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OFF17: General government, subsidies (to corporations)

grant type and time
1971-2024 (m DKK)

https://www.statbank.dk/OFF17

Tables in StatBank

AFTRYK2: Climate footprint (experimental statistics)

type of use, supplying industries, emitting industries, emitting countries and time
1990-2023 (Tonnes of CO2-equivalents)

https://www.statbank.dk/AFTRYK2

Tables in StatBank

FRA030: Absence in the private sector

occupation, sex, indicator of absence, cause of absence and time
2013-2023 (-)

https://www.statbank.dk/FRA030

Tables in StatBank

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 Denmark’s Data Portal. Research Services 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 Denmark’s Data Portal, 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 2025-1. 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, 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), 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

Access to business data

Business data refers to data on Danish enterprises and Danish trade and industry. This page gives an overview of who can get access to business data from Statistics Denmark and the possibilities to apply for an exemption to get access. , Business data and business data with limited access, Business data from Statistics Denmark covers a wide range of data on industries and data on the size, location, accounts, employment, development over time, etc. of Danish enterprises. Some types of business data involve competition- and market-sensitive information, which is why access is limited. For example when data concerns the activities or financial affairs of enterprises., See the overview of business data with limited access in Statistics Denmark (Excel, in Danish) , Note, : To protect competition- or market-sensitive information, business data with limited access is not available until at least one year after the reference year., Business data – who has access?, A person can get access to all (pseudonymised) business data in Statistics Denmark, including business data with limited access, if that person has an approved association agreement with a Danish institution that is authorised under Statistics Denmark’s Research scheme and that is , a Danish public institution, or , a private Danish institution in the category “interest organisation, think tank, etc.”, ‘Danish institution’ means an institution within the national community of Denmark, Greenland and the Faroe Islands. You can find the special rules for Greenland and the Faroe Islands under , Authorisation of institutions, ., Business data with limited access – who does not have access?, Generally, people employed in Danish private consultancies may not get access to business data with limited access., People employed by other Danish or foreign private companies (such as banks, pension funds and insurance companies) or by foreign consultancy firms are not permitted to access business data with restricted access., Business data with limited access – who can apply for an exemption? , In connection with specific projects, Danish private consultancies that do not have access in general to business data with limited access can apply for an exemption. This is only an option if:   , the data controller institution for the project is a public, Danish and authorised institution (see “a” above) or , the data controller institution for the project is a private and Danish institution in the category “interest organisation, think tank, etc.” (see “b” above) or, the data controller institution for the project is a public, Danish institution and an institution authorised as a client, which engages a private consultancy to perform an analysis for the institution for which business data with limited access is needed and the consultancy’s authorisation does not grant access to this data. , Read more under Authorisation of institutions, Apply for an exemption , If you are eligible to apply for an exemption (and thus comply with item 1, 2 or 3 above) and want to apply, please inform the project owner in Statistics Denmark early in the project proposal process. This ensures that the project owner can take this into consideration during the approval of the project proposal.  , Furthermore, you need to complete the request template from Statistics Denmark and send it to your project owner, when the project for which you are applying for business data with limited access has been approved., Template for request for exemption for business data with limited access (docx, template only available in Danish), Note, : The request template must be adjusted with your own official business stationery design, signed and sent (in Word or PDF format). If you need help filling in the template, for example purpose and description, you can consult Statistics Denmark’s guide on , how to create a project proposal, . , Request for exemption – how does it work?, For every request for exemption, Statistics Denmark makes a thorough assessment in four steps: , When the project proposal has been approved, the data controller institution completes a request template, adapt it with their own official business stationery design, sign it and send it to the project owner in Research Services., The project owner in Research Services assesses if there are grounds for an exemption. Note: The criteria for approval are the same as for a project proposal. , Read more in How to create a project proposal, The project owner in Research Services sends the request for exemption for approval by the Director General of Statistics Denmark., When the request for exemption has been approved, the approval is returned to the data controller institution and the consultancy charged with the task., If you have questions about exemption, please contact , Forskningsservice@dst.dk, or your project owner in Research Services. In the subject field, you should write: , ’Project number xxx - Re. exemption with respect to business data with limited access’, .,  

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

About microdata schemes

Statistics Denmark’s Research Services 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

Certification of users

All users working with data in one of Statistics Denmark’s microdata schemes must achieve certification. The certification ensures that everybody knows the data security rules under Statistics Denmark’s microdata schemes and feels safe using and transferring data. To ensure continued high focus on the data security rules, all users must subsequently achieve re-certification once a year.,  , Certification in practice, In practice, the certification takes place via Denmark’s Data Portal, where you must pass a test with questions on the data security rules described in , Research Services data security rules under the microdata schemes (pdf), . It is a good idea to read the rules before you start. You have three attempts per day to pass., See the video guide for user certification (in Danish), This is how you do it:, Log into Denmark’s Data Portal with your three/four-character ident and password., On your landing page, select the window ‘Learning and certification’. , Then select the tab ‘Certifications’. If it does not drop down automatically, click the small blue arrow., Answer the certification questions by clicking ‘Start certification’ and ‘OK’ in the info box that pops up., Answer the questions by clicking the option you believe to be correct., When you have answered all the questions, you click the button ‘Submit answer’, which has turned blue meanwhile., If you answer all ten questions correctly, you have passed the certification and you are considered able to handle data in accordance with our data security rules., Under ’Result’, your status will be indicated as ’Passed’, and a green info box appears with the text ’Congratulations, you have passed’., Under ’Resultat’ vil din status figurere som ’Bestået’, og der vises en grøn infoboks med teksten ’Tillykke, du har bestået’. , If you do not answer all ten questions correctly, you can see under ’Result’ how many questions you answered incorrectly in your attempt and how many remaining attempts you have. Furthermore, a red info box appears with the text ‘Sorry, you have not passed’., a) If you have more attempts left and want to re-take the test, press ’Certification front page’ and start over. Note that you have three attempts per day and that the questions change from time to time. Consider re-visiting the data security rules, before you try again., b) If you do not have any attempts left, your access to your projects will be locked for 24 hours. The small watch icon indicates when the 24 hours are up. After that, you can take the test again., The certification questions, The test contains questions about the data security rules (, data security rules under the microdata schemes (pdf), ). Since Statistics Denmark’s data security rules may differ from the practice in other institutions, it is important to read and know the rules under the microdata schemes. Knowing the rules is also the basis for answering the ten certification questions correctly. , Read more about the data security rules under Rules on transfer of analysis results , The questions are about access to researcher machines, pseudonymisation, transfer rules and working in general with data. Below you will find an example of a question that you can encounter in the certification test:, Question 1:, You have collected a survey that you are working on locally. You have registered the survey with the Danish Data Protection Agency, so the permits are in place. You have also sent the survey to Statistics Denmark to have the option of linking the survey with register data on the researcher server. Which is the correct statement?, Reply options:, a. You are allowed to download/transfer microdata from the survey that you have uploaded yourself., b. The only microdata you are not allowed to transfer, is microdata provided by Statistics Denmark to your project., c. Retrieval of microdata is never allowed regardless of data source.

https://www.dst.dk/en/TilSalg/data-til-forskning/brugeradgang/certificering-af-brugere

High performance computing

If you need high computing power for data analysis, modelling or machine learning, High Performance Computing (HPC) will give you access to an analysis platform that can handle complex calculations quickly and efficiently., HPC is a solution where your project is executed on a super computer in an HPC centre. Unlike traditional servers, HCP allows you to scale the computing power up or down according to your needs., Advantages of HCP:, High-performance: , Calculate and analyse large data volumes much faster than on regular servers., Adaptability: , Scale your capacity according to the project requirements., Storage: , HPC can store large volumes of data and quickly retrieve these from the memory., Not all projects require HCP. If you are working with small data volumes, which can be processed efficiently on a traditional server, a hosted server may be a more cost-efficient solution. If your analyses require extensive data processing, complex simulations or machine learning models, HPC will serve you well. To be able to use HPC for projects with data from Statistics Denmark, the HPC centre in question must have entered an agreement with Statistics Denmark. , HPC for health projects via the Danish National Genome Center (NGC), If you are working with data from Statistics Denmark and you have a project with a health-related purpose, we offer an HPC solution via the Danish National Genome Center (NGC). The HPC solution uses a , One-Node Architecture, , where calculations are carried out on one server at a time. The HPC centre is located outside Statistics Denmark, but the project will be created and controlled by Statistics Denmark in the same way as other projects. You will still manage the project via Denmark’s Data Portal. , To attach a new or existing project to NGC, you must:, Have created a supplementary agreement to your data processing agreement., (Re-)propose you project for approval with Research Services., Make an agreement with NGC., Be able to engage in dialogue with technical staff about the set-up of server access to NGC., Payment for use of the HPC centre is settled directly with the centre. For the use of NGC’s HPC infrastructure, you pay for installation, renting of hardware, operation and support. , Further information about conditions for login with NGC and prices for installation, renting of hardware, support and operation (pdf, in Danish), If you are interested in this solution, you can contact us at , forskningsservice@dst.dk, , to hear more about the possibilities or get help getting started with HPC., New opportunities for Data Sharing through the HPC Solution for Health Projects, If you wish to work with very large volumes of data, on a project with a health-related purpose, Statistics Denmark now offers a new project setup through our High Performance Computing (HPC) solution at the Danish National Genome Centre (NGC).,  , The Shared Secure Processing Environment—known as the SSPE solution—allows 3, rd, party data providers to make their data available within a closed environment which, in turn, enables users to analyze these data alongside data from Statistics Denmark. This makes it possible to support projects where the volume of the external data exceeds the storage and analyzing capacity of FSE's servers. Furthermore, it ensures that researchers bound by legal agreements not to transfer external data to third parties—including Statistics Denmark—can comply with those obligations., The SSPE solution is an advancement of the existing NGC setup. It is not meant as a replacement for the current setup, but as an additional offer to projects requiring greater analytical capacity. The same terms and conditions apply to projects wishing to use the SSPE solution as to those using the NGC solution., If you are interested in this solution, please contact us at , forskningsservice@dst.dk, if you wish to learn more about your options or to receive guidance on how to get started with the SSPE setup., Future , HCP solutions in Statistics Denmark, Statistics Denmark is cooperating with DeiC to extend the access to more HPC centres in 2025. We are working on opening for access to more HCP facilities, so that non-health-related projects can also use the analysis platform. This solution will use a , Multi-Node Architecture, , where calculations can be executed in parallel across several servers resulting in even higher performance.

https://www.dst.dk/en/TilSalg/data-til-forskning/analyseplatform/high-performance-computing

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 Denmark's Data Portal. , 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 Research Services must create the population, If Research Services is going to create the population for your project, this is done on the basis of a framework agreement. Under the population description in Denmark's Data Portal, you describe the population shortly and precisely (without involving technical details) and add that Statistics Denmark is going to create the population. When Research Services 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 , Research Services 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. Research Services 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, Research Services 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 additional 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 , Research Services 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 Denmark's Data Portal, 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 Research Services, we prepare a detailed extraction description of the case control population. The final extraction description is attached as an appendix to the project proposal. Please note that Research Services 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

ENE2HT: Energy Account in GJ (detailed table)

supply, type of energy and time
1966-2024 (GJ (gigajoule))

https://www.statbank.dk/ENE2HT

Tables in StatBank