dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion and conclusions In this paper, we present an up-to-date surface mass balance database for the entire Antarctic continent, including relevant information about the data (location, measurement methods, time period covered, specificity of the data, references) and recommendations for the use of data in particular regions. This database was carefully checked with a quality control. This method of selection was designed to keep only highly reliable data. The quality control led to a significant change in data distribution over Antarctica and in mean regional values; although, as already shown by  Magand et al. (2007) , this process removes suspicious data that could have a major impact on any kind of SMB interpolation (e. g. ,  Magand et al. , 2007; Genthon et al. , 2009; Verfaillie et al.
, 2012; Lenaerts et al. , 2012b). Inspection of the "A" rated dataset showed that our knowledge of SMB distribution is even less than previously supposed, because for large areas data are unreliable. This is particularly true in the Antarctic Peninsula, in West Antarctica, and along the margins of the ice sheet. Large scale field campaigns in these regions should thus be a scientific priority, with particular focus on elevations between 200 and 1000 m a. s. l. , because measurements are currently mainly located in low SMB areas and no measurements are available in large areas in which a significantly higher SMB is expected. Despite these limitations, the present work provided a new and more reliable database for climate model validation. The datasets described in this paper should make a correct assessment of model quality possible in several specific areas (see Table  5 ).
For model validation, similar approaches to those performed by  Agosta et al. (2012)  with the GS network should be extended to the whole of Antarctica, using any climate model and the selected datasets. In the present study, we demonstrated the interest of comparing field data with ERA-Interim outputs. On one hand, our comparison confirmed that ERA-Interim reasonably fits observations, even though the computed SMB presents significant dry biases. On the other hand, the comparison demonstrated that observations do not correctly sample the SMB between 200 and 1000 m a. s. l. , and that very few data are available for high SMB areas. New field data along the AIS margin and new traverses in unexplored areas are thus still required to validate climate models for Antarctica. To fill the knowledge gap, research should be performed in the Antarctic Peninsula, between Marie Byrd Land and the coast, on Ronne and Ross Ice Shelves, because these are areas where data are less reliable.
Important scientific and logistic stations are located in these regions (e. g. , McMurdo station, Byrd station), which are ideal opportunities to plan future traverses. Traverses may revisit routes that were already explored during the sixties and seventies, but using current techniques to offer more reliable SMB estimates. Explorations should associate GPR studies to pits and ice cores (with absolute dating techniques) to get continuous and accurate SMB data, as suggested by the ITASE program (e. g. ,  Anschütz et al. , 2009 Anschütz et al. , , 2011;; Fujita et al. , 2011; Verfaillie et al. , 2012). Finally, observation should focus where remote sensed data (passive microwave) are not reliable, i. e. in steep slopes, in wind glazed areas and where melting may occur. The current quality-checked database is now available to assess the temporal and spatial variability of the Antarctic SMB.
First, the dataset can be rescaled to obtain a temporally unbiased SMB climatology for the end of the 20th Century. This temporal rescaling step may be performed against ERA data. For this task, field data from each specific period and each region will be rescaled based on the SMB difference given by ERA between this specific period and a reference period. Second, collecting available GPR data in Antarctica into a similar database is highly relevant and is now timely. This is currently under process at NASA (by the SUMup working Group). When available, the data will be adapted to the current database format and will be included into the present database. Nevertheless, getting a correct estimate of the Antarctic SMB at a regional scale cannot be done with field measurements only, and cross comparison with remote sensing data is needed.
A step forward is the use of the database to apply the method of  Arthern et al. (2006)  based on passive microwave. The approach should allow the treatment or removal of serious biases in passive microwave data due to steep slopes, to melting at low elevations, and to erosion in wind glazed snow areas. The use of other sources of data (e. g. , altimetry) is also highly interesting here (e. g. ,  Helsen et al. , 2008; Shepherd et al. , 2012) , even if getting access to density is still an important limitation in this case. Finally, assessing the mean Antarctic SMB will need information given by atmospheric models at high resolution (∼ 10-20 km) to correctly account for the effects of local topography on precipitation and ablation processes (e. g. ,  Van de Berg et al. , 2006; Monaghan et al.
, 2006; Krinner et al. , 2008; Genthon et al, 2009; Lenaerts et al. , 2012b). Regional circulation models (e. g. , MAR, RACMO2, PMM5) are good candidates for this task. The present database is clearly a relevant tool for model calibration. This paper presented the most recent updated surface mass balance dataset for Antarctica. The database is freely available on the GLACIOCLIM-SAMBA website (http://www-lgge. ujf-grenoble. fr/ServiceObs/ SiteWebAntarc/database. php) for any scientific use. Continuous updating of the database is planned but will require data owners to share their published data. This will also be possible on the GS website.
Acknowledgements. The GLACIOCLIM-SAMBA observatory is supported by  IPEV  ( Institut Polaire Paul-Emile Victor ) and  INSU (Institut National des Sciences de l'Univers). We acknowledge the ice2sea project funded by the  European Commission 's  7th Framework Programme  through grant number  226375. We thank  Jérôme Chappellaz ,  Kenji Kawamura  and  Kumiko Azuma  who helped us contact data owners to obtain new unpublished data. We thank  M. Fily  and  ANR  fundings (" VANISH " project no. ANR-07-VULN-013 ) for providing several new unpublished data around Dome C. We are grateful to  O. Magand  and  P. Smeets  who provided partially updated databases, and we particularly thank  A. Bliss ,  M. Ding , and  H. Motoyama  who gave us access to the full information on published datasets over long traverses.
Then, in the late 1990s, advances in microscopy and computation to handle high-dimensional data brought the first computer-aided cell tracking  [124]. The authors used a new 3D multifocal time lapse system  [55]  to observe the early embryogenesis of Caenorhabditis elegans and the software Simi BioCell provided means for annotating and comparing the lineage trees of different embryos. The synergistic efforts of different disciplines brought further major advances. Developmental biology benefited from breakthroughs in fluorescent protein engineering  [14, 16, 84, 85] , microscopy imaging systems   [34, 60, 107, 127, 130] , image processing methods for cell segmentation and cell tracking  [69, [90] [91] [92] , the development of software for computer-aided data processing and visualization  [26] , and computer hardware for processing high dimensional datasets on computing grids  [115].
The digital reconstruction of Nematoda The reconstruction of the cell lineage tree of Caenorhabditis elegans led to the development of dedicated software applications. The first available tools were designed to perform manual tracking and annotations. Worth mentioning, as it was pioneering in the field, is the development of 3D-DIASemb  [50]  for performing manual tracking, nuclear and membrane segmentation, and cell motion analysis from Nomarski video microscopy imaging of Caenorhabditis elegans up to the 28cell stage. Simi BioCell, first mentioned in 1997, was used for manual tracking and annotation in Nematoda  [58, 59] , for combining the lineage tree with gene expression  [96] , and even lately for investigating cell lineages in the early mouse embryo  [94]. Semiautomatic tracking with StarryNite  [11]  and AceTree  [97]  (available at http://waterston.
The digital reconstruction of Ascidiacea As for the Nematodes, Ascidians exhibit a largely invariant cell lineage and digital embryos have been reconstructed from confocal images of fixed specimens at different stages of development. The most remarkable work has been collected in the Ascidian Network for In Situ Expression and Embryological Data (ANISEED) database  [133]  and the 3D Virtual Embryo software for visualization and interaction  [132] , both available through http://aniseed-ibdm. univ-mrs. fr/virtual_embryo. php. The 3D Virtual Embryo has been introduced as a tool for quantifying the geometry of cells and cell-cell contacts in an interactive three-dimensional environment and provides cell volumes, mathematical descriptors of cell shape, and cell-cell contacts. Initially used to describe the first developmental stages of Ciona intestinalis and a late 32cell stage of Halocynthia roretzi, the concept has been extended to other Ascidian species.
The ANISEED database integrating molecular, embryological, and anatomical data in the virtual embryo provides a new paradigm in the field of developmental biology. It should, however, be noted that so far, ANISEED has been based on the segmentation of fixed embryos. Further extending the concept will require the segmentation of live specimens. The Ascidian community has also contributed another interesting initiative. At first, dealing with raw data only, the Four-dimensional Ascidian Body Atlas, with interactive visualization of the development of Ciona intestinalis  [57]  is available online through http://chordate. bpni. bio. keio. ac. jp/faba/1. 4/top. html. More recently, Hotta et al. achieved the manual segmentation of Ciona intestinalis tailbud embryos, and the interactive visualization of the data is available through 3D pdf files  [98].
The sea urchin in modern developmental biology The sea urchin remains a leading model organism in developmental biology  [4] [5] [6] 32] , and the sea urchin community made a major breakthrough in building the architecture of the gene regulatory networks (GRN) underlying the embryo patterning and functional differentiation  [86, 110]. The corresponding resources including protocols, database, and data annotation are publicly available (Table  2 ). However, cell lineage and biomechanics have only been scarcely addressed. The fate of embryonic cells was identified by means of fluorescent dyes  [113, 120, 121]  and uncaging methods  [112] , but very little work has been done to achieve in vivo and in toto 3D digital reconstructions from time-lapse imaging, and the resulting data have not yet permitted 3D + time quantitative analysis of cell behavior.
The quantitative assessment of cell behavior from time-lapse video microscopy imaging suggested a possible dependency of cell division timing and position on the cell volume between the fourth and the tenth cell cleavage. In this work, cell volumes were calculated indirectly from the measurement of cell diameter under the assumption of a spherical cell shape. Conclusions derived from 2D analysis might, however, be revisited at some point with more precise 3D data. This paradigm has been explored-although the corresponding data did not have single cell resolution-for the 3D reconstruction of adult sea urchin soft tissue from magnetic resonance imaging  [138]. The method proposed for the inference of phylogenetic relationships was described together with a simple and attractive way to explore 3D structures interactively in digital specimens embedded in pdf files.
Digital specimens and the corresponding raw data should be made available to the entire community and scientists should all work to enrich a common database and contribute to the validation and correction of cell trajectories and cell shape. By sharing this 3D + time quantitative data for cohorts of individuals in defined genetic and environmental conditions, scientists will have the tools to bring a new perspective to classical questions of embryology. First, a series of questions would be settled with the analysis of cell clonal history and cell fate such as for ectoderm/ endoderm boundaries in the veg1 territory (reviewed by  [32] ) or the clonal origin of larval structures such as coelomic pouches. A second series of questions concerns the biomechanics of generic morphogenetic movements.
Conclusions We have depicted an ideal scenario for a complex systems approach to morphogenetic processes underlying early animal development. The proposed strategy, based on the evidence that morphogenesis is a multiscale dynamic process, requires the integration of quantitative data across spatio-temporal scales into a yet-to-be-defined theoretical framework. A new interdisciplinary community coming from different perspectives and sharing the same goals is progressing fast. However, the overall conceptual and experimental landscape is still disparate, resembling a huge puzzle whose pieces do not yet fit together. Synergistic efforts by the scientific community are producing public databases of genome and GRN components for many model organisms. State-of-the-art microscopy imaging has made it possible to observe embryo development in small transparent organisms in toto, in vivo, with resolution at the cellular level.
Software tools are being developed for visualizing, reconstructing and analyzing 3D + time image data. Computer hardware enables the processing of high-dimensional datasets on computing grids. Digital embryos can be directly manipulated and visualized in 3D to inspect and analyze cell clonal history and cell shape changes and cell-cell interactions over time. Much effort is being dedicated to the development of mathematical models of morphogenesis, based on either GRN architecture or the mechanical properties of tissue, for testing biological hypotheses and predicting the system's behavior. A new generation of digital resources anticipates the need to integrate multimodal and multiscale data and to provide tools for in silico experimentation. The availability of multimodal and multiscale quantitative data from the in vivo observation and reconstruction of cohorts of individuals should revolutionize practices in the field of developmental biology.
Acknowledgments We thank  Louise Duloquin  for the time-lapse imaging of live sea urchin embryos and our collaborators at the BioEmergences platform for their contribution. This work was supported by the  Centre National de la Recherche Scientifique , the  Agence Nationale de la Recherche , the  European Commission  and the  Region Ile de France. We thank  Paul Bourgine  for helpful discussions and  Yannick Kergosien  for critical reading of the manuscript. We apologize to the many authors whose work we were unable to cite owing to space constraints. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Experimental Crystal data (i) Àx þ 1; Ày; Àz þ 2; (ii) Àx; Ày; Àz þ 1; (iii) x À 1; y; z; (iv) Àx; Ày; Àz þ 2. Data collection: COLLECT  (Hooft, 1998) ; cell refinement: DIRAX/LSQ  (Duisenberg et al. , 2000) ; data reduction: EVALCCD  (Duisenberg et al. , 2003) ; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Putz, 2005) ; software used to prepare material for publication: WinGX publication routines  (Farrugia, 2012). Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: LH5670).
Computing details Data collection: COLLECT  (Hooft, 1998) ; cell refinement: DIRAX/LSQ  (Duisenberg et al. , 2000) ; data reduction: EVALCCD  (Duisenberg et al. , 2003) ; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Putz, 2005) ; software used to prepare material for publication: WinGX publication routines supplementary materials sup-2 Acta Cryst. (2014). E70, o3  (Farrugia, 2012).
2006 (Martin et al. , 2009;; Ibata et al. 2007; McConnachie et al. 2008; Richardson et al. 2011)  with total magnitudes ranging from M V ≃ -6. 0 to M V ≃ -10. 0. The great success of these surveys in unveiling the M31 satellite system also highlights their coverage limitations. And VI, VII & XXVIII are all examples of dwarf galaxies at projected distances of at least about 30 • (corresponding to ∼ 400 kpc M31-centric distances), yet SDSS has only surveyed regions that stay well away from the Milky Way disk, mostly south of M31, and PAndAS is limited to a cone of ∼ 10 • from M31. The Panoramic Survey Telescope and Rapid Response System 1 (Pan-STARRS1;  Kaiser et al. 2010 ) is a photometry survey that does not have these limitations and, with final stacked data reaching slightly deeper than the SDSS  (Metcalfe et al.
2013) , it has the potential to provide a much more complete understanding of the M31 dwarf galaxy satellite system out to and beyond the virial radius of Andromeda (∼ 300 kpc). In this paper, we present the discovery of the two dwarf galaxies found in stacked Pan-STARRS1 imaging data. As they are located in the Lacerta and Cas-siopeia constellations, but are also likely Andromeda satellites, we follow the dual-naming convention emphasized in  Martin et al. (2009)  to avoid ambiguity. We therefore dub the first dwarf galaxy both Lacerta I and Andromeda XXXI (Lac I/And XXXI) and the second one both Cassiopeia III and Andromeda XXXII (Cas III/And XXXII). The data set used for the discovery is summarized in §2, while the derivation of the dwarf galaxies' properties is described in §3, before we conclude in §4.
For systems that contain relatively few stars, a likelihood-based estimate of the structural parameters is far less biased and much more robust than estimates that rely on binning the data  (Martin et al. 2008). In this instance, the algorithm explores the likelihood of a family of exponential models with a constant background level to reproduce the distribution of PS1 stars around Lac I or Cas III. The parameters of the dwarf galaxy models are its centroid, its exponential halflight radius (r h ), the position angle of its major axis (θ), its ellipticity (ǫ = 1 -b/a, with a and b being, respectively, the major and minor axis scale lengths of the system), and the number of member stars within the chosen CMD selection box (N * ), which is tied to the background level through the number of stars in the data set.
In fact, if one is to look meticulously at the location of Lac I in the POSS plates, one can see a very faint fuzz of unresolved light that betrays the presence of the dwarf galaxy (Figure  5 ). The signal of this detection was likely too easily confused with the wispy Milky Way dust filaments, evermore present at this latitude, and disregarded at the time. Cas III, with its even larger size, does not appear as a significant detection in the POSS plates. Providing a detailed assessment of the stellar populations present in Lac I and Cas III is beyond the PS1 data and the scope of this discovery paper, even though we can nevertheless notice that the dwarf galaxies do not appear to harbor a population of very young and blue stars ( < ∼ 200 Myr). The strong signal of Lac I and Cas III in the PS1 data, along with the detectability of fainter systems such as And XXIX, highlights the ability of the PS1 3π stacked survey to expand our knowledge of the Andromedan satellite system and, beyond, that of the 1-Mpc heliocentric sphere.
NFM gratefully acknowledges the  CNRS  for support through  PICS  project  PICS06183. NFM & HWR ac-knowledge support by the  DFG  through the  SFB 881. CTS and EFB  acknowledge support from  NSF  grant  AST 1008342. The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh, Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
Among the selected glaciers, three belong to the GLACIO-CLIM observatory, which runs a permanent mass balance monitoring program. These three glaciers are Saint Sorlin Glacier, monitored since 1957 (# 31 in Table  1  and Fig. 1 ), Argentière Glacier monitored since 1975 (# 4 in Table  1  and Fig. 1 ), and Gébroulaz Glacier monitored since 1983 (# 28 in Table  1  and Fig. 1 ). Mass balance data available on these Table  1. List of the glaciers studied and the weather stations used in this study. Numbers (for the glaciers) and codes (for the weather stations) refer to Fig. 1. All glaciers are located in the French Alps except when specified in brackets: CH = Switzerland and IT = Italy. For the weather stations, letters in brackets identify the data producer: MF = Météo-France, EDF = Electricité de France, MS = Météo-Suisse.
Method used to fill data gaps in the snow line altitude time series In spite of the large number of images used in this study, there were still some gaps in the SLA time series. In the 27 yr study period, for the 43 glaciers studied, the SLA was measured in 85 % of the 1081 cases. Thus, data were missing in 15 % of the cases. To fill these gaps, a bi-linear interpolation was applied. Originally, this approach was used by  Lliboutry (1965 Lliboutry ( , 1974) )  to extract the spatial and temporal terms from a very incomplete table of mass balance measurements (70 % gaps) in the ablation zone of Saint Sorlin Glacier. This approach is based on the fact that the glacier mass balance depends on both the site and the time of measurement. This approach was subsequently extended to a set of glaciers in the Alps by  Reynaud (1980) , and later on, to glaciers in other mountain ranges by  Letréguilly and Reynaud (1990).
Meteorological data Table 1 also lists the weather stations we used, which are located in and around the study area (Fig. 1 ). Among the 105 weather stations available in the Météo-France database (including stations operated by Météo-France and Electricité de France) in the French departments (Hautes-Alpes, Isère, Savoie and Haute-Savoie) in the study area, 40 were selected on the basis of data availability and their proximity to the glacierized areas (except the Lyon-Bron weather station, which was selected as a regional reference). Among the 40 weather stations, only 20 of which had a continuous series of winter precipitation and summer temperatures over the period 1984-2010, and were finally used. Two weather stations in Switzerland were also used. The climate variables used in the analysis were (1) cumulative positive degree days (CPDD) from 15 May to 15 September for each year t, extrapolated to the altitude of 3000 m a.