cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/490c-rm17
title: An Effective Feature Selection and Classification Technique to Palmprint Biometric Identification Systems

description: Due to its excellent user-friendliness, including its non-invasive and hygienic features, palmprint recognition has attracted a lot of study interest. Most current palmprint identification works, such as Deep Learning (DL) techniques, usually learn discriminative attributes from palmprint pictures. These typically require significant labeled samples for an acceptable recognition performance. However, the availability of palmprint images is typically constrained due to the challenge of gathering sufficient samples, making most deep learning-based algorithms unsuccessful. To overcome the issues, we propose a deep-learning approach to palmprint recognition. Initially, (a) convert the original image to greyscale, (b) crop and resize the image, and (c) enhance the contrast utilizing Contrast-Limited Adaptive Histogram Equalization (CLAHE) approach. After that, extract the salient features from contrast-enhanced palm images using the ConvNeXt technique. Then, using Improved Spotted Hyena Optimizer (ISHO) algorithm to select the essential features by eliminating unnecessary extracted features. Finally, we employ the Darknet Convolutional Neural Network (DNetCNN) approach to classify whether the palmprint image is matched or not-matched. To improve the classification accuracy, utilize Sooty Tern Optimization Algorithm (STOA) to get higher classification accuracy and recognition accuracy. The proposed approach efficiently increases the Optimized DNetCNN's accuracy while decreasing the number of features and computing time. We validated the proposed methodology on three open palmprint databases, CASIA, Tongji, and IITD. A high recognition rate is achieved by our approach while utilizing a significantly smaller number of characteristics, according to experiments.
DOI: 10.6084/m9.figshare.22123214
title: Final Portuguese urban fires between 2013 and 2022

description: This file includes information regarding urban fires that took place in mainland Portugal from 2013 to 2022, after pre-processing raw data, including the steps of data transformation and cleaning. The dataset was validated by a set of technical procedures aiming to verify both data correctness and utility. This dataset provides the most recent multi-year record of Portuguese urban fires including 28 variables on 71015 events.
DOI: 10.6084/m9.figshare.22123214.v1
title: Final Portuguese urban fires between 2013 and 2022

description: This file includes information regarding urban fires that took place in mainland Portugal from 2013 to 2022, after pre-processing raw data, including the steps of data transformation and cleaning. The dataset was validated by a set of technical procedures aiming to verify both data correctness and utility. This dataset provides the most recent multi-year record of Portuguese urban fires including 28 variables on 71015 events.
DOI: 10.6084/m9.figshare.22123217
title: Raw Portuguese urban fires between 2013 and 2022

description: This file includes raw information regarding urban fires that took place in mainland Portugal from 2013 to 2022.
DOI: 10.6084/m9.figshare.22123217.v1
title: Raw Portuguese urban fires between 2013 and 2022

description: This file includes raw information regarding urban fires that took place in mainland Portugal from 2013 to 2022.
DOI: 10.17632/rvt536yz8x.1
title: Antigenic mapping of the hemagglutinin of the H9 subtype influenza A viruses using sera from Japanese quail (Coturnix c. japonica).

description: Raw hemagglutination inhibition data using quail sera produced against a library of field isolates and recombinant H9N2 influenza A viruses
DOI: 10.17632/fk97ybvz8r.1
title: Orignal data: USP10 strikes down β-catenin by dual-wielding deubiquitinase activity and phase separation potential

description: Western blot and gel source data of the article entitled "USP10 strikes down β-catenin by dual-wielding deubiquitinase activity and phase separation potential". The data was sorted by figure panels. Each tab in the excel file contains the data from an indicated figure panel. All images are the original scans and the sizes are draggable. Both chemiluminescence and bright field images are provided.
DOI: 10.17632/tm9wcjpjwx.1
title: Supplementary Table 1: ChatGPT Inputs, Outputs, and Categories of Suggested Diagnoses for Malignant Skin Neoplasms

description: Supplementary Table 1: ChatGPT Inputs, Outputs, and Categories of Suggested Diagnoses for Malignant Skin Neoplasms
DOI: 10.5281/zenodo.8276188
title: Calm registry dataset

description: Calm registry dataset without identifiers
DOI: 10.5281/zenodo.8276187
title: Calm registry dataset

description: Calm registry dataset without identifiers
DOI: 10.17632/tpkdd8dbgc.1
title: Dataset for Optimization of Direct Solar Steam Generation

description: The dataset includes preprocessed and experimental data used for the Optimization of Direct Solar Steam Generation (Interfacial and Volumetric Methods).
DOI: 10.17632/4sh78xh8zh.1
title: (is)phf11 in development of Enterococcus-regulated ems

description: (is)phf11 in development of Enterococcus-regulated ems
DOI: 10.5281/zenodo.8276190
title: InTheMED WP6 Data Archive – Tympaki, Greece: Simulation Results

description: This data archive includes the files “InTheMED_WP6_DS_ SimulatedGWLevelTimeSeriesGreece.mat” and “InTheMED_WP6_GDS_ MapsGreece.gdb” which contain the datasets that represent the results of the simulation runs of the Tympaki numerical groundwater model. More specifically, the timeseries of the groundwater level at the observation wells and the shapefiles that are necessary for the mapping of groundwater level and pumping rates at the optimization time-step.
description: This project is part of the PRIMA Program supported by the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement No 1923
DOI: 10.5281/zenodo.8276189
title: InTheMED WP6 Data Archive – Tympaki, Greece: Simulation Results

description: This data archive includes the files “InTheMED_WP6_DS_ SimulatedGWLevelTimeSeriesGreece.mat” and “InTheMED_WP6_GDS_ MapsGreece.gdb” which contain the datasets that represent the results of the simulation runs of the Tympaki numerical groundwater model. More specifically, the timeseries of the groundwater level at the observation wells and the shapefiles that are necessary for the mapping of groundwater level and pumping rates at the optimization time-step.
description: This project is part of the PRIMA Program supported by the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement No 1923
DOI: 10.6084/m9.figshare.24016827
title: Neuropsychological data Questionnaires Sample 1 and 2.sav

description: The SPSS data represent the individual mean values in the pre- and post-measurement after the cognitive training in the neuropsychological tests, the questionnaires and in the Cortisol levels for sample 1 and sample 2 described in the article Gajewski et al. (2023) Effects of cognitive and stress management training in middle-aged and older industrial workers in different socioeconomic settings: a randomized controlled study. Front. Psychol. 14:1229503. doi: 10.3389/fpsyg.2023.1229503. Further details and explanations of the variables and data sets on request.
DOI: 10.17632/pxcshrxdtf.1
title: CSES satellite data

description: The EFD-3 data includes observations of Very Low Frequency electric field. 
Data are stored in h5 format files that include different parameters.
In particular the file of data includes the WORKMODE flag that identifies the starting and ending time of burst mode (WORKMODE = 2) with higher data rate (original waveform is available) and the survey mode (WORKMODE  = 1) with lower data rate (only spectra are available).
Other parameters are: UTC, orbit number, geographic and magnetic latitude and longitude, altitude, etc.
DOI: 10.48366/r606599
title: Comparison of cybersecurity knowledge graphs

description: This comparison provides an overview of cybersecurity knowledge graphs.
DOI: 10.5517/ccdc.csd.cc21yfm2
title: CCDC 1905124: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Murray V. Baker, James L. Hickey|2019|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/w7nwbnsjvt.1
title: A transcriptomic based deconvolution framework for assessing differentiation stages and drug responses of AML

description: Count table for LUMC data.

We have downloaded the non-normalized count matrices (htseq-counts) and the meta files of the four discovery cohorts (TCGA-LAML, BEAT-AML, TARGET-AML and TARGET-ALL) from https://portal.gdc.cancer.gov. For LEUCEGENE, count data was downloaded from their dedicated site (https://data.leucegene.iric.ca/) along with their provided meta data. All meta/count data were pre-processed using R (v4.1.0). For the meta data, genomic aberration labels were relabeled to the main AML WHO 2016 classes, non-AML samples were removed from the down-stream analyses, ELN-classes were relabeled according to ELN 2017 recommendations. For the count data, ERCC spike-ins and mitochondrial genes were removed, and the count matrix was then sorted according genes standard deviation in order to remove the duplicated genes that had less variation thus providing less information, and lastly the gene ensembl ids were converted to gene symbols. Before converting ensembl ids into gene symbols, the stemness score for each patient was calculated via count-per-million (cpm) normalized libraries.
DOI: 10.17632/w7nwbnsjvt.2
title: A transcriptomic based deconvolution framework for assessing differentiation stages and drug responses of AML

description: Count table for LUMC data.

We have downloaded the non-normalized count matrices (htseq-counts) and the meta files of the four discovery cohorts (TCGA-LAML, BEAT-AML, TARGET-AML and TARGET-ALL) from https://portal.gdc.cancer.gov. For LEUCEGENE, count data was downloaded from their dedicated site (https://data.leucegene.iric.ca/) along with their provided meta data. All meta/count data were pre-processed using R (v4.1.0). For the meta data, genomic aberration labels were relabeled to the main AML WHO 2016 classes, non-AML samples were removed from the down-stream analyses, ELN-classes were relabeled according to ELN 2017 recommendations. For the count data, ERCC spike-ins and mitochondrial genes were removed, and the count matrix was then sorted according genes standard deviation in order to remove the duplicated genes that had less variation thus providing less information, and lastly the gene ensembl ids were converted to gene symbols. Before converting ensembl ids into gene symbols, the stemness score for each patient was calculated via count-per-million (cpm) normalized libraries.
DOI: 10.21227/pwew-sh91
title: Identification Systems

description: Due to its excellent user-friendliness, including its non-invasive and hygienic features, palmprint recognition has attracted a lot of study interest. Most current palmprint identification works, such as Deep Learning (DL) techniques, usually learn discriminative attributes from palmprint pictures. These typically require significant labeled samples for an acceptable recognition performance. However, the availability of palmprint images is typically constrained due to the challenge of gathering sufficient samples, making most deep learning-based algorithms unsuccessful. To overcome the issues, we propose a deep-learning approach to palmprint recognition. Initially, (a) convert the original image to greyscale, (b) crop and resize the image, and (c) enhance the contrast utilizing Contrast-Limited Adaptive Histogram Equalization (CLAHE) approach. After that, extract the salient features from contrast-enhanced palm images using the ConvNeXt technique. Then, using Improved Spotted Hyena Optimizer (ISHO) algorithm to select the essential features by eliminating unnecessary extracted features. Finally, we employ the Darknet Convolutional Neural Network (DNetCNN) approach to classify whether the palmprint image is matched or not-matched. To improve the classification accuracy, utilize Sooty Tern Optimization Algorithm (STOA) to get higher classification accuracy and recognition accuracy. The proposed approach efficiently increases the Optimized DNetCNN's accuracy while decreasing the number of features and computing time. We validated the proposed methodology on three open palmprint databases, CASIA, Tongji, and IITD. A high recognition rate is achieved by our approach while utilizing a significantly smaller number of characteristics, according to experiments.
DOI: 10.17632/rkt67g9wkp.1
title: Data for, Note on the reproductive biology of Ximenia americana (Ximeniaceae)

description: Data accompanying field studies in and near Palm Beach County Florida aimed at discerning the breeding system of Ximenia americana.
DOI: 10.17632/ps88j2w8y2.1
title: H-ABSC

description: This data set is completely for research purpose. It has annotated dataset with aspect term and aspect category.
DOI: 10.6084/m9.figshare.24018009
title: DRI DAS1 Data (ext).xlsx

description: Underlying data for a blog analysis on research integrity Trust Markers in academic research output. Data includes a breakdown of the percentage of Data Availability Statements and Data Repository types by specific countries explored in the blog.
DOI: 10.4228/zalf-mbrj-qf27
title: BIBS Dataset: Morphological traits of the aphid Metopeurum fuscoviride in urban vs. rural populations of Berlin, Germany.

description: Aphid colony traits and plant host traits collected in 2019 and 2020 along an urbanization gradient in Berlin, Germany. Colonies of the specialist pink tansy aphid (Metopeurum fuscoviride) were sampled in 12 grassland sites (11 from the CityScapeLab), along with plant and stem samples from the host plants. The dataset includes 4 tables: (1) Aphid morphological traits measured on 5 adult individuals from 1-3 colonies per site; (2) Colony parameters, including the aphid colony size, number of mutualistic ants, degree of parasitism, and plant host size and phenological stage; (3) Plant host leaf and stem traits; (4) plot information (i.e. site location and urbanization level).  This table contains the index of all tables forming this data collection.

Related datasets are listed in the metadata element 'Related Identifier'.
Dataset version 1.0
DOI: 10.5281/zenodo.8276241
title: Search strategies for 177 lu-PSMA in the treatment of metastatic castrate-resistant prostate cancer

description: The dataset includes the complete, reproducible search strategies for all literature databases searched during this project. The search strategies were designed to answer the following research questions: <strong>RQ1.</strong> Does the use of radioligand therapy using <sup>177</sup>Lu-PSMA lead to improved overall survival and progression-free survival, compared with other available treatment(s) in patients with metastatic, castrate-resistant prostate cancer? <strong>RQ2.</strong> Does the use of radioligand therapy using <sup>177</sup>Lu-PSMA lead to improved quality of life or symptom control, compared with other available treatment(s), in patients with metastatic, castrate-resistant prostate cancer? <strong>RQ3. </strong> What is the risk of adverse events and toxicity associated with radioligand therapy using <sup>177</sup>Lu-PSMA, compared with other available treatment in patients with metastatic, castrate-resistant prostate cancer?
DOI: 10.5281/zenodo.8276263
title: How do conservationists choose where to publish?

description: Accompanying data for: Natalie Yoh, Mukhlish Jamal Musa Holle, Jasmin Willis, Lauren F. Rudd, Iain M. Fraser, Diogo Veríssimo. (2023). How do conservationists choose where to publish? Conservation Letters. In Review.
DOI: 10.5281/zenodo.8276262
title: How do conservationists choose where to publish?

description: Accompanying data for: Natalie Yoh, Mukhlish Jamal Musa Holle, Jasmin Willis, Lauren F. Rudd, Iain M. Fraser, Diogo Veríssimo. (2023). How do conservationists choose where to publish? Conservation Letters. In Review.
DOI: 10.5281/zenodo.8004912
title: Core-loss EELS dataset and neural networks for element identification

description: We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification. The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra. Code on how to <br> - read the simulated data<br> - transform HDF5 format to TFRecord format<br> - train and evaluate neural networks using the simulated data<br> - use the trained networks for automated element identification<br> is available on GitHub at arnoannys/EELS_ID A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at: Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. <em>Sci Rep</em> 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7 [1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585<br> [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. __APL Mater__ 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)
description: {"references": ["Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585"]}
DOI: 10.5281/zenodo.8004911
title: Core-loss EELS dataset and neural networks for element identification

description: We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification. The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra. Code on how to <br> - read the simulated data<br> - transform HDF5 format to TFRecord format<br> - train and evaluate neural networks using the simulated data<br> - use the trained networks for automated element identification<br> is available on GitHub at arnoannys/EELS_ID A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at: Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. <em>Sci Rep</em> 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7 [1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585<br> [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. __APL Mater__ 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)
description: {"references": ["Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585"]}
DOI: 10.17632/8s3v73prwc.1
title: Experimental Statistics on GFRP- Reinforced Concrete Columns from Earlier Researchers.

description: A table describing samples of circular cross-section columns fully reinforced with GFRP in order to carry out a statistical study and link them to the famous international code equations.
DOI: 10.17632/8s3v73prwc.2
title: Experimental Statistics on GFRP- Reinforced Concrete Columns from Earlier Researchers.

description: A table describing samples of circular cross-section columns fully reinforced with GFRP in order to carry out a statistical study and link them to the famous international code equations.
DOI: 10.17632/8s3v73prwc.3
title: Experimental Statistics on GFRP- Reinforced Concrete Columns from Earlier Researchers.

description: A table describing samples of circular cross-section columns fully reinforced with GFRP in order to carry out a statistical study and link them to the famous international code equations.
DOI: 10.17632/8s3v73prwc.4
title: Experimental Statistics on GFRP- Reinforced Concrete Columns from Earlier Researchers.

description: A table describing samples of circular cross-section columns fully reinforced with GFRP in order to carry out a statistical study and link them to the famous international code equations.
DOI: 10.6084/m9.figshare.24016290
title: Fang RJ's data

description: Fang RJ's data
DOI: 10.6084/m9.figshare.24016290.v1
title: Fang RJ's data

description: Fang RJ's data