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Around 90% of the UK adult population reads newspapers and magazines with print publications still reaching more people than phones and tablets, a new industry-wide audience measurement system has found.

The figures released today from the Published Audience Measurement Company (PAMCo) follows a three-year search to produce a transparent method of measuring readership across all print and electronic devices.

The Sun tops first PAMCo data

The PAMCo data, replacing the previous National Readership Survey, uses the latest measurement techniques, together with 35,000 face-to-face interviews, to give a total audience reach figure. Advertisers had called for a more accurate representation of real readership.

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The first figures show that The Sun has replaced the Daily Mail /Mail Online as the UK’s most widely read newspaper across print and online.

The Sun has a monthly total brand reach of 33.3m readers compared to 31.2m for the Mail. The Sun edged ahead of its rival through its fast-growing phone readership. The Mirror papers are the third most widely read.

The figures show that local and regional titles such as The Scotsman (1.9m) and Yorkshire Post (1.1m) – owned by Johnston Press, publishers of i – enjoy a much wider total audience reach due to a rising number of digital readers.

Figures for the period up to the end of 2017, show the i recorded a total brand reach of 2.467 million readers. A rapid growth in the
website audience will be reported in the next PAMCo data.

The first figures show that established media brands which have built trust with readers have even more influence in an era of “fake news”.

Although those age 15-34 prefer to read content on digital devices, they retain an attachment towards print papers.

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Millennials Are Actually Workaholics, According to Research
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Millennials don’t have a reputation as a hard-working generation. The caricature of the Millennial worker is more or less a cartoon of anentitled recipient of hundreds of plastic participation trophies whocares less about paying his dues at work and moreabout perks like flex-time, beer carts, and nap rooms. Or perhaps I should say that “we” have that reputation, since I’m technically a Millennial — most demographers put the start-date forthis generation at 1981.

But according to a newsurvey by Project:Time Off and GfK,Millennials are actually more likely to see themselves — proudly — as “work martyrs” than older workers, and less likely to use all their vacation time. The researchers surveyedroughly 5,000 full-time employees who receive paid time off as a benefit, and found that Millennials were much more likely to agree with four statements they used to assess work martyrdom:

43% of work martyrs were Millennials, compared with just 29% of overall survey respondents. Millennials were also more likely to want to be seen as work martyrs than older workers; specifically, 48% of Millennials wanted their bosses to see them that way, while only 39% of Gen X did and 32% of Boomers did. 35% of Millennials thought it was good to be seen as a work martyr by colleagues, while only 26% and 20% of X’s and Boomers agreed, respectively.

Millennials are also more likely to forfeit unused vacation days than other groups — 24% of Millennials, 19% of Gen Xers, and 17% of Boomers forfeited time off that they’d earned. (This ought to be an especially bitter pill for Millennials to swallow given that, due to their juniority in the workforce, they earn less vacation time overall than older workers.)

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Research Article
Quantitative analysis of population-scale family trees with millions of relatives

These authors contributed equally to this work.

See all Hide authors and affiliations

01 Mar 2018: eaam9309 DOI: 10.1126/science.aam9309
Joanna Kaplanis
New York Genome Center, New York, NY 10013, USA. Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA.
Assaf Gordon
New York Genome Center, New York, NY 10013, USA. Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA.
Tal Shor
MyHeritage, Or Yehuda 6037606, Israel. Computer Science Department, Technion–Israel Institute of Technology, Haifa 3200003, Israel.
Omer Weissbrod
Computer Science Department, Weizmann Institute of Science, Rehovot 7610001, Israel.
Dan Geiger
Computer Science Department, Technion–Israel Institute of Technology, Haifa 3200003, Israel.
Mary Wahl
New York Genome Center, New York, NY 10013, USA. Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA. Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
Michael Gershovits
Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA.
Barak Markus
Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA.
Mona Sheikh
Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA.
Melissa Gymrek
New York Genome Center, New York, NY 10013, USA. Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA. Harvard Medical School, Boston, MA 02115, USA. Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02142, USA. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
Gaurav Bhatia
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
Daniel G. MacArthur
Harvard Medical School, Boston, MA 02115, USA. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Alkes L. Price
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
Yaniv Erlich
New York Genome Center, New York, NY 10013, USA. Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA. MyHeritage, Or Yehuda 6037606, Israel. Department of Computer Science, Fu Foundation School of Engineering, Columbia University, New York, NY, USA. Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, NY, USA.

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Abstract

Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data shared by genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights into the geographical dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.

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Quantitative analysis of population-scale family trees with millions of relatives

By Joanna Kaplanis , Assaf Gordon , Tal Shor , Omer Weissbrod , Dan Geiger , Mary Wahl , Michael Gershovits , Barak Markus , Mona Sheikh , Melissa Gymrek , Gaurav Bhatia , Daniel G. MacArthur , Alkes L. Price , Yaniv Erlich

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