cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25109225
title: Chlorophyll-a Data

description: The dataset consists of chlorophyll-a concentration data collected from 33 sites using AP-7000 sensors.
DOI: 10.2908/pipe_ec_employ
title: Beschäftigung in Ölfernleitungsunternehmen, nach Geschlecht (1990 - 2012)
title: Persons employed in oil pipeline enterprises by sex (2003-2012)
title: Employés dans les entreprises de transport par oléoducs, par sexe (2003-2012)
DOI: 10.2908/pipe_ec_ent
title: Ölfernleitungsunternehmen (1990-2012)
title: Oil pipeline enterprises (1990-2012)
title: Entreprises de transport par oléoducs (1990-2012)
DOI: 10.6084/m9.figshare.25109177
title: Code, raw and processed data for: <b>Central Arctic Ocean surface- atmosphere exchange of CO</b><b>2 </b><b>and CH</b><b>4 </b><b>constrained by direct measurements</b>John Prytherch et al., Biogeosciences, 2024

description: Code (MATLAB), raw and processed data corresponding to the results presented in <b>Central Arctic Ocean surface- atmosphere exchange of CO</b><b>2 </b><b>and CH</b><b>4 </b><b>constrained by direct measurements</b> (Prytherch et al., 2024)
DOI: 10.17632/hhwd3szm92.1
title: Data for Open-ended Song Learning in Hummingbirds

description: Data files for Open-ended song learning data.
DOI: 10.17632/ww5ks7g7mb.1
title: Anxiety, depression, and quality of life of caregivers of palliative care patients with cancer

description: Data Analysis
Data were tabulated in a Microsoft Excel database and then analyzed using SPSS Statistics version 26. Descriptive statistics were used to describe the sociodemographic characteristics; frequencies, means, and standard deviations were calculated. Spearman’s correlation coefficient and Kruskal-Wallis and Pearson’s chi-square tests were employed to determine the relationship between participants’ sociodemographic characteristics and their relationship with anxiety, depression, and quality of life.
DOI: 10.5281/zenodo.10590696
title: Raw data from "Morphology-resolved search for mutations in non- responders to anti-EGFR therapy in colorectal carcinoma" part 7/7

description: We performed targeted sequencing of morphological regions of primary colon cancer tumors for patients receiveing anti-EGFR (KRAS wild type) treatment as first line in metastatic setting. These are the raw FASTQ files, part 7 out of 7.
DOI: 10.5281/zenodo.10590697
title: Raw data from "Morphology-resolved search for mutations in non- responders to anti-EGFR therapy in colorectal carcinoma" part 7/7

description: We performed targeted sequencing of morphological regions of primary colon cancer tumors for patients receiveing anti-EGFR (KRAS wild type) treatment as first line in metastatic setting. These are the raw FASTQ files, part 7 out of 7.
DOI: 10.17632/ww5ks7g7mb.2
title: Anxiety, depression, and quality of life of caregivers of palliative care patients with cancer

description: Data Analysis
Data were tabulated in a Microsoft Excel database and then analyzed using SPSS Statistics version 26. Descriptive statistics were used to describe the sociodemographic characteristics; frequencies, means, and standard deviations were calculated. Spearman’s correlation coefficient and Kruskal-Wallis and Pearson’s chi-square tests were employed to determine the relationship between participants’ sociodemographic characteristics and their relationship with anxiety, depression, and quality of life.
DOI: 10.17632/xb3gtrdhrn.1
title: Space debris posing a potential threat to the ISS

description: Data on space objects whose orbit could potentially cross the orbit of the International Space Station. Data obtained from https://discosweb.esoc.esa.int/objects/ and https://in-the-sky.org/spacecraft_elements.php 24 Jan 2024.
DOI: 10.2908/pipe_go_tonkms
title: Oil pipeline transport within the national territory by type of transport operations and products (Mio Tkm)
title: Transports par oléoducs empruntant le territoire national par type de transport et de produits (Mio Tkm)
title: Transport in Ölfernleitungen innerhalb des nationalen Hoheitsgebietes - nach Typ der Beförderung und Produkttyp (Mio Tkm)
DOI: 10.2908/pipe_go_ton
title: Oil pipeline transport within the national territory by type of transport operations and products (1 000 t)
title: Transport par oléoducs empruntant le territoire national par type de transport et de produits (1 000 t)
title: Transport in Ölfernleitungen innerhalb des nationalen Hoheitsgebietes - nach Typ der Beförderung und Produkttyp (1 000 t)
DOI: 10.17632/h7hf7zj76f.1
title: PRESERVATION AND EXPANSION: EXPLORING THE RICH LITERARY TRADITIONS OF INDIA FROM VEDAS TO DARŚANAS

description: Abstract:
In the vast landscape of Indian literature and knowledge systems, the term 'literature' transcends written works to encompass an ancient tradition of oral compositions spanning over 5,000 years. Cāṇakya's categorization in the Arthaśāstra delineates essential knowledge into four domains, while the classical literature encompasses diverse subjects under chaturdaśa-vidyā-sthānas. The Vedas, recognized as the oldest literary compositions globally, embody profound insights presented in diverse formats. The meticulous preservation of the Vedas, employing techniques akin to modern hashing, underscores the reverence for knowledge in Indian culture. Contrastingly, the compilation and preservation of other ancient texts like the Bible and Quran reveal historical challenges and the dynamic nature of cultural transmission.
Exploring the Vedas, we encounter the intricate interplay of mantras, seers, deities, and metrical patterns, reflecting an unparalleled depth of ancient wisdom. The six Vedāṅgas provide indispensable tools for interpreting Vedic knowledge, with notable contributions from luminaries across disciplines. Itihāsa, Purāṇa, Dharmaśāstra, and Darśanas further enrich the literary tapestry, guiding societal conduct, preserving narratives, and elucidating philosophical frameworks.
The Vedas, as apauruṣeya revelations, epitomize timeless truths aligned with objective reality, serving as the foundation for smṛti adaptations across cultures and eras. The systematic transmission and interpretation of Vedic knowledge underscore the resilience and adaptability of Indian civilization amidst dynamic historical currents.
In contrast to Western legal traditions, Dharmaśāstra emphasizes ethical conduct and personal responsibility, fostering societal harmony and individual integrity. Darśanas offer diverse perspectives on existence and knowledge acquisition, reflecting the rich tapestry of Indian philosophical inquiry.
Understanding and preserving India's literary and philosophical heritage entails recognizing its dynamic evolution and enduring relevance across epochs and civilizations. The fusion of oral traditions, textual transmission, and philosophical inquiry embodies the timeless spirit of inquiry and reverence for knowledge permeating Indian culture through the ages.


Conclusion : 
In conclusion, the Vedas and their associated texts exemplify the depth and breadth of India's literary heritage. Through meticulous preservation techniques and profound reverence for knowledge, our predecessors ensured the continuity of these invaluable scriptures across millennia. In contrast to other cultures, where literary traditions faced deliberate alterations or destruction, Indian literature stands as an unaltered repository of ancient wisdom and profound insights into human existence. As we continue to explore and interpret these timeless texts, we deepen our understanding of our cultural heritage and the timeless principles that guide our lives.
DOI: 10.17632/ft4ztnkgtr.1
title: NNPE

description: A code that automatically calculates missing data of any group of weather stations based on the station with the most consistent records of the group, called Neural Network Precipitation Estimator (NNPE)

Initial preparation:
*Install Python:

If you don't have Python installed, download it from python.org and install it.
*Install the necessary libraries:

Open the terminal or command prompt (depending on your operating system) and run the following command to install the required libraries: pip install pandas numpy scikit-learn matplotlib openpyxl
#You can run this code from the terminal or command prompt:

Steps to run the code from the terminal or command prompt:
*Prepare the files:

Save the code in a text file with .py extension.
Make sure you have an Excel file called Input.xlsx in the same directory as the .py file. This file should have a structure similar to what the code expects.
*Execute the code:

Open the terminal or command prompt.
Navigate to the directory where your .py and Input.xlsx file are located.
Type python filename.py (replace filename.py with the actual name of your Python file) and press Enter.
*Interact with the program:

The program will ask you to enter a number, either 1 or 2, to determine the number of base stations to use.
Follow the instructions that appear in the terminal or command prompt depending on the questions the program asks.
Or You can run this code from Jupyter Lab:
Steps to run the code in Jupyter Lab:
Initial preparation:
*Install Jupyter Lab:
If you don't already have Jupyter Lab installed, you can do so by using pip in your terminal or command prompt with the command: pip install jupyterlab
*Open Jupyter Lab:
Open the terminal or command prompt.
Navigate to the directory where your .ipynb and Input.xlsx file is located.
Run the jupyter lab command. This will open Jupyter Lab in your default web browser.
Code execution:
*Create a new notebook:
In Jupyter Lab, click the "New" button and select "Notebook: Python" to create a new notebook.
*Copy and paste the code:
In a cell of the notebook, copy and paste the provided code.
*Execute the code in sections:
Divide the code into logical sections (cells). You can do this by separating each block of code with comments indicating sections #0, #1, #2, etc.
Click on a cell and press Shift + Enter to run that section of code.
*Interact with the program:
When the program prompts for input (as in section #6), enter the answer in the appropriate cell and press Enter.
*Observe the results:
If there are parts of the code that generate graphs, these will be displayed directly below the corresponding code cells.
*Save results (optional):
You can save the generated results to files if the part of the code that saves the graphs or data to CSV files has been uncommented.
*End execution:
Continue executing the cells until you have finished using the code.
Once finished, close Jupyter Lab or save and close the notebook if you want to resume work later.
DOI: 10.17632/rthybnm9r7.1
title: Spatial variability in Be isotopic distribution in the Indian Ocean

description: The results of Be isotopic measurements from the surface sediments covering the Indian Ocean.
DOI: 10.17632/rp9v9mvc36.1
title: Time-Series Dataset of Mangroves along the Southern Coast of Vietnam between 2016 through 2023

description: Here, we provide a time-series dataset of mangroves along the Southern Coast of Vietnam, captured at multispectral indices of Sentinel-2 and deep learning model from 2016 through 2023. The generated performance metrics, including overall accuracy, precision, recall, and F1-score were above 95% for whole years. The dataset encompasses a series of temporal observations, allowing for the analysis of changes and patterns in mangrove vegetation over time. Users can explore the dataset to gain insights into the evolution and health of mangrove ecosystems in this specific geographical region. The data includes various parameters relevant to mangrove classification, offering a valuable resource for researchers, environmental scientists, and practitioners engaged in the study and conservation of coastal ecosystems.
DOI: 10.48366/r661500
title: Probing Large Language Models for Scientific Synonyms

description: Machine-reusable expression of the research findings published in Thießen et al. (2023). Probing Large Language Models for Scientific Synonyms. CEUR-WS. https://ceur-ws.org/Vol-3510/
DOI: 10.17632/6hzdnzmx52.1
title: Journal of the American Academy of Dermatology Assessment of Effectiveness of COVID-Pandemic Scheduling Triage in an Academic Dermatology Clinic

description: Supplementary material for JAAD COVID triage paper
DOI: 10.5519/qd.y2l4jtty
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (14 records)
DOI: 10.17632/96cfzcwdmr.1
title: Optimizing Lordosis Preservation in Monosegmental Lumbar Spondylodesis: Evaluating the Efficacy of a Novel non-invasive Technique Using Intraoperative Hip Hyperextension

description: This study evaluates a new intraoperative technique for lumbar spondylodesis, using hip hyperextension and an inflatable bladder to maintain lordosis. In 100 patients, significant improvements in Distal Lordosis and Sacral Slope were observed, demonstrating the technique’s efficacy in preserving lordosis during spinal fusion.
DOI: 10.17632/cscnx5rgps.1
title: Inherited CD4 deficiency: long-read TCR single-cell RNA sequencing (scRNASeq) of stimulated TCRab+ memory T cells

description: To characterize the impact of inherited CD4 deficiency on TCR alpha/beta chains, long reads single-cell RNA sequencing was performed on isolated memory (defined as CD45RA-CCR7+/-) CD3+CD8-TCRαβ+ T cells from healthy donors (n=4) and CD4 deficient (P1-P5) patients or CD3+CD8+TCRαβ+ T cells from healthy donors (n=1). Following isolation, stimulation, 10X Genomics Chromium capture and cDNA library preparation (see short read ScRNAseq dataset), TCR contigs for single cells were obtained by the Repertoire and Gene Expression by Sequencing (RAGE-seq) method (Singh et al., 2019).
Basic repertoire features for the single cells were summarized in R (version 4.2.2, https://www.R-project.org/) via RStudio IDE (version 2022.12.0.353, http://www.posit.co/) R packages used for data manipulation and plotting included tidyverse (version 2.0.0), rstatix (version 0.7.2, https://CRAN.R-project.org/package=rstatix) and ggpuborg/package=ggpubr).
To classify single cells as CD4 or CD8 based on paired TRA:TRB paired sequences an XGBoost model as described in (Carter et al., 2019a) was used. Modelling was undertaken in R using the xgboost package (version 1.7.3.1, https://CRAN.R-project.org/package=xgboost). Model features included the TRA and TRB CDR3 lengths, TRA and TRB CDR3 charge, CDR3 amino acid proportions and one-hot encoding for TRBV and TRAV gene usage. The modelling objective was binary:logistic and the following parameters were used; nrounds=1000, booster=gbtree, eta=0.01, max_depth=10, gamma=1, subsample=0.8, colsample_bytree=0.8 and min_child_weight=5. Training and test sets used the paired single cell data from Carter et al. (Carter et al., 2019a) obtain from [https://github.com/JasonACarter/CD4_CD8-Manuscript] after excluding TRA:TRB pairs that were observed across both CD4 and CD8. Training and test dataset were a random sub-sampling of 14,000 CD4 and 14,000 CD8 across 10 iterations. To set thresholds for the binary logistic regression scores for assigning CD4 and CD8 the labelled test data was used to set the misidentification rate to 5% for each iteration. Cells that fell outside the threshold were considered ‘non-attributed’. Cell types for the single cells from RAGE-seq were predicted for TRA:TRB pairs for each of the 10 model iterations and the mean percentage of cells assigned CD4 and CD8 was calculated across the iterations for each donor.
DOI: 10.17632/bc2dvv2c4p.1
title: Trophic interactions in microbiomes influence plant host population size and ecosystem function

description: This dataset includes amplicon sequencing data for the manuscript, titled "Trophic interactions in microbiomes influence plant host population size and ecosystem function."
DOI: 10.17632/jyjvkzp4j3.1
title: Inherited CD4 deficiency: short read single-cell RNA sequencing (scRNASeq) of stimulated TCRab+ memory T cells

description: To identify transcriptional modules perturbed by inherited CD4 deficiency, single-cell RNA sequencing was performed on isolated memory (defined as CD45RA-CCR7+/-) CD3+CD8-TCRαβ+ and/or CD3+CD4-CD8-TCRαβ+ double negative (DN) T cells from healthy donors (n=4), FAS deficient (n=1), STAT3GOF (n=1) and CD4 deficient (P1-P5) patients. Isolated cells were cultured with TAE beads + IL-2 (50 IU/mL, #IL002, Millipore) for 20h. Cells were washed 3 times in cold PBS + 2% HI FBS and incubated with TotalSeq anti human hashtag antibodies (A0251-A0256, #394601, #394603, #394605, #394607, #394609, #394611, BioLegend) for 30 min on ice. Hashtagged cells were washed 3 times in cold PBS + 2% HI FBS and passed through a 35µm nylon mesh cell strainer (#352235, Falcon) to remove aggregates. Cells were resuspended at a concentration of 1000 cells/µl in cold PBS + 2% HI FBS, pooled, and loaded onto a 10X Genomics Chromium B or G chips. Single-cell capture, reverse transcription, and library preparation were performed with the Chromium Single-Cell 3’ Reagent Kits (v3 or v3.1), in accordance with the manufacturer’s instructions. The quality of the cDNA and feature barcode library was assessed with a TapeStation (Agilent). Transcriptome sequencing was performed on the S4 flow cells of an Illumina NovaSeq 6000 sequencer, while hashtag library sequencing was performed on NextSeq 500/550 system with High Output Kit v2.5 (Illumina). Cells from P1/P2/P3 and P4/P5 were processed in two batches of experiments, along with cells from two healthy donors per batch. For comparison, CD3+CD4-CD8 TCRαβ+ double-negative T cells from two healthy donors, one FAS-deficient patient, and one patient with a STAT3 GOF mutation were isolated and processed similarly. 
 Hashtag demultiplexing was performed using Seurat with default parameter settings. Hash tag doublets were excluded from subsequent analyses. Cells were further filtered based on standard QC metrics. Batch correction, unsupervised clustering, pseudobulk PCA, differential expression (DE) analysis, and gene-set enrichment analysis (GSEA) were performed as described previously (Ogishi et al., 2022). All analyses were conducted in R (version 4.2.2, https://www.R-project.org/).
DOI: 10.17632/s6cvks699v.1
title: deni-sapta-nugraha

description: The data comprise five recording files to provide the original sound of the English language produce by a 4-year-old kid. The recording reflects the transcription in table 1 to table 5 that has been respectively provided in the body of the article.
DOI: 10.17632/s6cvks699v.2
title: child language acquisition

description: The data comprise five recording files to provide the original sound of the English language produce by a 4-year-old kid. The recording reflects the transcription in table 1 to table 5 that has been respectively provided in the body of the article.
DOI: 10.17632/4xvbnsg2fc.1
title: Passion and Exercise Addiction in CrossFit vs. Other Exercise Forms

description: We compared 200+ regular CrossFit exercisers to 200+ other regular exercisers on the risk of exercise addiction, obsessive and harmonious passion. The former group scored higher on all these measures. We also compared motivation subdomains using the SMS-II, but no group differences emerged. Exercise intensity and frequency were statistically significant covariates the risk of exercise addiction, obsessive passion and harmonious passion.
DOI: 10.17632/z2bmdwwdf9.1
title: Supplementary Material for: Guidelines of care for the management of acne vulgaris

description: A multidisciplinary workgroup conducted a systematic review and applied the GRADE approach to provide evidence-based recommendations related to the management of acne vulgaris in adults, adolescents, and preadolescents aged 9 years or above.
DOI: 10.17632/ychxjr3cv6.1
title: Ethical decision-making -moral Intensity higher-order construct

description: Data set for PLS-SEM, IPMA and NCA
DOI: 10.17632/bg9jkb88y7.1
title: Assessment of Anticancer Drug Utilization Pattern from a hospital-based cancer registry—A Single Center Experience from Saudi Arabia

description: In recent years, various advancements in anticancer therapy have led to the development of multiple regimens and protocols. This study endeavors to provide an extensive evaluation of anticancer therapy prescription patterns from a hospital-based cancer registry.
Materials and Methods: From June 2014 to April 2022, 512 adult cancer patients who received chemotherapy in our cancer center were included in a retrospective study. Data encompassed demographic characteristics of patients and cancer, chemotherapy protocols or agents, antiemetics, and drug side effects. The prescribed drugs were assessed using the Essential Medicines List (EML), while the prescription’s rationality was determined using the World Health Organization (WHO) indicators.
DOI: 10.17632/rs2g777sws.1
title: Sovereign Debt and Economic Growth when Government is Myopic and Self-interested, by Acharya, Rajan and Shim, Journal of International Economics

description: Replication Code for Figures 2-6
DOI: 10.17632/88rc8wdwf7.1
title: Supplemental Table S4. Effects of feeding diets containing baleages harvested from alfalfa-grass mixture (ALF) or red-clover grass mixture (RC) on the milk proportion of fatty acids (FA)

description: Effect of diets containing alfalfa-grass or red clover-grass mixture baleage on selected milk fatty acids. This data set belongs to a study investigating the effect of diets containing alfalfa-grass or red clover-grass mixture on milk yield and composition, ruminal fermentation and microbiota taxa relative abundance, and nutrient utilization in dairy cows published in the Journal of Dairy Science (https://doi.org/10.3168/jds.2023-23836).
DOI: 10.17632/yy4dnbptm8.1
title: Supplemental Table S3. Amino acid and fatty acid (FA) profile of second and third cut legume-grass mixture baleages and concentrates used in the experimental diets

description: Amino acid and fatty acid profile of alfalfa-grass and red clover-grass mixture baleages. This data set belongs to a study investigating the effect of diets containing alfalfa-grass or red clover-grass mixture on milk yield and composition, ruminal fermentation and microbiota taxa relative abundance, and nutrient utilization in dairy cows published in the Journal of Dairy Science (https://doi.org/10.3168/jds.2023-23836).
DOI: 10.17632/ftx3yymckk.1
title: Mode of Purchase of Smartphones

description: This data set contains the 93 data obtained through proper surveying for the research. Though the data was derived through a systematically structured questionnaire that involved around 93 data only 90 data were used for the analysis to avoid outliers in the study. This dataset consists of the raw data which was further scrutinized for proper results of the study.
DOI: 10.5281/zenodo.10590862
title: IMMERSE Codebook - Parents of children aged from 10 to 18
DOI: 10.5281/zenodo.10590863
title: IMMERSE Codebook - Parents of children aged from 10 to 18
DOI: 10.5281/zenodo.10590776
title: augMENTOR-D2.1-RRI Survey Data M6

description: This dataset contains the responses to the RRI survey that took place on M6 for the project augMENTOR.

The data have been collected with the participants' informed consent. No personal or sensitive data were collected.

More information about the survey, the purpose and the results can be found in the deliverable D2.1 of the project.
DOI: 10.5281/zenodo.10590777
title: augMENTOR-D2.1-RRI Survey Data M6

description: This dataset contains the responses to the RRI survey that took place on M6 for the project augMENTOR.

The data have been collected with the participants' informed consent. No personal or sensitive data were collected.

More information about the survey, the purpose and the results can be found in the deliverable D2.1 of the project.