RDF2vec is a method for creating embeddings vectors for entities in knowledge graphs. In this talk, I introduce the basic idea of RDF2vec, as well as the latest extensions developments, like the use of different walk strategies, the flavour of order-aware RDF2vec, RDF2vec for dynamic knowledge graphs, and more.
Spark Summit EU 2015: Lessons from 300+ production usersDatabricks
At Databricks, we have a unique view into over a hundred different companies trying out Spark for development and production use-cases, from their support tickets and forum posts. Having seen so many different workflows and applications, some discernible patterns emerge when looking at common performance and scalability issues that our users run into. This talk will discuss some of these common common issues from an engineering and operations perspective, describing solutions and clarifying misconceptions.
Accelerating TensorFlow with RDMA for high-performance deep learningDataWorks Summit
Google’s TensorFlow is one of the most popular deep learning (DL) frameworks. In distributed TensorFlow, gradient updates are a critical step governing the total model training time. These updates incur a massive volume of data transfer over the network.
In this talk, we first present a thorough analysis of the communication patterns in distributed TensorFlow. Then we propose a unified way of achieving high performance through enhancing the gRPC runtime with Remote Direct Memory Access (RDMA) technology on InfiniBand and RoCE. Through our proposed RDMA-gRPC design, TensorFlow only needs to run over the gRPC channel and gets the optimal performance. Our design includes advanced features such as message pipelining, message coalescing, zero-copy transmission, etc. The performance evaluations show that our proposed design can significantly speed up gRPC throughput by up to 1.5x compared to the default gRPC design. By integrating our RDMA-gRPC with TensorFlow, we are able to achieve up to 35% performance improvement for TensorFlow training with CNN models.
Speakers
Dhabaleswar K (DK) Panda, Professor and University Distinguished Scholar, The Ohio State University
Xiaoyi Lu, Research Scientist, The Ohio State University
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
Raven: End-to-end Optimization of ML Prediction QueriesDatabricks
Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Knowledge graph embeddings are a mechanism that projects each entity in a knowledge graph to a point in a continuous vector space. It is commonly assumed that those approaches project two entities closely to each other if they are similar and/or related. In this talk, I give a closer look at the roles of similarity and relatedness with respect to knowledge graph embeddings, and discuss how the well-known embedding mechanism RDF2vec can be tailored towards focusing on similarity, relatedness, or both.
RDF2vec is a method for creating embeddings vectors for entities in knowledge graphs. In this talk, I introduce the basic idea of RDF2vec, as well as the latest extensions developments, like the use of different walk strategies, the flavour of order-aware RDF2vec, RDF2vec for dynamic knowledge graphs, and more.
Spark Summit EU 2015: Lessons from 300+ production usersDatabricks
At Databricks, we have a unique view into over a hundred different companies trying out Spark for development and production use-cases, from their support tickets and forum posts. Having seen so many different workflows and applications, some discernible patterns emerge when looking at common performance and scalability issues that our users run into. This talk will discuss some of these common common issues from an engineering and operations perspective, describing solutions and clarifying misconceptions.
Accelerating TensorFlow with RDMA for high-performance deep learningDataWorks Summit
Google’s TensorFlow is one of the most popular deep learning (DL) frameworks. In distributed TensorFlow, gradient updates are a critical step governing the total model training time. These updates incur a massive volume of data transfer over the network.
In this talk, we first present a thorough analysis of the communication patterns in distributed TensorFlow. Then we propose a unified way of achieving high performance through enhancing the gRPC runtime with Remote Direct Memory Access (RDMA) technology on InfiniBand and RoCE. Through our proposed RDMA-gRPC design, TensorFlow only needs to run over the gRPC channel and gets the optimal performance. Our design includes advanced features such as message pipelining, message coalescing, zero-copy transmission, etc. The performance evaluations show that our proposed design can significantly speed up gRPC throughput by up to 1.5x compared to the default gRPC design. By integrating our RDMA-gRPC with TensorFlow, we are able to achieve up to 35% performance improvement for TensorFlow training with CNN models.
Speakers
Dhabaleswar K (DK) Panda, Professor and University Distinguished Scholar, The Ohio State University
Xiaoyi Lu, Research Scientist, The Ohio State University
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
Raven: End-to-end Optimization of ML Prediction QueriesDatabricks
Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Knowledge graph embeddings are a mechanism that projects each entity in a knowledge graph to a point in a continuous vector space. It is commonly assumed that those approaches project two entities closely to each other if they are similar and/or related. In this talk, I give a closer look at the roles of similarity and relatedness with respect to knowledge graph embeddings, and discuss how the well-known embedding mechanism RDF2vec can be tailored towards focusing on similarity, relatedness, or both.
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
INKOMPASS Indonesia - A Philip Morris International Internship ProgramINKOMPASS
INKOMPASS is offering one of the best paid internships for college students in Indonesia. Find out how to apply to the INKOMPASS internship program in Indonesia.
Apache Sqoop Tutorial | Sqoop: Import & Export Data From MySQL To HDFS | Hado...Edureka!
** Hadoop Training: https://www.edureka.co/hadoop **
This Edureka PPT on Sqoop Tutorial will explain you the fundamentals of Apache Sqoop. It will also give you a brief idea on Sqoop Architecture. In the end, it will showcase a demo of data transfer between Mysql and Hadoop
Below topics are covered in this video:
1. Problems with RDBMS
2. Need for Apache Sqoop
3. Introduction to Sqoop
4. Apache Sqoop Architecture
5. Sqoop Commands
6. Demo to transfer data between Mysql and Hadoop
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
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Slides accompanying a day-long introduction to AtoM and Archivematica, presented by Dan Gillean and Justin Simpson at the UK National Archives as part of an AIM25 and Higher Education Archive Programme Network Meeting, December 2, 2016.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Hadoop Meetup Jan 2019 - Dynamometer and a Case Study in NameNode GCErik Krogen
Erik Krogen of LinkedIn presents regarding Dynamometer, a system open sourced by LinkedIn for scale- and performance-testing HDFS. He discusses one major use case for Dynamometer, tuning NameNode GC, and discusses characteristics of NameNode GC such as why it is important, and how it interacts with various current and future GC algorithms.
This is taken from the Apache Hadoop Contributors Meetup on January 30, hosted by LinkedIn in Mountain View.
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
MIPI DevCon Taipei 2019: Enabling MIPI Camera Applications Including Automoti...MIPI Alliance
Kelvin Xu, product marketing manager at Synopsys, describes automotive ADAS designs with MIPI camera interface solutions such as CSI-2℠ and D-PHY℠, and outlines other MIPI automotive protocols, including I3C® and DSI℠.
Apache Arrow is a new standard for in-memory columnar data processing. It is a complement to Apache Parquet and Apache ORC. In this deck we review key design goals and how Arrow works in detail.
Dossier sur les modalités de partenariat du nouveau groupe de travail que souhaite lancer la Fing en 2016 sur la rétroingénierie sociale des systèmes techniques.
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
INKOMPASS Indonesia - A Philip Morris International Internship ProgramINKOMPASS
INKOMPASS is offering one of the best paid internships for college students in Indonesia. Find out how to apply to the INKOMPASS internship program in Indonesia.
Apache Sqoop Tutorial | Sqoop: Import & Export Data From MySQL To HDFS | Hado...Edureka!
** Hadoop Training: https://www.edureka.co/hadoop **
This Edureka PPT on Sqoop Tutorial will explain you the fundamentals of Apache Sqoop. It will also give you a brief idea on Sqoop Architecture. In the end, it will showcase a demo of data transfer between Mysql and Hadoop
Below topics are covered in this video:
1. Problems with RDBMS
2. Need for Apache Sqoop
3. Introduction to Sqoop
4. Apache Sqoop Architecture
5. Sqoop Commands
6. Demo to transfer data between Mysql and Hadoop
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Slides accompanying a day-long introduction to AtoM and Archivematica, presented by Dan Gillean and Justin Simpson at the UK National Archives as part of an AIM25 and Higher Education Archive Programme Network Meeting, December 2, 2016.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Hadoop Meetup Jan 2019 - Dynamometer and a Case Study in NameNode GCErik Krogen
Erik Krogen of LinkedIn presents regarding Dynamometer, a system open sourced by LinkedIn for scale- and performance-testing HDFS. He discusses one major use case for Dynamometer, tuning NameNode GC, and discusses characteristics of NameNode GC such as why it is important, and how it interacts with various current and future GC algorithms.
This is taken from the Apache Hadoop Contributors Meetup on January 30, hosted by LinkedIn in Mountain View.
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
MIPI DevCon Taipei 2019: Enabling MIPI Camera Applications Including Automoti...MIPI Alliance
Kelvin Xu, product marketing manager at Synopsys, describes automotive ADAS designs with MIPI camera interface solutions such as CSI-2℠ and D-PHY℠, and outlines other MIPI automotive protocols, including I3C® and DSI℠.
Apache Arrow is a new standard for in-memory columnar data processing. It is a complement to Apache Parquet and Apache ORC. In this deck we review key design goals and how Arrow works in detail.
Dossier sur les modalités de partenariat du nouveau groupe de travail que souhaite lancer la Fing en 2016 sur la rétroingénierie sociale des systèmes techniques.
New OpenAIRE data providers: some of the most recent from September to Decemb...OpenAIRE
2016 OpenAIRE data providers: some of the most recent from September to December. Institutional repositories, publication repository aggregator, journals aggregator/publisher, journals. More info: https://www.openaire.eu/search/data-providers.
OpenAIRE webinar on Open Access in H2020 (OAW2016)OpenAIRE
OpenAIRE Webinar for project coordinators and researchers on Open Access to publications in H2020 - By Eloy Rodrigues and Pedro Principe (University of Minho, OpenAIRE Helpdesk & Training managers). Open Access Week 2016 initiatives.
Connecting the dots - e-Infra services for open scienceOpenAIRE
Starting from Open access towards services for open science, we present OpenAIRE, OpenMinTeD and OpenUP, three EU projects that build services to facilitate and accelerate open science.
Construire des outils pour la gestion des données de la recherche dans une co...Lesticetlart Invisu
Construire des outils pour la gestion des données de la recherche dans une communauté d’universités Aurore Cartier, Magalie Moysan et Nathalie Reymonet [Université Paris-Descartes, Université Paris-Diderot, Sorbonne Paris Cité]
The OpenChain Project has launched a series of bi-weekly free webinars that provide access to people and knowledge that we would otherwise obtain at events. We held our fifth meeting on Monday the 1st of June at 9am Pacific with two guest speakers.
This time we explored Software Heritage, an initiative whose goal is to collect, preserve, and share software code, and continued our discussion of containers from the perspective of scalable compliance.
Roberto Di Cosmo, Director at Software Heritage, explained why this initiative collects and preserves software in source code form with the understanding that software embodies key technical and scientific knowledge that humanity cannot afford to risk losing. His presentation helped provide insight into how such initiatives can link into activities like compliance automation in open source compliance, an area of immediate interest to the OpenChain community.
| www.eudat.eu | B2FIND Integration Version 4 February 2017: The aim of this presentation is to illustrate how metadata can be published in the B2FIND catalogue and how EUDAT’s B2FIND metadata catalogue can be integrated.
Software Heritage, a revolutionary infrastructure for software source code, O...OW2
Open Source Software is at the heart of our digital society and embodies a growing part of our technical and organisational knowledge, and this raises many questions: how to comply with the obligations of Open Source licenses? how to be sure that the source code of a key module we use will be still there when we need it in the future? do we really know what source code we are using, and where it comes from? how can we adress cybersecurity if we do not know? how do we share this information across the software supply chain?
Answering these questions and answering them well is quite a challenge.
In this presentation, you will discover Software Heritage, an open non-profit initiative, in partnership with Unesco, and supported by major IT players, and how the revolutionary infrastructure it is building changes the way we adress these issues.
Keynote presentation by Roberto Di Cosmo, Inria.
Abstract: With 8 billions unique source files from 120 million repositories, it is the largest archive of source code ever built.
The first workshop of the series "Services to support FAIR data" took place in Prague during the EOSC-hub week (on April 12, 2019).
Speaker: Baptiste Grenier
Slides of the presentation by Hugh Williams of OpenLink Software in the course of the LOD2 webinar: Virtuoso Universal Server on 20.12. 2011 - for more information please see: http://lod2.eu/BlogPost/webinar-series
Presentation given at Open Source Summit Japan 2016 about the state of the cloud native technology (Cloud Native Computing Foundation) and the standardization of container technology (Open Container Initiative)
Blockchain Beyond Finance - Cronos Groep - Jan 17, 2017BigchainDB
Towards the internet of value & trust.
"To develop shared global compute infrastructure,
we must first understand the status quo of infrastructure,
...and how to change it accordingly."
Dimitri De Jonghe, lead developer of BigchainDB talking about blockchain technology beyond the financial sector.
Publication of INSPIRE-based agricultural linked dataRaul Palma
Results of the publication of linked data from the agriculture sector within DATABio project, based on the agriculture data model developed in FOODIE project
The TIB|AV Portal : OSGeo conference videos as a resource for scientific res...Peter Löwe
This paper reports on new opportunities for research and education in Free and Open Source Geoinformatics as a translational part of Open Science, enabled the growing collection of OSGeo conference video recordings at the German National Library of Science and Technology (TIB). Since 2015, OSGeo conference recordings have been included to the collection sphere of TIB in information sciences. Currently, video content from selected national (FOSSGIS), regional (FOSS4G-NA) and global (FOSS4G) conferences is being actively collected. The annual growth exceeds 100 hours of new content relating to the OSGeo software projects and the OSGeo scientific-technical communities. This is seconded by retrospective acquisition of video material dating from past conferences, going back until 2002 to preserve this content, ensuring both long term availability and access. The audiovisual OSGeo-related content is provided through the TIB|AV Portal, a web-based platform for scientific audiovisual media providing state-of-the art multimedia analysis and retrieval. It implements the requirements by research libraries for reliable long term preservation. Metadata enhancement analysis provides extended search and retrieval options. Digital Object Identifiers (DOI) enable scientific citation of full videos, excerpts and still frames, use in education and also referral in social networks. This library-operated service infrastructure turns the audiovisual OSGeo-related content in a reliable source for science and education.
Zenodo and linking Open Science - Nielsen Lars HolmOpenAIRE
FAIR data in a generic data repository presented by Lars Holm Nielsen during the OpenAIRE workshop Services to support FAIR data, Vienna: https://www.openaire.eu/openaire-workshop-making-services-fair-vienna-april-24th-2019
RO-Crate: A framework for packaging research products into FAIR Research ObjectsCarole Goble
RO-Crate: A framework for packaging research products into FAIR Research Objects presented to Research Data Alliance RDA Data Fabric/GEDE FAIR Digital Object meeting. 2021-02-25
B2SHARE: Record lifecycle and HTTP API| www.eudat.eu | EUDAT
| www.eudat.eu | B2SHARE is a scientific data repository providing persistent storage and sharing data facilities. Building on the new Invenio 3.0 digital assets management platform, a new version of B2SHARE has been developed which is focused on an improved user experience. Answering the requests of the current user base, B2SHARE version 2 provides customizable metadata schemas and a simple but effective workflow for depositing user data, exposed in its RESTful HTTP API.
The presentation will introduce the B2SHARE service, its organizing principles and its basic operations. The metadata schemas and the dataset lifecycle, which are essentials in understanding the possibilities of the service, will be the main focus of the talk. The concrete output of the session can be a full paper expanding the presented topics.
Target Audience:Researchers of any scientific domain, which work with publishable data sets.
Open Source Tools for Making Open Source HardwareLeon Anavi
Is it worth making open source hardware using expensive proprietary software tools? Of course not! There are many open source software tools good enough for the job. In this presentation Leon Anavi will share his experience in combining open source hardware with free and open source software for fun and profit.
OpenAIRE Content Providers Community Call, November 4th, 2020
This call was focused on the PROVIDE future developments, functionalities wishlist and PROVIDE service in EOSC.
Was also an opportunity to share the most recent updates and novelties in the OpenAIRE Content Provider Dashboard, and to get feedback from community.
Recordings: https://youtu.be/wY4fOS767Us
Follow the Community activities at https://www.openaire.eu/provide-community-calls
OpenAIRE in the European Open Science Cloud (EOSC)OpenAIRE
Openness is the success factor for EOSC. OpenAIRE has been working in delivering an open access scholarly communication in Europe for the past 10 years and we now present how our work fits into the EOSC core developments
OpenAIRE Content Providers Community Call, October 7th, 2020
This call was focused on the OpenAIRE Broker Service, specifying how the service works to deploy the enrichment events to the Content Providers 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.
Recording: https://youtu.be/3sF4B58EGcs
Follow the Community activities at https://www.openaire.eu/provide-community-calls
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
OpenAIRE Content Providers Community Call. May 6th, 2020.
This Call focused the presentation of the new User Interface of Provide Dashboard and the presentation of 4 use cases using the Provide service.
Was also an opportunity to share the most recent updates and novelties in the OpenAIRE Content Provider Dashboard, and to get feedback from community.
Recording available here: https://youtu.be/J4m_ryRxtnY
20200504_OpenAIRE Legal Policy Webinar: GDPR and Sharing DataOpenAIRE
Presentation by Jacques Flores Dourojeanni (Research Data Management Consultant Utrecht University Library), as delivered during the OpenAIRE Legal Policy Webinar series on May 4th 2020.
More information and recordings: https://www.openaire.eu/item/openaire-legal-policy-webinars
20200504_Research Data & the GDPR: How Open is Open?OpenAIRE
Presentation by Prodromos Tsiavos (Senior Legal Advisor - ARC/ Director - Onassis Group) as delivered during the OpenAIRE Legal Policy Webinar series on May 4th 2020.
More information and recordings: https://www.openaire.eu/item/openaire-legal-policy-webinars
20200504_Data, Data Ownership and Open ScienceOpenAIRE
Presentation by Thomas Margoni (Senior Lecturer in Intellectual Property and Internet Law, Co-director, CREATe, University of Glasgow) as delivered during the OpenAIRE Legal Policy Webinar series on May 4th 2020.
More information and recordings: https://www.openaire.eu/item/openaire-legal-policy-webinars
20200429_Research Data & the GDPR: How Open is Open? (updated version)OpenAIRE
Presentation by Prodromos Tsiavos (Senior Legal Advisor - ARC/ Director - Onassis Group) as delivered during the OpenAIRE Legal Policy Webinar series on April 29th 2020.
More information and recordings: https://www.openaire.eu/item/openaire-legal-policy-webinars
20200429_Data, Data Ownership and Open ScienceOpenAIRE
Presentation by Thomas Margoni (Senior Lecturer in Intellectual Property and Internet Law, Co-director, CREATe, University of Glasgow) as delivered during the OpenAIRE Legal Policy Webinar series on April 29th 2020.
More information and recordings: https://www.openaire.eu/item/openaire-legal-policy-webinars
20200429_OpenAIRE Legal Policy Webinar: GDPR and Sharing DataOpenAIRE
Presentation by Jacques Flores Dourojeanni (Research Data Management Consultant Utrecht University Library), as delivered during the OpenAIRE Legal Policy Webinar series on April 29th 2020.
More information and recordings: https://www.openaire.eu/item/openaire-legal-policy-webinars
COVID-19: Activities, tools, best practice and contact points in GreeceOpenAIRE
Presentation from the webinar organized by the Greek OpenAIRE and RDA Nodes (Athena RC) and Elixir-GR to inform participants of EU and national efforts, in collaboration with the following research organizations: Flemming, CERTH, HEAL-Link, Demokritos, Univ. of Athens (Medical School).
Presentation of the 2nd Content Providers Community Call, targeting the following topics: 1) OpenAIRE Content provider dashboard updates; Main topic: DSpace-CRIS for OpenAIRE: implementation of the CRIS guidelines and beyond; 3) Community questions & comments.
Presentation of the 2nd Content Providers Community Call, targeting the following topics: 1) OpenAIRE Content provider dashboard updates;
2) OpenAIRE aggregation and enrichment processes: specifications and good practices;
3) Community questions & comments.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
3. Credit: Dave Hill, licensed under CC-BY-NC-SA 2.0. https://www.flickr.com/photos/dmh650/4031607067/in/gallery-wlef70-72157633022909105/
4. Horizon 2020
Credit: By Brian Herzog / licensed under CC-BY-NC-SA 2.0 (https://www.flickr.com/photos/herzogbr/6756173595/in/gallery-wlef70-72157633022909105/)
36. Horizon 2020
Accessible
Flexible access conditions:
REST-API: https://zenodo.org/dev
CERN Data Center:
● Already a home to over 100 PB
of data acquired from LHC
● Using CERN’s EOS disk cluster
38. Horizon 2020
Reusable
120 open licenses to choose from
(http://opendefinition.org/licenses/)
Licensing
Robust metadata model
● Simple where possible, verbose when required
● Storing all changes in metadata over record’s lifetime
(versioning)
● Supporting variety of data types
40. Horizon 2020
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Extended OpenAIRE grants support
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