Data for reuse & interpretation 
Data should be managed so that any scientist (including the collector or data originator) can discover, use and interpret the data after a period of time has passed
The comprehensive description of the data and contextual information that future researchers need to understand and use the data
Data sharing for Re-Use & Interpretation is good science
Rights in Data Re-use
Research Data Ownership
FAIR dealing in Data Re-Use
Re-Using Qualitative Data
Computational social science: Reflecting the changing data research landscape
A crucial part of ensuring that research data can be shared and reused by a wide range of researchers for a variety of purposes is by taking care that those data are accessible, understandable and (re)usable. Source: UK Data Service
Save time and money
Data reuse also enables colleagues to save time and money.
Data sharing for reuse & interpretation enables peer researchers to validate research findings
By sharing their data, researchers enable others to reproduce and validate their research findings, providing the researcher with transparency, accountability, and material support to strengthen their findings.
Protect institutions from reputation, financial and legal risk
Source: University of Pittsburgh & University of Oxford
Many kinds of data created as part of a research project are subject to the same rights as literary or artistic work. 
Such items acquire rights like copyright or more general Intellectual Property rights when they are created.
This gives the rights owner control over the exploitation of their work, such as the right to copy and adapt the work, the right to rent or lend it, the right to communicate it to the public and the right to license and distribute.
When data are shared or archived, the original copyright owner retains the copyright.
Copyright is an intellectual property right assigned automatically to the creator. It prevents unauthorised copying and publishing of an original work. Copyright applies to research data and plays a role when creating, sharing and reusing data. Source: UK Data Service
When publishing research data, researchers need to consider how they want their data to be reused by other researchers.
Thereafter, researchers need to specify their choice by licensing the data to match the intended uses. Source: UK Data Service
Creative Commons licenses
Creative Commons (CC) licenses allow creators to easily communicate the rights, which they wish to keep, and the rights, which they wish to waive in order for other people to make reuse of their intellectual properties. Source: UK Data Service
GOFAIR: Information about licenses
License Chooser: Tool that helps to select and apply a license to a resource, provided by Creative Commons
Guidelines for Legal Interoperability Of Research Data created by RDA & CODATA
Copyright is essential for data sharing and fair dealing 
When data are shared or archived, the original copyright owner retains the copyright. Source: UK Data Service
A data archive cannot archive data unless all rights holders are identified and give their permission for the data to be shared. Secondary users need to obtain copyright clearance before data can be reproduced. However, exceptions exist under the fair dealing concept. Source: UK Data Service
Under the fair dealing concept, data can be copied for non-commercial teaching or research purposes, private study, criticism or review without infringing copyright, provided that the owner of the work is sufficiently acknowledged. Source: UK Data Service 
In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.
Findable
The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
Accessible
Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.
Interoperable 
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
Reusable 
The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.
Information about Persistent Identifiers (PID)
DOI: List of current DOI registration agencies provided by the International DOI Foundation
Handle: Assigning, managing and resolving persistent identifiers for digital objects and other Internet resources provided by the Corporation for National Research Initiatives (CNRI)
PURL: Persistent Identifiers developed by the Online Computer Library Center (OCLC). Since 2016 hosted by the Internet Archive
URN: List of all registered namespaces provided by the Internet Assigned Numbers Authority (IANA)
Secondary analysis of qualitative data entails reusing data created from previous research projects for new purposes. Reuse provides a unique opportunity to study the raw materials of the recent or more distant past to gain insights for both methodological and substantive purposes.
Traditionally, secondary analysis has been less common and more contested for qualitative data than for quantitative data. However, secondary analysis of qualitative data has become more widely practised and accepted. This growth is explained by many factors such as the open data movement, research funders’ policies supporting data sharing, and researchers seeing benefits of sharing all manner of resources through social media.
Another factor enabling qualitative data reuse has been improved services and infrastructure, such as the UK Data Service, which provides access to hundreds of data collections. We dedicated to supporting quality research and education by documenting, disseminating and providing advice on using qualitative research data for secondary analysis. We want to ensure that access to qualitative data is as free, open, and as easy to access as possible, while upholding all ethical and legal standards. UK Data Service
New technologies, resources and methods are constantly changing how researchers interact with and use data. Many innovations, including those in modelling, simulation, big data, web-scraping, social media and more, have already made a huge impact on how researchers can and should access, manage, and explore data. The social sciences are no different, with many new forms of social data or methods of analysis that are more computationally intensive than most social scientists may be used to. The use of these computational tools, data and methods requires new skills as well as some new perspectives and expectations. Computational social science as a discipline applies these new tools and perspectives to data analysis. 
The UK Data Service supports researchers and data analysts by providing free learning resources relating to several innovative aspects of data-intensive social science research.
New technologies, resources and methods are constantly changing how researchers interact with and use data. Many innovations, including those in modelling, simulation, big data, web-scraping, social media and more, have already made a huge impact on how researchers can and should access, manage, and explore data. The social sciences are no different, with many new forms of social data or methods of analysis that are more computationally intensive than most social scientists may be used to. The use of these computational tools, data and methods requires new skills as well as some new perspectives and expectations. Computational social science as a discipline applies these new tools and perspectives to data analysis.
The UK Data Service supports researchers and data analysts by providing free learning resources relating to several innovative aspects of data-intensive social science research. UK Data Service