This protects you from government overreach, and protects your privacy, and makes identity theft a lot harder.

For example, personal information. Each governmental ministry might have any amount of information on a given voter. Finance knows about your tax, health knows about your medicare claims, justice knows about your criminal record, the RTA the status of your drivers licence. But they don’t know what the OTHER ministries know about you. The data is segregated. Just because one group of people knows about a given part of your life or existence, doesn’t mean that they can get access to everything else. You, by design, do not have one single file that has everything on you. The data is segregated.

To join the Privacy Shield Framework, a U.S.-based organization is required to self-certify to the Department of Commerce and publicly commit to comply with the Framework’s requirements. While joining the Privacy Shield is voluntary, the GDPR goes far beyond it.

GDPR is driving personal data breaches out into the open as companies face tough sanctions for non-disclosure.

None Of Your Business

Users have the right to obtain (in a machine readable format) their personal data for the purpose of transferring it from one controller to another, without being prevented from doing so by the data processor.

Users have the right to access their personal data and information about how their personal data is being processed. If the user requests it, data controllers must provide an overview of the categories of data being processed, a copy of the actual data and details about the processing. The details should include the purpose, how the data was acquired and with whom it was shared.

The Right to access is closely linked to the Right to data portability, but these two rights are not the same.

By choosing Matomo, you are joining an ever growing movement. You’re standing up for something that respects user-privacy, you’re fighting for a safer web and you believe your personal data should remain in your own hands, no one else’s.

Raw data access

Export and migrate your data between hosting options at any time

100% data ownership

With Matomo, the philosophy around data ownership is simple, you own your data, no one else.

People believe in us for that reason, and we will continue to champion the right for people to be in control of their data.

Data privacy now a global movementWe’re pleased to say we’re not the only ones who share this philosophy, web browsing companies like Brave have made it possible so you can browse the internet more privately; and you can use a search engine like DuckDuckGo to search with the freedom you deserve.

our values remain the same – advocating for 100% data ownership, respecting user-privacy, being reliable and encouraging people to stay secure. Complete analytics, that’s 100% yours.

Due to that, you have 100% data ownership as Matomo is hosted on your own servers and we have absolutely no way of gaining access to your data.

when you choose Matomo Cloud, we acknowledge in our Terms that you own all rights, titles, and interest to your users’ data. We obtain no rights from you to your users data. This means we can’t on-sell it to third parties, we can’t claim ownership of it, you can export your data anytime

the privacy of your users is respected

The relationship is between the website owner (you) and the visitor, with no external sources looking in

Own Your Encryption KeysYou would never trust a company to keep a record of your password for use anytime they want. Why would you do that with your encryption keys? With Graphite, you don't have to. You own and manage your keys so only YOU can decrypt your content.

To become compliant, organisations will need to either stop collecting the offending cookies or find a lawful ground to collect and process that data

Legitimate interest. When there is a genuine reason for processing personal data without consent. Interpretations of this legal ground may vary, but a good example would be risk assessment or checking children’s age, such as in an online liquor store.

Asking for consent when processing users’ personal data is one of the most important duties imposed on website owners by the GDPR.

The process of collecting and storing user consent from a technical perspective

it would appear impossible to require a publisher to provide information on and obtain consent for the installation of cookies on his own website also with regard to those installed by “third parties**”

Our solution goes a bit further than this by pointing to the browser options, third-party tools and by linking to the third party providers, who are ultimately responsible for managing the opt-out for their own tracking tools.

You are also not required to manage consent for third-party cookies directly on your site/app as this responsibility falls to the individual third-parties. You are, however, required to at least facilitate the process by linking to the relevant policies of these third-parties.

the publisher would be required to check, from time to time, that what is declared by the third parties corresponds to the purposes they are actually aiming at via their cookies. This is a daunting task because a publisher often has no direct contacts with all the third parties installing cookies via his website, nor does he/she know the logic underlying the respective processing.

When you think about data law and privacy legislations, cookies easily come to mind as they’re directly related to both. This often leads to the common misconception that the Cookie Law (ePrivacy directive) has been repealed by the General Data Protection Regulation (GDPR), which in fact, it has not. Instead, you can instead think of the ePrivacy Directive and GDPR as working together and complementing each other, where, in the case of cookies, the ePrivacy generally takes precedence.

Decision point #2 – Do you send any data to third parties, directly or inadvertently? <img class="alignnone size-full wp-image-10174" src="https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart.png" alt="GDPR cookie consent flowchart" width="1451" height="601" srcset="https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart.png 1451w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-300x124.png 300w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-981x406.png 981w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-761x315.png 761w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-611x253.png 611w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-386x160.png 386w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-283x117.png 283w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-600x249.png 600w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-1024x424.png 1024w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-50x21.png 50w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-250x104.png 250w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-241x100.png 241w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-400x166.png 400w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-350x145.png 350w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-840x348.png 840w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-860x356.png 860w, https://www.jeffalytics.com/wp-content/uploads/7deb832d95678dc21cc23208d76f4144_Flowchart-1030x427.png 1030w" sizes="(max-width: 1451px) 100vw, 1451px" /> Remember, inadvertently transmitting data to third parties can occur through the plugins you use on your website. You don't necessarily have to be doing this proactively. If the answer is “Yes,” then to comply with GDPR, you should use a cookie consent popup.

You must clearly identify each party that may collect, receive, or use end users’ personal data as a consequence of your use of a Google product. You must also provide end users with prominent and easily accessible information about that party’s use of end users’ personal data.

GDPR introduces a list of data subjects’ rights that should be obeyed by both data processors and data collectors. The list includes: Right of access by the data subject (Section 2, Article 15). Right to rectification (Section 3, Art 16). Right to object to processing (Section 4, Art 21). Right to erasure, also known as ‘right to be forgotten’ (Section 3, Art 17). Right to restrict processing (Section 3, Art 18). Right to data portability (Section 3, Art 20).

some examples of legitimate interest (although their main aim is to emphasize what rights, freedoms and so on override legitimate interest)

An example of reliance on legitimate interests includes a computer store, using only the contact information provided by a customer in the context of a sale, serving that customer with direct regular mail marketing of similar product offerings — accompanied by an easy-to-select choice of online opt-out.

This is no different where legitimate interests applies – see the examples below from the DPN. It should also be made clear that individuals have the right to object to processing of personal data on these grounds.

Individuals can object to data processing for legitimate interests (Article 21 of the GDPR) with the controller getting the opportunity to defend themselves, whereas where the controller uses consent, individuals have the right to withdraw that consent and the ‘right to erasure’. The DPN observes that this may be a factor in whether companies rely on legitimate interests.

Legitimate Interest may be used for marketing purposes as long as it has a minimal impact on a data subject’s privacy and it is likely the data subject will not object to the processing or be surprised by it.

15. Data sharing: practices and needs of the transport research community In view of the growing importance of data sharing in transportresearch and based on recommendations of a dedicated expert group on the Transport Research Cloud a new study will establish:1. What are the needs and objections of transports researchers in relation to data sharing:2. What are the training requirementsneeded for the transport research community to facilitate data sharing and3. What potential user communities would expect from a Transport Research Cloud?Type of Action: Provision of technical/scientific services by the Joint Research CentreIndicative timetable: Second quarter of 2020Indicative budget: EUR 0.20 million from the 2020 budget

multiple scandals have highlighted some very shady practices being enabled by consent-less data-mining — making both the risks and the erosion of users’ rights clear

Earlier this year it began asking Europeans for consent to processing their selfies for facial recognition purposes — a highly controversial technology that regulatory intervention in the region had previously blocked. Yet now, as a consequence of Facebook’s confidence in crafting manipulative consent flows, it’s essentially figured out a way to circumvent EU citizens’ fundamental rights — by socially engineering Europeans to override their own best interests.

The deceitful obfuscation of commercial intention certainly runs all the way through the data brokering and ad tech industries that sit behind much of the ‘free’ consumer Internet. Here consumers have plainly been kept in the dark so they cannot see and object to how their personal information is being handed around, sliced and diced, and used to try to manipulate them.

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.