Behavioral logs are traces of human behavior seen through the lenses of sensors that capture and record user activity.
Definition of log data
Behavioral logs are traces of human behavior seen through the lenses of sensors that capture and record user activity.
Definition of log data
Fig. 4
Graph is extremely unclear. Bad usage of point shapes
fast data visualization dominates the professional literature
One way to think about "core" biodiversity data is as a network of connected entities, such as taxa, taxonomic names, publications, people, species, sequences, images, collections, etc. (Fig. 1)
“It’s about embracing the inscrutable nature of human interactions,” says Chang. Evidence-based medicine was a massive improvement over intuition-based medicine, he says, but it only covers traditionally quantifiable data, or those things that are easy to measure. But we’re now quantifying information that was considered qualitative a generation ago.
Biggest challenges to redesigning the health care system in a way that would work better for patients and improve health
“Our biggest opportunity is leaning into that. It’s either embracing the qualitative nature of that and designing systems that can act just on the qualitative nature of their experience, or figuring how to quantitate some of those qualitative measures,” says Chang. “That’ll get us much further, because the real value in health care systems is in the human interactions. My relationship with you as a doctor and a patient is far more valuable than the evidence that some trial suggests.”
Biggest challenges to redesigning the health care system in a way that would work better for patients and improve health
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
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.
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)
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.
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...).
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.
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?
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.
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
De novo transcriptome profiling of highly purified human lymphocytes primary cells
research publications are not research data
they could be, if used as part of a text mining corpus, for example
Qualitative analysis
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.
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).
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]
When data is public domain it is recommended to use the CC0 Public Domain license for clarity.
8.5 million square kilometers
8500000
42,924 km2
42924
17,125,200 square kilometres
17125200
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.
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
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.
30.40
30.40
3,011.00
3011
43,600.00
43600
56.83%
56.83
Health Care System Index: 85.85
85.85
Safety Index: 36.52
36.52
45.24 Moderate
45.24
42.86 Moderate
42.86
70.00 High
70
83.33 Very High
83.33
5.56 Very Low
5.56
22.22 Low
22.22
75.00 High
75
50.00 Moderate
50
37.50 Low
37.50
75.00 High
75
0
0
83.21 Very High
83.21
12.86 Very Low
12.86
15.58 Very Low
15.58
57.48 Moderate
57.48
45.95 Moderate
45.95
45.79 Moderate
45.79
82.14 Very High
82.14
17.65 Very Low
17.65
15.28 Very Low
15.28
86.36
86.36
Dissatisfaction with Garbage Disposal
20.13
75.00 High
75
13.89 Very Low
13.89
13.89 Very Low
13.89
Safety Index: 76.31
76.31
Health Care System Index: 80.05
80.05
922.46
922.46
34.46
34.46
44,000.00
44000
39.83%
39.83
230,537
230537
689.59 km2 (266.25 sq mi)
689.59
Finland
Finland
Safety Index: 58.40
58.40
Health Care System Index: 79.32
79.32
74.11 High
74.11
41.07 Moderate
41.07
25.93 Low
25.93
4,182.14
4182.14
36.64
43,600.00
43600
58.27%
58.27
506,615
506615
47.87 km2 (18.48 sq mi)
47.87
France
France
51,300.00
51300
51,300.00
51300
51,300
43.51%
43.51
51,300.00
51300
Mortgage as Percentange of Income: 66.37%
66.37
43,600.00
43600
Mortgage as Percentange of Income: 107.95%
107.95
44,000.00
44000
69.68%
69.68
44,000.00
44000
46.99%
46.99
Safety Index: 85.60
85.60
4,122.00
4122
39.60
39.60
Health Care System Index: 69.44
69.44
528.03 km2 (203.87 sq mi)
528.03
Finland
Finland
277,375
277375
36.17
36.17
1,831.35
1831.35
36.17 Moderate
36.17
74.32 High
74.32
77.90 High
77.90
642,045
642045
715.48 km2 (276.25 sq mi)
715.48
Finland
Finland
CO2 Emission Index: 3,329.29
3329.29
Time Index (in minutes): 43.85
43.85
Health Care System Index: 74.23
74.23
Safety Index: 47.90
47.90
2,206,488
2206488
105.4 km2 (40.7 sq mi)
105.4
France
France
43.85 Moderate
43.85
74.23 High
74.23
47.90 Moderate
47.90
43.65 Moderate
43.65
Traffic Commute Time Index 15.00 Very Low
15
Health Care Index 91.67 Very High
91.67
Sweden
Sweden
151000
52.94 km2 (20.44 sq mi)
52.94
44.00 Moderate
44
49.87 Moderate
49.87
63.89 High
63.89
Safety Index: 43.65
43.65
Safety Index: 49.87
49.87
2,560.17
2560.17
21.96
21.96
Health Care System Index: 70.31
70.31
Safety Index: 55.34
55.34
Sweden
Sweden
572,779
572779
447.76 km2 (172.88 sq mi)
447
13
13
4
4
2
2
10
10
10
10
10
10
10
10
13
13
31
31
31
31
31
31
13
13
13
13
13
1,602,457
1602457
1,602,457
1602457
1,602,457
1602457
24,780,180
24780180
2,929,963 30 32.2 18 9,219,679 26 101.3 5
2929963
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
Score 85 / 100
85
Score 85 / 100
85
Score 85 / 100
85
Score 70 / 100
70
Score 70 / 100
70
Score 70 / 100
70
Score 84 / 100
84
Score 84 / 100
84
Score 84 / 100
84
0.855
0.855
0.847
0.847
0.847
0.847
0.847
0.847
0.839
0.839
0.855
0.855
0.839
0.839
0.839
0.839
0.855
0.855
0.855
0.855
CO2 Emission Index: 2,527.37
2,527.37
Time Index (in minutes): 40.30
40.30
Mortgage as Percentange of Income: 90.71%
90.71
Health Care System Index: 67.03
67.03
Safety Index: 52.05
52.05
€65,700 (US$74,000)
74,000
952,058
952,058
Sweden
Sweden
188 km2 (73 sq mi)
188
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
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
Vancouver British Columbia 2,463,431
2463431
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
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
2 Melbourne Vic 4,850,740
4850740