The Chinese place a higher value on community good versus individual rights, so most feel that, if social credit will bring a safer, more secure, more stable society, then bring it on
3,540 Matching Annotations
- Nov 2018
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www.abc.net.au www.abc.net.au
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multithreaded.stitchfix.com multithreaded.stitchfix.com
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Unless you need to push the boundaries of what these technologies are capable of, you probably don’t need a highly specialized team of dedicated engineers to build solutions on top of them. If you manage to hire them, they will be bored. If they are bored, they will leave you for Google, Facebook, LinkedIn, Twitter, … – places where their expertise is actually needed. If they are not bored, chances are they are pretty mediocre. Mediocre engineers really excel at building enormously over complicated, awful-to-work-with messes they call “solutions”. Messes tend to necessitate specialization.
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Local file Local file
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For the second, we could try to detect inconsistencies, eitherby inspecting samples of the class hierarchy
Yes, that's what I do when doing quality work on the taxonomy (with the tool wdtaxonomy)
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Possible relations between Items
This only includes properties of data-type item?! It should be made more clear because the majority of Wikidata classes has other data types.
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A KG typically spans across several domains and is built on topof a conceptual schema, orontology, which defines what types of entities (classes) are allowed inthe graph, alongside the types ofpropertiesthey can have
Wikidata differs from typical KG as it is not build on top of classes (entity types). Any item (entity) can be connected by any property. Wikidata's only strict "classes" in the sense of KG classes are its data types (item, lemma, monolingual string...).
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Entscheidend ist, dass sie Herren des Verfahrens bleiben - und eine Vision für das neue Maschinenzeitalter entwickeln.
Es sieht für mich nicht eigentlich so aus als wären wir jemals die "Herren des Verfahrens" gewesen. Und auch darum geht es ja bei Marx. Denke ich.
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digciz.org digciz.org
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Does the widespread and routine collection of student data in ever new and potentially more-invasive forms risk normalizing and numbing students to the potential privacy and security risks?
What happens if we turn this around - given a widespread and routine data collection culture which normalizes and numbs students to risk as early as K-8, what are our responsibilities (and strategies) to educate around this culture? And how do our institutional practices relate to that educational mission?
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- Oct 2018
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mfeldstein.com mfeldstein.com
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As a recap, Chegg discovered on September 19th a data breach dating back to April that "an unauthorized party" accessed a data base with access to "a Chegg user’s name, email address, shipping address, Chegg username, and hashed Chegg password" but no financial information or social security numbers. The company has not disclosed, or is unsure of, how many of the 40 million users had their personal information stolen.
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www.springboard.com www.springboard.com
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tl;dr: data engineer = software, coding, cleaning data sets data architects = structure the technology to manage data models and database admin data scientist = stats + math models business analysts = communication and domain expertise
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linter.structured-data.org linter.structured-data.org
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webdatacommons.org webdatacommons.org
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www.nature.com www.nature.com
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De novo transcriptome profiling of highly purified human lymphocytes primary cells
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www.textrelease.com www.textrelease.com
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research publications are not research data
they could be, if used as part of a text mining corpus, for example
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reswitched.tech reswitched.tech
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- Sep 2018
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bentaylor2.github.io bentaylor2.github.io
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drive.google.com drive.google.com
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Qualitative analysis
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nelsonslog.wordpress.com nelsonslog.wordpress.com
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I love the voice of their help page. Someone very opinionated (in a good way) is building this product. I particularly like this quote: Your data is a liability to us, not an asset.
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www.hpi.uni-potsdam.de www.hpi.uni-potsdam.de
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End-Users
Because Grafoscopio was used in critical digital literacy workshops, dealing with data activism and journalism, the intended users are people who don't know how to program necessarily, but are not afraid of learning to code to express their concerns (as activists, journalists and citizens in general) and if fact are wiling to do so.
Tool adaptation was "natural" of the workshops, because the idea was to extend the tool so it can deal with authentic problems at hand (as reported extensively in the PhD thesis) and digital citizenship curriculum was build in the events as a memory of how we deal with the problems. But critical digital literacy is a long process, so coding as a non-programmers knowledge in service of wider populations able to express in code, data and visualizations citizen concerns is a long time process.
Visibility, scalability and sustainablitiy of such critical digital literacy endeavors where communities and digital tools change each other mutually is still an open problem, even more considering their location in the Global South (despite addressing contextualized global problems).
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en.wikipedia.org en.wikipedia.org
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In October 2014 the Open Knowledge Foundation recommends the Creative Commons CC0 license to dedicate content to the public domain,[51][52] and the Open Data Commons Public Domain Dedication and License (PDDL) for data.[53]
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blog.okfn.org blog.okfn.org
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When data is public domain it is recommended to use the CC0 Public Domain license for clarity.
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en.wikipedia.org en.wikipedia.org
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8.5 million square kilometers
8500000
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en.wikipedia.org en.wikipedia.org
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42,924 km2
42924
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en.wikipedia.org en.wikipedia.org
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17,125,200 square kilometres
17125200
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www.statisticssolutions.com www.statisticssolutions.com
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predictive analysis
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.
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- Aug 2018
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autumm.edtech.fm autumm.edtech.fm
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this possibility of increased ownership and agency over technology and a somewhat romantic idea I have that this can transfer to inspire ownership and agency over learning
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techcrunch.com techcrunch.com
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A file containing personal information of 14.8 million Texas residents was discovered on an unsecured server. It is not clear who owns the server, but the data was likely compiled by Data Trust, a firm created by the GOP.
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www.numbeo.com www.numbeo.com
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30.40
30.40
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3,011.00
3011
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www.numbeo.com www.numbeo.com
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43,600.00
43600
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56.83%
56.83
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www.numbeo.com www.numbeo.com
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Health Care System Index: 85.85
85.85
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www.numbeo.com www.numbeo.com
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Safety Index: 36.52
36.52
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URL
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www.numbeo.com www.numbeo.com
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45.24 Moderate
45.24
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42.86 Moderate
42.86
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70.00 High
70
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www.numbeo.com www.numbeo.com
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83.33 Very High
83.33
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5.56 Very Low
5.56
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22.22 Low
22.22
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www.numbeo.com www.numbeo.com
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75.00 High
75
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50.00 Moderate
50
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37.50 Low
37.50
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www.numbeo.com www.numbeo.com
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75.00 High
75
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0
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0
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www.numbeo.com www.numbeo.com
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83.21 Very High
83.21
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12.86 Very Low
12.86
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15.58 Very Low
15.58
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www.numbeo.com www.numbeo.com
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57.48 Moderate
57.48
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45.95 Moderate
45.95
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45.79 Moderate
45.79
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www.numbeo.com www.numbeo.com
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82.14 Very High
82.14
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17.65 Very Low
17.65
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15.28 Very Low
15.28
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www.numbeo.com www.numbeo.com
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86.36
86.36
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Dissatisfaction with Garbage Disposal
20.13
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www.numbeo.com www.numbeo.com
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75.00 High
75
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13.89 Very Low
13.89
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13.89 Very Low
13.89
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www.numbeo.com www.numbeo.com
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Safety Index: 76.31
76.31
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URL
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www.numbeo.com www.numbeo.com
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Health Care System Index: 80.05
80.05
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URL
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www.numbeo.com www.numbeo.com
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922.46
922.46
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34.46
34.46
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www.numbeo.com www.numbeo.com
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44,000.00
44000
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39.83%
39.83
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en.wikipedia.org en.wikipedia.org
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230,537
230537
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689.59 km2 (266.25 sq mi)
689.59
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Finland
Finland
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www.numbeo.com www.numbeo.com
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Safety Index: 58.40
58.40
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URL
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www.numbeo.com www.numbeo.com
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Health Care System Index: 79.32
79.32
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URL
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www.numbeo.com www.numbeo.com
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74.11 High
74.11
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41.07 Moderate
41.07
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25.93 Low
25.93
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www.numbeo.com www.numbeo.com
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4,182.14
4182.14
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36.64
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www.numbeo.com www.numbeo.com
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43,600.00
43600
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58.27%
58.27
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en.wikipedia.org en.wikipedia.org
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506,615
506615
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47.87 km2 (18.48 sq mi)
47.87
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France
France
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www.numbeo.com www.numbeo.com
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51,300.00
51300
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www.numbeo.com www.numbeo.com
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51,300.00
51300
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51,300
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43.51%
43.51
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www.numbeo.com www.numbeo.com
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51,300.00
51300
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Mortgage as Percentange of Income: 66.37%
66.37
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www.numbeo.com www.numbeo.com
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43,600.00
43600
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Mortgage as Percentange of Income: 107.95%
107.95
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www.numbeo.com www.numbeo.com
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44,000.00
44000
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69.68%
69.68
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www.numbeo.com www.numbeo.com
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44,000.00
44000
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46.99%
46.99
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www.numbeo.com www.numbeo.com
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Safety Index: 85.60
85.60
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www.numbeo.com www.numbeo.com
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4,122.00
4122
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39.60
39.60
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www.numbeo.com www.numbeo.com
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Health Care System Index: 69.44
69.44
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en.wikipedia.org en.wikipedia.org
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528.03 km2 (203.87 sq mi)
528.03
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Finland
Finland
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277,375
277375
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URL
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www.numbeo.com www.numbeo.com
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36.17
36.17
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1,831.35
1831.35
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www.numbeo.com www.numbeo.com
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36.17 Moderate
36.17
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74.32 High
74.32
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77.90 High
77.90
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en.wikipedia.org en.wikipedia.org
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642,045
642045
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715.48 km2 (276.25 sq mi)
715.48
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Finland
Finland
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www.numbeo.com www.numbeo.com
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CO2 Emission Index: 3,329.29
3329.29
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Time Index (in minutes): 43.85
43.85
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www.numbeo.com www.numbeo.com
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Health Care System Index: 74.23
74.23
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www.numbeo.com www.numbeo.com
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Safety Index: 47.90
47.90
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URL
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en.wikipedia.org en.wikipedia.org
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2,206,488
2206488
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105.4 km2 (40.7 sq mi)
105.4
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France
France
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www.numbeo.com www.numbeo.com
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43.85 Moderate
43.85
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74.23 High
74.23
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47.90 Moderate
47.90
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www.numbeo.com www.numbeo.com
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43.65 Moderate
43.65
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Traffic Commute Time Index 15.00 Very Low
15
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Health Care Index 91.67 Very High
91.67
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en.wikipedia.org en.wikipedia.org
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Sweden
Sweden
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151000
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52.94 km2 (20.44 sq mi)
52.94
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www.numbeo.com www.numbeo.com
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44.00 Moderate
44
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49.87 Moderate
49.87
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63.89 High
63.89
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www.numbeo.com www.numbeo.com
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Safety Index: 43.65
43.65
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URL
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www.numbeo.com www.numbeo.com
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Safety Index: 49.87
49.87
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URL
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www.numbeo.com www.numbeo.com
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2,560.17
2560.17
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21.96
21.96
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www.numbeo.com www.numbeo.com
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Health Care System Index: 70.31
70.31
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www.numbeo.com www.numbeo.com
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Safety Index: 55.34
55.34
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URL
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en.wikipedia.org en.wikipedia.org
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Sweden
Sweden
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572,779
572779
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447.76 km2 (172.88 sq mi)
447
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www.oxfam.org.uk www.oxfam.org.uk
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13
13
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4
4
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2
2
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en.wikipedia.org en.wikipedia.org
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10
10
-
10
10
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10
10
-
10
10
-
13
13
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31
31
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31
31
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31
31
-
13
13
-
13
-
13
13
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-
en.wikipedia.org en.wikipedia.org
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1,602,457
1602457
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1,602,457
1602457
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1,602,457
1602457
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24,780,180
24780180
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2,929,963 30 32.2 18 9,219,679 26 101.3 5
2929963
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24,780,180
24780180
-
24,780,180
24780180
-
2,929,963 30 32.2 18 9,219,679 26 101.3 5
2929963
-
2,929,963 30 32.2 18 9,219,679 26 101.3 5
2929963
-
2,929,963 30 32.2 18 9,219,679 26 101.3 5
29290963
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www.transparency.org www.transparency.org
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Score 85 / 100
85
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Score 85 / 100
85
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Score 85 / 100
85
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www.transparency.org www.transparency.org
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Score 70 / 100
70
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Score 70 / 100
70
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Score 70 / 100
70
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www.transparency.org www.transparency.org
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Score 84 / 100
84
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Score 84 / 100
84
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Score 84 / 100
84
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en.wikipedia.org en.wikipedia.org
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0.855
0.855
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0.847
0.847
-
0.847
0.847
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0.847
0.847
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0.839
0.839
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0.855
0.855
-
0.839
0.839
-
0.839
0.839
-
0.855
0.855
-
0.855
0.855
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-
www.numbeo.com www.numbeo.com
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CO2 Emission Index: 2,527.37
2,527.37
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Time Index (in minutes): 40.30
40.30
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www.numbeo.com www.numbeo.com
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Mortgage as Percentange of Income: 90.71%
90.71
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www.numbeo.com www.numbeo.com
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Health Care System Index: 67.03
67.03
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-
www.numbeo.com www.numbeo.com
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Safety Index: 52.05
52.05
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URL
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en.wikipedia.org en.wikipedia.org
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€65,700 (US$74,000)
74,000
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952,058
952,058
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Sweden
Sweden
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188 km2 (73 sq mi)
188
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-
en.wikipedia.org en.wikipedia.org
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Largest census metropolitan areas in Canada by population (2016 Census) viewtalkedit CMA Province Population CMA Province Population Toronto Ontario 5,928,040 London Ontario 494,069 Montreal Quebec 4,098,927 St. Catharines–Niagara Ontario 406,074 Vancouver British Columbia 2,463,431 Halifax Nova Scotia 403,390 Calgary Alberta 1,392,609 Oshawa Ontario 379,848 Ottawa–Gatineau Ontario–Quebec 1,323,783 Victoria British Columbia 367,770 Edmonton Alberta 1,321,426 Windsor Ontario 329,144 Quebec Quebec 800,296 Saskatoon Saskatchewan 295,095 Winnipeg Manitoba 778,489 Regina Saskatchewan 236,481 Hamilton Ontario 747,545 Sherbrooke Quebec 212,105 Kitchener–Cambridge–Waterloo Ontario
5928040
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Largest census metropolitan areas in Canada by population (2016 Census) viewtalkedit CMA Province Population CMA Province Population Toronto Ontario 5,928,040 London Ontario 494,069 Montreal Quebec 4,098,927 St. Catharines–Niagara Ontario 406,074 Vancouver British Columbia 2,463,431 Halifax Nova Scotia 403,390 Calgary Alberta 1,392,609
4098927
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Vancouver British Columbia 2,463,431
2463431
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Largest census metropolitan areas in Canada by population (2016 Census) viewtalkedit CMA Province Population CMA Province Population Toronto Ontario 5,928,040 London Ontario 494,069 Montreal Quebec 4,098,927 St. Catharines–Niagara Ontario 406,074 Vancouver British Columbia 2,463,431 Halifax Nova Scotia 403,390 Calgary Alberta 1,392,609
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en.wikipedia.org en.wikipedia.org
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1 Sydney NSW 5,131,326 11 Hobart Tas 224,462 BrisbanePerth 2 Melbourne Vic 4,850,740 12 Geelong Vic 192,393 3 Brisbane Qld 2,408,223 13 Townsville Qld 178,864 4 Perth WA 2,043,138 14 Cairns Qld 150,041 5 Adelaide SA 1,333,927
5131326
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2 Melbourne Vic 4,850,740
4850740
-
4 Perth WA 2,043,138
2043138
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5 Adelaide SA 1,333,927
1333927
-
-
en.wikipedia.org en.wikipedia.org
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1,368,549
10,368,549
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en.wikipedia.org en.wikipedia.org
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Region Capital Area (km2) Area (sq mi) Population Nominal GDP EURO billions (2016)[148] Nominal GDP EURO per capita(2016) [149] Abruzzo L'Aquila 10,763 4,156 1,331,574 32 24,100 Aosta Valley Aosta 3,263 1,260 128,298 4 34,900 Apulia Bari 19,358 7,474 4,090,105 72 17,800 Basilicata Potenza 9,995 3,859 576,619 12 20,600 Calabria Catanzaro 15,080 5,822 1,976,631 33 16,800 Campania Naples 13,590 5,247 5,861,529 107 18,300 Emilia-Romagna Bologna 22,446 8,666 4,450,508 154 34,600 Friuli-Venezia Giulia Trieste 7,858 3,034 1,227,122 37 30,300 Lazio Rome 17,236 6,655 5,892,425 186 31,600 Liguria Genoa 5,422 2,093 1,583,263 48 30,800 Lombardy Milan 23,844 9,206 10,002,615 367 36,600 Marche Ancona 9,366 3,616 1,550,796 41 26,600 Molise Campobasso 4,438 1,713 313,348 6 20,000 Piedmont Turin 25,402 9,808 4,424,467 129 29,400 Sardinia Cagliari 24,090 9,301 1,663,286 34 20,300 Sicily Palermo 25,711 9,927 5,092,080 87 17,200 Tuscany Florence 22,993 8,878 3,752,654
3752654
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Liguria Genoa 5,422 2,093 1,583,263 48 30,800 Lombardy Milan 23,844 9,206 10,002,615 367 36,600
1583263
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Region Capital Area (km2) Area (sq mi) Population Nominal GDP EURO billions (2016)[148] Nominal GDP EURO per capita(2016) [149] Abruzzo L'Aquila 10,763 4,156 1,331,574 32 24,100 Aosta Valley Aosta 3,263 1,260 128,298 4 34,900 Apulia Bari 19,358 7,474 4,090,105 72 17,800 Basilicata Potenza 9,995 3,859 576,619 12 20,600 Calabria Catanzaro 15,080 5,822 1,976,631 33 16,800 Campania Naples 13,590 5,247 5,861,529 107 18,300 Emilia-Romagna Bologna 22,446 8,666 4,450,508 154 34,600 Friuli-Venezia Giulia Trieste 7,858 3,034 1,227,122 37 30,300 Lazio Rome 17,236 6,655 5,892,425 186 31,600 Liguria Genoa 5,422 2,093 1,583,263 48 30,800 Lombardy Milan 23,844 9,206 10,002,615 367 36,600 Marche Ancona 9,366 3,616 1,550,796 41 26,600 Molise Campobasso 4,438 1,713 313,348 6 20,000 Piedmont Turin 25,402 9,808 4,424,467 129 29,400 Sardinia Cagliari 24,090 9,301 1,663,286 34 20,300 Sicily Palermo 25,711 9,927 5,092,080 87 17,200 Tuscany Florence 22,993 8,878 3,752,654 112 30,000 Trentino-Alto Adige/Südtirol Trento 13,607 5,254 1,055,934 42 39,755 Umbria Perugia 8,456 3,265 894,762 21 24,000 Veneto Venice 18,399 7,104 4,927,596 156 31,700
4450508
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Time Index (in minutes): 34.00 39.27
39.27
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en.wikipedia.org en.wikipedia.org
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Land 825.56 km2
5110.2
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URL
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