dataesr/scientific-paragraphs-categorization huggingface dataset:
The chemistry of these materials allows us to obtain an essentially unlimited number of new materials  [7] [8] [9] [10] [11]. Indeed, in recent years the number of published synthesized nanoporous materials has grown exponentially  4. Yet, this growth is exceeded by the number of predicted structures, giving us libraries of millions of potentially interesting new materials. This sheer abundance of structures requires novel techniques from big data research to shed light on the existing libraries, as well as to facilitate the search for materials with optimal properties. In nanoporous materials the shape of the pores plays an essential role in the behaviour of the material  12, 13. Conventionally, pore structure is characterized by a set of traditional geometric descriptors such as pore volume, largest included sphere, surface area and so on.
These descriptors can be successfully optimized to search for materials with similar overall thermodynamic properties, but, as we will show, they capture partial geometric features only and do not encode enough geometric information to enable detection of materials that have similar overall pore shapes. There are computational techniques to quantify the similarity between crystal structures  14, 15. However, these algorithms are limited to identifying identical crystal structures, while we are interested in finding materials that may have different crystal structures or chemical compositions but similar pore geometries. Martin et al. 16  developed Voronoi network representations of pore geometries, which are useful as fingerprints but do not capture details of the local pore structure. We develop a mathematical quantification of geometric similarity by using topological data analysis (TDA).
In contrast, the pore system of DON consists of eight disjoint components, encoded by the eight long intervals in its 0D barcode. The 1D and 2D barcodes contain information on the shape of the cavities (see Supplementary Note 1). Identifying structures with similar pore shapes. The most elementary, but highly non-trivial, application of our approach is to identify porous materials with similar pore structures. As we have a database of over 3,000,000 nanoporous structures  19  , visual inspection is out of the question. Suppose we would like to know whether the library of hypothetical zeolites contains structures whose pore geometry is similar to a given material, for example, a synthesized zeolite. To see the effectiveness of our approach, it is instructive to take a structure and find the four structures that are most similar to the chosen one, selected once by conventional descriptors (ConD) and once using persistent homology (PerH).
To compare these two sets, we compute their average distances to the reference material, measured by the metric D CS of the conventional space as well as by the metric D TS of the barcode space (see Methods section for details). Figure  2a  shows the average distances of the two sets for each of the 146 experimentally known zeolite structures accessible to methane. The distances are normalized by the largest pairwise distance in the database. The TDA approach provides what one would expect: when persistent homology is used to identify similar pore structures, small D TS correlates well with small D CS , that is, similar persistence diagrams describing the pore shapes correlate with similar conventional geometric measures. Figure  2a  shows that the relatively few zeolites for which there are no four structures very similar to a given one with respect to PerH (large D TS ), the first four structures chosen by PerH might or might not have similar conventional geometric descriptors (small or large D CS ).
Such a large number of materials makes it simply impossible to compare the corresponding pore structures visually. Therefore, an important application of our methodology is that we can now readily identify similar pore structures. In Fig. 3 , we show some examples of materials from the CoRE-MOF database that have similar pore geometries. Our list of similar structures in much longer but what is specific to these examples is that the authors of the corresponding manuscripts did not report the similarities in the original references. Of course, this does not imply that the authors of these articles were not aware of these similarities, but given that there are over 10,000 experimental MOF structures, such similarities are easily overlooked. Methane storage case study. An important practical application of nanoporous materials is methane storage.
From molecular simulations we have determined the 13 best performing out of the 180 known zeolite structures, each having a deliverable capacity larger that 90 (v STP/v). We subsequently identified for each of these top-performing materials the 10 most similar structures in our database of 139,407 predicted zeolites. Figure  4a  shows that indeed 80% of these 130 new structures have a deliverable capacity that is similar to the 13 top-performing known zeolites. In Fig. 4b , we show a similar result for MOFs, where we used the 20 top-performing structures from the CoRE-MOF database and identified similar structure in the databases of 41,498 predicted MOF structures: 85% of these materials show high performance with a deliverable capacity larger than 150 (v STP/v). It is interesting that even for MOFs that have different chemical compositions (unlike zeolites), our method of identifying similar pore shapes illustrates the importance of pore geometry, and hence, of our methodology to quantify similarity for these types of materials.
We can also use our approach to study the topological diversity of the top-performing materials for methane storage. Bathia and Myers  21  analysed a small number of porous materials and concluded that top-performing materials should all have very similar heats of adsorption for given loading and de-charging pressures. Their work has had significant impact, as it provides a straightforward experimental recipe for optimizing the deliverable capacity of a material  22  : if all top-performing materials share a similar heat of adsorption, having a heat of adsorption close to this value should be a necessary condition for good performance. Given this impact it is surprising that the conclusion of Bathia and Myers  21  stands in sharp contrast with observations of Simon et al  12. Simon et al. 12  computed the deliverable capacity for over 200,000 zeolite structures, and their data (Fig.
Discussion Using topological data analysis, we have developed a topologybased methodology to quantify similarity of the chemical environment of adsorbed molecules. Quantifying similarity of pore structures allows us not only to find structures geometrically similar to top-performing ones, but also to organize the set of materials with respect to the similarity of their pore shapes. For our case study of methane storage, we find several distinct classes of pore shapes and conclude that each class requires a different optimization strategy, in contrast to the common belief that top-performing materials share a similar heat of adsorption. We give examples of geometrically almost indistinguishable MOFs whose similarities had previously been unreported (Fig. 3 ). The Supplementary Information shows the hypothetical zeolites that best resemble MOF-5 and CU-BTC (Supplementary Fig.
Methods Generating the barcodes. To assign the persistent homological descriptor to a material, we perform the following steps. We start by preparing a supercell of the material by expanding each unit cell to approximately the size of the largest unit cell of all considered materials, to compare materials that have unit cells of very different sizes. The pore system accessible to a gas molecule of interest is determined using the software package Zeo þ þ (ref. 16). The surface of this pore system is sampled with a fixed number of points per unit surface area. From these sampled points, filtered Vietoris-Rips complexes are constructed and their 0D, 1D and 2D persistence barcodes computed using the software package Perseus  26  We measure the distance between two barcodes by a combination of the L 2 -landscape distances of the barcodes from the dimensions 0,1 and 2 (see section below), using the Persistence Landscape Toolbox  27.
The program Zeo þ þ (ref. 16) detects the accessible void space inside a porous material using a periodic Voronoi network, modelling the framework atoms as hard spheres with radii taken from the Cambridge Structural Database  28, 29. The space accessible to a gas depends on the gas molecule size and is determined in terms of a probe gas molecule, where the size of the probe has to be chosen according to the specific application. We treat a probe gas molecule as a sphere with radius 1. 625, 1. 5, 1. 83 or 1. 98 Å for methane, carbon dioxide, krypton or xenon, respectively. These values are chosen smaller than usual to mimic by geometric constraints the accessibility of pore space as determined by energy barriers. Zeo þ þ encodes the pore structure as a large set of points situated on the pore surface which is defined as the boundary of the space where a probe can be placed.
On the other hand, the random sampling prevents picking up the grid structure in the barcodes. For the random sampling we choose one point per 2 Å 2 surface area while respecting a minimal distance r min between two sampled points where we decrease r min in steps of 0. 1 Å starting with r min ¼ 1. 3 Å until the given number of points has been selected. The grid size is 0. 5 Å and for each cube of the grid the point of the original point cloud is chosen that is closest to the midpoint of the cube. A point of the grid sampling is added to the random sampling whenever its distance to the randomly sampled points is greater than the final value of r min. The second step towards the persistent homological descriptor consists of calculating the persistence barcodes for a filtration of Vietoris-Rips complexes, obtained from the sets of points computed in the first step using the software package Perseus  26.
The distance D CS between two materials in the conventional descriptor space is estimated with a normalized euclidean distance of five conventional structural properties with an equal weight for each: D i (the diameter of largest included sphere), D f (the diameter of largest free sphere), r (density of a framework), ASA (accessible surface area), and AV (accessible volume). The dependence on the choice of the weights is shown in Supplementary Fig. 2. Mapper plot. We used Ayasdi 3. 0 Core software (www. ayasdi. com) to visualize our materials database (Fig. 6 ). Nodes in the network represent clusters of materials with similar pore shapes and edges connect nodes that contain structures in common. Each material is represented by a combination of its persistent barcodes and the metric in this space is D TS.
The lens used to bin the barcodes is the neighborhood lens (resolution 30, gain 3. 0 Â ). Further information can be found in the Ayasdi manual. Nodes are coloured by the average value of the heats of adsorption of the materials in a cluster (Red: high value, Blue: low value). 2. These figures were obtained with the Ayasdi Core software platform (www. ayasdi. com). Nodes are coloured by the average value of the heats of adsorption of the materials in a cluster (Red: high value, Blue: low value).
Figure2| Structures similar to a reference material. (a) For each known zeolite, the two sets of four most similar structures, once selected using the TDA descriptor (PerH, one blue dot for a set of four) and once selected by the conventional descriptor (ConD, one red dot per set) are compared. This is done by plotting their average distances D CS in conventional space (x-axis) and their average distance D TS in the barcode space (y axis) to the reference zeolite. The distances are normalized by the largest pairwise distance in the database. (b) The four structures most similar to the zeolite SSF respectively to IWV, as selected by either PerH or ConD. Their structural properties are given in Supplementary Table1. The inset in a highlights the four sets of four structures shown in b.
Acknowledgements During the early stage of the research Y. L. and B. S. were supported by the  Center for Gas Separations Relevant  to  Clean Energy Technologies , an  Energy Frontier Research Center  funded by the  DOE, Office of Science, Office of Basic Energy Sciences  under award  DE-SC0001015. Y. L. (during the later stages of the research) and S. B. were supported by the  National Center of Competence in Research (NCCR)  ' Materials' Revolution: Computational Design and Discovery of Novel Materials (MARVEL) ' of the  Swiss National Science Foundation  (SNSF). M. M. was supported by the  Deutsche Forschungsgemeinschaft  ( DFG, priority program SPP 1570 ). B. S. was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  666983 ) and by the  'Korean-Swiss Science and Technology Programme' (KSSTP)  grant number  162130  of the  Swiss National Science Foundation  (SNSF).
Processing of the large amount of data produced by the ATLAS experiment  [1]  requires fast and reliable access to what we call Auxiliary Data Files (ADF). These files, produced by Combined Performance, Trigger and Physics groups, contain conditions, calibrations, and other derived data used by the ATLAS software. In ATLAS this data has, thus far for historical reasons, been collected and accessed outside the ATLAS Conditions Database infrastructure and related software. For this reason, along with the fact that ADF are effectively read by the software as binary objects, this class of data appears ideal for testing the proposed Run 3 Conditions data infrastructure now in development. This paper will describe this implementation as well as the lessons learned in exploring and refining the new infrastructure with the potential for deployment during Run 2.
Usage of Auxiliary Data Files in ATLAS The Auxiliary Data Files consist of a variety of calibrations, alignments, efficiencies, weights and other useful constants, which are produced by experts using the physics data and are essential for user analysis in the context of ATLAS distributed computing infrastructure. These files are currently stored in a simple file system structure under an AFS (Andrew File System) dedicated area, that we call Calibration Area, at CERN and then propagated into the CVMFS (CERN Virtual Machine File System) storage area, which is accessible from external sites via Squid Proxy, and into the High Level Trigger (HLT) farm, located near the ATLAS experiment, for on-line processing. The Calibration Area on AFS The total volume of the calibration area is 2 GB of data.
This represents ≈ 0. 2% of data accessed via the Conditions Database infrastructure. The calibration area files are not updated very frequently, and in general their validity span a large range in time. For the moment this IOV structure is taken into account adding the time information in the name of the sub-directories inside a given package when needed, or even at the level of the file names. Every system has defined is own way to handle the internal file dependencies for a given package. The file type used is extremely heterogeneous: ASCII files (XML or TXT) and ROOT files can be used by the experts depending on their needs. Present Limitations The Calibration Area today: Directory structure determined essentially by the package experts Difficult to handle centrally and time-consuming to create a given calibration area release by the ASG (Analysis Software Group) manager Every system has adopted some model for tagging and versioning Needs to be exported to Point 1 -ATLAS detector and trigger computing farm location.
Managing the Auxiliary Data Files using the New LHC Run 3 Conditions Server A new system would make the file storage more uniform among different packages: provide a central way for tagging and versioning the files, simplify the creation of a calibration area release, and facilitate on-line synchronisation. The ATLAS and CMS experiments are exploring a common solution to manage conditions data with the time scale of Run 3 (2020). This solution foresees 3 main components: a back-end to persist the conditions data objects and the related meta-data like interval ranges, tags and global tags (for the moment based on Oracle), an intermediate web server providing access to the storage layer and delivering REST functions to clients, and finally a set of client libraries (Python and C++) in order to use the REST API from the data processing frameworks.
The data model at the level of Oracle consists today of a simple set of tables to handle the meta-data, and all conditions data objects are stored as files (BLOBs in the database language). A more detailed description can be found in the CHEP2015 article  [2]. This data model seems well adapted to contain the ADF. The proposed solution is to store the analysis ADF in the conditions database as any other conditions, and then to dump a directory structure in CVMFS in an automatized way in order to keep the file access as it is today.
Prototype to Manage Auxiliary Data Files in the Conditions Database For this prototype we have been gathering a subset of the ADF to verify the possibility to map the existing directory structure in the calibration area into the TAG and IOV based structure of the conditions data model. Some additional tables were added in order to store in the database the complete URI of the files uploaded. The strategy adopted in this first prototype was to map every file for a given calibration package in a TAG inside Oracle. Other possible mappings will be explored in future, to finally decide what is the optimal way of storing the data with the minimal amount of changes to be performed at the level of the software producing and or accessing these data. Python Client for Analysis ADF In order to provide the calibration experts with easy tools to manage their own data via the REST API, some special developments have been done based on an existing prototype for the conditions database.
At client level we have established a minimal set of functions that are needed: 1) calibcliadd < package >< path >< file >< description > Add a file for a given software package and in a given path. The path will be used later to migrate the file under AFS. This action does not upload data, but it is used only to create the relevant meta-data for later management of the specified auxiliary data file. 2) calibclicommit < package >< localfilepath >< destpath >< filetagextension >< destfile > Commit a local file into the conditions database. The file will be uploaded in this case, and stored in a tag using by default iov 0. 3) calibclitag < package >< packagetag >< filetagextension >< description > This will allow to associate a tag to a given package. This tag will reference all files uploaded for that package.
We present a summary of the campaign of remote observations that supported the European Space Agency's Rosetta mission. Telescopes across the globe (and in space) followed comet 67P/Churyumov-Gerasimenko from before Rosetta's arrival until nearly the end of mission in September 2016. These provided essential data for mission planning, largescale context information for the coma and tails beyond the spacecraft, and a way to directly compare 67P with other comets. The observations revealed 67P to be a relatively 'well behaved' comet, typical of Jupiter family comets and with activity patterns that repeat from orbit-to-orbit. Comparison between this large collection of telescopic observations and the in situ results from Rosetta will allow us to better understand comet coma chemistry and structure.
The coordination largely began with a meeting in London in 2012, sponsored by the European Union FP7 research infrastructure 'Europlanet' under its networking activity fund  2. Further meetings were hosted by the European Southern Observatory (ESO) and ESA (usually as parallel sessions to Rosetta Science Working Team meetings). Europlanet again sponsored a workshop in June 2016, at Schloss Seggau near Graz in Austria, towards the end of the parallel Rosetta observations, where results could be exchanged and further analyses of data planned. In addition to the wide range of observations from professional observatory facilities, a large number of amateur astronomers have collected a significant and useful data set. The amateur campaign was coordinated with support from the NASA Rosetta project office, in parallel with and as part of the main campaign, and is described in detail elsewhere  [3].
Analyses of the GNIRS and SpeX data are ongoing  [49]. The gas coma of comets is far more revealing, as emission features from various species can be measured across all possible wavelengths, for bright enough comets. Rosetta's own remote sensing spectrograph suite observed the gas coma from the UV through to the sub-mm (ALICE, VIRTIS, MIRO), detecting water already from June 2014 onwards and also mapping CO 2 , OH and CN, among other species  [50] [51] [52]. From the ground we were mostly limited to observations of 67P in the visible range, where emissions from so-called 'daughter' species are seen (e. g. fig. 7 ). Using the detection of these species to probe the composition of the parent ices in the nucleus requires the use of photochemistry models, which Rosetta presents a unique opportunity to test (by comparing these observations with in situ measurements of parent gasses escaping directly from the nucleus).
In terms of the widely used Af ρ parameter for quantifying cometary activity  [70] , we find that the peak in activity was around Af ρ ≈ 1000 cm. The Wendelstein data support an Af ρ power law dependence on heliocentric distance with an exponent of -3. 7 to -4. 2, depending on the phase function assumed, using data from mid-September to the end of December 2015  [30]. This is close to the Af ρ ∝ r -3. 4 post-perihelion from the prediction paper, and within the range of previous determinations discussed there  [23]. The total activity can also be measured in terms of gas production rates. The most abundant species released by the comet is water, but this is difficult to measure from the groundonly comets considerably brighter than 67P can be regularly observed with high resolution spectroscopy to separate cometary water emission lines (e.
2 × 10 27 molecules s -1. Further observations with ISIS were attempted until April 2016, but the OH production rate was only measurable relatively close to perihelion  [71]. Observations of OH emission with the Lowell 1. 1m and Kron photoelectric photometer (and narrowband filters) were used to derive water production rates of Q(H 2 O) = 7. perihelion data, positive post-perihelion). We include data from previous orbits  [4, 8, 13, 76] , narrowband photometry from TRAPPIST and the Lowell 1. 1 m around perihelion, and spectroscopy from the VLT, SAO 6 m and LT (see key in plot for symbols). Error bars are not included for clarity. The dashed line shows a scaled version of the dust scaling law plotted in fig. 10. In general, pre-perihelion data are only upper limits (marked with arrows) until relatively low r, when the rate climbs quickly pre-perihelion, with a similar slope to the dust fit.
Acknowledgements. We acknowledge the contribution of the Europlanet  EU FP7/H2020  framework in supporting meetings that initiated this campaign in 2012 and brought many of us together to discuss the results in 2016. We thank the  Royal Society  and the organisers of the ' Cometary science after Rosetta ' meeting for the invitation to present the results of the campaign. S. F. Green  acknowledges support from the  STFC  (Grant  ST/L000776/1 ). J. Kleyna is supported by  NSF  grant  1413736. H. J. Lehto ,  B. Zaprudin  and  A. Somero  acknowledge the support of the  Academy of Finland  (grant number  277375 ). J. Licandro  and  J. de León  acknowledge support from the  AYA2015-67772-R  ( MINECO, Spain). J. Lasue  and  A. C. Levasseur-Regourd  acknowledge support from  CNES , the  French Space Agency , for this work in relation with CONSERT and MIDAS on board  Rosetta.