cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28974116.v1
title: all-fpkm-aver_RT_master.table.new.zip

description: Transcription readthrough occurs when RNA polymerase bypasses canonical termination sites, producing elongated RNA molecules called readthrough (RT) transcripts or Downstream-of-Gene (DoG) transcripts. Although RT transcripts have been implicated in stress responses and pathological states, their roles in healthy human tissues are poorly understood. This study collected and analyzed RT events across 43 healthy human tissues, identifying 76,583 RT events from 36,335 transcripts across 11,875 genes. The dataset encompasses the sequences, locations, expression profiles, and comprehensive annotation information of corresponding genes for RT transcripts. It provides a thorough elucidation of RT transcriptomics and its significance in gene regulation, offering a wealth of benchmark data to facilitate further research on RT transcripts.<br>The file contains data on Downstream-of-Gene (DoG) transcripts with the following column headers:<br><b>gene_id</b>: Ensembl Gene ID for the gene associated with the DoG transcript.<b>chromosome</b>: Chromosome location of the gene.<b>start_position</b>: Start position of the gene on the chromosome.<b>end_position</b>: End position of the gene on the chromosome.<b>strand</b>: Strand orientation of the gene (e.g., + or -).<b>chromosome (DoG)</b>: Chromosome location of the DoG transcript.<b>dog_start_position</b>: Start position of the DoG transcript on the chromosome.<b>dog_end_position</b>: End position of the DoG transcript on the chromosome.<b>strand (DoG)</b>: Strand orientation of the DoG transcript.<b>sequence</b>: Nucleotide sequence of the DoG transcript.<b>mean-geneFPKM</b>: Average expression level (FPKM) of the gene across samples.<b>mean-dogFPKM</b>: Average expression level (FPKM) of the DoG transcript across samples.<b>tissue</b>: Tissue type from which the data was derived.<b>all-geneFPKM</b>: Expression levels (FPKM) of the gene across all samples.<b>all-dogFPKM</b>: Expression levels (FPKM) of the DoG transcript across all samples.<b>sample_ids</b>: Identifiers for the samples included in the dataset.<b>Symbol</b>: Official gene symbol for the DoG-associated gene.<b>Synonym</b>: Alternative names or synonyms for the gene.<b>Description</b>: Functional description of the gene.<b>DOG</b>: Unique identifier for the DoG transcript.This dataset provides comprehensive information about DoG transcripts, including their genomic locations, sequences, expression levels, and associated gene details across different samples and tissues.
DOI: 10.17632/7tdd6vngzm
title: Cu-Ni-Si Alloys Properties Dataset

description: This comprehensive dataset is specifically designed for the exploration of mechanical properties and electrical conductivity in Cu-Ni-Si alloys, offering detailed insights into chemical compositions, thermo-mechanical processing variables, and their impacts on alloy properties. The collection provides an extensive foundation for understanding and analyzing how various factors influence the performance and characteristics of Cu-Ni-Si alloys.

The dataset was curated to facilitate the development and validation of a predictive Hybrid Deep Learning (DL) and Ensemble Learning (EL) model that aims to fill the research gaps in the current understanding of Cu-Ni-Si alloys. It includes data on alloy compositions, processing conditions, and the resultant electrical and mechanical characteristics. The unique combination of DL and EL techniques provides a robust framework for predicting alloy behavior, which is demonstrated through superior predictive performance, showcased by near-perfect R2 values for both training and test datasets.

Moreover, for those looking to incorporate machine learning techniques into materials science, this dataset provides a unique opportunity to delve into the complex interplay between alloy composition, processing, and resultant properties. By offering a granular look at these relationships, the dataset opens up new avenues for innovation and research in material science and engineering.

The file "Cu-Ni-Si-alloys.xlsx" contains a detailed dataset on various properties of copper-nickel-silicon (Cu-Ni-Si) alloys. It includes columns for the composition of these alloys in terms of percentages of copper (Cu), aluminum (Al), cobalt (Co), chromium (Cr), magnesium (Mg), nickel (Ni), silicon (Si), tin (Sn), and zinc (Zn). Additionally, it provides data on their solid solution strengthening temperature (Tss in K), aging temperature and time, as well as their mechanical and electrical properties such as hardness (HV), yield strength (MPa), ultimate tensile strength (MPa), and electrical conductivity (%IACS). Each entry also includes a DOI link to its source and references for further reading. 

The dataset presented herein is extracted from the comprehensive collection of data on the mechanical properties and electrical conductivity of copper-based alloys curated by Gorsse, Stephane; Gouné, Mohamed; LIN, Wei-Chih; Girard, Lionel (2023) titled "Dataset of mechanical properties and electrical conductivity of copper-based alloys" available on figshare (Collection, DOI: https://doi.org/10.6084/m9.figshare.c.6475600.v1).
DOI: 10.6084/m9.figshare.12385721.v1
title: Dataset of pool-riffle adjustment due to changes in flow and sediment supply.

description: The repository contains the raw data and python 3 scripts (in the form of Jupyter Notebooks -- more information available at https://jupyter.org/). The data were measured over a 15-year longitudinal field study (for the East Creek Data) or in scaled laboratory flume experiments conducted by the Hassan Research Group at the University of British Columbia.<br>Further description of the methods and analyses can be found in:<br>Hassan, M. A., Radic, V., Buckrell, E., Chartrand, S. M., and McDowell, C. (2020). Pool-riffle adjustment due to changes in flow and sediment supply (in preparation).
DOI: 10.17632/3gxjgxkg76
title: A Spectral Dataset of both Liquid and Solid Milk with Impurities for some Milk Impurity Detection

description: This dataset was developed for a study focused on the fast, cost-effective, and accurate detection of milk impurities using machine learning algorithms in conjunction with Near-Infrared (NIR) Spectroscopy. Spectral data were gathered using pixel sensors equipped with on-chip filtering technology, incorporating up to 8 wavelength-selective photodiodes within a compact 9x9mm array. The spectrometer divided the spectrum into eight distinct bands, ranging from 400 to 1100 nm, to detect pure or impure milk for various additives in both liquid and solid forms. The additives analyzed include refined sugar, flour, coffee powder, and milk powder. The dataset comprises a total of 20,000 spectral samples.
DOI: 10.17632/rwrn9b37yc
title: NIs

description: Patient Population
All patients were sourced from the NICU department of Renmin Hospital of Wuhan University. The inclusion criteria are as follows: 1. Infants admitted to the NICU between January 2020 and June 2024, with a hospital stay of ≥48 hours. 2. Infants born at a gestational age of &lt;37 weeks. 3. Infants clinically diagnosed with late-onset sepsis, with the first blood culture result indicating bacterial growth. The exclusion criteria are as follows: 1. Infants with blood culture results indicating mixed infections. 2. Infants with incomplete clinical data. 3. Infants with preexisting disease, natural deformities, and genetic metabolic diseases.

This retrospective cohort study is part of the research on NIs in premature infants conducted at Renmin Hospital of Wuhan University, located in Wuhan, Hubei Province, China. This study received approval from the Medical Ethics Committee of Renmin Hospital of Wuhan University (Approval No.WDRY2023-K098), and during the data collection phase, the information of the infants was anonymized. Since the study does not involve patient privacy and will not adversely affect the rights and welfare of the infants, it was conducted after obtaining an informed consent waiver from the ethics committee (Approval No. WDRY2023-K098(X01)). However, the verbal informed consent was also obtained from the parents or legal guardians of the preterm infants.

Data Collection
Data were extracted from the medical record system in several aspects: 1. Demographic-related data, such as sex, gestational age, birth weight, the age of infection, corrected gestational age and weight of infection, urine output, body temperature, etc. 2. Vital signs and clinical manifestations at the time of infection, such as heart rate, response, skin color and condition, respiratory status, etc. 3. Relevant procedures performed within 7 days prior to the infection, such as whether mechanical ventilation was used and whether there was a presence of a Peripherally Inserted Central Catheter (PICC), etc. 4. Results of blood cultures, complete blood counts, and blood gas analyses obtained from the same batch at the time of suspected infection.
DOI: 10.6084/m9.figshare.19641420.v2
title: fair dataset metadata

description: <strong>Sunnybrook Cardiac Data</strong> <br> The <strong>Sunnybrook Cardiac Data (SCD)</strong>, also known as the <strong>2009 Cardiac MR Left Ventricle Segmentation Challenge</strong> data, consist of 45 cine-MRI images from a mixed of patients and pathologies: <em>healthy</em>, <em>hypertrophy</em>, <em>heart failure with infarction</em> and <em>heart failure without infarction</em>. Subset of this data set was first used in the automated myocardium segmentation challenge from short-axis MRI, held by a MICCAI workshop in 2009. The whole complete data set is now available in the CAP database with <strong>public domain license</strong>. <strong>Classification</strong> There are four pathological groups in this data set, which were classified based on (K Alfakih et al., JMRI 2003) paper, i.e.: 1. Heart failure with infarction (<strong>HF-I</strong>) group had ejection fraction (EF) &lt; 40% and evidence of late gadolinium (Gd) enhancement. 2. Heart failure without infarction (<strong>HF</strong>) group had EF &lt; 40% and no late Gd enhancement. 3. LV hypertrophy (<strong>HYP</strong>) group had normal EF (&gt; 55%) and a ratio of left ventricular (LV) mass over body surface area is &gt; 83 g/m2. 4. Healthy (<strong>N</strong>) group had EF &gt; 55% and no hypertrophy. The following table shows group statistics written as average (stddev) : <br> <strong>N (n=9)</strong> <br> <strong>HYP (n=12)</strong> <br> <strong>HF (n=12)</strong> <br> <strong>HF-I (n=12)</strong> End Diastolic Volume (ml) <br> 115.69 (36.89) <br> 114.39 (50.46) <br> 233.67 (63.21) <br> 244.92 (86.02) End Systolic Volume (ml) <br> 43.10 (14.74) <br> 43.11 (24.50) <br> 158.28 (56.34) <br> 174.34 (90.64) Ejection Fraction (%) <br> 62.93 (3.65) <br> 62.72 (9.22) <br> 33.09 (13.07) <br> 32.01 (12.27) Left Ventricular Mass (g) <br> 130.27 (32.69) <br> 175.87 (85.70) <br> 193.69 (39.01) <br> 201.32 (45.24) <strong>Availability</strong> The Cardiac Atlas Project provides the dissemination of the Sunnybrook data by hosting them in the CAP databases. Finite element models (see supporting files section below) derived from these data are also provided. The whole complete data are available for any users, including the guest user account. License and attribution of these data set, including its derivatives, follows the <strong>Public Domain (CC0 1.0 Universal)</strong>. If you are using this data in a publication, please cite the following reference: Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. “Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI.” <em>The MIDAS Journal</em> – Cardiac MR Left Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070 <strong>Data contributor</strong> 1. Perry Radau – Sunnybrook Health Sciences Centre, Toronto, Canada. <strong>More information</strong> · The original 2009 LV Segmentation Challenge webpage. · Promotional poster for the 2009 LV Segmentation Challenge. · The challenge results published in the MIDAS journal.
DOI: 10.6084/m9.figshare.24715227.v1
title: protocol.docx

description: protocol
DOI: 10.17632/3pp3p2hbdc
title: Data for: New method for denoising borehole transient electromagnetic data with discrete wavelet transform

description: Data_10512 is generated using Loki, which is a 3D forward modeling program (Raiche et al., 2007). Sferics is from a field survey. the three *.raw files are borehole TEM data recorded by SmarTem24.
DOI: 10.17632/jtbw57ftyv
title: Data for: Sentinel-1 and RADARSAT Constellation Mission InSAR assessment of slope movements in the Southern Interior of British Columbia, Canada

description: Senitnel-1 linear deformation rate maps of east-west and vertical movements over the Southern Interior of British Columbia, Canada (October 2017 to June 2021) & Sentinel-1 (20200618-20200922) and RCM DInSAR interferograms (June 2020 to September 2020); Refer to the corresponding paper published in 'Remote Sensing'  for details.
DOI: 10.17632/rk2xf4jmhm
title: Problem Instances for Vehicle Routing Problem with Server Drop-offs/Retrievals (VRPSDR)

description: Problem Instances for Vehicle Routing Problem with Server Drop-offs/Retrievals (VRPSDR)

Data Abstract:
The problem instances include:
- Q: vehicle capacity for demand (volume)
- N: number of drop-off or retrieval nodes
- K: number of vehicles
- coordX: x coordinates for nodes (first and last values are for departure/return depots)
- coordY: y coordinates for nodes (first and last values are for departure/return depots)
- q: volume (demand) at each node (first and last values are for departure/return depots)
- s: service time required at each node (first and last values are for departure/return depots)
- d: number of servers required at each node (first and last values are for departure/return depots)
- w: number of on-board servers for each vehicle
- Dist (for case study problem instances only): (2n+1)x(2n+1) distance matrix, index 0 referring to the departure/return depot node
- Time (for case study problem instances only): (2n+1)x(2n+1) time matrix, index 0 referring to the departure/return depot node
The solutions for the problem instances include:
- jobEnd: total job completion time [job completion] - for each vehicle
- travel: total travel time [travel time] - for each vehicle
- volume: total delivery volume (demand) [delivery volume (demand)] - for each vehicle
- visits: [node visit sequence] - for each vehicle
- arrive: [time of arrival at each node] - for each vehicle
- wait: [time waited at each node] - for each vehicle
- depart: [time of departure at each node] - for each vehicle
- server: [number of servers on-board after drop-off/retrieval at each node] - for each vehicle
DOI: 10.6084/m9.figshare.13235045.v1
title: Data file 2 - BUSCO report

description: Sample quality was measured with BUSCO v3.0.1 using 303 single-copy orthologous eukaryotic markers. <b>completeness</b>: percentage of orthologous genes, <br><b>single.copy</b>: percentage of single-copy genes, <br><b>duplicate</b>: percentage of duplicated genes,<br><b>fragmented</b>: percentage of fragmented genes,<br><b>missing</b>: percentage of missing genes,<br><b>total.genes</b>: total number of markers tested
DOI: 10.6084/m9.figshare.20306445.v1
title: Datasets fo DeepDrug

description: DeepDrug is a deep learning framework, using residual graph convolutional networks (RGCNs) and convolutional networks (CNNs) to learn the comprehensive structural and sequential representations of drugs and proteins in order to boost the drug-drug interactions(DDIs) and drug-target interactions(DTIs) prediction accuracy. DeepDrug is available at https://github.com/wanwenzeng/deepdrug. This repository includes processed Datasets for DeepDrug. Each dataset folder is constructed by : <strong>Drug/</strong> : drug SMILEs (<strong>drug.csv</strong>) and processed graph featrues (<strong>processed/data.pt</strong>). <strong>Target/ </strong>(only for DTI task) : target sequences (<strong>target.csv</strong>) and processed graph featrues (<strong>processed/data.pt</strong>). <strong>Binary_1vsX/</strong> or <strong>mutliclass/ </strong>or <strong>regression/ </strong>: DDI or DTI pairs (<strong>entry_pairs.csv</strong>), corresponding labels (<strong>pair_labels.csv</strong>) and 5-fold cross validation used in the research (<strong>cv_5fold.pkl</strong>).
DOI: 10.6084/m9.figshare.6727184.v3
title: Energy satellite for environmentally extended input-output analysis (Chile)

description: Energy satellite for Environmentally Extended Input-Output Analysis (EEIOA) of Chilean industry sectors.GJ/CLP(bil.) per year (2013).
DOI: 10.17632/369cky7n39
title: Tomato leaf image dataset

description: This dataset included two kinds of tomato diseased leaf images and healthy tomato leaf images.
DOI: 10.17632/5jsw5hcrb9
title: Pegylated liposomal doxorubicin-induced hand-foot syndrome predicted by serum metabolomic profiling and prevented by calcium dobesilate

description: Pegylated liposomal doxorubicin (PLD) is routinely used to treat patients with various types of cancers. However, it frequently causes hand–foot syndrome (HFS), one of the most common adverse events of cytotoxic chemotherapeutic agents, which can greatly affect the patient’s quality of life and may result in a dose reduction or even drug withdrawal. The pathogenesis and treatment for HFS remain unclear; thus, methods for predicting and preventing PLD-induced HFS are needed. Here, 106 pre-treatment serum specimens from breast cancer patients, who received PLD treatment, were collected and subjected to metabolomic profiling using liquid chromatography–tandem mass spectrometry. Metabolomic profiles showed that 45 metabolites were strongly associated with the incidence of PLD-induced HFS. An effective discriminant model was constructed by employing four of these metabolites that could predict PLD-induced HFS, prior to treatment, with a sensitivity of 88.2% and specificity of 80.8%. In vivo photoacoustic imaging of the skin capillary, Evans Blue assay, and targeted metabonomics showed that PLD could destroy the capillary endothelial cells, increase permeability, and increase PLD (doxorubicin) exudates blood vessels into skin tissue, leading to HFS. Calcium dobesilate (CaD) effectively reduced the occurrence of HFS in a rat model and in patients when administered together with PLD. Together, our data reveal an effective and convenient discriminant model that can accurately predict the occurrence of PLD-induced HFS, and CaD may be a potential drug to prevent HFS.
DOI: 10.17632/5ny883n8cz
title: Meteorological data measured by an urban climate monitoring station installed within a university campus in Ciudad Obregón, Sonora, Mexico

description: This dataset contains several meteorological variables recorded between June 2012 and January 2025 employing a meteorological station located in an urban environment. This station was deployed within the Instituto Tecnológico de Sonora's Nainari campus. However, due to station malfunction and the confinement caused by COVID19, some long periods with lack of data are observed. The observed variables include air temperature, relative humidity, barometric pressure, photosynthetically active radiation, solar radiation, wind speed, wind direction and precipitation.
DOI: 10.17632/c4zvyzftfg
title: Zaus-JBC_maxicircle

description: Zelonia australiensis mitochondrial genome
strain JB/C
DOI: 10.6084/m9.figshare.19786678.v4
title: Project CLEAR

description: MRSA Absence-Presence Dataset <br> MRSA isolates were collected as part of the CLEAR (Changing Lives by Eradicating Antibiotic Resistance) Trial. The trial was designed to compare the impact of a repeated decolonization protocol plus education on general hygiene and environmental cleaning to education alone on MRSA infection and hospitalization. Study subjects in the trial were recruited from hospitalized patients based on an MRSA positive culture or surveillance swabs. After recruitment, swabs were obtained from different body parts of subjects (nares, skin, throat, and wound) around the time of hospital discharge (ENRL) and at 1,3,6 and 9 months (V1-V4, respectively) following the initial visit, after which the swabs were cultured on chromogenic agar. The application of decolonization protocol lasted only for 6 months, and consequently, we only modeled visits until V3 in this study. We note that some enrolled study subjects, despite a positive culture (clinical or surveillance) during the hospital stay, did not have discharge swabs positive for MRSA at the first time point (ENRL). The data used in this study are collected from people aged over 18 (average age=56, SD=17) for both the education and decolonization groups, with eligibility requirements also including hospitalization within previous 30 days and positive testing for MRSA during the enrollment hospitalization or within the 30 days before or afterwards. Exclusion criteria included hospice care and allergy to the decolonization products. Over the course of the trial, 98 of 1063 participants (9.2%) in the education group and in 67 of 1058 (6.3%) in the decolonization group developed MRSA infections, and 84.8% of the MRSA infections resulted in hospitalization. More details about recruitment and eligibility, follow up, sample collection, and data preprocessing can be found in the reference https://www.nejm.org/doi/10.1056/NEJMoa1716771.
DOI: 10.6084/m9.figshare.26148589.v2
title: EduCodeVR Teaching Data.xlsx

description: Virtual reality (VR) technology revolutionizes Science, Technology, Engineering, Arts, and Mathematics (STEAM) education by simulating real-world environments, offering innovative learning methods. This study focuses on designing an educational system within VR for Arduino UNO microcontroller development. Students engage in graphical programming and hardware integration of the Arduino UNO within VR environments, implementing intelligent control of farms and traffic. This approach helps students grasp practical applications of the Arduino UNO in real production contexts, further cultivating their programming skills and fostering innovative thinking. We conducted Arduino courses tailored for programming beginners, assessing the usability of EduCodeVR and its impact on students through learning outcome tests and VR system usability assessments. Results demonstrate that EduCodeVR, using STEAM education methodologies, effectively enhances users' comprehensive literacy and computational thinking, highlighting the significant potential of integrating VR programming courses into STEAM education. Through this innovative teaching approach, students not only acquire knowledge but also enjoy the immersive pleasures of learning.
DOI: 10.6084/m9.figshare.4028634.v3
title: Processed expression data of 40 tissues for EST human

description: A tab-separated values (TSV) file for RefEx_expression_EST40_human<br>A value of "-1" means not applicable (N/A).<br>