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Test set

Francois Lang · OSF Preprints (OSF Preprints) · 2018

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Test set

Guo, Xuchao, Yao, Xiaochuang, Hao, Xia et al. · Figshare · 2024

The test set of AgCNER contains 5975 sentences.

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Test Set

Joshua Whittam · Figshare · 2015

JMP test set which shows no correlation

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Test Set

Whittam, Joshua · Figshare · 2015

JMP test set which shows no correlation

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test set

2024

Citation: 'test set' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.11524 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.

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Test set.

Siquan Hu (7866254), Ruixiong Ma (7866257), Haiou Wang (7866260) · Figshare · 2019

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Test Set

Keeler, Alex · Figshare · 2015

Test Set

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Test set.

Raphael Trevizani (3639214), Fábio Lima Custódio (3639205), Karina Baptista dos Santos (3639211) et al. · Figshare · 2017

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Test Set

Claude Sammut, Geoffrey I. Webb · Encyclopedia of Machine Learning and Data Mining · 2017

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test set

Martin H. Weik · 2000

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Test set

Francois Lang · OSF Preprints (OSF Preprints) · 2018

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Test Set

Alex Keeler · Figshare · 2015

Test Set

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Test set

Guo, Xuchao, Yao, Xiaochuang, Hao, Xia et al. · Figshare · 2024

The test set of AgCNER contains 5975 sentences.

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Test set.

Hao Wu (65943), Yuping Yang (3874810), Sijing Deng (13851249) et al. · Figshare · 2022

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training set and test set

Belter Xin · Figshare · 2020

s3-1_x_training_set.csv- separator: tab<br>- CID, mol_path (skeleton), fragment_cluster, fragment_cluster_smiles<br>s3-2_x_train_mol_sentence.csv- separator: space- fragment_cluster_smiles<br>s3-3_x_test_set.csv- separator: tab<br>- CID, mol_path (skeleton), fragment_cluster, fragment_cluster_smiles<br>s3-4_x_test_mol_sentence.csv- separator: space- fragment_cluster_smiles<br><br><br>

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training set and test set

Belter Xin · Figshare · 2020

s3-1_x_training_set.csv- separator: tab<br>- CID, mol_path (skeleton), fragment_cluster, fragment_cluster_smiles<br>s3-2_x_train_mol_sentence.csv- separator: space- fragment_cluster_smiles<br>s3-3_x_test_set.csv- separator: tab<br>- CID, mol_path (skeleton), fragment_cluster, fragment_cluster_smiles<br>s3-4_x_test_mol_sentence.csv- separator: space- fragment_cluster_smiles<br><br><br>

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Training set and test set.

Shichao Hou (18585070), Peng Zhao (128233), Peng Cui (33358) et al. · Figshare · 2024

&lt;div&gt;&lt;p&gt;The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pressing challenge at hand, it is imperative to find a solution for the intelligent recognition of hydrogenation catalyst images. To address the issue of low recognition accuracy caused by adhesion and stacking of hydrogenation catalysts, An image recognition algorithm of hydrogenation catalyst based on FPNC Net was proposed in this paper. In the present study, Resnet50 backbone network was used to extract the features, and spatially-separable convolution kernel was used to extract the multi-scale features of catalyst fringe. In addition, to effectively segment the adhesive regions of stripes, FPN (Feature Pyramid Network) is added to the backbone network for deep and shallow feature fusion. Introducing an attention module to adaptively adjust weights can effectively highlight the target features of the catalyst. The experimental results showed that the FPNC Net model achieved an accuracy of 94.2% and an AP value improvement of 19.37% compared to the original CenterNet model. The improved model demonstrates a significant enhancement in detection accuracy, indicating a high capability for detecting hydrogenation catalyst targets&lt;/p&gt;&lt;/div&gt;

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Test set-up.

Elke M. J. Devocht (3043653), A. Miranda L. Janssen (3043650), Josef Chalupper (3043656) et al. · Figshare · 2016

&lt;p&gt;Six loudspeakers were positioned in a circle of 1m around the participant, who was fitted with a cochlear implant (CI) and hearing aid (HA) in opposite ears. Speech was always presented in front (0°) while noise was simultaneously presented from the other five speaker locations. Participants used a touchscreen in front to self-administer all tests.&lt;/p&gt;

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Test set JMP

Rakesh Baboo · Figshare · 2015

Using JMP to test for correlation between descriptors for the test set, following the use of the training set

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Test set-up.

Anuschka Grobelny (3638449), Janina R. Behrens (3913120), Sebastian Mertens (4688173) et al. · Figshare · 2017

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