ics-publication-records
Publication records with co-authors network for Institute of Computer Science, CAS
Science Score: 31.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (2.9%) to scientific vocabulary
Repository
Publication records with co-authors network for Institute of Computer Science, CAS
Basic Info
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
ICS-publication-records
Publication records with co-authors network for Institute of Computer Science, CAS
Basic statistics of papers, monographs and conference submissions acknowledged to Institute of Computer Science, Czech Academy of Sciences since 1950 till now (as of April 26, 2017). Data was acquired from the repository of CAS as “UIVT-O” AND Document Type = (“B” OR “C” OR “G” OR “H” OR “J” OR “JI” OR “M” OR “P”)
Citation data was updated June 10, 2017 using the Web of Science Links Article Match Retrieval (WoS LAMR) service.
Owner
- Name: Nikola Jajcay
- Login: jajcayn
- Kind: user
- Location: Prague
- Company: National Institute of Mental Health // Seerlinq
- Website: jajcayn.github.io
- Repositories: 18
- Profile: https://github.com/jajcayn
computational neuroscience and other sciences
Citation (citation-stats.py)
#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import scipy.stats as sts
import pandas as pd
import plot_functions as pf # custom plotting
import matplotlib.pyplot as plt
df = pd.read_pickle("UI-data-w-WoS-LAMR-citations.bin")
print len(df)
print df.columns
# citations
yearly_cit = df.groupby(['RokVydání'])['PočetCitací'].sum()
total_cit = df['PočetCitací'].sum()
mean_cit, sd_cit, max_cit = df['PočetCitací'].mean(), df['PočetCitací'].std(), df['PočetCitací'].max()
print yearly_cit
# total citations, mean and SD, max cit
print total_cit, mean_cit, sd_cit, max_cit
pf.plot_year_time_series(yearly_cit.index, yearly_cit.values, ylabel = None, legend = ['citations'],
fname = "figs/yearly_citations.eps")
ifactor, cit = np.array(df['ImpFaktor']), np.array(df['PočetCitací'])
nans = np.logical_or(np.isnan(cit), np.isnan(ifactor))
plt.figure(figsize = (7,7))
plt.scatter(cit, ifactor, marker = 'x', s = 20, color = '#00734a')
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['left'].set_visible(False)
plt.gca().spines['bottom'].set_visible(False)
slope, intercept, _, _, _ = sts.linregress(cit[~nans], ifactor[~nans])
plt.plot(np.linspace(cit[~nans].min(), cit[~nans].max(), 200), slope*np.linspace(cit[~nans].min(), cit[~nans].max(), 200) + intercept,
linewidth = 1.4, color = "#00734a")
# plt.gca().set_xscale('log')
# plt.gca().set_yscale('log')
plt.xticks(size = 22)
plt.yticks(size = 22)
plt.ylabel("IF", size = 27)
plt.xlabel("CITATIONS", size = 27)
print sts.pearsonr(cit[~nans], ifactor[~nans])
plt.savefig("figs/IF-vs-citations.eps", bbox_inches = 'tight')
GitHub Events
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Last Year
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Last synced: over 1 year ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0