cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1251569
title: Open Access Levels Of Dutch Universities' Output 2016-2017 (Articles & Reviews): Green, Gold, Hybrid And Bronze - May 2018

description: Using Web of Science and Unpaywall data, we here provide an update of Open Access (OA) levels of Dutch universities, for 2016 and 2017.

Our previous analysis  (10.5281/zenodo.1133759 and 10.7287/peerj.preprints.3520v1) looked at OA classification as included in Web of Science (gold and green OA, based on Unpaywall data), and supplemented that with a breakdown of gold OA into pure gold, hybrid and bronze, taken from Unpaywall data (formerly OADOI) directly. Here, we improve on this by running all DOIs retrieved from WoS through Unpaywall data (using their web interface that allows batch checking of up to 10,000 DOIs at a time). Unlike WoS, Unpaywall data itself includes author-submitted versions in their green OA classification, resulting in more complete green OA levels. 

In addition, since our initial analysis of December 2017, Unpaywall data has considerably expanded its coverage of institutional repositories  (IRs) (see https://unpaywall.org/sources). This now includes coverage of the IRs from all Dutch universities. 

Taken together, the current data show higher levels of green open access, including author-submitted versions, compared to our previous analysis. 

In this update, we include output (articles and reviews) from 2016 and 2017 for all 14 universities in the Netherlands. 

The following categories are distinguished (description taken from Piwowar at al., 2018, doi: 10.7717/peerj.4375)


	<strong>Pure gold</strong>: Published in an open-access journal (as defined by the DOAJ)
	<strong>Hybrid</strong>: Free under an open license in a toll-access journal
	<strong>Bronze</strong>: Free to read on the publisher page, but without a license
	<strong>Green: </strong>Available from an institutional or disciplinary repository (including PubMedCentral)


Data for Dutch universities were collected from Web of Science using the organization-enhanced field. Only articles and reviews were included. DOIs were extracted from the Web of Science export, run through the Unpaywall data Simple Query Tool. From the resulting data from Unpaywall, OA classification was done using a simple formula in Excel (to be replaced by an R script in a future update). The Excel template used is included in this dataset, as is the OADOI API output for each Dutch university's article subset, and the lists of DOIs derived from Web of Science. The dataset also includes summarized data and three charts generated from these data, showing levels of different types of OA for 2016, 2017 and the two years compared.  

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DOI: 10.5281/zenodo.1251288
title: Toposat2 Log Files - Full Version

description: Full version of the log files of the experiments from the paper "Tuning Parallel SAT Solvers".
description: Funded by the German Federal Ministry of Education and Research, combined project 01IH15006A
DOI: 10.5281/zenodo.1251287
title: Toposat2 Log Files - Full Version

description: Full version of the log files of the experiments from the paper "Tuning Parallel SAT Solvers".
description: Funded by the German Federal Ministry of Education and Research, combined project 01IH15006A
DOI: 10.5281/zenodo.1251575
title: Toposat2_Logs

description: Log files for TopoSAT2 and HordeSAT for the Paper "Tuning Parallel SAT Solvers". 

For TopoSAT2, some non-relevant parts of the log files were removed. The full version can be found at http://doi.org/10.5281/zenodo.1251288
description: Funded by the German Federal Ministry of Education and Research, combined project 01IH15006A
DOI: 10.5281/zenodo.1251576
title: Toposat2_Logs

description: Log files for TopoSAT2 and HordeSAT for the Paper "Tuning Parallel SAT Solvers". 

For TopoSAT2, some non-relevant parts of the log files were removed. The full version can be found at http://doi.org/10.5281/zenodo.1251288
description: Funded by the German Federal Ministry of Education and Research, combined project 01IH15006A
DOI: 10.5281/zenodo.1251172
title: Junction Bam Files For Drosophila Species

description: Junction bam files for Drosophila species
DOI: 10.5281/zenodo.1251173
title: Junction Bam Files For Drosophila Species

description: Junction bam files for Drosophila species
DOI: 10.17632/j4pm6s3fzy.1
title: Data for: The upper bound of the misclassification risk for the long-term myoelectric signal recognition based on the adaptive learning

description: In order to provide the guideline for designing and evaluating the long-term myoelectric signal recognition methods based on the adaptive learning, we proposed theoretical models to describe how the boundary of the misclassification risk (BMR) change along parameters including, the adaptive learning times, the adaptive learning frequencies, the generalization ability of the predictive model, and the ratio of samples without supervised information during the adaptive learning. The models are built up based on the formulated adaptive learning process of the long-term myoelectric signal recognition, and the normalized definition of the concept drift and the concept sequence. Experiments based on both realistic long EMG data sequences (Realistic Concept-drift-rate Sequence, RCS) and simulated EMG data sequences with controllable permutations (Zero Concept-drift-rate Sequence, ZCS, and Constant Concept-drift-rate Sequence, CCS) are conducted to validate our theoretical analyses. All the data sequences are reorganized from the raw EMG dataset. The reorganization methods are presented in the paper. In the data set, we present the raw EMG dataset and the recognition result of the ZCSs, the CSSs and the RCSs. The folder “RawEMGData” contains the raw EMG dataset containing EMG data of 8 subjects noted as S1 to S8. For each subject, we acquire 24 data sessions, corresponding to the .dat files in the folder. The data sessions were acquired every a half hour. Each data session includes 4000 samples of 8 motion classes (500 samples for one motion class). Each row containing 8 data in the file represents a sample. The former 7 data in a row are the data acquired by 7 EMG channels, the last one data in a row is the motion label of the sample. The folder “CCSResult” contains the recognition result of the CCSs. The data are saved in the form of 5-dimention array in .mat files. The entry at position (i,j,k,l,m) saves the classification result of samples in lth session of kth session sequence, which are classified by the mth adaptive learner. The classification result is the number of samples with the ground truth label of j, and the classification result of i. The position varies from (1,1,1,1,1) to (8,8,100,L,8). The variable m=1 corresponds to the result of the IAL, m=2 to 8 correspond to the results of the RAL1 to RAL7. The maximum value of l is noted as L, which equals 8, 16, 18, 20, 22, 27, 32, 64, 128 for file “Bt_Seq1.mat” to file “Bt_Seq9.mat”.
DOI: 10.5258/soton/d0169
title: Data for Characterisation of platinum electrodeposits on a titanium micromesh stack in a rectangular channel flow cell

description: Dataset supports:
Arenas Martinez, L. F. et al (2017). Characterisation of platinum electrodeposits on a titanium micromesh stack in a rectangular channel flow cell. Electrochimica Acta.Abstract
Platinised titanium mesh is a common electrode material in industrial electrolytic cells and Ce- based redox flow batteries. In this work, the electrodeposition of platinum on a stack of titanium micromeshes is carried out from a flowing alkaline solution in a rectangular channel, divided flow cell. The morphology and distribution of platinum deposits are studied by SEM, EDS mapping and X-ray computer tomography. The active surface area of the electrode was assessed from the charge transfer current for the reduction of Ce(IV) and compared to that of planar and expanded metal mesh electrodes. The surface area was estimated by hydrogen electrosorption relative to that at a planar Pt/Ti electrode. As expected from the potential drop within the electrode channel, the individual micromesh near the cell separator displayed a higher platinum content. Pt/Ti micromesh offers an extended surface area and enhanced mass transport compared to planar electrodes and conventional expanded metal mesh anodes. The applications for these and alternative electrode structures are discussed.
DOI: 10.17632/j4pm6s3fzy.2
title: Data for: The upper bound of the misclassification risk for the long-term myoelectric signal recognition based on the adaptive learning

description: In order to provide the guideline for designing and evaluating the long-term myoelectric signal recognition methods based on the adaptive learning, we proposed theoretical models to describe how the boundary of the misclassification risk (BMR) change along parameters including, the adaptive learning times, the adaptive learning frequencies, the generalization ability of the predictive model, and the ratio of samples without supervised information during the adaptive learning. The models are built up based on the formulated adaptive learning process of the long-term myoelectric signal recognition, and the normalized definition of the concept drift and the concept sequence. Experiments based on both realistic long EMG data sequences (Realistic Concept-drift-rate Sequence, RCS) and simulated EMG data sequences with controllable permutations (Zero Concept-drift-rate Sequence, ZCS, and Constant Concept-drift-rate Sequence, CCS) are conducted to validate our theoretical analyses. All the data sequences are reorganized from the raw EMG dataset. The reorganization methods are presented in the paper. In the data set, we present the raw EMG dataset and the recognition result of the ZCSs, the CSSs and the RCSs. The folder “RawEMGData” contains the raw EMG dataset containing EMG data of 8 subjects noted as S1 to S8. For each subject, we acquire 24 data sessions, corresponding to the .dat files in the folder. The data sessions were acquired every a half hour. Each data session includes 4000 samples of 8 motion classes (500 samples for one motion class). Each row containing 8 data in the file represents a sample. The former 7 data in a row are the data acquired by 7 EMG channels, the last one data in a row is the motion label of the sample. The folder “CCSResult” contains the recognition result of the CCSs. The data are saved in the form of 5-dimention array in .mat files. The entry at position (i,j,k,l,m) saves the classification result of samples in lth session of kth session sequence, which are classified by the mth adaptive learner. The classification result is the number of samples with the ground truth label of j, and the classification result of i. The position varies from (1,1,1,1,1) to (8,8,100,L,8). The variable m=1 corresponds to the result of the IAL, m=2 to 8 correspond to the results of the RAL1 to RAL7. The maximum value of l is noted as L, which equals 8, 16, 18, 20, 22, 27, 32, 64, 128 for file “Bt_Seq1.mat” to file “Bt_Seq9.mat”.
DOI: 10.17632/z4v47fzz95.1
title: Hadoop Nodes Metric Data

description: This data contains collected metric data from 3 Hadoop nodes, one master, and two workers, under both normal and abnormal situations. it describes nodes behavior in the various situations which can further be analyzed to inspect anomalous times.
DOI: 10.5517/ccdc.csd.cc1zxgly
title: CCDC 1844611: Experimental Crystal Structure Determination

description: Related Article: Chao Ma, Xue-qiang Li, Jian Xu, Cou-Xi Chen|2013|Yaoxue Xuebao|48|1430|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxgr3
title: CCDC 1844616: Experimental Crystal Structure Determination

description: Related Article: Mark E Light and John E Drake|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxgs4
title: CCDC 1844617: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Scott G. Stewart, James E. Rixson|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxh7m
title: CCDC 1844631: Experimental Crystal Structure Determination

description: Related Article: Mark E Light and John E Drake|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxhr4
title: CCDC 1844647: Experimental Crystal Structure Determination

description: Related Article: Mark E Light, Michael A Paver and Jonathan S Joy|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxhw8
title: CCDC 1844651: Experimental Crystal Structure Determination

description: Related Article: Mark E Light, Michael A Paver and Jonathan S Joy|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/r45ht8hrnb.1
title: Cognitive emotion regulation strategies in types of anxiety and depression

description: This is a dataset from a study assessing cognitive emotion regulation strategies in types of anxiety (Arousal and Apprehension) and depression (Valence and Anhedonic). Here, anxiety and depression are considered as dimensional personality types. 1632 participants from a representative sample completed the Anxiety and Depression Questionnaire and the Cognitive Emotion Regulation Questionnaire. We conducted regression analyses with the affetive types as predictors. We found that the cognitive emotion regulation strategies use is related to the structure and functions (reactive or regulative) in stimulation processing of the types.
DOI: 10.5255/ukda-sn-7961-3
title: Annual Population Survey: Well-Being, April 2011 - March 2015: Secure Access
DOI: 10.5517/ccdc.csd.cc1zxk3k
title: CCDC 1844689: Experimental Crystal Structure Determination

description: Related Article: Thomais G. Tziotzi, Constantinos Milios, Tadeusz Lis|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1nt26r
title: CCDC 1543434: Experimental Crystal Structure Determination

description: Related Article: Syeda Shahzadi Batool, William T. A. Harrison, Quratulain Syed, Muhammad Salman Haider|2018|J.Coord.Chem.|71|1380|doi:10.1080/00958972.2018.1460471
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1nt27s
title: CCDC 1543435: Experimental Crystal Structure Determination

description: Related Article: Syeda Shahzadi Batool, William T. A. Harrison, Quratulain Syed, Muhammad Salman Haider|2018|J.Coord.Chem.|71|1380|doi:10.1080/00958972.2018.1460471
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxn3n
title: CCDC 1844782: Experimental Crystal Structure Determination

description: Related Article: Frank R. Fronczek|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/5j9766czzx.1
title: Grandiose/Vulnerable Narcissism predicting spiteful punishment after self-threat

description: NPI items were re-coded such that the narcissistic response was always scored as 1, and the non-narcissistic response was always scored as 0. The Vanity, Exhibitionism, Superiority, and Entitlement subscales were positively skewed, and were Log-transformed. Power analyses showed that the desired sample size given a medium effect size, .80 power, and an α of .05, was 168 participants. Given that the dataset was more than double that, in order to avoid inflated p values, the total dataset was split into a training (n = 221) and a validation (n = 233) sample based on a random selection of 50% of the total cases. This serves as an internal replication for performed analyses, as suggested by Hair and colleagues (2009). Initial preference scores were calculated using an algorithm created by LeBel and Gawronski (2009). Hypothesis 1: in the no threat condition, high and low vulnerable narcissistic participants (MCNS) would not differ in their endorsements of spiteful or strategic punishment. However, it was predicted that participants high in vulnerable narcissism in the self-threat condition would favor more spiteful punishment than threatened low narcissistic participants. This was tested using a hierarchical linear regression entering in MCNS and Condition in step 1, and adding MCNS x Condition in step 2. Hypothesis 2: in the no threat condition, participants high in grandiose narcissism would not differ from low grandiose narcissism participants in their endorsement of spiteful punishments. But in the self-threat condition, participants high in grandiose narcissism would show greater endorsement of spiteful punishment compared to participants low in grandiose narcissism. This was analyzed using total NPI scores (Hypothesis 2a) and NPI subscales separately (Hypothesis 2b). This was tested using hierarchical linear regressions entering in NPI measures and Condition in step 1, and adding NPI x Condition interactions in step 2. Hypothesis 3: vulnerable narcissism would predict more of the variance in spiteful punishments than grandiose narcissism. This was tested using Fisher's r to z transformation to analyze correlation coefficients between models. Hypothesis 4: participants higher in vulnerable narcissism would be less in favor of strategic punishment after self-threat compared to those lower in narcissism and non-threatened participants. This was tested using a hierarchical linear regression entering in MCNS and Condition in step 1, and adding MCNS x Condition in step 2. Hypothesis 5: participants higher in grandiose narcissism would be less in favor of strategic punishment after self-threat compared to those lower in narcissism and non-threatened participants. This was tested using hierarchical linear regressions entering in NPI measures and Condition in step 1, and adding NPI x Condition interactions in step 2. Hypothesis 6 threatened vulnerable narcissists should show increased implicit self-esteem after given the ability to punish.
DOI: 10.17632/k4py2wfk49.1
title: Subplate neurons can be derived in vitro from a male hPSC line (RUES1) via a preplate stage and their fate is also modulated post-mitotically via WNT signaling.
DOI: 10.5517/ccdc.csd.cc1zt082
title: CCDC 1841284: Experimental Crystal Structure Determination

description: Related Article: X.-B. Zhou, J.-R. Zhu, J.-M. Gu, X.-R. Hu|2018|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|74|690|doi:10.1107/S2053229618006836
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.48668/sk0hf6/i2mwe9
title: output_amusementnews19540719_001_0.jpg

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DOI: 10.48668/sk0hf6/mylmt4
title: output_amusementnews19530709_001_1.jpg

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DOI: 10.48668/sk0hf6/4gcuqz
title: output_amusementnews19530529_001_0.jpg

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DOI: 10.48668/sk0hf6/he71xr
title: output_amusementnews19530809_001_6.jpg

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DOI: 10.48668/sk0hf6/atnejj
title: output_amusementnews19530612_001_1.jpg

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DOI: 10.48668/sk0hf6/gz22mw
title: output_amusementnews19540413_001_5.jpg

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DOI: 10.48668/sk0hf6/q0l662
title: output_amusementnews19540621_001_5.json

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DOI: 10.48668/sk0hf6/yjey0b
title: output_amusementnews19550603_004_2.json

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DOI: 10.48668/sk0hf6/atxrpt
title: output_amusementnews19561202_004_0.jpg

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DOI: 10.48668/sk0hf6/t6mtln
title: output_amusementnews19540627_001_0.json

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DOI: 10.48668/sk0hf6/xpuy42
title: output_amusementnews19550506_004_1.json

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DOI: 10.48668/sk0hf6/rekx4d
title: output_amusementnews19540522_001_0.jpg

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DOI: 10.48668/sk0hf6/zq9qga
title: output_amusementnews19540621_004_0.json

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DOI: 10.48668/sk0hf6/lgyrhr
title: output_amusementnews19550429_004_7.json

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DOI: 10.48668/sk0hf6/kwvyzf
title: output_amusementnews19540524_004_3.json

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DOI: 10.48668/sk0hf6/nhp570
title: output_amusementnews19540527_001_1.jpg

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DOI: 10.48668/sk0hf6/bsg33v
title: output_amusementnews19540620_001_4.json

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DOI: 10.48668/sk0hf6/wfspus
title: output_amusementnews19530823_001_0.jpg

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DOI: 10.48668/sk0hf6/hsks4u
title: output_amusementnews19540606_004_0.jpg

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DOI: 10.48668/sk0hf6/gnqhub
title: output_amusementnews19550110_004_2.json

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DOI: 10.48668/sk0hf6/5rg85n
title: output_amusementnews19550527_001_0.jpg

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DOI: 10.48668/sk0hf6/p8e64y
title: output_amusementnews19540621_001_0.json

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