This document provides an overview of research data management and what it will mean for the School of Engineering at the University of Lincoln. It discusses the need to manage research data due to funder and institutional requirements. It also describes benefits like reducing duplicated work and supporting collaboration. The document then provides examples of research data sharing benefits. It outlines support available, including from data archives, the Digital Curation Centre, JISC and UKOLN. Finally, it discusses JISC's Managing Research Data program and projects around research data management infrastructure and planning.
This document summarizes a workshop on data management. It outlines the typical research lifecycle including proposal planning, project start up, data collection, analysis, sharing, and end of project. It discusses support for researchers within areas like data mining, curation, and preservation. It also discusses support from outside through infrastructure, policy, and best practices. Finally, it identifies 9 key skills gaps for librarians in advising researchers on data management tasks.
A Big-Data Process Consigned Geographically by Employing Mapreduce Frame WorkIRJET Journal
This document discusses frameworks for processing big data that is distributed across geographic locations. It begins by introducing the challenges of geo-distributed big data processing and then describes several MapReduce-based frameworks like G-Hadoop and G-MR that can process pre-located geo-distributed data. It also covers Spark-based systems like Iridium and frameworks that partition data across geographic locations, such as KOALA grid-based systems. The document analyzes key aspects of geo-distributed big data processing systems like data distribution, task scheduling, and fault tolerance.
A Survey on Medical Image Retrieval Based on HadoopAkshay Mamulwar
This document proposes a Hadoop-based medical image retrieval system to address the challenges of storing and retrieving large amounts of medical image data. It describes how Hadoop uses HDFS for distributed storage and MapReduce for parallel processing to improve efficiency. Feature extraction from query images is performed using MapReduce jobs. Features are then matched to those in a library stored on HDFS to retrieve similar images. This system is expected to reduce retrieval times compared to single-node systems when handling large medical image datasets.
The document describes the Semantic Scout, a framework developed by CNR Semantic Technology Lab for searching, presenting, and analyzing entities from CNR data sources using semantic web, linked open data, natural language processing, and information retrieval techniques. It summarizes the goals and architecture of the Semantic Scout, including how it converts CNR data into ontologies and triples, publishes and links the data, and allows users to search and explore the data through a SPARQL endpoint and other interfaces. The document also provides an example of how the Semantic Scout can be used to identify experts on a topic by searching the integrated CNR data cloud.
Post Event Investigation of Multi-stream Video Data Utilizing Hadoop Cluster IJECEIAES
Rapid advancement in technology and in-expensive camera has raised the necessity of monitoring systems for surveillance applications. As a result data acquired from numerous cameras deployed for surveillance is tremendous. When an event is triggered then, manually investigating such a massive data is a complex task. Thus it is essential to explore an approach that, can store massive multi-stream video data as well as, process them to find useful information. To address the challenge of storing and processing multi-stream video data, we have used Hadoop, which has grown into a leading computing model for data intensive applications. In this paper we propose a novel technique for performing post event investigation on stored surveillance video data. Our algorithm stores video data in HDFS in such a way that it efficiently identifies the location of data from HDFS based on the time of occurrence of event and perform further processing. To prove efficiency of our proposed work, we have performed event detection in the video based on the time period provided by the user. In order to estimate the performance of our approach, we evaluated the storage and processing of video data by varying (i) pixel resolution of video frame (ii) size of video data (iii) number of reducers (workers) executing the task (iv) the number of nodes in the cluster. The proposed framework efficiently achieve speed up of 5.9 for large files of 1024X1024 pixel resolution video frames thus makes it appropriate for the feasible practical deployment in any applications.
Calculations using standard enthalpies of formationulcerd
1. The document provides standard enthalpies of formation (ΔfH°) for various compounds and asks the reader to use these values to calculate the enthalpy change for 10 chemical reactions and processes.
2. It lists the standard enthalpies of formation for common compounds such as water, carbon dioxide, benzene, cyclohexane, calcium carbonate, quicklime, sulfur dioxide, sulfuric acid, ethanol, bromoethane and more.
3. The reader is to apply Hess's law and add or subtract the given standard enthalpies of formation to determine the enthalpy change for the 10 processes listed, such as the enthalpy of solution of hydrogen bromide gas and
‘Pencils and Pixels’ is a learning resource aimed at helping you to improve your communication skills through drawing. An important part of the design process is to develop ideas from the imagination and share those ideas in the wider world. Whether you are having a conversation with yourself or with others, improving your drawing skills will help you to explain that most important of questions, ‘but what will it look like?
For more information and related videos, visit: http://pencilsandpixels.blogs.lincoln.ac.uk/lessons/lesson-1/
This document provides an overview of research data management and what it will mean for the School of Engineering at the University of Lincoln. It discusses the need to manage research data due to funder and institutional requirements. It also describes benefits like reducing duplicated work and supporting collaboration. The document then provides examples of research data sharing benefits. It outlines support available, including from data archives, the Digital Curation Centre, JISC and UKOLN. Finally, it discusses JISC's Managing Research Data program and projects around research data management infrastructure and planning.
This document summarizes a workshop on data management. It outlines the typical research lifecycle including proposal planning, project start up, data collection, analysis, sharing, and end of project. It discusses support for researchers within areas like data mining, curation, and preservation. It also discusses support from outside through infrastructure, policy, and best practices. Finally, it identifies 9 key skills gaps for librarians in advising researchers on data management tasks.
A Big-Data Process Consigned Geographically by Employing Mapreduce Frame WorkIRJET Journal
This document discusses frameworks for processing big data that is distributed across geographic locations. It begins by introducing the challenges of geo-distributed big data processing and then describes several MapReduce-based frameworks like G-Hadoop and G-MR that can process pre-located geo-distributed data. It also covers Spark-based systems like Iridium and frameworks that partition data across geographic locations, such as KOALA grid-based systems. The document analyzes key aspects of geo-distributed big data processing systems like data distribution, task scheduling, and fault tolerance.
A Survey on Medical Image Retrieval Based on HadoopAkshay Mamulwar
This document proposes a Hadoop-based medical image retrieval system to address the challenges of storing and retrieving large amounts of medical image data. It describes how Hadoop uses HDFS for distributed storage and MapReduce for parallel processing to improve efficiency. Feature extraction from query images is performed using MapReduce jobs. Features are then matched to those in a library stored on HDFS to retrieve similar images. This system is expected to reduce retrieval times compared to single-node systems when handling large medical image datasets.
The document describes the Semantic Scout, a framework developed by CNR Semantic Technology Lab for searching, presenting, and analyzing entities from CNR data sources using semantic web, linked open data, natural language processing, and information retrieval techniques. It summarizes the goals and architecture of the Semantic Scout, including how it converts CNR data into ontologies and triples, publishes and links the data, and allows users to search and explore the data through a SPARQL endpoint and other interfaces. The document also provides an example of how the Semantic Scout can be used to identify experts on a topic by searching the integrated CNR data cloud.
Post Event Investigation of Multi-stream Video Data Utilizing Hadoop Cluster IJECEIAES
Rapid advancement in technology and in-expensive camera has raised the necessity of monitoring systems for surveillance applications. As a result data acquired from numerous cameras deployed for surveillance is tremendous. When an event is triggered then, manually investigating such a massive data is a complex task. Thus it is essential to explore an approach that, can store massive multi-stream video data as well as, process them to find useful information. To address the challenge of storing and processing multi-stream video data, we have used Hadoop, which has grown into a leading computing model for data intensive applications. In this paper we propose a novel technique for performing post event investigation on stored surveillance video data. Our algorithm stores video data in HDFS in such a way that it efficiently identifies the location of data from HDFS based on the time of occurrence of event and perform further processing. To prove efficiency of our proposed work, we have performed event detection in the video based on the time period provided by the user. In order to estimate the performance of our approach, we evaluated the storage and processing of video data by varying (i) pixel resolution of video frame (ii) size of video data (iii) number of reducers (workers) executing the task (iv) the number of nodes in the cluster. The proposed framework efficiently achieve speed up of 5.9 for large files of 1024X1024 pixel resolution video frames thus makes it appropriate for the feasible practical deployment in any applications.
Calculations using standard enthalpies of formationulcerd
1. The document provides standard enthalpies of formation (ΔfH°) for various compounds and asks the reader to use these values to calculate the enthalpy change for 10 chemical reactions and processes.
2. It lists the standard enthalpies of formation for common compounds such as water, carbon dioxide, benzene, cyclohexane, calcium carbonate, quicklime, sulfur dioxide, sulfuric acid, ethanol, bromoethane and more.
3. The reader is to apply Hess's law and add or subtract the given standard enthalpies of formation to determine the enthalpy change for the 10 processes listed, such as the enthalpy of solution of hydrogen bromide gas and
‘Pencils and Pixels’ is a learning resource aimed at helping you to improve your communication skills through drawing. An important part of the design process is to develop ideas from the imagination and share those ideas in the wider world. Whether you are having a conversation with yourself or with others, improving your drawing skills will help you to explain that most important of questions, ‘but what will it look like?
For more information and related videos, visit: http://pencilsandpixels.blogs.lincoln.ac.uk/lessons/lesson-1/
‘Pencils and Pixels’ is a learning resource aimed at helping you to improve your communication skills through drawing. An important part of the design process is to develop ideas from the imagination and share those ideas in the wider world. Whether you are having a conversation with yourself or with others, improving your drawing skills will help you to explain that most important of questions, ‘but what will it look like?
For more information and related videos, visit: http://pencilsandpixels.blogs.lincoln.ac.uk/lessons/lesson-1/
Analytics for http://forensicchemistry.lincoln.ac.uk, Feb/March 2011ulcerd
The document summarizes website analytics for the forensicchemistry.lincoln.ac.uk site from February 20, 2011 to March 21, 2011. It received 45 visits from 11 countries over this period. The majority of traffic came from search engines (53.33%) and referring sites (37.78%). The United Kingdom contributed the most visits (14) and had the highest average pages per visit (2.14) and average time on site (1 minute, 44 seconds). The overall bounce rate was 64.44% and 93.33% of visits were from new visitors.
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
1. The document discusses moles, molar mass, molarity, and provides example calculations involving these concepts. Molarity is defined as the number of moles of solute per liter of solution.
2. The document then provides 21 practice problems calculating things like the number of moles or grams of various substances, the molarity of different solutions, and multi-step dilution problems.
3. Students are asked to use the concepts of moles, molar mass, and molarity to solve quantitative chemistry problems involving substances in solutions.
Organic Chemistry: Classification of Organic Compounds: Seminarulcerd
This document discusses various organic functional groups including alcohols, aldehydes, ketones, carboxylic acids, esters, ethers, amines, amides, halogenoalkanes, nitriles, nitro compounds, and thiols. It provides examples of each functional group and discusses their classifications. Primary, secondary, and tertiary alcohols and amines are defined. Common illegal and recreational drugs like amphetamines, aspirin, cannabis, LSD, cocaine, morphine, and codeine are analyzed in terms of their functional group components.
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
EPrints Analytics - Forensic Chemistry OER Course site, Feb/March 2011ulcerd
This document summarizes webpage analytics for content from the Introductory Chemistry institutional repository from February 21 to March 21. It shows the number of pageviews and unique pageviews for each page, as well as average time on page, bounce rate, exit percentage, and cost index for the overall page and individual pages. The most visited page was /2366/ with 15 pageviews and 14 unique pageviews. The average time on page for all content was 53 seconds, with bounce and exit rates lower than the site average.
I shall provide a summary of JISC work in the area of ‘Big Data’. My primary focus will be on how to manage the huge amount of research data produced in UK Universities. I shall cover the history of JISC interventions to improve research data management and look at next steps. I shall touch on some other areas of work like ‘Digging into Data’ and web archiving which also deal with ‘big data’.
Scientific discovery and innovation in an era of data-intensive science
William (Bill) Michener, Professor and Director of e-Science Initiatives for University Libraries, University of New Mexico; DataONE Principal Investigator
The scope and nature of biological, environmental and earth sciences research are evolving rapidly in response to environmental challenges such as global climate change, invasive species and emergent diseases. Scientific studies are increasingly focusing on long-term, broad-scale, and complex questions that require massive amounts of diverse data collected by remote sensing platforms and embedded environmental sensor networks; collaborative, interdisciplinary science teams; and new tools that promote scientific data preservation, discovery, and innovation. This talk describes the challenges facing scientists as they transition into this new era of data intensive science, presents current solutions, and lays out a roadmap to the future where new information technologies significantly increase the pace of scientific discovery and innovation.
This document provides an introduction and overview of the DBM630: Data Mining and Data Warehousing course. It outlines the course syllabus, textbooks, assessment tasks, schedule, prerequisites, and provides a high-level introduction to data mining and data warehousing concepts including definitions, processes, applications and evolution of database technologies.
This document provides an introduction and overview of the DBM630: Data Mining and Data Warehousing course. It outlines the course syllabus, textbooks, assessment tasks, schedule, prerequisites, and introduces concepts related to data mining and data warehousing including definitions, processes, applications, and evolution of database technology. The goal of the course is to teach students about data warehousing, data mining techniques such as association rule mining, classification, clustering, and current trends in the field.
Supporting Libraries in Leading the Way in Research Data ManagementMarieke Guy
Marieke Guy, Institutional Support Officer, Digital Curation Centre, UKOLN, University of Bath, UK presents on Supporting Libraries in Leading the Way in Research Data Management at Online Information, London 20th -21st November 2012
SEAD is a NSF DataNet project that aims to provide cyberinfrastructure for long tail data in sustainability science research. It develops tools for active and social curation of data including an Active Curation Repository (ACR) and VIVO profiles. It also creates a Virtual Archive to facilitate long-term access and preservation of datasets across multiple institutional repositories. The presentation provides an overview of SEAD's approach and highlights pilots with the National Center for Earth Surface Dynamics, including ingesting their data collections into the ACR and Virtual Archive and building a social network in VIVO.
Presentation Title: Grand Challenges and Big Data: Implications for Public Participation in Scientific Research
Presenter: William Michener, Professor and PI/Director of DataONE, University Libraries, University of New Mexico
This document discusses knowledge discovery in databases (KDD) through the LON-CAPA online educational system. [1] It defines KDD and data mining, describing the tasks, methods, and applications of KDD. [2] The goals are to obtain predictive models of students, help students and instructors use resources more effectively, and provide information to increase student learning. [3] It then discusses the KDD process and data mining methods like classification, clustering, and dependency modeling that can be applied to discover knowledge from educational data.
1. Find all frequent itemsets of length 1 by scanning the database to count item occurrences.
2. Iteratively generate candidate itemsets of length k from frequent itemsets of length k-1, and prune unpromising candidates using the Apriori property.
3. Scan the database to determine truly frequent itemsets.
4. Generate association rules from frequent itemsets by adding items to the antecedent and consequent of rules if they meet minimum confidence.
This document provides an overview of artificial neural networks and their application in data mining techniques. It discusses neural networks as a tool that can be used for data mining, though some practitioners are wary of them due to their opaque nature. The document also outlines the data mining process and some common data mining techniques like classification, clustering, regression, and association rule mining. It notes that neural networks, as a predictive modeling technique, can be useful for problems like classification and prediction.
‘Pencils and Pixels’ is a learning resource aimed at helping you to improve your communication skills through drawing. An important part of the design process is to develop ideas from the imagination and share those ideas in the wider world. Whether you are having a conversation with yourself or with others, improving your drawing skills will help you to explain that most important of questions, ‘but what will it look like?
For more information and related videos, visit: http://pencilsandpixels.blogs.lincoln.ac.uk/lessons/lesson-1/
Analytics for http://forensicchemistry.lincoln.ac.uk, Feb/March 2011ulcerd
The document summarizes website analytics for the forensicchemistry.lincoln.ac.uk site from February 20, 2011 to March 21, 2011. It received 45 visits from 11 countries over this period. The majority of traffic came from search engines (53.33%) and referring sites (37.78%). The United Kingdom contributed the most visits (14) and had the highest average pages per visit (2.14) and average time on site (1 minute, 44 seconds). The overall bounce rate was 64.44% and 93.33% of visits were from new visitors.
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
1. The document discusses moles, molar mass, molarity, and provides example calculations involving these concepts. Molarity is defined as the number of moles of solute per liter of solution.
2. The document then provides 21 practice problems calculating things like the number of moles or grams of various substances, the molarity of different solutions, and multi-step dilution problems.
3. Students are asked to use the concepts of moles, molar mass, and molarity to solve quantitative chemistry problems involving substances in solutions.
Organic Chemistry: Classification of Organic Compounds: Seminarulcerd
This document discusses various organic functional groups including alcohols, aldehydes, ketones, carboxylic acids, esters, ethers, amines, amides, halogenoalkanes, nitriles, nitro compounds, and thiols. It provides examples of each functional group and discusses their classifications. Primary, secondary, and tertiary alcohols and amines are defined. Common illegal and recreational drugs like amphetamines, aspirin, cannabis, LSD, cocaine, morphine, and codeine are analyzed in terms of their functional group components.
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
EPrints Analytics - Forensic Chemistry OER Course site, Feb/March 2011ulcerd
This document summarizes webpage analytics for content from the Introductory Chemistry institutional repository from February 21 to March 21. It shows the number of pageviews and unique pageviews for each page, as well as average time on page, bounce rate, exit percentage, and cost index for the overall page and individual pages. The most visited page was /2366/ with 15 pageviews and 14 unique pageviews. The average time on page for all content was 53 seconds, with bounce and exit rates lower than the site average.
I shall provide a summary of JISC work in the area of ‘Big Data’. My primary focus will be on how to manage the huge amount of research data produced in UK Universities. I shall cover the history of JISC interventions to improve research data management and look at next steps. I shall touch on some other areas of work like ‘Digging into Data’ and web archiving which also deal with ‘big data’.
Scientific discovery and innovation in an era of data-intensive science
William (Bill) Michener, Professor and Director of e-Science Initiatives for University Libraries, University of New Mexico; DataONE Principal Investigator
The scope and nature of biological, environmental and earth sciences research are evolving rapidly in response to environmental challenges such as global climate change, invasive species and emergent diseases. Scientific studies are increasingly focusing on long-term, broad-scale, and complex questions that require massive amounts of diverse data collected by remote sensing platforms and embedded environmental sensor networks; collaborative, interdisciplinary science teams; and new tools that promote scientific data preservation, discovery, and innovation. This talk describes the challenges facing scientists as they transition into this new era of data intensive science, presents current solutions, and lays out a roadmap to the future where new information technologies significantly increase the pace of scientific discovery and innovation.
This document provides an introduction and overview of the DBM630: Data Mining and Data Warehousing course. It outlines the course syllabus, textbooks, assessment tasks, schedule, prerequisites, and provides a high-level introduction to data mining and data warehousing concepts including definitions, processes, applications and evolution of database technologies.
This document provides an introduction and overview of the DBM630: Data Mining and Data Warehousing course. It outlines the course syllabus, textbooks, assessment tasks, schedule, prerequisites, and introduces concepts related to data mining and data warehousing including definitions, processes, applications, and evolution of database technology. The goal of the course is to teach students about data warehousing, data mining techniques such as association rule mining, classification, clustering, and current trends in the field.
Supporting Libraries in Leading the Way in Research Data ManagementMarieke Guy
Marieke Guy, Institutional Support Officer, Digital Curation Centre, UKOLN, University of Bath, UK presents on Supporting Libraries in Leading the Way in Research Data Management at Online Information, London 20th -21st November 2012
SEAD is a NSF DataNet project that aims to provide cyberinfrastructure for long tail data in sustainability science research. It develops tools for active and social curation of data including an Active Curation Repository (ACR) and VIVO profiles. It also creates a Virtual Archive to facilitate long-term access and preservation of datasets across multiple institutional repositories. The presentation provides an overview of SEAD's approach and highlights pilots with the National Center for Earth Surface Dynamics, including ingesting their data collections into the ACR and Virtual Archive and building a social network in VIVO.
Presentation Title: Grand Challenges and Big Data: Implications for Public Participation in Scientific Research
Presenter: William Michener, Professor and PI/Director of DataONE, University Libraries, University of New Mexico
This document discusses knowledge discovery in databases (KDD) through the LON-CAPA online educational system. [1] It defines KDD and data mining, describing the tasks, methods, and applications of KDD. [2] The goals are to obtain predictive models of students, help students and instructors use resources more effectively, and provide information to increase student learning. [3] It then discusses the KDD process and data mining methods like classification, clustering, and dependency modeling that can be applied to discover knowledge from educational data.
1. Find all frequent itemsets of length 1 by scanning the database to count item occurrences.
2. Iteratively generate candidate itemsets of length k from frequent itemsets of length k-1, and prune unpromising candidates using the Apriori property.
3. Scan the database to determine truly frequent itemsets.
4. Generate association rules from frequent itemsets by adding items to the antecedent and consequent of rules if they meet minimum confidence.
This document provides an overview of artificial neural networks and their application in data mining techniques. It discusses neural networks as a tool that can be used for data mining, though some practitioners are wary of them due to their opaque nature. The document also outlines the data mining process and some common data mining techniques like classification, clustering, regression, and association rule mining. It notes that neural networks, as a predictive modeling technique, can be useful for problems like classification and prediction.
The document discusses the growing trend of big data and how cloud storage is a viable option for enterprise data storage needs. It notes that while cloud storage adoption has been slow, offerings continue to mature to handle larger data volumes, varieties, and velocities. The document recommends that organizations prepare their storage environments, evaluate emerging big data solutions, and rationalize their data to take advantage of next generation cloud-based storage architectures optimized for big data.
The document is a chapter from a textbook on data mining written by Akannsha A. Totewar, a professor at YCCE in Nagpur, India. It provides an introduction to data mining, including definitions of data mining, the motivation and evolution of the field, common data mining tasks, and major issues in data mining such as methodology, performance, and privacy.
This document provides information about Dr. Sunil Bhutada, including his educational background and professional experience. It then outlines the syllabus for a course on data warehousing and data mining, including an introduction to key concepts and textbooks. Finally, it shares slides on additional topics related to data warehousing, data mining, and business intelligence.
Educating a New Breed of Data Scientists for Scientific Data Management Jian Qin
This presentation reports the data science curriculum development and implementation at Syracuse iSchool, which has shaped by the fast changing data-intensive environment not only for science but also for business and research at large.
Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...Connected Data World
"The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker", said Peter F. Drucker in 1999, and time has proven him right.
Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, IT logs, and all kinds of other data stored nation-wide.
The data is growing in terms of variety, velocity, volume, value and veracity. NASA needs to provide accessibility to engineering data sources, whose visibility is currently limited. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary.
This is what David Meza, Acting Branch Chief - People Analytics, Sr. Data Scientist at NASA, calls "Knowledge Architecture": the people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations.
To convert data to knowledge, a convergence of knowledge management, information architecture, and data science is necessary. This is called knowledge architecture. Knowledge architecture involves designing and implementing an organization's intellectual infrastructure to capture, organize, analyze, and utilize information. It transforms information into knowledge through applying context. NASA faces challenges with large amounts of growing and siloed data. Opportunities for knowledge architecture at NASA include improving search capabilities, developing metadata standards, using analytics and visualization, and creating a lessons learned knowledge graph. This could help NASA make better decisions by leveraging past knowledge and reducing waste.
Starfish-A self tuning system for bigdata analyticssai Pramoda
Starfish is a self-tuning system for improving performance in Hadoop big data analytics. It collects execution profiles from Hadoop clusters, then uses a what-if engine and optimizers to search for and estimate the impact of different tuning configurations on jobs, workflows, and workloads. The goal of Starfish is to enable users and applications to get good performance automatically throughout the data lifecycle in Hadoop.
Chemical and Physical Properties: Practical Sessionulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical and Physical Properties: Isotopes and Forensic Scienceulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical and Physical Properties: Chemical vs. Physical Propertiesulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Organic Chemistry: Classification of Organic Compoundsulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical Structure: Structure of Matter. Elements, Ions & Isotopes ulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical Structure: Chemical Nomenclature. Inorganic Compoundsulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical Structure: Chemical Bonding. Properties of Coordination Compounds ulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical Structure: Chemical Bonding. Ionic, Metallic & Coordinate Bondsulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical Structure: Chemical Bonding. Polar Bondsulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical Structure: Chemical Bonding. Molecular Orbitalsulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical Structure: Chemical Bonding. Homonuclear Covalent Bondsulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
This document discusses various topics in thermochemistry including:
- Enthalpy changes in chemical reactions and how they are measured using calorimetry. Exothermic and endothermic reactions are explained.
- Hess's law, which states that the enthalpy change of a reaction is independent of the reaction pathway. It can be used to calculate enthalpy changes.
- Standard enthalpies of formation and how they allow calculation of enthalpy changes using Hess's law and bond dissociation enthalpies.
- Measuring enthalpy changes using bomb calorimetry and coffee cup calorimetry. Limitations of each method are discussed.
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical and Physical Properties: Radioactivity & Radioisotopes ulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Chemical Structure: Structure of Matter. Atoms – the building blocks of matterulcerd
Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
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You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
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Answers about how you can do more with Walmart!"
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إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
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تتميز هذهِ الملزمة بعِدة مُميزات :
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1. Managing Engineering Research Data
at the University of Lincoln
Part 2 of 2
Mansur Darlington
20 January 2012
1 Orbital DMP Meeting 20.01.12
2. THE NATURE OF ENGINEERING RESEARCH DATA
2 Orbital DMP Meeting 20.01.12
3. ERIM
Engineering Research Information
Management
JISC MRD Programme Phase 1
http://www.ukoln.ac.uk/projects/erim/
3 Orbital DMP Meeting 20.01.12
4. ERIM Project Overview
• Primarily associated with the engineering
research domain.
• To better understand the research data that
are collected, generated and used in
engineering research activities.
• To better understand the context in which the
data are collected, generated and used.
• To inform the way that the data can be
managed so that they are more easily used
or re-used.
• To increase their value to the community.
4 Orbital DMP Meeting 20.01.12
5. The Aims
• Achieving an understanding of the diversity
and character of research data.
• Devising a means by which research data
can be classified in respect of research data
management.
• Developing models of the research data life-
cycle which characterize the information flow
in the research process and identify critical
points in the management process.
• Providing exemplars of best-practice
research data management strategies.
5 Orbital DMP Meeting 20.01.12
6. The Objectives
• Identify opportunities for and the benefits of
research data re-use and re-purposing.
• Identify the contextual, technical, legal and
social barriers to the re-use and repurposing
of research data.
• Establish whether and what data might be
used in a raw form, what data would require
reprocessing and how this might be achieved.
• Understand what contextual information is
required for research data to be understood
for the purpose of re-use.
6 Orbital DMP Meeting 20.01.12
7. Theoretical Elements to the Research
1. Understanding & Defining the ‘Space’
2. Terminology
https://wiki.bath.ac.uk/display/ERIMterminology/ERIM+Terminology+V4
3. Identifying the Objects in the space
4. Understanding the Relationships between
Objects
5. Modelling the Relationships
6. Understanding the Outcomes
7 Orbital DMP Meeting 20.01.12
8. Understanding & Defining the ‘Space’
for Current Research
known
Making data available and fit for the current
CURRENT-ACTIVITY MANAGEMENT SPACE
PURPOSING:
Management for A by A
purpose
FUTURE-ACTIVITY MANAGEMENT SPACE
Management for X by Y,
where Y can be X Management for X by Y
for
supporting data RE-USE: RE-PURPOSING:
Raw Future
Research Managing data such that it will be available Making data available and Research
Data for a future unknown purpose fit for a future known purpose
8 Orbital DMP Meeting 20.01.12
9. ERIM Project Terminology for
Research Data Mangement
https://wiki.bath.ac.uk/display/ERIMterminolo
gy/ERIM+Terminology+V4
(source definitions for terms found in slides 10-14)
9 Orbital DMP Meeting 20.01.12
10. 2. The Terminology I
Preparation Activities:
• Data Purposing Making research data available and fit for the
current research activity.
• Data Re-purposing Making existing research data available
and fit for a future known research activity
• Supporting Data Re-use: Managing existing research data
such that it will be available for a future unknown research activity
Use Activities
• Data Use Using research data for the current research
purpose/activity to infer new knowledge about the research subject.
• Data Re-use Using research data for a research purpose other
than that for which it was intended.
10 Orbital DMP Meeting 20.01.12
11. 2. The Terminology II
What do we Mean by DATA?
• Data. Reinterpretable representations of information
in a formalized manner suitable for communication,
interpretation or processing.
• Information. Any type of knowledge that can be
exchanged. In an exchange, it is represented by
data.
***********************
• Data Object. Either a physical object or a digital
object containing data.
• Data Record. A data object created, received and
maintained as evidence of an activity.
• Data Case. The set of data records associated with
some discrete research activity (project, task,
experiment, etc.).
11 Orbital DMP Meeting 20.01.12
13. 4. Understanding the Relationships
Data Preparation in
Data Development Purposing Supporting Re-use Re-
purposing
Association
Aggregation
Annotation
Augmentation
Collection
Collation
Generation
Derivation
Refinement
Migration
13 Orbital DMP Meeting 20.01.12
14. 6. Understanding the Outcomes
Management and Development Side-effects
• Information Loss
• Information Gain
• Function Loss
• State Loss
14 Orbital DMP Meeting 20.01.12
15. 5. Modelling the Objects’ Relationships
RESEARCH TIME LINE
Data level
Gather RDR1 Refine RDR1' Refine RDR1''
Associate
Derive RDR4
Gather RDR2 Derive RDR3
CDR1 Aggregate RDR6
RDR5
15 Orbital DMP Meeting 20.01.12
17. Research Data Scoping Survey
• 12 Research Cases
• 46 Data Assets
• 12 questions for each
• Researchers from:
Bath, Lancaster, Leeds,
Salford, Strathclyde,
Heriot-Watt
17 Orbital DMP Meeting 20.01.12
18. Scoping Survey Targets
1. Airframe Stress Data Reuse
2. Snow Mobile Design Activity Observation
3. Aerospace Cost Forecasting
4. Large-Scale Metrology Shared Resources
5. Form-fill-feed Packaging Modelling
6. CNC Machine Measurement
7. Cryogenic Machining
8. Information Management Tool
9. Knowledge Enhanced Notes
10. Service Design Research
11. Design Activity & Knowledge Capture Research
12. Understanding the Learning Organization
18 Orbital DMP Meeting 20.01.12
19. Selecting 5 Case Studies using Binary Classification
Research Generated Data vs Pre-existing Data
Homogeneous Media vs Heterogeneous Media
1. Costing
Descriptive vs Prescriptive
2. Company
case studies
Real vs Simulated 3. Programming
4. Interview analysis 5. Metrology
19 Orbital DMP Meeting 20.01.12
20. ERIM Research Summary
• Designed and carried out scoping survey.
• Theory development and revision
• Designed and carried out audit on 5 cases
• RAID Modelled the case research activities.
• Complete analysis and characterization of
audit cases
• Identified barriers to data re-use and
strategies to mitigate.
• Established critical points in Information Flow.
• Developed research data management plan
process ‘cascade’.
20 Orbital DMP Meeting 20.01.12
21. Key ERIM Research Findings I
• Great diversity of data type and quality.
• Complex and chaotic nature of data
development.
• Outputs not linked to data.
• Supporting documents not situated with the data
files.
• Little use of metadata to support future use.
• Immature understanding of benefits of sharing
and thus need for management.
• Limited understanding of the barriers to or
opportunities for information sharing and re-use.
21 Orbital DMP Meeting 20.01.12
22. Key ERIM Research Findings II
Poor framework for:
• pre-project considerations of data management.
• data management during the research.
• during-project data management for post-project re-
use.
Poor knowledge of context in which data were
generated:
• engineering research data are very diverse.
• large number of diverse research data records.
• relations between data records complex.
Knowing the context is vital for understanding data.
22 Orbital DMP Meeting 20.01.12
23. What Needs Managing?
• Research Data:
– Data Data Objects Information.
• Their life cycle processes:
– Collection
– Generation
– Development
– Organization
– Disposal, etc.
• The process of data management itself.
23 Orbital DMP Meeting 20.01.12
25. What is the Purpose of RDM Planning?
• Reduction in duplicated work.
• Inspiration for new/continuation research &
funding.
• Greater transparency of research.
• Improved basis for validation.
• Obviating the need for re-collection and
generation.
• Providing basis for reliable data citation.
• Increasing scholarly output.
Relies upon RE-USING DATA
25 Orbital DMP Meeting 20.01.12
26. Amenability Criteria & ‘Re-usefulness’
‘To manage research data such that they are
highly amenable to re-use.’
‘What is the nature of these data that makes them more
or less amenable to re-use?’
• Findability
• Readability
• Comprehensibility
• Interpretability
• Admissibility
• Desirability
Data ‘RE-USEFULNESS’
(some data will remain forever re-useless)
26 Orbital DMP Meeting 20.01.12
28. DMP Task Dimensions and Topics
• Access, Data Sharing and Re-use.
• Data Types, Format, Standards and Capture
Methods.
• Ethical and Privacy Concerns.
• Resourcing.
• Short-term Storage and Data Management.
• Deposit and Long-term preservation.
• DMP Adherence and Review.
DCC DMP Checklist
28 Orbital DMP Meeting 20.01.12
29. Data Management is not just about
STORAGE!
29 Orbital DMP Meeting 20.01.12
30. Guidance Required to Support Tasks
• Data management planning.
• Data management execution.
• Bid submission.
• Project planning.
• During-project management whilst doing the
research.
• Collaboration with colleagues, industry and
others.
• Supporting data use, re-use, re-purposing.
• Preparation for long-term preservation.
• End-of-life concerns.
30 Orbital DMP Meeting 20.01.12
31. The Two Stages of RDM
‘Managing research data such that they are
highly amenable to re-use.’
• Good DM planning provides the
potential to increase data re-usefulness.
• The execution of good DMPs promotes
data re-usefulness.
31 Orbital DMP Meeting 20.01.12
32. Key Building Blocks for Practical Data Management
1. DMP Guidance, Documentation &
Procedures.
2. Storage and security
3. Data Organization
4. Data Documentation
32 Orbital DMP Meeting 20.01.12
33. 1. DMP Guidance, Documentation & Procedures
Principles for Engineering Research Thematic Analysis of Data Management
Data Management. Plan Tools and Exemplars.
(erim6rep101028mjd) (erim6rep100701ab)
Engineering Research Data Management
1 Plan Requirement Specification Being a specification for 2
(erim6rep100901ab)
REDm-MED
The Draft IdMRC Projects Data
Model DMP for
Being an implementation of 1 2 Management Plan
Mech. Eng. Depts.
(erim6rep101015mjd)
RAIDmap Use Cases RAID Associative Tool Specification Prototype
(erim6rep101125mjd) (erim6rep101109mjd) RAIDmap Tool
33 Orbital DMP Meeting 20.01.12
34. Security: Document/Data Access Levels
Level 1 – In the public domain with an unrestricted
readership and can distributed at will.
Level 2 – Viewable by any individual who has
password access to the main project web site.
Level 3 – Viewable by any individual who has
password access to the main project file store and is
a project-affiliated member of a university research
team.
Level 4 – For sensitive documents. Must carry a
distribution list identifying for whom it is intended and
be disseminated through a nominated secure
passwork-protected portal. Distribution controlled by
PI and Collaborator liaison officer.
34 Orbital DMP Meeting 20.01.12
39. Work package Task Author initials
number number
Project
name
Revision
number
kim12rep05pjw01.doc
Document Document File type
type rank number
39 Orbital DMP Meeting 20.01.12
40. 3 Data Organization: 3 steps to document happiness
1. Fill in the document properties (use
metadata!).
2. Make them visible by browsing or by search
– in particular rehabitate the record TITLE.
3. Use a file NAME coding convention that
captures human-readable context
information.
40 Orbital DMP Meeting 20.01.12
41. 4. Data Documentation
• RAID-mapping: provision of context.
• Project Document Records:
– Existence Location of key Project Documents
• Project Plan
• Project data management plan
• Project document manifest
– Location of Data Records
– Some description of relations between data
records
41 Orbital DMP Meeting 20.01.12