The government needs to place tough restrictions on data collection and storage by businesses to limit the amount of damage in the event of a cyber breach.

DXtera Institute is a non-profit, collaborative member-based consortium dedicated to transforming student and institutional outcomes in higher education.

Data journalism produced by two of the nation’s most prestigious news organizations — The New York Times and The Washington Post — has lacked transparency, often failing to explain the methods journalists or others used to collect or analyze the data on which the articles were based, a new study finds. In addition, the news outlets usually did not provide the public with access to that data

Worse yet, it wouldn’t surprise me if we saw more unethical people publish data as a strategic communication tool, because they know people tend to believe numbers more than personal stories. That’s why it’s so important to have that training on information literacy and methodology.”

fall short of the ideal of data journalism

set; if this is higher, the tree 2can be considered to fit the data less well

For example, the idea of “data ownership” is often championed as a solution. But what is the point of owning data that should not exist in the first place? All that does is further institutionalise and legitimate data capture. It’s like negotiating how many hours a day a seven-year-old should be allowed to work, rather than contesting the fundamental legitimacy of child labour. Data ownership also fails to reckon with the realities of behavioural surplus. Surveillance capitalists extract predictive value from the exclamation points in your post, not merely the content of what you write, or from how you walk and not merely where you walk. Users might get “ownership” of the data that they give to surveillance capitalists in the first place, but they will not get ownership of the surplus or the predictions gleaned from it – not without new legal concepts built on an understanding of these operations.

These models are emerging, which is why its exciting to be involved in the ground floor of this sector, however some models clearly make sense already and thats largely because they closely follow the models free software itself has shaped. If you want status, then you can make a name for yourself by leading a team to write the docs ala free software itself, if you want money then build the reputation for the documentation team and contract out your knowledge (eg. extend the docs on contract ala free software).

Dissecting Flavivirus Biology in Salivary Gland Cultures from Fed and Unfed Ixodes scapularis (Black-Legged Tick)

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Nyhan and Reifler also found that presenting challenging information in a chart or graph tends to reduce disconfirmation bias. The researchers concluded that the decreased ambiguity of graphical information (as opposed to text) makes it harder for test subjects to question or argue against the content of the chart.

Big Data is a buzzword which works on the platform of large data volume and aggregated data sets. The data sets can be structured or unstructured.  The data that is kept and stored at a global level keeps on growing so this big data has a big potential.  Your Big Data is generated from every little thing around us all the time. It has changed its way as the people are changing in the organization. New skills are being offered to prepare the new generated power of Big Data. Nowadays the organizations are focusing on new roles, new challenges and creating a new business.  

doing the research

where any 10 of 30 segments can fully recover a user's files

y bosses want to see quick wins, but I know we can achieve big w

The main thing Smith has learned over the past seven years is “the importance of ownership.” He admitted that Tumblr initially helped him “build a community around the idea of digital news.” However, it soon became clear that Tumblr was the only one reaping the rewards of its growing community. As he aptly put it, “Tumblr wasn’t seriously thinking about the importance of revenue or business opportunities for their creators.”

You may not access or use the Site in any manner that could damage or overburden any MIT server, or any network connected to any MIT server. You may not use the Site in any manner that would interfere with any other party’s use of the Site.

Adoption of good practice to generate high quality data will depend on sharing the burden of capacity building in some way. That in turn, can-not happen until there is a framework that provides sufficient trust to allow the sharing and compar-ison of data and its management.

I tried very hard in that book, when it came to social media, to be platform agnostic, to emphasize that social media sites come and go, and to always invest first and foremost in your own media. (Website, blog, mailing list, etc.)

Outliers : All data sets have an expected range of values, and any actual data set also has outliers that fall below or above the expected range. (Space precludes a detailed discussion of how to handle outliers for statistical analysis purposes, see: Barnett & Lewis, 1994 for details.) How to clean outliers strongly depends on the goals of the analysis and the nature of the data.

Understanding the structure of the data : In order to clean log data properly, the researcher must understand the meaning of each record, its associated fi elds, and the interpretation of values. Contextual information about the system that produced the log should be associated with the fi le directly (e.g., “Logging system 3.2.33.2 recorded this fi le on 12-3-2012”) so that if necessary the specifi c code that gener-ated the log can be examined to answer questions about the meaning of the record before executing cleaning operations. The potential misinterpretations take many forms, which we illustrate with encoding of missing data and capped data values.

Data transformations : The goal of data-cleaning is to preserve the meaning with respect to an intended analysis. A concomitant lesson is that the data-cleaner must track all transformations performed on the data .

Data Cleaning A basic axiom of log analysis is that the raw data cannot be assumed to correctly and completely represent the data being recorded. Validation is really the point of data cleaning: to understand any errors that might have entered into the data and to transform the data in a way that preserves the meaning while removing noise. Although we discuss web log cleaning in this section, it is important to note that these principles apply more broadly to all kinds of log analysis; small datasets often have similar cleaning issues as massive collections. In this section, we discuss the issues and how they can be addressed. How can logs possibly go wrong ? Logs suffer from a variety of data errors and distortions. The common sources of errors we have seen in practice include:

In addition, real world events, such as the death of a major sports fi gure or a political event can often cause people to interact with a site differently. Again, be vigilant in sanity checking (e.g., look for an unusual number of visitors) and exclude data until things are back to normal.

Recording accurate and consistent time is often a challenge. Web log fi les record many different timestamps during a search interaction: the time the query was sent from the client, the time it was received by the server, the time results were returned from the server, and the time results were received on the client. Server data is more robust but includes unknown network latencies. In both cases the researcher needs to normalize times and synchronize times across multiple machines. It is common to divide the log data up into “days,” but what counts as a day? Is it all the data from midnight to midnight at some common time reference point or is it all the data from midnight to midnight in the user’s local time zone? Is it important to know if people behave differently in the morning than in the evening? Then local time is important. Is it important to know everything that is happening at a given time? Then all the records should be converted to a common time zone.

Log Studies collect the most natural observations of people as they use systems in whatever ways they typically do, uninfl uenced by experimenters or observers. As the amount of log data that can be collected increases, log studies include many different kinds of people, from all over the world, doing many different kinds of tasks. However, because of the way log data is gathered, much less is known about the people being observed, their intentions or goals, or the contexts in which the observed behaviors occur. Observational log studies allow researchers to form an abstract picture of behavior with an existing system, whereas experimental log stud-ies enable comparisons of two or more systems.

Two common ways to partition log data are by time and by user. Partitioning by time is interesting because log data often contains signifi cant temporal features, such as periodicities (including consistent daily, weekly, and yearly patterns) and spikes in behavior during important events. It is often possible to get an up-to-the- minute picture of how people are behaving with a system from log data by compar-ing past and current behavior.

An important characteristic of log data is that it captures actual user behavior and not recalled behaviors or subjective impressions of interactions.

Table 1 Different types of user data in HCI research

Large-scale log data has enabled HCI researchers to observe how information diffuses through social networks in near real-time during crisis situations (Starbird & Palen, 2010 ), characterize how people revisit web pages over time (Adar, Teevan, & Dumais, 2008 ), and compare how different interfaces for supporting email organi-zation infl uence initial uptake and sustained use (Dumais, Cutrell, Cadiz, Jancke, Sarin, & Robbins, 2003 ; Rodden & Leggett, 2010 ).

Behavioral logs are traces of human behavior seen through the lenses of sensors that capture and record user activity.

Ethnographic findings are not privileged, just particular: another country heard from. To regard them as anything more (or anything less) than that distorts both them and their implications, which are far profounder than mere primitivity, for social theory.

With Alphabet Inc.’s Google, and Facebook Inc. and its WhatsApp messaging service used by hundreds of millions of Indians, India is examining methods China has used to protect domestic startups and take control of citizens’ data.

Fig. 4

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.

“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.”

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

Possible relations between Items

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

Entscheidend ist, dass sie Herren des Verfahrens bleiben - und eine Vision für das neue Maschinenzeitalter entwickeln.

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?

De novo transcriptome profiling of highly purified human lymphocytes primary cells

research publications are not research data

Third, the post-LMS world should protect the pedagogical prerogatives and intellectual property rights of faculty members at all levels of employment. This means, for example, that contingent faculty should be free to take the online courses they develop wherever they happen to be teaching. Similarly, professors who choose to tape their own lectures should retain exclusive rights to those tapes. After all, it’s not as if you have to turn over your lecture notes to your old university whenever you change jobs.

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

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

42,924 km2

17,125,200 square kilometres

predictive analysis

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

30.40

3,011.00

43,600.00

56.83%

Health Care System Index: 85.85

Safety Index: 36.52

45.24 Moderate

42.86 Moderate

70.00 High

83.33 Very High

5.56 Very Low

22.22 Low

75.00 High

50.00 Moderate

37.50 Low

75.00 High

83.21 Very High

12.86 Very Low

15.58 Very Low

57.48 Moderate

45.95 Moderate

45.79 Moderate

82.14 Very High

17.65 Very Low

15.28 Very Low

86.36

75.00 High

13.89 Very Low

13.89 Very Low

Safety Index: 76.31

Health Care System Index: 80.05

922.46

34.46

44,000.00

39.83%

230,537

689.59 km2 (266.25 sq mi)

Finland

Safety Index: 58.40

Health Care System Index: 79.32

74.11 High

41.07 Moderate

25.93 Low

4,182.14

43,600.00

58.27%

506,615

47.87 km2 (18.48 sq mi)

France

51,300.00

51,300.00

43.51%

51,300.00

Mortgage as Percentange of Income: 66.37%

43,600.00

Mortgage as Percentange of Income: 107.95%

44,000.00

69.68%

44,000.00

46.99%

Safety Index: 85.60

4,122.00

39.60

Health Care System Index: 69.44

528.03 km2 (203.87 sq mi)

Finland

277,375

36.17

1,831.35

36.17   Moderate

74.32   High

77.90   High

642,045

715.48 km2 (276.25 sq mi)

Finland

CO2 Emission Index: 3,329.29

Time Index (in minutes): 43.85