RDAP 15: Beyond Metadata: Leveraging the “README” to support disciplinary Doc...ASIS&T
Research Data Access and Preservation Summit, 2015
Minneapolis, MN
April 22-23, 2015
Part of “Beyond metadata: Supporting non-standardized documentation to facilitate data reuse”
This document outlines best practices for creating research data. [1] It recommends using consistent data organization with standardized formats and descriptive file names. [2] Researchers should perform quality assurance checks and use scripted programs to analyze data while keeping notes. [3] All aspects of data collection and analysis should be thoroughly documented. Following these practices will improve data usability, sharing, and reproducibility.
A scientific database is an electronic index of bibliographic records, often containing citations, abstracts, and full text of journal or magazine articles. They are created using tools like web servers, database management systems, and programming languages. Scientific databases are useful for improving data quality, reducing costs, enabling long-term studies, and allowing data synthesis. They must contain wanted, up-to-date, complete data presented attractively and compatibly for their users. Examples include forensic DNA databases and geographical databases.
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.
This poster presents guidelines for researchers to improve reproducibility in scientific research by better documenting the key entities of research: data, software, workflow, and research output. It recommends documenting data sources and processing steps, writing descriptive code with examples, and using tools like Docker, Jupyter notebooks, LaTeX, and data repositories to capture the experimental environment and research process. Following these guidelines helps researchers communicate and verify their work, allowing others to build on their research findings.
This presentation discusses managing research data through the data life cycle. It begins with an overview of the research life cycle and embedding the data life cycle within it. Key aspects of data management are then covered, including why manage data, ethical and legal issues, requirements for data sharing and retention, and creating a data management plan. The rest of the presentation delves into each stage of the data life cycle, providing best practices for data collection, organization, security, storage, documentation, processing, analysis, and long-term preservation or sharing. File formats, metadata, repositories, and bibliographic resources are also addressed.
RDAP 15: Beyond Metadata: Leveraging the “README” to support disciplinary Doc...ASIS&T
Research Data Access and Preservation Summit, 2015
Minneapolis, MN
April 22-23, 2015
Part of “Beyond metadata: Supporting non-standardized documentation to facilitate data reuse”
This document outlines best practices for creating research data. [1] It recommends using consistent data organization with standardized formats and descriptive file names. [2] Researchers should perform quality assurance checks and use scripted programs to analyze data while keeping notes. [3] All aspects of data collection and analysis should be thoroughly documented. Following these practices will improve data usability, sharing, and reproducibility.
A scientific database is an electronic index of bibliographic records, often containing citations, abstracts, and full text of journal or magazine articles. They are created using tools like web servers, database management systems, and programming languages. Scientific databases are useful for improving data quality, reducing costs, enabling long-term studies, and allowing data synthesis. They must contain wanted, up-to-date, complete data presented attractively and compatibly for their users. Examples include forensic DNA databases and geographical databases.
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.
This poster presents guidelines for researchers to improve reproducibility in scientific research by better documenting the key entities of research: data, software, workflow, and research output. It recommends documenting data sources and processing steps, writing descriptive code with examples, and using tools like Docker, Jupyter notebooks, LaTeX, and data repositories to capture the experimental environment and research process. Following these guidelines helps researchers communicate and verify their work, allowing others to build on their research findings.
This presentation discusses managing research data through the data life cycle. It begins with an overview of the research life cycle and embedding the data life cycle within it. Key aspects of data management are then covered, including why manage data, ethical and legal issues, requirements for data sharing and retention, and creating a data management plan. The rest of the presentation delves into each stage of the data life cycle, providing best practices for data collection, organization, security, storage, documentation, processing, analysis, and long-term preservation or sharing. File formats, metadata, repositories, and bibliographic resources are also addressed.
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.
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Using data management plans as a research tool: an introduction to the DART Project
Amanda L. Whitmire, Ph.D., Assistant Professor, Data Management Specialist, Oregon State University Libraries & Press
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 discusses several topics that will drive the future of digital libraries, including data management plans, data citation, curation service models, sustainability, training data practitioners, and more. Specific issues covered include scientific data support, data identifiers, curation best practices, cost models, educating librarians in data management, and the role of digital libraries in enabling reproducible science through 2050.
This document discusses research data management services at the University of Western Australia (UWA). It provides information on the Institutional Research Data Store (IRDS), a no-cost research data storage option for UWA researchers that provides 25GB of secure storage. It also discusses requirements for research data management and sharing from funding bodies like the Australian Research Council, and options for making data available through UWA's Research Data Online platform. Contact information is provided for the Research Data Coordinator for any questions.
The document discusses using computer software to analyze qualitative data, describing different types of analysis software and their functions. It also provides examples of research studies that used various computer-assisted qualitative data analysis software packages like MS Word, NVivo, and NUD*IST to code and analyze interview transcripts, field notes, and other qualitative data sources. The document emphasizes that the choice of software depends on the researcher's methodology, data types and amount, and analysis approach.
RDAP14: An analysis and characterization of DMPs in NSF proposals from the Un...ASIS&T
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
Lightning Talks
William Mischo, University of Illinois at Urbana-Champaign
Research Data Service at the University of EdinburghRobin Rice
The University of Edinburgh provides research data management services and resources to support researchers through the entire data lifecycle. These include tools for creating data management plans, storing and sharing research data securely, and preserving data in the long term. The Research Data Service aims to help researchers comply with open science principles and data policies through a range of training programs, online guidance, and technical infrastructure. It has developed a multi-year roadmap and maturity model to continuously improve services based on researchers' needs and priorities like relationship building, communication skills, and consultation.
Efficient and effective data management for ILRI research projects: A holisti...ILRI
This document discusses ILRI's approach to data management for research projects and proposes a holistic framework to improve current practices. Currently, ILRI's data management has been ad-hoc with data stored decentralized across different databases and locations. The proposed framework includes standardizing data collection technologies and indicator templates, implementing near real-time data relay and quality checks, centralized storage and multiple access methods, and making data publicly accessible. This will help ensure high quality data management throughout the entire research project lifecycle.
This document discusses creating a data management plan. It explains that a data management plan is a comprehensive plan for managing research data throughout a project's lifecycle and briefly describing how data will be shared per a funder's policy. It provides an overview of key elements to include in a plan such as file formats, organization, sharing, and preservation. The document also reviews funder requirements and available tools to create plans, noting they can be tailored to different funders' guidelines.
Presentation given to the High Performance Computing Summer School as part of a hands-on workshop developing software management plans and looking at software as data within the context of research data management best practices.
Feb 26 NISO Training Thursday
Crafting a Scientific Data Management Plan
About the Training
Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors.
This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth.
About the Instructors
Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries
Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Learning to Curate Research Data
Jennifer Doty, Research Data Librarian, Emory Center for Digital Scholarship, Emory University, Robert W. Woodruff Library
The document outlines five starter steps for research libraries to become partners in research data management. The steps are: 1) prepare a use case for data management, 2) coordinate stakeholders like the IT department and research units, 3) adapt current skills to research data management, 4) initiate a pilot project to test tools and develop workflows, and 5) get to know the current landscape of data requirements, repositories, and lessons from other libraries. The library is well positioned to play a leadership role in research data management by utilizing its skills in preservation, access, and curation.
Improving Integrity, Transparency, and Reproducibility Through Connection of ...Andrew Sallans
The Center for Open Science (COS) was founded as a non-profit technology start-up in 2013 with the goal of improving transparency and reproducibility by connecting the scholarly workflow. COS achieves this goal through the development of a free, open source web application called the Open Science Framework (OSF), providing features like file sharing and citing, persistent urls, provenance tracking, and automated versioning. Initial workflow API connections focused on storage services and included Figshare, GitHub, Amazon S3, Dropbox, and Dataverse. The team is now working to connect other parts of the workflow with services like DMPTool, Databib/re3data, and Databrary. This session will introduce the core architecture and the problems that it solves, and illustrate how connecting services can benefit everyone involved in supporting the research ecosystem. COS is funded through the generosity of grants from the Laura and John Arnold Foundation, the John Templeton Foundation, the Alfred P. Sloan Foundation, the Association of Research Libraries, and others.
Presented at CNI Fall 2014, Washington, DC.
February 18 2014 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Capacity Building: Leveraging existing library networks to take on research data
Heidi Imker, Director of the Research Data Service, University of Illinois at Urbana-Champaign
UVa Library Scientific Data Consulting Group (SciDaC): New Partnerships and...Andrew Sallans
The UVA Library Scientific Data Consulting Group (SciDaC) provides new partnerships and services to support scientific data management in research. SciDaC was formed in 2010 to focus on data consulting after restructuring from the Research Computing Lab. SciDaC conducts data interviews and assessments, assists with NSF Data Management Plan requirements, and works to integrate research data into the institutional repository. Future work includes expanding disciplinary support, integrating into the research proposal process, and advising on data policy.
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 explosion in growth of the Web of Linked Data has provided, for the first time, a plethora of information in disparate locations, yet bound together by machine-readable, semantically typed relations. Utilisation of the Web of Data has been, until now, restricted to the members of the community, eating their own dogfood, so to speak. To the regular web user browsing Facebook and watching YouTube, this utility is yet to be realised. The primary factor inhibiting uptake is the usability of the Web of Data, where users are required to have prior knowledge of elements from the Semantic Web technology stack. Our solution to this problem is to hide the stack, allowing end users to browse the Web of Data, explore the information it contains, discover knowledge, and use Linked Data. We propose a template-based visualisation approach where information attributed to a given resource is rendered according to the rdf:type of the instance.
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.
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Using data management plans as a research tool: an introduction to the DART Project
Amanda L. Whitmire, Ph.D., Assistant Professor, Data Management Specialist, Oregon State University Libraries & Press
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 discusses several topics that will drive the future of digital libraries, including data management plans, data citation, curation service models, sustainability, training data practitioners, and more. Specific issues covered include scientific data support, data identifiers, curation best practices, cost models, educating librarians in data management, and the role of digital libraries in enabling reproducible science through 2050.
This document discusses research data management services at the University of Western Australia (UWA). It provides information on the Institutional Research Data Store (IRDS), a no-cost research data storage option for UWA researchers that provides 25GB of secure storage. It also discusses requirements for research data management and sharing from funding bodies like the Australian Research Council, and options for making data available through UWA's Research Data Online platform. Contact information is provided for the Research Data Coordinator for any questions.
The document discusses using computer software to analyze qualitative data, describing different types of analysis software and their functions. It also provides examples of research studies that used various computer-assisted qualitative data analysis software packages like MS Word, NVivo, and NUD*IST to code and analyze interview transcripts, field notes, and other qualitative data sources. The document emphasizes that the choice of software depends on the researcher's methodology, data types and amount, and analysis approach.
RDAP14: An analysis and characterization of DMPs in NSF proposals from the Un...ASIS&T
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
Lightning Talks
William Mischo, University of Illinois at Urbana-Champaign
Research Data Service at the University of EdinburghRobin Rice
The University of Edinburgh provides research data management services and resources to support researchers through the entire data lifecycle. These include tools for creating data management plans, storing and sharing research data securely, and preserving data in the long term. The Research Data Service aims to help researchers comply with open science principles and data policies through a range of training programs, online guidance, and technical infrastructure. It has developed a multi-year roadmap and maturity model to continuously improve services based on researchers' needs and priorities like relationship building, communication skills, and consultation.
Efficient and effective data management for ILRI research projects: A holisti...ILRI
This document discusses ILRI's approach to data management for research projects and proposes a holistic framework to improve current practices. Currently, ILRI's data management has been ad-hoc with data stored decentralized across different databases and locations. The proposed framework includes standardizing data collection technologies and indicator templates, implementing near real-time data relay and quality checks, centralized storage and multiple access methods, and making data publicly accessible. This will help ensure high quality data management throughout the entire research project lifecycle.
This document discusses creating a data management plan. It explains that a data management plan is a comprehensive plan for managing research data throughout a project's lifecycle and briefly describing how data will be shared per a funder's policy. It provides an overview of key elements to include in a plan such as file formats, organization, sharing, and preservation. The document also reviews funder requirements and available tools to create plans, noting they can be tailored to different funders' guidelines.
Presentation given to the High Performance Computing Summer School as part of a hands-on workshop developing software management plans and looking at software as data within the context of research data management best practices.
Feb 26 NISO Training Thursday
Crafting a Scientific Data Management Plan
About the Training
Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors.
This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth.
About the Instructors
Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries
Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Learning to Curate Research Data
Jennifer Doty, Research Data Librarian, Emory Center for Digital Scholarship, Emory University, Robert W. Woodruff Library
The document outlines five starter steps for research libraries to become partners in research data management. The steps are: 1) prepare a use case for data management, 2) coordinate stakeholders like the IT department and research units, 3) adapt current skills to research data management, 4) initiate a pilot project to test tools and develop workflows, and 5) get to know the current landscape of data requirements, repositories, and lessons from other libraries. The library is well positioned to play a leadership role in research data management by utilizing its skills in preservation, access, and curation.
Improving Integrity, Transparency, and Reproducibility Through Connection of ...Andrew Sallans
The Center for Open Science (COS) was founded as a non-profit technology start-up in 2013 with the goal of improving transparency and reproducibility by connecting the scholarly workflow. COS achieves this goal through the development of a free, open source web application called the Open Science Framework (OSF), providing features like file sharing and citing, persistent urls, provenance tracking, and automated versioning. Initial workflow API connections focused on storage services and included Figshare, GitHub, Amazon S3, Dropbox, and Dataverse. The team is now working to connect other parts of the workflow with services like DMPTool, Databib/re3data, and Databrary. This session will introduce the core architecture and the problems that it solves, and illustrate how connecting services can benefit everyone involved in supporting the research ecosystem. COS is funded through the generosity of grants from the Laura and John Arnold Foundation, the John Templeton Foundation, the Alfred P. Sloan Foundation, the Association of Research Libraries, and others.
Presented at CNI Fall 2014, Washington, DC.
February 18 2014 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Capacity Building: Leveraging existing library networks to take on research data
Heidi Imker, Director of the Research Data Service, University of Illinois at Urbana-Champaign
UVa Library Scientific Data Consulting Group (SciDaC): New Partnerships and...Andrew Sallans
The UVA Library Scientific Data Consulting Group (SciDaC) provides new partnerships and services to support scientific data management in research. SciDaC was formed in 2010 to focus on data consulting after restructuring from the Research Computing Lab. SciDaC conducts data interviews and assessments, assists with NSF Data Management Plan requirements, and works to integrate research data into the institutional repository. Future work includes expanding disciplinary support, integrating into the research proposal process, and advising on data policy.
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 explosion in growth of the Web of Linked Data has provided, for the first time, a plethora of information in disparate locations, yet bound together by machine-readable, semantically typed relations. Utilisation of the Web of Data has been, until now, restricted to the members of the community, eating their own dogfood, so to speak. To the regular web user browsing Facebook and watching YouTube, this utility is yet to be realised. The primary factor inhibiting uptake is the usability of the Web of Data, where users are required to have prior knowledge of elements from the Semantic Web technology stack. Our solution to this problem is to hide the stack, allowing end users to browse the Web of Data, explore the information it contains, discover knowledge, and use Linked Data. We propose a template-based visualisation approach where information attributed to a given resource is rendered according to the rdf:type of the instance.
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/.
Incentivising the uptake of reusable metadata in the survey production processLouise Corti
This document discusses incentivizing the uptake of reusable metadata in survey production. It notes that there is no universal language used to document survey questions and variables, leading to wasted resources. The Data Documentation Initiative (DDI) is proposed as a standard. Barriers to adopting metadata best practices include legacy systems, manual processes, and reluctance to change. The document outlines ideas to incentivize metadata use such as specifying documentation requirements in funding calls and improving documentation tools and workflows. Showing tangible benefits through applications like question banks and data exploration systems is also suggested.
ESI Supplemental 1 E-research Support SlidesDuraSpace
E-Research Support at
Johns Hopkins University & Purdue University
Supplemental Webinar
Wednesday, October 17, 2012
Presented by Sayeed Choudhurry & James Mullins
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.
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.
Relationship Building and Advocacy Across the CampusUCD Library
Presentation given by Julia Barrett, Research Services Manager at University College Dublin Library, to the ANLTC Seminar: Supporting the Activities of Your Research Community - Issues and Initiatives, held on December 3, 2014 at the Royal Irish Academy, Dublin, Ireland.
It's 2015. Do You Know Where Your Data Are?Patricia Hswe
This document summarizes a presentation on research data management. It discusses definitions of research data and why data should be shared. It provides tips for best practices in file naming, description standards, formats and storage. Tools, resources and services for research data management from Penn State and beyond are presented, including ScholarSphere and DMPTool. The importance of having an online presence and sharing research is discussed.
This document discusses research lifecycles and data management. It begins by outlining typical stages in a research lifecycle from planning to publication. It then discusses how data is created and managed at various stages, and raises questions researchers should consider around formatting, documenting, storing, sharing and preserving data. The document provides examples of research lifecycle models and gives advice on best practices for managing data at each stage of the research process to support reuse and ensure data is well documented and preserved.
Data Sets, Ensemble Cloud Computing, and the University Library:Getting the ...SEAD
This document discusses research data management and the role of university libraries. It describes the SEAD (Sustainable Environment Actionable Data) project, which provides data services like curation, preservation, and a social community network to support research data across its lifecycle. SEAD aims to support interdisciplinary research by allowing researchers to define and manage related collections of data and metadata called Research Objects in a scalable way. The document argues that research organizations are best positioned to provide comprehensive long-term data services that integrate across the entire research process.
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
Staffing Research Data Services at University of EdinburghRobin Rice
Invited remote talk for Georg-August University of Göttingen workshop: RDM costs and efforts on 28 May in Göttingen. Organised by the project Göttingen Research Data Exploratory (GRAcE).
Flying solo: data librarians working outside (traditional) librariesJane Frazier
I used these slides for my portion of the "Flying Solo" ANDS webinar:
Did you know there are data librarians who work outside of (traditional) libraries? For some, being a data librarian means leaving the relative comfort of the library behind and ‘flying solo’ into unchartered territory. These are new and demanding roles that require a steep learning curve with minimal support. In this webinar, three data librarians working outside of libraries will share their experience of going it alone, reflecting on these challenging yet rewarding roles that push the boundaries of librarianship and open new opportunities for the profession.
Siobhann McCafferty is based at QUT’s Institute for Future Environments in Brisbane and is the Research Data Coordinator for the National Agricultural Nitrous Oxide Research Program (NANORP). She is embedded in the Healthy Ecosystems and Environmental Management group at IFE and works with researchers from across Australia to store program data and make it discoverable and reusable.
Jane Frazier is a Data Librarian at ANDS. She has previously worked in the University of North Carolina Music Library cataloging 20th century American vocal sheet music, as curatorial assistant at the Dryad digital repository, and at the UNC Metadata Research Center exploring automatic subject indexing processes for Dryad. From 2013 to 2014 Jane led the research and development of a new web-based cataloging system for collectible items with Stanley Gibbons, one of the world’s oldest stamp collecting firms.
-- Michelle Teis has more than 25 years’ industry experience, senior consultant
Michelle Teis is an enterprise information management expert specialising in content, data and knowledge management, and information privacy. http://www.glentworth.com/about-us/our-key-people/michelle-teis/
RDAP 15: “This is just for me”: Researchers on their data documentation pract...ASIS&T
This document summarizes a panel discussion on researchers' data documentation practices. It finds that metadata is often not sufficient because data itself is messy and domain-specific. Researchers in different disciplines like chemistry, ecology, computer science, microbiology, and earth sciences approach documentation differently based on norms and needs within their fields. For example, chemists use numbering systems and documentation protocols while microbiologists emphasize keeping detailed lab notebooks. The document concludes more interviews are needed to better understand disciplinary practices and tailor library services accordingly.
Similar to Context in context: applying a context-driven approach in an academic library (20)
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
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.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
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.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Context in context: applying a context-driven approach in an academic library
1. Context in context: applying a
content-driven approach in an
academic library
Kathleen Fear
@kmfear
2.
3. • Relatively low usage, with a few exceptions
• Electronic theses and dissertations; musical scores
• Needed to think about changing software
• Usability research never focused on metadata
• Metadata form was challenging for users
• Data can be deposited to UR Research, but it wasn’t a focus
of the repository design process
4. DATA
&
DOCUMENTATION
.csv, codebooks, research design,
survey, images, images, notes,
shape files, specimens,
artifacts, etc.
DATA DEPOSIT
REQUIREMENTS
description, creator, title, publisher,
date, donor, rights, collector, taxon,
documents, subject, coverage,
methods, etc.Data
producer
Repository
staff
Data Reuser
But based on whose needs?
A Context-driven Approach to Data Curation for Reuse
DATA
&
DOCUMENTATION
data collection, data producer, and
repository information, prior reuse, missing data,
research objectives, provenance, advise on reuse, etc.
The DSpace Digital Repository Model
Metadata
librarians
Data services
staff
Outreach
librarians
Project staff
HPC staff?
5. Scoping down to data…
For your purposes, what does a data repository need to do?
Producers / Reusers
Data curation profiles
Walk-throughs of existing systems
Staff
IR situation analysis
Interviews (informal)
What systems match those needs?
6. What contextual information is needed to
effectively support reuse?
What resources can we devote to developing and
maintaining our repository?
…looking back out to library context
What use cases / user needs are in and out of
scope?
Should data be treated the same as other scholarly
resources in the IR?
What is the library’s strategic vision for data?
User context
IT service model
Library strategic plan
8. • Kyle Parry, CLIR fellow for data curation for
visual and cultural studies
• Nora Dimmock, Asst Dean for IT
• Marcy Strong, Head of Cataloging
• Kathleen Fear, Data librarian
9. Conflict and consensus
• What contextual information supports reuse
of these images serving multiple goals
(artistic, political, documentary, etc.)?
• Where are instances of conflict in what
repository staff and different users prioritize?
How can these conflicts be resolved?
Editor's Notes
Rochester’s emphasis on user-centered repository design = good fit for context-driven approach to data curation
Base assumptions
Step 1: identify stakeholders and the designated community
photo credit for the image of the data producer, repository staff, and data reuser: <a href="http://www.flickr.com/photos/32066106@N06/3000043099">Passing time</a> via <a href="http://photopin.com">photopin</a> <a href="https://creativecommons.org/licenses/by-nc-nd/2.0/">(license)</a>
photo credit for The DSpace Digital Repository Model image: http://www.ariadne.ac.uk/issue55/vandeventer-pienaar#27, Figure 4
Trying to find the edges of our designated community. We aren’t and can’t be a disciplinary repository, so whose needs are in scope and whose are out?
What we got was huge diversity, and a range of systems that could do some, but not all of the needs – you can do anything, but you can’t do everything.
Attempting to focus in on user context for data blew us back out to look at the library context for the repository.
In order to interpret and implement the things users tell us they need, we need to have clear justification for who we determine are users. When building a repository that necessarily serves a very broad and very diverse set of users, there will always be competing priorities. Previous repository has a gazillion metadata fields, 99% of them optional, and they’re a major barrier.