dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , some actors are not black-boxes) to optimize the workflow execution. Additionally, basing the SciFloware implementation on the middleware SON  [12]  which follows the SAAS concept (Software as a Service) makes it very modular and flexible. Last but not least, we have developed a large series of reproducibilityfriendly features. Our approach allows users to share their experiments in the spirit of  [39] , understand and compare their results  [40]  and possibly refine their analysis process to augment quality of their data sets. To do so, we have followed the recommendations and current standards on provenance  [41]   [19]  and introduced a generator of IPython/Jupyter notebooks  [2].
Future work includes considering automatic transformation of scripts designed by scientists into OpenAlea scientific workflows to augment the size of the workflow library. We are also working on techniques to augment the reuse of workflows (and scripts) by guiding the design of such experiments (  [42, 43] ). Another very important point we are actively working on is the reproducibility of scientific results. From the data point-of-view, one of the main challenge lies in finding the right level of granularity at which visualizing and, to some extent, recording the data  [44]. Currently, the complete data sets are kept while we investigate several techniques (inspired from  [42] ) to reduce the amount of data stored while ensuring a good level of reproducibility. From the environment point-of-view, we are currently able to consider virtual machine techniques to reproduce a given experiment in the exact same conditions (same OS, software versions.
Acknowledgement The authors acknowledge the support of  France-Grilles  for providing computing resources on the  French National Grid Infrastructure. This work has been performed in the context of the  IBC (Institute of Computational Biology) in Montpellier, France. MM has received the support of the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills fellowship  under grant agreement n  267196. Authors would like to thank  Julien Coste  (from  INRIA ) for his help in the OpenAlea server.
Mass distribution in Abell 2744 We constructed a high-precision mass model of galaxy cluster Abell 2744 at z = 0. 308, based on a strong-gravitational-lensing analysis of the entire HSTFF imaging data set, that includes both the ACS and WFC-3 observations. With the depth of this dataset in the visible and near-infrared, we identified 34 new multiply imaged background galaxy systems listed in  Jauzac et al. (2015b) , bringing the total up to 61, leading to a final tally of 181 individual lensed images. While doing so, we corrected earlier erroneous identifications and inaccuracies in the positions of multiple systems in the northern part of the cluster core, namely the image System #3). We then culled the multiple images that were less reliable after which running LENSTOOL with 54 multiply-imaged systems (154 total images) that were determined to be the most secure ones amongst  the 61 listed in  Jauzac et al.
3 Rvir. With the exquisite data from the HSTFF program for these clusters, and during the process of constructing the highest resolution mass models to date, we obtained constraints on subhalos associated with cluster member galaxies. Using the LENSTOOL software, the mass is partitioned into the large-scale clumps and galaxy-scale subhaloes as permitted by the input observed lensing constraints. LENSTOOL uses a Bayesian scheme with an MCMC algorithm to provide the best-fit suite of models given the priors while delineating the degeneracies amongst model parameters  (Jullo et al. 2007 ). In Figure  2 , we plot the luminosity distribution of the selected cluster members in all 3 clusters. We adopted a uniform luminosity cut and selected all cluster members with L > 0. 01L *. This however yields different numbers for each cluster given their redshifts.
Given that we have only two massive clusters that are truly comparable to the HSTFFs, this is the scheme we adopted to at arrive a rough estimate of the scatter. From the 10 14 Illustris Haloes and iCluster Zooms, we selected only the subhaloes that hosted luminous cluster members. We compare the data and simulations within the same projected area as spanned by the HSTFF lens models. he lens models extend out only to a fraction of the virial radius to only about ∼ 30-50%. Within this region we then count match by mimic-ing the selection of the same number of bright galaxies from the simulation and construct the mass function of their host dark matter sub haloes. TThe results of the comparisons of the count-matched sub haloes are detailed in the next section. In order to make an abundance matched comparison with our HSTFF datasets, we selected the subhaloes that hosted the 733 brightest galaxies in the iCluster Zooms and the 10 14 Illustris Haloes for Abell 2744, 175 galaxies for comparison with MACSJ 0416 and 217 for MACSJ 1149.
For the analysis presented in this paper, it was thus crucial to choose simulations that self-consistently include the effects of baryonic physics. We chose the Illustris suite of simulations which deploys the moving-mesh code AREPO and includes sub-grid models for galaxy formation Once again the iCluster Zoom 1 snapshot from the full physics run was selected, however here only the dark matter subhaloes associated with the brightest 217 cluster galaxies -number matched within 0. 5R vir to the HFF derived SHMF are plotted. Figure  23. Comparison with the analytically predicted SHMF: the lensing derived SHMF (with model errors) for the two independent scaling laws is overplotted along with the analytically calculated SHMF for a cluster halo with mass equivalent to that of MACSJ 1149. This analytic estimate includes the halo-to-halo scatter that is shown in the dull green band.
(2016) , report not finding any cluster scale haloes with the equivalent number and radial distribution of massive substructures (> 5 × 10 13 M ) similar to what is inferred from the HSTFF data of Abell 2744. Using extreme value statistics, they conclude that a simulation volume ten times larger than the Millennium XXL would be needed to find a cluster equivalent to Abell 2744. In addition, cosmological simulations (including the Illustris suite) that employ AGN feedback mechanisms to regulate star formation in massive galaxies are known to inadequately reproduce the observed luminosities, sizes and masses of cluster galaxies at the present time. Therefore, the disagreements that we find can be entirely explained by the paucity of simulated clusters that can be considered appropriate equivalents in both mass and complexity to the HSTFF clusters and our incomplete understanding of galaxy formation.
ACKNOWLEDGEMENTS P. N. acknowledges many useful discussions with  Dan Coe  and  Jennifer Lotz  on the HSTFF clusters. She also thanks  Angelo Ricarte  for his help with the 3D rendering of the substructure distribution in these 3 HSTFF clusters. P. N. gratefully acknowledges support from an  NSF   theory program  via the grant  AST-1044455  that supported the early stages of this work. MJ's work was supported by the  Science and Technology Facilities Council  [grant number  ST/L00075X/1 & ST/F001166/1 ] and used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk [www. dirac. ac. uk]). This equipment was funded by  BIS National E-infrastructure capital  grant  ST/K00042X/1 ,  STFC  capital grant  ST/H008519/1 , and  STFC DiRAC Operations  grant  ST/K003267/1  and  Durham University.
DiRAC is part of the  National E-Infrastructure. JR acknowledges support from the  ERC starting  grant  336736  (CALENDS). EJ acknowledges the support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* Midex  project ( ANR-11-IDEX-0001-02 ). MM acknowledges support from the  Italian Ministry of Foreign Affairs and International Cooperation, Directorate General for Country Promotion , from  INAF  via  PRININAF2014 1. 05. 01. 94. 02 , and from  ASI  via contract  ASI/INAF/I/023/12/0.
In this paper we demonstrate the role of electricity storage for the integration of high shares of Variable Renewable Energy Sources (VRES 3 ) in the long-term evolution of the power system. For this a new electricity module is developed in POLES (Prospective Outlook on Long-term Energy Systems). It now takes into account the impacts of VRES on the European power system. The power system operation relies on EUCAD (European Unit Commitment And Dispatch), which includes daily storage and other inter-temporal constraints. The innovative aspect of our work is the direct coupling between POLES and EUCAD, thus combining a long-term simulation horizon and a short-term approach for the power system operation. The storage technologies represented are pumped-hydro storage, lithium-ion batteries, adiabatic Compressed Air Energy Storage (a-CAES) and electric vehicles (charging optimisation and vehicle-to-grid).
Power system operation The representation of the power system operation requires technical and temporal detail. For example, the variability of wind and solar power is based on inter-temporal constraints (VRES production changes from one hour to another). Electricity storage also needs this type of constraints (it has to be balanced across time), which suggest an optimisation approach. Therefore, for European countries we base the power system operation on the newly developed EUCAD (European Unit Commitment And Dispatch), detailed in  (Després, 2015b). The other countries still rely on simulation equations in POLES, not detailed here (see the supplementary materials in appendix D). EUCAD uses the GAMS optimisation language, while POLES uses the Vensim language; the interface is managed with a specific library of functions developed at the European Joint Research Centre of Seville (IPTS).
8 Grid capacities are based on data from the ENTSO-E for 2010 and 2025, gathered in  (Brancucci Martínez-Anido, 2013). After 2025, the connection lines are developed proportionately to their number of full load hours, which is assumed to be a proxy for the congestion costs; the maximum installable potential is assumed to be twice the level of 2025. See appendix D for more details. 9 A proxy of the reservoir size is evaluated and shown in appendix B. Adiabatic CAES develops slowly (2. 5 GW at the end of the century), penalised by its low efficiency (65%). However, the longer-term services of storage (over 24 h), not represented here, could lead to stronger investments in a-CAES or pumped hydro. In the right part of the Figure  5 , we see that the technologies with highest efficiency (lithiumion batteries, either stationary or in EV) are used in priority in the daily operation optimisation.
A  C C E P T E D M A N U S C R I P T Acknowledgements  The research leading to these results has received funding from the  European Union 's Seventh Programme FP7/2007-2013 under grant agreement n°  308329  (ADVANCE). The author would like to thank  Robert Pietzcker  and  Gunnar Luderer  for their collaboration and the detailed comments they provided, and  Bénédicte Champel  and  Julie Mougin  for their careful proofreading. Jacques Després holds a doctoral fellowship from the  Région Rhône-Alpes.
Image data In this work, two datasets have been used. A High Resolution (HR) set of 5 MR T1-weighted images has been used to construct the library of manually labeled cases. A second dataset of 20 standard resolution images has been used to evaluate the proposed method and to compare it with other related approaches. The HR images are publicly available at the CoBrALab website (http://cobralab. ca/atlases/Cerebellum. html). Both the HR images used as input and the manually labeled validation dataset are the same as those used in  Park et al. (2014).
HR dataset These cerebellar atlases were created based on HR MR images from 5 healthy volunteers (2 male, 3 female, aged 29-57). We used T1-weighted images that were acquired on a GE Discovery MR 750 3 T system (General Electric, Waukesha, WI) using an 8-channel head coil. High-resolution T1weighted images were acquired using the 3D inversion-prepared fast spoiled gradient-recalled echo acquisition, FSPGR-BRAVO, in a scan time of ~ 20 min with the following parameters: TE/TR = 4. 3 ms/9. 2 ms, TI = 650 ms, α = 8°, 2NEX, FOV = 22 cm, slice thickness = 0. 6 mm, 384 × 384 inplane steps for an approximate isotropic resolution of 0. 6 mm dimension voxels. Reconstruction filters, ZIPX2 and ZIP512, were also used resulting in isotropic 0. 3 mm 3 voxels with a final matrix size of 489x734x503 voxels  (Park et al.
Standard resolution dataset MR images from 10 healthy control and 10 patients with schizophrenia (15 male, 5 female, average 30. 1 years age, range 25-35 years) were used to create a validation set. Imaging data were acquired using an 8-channel head coil on a 1. 5 T GE Echospeed system (General Electric Medical Systems, Milwaukee, WI), which permits maximum gradient amplitudes of 40 mT/m. Axial inversion recovery prepared spoiled gradient recall images were acquired: echo time (TE) = 5. 3 ms, repetition time (TR) = 12. 3 ms, time to inversion (TI) = 300 ms, flip angle = 20°, number of excitations (NEX) = 1 (124 contiguous images, 1. 5 mm thickness, 0. 78 x 0. 78 mm in plane resolution). These images were linearly registered to the MNI space using MRITotal software  (Collins et. al, 1994)  to perform the manual labeling thus resulting in a 1 mm 3 image resolution and a matrix size of 181x217x181 voxels.
The whole cerebellum is manually segmented on these 20 cases dataset and the left cerebellum was segmented on 10 of the cases while in the other 10 the right cerebellum was segmented (24 lobules: I-II, III, IV, V, VI, Crus I, Crus II, VIIb, VIIIa, VIIIb, IX, and X). The definition of the lobules is the same as the atlases and it is shown in Figure  1. One of the authors of this manuscript, Min Tae M. Park, was one of the manual raters of these data.
To this end, a customized subject-specific library is created by concatenating the forward non-linear transformation to the MNI152 atlas of each library case and the inverse non-linear transformation of the subject to be segmented. By doing this we are applying the transformation once, so only one interpolation is needed. Although the custom library has interpolation errors these are minimized during the label fusion step. The registration step has a huge impact on segmentation accuracy. We tested 3 different resolution levels (low=4 mm 3 , medium=2 mm 3 and high=1 mm 3 ). In general, a high dimensional transformation (1 mm 3 ) is expected to better match the reference anatomy. However, this is highly dependent on the data topology and we found that the low dimensional registration (4 mm 3 ) provided the best segmentation results (being also the most efficient in terms of computational load).
Library construction To construct the library of templates, the HR dataset mentioned in Section 2. 1 was preprocessed as described in Section 2. 2. Label maps delineated on native space were linearly mapped to the MNI space. Then, images and labels were left-right flipped to double the library size (from 5 to 10 labeled cases). The fact that the cerebellum is nearly symmetric allows the segmentation process to take benefit from this step as long as we are increasing the number of anatomical references (patterns) locally. Images and labels were then cropped to the cerebellum region. Afterwards, the non-linear transformation of these cropped images to the cropped MNI atlas was estimated. These transformations are used to non-linearly register the entire library to the space of the case to be segmented in a similar way as done in  (Weier et al.
Multi-scale OPAL label fusion In patch-based segmentation, the patch size is a key parameter that is strongly related to the structure to be segmented and the image resolution. This can be empirically optimized for a specific dataset. It can be seen in the literature that multi-scale approaches improve segmentation results  (Manjón et al. , 2014). In OPAL  (Giraud et al. , 2015)  independent and simultaneous multiscale and multi-feature ANN fields were computed. In this paper, a multi-scale approach was also used but only voxel intensities are considered as a feature. Finally, several different ANNs are computed for different scales resulting in different label probability maps and have to be combined. In OPAL, the final estimation is obtained by simply averaging these maps by using a late fusion  (Snoek, Worring and Smeulders, 2005).
𝑝(𝑙) = 𝛼(𝑙)𝑝 1 (𝑙) + (1 -𝛼(𝑙))𝑝 2 (𝑙) (3) where p1(l) is the probability map of scale 1 for label l, p2(l) the probability map of scale 2 for label l, p(l) is the final probability map for label l and () ∈ [0,1] is the mixing coefficient for label l optimized using a gradient descent technique. For this optimization, we minimized the segmentation error (i. e. , maximizing DICE coefficient). For this process, since the training dataset was very small (N=5) we used the validation dataset using a leave-one-out approach to avoid bias in the method comparison section. Specifically, we performed 20 estimation rounds (removing one case each time). Details are given in the experiments section.
Library extension To mitigate the effect of the reduced size of the library of manually segmented templates, an automatic library extension strategy was used. To this purpose, 100 randomly selected unlabeled images from healthy subjects were used. The images belong to the IXI -Information eXtraction from Images (EPSRC GR/S21533/02) dataset which is publicly available at its website (http://biomedic. doc. ic. ac. uk/brain-development). The library extension strategy consisted of applying the described segmentation pipeline to the new set of images and to add these images and the corresponding segmentations to the library as done in some previous works  (Eskildsen et al. , 2012; Giraud et al. , 2015).
Impact of label regularization We performed a similar experiment to measure the effect of the label regularization on the segmentation results. Table  4  shows the results of the proposed regularization method computed over the 20 subjects within the validation dataset. Note that we optimized these coefficients 20 times using leave-one-out cross validation as previously described and the final DICE coefficients shown in table  4  were computed using the corresponding parameter set from the leave-one-out estimation. Also, in the following experiments, the 20 different sets of beta coefficients will be used to prevent the validation cases to be included in its own segmentation process. Despite the fact that the global improvement is limited, an important increase in lobule I-II, which is a difficult structure to segment given its high variability within subjects, can be seen due to the regularization.
Impact of extended library size In this experiment we evaluated the impact of the number of subjects from the extended library used to perform the labeling step. The extended library included the original 10 manually labeled templates and 100 automatically segmented images from IXI dataset. From the library, the N most similar cases (in the mean squared error sense) to the target are selected. As can be noticed in Figure  3  the plateau is reached around 50 cases. Note that we have a noticeable oscillation for values higher than 50. As explained before, PatchMatch algorithm involves a random search step that is performed iteratively. In contrast to an exhaustive search PatchMatch converges faster because it doesn't explores the whole library. Thus we obtain an approximated nearest neighbor field.
Method comparison The proposed method (with all the optimizations and the extended library) was compared with 3 state-of-the-art methods: SUIT  (Diedrichsen, 2006) , MAGeT  (Park et al. , 2014; Chakravarty et al. , 2013)  and RASCAL  (Weier et al. , 2014). The different compared methods were applied to segment the validation dataset to evaluate their performance. In Table  5  CERES method is compared with MAGeT and RASCAL methods for all 12 structures and the whole cerebellum. As can be noticed, CERES outperformed both method overall (only MAGeT had a slightly better result for lobule VI). As SUIT works with a different set of labels, the output segmentations of MAGeT, RASCAL and CERES were adapted by fusing lobules I-II, III and IV for a meaningful comparison as done in  Park et al. (2014).
Acknowledgements We want to thank the  IXI -Information eXtraction from Images  ( EPSRC GR/S21533/02 ) datasets promoters for making available a valuable resource to the scientific community. This study has been carried out with financial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of the Investments for the future  Programme IdEx Bordeaux  ( HL-MRI ANR-10-IDEX-03-02 ), Cluster of excellence CPU, TRAIL ( HR-DTI ANR-10-LABX-57 ) and the  CNRS  multidisciplinary project " Défi ImagIn ". This research was also supported by the  Spanish  grant  TIN2013-43457-R  from the  Ministerio de Economia y competitividad.
The reference ontologies include the Plant Ontology, Plant Trait Ontology and the Plant Experimental Conditions Ontology developed by the Planteome project, along with the Gene Ontology, Chemical Entities of Biological Interest, Phenotype and Attribute Ontology, and others. The project also provides access to species-specific Crop Ontologies developed by various plant breeding and research communities from around the world. We provide integrated data on plant traits, phenotypes, and gene function and expression from 95 plant taxa, annotated with reference ontology terms. The Planteome project is developing a plant gene annotation platform; Planteome Noctua, to facilitate community engagement. All the Planteome ontologies are publicly available and are maintained at the Planteome GitHub site (https://github.
The portal also hosts eight species-specific Crop Ontologies (CO) describing traits and phenotype scoring standards being adopted by international breeding projects on maize (Zea mays), sweet potato (Ipomoea batatas), soybean (Glycine max), pigeon pea (Cajanus cajan), rice (Oryza sativa), cassava (Manihot esculenta), lentil (Lens culinaris) and wheat (Triticum aestivum). The CO adoption is an integral part of the Integrated Breeding Platform and tools developed by the Consultative Group on International Agriculture Research (http://www. cgiar. org/). All ontologies and annotated data are available from the project website and browser, and through our web services and Application Programming Interface (API) for integration with software tools designed for data collection and curation, genome annotation and analysis.
THE PLANTEOME DATABASE The Planteome database is accessible from our web site (http://planteome. org/) designed in Drupal version 6. It features an ontology browser and faceted search options to access ontologies and ontology-based annotations of various bioentities. The ontology browser (http://browser. planteome. org/amigo) is a customized adoption of the AmiGO browser (1), developed by the Gene Ontology Con-sortium. All data and ontologies are stored in a SOLR (http: //lucene. apache. org/solr) index system that allows for fulltext searches through the ontology browser. The schema and index files for the design of the data store are available at the GitHub repository https://github. com/Planteome/ amigo. In the current Planteome 2. 0 Release, the Planteome database provides access to reference (Table  1 ) and speciesspecific ontologies and approximately 2 million (M) bioentities, or data objects, including proteins, genes, RNA transcripts, gene models, germplasm and QTLs (Quantitative Trait Loci; Table  2 ).