Slides from Wednesday 1st August - Data in the Scholarly Communications Life Cycle Course which is part of the FORCE11 Scholarly Communications Institute.
Presenter - Natasha Simons
Data Management Planning for researchersSarah Jones
This document provides information about creating a data management plan (DMP) for researchers. It begins with defining what a DMP is - a short plan that outlines what data will be created, how it will be managed and stored, and plans for sharing and preservation. It then discusses the common components of a DMP, including describing the data, standards and methodologies, ethics and intellectual property, data sharing plans, and preservation strategies. The document provides examples of DMP requirements and recommendations from funders. It offers tips for creating a good DMP, including thinking about the needs of future data re-users, consulting stakeholders, grounding plans in reality, and planning for sharing from the outset. Finally, it discusses tools and resources
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
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
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 document discusses sharing research data. It describes the Data Services Center, which provides data services including finding and providing access to datasets. It notes that funders and publishers require data sharing, and that shared data receives more citations. It recommends sharing the minimum data needed to reproduce results, and considering timing, usability and granularity of data sharing. For sharing methods, it recommends using disciplinary or general repositories like UR Research, Dryad and REACTUR, which provide long-term preservation and access. Workshops and help are available for data management and sharing.
The document provides guidance on writing a data management plan (DMP). It explains that DMPs are now required by many funders to accompany grant applications. A DMP outlines how research data will be managed and shared during and after a project. It should address issues like the type of data being collected, documentation, storage and backup plans, data sharing and reuse, legal and ethical concerns, and long-term preservation. Writing a DMP helps ensure good data management practices and that a project is compliant with funder policies supporting open access to research data.
Data Management Planning for researchersSarah Jones
This document provides information about creating a data management plan (DMP) for researchers. It begins with defining what a DMP is - a short plan that outlines what data will be created, how it will be managed and stored, and plans for sharing and preservation. It then discusses the common components of a DMP, including describing the data, standards and methodologies, ethics and intellectual property, data sharing plans, and preservation strategies. The document provides examples of DMP requirements and recommendations from funders. It offers tips for creating a good DMP, including thinking about the needs of future data re-users, consulting stakeholders, grounding plans in reality, and planning for sharing from the outset. Finally, it discusses tools and resources
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
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
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 document discusses sharing research data. It describes the Data Services Center, which provides data services including finding and providing access to datasets. It notes that funders and publishers require data sharing, and that shared data receives more citations. It recommends sharing the minimum data needed to reproduce results, and considering timing, usability and granularity of data sharing. For sharing methods, it recommends using disciplinary or general repositories like UR Research, Dryad and REACTUR, which provide long-term preservation and access. Workshops and help are available for data management and sharing.
The document provides guidance on writing a data management plan (DMP). It explains that DMPs are now required by many funders to accompany grant applications. A DMP outlines how research data will be managed and shared during and after a project. It should address issues like the type of data being collected, documentation, storage and backup plans, data sharing and reuse, legal and ethical concerns, and long-term preservation. Writing a DMP helps ensure good data management practices and that a project is compliant with funder policies supporting open access to research data.
1) There is increasing pressure to make research data available for transparency and reproducibility, but privacy concerns exist as data is shared more broadly.
2) Tools for data management and sharing are improving, but policy frameworks around copyright, credit, and privacy need more focus. Data sets can contain private human subject information or be de-anonymized when combined with other data.
3) The RDA-NISO Data Privacy Interest Group aims to develop guidelines and metadata standards to address privacy issues and enable responsible data sharing. This will help improve understanding and reduce risks around research data reuse and access.
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.
Who owns the data? Intellectual property considerations for academic research...Rebekah Cummings
Intellectual property (IP) is often complicated but is even more so as it pertains to data, as “facts” are not eligible for copyright protection under United States copyright law. The IP issues surrounding data in academic research environments are often exacerbated by the fact that data ownership has rarely been discussed in university environments prior to NSF’s data management plan requirement in 2011. Researchers retained custody over their datasets and other stakeholders – namely universities and funding agencies – rarely contested ownership. Now, as datasets are increasingly seen as valuable outputs of research alongside publications, questions of data ownership are coming to the fore. This presentation will frame the complex issues surrounding data ownership in an academic research setting and will discuss strategies for educating and advising your researchers on intellectual property issues related to research data.
Open Science is a movement to make scientific research, its data and dissemination accessible to all levels of society. This movement considers aspects such as Open Access, Open Data, Reproducible Research and Open Software.
Each of these aspects presents discreteness that need to be evaluated and discussed by the scientific community so that guidelines are established that facilitate the dissemination of scientific information.
The great challenge is to establish effective and efficient practices that allow journals to add these demands in their editorial processes, so as not only to allow data, software and methods to be accessible, but also to encourage the community to do so.
Considering these questions, this panel has as a proposal to discuss important aspects about the advancement of research communication. Some of these aspects are placed in the SciELO indexing criteria, as is the case of referencing research materials in favor of transparency and reproducibility.
Syllabus
FAIR criteria, concepts and implementation; challenges for the publication of data and methods; institutional policies for open data; adoption of TOP guidelines (Transparency and Openness Promotion); software repositories; thematic areas data repositories.
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 open data and data sharing, including defining open data, the benefits of open data, overcoming barriers to opening data such as concerns about scooping and sensitive data, best practices for making data open through formats, licensing and description, and the role of research databases and data citation in promoting open data.
This document discusses licensing research data for reuse. It begins by providing a scenario where a user has downloaded a dataset but is unsure what they can do with the data due to licensing. It then discusses that licensing is critical to enabling data reuse and citation. It provides information on AusGOAL, the Australian open access and licensing framework, and notes it is recommended for data publishing by ANDS partners. It also includes links to licensing guides and FAQs. In summary, the document emphasizes the importance of data licensing for enabling reuse and outlines Australia's recommended licensing system.
The format for the data management plans for PhD students at Wagenigen UR explained. This format was developed by the library in cooperation with the Wageningen Graduate Schools.
This document discusses the FAIR data principles and increasing adoption of FAIR. It begins by explaining the 15 FAIR principles for findable, accessible, interoperable and reusable data. It then discusses how adoption is increasing through funder requirements, the role of FAIR within EOSC, and related projects. However, it notes that most data is still not managed or shared according to FAIR principles due to barriers like time and effort required as well as lack of incentives and rewards. The document argues that both cultural and technical aspects must be addressed to fully implement FAIR.
Managing environmental- molecular- and associated meta-data: The Micro B3 Inf...Renzo Kottmann
A 5 minutes lightning talk about the approach the Micro B3 Information System takes to deliver integrated environmental and molecular data with associated metadata. Presented at Biodiversity Informatics Horizon 2013 conference (see http://conference.lifewatch.unisalento.it/index.php/EBIC/BIH2013)
DataONE Education Module 10: Legal and Policy IssuesDataONE
This document discusses legal, ethical and policy issues related to managing research data. It defines key concepts like copyright, licenses and waivers, and explains why identifying ownership and control is important. Restrictions on data use and sharing are discussed, including protecting privacy and following regulations. Open licensing is presented as a way to facilitate sharing while still giving credit. The importance of behaving ethically and respecting licenses is emphasized.
The document outlines a 23 Things program for research data management training, which releases weekly activities and has monthly webinars, and provides a calendar of events and list of coordinators for the program at UWA.
This talk was given by Brianna Marshall, Digital Curation Coordinator, at the UW-Madison Digital Humanities Research Network meeting on December 2, 2014.
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.
Managing and Sharing Research Data - Workshop at UiO - December 04, 2017Michel Heeremans
These slides were presented during a workshop on Research Data Management, given at the University of Oslo, Department of Geosciences on December 04, 2017
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
State of the Art Informatics for Research Reproducibility, Reliability, and...Micah Altman
In March, I had the pleasure of being the inaugural speaker in a new lecture series (http://library.wustl.edu/research-data-testing/dss_speaker/dss_altman.html) initiated by the Libraries at the Washington University in St. Louis Libraries -- dedicated to the topics of data reproducibility, citation, sharing, privacy, and management.
In the presentation embedded below, I provide an overview of the major categories of new initiatives to promote research reproducibility, reliability, and reuse and related state of the art in informatics methods for managing data.
Research Data Management and Sharing for the Social Sciences and HumanitiesRebekah Cummings
This document summarizes a presentation on research data management for social and behavioral sciences and humanities. The presentation covered topics such as what data management is, why it is important to manage and share data, how to create data management plans, organize data files through naming conventions and folder structures, describe data through metadata and codebooks, issues around data ownership, and data storage, archiving and sharing options. The presentation was aimed at providing guidance to researchers at the University of Utah on best practices for managing and sharing their research data.
The document discusses challenges trainers may face when training researchers on data management planning and potential strategies to address these challenges. It identifies six common challenges: 1) researchers not understanding the need for data management planning, 2) researchers being unfamiliar with the concept of research data, 3) researchers not knowing how to describe their data, 4) researchers not thinking ethics and legal compliance applies to their work, 5) the complexity of data privacy and GDPR topics, and 6) researchers not understanding documentation and metadata. The document provides examples and explanations to help trainers overcome these challenges and motivate researchers on the importance of data management planning and its components.
Responsible conduct of research: Data ManagementC. Tobin Magle
A presentation for the Food and Nutrition Science Responsible conduct of research class on data management best practices. Covers material in the context of writing a data management plan.
The document discusses requirements for data management plans from the National Science Foundation. It notes that as of January 2011, NSF will require a data management plan for all new grant proposals as well as existing grants. The plan must address what data will be collected and how it will be organized, preserved, shared, and accessed. It emphasizes the importance of effective data management for facilitating research by both the principal investigators and other researchers. The document provides guidance on developing a data management plan that meets NSF's criteria and effectively manages research data.
1) There is increasing pressure to make research data available for transparency and reproducibility, but privacy concerns exist as data is shared more broadly.
2) Tools for data management and sharing are improving, but policy frameworks around copyright, credit, and privacy need more focus. Data sets can contain private human subject information or be de-anonymized when combined with other data.
3) The RDA-NISO Data Privacy Interest Group aims to develop guidelines and metadata standards to address privacy issues and enable responsible data sharing. This will help improve understanding and reduce risks around research data reuse and access.
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.
Who owns the data? Intellectual property considerations for academic research...Rebekah Cummings
Intellectual property (IP) is often complicated but is even more so as it pertains to data, as “facts” are not eligible for copyright protection under United States copyright law. The IP issues surrounding data in academic research environments are often exacerbated by the fact that data ownership has rarely been discussed in university environments prior to NSF’s data management plan requirement in 2011. Researchers retained custody over their datasets and other stakeholders – namely universities and funding agencies – rarely contested ownership. Now, as datasets are increasingly seen as valuable outputs of research alongside publications, questions of data ownership are coming to the fore. This presentation will frame the complex issues surrounding data ownership in an academic research setting and will discuss strategies for educating and advising your researchers on intellectual property issues related to research data.
Open Science is a movement to make scientific research, its data and dissemination accessible to all levels of society. This movement considers aspects such as Open Access, Open Data, Reproducible Research and Open Software.
Each of these aspects presents discreteness that need to be evaluated and discussed by the scientific community so that guidelines are established that facilitate the dissemination of scientific information.
The great challenge is to establish effective and efficient practices that allow journals to add these demands in their editorial processes, so as not only to allow data, software and methods to be accessible, but also to encourage the community to do so.
Considering these questions, this panel has as a proposal to discuss important aspects about the advancement of research communication. Some of these aspects are placed in the SciELO indexing criteria, as is the case of referencing research materials in favor of transparency and reproducibility.
Syllabus
FAIR criteria, concepts and implementation; challenges for the publication of data and methods; institutional policies for open data; adoption of TOP guidelines (Transparency and Openness Promotion); software repositories; thematic areas data repositories.
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 open data and data sharing, including defining open data, the benefits of open data, overcoming barriers to opening data such as concerns about scooping and sensitive data, best practices for making data open through formats, licensing and description, and the role of research databases and data citation in promoting open data.
This document discusses licensing research data for reuse. It begins by providing a scenario where a user has downloaded a dataset but is unsure what they can do with the data due to licensing. It then discusses that licensing is critical to enabling data reuse and citation. It provides information on AusGOAL, the Australian open access and licensing framework, and notes it is recommended for data publishing by ANDS partners. It also includes links to licensing guides and FAQs. In summary, the document emphasizes the importance of data licensing for enabling reuse and outlines Australia's recommended licensing system.
The format for the data management plans for PhD students at Wagenigen UR explained. This format was developed by the library in cooperation with the Wageningen Graduate Schools.
This document discusses the FAIR data principles and increasing adoption of FAIR. It begins by explaining the 15 FAIR principles for findable, accessible, interoperable and reusable data. It then discusses how adoption is increasing through funder requirements, the role of FAIR within EOSC, and related projects. However, it notes that most data is still not managed or shared according to FAIR principles due to barriers like time and effort required as well as lack of incentives and rewards. The document argues that both cultural and technical aspects must be addressed to fully implement FAIR.
Managing environmental- molecular- and associated meta-data: The Micro B3 Inf...Renzo Kottmann
A 5 minutes lightning talk about the approach the Micro B3 Information System takes to deliver integrated environmental and molecular data with associated metadata. Presented at Biodiversity Informatics Horizon 2013 conference (see http://conference.lifewatch.unisalento.it/index.php/EBIC/BIH2013)
DataONE Education Module 10: Legal and Policy IssuesDataONE
This document discusses legal, ethical and policy issues related to managing research data. It defines key concepts like copyright, licenses and waivers, and explains why identifying ownership and control is important. Restrictions on data use and sharing are discussed, including protecting privacy and following regulations. Open licensing is presented as a way to facilitate sharing while still giving credit. The importance of behaving ethically and respecting licenses is emphasized.
The document outlines a 23 Things program for research data management training, which releases weekly activities and has monthly webinars, and provides a calendar of events and list of coordinators for the program at UWA.
This talk was given by Brianna Marshall, Digital Curation Coordinator, at the UW-Madison Digital Humanities Research Network meeting on December 2, 2014.
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.
Managing and Sharing Research Data - Workshop at UiO - December 04, 2017Michel Heeremans
These slides were presented during a workshop on Research Data Management, given at the University of Oslo, Department of Geosciences on December 04, 2017
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
State of the Art Informatics for Research Reproducibility, Reliability, and...Micah Altman
In March, I had the pleasure of being the inaugural speaker in a new lecture series (http://library.wustl.edu/research-data-testing/dss_speaker/dss_altman.html) initiated by the Libraries at the Washington University in St. Louis Libraries -- dedicated to the topics of data reproducibility, citation, sharing, privacy, and management.
In the presentation embedded below, I provide an overview of the major categories of new initiatives to promote research reproducibility, reliability, and reuse and related state of the art in informatics methods for managing data.
Research Data Management and Sharing for the Social Sciences and HumanitiesRebekah Cummings
This document summarizes a presentation on research data management for social and behavioral sciences and humanities. The presentation covered topics such as what data management is, why it is important to manage and share data, how to create data management plans, organize data files through naming conventions and folder structures, describe data through metadata and codebooks, issues around data ownership, and data storage, archiving and sharing options. The presentation was aimed at providing guidance to researchers at the University of Utah on best practices for managing and sharing their research data.
The document discusses challenges trainers may face when training researchers on data management planning and potential strategies to address these challenges. It identifies six common challenges: 1) researchers not understanding the need for data management planning, 2) researchers being unfamiliar with the concept of research data, 3) researchers not knowing how to describe their data, 4) researchers not thinking ethics and legal compliance applies to their work, 5) the complexity of data privacy and GDPR topics, and 6) researchers not understanding documentation and metadata. The document provides examples and explanations to help trainers overcome these challenges and motivate researchers on the importance of data management planning and its components.
Responsible conduct of research: Data ManagementC. Tobin Magle
A presentation for the Food and Nutrition Science Responsible conduct of research class on data management best practices. Covers material in the context of writing a data management plan.
The document discusses requirements for data management plans from the National Science Foundation. It notes that as of January 2011, NSF will require a data management plan for all new grant proposals as well as existing grants. The plan must address what data will be collected and how it will be organized, preserved, shared, and accessed. It emphasizes the importance of effective data management for facilitating research by both the principal investigators and other researchers. The document provides guidance on developing a data management plan that meets NSF's criteria and effectively manages research data.
Aim:- To show how research data management can contribute to the success of your PhD.
*What is research data and why it is important?
*The Research Data lifecycle
* Research Data – more than just your results
* FAIR data and Open Research
* DMP online tool
Presentation given at the Consorcio Madrono conference on Data Management Plans in Horizon 2020 http://www.consorciomadrono.es/info/web/blogs/formacion/217.php
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.
Writing a successful data management plan with the DMPToolkfear
This document provides an overview of how to write an effective Data Management Plan (DMP) using the DMPTool. It discusses the key components of a DMP including data products, standards, access and sharing, preservation, and documentation. The goals are to help researchers generate a DMP, understand the basic elements, and recognize how good data management leads to a strong plan. Writing a thorough DMP is now required by many funders and helps ensure data is organized, accessible, and preserved for future use.
Session presented by Judith Carr, Research Data Manager at the University of Liverpool on Research Data Management and your PhD.
Aim:- To show how research data management can contribute to the success of your PhD.
Covers:
* What is research data and why it is important?
* The Research Data lifecycle
Research Data – more than just your results
* FAIR data and Open Research
DMP online tool
Getting to grips with research data management Wendy Mears
This document provides an overview of research data management. It defines research data management and discusses its importance. It also outlines the data lifecycle model and provides guidance on sharing data, working with data, planning for data management, and useful resources for research data management. The document aims to help researchers effectively manage the data created throughout the research process.
The document provides an overview of research data management and the importance of avoiding a "DATApocalypse" or data disaster. It discusses the definition of research data, why data management is important, questions to consider, best practices for data management planning, documentation, and long-term preservation. The goal is to help researchers and institutions properly manage data to enable sharing and preservation, as required by most major funders.
- Persistent identifiers (PIDs) play a key role in discoverability, accessibility, and reproducibility of research by providing long-lasting references to digital resources like publications, data, software, and people.
- There are many PID systems that vary in purpose, governance, metadata collected, and other factors such as Handles, DOIs, and ORCIDs. DOIs are most widely used for research data.
- When choosing a PID, factors to consider include purpose, scope, underlying technology, governance, and trustworthiness to ensure the PID remains long-lasting. It is important that PID systems and their social infrastructure are maintained to avoid failures.
This document summarizes Rob Grim's presentation on e-Science, research data, and the role of libraries. It discusses the Open Data Foundation's work in promoting metadata standards like DDI and SDMX. It also outlines the research data lifecycle and how metadata management can help libraries support research through services like data registration, archiving, discovery and access. Finally, it provides examples of how Tilburg University library supports research data through services aligned with data availability, discovery, access and delivery.
Persistent Identifiers (PiDs) for research – why we have them, why there are so many PiD systems, how they work looking at a few examples (Handles, DOIs, ORCIDs), how to choose one, can PiD systems fail and what’s happening in the international PiD community
Overview of the Research on Open Educational Resources for Development (ROER4D) Open Data initiative, highlighting data management principles, the five pillars of the ROER4D data publication approach and the project de-identification approach.
"Open Science, Open Data" training for participants of Software Writing Skills for Your Research - Workshop for Proficient, Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Telegrafenberg, December 16, 2015
Share and Reuse: how data sharing can take your research to the next levelKrzysztof Gorgolewski
This document discusses the benefits of sharing neuroimaging data. It provides examples of large neuroimaging datasets including the NKI Enhanced dataset and the Human Connectome Project. It notes that data sharing can save money by reducing data reacquisition costs. While there are fears associated with data sharing like being scooped, studies show higher statistical quality and citation rates when data is shared. The document promotes sharing statistical maps and outlines standards like BIDS to facilitate sharing. It provides resources for sharing including repositories like OpenfMRI and tools to build data papers and communities around shared data.
FAIRDOM data management support for ERACoBioTech ProposalsFAIRDOM
This document provides information about a webinar from the FAIRDOM Consortium on data management for ERACoBioTech full proposals. It includes:
- Details on how to budget for and include a data management plan in proposals
- A checklist for developing a data management plan covering topics like the types and volumes of data, data sharing and reuse, and making data FAIR
- An overview of the FAIRDOM services and software platform that can help with project data management and stewardship
The document discusses NSF requirements for data management plans for grant proposals. It notes that as of January 2011, proposals must include a data management plan that addresses how data will be organized, preserved, and shared. The plan must provide enough detail for reviewers to understand how data will be managed during and after the project. Guidelines are provided on the key elements to address in a data management plan, including what data will be collected, how it will be formatted and documented, how others can access and use the data, and how the data will be preserved long-term. Resources for developing effective data management plans are suggested.
The document discusses requirements for National Science Foundation (NSF) Data Management Plans (DMPs). Starting in 2011, DMPs describing how research data will be organized, preserved, and shared are required as part of NSF grant proposals. DMPs must address data standards, access and sharing policies, and long-term preservation and access. Resources for writing DMPs are provided, including tools, best practices examples, and experts available for consultation.
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.
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.
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.
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.
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.
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.
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 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.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Juneteenth Freedom Day 2024 David Douglas School District
Fsci 2018 wednesday1_august_am6
1. Natasha Simons
#AM6 Data in the Scholarly
Communications Lifecycle
FORCE11 Scholarly Communications Institute
Monday 30 July – Friday 3 August 2018
San Diego, USA
2. Wednesday 1 August
Today’s course outline
• Debate: for and against open data (con’t from Tuesday)
• Managing working data – are you being FAIR to the future you?
• Hands-on with the Open Science Framework
• Introduction to the Carpentries
Link to slides: https://tinyurl.com/y9hmu7q4
5. Are you being FAIR to the future you?
In 5 years time will my research data be:
• Findable – A top draw of USB drives and sticks isn’t always a good data archive
• Accessible – My new desktop doesn't have a DVD drive or what was the password on that
encrypted data drive?
• Interoperable – Wonder where I put my old copy of that software that compiles this binary
data file?
• Reusable – How accurate was that sensor network I used to gather these observations? Am
I allowed to reuse this data?
FAIR - Working Data | John Morrissey5 |
Slide credit: John Morrisey, CSIRO
6. FAIR Working Data
Findable by whom? How? Minimum viable metadata?
• Standardized naming conventions for folders and files
• Consider using Readme.txt files to describe content? Maybe you could include
metadata.txt or metadata.json files embedded in folders
• Think about what persistent identifiers are useful in your project.
• Do you need a basic registry to manage metadata?
FAIR - Working Data | John Morrissey6 |
Slide credit: John Morrisey, CSIRO
7. FAIR Working Data
Accessible by: Whom? How? What?
• How will you manage identity and access control?
• Shared storage resources – where?
• Will you use simple storage or a higher level platform like a shared eLab notebook
or database?
• What categories of data will you hold/share and which data assets need to be kept
long term?
FAIR - Working Data | John Morrissey7 |
Slide credit: John Morrisey, CSIRO
8. FAIR Working Data
Interoperable:
• What are the key standards currently applied to the projects domain/s?
• Are my data producing assets standards compliant? Do they need to be? What do I
have to do to convert my data assets to the correct format?
• Do we have a set of vocabularies we want to use within our project? Where are
they?
• Who can help me with my standards compliance work? (Librarians? IT Specialists?
Information Management Specialists?)
FAIR - Working Data | John Morrissey8 |
Slide credit: John Morrisey, CSIRO
9. FAIR Working Data
Reusable:
• Agree on a licencing framework before the project starts producing data
• What data assets need to be preserved long term?
• What data assets will we publish?
• Where will we publish?
• Who has contributed to the data asset and how will they be represented when
the data published
• Who will manage the long-term data archive?
FAIR - Working Data | John Morrissey9 |
Slide credit: John Morrisey, CSIRO
13. With the exception of third party images or where otherwise indicated, this work is licensed under the Creative
Commons 4.0 International Attribution Licence.
ANDS, Nectar and RDS are supported by the Australian Government through the National Collaborative Research
Infrastructure Strategy Program (NCRIS).
Natasha Simons
Associate Director, Skilled Workforce| Australian Research Data Commons
Industry Fellow | The University of Queensland
T: +61 7 3346 9991 | E: natasha.simons@ands.org.au | W: ands.org.au
ORCID: https://orcid.org/0000-0003-0635-1998 Tw: @n_simons
Thank you!