Dataset Information

A data-based study in support of Blackbuck related cases from Haryana.

ABSTRACT: Haryana State (located in the northern part of India), is lacking in natural forest, but it has rich biodiversity of some wild animals, especially the Blackbuck antelopes. The maximum population of Blackbucks in the state is living in open cultivated fields. Blackbucks were once found abundantly throughout Haryana, but now they are limited to the south-west part of the state, driven almost to extinction in the rest of Haryana, mainly because of habitat destruction and wildlife crime. This data report is an outcome of six years (January 2012-September 2017), based on assessment of records in terms of location, year and month wise frequency of death and rescued cases related to Blackbuck reported, as registered by the State Wildlife Department. It is envisioned that this data report will provide appropriate information for the conservationist to plan future conservation strategy for Blackbucks in Haryana.

SUBMITTER: Kumar V

PROVIDER: S-EPMC5966515 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A data-based study in support of Blackbuck related cases from Haryana.

Kumar Vikas V Sharma Neelkamal N Sharma Arun A Verma Kapil K Singal Kusum K Kumar Mahesh M

Data in brief 20180216

Haryana State (located in the northern part of India), is lacking in natural forest, but it has rich biodiversity of some wild animals, especially the Blackbuck antelopes. The maximum population of Blackbucks in the state is living in open cultivated fields. Blackbucks were once found abundantly throughout Haryana, but now they are limited to the south-west part of the state, driven almost to extinction in the rest of Haryana, mainly because of habitat destruction and wildlife crime. This data r ...[more]

PMID: 29845095

Similar Datasets

Project description:BackgroundIn Japan, challenges were reported in accurately estimating the prevalence of schizophrenia among the general population. Retrieving previous studies, we investigated that patients with schizophrenia were more likely to experience poor subjective well-being and various physical, psychiatric, and social comorbidities. These factors might have great potential for precisely classifying schizophrenia cases in order to estimate the prevalence. Machine learning has shown a positive impact on many fields, including epidemiology, due to its high-precision modeling capability. It has been applied in research on mental disorders. However, few studies have applied machine learning technology to the precise classification of schizophrenia cases by variables of demographic and health-related backgrounds, especially using large-scale web-based surveys.ObjectiveThe aim of the study is to construct an artificial neural network (ANN) model that can accurately classify schizophrenia cases from large-scale Japanese web-based survey data and to verify the generalizability of the model.MethodsData were obtained from a large Japanese internet research pooled panel (Rakuten Insight, Inc) in 2021. A total of 223 individuals, aged 20-75 years, having schizophrenia, and 1776 healthy controls were included. Answers to the questions in a web-based survey were formatted as 1 response variable (self-report diagnosed with schizophrenia) and multiple feature variables (demographic, health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities). An ANN was applied to construct a model for classifying schizophrenia cases. Logistic regression (LR) was used as a reference. The performances of the models and algorithms were then compared.ResultsThe model trained by the ANN performed better than LR in terms of area under the receiver operating characteristic curve (0.86 vs 0.78), accuracy (0.93 vs 0.91), and specificity (0.96 vs 0.94), while the model trained by LR showed better sensitivity (0.63 vs 0.56). Comparing the performances of the ANN and LR, the ANN was better in terms of area under the receiver operating characteristic curve (bootstrapping: 0.847 vs 0.773 and cross-validation: 0.81 vs 0.72), while LR performed better in terms of accuracy (0.894 vs 0.856). Sleep medication use, age, household income, and employment type were the top 4 variables in terms of importance.ConclusionsThis study constructed an ANN model to classify schizophrenia cases using web-based survey data. Our model showed a high internal validity. The findings are expected to provide evidence for estimating the prevalence of schizophrenia in the Japanese population and informing future epidemiological studies.

Dataset Information

A data-based study in support of Blackbuck related cases from Haryana.

Publications

A data-based study in support of Blackbuck related cases from Haryana.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets