Once the awarding and registration systems are in place, institutions should also integrate with a modern CRM solution to attract and manage student interest, support, and personalized communications to increase enrollment and engagement. The CRM needs to support career services and other experiential learning departments as the school looks to build outside relationships with organizations and industry partners to provide real-world learning experiences and assessment opportunities for students

Why do so many businesses share their data openly, for free? Most often, the answer is scale. As companies grow, the staff within those companies realize they have more ideas than they have the time and resources to develop them. It’s typically easier to work with other external companies that specialize in these ideas than build them in-house. By creating APIs, a company allows third-party developers to build applications that improve adoption and usage of its platform. That way, a business can build an ecosystem that becomes dependent on the data from their API, which often leads to additional revenue opportunities.

After struggling with this problem for a while and still being far from solving this issue, I realized that I was making too many requests to the website; which made me come up with the idea of saving all the pages I needed to scrape on my local computer. Next, I started sending requests to these local HTML files instead and kept adapting my code.

[There’s also] a big new study from Cambridge University, in which researchers looked at 84,000 people…and found that social media was strongly associated with worse mental health during certain sensitive life periods, including for girls ages 11 to 13…One explanation is that teenagers (and teenage girls in particular) are uniquely sensitive to the judgment of friends, teachers, and the digital crowd.

In order to throw light on the question whether exceptionally bright children are specially likely to be one-sided, nervous, delicate, morally abnormal, socially unadaptable, or otherwise peculiar, the writer has secured rather extensive information regarding 31 children whose mental age was found by intelligence tests to be 25 per cent above the actual age. This degree of intelligence is possessed by about 2 children out of 100, and is nearly as far above average intelligence as high-grade feeble-mindedness is below. The supplementary information, which was furnished in most cases by the teachers, may be summarized as follows: -- Ability special or general. In the case of 20 out of 31 the ability is decidedly general, and with 2 it is mainly general. The talents of 5 are described as more or less special, but only in one case is it remarkably so. Doubtful 4. Health. 15 are said to be perfectly healthy; 13 have one or more physical defects; 4 of the 13 are described as delicate; 4 have adenoids; 4 have eye-defects; 1 lisps; and 1 stutters. These figures are about the same as one finds in any group of ordinary children. Studiousness. "Extremely studious," 15; "usually studious" or "fairly studious," 11; "not particularly studious," 5; "lazy," 0. Moral traits. Favorable traits only, 19; one or more unfavorable traits, 8; no answer, 4. The eight with unfavorable moral traits are described as follows: 2 are "very self-willed"; 1 "needs close watching"; 1 is "cruel to animals"; 1 is "untruthful"; 1 is "unreliable"; 1 is "a bluffer"; 1 is "sexually abnormal," perverted," and "vicious." It will be noted that with the exception of the last child, the moral irregularities mentioned can hardly be regarded, from the psychological point of view, as essentially abnormal. It is perhaps a good rather than a bad sign for a child to be self-willed; most children "need close watching"; and a certain amount of untruthfulness in children is the rule and not the exception. Social adaptability. Socially adaptable, 25; not adaptable, 2; doubtful, 4. Attitude of other children. "Favorable," "friendly," "liked by everybody," "much admired," "popular," etc., 26; "not liked," 1; "inspires repugnance," 1; no answer, 1. Is child a leader? "Yes," 14; "no," or "not particularly," 12; doubtful, 5. Is play life normal? "Yes," 26; "no," 1; "hardly," 1; doubtful, 3. 1s child spoiled or vain? "No," 22; "yes," 5; "somewhat," 2; no answer, 2. According to the above data, exceptionally intelligent children are fully as likely to be healthy as ordinary children; their ability is far more often general than special, they are studious above the average, really serious faults are not common among them, they are nearly always socially adaptable, are sought after as playmates and companions, their play life is usually normal, they are leaders far oftener than other children, and notwithstanding their many really superior qualities they are seldom vain or spoiled.

There are two main reasons to use logarithmic scales in charts and graphs.

One reason for choosing a dot plot rather than a bar chart is that it is less cluttered. We will be learning other benefits of dot plots in this and future posts.

A new breach involving data from nine million AT&T customers is a fresh reminder that your mobile provider likely collects and shares a great deal of information about where you go and what you do with your mobile device — unless and until you affirmatively opt out of this data collection. Here’s a primer on why you might want to do that, and how.

"For this campaign, we surveyed 930 Americans to explore their retirement plans. Among them, 16% were retired, 22% were still working, and 62% were retirees who had returned to work."So, 149 of those surveyed were retired. Of those 149, 25 (1 in 6) are considering returning to work. 13 of those want remote positions.

Back to the basics: Identifying and addressing underlying challenges in achieving high quality and relevant health statistics for indigenous populations in Canada

The compiled dataset is made available on Zenodo.

Where information that a controller would otherwise be required to provide to a datasubject pursuant to subsection (1) includes personal data relating to another individualthat would reveal, or would be capable of revealing, the identity of the individual, thecontroller—(a)shall not, subject to subsection (8), provide the data subject with the informationthat constitutes such personal data relating to the other individual, and(b)shall provide the data subject with a summary of the personal data concernedthat—(i)in so far as is possible, permits the data subject to exercise his or her rightsunder this Part, and

Subject to subsection (2), a controller, with respect to personal data for which it isresponsible, may restrict, wholly or partly, the exercise of a right of a data subjectspecified in subsection (4)

Subsection (1) shall not apply—(a)in respect of personal data relating to the data subject that consists of anexpression of opinion about the data subject by another person given inconfidence or on the understanding that it would be treated as confidential, or(b)to information specified in paragraph (b)(i)(III)of that subsection in so far as arecipient referred to therein is a public authority which may receive data in thecontext of a particular inquiry in accordance with the law of the State.

Data Protection Act 2018

And it constitutes an important but overlooked signpost in the 20th-centuryhistory of information, as ‘facts’ fell out of fashion but big data became big business.

a peer-reviewed article

I used SjoerdV / ConvertOneNote2MarkDown PowerShell script. The key is running PowerShell and OneNote as Administrator.It will crash a bunch of times depending on the size of your OneNote repository. However, if you keep restarting the program as administrator it seems to start back were it left off.Here are my notes: https://www.dropbox.com/s/au66hamcv71sggk/202211151246%20OneNote%20to%20Markdown%20Procedure.pdf?dl=0

student outcomes, including learning, persistence, or attitudes.

Microsoft today announced it intends to build a new datacenter region in Southern Finland.

Big tech has benefited from an educational dynamic that consistently underfunds public education but demands increased technology to prepare the workers of the future, providing low-cost solutions in exchange for data and the potential for future product loyalty

个人学习可能取决于他人行为的主张突出了将学习环境视为一个涉及多个互动参与者的系统的重要性

In March, Fortum and Microsoft announced our joint plan for a ground-breaking data centre region in the Helsinki, Finland metro­politan area.

Blind news audiences are being left behind in the data visualisation revolution: here's how we fix that

When engaging in data literacy work in our classrooms, it’s helpful to keep two ideas at play at once: on the one hand, these algorithmic systems are nowhere near as “smart” as these platforms want to lead us to believe they are; and on the other hand, concerns about accuracy can distract us from the bigger picture, that these platforms are built on a logic of prediction that, one nudge at a time, may ultimately infringe upon users’ ability to make up their own mind.

If you have experienced trouble in rememberingdates try the following system which has proved beneficial to at least onestudent.

ProPublica recently reported that breathing machines purchased by people with sleep apnea are secretly sending usage data to health insurers, where the information can be used to justify reduced insurance payments.

Layoffs increase the odds of suicide by two and a half times.

Actually I’m not sure most people do this, I just hope I’m not the only one.

We believe that the numeric notational marks associated with the animals constituted a calendar, and given that it references natural behaviour in terms of seasons relative to a fixed point in time, we may refer to it as a phenological calendar, with a meteorological basis.

We have proposed the existence of a notational system associated with an unambiguous animal subject, relating to biologically significant events informed by the ethological record, which allows us for the first time to understand a Palaeolithic notational system in its entirety. This utilized/allowed the function of ordinality (and, later, place value), which were revolutionary steps forward in information recording.

Data Viz with Python and RLearn to Make Plots in Python and R

We can have a machine learning model which gives more than 90% accuracy for classification tasks but fails to recognize some classes properly due to imbalanced data or the model is actually detecting features that do not make sense to be used to predict a particular class.

According to an analysis from the Wall Street Journal, the top 1% of Twitch streamers made over 50% of all money paid out by the platform in 2021. Furthermore, just 5% of users had made over $1,000 in the same year. Only 0.06% had made over the U.S. median household income of $67,521. In a survey of 5,000 community members composed of smaller Twitch streamers, Stream Scheme found that 76% were not able to reach Twitch’s $100 minimum payout threshold. Most others were making between $25-130 per month on the platform. 

In a 2021 leak of Twitch’s user data that included creator payouts, it was revealed that from August 2019 to October 2021, the top 100 streamers on the platform made anywhere between $9,626,712.16 and $886,999.17. 

Best times to post on social media overall: Tuesdays through Thursdays at 9 a.m. or 10 a.m. Best days to post on social media: Tuesdays through Thursdays Worst days to post on social media: Sundays

Data Services

How to Manage your Research Data Effectively

Introduction of the Compose specification makes a clean distinction between the Compose YAML file model and the docker-compose implementation.

One interesting concept in organizational sociology is “normal accidents theory.” Studying Three Mile Island, Charles Perrow created a 2x2 grid

hat we want is to be able to leave Facebook and still talk to our friends, instead of having many Facebooks.

Our annotators achieve thehighest precision with OntoNotes, suggesting thatmost of the entities identified by crowdworkers arecorrect for this dataset.

an algorithm with high precision on LitBank orOntoNotes would miss a huge percentage of rele-vant mentions and entities on other datasets (con-straining our analysis)

Procedure: We first launch an annotation tutorial(paid $4.50) and recruit the annotators on the AMTplatform.9 At the end of the tutorial, each annotatoris asked to annotate a short passage (around 150words). Only annotators with a B3 score (Bagga

Annotation structure: Two annotation ap-proaches are prominent in the literature: (1) a localpairwise approach, annotators are shown a pairof mentions and asked whether they refer to thesame entity (Hladká et al., 2009; Chamberlain et al.,2016a; Li et al., 2020; Ravenscroft et al., 2021),which is time-consuming; or (2) a cluster-basedapproach (Reiter, 2018; Oberle, 2018; Bornsteinet al., 2020), in which annotators group all men-tions of the same entity into a single cluster. InezCoref we use the latter approach, which can befaster but requires the UI to support more complexactions for creating and editing cluster structures.

owever, these datasets vary widelyin their definitions of coreference (expressed viaannotation guidelines), resulting in inconsistent an-notations both within and across domains and lan-guages. For instance, as shown in Figure 1, whileARRAU (Uryupina et al., 2019) treats generic pro-nouns as non-referring, OntoNotes chooses not tomark them at all

Specifically, our work investigates the quality ofcrowdsourced coreference annotations when anno-tators are taught only simple coreference cases thatare treated uniformly across existing datasets (e.g.,pronouns). By providing only these simple cases,we are able to teach the annotators the concept ofcoreference, while allowing them to freely interpretcases treated differently across the existing datasets.This setup allows us to identify cases where ourannotators disagree among each other, but moreimportantly cases where they unanimously agreewith each other but disagree with the expert, thussuggesting cases that should be revisited by theresearch community when curating future unifiedannotation guidelines

this work, we developa crowdsourcing-friendly coreference annota-tion methodology, ezCoref, consisting of anannotation tool and an interactive tutorial. Weuse ezCoref to re-annotate 240 passages fromseven existing English coreference datasets(spanning fiction, news, and multiple other do-mains) while teaching annotators only casesthat are treated similarly across these datasets

One example could be putting all files into an Amazon S3 bucket. It’s versatile, cheap and integrates with many technologies. If you are using Redshift for your data warehouse, it has great integration with that too.

Dr. Miho Ohsaki re-examined workshe and her group had previously published and confirmed that the results are indeed meaningless in the sensedescribed in this work (Ohsaki et al., 2002). She has subsequently been able to redefine the clustering subroutine inher work to allow more meaningful pattern discovery (Ohsaki et al., 2003)

Eamonn Keogh is an assistant professor of Computer Science at the University ofCalifornia, Riverside. His research interests are in Data Mining, Machine Learning andInformation Retrieval. Several of his papers have won best paper awards, includingpapers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF CareerAward for “Efficient Discovery of Previously Unknown Patterns and Relationships inMassive Time Series Databases”.

This is different than row-level security because row-level security is going to allow you to restrict the actual data that’s shown to them not the actual report that’s shown

It took me a while to grok where dbt comes in the stack but now that I (think) I have it, it makes a lot of sense. I can also see why, with my background, I had trouble doing so. Just as Apache Kafka isn’t easily explained as simply another database, another message queue, etc, dbt isn’t just another Informatica, another Oracle Data Integrator. It’s not about ETL or ELT - it’s about T alone. With that understood, things slot into place. This isn’t just my take on it either - dbt themselves call it out on their blog:

Of course, despite what the "data is the new oil" vendors told you back in the day, you can’t just chuck raw data in and assume that magic will happen on it, but that’s a rant for another day ;-)

Working with the raw data has lots of benefits, since at the point of ingest you don’t know all of the possible uses for the data. If you rationalise that data down to just the set of fields and/or aggregate it up to fit just a specific use case then you lose the fidelity of the data that could be useful elsewhere. This is one of the premises and benefits of a data lake done well.

binary string (i.e., a string in which each character in the string is treated as a byte of binary data)

okay so remind you what is a sheath so a sheep is something that allows me to 00:05:37 translate between physical sources or physical realms of data and physical regions so these are various 00:05:49 open sets or translation between them by taking a look at restrictions overlaps 00:06:02 and then inferring

it seems like a perversion of my beautiful REST/JSON server

I also think being able to self-host and export parts of your data to share with others would be great.

Your data could warm you up this winter, here’s how

with Prisma you never create application models in your programming language by manually defining classes, interfaces, or structs. Instead, the application models are defined in your Prisma schema

high friction and cost of discovering, understanding, trusting, and ultimately using quality data. If not addressed, this problem only exacerbates with data mesh, as the number of places and teams who provide data - domains - increases.

building common infrastructure

A data product owner makes decisions around the vision and the roadmap for the data products, concerns herself with satisfaction of her consumers and continuously measures and improves the quality and richness of the data her domain owns and produces. She is responsible for the lifecycle of the domain datasets, when to change, revise and retire data and schemas. She strikes a balance between the competing needs of the domain data consumers.

only by examining a constellation of metrics in tension can we understand and influence developer productivity

Software engineers typically stay at one job for an average of two years before moving somewhere different. They spend less than half the amount of time at one company compared to the national average tenure of 4.2 years.

The average performance pay rise for most employees is 3% a year. That is minuscule compared to the 14.8% pay raise the average person gets when they switch jobs.

There are a lot of PostgreSQL servers connected to the Internet: we searched shodan.io and obtained a sample of more than 820,000 PostgreSQL servers connected to the Internet between September 1 and September 29. Only 36% of the servers examined had SSL certificates. More than 523,000 PostgreSQL servers listening on the Internet did not use SSL (64%)

At most 15% of the approximately 820,000 PostgreSQL servers listening on the Internet require encryption. In fact, only 36% even support encryption. This puts PostgreSQL servers well behind the rest of the Internet in terms of security. In comparison, according to Google, over 96% of page loads in Chrome on a Mac are encrypted. The top 100 websites support encryption, and 97 of those default to encryption.

one recognizes in the tactile realitythat so many of the cards are on flimsy copy paper, on the verge of disintegration with eachuse.

Toutefois, à bien des égards, la loi 25 est le plus rigoureux des trois régimes.

En cas de non-respect de la Loi, la Commission d’accès à l’information pourra imposer des sanctionsimportantes, qui pourraient s’élever jusqu’à 25 M$ ou à 4 % du chiffre d’affaires mondial. Cette sanctionsera proportionnelle, notamment, à la gravité du manquement et à la capacité de payer de l’entreprise.ENTREPRISES

certains renseignements détenus par le ministère del’Éducation et de l’Enseignement supérieur ont été dérobés. Ainsi, 360 000 enseignantspeuvent être des victimes potentielles.

First, to clarify - what is "code", what is "data"? In this article, when I say "code", I mean something a human has written, that will be read by a machine (another program or hardware). When I say "data", I mean something a machine has written, that may be read by a machine, a human, or both. Therefore, a configuration file where you set logging.level = DEBUG is code, while virtual machine instructions emitted by a compiler are data. Of course, code is data, but I think this over-simplified view (humans write code, machines write data) will serve us best for now...

The authors propose, based on these experiences, that the cause ofa number of unexpected difficulties in human-computer interaction lies in users’ unwillingness orinability to make structure, content, or procedures explicit

no

In practice, a system in which different parts of the web have different capabilities cannot insist on bidirectional links. Imagine, for example the publisher of a large and famous book to which many people refer but who has no interest in maintaining his end of their links or indeed in knowing who has refered to the book.

If the key, or the de-vice on which it is stored is compromised, or if avulnerability can be exploited, then the data assetcan be irrevocably stolen

If akey is lost, this invariably means that the secureddata asset is irrevocably lost