Denmark's Data Portal makes data available to authorised institutions for specific research, fact-finding and analytical tasks. Access to data can be granted under various data schemes depending on the institution or the project to which you seek access., The researcher scheme , Researchers and other analysts from authorised institutions can create a project with access to Statistics Denmark’s register data. , Read more about authorisation of institutions, The project database scheme , The project database scheme is intended for institutions that are continuously creating projects with significant overlap in data content. Under this scheme, it is not allowed to carry out research directly on the project database, and the scheme must not be used for projects or tasks that are not directly related to the purpose of the project database. Furthermore, the institution must have one or more employees at who can serve as project database managers, of whom at least one can functions as an administrator. The duties of the project database manager include population generation, data extraction etc. as well as ongoing communication with Statistics Denmark., If you want to apply for a project database to be set up, you must contact the Project database group at , FSEProjektdatabase@dst.dk, ., More on the project database scheme, An authorised institution can have a maximum of one project database. The project database is a collection of pseudonymised microdata. It is used over time for multiple projects (called subprojects) under the relevant project database scheme., For the project database, data is selected from Statistics Denmark’s databank of basic data and, if relevant, data from other sources (such as the institution’s own data). The data content in project databases is subject to the data minimisation principle, and for that reason, data in a project database must be applied in several subprojects., In the project database scheme, the project database is called the main project. Other projects in the project database scheme are subprojects of the project database. The authorised institution that owns the project database therefore owns both the main project and the subprojects in the scheme., The target group of the project database scheme is institutions that:, are authorised for microdata schemes at Statistics Denmark., have at least five active projects with significantly overlapping data., continuously extend their project portfolio with new subprojects with significant overlap in the underlying data., Terms of a project database scheme, Project databases are subject to the following terms:, The institution is required to appoint one to three experienced project database managers who will be the assigned liaison officers with Statistics Denmark. Only project database managers get access to the actual project database., The project database and subprojects are subject to the data minimisation principle., The user must pay for all costs associated with the creation, operation and maintenance of the relevant project database. Subprojects are considered regular projects and are handled and invoiced separately., You can keep a project database going for as long as it is used for active subprojects. The project database can only be preserved as long as it is used for subprojects to an extend that is consistent with the data made available in the project database. The project database can thus be limited or discontinued if Statistics Denmark estimates that this is no longer the case., The authority scheme, The authority scheme makes microdata available to Danish institutions that carry out tasks for the authorities, i.e. departments, agencies and directorates, regions and municipalities. The scheme meets the demand for ad hoc analyses with tight deadlines. , Read more about the Authority scheme, (in Danish), Data confidentiality and access rules, Access to data is given in agreement with the principles of the General Data Protection Regulation, especially article 5(1)(c): , “Personal data shall be adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed (‘data minimisation’).” , This also applies to section 10 of the Danish Data Protection Act: , “Data as mentioned in Article 9(1) and Article 10 of the General Data Protection Regulation may be processed where the processing takes place for the sole purpose of carrying out statistical or scientific studies of significant importance to society and where such processing is necessary in order to carry out these studies.” , Read more on Statistics Denmark’s Data confidentiality policy and Information security policy

https://www.dst.dk/en/TilSalg/data-til-forskning/mikrodataordninger/om-mikrodataordninger

Registers and reference types

Statistics Denmark has gathered a vast series of historical register data in our databank of basic data, which users can access via the platform DDP App. Denmark’s Data Portal manages the databank of basic data and handles access to the platform, support, etc. Most registers in the databank are updated at least once a year in connection with release of the register-based statistics (, see Scheduled releases, ). , The data safari and the List of registers and variables (below) both show the registers in DDP App, and here you can see variables for the individual registers. The documentation of variables is available in Statistics Denmark’s , documentation system, ., Go to Data safari , Go to List of registers and variables (in Danish), , Overview of rerun registers (in Danish), Genkørte registre 2025-3. kvt (pdf), Genkørte registre 2025-2. kvt (pdf), Genkørte registre 2025-1. kvt (pdf), Genkørte registre 2024-4. kvt (pdf), Genkørte registre 2024-3. kvt (pdf), Genkørte registre 2024-2. kvt (pdf), Genkørte registre 2024 - 1. kvt (pdf), Genkørte registre 2023 - 4. kvt (pdf), Genkørte registre 2023 - 3. kvt (pdf), Genkørte registre 2023 - 2. kvt (pdf) , Genkørte registre 2023 - 1. kvt (pdf), Genkørte registre 2022 (pdf), , Reference types, Registers in the basic data overview are compiled by means of different reference types. Next to each register in the basic data overview, you can see which reference type a register has: ’Status’, ’Statusperiode’ (status period), ’Forløb’ (longitudinal) or ’Hændelse’ (incident)., Status, The reference type shows the status for a given date. For example, LONN (structure of earnings), which shows what a citizen earns as of the register date (e.g. 31 December 2021). Or BEF, which shows the population as of the quarter date (including status of residence, age, family, etc.)., Data definition: Clear status as of a given date. The population delimitation and all data content is focused on the date., Status period, This reference type shows the period status, where the population is delimited as of a given date, but the variables contain summed up data for a specific period. For example, IND, which contains the labour income for a year (the period appears from ’Opdateringsfrekvens’ (update frequency) in the basic data overview). Other examples of status period registers: PERSBEST (board members and managers), MFR (medical birth register), HANDICB (financial support for disability cars), DMRB (motor vehicles). It is not always easy to see what is being summed up., Data definition: The population delimitation is made as of a given date, but the content of the variables is accumulated over a given period. The period cannot be deduced from dates in microdata, but from the indicated period (shown under ‘Opdateringsfrekvens’ (update frequency)) – meaning that content in for example amounts, volumes, quantities etc. is aggregated over the indicated period (e.g. a quarter, a year)., Longitudinal, Here, data covers a longitudinal study. There will always be just one version of the register available. For example, UDD, which contains Highest educational attainment. Or BEFADR, which is an address key register (where e.g. 1.4m addresses changed key on 1 January 2007 in connection with the local government reform). When a longitudinal register is updated, the individual dataset is updated. This is why there is always only one dataset for a longitudinal register., Data definition: The definition of longitudinal data is that data contains a start date and an end date., Incident, Here, data covers an incident. For example, UDFK, which contains primary and lower secondary school marks (does not include a date but a school year), or OPHGIN (basis of right of residence for immigrants). When a longitudinal register is updated, the individual dataset is updated with new incidents. This is why there is always only one dataset., Data definition: The definition of incidents is first and foremost that data contains a date - only one date - for the occurrence of the incident, and will usually also have one incident type attached., Documentation for the use of registers and data packages, Statistics Denmark has prepared a memo describing the coherence between several of the most used registers in Statistics Denmark’s microdata scheme and their connection with the published statistics., The social statistics registers in Statistics Denmark consist of comprehensive data collections, which have been built and extended since the early 1980s. Data is of high quality and comprises the whole population. This gives the users of data unique possibilities of analysis, allowing them to analyse both status at a given point in time and the development over time., The memo is primarily intended for researchers, analysts and other users of microdata who want to obtain deeper insight into the quality of the coherence between the different registers. , Read more on Documentation for the use of registers (in Danish), Datapackages (pdf - in Danish), Especially on the Data Warehouse for Business Statistics, In January 2024, Statistics Denmark launched the new Data Warehouse for Business Statistics – a significant extension and improvement of the existing business registers. , The new warehouse ensures wider and better access to anonymised data on enterprises and facilitates extraction of unique data by linking data across more statistical registers. The data warehouse also facilitates linking of business statistics and social statistics at micro level, the so-called ‘Linked Employer-Employee Data’ (LEED). , Read more in , this brochure (pdf), or see , the presentationen of The Data Warehouse for Business Statistics on 30 November 2023 (pdf), .

https://www.dst.dk/en/TilSalg/data-til-forskning/generelt-om-data/registre-og-referencetyper

Population description

In the project proposal, you must describe the population shortly and precisely (without technical terms, details or data specifications), and document who creates the population. You do so under the population description in the DDP App. , Private institutions are able to create the population themselves and get a full register extraction if the project is surveying a major group of entities. To get a full register extraction, private institutions must give reasons for this need based on the size of the population. , , When Denmark’s Data Portal must create the population, If Denmark’s Data Portal is going to create the population for your project, this is done on the basis of a framework agreement. Under the population description in the DDP App, you describe the population shortly and precisely (without involving technical details) and add that Statistics Denmark is going to create the population. When Denmark’s Data Portal have received the project proposal, they will contact you about the creation of the population. , Examples of population descriptions:, `The population consists of all persons who have been hospitalised with asthma, which is matched with five controls on sex and birth year per case. The controls must be alive and be residing in Denmark on the index data of the case. Statistics Denmark creates the population.', `The population consists of persons who have had residence permits as refugees, and family members reunited with refugees. Statistics Denmark creates the population.', Framework agreement for extraction description and population creation , Denmark’s Data Portal prepares a framework agreement, which covers counselling regarding the extraction description as well as the subsequent population creation. Based on the framework agreement, we prepare a detailed extraction description in collaboration with the relevant institution. Denmark’s Data Portal uses the extraction description for the final population creation. Based on the institution's criteria and needs regarding the population, we give advice on which registers, variables and variable values that are necessary to create the wanted population. The final extraction description is attached as an appendix to the project proposal. When the extraction description is ready, Denmark’s Data Portal creates the population for the project., How to make the extraction description for the population?, The following elements must be uncovered for the extraction description:, Registers or external data to be used, Periods, including if you want to use registers that are updated annually, quarterly or monthly (for example, BEF (population) is updated quarterly), Conditions based on specific variables and delimitation on specific variable values (for example, if the population must be delimited by age from 15-76 years), How registers must be linked (if several registers are applied), including linking based on specific variables and, if relevant, key register, , Especially about case control populations , Denmark’s Data Portal uses the term 'case control populations' for analyses where cases (e.g. exposed) are compared with a reference group (controls). The term is used regardless of the type of study. Under the population description in the DDP App, enter a short and precise description of the criteria for cases and controls in the case control population, without involving technical details (including registers and variables). , In collaboration with Denmark’s Data Portal, a detailed extraction description of the case control population is prepared. The final extraction description is attached as an appendix to the project proposal. Please note that the DDP App only creates case control populations based on date and register criteria, not based on more complicated statistical methods such as for example Propensity Score Matching., How to make the extraction description for the case control population?, The following elements must be uncovered for the extraction description: , What characterises cases:, Registers, periods, conditions, and how registers are linked (see description below), If relevant, index date (for example date of first completed vocational education, first hospital discharge date), What characterises the pool of possible controls:, Registers to be used for creating the pool of possible controls, Inclusion and exclusion criteria based on specific variables and variable values (for example sex = 2 (women), municipality = 607 (Fredericia), residence in the period 01-01-2020 until 31-12-2023), Specific criteria for the case control population including:, How many controls are extracted per case?, Whether cases are allowed to be controls of other cases, If controls are allowed to change status in the inclusion period, Extraction with or without replacement: , is a control allowed to be used as a control for more than one case (replacement)?, or can a control only be a control for a specific case (without replacement)?

https://www.dst.dk/en/TilSalg/data-til-forskning/anmodning-om-data/populationsbeskrivelse

How to create a data order

A data order is a request used to specify which , registers, , , periods, , , populations, , and , external data, you wish to use in the project., , Via Projects and Data, Open , Projects and Data, Select , Reorder, Choose the project to which the data order relates, Via the Project Proposal, Go to the Data packages section in the project proposal, Follow the link Create a new order in the text at the top of the page, A new data order is created automatically, Once the data order has been created, a new section called , Order, appears in the navigation menu. Here, you can choose whether to order , register data, , , external data, , or , population creation, ., Please note that for a new project, a data order can be created once a project proposal has been set up, but it can only be submitted after the project proposal has been submitted., How to Order Register Data, Under Register Data, select the registers that should be included in or updated for the project. You can only choose registers from data packages that have either been requested or approved in the project proposal., If the project proposal has already been approved and you wish to use registers from data packages that are not yet approved, you must create a resubmission. Registers from new data packages will only be processed once the resubmission has been approved., Before selecting registers, you may specify the , Preferred order period, at the top of the page if the data should only be delivered for a limited time period., By default, all variables within a given register are selected. If you wish to limit the selection at the variable level, click the ˅ next to Add and then select , Limit variables, ., Under , Choose populations, , choose which populations the registers should be extracted for. Under , Distribution of registers on populations, , specify which registers should be extracted for which populations., If a full register extract is required, this must be indicated in the project proposal under the population description. The full register will then automatically appear as a population in the data order. If the project has previously been approved for a full population but this option does not appear, you should contact the project owner at Statistics Denmark., If a population is uploaded to the project, it must be ordered as a population creation, as this is a prerequisite for it to be selected., How to Order External Data, If the project uses data that is not register data, this should be added as , External Data, ., External data is added by:, Creating an order (see above), Selecting the plus sign next to , Processing of external data, Entering a title, data source type (dropdown menu), and a brief description, Uploading relevant attachments and variable lists. Read more about the requirements for , linking of external data, External data is currently linked to populations in the same way as register data, and the distribution is specified per population., How to Create a Population, If there is a need to create populations or if external populations are used, these must be created under , Population creation, . Once the population has been created, it can be selected in the data order., To create a new population:, Click , Description, under , Population creation, in the navigation menu, Select , Add Population, Provide a descriptive title, Briefly and precisely describe who the population includes and how it is defined, Upload any relevant attachments, You must also indicate whether the population is uploaded or should be created by Denmark’s Data Portal. If the population is created from a project database, this should be marked with a checkmark., If an existing population needs to be updated, select , New Version, . An overview of the project’s populations will be displayed. Select one, and the system will automatically create a new version that can be further edited., Summary, Once the data order has been completed, a full summary is displayed. It is recommended to review this carefully to ensure that all necessary data has been included. The data order can then be submitted., Note:, For new projects, the data order can only be submitted once the project proposal has been submitted. For already approved projects, a data order can always be submitted within the framework of the most recently approved project proposal.

https://www.dst.dk/en/TilSalg/data-til-forskning/anmodning-om-data/oprettelse-af-databestilling

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About microdata schemes

Registers and reference types

Population description

How to create a data order