cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/6mmwf3y864
title: Raw data of gas hydrate equilibria and PXRD measurements in system C3H8/CH4 – H2O – Urea

description: The raw data from the measurements of the gas hydrate equilibrium conditions in the methane/propane-water-urea system are given in the “Hydrate equilibrium (C3H8+CH4-Urea-H2O)” archive. A model gas mixture of 4.34% C3H8 + 95.66% CH4 (mol%) was used as a hydrate-forming gas. A total of 39 equilibrium points have been measured. For each of them there is a separate .xlsx file in the archive with columns of numerical values of the following parameters measured by sensors of GHA350 autoclave: time, temperature (°C), gauge pressure (bar), stirrer speed (rpm). The file name represents the sample under study and the point number. For example, the file name “30% Urea_point 24” corresponds to the raw data obtained from measurements of the 24th equilibrium point (numbering from Table 1 of data paper [2]) for a 30 mass% aqueous solution of urea. Cells D1:H3 of each file (except for 0% Urea sample) display numerical values of the masses and concentrations of urea and water in the aqueous phase.
The raw data presented in the archive “PXRD analysis (C3H8+CH4-Urea-H2O)” are related to the powder X-ray diffraction study of the phase composition of gas hydrate samples synthesized from urea aqueous solutions. Diffractograms were recorded for 5 samples. Two of them were recorded for the samples obtained from 20 mass% aqueous urea solution (samples 1 and 2). For samples 1 and 2, the pressure of the hydrate-forming gas during the synthesis was 1.5 and 6 MPa, respectively. Two diffractograms were acquired for the samples obtained from 40 mass% urea solution (samples 3 and 4). For samples 3 and 4, the gas pressure during hydrate synthesis was 1.5 and 6 MPa, respectively. One diffractogram was obtained for a frozen 40 mass% urea solution (sample 5) without methane/propane hydrates. For each sample, a separate file containing numerical values of 2θ (degree) and signal intensity (a.u.) is provided.

The raw data is linked to the papers:
[1] Yinghua Gong, Rais I. Mendgaziev, Wei Hu, Yingzhou Li, Zhi Li, Andrey S. Stoporev, Andrey Yu. Manakov, Vladimir A. Vinokurov, Tianduo Li, Anton P. Semenov (2022) Urea as a Green Thermodynamic Inhibitor of sII Gas Hydrates // Chemical Engineering Journal, 429, 132386 DOI: 10.1016/j.cej.2021.132386
[2] Anton Semenov, Yinghua Gong, Rais Mendgaziev, Andrey Stoporev, Vladimir A. Vinokurov, Tianduo Li (2023) Dataset for the phase equilibria and PXRD studies of urea as a green thermodynamic inhibitor of sII gas hydrates // Data in Brief, 49, 109303 DOI: 10.1016/j.dib.2023.109303
DOI: 10.17632/k3bwtx74tg
title: Provenance data for Paleogene Sediments in SE Asia

description: Major element compositions of sedimentary rocks in Vietnam, as well as matching zircon U-Pb ages from these basins and similar aged material from Jianchuan and Yuanjiang Basins Southwest China designed to understand the overall composition in order to determine their provenance
DOI: 10.6084/m9.figshare.2009070.v1
title: "Variation of outdoor illumination as a function of solar elevation and light pollution" (Spitschan et al.) – Raw Data

description: <br><b>"Variation of outdoor illumination as a function of solar elevation and light pollution" (Spitschan <i>et al.</i>) – Raw Data</b>This is the raw data collected for the data base of outdoor illumination spectra described by Spitschan, Aguirre, Brainard & Sweeney (2016). The spectra provided here are raw, uncalibrated and unprocessed.Code to process these data is provided at https://github.com/spitschan/IlluminationSpectraDataset. MD5 (calibrationdata.tar.gz) = 92102c29bbc54f3d426d2d17f67ad526MD5 (dataraw.tar.gz) = 351c6d0c5dd44e748af483d035954504
DOI: 10.6084/m9.figshare.21776456.v1
title: POCI CSV dataset of the provenance information of all the citation data

description: This dataset contains the provenance information (in CSV format) of all the citation data included in POCI, released on 27 December 2022. In particular, each line of the CSV file defines a citation, and includes the following information: <strong>[field "oci"]</strong> the Open Citation Identifier (OCI) for the citation; <strong>[field "snapshot"]</strong> the identifier of the snapshot; <strong>[field "agent"]</strong> the name of the agent that have created the citation data; <strong>[field "source"]</strong> the URL of the source dataset from where the citation data have been extracted; <strong>[field "created"]</strong> the creation time of the citation data. <strong>[field "invalidated"]</strong> the start of the destruction, cessation, or expiry of an existing entity by an activity; <strong>[field "description"]</strong> a textual description of the activity made; <strong>[field "update"]</strong> the UPDATE SPARQL query that keeps track of which metadata have been modified. The size of the zipped archive is 5 GB, while the size of the unzipped CSV file is 122 GB.Additional information about POCI at official webpage.
DOI: 10.17632/99j9t7f8kj
title: Home Energy Management System Dataset

description: Of course, here's the improved version in English:

---

The present dataset was collected in a controlled environment. For this purpose, a test bench was created that includes five typical household appliances: TV, air conditioner, fan, computer, and lamp. Each of these devices is connected to individual smart meters, allowing for detailed monitoring of their energy consumption, thereby forming a Home Energy Management System (HEMS). Additionally, a meter was installed in the distribution box to measure the total consumption generated by the appliances connected to the test bench.

The dataset used to train the model consists of real-time data collected by the HEMS. This dataset includes detailed records of the energy consumption of each appliance connected to the test bench. The data was collected at regular intervals of fifteen minutes, capturing daily patterns over a 30-day period.
DOI: 10.17632/bg5nms9y65
title: Data of qEEG and MMPI

description: This data consists of data of MMPI, qEEG of eyes closed and qEEG of eyes open. All data were in standardized scores.
DOI: 10.17632/pprkvz3vbd
title: Full Scale Wastewater Treatment Plant Data

description: Energy consumption, climate, and wastewater characteristics of Melbourne eastern wastewater treatment plant for preiod of six years (2014-2019). https://www.researchgate.net/profile/Faramarz-Bagherzadeh-2
DOI: 10.17632/xgb72fhh6d
title: Data for: Discovery of Potent Proteolysis Targeting Chimera Degrader of IDO1

description: Supplementary Information for Discovery of Potent Proteolysis Targeting Chimera Degrader of IDO1
DOI: 10.6084/m9.figshare.25962676.v2
title: <b>the impact of smoking and hypertension on cataract development in diabetic patients </b>

description: data on the effect of smoking and HTN on cataract development
DOI: 10.17632/532fxhnwbb
title: BDFoodSent: A Large-Scale Sentiment-Labeled Restaurant Review Dataset from Bangladesh

description: BDFoodReview is a large-scale dataset containing 334,119 restaurant reviews collected from "Foodpanda Bangladesh". The dataset includes customer reviews in mixed languages (Bangla, English, and Banglish), translated into English, along with their corresponding ratings and sentiment labels.

Dataset Statistics
Total Reviews: 334,119
Features/Columns: 19

Potential Applications
Sentiment Analysis
Restaurant Review Classification
Customer Satisfaction Analysis
Opinion Mining
Natural Language Processing Research
Food Service Industry Analysis
DOI: 10.17632/z3yc9z84tk
title: Monitoring of structural performance of cracked reinforced concrete using DIC and CMfM

description: Complete information on data collection and the dataset can be found in the paper:
A. Sjölander, V. Belloni, V. Peterson, J. Ledin. Experimental dataset to assess the structural performance of cracked reinforced concrete using Digital Image Correlation techniques with fixed and moving cameras. Data in Brief. Volume 51, December 2023, 109703.

Version History.
v1. Upload of original data.
v2. Change of title of dataset to comply with new title of method, DfM changed to CMfM.
v3. Editorial changes of title.
v4. Added link to Paper
DOI: 10.6084/m9.figshare.28690556.v1
title: <b>High</b><b>-</b><b>entropy</b><b>-</b><b>doping effect in a rapid</b><b>-</b><b>charging</b><b> </b><b>Nb</b><b>2</b><b>O</b><b>5</b><b> </b><b>lithium-ion</b><b> </b><b>battery negative electrode</b>

description: <b>F</b><b>or </b><b>host lattice like </b><b>Nb</b><b>2</b><b>O</b><b>5</b><b>,</b><b> </b><b>doping without causing phase change is </b><b>quite </b><b>difficult</b><b>.</b><b> </b><b>Here </b><b>we </b><b>introduce </b><b>a</b><b>n emergent and rigid </b><b>high-entropy</b><b>-</b><b>doping effect </b><b>in Nb</b><b>2</b><b>O</b><b>5</b><b>. </b><b>Unlike traditional doping approaches, high-entropy-doping disregards the chemical functions of doping elements and focuses solely on their quantities. </b><b>The high-entropy</b><b>-</b><b>doping effect holds promise for introducing more phenomena in material research.</b>
DOI: 10.17632/cxbtk83r43
title: Data for: Broad spectrum antiviral remdesivir inhibits human endemic and zoonotic deltacoronaviruses with a highly divergent RNA dependent RNA polymerase
DOI: 10.17632/t6chv2vdwm
title: Parametric residential heating energy, thermal comfort and CO2 concentration data for an energy-efficient semi-detached house in Ireland

description: The dataset is a synthetic, simulation-based collection of numerical data representing the energy performance, thermal comfort, and indoor air quality (specifically CO₂ concentration) of a low-energy, naturally ventilated semi-detached residential dwelling in Ireland, an oceanic temperate climate zone. Generated through 45,000 parametric simulations using EnergyPlus and the jEPlus tool, the dataset captures a wide range of building design and operational scenarios for an A-rated semi-detached house, the most prevalent residential archetype in Ireland. It is stored in a comma-separated values (.csv) format and hosted on Mendeley Data, with the data originating from University College Dublin, Ireland. The dataset comprises 19 input parameters and 24 output parameters, detailed as follows:

Input Parameters

Sampled via LHS from Irish EPCs and TUS data:
Window U-value (0.53–3.21 W/m²·K): Glazing heat loss.
Floor U-value (0.06–0.85 W/m²·K): Floor insulation.
Roof U-value (0.06–0.88 W/m²·K): Roof insulation.
Wall U-value (0.12–0.85 W/m²·K): Wall heat loss.
Orientation (0–360°): Solar exposure.
HSPT (18–22°C): Heating setpoint.
Lighting Density (1–6 W/m²): Lighting load.
Occupancy Density (0.0125–0.3289 persons/m²): Occupant load.
Equipment Density (16–24 W/m²): Equipment load.
HVAC Efficiency (0–1): Heating efficiency.
Infiltration (0.35–1.5 ACH): Air leakage.
Metabolic Rate (60–360 W/person): Occupant heat.
Clothing Value (0–1 clo): Thermal insulation.
SHGC (0.1–1): Solar gain factor.
WWR (40–90%): Window area ratio.
Window Open Time (10–180 min): Ventilation duration.
Window Close Time (10–180 min): Closed duration.
Window Area (0.1–1 factor): Openable area.
Ventilation (3.35–6.5 ACH): Air exchange rate.


Output Parameters

Calculated per simulation:
c0: Heating (kWh/year): Annual heating energy.
c1: Lighting (kWh/year): Annual lighting energy.
c2–c8: Thermal Discomfort (hours/year): Hours outside comfort for kitchen, dining, sitting, bedrooms 1–3, facility.
c9–c15: Setpoint Not Met (hours/year): Unmet heating hours for kitchen, dining, sitting, bedrooms 1–3, facility.
c16–c20: CO₂ &gt; 1000 ppm (hours/year): High CO₂ hours for kitchen, dining, bedrooms 1–3.
c21: EUI (kWh/m²·year): Energy intensity.
c22: Total Site Energy (kWh): Total energy use.
c23: CO₂ Average (hours/year): Mean CO₂ &gt; 1000 ppm hours.
c24–c33: Fanger Index: PMV (-3 to +3) for 10 zones (kitchen, dining, corridor, sitting, top toilet, bedrooms 1–3, top corridor, bath).

We see this dataset as a vital resource for understanding how heating energy, thermal comfort, and CO₂ levels interplay in low-energy, naturally ventilated homes in Ireland. It’s useful for us as architects to optimise building designs, for engineers to refine HVAC and ventilation systems, for policymakers to shape energy-efficient regulations, and for researchers like us to study occupancy impacts on indoor environmental quality.
DOI: 10.6084/m9.figshare.23488436.v2
title: Financial Data Analysis Process

description: · Financial expenses1 dataset: This dataset consists of simulated event logs generated from the financial expense data analysis process model. Each trace provides a detailed description of the process of analyzing office expense data. · Financial expenses2 dataset: This dataset consists of simulated event logs generated from the travel expense data analysis process model. Each trace provides a detailed description of the process of analyzing travel expense data. · Financial expenses3 dataset: This dataset consists of simulated event logs generated from the sales expense data analysis process model. Each trace provides a detailed description of the process of analyzing sales expense data. · Financial expenses4 dataset: This dataset consists of simulated event logs generated from the management expense data analysis process model. Each trace provides a detailed description of the process of analyzing management expense data. · Financial expenses5 dataset: This dataset consists of simulated event logs generated from the manufacturing expense data analysis process model. Each trace provides a detailed description of the process of analyzing manufacturing expense data. · Financial expenses6 dataset: This dataset consists of simulated event logs generated from the financial statement data analysis process model. Each trace provides a detailed description of the process of analyzing financial statement data.
DOI: 10.17632/mmrtv3nnt7
title: CO2 footprint from industrial facilities in Saudi Arabia

description: Details are in : https://doi.org/10.1016/j.ecmx.2022.100299

Version 1 database: 2022
Version 2 database: update 2025

The Kingdom of Saudi Arabia (KSA) is among the countries that committed to taking measures to cut greenhouse gas emissions in accordance with the 2015 Paris Climate Agreement. KSA has rolled out the 2030 Vision aiming at creating a more diverse and sustainable economy that cascaded into a series of initiatives, including the circular carbon economy, Saudi green initiative, and the national renewable energy program. Furthermore, KSA has recently announced an ambitious goal to reach net-zero goal by 2060. In its updated nationally determined contribution (NDC), the Kingdom committed to reducing its carbon emissions by 278 million tons of CO2eq (equivalent) annually by 2030. This ambition is more than a two-fold increase versus the previously announced target (130 million tons of CO2eq). With no current plans to change its hydrocarbon production rates, this reduction in emissions would be achieved mainly through diversifying its energy mix, increasing the efficiency of industrial processes, and deploying carbon capture utilization and storage (CCUS). To achieve this goal, it is vital to establish a detailed register for CO2 emissions from stationary industrial sources to design optimum and effective CCUS applications. This register includes details about the emission source locations, rates, and characteristics. For the first time, this paper provides a country-wide extensive study that maps out CO2 emissions from stationary industrial emitters associated with the leading six industries in the country, which are electricity generation, desalination, oil refining, cement, petrochemicals, and iron & steel. Moreover, CO2 concentrations within the emitted flue gas from these resources are estimated, which is crucial to determine the capture cost. This study aims to provide a vital resource for researchers and policymakers who seek to reduce greenhouse gas emissions by promoting renewable energy, improving the efficiency of existing fossil-fuel-based industries, and evaluating the potential of CCUS in KSA.
DOI: 10.17632/sh9kd7vdm7
title: Country risk premium for MNC

description: Multinational companies operate on several markets. But nobody knows which approach for shares' return estimation should be chosen be investors. The key question is: does country risk premium matter in shares' return calculation or not? The data which contains in this dataset, relates to 70 companies, 41 ones are incorporated in developed countries, 29 ones - in developing countries. The sample is representative as we include all major economic sectors like oil and gas, metal and mining, services, manufacture, electronics etc. (analysis carried out by sectors was included at the end of the dataset) and a long period of time (2010-2019). The data was get from different sources: daily shares' and indexes' prices were taken from open sources (Yahoo finance, Investing), country risk premium was taken from Aswath Damodaran's web-site, weights for country risk premium estimation were determined based on data on each company's revenue / fixed assets structure which were taken form each company's annual report (2018-2019). The key hypothesis for this research was: In some cases it is necessary to adjust the estimation of multinational company's cost of equity for country risk in order to get more accurate result. The research showed that contrary to expectations, use of more complicated models does not make the outcome of calculations much more accurate. The exception are developing markets: when an investor intends to invest money in shares of a multinational company which is incorporated in developing countries, he should use more complicated approaches which take into account weighted country risk premium as they help to get more accurate outcomes. All the calculations are included in this dataset.
DOI: 10.6084/m9.figshare.4902941.v1
title: TARA-NON-REDUNDANT-MAGs-SPLIT

description: Anvi'o profiles for each MAG in Delmont et al.<br>Each of which can be visualized using anvi'o version 2.3.0 or later.
DOI: 10.17632/zkdkvv9gfd
title: Data for: Sizing Rupture Disk Vent Line Systems for High-velocity Gas Flows
DOI: 10.17632/5p8yvmcf5t
title: a dataset of 26,935 comments on YouTube videos related to Hanfu

description: The research data were extracted using Python and the YouTube Data API provided by Google. A total of 1,100 Hanfu-related video samples were collected using the keyword "Chinese Hanfu". This keyword was selected based on preliminary research and careful consideration, as it was found to be the most direct and widely used term relevant to the focus of this study. Additionally, an analysis of search trend charts on YouTube indicated that " Chinese Hanfu " has experienced a notable surge in search volume, which may be associated with China’s visa-free entry policy introduced for multiple countries in 2024.
To balance data quality and collection efficiency, an exponential-logarithmic sampling formula was employed. The number of comments sampled （C） was calculated according to the following equation:
C=50×log (Video Views)+10 
To address the large variance in video popularity, the logarithm of video views was applied to compress the influence of high view counts. A coefficient of 50 and a constant of 10 were used to adjust the overall sampling scale and to ensure adequate comment coverage for videos with lower view counts. Specifically, for videos with fewer than 1,000 views, all comments were collected. For videos with more than 1,000 views, the number of sampled comments was determined using the specified formula. Compared to simple random sampling, this approach helps reduce data redundancy, while balancing the representativeness of both high-traffic and niche videos, thereby providing a robust and efficient foundation for trend analysis.
All selected videos were published before 31 December 2024, the end date of data collection. No fixed start date was set, as the purpose of this study was to support subsequent time-series analyses of keyword trends. To capture long-term dynamics and patterns, videos were sampled across an extended time frame. This design ensured both the breadth and continuity of the dataset, facilitating the exploration of long-term behavioral patterns and structural regularities across a broad temporal spectrum.
The following ten categories of metadata were retrieved using a web crawler: video URL, upload date, title, textual description, number of views, number of comments, content of each comment, number of likes per comment, timestamp of each comment, and total number of comments per video. In total, 26935valid comments were collected.
DOI: 10.17632/cbv7jyx4p9
title: HUST bearing: a practical dataset for ball bearing fault diagnosis

description: In this work, we introduce a practical dataset named HUST bearing, that provides a large set of vibration data on different ball bearings. This dataset contains 99 raw vibration data of 6 types of defects (inner crack, outer crack, ball crack, and their 2-combinations) on 5 types of bearing at 3 working conditions with the sample rate of 51,200 samples per second.
DOI: 10.17632/j6zg95s7nn
title: ISCIENCE-D-22-01108R2

description: Western blot images, liposome permeabilization, thermal shift, cell death, BH3 profiling mitochondrial permeabilization
DOI: 10.17632/jgpsf32f86
title: Supplemental Materials for JAAD Publication JAAD-D-21-01712R3, Phase 2 Randomized, Dose-Ranging Trial of CTP-543, a Selective Janus Kinase Inhibitor, in Moderate to Severe Alopecia Areata

description: These are supplemental data sets to the JAAD publication entitled "Phase 2 Randomized, Dose-Ranging Trial of CTP-543, a Selective Janus Kinase Inhibitor, in Moderate to Severe Alopecia Areata". Supplemental figures include: Figure S1: Enrollment and Patient Disposition; Figure S2: Patient SALT Improvement Thresholds; Figure S3: Percentage of Patients Achieving SALT score ≤20 Over Treatment Period; Figure S4. Patient-Reported Outcomes.
DOI: 10.17632/zt5n7cj7xw
title: Gray et al. (2024)

description: Data corresponding to submitted manuscript titled "Challenges of measuring volatiles in enstatite chondrites and evaluating their contribution to Earth's volatile inventory".
DOI: 10.6084/m9.figshare.3562272.v1
title: Supplement 1. Plot x species matrix table and a species x traits matrix table.

description: File List plot_x_species.txt (MD5: 6b850f1de36a0eb9ed03fa4a9713e30d) species_trait.txt (MD5: 00f69112ddfc37a6081b20069bcd89ee) Description plot_x_species.txt<br> variable names: plot: a column with plot names ACABOR … ZIZMUC (105 columns in total): columns with abundances of different species. Species names are abbreviated with the first three letters of the genus names and the first three letters of the species name. species_trait.txt<br> variable names: species: a column with species names SLA, LA, FD, SL, SD, BA, WD, polyphenols, C, N, P, S, K, Ca, Mg, Fe, Mn, Zn, B, Cu: columns with trait values. For more information on trait descriptions, see Appendix.
DOI: 10.17632/4j9g637nd3
title: Western blot: Evidence of RNA polymerase III recruitment and transcription at protein-coding gene promoters

description: Evidence of RNA polymerase III recruitment and transcription at protein-coding gene promoters