dataesr/scientific-paragraphs-categorization huggingface dataset:
A posteriori verification of the NRT dataset is important to qualify this specific product, which is increasingly requested by users. Understanding the reasons for differences between NRT and validated datasets will also help improve the automatic processing of the measurements. Figure  10  shows the differences for the hourly means. The most evident feature of the differences for all sites is the linear drift correction between two calibration sequences (≈ 2 to 4 weeks). At the Amsterdam Island station we see a reverse slope for a short period (2 weeks) in early July 2014, with the drift changing towards a smaller bias with time. This is due to a revision of the calibration performed on 1 July, after the correction of an erroneous injection of one calibration gas. In most cases (95 %), the differences are within ±0.
06 µmol mol -1 for CO 2 and ±0. 75 nmol mol -1 for CH 4. The statistics of the validated minus NRT mole fractions are shown for each site in Fig. 11. It is worth noting that for most of the stations, the median differences are less than or equal to zero. Only three instruments show a positive median difference for CO 2 (Lamto station -identified by the three-letter code LTO with instrument no. 192; PDM, no. 222; Ivittuut station -identified by the three-letter code IVI with instrument no. 93) and one for CH 4 (IVI -no. 93). This means that almost all instruments have a tendency to drift positively; consequently, when a NRT dataset is revised after a few days or weeks with the new calibration sequence, its value is slightly decreased. This tendency for a positive drift for CH 4 measurements by CRDS analyzers was also noticed by  Yver-Kowk et al.
(2015). In addition to the data corrections due to instrumental drift, we also detect in Fig. 10  some isolated events that present a different profile of variability, and there are also a few outliers. For example, not visible in this figure (out of scale) is a 5-day period (10-15 July) at the Mace Head station (no. 41) with very high differences between NRT and validated mole fractions: up to -25 µmol mol -1 for CO 2 and -250 nmol mol -1 for CH 4. This event corresponds to the installation of a new calibration scale at the Mace Head station, with erroneous values of the standard gases entered into the database. Consequently, the mole fractions calculated in NRT were wrong, and a few days were required to identify the problem and reprocess the dataset. Another example is the relatively constant differences observed at the Finokalia station (identified by the three-letter code FKL) from 5 to 20 June 2014: +0.
The zoom into June 2014 (Fig. 10 , right) also shows small oscillations in the CO 2 differences at the Lamto station. This feature is related to the strong diurnal cycle observed at this tropical site (typically 50 ppm). Since the correction applied to the data depends on concentration, the differences between NRT and validated data also display a diurnal cycle. We also observe for some periods a relatively high random variability of the mole fraction differences for the Trainou station instrument (no. 108). This is due to the leakage of one valve that is used to evacuate the liquid water from a water trap setup inside a refrigerator. This problem caused contamination for a few minutes. These contaminated values were used in the NRT data processing, whereas they were excluded after the quality control of the measurements performed by the station PI, which explains the differences between the two datasets.
Data time aggregation and associated metadata Further processing consists of aggregating the data in time. The 1 min, hourly and daily means are computed for in situ data. The 1 min means and injection means are computed for tank data (calibration and target gases). As recommended by the World Data Centre for Greenhouse Gases (WDCGG; WMO, 2012), we calculate the means using data from the nearest time aggregation level and not always using the raw data. This implies that raw data are used to calculate 1 min averages, which are then used to calculate hourly averages and so on. For each single averaged data point, we provide the number of data used to compute the average and the standard deviation. The measurement time associated with an average dataset corresponds to the beginning of the averaging period (e.
g. , the hourly means at 13:00 are calculated from the 1 min means from 13:00 to 13:59), which is also in line with the recommendation of WDCGG  (WMO, 2012). The times provided to the users are always universal time. The time difference between local time and universal time is provided in the metadata of the station. Different data output formats can be provided to fit user needs. The files provided to the users always include the following information for each average mole fraction in dry air: time/date of the measurement, site and instrument identifiers, number of data and standard deviation, user flag and an internal identifier tracing all processing parameters (an example can be found in Table  7 ). In addition, the header of the file provides metadata including the station coordinates, the measurement calibration scale, the name of a contact person and the institute in charge of the monitoring program.
Conclusion and perspectives The provision of atmospheric GHG mole fractions in NRT is useful for early detection of anomalies, whether they are instrumental or geophysical, and data assimilation schemes. As part of the construction of the ICOS ATC data center, we have developed a framework for fast delivery (24 h) of the atmospheric greenhouse gases dataset. The setup of the hardware and software needed for data collection, data processing, configuration of measurements and quality control of the time series have been performed over the past years in close collaboration with experimentalists in charge of running stations during the demonstrator phase of ICOS. In the last few years, we moved from a situation in which each European station was performing its own data processing to the ICOS configuration with a central database and a set of software codes processing the raw data transferred from all ICOS sites daily.
05 µmol mol -1 and ±0. 7 nmol mol -1 for CO 2 and CH 4 , respectively. Outliers may occur, which are generally associated with an error in the metadata information provided by the station PI (e. g. , error in the attributed value of the calibration gas). ICOS aims to maintain very high-precision measurements with a high level of data recovery, traceability and fast delivery. Rapid access to processed data and their associated metadata, as well as a catalogue of data products updated daily, is intended to facilitate the verification of the measurements. In 2013, 17. 8 GB of data files and data products were viewed by users on the ICOS ATC website (https://icos-atc. lsce. ipsl. fr), which corresponds to more than 17 000 hits and more than 380 000 pages viewed. Traceability of the downloads, longterm archival and data policies beyond the scope of this paper are being designed in collaboration with the carbon portal of ICOS.
Thus far, the NRT dataset has been provided to the participants of the ICOS Preparatory Phase and the following projects: InGOS (http://ingos-atm. lsce. ipsl. fr/), ICOS-INWIRE (http://www. icos-inwire. lsce. ipsl. fr/) and MACC-III/COPERNICUS (http://www. copernicus-atmosphere. eu/ d/summary/macc/gac/verif/ghg/icos/). The format of the files provided to the users was adapted to their needs, and the identifier which allows for the traceability of the measurements is part of the compulsory information. The MACC-III project is using the CO 2 data in NRT time to evaluate their assimilation and forecasting system developed at the European Centre for Medium-range Weather Forecasts  (Agustí-Panareda et al. , 2014). In another study, the authors performed a CH 4 inversion to test the ability of the European network of atmospheric observations to detect the leakage of an offshore oil platform at Elgin Field, North Sea  (Berchet et al.
, 2013). The continuous enhancement of automatic processing is important, and new developments are in progress. This includes the evaluation of spike detection algorithms that would allow the automatic identification of data being significantly influenced by local processes. Another perspective is to interface the database with the electronic logbooks of the station operations (maintenance, troubleshooting, etc. ), as a support of the quality control of the time series. One important issue is the estimation of time-varying uncertainties based on regular measurements of the target gases, comparison of in situ and flask measurements and analysis of specific tests. Evaluation of algorithms to estimate random and systematic errors was performed by the INGOS and ICOS-INWIRE European projects, and we have started to transfer some of them into the ICOS data processing.
Acknowledgements. The authors acknowledge the scientists and engineers from the stations contributing to the development of the ICOS data center. Their regular feedback and comments are essentials for the success of the project. We wish to acknowledge the valuable comments of  M. Steinbacher ,  H. Chen ,  L. Rivier ,  J. D. Paris ,  M. Delmotte  and  N. Schneider  as well as the entire staff of the  ICOS Atmospheric Data Center. This study was funded in part by the  European Commission  under the  EU Seventh Research Framework Programme through ICOS  (grant agreement no. 211574 ) and  ICOS-INWIRE  (grant agreement no. 313169 ). Edited by:  D. Brunner Reviewed  by:  J. Klausen  and one anonymous referee.
Overview The workflow implemented by caspo is depicted in Figure  1. It starts with a PKN describing signed and directed signaling interactions and a dataset providing phosphorylation activities of a set of readouts with respect to several perturbations combining stimuli and inhibitions. Learn. Given the PKN and the phosphoproteomics dataset, we aim at learning all logical networks compatible with the given topology and explaining the experimental observations similarly well  (Guziolowski et al. , 2013; Videla et al. , 2015b). Note that the number of logical networks in the family depends on the level of tolerance with respect to optimal fitness and size. In fact, this allows us to consider the uncertainty in observations and relax the parsimonious principle adopted for the learning. Classify and predict.
Thus, given a set of input-output behaviors, we are interested in designing new experimental perturbations which would allow for an optimal discrimination of rival models at hand  (Videla et al. , 2015a). Once the experiments are carried out in the wet lab, the new experimental observations would be combined with the previously available dataset and the workflow could start over. Control. To conclude, given a family of logical networks we are interested in identifying key-players that would allow to control the response in every network. More precisely, we aim at finding minimal intervention sets that would force a set of targets into a desired steady state under various environmental conditions or constraints  (Kaminski et al. , 2013). For example, this could allow to find new therapeutic targets relevant for drug development or diagnosis.
Implementation Our software provides a command line interface (CLI) as well as an application programming interface (API) in order to facilitate the integration with other software packages and tools. Next, we provide a brief description of the CLI and refer the reader to the online documentation for an in-depth description of installation and usage (http:// caspo. readthedocs. io). The CLI consists of various subcommands, viz. , learn, classify, design, predict, control, visualize and test. Each subcommand provides its own help message describing required inputs and available options. Subcommands learn, classify, design, predict and control implement all the steps in the workflow described before and depicted in Figure  1. Each subcommand will output one or more files and some default visualizations.
In this context, the classification of networks into input-output behaviors, the design of experiments in order to discriminate such behaviors, and the predictions made from such behaviors also assume logical networks without loops. In contrast, the control of logical networks is more general as it accepts any logical network (with or without loops). In caspo learn, classify, design and predict we assume an experimental setup fixed, i. e. a fixed set of possible stimuli, inhibitors and readouts. Further, in caspo learn we assume a dataset describing the response of the system to multiple experimental perturbations combining stimuli and inhibitors. Such a system response must be provided in terms of measurements over the set of readouts at a single time-point and normalized in the range [0,1].
Although the methods implemented in caspo were initially developed for signaling networks and phosphoproteomics datasets, any PKN and dataset satisfying these assumptions could also work. Case studies. We consider three real-world and recently published case studies modeling signaling pathways of primary human hepatocytes in liver cells. In Table  1  we describe the main characteristics for each PKN and dataset. Apart from number of nodes and edges for each PKN, we also show the number of possible hyperedges h derived from the PKN. Such a number is particularly relevant for learning logical networks since the number of possible networks to explore is given by 2 h (Saez-  Rodriguez et al. , 2009). The small-scale case study (S) consists of a PKN and a dataset from the DREAM4 challenge  (Prill et al.
, 2011). The medium-scale case study (M) consists of a PKN and a dataset introduced in Saez-Rodriguez et al. (2009). Finally, the large-scale case study (L) consists of a PKN and a dataset published by  Melas et al. (2012). Further, in this case we consider either to limit the number of sources per hyperedge to 2 (up to 2 inputs per AND gate) or not (XL). Notably, this has a direct impact in the search space of logical networks to explore during learning. Performance. We illustrate the performance of caspo over each case study for learn, classify, and design. For control, we refer the reader to  Kaminski et al. (2013). Benchmarks shown in Table  1  were run using a dedicated server with 16 cores and 60G of RAM. Notably, running using 16 threads allows us to explore the search space using 16 different search heuristics in parallel.
Conclusion The caspo toolbox provides a logic-based implementation of the hypotesis-driven research loop in systems biology. Combining various steps, viz. , learn, classify, design, predict and control, it enables a complete study of the variability of logical networks families from various perspectives. Our software could be extended in several ways. In particular, the learning of logical networks could allow users to specify a set of fixed logic rules and learn the rest. Also, feasible interactions are likely to be missing from any given PKN. Thus, the ability to infer new links that would improve the fitness to a given dataset could be very useful. Moreover, links in the PKN could be assigned to different levels of confidence depending on the methods used to build such a PKN and weighted accordingly in the objective function  (Eduati et al.
, 2012). Regarding performance, while caspo is able to cope with real-world case studies very efficiently, various factors should be taken into account, especially for learning logical networks. Of course, the size of the PKN is determinant but also the amount and type (single versus combinatorial) of experimental perturbations is critical in order to constrain the search space. Further, tolerances with respect to optimum MSE and size could lead to an intractable number of logical networks if they are not chosen carefully and rather conservatively. Altogether, the caspo toolbox is a powerful software that we expect to keep improving as more researchers start using it for their investigations.
Introduction Temporal datasets are ubiquitous but notoriously hard to visualize, especially rich datasets that involve more than one dimension in addition to time, such as videos, dynamic networks, multivariate and geo-spatial data. Previous work on novel visualizations for temporal data has dramatically advanced the field of information visualization (Infovis). However, there are so many different techniques today that it has become hard for both researchers and designers to get a clear picture of i) what has been done, ii) how to compare and chose visualizations, and iii) how much of the design space of temporal data visualizations remains to be explored. For similar reasons, teaching this research topic to students is challenging. Therefore, there is a clear need to structure and organize previous work in the area of temporal data visualization.
Names can also be ambiguous. For example, the term small multiples is commonly used to refer to a specific type of temporal data visualization  [Tuf86]. But Figure  3  shows that two visualizations can be based on small multiples despite being different with respect to which aspect of the data they show. Recent effort at proposing taxonomies, conceptual models, and design spaces for temporal visualizations mainly categorize visualizations based on analytical tasks or data types (e. g. , object movement data [AAH11, AAB * 11, AA12], video data [BCD * 12], or datasets with different temporal and spatial structures [AMM * 07]). While descriptions of visualization techniques by tasks and data types can help choosing a visualization technique for a specific problem, we aim to describe visualizations in their own visualization space, independent from data and task.
2. A generalized space-time cube does not need to involve geo-spatial data. Many visualizations (e. g. , scatterplots or node-link diagrams) convey abstract, non-spatial data. Those (non-temporal) visualizations all embed data into an abstract and 2D visualization space. When data changes over time, such as in GapMinder's animated 2D scatterplots  [Ros06] , each animation frame can be conceptually thought of as a slice of a space-time cube. In the model of the generalized space-time cube, "space" therefore refers to an abstract 2D substrate that is used to visualize (non-temporal) data at a specific time. We call this space base plane. Thus, generalized space-time cubes include traditional geo-spatial space-time cubes, video cubes, matrix cubes, as well as a subset of OLAP cubes as we discuss later.
It is important to stress that this article is not about spacetime cube visualizations, and that 3D space-time cube representations, like the one in Figure  4 , represent only a very small subset of the visualizations we aim to cover. In the remainder of this article, the term space-time cube refers to our model, if not indicated otherwise. Finally, our framework does not consider how space-time cubes are built for a given dataset. A general heuristic is to decide on a 2D visual representation of the data for a given time slice, and then extrude it over time. For example, networks can be shown as node-link diagrams or matrices, multivariate data as scatterplots or barcharts. Such decisions are independent from the cube. We admittedly do not offer a detailed framework that clarifies the different ways concrete datasets can be turned into concrete space time cubes.
Instead, we assume that a conceptual 3D space-time cube is already given and focus on how this cube can be transformed to accommodate 2D media like computer displays and pa-per while remaining legible. We show how such operations are enough to capture most known techniques for visualizing temporal datasets. We mainly consider datasets that involve two dimensions plus time, although we later discuss how our model can be extended to higher dimensionalities. Datasets with a single data dimension plus time may not require a space-time cube and can be visualized with timelines and visualizations for time series. In this paper, we first review common temporal data visualization techniques, and explain how they can be all seen as operations on a generalized space-time cube. We then describe our framework in more detail by providing definitions of key concepts, as well as a taxonomy of elementary space-time cube operations and how they can be combined into compound operations.
Then, we explain how choosing effective space-time cube operations can be motivated by the characteristics of the dataset, mostly because different datasets yield different "shapes" or inner structures for space-time cubes. We provide a way to characterize such inner structures, based on characteristics of data. We then informally discuss the relative benefits and drawbacks of different space-time cube operations and briefly review empirical evidence from user studies. We finally discuss interactive visual exploration systems that support a range of spacetime cube operations, discuss limitations of our framework, and suggest avenues for future work. A catalog of temporal visualizations classified according to our framework can be found at www. spacetimecubevis. com.
Decomposing Space-Time Cubes We assume that any space-time cube can be subdivided into lower-level space-time objects (see Section 3. 1). A common example is a set of 3D curves or tubes generated by moving objects such as cars, persons, nodes in a network, or points in a scatterplot (see Figure  26 1  ). These curves can yield different patterns depending on the data 2 3 4. Another typical structure is obtained from dense matrix data such as videos or dynamic heatmap visualizations. For these datasets, the object of interest (e. g. , a pixel) is fixed in space but its color changes over time. The space-time objects are therefore multicolored lines parallel with the time axis and with no intervening space 5. Again, different patterns of variation are possible 6. A space-time cube can be subdivided in different ways.
Density: The density of a space-time cube refers to how much non-empty space it contains. The inner structure of a space-time cube can be dense (Figure  26  1 2 5 6 ) or sparse (Figure  26  3 4 7 8 ). Datasets like videos (Figure  26  ( 5 6 ) produce inner structures that are maximally dense, and which will be referred to as complete. Furthermore, a space-time cube can be spatially dense but temporally sparse (e. g. , a few photo snapshots), or vice versa (e. g. , a GPS log over several days). Density is a key property of inner structures, and it is the only dimension that is independent from the decomposition used.
Thereby, it does not matter whether the initial data is discrete or continuous in time or space. Discrete and continuous are two complementary aspects, both encompassed by the generalized space-time cube. For example, repeated (time chopping + time flattening) can transform a continuous time dimension into a discrete time dimension. On the other hand, time interpolation can yield a continuous impression of otherwise discrete data. In the next section we will see why a dataset's characteristics matter, and how the inner structure of a space-time cube can be altered with space-time operations.
However, only a small subset of all possible operations have been covered by user studies; many of the existing findings are hard to generalize beyond the domain, type of data, and operation parameters used in the stimuli. Thanks to the ability of our framework to reduce complex techniques into elementary operations, useful advice can often be provided based on more general knowledge on visual perception and on common sense. With respect to data type, the inner structure of a space-time cube can in many cases inform the suitability of certain operations. For example, if a dataset produces maximally dense space-time cubes, a 3D representation will always be problematic unless other operations (filtering, cutting) are used.