cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/mw8yd7z9wc.2
title: The Climate Change Twitter Dataset

description: If you use the dataset, cite the paper: https://doi.org/10.1016/j.eswa.2022.117541

The most comprehensive dataset to date regarding climate change and human opinions via Twitter. It has the heftiest temporal coverage, spanning over 13 years, includes over 15 million tweets spatially distributed across the world, and provides the geolocation of most tweets. Seven dimensions of information are tied to each tweet, namely geolocation, user gender, climate change stance and sentiment, aggressiveness, deviations from historic temperature, and topic modeling, while accompanied by environmental disaster events information. These dimensions were produced by testing and evaluating a plethora of state-of-the-art machine learning algorithms and methods, both supervised and unsupervised, including BERT, RNN, LSTM, CNN, SVM, Naive Bayes, VADER, Textblob, Flair, and LDA.

The following columns are in the dataset:

➡ created_at: The timestamp of the tweet.
➡ id: The unique id of the tweet.
➡ lng: The longitude the tweet was written.
➡ lat: The latitude the tweet was written.
➡ topic: Categorization of the tweet in one of ten topics namely, seriousness of gas emissions, importance of human intervention, global stance, significance of pollution awareness events, weather extremes, impact of resource overconsumption, Donald Trump versus science, ideological positions on global warming, politics, and undefined.
➡ sentiment: A score on a continuous scale. This scale ranges from -1 to 1 with values closer to 1 being translated to positive sentiment, values closer to -1 representing a negative sentiment while values close to 0 depicting no sentiment or being neutral.
➡ stance: That is if the tweet supports the belief of man-made climate change (believer), if the tweet does not believe in man-made climate change (denier), and if the tweet neither supports nor refuses the belief of man-made climate change (neutral).
➡ gender: Whether the user that made the tweet is male, female, or undefined.
➡ temperature_avg: The temperature deviation in Celsius and relative to the January 1951-December 1980 average at the time and place the tweet was written.
➡ aggressiveness: That is if the tweet contains aggressive language or not.

Since Twitter forbids making public the text of the tweets, in order to retrieve it you need to do a process called hydrating. Tools such as Twarc or Hydrator can be used to hydrate tweets.
DOI: 10.17632/446sbvc3mj.1
title: Data for: Anxiety or Pain? The Impact of Tariffs and Uncertainty on Chinese Firms in the Trade War

description: Please refer to the readme file.
DOI: 10.17632/dv8xt3t3b3.3
title: Timed-Up and Go Data retrieved from Centre of Portugal

description: Data collected with a Smartphone equipped with accelerometer and magnetometer sensors, and a BITalino device with ECG and EEG sensors.
DOI: 10.17632/cjcg7gwt52.1
title: Data for: Identification of changes in volatile compounds in dry-cured fish during storage using HS-GC-IMS

description: Raw dataset of volatile flavor compounds in dry-cured fish identified by HS-GC-IMS during storage (peak position and signal intensity of each substance, sample similarity, relative peak height, and fingerprint). In addition, the number "1" represents the 4 ℃ experimental group, and the number "2" represents the 25 ℃ experimental group.
DOI: 10.17632/n666tk4mw9.1
title: Multimodal Tactile Texture Dataset

description: The objective of this dataset is to provide a comprehensive collection of data that explores the recognition of tactile textures in dynamic exploration scenarios. The dataset was generated using a tactile-enabled finger with a multi-modal tactile sensing module. By incorporating data from pressure, gravity, angular rate, and magnetic field sensors, the dataset aims to facilitate research on machine learning methods for texture classification.

The data is stored in pickle files, which can be read using Panda’s library in Python. The data files are organized in a specific folder structure and contain multiple readings for each texture and exploratory velocity. The dataset contains raw data of the recorded tactile measurements for 12 different textures and 3 different exploratory velocities stored in pickle files.

Pickles_30 - Folder containing pickle files with tactile data at an exploratory velocity of 30 mm/s.
Pickles_40 - Folder containing pickle files with tactile data at an exploratory velocity of 40 mm/s.
Pickles_45 - Folder containing pickle files with tactile data at an exploratory velocity of 45 mm/s.
Texture_01 to Texture_12 - Folders containing pickle files for each texture, labelled as texture_01, texture_02, and so on.
Full_baro - Folder containing pickle files with barometer data for each texture.
Full_imu - Folder containing pickle files with IMU (Inertial Measurement Unit) data for each texture.

The "reading-pickle-file.ipynb" file is a script for reading and plotting the dataset.
DOI: 10.15468/dl.733qca
title: Occurrence Download

description: A dataset containing 674 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus thunbergii Parl.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 674 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009259-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/syft7bhs5n.2
title: Effectiveness of azelaic and pyruvic acids in the treatment of acne vulgaris

description: The present study aims to compare azelaic and pyruvic acid peels for their efficacy in the treatment of acne vulgaris in 120 young women (mean age 22 years old).
DOI: 10.17632/nhs6mjg6yy.1
title: LOCBEEF:  Beef Quality Image dataset for Deep Learning Models

description: The LOCBEEF dataset contains 3268 images of local Aceh beef collected from 07:00 a.m - 22:00 p.m, more information about the clock is shown in Fig. The dataset contains two categories of directories, namely train, and test. Furthermore, each subdirectory consists of fresh and rotten. An example of the image can be seen in Figs. 2 and 3. The directory structure for the data is shown in Fig. 1. The image directory for train contains 2228 images each subdirectory contains 1114 images, and the test directory contains 980 images for each subdirectory containing 490 images. For images have a resolution of 176 x 144 pixel, 320 x 240 pixel, 640 x 480 pixel, 720 x 480 pixel, 720 x 720 pixel, 1280 x 720 pixel, 1920 x 1080 pixel, 2560 x 1920 pixel, 3120 x 3120 pixel, 3264 x 2248 pixel, and  4160 x 3120 pixel.

The classification of LOCBEEF datasets has been carried out using the deep learning method of Convolutional Neural Networks with an image composition of 70% training data and 30% test data. Images with the mentioned dimensions are included in the LOCBEEF dataset to apply to the Resnet50.
DOI: 10.17632/cb8pp65p5n.1
title: LC-MS data set Ceropia species Panama

description: Crude LC-MS data from Thermo Scientific. We also added the .csv file for the Metaboanalyst workflow.
DOI: 10.17632/y6g95pcgr6.1
title: Data for research about challenges faced by school leaders and their impacts on school culture, teacher performance, and student outcomes

description: This Excel file contains 99 peer-reviewed journal articles sourced from the Education Resources Information Center (ERIC) database, focusing on studies published within the last five years. The collection includes research from various countries, providing insights from a wide range of global communities.
DOI: 10.17632/pxyzbg2h9k.2
title: Data for: Chlorhexidine-impregnated gel dressing compared to transparent polyurethane dressing in the prevention of catheter-related infections in critically ill adult patients: a randomized controlled trial

description: Data Base
DOI: 10.17632/6zwcgrttkp.1
title: Data from: Over eight hundred cannabis strains characterized by the relationship between their subjective effects, perceptual profiles, and chemical compositions

description: Commercial cannabis strains have multiplied in recent years as a consequence of regional changes in legislation for medicinal and recreational use. The lack of a standardized system to label plants and seeds hinders the consistent identification of particular strains with their elicited psychoactive effects. We analyzed a large publicly available dataset where users freely reported their experience with different strains, including subjective effects and flavour associations. Metrics of strain similarity based on self- reported effects and flavour tags allowed machine learning classification into three major clusters associated with species (Cannabis sativa, Cannabis indica, and hybrids). Synergy between terpene and cannabinoid content was suggested by significative correlations between psychoactive effects and flavour tags. The use of predefined tags was validated by the application of Latent Semantic Analysis (LSA) to unstructured written reviews, also providing breed-specific topics consistent with their purported medicinal and subjective effects. While cannabinoid content was variable even within individual strains, terpene profiles matched the perceptual characterizations made by the users and could be used to predict psychoactive effects. Our work represents the first data-driven synthesis of self-reported and chemical information on a large number of cannabis strains. Since terpene content is robustly inherited and less influenced by environmental factors, flavour perception could represent a reliable marker for the prediction of psychoactive effects of cannabis. Our novel methodology contributes towards the demands for reliable strain classification and characterization in the context of an ever-growing market for medicinal and recreational cannabis.
DOI: 10.17632/y5gfmbk5p6.2
title: Number of Monthly Tourist Arrivals - ASEAN-5

description: Monthly (Jan. 2000 - Dec. 2019) Data for the number of tourist arrivals to Indonesia, Malaysia, Philippines, Singapore and Thailand (ASEAN-5).
Data sources are: Statistics, Indonesia; Tourism Malaysia; Department of Tourism; Philippines; Singapore Tourism Board; Office of Tourism development, Thailand. Data is available at  https://sites.google.com/site/fumitakafuruokaswebpage/data-and-oxgauss-codes-iii/paper-41
DOI: 10.17632/mpd6m6jp4f.1
title: Dataset exploring customer purchase intention and online buying behaviour through theory of planned behaviour

description: The aim of this research is to explore the theory of planned behaviour about consumer behaviour and purchase decision from the social psychology perspective.  We understand the customer purchase intention to use online channels and, in this way, determine the precursors of online customer purchase intention in a spiritual city and a handloom hub city. we understand the role of personalized ads, views about shopping website brands and TPB. 
Hypotheses:
H1: Customer purchase intention positively relates to overall online buying behaviour.
H1a: Age moderates the effect of customer purchase intention on online buying behaviour.
H2: Personalized advertisements positively influence customer attitudes towards shopping website brands.
H2a: Age moderates the effect of personalized advertisements on customer attitudes towards shopping website brands.
H3: Personalized advertisements significantly affect customer purchase intention.
H3a: Age moderates the effect of personalized advertisements on customer purchase intention.
H4: Attitude towards shopping website brands positively affects customer purchase intention.
H4a: Age moderates the effect of attitude towards shopping website brands on customer purchase intention.
H5:  Subjective norms affect customer purchase intention.
H5a: Age moderates the effect of subjective norms on customer purchase intention.
H6: PBC affects customer purchase intention. 
H6a: Age moderates the effect of PBC on customer purchase intention.
DOI: 10.17632/2j77n3ynnc.1
title: Data for: A rapid LC-MS/MS method for lutein quantification in spinach (Spinacia oleracea)

description: Supplementary material for the Microchemical Journal, title: A rapid LC-MS/MS method for lutein quantification in spinach (Spinacia oleracea) Masashi Miura, Miho Sakai, Mamiko Nogami, Miwa Sato, Takeshi Yatsushiro Appendix A, which includes Fig. A.1 and A.2 and Tables A.1 to A.5.
DOI: 10.17632/87s2br3ptb.2
title: TASD-Dataset: Text-based Early Autism Spectrum Disorder Detection Dataset for Toddlers

description: The TASD-Dataset is tailored for early ASD detection in toddlers, providing detailed textual sequences that describe the daily situations of toddlers both with and without ASD. It incorporates essential ASD assessment features such as Attention Response, Word Repetition, Emotional Empathy, and introduces new features including Noise Sensitivity, Sharing Interest, Sign Communication, and Tiptoe Flapping. Each feature is intricately linked with specific toddler behaviors, offering nuanced insights for parents and aiding in the identification of ASD-related signals. By emphasizing these features, the dataset facilitates the development of machine learning models to explore behavioral markers crucial for early ASD risk detection. 

Here's a summary of the key features:

- Attention Response: Reflects how toddlers respond to external stimuli, indicating differences in sensory processing or responsiveness.

- Change Reaction: Assesses how toddlers adapt to changes, potentially indicating challenges in flexibility or coping mechanisms.

- Word Repetition: Involves repetitive use of words or phrases, often seen in children with ASD as echolalia or repetitive speech.

- Eye Contact: Indicates the frequency and quality of a child's eye contact during social interactions, impacting social communication abilities.

- Emotional Empathy: Measures a child's ability to comprehend and respond to others' emotions.

- Finger Movements: Observes repetitive finger movements or hand gestures, often indicative of self-stimulatory behaviors.

- Focused Attention: Evaluates a child's capacity to maintain concentration on a task, potentially indicating attention deficits.

- Follow Pointing: Assesses a child's ability to follow pointing gestures, a skill crucial for social communication.

- Repetitive Behavior: Encompasses persistent and ritualistic actions or interests commonly seen in ASD.

- Toy Arranging: Examines a child's inclination or patterns in arranging toys, reflecting preferences or behaviors seen in ASD.

- Noise Sensitivity: Gauges a toddler's sensitivity to auditory stimuli, including reactions to loud noises or certain frequencies.

- Sharing Interest: Assesses a child's ability to engage in joint attention and reciprocal interactions with others.

- Sign Communication: Evaluates the use of gestures or signs as a means of communication, crucial for early communication skills.

- Tiptoe Flapping: Observes repetitive behaviors such as tiptoe walking combined with hand flapping, potentially associated with sensory-seeking behaviors seen in ASD.

The dataset for Early ASD Detection in Toddlers offers valuable insights into behavioral features associated with ASD, providing researchers and practitioners with a rich resource for advancing early intervention strategies and improving outcomes for children with ASD.
DOI: 10.17632/7s4bsypk5n.1
title: Data for: Understanding the mechanisms through which adverse childhood experiences affect lifetime economic outcomes

description: This file contains three files: Data extracted from the National Child Development Study: - ncds-main.dta (Data to be opened in Stata 15 or above) - NCDS_pay.dta (Data to be opened in Stata 15 or above) Do files: Do-file Schurer et al.do (to be opened in Stata, any release). User needs to change paths to where data is saved.
DOI: 10.17632/v7skdj677z.1
title: Calamansi Juice with Camote Leaf Extract: A Product Development

description: Calamansi is an abundant naturally-grown citrus tree and is one of the sources of staple fruit juice in the Philippines. Hence, this research study aimed to develop an acceptable mocktail drink that utilized the extract of Calamansi Juice with Camote Leaves. The conceptual framework utilized was the Product Development framework, which is a systematic, commercially oriented research to examine what constitutes a new developed product that satisfies every consumer. Factors considered in the product testing included the appearance, taste, color, consistency, aroma and general acceptability that distinguished food product development which are the ethical considerations of producing large volume of safe and healthy food product for human consumption. In line with this, a product testing with one product sample was conducted and participated by the three hundred fifty four (354) student respondents, eighty four (84) teacher respondents, and ten (10) entrepreneur respondents. An evaluation sheet using the modified nine-point Hedonic scale was devised for the conduct of product testing that established the overall acceptability of the product from the respondents.
DOI: 10.17632/48ygt7bx7f.2
title: Pollen Surface Samples Morocco

description: Pollen surface sample assemblage data for sites in the Middle Atlas, and High Atlas, Morocco. Data is provided as the pollen percentage for each vegetation type identified in each surface sample. High quality version of the pollen diagram as .pdf Associated paper is published as: "Modern surface pollen assemblages from the Middle and High Atlas, Morocco: insights into pollen representation and transport", and is available here: http://dx.doi.org/10.1080/00173134.2015.1108996
DOI: 10.17632/jmy6pt5zhj.1
title: Researcher mobility - customizable research tool

description: A set of four nested PySpark notebooks on Elsevier’s cloud-based computational platform powered by Databricks that constitute a customisable research tool for studying researcher mobility.
DOI: 10.17632/bfyrwb4h7v.1
title: Data for: Stock Liquidity and Issuing Activity

description: SAS codes
DOI: 10.17632/p3v94dft2y.3
title: Ransomware PE Header Feature Dataset

description: This dataset contains headers of 2157 binary executable samples comprising 1134 legitimate software (goodware) and 1023 ransomware, grouped into 25 ransomware families. The dataset was retrieved by extracting raw information of the PE header (first 1024 bytes).

The CSV file columns are sample ID, filename, target class (GR), family ID, and numerical columns from 0 to 1023, as follows:
 | ID | filename | GR | family | 0 - 1023 |
Goodware | 10000 to 11133 | Their name.exe | 0 | 0 | Numerical features ranging from 0 to 255 |
Ransomware | 20000 to 21022 | Their SHA-256 hash | 1 | 25 family IDs | Numerical features ranging from 0 to 255 |

Family IDs:
Avaddon 1
Babuk 2
Blackmatter 3
Conti 4
Darkside 5
Dharma 6
Doppelpaymer 7
Exorcist 8
Gandcrab 9
Lockbit 10
Makop 11
Maze 12
Mountlocker 13
Nefilim 14
Netwalker 15
Phobos 16
Pysa 17
Ragnarok 18
RansomeXX 19
Revil 20
Ryuk 21
Stop 22
Thanos 23
Wastedlocker 24
Zeppelin 25

A complete description of this dataset can be found in the paper "Moreira, C.C., Moreira, D.C., Sales Jr., C. de S. de:
Improving Ransomware Detection based on Portable Executable Header using Xception Convolutional Neural Network. Computers & Security, 103265 (2023), DOI: https://doi.org/10.1016/j.cose.2023.103265, URL: https://www.sciencedirect.com/science/article/pii/S016740482300175X "

Please, reference our work when using this dataset.
DOI: 10.17632/xhr8tgj95g.2
title: Green Manufacturing Research Corpus

description: The "Green Manufacturing Research Corpus" is a meticulously curated dataset that comprises a comprehensive collection of documents dedicated to the domain of green manufacturing. This dataset was meticulously assembled through an extensive search, with a primary focus on documents delving into the intersection of green, sustainable, eco-friendly, and low-carbon practices within the manufacturing sector. Our search strategy involved utilizing a combination of keywords such as green, sustainable, environment, eco-friendly, clean, renewable, zero-waste, and low carbon, alongside the term manufacturing, all within the article titles. This precision ensured that the dataset exclusively features precise and pertinent articles directly tied to the subject of green manufacturing, as of March 6, 2023. In total, the initial search yielded an impressive 3,577 documents, encompassing a diverse array of articles exploring the convergence of green manufacturing with environmental and sustainability dimensions. This dataset forms the cornerstone for our systematic review, allowing us to furnish an authentic portrayal of the current state of the field. It also empowers us to identify emerging trends and confront existing challenges within the realm of green manufacturing. Moreover, we meticulously refined the dataset by excluding specific document types, thereby ensuring that our analysis predominantly centered on original research articles. Document categories excluded from the dataset encompassed review articles (148), conference reviews (39), errata (10), retracted articles (3), and reports (1). This scrupulous curation process culminated in a dataset of 3,376 original research articles. These articles served as the bedrock for our assessment of the present state of green manufacturing research, as well as the identification of key trends, challenges, and opportunities within the field. By relying on this rigorously assembled dataset, we assure that our analysis is firmly anchored in primary sources, capturing recent and relevant discoveries within the dynamic and evolving domain of green manufacturing. Note: This dataset consists of raw files sourced from Scopus. This dataset stands as a valuable resource for researchers, scholars, and policymakers with an interest in the dynamic and ever-evolving field of green manufacturing. It provides a comprehensive overview of the current research landscape, rendering it an indispensable tool for those seeking to gain insights into and contribute to the advancement of sustainable manufacturing practices.
DOI: 10.17632/9trdxs83df.1
title: Ensayo: Relaciones del neuromarketing con la posibilidad de modelación del comportamiento del consumidor respecto a productos agropecuarios

description: Este escrito considera la complejidad que supera la contemplación del precio como el factor más relevante en el proceso de consumo, se exponen también acercamientos a la toma de decisiones desde una perspectiva lógica hasta técnicas heurísticas, como también métodos matemáticos empleados para el desarrollo de modelos (predictivos o para la corroboración de hipótesis). Finalmente expone el estado actual del mercado agropecuario y de commodities frente a las dinámicas del consumidor, junto con los puntos a tener en cuenta para los nuevos modelos del comportamiento del consumidor en el sector agropecuario..
DOI: 10.17632/89j7snbm2r.2
title: Arabidopsis_Ribo_seq_ORFeome

description: This dataset includes files from a Ribosome profiling study for ORF discovery in Arabidopsis. The associated code files: https://github.com/hsinyenwu/ORFeome abundance.txt is the Kallisto quantification with the max 2 isoforms