner included previous work as background in the tools?
  • Who owns the IPR to this work and how does it affect the project?
  • How can data be re-used if the data is owned by one partner and this partner ceases operation or leaves the project?
Storage and download of data
  • How will the data be stored—centrally or distributed?
  • What are the general requirements for data protection and how are they assured?
  • Shall all/part of the data be downloadable for all partners and if so, under which conditions?
  • Shall all/part of the data be downloadable for third parties and if so, under which conditions?
  • Is there a time limit for requesting data for download?
  • Is there a time limit for keeping the data?
Access methods
  • Can the data be downloaded, remotely accessed, or only accessed at the premises of any partner?
  • Shall a specific access procedure be used, and if so by whom?
  • Who should manage the access procedure?
  • How will the data be accessed?
  • What are the requirements for data protection for partners/third parties analysing the data?
Areas of use
  • Shall it be possible to use the data for education, research and commercial purposes?
  • Are there special conditions for commercial use?
  • In which research/commercial areas could the data be used? (i.e., safety, mobility, etc.)
Post-project re-use of data
  • Which partner is responsible for maintaining the data after the project?
  • Shall a non-partner be the provider of the project data after the project?
  • Which application procedure shall be used?
  • Who will grant access to the data after the project?
  • Are there conditions, such as legal and ethical constraints and availability of funding for data storage and access services, to be considered?
  • Are there time limits after which the data need to be deleted?
Post-project financing
  • How will the storage and support services for data re-use be financed after the project?
  • Known or to be decided?
  • How will this funding be distributed?
Participant agreements including consent forms

The participant agreement explains the project to the participant and outlines the commitments required of both the project and the participant. It includes informed consent on several topics (e.g., the participants release their data for research). As the participants allow the project to follow them in their private lives for a period from a few weeks up to several years, it is important to be very clear on the use of the data during and after the project.

From a data-sharing standpoint, it is especially important to describe:

  • what data are collected;
  • where and for how long time the data will be stored;
  • who is responsible for the data;
  • who (project partners/third parties) will have access to what data and on what       conditions, during and potentially after the project;
  • an overview of the access procedures;
  • how pseudonymization and anonymity will be ensured;
  • the rights of the ‘data subject’ defined in GDPR Art.12;
  • the responses to the three YES/NO options below, directly related to data sharing.

It is recommended that the participant actively consent to these vital aspects of data sharing. Example text, which needs to be adapted to adhere to specific national regulations, is provided here for European conditions:

I hereby agree to participate in the above-described research study. I consent to having the material transferred and shared with research partners in a third country (e.g., a country outside EES).

 Yes No

I also consent to video recordings or pictures being published or shown in public events (e.g., research reports or conferences).

 Yes No

I also consent to the collected data (including video recordings and pictures and location data) being re-used in other research projects by research partners/third parties, focusing on factors regarding:

  • the driver (e.g., drowsiness, distraction, driving style);and/or
  • the vehicle (e.g., fuel consumption, system activation, automation level);and/or
  • the traffic environment(e.g., road geometry, weather conditions); and/or
  • … (to be completed by the specific project)

 Yes  No

External data provider agreements

External data providers could be companies providing sensor systems, map data, weather data or other services that the project needs to enhance the dataset. Contracts and NDAs should be signed. It is important to be aware of topics that can affect future research due to possible restrictions in data use. Attention from a data-sharing perspective should be given to answering the following questions:

  • Who owns the data (i.e. the data provider, the consortium or a specific project member)?
  • What is regarded as confidential information and what can be shared?
  • Can confidential data be anonymised/changed/aggregated, to allow for more open access?
  • Can the data be accessed by another project partner/third party?
  • Can the data be transferred to another project partner/third party?
  • Are there restrictions on what the data can be used for?
  • Are there special conditions for sharing and re-using the data after the project?
  • What happens if the external data provider is bought by another company?


Training on data protection related to personal data and IPR

All personnel handling data from FOT and NDS need to undergo training in data protection, if the data is personal or conditioned based on intellectual property rights (IPR). Personal data are any data that could reveal a person´s identity—such as video, national identification number, address or GPS positioning, and any data that could be connected to identifying data. Persons or organisations collecting and managing personal information must protect it from misuse and respect certain rights of the data owners.

Protection of intellectual property rights is another important aspect when working with extensive datasets, including video, especially when research partnerships include industrial partners. The data could reveal algorithms of certain systems if re-engineered, and therefore need to be protected.

Training on personal privacy issues and IPR needs to accompany the general training on the data security measures put in place to protect the data. The level of training should be adjusted to the content of the specific dataset to be protected.

Set-up and content of the training

Who and when?

In order to ensure protection of personal data and IPR, training procedures must be in place and provided prior to any data access. Training material and procedures can be created by the organisation providing the training or possibly bought from the data provider´s Support Services. Training must be given to analysts, video annotators, those responsible for the database, visiting researchers and all other staff handling, analysing or looking at personal or IPR data. Even persons to whom data are shown (during a demonstration, for example) must be informed about relevant data protection and IPR issues beforehand.

What?

The training needs to cover the following topics (the level of detail can be adapted to target audience’s needs):

  • description of the data with special focus on personal data and IPR:
    • What are personal data, in general and in this specific context?
    • What are intellectual property rights, in general and in this specific context?
    • What data are collected with (for example) video, questionnaires or GPS tracks?
    • Information about data ownership and access rights for partners/third parties.
  • data-handling requirements originating from national and other applicable laws, regulations, and rules. Explain the purpose and implementation of each of the data-protection principles listed below; give practical examples and answer frequently asked questions. Personal data must be:
    • processed fairly and lawfully;
    • obtained for specified and lawful purposes;
    • adequate, relevant and not excessive;
    • accurate and up-to-date;
    • not kept any longer than necessary;
    • processed in accordance with the participants´ rights and acceptance;
    • securely kept;
    • not transferred to any other country without adequate protection in situ.
  • explanation of the consent form content, especially the specific active consents related to data sharing (voluntariness, comprehension and disclosure):
    • How should the study participant be informed about data collection, purpose, handling, storage, and access—including re-use after the project ends?
    • What is included/excluded in the participant’s consent? (For example, participants give their consent to collect videos for analysis purposes and to video of them being shown in conference presentations).
  • explanation of data-handling procedures:
    • practical rules and procedures for data access (rooms and workspaces with limited access, personalized keys, password protection);
    • data structure;
    • how the data are anonymised, pseudo-identified and/or encrypted;
    • how the data are accessed, in order to (for example) perform analysis;
    • the contact persons for different procedures including the data protection responsible;
    • whom to inform in case of deviations.
  • information about publication rights.

How

It is recommended that a personal training session be organised in order to answer questions and make sure that all staff members know their responsibilities. Online courses might be helpful to provide additional valuable information, but they are not considered sufficient on their own as they cannot cover local implementation of the security precautions. For illustration purposes, videos on data protection (e.g., Data Protection Act training video (https://www.youtube.com/watch?v=wAe4358amJc&list=PLBEEA03BA780B128E&index=1, accessed on December 27th 2016) or case studies (e.g., https://dataprotection.ie/docs/CASE-STUDIES-2013/1441.htm#CS6, accessed on December 27th 2016) can be included in the training material.

The US NIH online training course (http://phrp.nihtraining.com/) can complement the training session by providing a basic understanding of the three principles essential to the ethical conduct of research with humans: respect, beneficence and justice.

How to document

Documentation of all training is recommended, most conveniently recorded on the analyst’s information sheet, which the participant needs to sign. Although analysts might have an NDA in their certificate of employment, the process of signing the document enhances the protective level of the data. It is recommended that the following records be kept:

  • persons who have undergone training;
  • training procedures;
  • process descriptions;
  • contact persons for different procedures including the data protection responsible.

Application procedure

The partners should agree on an application procedure for re-use of data early on in the project, so that all project partners (and any possible third parties) know the conditions for additional research using the specific dataset. This will provide the necessary information so that new research applications to utilize the data can already take the data application time and potential costs for re-using the data into consideration during the proposal phase, before the application is sent to the targeted call. The feasibility of disseminating this information in the proposal phase should be investigated. It should be noted that these procedures are often much more time- and resource-consuming than expected.

Contents of the application procedure

The application procedure shall address the following items (at least):

  • where to apply:
    • information regarding where and how to send in the application;
    • contact person for questions regarding the application.
  • information needed in order to be able to evaluate the application (see suggested content below);
  • person/organisation approving an application;
  • response times, and conditions to be taken into account in the approval decision;
  • requirements for mandatory training in data protection and privacy issues;
  • requirement for signing an NDA;
  • information on the data-access procedure;
  • requirements for data protection, including possible certification of data-protection implementation;
  • conditions for access and use of the data;
  • potential costs for data storage, access, support and research services;
  • requirements for acknowledgements on publications, reports and presentations;
  • documentation of data applications and the related approval decision(s).
Contents of the application form

The suggested information to be provided by the applicant for a decision within the set response time:

  • applicant details:
    • organisation(s) applying;
    • contact person(s) for each organisation;
    • project partners applying (when applicable)—list of partners that want data access for project analysis.
  • short project description;
  • requested dataset:
    • which dataset (if many available);
    • specific data requested (which time-series data, video, GPS, questionnaires, etc.)
  • use and expected results:
    • What research questions are the data expected to answer?
    • How is the data to be accessed?
    • What are the expected results?
  • information on the intended publication of the data:
    • How will the results be disseminated?
    • What data, graphs, etc. is intended to be disseminated?
  • list of persons to get access, and the related access time period:
  • need for training in data protection and privacy issues:
    • Have the researchers had previous training? If so, what kind?
    • Is training related to data protection required, or only in data analysis setup at the data provider?
  • need for support and research services:
    • Level of knowledge of the concerned analysis tools? Using self-supplied tools or needing training on provided tools?
    • Other support needs for the analysis, such as extracting datasets, etc.?
    • Should the research facilities do part or all of the analyses/research

Support and research services

Support and research services are essential to data sharing. Depending on the knowledge and responsibilities of the persons re-using a dataset, either support services alone are provided, or research services are also required.

Support services comprise all activities in which support is being provided for successful data re-use. Support can be provided in various forms, starting with supplying information and ending with assistance with data analysis methods and procedures.

Research services comprise all activities where research work is carried out for the client, ranging from advice on specific research questions in different research stages to a more complete research endeavour providing a detailed analysis of specific research questions. The services are more targeted to the latter.

Analysis tools are an integral part of support and research services. The efforts and costs are to be included in the business model for the re-use of the data.

Support services

Support starts as early as the application stage, with discussions on the suitability of the data to answer the specific research questions at hand. Support services target the researcher’s ability to perform analysis and re-use existing data. The services are divided into different stages depending on the degree and impact of the support. These stages are:

  • information and data provision;
  • supporting tools;
  • assistance with dedicated research needs;
  • data-protection and analysis facilities.


Information and data provision

The first stage of support is to make researchers aware of available datasets and tools for data handling. This information is usually provided in online data catalogues. Furthermore, discussions may be necessary to answer questions about data usability (based on feedback from initial data analysis or from already performed data re-use) and which procedures have been established and proved to be successful. Metadata and other detailed background information on the data collection and initial study design can provide a better understanding of the dataset and improve data handling. Additional services, such as basic data aggregation and data extraction and transfer, could also be provided.

Supporting tools

Tools are an integral part of the support services. These tools consist of viewing and annotation tools, scripts to extract useful datasets from a database and licensed SW—and can also include entire frameworks for retrieving, processing and uploading data back into a database. However, it is important that the analysts are free to choose what tools to use without being constrained by factors other than the raw data formats and data descriptions (for example, by complex frameworks with graphical interfaces). It is, as mentioned in Chapter 5, important that raw data can be read in a clearly described format directly from the data storage source (e.g., database or file storage), regardless of what analysis tools are used in the project. Note that appropriate access restrictions should always apply. Allowing analysts to choose their tools is important, since different analysts have different ways of analysing data. Support services should impose as few constraints as possible on what processes analysts can use to analyse the data (within the data-protection framework). Examples of different ways to analyse data are given in Chapter 5. Data description formats and data formats will have to be able to deal with different analysis processes, in order to be accepted and used by as large a community as possible. It is also important that the dependency on third-party software for access is kept to a minimum.

The FOT tools are available online on the FOT-Net website at the following link: http://wiki.fot-net.eu/index.php?title=Tools_for_FOTs (accessed on December 27th 2017) and the content is described in the deliverable D4.2 Tool Catalogue. The tools are divided into three sections:

  • Tools for Preparing: Operationalization of high-level FOT goals to specific study design and measures;
  • Tools for Using: FOT operation and data acquisition;
  • Tools for Analysing: Data handling and evaluation.

Support may consist of providing dedicated tools for specific tasks (if available) and setup and basic maintenance of the analysis tools. Due to the complexity of data analysis, the setup of these tools requires a profound understanding of the datasets. Further developments of the tools fall under the stage Assistance with dedicated research needs of the support services.

Assistance with dedicated research needs

Assistance, the most advanced stage of support services, can take the form of dedicated advice on analysis methods and the custom modification of tools. In a strict sense, analysis methods are not applied (this would be part of research services, see 8.2); instead, this service selects, provides and adjusts analysis methods.

Data protection and analysis facilities

The following support services can also be provided:

  • analyst training;
  • support relating to privacy issues;
  • data-protection measures;
  • secure facilities for analysis work.

The researcher could be given training in security and privacy matters, thus gaining a deeper understanding of the sensitivity of the data. Training in using analysis tools could also be included (see Chapter 7).

Support for new research projects on confidentiality and privacy issues is a common role for data warehouses.

Advice and support could be given on the need for data-protection measures.

Certain data warehouses offer secure sites/rooms for analysis. In these cases, the data may not be transferred, but must be analysed on-site to fulfil security requirements.

Research services

Research services have a role beyond the initial start-up provided by the support services. In this case, the data provider takes part in the actual research to be performed, if required by the analyst. If the analyst comes from another discipline and/or is unfamiliar with the type of data and therefore would like to have it aggregated to a more suitable format, the research services (sometimes called the data extractionist) can assist. A deep understanding of the research questions is necessary in order to aggregate the raw data in the best way without losing relevant information. The work performed by the research services can extend as far as performing the complete analysis, answering specific research questions.

The three levels of research services are:

  • research advice on methodology;
  • research involvement/research support;
  • complete research performance.

The three levels are not necessarily distinct, but can overlap each other.

Research advice

On this level, advice is provided on data analysis. The advice, based on experience from data collection or previously performed analysis of the dataset, focuses on the best practice to answer the actual research questions and the related hypothesis. That is, the advice does not deal with how to solve a problem (using tools, data handling, data protection and/or data processing), but focuses on what methods should be used to get to the desired results.

Examples of research advice are:

  • determine whether a dataset can be re-used;, review the scientific approach/method for re-using data;
  • review whether a dataset is appropriate for the research questions, hypothesis and indicators.
Research involvement/research support

The second stage of research services is an active involvement in the research to be performed in terms of:

  • support in the identification/selection of data for re-use;
  • development of specific tools for:
    • data handling
    • data analysis and evaluation.
  • performing parts of the analysis, such as:
    • formulating research questions based on research content,
    • formulating a hypothesis based on research questions,
    • deriving indicators from hypothesis,
    • applying data analysis based on indicators,
    • statistical analysis of data.
Complete research performance

Finally, the highest level of research services consists of the data provider, or a third party, performing all the research. In this case, complete work packages for research on the datasets are taken over by the research service provider. Work packages can consist of work in one or more of the following fields:

  • selection and provision of data;
  • selection and/or development of specific analysis tools;
  • complete analysis;
  • scientific reporting.

Financial models

Efficient management of FOT datasets is the key for successful re-use. If data sharing is not economically feasible for data owners and potential data re-users, re-use of data does not take place and the benefits of data sharing aren’t achieved. Thus, in order for data sharing to gain popularity within ITS and FOT research, financial models are needed that cover data management costs.

Organisations supported by public funding are facing new requirements to plan long-term data preservation and management. Future work on financial models will have to take into account both the changing conditions of public funding to promote data sharing and the current trend opening ITS data for use in new services.

This chapter discusses options for organisations carrying out field trials to fund the sharing and upkeep of datasets after the project.

Data management costs

In terms of cost items, FOT data management has many things in common with open data efforts and large-scale user tests in various scientific disciplines. Documentation and user support have heightened roles, though, as FOT datasets are generally in non-standard form and have their origins in studies with specific goals. In addition, strict requirements to uphold user privacy and product IPR may require secure facilities and processes, raising the management costs of such datasets higher than those of fully open datasets.

Table Data-sharing costs lists the items requiring funding in FOT data management. The items are related to data management after a project—or more generally, after the data collection has ended.

Clearly, storing a massive dataset and organising proper backups to avoid losing data incurs costs. Data may also have to be anonymised to enable wider sharing. When sharing a dataset, licences/agreements usually need to be completed, as well as financial arrangements. Further, to justify the benefits of data sharing to funding organisations, it is important to collect information on the use of the data. As a result of such requirements, the list of data management cost items can grow long. However, that does not necessarily mean that data sharing causes a huge burden on organisations. Effective processes, support and tools provided internally or externally by professionals, can reduce the stress on participants in single projects. Basic preparations to share data should become part of good scientific practise.

Considering the general costs of data management, it is unlikely that all test data can be stored for future science. A selection process is foreseen that would concentrate the efforts and funding on promising and valuable datasets. This selection could be carried out by those who fund the costs of data sharing and supported by the experts who collected the data for the original project. The selection could be based on the following criteria:

  • potential for re-use, from both scientific and business perspectives;
  • efforts needed to store the dataset;
  • quality and amount of data.

Table Data-sharing costs presents cost items and tasks involved in data sharing after data collection has ended. It is assumed that tasks enabling data sharing, such as concluding legal agreements, metadata documentation and data quality checking, have already been performed in the original project that collected the data. Some of the cost items in Table Data-sharing costs are optional, such as advertising datasets or participation in international harmonisation/standardisation efforts of data collection and data catalogues. However, such tasks are common in professional data management services and can also be foreseen in FOT data-sharing activities that have achieved an established status.

Table Data-sharing costs

Cost itemCommentsTiming of cost
Data selection, enhancement of documentation (metadata), creation of entries in relevant data cataloguesFinalisation and structuring of data. As a pre-requisite for sharing, the datasets need to be comprehensively documented.When project/data collection ends
Anonymizing dataThe level of anonymization and related efforts depend on how widely the data will be shared.Before data is shared
Management & coordination personnel costsBasic management of e-infrastructure, including user support, data catalogue operations and updates, data import to archives, backups, compilation of usage statistics, license management, agreements and financesContinuous
IT operationsDatabase servers, storage, licenses and IT personnel costsContinuous
Analysis or data handling facilitiesPhysically secure work spaceContinuous
Analysis support servicesExpert support at different levelsWhen data is shared and during analysis efforts
Promotion and advertisement

Informing potential data re-users and data-sharing funders

Optional: Direct funding of further analysis projects, to ensure good use of valuable datasets

Optional: Direct advertisement of datasets for potential research projects and those planning new projects, beyond common catalogues

When project ends/Continuous
Optional: Standardisation and collaboration regarding dataset formatsTaking part in national and international collaborations regarding dataset formatsContinuous

Financial models

This section suggests financial models for data sharing, starting mainly from the point of view of the organisation that has collected the dataset. As the main funding for transport-related research today comes from direct governmental grants, this is also likely to be the case for the re-use of FOT data. Future funding might be directed toward established data-sharing and e-infrastructure activities. In fact, the first two financial models in this chapter (A and B), are based on such activities.

Project-based funding is one of the current methods for financing data sharing. The models C–E consider the pros and cons of directly including data sharing and re-use in the project activities. In the models F–H, the costs fall mainly on the end user (e.g., through membership fees or licenses). Several funding sources might be required to keep data available and provide services for third parties. Therefore, the financial models can also be complementary.

Financial model examples

A) Organisations’ core activity

Digital preservation becomes a part of organisations’ core activities. This model is motivated by conditions set by public grant agreements. A part of the grant for the original projects that collected the data will be directed toward central data preservation activity inside the organisation. This would cover data management and sharing for a certain period after the project is finished. The data availability for third parties should be based on reasonable conditions and costs.

A selection process may be required to decide which data will be stored, the way they will be stored and for how long. The operation of a repository can also be outsourced. However, when a dataset containing personal data is stored by a third party, it needs to be strongly encrypted to avoid misuse and liability problems.

Table Model A (example: social sciences universities, possibly larger FOT/NDS datasets)

ProsCons

·        Data would be considered IPR, valuable datasets would not be lost

·        Dedicated professionals would enhance the quality of the data provision procedures and analysis tools

·        A burden for small organisations not prepared for such requirements

·        No existing selection process for funding

B) e-Infrastructures

Public funding is directed to data infrastructures, serving multiple organisations and disciplines. Centralised data management could offer professional data management services, general harmonisation and possibly greater cost-effectiveness when compared to distributed approaches. The roles of public infrastructure would cover certain tasks, but project-specific funding would still be needed when data-users or data owners request additional services.

Table Model B (example: Supercomputing infrastructures and their services to universities)

ProsCons

·        Professional data management services

·        Data and processing services are free (i.e., for academic re-use)

·        The operators of the data infrastructure will have limited knowledge and means to provide detailed support for analysts, other than existing documentation

·        Dataset confidentiality sets limitations for storage by third party services

·        No selection process for funding exists

·        Valuable datasets from smaller projects might not be considered

C) Archiving included in project budget

Project budget allows for dataset finalisation and archiving in commercial services. In this model, the project budget allows for final cleaning, documentation and fees for archiving in selected data storages for a fixed period (e.g., 10 years). The project creates entries in relevant data catalogues.

Table Model C (example: Research team storing its data—or making them open-source)

ProsCons
·        The commercial service could get part of their funding from advertising, even enabling free storage

·        Who answers questions regarding the dataset after a few years have passed?

·        Is the documentation of the required quality?

·        No existing selection process for funding

D) Project extension

The project is awarded a continuation to maintain its data.  Model D is like model C, except the dataset is archived by the project partners. For notable projects, separate grants for operation (including data storage, promotion, calls for analysis proposals, etc.) would be awarded based on a review board decision, under specific conditions.

Table Model D (example: Large research projects apply for extensions)

ProsCons
·        Targeted promotion activities for datasets can also include funding for analysis activities and effective monitoring of results

·        No selection process for funding exists

·        Valuable datasets from smaller projects might not be considered

E) New project funding

New projects finance maintenance or revival of a dataset. In a chain of projects, the benefit of using past data is obvious, encouraging efforts to be put into maintaining and exploiting the old dataset. Depending on the follow-up activity, the data owner might also be motivated to share data with third parties, to extend analyses for mutual or customer benefit (e.g., offering material for thesis work, benefiting the customer who originally funded the data collection).

If a data request from outside of the organisation meets the business interests of the data owner, it is welcome. Otherwise, it fails to motivate the efforts needed for data sharing.

Table Model E (example: Various research projects analysing and benefiting from previous dataset)

ProsCons

·        No changes to current funding methods (additions are needed in call texts to promote existing datasets)

·        When data is re-used by those who collected the data in a previous project, the re-use is very efficient

·        Plain project-based funding may not be sufficient to keep datasets available and it should be seen instead as a complementary funding source

·        Project owners have difficulties estimating the costs required to access a dataset, when they are making an initial project plan/offer

F) Established network

A network of organisations with participation fees arranges data management jointly.  Organisations within the same discipline form networks that share and promote data. Datasets are collected, documented and catalogued using agreed-on/standardised methods. The networks are likely to be formed for handling continuous operational data which meet their business interests. There could be various levels of memberships and fees.

Table Model F (example: Accident data collection and sharing)

ProsCons

·        Business aspects can be applied on fees, high-quality harmonised data

·        Could include freemium services, where the basic information is available for free but advanced services have a cost

·        Facilitates cooperation in research

·        Only certain disciplines seem to reach this status

G) Analysis services

An organisation with several valuable datasets uses them to create business, offering both data and related services. This model can enable the original group that carried out a study to get further funding for their work. The model is for organisations with a prominent role in a discipline.

Table Model G (example: Notable data owners/Data providers)

ProsCons
·        Continuous research quite possibly results in high-quality results.

·        Small organisations and partnership projects have difficulties setting up this sort of business and their data easily get lost.

·        Even valuable datasets become old and lose value for organisations purchasing analysis services.

H) Data integrators

Companies­ acquire and market FOT datasets along with transport and other related datasets. In this model, a data integrator markets particularly useful FOT datasets (among others, such as those containing real-time traffic data) licensed from original sources. Customers are offered a single access point for data so they don’t have to go through negotiations with several parties, facilitating (for example) the development of mobile applications. In order for a dataset to be shared without fees for the re-users, the maintenance would have to be financed through the organisation that contributed the dataset—or supporting business operations.

Table Model H (example: Road operators putting together information services)

ProsCons
·        Easy licensing of various high-quality information resources·        Data integrators may have little interest in non-commercial academic work
Distribution of costs

Depending on the financial model and the activities set up for data sharing, the costs are shared differently among the project that collects the data, the organisation(s) owning the data and, finally, the re-users.

The table Funding source and re-use costs in different financial models considers the funders for data management and re-use in the different financial models presented previously. The costs are divided into three classes:

  • dataset finalisation, costs that often come at the end of a project;
  • continuous costs coming from management and upkeep of data;
  • costs when data are shared, e.g., selection of data and user support.

Additionally, the table considers the cost for the re-user in each financial model.

Those costs that are potentially funded by an external party or the organisation’s non-project funding (i.e., part of the organisation that is not involved in sharing or re-using the data) are highlighted.

Table Funding source and re-use costs in different financial models

Financial modelFunderCosts for re-user
Dataset finalisationContinuous costsCosts when data are shared
A. Organisation’s core activityProject/ Organisation’s selection processOrganisation (with public funding)Organisation/ Re-userNon-profit price
B. e‑InfrastructuresProjectPublicly funded e-infra, where organisation may have a rolePublicly funded e-infra, additional services have a priceFree (basic services)
C. Archiving included in project budgetProjectProject or e.g., data storage service supported by advertisingBoth project and re-userFree or non-profit price
D. Project extensionProjectProjectProjectFree or non-profit price, even calls for analysis proposals
E. New project fundingRe-userRe-userRe-userCommercial price
F. Established networkProjectRe-users via participation feesRe-userDifferent levels of memberships and fees
G. Analysis servicesProjectOrganisationRe-userCommercial price
H. Data integratorsProjectIntegratorRe-userCommercial price

Data protection recommendations

Data protection is the key to creating trust between a data provider, data owner(s) and the researcher. The data provider is responsible towards the data owner(s) to ensure that data are being handled according to agreements or contracts as well as the legal context in the country where the data is managed. Subsequently, if the data provider knows that the researchers have good, proven procedures in place to keep control of who is using the data, and that the researchers have knowledge of the legislation surrounding the handling of personal and IPR data, they will be more willing to allow access to or share data.

This chapter applies whenever the data are shared between two organisations. There are many different scenarios where data can be shared and the organisations must discuss the following questions beforehand:

  • How are the data going to be accessed between the organisations?
  • Should each organisation have a dataset?
  • How can the data be transferred?
  • What physical security requirements must be in place?
  • When must data be erased?

When data are collected and used within the same organisation there might be greater control of how the data is handled, but this chapter could still be applicable.

This chapter discusses the different demands imposed on data protection by different kinds of data. The scope of data protection includes unauthorized access, data theft, data loss and the proper documentation of the implementation. The chapter also includes a suggestion for data-protection requirements to facilitate the setup of the necessary data-protection framework, for a data provider in the role of a data centre (DC) and a data user in the role of an analysis site (AS).

Stakeholders

There can be many stakeholders involved when two or more organisations decide to share data. The researcher, later referred to as the data user, is the person who will use the data for analysis. The data provider shares the data with another organisation. The data owner is the organisation that owns the data according to contracts with the data provider(s). In many cases the data provider and data owner are the very same organisation.

In addition to these three, this document defines a data centre as the organisation that makes FOT/NDS data available to more than one data user. The distinction between the data centre and the data provider is that the latter shares data with another organisation, but does not interface directly with the data users at an analysis site. This document also defines analysis site as the organisation establishing data access for a group of data users.

It is important to state that a single organisation can act in one, many, or even all, of the roles.

A study participant is defined as the person who generates the data being collected. This is an important definition, as this person is protected by legal rights concerning the usage of the data.

Data centre

The data centre must implement appropriate data-protection means to ensure responsibility and liability, as stated in agreements with data providers, data owners and the study participants. A person downloading a dataset to a computer is not considered a data centre unless the data is made available to others.

When transferring the data to another organisation, the data centre organisation itself becomes a data provider. This can lead to chains where many organisations host data as data centres, while also sharing data as data providers. It is important for all parties in the chain to have a clear picture of the data flow, comply with the data-protection requirements and thoroughly understand the data ownership and privacy laws in the country where the data are being managed. In fact, a dataset (or parts thereof) can be owned by one or more organisations. The data ownership and usage are regulated in the agreements between the organisations (see Chapter 4).

Data user / Analysis site

A data user might be allowed to download data from a data centre or operate within an analysis site. An organisation can establish an analysis site where the requirements stated by the data centre are implemented. The data users within an analysis site must accept and follow the data-protection principles. In many cases an organisation establishing a data centre also acts as an (internal) analysis site, although it might be practical to keep the distinction between the two, especially in large organisations when managing personal and/or confidential data.

Data classification

The level of data protection required depends on the harm the data could do if revealed and the legal requirements. If the dataset consists of personal or confidential commercial data, it is mandated by law that action is taken to ensure data protection, regardless of the size of the dataset. Confidential commercial data is usually accompanied by agreements stating the conditions for access and use, whereas the use of personal data is regulated by law and the agreement with the participant (via consent). This document classifies data into personal, special categories of personal data, confidential, and non-sensitive data. This classification will be used frequently in this document and it is therefore important to understand it; the different categories are defined below.

Personal data that need protection

The General Data Protection Regulation (GDPR, REGULATION (EU) 2016/679) will reform the usage of personal data in Europe; the regulation came into force on May 25th 2018. GDPR strengthens the rights of the individuals and set a common legal framework for all European Union countries. It is absolutely vital for any organisation that host or process personal data in the European Union to ensure that personal data are managed according to the law. GDPR states in Art. 3 that the law applies also to processing of personal data monitoring of person behaviour taking place within European Union, regardless of the processing being done within or outside of European Union. Any organisation planning to share personal data to third countries outside of European Union must pay great attention to what can be shared and how.

It is although important for organisations affected by GDPR to also investigate the research data laws that will be implemented country-wise.

Even if GDPR harmonizes the regulations in a European Union context there will still be differences in implementation between the US, Australia and Asian countries.  For example in the US, ‘personal data’ are known as ‘personal identifiable information’ (PII) and ‘specific categories of personal data’ are known as ‘sensitive personal information’ (SPI or SPII). The definitions are not identical to the ones being stated in Europe, and it is therefore advised to take any necessary actions to ensure that data are managed according to the laws of the country/ies where the data are located.

The term personal data is defined in GDRP Art. 4:

‘personal data’  means any  information relating to  an  identified or  identifiable  natural person (‘data  subject’);  an identifiable natural person is  one  who  can  be  identified,  directly or  indirectly,  in  particular by  reference to  an identifier such as a name, an identification number, location data, an online identifier or  to one or  more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person

There are also special categories of personal data that requires additional consideration defined in GDPR Art. 9:

Processing of personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership, and the processing of genetic data, biometric data for  the purpose of uniquely identifying a natural person, data  concerning  health or  data  concerning  a  natural person’s sex  life  or  sexual orientation shall  be prohibited.

The suggested data-protection requirements in this chapter aim to guide the data centres and analysis sites in setting up a data-protection concept that meets the regulations and respects the will of the participants as stated in the consent form.

Confidential commercial data that need protection

Confidential commercial data is information which an organisation has taken steps to protect from disclosure, because disclosure might help a competitor. The sensitivity of confidential commercial data usually dictates the data-protection requirements stated in the data-sharing agreements. When contracts for providing the data are being signed, it is advisable for both parties to discuss, and agree on, the level of protection level that will be suitable. Some data might be less sensitive whereas some might not be sharable at all.

Table Categorisation of confidential commercial data

Data CategoryAccessOwnership
OpenOpen for all analysts/all project partners/certain project partnersOwned by all/part of the project consortium
LicensedConfidential commercial data shared with all/certain project partners during the project. Available on a per-project with approval by the owner.Data provider (usually the data owner who holds the IP rights)
ProprietaryConfidential commercial data that are never shared, as the commercial value of potential loss or misuse of the data is too high to allow data sharing.Data provider (usually the data owner who holds the IP rights)

Licensed data could be made more open by, for example, aggregating some signals in order to produce a non-sensitive level of information—thus avoiding commercially harmful misuse of the original data.

Non-sensitive data

The definition of non-sensitive data is data that are completely anonymised and do not include any confidential commercial elements (unless an agreement with a data owner allows public usage). This means that no personal identifiable data are available in the dataset (i.e. video, images or GPS traces). Actually, by the definition of anonymized data, you shall not be able to reverse engineer the identity of the data subject in any related dataset. In most cases, this means that the original dataset must be deleted. If video or image material is included in the dataset, any identifiable traffic participant and any other objects that can be used to identify a person (e.g., number plates) must be anonymised (e.g., by blurring) to ensure confidentiality. If GPS traces (including position and time) are included in the data it is important to use proper methods to protect the participant from being identified. For more information on anonymization, see Section 6.3.

If the data are classified as non-sensitive, there are few, if any, mandatory requirements for data-protection, but it is still recommended that all possible requirements be investigated—including those regarding reliable data storage. 

Data access methods

This section presents data accessed in one of four different ways: 1) downloaded via a public website, 2) transferred on hard drives to the research organisation, 3) remotely accessed at the data provider, or 4) accessed exclusively at the premises of the data provider. Each method has its own implications; usually, the data category has the greatest impact on method selection.

Public download

This means that the dataset is downloadable from a public space (e.g., a web or an ftp server). This option is suited for non-sensitive data, as it is not possible for the data centre to control the use of the data. The dataset could be under a license that sets conditions or restricts the usage of data. The license could also state that any papers or public material must include a reference to the data provider. The organisation downloading the data will, by definition, be considered a DC—and also an AS, if it performs analyses on the data.

Conditioned download

This means that the dataset is transferred between two (or more) parties that agree on the conditions. The data is transferred from the data provider to the requesting organisation using portable disks or by an agreed-on Internet protocol. There are no restrictions on data categories but it is mandatory for the parties to consider all related agreements. The dataset could be under a license, agreed on between the parties, that sets conditions or restricts the usage of the data. The requesting organisation downloading (and therefore managing) the data will by definition be considered a DC and, as noted, if it performs analyses on the data it will also be considered an AC.

Remote access

In this case, the data will not leave the data centre; all analysis is performed within the data provider’s IT-infrastructure. There are no restrictions that depend on data classification but it is mandatory for the parties to consider all related agreements. The dataset could be under a license agreement between the parties that conditions or restricts usage of the data. The requesting organisation is considered an AS.

On-site access

When remote access is not possible due to network bandwidth limitations, or legal, contractual or data-protection requirements, on-site access might be the only option. It is then up to the data provider to allow external partner(s) access to the data on the premises. In this case the data provider will be acting both as a DC and an AS.

Requirements on data protection at data centres

It is imperative that any organisation hosting FOT/NDS data document its data management processes. Depending on the level of sensitivity of the data, different levels of precautions have to be taken. If the data include personal identifiable data or confidential data, stronger requirements need to be formulated. The data handling needs to be documented; there are frameworks that must be considered (if not already established) to ensure that the necessary processes are documented and traceable. For example, ISO 9001:2008 for Quality management systems, ISO/IEC 27001:2013 for Information security management, ITIL (IT Infrastructure Library), or the UK initiative Cyber Essentials could be used. GDPR certification to demonstrate compliance is a future alternative but by May 2018 there yet any certification body approved. Additionally, similar (although not formally acknowledged) quality assurance procedures might also be suitable; the most important consideration is that the organisation reflects on data security and access—and implements routines that ensure data protection.

It is important that third-party organisations (e.g., a cloud-based data-hosting company or a third-party organisation managing parts of the IT infrastructure) comply with the requirements. GDPR addresses this in Art. 28 and Art. 29.

It is also stated in GDPR Art. 35, that any organisation managing personal data must make an impact assessment on the risk in case of a data breach.

DC1:      The DC must document its data-protection implementation.

The DC data-protection implementation must be documented. It is recommended that the documentation be accepted by the data providers and owners.

A data centre shall not be allowed to manage personal or confidential data before the data-protection implementation is documented. The data centre must fulfil legal requirements and document how to meet requirements DC2-DC8. It is recommended that there be a transparent process with the data providers and legal instances. It could be valuable to get an independent review of the implementation by an external party. The following steps should be part of the review process:

Implementation guidelines:

  • Appoint an individual as DC data supervisor. The data supervisor is responsible for mapping, implementing and following the requirements for data protection.
  • If personal data are included, the organisation (in a European context) handling the data also assumes the responsibilities of being a data controller.
  • Data hosting should not be allowed before the DC and the data provider(s) have agreed on the level of data protection.
  • Compile documentation meeting the requirements stated for a DC.
  • The DC documentation should be reviewed by the data provider or a third party organisation.

The DC must address and document the data-protection implementation by:

  • Presenting the DC.
  • Defining the start and end date (if applicable) for data hosting.
  • Providing name of the appointed DC data supervisor and description of organisational structure.
  • Providing an overview of personnel who will have access to data.
  • Briefly analysing the responsibilities of DC in the context of data protection and privacy issues.
  • Analysing GDPR and national legal compliance; what legal issues must be handled specifically for the DC, and how will this be done?
  • Describing in detail the compliance by the DC with numbered requirements. In the documentation, known deliberate deviations from requirements should be listed, analysed, and motivated separately. Why is compliance not needed, and how will issues be addressed instead? Any changes (additions, modifications, deletions) to the implementation must be documented.
  • Providing status of the described implementation; is it planned or already implemented? Provide time plan with technical details where applicable.
  • Providing disaster recovery plan, with risk assessments.
  • Providing incident response plan for data security breaches, with risk assessments.
  • Providing relevant internal routines/guidelines, as well as training for personnel.
  • Describing relevant contracts/agreements.

DC2:      Data stored and processed at a DC must be protected from unauthorized access.

Servers, computing environments (physical as well as virtual) and network connections must be protected, using measures sufficient to prohibit access to unauthorized parties.

Implementation guidelines:

This requirement covers many aspects of operating a data centre, but the requirements should focus on the most important processes for protecting data from unauthorized access.

Physical protection

The servers and other equipment must be kept in a secure environment to stop physical intrusion; within the organisation only entitled personnel should have access to the server rooms. Logging of individuals’ access/activities regarding the servers might also be required. This rigid requirement should be applied when called for by the type of data and the associated data protection requirements.

Logical protection

The personnel having access to the data need to be identified. It is important to consider not only the analysts, but also IT-administrative staff members who might have full privileges on the servers.

The FESTA handbook recommends using group-based privileges instead of giving individual users access to very specific parts of the data. This might cause overhead in the initial phase but in the end it will help control data access.

It is recommended that future use of the data be considered when designing the data access patterns, even in the main data collection project, as this can avoid costly updates later on. As a starting point it might be suitable to consider the main data-collection project equal to any other post-project analysis.

The use of Internet firewalls is recommended when managing personal or confidential data, to restrict traffic to the services in the data environment. One way to implement this is to allow access only from a limited number of analysis workstations to the services that host the data. It is recommended that only specific workstations have the special privileges required to perform data uploading. The network traffic between the firewalls should be encrypted if transferring any personal or confidential data. Be aware that merely using the IP address is rather insecure (due to ‘spoofing’), and more efficient measures should be investigated.

Disk cabinets and USB sticks can be used for transporting data but should not be used as either main data storage or backup. If used for transferring personal or confidential data the disks should be entirely encrypted.

DC3:      Data stored and handled at a DC must be protected from accidental deletion or corruption.

Secure backup and disaster recovery solutions must be in place.

Implementation guidelines:

Corruption or accidental deletion of data can result from user error (unintended deletion), malware (e.g., ransomware) or alteration of physical media (hardware failure or a disaster such as flooding, fire or theft).

The consequences vary depending on the type of data: loss of users’ own data (such as their own processes, algorithms, results and derived measures) will affect only that user and those depending on the outputs—whereas loss of original experimental data will affect all users, and therefore needs to be considered more critical. Further, it is frequently a legal requirement that the data be kept a certain number of years in order to re-do analyses (in case of doubts about scientific results, etc.).

Several good practices can limit the impact of accidental data deletion or corruption. First, to prevent alteration of storage media, only a minimum necessary amount of trained IT professional should have access to the actual storage places (server rooms in particular). Professional security measures must be taken in order to prevent unauthorized access. It could be a part of a quality management system. Proper disaster prevention and mitigation measures, such as fire detectors and extinguishers and staff training, also have to be taken in order to avoid storage destruction.

Data must also be replicated, preferably in different locations, in order to survive a disaster scenario. Given the size and confidential nature of the typical datasets involved, this may prove challenging for technical or data-protection-related reasons. For instance, synchronizing data in an uncontrolled cloud service would pose a bandwidth issue as well as a lot of legal problems. One possible solution which, although non-ideal, is easily implemented, is to keep the original data collection media (such as hard drives) in antistatic sleeves in a fire-resistant safe, in a building other than the data centre.

Appropriate IT measures should also be taken to ensure resilience to hardware and/or software failures: hard drives in RAID arrays with sufficient redundant hard-drives and hot-swap units would handle multiple simultaneous failures of different hard drives before any data would be lost. Regular backups of machines and virtual machines must also be scheduled.

In order to avoid unintended data deletion by users (whether the data are in files or databases), the users’ access rights must prevent them from deleting any part of the original data without authorisation.

If parts of the original data have to be deleted on some occasions (data privacy laws specify, for instance, that participants in data collection can request the deletion of some of their data), the deletion has to be done in a controlled way, following strict procedures which ensure that only the data to be deleted are actually deleted.

Users’ own data have to be backed up on a regular basis using standard methods (such as a backup server, associated with a magnetic tape archive). The impact of any action from the users´ side must be made clear to them, both through the user interface of the tools they are using and their initial training. The training should include information regarding which data are backed up and which are not. It must also be explained that data can be permanently lost if they are created/modified and then deleted before a backup point, or deleted and not retrieved after the archiving period. Additionally, users should be trained in procedures to recover unintentionally deleted data and receive a general introduction to structural metadata (see Chapter 5).

DC4:      Confidentiality agreements for any involved personnel must be in place.

The DC must require signed confidentiality agreements from all involved personnel before they start handling the FOT/NDS data. Agreements can either be explicit, for the specific project (for guest researchers, students, etc.), or implicit, through employment contracts.

Implementation guidelines:

Employees usually have a confidentiality statement in their employment contracts. It is important to understand what is applicable for the specific organisation before starting to manage personal or confidential data. If consultants, students and other temporary personnel are involved but not covered by an agreement with a confidentiality statement, then they must sign a separate NDA.

DC5:      Data protection must be ensured by the DC after end of project.

The data must be stored and protected at the DC after the end of the project to facilitate data re-use and sharing.

Implementation guidelines:

A set of policies needs to be in place for storing and protecting the data at the DC after the end of a project. These policies should permit effective data re-use and be proportionate to foreseen risks and damages from leaking or losing collected data.

It is also important to make decisions about what to do with the data; they could vary depending on data type. Agreements could include requirements regarding when data need to be erased. The responsible organisation must adhere to national or other applicable legislation when deciding if the data are to be:

  • Kept online

The organisation must guarantee the same data-protection level after the main data collection project.

  • Anonymised

Personal data could be anonymised or essential features could be extracted from the data and shared, leaving the original data secure. Commercial confidential data could be aggregated to a sufficient level that the owner might be willing to allow their less restrictive use. Combined, these two possibilities could result in data considered non-sensitive, lowering the data-protection requirements and possibly allowing public usage.

  • Archived

If there is no possibility of keeping the data online, all or parts of the data could be archived. It is important to evaluate the overall requirements for infrastructure and data management during the archiving period and also to decide when and how the data shall be erased. If personal data are to be stored as such at all, they must be encrypted. The same applies for confidential data; separate agreements usually set rules for deletion and archiving.

  • Erased

Policies should describe how to erase data securely. It is important to consider all media where data have been stored (e.g., storage and backup systems, portable storage such as USB drives, and even paper copies). The policies have to be in accordance with the requirements for recreation of results (see DC2).

DC6:      Data sent between a DC and an AS must be encrypted.

Data may be transferred between a DC and an AS by electronic means or, alternatively, transported on physical media. The DC must ensure that the data cannot be accessed during the transfer.

Implementation guidelines:

Encryption should be applied to all data transfers containing personal or confidential information. Files exchanged on portable media (e.g., USB drives) or sent over the Internet should be encrypted using software. For data streams between a DC and an AS, encryption practices are needed to ensure that the transfer cannot be listened to by outsiders.

Re-using personal data requires the laws and standards of all involved countries to be investigated. Several countries have standards in place for encryption and protection of personal data, e.g., British Standard 10012:2009 and FIPS 140-2 (Federal Information Processing Standards in the US).

Encryption methods and standards evolve. The chosen encryption product should be checked to ensure it has been certified to meet current standards.

Use of encryption software is usually straightforward, as it is often integrated into normal processes by design. However, a specialist could be consulted for the selection of encryption software if the data-protection requirements are high.

DC7:      Data downloads are regulated by the project agreement(s) and the informed consent of the driver.

Data sharing could in some cases involve actual downloading of part/all of a project’s data. The project agreement should regulate the possibilities of doing this. Also, the participants must have given their consent to the data being disseminated outside the project partners.

Implementation guidelines:

Any data downloaded for data sharing must comply with the terms of the project agreement(s), data ownership, and the participants’ signed consent form.

The receiving partner should operate under the same requirements as the DC. It is recommended that the same approval process be used for both of them. Depending on the data categories shared, some requirements might not be applicable—but it is important to justify and document any deviations.

DC8:      Data extractions for specific purposes must be in accordance with the consent forms and project agreement, and the extraction must be documented.

The difference between data extraction and data download is that when extracting data, the recipient will not become a DC; the data extracted is the output of analysis (e.g., plots, statistics or images), not data for re-use. Depending on what the participants have agreed to in the consent forms, different extraction policies can be used. Video and GPS extraction must be treated with special care. The recommendation is to anonymise the personal data content in the videos, especially faces and vehicle number plates. Each extraction must be in accordance with the project agreement(s).

Implementation guidelines:

To facilitate data extraction, information describing the level of consent from all participants (e.g., a participant did or did not permit third parties to access recorded video) should be accessible within the analysis environment. This will make it possible to keep track of which data can be extracted and under which circumstances. It is recommended that all parties agree on and implement a process for data extraction within the project. All extractions must be documented. A person (the data supervisor in Figure 5) shall be responsible for managing data extraction requests and forwarding the decision to the person performing the extraction (data extractionist). This schema considers three questions:

  • Do the requested data relate to participant consent and is the extraction valid?
  • Do the requested data include data that need approval from the data owner?
  • Do the requested data meet the size requirements for extractions defined in the project?

Documents

The following specific documents within the context of the data centre are identified:

  • an agreement and/or a data license with a data provider (if applicable);
  • an agreement with an external IT infrastructure provider (if applicable);
  • a confidentiality-disclosure agreement (CDA) or NDA for involved personnel;
  • data-protection implementation documentation;
  • data-extraction requests.

Requirements on data protection at analysis sites

An AS gets access to the data hosted by a DC by downloading or remotely accessing them. The AS must document the data-protection implementation plan, which should be agreed on with the DC. Depending on the sensitivity level of the data, different levels of precautions have to be taken. If the data include personal or confidential data, stronger requirements need to be fulfilled.

AS-1:     The AS organisation must document its data-protection implementation.

In order for data access to be granted to the analysts from a research organisation, the data-protection implementation must be documented and it is recommended that it be agreed on by the DC.

Implementation guidelines:

An AS shall not be allowed to analyse any data before the data-protection implementation is documented and accepted. It could be valuable to have an external partner (or partners) provide an independent view of the implementation. The AS must fulfil the legal requirements and document how to meet the requirements AS2-AS10. The following process is recommended:

  • Appoint an individual to be AS data supervisor. The data supervisor is responsible for mapping, implementing and following the requirements for data protection.
  • Compile documentation that meets the requirements specified for an AS.
  • The AS documentation should also be approved by the DC.

The AS must address and document the data-protection implementation by:

  • Presenting the AS and intended data usage.
  • Defining the start and end dates for data usage.
  • Providing name of appointed AS supervisor and description of organisational structure.
  • Providing an overview of personnel to be granted access to data.
  • Briefly analysing responsibilities of the AS in the context of data protection and privacy issues.
  • Analysing GDPR and national legal compliance; what legal issues must be handled specifically for the AS, and how will this be done?
  • Describing in detail the compliance by the AS with numbered requirements. In the documentation, known deliberate deviations from requirements should be listed, analysed, and motivated separately. Why is compliance not needed, and how will issues be addressed instead? Any changes (additions, modifications, deletions) to the implementation must be documented.
  • Providing status of the described implementation; is it planned or already implemented? Provide a detailed time plan with technical details where applicable.
  • Providing incident response plan for data security breaches, with risk assessments.
  • Providing relevant internal routines/guidelines, as well as training for personnel.
  • Describing relevant contracts/agreements.
  • Determining whether the intended data usage requires approval from a national ethics committee.

AS-2:     The analysis work stations must be physically and logically protected.

Analysis work stations (used either for remote virtual access to the DC or for handling downloaded data) must be protected in such a way that unauthorized access is prohibited.

Implementation guidelines:

For physical protection, work stations must be placed in locked rooms, or otherwise placed so that the screen content can only be seen by the data user. Some organisations also restrict users from bringing mobile phones or laptops into the analysis rooms, a stringent measure that requires security controls and usually limits the access to the computers. These guidelines have to be adapted to the level of confidentiality of the data in question.

As for logical protection, here are a few other ways to reduce the risk of intentional or unintentional data re-distribution:

  • Restrict the computer’s access to network services. The analysis computer should only be allowed to communicate with the necessary services. This could be configured in the Windows or Symantec firewall or, if using Linux, iptables. The ruling principle should be: deny all access, and then open up only the necessary services.
  • Restrict the ports (USB, printer, SATA) of the computer. This could be done using Group Privilege Objects in Microsoft Windows. Or, even more drastically, by physically disabling the ports.
  • Restrict the analyst from having administrator or root privileges in the operating system, since this capacity could eliminate the restrictions above.
  • If unwarranted attempts to re-distribute data are detected, they should be reported automatically to the data supervisor.

AS-3:     Analysts must have received relevant training in data protection and confidentiality issues.

Before data access can be granted, analysts must present proof that they received mandatory training, possibly prescribed by the initial project (e.g., US NIH education: http://phrp.nihtraining.com, accessed on December 27th 2016).

Implementation guidelines:

The analysts must have relevant knowledge in data protection and confidentiality issues if the dataset includes personal or confidential data. The web-based NIH training could be used, but the project could also choose to design and set up a training package. In either case, it is important to provide information regarding the local implementation of the security precautions, such as the data-protection procedures and the analysis environment capabilities and restrictions. The training should cover rules for the specific dataset at hand and provide analysts with general, basic information about the study. Further, the material should be designed according to the national regulations that concern personal data.

AS-4:     A confidentiality agreement for any involved AS personnel must be in place.

The research organisation must require signed confidentiality agreements from all personnel, before data access can be granted. Agreements can either be explicit, for the specific project (for guest researchers, students, etc.), or implicit, through employment contracts.

Implementation guidelines:

Employees usually have a confidentiality statement in their employment contracts. It is important to investigate what is applicable for the specific organisation before starting to manage personal or confidential data. If consultants, students and other temporary personnel are involved but not covered by an agreement that includes a confidentiality statement, a separate NDA must be signed.

AS-5:     The AS data supervisor administers access requests and forwards them to the DC data access dispatcher.

It is recommended that the number of persons having accounts for the data be restricted. The fewer persons are involved, the easier it is to keep track of users, accounts and privileges.

Implementation guidelines:

The process is exemplified by the following schema:

The analyst asks for access from the AS dispatcher, the person in the AS organisation who is responsible for providing access to the DC. At the DC a person is appointed DC dispatcher. The DC dispatcher then forwards the information to the account creator, and the credentials or privileges are extended to the AS analyst. Depending on the size of the AS, the AS dispatcher and the AS analyst can be the same person. Similar simplifications could be suitable for the DC’s involved personnel.

AS-6:     Specified procedures for data extraction must be used.

Extraction of a portion of the data, including video snippets and screen shots, must be performed according to the participant’s consent form and the data-extraction procedures. All extraction is administered through the DC.

Implementation guidelines:

The DC and AS should agree on, implement and document the process for data extractions. The AS data user must send a data-extraction request, approved by the AS data supervisor, to the DC data supervisor for approval. The request could include:

  • intended use of the extracted data;
  • list of data types;
  • text description of the data;
  • total size of the data;
  • list of files or folders to extract.

AS-7:     The analyst must not extract or re-distribute data.

Regulations for data extraction procedures are in place; the analyst must not circumvent these procedures or disclose data beyond the AS in any way.

Implementation guidelines:

This information should be included in the training package, which should be given to the analysts before they get access to the data.

AS-8:     The project data must not be used for research areas not covered by the consent forms in the project.

The data must not be used for any purposes other than those stated in the consent forms, except with ethical review board approval. Even if national law requires approval from an ethical review board (or similar) for the original project, other usage of personal data is normally not permitted, unless further approval is sought explicitly.

Implementation guidelines:

The need for ethical review board approval depends on the national legislation. The consent form signed by the participant defines what research can be done using the data. The participants give their consent to specified organisations to perform certain research during, and possibly after, the project. Special care should be given to the selection of research areas. The only ways to use the data for other purposes later are to ask the participant for another approval or get approval from an ethical review board using the regular national processes. Also, if ethical approval is mandatory and has already been given for a specific use of the data, additional ethical approval is still needed if new research areas are to be investigated. The specific procedures might differ between countries, but the overall principle must always be applied.

AS-9:     Visitors/guests to the AS should sign a confidentiality agreement.

If confidential or personal data are to be presented to a visitor, the visitor is required to sign a confidentiality agreement. As a general rule, visitors do not have access to the data and are always accompanied by an authorized person. Generally, confidentiality agreements between organisations are preferred over personal agreements with visitors.

AS-10:   All post-project research must investigate the need for approval

The drivers’ consent forms and national ethics regulations together with project agreements set the conditions for post-project research. All research proposals, but especially those not previously covered by the project, might need to be submitted to a local ethics committee and/or national authority for approval. Additional consent might even be needed from the drivers. Project agreements, including agreements with sensor providers, might also restrict the use of the data.

Implementation guidelines:

Early in the proposal phase of the project that will be re-using the data, possible data providers should be contacted to figure out the boundary conditions for re-using the data. The conditions might affect research areas, data availability, data access restrictions and approvals and could therefore affect the proposal content. The data providers should be able to present an overview of the requirements for re-using the data, including the necessity for additional approvals. The responsibility remains, though, with the researchers performing the new research to investigate the need for approvals. Obtaining additional approvals can be time-consuming; it may involve former participants, an ethical review board and external data providers to the original project (e.g., providers of map data) and thus the additional time required should be considered in the time plan for the project.

Documents

The following documents are specifically required by the AS:

  • CDA or NDA between analyst/visitors and AS organisation;
  • agreement and/or data license with DC;
  • approved training certificate / documentation for analyst;
  • data-protection implementation documentation;
  • approval from ethics committee for intended research (if applicable); data extraction request.


Data and metadata descriptions

FOT/NDS studies collect a large amount of raw data, especially when continuous data-logging is favoured over event-based data collection. Moreover, these studies also generate considerable amounts of derived data. Derived data can take different forms to address different needs. They can, for instance, be very similar to the raw data, simply representing the same information in a different format (e.g., in-vehicle signal values decoded from raw CAN frames). They can also be cleaned-up, filtered and/or discretized versions of raw measures. They can be a derived measure, where several pieces of information have been combined together to compute a new, more directly interpretable measure (e.g., time headway is the distance to the forward vehicle divided by speed; traffic density is calculated from traffic volume and speed). Lastly, they can be aggregated data, obtained using a data-reduction process, in which the most important aspects of the dataset have been summarised. The summarised data generally consist of a list of relevant events or driving situations and their associated attributes, the result of a mix of algorithm and annotation-based processes.

Depending on the aim of data re-use, simply re-using data in their most transformed/aggregated form may be sufficient. Occasionally, and when not prevented by intellectual property agreements (e.g., in the case of CAN data provided by vehicle manufacturers), it might be necessary to go back to the original, raw form. In most cases, however, cleaned-up, derived, annotated data will be the most useful. Whichever form of data is used, the core of data sharing is that the data provided are valid, or at least documented to a level where an assessment of the level of validity can be performed. This is potentially problematic if the data re-user was not part of the project and does not know in detail how the tests were performed, which sensor/version was used or how the data were processed from the raw data. The main problem is usually that the data are insufficiently described.

Data re-use requires precise knowledge about the data. Therefore, it is vital to have extensive and high-quality metadata, providing the following information:

  • the conditions in which they have been collected,
  • the purpose;
  • how they have been stored, cleaned-up, processed and aggregated; and
  • how they can be accessed.

A well-documented dataset inspires trust when being used, and also reduces the risk of less confident conclusions—something that all stakeholders benefit from.

In addition, before researchers/analysts/business developers even start to use a dataset, it has to be identified as potentially interesting and then selected as relevant for their purpose. These first steps only require a subset of the aforementioned documentation, which gives an overview sufficient to compare several datasets but is compact enough to ensure efficiency both in terms of creation and consultation. This results in the choice of items to be documented in the data catalogue. The content and structure of the FOT-Net Data Catalogue were described in FOT-Net Data deliverable D4.1 (Innamaa S. & Koskinen, S., 2017).

The aim of this chapter is to address these issues and provide methods for efficiently describing a dataset and the associated metadata. It suggests good practices for documenting a data collection and datasets in a structured way.

Definitions

Data

This document defines data as ‘any information whose value might be used during analysis and impact its result’.

This means that information which may be considered ‘contextual’, such as, for instance, participants’ characteristics or weather, traffic and driving conditions, is considered data, and part of the dataset.

A categorization of data is proposed in the following chapter.

Metadata

This document defines metadata as ‘any information that is necessary in order to use or properly interpret data’.

This document presents four different categories of metadata, each providing a different kind of information about the data. The categories are described below and in Figure 2:

  1. FOT/NDS study design and execution documentation, which corresponds to a high-level description of the data collection—its initial objectives and how they were met, description of the test site, etc.;
  2. descriptive metadata, which precisely describe each individual category of data, including information about its origin and quality;
  3. structural metadata, which describe how the data are organized; and
  4. administrative metadata, which set the conditions for accessing the data and how access is to be implemented

In the following sections, FOT/NDS data are introduced and classified, including examples and recommendations for which data should­­­­­­­­­­­­­ be systematically collected. The four metadata classes are more precisely defined, and what should be documented (according to good practice) for each of them is described.

The recommendations are based on best practices from NDS and FOT projects—but additional information will probably be needed for each specific study.

Data categories

Data can take many successive forms, from raw collected data to very high-level aggregated data, with many steps in between. A dataset is not only the result of data collection, but also of an iterative process, comprising pre-processing, integration of different data sources, calculation of derived measures and manual and/or automatic data reduction. Aggregated data are usually the easiest to use, but may only be suitable for analysing research questions similar to the initial study. In contrast, raw data can meet a larger variety of needs, but usually requires a deep technical understanding of the data collection process and sufficient data storage and operational capacity in order to be used in a relevant and efficient way. A trade-off, using intermediary states of the data, generally has to be found.

As a result, a data re-use case will typically require a combination of very different forms of data.  This document proposes a way to classify them, based on two characteristics: the relations between the different entities (vehicles, users or operators in an AD setup, infrastructure, etc.) addressed during FOT/NDS data collection, and the information which typically captures the entities’ different aspects (measures). This classification system is as close as possible, but also complementary, to the definitions in FESTA (FESTA, 2018), which essentially relate to data collection and analysis. This system emphasizes the typical structure of an FOT/NDS dataset and contains the following main categories: context data, acquired and derived data and aggregated data (see Figure 4). The sub-categories are described further in the following sections. Each sub-category may contain either objective data (which is normally quantitative data), subjective data (which can be either qualitative or quantitative data), or a mix of both.

Objective data are collected through direct physical measurement, without any influence from the experimenter or the participant’s subjective impression. They are collected using sensors, which can be pre-existing or installed on purpose, and data acquisition systems, which can be installed inside vehicles or on the roadside.

Subjective data are provided by the participants or observers, based on their impressions, feelings, memories or opinions—collected (for example) by questionnaires, travel diaries (usually quantitative data) or interviews and focus groups (qualitative data).

This categorization will be used as a basis for recommendations regarding what should be recorded in an FOT/NDS study, and how the corresponding metadata should be created.

Context data

Context data correspond to all information which doesn’t change during the study, but helps explain the observations or document their values. They may be directly collected, generated for the purpose of the experiment, already exist or retrieved from external data sources.

They contain, for instance, background information—such as infrastructure characteristics (e.g., map data) and vehicle /driver characteristics and roles during automated driving, including questionnaire results.

Questionnaires collect qualitative and quantitative data reported by each individual participant. They typically cover basic data, such as age, gender and general attitudes about driving. They can also cover more specific aspects, such as personality traits (e.g., sensation-seeking, introverted). Quantitative data is obtained by means of closed questions (e.g., multiple choice, scales) whereas qualitative data is obtained when specific questions are open for rich text information, often of a more interpretive nature.

Acquired or derived data

Acquired data are all data collected during the course of the study for the sole purpose of the analysis.

Derived data are obtained by transforming raw data into more directly usable data through, for instance: data fusion, filtering, classification and reduction. They typically contain derived measures (such as, for instance, time-headway, which derives from both longitudinal speed and headway), and performance indicators (PI), referring to time- and location-based segments such as particular events.

In most cases, transforming acquired measures into derived measures during pre-processing or processing doesn’t change their nature, established that no information loss occurred. For instance, an acceleration low-pass-filtered to remove noise doesn’t cease to be a vehicle-dynamics measure; the depression of a pedal converted to a discrete pressed/not-pressed state doesn’t cease to be a driver-action measure. As a result, in most cases, the subclasses presented below apply to both acquired and derived data.

However, in some cases, several kinds of measures are combined together to form new, more interpretable measures, which can’t be categorized simply. For instance, speed and acceleration from several vehicles can be combined together to form a time-to-collision variable.

This category includes both objective data, in the form of measures from sensors (referred to as sensor data in FESTA), and subjective data, collected from either the participants (referred to as self-reported measures in FESTA) or analysts. Subjective data can be as varied as time-history data, subjective classification of time segments, or rich-text information from travel diaries, interviews, and focus group discussions. Questionnaires can also be seen as acquired data when collected periodically during the project (compared to the static questionnaire data described in 5.2.1).

Time-history data

Time-history data describe the history of a measurement over time. Time-history data can be collected with a specific measurement frequency, or when triggered by an event, typically a value change.

Time-history data may consist of the variation over time of single physical values (e.g., speed), a collection of physical values (e.g., 3-axis acceleration) or more complex media, such as sound or video.

Time-history data can either be collected from the vehicle perspective, by means of (for instance) an instrumented vehicle, smart device application or travel diary, or from the infrastructure perspective, by means of roadside measurements. They can be historical or real-time observations, or by measurements done in a mobile phone app from persons inside or outside of a vehicle.

Time-history data consist of both direct measures, i.e. raw data measured over time, or derived measures, after any kind of transformation (such as resampling, offset correction, filtering and removal of incorrect values) has been performed.

In-vehicle measures

Instrumenting vehicles enables the collection of vast amounts of data, using either original sensors (tapping their communication networks, such as CAN) or additional sensors. Applications on smart devices (i.e. smartphones) can also collect important information in the following categories, and the data they collect can basically be treated the same way as the data from instrumented vehicles.

Vehicle dynamics

Vehicle-dynamics measurements describe the motion of the vehicle. Typical measurements are longitudinal speed, longitudinal and lateral acceleration, yaw rate and slip angle.

Driver state and actions

In addition to variables describing driver actions which command the vehicle, like steering wheel angle, pedal activation or HMI button press, variables characterizing the physical and emotional state of the driver can also be measured. For instance, cameras and computer vision can measure driver position, detect engagement in a secondary task or detect eyelid closure (which highly correlates with alertness). In fully autonomous vehicles (e.g. a shuttle) the driver might be a remote operator having the responsibility to act in the case of problems that the AD-system cannot solve. The actions are of course important to record, in the vehicle but also in the remote operator system.

In-vehicle systems state

The state of in-vehicle systems can be accessed by connecting to the embedded controllers. The data category comprises continuous measures, like engine RPM, or categorical values, like ADAS and active safety systems activation, and automation level.

It is important to document the system state when in baseline mode. As new vehicles have numerous active systems installed, there is a need to know the settings also for baseline data. The AD-level (SAE, 2018) is important both for baseline and treatment data when comparing an autonomous function in level 4 in treatment and in level 1 for the baseline data.

Environment detection

A precise understanding of the environment can be obtained by advanced sensors like radars, LIDARs, cameras and computer vision, or by simpler sensors (e.g., optical or temperature). For instance, luminosity (indicating the presence of rain), characteristics and dynamics of the infrastructure (e.g., lane width, road curvature) and surrounding objects (e.g., type, relative distance and speed) can all be measured from within a vehicle.

Vehicle positioning

The geographical location of a vehicle is most frequently determined with global navigation satellite systems (GNSS) and the aforementioned advanced sensors. It can also be determined by information from the cell phone network, surrounding Wi-Fi networks, or a combination of these and GNSS.

Media

Media data are usually video, but in some data-collection projects audio is recorded. Media data also include the index files used to synchronize these data with other data categories.

Human behaviour measures

The actions of drivers can be measured from the vehicle perspective but also there is with the introduction of AD function also a need to view the driver as a passenger and also to understand the whereabouts of other passengers. In some cases you can collect objective data from sensors in the vehicle or devices attached to a mobile app (e.g., a device measuring heart rate and transferring this to a mobile app).

Complementary to sensors and instrumentation, some continuous measures can also be built through the perception of analysts or annotators using video data. Eye glance and driver state (e.g., drowsy, impaired, angry) can be evaluated manually by analysing video from driver-face-oriented cameras. This is even more important in the context of autonomous vehicles, where rich data from video could automatically be converted into individual measures of interest (e.g., hands on steering wheel, head and body pose).

Roadside measures

Roadside measures comprise vehicle counting, speed measurement and positioning—using radar, LIDAR or simpler rangefinders, video-based counting, inductive loops or pressure hose.

In the case of ITS systems, they may also contain more complex information remotely transferred from vehicles to or from roadside units. These messages are given more attention in an autonomous driving scenario where vehicles could communicate with each other to warn or inform. One challenge is to have a common time reference on timestamps both when the message was transmitted and received at the sender and receiver.

Media data (typically video—for instance in traffic conflict observations) are also often collected from beside or above the roadside. Roadside measures are evolving rapidly, with data being collected by drones or open-data services, for example.

Experimental conditions

Experimental conditions are the external factors which may have an impact on participants’ behaviour. They may be directly collected during the experiment, or integrated from external sources. Typical examples are traffic density and weather conditions. Controlled factors, such as the ability to use a system, also need to be included in the dataset, depending on which phase of the experimental plan a participant is currently participating in.

Time and location segments

For the purpose of the analysis it can be relevant to analyse the data aggregated for a delimited period in time or space (such as journeys, certain events as defined in FESTA or e.g. road segments). These data segments are defined by a combination of specific conditions and characterised by specific attributes, some which are automatically computed, and some which are manually annotated from video. The attributes mostly consist of situational variables and/or PI, depending on the studied phenomena and its expected contributing factors; they can also consist of links to other segments or contextual data. For instance, each trip might link to a specific driver and vehicle, each of which have their own characteristics. Finally, the segments might serve as a container for time-history data: a trip can contain the history of the vehicle speed and an event may contain successive eye-glance values, manually coded by an annotator. As a result, the segments contain a large amount of initial data, which is structured, reduced and summarized into more manageable tables, suitable for data analysis.

The creation of the segments can either be automated (i.e., they are created in response to a specific value or threshold of one or more variables), manual (when a specific event is observed on a video), or a combination of both (e.g., automatic detection of candidate events, accepted or rejected in video annotation). In the same way, attribute values can either be automatically computed (i.e., the mean or maximum value of a measure during a time segment) or manually annotated, typically from video. In the latter case, standardized annotation schemas are used to enrich data with information available from video recording. Annotation variables are thus a subjective assessment of the situation by an analyst or annotator. They can be quantitative, using single or multiple choices (i.e., present/not present or level of rain); they can consist of specific time stamps, for example when the driver is first aware of a hazard; or they can be qualitative narratives, which describe a specific event or situation.

Finally, subjective, participant-reported data can be collected as certain kinds of segments, such as self-declared events, or they can populate some segment attributes (e.g., travel diaries that contribute some characteristics to trips).

Time segments

Time segments are the most common type of segments, collected and/or generated during data reduction. They correspond to a time period when some specific conditions are met. Depending on the kind of conditions which define them, their typical duration, and the researcher’s own vocabulary, they are identified as trips (a vehicle is started, driven for a period of time by a driver, then stopped), events (typically a short period of time with very particular characteristics), situations or chunks (division of the complete dataset into segments of comparable size according to a combination of situational variables, characterized with PI).

Locations

While time segments take the perspective of a driver in a vehicle during a trip, locations take the perspective of a place, where multiple trips might pass through. Roadside observations will typically generate locations, and typical location attributes are vehicle counts or speed measurements, which can also be associated with the infrastructure attributes. Furthermore, using geographical information systems (GIS), data from in-vehicle collection can also be projected over a geographical reference system to characterize, for instance, one or several participating drivers’ behaviour at a specific location such as an intersection.

Just like trips, locations can be divided into smaller segments. This could be parts of a road stretch, especially when a fixed trajectory is followed. The segments can be used individually or be linked in a chain.

Aggregated data

Using relations between segments, reduced data (e.g., segment attributes) are typically aggregated into smaller, more usable tables, suitable for data analysis or data interpretation. For instance, driver characteristics can be grouped together with attributes from one type of situation, to evaluate the impact of drivers’ characteristics on their behaviour in that situation. The data resulting from aggregating different kinds of reduced data together are called aggregated data. Although they are generally linked to a specific research question, the aggregated data may be re-used with different statistical methods, or re-aggregated with other data, to quickly answer new questions without the need to go back to harder-to-use, raw data.

As they don’t contain instantaneous values, they don’t allow potentially problematic re-use, such as pinpointing illegal behaviour from one specific driver or benchmarking a driving assistance system without authorization from its supplier. As a result, aggregated data are generally easier to share than other categories of data.

Metadata

Metadata basically provide information about data and can be divided into different types, describing different traits of a dataset. Descriptive metadata, describing the content of a dataset, is perhaps the most useful type for data analysis. In contrast, structural metadata are the prerequisite that helps the analyst understand the structure of the dataset, by describing ‘data about the containers of data’ (Roebuck K., 2012). Administrative metadata are collected for the effective operation and management of data storage. Finally, the FOT/NDS study documentation provides an overall description of how the study was performed.

Descriptive metadata

Descriptive metadata shall include detailed information needed to understand each part of a dataset. The purpose is to describe the dataset and build trust in it—by providing not only the characteristics of each measure or component, but also information about how the data were generated and collected.

Descriptive metadata shall preferably be available close to the actual data to facilitate analysis. The descriptive metadata need to define the dataset and include detailed descriptions of measures, PI, time and location segments and their associated values. In addition, external data sources, subjective data from self-reported measures and situational data from video coding must be described in detail. Not only must the output of the data be described, but how the data were generated and processed is equally important; this is where one can build trust in the dataset. The more thoroughly the origin of a measure is described, the greater the trust. The proposed structure of descriptive metadata follows the data categories in 5.2.

Context data description

The level of detail when describing contextual data can vary. Information about drivers and vehicles is often obvious from the name of the variable (e.g., gender and age for participants, and model, brand and year for vehicles). Other information, such as questionnaire data acquired from participants, might need a more in-depth description (e.g., a definition of the self-assessed sensation-seeking measure).

As FOT/NDS databases often consist of a variety of different external data sources, it is very important to document them all to get a full picture of the data. The external data sources can include static contextual data from map databases or dynamic data from weather services and traffic management services. In these cases, a more in-depth description is needed where it is important to describe the origin of the data, the methods used to match the different datasets (e.g., a description of the map-matching algorithm), and each output variable.

Some additional data might be merged with the acquired data (e.g., map attributes or weather codes). These data are described in their respective sections below.

Acquired or derived data description

A description of every measure in a dataset is mandatory, making the data re-usable for future analysis. The origin of the data and the processing steps performed are equally important for drawing correct conclusions in the analysis.

It is important to include definitions of time and location segments in descriptive metadata, as the definitions vary between different datasets depending on the purpose of selected segments. The segments need to be defined (i.e. how the segment start and stop times are calculated), and so do the associated attributes (e.g., summaries, situational variables and PI). The different types of time and location segments are often important products of the dataset, providing easy-to-use references to the actual data.

This section also includes a suggestion for describing PI and summaries—data which are often attached to time or location segments, but may also be used independently of them.

Direct or derived measures in time-history data description

The description of direct measures is often beyond the project’s control and needs to be requested from the supplier of the equipment generating the data. If the data are acquired from the CAN bus of a vehicle, the OEM can supply information which describes the data. Understanding the origin and full history of direct-measure data is important, but often overlooked. To get access to this information, the use and restrictions of direct-measure metadata should be included in the contracts and NDAs with the suppliers. The origin of the measure should at a minimum include where the data were generated (e.g., sensors, ECU) and acquired (e.g., CAN or other equipment/channels), the frequency, the units, the range, the resolution, whether they were derived from other data and error codes.

When direct measures are being processed into derived measures, it is important to document all the data processing steps. Derived measures are often processed several times, and the final product might consist of more than one measure. The need for a detailed description is crucial for creating trust for data re-use.

The output of the data processing must be documented and include information on data precision, unit and sample rate. This metadata must also include information about how the data were processed (e.g., synchronization policies, re-sampling filters, harmonization rules). In an ideal scenario, an analyst performing an analysis can quickly understand not only the meaning of the measure, but also its origin and history, and use this information to interpret the results.

Proper naming conventions for all data containers can go a long way towards helping interpret data’s origin and understanding how it can be used. Tags describing the data type and origin can, for instance, be used. However, naming conventions are always a trade-off between comprehensiveness and legibility, and although necessary, are not sufficient for the proper documentation of a dataset.

Preferably all information in the table below should be included for each major data-processing step. As an example, interpolation filters must be documented in detail, so that the analyst can understand whether the measure can be used for a specific research question. Additionally, the tolerance for missing data (e.g., the  number of frames or seconds) and how these values are stored should also be described in the metadata, because the values are often managed differently in different data formats (e.g., NaN in MATLAB, but NULL in Java and relational databases). Describing the measure in detail avoids misinterpretation.

Table Metadata attributes of time-history data measures

Data description itemInstruction/example
Data precisionWhat is the data precision of the measure? The terminology is derived from database technologies where the precision is the maximum allowed number of digits (either the maximum length for the data type or the specified length). If not specified, the data type will define the maximum allowed precision. When measuring the signal this is the resolution. This information, as well as the precision and accuracy of the measurement, should be provided in the origin section below.
UnitWhat is the unit of the measure (e.g., m/s, RPM or if an enumeration)?
Sample rateWhat is the current frequency of the measure (e.g., speed resampled at 10 Hz or 1 Hz)?
FilterWhich filters were applied (e.g., low-pass, interpolation or outlier filters)? This could also include the maximum allowed time during loss of signal data for the filter to be applied. The value can be very different depending on the measure (e.g. interpolation might be implemented on the speed signal unless the next available sample is less than two seconds later).
Origin How was the measure generated and from what data source? This includes information about precision, accuracy and resolution of the measurement. For instance, it is important to know if the speed measures originated from CAN at 20 Hz or GPS at 1 Hz. It is also important to know how precise and accurate the measurement was done, as well as the resolution of measuring device and the logger system translating the signal. This could also refer to another described measure.
TypeIs the measure an integer, float, string or picture file?
RangeWhat is the expected range (minimum and maximum values) of the measure?
Error codesWhich values trigger error codes? What is a null value? It is also important to describe how the errors are managed.
QualityAre there any quality measures related to this measure and how are they defined? The quality could be set on a per-trip, per-measure or even per-sample level (e.g., for GNSS data: HDOP, number of satellites).
Offset Is there a known offset of the measure? The information is related to the actual measurement and data logger. If an offset is known this should be included in the metadata of the measure.
Enumeration specificationCan enumerations be translated into readable values (e.g., 1 means ‘left’ and 2 means ‘right’ for the turn indicator)?
AvailabilityCan the measure be shared? What are the conditions to access it?

Time segment data description

Calculated time segments or triggered events represent singularities over time, which may be as short as a single time instance, or longer based on a specific set of criteria. The definitions of time segments differ among datasets; the more common ones are trips, legs and events. This variation makes it even more important to describe the purpose and how the segments were designed, including their origins. It is also important to understand the conditions that define the start and stop of a time segment.

Events are often described by type, which explains why an event was triggered or threshold met. To understand the event properly, event type descriptions must include references to the measures and method used to calculate the event, as well as threshold values.

Different segments can have different associated PI, summaries or attributes, and these should also be described: for example, a trip record might include the duration, distance travelled, average speed, number of times passing intersections, or just the number of samples. Time segments should include the attributes in .the tabel below.

Table Metadata attributes of time segments

Data description itemInstruction/example
TypeWhat is the purpose of the trigger (e.g., a hard braking event, swerving at high speeds, overtaking or entering an intersection)?
DefinitionWhat is the definition of the time interval? How are the time series grouped? The output could be a single point, fixed or variable in time.
OriginWhich measures were used to create the entity? What was the overall principle of the data computation that generated the entity?
UnitWhat is the unit of any output value (defined by type)?
Enumeration specificationDescription of enumeration values.
Attribute, PI or summary  specificationTime segments might have associated data that need description. It could be attributes, such as driver ID or duration. It could also be computed data, such as PI or summaries (e.g., distance travelled, number of intersections passed, average speed or the number of times a button was pressed). The definition of all PI and summaries associated with the object are described later in this chapter.
AvailabilityCan the segment be shared? What are the conditions for accessing it?

Location data description

In many studies the vehicle is not the main entity; rather it simply provides values for locations. Locations must be defined, usually by position or a set of positions. This could be an intersection, a sharp bend, the specific position of a roadside unit or a stretch of road (anything from a city street to a European highway). The definition is of great importance because of this great variance. As with time segments, the value of the locations is not only the encapsulation of time or position, but also the determination of associated attributes and the output of computations. The metadata attributes of location segments are presented below.

Table Metadata attributes of locations

Data description itemInstruction/example
TypeWhat is the purpose of the location segment?
DefinitionWhat is the definition of the location, in terms of position, scenario or equipment? Can locations be grouped or arranged in a hierarchy?
Attribute, PI or summary  specificationLocation segments might have associated data that need description. It could be attributes, such as number of exits at a roundabout. It could also be computed data, such as PI or summaries (e.g., number of vehicles passing or average speed). The definition of all PI and summaries associated with the object are described later in this chapter.

PI and summaries definitions

PIs are used to measure the performance of one or more measures, and are often associated with a specific analysis project, although some might be re-used for other purposes. Each implementation of a PI should therefore be described precisely; see metadata attributes in Table 6.

PIs as summary tables are pre-computed data, used to make the analysis more efficient. The summaries are stored as attributes, often with time or location segments as a base; the summaries could, for example, describe the mean speed of a trip or the number of passes through an intersection. Summaries are convenient in data reduction. They are especially useful in a larger dataset for excluding data not needed for the analysis.

Table Metadata attributes of PI or summaries

Data description itemInstruction/example
PurposeWhat is the purpose of the PI or summary?
DefinitionDetails about how the PI or summary was calculated and the denominator (e.g., per time interval, per distance or location),
OriginWhich measures were used to create the entity? What was the overall principle of the data computation generating the entity? 
UnitWhat is the unit of the output value?
VariabilityWhat is the variability of the PI or summary?
BiasIs there a known bias of the PI or summary?
Data precisionDetails on the data type and the resolution of the output value.
AvailabilityCan the attribute be shared? What are the conditions for accessing it?

Description of video annotation codebook

Documenting the video annotation codebook is important for helping the person coding the data to understand the instructions, but also for defining enumerations (for incident severity there are the conditions that define a crash, near-crash, increased risk or normal driving). It is also important to document the process of coding the data, whether inter-rater reliability testing was conducted, and other important aspects of the persons coding the data; typically this information is part of FOT/NDS study design.  For each measure (as part of the video annotation codebook) the recommendation is documented in the table below.

Often the reduced data are coupled to time or location segments. Because it is important to know why those segments where selected for video coding, the reference must be documented.

Table Metadata attributes of video annotation code book measures

Data description itemInstruction/example
DescriptionWhat is the purpose of the measure?
InstructionsIn what way was this measure described to the person coding the data?
TypeWhat type of input is expected (single or multiple choice: e.g., present/not present or level of rain, continuous, free text or voice)?
OptionsWhat are the possible alternatives (often coded as enumerations)? How reliable are the data expected to be?

Description of self-reported measures

Other subjective data include travel diaries, interviews, and documentation from focus groups. These data are often in rich text format and the data description should cover why, when and how the data were collected. Questionnaires acquired during the data collection period should also be described in this section. These data are very similar to video annotations and could be described by answering the questions in Table 8.

Table Metadata attributes of self-reported data

Data description itemInstruction/example
DescriptionWhat is the purpose of the self-reported measure?
InstructionsIn what way has this measure been described to the participants?
TypeWhat type of data is expected (single or multiple values, continuous, free text or voice)?
OptionsDescriptions of possible alternatives (often coded as enumerations) and how non-answers should be handled.

Aggregated data description

The shape of aggregated data can vary to such a degree that it is difficult to propose a structured format. Depending on the level of aggregation, the data could be described as time history measures or time segments. Also, in many cases the aggregated data are shared with the promise that the underlying data will not be revealed; the algorithms are not described in depth (to eliminate the risk of making raw data information available by means of reverse engineering), and only a high-level description is allowed. The trust in this data will be reduced and it is up to the recipient to judge if it is good enough for re-use. An appropriate set of metadata questions is proposed in the table below.

Table Metadata attributes of aggregated data

Data description itemInstruction/example
DescriptionWhat is the purpose of the aggregated data?
DefinitionWhich algorithms were applied to the underlying measures?
OriginWhich underlying measures were used to calculate the aggregated data?
UnitWhat is the unit of the output value?
VariabilityWhat is the variability of the data?
BiasIs there a known bias of the data?
Data precisionDetails on the data type and the resolution of the output value.
Structural metadata

In a typical FOT/NDS study, different parts of the dataset will use different storage technology, such as file systems, SQL and Not-Only SQL databases.

Structural metadata are used to describe how the data are structured in relation to other data. Data are organized into a system (e.g., a database and/or file system), a structure or database schema and a data content format. The aim of structural metadata is to facilitate the initial phase of data re-use by providing the necessary documentation about how the data is organized. The description should include the file system, the file structure and how to interpret the contents of a data container. All components of the dataset need to be described.

Since data may be stored for a very long time, it also becomes important to describe and preserve tools that can read the data. This issue is highlighted when it comes to data archives. Even only five years after a project has ended, the knowledge about specific tools might have been lost and the cost of building up the competence again might exceed the data’s value. It is therefore recommended that the tools, platform and prerequisites be described—in even more depth if using a non-standard data container, file format or file structure.

File system/Database

At the lowest level the file system format, or encapsulation, must be known. This information gets especially important as the years go by, as tools and formats slowly depreciating and are replaced by newer technologies.

Popular formats include NTFS (for Windows), EXT4 and XFS for Linux, or FAT32 (supported on many platforms). However, the particular demands and scale of NDS/FOT studies might require less common file systems. Examples are ZFS (Unix) and ReFS (Windows), which offer superior reliability for large volumes. Some file systems also contain metadata for each file, such as the ‘forks’ in HFS. For large projects requiring scalability and distribution of calculations over many servers, data may also be stored on a distributed file system such as HDFS.

If data are stored or archived in a relational database (e.g., Oracle, MySQL) or a Not-Only SQL database (e.g. Cassandra or MongoDB), it is important to know the type and version, to facilitate data import to an identical system or conversion to a different product.

Files themselves can also be encapsulated in archives (with or without compression and/or encryption) or in binary objects in databases.

File structure/database design

The file structure should be described. As an example, it could be described as Vehicle/Year/Month/Trip.

Files might not always be accessed with a traditional file system; if not, it is also important to describe how to access them. Examples include Content-Addressable Storage (CAS). The analyst accesses the content, without knowing its location, using a key.

It is recommended that the schema be documented graphically to indicate the relations between the different tables, a task usually easily accomplished using data management software. This principle should be applied whether data are stored in a relational database data or an alternative (i.e., in a file system or Not-Only SQL environment)

Data container

The data container describes the format of a file. This could be avi for a media file, csv for a text file or mat-file for data used by MATLAB. With a non-standard format it is important to describe it in detail, including file content structure, header length, data type and indices. It is also good practice to include information about tools that can interpret the data format of the container.

Content

The content description should include how the data are organized in a file or object. Thus codec and indices could be provided for an avi file, the description of a row for a csv file and the object design for a mat-file. It is recommended that the data descriptions be kept in a readable format; XML is recommended, since most tools/programming languages have built-in methods for reading xml files. A description of the file contents gets even more important if a non-standard format is used. Similarly, when different data types are mixed in the same file (e.g., video and CAN data) it is vital to have a precise description of the content. The content description of a database includes detailed information about the tables, such as columns and their respective data types, indexes, triggers, sequences and views.

Administrative metadata

Administrative metadata are collected for the effective operation and management of data storage and catalogues. This administrative information, covering various topics, is stored along with the datasets. From a FOT data re-use perspective, the key role of administrative metadata is to cover access conditions, rights, ownership and constraints. Generally, administrative metadata can include (Puglia, S., Reed, J., and Rhodes E., 2004):

  • version number
  • archiving date
  • information about rights, reproduction and other access requirements
  • archiving policy
  • digital asset management logs
  • documentation of processes
  • billing information
  • contractual agreements
  • end of life of the data.

The method for storing administrative metadata depends on the specific repository/catalogue. Many of the items above need to be stored at least as supplementary documentation, according to repository/catalogue guidelines, if not directly as attributes of a dataset. The administrative metadata also have a role in data protection: defining processes, personal data management, access rights and keeping track of (for example) periodic backups.

For online FOT catalogues, information about a contact person/organisation and licensing options or required agreements must be included, so potential analysts know how to gain access to a dataset. Another required administrative feature of a catalogue is usage logs of information queries and retrieved data, in order to be able to summarize the level of interest for different datasets.

Assigning persistent identifiers for datasets is necessary for references and citations. Some persistent identifiers like the Digital Object Identifier (DOI) also support dataset version management. Each time there’s a change in the data, a new DOI is assigned and a log of changes collected. As an alternative, a fixed dataset reference could be used in publications when the dataset has been used. One example of this is the “SHRP2 Insight” portal where a dataset reference is regulated in the terms of use (https://insight.shrp2nds.us/terms).

FOT/NDS study design and execution documentation

The study design and experimental procedures must be documented well enough so that persons and partners who did not take part in executing the test can perform analyses. The main purpose of this documentation is to describe in free form the purpose of the data collection, the experimental procedures and the important details of the actual execution—including a description of the test site, which must be known before the data are interpreted. As a result, this documentation should contain not only initial plans, but also the final details of the study. More information is available in the FESTA Handbook. The document should give an overview of the following (at least):

  • purpose of the field tests or data collection;
  • research questions;
  • sample selection criteria and overall description of recruitment;
  • possible grouping of participants (e.g., test groups 1 and 2 and a reference group); description of the groups;
  • overall description of equipment used, functions, HMI, additional driver support in the vehicle (navigators, etc.) and vehicle fleet—preferably with links to videos demonstrating usage;
  • description of the test site (if it was within a specified perimeter), including maps and photos;
  • date and timing of different phases of the study;
  • description of scenarios/test runs/study phases, if relevant—with photos of key locations and views from participants’ perspectives;
  • test plan and execution, describing (for example) what the participants were asked to do, how and when the briefing was given, what questionnaires were administered or what interviews were given;
  • in the case of a FOT, how the participants were introduced to the system;
  • how contact was maintained during the study;
  • special events and changes that may affect data analysis (e.g., roadwork, strikes, economical changes, special weather);
  • summary information of the project and cooperation partners, duration, budget etc.

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