cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/cs23mhcd5w.1
title: The Effect of Social Assistance on Vote Results for the 2024 Presidential and Vice Presidential Election in Indonesia: A Bibliometric Analysis and Systematic Literature Review

description: This datasets was for the article entitled The Effect of Social Assistance on Vote Results for the 2024 Presidential and Vice Presidential Election in Indonesia: A Bibliometric Analysis and Systematic Literature Review
DOI: 10.17632/2kkn2975mp.1
title: Fractal characteristics of the spatial heterogeneity in trace element abundance in undisturbed surface soils and implications for soil pollution assessment

description: Data for all figures and tables in the manuscript entitled "Fractal characteristics of the spatial heterogeneity in trace element abundance in undisturbed surface soils and implications for soil pollution assessment".
DOI: 10.17632/zfj37vkbdn.1
title: GHG Emissions Nuclear Power data 2024

description: This data contributes to the ongoing dialogue on nuclear energy's role in climate change mitigation strategies, providing a nuanced view of its environmental implications while acknowledging the complexities inherent in life cycle assessments.
DOI: 10.15468/dl.r576a5
title: Occurrence Download

description: A dataset containing 242 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Fistularia commersonii Rüppell, 1838",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 242 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009252-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ryb93tmj67.1
title: Trem2/PPARγ/STAT6 axis contributes to the host protection against Toxoplasma gondii-induced adverse pregnancy outcomes via decidual macrophages

description: Figure 1. T. gondii inhibited the expressions of Trem2 and its downstream effectors. (A) Representative images of uteri and fetuses at G 17.5 in PBS and T. gondii-infected WT mice. (B) Flow cytometry was employed to identify the percentage of CD11b+ DMs in PBS and T. gondii-infected mouse placentas at G 17.5 and representative histograms were shown . (C) Flow cytometry was employed to assay the MFI of Trem2 on DMs in PBS and T. gondii-infected mouse placentas at G 17.5 and representative histograms were shown . (E-G) Representative IHC staining images of Trem2 (E), P-STAT6 (F), and PPARγ (G) in PBS and T. gondii-infected mouse placentas. Figure 2. T. gondii antigens inhibited the expressions of Trem2, P-STAT6, and PPARγ. Figure 2. (A) Trem2 expression was determined with Western blot. (B) Expressions of PPARγ, phosphorylated (P), and total STAT6 were assayed by Western blot. (C-D) P-STAT6 and PPARγ expression were measured by cellular immunofluorescence staining. Figure 3. The effect of Trem2 deficiency on its downstream pathway during T. gondii infection in mice. (A) Representative images of fetuses at G 17.5 from WT and Trem2-/- mice with or without T. gondii infection. Fetal development was assessed by fetal size (CRL×OF) and fetal weight (n = 5-7 biologically independent mice). (B) Flow cytometry was utilized to identify the percentage of CD11b+ DMs in WT and Trem2-/--infected mouse placenta and representative histograms were shown. (C) P-STAT6, STAT6, and PPARγ proteins were analyzed by Western blot in mouse placentas from Trem2-/- and WT-infected mice. (D-E) Expressions of P-STAT6, STAT6, and PPARγ were assayed by Western blot. Figure 4. The effect of Trem2 deficiency on its downstream pathway during TgAg stimulation in BMDMs. (A) BMDMs extracted from WT and Trem2-/- mice were experimented with following the protocol described in the schematic. The graphic was created with BioRender.com. (B-C) Expressions of P-STAT6, STAT6, PPARγ, and Trem2 in BMDMs were assayed by Western blot. (D) Confocal microscopy was employed to assay the expression of P-STAT6, PPARγ, and Trem2 in BMDMs from WT mice. Figure 5. T. gondii infection down-regulated Trem2 by affecting the PPARγ/STAT6 signaling pathway. (A) Western blot was utilized to evaluate the expression of PPARγ, P-STAT6, STAT6, and Trem2. (B) Expressions of PPARγ, P-STAT6, and STAT6 were measured by Western blot. Figure S1. P-STAT6 or STAT6 interacts with PPARγ. (A-B) Co-immunoprecipitation of STAT6 with PPARγ. HEK293T cells were transfected with flag-STAT6, myc-PPARγ, or flag-STAT6+myc-PPARγ, respectively. Whole-cell lysates were incubated with myc or flag antibodies, and the presence of flag-STAT6 (A) or myc-PPARγ (B) was analyzed by immunoblotting. (C) Co-immunoprecipitation of P-STAT6 with PPARγ. (D) Confocal microscopy of the expression of P-STAT6, PPARγ, and Trem2 in Raw264.7 cells treated with or without IL-4 (20 ng/ml) for 1 h.
DOI: 10.17632/j5wvg6ccb7.1
title: How consumers' digital engagement affects regional innovation capacity in China

description: Using data of 211 cities from 2015 to 2018 in China, this study investigates whether and how consumers’ digital engagement affects regional innovation capacity. Our empirical results show that cities with greater knowledge diffusion embedded in consumer digital engagement benefit more from increased regional innovation capacity. Further analyses show that cities with higher digital consumer participation have higher innovation output, more entrepreneurial activities, and increased financial access, boosting city-level knowledge generation and commercialization. Our findings also reveal that consumers’ digital engagement is more beneficial in relatively developing regions, including western, peripheral, and less innovative cities, thereby alleviating the concentration of innovation to some extent.
DOI: 10.15468/dl.qba8cw
title: Occurrence Download

description: A dataset containing 78 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sepioteuthis lessoniana d'Orbigny, 1826",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 78 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009253-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/vt93yw3htg.1
title: Data for: Quantifying optimal magmatic oxidation conditions for the formation of giant porphyry copper deposits

description: Supporting Materials 1 and 2 for the manuscript titled "Quantifying optimal magmatic oxidation conditions for the formation of giant porphyry copper deposits" submitted to Geochimica et Cosmochimica Acta.
DOI: 10.17632/53wrc86y6s.1
title: Therapeutic application of extracellular vesicular EGFR isoform D as a co-drug to target squamous cell cancers with tyrosine kinase inhibitors

description: Supplementary Information for manuscript "Therapeutic application of extracellular vesicular EGFR isoform D as a co-drug to target squamous cell cancers with tyrosine kinase inhibitors".
DOI: 10.5281/zenodo.12615622
title: Resolving single-cell expression profiles by pseudo-temporal integration of transcriptomic and proteomic datasets.

description: Raw and processed single cell proteomics (scp-MS) and scRNA-Seq data of HEK293-PIP-FUCCI cells which were challanged with hypoxia. The repository contains data for recreating the pseudo-temporal alignment analysis of transcription-translation profiles.
DOI: 10.5281/zenodo.12615623
title: Resolving single-cell expression profiles by pseudo-temporal integration of transcriptomic and proteomic datasets.

description: Raw and processed single cell proteomics (scp-MS) and scRNA-Seq data of HEK293-PIP-FUCCI cells which were challanged with hypoxia. The repository contains data for recreating the pseudo-temporal alignment analysis of transcription-translation profiles.
DOI: 10.5281/zenodo.6399494
title: Multicolor flow cytometry of monocultures and co-cultures of Bacteroides species

description: Dataset of FCS (Flow Cytometry Standard) files, along with meta-data, related to a flow cytometry analysis of monocultures and co-cultures of Bacteroides species under several different conditions. 

Data Collection. This dataset accompanies a journal artcle which was published in Frontiers in Microbiology (https://doi.org/10.3389/fmicb.2022.910390). The "Methods and Materials" section in this article fully describes the biological nature of these samples and how the samples were processed for flow analysis and analyzed with flow cytometry. 

Data Organization. Dataset includes 1832 samples. See mapping.xlsx and mapping_key.xlsx for list of samples and their meta-data. Folders are formatted as {run_data}_{time_point} and contains only samples belonging to either a run performed on 2018/07/17 or 2018/07/21 for time points of either 0, 24, 48, 72, or 102 hours. 

Data Analysis. Code used for manipulating and analyzing these samples is publicly available (https://github.com/firasmidani/BacteroidesFlowCytometry).

Data Integrity. "hardac-hashes.txt" stores the MD5 hashes of the original folders created by the authors prior to uploading data to Zenodo.
DOI: 10.5281/zenodo.6399495
title: Multicolor flow cytometry of monocultures and co-cultures of Bacteroides species

description: Dataset of FCS (Flow Cytometry Standard) files, along with meta-data, related to a flow cytometry analysis of monocultures and co-cultures of Bacteroides species under several different conditions. 

Data Collection. This dataset accompanies a journal artcle which was published in Frontiers in Microbiology (https://doi.org/10.3389/fmicb.2022.910390). The "Methods and Materials" section in this article fully describes the biological nature of these samples and how the samples were processed for flow analysis and analyzed with flow cytometry. 

Data Organization. Dataset includes 1832 samples. See mapping.xlsx and mapping_key.xlsx for list of samples and their meta-data. Folders are formatted as {run_data}_{time_point} and contains only samples belonging to either a run performed on 2018/07/17 or 2018/07/21 for time points of either 0, 24, 48, 72, or 102 hours. 

Data Analysis. Code used for manipulating and analyzing these samples is publicly available (https://github.com/firasmidani/BacteroidesFlowCytometry).

Data Integrity. "hardac-hashes.txt" stores the MD5 hashes of the original folders created by the authors prior to uploading data to Zenodo.
DOI: 10.5281/zenodo.12629217
title: Supplementary materials
DOI: 10.5281/zenodo.12629218
title: Supplementary materials
DOI: 10.17632/5kcff93f5j.1
title: Language and Citations in Medical Articles Published by Cuban Authors (2000-2024)

description: A search was carried out of all medical articles by authors with Cuban affiliations who had published in journals indexed in Scopus, from the year 2000 to date. The formulation used was as follows:
AFFILCOUNTRY ( cuba ) AND PUBYEAR &gt; 1999 AND PUBYEAR &lt; 2025 AND ( LIMIT-TO ( SUBJAREA , "MEDI" ) ) AND ( LIMIT-TO ( DOCTYPE , "ar" ) )
Data were filtered to include only articles in English and Spanish and the ANOVA (or Kruskal-Wallis if assumptions are not met) statistical test was performed. Significant values were considered when the p value was equal to or less than 0.05.
The data frame was downloaded in CSV format and converted to XLSX for Excel. Subsequently, an analysis of the same was carried out in RStudio ®.
DOI: 10.17632/5kcff93f5j.2
title: Language and Citations in Medical Articles Published by Cuban Authors (2000-2024)

description: A search was carried out of all medical articles by authors with Cuban affiliations who had published in journals indexed in Scopus, from the year 2000 to date. The formulation used was as follows:
AFFILCOUNTRY ( cuba ) AND PUBYEAR &gt; 1999 AND PUBYEAR &lt; 2025 AND ( LIMIT-TO ( SUBJAREA , "MEDI" ) ) AND ( LIMIT-TO ( DOCTYPE , "ar" ) )
Data were filtered to include only articles in English and Spanish and the ANOVA (or Kruskal-Wallis if assumptions are not met) statistical test was performed. Significant values were considered when the p value was equal to or less than 0.05.
The data frame was downloaded in CSV format and converted to XLSX for Excel. Subsequently, an analysis of the same was carried out in RStudio ®.
DOI: 10.15468/dl.rfchff
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sepioteuthis lessoniana Lesson, 1830",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 32 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009254-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12575451
title: Target Speech Extraction Dataset for Knowledge Boosting (Part 1)

description: Part 1 of the Target Speech Extraction Dataset as described in Knowledge boosting during low-latency inference (Interspeech 2024)

Abstract: Models for low-latency, streaming applications could benefit from the knowledge capacity of larger models, but edge devices cannot run these models due to resource constraints. A possible solution is to transfer hints during inference from a large model running remotely to a small model running on-device. However, this incurs a communication delay that breaks real-time requirements and does not guarantee that both models will operate on the same data at the same time. We propose knowledge boosting, a novel  technique that allows a large model to operate on time-delayed input during inference, while still boosting small model performance. Using a  streaming neural network that processes 8 ms chunks, we evaluate different speech separation and enhancement tasks with communication delays of up to six chunks or 48 ms. Our results show larger gains where the performance gap between the small and large models is wide, demonstrating a promising method for large-small model collaboration for low-latency applications.
DOI: 10.5281/zenodo.12575452
title: Target Speech Extraction Dataset for Knowledge Boosting (Part 1)

description: Part 1 of the Target Speech Extraction Dataset as described in Knowledge boosting during low-latency inference (Interspeech 2024)

Abstract: Models for low-latency, streaming applications could benefit from the knowledge capacity of larger models, but edge devices cannot run these models due to resource constraints. A possible solution is to transfer hints during inference from a large model running remotely to a small model running on-device. However, this incurs a communication delay that breaks real-time requirements and does not guarantee that both models will operate on the same data at the same time. We propose knowledge boosting, a novel  technique that allows a large model to operate on time-delayed input during inference, while still boosting small model performance. Using a  streaming neural network that processes 8 ms chunks, we evaluate different speech separation and enhancement tasks with communication delays of up to six chunks or 48 ms. Our results show larger gains where the performance gap between the small and large models is wide, demonstrating a promising method for large-small model collaboration for low-latency applications.
DOI: 10.5281/zenodo.10119283
title: Predicting the First Response Latency of Maintainers and Contributors in Pull Requests

description: Dataset for the "Predicting the First Response Latency of Maintainers and Contributors in Pull Requests" paper submitted to the IEEE Transactions on Software Engineering (TSE) journal.
DOI: 10.5281/zenodo.10119284
title: Predicting the First Response Latency of Maintainers and Contributors in Pull Requests

description: Dataset for the "Predicting the First Response Latency of Maintainers and Contributors in Pull Requests" paper submitted to the IEEE Transactions on Software Engineering (TSE) journal.
DOI: 10.5281/zenodo.12628883
title: Predicting the First Response Latency of Maintainers and Contributors in Pull Requests

description: Dataset for the "Predicting the First Response Latency of Maintainers and Contributors in Pull Requests" paper submitted to the IEEE Transactions on Software Engineering (TSE) journal.
DOI: 10.5281/zenodo.10815611
title: Microseismic monitoring of tailings dams - signal and noise evaluation

description: The dataset originates from the manuscript titled "Microseismic Monitoring of Tailings Dams: Evaluation of Signal and Noise Sources," currently under submission to Seismica.

The database is organized into four distinct categories, each serving a specific purpose:





Signal: This category comprises common signals, which are likely to be observed at various stations worldwide. It includes data from both blast events and natural seismic occurrences;




Site-specific Signal: These signals are unique to the specific location or a single station. They primarily consist of data obtained from pulse tests conducted at the site;




Noise: Within this category, common noise sources are documented, which are also anticipated to be observed at other stations globally. The noise sources encompass mechanical disturbances such as those from backhoes, drills, excavators, and trucks. Additionally, data related to natural phenomena such as lightning, thunder, and combinations thereof (lightning accompanied by thunder) are also cataloged here;




Site-specific Noise: This category comprises noise sources that are specific to the particular location or a single station. The primary source identified here is digitizer interference.
DOI: 10.15468/dl.yequ5a
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lamprotornis albicapillus (Blyth, 1855)"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009257-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/9g3pxyktyh.1
title: Supplementary material. The challenge of measuring fetal death: a cross-reference of available information sources in Mexico (2014-2022)

description: In the manuscript entitled "The challenge of measuring fetal death: a cross-reference of available information sources in Mexico (2014-2022)" , we performed a cross-reference study of routinely collected fetal mortality data in Mexico, including health system-based data and vital statistics from the National Institute for Statistics and Geography (INEGI). We examined reporting practices across different data sources and estimated underreporting and fetal mortality rates using several definitions and gestational age cutoffs. 
When analyzing the Automated Subsystem for Hospital Discharges (SAEH) database for 2016, we discovered and eliminated several duplicated records. In this supplementary material we provide the 2016 fetal death data by gestational age and fetal weight, as reported in the SAEH, both before and after the removal of duplicate
records.
DOI: 10.5281/zenodo.12616999
title: Data for "Revisiting the reanalysis-model discrepancy in Southern Hemisphere winter storm track trends"

description: The dataset supporting the conclusion of the submitted paper is uploaded here.

The data are labeled after each figure. The npz files include data required to reproduce our results in python arrays.
DOI: 10.5281/zenodo.12617000
title: Data for "Revisiting the reanalysis-model discrepancy in Southern Hemisphere winter storm track trends"

description: The dataset supporting the conclusion of the submitted paper is uploaded here.

The data are labeled after each figure. The npz files include data required to reproduce our results in python arrays.
DOI: 10.15468/dl.tb9a2j
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sepioteuthis lessoniana Ferussac, 1831,1830",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009256-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pmc4g589gk.1
title: Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models

description: Maxillofacial fracture detection and classification in computed tomography images
using convolutional neural network-based models
DataCreated By :Prof: Eldirdiri Fadol Ibrahim Fadol Khalili

 Here is the description of the dataset maxillofacial_fracture_data:

 Dataset: maxillofacial_fracture_data

 Number of Samples: 100

 Image Dimensions: 64x64 pixels

 Number of Classes: 5 (Class labels ranging from 0 to 4)

Columns in the Dataset:

 pixel_0, pixel_1, ..., pixel_4095:

Description: Each column represents the pixel value at a specific position in a flattened 64x64 image. The pixel values are normalized and range between 0 and 1.

 Data Type: Float

 label:

 Description: The class label of the image. It is an integer value representing the class to which the image belongs.

 Data Type: Integer

 Detailed Description
Image Data:

 The image data is flattened from a 2D array (64x64) into a 1D array (4096). Each image has 4096 pixel values, and each value represents the intensity of a pixel in the image.
 These pixel values are normalized, meaning they lie between 0 and 1. Normalization is a common preprocessing step in image processing to make the training of neural networks more efficient.

Class Labels:

 The class label indicates the type of maxillofacial fracture detected in the image. The labels are integers from 0 to 4, representing different types or categories of fractures.
 The exact meaning of each label (e.g., what type of fracture each number represents) should be defined based on your specific use case or dataset documentation.
import pandas as pd

# Define the file path for the dataset
file_path_data = "E:\\maxillofacial_fracture_data.xlsx" # Update this path as necessary

# Load the dataset
df = pd.read_excel(file_path_data)

# Display the first few rows of the dataframe
print("First few rows of the dataset:")
print(df.head())

# Display the summary of the dataframe
print("\nSummary statistics of the dataset:")
print(df.describe())

# Display the information about the dataframe
print("\nInformation about the dataframe:")
df.info()
Detecting and classifying maxillofacial fractures in computed tomography (CT) images
using convolutional neural network (CNN)-based models is a cutting-edge application of deep
learning in medical imaging.
Here's a detailed approach to implement this using R and TensorFlow/Keras libraries
DOI: 10.15468/dl.b2yqdv
title: Occurrence Download

description: A dataset containing 113 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Jasminum multiflorum (Burm.f.) Andrews"
 ]
}

 The dataset includes 113 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009258-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.