Table of Contents
1. General Information
1.3 Name and contact details of the lead researcher including their ORCID
The lead researcher can be the principal investigator or supervisor of the project. Principal Investigator (PI) refers here to the holder of an independent grant and is usually the lead researcher for the grant project.
ORCID provides a persistent digital identifier (an ORCID iD) that you own and control, and that distinguishes you from every other researcher. You can connect your iD with your professional information — affiliations, grants, publications, peer review, and more. You can use your iD to share your information with other systems, ensuring you get recognition for all your contributions, saving you time and hassle, and reducing the risk of errors.
1.4 Name and contact details of the executive researcher including ORCID
Contact Details
This can be a single person (e.g. the researcher collecting the data), but also multiple names can be mentioned to assure findability of the research output (data, research protocol, publications, etc.) during and after execution.
ORCID provides a persistent digital identifier (an ORCID iD) that you own and control, and that distinguishes you from every other researcher. You can connect your iD with your professional information — affiliations, grants, publications, peer review, and more. You can use your iD to share your information with other systems, ensuring you get recognition for all your contributions, saving you time and hassle, and reducing the risk of errors.
2. Legislation
2.1b I confirm that I am aware of and compliant with laws and regulations concerning privacy sensitive data
General Data Protection Regulation (GDPR):
Each research that processes personal data must be registered initially in the so called GDPR Processing Activities Register. This registration is for review and assessment of the principles of processing data, (remember to obtain informed consent of data subjects) with regard to the technical and organizational measures for protecting personal data. In case third parties are involved in processing personal data, a data processing agreement is mandatory.
You can find more information on the privacy page of Maastricht University.
For any privacy related concerns, you can contact one of the Data Protection Officers:
Maastricht University: fg@maastrichtuniversity.nl
2.1c My research project is registered at my institution by the Local Information and Security Officer (LISO).
Local Information and Security Officer
The Local Information and Security Officer is in most cases is the Information Manager of your faculty. He/she maintains an active registry of research projects. Registering your research in their respective GDPR registry fulfills this requirement.
For FHML, you do not need to contact the Information Manager directly, but you can fill out the GDPR Registration.
Within the MUMC+ this is accomodated by the Quality and Security Department (Afdeling Kwaliteit en Veiligheid).
Registration Requirement
If you are processing personal data, you are required to register your project. This has been specified in the Privacy Regulations.
2.1d My study research file (e.g. protocol) has been or will be submitted to the relevant Ethics Committee.
The Medical Ethics Committee (METC)
The medical ethics committee of the University Hospital Maastricht and Maastricht University evaluates medical research involving humans with regards to the Medical Research Involving Human Subjects.
The Board of Directors of the University Hospital Maastricht and the Executive Board of Maastricht University have determined that all medical scientific research proposals not subject to the WMO should be assessed by the METC azM / UM.
Ethics Review Committee Health, Medicine and Life Sciences (FHML-REC)
Research involving human participants or personal data conducted in FHML should be submitted for ethics review. The work typically undertaken in FHML falls under either the METC for research that is regulated by the Medical Research Involving Human Subjects Act (WMO), or the FHML-REC for research that is outside that legal requirement.
Ethics Review Committee Inner City faculties (ERCIC)
ERCIC encourages researchers to submit their study protocols involving human participants or personally identifiable data for ethical review before the start of research activities. Review by ERCIC at the moment takes place on a voluntary basis.
Ethics Review Committee Psychology and Neuroscience (ERCPN)
Ethical review of scientific research involving human participants or personally identifiable data is carried out by the Ethics Review Committee Psychology and Neuroscience (ERCPN). If studies fall under the Medical Research Involving Human Subjects Act (WMO), review by the accredited review committee METC is mandatory.
Animal Ethics Committee (DEC)
As from 18-12-2014 the Wet op de Dierproeven (WOD) has changed. This has changed the role of the Animal Ethics Committees (DECs) as well. The DEC-UM now reviews not only ethically, but also scientifically and offers its opinion to the Central Commission Animal testing (CCD). For information about the application of a project authorisation you can visit the website of the CCD. The Central Commission Animal testing (CCD) is the only authority that can act throughout the Netherlands to grant licenses for animal testing.
2.1f The 'Act medical scientific research with human beings applies to my project and I will comply with the 'Quality Assurance for Research Involving Human Subjects'
The 'Act medical scientific research with human beings (Dutch: 'Wet medisch-wetenschappelijk onderzoek met mensen, WMO) applies to my project and I will comply with the 'Quality Assurance for Research Involving Human Subjects' (Dutch: Kwaliteitsborging Mensgebonden Onderzoek).
If you intend to submit a new research proposal to the METC, it is first important to determine whether your research falls within the scope of the Act Medical Scientific Research with Human Beings (WMO).
2.1g I will be doing research involving human subjects and I am aware that informed consent is required from the participants for collecting or reusing their data.
Informed Consent
Consent must be freely given, informed, specific, and unambiguous. Consent must be a statement or clear affirmative action signifying agreement to the processing.
Exceptions to Informed Consent
Within all types of (medical) scientific (clinical) research informed consent should be the basis for the use of personal data. However, based on article 24 of the Dutch implementation law of the GDPR, there are some (cumulative) exceptions. The Ethics Committee (EC) needs to approve this deviation, based on solid argumentation in the protocol. The following criteria need to be taken into account:
the processing is absolutely necessary for scientific or historical research or statistical purposes
the research mentioned above serves a common interest
requesting explicit permission proves impossible or takes a disproportionate effort
the execution of the research has been provided with safeguards such that the privacy of the subject is not disproportionately harmed
Informed Consent Templates
METC
ERCIC
2.1k I will be doing research involving human subjects and I have taken privacy protection measurements.
Anonymisation
The process of anonymising data means that all identifying elements are eliminated from a set of personal data so that the data subject is no longer identifiable.
Pseudonymisation
EU law defines ‘pseudonymisation’ as “the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person”. Contrary to anonymised data, pseudonymised data are still personal data and are therefore subject to data protection legislation. Pseudonymisation is mandatory when conducting scientific medical research with human subjects.
2.2a In collecting new data, I will be collaborating with other parties.
Collaborating Parties
In the case of collaborating parties there will be a cooperation within a project in using data. You have to make agreements concerning the data with the particular researcher(s) who will be sharing with you.
We speak of a consortium if the included partners conduct the entire project together. Data management and sharing of data is hereby an agreement between the entire consortium.
2.2c I am a member of a consortium of 2 or more partners.
Consortium Agreement
A consortium agreement is an agreement in which more than two parties are involved to collaborate for research, development or other purposes. This agreement includes provisions necessary for the execution process, the grants, confidentiality, intellectual property(IP), co-authorship, conditions for reusing data and publications. In contrast to a cooperation agreement, a consortium agreement is more comprehensive and it describes among other subjects, the structure of the management.
If a project is being executed in collaboration with other academic partners (for instance researchers from another university) and/or peripheral hospitals, this collaboration is called a consortium cooperation. In this case it is very important to confirm agreements in the form of a consortium agreement.
When the project is carried out by different departments within the same institute, determining consortium agreements about internal management, about the consortium and/or processor agreements, are not mandatory. However, it is advisable to construct mutual agreements and to record them in writing. You should always contact the relevant legal department when constructing a consortium agreement:
FHML and MUMC+: Research Office
Other Faculties: Legal Affairs
2.2d Agreements have been made regarding research data management and intellectual property recorded in a collaboration or consortium agreement.
Consortium Cooperation and Personal Data
If there is a consortium agreement, and during the research, personal data will be processed, it is important to think carefully about the relationships / roles of the consortium partners with regard to these personal data. In other words: Who is responsible for the data management? Multiple persons responsible or only one?
Consortium Agreements and the DMP
Legal agreements such as a consortium agreement, should not be included in a DMP. DMP is a living document which serves another purpose namely describing the many aspects of data management. It is a document that can be altered and therefore it is not suitable for irrevocable, binding legal agreement. In case of a consortium, reference to the consortium agreement document should be logged in the DMP.
Co-authorship
In accordance with the ethical guidelines, (rules of the Vancouver convention: Davidoff F et al., Sponsorship, authorship and accountability, NEJM 345:825-826, 2001), agreements regarding co-authorship can be made.
3. Data Preparation and Collection
3.1a In collecting data for my project, I will be reusing or combining existing data
Reuse of Data and Informed Consent (IC)
Anonymized Data: When data are anonymized, any detail that can be linked to an individual, is altered in such a way that only anonymous details remain. When data can no longer be qualified as personal data, Informed Consent for reusing these data is not necessary
Pseudonymized Data: the pseudonymization of data is encouraged because it leaves the possibility of linking data open.
If you opt to pseudonymise data, you will often need the permission of the subjects to reuse the data. The data may be linked only once subjects have given their explicit consent. If the data are used for another purpose in the future (further use, or ‘secondary processing’), the researcher must again seek the permission of the subjects before linking the data.
3.1b I have the owner's permission for reusing the data
Reusing Data
If data is used from former projects/databases, it should be clear the subjects already consented to the re-use during the original collection of their data. When using data from other authors or sources, take into account that you have to acquire permission or deal with user agreements and licenses first.
Even when reusing data, you have to take into account different retention period for your particular type of research. You are sometimes required to delete the data you are reusing immediately after your research.
There are some legal bases on which no permission is required, for instance when the information is public domain. Please check the legal bases on which the data was collected.
3.3a The following type of data will be used/collected
Standard Personal Data
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. NB: National identification numbers cannot be used without a legal obligation!
Special Categories of Personal Data (Sensitive Data)
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;
Clarification: data collected within clinical scientific research qualifies as a special category of personal data: also when it is collected in a pseudonomized form!
Anonymous Data
This is only possible when the data a) was already anonymous when extracted from an existing database or b) will be irreversibly anonymised before providing the data to the researcher. Consent is not needed.
If you are uncertain to which category the data belongs, you should get in touch with your faculty contact person (Local Information and Security Officer or Information Manager). Or you could sent an email to privacy@maastrichtuniversity.nl for UM or secretariaat.kwaliteitenveiligheid@mumc.nl for MUMC+ research.
3.6a I will select a metadata standard/terminology for recording my data that allows my dataset to be linked or integrated with other datasets
(Meta)data use vocabularies that follow FAIR principles.
It is recommended to make use of metadata standards at an early stage in the research. Within the MUMC+ the next metadata standards are advised:
In the near future, a library will be developed within MUMC+, where standardized and validated questionnaires are provided with coding like SNOMED and/or LOINC. For more information, advice and assistance for the choice and implementation of a metadata standard, please contact: datahub@maastrichtuniversity.nl
Answer when using DataHub Infrastructure:
The use of an already existing meta-data standard (ISA) combined with the use of ontologies from various open ontologies provided by EBI's Ontology Lookup Service, will assure that the interoperability of the data gathered in this project is optimal. Our vision is to structure (meta)data as early as possible in the process. To provide optimal interoperability, we will try to use as much of the standard vocabulary as possible. For some metadata this is already forced by the DataHub infrastructure for interoperability reasons. Some fields will be annotated with an ontology that will be research domain specific. When possible, we will provide a mapping to a more commonly used ontology.
3.7 Give an estimation of the size of the data collection
Estimating Size
This can be rough estimate before the start of your project. Try to indicate whether it will be in the MB/GB/TB range. The volume of the data collection is determined by the type of data, for example:
(alpha)numeric data
image footage, images
audio files
The number of participants is also of great importance. Besides, you have to take into account the reproducibility of the research and the data. All the material (i.e. data, code, software) needed for the reproduction of the research must be saved.
4. Data Processing and Analysis
4.1a During the project, I will have access to sufficient storage capacity/sites and a backup of my data will be available
There are several options for storing data during the research project. It is highly recommend that you use UM/MUMC+ infrastructures whenever possible. Check with your faculty whether agreements already have been made with regards to storage and whether any procedures are in place.
4.2a I will ensure that the data and their documentation will be of sufficient quality to allow other researchers to interpret and reuse them (in a replication package).
Meta(data) are richly described with a plurality of accurate and relevant attributes.
Documentation of the Research Process
Study protocols, CV’s, orientations schedules and certificates of persons working within the research, standard operating procedures (SOPs), old and new versions of information on test subjects, methods of blinding etc. Digital information stored on a secured server and all paper information in a safely locked cabinet whilst specifying its location.
Quality Control during Data Collection
Incorporate, as much as possible, restrictions and validation rules when using online data collecting tools like Castor EDC, apps or tailor-made software. This in order to prevent data entry mistakes and to increase the quality of the data.
Examples of restrictions and validation rules are:
automated routing: only relevant questions will be available
indicate ranges
use mainly fixed response questions and try to avoid open text questions
mandatory questions
All data collection tools must have an audit layer, all data changes will be logged (by whom, what and when).
Quality control after data collection
For additional quality control, a data-control and data-cleaning plan can be developed.
In combination with the code-book you can determine which controls should be carried out.
The controls described will be translated to a script (e.g. SPSS-syntax). With this script an error summary can be generated. Based on the summary corrections can be made.
These corrections will be included in a script and will be documented in the data cleaning plan. This method will ensure that:
controls and corrections are made in a uniform manner
all steps will be documented
In this way reproducability of the data will be guaranteed.
Software for Data Collection
Within the MUMC+ various standard tools are being used for data collection. See the RDM Portal.
4.7a Data wil be shared and transferred in a secure way
Within the UM/MUMC+ the use of SURFfilesender is recommended. This allows you to send files up to 500GB for free. With SURFfilesender, your files are sent securely. The uploaded files are stored in the Netherlands for no more than 21 days. Although SURFfilesender is already secure as it is, you can also opt for additional security in the form of encryption. Files up to 2GB can be send using encryption. All you have to do is send the recipient a ‘key’ via a second channel: telephone or SMS, for example. The recipient then enters this key, which allows them to download the file. This way, you can determine who is allowed access to your valuable research data or confidential files.
SURFdrive
Store, synchronise and share your documents easily with SURFdrive. SURFdrive is a personal cloudservice for the Dutch education and research. Your documents are kept safe and sound in our communitycloud.
The University Library offers the Virtual Research Environment (VRE) powered by Microsoft SharePoint as a collaborative platform. The VRE facilitates the production of joint research papers. Multiple collaborators from different institutions and locations can edit shared documents within a VRE.
A Virtual Research Environment has a secured place on a UM server, so collaboration takes place in a secure environment, accessible anytime and anyplace and is fully integrated with Microsoft. VREs facilitate online communication and sharing sources of information with integrated tools like wikis, blogs, shared calendars and discussion forums.
Other Tools for Encryption
7-Zip and WinZip are tools that support several different data compression, encryption and pre-processing algorithms. The contents of the files that you want to protect are encrypted based on a password that you specify. In order to later extract the original contents of the encrypted files, the correct password must again be supplied.
5. Data Archiving and Open Access
5.1a I will select a data format, which will allow other researchers and their computers (machine actionable) to read my data collection
(Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
To ensure long-term usability, accessibility and preservation of data, it is recommended that you use a ‘preferred’ file format. DANS has made an extensive list of preferred formats.
DANS is confident that preferred formats will offer the best long-term guarantees in terms of usability, accessibility and sustainability.
5.2a I will use a metadata scheme for the description of my data collection (for describing the dataset as a whole)
Data are described with rich metadata.
To make your dataset (re)usable, you should use a metadata scheme to describe your data. It is advised that you start with this fairly early in your project. After the project it is generally a lot harder and more work to do. There are already multiple standards available for different fields of research.
Metadata Standards and Schemas
A key component of metadata is the schema. Metadata schemes provide the overall structure for the metadata. It describes how the metadata is set up, and usually addresses standards for common components of metadata like dates, names, and places.
One of the most generic and commonly employed metadata schemes is the Dublin Core and can always be applied in case you are unsure about which schema to select.
List of Metadata Standards by the Digital Curation Centre
The Digital Curation Centre has provided a list with links to information about discipline specific metadata standards, including profiles, tools to implement the standards, and use cases of data repositories currently implementing them.
FAIRsharing.org provides a more extensive catalogue of standards used in biomedical research.
When you register your dataset in an online catalogue you are in fact already asked to provide a set of metadata. The metadata asked for an online catalogue are mainly focused on improving the find-ability and accessibility of your data.
However, the metadata in schemes like those listed on FAIRsharing.org are far more detailed and domain specific. These specific metadata schemes are aimed at improving the interoperability and reusability of the data.
5.3a I will make the following end products available for further research and verification
NWO Requirements
NWO expects you to preserve the data resulting from your project for at least ten years, unless legal provisions or discipline-specific guidelines dictate otherwise. As much as possible, research data should be made publicly available for re-use. As a minimum, NWO requires that the data underpinning research papers should be made available to other researchers at the time of the article’s publication, unless there are valid reasons not to do so. The guiding principle here is 'as open as possible, as closed as necessary.' Due consideration is given to aspects such as privacy, public security, ethical limitations, property rights and commercial interests. In relation to research data, NWO recognizes that software (algorithms, scripts and code developed by researchers in the course of their work) may be necessary to access and interpret data. In such cases, the data management plan will be expected to address how information about such items will be made available.
Availability
Conditions for the availability of the data, can be determined in terms of use. Terms of use can be included in the cooperation agreement or consortium agreement.
Verification
For verification, data will be made available for people who have the necessary permissions/rights. These permissions/rights can be captured in terms of use.
End Products of the Project
Give a brief description of the end products available for follow up research and verification:
raw data
processed data, e.g. SPSS files
data documentation, for instance codebooks with metadata
documentation about the data control and data cleaning process: description of the findings in a Word-document
TMF (Trial Master File) and separate ISF's (Investigator Site File): documentation files with documentation about the research process (including digital documentation about the data and syntaxes) and signed IC's. These will be kept safely in the appropriate center in a locked cabinet for 15 years.
study flow and logistics
study protocol
scripts/syntaxes for data review and data cleaning, analyses
questionnaires and eCRF’s
other products like text documents, spreadsheets, (lab) logbooks, models and algorithms, transcripts, codebooks, samples, artifacts, models, scripts and other data files like literature review files, email archives, etc.
5.5a The data collection of my project will be findable for subsequent research
(Meta)data are registered or indexed in a searchable resource.
In addition to the persistent identifier, the find-ability of your dataset will be enhanced by registering the dataset in an online catalogue or web portal with a search engine. In this way, the dataset is find-able for other potential users. Research Data Alliance has formulated a clear definition.
You can register the dataset on such a catalogue. This does not mean however that the data themselves are stored there. Rather, you register information about your dataset (metadata), and provide a reference through a persistent identifier.
The information about the dataset may include title, description, research goal and contact information and conditions for getting access to the data, etc. It may be generic information, or specific information aimed at a research community.
DataverseNL is an online research data repository to register, store and share research data in accordance with the FAIR principles. Every researcher at UM can make use of DataVerseNL during the research period and up to the prescribed term of ten years after the last publication based on the data. For questions and support, contact your faculty data steward or the University Library Research Data Management specialists.
The Maastricht Data Repository, offered by DataHub, is available within the MUMC+ for storage and archiving of metadata as well as research data, holding into account the drafted directives. The research data will be stored according to the FAIR principles. DataHub meets the requirements of current and future legislation.
If a dataset is stored in the Maastricht Data Repository, a Unique Persistent Identifier (PID) is automatically generated for this dataset. This unique PID refers to the corresponding data set stored in the Maastricht Data Repository. The dataset receives a Handle PID.
DataHub also provides the ability to transfer your metadata or even your complete dataset onto DataVerseNL. For more information please contact DataHub:
5.6a I will be using a persistent identifier as a permanent link to my data collection
(Meta)data are assigned a globally unique and persistent identifier.
A Persistent Identifier (PID) is an online permanent referral to a digital object that is independent of its storage location. The digital object in this case is the dataset itself, or metadata that describe what the dataset is about (see key item 7). This PID is a unique ‘label’ (usually in the form of a code) and is created by a (certified) data archive or repository. With a PID the dataset, or a description of it, can always be found on the internet, even when the name or location of it is changed since its creation. They are essential for ensuring Findability and sustainable archiving of your dataset. In addition, a PID enables you to cite your data in publications. Examples of PID’s are DOI, Handle, URN of ARK.
For more information on PIDs, see the web pages of the International DOI Foundation (IDF) and Datacite (TUDelft).
The course of RDNL is also a good option to learn more about the storing, managing, archiving and sharing of data.
DataHub/DataVerseNL
If a dataset is stored in the Maastricht Data Repository or in DataverseNL, a Unique Persistent Identifier (PID) is automatically generated for this dataset. This unique PID refers to the corresponding data set stored in the Maastricht Data Repository or in DataversNL. The dataset receives a Handle PID.
Resulting publication(s) related to the dataset, are identified with a Digital Object Identifier (DOI) submitted by the publisher. In the publication(s) it will probably be required to include a reference to the corresponding dataset's Handle PID.
DOI
The Digital Object Identifier is a unique and stable identifier that ensures that a digital object can be permanently found on the World Wide Web, regardless of changes in the URL where the object is found. A central registry ensures that the user of a DOI will be referred to its current location.
5.7a Once the associated article is published and/or the project has ended, (part of) my data will be accessible for further research and verification
(Meta)data are retrievable by their identifier using a standardised communications protocol.
Restricted Access – Embargo Period
By determining an embargo period, the accessibility of data can be delayed. One of the reasons for delayed access to data can be the marketing of the acquired knowledge.
A distinction in accessibility can be made between the access of raw data and aggregated research data (for scientific publication). Agreements on embargo period and restricted access should be captured in Terms of use.
5.7c Once the project has ended, my data collection will be publicly accessible
(Meta)data are retrievable by their identifier using a standardised communications protocol.
Accessibility to data is a key item for ZonMW. Requirements for the accessibility of data can be laid down in guidelines for terms of use. Open access is not needed. The data must be findable via an online catalogue or metadata catalogue, foreseen in the terms of use.
5.7e I have a set of terms of use available to me, which I will use to define the requirements of access to my data collection once the project has ended
(Meta)data are released with a clear and accessible data usage license.
UM/MUMC+ promotes researchers to create FAIR data. FAIR data, however, are not necessarily OPEN for anybody. With the ‘A’ of accessible in FAIR, you as a researcher can state the conditions by which the data will be shared.
If the reuse of your dataset is bound to specific conditions, or in other words there is restricted access to your dataset, other researchers must be able to view the terms of use. These must be findable online, e.g. through the website of your institute, or the catalogue or repository.
The terms of use have to be made available by your institute or research group and should not be personal. Other researchers should be able to find out who they need to contact if they want to make use of the data.
The legal status of the licenses and conditions for reusing the data have to be clear. You can use international standards for the terms of use, or you can formulate them yourself together with a legal advisor.
Terms of Use - UM/MUMC+
A Terms of Use Agreement is used to set the rules to which your users must agree to in order to use your data collection. One useful disclosure of a terms of use agreement, can be a clause covering Intellectual Property.
At the moment no sample Terms of Use template is available within UM and MUMC +. Terms of use should therefore be drawn up by a legal advisor. Within the UM you can contact:
Using a Creative Commons license the researcher can easily capture a basic Terms of Use for their data. The licences come in 6 different flavors. By using the online tool it is easy to select the licence that most fits your project.
5.7f In the terms of use restricting access to my data, I have included at least the following:
(Meta)data are released with a clear and accessible data usage license.
Examples of Terms of Use for Reusing Data
An appointed committee will decide on the approval of future data requests and will consist of the following persons, being XXXXXX
The purpose of the data access is scientific and not in any way for commercial purposes;
The purpose of the data request needs to be of social interest. Research questions of the applicant for accessing data need to be related to the research topic of the original research;
The dataset may not be linked to an external dataset (due to privacy);
Use of the data set in not infinite, but only for a predetermined period, a time interval required for analyses of data up to a maximum of 3 months (and one-time only for this specific question by this applicant).
If the purpose of data-access is for scientific publication, prior agreements must be made regarding to co-authorship, access to final manuscript and possibility to stop publication of data up to a period of 3 months if no agreement can be reached between researchers and the applicant.
5.8a I will select an archive or repository for (certified) long-term archivig of my data collection once hte project has ended
(Meta)data are retrievable by their identifier using a standardised communications protocol.
At the end of your project, you are required to deposit your data sustainably in a data repository (or a data archive). Preferably, this should be done in a certified repository (core trust seal). A certified repository ensures that data can be shared in the long run.
It is recommended that you use a repository provided by UM/MUMC+. If you are required by co-funder or a scientific journal to use an international repository, you are free to do so. However, it is preferred that you register your dataset at least in one of the repositories provided by your institution. Contact the data steward of your faculty or department for more information.
DataverseNL is an online research data repository to register, store and share research data. Every researcher at UM can make use of DataVerseNL during the research period and up to the prescribed term of ten years after the last publication based on the data. For questions and support, contact the University Library Research Data Management specialists.
Maastricht Data Repository
Datasets from your project can be deposited in the Maastricht Data Repository, offered by DataHub Maastricht. The Maastricht Data Repository provides services on sustainability and access during and after the research project. For long-term storage an agreement will be made on storing at DataHub Maastricht, possibly in combination with an external domain specific repository. Data in the Maastricht Data Repository will be stored in accordance with funder and university data policies.
The Maastricht Data Repository has an access management layer available within the infrastructure. Only people with the correct permissions will be able to upload new data and access existing data. After an agreed period, the data will be made available to the DataHub Maastricht community or beyond. Personal sensitive data on human subject will not be made available for ethical/privacy reasons. For more information please contact DataHub:
5.9a Once the project has ended, I will ensure that all data (digital and paper), software codes and research materials, published or unpublished, are managed and securely stored.
UM Code of Conduct
Maastricht University has established an Integrity Code of Conduct based on the codes of conduct applied by the Association of Universities in the Netherlands (VSNU) in the fields of education, research and management.
To the extent that a long term is not required by a law, rule, contract, subsidy or faculty guideline, all research results must be stored for a period of at least ten years after the final publication of the relevant data.
If your research falls outside the scope of medical scientific research with human beings (WMO), it is recommend to store your data for 10 years. If your research meets the WMO requirement, you have to keep the data for 15 years after the last publication on that data.
Informed Consent
You are required to keep the informed consents at least 15 years after the inclusion of the final participant or as long as your dataset is stored.
5.10a Once the project has ended and the data have been selected, I can make an estimate of the size of the data collection (in GB/TB) to be preserved for storage or archival
The volume of data is determined by the type of data, for example:
(alpha)numeric data
image footage, images
audio files
The number of participants is also of great importance. Besides, you have to take into account the reproducibility of the research and data. All the material needed for the reproduction of the research (data, codes and software) must be saved. An estimate is sufficient, for instance <1 GB.
5.12a Once the project has ended and the data have been selected, I can make an estimation of the costs involved for storage
Answer when using the Maastricht Data Repository
The cost for data stored in the Maastricht Data Repository is calculated on a price per GB per year. Another factor that influences the cost is the number of replica’s that you require for your data.
We currently offer data replication to two geographically separated storage backends. /wiki/spaces/IN/pages/15302946 shows the (estimated) prices.
