RDM –RESEARCH DATA MANAGEMENT

RDM basics

University of Helsinki DataSupport

Spring 2019

1. Introduction:Data and Open Science and research; Definition of data and materials

2. Description of data; Metadata; Quality and integrity

3. Ethical and legal issues

4. Solutions for storing data

5. Publishing and archiving (static phase) data (after the research project)

6. DMPTuuli and Help and support

RDM BASICS –RESEARCH DATA MANAGEMENT

TOPICS FOR TODAY

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RESEARCH DATA AND OPEN SCIENCE

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TYPICAL CONCERNABOUT OPEN SCIENCE

• Researchers are puzzled, especially, by the demand to open data

• Open science principles does not force to open everything but to teach researchers how and on what grounds publications, data and processes can be opened

• What sort of ethical issues are related to research data management

• Researcher can explain why certain data cannot be opened

• “Super sensitive data” cannot be opened but its metadata (description) mostly likely can be opened

”As open as possible, as closed as necessary”

‒ European Commission

Source: https://ec.europa.eu/research/openscience/pdf/openaccess/ord_extension_faqs.pdf

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OPEN SCIENCE IS NOT ABOUTOPENING EVERYTHING

Source Open science and research handbook / http://openscience.fi/handbook

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• For example, four stages of dataset availability in the Finnish Social Science Data Archive AILA

OPEN SCIENCE IS NOT ABOUTOPENING EVERYTHING

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RESEARCH DATA AND MATERIAL

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DEFINITIONS OF RESEARCH MATERIAL

AND RESEARCH DATA

• Research material = Data

• In an everyday situation definitions are not clear – in Finnish at least

• Used as synonyms

• Data can be understood as a general term which requires a prefix such as qualitative, quantitative, numeric, computational, interpreted etc.

• Research material ≠ Data

• In many cases data only refers to digital material

• Data is considered as computational, quantitative, machine-readable (e.g. Big data)

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• Practical definition for data

• Data can be understood as observations from any material; observations which does not contain any semantic meaning; meaning is constructed through analysis and interpretation only when contextualized

• Data life-cycle

• From raw material to data From data to analysis

From analysis to interpretation From interpretation to research results

• Interpreted data produced in one project can be reused as raw data in another

• Data can be duplicated, copied, enhanced, developed etc. from one project to another

DEFINITIONS OF RESEARCH MATERIAL

AND RESEARCH DATA

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• In the RDM plan data is understood as a broad term including:

• research material (such as any kind of physical artifacts)

• research sources (such as various archive material)

• data produced during the research (such as digitized copies of the aforementioned physical artifacts)

• data collected by various methods (such as surveys, interviews, measurements, imaging techniques etc.)

• curated collections

• annotation and coding of the material on various levels

• all revisions of a data set produced in/for the analysis process

• physical and electronic lab journals

• source codes, algorithms and software

• etc.

Everything that your research is based on!

DEFINITIONS OF RESEARCH MATERIAL

AND RESEARCH DATA

“All information that is needed to replicate a

study should be preserved, and everything

that is potentially useful for others.”

– Sarah Jones /DCC RDM basics

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CATEGORIZATION OF DATASETS

• General description of data

• What kinds of data are collected or reused? What file formats will the data be in?

• Here, outline a structure which is followed throughout the whole DMP

= describe/list all datasets and material which are discussed later in the plan, e.g.

A) Data collected by yourself

various locations; raw, non-catalogued private collections

various types

B) Data reused in your project

ready-made dataset in an archive

remember to cite the original creator or collector in your work!

C) Data produced during your project

notebooks, research diaries, field notes, comments, annotations, coding, and so on register a

PID for your datasets so you can be cited!

D) Managerial documents, agreements, contracts etc.

• Can be presented in a table together with file formats to save space!RDM basics

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DATASETS TABLE EXAMPLE

Data type File

format

Personal or

sensitive data

Storing data and

backups during the

project

Documentation and

metadata

Ownership and

Agreements

Opening or

publishing data after

the project

Measurements .xls

.csv

No Personal storage at UH

(home folder)

readme.txt

codebooks

UH and LUKE

Agreement

Opening via

publication at DRYAD

or Zenodo

Gene sequences .txt

fasta

No. Collecting only

from plants.

Group storage space .FASTA PI NCBI Genome

Programme codes .xml

ASCII

R-code

No GitLab & Shared

network drive hosted

by UH

GitLab & readme.txt Co-ownership of the

research group

Via publication and

Zenodo

Microscopy images .tiff No Server storage space OME-TIFF PI Electron Microscopy

Public Image Archive

(EMPIAR)

Lab notes .doc

.txt

.pdf

No

Patenting or

commercializing?

Electronic lab notebook

Scinote

Cloud service

Programme generates

metadata by itself

PI and me No

Samples

(applying from THL

Biobank)

.xls Anonymization will

be done by Biobank

Freezer at the Institute

of Biotechnology (PI’s

lab)

Unique identifier code THL Biobank-licence

Research agreement

DMP

Samples discarded

one year after

publishing the results.

Questionnaire forms Paper

forms

Yes

Data Controller UH

Locked filing cabinets

in PI office.

codebook

readme.txt

PI

Informing participants

No, only metadata will

be open in FSD.

Forms discarded 2

years after project

ends.

Spatial data about

land use and forest

stand

.tiff,

Coloured

No, open data Datacloud at UH

(service coming soon)

Supplements at Etsin National land survey of

Finland: license CCBY

Processed data at

Zenodo

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DOCUMENTATION AND QUALITY

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DOCUMENTATION AND QUALITY

• Documentation and quality

• How will the data be documented? How will the consistency and quality of data be controlled and documented?

• How do you describe your data in a way that it can be…

• found,

• accessed,

• linked, merged or integrated with other similar data (interoperability),

• reused,

by you or by someone else in the future? (https://www.force11.org/group/fairgroup/fairprinciples)

• Metadata = data about your data = description of your data

• Metadata can be opened e.g. in Research Data Finder Etsin (https://etsin.avointiede.fi/en/)

• How do you ensure no data will be lost if converted into another format?

• How do you ensure your data maintains the original information of your source?

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DATA DOCUMENTATION, METADATA

• Data documentation means describing your data

• Document your data at the very beginning of your research project!

• make a note of all file names and formats associated with the project, how the data is organized, how the data was generated (including any equipment or software used), and information about how the data has been altered or processed.

• include an explanation of codes, abbreviations, or variables used in the data or in the file naming structure.

• keep notes about where you got the data so that you and others can find it.

• At minimum, store this documentation in a readme.txt file or the equivalent, together with the data.

• Data documentation Guide: 10.5281/zenodo.1683181

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STEPS OF DOCUMENTING DATA PROPERLY

• File formats: What formats are used during the project and what formats are convenient for data publishing, archiving and for long term storage?

• Filenames: Naming files should be standardized in the project in order to understand the content from the name.

• File naming convention helps you to stay organized, contain quickly information from the title as well as assists others to navigate in your directories.

• Use unique file names in case of directory break down.

• Directory structure: How will files be organized and who has access to which directory?

• Create a folder structure to suit your project needs.

• If you work with sensitive data, a clear folder system helps also in access control.

• Balance between shallow and deep folder hierarchy to keep files findable.

• Version control: Being able to go back to an older version of a particular file can for example save you from data loss. Version control can be done automatically or manually.

• Metadata: Describing the data makes it understandable. This can be done by using e.g. metadata standards, readme-files, data dictionaries, and codebooks.

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DATA DOCUMENTATION, METADATA

What you should include in the documentation of your data

• TITLE: Name of the dataset or research project that produced it

• CREATOR: Names and addresses of the organization or people who created the data

• IDENTIFIER: Number used to identify the data, even if it is just an internal project reference number

• DATES: Key dates associated with the data, including project start and end date, data modification data release date, and time period covered by the data

• SUBJECT: Keywords or phrases describing the subject or content of the data

• FUNDERS: Organizations or agencies who funded the research

• RIGHTS: Any known intellectual property rights held for the data

• LANGUAGE: Language(s) of the intellectual content of the resource, when applicable

• LOCATION: Where the data relates to a physical location, record information about its spatial coverage

• METHODOLOGY: How the data was generated, including equipment or software used, experimental protocol, other things you might include in a lab notebook

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METADATA STANDARDS

• Describe data in a controlled way

• Field and disciplinary specific

• Should always be favored, if suitable for the project.

• Disciplinary Metadata / Digital Curation Centre DCC

• Metadata Standards by Subject / Research Data Alliance RDA

• General Research Data / Digital Curation Centre DCC.

• Search for standards / Biosharing.org

• Metadata Tools / Standford University Library

• Curation Tools and Services / ICPSR

Examples (by Standford University Libraries)Basic MetadataAdvanced Metadata

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NAMING TIPS

• Balance with the amount of elements in the name: too few making it too general vs. too many hinder understandability. Limit the name to 32 characters or less.

• Use meaningful abbreviations

• Order the elements from general to specific

• Use the underscore (_) as element delimiter and hyphen (-) or capitalizer to delimit words within an element. Don’t use special characters: & , * % # ; * ( ) ! @$ ^ ~ ' { } [ ] ? < >.

• Time should be ordered: year, month, day (YYYYMMDD) or more specifically if needed: hours, minutes, seconds (HHMMSS)

• For version control use the letter V followed minimum by two digits (V06), and extend it if needed for minor changes (V06-02). Remember the leading zeros to make sure files sort correctly.

• Write a read me –file about the naming system and explain abbreviations M

• Make your research group & collaborators use the file naming system.

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DATA QUALITY

• Quality control of data is an integral part of all research and takes place at various stages: during data collection, data entry or digitisation and data checking.

• It is important to assign clear roles and responsibilities and to develop suitable procedures before data gathering starts.

• During data collection, researchers must ensure that the data recorded reflect the actual facts, responses, observations and events.

Quality assurance processes can be such as calibration, repeat samples or measurements, standardized data capture or recording, data entry validation, peer review of data or representation with controlled vocabularies.

• The quality assurance processes adopted will be documented and results is recorded in the metadata.

Sources: Quality assurance (Openscience.fi), Quality assurance (UK Data archive)

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ETHICAL AND LEGAL ISSUES

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ETHICAL AND LEGAL ISSUES

Ethical and legal compliance

• How will ethical issues be managed? – e.g. good scientific practice

• Legislation - e.g. how will ownership, copyright and Intellectual Property Right (IPR) issues be managed?

• Who has rights to your data – who owns your data(ongoing debate whether the term ownership should be used in relation to data)

• Have you collected your data yourself?

• Are you using data collected by someone else?

• Are there copyrights or Intellectual Property Rights you should take into account?

The University of Helsinki does not own your data unless specifically agreed upon

• Who has usage rights to your data?

• Who has the right to issue re-use of your data?

Make agreements with your research team and your organization in time

University of Helsinki - Data Processing Agreement (UH as controller)

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YOU ALWAYS NEED TO CONTACT UNIV OF HELSINKI LEGAL AFFAIRS

TUTKIMUKSENJURISTIT@HELSINKI.FI IF YOU MAKE AGREEMENTS WITH

A PERSON OR ORGANIZATION OUTSIDE THE UNIV OF HELSINKI

OUTSIDE FUNDING

• Researchers have to make an agreement on the transfer of rights to the Univ of Helsinki if outside funding e.g. from the Academy of Finland or the EU

• The Principal Investigator is responsible for all researchers in a research team signing thisagreement

• You always need to contact U of H Legal Affairs tutkimuksenjuristit@helsinki.fi when making suchagreements

• Such agreements are always made in the name of the U of H not the individual researcher

TRANFERING YOUR RESEARCH DATA OUTSIDE THE U of H

• Unpublished research data that you collect or produce while employed by the University of Helsinki cannot be handed to persons or organizations outside the U of H without legally valid agreements

• There might be data protection issues if handing over the data

• Agreements are needed on e.g. confidentiality and on how the data can be used

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2019

INFORMING RESEARCH PARTICIPANTS

• The General Data Protection Regulation (GDPR) dictates the minimum requirements on the processing of personal information in the EU

• The new Finnish Data Protection Act took effect on Jan 1, 2019

• Balancing between intelligibility and comprehensiveness can be difficult when informing researchparticipants

• Univ of Helsinki Legal Affairs’ document on informing participantsPrivacy Notice for Scientific Research v2_2019EN

- not compulsory but useful as an outline for what you need to include

- always consult Legal Affairs before using this document as not always applicable

• GDPR for researchers - U of H Legal Affairs

• Data Protection Guide for Researchers (U of H Legal Affairs)

https://flamma.helsinki.fi/en/HY375934

• FSD’s guide on Informing Research Participants

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ANONYMISATION AND PSEUDONYMISATION

• Anonymous data: An individual data unit (person) cannot be re-identified with reasonable effort based on the data provided or by combining the data with additional data points. Completely anonymous data do not exist, but with well-executed procedures one can achieve a result where individual persons cannot be identified with reasonable effort. Anonymisation refers to the various techniques and tools used to achieve anonymity.

• Pseudonymous data: An individual data unit cannot be re-identified based on the pseudonymised data without additional, separate information. Pseudonymisation refers to the removal or replacement of identifiers with pseudonyms or codes, which are kept separately and protected by technical and organisational measures. The data remain pseudonymous as long as the additional identifying information exists.

• After anomymisation not possible for a research participant to withdraw from a study

Source: http://www.fsd.uta.fi/aineistonhallinta/en/anonymisation-and-identifiers.htmlRDM basics

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SENSITIVE DATA

• Sensitivity of data often needs to be determined on a case to case basis

• Personal information relating to ethnic origin, health, political views, sexual orientation

are examples of sensitive data

• Anonymisation prerequisite for archiving sensitive data

• Anonymisation can be tricky – the Finnish Social Science Data Archive has to further

anonomise about 80 percent of the anonomised data they receive

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ETHICAL REVIEW

Finnish Advisory Board on Research Integrity (TENK)

Ethical review of medical research:

• The ethics committees of hospital districts

• The National Committee on Medical Research Ethics TUKIJA

Ethical review of non-medical research:

• University of Helsinki Ethical Review Board in the Humanities and Social and Behavioural Sciences

• Viikki Campus Research Ethics Committee

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STORING SOLUTIONS BY IT DEPARTMENT

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STORAGE SOLUTIONS

Storage and back up

• How will the data be stored and backed up? How will you control access to keep the data secure?

• During the active phase of your project

• Describe how you will keep your data secured – and share it safely with your collegues

• Describe the management of all your data and material (see documentation and quality)

• Suitable storage solutions discussed more thoroughly by Janne/Ville from IT services

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PUBLISHING AND ARCHIVING DATA

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• Digital preservation is the reliable storage of digital information for a long time. It

enables future generations to use the data and ensures an unbroken chain of

evidence in research data.

• Digital preservation allows for seamless co-operation and the common use of data

across organisational lines. It improves the possibilities for developing competence

and reduces the human factor. Therefore it improves the quality of processes and

services. https://openscience.fi/what-is-digital-preservation

• The challenge is that although hardware, software and file formats age, the

information must still be kept usable.

• Reliable preservation of bits is not alone enough to guarantee the reusability of data

by future generations - functional, mental, skill-based, financial and legal capabilities

are also needed. Always remember that without metadata the dataset is useless!

DIGITAL PRESERVATION

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PUBLISHING AND ARCHIVING DATA: CERTIFIED ARCHIVES

• When publishing data, it is advisable to useCore Trust Seal certified archives – they havebeen found to be trustworthy by experts.

• https://assessment.datasealofapproval.org/assessment_109/seal/html/

• Core Trust Seal certified archives in Finland:

‒ The Language Bank of Finland

‒ The Finnish Social Science Data Archive

• Note that Academy of Finland also acceptsmany other data archives, such as IDA, Zenodo, Figshare etc. for publishing and archiving data.

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• IDA is a national storage service funded by the Ministry of Education and Culture, aimed at research groups and projects. IDA is produced by CSC – IT Center for Science.

• IDA is free, safe and user-friendly storage service for data and metadata, both raw data and processed datasets. Several petabytes of storage capacity is available. Owners of the data decide on the openness and usage policies of their own data.

• Higher education institutions each have their own quota in the IDA, with each institution itself granting user rights to projects, i.e. user groups. Quota is also offered to researchers funded by the Academy of Finland.

• Collaborative projects may utilise a shared storage space not tied to a single organization.

• Data stored in IDA can be used as a part of a dataset description, which has a Persistent identifier enabling linking from e.g. related publications

• Note that IDA is not intended for data containing sensitive personal data and that it is not recommended to use IDA for datasets which are still actively processed.

• https://ida.fairdata.fi/login

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OTHER ARCHIVES:

https://zenodo.org/

• Research. Shared. — all research outputs from across all fields of research

are welcome!

• Citeable. Discoverable. — uploads gets a Digital Object Identifier (DOI)

to make them easily and uniquely citeable.

• Communities — create and curate your own community for a workshop,

project, department, journal, into which you can accept or reject uploads.

Your own complete digital repository!

• Funding — identify grants, integrated in reporting lines for research funded by

the European Commission via OpenAIRE.

• Flexible licensing — because not everything is under Creative Commons.

• Safe — your research output is stored safely for the future in the same cloud

infrastructure as CERN's own LHC research data.

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OTHER ARCHIVES

• Figshare is a repository where users can make all of their research outputs available in a citable, shareable and discoverable manner

• Allows users to upload any file format to be previewed in the browser so that any research output, from posters and presentations to datasets and code, can be disseminated in a way that the current scholarly publishing model does not allow

• By making data public on figshare for the most part you are doing so under the creative commons 4.0 licences(http://creativecommons.org/licenses/)

• Figshare uses datacite dois (what is a DOI?) for persistent data citation

https://figshare.com/abouthttps://figshare.com/features

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OTHER ARCHIVES

REGISTRY OF RESEARCH DATA REPOSITORIES

• re3data.org is a global registry of research data repositories that covers research data repositories from different academic disciplines.

• It presents repositories for the permanent storage and access of data sets to researchers, funding bodies, publishers and scholarly institutions.

• A tool for the easy identification of appropriate data repositories to store research data.

• https://www.re3data.org/

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CREATIVE COMMONS LICENSES

• Licensing your work makes possible a wide distribution of your work, as the license tells the users how you would like it to be shared. The Creative commons -licenses (latest version 4.0) have become a standard for open publishing (https://creativecommons.org/)

• You can choose the license when you publish your work (for example, in Tuhat -publication database or in data-archive of your choice, but not in commercial publishing platforms like Academia.edu or Research Gate).

• University of Helsinki recommendations: CC 0 for research data: author gives up all copyrights, but when material is used in research, the guidelines on good research practice should be followed, that is, the sources and authors should be named. Use CC BY if you want to be mentioned as creator of the data. Avoin CC BY NC (non-commercial)!

• On different CC-licenses, see our License guide: http://libraryguides.helsinki.fi/oa/eng/license

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HOW TO CITE DATA

• Data should be considered legitimate, citable products of research. Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse.

• Required information: Author - Who is the creator of the data set? This can be an individual, a group of individuals, or an organization, Title - What is the name of the data set, or what is the name of the study? Publication year, Producer - The organization that made the creation of the data set possible, Distributor - The organization that makes the data set available for downloading and use, Edition or Version - Is there a version or edition number associated with the data set? Material designator - What type of file is the data set?, Access information - URL or other persistent identifier like DOI.

• Tracing data : Data citation roadmap for Finland: http://urn.fi/URN:NBN:fi-fe201804106446

• https://www.force11.org/datacitationprinciples

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DATA MANAGEMENT PLANNING AND DMPTUULI

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• DMP is an integral part of good research practice and ensures research integrity and quality. It helps you to manage your data, meet funder requirements and help others to use your data if shared

• DMP is a living document – it is updatable and reviewable. Create your data management plan early and review it regularly throughout the research project.

• DMP length: 1-2 pages

• DMP is not a research plan – it complements it. Avoid redundancy/overlapping with the research plan!

Figure:

http://www.fsd.uta.fi/aineistonhallinta/en/why-are-research-data-managed-and-

reused.html#research-data-life-cycle

DATA MANAGEMENT PLAN (DMP)

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DATA MANAGEMENT PLAN (DMP)

• Even if the research project does not produce any data, the DMP must be returned with a clear statement of the situation

• DMP has to follow the Academy of Finland’s guidelines; answer questions which areapplicable. If some set of questions is not applicable, justify why!

write only something you yourself understand!

THAT IS: Reflect your own project and data; NO COPY PASTING OF EXAMPLE ANSWERS!

”…I will use XML…” and XML was never used or even mentioned elsewhere.

• A good DMP is clearly written and consistent (within itself and as a part of the whole plan):

• “We promote open access… We aim to publish in a [journal behind paywall; no explanation]…”

• “We archive data in archive X… Data is shared in publications and in conference papers…”

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• DMPTuuli is an easy way to do a data management plan! Go to www.dmptuuli.fi

• Click on ‘Create account’ and register for a new account or use HAKA registration

• You’ll need your ORCID-number and the plan number from the Academy of Finland electronic application portal

• In Tuuli you will find a number of templates for different funders and institutions and generic and specific guides to help you answer the DMP questions

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HELP AND SUPPORT

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DATA SUPPORT AT THE UNIVERSITY OF HELSINKI

More information: https://www.helsinki.fi/en/research/university-of-helsinki-datasupport

Data Support

datasupport@helsinki.fi

Data protection

Central Archives

Legal Affairs

Research Affairs

IT services

Library

Niku

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FIND SERVICES ON RESEARCH DATA MANAGEMENT

http://datasupport.helsinki.fi

Send comments or feedback: datasupport@helsinki.fi

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• Research Data Management Guide https://www.helsinki.fi/en/research/research-environment/research-data-management

• Might be handy to keep the page open in another window while outlining a DMP with Tuuli

• University of Helsinki Research

Data Policy defines high-level principles

regarding the collection, storing, use,

and management of research data

http://www.helsinki.fi/kirjasto/en/get-

help/management-research-

data/research-data-policy/

RDM RESEARCH GUIDE

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• FSD’s Data Management Guidelineshttp://www.fsd.uta.fi/aineistonhallinta/en/

• Might be handy to keep the page open in another window while outlining a DMP with Tuuli

FINNISH SOCIAL SCIENCE DATA ARCHIVE & THEIR DATA MANAGEMENT GUIDELINES

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• University of Helsinki DMP archive in Zenodohttps://zenodo.org/communities/uh_dmp/

• FAIR principleshttps://www.force11.org/group/fairgroup/fairprinciples

• Five star datahttp://5stardata.info/en/

• Guidance for choosing cloud serviceshttps://wiki.eduuni.fi/display/pilviohje/Pilviohje

• Jyrki Hakapää (Academy of Finland) on Open Sciencehttps://www.youtube.com/watch?v=Bvg3CNsBeWo

MORE HELP

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https://elomake.helsinki.fi/lomakkeet/88701/lomake.html

WE NEED YOUR HELP –GIVE US FEEDBACK!

THANK YOU!

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