Adam Etkin's Flash Presentation from STM Spring 2014Adam Etkin
PRE-Score is a proposed product that aims to provide standardized metrics and filters to evaluate the peer review process of scholarly journals. It would assign a PRE-Score to journals based on factors like the rigor of the peer review process, qualifications of reviewers, and best practices followed. This score would provide readers a "leading indicator" of journal quality rather than lagging metrics. It is presented as addressing the lack of consistent ways to evaluate peer review across different journals and as an incentive for journals to improve their peer review practices.
Unique schools can point out numerous distinctive expositions as proposal composition prerequisites to fulfill their particular scholastic necessities, and due to this the first thing you should figure out from your school is which to utilize. In the event that you require help in paper subject choice or other task you can approach the Dissertation consulting Services.
A Comparison of Non-Dictionary Based Approaches to Automate Cancer Detection Using Plaintext Medical Data with Dr. Shaun Grannis, Dr. Brian Dixon et. al. presented at the Regenstrief WIP (7th Jan 2015)
Presentation on Evaluating Methods for the Identification of Cancer in Free-Text Pathology Reports Using alternative Machine Learning and Data Preprocessing Approaches
1. The document discusses preparing researchers for the next Research Excellence Framework (REF) assessment in the UK. It covers open access policies, bibliometrics, altmetrics, and ORCID identifiers.
2. Open access requirements for REF submissions are that journal articles and conference papers be made publicly available within 3 months of acceptance in an institutional repository.
3. Bibliometrics like citation counts and journal impact factors may play a larger role in REF assessments in the future, though peer review will still be primary. Concerns about gaming the system and disciplinary biases remain.
This document discusses developing a FHIR-based API for OpenMRS to improve interoperability. It covers the need for interoperability in healthcare and limitations of current standards like HL7 V2. FHIR is presented as a promising new standard that addresses many issues. The document outlines plans to build basic FHIR import/export capabilities in OpenMRS to allow resource exchange and integration with platforms like SMART. The goal is to explore how far a FHIR-based approach can go in supporting interoperability and establishing FHIR as a core OpenMRS standard.
From Replication Crisis to Credibility RevolutionKoki Ikeda
The document discusses issues related to the replication crisis in psychology and potential solutions. It notes that questionable research practices like p-hacking and HARKing are common but unintentional. Solutions proposed include transparency through open science, pre-registration of studies including pre-reviews, direct replication of studies, and higher evidentiary standards. Institutional changes are also needed to incentivize practices like pre-registration and increasing acceptance of replication studies. While rigorous methods may initially lower productivity, they can increase it long-term by allowing easier reuse of materials and data and identifying reliable findings sooner.
Text mining and summarization technologies can help researchers in 3 key ways:
1) By systematically screening the large volume of literature in their field to quickly assess relevance and quality of papers.
2) By providing quick informative overviews and summaries of academic papers in bullet points highlighting limitations to save researchers time.
3) By extracting references, figures, tables and datasets to allow researchers to analyze information in more depth and follow citation trails more efficiently.
Adam Etkin's Flash Presentation from STM Spring 2014Adam Etkin
PRE-Score is a proposed product that aims to provide standardized metrics and filters to evaluate the peer review process of scholarly journals. It would assign a PRE-Score to journals based on factors like the rigor of the peer review process, qualifications of reviewers, and best practices followed. This score would provide readers a "leading indicator" of journal quality rather than lagging metrics. It is presented as addressing the lack of consistent ways to evaluate peer review across different journals and as an incentive for journals to improve their peer review practices.
Unique schools can point out numerous distinctive expositions as proposal composition prerequisites to fulfill their particular scholastic necessities, and due to this the first thing you should figure out from your school is which to utilize. In the event that you require help in paper subject choice or other task you can approach the Dissertation consulting Services.
A Comparison of Non-Dictionary Based Approaches to Automate Cancer Detection Using Plaintext Medical Data with Dr. Shaun Grannis, Dr. Brian Dixon et. al. presented at the Regenstrief WIP (7th Jan 2015)
Presentation on Evaluating Methods for the Identification of Cancer in Free-Text Pathology Reports Using alternative Machine Learning and Data Preprocessing Approaches
1. The document discusses preparing researchers for the next Research Excellence Framework (REF) assessment in the UK. It covers open access policies, bibliometrics, altmetrics, and ORCID identifiers.
2. Open access requirements for REF submissions are that journal articles and conference papers be made publicly available within 3 months of acceptance in an institutional repository.
3. Bibliometrics like citation counts and journal impact factors may play a larger role in REF assessments in the future, though peer review will still be primary. Concerns about gaming the system and disciplinary biases remain.
This document discusses developing a FHIR-based API for OpenMRS to improve interoperability. It covers the need for interoperability in healthcare and limitations of current standards like HL7 V2. FHIR is presented as a promising new standard that addresses many issues. The document outlines plans to build basic FHIR import/export capabilities in OpenMRS to allow resource exchange and integration with platforms like SMART. The goal is to explore how far a FHIR-based approach can go in supporting interoperability and establishing FHIR as a core OpenMRS standard.
From Replication Crisis to Credibility RevolutionKoki Ikeda
The document discusses issues related to the replication crisis in psychology and potential solutions. It notes that questionable research practices like p-hacking and HARKing are common but unintentional. Solutions proposed include transparency through open science, pre-registration of studies including pre-reviews, direct replication of studies, and higher evidentiary standards. Institutional changes are also needed to incentivize practices like pre-registration and increasing acceptance of replication studies. While rigorous methods may initially lower productivity, they can increase it long-term by allowing easier reuse of materials and data and identifying reliable findings sooner.
Text mining and summarization technologies can help researchers in 3 key ways:
1) By systematically screening the large volume of literature in their field to quickly assess relevance and quality of papers.
2) By providing quick informative overviews and summaries of academic papers in bullet points highlighting limitations to save researchers time.
3) By extracting references, figures, tables and datasets to allow researchers to analyze information in more depth and follow citation trails more efficiently.
The document discusses researchers' perspectives on data publication based on a survey of 249 researchers. It finds that "data publication" has different meanings for researchers, including making data available in a repository, associating it with a traditional research paper, or associating it with a dedicated data paper. Researchers value peer review but do not immediately understand or value the concept of data publication itself. The document aims to better understand how researchers interpret and value data publication.
Measure for Measure: The role of metrics in assessing research performance - ...Michael Habib
The document discusses the role of metrics in assessing research performance. It notes that the choice of metric depends on the level and type of impact being assessed, such as article, journal, researcher or institution. While impact factor is the most widely known metric, the document recommends using a variety of metrics to provide a richer view. It promotes transparent, standard metrics like Scopus Cited-by counts, SNIP and SJR which can be easily embedded. The document also notes that awareness of metrics affects their acceptance and recommends raising awareness of newer alternative metrics.
preSCORE Presentation by Adam Etkin for Highwire Fall 2013 MeetingAdam Etkin
preSCORE, an organization formed to assist members of the scholarly publishing community who are committed to preserving an ethical, rigorous peer review process, was unveiled at the 2013 Fall Highwire meeting.
In this webinar, we will go over how to determine the appropriate sample size for a quantitative study by using power analysis. The presentation includes an explanation of what a power analysis is and examples of how to conduct power analyses for common statistical tests. The presentation will also focus on power analysis using G*Power and Intellectus Statistics software programs. Sample size calculations for more advanced analyses will be briefly discussed.
June 18, 2014
NISO Virtual Conference: Transforming Assessment: Alternative Metrics and Other Trends
NISO Altmetrics Initiative: A Project Update
- Martin Fenner, Technical Lead for the PLOS Article-Level Metrics project
Scientific research: What Anna Karenina teaches us about useful negative resultsDaniel S. Katz
a panel talk for the 1st Workshop on E-science ReseaRch leading tO negative Results (ERROR), held in conjunction with the 11th eScience conference on 3 September 2015 in Munich, Germany
This document provides tips for fast-tracking a quantitative methodology dissertation, including establishing clear goals and communication with your committee, developing strong research questions and variables of interest, planning an appropriate data collection and analysis strategy using validated instruments and statistical software, and maintaining consistency throughout the process. Key recommendations include running a power analysis, using pre-existing surveys when possible, and having a detailed plan for addressing each research question and analyzing the data. Following these tips can help students efficiently complete the methodology chapter and overall dissertation.
June 18, 2014
NISO Virtual Conference: Transforming Assessment: Alternative Metrics and Other Trends
Snowball Metrics: University-owned Benchmarking to Reveal Strengths within All Activities
- Dr. Lisa Colledge, Snowball Metrics Program Director, Elsevier
Risk Based De-identification for Sharing Health DataKhaled El Emam
This presentation describes a methodology, tools, and experiences for the de-identification of health information. The objective is to support data sharing for the purpose of research and public health.
LQAS: Pitfalls, Controvery & Addressing Concerns_Luna, Nitkin, Yaggy_5.10.11CORE Group
This document summarizes an upcoming LQAS session that will discuss common mistakes in LQAS and ways to avoid them. The session will include an overview of LQAS, experiences from organizations that use it, and small group work. While LQAS can be useful for monitoring, it is controversial and best not used for evaluation. Common mistakes include incorrectly interpreting findings, improper descriptions of actions taken, and not collecting adequate information. The American Statistical Association recommends carefully stating LQAS conclusions to avoid mistakenly drawing conclusions about a supervision area's performance.
1) Norway has established an archetype governance model to develop shared clinical information models using openEHR in order to achieve semantic interoperability across its healthcare system.
2) The governance model involves national editorial committees, regional representatives, and clinicians collaborating online using specific tools to develop, review, and approve archetypes according to a formalized process.
3) Over 60 archetypes have been published so far covering basic clinical concepts like observations, diagnoses, and medications, though engagement of certain specialties remains a challenge.
This document discusses checklists as a risk management tool. It provides examples of how checklists can be structured and used to identify risks for a project. Checklists have pros such as being low cost, flexible, and easy to use tools that can achieve high accuracy. However, checklists also have cons like not being able to identify all risks and potentially discouraging original thinking. The document concludes with an exercise to test understanding of how checklists can support risk management.
This document outlines a syllabus for a course on business analytics. It covers topics like introduction to analytics, statistics for business analytics, advanced Excel, R, data mining techniques like decision trees and clustering in R, time series forecasting, predictive modeling with logistic regression in R, and an overview of big data and Hadoop. It also defines key concepts like data analysis, data analytics, data mining. Descriptive, predictive, and prescriptive analytics techniques are discussed. Applications of business analytics in various domains like finance, marketing, HR, CRM, manufacturing, and credit cards are provided.
This document is a resume for Zeyu Xie that outlines his education and professional experience. He received a Master's degree in Financial Engineering from Claremont Graduate University and a Bachelor's degree in Actuarial Science and Applied Statistics from Purdue University. For professional experience, he has conducted research and analysis for insurance companies, universities, and an investment firm. His skills include programming languages, Bloomberg certification, and being fluent in Mandarin.
Selecting an Ideal Survey Instrument for a Quantitative StudyStatistics Solutions
During this presentation, we will discuss the key components for selecting an appropriate survey instrument for your research. We will examine sources for identifying surveys and the reliability/validity statistics to incorporate into the methodology chapter.
You Got Your Engineering in my Data Science - Addressing the Reproducibility ...jonbodner
Presented at PyData DC 2016.
Data science is the backbone of modern scientific discovery and industry. It makes sense of everything from cancer trials to package delivery logistics. But all is not well with data science. Over the past decade, multiple studies have been found to be unreliable and non-reproducible when other scientists tried to recreate their results. This is due to a variety of factors, including fraud, pressure to publish, improper data handling practices, and bugs in analytic tools.
The problems faced by data science mirror problems that software engineering has been trying to solve. While there are no silver bullets to guarantee quality software, techniques have been developed over time that have improved quality and reliability. Some of these techniques, including open source, version control, automation, and fuzzing could be adapted to the data science domain to improve reliability and help address the reproducibility crisis.
Umm, how did you get that number? Managing Data Integrity throughout the Data...John Kinmonth
We live at the intersection of data and people. Data integrity is a function of the decisions that people make throughout the data lifecycle.
Dave De Noia, Pointmarc lead solution architect in data management, gives his take on the processes and people that affect data integrity throughout organizations at DRIVE 2014 (Data, Reporting, Intelligence, and Visualization Exchange)
Whether you're a retailer merging web analytics data with offline numbers or a healthcare company adding new data management software, De Noia explains how to avoid logic wobble and establish shared data structures.
About Dave:
Dave De Noia lives in the balance of chaos and order inherent to working with data. Starting his career at Microsoft building analyses in both SQL and big data environments, Dave later moved onto Redfin where he created and managed data infrastructure for analysis and reporting projects. Dave now serves as the senior solution and data architect at Pointmarc, a Bellevue-based digital analytics consultancy, where he helps some of the world’s largest brands get value from their data. Naturally functioning as a bridge between business and technical teams, Dave’s professional passion lies at the intersection of data and people.
About Pointmarc:
Pointmarc is a leading digital analytics agency providing actionable marketing insight and analytics platform instrumentation services for Fortune 500 clients within retail, technology, financial, media and pharmaceutical industries. With offices in Seattle, Boston, San Francisco and Portland, Pointmarc’s immersive approach to analytics empowers businesses to dive deeper into their data.
Email info@pointmarc.com for more information on data management or analytics instrumentation, and follow @pointmarc on Twitter for the latest in analytics.
The document discusses researchers' perspectives on data publication based on a survey of 249 researchers. It finds that "data publication" has different meanings for researchers, including making data available in a repository, associating it with a traditional research paper, or associating it with a dedicated data paper. Researchers value peer review but do not immediately understand or value the concept of data publication itself. The document aims to better understand how researchers interpret and value data publication.
Measure for Measure: The role of metrics in assessing research performance - ...Michael Habib
The document discusses the role of metrics in assessing research performance. It notes that the choice of metric depends on the level and type of impact being assessed, such as article, journal, researcher or institution. While impact factor is the most widely known metric, the document recommends using a variety of metrics to provide a richer view. It promotes transparent, standard metrics like Scopus Cited-by counts, SNIP and SJR which can be easily embedded. The document also notes that awareness of metrics affects their acceptance and recommends raising awareness of newer alternative metrics.
preSCORE Presentation by Adam Etkin for Highwire Fall 2013 MeetingAdam Etkin
preSCORE, an organization formed to assist members of the scholarly publishing community who are committed to preserving an ethical, rigorous peer review process, was unveiled at the 2013 Fall Highwire meeting.
In this webinar, we will go over how to determine the appropriate sample size for a quantitative study by using power analysis. The presentation includes an explanation of what a power analysis is and examples of how to conduct power analyses for common statistical tests. The presentation will also focus on power analysis using G*Power and Intellectus Statistics software programs. Sample size calculations for more advanced analyses will be briefly discussed.
June 18, 2014
NISO Virtual Conference: Transforming Assessment: Alternative Metrics and Other Trends
NISO Altmetrics Initiative: A Project Update
- Martin Fenner, Technical Lead for the PLOS Article-Level Metrics project
Scientific research: What Anna Karenina teaches us about useful negative resultsDaniel S. Katz
a panel talk for the 1st Workshop on E-science ReseaRch leading tO negative Results (ERROR), held in conjunction with the 11th eScience conference on 3 September 2015 in Munich, Germany
This document provides tips for fast-tracking a quantitative methodology dissertation, including establishing clear goals and communication with your committee, developing strong research questions and variables of interest, planning an appropriate data collection and analysis strategy using validated instruments and statistical software, and maintaining consistency throughout the process. Key recommendations include running a power analysis, using pre-existing surveys when possible, and having a detailed plan for addressing each research question and analyzing the data. Following these tips can help students efficiently complete the methodology chapter and overall dissertation.
June 18, 2014
NISO Virtual Conference: Transforming Assessment: Alternative Metrics and Other Trends
Snowball Metrics: University-owned Benchmarking to Reveal Strengths within All Activities
- Dr. Lisa Colledge, Snowball Metrics Program Director, Elsevier
Risk Based De-identification for Sharing Health DataKhaled El Emam
This presentation describes a methodology, tools, and experiences for the de-identification of health information. The objective is to support data sharing for the purpose of research and public health.
LQAS: Pitfalls, Controvery & Addressing Concerns_Luna, Nitkin, Yaggy_5.10.11CORE Group
This document summarizes an upcoming LQAS session that will discuss common mistakes in LQAS and ways to avoid them. The session will include an overview of LQAS, experiences from organizations that use it, and small group work. While LQAS can be useful for monitoring, it is controversial and best not used for evaluation. Common mistakes include incorrectly interpreting findings, improper descriptions of actions taken, and not collecting adequate information. The American Statistical Association recommends carefully stating LQAS conclusions to avoid mistakenly drawing conclusions about a supervision area's performance.
1) Norway has established an archetype governance model to develop shared clinical information models using openEHR in order to achieve semantic interoperability across its healthcare system.
2) The governance model involves national editorial committees, regional representatives, and clinicians collaborating online using specific tools to develop, review, and approve archetypes according to a formalized process.
3) Over 60 archetypes have been published so far covering basic clinical concepts like observations, diagnoses, and medications, though engagement of certain specialties remains a challenge.
This document discusses checklists as a risk management tool. It provides examples of how checklists can be structured and used to identify risks for a project. Checklists have pros such as being low cost, flexible, and easy to use tools that can achieve high accuracy. However, checklists also have cons like not being able to identify all risks and potentially discouraging original thinking. The document concludes with an exercise to test understanding of how checklists can support risk management.
This document outlines a syllabus for a course on business analytics. It covers topics like introduction to analytics, statistics for business analytics, advanced Excel, R, data mining techniques like decision trees and clustering in R, time series forecasting, predictive modeling with logistic regression in R, and an overview of big data and Hadoop. It also defines key concepts like data analysis, data analytics, data mining. Descriptive, predictive, and prescriptive analytics techniques are discussed. Applications of business analytics in various domains like finance, marketing, HR, CRM, manufacturing, and credit cards are provided.
This document is a resume for Zeyu Xie that outlines his education and professional experience. He received a Master's degree in Financial Engineering from Claremont Graduate University and a Bachelor's degree in Actuarial Science and Applied Statistics from Purdue University. For professional experience, he has conducted research and analysis for insurance companies, universities, and an investment firm. His skills include programming languages, Bloomberg certification, and being fluent in Mandarin.
Selecting an Ideal Survey Instrument for a Quantitative StudyStatistics Solutions
During this presentation, we will discuss the key components for selecting an appropriate survey instrument for your research. We will examine sources for identifying surveys and the reliability/validity statistics to incorporate into the methodology chapter.
You Got Your Engineering in my Data Science - Addressing the Reproducibility ...jonbodner
Presented at PyData DC 2016.
Data science is the backbone of modern scientific discovery and industry. It makes sense of everything from cancer trials to package delivery logistics. But all is not well with data science. Over the past decade, multiple studies have been found to be unreliable and non-reproducible when other scientists tried to recreate their results. This is due to a variety of factors, including fraud, pressure to publish, improper data handling practices, and bugs in analytic tools.
The problems faced by data science mirror problems that software engineering has been trying to solve. While there are no silver bullets to guarantee quality software, techniques have been developed over time that have improved quality and reliability. Some of these techniques, including open source, version control, automation, and fuzzing could be adapted to the data science domain to improve reliability and help address the reproducibility crisis.
Umm, how did you get that number? Managing Data Integrity throughout the Data...John Kinmonth
We live at the intersection of data and people. Data integrity is a function of the decisions that people make throughout the data lifecycle.
Dave De Noia, Pointmarc lead solution architect in data management, gives his take on the processes and people that affect data integrity throughout organizations at DRIVE 2014 (Data, Reporting, Intelligence, and Visualization Exchange)
Whether you're a retailer merging web analytics data with offline numbers or a healthcare company adding new data management software, De Noia explains how to avoid logic wobble and establish shared data structures.
About Dave:
Dave De Noia lives in the balance of chaos and order inherent to working with data. Starting his career at Microsoft building analyses in both SQL and big data environments, Dave later moved onto Redfin where he created and managed data infrastructure for analysis and reporting projects. Dave now serves as the senior solution and data architect at Pointmarc, a Bellevue-based digital analytics consultancy, where he helps some of the world’s largest brands get value from their data. Naturally functioning as a bridge between business and technical teams, Dave’s professional passion lies at the intersection of data and people.
About Pointmarc:
Pointmarc is a leading digital analytics agency providing actionable marketing insight and analytics platform instrumentation services for Fortune 500 clients within retail, technology, financial, media and pharmaceutical industries. With offices in Seattle, Boston, San Francisco and Portland, Pointmarc’s immersive approach to analytics empowers businesses to dive deeper into their data.
Email info@pointmarc.com for more information on data management or analytics instrumentation, and follow @pointmarc on Twitter for the latest in analytics.
Presented by Ms Bernadette Lewis, Secretary General, Caribbean Telecommunications Union at the LEARN Caribbean Research Data Workshop. http://learn-rdm.eu/en/workshops/eclac-mini-workshops/3rd-mini-workshop
Open Data in a Big World by Fernando Ariel López LEARN Project
Este documento presenta los principios de datos abiertos desarrollados por un grupo de trabajo de cuatro organizaciones científicas internacionales. Describe las responsabilidades de científicos, instituciones, editores, financiadores, asociaciones profesionales y bibliotecas para implementar y promover la apertura de datos. También cubre los límites a la apertura de datos y prácticas como la citación, interoperabilidad y reutilización de datos abiertos.
Avances en torno a la Ley 26.899 e iniciativa regional de datos primarios de...LEARN Project
Avances en torno a la Ley 26.899 e iniciativa regional de datos primarios de investigación by Alberto Apollaro - presented at the 4th LEARN RDM Workshop in Santiago, Chile: http://learn-rdm.eu/
Gestion de datos para la investigacion: el caso peruano by Edward Mezones, Su...LEARN Project
Este documento describe la gestión de datos para la investigación en Perú. Explica que el gobierno peruano ha promovido la apertura de datos gubernamentales a través de portales de datos abiertos y la Ley 30035, que establece un repositorio nacional digital de acceso abierto llamado ALICIA. También describe cómo SUSALUD, la agencia de seguro de salud de Perú, ha integrado su repositorio de datos abiertos a ALICIA para facilitar la investigación en salud.
Gestion de Datos de Investigacion, Fernando López, CAICYT-CONICET (Argentina) - presented at the 4th LEARN RDM Workshop in Santiago, Chile: http://learn-rdm.eu/
Este documento describe los objetivos y tipos de datos de un centro de datos. El centro busca recopilar y sistematizar datos existentes en Colombia para mejorar la calidad e acceso a la información. Provee acceso libre a datos administrativos de entidades gubernamentales y de investigación realizada por profesores. El centro también busca documentar los metadatos de acuerdo a estándares internacionales y dar a conocer los datos a través de boletines y su plataforma.
LEARN: Helping Institutions to Build and Implement a Policy on research DataLEARN Project
LEARN: Helping Institutions to Build and Implement a Policy on research Data, presentation by Ignasi Labastida, CRAI Universitat de Barcelona, at the OpenAIRE National Workshop, Rome, 30-31 May 2016
Datos Abiertos de Investigacion - Caso MexicoLEARN Project
Este documento describe los datos abiertos de investigación en México. Define datos abiertos como datos primarios disponibles de forma gratuita para ser utilizados, reutilizados y redistribuidos sin permiso especial. Explica que México tiene 11 repositorios de datos de investigación registrados en Re3data.org, que cubren áreas como medicina, ciencias físicas y sociales. Los repositorios existentes son anteriores a las nuevas leyes mexicanas sobre acceso abierto a la investigación y se financian con fondos públicos. México trabaja con otros países de Am
TALLER LEARN SOBRE DATOS DE INVESTIGACIÓN IMPLEMENTACIÓN DE POLÍTICAS Y ESTRA...LEARN Project
TALLER LEARN SOBRE DATOS DE INVESTIGACIÓN IMPLEMENTACIÓN DE POLÍTICAS Y ESTRATEGIAS EN AMÉRICA LATINA Y EL CARIBE by Miguel Ángel Márdero Arellano, IBICT (Brazil) - presented at the 4th LEARN RDM Workshop in Santiago, Chile: http://learn-rdm.eu/
Open Data in the world of Science” by Dr. Claudio GutiérrezLEARN Project
Este documento resume los principales puntos de una presentación sobre datos abiertos en ciencias. Explora las definiciones de datos, datos abiertos y datos científicos. También discute los desafíos de gestionar y jerarquizar la gran cantidad de datos disponibles, así como la necesidad de cambiar las mentalidades sobre la propiedad y el secreto de los datos. Finalmente, propone algunas soluciones como mejorar las interfaces de visualización y enlazar los datos para proveer contexto.
Data management: The new frontier for librariesLEARN Project
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”, by Kathleen Shearer, COAR, CARL/ABCR, RDC/DCR, ARL, SSHRC/CSRH.
This document summarizes three projects on rapid reviews conducted as part of a Master's in Epidemiology at uOttawa. It presents the objectives and methods of defining rapid reviews through a consensus approach using a modified Delphi method with experts, exploring attitudes and perceptions towards rapid reviews using Q methodology, and a pragmatic approach to defining rapid reviews. Key findings from the consensus approach include that rapid reviews are conducted in less time than systematic reviews, require a protocol, tailor systematic review methods to be completed more quickly, and balance rigor with meeting user needs within the timeline. The document outlines the need for further research on rapid reviews to improve understanding.
This document provides an overview of risk-based monitoring (RBM) for clinical trials. It discusses the history and evolution of RBM, which originated from quality by design principles. The document outlines an 8-step RBM methodology involving assessing risks, determining key risk indicators and performance thresholds, defining response plans, communication plans, and adjusting the plan based on monitoring results. It also discusses how electronic tools can facilitate remote, centralized RBM using metrics and dashboards. The role of clinical research associates is shifting from on-site monitoring to focusing monitoring efforts based on risk-driven data.
PRE-val is a service that independently validates peer review processes for scholarly journals. It provides a badge that journals can display to signal that a given article has undergone quality peer review. PRE-val aims to increase transparency and trust in peer review by answering whether an article has truly been peer reviewed and providing information about the journal's review process. It was created due to increasing criticism of traditional peer review and problems like predatory publishers. PRE-val supports best practices in peer review to encourage high-quality review.
This document discusses planning and conducting a systematic literature review. It begins by explaining that systematic reviews aim to aggregate all relevant evidence on a topic in a fair and repeatable manner. The document then outlines the key steps in planning a review, including specifying the research question, developing a review protocol, and evaluating the protocol. It also covers conducting the review, such as identifying relevant research, selecting primary studies, assessing study quality, and extracting and synthesizing data. The importance of transparency and replicability in the review process is emphasized.
The New Dimensions in Scholcomm: How a global scholarly community collaborati...NASIG
Digital Science and 100+ global research institutions have spent the better part of the last two years collaborating to solve three distinct challenges in the existing research landscape:
* Research evaluation focuses almost exclusively on publications and citations data
* Research evaluation tools are siloed in proprietary applications that rarely speak to each other
* The gaps amongst proprietary data sources made generating a complete picture of impact extremely difficult (and expensive)
The goal of this collaboration amongst publishers, funders, research administrators, libraries, and Digital Science was to transform the research landscape by attempting to solve the problems resulting from expensive, siloed data research evaluation data.
Research protocol & Systematic Review.docxAmaraZahid
The document provides information on writing research protocols and conducting systematic reviews. It defines a research protocol as a document that describes the objectives, design, methodology, and organization of a clinical trial. A systematic review aims to identify, evaluate and summarize all available evidence on a health-related issue. Key steps in writing a research protocol include developing objectives, methodology, and a plan for analysis. Key steps in conducting a systematic review include formulating a question, developing a protocol, searching literature, selecting studies, analyzing results, and reporting findings. Both research protocols and systematic reviews follow a rigorous process to comprehensively gather and evaluate evidence to answer research questions.
The document outlines the STATIK framework for implementing Kanban, which involves understanding the system's purpose, sources of dissatisfaction, demand and capability, knowledge discovery processes, classes of service, Kanban systems design, and rollout. It emphasizes understanding multiple perspectives, balancing demand and capability, discovering needs through collaboration and validation, recognizing different expectations through classes of service, and designing systems to pursue sustained evolutionary change through Kanban values like transparency and respect.
The research data spring project "Collaboration for Research Enhancement by Active use of Metadata" slides for the third sandpit workshop. Project partners:
University of Southampton: Simon Coles (Principal Investigator); Jeremy Frey; Colin Bird (Project Coordinator); Cerys Willoughby
University of Edinburgh: Mike Mineter; Magnus Hagdorn
University of the Arts London: Athanasios Velios; Iris Garrelfs
Science & Technology Facilities Council (STFC): Tom Griffin; Brian Matthews
Nine by Nine: Graham Klyne
This presentation was provided by Carly Strasser of the Chan Zuckerberg Initiative during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
This document discusses the role of libraries in assessing and reporting research impact. It begins by outlining common metrics used to measure impact, such as citations, social media mentions, and altmetrics. It then discusses frameworks that can help contextualize impact, such as the Becker Model. The document emphasizes moving beyond simple counts to understand the true impact of research. It proposes strategies libraries can take, such as integrating altmetrics into institutional repositories and using research networking systems to map the diffusion of research outputs. Overall, the key points are that understanding research impact is complex, libraries can play an important role by supporting impact assessment, and stakeholder engagement is critical for local success.
A basic introduction to rapid reviews, created for a graduate student workshop, March 2018, presented by PF Anderson from the University of Michigan. Includes links to more resources, standards and guidelines, tools, software, and more.
This document outlines the process and key stages of conducting a systematic review. It aims to define what a systematic review is, discuss the formulation of a review question and development of a protocol, and explain the stages of searching for studies, selecting studies, extracting data, and synthesizing evidence. A systematic review attempts to comprehensively and methodically identify and synthesize all empirical evidence that fits pre-specified eligibility criteria to answer a specific research question and minimize bias in order to draw reliable conclusions.
Systematic literature review | Meta analysis | Retrospective versusPubrica
Systematic review for prospective studies is a meticulous and essential process ensuring research findings’ reliability and validity. The key to success lies in adhering to a well-structured methodology that includes defining the research question, developing a comprehensive search strategy, screening studies based on pre-defined criteria, and critically appraising the selected articles.
Read more @ https://pubrica.com/academy/manuscript-editing/conduct-a-systematic-review-for-prospective-studies/
Presentation from a workshop given at ACRL 2011 conference, Data-Driven Library Web Design: Making Usability Testing Work with Collaborative Partnerships
Curlew Research Brussels 2014 Electronic Data & Knowledge ManagementNick Lynch
Life Science externalisation and collaboration overview and the challenges that Life Science companies face in delivering successful data sharing with their partners in either Open Innovation or pre-competitive workflows
The ROER4D Curation & Dissemination team provides an overview of the ROER4D open data initiative as well as some key insights and challenges experienced.
The document discusses research methodology. It defines methodology as the systematic process used to solve a research problem. It lists the key parts of methodology as the research design, sample size determination, sampling techniques, subjects, research instruments, validation of instruments, data gathering procedures, data processing methods, and statistical treatment. It explains the importance of methodology to customers, business partners, suppliers, and professionals. It also outlines some key characteristics of methodology such as rationale, aims, description, and tips for determining sample size.
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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
2. TWO
FLAVOURS
AND
ONE
PRESENTATION
Share
experiences
in
openness
from
the
machine
learning
community
To
propose
potential
solutions
to
the
problem
of
sharing
sensible
data
4. THE
ROLE
OF
DATA
IN
MACHINE
LEARNING
• Machine
learning
is
sometimes
called
learning
from
data.
Thus
we
are
talking
about
building/coding
algorithms
that
learn
from
data.
• Data
is
the
key
component
in
most
research,
but
it
is
obviously
crucial
in
machine
learning.
6. CURRENT
MACHINE
LEARNING
RESEARCH
FOCUS
• Improving
results
in
terms
of
generalization,
robustness,
speed,
and
interpretability.
• Comparison
with
other
techniques
is
fundamental.
• Many
different
data
sets
are
needed
to
assess
each
property
we
are
tackling
in
the
research,
e.g.
we
need
large
datasets
for
speed
and
big
data.
• Sometimes,
their
application
largely
depends
on
the
domain,
e.g.,
financial
products
forecasting,
targeted
gene
identification.
7. AS
A
RESEARCHER
DISEMINATION:
REPORTING
PROCESS,
CLAIMING
AUTHORSHIP
QUALITY:
COMMUNITY
VALIDITY
ASSESSMENT,
PEER
REVIEW
(KPI
USED
BY
ORGANISATIONS
FOR
RESEARCH
QUALITY)
We
pursue
to
contribute
to
advance
science:
reproducibility
of
experimentation
that
helps
to
eliminate individual
biases.
8. FOR
THIS
REASON
WE
USE
PUBLISHING
DISSEMINATION
QUALITY
ASSESSMENT
9. FOR
THIS
REASON
WE
USE
PUBLISHING
LONG
PROCESS
>
1
YEAR
NOT
A
GOOD
DISEMINATION
SOURCE,
OR
ATTRIBUTION
PROC.
PEER
QUALITY
ASSESSMENT
CRITICAL
FEEDBACK
GUARD
AGAINST
ERRORS
MAJOR
SOURCE
FOR
RANKING
RESEARCHERS
NON-‐PUBLISHED
RESEARCH
IS
NOT
RESEARCH
10. FOR
THIS
REASON
WE
USE
PUBLISHING
FAIRER
PROCESS >
3-‐6
month
QUALITY
ASSESSMENT
RANKING
RESEARCHERS
NON-‐PUBLISHED
RESEARCH
IS
NOT
RESEARCH
11. THE
NIPS
CASE:
WHEN
CONFERENCE
PAPERS
ARE
OLD
NEWS
IN
MACHINE
LEARNING
3-‐6
MONTHS
IS
TOO
LONG
DECEMBER
5TH – 10TH ,
2016
13. 24/7
PUBLISHING
CYCLE
IMMEDIACY,
24/7
PUBLISHING
CYCLE
IMMEDIATE
ATTRIBUTION
NO
QUALITY
ASSESSMENT
RANKING
RESEARCHERS
HOW
TO
DISTINGUISH
GOOD
RESEARCH
FROM
FRAUD
19. Data
Sets -‐ Raw
data
as
a
collection
of
similarily structured
objects.
Material
and
Methods -‐
Descriptions
of
the
computational
pipeline.
Learning
Tasks -‐ Learning
tasks
defined
on
raw
data.
Challenges -‐ Collections
of
tasks
which
have
a
particular
theme.
MACHINE
LEARNING
DATASET
REPOSITORIES
20. • The
U.S.
Securities
and
Exchange
Commission
(SEC)
2010
proposal,
financial
products
must
include
Python
code.
WHEN
DESCRIPTION
AND
DATA
IS
NOT
ENOUGH
28. BOTTOM
LINE
1. SCIENCE
IS
MORE
THAN
A
PUBLICATION
2. AT
ITS
CORE
LIES
REPRODUCIBILITY…THIS
INVOLVES
DATA
3. BUT
DATA
IS
NOT
EVEN
ENOUGH…
CODE
AND
PRECISE
EXPERIMENT
REPRODUCIBILITY
4. DATA
AND
PROGRAMMING
LITERACY
IS
NEEDED
29. BUT
…
SOMETHING
ELSE
ABOUT
QUALITY
ASSESSMENT:
OPEN
REVIEW
CASE
30. THE
RULES
• Submissions
posted
on
arXiv before
being
submitted
to
the
conference
• Program
committee
assigns
blind
reviewers
(as
always)
marked
as
designated
reviewers
• Anyone
can
ask
the
program
chairs
for
permission
to
become
an
anonymous
designated
reviewer
(open
bidding).
• Open
commenters
will
have
to
use
their
real
names,
linked
with
their
Google
Scholar
profiles.
• Papers
that
are
presented
in
the
workshop
track
or
are
not
accepted
will
be
considered
non-‐archival,
and
may
be
submitted
elsewhere
(modified
or
not),
although
the
ICLR
site
will
maintain
the
reviews,
the
comments,
and
the
links
to
the
arXiv versions
31. SOME
POINTS
ABOUT
OPEN
REVIEW
• Reading
the
answers
from
others
is
valuable
• Rejected
papers
have
influence
• May
become
a
popularity
contest
41. Research is based on reproducibility:
documentation, data, code, and details.
Machine learning methods can help in the
challenge for democratizing data.
CONCLUSIONS