cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/zyhy8z69kt.1
title: Experimental Babesia rossi infection data

description: This includes supplementary data with the statistical analysis as well as the original data for independent assessment.
DOI: 10.17632/vpbsd5bryy.1
title: Boxing Data

description: This data set comes from purse information provided by the Nevada State Athletic Commission (NSAC) and the California State Athletic Commission (CSAC). The NSAC provided purse information on professional boxing matches in that state from 2009 to 2017. The CSAC provided purse information for the period June 2012 to December 2015 and its website provided purse information for 12 additional boxing cards held between 2016 and 2017, which are also included in the data set. After excluding female boxers and a few other minor exclusions, the result is an unbalanced panel of 1,212 boxers observed an average of 3.85 fights.
DOI: 10.17632/9pvpxyfdrv.1
title: Messaging organizational change: how regulatory fit relates to openness to change through fairness perceptions. STUDY 2

description: [THIS IS DATA FOR STUDY 2] Regulatory fit theory predicts that matching a message to individuals’ motivational orientation feels right, thereby enhancing fairness perceptions. In two studies we tested whether, through fairness perceptions, regulatory fit relates to greater openness to change, especially when that change has negative consequences for its recipients. Study 1 was a field experiment conducted among primary and middle school teachers who, respectively, expected positive versus negative outcomes from an actual major education change in Poland. We manipulated message framing, describing the prospected change in a promotion-related or prevention-related way. We measured processing fluency, engagement, process fairness, and change openness. Study 2 was a vignette experiment where college graduates assessed their openness to a change in university policy regarding credits for research participation. We randomized change framing (promotion vs. prevention) and outcome (positive vs. negative) between the participants. Supporting the predictions, the decision process regarding the change was perceived as more fair when the message framing matched recipients’ regulatory focus. In line with our expectations, perceived fairness was linked with increased openness toward the change especially among individuals who could be hurt by the changes. In both studies, the fairness mechanism was crucial under these circumstances as compared to the competing mechanism of engagement (and fluency in Study 1).
DOI: 10.17632/btf2mz4fhf.3
title: Data for: PAPILA dataset: a regional emission inventory of reactive gases for South America based on the combination of local and global information

description: PAPILA dataset is a collection of annual emission inventories of reactive gases (CO, NOx, NMVOCs, NH3 and SO2) from anthropogenic sources in South America, for the period 2014–2016. The dataset was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4.1 (developed by joining CEDS trends and EDGARv4.3.2 historical data), enriching it with derived data from locally available emission inventories for Argentina, Chile and Colombia. The inventories are presented as NetCDF4 files, one for each species and year, gridded with a spatial resolution of 0.1° x 0.1° covering the domain 32° W–120° W and 34° N–58° S. Each file contains 12 variables corresponding to the emissions in Tg/y from the following categories, which are organized and denominated using the nomenclature given by CAMS: thermal power plants (ENE); residential and commercial combustion (RES); road transportation (TRO); non-road transportation (TNR); fugitive emissions (FEF); industries (including fuel consumption in manufacturing industries and construction, refineries, industrial processes and solvent and other products use) (IND); agricultural soils (AGS); agriculture livestock (AGL); navigation (SHP); waste (including solid waste, wastewater and incineration) (SWD); and the sum of all sectors (SUM).
DOI: 10.17632/9nhjb5gbhn.1
title: Data for: Generalized additive modeling to analyze dynamic phonetic data: a tutorial focusing on articulatory differences between L1 and L2 speakers of English

description: This zip file contains both the data and the analysis discussed in the linked paper "Analyzing dynamic phonetic data using generalized additive mixed modeling: a tutorial focusing on articulatory differences between L1 and L2 speakers of English". Note that the most recent version of the analysis can be found at http://www.let.rug.nl/~wieling/Tutorial.
DOI: 10.17632/pbmz29tw47.4
title: Nurses’ Knowledge, Skills and Preparedness for Disaster Management of a Megapolis

description: Dataset of Nurses’ Knowledge, Skills and Preparedness for Disaster Management of a Megapolis (n=410)
DOI: 10.17632/63y2gbj45w.1
title: Biological Interpretation of Metabolic profiling fingerprinting of chemotherapy-resistant prostate cancer stem cells. A metabolomic approach by liquid chromatography–high resolution mass spectrometry

description: Chemoresistance is one of the most important challenges in cancer therapy. The presence of cancer stem cells within the tumor may contribute to chemotherapy resistance since these cells express high levels of extrusion pumps and xenobiotic metabolizing enzymes that inactivate the therapeutic drug. Despite the recent advances in cancer cell metabolism adaptations, little is known about the metabolic adaptations of the cancer stem cells resistant to chemotherapy. In this study, we have undertaken a metabolomic analysis by western blot, quantitative real-time polymerase chain reaction (qPCR), and fatty acid oxidation in a prostate cancer cell line resistant to the antiandrogen 2-hydroxiflutamide with features of cancer stem cells, compared to its parental androgen-sensitive cell line.
DOI: 10.17632/v6p8tgmmbc.1
title: Data for: Depositional setting, taphonomy and geochronology of new fossil sites in the Catskill Formation (Upper Devonian) of northern Pennsylvania, USA, including a new early tetrapod record

description: Supplemental geochronologic data file and methods, fossil images, and lithofacies descriptions.
DOI: 10.17632/76s5pymmp3.1
title: GESCAM : A Dataset and Method on Gaze Estimation for Classroom Attention Measurement

description: This dataset was meticulously generated using 3D modelling, animation, simulation, and rendering techniques comprising 60,000 images with 650,000 instances of individuals (students, teachers) engaged in various activities, including looking at blackboard, notebooks, mobile phones etc. 

For full dataset access, please contact us at the email provided on GESCAM project page.
DOI: 10.17632/ybmxsfdf33
title: Understanding Challenges and AI Adoption in the Journey to Software Development Careers

description: This study explores the challenges faced and the adoption of AI in the transition to software development careers. The aim is to understand the motivations, transition processes, skill development, and experiences of individuals who have transitioned into software development careers through semi-structured interviews.
DOI: 10.17632/f45bkkt8pr.1
title: SMS PHISHING DATASET FOR MACHINE LEARNING AND PATTERN RECOGNITION

description: The dataset is a set of labelled text messages that have been collected for SMS Phishing research. It has 5971 text messages labeled as Legitimate (Ham) or Spam or Smishing. It includes 489 spam messages, 638 smishing messages, and 4844 ham messages. This dataset contains raw message content that can be used as labelled data in Deep Learning or for extracting further attributes. The dataset contains extracted attributes from malicious messages that can be used for Classification of messages as malicious or legitimate. This dataset also includes python code that are used for extracting attributes. The data has been collected by converting the images obtained from the Internet to text using Python code. Attributes have been selected based on their relevance. The details of dataset attributes are given below: 

LABEL- Classification label categorizing the message as ham, spam, or Smishing
TEXT- The raw content of the message. 
URL- Gives out whether the message contains a URL or not.
EMAIL- Gives out whether the message contains an email id or not.
PHONE - Gives out whether the message contains a phone number or not.
Python code for extraction of the above listed dataset attributes is attached. The snapshot of this dataset is also attached. Frequency chart of the attributes are also attached.
DOI: 10.17632/nt46yh6t73.1
title: Nepalese Large Cardamom: Production, Growth, trade and Trend

description: Data related to Nepalese Large Cardamom 1. Production of Nutmeg, mace, and cardamoms in Nepal (Tons) 2. Cardamom Production area of Nepal (Area, Production, and Yield) 3. Large Cardamom Trade (Import, Export and Trade Balance) 4. Major Exporting and Importing Nation 5. Global Market of Large Cardamom 6. Share in the World Import Value of Large Cardamom Cardamom 7. Revealed Comparative Advantage of Top Ten Exporters in the World
DOI: 10.17632/4k8f4t352y.1
title:  Synthetic data for Li-ion Batteries Degradation Maps with degradation example

description: This dataset contains the data associated with the publication "Best Practices for Incremental Capacity Analysis" in Frontiers in Energy Research. It consists of synthetic data for the main Li-ion chemistries for different degradations.

Available chemistries:
GIC/NMC111
GIC/NMC532
GIC/LCO
GIC/NCA
GIC/LMO
GIC/(NMC,LMO)
GIC/LFP
(GIC/Si)/NMC811
LTO/NMC
LTO/LMO
LTO/(NCA/LCO)

Map files contains data for up to 30% of each degradation mode individually (or 500% for kinetic and resistance variations) at C/25 in charge and discharge.
The variable Mapindex summarizes the order for the data in the Params and Voltage variables (Vmc and Vmd, for charge and discharge respectively).
The variable Params summarizes the simulation parameters (column index in Paramindex). Qmc and Qmd contains the capacity values in charge and discharge respectively.

Example files contains data for up to 15% LLI, 10%LAMPE, and 10%LAMNE C/25 simulations in charge and discharge with 3 % increments (for LLI) and 2% increments (for LAMs).
The variable Params summarizes the simulation parameters (column index in Paramindex). Qc and Qd contains the capacity values in charge and discharge respectively and Vc, and Vd the voltage response.

All simulations were performed with the 2022 version of the alawa toolbox using stock electrode data. Voltage and kinetics of electrodes from different manufacturers, with different composition, or with different architecture might differ significantly.
DOI: 10.15468/dl.7tvzp3
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Bangladesh",
 "TaxonKey is Ptyas korros (Schlegel, 1837)"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009344-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/724zxrrsvg.1
title: SPSS sav file
DOI: 10.17632/fxjm8fb6mw.1
title: Knee X-ray Osteoporosis Database

description: Osteoporosis is the leading pathological disorder of bones after arthritis affecting millions of people worldwide. The diagnosis of osteoporosis is done by Dual Energy X-ray Absorptiometry (DXA), although it gives good results but its cost is very high and is not readily available and the treatment cost of osteoporosis is also very high so there is a need for some cost-effective and easily available detection system. The researchers are in continuous search of finding new ways of detecting osteoporosis at early stages with less cost and in less time. The researchers mostly use their build in datasets instead of publically available datasets that make the study limited to particular regions and particular populations. To make a detection system, that is accepted worldwide and used as an alternative pre-screening tool there is a need to make the research data-centric which implies researchers from different areas of research and different countries should work on some particular datasets and find a common solution. Inspired by this scenario, we have presented the database for the detection of osteoporosis in this article. The database consists of clinical factors like age, gender, menopause age, other diseases like diabetes, thryroidism, fracture history, lifestyle factors, etc responsible for osteoporosis with the T-score values obtained from the Quantitative Ultrasound System and Knee X-ray for each participant. The main objective of this dataset is to make the research of finding the cost-effective, timely, and accurate osteoporotic detection system data-centric.
DOI: 10.17632/b3yjvcn7k2.1
title: The First Complete Mitochondrial Genome Data of Hippocampus kuda Originating from Malaysia

description: Mitogenome sequences assembled using raw data for two Malaysian Hippocampus kuda (HK1 and HK2). Genomic DNA was extracted using Qiagen Blood and Tissue Kit (Qiagen, Valencia, CA) following the manufacturer’s instructions. All libraries were prepared using BEST protocol as described in Mak et al., (2017) and were sequenced on an BGISEQ-500 platform (100PE). Raw sequence reads were assembled into individual mitogenome sequence using the MITOBIM v1.8 (reference genome assembly) pipelines. Mitogenome mapping quality was assessed using PALEOMIX and further annotated using MitoAnnotator and GB2sequin annotation web application. The mitogenome data is available in Genbank with the accession numbers MT221436 and MT221436.
DOI: 10.17632/pysxmjpkr4.1
title: Survey data for COVID-19 vaccine preference analysis in the United Arab Emirates

description: Survey data collected between July 4 to August 4, 2020; and describing individuals preferences for COVID-19 vaccine in the United Arab Emirate (UAE). The data contains 1109 Observations, on 50 variables, and covers all 7 Emirates of the UAE. Further details are available upon request from the corresponding authors at : https://business.aau.ac.ae/en/business/academic-staff/staff/view/321/
DOI: 10.17632/9fckvw84z8.1
title: Natural Data-augmentation for Skin Lesions (ISIC-2017 Challenge Dataset)

description: Data augmentation techniques may mitigate many limitations of datasets, such as imbalanced data among the classes of skin lesions and heterogeneous sources of data, by adding augmented samples with different image transformations, such as rotation, random crop, horizontal and vertical flip, translation, shear, color jitter, and colorspace. It is proven in many studies that data augmentation improved the diagnosis of skin cancer. In the HAM10000 dataset, the skin lesion images were captured at different magnifications or angles or with different cameras, a process known as natural data augmentation. We used a deep learning architecture called Faster R-CNN to develop the algorithm to generate augmented copies similar to the natural data-augmentation for ISIC-2017 challenge dataset.
DOI: 10.17632/c9bbgdhd7v.1
title: Cultivate intercultural sensitivity to motivate U.S. foreign language learners: A quasi-experimental study

description: Through a four-week experimental intervention based on the intergroup contact theory, this study sought to (a) explore its effects on U.S. foreign language learners’ Intercultural Sensitivity; and (b) investigate the interactions between Intercultural Sensitivity, Foreign Language Motivation, and Language and Cultural Backgrounds. Three findings were noted: (a) Intercultural Sensitivity significantly related to Foreign Language Motivation, after controlling for learners’ Language and Cultural Background factors; (b) the four-week intervention failed to produce statistically significant impact as intended; and (c) Parental Encouragement, Exposure to the Target Language Culture, and Chinese Learning History significantly related to learners’ responsiveness towards innovative culture learning.
DOI: 10.17632/s4g2y7c7c7.2
title: Legacy and Emerging Plasticizers and Stabilizers in PVC Floorings - Rawdata

description: This dataset contains data acquisition methods, measured raw data, and processing scripts for the study "Legacy and emerging plasticizers and stabilizers in PVC floorings and implications for recycling". This study is published in ES&T (DOI: 10.1021/acs.est.3c04851).

Specifically these data are given:
• Supporting Information File 4 (SI4): Zip file containing Agilent method files for the GC-MS analyses.
• Supporting Information File 5 (SI5): Zip file containing python scripts for data analysis
• Supporting Information File 6 (SI6): Zip file containing the raw data from all conducted ATR FTIR measurements, as csv, spa, spc and dx files for each spectrum
• Supporting Information File 7 (SI7): Zip file containing the raw data from all GC-MS or-tho-phthalate quantification runs, as Agilent files for each run and Excel files from Mas-shunter quantification
• Supporting Information File 8 (SI8): Zip file containing the raw data from all GC-MS suspect screening runs, as Agilent files for each run and Excel files from Masshunter identification, Python semi-quantification and substance table
DOI: 10.15468/dl.hbw3n9
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((92.24557 12.82190,112.75443 12.82190,112.75443 27.17810,92.24557 27.17810,92.24557 12.82190))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Melocalamus Benth."
 ]
}

 The dataset includes 36 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009345-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2dc2dhcxyr.1
title: Master Thesis - XU TING 2021

description: for my master thesis
DOI: 10.17632/98mzk82wbb.1
title: Indian Sign Language Video and Text dataset for sentences (ISLVT)

description: This video and gloss-based dataset has been meticulously crafted to enhance the precision and resilience of ISL (Indian Sign Language) gesture recognition and generation systems. Our goal in sharing this dataset is to contribute to the research community, providing a valuable resource for fellow researchers to explore and innovate in the realm of sign language recognition and generation.Overview of the Dataset: Comprising a diverse array of ISL gesture videos and gloss datasets. The term "gloss" in this context often refers to a written or spoken description of the meaning of a sign, allowing for the representation of sign language in a written form. The dataset includes information about the corresponding spoken or written language and the gloss for each sign. Key components of a sign language gloss dataset include ISL grammar that follows a layered approach, incorporating specific spatial indices for tense and a lexicon with compounds. It follows a unique word order based on noun, verb, object, adjective, or part of a question. Marathi sign language follows the subject-object-verb (SOV) form, facilitating comprehension and adaptation to regional languages. This Marathi sign language gloss aims to become a medium for everyday communication among deaf individuals. This dataset reflects a careful curation process, simulating real-world scenarios. The original videos showcase a variety of gestures performed by a professional signer capturing a broad spectrum of sign language expressions. Incorporating Realism with green screen with controlled lighting conditions. All videos within this dataset adhere to pixels, ensuring uniformity for data presentation and facilitating streamlined pre-processing and model development stored in a format compatible with various machine and Deep learning frameworks, these videos seamlessly integrate into the research pipeline
DOI: 10.17632/6574nhzm8x.1
title: FabricSpotDefect: An Annotated Dataset for Identifying Spot Defects in Different Fabric Types

description: Content: This FabricSpotDefect dataset is designed to improve fabric spot defect detection using computer vision. It includes various types of fabrics such as cotton, linen, silk, denim, and both plain and patterned textiles, featuring spot defects like stains, discolorations, oil marks, and more.

Format: The images are in 2D RGB .jpg format of varying sizes.

Original Images: 1,014 raw images with 3,286 spot defect labels.

Augmented Images: After applying six types of augmentation techniques, the dataset expands to 2,300 images with 7,641 labeled spots. 

Data Features: The dataset features both original and augmented images organized into “original” and “augmented” folders, each containing subfolders for training, validation, and testing. All images are resized to 416×416 pixels and undergo six augmentation techniques: flipping, rotation, shearing, saturation, brightness, and noise addition.

How data are acquired: The FabricSpotDefect dataset images were collected from everyday home fabrics under natural lighting using three smartphones( Samsung Galaxy Note20, Samsung Galaxy S20 FE, and Samsung Galaxy A53 5G).

Use Case: To develop an AI model that will identify spot defects in fabrics, so that the model could be used to improve the quality control process in textile production.
DOI: 10.17632/ny4y6mctbm.1
title: Probabilities of Low and medium level activities and Locations based on "OPPORTUNITY Activity Recognition Dataset

description: The OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors (hereafter OPPORTUNITY dataset) is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc). Current dataset contains probabilities of subjects' Location and compass, doors states, gestures, gesture, low-level activities for both hands including hand movements, and interactions with specific objects. Thre of most probable candidates are reported as A,B and C. For locations they are calculated for all instances with no label. For other activities, we used activities 1, 2, 3 and 9 (drill) as training and probabilities are calculated for activities 4 and 5. For original signals received from sensors, download the OPPORTUNITY dataset. CITATION ----------- Use of this dataset in publications must be acknowledged by referencing BOTH of the following publication [1] and [2]. [1] Pouya Foudeh, A Khorshidtalab, and Naomie Salim. "A Probabilistic Data-Driven Method for Human Activity Recognition", Journal of Ambient Intelligence and Smart Environments, 2018 [2] Ricardo Chavarriaga, Hesam Sagha, Alberto Calatroni, Sundaratejaswi Digumarti, Gerhard Tröster, José delR. Millán, Daniel Roggen. "The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition", Pattern Recognition Letters, 2013 Legend ------ ""Couloms"": id Instant serial number subject A two digit number. Left digit number of subject (1-4) Right digit number of activity (1-5) time Time in seconds Angle Electric compass Doors State: _w Washing machine _f Fridge _1 Upper drawer (1) _2 Middle drawer (2) _3 Lower drawer (3) Locations, Postures, Gestures, Right and Left hands Interactions and Movements: (Note: probable choices are only available in test sets, activity 4 and 5 of each subject) ..A Square number of A, the most probable label. ..B Square number of B, 2nd most probable label. ..C Square number of C, 3rd most probable label. ..Ap Probability of A ..Bp Probability of B ..Cp Probability of C .._labl Manually Generated Label
DOI: 10.17632/htpcv69w9z.1
title: Microsatellite data of Acer caudatifolium

description: Diploid genotype from 16 microsatellite loci of Taiwanese snakebark maple (Acer caudatifolium) in GenAlEX format. 
Missing data was labelled as -9.