Materials for introduction to adaptive learning and learning analytics as well as efforts of interoperability standardization. This slides treats brief concept of adaptive learning, reference model of learning analytics, data APIs for learning analytics, and topic list of standardization community (ISO/IEC JTC1 SC36).
Slides | Research data literacy and the libraryColleen DeLory
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Sarah Wright, Christian Lauersen and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
Materials for introduction to adaptive learning and learning analytics as well as efforts of interoperability standardization. This slides treats brief concept of adaptive learning, reference model of learning analytics, data APIs for learning analytics, and topic list of standardization community (ISO/IEC JTC1 SC36).
Slides | Research data literacy and the libraryColleen DeLory
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Sarah Wright, Christian Lauersen and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
With approximately 1.x years of delay to the US, the term "Data Science" is also gaining speed in Europe. We see more and more job openings for- and business cards of data scientists, new events dedicated to the topic and an increased demand in related education literally every month. In response to this trend, Zurich University of Applied Sciences founded the ZHAW Data Science Laboratory (Datalab) last year.
This talk is to give an updated overview of Data Science in Europe by the example of the Datalab's activities in Switzerland. After a definition and classification of the field, a presentation of real technical projects sets the stage for what Data Science looks like here, offside of internet behemoths and big data clichés. Then, conclusions on the state of the art at least in Switzerland are drawn from evaluating the recent "1st Swiss Workshop on Data Science" event and ZHAW's professional education programme "DAS in Data Science".
With the help of the audience during the subsequent discussion, these results can eventually be extrapolated to the wider European community.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
This slide was presented in International the 2015 Conference on Education Research.
I aggregated several my other partial slides and reports to describe adaptive learning model pertaining to concept of learning analytics as well as LOD for curriculum standards and digital resources. There is short introduction to the project of ISO/IEC 20748 Learning analytics interoperability - Part 1: Reference model.
Machine learning for data management -Findings and implications for data management:
Machine learning has significant potential to improve data quality, but will at the same time disrupt data management processes and practices.
Data management processes will be redesigned:
- Highly repetitive and simple cases will be automated by machine, but human needs to intervene in more difficult and complex cases
--> Machine takes over prediction
--> Human judges output and confirms
There are some important prerequisites:
- Machine learning techniques depend on high quality data-->(Garbage in – garbage out)
- New roles and skills are required to explore and productize machine learning
Introduction to research data managementMichael Day
Slides from a presentation given at the JIBS User Group / RLUK joint event "Demystifying research data: don't be scared, be prepared" held at the SOAS Brunei Gallery, London, 17 July 2012.
Special Issue on: "Advances in Neural Network Models and Algorithms for Data ...gerogepatton
Recent advances in storage, hardware, information technology, communication, and networking have resulted in increasingly large and complex heterogeneous data. This has powered the demand to extract useful and actionable insights from data in an automatic, reliable and scalable way. Neural networks are widely used learning machines with powerful learning ability and adaptability, which have achieved remarkable performance in the data analytical tasks, such as computer vision, face/speech recognition, video surveillance, document summarization, distributed and/or real-time resource allocation, etc. Recently there is a surge of research activities devoted to theoretical development of scalable and robust learning models on deep neural networks, neurodynamics, combinatorial optimization techniques.
ANDS Webinar. Data Management Policies and PeopleJulia Gross
Over nine months in 2011 Edith Cowan University Library successfully completed an ANDS funded Seeding the Commons project. The project team were tasked with developing a data management plan and policy, identifying and describing a selection of datasets and producing training for researchers at the university. As part of the project, the library team learned new skills, including conducting data interviews, describing data using RIF-CS, and understanding the many issues surrounding the management of research data. In this webinar Constance and Julia will discuss how they approached the project, the lessons learned along the way, and how the benefits are being taken forward in 2012
This presentation was provided by Kristi Holmes of Northwestern University during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
Fairification experience clarifying the semantics of data matricesPistoia Alliance
This webinar presents the Statistics Ontology, STATO which is a semantic framework to support the creation of standardized analysis reports to help with review of results in the form of data matrices. STATO includes a hierarchy of classes and a vocabulary for annotating statistical methods used in life, natural and biomedical sciences investigations, text mining and statistical analyses.
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 effect of technology-organization-environment on adoption decision of bi...IJECEIAES
Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organizationenvironment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
With approximately 1.x years of delay to the US, the term "Data Science" is also gaining speed in Europe. We see more and more job openings for- and business cards of data scientists, new events dedicated to the topic and an increased demand in related education literally every month. In response to this trend, Zurich University of Applied Sciences founded the ZHAW Data Science Laboratory (Datalab) last year.
This talk is to give an updated overview of Data Science in Europe by the example of the Datalab's activities in Switzerland. After a definition and classification of the field, a presentation of real technical projects sets the stage for what Data Science looks like here, offside of internet behemoths and big data clichés. Then, conclusions on the state of the art at least in Switzerland are drawn from evaluating the recent "1st Swiss Workshop on Data Science" event and ZHAW's professional education programme "DAS in Data Science".
With the help of the audience during the subsequent discussion, these results can eventually be extrapolated to the wider European community.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
This slide was presented in International the 2015 Conference on Education Research.
I aggregated several my other partial slides and reports to describe adaptive learning model pertaining to concept of learning analytics as well as LOD for curriculum standards and digital resources. There is short introduction to the project of ISO/IEC 20748 Learning analytics interoperability - Part 1: Reference model.
Machine learning for data management -Findings and implications for data management:
Machine learning has significant potential to improve data quality, but will at the same time disrupt data management processes and practices.
Data management processes will be redesigned:
- Highly repetitive and simple cases will be automated by machine, but human needs to intervene in more difficult and complex cases
--> Machine takes over prediction
--> Human judges output and confirms
There are some important prerequisites:
- Machine learning techniques depend on high quality data-->(Garbage in – garbage out)
- New roles and skills are required to explore and productize machine learning
Introduction to research data managementMichael Day
Slides from a presentation given at the JIBS User Group / RLUK joint event "Demystifying research data: don't be scared, be prepared" held at the SOAS Brunei Gallery, London, 17 July 2012.
Special Issue on: "Advances in Neural Network Models and Algorithms for Data ...gerogepatton
Recent advances in storage, hardware, information technology, communication, and networking have resulted in increasingly large and complex heterogeneous data. This has powered the demand to extract useful and actionable insights from data in an automatic, reliable and scalable way. Neural networks are widely used learning machines with powerful learning ability and adaptability, which have achieved remarkable performance in the data analytical tasks, such as computer vision, face/speech recognition, video surveillance, document summarization, distributed and/or real-time resource allocation, etc. Recently there is a surge of research activities devoted to theoretical development of scalable and robust learning models on deep neural networks, neurodynamics, combinatorial optimization techniques.
ANDS Webinar. Data Management Policies and PeopleJulia Gross
Over nine months in 2011 Edith Cowan University Library successfully completed an ANDS funded Seeding the Commons project. The project team were tasked with developing a data management plan and policy, identifying and describing a selection of datasets and producing training for researchers at the university. As part of the project, the library team learned new skills, including conducting data interviews, describing data using RIF-CS, and understanding the many issues surrounding the management of research data. In this webinar Constance and Julia will discuss how they approached the project, the lessons learned along the way, and how the benefits are being taken forward in 2012
This presentation was provided by Kristi Holmes of Northwestern University during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
Fairification experience clarifying the semantics of data matricesPistoia Alliance
This webinar presents the Statistics Ontology, STATO which is a semantic framework to support the creation of standardized analysis reports to help with review of results in the form of data matrices. STATO includes a hierarchy of classes and a vocabulary for annotating statistical methods used in life, natural and biomedical sciences investigations, text mining and statistical analyses.
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 effect of technology-organization-environment on adoption decision of bi...IJECEIAES
Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organizationenvironment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
Pleasure to present this introduction to IBM cognitive business to business leaders in Hamilton, Ontario. Covers: what cognitive computing is, how businesses are using it to their advantage, and steps to getting started. Includes links to videos "IBM Today" and "IBM Woodside Energy".
Adapted from Nancy Pearson, VP Cognitive Business Marketing "Intelligent enterprise: Cognitive Business" presentation from World of Watson Oct 2016.
Preparing the next generation for the cognitive era - NFAIS KeynoteSteven Miller
Keynote address at NFAIS 2016 in Philadelphia PA on February 21st 2016 focused on how the Cogntive Era is transforming our lives, creating new careers, and inspiring innovation.
Evolution of Enterprise Content ManagementJoel Oleson
Enterprise Content Management has changed dramatically over the past 20 years. It’s no longer about the repository. In this day and age it’s about digital experience, mobility, flexibility, choice and automation. In this session we’ll explore the evolution of ECM and what the future of productivity looks like in the Future of SharePoint & Office 365.
Slides for Michael J. Salvo's introduction to session C4, Experience Architecture: A Possible Future for Scholarship & Curriculum with Liza Potts, Patricia Sullivan, Cheryl Geisler, Jennifer Sano-Francini, and Douglas Walls.
User Experience: The good, the bad, and the ugly IxDA Chicago
General Assembly and IxDA paired up to bring in the experts to discuss their thoughts on the subject in an effort to teach others how to do more of what we love in UX/UI and to stop doing what we don't.
We discussed awesome and not-so-great examples of user experience and user interface design, straight from the experts themselves.
This presentation was provided by Leslie D. McIntosh, during the eighth and final session of our Spring 2023 NISO Training Series "Quality Assurance of Data Sets." The class focused on Data Quality Management, and was held June 15, 2023.
SGCI Science Gateways: Harnessing Big Data and Open Data 03-19-2017Sandra Gesing
The importance of Big Data and Open Data to achieve scientific advancements in precision medicine is beyond doubt and evident in many different projects and initiatives such as the Precision Medicine Initiative (All of Us), ICTBioMed, NCIP Hub, 100K Genomics England Project, NIH Cancer Moonshot, and the Million Veterans Program. In April 2013, McKinsey & Company proclaimed that Big Data has the ability to revolutionize pharmaceutical research and development within clinical environments, by using data for better informed decision making and targeting the diverse user roles including physicians, consumers, insurers, and regulators. Companies from a wide spectrum such as Oracle Health Sciences, Google, and Data4Cure build solutions that help address efficient and secure data sharing with the patient or clinician in mind. Open data can be maintained and shared by patient communities such as PatientsLikeMe.com and build an invaluable resource for further data mining.
Even with all these advances there are still challenges to address including a recent Precision Medicine World Conference announcement in November 2016: “We are missing easy-to-use solutions to share patient data.” Science gateways are a solution to fill the gap and help form by definition end-to-end solutions – web-based, mobile or desktop applications - that provide intuitive access to advanced resources and allow researchers to focus on tackling today’s challenging science questions. Science Gateways abstract the complex underlying computing and data infrastructure as far as feasible and desired by the stakeholder and can be tailored to different target groups with diverse backgrounds, demands, and technical knowledge.
Science Gateways have existed for over a decade and a wide variety of frameworks and APIs have been developed to support the efficient creation of science gateways and ease the implementation of connections to Cloud infrastructures and distributed data on a large scale. The importance of science gateways has been recognized by NSF by funding the creation of a Science Gateways Community Institute (SGCI) to serve the community with free resources, services, experts, and ideas for creating and sustaining science gateways. To achieve this goal, the SGCI serves the community with five areas that have diverse foci and which also closely interact: Incubator, Extended Developer Support, Scientific Software Collaborative, Community Engagement and Exchange and Workforce Development.
The Institute is technology-agnostic and serves the community by offering a wide variety of services and using technologies that are the best fitting solution for the use case. Gateways allow for precision medicine to be more efficiently developed or adapted by lowering the barriers to data sharing and Big Data analysis.
Data Management and Broader Impacts: a holistic approachMegan O'Donnell
[please download to view at full resolution]
The National Science Foundation’s (NSF) Broader Impacts Criterion asks scientists to frame their research beyond “science for science’s sake.” Examining data and data management through a Broader Impacts lens highlights the benefits of good data management, data management plans (DMPs), and strengthens the argument for better Data Information Literacy (DIL) in the sciences.
Slides from Monday 30 July - Data in the Scholarly Communications Life Cycle Course which is part of the FORCE11 Scholarly Communications Institute.
Presenter - Natasha Simons
Preparing the next generation for the cognitive eraSteven Miller
Short version of my latest presentation used during a panel session at the ASA Research Symposium at Southern Illinois University Carbondale on November 21st 2015
The data economy is driving an incredible rate of innovation. New job roles are emerging, existing job roles are evolving. While much of the hype has focused on the data scientist role it's just one of many.
Data Curation: Retooling the Existing WorkforceSteven Miller
My presentation given at the Symposium on Digital Curation in the Era of Big Data: Career Opportunities and Educational Requirements held at the National Academy of Sciences on July 19, 2012.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Driving Data and Cognitive Sciences Curriculum at the Nexus of Society, Policy, and Ethics
1. Driving Data and Cognitive Sciences
Curriculum at the Nexus of Society,
Policy, and Ethics
Nitesh Chawla, PhD
Frank M. Freimann Professor of Computer Science & Engg.
Director, iCeNSA
@nvchawla
2. Data and cognitive science is …
...multidisciplinary
… curiosity + science
...a process
…about augmenting human ability and intelligence
… about deriving value
3. Brendan Tierney, 2012. Source: kdnuggets Steven Geringer, 2014. Source: kdnuggets
Data Science Venn Diagrams Galore
8. Building and managing big data infrastructure is hard
Processing and managing data at scale is hard
Modeling and analytics is hard
Discovering and communicating new game-changing insights
is hard
But it is even harder to actually drive deep
understanding and action.
9. Insights don’t exist in a vacuum
e.g.
• (Class-aware) Rules
X = Rule(supp, conf) → Y =f: ?
10. X = Rule(supp, conf) → Y =f: Dollar value of prediction
Original:
11. X = Rule(supp, conf) → Y =f: Insurance Profitability
Original:
12. Net Present Value of Analytics
What’s the best
choice?
What’s the best
choice given a
fixed budget?
Which predictive
model goes best
with which
external data
strategy?
13. The Nation must promote ethics in Big Data by ensuring that technologies
do not propagate errors or disadvantage certain groups, either explicitly or
implicitly. Efforts to explore ethics-sensitive Big Data research would enable
stakeholders to better consider values and societal ethics of Big Data
innovation alongside utility, risk, and cost.” White House Big Data
Strategic Research Plan.
“To reap the societal benefits of AI systems, we will first need to trust it.”
Learning to trust artificial intelligence systems, IBM.
14. “Scientific rigor and transparency in conducting biomedical research is key
to the successful application of knowledge toward improving health
outcomes.” National Institute of Health.
“The confidence in and reliability of science and engineering research is
truly invaluable and especially so at the National Science Foundation (NSF).
“Reproducibility”, “replicability” and “robustness” are broad terms that
encompass research aspects that relate to confidence in published
findings.” NSF.
17. Evolving Data and Cognitive Science
Curriculum
• Not just a product or project, but a process and
experience
• Encapsulate data science process with elements of design
and systems thinking
– Example: participatory design, interactions among elements
• Experimentation and validation
• Interpretability and comprehensibility
• Enabling action
18. Evolving Data and Cognitive Science
Curriculum
• Extends beyond data science methods to include social
engagement and responsibility, ethics, trust and safety, and
policy
• Reproducibility, rigor, repeatability, and responsibility
• Community and society engagement
• Identify, communicate, and address bias and sources of
distrust in data-enabled science
• Increased public scientific literacy and public engagement
In this era of big data, augmentation of human capabilities, and increased
automation, these particular issues have never been so important!
19. Augmenting Human Intelligence and Creativity: The
Evolving Paradigm
Ethics,
Policy,
Law
Reproducibility and
Rigor
Methods,
Algorithms,
Tools, System
Design and
Systems
thinking
Society
20. Augmenting Human Intelligence and Creativity:
Evolving Paradigm
Ethics,
Policy,
Law
Reproducibility
and Rigor
Methods,
Algorithms,
Tools,
System
Design and
Systems
thinking
Society
Source: IBM Smarter Planet
21. Vulnerability
Readiness
0 .1 .2 .
3
.4 .5 .6 .7 .8
.2
.3
.4
.5
.6
.1
Cote d’Ivoire
Laos
Guinea
Solomon
IslandsRwanda
Philippine
s
Mongolia
Poland
Russia
Georgia
C
I
V
S
L
B
R
W
A
G
I
N
L
A
O
P
H
L
M
N
G
G
E
O
P
O
LR
U
S
Invasive Species Climate Change Adaptation