cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4b26xzs5jj.1
title: Commercial vaccines used in poultry, cattle, and aquaculture

description: Currently, maintaining or expanding on these successes poses a significant challenge for the global biopharmaceutical industry. Meeting the high demand for vaccines tailored to the specificities of each pathogen and species requires robust production systems capable of producing in a stable and high yielding manner (9, 10). There are several technological platforms (traditional or modern) available for this purpose, as well as different options for obtaining effective, stable, and safe vaccines (2). Knowing the best option for each species and infectious agent in advance is critical for the industry in this highly diverse and complex environment. It will allow manufacturers to make the best decisions with a minimal risk of failure, quickly enter the market, and optimally take advantage of all available resources. Based on these facts, an investigation was conducted with the aim of comparing global trends in the manufacture and marketing of avian, bovine, and fish vaccines.
DOI: 10.17632/kcf54cgk78.1
title: Dataset: UTAUT, Combining PLS-SEM and selected machine learning algorithms

description: This dataset accompanies the manuscript by Richter/Tudoran on "Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms" (at the time of upload, in-press at: Journal of Business Research). 
In this manuscript, to illustrate the combined use of PLS-SEM and selected ML algorithms, we used PLS-SEM on a Unified Theory of Acceptance and Use of Technology (UTAUT) model to create latent variable scores. The analysis made use of raw indicator data provided by 
Al-Gahtani, S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information & Management, 44, 681-691.
The dataset uploaded contains the latent variable scores for two endogenous constructs, the behavioral intention (BI) and the use behavior (USE), as well as four exogenous constructs, performance expectancy (PE), effort expectancy (EE), social influence (SN), and facilitating conditions (FC).
DOI: 10.17632/bkym8ksp3h.1
title: Hyndai Car Dataset

description: Description:
The dataset contains information on 100 cars, with each row representing a single car. The data was generated to simulate various characteristics and maintenance aspects of cars, including engine temperature, brake pad thickness, tire pressure, maintenance type, and anomaly indication variables.

Variables:

Engine Temperature (Continuous):
Represents the temperature of the engine in degrees Celsius.
Values range from approximately 70 to 100 degrees Celsius.
Brake Pad Thickness (Continuous):
Indicates the thickness of brake pads in millimeters.
Values range from approximately 5 to 15 millimeters.
Tire Pressure (Continuous):
Represents the pressure of tires in pounds per square inch (PSI).
Values range from approximately 28 to 36 PSI.
Maintenance Type (Categorical):
Indicates the type of maintenance performed on the car.
Categories include "routine maintenance," "component replacement," and "repair."
Anomaly Indication (Binary):
Indicates the presence or absence of anomalies detected in sensor readings.
Binary variable where 0 represents the absence of anomalies, and 1 represents the presence of anomalies.
Usage:

This dataset can be used for various analytical purposes, including exploratory data analysis, predictive modeling, and anomaly detection.
Researchers and analysts can analyze the relationships between different variables to identify patterns and insights related to car maintenance and performance.
Machine learning models can be trained using this data to predict maintenance requirements, detect anomalies in sensor readings, or optimize maintenance schedules for improved efficiency and cost-effectiveness.
DOI: 10.17632/7397w9h5tb.1
title: Sweet Pumpkin Disease Recognition Dataset

description: 1. The Sweet Pumpkin (scientific name: Cucurbita moschata) belongs to the Cucurbitaceae family. It is a type of fruiting vegetable and is also known as Sweet Gourd. The origin of the Sweet Pumpkin plant is in Central America or the northern part of South America. Apart from Cucurbita moschata, there are several other varieties of sweet pumpkins, such as Cucurbita mixta, C. pepo, C. maxima, C. alba, and C. moschata, each with different varieties and characteristics. Among them, C. moschata is most commonly seen in Bangladesh.
2. The shape of the Sweet Pumpkin is generally round and in a ripe state, its inner part has a bright orange color. It contains Vitamin A and beta-carotene. It is primarily used for cooking in various dishes and has a slightly sweet taste.
3. The Sweet Pumpkin is a well-known vegetable in our country. Not only is it visually appealing, but its benefits are also significant. Whether fried, cooked, mashed, or prepared in a curry, Sweet Pumpkin is used to make delicious halwa. If you're not fond of eating Sweet Pumpkin, you might be missing out on numerous health benefits.
4. The Sweet Pumpkin is rich in various nutrients, including Vitamin A, B-complex, C, and E, as well as potassium, magnesium, calcium, manganese, iron, zinc, phosphorus, copper, carotenoids, and other antioxidants. Additionally, the abundance of beta-carotene in this vegetable helps in the formation of cancer-fighting cells in our body.
5. Diseases in crops are a widespread problem in agricultural production, and they have an adverse impact on the quality and productivity of the crops. Due to non-biological environmental factors, Sweet Pumpkin diseases adversely affect the yield and quality of Sweet Pumpkin, causing significant economic losses to farmers. The traditional identification of crop diseases is often time-consuming, labor-intensive, ineffective, and subjective.
6. In recent times, computer vision has shown great promise in conducting the classification and identification tasks of this kind.
7. Five distinct kinds of Sweet Pumpkin diseases are shown in this large dataset, which can be used to develop machine vision-based techniques: Healthy Leaf, Downy Mildew Disease, Leaf Curl Disease, Mosaic Disease, and Red Beetle.
8. In this dataset, 1000 images of Sweet Pumpkin were initially collected from the field. Subsequently, to increase the quantity of data points, various image processing techniques were applied, such as shifting, flipping, zooming, shearing, brightness enhancement, and rotation, resulting in a total of 7000 augmented images derived from the original images.
DOI: 10.17632/xn447zrnk4.2
title: File Extension Base Crypto-Ransomware Vaccine

description: Crypto-ransomware attacks have become one of the biggest cybersecurity threats to enterprises and government institutes. Traditional ransomware protection solutions have limited prevention techniques. This study focuses on file extensions to suggest a new ransomware vaccine solution for preventing ransomware encryption. Analysis and test results show that blurring file
extensions dramatically reduces encrypted files on the system. Therefore, the implications of this study could be used for approaching ransomware prevention differently and developing new methods or tools.
DOI: 10.17632/8y2h89z46s.3
title: Connection to Nature Data

description: This data file contains demographic information along with established scale scores of 515 both allotment and non-allotment owners to explore their time in nature and the impacts of this on mental health. Also, questions were asked around time and activities in nature. The age range was between 18-85 years old. The data had approval from the psychology research ethics committee. Before data collection began, full informed consent was obtained from participants as well as information sheets provided to them and all participants were fully debriefed afterwards. Identifying information was removed to ensure confidentiality and anonymity. The scores for the scales on mental health (Clarke et al, 2011; NHS health scotland,2008), self-esteem (Robins, Hendin & Trzesniewski, 2001), physical health (DeSalvo et al, 2006), connection to nature (Nisbet, & Zelenski,2013), social identity (Sani, Madhok, Norbury, Dugard & Wakefield, 2014), loneliness (De Jong Gierveld & Van Tilburg, 2006), social support (Van Dick & Haslam, 2012) and self- efficacy (Tambs, & Røysamb, 2014) are included.
DOI: 10.17632/w35fvmtnf2.1
title: Centrality and seed-based correlation maps obtained with functional MRI

description: The data set offers individual brain connectivity maps of 48 patients with functional movement disorder (FMD) and 65 healthy controls obtained with functional MRI (Mueller, et al., 2022). For each participant, the data set provides five variants of network centrality maps and two types of seed-based correlation maps using the precuneus and the left temporoparietal junction (TPJ) as seed regions.

The data set contains seven zip-files related to seven types of brain connectivity maps (five variants of brain network centrality and two types of seed-based connectivity maps):
‘GCOR.zip’: global correlation;
‘GCOR_no_denoising.zip’: global correlation without using denoising in image pre-processing;
‘ICC.zip’: intrinsic connectivity;
‘ECM_add.zip’: eigenvector centrality with the ‘add’ approach adding one to all correlations;
‘ECM_rlc.zip’: eigenvector centrality with the ‘rlc’ approach using the rectified linear unit filter;
‘SBC_precuneus.zip’: seed-based correlation with precuneus as seed-region;
‘SBC_TPJ.zip’: seed-based correlation with the left TPJ as seed-region;

After unzipping the files, there will be seven directories with the same names: ‘GCOR’, ‘GCOR_no_denoising’, ‘ICC’, ‘ECM_add’, ‘ECM_rlc’, ‘SBC_precuneus’, and ‘SBC_TPJ’.

Each of the seven directories contains a subdirectory ‘*_data’ containing all the individual centrality/correlation maps in the Neuroimaging Informatics Technology Initiative (NIfTI) file format. Patient maps are named with ‘FPH??_*.nii’ while control subjects are denoted by ‘CSS??_*.nii’ and ’CSZ??_*.nii’. The notation ‘??’ stands for consecutive numbering.

Each of the seven directories contains a subdirectory ‘SPM_fullmodel’ including an SPM12 analysis with group comparisons, e. g. the comparison of connectivity between FMD patients with and without FW, or between FW patients and healthy controls. In addition, each of the five centrality directories contains a further subdirectory ‘SPM_corr_SFMDRS’ including an SPM12 analysis for investigating a potential relationship between centrality and disease severity using the simplified FMD rating scale (SFMDRS).

All analysis subdirectories contain files of a standard SPM12 analysis including the ‘SPM.mat’. Parameter estimates are shown in the files ‘beta_00??.nii’, T-maps are named with ‘spmT_00??.nii’, and contrast images are named with ‘con_00??.nii’ (‘??’ stands for consecutive numbering). The analysis space (aka ‘brain mask’) is saved in the file ‘mask.nii’. Tables listing all clusters, coordinates, p-, T-, and Z-values for all available contrasts are also saved in the postscript file ‘spm_2022Feb25.ps.

Reference:
K. Mueller, F. Růžička, M. Slovák, Z. Forejtová, P. Dušek, P. Dušek, R. Jech, T. Serranová, Symptom-severity-related brain connectivity alterations in functional movement disorders, Neuroimage: Clinical (2022), In Press.
DOI: 10.17632/gjmr63rz2r.1
title: Dataset for Forest Fire Detection

description: We have curated a dataset to address the problem of forest fire detection. All images in the dataset are 3-channeled with spatial resolution of 250 × 250. We have retrieved these images by searching various search terms in multiple search engines. Afterwards, we thoroughly investigated these images to crop and remove the inappropriate components such as people, fire-extinguishing machinery etc. We ensured that each image only contain the relevant fire region. The dataset is designed for binary problem of fire or no-fire detection in the forests landscape. It is a balanced dataset consisting of 1900 images in total, where 950 images belong to each class. We have divided the dataset into 80:20 for training and testing purposes in our study.
DOI: 10.17632/dtgtmdtdnc.1
title: Data for: Cross-Linguistic Frequency and the Learnability of Semantics: Artificial Language Learning Studies of Evidentiality

description: Participants' responses (raw data) for each experiment described in the manuscript.
DOI: 10.17632/dddb2prk4p.1
title: Reflectance spectra of boreal forest floor elements: ferns, herbaceous plants and decaying wood

description: This dataset contains spectral reflectance factors at wavelengths from 350 to 2500 nm measured on boreal forest floor elements in three sub-sites around the Hyytiälä forest station (61◦ 51’N, 24◦ 18’E) in Finland. The data were collected in situ at the peak growing season from July 19 to 29, 2023 on 11 ferns, 11 common wood sorrel (Oxalis acetosella L.), 10 May lily (Maianthemum bifolium (L.) F.W. Schmidt) and 15 decaying wood samples. In addition, field measurements were associated with metadata describing the target and its surrounding environment. The table "spectral_measurements.csv" contains the hemispherical-conical reflectance factors for each target. The table "description.csv" contains metadata for each target. The file "sample_photos.pdf" contains photographs of the targets and their surrounding environment. For further details on data collection, the measurement process and the content of the data files, see README.pdf. If using the data, please cite the Mendeley Dataset: Mercier, Audrey; Karlqvist, Susanna; Hovi, Aarne; Rautiainen, Miina (2023), “Reflectance spectra of boreal forest floor elements: ferns, herbaceous plants and decaying wood”, Mendeley Data, V1, doi: 10.17632/dddb2prk4p.1 Funding: European Union – NextGenerationEU as part of the Research Council of Finland project ARTISDIG (decision number 348152).
DOI: 10.17632/3sbnmyzgtf.1
title: How informative is question-and-answer similarity to financial analysts? Evidence from Chinese earnings communication conferences

description: This is the replication package for the results presented in the paper "How informative is question-and-answer similarity to financial analysts? Evidence from Chinese earnings communication conferences".
DOI: 10.17632/bf96rjxnky.1
title: MaRTIn – Massive Recursive Tensor Integration

description: We present MaRTIn, an extendable all-in-one package for calculating amplitudes up to two loops in an expansion in external momenta or using the method of infrared rearrangement. Renormalisable and non-renormalisable models can be supplied by the user; an implementation of the Standard Model is included in the package. In this manual, we discuss the scope and functionality of the software, and give instructions of its use.
DOI: 10.17632/zxfxd59554.1
title: PyFEST - a Python code for accurate frequency estimation

description: The data consist of an application, namely PyFEST, written in Python language, and a file with instructions to install and use the application. It can be used to estimate the frequencies of short-time signals with high accuracy. Along with the application, examples with generated signal (single-ton, multi-tone, noisy, damped etc.) and measured signals are delivered for testing purposes. The frequencies of the harmonic components are evaluated one-by-one with high accuracy. Because the actions performed do not imply previous expertise, the results are not influenced by human intervention.
DOI: 10.17632/br67trdjyj.1
title: Replication material for Common Trade Exposure and Business Cycle Comovement

description: This repository contains all codes and data necessary to replicate the results in Avila-Montealegre and Mix (2024).
DOI: 10.17632/59jmfjpjzr.1
title: Predicting Customer Churn: Using the Best-Worst Method to Compare Models on more than just Accuracy

description: This is a collection of the data used for analysis (master dataset, training data, test data), and the code and processes that have been used to conduct the analysis for this research project.
DOI: 10.17632/c4n7fckkz3.3
title: DNS Exfiltration Dataset

description: DNS exfiltration dataset was recorded in a realistic network environment. More than 50 million DNS requests were recorded on one of the ISP's DNS servers. The data in the dataset was anonymised by changing all IP addresses using injective mapping. 
Features in the dataset are split into single request and aggregate features. Single request or DNS label-based features can be calculated for each DNS request independently using only the textual characteristics of the request. On the other hand, aggregate features are calculated using multiple subsequent request from one client to a particular TLD. This reduces the size of the dataset to about 35 million records. The complete list of features with descriptions can be found in dataset_description.txt file. For all of the features which are based on finding English words in the request we used about 60.000 most commom English words. The list of used words can be found in english_words.txt.
The main dataset (dataset.csv) contains regular requests and exfiltrations performed using DNSExfiltrator and Iodine tools. Additional dataset (dataset_modified.csv) contains only exfiltrations executed with modified DNSExfiltrator tool. Waiting times between two consecutive requests in this dataset are randomised and the requests also have lower entropy causing the detection to be much harder.

If you use this dataset for your research, please cite: Žiža, K., Tadić, P. & Vuletić, P. DNS exfiltration detection in the presence of adversarial attacks and modified exfiltrator behaviour. Int. J. Inf. Secur. (2023). https://doi.org/10.1007/s10207-023-00723-w
DOI: 10.17632/zrdxwfcxfs.1
title: Friend or Foe dataset

description: This dataset was extracted from an American game show, Friend or Foe. In this show, pairs of contestants played a variant of the prisoner’s dilemma to decide the allocation of an often-large monetary prize. From video recordings of contestants’ interactions prior to their decision-making, we extract a broad range of multimodal behavioral features (i.e., eye gaze, head pose, facial expressions, body movement, and language characteristics) and socio-demographic information.
DOI: 10.17632/3bnjsssxph.1
title: VRP with ODs reservation

description: These data have been used in our work "An Effective Mitigation Strategy to Hedge Against
Absenteeism of Occasional Drivers"
DOI: 10.17632/6m3v4ksd7y.1
title: Dataset on eco-friendly cement production using iron ore tailings from Brazil and evaluation of grinding process on the synthesis of geopolymers

description: The use of iron ore tailings in Brazil is an important topic after two recent tragedies. The data was collected in order to understand the geomechanical feasibility of eco-friendly cements using iron ore tailings and evaluate the impact of the grinding process on the compressive strength of geopolymers. The data is based on two major studies using the same methodology. The first one is the examination of the material best composition in terms of uniaxial compression resistance by varying its proportions of iron ore tailings, metakaolin and alkaline solution (composed by 1:3 ratio of sodium hydroxide (NaOH) and sodium silicate (Na2SiO3)). The second one is the evaluation of the effects in compressive strength when grinding the iron ore tailings for 1, 2 and 3 hours for producing the geopolymer. The best proportion consisted in 50% of iron ore tailings, 25% of metakaolin and 25% of a alkaline solution obtained an uniaxial compressive strength resistance of 23.5 MPa in 3 days. Also, the gain obtained with the ground tailings after 3 hours only occurs in the firsts days of cure, which does not justify grinding for this iron ore tailings due to the high cost of this operation. Tests with 1 and 2 hours of tailings grinding showed lower results of UCS when compared with IOT without milling, which is unexpected and needs further investigation to understand this behavior. One possible explanation is material agglomeration during the initial grinding times, and it is feasible that the clustering of particles limited the reaction. Finally, it appears that geopolymer mortars seem to be a viable alternative for the sustainable use of IOT in the state of Minas Gerais, in Brazil, since they present satisfactory results of compressive strength.
DOI: 10.17632/8zdymg4y96.1
title: ISI-161: Spreadsheet tables

description: This dataset (corpus) contains canonical tables describing the basic concepts and relationships in the field of industrial safety inspection (ISI). Tables are obtained by converting arbitrary tables from ISI reports using the TabbyXL. The numeric values in the cells are noised.
DOI: 10.17632/czh96my8cf.1
title: Enhancing microglial phagocytosis analysis through deep learning: A U-Net based approach

description: This dataset provides a trained U-Net (called AIstain), an DEMO analysis assay and the training dataset used in our corresponding Manuscript: "Enhancing microglial phagocytosis analysis through deep learning: A U-Net based approach" (Zähringer et al.) submitted to Cell Reports Methods.
The neural network can be used on Olympus ScanR systems with the analysis software v3.4 and higher to perform label-free live cell imaging experiments.
Further information and how to use the U-Net can be found in the publication.
DOI: 10.17632/ynn627w7jw.1
title: Data for: Green Innovation Transformation, Economic Sustainability and Energy Consumption during China's New Normal Stage

description: The zip file includes one command file, one instruction, and data for the manuscript “Green Innovation Transformation, Economic Sustainability and Energy Consumption during China's New Normal Stage”. The Data file consists of two parts: the micro and macro empirical data. Both of them are uploaded by two types, dta format (for STATA software) and excel format. The Command.do file is uesed for the STATA software. The instruction describe how to use data and command files in steps.
DOI: 10.17632/4xjjpxrjhk.1
title: Data for: Mapping social-ecological systems to understand the challenges underlying wildlife management

description: The file contains the min-max normalized data for all 19 indicators (representing 15 variables) measured across 20 counties, which were used for the principal component analysis. Please see Appendix B for a detailed description of included variables.
DOI: 10.17632/m2tk4652bm.1
title: Data for: Sexual assault victimization and psychopathology: A review and meta-analysis

description: This dataset contains coded effect sizes from articles comparing psychopathology in sexually assaulted to unassaulted groups.
DOI: 10.17632/7n63b5r87v.1
title: Panel Data for DEA and Econometric Analyses

description: This study used two inputs variables; total hydropower installed capacity (megawatts per hour) and long-term average annual precipitation (in volume), and inputs costs; mean renewable energy feed-in tariffs (USD), and dam capacity per capita. One output variable which is hydroelectricity generation (terawatts per hour), was used to measure the cost-efficiency and its decompositions. Static panel regression analysis in the second stage with a partial log form of the variables in each of the three model estimated. where Cost Eﬃciency (CE) of country (i) in period (t) obtained from the DEA in the first stage, inflation rate based on consumer price index (INF), log of gross domestic product (GDP), log of labour input (numbers of labours), investment proxy for research & development (INV).
DOI: 10.15468/dl.h5z7ut
title: Occurrence Download

description: A dataset containing 3796 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pyrus communis L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 3796 records from 288 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009381-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.