cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/82jswfyz5p.1
title: Solar Panel Data

description: A solar photovoltaic (PV) plant (operated by Ultra Vibrant Solar Energy Pvt. Ltd.) was installed on the rooftop of Fitpack Textiles in Jaipur, Rajasthan, situated at coordinates 26.76640°N, 75.8326°E and at location: https://maps.app.goo.gl/pHLiy3EW7UEeTTLp6 within the state of Rajasthan, India. The site incorporated two inverters, with Inverter 1 having a capacity of 20 kW and Inverter 2 with a capacity of 100 kW. The collection of data was conducted during the year 2022. The output current and voltage of the panel are measured from 6 AM to 3 PM at every 30 minutes of interval. The daily electrical output collected from INV 1 and INV 2 has been given. For the record there was a technical breakdown in the month of December, and the plant malfunctioned, resulting in negligible data available for many days in December. 
The study examined the variation in electrical output power of solar PV panels with respect to environmental parameters, solar radiation, atmospheric conditions, and external events such as solar flares and CMP outbursts in 2022. The atmospheric conditions analyzed included air temperature, cloud opacity, DNI, DHI, GHI, relative humidity, and wind speed, obtained with the help of data produced by Solcast API (https://solcast.com/Solcast PV). Table 1 denotes the monthly Average Radiation Data obtained from Solcast API. Additionally, wind direction (WD) in degrees and barometric pressure (BP) data, as well as pollution parameters, were sourced from Central Pollution Control Board (CPCB) (All India Central Control Room for Air Quality Management center, Adarsh Nagar, Jaipur). The parameters considered were PM2.5 (µg/m³), PM 10 (µg/m³), NOx (ppb), NH3 (µg/m³), SO2 (µg/m³), CO (mg/m³), Ozone (µg/m³), Benzene (µg/m³), MP-Xylene (µg/m³), and O Xylene (µg/m³). Solar flares and Coronal Mass Ejection (CME) data from the year 2022 were acquired from the NASA website NASA Solar Flare Data and NASA CME Data where DONKI is a comprehensive online database of space weather events for the community. Only solar flares categorized as M and X class were specifically targeted to gauge their impact on the solar output of the system. The above data has been arranged in Tabular form in the given excel sheet. The * in the table indicates the days of flares and CME occurrences. The above data can be processed further for preparing the machine learning (ML)-based output power prediction model.
DOI: 10.17632/mhszjt942k
title: Frequency and characteristics of occupational injuries and infections in senior medical students: A 5-year retrospective study

description: : In this descriptive cross-sectional study analyzed the occupational health and safety unit and hospital electronic records of needlestick and sharp injuries, mucosal exposure and occupational trauma injuries from January 1, 2019, to December 31, 2023.
DOI: 10.15468/dl.adcegh
title: Occurrence Download

description: A dataset containing 277 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prunus triloba Lindl.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 277 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009355-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/mhcsf96tz4.1
title: Exploring the Integration of Informed Machine Learning in Engineering Applications: A Comprehensive Review

description: Integrating Artificial Intelligence (AI) and Machine Learning (ML) into mechanical engineering catalyzes a transformative shift within Industry 4.0, offering unprecedented opportunities for innovation, efficiency, and problem-solving. This paper explores the pivotal role of AI and ML in reshaping mechanical engineering practices, from predictive maintenance and design optimization to quality control and supply chain management. By leveraging sophisticated algorithms and vast datasets, AI and ML enable mechanical systems to achieve higher autonomy, performance, and reliability levels. However, adopting these technologies also presents challenges, including technical hurdles, ethical considerations, and the need for specialized knowledge. Through a series of case studies, the paper illustrates successful implementations of AI and ML in mechanical engineering projects, highlighting the benefits and addressing the limitations encountered. Furthermore, it discusses the evolving role of mechanical engineers in this new landscape, emphasizing the importance of continuous learning and interdisciplinary collaboration to harness the full potential of AI and ML in Industry 4.0. The paper concludes with a forward-looking perspective on future research directions, underscoring the critical role of ethical AI and the development of robust algorithms to navigate the complexities of real-world applications.
DOI: 10.17632/rxxch9vw59.2
title: BanglaLekhaImageCaptions

description: This dataset consists of images and annotations in Bengali. The images are human annotated in Bengali by two adult native Bengali speakers. All popular image captioning datasets have a predominant western cultural bias with the annotations done in English. Using such datasets to train an image captioning system assumes that a good English to target language translation system exists and that the original dataset had elements of the target culture. Both these assumptions are false, leading to the need of a culturally relevant dataset in Bengali, to generate appropriate image captions of images relevant to the Bangladeshi and wider subcontinental context. The dataset presented consists of 9,154 images.
DOI: 10.17632/t4d3fgyc6z.1
title: Spatiotemporal examination of seasonal surface water changes

description: The dataset supports spatiotemporal analysis of interactions between seasonal water, climate, land use, policy, and socioeconomic changes. The seasonal water area data was extracted from The JRC Monthly Water History, V1.2. This entire history collection includes 430 monthly water detection images between March 1984 and December 2019. Each image contains 30m pixels with three possible cell values: 1 - not water (i.e., land), 2 - seasonal water, and 3 - permanent water. We aimed at examining the whole Hulun-Buir basin consisting of 12 banners. We counted the total number of cells with a 2 value each year over each banner from 2000 to 2014. We multiplied that number by 30m × 30m to get the yearly seasonal water areas by banners. The data of precipitation and temperature were interpolated from the 50 weather stations in Inner Mongolia Autonomous Region (IMAR) of China (http://cdc.cma.gov.cn). The annual precipitation and the average temperature were spatially interpolated as two grid maps at 250m×250m using ArcGIS Inverse Distance Weighted (IDW). The cell values were resampled as the averaged values by counties. The data of land-use and land-cover changes (LULC) primarily came from The NASA MCD12Q1 Data Product (https://lpdaac.usgs.gov/products/mcd12q1v006/) at 500 m resolution. 16 out of the 17 International Geosphere-Biosphere Program LULC types were found in the study area, except "Evergreen Broadleaf Forest" (http://www.igbp.net/). The socioeconomic variables were entered from the statistic yearbooks of the IMAR from 2000 to 2015 (IMAR Statistical Bureau, 2001 - 2016). All data are stored in the excel file, titled as HulunBuir_Seasonal_Water_Change_Research_Data_Sources. The worksheet called ‘Data’ contains all data items; the worksheet of ‘Explanation’ provides full names and measurement units of the abbreviated variable names; and the worksheet of ‘Banner_Names’ includes the Chinese and English names along with the index ID for the counties involved with the dataset. Banner is the Mongolian term of the English word, County.
DOI: 10.17632/6rdzdjp624.1
title: KrasG12D/+ co-mutation alters the landscape of lung adenocarcinoma

description: Tumor pieces from Kras-mutant genetically engineered lung adenocarcinoma mouse models with loss Lkb1 and/or Keap1 were assessed. 
Steady-state LC-MS analysis (M03818) was performed on tumor pieces; or normal lung pieces. Location is annotated.
13C,15N-Glutamine 15 minute pulse-labeling (M04050) of mice was performed at defined timepoints when tumor burden is known to be significantly progressed. Tumor pieces and plasma were isolated from n=5 mice/genotype.
Related to manuscript: 
Best et al., Glutaminase inhibition impairs CD8 T cell activation in STK11/Lkb1 deficient lung cancer. Cell Metabolism, 2022
DOI: 10.17632/7rpwxrphws.1
title: Dataset of Certified Food Dye Levels in Food Samples Consumed by Children for Updated Exposure Levels

description: This is dataset for the levels of Food, Drug, & Cosmetic (FD&C) dye in juice drinks, breakfast cereals, frozen desserts, ice cream cones, soft drinks (fruit flavored), frostings & icings, fruit snacks/candy, decoration chips for baking, water enhancers, and flavored fruit drink powder organized by absolute values, averages, SDs and % RSD.
DOI: 10.17632/hc8jzfh899.1
title: Fertility and Fertility Preservation Knowledge in Portuguese Women

description: This dataset was used in a study aimed to describe general fertility knowledge and factors affecting fertility, fertility preservation knowledge and attitudes, and the desire to access more information on this topic in a sample of reproductive-age Portuguese women. The sample comprised 257 Portuguese women aged between 18 and 45, single (77.4%) and nulliparous (87.5%). A questionnaire was developed explicitly for this study and disseminated through social media advertisements using an exponential non-discriminative snowball sampling method for data collection. The main reasons mentioned for postponing childbearing were career building/development (35%), financial stability (26.5%), and not having a partner (8.2%). Participants were aware of the impact of ageing, lifestyle, and sexual health factors on fertility but were not well-informed regarding the age range for fertility decline. They reported awareness of fertility preservation techniques (81.7%), but 68.9% showed no interest in using them. Most participants agreed that information about fertility and fertility preservation should be available. However, a meaningful percentage (38.1%) were not interested in having more information. The current study identified that more information regarding fertility and fertility preservation is relevant to ensure that more women can make informed decisions regarding their reproductive life.
DOI: 10.17632/dz4bv26ncd.1
title: Airfoil 360 v2022: Wind Tunnel Data

description: This data includes lift and drag coefficient data through 360 degrees angles of attack for three airfoils: (1) NACA 0012, (2) NACA 2412, and (3) Selig s1210.  Wind tunnel data was captured for each airfoil at two Reynolds Numbers: 50,000 and 100,000.  Experimental data that captures full circle angles of attack are necessary to generate models that can accurately predict the performance of vertical-axis wind turbines.

Such a set of models is presented in Stringer, D. Blake (2022), “Airfoil 360 v2022: Airfoil Model Matlab(R) Functions”, Mendeley Data, V1, doi: 10.17632/zrhwpwdfn9.1.

Standard wind tunnel correction factors have been applied as outlined in
P. D. Chappell, “Lift-Interference and Blockage Corrections for Two-Dimensional Subsonic Flow in Ventilated and Closed Wind Tunnels,” ESDU, ESDU 76028, 1995. doi: 10.1912/ESDU76028a.

This data was taken in the Kent State University Subsonic Wind Tunnel between 2016 and 2022 by the following undergraduate students:
Sydney Bihn, Dakota Bunner, & Adam Fertig.
DOI: 10.17632/63p8ksm3vc.1
title: UNVEILING JOLAHA: INSIGHTS INTO THE POLLUTION CHALLENGES FACED BY A MUSLIM CASTE

description: Abstract : This study will elucidate the characteristics of the caste system prevalent among the Muslims of Bangladesh, specifically focusing on the Jolaha community. The data for this research was gathered using the anthropological technique of participant observation in a hamlet in Bangladesh where the Jolaha, a dominant occupational group, resides. The weavers of South Asia are categorised into two categories based on their religious beliefs. Tanti is the name used to designate to Hindu weavers, whereas Muslim weavers are known as Jolha, Jolaha, Julaa, and so on. However, one common characteristic between them is that they both belong to the bottom echelons of our hierarchical South Asian society. Regarding the Jolahas, there is strong evidence to suggest that they were members of a marginalised Hindu caste known as Tanti, who collectively converted to the Islamic faith. However, after their religious conversion, the Jolahas continue to be regarded as a socially disadvantaged occupational caste. They are seen as tainted and stigmatised by Muslims in general because to their inherited profession. The contemporary perception of the Jolaha occupation no longer deems it as unclean. There has been a certain degree of shift in the pattern of movement in recent years. However, it is regrettable that even after many generations, people continue to identify them as Jolaha despite their mobility. The non-Jolaha Muslims see them as individuals belonging to inferior castes. Keywords: Caste system, Jolaha community, Anthropological research, Occupational groups, South Asian weavers, Tanti (Hindu weavers), Jolha, Jolaha, Julaa (Muslim weavers), Social hierarchy, Marginalized Hindu caste, Stigmatization, Occupational caste, Social mobility, Identity, Inferior castes.
DOI: 10.17632/m7dvz8xmfp.1
title: Data for: 3622221

description: Dataset for the spatial econometric analysis of the change of greenhouse gas emissions during the Covid-19 pandemic (March-April 2020) for Southern Germany
DOI: 10.17632/4cbf9sxj7x.7
title: Data for: Predicting Scientific Research Trends Based on Link Prediction in Keyword Networks

description: The provided data contains Journal and Conference articles' keywords co-occurrence network from 2000 to 2017. The data is in GEXF format and contains time stamps for each keyword. Time intervals are on a yearly basis.
DOI: 10.17632/m585w9g6r6.1
title: The Guilt Emotion Reduces the Gaze-Cueing Effect

description: Previous studies have found that the gaze-cueing effect, an enhancement of the reaction in detecting targets appearing in a gazed-at location compared with those appearing in other locations, can be modulated by prior social interaction. In the present study, we investigated whether a feeling of guilt established from prior interaction with a cueing face could modulate the gaze-cueing effect. Participants first completed a guilt-induction task using a modified dot-estimation paradigm to associate the feeling of guilt with a specific face, and then the face that established the binding relationship was used as the stimulus in a gaze-cueing task. The results showed that guilt-directed faces and control faces induce equal magnitudes of gaze-cueing effect in 200 ms stimulus onset asynchrony (SOA), while guilt-directed faces induce a smaller gaze-cueing effect than control faces in 700 ms SOA. These findings provide evidence for the role of social factors on the gaze-cueing effect.
DOI: 10.17632/mdyhynn24x.1
title: longitude and latitude details of classroom for attendence system

description: This dataset contains classroom details in a college such as latitude, longitude, place, and room_number for student's attendance system. This dataset helps to get face recognition based attendance system using Deep Learning.
DOI: 10.17632/2cfjdf5zh8.1
title: Archetype Commercial Building Energy Models

description: Building energy models used for analysis of "Spatio-temporal electrical grid emission factors effects on calculated GHG emissions of commercial buildings in mixed-grid environments"
DOI: 10.17632/kc5znhxwxv.2
title: Risk free rate in Vietnam market

description: Our dataset contains monthly risk free rate in the Vietnam stock market from July 2010 to June 2021. The folders contain the data files in an Excel file (.xlsx).
DOI: 10.17632/9mh4dg4rfn.5
title: Data on departure reasons in US CEO turnover over fiscal years 1992-2020

description: We present a dataset of US CEO turnover types created with manual data collection through searches of news stories related to CEO turnover. The data identify the fiscal year of turnover and its type for top managers in publicly-traded US firms. We identify turnover cases based on changes in the CEO of the firm as recorded in secondary data sources of Compustat and Execucomp, and then we classify turnover types based on the examination of full-text news. 
 
We start dataset construction with the ExecuComp executive-level data for the fiscal years from 1992 through 2020. These data are merged with the CompuStat dataset of financial variables. As the dataset is intended for research on CEO turnover, we exclude observations in which the CEO at the start of the fiscal year is not well-defined. The data set also excludes firm/year combinations that involve a restructuring of the firm – spinoff, buyout, merger, or bankruptcy. 
 
We identify the CEO at the start of each year for each firm. To identify CEO turnover based on changes in the CEO from year to year, we require firm observations to extend over at least six contiguous years. Cases involving the last year the firm is in the sample are excluded. We also exclude from the dataset cases when there was an interim CEO who stayed in the position for less than 2 years. This results in a sample of 3,100 firms reflecting 41,773 firm/year combinations. 
 
For this sample, we examine news articles related to CEO turnover to confirm the reasons for each CEO departure case. We use the ProQuest full-text news database and search for the company name, the executive name, and the departure year. We identify news articles mentioning the turnover case and then classify the explanation of each CEO departure case into one of five categories of turnover. These categories represent CEOs who resigned, were fired, retired, left due to illness or death, and those who left the position but stayed with the firm in a change of duties, respectively. 

The published data file does not include proprietary data from ExecuComp and CompuStat such as executive names and firm financial data. These data fields may be merged with the current data file using the provided ExecuComp and CompuStat identifiers.
 
The dataset consists of a single table containing the following fields:
• gvkey – unique identifier for the firms retrieved from CompuStat database
• firmid – unique firm identifier to distinguish distinct contiguous time periods created by breaks in a firm’s presence in the dataset
• coname – company name as listed in the CompuStat database
• execid – unique identifier for the executives retrieved from ExecuComp database
• year – fiscal year
• reason – reason for the eventual departure of the CEO executive from the firm, this field is blank for executives who did not leave the firm during the sample period
• ceo_departure – dummy variable that equals 1 if the executive left the firm in the fiscal year, and 0 otherwise
DOI: 10.17632/tzf6zy8kxt.1
title: Ubiquitous Learning Research Data (1993-2020)

description: The ubiquitous learning research and publication dataset, which was indexed by Scopus from 1984 to 2021. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
DOI: 10.17632/rr4rzzrjfc.2
title: Machine Learning for Prediction of Glycemic Control in Diabetes Mellitus

description: This study aims to predict glycemic control status three years after diagnosis and document the most important factors for glycemic control in diabetes mellitus. The data belongs to the people living in Istanbul. We identified newly diagnosed diabetes mellitus patients in Istanbul Province in 2017. The criteria for having diabetes were a diabetes diagnosis (ICD-10 codes E10-E14) or prescription of antidiabetic medications (except metformin) or having an HbA1c over 6.5. In addition, all the diabetic patients were checked if they had initial serum creatinine, lipid profile, at least four HbA1c measurements (roughly once a year). The patients were divided into two groups according to their profile of HbA1c levels; under control (the last two HbA1c values are under 7) or uncontrolled.
id: Patient id no
HbA1c: Serum level at diagnosis (%)
Hba1c_change: %, (level at 1st year-level at diagnosis)
sex: 1: female, 2: male
age: years
LDL: mg/dL, level at diagnosis
Cholesterol: mg/dL, level at diagnosis
HDL: mg/dL, level at diagnosis
Creatinine: mg/dL, level at diagnosis
Triglyceride: mg/dL, level at diagnosis
infectious_diseases;Malign_neoplasms;Obesity;Thyroid_dis;neoplasms_unknown;anemia;vitamin_deficiency;lipoprotein_met_dis;hematologic_dis;endocrine_other;bipolar_affective_dis;depression;anxiety_dis;Other_mental_dis;neuropathies;diabetic_nueropathy;nervous_sys_dis;cataract;retinopathy;refraction_dis;impacted_cerumen;tinnitus_h93_1;eye_other;otitis_externa_h60;mastoid_h60_h95;hypertension_i10;ischemic_heart_dis;cardiomyopathies;cerebrovascular;other_circulatory;respiratory_sys;oral_dis;gastro_oes_reflux;dyspepsia;digestive_sys_dis;skin_dis;musculoskeletal_dis;nephropaties;kidney_failure;other_genitourinary;pregnancy;birth;ceserian_multiple;other_pregnancy: 1: present in the first year of diagnosis, 0: absent in the first year of diagnosis
digestive_drugs;antiobesity;other_digestive;hematologic_drugs;cardiovascular_drugs;lipid_modifying;dermatologic_drugs;gynecologic_drugs;sex_hormones;systemic_hormones;glucagon;calcium_homeostasis_drugs;antiinfectives;vaccines;antineoplastics;endocrin_drugs;immunostimulants;immunosupresants;musculosceletal_drugs;anesthetics;analgesics;antiepileptics;antiparkinson;antipsychotics;anxiolytics;pshycoanaleptics;other_nervous_drugs;antiparasitic;respiratory_sys_drugs;eye_ear_drugs;various_drugs: 1: prescribed in the first year of diagnosis, 0: not prescribed in the first year of diagnosis.
akarboz;dapagliflozin;eksenatid;gliklazid;glimepirid;glipizid;linagliptin;nateglinid;pioglitazon_hcl;repaglinide;saksagliptin;sitagliptin;vildagliptin: prescribed amount in the first year after diagnosis (gram/year)
insulin_aspart;insulin_detemir;insulin_glarjin;insulin_glusilin;insulin_lispro;insulin_nph;insulin_reguler: prescribed amount in the first year after diagnosis (1000IU/year)
metformin_hcl: prescribed amount in the first year after diagnosis (kg/year)
Glycemic_control: 0: Under control, 1: Poor control
DOI: 10.17632/nj4cz7mmj5.1
title: Combined microbiome and metabolome analysis reveals a novel interplay between intestinal flora and serum metabolites in lung cancer

description: 16S ribosomal RNA (rRNA) gene sequencing and liquid chromatography mass spectrometry (LC-MS) technology were uesd to analyze intestinal microbiota composition and serum metabolism profile.
DOI: 10.17632/689dzcy8wv.1
title: Impacts of Thunderstorm-Generated Gravity Waves on the Ionosphere-Thermosphere using TIEGCM-NG/MAGIC Simulations and Comparisons with GNSS TEC, ICON and COSMIC-II Observations

description: TIEGCM-nested grid simulation for the April 13, 2020 thunderstorm/tornado event, along with the codes for reading data and plotting figures.