This is module 11 in the EDI Data Publishing training course. In this module, you will learn the procedure to upload a data package to the EDI Repository.
This is module 12 in the EDI Data Publishing training course. In this module, you will learn how to correctly cite an EDI dataset and create provenance metadata for a derived dataset.
This is module 10 in the EDI Data Publishing training course. In this module, you will receive an introduction to what a data package is, how DOIs are assigned to data packages, and the repository's steps to insert a data package.
This is module 6 in the EDI Data Publishing training course. In this module, you will learn how to create quality metadata and be introduced to the landscape of data repositories and their functions.
Introduction to the Environmental Data Initiative (EDI)Corinna Gries
The Environmental Data Initiative enables the environmental science community to maximize knowledge development through the reusability of FAIR environmental data by providing curation services, training, and a robust and modern data repository.
Please cite as: Gries, Corinna. (2018, December). Introduction to the Environmental Data Initiative (EDI) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.4672376
This is module 5 in the EDI Data Publishing training course. In this module, you will learn how to properly format a data file for publishing in the EDI Repository.
This is module 2 in the EDI Data Publishing training course. In this module, you will learn about the Environmental Data Initiative, the project that created these trainings. EDI operates the EDI Data Repository and has curators on staff to help scientists deposit their data.
This is module 12 in the EDI Data Publishing training course. In this module, you will learn how to correctly cite an EDI dataset and create provenance metadata for a derived dataset.
This is module 10 in the EDI Data Publishing training course. In this module, you will receive an introduction to what a data package is, how DOIs are assigned to data packages, and the repository's steps to insert a data package.
This is module 6 in the EDI Data Publishing training course. In this module, you will learn how to create quality metadata and be introduced to the landscape of data repositories and their functions.
Introduction to the Environmental Data Initiative (EDI)Corinna Gries
The Environmental Data Initiative enables the environmental science community to maximize knowledge development through the reusability of FAIR environmental data by providing curation services, training, and a robust and modern data repository.
Please cite as: Gries, Corinna. (2018, December). Introduction to the Environmental Data Initiative (EDI) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.4672376
This is module 5 in the EDI Data Publishing training course. In this module, you will learn how to properly format a data file for publishing in the EDI Repository.
This is module 2 in the EDI Data Publishing training course. In this module, you will learn about the Environmental Data Initiative, the project that created these trainings. EDI operates the EDI Data Repository and has curators on staff to help scientists deposit their data.
The FDA requires nonclinical data in all submissions for carcinogenicity and general toxicology studies initiated after December 17, 2016, to comply with data standards specifications. Learn more about the new SEND regulatory requirements and how Covance can help you get SEND-ready.
Rachael LammeyCrossref Mary Hirsch DataCite
The underlying data created and/or reused and remixed for research is becoming as crucial as the resulting text-based output. This is your opportunity to dig into the what, the why, and the how of data publication, data citation, and data sharing. Workshop hosts will cover this topic from a range of perspectives. Let’s review the best practices and case studies in data citation and data publishing, add to our collective understanding of why this is so important, and contribute to the next steps in building solutions to improving infrastructure for research data
A presentation of the Dutch Techcentre for Life Sciences FAIR Data ecosystem given at the BlueBridge workshop, a pre-event of the Research Data Alliance's 9th Plenary
How OpenAIRE uses persistent identifiers for discovery, enrichment, and linki...OpenAIRE
Presentation from a joint FREYA and OpenAIRE webinar "New developments in the field of Persistent Identifiers" (PIDs) that covers OpenAIRE Content Acquisition Policy, role of PIDs in OpenAIRE, OpenAIRE Guidelines and their objectives, use of PIDs for different kinds of entities and provides some examples.
Role of PIDs in connecting scholarly worksOpenAIRE
Presentation from a joint webinar FREYA and OpenAIRE: New developments in the field of Persistent Identifiers by Dr. Amir Aryani, Director, Research Graph Foundation
A podium abstract presented at AMIA 2016 Joint Summits on Translational Science. This discusses Data Café — A Platform For Creating Biomedical Data Lakes.
Presentation from a joint FREYA and OpenAIRE webinar "New developments in the field of Persistent Identifiers" (PID) on FREYA-WP3: New PID developments by Ketil Koop-Jakobsen, PANGAEA, Bremen University, Germany
A basic course on Research data management, part 3: sharing your dataLeon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
A basic course on Reseach data management, part 2: protecting and organizing ...Leon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
B2STAGE- how to shift large amounts of data| www.eudat.eu | EUDAT
| www.eudat.eu | B2STAGE is a reliable, efficient, light-weight and easy-to-use service to transfer research data sets between EUDAT storage resources and high-performance computing (HPC) workspaces.
The FDA requires nonclinical data in all submissions for carcinogenicity and general toxicology studies initiated after December 17, 2016, to comply with data standards specifications. Learn more about the new SEND regulatory requirements and how Covance can help you get SEND-ready.
Rachael LammeyCrossref Mary Hirsch DataCite
The underlying data created and/or reused and remixed for research is becoming as crucial as the resulting text-based output. This is your opportunity to dig into the what, the why, and the how of data publication, data citation, and data sharing. Workshop hosts will cover this topic from a range of perspectives. Let’s review the best practices and case studies in data citation and data publishing, add to our collective understanding of why this is so important, and contribute to the next steps in building solutions to improving infrastructure for research data
A presentation of the Dutch Techcentre for Life Sciences FAIR Data ecosystem given at the BlueBridge workshop, a pre-event of the Research Data Alliance's 9th Plenary
How OpenAIRE uses persistent identifiers for discovery, enrichment, and linki...OpenAIRE
Presentation from a joint FREYA and OpenAIRE webinar "New developments in the field of Persistent Identifiers" (PIDs) that covers OpenAIRE Content Acquisition Policy, role of PIDs in OpenAIRE, OpenAIRE Guidelines and their objectives, use of PIDs for different kinds of entities and provides some examples.
Role of PIDs in connecting scholarly worksOpenAIRE
Presentation from a joint webinar FREYA and OpenAIRE: New developments in the field of Persistent Identifiers by Dr. Amir Aryani, Director, Research Graph Foundation
A podium abstract presented at AMIA 2016 Joint Summits on Translational Science. This discusses Data Café — A Platform For Creating Biomedical Data Lakes.
Presentation from a joint FREYA and OpenAIRE webinar "New developments in the field of Persistent Identifiers" (PID) on FREYA-WP3: New PID developments by Ketil Koop-Jakobsen, PANGAEA, Bremen University, Germany
A basic course on Research data management, part 3: sharing your dataLeon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
A basic course on Reseach data management, part 2: protecting and organizing ...Leon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
B2STAGE- how to shift large amounts of data| www.eudat.eu | EUDAT
| www.eudat.eu | B2STAGE is a reliable, efficient, light-weight and easy-to-use service to transfer research data sets between EUDAT storage resources and high-performance computing (HPC) workspaces.
The Oracle Web ADI makes task easy by making it convenient in Microsoft Excel and Word to complete your Oracle E-Business Suite tasks. It works via Internet, presents Oracle E-Business Suite Data in a spreadsheet interface, validates data, enables customization and automatically imports data. The Oracle E-Business Suite task you perform on the desktop is determined by the integrator you select in Oracle Web Applications Desktop Integrator. Each seeded integrator is delivered with the Oracle E-Business Suite product that provides the functionality being integrated with the desktop.
Retail Analytics, with Oracle Data Integrator 11G.
Points about ODI Objects, Interfaces, Variables, Packages, Scenarios, Load Plans, Scheduling.
Batch Scheduling with RA 14.2, UAF in 14.2, Error Managment in RA 14.2
DAC Notes. We provide best training and placement in Data warehousing and big data analytics . Mainily we offer training on
1) OBIEE
2)ODI
3)OBIA
4)INFORMATICA
5)HADOOP
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
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We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
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Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. 2
Background
Publishing a data package to the EDI Data Repository only requires an authorized
user account and a modern web browser, along with your data and metadata
available on your desktop or laptop computer.
3. 3
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Objectives
Learn the steps of publishing your data package through the EDI Data Portal,
including problem diagnosis with the Quality Report
Understand the EDI Dashboard and what it can tell you about your published data
package
4. The four steps to EDI data happiness
1. Login to the EDI Data Portal using your unique user id
2. Reserve a data package identifier (to avoid namespace collision)
3. Evaluate your data package (and correct any issues)
4. Upload (aka publish) your data package
4
17. 17
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Summary
Evaluating and uploading data packages to the EDI Data Repository requires only 4
simple steps: 1) Login, 2) Identifier reservation, 3) Evaluate, and 4) Upload
The EDI Dashboard provides useful information for understanding the state of the
data repository, along with information about your evaluation/upload process, and
data package status