cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24520546.v1
title: Another Arabic Voice Command Dataset for Multiple Speech Processing Tasks

description: The expansion of Internet connectivity has revolutionized our daily lives, with people increasingly relying on smartphones and laptops for various tasks. This technological evolution has prompted the development of innovative solutions to enhance the quality of life for diverse populations, including the elderly and individuals with disabilities. Among the most impactful advancements are voice-command-enabled technologies such as SIRI and Google voice commands, which are built upon the foundation of Speech Recognition modules, a critical component in facilitating human-machine communication.Automatic Speech Recognition (ASR) has witnessed significant progress in achieving human-like performance through data-driven methods. In the context of our research, we have meticulously crafted an Arabic voice command dataset to facilitate advancements in ASR and other speech processing tasks. This dataset comprises 10 distinct commands spoken by 10 unique speakers, each repeated 10 times. Despite its modest size, the dataset has demonstrated remarkable performance across a range of speech processing tasks.The dataset was rigorously evaluated, yielding exceptional results. In ASR, it achieved an accuracy of 95.9%, showcasing its potential for effectively transcribing spoken Arabic commands. Furthermore, the dataset excelled in speaker identification, gender recognition, accent recognition, and spoken language understanding, with macro F1 scores of 99.67%, 100%, 100%, and 97.98%, respectively.This Arabic Voice Command Dataset represents a valuable resource for researchers and developers in the field of speech processing and human-machine interaction. Its quality and diversity make it a robust foundation for developing and testing ASR and other related systems, ultimately contributing to the advancement of voice-command technologies and their widespread accessibility.
DOI: 10.5517/ccdc.csd.cc2cypqr
title: CCDC 2203285: Experimental Crystal Structure Determination

description: Related Article: Y. Bai, P. Nasr, G. King, J. W. Reid, A. F. G. Leontowich, M. G. Corradini, R. G. Weiss, F.-I. Auzanneau, M. A. Rogers|2023|Nanoscale|15|16933|doi:10.1039/D3NR03988A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cyprs
title: CCDC 2203286: Experimental Crystal Structure Determination

description: Related Article: Y. Bai, P. Nasr, G. King, J. W. Reid, A. F. G. Leontowich, M. G. Corradini, R. G. Weiss, F.-I. Auzanneau, M. A. Rogers|2023|Nanoscale|15|16933|doi:10.1039/D3NR03988A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dkvb5
title: CCDC 2221687: Experimental Crystal Structure Determination

description: Related Article: Idan Avigdori, Kuldeep Singh, Natalia Fridman, Mark Gandelman|2023|Chemical Science|14|12034|doi:10.1039/D3SC04268E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dkvc6
title: CCDC 2221688: Experimental Crystal Structure Determination

description: Related Article: Idan Avigdori, Kuldeep Singh, Natalia Fridman, Mark Gandelman|2023|Chemical Science|14|12034|doi:10.1039/D3SC04268E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dkvd7
title: CCDC 2221689: Experimental Crystal Structure Determination

description: Related Article: Idan Avigdori, Kuldeep Singh, Natalia Fridman, Mark Gandelman|2023|Chemical Science|14|12034|doi:10.1039/D3SC04268E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dkvf8
title: CCDC 2221690: Experimental Crystal Structure Determination

description: Related Article: Idan Avigdori, Kuldeep Singh, Natalia Fridman, Mark Gandelman|2023|Chemical Science|14|12034|doi:10.1039/D3SC04268E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dqsgd
title: CCDC 2226434: Experimental Crystal Structure Determination

description: Related Article: Li-Xia Liu, Wen-Jun Huang, Chang-Bin Yu, Yong-Gui Zhou|2023|Org.Biomol.Chem.|21|8516|doi:10.1039/D3OB01315D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f9pv9
title: CCDC 2243651: Experimental Crystal Structure Determination

description: Related Article: Jan Adamek, Paulina H. Marek-Urban, Krzysztof Woźniak, Krzysztof Durka, Sergiusz Luliński|2023|Chemical Science|14|12133|doi:10.1039/D3SC03876A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f9pwb
title: CCDC 2243652: Experimental Crystal Structure Determination

description: Related Article: Jan Adamek, Paulina H. Marek-Urban, Krzysztof Woźniak, Krzysztof Durka, Sergiusz Luliński|2023|Chemical Science|14|12133|doi:10.1039/D3SC03876A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f9pxc
title: CCDC 2243653: Experimental Crystal Structure Determination

description: Related Article: Jan Adamek, Paulina H. Marek-Urban, Krzysztof Woźniak, Krzysztof Durka, Sergiusz Luliński|2023|Chemical Science|14|12133|doi:10.1039/D3SC03876A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f9pyd
title: CCDC 2243654: Experimental Crystal Structure Determination

description: Related Article: Jan Adamek, Paulina H. Marek-Urban, Krzysztof Woźniak, Krzysztof Durka, Sergiusz Luliński|2023|Chemical Science|14|12133|doi:10.1039/D3SC03876A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f9pzf
title: CCDC 2243655: Experimental Crystal Structure Determination

description: Related Article: Jan Adamek, Paulina H. Marek-Urban, Krzysztof Woźniak, Krzysztof Durka, Sergiusz Luliński|2023|Chemical Science|14|12133|doi:10.1039/D3SC03876A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f9q0h
title: CCDC 2243656: Experimental Crystal Structure Determination

description: Related Article: Jan Adamek, Paulina H. Marek-Urban, Krzysztof Woźniak, Krzysztof Durka, Sergiusz Luliński|2023|Chemical Science|14|12133|doi:10.1039/D3SC03876A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f9q1j
title: CCDC 2243657: Experimental Crystal Structure Determination

description: Related Article: Jan Adamek, Paulina H. Marek-Urban, Krzysztof Woźniak, Krzysztof Durka, Sergiusz Luliński|2023|Chemical Science|14|12133|doi:10.1039/D3SC03876A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fc9jn
title: CCDC 2245191: Experimental Crystal Structure Determination

description: Related Article: Swati Lekha Mondal, Vinod Bhajammanavar, Isai Ramakrishna, Mahiuddin Baidya|2023|Chem.Commun.|59|13211|doi:10.1039/D3CC03810F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fmfs9
title: CCDC 2253011: Experimental Crystal Structure Determination

description: Related Article: Amandine Luc, João C.A. Oliveira, Philipp Boos, Nicolas Jacob, Lutz Ackermann, Joanna Wencel-Delord |2023|Chem Cat.|3|100765|doi:10.1016/j.checat.2023.100765
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: 10.6084/m9.figshare.24522883
title: <b>Digital mammography Dataset for Breast Cancer Diagnosis Research (DMID)</b>

description: This dataset contains images of mammograms and <b>can be used for research and education purposes only.</b> The dataset contains DCM images, TIFF images, a Radiology report, a Segmented mask, and pixel level annotation on abnormal regions and csv file that contains other metadata.
DOI: 10.5519/qd.sjctospn
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4138 records)
DOI: 10.25612/837.8ljz1nakzv4y
title: Licensed Child Care Operations in the Houston-Galveston Area Council Region
title: HHSC CCL Daycare and Residential Operations Data - H-GAC Region

description: List of licensed child care operations (facilities) in the 13 counties of the Houston-Galveston Area Council region as of January 2023.
DOI: 10.25612/837.p4a7gj20e6vp
title: Licensed Childcare Operations - Counts and Capacity by Community Tabulation Area

description: Count and capacity of licensed childcare operations in Harris County (TX) by CTA. Subtotals by operation type and acceptance of subsidies are also provided.
DOI: 10.6084/m9.figshare.24525757
title: Table S4.3.xlsx

description: Thesis Table S4.3 Relative abundance of the top 20 prevalent species, detected by 16S rDNA sequencing Sample info is stored in sheet "metadata_mdf"Relative abundance data is stored in sheet "genus_top20_meta"
DOI: 10.6084/m9.figshare.24525757.v2
title: Table S4.3.xlsx

description: Thesis Table S4.3 Relative abundance of the top 20 prevalent species, detected by 16S rDNA sequencing Sample info is stored in sheet "metadata_mdf"Relative abundance data is stored in sheet "genus_top20_meta"
DOI: 10.6084/m9.figshare.24525757.v1
title: Table S4.3.xlsx

description: Thesis Table S4.3 Relative abundance of the top 20 prevalent species, detected by 16S rDNA sequencing Sample info is stored in sheet "metadata_mdf"Relative abundance data is stored in sheet "genus_top20_meta"
DOI: 10.5281/zenodo.10084293
title: platyrinchus_mystaceus_decontaminated_FSCR.fasta.gz

description: platyrinchus_mystaceus_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10084292
title: platyrinchus_mystaceus_decontaminated_FSCR.fasta.gz

description: platyrinchus_mystaceus_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10081204
title: Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"

description: The tar file contains two directories: data and models.  Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data).  The data in these directories are unnormalized.  The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file.  These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively.  To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths.  The other arguments should be as follows:
--uniformize=1
--predictor_norm_type_string="z_score"
--vector_target_norm_type_string=""
--scalar_target_norm_type_string=""
 
Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data.  To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.
DOI: 10.5281/zenodo.10081205
title: Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"

description: The tar file contains two directories: data and models.  Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data).  The data in these directories are unnormalized.  The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file.  These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively.  To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths.  The other arguments should be as follows:
--uniformize=1
--predictor_norm_type_string="z_score"
--vector_target_norm_type_string=""
--scalar_target_norm_type_string=""
 
Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data.  To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.
DOI: 10.5281/zenodo.10084373
title: poliocrania_exsul_decontaminated_FSCR.fasta.gz

description: poliocrania_exsul_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10084374
title: poliocrania_exsul_decontaminated_FSCR.fasta.gz

description: poliocrania_exsul_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10084382
title: pouteria_campechiana_decontaminated_FSCR.fasta.gz

description: pouteria_campechiana_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10084381
title: pouteria_campechiana_decontaminated_FSCR.fasta.gz

description: pouteria_campechiana_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10084391
title: premnoplex_brunnescens_decontaminated_FSCR.fasta.gz

description: premnoplex_brunnescens_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.8185092
title: mzML test data for the jullia programming language libraries

description: The repository contains three mzML mass spectrometry datasets: Col_1.mzML is a liquid chromatography (LC) ESI MS dataset from an Arabidopsis extraction published in: Sotelo-Silveira, M., Chauvin, A.-L., Marsch-Martínez, N., Winkler, R. & De Folter, S. Metabolic fingerprinting of Arabidopsis thaliana accessions. Frontiers in Plant Science 6, 1–13 (2015). https://doi.org/10.3389/fpls.2015.00365 Cytochrome_C.mzML is an electrospray mass spectrometry (ESI MS) dataset of Cytochrome C. The data were discussed in: Winkler, R. ESIprot: a universal tool for charge state determination and molecular weight calculation of proteins from electrospray ionization mass spectrometry data. Rapid Communications in Mass Spectrometry 24, 285- 294 (2010). https://doi.org/10.1002/rcm.4384 T9_A1.mzML is a low-temperature plasma (LTP) MS dataset of the interaction between Arabidopsis and Trichoderma, published in 1. Torres-Ortega, R. et al. In Vivo Low-Temperature Plasma Ionization Mass Spectrometry (LTP-MS) Reveals Regulation of 6-Pentyl-2H-Pyran-2-One (6-PP) as a Physiological Variable during Plant-Fungal Interaction. Metabolites 12, 1231 (2022). https://doi.org/10.3390/metabo12121231
DOI: 10.5281/zenodo.10084390
title: premnoplex_brunnescens_decontaminated_FSCR.fasta.gz

description: premnoplex_brunnescens_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10083599
title: Anomalous Nernst effect in the topological and magnetic material MnBi4Te7 (Data)
DOI: 10.5281/zenodo.10083598
title: Anomalous Nernst effect in the topological and magnetic material MnBi4Te7 (Data)
DOI: 10.6084/m9.figshare.24526645
title: Table S4.2.xlsx

description: Thesis Table S4.2 Correlation of aac(6’)-class gene prevalence with Amikacin (AMK) MICs of Salmonella and non-Salmonella species tested by Agar dilution using M9 agar This datasheet contains raw data used to generate Fig. 4-4 and Fig. 4-5 in chapter 4.The aac(6’)-gene prevalence and AMK MIC of each strain, as well as metadata containing the strain info and the culture conditions (i.e. incubation duration, formulation of M9 agar, etc.)<br>
DOI: 10.6084/m9.figshare.24526645.v1
title: Table S4.2.xlsx

description: Thesis Table S4.2 Correlation of aac(6’)-class gene prevalence with Amikacin (AMK) MICs of Salmonella and non-Salmonella species tested by Agar dilution using M9 agar This datasheet contains raw data used to generate Fig. 4-4 and Fig. 4-5 in chapter 4.The aac(6’)-gene prevalence and AMK MIC of each strain, as well as metadata containing the strain info and the culture conditions (i.e. incubation duration, formulation of M9 agar, etc.)<br>
DOI: 10.6084/m9.figshare.24526441
title: Cold tolerance of the mealybug parasitoid <i>Anagyrus vladimiri</i>

description: this data set contains the raw data from the publication Cold tolerance of the mealybug parasitoid <i>Anagyrus vladimiri</i>
DOI: 10.6084/m9.figshare.24526441.v1
title: Cold tolerance of the mealybug parasitoid <i>Anagyrus vladimiri</i>

description: this data set contains the raw data from the publication Cold tolerance of the mealybug parasitoid <i>Anagyrus vladimiri</i>
DOI: 10.5066/f71n7z7v
title: Phytoplankton data for Cheney Reservoir near Cheney, Kansas, June 2001 through November 2015

description: This U.S. Geological Survey (USGS) Data Release provides phytoplankton data collected from Cheney Reservoir, Kansas, during June 2001 through November 2015. All data are reported as raw calculated values and are not rounded to USGS significant figures. This data release was produced in compliance with the open data requirements as a way to make scientific products associated with USGS research efforts and publications available to the public. The dataset includes all routine and quality assurance/quality control samples collected at the long-term sampling site on Cheney Reservoir located near the dam (USGS station identification number 07144790). Phytoplankton were identified to the lowest possible taxonomic level and abundance (density) and biovolume are reported.
DOI: 10.6073/pasta/051bc8d59d4667f1a733c38a3dcc1849
title: Long-term (1993-2019) tree population measurements from a mapped 2.9-ha permanent plot in old-growth northern hardwood forest, Dukes Research Natural Area, Marquette Co., MI, USA

description: The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 permanent monitoring plots (data to be provided in a separate package). In 1993-95, a macroplot of 2.91 ha was established in a mixed mesic upland forest area within the RNA, in which all woody stems &gt;2 cm diameter at breast height (DBH) were identified, measured, and mapped. In 1999 and again every five years subsequently through 2019, the macroplot was recensused; all stems were remeasured, stems newly recruited (&gt;2 cm DBH) were measured and mapped, and any mortality since previous census was noted and described. A severe storm in 2002 resulted in extensive mortality throughout the RNA, particularly in the area in and around the macroplot.
DOI: 10.5281/zenodo.10084533
title: TABIIY UGLEVODOROD GAZLARINI QURITISH JARAYONINI TADQIQ QILISH
DOI: 10.5281/zenodo.10084532
title: TABIIY UGLEVODOROD GAZLARINI QURITISH JARAYONINI TADQIQ QILISH