cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/68mt8gms4j.2
title: Data for: Effectiveness of flip teaching on engineering students' performance in the physics lab

description: Raw data for the Effectiveness of flip teaching on engineering students' performance in the physics lab. The progressive introduction of the flip teaching (FT) instructional model into higher education has accelerated in recent years. The FT methodology seems to be especially suitable for laboratory practice sessions: before the lab session the students are given documents and videos that explain the theoretical contents and the experimental procedure. When this material is studied in advance, the practice session can be devoted to the discussion, clarification and practical application of the acquired knowledge. This paper describes the effect of the FT methodology on the students’ academic performance when it was applied to the laboratory practice in two subjects, Physics and Electricity, of a technical degree. The laboratory and final grades of these subjects were compared in four consecutive years. The characteristics of all four years were quite similar, except that the traditional teaching method (TM) was used in two, while FT was applied in the other two. The statistical analysis shows that the academic results of the students were better in both subjects under FT than those obtained using TM, and that the difference was statistically significant.
DOI: 10.17632/5zrtmp7gwd.2
title: Electric Vehicle Charging Dataset

description: This electric vehicle (EV) charging dataset includes the EV connection time, charging duration, energy consumption, and day number each corresponding respectively to connectionTime_decimal, chargingDuration, kWhDelivered, dayIndicator columns in the dataset for an EV charging parking lot. The data is generated using conditional tabular generative adversarial networks (CTGAN) and kernel density estimation (KDE) from the Caltech dataset to maintain a realistic load profile, accurately model EV owner behaviors, and preserve the relationship between the columns of the dataset. This dataset includes EV data for 29,600 days while the original Caltech dataset includes data for only 185 days. This data can be useful in training machine learning algorithms, specifically, reinforcement learning algorithms. The connection time range is 0-24. The unit for charging duration is hour and energy consumption is in kWh.
DOI: 10.17632/73rb78pmzw.1
title: Nairobi Securities Exchange (NSE) All Stocks Prices 2013-2020

description: This compilation consists of historical data of the Nairobi Securities Exchange (NSE), the stock exchange market of Kenya. The data is raw daily trade data and is of value to any machine learning algorithm that requires training and testing data. An earlier dataset (2007-2012) was initially compiled as part of a research project to predict next day stock price prediction, based on the previous five days by use of Artificial Neural Networks (ANN). The research [1],[2] tested 6 stocks [3] using an ANN of configuration 5:21:21:1. However, the whole of 2007-2012 dataset of all NSE stocks is also available [4]. Since then, the NSE has continued to operate and still generates new data daily. This has necessitated compilation of new data. This new NSE 2013 to 2020 (upto June 30) data was once again scrapped from public website [5] of company, licensed by NSE. The processing involved - raw data export to spreadsheets then removal of unnecessary data elements. Each stock data row has the following 13 data columns (1) Date (2) Stock Code (3) Stock Name (4) 12-month Low price (5) 12-month High price (6) Day's Low price (7) Day's High price (8) Day's Final Price (9) Previous traded price (10) Change in price value (11) Change in price % (12) Volume traded (13) Adjusted price. The original data also had a column on the price direction (arrow graphic), but this column was dropped from this final compilation. Two additional CSV files (2013 and 2020) are introduced to show market sectors that each stock belongs. The column headings are: (1) Market sector (2) Stock Code (3) Stock Name. This new dataset gives researchers a larger dataset (2007-2020), bigger pool of stocks and indices (64), market sector data and many other data attributes, over and above the test cases that were presented in the earlier research. List of data files on this dataset: NSE_data_all_stocks_2013.csv NSE_data_all_stocks_2014.csv NSE_data_all_stocks_2015.csv NSE_data_all_stocks_2016.csv NSE_data_all_stocks_2017.csv NSE_data_all_stocks_2018.csv NSE_data_all_stocks_2019.csv NSE_data_all_stocks_2020_to_jun30.csv NSE_data_stock_market_sectors_2013.csv NSE_data_stock_market_sectors_2013.csv References: [1] Wanjawa, B. W. (2014). A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange (Dissertation, University of Nairobi). [2] Wanjawa, B. W., & Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. arXiv preprint arXiv:1502.06434. [3] Wanjawa, Barack (2020), “Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks”, Mendeley Data, v3, http://dx.doi.org/10.17632/95fb84nzcd.3 [4] Wanjawa, Barack (2020), “Nairobi Securities Exchange All Stocks Prices 2007-2012”, Mendeley Data, v1, http://dx.doi.org/10.17632/5hk4zw32f5.1 [5] Synergy Systems Ltd. (2020). MyStocks. Retrieved July 27, 2020, from http://live.mystocks.co.ke/
DOI: 10.17632/bdhb6v9cyy.1
title: Petrographic details of Ar-Ar geochronology samples from western Lesbos Greece

description: Backscattered electron images and energy dispersive spectroscopy mineral analyses of geochronological samples reported by Pe-Piper, Piper, Zouros and Anastasakis (2019) Age, stratigraphy, sedimentology and tectonic setting of the Sigri Pyroclastic Formation and its fossil forests, Early Miocene, Lesbos, Greece. Basin Research.
DOI: 10.17632/cydmwsfztj.1
title: A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry

description: Data related to the publication: "Imaging Mass Cytometry Analysis Sheds Light on Type 1 Diabetes Progression" (manuscript submitted to Cell Metabolism). We used imaging mass cytometry to simultaneously image 37 biomarkers with single-cell and spatial resolution in pancreas sections from 12 human donors at different stages of type 1 diabetes. CODE: - Python script for coordinate transformation - Functions for custom histoCAT neighborhood analysis DATA: - Single-cell data - Islet-level data - Relationships between cells and islets IMAGES: - Image stacks (37 channels) for all areas imaged (numbers indicate nPOD case IDs) - Panel file with information related to antibodies and metal tags - Metadata file with patient information for all images
DOI: 10.17632/db8vhwm589.1
title: Deep mutational scanning identifies SARS-CoV-2 Nucleocapsid escape mutations of currently available rapid antigen tests. Frank et al. Supplementary Tables 1 and 2

description: Deep mutational scanning identifies SARS-CoV-2 Nucleocapsid escape mutations of currently available rapid antigen tests. Frank et al. Supplementary Tables 1 and 2
DOI: 10.17632/z3g26tphnv.4
title: MEFAR Dataset: Neurophysiological and Biosignal Data

description: The dataset was carefully generated with the purpose of investigating mental fatigue through the analysis of physiological signals. The signals that are included are as follows: Electroencephalography (EEG) is a technique utilized to assess the electrical activity of the brain. The blood volume pulse (BVP) is a measurement technique that records fluctuations in blood volume inside the peripheral blood vessels. Electrodermal activity (EDA) is a physiological measure that monitors variations in the electrical conductivity of the skin. The heart rate (HR) is data that measures the frequency of heartbeats within a given timeframe, typically expressed as the number of beats per minute. The temperature sensor is responsible for monitoring the body temperature of the participants. The 3-axis accelerometer (ACC) data quantifies the rate of change in velocity experienced by the individuals involved. Data was gathered from a sample of 23 individuals during both morning and evening sessions. The Chalder Fatigue Scale was employed in order to assess the levels of mental weariness among the participants. The scale utilized in this study offers a numerical score to each participant, which is determined based on their responses. A score of 12 or above on this scale is indicative of a positive mental fatigue condition. The dataset includes both unprocessed/raw and processed data. The term "raw data" pertains to unaltered recorded signals, whereas "processed data" generally includes signal preprocessing methods such as filtering, artifact removal, and feature extraction. The processed data comprises data that has been processed using various sampling frequencies, namely 1 Hz, 32 Hz, and 64 Hz. The operations encompass downsampling, midsampling, and upsampling. The datasets that have undergone processing are denoted as MEFAR_DOWN, MEFAR_MID, and MEFAR_UP, representing the treated data with downsampling, midsampling, and upsampling, respectively.
 
Furthermore, comprehensive demographic data pertaining to the participants can be found in an Excel spreadsheet named "general_info." Maintaining the identity and privacy of participants is crucial in order to adhere to ethical requirements.
This dataset can be shared among researchers that are interested in analyzing mental exhaustion, enabling them to conduct more investigations, develop algorithms, and validate their findings.
DOI: 10.17632/yzzxty4yww.1
title: Autonomous Vehicles Crash and Disengagement Report (CA DMV)

description: Autonomous Vehicles (AVs) are being extensively tested on the public roads in several states in the USA, such as California, Florida, Nevada and Texas. AV utilisation is expected to increase into the future, given rapid advancement and development in sensing and navigation technologies. For the transparent deployment of AVs in California, California Department of Motor Vehicles (CA DMV) commissioned AV manufacturers to draft and publish reports on disengagements and crashes.  The data is in the form of reports that need to be processed before running any analysis.  This data set contains processed crash and disengagement data. Additionally, supplementary information such as land-use characteristics and shapefiles are also provided. This data is updated every year since CA DMV publishes new data annually. The processed data, along with the supplementary data, will assist in accelerating the research in this area.
DOI: 10.17632/nsj3yv6478.1
title: NigeriaCOVID-19 Daily Progression

description: The Coronavirus disease known as COVID-19 at this point in writing is a pandemic that calls for a serious global emergency ravaging the world. It is regarded as a global pandemic and in some quarters an outbreak that spread like fire. Every possible effort should be made in the application of predictive models, artificial intelligence, machine learning, geographic information system, and other resources to contain the spread of this disease. In this dataset, the daily total number of COVID-19 cases in Nigeria from February 27, 2020, to June 7, 2020, were automatically mined and monitored every 24 hours from the official websites of NCDC and Wikipedia. The initial data is in raw Excel file format COVID19 NIGERIA_7th June_2020_ Daily Progression.csv. Data with regularly updated case numbers in new-real time can be found in Daily Progression_Covid19.xlsx. These times series data are useful as they present facts that drive analytics on COVID-19 cases in Nigeria. At the same time, public health agencies, non-governmental organizations, academic institutions researchers, students can use these data to timely track and study the mutation sequence of the epidemic in each region and state in Nigeria. The data visualization through power bi present a dashboard with daily updates, scientific reports showing and hyper-linking cases of the epidemic highlighting death by states, confirmed cases by top states, new deaths cases by top states, and new cases by to states respectively.
DOI: 10.17632/887mp9spng
title: Dataset publication

description: This is a dataset.
DOI: 10.17632/dvpfx9p342.1
title: LBsoft: A parallel open-source software for simulation of colloidal systems

description: We present LBsoft, an open-source software developed mainly to simulate the hydro-dynamics of colloidal systems based on the concurrent coupling between lattice Boltzmann methods for the fluid and discrete particle dynamics for the colloids. Such coupling has been developed before, but, to the best of our knowledge, no detailed discussion of the programming issues to be faced in order to attain efficient implementation on parallel architectures, has ever been presented to date. In this paper, we describe in detail the underlying multi-scale models, their coupling procedure, along side with a description of the relevant input variables, to facilitate third-parties usage. The code is designed to exploit parallel computing platforms, taking advantage also of the recent AVX-512 instruction set. We focus on LBsoft structure, functionality, parallel implementation, performance and availability, so as to facilitate the access to this computational tool to the research community in the field. The capabilities of LBsoft are highlighted for a number of prototypical case studies, such as pickering emulsions, bicontinuous systems, as well as an original study of the coarsening process in confined bijels under shear.
DOI: 10.17632/xn73ym9348.1
title: 5α-reductase inhibitor exposure for dermatologic conditions does not increase the risk of female breast or gynecologic cancers: A population-based propensity score-matched cohort study. JAAD. 2024.

description: Supplemental methods and results.
DOI: 10.17632/txhzhxy7th.1
title: Data for: ARTIFICIAL NEURAL NETWORK MODELLING IN THE PREDICTION OF BANANAS' HARVEST
DOI: 10.17632/znzvysxx6f.1
title: Machakos Cancer Registry Data 2015-2019

description: Background: In Kenya cancer is an increasing public health challenge, with an estimated 48,000 new cancer cases and over 33,000 deaths recorded each year. Machakos county, Kenya, recently opened a cancer centre given the increasing burden of cancer care with an aim of bringing cancer services such as surgical oncology, chemotherapy, radiotherapy, and pathological services closer to the residents of Machakos county. The county is also one of four counties selected for the universal health coverage pilot programme making it uniquely poised to inform cancer control programs at the local, national, and international levels. Methods: This study aimed to build a cancer database and comprehensively to enable future population-based cancer studies by reviewing cancer diagnosis records for selected major public hospitals in Machakos county from 2015-2019. Medical records data were retrieved from Machakos Level 5 Hospital and Kangundo, Matuu, Kathiani and Mwala Level 4 Hospitals. Results: A total of 522 cancer cases were recorded across the study period with more than a third (N=172, 33%) diagnosed August-December of 2019 when the Machakos Cancer Centre opened. Among the cancers diagnosed the majority were in women (59.2%) with cervix uteri (n=106, 34.3%) followed by breast (n=62, 20.1%) as the most common cancers. For males, oesophagus (n=52, 24.4%) followed by prostate (n=43, 20.2%) were the most common cancer types. Conclusion: It is clear that access to cancer care treatment will change referral patterns for residents in Machakos county and with the establishment of this database we expect to enable future population-based surveillance of the cancer burden to inform cancer control programs.
DOI: 10.17632/438ytjyzxk.3
title: Dataset of German FinTech companies: A market overview

description: This dataset comprises a hand collected market overview of the FinTech market in Germany as of December 2021. It includes various verified properties of 978 unique firms, which can be attributed to the financial technology sector and are operating in Germany. Each observation represents one company with 24 variables, including name, address, legal form, founders with corresponding LinkedIn accounts, register number or company-ID, attribution to FinTech segments and subsegments, bank cooperation, URL address, local court, former name, operating status. The dataset contains established companies as well as start-ups. Since the market in Germany and the nature of FinTech companies itself are dynamic as well as changing there is no complete overview of the market. Furthermore, the total number, the operating status as well as specific properties of FinTechs cannot be found in one accumulated data base. The dataset contains valuable information for researchers, practitioners as well as for supervising authorities. We provide the description of variables as well as a taxonomy for categorizing FinTechs. The nature of the dataset enables further cross-sectional and the possibility of longitudinal analyses of the complete market. The aim of the collection procedure was to find and identify all relevant FinTechs operating in Germany with a structured approach. Different databases and websites (see below) were used to obtain an overview of the market. The dataset was repeatedly updated and verified throughout the years within this process. An association to the segment of operations was conducted. Through structured Google searches the operating status was checked.    

The corresponding paper with a detailed description of the variables and volume estimates can be downloaded here:  
https://elibrary.duncker-humblot.com/article/72485/german-fintech-companies-a-market-overview-and-volume-estimates
DOI: 10.17632/s27zfxgbpj.1
title: Study dataset: Real-time ultrasound demonstration of uterine isthmus contractions during pregnancy

description: This dataset includes transvaginal ultrasound recordings of the uterine cervix in the form of extended video clips (over 15 minutes) from 30 individuals in their second trimester of pregnancy. Each individual also has a fast-motion video clip (at 40x speed) available. The accompanying .csv file provides demographic information about the cohort. 
The full analysis of this dataset is published in the American Journal of Obstetrics and Gynecology as an original research paper: https://doi.org/10.1016/j.ajog.2023.07.025

The .csv file consists of the following columns: 
- Individual (number) 
- SG_eco: completed weeks of pregnancy 
- Dies_eco: completed days of the current week of pregnancy 
- Paritat: number of previous pregnancies ≥23.0wk 
- Cesaries: number of previous cesarean sections 
- Edat: maternal age at the time of ultrasound examination (in years) 
- Pes: maternal weight at the start of the pregnancy (in kg) 
- Talla: maternal height (in cm) 
- Contraccio: 0=no, 1=yes (presence of isthmic contraction during the examination, subjectively assessed) 
- Orina: volume of urine collected before the ultrasound examination (in ml)
DOI: 10.17632/gszt9767t6.1
title: Kosumphisai lipids

description: a data set of laboratory testing on human lipid profile tests with date indicated
DOI: 10.17632/pwjh8gpz93.1
title: Dataset on the deposits of the semi-circular rampart around the former Viking settlement Hedeby and its vicinity

description: Dataset on the physicochemical analysis and morphological description of the material of the semi-circular rampart around the former Viking settlement Hedeby and its vicinity. The drilling cores were taken along a coring transect across the semi-circular rampart. The material of the cores was sampled. The following properties were determined: pH, weight percentage of gravel, charcoal, artefacts, and bones, loss on ignition, magnetic susceptibility, grain size distribution ≤2 mm, elemental concentrations.
DOI: 10.17632/2wnh3kxt4r.1
title: Data for: Geotechnical and Geomorphological Investigation of Rainfall Induced Shallow Landslide at Okeigbo, Ondo State, southwestern Nigeria

description: Rainfall and spot height data in .DAT format. Stress diagrams in .eps format
DOI: 10.17632/j55fyhtxn4.2
title: PlasmoCount: Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

description: PlasmoCount is an online tool for automated detection of infected red blood cells by Plasmodium (malaria) parasites. The tool was built using a machine learning approach (convolutional neural networks) developed from training data sets of parasite infected smears (Giemsa-smears) and then subsequently tested on blind data sets to validate accuracy and reproducibility. The data sets behind the work (first described in the preprint of our paper on MedRxiv - https://doi.org/10.1101/2021.01.26.21250284) are supplied here to enable others to build improved models or alternative strategies to automated cell detection. More information about the data sets or model can be gained from contacting the Baum laboratory.
DOI: 10.17632/fzp4gk22gr.1
title: Data for: Laboratory Hydraulic Fracturing of Granite: Acoustic Emission Observations and Interpretation

description: Processed AE data from set of hydraulic fracture experiments on Barre granite. Raw waveforms are excluded for ease of upload/download.
DOI: 10.17632/2dsgypk45h.1
title: Millennial-timescale quantitative estimates of climate dynamics in central Europe from earthworm calcite granules in loess deposits

description: This database contains Supplementary Data 1 to 3 and the description published in the following article Millennial-timescale quantitative estimates of climate dynamics in central Europe from earthworm calcite granules in loess deposits by Prud'homme et al., in Communications in Earth and Environment (2022)
DOI: 10.17632/c7nh26z8p3.1
title: Data from a performance comparison of four commercially available flow cytometers using polystyrene beads

description: Accurate comparison of flow cytometric data requires an understanding of how the cytometric fingerprint of a sample may vary from instrument to instrument. Key sources of variability include the number, wavelengths, and power of excitation lasers; the number and types of emission detectors; sample-handling systems and options; and whether fixed or dynamic detector voltages are used. To explore this variability, identical suspensions of three sizes (0.2, 0.5, and 0.8 m-diameter) of solid, fluorescent, polystyrene beads were prepared. The suspensions were then run at on four commercially available flow cytometers, keeping instrument settings as consistent as possible. The results are displayed graphically in Figure 3 of the article “Flow cytometry applications in water treatment, distribution, and reuse: A review” (DOI: 10.1016/j.watres.2018.12.016). This dataset contains the complete .FCS files generated from the experimental comparison.
DOI: 10.17632/x3wxfycggn.1
title: Data Set -assessing the validity of self-reported social media use

description: Aim: Examine the degree of association between single self-estimate social media use, the problematic use of social networking scale and objective mobile social media use in a sample of both Android and iPhone users. 

Variable Description
Variable Name (w) = variable winzorised
Gender (Female = 1, Male = 2, not specified = 3)
Android v iPhone (iPhone user = 1, Android user = 2)
SR_SM_use_minutesweek = self-report single-estimate of weekly mobile social media use ("On average, how many hours a week do you think you spend viewing social media on your phone?")
PUSNS_# = problematic use of social networking scale item #
PUSNS = problematic use of social networking scale total
TST = total mobile screen time over last 7 days
Facebook = total mobile Facebook use over last 7 days
Instagram = total mobile Instagram use over last 7 days
Snapchat = total mobile Snapchat use over last 7 days
Twitter = total mobile Twitter use over last 7 days
TikTok = total mobile TikTok use over last 7 days
SMU = objective mobile social media use (Facebook, Instagram, Snapchat, Twitter, TikTok) over last 7 days
PickUps = total smartphone pick-ups over last 7 days