Embedded Files
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 licence and distribute.
Discover
Discover
Data discovery
Data discovery
Data discovery is the process of visually navigating data and applying analytics in order to detect patterns, gain insight, answer specific questions, and derive value from the data.
This stage of the Research Process is a time for reviewing the RDM plan
New data discovery and data creation
Integration of new data into current data
Data analysis
Attributions of original data sets
New data discovery and data creation
New data discovery and data creation
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.
Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.
Locating existing data
Locating existing data
Identifying and locating sources of existing data can be important for a variety of reasons, including:
asking new questions or providing a new analysis of the data
comparing results from various studies
replicating and validating previous results
developing or testing computational models
extending a study across time, geography, or other variables by incorporating data from multiple datasets.
List of resources to locate existing data
List of resources to locate existing data
Data Directories
These online directories maintain lists of data sources and repositories across a wide range of disciplines.
re3data - A global registry of research data repositories covering a wide variety of academic subjects in the sciences, social sciences, and humanities.
Open Access Directory of Data Repositories - Lists of open access data repositories for a wide range of subject areas.
General repositories
These repositories maintain data from a wide range of subject areas and are not limited to a particular discipline.
figshare - A repository for sharing all types of research output in any subject - includes papers, figures, posters, slides.
Amazon Web Services Public Data Sets * - Hosts a variety of large public datasets, such as Landsat, census, and genomic data. Creating an account may be required and charges may apply for computing time and data transfer.
Discipline related repositories
The following are examples of data repositories that focus on a particular subject area, discipline, or cluster of related disciplines within the broad categories of humanities, sciences, social sciences, and government.
HUMANITIES
LINGUISTICS
OLAC – Open Language Archives Community - An international partnership “creating a worldwide virtual library of language resources,” currently with 58 participating archives.
TROLLing-Tromsø Repository of Language and Linguistics - An open access repository of linguistic data and statistical code.
MUSIC
Mutopia Project - Free sheet music.
SCIENCES
BIOLOGY / LIFE SCIENCES
DRYAD - General purpose repository for data underlying scientific and medical publications, historically with a concentration in life sciences.
Gene Expression Atlas - Information on gene expression patterns under different biological conditions, such as different cell types, organism parts, or diseases. ?
genenames.org (HUGO Gene Nomenclature Committee) - Curated repository of HGNC approved gene names and symbols, gene families, and links to related genomic, proteomic, and phenotypic information.
NCBI (National Center for Biotechnology Information) - Provides access to a variety of sources for biomedical and genomic data, including:
Conserved Domain Database (CDD) - Sequence alignments and profiles representing protein domains conserved in molecular evolution.
Gene - Gene data from a variety of species with related information, such as nomenclature, chromosome location, phenotypes, etc.
Database of Genotypes and Phenotypes (dbGaP) - Data and results from investigations of the interaction of genotypes and phenotypes in humans.
WormBase - Data on the genetics, genomics, and biology of C. elegans and some related nematodes.
UniProt (The Universal Protein Resource) - Collection of databases that provide a comprehensive source for protein sequence and annotation data, including a repository for metagenomics and environmental data.
CHEMISTRY
eCrystals - Mostly open access source of fundamental and derived data from single crystal X-ray structure determinations from the University of Southampton and EPSRC UK National Crystallography Service.
PubChem - Database of chemical substances with descriptive and property information along with bioactivity screening data.
Zinc15 - Database of commercially available compounds with 3-D structure representations in a format ready for virtual screening for potential biological activity.
SOCIAL SCIENCES
ECONOMICS
GTAP Database – Global Trade Analysis Project - Global database describing bilateral trade patterns, production, consumption and intermediate use of commodities and services.
GeoFRED® - Geographical Economic Data - Maps of data contained in FRED®. Create customized maps and download data.
Data Journals
Many journals can be helpful tools in locating data, although they can play different roles as noted below.
Traditional Articles that Publish Data
These traditional "data journals" publish only articles that focus on presenting data, either experimental or computational, or may review experimental methods.
Journal of Physical and Chemical Reference Data - Publishes articles reporting critically evaluated reference data and property measurements.
Journal of Chemical and Engineering Data - Publishes both experimental and computational data.
Data Journals or "Data Paper" Journals
These newer style "data journals" primarily publish articles that describe publicly available datasets and link to those datasets.They may also publish articles on data-related topics, such as describing or reviewing certain analytical or statistical methods. However, traditional research articles that actually analyze the data and draw conclusions from that analysis are generally outside the scope of these journals.
Biodiversity Data Journal - Community peer-reviewed and open-access. Promotes the publishing, dissemination and sharing of biodiversity-related data of any kind. Publishes data papers, general articles, software descriptions, species inventories, and more.
Earth System Science Data - An international interdisciplinary journal that provides a distinctive model for publishing papers about original research data sets and encouraging the reuse of high quality data. Includes methods and review articles and a "living data" process for handling datasets that undergo regular updating or extension.
IUCrData - Open-access and peer-reviewed. Provides descriptions of crystallographic datasets and datasets from related disciplines.
Scientific Data - Open-access and peer-reviewed. Its Data Descriptor articles describe data sets, the method of data collection and analyses relating to the quality of the data. They also link to one or more published sources of the data.
Mixed Journals
These journals publish a mixture of article types, including "data papers" that describe datasets along with traditional research articles and other formats.
International Journal of Robotics Research - Publishes peer-reviewed data papers and multimedia extensions in addition to articles.
Internet Archaelogy - Open access and peer-reviewed. Publishes data papers as well as research articles, methodologies, reviews and more.
Nucleic Acids Research - For more than 20 years has published a special issue in January that reports on databases containing data related to bioinformatics generally, including nucleic acids, proteins, and genomics.
These are only a few examples of journals that can point you to useful data. For more complete listings, check these sites:
Sources of Dataset Peer Review (from the Edinburgh DataShare Wiki)
A Growing List of Data Journals (from Data@MLibrary)
Open Data Journals (from the FOSTER project)
Data Visualisation
Data Visualisation
Data Visualisation is the visual representation of data, and is used to enable people to both understand and communicate information through:
graphical (Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data )
schematic (It means that right data must be identified before. Semantic technologies provide new ways for accessing data and acquiring knowledge. The underlying structures allow finding information easier, gathering meanings and associations of the data entities and associating the data to users' knowledge)
Data Visualisation tools
Data Visualisation tools
A variety of tools are available that support data discovery, integration, analysis, and visualization
Therefore the tools enable
tracing the use of data sets and data elements
attribution to the creators of the original data sets, and
identifying effects of errors in the original data sets or elements of those sets on derived data sets
Data Analysis
Data Analysis
Data interpretation and analysis is the process of assigning meaning to the gathered information and ascertaining “the conclusions, significance, and implications of the findings. Source: University of Pittsburgh & University of Oxford
Reuse
Reuse
Data for reuse & interpretation
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 reuse & interpretation is good science
Data sharing for reuse & interpretation is good science
Data sharing for reuse & interpretation is good science.
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.
Well-managed and accessible data allows others to validate and replicate findings, and to ensure research integrity
Research data management facilitates sharing of research data and, when shared, data can lead to valuable discoveries by others outside of the original research team
Protect institutions from reputation, financial and legal risk
Source: University of Pittsburgh & University of Oxford
Rights in data reuse
Rights in data reuse
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
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)
Sharing and licensing for reuse
Sharing and licensing for reuse
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
Research data ownership
Research data ownership
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
Creative Commons is a nonprofit organization that helps overcome legal obstacles to the sharing of knowledge and creativity to address the world’s pressing challenges.
Authors give away the copyright rights to their work to the publisher when the article is published in the traditional publication process.
However, when authors publish their work via the Open Access process, they retain the copyright of that work. It is important that authors assign a Creative Commons license to determine how their work may be used and shared.
Cite
Cite
Make data easy to reuse & cite
Make data easy to reuse & cite
This requires clear and detailed data description and annotation. Besides the information that is needed to reuse the data, data also need to be accompanied by information for citing and discovering the data. UK Data Service
Why make data easy to cite?
By documenting your data and recommending appropriate ways to cite your data, you can be sure to get credit for your data products
Citing data
Citing data
If you’re reusing a dataset to inform your own work, you’ll want to make sure that you are providing proper recognition.
Datasets are scholarly products and should be cited as such.
If you are using a dataset that was deposited in a disciplinary data repository, you may find that the repository has a recommended citation standard.
ICPSR provides useful guidance on data citations and suggests that a citation for a dataset should include the following basic elements:
Title
Author
Date
Version
Persistent identifier
For general information about citing a dataset, see the following resources:
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