[Tex/LaTex] URL issues in bibliography

datenatbiburl

I am having problems building my bibliography and would like some advice. The main issues are:

  1. 'URL address' and 'date accessed' not always included in the bibliography entries for websites.
  2. URL addresses in the bibliography not always in line with other text (please see screenshot).
  3. Bibliography entries are not always numbered sequentially in the article and bibliography.
  4. Maintain same style for referening articles (as shown in the example).

I have tried several bibliography styles and looked through similar posts on tex.stackexchange but none seem to solve all of the problems in one go i.e. in the screenshot example, only 3. is solved but in other attempts, it will be 1. etc…I also tried using Biblatex and Biber but I get the following error message:

did you pass the "backend=biber" option to BibLaTeX?

I am using pdfLaTeX+MakeIndex+BibteX typeset to compile my document. The .bib library file is created using Mendeley. I attach my Bibtex, input code and a screenshot of the output.

Bibtex

@misc{Car2018,
title = {{Carillion fallout: 'They've literally locked the gate' - BBC News}},
url = {http://www.bbc.co.uk/news/business-42690807},
urldate = {2018-01-15}

@misc{Wik2018,
title = {{List of software for Monte Carlo molecular modeling - Wikipedia}},
url = {https://en.wikipedia.org/wiki/List{\_}of{\_}software{\_}for{\_}Monte{\_}Carlo{\_}molecular{\_}modeling},
urldate = {2018-01-15}
}

@article{McKenna2010,
abstract = {Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.},
author = {McKenna, Aaron and Hanna, Matthew and Banks, Eric and Sivachenko, Andrey and Cibulskis, Kristian and Kernytsky, Andrew and Garimella, Kiran and Altshuler, David and Gabriel, Stacey and Daly, Mark and DePristo, Mark A},
doi = {10.1101/gr.107524.110},
issn = {1549-5469},
journal = {Genome research},
month = {sep},
number = {9},
pages = {1297--303},
pmid = {20644199},
publisher = {Cold Spring Harbor Laboratory Press},
title = {{The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20644199 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2928508},
volume = {20},
year = {2010}
}

Input code (using MikTex 2.9, Texworks)

\documentclass[a4paper,11pt]{article}
\usepackage{color}
\usepackage{graphicx}
\usepackage{times}
\usepackage{adjustbox}
\usepackage[numbers]{natbib}
\bibliographystyle{unsrtnat}

\begin{document}

Webpage test 1 \cite{Car2018}.
Webpage test 2 \cite{Wik2018}.
Article test 1 \cite{McKenna2010}

\bibliography{library}

\end{document}

enter image description here

I know there are several problems here but I'm not sure which one is attributed to Mendeley or TexWorks which is why I have put them in one post. Any advice you can give would be great!

Best Answer

Use url. This is a version of your source

\documentclass[a4paper,11pt]{article}
\usepackage{color}
\usepackage{graphicx}
\usepackage{times}
\usepackage{adjustbox}
\usepackage[numbers]{natbib}
\bibliographystyle{unsrtnat}


\usepackage[hyphens]{url}
\usepackage[T1]{fontenc}

\begin{document}

Webpage test 1 \cite{Car2018}.
Webpage test 2 \cite{Wik2018}.
Article test 1 \cite{McKenna2010}

\bibliography{library}

\end{document}

The unsrtnat style does not make use of the urldate entry. But you could use note instead

 @misc{Car2018,
 title = {{Carillion fallout: 'They've literally locked the gate' - BBC News}},
 url = {http://www.bbc.co.uk/news/business-42690807},
 note = {Visited: 2018-01-15}
 }

 @misc{Wik2018,
 title = {{List of software for Monte Carlo molecular modeling - Wikipedia}},
 url = {https://en.wikipedia.org/wiki/List_of_software_for_Monte_Carlo_molecular_modeling},
 note = {Visited: 2018-01-15}
 }

 @article{McKenna2010,
 abstract = {Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.},
 author = {McKenna, Aaron and Hanna, Matthew and Banks, Eric and Sivachenko, Andrey and Cibulskis, Kristian and Kernytsky, Andrew and Garimella, Kiran and Altshuler, David and Gabriel, Stacey and Daly, Mark and DePristo, Mark A},
 doi = {10.1101/gr.107524.110},
 issn = {1549-5469},
 journal = {Genome research},
 month = {sep},
 number = {9},
 pages = {1297--303},
 pmid = {20644199},
 publisher = {Cold Spring Harbor Laboratory Press},
 title = {{The Genome Analysis Toolkit: a MapReduce framework for 
 analyzing next-generation DNA sequencing data.}},
 url = {http://www.ncbi.nlm.nih.gov/pubmed/20644199 
 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2928508},
 volume = {20},
 year = {2010}
 }

enter image description here

Related Question