An overview of the LSHTM Research Data Management Policy, outlining the motivations for its introduction, obligations that need to be met and the support available
A presentation offering an introduction to managing and sharing research data given at the Czech Open Science days as part of the EC-funded FOSTER project.
An introduction to Research Data Management and Data Management Planning for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
Management of research data specifically for Engineering and Physical Science. Delivered by Stuart Macdonald at the "Support for Enhancing Research Impact" meeting at the University of Edinburgh on 22 June 2016.
This document provides an overview of a webinar on digital curation and research data management for universities. The webinar covers an introduction to digital curation, the benefits and drivers for research data management, current initiatives in UK universities, and the role of libraries in supporting research data management. Libraries are increasingly involved in developing institutional policies, providing training, and advising researchers on writing data management plans and sharing data. The webinar highlights training opportunities for librarians to develop skills in research data management and digital curation.
Presentation given at the European Research Council workshop on research data management and sharing in Brussels on 18th-19th September 2014. The presentation covers the benefits and drivers for RDM, points to relevant tools and resources and closes with some open questions for discussion.
OU Library Research Support webinar: Data sharingDaniel Crane
Slides from a webinar delivered on 06th February 2018 for OU research staff and students. Covers data sharing policies; Benefits of data sharing; Data repositories; Preparing data for sharing; and Re-using data.
Writing successful Data Management Plansdancrane_open
The document discusses writing successful data management plans (DMPs). It explains that a DMP is a project document that describes how data will be collected, stored, backed up, archived, and accessed. It provides guidance on what to include in a DMP, such as data collection methods, documentation, ethics, storage, sharing, and responsibilities. It recommends consulting advice and using online tools like DMP Online to help write funder-compliant DMPs.
This document discusses legal and ethical issues related to data sharing. It covers rights and copyright regarding data, how to address ethics when sharing personal data under GDPR, and obtaining consent from participants. Guidelines are provided for discovering and accessing shared data from repositories. Questions about data sharing are welcomed.
A presentation offering an introduction to managing and sharing research data given at the Czech Open Science days as part of the EC-funded FOSTER project.
An introduction to Research Data Management and Data Management Planning for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
Management of research data specifically for Engineering and Physical Science. Delivered by Stuart Macdonald at the "Support for Enhancing Research Impact" meeting at the University of Edinburgh on 22 June 2016.
This document provides an overview of a webinar on digital curation and research data management for universities. The webinar covers an introduction to digital curation, the benefits and drivers for research data management, current initiatives in UK universities, and the role of libraries in supporting research data management. Libraries are increasingly involved in developing institutional policies, providing training, and advising researchers on writing data management plans and sharing data. The webinar highlights training opportunities for librarians to develop skills in research data management and digital curation.
Presentation given at the European Research Council workshop on research data management and sharing in Brussels on 18th-19th September 2014. The presentation covers the benefits and drivers for RDM, points to relevant tools and resources and closes with some open questions for discussion.
OU Library Research Support webinar: Data sharingDaniel Crane
Slides from a webinar delivered on 06th February 2018 for OU research staff and students. Covers data sharing policies; Benefits of data sharing; Data repositories; Preparing data for sharing; and Re-using data.
Writing successful Data Management Plansdancrane_open
The document discusses writing successful data management plans (DMPs). It explains that a DMP is a project document that describes how data will be collected, stored, backed up, archived, and accessed. It provides guidance on what to include in a DMP, such as data collection methods, documentation, ethics, storage, sharing, and responsibilities. It recommends consulting advice and using online tools like DMP Online to help write funder-compliant DMPs.
This document discusses legal and ethical issues related to data sharing. It covers rights and copyright regarding data, how to address ethics when sharing personal data under GDPR, and obtaining consent from participants. Guidelines are provided for discovering and accessing shared data from repositories. Questions about data sharing are welcomed.
Data sharing is the practice of making research data openly available to others. It has many benefits including enabling innovation, improving transparency and research integrity, and increasing citations and impact. Major funders now require data sharing as a condition of funding. To share data, it must be prepared by documenting it with metadata and supporting files. This allows others to understand and use the data. Researchers are encouraged to share data in open repositories to maximize access and reuse. Proper preparation of data for sharing helps ensure data is FAIR - Findable, Accessible, Interoperable and Reusable.
The document summarizes a pilot project at the University of Edinburgh to support the development of a UK Research Data Discovery Service. PhD interns engaged with researchers from various schools to describe and deposit research datasets in the university's systems to be harvested by the discovery service. Observations found mixed results across schools, with humanities researchers less comfortable sharing data due to copyright and reluctance to share interpretations. Other schools had established data repositories causing less interest in the university's system. Building research data management practices will require tailored approaches and more training over time.
Research data spring: extending the OPD to cover RDMJisc RDM
The research data spring project "Extending the Organisational Profile Document to cover Research Data Management" slides for the third sandpit workshop. Project led by Joy Davidson from the Digital Curation Centre.
Presentation given by Sarah Jones at a seminar run by LSHTM on 6th November 2012. http://www.lshtm.ac.uk/newsevents/events/2012/11/developing-data-management-expertise-in-research---half-day-event
The document provides an overview of research data management (RDM) and the RDM services that Lancaster University plans to offer. It discusses that RDM involves maintaining and preserving digital research data throughout its lifecycle. It also notes that funder requirements and policies are driving universities to improve RDM practices to ensure long-term access and reuse of research data. Lancaster University plans to offer storage, advocate for RDM, provide training and support, help with data management plans, and collaborate with other universities and groups like N8 on RDM issues.
This document discusses data management plans (DMPs), which are brief plans that define how research data will be created, documented, stored, shared, and preserved. DMPs are often required as part of grant applications. The document provides an overview of why DMPs are important, how they benefit researchers and institutions, and key aspects to address in a DMP such as data organization, stakeholders, and making data FAIR (findable, accessible, interoperable, and reusable). Examples of DMPs from real projects are also presented.
Research data management at TU EindhovenLeon Osinski
The document discusses research data management at TU Eindhoven. It outlines the long process of developing RDM practices since 2008. It describes the current organization and governance structure for RDM. Key external requirements for RDM from funders, regulations, and integrity standards are also summarized. The document concludes by outlining RDM support services available and the benefits of good RDM practices.
This document discusses best practices for preparing and sharing research data. It emphasizes obtaining proper consent from participants, performing a risk analysis to avoid re-identification, and considering appropriate sharing methods such as data repositories. Sharing data benefits the research community by encouraging new collaborations and validation of results, but must be balanced with obligations to protect participants and intellectual property. The document provides guidance on topics like data licensing, anonymization, and the policies of research institutions and journals regarding data sharing.
Research data management involves organizing data throughout the research lifecycle to ensure reliable verification of results and allow new research. It includes developing policies, storing and organizing data appropriately, and addressing requirements for working with personal or sensitive information. The Open University provides support and resources to help researchers effectively manage their data, including training, data storage options, and a research data repository.
Presentation given to EC project officers as part of workshops run by the FOSTER (foster open science) project. The presentation covers the Horizon 2020 open data pilot.
Stop press: should embargo conditions apply to metadata?Jisc RDM
Sarah Middle of Cambridge University discusses whether embargo conditions should apply to metadata. Session held at the Research Data Network event in May 2016, Cardiff University.
Research Data Management: Approaches to Institutional PolicyRobin Rice
This document summarizes research data management policies from several universities. It discusses the purpose statements, tones, roles and responsibilities outlined in the policies of universities in the UK, Australia, and US. The University of Edinburgh policy takes a partnership approach, sharing responsibilities between the university and researchers. It aims to support research excellence through managing data to high standards across the research lifecycle.
Research Data Management: An Introductory Webinar from OpenAIRE and EUDATTony Ross-Hellauer
OpenAIRE and EUDAT co-present this webinar which aims to introduce researchers and others to the concept of research data management (RDM). As well as presenting the benefits of taking an active approach to research data management – including increased speed and ease of access, efficiency (fund once, reuse many times), and improved quality and transparency of research – the webinar will advise on strategies for successful RDM, resources to help manage data effectively, choosing where to store and deposit data, the EC H2020 Open Data Pilot and the basics of data management, stewardship and archiving.
Webinar recording available: http://www.instantpresenter.com/eifl/EB57D6888147
Stuart Macdonald steps through the process of creating a robust data management plan for researchers. Presented at the European Association for Health Information and Libraries (EAHIL) 2015 workshop, Edinburgh, 11 June 2015.
The document provides information on creating a data management plan (DMP) for grant applications. It discusses what a DMP is, why they are important, and what funders require in a DMP. A DMP outlines how research data will be collected, documented, stored, shared, and preserved. The document recommends addressing six key themes in a DMP: data types and standards; ethics and intellectual property; data access, sharing and reuse; short-term storage and management; long-term preservation; and resourcing. Developing a strong DMP helps researchers manage data effectively and makes data available and reusable by others.
Data sharing is the practice of making research data openly available to others. It has many benefits including enabling innovation, improving transparency and research integrity, and increasing citations and impact. Major funders now require data sharing as a condition of funding. To share data, it must be prepared by documenting it with metadata and supporting files. This allows others to understand and use the data. Researchers are encouraged to share data in open repositories to maximize access and reuse. Proper preparation of data for sharing helps ensure data is FAIR - Findable, Accessible, Interoperable and Reusable.
The document summarizes a pilot project at the University of Edinburgh to support the development of a UK Research Data Discovery Service. PhD interns engaged with researchers from various schools to describe and deposit research datasets in the university's systems to be harvested by the discovery service. Observations found mixed results across schools, with humanities researchers less comfortable sharing data due to copyright and reluctance to share interpretations. Other schools had established data repositories causing less interest in the university's system. Building research data management practices will require tailored approaches and more training over time.
Research data spring: extending the OPD to cover RDMJisc RDM
The research data spring project "Extending the Organisational Profile Document to cover Research Data Management" slides for the third sandpit workshop. Project led by Joy Davidson from the Digital Curation Centre.
Presentation given by Sarah Jones at a seminar run by LSHTM on 6th November 2012. http://www.lshtm.ac.uk/newsevents/events/2012/11/developing-data-management-expertise-in-research---half-day-event
The document provides an overview of research data management (RDM) and the RDM services that Lancaster University plans to offer. It discusses that RDM involves maintaining and preserving digital research data throughout its lifecycle. It also notes that funder requirements and policies are driving universities to improve RDM practices to ensure long-term access and reuse of research data. Lancaster University plans to offer storage, advocate for RDM, provide training and support, help with data management plans, and collaborate with other universities and groups like N8 on RDM issues.
This document discusses data management plans (DMPs), which are brief plans that define how research data will be created, documented, stored, shared, and preserved. DMPs are often required as part of grant applications. The document provides an overview of why DMPs are important, how they benefit researchers and institutions, and key aspects to address in a DMP such as data organization, stakeholders, and making data FAIR (findable, accessible, interoperable, and reusable). Examples of DMPs from real projects are also presented.
Research data management at TU EindhovenLeon Osinski
The document discusses research data management at TU Eindhoven. It outlines the long process of developing RDM practices since 2008. It describes the current organization and governance structure for RDM. Key external requirements for RDM from funders, regulations, and integrity standards are also summarized. The document concludes by outlining RDM support services available and the benefits of good RDM practices.
This document discusses best practices for preparing and sharing research data. It emphasizes obtaining proper consent from participants, performing a risk analysis to avoid re-identification, and considering appropriate sharing methods such as data repositories. Sharing data benefits the research community by encouraging new collaborations and validation of results, but must be balanced with obligations to protect participants and intellectual property. The document provides guidance on topics like data licensing, anonymization, and the policies of research institutions and journals regarding data sharing.
Research data management involves organizing data throughout the research lifecycle to ensure reliable verification of results and allow new research. It includes developing policies, storing and organizing data appropriately, and addressing requirements for working with personal or sensitive information. The Open University provides support and resources to help researchers effectively manage their data, including training, data storage options, and a research data repository.
Presentation given to EC project officers as part of workshops run by the FOSTER (foster open science) project. The presentation covers the Horizon 2020 open data pilot.
Stop press: should embargo conditions apply to metadata?Jisc RDM
Sarah Middle of Cambridge University discusses whether embargo conditions should apply to metadata. Session held at the Research Data Network event in May 2016, Cardiff University.
Research Data Management: Approaches to Institutional PolicyRobin Rice
This document summarizes research data management policies from several universities. It discusses the purpose statements, tones, roles and responsibilities outlined in the policies of universities in the UK, Australia, and US. The University of Edinburgh policy takes a partnership approach, sharing responsibilities between the university and researchers. It aims to support research excellence through managing data to high standards across the research lifecycle.
Research Data Management: An Introductory Webinar from OpenAIRE and EUDATTony Ross-Hellauer
OpenAIRE and EUDAT co-present this webinar which aims to introduce researchers and others to the concept of research data management (RDM). As well as presenting the benefits of taking an active approach to research data management – including increased speed and ease of access, efficiency (fund once, reuse many times), and improved quality and transparency of research – the webinar will advise on strategies for successful RDM, resources to help manage data effectively, choosing where to store and deposit data, the EC H2020 Open Data Pilot and the basics of data management, stewardship and archiving.
Webinar recording available: http://www.instantpresenter.com/eifl/EB57D6888147
Stuart Macdonald steps through the process of creating a robust data management plan for researchers. Presented at the European Association for Health Information and Libraries (EAHIL) 2015 workshop, Edinburgh, 11 June 2015.
The document provides information on creating a data management plan (DMP) for grant applications. It discusses what a DMP is, why they are important, and what funders require in a DMP. A DMP outlines how research data will be collected, documented, stored, shared, and preserved. The document recommends addressing six key themes in a DMP: data types and standards; ethics and intellectual property; data access, sharing and reuse; short-term storage and management; long-term preservation; and resourcing. Developing a strong DMP helps researchers manage data effectively and makes data available and reusable by others.
Stuart Macdonald reviews what researchers need to do to comply with the new EPSRC framework concerning the management and provision of access to publicly-funded research data. Presented at the Mobility, Mood and Place Research Committee Meeting workshop at the Edinburgh College of Art, 16 June, 2015.
This document provides an introduction to research data management for geoscience PhD students. It defines research data and different data types. It discusses the importance of managing data throughout its lifecycle for efficient and valid research. It outlines funder requirements, university policies, and activities involved in good research data management like data planning, documentation, storage, sharing and preservation.
This slide deck provides an overview and resources to respond to the OSTP memo with the subject: Increasing Access to the Results of Federally Funded Scientific Research issued by John P. Holdren in February 2013. It provides resources and information agencies, foundations, and research projects can use to assemble achieve public access to scientific data in digital formats.
Presentation by Gareth Knight of the London School of Hygiene & Tropical Medicine. It was presented at the LSHTM Research Data Services workshop on June 30th 2015, an event organised to mark the end of LSHTM's Wellcome Trust funded RDM project.
The document provides information about research data management (RDM) services and initiatives at the University of Edinburgh. It describes the EDINA National Data Centre and Data Library, which provide online resources and data management support. It outlines several JISC-funded RDM projects undertaken by the Data Library, including building the Edinburgh DataShare repository. It also summarizes the Research Data MANTRA training module and the university's RDM roadmap, which lays out a multi-phase plan to improve RDM support and services by 2015 in line with funder requirements.
Creating a Data Management Plan for your ResearchRobin Rice
This document provides an overview of creating a data management plan (DMP). It discusses what a DMP is, the benefits of creating one, and what funders require. A DMP defines what data will be collected, documented, stored, shared, and preserved. Developing a DMP helps avoid problems and ensures data are reliable and secure. The document outlines six key themes a DMP should address: data types and standards, ethics, access and sharing, storage, preservation, and resources. Support is available to help researchers develop effective DMPs.
Policies from funders, publishers, and universities increasingly require researchers to share their data. Sharing data brings benefits like enabling replication and innovation by other researchers, safeguarding research integrity, and potentially increasing citations. Researchers should select what data to share, prepare it with good documentation and open file formats, and consider using repositories. The library provides support for data management plans, preparation, and sharing through services like Open Research Data Online.
The document summarizes the activities of EDINA and the Data Library at the University of Edinburgh related to research data management. It describes EDINA as a national data center that provides online resources for education and research. The Data Library assists university researchers with discovering, accessing, using and managing research datasets. It also outlines several projects the Data Library is involved in to develop training, policies and services to support best practices in research data management according to funder requirements. This includes developing an institutional research data management roadmap to help the university meet funder expectations by 2015.
This document provides information about developing a data management plan for grant proposals. It discusses the goals of the workshop which are to learn about data management planning, available resources, develop a draft plan, and receive feedback. It then covers what good data management involves, who requires data management plans, examples of requirements from agencies like NSF, and parts of a generic data management plan. Finally, it discusses resources available for creating plans like the DMPTool.
Presentación de Joy Davidson, Digital Curation Centre (UK) en FOSTER event: Data Management Plan and Social Impact of Research. Universitat Jaume I, 27 mayo 2016
The academic research data lifecycle. Session 1.4 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Managing Your Research Data for Maximum Impact -Rob Daley 300616_SharedRob Daley
This document provides an overview of best practices for managing research data. It discusses why data management is important given changing policies from funders that require making data openly available. It outlines challenges for researchers in managing data and provides guidance on developing a data management plan to address issues like data types, access, storage, and long-term preservation. The document also covers topics like formatting data, addressing legal and ethical concerns, publishing and citing data, and tools like ORCID and DOIs to help maximize the impact of research data.
The document provides background information on RDM services at the University of Edinburgh. It summarizes that EDINA and the University Data Library provide research data management support and online resources. It then overviews key RDM services including DataStore for active research data storage, DataShare for open data publication, and plans for a long-term DataVault archive. The document also discusses RDM training and the university's RDM policy implemented through a multi-phase roadmap.
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
LSHTM Research Data Management Policy: An Overview
1. LSHTM’S RESEARCH DATA MANAGEMENT POLICY
This work is licensed under aCreative Commons Attribution 2.0 UK: England & Wales License
Gareth Knight
gareth.knight@lshtm.ac.uk
6 -7 November 2014
An overview of School expectations
2. Research Data Management Policy
•Research data is a key asset that contributes to the School’s objectives of encouraging rigorous scientific enquiry
•Data management is an integral part of good research practice that protects the intellectual and financial investment made in the data’s creation and enables it to be shared and validated.
•The RDM Policy describes a set of principles that contribute to the management of research data in accordance with good research practice
Web version:
http://www.lshtm.ac.uk/research/researchdataman/rdm_policy_summary.html
PDF version:
http://researchonline.lshtm.ac.uk/612422/
3. Data Management Principles
1.Understand the legal, regulatory & contractual environment in which you're performing research
2.Clarify rights for ownership, access and use of data
3.Write a Data Management Plan before you start a project
4.Create a metadata record to recognisedata's existence
5.Store research data in a managed environment while it's being developed
6.Offer research data to a repository or enclave when you've finished it
7.Consider making research data available for others to access & use
8.Cite the metadata record of data so others can find it
9.Ask your research funder to fund data management and sharing activities
Identify research topic & funding
Perform literature review
Develop research plan
Upgrading seminar
Perform research
Write-up results
Produce several drafts
Finalise & submit
4. 1. Research Environment
“Research data must be created, maintained and shared in accordance with contractual, legislative, regulatory, ethical & other relevant requirements”
•Recognise that research environment is highly regulated, subject to obligations:
–Local (see right)
–National (FOI)
–International (EU Directive)
•Researchers expected to maintain awareness of obligations and apply appropriate practices.
Guidelines on Good Research Practicehttp://www.lshtm.ac.uk/research/about/ guidelines_on_good_research_practice.pdf
LSHTM Data Protection Policyhttp://intra.lshtm.ac.uk/infoman/data/index.html
LSHTM Information Management and Security Policyhttp://www.lshtm.ac.uk/its/informationsecurity/ policy/index.html
LSHTM Records Retention & Disposal Schedulehttp://intra.lshtm.ac.uk/infoman/records/ retention.html
Clinical Trial Data Management SOPhttps://intra.lshtm.ac.uk/trials/sops/index.html
5. 2. Rights Framework
“Rights assigned to research data should not unnecessarily
restrict its management, sharing, or use”
•IPR should be documented in a clear, unambiguous manner at appropriate granularity.
–Moral rights
–Ownership rights
–Access rights & permitted use
•Don’t sign away rights, unless necessary
•A non-exclusive licence encouraged, enabling user by multiple people
•Common examples include Creative Commons, Open Data Commons & Data Transfer Agreement
LSHTM: Choose a Data Licence
http://www.lshtm.ac.uk/research/researchdataman/share/choose_licence.html
6. 3. Data Management Plan
“A Data Management Plan should be produced for
all research projects that are creating or capturing data”
•A DMP will be required by all research projects that are:
–Led by LSHTM
–Submitted from Jan 2015 & receive funding confirmation
–Supported using public/private funding sources
–Are creating primary data in digital form.
•PI may submit a DM Plan using the:
–Research funder template
–School template
•MPhil/PhD/DrPHstudents encouraged to agree a DMP with supervisor & submit in Upgrading or Review report
Research projects:http://www.lshtm.ac.uk/research/researchdataman/plan/lshtm_dmplan.html
Student Projects: http://www.lshtm.ac.uk/research/researchdataman/plan/student_dmp.html
7. 4. Recognise data’s existence
“Research data created or captured by researchers must be registered with the School, irrespective of whether it is hosted at the School or elsewhere”
•Description of data that is suitable for sharing:
–What is it?
–When was it created?
–How was it captured?
–Who created it?
•Time period for capture will depend upon project length:
–Final 3 months of funding for most projects
–Completion of work unit in longitudinal study
•Metadata made public in Data Repository, unless requested otherwise
8. 5. Data Storage
“Research data must be held in a managed storage environment throughout the period of retention”
•Data must be kept for minimum 10 years after project end
•Storage system protect against loss and corruption, unauthorised access and modification
•System may be operated by School or 3rdparty
•If institutional storage not available, researcher should maintain data integrity and security
Keep Data Secure: http://www.lshtm.ac.uk/research/researchdataman/store/
Information Security: http://www.lshtm.ac.uk/its/informationsecurity/
9. 6. Data Repository
“Research data… should be offered to an appropriate data repository or enclave designated by the School or Funder, except in circumstances that would breach IPR, ethical, confidentiality, or other obligations”
•Data should be curated & preserved after project end
•Data repositories better equipped to handle process
•Many data repositories exist, including LSHTM, Figshare, UK Data Service, and others
•Most repositories will accept data for preservation only & won’t share it, but not all
LSHTM: Locate a Data Repository
http://www.lshtm.ac.uk/research/researchdataman/share/locate_datarepository.html
10. 7. Data Sharing
“Research data that substantiate research findings should be made available for access and use in a timely manner, within the boundaries of… contractual, legislative, ethical, or other requirements”
•Data sharing advances research & education in public health
•Not all data is suitable for sharing, but if it is…
•Data may be shared at different intervals:
–During project lifetime
–Same time as publication
–Project close
–Following embargo (e.g. 1-years)
•Liaise with RDM Service to determine sharing needs
Sharing Research Data: Identifying what to share & when
http://www.lshtm.ac.uk/research/researchdataman/share/
11. 8. Data Citation
“Research data produced and/or used during
research must be cited in research outputs”
•Good research practice built on recognition of all source material
•Enables validation of findings and enables unique contribution of data creator to be recognised
•Funders & journals request an Access statement indicating how data is accessed
•A persistent ID scheme should be used, e.g. Digital Object Identifier (DOI), Handle
LSHTM: Cite Data
http://www.lshtm.ac.uk/research/researchdataman/cite
12. 9. Data Management Costs
“Management and sharing of research data should be supported through the allocation of research funding, where permitted”
•No expenditure can be ‘double funded' using Direct & Indirect costs
•Post-project costs can be claimed as direct cost if invoiced before project end
•Budget & Justification of Resources should broadly indicate where RDM costs arise (e.g. data capture & cleaning)
•Long-term storage cannot be claimed if funder requires deposit in a funder repository
•Unforeseen data management costs incurred after project end will not be supported by the funder
Funder
RDMCosts
RCUK (MRC, NERC,etc.)
Yes
WellcomeTrust
Yes
Gates Foundation
Yes
Deptfor International Developments (DfID)
Yes
Cancer Research UK
No
World Cancer Research Fund
No
Others
Unknown
LSHTM: Calculating Data Management Costs
http://www.lshtm.ac.uk/research/researchdataman/plan/rdm_costs.html
13. Further Information
Guides & tutorials on:
•Data Management Plans
•File formats and software
•Data storage & security
•Data labelling and organisation
•Documentation & metadata
•Data sharing & citation
Available on the RDM website
One-to-one advice & guidance:
•Email: researchdatamanagement@lshtm.ac.uk
•Telephone: #2564
http://www.lshtm.ac.uk/research/researchdataman/