cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5801914
title: Data for Cyrillic Reference Parsing

description: We provide a <strong>synthetic reference data set</strong> covering over 100,000 labeled references (mostly Russian language) and a manually annotated set of real references (771 in number) gathered from multidisciplinary <strong>Cyrillic script publications</strong>. <strong>Background:</strong> Extracting structured data from bibliographic references is a crucial task for the creation of scholarly databases. While approaches, tools, and evaluation data sets for the task exist, there is a distinct lack of support for languages other than English and scripts other than the Latin alphabet. A significant portion of the scientific literature that is thereby excluded consists of publications written in Cyrillic script languages. To address this problem, we introduce a new multilingual and multidisciplinary data set of over 100,000 labeled reference strings. The data set covers multiple Cyrillic languages and contains over 700 manually labeled references, while the remaining are generated synthetically. With random samples of varying size of this data, we train multiple well-performing sequence labeling BERT models and thus show the usability of our proposed data set. To this end, we showcase an implementation of a multilingual BERT model trained on the synthetic data and evaluated on the manually labeled references. Our model achieves an F1 score of 0.93 and thereby significantly outperforms a state-of-the-art model we retrain and evaluate on our data. The code for generating the data set is available at https://github.com/igor261/Sequence-Labeling-for-Citation-Field-Extraction-from-Cyrillic-Script-References When using the data set, please cite the following paper: <em>Igor Shapiro, Tarek Saier, Michael Färber: "Sequence Labeling for Citation Field Extraction from Cyrillic Script References". In Proceedings of the AAAI-22 Workshop on Scientific Document Understanding (SDU@AAAI'22), 2022.</em>
DOI: 10.5281/zenodo.5801913
title: Data for Cyrillic Reference Parsing

description: We provide a <strong>synthetic reference data set</strong> covering over 100,000 labeled references (mostly Russian language) and a manually annotated set of real references (771 in number) gathered from multidisciplinary <strong>Cyrillic script publications</strong>. <strong>Background:</strong> Extracting structured data from bibliographic references is a crucial task for the creation of scholarly databases. While approaches, tools, and evaluation data sets for the task exist, there is a distinct lack of support for languages other than English and scripts other than the Latin alphabet. A significant portion of the scientific literature that is thereby excluded consists of publications written in Cyrillic script languages. To address this problem, we introduce a new multilingual and multidisciplinary data set of over 100,000 labeled reference strings. The data set covers multiple Cyrillic languages and contains over 700 manually labeled references, while the remaining are generated synthetically. With random samples of varying size of this data, we train multiple well-performing sequence labeling BERT models and thus show the usability of our proposed data set. To this end, we showcase an implementation of a multilingual BERT model trained on the synthetic data and evaluated on the manually labeled references. Our model achieves an F1 score of 0.93 and thereby significantly outperforms a state-of-the-art model we retrain and evaluate on our data. The code for generating the data set is available at https://github.com/igor261/Sequence-Labeling-for-Citation-Field-Extraction-from-Cyrillic-Script-References When using the data set, please cite the following paper: <em>Igor Shapiro, Tarek Saier, Michael Färber: "Sequence Labeling for Citation Field Extraction from Cyrillic Script References". In Proceedings of the AAAI-22 Workshop on Scientific Document Understanding (SDU@AAAI'22), 2022.</em>
DOI: 10.5281/zenodo.5802914
title: Controlling Spin-Correlated Radical Pairs with Donor–Acceptor Dyads: A New Concept to Generate Reduced Metal Complexes for More Efficient Photocatalysis

description: Raw data used for the publication in <em>Chem. Eur. J.</em> <strong>2021</strong>, <em>27</em>, 4115-4123; doi: 10.1002/chem.202004638.
DOI: 10.5281/zenodo.5802913
title: Controlling Spin-Correlated Radical Pairs with Donor–Acceptor Dyads: A New Concept to Generate Reduced Metal Complexes for More Efficient Photocatalysis

description: Raw data used for the publication in <em>Chem. Eur. J.</em> <strong>2021</strong>, <em>27</em>, 4115-4123; doi: 10.1002/chem.202004638.
DOI: 10.5517/ccdc.csd.cc25ssrj
title: CCDC 2019828: Experimental Crystal Structure Determination

description: Related Article: Tatsuki Yoshida, Yuki Honda, Tatsuya Morofuji, Naokazu Kano|2021|Org.Lett.|23|9664|doi:10.1021/acs.orglett.1c03986
DOI: 10.5517/ccdc.csd.cc23z3fn
title: CCDC 1965351: Experimental Crystal Structure Determination

description: Related Article: Hyunyong Kim, Hyunseok Kim, Kimoon Kim, Eunsung Lee|2021|Inorg.Chem.|60|18687|doi:10.1021/acs.inorgchem.1c02070
DOI: 10.5517/ccdc.csd.cc23z3gp
title: CCDC 1965352: Experimental Crystal Structure Determination

description: Related Article: Hyunyong Kim, Hyunseok Kim, Kimoon Kim, Eunsung Lee|2021|Inorg.Chem.|60|18687|doi:10.1021/acs.inorgchem.1c02070
DOI: 10.5517/ccdc.csd.cc23z3hq
title: CCDC 1965353: Experimental Crystal Structure Determination

description: Related Article: Hyunyong Kim, Hyunseok Kim, Kimoon Kim, Eunsung Lee|2021|Inorg.Chem.|60|18687|doi:10.1021/acs.inorgchem.1c02070
DOI: 10.5517/ccdc.csd.cc23z3jr
title: CCDC 1965354: Experimental Crystal Structure Determination

description: Related Article: Hyunyong Kim, Hyunseok Kim, Kimoon Kim, Eunsung Lee|2021|Inorg.Chem.|60|18687|doi:10.1021/acs.inorgchem.1c02070
DOI: 10.5517/ccdc.csd.cc28m9rz
title: CCDC 2103931: Experimental Crystal Structure Determination

description: Related Article: Nikolay V. Shcherbakov, Elena I. Chikunova, Dmitry Dar’in, Vadim Yu. Kukushkin, Alexey Yu. Dubovtsev|2021|J.Org.Chem.|86|17804|doi:10.1021/acs.joc.1c02119
DOI: 10.5517/ccdc.csd.cc28mcz8
title: CCDC 2104000: Experimental Crystal Structure Determination

description: Related Article: Nikolay V. Shcherbakov, Elena I. Chikunova, Dmitry Dar’in, Vadim Yu. Kukushkin, Alexey Yu. Dubovtsev|2021|J.Org.Chem.|86|17804|doi:10.1021/acs.joc.1c02119
DOI: 10.5517/ccdc.csd.cc28q7bl
title: CCDC 2106739: Experimental Crystal Structure Determination

description: Related Article: Deniz F. Bekiş, Tim Küllmey, Mohammad R. Ghazanfari, Robert Burda, Elena Voloshina, Clara A. von Randow, Friederike Fuß, Moritz Liesegang, Beate Paulus, Ann-Christin Pöppler, Günther Thiele|2021|Inorg.Chem.|60|19145|doi:10.1021/acs.inorgchem.1c02943
DOI: 10.5517/ccdc.csd.cc28q7cm
title: CCDC 2106740: Experimental Crystal Structure Determination

description: Related Article: Deniz F. Bekiş, Tim Küllmey, Mohammad R. Ghazanfari, Robert Burda, Elena Voloshina, Clara A. von Randow, Friederike Fuß, Moritz Liesegang, Beate Paulus, Ann-Christin Pöppler, Günther Thiele|2021|Inorg.Chem.|60|19145|doi:10.1021/acs.inorgchem.1c02943
DOI: 10.5517/ccdc.csd.cc28q7dn
title: CCDC 2106741: Experimental Crystal Structure Determination

description: Related Article: Deniz F. Bekiş, Tim Küllmey, Mohammad R. Ghazanfari, Robert Burda, Elena Voloshina, Clara A. von Randow, Friederike Fuß, Moritz Liesegang, Beate Paulus, Ann-Christin Pöppler, Günther Thiele|2021|Inorg.Chem.|60|19145|doi:10.1021/acs.inorgchem.1c02943
DOI: 10.5517/ccdc.csd.cc28ylnh
title: CCDC 2113817: Experimental Crystal Structure Determination

description: Related Article: Bethany M. Stratakes, Kaylee A. Wells, Daniel A. Kurtz, Felix N. Castellano, Alexander J. M. Miller|2021|J.Am.Chem.Soc.|143|21388|doi:10.1021/jacs.1c10628
DOI: 10.25919/zb9w-vj83
title: Parkes observations for project P1129 semester 2021OCTS_04

description: PSR J0837-2454 is a young, high-Galactic-latitude pulsar, whose distance derived from dispersion measure (DM) implies a height of $&gt;$1~kpc above the Galactic plane. The young age (28kyr) and position can be explained if (i) the pulsar was born at this high Galactic $z$-height from a runaway or walkaway O/B star; or (ii) the DM-derived distance to this pulsar is incorrect and the pulsar is closer to the plane of the Galaxy. Obtaining a coherent timing solution for the pulsar over the coming year will allow gated VLBA observations which, along with an independent estimate of the proper motion, will also provide us with a distance to the pulsar through a measurement of the parallax, if the pulsar is closer than the DM implies. These measurements are crucial to determining whether or not this pulsar represents the remnants of a rare Milky Way halo O/B star. In addition, studying the scattering and scintillation will allow us to test whether there is any additional source of DM along the line of sight to the pulsar that might inflate its DM-derived distance, and hence its height above the Galactic plane. Finally, measuring the polarisation properties of the pulsar will allow us to study its beam geometry, as well as further investigate the ISM and the Galactic magnetic field in this direction.
DOI: 10.25919/6t45-rw79
title: Parkes observations for project P971 semester 2021OCTS_03

description: We propose further timing observations of the relativistic pulsar - white-dwarf binary, PSR J1141$-$6545. This unique system offers access to a wide range of physics, from relativistic phenomena through pulsar emission cone studies, to the structure and distribution of interstellar plasma along our line-of-sight. The analysis of existing data has already revealed the first detection of Lense-Thirring precession in a binary star system, and hinted at complex emission beam structure probed via geodetic precession. Further timing of this system for the next few years is needed for studies of these effects at higher precision. From this semester, we are converting this project into a large project proposal. We request the TAC to award us pre-graded status for the next semester.
DOI: 10.5281/zenodo.546238
title: RuSim1000

description: The dataset for evaluating Russian-language distributional models that focus on revealing similarity (possibly accompanied by association) as opposed to pure association.
DOI: 10.34740/kaggle/dsv/2964345
title: AutoXGB
DOI: 10.34740/kaggle/dsv/2964368
title: Cryptocurrency extra data - Ethereum Classic
DOI: 10.5281/zenodo.5803015
title: RuSim1000

description: The dataset for evaluating Russian-language distributional models that focus on revealing similarity (possibly accompanied by association) as opposed to pure association.
DOI: 10.5281/zenodo.795920
title: RuSim1000

description: The dataset for evaluating Russian-language distributional models that focus on revealing similarity (possibly accompanied by association) as opposed to pure association.
DOI: 10.34740/kaggle/dsv/2964342
title: LUFlow Network Intrusion Detection Data Set
DOI: 10.34740/kaggle/dsv/2964444
title: Omicron daily cases by country (COVID-19 variant)
DOI: 10.6084/m9.figshare.17463473
title: Supplementary materials

description: Those data were the supplementary material of Genetic diversity and structure of tropical <i>Porites lutea</i> populations highlight their high adaptive potential to environmental changes in the South China Sea Luo Yanqiu<sup>1,†</sup>, Huang Wen<sup>1,†</sup>, Yu Kefu<sup>1,2*</sup>, Li Ming<sup>1</sup>, Chen Biao<sup>1</sup>, Huang Xueyong<sup>1</sup>, Qin Zhenjun<sup>1</sup>
DOI: 10.6084/m9.figshare.17463473.v1
title: Supplementary materials

description: Those data were the supplementary material of Genetic diversity and structure of tropical <i>Porites lutea</i> populations highlight their high adaptive potential to environmental changes in the South China Sea Luo Yanqiu<sup>1,†</sup>, Huang Wen<sup>1,†</sup>, Yu Kefu<sup>1,2*</sup>, Li Ming<sup>1</sup>, Chen Biao<sup>1</sup>, Huang Xueyong<sup>1</sup>, Qin Zhenjun<sup>1</sup>
DOI: 10.5281/zenodo.5803023
title: A Deep Learning Estimation of the Earth Resistivity Model for ATEM

description: The Transient Electromagnetic (TEM) method is a geophysical method based on the law of electromagnetic induction. The traditional TEM method has low efficiency because of the ground observation. The airborne TEM (ATEM) was introduced to overcome this limitation. However, due to the flight-movement observation, the useful signal in ATEM data is relatively weak, while the characteristics of noises are complex. So far the denoising process generally consists of many steps, and each step deals with one kind of noise, so the requirements placed on the processing personnel will be very high. After the denoising is completed, the data will be brought into the inversion to obtain an estimate of the earth resistivity model. The inversion generally cannot further distinguish whether there are residual noises in the processed data. Therefore, the reliability of the estimated earth resistivity model will be seriously affected due to the probable disadvantages in the denoising process. Here, we uploaded the data and code used to train a SAE network for estimating earth resistivity from noisy ATEM data. For training of the SAE network, a common solution is to train each sub-network separately, and then form the entire network, in particular: (1) the sub-network 1 will be trained firstly as a separate denoising autoencoder, and at this time, the sub-network 2 and the sub-network 3 will be not considered. (2) After the convergence of sub-network 1 is achieved, the joint structure of sub-network 1 and sub-network 2 will be trained. At this time, the parameters of sub-network 1 will be locked, and only sub-network 2 will be updated. (3) After the convergence of sub-network 2 is achieved, sub-network 3 will be trained in a similar way. When the three sub-networks converge respectively, the training of the SAE can be completed after several joint training of the entire structure, so that the three sub-networks are finally integrated.
DOI: 10.5281/zenodo.5803022
title: A Deep Learning Estimation of the Earth Resistivity Model for ATEM

description: The Transient Electromagnetic (TEM) method is a geophysical method based on the law of electromagnetic induction. The traditional TEM method has low efficiency because of the ground observation. The airborne TEM (ATEM) was introduced to overcome this limitation. However, due to the flight-movement observation, the useful signal in ATEM data is relatively weak, while the characteristics of noises are complex. So far the denoising process generally consists of many steps, and each step deals with one kind of noise, so the requirements placed on the processing personnel will be very high. After the denoising is completed, the data will be brought into the inversion to obtain an estimate of the earth resistivity model. The inversion generally cannot further distinguish whether there are residual noises in the processed data. Therefore, the reliability of the estimated earth resistivity model will be seriously affected due to the probable disadvantages in the denoising process. Here, we uploaded the data and code used to train a SAE network for estimating earth resistivity from noisy ATEM data. For training of the SAE network, a common solution is to train each sub-network separately, and then form the entire network, in particular: (1) the sub-network 1 will be trained firstly as a separate denoising autoencoder, and at this time, the sub-network 2 and the sub-network 3 will be not considered. (2) After the convergence of sub-network 1 is achieved, the joint structure of sub-network 1 and sub-network 2 will be trained. At this time, the parameters of sub-network 1 will be locked, and only sub-network 2 will be updated. (3) After the convergence of sub-network 2 is achieved, sub-network 3 will be trained in a similar way. When the three sub-networks converge respectively, the training of the SAE can be completed after several joint training of the entire structure, so that the three sub-networks are finally integrated.
DOI: 10.5281/zenodo.5801711
title: Generated RGBD Tracking Benchmarks (Got10K) 4001-4300

description: We convert the existing RGB tracking benchmakrs, eg. Got10K, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular estimation methods. Please visit the Got10K page for the RGB images and the groundtruths.
DOI: 10.5281/zenodo.5801710
title: Generated RGBD Tracking Benchmarks (Got10K) 4001-4300

description: We convert the existing RGB tracking benchmakrs, eg. Got10K, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular estimation methods. Please visit the Got10K page for the RGB images and the groundtruths.
DOI: 10.5281/zenodo.5801742
title: Generated RGBD Tracking Benchmarks (Got10K) 4301-4500

description: We convert the existing RGB tracking benchmakrs, eg. Got10K, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular estimation methods. Please visit the Got10K page for the RGB images and the groundtruths.
DOI: 10.5281/zenodo.5801741
title: Generated RGBD Tracking Benchmarks (Got10K) 4301-4500

description: We convert the existing RGB tracking benchmakrs, eg. Got10K, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular estimation methods. Please visit the Got10K page for the RGB images and the groundtruths.
DOI: 10.21227/7eck-8z89
title: Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging

description: Drought has become one of the main challenges facing global agricultural production and crop safety. Drought stress will lead to the termination of crop photosynthesis and metabolic disorders, which will seriously affect the growth and development of crops. We aimed to study a method for identificaton of the drought stress in tomato seedlings using chlorophyll fluorescence imaging. In this study, chlorophyll fluorescence parameters and there corresponding chlorophyll fluorescence images of 4 different drought stress levels were collected. Then three feature optimization algorithms which were Successive Projections Algorithm (SPA), Iteratively Retains Informative Variables (IRIV) and Variable Iterative Space Shrinkage Approac (VISSA) were used to choose important parameters. The common parameters extracted by the three algorithms are the actual light quantum efficiency at L2 during the light adaptation process(QY_L2), the non-actinic fluorescence quenching at L3 during the light adaptation process(NPQ_L3), and the light adaptation photochemical quenching at L2 time(qL_L2), steady-state light adaptation photochemical quenching(qL_Lss), and the light adaptation photochemical quenching at D3 time during the dark relaxation process(qL_D3). Then, the corresponding chlorophyll fluorescence images of the five common parameters were selected. And two types of image features were used to study and analyze drought stress classes: histogram features and texture features. The Pearson correlations of the features were calculated and the high correlated features were input into three models, which were Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and k-Nearest Neighbor (KNN), to identify drought stress classes. The recognition accuracy rate of LDA, SVM and KNN were 86.8%, 87.1% and 76.5% respectively. Our experiment results showed that the five common fluorescence parameters and there corresponding image features could be used to evaluate the drought stress classes of tomato seedlings, and had a good evaluation effect. This research provideed a new method for monitoring drought stress classes and had considerable prospects for non-destructive diagnosis of plant drought stress
DOI: 10.25919/h1jb-b741
title: Parkes observations for project P885 semester 2021OCTS_06

description: We request a reduced monitoring cadence for the magnetars 1E 1547.0-5408 and Swift J1818.0-1607 to once every 10 days, to track their rotation, flux density, and polarisation. Swift J1818.0-1607 remains very dynamic, and the unique UWL capabilities promise new opportunities to probe still mysterious radio emission mechanisms in magnetars. Our timing results indicate that 1E 1547.0-5408 is currently a more stable rotator, supporting the diminished cadence. We will observe XTE J1810-197 and PSR J1622-4950 less frequently, to complement monitoring elsewhere. The overall request is 18 hours.
DOI: 10.34740/kaggle/dsv/2964542
title: Cryptocurrency extra data - Ethereum
DOI: 10.6084/m9.figshare.17493587.v1
title: Aging and female mate choice

description: Each worksheet has a key and data handling steps are descriped in the xls files. If anything is unclear please do not hesitate to contact me at rutharcher0 -at- gmail.com.
DOI: 10.6084/m9.figshare.17493587
title: Aging and female mate choice

description: Each worksheet has a key and data handling steps are descriped in the xls files. If anything is unclear please do not hesitate to contact me at rutharcher0 -at- gmail.com.
DOI: 10.6084/m9.figshare.17493599
title: supplementary material for " Genetic diversity and structure of tropical Porites lutea populations highlight their high adaptive potential to environmental changes in the South China Sea"

description: supplementary material for "Genetic diversity and structure of tropical <i>Porites lutea</i> populations highlight their high adaptive potential to environmental changes in the South China Sea"
DOI: 10.6084/m9.figshare.17493599.v1
title: supplementary material for " Genetic diversity and structure of tropical Porites lutea populations highlight their high adaptive potential to environmental changes in the South China Sea"

description: supplementary material for "Genetic diversity and structure of tropical <i>Porites lutea</i> populations highlight their high adaptive potential to environmental changes in the South China Sea"
DOI: 10.17903/fk2/cgmui2
title: Στατιστικές Εκπαίδευσης κατά το έτος 1959-1960

description: Περιέχει δεδομένα από τις Στατιστικές Μέσης και Ανώτατης Εκπαίδευσης κατά το σχολικό και ακαδημαϊκό έτος 1959-1960
DOI: 10.5281/zenodo.5795132
title: Affordable prices without threatening the oncological R&D pipeline - An economic experiment on transparency in price negotiations

description: This is the data repository of an economic experiment on the effects of transparency regulations, conducted by the Netherlands Cancer Institute and the University of Amsterdam. We replicated the EU pharmaceutical market in a laboratory setting. In a randomized-controlled study, we analyzed how participants, 400 students located in 4 European countries, negotiated in the current system of Price Secrecy in comparison to innovative bargaining settings where either prices only (Price Transparency) or prices and R&D costs (Full Transparency) were made transparent to buyers.
DOI: 10.5281/zenodo.5795131
title: Affordable prices without threatening the oncological R&D pipeline - An economic experiment on transparency in price negotiations

description: This is the data repository of an economic experiment on the effects of transparency regulations, conducted by the Netherlands Cancer Institute and the University of Amsterdam. We replicated the EU pharmaceutical market in a laboratory setting. In a randomized-controlled study, we analyzed how participants, 400 students located in 4 European countries, negotiated in the current system of Price Secrecy in comparison to innovative bargaining settings where either prices only (Price Transparency) or prices and R&D costs (Full Transparency) were made transparent to buyers.
DOI: 10.17903/fk2/mqinhb
title: Στατιστικές Εκπαίδευσης κατά το έτος 1960-1961

description: Περιέχει δεδομένα από τις Στατιστικές Μέσης και Ανώτατης Εκπαίδευσης κατά το σχολικό και ακαδημαϊκό έτος 1960-1961
DOI: 10.5281/zenodo.5803071
title: msd-errors - Random walk data

description: The data related to the random walk components of the msd-error work, including truly random and temporarily anti-correlated random walks at a series of system and step size. These have either be analysed numerically or analytically (using kinisi).<br><br>Created using showyourwork from this GitHub repo.<br>