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Mendeley readers
Attention Score in Context
| Title |
Building the Ferretome
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, May 2016
|
| DOI | 10.3389/fninf.2016.00016 |
| Pubmed ID | |
| Authors |
Dmitrii I. Sukhinin, Andreas K. Engel, Paul Manger, Claus C. Hilgetag |
| Abstract |
Databases of structural connections of the mammalian brain, such as CoCoMac (cocomac.g-node.org) or BAMS (https://bams1.org), are valuable resources for the analysis of brain connectivity and the modeling of brain dynamics in species such as the non-human primate or the rodent, and have also contributed to the computational modeling of the human brain. Another animal model that is widely used in electrophysiological or developmental studies is the ferret; however, no systematic compilation of brain connectivity is currently available for this species. Thus, we have started developing a database of anatomical connections and architectonic features of the ferret brain, the Ferret(connect)ome, www.Ferretome.org. The Ferretome database has adapted essential features of the CoCoMac methodology and legacy, such as the CoCoMac data model. This data model was simplified and extended in order to accommodate new data modalities that were not represented previously, such as the cytoarchitecture of brain areas. The Ferretome uses a semantic parcellation of brain regions as well as a logical brain map transformation algorithm (objective relational transformation, ORT). The ORT algorithm was also adopted for the transformation of architecture data. The database is being developed in MySQL and has been populated with literature reports on tract-tracing observations in the ferret brain using a custom-designed web interface that allows efficient and validated simultaneous input and proofreading by multiple curators. The database is equipped with a non-specialist web interface. This interface can be extended to produce connectivity matrices in several formats, including a graphical representation superimposed on established ferret brain maps. An important feature of the Ferretome database is the possibility to trace back entries in connectivity matrices to the original studies archived in the system. Currently, the Ferretome contains 50 reports on connections comprising 20 injection reports with more than 150 labeled source and target areas, the majority reflecting connectivity of subcortical nuclei and 15 descriptions of regional brain architecture. We hope that the Ferretome database will become a useful resource for neuroinformatics and neural modeling, and will support studies of the ferret brain as well as facilitate advances in comparative studies of mesoscopic brain connectivity. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 20% |
| France | 1 | 10% |
| Japan | 1 | 10% |
| United States | 1 | 10% |
| Australia | 1 | 10% |
| Canada | 1 | 10% |
| Russia | 1 | 10% |
| Switzerland | 1 | 10% |
| Unknown | 1 | 10% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 80% |
| Scientists | 2 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 28 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 39% |
| Student > Ph. D. Student | 6 | 21% |
| Student > Bachelor | 2 | 7% |
| Student > Master | 2 | 7% |
| Other | 1 | 4% |
| Other | 3 | 11% |
| Unknown | 3 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 7 | 25% |
| Agricultural and Biological Sciences | 4 | 14% |
| Computer Science | 2 | 7% |
| Arts and Humanities | 1 | 4% |
| Mathematics | 1 | 4% |
| Other | 6 | 21% |
| Unknown | 7 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 June 2017.
All research outputs
#6,797,034
of 25,196,456 outputs
Outputs from Frontiers in Neuroinformatics
#301
of 823 outputs
Outputs of similar age
#90,500
of 311,610 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#6
of 12 outputs
Altmetric has tracked 25,196,456 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 823 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 311,610 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.