1,720,958 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
github.com/aofarrel/SRANWRP/pull_FASTQs_from_SRA_by_run
No full text# SRAnwrp [](https://hub.docker.com/r/ashedpotatoes/sranwrp/tags)
SRAnwrp ("Saran Wrap") envelops several SRA-related tools in the warm, polyethylene embrace of a single Ubuntu-based Docker image and some optional assorted workflows. For the sake of simplicity, releases on main follow the same versioning scheme as the Docker image.
## What tasks can it perform?
The combination of e-direct and sra-tools allows it do basically anything you can do from SRA's website. These exist in the form of WDL workflows -- [more on WDL here](./wdl.md).
### Pulling FASTQs
* [Pull paired FASTQs from a list of run accessions (SRR/ERR/DRR)](./workflows/pull_paired_FASTQ_by_run_accession.wdl)
* [Pull paired FASTQs from a lit of BioSample accessions - can be SRS or SAME notation](./workflows/pull_paired_FASTQ_by_biosample.wdl)
* Plus some bonus [non-workflow pulling tasks](./tasks/pull_fastqs.wdl)
* *Note* -- as a pre-3.0.5 version of fasterq-dump is being used, pulling non-Illumina fastqs is not supported.
* *Note* -- it is recommended you set the disk_size variable to 20x the size of the largest .sra that you want to download.
### Getting Organism + TaxID from a list of BioProject/BioSample accessions
There's a lot of BioProjects on SRA, and some of them are multi-species. Use [this workflow](./workflows/get_organisms_from_bioproject.wdl) to get a list of all run accessions, and said run accessions' species and TaxIDs, from a list of BioProject accessions. If you instead have a list of BioSamples, use [this workflow](./workflows/get_organisms_from_biosample.wdl) to get species and taxid (as well as a list of all run accessions).
### Getting sample accessions from run accessions (SRR/ERR/DRR)
If you have a list of run accessions, [this workflow](./workflows/get_samples_from_runs.wdl) will get a list of sample accessions that they cover. Some samples have more than one run -- those samples will only appear in the output once.
### Other stuff?
Here's [some other tasks](./tasks/processing_tasks.wdl) that can help you convert between data types.
## What's included in the Docker image?
Non-exhaustive list:
* The TB reference genome and a BED of its commonly masked regions
* bash-5.1.16(1)-release
* [bedtools-latest](https://github.com/arq5x/bedtools2)
* [bc-latest](https://www.gnu.org/software/bc/)
* bcftools-1.16
* cpan-latest
* curl-latest
* [entrez-direct-latest](https://www.ncbi.nlm.nih.gov/books/NBK179288/) (aka edirect)
* gcc-latest
* git-latest
* htslib-1.16
* make-latest
* Matplotlib-latest
* [numpy-latest](https://github.com/numpy/numpy)
* [pandas-latest](https://github.com/pandas-dev/pandas)
* [pigz-latest](https://github.com/madler/pigz)
* python-3.12
* **note:** must be called with `python3` instead of `python` (and `pip3` instead of `pip`) when running non-interactively
* [samtools-1.16](https://github.com/samtools/samtools)
* mpileup, minimap2, fixmate, etc
* [seqtk-latest](https://github.com/lh3/seqtk)
* [sra-tools-3.0.1](https://github.com/ncbi/sra-tools) (aka SRAtools, SRA tools, SRA toolkit, etc)
* align-info, fastq-dump, fasterq-dump, prefetch, sam-dump, sra-pileup, etc
* fyi: [ncbi/ncbi-vdb](https://github.com/ncbi/ncbi-vdb) was merged with sra-tools in sra-tools-3.0.0 and vdb-get was retired in 3.0.1
* sudo-latest
* [taxoniumtools-latest](https://github.com/theosanderson/taxonium/tree/master/taxoniumtools)
* tree-latest
* vim-latest
* wget-latest
## Who builds?
Right now, the image is built and pushed manually. You'll need to include your own copy of the TB reference tarball -- it can be created with clockwork refprep, or downloaded from [this Google bucket](https://console.cloud.google.com/storage/browser/_details/topmed_workflow_testing/tb/ref/index_H37Rv_reference_output/Ref.H37Rv.tar). MD5s are provided in this repo as a double-check.
## Why?
* Docker Hub's latest version of staphb/sratoolkit, as of my writing this in October 2022, [runs version 2.9.2 (see command 15)](https://hub.docker.com/layers/staphb/sratoolkit/latest/images/sha256-84fc990e6d04f263d7bea82dcbff7f5dd9182ab5234314bb0daf2e2db977e4a0?context=explore), which [doesn't work at all anymore](https://github.com/ncbi/sra-tools/issues/714)
* Existing Docker images tend to contain either the SRA toolkit or Entrez Direct, not both
* Building SRA Toolkit on your own, without conda, is not intuitive
* Building SRA Toolkit on your own, with conda, is also not intutive (you usually end up with v2.10 which only sometimes works)
* No need to run `vdb-config --interactive` or any other interactive process before using anything in this image; SRA Toolkit's config file is generated while building the imag
github.com/aofarrel/SRANWRP/pull_FASTQs_from_SRA_by_run
No full text# SRAnwrp [](https://hub.docker.com/r/ashedpotatoes/sranwrp/tags)
SRAnwrp ("Saran Wrap") envelops several SRA-related tools in the warm, polyethylene embrace of a single Ubuntu-based Docker image and some optional assorted workflows. For the sake of simplicity, releases on main follow the same versioning scheme as the Docker image.
## What tasks can it perform?
The combination of e-direct and sra-tools allows it do basically anything you can do from SRA's website. These exist in the form of WDL workflows -- [more on WDL here](./wdl.md).
### Pulling FASTQs
* [Pull paired FASTQs from a list of run accessions (SRR/ERR/DRR)](./workflows/pull_paired_FASTQ_by_run_accession.wdl)
* [Pull paired FASTQs from a lit of BioSample accessions - can be SRS or SAME notation](./workflows/pull_paired_FASTQ_by_biosample.wdl)
* Plus some bonus [non-workflow pulling tasks](./tasks/pull_fastqs.wdl)
* *Note* -- as a pre-3.0.5 version of fasterq-dump is being used, pulling non-Illumina fastqs is not supported.
* *Note* -- it is recommended you set the disk_size variable to 20x the size of the largest .sra that you want to download.
### Getting Organism + TaxID from a list of BioProject/BioSample accessions
There's a lot of BioProjects on SRA, and some of them are multi-species. Use [this workflow](./workflows/get_organisms_from_bioproject.wdl) to get a list of all run accessions, and said run accessions' species and TaxIDs, from a list of BioProject accessions. If you instead have a list of BioSamples, use [this workflow](./workflows/get_organisms_from_biosample.wdl) to get species and taxid (as well as a list of all run accessions).
### Getting sample accessions from run accessions (SRR/ERR/DRR)
If you have a list of run accessions, [this workflow](./workflows/get_samples_from_runs.wdl) will get a list of sample accessions that they cover. Some samples have more than one run -- those samples will only appear in the output once.
### Other stuff?
Here's [some other tasks](./tasks/processing_tasks.wdl) that can help you convert between data types.
## What's included in the Docker image?
Non-exhaustive list:
* The TB reference genome and a BED of its commonly masked regions
* bash-5.1.16(1)-release
* [bedtools-latest](https://github.com/arq5x/bedtools2)
* [bc-latest](https://www.gnu.org/software/bc/)
* bcftools-1.16
* cpan-latest
* curl-latest
* [entrez-direct-latest](https://www.ncbi.nlm.nih.gov/books/NBK179288/) (aka edirect)
* gcc-latest
* git-latest
* htslib-1.16
* make-latest
* Matplotlib-latest
* [numpy-latest](https://github.com/numpy/numpy)
* [pandas-latest](https://github.com/pandas-dev/pandas)
* [pigz-latest](https://github.com/madler/pigz)
* python-3.12
* **note:** must be called with `python3` instead of `python` (and `pip3` instead of `pip`) when running non-interactively
* [samtools-1.16](https://github.com/samtools/samtools)
* mpileup, minimap2, fixmate, etc
* [seqtk-latest](https://github.com/lh3/seqtk)
* [sra-tools-3.0.1](https://github.com/ncbi/sra-tools) (aka SRAtools, SRA tools, SRA toolkit, etc)
* align-info, fastq-dump, fasterq-dump, prefetch, sam-dump, sra-pileup, etc
* fyi: [ncbi/ncbi-vdb](https://github.com/ncbi/ncbi-vdb) was merged with sra-tools in sra-tools-3.0.0 and vdb-get was retired in 3.0.1
* sudo-latest
* [taxoniumtools-latest](https://github.com/theosanderson/taxonium/tree/master/taxoniumtools)
* tree-latest
* vim-latest
* wget-latest
## Who builds?
Right now, the image is built and pushed manually. You'll need to include your own copy of the TB reference tarball -- it can be created with clockwork refprep, or downloaded from [this Google bucket](https://console.cloud.google.com/storage/browser/_details/topmed_workflow_testing/tb/ref/index_H37Rv_reference_output/Ref.H37Rv.tar). MD5s are provided in this repo as a double-check.
## Why?
* Docker Hub's latest version of staphb/sratoolkit, as of my writing this in October 2022, [runs version 2.9.2 (see command 15)](https://hub.docker.com/layers/staphb/sratoolkit/latest/images/sha256-84fc990e6d04f263d7bea82dcbff7f5dd9182ab5234314bb0daf2e2db977e4a0?context=explore), which [doesn't work at all anymore](https://github.com/ncbi/sra-tools/issues/714)
* Existing Docker images tend to contain either the SRA toolkit or Entrez Direct, not both
* Building SRA Toolkit on your own, without conda, is not intuitive
* Building SRA Toolkit on your own, with conda, is also not intutive (you usually end up with v2.10 which only sometimes works)
* No need to run `vdb-config --interactive` or any other interactive process before using anything in this image; SRA Toolkit's config file is generated while building the imag
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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