cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6408602
title: Face mask detection and masked facial recognition dataset (MDMFR Dataset)

description: The unavailability of a unified standard dataset for face mask detection and masked facial recognition motivated us to develop an in-house MDMFR dataset (MDMFR, 2022) to measure the performance of face mask detection and masked facial recognition methods. Both of these tasks have different dataset requirements. Face mask detection requires the images of multiple persons with and without mask. Whereas, masked face recognition requires multiple masked face images of the same person. Our MDMFR dataset consists of two main collections, 1) face mask detection, and 2) masked facial recognition. There are 6006 images in our MDMFR dataset. The face mask detection collection contains two categories of face images i.e., mask and unmask. Our detection database consists of 3174 with mask and 2832 without mask (unmasked) images. To construct the dataset, we captured multiple images of the same person in two configurations (mask and without mask). The masked facial recognition collection contains a total of 2896 masked images of 226 persons. More specifically, our dataset includes the images of both male and female persons of all ages including the children. The images of our dataset are diverse in terms of gender, race, and age of users, types of masks, illumination conditions, face angles, occlusions, environment, format, dimensions, and size, etc. Before being fed to our DeepMaskNet model, all images are scaled to a width and height of 256 pixels. All images have a bit depth of 24. We prepared the images of our dataset for the proposed DeepMaskNet model during preprocessing where images are cropped in Adobe-Photoshop to exclude the extra information like neck and shoulder. As the input size of our Deepmasknet model was 256-by-256, so images were resized to 256-by-256 in publicly available Plastiliq Image Resizer software (Plastiliq, 2022).
DOI: 10.6084/m9.figshare.19530547.v1
title: Effectiveness of a Training Workshop (raw data).xlsx

description: Anonymised dataset to show effectiveness of workshop based on the pre, post and 1 year post workshop surveys.
DOI: 10.6084/m9.figshare.19545061.v1
title: data.zip

description: The supplementary data of the calculation result for five enzymes
DOI: 10.5281/zenodo.4765992
title: MuSe-Physio: Multimodal Physiological-Arousal (MuSe2021)

description: <strong>MuSe-Physio of the 2nd Multimodal Sentiment Challenge! </strong><br> redicting the level of psycho-physiological arousal from a) human annotations fused with b) galvanic skin response (also known as Electrodermal Activity (EDA)) signals from the stressed people as regression task. Audio-visual recordings as well as other biological signals (heart rate and respiration) are offered for modelling. This package includes only MuSe-Physio features (all partitions) and labels of the training and development set (test scoring via the MuSe website). More: https://www.muse-challenge.org/muse2021 <strong>General: </strong>The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines. We introduce the novel dataset Ulm-TSST database, supplying a multimodal richly annotated dataset of external dimensional ratings of emotion and mental well-being. After a brief period of preparation the subjects are asked to give an oral presentation, within a job-interview setting. Ulm-TSST includes biological recordings, such as Electrocardiogram (ECG), Electrodermal Activity (EDA), Respiration, and Heart Rate (BPM) as well as continuous arousal and valence annotations. With 105 participants (69.5% female) aged between 18 and 39 years, a total of 10 hours were accumulated.
DOI: 10.5281/zenodo.4660292
title: MuSe-Physio: Multimodal Physiological-Arousal (MuSe2021)

description: <strong>MuSe-Physio of the 2nd Multimodal Sentiment Challenge! </strong><br> redicting the level of psycho-physiological arousal from a) human annotations fused with b) galvanic skin response (also known as Electrodermal Activity (EDA)) signals from the stressed people as regression task. Audio-visual recordings as well as other biological signals (heart rate and respiration) are offered for modelling. This package includes only MuSe-Physio features (all partitions) and labels of the training and development set (test scoring via the MuSe website). More: https://www.muse-challenge.org/muse2021 <strong>General: </strong>The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines. We introduce the novel dataset Ulm-TSST database, supplying a multimodal richly annotated dataset of external dimensional ratings of emotion and mental well-being. After a brief period of preparation the subjects are asked to give an oral presentation, within a job-interview setting. Ulm-TSST includes biological recordings, such as Electrocardiogram (ECG), Electrodermal Activity (EDA), Respiration, and Heart Rate (BPM) as well as continuous arousal and valence annotations. With 105 participants (69.5% female) aged between 18 and 39 years, a total of 10 hours were accumulated.
DOI: 10.6084/m9.figshare.19552054
title: Suspended particle parameters in the bottom water of North Yellow Sea in autumn

description: This file shows the measured and calculated parameters of suspended particulate matter in the near bottom water of North Yellow Sea in autumn. The manuscript analyzing this data entitled "Evolution of suspended particles in the North Yellow Sea in autumn: Insights from grain-size distributions" was submitted to Marine Geology.
DOI: 10.6084/m9.figshare.19552054.v1
title: Suspended particle parameters in the bottom water of North Yellow Sea in autumn

description: This file shows the measured and calculated parameters of suspended particulate matter in the near bottom water of North Yellow Sea in autumn. The manuscript analyzing this data entitled "Evolution of suspended particles in the North Yellow Sea in autumn: Insights from grain-size distributions" was submitted to Marine Geology.
DOI: 10.5281/zenodo.6424926
title: GEOS-Chem 2015 Speciated PM2.5 Outputs for "Impact of circular- and single-sector waste-heat reuse pathways on PM2.5-air quality, CO2 emissions, and human health in India; material exchanges more viable to achieve sustainability targets"

description: Daily PM2.5 and species outputs from GEOS-Chem Modeling over India (0.5° x 0.625°)
DOI: 10.5281/zenodo.6424925
title: GEOS-Chem 2015 Speciated PM2.5 Outputs for "Impact of circular- and single-sector waste-heat reuse pathways on PM2.5-air quality, CO2 emissions, and human health in India; material exchanges more viable to achieve sustainability targets"

description: Daily PM2.5 and species outputs from GEOS-Chem Modeling over India (0.5° x 0.625°)
DOI: 10.34740/kaggle/dsv/3427540
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.19551883
title: Appendix A.xlsx

description: The supplement provides all the coefficients derived in Icen and Sandıkkaya 2022 study.
DOI: 10.6084/m9.figshare.19545061.v2
title: data.zip

description: The supplementary data of the calculation result for five enzymes
DOI: 10.5281/zenodo.6416097
title: Dataset for the publication: First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries

description: This dataset contains the input and output files from the calculation of the atomistic properties of metallic magnesium, such as bulk, surface, adsorption, and diffusion properties. The discussion of the results were published in the ChemSusChem article: 'First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries' (https://doi.org/10.1002/cssc.202200414). A preprint of the publication is further available under: https://doi.org/10.26434/chemrxiv-2022-qz055. All calculations were performed using the density function theory code Vienna <em>ab initio</em> simulation package (VASP). The dataset contains all raw data for the performed convergence studies and calculated bulk-, surface-, adsorption-, and diffusion properties. An overview of the folder structure of the Zip archive, more precisely in which folders the data for the respective figures or tables of the underlying publication (https://doi.org/10.1002/cssc.202200414) are stored, is provided in the following table: Convergence_study Figure S1 Bulk_properties Table S3 Surface_properties Table 1, Table 2, Figure 1, Table S5 Adsorption_properties Monomer: Table S6; Dimer: Table 4, Table 5, Table 6; Islands: Figure S5, Table S9 Diffusion_properties Table 3, Table 7, Table 8, Table 9, Table 10, Table 11, Table 12, Figure 13, Figure 14, Table S7, Table S8, Table S10 Table S11, Figure S4, Figure S7, Figure S9
DOI: 10.5281/zenodo.6416096
title: Dataset for the publication: First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries

description: This dataset contains the input and output files from the calculation of the atomistic properties of metallic magnesium, such as bulk, surface, adsorption, and diffusion properties. The discussion of the results were published in the ChemSusChem article: 'First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries' (https://doi.org/10.1002/cssc.202200414). A preprint of the publication is further available under: https://doi.org/10.26434/chemrxiv-2022-qz055. All calculations were performed using the density function theory code Vienna <em>ab initio</em> simulation package (VASP). The dataset contains all raw data for the performed convergence studies and calculated bulk-, surface-, adsorption-, and diffusion properties. An overview of the folder structure of the Zip archive, more precisely in which folders the data for the respective figures or tables of the underlying publication (https://doi.org/10.1002/cssc.202200414) are stored, is provided in the following table: Convergence_study Figure S1 Bulk_properties Table S3 Surface_properties Table 1, Table 2, Figure 1, Table S5 Adsorption_properties Monomer: Table S6; Dimer: Table 4, Table 5, Table 6; Islands: Figure S5, Table S9 Diffusion_properties Table 3, Table 7, Table 8, Table 9, Table 10, Table 11, Table 12, Figure 13, Figure 14, Table S7, Table S8, Table S10 Table S11, Figure S4, Figure S7, Figure S9
DOI: 10.11588/data/h3kpeu
title: Inter-Nucleosomal Potentials from Nucleosomal Positioning Data [Research Data]

description: This dataset accompanies the paper "Inter-Nucleosomal Potentials from Nucleosomal Positioning Data." In this paper, a systematic ​method to develop inter-nucleosomal potentials between nucleosomes along a chromosome is focused. To this purpose, a modified Lennard-Jones potential is applied based on nucleosomal positioning data. The method is tested on the Candida albicans genome. The calculated potentials for all chromosomes for Candida albicans and their compressibility derived from potential-based simulations are included. In this dataset: "Potential01_100000.like_bed" contains the inter-nucleosomal potentials between nucleosomes for Candida albicans. Potentials are represented by four crucial parameters: parameter sigma is the scale for the modified Lennard-Jones potential; epsilon is the amplitude; delta is the exponents for the first term; and nu is the exponents for the second term. The meaning of columns are: chromosome_name, start_position, end_posiiton, section_index, sigma, epsilon, delta, nu, compressibility. “compress01_100000.out” contains the block density result for chromosome 2. The header is three “0” + the number of bins in the separation. The rest is section_index, slope, intercept + the block density result of the corresponding bin separation. "ICPC-source.tar.gz" is the source code.
DOI: 10.5517/ccdc.csd.cc2bgztm
title: CCDC 2159361: Experimental Crystal Structure Determination

description: Related Article: Julia Lingnau, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgzvn
title: CCDC 2159362: Experimental Crystal Structure Determination

description: Related Article: Julia Lingnau, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgzwp
title: CCDC 2159363: Experimental Crystal Structure Determination

description: Related Article: Julia Lingnau, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.15468/dl.n7kuew
title: Occurrence Download

description: A dataset containing 65 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Happywhale - Polar bear in Arctic Ocean"
 ]
}

 The dataset includes 65 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021218-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0880749.10fu
title: SH0880749.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0880750.10fu
title: SH0880750.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.13359621
title: Materials for "Revisiting the Relationship between Induced Polarization and Surface Conductivity: Ratios from Laboratory to Field"

description: These files are for the work "Revisiting the Relationship between Induced Polarization and Surface Conductivity: Ratios from Laboratory to Field". 

They include two parts of Experiments and Simulations, details of each part being explained in the Readme.txt therein. 

Through these materials, results presented in this work can be independently validated.
DOI: 10.15156/bio/sh0880751.10fu
title: SH0880751.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0880752.10fu
title: SH0880752.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0880753.10fu
title: SH0880753.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0880754.10fu
title: SH0880754.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0880755.10fu
title: SH0880755.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0880756.10fu
title: SH0880756.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0880757.10fu
title: SH0880757.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.