dataesr/scientific-paragraphs-categorization huggingface dataset:
Tile-to-tile zero-point homogenisation The CFHTLS data are provided in single tiles of ∼1 deg side, overlapping each other by ∼2 arcmin to allow for crosscalibration. These are shown in Fig. 1  for the W1 and W4 fields, together with the position of the two VIPERS areas. To build the VIPERS global catalogue we merged adjacent tiles, eliminating duplicated objects. In these cases, the object in a pair having the best Terapix flag was chosen; if the flags were identical, the object at the greater distance from the tile border was chosen. Tiles were merged proceeding first in right ascension rows and then merging the rows into a single catalogue. For any galaxy survey planning to measure large-scale clustering it is crucial that the photometric or colour selection is as homogeneous as possible over the full survey area in order to avoid creating spurious object density fluctuations that could be mistaken as real inhomogeneities.
The most effective criteria are shown in Fig. 3  applied to the VVDS data. Galaxies are retained in the source list if their colours obey the following relationship: (r -i) > 0. 5(u -g) OR (r -i) > 0. 7. ( 9 ) The resulting distribution of the true redshifts for the selected samples is shown in Fig. 4 , with the corresponding level of completeness shown in Fig. 5. To compute the latter quantity, we used the VVDS data (both Deep and Wide), and plot the ratio of the numbers of objects in a VIPERS-like selected sample, to the original total redshift sample. We call this quantity the Colour Sampling Rate (CSR). As indicated by the combination of these two figures, the VIPERS selection does not introduce any significant colour bias (i. e. it selects virtually all galaxies) above z ∼ 0. 6, with an acceptable contamination (∼5%) of low-redshift interlopers.
Data reduction, redshift measurement and validation VIPERS is the first VIMOS redshift survey for which the data reduction is performed with a fully automated pipeline, starting from the raw data and down to the calibrated spectra and redshift measurements. The pipeline includes and updates algorithms from the original VIPGI system  (Scodeggio et al. 2005 ) within a complete purpose-built environment. Within it, the standard CCD data reduction, spectral extraction and calibration follow the usual recipes discussed in previous VIMOS papers  (Le Fèvre et al. 2005; Lilly et al. 2009 ). The difference in the case of VIPERS is that the only operation for which we still require human intervention is the verification and validation of the measured redshift. All data reduction has been centralised in our data reduction and management centre at INAF -IASF Milano.
When ready, the fully reduced data are made available to the team within a dedicated database. The full management of these operations within the "EasyLife" environment is described in  Garilli et al. (2008). Figure  7  shows a few examples of VIPERS spectra, for galaxies with varying redshift and quality flag. In common with previous VIMOS surveys (e. g. Le Fèvre et al. 2005; Lilly et al. 2009 ), all redshifts have been validated independently by two scientists but with some simplification to increase efficiency given the very large number of spectra. Nevertheless, this required a very strong team effort. Two team members are assigned the same VIMOS field to review, with one of the two being the primary person responsible for that pointing. At the end of the process discrepant redshifts resulting from the two reviewers are discussed and reconciled.
The masks are stored in DS9 region file format using the polygon data structure. Terapix included a bright star mask as part of the T0006 data release consisting of star-shaped polygons centred on the stellar halos. We found this mask to be too restrictive for VIPERS; in particular, we found that the area lost was excessive near diffraction spikes and within stellar halos. We follow the same strategy in constructing the VIPERS mask, but instead use a circular A108, page 10 of 21 template with a cross pattern. The angular size of the template is scaled based upon the magnitude of the star. Our starting point for the bright star mask was the USNO-B 1. 0 catalogue  (Monet et al. 2003) , from which we selected a sample of stars with B Vega < 17. 5. Using the full CFHTLS area (130 deg 2 ), we measured the mean source density in the photometric catalogue as a function of distance from a bright star.
Spectroscopic mask and weights Although the general layout of VIMOS is well known, the precise geometry of each quadrant's observations need to be specified carefully, in order to perform precise clustering measurements with the VIPERS data. Although it happens rarely, a quadrant may be partly vignetted by the guide probe arm, in those cases in which no better located guide star could be found. In addition, the accurate size and geometry of each quadrant was changed between the pre-and the post-refurbishment data (i. e. from mid-2010 on), due to the dismounting of the instrument and the technical features of the new CCDs. We had therefore to build our own extra mask of the spectroscopic data, accounting for all these aspects at any given point on the sky covered by the survey. The masks for the W1 and W4 data were constructed from the pre-imaging observations by running an image analysis routine that identifies "good" regions.
Results and perspectives Experience with the first half of the VIPERS dataset fully confirms the expected general performance and science potential of the survey. As shown here, the average quality of the redshifts is as expected, with typical redshift measurement errors that are even better than in previous similar surveys with VIMOS. Figure  13  shows the redshift distribution of the data collected so far in the two fields. The combination of the two fields provides an impressively smooth distribution, averaging over local structure. As discussed earlier, the survey is complete beyond z = 0. 6, with a transition region at 0. 4 < z < 0. 6 produced by the colourcolour selection. A substantial tail of galaxies out to z = 1. 4 is also apparent. This redshift range benefits particularly strongly from the increased sensitivity and lack of substantial fringing with the refurbished VIMOS CCDs, allowing a clearer detection of the [OII]3727 line or the 4000 Å break beyond 8000 Å.
The scientific investigations of the VIPERS Team using this rich dataset have focused so far on a series of goals, which we briefly list here: -To measure the growth of structure between z = 1. 2 and 0. 5, by modelling the anisotropy of clustering (de la  Torre et al. 2013). The initial application is to the galaxy population treated as a whole, but the high sampling and good spectroscopic completeness means that we will be able to exploit the use of multiple populations to reduce statistical and systematic errors in this measurement. -To measure in detail the clustering of galaxies on small/intermediate scales at 0. 5 < z < 1, quantifying its dependence on luminosity and stellar mass  (Marulli et al. 2013). The final goal here is to describe the relation between baryons and dark matter, measuring the evolution of the galaxy HOD (de la  Torre et al.
). -To construct a massive spectroscopic and multi-band photometric database, with automatic spectral classifications through SED-fitting, Principal Component Analysis  (Marchetti et al. 2013 ) and other techniques, such as supervised learning algorithm methods  (Małek et al. 2013 ). -To cross-correlate the detailed 3D maps of the galaxy distribution with the dark-matter maps reconstructed using weak lensing from the CFHTLS high-quality images. -To measure the faint end of the AGN luminosity function and their correlation with large-scale structure, through a dedicated sub-sample. This is a substantial list of what should prove to be exciting developments, representing a major advance in our knowledge of the structure in the Universe around redshift unity. But all these applications should benefit from more detailed investigation, and there are many fruitful topics beyond those listed above.
We hope, and expect, that VIPERS will follow in the path of the major low-redshift surveys in generating many more important papers from open use of the public data. We therefore encourage readers to start using the PDR-1 data release  4  , described in detail in its accompanying paper  (Garilli et al. 2014). This should serve to increase anticipation for what may be achieved with the final VIPERS dataset, which, given the current statistics, is expected to include around 90 000 redshifts. Fig. 16. Zoom on the galaxy distribution within the VIPERS W1 field, where now the additional dimension of galaxy rest-frame colours has been added. Galaxies are here marked in blue, green or reddish, depending on whether their U -B rest-frame colour is respectively <0. 9, between 0. 9 and 1. 2, or >1. 2.
Acknowledgements. We acknowledge the crucial contribution of the  ESO  staff for the management of service observations. In particular, we are deeply grateful to  M. Hilker  for his constant help and support to this program. Italian participation to VIPERS has been funded by  INAF  through  PRIN 2008  and 2010 programs. L. G. , B. R. G. and J. B. acknowledge support from the  European Research Council  through the  Darklight ERC Advanced Research Grant  (#  291521 ). O. L. F. acknowledges support from the  European Research Council  through the  EARLY ERC Advanced Research Grant  (#  268107 ). Polish participants have been supported by the  Polish Ministry of Science  (grant  N203 51 29 38 ), the  Polish-Swiss Astro  Project (co-financed by a grant from  Switzerland , through the  Swiss Contribution  to the enlarged European Union), the  European Associated Laboratory Astrophysics Poland-France HECOLS  and a  Japan Society for the Promotion of Science (JSPS)   Postdoctoral Fellowship for Foreign Researchers.
2011) is a notable recent addition, with ∼120 000 spectra for galaxies at z < 1, collected over 7 fields for a total area of 9 deg 2. Redshifts are obtained with a low-resolution prism (Cool et al. 2013) , yielding typical errors one order of magnitude larger than those of the VIMOS surveys (see alsoSect. 5. 3). As such, analyses of these data have concentrated on galaxy evolution studies requiring lower precision on galaxy distances. Nevertheless, while we were revising this paper, a first detailed study of the clustering of galaxies as a function of luminosity and colour was published in the arXiv (Skibba et al. 2014 ).
To achieve this goal, neXtProt is built on a corpus containing both curated knowledge originating from the UniProtKB/Swiss-Prot knowledgebase, and carefully selected and filtered high-throughput data pertinent to human proteins. The CALIPHO group also develops a tool, the BioEditor, to capture annotations directly from the literature. With this tool we have annotated 300 protein kinases with precise descriptions of their molecular and biological functions, as well as their roles in diseases or as disease diagnosis tools. This presentation will give an overview of the different activities of the CALIPHO group in bioinformatics.
MAP Kinase RESOURCE: A DATABASE AND KNOWLEDGE PLATFORM FOR MAPKs Jonas Cicenas, The advent of internet has altered the way of data storage, accession and analysis. The web has modernized the information processing, allowing more concentrated and immediately accessible data. Scientific data is no longer limited to the format and limitations of the traditional printed media of scientific communication. Internet provides limitless stream of information, enables communication between researchers, and equips researchers with a wide pool of tools to analyze and cross-reference their data. However, there are some drawbacks for the scientists, such as too vast amount of information, the quality of information, the difficulty to find relevant data, etc. Thus, although there are a lot of databases, which contain information on MAP kinases, the more focused and concentrated knowledgebase was created, to meet the needs of scientists, mostly focused on MAP kinases, as well as those who are new to the subject and are willing to learn more.
NAVIGATING HUMAN PHOSPHORYLATION NETWORKS WITH THE SIGNET SUITE OF ON-LINE KNOWLEDGE BASES Steven Pelech Kinexus Bioinformatics Corporation, Vancouver, Canada The SigNET KnowledgeBank is a suite of open-access knowledgebases with empirical and predictive data on protein expression and phosphorylation, and the interactions of protein kinases with other signaling proteins and protein kinase inhibitors. The PhosphoNET KnowledgeBase (www. phosphonet. ca) provides data on about 200,000 known and another 660,000 predicted human phosphosites, including their evolutionary conservation in 20 other species and prediction of the protein kinases that target these sites. The TranscriptoNET KnowledgeBase documents the mRNA expression levels of 21,000 human genes in 600 human tissues, tumours and cancer cell lines.
The DrugKiNET Knowledgebase features data on over 106,000 known kinase drug pair interactions as well as 200,000 predicted kinase drug pairs. These findings are graphically integrated in the KinATLAS website (http://www. kinatlas. ca) that visualizes predicted and experimentally confirmed interactions between protein kinases and their substrates in a tissueand cell-specific manner. These websites are being used at Kinexus to guide the creation of hundreds of antibodies and kinase substrate peptides to further map, track and interpret cell signaling networks with microarrays and other antibody-based platforms.
Mindaugas Valius Proteomics Center, Vilnius University Institute of Biochemistry, Lithuania Small Ras GTPases are well-known regulators of various MAP kinases. The balance of GTP-and GDP-bound Ras is maintained by various GEF and GAP family proteins. Although GEF-dependent branch of positive regulation is elucidated in details, GAP-dependent branch and downregulation of Ras GTPase activity is less investigated. We have demonstrated that negative regulator of Ras, p120 RasGAP associate with adaptor protein Nck. In this presentation the molecular mechanism of p120 RasGAP-Nck complex formation, intracellular localization and the role in regulation of p120 RasGAP catalytic activity will be demonstrated. The data suggest that adaptor proteins are involved in the control of not only GEF but also GAP branch of Ras regulators.
The upregulation of iHsp70 correlated with sustained phosphorylation of MAP kinases ERK1,2, whereas activation of p38 and JNK1,2 was significantly inhibited. However, the exposure of cells to specific MAPKs inhibitors revealed JNK1,2 and p38 being an upstream, and ERK1,2 a downstream targets in Hsp70 induction. iHsp70 did not autocrinelly suppressed activation and total amount of transcription factor c-Jun, suggesting it's involvement in protection of myogenic stem sell following heat shock. Our data also revealed that translocation of MAPK to nucleus depended on the intensity of stress and duration of recovery period. In conclusion, our data show that pro-apoptotic stress kinases JNK1,2 and p38 initially participate in iHsp70 induction and stem cell protection mechanism against various stresses.
Data warehouse performance is usually achieved through physical data structures such as indexes or materialized views. In this context, cost models can help select a relevant set ofsuch performance optimization structures. Nevertheless, selection becomes more complex in the cloud. The criterion to optimize is indeed at least two-dimensional, with monetary cost balancing overall query response time. This paper introduces new cost models that fit into the pay-as-you-go paradigm of cloud computing. Based on these cost models, an optimization problem is defined to discover, among candidate views, those to be materialized to minimize both the overall cost of using and maintaining the database in a public cloud and the total response time ofa given query workload. We experimentally show that maintaining materialized views is always advantageous, both in terms of performance and cost.
Running example To illustrate our work, we rely on a simulated dataset storing the sales of an international supply chain. Business users need to analyze the total profit per day, month, and year; and per administrative department, region, and country. Our full dataset stores 10 years  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010)  of sale data. Its size is 500 GB. We run over this dataset a query workload Q that includes such queries as Q1= "sales per year and country", whose processing time is 0. 2 hour. The size of Q's result is 10 GB. A typical materialized view we may consider to optimize overall response time is V1 = "sales per month and country", whose processing time is 0. 1 hour. The whole set of selected materialized views is denoted V. V's size is 50 GB.
Cloud pricing policies Cloud Service Providers (CSPs) supply a pool of resources, such as hardware (CPU, storage, networks), development platforms, and services. There are many CSPs on the market, such as Amazon, Google, and Microsoft. Each CSP offers different services and pricing. This paper relies on limited, yet representative enough, models that include the main, commonly billed elements, i. e. , CPU, storage, and bandwidth consumption 1. These models are fully compliant with both relational (Amazon RDS, SQL Azure, Google Cloud SQL) and data intensive systems (MapReduce, Pig, SCOPE, Hive, Jaql), as for now, query response times are considered parameters of these models. In order for the reader to have an overview of the pricing policies taken into account in the proposed models, we present in this section an example for both Microsoft Azure and Amazon Web Service (AWS).
Even if the performance (response times and storage volume) differs from a system to another, identical values will be used in the example for clarity reasons. The objective of this work is indeed not to compare the different providers. (Amazon, 2013) provide computing resources. Different instance configurations can be rented (micro, extra small, small, large, extra large, etc. ) at various prices, as illustrated in Table 1 andTable 2. For example, the costs for a small instance (consisting in a 1. 7 GB RAM, 1 EC2 Computing Unit, 160 GB of local storage under Linux for Amazon EC2; and a 1. 75 GB RAM, 1 Computing Unit, 224 GB of local storage under Windows for Azure), are respectively $0. 06 and $0. 09 per hour for Amazon and Azure. 1 Future works shall propose a generic framework to map to any CSP.
Output data First 5 GB Free Up to 10 TB $0. 12 per GB  Bandwidth consumption is billed with respect to data volume (Table  3 ). Within Amazon and Azure models, input data transfers are free, whereas output data transfer cost varies with respect to data volume. Note that the same prices are applied by both providers. Price per month $0. 10 per GB  Finally, CSPs supply storage capabilities. Prices usually vary with respect to data volume. However, as mentioned previously, CSPs provide different services, the pricing model differing from one to another. Amazon EBS proposes a per instance model, whereas Amazon S3 and SQL Azure enable a global storage. Amazon EBS (Table  4 ) proposes a fixed price, whereas Amazon S3 (Table  5 ) and SQL Azure (Table  6 ) enable an earned rate when volume increases.
Materialized views In Database Management Systems, a view is a virtual table associated to a query answer. Views help indirectly save complex queries, format the same data in different forms, support logical independence, and reinforce security by masking some pieces of data from unauthorized users. Materializing a view, i. e. , storing it physically into a table, further helps improve response time by avoiding recomputing the corresponding query each time the view itself is queried. However, materialized views must be refreshed when source data are updated, which induces some maintenance overhead. In this work, we assume that we have at our disposal a set of candidate views for materialization that have already been preselected by an existing view selection method (e. g. ,  (Baril & Bellahsene, 2003) ).
Data transfer cost Data transfer cost C t depends on the size of uploaded data, i. e. , queries Q i and dataset D (including the initial dataset and additional inserted data if any), on the size of downloaded data, i. e. , query answers A i , and on the pricing model applied by the provider P. The cost can be decomposed into an upload transfer cost C t -and a download transfer cost C t + :   (, , , ) =   -(, , ) +   + (, ). (3) Note that most cloud providers, such as Amazon or Microsoft, do not charge for input data transfers, so input queries, initial and inserted data can be ignored for now. As a consequence, data transfer cost can be reduced to C t + (A, P) and thus depends only on A and P:   (, ) =   + (, ). (4) The pricing of Azure and Amazon EC2 is variable. It is null for the first 5 GB.
Storage cost Storage cost C s depends on the size and storage time of the whole dataset D, the instance configuration IC, and the pricing policy of provider P: C s (D, IC, P). Storage time t(D) of dataset D can be divided into periods such that t(D) = t n D k=1 (D k ) , where D k represents the whole dataset for period k. In each period k, the size s(D k ) of the stored data is fixed. Total storage cost is thus the sum of the price to be paid for each period: 𝐶 𝑠 (𝐷, 𝐼𝐶, 𝑃) = 𝐶 𝑠 𝑛 𝐷 𝑘=1 (𝐷 𝑘 , 𝐼𝐶, 𝑃). ( 6 ) Within Amazon EBS, total storage cost is the product of data size by storage time, fixed price c s EBS per GB, and the number of computing instances n IC. Indeed, as mentioned previously, each EC2 instance depends on a different EBS volume. Then storage cost can be expressed by: 𝐶 𝑠 (𝐷, 𝐼𝐶, 𝐸𝐵𝑆) = 𝑐 𝑠 𝐸𝐵𝑆 𝑛 𝐷 𝑘=1 × 𝑠(𝐷 𝑘 ) × 𝑡(𝐷 𝑘 ) × 𝑛 𝐼𝐶.
(10) If a view is materialized, its associated query must be executed, which must be paid for in the cloud. Let the materialization time of view V k be t mat P (V k ). The total materialization time is: 𝑇 𝑚𝑎𝑡 (𝑉, 𝑃) = 𝑡 𝑚𝑎𝑡 𝑃 𝑉 𝑘 ∈𝑉 (𝑉 𝑘 ). (11) The maintenance cost of materialized views is directly proportional to the time required for updating materialized views when they are impacted by modifications of the source dataset. Note that we consider that querying and maintenance do not occur at the same time. For example, queries are posed during day-time and maintenance is performed during night-time. Let the maintenance time of view V k be t maint P (V k ). Then, the total maintenance time of V is: 𝑇 𝑚𝑎𝑖𝑛𝑡 (𝑉, 𝑃) = 𝑡 𝑚𝑎𝑖𝑛𝑡 𝑃 𝑉 𝑘 ∈𝑉 (𝑉 𝑘 ). (12) When using materialized views, query processing time is defined by two main parameters: query workload Q and set of materialized views V.
Storage cost Using materialized views does not impact the storage cost model as presented by Formulas 6, 7 and 8. Exploiting materialized views to enhance query performance implies storing them in the cloud and paying the corresponding cost. As a consequence, some data can be duplicated. In that case, the size of D + V, i. e. , s(D) + s V k ∈V (V k ), is used instead of the size s(D) of D alone in the storage cost model. Therefore, the storage cost model depending on D and V can be expressed as:   (, , , ) =   ( + , , ). ( ) 14 Note that we assume that original data and materialized views are stored for the whole considered storage period. Example 5. In our running example with Amazon S3, the dataset (0. 5 TB) has been stored for a year, the size of duplicated data due to materialized views is 50 GB.