cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sz5558wrd4.3
title: Bangla Multilabel Cyberbully, Sexual Harrasment, Threat and Spam Detection Dataset

description: Dataset Overview
The Bangla Multilabel Cyberbully, Sexual Harassment, Threat, and Spam Detection Dataset is designed to facilitate the development of machine learning models to detect and classify various types of abusive content in Bangla social media text. This dataset contains a collection of comments annotated for multiple types of abuse, making it suitable for multilabel classification tasks. It aims to support research and development in natural language processing (NLP) to enhance online safety and moderate harmful content on Bangla language social media platforms.

Purpose
1. Train and evaluate machine learning models for detection of cyberbullying, sexual harassment, religious hate speech, threats, and spam in Bangla comments.
2. Support research in NLP and machine learning focused on Bangla, a low-resource language.
3. Aid in developing automated moderation systems for social media platforms to ensure safe and respectful communication.

Data Collection
Initially, we collected around 30,000 comments from social media platforms like Facebook and TikTok. These comments were in Bangla, English, and Banglish (Bangla written using English characters). Since our research focuses on Bangla abusive text detection, we refined the dataset through the following steps:

1. We filtered out all comments written in English to focus on the Bangla text.
2. To ensure data quality, We eliminated duplicate entries and rows with missing or null values.
3. We removed any remaining English characters and both Bangla and English numerical values to ensure the analysis was based solely on Bangla text.

After these steps, we obtained a final dataset of 12,557 comments. Each comment was manually labeled into five classes: bully, sexual, religious, threat, and spam. This dataset supports multi-class labeling, meaning a comment can simultaneously belong to more than one class.

Dataset Columns
1. Gender: Indicates the gender of the person who received the bullying.
2. Profession: Indicates the profession of the person who received the bullying. 
3. Comment: Contains the text of the comment in Bangla. 
4. Bully: Binary label indicating whether the comment contains bullying content. (0 for no, 1 for yes)
5. Sexual: Binary label indicating whether the comment contains sexual harassment content. (0 for no, 1 for yes)
6. Religious: Binary label indicating whether the comment contains religious hate speech. (0 for no, 1 for yes)
7. Threat: Binary label indicating whether the comment contains threats. (0 for no, 1 for yes)
8. Spam: Binary label indicating whether the comment is considered spam. (0 for no, 1 for yes)

Applications
1. Training and testing machine learning models for multilabel classification.
2. Research on natural language processing (NLP) and cyberbullying detection in low-resource languages like Bangla.
3. Developing automated systems for monitoring and moderating online content on social media platforms to ensure safe and respectful communication.
DOI: 10.17632/5m68fkzwzn.1
title: Inclusion-Exclusion Process

description: Inclusion-Exclusion Process
DOI: 10.17632/x3fz2nfm4w.1
title: SalmonScan: A Novel Image Dataset for Fish Disease Detection in Salmon Aquaculture System

description: The SalmonScan dataset is a collection of images of salmon fish, including healthy fish and infected fish. The dataset consists of two classes of images:

Fresh salmon 🐟
Infected Salmon 🐠

This dataset is ideal for various computer vision tasks in machine learning and deep learning applications. Whether you are a researcher, developer, or student, the SalmonScan dataset offers a rich and diverse data source to support your projects and experiments.

So, dive in and explore the fascinating world of salmon health and disease!

The SalmonScan dataset consists of approximately 1,208 images of salmon fish, classified into two classes:

- Fresh salmon (healthy fish with no visible signs of disease), 456 images
- Infected Salmon containing disease, 752 images

Each class contains a representative and diverse collection of images, capturing a range of different perspectives, scales, and lighting conditions. The images have been carefully curated to ensure that they are of high quality and suitable for use in a variety of computer vision tasks.

Data Preprocessing

The input images were preprocessed to enhance their quality and suitability for further analysis. The following steps were taken:

Resizing 📏: All the images were resized to a uniform size of 600 pixels in width and 250 pixels in height to ensure compatibility with the learning algorithm.

Image Augmentation 📸: To overcome the small amount of images, various image augmentation techniques were applied to the input images. These included:

Horizontal Flip ↩️: The images were horizontally flipped to create additional samples.
Vertical Flip ⬆️: The images were vertically flipped to create additional samples.
Rotation 🔄: The images were rotated to create additional samples.
Cropping 🪓: A portion of the image was randomly cropped to create additional samples.
Gaussian Noise 🌌: Gaussian noise was added to the images to create additional samples.
Shearing 🌆: The images were sheared to create additional samples.
Contrast Adjustment (Gamma) ⚖️: The gamma correction was applied to the images to adjust their contrast.
Contrast Adjustment (Sigmoid) ⚖️: The sigmoid function was applied to the images to adjust their contrast.

Usage

To use the salmon scan dataset in your ML and DL projects, follow these steps:

- Clone or download the salmon scan dataset repository from GitHub.
- Unzip the file to access the two folders (FreshFish and InfectedFish).
- Load the images into your preferred programming environment, such as Python.
- Use standard libraries such as numpy or pandas to convert the images into arrays, which can be input into a machine learning or deep learning model.
- Split the dataset into training, validation, and test sets as per your requirement.
- Preprocess the data as needed, such as resizing and normalizing the images.
- Train your ML/DL model using the preprocessed training data.
- Evaluate the model on the test set and make predictions on new, unseen data.
DOI: 10.15468/dl.zedw86
title: Occurrence Download

description: A dataset containing 825 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Podocarpus macrophyllus (Thunb.) Sweet",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 825 records from 101 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009281-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/4rv6djd642.1
title: Teachers Job satisfaction

description: This study aimed to examine the influence of various factors on teacher job satisfaction, service quality, and performance in Sidama Regional State public secondary schools. Moreover, the research aimed to establish or reject four hypotheses to achieve its primary objective, and these hypotheses were:
H1. Working conditions have a very positive impact on teachers' job happiness.
H2. The level of job satisfaction among teachers would increase with compensation and incentives.
H3. Professional development opportunities would improve instructors' job satisfaction.
H4. Coworker and other relationships have a strong favorable effect on teachers' job satisfaction.
Survey design and mixed (quantitative and qualitative) methods were used to collect and analyze the data needed to conduct the research. Sidama Regional State public secondary school teachers and principals were used as the study population. A sample size of 170 teachers and 10 school principals and supervisors (total N = 180) was chosen as a sample by using simple random sampling techniques. Closed-ended questionnaires and semi-structured interviews were used to generate the primary data for the study from teachers and school principals, respectively. This study used descriptive correlational analysis, multiple regression analysis, frequency, percentage, mean, and standard deviation to measure the relationship between variables and the effect of independent factors on the dependent variable. The results showed that teachers' job satisfaction was found to be positively correlated with working conditions, salary and reward systems, relationships with colleagues, and professional development opportunities (predictors). These factors also had a substantial impact on teachers' performance and the quality of service they provided to stakeholders.
DOI: 10.17632/tbshh5b542.1
title: GREEN MARKETING

description: the data contains respondents' opinions regarding how green marketing practices, including eco-product, sustainable packaging and green image influence their purchasing intentions toward cosmetic products in Ghana. The data also contain information regarding the consumers' green awareness and loyalty toward the cosmetic products. Hence, the data is relevant for researches related to green marketing, green loyalty, green awareness and consumer purchasing intentions, in relation to cosmetic products and consumption in Ghana. Based on the data, it was discovered that the green marketing practices positively affect consumers' purchasing intentions toward cosmetic products, while green awareness and green loyalty significantly, positively and partially mediate the linkages between green marketing practices and consumer purchasing intention, particularly within the cosmetic industry in a developing country like Ghana.
DOI: 10.17632/27h9wzm8x2
title: Fed Information Effects: Evidence from the Equity Term Structure (Replication)

description: This folder contains the replication codes for the paper “Fed Information Effects: Evidence from the Equity Term Structure” by Benjamin Golez and Ben Matthies.
DOI: 10.17632/xbzw29tjz4.2
title: Dataset for: Identification of the Dominant Recombination Process for Perovskite Solar Cells Based on Machine Learning

description: Code and data used in the publication "Identification of the Dominant Recombination Process for Perovskite Solar Cells Based on Machine Learning" The dataset saved in the .csv files "balanced_dataset.csv" consist of over 10<sup>6</sup> simulated perovskite solar cells with their performance (V<sub>OC</sub>,J<sub>SC</sub> and FF) at (0.1,0.18,032,0.56,1) sun illuminations as well as the ideality factor (n) . This dataset was built using drift-diffusion open-source code SIMsalabim. (https://github.com/kostergroup/SIMsalabim)
DOI: 10.17632/wrvjmdtjd9.1
title: Dataset: Online Advertisement Click-Through Rates

description: This dataset offers a comprehensive view of how users interact with online ads, which is vital for refining ad targeting strategies. This dataset covers a wide range of user demographics, including age, gender, income, and location, as well as detailed ad characteristics like types, topics, and placements. By simulating user behavior metrics such as clicks per user, click times, and conversion rates, it provides insights into how users engage with ads. Additionally, factors like the day of the week and device type add context to the data, allowing for a deeper analysis of ad performance metrics like click-through rates (CTR). With its wealth of information, this dataset is valuable for marketers and advertisers looking to optimize their ad campaigns and maximize their impact in the online advertising realm.
DOI: 10.17632/h293czdh29.1
title: SPSS Raw Data

description: The overall objective of this study is to analyze the interaction between the expansion of informal settlements and sustainable urbanization in Bahir Dar from the perspective of land management. The data shows socio-economic characteristics of 156 informal settlers in two sub-cities of Bahir Dar, Ethiopia. It is raw data inserted in SPSS ready to extract information related to the objective of the study. The data was gathered with the support of trained data enumerators with close supervision of the researcher. However, the findings of the study is triangulated with additional data collected from the study site including interviews, focus group discussions and cases.
DOI: 10.17632/pjkmzh8tf8.1
title: A simple model of flood peak attenuation: validation dataset and example

description: Supplementary data for the paper “A simple model of flood peak attenuation” published to Water Resources Research. This dataset demonstrates sensitivity analyses and validation. It contains useful information and a simple spreadsheet with an example for practical application such as first assessments of natural and dam-break floods, engineering design and analyses of large river networks using remote sensing technology. 

Please read the paper and the associated supporting information before using the data. 

Contact authors if you have any further questions: Stefany Lima (stefglima@gmail.com); Rodrigo Paiva (rodrigo.paiva@ufrgs.br).
DOI: 10.17632/kwt87455kc.4
title: Indicadores Economicos Mensuales de Puerto Rico 2019
DOI: 10.17632/mvwdtmzhcw.1
title: simulated data for clustering experiments

description: 182 simulated datasets (first set contains small datasets and second set contains large datasets) with different cluster compositions – i.e., different number clusters and separation values – generated using clusterGeneration package in R. Each set of simulation datasets consists of 91 datasets in comma separated values (csv) format (total of 182 csv files) with 3-15 clusters and 0.1 to 0.7 separation values. Separation values can range between (−0.999, 0.999), where a higher separation value indicates cluster structure with more separable clusters. Size of the dataset, number of clusters, and separation value of the clusters in the dataset is printed in file name. size_X_n_Y_sepval_Z.csv: Size of the dataset = X number of clusters in the dataset = Y separation value of the clusters in the dataset = Z
DOI: 10.17632/v5x4vpnzds.1
title: Global LiDAR Lowland DTM (GLL_DTM_v1)

description: The Global LiDAR Lowland DTM (GLL_DTM_v1) at 0.05-degree resolution (~5*5 km) is created from ICESat-2 data (v003) collected between 14 October 2018 and 13 May 2020 as published in Vernimmen et al. 2020: New ICESat-2 satellite LiDAR data allow first global lowland DTM suitable for accurate coastal flood risk assessment
DOI: 10.17632/76cr3wfhff.1
title: Fish-gres Dataset for Fish Species Classification

description: Fish-gres dataset consists of 8 fish species, where the number of images on each species varies from 240 images to 577 images. This difference is due to the availability of random samples taken in traditional markets in Gresik, East Java, Indonesia. All of the species can easily be found in the traditional markets where the dataset was created. Image acquisition was carried out under various background conditions and the number of fish species varies in the traditional market in Gresik Regency, East Java, Indonesia. The resolution of the acquisition image is 4160x3120 pixels, then we resize the image to 390x520 pixels. The species of the fish are as follows, Chanos Chanos (500 images), Johnius Trachycephalus (240 images), Nibea Albiflora (252 images), Rastrelliger Faughni (544 images), Upeneus Moluccensis (577 images), Eleutheronema Tetradactylum (240 images), Oreochromis Mossambicus (331 images), and Oreochromis Niloticus (564 images), respectively.
DOI: 10.17632/8b6nw2xxhn.1
title: MannGallen_2017_CurrentBiology

description: Alignment of anatomical data, template brain, and atlas ROIs (anatomical.zip) 1- Template and atlas from Virtual Fly Brain project: /Original_atlas_template 2- Mean brain and the template/atlas warped to it: /Meanbrain_aligned 3- Live brain and the meanbrain/atlas warped to it: /livebrain_aligned Functional and atlas data to extract ROI time series (roi_timeseries_data.zip) 1- File of atlas ROIs: /vfb_nn/vfb_nn_Original.nii.gz 2- Functional imaging data: /functional/brain03gc6m.nii.gz Sample 'video' of motion-corrected raw data (sample_video.tiff.zip)
DOI: 10.17632/dkbxwxmtfv.2
title: Supplemental Materials: Orismilast in moderate-to-severe psoriasis: Efficacy and safety from a 16-week, randomized, double-blinded, placebo-controlled, dose-finding, phase 2b trial (IASOS)_JAAD

description: Supplemental information for the manuscript in the Journal of the American Academy of Dermatology (JAAD): "Orismilast in moderate-to-severe psoriasis: Efficacy and safety from a 16-week, randomized, double-blinded, placebo-controlled, dose-finding, phase 2b trial (IASOS)"
DOI: 10.17632/923xvkk5mm.1
title: Benchmark data sets

description: A total of 12 software defect data sets from NASA were used in this study, where five data sets (part I) including CM1, JM1, KC1, KC2, and PC1 are obtained from PROMISE software engineering repository (http://promise.site.uottawa.ca/SERepository/), the other seven data sets (part II) are obtained from tera-PROMISE Repository (http://openscience.us/repo/defect/mccabehalsted/).
DOI: 10.17632/48whrmmkjg.1
title: Energy Consumption in KWh of a Typical House Sincelejo Colombia

description: El conjunto de datos presentado corresponde al registro del consumo energético de cinco dispositivos en una vivienda típica de una familia de 4 miembros en la ciudad de Sincelejo, Colombia. Los datos han sido recolectados durante seis meses consecutivos utilizando un Sistema de Gestión Energética en el Hogar (HEMS) que monitorea en tiempo real el consumo de energía. Este conjunto de datos incluye información detallada sobre el consumo horario del ventilador, PC, aire acondicionado, lámpara y TV, capturando patrones de uso en diferentes momentos del día. El registro de consumo se ha realizado a intervalos de un minuto, con el objetivo de proporcionar una visión precisa de los hábitos de consumo de los dispositivos. La base de datos contiene un total de 3626 filas, incluyendo una fila de encabezado y las entradas correspondientes a los datos recolectados, distribuidas en 6 columnas: una columna de marcas temporales (TimeStamp) y cinco columnas adicionales que representan el consumo de cada uno de los dispositivos mencionados en kilovatios hora (kWh).
DOI: 10.17632/72ggfgw9f7.2
title: Top-down_Dali_Data_SchynsLab_YY

description: Raw data, analyzed data and processing code for Neural Representation Strength of Predicted Category Features Biases Decision Behavior
DOI: 10.17632/tvnyyz6t8w.1
title: DatainBrief Mothibe ME May 2019 (01)

description: •The data presented here provides the in vitro effect of herbal medicines on the platelet activity, measured as luminescence. •It shows the capacity of the platelets to release reactive oxygen species when activated after being exposed to the herbal medicines. •It shows the mechanisms by which the effects are mediated in the platelets, either through cell surface receptor- formyl peptide receptor, or intracellular protein kinase C . These tables show the actual calculated % inhibition for each herbal medicine and its diluted standards per time interval- 0, 5, 10, 15, 20, 25, 30, 45 and 60 minutes. The calculated average % inhibition is also presented in the last column of each table and the standard deviations. The data is presented for when the platelets were activated with fMLP and with PMA. Positive values represent inhibition and negative values represent stimulation of the platelets
DOI: 10.17632/x747rrhmvb.1
title: The Effectiveness of Utilizing Web-learning Media Towards Islamic Education Learning (PAI) Outcome in The Era of Industrial Revolution 4.0

description: This study aims to analyze the effectiveness of e-learning in the Al-Irsyad Al-Islamiyyah Woman Junior High School in Malang. The study was conducted on students in grades VII at Al-Irsyad Al-Islamiyyah Woman Junior High School in Malang, by using a quantitative approach and the type of research survey. The primary data sources were students in grades VIIA and VIIB, school web data and teachers at the Al-Irsyad Al-Islamiyyah Woman Junior High School in Malang. The instruments of data collection used questionnaires, observation and documentation of archives in Al-Irsyad Al-Islamiyyah Woman Junior High School in Malang as the object of research. Sampling data sources is done purposively. Data were analyzed using a test instrument validity, reliability test, as well as a descriptive analysis of data pre-test and post-test. Furthermore, the data elaborated and described T-test using SPSS 21,00. The results of the study show that the class learning based on e-learning strategy in the experimental class (VIIA) is more effective than learning in the control class (VIIB) in terms of student learning outcomes. Based on the t-test obtained P (0.698 ) &gt; oc (0, 05), thus H0 be accepted. Through the calculation of normalized gain values between the experimental classes shows a higher number than the control class, namely g = 0.70 in the experimental class and g = 0, 60 in the control class
DOI: 10.17632/k2v9sbmr8r.1
title: Raw data of Users' comments on Online Food Delivery Services

description: The following dataset (in R format) was built after collecting data from a Colombian website of Online Fast Food Delivery with a web scraping procedure conducted with Agenty. The dataset contains the following variables Column1 (doc_id): An integer number that identifies each comment. Column2 (text): A Spanish written comment posted by users of the website Column3 (Category): Restaurants are classified into six categories (Asian food, Burgers, Chicken, Alcoholic beverages, Meats, and Pizzas). Column4 (Provider): The commercial name of restaurants Column5 (Rating): A number (between 0 and 100) that represents the satisfaction of the customer with both the delivery service and the food. Column6 (TotalC): The number of comments that each restaurant has received at the moment we collected the data.
DOI: 10.17632/ytbnp523n9.1
title: Protocol for qPCR analysis that corrects for cDNA amplification efficiency - supplemental data

description: "Example qPCR" is the file that has been used to create the figures for the associated Star Protocol.
"Proof of concept" contains 7 sheets with different scenarios and explanatory notes that compare the original 2-ΔΔCT method to the adjusted version from the associated Star Protocol.

Star Protocol DOI: 10.1016/j.xpro.2022.101515
DOI: 10.17632/28xhc4kyfp.1
title: CG-1050 v2: Original and tampered images

description: This dataset complements the dataset known as CG-1050 (v1) in which 1050 tampered images and their corresponding original counterpart were provided. Now, the objective is to provide the specific block (cropped image) in which the manipulation was performed. It is organized in two directories: training and validation. In each directory there are two sub-directories, one for the original cropped images, and the other for the tampered cropped images. The manipulated region is around the 25% to 75% of the cropped image. This dataset can be used for training and validation machine learning based models for classification of tampered images.