{
  "authors": [
    {
      "name": "0728a400aa1c86fe"
    }
  ], 
  "end_iso_date": "cur.", 
  "notes": [
    {
      "italic": "yes", 
      "note": "Opened to public in 2015"
    }, 
    {
      "italic": "yes", 
      "note": "Pre-released in May, 2015 (V1.2, permissive and simplified BSD license)"
    }, 
    {
      "italic": "yes", 
      "note": "Partially funded by EU FP7 TETRACOM 6-months grant [$#cm_5ae0ce43ceb4d73c_72d7c56eac47e48d#$]"
    }, 
    {
      "italic": "yes", 
      "note": "Supports our new publication model in computer engineering where all research artifacts (tools, benchmarks, datasets, models) are continuously shared and validated by the community [$#cm_29db2248aba45e59_40a4b58adfb594a8#$,$#cm_77154d189d2e226c_7dd14d78bfdb9af2#$,$#cm_77154d189d2e226c_9aad4281e1c4e42a#$,$#cm_77154d189d2e226c_cc51a9904ca8ad12#$,$#cm_77154d189d2e226c_b48be4e687d9401f#$,$#cm_77154d189d2e226c_be9926d412125773#$]"
    }, 
    {
      "italic": "yes", 
      "note": "Awards [$#cm_5ae0ce43ceb4d73c_ab884a345fe99806#$]"
    }, 
    {
      "italic": "yes", 
      "note": "Guest lectures [$#cm_7b226dd503e5461e_b0d0a774a6da0ac1#$]"
    }, 
    {
      "italic": "yes", 
      "note": "Associated publications [$#cm_29db2248aba45e59_cd11e3a188574d80#$, $#cm_29db2248aba45e59_9671da4c2f971915#$, $#cm_29db2248aba45e59_6f40bc99c4f7df58#$, $#cm_29db2248aba45e59_0c44d9a2db3de3c9#$, $#cm_29db2248aba45e59_cd11e3a188574d80#$]"
    }, 
    {
      "italic": "yes", 
      "note": "Associated events [$#cm_77154d189d2e226c_7dd14d78bfdb9af2#$,$#cm_77154d189d2e226c_9aad4281e1c4e42a#$,$#cm_77154d189d2e226c_cc51a9904ca8ad12#$,$#cm_77154d189d2e226c_b48be4e687d9401f#$,$#cm_77154d189d2e226c_be9926d412125773#$]"
    }, 
    {
      "italic": "yes", 
      "note": "Associated live repository [$#cm_4d63887b5cda54aa_df354bed9593719c#$] - hosting in Azure is sponsored by Microsoft"
    }, 
    {
      "italic": "yes", 
      "note": "ReQuEST: 1st open, reproducible and Pareto efficient SW/HW co-design competition for deep learning (speed, accuracy, costs) uses CK to put ML and systems researchers on the same page and share produced artifacts as reusable, portable and customizable components (open science) [$#cm_77154d189d2e226c_30191f423b87244b#$]"
    }
  ], 
  "start_iso_date": "2014-11-01", 
  "title": "Collective Knowledge Framework and Repository (CK aka cTuning4, BSD-license)", 
  "type": {
    "name": "software"
  }, 
  "urls": [
    {
      "title": "Website", 
      "url": "http://cKnowledge.org"
    }, 
    {
      "title": "Partners / users", 
      "url": "http://cKnowledge.org/partners.html"
    }, 
    {
      "title": "Use cases", 
      "url": "http://cKnowledge.org/use_cases.html"
    }, 
    {
      "title": "Public repository to crowdsource optimizations", 
      "url": "http://cKnowledge.org/repo"
    }, 
    {
      "title": "ACM project to integrate CK with Digital Library funded by Sloan Foundation", 
      "url": "https://dl.acm.org/docs/reproducibility.cfm"
    }, 
    {
      "title": "Artifact Evaluation", 
      "url": "http://cTuning.org/ae"
    }, 
    {
      "title": "Development GitHub Website", 
      "url": "http://github.com/ctuning/ck"
    }, 
    {
      "title": "Documentation", 
      "url": "http://github.com/ctuning/ck/wiki"
    }, 
    {
      "title": "Google Group discussions", 
      "url": "https://groups.google.com/d/forum/collective-knowledge"
    }
  ], 
  "why": "- Collective Knowledge (CK) is a light-weight, portable, modular and python-based framework, repository, web service and SDK to organize, describe, cross-link and share user code, data, experimental setups and meta information as unified and reusable components with JSON API via standard Git services (such as GITHUB or BitBucket)."
}

{}