import requests
from pprint import pprint as pp
USER='jezcope'
API_TOKEN='Get your own API token!'
GIT_API_URL='https://api.github.com'
r = requests.get(GIT_API_URL + "/users/datacarpentry/repos?per_page=100", auth=(USER, API_TOKEN))
13:58:34.143 [ refine_server] Starting Server bound to '127.0.0.1:333 | |
3' (0ms) | |
13:58:34.196 [ refine_server] Initializing context: '/' from '\\<netw | |
ork path>\Data wrangling\openrefine-2.6-beta.1\webapp' (53ms) | |
13:58:39.001 [ refine_server] Failed to use jdatapath to detect user | |
data path: resorting to environment variables (4805ms) | |
13:58:39.003 [ refine_server] Failed to use jdatapath to detect user | |
data path: resorting to environment variables (2ms) | |
13:58:39.109 [ refine] Starting OpenRefine 2.6-beta.1 [TRUNK]. | |
.. (106ms) |
<?xml version="1.0" encoding="UTF-8"?> | |
<opml version="1.0"> | |
<head> | |
<title>Jez subscriptions in feedly Cloud</title> | |
</head> | |
<body> | |
<outline text="Research data management" title="Research data management"> | |
<outline type="rss" text="data.bris" title="data.bris" xmlUrl="http://data.blogs.ilrt.org/feed/atom/" htmlUrl="http://data.bris.ac.uk"/> | |
<outline type="rss" text="The CMM for RDM Blog" title="The CMM for RDM Blog" xmlUrl="http://rdm.ischool.syr.edu/xwiki/bin/view/Blog/GlobalBlogRss?xpage=plain" htmlUrl="http://rdm.ischool.syr.edu/xwiki/bin/view/Main/WebHome"/> |
logged: server *esup-server*, connection *esup-server* <127.0.0.1:40644>, message accept from 127.0.0.1 | |
name: *esup-server* <127.0.0.1:40644>, sentinel: proc: *esup-server* <127.0.0.1:40644>, event open from 127.0.0.1 | |
logged: server *esup-server*, connection *esup-server* <127.0.0.1:40646>, message accept from 127.0.0.1 | |
name: *esup-server* <127.0.0.1:40646>, sentinel: proc: *esup-server* <127.0.0.1:40646>, event open from 127.0.0.1 | |
Set information from port 40644 to be the log process | |
Set information from port 40646 to be the results process |
Jez Cope, Imperial College London/University of Sheffield, United Kingdom
Reproducibility has always been a central pillar of scientific research. The journal article is the gold standard in enabling this, describing the motivation, methods, results and conclusions of a piece of work. It is generally impossible to include the full underlying data within a paper, so authors instead make do with summaries, statistics and carefully-selected subsets. This approach makes it difficult to validate the conclusions of the paper, and to overcome this shortcoming there is increasing pressure on researchers to improve access to their underlying datasets.
However, the data is only half of the story. The calculations required to generate or analyse it are often too complex to give more than a general sketch in the methods section. When another researcher tries to reproduce the analysis, they quickly discover that there are many implementation details and e
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# A minimalist workout generator: | |
# | |
# - No commandline options | |
# - All configuration and data in this file | |
import random | |
GROUPS = { | |
'Core front': [ | |
'Crunches', |