dataesr/scientific-paragraphs-categorization huggingface dataset:
We then ran each key analysis (those in the figures, either GEE, fitPagel or phylosig functions) using the different trees. The distribution of the 100 P values of each analysis is presented in S5 Fig. We tested the significance of the co-occurrence of capsule groups, with the default method (fitMk) of the fitPagel function from the phytools package (v0. 5-52 maps v3. 1. 0). This function assumes an ARD-all rates different, which allows different rates at all transitions-substitution model for both characters and gives the probability that they are independent (the rates of transitions of each character are independent of the other character). We controlled the associations between traits for phylogenetic dependence whenever one of their lambda's P values was less than 0. 05. We used the pic function to make independent contrast analysis of continuous data and the compar.
Metagenome analyses We selected from MG-RAST the metagenomes matching at least one species of our complete genome database and obtained from four environmental categories (subclasses indicated in S8 Table ): (i) water (fresh, marine and spring water), (ii) soil (agricultural, dessert, forest, tundra and grasslands), (iii) air (indoor, mammal), and (iv) host-associated (human, other mammals, arthropods, aquatic organisms and plant). These categories are broad and heterogeneous (they put together many different environments). They are used to provide a very coarse-grained classification of the type of environment of each species. We used 16S rRNA assembled reads to identify and quantify the presence of species from the complete genome dataset in the environmental samples. All analyses were performed at the species level rather than at the strain level because 16S rRNA does not allow resolving phylogenetic structure below the species level.
For consistency with previous analyses, Archaea were also excluded from the 16S environmental datasets. First, for each metagenome we identified the 16S matching each of the species in our database using BLASTN v 2. 2. 28 (selected hits with more than 97% sequence identity and with alignments covering at least 90% of the query sequence). The relative abundance of each species was then calculated by dividing the number of 16S rRNA sequences in each metagenome by the total number of sequences. This.
Availability We have made publicly available the methods to detect capsules. CapsuleFinder can be used locally using the program MacSyFinder  [33] , freely available for download at https://github. com/gem-pasteur/macsyfinder. We recommend the use of our models without the option "all' (as recommended in the documentation of the program). It can also be queried on a dedicated webserver within the Galaxy platform (https://galaxy. pasteur. fr/root?tool_id=toolshed. pasteur. fr/repos/odoppelt/capsulefinder/CapsuleFinder/1. 0. 2). The protein profiles and capsule models used in this study are accessible at https://research. pasteur. fr/fr/tool/capsulefinder/. The models are written in a simple XML grammar in plain text files to allow user modifications (see documentation in http://macsyfinder.
readthedocs. io/en/latest/). The results of MacSyFinder can be visualized with MacSyView, available online at http://macsyview. web. pasteur. fr. The capsules detected in this study, their genomic localization and organization are collected in an accessible database, CapsuleDB, http://macsydb. web. pasteur. fr/capsuledb/_design/ capsuledb/index. html.
The energy drawn by Cloud data centers is reaching worrying levels, thus inciting providers to install on-site green energy producers, such as photovoltaic panels. Considering distributed Clouds, workload managers need to geographically allocate virtual machines according to the green production in order not to waste energy. In this paper, we propose SAGITTA: a Stochastic Approach for Green consumption In disTributed daTA centers. We show that compared to the optimal solution, SAGITTA consumes 4% more brown energy, and wastes only 3. 14% of the available green energy, while a traditional round-robin solution consumes 14. 4% more energy overall than optimum, and wastes 28. 83% of the available green energy.
INTRODUCTION The rapid increase of demand for Internet services leads Cloud providers to build more and more data centers for hosting these services. The data centers that constitute the Cloud infrastructures are usually geographically distributed for security reasons or to offer lower latency for their clients. This infrastructure increase comes with a dramatic growth of the power consumption globally drawn by data centers. As an example, in 2014, data centers in the U. S. consumed an estimated 70 billion kWh, representing about 1. 8% of total U. S. electricity consumption  (Shehabi et al. , 2016). To reduce this impact, Cloud providers resort to renewable energy sources which are either on-site or off-site  (Tripathi et al. , 2016). Such energy sources are mostly intermittent by nature (wind, sun, etc.
) with high variations, and periods of time without any production (during night for instance for photovoltaic panels). Energy storage devices can help to overcome this issue. But, they constitute a costly investment and they intrinsically lose part of the energy stored  (Wang et al. , 2012). Thus, without storage, renewable energy has to be consumed upon production or it is wasted. In this context, optimizing renewable energy consumption requires to know local availability for the distributed cloud infrastructure, in order to adequately allocate computing resources to incoming user requests. The goal is to geographically distribute the workload among the data centers so that, it fits at best the on-site renewable energy production, which is variable and not known in advance. In this paper, we consider the problem of scheduling workload across multiple data centers for minimizing renewable energy loss.
RELATED WORK Cloud infrastructures consist in geographically distributed data centers which are linked through com-munication networks  (Wang et al. , 2008). With the emergence of the Future Internet and the dawning of new IT models such as cloud computing, the usage of data centers, and consequently their power consumption, increases dramatically. As an example, for 2010, Google used 900,000 servers which consumed 1. 9 billion kWh of electricity  (Koomey, 2011). Other major Cloud companies present similar figures and similar issues  (Katz, 2009). Virtualization technology and its ability to pool resources through transparent sharing should have minimized worldwide data center consumption. But, the energy consumption of state-of-the-art servers grows inexorably as they embed more and more powerful cores and advanced features and technologies.
Problem formulation We consider a system of M data centers spread over a large area. A data center DC i is characterized by its number S i of servers. Servers are considered homogeneous over the different data centers, in term of computing capabilities and energy consumption. As for the application model, we consider identical VMs submitted at unpredictable rate. The VMs are supposed to be executable in less than one time slot. We can thus describe both computing and memory requirement of VMs by the number C of VMs that a server can complete in a single time slot. We consider that a server consumes at full capacity a power of P s. Finally, the energy consumption of a data center DC i is proportional to its number of servers ON at current time slot t, U i (t). The total power consumed by the system is thus M ∑ i=1 P s ×U i (t).
This power requirement is to be compared with the green power produced at each data center. We model the green power available at time slot t in data center DC i as a random variable G i (t) that follows a truncated normal distribution of mean Eg i (t) and standard deviation p i (t), with lower limit 0. Thus, the brown power consumed at time slot t in DC i is equal to max(P s ×U i (t) -G i (t), 0). Our problem consists in allocating VMs to data centers, in order to minimize the consumption of brown energy. VMs are allocated by time slots. Then, our objective is to turn ON the adequate number of servers on the better locations for this criteria. We denote N(t) the number of waiting VMs at time slot t. We thus need to have enough servers ON for all waiting VMs at time slot t: M ∑ i=1 U i (t) ≥ N(t)/C.
Algorithms description Our SAGITTA approach uses several algorithms to take decisions and allocate VMs to physical servers. These algorithms are designed to determine at any time slot on which data center to turn ON and OFF servers. At each time slot, our constraint is to turn ON the minimum number of servers that allows for executing all requested VMs, that is N(t)/C. The general algorithm (Algorithm 1) is designed as follows. It first determines if the number of servers available is under or over the requested number. If there is not enough servers ON, Algorithm 2 determines the location of servers to switch on. If some servers are unnecessary, Algorithm 3 determines where servers should be shut down. These decisions are done regarding the expected green energy Algorithm 1: General algorithm if ∑ 1≤i≤M U i (t) < N(t) C then Switch on decision; (Algorithm 2) else if ∑ 1≤i≤M U i (t) > N(t) C then Switch off decision; (Algorithm 3) end if Transfer decision; (Algorithm 4) for 1 ≤ i ≤ M do Let U i (t) servers on and fill them, switch off the rest; end for production in the different data centers.
More precisely, Algorithm 2 compares the expected extra cost in brown energy consumption c i induced by an additional server ON on any datacenters, and selects the data center with minimum expected extra cost. The variable U i (t) is then incremented, but the servers are only switched on at end of Algorithm 1, when all decisions are taken on any data centers. The same way, Algorithm 3 selects one by one the servers to switch OFF to maximize the expected gain. Finally, Algorithm 4 evaluates if the brown power consumption could be reduced by transferring the available processing power from one data center to another. More precisely, the algorithm determines some location where a fixed number of servers is turned off, and a new location where the same number of servers is turned on. One server is selected for switch OFF on the data center of maximum gain and another one to switch ON on the data center of minimum cost, if the gain on the first data center exceed the cost on the second one.
However, an excessive Algorithm 2: Switch on decision for 1 ≤ i ≤ M do if U i (t) < S i then Compute c i = Ec i (U i (t) + 1) -Ec i (U i (t)); else c i = C × P s + 1; end if end for while ∑ 1≤i≤M U i (t) < N(t) C do Find j such that c j = min 1≤i≤M c i ; U j (t) + +; Recompute c j ; end while Algorithm 3: Switch off decision for 1 ≤ i ≤ M do if U i (t) > 0 then Compute g i = Ec i (U i (t)) -Ec i (U i (t) -1); else g i = -1; end if end for while ∑ 1≤i≤M U i (t) > N(t) C do Find j such that g j = max 1≤i≤M g i ; U j (t) --; Recompute g j ; end while turnover of workload between data centers could degrade the quality of the proposed solution. To avoid this, an additional criteria Z is added. Varying cases for this criterion are tested in Section 5. 3. After running Algorithm 4, general Algorithm 1 applies all these decisions.
VALIDATION FRAMEWORK We evaluate our algorithm through a modeling and simulation (M&S) process. In the following, we first give an overview of the whole cloud implementation model (Section 4. 1). We then detail our implementation of the data centers (Section 4. 2), of the green power production (Section 4. 3), of the cloud workload (Section 4. 4), of the algorithm implementation (Section 4. 5), and the different simulations performed (Section 4. 6).
Simulation overview The whole cloud implementation model is described in Figure  2. We simulate data centers using the DCSim (Data Center Simulator) discrete-event M&S tool  (Tighe et al. , 2012). This simulator provides the power consumption of each data center as a function of time. We implement our algorithm in an ad-hoc way using the Java language into a simulated cloud controller. This simulator receives as inputs the green power production for each data center as well as the cloud workload (i. e. the number of VMs to deploy on the cloud for each time slot). Based on these inputs and on SAGITTA's algorithms, the controller generates for each server the VM allocation and the instructions which are directly sent to the simulated data center manager. Note that we do not explicitly model the brown power production as we assume it to be infinite (at the scale of the cloud).
We also ignore the telecommunication network as we assume it to have negligible impact on the system functioning (we assume network to be oversized for our scenario), and an almost constant power consumption over time if no energysaving technique is applied  (Orgerie et al. , 2014). Finally, we do not take into account here the energy consumed by the data centers' cooling systems. In order to perform the simulations, we connect all these heterogeneous models using the MECSYCO (Multi-agent Environment for Complex-SYstem-COsimulation) M&S platform  (Camus et al. , 2016a; Camus et al. , 2016b)  which is based on the DEVS (Discrete-EVent System specification) formalism  (Zeigler et al. , 2000). We have defined a DEVS interface for DCSim, and implemented it in MECSYCO.
Data center simulation Our cloud consists in five homogeneous data centers composed of five clusters. Each of these clusters contains 80 homogeneous nodes, so overall, the cloud comprises a total of 400 servers. The characteristics of each server are based on the Taurus servers of the French experimental testbed Grid'5000 1. These Taurus servers are equipped with 2 Intel Xeon E5-2630 CPU with 6 cores each, 32GB memory, 598GB storage and a 10 Gigabit Ethernet interface. In order to determine the power consumption of each node, we implement the power model of  (Li et al. , 2015) , which is based on real measurements made on Taurus nodes. These measurements notably state that a Taurus server consumes 8W when powered OFF, 97W when idle, and 220W at 100% CPU load (i. e. P s = 220W for our algorithm).
Green power production In order to feed the controller during the simulation, we use real recordings of green power production and real workload traces. We get the former from the Photovolta project 3  of the University of Nantes. These recordings correspond to the power produced by a sin-gle Sanyo HIP-240-HDE4 photovoltaic panel updated every five minutes over one week. In order to have heterogeneous trajectories between data centers (and thus to represent solar irradiance differences between sites spread across a country), we select recordings starting at different dates, namely: 4th of September 2016, 2nd of February 2014, 8th of June 2014, 22nd of June 2015 and 21st of December 2014. We consider here that 30 photovoltaic panels (for a surface of 165. 6m 2 ) are installed at each data center.
Algorithm implementation The controller implementing our SAGITTA approach is run at each time slot (i. e. each five minutes). It saves all the data received from the green power sources during the current day. The controller computes at each time slot the standard deviations p i (t) using this history. It computes each expected green power production Eg i (t) by averaging a reference green power production trajectory (the Photovolta project recording of the 20th of August 2013 in our case which is the day with the best yield) scaled according to the last green power production received from i. More precisely, we denote P re f (t) the green power production at corresponding hour the day of reference (see Figure  4 ). We obtain the following formula: Eg i (t) = max 0, PV i (t -1) + P re f (t) -P re f (t -1) 2.
Simulated approaches We compare SAGITTA performance against two Round-Robin inspired algorithms: • Round-Robin-VM distributes the VMs fairly between the data centers regardless their green power production. • Round-Robin-DC starts filling with VMs the first data center (in an arbitrary predefined order). If this data center becomes full, the algorithm starts using the next one, and so on. Like SAGITTA, these two algorithms employ in priority the nodes already ON. As the performance of Round-Robin-DC strongly depends on the order of the data centers, we test two opposite configurations corresponding to the best and the worst possible contexts. To define these contexts, we sort the photovoltaic traces according to the total amount of green energy they provide. We assign then the traces to the data centers following this order.
This plot highlights the usefulness of the transfer algorithm. For instance, at time 173,700 s. which corresponds to early morning, DC 2 starts producing green energy slightly earlier than DC 0. SAGITTA takes then advantage of this situation by performing 24 transfers from DC 0 to DC 2. Transfers are highly correlated with discontinuities in the green power production trajectories. Thus, the transfer decision may enable adapting the VM allocation, and consequently the energy consumption, to unforeseen increases and decreases of the green power production. In the absence of transfer, the switch on and off decisions enable adapting the DC workload to their green power productioni. e. the data centers with higher power production are generally more used than the others. For the sake of simplicity, in the following, we will consider the best case for the Round-Robin-DC algorithm (with data centers ranked by their overall green energy production).
Scalability of SAGITTA In order to check if the SAGITTA's energy savings scale up, we simulate the power consumption of distributed clouds with a larger number of data centers. For these different clouds, we progressively increase the number of data centers, and so the number of green power sources (still taken from the Photovolta project), while maintaining the same total number of nodes (and so an unchanged input workload). The total photovoltaic energy production is also kept as steady as possible by progressively decreasing the number of photovoltaic panels per data centers. Yet we decided not to consider fractions of panels, so the number of panels slightly varies between the scenarios to keep whole numbers. The compositions of these clouds are summed up in Table  4. As shown in Figure  10 , the simulation results discloses that SAGITTA scales up: it maintains its ergy gains in larger clouds, and always consumes less brown energy than the other approaches.
CONCLUSION In this paper, we propose SAGITTA: a Stochastic Approach for Green consumption In disTributed daTA centers. It aims at allocating virtual machines in an energy-efficient way for a distributed cloud comprising several data centers that are geographically dis- tributed and that embed on-site photovoltaic panels. To reduce brown energy consumption, SAGITTA employs a stochastic approach to estimate the expected green energy consumption and to adequately allocate virtual machines on data centers depending on their green energy production. It also switches off unused servers to save energy, while taking into account the energy cost of switching on and off servers. We conducted a simulation-based evaluation using real workload traces, wattmeter measurements on testbed servers and real production traces from photovoltaic panels.
access to primary research data is vital for the advancement of science. to extend the data types supported by community repositories, we built a prototype Image data Resource (IdR). IdR links data from several imaging modalities, including high-content screening, multi-dimensional microscopy and digital pathology, with public genetic or chemical databases and cell and tissue phenotypes expressed using controlled ontologies. Using this integration, IdR facilitates the analysis of gene networks and reveals functional interactions that are inaccessible to individual studies. to enable reanalysis, we also established a computational resource based on Jupyter notebooks that allows remote access to the entire IdR. IdR is also an open-source platform for publishing imaging data. thus IdR provides an online resource and a software infrastructure that promotes and extends publication and reanalysis of scientific image data.
Furthermore, conventional publications do not include the metadata that define imaging protocols, biological systems and perturbations or the processing and analytic outputs that convert the image data into quantitative measurements. Several public image databases have appeared over the past few years. These provide online access to image data, enable browsing and visualization and, in some cases, include experimental metadata. The Allen Brain Atlas, the Human Protein Atlas and the Edinburgh Mouse Atlas all synthesize measurements of gene expression, protein localization and/or other analytic metadata with coordinate systems that place biomolecular localization and concentration into a spatial and biological context 1-3. There are many other examples of dedicated databases for specific imaging projects, each tailored for specific aims and target communities  [4] [5] [6] [7] [8].
A number of public resources serve as scientific, structured repositories for image data-i. e. , they collect, store and provide persistent identifiers for long-term access to submitted data sets and provide rich functionalities for browsing, search and query. One archetype is the EMDataBank, the definitive community repository for molecular reconstructions recorded by electron microscopy 9. The Journal of Cell Biology has built the JCB DataViewer, which publishes image data sets associated with its online publications. The CELL Image Library includes several thousand community-submitted images, some of which are linked to publications 10. Figshare stores 2D pictures derived from image data sets and can provide links to download image data. The EMDataBank recently released a prototype repository for 3D tomograms, the EMPIAR resource 11.
Finally, the BioStudies and Dryad archives include support for browsing and downloading image data files linked to studies or publications 12. Some of these provide a resource for a specific imaging domain (for example, EMDataBank) or experiment (MitoCheck), whereas others archive data sets and provide links to related publications at external journal websites (BioStudies). However, no existing resource links independent biological imaging data sets to provide an 'added-value' platform similar to Expression Atlas, for gene expression data 13 , or UniProt, for protein sequence and function data 14. Inspired by these added-value resources, we built IDR, an addedvalue platform that combines data from multiple independent.
imaging experiments and imaging modalities and integrates them into a single resource for reanalysis in a convenient, scalable form. IDR provides a prototyped resource that supports browsing, search, visualization and computational processing within and across data sets acquired from a wide variety of imaging domains. For each study, image data are stored along with metadata related to the experimental design, data acquisition and analysis and made available for search and query through a web interface and a single application program interface (API). Where possible, we have mapped the phenotypes determined by data set authors to a common ontology. For several studies, we have calculated comprehensive sets of image features that can be used by others for reanalysis and the development of phenotypic classifiers.