This presentation was provided by Mark Llauferseiler of the University of Oklahoma, during part one of the NISO two-part webinar "Labor and Capacity for Research Data Management," which was held on March 11, 2020.
This presentation was provided by Stephanie Labou of The University of California - San Diego, during part two of the NISO two-part webinar "Building Data Science Skills: Strategic Support for the Work, Part Two" which was held on March 18, 2020.
The document summarizes the role and challenges of research data management (RDM) information professionals from the perspective of a library practitioner. It discusses how RDM professionals educate researchers on topics like data management planning and repositories, consult on issues like workflows and publishing, and curate data to ensure findability, understandability and reuse. However, navigating relationships with different university offices, building shared understanding of technical concepts, and managing expectations with limited resources present challenges. Key principles for RDM professionals include keeping researchers central, considering future data re-users, and contributing to communities of practice. Ongoing gaps include supporting restricted and large data as well as developing actionable policies and training new professionals.
This presentation was provided by Julie Goldman of Harvard University, during part two of the NISO two-part webinar "Building Data Science Skills: Strategic Support for the Work, Part Two," which was held on March 18, 2020.
This document discusses best practices for content delivery platforms to support artificial intelligence projects. It recommends that platforms (1) accept that they do not have all the data needed and should integrate third-party sources, (2) provide consistent tagging of content, (3) offer a lightweight programmatic interface, (4) embrace allowing large amounts of content to be taken offline for analysis, and (5) enable complex filtering and selection of data. The document also suggests platforms could consider offering preprocessed datasets or AI tools as new products.
This presentation was provided by Courtney R. Butler of The Federal Reserve Bank - Kansas City, during part two of the NISO two-part webinar "Building Data Science Skills: Strategic Support for the Work, Part Two," which was held on March 18, 2020.
This presentation was provided by Carolyn Hansen of the University of Cincinnati during the NISO Training Thursday event, Metadata and the IR, held on Thursday, February 23, 2017.
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.
Slides | Targeting the librarian’s role in research servicesLibrary_Connect
Slides from the Nov. 8, 2016 Library Connect webinar "Targeting the librarian’s role in research services" with Nina Exner, Amanda Horsman and Mark Reed. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=223121
This presentation was provided by Stephanie Labou of The University of California - San Diego, during part two of the NISO two-part webinar "Building Data Science Skills: Strategic Support for the Work, Part Two" which was held on March 18, 2020.
The document summarizes the role and challenges of research data management (RDM) information professionals from the perspective of a library practitioner. It discusses how RDM professionals educate researchers on topics like data management planning and repositories, consult on issues like workflows and publishing, and curate data to ensure findability, understandability and reuse. However, navigating relationships with different university offices, building shared understanding of technical concepts, and managing expectations with limited resources present challenges. Key principles for RDM professionals include keeping researchers central, considering future data re-users, and contributing to communities of practice. Ongoing gaps include supporting restricted and large data as well as developing actionable policies and training new professionals.
This presentation was provided by Julie Goldman of Harvard University, during part two of the NISO two-part webinar "Building Data Science Skills: Strategic Support for the Work, Part Two," which was held on March 18, 2020.
This document discusses best practices for content delivery platforms to support artificial intelligence projects. It recommends that platforms (1) accept that they do not have all the data needed and should integrate third-party sources, (2) provide consistent tagging of content, (3) offer a lightweight programmatic interface, (4) embrace allowing large amounts of content to be taken offline for analysis, and (5) enable complex filtering and selection of data. The document also suggests platforms could consider offering preprocessed datasets or AI tools as new products.
This presentation was provided by Courtney R. Butler of The Federal Reserve Bank - Kansas City, during part two of the NISO two-part webinar "Building Data Science Skills: Strategic Support for the Work, Part Two," which was held on March 18, 2020.
This presentation was provided by Carolyn Hansen of the University of Cincinnati during the NISO Training Thursday event, Metadata and the IR, held on Thursday, February 23, 2017.
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.
Slides | Targeting the librarian’s role in research servicesLibrary_Connect
Slides from the Nov. 8, 2016 Library Connect webinar "Targeting the librarian’s role in research services" with Nina Exner, Amanda Horsman and Mark Reed. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=223121
The document discusses the need for an ecosystem to better manage research data through its entire lifecycle, from creation to publication to sharing and reuse. It proposes that libraries can play a key role in this ecosystem by providing services like curation repositories, identifiers, metadata, and tools to help researchers publish, share, and get credit for their data. The goal is to improve data discovery, access, attribution, and incentivize data sharing to make research data as integral to the scholarly record as journal articles.
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Building Best Practices in Research Data Management: Tisch Library’s Initiatives
Regina F. Raboin, Science Research and Instruction Librarian/ Data Management Services Group Coordinator, Tisch Library, Tufts University
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
Jared Lyle, ICPSR
Jennifer Doty, Emory University
Joel Herndon, Duke University
Libbie Stephenson, University of California, Los Angeles
SLIDES | 12 time-saving tips for research supportLibrary_Connect
The document provides 25 tips for using various tools to work smart, work together, and stay up-to-date as a researcher. The tips include creating a document library, downloading and marking up documents, using an electronic lab notebook, joining a research ecosystem, setting alerts, following researchers, analyzing search results, and more. The overall message is that new tools can help researchers organize the growing amount of data, connect with collaborators, and maintain novelty in their work.
This document summarizes research data support services at Tufts University. It discusses the context at Tufts including relevant support organizations. It describes collaborations between the libraries, technology services, and research centers to provide data management resources like the Tufts Data Lab, a data management team, and Carpentries data workshops. Ongoing work includes developing guidance on data storage, a centralized support website, and expanding the use of the Dataverse sharing platform.
Rscd 2017 bo f data lifecycle data skills for libsSusanMRob
This document discusses the data skills required of librarians and presents a matrix of factors that influence these skills, including the librarian's role, the data lifecycle services provided by the library, and the research intensity of the institution. It notes the wide range of possible data-related skills and acknowledges that no individual can master all of them, emphasizing the need for librarians to work as a team with complementary skills. The document also examines questions around how librarians can become more involved in data science and what their future roles may be in supporting data-intensive research.
Slides | Research data literacy and the libraryColleen DeLory
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Sarah Wright, Christian Lauersen and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
Using a Case Study to Teach Data Management to LibrariansSherry Lake
This document outlines the agenda and learning objectives for a workshop on research data management for libraries. The workshop uses a case study approach and hands-on activities to teach librarians best practices for data collection, organization, documentation, backup/storage, and sharing/preservation. The goal is to prepare librarians to teach researchers about data management and illustrate opportunities for library involvement in the area. Based on a survey after the workshop, most attendees felt their expectations were met or exceeded, and they found the hands-on case study activities and practical tips to be most useful.
At Utah State University, a pilot project is under development to evaluate the benefits of tracking data sets and faculty publications using the online catalog and the Library’s institutional repository.
With federal mandates to make publications and data open, universities look for solutions to track compliance. At Utah State University, the Sponsored Programs Office follows up with researchers to determine where data has been or will be deposited, per the terms of their grant.
Interested in making this publicly discoverable, the Library, Sponsored Programs, and Research Office are working together to pilot a project that enables the creation of publicly accessible MARC and Dublin Core records for data deposited by USU faculty. This project aims to make data sets, as well as publications, visible in research portals such as WorldCat, as well through Google searches.
This presentation will describe the project and anticipated benefits, as well as outline the roles of the cataloging staff and data librarian, and the involvement of the Research Office.
Presentation and workshop notes from session on how to apply the Researcher Development Framework to library and information service provision for research/e support
Uses case studies of different types of researchers.
Workshop notes integrated into the presentation
This document discusses research data management (RDM). It defines research data and describes the RDM lifecycle. Key aspects of RDM include creating data management plans, documenting and organizing data, and ensuring long-term preservation and sharing of data. The document outlines best practices for RDM, such as using appropriate file formats and metadata standards. It also discusses challenges around sensitive data and guidelines for data sharing and citation. The roles libraries can play in supporting RDM are identified, such as developing RDM policies, training researchers, and setting up data repositories.
With big data research all the rage, how are librarians being asked to engage with data? As big data research takes off across Business, Science, and the Humanities, librarians need to understand big data and the issues around its storage and curation. How can it be made accessible? What tools and resources are required to use and analyze big data? In this webinar, panelists Caroline Muglia and Jill Parchuck share how big data is being used on their campuses and how they, as librarians, are supporting the sourcing and storage of this data.
Giving Credit Where Credit is Due: Author and Funder IDsAndrea Payant
A process to include standardized funder and author identifiers into institutional repository and ILS records which are associated with funded research data
This presentation was provided by Anne Washington of the University of Houston during the NISO virtual conference, Open Data Projects, held on Wednesday, June 13, 2018.
This presentation was provided by Carly Strasser of the Chan Zuckerberg Initiative during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
Presentation at "International knowledge graph workshop" at KDD 2020. The short overview talk shows how we have moved from Semantic Web to Linked Data to Knowledge Graphs. We argue that the same "a little semantics goes a long way" principle from the early days of the Semantic Web still is needed today -- some lessons learned and steps ahead are outlined.
This document provides orientation information for Columbia University students. It introduces Jeffrey Lancaster as the Emerging Technologies Coordinator and provides information about the Digital Science Center (DSC) including available software, presentation rooms, workstations, scanners, and wireless access. Tips are given for file naming, using subject librarians and reference management software, and downloading required software. Contact information is provided for several subject librarians. Students are encouraged to connect with the Science and Engineering Library via blogs, social media, and upcoming events in September/October.
Curation-Friendly Tools for the Scientific Researcherbwestra
Presentation for Online Northwest Conference, in Corvallis Oregon, February 10, 2012.
Highlights electronic lab notebooks (ELN) and OMERO (Open Microscopy Environment) as two tools that enable researchers to better manage their research data.
The document discusses the need for an ecosystem to better manage research data through its entire lifecycle, from creation to publication to sharing and reuse. It proposes that libraries can play a key role in this ecosystem by providing services like curation repositories, identifiers, metadata, and tools to help researchers publish, share, and get credit for their data. The goal is to improve data discovery, access, attribution, and incentivize data sharing to make research data as integral to the scholarly record as journal articles.
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Building Best Practices in Research Data Management: Tisch Library’s Initiatives
Regina F. Raboin, Science Research and Instruction Librarian/ Data Management Services Group Coordinator, Tisch Library, Tufts University
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
Jared Lyle, ICPSR
Jennifer Doty, Emory University
Joel Herndon, Duke University
Libbie Stephenson, University of California, Los Angeles
SLIDES | 12 time-saving tips for research supportLibrary_Connect
The document provides 25 tips for using various tools to work smart, work together, and stay up-to-date as a researcher. The tips include creating a document library, downloading and marking up documents, using an electronic lab notebook, joining a research ecosystem, setting alerts, following researchers, analyzing search results, and more. The overall message is that new tools can help researchers organize the growing amount of data, connect with collaborators, and maintain novelty in their work.
This document summarizes research data support services at Tufts University. It discusses the context at Tufts including relevant support organizations. It describes collaborations between the libraries, technology services, and research centers to provide data management resources like the Tufts Data Lab, a data management team, and Carpentries data workshops. Ongoing work includes developing guidance on data storage, a centralized support website, and expanding the use of the Dataverse sharing platform.
Rscd 2017 bo f data lifecycle data skills for libsSusanMRob
This document discusses the data skills required of librarians and presents a matrix of factors that influence these skills, including the librarian's role, the data lifecycle services provided by the library, and the research intensity of the institution. It notes the wide range of possible data-related skills and acknowledges that no individual can master all of them, emphasizing the need for librarians to work as a team with complementary skills. The document also examines questions around how librarians can become more involved in data science and what their future roles may be in supporting data-intensive research.
Slides | Research data literacy and the libraryColleen DeLory
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Sarah Wright, Christian Lauersen and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
Using a Case Study to Teach Data Management to LibrariansSherry Lake
This document outlines the agenda and learning objectives for a workshop on research data management for libraries. The workshop uses a case study approach and hands-on activities to teach librarians best practices for data collection, organization, documentation, backup/storage, and sharing/preservation. The goal is to prepare librarians to teach researchers about data management and illustrate opportunities for library involvement in the area. Based on a survey after the workshop, most attendees felt their expectations were met or exceeded, and they found the hands-on case study activities and practical tips to be most useful.
At Utah State University, a pilot project is under development to evaluate the benefits of tracking data sets and faculty publications using the online catalog and the Library’s institutional repository.
With federal mandates to make publications and data open, universities look for solutions to track compliance. At Utah State University, the Sponsored Programs Office follows up with researchers to determine where data has been or will be deposited, per the terms of their grant.
Interested in making this publicly discoverable, the Library, Sponsored Programs, and Research Office are working together to pilot a project that enables the creation of publicly accessible MARC and Dublin Core records for data deposited by USU faculty. This project aims to make data sets, as well as publications, visible in research portals such as WorldCat, as well through Google searches.
This presentation will describe the project and anticipated benefits, as well as outline the roles of the cataloging staff and data librarian, and the involvement of the Research Office.
Presentation and workshop notes from session on how to apply the Researcher Development Framework to library and information service provision for research/e support
Uses case studies of different types of researchers.
Workshop notes integrated into the presentation
This document discusses research data management (RDM). It defines research data and describes the RDM lifecycle. Key aspects of RDM include creating data management plans, documenting and organizing data, and ensuring long-term preservation and sharing of data. The document outlines best practices for RDM, such as using appropriate file formats and metadata standards. It also discusses challenges around sensitive data and guidelines for data sharing and citation. The roles libraries can play in supporting RDM are identified, such as developing RDM policies, training researchers, and setting up data repositories.
With big data research all the rage, how are librarians being asked to engage with data? As big data research takes off across Business, Science, and the Humanities, librarians need to understand big data and the issues around its storage and curation. How can it be made accessible? What tools and resources are required to use and analyze big data? In this webinar, panelists Caroline Muglia and Jill Parchuck share how big data is being used on their campuses and how they, as librarians, are supporting the sourcing and storage of this data.
Giving Credit Where Credit is Due: Author and Funder IDsAndrea Payant
A process to include standardized funder and author identifiers into institutional repository and ILS records which are associated with funded research data
This presentation was provided by Anne Washington of the University of Houston during the NISO virtual conference, Open Data Projects, held on Wednesday, June 13, 2018.
This presentation was provided by Carly Strasser of the Chan Zuckerberg Initiative during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
Presentation at "International knowledge graph workshop" at KDD 2020. The short overview talk shows how we have moved from Semantic Web to Linked Data to Knowledge Graphs. We argue that the same "a little semantics goes a long way" principle from the early days of the Semantic Web still is needed today -- some lessons learned and steps ahead are outlined.
This document provides orientation information for Columbia University students. It introduces Jeffrey Lancaster as the Emerging Technologies Coordinator and provides information about the Digital Science Center (DSC) including available software, presentation rooms, workstations, scanners, and wireless access. Tips are given for file naming, using subject librarians and reference management software, and downloading required software. Contact information is provided for several subject librarians. Students are encouraged to connect with the Science and Engineering Library via blogs, social media, and upcoming events in September/October.
Curation-Friendly Tools for the Scientific Researcherbwestra
Presentation for Online Northwest Conference, in Corvallis Oregon, February 10, 2012.
Highlights electronic lab notebooks (ELN) and OMERO (Open Microscopy Environment) as two tools that enable researchers to better manage their research data.
Research Data Management Fundamentals for MSU Engineering StudentsAaron Collie
This document discusses the importance of research data management and outlines best practices. It notes that data is expensive to produce but is the primary output of research. Funding agencies now require data management plans to facilitate data sharing and reuse. The document recommends storing data on multiple types of storage, avoiding single points of failure, creating backup strategies, documenting projects and data, and selecting open file formats. Overall, it emphasizes that data management is an important skill for researchers.
Librarian building blocks; or, how to make the ideal librarianDom Bortruex
"Librarian building blocks" will explore recent changes and needs in librarianship, introduce strategies for learning new skills, and inspire participants to implement these skills. This presentation is for a general audience and will cover skills for all libraries. To build the ideal librarian, we determined what skills and knowledge a contemporary librarian needs to succeed. Since job postings and MLIS curriculum reflect current, popular trends in librarianship, we developed a data harvesting Python script that gathered the data for more than 600 librarian job postings and MLIS curriculum content. Based on this data, we will present which skills are being taught and which skills need to be taught. The presentation will explore what these changes in technology and librarianship mean for current librarians and how they can stay up to date in the continuously evolving field of librarianship.
The document summarizes the collaboration between research libraries and computational research. It discusses how libraries traditionally provided curation, preservation, and sharing functions but now face challenges in continuing these roles with large computational analyses. The libraries must collaborate with research computing to address issues like data preservation requirements conflicting with computational resource needs. Recent projects between Hesburgh Libraries and research computing are highlighted as successful examples of such collaboration, including initiatives to develop tools for reproducible computational research and preservation of executable software and datasets.
This document outlines a modular approach for teaching research data management (RDM) skills. It provides examples of RDM modules focused on data organization for science, technology, engineering and humanities disciplines. Each module introduces key RDM concepts and includes hands-on activities. For example, the data organization module for science discusses file naming conventions and organizing physical samples. It involves a group activity to discuss better file organization strategies. The overall workshop is meant to be tailored depending on the audience and their needs.
Engaging Students with Research Data Management: The Modular ApproachClaire Sewell
This document outlines a modular approach for teaching research data management (RDM) skills. It provides examples of RDM modules focused on data organization for science, technology, engineering and humanities disciplines. Each module introduces key RDM concepts and includes hands-on activities. For example, the data organization module for science discusses file naming conventions and organizing physical samples. It encourages audience participation to discuss best practices. The document emphasizes tailoring RDM teaching to different subject areas and audiences through customizable modules and activities.
Scholarly Information Practices In The Online EnvironmentOCLC Research
The document discusses opportunities for libraries to develop shared service frameworks based on scholars' core activities in the online environment. It analyzes literature on scholarly information practices to identify common themes across disciplines. Key findings show convergence in practices like searching, collecting, and collaborating. This implies a need for generic models of core library services to support current research workflows. Frameworks based on scholars' information needs could help libraries invest strategically and avoid duplicating efforts.
Linked Data at the OU - the story so farEnrico Daga
The document discusses the Open University's use of linked open data and their data.open.ac.uk platform. It provides an overview of linked data principles and the data.open.ac.uk platform. Key services of the Open University rely on data.open.ac.uk to support users in various ways such as the student help center and OpenLearn platform. While linked data is useful for centralized data publishing, it does not replace traditional data management and requires developers to integrate it with existing workflows.
The Learning Resource Centre (LRC) at the Indian School of Business (ISB) supports the school's mission to build a top-ranked research institution. The LRC acquires and organizes information resources to meet the needs of the ISB community. It provides access to resources anytime, from anywhere. The LRC's mission is to establish a knowledge hub and provide innovative, responsive services. It pursues this mission through its 3C strategy of Content, Connectivity, and Customer Care. The LRC offers a variety of resources including e-books, databases, journals, audio/visual materials and more. It provides many services to support teaching, learning and research for faculty, students, staff and departments.
PIDs, Data and Software: How Libraries Can Support Researchers in an Evolving...Sarah Anna Stewart
Presentation given at the M25 Consortium of Academic Libraries, CPD25 Event on 'The Role of the Library in Supporting Research'. Provides an introduction to data, software and PIDs and a brief look at how libraries can enable researchers to gain impact and credit for their research data and software.
This presentation was provided by Todd Digby of The University of Florida, during the NISO event "Privacy in the Age of Surveillance: Everyone's Concern." The virtual conference was held on September 16, 2020.
Defining the Libraries' Role in Research: A Needs Assessment Case StudyKathryn Crowe
This document summarizes the results of a needs assessment survey conducted at UNC Greensboro to understand faculty research data management needs. Key findings include: the most common data formats are text, PDFs, and spreadsheets; most faculty back up data themselves but do not follow best practices; the top priorities for support are storage/backup, meeting sharing requirements, and assistance with data management plans. Barriers to sharing include large data sizes and lack of knowledge about requirements and options. The survey informed new research data services from the libraries and other campus units, including data storage, curation, and consultation on data management plans and sharing requirements.
Frances McNamara - Kuali OLE Implementation at University of ChicagoKuali Days UK
Presented by Frances McNamara, Director, Integrated Library Systems and Administrative and Desktop Systems at the University of Chicago at the Kuali Days UK conference, 29 October 2013.
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 | Research data literacy and the libraryLibrary_Connect
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Christian Lauersen, Sarah J. Wright and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
This document summarizes a presentation about Myria, a relational algorithmics-as-a-service platform developed by researchers at the University of Washington. Myria allows users to write queries and algorithms over large datasets using declarative languages like Datalog and SQL, and executes them efficiently in a parallel manner. It aims to make data analysis scalable and accessible for researchers across many domains by removing the need to handle low-level data management and integration tasks. The presentation provides an overview of the Myria architecture and compiler framework, and gives examples of how it has been used for projects in oceanography, astronomy, biology and medical informatics.
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.
Introduction to apache spark and machine learningAwoyemi Ezekiel
This document provides an introduction to Apache Spark and machine learning. It discusses what Apache Spark is, how it compares to other big data frameworks, and the Spark program lifecycle. It also defines what big data is and where it comes from. Additionally, it discusses data science goals of deriving knowledge from big data efficiently and intelligently, and provides examples of machine learning applications. Finally, it includes two coding examples - one involving text analysis on Shakespeare's works, and another involving movie recommendations from movie rating data.
Similar to Llauferseiler "OU Libraries: Opportunities Supporting Research and Education" (20)
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the closing segment of the NISO training series "AI & Prompt Design." Session Eight: Limitations and Potential Solutions, was held on May 23, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the seventh segment of the NISO training series "AI & Prompt Design." Session 7: Open Source Language Models, was held on May 16, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the sixth segment of the NISO training series "AI & Prompt Design." Session Six: Text Classification with LLMs, was held on May 9, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the fifth segment of the NISO training series "AI & Prompt Design." Session Five: Named Entity Recognition with LLMs, was held on May 2, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the fourth segment of the NISO training series "AI & Prompt Design." Session Four: Structured Data and Assistants, was held on April 25, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the third segment of the NISO training series "AI & Prompt Design." Session Three: Beginning Conversations, was held on April 18, 2024.
This presentation was provided by Kaveh Bazargan of River Valley Technologies, during the NISO webinar "Sustainability in Publishing." The event was held April 17, 2024.
This presentation was provided by Dana Compton of the American Society of Civil Engineers (ASCE), during the NISO webinar "Sustainability in Publishing." The event was held April 17, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the second segment of the NISO training series "AI & Prompt Design." Session Two: Large Language Models, was held on April 11, 2024.
This presentation was provided by Teresa Hazen of the University of Arizona, Geoff Morse of Northwestern University. and Ken Varnum of the University of Michigan, during the Spring ODI Conformance Statement Workshop for Libraries. This event was held on April 9, 2024
This presentation was provided by William Mattingly of the Smithsonian Institution, during the opening segment of the NISO training series "AI & Prompt Design." Session One: Introduction to Machine Learning, was held on April 4, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the eight and final session of NISO's 2023 Training Series on Text and Data Mining. Session eight, "Building Data Driven Applications" was held on Thursday, December 7, 2023.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the seventh session of NISO's 2023 Training Series on Text and Data Mining. Session seven, "Vector Databases and Semantic Searching" was held on Thursday, November 30, 2023.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the sixth session of NISO's 2023 Training Series on Text and Data Mining. Session six, "Text Mining Techniques" was held on Thursday, November 16, 2023.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the fifth session of NISO's 2023 Training Series on Text and Data Mining. Session five, "Text Processing for Library Data" was held on Thursday, November 9, 2023.
This presentation was provided by Todd Carpenter, Executive Director, during the NISO webinar on "Strategic Planning." The event was held virtually on November 8, 2023.
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#فهم_ماكو_درخ
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واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
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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,
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
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Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
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Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Gender and Mental Health - Counselling and Family Therapy Applications and In...
Llauferseiler "OU Libraries: Opportunities Supporting Research and Education"
1. What will you do at the intellectual
crossroads of the University of Oklahoma?
OU Libraries: Opportunities Supporting
Research & Education
NISO Labor and Capacity for Research Data Management
2. What will you do at the intellectual
crossroads of the University of Oklahoma?
Who am I or why me?
Local repository services
Outreach and other
researcher services
Outline
3. What will you do at the intellectual
crossroads of the University of Oklahoma?
Education
BS Physics
University of Kentucky
MS Meteorology
Pennsylvania State University
PhD Meteorology
Pennsylvania State University
Work
• DoE Support scientist (1994-1996)
• Atmospheric Radiation Measurement program
o Truly ”Big” Data
o May be the first ”official” documented data
management plan
• Computer Specialist (1996-1999)
Florida State Department of Meteorology
o more Data
• Computer Systems Coordinator and Adjunct
Faculty (1999-2913)
• OU School of Meteorology
o Data, and more data
? Nontraditional PhD ?
4. What will you do at the intellectual
crossroads of the University of Oklahoma?
Data Management ARM program: A taste
Processed Data
An example netCDF data file name is depicted below:
The sgp5mwravgB4.c1.20040706.020415.cdf file contains 5-minute averaged microwave
radiometer data from the Southern Great Plains Vici site from July 6, 2004. The data level is “c1”
indicating the data was derived or calculated via Value-Added Processing (see Data Levels).
ARM netCDF files shall be named according to the following naming convention:
(sss)(nn)(inst)(qqq)(Fn).(dl).YYYYMMDD.hhmmss.cdf.
5. What will you do at the intellectual
crossroads of the University of Oklahoma?
sss
is the site identifier (e.g., sgp, twp, nsa)
nn
is the data integration period (e.g., 1, 5, 15, 30, 1440)
inst
is the instrument abbreviation (e.g., mwr, wsi, mpl)
qqq
is an optional qualifier that distinguishes these data from other data sets produced by the same instrument
Fn
is the facility designation (e.g., C1, E13, B4)
dl
is the data level (e.g., a0, a1, b1, c1)
The length constraints are:
sss: 3 characters
Fn: 2 or 3 characters
dl: 2 characters
(sss)(nn)(inst)(qqq)(Fn).(dl).YYYYMMDD.hhmmss.cdf
• (sss)(nn)(inst)(qqq)(Fn).(dl): MUST be 33 characters or less.
• “The TOTAL length of a filename sent to the ARM Data Center MUST be 61
characters or less.”
• https://www.arm.gov/policies/datapolicies/formatting-and-file-naming-protocols
6. What will you do at the intellectual
crossroads of the University of Oklahoma?
Florida State
• Live and archive data
manager
• WX software/tools
maintainer
Research IT support
University of Oklahoma
• All that at FSU plus
• Data liaison with NOAA
agencies
• Capstone (senior thesis)
• Instructor of record for
intro meteo lab for
undergrads
Rest of my life
7. What will you do at the intellectual
crossroads of the University of Oklahoma?
2013 Research Data Specialist
2019 Head of Data Analytics, Visualization, and
Informatics Syndicate (DAVIS)
OU/Libraries Organizational Contact for
• The Carpentries
• ORCID
• OSF
• DMPTool
OU University Libraries
8. What will you do at the intellectual
crossroads of the University of Oklahoma?
Repository Services
collaboratively develops and
manages the operations of
the university's local and
shared institutional
repositories, and works to
assure the long-term
preservation and
accessibility of their content.
SHAREOK
Journals @ SHAREOK
Commons@SHAREOK
…not “big” data…
ShareOK
9. What will you do at the intellectual
crossroads of the University of Oklahoma?
PetaStore
• Tape system
o Tapes purchased by PI’s
• Designed for larger data sets
• TB storage
• Not publicly accesable
OURRStore
Now being built
Upgrade to PetaStore
Publicly accessible via Globus
• Local Library support
• Check before release
• Globus endpoint is what
then is reference in local
repository (ShareOK)
OU Supercomputing Center for Education and Research
10. What will you do at the intellectual
crossroads of the University of Oklahoma?
Consultation & Collaborative Spaces: DAVIS
11. What will you do at the intellectual
crossroads of the University of Oklahoma?
Data Analysis Visualization Informatics Syndicate
12. What will you do at the intellectual
crossroads of the University of Oklahoma?
Information Specialists
13. What will you do at the intellectual
crossroads of the University of Oklahoma?
The Carpentries
14. What will you do at the intellectual
crossroads of the University of Oklahoma?
Trained instruction
Introduce you to evidence-
based best-practices of
teaching.
Teach you how to create a
positive environment for
learners at your workshops.
Provide opportunities for you
to practice and build your
teaching skills.
Help you become
integrated into the
Carpentries community.
Prepare you to use these
teaching skills in teaching
Carpentries workshops.
The Carpentries Pedagogy
15. What will you do at the intellectual
crossroads of the University of Oklahoma?
The Carpentries
Intellectual Crossroads of the University
The Carpentries
Intellectual Crossroads of the University
The Carpentries
16. What will you do at the intellectual
crossroads of the University of Oklahoma?
University Libraries Workshops
17. What will you do at the intellectual
crossroads of the University of Oklahoma?
UL Workshops
18. What will you do at the intellectual
crossroads of the University of Oklahoma?
University Libraries Workshops
19. What will you do at the intellectual
crossroads of the University of Oklahoma?
University Libraries Workshops
20. What will you do at the intellectual
crossroads of the University of Oklahoma?
Open Science Framework https://osf.ou.edu
21. What will you do at the intellectual
crossroads of the University of Oklahoma?
Digital Object Identifiers DOIs
22. What will you do at the intellectual
crossroads of the University of Oklahoma?
ORCID
23. What will you do at the intellectual
crossroads of the University of Oklahoma?
Scenes from DAVIS
• Researcher problems around
• Coding
• Python, R, some MatLab
• Applying Statistical Methods
• Specialists not necessarily stats experts
• Wish: Libraries hire a stats person
• Machine setups
• Spend time fixing software installs
• Trying to have faculty/instructor use the UL as a resource for classes
• Research Data Management
• How to do better
• DMP reviews
24. What will you do at the intellectual
crossroads of the University of Oklahoma?
Dr. Mark Laufersweiler
Research Data Specialist
University Libraries
laufers@ou.edu
@laufers
@OU_Libraries
ORCID: 0000-0001-5544-0976
Thank you
25. What will you do at the intellectual
crossroads of the University of Oklahoma?
ARM naming
• https://www.arm.gov/policies/datapolicies/for
matting-and-file-naming-protocols
OU Libraries (UL)
• https://libraries.ou.edu
OU UL DAVIS
• https://libraries.ou.edu/DAVIS
OU UL Carpentries
• https://libraries.ou.edu/carpentries
OU UL Workshops
• https://libraries.ou.edu/content/university-
libraries-request-workshops
ShareOK (UL Repository Services)
• https://libraries.ou.edu/content/repository-
services
OU UL Research Data Management
• https://libraries.ou.edu/content/research-data-
management
OU UL How to create a README.txt
• https://libraries.ou.edu/content/how-make-
readmetxt-file
OU UL DOI’s
• https://libraries.ou.edu/content/doi-digital-assets
OU Supercomputing (OSCER)
• https://www.ou.edu/oscer
• PetaStore
o http://ou.edu/oscer/support/petastore_storag
e
Links