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Two months later, Science and Dryad are publicly releasing a data set of 28 million download request records from 1 September 2015 through 29 February 2016, timestamped down to the second. Each includes the DOI of the paper, allowing as rich a bibliographic exploration as you have CPU cycles to burn. The 3 million IP addresses have been converted into arbitrary codes. Elbakyan converted the IP addresses into geolocations using a database I purchased from the company Maxmind. She then clustered each geolocation to the coordinates of the nearest city using the Google Maps API. Sci-Hub users cluster to 24,000 unique locations.

The big take-home? Sci-Hub is everywhere. Most papers are being downloaded from the developing world: The top 3 countries are India, China, and Iran. But the rich industrialized countries use Sci-Hub, too. A quarter of the downloads came from OECD nations, and some of the most intense download hotspots correspond to the campuses of universities in the US and Europe, which supposedly have the most comprehensive journal access.

But these data have many more stories to tell. How do the reading habits of researchers differ by city? What are the hottest research topics in Indonesia, Italy, Brazil? Do the research topics shift when the Sci-Hub night owls take over? My analysis indicates a bimodal distribution over the course of the day, with most locations surging around lunchtime, and the rest peaking at 1am local time. The animated map above shows just 2 days of the data.

Something everyone would like to know: What proportion of downloaded articles are actually unavailable from nearby university libraries? Put another way: What is the size of the knowledge gap that Sci-Hub is bridging?

Download the data yourself and let the world know what you find.

The data:

http://dx.doi.org/10.5061/dryad.q447c

My analysis of the data in Science:

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— John Bohannon

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Biogeosciences Meeting Report

Treatment of the Climatic Signal in Time and Space: From Instrumental and Proxy Data to Modelling; Rouen, France, 18–20 April 2017

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A desire for a full understanding of the climate dynamics driven by natural processes versus human activities motivates much of the research on analyzing observations of climate and ocean changes. Comprehensive instrumental climate databases with high spatial and temporal resolution have been available on the global scale since the 1970s, but few time series cover more than a century. Proxy data (e.g., sediment, ice cores, and tree rings) provide information about events in the distant past. However, linking the instrumental records of climate and the paleoproxy time series remains a challenge.

Climatologists can use instruments to observe a long-term tendency (multidecadal to secular); however, because of the limited timescale covered, they are unable to draw conclusions on the driving mechanisms. Our “paleo–time series” workshop addressed this issue by discussing how the paleoclimatological community can help in understanding instrumentally observed long-term tendencies.

The participants identified the need to increase the quality of the dating control and to properly integrate the dating uncertainties into the compilation, factors that are still overlooked despite valuable efforts in developing age-modeling software . In addition, an improvement in the time resolution of paleodata and spatial distribution of the paleo–time series is necessary. This is especially true at high latitudes and in the ocean realm, where proxy data are very rare and still missing in most available compilations, even the most recent ones .

Another important question that participants raised concerned how to process the time series. Statistical techniques, together with times series analysis methods such as continuous wavelet transform , are useful for comparisons of the high-frequency variability in both instrumental and proxy records. These methods could also be applied for documenting the low-frequency variability captured in the paleorecords .

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