Low emittance rings: magnet comparison

 

For different light sources magnet data were gathered. The main field parameters and the multipoles were collected for the different magnets.

Instructions: 

Main field and multipoles for the individual machines.

Typically read by pandas dataframes but similar software packages should be able to handle the data.

Main field gives main strength (dipole, gradient, ...), pole radius or pole gap and so on

Multipoles are listed for the different machines in separate files at the reference radius of the machine.

 

ESRF-EBS uses different reference radii internally (7 and 13 mm). Data were recalculated for a reference radius of 10 mm for this machine.

 

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data of fishing operations from beidou, AIS and policy.

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A dataset containing system and service performance metrics, and user-facing quality metrics generated by running load tests against a microservice-based system under varying environmental and service configuration conditons.

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Self-Admitted Technical Debt (SATD) is a form of Technical Debt where developers document the debt using source code comments (SATD-C) or  issues (SATD-I). However, it is still unclear the circumstances that drive developers to choose one or another. In this paper, we survey authors of both types of debts using a large-scale dataset containing 74K SATD-C and 20K SATD-I instances, extracted from 190 GitHub projects. As a result, we provide 13 guidelines to support developers to decide when to use comments or issues to report Technical Debt.

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This dataset is a Verilog-a implementation of a dynamic compact model of ferroelectric capacitance. It can be run with a SPICE-type circuit simulator. 

Researchers using this dataset should cite it as follows: Ning Feng, Hao Li, Chang Su, Lining Zhang, Qianqian Huang,Runsheng Wang, and Ru Huang,  A Dynamic Compact Model for Ferroelectric Capacitance, IEEE Electron Device Letters, DOI: 10.1109/LED.2022.3141413

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We have long known that the characterization of protein three-dimensional structure is key to obtaining a detailed understanding of protein function. Computational approaches to protein structure characterization have largely addressed a narrow formulation of the problem, where the goal is the determination of one structure, also known as the native structure, from a given protein amino-acid sequence. However, many researchers over the years have argued for broadening our view of proteins to account for the multiplicity of native structures.

Instructions: 

The .zip file contains 3 folders when unzipped. We provide the details of each folder below.

 

“monomorphic_benchmark_targets” folder: Contains 20 protein targets organized into 20 subfolders. Data for each protein is provided in a subfolder named with its pdb id. Each such subfolder contains the following 4 files.

  1. A .fasta file containing the amino-acid sequence of the protein.

  2. A .pdb file containing the native tertiary conformation coordinates. Detailed format for a .pdb file can be found in http://www.wwpdb.org/documentation/file-format

  3. A .frag3 file containing the fragments of length 3 for the protein sequence generated from http://old.robetta.org/

  4. A .frag9 file containing the fragments of length 9 for the protein sequence generated from http://old.robetta.org/

 

“monomorphic_casp_targets” folder: Contains 10 protein targets organized into 10 subfolders. Data for each protein is provided in a subfolder named with its casp id. Each such subfolder contains the following 4 files.

  1. A .fasta file containing the amino-acid sequence of the protein.

  2. A .pdb file containing the native tertiary conformation coordinates.

  3. A .frag3 file containing the fragments of length 3 for the protein sequence generated from http://old.robetta.org/

  4. A .frag9 file containing the fragments of length 9 for the protein sequence generated from http://old.robetta.org/

 

“metamorphic_benchmark_targets” folder: Contains 18 pairs of protein targets organized into 18 subfolders. Data for each target pair is provided in a subfolder named with its pair id (as indicated in the paper). Each such subfolder contains the following 5 files.

  1. A .fasta file containing the amino-acid sequence common to the pair of target proteins.

  2. A .pdb file containing the native tertiary conformation coordinates for the first target in the target pair.

  3. A .pdb file containing the native tertiary conformation coordinates for the second target in the target pair.

  4. A .frag3 file containing the fragments of length 3 for the protein sequence generated from http://old.robetta.org/

  5. A .frag9 file containing the fragments of length 9 for the protein sequence generated from http://old.robetta.org/

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Any work using this dataset should cite the following paper:

Nirmalya Thakur, Saumick Pradhan, and Chia Y. Han, “Investigating the impact of COVID-19 on Online Learning-based Web Behavior”, Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022 (Accepted for publication)

Abstract

Instructions: 

For details on instructions on how to use the dataset, the above mentioned paper may be studied.

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Any work using this dataset should cite the following paper:

Nirmalya Thakur, Isabella Hall, and Chia Y. Han, “Investigating the Emergence of Online Learning in Different Countries using the 5 W’s and 1 H Approach”, Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022

Abstract

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