Introduction to the ELIXIR-UK Biomedical Atlas Centre presented by Richard Baldock at the ELIXIR-UK Workshop during Genome Science 2016 in Liverpool on 31st August 2016
Niffler is an efficient DICOM Framework for machine learning pipelines and processing workflows on metadata. It facilitates efficient transfer of DICOM images on-demand and real-time from PACS to the research environments, to run processing workflows and machine learning pipelines.
https://github.com/Emory-HITI/Niffler/
We propose Niffler (https://github.com/Emory-HITI/Niffler), an open-source ML framework that runs in research
clusters by receiving images in real-time using DICOM protocol from hospitals' PACS.
This document discusses Bioschemas, which aims to enable findability and interoperability of life sciences data on the web. It defines schemas using Schema.org for different types of life sciences data, including datasets and data catalogs. Bioschemas has over 200 members across 35 organizations that have deployed Bioschemas markup. It has ongoing work to increase adoption of Bioschemas across different types of life sciences resources and provide training and events for the community.
Building on the Atlas (of Living Australia)Andrew Treloar
Presentation given at Atlas of Living Australia Science Symposium 2013. Discusses Australian National Data Service Applications program and two specific projects: Soils to Satellites (also involving TERN), and Edgar Bird Species distribution.
Curvature Quantum Curvature and Feynman DiagramsDann Passoja
This is the presentation of material and the derivation of the equation E=hω(χ(n)) which introduces a relationship between energy and the Euler characteristic, a topological term. The curvature of Feynman diagrams are measured indicating that with the proper calibration that such an equation can be used.
La unidad habla sobre estructuras definidas y relaciones espaciales en ergonomía, así como principios ordenadores para organizar el espacio de trabajo de manera eficiente.
Niffler is an efficient DICOM Framework for machine learning pipelines and processing workflows on metadata. It facilitates efficient transfer of DICOM images on-demand and real-time from PACS to the research environments, to run processing workflows and machine learning pipelines.
https://github.com/Emory-HITI/Niffler/
We propose Niffler (https://github.com/Emory-HITI/Niffler), an open-source ML framework that runs in research
clusters by receiving images in real-time using DICOM protocol from hospitals' PACS.
This document discusses Bioschemas, which aims to enable findability and interoperability of life sciences data on the web. It defines schemas using Schema.org for different types of life sciences data, including datasets and data catalogs. Bioschemas has over 200 members across 35 organizations that have deployed Bioschemas markup. It has ongoing work to increase adoption of Bioschemas across different types of life sciences resources and provide training and events for the community.
Building on the Atlas (of Living Australia)Andrew Treloar
Presentation given at Atlas of Living Australia Science Symposium 2013. Discusses Australian National Data Service Applications program and two specific projects: Soils to Satellites (also involving TERN), and Edgar Bird Species distribution.
Curvature Quantum Curvature and Feynman DiagramsDann Passoja
This is the presentation of material and the derivation of the equation E=hω(χ(n)) which introduces a relationship between energy and the Euler characteristic, a topological term. The curvature of Feynman diagrams are measured indicating that with the proper calibration that such an equation can be used.
La unidad habla sobre estructuras definidas y relaciones espaciales en ergonomía, así como principios ordenadores para organizar el espacio de trabajo de manera eficiente.
Foundations for the future of science discusses using artificial intelligence and machine learning to advance scientific research. Key points discussed include using AI to analyze large datasets, develop scientific models, and automate experimental workflows. The document also outlines several examples of how the Globus data platform is currently enabling AI-powered scientific applications across multiple domains. Overall, the document advocates that embracing "AI for science" has the potential to accelerate scientific discovery by overcoming limitations in human analysis capabilities and computational resources.
Using Lucene/Solr to Build CiteSeerX and Friendslucenerevolution
Presented by C. Lee Giles, Pennsylvania State University - See complete conference videos - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012
Cyberinfrastructure or e-science has become crucial in many areas of science as data access often defines scientific progress. Open source systems have greatly facilitated design and implementation and supporting cyberinfrastructure. However, there exists no open source integrated system for building an integrated search engine and digital library that focuses on all phases of information and knowledge extraction, such as citation extraction, automated indexing and ranking, chemical formulae search, table indexing, etc. We propose the open source SeerSuite architecture which is a modular, extensible system built on successful OS projects such as Lucene/Solr and discuss its uses in building enterprise search and cyberinfrastructure for the sciences and academia. We highlight application domains with examples of specialized search engines that we have built for computer science, CiteSeerX, chemistry, ChemXSeer, archaeology, ArchSeer. acknowledgements, AckSeer, reference recommendation, RefSeer, collaboration recommendation, CollabSeer, and others, all using Solr/Lucene. Because such enterprise systems require unique information extraction approaches, several different machine learning methods, such as conditional random fields, support vector machines, mutual information based feature selection, sequence mining, etc. are critical for performance.
Using Lucene/Solr to Build CiteSeerX and Friendslucenerevolution
The document discusses Prof. C. Lee Giles' work using Lucene/Solr to build specialty search engines and digital libraries, including CiteSeerX. It describes how these "Seer" tools provide scalable and automated cyberinfrastructure for indexing and searching large heterogeneous datasets. They support research in domains like computer science, chemistry, and more by facilitating knowledge integration and discovery through intelligent search and data mining algorithms.
2nd Microscopy Congress: Public archiving of bio-imaging data - perspectives,...Ardan Patwardhan
The open and public access to structural data is of utmost importance for validation, development, testing and training. The Electron Microscopy Data Bank (EMDB) archive is the authoritative source for 3DEM data. In 2014 PDBe started EMPIAR – the electron microscopy pilot image archive to store raw image data related to EMDB structures. The challenge here has been in dealing with the storage and transfer of large datasets. EMPIAR is now fully functional with routine uploads and downloads in the Terabyte range. The success of EMPIAR has spurred interest in wider bio-imaging circles as a working example of image archiving and possibly even a prototype for a broader bio-imaging archive. I will describe EMPIAR and discuss the prospects for public archiving of bio-imaging data.
Escaping Flatland: Interactive High-Dimensional Data Analysis in Drug Discove...Spark Summit
This document describes a project at Novartis to use Apache Spark for high-dimensional data analysis from drug screening. Large datasets from various screening technologies were analyzed using Spark pipelines for quality control, normalization, and classification. Visualizations were built using WebGL. The goals were to speed up multi-day batch jobs, create a unified analysis workflow, and build an application for scientists. Future work includes elastic infrastructure, supervised learning of cell phenotypes, and contributing methods to open source.
Deep learning is finding applications in science such as predicting material properties. DLHub is being developed to facilitate sharing of deep learning models, data, and code for science. It will collect, publish, serve, and enable retraining of models on new data. This will help address challenges of applying deep learning to science like accessing relevant resources and integrating models into workflows. The goal is to deliver deep learning capabilities to thousands of scientists through software for managing data, models and workflows.
This document discusses Scratchpad virtual research environments for sharing, linking, and publishing biodiversity data. It notes that most biodiversity data is currently not in digital, openly accessible, or linked formats. Scratchpads are introduced as hosted websites for biodiversity data, which allow researchers to create virtual research platforms, publish data openly and flexibly. The document outlines the types of biodiversity data that can be incorporated into Scratchpads, including taxon pages, maps, images, literature, and matrices. It also summarizes the goals and funding of the ViBRANT project, which aims to develop a federated network of biodiversity informatics infrastructures through Scratchpads and other virtual research environments.
ALIAOnline Practical Linked (Open) Data for Libraries, Archives & MuseumsJon Voss
This document discusses practical applications of Linked Open Data (LOD) for libraries, archives, and museums. It describes how LOD allows these institutions to publish structured data on the web in ways that are interoperable and can be connected to other open datasets. Examples are given of how LOD is being used by various institutions to share metadata, images, and other cultural heritage assets on the web in open, machine-readable formats. The presenter argues that LOD represents a new paradigm that these cultural organizations should embrace to make their collections more accessible and useful on the web.
Time to Science/Time to Results: Transforming Research in the CloudAmazon Web Services
This session demonstrates how cloud can accelerate breakthroughs in scientific research by providing on-demand access to powerful computing. You will gain insight into how scientific researchers are using the cloud to solve complex science, engineering, and business problems that require high bandwidth, low latency networking and very high compute capabilities. You will hear how leveraging the cloud reduces the costs and time to conduct large scale, worldwide collaborative research. Researchers can then access computational power, data storage, and supercomputing resources, and data sharing capabilities in a cost-efficient manner without implementation delays. Disease research can be accomplished in a fraction of the time, and innovative researchers in small schools or distant corners of the world have access to the same computing power as those at major research institutions by leveraging Amazon EC2, Amazon S3, optimizing C3 instances and more to increase collaboration. This session will provide best practices and insight from UC Berkeley AMP Lab on the services used to connect disparate sets of data to drive meaningful new insight and impact.
The document discusses SCAR-MarBIN and ANTABIF, which provide free and open access to Antarctic biodiversity data. Their goals are to exchange scientific data and results from Antarctica freely to promote international cooperation and adaptive conservation/management. They have developed web portals and databases containing over 850,000 visitors and 35 million data records downloaded. Their philosophy is to build an open electronic ecosystem offering access to taxonomic and geospatial biodiversity data using open source solutions.
Accelerating Discovery via Science ServicesIan Foster
[A talk presented at Oak Ridge National Laboratory on October 15, 2015]
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In big-science projects in high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to develop suites of science services to which researchers can dispatch mundane but time-consuming tasks, and thus to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers. I use examples from Globus and other projects to demonstrate what can be achieved.
Exploration of multidimensional biomedical data in pub chem, Presented by Lia...Lucidworks (Archived)
The document discusses the development of a new search system for PubChem to allow for exploration of multidimensional biomedical data. The new system was needed to address the challenges of handling large and heterogeneous datasets with many relationships between data types in a way that allows for fast querying. The system leverages Apache SOLR to provide features like full text search, faceting, molecule structure searching and joining of related data. It includes backend components like SOLR, SQL and specialized search engines as well as web APIs and frontend interfaces like reusable widgets and a new search interface.
The Discovery Cloud: Accelerating Science via Outsourcing and AutomationIan Foster
Director's Colloquium at Los Alamos National Laboratory, September 18, 2014.
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. In this talk, I explore the past, current, and potential future of large-scale outsourcing and automation for science.
Our access to scientific information has changed in ways that were hardly imagined even by the early pioneers of the internet. The immense quantities of data and the array of tools available to search and analyze online content continues to expand while the pace of change does not appear to be slowing. ChemSpider is one of the chemistry community’s primary online public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data tens of thousands of chemists every day and it serves as the foundation for many important international projects to integrate chemistry and biology data, facilitate drug discovery efforts and help to identify new chemicals from under the ocean. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of the solutions that it helps to enable. We will also discuss the possibilities it offers in the domain of crowdsourcing and open data sharing. The future of scientific information and communication will be underpinned by these efforts, influenced by increasing participation from the scientific community and facilitated collaboration and ultimately accelerate scientific progress.
A Data Ecosystem to Support Machine Learning in Materials ScienceGlobus
This presentation was given at the 2019 GlobusWorld Conference in Chicago, IL by Ben Blaiszik from University of Chicago and Argonne National Laboratory Data Science and Learning Division.
- Data transfer
- Compute access
- Training
Data Hub:
- Secure data sharing
- Data management
- Metadata catalogues
AAI:
- Single sign-on
- User attributes
- Group management
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This document provides an introduction to big data, including:
- Big data is characterized by its volume, velocity, and variety, which makes it difficult to process using traditional databases and requires new technologies.
- Technologies like Hadoop, MongoDB, and cloud platforms from Google and Amazon can provide scalable storage and processing of big data.
- Examples of how big data is used include analyzing social media and search data to gain insights, enabling personalized experiences and targeted advertising.
- As data volumes continue growing exponentially from sources like sensors, simulations, and digital media, new tools and approaches are needed to effectively analyze and make sense of "big data".
The document proposes a reproducible framework powered by Globus to help researchers share and reproduce scientific models and simulations. It describes challenges with current methods for sharing work, like dependencies or configuration issues, that prevent easy reproducibility. The framework aims to [1] capture scientific activities, code, data and environments; [2] preserve them as standardized packages called "SciUnits"; [3] share and distribute SciUnits so others can [4] re-execute and re-analyze the work without installation or configuration problems. Key components are outlined for establishing this framework to support reproducibility across different scientific domains.
ELIXIR-UK and the ELIXIR Interoperability PlatformELIXIR UK
Introduction to ELIXIR-UK and the ELIXIR Interoperability Platform presented by Carole Goble at the ELIXIR-UK Workshop during Genome Science 2016 in Liverpool on 31st August 2016
Foundations for the future of science discusses using artificial intelligence and machine learning to advance scientific research. Key points discussed include using AI to analyze large datasets, develop scientific models, and automate experimental workflows. The document also outlines several examples of how the Globus data platform is currently enabling AI-powered scientific applications across multiple domains. Overall, the document advocates that embracing "AI for science" has the potential to accelerate scientific discovery by overcoming limitations in human analysis capabilities and computational resources.
Using Lucene/Solr to Build CiteSeerX and Friendslucenerevolution
Presented by C. Lee Giles, Pennsylvania State University - See complete conference videos - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012
Cyberinfrastructure or e-science has become crucial in many areas of science as data access often defines scientific progress. Open source systems have greatly facilitated design and implementation and supporting cyberinfrastructure. However, there exists no open source integrated system for building an integrated search engine and digital library that focuses on all phases of information and knowledge extraction, such as citation extraction, automated indexing and ranking, chemical formulae search, table indexing, etc. We propose the open source SeerSuite architecture which is a modular, extensible system built on successful OS projects such as Lucene/Solr and discuss its uses in building enterprise search and cyberinfrastructure for the sciences and academia. We highlight application domains with examples of specialized search engines that we have built for computer science, CiteSeerX, chemistry, ChemXSeer, archaeology, ArchSeer. acknowledgements, AckSeer, reference recommendation, RefSeer, collaboration recommendation, CollabSeer, and others, all using Solr/Lucene. Because such enterprise systems require unique information extraction approaches, several different machine learning methods, such as conditional random fields, support vector machines, mutual information based feature selection, sequence mining, etc. are critical for performance.
Using Lucene/Solr to Build CiteSeerX and Friendslucenerevolution
The document discusses Prof. C. Lee Giles' work using Lucene/Solr to build specialty search engines and digital libraries, including CiteSeerX. It describes how these "Seer" tools provide scalable and automated cyberinfrastructure for indexing and searching large heterogeneous datasets. They support research in domains like computer science, chemistry, and more by facilitating knowledge integration and discovery through intelligent search and data mining algorithms.
2nd Microscopy Congress: Public archiving of bio-imaging data - perspectives,...Ardan Patwardhan
The open and public access to structural data is of utmost importance for validation, development, testing and training. The Electron Microscopy Data Bank (EMDB) archive is the authoritative source for 3DEM data. In 2014 PDBe started EMPIAR – the electron microscopy pilot image archive to store raw image data related to EMDB structures. The challenge here has been in dealing with the storage and transfer of large datasets. EMPIAR is now fully functional with routine uploads and downloads in the Terabyte range. The success of EMPIAR has spurred interest in wider bio-imaging circles as a working example of image archiving and possibly even a prototype for a broader bio-imaging archive. I will describe EMPIAR and discuss the prospects for public archiving of bio-imaging data.
Escaping Flatland: Interactive High-Dimensional Data Analysis in Drug Discove...Spark Summit
This document describes a project at Novartis to use Apache Spark for high-dimensional data analysis from drug screening. Large datasets from various screening technologies were analyzed using Spark pipelines for quality control, normalization, and classification. Visualizations were built using WebGL. The goals were to speed up multi-day batch jobs, create a unified analysis workflow, and build an application for scientists. Future work includes elastic infrastructure, supervised learning of cell phenotypes, and contributing methods to open source.
Deep learning is finding applications in science such as predicting material properties. DLHub is being developed to facilitate sharing of deep learning models, data, and code for science. It will collect, publish, serve, and enable retraining of models on new data. This will help address challenges of applying deep learning to science like accessing relevant resources and integrating models into workflows. The goal is to deliver deep learning capabilities to thousands of scientists through software for managing data, models and workflows.
This document discusses Scratchpad virtual research environments for sharing, linking, and publishing biodiversity data. It notes that most biodiversity data is currently not in digital, openly accessible, or linked formats. Scratchpads are introduced as hosted websites for biodiversity data, which allow researchers to create virtual research platforms, publish data openly and flexibly. The document outlines the types of biodiversity data that can be incorporated into Scratchpads, including taxon pages, maps, images, literature, and matrices. It also summarizes the goals and funding of the ViBRANT project, which aims to develop a federated network of biodiversity informatics infrastructures through Scratchpads and other virtual research environments.
ALIAOnline Practical Linked (Open) Data for Libraries, Archives & MuseumsJon Voss
This document discusses practical applications of Linked Open Data (LOD) for libraries, archives, and museums. It describes how LOD allows these institutions to publish structured data on the web in ways that are interoperable and can be connected to other open datasets. Examples are given of how LOD is being used by various institutions to share metadata, images, and other cultural heritage assets on the web in open, machine-readable formats. The presenter argues that LOD represents a new paradigm that these cultural organizations should embrace to make their collections more accessible and useful on the web.
Time to Science/Time to Results: Transforming Research in the CloudAmazon Web Services
This session demonstrates how cloud can accelerate breakthroughs in scientific research by providing on-demand access to powerful computing. You will gain insight into how scientific researchers are using the cloud to solve complex science, engineering, and business problems that require high bandwidth, low latency networking and very high compute capabilities. You will hear how leveraging the cloud reduces the costs and time to conduct large scale, worldwide collaborative research. Researchers can then access computational power, data storage, and supercomputing resources, and data sharing capabilities in a cost-efficient manner without implementation delays. Disease research can be accomplished in a fraction of the time, and innovative researchers in small schools or distant corners of the world have access to the same computing power as those at major research institutions by leveraging Amazon EC2, Amazon S3, optimizing C3 instances and more to increase collaboration. This session will provide best practices and insight from UC Berkeley AMP Lab on the services used to connect disparate sets of data to drive meaningful new insight and impact.
The document discusses SCAR-MarBIN and ANTABIF, which provide free and open access to Antarctic biodiversity data. Their goals are to exchange scientific data and results from Antarctica freely to promote international cooperation and adaptive conservation/management. They have developed web portals and databases containing over 850,000 visitors and 35 million data records downloaded. Their philosophy is to build an open electronic ecosystem offering access to taxonomic and geospatial biodiversity data using open source solutions.
Accelerating Discovery via Science ServicesIan Foster
[A talk presented at Oak Ridge National Laboratory on October 15, 2015]
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In big-science projects in high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to develop suites of science services to which researchers can dispatch mundane but time-consuming tasks, and thus to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers. I use examples from Globus and other projects to demonstrate what can be achieved.
Exploration of multidimensional biomedical data in pub chem, Presented by Lia...Lucidworks (Archived)
The document discusses the development of a new search system for PubChem to allow for exploration of multidimensional biomedical data. The new system was needed to address the challenges of handling large and heterogeneous datasets with many relationships between data types in a way that allows for fast querying. The system leverages Apache SOLR to provide features like full text search, faceting, molecule structure searching and joining of related data. It includes backend components like SOLR, SQL and specialized search engines as well as web APIs and frontend interfaces like reusable widgets and a new search interface.
The Discovery Cloud: Accelerating Science via Outsourcing and AutomationIan Foster
Director's Colloquium at Los Alamos National Laboratory, September 18, 2014.
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. In this talk, I explore the past, current, and potential future of large-scale outsourcing and automation for science.
Our access to scientific information has changed in ways that were hardly imagined even by the early pioneers of the internet. The immense quantities of data and the array of tools available to search and analyze online content continues to expand while the pace of change does not appear to be slowing. ChemSpider is one of the chemistry community’s primary online public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data tens of thousands of chemists every day and it serves as the foundation for many important international projects to integrate chemistry and biology data, facilitate drug discovery efforts and help to identify new chemicals from under the ocean. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of the solutions that it helps to enable. We will also discuss the possibilities it offers in the domain of crowdsourcing and open data sharing. The future of scientific information and communication will be underpinned by these efforts, influenced by increasing participation from the scientific community and facilitated collaboration and ultimately accelerate scientific progress.
A Data Ecosystem to Support Machine Learning in Materials ScienceGlobus
This presentation was given at the 2019 GlobusWorld Conference in Chicago, IL by Ben Blaiszik from University of Chicago and Argonne National Laboratory Data Science and Learning Division.
- Data transfer
- Compute access
- Training
Data Hub:
- Secure data sharing
- Data management
- Metadata catalogues
AAI:
- Single sign-on
- User attributes
- Group management
National
resources
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This document provides an introduction to big data, including:
- Big data is characterized by its volume, velocity, and variety, which makes it difficult to process using traditional databases and requires new technologies.
- Technologies like Hadoop, MongoDB, and cloud platforms from Google and Amazon can provide scalable storage and processing of big data.
- Examples of how big data is used include analyzing social media and search data to gain insights, enabling personalized experiences and targeted advertising.
- As data volumes continue growing exponentially from sources like sensors, simulations, and digital media, new tools and approaches are needed to effectively analyze and make sense of "big data".
The document proposes a reproducible framework powered by Globus to help researchers share and reproduce scientific models and simulations. It describes challenges with current methods for sharing work, like dependencies or configuration issues, that prevent easy reproducibility. The framework aims to [1] capture scientific activities, code, data and environments; [2] preserve them as standardized packages called "SciUnits"; [3] share and distribute SciUnits so others can [4] re-execute and re-analyze the work without installation or configuration problems. Key components are outlined for establishing this framework to support reproducibility across different scientific domains.
ELIXIR-UK and the ELIXIR Interoperability PlatformELIXIR UK
Introduction to ELIXIR-UK and the ELIXIR Interoperability Platform presented by Carole Goble at the ELIXIR-UK Workshop during Genome Science 2016 in Liverpool on 31st August 2016
Introduction to ELIXIR-UK's training activities presented by Rita Hendricusdottir at the ELIXIR-UK Workshop during Genome Science 2016 in Liverpool on 31st August 2016
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.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
BREEDING METHODS FOR DISEASE RESISTANCE.pptxRASHMI M G
Plant breeding for disease resistance is a strategy to reduce crop losses caused by disease. Plants have an innate immune system that allows them to recognize pathogens and provide resistance. However, breeding for long-lasting resistance often involves combining multiple resistance genes
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
1. European LifeSciences Infrastructure for Biological Information
www.elixir-uk.org
ELIXIR-UK: Biomedical
Atlas Centre
the UK Node of the
Richard Baldock, Dave Burt, Albert Burger, Susan
Lindsay, Alan Archibald
2. Why an atlas?
• Organism & tissue level framework for spatio-temporal data –
collate, compare, analyse
• Knowledge repository – anatomy, definition of terms
• “Dumb” image coordinates to anatomy/histology/biological “space”
• Framework for interoperability – individuals, species, development
• Framework for systems modelling
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3. Biomedical Atlas Centre
• University of Edinburgh
• IGMM – Richard Baldock
• Roslin – Dave Burt, Alan
Archibald
• Newcastle University
• IHG - Susan Lindsay
• Heriot Watt University
• CS - Albert Burger
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emouseatlas.org
hudsen.org
echickatlas.org
gudmap.org
eurexpress.org
macs.hw.ac.uk/bisel/
9. ELIXIR Themes – Data
• Spatially & anatomically mapped gene-expression data
• ~37K in situ assays, ~400K images
• ~ 5K sample based assays – microarray & RNA-seq
• Atlas Models & 3D datasets
• Developmental sequence 3D models, mouse, human, chick
• Atlas models most stages mouse, human, chick
• Full 3D human data-sets 100 normal, 75 abnormals
• High-resolution Histology & Ontology
• Histology series/atlases mouse & human embryo, mouse placenta
• Developmental anatomy ontologies for mouse & human, beta-
anatomy for chick
• Data sets published with DOIs & in DataCite
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10. ELIXIR Themes – Tools
• 3D reconstruction & anatomy delineation applications
• complex 3D spatial mapping (constrained distance
transform)
• high-performance image processing for pattern
comparison and analysis (woolz – C, Java & python)
• Data-structures for complex mesh-based transforms
• 3D tile-server tools for very large image data (100GB+)
• Web-based visualisation tools & interfaces (MARender)
• DB & web-application for 3D spatial data.
• All open-source – “matech” on GitHub
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11. ELIXIR Themes – Compute
• Web-servers for spatial data - dynamic spatial
comparison & sorting
• Web-services for gene-expression, atlas data & DB query
• 3D image tile-servers, arbitrary re-sectioning
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12. ELIXIR Themes – Interoperability
• Ontologies linked through Uberon & direct mappings
mouse-human-chick
• spatial-mapping services (beta)
• DAI implementation for “Waxholm” space (INCF)
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13. ELIXIR Themes – Training
• Training courses
• Online videos etc
• Online anatomy teaching materials
• Model meshes for 3D printing (HuDSeN)
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14. Future
• Biomedical Atlas Centre
• Convergence of systems common software tools
• Build on infrastructures e.g. OMERO
• Develop interoperability standards
• Implement “ELIXIR” services & Integration
• Web-based tools for 3D image data browsing & “visual-
analysis”
• Atlas hosting other species
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15. Biological direction – the missing part
• Biological Directions
in image space
• left-right
• dorsal-ventral
• anterior-posterior
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