cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/2cvn75fkw4
title: DRE-YOLO

description: Detection of Unexploded Ordnance (UXO) based on drone platforms is crucial for public safety; however, it faces three significant challenges in complex outdoor environments: abrupt variations in target scale, degradation of edge information due to vegetation occlusion, and a high false detection rate caused by the similarity in texture between targets and backgrounds.Existing methods for detecting rotating targets exhibit inherent limitations: the traditional fixed receptive field design results in insufficient discriminability of small target features; in scenarios with vegetation occlusion, upsampling operations struggle to mitigate the degradation of geometric information at target edges; and significant background noise substantially reduces detection reliability.To address these challenges, this paper introduces DRE-YOLO, a multi-scale dynamic refinement network framework. First, a Deformable Adaptive Module (DAM-C3) is designed to dynamically adjust the receptive field size through deformable convolutional kernels while integrating local-global contexts, significantly enhancing scale adaptability. Secondly, an Efficient Shift-Upsampling (ES-Upsample) is developed, which combines a decoupled upsampling mechanism with a channel displacement fusion layer to effectively suppress edge degradation caused by vegetation occlusion. Finally, a Dual-Polarity Attention (DPA) is introduced in the detection head, utilizing a dynamic feature scaler and differential polarity modeling to substantially mitigate interference from background textures that are similar to the targets.
DOI: 10.17632/2cvn75fkw4.1
title: DRE-YOLO

description: Detection of Unexploded Ordnance (UXO) based on drone platforms is crucial for public safety; however, it faces three significant challenges in complex outdoor environments: abrupt variations in target scale, degradation of edge information due to vegetation occlusion, and a high false detection rate caused by the similarity in texture between targets and backgrounds.Existing methods for detecting rotating targets exhibit inherent limitations: the traditional fixed receptive field design results in insufficient discriminability of small target features; in scenarios with vegetation occlusion, upsampling operations struggle to mitigate the degradation of geometric information at target edges; and significant background noise substantially reduces detection reliability.To address these challenges, this paper introduces DRE-YOLO, a multi-scale dynamic refinement network framework. First, a Deformable Adaptive Module (DAM-C3) is designed to dynamically adjust the receptive field size through deformable convolutional kernels while integrating local-global contexts, significantly enhancing scale adaptability. Secondly, an Efficient Shift-Upsampling (ES-Upsample) is developed, which combines a decoupled upsampling mechanism with a channel displacement fusion layer to effectively suppress edge degradation caused by vegetation occlusion. Finally, a Dual-Polarity Attention (DPA) is introduced in the detection head, utilizing a dynamic feature scaler and differential polarity modeling to substantially mitigate interference from background textures that are similar to the targets.
DOI: 10.48436/sheh1-77176
title: Research data for "Epoxide-functionalized hyperbranched polyethylene and its applications in functional polymer synthesis and hot lithography photopolymer toughening"

description: Context and methodology

This dataset was collected during a research project on photopolymers for 3D printing within the framework of a PhD thesis at the Department of Polymers of University of Chemistry and Technology in Prague and at the Institute of Applied Synthetic Chemistry, TU Wien. It provides the raw data of results presented and discussed therein. The data is published within below-referenced publication.

Technical details

The dataset entails the raw data of various analyses collected in an Excel file Raw data figures:Tab 1: "Figure 3a PhotoDSC": PhotoDSC data obtained during curing of PE-GU formulation.

Tab 2: "Figure 3b RhotoRheo": PhotoRheology data obtained during curing of PE-GU formulation.

Tab 3: "Figure 5a Tensile test": Stress-strain curves obtained during tensile testing of PE-GU, PE-EU and CE matrix formulations.

Tab 4: "Figure 5b DMTA": Dynamic mechanical thermal analysis data obtained during tensile testing of PE-GU, PE-EU and CE matrix formulations.NMR data are included in zip (.rar) archive as raw FID data which can be opened with community-standard programmes (e.g. MestRe Nova, TopSpin).

Abbreviations

CE matrix: Reference formulation containing cycloaliphatic epoxide 3,4-Epoxycyclohexylmethyl-3',4'-epoxycyclohexane carboxylateDMTA: Dynamic mechanical thermal analysis

DSC: Differential scanning calorimetryFID: Free induction decay

NMR: Nuclear magnetic resonance spectroscopyPE-EU: Cycloaliphatic epoxide formulation containing polyethylene rubber functionalized with ethyl ester groups.PE-GU: Cycloaliphatic epoxide formulation containing polyethylene rubber functionalized with glycidyl groups.
DOI: 10.48436/zbw6r-n1g73
title: Research data for "Epoxide-functionalized hyperbranched polyethylene and its applications in functional polymer synthesis and hot lithography photopolymer toughening"

description: Context and methodology

This dataset was collected during a research project on photopolymers for 3D printing within the framework of a PhD thesis at the Department of Polymers of University of Chemistry and Technology in Prague and at the Institute of Applied Synthetic Chemistry, TU Wien. It provides the raw data of results presented and discussed therein. The data is published within below-referenced publication.

Technical details

The dataset entails the raw data of various analyses collected in an Excel file Raw data figures:Tab 1: "Figure 3a PhotoDSC": PhotoDSC data obtained during curing of PE-GU formulation.

Tab 2: "Figure 3b RhotoRheo": PhotoRheology data obtained during curing of PE-GU formulation.

Tab 3: "Figure 5a Tensile test": Stress-strain curves obtained during tensile testing of PE-GU, PE-EU and CE matrix formulations.

Tab 4: "Figure 5b DMTA": Dynamic mechanical thermal analysis data obtained during tensile testing of PE-GU, PE-EU and CE matrix formulations.NMR data are included in zip (.rar) archive as raw FID data which can be opened with community-standard programmes (e.g. MestRe Nova, TopSpin).

Abbreviations

CE matrix: Reference formulation containing cycloaliphatic epoxide 3,4-Epoxycyclohexylmethyl-3',4'-epoxycyclohexane carboxylateDMTA: Dynamic mechanical thermal analysis

DSC: Differential scanning calorimetryFID: Free induction decay

NMR: Nuclear magnetic resonance spectroscopyPE-EU: Cycloaliphatic epoxide formulation containing polyethylene rubber functionalized with ethyl ester groups.PE-GU: Cycloaliphatic epoxide formulation containing polyethylene rubber functionalized with glycidyl groups.
DOI: 10.17632/tjp3y8h46z.1
title: DCPL

description: This study constructs a new dataset named Documents under Complex Page Layouts (DCPL). The dataset consists of grayscale scanned images of real documents, covering various types such as journals, magazines, and academic papers. It features diverse layout styles and heterogeneous content structures. The documents include formulas, tables, illustrations, and multilingual text, with languages including but not limited to English, Chinese, and Russian.
The page layouts exhibit complex and highly variable structures, including two-column formats, embedded images and text, mixed text–image arrangements, and irregular paragraph layouts, effectively representing a wide range of document styles encountered in real-world scenarios. The entire DCPL dataset contains 900 document pages.
DOI: 10.17632/3nt8rkdd4z
title: 4G LTE network performance Indicators

description: The study used a real‑world dataset from a Nigerian network provider that contains 910 data points with 257 features capturing various measurements relevant to 4G LTE network performance.
DOI: 10.17632/3nt8rkdd4z.1
title: 4G LTE network performance Indicators

description: The study used a real‑world dataset from a Nigerian network provider that contains 910 data points with 257 features capturing various measurements relevant to 4G LTE network performance.
DOI: 10.5281/zenodo.18094606
title: Peptidomic profiling reveals extracellular matrix remodeling signatures discriminative of multiple myeloma

description: Multiple myeloma (MM) evolves from monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM (SMM) with annual progression rates of 1% and 10%, respectively. Current risk models don't fully capture the underlying dynamic molecular processes. We hypothesized that urinary peptides reflect disease-specific microenvironmental alterations in plasma cell dyscrasias. To test this hypothesis, capillary electrophoresis–mass spectrometry CE–MS was applied to profile the urinary peptidome of 314 individuals, including a discovery group (42 MGUS, 27 SMM, 14 MM), an independent validation group (45 MGUS, 9 SMM, 7 MM, 9 with plasmacytoma), 86 without underlying malignancy, and 75 patients with impaired kidney function. 121 peptides were significantly altered between MM and MGUS and displayed a monotonic abundance trend across the MGUS–SMM–MM continuum. These peptides predominantly derived from collagens, beta-2 microglobulin, alpha-1 antitrypsin, and antithrombin-III. Integration of these 121 peptides into a support vector machine classifier achieved an area under the curve of 0.94 (0.85–0.99; 95% CI) in the independent validation cohort, with 100% sensitivity and 82% specificity for MM detection. The finding that urinary peptides enable non-invasive molecular discrimination of MM from precursor states represents a solid basis for a prospective evaluation in prognosis and detection of progression.
DOI: 10.5281/zenodo.18094607
title: Peptidomic profiling reveals extracellular matrix remodeling signatures discriminative of multiple myeloma

description: Multiple myeloma (MM) evolves from monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM (SMM) with annual progression rates of 1% and 10%, respectively. Current risk models don't fully capture the underlying dynamic molecular processes. We hypothesized that urinary peptides reflect disease-specific microenvironmental alterations in plasma cell dyscrasias. To test this hypothesis, capillary electrophoresis–mass spectrometry CE–MS was applied to profile the urinary peptidome of 314 individuals, including a discovery group (42 MGUS, 27 SMM, 14 MM), an independent validation group (45 MGUS, 9 SMM, 7 MM, 9 with plasmacytoma), 86 without underlying malignancy, and 75 patients with impaired kidney function. 121 peptides were significantly altered between MM and MGUS and displayed a monotonic abundance trend across the MGUS–SMM–MM continuum. These peptides predominantly derived from collagens, beta-2 microglobulin, alpha-1 antitrypsin, and antithrombin-III. Integration of these 121 peptides into a support vector machine classifier achieved an area under the curve of 0.94 (0.85–0.99; 95% CI) in the independent validation cohort, with 100% sensitivity and 82% specificity for MM detection. The finding that urinary peptides enable non-invasive molecular discrimination of MM from precursor states represents a solid basis for a prospective evaluation in prognosis and detection of progression.
DOI: 10.17632/x6bgc8j4ms
title: _dataset_performance

description: The aggregated experimental results that support the findings of this study are provided as a supplementary dataset file: sustainability_dataset_performance.csv (Table I metrics; hydropower and CT case studies). The dataset is reported at an aggregated level (mean ± SD) and does not include run-level raw measurements. In this study, N refers to repeated runs/sessions per domain and condition. A brief description of the dataset and scope is provided in _dataset_readme.md.

For submission convenience, the supplementary materials are also provided as a single attachment package: sustainability_dataset_package.zip (includes the dataset CSV, README, data dictionary, and methods file).
DOI: 10.17632/x6bgc8j4ms.2
title: _dataset_performance

description: The aggregated experimental results that support the findings of this study are provided as a supplementary dataset file: sustainability_dataset_performance.csv (Table I metrics; hydropower and CT case studies). The dataset is reported at an aggregated level (mean ± SD) and does not include run-level raw measurements. In this study, N refers to repeated runs/sessions per domain and condition. A brief description of the dataset and scope is provided in _dataset_readme.md.

For submission convenience, the supplementary materials are also provided as a single attachment package: sustainability_dataset_package.zip (includes the dataset CSV, README, data dictionary, and methods file).
DOI: 10.15468/dl.7g3crd
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.38158300000001 26.378624, 89.303209 26.373047, 89.226787 26.356453, 89.154219 26.329258, 89.087306 26.292136, 89.02770599999999 26.246011, 88.97689 26.192028, 88.936106 26.131527, 88.906347 26.066005, 88.88833 25.997083, 88.88248 25.926462, 88.888918 25.855882, 88.907464 25.78708, 88.93764400000001 25.721745, 88.97869799999999 25.661479, 89.029607 25.607758, 89.089114 25.561894, 89.15575699999999 25.525008, 89.22790500000001 25.497999, 89.303797 25.481526, 89.38158300000001 25.47599, 89.459369 25.481526, 89.53526100000001 25.497999, 89.607409 25.525008, 89.674052 25.561894, 89.733558 25.607758, 89.78446700000001 25.661479, 89.82552099999999 25.721745, 89.855701 25.78708, 89.87424799999999 25.855882, 89.880686 25.926462, 89.874835 25.997083, 89.856818 26.066005, 89.82705900000001 26.131527, 89.786275 26.192028, 89.73545900000001 26.246011, 89.67586 26.292136, 89.608947 26.329258, 89.536378 26.356453, 89.45995600000001 26.373047, 89.38158300000001 26.378624))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081729-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/3f345tdg9v
title: Papaya Leaf Images

description: The Dataset comprises 140 papaya leaf images, each with a resolution of 150 × 150 pixels. This dataset is suitable for conducting diverse image processing experiments and serves as a valuable resource for the development and evaluation of computational models in the field of computer vision.

The dataset is classified into five major classes: Boron, Healthy, Nitrogen, Phosphorus, and Potassium. The dataset is split and distributed into a Training set with 80 images, a Validation set with 40 images, and a Testing set with 20 images. Each set of leaves in the training dataset was manually annotated and categorized with the guidance of agricultural experts
DOI: 10.17632/3f345tdg9v.1
title: Papaya Leaf Images

description: The Dataset comprises 140 papaya leaf images, each with a resolution of 150 × 150 pixels. This dataset is suitable for conducting diverse image processing experiments and serves as a valuable resource for the development and evaluation of computational models in the field of computer vision.

The dataset is classified into five major classes: Boron, Healthy, Nitrogen, Phosphorus, and Potassium. The dataset is split and distributed into a Training set with 80 images, a Validation set with 40 images, and a Testing set with 20 images. Each set of leaves in the training dataset was manually annotated and categorized with the guidance of agricultural experts
DOI: 10.17632/grv5mgysg5
title: Data for manuscript “How Does Open Government Data Affect Regional Economic Resilience? Empirical Evidence from China.”

description: This repository provides the data and replication code for the study “How Does Open Government Data Affect Regional Economic Resilience? Empirical Evidence from China.”
The paper examines the causal impact of Open Government Data (OGD) initiatives on regional economic resilience using a multi-period Difference-in-Differences (DID) framework.

The dataset is a balanced panel of Chinese prefecture-level cities covering multiple years, integrating information from official statistical yearbooks, government open data platform records, and publicly available socioeconomic indicators. The key explanatory variable captures the timing and intensity of local OGD policy implementation, while the dependent variable measures regional economic resilience through composite indicators reflecting resistance and recovery capacity.

The empirical strategy employs a staggered-adoption, multi-period DID model with city and year fixed effects, controlling for time-varying regional characteristics. Standard errors are clustered at the city level. Robustness checks include alternative resilience measures, placebo tests, and parallel trend verification.

All data processing, variable construction, and econometric analyses are fully documented in the provided code files. Running the code in the specified software environment reproduces all main tables and figures reported in the paper. This repository is intended to support transparency, reproducibility, and further research on open government data and regional economic development.
DOI: 10.17632/grv5mgysg5.1
title: Data for manuscript “How Does Open Government Data Affect Regional Economic Resilience? Empirical Evidence from China.”

description: This repository provides the data and replication code for the study “How Does Open Government Data Affect Regional Economic Resilience? Empirical Evidence from China.”
The paper examines the causal impact of Open Government Data (OGD) initiatives on regional economic resilience using a multi-period Difference-in-Differences (DID) framework.

The dataset is a balanced panel of Chinese prefecture-level cities covering multiple years, integrating information from official statistical yearbooks, government open data platform records, and publicly available socioeconomic indicators. The key explanatory variable captures the timing and intensity of local OGD policy implementation, while the dependent variable measures regional economic resilience through composite indicators reflecting resistance and recovery capacity.

The empirical strategy employs a staggered-adoption, multi-period DID model with city and year fixed effects, controlling for time-varying regional characteristics. Standard errors are clustered at the city level. Robustness checks include alternative resilience measures, placebo tests, and parallel trend verification.

All data processing, variable construction, and econometric analyses are fully documented in the provided code files. Running the code in the specified software environment reproduces all main tables and figures reported in the paper. This repository is intended to support transparency, reproducibility, and further research on open government data and regional economic development.
DOI: 10.17632/5w3hzrkcw8
title: Qualitative Interview Dataset on Digital Transformation and Sustainability in Gold Jewelry Manufacturing: A Case Study of PT Hantar Hamparan Hasil

description: This dataset contains qualitative interview data from a case study on digital transformation and sustainability in gold jewelry manufacturing at PT Hantar Hamparan Hasil, Indonesia. Data were collected through semi-structured interviews with key stakeholders, including the Chief Executive Officer, a jewellery designer, and a production head, representing strategic, design, and manufacturing perspectives.
The interviews were structured using a SWOT analysis framework to examine internal and external factors influencing pre-production processes, design visualization, material efficiency, waste management, and readiness for digital technology adoption. The dataset supports thematic analysis focused on challenges arising from manual production methods, design communication gaps, and material waste due to trial-and-error practices.
This dataset provides empirical insights for research on sustainable manufacturing, digital visualization technologies (AR/VR), Digital Twin adoption, and human-centered digital transformation in the jewelry industry.
DOI: 10.17632/5w3hzrkcw8.1
title: Qualitative Interview Dataset on Digital Transformation and Sustainability in Gold Jewelry Manufacturing: A Case Study of PT Hantar Hamparan Hasil

description: This dataset contains qualitative interview data from a case study on digital transformation and sustainability in gold jewelry manufacturing at PT Hantar Hamparan Hasil, Indonesia. Data were collected through semi-structured interviews with key stakeholders, including the Chief Executive Officer, a jewellery designer, and a production head, representing strategic, design, and manufacturing perspectives.
The interviews were structured using a SWOT analysis framework to examine internal and external factors influencing pre-production processes, design visualization, material efficiency, waste management, and readiness for digital technology adoption. The dataset supports thematic analysis focused on challenges arising from manual production methods, design communication gaps, and material waste due to trial-and-error practices.
This dataset provides empirical insights for research on sustainable manufacturing, digital visualization technologies (AR/VR), Digital Twin adoption, and human-centered digital transformation in the jewelry industry.
DOI: 10.17632/k3xtwnzpyn.1
title: iPSC-CD4 generation and characterization
DOI: 10.17632/g9vv6xv4ct
title: Divergence of cytokinesis and dimorphism control by myosin II regulatory light chain in fission yeasts

description: Non-muscle myosin II activation by regulatory light chain (Rlc1Sp) phosphorylation at Ser35 is crucial for cytokinesis during respiration in the fission yeast Schizosaccharomyces pombe. We show that in the early divergent and dimorphic fission yeast S. japonicus non-phosphorylated Rlc1Sj regulates the activity of Myo2Sj and Myp2Sj heavy chains during cytokinesis. Intriguingly, Rlc1Sj-Myo2Sj nodes delay yeast to hyphae onset but are essential for mycelial development. Structure-function analysis revealed that phosphorylation-induced folding of Rlc1Sp α1 helix into an open conformation allows precise regulation of Myo2Sp during cytokinesis. Consistently, inclusion of bulky tryptophan residues in the adjacent α5 helix triggered Rlc1Sp shift and supported cytokinesis in absence of Ser35 phosphorylation. Remarkably, unphosphorylated Rlc1Sj lacking the α1 helix was competent to regulate S. pombe cytokinesis during respiration. Hence, early diversification resulted in two efficient phosphorylation-independent and -dependent modes of Rlc1 regulation of myosin II activity in fission yeasts, the latter being conserved through evolution.