The presentation introduce research data management services at University of Cape Town (UCT) with more focus on UCT's Data management planing online tool.
Research Data Support at the University of EdinburghRobin Rice
The document summarizes the research data support services at the University of Edinburgh. It describes the university's background and information services department. It then outlines the maturity model that guides the research data management (RDM) services, the governance structure overseeing the RDM service, and the funding model that supports it. The document also summarizes the university's RDM policy and the various tools and support provided across the research data lifecycle, from creating data management plans and storing data to publishing and preserving data in the long term.
DMAOnline - data management administration onlineJisc
The document summarizes the progress and next steps of the DMAOnline project. In phase 1, they refined use cases and developed a prototype dashboard with real university data. For phase 2, they plan to: deliver a version 2 prototype with live data ingestion and improved visualization; scale the backend to manage multi-institutional data; and have live university data and at least 3 early adopters using the system. Funding of £36,198 is requested to support developer and manager time as well as project meetings with early adopters.
The document outlines a proposed data science model curriculum. It discusses placing data science occupations into top-level hierarchies including managers, professionals, and technicians. It then proposes new third-level occupation groups for data science managers, professionals, and technology professionals. Finally, it describes five core competencies for data science: data analytics, data management, data science engineering, scientific/research methods, and data science domain knowledge.
UK Research Data Discovery Service metadata schemaJisc RDM
An overview of the metadata schema being developed for the UK research data discovery service. Dom Fripp at the Research Data Network event at Cardiff University, May 2016.
This document summarizes a webinar for the Research Data Discovery Service Phase 3 project. The webinar agenda included project updates, a review of the latest system status including harvesting and requirements, a discussion of metadata, an overview of next steps for Phase 3, and time for questions. Participants were encouraged to provide feedback and help test the beta version of the system as it is further developed into a production research data discovery service.
National data services lightening talk at the RDAJisc RDM
Our slides for the lightening talk at the annual RDA in Tokyo. All about the national shared services to support research data infrastructure. March 2016.
Research Data Support at the University of EdinburghRobin Rice
The document summarizes the research data support services at the University of Edinburgh. It describes the university's background and information services department. It then outlines the maturity model that guides the research data management (RDM) services, the governance structure overseeing the RDM service, and the funding model that supports it. The document also summarizes the university's RDM policy and the various tools and support provided across the research data lifecycle, from creating data management plans and storing data to publishing and preserving data in the long term.
DMAOnline - data management administration onlineJisc
The document summarizes the progress and next steps of the DMAOnline project. In phase 1, they refined use cases and developed a prototype dashboard with real university data. For phase 2, they plan to: deliver a version 2 prototype with live data ingestion and improved visualization; scale the backend to manage multi-institutional data; and have live university data and at least 3 early adopters using the system. Funding of £36,198 is requested to support developer and manager time as well as project meetings with early adopters.
The document outlines a proposed data science model curriculum. It discusses placing data science occupations into top-level hierarchies including managers, professionals, and technicians. It then proposes new third-level occupation groups for data science managers, professionals, and technology professionals. Finally, it describes five core competencies for data science: data analytics, data management, data science engineering, scientific/research methods, and data science domain knowledge.
UK Research Data Discovery Service metadata schemaJisc RDM
An overview of the metadata schema being developed for the UK research data discovery service. Dom Fripp at the Research Data Network event at Cardiff University, May 2016.
This document summarizes a webinar for the Research Data Discovery Service Phase 3 project. The webinar agenda included project updates, a review of the latest system status including harvesting and requirements, a discussion of metadata, an overview of next steps for Phase 3, and time for questions. Participants were encouraged to provide feedback and help test the beta version of the system as it is further developed into a production research data discovery service.
National data services lightening talk at the RDAJisc RDM
Our slides for the lightening talk at the annual RDA in Tokyo. All about the national shared services to support research data infrastructure. March 2016.
Northumbria University is working to implement a robust research data management (RDM) solution. It has engaged in several activities to assess current RDM practices and infrastructure needs, including interviews with grant holders, a survey of researchers, and workshops with the Digital Curation Centre. Through these workshops, the university used the RISE model to evaluate its capabilities for data ingest, access, preservation, and more across several potential repository platforms. This helped provide evidence to secure budget and staffing to pilot and roll out a new RDM system starting in 2018. The university aims to go to procurement in September 2017 after finalizing business requirements and an options appraisal.
Jisc Research Data Discovery Service ProjectJisc RDM
This document summarizes the UK Research Data Discovery Service (UKRDDS) project run by Jisc from 2013-2016. The project had two phases: an initial pilot to evaluate options for a research data registry and a second phase to build a test service based on the CKAN platform. The project engaged universities and data centers to pilot the service and provide feedback. It focused on developing a core metadata schema and getting stakeholder input to define requirements and priorities through an advisory group structure. The timeline outlines milestones like prototyping the service, implementing pilots, and developing plans to transition the service to ongoing operations.
This document discusses open data and the process of archiving, documenting, quality checking, integrating, publishing, and redistributing data. It describes how data is archived at the Marine Data Archive and documented with metadata. Quality control ensures data is correctly interpreted and usable. Data is integrated and published through the Integrated Marine Information System with discovery metadata. A data policy advocates open data exchange and making data publicly available while recognizing the original source. The main challenges are convincing scientists to openly share data and having no mandates for participation.
Jisc Research Data Shared Service - Spring UpdateJisc RDM
This document provides an overview and update on Jisc's Research Data Shared Service. It discusses the vision, goals, and key requirements of creating a shared research data infrastructure. It also provides details on the supplier framework, consultant support, pilot engagements, and strategic view of the service. The service aims to make research data management easier for researchers and help institutions meet requirements in a cost-effective, interoperable manner.
AMASED: Access methods for analysing sensitive dataJisc
The document summarizes the goals and progress of the AMASED project, which aims to develop methods for analyzing sensitive research data using the DataSHIELD software. Key goals include developing text analysis packages for the British Library dataset, implementing a pilot with F1000 Research, and scoping a user interface. Recent progress includes successful text analysis of unrestricted library data and further defining challenges of integrating data cleaning tools. Next steps include establishing an advisory group, developing proofs of concept for analyzing library and research paper data, and scoping a user interface. Funding of £59,406 is requested from Jisc and the University of Bristol to complete these goals.
Gold, silver, bronze - research data networkJisc RDM
This document discusses the development of a scalable data model to meet researcher metadata requirements. It describes conceptual and practical processes used, including aligning with standards and popular data models. An example shows over 1500 lines of metadata XML for one data package. A research data shared service is proposed to provide bronze, silver, or gold ratings for metadata completeness. Focus groups with researchers are evaluating metadata fields and use cases to test the infrastructure. Exercises are used to gather information about researchers' metadata production and needs at different research lifecycle stages.
The document outlines the mission and aims of establishing a business case and costing process for research data management (RDM) in a more efficient and effective manner. It discusses commissioning work from Research Consulting to deliver a high-level business case for RDM and from Cambridge Econometrics to analyze methods to quantify the economic benefits of RDM. The next steps include publishing the commissioned reports and resources in May 2016 to provide RDM costing schemas, budgets, templates, and awareness materials.
The document outlines Research Data Spring, a program that supports partnerships to improve the research data lifecycle. It aims to find new tools and solutions for researchers' data management and use. The program has funded several phases of projects, with Phase II including 11 projects and Phase III focusing on 7 continued projects. Upcoming work includes developing the projects into robust solutions and services and showcasing results in autumn 2016.
1. Metrics are being developed to track downloads and reuse of research data to understand impact and reassure researchers. A new service called IRUS for Data will provide metrics for data repositories across different platforms.
2. There is debate around what data citations mean and how they should be used and understood. Projects are working to develop best practices and encourage responsible use of citation metrics for data.
3. Ensuring research data sharing is recognized in existing systems like journal policies is challenging due to lack of standards. Initiatives are working with publishers and repositories to develop guidance and implement principles for data citation.
University of Edinburgh RDM Training: MANTRA & beyondRobin Rice
The document summarizes training provided at the University of Edinburgh on research data management (RDM). It describes a training matrix that includes the MANTRA online course, bespoke sessions, workshops on information skills, and a new MOOC. The MANTRA course and workshops on topics like data management planning and working with sensitive data are well attended. Feedback from participants indicates the training is useful and enlightening. New developments include data and software carpentry workshops and transitioning to a Research Data Service to provide comprehensive RDM support.
**Researcher engagement resources: a demonstration**
*Rosie Higman, University of Cambridge/Manchester, Hardy Schwamm, Lancaster University*
Research Data Network
A Data Curation Framework: Data Curation and Research Support ServicesSusanMRob
The document presents a data curation framework to help align research support services with university eResearch needs. The framework has four components: data processing, storage, archiving, and research data management. It identifies current support services around information literacy, collections access, and scholarship. The framework is intended as a tool to highlight areas for collaboration between research support services and eResearch providers as services evolve over time.
Managing data behind creative masterpiecesJisc RDM
The document discusses research data in creative fields and Jisc's Research Data Shared Service (RDSS) to help manage such data. RDSS aims to enable open science through efficient capture, preservation and reuse of research data. It will provide core functions like deposit, description, storage, publication and preservation of data, as well as reporting and advisory services. RDSS addresses key issues in research data management to help reduce costs and risks for researchers and institutions.
Slides from the UCT eResearch Emerging Researcher seminar on Research Data Management (RDM), by Erika Mias and Kayleigh Lino. The seminar gave attendees a brief outline of the pending UCT RDM Policy, with information about the researcher's responsibilities regarding adherence to it. The presenters gave attendees a hands-on demonstration of the Library services that have been implemented to assist researchers with managing, collaborating on, and sharing research data. They also demonstrated tools and tips for RDM best practices.
The document summarizes the research data management program at the University of Edinburgh. It discusses the services provided, including a data management planning tool, a data repository for publication and preservation, and a data storage system. Training and support are also offered to help researchers with best practices in organizing, documenting, sharing, and preserving their research data over its entire lifecycle. The program aims to implement the University's research data policy and support funder requirements by establishing these research data management services.
Northumbria University is working to implement a robust research data management (RDM) solution. It has engaged in several activities to assess current RDM practices and infrastructure needs, including interviews with grant holders, a survey of researchers, and workshops with the Digital Curation Centre. Through these workshops, the university used the RISE model to evaluate its capabilities for data ingest, access, preservation, and more across several potential repository platforms. This helped provide evidence to secure budget and staffing to pilot and roll out a new RDM system starting in 2018. The university aims to go to procurement in September 2017 after finalizing business requirements and an options appraisal.
Jisc Research Data Discovery Service ProjectJisc RDM
This document summarizes the UK Research Data Discovery Service (UKRDDS) project run by Jisc from 2013-2016. The project had two phases: an initial pilot to evaluate options for a research data registry and a second phase to build a test service based on the CKAN platform. The project engaged universities and data centers to pilot the service and provide feedback. It focused on developing a core metadata schema and getting stakeholder input to define requirements and priorities through an advisory group structure. The timeline outlines milestones like prototyping the service, implementing pilots, and developing plans to transition the service to ongoing operations.
This document discusses open data and the process of archiving, documenting, quality checking, integrating, publishing, and redistributing data. It describes how data is archived at the Marine Data Archive and documented with metadata. Quality control ensures data is correctly interpreted and usable. Data is integrated and published through the Integrated Marine Information System with discovery metadata. A data policy advocates open data exchange and making data publicly available while recognizing the original source. The main challenges are convincing scientists to openly share data and having no mandates for participation.
Jisc Research Data Shared Service - Spring UpdateJisc RDM
This document provides an overview and update on Jisc's Research Data Shared Service. It discusses the vision, goals, and key requirements of creating a shared research data infrastructure. It also provides details on the supplier framework, consultant support, pilot engagements, and strategic view of the service. The service aims to make research data management easier for researchers and help institutions meet requirements in a cost-effective, interoperable manner.
AMASED: Access methods for analysing sensitive dataJisc
The document summarizes the goals and progress of the AMASED project, which aims to develop methods for analyzing sensitive research data using the DataSHIELD software. Key goals include developing text analysis packages for the British Library dataset, implementing a pilot with F1000 Research, and scoping a user interface. Recent progress includes successful text analysis of unrestricted library data and further defining challenges of integrating data cleaning tools. Next steps include establishing an advisory group, developing proofs of concept for analyzing library and research paper data, and scoping a user interface. Funding of £59,406 is requested from Jisc and the University of Bristol to complete these goals.
Gold, silver, bronze - research data networkJisc RDM
This document discusses the development of a scalable data model to meet researcher metadata requirements. It describes conceptual and practical processes used, including aligning with standards and popular data models. An example shows over 1500 lines of metadata XML for one data package. A research data shared service is proposed to provide bronze, silver, or gold ratings for metadata completeness. Focus groups with researchers are evaluating metadata fields and use cases to test the infrastructure. Exercises are used to gather information about researchers' metadata production and needs at different research lifecycle stages.
The document outlines the mission and aims of establishing a business case and costing process for research data management (RDM) in a more efficient and effective manner. It discusses commissioning work from Research Consulting to deliver a high-level business case for RDM and from Cambridge Econometrics to analyze methods to quantify the economic benefits of RDM. The next steps include publishing the commissioned reports and resources in May 2016 to provide RDM costing schemas, budgets, templates, and awareness materials.
The document outlines Research Data Spring, a program that supports partnerships to improve the research data lifecycle. It aims to find new tools and solutions for researchers' data management and use. The program has funded several phases of projects, with Phase II including 11 projects and Phase III focusing on 7 continued projects. Upcoming work includes developing the projects into robust solutions and services and showcasing results in autumn 2016.
1. Metrics are being developed to track downloads and reuse of research data to understand impact and reassure researchers. A new service called IRUS for Data will provide metrics for data repositories across different platforms.
2. There is debate around what data citations mean and how they should be used and understood. Projects are working to develop best practices and encourage responsible use of citation metrics for data.
3. Ensuring research data sharing is recognized in existing systems like journal policies is challenging due to lack of standards. Initiatives are working with publishers and repositories to develop guidance and implement principles for data citation.
University of Edinburgh RDM Training: MANTRA & beyondRobin Rice
The document summarizes training provided at the University of Edinburgh on research data management (RDM). It describes a training matrix that includes the MANTRA online course, bespoke sessions, workshops on information skills, and a new MOOC. The MANTRA course and workshops on topics like data management planning and working with sensitive data are well attended. Feedback from participants indicates the training is useful and enlightening. New developments include data and software carpentry workshops and transitioning to a Research Data Service to provide comprehensive RDM support.
**Researcher engagement resources: a demonstration**
*Rosie Higman, University of Cambridge/Manchester, Hardy Schwamm, Lancaster University*
Research Data Network
A Data Curation Framework: Data Curation and Research Support ServicesSusanMRob
The document presents a data curation framework to help align research support services with university eResearch needs. The framework has four components: data processing, storage, archiving, and research data management. It identifies current support services around information literacy, collections access, and scholarship. The framework is intended as a tool to highlight areas for collaboration between research support services and eResearch providers as services evolve over time.
Managing data behind creative masterpiecesJisc RDM
The document discusses research data in creative fields and Jisc's Research Data Shared Service (RDSS) to help manage such data. RDSS aims to enable open science through efficient capture, preservation and reuse of research data. It will provide core functions like deposit, description, storage, publication and preservation of data, as well as reporting and advisory services. RDSS addresses key issues in research data management to help reduce costs and risks for researchers and institutions.
Slides from the UCT eResearch Emerging Researcher seminar on Research Data Management (RDM), by Erika Mias and Kayleigh Lino. The seminar gave attendees a brief outline of the pending UCT RDM Policy, with information about the researcher's responsibilities regarding adherence to it. The presenters gave attendees a hands-on demonstration of the Library services that have been implemented to assist researchers with managing, collaborating on, and sharing research data. They also demonstrated tools and tips for RDM best practices.
The document summarizes the research data management program at the University of Edinburgh. It discusses the services provided, including a data management planning tool, a data repository for publication and preservation, and a data storage system. Training and support are also offered to help researchers with best practices in organizing, documenting, sharing, and preserving their research data over its entire lifecycle. The program aims to implement the University's research data policy and support funder requirements by establishing these research data management services.
This document discusses data management plans (DMPs), which are brief plans that define how research data will be created, documented, stored, shared, and preserved. DMPs are often required as part of grant applications. The document provides an overview of why DMPs are important, how they benefit researchers and institutions, and key aspects to address in a DMP such as data organization, stakeholders, and making data FAIR (findable, accessible, interoperable, and reusable). Examples of DMPs from real projects are also presented.
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 discusses the importance of developing a data management plan (DMP) when conducting research. A DMP is a brief document written at the start of a research project that outlines how research data will be collected, documented, shared, and preserved. It addresses issues such as data formats, metadata, ethics, and long-term storage. Developing a DMP helps researchers manage their data effectively and address funder requirements for data sharing and archiving. The presentation provides examples and guidance on the key components of a DMP and resources for creating DMPs according to different funder templates.
The DMP Tool provides a centralized location for research data management planning templates based on funder requirements. It offers a CMS-like interface for collaboratively writing, updating, and sharing data management plans. The tool supports the data management planning process through its templates, examples, and information resources. It also has an API that allows other services to integrate its data. However, the DMP Tool itself does not store, share, review, or access research data.
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.
The document provides background information on RDM services at the University of Edinburgh. It summarizes that EDINA and the University Data Library provide research data management support and online resources. It then overviews key RDM services including DataStore for active research data storage, DataShare for open data publication, and plans for a long-term DataVault archive. The document also discusses RDM training and the university's RDM policy implemented through a multi-phase roadmap.
The document provides information about research data management (RDM) services and initiatives at the University of Edinburgh. It describes the EDINA National Data Centre and Data Library, which provide online resources and data management support. It outlines several JISC-funded RDM projects undertaken by the Data Library, including building the Edinburgh DataShare repository. It also summarizes the Research Data MANTRA training module and the university's RDM roadmap, which lays out a multi-phase plan to improve RDM support and services by 2015 in line with funder requirements.
An introduction to Research Data Management and Data Management Planning for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
The document summarizes the activities of EDINA and the Data Library at the University of Edinburgh related to research data management. It describes EDINA as a national data center that provides online resources for education and research. The Data Library assists university researchers with discovering, accessing, using and managing research datasets. It also outlines several projects the Data Library is involved in to develop training, policies and services to support best practices in research data management according to funder requirements. This includes developing an institutional research data management roadmap to help the university meet funder expectations by 2015.
Librarians can provide valuable data management services to researchers on campus. An effective strategy includes surveying researchers to identify needs, communicating service offerings through workshops and consultations, and providing in-depth guidance on data management plans and long-term data preservation. Developing workshops involves setting learning objectives, evaluating content, and securing resources like space and food. Consultations allow librarians to help with specific topics like choosing file formats or finding metadata standards. Creating a data management plan requires detailing a data inventory, metadata description, long-term preservation and access methods. Trusted disciplinary repositories and use of stable identifiers help ensure long-term findability and access.
Presentation given by Sarah Jones at a seminar run by LSHTM on 6th November 2012. http://www.lshtm.ac.uk/newsevents/events/2012/11/developing-data-management-expertise-in-research---half-day-event
Getting to grips with research data management Wendy Mears
This document provides an overview of research data management. It defines research data management and discusses its importance. It also outlines the data lifecycle model and provides guidance on sharing data, working with data, planning for data management, and useful resources for research data management. The document aims to help researchers effectively manage the data created throughout the research process.
This document provides an overview of research data management (RDM) priorities, stakeholders, and practices from the perspective of the University of Edinburgh. It discusses the university's RDM roadmap, which aims to implement RDM services and support over multiple phases by April 2015. Key services discussed include general RDM support and consultancy, support for data management planning, storage and collaboration facilities, and tools for long-term data management and deposit. The roles of key university committees in overseeing the RDM program are also outlined. Finally, the document discusses the university's communications plan to raise awareness of RDM among researchers and support staff.
This document summarizes a workshop on planning for research data management. The workshop covered what research data management is, why it is important, and how to plan for it. Key points included defining the data that will be collected, how it will be stored and backed up, file naming and formatting standards, documentation and metadata, ethics and legal compliance, data sharing and preservation plans, and allocating roles and resources. Attendees then discussed challenges and needs for managing their own research data. The presenter emphasized starting planning early and seeking advice, and provided information on resources and tools available to support research data management.
The document summarizes a workshop on planning for research data management. It discusses what research data management is, including definitions and lifecycle models. It emphasizes the importance of planning for RDM from the beginning of a research project, including developing a data management plan that addresses data collection, documentation, storage, sharing, and long-term preservation. The workshop also covered naming conventions, file formats, metadata, and tools and resources available to support RDM.
To create an Adobe ID, visit www.adobe.com and click "Sign-in" which will guide you through the process of setting up an Adobe ID. For additional assistance, check the Adobe Support page or contact the listed email address or library guide "Ask-A-Librarian" tab with any questions.
A brief guide on how to access electronic resources at UCT Libraries. There are two options. In both options you would need to have UCT network credentials.
Primo is the UCT Libraries' search tool that allows users to search the entire library collection, including physical and electronic resources, with a single search. It provides access to UCT's catalog, databases, ebooks, articles, and other materials. Users can sign in to Primo to save searches and items to their personal folders. Searches can be conducted by title, author, keyword, or subject, and results will show if items are available online or in the physical collection. Links are provided to access full text for electronic resources directly from the search results.
This document provides an overview of library resources and skills training offered by Awot Kiflu Gebregziabher at the University of Cape Town (UCT) libraries. The training covers topics such as literature searching, keeping up-to-date in research, citing and referencing, and research data management. The objective is to help students know ways to search for scholarly resources, be aware of tools that facilitate research and studies, and know library services available. Methods for conducting literature searches are outlined, along with tools for searching the library's collections, databases, and other resources. Off-campus access and interlibrary loans are also discussed.
To locate your h-index in Google Scholar, you must first create a Google Scholar profile by following the steps in the "How to set up Google Scholar Profile" slides. Next, perform an advanced search in Google Scholar by typing your name in the "Return articles authored by" search box. Then select the right name and affiliation combination listed under the "User Profiles for author" section to view your h-index and other citation metrics. If additional help is needed, contact your subject librarian at UCT Libraries.
This document provides instructions for locating your h-index in the Scopus database. It outlines the steps to search for an author by entering your surname, initials and affiliation. This will display your citation overview page containing your h-index. From there, you can update the analytics to exclude self-citations and request merging author profiles if publications are separated. If additional help is needed, users can contact their subject librarian at UCT Libraries.
The slides introduces you to Primo and how to use the platform. Additionally you will be introduced to UCT's interlibrary loans service as well as how you can access resources from off-campus.
This document provides instructions for setting up a Google Scholar profile. It notes that a Google Scholar profile allows for unique identification and visibility to a diverse audience. It also accommodates different publication types like articles, book chapters, and theses. However, it is possible for other researchers' publications to be incorrectly listed under your profile. The document then provides a step-by-step guide to setting up a Google Scholar profile which involves logging into a personal Google account, clicking "My Profile" and adding publications. It emphasizes using a personal Gmail account to maintain the profile permanently.
This document provides guidance on finding scholarly resources and proper referencing. It discusses searching strategies such as known item searches and searching for unknown items using keywords and Boolean logic. It also outlines where to search, including library databases and catalogs. The document then covers the two stages of referencing - in-text citations and reference lists. It provides examples of how to cite sources in the text and format references for different source types, such as books and journal articles, based on the UCT Author-Date referencing style.
EndNote is a reference management software that allows users to organize references and citations. The document provides instructions on downloading and installing EndNote, setting preferences, importing references from databases and folders, and using features like organizing references into groups and inserting citations into documents. Key features covered include syncing libraries across devices, searching references, and backing up libraries for protection.
The document provides tips for finding journal articles using Boolean operators like "and", "or", and "not" to combine or exclude search terms. It also describes using wildcard and truncation symbols to account for unknown characters or multiple spellings/endings. Useful databases for oceanography are listed, and it recommends creating database accounts to set up article alerts that will keep you up to date on the latest research.
The document provides an overview of a library training session for the African Climate and Development Unit on various library resources and search strategies. It covers the library webpage, databases like Scopus and Web of Science, searching for known and unknown items, keeping search strategies structured using Boolean operators, tracking author publications and journal impact factors. Tips are provided on creating personal accounts in databases, setting up search alerts, and ways to stay updated in one's field through table of contents alerts and professional organizations.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
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
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 Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
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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.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
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.
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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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2. • involves thinking and planning about the future
of your research data
Research data management (RDM)
• collection
• filing
• storage
• format
• describing
• sharing and accessibility
• archiving and
conserving
3. Research data management (RDM) cont. …
• Purpose
• to satisfy funder requirements
• to easily find, access, share and re-use your
research data
• to get cited and increase your visibility in
research
4. Research data management (RDM) cont. …
Well managed research data allows
• Verification of published research results
• Reduces the potential for scientific fraud
• Promotes new research through the re-use of
existing data
• Provides resources for training new researchers
• Discourages unintentional redundancy in research
5.
6. • RDM is effective handling of research data
during and after research project to insure
findability, accessibility, interoperability and
re-usability.
Research data management (RDM) cont. …
7. Data Management Plan (DMP)
“…a formal document that describes the data
produced in the course of a research project. [It also]
outlines the data management strategies that will be
implemented both during the active phase of the
research project and after the project ends.”
Sarah Jones (DCC)
Research data management (RDM) cont. …
10. Research data management (RDM) cont. …
• Available RDM Services and tools at UCT
• Research Data Services: technical support
• Data Management Plan Online (DMPonline)
o Planning tool
o Enables updating your plans anytime
• Institutional data Repository: ZivaHub: Open
Data UCT
o Archiving, publishing and sharing platform
11. Access UCT Research Data Services from
library homepage (www.lib.uct.ac.za )
15. Data Management Plan Online (DMPonline)
o Planning tool with
o Guiding questions and information
o Contact link for technical support
o Enables you
o To updating your plans anytime
o Share your plan to your research collaborators
o Export the plan to any file formats
o Present your plan with your research proposal
29. • For announcements & relevant information
• To post queries
• View self help video clips at
Follow us on social media
@UCTLibrary UCT Libraries
UCTLibraries
30. Thank You
For more information and in-depth training check:
Library guides: Biological Sciences, Environmental
and Geographical Sciences
Trainings: Savvy Researcher Series, List of potential
topics for library skills training
Specific Query: use Ask a Librarian facility Or email
me at awot.Gebregziabher@uct.ac.za