Data discovery and metadata - Natasha Simons
Research Data Management workshop at the iSchools Data Science Winter Institute, 7-9 December 2017, University of Hong Kong
Data management basics, for UC Davis EDU 292Phoebe Ayers
This document provides information and guidance about data management for EDU 292. It lists resources for data management from UC Davis Libraries and highlights key reasons for properly managing research data such as reproducibility, credibility, and fulfilling requirements. It discusses metadata, storage options, backups, file formats, and security. It also covers citing data sources accurately and linking works together. The document encourages participants to consider aspects like long-term maintenance, access, and version control for research data and raises questions to facilitate planning proper data management practices.
This document summarizes Sherry Lake's presentation on re-tooling libraries to support data management. Some key points:
- The University of Virginia restructured its research support model in 2010 to focus on data management and created the Scientific Data Consulting Group.
- Other models discussed include groups at Purdue, Johns Hopkins, Cornell, Wisconsin, and Rutgers that provide data management consulting and services.
- Re-skilling existing staff involves training librarians through courses, workshops, and data interviews to build expertise in areas like data formats, metadata, and data management plans.
- Multiple areas of competency are important for supporting research data, including information science, computer science, domain expertise, management
The document discusses methods for tracking the reuse of data from scientific repositories through citation analysis. It outlines initial questions around how data is currently cited and levels of reuse. Methods tested include searching repositories like TreeBASE, Pangaea and ORNL DAAC, as well as databases like ISI Web of Science, Scirus and Google Scholar. Preliminary findings suggest search terms like repository name, DOI and author name had varying effectiveness across sources. Further analysis is needed to solidify conclusions and examine additional repositories, search terms and databases.
No more waiting! Tools that work Today to reveal dataset useHeather Piwowar
This document discusses the need to better understand the impact of datasets beyond just citations. It notes that datasets can be engaged with in many ways, such as through views, saves, discussions, and recommendations, by various groups like researchers, teachers, students, and policymakers. It calls for exposing more metrics of engagement, supporting more tools for interacting with datasets at all stages, and making metrics and data more openly available to help reveal how datasets are being used.
This document provides instructions and guidance for students completing a search strategy project on academic databases. It discusses analyzing the content, structure, and interface of a database; indexing and filtering documents; field searching; reading sample records; and transitioning between different types of searches. The document also defines key concepts like journals, citations as metadata, and Chicago citation style.
2013 DataCite Summer Meeting - Making Research better
DataCite. Co-sponsored by CODATA.
Thursday, 19 September 2013 at 13:00 - Friday, 20 September 2013 at 12:30
Washington, DC. National Academy of Sciences
http://datacite.eventbrite.co.uk/
Data and Donuts: How to write a data management planC. Tobin Magle
This presentation describes best practices for how to write a data management plan for your research data. Additionally, it provides information about finding funder requirements, metadata standards, and repositories.
Data management basics, for UC Davis EDU 292Phoebe Ayers
This document provides information and guidance about data management for EDU 292. It lists resources for data management from UC Davis Libraries and highlights key reasons for properly managing research data such as reproducibility, credibility, and fulfilling requirements. It discusses metadata, storage options, backups, file formats, and security. It also covers citing data sources accurately and linking works together. The document encourages participants to consider aspects like long-term maintenance, access, and version control for research data and raises questions to facilitate planning proper data management practices.
This document summarizes Sherry Lake's presentation on re-tooling libraries to support data management. Some key points:
- The University of Virginia restructured its research support model in 2010 to focus on data management and created the Scientific Data Consulting Group.
- Other models discussed include groups at Purdue, Johns Hopkins, Cornell, Wisconsin, and Rutgers that provide data management consulting and services.
- Re-skilling existing staff involves training librarians through courses, workshops, and data interviews to build expertise in areas like data formats, metadata, and data management plans.
- Multiple areas of competency are important for supporting research data, including information science, computer science, domain expertise, management
The document discusses methods for tracking the reuse of data from scientific repositories through citation analysis. It outlines initial questions around how data is currently cited and levels of reuse. Methods tested include searching repositories like TreeBASE, Pangaea and ORNL DAAC, as well as databases like ISI Web of Science, Scirus and Google Scholar. Preliminary findings suggest search terms like repository name, DOI and author name had varying effectiveness across sources. Further analysis is needed to solidify conclusions and examine additional repositories, search terms and databases.
No more waiting! Tools that work Today to reveal dataset useHeather Piwowar
This document discusses the need to better understand the impact of datasets beyond just citations. It notes that datasets can be engaged with in many ways, such as through views, saves, discussions, and recommendations, by various groups like researchers, teachers, students, and policymakers. It calls for exposing more metrics of engagement, supporting more tools for interacting with datasets at all stages, and making metrics and data more openly available to help reveal how datasets are being used.
This document provides instructions and guidance for students completing a search strategy project on academic databases. It discusses analyzing the content, structure, and interface of a database; indexing and filtering documents; field searching; reading sample records; and transitioning between different types of searches. The document also defines key concepts like journals, citations as metadata, and Chicago citation style.
2013 DataCite Summer Meeting - Making Research better
DataCite. Co-sponsored by CODATA.
Thursday, 19 September 2013 at 13:00 - Friday, 20 September 2013 at 12:30
Washington, DC. National Academy of Sciences
http://datacite.eventbrite.co.uk/
Data and Donuts: How to write a data management planC. Tobin Magle
This presentation describes best practices for how to write a data management plan for your research data. Additionally, it provides information about finding funder requirements, metadata standards, and repositories.
RDAP13 Elizabeth Moss: The impact of data reuseASIS&T
Kathleen Fear, ICPSR, University of Michigan
“The impact of data reuse: a pilot study of 5 measures”
Panel: Data citation and altmetrics
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
This document discusses best practices for research data management. It recommends creating a data management plan that considers short and long-term storage needs for oneself, colleagues, funders and journals. The plan should include all raw, processed and supplementary materials needed to understand and reproduce the research. Short term storage options like personal computers, servers and cloud services are discussed alongside folder structure, file naming conventions and documentation standards. Long term archiving through discipline repositories is also covered to ensure research can be understood, verified and built upon by others in the future.
This document provides an overview of the Dataverse Network Project, which is a repository for research data hosted at Harvard University. It allows researchers to deposit, share, and organize their data in a curated network. Key features include long-term preservation of data and metadata, access and sharing capabilities, and archiving best practices to promote data access and reproducibility. Researchers can create individual dataverses to organize their studies and deposit data through a web interface or via software installation. The network supports various file types and formats and provides data citation and version control.
This document provides best practices for digital file management, including file naming, version control, file organization, and use of README files. It recommends using descriptive yet concise file names without special characters or spaces. For version control, it suggests numbering files with leading zeros and using date formats like YYYYMMDD. The document also advises having a clear and consistent folder structure with mutually exclusive top-level folders and explanatory README files. It announces additional training workshops on these topics.
What role can publishers play in the open data ecosystem?Varsha Khodiyar
Presentation at session 3 of the NIH workshop 'Role of Generalist Repositories to Enhance Data Discoverability and Reuse' on Feb 11th, at the NIH Main Campus.
This is the PowerPoint for my "Data Management for Undergraduate Researchers" workshop for the Office of Undergraduate Research Seminar and Workshop Series. Major topics include motivations behind good data management, file naming, version control, metadata, storage, and archiving.
This document summarizes a seminar on data management for undergraduate researchers. It discusses what data is, why it needs to be managed, and key aspects of the data management process such as data organization, metadata, storage, and archiving. Topics covered include file naming best practices, version control, documentation, metadata standards, storage options, and long-term archiving. The goal is to help researchers organize and document their data so it can be understood, preserved, and reused.
Keynote presentation at 2020 NIH/NLM workshop on generalist repositories. Central themes include software as a richer pathway to data than articles, the development of new metrics for software (such as the CHAOSS framework), working with the technology companies through organizations like the Eclipse Foundation, and the importance of linked data. In particular, the concept of the "value line" as a means to map generalist repositories represents an important opportunity.
Written and presented by Carole Goble (University of Manchester) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
This document discusses best practices for organizing research data. It recommends creating a hierarchical folder structure with the most important attributes of the data ranked highest. Descriptive and consistent file naming conventions are also important for both human and machine readability. Spreadsheets should be used wisely by having each column represent a variable and each row an observation to create tidy data that is efficient for analysis. The Open Science Framework is introduced as a tool for collaboration and organizing research components online.
Improving Scientific Information Sharing by Fostering Reuse - Presentation at...3 Round Stones
Most scientific developments are recorded in published papers and communicated via presentations. Scientific findings are presented within organizations, at conferences, via Webinars and other fora. Yet after delivery to an audience, important information is often left to wither on hard drives, document management systems and even the Web. Accessing the underlying data for scientific findings has been the Achilles Heel of researchers due to closed and proprietary systems. This presentation shows an alternative to sharing scientific information using a Linked Data approach.
Lightning Talk, Konkiel: Bootstrapping Library Data Management Services for E...ASIS&T
This document discusses how libraries can provide data management services to support epidemiology research. It describes the characteristics of epidemiology data, including its sensitive nature and complexity. It outlines some of the needs of epidemiology researchers, such as secure storage, training, and tools for data sharing and citation. The document proposes several library services to address these needs, such as repositories for long-term preservation of epidemiology data with access controls, training in data management and standards, and assigning persistent identifiers to data. Finally, it provides examples of resources on related topics like informed consent workflows and disciplinary metadata standards.
This document provides an overview of library resources available at CUT to support research. It discusses information skills resources for various stages of research, how to search the library catalog and databases. It introduces key databases like IEEE Xplore, Science Direct and Scopus. Standards available through CYS are mentioned. Services like interlibrary loans and the virtual private network for off-campus access are highlighted. Contact information for the subject librarian is provided for research support.
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2015-02-09. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
A presentation on research data management presented at the Utah Library Association conference in May 2015. Main topics included federal mandates, data repositories, metadata, and file naming conventions. Presenters: Rebekah Cummings, Elizabeth Smart, Becky Thoms, and Brit Faggerheim.
Written and presented by Wolfgang Müller (HITS) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
Slides from Thursday 2nd August 2018 - Data in the Scholarly Communications Life Cycle Course which is part of the FORCE11 Scholarly Communications Institute.
Presenter - Natasha Simons
Documentation and Metdata - VA DM BootcampSherry Lake
This document discusses documentation and metadata for research data. It begins with an overview of why documentation is important at different stages of the research data lifecycle from collection through archiving. Key elements to document include how the data was created, its content and structure, who created and maintains it, and how it can be accessed and cited. The document then discusses common documentation formats like readmes, data dictionaries, and codebooks. It also introduces metadata as structured information that describes resources and explains common metadata standards and tools for creating structured metadata files. Exercises guide creating documentation in these formats for a weather dataset example.
RDAP13 Elizabeth Moss: The impact of data reuseASIS&T
Kathleen Fear, ICPSR, University of Michigan
“The impact of data reuse: a pilot study of 5 measures”
Panel: Data citation and altmetrics
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
This document discusses best practices for research data management. It recommends creating a data management plan that considers short and long-term storage needs for oneself, colleagues, funders and journals. The plan should include all raw, processed and supplementary materials needed to understand and reproduce the research. Short term storage options like personal computers, servers and cloud services are discussed alongside folder structure, file naming conventions and documentation standards. Long term archiving through discipline repositories is also covered to ensure research can be understood, verified and built upon by others in the future.
This document provides an overview of the Dataverse Network Project, which is a repository for research data hosted at Harvard University. It allows researchers to deposit, share, and organize their data in a curated network. Key features include long-term preservation of data and metadata, access and sharing capabilities, and archiving best practices to promote data access and reproducibility. Researchers can create individual dataverses to organize their studies and deposit data through a web interface or via software installation. The network supports various file types and formats and provides data citation and version control.
This document provides best practices for digital file management, including file naming, version control, file organization, and use of README files. It recommends using descriptive yet concise file names without special characters or spaces. For version control, it suggests numbering files with leading zeros and using date formats like YYYYMMDD. The document also advises having a clear and consistent folder structure with mutually exclusive top-level folders and explanatory README files. It announces additional training workshops on these topics.
What role can publishers play in the open data ecosystem?Varsha Khodiyar
Presentation at session 3 of the NIH workshop 'Role of Generalist Repositories to Enhance Data Discoverability and Reuse' on Feb 11th, at the NIH Main Campus.
This is the PowerPoint for my "Data Management for Undergraduate Researchers" workshop for the Office of Undergraduate Research Seminar and Workshop Series. Major topics include motivations behind good data management, file naming, version control, metadata, storage, and archiving.
This document summarizes a seminar on data management for undergraduate researchers. It discusses what data is, why it needs to be managed, and key aspects of the data management process such as data organization, metadata, storage, and archiving. Topics covered include file naming best practices, version control, documentation, metadata standards, storage options, and long-term archiving. The goal is to help researchers organize and document their data so it can be understood, preserved, and reused.
Keynote presentation at 2020 NIH/NLM workshop on generalist repositories. Central themes include software as a richer pathway to data than articles, the development of new metrics for software (such as the CHAOSS framework), working with the technology companies through organizations like the Eclipse Foundation, and the importance of linked data. In particular, the concept of the "value line" as a means to map generalist repositories represents an important opportunity.
Written and presented by Carole Goble (University of Manchester) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
This document discusses best practices for organizing research data. It recommends creating a hierarchical folder structure with the most important attributes of the data ranked highest. Descriptive and consistent file naming conventions are also important for both human and machine readability. Spreadsheets should be used wisely by having each column represent a variable and each row an observation to create tidy data that is efficient for analysis. The Open Science Framework is introduced as a tool for collaboration and organizing research components online.
Improving Scientific Information Sharing by Fostering Reuse - Presentation at...3 Round Stones
Most scientific developments are recorded in published papers and communicated via presentations. Scientific findings are presented within organizations, at conferences, via Webinars and other fora. Yet after delivery to an audience, important information is often left to wither on hard drives, document management systems and even the Web. Accessing the underlying data for scientific findings has been the Achilles Heel of researchers due to closed and proprietary systems. This presentation shows an alternative to sharing scientific information using a Linked Data approach.
Lightning Talk, Konkiel: Bootstrapping Library Data Management Services for E...ASIS&T
This document discusses how libraries can provide data management services to support epidemiology research. It describes the characteristics of epidemiology data, including its sensitive nature and complexity. It outlines some of the needs of epidemiology researchers, such as secure storage, training, and tools for data sharing and citation. The document proposes several library services to address these needs, such as repositories for long-term preservation of epidemiology data with access controls, training in data management and standards, and assigning persistent identifiers to data. Finally, it provides examples of resources on related topics like informed consent workflows and disciplinary metadata standards.
This document provides an overview of library resources available at CUT to support research. It discusses information skills resources for various stages of research, how to search the library catalog and databases. It introduces key databases like IEEE Xplore, Science Direct and Scopus. Standards available through CYS are mentioned. Services like interlibrary loans and the virtual private network for off-campus access are highlighted. Contact information for the subject librarian is provided for research support.
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2015-02-09. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
A presentation on research data management presented at the Utah Library Association conference in May 2015. Main topics included federal mandates, data repositories, metadata, and file naming conventions. Presenters: Rebekah Cummings, Elizabeth Smart, Becky Thoms, and Brit Faggerheim.
Written and presented by Wolfgang Müller (HITS) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
Slides from Thursday 2nd August 2018 - Data in the Scholarly Communications Life Cycle Course which is part of the FORCE11 Scholarly Communications Institute.
Presenter - Natasha Simons
Documentation and Metdata - VA DM BootcampSherry Lake
This document discusses documentation and metadata for research data. It begins with an overview of why documentation is important at different stages of the research data lifecycle from collection through archiving. Key elements to document include how the data was created, its content and structure, who created and maintains it, and how it can be accessed and cited. The document then discusses common documentation formats like readmes, data dictionaries, and codebooks. It also introduces metadata as structured information that describes resources and explains common metadata standards and tools for creating structured metadata files. Exercises guide creating documentation in these formats for a weather dataset example.
Planning for Research Data Management: 26th January 2016IzzyChad
This document provides an overview of a session on planning for research data management. It discusses what research data management is, why it is important, and walks through the steps for creating a data management plan. The presenter explains the benefits of effective data management, such as helping researchers work more efficiently and enabling data sharing. Key aspects of a data management plan are also outlined, including describing the data, addressing ethics and intellectual property, determining how data will be stored and preserved, and making plans for data sharing and access.
This document provides an overview of research data management and outlines the steps for creating a data management plan. It discusses why research data management is important, including enabling data reuse and sharing and meeting funder requirements. The document then walks through creating a data management plan, covering topics like the types and formats of data that will be generated, ethical and intellectual property issues, how data will be stored and backed up, and long-term preservation and deposition of data. It emphasizes that planning early helps ensure accurate, complete and secure data, and avoids problems down the line.
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 Indiana University School of Medicine's Ruth Lilly Medical Library. Contains information and resources specific to Indiana University Purdue University Indianapolis (IUPUI). For full class materials, see LYD17_IUPUIWorkshop folder here: https://osf.io/r8tht/.
Spring 2014 Data Management Lab: Session 2 Slides (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
This document summarizes a session from the Force 11 Scholarly Communications Institute Summer School on data discovery. The session covered metadata, including what it is, types of metadata, and standards. It discussed how people search for and find data through various sources. The session also explored the FAIR data principles of findable, accessible, interoperable and reusable data and had breakout groups discuss applying these principles in practice.
This document provides an introduction to research data management for geoscience PhD students. It defines research data and different data types. It discusses the importance of managing data throughout its lifecycle for efficient and valid research. It outlines funder requirements, university policies, and activities involved in good research data management like data planning, documentation, storage, sharing and preservation.
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
Managing data throughout the research lifecycleMarieke Guy
This document summarizes a presentation about managing data throughout the research lifecycle. It discusses the stages of the research lifecycle, including planning, data creation, documentation, storage, sharing, and preservation. It provides examples of research lifecycle models and addresses key questions to consider at each stage, such as what formats to use, how to document data, where to store it, and how to share and preserve it. The presentation emphasizes making informed decisions about data management and talking to colleagues for support and advice.
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.
Research Data Management: What is it and why is the Library & Archives Servic...GarethKnight
This document summarizes research data management and the library and archives service's involvement. It defines research data, explains why data needs to be managed, and outlines the key drivers for data management and publication. It then describes the library and archives service's knowledge of data management, the research data management support service being established, and the guidance, training, and tools being developed to help researchers with data management.
Data Literacy: Creating and Managing Reserach Datacunera
This document discusses best practices for creating and managing research data. It covers defining data, the importance of data management, developing a data management plan, file naming conventions, metadata, data sharing and preservation. Key points include making a data management plan addressing types of data, standards, access and sharing policies; using descriptive file names with dates; storing multiple versions of data; and including metadata to explain the data. Resources for data management support are provided.
http://kulibrarians.g.hatena.ne.jp/kulibrarians/20170222
Presentation by Cuna Ekmekcioglu (The University of Edinburgh)
- Creating and Managing Digital Research Data in Creative Arts: An overview (2016)
CC BY-NC-SA 4.0
Here are the results of the dotmocracy voting:
- "Libraries are the best departments at universities to take on research data archiving." Received the most dots.
- "High cost research facilities should be obliged to share (and preserve) their data." Received the second most dots.
- "Each dataset should also include the data in its rawest form." Received the third most dots.
The top three propositions that received the most votes were:
1. Libraries are the best departments at universities to take on research data archiving.
2. High cost research facilities should be obliged to share (and preserve) their data.
3. Each dataset should also include the data in its
This document provides an overview of key concepts for effective data management, including why data management is important, common data types and stages, best practices for storage, versioning, naming conventions, metadata, standards, sharing, and archiving. It emphasizes that properly managing data helps ensure reproducibility, enables data sharing and reuse, satisfies funder requirements, and supports student work. The presentation covers terminology like metadata ("data about data") and standards like ISO and EML and provides examples to illustrate best practices for documentation to help others understand and use research data. It aims to bring together these concepts to help researchers develop effective Data Management Plans as required by funders like NSF.
Some Ideas on Making Research Data: "It's the Metadata, stupid!"Anita de Waard
This document discusses challenges and opportunities around research data management. It notes that while the majority of research data is currently stored locally on hard drives, funding agencies and researchers are increasingly focused on sharing, curating and ensuring long-term access to data. However, there are open questions around how to incentivize researchers to share data, ensure sustainable funding models for repositories, and develop interoperable metadata standards. The document explores potential roles for libraries, institutions, publishers and domain-specific repositories in addressing these issues.
The challenge of sharing data well, how publishers can helpVarsha Khodiyar
Researchers, academic institutes and funders are increasingly recognizing the importance of data sharing for reproducible science. However, it is not always straightforward and clear to researchers as to how best to share data in a useful way. At Springer Nature we are working on several initiatives to help facilitate the sharing of research data in a reusable way, with our overarching goal being to publish research that is robust and reproducible. I will talk about the effort that goes into our flagship data journal, Scientific Data, to facilitate best practices in publication and sharing of research data, and share some of our experiences publishing Challenge datasets. I will also describe some of the newer Research Data Services that are now available to help all researchers (not only Springer Nature authors) to share their data in a useful way.
This document provides an introduction to data management. It discusses the importance of data management and introduces best practices. These include making a data management plan, properly organizing and naming files, adding descriptive metadata, securely storing and backing up data, considering legal and ethical issues, enabling sharing and reuse, and ensuring long-term preservation. Effective data management is important across all disciplines and throughout the entire data lifecycle from creation to archiving.
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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.
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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.
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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.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
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Answers about how you can do more with Walmart!"
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Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
3. Why do people search for data*?
•Exploratory/Scoping
•Reuse/Secondary data analysis
•Can be starting point or ad hoc
•Peer review
•Reproduce/extend results
•Repurpose (e.g. for mashups, visualisations, simulations)
•Verify claims (e.g. report findings)
*Not in any order; not exhaustive!
5. How do people find data*?
•Google
•Ask a colleague
•Find link to data in a journal article
•Data journals
•Data registries e.g. re3data
•Open data portals e.g. data.gov
•Institutional repositories
•Data / Discipline repositories e.g. Dryad
•Project website
•Data discovery aggregators like Research Data Australia
•Library catalogues, databases
*Not in any order; not exhaustive!
6. Characteristics of finding data
When creating metadata records, keep in mind that finding data is:
● Movable feast / changing beast
● No standard practice, universal standard or vocab
● Databases are non-exhaustive
● Methods for searching and terms driven by why people are
looking and how the data is stored
7. FAIR Data
To aid discovery and reuse, data needs to be:
● Findable
● Accessible
● Interoperable
● Reusable
More on FAIR Data:
● FAIR Data Principles (FORCE11): https://www.force11.org/group/fairgroup/fairprinciples
● ANDS and FAIR Data: https://www.ands.org.au/working-with-data/fairdata
● FAIR Data ANDS Webinar series: https://www.youtube.com/user/andsdata (FAIR Data Playlist)
ANDS/Nectar/RDS
“FAIRground” booth
at eResearch
Australasia 2017
8. Hands-on exercise: data description
Your task:
1. Divide into pairs
2. Each pair take one of the CSV data files
3. Describe the data by creating a metadata record. Think about:
title, creators, date, short description and so on.
You have 15 minutes - go!!
If you are unfamiliar with metadata, take few minutes
to view the introductory video at:
https://www.youtube.com/watch?v=ABF2FvSPVYE
9. Class discussion
How did you go?
What did you learn?
Here are the original metadata descriptions:
CSV dataset #1 - https://data.qld.gov.au/dataset/marine-oil-spills-
data
CSV dataset #2 –
https://data.qld.gov.au/dataset/koala-hospital-data
11. Open data case study
University of Tasmania - IMAS Marine Data
https://www.youtube.com/watch?v=_Bs56PnYK9g
More Open Data project stories: https://www.youtube.com/user/andsdata
(Open Data Playlist)
18. 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@ands.org.au
@n_simons
orcid.org/0000-0003-0635-1998
Natasha Simons
Editor's Notes
[Kate]
Thank you and please feel free to contact us.