This talk was given by Brianna Marshall, Digital Curation Coordinator, at the UW-Madison Digital Humanities Research Network meeting on December 2, 2014.
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.
Practical Data Management - ACRL DCIG WebinarKristin Briney
This document summarizes a webinar on practical data management. It discusses best practices for file organization, naming conventions, documentation, storage, backups, and ensuring future usability. Key recommendations include organizing files logically by project or type, using consistent naming conventions, thoroughly documenting data collection and analysis methods, storing data in multiple locations both on and off-site, backing up data regularly including testing backups, and future-proofing data through file format conversion and migration to new media. Resources for further information on data management best practices are also provided.
Scholars and researchers are being asked by an increasing number of research sponsors and journals to outline how they will manage and share their research data. This is an introduction to data management and sharing practices with some specific information for Columbia University researchers.
The document provides guidance on early planning for data management, including becoming familiar with funder requirements, planning for the types and formats of data that will be created, designing a system for taking notes, organizing files through consistent naming schemes and use of folders, adding metadata to files to aid in documentation and discovery, and using RSS feeds to organize web-based information. It also touches on issues like plagiarism, data protection, intellectual property rights, and remote access to and backup of data.
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.
This presentation is an updated version of my Data Management 101 talk, which covers the basics of research data management in the categories of: storage and backup, documentation, organization, and making files usable for the future.
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.
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.
Practical Data Management - ACRL DCIG WebinarKristin Briney
This document summarizes a webinar on practical data management. It discusses best practices for file organization, naming conventions, documentation, storage, backups, and ensuring future usability. Key recommendations include organizing files logically by project or type, using consistent naming conventions, thoroughly documenting data collection and analysis methods, storing data in multiple locations both on and off-site, backing up data regularly including testing backups, and future-proofing data through file format conversion and migration to new media. Resources for further information on data management best practices are also provided.
Scholars and researchers are being asked by an increasing number of research sponsors and journals to outline how they will manage and share their research data. This is an introduction to data management and sharing practices with some specific information for Columbia University researchers.
The document provides guidance on early planning for data management, including becoming familiar with funder requirements, planning for the types and formats of data that will be created, designing a system for taking notes, organizing files through consistent naming schemes and use of folders, adding metadata to files to aid in documentation and discovery, and using RSS feeds to organize web-based information. It also touches on issues like plagiarism, data protection, intellectual property rights, and remote access to and backup of data.
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.
This presentation is an updated version of my Data Management 101 talk, which covers the basics of research data management in the categories of: storage and backup, documentation, organization, and making files usable for the future.
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.
This presentation covers a number of best practices for managing research data. The main topics include: file naming and organization conventions, data documentation, and data storage and backups.
Responsible Conduct of Research: Data ManagementKristin Briney
This presentation was given by myself and Brad Houston (http://www.slideshare.net/herodotusjr), for UWM's Responsible Conduct of Research (RCR) series in Fall of 2013. It covers data management plans and practical data management tips. The corresponding handout is also available on Slideshare: http://www.slideshare.net/kbriney/rcr-data-management-handout
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.
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.
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.
Brief overview of open data, big data and sharing data ; discussion followed (based on Alastair Croll's presentation at ALA). robin fay @georgiawebgurl ; peter murray (lyrasis)
Slides from NCURA's webinar "Part I: Public Access: Practical Ways To Assist Faculty To Comply With Public Access Policies". This is the last section on the webinar on open data.
University of Bath Research Data Management training for researchersJez Cope
Slides from a workshop on Research Data Management for research staff and students at the University of Bath.
Part of the Research360 project (http://blogs.bath.ac.uk/research360).
Authors: Cathy Pink and Jez Cope, University of Bath
Presentation on electronic records management and archival issues. Originally presented at the Fall 2008 meeting of the Southeastern Wisconsin Archivists Group
Presentation from a University of York Library workshop on research data management. The workshop provides an introduction to research data management, covering best practice for the successful organisation, storage, documentation, archiving, and sharing of 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
Shared data and the future of librariesRegan Harper
Big data refers to large amounts of diverse data that are growing exponentially due to increased digital activity. Shared data connects these disparate sources of information through linking related data points. This allows data to be reused, corrected efficiently, and shared in potentially useful ways. For libraries, big data could include patron records, bibliographic data, and more. Linked data in particular supports library goals by making information reusable, correctable, and shareable across systems through relationships between data. However, privacy and potential misuse of inferences from big data are ongoing concerns that must be addressed.
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2017-02-22. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
The liaison librarian: connecting with the qualitative research lifecycleCelia Emmelhainz
A discussion of user needs in anthropology and ways in which academic liaison librarians could support the lifecycle of qualitative research in a holistic way.
This slideshow was used in a Research Data Management Planning course taught at IT Services, University of Oxford, on 2015-02-18 and 2015-05-13. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
This slideshow was used in a research data management planning course taught at IT Services, University of Oxford, on 2017-02-01. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one. (The presentation has been very slightly edited: references to resources provided to course participants have been replaced with web links.)
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 2017-02-15. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
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.
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
This presentation covers a number of best practices for managing research data. The main topics include: file naming and organization conventions, data documentation, and data storage and backups.
Responsible Conduct of Research: Data ManagementKristin Briney
This presentation was given by myself and Brad Houston (http://www.slideshare.net/herodotusjr), for UWM's Responsible Conduct of Research (RCR) series in Fall of 2013. It covers data management plans and practical data management tips. The corresponding handout is also available on Slideshare: http://www.slideshare.net/kbriney/rcr-data-management-handout
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.
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.
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.
Brief overview of open data, big data and sharing data ; discussion followed (based on Alastair Croll's presentation at ALA). robin fay @georgiawebgurl ; peter murray (lyrasis)
Slides from NCURA's webinar "Part I: Public Access: Practical Ways To Assist Faculty To Comply With Public Access Policies". This is the last section on the webinar on open data.
University of Bath Research Data Management training for researchersJez Cope
Slides from a workshop on Research Data Management for research staff and students at the University of Bath.
Part of the Research360 project (http://blogs.bath.ac.uk/research360).
Authors: Cathy Pink and Jez Cope, University of Bath
Presentation on electronic records management and archival issues. Originally presented at the Fall 2008 meeting of the Southeastern Wisconsin Archivists Group
Presentation from a University of York Library workshop on research data management. The workshop provides an introduction to research data management, covering best practice for the successful organisation, storage, documentation, archiving, and sharing of 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
Shared data and the future of librariesRegan Harper
Big data refers to large amounts of diverse data that are growing exponentially due to increased digital activity. Shared data connects these disparate sources of information through linking related data points. This allows data to be reused, corrected efficiently, and shared in potentially useful ways. For libraries, big data could include patron records, bibliographic data, and more. Linked data in particular supports library goals by making information reusable, correctable, and shareable across systems through relationships between data. However, privacy and potential misuse of inferences from big data are ongoing concerns that must be addressed.
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2017-02-22. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
The liaison librarian: connecting with the qualitative research lifecycleCelia Emmelhainz
A discussion of user needs in anthropology and ways in which academic liaison librarians could support the lifecycle of qualitative research in a holistic way.
This slideshow was used in a Research Data Management Planning course taught at IT Services, University of Oxford, on 2015-02-18 and 2015-05-13. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
This slideshow was used in a research data management planning course taught at IT Services, University of Oxford, on 2017-02-01. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one. (The presentation has been very slightly edited: references to resources provided to course participants have been replaced with web links.)
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 2017-02-15. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
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.
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
This document provides guidance on creating a data management plan (DMP). It explains that DMPs are required by many funders to help researchers better organize, document, and preserve their data. The key parts of a DMP include describing the data, metadata standards, data security, archiving and preservation, and access. The presenter provides tips for addressing each part, such as using open formats and partnering with repositories. Resources for creating a DMP at the University of Wisconsin-Milwaukee are also listed.
Data Analytics: HDFS with Big Data : Issues and ApplicationDr. Chitra Dhawale
This document provides information about a course on data analytics. It outlines the course outcomes, which include developing scalable systems using Apache and Hadoop, writing MapReduce applications, differentiating SQL and NoSQL, and analyzing and developing big data solutions using Hive and Pig. The document also describes some of the topics that will be covered in the course, including distributed file systems and their issues, an introduction to big data, characteristics of big data, types of big data, and comparisons between traditional and big data approaches.
This document provides guidance on research data management and developing data management plans. It discusses why managing research data is important, including making research easier to conduct, avoiding accusations of fraud or bad science, and getting credit for data produced. The document outlines what is involved in research data management and considerations for sharing and preserving data, such as file formats, documentation, and standards. It emphasizes the importance of data management planning and provides tips on developing plans to meet funder requirements.
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
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/.
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.
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
This document provides information and recommendations for preventing data loss through proper storage, organization, and backup of research files. It discusses developing a consistent file naming convention and folder structure for projects. The document also recommends storing multiple copies of important files in different locations and using version control software to track changes over time. Activities are included to help attendees evaluate their current practices and develop improved plans for organizing, backing up, and locking important versions of their data and files.
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
Meeting Federal Research Requirements for Data Management Plans, Public Acces...ICPSR
These slides cover evolving federal research requirements for sharing scientific data. Provided are updates on federal agency responses to the 2013 OSTP memo, guidance on data management plans, resources for data management and curation training for staff/researchers, and tips for evaluating public data-sharing services. ICPSR's public data-sharing service, openICPSR, is also presented. Recording of this presentation is here: https://www.youtube.com/watch?v=2_erMkASSv4&feature=youtu.be
This document provides an overview of data management basics for graduate students. It discusses why managing data is important, including requirements from funders and for responsible research. It then covers topics like organizing data through file naming, versioning, backup and storage strategies, and post-project activities. Resources for developing data management plans and tools are also listed. The overall message is that planning is key to prevent data loss and enable efficient and ethical research.
Research Data (and Software) Management at Imperial: (Everything you need to ...Sarah Anna Stewart
A presentation on research data management tools, workflows and best practices at Imperial College London with a focus on software management. Presented at the 2017 session of the HPC Summer School (Dept. of Computing).
1. The document provides an overview of key concepts in data science and machine learning including the data science process, types of data, machine learning techniques, and Python tools used for machine learning.
2. It describes the typical 6 step data science process: setting goals, data retrieval, data preparation, exploration, modeling, and presentation.
3. Different types of data are discussed including structured, unstructured, machine-generated, graph-based, and audio/video data.
4. Machine learning techniques can be supervised, unsupervised, or semi-supervised depending on whether labeled data is used.
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3. Time to ponder
Can you still access your data from…
– 20 years ago?
– 10 years ago?
– 5 years ago?
– 1 year ago?
Let’s talk about the data you’ve kept and lost.
4. Horror stories
Data that is…
– Lost (disorganized)
– Unreadable
– Without context
– Gone (deleted)
5. Data management basics
File organization
• Is your data organized meaningfully or jumbled together? Do you
know where your data is?
Documentation
• How much contextual information accompanies your data? Can you
understand it? Can a stranger understand it?
Storage & backup
• Where is your data stored and backed up? Could you recover from
hardware failure or accidental deletion?
Media obsolescence
• Do you know how the software, hardware, and file formats you use
will impact your data’s readability in the future?
6. 1. Organization
• Create a system at the beginning of the
project.
• Make sure the entire team is on board.
• The more collaborators, the more
important your system becomes!
• Any system is better than none.
7. File naming conventions
• Use them any time you have related files
• Consistent
• Short yet descriptive
• Avoid spaces and special characters
example: File001.xls vs. Project_instrument_location_YYYYMMDD.xls
8. Directory/folder organization
• Lots of possibilities, so consider what makes
sense for your project
– File type
– Date
– Type of analysis
example: MyDocumentsResearchSample12.tiff vs.
C:NSFGrant01234WaterQualityImagesLakeMendota_20141030.tiff
9. Retroactive organization
• Do a data inventory. List all the places
where your data lives (both physical and
digital)
• Make a plan for consolidating – follow the
rule of 3, not the rule of 17
11. Document on many levels
Project- & folder-level
– Create a readme file. (Good example located here:
http://hdl.handle.net/2022/17155)
– Document any data processing and analyses.
– Don’t forget written notes.
Item-level
– Remember the importance of file names for conveying
descriptive information.
– Find and adhere to disciplinary metadata standards
• TEI (XML)
• Dublin Core
12. 3. Storage & backup
storage = working files.
The files you access regularly and change frequently. In
general, losing your storage means losing current versions
of the data.
backup = regular process of copying data separate from storage.
You don’t really need it until you lose data, but when you
need to restore a file it will be the most important process
you have in place.
13. Rule of 3
• Keep THREE copies of your data
– TWO onsite
– ONE offsite
• Example
– One: Network drive
– Two: External hard drive
– Three: Cloud storage
• This ensures that your storage and backup is not
all in the same place – that’s too risky!
14. Evaluating cloud services
• Lots of options out there – and not all are
created equal
• Read the Terms of Service!
• Servers get hacked all the time. Whatever you’re
storing, you don’t want your provider to have
access to it.
• Data encryption is your friend.
15. 4. Media obsolescence
• software
• hardware
• file formats
CC
image
by
Flickr
user
wlef70
16. Thwarting obsolescence
• You can’t.
• Today’s popular software can become
obsolete through business deals, new
versions, or a gradual decline in user base.
(Consider WordPerfect.)
• Anticipate average lifespan of media to be
3-5 years. Migrate your files every few years,
if not more frequently!
17. Thwarting obsolescence
• Some file formats are less susceptible to
obsolescence than others
– Open, non-proprietary formats (pick TXT
over DOCX, CSV over XSLX, TIF over
JPG)
– Wide adoption
– History of backward compatibility
– Metadata support in open format (XML)
18. Back to (data management) basics
File organization
• Is your data organized meaningfully or jumbled together? Do you
know where your data is?
Documentation
• How much contextual information accompanies your data? Can you
understand it? Can a stranger understand it?
Storage & backup
• Where is your data stored and backed up? Could you recover from
hardware failure or accidental deletion?
Media obsolescence
• Do you know how the software, hardware, and file formats you use
will impact your data’s readability in the future?
19. Federal funding requirements
Data management plans (DMPs) are required by
all federal funding agencies.
2013 OSTP mandate:
• Public access to data and publications.
• Individual agencies create their own
requirements.
• Goal is to make publically-funded research
reproducible.
20. NEH Office of Digital
Humanities DMP
2 page document that answers:
• What data are generated by your research?
• What is your plan for managing the data?
NEH will also release requirements for public
data access soon.
22. My suggestion?
Grant or not, start new projects with a data
management plan compiled by project leaders.
The DMP should cover:
• Organization & naming
• Documentation & metadata
• Storage & sharing
• Any and all other pertinent details. (The more the
better; it’ll save you headaches later.)
The DMP should be actively revisited and
adapted as needed throughout the project.
23. Final thoughts
• Think about how your data organization,
documentation, and storage impacts your
ability to access your data years from now.
• If organizing retroactively, prioritize your
most important research.
• Any plan is better than no plan at all. Start
today. Ask for help.
24. Get in touch
Brianna Marshall
Digital Curation Coordinator
General Library System
Lead, Research Data Services
brianna.marshall@wisc.edu
25. Thank you!
Adapt this presentation as needed!
Creative Commons Attribution:
Some content adapted from the wise Kristin Briney.
Find all RDS slides at: www.slideshare.net/UW_RDS/