Over the past 10 years, research systems have evolved from systems that focused on how to structure and record information on research, to systems capable of allowing significant insights to be derived based upon years of high quality information. In 2015, the maturity of the information now collected within many Current Research Information Systems, and the insights that this can provide is of equal or greater value than the insights that could be gleaned from established externally provided research metrics platforms alone. The ability to intersect these external and internal worlds provides new levels of strategic insight not previously available. With the addition of platforms that track altmetrics, and their ability to connect university publications data with a constant flow of real time attention level metrics, an image of a dynamic network of systems emerges, connected together by ever turning ‘cogs’ pushing and translating information. Add to this, the success of ORCID as pervasive researcher identifier infrastructure, and CASRAI as the emerging social contract for information exchange, and it becomes possible to extend this network back from the systems that track and record research information, through to the platforms through which research knowledge is created. The ‘Mechanics’ of this network of systems is more than just getting the ‘plumbing’ right. As research information moves through the network, its audience and purpose changes, the requirements for contextual metadata can also change. This presentation will explore the lived experience of Research Data Mechanics at Digital Science though illustrating how connections between Figshare, Altmetric, Symplectic Elements, and Dimensions can both enhance research system capability and reduce the burden on researchers, and research administration.
Data science remains a high-touch activity, especially in life, physical, and social sciences. Data management and manipulation tasks consume too much bandwidth: Specialized tools and technologies are difficult to use together, issues of scale persist despite the Cambrian explosion of big data systems, and public data sources (including the scientific literature itself) suffer curation and quality problems.
Together, these problems motivate a research agenda around “human-data interaction:” understanding and optimizing how people use and share quantitative information.
I’ll describe some of our ongoing work in this area at the University of Washington eScience Institute.
In the context of the Myria project, we're building a big data "polystore" system that can hide the idiosyncrasies of specialized systems behind a common interface without sacrificing performance. In scientific data curation, we are automatically correcting metadata errors in public data repositories with cooperative machine learning approaches. In the Viziometrics project, we are mining patterns of visual information in the scientific literature using machine vision, machine learning, and graph analytics. In the VizDeck and Voyager projects, we are developing automatic visualization recommendation techniques. In graph analytics, we are working on parallelizing best-of-breed graph clustering algorithms to handle multi-billion-edge graphs.
The common thread in these projects is the goal of democratizing data science techniques, especially in the sciences.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
The goal of the Very Open Data Project is to provide a software-technical foundation for this exchange of data, more specifically to provide an open database platform for data from the raw data coming from experimental measurements or models through intermediate manipulations to finally published results. The sheer expanse of the amount data involved creates some unique software-technical challenges. One of these challenges is addressed in the part of the study presented here, namely to characterize scientific data (with the initial focus being detailed chemistry data from the combustion kinetic community), so that efficient searches can be made. A formalization of this characterization comes in the form of schemas of descriptions of tags and keywords describing data and ontologies describing the relationship between data types and the relationship between the characterizations themselves. These will be translated to meta-data tags connected to the data points within a non-relational data of data for the community.
The focus of the initial work will be on data and its accessibility. As the project progresses, the emphasis will shift on not only having available data accessible for the community, but that the community itself will be able to, with emphasis on minimal effort, will be able contribute their own data. This will involve, for example, the concepts of the ‘electronic lab notebook’ and the existence and availability of extensive concept extraction tools, primarily from the chemical informatics field.
Data science remains a high-touch activity, especially in life, physical, and social sciences. Data management and manipulation tasks consume too much bandwidth: Specialized tools and technologies are difficult to use together, issues of scale persist despite the Cambrian explosion of big data systems, and public data sources (including the scientific literature itself) suffer curation and quality problems.
Together, these problems motivate a research agenda around “human-data interaction:” understanding and optimizing how people use and share quantitative information.
I’ll describe some of our ongoing work in this area at the University of Washington eScience Institute.
In the context of the Myria project, we're building a big data "polystore" system that can hide the idiosyncrasies of specialized systems behind a common interface without sacrificing performance. In scientific data curation, we are automatically correcting metadata errors in public data repositories with cooperative machine learning approaches. In the Viziometrics project, we are mining patterns of visual information in the scientific literature using machine vision, machine learning, and graph analytics. In the VizDeck and Voyager projects, we are developing automatic visualization recommendation techniques. In graph analytics, we are working on parallelizing best-of-breed graph clustering algorithms to handle multi-billion-edge graphs.
The common thread in these projects is the goal of democratizing data science techniques, especially in the sciences.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
The goal of the Very Open Data Project is to provide a software-technical foundation for this exchange of data, more specifically to provide an open database platform for data from the raw data coming from experimental measurements or models through intermediate manipulations to finally published results. The sheer expanse of the amount data involved creates some unique software-technical challenges. One of these challenges is addressed in the part of the study presented here, namely to characterize scientific data (with the initial focus being detailed chemistry data from the combustion kinetic community), so that efficient searches can be made. A formalization of this characterization comes in the form of schemas of descriptions of tags and keywords describing data and ontologies describing the relationship between data types and the relationship between the characterizations themselves. These will be translated to meta-data tags connected to the data points within a non-relational data of data for the community.
The focus of the initial work will be on data and its accessibility. As the project progresses, the emphasis will shift on not only having available data accessible for the community, but that the community itself will be able to, with emphasis on minimal effort, will be able contribute their own data. This will involve, for example, the concepts of the ‘electronic lab notebook’ and the existence and availability of extensive concept extraction tools, primarily from the chemical informatics field.
La comparsa, pochi decenni fa, di Internet e della connettività globale ha dato origine ad un fenomeno assolutamente nuovo: un accumulo di enormi quantità di dati conservati in banche digitali, la cui quantità raddoppia ogni pochi giorni e in prospettiva ogni poche ore. E’ la realtà dei Big Data, di cui molto si parla e discute, sovente con toni entusiastici. Ma Big Data vuol dire anche problemi di utilizzo, di interpretazione e rischi di distorsioni. Se questo è rilevante per i dati che hanno un valore economico, l’accumulo di informazione e il come viene trattata ha risvolti altrettanto rilevanti sulla formazione di conoscenza.
Per affrontare queste sfide, cruciali sono il rapporto fra etica e scienza, l’analisi critica su come i dati vengono prodotti e proposti, e il coinvolgimento di tutti i soggetti sociali chiamati in causa.
12 settembre 2019 | Torino, Polo del '900
The literature contains a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data reuse. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data.
Managing Metadata for Science and Technology Studies: the RISIS caseRinke Hoekstra
Presentation of our paper at the WHISE workshop at ESWC 2016 on requirements for metadata over non-public datasets for the science & technology studies field.
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need.
However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
The Roots: Linked data and the foundations of successful Agriculture DataPaul Groth
Some thoughts on successful data for the agricultural domain. Keynote at Linked Open Data in Agriculture
MACS-G20 Workshop in Berlin, September 27th and 28th, 2017 https://www.ktbl.de/inhalte/themen/ueber-uns/projekte/macs-g20-loda/lod/
The Rensselaer Institute for Data Exploration and Applications is addressing new modes of data exploration and integration to enhance the work of campus researchers (and beyond). This talk outlines the "data exploration" technologies being explored
OSi Geographic Information Research & Development Initiatives Launch
Ordnance Survey Ireland GI R&D Initiatives
Tuesday, 22 March 2016, 13:00 to 20:30 (GMT) , Maynooth University
Slides to accompany Dr Louise Cooke's workshop session "An introduction to social network analysis" presented at DREaM Event 2.
For more information about the event, please visit http://lisresearch.org/dream-project/dream-event-2-workshop-tuesday-25-october-2011/
Social media provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens share information, express opinions, and engage in discussions. Often such a Online Citizen Sensor Community (CSC) has stated or implied goals related to workflows of organizational actors with defined roles and responsibilities. For example, a community of crisis response volunteers, for informing the prioritization of responses for resource needs (e.g., medical) to assist the managers of crisis response organizations. However, in CSC, there are challenges related to information overload for organizational actors, including finding reliable information providers and finding the actionable information from citizens. This threatens awareness and articulation of workflows to enable cooperation between citizens and organizational actors. CSCs supported by Web 2.0 social media platforms offer new opportunities and pose new challenges. This work addresses issues of ambiguity in interpreting unconstrained natural language (e.g., ‘wanna help’ appearing in both types of messages for asking and offering help during crises), sparsity of user and group behaviors (e.g., expression of specific intent), and diversity of user demographics (e.g., medical or technical professional) for interpreting user-generated data of citizen sensors. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues in CSC, and allow better accessibility to user-generated data at higher level of information abstraction for organizational actors. This study presents a novel web information processing framework focused on actors and actions in cooperation, called Identify-Match-Engage (IME), which fuses top-down and bottom-up computing approaches to design a cooperative web information system between citizens and organizational actors. It includes a.) identification of action related seeking-offering intent behaviors from short, unstructured text documents using both declarative and statistical knowledge based classification model, b.) matching of intentions about seeking and offering, and c.) engagement models of users and groups in CSC to prioritize whom to engage, by modeling context with social theories using features of users, their generated content, and their dynamic network connections in the user interaction networks. The results show an improvement in modeling efficiency from the fusion of top-down knowledge-driven and bottom-up data-driven approaches than from conventional bottom-up approaches alone for modeling intent and engagement. Several applications of this work include use of the engagement interface tool during recent crises to enable efficient citizen engagement for spreading critical information of prioritized needs to ensure donation of only required supplies by the citizens. The engagement interface application also won the United Nations ICT agency ITU's Young Innovator 2014 award.
La comparsa, pochi decenni fa, di Internet e della connettività globale ha dato origine ad un fenomeno assolutamente nuovo: un accumulo di enormi quantità di dati conservati in banche digitali, la cui quantità raddoppia ogni pochi giorni e in prospettiva ogni poche ore. E’ la realtà dei Big Data, di cui molto si parla e discute, sovente con toni entusiastici. Ma Big Data vuol dire anche problemi di utilizzo, di interpretazione e rischi di distorsioni. Se questo è rilevante per i dati che hanno un valore economico, l’accumulo di informazione e il come viene trattata ha risvolti altrettanto rilevanti sulla formazione di conoscenza.
Per affrontare queste sfide, cruciali sono il rapporto fra etica e scienza, l’analisi critica su come i dati vengono prodotti e proposti, e il coinvolgimento di tutti i soggetti sociali chiamati in causa.
12 settembre 2019 | Torino, Polo del '900
The literature contains a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data reuse. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data.
Managing Metadata for Science and Technology Studies: the RISIS caseRinke Hoekstra
Presentation of our paper at the WHISE workshop at ESWC 2016 on requirements for metadata over non-public datasets for the science & technology studies field.
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need.
However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
The Roots: Linked data and the foundations of successful Agriculture DataPaul Groth
Some thoughts on successful data for the agricultural domain. Keynote at Linked Open Data in Agriculture
MACS-G20 Workshop in Berlin, September 27th and 28th, 2017 https://www.ktbl.de/inhalte/themen/ueber-uns/projekte/macs-g20-loda/lod/
The Rensselaer Institute for Data Exploration and Applications is addressing new modes of data exploration and integration to enhance the work of campus researchers (and beyond). This talk outlines the "data exploration" technologies being explored
OSi Geographic Information Research & Development Initiatives Launch
Ordnance Survey Ireland GI R&D Initiatives
Tuesday, 22 March 2016, 13:00 to 20:30 (GMT) , Maynooth University
Slides to accompany Dr Louise Cooke's workshop session "An introduction to social network analysis" presented at DREaM Event 2.
For more information about the event, please visit http://lisresearch.org/dream-project/dream-event-2-workshop-tuesday-25-october-2011/
Social media provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens share information, express opinions, and engage in discussions. Often such a Online Citizen Sensor Community (CSC) has stated or implied goals related to workflows of organizational actors with defined roles and responsibilities. For example, a community of crisis response volunteers, for informing the prioritization of responses for resource needs (e.g., medical) to assist the managers of crisis response organizations. However, in CSC, there are challenges related to information overload for organizational actors, including finding reliable information providers and finding the actionable information from citizens. This threatens awareness and articulation of workflows to enable cooperation between citizens and organizational actors. CSCs supported by Web 2.0 social media platforms offer new opportunities and pose new challenges. This work addresses issues of ambiguity in interpreting unconstrained natural language (e.g., ‘wanna help’ appearing in both types of messages for asking and offering help during crises), sparsity of user and group behaviors (e.g., expression of specific intent), and diversity of user demographics (e.g., medical or technical professional) for interpreting user-generated data of citizen sensors. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues in CSC, and allow better accessibility to user-generated data at higher level of information abstraction for organizational actors. This study presents a novel web information processing framework focused on actors and actions in cooperation, called Identify-Match-Engage (IME), which fuses top-down and bottom-up computing approaches to design a cooperative web information system between citizens and organizational actors. It includes a.) identification of action related seeking-offering intent behaviors from short, unstructured text documents using both declarative and statistical knowledge based classification model, b.) matching of intentions about seeking and offering, and c.) engagement models of users and groups in CSC to prioritize whom to engage, by modeling context with social theories using features of users, their generated content, and their dynamic network connections in the user interaction networks. The results show an improvement in modeling efficiency from the fusion of top-down knowledge-driven and bottom-up data-driven approaches than from conventional bottom-up approaches alone for modeling intent and engagement. Several applications of this work include use of the engagement interface tool during recent crises to enable efficient citizen engagement for spreading critical information of prioritized needs to ensure donation of only required supplies by the citizens. The engagement interface application also won the United Nations ICT agency ITU's Young Innovator 2014 award.
Provincial Perspectives on Research Impacts: Eddy Nason, Renata Osika, Krista...CASRAI
When we say “Research Impact” many things come to mind and the reasons for why we are concerned with it vary. The underlying concepts are complex and often require expert knowledge, and there is also no one single interpretation or answer. Stakeholders are diverse and so are the means of communication. Therefore across Canada, we continue to seek more consistent and harmonized ways of telling the “Impact Story.” The panel will reflect on harmonization efforts across provinces.
Julian Omidi recently refocused his main energy on philanthropic activities and providing aid to the less fortunate. Earlier this year, he and his brother Michael Omidi, MD founded the charity No More Poverty. The organization seeks to end poverty at home and abroad by supporting the efforts of like-minded charities and agencies. Current efforts are focused on increasing awareness of and donations to charities already doing great work to address poverty and its staggering effects throughout the world.
Singapore Management UniversityInstitutional Knowledge at Si.docxjennifer822
Singapore Management University
Institutional Knowledge at Singapore Management University
Research Collection Lee Kong Chian School Of
Business
Lee Kong Chian School of Business
10-2016
Big data and data science methods for management
research: From the Editors
Gerard GEORGE
Singapore Management University, [email protected]
Ernst C. OSINGA
Singapore Management University, [email protected]
Dovev LAVIE
Technion
Brent A. SCOTT
Michigan State University
DOI: https://doi.org/10.5465/amj.2016.4005
Follow this and additional works at: https://ink.library.smu.edu.sg/lkcsb_research
Part of the Management Sciences and Quantitative Methods Commons, and the Strategic
Management Policy Commons
This Editorial is brought to you for free and open access by the Lee Kong Chian School of Business at Institutional Knowledge at Singapore
Management University. It has been accepted for inclusion in Research Collection Lee Kong Chian School Of Business by an authorized administrator
of Institutional Knowledge at Singapore Management University. For more information, please email [email protected]
Citation
GEORGE, Gerard; Ernst C. OSINGA; LAVIE, Dovev; and SCOTT, Brent A.. Big data and data science methods for management
research: From the Editors. (2016). Academy of Management Journal. 59, (5), 1493-1507. Research Collection Lee Kong Chian School
Of Business.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/4964
https://ink.library.smu.edu.sg?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://ink.library.smu.edu.sg/lkcsb?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://doi.org/10.5465/amj.2016.4005
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
http://network.bepress.com/hgg/discipline/637?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
http://network.bepress.com/hgg/discipline/642?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
http://network.bepress.com/hgg/discipline/642?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
mailto:[email protected]
1
FROM THE EDITORS
BIG DATA AND DATA SCIENCE METHODS FOR MANAGEMENT RESEARCH
Published in Academy of Management Journal, October 2016, 59 (5), pp. 1493-1507.
http://doi.org/10.5465/amj.2016.4005
The recent advent of remote sensing, mobile technologies, novel transaction systems, and
high performance computing offers opportunities to un.
Singapore Management UniversityInstitutional Knowledge at Si.docxedgar6wallace88877
Singapore Management University
Institutional Knowledge at Singapore Management University
Research Collection Lee Kong Chian School Of
Business
Lee Kong Chian School of Business
10-2016
Big data and data science methods for management
research: From the Editors
Gerard GEORGE
Singapore Management University, [email protected]
Ernst C. OSINGA
Singapore Management University, [email protected]
Dovev LAVIE
Technion
Brent A. SCOTT
Michigan State University
DOI: https://doi.org/10.5465/amj.2016.4005
Follow this and additional works at: https://ink.library.smu.edu.sg/lkcsb_research
Part of the Management Sciences and Quantitative Methods Commons, and the Strategic
Management Policy Commons
This Editorial is brought to you for free and open access by the Lee Kong Chian School of Business at Institutional Knowledge at Singapore
Management University. It has been accepted for inclusion in Research Collection Lee Kong Chian School Of Business by an authorized administrator
of Institutional Knowledge at Singapore Management University. For more information, please email [email protected]
Citation
GEORGE, Gerard; Ernst C. OSINGA; LAVIE, Dovev; and SCOTT, Brent A.. Big data and data science methods for management
research: From the Editors. (2016). Academy of Management Journal. 59, (5), 1493-1507. Research Collection Lee Kong Chian School
Of Business.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/4964
https://ink.library.smu.edu.sg?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://ink.library.smu.edu.sg/lkcsb?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://doi.org/10.5465/amj.2016.4005
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
http://network.bepress.com/hgg/discipline/637?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
http://network.bepress.com/hgg/discipline/642?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
http://network.bepress.com/hgg/discipline/642?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
mailto:[email protected]
1
FROM THE EDITORS
BIG DATA AND DATA SCIENCE METHODS FOR MANAGEMENT RESEARCH
Published in Academy of Management Journal, October 2016, 59 (5), pp. 1493-1507.
http://doi.org/10.5465/amj.2016.4005
The recent advent of remote sensing, mobile technologies, novel transaction systems, and
high performance computing offers opportunities to un.
On November 21st 2014 at the Tufts University Medford campus and November 25th 2014 at the campus of the University of Massachusetts Medical School in Worcester, the BLC and Digital Science hosted a workshop focused on better understanding the research information management landscape.
Jonathan Breeze, CEO of Symplectic, reflected on the emergence of research information management systems and the resulting benefits they can provide.
Linked Data Love: research representation, discovery, and assessment
#ALAAC15
The explosion of linked data platforms and data stores over the last five years has been profound – both in terms of quantity of data as well as its potential impact. Research information systems such as VIVO (www.vivoweb.org) play a significant role in enabling this work. VIVO is an open source, Semantic Web-based application that provides an integrated, searchable view of the scholarly activities of an organization. The uniform semantic structure of VIVO-ISF data enables a new class of tools to advance science. This presentation will provide a brief introduction and update to VIVO and present ways that this semantically-rich data can enable visualizations, reporting and assessment, next-generation collaboration and team building, and enhanced multi-site search. Libraries are uniquely positioned to facilitate the open representation of research information and its subsequent use to spur collaboration, discovery, and assessment. The talk will conclude with a description of ways librarians are engaged in this work – including visioning, metadata and ontology creation, policy creation, data curation and management, technical, and engagement activities.
Kristi Holmes, PhD
Director, Galter Health Sciences Library
Director of Evaluation, NUCATS
Associate Professor, Preventive Medicine-Health and Biomedical Informatics
Northwestern University Feinberg School of Medicine
Crossref LIVE: The Benefits of Open Infrastructure (APAC time zones) - 29th O...Crossref
In November 2020, Crossref formally adopted the “Principles of Open Scholarly Infrastructure” (POSI). POSI is a list of sixteen commitments that will now guide the board, staff, and Crossref’s development as an organisation into the future.
This webinar took place on the 29th October at 03:00 PM AEST (UTC+10) and covered:
- What are the Principles of Open Scholarly Infrastructure (POSI) and why are they needed?
- Why POSI is important for Crossref and how it will help realise the Research Nexus
- Open metadata and infrastructure services from Crossref
Presented in English by Cameron Neylon, Professor of Research Communications, Centre for Culture and Technology, at Curtin University, Amanda Bartell, Head of Member Experience at Crossref, and Vanessa Fairhurst, Community Engagement Manager at Crossref.
A Big Picture in Research Data ManagementCarole Goble
A personal view of the big picture in Research Data Management, given at GFBio - de.NBI Summer School 2018 Riding the Data Life Cycle! Braunschweig Integrated Centre of Systems Biology (BRICS), 03 - 07 September 2018
Optimising benefits from Canadian Research - Jim WoodgettCASRAI
Janet Halliwell, Chair CASRAI; Co-Chair Admin Burden Canada collective; Chair CSPC
Dominique Bérubé, Vice-President Research Programs, SSHRC
Jim Woodgett, Director of Research, Lunenfeld-Tanenbaum Research Institute
Optimising benefits from Canadian Research - Janet HalliwellCASRAI
Janet Halliwell, Chair CASRAI; Co-Chair Admin Burden Canada collective; Chair CSPC
Dominique Bérubé, Vice-President Research Programs, SSHRC
Jim Woodgett, Director of Research, Lunenfeld-Tanenbaum Research Institute
Admin Burden in Canada (ABC) Introductory Panel Discussion (CA, UK and US ove...CASRAI
Admin Burden in Canada (ABC) Introductory Panel Discussion (CA, UK and US overview)
David Robinson
Executive Vice Provost & Professor
Oregon Health & Science University (US)
ABC Project 1 - Piloting Auto-upload of Standardized Funding Award Results - ...CASRAI
ABC Project 1 - Piloting Auto-upload of Standardized Funding Award Results
Judith L. Chadwick
Assistant Vice-President, Research Services
University of Toronto
Bob Dirstein
Dirstein Consulting Inc.
w/University of Toronto
ABC Project 2 - Launching an ORCID Consortia in Canada - Clare Appavoo & Geof...CASRAI
Launching an ORCID Consortia in Canada
Clare Appavoo
Executive Director
Canadian Research Knowledge Network (CRKN)
Geoffrey Harder
Associate University Librarian
University of Alberta
Mark Leggott
Executive Director
Research Data Canada (RDC)
Introduction to the Federal Demonstration Partnership (FDP) of the US - David...CASRAI
Introduction to the Federal Demonstration Partnership (FDP) of the US
David Robinson
Executive Vice Provost & Professor
Oregon Health & Science University (US)
Tutorial: the new Portage Research Data Management Planning Tool - Chuck Hump...CASRAI
Tutorial: the new Portage Research Data Management Planning Tool
Chuck Humphrey
Director, Portage Network
University of Alberta
Dylanne Dearborn
Physics Library
University of Toronto Libraries
How Do I Know Thee? Let Me Count the Ways: Panel 2: Jeffrey Alexander & Patri...CASRAI
All R&D organizations classify their research activities, either implicitly (e.g., by laboratory or department) or explicitly (e.g., by creating taxonomies to define and map research disciplines and domains). However the lack of clear standards for doing so impedes the sharing and aggregation of data on R&D activities. In this panel the speakers will provide an overview of the organizational needs driving the development of a classification of R&D activities, use cases for such a classification, and the potential advantages of international coordination across such classifications.
Classifying R&D: Why and How Organizations Develop Taxonomies for Research Fi...CASRAI
All R&D organizations classify their research activities, either implicitly (e.g., by laboratory or department) or explicitly (e.g., by creating taxonomies to define and map research disciplines and domains). However the lack of clear standards for doing so impedes the sharing and aggregation of data on R&D activities. In this workshop, Jeff Alexander and Patrick Lambe will provide an overview of the organizational needs driving the development of a classification of R&D activities, use cases for such a classification, and the potential advantages of international coordination across such classifications. The workshop, based heavily on a study they conducted for the National Center for Science & Engineering Statistics at the U.S. National Science Foundation, will review alternate approaches to both developing R&D classifications, and streamlining the process of classifying research programs and projects. Topics to be covered include examples of international R&D classifications and their development (such as the Australia-New Zealand Standard Research Classification), design principles for R&D classifications, and new automated and semi-automated classification techniques using semantic analysis and machine learning.
How Do I Know Thee? Let Me Count the Ways: Sarah Moreault, Monica Valsangkar-...CASRAI
Classification of research plays an integral role in the functioning of research funding organizations. As such it is important to have a classification system for efficient research data collection, use, analysis and reporting. Hear about lessons learned as well as key limitations and challenges for the implementation of a standard approach to classification through the analyses of different international standards currently in use with respect to their governance, development, implementation and maintenance
Lightning Reports on 2015 CASRAI Standards Work: Data Management PlanCASRAI
Get an overview of all CASRAI standards projects from the past year delivered by the project leads. Includes Project CRediT, Peer Review Citations, Snowball Metrics, Data Management Plans, Open Access Reporting and Organizational ID standards.
Closing the Loop - Technology ImplementationsCASRAI
Thorsten Hoellrigl, Thomas Vestam. Targeted at representatives of IT departments and software suppliers. Hearing from early technology adopters of CASRAI standards on progress and lessons learned.
A process server is a authorized person for delivering legal documents, such as summons, complaints, subpoenas, and other court papers, to peoples involved in legal proceedings.
Canadian Immigration Tracker March 2024 - Key SlidesAndrew Griffith
Highlights
Permanent Residents decrease along with percentage of TR2PR decline to 52 percent of all Permanent Residents.
March asylum claim data not issued as of May 27 (unusually late). Irregular arrivals remain very small.
Study permit applications experiencing sharp decrease as a result of announced caps over 50 percent compared to February.
Citizenship numbers remain stable.
Slide 3 has the overall numbers and change.
Understanding the Challenges of Street ChildrenSERUDS INDIA
By raising awareness, providing support, advocating for change, and offering assistance to children in need, individuals can play a crucial role in improving the lives of street children and helping them realize their full potential
Donate Us
https://serudsindia.org/how-individuals-can-support-street-children-in-india/
#donatefororphan, #donateforhomelesschildren, #childeducation, #ngochildeducation, #donateforeducation, #donationforchildeducation, #sponsorforpoorchild, #sponsororphanage #sponsororphanchild, #donation, #education, #charity, #educationforchild, #seruds, #kurnool, #joyhome
What is the point of small housing associations.pptxPaul Smith
Given the small scale of housing associations and their relative high cost per home what is the point of them and how do we justify their continued existance
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
ZGB - The Role of Generative AI in Government transformation.pdfSaeed Al Dhaheri
This keynote was presented during the the 7th edition of the UAE Hackathon 2024. It highlights the role of AI and Generative AI in addressing government transformation to achieve zero government bureaucracy
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
Presentation by Jared Jageler, David Adler, Noelia Duchovny, and Evan Herrnstadt, analysts in CBO’s Microeconomic Studies and Health Analysis Divisions, at the Association of Environmental and Resource Economists Summer Conference.
PNRR MADRID GREENTECH FOR BROWN NETWORKS NETWORKS MUR_MUSA_TEBALDI.pdf
Research Metadata Mechanics - Simon Porter
1. Work smart. Discover more.
The New Research Data
Mechanics…
Simon Porter
VP Research Engagement Knowledge Architecture
Digital Science
@sjcporter #CASRAI15
also presented at #VIVO15
http://dx.doi.org/10.6084/m9.figshare.1509911
2. Work smart. Discover more.
Before we begin…
This work extends on work and concepts that I began
whilst atThe University of Melbourne. I am grateful for the
permission to build upon it at Digital Science.
3. Work smart. Discover more.
Expectations around Research
Information Systems are undergoing a
period of rapid transformation
Images
Modified
from
Louis
K,
C-‐0T
Autobot
Transforma;on
And
h=ps://www.flickr.com/photos/ppapadimitriou/
Blocks
source
Flikr
Paper based Administration
-mid late 90’s
Current Research Information Systems
-mid 2000’s Late 2000’s onwards:VIVO/ORCID’s/
Research Data Management /OA
compliance/ Altmetrics/Open Science/
Team Building/Interdisciplinary
Collaborations
4. Work smart. Discover more.
How do we
describe the
discipline that
provides the
foundations to
make these
aspirations
happen?
? ?
? ? ?
5. Work smart. Discover more.
Why is it safe to raise these expectations now?
We know that Universities can be good at
managing information about their research
6. • htcacheclean
-‐d5
-‐n
-‐i
-‐p/servers/
apache_mod_proxy
-‐l150M
AOer
14
years
of
publica;ons
repor;ng,
there
are
over
150,000
data
points
on
this
visualiza;on
(presented
at
VIVO14)
Porter,
S
Examples
From
the
University
of
Melbourne
7. The
Funding
Pipeline
Funds
awarded
in:
q 2006
q 2007
q 2008
q 2009
q 2010
q 2011
q 2012
q 2013
q 2014
q 2015
In
2017,
almost
all
Research
will
be
funded
by
awards
yet
to
be
won
$
Total
Funding
by
Alloca;on
Year
for
Department
X
2014
9
years
of
sustained
quality
informa;on
on
agreements
went
into
construc;ng
this
pipeline
(presented
at
VIVO14,
Porter,
S)
Examples
From
the
University
of
Melbourne
10. Work smart. Discover more.
The Evolution from Data Entry to Data Glue
• Data Entry - 2009
• Harvesting a single source (like WOS or )-
2010
• Harvesting multiple sources (WOS, Scopus,
Repec,Arxiv, pubmed, …) 2012 (Symplectic)
• Over this time, researcher interaction has
moved from data entry (or email) to:
“we think this is yours, please confirm”
An
example
from
the
University
of
Melbourne
17. Examples
From
the
University
of
Melbourne
Presented
at
Digital
Science
Showcase
2015
18. Melbourne
Ar;cles
with
the
highest
Altmetric
scores…
Examples
From
the
University
of
Melbourne
Presented
at
Digital
Science
Showcase
2015
19. Examples
From
the
University
of
Melbourne
Presented
at
Digital
Science
Showcase
2015
20. At
least
a
year
too
late…
Examples
From
the
University
of
Melbourne
Presented
at
Digital
Science
Showcase
2015
21. Using
Altmetrics
to
their
fullest
poten;al
demands
a
different
way
of
engaging
with
informa;on…
Examples
From
the
University
of
Melbourne
Presented
at
Digital
Science
Showcase
2015
22. From
Data
Glue
to
Data
Mechanics…
h=ps://www.flickr.com/photos/ronwls/13987847602/
in/photolist-‐nj4nLf-‐F329z
23. The
Goal
of
Research
Data
Mechanics
1) In
all
cases,
we
seek
to
replace
manual
interven;on
with
cogs
turning
between
an
understood
system
of
research
2) To
build
and
increase
the
trust
network
of
researchers,
ins;tu;ons,
funding
bodies,
publishers,
and
internal
and
external
service
providers
24. Work smart. Discover more.
Another perspective on Research Data Mechanics:
In
the
case
of
QM
or
Classical
Mechanics
these
laws
of
mo;on
are
determined
by
the
forces
felt
by
the
par;cle
...in
the
case
of
Research
Data
Mechanics,
our
par;cles
are
items
of
data
and
the
underlying
laws
of
mo;on
are
university,
government,
publisher
and
funder
policies
and
prac;ces.
36. Research
data
can
be
enhanced
as
it
travels
through
systems…
Enriched
data
publica;on
links
Research
grants…
Research
Data
as
it
is
shared
What
become
possible…..
37. And
another
thing…
Both
are
examples
of
reducing
barriers
between
the
act
of
research
collabora;on,
and
the
knowing
of
it
39. A
Generic
System
Component
Component
Policy
(Informa;on
Transformed
by
People
processes)
Component
configura;on
and
behavior
is
Influenced
by
the
upstream
and
downstream
components
40. System
components
in
the
context
of
one
possible
VIVO
configura;on
HR
Policy
Finance
Policy
Policy
Grant
Management
Policy
{
{
J
J
J
J
F
F
F
F
41. Inves;ga;ve
Power
with
reference
to
the
system
• Examples
– University
Level
Benchmarking
– Compara;ve
Inter
-‐
Department
Data
Analysis
JJJJ
JJJJ
42. Inves;ga;ve
Power
with
reference
to
the
system
– University
Level
Benchmarking
(Grants
Awarded)
– University
Level
Funding
Pipeline
Analysis
– University
Level
Funding
Pipeline
Analysis
(difficult)
FFFF
FFF
Grant
Management
F
F
F
Grant
Manag
ement
F
F
F
Grant
Manag
ement
F
F
F
Grant
Manag
ement
F
F
F
Grant
Manag
ement
F
F
F
Grant
Manag
ement
?
43. A
Deeper
view
of
Research
Data
Mechanics
STAR
METRICS
(2009)
FFF
Grant
Management
Finance
System
DUNS
database
Payroll
System
h=p://www.nsf.gov/sbe/sosp/workforce/lane.pdf
(an
extended
version
of
research
data
mechanics)
44. Work smart. Discover more.
Some challenges for Research Data Mechanics
• Extending
the
system
of
components
and
the
trust
network
• Crea;ng
common
‘core’
capacity
across
all
research
ins;tu;ons,
Funding
bodies,
Publishers
• Crea;ng
a
research
data
‘machine’
equally
capable
of
preserving
the
history
of
research,
as
well
facilita;ng
the
needs
of
the
‘now’
45. Work smart. Discover more.
Challenge 1) Identifying and removing system
boundaries
– System
boundaries
cause
• informa;on
that
is
already
know
to
be
recreated
• Informa;on
Loss
– Reasons
for
systems
boundaries
include
• Too
much
data
fric;on
created
from
a
lack
of
standards/apis
for
communica;ng
informa;on
• Insufficiently
structured
informa;on
at
the
source
of
crea;on
• Misconfigured
policy
• Insufficiently
developed
trust
networks
• A
lack
of
awareness
of
possibility
46. Work smart. Discover more.
Practical Ways thatVIVO is extending boundaries
HR
Policy
Finance
Policy
Policy
Grant
Management
Policy
Department
Websites
Department
Websites
Department
Websites
Department
Websites
47. Work smart. Discover more.
2) Creating common ‘core’ capacity across all research
institutions
• If
your
ins;tu;on
can
produce
‘sustainable’
VIVO
data
capable
of
represen;ng
your
en;re
research
ins;tu;on,
then,
as
of
now,
you
have
reached
core
capacity…
• What
is
the
core
capacity
for
a
funding
body?
• For
a
publisher?
48. Work smart. Discover more.
3) Creating a machine capable of writing history
C
RIS
50. Work smart. Discover more.
h=ps://en.wikipedia.org/wiki/Aqueduct_(water_supply)#/media/File:Pont_du_Gard_Oct_2007.jpg
In Research Data Mechanics
we are not just building pipes…