cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7674350
title: Bioprocess optimization for lactic and succinic acid production from a pulp and paper industry side stream

description: The data gathered in this file represent measurements and results obtained of during hydrolysis experiments done by RISE and fermentations performed by ATB. This information was necessary to optimize the lactic and succinic acid production via small scale fermentation trials. For lactic acid fermentation two Bacillus coagulans strains were used: A162 and A541. Simillarly, two succinic acid producers were used in this study: Actinobacillus succinogenes B1 and Basfia succiniciproducens B2. Moreover, the enzyme loading is described, what directly shows the hydrolysis performance.
DOI: 10.14459/2022mp1692519
title: Measurement data of longitudinal and lateral behaviour of a bicycle tire for cargo trailers

description: As part of a cooperation between the Ravensburg-Weingarten University of Applied Sciences (RWU) and the Bern University of Applied Sciences (BFH), the Pick-Up bicycle tire from the manufacturer Schwalbe in size 55-406 ETRO (20x2.15 in) was measured at the mobile tire testing laboratory "MoReLab" at BFH in Vauffelin. As part of the investigation, the longitudinal force was measured as a function of wheel slip, as well as the lateral force and the self aligning torque of the tire as a function of the slip angle.
DOI: 10.5281/zenodo.7674274
title: AL preprocessed data used in paper "Multi variables time series information bottleneck"

description: Preprocessed AL data used in paper "Multi variables time series information bottleneck" with the GitHub code This dataset is created from a public available dataset of solar power data collected in Alabama by NREL. The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow. Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=137 representing 137 solar plants ordered like in NREL). Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data Data file info :<br> Type: .npz<br> Size: 34.48MB<br> *** Key: 'data_TR_AL'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137] *** Key: 'data_VAL_AL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137] *** Key: 'data_TE_AL'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137] *** Key: 'data_TR'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137] *** Key: 'data_VAL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137] *** Key: 'data_TE'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137] *** Key: 'position_TR_AL'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_VAL_AL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TE_AL'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TR'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_VAL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TE'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}
DOI: 10.5281/zenodo.7674273
title: AL preprocessed data used in paper "Multi variables time series information bottleneck"

description: Preprocessed AL data used in paper "Multi variables time series information bottleneck" with the GitHub code This dataset is created from a public available dataset of solar power data collected in Alabama by NREL. The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow. Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=137 representing 137 solar plants ordered like in NREL). Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data Data file info :<br> Type: .npz<br> Size: 34.48MB<br> *** Key: 'data_TR_AL'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137] *** Key: 'data_VAL_AL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137] *** Key: 'data_TE_AL'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137] *** Key: 'data_TR'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137] *** Key: 'data_VAL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137] *** Key: 'data_TE'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137] *** Key: 'position_TR_AL'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_VAL_AL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TE_AL'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TR'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_VAL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TE'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}
DOI: 10.6084/m9.figshare.22106153.v8
title: LeakPair: Proactive Repairing of Memory Leaks in Single Page Web Applications

description: LeakPair: Proactive Repairing of Memory Leaks in Single Page Web Applications
DOI: 10.6084/m9.figshare.22133126.v2
title: 3D Visual Sustainable Management of Rubber Forest Based on Terrestrial Photogrammetry System

description: As an important part of agricultural cash crops in Hainan Province, more attention should be paid to monitoring sample plots of rubber trees. We developed a ground continuous photogram-metry system combined with 3D point-cloud reconstruction technology based on the structure from motion with Multiview Stereo method and sample plot survey data. Error analyses and ac-curacy evaluations of quadrat information were performed in the study area, covering different age classes and morphological characteristics, to explore its practical value in monitoring rubber plantation sample plots. Furthermore, the relationship between under branch height, diameter at breast height (DBH), and rubber tree volume was explored, and a rubber tree binary volume model was established. Here, we innovatively proposed a planning scheme for a terrestrial photogrammetry system for the sus-tainable management of rubber forests, to provide a novel solution to the issues faced by current sample plot monitoring practices. In the future, the application of a ground continuous photo-grammetry system to other types of forest monitoring will gradually be explored.The following is the data package of our research: 1. The data table in the folder (Data) is the data summary table of our study, which includes the measurement value of rubber forest volume, the measurement value of rubber forest DBH, the measurement value of under branch height and the specific information of each rubber tree in 19 sample plots. 2. The data in the folder (Figures) is the relevant illustration in the research paper.
DOI: 10.6084/m9.figshare.22147283.v1
title: soil d15N and isotope benchmarking based fdenit and N loss

description: soil d15N and isotope benchmarking based fdenit and N loss
DOI: 10.34740/kaggle/dsv/5056820
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.7674366
title: PB preprocessed data used in paper "Multi variables time series information bottleneck"

description: Preprocessed PB data used in paper "Multi variables time series information bottleneck" with the GitHub code This dataset is created from a public available dataset of solar power data collected in Alabama by CalTrans. The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow. Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=325 representing 325 traffic detectors ordered like in CalTrans). Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data Data file info :<br> Type: .npz<br> Size: 114.23MB<br> *** Key: 'data_TR_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325] *** Key: 'data_VAL_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325] *** Key: 'data_TE_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325] *** Key: 'data_TR'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325] *** Key: 'data_VAL'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325] *** Key: 'data_TE'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325] *** Key: 'position_TR_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_VAL_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TE_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TR'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_VAL'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TE'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}
DOI: 10.5281/zenodo.7674365
title: PB preprocessed data used in paper "Multi variables time series information bottleneck"

description: Preprocessed PB data used in paper "Multi variables time series information bottleneck" with the GitHub code This dataset is created from a public available dataset of solar power data collected in Alabama by CalTrans. The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow. Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=325 representing 325 traffic detectors ordered like in CalTrans). Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data Data file info :<br> Type: .npz<br> Size: 114.23MB<br> *** Key: 'data_TR_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325] *** Key: 'data_VAL_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325] *** Key: 'data_TE_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325] *** Key: 'data_TR'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325] *** Key: 'data_VAL'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325] *** Key: 'data_TE'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325] *** Key: 'position_TR_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_VAL_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TE_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TR'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_VAL'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'} *** Key: 'position_TE'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}
DOI: 10.6084/m9.figshare.22154495
title: Resistance Development to Insecticides in Aedes aegypti

description: A laboratory strain of <em>Aedes aegypti</em> was subjected repeatedly to larval selection pressure with two bacterial insecticides spinosad (<em>Saccharopolyspora spinos</em>a) and bacilod (<em>Bacillus thuringiensis israelensis</em>)
DOI: 10.6084/m9.figshare.22154495.v1
title: Resistance Development to Insecticides in Aedes aegypti

description: A laboratory strain of <em>Aedes aegypti</em> was subjected repeatedly to larval selection pressure with two bacterial insecticides spinosad (<em>Saccharopolyspora spinos</em>a) and bacilod (<em>Bacillus thuringiensis israelensis</em>)
DOI: 10.6084/m9.figshare.22147283.v2
title: Soil d15N and isotope benchmarking based fdenit and N loss

description: Soil d15N and isotope benchmarking based fdenit and N loss
DOI: 10.5281/zenodo.7198117
title: A flexible operation-based infrastructure for collaborative model-driven engineering (Evaluation Data)

description: Repository containing the UML models used in the evaluation of the paper entitled: "A flexible operation-based infrastructure for collaborative model-driven engineering" submitted to ECMFA 2023.
DOI: 10.5281/zenodo.7198116
title: A flexible operation-based infrastructure for collaborative model-driven engineering (Evaluation Data)

description: Repository containing the UML models used in the evaluation of the paper entitled: "A flexible operation-based infrastructure for collaborative model-driven engineering" submitted to ECMFA 2023.
DOI: 10.6084/m9.figshare.22154336
title: Experimento neuromanagement

description: A simulation of a selection of personnel has been carried out after the creation of a tool to help them with neurosciences. A real selection of personnel has not been carried out, the important thing about this work is that a certain type of data has been collected that can provide a solution to an existing problem of personnel selection and not the results of the selection itself. The names are fictitious and if there has been any data that it has not been possible to use due to artifacts, etc. They have been used from another individual that were ok to be used. It is repeated that the result of the experiment is not transcendent in terms of who should be selected, but to show that the created tool could be used in the way it is done in the article.
DOI: 10.6084/m9.figshare.22154336.v1
title: Experimento neuromanagement

description: A simulation of a selection of personnel has been carried out after the creation of a tool to help them with neurosciences. A real selection of personnel has not been carried out, the important thing about this work is that a certain type of data has been collected that can provide a solution to an existing problem of personnel selection and not the results of the selection itself. The names are fictitious and if there has been any data that it has not been possible to use due to artifacts, etc. They have been used from another individual that were ok to be used. It is repeated that the result of the experiment is not transcendent in terms of who should be selected, but to show that the created tool could be used in the way it is done in the article.
DOI: 10.6084/m9.figshare.21989697
title: Specification Tests for GARCH Processes with Nuisance Parameters on the Boundary

description: This article develops tests for the correct specification of the conditional variance function in GARCH models when the true parameter may lie on the boundary of the parameter space. The test statistics considered are of Kolmogorov-Smirnov and Cramér-von Mises type, and are based on empirical processes marked by centered squared residuals. The limiting distributions of the test statistics depend on unknown nuisance parameters in a nontrivial way, making the tests difficult to implement. We therefore introduce a novel bootstrap procedure which is shown to be asymptotically valid under general conditions, irrespective of the presence of nuisance parameters on the boundary. The proposed bootstrap approach is based on shrinking of the parameter estimates used to generate the bootstrap sample toward the boundary of the parameter space at a proper rate. It is simple to implement and fast in applications, as the associated test statistics have simple closed form expressions. Although the bootstrap test is designed for a data generating process with fixed parameters (i.e., independent of the sample size <i>n</i>), we also discuss how to obtain valid inference for sequences of DGPs with parameters approaching the boundary at the n−1/2 rate. A simulation study demonstrates that the new tests: (i) have excellent finite sample behavior in terms of empirical rejection probabilities under the null as well as under the alternative; (ii) provide a useful complement to existing procedures based on Ljung-Box type approaches. Two data examples illustrate the implementation of the proposed tests in applications.
DOI: 10.6084/m9.figshare.21989697.v1
title: Specification tests for GARCH processes with nuisance parameters on the boundary

description: This paper develops tests for the correct specification of the conditional variance function in GARCH models when the true parameter may lie on the boundary of the parameter space. The test statistics considered are of Kolmogorov-Smirnov and Cramér-von Mises type, and are based on empirical processes marked by centered squared residuals. The limiting distributions of the test statistics depend on unknown nuisance parameters in a non-trivial way, making the tests difficult to implement. We therefore introduce a novel bootstrap procedure which is shown to be asymptotically valid under general conditions, irrespective of the presence of nuisance parameters on the boundary. The proposed bootstrap approach is based on shrinking of the parameter estimates used to generate the bootstrap sample toward the boundary of the parameter space at a proper rate. It is simple to implement and fast in applications, as the associated test statistics have simple closed form expressions. Although the bootstrap test is designed for a data generating process with fixed parameters (i.e., independent of the sample size <i>n</i>), we also discuss how to obtain valid inference for sequences of DGPs with parameters approaching the boundary at the n−1/2 rate. A simulation study demonstrates that the new tests: (i) have excellent finite sample behaviour in terms of empirical rejection probabilities under the null as well as under the alternative; (ii) provide a useful complement to existing procedures based on Ljung-Box type approaches. Two data examples illustrate the implementation of the proposed tests in applications.
DOI: 10.6084/m9.figshare.22153049.v1
title: Ranking mundial de periódicos de Ciência da Informação de acordo com SJR (2021).xlsx

description: Lista o ranking mundial de periódicos de Ciência da Informação referente ao ano de 2021, disponibilizado no site do SCImago Journal Rank (SJR), no formato csv. Traz, entre outras informações, o nome dos periódicos, o número total de documentos, de referências e de citação de cada um deles, além do país e da editora.
DOI: 10.6084/m9.figshare.22153049.v2
title: Ranking mundial de periódicos de Ciência da Informação de acordo com SJR (2021).xlsx

description: Lista o ranking mundial de periódicos de Ciência da Informação referente ao ano de 2021, disponibilizado no site do SCImago Journal Rank (SJR), nos formatos csv. Traz, entre outras informações, o nome dos periódicos, o número total de documentos, de referências e de citação de cada um deles, além do país e da editora.
DOI: 10.6084/m9.figshare.22153049
title: Ranking mundial de periódicos de Ciência da Informação de acordo com SJR (2021).xlsx

description: Lista o ranking mundial de periódicos de Ciência da Informação referente ao ano de 2021, disponibilizado no site do SCImago Journal Rank (SJR), no formato csv. Traz, entre outras informações, o nome dos periódicos, o número total de documentos, de referências e de citação de cada um deles, além do país e da editora.
DOI: 10.6084/m9.figshare.22153049.v4
title: Ranking mundial de periódicos de Ciência da Informação de acordo com SJR (2021).xlsx

description: Lista o ranking mundial de periódicos de Ciência da Informação referente ao ano de 2021, disponibilizado no site do SCImago Journal Rank (SJR), no formato csv. Traz, entre outras informações, o nome dos periódicos, o número total de documentos, de referências e de citação de cada um deles, além do país e da editora.
DOI: 10.6084/m9.figshare.22153049.v3
title: Ranking mundial de periódicos de Ciência da Informação de acordo com SJR (2021).xlsx

description: Lista o ranking mundial de periódicos de Ciência da Informação referente ao ano de 2021, disponibilizado no site do SCImago Journal Rank (SJR), nos formatos csv. Traz, entre outras informações, o nome dos periódicos, o número total de documentos, de referências e de citação de cada um deles, além do país e da editora.
DOI: 10.11588/data/l2krzo/pj4f87
title: CiXIV131-4_1877.zip

description: :unav
DOI: 10.11588/data/l2krzo/qw7edq
title: CiXIV130_1874.zip

description: :unav
DOI: 10.11588/data/l2krzo/3t0ugd
title: CiXIV270.zip

description: :unav
DOI: 10.11588/data/l2krzo/zwubar
title: CiXIV285_1850.zip

description: :unav
DOI: 10.11588/data/l2krzo/gtif3d
title: 39A8599.zip

description: :unav
DOI: 10.11588/data/l2krzo
title: Ground Truth data for printed Malayalam

description: Ground Truth (GT) data (JPG, PAGE and ALTO XML files) which can be used to train OCR models that recognize printed text in Malayalam script. The training material is gathered from 19th and 20th centuries prints. The GT data was trained in Transkribus with the HTR+ and the PyLaia engine with a resulting CER of 2.29% on validation set with HTR+ and 3,20% with PyLaia. The training was performed on 43 pages with appr. 9,000 words. The validation set consisted of 5 pages (ca. 1,000 words). Transcription was performed by Tübingen University Library, the Ground Truth data was created by Elena Mucciarelli (University of Groningen) with support and model training by Dorothee Huff (Tübingen University Library).
DOI: 10.5281/zenodo.7602367
title: yRCA Chaos testing logs

description: The uploaded files document the work done with Online Boutique as reference application to access yRCA capabilities in detecting and explaining failures generated with a personalized chaos test tool.
DOI: 10.5281/zenodo.7602366
title: yRCA Chaos testing logs

description: The uploaded files document the work done with Online Boutique as reference application to access yRCA capabilities in detecting and explaining failures generated with a personalized chaos test tool.