I work for a science research discovery platform that allows users to group and share articles intuitively, then leverages machine learning to provide them with recommendations. At its core, our mission is to make science more accessible by improving the access experience: in other words, to help level the playing field when it comes to public engagement with science, a field which for decades (if not centuries) has been stubbornly tilted in favour of those with significant levels of both education and money. Our problem? Plenty of the articles we index require a fee to be read in full. How can we level anything when the scientific publishing industry insists on stacking up the field against us?Many of our users are PhDs and postdocs; not only highly educated in the subject they’re researching, they’re also almost certainly affiliated with an institution that provides free journal access. Whilst open access is no less important for this group, to ensure that the most fruitful collaborative work can take place, the users who are left behind from the start are those we term ‘citizen scientists’. These are members of the public who are less likely to be familiar with the vocabulary of the subject they have set out to research, who almost certainly do not have paid access to subscription journals, and who may be seeking out research for personal reasons, such as to find out more about a recent diagnosis for themselves or for a loved one, to help them decide on a field of future study, or simply for personal interest. They're stuck at the lowest end of the playing field. The problem with open access, the argument goes, is that it’s useless: citizens who do not hold advanced degrees need scientific training to understand the articles in the first place. Yet fostering an ‘open culture’ will build public scientific literacy from the ground up by encouraging people to inform themselves about science from primary sources. And - to co-opt the slogan that decorates a building not far from our offices in Shoreditch, London - more scientific literacy, more power. The effects of a cultural sea change like this could be dramatic. Nature called the impact of releasing papers to the public “astonishing”, apparently surprised that anybody not holed up in an ivory tower would have any interest in research that is ostensibly done for the good of the public. The shock that public engagement actually works isn’t new: consider the resurgence of citizen science, a flourishing movement borne of scientists’ realisation that patterns or anomalies in data might just as well be spotted by laypeople as by trained scientists, which has produced democratic and cultural movements to the surprise of many of its practitioners. Steven Bishop writes how citizen science not only permits but delights in the participation of those who do not fit inside the narrow categories of "affluent, literate and educated". Perhaps scientific publishing has something to learn from this radical ethos - and its radical success.Concretely, what could an open culture do for us? A lot, is the answer. Pupils at schools too underfunded to afford journal subscriptions could nevertheless be introduced to advanced science and scientific inquiry, helping to shape their powers of critical thought in a critical age - maybe even giving them the tools to begin contributing to science, and by doing so to start fighting the systemic inequality that continues to plague it. Would-be higher education students unable to afford university fees could take their education into their own hands by learning for themselves what was previously hidden behind a paywall too high to scale without institutional help. Any person able to read about science for themselves is far less likely to be duped by ‘fake news’, a tide that threatens, however distantly, to engulf rational inquiry.  The spread of open culture - a different kind of tide - would begin to beat at the barriers that have stood for centuries between academia and the public, barriers that have largely served only to increase resentment and distrust of science itself. Open scholarship raises the bar that sets our preconceptions about what science and its practitioners should look like, and invites us to join it. It hints at a world where researchers, far from being forced to chase economic stability or arbitrary impact factors, can pursue research for knowledge’s sake: research from which members of the public can directly, explicitly benefit. Open begins to level out the playing field.

In this study, we analyzed whether the 'Customer' are more likely than 'Subscriber' to use Citibike during working hours (9:00 - 17:00). Using one-tailed Z-test and Chisq test, we found that the percentage of riders used CitiBike during working hours is significantly higher for 'Customer' than for 'Subscriber'.

We wanted to investigate the larger question regarding how citibike trip times related to the gender of the rider. Using the dataset from July 2017, we looked at the distributions of male and female riders. We found that the distributions of male and female riders are not statistically different from each other using the KS test. This knowledge is important when interpreting Citibike data in our future analysis. 

Citibike Dataset Analysis (For NYU CUSP PUI2017 HW7)<Zhiao Zhou, zz1749>AbstractNew York Times has reported that there were more male bike-share members in NYC where about a third of members were female, who cared more about safety and convenience. However, it was also mentioned that quite a few women liked biking to work.\citep{gap} So it would be interesting to find out if the ratio of men biking at morning (commuting period for most people) over man biking the whole day is smaller than the ratio of women which would help balance the gender disparity.  Here we carried out a z test between proportions in iPython notebook to test my hypothesis using a sample of 201706 Citibike (The most popular bike-sharing system in NYC) public datasets. It turned out that the Z-score is 9.9977 and the p-value is 7.7958e-24. So we could accept our alternative hypothesis that women actually bike more at morning which would be useful for future analysis since the existing gender disparity seems to result from lack of infrastructure and safety for women. IntroductionFirst launched in 2013, Citibike has now totals of 706 stations and 12,000 bikes which pushed itself to become the biggest bike-sharing system in the USA.\citep{wikipedia}  However, Citibike has been struggling to figure out why men far outnumber women in using their services, with the number of men riders double that of women riders, as Sarah M. Kaufman,  the assistant director of tech programming at the Rudin Center for Transportation at NYU, said that women became early indicators of a successful bike system which means that if you had more women riders, it means that it would be convenient and safe. \citep{fitzsimmons2015} This phenomenon also emerged in Chicago and Washington where bike-sharing systems attracted more men. And till now it's still not solved yet what triggers this gender disparity.  The Citibike company was trying to introduce new stylish bikes or add new stations to woo women.What was fun was that there seemed to be a number of women who loved to commute by public-sharing bike. If we could find out that in fact, women bike more than men at morning, the company could focus more on service for women during a commute. Additionally, this hypothesis was untested, we could easily test it using z-test, nonetheless.  Figure 1 shows my null hypothesis and its corresponding maths expression as well as my significance level.

As more people want to fly each year, it is important to make "flying things" work better. A great way to do this is to make the "fire" which keeps them in the air burn hotter. With a hotter fire, we need less stuff for burning and we can save money. However, a hotter fire also means that stuff around it might become hotter than it should, which is a big problem. My work is coming up with new types of stuff to be used in the area close to the fire. First, I use big computers to get an idea of what kind of new stuff might work well. Second, with help from some friends, I make it. Third, I use new ways of checking how good this stuff really is by making it hot and then pulling it until it breaks. Last, I talk to other people and hope they will use this new stuff inside their "flying things" soon.

Imagine you are at your friend's party, and you are listening to a story about her last trip. Even though music is playing and other people are talking, you can probably understand what she is saying. But if you have hearing problems, you might find it hard to follow the conversation. Why is that? Our brains only pay attention to parts of the sound that we are trying to understand. In this case in your friend's voice there are some parts that help your brain listen to her instead of the music. I study how the parts of the brain that have the job of making you hear do exactly this. I want to help people with hearing problems to understand their friends voices even when there is loud music.

Sick people need making-better-stuffs to get well. We use computers to work out which making-better-stuffs should work best for different people. All people have slightly different small inside body things. Doctors want making-better-stuffs to stick to the right inside body things as well as possible to fix the problems that make people sick. In our computers we make pretend small inside body things just like those in different people and move them around like they do really. We use them to find out which make-better-things stick the best without giving many making-better-stuffs to people which is slow and sometimes makes other things go wrong. We can also use our pretend small inside body things to help people put together new, better, making-better-stuffs.

Cells move around inside us. They use tiny feet that stick to all the not-cell stuff around them. These tiny feet kind of help cells if they are alone. But not really that much. Do the tiny feet help cells if they are in a group? My work asked this question outside of a body. I put cells inside the same not-cell stuff from our body, and I watched if they moved in a group or not. They really needed the tiny feet in order to move through the not-cell stuff from our body. To make the tiny feet strong, cells use the same little bits that make a body strong to lift heavy things. Those little bits are in every cell in your body! They put together the tiny feet and hold them together. Without all that a group of cells can't move around inside our bodies. This is important for when cells don't stop growing and make a bad spot that keeps getting bigger.

The human body carries a lot of good tiny cells that are important for our well-being. One way that our tiny cells are good for us is that they stop bad tiny cells from growing in our bodies. However, how the good tiny cells stop bad tiny cells from growing and staying in our bodies is not well understood. When entering from the mouth and down the stomach, bad tiny cells and good tiny cells eventually find each other and have to fight for space and food inside our bodies. One type of bad tiny cell likes to grow by "breathing," much the same as we breathe air so that the cells in our bodies can live. However, "air" is low after entering the stomach. This is because in the normal body, the good tiny cells are always making things that keep the air low. After entering the stomach and moving down, bad tiny cells make things that hurt our bodies. Our bodies respond by making things that kill both good and bad tiny cells. However, the bad tiny cells have figured out ways to not die during the attack by our body, while the good tiny cells have not, and get killed. When the good tiny cells die, they can no longer keep the air low and the bad tiny cells are able to breathe and grow to very high numbers. So, one way that our good tiny cells keep us well is by stopping the bad tiny cells from breathing and growing in our bodies. However, some bad tiny cells have figured out a way to wipe out good tiny cells from our bodies. This is one way that bad tiny cells can make us sick.From \cite{Rivera_Ch_vez_2016}.

[Compensatory evolution via cryptic genetic variation: Distinct trajectories to phenotypic and fitness recovery]  [Sudarshan Chari, Christian Marier, Cody Porter, Emmalee Northrop, Alexandra Belinky and Ian Dworkin   , october 10, 2017, version 1, BioRXiv] 

To make the most of bringing people together to talk about deciding something, you need to decide between different good ways of bringing people together, don't try to make all the good ways work for everyone at once. It is better to let people end with different thoughts than pretend everyone agrees. You should write down what people thought and make it clear how that is used in deciding something. Plan to share in open ways, so others can learn from what happened and what people thought - others might use what you shared in ways you didn't plan, but that might help bringing people together mean more.

Up-goer Five entry: herePhD proposal: Designing Definition Discovery - Read, Recognize, Reflect, Repeat

Up-goer Five EntryThe body is made up of lots of cells and cells have sticks that make them what they are. Normal cells have a normal number of cell sticks. Bad cells, which can kill a body, have the wrong number of cell sticks. More cell sticks in a cell can cause cell problems that can lead to even more cells sticks in a cell. When this happens more cells take over a body and kill it. Some cell sticks when added cause new changes to happen in a cell as well that can lead to a problem when all cell sticks have to be given to new cells.  Peer-Reviewed PublicationCancer cells display aneuploid karyotypes and typically mis-segregate chromosomes at high rates, a phenotype referred to as chromosomal instability (CIN). To test the effects of aneuploidy on chromosome segregation and other mitotic phenotypes we used the colorectal cancer cell line DLD1 (2n = 46) and two variants with trisomy 7 or 13 (DLD1+7 and DLD1+13), as well as euploid and trisomy 13 amniocytes (AF and AF+13). We found that trisomic cells displayed higher rates of chromosome mis-segregation compared to their euploid counterparts. Furthermore, cells with trisomy 13 displayed a distinctive cytokinesis failure phenotype. We showed that up-regulation of SPG20 expression, brought about by trisomy 13 in DLD1+13 and AF+13 cells, is sufficient for the cytokinesis failure phenotype. Overall, our study shows that aneuploidy can induce chromosome mis-segregation. Moreover, we identified a trisomy 13-specific mitotic phenotype that is driven by up-regulation of a gene encoded on the aneuploid chromosome. From \cite{Nicholson_2015}.

Various age-related neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS), Alzheimer’s, Huntington’s, and Parkinson’s, are associated with the same basic disorder: the loss of nerve cells capacity to fold their proteins correctly, which causes protein aggregations that form "clumps" that end up generating the cell death (Figure 1). To better understand protein folding let's visualize folded proteins as a folded paper plane when both have a correct 3D conformation they can accomplish their respective functions, catalyze a reaction, in the case of some proteins or fly in the case of the plane. Incorrect folding cause that proteins lose their normal functions and cause protein aggregations that form "clumps" that end up generating the cell death. Analogously, a paper plane incorrectly folded would not be able to fly and would form a paper ball with other defective paper planes. With this example is easier to understand the conformation or shapes that some proteins acquire (Figure 1). 

Con motivo de la reciente OpenCon Santiago 2017, queremos demostrar nuestra inquietud respecto a diversos temas relacionados con la difusión y comunicación del conocimiento, así como la valoración de su producción, donde a la fecha no ha existido una discusión en los espacios oficiales para ello. Esta situación se hace más crítica al no haberse considerado como uno de los puntos a discutir en el proyecto de creación del nuevo Ministerio de Ciencia y Tecnología de Chile.

We demonstrate that attention flows manifest knowledge, and the distance (similarity) between crypto economies has predictive power to understand whether a fork or fierce competition within the same token space will be a destructive force or not. When dealing with hundreds of currencies and thousands of tokens investors have to face a very practical constraint: attention quickly becomes a scarce resource. To understand the role of attention in trustless markets we use Coase's theorem. For the theorem to hold, the conditions that the crypto communities that will split should meet are: (i)Well defined property rights: the crypto investor owns his attention; (ii) Information symmetry: it is reasonable to assume that up to the moment of the hard fork market participants are at a level ground in terms of shared knowledge. Specialization (who becomes the expert on each new digital asset) will come later; (iii) Low transaction costs: Just before the chains split there is no significant cost in switching attention. Other factors (such as mining profitability) will play a role after the fact, and any previous conditions (e.g. options sold on the future new assets) are mainly speculative. The condition of symmetry refers to the “common knowledge” available at t-1 where all that people know is the existing asset. Information asymmetries do exist at the micro level -we cannot assume full efficiency because transaction costs are really never zero. Say’s Law states that at the macro level, aggregate production inevitably creates an equal aggregate demand. Since a fork is really an event at the macroeconomic level (in this case, the economy of bitcoin cash vs the economy of bitcoin), the aggregate demand for output is determined by the aggregate supply of output — there is a supply of attention before there was demand for attention. The Economic Complexity Index (ECI) introduced by Hidalgo and Hausmann allows to predicting future economic growth by looking at the production characteristics of the economy as a whole, rather than as the sum of its parts i.e. the present information content of the economy is a predictor of future growth. Say’s Law and the ECI approach are about aggregation of dispersed resources, and that’s what makes those relevant to the study of decentralized systems. While economic complexity is measured by the mix of products that countries are able to make, crypto economy complexity depends on the remixing of activities. Some services are complex because few crypto economies consume them, and the crypto economies that consume those tend to be more diversified. We should differentiate between the structure of output (off-chain events) vs aggregated output (on-chain, strictly transactional events). It can be demonstrated that crypto economies tend to converge to the level of economic output that can be supported by the know-how that is embedded in their economy — and is manifested by attention flows. Therefore, it is likely that a crypto economy complexity is a driver of prosperity when complexity is greater than what we would expect, at a given level of investment return. As members of the community specialize in different aspects of the economy, the structure of the network itself becomes an expression of the composition of attention output. We use genetic programming to find drivers — in other words, to learn the rankings. Such a ranking score function has the form, returns_tokenA > returns_tokenB = f (sources_tokenA > sources_tokenB). Ultimately, the degree of complexity is an issue of trust or lack thereof, and that is what the flow of attention and its conversion into transactional events reveal.

In the summer of 2017, we conducted a survey to assess scientists' opinions on the value and potential barriers related to reading and reviewing preprints at journal clubs. In this short article we present and discuss the results of the survey as well as how these results helped us shape our approach at PREreview.

Simon James Goring*1¶, Kaitlin Stack Whitney2¶, Emilio M. Bruna3,4, Aerin L. Jacob⁵, Timothée Poisot⁶ ¹Department of Geography, University of Wisconsin, Madison, Wisconsin, United States of America ²Science, Technology, & Society Department, Rochester Institute of Technology, Rochester, New York, United States of America ³Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America ⁴Center for Latin American Studies, University of Florida, Gainesville, Florida, United States of America ⁵Yellowstone to Yukon Conservation Initiative, Canmore, Alberta, Canada ⁶Département de Sciences Biologiques, Université de Montréal, Montréal, Quebec, Canada *Corresponding Author E-mail: goring@wisc.edu (SG) ¶ These authors contributed equally to this work.

Plants, animals and humans are adapted to Earth's rotationFor many years we have known that living organisms, including humans, have an internal biological clock that helps them anticipate and adapt to the regular rhythm of the day.  For example, the mimosa plant opens its leaves towards the sun during the daytime and closes them at dusk; and dogs sleep at night and remain pretty active during the daytime. The circadian rhythm – from the Latin words circa meaning "around" and dies meaning "day" – explains how plants, animals and humans adapt their biological rhythm so that it is synchronized with oscillations of day and night caused by Earth's rotations \cite{release}.These circadian rhythms are ancient and highly conserved throughout evolution. Circadian rhythms have been observed in unicellular organisms, including fungi, as well as plants, insects, rodents, and humans. This suggests that the ability to optimize behavior according to the amount of light in our environment has been incredibly important for the success of organisms \cite{ibez}... so important, that a group of scientists was recently awarded a Nobel Prize for discovering more about how this all works.

Web DesignIl web design è il futuro del grafico  pubblicitario e della pubblicità stessa, come si è potuto constatare nell'ultimo decennio con l'avvento degli smartphone.Prima per accedere al web serviva un pc e si era vincolati dal dover essere fisicamente a casa, mentre ora ovunque ci si trovi, si ha pieno accesso ad esso.Vista l'importanza abbiamo deciso di esaminare i  siti  delle principali  marche di smartphone Android, quali "LG", "Samsung" e "Huawei".

Suppose we were to treat the Lagrangians of the theories we wish to unite as coordinates in a space of Lagrangians. In this space the Lagrangians for general relativity (Einstein-Hilbert), the standard model, and the dark sector act as coordinate axes. From here a functional derivative equation is set up for a functional, T, which gives as a result another functional, U, which denotes the Lagrangian for our universe. From there the action (𝒮) due to U is computed and shown to converge even at infinite energy. Finally with 𝒮 a generating functional for all possible interactions is computed and a one inch equation of everything is given(Z = ei𝒮). It is the assertion of this paper that with the generating functional detailed herein one may compute any physical quantity of interest including gravitational and dark sector corrections and arrive at a finite result. Specifically graviton-graviton and graviton photon interactions are shown to result in finite observable quantities. It is noted that this is a fully worked out model where GR and QFT are treated on an equal footing by relativizing QFT, or put simply GR=QFT.

 Este texto é uma proposta incentivada na disciplina de Modelagem Geoestatística do Ambiente, ministrada no Programa de Pós Graduação em Ciência do Solo da Universidade Federal de Santa Maria. Ele é desenvolvido com base nos conhecimentos adquiridos a cada aula da disciplina, sendo movido e motivado por um trecho extraído do livro Geostatistics for Natural Resources Evaluation, de Pierre Goovaerts (1997, p.442): “[…] beware that uncertainty is not intrinsic to the phenomenon under study: rather it arises from our imperfect knowledge of that phenomenon, it is data-dependent and most importantly model-dependent, that model specifying our prior concept (decisions) about the phenomenon. No model, hence no uncertainty measure, can ever be objective: the point is to accept that limitation and document clearly all aspects of the model.”     Isso nos permite refletir sobre os modelos usados para estudar os fenômenos espaciais de interesse na ciência do solo, considerando aspectos relacionados tanto ao surgimento como aos rumos tomados por esse campo ao longo da história.     Não é de hoje que sabemos da importância de obter informações sobre o solo. Contudo, a necessidade de que os usuários tenham uma forma mais dinâmica de visualizar tais informações instigou – e ainda instiga - muitos pesquisadores a desenvolver formas para que isso se tornasse possível. Uma das formas mais práticas e interativas é a criação de uma forma visual por meio da criação de um mapa, como apresentado na Figura 1, tornando a informação mais intuitiva ao usuário final, que tem nele a variação das informações na paisagem. O objetivo do mapeamento é permitir que possamos dizer algo preciso sobre partes específicas da área e que não poderíamos dizer sobre o todo (\citealp*{beckett1968}).

   Lugrin R., Nyffeler C., and Ruas M.    Ecole Polytechnique Fédérale de Lausanne  Introduction In populated areas noise can be one of the major sources of pollution. If the presence of noise seems to go hand in hand with urban areas the introduction of vegetation could possibly reduce the discomfort it can cause to people and the possible effects it has on their health. The aim of this paper is therefore to study the correlation, which exists between greenness in urban areas and noise levels that can be measured both during day and night.As of now several studies have been published, which try to relate the presence of vegetation noise levels and the impact both have on the population. One work by Donald Aylor \cite{Aylor_1972} explored the way different types of vegetation hindered the effects of noise in the presence of different environmental factors (Wind, soil among others). After the observations, models were developed to further enlarge the scope of the study. Results showed vegetation could effectively reduce loudness. The effectiveness of this reduction relied heavily on the vegetation's structure: foliage, stems, ground structure.Another work \cite{Dzhambov_2015} aimed to link the presence of green areas with the attenuation of both psychological stress and physical health issues related to noise pollution. A survey was conducted correlating the presence of greenness and the perception of noise the population had. Results showed that the "Noise sensitivity" was clearly reduced when the access to green spaces was increased.For the present study the initial statement is that in areas with elevated greenness the noise decreases.  The area of study is the municipality of Vernier (Geneva, Switzerland). Day and night levels of noise were measured and a correlation was established with the green areas of the municipality based on satellite pictures in order to verify the hypothesis mentionned above.Data Several raster and vector layers around the municipality of Vernier were used.Four orthophotos cover the zone of interest. They are RVB images already georeferenced and have a ground resolution of 0.5 meter. They probably originate from the federal office of topography Swisstopo but this is not clearly mentioned in the data, likewise for the height model. This height model has a ground resolution of 1 meter.The noise data is provided by the Swiss noise databas \cite{nokey_f47c8}. The information is given in a raster file. It contains values of noise that were predicted by models and calculations based on noise sources data, traffic, urban fabric and terrain configuration  \cite{nokey_ecc2a}. Noise attenuation due to vegetation has not been taken into account to produce this databaseThe boundary of the municipality is stored in a shapefile as a polygon. It is projected according to the Swiss coordinates system SCR EPSG21781 as all the other georeferenced files used here.Methods  In order to obtain the results needed to do the study, both QGIS and GeoDa softwares were used following the respective "QGIS User Guide"\cite{nokey_13188} and "GeoDa User Guide" \cite{nokey_9ebde}. The data needed for the analysis was obtained from four RVB satellite images encompassing the municipality of Vernier, two raster layers with the sound levels around the municipality of interest, and one vector layer defining the boundaries of Vernier. All this content was imported to QGIS. A Virtual Raster Catalogue was then created to merge the four RVB images. Using the style properties of the catalog, both Red and Blue bands were removed so that only the green band remained visible. This allowed to keep only the reflectance information of the area, which is important to detect the vegetation. After that a 50x50m grid was created with its extent around the limits of the municipality of Vernier. With this grid all information contained in the green band raster and the day and night rasters was gathered in one single attribute table: levels of sound during day and night and of greenness in the area selected. This task was performed using the zonal statistical tool. Then, the main tool of analysis was created, namely selecting only the cells confined within the boundaries of the municipality to extract precisely the information of interest. This operation was made possible with the "Spatial Query" tool available in QGIS.In GeoDa this finer grid was exploited to extract both box maps and scatter plots. Those allow to establish comparisons between the different values of greenness and sound. They are also of interest to verify the spatial correlation between the levels of vegetation and noise, which allow for the verification of the hypothesis.Results The average values of each dataset for the commune of Vernier are shown in table \ref{350700}. There is quite a high range of values for the vegetation indicator, while the sound values for both day and night vary less. The data obtained during nighttime shows lower values as during daytime.

A document by Noemi Romano. Click on the document to view its contents.