• “Detecting radio-astronomical "Fast Radio Transient Events" via an OODT-based metadata processing pipeline”, Chris Mattmann, Andrew Hart , Luca Cinquini, David Thompson, Kiri Wagstaff, Shakeh Khudikyan. ApacheCon NA 2013, Februrary 2013
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.
• “Detecting radio-astronomical "Fast Radio Transient Events" via an OODT-based metadata processing pipeline”, Chris Mattmann, Andrew Hart , Luca Cinquini, David Thompson, Kiri Wagstaff, Shakeh Khudikyan. ApacheCon NA 2013, Februrary 2013
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.
A Recommender Story: Improving Backend Data Quality While Reducing CostsDatabricks
A recommender story: improving backend data quality while reducing costsnInformation overload is one of the biggest challenges academics face on a daily basis while finding the right knowledge to advance science. With around 7k research articles being published every day, how do you find the right ones?
Elsevier is a global information analytics business that helps institutions and professionals advance healthcare, open science and improve performance. With many data sources and signals being available, data science and big data engineering provide the perfect opportunity to deliver more value to researchers.
Here we will focus on Mendeley, an open (free of charge) academic content platform to help researchers discover new information via functionalities such as a crowd sourced collection of academic related documents (Catalogue) and various personalized recommender systems. MendeleySuggest, the recommender system, helps millions of researchers worldwide to find documents and people relevant to their research field, they did not yet know exist. The personalised recommenders are powered by Mendeley Catalogue, clustering 2 billion records correctly into canonical records, state of the art algorithms and big data solutions (e.g. Spark).
In the past few years, we noticed that with our content growth, quality of the canonical records started drifting due to scalability issues. As a result, we faced clustering accuracy problems and, in turn, impacting also the recommenders. In this talk we will highlight how we rearchitected the fabrication of Mendeley Catalogue to improve its scalability and accuracy. In addition, we will show how the migration from Hadoop Map Reduce to Spark has helped us reduce costs as well as improving maintainability.
My talk at the Winter School on Big Data in Tarragona, Spain.
Abstract: 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. 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.
Overview of the W3C Semantic Sensor Network (SSN) ontologyRaúl García Castro
The slides include an overview of the W3C Semantic Sensor Network (SSN) ontology along with an example of its use in a coastal flood emergency planning use case in the FP7 SSG4Env project.
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy SciencesIan Foster
Argonne’s Discovery Engines for Big Data project is working to enable new research modalities based on the integration of advanced computing with experiments at facilities such as the Advanced Photon Source (APS). I review science drivers and initial results in diffuse scattering, high energy diffraction microscopy, tomography, and pythography. I also describe the computational methods and infrastructure that we leverage to support such applications, which include the Petrel online data store, ALCF supercomputers, Globus research data management services, and Swift parallel scripting. This work points to a future in which tight integration of DOE’s experimental and computational facilities enables both new science and more efficient and rapid discovery.
Semantically-Enabling the Web of Things: The W3C Semantic Sensor Network Onto...Laurent Lefort
Presentation of the SSN XG results at eResearch Australia 2011 https://eresearchau.files.wordpress.com/2012/06/74-semantically-enabling-the-web-of-things-the-w3c-semantic-sensor-network-ontology.pdf
Scaling People, Not Just Systems, to Take On Big Data ChallengesMatthew Vaughn
Here, I describe how the Texas Advanced Computing Center has shifted its focus from traditional modeling and simulation towards fully embracing big data analytics performed by users with diverse technical backgrounds.
Course: Bioinformatics for Biomedical Research (2014).
Session: 2.2- Introduction to Galaxy. A web-based genome analysis platform.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
In 2001, as early high-speed networks were deployed, George Gilder observed that “when the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances.” Two decades later, our networks are 1,000 times faster, our appliances are increasingly specialized, and our computer systems are indeed disintegrating. As hardware acceleration overcomes speed-of-light delays, time and space merge into a computing continuum. Familiar questions like “where should I compute,” “for what workloads should I design computers,” and "where should I place my computers” seem to allow for a myriad of new answers that are exhilarating but also daunting. Are there concepts that can help guide us as we design applications and computer systems in a world that is untethered from familiar landmarks like center, cloud, edge? I propose some ideas and report on experiments in coding the continuum.
Data Science with Spark - Training at SparkSummit (East)Krishna Sankar
Slideset of the training we gave at the Spark Summit East.
Blog : https://doubleclix.wordpress.com/2015/03/25/data-science-with-spark-on-the-databricks-cloud-training-at-sparksummit-east/
Video is posted at Youtube https://www.youtube.com/watch?v=oTOgaMZkBKQ
Scalable Data Science and Deep Learning with H2Oodsc
The era of Big Data has passed, and the era of sensory overload – that is, the proliferation of sensor data – is upon us. The challenge today is how to create the next generation of business and consumer applications that transform how we interact with sensors themselves. Applications need to learn from every user interaction and data point and predict what can happen next. The future depends on Machine Learning, as much as it depends on the data itself, to change the way we interact with these systems.
In this talk, we explain H2O’s scalable distributed in-memory math architecture and its design principles. The platform was built alongside (and on top of) both Hadoop and Spark clusters and includes interfaces for R, Python, Scala, Java, JavaScript and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. We outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. By the end of this presentation, you will know how to create your own machine learning workflows on your data using R, Python (iPython Notebooks) or the Flow GUI.
A Recommender Story: Improving Backend Data Quality While Reducing CostsDatabricks
A recommender story: improving backend data quality while reducing costsnInformation overload is one of the biggest challenges academics face on a daily basis while finding the right knowledge to advance science. With around 7k research articles being published every day, how do you find the right ones?
Elsevier is a global information analytics business that helps institutions and professionals advance healthcare, open science and improve performance. With many data sources and signals being available, data science and big data engineering provide the perfect opportunity to deliver more value to researchers.
Here we will focus on Mendeley, an open (free of charge) academic content platform to help researchers discover new information via functionalities such as a crowd sourced collection of academic related documents (Catalogue) and various personalized recommender systems. MendeleySuggest, the recommender system, helps millions of researchers worldwide to find documents and people relevant to their research field, they did not yet know exist. The personalised recommenders are powered by Mendeley Catalogue, clustering 2 billion records correctly into canonical records, state of the art algorithms and big data solutions (e.g. Spark).
In the past few years, we noticed that with our content growth, quality of the canonical records started drifting due to scalability issues. As a result, we faced clustering accuracy problems and, in turn, impacting also the recommenders. In this talk we will highlight how we rearchitected the fabrication of Mendeley Catalogue to improve its scalability and accuracy. In addition, we will show how the migration from Hadoop Map Reduce to Spark has helped us reduce costs as well as improving maintainability.
My talk at the Winter School on Big Data in Tarragona, Spain.
Abstract: 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. 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.
Overview of the W3C Semantic Sensor Network (SSN) ontologyRaúl García Castro
The slides include an overview of the W3C Semantic Sensor Network (SSN) ontology along with an example of its use in a coastal flood emergency planning use case in the FP7 SSG4Env project.
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy SciencesIan Foster
Argonne’s Discovery Engines for Big Data project is working to enable new research modalities based on the integration of advanced computing with experiments at facilities such as the Advanced Photon Source (APS). I review science drivers and initial results in diffuse scattering, high energy diffraction microscopy, tomography, and pythography. I also describe the computational methods and infrastructure that we leverage to support such applications, which include the Petrel online data store, ALCF supercomputers, Globus research data management services, and Swift parallel scripting. This work points to a future in which tight integration of DOE’s experimental and computational facilities enables both new science and more efficient and rapid discovery.
Semantically-Enabling the Web of Things: The W3C Semantic Sensor Network Onto...Laurent Lefort
Presentation of the SSN XG results at eResearch Australia 2011 https://eresearchau.files.wordpress.com/2012/06/74-semantically-enabling-the-web-of-things-the-w3c-semantic-sensor-network-ontology.pdf
Scaling People, Not Just Systems, to Take On Big Data ChallengesMatthew Vaughn
Here, I describe how the Texas Advanced Computing Center has shifted its focus from traditional modeling and simulation towards fully embracing big data analytics performed by users with diverse technical backgrounds.
Course: Bioinformatics for Biomedical Research (2014).
Session: 2.2- Introduction to Galaxy. A web-based genome analysis platform.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
In 2001, as early high-speed networks were deployed, George Gilder observed that “when the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances.” Two decades later, our networks are 1,000 times faster, our appliances are increasingly specialized, and our computer systems are indeed disintegrating. As hardware acceleration overcomes speed-of-light delays, time and space merge into a computing continuum. Familiar questions like “where should I compute,” “for what workloads should I design computers,” and "where should I place my computers” seem to allow for a myriad of new answers that are exhilarating but also daunting. Are there concepts that can help guide us as we design applications and computer systems in a world that is untethered from familiar landmarks like center, cloud, edge? I propose some ideas and report on experiments in coding the continuum.
Data Science with Spark - Training at SparkSummit (East)Krishna Sankar
Slideset of the training we gave at the Spark Summit East.
Blog : https://doubleclix.wordpress.com/2015/03/25/data-science-with-spark-on-the-databricks-cloud-training-at-sparksummit-east/
Video is posted at Youtube https://www.youtube.com/watch?v=oTOgaMZkBKQ
Scalable Data Science and Deep Learning with H2Oodsc
The era of Big Data has passed, and the era of sensory overload – that is, the proliferation of sensor data – is upon us. The challenge today is how to create the next generation of business and consumer applications that transform how we interact with sensors themselves. Applications need to learn from every user interaction and data point and predict what can happen next. The future depends on Machine Learning, as much as it depends on the data itself, to change the way we interact with these systems.
In this talk, we explain H2O’s scalable distributed in-memory math architecture and its design principles. The platform was built alongside (and on top of) both Hadoop and Spark clusters and includes interfaces for R, Python, Scala, Java, JavaScript and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. We outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. By the end of this presentation, you will know how to create your own machine learning workflows on your data using R, Python (iPython Notebooks) or the Flow GUI.
Building a National Virtual Observatory: The Case of the Spanish Virtual Obse...Joint ALMA Observatory
Talk at the presentation workshop for the Chilean Virtual Observatory (ChiVO; http://www.chivo.cl/workshop-program), on lessons that can be learned from the development of the Spanish Virtual Observatory, and how ChiVO has already applied most of them.
Astronomical Data Processing on the LSST Scale with Apache SparkDatabricks
The next decade promises to be exciting for both astronomy and computer science with a number of large-scale astronomical surveys in preparation. One of the most important ones is Large Scale Survey Telescope, or LSST. LSST will produce the first ‘video’ of the deep sky in history by continually scanning the visible sky and taking one 3.2 giga-pixel image every 20 seconds. In this talk we will describe LSST’s unique design and how its image processing pipeline produces catalogs of astronomical objects. To process and quickly cross-match catalog data we built AXS (Astronomy Extensions for Spark), a system based on Apache Spark. We will explain its design and what is behind its great cross-matching performance.
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
A talk given at the annual Computer Science for High School Teachers event at Victoria University of Wellington. I presented on some basics of the World Wide Web and why it's worth to preserve it, our work on non-expert tools to populate semantically enriched content, a current project to identify NZ native birds based on their calls that involves citizen science and contemporary deep learning using TensorFlow, a project that investigates the impact of online citizen science on the development of science capabilities of primary school children, and my collaboration with Adam Grener from the School of English, Film, Theater and Media Studies at VUW with whom I am working on computational tools for the literature studies.
Similar to VO web-services-based astronomy workflows (20)
Jupyter notebooks have arrived to stay as a means to document the scientific analysis protocol, as well as to provide executable recipes shared seamlessly among the community. This has triggered the rise of a plethora of complementary tools and services associated to them. This talk will cover different possibilities to use Jupyter notebooks and JupyterLab interface. We will start with the description of their basic functionalities, as well as functionality extensions not widely known by the community. We will describe how to take advantage of their cross-language capabilities to enhance collaborative work, and also use them as complementary assets in the paper publication process to provide reproducibility of the results. Other aspects on how to deal with modularity and scalability of long complex notebooks will be covered, and we will see several platforms for rendering and execution others then the browser and the local desktop. We will finish on how they are actually being used together with Docker and Binder as part of the versioned executable documentation of a project like Gammapy.
Los IPython Notebooks nos han proporcionado una sustancial mejora en la documentación del scripts, así como su inspección y una mayor re-utilización. Los IPython Notebooks también permiten acceder a distintos lenguajes de programación (Fortran, IDL, R, Shell,..) en un mismo script, lo que unido a su modo de acceso Web les hace ser un elemento ideal para el trabajo colaborativo (multi-lenguaje, multi-usuario, multi-plataforma, etc..) Os contaré qué tipo de cosas pueden hacerse con IPython Notebooks, desde desarrollo colaborativo de código multi-lenguaje, pasando por la reutilización de tutoriales, visualización interactiva de resultados, hasta la distribución de código más modular, y la publicación final de un experimento digital verificable y reproducible: el preámbulo de los papers ejecutables.
Astronomy is a collaborative science, but it has also become highly specialized, as many other disciplines. Improvement of sharing, discovery and access to resources will enable astronomers to greatly benefit from each other’s highly specialized knowhow. Some initiatives led by scientists and publishers, complement traditional paper publishing with assets published in more interactive digital formats. Among the main goals of these efforts are improving the reproducibility and clarity of the scientific outcome, going beyond the static PDF file, and fostering re-use, which turns into a more efficient exploitation of available digital resources.
The science performed in Astronomy is digital science, from observing proposals to final publication, including data and software used: each of the elements and actions involved in the scientific output could be recorded in electronic form.
This fact does not prevent the final outcome of an experiment is still difficult to reproduce. An exhaustive process of documentation can be long, tedious, where access to all the resources must be granted, and after all, the repeatability of results is not even guaranteed. At the same time, we have access to a wealth of files, observational data and publications that could be used more efficiently with a better visibility of the scientific production, avoiding duplication of effort and reinvention.
Digital Science: Reproducibility and Visibility in AstronomyJose Enrique Ruiz
The science done in Astronomy is digital science, from observing proposals to final publication, to data and software used: each of the elements and actions involved in scientific output could be recorded in electronic form. This fact does not prevent the final outcome of an experiment is still difficult to reproduce. This procedure can be long, tedious, not easily accessible or understandable, even to the author. At the same time, we have a rich infrastructure of files, observational data and publications. This could be used more efficiently if we reach greater visibility of the scientific production, which avoids duplication of effort and reinvention.
Reproducibility is a cornerstone in scientific method, and extraction of relevant information in the current and future data flood is key in Astronomy. The AMIGA group (Analysis of the interstellar Medium of Isolated GAlaxies, IAA-CSIC, http://amiga.iaa.es) faces these two challenges in the European project "Wf4Ever: Advanced technologies for enhanced preservation workflow Science" to enable the preservation of the methodology in scalable semantic repositories to facilitate their discovery, access, inspection, exploitation and distribution. These repositories store the experiments on "Research Objects" whose main constituents are digital scientific workflows. These provide a comprehensive view and clear scientific interpretation of the experiment as well as the automation of the method, going beyond the usual pipelines that normally end up in data processing.
The quantitative leap in volume and complexity of the next generation of archives will need analysis and data mining tasks to live closer to the data, in computing and distributed storage environments, but they should also be modular enough to allow customization from scientists and be easily accessible to foster their dissemination among the community. Astronomy is a collaborative science, but it has also become highly specialized, as many other disciplines. Sharing, preservation, discovery and a much simplified access to resources in the composition of scientific workflows will enable astronomers to greatly benefit from each other’s highly specialized knowhow, they constitute a way to push Astronomy to share and publish not only results and data, but also processes and methodologies.
We will show how the use of scientific workflows can help to improve the reproducibility of the experiment and a more efficient exploitation of astronomical archives, as well as the visibility of the scientific methodology and its reuse.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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.
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.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
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.
3. Wf4Ever!
Curating and preserving collaborative digital experiments
1. Intelligent Software Components (ISOCO, Spain)!
2. University of Manchester (UNIMAN, UK)!
2 7
3. Universidad Politécnica de Madrid (UPM, Spain)!
5! 4!
4. Poznan Supercomputing and Networking Centre
(PSNC, Poland)!
5. Universisty of Oxford (OXF, UK)!
6. Instituto Astrofísica Andalucía (IAA-CSIC, Spain)!
1! 3! 7. Leiden University Medical Centre (LUMC, NL)!
6!
4. Who are you ?!
The AMIGA Group!
Analysis of the interstellar Medium of Isolated Galaxies!
!
Statistical baseline of isolated galaxies to compare!
with the behaviour of galaxies in denser environments!
Multi study of ~1000 galaxies!
!
Instituto Astrofisica de Andalucia - CSIC!
Univ . Granada, Obs. Marseille, Obs. Paris, !
NAOJ, FCRAO, UNAM, Univ. Edinburgh, !
IRAM, ESO, Kapteyn Astronomical Institute.!
!
P.I. Lourdes Verdes-Montenegro!
http://amiga.iaa.es!
5. Who are you ?!
VO Virtual Observatory!
• International Virtual Observatory Alliance (IVOA)!
• Interoperability and Discovery!
• Publishing and Accessing Data!
• Service Oriented Architecture (SoA)!
• Integration of Software and Data!
• Distributed Resources!
• Panchromatic Astronomy!
• Data Models!
• Web Services!
• Semantics!
!
10. Who are you ?!
VO Archives Developments
Robledo DSS-63!
• Madrid Deep Space Communication Complex (MDSCC)!
• 70m single dish in Robledo de Chavela (Madrid)!
• 5% operational time for observations!
• K band Spectra (18 - 26 GHz)!
• H2O Masers, methanol, NH3,..!
!
!
TAPAS – IRAM 30m!
• Telescope Archive for Public Access System!
• Bolometric observations, maps, spectra!
• Rotational molecular transitions!
• ~200 scientific projects / year, 1TB!
Radio Astronomy DAta Model for Single-dish telescopes!
11. Who are you ?!
The AMIGA Group!
Analysis of the interstellar Medium of Isolated Galaxies!
!
Statistical baseline of isolated galaxies to compare!
with the behaviour of galaxies in denser environments!
!
Multi study of ~1000 galaxies!
+!
Need of intensive and complex analysis of 3D data!
2D spatial + 1 Velocity!
12. Who are you ?!
Velocity Datacubes!
!
M. Krips – ESO 3D2008 Workshop – Garching!
13. Who are you ?!
GIPSY!
Groningen Image Processing SYstem!
Connectivity !
• VO Archives !
• VO Software!
!
Accessibility!
• Usability GUI!
• VO Web Services!
!
Kapteyn Astronomical Institute!
IAA - CSIC!
14. Who are you ?!
B0DEGA Below 0 DEgrees GAlaxies!
P.I. : D. Espada!
Legacy project of Submillimiter Array interferometer (SMA)!
http://b0dega.iaa.es!
!
IAA-CSIC!
CfA (Harvard-Smithsonian Center for Astrophysics)!
ASIAA (Institute of Academia Sinica Astronomy and Astrophysics) !
!
Molecular gas properties of a survey of nearby galaxies.!
30 processed and reduced datacubes of galaxies!
15. Who are you ?!
The B0DEGA 3D VO Catalog!
The Data and Service provider!
Aladin VO Software!
16. The Virtual Observatory!
The Virtual Observatory!
Infrastructure of interoperable data and services. Standards for:!
• Providers to share data and services!
• Developers to discover the services, find and access the data!
Goal: astronomers to use this infrastructure in a seamless way!
17. The Virtual Observatory!
Standards for Web Services!
• Most of the Web Services in Astronomy!
• They are registered and curated !
• VO Registry!
• WS for Humans!
• Data discovery and data access!
• Accessed with local software (Europe)!
• Integrated in web portals (USA)!
• WS for Machines!
• Storage, transport, authentication, etc.!
18. The Virtual Observatory!
The VO Registry!
• If you are not registered, you are not in the VO!
• Web forms to register services!
• Three VO Registries!
• Euro-VO!
• National Virtual Observatory (USA)!
• AstroGrid (UK)!
• Harvesting among registries!
• A VO Registry register resources!
• Organizations!
• Authorities!
• Data collections!
• Services!
19. The Virtual Observatory!
WS for Humans!
• Most WS provide “just” Data Discovery and Access!
• Associated to a very specific Archive!
• Designed to discover!
• VO Services!
• Catalogs!
• Images!
• Spectra!
• Parameters-based -> Standards!
• Responses are always VOTables!
• Characterization of data!
• Actual data values !
• List of services !
• Spreadsheets for catalogues!
• Links to binaries for images and spectra!
20. The Virtual Observatory!
WS for Humans!
• Sesame name resolver is one of the most used!
• Resolves objects names into coordinates!
• Provided by Centre de Données de Strasboug (CDS) !
• Data Discovery and Access (RESTful)!
• ConeSearch!
• Simple Image Access!
• Simple Spectra Access!
• Parameters: RA, DEC, SIZE !
• Table Access Protocol (TAP), OpenSkyQuery, SkyNodes!
• Astronomical Data Query Langage (ADQL) requests!
• Sparse complex services (SOAP)!
• Mosaicing of images, footprint of regions, spectral
building and fitting, principal components analysis in
spectra..!
• Common Execution Architecture (AstroGrid)- not took off!
21. The Virtual Observatory!
WS for Machines!
• Implementation in progress!
• More standards than implemented services!
• Universal Worker Service (Grid oriented)!
• asynchronous!
• stateful!
• job oriented services!
• VOSpace!
• distributed storage!
• will be provided for Big Data archives!
• Single Sign-On and Credential Delegation!
• Registry Interfaces: services acting on the Registry!
22. The Virtual Observatory!
VOSI!
• VO Services Support Interface (REST binding)!
• In progress of implementation!
• Provides interoperability among services!
• Common Contract for all VO services!
• Self-descriptive services!
"- operations and data!
/capabilities /tables!
- state of the service !
/availability /upSince /downAt /backAt /note!
• XML/VOTable VOSI files!
• VOSI files stored in service provider server!
• Files are scanned by VO Regrisries!
• Provide also state of the service!
23. The Virtual Observatory!
VOTables!
!
XML Format!
• Characterization of Data!
• Semantics!
• UCDs (Universal Content Descriptors)!
• Data Models!
• UTypes!
• Actual Data!
• Tabular data!
• Links to binary data!
31. A Cloud of Services!
The next generation of archives!
!
Much wider FoV and spectral coverage!
• Large volumes for an observed datacube!
• Subproducts are Virtual Data generated on-the-fly!
Automated surveys !
• Huge amounts of tabular data!
• Services for Knowledge Discovery in Databases!
32. A Cloud of Services!
Cube sizes!
!
ASKAP Cubes!
Prof. Kevin Vinsen !
33. A Cloud of Services!
The overall picture!
!
Distributed, scalable and flexible infrastructure!
• Grid + Cloud may solve storage and processing!
• Bandwidth is the issue!
Big Data Science performance is highly dependent
upon I/O data rates (local and transfer)!
!
The data is the infrastructure!
• Interconnected and interoperable archives!
• Distributed, multi-wavelength and multi-facilities!
!
Archives speaking Web Services!
ALMA, LSST, ASKAP, MeerKAT, LOFAR, Apertif,...!
34. A Cloud of Services!
The overall picture!
!
We are moving into a world where !
• computing and storage are cheap !
• data movement is death!
!
Archives should evolve from data providers into virtual data
and services providers, where web services may help to solve
bandwidth issues.!
!
Web Services!
• Smaller virtual data subproducts!
• Distributed, multi-archive, multi-wavelength astronomy!
• Workflows as a disruptive working methodology!
!
35. A Cloud of Services!
3D Data Services!
• Cutout!
• Resample!
• Spectrum extraction!
• 2D slice extraction!
• Dimensional reduction!
• Filtering/Flagging!
• 2D Moments!
• Complex transformations!
!
36. Scientific Use Cases!
Exploration services!
KDD - Knowledge Discovery in Databases!
Understand what information is contained within the
data in order to know how we can efficiently extract it !
• Anomaly detection!
• Cross-matching data!
• Dimensionality reduction!
!
Extraction of scientifically !
relevant information from a!
multidimensional parameter space.! visIt software
!
37. Scientific Use Cases!
Data Mining!
Some key astronomy problems that can be addressed with data mining
techniques:!
!
• Cross-Match objects from different catalogues!
• The distance problem (e.g., Photometric Redshift estimators)!
• Star-Galaxy Separation!
• Cosmic-Ray Detection in images!
• Supernova Detection and Classification!
• Morphological Classification (galaxies, AGN, gravitat. lenses, ...)!
• Class and Subclass Discovery (brown dwarfs stars, ...)!
• Dimension Reduction = Correlation Discovery!
• Learning Rules for improved classifiers !
• Classification of massive data streams!
• Real-time Classification of Astronomical Events !
• Clustering of massive data collections!
• Novelty, Anomaly, Outlier Detection in massive databases!
!
43. Scientific Use Cases!
Self Organizing Map!
Organizing information in complex data collections!
Find hidden relationships and patterns!
Based on links among keywords and metadata !
!
!
!
44. Scientific Use Cases!
The time domain!
• VO Sky Event reporting metadata!
• What, Where, Who, How ?!
• Stars flares ,GRBs, solar, atmospheric particle bursts,..!
!
The Helio-VO Project!
!
!
!
!
!
!
!
!
45. Scientific Use Cases!
The VO-Experiment!
• Data Mining Oriented!
• VO Services !
• Discovery !
• Access!
• Waiting for analysis services!
• Local software (also some Web portals)!
• Crossmatching!
• Inspection!
• Visualization!
• Web services associated to archives of big facilities!
• Hinders cross-boundary science!
!
!
!
46. Scientific Use Cases!
XMM Observations of the AMIGA Sample!
!
!
TopCat Hands-On !
Let’s do some science !!
!
!
52. Wf4Ever!
Workflows Preservation!
!
All components related to the!
research lifecycle should be available. !
!
Preserved and easily retrievables !
!
• Proposals!
• Data!
• Processes!
• Workflows!
• Publications!
!
!
53. IVOA Wf!
Open questions for Web Services!
In the Virtual Observatory!
!
• Curation and preservation (identifiers)!
• Discovery (semantics) of web services!
• Characterization: input, outputs, functionality, etc.!
• Copies (authenticity) or similar used as alternates !
• Permissions (authentication), licenses, platform, costs,..!
• Metrics for quality: popularity, use stats, logs uptime, etc.!
• Versioning and authoring (referenced and acknowledged)!
!
In a cloud of services and data, Web Services should benefit
of the same privileges acquired by Data.!
57. Taverna!
Simple AMIGA ConeSearch!
• Xpath plugin not a useful for extracting info from VOTable!
• Helio-VO beanshell used instead (Thanks !)!
• Visualization of results.. (VOTables) !
58. Taverna!
XMM Multi-ConeSearch!
• Lot of previous VOTable parsing ..!
• The response is 1051 VOTables !!
• VOTable merging tool needed!
59. Taverna!
AMIGA Multi-ConeSearch!
• Lot of beanshells for VOTabl and CSV parsing ..!
• Beanshells development needed for splitting lists into values!
• STILTS Library needed for VOTable crossmatching!
60. Taverna!
The VO-experiment!
• Discover Services!
• Multi-query!
• Crossmatching!
• Inspection!
• Visualization and Comparison!
!
Proposed shortcuts for Taverna!
• VORegistry Access Perspective!
• STILTS VOTable Library !
• SAMP (Connectivity with VO Software)!
• Python based beanshells!
• Simple standard astronomy functions!
!
