Every time your child opens the email, that person knows generally where they are (or specifically, if they have other info to triangulate against).

In contrast to such pseudonymous social networking, Facebook is notable for its longstanding emphasis on real identities and social connections.

our sum of squares is 41.187941.187941.1879

it acts as PCA quantitative variables and as MCA for qualitative variables.

in clustering analyses, standardization may be especially crucial in order to compare similarities between features based on certain distance measures. Another prominent example is the Principal Component Analysis, where we usually prefer standardization over Min-Max scaling, since we are interested in the components that maximize the variance

As a rule of thumb I’d say: When in doubt, just standardize the data, it shouldn’t hurt.

driven by data—where schools use data to identify a problem, select a strategy to address the problem, set a target for improvement, and iterate to make the approach more effective and improve student achievement.

Balloon plot

how the features are all on the same relative scale. The relative spaces between each feature’s values have been maintained.

many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the l1 and l2 regularizers of linear models) assume that all features are centered around zero and have variance in the same order. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.

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missing values are managed, without any pre-processing, by the model used to cluster with the assumption that values are missing completely at random.

Success ina data science project comes not from access to any one exotic tool, but from having quantifiablegoals, good methodology, crossdiscipline interactions, and a repeatable workflow.

Academicsarealsoatfaulthere:arecentanalysisof29millionpapersinover15,000peer-reviewedtitlespublishedaroundthetimeoftheZikaandEbolaepidemicsfoundthatlessthan1%exploredthegenderedimpactoftheoutbreaks

However, this doesn’t mean that Min-Max scaling is not useful at all! A popular application is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.

The result of standardization (or Z-score normalization) is that the features will be rescaled so that they’ll have the properties of a standard normal distribution with μ=0μ=0\mu = 0 and σ=1σ=1\sigma = 1 where μμ\mu is the mean (average) and σσ\sigma is the standard deviation from the mean

Threshold values of 0.8-0.9 are recommended for well separated clusters; to allow for overlapping clusters, we chose a threshold of 0.6.

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Virtually all BPMs have utilities for creating simple, data-gathering forms. And in many types of workflows, these simple forms may be adequate. However, in any workflow that includes complex document assembly (such as loan origination workflows), BPM forms are not likely to get the job done. Automating the assembly of complex documents requires ultra-sophisticated data-gathering forms, which can only be designed and created after the documents themselves have been automated. Put another way, you won't know which questions need to be asked to generate the document(s) until you've merged variables and business logic into the documents themselves. The variables you merge into the document serve as question fields in the data gathering forms. And here's the key point - since you have to use the document assembly platform to create interviews that are sophisticated enough to gather data for your complex documents, you might as well use the document assembly platform to generate all data-gathering forms in all of your workflows.

El ritmo de las actividades de diseño e instalación de redes comunitarias en veredas del municipio de Fusagasugá se ve acrecentado por las convocatorias internas de investigación de la Universidad de Cundinamarca que a lo largo del tiempo de vida de Red FusaLibrehan sido un músculo financiero que les permite acelerar los proc

Chile has developed from 1990 to date, a considerable decrease in poverty rates, which fell from 40.5% in 1990 to 8.5% in 2015.

Developing economies’ copper demand has steadily grown over the last decades, fueling economic and social improvement. By 2011, China already represented 40% of the demand.

Codelco is a state-owned Chilean mining company and the world’s largest copper producer. Based on their annual report and USGS statistics, they produced ~10% of the world’s copper in 2015 and own 8% of global reserves. They are also a large producer of greenhouse gas emissions. Last year, Codelco produced 3,2 t CO2e/millions tmf from both indirect and direct effects, and in 2011 it consumed 12% of the total national electricity supply.

Methodology The classic OSINT methodology you will find everywhere is strait-forward: Define requirements: What are you looking for? Retrieve data Analyze the information gathered Pivoting & Reporting: Either define new requirements by pivoting on data just gathered or end the investigation and write the report.

Normalización de las entradas descriptivas: Personas, Lugares, Instituciones (utilización de Linked Open Data (LOD) cuando sea posible.

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

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

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.

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.

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

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