Slides for the webinar: Access the world’s research outputs through the CORE API, 13th January 2022.
Link to the webinar video: https://youtu.be/acRLJNpq4W4
In this webinar, we present our new CORE APIv3.
Presenters Petr Knoth and Matteo Cancellieri walk you through the new features.
At a glance the new APIv3 offers:
- An extended model of the CORE resources to link different versions of a paper.
- Support for medium-size datasets collection.
- Improved analytical tools.
- User management made easier.
- Better documentation.
- A gallery to kick start your journey with the API.
The webinar contains also a quick demo showing the API features and tries to reply to the question "Did research stop during COVID?"
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
Getting real-time analytics for devices/application/business monitoring from trillions of events and petabytes of data like companies Netflix, Uber, Alibaba, Paypal, Ebay, Metamarkets do.
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
DataOps is the transformation of data processing from a craft with manual processes to an automated data factory. Lean principles, which have proven successful in manufacturing, are equally applicable for data factories. We will describe how lean principles can be applied in practice for successful data processing.
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
Getting real-time analytics for devices/application/business monitoring from trillions of events and petabytes of data like companies Netflix, Uber, Alibaba, Paypal, Ebay, Metamarkets do.
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
DataOps is the transformation of data processing from a craft with manual processes to an automated data factory. Lean principles, which have proven successful in manufacturing, are equally applicable for data factories. We will describe how lean principles can be applied in practice for successful data processing.
Drug and Vaccine Discovery: Knowledge Graph + Apache SparkDatabricks
RDF, Knowledge Graphs, and ontologies enable companies to produce and consume graph data that is interoperable, sharable, and self-describing. GSK has set out to build the world’s largest medical knowledge graph to provide our scientists access to the world’s medical knowledge, also enable machine learning to infer links between facts.
These inferred links are the heart of gene to disease mapping and is the future of discovering new treatments and vaccines. To power RDF sub-graphing, GSK has developed a set of open-source libraries codenamed “Project Bellman” that enable Sparql queries over partitioned RDF data in Apache Spark.
These tools provide the ability to scale up to Sparql querying over trillions of RDF triples, provide point-in-time queries, and provide incremental data updates to downstream consumer applications. These tools are used by both GSK’s Ai/ML team to discover gene to disease mappings, and GSK’s scientists to query over the world’s medical knowledge.
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
HPC + Ai: Machine Learning Models in Scientific Computinginside-BigData.com
In this video from the 2019 Stanford HPC Conference, Steve Oberlin from NVIDIA presents: HPC + Ai: Machine Learning Models in Scientific Computing.
"Most AI researchers and industry pioneers agree that the wide availability and low cost of highly-efficient and powerful GPUs and accelerated computing parallel programming tools (originally developed to benefit HPC applications) catalyzed the modern revolution in AI/deep learning. Clearly, AI has benefited greatly from HPC. Now, AI methods and tools are starting to be applied to HPC applications to great effect. This talk will describe an emerging workflow that uses traditional numeric simulation codes to generate synthetic data sets to train machine learning algorithms, then employs the resulting AI models to predict the computed results, often with dramatic gains in efficiency, performance, and even accuracy. Some compelling success stories will be shared, and the implications of this new HPC + AI workflow on HPC applications and system architecture in a post-Moore’s Law world considered."
Watch the video: https://youtu.be/SV3cnWf39kc
Learn more: https://nvidia.com
and
http://hpcadvisorycouncil.com/events/2019/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
Data Mining For Supermarket Sale Analysis Using Association Ruleijtsrd
Data mining is the novel technology of discovering the important information from the data repository which is widely used in almost all fields Recently, mining of databases is very essential because of growing amount of data due to its wide applicability in retail industries in improving marketing strategies. Analysis of past transaction data can provide very valuable information on customer behavior and business decisions. The amount of data stored grows twice as fast as the speed of the fastest processor available to analyze it.Its main purpose is to find the association relationship among the large number of database items. It is used to describe the patterns of customers purchase in the supermarket. This is presented in this paper. Rajeshri Shelke"Data Mining For Supermarket Sale Analysis Using Association Rule" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd94.pdf http://www.ijtsrd.com/engineering/computer-engineering/94/data-mining-for-supermarket-sale-analysis-using-association-rule/rajeshri-shelke
Archives work is messy -- in many cases archivists have to organize and make accessible large amounts of mixed data in a variety of formats, both physical and digital. Thankfully, there are a variety of technology tools available to help solve the messiness problem and make collections more accessible. In this session, audience members will learn about current and emerging archival technology tools, the pros and cons of the major tools, and resources for further education.
Apache Hive is a data warehousing system for large volumes of data stored in Hadoop. However, the data is useless unless you can use it to add value to your company. Hive provides a SQL-based query language that dramatically simplifies the process of querying your large data sets. That is especially important while your data scientists are developing and refining their queries to improve their understanding of the data. In many companies, such as Facebook, Hive accounts for a large percentage of the total MapReduce queries that are run on the system. Although Hive makes writing large data queries easier for the user, there are many performance traps for the unwary. Many of them are artifacts of the way Hive has evolved over the years and the requirement that the default behavior must be safe for all users. This talk will present examples of how Hive users have made mistakes that made their queries run much much longer than necessary. It will also present guidelines for how to get better performance for your queries and how to look at the query plan to understand what Hive is doing.
In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.
Presentation of the CORE APIv3 which provides seamless programmable access to the metadata and content from across the global repositories network delivered at Open Repositories 2022.
OpenAIRE Content Providers Community Call, July 1st, 2020
This call was focused on Data Repositories namely the OpenAIRE Research Graph and Data Repositories, the OpenAIRE Content Acquisition Policy, and the Guidelines for Data Archive Managers.
Was also an opportunity to share the most recent updates and novelties in the OpenAIRE Content Provider Dashboard, and to get feedback from community.
Follow the Community activities at https://www.openaire.eu/provide-community-calls
Drug and Vaccine Discovery: Knowledge Graph + Apache SparkDatabricks
RDF, Knowledge Graphs, and ontologies enable companies to produce and consume graph data that is interoperable, sharable, and self-describing. GSK has set out to build the world’s largest medical knowledge graph to provide our scientists access to the world’s medical knowledge, also enable machine learning to infer links between facts.
These inferred links are the heart of gene to disease mapping and is the future of discovering new treatments and vaccines. To power RDF sub-graphing, GSK has developed a set of open-source libraries codenamed “Project Bellman” that enable Sparql queries over partitioned RDF data in Apache Spark.
These tools provide the ability to scale up to Sparql querying over trillions of RDF triples, provide point-in-time queries, and provide incremental data updates to downstream consumer applications. These tools are used by both GSK’s Ai/ML team to discover gene to disease mappings, and GSK’s scientists to query over the world’s medical knowledge.
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
HPC + Ai: Machine Learning Models in Scientific Computinginside-BigData.com
In this video from the 2019 Stanford HPC Conference, Steve Oberlin from NVIDIA presents: HPC + Ai: Machine Learning Models in Scientific Computing.
"Most AI researchers and industry pioneers agree that the wide availability and low cost of highly-efficient and powerful GPUs and accelerated computing parallel programming tools (originally developed to benefit HPC applications) catalyzed the modern revolution in AI/deep learning. Clearly, AI has benefited greatly from HPC. Now, AI methods and tools are starting to be applied to HPC applications to great effect. This talk will describe an emerging workflow that uses traditional numeric simulation codes to generate synthetic data sets to train machine learning algorithms, then employs the resulting AI models to predict the computed results, often with dramatic gains in efficiency, performance, and even accuracy. Some compelling success stories will be shared, and the implications of this new HPC + AI workflow on HPC applications and system architecture in a post-Moore’s Law world considered."
Watch the video: https://youtu.be/SV3cnWf39kc
Learn more: https://nvidia.com
and
http://hpcadvisorycouncil.com/events/2019/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
Data Mining For Supermarket Sale Analysis Using Association Ruleijtsrd
Data mining is the novel technology of discovering the important information from the data repository which is widely used in almost all fields Recently, mining of databases is very essential because of growing amount of data due to its wide applicability in retail industries in improving marketing strategies. Analysis of past transaction data can provide very valuable information on customer behavior and business decisions. The amount of data stored grows twice as fast as the speed of the fastest processor available to analyze it.Its main purpose is to find the association relationship among the large number of database items. It is used to describe the patterns of customers purchase in the supermarket. This is presented in this paper. Rajeshri Shelke"Data Mining For Supermarket Sale Analysis Using Association Rule" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd94.pdf http://www.ijtsrd.com/engineering/computer-engineering/94/data-mining-for-supermarket-sale-analysis-using-association-rule/rajeshri-shelke
Archives work is messy -- in many cases archivists have to organize and make accessible large amounts of mixed data in a variety of formats, both physical and digital. Thankfully, there are a variety of technology tools available to help solve the messiness problem and make collections more accessible. In this session, audience members will learn about current and emerging archival technology tools, the pros and cons of the major tools, and resources for further education.
Apache Hive is a data warehousing system for large volumes of data stored in Hadoop. However, the data is useless unless you can use it to add value to your company. Hive provides a SQL-based query language that dramatically simplifies the process of querying your large data sets. That is especially important while your data scientists are developing and refining their queries to improve their understanding of the data. In many companies, such as Facebook, Hive accounts for a large percentage of the total MapReduce queries that are run on the system. Although Hive makes writing large data queries easier for the user, there are many performance traps for the unwary. Many of them are artifacts of the way Hive has evolved over the years and the requirement that the default behavior must be safe for all users. This talk will present examples of how Hive users have made mistakes that made their queries run much much longer than necessary. It will also present guidelines for how to get better performance for your queries and how to look at the query plan to understand what Hive is doing.
In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.
Presentation of the CORE APIv3 which provides seamless programmable access to the metadata and content from across the global repositories network delivered at Open Repositories 2022.
OpenAIRE Content Providers Community Call, July 1st, 2020
This call was focused on Data Repositories namely the OpenAIRE Research Graph and Data Repositories, the OpenAIRE Content Acquisition Policy, and the Guidelines for Data Archive Managers.
Was also an opportunity to share the most recent updates and novelties in the OpenAIRE Content Provider Dashboard, and to get feedback from community.
Follow the Community activities at https://www.openaire.eu/provide-community-calls
Conference Opening Science to Meet Future Challenges, Warsaw, March 11, 2014, organized by Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw.
How serendipitous is discovery for users? Like many a teenager, OpenURL linking can behave inappropriately. What can we do to smooth out the bumps on the road and what other tools are available? This breakout session will walk swiftly through linking to discovery targets, from OpenURL 0.1/1.0, to Index-Enhanced Direct Linking, Link 2.0 and beyond …
Presented by Michael Victor, Abenet Yabowork, Jane Poole, Harrison Njamba, Erick Rutto and Peter Ballantyne at the ILRI open access week workshop, ILRI, Nairobi, 23-25 October 2019
Enabling better science - Results and vision of the OpenAIRE infrastructure a...Paolo Manghi
Enabling better science: presentation on the results and vision of the OpenAIRE infrastructure and RDA Publishing Data Services Working Group in this direction.
This presentation was provided by Karen Hawkins of IEEE during the NISO event "Next Generation Discovery Tools: New Tools, Aging Standards," held March 27 - March 28, 2008.
UK e-Infrastructure: Widening Access, Increasing ParticipationNeil Chue Hong
A talk given at the ICHEC Annual Seminar by Neil Chue Hong, reflecting on the rise of Grid and Web 2.0, and how this might enable increased participation and use of computing infrastructure for e-Science and research.
Similar to Access the world’s research outputs through the CORE API (20)
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Home assignment II on Spectroscopy 2024 Answers.pdf
Access the world’s research outputs through the CORE API
1. Access the world’s research outputs
through the CORE API
Petr Knoth, Matteo Cancellieri, Knowledge Media institute, The Open University
https://core.ac.uk
https://core.ac.uk/services/api
https://bit.ly/core-apiv3
@oacore
2. • What can you do with the CORE API?
• Lessons learned from v2 and new features in v3
• Live tutorial: Did research stop during COVID?
Outline
Questions? https://bit.ly/core-apiv3
3. In doing so, we:
● enrich scholarly data using state-of-the-art text and data
mining technologies to aid discoverability,
● enable others to develop new tools and use cases on top
of the CORE platform,
● support the network of open access repositories and
journals with innovative technical, solutions and,
● facilitate a scalable, cost-effective route for the delivery of
open scholarship.
CORE’s mission
CORE’s mission is to aggregate all open access research worldwide and deliver
unrestricted access for all.
Questions? https://bit.ly/core-apiv3
4. ~97 million
Data providers
10,372
28,468,748 > 90
Countries
250
Metadata records
218,808,331
Full texts hosted
directly by CORE
Languages
Free to read links to full
text papers
Questions? https://bit.ly/core-apiv3
5. CORE services
Content discovery Raw data services Managing content
Discovery
Recommender
API
Dataset
FastSync
Repository Dashboard
Repository Edition
Search
Questions? https://bit.ly/core-apiv3
6. ● An extended model of the CORE resources to
link different versions of a paper.
● Support for medium-size datasets collection
● Improved analytical tools
● User management made easier
● Better documentation
● A gallery to kick start your journey with the API
What's new on the CORE API
Questions? https://bit.ly/core-apiv3
7. 🖋 Documentation in Swagger
🖋 PHP + Symfony implementation
🚀 Elasticsearch
API clients
•Java https://github.com/oacore/oacore4j
•Python https://github.com/oacore/pyoacore
•R https://github.com/ropensci/rcoreoa
CORE API: where are we?
> 2,500 registered users 252 active users
(in the last two months )
Questions? https://bit.ly/core-apiv3
8. Works
A deduplicated and polished item, it is made with the best metadata we can use from multiple articles
from different sources, it includes enrichments.
Article (old name) /Output (new name)
It is data coming directly from the data providers. It mostly comes from OAI-PMH but there also other
different data providers. The data is uniform so all the different data providers lead to a single metadata
format.
Data provider
It contains repositories (institutional
and disciplinary), preprint servers, journals and
publishers.
Journal
This dataset contains all journal titles included in
the CORE collection.
How CORE sees the world
1...n versions
contains contains
Questions? https://bit.ly/core-apiv3
9. Improved search queries
(
(
"Neural networks"
AND
yearPublished<=2018
)
OR
(
title:"deep learning"
AND yearPublished>2019
)
)
AND
_exists_:doi
+ better sorting
+ better filtering
Questions? https://bit.ly/core-apiv3
10. Large dataset access
The API now support querying
for medium size datasets
(1,000-100,000 records)
through the scroll parameter.
For large datasets (>100,000),
consider the CORE dataset
Questions? https://bit.ly/core-apiv3
11. Better analytical tools (coming soon) CORE
Analytics
Meaningful statistics for all
the entities in CORE
Search aggregation to help you orientate
while searching
Questions? https://bit.ly/core-apiv3
15. Feedback
Please cite CORE https://core.ac.uk/about/research-outputs
Show us how you are using the API
Let us know what do you think
Questions? https://bit.ly/core-apiv3
Not-for-profit service run by the Open University with the support of Jisc. CORE aggregates outputs from around the world, but also acts as the national aggregator for research outputs.
Focus on the languages and language detection.
Open, comprehensive, free, seamless
Jointly funded service between The Open University and Jisc
Global aggregator of full text and metadata (over 200 million metadata records from 10k repositories and 40 million active users)