design choices are being selected to be intentionally deceptive. To nudge the user to give up more than they realize. Or to agree to things they probably wouldn’t if they genuinely understood the decisions they were being pushed to make.

For mainly two reasons: I pay for things that bring value to my life, and when something's "free", you're usually really just giving away your privacy without being aware.

sleep Student's Sleep Data

Pulse Pulse Rates and Exercise

HSAUR agefat Total Body Composision Data

Google Recaptcha and personal dataBut we all know: there’s no such thing as a free lunch right? So what is the price we pay for this great feature? Right: it’s personal data.

Consent is one of six lawful grounds for processing data. It may be arguable that anti-spam measures such as reCaptcha can fall under "legitimate interests" (ie you don't need to ask for consent)

A 1% sample of AddThis Data (“Sample Dataset”) is retained for a maximum of 24 months for business continuity purposes.

Not only are public transport datasets useful for benchmarking route planning systems, they are also highly useful for benchmarking geospatial [13, 14] and temporal [15, 16] RDF systems due to the intrinsic geospatial and temporal properties of public transport datasets. While synthetic dataset generators already exist in the geospatial and temporal domain [17, 18], no systems exist yet that focus on realism, and specifically look into the generation of public transport datasets. As such, the main topic that we address in this work, is solving the need for realistic public transport datasets with geospatial and temporal characteristics, so that they can be used to benchmark RDF data management and route planning systems. More specifically, we introduce a mimicking algorithm for generating realistic public transport data, which is the main contribution of this work.

Generating random data

Right now, if you want to know what data Facebook has about you, you don’t have the right to ask them to give you all of the data they have on you, and the right to know what they’ve done with it. You should have that right. You should have the right to know and have access to your data.

Good data privacy practices by companies are good for the world. We wake up every day excited to change the world and keep the internet that we all know and love safe and transparent.

startup focused on creating transparency in data. All that stuff you keep reading about the shenanigans with companies mishandling people's data? That's what we are working on fixing.

 Get a convenient overview of your test results

1947 to 2004

Data Model

Research ethics concerns issues, such as privacy, anonymity, informed consent and the sensitivity of data. Given that social media is part of society’s tendency to liquefy and blur the boundaries between the private and the public, labour/leisure, production/consumption (Fuchs, 2015a: Chapter 8), research ethics in social media research is par-ticularly complex.

One important aspect of critical social media research is the study of not just ideolo-gies of the Internet but also ideologies on the Internet. Critical discourse analysis and ideology critique as research method have only been applied in a limited manner to social media data. Majid KhosraviNik (2013) argues in this context that ‘critical dis-course analysis appears to have shied away from new media research in the bulk of its research’ (p. 292). Critical social media discourse analysis is a critical digital method for the study of how ideologies are expressed on social media in light of society’s power structures and contradictions that form the texts’ contexts.

t has, for example, been common to study contemporary revolutions and protests (such as the 2011 Arab Spring) by collecting large amounts of tweets and analysing them. Such analyses can, however, tell us nothing about the degree to which activists use social and other media in protest communication, what their motivations are to use or not use social media, what their experiences have been, what problems they encounter in such uses and so on. If we only analyse big data, then the one-sided conclusion that con-temporary rebellions are Facebook and Twitter revolutions is often the logical conse-quence (see Aouragh, 2016; Gerbaudo, 2012). Digital methods do not outdate but require traditional methods in order to avoid the pitfall of digital positivism. Traditional socio-logical methods, such as semi-structured interviews, participant observation, surveys, content and critical discourse analysis, focus groups, experiments, creative methods, par-ticipatory action research, statistical analysis of secondary data and so on, have not lost importance. We do not just have to understand what people do on the Internet but also why they do it, what the broader implications are, and how power structures frame and shape online activities

Challenging big data analytics as the mainstream of digital media studies requires us to think about theoretical (ontological), methodological (epistemological) and ethical dimensions of an alternative paradigm

Who communicates what to whom on social media with what effects? It forgets users’ subjectivity, experiences, norms, values and interpre-tations, as well as the embeddedness of the media into society’s power structures and social struggles. We need a paradigm shift from administrative digital positivist big data analytics towards critical social media research. Critical social media research combines critical social media theory, critical digital methods and critical-realist social media research ethics.

de-emphasis of philosophy, theory, critique and qualitative analysis advances what Paul Lazarsfeld (2004 [1941]) termed administrative research, research that is predominantly concerned with how to make technologies and administration more efficient and effective.

Big data analytics’ trouble is that it often does not connect statistical and computational research results to a broader analysis of human meanings, interpretations, experiences, atti-tudes, moral values, ethical dilemmas, uses, contradictions and macro-sociological implica-tions of social media.

Such funding initiatives privilege quantitative, com-putational approaches over qualitative, interpretative ones.

There is a tendency in Internet Studies to engage with theory only on the micro- and middle-range levels that theorize single online phenomena but neglect the larger picture of society as a totality (Rice and Fuller, 2013). Such theories tend to be atomized. They just focus on single phenomena and miss soci-ety’s big picture

Data extraction is a piece of a larger puzzle called data integration (getting the data you want to a single place, from different systems, the way you want it), which people have been working on since the early 1980s.

For n < 5 we recommend showing the individual data points.

Thanks also to this example from the D3 gallery for demonstating how to create sunburst charts.

The main priorities of different ELN features

GIS and Spatial Analytics Market: Global Size

Size of the warehouse management systems (WMS) market worldwide, from 2015 to 2024

Let’s take a look at one more visitor, Arel::Visitors::Dot. The visitor generates the Graphviz’s Dot format and we can use it to create diagrams out of an AST.

Sociedad Quimica y Minera de Chile (NYSE:SQM): Known as SQM, this company also extracts potash from the Atacama Desert and is Chile’s largest fertilizer producer.

Annotation extends that power to a web made not only of linked resources, but also of linked segments within them. If the web is a loom on which applications are woven, then annotation increases the thread count of the fabric. Annotation-powered applications exploit the denser weave by defining segments and attaching data or behavior to them.

The Web Annotation Data Model specification describes a structured model and format to enable annotations to be shared and reused across different hardware and software platforms.

A final word: when we do not understand something, it does not look like there is anything to be understood at all - it just looks like random noise. Just because it looks like noise does not mean there is no hidden structure.

You can create sub-projects (or sub-contexts) by adding a backslash

Double click on a project/context select all there sub-projects/contexts, therefore show their tasks

arborescence

Your task list is a plain text file, not some proprietary format owned by a company or locked to a specific application.

A simple and timeless format Plain text is the simplest file format there is. It will always be accessible, by some kind of application, forever.

And since it’s just a client, you can always use the todo.txt format text file created by it in the very beginning with any other client. Give it a try.

Avoiding complicated outlining or mind-mapping software saves a bunch of mouse clicks or dreaming up complicated visualizations (it helps if you are a linear thinker).

Plaintext files are tiny, simple, quick to work with, editable by tons of great programs, searchable by all modern operating systems, easy to back up, perfect for versioning, trivial to sync between devices, and are amazingly flexible in their uses and formats.

In this system, plaintext files are used for most of the backbone of your organizational system.

made to work alongside the various plain-text, Dropbox syncing mobile notes apps such as Denote for Android and Jottings for iPhone from an app for the Ubuntu desktop. Plain text notes anywhere you want. Easily synced between your desktop and phone. Notes, plain and simple.

Future proofs your journal entries by saving them as plain text and organizing them as you go. This means you can read or create entries when you don’t have DayJournal.

It’s flexible in precisely the way so many modern apps aren’t, and if you like tweaking things until they’re just right, I can’t recommend it enough.

And if all else fails, you can just use a text editor.

Plain text is software and operating system agnostic. It's searchable, portable, lightweight, and easily manipulated. It's unstructured. It works when someone else's web server is down or your Outlook .PST file is corrupt. There's no exporting and importing, no databases or tags or flags or stars or prioritizing or insert company name here-induced rules on what you can and can't do with it.

Countless productivity apps and sites store your tasks in their own proprietary database and file format. But you can work with your todo.txt file in every text editor ever made, regardless of operating system or vendor.

Most to-do lists give you no control over your data. Your tasks live inside the app, not in a document you can edit, and syncing is handled by whichever company made the app. If you don't like this, todo.txt is a great alternative.

greater integration of data, data security, and data sharing through the establishment of a searchable database.

platform

Foundation finds its grantees 'significantly outperform' similar charities

Remarkably, studies receiving mainly public funding can, a quarter of a century on, still survive without making their data available in a useful way. In the UK a series of studies—the Avon Longitudinal Study of Parents and Children (ALSPAC) (100), UK Biobank (101), and Born in Bradford (102), among others—have surely been exemplary in promoting data accessibility.

view-helpers form-helpers form-helper view-helper button buttons form forms

Bastian Greshake Tzovaras

I'll give a little bit of the history to provide context. My own involvement in this started around 2008 after we had shipped our key-value store. My next project was to try to get a working Hadoop setup going, and move some of our recommendation processes there. Having little experience in this area, we naturally budgeted a few weeks for getting data in and out, and the rest of our time for implementing fancy prediction algorithms. So began a long slog. We originally planned to just scrape the data out of our existing Oracle data warehouse. The first discovery was that getting data out of Oracle quickly is something of a dark art. Worse, the data warehouse processing was not appropriate for the production batch processing we planned for Hadoop—much of the processing was non-reversable and specific to the reporting being done. We ended up avoiding the data warehouse and going directly to source databases and log files. Finally, we implemented another pipeline to load data into our key-value store for serving results. This mundane data copying ended up being one of the dominate items for the original development. Worse, any time there was a problem in any of the pipelines, the Hadoop system was largely useless—running fancy algorithms on bad data just produces more bad data. Although we had built things in a fairly generic way, each new data source required custom configuration to set up. It also proved to be the source of a huge number of errors and failures. The site features we had implemented on Hadoop became popular and we found ourselves with a long list of interested engineers. Each user had a list of systems they wanted integration with and a long list of new data feeds they wanted. ETL in Ancient Greece. Not much has changed.

Effective use of data follows a kind of Maslow's hierarchy of needs. The base of the pyramid involves capturing all the relevant data, being able to put it together in an applicable processing environment (be that a fancy real-time query system or just text files and python scripts). This data needs to be modeled in a uniform way to make it easy to read and process. Once these basic needs of capturing data in a uniform way are taken care of it is reasonable to work on infrastructure to process this data in various ways—MapReduce, real-time query systems, etc. It's worth noting the obvious: without a reliable and complete data flow, a Hadoop cluster is little more than a very expensive and difficult to assemble space heater. Once data and processing are available, one can move concern on to more refined problems of good data models and consistent well understood semantics. Finally, concentration can shift to more sophisticated processing—better visualization, reporting, and algorithmic processing and prediction. In my experience, most organizations have huge holes in the base of this pyramid—they lack reliable complete data flow—but want to jump directly to advanced data modeling techniques. This is completely backwards. So the question is, how can we build reliable data flow throughout all the data systems in an organization?

Data integration is making all the data an organization has available in all its services and systems.

1. Introduction to eXtensible Time Series, using xts and zoo for time series Introducing xts and

Filter

Cell Editor

Second, uBlock Origin does not have a dedicated server, it can't "phone home" with your browsing data, there is only GitHub, and GitHub is completely unrelated to uBlock Origin.

Is there a home server?

TrackMeNot is user-installed and user-managed, residing wholly on users' system and functions without the need for 3rd-party servers or services. Placing users in full control is an essential feature of TrackMeNot, whose purpose is to protect against the unilateral policies set by search companies in their handling of our personal information.

What’s the Difference Between AI, Machine Learning and Data Science?

landscape vs. portrait.

how to nest grids inside grids using a Master / Detail configuration

Google has confirmed that it partnered with health heavyweight Ascension, a Catholic health care system based in St. Louis that operates across 21 states and the District of Columbia.

Speaking with MIT Technology Review, Rohit Prasad, Alexa’s head scientist, has now revealed further details about where Alexa is headed next. The crux of the plan is for the voice assistant to move from passive to proactive interactions. Rather than wait for and respond to requests, Alexa will anticipate what the user might want. The idea is to turn Alexa into an omnipresent companion that actively shapes and orchestrates your life. This will require Alexa to get to know you better than ever before.

SEL measurement is being done in myriad ways, involving multiple different conceptualizations of SEL, different political positions, and different sectoral interests.

This booklet itells you how to use the R statistical software to carry out some simple analyses that are common in analysing time series data.

"While we hope that Google will lift these unwarranted sanctions for AdNauseam, it highlights a much more serious problem for Chrome users," the AdNauseam team adds. "It is frightening to think that at any moment Google can quietly make your extensions and data disappear, without so much as a warning."

"Element" SelectorsEach component has a data-reach-* attribute on the underlying DOM element that you can think of as the "element" for the component.

Data Scientist

I frequently talk with people who are not that concerned about surveillance, or who feel that the positives outweigh the risks. Here, I want to share some important truths about surveillance: Surveillance can facilitate human rights abuses and even genocide Data is often used for different purposes than why it was collected Data often contains errors Surveillance typically operates with no accountability Surveillance changes our behavior Surveillance disproportionately impacts the marginalized Data privacy is a public good We don’t have to accept invasive surveillance

Terminar los proyectos que empezamos en 2019, con prioridad en Documentatón, ya que no es un cover, sino que es nuestro propio libro.

Selección comunitaria de temas para Data Weeks o Data Rodas. Apoyo de proyectos de los participantes de la comunidad. Reuniones periódicas de la Comunidad, algo así como Data Roda el primer viernes de cada mes, así sea para saludarnos síncronamente y ver en qué andamos y hacer un encera - brilla de bacanes.

Si puedo aportar una herramienta más a Grafoscopio, quiero que sea la querendura.

if n is very small (for example n = 3), rather than showing error bars and statistics, it is better to simply plot the individual data points.

Keep the ergonomics of stable reference and directly mutable objects. In other words; be able to have a variable pointing to an object, and make subsequent reads or writes to it. Without needing to fear that you’re working with old data. While, in the background,..State is stored in an immutable, structurally shared tree.

With MobX you don't need to normalize your data.