The document discusses best practices for research data management, including creating a data management plan that addresses what data will be generated, how it will be organized and documented, how it will be stored securely and backed up, whether and how it can be shared and reused, and how it will be preserved and archived in the long term such as by depositing it in an institutional repository. It provides guidance on the key elements to address in a data management plan and resources for creating plans and managing research data over its entire lifecycle.
FAIR Data in Trustworthy Data Repositories Webinar - 12-13 December 2016| www...EUDAT
| www.eudat.eu | This webinar was co-organised by DANS, EUDAT and OpenAIRE and was held on 12th and 13th December 2016.
Everybody wants to play FAIR, but how do we put the principles into practice?
There is a growing demand for quality criteria for research datasets. In this webinar we will argue that the DSA (Data Seal of Approval for data repositories) and FAIR principles get as close as possible to giving quality criteria for research data. They do not do this by trying to make value judgements about the content of datasets, but rather by qualifying the fitness for data reuse in an impartial and measurable way. By bringing the ideas of the DSA and FAIR together, we will be able to offer an operationalization that can be implemented in any certified Trustworthy Digital Repository.
In 2014 the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) were formulated. The well-chosen FAIR acronym is highly attractive: it is one of these ideas that almost automatically get stuck in your mind once you have heard it. In a relatively short term, the FAIR data principles have been adopted by many stakeholder groups, including research funders.
The FAIR principles are remarkably similar to the underlying principles of DSA (2005): the data can be found on the Internet, are accessible (clear rights and licenses), in a usable format, reliable and are identified in a unique and persistent way so that they can be referred to. Essentially, the DSA presents quality criteria for digital repositories, whereas the FAIR principles target individual datasets.
In this webinar the two sets of principles will be discussed and compared and a tangible operationalization will be presented.
An introduction to the FAIR principles and a discussion of key issues that must be addressed to ensure data is findable, accessible, interoperable and re-usable. The session explored the role of the CDISC and DDI standards for addressing these issues.
Presented by Gareth Knight at the ADMIT Network conference, organised by the Association for Data Management in the Tropics, in Antwerp, Belgium on December 1st 2015.
Essentials 4 Data Support: a fine course in FAIR Data SupportEllen Verbakel
The document summarizes the Essentials 4 Data Support (E4DS) course, which teaches people how to support researchers in storing, managing, archiving, and sharing research data according to FAIR principles. The course covers topics like data documentation, identifiers, formats, metadata, and licensing. It is offered online or in a blended format over 6 weeks. The goal is to educate data supporters so that researchers can find, access, interoperate with, and reuse each other's data in a fair manner.
Ownership, intellectual property, and governance considerations for academic ...Rebekah Cummings
This document discusses ownership, intellectual property, and governance considerations for academic research data. It frames some of the complications around data ownership and intellectual property by looking at the different stakeholders involved, including researchers, universities, funding agencies, and the public. It then shares the policies at the University of Utah, which state that the university retains ownership and stewardship of research data produced using university resources. However, intellectual property laws and policies are complex, and ownership depends on factors like copyright, patents, and contractual agreements. The document concludes by discussing strategies librarians can use to educate researchers and encourage open sharing of data.
Research data management & planning: an introductionMaggie Neilson
This document provides an introduction to research data management (RDM). It defines RDM as the organization and stewardship of research data throughout a research project and beyond. Key components of RDM include data management plans, metadata, sharing and preservation, and ethical and legal obligations. The document discusses why RDM is important, outlines the goals of the Tri-Agency Statement on digital data management, and provides resources for writing data management plans, creating metadata, sharing data, and addressing privacy and ethics.
FAIR Data in Trustworthy Data Repositories Webinar - 12-13 December 2016| www...EUDAT
| www.eudat.eu | This webinar was co-organised by DANS, EUDAT and OpenAIRE and was held on 12th and 13th December 2016.
Everybody wants to play FAIR, but how do we put the principles into practice?
There is a growing demand for quality criteria for research datasets. In this webinar we will argue that the DSA (Data Seal of Approval for data repositories) and FAIR principles get as close as possible to giving quality criteria for research data. They do not do this by trying to make value judgements about the content of datasets, but rather by qualifying the fitness for data reuse in an impartial and measurable way. By bringing the ideas of the DSA and FAIR together, we will be able to offer an operationalization that can be implemented in any certified Trustworthy Digital Repository.
In 2014 the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) were formulated. The well-chosen FAIR acronym is highly attractive: it is one of these ideas that almost automatically get stuck in your mind once you have heard it. In a relatively short term, the FAIR data principles have been adopted by many stakeholder groups, including research funders.
The FAIR principles are remarkably similar to the underlying principles of DSA (2005): the data can be found on the Internet, are accessible (clear rights and licenses), in a usable format, reliable and are identified in a unique and persistent way so that they can be referred to. Essentially, the DSA presents quality criteria for digital repositories, whereas the FAIR principles target individual datasets.
In this webinar the two sets of principles will be discussed and compared and a tangible operationalization will be presented.
An introduction to the FAIR principles and a discussion of key issues that must be addressed to ensure data is findable, accessible, interoperable and re-usable. The session explored the role of the CDISC and DDI standards for addressing these issues.
Presented by Gareth Knight at the ADMIT Network conference, organised by the Association for Data Management in the Tropics, in Antwerp, Belgium on December 1st 2015.
Essentials 4 Data Support: a fine course in FAIR Data SupportEllen Verbakel
The document summarizes the Essentials 4 Data Support (E4DS) course, which teaches people how to support researchers in storing, managing, archiving, and sharing research data according to FAIR principles. The course covers topics like data documentation, identifiers, formats, metadata, and licensing. It is offered online or in a blended format over 6 weeks. The goal is to educate data supporters so that researchers can find, access, interoperate with, and reuse each other's data in a fair manner.
Ownership, intellectual property, and governance considerations for academic ...Rebekah Cummings
This document discusses ownership, intellectual property, and governance considerations for academic research data. It frames some of the complications around data ownership and intellectual property by looking at the different stakeholders involved, including researchers, universities, funding agencies, and the public. It then shares the policies at the University of Utah, which state that the university retains ownership and stewardship of research data produced using university resources. However, intellectual property laws and policies are complex, and ownership depends on factors like copyright, patents, and contractual agreements. The document concludes by discussing strategies librarians can use to educate researchers and encourage open sharing of data.
Research data management & planning: an introductionMaggie Neilson
This document provides an introduction to research data management (RDM). It defines RDM as the organization and stewardship of research data throughout a research project and beyond. Key components of RDM include data management plans, metadata, sharing and preservation, and ethical and legal obligations. The document discusses why RDM is important, outlines the goals of the Tri-Agency Statement on digital data management, and provides resources for writing data management plans, creating metadata, sharing data, and addressing privacy and ethics.
The document provides information about MANTRA, a free online course for research data management created by the University of Edinburgh. MANTRA teaches best practices for managing research data through open educational modules aligned with the research data lifecycle. It is available for reuse and repurposing under an open license. The course covers topics like data planning, organization, documentation, storage, security, and sharing.
EUDAT & OpenAIRE Webinar: How to write a Data Management Plan - July 7, 2016|...EUDAT
| www.eudat.eu | 1st Session: July 7, 2016.
In this webinar, Sarah Jones (DCC) and Marjan Grootveld (DANS) talked through the aspects that Horizon 2020 requires from a DMP. They discussed examples from real DMPs and also touched upon the Software Management Plan, which for some projects can be a sensible addition
The state of global research data initiatives: observations from a life on th...Projeto RCAAP
The document discusses research data management and provides guidance on best practices. It defines research data management as the active management of data over its lifecycle. It recommends writing a data management plan to document how data will be created, stored, shared, and preserved. It also provides tips for making data accessible and reusable through use of metadata standards, documentation, open licensing, and depositing data in repositories with persistent identifiers. The goal is to help researchers manage and share their data effectively to increase access and reuse.
The document discusses the components and design of information storage and retrieval systems (ISRS). It describes ISRS as having three main components: the user interface, knowledge base, and search agent. The user interface allows users to input queries and view results, and should be intuitive. The knowledge base stores the information to be retrieved in a database. And the search agent acts to translate user queries and match them to the knowledge base to retrieve relevant information. The document provides details on each of these components and discusses best practices for designing an effective ISRS.
This document summarizes a session from the Force 11 Scholarly Communications Institute Summer School on data discovery. The session covered metadata, including what it is, types of metadata, and standards. It discussed how people search for and find data through various sources. The session also explored the FAIR data principles of findable, accessible, interoperable and reusable data and had breakout groups discuss applying these principles in practice.
This document provides guidance on research data management and developing data management plans. It discusses why managing research data is important, including making research easier to conduct, avoiding accusations of fraud or bad science, and getting credit for data produced. The document outlines what is involved in research data management and considerations for sharing and preserving data, such as file formats, documentation, and standards. It emphasizes the importance of data management planning and provides tips on developing plans to meet funder requirements.
This document discusses challenges and opportunities around data management for integrated structural biology research. It notes that structural biology projects often use multiple experimental techniques and facilities, generating data in different formats. There is a need for metadata standards and repositories that can integrate diverse structural biology data types. A proposed solution is a virtual research environment portal that provides collaborative data sharing, processing and analysis tools, along with mechanisms for data quality control, citation and recognition. Such a system could help address the challenges of managing multi-technique structural biology data throughout the research lifecycle.
Information Storage and Retrieval : A Case StudyBhojaraju Gunjal
Bhojaraju.G, M.S.Banerji and Muttayya Koganurmath (2004). Information Storage and Retrieval: A Case Study, In Proceedings of International Conference on Digital Libraries (ICDL 2004), New Delhi, Feb 24-27, 2004.
(Best Poster Presentation Award)
Presentation on data sharing that outlines five layers that must be addressed to enable data to be located, obtained, access, understood and use, and cited.
Research Data Management Introduction: EUDAT/Open AIRE Webinar| www.eudat.eu | EUDAT
This webinar discusses research data management. It explains why managing data is important for reproducibility, avoiding data loss, and meeting funder requirements. It outlines Horizon 2020's requirements for open data and describes services from EUDAT and OpenAIRE that can help with the entire data lifecycle from creation to long-term preservation and sharing. The webinar covers best practices like creating data management plans, metadata, using standards, licensing, and selecting repositories to archive and share research data.
How Portable Are the Metadata Standards for Scientific Data?Jian Qin
The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure.
A presentation on research data management presented at the Utah Library Association conference in May 2015. Main topics included federal mandates, data repositories, metadata, and file naming conventions. Presenters: Rebekah Cummings, Elizabeth Smart, Becky Thoms, and Brit Faggerheim.
workshop session delivered alongside 'Making your thesis legal' workshop in July and September 2013 to PhD, MPhil, DrPh students who are completing their thesis. Discusses standards for sharing data, issues that need addressing, formats, data protection, usability, licenses
D4Science Data infrastructure: a facilitator for a FAIR data managementResearch Data Alliance
D4Science is a hybrid data infrastructure that integrates technologies to provide elastic access and usage of data and data management capabilities. It hosts over 50 virtual research environments for over 2500 scientists across 44 countries. D4Science aims to facilitate FAIR (Findable, Accessible, Interoperable, Re-usable) data management by assigning unique identifiers and rich metadata to resources, publishing catalogs to enable discovery, making resources available through standards, adding metadata in multiple formats, and requiring licenses and provenance to promote reuse.
This presentation gives an overview of the key things that we need to consider before publishing data from the repository. It briefly discusses research data management, research data lifecycle, FAIR principles of research data management and then move on to key elements that should be considered while preparing datasets for publishing through repository.
Scholars and researchers are being asked by an increasing number of research sponsors and journals to outline how they will manage and share their research data. This is an introduction to data management and sharing practices with some specific information for Columbia University researchers.
The document provides information about MANTRA, a free online course for research data management created by the University of Edinburgh. MANTRA teaches best practices for managing research data through open educational modules aligned with the research data lifecycle. It is available for reuse and repurposing under an open license. The course covers topics like data planning, organization, documentation, storage, security, and sharing.
EUDAT & OpenAIRE Webinar: How to write a Data Management Plan - July 7, 2016|...EUDAT
| www.eudat.eu | 1st Session: July 7, 2016.
In this webinar, Sarah Jones (DCC) and Marjan Grootveld (DANS) talked through the aspects that Horizon 2020 requires from a DMP. They discussed examples from real DMPs and also touched upon the Software Management Plan, which for some projects can be a sensible addition
The state of global research data initiatives: observations from a life on th...Projeto RCAAP
The document discusses research data management and provides guidance on best practices. It defines research data management as the active management of data over its lifecycle. It recommends writing a data management plan to document how data will be created, stored, shared, and preserved. It also provides tips for making data accessible and reusable through use of metadata standards, documentation, open licensing, and depositing data in repositories with persistent identifiers. The goal is to help researchers manage and share their data effectively to increase access and reuse.
The document discusses the components and design of information storage and retrieval systems (ISRS). It describes ISRS as having three main components: the user interface, knowledge base, and search agent. The user interface allows users to input queries and view results, and should be intuitive. The knowledge base stores the information to be retrieved in a database. And the search agent acts to translate user queries and match them to the knowledge base to retrieve relevant information. The document provides details on each of these components and discusses best practices for designing an effective ISRS.
This document summarizes a session from the Force 11 Scholarly Communications Institute Summer School on data discovery. The session covered metadata, including what it is, types of metadata, and standards. It discussed how people search for and find data through various sources. The session also explored the FAIR data principles of findable, accessible, interoperable and reusable data and had breakout groups discuss applying these principles in practice.
This document provides guidance on research data management and developing data management plans. It discusses why managing research data is important, including making research easier to conduct, avoiding accusations of fraud or bad science, and getting credit for data produced. The document outlines what is involved in research data management and considerations for sharing and preserving data, such as file formats, documentation, and standards. It emphasizes the importance of data management planning and provides tips on developing plans to meet funder requirements.
This document discusses challenges and opportunities around data management for integrated structural biology research. It notes that structural biology projects often use multiple experimental techniques and facilities, generating data in different formats. There is a need for metadata standards and repositories that can integrate diverse structural biology data types. A proposed solution is a virtual research environment portal that provides collaborative data sharing, processing and analysis tools, along with mechanisms for data quality control, citation and recognition. Such a system could help address the challenges of managing multi-technique structural biology data throughout the research lifecycle.
Information Storage and Retrieval : A Case StudyBhojaraju Gunjal
Bhojaraju.G, M.S.Banerji and Muttayya Koganurmath (2004). Information Storage and Retrieval: A Case Study, In Proceedings of International Conference on Digital Libraries (ICDL 2004), New Delhi, Feb 24-27, 2004.
(Best Poster Presentation Award)
Presentation on data sharing that outlines five layers that must be addressed to enable data to be located, obtained, access, understood and use, and cited.
Research Data Management Introduction: EUDAT/Open AIRE Webinar| www.eudat.eu | EUDAT
This webinar discusses research data management. It explains why managing data is important for reproducibility, avoiding data loss, and meeting funder requirements. It outlines Horizon 2020's requirements for open data and describes services from EUDAT and OpenAIRE that can help with the entire data lifecycle from creation to long-term preservation and sharing. The webinar covers best practices like creating data management plans, metadata, using standards, licensing, and selecting repositories to archive and share research data.
How Portable Are the Metadata Standards for Scientific Data?Jian Qin
The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure.
A presentation on research data management presented at the Utah Library Association conference in May 2015. Main topics included federal mandates, data repositories, metadata, and file naming conventions. Presenters: Rebekah Cummings, Elizabeth Smart, Becky Thoms, and Brit Faggerheim.
workshop session delivered alongside 'Making your thesis legal' workshop in July and September 2013 to PhD, MPhil, DrPh students who are completing their thesis. Discusses standards for sharing data, issues that need addressing, formats, data protection, usability, licenses
D4Science Data infrastructure: a facilitator for a FAIR data managementResearch Data Alliance
D4Science is a hybrid data infrastructure that integrates technologies to provide elastic access and usage of data and data management capabilities. It hosts over 50 virtual research environments for over 2500 scientists across 44 countries. D4Science aims to facilitate FAIR (Findable, Accessible, Interoperable, Re-usable) data management by assigning unique identifiers and rich metadata to resources, publishing catalogs to enable discovery, making resources available through standards, adding metadata in multiple formats, and requiring licenses and provenance to promote reuse.
This presentation gives an overview of the key things that we need to consider before publishing data from the repository. It briefly discusses research data management, research data lifecycle, FAIR principles of research data management and then move on to key elements that should be considered while preparing datasets for publishing through repository.
Scholars and researchers are being asked by an increasing number of research sponsors and journals to outline how they will manage and share their research data. This is an introduction to data management and sharing practices with some specific information for Columbia University researchers.
The document provides guidance on early planning for data management, including becoming familiar with funder requirements, planning for the types and formats of data that will be created, designing a system for taking notes, organizing files through consistent naming schemes and use of folders, adding metadata to files to aid in documentation and discovery, and using RSS feeds to organize web-based information. It also touches on issues like plagiarism, data protection, intellectual property rights, and remote access to and backup of data.
Data Management for Postgraduate students by Lynn Woolfreypvhead123
This document discusses research data management for postgraduates. It explains that research data management refers to storing, accessing, and preserving research data. It notes that funders and universities now require data management plans for funding proposals and research. The document provides reasons for doing research data management, such as ensuring long-term data preservation, preventing fraud, and enabling data reuse. It outlines elements to include in a data management plan and resources for writing plans. The document advises that data services can help take the burden of research data management off researchers.
Introduction to research data managementdri_ireland
An Introduction to Research Data Management: slides from a presentation given online on May 12 2022, by Beth Knazook, Project Manager, Research Data. Covers topics such as: what are research data; why share research data; why DMPs are important; and where should you share your data?
This document summarizes strategies for creating data management plans and developing sustainable research data management services. It discusses defining research data and data management, federal public access mandates from agencies like NIH and NSF, resources for librarians, workflows for data management plan consultations, and developing scalable research data management services. It provides an overview of common elements to include in data management plans, such as data products, repositories, metadata, documentation, and access, and lessons learned from establishing research data management services at one university.
Paper was presented at European Survey Research Association 2013, in the session Research Data Management for Re-use: Bringing Researchers and Archivists closer.
FAIRy stories: the FAIR Data principles in theory and in practiceCarole Goble
https://ucsb.zoom.us/meeting/register/tZYod-ippz4pHtaJ0d3ERPIFy2QIvKqjwpXR
FAIRy stories: the FAIR Data principles in theory and in practice
The ‘FAIR Guiding Principles for scientific data management and stewardship’ [1] launched a global dialogue within research and policy communities and started a journey to wider accessibility and reusability of data and preparedness for automation-readiness (I am one of the army of authors). Over the past 5 years FAIR has become a movement, a mantra and a methodology for scientific research and increasingly in the commercial and public sector. FAIR is now part of NIH, European Commission and OECD policy. But just figuring out what the FAIR principles really mean and how we implement them has proved more challenging than one might have guessed. To quote the novelist Rick Riordan “Fairness does not mean everyone gets the same. Fairness means everyone gets what they need”.
As a data infrastructure wrangler I lead and participate in projects implementing forms of FAIR in pan-national European biomedical Research Infrastructures. We apply web-based industry-lead approaches like Schema.org; work with big pharma on specialised FAIRification pipelines for legacy data; promote FAIR by Design methodologies and platforms into the researcher lab; and expand the principles of FAIR beyond data to computational workflows and digital objects. Many use Linked Data approaches.
In this talk I’ll use some of these projects to shine some light on the FAIR movement. Spoiler alert: although there are technical issues, the greatest challenges are social. FAIR is a team sport. Knowledge Graphs play a role – not just as consumers of FAIR data but as active contributors. To paraphrase another novelist, “It is a truth universally acknowledged that a Knowledge Graph must be in want of FAIR data.”
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
This document provides biographical and contact information for Professor Aboul Ella Hassanien, including that he is the founder and chair of the Scientific Research Group in Egypt and formerly served as dean of the faculty of computers and information at Beni-Suef University. It announces an upcoming presentation by Professor Hassanien on sharing scientific data, ethics, and consent taking place on January 20, 2018 at Cairo University.
This presentation was delivered at the Elsevier Library Connect Seminar on 6 October 2014 in Johannesburg, 7 October 2014 in Durban and 9 October 2014 in Cape Town and gives an overview of the potential role that librarians can play in research data management
This document provides an overview of preparing research data for long-term preservation and sharing. It discusses defining data, following data management policies, documenting data with metadata, securely storing data through backups and appropriate file formats and storage media. It also addresses sharing data by depositing in repositories, making data publicly available through services like Figshare, and using licenses. The document emphasizes planning for data management from the start of a research project through drafting a data management plan and seeking advice from university support services.
Data management plans archeology class 10 18 2012Elizabeth Brown
This document summarizes a presentation about developing and implementing NSF Data Management Plans. It discusses the types of data that may be generated from research projects, how to describe those data in a Data Management Plan, and policies around sharing, accessing, and preserving research data in the long term. The presentation aims to help researchers understand NSF data policy requirements, identify library services to support developing Data Management Plans, and plan for long-term preservation of data from funded projects.
Brad Houston presented information on data management plans (DMPs) required by the National Science Foundation (NSF) for grant proposals. He explained that DMPs must describe the data to be collected or generated, how it will be organized and formatted, and how it will be preserved and shared. He emphasized using open standards and preparing metadata to help others understand and find the data. Researchers were advised to consider long-term preservation and to partner with libraries or repositories to ensure access over time. Contact information was provided for those needing assistance developing their DMP.
Presentation given at the Consorcio Madrono conference on Data Management Plans in Horizon 2020 http://www.consorciomadrono.es/info/web/blogs/formacion/217.php
Linking Data to Publications through Citation and Virtual ArchivesMicah Altman
This document discusses linking data to publications through citation and virtual archives. It argues that data citation and sharing infrastructure are necessary for scientific reproducibility and open data. It outlines elements of data management plans and requirements for data sharing infrastructure, including persistence, provenance, access control and incentives. The document advocates for data citations as first-class objects and emerging practices like assigning DOIs to datasets. It presents several use cases for the Dataverse network, a virtual archive designed for research data sharing through federated and organizational models.
Data Access & Storage @ UWA - UWA Research Week September 2017Katina Toufexis
The document discusses research data management services provided by the University of Western Australia (UWA) Library. It notes that funders like the Australian Research Council (ARC) and National Health and Medical Research Council (NHMRC) require research data to be managed and shared. UWA policies also require research data related to publications to be available through the UWA Research Repository. The document provides guidance on creating data management plans, using appropriate licenses, and securely storing data long-term using the Institutional Research Data Storage (IRDS) system rather than third-party cloud services like Dropbox.
Meeting the NSF DMP Requirement June 13, 2012IUPUI
The document provides guidance on developing a data management plan (DMP) to meet requirements for National Science Foundation grant proposals. It discusses the context and rationale for federal data policies, defines the key elements required for a DMP, and provides examples of DMPs for different types of research data. The main points are: understanding the NSF data policy aims to increase research impact and data sharing/reuse; a DMP must address the types of data generated, metadata standards, data access/sharing plans, long-term preservation, and associated costs; and good planning helps ensure data remains accessible, usable and preserved into the future. Resources and guidance are available to help researchers develop robust and fundable DMPs.
This slideshow was used in a Preparing Your Research Data for the Future course taught in the Medical Sciences Division, University of Oxford, on 2015-06-08. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
Similar to Research Data Services Best Practices by Dalal Rahme (20)
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
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This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
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Research Data Services Best Practices by Dalal Rahme
1. Research Data Management Best Practices
Dalal Rahme
Data Services Librarian
American University of Beirut
2. American University of Beirut
Research Data
“Recorded factual
material commonly accepted in
the scientific community as
necessary to validate research
findings.” (NIH, 2003)
dalal.rahme@aub.edu.lb
3. American University of Beirut
Research Data Lifecycle
Create
Process
Analyse
???
@DalalRahme
6. American University of Beirut
Digital obsolescence
Software: the software
needed to access the digital
file becomes obsolete.
Hardware: the hardware
needed to access the digital
file becomes obsolete
7. American University of Beirut
Making Data FAIR
Findable
• Persistent identifier (like a DOI or Handle)
• Rich metadata for description
• Available via discovery portals
Accessible
• Clarity and transparency around the conditions governing access and reuse
Interoperable
• Use of community agreed formats, language and vocabularies for data and metadata
Reusable
• Rich contextual information
• Information on how data was formed
• Machine readable metadata and licensing information
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8. American University of Beirut
Research Data Management
Involves curation and preservation of
both data and associated metadata
Extents beyond the project life-cycle
(long-term) to ensure sustained
accessibility and re-use
Requires effective planning – i.e.
Research Data Management Plan (RDMP or DMP)
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Digital
Object
Metadata+
11. American University of Beirut
A Data Management Plan
A short document
A living document
Required by many funding agencies
Includes 2 topics:
What data will your research generate?
What is your plan for managing the data?
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12. American University of Beirut
I. Data Production
What data will you collect or create?
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13. American University of Beirut
How will the data be collected or created?
Tools/ Software
Scripts
Time frame
Data Production
Proprietary
vs
Non-
Proprietary
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In which format the data
will be kept?
15. American University of Beirut
II. Description and Organization
Describing and organizing
your data makes analysis
easier for you and
provides context for those
you want to share the data
with.
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16. American University of Beirut
Documentation
• Detailing your methods
helps people understand
what you did
• And helps make your work
reproducible
• Conclusions can be verified
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17. American University of Beirut
Metadata
“Metadata is structured information that describes, explains, locates,
or otherwise represents something else.” (NISO, 2004)
Some fields of research have already defined their metadata
standards: Darwin Core (Biology), VRA Core (Visual Art), EBU
Core (Audiovisual Content)
Repositories ask you as depositor to provide metadata along with
your file.
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18.
19. American University of Beirut
Naming conventions
Consider including:
• Unique Identifiers such as project name or grant number
• Conditions such as lab instrument, solvent..etc
• Date
• Version number
Example1:
aub_aco000001_000001_m.tif
aub_aco000001_000001_d.tif
(organization_projectnamebooknumber_page number_master/derivative copy. format
Example 2:
micro_hhl_20170620_dr_001.tif
(instrument_insectname_date_initials_sequential number.format)
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21. Sharing Research Data is important
because…
• It promotes innovation and potential new data uses
• It maximizes transparency and accountability
• It encourages the improvement and validation of research methods
• It reduces the cost of duplicating data collection
• It increases the impact and visibility of research
• It promotes the research that created the data and its outcomes
• It provides a direct credit to the researcher as a research output in its own
right
22. American University of Beirut
III. Access and Security
Your DMP should include information on how to protect
and secure your data.
How will the data be backed up? And how frequent?
Who will be responsible for the backing up?
How will the data be recovered in the event of an incident?
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23. American University of Beirut
III. Access and Security
Consider AUB campus specific information on Security:
IT security: Data Classification Policy
IRB at AUB
AUBMC Access to medical records for research
AUB Data Bank Institutional Repository Policy
Consider Laws and Legislations:
Access to Information Law in Lebanon
HIIPA (Health Insurance Portability and Accountability Act)
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24. American University of Beirut
IV. Reuse and Sharing
Creative Commons
licenses provide
options for selecting
how you would like to
allow others to use
your data.
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25. American University of Beirut
V. Archiving and Preservation
Think about what will
happen to your data
long-term, beyond its
current use in the
project.
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26. American University of Beirut
V. Archiving and Preservation
Deposit your data
in an institutional
repository:
AUB Scholarworks
AUB Data Bank
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27. American University of Beirut
• Storage
• Backup
• Discoverability
• Structured
Metadata
Preservation
• Accessibility
• Citation
• Unique identifier
(handle)
Visibility • Embargo
• Access Restriction
• Institutional
Support
Control
28. American University of Beirut
V. Archiving and Preservation
There are lots of general and subject specific repositories
Searchable list of repositories by subject or country: https://www.re3data.org/
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33. American University of Beirut
Thank you
More info
http://tiny.cc/dataservices
dalal.rahme@aub.edu.lb
Editor's Notes
All original sources or material (digital or not) created or collected to conduct a research project.
All data that is analysed to answer the research question(s).
Research data “may be raw, abstracted or analysed, experimental or observational.” (UCL Research Data Policy)
Examples include: questionnaires, audio recordings, database entries, blood samples, lab notebooks, list of measurements, photographs, maps etc.
“AUB is proud to produce more than half of Lebanon’s entire research and we are also proud to have the highest research output per faculty member in the Arab world”
The rapid evolution and proliferation of different kinds of computer hardware, modes of digital encoding, operating systems and general or specialized software ensures that digital obsolescence will become a problem in the future. Many versions of word-processing programs, data-storage media, standards for encoding images and films are considered "standards" for some time, but in the end are always replaced by new versions of the software or completely new hardware. Files meant to be read or edited with a certain program (for example Microsoft Word) will be unreadable in other programs, and as operating systems and hardware move on, even old versions of programs developed by the same company become impossible to use on the new platform (for instance, older versions of Microsoft Works, before Works 4.5, cannot be run under Windows 2000 or later). This is why we need to take all of those changes into consideration when working with our research data.
So what we want to do is to make our data FAIR> The FAIR Data Principles are a hot topic in research data management. Their adoption within the Horizon 2020 funding programme means researchers now have to pay much more attention to how their share, publish and archive their data. Horizon 2020 is the biggest EU Research and Innovation programme
We have to ensure that our archives are in alignment with those principles.
A Data Management Plan (DMP) is a document you create that sets out how you will organize, store and share your research data at each stage in your project. A DMP is a living document that can be modified to accommodate changes in the course of your research. A DMP is a high level plan: no private data is exposed.
IN the first part we are going to talk about the kind of data you are collecting, whether it is in a digital format such as an image or a video, textual or numerical, spreadsheets or interview transcript (provide pics), or even physical. We are only thinking in the digital context here but the same applies to physical data .
Knowing what you are collecting, how much are you collecting of it and how much storage do you need. Think in advance about the space you need and how quickl;y you are gong to need it.
You are going to think about the formats you are saving your data in. how will you save your data. This is an example of all the way you can save a spreadsheet.
It is always advised to use open formats.
Closed format: Can only be used, opened by a specific type of hardware or software, such as Word by Microsoft (office).
Open format: Available to anyone to read and study and even modify.
Those are some few examples.
More can be found under www.openformats.org
This is a huge topic and we are going to talk about it in details in other session
Readme file and dictionaries.
Open AUB scholarworks and show metadata.
Be aware of the laws, legislations, organization policies and departments policies when it comes to data security and privacy. Talk to the IT at your department and learn more about their proceedures.
HIPAA (Health Insurance Portability and Accountability Act of 1996) is United States legislation that provides data privacy and security provisions for safeguarding medical information.
The IRB is the committee formally designated by the Human Research Protection Program to review and approve the conduct of research involving human subjects who are recruited to participate in research activities conducted at AUB/AUBMC and/or by AUB/AUBMC faculty, students and staff, regardless of the funding source or the location of the research.
AUB scholarworks policy, back up every night, several copies are kept on different servers. Authentication is available through username and password whenever needeed.
This is especially for special data. Some data can be reproduced, other data cannot, such as earthquake data cannot be reproduced…etc so that dataset is a little bit more special.
You may charge people who are requesting your data a fee if it is going to cost you something but it should be no more than incremental cost. You cannot charge like a $1000.
So if you are sharing data think about stipulation to put on your data. CC.
Explain each license.
The final part is archiving and preservation. You want to have it accessible in the future not only to you but to other people from different disciplines maybe.
There are several options:
AUB databank: talk about benefits. Free repository handled by the institution.
Subject specific repositories are usually not for free and handled by other institutions.
The DMPTool is an online tool that includes data management plan templates for many of the large funding agencies that require such plans
The tool includes general guidance, links to helpful documentation, issues to consider, and specific questions to think about as you prepare your data management plan. Space is provided to compose a response for each of the main areas that your funding agency would like for you to address in your plan. You can save and come back to your plan as often as you like. When you are finished, you can export your plan in plain text format and insert it into your grant proposal.
You can share your plan with your research group and with us to review it before sending it.