The best way to publish and share research data is with a research data repository. A repository is an online database that allows research data to be preserved across time and helps others find it.
What is Data Commons and How Can Your Organization Build One?Robert Grossman
This is a talk that I gave at the Molecular Medicine Tri Conference on data commons and data sharing to accelerate research discoveries and improve patient outcomes. It also covers how your organization can build a data commons using the Open Commons Consortium's Data Commons Framework and the University of Chicago's Gen3 data commons platform.
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
What is Data Commons and How Can Your Organization Build One?Robert Grossman
This is a talk that I gave at the Molecular Medicine Tri Conference on data commons and data sharing to accelerate research discoveries and improve patient outcomes. It also covers how your organization can build a data commons using the Open Commons Consortium's Data Commons Framework and the University of Chicago's Gen3 data commons platform.
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
Leveraging Open Source Technologies to Enable Scientific Archiving and Discovery; Steve Hughes, NASA; Data Publication Repositories
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
IMPLEMENTATION OF DIGITAL LIBRARY SYSTEM BY USING DSPACE & ANDROID APPS AT AM...IAEME Publication
Developing countries face serious problems on building and using digital libraries
(DL) due to low computer and Internet penetration rates, lack of financial resources,
etc. Thus, since mobile phones are much more used than computers in these countries,
they might be a good alternative for accessing DL. Moreover, in the developed world
there has been an exponential growth on the usage of mobile phones for data traffic,
establishing a good ground for accessing DL on mobile devices. This paper presents a
design proposal for making DSpace-based digital libraries accessible on mobile
phones. Since DSpace is a popular free and open source DL system used around the
world, making it accessible through mobile devices might contribute for improving the
global accessibility of scientific and academic publications.
Nelson Piedra , Janneth Chicaiza
and Jorge López, Universidad Técnica Particular de Loja, Edmundo
Tovar, Universidad Politécnica de Madrid,
and Oscar Martínez, Universitas
Miguel Hernández
Explore the advantages of using linked data with OERs.
Opening talk at the "Interdisciplinary Data Resources to Address the Challenges of Urban Living” Workshop at the Urban Big Data Centre, University of Glasgow, 4 April 2016
New forms of data for the social sciences: Smarter cities, more efficient organisations, and healthier communities. Wednesday 3rd November 2015, UCL, London, United Kingdom
As the volume and complexity of data from myriad Earth Observing platforms, both remote sensing and in-situ increases so does the demand for access to both data and information products from these data. The audience no longer is restricted to an investigator team with specialist science credentials. Non-specialist users from scientists from other disciplines, science-literate public, to teachers, to the general public and decision makers want access. What prevents them from this access to resources? It is the very complexity and specialist developed data formats, data set organizations and specialist terminology. What can be done in response? We must shift the burden from the user to the data provider. To achieve this our developed data infrastructures are likely to need greater degrees of internal code and data structure complexity to achieve (relatively) simpler end-user complexity. Evidence from numerous technical and consumer markets supports this scenario. We will cover the elements of modern data environments, what the new use cases are and how we can respond to them.
FAIRy stories: the FAIR Data principles in theory and in practiceCarole Goble
https://ucsb.zoom.us/meeting/register/tZYod-ippz4pHtaJ0d3ERPIFy2QIvKqjwpXR
FAIRy stories: the FAIR Data principles in theory and in practice
The ‘FAIR Guiding Principles for scientific data management and stewardship’ [1] launched a global dialogue within research and policy communities and started a journey to wider accessibility and reusability of data and preparedness for automation-readiness (I am one of the army of authors). Over the past 5 years FAIR has become a movement, a mantra and a methodology for scientific research and increasingly in the commercial and public sector. FAIR is now part of NIH, European Commission and OECD policy. But just figuring out what the FAIR principles really mean and how we implement them has proved more challenging than one might have guessed. To quote the novelist Rick Riordan “Fairness does not mean everyone gets the same. Fairness means everyone gets what they need”.
As a data infrastructure wrangler I lead and participate in projects implementing forms of FAIR in pan-national European biomedical Research Infrastructures. We apply web-based industry-lead approaches like Schema.org; work with big pharma on specialised FAIRification pipelines for legacy data; promote FAIR by Design methodologies and platforms into the researcher lab; and expand the principles of FAIR beyond data to computational workflows and digital objects. Many use Linked Data approaches.
In this talk I’ll use some of these projects to shine some light on the FAIR movement. Spoiler alert: although there are technical issues, the greatest challenges are social. FAIR is a team sport. Knowledge Graphs play a role – not just as consumers of FAIR data but as active contributors. To paraphrase another novelist, “It is a truth universally acknowledged that a Knowledge Graph must be in want of FAIR data.”
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
The presentation for the W3C Semantic Web in Health Care and Life Sciences community group by Slava Tykhonov, DANS-KNAW, the Royal Netherlands Academy of Arts and Sciences (October 2020). The recording is available https://www.youtube.com/watch?v=G9oiyNM_RHc
Leveraging Open Source Technologies to Enable Scientific Archiving and Discovery; Steve Hughes, NASA; Data Publication Repositories
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
IMPLEMENTATION OF DIGITAL LIBRARY SYSTEM BY USING DSPACE & ANDROID APPS AT AM...IAEME Publication
Developing countries face serious problems on building and using digital libraries
(DL) due to low computer and Internet penetration rates, lack of financial resources,
etc. Thus, since mobile phones are much more used than computers in these countries,
they might be a good alternative for accessing DL. Moreover, in the developed world
there has been an exponential growth on the usage of mobile phones for data traffic,
establishing a good ground for accessing DL on mobile devices. This paper presents a
design proposal for making DSpace-based digital libraries accessible on mobile
phones. Since DSpace is a popular free and open source DL system used around the
world, making it accessible through mobile devices might contribute for improving the
global accessibility of scientific and academic publications.
Nelson Piedra , Janneth Chicaiza
and Jorge López, Universidad Técnica Particular de Loja, Edmundo
Tovar, Universidad Politécnica de Madrid,
and Oscar Martínez, Universitas
Miguel Hernández
Explore the advantages of using linked data with OERs.
Opening talk at the "Interdisciplinary Data Resources to Address the Challenges of Urban Living” Workshop at the Urban Big Data Centre, University of Glasgow, 4 April 2016
New forms of data for the social sciences: Smarter cities, more efficient organisations, and healthier communities. Wednesday 3rd November 2015, UCL, London, United Kingdom
As the volume and complexity of data from myriad Earth Observing platforms, both remote sensing and in-situ increases so does the demand for access to both data and information products from these data. The audience no longer is restricted to an investigator team with specialist science credentials. Non-specialist users from scientists from other disciplines, science-literate public, to teachers, to the general public and decision makers want access. What prevents them from this access to resources? It is the very complexity and specialist developed data formats, data set organizations and specialist terminology. What can be done in response? We must shift the burden from the user to the data provider. To achieve this our developed data infrastructures are likely to need greater degrees of internal code and data structure complexity to achieve (relatively) simpler end-user complexity. Evidence from numerous technical and consumer markets supports this scenario. We will cover the elements of modern data environments, what the new use cases are and how we can respond to them.
FAIRy stories: the FAIR Data principles in theory and in practiceCarole Goble
https://ucsb.zoom.us/meeting/register/tZYod-ippz4pHtaJ0d3ERPIFy2QIvKqjwpXR
FAIRy stories: the FAIR Data principles in theory and in practice
The ‘FAIR Guiding Principles for scientific data management and stewardship’ [1] launched a global dialogue within research and policy communities and started a journey to wider accessibility and reusability of data and preparedness for automation-readiness (I am one of the army of authors). Over the past 5 years FAIR has become a movement, a mantra and a methodology for scientific research and increasingly in the commercial and public sector. FAIR is now part of NIH, European Commission and OECD policy. But just figuring out what the FAIR principles really mean and how we implement them has proved more challenging than one might have guessed. To quote the novelist Rick Riordan “Fairness does not mean everyone gets the same. Fairness means everyone gets what they need”.
As a data infrastructure wrangler I lead and participate in projects implementing forms of FAIR in pan-national European biomedical Research Infrastructures. We apply web-based industry-lead approaches like Schema.org; work with big pharma on specialised FAIRification pipelines for legacy data; promote FAIR by Design methodologies and platforms into the researcher lab; and expand the principles of FAIR beyond data to computational workflows and digital objects. Many use Linked Data approaches.
In this talk I’ll use some of these projects to shine some light on the FAIR movement. Spoiler alert: although there are technical issues, the greatest challenges are social. FAIR is a team sport. Knowledge Graphs play a role – not just as consumers of FAIR data but as active contributors. To paraphrase another novelist, “It is a truth universally acknowledged that a Knowledge Graph must be in want of FAIR data.”
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
The presentation for the W3C Semantic Web in Health Care and Life Sciences community group by Slava Tykhonov, DANS-KNAW, the Royal Netherlands Academy of Arts and Sciences (October 2020). The recording is available https://www.youtube.com/watch?v=G9oiyNM_RHc
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.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
1. Dataset Sources
Repositories data
Supervised by
Asst. Prof. Dr. Ahmed Aljanabi
Department of Computer Science
Faculty of CS and Mathematics
University of Kufa
Muntathar manhar muhsin
Ph D in Computer Science
2. Introduction
The best way to publish and share research data is with a research
data repository. A repository is an online database that allows
research data to be preserved across time and helps others find it.
The aim of the module is to introduce to the students the topics
that include:
1. Lincoln Centre for Autonomous Systems (LCAS)
2. Mendeley Data
3. Nasdaq Data Link
4. Harvard Dataverse
5. Harvard Dataverse
6. Dryad Digital Repository
7. Network Repository
3. Lincoln Centre for Autonomous Systems
(LCAS)
Research center based at the University of Lincoln in UK.
Cross-Disciplinary in Robotics Research.
Specializes in technologies for perception, learning, decision-making,
control, and interaction in autonomous systems, especially mobile
robots and robotic manipulators,
and the integration of these capabilities in application domains
including
Agri-food,
Healthcare,
Intelligent transportation,
Logistics,
Nuclear robotics,
Service robotics, and
Space robotics.
The website : https://lcas.lincoln.ac.uk/wp/
4. Lincoln Centre for Autonomous Systems
(LCAS)
The link to access the website is
https://lcas.lincoln.ac.uk/wp/
Website interface:
5. Mendeley Data
Mendeley Data is a free and secure cloud-based
communal repository.
Research data can be found in fields, including to:
Natural Sciences and Mathematics
Engineering
Life sciences
Medical and health sciences
Social sciences
Humanities.
The website : https://data.mendeley.com/
6.
7. Nasdaq Data Link
Nasdaq Data Link A premier source for financial, economic
and alternative datasets.
Data Type
Prices & Volumes
Estimates
Fundamentals
Corporate Actions
Sentiment
Derived Metrics
National Statistics
Technical Analysis
Others
The website :
8.
9. figshare
Open access data repository.
Datasets: images, and videos.
Figshare allows researchers to upload any file format
and assigns a digital object identifier (doi) for
citations.
Free accounts on figshare can upload files of up to 5gb
and get 20gb of free storage.
fields:
Software and Code
Models and Simulations
Machine Learning and Data Science
The website : https://figshare.com/
10.
11. Harvard Dataverse
The harvard dataverse repository is a free data repository
open to all researchers from any discipline, both inside and
outside of the harvard community,
Can share, archive, cite, access, and explore research data.
You can open your data to the general public, or restrict
access and define customizable terms of use. When you
publish your data, you automatically get a standard data
citation with a digital object identifier (DOI).
Powered by the open-source web application dataverse,
developed by the insitute of quantitative social science at
harvard.
Is free and has a limit of 2.5 GB per file and 10 GB per
dataset.
Website source : https://dataverse.harvard.edu/
12.
13. Dryad Digital Repository
Dryad is a curated general-purpose repository that
makes data discoverable, freely reusable, and citable.
Most types of files can be submitted (e.g., text,
spreadsheets, video, photographs, software code)
including compressed archives of multiple files.
Since a guiding principle of Dryad is to make its
contents freely available for research and educational
use, there are no access costs for individual users or
institutions. Instead, Dryad supports its operation by
charging a $120US fee each time data is published.
Website source : https://datadryad.org/stash
14.
15. Network Repository
The first interactive data and network data repository with real-time
visual analytics. Network repository is not only the first interactive
repository, but also the largest network repository with thousands of
donations in 30+ domains (from biological to social network
data). This large comprehensive collection of network graph data is
useful for making significant research findings as well as
benchmark network data sets for a wide variety of applications and
domains (e.g., network science, bioinformatics, machine learning,
data mining, physics, and social science) and includes relational,
attributed, heterogeneous, streaming, spatial, and time series
network data as well as non-relational machine learning data. All
graph data sets are easily downloaded into a standard consistent
format. We also have built a multi-level interactive graph analytics
engine that allows users to visualize the structure of the network
data as well as macro-level graph data statistics as well as
important micro-level network properties of the nodes and edges.
Check out GraphVis: the interactive visual network mining and
machine learning tool.
Website source : https://networkrepository.com/
