?” Paper presented at the International Conference on Information Management and Libraries (ICIML), November 10-13, University of the Punjab, Lahore, Pakistan.
This document summarizes several research projects related to big data and social science knowledge. It discusses projects that analyzed large social media platforms like Facebook, Twitter, and Wikipedia to study information diffusion and social influences. It also discusses challenges like securing access to commercial data and ensuring replicability of findings. Examples demonstrate how big data can provide novel insights but are limited by the objects studied and incomplete representation of populations. The document discusses debates around the implications of big data for privacy, prediction, exclusion, and manipulation. It argues that knowledge depends on how research technologies advance knowledge within ethical and legal frameworks.
Curriculum Development at the Tetherless World Constellation - Peter Fox - RD...ASIS&T
The document summarizes curriculum development at the Tetherless World Constellation focusing on data science and related fields. It discusses themes like data science, semantic science, knowledge provenance and ontology engineering. It notes the Constellation involves over 35 faculty, post-docs, grad and undergrad students across multiple departments. It also lists some application themes like government data, environmental informatics and health/life sciences. Finally, it advocates teaching data science methodology and principles over technology in an interdisciplinary way and emphasizing collaboration.
From Web Data to Knowledge: on the Complementarity of Human and Artificial In...Stefan Dietze
Inaugural lecture at Heinrich-Heine-University Düsseldorf on 28 May 2019.
Abstract:
When searching the Web for information, human knowledge and artificial intelligence are in constant interplay. On the one hand, human online interactions such as click streams, crowd-sourced knowledge graphs, semi-structured web markup or distributional semantic models built from billions of Web documents are informing machine learning and information retrieval models, for instance, as part of the Google search engine. On the other hand, the very same search engines help users in finding relevant documents, facts, or data for particular information needs, thereby helping users to gain knowledge. This talk will give an overview of recent work in both of the aforementioned areas. This includes 1) research on mining structured knowledge graphs of factual knowledge, claims and opinions from heterogeneous Web documents as well as 2) recent work in the field of interactive information retrieval, where supervised models are trained to predict the knowledge (gain) of users during Web search sessions in order to personalise rankings. Both streams of research are converging as part of online platforms and applications to facilitate access to data(sets), information and knowledge.
Brown Bag: New Models of Scholarly Communication for Digital Scholarship, by ...Micah Altman
In his talk for the MIT Libraries Program on Information Science, Steve Griffin discusses how how research libraries can play a key and expanded role in enabling digital scholarship and creating the supporting activities that sustain it.
Advancing Science through Coordinated CyberinfrastructureDaniel S. Katz
How local, regional, and national cyberinfrastructure can be coordinated and linked to advance science and engineering, based on experiences and lessons from the Center for Computation & Technology at LSU (ideas, funding, implementation), plus some thoughts on what might be done differently if we were starting today. Presented at First Workshop - Center for Computational Engineering & Sciences, Unicamp, Campinas, Brazil 10 APR 2014
This document summarizes several research projects related to big data and social science knowledge. It discusses projects that analyzed large social media platforms like Facebook, Twitter, and Wikipedia to study information diffusion and social influences. It also discusses challenges like securing access to commercial data and ensuring replicability of findings. Examples demonstrate how big data can provide novel insights but are limited by the objects studied and incomplete representation of populations. The document discusses debates around the implications of big data for privacy, prediction, exclusion, and manipulation. It argues that knowledge depends on how research technologies advance knowledge within ethical and legal frameworks.
Curriculum Development at the Tetherless World Constellation - Peter Fox - RD...ASIS&T
The document summarizes curriculum development at the Tetherless World Constellation focusing on data science and related fields. It discusses themes like data science, semantic science, knowledge provenance and ontology engineering. It notes the Constellation involves over 35 faculty, post-docs, grad and undergrad students across multiple departments. It also lists some application themes like government data, environmental informatics and health/life sciences. Finally, it advocates teaching data science methodology and principles over technology in an interdisciplinary way and emphasizing collaboration.
From Web Data to Knowledge: on the Complementarity of Human and Artificial In...Stefan Dietze
Inaugural lecture at Heinrich-Heine-University Düsseldorf on 28 May 2019.
Abstract:
When searching the Web for information, human knowledge and artificial intelligence are in constant interplay. On the one hand, human online interactions such as click streams, crowd-sourced knowledge graphs, semi-structured web markup or distributional semantic models built from billions of Web documents are informing machine learning and information retrieval models, for instance, as part of the Google search engine. On the other hand, the very same search engines help users in finding relevant documents, facts, or data for particular information needs, thereby helping users to gain knowledge. This talk will give an overview of recent work in both of the aforementioned areas. This includes 1) research on mining structured knowledge graphs of factual knowledge, claims and opinions from heterogeneous Web documents as well as 2) recent work in the field of interactive information retrieval, where supervised models are trained to predict the knowledge (gain) of users during Web search sessions in order to personalise rankings. Both streams of research are converging as part of online platforms and applications to facilitate access to data(sets), information and knowledge.
Brown Bag: New Models of Scholarly Communication for Digital Scholarship, by ...Micah Altman
In his talk for the MIT Libraries Program on Information Science, Steve Griffin discusses how how research libraries can play a key and expanded role in enabling digital scholarship and creating the supporting activities that sustain it.
Advancing Science through Coordinated CyberinfrastructureDaniel S. Katz
How local, regional, and national cyberinfrastructure can be coordinated and linked to advance science and engineering, based on experiences and lessons from the Center for Computation & Technology at LSU (ideas, funding, implementation), plus some thoughts on what might be done differently if we were starting today. Presented at First Workshop - Center for Computational Engineering & Sciences, Unicamp, Campinas, Brazil 10 APR 2014
Yaşar Tonta, “Açık Erişim ve Açık Bilim” (panel sunuşu). Bilimsel Yayınlar ve Açık Erişim Paneli. 26 Mart 2015, Atılım Üniversitesi Kütüphanesi, Ankara
Research Assessment Using Bibliometric and Scientometric Measures: The Good, ...Yasar Tonta
Yaşar Tonta, “Research Assessment Using Bibliometric and Scientometric Measures: The Good, the Bad, and the Ugly” (sunuş). 3rd International Conference on Scientific Communication in the Digital Age, 10-12 March 2015, Kiev, Ukraine.
Bibliometric and scientometric measures like journal impact factors and the h-index are commonly used to evaluate research performance, but they have significant limitations. They should not be used alone to assess the quality of individual researchers or institutions. While useful as collective measures, on their own they fail to account for differences between disciplines and can incentivize behaviors that do not align with quality research. Proper evaluation requires peer review and a holistic approach, not overreliance on quantitative indicators.
Research Data Management: Pushing the Frontiers of Good Research PracticeYasar Tonta
Research Data Management: Pushing the Frontiers of Good Research Practice
Abstract. Research data can be defined as factual records (e.g., digital and textual data, audio and video recordings) that are used as the main source to validate the research findings. Therefore, the US and the EU spend large sums of money to collect, mine, analyze, synthesize and visualize research data to carry out cutting-edge research. The amount of research data increases 30% per annum. Data is the lifeblood of research, and the management of research data constitutes the crucial part of good research practice even though most research data tend to be locked in researchers’ own hard disks or in their proprietary institutional servers. G8 countries have recently acknowledged the importance of research data and endorsed the Open Data Charter (2013). Managing use and re-use of atmospheric research data through the British Atmospheric Data Centre (BADC) provides somewhere between 400% and 1,200% returns on investment (Beagrie & Houghton, 2013, p. 7). Yet, very many countries have yet to establish their research data centers, infrastructure and policies. This talk will provide an overview of major research data management (RDM) issues, underline the importance of effective RDM, explore the benefits of RDM in terms of pushing the frontiers of good research practice.
The document discusses the future of cultural heritage. It defines cultural heritage as the legacy inherited from past generations and maintained in the present for future generations. This includes physical artifacts and intangible attributes of a society. Cultural heritage is sometimes neglected due to definitions of citizenship in nation-states and intentional collective amnesia that contributes to nation-building. The preservation of cultural heritage collaboratively through a game theory approach is discussed as a potential solution.
“Support Programs to Increase the Number of Scientific Publications Using Bib...Yasar Tonta
This document discusses support programs in Turkey that aim to increase the number of scientific publications. It analyzes Turkey's Support Program for International Scholarly Publications, which was previously based on journal impact factors (JIFs) but now uses other metrics like Article Influence Scores. The author examines the impact of recent changes to the program's algorithms in 2013-2014 compared to pre-2012. Through analyzing a stratified sample of journals, the purpose is to understand the motives behind changes and their effects on determining the monetary support provided by TUBITAK.
Everything we do, create and produce such as intellectual and artistic works, performances, etc. can be defined as culture. We own a very rich cultural heritage of the past. Yet, the common cultural heritage that belongs to the humanity continues to be looted and destroyed due to negligence, armed conflicts and wars. Even though destroying cultural heritage is a crime according to international law, common cultural heritage has been harmed to a great extent during the 20th century. The main reason for this has been the ongoing process of building “nation-states” taking place around the world since the beginning of the last century. The cultural heritage of the “other” gets neglected, to say the least, during the building stages of nation-states. But the destruction of cultural heritage that belongs to the “other” is not, if we are to use the concepts of game theory, a “zero-sum game” in which one party wins while the other loses. In fact, it is not even a lose-lose game in which both parties lose. In such conflicts the humanity loses part of its very precious and irreplaceable common cultural heritage forever. In this paper the causes of the destruction of cultural heritage and the question of to whom the neglected cultural heritage belongs are discussed and the economic and social values of cultural heritage are examined by means of the game theory. It is stressed that the future of cultural heritage along with its preservation, sharing and transmission to next generations is the common concern and responsibility of all countries and humanity.
Can Bibliometric and Scientometric Measures be Used to Assess Research Quali...Yasar Tonta
The quality of research output has traditionally been assessed by peer review. Yet, bibliometric and scientometric measures are increasingly being used nowadays to support or even supplant peer review for research assessment. The main reasons for the popularity of such measures are that they can be obtained easily and that they are considered to be more "objective" in comparison to peer review. This paper explores the misuse of bibliometric and scientometric measures to assess research quality, provides recommendations of San Francisco Declaration on Research Assessment and the Leiden Manifesto for Research Metrics, and briefly addresses "responsible metrics" introduced in "The Metric Tide", a report of independent review chaired by James Wilsdon (Wilsdon, et al. 2015).
RDA / Kaynak Tanımlama ve Erişim Standardı’nın Yazma Eserler İçin Uygulanabil...Emine Gür
RDA standardının, Yazma Eserlerin tanımlanması ve erişimi için uygulanabilirliğini görmek
FRBR mantığının yazma eser kataloglarında kullanılabilir olup olmadığını örnekler üzerinden ölçmek.
Yazma Eserlerin tanımlanmasında kullanılacak MARC alanları için öneri sunmak
This document discusses the roles and responsibilities of librarians as datamediaries. It defines data literacy as the ability to understand, work with, and interpret data in context. Key stakeholders for data literacy include learners, educators, researchers, policymakers, and information institutions like libraries. Libraries are well-positioned to respond to the data literacy challenge through community partnerships and building on existing information literacy practices. As datamediaries, libraries can help engage communities and promote social justice through collaborative data literacy education.
This document summarizes a presentation on revisiting data literacy in the big data landscape. It discusses trends in technology, pedagogy, and information literacy as they relate to data. It also explores different conceptions of data literacy from social science and STEM perspectives. Additionally, it examines current approaches to teaching data literacy and emerging issues around big data initiatives like precision medicine. The document advocates that librarians play important roles in developing data literacy and managing ethical implications of large-scale data projects.
Digital data is increasingly being used to track and analyze human activities like work, learning, and living. This document discusses how the "datafication" of these areas is redistributing responsibilities between humans and algorithms. It explores issues around accountability, control, and transparency when important decisions are made based on data. The author advocates developing new "literacies" to ensure data practices align with public interests and values, and calls for a posthuman perspective that sees humans and technology as deeply entangled.
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
State of the Art Informatics for Research Reproducibility, Reliability, and...Micah Altman
In March, I had the pleasure of being the inaugural speaker in a new lecture series (http://library.wustl.edu/research-data-testing/dss_speaker/dss_altman.html) initiated by the Libraries at the Washington University in St. Louis Libraries -- dedicated to the topics of data reproducibility, citation, sharing, privacy, and management.
In the presentation embedded below, I provide an overview of the major categories of new initiatives to promote research reproducibility, reliability, and reuse and related state of the art in informatics methods for managing data.
Digital literacy is an important skill for social services practitioners to effectively access and use online information resources. It includes skills like identifying trustworthy online information, communicating digitally, and participating in online communities of practice. Developing digital literacy can help practitioners overcome barriers like limited internet access and preference for verbal communication, and allow for knowledge sharing networks. A pilot project created an online community for practitioners to discuss cases, share evidence, and build research and information literacy skills through supported collaboration.
Yaşar Tonta, “Açık Erişim ve Açık Bilim” (panel sunuşu). Bilimsel Yayınlar ve Açık Erişim Paneli. 26 Mart 2015, Atılım Üniversitesi Kütüphanesi, Ankara
Research Assessment Using Bibliometric and Scientometric Measures: The Good, ...Yasar Tonta
Yaşar Tonta, “Research Assessment Using Bibliometric and Scientometric Measures: The Good, the Bad, and the Ugly” (sunuş). 3rd International Conference on Scientific Communication in the Digital Age, 10-12 March 2015, Kiev, Ukraine.
Bibliometric and scientometric measures like journal impact factors and the h-index are commonly used to evaluate research performance, but they have significant limitations. They should not be used alone to assess the quality of individual researchers or institutions. While useful as collective measures, on their own they fail to account for differences between disciplines and can incentivize behaviors that do not align with quality research. Proper evaluation requires peer review and a holistic approach, not overreliance on quantitative indicators.
Research Data Management: Pushing the Frontiers of Good Research PracticeYasar Tonta
Research Data Management: Pushing the Frontiers of Good Research Practice
Abstract. Research data can be defined as factual records (e.g., digital and textual data, audio and video recordings) that are used as the main source to validate the research findings. Therefore, the US and the EU spend large sums of money to collect, mine, analyze, synthesize and visualize research data to carry out cutting-edge research. The amount of research data increases 30% per annum. Data is the lifeblood of research, and the management of research data constitutes the crucial part of good research practice even though most research data tend to be locked in researchers’ own hard disks or in their proprietary institutional servers. G8 countries have recently acknowledged the importance of research data and endorsed the Open Data Charter (2013). Managing use and re-use of atmospheric research data through the British Atmospheric Data Centre (BADC) provides somewhere between 400% and 1,200% returns on investment (Beagrie & Houghton, 2013, p. 7). Yet, very many countries have yet to establish their research data centers, infrastructure and policies. This talk will provide an overview of major research data management (RDM) issues, underline the importance of effective RDM, explore the benefits of RDM in terms of pushing the frontiers of good research practice.
The document discusses the future of cultural heritage. It defines cultural heritage as the legacy inherited from past generations and maintained in the present for future generations. This includes physical artifacts and intangible attributes of a society. Cultural heritage is sometimes neglected due to definitions of citizenship in nation-states and intentional collective amnesia that contributes to nation-building. The preservation of cultural heritage collaboratively through a game theory approach is discussed as a potential solution.
“Support Programs to Increase the Number of Scientific Publications Using Bib...Yasar Tonta
This document discusses support programs in Turkey that aim to increase the number of scientific publications. It analyzes Turkey's Support Program for International Scholarly Publications, which was previously based on journal impact factors (JIFs) but now uses other metrics like Article Influence Scores. The author examines the impact of recent changes to the program's algorithms in 2013-2014 compared to pre-2012. Through analyzing a stratified sample of journals, the purpose is to understand the motives behind changes and their effects on determining the monetary support provided by TUBITAK.
Everything we do, create and produce such as intellectual and artistic works, performances, etc. can be defined as culture. We own a very rich cultural heritage of the past. Yet, the common cultural heritage that belongs to the humanity continues to be looted and destroyed due to negligence, armed conflicts and wars. Even though destroying cultural heritage is a crime according to international law, common cultural heritage has been harmed to a great extent during the 20th century. The main reason for this has been the ongoing process of building “nation-states” taking place around the world since the beginning of the last century. The cultural heritage of the “other” gets neglected, to say the least, during the building stages of nation-states. But the destruction of cultural heritage that belongs to the “other” is not, if we are to use the concepts of game theory, a “zero-sum game” in which one party wins while the other loses. In fact, it is not even a lose-lose game in which both parties lose. In such conflicts the humanity loses part of its very precious and irreplaceable common cultural heritage forever. In this paper the causes of the destruction of cultural heritage and the question of to whom the neglected cultural heritage belongs are discussed and the economic and social values of cultural heritage are examined by means of the game theory. It is stressed that the future of cultural heritage along with its preservation, sharing and transmission to next generations is the common concern and responsibility of all countries and humanity.
Can Bibliometric and Scientometric Measures be Used to Assess Research Quali...Yasar Tonta
The quality of research output has traditionally been assessed by peer review. Yet, bibliometric and scientometric measures are increasingly being used nowadays to support or even supplant peer review for research assessment. The main reasons for the popularity of such measures are that they can be obtained easily and that they are considered to be more "objective" in comparison to peer review. This paper explores the misuse of bibliometric and scientometric measures to assess research quality, provides recommendations of San Francisco Declaration on Research Assessment and the Leiden Manifesto for Research Metrics, and briefly addresses "responsible metrics" introduced in "The Metric Tide", a report of independent review chaired by James Wilsdon (Wilsdon, et al. 2015).
RDA / Kaynak Tanımlama ve Erişim Standardı’nın Yazma Eserler İçin Uygulanabil...Emine Gür
RDA standardının, Yazma Eserlerin tanımlanması ve erişimi için uygulanabilirliğini görmek
FRBR mantığının yazma eser kataloglarında kullanılabilir olup olmadığını örnekler üzerinden ölçmek.
Yazma Eserlerin tanımlanmasında kullanılacak MARC alanları için öneri sunmak
This document discusses the roles and responsibilities of librarians as datamediaries. It defines data literacy as the ability to understand, work with, and interpret data in context. Key stakeholders for data literacy include learners, educators, researchers, policymakers, and information institutions like libraries. Libraries are well-positioned to respond to the data literacy challenge through community partnerships and building on existing information literacy practices. As datamediaries, libraries can help engage communities and promote social justice through collaborative data literacy education.
This document summarizes a presentation on revisiting data literacy in the big data landscape. It discusses trends in technology, pedagogy, and information literacy as they relate to data. It also explores different conceptions of data literacy from social science and STEM perspectives. Additionally, it examines current approaches to teaching data literacy and emerging issues around big data initiatives like precision medicine. The document advocates that librarians play important roles in developing data literacy and managing ethical implications of large-scale data projects.
Digital data is increasingly being used to track and analyze human activities like work, learning, and living. This document discusses how the "datafication" of these areas is redistributing responsibilities between humans and algorithms. It explores issues around accountability, control, and transparency when important decisions are made based on data. The author advocates developing new "literacies" to ensure data practices align with public interests and values, and calls for a posthuman perspective that sees humans and technology as deeply entangled.
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
State of the Art Informatics for Research Reproducibility, Reliability, and...Micah Altman
In March, I had the pleasure of being the inaugural speaker in a new lecture series (http://library.wustl.edu/research-data-testing/dss_speaker/dss_altman.html) initiated by the Libraries at the Washington University in St. Louis Libraries -- dedicated to the topics of data reproducibility, citation, sharing, privacy, and management.
In the presentation embedded below, I provide an overview of the major categories of new initiatives to promote research reproducibility, reliability, and reuse and related state of the art in informatics methods for managing data.
Digital literacy is an important skill for social services practitioners to effectively access and use online information resources. It includes skills like identifying trustworthy online information, communicating digitally, and participating in online communities of practice. Developing digital literacy can help practitioners overcome barriers like limited internet access and preference for verbal communication, and allow for knowledge sharing networks. A pilot project created an online community for practitioners to discuss cases, share evidence, and build research and information literacy skills through supported collaboration.
Accessing and Using Big Data to Advance Social Science KnowledgeJosh Cowls
This document summarizes a project investigating the use of big data to advance social science knowledge. It introduces the project leaders and discusses data sources and scope. It then focuses on defining big data, discussing how digital data represents real-world objects and phenomena, and the opportunities and limits this presents. Challenges of using big data to gauge public opinion are also examined, such as issues of representativeness, reliability, and replicability. The document concludes by listing project papers on this topic.
From Text to Data to the World: The Future of Knowledge GraphsPaul Groth
Keynote Integrative Bioinformatics 2018
https://docs.google.com/document/d/1E7D4_CS0vlldEcEuknXjEnSBZSZCJvbI5w1FdFh-gG4/edit
Can we improve research productivity through providing answers stemming from knowledge graphs? In this presentation, I discuss different ways of building and combining knowledge graphs.
Slide 2: Etymology: The etymology of the term ‘Big Data’ can be traced back to the mid-1990s, when it was first used by John Mashey to refer to handling and analysis of massive datasets. However, by 2013, ‘Big Data’ was already being declared obsolescent as a meaningful term by some, as it was too wide ranging and vague in definition (e.g. de Goes, 2013).
Side 6: Vagaries: Kitchin argues that it is velocity and these additional key characteristics that set Big Data apart and make them a “disruptive innovation – one that radically changes the nature of data and what can be done with them” (Kitchin, 2014). However, there is no one characteristic profile that all Big Data fit and they can take multiple forms.
Slide 8: Ethics: Several ethical questions have been raised about the scope of data being generated and retained; such as those concerning privacy, informed consent, and protection from harm.
These questions raise wider issues about what kinds of data should be combined and analysed, and the purposes to which the resulting information should be put.
Slide 9: Inequalities: Challenges of inequality have also been posed:
Whose data traces will be analysed? It is likely that only those who are better off will be represented (as they are more likely to use social media, etc.)
Access and use of open data is unlikely to be equally available to everyone due to existing structural inequalities (Eynon, 2013)
Slide 11: What do Big Data actually tell us? Eynon (2013) argues that Big Data is concerned with capturing and examining patterns, and tells us more about what people actually do than about what they say they do. However, this is not sufficient for all kinds of social science research. We need to understand the meanings of behaviours which cannot be inferred simply from tracking specific patterns.
In order that Big Data are used appropriately, we need to ensure understanding of what kinds of research can or cannot be carried out using them. Big Data should not be seen as [a] “technical fix” for research, but should be used to empower, support and facilitate practice and critical research.
The document summarizes research on generational differences and how they relate to technology use. It discusses definitions of generations and characterizations of groups like the Silent Generation, Baby Boomers, Gen X, and Net Generation. It also explores the idea of "digital natives" and how younger generations' upbringing alongside technology has impacted how their brains process information. However, more recent research finds individual factors better predict technology use than generational labels. The document advocates for developing digital literacy skills and rigorous research to understand students' diverse learning experiences.
Science as an Open Enterprise – Geoffrey BoultonOpenAIRE
Science as an Open Enterprise – Geoffrey Boulton, University of Edinburgh.
University of Minho Open Access Seminar & OpenAIRE Interoperability Workshop (7 Feb. 2013) - Session: Open Science, Open Data and Repositorie.
How open data contribute to improving the world. The life science use case. The technical, social, ethical issues.
This was a talk given within the iGEM 2020 programme by the London Imperial College students group (https://2020.igem.org/Team:Imperial_College), in a webinar organised by the SOAPLab group on the topic of Ethics of Automation. Excellent Dr Brandon Sepulvado was the other speaker of the day.
The document discusses the impact of information in society. It covers several topics:
1. How information is used in daily activities through different senses and how the amount of new information created is escalating rapidly.
2. The use of information devices, services, and how information is used by individuals for decision making, resolving uncertainty, and problem solving.
3. The impact of information technology on society from oral societies to print and digital/electronic societies and how this has changed communication and a sense of community.
4. An overview of the field of information studies, how it relates to information systems and computer science, and how it connects systems and technology to context.
Data Science and AI in Biomedicine: The World has ChangedPhilip Bourne
This document discusses the changing landscape of data science and AI in biomedicine. Some key points:
- We are at a tipping point where data science is becoming a driver of biomedical research rather than just a tool. Biomedical researchers need to become data scientists.
- Data science is interdisciplinary and touches every field due to the rise of digital data. It requires openness, translation of findings, and consideration of responsibilities like algorithmic bias.
- Advances like AlphaFold2 show the power of large collaborative efforts combining data, computing resources, engineering, and domain expertise. This points to the need for public-private partnerships and new models of open data sharing.
- The definition of
Thoughts on Knowledge Graphs & Deeper ProvenancePaul Groth
Thinking about the need for deeper provenance for knowledge graphs but also using knowledge graphs to enrich provenance. Presented at https://seminariomirianandres.unirioja.es/sw19/
The "Supporting Students with TEL" is a module within the PGCLT(HE) at Canterbury Christ Church University. This is the presentation that was given to academic staff that puts TEL in an historical and cultural context before looking at what CCCU does now
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
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In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
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Developments in Education for Information: Will ‘Data’ Trigger the Next Wave of Curriculum Changes in LIS Schools?
1. Developments
in
Educa2on
for
Informa2on:
Will
"Data"
Trigger
the
Next
Wave
of
Curriculum
Changes
in
LIS
Schools?
Yaşar
Tonta
Hace&epe
University
Department
of
Informa5on
Management
06800
Beytepe,
Ankara,
Turkey
yunus.hace&epe.edu.tr/~tonta/tonta.html
yasartonta@gmail.com
@yasartonta
ICIML
2015,
November
10-‐13,
2015,
University
of
the
Punjab,
Lahore,
Pakistan
2. Plan
• Introduc5on
• Educa5on
for
Informa5on
(1887-‐-‐
)
• Data
(science,
analy5cs,
mining,
cura5on
.
.
.)
• Data-‐centric
curriculum
changes
in
LIS
educa5on
• Conclusions
3. Introduc5on
• Informa5on
• Data
deluge
• Big
data
• Informa5on
science:
bridge
between
Math
&
Computer
Engineering
• Bioinforma5cs,
ecoinforma5cs,
genomics.
.
.
• Scientomics
(“the
living
existence
is
informa-onal”
(Del
Moral
et
al.,
2011)
5. First
period:
1887-‐1963
• Columbia
U.
School
of
Library
Economy
(1887)
• ALA
(1876),
DDC
(1876),
LC
(1897),
LCSH
(1909)
• Chicago
U.
School
of
Library
Economy
(1926)
offering
Ph.D.
for
the
first
5me
• Library
educa5on
was
largely
based
on
“appren5ceship”
• Focus
was
on
Informa2on
– Courses
on
cataloging,
classifica5on
and
indexing
– Technology
was
limited
6. Second
period:
1964-‐1993
• Informa5on
explosion
following
WWII
• Computers,
bibliographic
databases,
MARC
• Focus
was
on
Informa2on
+
Technology
– Courses
on
programming
languages,
DBMS,
informa5on
retrieval,
etc.
• Name
changes:
UPi&
LS
became
LIS
(1964)
• ADI
(1935)
became
ASIS
(1968)
• Survival
period
(25%
of
LS/LIS
schools
closed
in
this
period)
• “Pandra
syndrome”
(Van
House
&
Su&on,
1996)
7. Third
period:
1994-‐-‐
• Internet,
WWW,
Google,
mobile,
digital
na5ves,
personaliza5on
• Focus
is
on
Informa2on
+
Technology
+
People
– Courses
on
social
media,
informa5on
seeking
models,
personaliza5on
(e.g.,
sharing,
tagging,
ra5ng,
etc.)
• Dropping
“L”
word
(UC
Berkeley
SIMS,
1994;
UMich
SI,
1996)
• iSchools
(2005-‐-‐
)
– Research
on
“the
rela5onship
between
informa5on,
technology
and
people”
– “learning
and
understanding
the
role
of
informa5on
in
human
endeavors”
– “I-‐den5ty
crisis”
(Cronin,
2005)
9. Research
interests
at
iSchools
Co-‐word
map
of
the
research
interests
at
iSchools.
Source:
Holmberg,
Tsou
and
Sugimoto
(2013)
• computer
informa5on
(incl.
HCI
&
compu5ng
informa5on,
e.g.,
informa5cs);
• informa5on
retrieval
and
data
mining;
• social
media
and
informa5on
systems;
• educa5on
and
informa5on
technology;
• informa5on
seeking
and
digital
libraries;
• libraries
and
library
services;
• data
analy5cs
and
compu5ng
10. iSchools
Faculty
PhDs
(N=769)
Computer
Science
Informa5on
Librarianship
Soc.
&
Behav.
Sci.
Mgmt
&
Poli5cs
Educa5on
Humani5es
Communica5on
11%
30%
9%
9%
8%
7%
5%
Source:
Wiggins
and
Sawyer
(2012,
p.
13;
chart
is
based
on
figures
in
the
first
column
of
Table
3)
10%
11. Next
.
.
.
• Internet
of
Things
(IoT)
• Cloud
compu5ng
• “Industry
4.0”:
“a
collec5ve
term
for
technologies
and
concepts
of
value
chain
organiza5on”
which
draws
together
Cyber-‐
Physical
systems,
IoT,
and
cloud
compu5ng
(h&ps://en.wikipedia.org/wiki/
Industry_4.0)
Data
pyramid.
Source:
Gray
(2009,
p.
xxvi)
12. Next
.
.
.
(2)
• Data
intensive
science
(SKA
generates
700TB
of
data
per
second)
• Big
data:
“high-‐volume,
high-‐velocity
and/or
high-‐
variety
informa5on
assets
that
demand
cost-‐
effec5ve,
innova5ve
forms
of
processing
that
enable
enhanced
insight,
decision-‐making,
and
process
automa5on”
(h&p://www.gartner.com/it-‐glossary/big-‐data)
• Merger
of
digital
archives
and
science-‐compu5ng
facili5es
(Ma&mann,
2013,
p.
474)
13. Data
X
• Data
science:
“the
transforma-on
of
data
using
mathema-cs
and
sta-s-cs
into
valuable
insights,
decisions,
and
products”
(Foreman,
2014,
p.
xiv)
• Data
analy5cs
• Data
mining
• Data
cura5on
• .
.
.
14. Research
Data
Management
(RDM):
“a
wicked
problem”?
• “.
.
.
is
one
that
is
unique
and
highly
complex
whose
defini5on
itself
is
disputed
by
those
involved,
and
whose
solu5on
is
likely
to
remain
unclear”
(Cox,
Pinfield
&
Smith,
2014,
p.
2).
• open
data
and
open
science,
big
data,
disciplinary
data
diversity
(Lyon
&
Brenner,
2015,
p.
112).
• Need
for
data
scien5sts,
data
curators,
data
miners
.
.
.
• Yet,
few
LIS
schools
have
data
science/data
cura5on
programs/courses
(UofAZ,
UCB,
UIUC,
UNC-‐CH,
SJSU)
15. Conclusions
• So,
will
“data”
trigger
curricular
changes
in
LIS
schools?
• Yes,
it
already
has:
One
third
of
LIS
schools
offer
data
cura5on
courses
• iSchools
specialize
in
informa5on
retrieval
and
data
mining,
data
analy5cs
and
compu5ng,
and
informa5cs
• Data
science,
big
data
analy5cs
and
data
mining
programs
exist
mostly
in
non-‐LIS
schools
• Too
early
to
say
if
the
“D”
word
(Data
Science)
will
be
added
to
the
LIS
schools’
names
Source:
h&p://www.informa5onweek.com/big-‐data/big-‐data-‐analy5cs/
big-‐data-‐analy5cs-‐masters-‐degrees-‐20-‐top-‐programs/d/d-‐id/1108042?
16. References
• Cox,
A.M.,
Pinfield,
S.
&
Smith,
J.
(2014).
Moving
a
brick
building:
UK
libraries
coping
with
research
data
management
as
a
‘wicked’
problem.
Journal
of
Librarianship
and
Informa-on
Science,
1–15.
h&p://
lis.sagepub.com/content/early/2014/05/13/0961000614533717.full.pdf+html.
• Cronin,
B.
(2005).
An
I-‐den5ty
crisis?
The
informa5on
schools
movement.
Interna-onal
Journal
of
Informa-on
Management,
25,
363–365.
• Del
Moral,
R.,
González,
M.,
Navarro,
J.
&
Marijuán,
P.C.
(2011).
From
genomics
to
scientomics:
expanding
the
bioinforma5on
paradigm.
Informa-on,
2(4):
651-‐671,
DOI:
10.3390/info2040651.
• Foreman,
J.W.
(2014).
Data
smart:
Using
data
science
to
transform
informa-on
into
insight.
Indianapolis,
IN:
Wiley.
• Gray,
J.
(2009).
Jim
Gray
on
eScience:
A
transformed
scien5fic
method.
In
T.
Hey,
S.
Tansley
&
K.
Tolle
(Eds.).
The
fourth
paradigm:
Data
intensive
scien-fic
discovery
(pp.
xix-‐xxxii).
Redmond,
WA:
Microsoy
Research.
h&p://
research.microsoy.com/en-‐us/collabora5on/fourthparadigm/4th_paradigm_book_jim_gray_transcript.pdf.
• Holmberg,
K.,
Tsou,
A.
&
Sugimoto,
C.R.
(2013).
The
conceptual
landscape
of
iSchools:
examining
current
research
interests
of
faculty
members.
Informa-on
Research,
18(3)
paper
C32.
h&p://Informa5onR.net/ir/18-‐3/colis/
paperC32.html.
• Lyon,
L.
&
Brenner,
A.
(2015).
Bridging
the
data
talent
gap:
Posi5oning
the
iSchool
as
an
agent
for
change.
Interna-onal
Journal
of
Digital
Cura-on,
10(1):
111-‐122.
• Ma&mann,
C.A.
(2013,
January
24).
A
vision
for
data
science.
Nature,
493:
473-‐475.
• Van
House,
N.A.
&
Su&on,
S.A.
(1996).
The
Panda
Syndrome:
An
ecology
of
LIS
educa5on.
Journal
of
Educa-on
for
Library
and
Informa-on
Science,
37,
131-‐147.
h&p://faculty.washington.edu/sasu&on/panda.htm.
• Wiggins,
A.
&
Sawyer,
S.
(2012).
Intellectual
diversity
and
the
faculty
composi5on
of
iSchools.
Journal
of
the
American
Society
for
Informa-on
Science
and
Technology,
63,
8-‐21.
• Yu,
C.
&
Baeg,
J.H.
(2012).
The
evolu5on
of
a
discipline:
A
fractal
representa5on
of
informa5on
science.
In
Proceedings
of
iConference
2012
February
7–10,
2012,
Toronto,
Ontario,
Canada
(pp.
548-‐549).
New
York:
ACM.
17. Developments
in
Educa2on
for
Informa2on:
Will
"Data"
Trigger
the
Next
Wave
of
Curriculum
Changes
in
LIS
Schools?
Yaşar
Tonta
Hace&epe
University
Department
of
Informa5on
Management
06800
Beytepe,
Ankara,
Turkey
yunus.hace&epe.edu.tr/~tonta/tonta.html
yasartonta@gmail.com
@yasartonta
ICIML
2015,
November
10-‐13,
2015,
University
of
the
Punjab,
Lahore,
Pakistan