cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/ccj0-q561
title: Physical parameter-based S parameters

description: In this work, physical parameter‐based modeling of small signal parameters for a metal‐semiconductor field‐effect transistor (MESFET) has been carried out as continuous functions of drain voltage, gate voltage, frequency, and gate width. For this purpose, a device simulator has been used to generate a big dataset of which the physical device parameters included material type, doping concentration and profile, contact type, gate length, gate width, and work function. Five state‐of‐the‐art algorithms: multi‐layer perceptron (MLP), IBk, K*, Bagging, and REPTree have been used for creating a regression model. The symbolic regression algorithm has been used to obtain analytical expressions of the real and imaginary parts of the Scattering (S) parameters using the physics‐based generated dataset. The regression performances of all the benchmarks and the symbolic regression have been compared to references from the device simulator results. The results of the derived equations and the best algorithms have been then compared to the device simulator results, with case studies for validation. The DC performance characteristics of the MESFET have been also obtained. The proposed model can be used to predict the small signal parameters of new devices prior to development, and allows for both the device and circuit to be optimized for specific applications.
DOI: 10.21227/156h-5208
title: Physical parameter-based data-driven modeling of small signal parameters of a metal-semiconductor field-effect transistor

description: In this work, physical parameter‐based modeling of small signal parameters for a metal‐semiconductor field‐effect transistor (MESFET) has been carried out as continuous functions of drain voltage, gate voltage, frequency, and gate width. For this purpose, a device simulator has been used to generate a big dataset of which the physical device parameters included material type, doping concentration and profile, contact type, gate length, gate width, and work function. Five state‐of‐the‐art algorithms: multi‐layer perceptron (MLP), IBk, K*, Bagging, and REPTree have been used for creating a regression model. The symbolic regression algorithm has been used to obtain analytical expressions of the real and imaginary parts of the Scattering (S) parameters using the physics‐based generated dataset. The regression performances of all the benchmarks and the symbolic regression have been compared to references from the device simulator results. The results of the derived equations and the best algorithms have been then compared to the device simulator results, with case studies for validation. The DC performance characteristics of the MESFET have been also obtained. The proposed model can be used to predict the small signal parameters of new devices prior to development, and allows for both the device and circuit to be optimized for specific applications.
DOI: 10.21258/1727907
title: Swiss national forest inventory - Result table No. 891930
DOI: 10.21258/1752992
title: Swiss national forest inventory - Result table No. 899464
DOI: 10.21258/1757975
title: Swiss national forest inventory - Result table No. 899322
DOI: 10.21258/1763002
title: Swiss national forest inventory - Result table No. 899270
DOI: 10.5281/zenodo.4026861
title: leonardlab/Source Data for "Elucidation and refinement of synthetic receptor mechanisms"

description: This source data accompanies the manuscript "Elucidation and refinement of synthetic receptor mechanisms".
DOI: 10.5281/zenodo.4026860
title: leonardlab/Source Data for "Elucidation and refinement of synthetic receptor mechanisms"

description: This source data accompanies the manuscript "Elucidation and refinement of synthetic receptor mechanisms".
DOI: 10.21258/1754220
title: Swiss national forest inventory - Result table No. 891908
DOI: 10.21258/1764958
title: Swiss national forest inventory - Result table No. 897388
DOI: 10.5291/ill-data.9-13-806
title: Effect of oxidative stress on polyunsaturated lipid bilayers modulated by cholesterol and head group composition
DOI: 10.21258/1739328
title: Swiss national forest inventory - Result table No. 902063
DOI: 10.21258/1765720
title: Swiss national forest inventory - Result table No. 898954
DOI: 10.21258/1756132
title: Swiss national forest inventory - Result table No. 898055
DOI: 10.21258/1766024
title: Swiss national forest inventory - Result table No. 897625
DOI: 10.6084/m9.figshare.13488465
title: Supplementary Table 1

description: Concentration of exosomal lipids that significantly changed after Tempol treatment.
DOI: 10.6084/m9.figshare.13488465.v1
title: Supplementary Table 1

description: Concentration of exosomal lipids that significantly changed after Tempol treatment.
DOI: 10.21258/1741824
title: Swiss national forest inventory - Result table No. 902246
DOI: 10.21258/1767042
title: Swiss national forest inventory - Result table No. 897536
DOI: 10.21258/1726892
title: Swiss national forest inventory - Result table No. 893005
DOI: 10.21258/1747059
title: Swiss national forest inventory - Result table No. 892048
DOI: 10.21227/gdqj-am79
title: Neural Ordinary Differential Equation Control of Dynamics on Graphs

description: We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we introduce a neural-ODE control (NODEC) framework and find that it can learn control signals that drive graph dynamical systems into desired target states. While we use loss functions that do not constrain the control energy, our results show that NODEC produces control signals that are highly correlated with optimal (or minimum energy) control signals. Finally, we empirically showcase the high performance and versatility of NODEC for various (non-)linear dynamics and loss functions on different graphs.
DOI: 10.21227/sndx-k161
title: Neural Ordinary Differential Equation Control of Dynamics on Graphs

description: We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we introduce a neural-ODE control (NODEC) framework and find that it can learn control signals that drive graph dynamical systems into desired target states. While we use loss functions that do not constrain the control energy, our results show that NODEC produces control signals that are highly correlated with optimal (or minimum energy) control signals. Finally, we empirically showcase the high performance and versatility of NODEC for various (non-)linear dynamics and loss functions on different graphs.
DOI: 10.21258/1740371
title: Swiss national forest inventory - Result table No. 897225
DOI: 10.21227/a6s9-sr89
title: Neural Ordinary Differential Equation Control of Dynamics on Graphs

description: We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we introduce a neural-ODE control (NODEC) framework and find that it can learn control signals that drive graph dynamical systems into desired target states. While we use loss functions that do not constrain the control energy, our results show that NODEC produces control signals that are highly correlated with optimal (or minimum energy) control signals. Finally, we empirically showcase the high performance and versatility of NODEC for various (non-)linear dynamics and loss functions on different graphs.
DOI: 10.21258/1765839
title: Swiss national forest inventory - Result table No. 898920
DOI: 10.21258/1758558
title: Swiss national forest inventory - Result table No. 897969
DOI: 10.21258/1741492
title: Swiss national forest inventory - Result table No. 899041
DOI: 10.21258/1766702
title: Swiss national forest inventory - Result table No. 897322
DOI: 10.21258/1733424
title: Swiss national forest inventory - Result table No. 897173
DOI: 10.21258/1767224
title: Swiss national forest inventory - Result table No. 892899
DOI: 10.21258/1757908
title: Swiss national forest inventory - Result table No. 897659
DOI: 10.21258/1732557
title: Swiss national forest inventory - Result table No. 899552
DOI: 10.21258/1728056
title: Swiss national forest inventory - Result table No. 897810
DOI: 10.21258/1769024
title: Swiss national forest inventory - Result table No. 898190
DOI: 10.21258/1731927
title: Swiss national forest inventory - Result table No. 891884
DOI: 10.21258/1749541
title: Swiss national forest inventory - Result table No. 902260
DOI: 10.21258/1769174
title: Swiss national forest inventory - Result table No. 892836
DOI: 10.21258/1761502
title: Swiss national forest inventory - Result table No. 896993
DOI: 10.21258/1767098
title: Swiss national forest inventory - Result table No. 891824
DOI: 10.21258/1727229
title: Swiss national forest inventory - Result table No. 891678
DOI: 10.21258/1732491
title: Swiss national forest inventory - Result table No. 898787
DOI: 10.21258/1727226
title: Swiss national forest inventory - Result table No. 891812
DOI: 10.21258/1751304
title: Swiss national forest inventory - Result table No. 897159
DOI: 10.21258/1731589
title: Swiss national forest inventory - Result table No. 892011
DOI: 10.21258/1761644
title: Swiss national forest inventory - Result table No. 897654
DOI: 10.21258/1760479
title: Swiss national forest inventory - Result table No. 892077
DOI: 10.21258/1759025
title: Swiss national forest inventory - Result table No. 899670
DOI: 10.21258/1737513
title: Swiss national forest inventory - Result table No. 898075
DOI: 10.17632/wxwmgg3j95.1
title: Data for: Embedded Stacked Group Sparse Autoencoder with L1 regularization and manifold reduction

description: Data and codes for: Embedded Stacked Group Sparse Autoencoder with L1 regularization and manifold reduction
DOI: 10.25919/9kw7-gq38
title: Parkes observations for project PX501 semester 2018OCTS_BPSR

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX500) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first (PX500) will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time (relating to this proposal) will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.21258/1729457
title: Swiss national forest inventory - Result table No. 898694
DOI: 10.21258/1733899
title: Swiss national forest inventory - Result table No. 899433
DOI: 10.21258/1741453
title: Swiss national forest inventory - Result table No. 892901
DOI: 10.21258/1725436
title: Swiss national forest inventory - Result table No. 897088
DOI: 10.21258/1744437
title: Swiss national forest inventory - Result table No. 897340
DOI: 10.21258/1760955
title: Swiss national forest inventory - Result table No. 891787
DOI: 10.21258/1730258
title: Swiss national forest inventory - Result table No. 897795
DOI: 10.21258/1745619
title: Swiss national forest inventory - Result table No. 891705
DOI: 10.21258/1761655
title: Swiss national forest inventory - Result table No. 898273
DOI: 10.21258/1738591
title: Swiss national forest inventory - Result table No. 898916
DOI: 10.21258/1732468
title: Swiss national forest inventory - Result table No. 898857
DOI: 10.21258/1726211
title: Swiss national forest inventory - Result table No. 891680
DOI: 10.21258/1761492
title: Swiss national forest inventory - Result table No. 902264
DOI: 10.21258/1739206
title: Swiss national forest inventory - Result table No. 897815
DOI: 10.21258/1766525
title: Swiss national forest inventory - Result table No. 899551
DOI: 10.21258/1756333
title: Swiss national forest inventory - Result table No. 897619
DOI: 10.21258/1728004
title: Swiss national forest inventory - Result table No. 897933
DOI: 10.21258/1768948
title: Swiss national forest inventory - Result table No. 893460
DOI: 10.17638/datacat.liverpool.ac.uk/1133
title: Carved in Stone

description: See included README file.
DOI: 10.21258/1755869
title: Swiss national forest inventory - Result table No. 892074
DOI: 10.21258/1768650
title: Swiss national forest inventory - Result table No. 892914
DOI: 10.21258/1760009
title: Swiss national forest inventory - Result table No. 898955
DOI: 10.21258/1765775
title: Swiss national forest inventory - Result table No. 892029
DOI: 10.21258/1745487
title: Swiss national forest inventory - Result table No. 898524
DOI: 10.21258/1762499
title: Swiss national forest inventory - Result table No. 897671
DOI: 10.21258/1735889
title: Swiss national forest inventory - Result table No. 896937
DOI: 10.21258/1745032
title: Swiss national forest inventory - Result table No. 898262
DOI: 10.21258/1736465
title: Swiss national forest inventory - Result table No. 898783
DOI: 10.21258/1763143
title: Swiss national forest inventory - Result table No. 892835
DOI: 10.21258/1753305
title: Swiss national forest inventory - Result table No. 897529
DOI: 10.21258/1761921
title: Swiss national forest inventory - Result table No. 891793
DOI: 10.21258/1754871
title: Swiss national forest inventory - Result table No. 897293
DOI: 10.34740/kaggle/dsv/1778808
title: US counties COVID 19 dataset
DOI: 10.21258/1755015
title: Swiss national forest inventory - Result table No. 899304
DOI: 10.21258/1766654
title: Swiss national forest inventory - Result table No. 898677
DOI: 10.21258/1769368
title: Swiss national forest inventory - Result table No. 898196
DOI: 10.21258/1737853
title: Swiss national forest inventory - Result table No. 891950
DOI: 10.21258/1753408
title: Swiss national forest inventory - Result table No. 899282
DOI: 10.21258/1769560
title: Swiss national forest inventory - Result table No. 899534
DOI: 10.21258/1758746
title: Swiss national forest inventory - Result table No. 891759
DOI: 10.21258/1749420
title: Swiss national forest inventory - Result table No. 898548
DOI: 10.21258/1761928
title: Swiss national forest inventory - Result table No. 898701
DOI: 10.6084/m9.figshare.13488606
title: Supplemental Data to Endocrinology en.2020-01022-R1

description: This is Table S1, the supplemental data to accompany a paper from Wu S et al. to appear in Endocrinology (en.2020-01022-R1)
DOI: 10.6084/m9.figshare.13488606.v1
title: Supplemental Data to Endocrinology en.2020-01022-R1

description: This is Table S1, the supplemental data to accompany a paper from Wu S et al. to appear in Endocrinology (en.2020-01022-R1)
DOI: 10.21258/1741961
title: Swiss national forest inventory - Result table No. 897010
DOI: 10.21258/1743927
title: Swiss national forest inventory - Result table No. 898175
DOI: 10.21258/1755654
title: Swiss national forest inventory - Result table No. 902283
DOI: 10.21258/1731123
title: Swiss national forest inventory - Result table No. 902165
DOI: 10.21258/1749409
title: Swiss national forest inventory - Result table No. 899526
DOI: 10.21258/1769330
title: Swiss national forest inventory - Result table No. 898550
DOI: 10.21258/1726861
title: Swiss national forest inventory - Result table No. 897003
DOI: 10.21258/1735127
title: Swiss national forest inventory - Result table No. 898365
DOI: 10.21258/1755295
title: Swiss national forest inventory - Result table No. 891850
DOI: 10.21258/1750287
title: Swiss national forest inventory - Result table No. 892013
DOI: 10.5281/zenodo.4392862
title: NVivo files for paper on Corporate Social (ir)Responsibility and Dam Building (Journal of Business Ethics)

description: These NVivo (.npv) files contain the coded sources in .pdf format for a paper to be submitted to the Journal of Business Ethics on 25 December 2020
DOI: 10.5281/zenodo.4392861
title: NVivo files for paper on Corporate Social (ir)Responsibility and Dam Building (Journal of Business Ethics)

description: These NVivo (.npv) files contain the coded sources in .pdf format for a paper to be submitted to the Journal of Business Ethics on 25 December 2020
DOI: 10.5517/ccdc.csd.cc24bzp5
title: CCDC 1976767: Experimental Crystal Structure Determination

description: Related Article: Yilin Chen, Jiancheng Li, Wenjun Jiang, Jinbo Zhao, Hongping Zhu, Shahila Muhammed, Pattiyil Parameswaran, Herbert W. Roesky|2020|Organometallics|39|4282|doi:10.1021/acs.organomet.0c00368
DOI: 10.5517/ccdc.csd.cc24dmv1
title: CCDC 1978353: Experimental Crystal Structure Determination

description: Related Article: Yilin Chen, Jiancheng Li, Wenjun Jiang, Jinbo Zhao, Hongping Zhu, Shahila Muhammed, Pattiyil Parameswaran, Herbert W. Roesky|2020|Organometallics|39|4282|doi:10.1021/acs.organomet.0c00368
DOI: 10.5517/ccdc.csd.cc24c03n
title: CCDC 1976780: Experimental Crystal Structure Determination

description: Related Article: Yilin Chen, Jiancheng Li, Wenjun Jiang, Jinbo Zhao, Hongping Zhu, Shahila Muhammed, Pattiyil Parameswaran, Herbert W. Roesky|2020|Organometallics|39|4282|doi:10.1021/acs.organomet.0c00368
DOI: 10.5517/ccdc.csd.cc24c04p
title: CCDC 1976781: Experimental Crystal Structure Determination

description: Related Article: Yilin Chen, Jiancheng Li, Wenjun Jiang, Jinbo Zhao, Hongping Zhu, Shahila Muhammed, Pattiyil Parameswaran, Herbert W. Roesky|2020|Organometallics|39|4282|doi:10.1021/acs.organomet.0c00368
DOI: 10.5517/ccdc.csd.cc24gxm5
title: CCDC 1980547: Experimental Crystal Structure Determination

description: Related Article: Yilin Chen, Jiancheng Li, Wenjun Jiang, Jinbo Zhao, Hongping Zhu, Shahila Muhammed, Pattiyil Parameswaran, Herbert W. Roesky|2020|Organometallics|39|4282|doi:10.1021/acs.organomet.0c00368
DOI: 10.5517/ccdc.csd.cc24c05q
title: CCDC 1976782: Experimental Crystal Structure Determination

description: Related Article: Yilin Chen, Jiancheng Li, Wenjun Jiang, Jinbo Zhao, Hongping Zhu, Shahila Muhammed, Pattiyil Parameswaran, Herbert W. Roesky|2020|Organometallics|39|4282|doi:10.1021/acs.organomet.0c00368
DOI: 10.21258/1725767
title: Swiss national forest inventory - Result table No. 891818
DOI: 10.5517/ccdc.csd.cc24xxxx
title: CCDC 1994010: Experimental Crystal Structure Determination

description: Related Article: Aidan M. Kelly, Peng-Jui Chen, Jenna Klubnick, Daniel J. Blair, Martin D. Burke|2020|Org.Lett.|22|9408|doi:10.1021/acs.orglett.0c02449
DOI: 10.5517/ccdc.csd.cc25tvvq
title: CCDC 2020854: Experimental Crystal Structure Determination

description: Related Article: Masaichi Saito, Jumpei Hamada, Shunsuke Furukawa, Masahiko Hada, Libor Dostál, Aleš Růžička|2020|Organometallics|39|4191|doi:10.1021/acs.organomet.0c00534
DOI: 10.5517/ccdc.csd.cc25tvwr
title: CCDC 2020855: Experimental Crystal Structure Determination

description: Related Article: Masaichi Saito, Jumpei Hamada, Shunsuke Furukawa, Masahiko Hada, Libor Dostál, Aleš Růžička|2020|Organometallics|39|4191|doi:10.1021/acs.organomet.0c00534
DOI: 10.21258/1755359
title: Swiss national forest inventory - Result table No. 897001
DOI: 10.21227/dsqp-4609
title: Food/Non-food Image Classification

description: Recently food recognition is a vital topic among researchers, as it automates the food logging process besides having applications in Food Safety, Food Quality Detection, Inspection, etc. The advancement in deep learning technology on edge computing and cloud computing leads to the development of hybrid solutions by considering the advantages of these computing platforms. For large scale, experiments, we gathered and prepared a Food/Non-Food dataset created from the existing image datasets of UECFOOD256, Caltech 256, Instagram Images, Flickr Image Dataset, Food101, Malaysian Food Dataset(gathered and crawled by us), Indoor Scene recognition Dataset, 15 scene dataset. The dataset is divided into two classes and is currently the largest Food/NonFood dataset available to the best of our knowledge. Our main contribution for this repository is the Food/Non-Food dataset, using foods of different cultures, existing food datasets, and the images of the users on the popular social media platforms. We manually cleaned the datasets for this purpose.
DOI: 10.21227/31m0-rr63
title: Food/Non-food Image Classification

description: Recently food recognition is a vital topic among researchers, as it automates the food logging process besides having applications in Food Safety, Food Quality Detection, Inspection, etc. The advancement in deep learning technology on edge computing and cloud computing leads to the development of hybrid solutions by considering the advantages of these computing platforms. For large scale, experiments, we gathered and prepared a Food/Non-Food dataset created from the existing image datasets of UECFOOD256, Caltech 256, Instagram Images, Flickr Image Dataset, Food101, Malaysian Food Dataset(gathered and crawled by us), Indoor Scene recognition Dataset, 15 scene dataset. The dataset is divided into two classes and is currently the largest Food/NonFood dataset available to the best of our knowledge. Our main contribution for this repository is the Food/Non-Food dataset, using foods of different cultures, existing food datasets, and the images of the users on the popular social media platforms. We manually cleaned the datasets for this purpose.
DOI: 10.21227/vqg5-kn50
title: Food/Non-food Image Classification

description: Recently food recognition is a vital topic among researchers, as it automates the food logging process besides having applications in Food Safety, Food Quality Detection, Inspection, etc. The advancement in deep learning technology on edge computing and cloud computing leads to the development of hybrid solutions by considering the advantages of these computing platforms. For large scale, experiments, we gathered and prepared a Food/Non-Food dataset created from the existing image datasets of UECFOOD256, Caltech 256, Instagram Images, Flickr Image Dataset, Food101, Malaysian Food Dataset(gathered and crawled by us), Indoor Scene recognition Dataset, 15 scene dataset. The dataset is divided into two classes and is currently the largest Food/NonFood dataset available to the best of our knowledge. Our main contribution for this repository is the Food/Non-Food dataset, using foods of different cultures, existing food datasets, and the images of the users on the popular social media platforms. We manually cleaned the datasets for this purpose.
DOI: 10.21258/1725802
title: Swiss national forest inventory - Result table No. 897132
DOI: 10.21258/1768630
title: Swiss national forest inventory - Result table No. 899019
DOI: 10.21258/1739005
title: Swiss national forest inventory - Result table No. 897229
DOI: 10.21258/1725894
title: Swiss national forest inventory - Result table No. 897061
DOI: 10.17632/3pwr54mgvv.1
title: Data for: Technical Regulations, Intermediate Inputs and Performance of Firms: Evidence from India

description: replication codes for manuscript titled "Technical Regulations, Intermediate Inputs and Performance of Firms: Evidence from India"
DOI: 10.21258/1746493
title: Swiss national forest inventory - Result table No. 902244
DOI: 10.21258/1730772
title: Swiss national forest inventory - Result table No. 898072
DOI: 10.3886/e129861
title: JLS_2020

description: Data for replication of the article: President and Congress in Paraguay: legislative success in foreign and domestic policy, published in The Journal of Legislative Studies, 2020.
DOI: 10.3886/e129861v1
title: JLS_2020

description: Data for replication of the article: President and Congress in Paraguay: legislative success in foreign and domestic policy, published in The Journal of Legislative Studies, 2020.
DOI: 10.3886/e129862
title: Ideologia e Discurso: o posicionamento do Partido dos Trabalhadores na oposição e no governo

description: Dados para replicação do artigo: https://www.scielo.br/pdf/rbcpol/n28/2178-4884-rbcpol-28-161.pdf