Changes in National Ethics Policy for Managing and Sharing Human Research DataARDC
The document discusses data management and sharing in research. It summarizes key points from the National Statement on Ethical Conduct in Human Research regarding obtaining appropriate consent for data reuse, and developing data management plans. While open sharing of data is ideal, mediated access through committees or archives is also possible. Consent forms should specify how data will be governed and accessed now and in the future to facilitate ethical data sharing and reuse.
Jeff Haywood - Research Integrity: Institutional ResponsibilityJisc
1) The document discusses challenges and solutions related to research data management (RDM) at the University of Edinburgh. It outlines the university's RDM policy and implementation plan to provide training, support, and services for storing, backing up, and sharing research data.
2) The RDM working group at the university recommended establishing a research data service strategy to provide archiving of data, globally accessible storage, and support for mobile access and collaboration.
3) Key challenges going forward include securing sustainable funding, integrating new services with existing practices, developing support staff skills, and encouraging researcher engagement with new RDM practices.
Introduction to the Research Integrity Advisor Data Management Workshop, Bris...ARDC
Dr Jacobs' introduction to the RIA Data Management Workshop in Brisbane on 31 March 2017. The RIA Data Management Workshop series is a joint collaboration of the Australian Research Council, the National Health and Medical Research Council, the Australasian Research Management Society and the Australian National Data Service.
This document provides an overview of data management plans (DMPs), including their purpose, origins, requirements by major funders, and benefits. It discusses key components of DMPs such as the data types and standards, access and sharing policies, and long-term preservation. The document also examines researchers' attitudes toward DMPs and data sharing, which can include fears about misuse or losing competitive advantage. Finally, it poses questions about developing research data management services at Villanova University through collaboration between libraries, support services, and researchers.
Research Data Management in practice, RIA Data Management Workshop Brisbane 2017ARDC
The Australian National Data Service (ANDS) aims to make Australian research data more valuable by partnering with research organizations and funding data projects. In 2015, ANDS conducted over 100 workshops and events with over 4,000 participants and developed online resources. ANDS provides guides on topics like data management and the FAIR data principles. ANDS also advocates for practices like data citation and publishing to ensure research data is preserved and reusable over time. The presentation outlines ANDS' role in supporting good research data management practices and sharing to ensure the integrity and impact of research evidence.
Presentation by Hugo Leroux and Liming Zhu, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Changes in National Ethics Policy for Managing and Sharing Human Research DataARDC
The document discusses data management and sharing in research. It summarizes key points from the National Statement on Ethical Conduct in Human Research regarding obtaining appropriate consent for data reuse, and developing data management plans. While open sharing of data is ideal, mediated access through committees or archives is also possible. Consent forms should specify how data will be governed and accessed now and in the future to facilitate ethical data sharing and reuse.
Jeff Haywood - Research Integrity: Institutional ResponsibilityJisc
1) The document discusses challenges and solutions related to research data management (RDM) at the University of Edinburgh. It outlines the university's RDM policy and implementation plan to provide training, support, and services for storing, backing up, and sharing research data.
2) The RDM working group at the university recommended establishing a research data service strategy to provide archiving of data, globally accessible storage, and support for mobile access and collaboration.
3) Key challenges going forward include securing sustainable funding, integrating new services with existing practices, developing support staff skills, and encouraging researcher engagement with new RDM practices.
Introduction to the Research Integrity Advisor Data Management Workshop, Bris...ARDC
Dr Jacobs' introduction to the RIA Data Management Workshop in Brisbane on 31 March 2017. The RIA Data Management Workshop series is a joint collaboration of the Australian Research Council, the National Health and Medical Research Council, the Australasian Research Management Society and the Australian National Data Service.
This document provides an overview of data management plans (DMPs), including their purpose, origins, requirements by major funders, and benefits. It discusses key components of DMPs such as the data types and standards, access and sharing policies, and long-term preservation. The document also examines researchers' attitudes toward DMPs and data sharing, which can include fears about misuse or losing competitive advantage. Finally, it poses questions about developing research data management services at Villanova University through collaboration between libraries, support services, and researchers.
Research Data Management in practice, RIA Data Management Workshop Brisbane 2017ARDC
The Australian National Data Service (ANDS) aims to make Australian research data more valuable by partnering with research organizations and funding data projects. In 2015, ANDS conducted over 100 workshops and events with over 4,000 participants and developed online resources. ANDS provides guides on topics like data management and the FAIR data principles. ANDS also advocates for practices like data citation and publishing to ensure research data is preserved and reusable over time. The presentation outlines ANDS' role in supporting good research data management practices and sharing to ensure the integrity and impact of research evidence.
Presentation by Hugo Leroux and Liming Zhu, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
RDAP14: Policy Recommendations for Institutions to Serve as Trustworthy Stewa...ASIS&T
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
J. Steven Hughes
NASA Jet Propulsion Laboratory
Robert R. Downs
Center for International Earth Science Information Network (CIESIN), Columbia University
David Giaretta
Alliance for Permanent Access
Presentation by Dr Steve McEachern, ADA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
FAIR - Working Data - It's not just about FAIR publishing. Presented by John Morrissey from CSIRO at the C3DIS post conference workshop: Managed data – trusted research: an introduction to Research Data Management 31 may 2018 in Melbourne
Practical and Conceptual Considerations of Research Object PreservationSEAD
This document discusses research object (RO) frameworks for preserving digital research data. It addresses the challenges of research spanning long periods of time and involving complex, heterogeneous data that changes states. The research object framework aims to capture agents, states, relationships, and content to enable automation, reproducibility, and reuse of research. The framework defines three states for research objects - live, curated, and published. Live objects are works in progress, curated objects are packaged for preservation, and published objects are immutable and citable. The framework allows documentation of research processes and outputs to build trust and facilitate reuse.
How to get there from here- Research data Managment training. presented by Sue Cook, CSIRO, at the C3DIS post conference workshop; Managed data – trusted research: an introduction to Research Data Management in Melbourne 31st May 2018
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
International perspective for sharing publicly funded medical research dataARDC
Presentation by Olivier Salvado, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Introduction to research data management. Presented by Natasha Simons at the C3DIS post conference workshop: Managed data – trusted research: an introduction to Research Data Management, Melbourne 31st may 2018
This document provides guidance on research data management for postgraduates. It discusses why research data management is important, including ensuring long-term preservation and discoverability of data, supporting more rigorous research through record keeping, and preventing academic fraud. It also outlines the key elements to include in a research data management plan, such as data type and format, storage and security, ownership and sharing. Developing a data management plan early in the research process is recommended to facilitate data preservation and sharing.
This document discusses privacy, policy, and data governance challenges at universities. It summarizes different policies from funding agencies regarding data sharing and management plans. It then discusses scenarios and recommendations from UCLA's Data Governance Task Force, including the scope of data to be governed, developing an inventory, establishing best practices, extending existing governance structures, and recommended activities to support effective data governance. Key issues addressed include student and faculty records, appropriate data uses, and developing workable governance processes.
The document summarizes plans by the Australian Government to establish new legislation and institutions to streamline access to and use of public sector data. Key points include:
- A new Commonwealth Data Sharing and Release Act will be introduced in 2019 to provide consistent rules for sharing data and establish a National Data Commissioner to oversee the system.
- The National Data Commissioner will ensure transparency, accountability, security, and appropriate risk management in data sharing.
- New rules will focus on enabling data to be shared for purposes like research and policy-making, while protecting privacy and building public trust in data use.
- The government will continue consulting stakeholders on the legislation to address concerns and help the public understand the reforms.
A FAIR Data Sharing Framework for Large-Scale Human Cancer ProteogenomicsBrett Tully
A FAIR Data Sharing Framework for Large-Scale Human Cancer Proteogenomics
Islam M1,2, Christiansen J3, Mahboob S4, Valova V4, Baker M4, Capes-Davis D4, Hains P4, Balleine R1,4, Zhong Q1,4, Reddel R1,4, Robinson P1,4, Tully B4
1 The University of Sydney, Camperdown, Sydney, NSW, 2050, Australia
2 Intersect, Level 13/50 Carrington St, Sydney, NSW, 2000, Australia
3 Queensland Cyber Infrastructure Foundation Ltd, Axon Building 47, University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
4 Children’s Medical Research Institute, Westmead, NSW, 2145, Australia
Background
The ACRF International Centre for the Proteome of Cancer (ProCan) at Children’s Medical Research Institute (CMRI) is an “industrial scale” program specialising in small-sample proteomics analysis from human cancer tissue.
ProCan seeks to generate both a wide and deep analytics pipeline and requires an enabling data framework. The framework must accommodate initial analysis and proteomic profiling of a large number of tumor samples, along with the clinical and demographic information, subsequent multi-omics studies, and any previously recorded responses to treatment. The curated datasets will provide a valuable resource beyond their primary use and ProCan is committed to making its data accessible to collaborators and the wider scientific community.
Objectives
The objective of the project is to an establish efficient, reliable, secure and ethical data sharing and publication framework based on the best practice data sharing principles, such as the FAIR principle. The framework must address various challenges that stem from the scale and complexity of the program, and ProCan’s focus on human-derived data and associated challenges presented in sharing these data while maintaining the privacy of any research participants.
Method
The project adopted a requirements-driven methodology and engaged with a wide range of ProCan stakeholders nationally and internationally. Together, various industrial-scale proteomics data management and sharing scenarios were explored such that robust and ethical sharing of the data would be achieved.
Results
The project developed a data sharing framework based on the FAIR principle that currently forms the basis of ongoing implementation work within the ProCan program.
This document discusses guidelines for making archaeological data findable, accessible, interoperable, and reusable (FAIR). It is funded by the European Commission and aims to support the creation and sharing of FAIR archaeological data. The document introduces the FAIR data principles, which are designed to make data more easily discoverable and usable. It also discusses the role of metadata, identifiers, standards, and repositories in making archaeological data FAIR. Guidelines are provided around each letter of FAIR to help researchers and institutions implement good research data management practices.
This document presents an agile conceptual framework called "Agile Data Curation" for improving data management practices. It discusses why data management is important both internally for research and externally for public access and reproducibility. The framework is based on principles adapted from agile software development, focusing on early and continuous data sharing, incremental improvements, and community involvement. The presenters provide background on developing the framework through discussions at conferences and within the Research Data Alliance community of interest.
This document provides information about research data management for postgraduates. It discusses why research data management is important, including ensuring long-term preservation and discoverability of data, supporting more rigorous research by allowing data verification, and enabling reuse of data. It outlines the key elements to include in a research data management plan such as the type and format of data, description standards, storage and security, responsibilities, intellectual property and data sharing. Finally, it provides some online tools that can help with creating a data management plan.
Data sharing promotes many goals of the NIH research endeavor. It is particularly important for unique data that cannot be readily replicated. Data sharing allows scientists to expedite the translation of research results into knowledge, products, and procedures to improve human health. Do you know what a data sharing plan should include? Are you aware of common practices and standards for data sharing? Do you know what services are available to help share your data responsibly? This workshop will begin to address these questions. Q&A will follow the presentation. Anyone interested in or planning to apply for NIH funding should attend. Note: The NIH data-sharing policy applies to applicants seeking $500,000 or more in direct costs in any year of the proposed research.
Presentation by Dr Davina Ghersi, NHMRC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Prof Lisa Askie, ANZCTR, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
The document summarizes the Jisc Managing Research Data Programme which aims to support universities in improving research data management. It discusses why managing research data is important, highlighting funder policies and the benefits of open data. It provides an overview of Jisc's activities including training projects, guidance resources, and funding for institutional infrastructure services and repositories. The presentation emphasizes the importance of institutional policies, support services, skills development and cultural change to effectively manage research data in line with funder expectations.
RDAP14: Policy Recommendations for Institutions to Serve as Trustworthy Stewa...ASIS&T
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
J. Steven Hughes
NASA Jet Propulsion Laboratory
Robert R. Downs
Center for International Earth Science Information Network (CIESIN), Columbia University
David Giaretta
Alliance for Permanent Access
Presentation by Dr Steve McEachern, ADA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
FAIR - Working Data - It's not just about FAIR publishing. Presented by John Morrissey from CSIRO at the C3DIS post conference workshop: Managed data – trusted research: an introduction to Research Data Management 31 may 2018 in Melbourne
Practical and Conceptual Considerations of Research Object PreservationSEAD
This document discusses research object (RO) frameworks for preserving digital research data. It addresses the challenges of research spanning long periods of time and involving complex, heterogeneous data that changes states. The research object framework aims to capture agents, states, relationships, and content to enable automation, reproducibility, and reuse of research. The framework defines three states for research objects - live, curated, and published. Live objects are works in progress, curated objects are packaged for preservation, and published objects are immutable and citable. The framework allows documentation of research processes and outputs to build trust and facilitate reuse.
How to get there from here- Research data Managment training. presented by Sue Cook, CSIRO, at the C3DIS post conference workshop; Managed data – trusted research: an introduction to Research Data Management in Melbourne 31st May 2018
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
International perspective for sharing publicly funded medical research dataARDC
Presentation by Olivier Salvado, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Introduction to research data management. Presented by Natasha Simons at the C3DIS post conference workshop: Managed data – trusted research: an introduction to Research Data Management, Melbourne 31st may 2018
This document provides guidance on research data management for postgraduates. It discusses why research data management is important, including ensuring long-term preservation and discoverability of data, supporting more rigorous research through record keeping, and preventing academic fraud. It also outlines the key elements to include in a research data management plan, such as data type and format, storage and security, ownership and sharing. Developing a data management plan early in the research process is recommended to facilitate data preservation and sharing.
This document discusses privacy, policy, and data governance challenges at universities. It summarizes different policies from funding agencies regarding data sharing and management plans. It then discusses scenarios and recommendations from UCLA's Data Governance Task Force, including the scope of data to be governed, developing an inventory, establishing best practices, extending existing governance structures, and recommended activities to support effective data governance. Key issues addressed include student and faculty records, appropriate data uses, and developing workable governance processes.
The document summarizes plans by the Australian Government to establish new legislation and institutions to streamline access to and use of public sector data. Key points include:
- A new Commonwealth Data Sharing and Release Act will be introduced in 2019 to provide consistent rules for sharing data and establish a National Data Commissioner to oversee the system.
- The National Data Commissioner will ensure transparency, accountability, security, and appropriate risk management in data sharing.
- New rules will focus on enabling data to be shared for purposes like research and policy-making, while protecting privacy and building public trust in data use.
- The government will continue consulting stakeholders on the legislation to address concerns and help the public understand the reforms.
A FAIR Data Sharing Framework for Large-Scale Human Cancer ProteogenomicsBrett Tully
A FAIR Data Sharing Framework for Large-Scale Human Cancer Proteogenomics
Islam M1,2, Christiansen J3, Mahboob S4, Valova V4, Baker M4, Capes-Davis D4, Hains P4, Balleine R1,4, Zhong Q1,4, Reddel R1,4, Robinson P1,4, Tully B4
1 The University of Sydney, Camperdown, Sydney, NSW, 2050, Australia
2 Intersect, Level 13/50 Carrington St, Sydney, NSW, 2000, Australia
3 Queensland Cyber Infrastructure Foundation Ltd, Axon Building 47, University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
4 Children’s Medical Research Institute, Westmead, NSW, 2145, Australia
Background
The ACRF International Centre for the Proteome of Cancer (ProCan) at Children’s Medical Research Institute (CMRI) is an “industrial scale” program specialising in small-sample proteomics analysis from human cancer tissue.
ProCan seeks to generate both a wide and deep analytics pipeline and requires an enabling data framework. The framework must accommodate initial analysis and proteomic profiling of a large number of tumor samples, along with the clinical and demographic information, subsequent multi-omics studies, and any previously recorded responses to treatment. The curated datasets will provide a valuable resource beyond their primary use and ProCan is committed to making its data accessible to collaborators and the wider scientific community.
Objectives
The objective of the project is to an establish efficient, reliable, secure and ethical data sharing and publication framework based on the best practice data sharing principles, such as the FAIR principle. The framework must address various challenges that stem from the scale and complexity of the program, and ProCan’s focus on human-derived data and associated challenges presented in sharing these data while maintaining the privacy of any research participants.
Method
The project adopted a requirements-driven methodology and engaged with a wide range of ProCan stakeholders nationally and internationally. Together, various industrial-scale proteomics data management and sharing scenarios were explored such that robust and ethical sharing of the data would be achieved.
Results
The project developed a data sharing framework based on the FAIR principle that currently forms the basis of ongoing implementation work within the ProCan program.
This document discusses guidelines for making archaeological data findable, accessible, interoperable, and reusable (FAIR). It is funded by the European Commission and aims to support the creation and sharing of FAIR archaeological data. The document introduces the FAIR data principles, which are designed to make data more easily discoverable and usable. It also discusses the role of metadata, identifiers, standards, and repositories in making archaeological data FAIR. Guidelines are provided around each letter of FAIR to help researchers and institutions implement good research data management practices.
This document presents an agile conceptual framework called "Agile Data Curation" for improving data management practices. It discusses why data management is important both internally for research and externally for public access and reproducibility. The framework is based on principles adapted from agile software development, focusing on early and continuous data sharing, incremental improvements, and community involvement. The presenters provide background on developing the framework through discussions at conferences and within the Research Data Alliance community of interest.
This document provides information about research data management for postgraduates. It discusses why research data management is important, including ensuring long-term preservation and discoverability of data, supporting more rigorous research by allowing data verification, and enabling reuse of data. It outlines the key elements to include in a research data management plan such as the type and format of data, description standards, storage and security, responsibilities, intellectual property and data sharing. Finally, it provides some online tools that can help with creating a data management plan.
Data sharing promotes many goals of the NIH research endeavor. It is particularly important for unique data that cannot be readily replicated. Data sharing allows scientists to expedite the translation of research results into knowledge, products, and procedures to improve human health. Do you know what a data sharing plan should include? Are you aware of common practices and standards for data sharing? Do you know what services are available to help share your data responsibly? This workshop will begin to address these questions. Q&A will follow the presentation. Anyone interested in or planning to apply for NIH funding should attend. Note: The NIH data-sharing policy applies to applicants seeking $500,000 or more in direct costs in any year of the proposed research.
Presentation by Dr Davina Ghersi, NHMRC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Prof Lisa Askie, ANZCTR, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
The document summarizes the Jisc Managing Research Data Programme which aims to support universities in improving research data management. It discusses why managing research data is important, highlighting funder policies and the benefits of open data. It provides an overview of Jisc's activities including training projects, guidance resources, and funding for institutional infrastructure services and repositories. The presentation emphasizes the importance of institutional policies, support services, skills development and cultural change to effectively manage research data in line with funder expectations.
This presentation introduced participants to the DC 101 course and was given at the Digital Curation and Preservation Outreach and Capacity Building Workshop in Belfast on September 14-15 2009.
http://www.dcc.ac.uk/events/workshops/digital-curation-and-preservation-outreach-and-capacity-building-workshop
This document discusses data publishing and management. It introduces the advantages of publishing research data, including increasing citations, recognition and meeting grant requirements. It outlines best practices for data management planning and provides examples of data publishing platforms like SHaRED. The document advises that major journals and funding bodies now require data publication in open repositories to promote open access and data sharing in science.
Presentation during the 14th Association of African Universities (AAU) Conference and African Open Science Platform (AOSP)/Research Data Alliance (RDA) Workshop in Accra, Ghana, 7-8 June 2017.
Research Data Management Services at UWA (November 2015)Katina Toufexis
Research Data Management Services at the University of Western Australia (November 2015).
Created by Katina Toufexis of the eResearch Support Unit (University Library).
CC-BY
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
Digital transformation to enable a FAIR approach for health data scienceVarsha Khodiyar
Invited talk for ConTech Pharma on 1st March 2022
Abstract
Health Data Research UK is the UK’s national institute for health data science, with a mission to unite the UK’s health data to enable discoveries that improve people’s lives. In this talk, Dr Varsha Khodiyar will outline how HDR UK is bringing together disparate health data from all four countries of the United Kingdom, creating the infrastructure to enable discovery of and access to health data, and the convening standards making bodies to improve data linkage and data reuse. Varsha will also discuss how HDR UK is moving beyond the traditional confines of FAIR data to also ensure that data sharing and data use is transparent and ‘fair’ for the patients and lay public who are the subjects of these datasets.
The document discusses the importance of managing research data. It notes that data management saves time, makes long-term data preservation easier, and supports sharing data with others. Data sharing is now required by most major funding agencies and academic journals. The document provides examples of problems caused by poor data management practices and outlines the key components of a data management plan, such as describing the data, file formats, sharing and archiving policies, and responsibilities. Researchers are encouraged to seek help from scientific consulting services for creating data management plans.
The UK Data Archive acquires, curates, and provides access to the UK's largest collection of social and economic data. It has the largest collection of digital data in the social sciences and humanities in the United Kingdom. The data lifecycle involves creating, processing, and analyzing data, as well as preserving, giving access to, and reusing data with the goal of advancing scientific inquiry.
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 discusses managing research data and the benefits of developing a data management plan. It notes that managing research data enables verification, sharing and citation of results. Developing a data management plan structures how data will be created, managed, stored, shared and preserved. The plan should address what data will be created, data management practices, storage and access, and long-term preservation strategies. With good planning, researchers can avoid errors and losing data. The document provides resources for developing plans and getting help with data management.
Practical Research Data Management: tools and approaches, pre- and post-awardMartin Donnelly
This document provides an overview of a presentation on practical research data management. It discusses the importance of research data management, who is involved in the process, and the benefits it provides, such as increased efficiency and accessibility of data. It emphasizes that data management planning is a shared activity that should involve researchers, support staff, and other stakeholders. Effective data management planning helps ensure data is organized, documented, preserved, and shared appropriately. The presentation also provides examples of what a data management plan may include and why creating one is important for collaborative research projects.
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.
Supporting Research Data Management in UK Universities: the Jisc Managing Res...L Molloy
Research data management in the UK: interventions by the Jisc Managing Research Data programme and the Digital Curation Centre. Specifies the importance of academic librarians for RDM. Includes links to openly available training resources. Presentation by L Molloy to ExLibris event, 'Excellence in Academic Knowledge Management', Utrecht, 29 October 2013.
Using a behavioral framework to understand researchers data management practi...ARDC
Using a behavioral framework to understand researchers data management practices.
Kylie Poulton
Presented at Brisbane: Train the (data skills) trainer Dec 6th 2017
Survey of research data management practices up2010digschol2011heila1
An analysis of data management practices at a large South African university was conducted through interviews with researchers and students to identify needs and challenges. The findings showed that while data collection methods vary, data storage is often ad hoc with no centralized support or resources. Researchers expressed a need for a central university server or repository for secure data storage and assistance with time constraints. It was concluded that a formal research data management program and staff support are needed to improve current practices.
This document discusses the importance of research data management. It covers the data lifecycle and components of a data management plan. The data lifecycle includes collecting, processing, analyzing, storing, preserving, and sharing data. A data management plan outlines how data will be managed and preserved during and after a research project. It includes information about the data, metadata, data sharing policies, long-term storage, and budget. Developing a data management plan helps keep data organized, track processes, control versions, prepare data for sharing and reuse, and ensure long-term access.
This document summarizes Simon Hodson's presentation on open science and FAIR data developments globally. Some key points:
1) There is a growing policy push for open research data, with funders and organizations adopting data sharing policies based on FAIR data principles of findability, accessibility, interoperability, and reusability.
2) Initiatives are working to build the international ecosystem of open science, including components for reporting research outputs, persistent identifiers, data standards, data repositories, and criteria for trustworthy data.
3) The African Open Science Platform aims to lay the foundations for open science in Africa through frameworks for policy, incentives, training, and technical infrastructure development.
4) International
Similar to Data Management, Research Integrity and Ethics (20)
Presentation by Prof Chris Rowe, ADNet, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Investigator-initiated clinical trials: a community perspectiveARDC
Presentation by Miranda Cumpston, ACTA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Dr Merran Smith, PHRN, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Dr Adrian Burton, ARDC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
FAIR for the future: embracing all things dataARDC
FAIR for the future: embracing all things data - Natasha Simons, Keith Russell and Liz Stokes, presented at Taylor & Francis Scholarly Summits in Sydney 11 Feb 2019 and Melbourne 14 Feb 2019.
How to make your data count webinar, 26 Nov 2018ARDC
This document outlines the Make Data Count (MDC) initiative to standardize and promote the tracking of research data usage metrics. MDC has developed a Code of Practice for data usage logs, built an open hub to aggregate standardized usage data, and implemented tracking and display of usage metrics at their own repositories. They encourage other repositories to follow five simple steps to Make Their Data Count: 1) Read the Code of Practice, 2) Process usage logs, 3) Send logs to the hub, 4) Pull usage metrics from the hub, and 5) Display metrics. Future work includes outreach, iteration on implementations, and expanding metrics beyond DOIs.
The document summarizes a BoF (Birds of a Feather) session on connecting data management plans (DMPs) to power up research. The agenda included lightning talks on various DMP tools that connect DMPs to other systems and the benefits of those connections. Speakers discussed DMPs at different universities and tools like a comparative table, machine-readable DMPs, RedBox, and DASH-R for connecting DMPs. The session concluded with questions and discussion on building the DMP community.
The document discusses resources for making data FAIR (Findable, Accessible, Interoperable, and Reusable). It summarizes a survey that showed awareness of the FAIR principles was varying but growing, and most respondents would recommend them. It then describes training materials and a self-assessment tool to help make data more FAIR. Finally, it outlines future plans to provide more guidance and tools to implement FAIR practices.
The document discusses various ways that individuals can get the most out of participating in the Research Data Alliance (RDA). It lists activities like attending plenaries, preparing posters, co-chairing working groups, working on joint papers, discussing projects, and meeting people in the field. Through these engagements, participants can gain skills, enhance their reputation, build international networks, and influence strategies. The RDA provides infrastructure and governance to support collaboration and help participants leverage expertise to address issues and better understand policies and requirements in their own work.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Data Management, Research Integrity and Ethics
1. Building on the past, planning for the future
The ARDC is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program
2. Data Management, Research Integrity and Ethics
• Importance of data management
• Reproducibility crisis
• Code for Responsible Conduct of Research
• National Statement on Ethical Conduct in Human Research
• Funders and Publishers
• What is data management
• Resources for data management and ethics policies
3. Reproducibility crisis
“the results of many scientific studies are difficult or impossible to replicate
or reproduce on subsequent investigation, either by independent researchers
or by the original researchers themselves.” https://en.wikipedia.org/wiki/Replication_crisis
One of the factors is lack of management, availability or retention of
research data (Nature 533, 452–454 (26 May 2016) doi:10.1038/533452a).
Good management, retention and appropriate sharing of data improves
reproducibility of research
Institutional policies (data management, ethics) – data
storage, management, retention, sharing
4. Code for Responsible Conduct of Research:
data management
Institutions: R8 Provide access to facilities for the safe and secure storage and
management of research data, records and primary materials and, where possible
and appropriate, allow access and reference.
Researchers: R22 Retain clear, accurate, secure and complete records of all
research including research data and primary materials. Where possible and
appropriate, allow access and reference to these by interested parties.
‘Management of Data and Information in Research’ guide accompanying the Code
R27 Cite and acknowledge other relevant work appropriately and
accurately. Data can also be cited!
https://www.ands.org.au/working-with-data/citation-and-identifiers/data-citation
5. National Statement on Ethical Conduct in Human
Research: Data management
Element 4: Collection, Use and Management of Data and
Information
7. Data management, Research Integrity and Ethics
Planning for the management of research data early in a research
project can improve research efficiency, guard against data loss,
enhance data security, and ensure research data integrity and
replication.
QUT Library ‘Managing your research data’
https://www.library.qut.edu.au/research/data/
Australian Universities’ data management policies, procedures, training
https://projects.ands.org.au/policy.php
9. Data management plans in new version of the Statement
3.1.45 For all research, researchers should develop a data management plan that addresses their
intentions related to generation, collection, access, use, analysis, disclosure, storage, retention,
disposal, sharing and re-use of data and information, the risks associated with these activities and any
strategies for minimising those risks. The plan should be developed as early as possible in the research
process and should include, but not be limited to, details regarding:
(a) physical, network, system security and any other technological security measures;
(b) policies and procedures;
(c) contractual and licensing arrangements and confidentiality agreements;
(d) training for members of the project team and others, as appropriate;
(e) the form in which the data or information will be stored;
(f) the purposes for which the data or information will be used and/ or disclosed;
(g) the conditions under which access to the data or information may be granted to others; and
(h) what information from the data management plan, if any, needs to be communicated to potential
participants.
Researchers should also clarify whether they will seek:
(i) extended or unspecified consent for future research (see paragraphs
2.2.14 to 2.2.16); or
(j) permission from a review body to waive the requirement for consent
(see paragraphs 2.3.9 and 2.3.10).
12. A note on mediated access for sensitive data
• Not all data for sharing has to be open!
• F.A.I.R. data
• Findable
• Accessible
• Interoperable
• Reusable
• Five Safes risk management framework
• Safe projects: is the use of the data appropriate?
• Safe people: can the users be trusted to use it in an appropriate manner?
• Safe settings: does the access facility limit unauthorised use?
• Safe data: is there a disclosure risk in the data itself?
• Safe outputs: are the statistical results non-disclosive?
13. Data Management, Research Integrity and Ethics
Good data management practice improves
integrity of research
Institutional data management policies and
procedures, and ethics policies can support
data management and appropriate reuse of
research data, and therefore improve
reproducibility and integrity of research
14. With the exception of third party images or where otherwise indicated, this work is licensed under the Creative Commons 4.0 International Attribution Licence.
Kate LeMay
Senior Research Data Specialist
kate.lemay@ardc.edu.au
@katelemayardc
The ARDC is supported by the
Australian Government through the
National Collaborative Research
Infrastructure Strategy Program