Data in Brief

HVDROPDB_RetCam_Neo_Segmentation and HVDROPDB_RetCam_Neo_Classification are the first datasets to be published for the retinal structure segmentation to identify the Retinopathy of Prematurity (ROP). They are prepared by screening the preterm infants visiting PBMA's H.V. Desai Eye Hospital, Pune with two diverse imaging systems RetCam and Neo. The Segmentation dataset contains sub-datasets for the segmentation of optic disc, blood vessels, and demarcation line/ridge from the fundus images of preterm infants, annotated by a group of ROP experts. Each sub-dataset contains retinal fundus images of premature infants with the ground truths prepared manually to assist researchers in developing an explainable automated ROP screening system. The Classification sub-datasets contain ROP and Normal images.

The dataset that we published in this data repository can be used to build neural networks-based inverse kinematics for NAO robot arms. This dataset is named ARKOMA. ARKOMA is an acronym for ARif eKO MAuridhi, all of whom are the creators of this dataset. This dataset contains input-output data pairs. In this dataset, the input data is the end-effector position and orientation in the three-dimensional cartesian space, and the output data is a set of joint angular positions. These joint angular positions are in radians. For further applications, this dataset was split into the training dataset, validation dataset, and testing dataset. The training dataset is used to train neural networks. The validation dataset is utilized to validate neural networks’ performance during the training process. Meanwhile, the testing dataset is employed after the training process to test the performance of trained neural networks. From a set of 10000 data, 60% of data was allocated for the training dataset, 20% of data for the validation dataset, and the other 20% of data for the testing dataset. It should be noted, this dataset is compatible with NAO H25 v3.3 or later.

This paper presents Modern Standard Arabic data for the automatic estimation of the risk of depression for online personas based on their daily Arabic tweets. The data was collected during 1-1-2020 and 1-1-2021 using automatically collecting samples of depression and non-depression tweets. The data contains 1230 records. This data can be used to develop machine learning tools to identify the risk of an individual being depressed and to build recommender systems that monitor depression.

1. These data are particularly valuable for researchers that aim to model lecturer performance based on multi-rater approach. The data may also be used to compare the error of measurement between classical measurement model and modern measurement model (graded response model or partial credit model). 2.The data may be useful for researchers to find the fittest model of lecturer performance by combining demographics, perception data, and documentation data.

This survey dataset examines COVID-19-related knowledge, attitudes, and adoption of prevention behaviors. The survey was conducted among non-random sample of 464 Connecticut residents in the U.S in the early stage of social distancing and shutdown from March 23 to March 29, 2020. The questionnaires were developed by using Qualtrics software. Participants were purposively recruited. Participants could choose a hyperlink for self-administration of the survey online or were interviewed over the phone or other means of communication and record their answers online. Data was transferred from Qualtrics to SPSS Version 26.0 for analysis. Data were analyzed using frequencies, percentages, means, and standard deviations.

An online survey was administered through Qualtrics (one of the leading tools to collect data online). We used social networking sites, personal connections, and emails to collect representative data from Pakistani nationals currently living in Pakistan. Anyone who had access to the Internet could take the survey. The survey questionnaire is provided as a supplementary file.

Lactobacillus sakei MBEL1397(=KCTC14037BP) was isolated from kimchi, a traditional Korean fermented food, in Gangwon province, Republic of Korea. MBEL1397 is an acid-tolerant strain with antimicrobial activity and α-glucosidase inhibitory activity, which might be preliminary indications of its probiotic properties. Complete genome sequencing of L. sakei MBEL1397 was performed using the PacBio RSII platform. MBEL1397 has a 1,994,569 bp circular chromosome with 41.04% G+C content. The genome includes 1,946 protein-coding genes, 66 transfer RNA genes, and 21 ribosomal RNA genes. The BioProject has been deposited at DDBJ/EMBL/GenBank. The GenBank accession numbers are PRJNA598112 for the BioProject, SAMN13698554 for the BioSample, and CP048116 for the chromosome.

This dataset is used to clarify the nexus between effective parenting practices, low self-control, and anti-social behaviors in Gottfredson and Hirschi’s General Theory of Crime (GTC). In order to acquire the data, we conducted the electronic search through ProQuest, PsycINFO, Scopus, and Web of Science, and also the American Society of Criminology, National Criminal Justice Reference Service [NCJRS], Criminal Justice Abstracts. Because the GTC was introduced in 1990, the time frame of this research ranged from 1 January 1990 to 23 September 2019 for all published and non-published research.

Metagenomic DNA isolated and analyzed from soils of three varillal forests from the Allpahuayo-Mishana National Reserve, Loreto Region, Peru

Coral reef percent cover data and analysis for monitoring